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    "# set up behind the scenes\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.gridspec import GridSpec\n",
    "plt.style.use('./matplotlibrc_notebook')\n",
    "import numpy as np\n",
    "from scipy import stats\n",
    "import pandas as pd\n",
    "from fooof import FOOOF, FOOOFGroup\n",
    "from neurodsp import sim, spectral\n",
    "from statsmodels.tsa.stattools import acf\n",
    "import altair as alt\n",
    "\n",
    "# For compatability with Stencila output Altair plots\n",
    "# using MIME type renderer\n",
    "alt.renderers.enable('mimetype')\n",
    "\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "C_ORD = plt.rcParams['axes.prop_cycle'].by_key()['color']\n",
    "\n",
    "import nibabel as ni\n",
    "from surfer import Brain\n",
    "\n",
    "\n",
    "def compute_perm_corr(x, y, y_nulls, corr_method='spearman'):\n",
    "    corr_func = stats.spearmanr if corr_method == 'spearman' else stats.pearsonr\n",
    "    rho, pv = corr_func(x,y)\n",
    "    rho_null = np.array([corr_func(x, n_)[0] for n_ in y_nulls])\n",
    "    pv_perm = (abs(rho)<abs(rho_null)).sum()/y_nulls.shape[0]\n",
    "    return rho, pv, pv_perm, rho_null\n",
    "\n",
    "def convert_knee_val(knee, exponent=2.):\n",
    "    \"\"\"\n",
    "    Convert knee parameter to frequency and time-constant value.\n",
    "    Can operate on array or float.\n",
    "\n",
    "    Default exponent value of 2 means take the square-root, but simulation shows\n",
    "    taking the exp-th root returns a more accurate drop-off frequency estimate\n",
    "    when the PSD is actually Lorentzian.\n",
    "    \"\"\"\n",
    "    knee_freq = knee**(1./exponent)\n",
    "    knee_tau = 1./(2*np.pi*knee_freq)\n",
    "    return knee_freq, knee_tau\n",
    "\n",
    "def sig_str(rho, pv, pv_thres=[0.05, 0.01, 0.005, 0.001], form='*', corr_letter=r'$\\rho$'):\n",
    "    \"\"\"Generates the string to print rho and p-value.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    rho : float\n",
    "    pv : float\n",
    "    pv_thres : list\n",
    "        P-value thresholds to for successive # of stars to print.\n",
    "    form : str\n",
    "        '*' to print stars after rho, otherwise print p-value on separate line.\n",
    "\n",
    "    Returns\n",
    "    -------\n",
    "    str\n",
    "    \"\"\"\n",
    "    if form == '*':\n",
    "        s = corr_letter+' = %.2f '%rho + np.sum(pv<=np.array(pv_thres))*'*'\n",
    "    else:\n",
    "        if pv<pv_thres[-1]:\n",
    "            s = corr_letter+' = %.2f'%rho+ '\\np < %.3f'%pv_thres[-1]\n",
    "        else:\n",
    "            s = corr_letter+' = %.2f'%rho+ '\\np = %.3f'%pv\n",
    "    return s"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction\n",
    "\n",
    "Human brain regions are broadly specialized for different aspects of behavior and cognition, and the temporal dynamics of neuronal populations across the cortex are thought to be an intrinsic property (i.e., neuronal timescale) that enables the representation of information over multiple durations in a hierarchically embedded environment (@bib57). For example, primary sensory neurons are tightly coupled to changes in the environment, firing rapidly to the onset and removal of a stimulus, and showing characteristically short intrinsic timescales (@bib70; @bib76). In contrast, neurons in cortical association (or transmodal) regions, such as the prefrontal cortex (PFC), can sustain their activity for many seconds when a person is engaged in working memory (@bib105), decision-making (@bib40), and hierarchical reasoning (@bib77). This persistent activity in the absence of immediate sensory stimuli reflects longer neuronal timescales, which is thought to result from neural attractor states (@bib94; @bib102) shaped by N-methyl-D-aspartate receptor (NMDA)-mediated recurrent excitation and fast feedback inhibition (@bib95; @bib93), with contributions from other synaptic and cell-intrinsic properties (@bib23; @bib37). How connectivity and various cellular properties combine to shape neuronal dynamics across the cortex remains an open question.\n",
    "\n",
    "Anatomical connectivity measures based on tract tracing data, such as laminar feedforward vs. feedback projection patterns, have classically defined a hierarchical organization of the cortex (@bib26; @bib46; @bib85). Recent studies have also shown that variations in many microarchitectural features follow continuous and coinciding gradients along a sensory-to-association axis across the cortex, including cortical thickness, cell density, and distribution of excitatory and inhibitory neurons (@bib49; @bib98). In particular, gray matter myelination (@bib39)—a noninvasive proxy of anatomical hierarchy consistent with laminar projection data—varies with the expression of genes related to microcircuit function in the human brain, such as NMDA receptor and inhibitory cell-type marker genes (@bib10). Functionally, specialization of the human cortex, as well as structural and functional connectivity (@bib64), also follow similar macroscopic gradients. Moreover, in addition to the broad differentiation between sensory and association cortices, there is evidence for an even finer hierarchical organization within the frontal cortex (@bib77). For example, the anterior-most parts of the PFC are responsible for long timescale goal-planning behavior (@bib2; @bib88), while healthy aging is associated with a shift in these gradients such that older adults become more reliant on higher-level association regions to compensate for altered lower-level cortical functioning (@bib17).\n",
    "\n",
    "Despite convergent observations of cortical gradients in structural features and cognitive specialization, there is no direct evidence for a similar gradient of neuronal timescales across the human cortex. Such a gradient of neuronal dynamics is predicted to be a natural consequence of macroscopic variations in synaptic connectivity and microarchitectural features (@bib13; @bib22; @bib48; @bib49; @bib98), and would be a primary candidate for how functional specialization emerges as a result of hierarchical temporal processing (@bib57). Single-unit recordings in rodents and non-human primates demonstrated a hierarchy of timescales that increase, or lengthen, progressively along a posterior-to-anterior axis (@bib21; @bib68; @bib76; @bib99), while intracranial recordings and functional neuroimaging data collected during perceptual and cognitive tasks suggest likewise in humans (@bib3; @bib47; @bib61; @bib100). However, these data are either sparsely sampled across the cortex or do not measure neuronal activity at the cellular and synaptic level directly, prohibiting the full construction of an electrophysiological timescale gradient across the human cortex. As a result, while whole-cortex data of transcriptomic and anatomical variations exist, we cannot take advantage of them to dissect the contributions of synaptic, cellular, and circuit connectivity in shaping fast neuronal timescales, nor ask whether regional timescales are dynamic and relevant for human cognition.\n",
    "\n",
    "Here we combine several publicly available datasets to infer neuronal timescales from invasive human electrocorticography (ECoG) recordings and relate them to whole-cortex transcriptomic and anatomical data, as well as probe their functional relevance during behavior ([Figure 1A](#fig1A) for schematic of study; [Tables 1](#table1) and [2](#table2) for dataset information). Unless otherwise specified, (_neuronal_) _timescale_ in the following sections refers to ECoG-derived timescales, which are more reflective of fast synaptic and transmembrane current timescales than single-unit or population spiking timescales ([Figure 1A](#fig1A), left box), though we demonstrate in macaques a close correspondence between the two. In humans, neuronal timescales increase along the principal sensorimotor-to-association axis across the cortex and align with macroscopic gradients of gray matter myelination (T1w/T2w ratio) and synaptic receptor and ion channel gene expression. Finally, we find that human PFC timescales expand during working memory maintenance and predict individual performance, while cortex-wide timescales compress with aging. Thus, neuronal timescales follow cytoarchitectonic gradients across the human cortex and are relevant for cognition in both short and long terms, bridging microcircuit physiology with macroscale dynamics and behavior.\n",
    "\n",
    "table: Table 1.\n",
    ":::\n",
    "## Summary of open-access datasets used.\n",
    "\n",
    "| Data                                                               | Ref.                           | Specific source/format used                                                              | Participant info                                                                | Relevant figures                                                    |\n",
    "| ------------------------------------------------------------------ | ------------------------------ | ---------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------- | ------------------------------------------------------------------- |\n",
    "| MNI Open iEEG Atlas                                                | @bib29; @bib30                 |                                                                                          | N = 105 (48 females)  Ages: 13–65, 33.4 ± 10.6                                  | [Figure 2A–D](#fig2),  [Figure 3](#fig3),  [Figure 4E and F](#fig4) |\n",
    "| T1w/T2w and cortical thickness maps from  Human Connectome Project | @bib38; @bib39                 | Release S1200, March 1, 2017                                                             | N = 1096 (596 females)  Age: 22–36+ (details restricted due to identifiability) | [Figure 2C and D](#fig2),  [Figure 3D–F](#fig3)                     |\n",
    "| Neurotycho macaque ECoG                                            | @bib69; @bib103                | Eyes-open state from anesthesia datasets (propofol and ketamine)                         | Two animals (Chibi and George)  four sessions each                              | [Figure 2E–G](#fig2)                                                |\n",
    "| Macaque single-unit timescales                                     | @bib68                         | [Figure 1](#fig1) of reference                                                           |                                                                                 | [Figure 2E–G](#fig2)                                                |\n",
    "| Whole-cortex interpolated Allen Brain Atlas human gene expression  | @bib42; @bib43                 | Interpolated maps downloadable from <http://www.meduniwien.ac.at/neuroimaging/mRNA.html> | N = 6 (one female)  Age: 24, 31, 39, 49, 55, 57 (42.5 ± 12.2)                   | [Figure 3](#fig3)                                                   |\n",
    "| Single-cell timescale-related genes                                | @bib5; @bib81                  | Table S3 from @bib81, Online Table 1 from @bib5                                          | N = 170 (@bib81) and 4168 (@bib5) genes                                         | [Figure 3C and D](#fig3)                                            |\n",
    "| Human working memory ECoG                                          | @bib54; @bib53; @bib51, @bib52 | CRCNS fcx-2 and fcx-3                                                                    | N = 14 (five females)  Age: 22–50, 30.9 ± 7.8                                   | [Figure 4A–D](#fig4)                                                |\n",
    ":::\n",
    "{#table1}\n",
    "\n",
    "table: Table 2.\n",
    ":::\n",
    "## Reproducing figures from code repository.\n",
    "\n",
    "All IPython notebooks @bib35: <https://github.com/rdgao/field-echos/tree/master/notebooks>\n",
    "\n",
    "| Notebook                                                                                     | Results                                                                                                                                                                                                                                                                                                             |\n",
    "| -------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |\n",
    "| 1_sim_method_schematic.ipynb                                                                 | Simulations: [Figure 1B–E](#fig1)                                                                                                                                                                                                                                                                                   |\n",
    "| 2_viz_NeuroTycho-SU.ipynb                                                                    | Macaque timescales: [Figure 2E–G](#fig2), [Figure 2—figure supplement 4](#fig2s4)                                                                                                                                                                                                                                   |\n",
    "| 3_viz_human_structural.ipynb                                                                 | Human timescales vs. T1w/T2w and gene expression:   [Figure 2A–D](#fig2), [Figure 2—figure supplements 1](#fig2s1) and [3](#fig2s3), [Figure 3](#fig3), [Figure 3—figure supplements 1](#fig3s1) and [2](#fig3s2), [Supplementary file 1–](#supp1), [Supplementary file 2](#supp2), [Supplementary file 3](#supp3). |\n",
    "| 4b_viz_human_wm.ipynb                                                                        | Human working memory: [Figure 4A–D](#fig4), [Figure 4—figure supplement 1](#fig4s1)                                                                                                                                                                                                                                 |\n",
    "| 4a_viz_human_aging.ipynb                                                                     | Human aging: [Figure 4E and F](#fig4), [Figure 4—figure supplement 2](#fig4s2)                                                                                                                                                                                                                                      |\n",
    "|  supp_spatialautocorr.ipynb                                                                  | Spatial autocorrelation-preserving nulls:                                                                                                                                                                                                                                                                           |\n",
    "| supp_spatialautocorr.ipynb                                                                   | Spatial autocorrelation-preserving nulls: [Figure 2—figure supplement 2](#fig2s2)                                                                                                                                                                                                                                   |\n",
    ":::\n",
    "{#table2}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "figure: Figure 1.\n",
    ":::\n",
    "\n",
    "## Schematic of study and timescale inference technique.\n",
    "\n",
    ":::\n",
    "{#fig1}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "figure: Figure 1A.\n",
    ":::\n",
    "![](static_figs/fig_1A.jpg)\n",
    "\n",
    "(**A**) In this study, we infer neuronal timescales from intracranial field potential recordings, which reflect integrated synaptic and transmembrane current fluctuations over large neural populations (@bib12). Combining multiple open-access datasets ([Table 1](#table1)), we link timescales to known human anatomical hierarchy, dissect its cellular and physiological basis via transcriptomic analysis, and demonstrate its functional modulation during behavior and through aging.\n",
    "\n",
    ":::\n",
    "{#fig1A}"
   ]
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   "source": [
    "# simulate noise\n",
    "T = 240\n",
    "fs = 2000.\n",
    "t_ds = np.arange(0.005,0.08,0.01)\n",
    "f_to_plot=100\n",
    "noise, ac = [], []\n",
    "for t_d in t_ds:\n",
    "    noise.append(sim.sim_synaptic_current(T, fs, tau_d = t_d))\n",
    "    ac.append(acf(noise[-1], nlags=int(fs), fft=True))\n",
    "\n",
    "noise = np.vstack(noise)\n",
    "ac = np.vstack(ac).T\n",
    "f_axis, PSD = spectral.compute_spectrum(noise,fs)\n",
    "\n",
    "# FOOOF PSDs without knee\n",
    "fg = FOOOFGroup(aperiodic_mode='knee', max_n_peaks=0, verbose=False)\n",
    "fg.fit(freqs=f_axis, power_spectra=PSD, freq_range=(2,200))\n",
    "fit_knee = fg.get_params('aperiodic_params', 'knee')\n",
    "fit_exp = fg.get_params('aperiodic_params', 'exponent')\n",
    "knee_freq, taus = convert_knee_val(fit_knee, fit_exp)\n",
    "P_knee = [PSD[i,np.argmin(np.abs((f_axis[:f_to_plot]-(knee_freq[i]))))] for i in range(len(t_ds))]"
   ]
  },
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   "metadata": {
    "caption": "(**B**) Simulated time series...",
    "execution": {
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    "id": "fig1B",
    "label": "Figure 1B"
   },
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\n",
      "text/plain": [
       "<Figure size 864x216 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### simulation data and analysis ###\n",
    "color = plt.cm.inferno(np.linspace(0,1,len(t_ds)))\n",
    "plt.rcParams['axes.prop_cycle'] = plt.cycler('color', color)\n",
    "i_plot = [0,3,7]\n",
    "t = np.arange(0,T,1/fs)\n",
    "labels = ['fast', 'medium', 'slow']\n",
    "plt.figure(figsize=(12,3))\n",
    "for p in range(3):\n",
    "    plt.subplot(3,1,p+1)\n",
    "    plt.plot(t[:int(fs)], noise.T[:int(fs),i_plot[p]], alpha=0.9, color=np.array(plt.cycler('color', plt.cm.inferno(np.linspace(0,0.85,3))))[p]['color'])\n",
    "    plt.xticks([]); plt.yticks([])\n",
    "    plt.xlim([0,1]); plt.ylabel(labels[p])\n",
    "    \n",
    "    \n",
    "plt.xlabel('time (s)', labelpad=-10)\n",
    "plt.xticks([0,1], fontsize=15); \n",
    "plt.subplot(3,1,1)\n",
    "plt.title('simulated data')\n",
    "plt.tight_layout(pad=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "caption": "(**C**) ...and their autocorrelation functions (ACFs), with increasing (longer) decay time constant, _τ_ (which neuronal timescale is defined to be).",
    "id": "fig1C",
    "label": "Figure 1C"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4,4))\n",
    "plt.rcParams['axes.prop_cycle'] = plt.cycler('color', plt.cm.inferno(np.linspace(0,0.85,3)))\n",
    "plt.plot(t[:500]*1000, ac[:500,i_plot]/ac[0,i_plot])\n",
    "plt.xlim([-5,150]);\n",
    "plt.yticks([0,np.exp(-1), 1], ['0','e','1'])\n",
    "plt.xlabel('lag time (ms)'); plt.ylabel('autocorrelation')\n",
    "plt.legend(['%i ms'%np.round(tt) for tt in t_ds[i_plot]*1000], frameon=False, loc='upper right', title='decay time constant');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "caption": "(**D**) Example human electrocorticography (ECoG) time series (top), autocorrelation function (bottom left), and power spectral density (PSD, bottom right) showing the aperiodic component fit (red dashed), and the ‘knee frequency’ at which power drops off (${f}_{k}$, red circle).",
    "id": "fig1D",
    "label": "Figure 1D"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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9pKSkFPrznj17SEhIyHfrpk6dOpx00kn07duX9u3b07NnT04//XSCgoIA53ZQUlISW7ZsybtNVpTmzZvToEGDSn2tJalbty7BwcF06dKFX375JS8pTUxMzBtvy1Q+v0xWROQa4HXgceBX4A7gOxE5WVU3FbLLcKB2gXV3A+cCk13HDAbaAg8ACzzKlXt4Smu3Yszx7cCmTay6+GIUwPXN3/1zRmQkCWefjaqiqnT88kskN5cGqhw5coSMjAwyMzPJzs5mVfPmJMbHA9Bo9246u8Zn8qxNyAV2qPL2KaeggYHUqVOHf6xZQ0xaGgEBAYgIitMeRlXZERfHqrZtycnJIeTgQU5dsQLNzSVX1XkE5zE3l/nNm7MjMJCMjAzab99Oq337yMrO5kh2NllZWWRlZ5OVnU1Sdjbv5OTk/UO+CRBXbLlADhBYqxbh4eGk1K9P7caNGTJkCKc2akS3wECatmpFXOPGBNSuDUFBzhISQlp6Olu3bmX37t2kJCeTW4r3Pioq6piuy74gInm3gubMmcOGDRto27YtCQkJNGvWzKdJZU3id++yODdwHwcmedSI/ACsBUYDdxbcR1V/L3CM7jgJzI2qusa1uj0QBMzwWFchlqwYc3w7tGUL7YsYS2klcMV77+U9z+DYb0xu3yxdyn9dP1+D842pKBctX84R188XAr2LKPc+MD4wkICAADqJMPbIkSJKwme7drEmOpqQkBBG7trFiJ07Cy2XFBtL/Ztvpnbt2kRHR3PL6NEEZmfnL5SdDfv3w2OPwZ2uy/H778PIkUWef8GUKeTUdt6d3g88QPS6deQEB5Ndpw5ZYWHOEh5OcpcubBs2jBYtWtC2SRMC58yBRo2cJTYWfNS+54QTTuDkk08GnHYrbdu25ciRI2zYsIF27dr5JKaaxu+SFeAEnFs6X7lXqGqWiHwNDCrlMV7GqZF5z2NdZ5zrSYJ3wjyarNjAcMYcn2LbtGHXgAEggkDeP0sB6sXEsObKK53nIqS+9hqHcnPztuftAzzQpw/3dOtGZmYmhxcvZs2335KRkXHM+USEt4YMQQMDERECFi7kr717EZFjlvYtWzKtd28CAwOJycoiadYsgoODCa5dm9pBQdSqVctpvKvK5zfeCM1dd8pnzIDFi52aogJLfHw8T9x339GA1q2DrCzIzYWcHOfR/fOJJx4t16gRnHsuHDlCdno6GampZKenIzk5BGRlkeO6BQQQeOQIAdnZBGRnE3T4MKEeI8KGNm/OiQMGOLeM/vjDOWbexlBo0wbatoV27eDmm6GKeghFRkbSoUMHWrVqxYoVK7jkkksAZ66gFi1aULt2UWmq8RZ/TFbauh7XF1i/EWgtIoGqWmRLNhEZCvQCemv+FludgRRgsoicjVObOwUYraqpRRxrvuvHLoVtt5oVY45vIY0a0WDixCK3x3s+eeGFIstFez5p2xZGjiQ7O5vdu3fntXPJdtVg1PUomjxkCCWNl5qTk0NyQADJQ4bkWx8YGEhERATh4eFEZGURnpREWFgYYRdcQMDQoSUc1eXll0tXbuBA9p5yCuvWrStxhNefnnuOgOxsAjMyqJWeTkRODs0iI6kfHExU06bOrSNwEsMBA2DHDti5E/btgz//dBaAq68+etD//he2bIHevaF/f4iMLF3cZdCuXTu6devG9OnTSU9PJzQ0lJycHBITE612pQr4Y7Li/isrmECk4gxiFwYUN9jBaODnQhrjdsa5tvwBvISTgDwOtAT6lydQS1aMMeVVq1YtGjVqRKNGjcjNzSUlJSWvW3RhtS5llZOTw/79+9m/f3++9SJCaGgo4eHhhIeHExYWlvdzSEhImc+TnJxMQkICKSkppdtBhOCICOq3bk2DBg1o0KBB4d23O3eGH344+vzAAaemZ906WLsWPBu3fvghuGZGJigIzjgDzjsPhg4F1zxuFVWnTh3OOeccpk6dmjfRIjjX/7Zt2x7XXdD9gfjbfBYicgXwMRCvqrs81o8CJgERqnqoiH3bAWuAi1V1aoFtXYHaqvqLx7pLgc+AM1X1p2Jimt+nT58+8+fPz7d+//79REdHU6dOHQ4dOmR/rMYUrlp+MIr63Fc2VeXAgQPs3bs3r5tzampqXs1LZQoMDMxLXoprOOr+v5GamnpMMlQYESE6Opr69evToEEDIr1d8/Hdd7BoEfz4IyxZ4tyqcnvkEXj8ca+cZv/+/TRo0IBzzjmHUaNG5a0/7bTTqF+/vlfOcRzx6ufeH2tWDrgeIwDPfm3hOA3S04rZdyhwCJhVcEPBRrgu37oeTwaKTFaKEhUVRXh4OIcOHWLfvn3ExMSU9RDGGJOPiFC3bl3q1q2bb31GRka+5KUykpicnBwOHjzotRmlQ0NDOeGEE2jUqFHljpVyzjnO8vjjkJIC338Ps2bB9OnQq9fRcomJEBzstLEph7p169K1a9e8offdNm/ebMlKJfPHZMXdALYV+duttALWavFVQYOA2aqarw5VRGoBVwF/FEha3BNNlGvOb/fAcGvWrGHbtm2WrBhjKk1ISAghISHExcXlW18VSUxZhYWF0aZNGxo3blz1A6fFxsLllztLairUqXN02z33wNdfww03wAMPlKuB7qBBg3jsscfYs2cP9eo5Q3Tt2rWLjIyMct1GM6Xjj8PvJQBbgWHuFSISBJwHzC1qJ1eX5+7ALwW3qWo28BgwrsCmi3DGWSl0sLnSaNy4MQA7i+gKaIwxlcmdwLRq1YqTTz6Z008/nXPPPZeBAwfSs2dPOnToQPPmzfOmCKlMERERdOvWjX79+tG0aVPfj/AaEQHu2a+zsyEgADIz4dVXnYbOTz7pPC+D4cOHA+SrXVFVa7tYyfyuZkVVVUTGA6+IyD5gEXA7UA94AUBEWgNxnu1PcLo7R+CMx1KYJ4E3ROQlYCbQA3gUeFlVN5c33kau6sQdO3aU9xDGGON1RdXEZGdnk5aWxqFDh/Ie3T+XtzYmKiqKtm3bFt1Y1h/UqgVTpsDKlc4YMVOnwsMPwwcfOMnLgAGlOkznzp3zht4f4LFPYmIirVu39n2Cdpzyu2QFQFUnikgo8C+c3j0rgHNUdaOryCPAteRvwOO+Ybi/iGNOEpEjOCPb3ggkAf8Bxlck1oYNGwKWrBhjqodatWoRFRVFVFTUMdsyMjJIS0vj8OHD5LrHjCkm+RARIiMjCz2W3+rY0Ula5s6F22+HNWtg0CBYvx5KMTmhiDBw4EBmzZpFTk4Oga6am4yMDLZv357XS9R4V6mTFRGJx6nZGIzTffiYv2BVDfRWYKr6HPBcEdtGAiMLrFtaWEwFyrxH/oHiKsxqVowxxwt3bUxsbKyvQ6l8/fs7A889+6xze6gMsygPGTKETz/9lISEBE70GBxvw4YNlqxUkrLUrLwG9APeArZBqaZ3OO5ZsmKM/xCRAJxBIfsBLYAonMEgtwDfq+oy30Vn/E5wMIwZk3/dwoWQnu70LirCOeecQ0BAAMuWLcuXrKSmprJ7927rGVQJypKsnA3crKofVlYw1ZE7WbEGtsb4jojUBm7GuXXcgqMJymHgJKAR8ISIbAUm4Mw9VraWleb4t307DB/uDED3yivOkP6FiI6Opnfv3qxYsYKrrroq3zbrxlw5ytIS6CCUOPJzjWM1K8b4loicCfwFXA78F2ihqnGqeoqqnqGqHVU1Bmcy0+eB64HVItLXVzEbP9WwoZOg5OTALbfAgw/mzbZd0Hnnnce6deuOGbk3JSWF4kfYMOVRlpqVt4G7RWSuqmZVVkDVjbuB7c6dO8nNzbWW4MZUvWeAG1R1YXGFXLOtrwFeEpEBrv1OrYL4THUREOB0Z27TBkaNgvHj4cgRmDDBqXX58EOYPBkOHOBftWuzH9j000/EDhuWd4isrCxSU1O9P0pvDVeWZCUUp7vvDhH5A6d61ZOqailnxzp+hISEEBMTw969e9mzZ49V/xlTxVS1Zzn2mQPMqYRwzPFg5EiIjoaLL4bnn4etW50uz5mZEBYGMTGEZGVxc2AgfPYZ29q1Y99JJ+XtvnfvXktWvKws1QDdcLoQrwQCccY08Vxq7G/GbgUZ4x9E5BoRKfQbg4i0EpF3qjomU00NHQpffulMjDhlitPoNjraaZQrggQHI9HRpKen0/nllwnZc3Qg9L179/ow8ONTqZMVVe1X0lKZgfozS1aM8RvvAb+JyD8K2RaHMz6TMaVz/vnOsP0NGjjD+BdQNzqaNFWy09Jo4jHhZalnoDalVuYGFiJynog8IyKvi8iTInKu+O2QhVXDkhVj/Mp24EcRGe3rQMxx4I8/wPP2vkfj2YiICAIDA9mXlUWjn47OhZuRkcHhwwVbSpiKKHWyIiJhIjIPZ6j664E+wK3A18BCEQmrnBD9n3VfNsav3AE8BDwjIp+LSLivAzLV2IEDzq0gcCZGXLkSXIlIgGuG7AOHD1MrLS3fbnYryLvKUrMyHugEDFLVWFU9SVWjgXOBE3Hm3qmRbMh9Y/yLawTsc3EGh/tVRE7CmbTUmLKJioIs15/O3r1O76ANG5yJEYHounUJVOVggRsMlqx4V1mSlYuBh1T1e8+VqvodMMa1vUay20DG+B9Xj59TgUxgKc7M7caUzaWXgrvWpFkzpzeQO2FRJTIqiggRvomIyLebJSveVZZkJRQoanbizUBMxcOpnixZMcY/qeomnOH3vwYe83E4pjq6+mqoXdu59SMCrVs7t4UOHYKdOwlITyc4PJwJSUnk5OTk7ZaamlruWazNscqSrKyg6Jb01+F0aa6RLFkxxm9cB2zwXKGq6ap6GXAfUOzAccYco0kTeOst5+d9+5wGtu5JD3fuhLQ0Nj70EGsOHeL333/Pt+v+/furNNTjWVmSlUeAS0TkZxG5S0Qudz0uAkZQg7+1xMfHA5BUILM2xlQtVX1fVQvtN6qqE2ryEAumAnr3hu+/d0a1DQx02qu4rvukp9PzppuIiopi7ty5+Xbbt2+fD4I9PpV6BFtVXSgiFwBPAM8BAihOjcswVZ1VKRFWA8HBwcTFxZGcnMzu3bvzGtwaYyqfiHQrS3lV/a2yYjHHsSZN4IEHnAWc+YOuuw6uvZbg6GiGDRvGxx9/zKFDhwgPdzqgWc2K95RluH1UdTYw29VNuS6wX1XTit+rZmjUqBHJycns3LnTkhVjqtYynC9OJXF/wQqs3HBMjRAYCB98kPd05MiRvP/++/z000+ce+65gNWseFOxyYqIXAj8qKr7XT8XVibvZ1X90rvhVR+NGjXijz/+YMeOHXTrVqYvesaYiil4a6cW8ANwM7C26sMxNdEZOTn0atyYuXPn5iUrmZmZpKenExoa6uPoqr+SalamAj1xuv1NLaFsjf7GYo1sjfENVV3g+VxE3NehZXbLx1SJ998ncORIJjVrRqd160hMTKSFqxFuSkoKTZo08W18x4GSGti2xGmT4v65uKVV5YRYPViyYowxNdTZZ0NUFB23bOGqwEC++eabvE1//vknR44c8WFwx4dikxVV3ayq7nf5WiDLtS7f4tp+T6VG6udsFFtjjKmhGjaECRMA+G9AAH/++COHDh0CICcnh0WLFllP0QoqNlkRkRjXEguMBdp7rMtbgLOBUVURsL+y+YGMMaYGu/56cvv0oW5WFuOOHMnXjfnQoUNs2LChmJ1NSUq6DfQxkAzsdj3/zvW84PI6MK+SYqwWrGbFGL9Tmh5CxniHCAGvv44GBTEKSJo2LV9typYtW1C1P8nyKqmB7Q3AAJwuf+/gjLFSMD3MAfYDc6nB3MmK1awYU7VE5KuCq1yPL4rIgQLbVFWHVkFYpiY68UTknntg/Hju3ruXH/73P3r37g1Aeno6e/bsIS4uzsdBVk/FJiuquh14H0BEFPhaVfdURWDVTYMGDQDYtWsXOTk5BAbW2I5RxlS1iELWLShmmzGV5+GHyc3IYPRnn5Hx5Zf06tUrb4iPLVu2WLJSTmUZwfZ9EaklIu2B2hz99iJAHaCXqj5TCTFWC56j2CYnJ+cNwW+MqXQv4IwHdcjXgRhDWBgBL7zA5Q0a8OCDD/L333/TsWNHwJmSJSMjg5CQEB8HWf2Uem4gETkDZ3blv3BGjPzVtSwF5gMPV0J81Yq1WzHGJx4D9ojIfBF5qKzD7xtTGe644w5iIyPZ+O67eetyc3NZvXq1D6OqvsoykeF4nLYpFwHTgC+B84FXcRqynevt4Koba7diTNVT1a5Ac+BtoD3OlCC7ReQTEblWRGz+C1PlwgID+SsoiIkJCexZtChv/bZt22wY/nIoS7LSBRinqtOBr4AWqjpbVe/E6Q001puBicgoEUkQkXQRWSIivUooP0tEtJAl3KPMGSLyPxE57Dr2P70ZsyUrxviGqu5S1Q9V9SpVbQAMwqkFvg5IFJE/ReRZERno20hNjRESQvTgwQQBLSdNyrfJalfKrizJCsAu1+NaoIOIuPf/EjjZW0GJyDU4CdBHODU5+4HvRKRlMbt1Bl4CehVYDruOeRLwLbAJuBCYCbwtIiO8FbeNtWKMf1DV31T1KVXtC8Ti3KauA0z0aWCmRgl5+mkyg4Lou28fGT/8kLc+JSWFzMxMH0ZW/ZQlWfkb6OP6eQ1OI1v3veFowCsthsRpNv04MElVH1PVb4AhwB5gdBH71AWaAt+q6i8FllxXsQeAROByVf1WVe/GSYYe9UbcYG1WjPFHqnpIVb9S1dtUtY2v4zE1SMOG5N7jDO7e/p13wGOcleTkZF9FVS2VJVl5CRgnIi+q6gGcWoqPRGQc8Byw2EsxnYBz/zlv7ARVzQK+xqnaLUxn1+OfxRx3ADBL84/KMx3oJCKNyh2tB7sNZEzlEJEgEblHRF4SkX4Ftv3HV3EZU5LQhx/mQJ06dEhLI2D69Lz1lqyUTamTFVX9GLgUcP8n/iewA7gPp5fQbV6Kqa3rcX2B9RuB1h4zqnrqDGQCT4hIiqtNyhQRiQcQkTCgURHH9DxnhViyYkyleQPnVvNW4AMRecBj23m+CcmYUggLI/O++wDo+OmniGtU2+TkZBvRtgxKPc4KgKpO9fh5F3CW1yOCSNdjaoH1qTjJVRhwsMC2zji3pVKB4TgzQD8B/CgiXUs4puc58xGR+a4fu5QmcEtWjKk03VW1M4CIfAzMEZGDqjqRo2M+GeOXYu+/n1UTJ/Lc7t103riRVm3akJmZSWpqKpGRhf77MQUUm6yUdbwCVf2tYuE4p3Ufroj1uRzreeBTVXXPT7RQRFYDvwCXcHQqgLIcs8w8k5Xc3FwCAsraftkYU4QAEQlR1QxV3Skig4GfRCQJmwPI+LnAkBC2f/QRk4cPZ+XkyTz8sDMs2e7duy1ZKaWSalaWUboLgbjKeWOMefdcHhEc7X0EEI6TVKQV3EFV1+A0+vVc9z8R2Y9TdTzN45ie3N2aC84f4j5GX8irYelTWBlPISEhREdHs2/fPlJSUmxYZWO85x3gfyJyu6r+pKqbRWQIMBsbUt9UA61atWLo0KF88sknbFi7ltbt2rF161ZOOOEEX4dWLZSUrPQrYXtlSHA9tiJ/G5NWwFot5CafiFwG7FDVhR7rBOfW0B5VPSQiO13H8OR+vs5bwTds2JB9+/axc+dOS1aM8RJVfV5Efgb2eaxbISKdgbt8FpgxpRQfH8/QQYPoMmUKIx5+mD8//JBDON2YY2NjfR2e3yv2PoWqLihsARbhjLWyqMB6b0jAaUQ3zL1CRIJwGtEVNbPzLcBLHuO+AAwGQgF3AjMXuKBAA91hwEpX+xuvsHYrxlQOVV2qqgkF1iUDK0XEJlsxfi0sLIwmrVpxYWQkTTIzCf/wQwA2b97s48iqhzI1qhCRniIyBzgEbAM6i8hHIvKEtwJy1ZyMB24WkSdd96ZnAPVwJixDRFqLSE+P3f4P53bPRyIyUERuAz4EvlBVd5fqCUA7YIqInCsizwFX4Yzp4jU2MJwxVe5jIElE3ihppGtjfKlFy5Zsu/56ADrOnk1gRgY7d+4kPT3dx5H5v7JMZHgWR6ddH8PRxqkrgQdE5G5vBeVq4f9v4GpgKlAXOEdV3V2NHwGWeJT/DmfguBNwxk4Zg3OP+2qPMn8AF+Dc+pnm+vk6VZ3irbjBBoYzxgdOxhnx+hzgZxFZ55rQsKmP4zImn/j4eDL69WN9bCwx2dlEfPABubm5rFmzpuSda7iy1Kw8DUxW1QE4A8QJgKqOB54EbvRmYKr6nKo2U9U6qtpbVT2Tk5GqKgXKz1LVU1U1TFUbqeq9qppeoMx3qtpFVUNUta2qvufNmMFuAxlT1VT1L1V9QFVb4LSzmwfcDWwSkTkicqWIhPo0SGOAgIAAmrdowbYbbgCg47ffEpiRwbZt2zhwoNB+HsalLMlKR5zh6eHYHkLzcEadrfEsWTHGd1R1oareBDTEmVdsDzAJ5zbR2yJyuk8DNDVe69atyezbl/WxsURnZxPxyScAJCQklLBnzVaWZGU3zvTrhTnJtb3Gs2TFGN9T1SxVnaGqlwENgJuA0zl6K9sYnwgKCqJtu3Zs++c/ATjy008AJCUlkZGR4cvQ/FpZkpX3gf+IyHWAu09uLREZAIwDPvFybNWSu4GttVkxxrfE0R9n0MgXgWbAFz4NyhigefPmZA0YwEP9+zNw7162bt2KqpKYmOjr0PxWWZKVx3Aau74NbHetWwx8h9M92GuzF1dnnjUrNu+DMVVPRM4QkVdx5jH7AeiE84Wqoape4svYjIGjbVc6XHcdwcHBTJ3qzGSzZcsWcnO9MqD6cafUcwOpag5wnYiMB/oCsTgjv/7s6mljcPrSR0REkJqayv79+4mOjvZ1SMYc91xDGVwKXIzTXmUr8BbwfsGxWYzxB82aNWPdunUMGjSIzV99RUCXLmT262ejnxeh1MmKiPwE/J+qzsYZEM4UoWHDhqSmprJz505LVoypZCKSCDQFDuPc5nnfY54wY/xSSEgI8fHx3HziiQyaMYMdb7zBb2eeya5duyxZKURZewNZ659SsHYrxlSp9cBIoIFrWANLVEy10LBhQ7J79mRHaCiNDh8m4rvvSEpK8nVYfqksycq7wGMi0tXGLCie9QgypkotBKaq6uHS7iAiYSIyrvJCMqZk9evXh1q1WH/hhQA0/+QT0g8f5uDBgz6OzP+UJVk5A+iJMxPzIRE5WGCxEW1cLFkxpkpFAOtE5AERaVlcQRFpKSKP48xBZrM1G58KCgoiJiaG1AsvZE9wMC0OHiRi0SK2b99e8s41TKnbrACzXIspgSUrxlQdVf23iHyOM6fYkyLyJ/AbTiPbw0AU0AToBbTGGWtlhMe8Ycb4THx8PCkpKawZNIjTv/qK+PfeY8OZZ9K8eXPq1Knj6/D8RlmSlX3AbGtZXzKbzNCYqqWqvwL9RaQrcBnOsPsX4CQqKTiJy1TgS1Vd5rNAjSmgUaNGrF69mtQrriD16685cfdudqxYwer4eE455RRfh+c3ypKsjMNpyGbJSglsMkNjfENVfwd+93UcxpRWSEgIrVq1Yv369fzerx9/z5nDttWr6XXyyWRkZBASEuLrEP1CWdqsrAUszSsFuw1kjDGmtNq0aUPt2rXZf8cdvNi2Le/MnUt2drZ94fVQlmRlPvCwiPwpIp+JyMsFlpcqKcZqx5IVY4wxpVWrVi2aNWuGiHDhhReSlJTEkiVL2LZtm69D8xtlSVYuA3bgtKA/Ded+cMHFAJGRkYSGhpKWlkZqaqqvwzHGGOPnmjRpAsBpp53GzXXrctnLL5O+aRN79+71cWT+odTJiqq2LGFpVZmBViciYgPDGWOMKbXw8HDq1q1LYGAgN9WtS9eMDMLfe48VK1aQk5Pj6/B8riw1KwCISEcRudU1psENInJSZQRW3dmtIGOqnohcKCI2Vrmplpo3bw7AvhtuAKDjTz+RsWcPq1at8mVYfqHUyYqI1BKRD4A/gFdwZlmeBKwUkU9FJLCSYqyWLFkxxifeAM70dRDGlEfTpk2Jjo7mUOfOrIuNJSonh4ipU0lMTGTXrl2+Ds+nylKz8igwArgFiFLVOkA0cCtOe5WHvR9e9WXJijE+sQuo5+sgjCkPEaFLly4EBASw44orAGg9axbk5LBmzRofR+dbZRlnZSTwqKpOcq9Q1QPAGyISgZO0PObd8Kova7NijE98DLwkIoOB1cDuAttVVV+o+rCMKZ3w8HA6dOjAX1lZ7HjrLRqlp5Pw448cHDiwRo+7UpZkJQZYUcS2P4CGFY7mOGI1K8b4xJOux6J6KCpgyYrxay1atGDjxo1suOACGn3+OVFTp3Jw4ECSk5Np2rSpr8PzibLcBloFDC1i21BgQ8XDOX5YsmJM1VPVgBIWa1tnqoWGDRuSOmIE/9e8OeempZGZmUlycrKvw/KZstSsPAV8ISLROHNs7AIaABfjjMFyvffDq74sWTHGt0SkOU6N71+AqOohH4dkTKnVq1eP9SEh6KhRJD38MAsWLCAyMhJVRUR8HV6VK8s4K9OAm4GBwJfAz67HgcCdqvpeZQRYXdn8QMb4hohcJCIJwCac61Q74GMR+UhEgnwbnTGlExsbS0BAAJ06daJly5bMmT6dnD17OHSoZubcZRpnxdW4Nh7oiNM9sCPQQFVfrYTYqrWYmBiCg4M5ePAghw8f9nU4xtQIInIJ8DmwALiEo9e4acBwnF6Nxvi9gIAAYmJiEBHGnngii7dto+6rr7Jv3z5fh+YTZUpWROQ+YJqqrlLVRUAcsEVEbq2U6KoxEbFbQcZUvUeBl1T1BpwEBQBXze/DwJU+isuYMouOjgagab9+RAMdf/mF/Vu3+jYoHynLoHAPAv/B6Q7oth74BJggIrd4ObZqr7omKzt37uTBBx+kU6dO1K1bl5YtW3LVVVexcOFCX4dmTElOAL4pYtvvWK9FU43ExMQAkHbiiayJiyMiN5fDr73m46h8oyw1K6OA+1T1QfcKVd2uqvfjfGO5y8uxVXvVMVn59NNPadeuHePHj2flypUcOHCAxMREPv74Y/r06cNll11W40dSNH5tC3B6EdtOBWrm11JTLblrVgB2uQaJa/bll6QdPOirkHymLMlKA5zuy4X5E2hW8XCOEpFRIpIgIukiskREepVQvreIzBOR/SKyQ0Q+EJEGBcqsFBEtsOzxZtyeqtvAcK+++ipXXHEFqampnHfeecyfP589e/awcuVKHnnkEUJDQ5k8eTI9evSwuSqMv3oFeEhEHgO644yr0lhEbgDG4EwRYky1EBQURHh4OAAH+/ZlZ2gojdLT+fOpp8jOzvZxdFWrrOOsXFrEtouBtRUPxyEi1wCvAx8BFwH7ge9EpGUR5U8C5gKpwOXAvcA/XPsEucoEA22BB4BeHss53oq7oOpUszJz5kzuvPNOAF544QVmzpxJnz59iI2NpUOHDjz++OOsWrWKXr16sXXrVv7xj3+wfPlyH0dtTH6q+jLOwHD/BhYDAswAXgUmqeoEH4ZnTJm5bwURGMj6QYMAiHrvPbZt2+bDqKpeWcdZmSoizYBZOMNYxwHnAwNwWt5XmDgdyB/HubA85lr3A04yNBq4s5Ddbgd2AhepapZrnwRgKU7X6m+A9kAQMENVq2SSBXfNyvbt26vidOW2fv16Lr/8cnJzc3nssce46667Ci3XokUL5s6dy+WXX86MGTMYPHgwS5YsoVWrVlUbsDHFUNXHRORFoCcQCxwA/qeqlVaLakxladmyJVu2bAHg0CWXkDZjBhnJyaRv2UKLFi18G1wVKss4K1/i1KDUA17EaVj7Ek5X5ktU9QsvxXQC0Bz4yuPcWcDXwKAi9vkbeM6dqLi4a3rctTGdgQwgwUtxlqhJkyYAfp0Bqyo33XQTaWlpXHLJJTzyyCPFlg8NDeXzzz9n4MCB7N69m0GDBnHgwIEqitaY4onIIBGpraoHVPU7Vf1EVb+2RMVUV5GRkbRp0waA7LAwXr/9dk7JyWHWvHk+jqxqlXWclS9UtRtQB2gCRKrqyV5MVMC5VQNOTyNPG4HWInLMcNmqOrGQsV7c84K4a1E6AynAZBE5KCIHROQt1ySMhRKR+SIyH+hSxtcAkDeHgz8nK++//z4//vgj9erV49VXXy3VyIjBwcF88cUXdO7cmYSEBG644QZUtQqiNaZEXwN7ReRrEbm1qFvHxlQnzZodbRJ6wlln0bBhQyZPnuzDiKpemZIVABE5D+c2zaPAAyJyrnh37N9I12NqgfWpOPGGlSLGpsAEYBnwo2t1Z5xaoD+A83B6MF0ETK9wxEXwrFnxx3/maWlp3H///YDTTqVevXql3jciIoIvvviCiIgIpk6dyquv2riAxi80wOm5uBt4CNggImtE5HkRGWAj2JrqKDQ0lIAA5991QEAA559/Prp6NSufe87HkVWdsoyzEiYi84CZOPMA9QFuw/kms1BESkwiSnsq12PB/+7u9bklxNkUp7FtAHCZHs0S7gdOV9X/qOpPqvpfnOkDzhKRMwo7lqr2VdW+FD3bdLHCw8OpW7cuGRkZpKSklOcQleq1115j9+7dnHrqqVx5ZdnHyjrhhBN45513ALjvvvtYt26dt0M0pkxUdY/r1s91qtoE6ITTWL8X8B1O7aox1YqI5PUKAhjRogWrgUYPPwxZWUXveBwpS83KeJwP/iBVjVXVk1Q1GjgXOJGjU7NXlLsBRMHbM+E4iUpaUTuKSEecHgCRwEBVzZsJWlV/V9VfCuzyrevx5ApFXAx37cpWPxt1MC0tjWeeeQaAcePGlXtirBEjRnDNNdeQnp7OddddR05OjjfDNKZcRKShiFwM3ASMxBljJROnttWYaici4ui/xMyOHdkSHk5MRgb73nrLh1FVnbIkKxcDD6nq954rVfU7nPELLvZSTO4GsAW7mLQC1moR91NE5DRgIZADnKGqf3psqyUiI0Wka4HdQl2Pldb4zl/brUyaNInk5GROO+00Bg0qqt1y6bz44os0atSIxYsX8+KLL3onQGPKQUTeFZH1wDbgTZw2cJNx5jKLUtWzfBmfMeXlWbOCCIlDhgCQ+n//56OIqlZZkpVQYHMR2zYDMRUPB3CSla3AMPcK133m83Bu7xxDRFoAs4FdQG9VzdfjR1WzgceAcQV2vQjIApZ4JfJC+GPNSm5ubl4bkwcffLDC041HR0fz5ptvAjBmzBi7HWR86VqcLzYrcMZbul1Vn1LVRQV6CxpTrURGRuZ7fmjYMA4GBtJs2zaOLF7so6iqTlmSlRU4F4LCXAesrHA0gKvmZDxws4g8KSKDcQZ1qge8ACAirUWkp8duL+Hc+nkcaCYiPT0W91wgTwJDROQlV0O7B3Ea4b6sqkUlYRXmj92Xv//+ezZs2EDz5s05//zzvXLMwYMHc+2115KZmcltt93mlw2KTY0QgzO78kKcNnVrRGS7iHzm6h3U0bfhGVM+9evXJyQkJO95TkgIK087DYAt//63r8KqMmVJVh4BLhGRn0XkLhG53PW4CBiBU3PhFao6EWcEyquBqUBd4BxV3egRyxLIq3UZDATijP2ypMBypeuYk3CSqn44jYRvxJmY8T5vxV0Y920gf6pZmThxIgA333wzgYHH9AQvtwkTJhATE8OcOXNqXLc64x9Udb+qzlDV0araFedLzs1ACM5Q/H/4NMAaxL6weFdAQADdunXLVxN+eORIcoFmS5bAcT5nW1kGhVuIM3ZJKPAc8LHrMQQYpqqzvBmYqj6nqs1UtY6q9lbVJR7bRqqquH7OUtUgVZUilgke+72nqp1VNVRVW6rq/6lqsb2LKsrfala2bNnCrFmzCA4O5vrrr/fqsevVq5fXaHf06NE2WJzxGRFpKSLX4yQorwNDcKYMed6ngVVTffv2LVMt7JtvvlniAJOm7GJjY/ONuZIeH8+yE07gfVWW/1Kw/8jxpayDws1W1VNwbrk0xRkU7hRVnSUijSolwmrO32pWPvnkE1SVYcOGERcX5/XjX3fddfTu3ZukpCQefvhhrx/fmOKIyDsisglnUMnncL5cjQOaqWpHVT3+68v9wJNPPsn+/ft9HcZxqW3btnljrgBs+s9/uKtOHZ768EMfRlX5yjLOSo6I9ABQ1TRV3a6qaa5tZwDWqrIQ/jQwnKryoesP+uqrr66UcwQEBPD6668TGBjIq6++yrJl1lPUVKmuwGc4t3vrqepFqvqmqvpH1aYxFRQSEpLvi2adsDD69+/PjBkz2Ly50ppf+lyxyYqI/EdEXhaRl3EGZbvX/dxzwWkQW+T4JzWZe2C4zMxM9uzx7fQkK1asYNWqVcTGxnLOOZU22TSdOnVi9OjRqCo333yzjb1iqoyqdlXVB4GfgLauRvZtfB1XdXLo0CFGjRpFTEwM9erV4+mnn863fefOnfzzn/+kUaNGBAUF0ahRI+666y4yMzMBZ8LTzZs3HzN9x8cff0yPHj2oU6cOderUoXfv3ixcuLBKX9vxwj1JrtsFgwZxUXY2awcO9Pn/mcpS0qzLm3HGUAFnRNkzcAZW8pQD7Adu8Wpkx5EmTZqwf/9+tm3bVim3Xkrro48+AuCyyy4jKKhyRx0fO3YskydPZvny5bz22mvcfvvtlXo+Y9xE5J/A/+HMCi+AishuYJyqvlHZ5585c2Zln6LMLrjggpILuVx22WX88ssvTJgwgdjYWMaOHcuqVas4++yzyc3NZdCgQYgIr776KlFRUXz33Xc888wztG7dmjvuuINp06YxePBgTj/9dO655x4Apk6dytVXX82jjz7Ks88+y65du3jssce49NJL2bx5M8HBwZX10o9LDRo0ICAggNxcp8ll43r1eDMwkIiEBBZ/9hn1jsPrbbE1K6r6lqshaktgCzDY/dxjOUFVu7tmZTaF8Id2K6rK559/DlCuofXLKjw8nJdffhlwxl7ZsWNHpZ/TGBG5HHgLZ06w4TjD7F8IzAcmishlvovO//355598/fXXvPrqq/zzn/9k6NChzJ49O6+NxPbt24mOjuaDDz5g+PDhnHXWWTz99NN07tyZBQsWANC1a1dq165NgwYN6NnTGWFi/fr13HbbbYwbN46+ffty6aWX8uSTT5KUlGTjMpVDUFBQvi++OaGhrOzeHYBab7zBvn37fBVapSmpZiWPK2Ex5eAPPYKWL1/Otm3baNy4Mae5+uZXtqFDh3LBBRcwc+ZM7rzzTqZOnVol560MBw4cYPHixSxatIg//viDHTt2sHPnTrKzswkODiY8PJyWLVvSqlUrunXrxumnn07btm0rPOCeKbMHgddV9dYC62eIyF6coQo+q/qwqodFixYBcO655+ata9iwIb169QKcL17z588nNzeXhIQE1q1bxx9//MGuXbvy9VIp6IEHHgBg//79rFmzhrVr1/LVV18B5N0+MmXTsGFDdnl0V04bOZLc//2Pk//+m8RVq4j+xz98GJ33lTpZEZEfSypjQ1kXzh9qVqZNmwY4CYRnS/LK5K4qnjdvHl988QUzZsxg6NChVXJubzhy5AizZs3iww8/5OuvvyarhAnD1q5dm+95gwYNuOCCCxg+fDgDBgywqu6q0Qa4u4ht03HGWjJF2LdvH0FBQceMlhofH09qaioAb7/9NmPGjGHXrl00bNiQ0047jdDQ0GI7ECQlJXH99dcze/ZsgoOD6dChAy1atABsPJbyio+Pz3crKL1xY1Y2aULnbdvYN2ECHGfJSln+ax3EmWTQc8kBOgMdgeVej+444Q81K9OnTwdg+PDhVXrepk2b8uSTzhyXt912GwcPHqzS85fHkSNHeOONN2jTpg0XXXQR06dPJycnh169evHvf/+bKVOm8L///Y8tW7aQlJTEli1b+Ouvv5gxYwbPPvssI0aMID4+nl27dvHWW29x3nnn0bRpU+6//34SEhJKDsBUxGacCVcL0xmbdblYsbGxZGVlHdPt2D1r/IIFCxg1ahQ33XQTu3fvZseOHUybNq3EtnhXXHEFCQkJLFmyhEOHDrF8+XKvj/NU0wQFBdGuXbt86/ZecQUALb/9lnRXcnncUNUKLUAdnDl7xlT0WP66APP79Omj5fX9998roBU5RkWsXbtWAY2KitIjR45U+fmzs7O1R48eCugdd9xR5ecvi+nTp2vz5s0Vp0G5nnjiiTphwgTdsWNHmY6Tm5urf/zxh44dO1bbt2+fdzxABw4cqHPmzNHc3NxKehV+pyo/qw8AqcANQF3XurrAKNf6p8pwrAp97qujNWvWKKBvv/123rq9e/dqRESEnnfeeTp+/HgNCAjIdx3Zvn27hoSE6ODBg/PWtWrVSm+77ba85yEhIfrII4/kO9cdd9yhgC5durQSX9HxLzk5Wb/66itnmT5dt9epowqa+MILvg7Nq5/tUt8GKibZOSwizwLv4My/Ywrw9czLM2bMAOD888+v9F5AhQkMDOTNN9/klFNO4ZVXXuHyyy/PuwfuL7Zt28att96a15Ojffv2PProo4wYMaJcUxKICJ07d6Zz586MHTuWX375hUmTJjF58mR++OEHfvjhB3r06MEDDzzAsGHDquzWnDdkZ2eze/dukpKSSElJITs7m5ycHFSViIgIoqOjiYmJoX79+tSuXbuqw5sAnAxMAt4QkWyc290CfAE8WtUBVSft2rXjqquu4q677iIjI4NmzZrx1FNP5d0C7dGjB7m5udx1111cfPHFbNmyhSeffJLMzEwOHz6cd5y6deuyfPlyFi5cyBlnnEGPHj1477336Ny5M9HR0UybNo3XXnsNIN9+puzq1atH69at2bBhAwQEsHLECJ764APC1q5lvK+D8yZvZDw4c/gc8nYm5S8LFfyGlZqaqoDWrl3bJ9+me/XqpYBOmTKlys/t6f7771dA27Rpo4cOHfJpLJ7mzJmj9erVU0AjIiL0pZde0qysrEo51969e/WJJ57IOx+g7dq103feeUczMzMr5ZwVsXv3bp0yZYo+9NBDev7552uzZs1URPLVFBW3NGzYUNU3n9mOOInJIziNbjuW4xg1rmZFVTUzM1NHjx6t9erV08jISB09erRefPHFet5556mq6vPPP6/NmjXTkJAQPeGEE/Shhx7SsWPHalRUlGZkZKiq6tSpU7VevXoaGhqqW7du1fXr1+vZZ5+t4eHhWq9ePT3rrLN04cKFGhYWpk899ZQvX+5xYefOnUdrV776Sjt16qT169fXAwcO+DIsr36mRbV0jZtEpLBGawFAI5xGa/NU9cKypUrVg4jM79OnT5/58+eX+xjR0dHs37+fXbt2Ub9+fe8FV4KdO3fSuHFjgoOD2bNnD+Hh4VV27oIyMjLo0aMHK1eu5NZbb+XVV1/1WSzgJOpPP/00Y8aMITc3l7PPPpt33333mAGXKsPhw4d55513ePbZZ9myZQvgtG265557GDVqFGFhYZUeQ2FycnJYtGgRM2fOZM6cOaxYseKYMiJCXFwc8fHxxMbGEhwcTGBgICJCamoqe/fuZe/evezatctd41KlXaJccwI9BLTwWL0GeFhVp5XhOBX+3BtTFbKysvjuu+/cSTaLFy9m/PjxPPfss9x9772+Csu7n/vSZjVAbhHLfuBTIN7bmZS/LHjhG1aXLl0U0P/9738VOk5Zvf322wrkfSvytd9//12DgoIU0NmzZ/ssjgMHDujw4cPzagAeeeQRzc7OrvI4jhw5oh988EG+di2xsbH62GOPaUpKSpXEkJ2drfPnz9fbbrtN4+Pj89WM1K5dW/v3768PPfSQTp48WVevXl3qWqesrCzdvHmzatV+Vm9zXZc+x5lx/WzgKpxbQNnAhWU4Vo2sWTHV0x9//JFXszJt6lR9ITRUtwQHa/quXb4Kybufba8dyJKVYg0bNkwBnTx5coWOU1aXX365Avryyy9X6XmL89RTTymgDRo0KHPDVW9YuXKltm3bNq/R8VdffVXlMRSUk5Oj06dP19NOOy0vUQgLC9PbbrtNf//990o536JFi/SOO+44JkFp0aKF3nvvvTpnzhw9fPiwN05XlZ/VjcCEIra9BPxZhmNZsmKqjSNHjujMmTPzEpbV9eurgi649FJf3Xb37me71AWdmZafBZYCfwB/upa/gA1AlreD85fFGxet0aNHK6Djx4+v0HHKIicnR+Pi4hTQ1atXV9l5S5Kdna19+/ZVQP/xj39UaVuNzz77TMPCwhTQTp06aUJCQpWduzRyc3N13rx5evbZZ+dLILp3766vvPKKbt26tdzHzszM1AULFug999yjTZs2zXf8li1b6n333ae//vprZbSrqsrPahpwdhHbBgJpZTiWJSumWlmyZElesjL/tttUQTeHh+vvv/3mi3C8+tkuS2+gl4ErgG+A9sBhYC1wOtAAKDhipPHgHgApMTGxys75559/kpycTJMmTY7pj+9LgYGBfPbZZ5xyyiksWrSIe+65h//+97+Ves6srCzuv/9+XnjhBcAZ92HSpEk+axtSFBGhb9++9O3blz///JO33nqLDz/8kGXLlrFs2TJuv/12unXrRp8+fTj11FM5+eSTadas2TGvIycnh127dvHXX3/xxx9/sGDBAhYsWEBa2tH5Rps2bcoll1zCpZdeSvfu3Y+X0XZ/wGnw/30h24YAC6o2HGOqTkxMDMnJyQCk9u9P8ptv0uzQIbZ9/TV07erj6CqmLMnKYJyxVJ4VkdHAAFW9VETCcObh6FwpER4nWrZ0ZivYtGlTlZ3zhx9+AGDgwIF+94+oQYMGfPHFF5x55pm88sordOrUiRtvvLFSzrVr1y4uvfRSFixYQK1atXj++ee5/fbb/e49Kahz5868/PLLPP3000ybNo2pU6fy3Xff8dtvv/Hbb7/lKxsdHU1oaChBQUGkp6ezZ8+evJEtPbVv356zzz6biy++mJ49e1arLtOl9A0wXkQW4QyrnwTEAucDg4BnPDoLqKq+4JswjfG+mJiYvJ+1Vi3W9ulD3A8/UOedd0gbPdrvvpyVRVmSlSjgf66fV+IMvoSqponIc3B8den2Nl/UrHgmK/7otNNO49VXX2XUqFHccsstxMXFeX2E3cWLF3PxxRezY8cOGjZsyJQpU/hHNRuGOjQ0lCuuuIIrrriC9PR0fvrpJ3755Rf+97//sXbtWrZu3cq+ffuOmbwsNjaW9u3bc/LJJ9OjRw8GDBhQJT2dfOx112Mv11LQAx4/K2DJijluxMbGEhkZmTdSeNqVV5L1ww902rSJnX/+SZifjW9VFmVJVnYC8a6f1wH1RKShqu4Ekj22mUJ4Jiu5ubmV/o02IyODn376CYD+/ftX6rkq4oYbbmDbtm089thjXHbZZUyZMoUhQ4ZU+Liqyosvvsj9999PVlYWZ5xxBp9//jnx8dX7zzQ0NJSzzz6bs88+O29dbm4uKSkpZGZmkpWVRUhICPXq1fPJAIC+pqrHXVWRMaUlIvTo0YO5c+cCcCQmhj/btOGUhAS2PP88TaZM8XGE5VeWD/YMnOrVgaq6GdgEPCoiLYBbcObkMEWIiIggNjaWzMzMfDNlVpaff/6ZjIwMTj755Cod16U8xo4dy5133smRI0e46KKL+Oijjyp0vJSUFIYOHcrdd99NVlYWd911F3Pnzq32iUpRAgICiIuLo0mTJrRs2ZKGDRvWyETFGAN16tShd+/eec9Tbr6ZQaGh3L9zJzk5OT6MrGLKkqw8DKwC7nE9vxtnMLgNwHDgMe+GdvypynYr/n4LyJOI5NWCZGdnc/XVV3PvvfeWOMtxQarKp59+SocOHZg5cyZ169Zl2rRpvPDCC/bP2xhTY8TExOTN8p7Zpg2h55zDkl9+4a+//vJxZOVX6mRFVVNV9XxgqOv5VzhDWl8OtFfVzyonxONHVbZbmTNnDlA9khVwEpannnqKV155hVq1avHcc8/RrVs3fv7551Lt/+uvv3L22WdzxRVXsGvXLs444wxWrFjBsGHDKjdwY0ylUC3d6OrmWCJCgwYN8p6fe+655Obm8tH46tu0tMz3d1U10+Pn9ar6uaravPelUFU1K3v27OH333+ndu3anHHGGZV6Lm8SEW677TbmzZtH69atWblyJWeccQZ9+vTh/fffJykpKe8Cpqps3LiRN998k759+3LqqacyZ84cYmJieOutt5g/fz7Nmzf38SsypvrZsmULvXv3JiQkhC5dupRqn5EjR9KxY8e85yLChAkTiiy/f/9+Bg8eTGhoKPHx8UyaNAkRYc+ePYBzG3vEiBEVeh01nWfPoIYNG/JN3bo8M3kyG774wodRlV+FZ102pVdVNStz585FVTn99NMJDQ2t1HNVhtNPP52//vqL8ePH89JLL7Fw4UIWLlwIQHh4OOHh4Rw8eDDfbK3h4eHceuut3HvvvcTFxfkqdGOqvZdeeokVK1YwefJkmjRpUinn+PDDD5k9ezbvvfcebdq0oU2bNnTu3Jm6desC8NZbb7F27dpKOXdNERUVlf95mzYELF3Knv/8h2ZDhlS7W+OWrFQhd83Kxo0bK/U81am9SlFCQ0N57LHHuOeee/joo4+YOXMmP//8M4cOHeLQoUMAxMXFceqpp3LRRRcxfPjwvAudMab89u7dS8uWLRk6dGilnqNOnTpce+21eevsS4Z3RURE5Hueds01sHQpnf78kwObN1PvhBN8FFn5WLJShU5w/XEkJFTeXTNVPS6SFbfIyEhuvfVWbr31VlSVffv2kZ6eTlhYGFFRUX4/sJsx1UmLFi3YvNnp2CkivPvuuyQmJjJhwoS8LwkAK1asoGvXrsybN4++ffuW6Rx9+/ZlwYIFeecYO3YsLVq04LrrriM5OZl7772X999/P297ec5hnF6CYWFheaNWpzdrxsqGDem4cyebnnmGepMm+TjCsrExCapQixYtqFWrFlu3biU9Pb1SzpGQkMCWLVuoV69eqe83VxciQkxMDI0bN6Zu3bqWqBjjZdOmTWPw4MG0atWKJUuWcN5553n9HBMnTuT6668nNDSUJUuWcMMNN+Tb/sgjj+SLoVu3bl6PoaaIjY3N9zzl0ksBiP7kEyhkhGt/5rc1KyIyCrgPaAKsAO5W1SXFlO+IM6vqacBe4FXgGfVoUi4iZwATgE7AduApVX2nsl5DQUFBQbRs2ZKEhATWr19Pp06dvH4Od61K//79j8eh1I2pFu666y5WrFjh0xi6dOnCiy++WKZ9unbtSlxcHJs3b6Znz56VElf79u1p0qQJAQEBhZ6jdevWlR5DTdG6dWu2bNmS9zy1Tx92vfYajdLS2PHhhzTyuA3n7/zyv5mIXIMzbPZHwEXAfuA7EWlZRPn6wByc4bMvASYBT3J0TBhE5CTgW5zB7C4EZgJvi0iVNjlv27YtUHm3gtxdlgcMGFApxzfGGFM9hIeH069fv7znGhhIQr9+ZAKL3367zGNZ+ZLf1ayIU7f/ODBJVR9zrfsBZ4bn0cCdhex2G85rGaKqh4FvRKQ28KCIvKSqWThzgiQCl7tqW74VkTjgUWBqJb+sPG3atAFg3bp1Xj92dnY2P/74I3B8tFcxproqa42GMZUlPDycxo0bs337dgAOXn01Z65Zw87ERDpu2MCJJ57o4whLxx9rVk4AmgNfuVe4ko2vcWZNLcwAYK4rUXGbDsQAPTzKzNL8Iw1NBzqJSJXN7uauWamMZOXXX3/l4MGDtGnTxsYYMcZ4hYgcM4O3Z2Nb4/86d+6c134lJyKCnkOGsHXrVmbMmEFGRoaPoysdf0xW2roe1xdYvxFoLSKBRexTWHmAtiISBjQqrkxhgYjIfBGZD3QpOezSqczbQMdTLyBjjH+IjIwkPT2d/fv3561zT5JaWQIDC7vMm/KqVasWXbt2zeuUcMYZZxAVFsbe995j2aJF1WK0YH9MViJdj6kF1qfixBtWxD6FlXdvK+6YnucsUmJiIiKStyxfvpzly5fnWzdu3DgAGjVqlLfulFNOAeDGG29ERPLakqxevZqZM2fm23+SqyuZ57oLLrgAgAsuuCDfeiBv1Ef38tlnzowHEydOzFt34403AnDKKafkrWvUyKlIGjdunFdek3vZsWOH11/TzJkz2bFjR7519pqq12sy1du5554LwPXXX8+8efN44YUXmDhxYqWes27dumzbto0ffviBffv2Veq5aorQ0NC8a0rt2rWZEx7O02vWkPP55yxfvtzH0ZWCqvrVAlyB01C2QYH1o1zrwwvZ5whwf4F1tVzlb8epVVHg0gJl2rjWn19CTPP79Omj3pCTk6MhISEK6P79+71yTFXVgwcPaq1atTQwMNCrxzXGC3x+XSnP4s3PfXVy7bXXaocOHfKte+utt7RFixZau3ZtPfPMM/WXX35RQOfNm1foPoA+++yzRZ5j7NixGhYWlvf83XffVUCTk5NVVXXt2rXatm1bDQ4O1s8++8yLr65mS0tL09mzZ+tXX32lC664QhV0dVycfvXVV7p8+XLNzc315um8+nkU9bPqHxE5D5gFtFHV9R7rR+N0O66lBYIWkWTgDVV92GNdNE4X5muAaTi1KKNU9S2PMl2B34AzVbXIek0Rmd+nT58+8+fP98IrhE6dOrFy5Up+/fVXunfv7pVjzpw5kyFDhtCrVy8WL17slWMa4yXVckAcb3/ujfEHe/fuZdGiRQSmp3Pm5ZcTnpvL3Oef5/AJJ3DSSSflDV7qBV793PvjbSB3Y45WBda3AtYWTFQ89imsPK59DgE7iynj/dauxaiMRrbWXsUYY0xJYmJiqF+/PjmhofzlGnAv/IMPAFi/fv0xjan9hb8mK1uBYe4VIhIEnAfMLWKfucAAV0Nat2FACs6Acu4yFxRooDsMWKmqu7wQd6mddNJJAPz9999eO6YlK8YYY0qjadOmABy+5hoAOv35J7UOHyYrK4vU1IJNO/2D3yUrrpqT8cDNIvKkiAwGZgD1gBcARKS1iHgObTgRCMYZX+V8ERkDPAiMV9UjrjITgHbAFBE5V0SeA67CGdOlSrlHrv3rr7+8crytW7eyZs0aIiIiOO2007xyTGOMMcenhg0bEhcXx+EWLVjVoAF1cnMJ//JLAFJSUnwcXeH8LlkBUNWJwL+Bq3EGbKsLnKOq7q7GjwBLPMrvxBlHpZar/I3AGFWd4FHmD+ACnFs/01w/X6eqUyr79RTk7WTl+++/B+Css86qdtN+G2OMqVoiwmmnnUZ0dDTJF1/MRmCZq1nC2rVr/bIrs9+NYOumqs8BzxWxbSQwssC6ZcA/Sjjmd8B33omw/Nq0aUNwcDCJiYmkpqYeM5V3WX33nfOSzj77bG+EZ4wx5jgnIrRr145f+vfnhj//ZOny5bybkUFISAjbt2+nSZMmvg4xH7+sWTneBQUF5bVbWblyZYWOlZOTkzcf0DnnnFPh2IwxxtQMMTExBIWEcM7gwaSlpbFw4UIAkpKSfBzZsSxZ8RFv3QpatmwZ+/bto1WrVrRu3doboRljjKkBAgMDOfHEEznppJPo0aQJrT76iOCUFJKTk/2uV5AlKz7irWTF3V7FbgEZY4wpq2bNmhEUFMTrwcGM3r+fsE8+ITs7m61bt/o6tHwsWfERbyUr7vYqdgvIGGNMWQUEBBAfH8+Byy8HoN3ChUhODomJib4NrABLVnzEM1kpb8vrAwcO8MsvvxAYGEi/fv28GZ4xxpgy8sdeNKXRunVrDp16KlvDwojLzCRi/nwOHjzoV7NrW7LiI40bNyYmJoa9e/eyZcuWch3jxx9/JCcnh169ehEVFeXlCI0xpuq1aNGC22+/vULHmD9/PiLCsmXLABg5ciQdO3as0DHvvvtu6tatS2RkJIsWLUJEmDp1KgDbtm1j0KBBfjtGSUkiIyOpGx1N4qBBANT7/HMA5s2bx5EjR4rbtcpYsuIj7n7uAL/88ku5jjFr1iwABrn+wIwxprqbNm0a9957r1eP+cgjj/DJJ5+Ue/+//vqLF154geuuu45Zs2bRsWNHlixZwllnnQXAnDlz8m7JV1eNGjXi0EUXkS5Ch507CXW1Wfntt998HJnDkhUf6tnTGYR3yZIlJZQ8Vk5ODjNnzgRg6NChXo3LGGN8pWvXrrRo0cKrx2zdujWdO3cu9/579+4F4IorruDMM88kKiqKnj17EhMT460Qfa5x48Zkh4fzl6sGKsw1X1BycnK5a/+9yZIVH+rVqxdQvpqVpUuXkpycTMuWLenQoYO3QzPG1FAHDx7kX//6F82bNyc4OJi4uDiuvfZa9u/fn1dGRHjjjTcYOnQoderUoWXLlrzyyiv5jpOdnc2jjz5Ks2bNCAkJoXv37syde3R6N/etmjfeeIOGDRvStGlTEhMTj7kNlJiYyCWXXEKDBg2IiIhg6NChJCQk5DvXN998Q9euXQkNDaV3795s2rQp3/aCt4HS09P597//TZMmTQgPD6d379789NNPhb4f48aNo2/fvgCceuqp9O3bl8TExLzbQO+99x7XXXcdAHFxcYwbN67U77U/CQkJIS4ujkPXXMPXtWrxgcfv+48//mDjxo1F71wFLFnxoVNPPRUR4bfffiMjI6NM+3711VcADBkyBBGvzsRtjKnBrrjiCmbMmMH48eP5/vvvuffee/nkk0/4z3/+k6/c/fffT3h4OF9++SXDhw/njjvu4M0338zbPmrUKJ577jn+9a9/MX36dE488UTOPfdcFi9enO84//nPf5g0aRL/93//d0yNyrZt2zj11FNJSEhg4sSJvPvuu2zatInTTz+dHTt2AM6XvSFDhtCuXTumTZvGgAEDuOWWW4p9jZdddhmTJk3ivvvuY/r06TRo0IBzzz2X9evXH1P2hhtu4NVXXwXg3XffZeLEifm2n3feeTz88MMAfPvtt9xwww3FntufdejQgbR27fjgwgt5Y906du06Osfv33//zeHDh30XnKraUsICzO/Tp49Whg4dOiigixcvLtN+7du3V0Dnzp1bKXEZ40U+/wyXZ6nw5x6KXt5442i5N94ovqynbt2KLjdq1NFyy5aVK+T09HQdOHCgzp49O9/6IUOG6CmnnOLx0tDTTjstX5kRI0ZoixYtVFV19erVCuibb76Zr8xZZ52l/fr1U1XVefPmKaDPPvtsvjLNmzfX2267TVVV7777bg0PD9fk5OS87cnJyRoREaF33323qqoOHz5cTzrpJM3Nzc0rc8cddyigv/76q6qqXnvttdqhQwdVVV2xYoUC+sEHH+SVz8zM1Pbt2+t7771X6PvijtV9vE2bNimgU6ZMUVXVd999V4F8cVZXWVlZ+vbbb2tAQICOGDFCv/rqq7xl48aNZTmUVz+PVrPiY+5bQWVpt7JmzRpWrVpFVFQUZ5xxRmWFZoypYUJCQvj+++8ZNGgQiYmJfP/99zz//POsWrWKzMzMfGUvu+yyfM+HDh1KYmIi27ZtY/78+QAMHjyY7OzsvGXw4MH8/PPP+XqYtG/fvsh4Fi5cSL9+/ahXr17eunr16tG/f38WLFgAwKJFixg0aFC+GuaLLrqoyGO6a3YuuOCCvHXBwcH8/fffXHvttUXuV1PUqlWLgQMHckO7dlw1fTphq1fnbfPlrSBLVnzMnawsWrSo1Pt8+umngPOBtFmWjfFTxdWX3Hjj0XI33lh8WU/LlxddbtKko+VOOaXcYX/11Ve0bt2ali1bcuWVV/LDDz9Qp04dd21TnoYNG+Z7HhcXBziNUd1deBs3bkxQUFDecu+995KVlcWePXvy9qtfv36Rsezbt48GDRocs75BgwYcPHgwr4xnMgMQHx9f5DH37t1LUFAQdevWLbJMTde4cWOurVuXodnZRLga2gIcPnzYZyPb+u2syzVFnz59AKc/e05ODoGBgcWWV9W8LnhXXHFFpcdnjKk5EhISuPjii7n22mtZsGBB3sy7l1xyCatWrcpXtuCYIrt37wacpCUqKgoRYdGiRYV+oapXrx7r1q0rMZ6YmJh87SbckpKSiI2NBSA2Njbv3EXF5ikqKoqsrCwOHDiQb3yqJUuWEB0dzYknnlhiXMe7gIAATn7tNejShU6rVpGSmkpWRATg1OzHx8dX+Rdlq1nxsdatW9OqVSv27dvH8uXLSyy/bNky1q9fT3x8fF4LdWOM8YbffvuNI0eO8MADD+QlKmlpafz888/H1Ky4x3lyczeibdiwIaeffjqqSmpqKt27d89b5syZwwsvvECtWqX7nnz66aczb968fDUxe/bsYe7cufzjH/8AoF+/fsycOZPs7Oy8Mt98802Rx+zdu/cx8R85coRLLrmEDzxqEcqipC+Z1VHYySeT0KoVIaqETZmStz4jI4Nvv/22yuOxZMUPuOf1mT17dollP/74YwAuvfTS4/IDYozxna5duxIYGMj999/Pjz/+yOeff06/fv1ISko6pifIt99+y+23387333/Pv/71L6ZNm5bXY6hLly5cdNFFXHXVVUycOJF58+YxduxYxowZQ7NmzQgIKN2/ntGjRxMUFMTAgQP58ssv+eKLLxg4cCDBwcHcddddAIwZM4YdO3YwbNgwZs+ezdNPP31MN2pP3bp14/zzz+eOO+7gtdde44cffuDyyy8nLS2Nm266qVzvm/uW0pdfful3c+pURANXN+xW338PBWZh9kwgq4IlK37g/PPPB5w/9OKkpaXx/vvvA3DVVVdVelzGmJqlbdu2fPDBB/z5558MHjyY++67j+7duzNx4kS2bNmS110Y4L777mP9+vUMHTqUuXPnMnnyZEaMGJG3/eOPP+a6667jqaeeYtCgQXz66aeMHz+e//u//yt1PE2bNuWnn36iUaNGXHPNNVx//fU0b96cJUuW5NX8dOjQgR9++IE9e/Zw4YUX8umnn+Z1NS7K5MmTufrqq3nssccYPnw4e/fuZe7cuTRv3ryM75ijf//+nHPOOdxxxx1MmDChXMfwR5GXX05KnTo0PnyYiKVL821bu3ZtlcYiBav2zLFEZH6fPn36uFu4e1tmZib169fn4MGDrF27lrZt2xZa7rXXXuPWW2+lV69ex4xVYIwfq5YDAVX25746ExGeffZZrw+Lb/zP1ptuoumkSfzesiXbXnop3zb3aL5F8Orn3mpW/EDt2rXzhswv6p5pbm4uL7n+UNzVn8YYY0xlajJ2LK/FxXHrkSP52i3Vrl2b9PT0KovDkhU/4R718O233yYrK+uY7d988w1r166ladOmXHjhhVUdnjHGmBpIGjUi8Ikn+GX7djZs2EB0dDSdO3dmwIABxXYR9zZLVvzEGWecQfv27UlKSjqmdiU7O5sHHngAcBqclbYlvTHGVAZVtVtANciVV15JZGQky3/9ldN79aJ58+albiTtLZas+AkRyZtfYty4cRw4cCBv21NPPcXff/9NixYtuPXWW30VojHGmBooLCyMV3r2ZOynn3LA1cmjqlmy4kcuvfRSunfvzrZt27jqqqtITU3l7bffzpvF880336R27dq+DdIYY0yNM7B7d9oA+8rQm8ubLFnxIwEBAXz88cdERUUxa9YsIiMjueGGG8jNzeWJJ55gwIABvg7RGGNMDRR/332kBwQQs3EjOQVGDK4Klqz4mbZt2/Lzzz/Ts2dPwJl/480332TMmDE+jswYY0yNFRXFL48+ynknn0xSIZ1AKpu11PRDHTt2ZMmSJWRkZFC7du18s4kaY4wxvtD30Uf5aexYn5zbkhU/FhIS4usQjDHGGACffnH2y9tAItJRROaKyCER2SIi90sJ75KIxIjIRBHZLCIHRWSJiPQvUOZeEdFClvMr9xUZY4wxprz8rmZFROoDc4CVwCVAN+BJIAcodNIFVyIzFWgLPAzsAK4DvheR01V1iatoZ+An4L4Ch1jj5ZdhjDHGGC/xu2QFuA0nriGqehj4RkRqAw+KyEuqWljLnu5AP2CAqs4FEJE5QAdgNOCZrHyrqr9U9oswxhhjjHf4422gAcBcV6LiNh2IAXoUsU8u8BawyL1CVXOB9UBLABGpBZwI/On9kI0xxhhTWfwxWWmLk2R42uix7RiqulxVR6lqhnudiEQCZ3L0Fs+JQG1gkKtdS5arXctpRQUiIvNFZD7QpVyvxBhjjDEVVqW3gUQkCGhdTJFdQCSQWmC9+3lkGU73KhAFPO963tn1GA/cAIQC9wM/isgpqmrtVowxxhg/VNVtVhoDq4vZPhoQQIvYnlvSCVyNbV8BrgLuVNXfXZt+BC4AvnO3exGReTi1OP8Gri94LFXt6yq3bcWKFVF9+/Yt6fTGmAIWLFjwoqre5es4yuGEFStWYJ97Y8rO2597US0qL/ANEUkG3lDVhz3WRQN7gWtU9cNi9g0GPsTpRfSAqj5divNNA5qqavdiyqQCwRxtqGvKp4vrcYUPYzhedHE9rvBhDKW1ojomK37wue/ielzhg+OUdp+SyhW3vahtha0v7bqq4q1zl+c4pd2npHLFbS9qW2nXdwEOqWqTooIrK3/sDZQAtCqwzv18bVE7iUgoMBOnV9Atqvp6ge1nAo1U9bMCu4YCe0qIaTkcrWkx5eNq/2PvoxfYe1klfPq599bvuDzHKe0+JZUrbntR2wpbX9p1VcV+N8Wvdz/3Jn9MVuYCN4lImKqmudYNA1IoPpP8GOgDXK6qnxeyvT/wgIjMV9UkABGJB/4B+GYaSWOMKYK3/gmX5zil3aekcsVtL2pbYev9LSm33035YqoIf+wNNBGn6vUbETlfRMYADwLjVfUIOD19RKSniMS5ng8HhgOfAFtc29yLu2HtGzgNdb8WkSEicjHO4HMpwH+r9BUaY4wxptT8rs0KgIh0B14CTsHpITTRs/2JiPQF5gHXqep7IvIecG0Rh/tbVTu69msPPAP0BgKB74F7VHVL5bwSY4wxxlSUXyYrxhhjjDFu/ngbyBhjjDEmjyUrxhhjjPFrlqyUQERGiUiCiKS7hufv5euY/Jmr8XJqgXUiImNEZIuIHBaRH0TkxAJlaovICyKSJCKpIjJVRBpVbfS+JSKBInK3iKwWkTQRWSUit7sGOrT30UcK+5s2vlHe64vxviq/1quqLUUswDVADjAWGAzMBg4CLX0dmz8uOA2XD+IMBuS5fiyQDtwJDAGWAtuBKI8y7+L0zBoJjMAZb2cFEOjr11WF7984IAMYg9PVfhyQDdxn76PPfieF/k3b4j+/i9J8Lmzxn99Fea9RPn/R/rrgDPufCLzmsS4IZ1LFl30dnz8tOBNE3gdk4ow0fMhjWwROl/H7PdZFu/7Q73Y9b+1KCi/1KNMGZ3qFC339+qroPQxwvSf/KbD+VWC3vY9V/vso8m/aFv/5XZTmc2GL//wuKnKNsttARTsBaA585V6hzpxCXwODfBWUnzoXZyycf3PsmDU9gXDyv4/7gAUcfR/Pcj3O8iiTAPxNzXmvo4APgC8LrF8LxOG8R/Y+Vp3i/qZN1aro9cV4j8+u9ZasFK2t63F9gfUbgdYiEljF8fizX3Fujb3MsZNQut/HDQXWb/TY1hZI0qMjFhdW5rimqvtU9XY9OvGm2wXANsA9x4a9j1WjuL9pU7Uqen0x3uOza70/DrfvLyJdjwUb1qXiJHlhONVbNZ6qbi9mcySQqa7Rhz2kcvQ9juTY99ldpmnFI6yeROQGYADO/V97H6tQCX/Tpgp54fpivMSX13qrWSmauB4LZo/u9blVGEt1JhT+zVQ4+h6WpkyNIiJXAq8DU4FXsPfRmMLY37z/qNRrlCUrRTvgeowosD4c500tWI1lCncAqC0iQQXWh3P0PT7Ase9zwTI1hoiMBj7Eua97pTqt0Ox9NOZYpflcmKpRqdcoS1aKluB6bFVgfStgresfiClZAk7W3LLA+lY4jUfdZeJFJLSYMjWCiPwf8DxOsjLCo0rV3kdjjlWaz4WpGpV6jbJkpWgJwFZgmHuFK2M8D5jro5iqo8U4Y4cMc68QkWigD0ffx7k4E0te4FGmDdCBGvRei8i/cFravwSMVNVsj832PhpzrNJ8LkzVqNRrlDWwLYKqqoiMB14RkX3AIuB2oB7wgk+Dq0ZU9ZCI/Bd4QkRygXU4g54dBN5yldkgIlOAN0UkCtgHPAX8CUz3SeBVTEQaAk8DfwGfAae5Bq51W4bTVdDeR2NcSnN9MVWjsq/1lqwUQ1Unuqqr/gWMxhll7xxV3ejTwKqfh3Da+dyLc29yMXCtqnreo7wOJwl8GqfGbw5wp6rmVHGsvnIOzoBLnYAlhWyPw95HYwpTms+FqRqVdo0Sa3phjDHGGH9mbVaMMcYY49csWTHGGGOMX7NkxRhjjDF+zZIVY4wxxvg1S1aMMcYY49csWTE+IwUGEjHGGGMKY8mKyUdEVETurYLznI4zSZ/7+UjXuetV9rmLiCdARH4Rkb6VeI47ROSdyjq+MSURkWYislhEMkRkha/j8XcFrwsi8p6IrCyibAvXNWxEKY99soisFJHa3ov4+GXJivGVG4B2Hs+/BnoB+30SDdwFJKvq/Eo8x2vA6SJydiWew5ji/AvoAlwKXO/bUKqFu6ik64Kq/oEzMvWj3j728chGsDV+QVWTgWRfnFtEInAuGOdV5nlUNVtEXgCeAb6vzHMZU4QYYJOqzvB1IP6uiq4LTwO/i8h/VTWpEs9T7VnNiimWiJwqIt+IyH4ROSIia0XkpgJlThaRH0UkTUQ2ishVIrJeRMYVccz3gGuBDq5q074FbwOJSKKI3C8ib4rIARHZIyKPiUiUiHwkIodEZLOIjCxw7FNEZK6IHBaRZBH5r4jUKeFl3oAzf8Vij+OU+fwiEiYib4nIThFJF5HfROTCAueaCnQUkYElxGSMV4lIIjASaO/6rI0UkXEiskxEXnB9xhe5ytYSkcdFZIvrltEyEelf4Hgnicj3rs/CehG50PV4r2v7Mbd2RaSu+9we604QkekikuqK4cMC+7wnIlNF5F+uz1y6iMwTkZMKxHOhK87DIrJJRB4SxwWuc3YvUP4hEdklIkV9aT/mulAWIjLfdd5jFncZVV2NM4fOHeU5R01iyYopkog0A+YBh4CLgaE4H6zXRaSzq0wDV5lQ4DKcbwovA02LOfR/gG+AjTi3fn4rotwYnBk6hwOf43zLWQokASOA1cAkV5yISHtgIaDAJcD9ONXdn5fwUi8Hpumxc0+U6fzABOAs4E6cb2OrgCmeF1VXDdJPOO+VMVVpOPk/d1+71p8M9AAuAv7Pte5N4B6cGcCHAWuA2SLSG/Jm050PNACuBJ4F3gDiyxKQ6/rxM9AcuAa42RXb9yIS7FF0AM4XnH8BVwFtgPc8jnMR8AXORKDDca5B43CuAbNxam0vL3D6K4FPCsxu7qmo64I7mcu34FwrPN3qei3u5WLgCPB+gXJfYNeDkqmqLbbkLTj/6O91/XwuziRTQR7bY1xlbnc9fxKnnUldjzIXucqMK+Y87wErPZ6PdO1Tz/U8EScZcM9fFQrkAD967NPatc8w1/NPcS7EtT3KnOEqc2YRcUQC2TiTbXmuL8/5/wbe8NgeDDwHdCpw7OeAjb7+XdtS85ZCPnfjXH/D3T3Wnehad0OBfee6//6Bu4EsoLnH9ssKXD/yfaZd6+q61o10PX/Kdf3wLNPS9Zm8xiPmHKChR5k7XceJdT3/HZhbIN6nga9cP78IbAMCXM+7uPbvVsT7VNR14T3XfsUtIwo5XgjwK85kuKEFtl3g2q95YbHY4ixWs2KKpKqzVXUAECjOrZ4RON9UwJkhGKAvMF9V93vsOh3ng15RS9X1aVbVdCAVp0GaW4rrsa7rsR9OW5Acj287S3CqcvNVYXtoivONaKsXzr8YGCUiX4nIjTgX4HtU9a8Cx90MNBMR+/wZf7Ha4+e+rsdvCtQcfIPTQDwYp6Zgpapu9thvCmX/3PfD+Yzu9zjPVpxaSc/P7GZV3enxfJvrMUxEQnGSj5meB1bV+1V1iOvpB0Bj4EzX86uAVapaVK1ucdeFDTg1UQWXIYWUdXsdpzZohOta4sn9HjYvZv8azxrYmiKJSCBOLcBNOLUEG3BuswC4x0iph3NhyaOqOSKyxwshpBay7nAx5WNxYr2pkG0Ni9gnqpjjlvX8dwI7gKtxvi3lisg04J+qerDAMQKBsCLOYUxVSlPVNI/nsa7H7UWUr4eToOdrEO/63O8q47ljgdNwamkK8mxwWvBzl+t6DMCp7QXYXdRJVPU3cbocXy4iC3FqgV4pJq7irgsZqrqs4EoRaVHYgUTkTpxbXMNVdX0hRdzniCpkm3GxZMUUZwxwI84H7RtVTXM1VvXs8rgDiPPcyVVjEEvVOwDMwOkiXFBRyZO7dqTCFwrXN6axwFgRaYfTruURnOroWzyKRuPcuz5U0XMaUwkO4NyW+AeFJxF7XMtJhWyL9vjZ3dbDswYxvJBzzabw7rulTeTdXwQKXoeaACcAP6lqDk7tyr+BD4FGwMfFHNMr1wVxxmd5DnhGi+6B5X7PUorYbrAGtqZ4vYBlqjrF45vXINeju2ZlIdBXRCI99jsXCCrh2DneCzPPzzj325er6jLXt5+twHigYxH7bMf5ltakIicWkUBxBni6C0BV16rqkzhV3M0KFG8MbHXfYjLGz/yM8/mOcH+OXJ+lAcBonFs983F6tbVx7+T6x+zZ886dRDTyWHdGIec6EfjL4zwrcdrSnF6aYFU1Fadh7fkFNt2Bk5C4a2E+wqmF+T+cW9eF3eJxq/B1wdXw/nOcBvVjiina2PW4pbznqgksWTHF+RXoKSK3i0gfV5fE93C+MbkvSi/jfKi/FpHzReQ64C3XttyCB/SwH2giIgNdPQu84T9Ad+BzETlXRIbjfGs7GacB3jFU9RBOD59eFTmx65vb/3BqVW4Wpzv2AzgX5y8LFO8F/FCR8xlTWVR1BU4PlY9E5FYR6Scij+E0pt+iqrk4tRTrgFkiMkJEruDYXi7zgAzgJdfn/HqcBrWZHmWex7mlNFtEhorIYJxeSmcBy8sQ9uPAABGZ5DrX3Tg9h8Z7tDvbidNh4Ayc2pXi3oMKXRdc7Xqm4XxpewI4RUR6eiyeX+56AWtUdVthxzIOS1ZMccbjXIDGArOAK3C+rfyA60OsqinAQJy/pak4tz1Gu/Yv7jbHG8Au13G9MqKrqi7HucjF4Vxs38b5htRXVYu6/w5OMjFQpMJzFd2JcxEcA3wH/BO4R1XfdhdwjR/RlWMTGGP8yZXAu8CDwLc43XgfAB6CvFue/XFqNN7HuVY87HkAV6P7S3A+j7OA23Dacx3yKLMFpwblME7Nx2c415IBrqSpVFR1qutcPV3nugWnV9J/CxSdDaTjXB9KUpHrQiOgG04iNhfni8wSj6WbR9mzsetBicRqok1FiEgvoI6qzvVY1xZYCwxV1a98FlwpiUgUTov8Eao6p5LPdTdwtap2rczzGOMLrgHP/q2qE3wdS2FE5Btgr6peVYqylX5dEJGuOL0IW6qNYFssq1kxFdUapwr3HhE5U0QuxrlPu45qMqS8qh7AaQR7T2Wex1U1fBvF3782xniZiIwWZ+TsQTi3rktURdeFewAbar8ULFkxFaKqHwH34QxN/R3wKk4DuX6qmuHL2MroWaCeiJxViee4BVikqt9U4jmMMcfqjzOy7f2qurQM+1XadUFEugCn4NxmNyWw20DGGGOM8WtWs2KMMcYYv2bJijHGGGP8miUrxhhjjPFrlqwYY4wxxq9ZsmKMMcYYv/b/rIMO0wvMDIUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# load example macaque data\n",
    "data_load = np.load('./data/fig1D_data.npz')\n",
    "data, fs = data_load['data'], data_load['fs']\n",
    "data_load.close()\n",
    "\n",
    "# compute autocorrelation function\n",
    "max_lag=250\n",
    "t_ac = np.arange(0,max_lag+1)/fs\n",
    "ac = acf(data, nlags=max_lag, fft=True)\n",
    "# compute psd\n",
    "fit_range=[1,80]\n",
    "plt_inds = np.arange(fit_range[0],fit_range[1]+1)\n",
    "faxis, psd = spectral.compute_spectrum(data,fs,avg_type='median')\n",
    "# fit fooof with knee\n",
    "fok = FOOOF(max_n_peaks=2, aperiodic_mode='knee', verbose=False)\n",
    "fok.fit(faxis, psd, fit_range)\n",
    "offset, knee, exp = fok.get_params('aperiodic_params')\n",
    "kfreq, tau = convert_knee_val(knee,exp)\n",
    "\n",
    "# plot time series\n",
    "plt.figure(figsize=(8,2))\n",
    "plt.plot(np.arange(0,fs)/fs, data[int(fs*1):int(fs*2)])\n",
    "plt.xlim([0,1]); \n",
    "plt.xlabel('time (s)');plt.ylabel('voltage (au)');\n",
    "plt.title('example macaque ECoG')\n",
    "plt.tight_layout()\n",
    "\n",
    "plt.figure(figsize=(8,4))\n",
    "# plot acf\n",
    "plt.subplot(1,2,1)\n",
    "plt.plot(t_ac*1000, ac, 'k', lw=2)\n",
    "plt.axhline(0, lw=1, ls='--', color='k')\n",
    "plt.xlim([0,250])\n",
    "plt.xlabel('lag time (ms)'); plt.ylabel('autocorrelation')\n",
    "# plot psd\n",
    "plt.subplot(1,2,2)\n",
    "plt.loglog(faxis[plt_inds], psd[plt_inds], 'k', lw=5, alpha=0.3)\n",
    "plt.loglog(faxis[plt_inds], 10**fok.fooofed_spectrum_, 'k-')\n",
    "plt.loglog(faxis[plt_inds], 10**offset/(knee+faxis**exp)[plt_inds], '--r', lw=2)\n",
    "plt.xlim([1,100])\n",
    "plt.xlabel('frequency (Hz)');plt.ylabel('power');\n",
    "plt.xticks([1, 10, 100], ['1','10', '100']);plt.yticks([]);\n",
    "plt.legend(['data', 'full fit', 'aperiodic fit'], frameon=False)\n",
    "plt.yticks([]); plt.tick_params('y', which='minor', left=False, labelleft=False)\n",
    "plt.xlabel('frequency (Hz)'); plt.ylabel(r'power ($V^2/Hz$)')\n",
    "# plot dot at knee frequency\n",
    "plt.plot(kfreq, 10**offset/(knee+faxis**exp)[np.where(faxis==np.round(kfreq))[0]], 'ro', ms=10, alpha=0.8)\n",
    "plt.tight_layout()\n",
    "# print('knee frequency: %.3fHz, time constant: %.3fms, exponent: %.3f'%(kfreq, 1000*tau, exp))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "caption": "(**E**) Estimation of timescale from PSDs (left) of simulated time series in (**B**), where the knee frequency, ${f}_{k}$, is converted to timescale, _τ_. Right: correlation between ground truth and estimated timescale values.",
    "id": "fig1E",
    "label": "Figure 1E"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,4))\n",
    "plt.rcParams['axes.prop_cycle'] = plt.cycler('color',color)\n",
    "plt.subplot(1,2,1)\n",
    "plt.loglog(f_axis[:f_to_plot], PSD[:,:f_to_plot].T);\n",
    "plt.scatter(knee_freq,P_knee, c=color, s=50, edgecolor='k', zorder=100)\n",
    "plt.yticks([]); plt.tick_params('y', which='minor', left=False, labelleft=False)\n",
    "plt.xlabel('frequency (Hz)'); plt.ylabel(r'power ($V^2/Hz$)')\n",
    "plt.xticks([1, 10, 100], ['1','10','100']); \n",
    "plt.xlim([1,200]);\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.scatter(t_ds*1000,taus*1000, c=color, s=50, edgecolor='k', zorder=100)\n",
    "plt.xlim([0,t_ds.max()*1200]);plt.ylim([0,t_ds.max()*1200])\n",
    "plt.plot(plt.xlim(), plt.xlim(), 'k--', alpha=0.8);\n",
    "plt.xlabel('simulated time constant (ms)'); plt.ylabel('fit time constant (ms)')\n",
    "plt.annotate('r = %.2f'%stats.spearmanr(t_ds,taus)[0]**2, xy=(0.75, 0.25), xycoords='axes fraction')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-13T22:26:04.310421Z",
     "iopub.status.busy": "2021-04-13T22:26:04.310025Z",
     "iopub.status.idle": "2021-04-13T22:26:04.358081Z",
     "shell.execute_reply": "2021-04-13T22:26:04.355568Z",
     "shell.execute_reply.started": "2021-04-13T22:26:04.310391Z"
    }
   },
   "outputs": [],
   "source": [
    "# defining the simulation, analysis, and plotting function\n",
    "def sim_timescale_schematic(tau_sim=0.025, osc_freq=10.5, rel_osc_amp=0.2):\n",
    "    \"\"\"\n",
    "    simulates the neural signal with a given decay timescale and oscillation\n",
    "    \n",
    "    \"\"\"    \n",
    "    # set fit frequency range\n",
    "    fit_range=[1,100]\n",
    "    plt_inds = np.arange(fit_range[0],fit_range[1]+1)\n",
    "    # simulation time and sampling frequency\n",
    "    T, fs = 120, 2000\n",
    "    # PSD parameters\n",
    "    nperseg = int(fs)\n",
    "    noverlap = int(0.5*nperseg)\n",
    "\n",
    "    # simulate signal with given timescale and oscillation\n",
    "    # Define the components of the combined signal to simulate\n",
    "    components = {'sim_synaptic_current' : {'tau_d' : tau_sim},\n",
    "              'sim_bursty_oscillation' : {'freq' : osc_freq}}\n",
    "    component_variances = [1, rel_osc_amp]\n",
    "    x = sim.sim_combined(T, fs, components, component_variances)\n",
    "    t = np.arange(0,T, 1/fs)\n",
    "\n",
    "    # compute autocorrelation function and PSD\n",
    "    autocor = acf(x, nlags=int(fs), fft=True)\n",
    "    f_axis, psd = spectral.compute_spectrum(x, fs, nperseg=nperseg, noverlap=noverlap)\n",
    "    \n",
    "    # fit with spectral parametrization and get fit values\n",
    "    # and compute timescale\n",
    "    ff = FOOOF(max_n_peaks=2, aperiodic_mode='knee', verbose=False)\n",
    "    ff.fit(f_axis, psd, fit_range)\n",
    "    offset, knee, exp = ff.get_params('aperiodic_params')\n",
    "    knee_freq = knee**(1./exp)\n",
    "    tau_fit = 1./(2*np.pi*knee_freq)\n",
    "    knee_power = 10**offset/(knee+f_axis**exp)[np.where(f_axis==np.round(knee_freq))[0]]\n",
    "    fit_spectrum = 10**offset/(knee+f_axis**exp)[plt_inds]\n",
    "\n",
    "    ### plotting ###\n",
    "    fig = plt.figure(figsize=(12,8))\n",
    "    gs = GridSpec(3,2, figure=fig)\n",
    "\n",
    "    # plot time series\n",
    "    ax1 = fig.add_subplot(gs[0,:])\n",
    "    ax1.plot(t[:1000],x[:1000])\n",
    "    ax1.set_xlabel('time (s)'); ax1.set_ylabel('voltage (au)');\n",
    "    ax1.set_xlim([0,t[1000]])\n",
    "    if tau_sim==0.042: print('you found easter egg! nice.')\n",
    "\n",
    "    # plot autocorrelation\n",
    "    ax2 = fig.add_subplot(gs[1:,0])\n",
    "    ax2.plot(t[:1001], autocor[:1001], label='data autocorrelation', lw=2, alpha=0.8)\n",
    "    ax2.axvline(tau_sim, ls='--', lw=2, label='true tau: %.2f ms'%(tau_sim*1000))\n",
    "    ax2.axvline(tau_fit, color='r', lw=4, alpha=0.5, label='fit tau: %.2f ms'%(tau_fit*1000))\n",
    "    ax2.set_xticks([0,tau_sim, 0.1,0.2]); ax2.set_xlim([0,0.2])\n",
    "    ax2.set_ylim([autocor.min(),1])\n",
    "    ax2.set_xlabel('lag time (s)'); ax2.set_ylabel('acf');\n",
    "    ax2.legend()\n",
    "\n",
    "    # plot spectrum\n",
    "    ax3 = fig.add_subplot(gs[1:,1])\n",
    "    ax3.loglog(f_axis[plt_inds], psd[plt_inds], lw=2, label='data PSD')\n",
    "    ax3.loglog(f_axis[plt_inds], 10**ff.fooofed_spectrum_, 'r-', alpha=0.4, lw=5, label='fit')\n",
    "    ax3.plot(knee_freq, knee_power, 'ro', ms=20, mec='k', alpha=0.8, label='knee frequency')\n",
    "    ax3.set_xlim([1,None])\n",
    "    ax3.set_xlabel('frequency (Hz)'); ax3.set_ylabel('power (V^2/Hz)');\n",
    "    ax3.legend()\n",
    "\n",
    "    plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "caption": "**Interactive demo of timescale and fitting in spectral domain.** Simulated time series (top), its autocorrelation function and fit time constant (bottom left), and the power spectral density. Edit the code above to vary the ground truth timescale and oscillation frequency, as well as the relative amplitude between the periodic and aperiodic components.",
    "execution": {
     "iopub.execute_input": "2021-04-13T22:26:04.360465Z",
     "iopub.status.busy": "2021-04-13T22:26:04.360143Z",
     "iopub.status.idle": "2021-04-13T22:26:05.399832Z",
     "shell.execute_reply": "2021-04-13T22:26:05.398458Z",
     "shell.execute_reply.started": "2021-04-13T22:26:04.360428Z"
    },
    "id": "fig1s1",
    "label": "Figure 1-figure supplement 1."
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# compute and plotting function is defined in the cell above\n",
    "# here, try different timescale values for the simulation by changing `tau_sim`\n",
    "# there is stochasticity in the simulation so run it multiple times\n",
    "# try to stay within 0.005 to 0.15 seconds\n",
    "\n",
    "# you can also change the frequency and relative power of the bursty oscillation\n",
    "# to see how the autocorrelation is corrupted by the oscillatory component\n",
    "sim_timescale_schematic(tau_sim = 0.02, osc_freq=22.5, rel_osc_amp=0.1)\n",
    "\n",
    "# also see here for great autocorrelation gif: https://twitter.com/saydnay/status/1355228493089361921"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Results\n",
    "\n",
    "## Neuronal timescale can be inferred from the frequency domain\n",
    "\n",
    "Neural time series often exhibit time-lagged correlation (i.e., autocorrelation), where future values are partially predictable from past values, and predictability decreases with increasing time lags. For demonstration, we simulate the aperiodic (non-rhythmic) component of ECoG recordings by convolving Poisson population spikes with exponentially decaying synaptic kernels with varying decay constant ([Figure 1B](#fig1B)). Empirically, the degree of self-similarity is characterized by the autocorrelation function (ACF), and ‘timescale’ is defined as the time constant (_τ_) of an exponential decay function (${e}^{-\\frac{t}{\\tau }}$) fit to the ACF, i.e., the time it takes for the autocorrelation to decrease by a factor of _e_ ([Figure 1C](#fig1C)).\n",
    "\n",
    "Equivalently, we can estimate timescale in the frequency domain from the power spectral density (PSD). PSDs of neural time series often follow a Lorentzian function of the form $\\frac{1}{{f}_{k}{}^{2}+{f}^{2}}$, where power is approximately constant until the ‘knee frequency’ (_f~k~_, [Figure 1D](#fig1D)), then decays following a power law. This approach is similar to the one presented in @bib14, but here we further allow the power law exponent (fixed at two in the equation above) to be a free parameter representing variable scale-free activity (@bib45; @bib65; @bib75; @bib89). We also simultaneously parameterize oscillatory components as Gaussians peaks, allowing us to remove their effect on the power spectrum, providing more accurate estimates of the knee frequency. From the knee frequency of the aperiodic component, neural timescale (decay constant) can then be computed exactly as $\\tau =\\frac{1}{2\\pi {f}_{k}}$.\n",
    "\n",
    "Compared to fitting exponential decay functions in the time domain (e.g., @bib68)—which can be biased even without the presence of additional components (@bib104)—the frequency domain approach is advantageous when a variable power law exponent and strong oscillatory components are present, as is often the case for neural signals (example of real data in [Figure 1D](#fig1D)). While the oscillatory component can corrupt naive measurement of _τ_ as time for the ACF to reach 1/e ([Figure 1D](#fig1D), inset), it can be more easily accounted for and removed in the frequency domain as Gaussian-like peaks. This is especially important considering neural oscillations with non-stationary frequencies. For example, a broad peak in the power spectrum (e.g., ~10 Hz in bandwidth in [Figure 1D](#fig1D)) represents drifts in the oscillation frequency over time, which is easily accounted for with a single Gaussian, but requires multiple cosine terms to capture well in the autocorrelation. Therefore, in this study, we apply spectral parameterization to extract timescales from intracranial recordings (@bib20). We validate this approach on PSDs computed from simulated neural time series and show that the extracted timescales closely match their ground-truth values ([Figure 1E](#fig1E)).\n",
    "\n",
    "## Timescales follow anatomical hierarchy and are ~10 times faster than spiking timescales\n",
    "\n",
    "Applying this technique, we infer a continuous gradient of neuronal timescales across the human cortex by analyzing a large dataset of human intracranial (ECoG) recordings of task-free brain activity (@bib29). The MNI-iEEG dataset contains 1 min of resting state data across 1772 channels from 106 patients (13–62 years old, 48 females) with variable coverages, recorded using either surface strip/grid or stereoEEG electrodes, and cleaned of visible artifacts. [Figure 2A](#fig2A) shows example data traces along the cortical hierarchy with increasing timescales estimated from their PSDs ([Figure 2B](#fig2B); circles denote fitted knee frequency). Timescales from individual channels were extracted and projected from MNI coordinates onto the left hemisphere of HCP-MMP1.0 surface parcellation (@bib38) for each patient using a Gaussian-weighted mask centered on each electrode. While coverage is sparse and idiosyncratic in individual patients, it does not vary as a function of age, and when pooling across the entire population, 178 of 180 parcels have at least one patient with an electrode within 4 mm ([Figure 2—figure supplement 1A–F](#fig2s1))."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-13T22:26:06.390728Z",
     "iopub.status.busy": "2021-04-13T22:26:06.390353Z",
     "iopub.status.idle": "2021-04-13T22:26:09.807075Z",
     "shell.execute_reply": "2021-04-13T22:26:09.806450Z",
     "shell.execute_reply.started": "2021-04-13T22:26:06.390694Z"
    }
   },
   "outputs": [],
   "source": [
    "# load data\n",
    "# timescale data\n",
    "df_tau = pd.read_csv('./data/df_tau.csv', index_col=0)\n",
    "df_tau.columns=['timescale (ms)', 'log10 timescale (ms)']\n",
    "# timescale SAP-surrogates\n",
    "msr_nulls = pd.read_csv('./data/df_tau_shuffles.csv', index_col=0).values.T\n",
    "\n",
    "# structural data\n",
    "df_struct = pd.read_csv('./data/df_structural_avg.csv', index_col=0)\n",
    "df_struct.columns = df_struct.columns.str.upper()\n",
    "\n",
    "# load macroparcel data\n",
    "df_macro = pd.read_csv('./data/df_human_features_macro.csv')\n",
    "df_macro.columns = df_macro.columns.str.upper()\n",
    "df_macro.columns = ['index', 'timescale (ms)'] + list(df_macro.columns[2:])\n",
    "df_macro['timescale (ms)'] = df_macro['timescale (ms)']*1000\n",
    "\n",
    "## load example ECoG data\n",
    "data_load = np.load('./data/fig2AB_data.npz')\n",
    "fs, data, psds, f_axis = data_load['fs'], data_load['data'], data_load['psds'], data_load['f_axis']\n",
    "data_load.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "figure: Figure 2.\n",
    ":::\n",
    "\n",
    "## Timescale increases along the anatomical hierarchy in humans and macaques.\n",
    "\n",
    ":::\n",
    "{#fig2}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "caption": "(**A**) Example time series from five electrodes along the human cortical hierarchy (M1: primary motor cortex; SMC: supplementary motor cortex; OFC: orbitofrontal cortex; ACC: anterior cingulate cortex; MTL: medial temporal lobe).",
    "execution": {
     "iopub.execute_input": "2021-04-13T22:26:09.810864Z",
     "iopub.status.busy": "2021-04-13T22:26:09.810644Z",
     "iopub.status.idle": "2021-04-13T22:26:11.469384Z",
     "shell.execute_reply": "2021-04-13T22:26:11.468784Z",
     "shell.execute_reply.started": "2021-04-13T22:26:09.810843Z"
    },
    "id": "fig2A",
    "label": "Figure 2A"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 972x216 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot example time series\n",
    "labels = ['M1', 'SMC', 'OFC', 'ACC', 'MTL']\n",
    "\n",
    "# these are the indices of the channels in the MNI dataset\n",
    "# but only these 5 are included here, you can find the whole .mat file online\n",
    "plt_inds = [125, 220, 1123, 1666, 573] \n",
    "c_ord = [3, 2, 0, 4, 5]\n",
    "\n",
    "plt.figure(figsize=(13.5,3))\n",
    "for i, i_p in enumerate(plt_inds):\n",
    "    plt.plot(stats.zscore(data[:, i])-4*i, color=C_ORD[c_ord[i]], label=labels[i])\n",
    "\n",
    "plt.xticks([0, fs*5], ['0', '5']); plt.yticks([]);\n",
    "plt.xlabel('time (s)');plt.ylabel('voltage (au)');\n",
    "plt.xlim([0, fs*5])\n",
    "plt.legend(loc='lower left', bbox_to_anchor= (1, 0), ncol=1, frameon=False, handletextpad=0.5)\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "caption": "(**B**) ...and their corresponding power spectral densities (PSDs, left) computed over 1 min. Circle and dashed line indicate the knee frequency for each PSD, derived from the aperiodic component fits (right). Data: MNI-iEEG database, N = 106 participants.",
    "id": "fig2B",
    "label": "Figure 2B"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot example PSDs and fit\n",
    "plt.figure(figsize=(8,4))\n",
    "for i, i_p in enumerate(plt_inds):    \n",
    "    fit_range=[1,70]\n",
    "    fok = FOOOF(max_n_peaks=3, aperiodic_mode='knee', verbose=False)\n",
    "    fok.fit(f_axis, psds[:,i], fit_range)\n",
    "    offset, knee, exp = fok.get_params('aperiodic_params')\n",
    "    kfreq, tau = convert_knee_val(knee,exp)\n",
    "    ap_spectrum = (10**offset/(knee+f_axis**exp))\n",
    "    \n",
    "    color = C_ORD[c_ord[i]]\n",
    "\n",
    "    plt.subplot(1,2,1)\n",
    "    plt.loglog(f_axis[1:100], psds[1:100,i]/psds[2,i], lw=2, color=color)\n",
    "    plt.axvline(kfreq, ls='--', color=color, lw=2, alpha=0.3)\n",
    "    plt.plot(kfreq, 23, 'o', color=color, ms=10, label=labels[i])\n",
    "    plt.xticks([1, 10, 100], ['1', '10','100']); plt.yticks([]); plt.xlabel('frequency (Hz)'); plt.ylabel(r'power ($V^2/Hz$)')\n",
    "    plt.xlim([1,100]); plt.ylim([None,30])\n",
    "\n",
    "    \n",
    "    plt.subplot(1,2,2)\n",
    "    plt.loglog(f_axis[2:100], ap_spectrum[2:100]/ap_spectrum[1], '-', color=color, lw=4, alpha=0.8, label=labels[i])    \n",
    "    plt.xticks([1, 10, 100], ['1', '10','100']); plt.yticks([]); plt.xlabel('frequency (Hz)'); plt.ylabel(r'power ($V^2/Hz$)')\n",
    "    plt.xlim([2,100]);\n",
    "    plt.legend(loc='lower left', bbox_to_anchor= (0, 0.01), ncol=1, frameon=False, handletextpad=1)\n",
    "    \n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "figure: Figure 2C.\n",
    ":::\n",
    "![](static_figs/fig_2C.jpg)\n",
    "\n",
    "(**C**) Human cortical timescale gradient (left) falls predominantly along the rostrocaudal axis, similar to T1w/T2w ratio (right; z-scored, in units of standard deviation). Colored dots show electrode locations of example data.\n",
    ":::\n",
    "{#fig2}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "caption": "(**D**) Neuronal timescales are negatively correlated with cortical T1w/T2w, thus increasing along the anatomical hierarchy from sensory to association regions (Spearman correlation; p-value corrected for spatial autocorrelation, [Figure 2—figure supplement 2A–C](#fig2s2)).",
    "id": "fig2D",
    "label": "Figure 2D"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4,4))\n",
    "x=stats.zscore(df_struct['T1T2'])\n",
    "y=df_tau['log10 timescale (ms)']\n",
    "rho, pv, pv_perm, rho_null = compute_perm_corr(x,y.values,msr_nulls)\n",
    "m,b,_,_,_ = stats.linregress(x,y)\n",
    "plt.plot(x, y, 'o', color=C_ORD[3], alpha=0.5, ms=5)\n",
    "XL= np.array(plt.xlim())\n",
    "plt.plot(XL,XL*m+b, '--', lw=2, color=C_ORD[3], alpha=0.8)\n",
    "plt.xlabel(r'z-scored T1w/T2w ($\\sigma$)');  plt.ylabel('timescale (ms)');\n",
    "plt.tick_params('y', which='minor', left=False, labelleft=False)\n",
    "plt.yticks(np.log10(np.arange(10,60,10)), (np.arange(10, 60, 10)).astype(int))\n",
    "s = sig_str(rho, pv_perm, form='text')\n",
    "plt.annotate(s, xy=(0.55, 0.75), xycoords='axes fraction');"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "figure: Figure 2—figure supplement 1.\n",
    ":::\n",
    "![](elife-61277.xml.media/fig2-figsupp1.jpg)\n",
    "\n",
    "### MNI-iEEG dataset electrode coverage.\n",
    "\n",
    "(**A**) Per-parcel Gaussian-weighted mask values showing how close the nearest electrode was to a given HCP-MMP1.0 parcel for each participant. Brighter means closer, 0.5 corresponds to the nearest electrode being 4 mm away. (**B**) Maximum mask weight for each parcel across all participants. Most parcels have electrodes very close by in at least one participant across the entire participant pool. (**C**) The number of valid HCP-MMP parcels each participant has above the confidence threshold of 0.5 is uncorrelated with age. (**D**) Cortical map of the number of participants with confidence above threshold at each parcel. Sensorimotor, frontal, and lateral temporal regions have the highest coverage. (**E**) Cortical map of the average age of participants with confidence above threshold at each parcel. (**F**) Age distribution of participants with confidence above threshold at each parcel. Average age per parcel (red line) is relatively stable while age distribution varies from parcel to parcel (each subject is a black dot). (**G**) Average neuronal timescale when further aggregating the 180 Glasser parcels into 21 macro-regions (mean ± s.e.m across parcels within the macro-region).\n",
    ":::\n",
    "{#fig2s1}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "figure: Figure 2—figure supplement 2.\n",
    ":::\n",
    "![](elife-61277.xml.media/fig2-figsupp2.jpg)\n",
    "\n",
    "### Comparison of spatial autocorrelation-preserving null map generation methods.\n",
    "\n",
    "(**A**) Distributions of Spearman correlation values between empirical T1w/T2w map and 1000 spatial-autocorrelation preserving null timescale maps generated using Moran Spectral Randomization (MSR), spatial variogram fitting (VF), and spin permutation. Red dashed line denotes correlation between empirical timescale and T1w/T2w maps, p-values indicate two-tailed significance, i.e., proportion of distribution with values more extreme than empirical correlation. (**B**) Spatial variogram for empirical timescale map (black) and 10 null maps (blue) generated using MSR (left) and VF (right). Inset shows distribution of distances between pairs of HCP-MMP parcels. (**C**) Distribution of Spearman correlations between empirical and 1000 null timescale maps generated using MSR (green) and VF (red), showing similar levels of correlation between empirical and null maps for both methods.\n",
    ":::\n",
    "{#fig2s2}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "figure: Figure 2—figure supplement 3.\n",
    ":::\n",
    "![](elife-61277.xml.media/fig2-figsupp3.jpg)\n",
    "\n",
    "### Cortical thickness.\n",
    "\n",
    "Cortical thickness from the HCP dataset is positively correlated with neuronal timescale (left) and negatively correlated with T1w/T2w, i.e., thicker brain regions have longer (slower) timescales and less gray matter myelination, corresponding to higher-order association areas.\n",
    ":::\n",
    "{#fig2s3}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "caption": "(**E**) Example PSDs from macaque ECoG recordings (left) and aperiodic fit (right), similar to (**B**) (LIP: lateral intraparietal cortex; LPFC: lateral prefrontal cortex; S1 and S2: primary and secondary somatosensory cortex). PSDs are averaged over electrodes within each region. Data: Neurotycho, N = 8 sessions from two animals.",
    "execution": {
     "iopub.execute_input": "2021-04-13T22:26:11.478797Z",
     "iopub.status.busy": "2021-04-13T22:26:11.478565Z",
     "iopub.status.idle": "2021-04-13T22:26:12.627388Z",
     "shell.execute_reply": "2021-04-13T22:26:12.626723Z",
     "shell.execute_reply.started": "2021-04-13T22:26:11.478776Z"
    },
    "id": "fig2E",
    "label": "Figure 2E"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# get mean population time constants, values grabbed from Murray et al., 2014\n",
    "cell_ts = {'MT':[77.,64.], 'LIP':[138., 91.], 'LPFC':[184.,180.,195.,162.], 'OFC':[176.,188.], 'ACC':[313.,340.,257.], 'S1':[65.], 'S2':[149.]}\n",
    "cell_ts_avg = {k: np.array([np.mean(np.array(v)), stats.sem(np.array(v))]) for k,v in cell_ts.items()}\n",
    "\n",
    "# electrode indices for each of the corresponding areas in each monkey\n",
    "loc_inds_chibi = {'MT':[3,4,109], 'LIP':[10,11], 'LPFC':[14,15,25,26], 'OFC':[23,34,45], 'ACC':[52,57,58,59], 'S1':[9,19], 'S2':[95,108]}\n",
    "loc_inds_george = {'MT':[4,13,22], 'LIP':[10,11,20,21], 'LPFC':[15,24,25,26], 'OFC':[45,66], 'ACC':[52,57,58,59], 'S1':[18,19,30], 'S2':[1,2,9,108]}\n",
    "loc_inds = {'Chibi': loc_inds_chibi, 'George': loc_inds_george}\n",
    "\n",
    "area_ord = [3,1,2,0,4,6,5] # color order to match Murray figure\n",
    "\n",
    "##### plot example PSDs ######\n",
    "data_load = np.load('./data/fig2E_data.npz')\n",
    "psds, f_axis = data_load['psds'], data_load['f_axis']\n",
    "data_load.close()\n",
    "plt.figure(figsize=(8,4))\n",
    "for i_r, (reg, inds) in enumerate(loc_inds_chibi.items()):\n",
    "    \n",
    "    psds_reg = np.mean(np.log10(psds[np.array(inds)-1]),0)\n",
    "    psds_reg = psds_reg-psds_reg[40]\n",
    "    fit_range=[1,70]\n",
    "    plt_inds = np.arange(fit_range[0],fit_range[1]+1)\n",
    "    fok = FOOOF(max_n_peaks=3, aperiodic_mode='knee', verbose=False)\n",
    "    fok.fit(f_axis, 10**psds_reg, fit_range)\n",
    "    offset, knee, exp = fok.get_params('aperiodic_params')\n",
    "    kfreq, tau = convert_knee_val(knee,exp)\n",
    "    ap_spectrum = np.log10((10**offset/(knee+f_axis**exp)))\n",
    "\n",
    "    plt.subplot(1,2,1)    \n",
    "    plt.semilogx(f_axis[3:100], psds_reg[3:100] , color=C_ORD[area_ord[i_r]], label=reg, lw=2)\n",
    "    plt.axvline(kfreq, ls='--', color=C_ORD[area_ord[i_r]], lw=2, alpha=0.3)\n",
    "    plt.plot(kfreq, 1.9, 'o', color=C_ORD[area_ord[i_r]], ms=10)\n",
    "    plt.xticks([10, 100], ['10','100']); plt.yticks([]); plt.xlabel('frequency (Hz)'); plt.ylabel(r'power ($V^2/Hz$)')\n",
    "    plt.xlim([3,100]); plt.ylim([None,2])    \n",
    "\n",
    "    plt.subplot(1,2,2)\n",
    "    plt.semilogx(f_axis[3:100], ap_spectrum[3:100], '-', color=C_ORD[area_ord[i_r]], lw=4, alpha=0.8)\n",
    "    plt.xticks([10, 100], ['10','100']); plt.yticks([]); plt.xlabel('frequency (Hz)'); plt.ylabel(r'power ($V^2/Hz$)')\n",
    "    plt.xlim([3,100]); plt.ylim([None,2])\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "figure: Figure 2—figure supplement 4.\n",
    ":::\n",
    "![](elife-61277.xml.media/fig2-figsupp4.jpg)\n",
    "\n",
    "### Macaque ECoG and single-unit coverage.\n",
    "\n",
    "(**A**) Locations of 180-electrode ECoG grid from two animals in the Neurotycho dataset; colors correspond to locations used for comparison with single-unit timescales. (**B**) Electrode indices of the sampled areas from the two animals, corresponding to those colored in (**A**).\n",
    ":::\n",
    "{#fig2s4}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "caption": "(**F**) Macaque ECoG timescales track published single-unit spiking timescales (@bib68) in corresponding regions (error bars represent mean ± s.e.m).",
    "id": "fig2F",
    "label": "Figure 2F"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "##### plot stats and results ######\n",
    "# load ecog results dataframe\n",
    "df_combined = pd.read_csv('./data/df_macaque.csv', index_col=0)\n",
    "\n",
    "# define querying condition\n",
    "feature = 'tau'\n",
    "cond_query = 'EyesOpen'\n",
    "df_cond = df_combined[df_combined['cond']==cond_query] \n",
    "\n",
    "# plot\n",
    "plt.figure(figsize=(5,4))\n",
    "ecog_ts_avg = {}\n",
    "\n",
    "# plot per session average across electrodes\n",
    "for i, k in enumerate(cell_ts.keys()):\n",
    "    sesh_mkrs = [] # hack to save the session marker for next plot\n",
    "    ecog_ts_avg[k] = []\n",
    "    for s in df_cond['session_id'].unique():\n",
    "        df_sesh = df_cond[df_cond['session_id']==s]\n",
    "        patient = df_sesh['patient'].iloc[0]\n",
    "\n",
    "        # loc_inds has the ecog electrode indices that fall into each area\n",
    "        region_inds = loc_inds[patient][k] \n",
    "        marker = 's' if patient == 'George' else '^'\n",
    "        sesh_mkrs.append(marker)\n",
    "        ecog_ts_sess_avg = df_sesh.loc[df_sesh['chan'].isin(region_inds)].mean()[feature]*1e3 # use ms\n",
    "        ecog_ts_avg[k].append(ecog_ts_sess_avg)\n",
    "\n",
    "# plot grand average\n",
    "for i,k in enumerate(cell_ts.keys()):\n",
    "    plt.errorbar(cell_ts_avg[k][0], np.mean(ecog_ts_avg[k]), xerr=cell_ts_avg[k][1], yerr=stats.sem(ecog_ts_avg[k]), fmt='o', color=C_ORD[area_ord[i]], ms=10, label=k)\n",
    "    \n",
    "# fit & plot line\n",
    "ts_mat = np.array([(cell_ts_avg[k][0], np.mean(ecog_ts_avg[k])) for k in cell_ts.keys()])\n",
    "m,b,r,pv,stderr = stats.linregress(ts_mat)\n",
    "XL = np.array(plt.xlim())\n",
    "plt.plot(XL, m*XL+b, 'k--', lw=1)\n",
    "\n",
    "rho, pv = stats.spearmanr(ts_mat[:,0], ts_mat[:,1])\n",
    "s = sig_str(rho, pv, form='text')\n",
    "plt.annotate(s, xy=(0.05, 0.8), xycoords='axes fraction')\n",
    "\n",
    "plt.tick_params('x', which='minor', bottom=False, labelbottom=False)\n",
    "plt.tick_params('y', which='minor', left=False, labelleft=False)\n",
    "plt.xlim([-10,400]);plt.ylim([8,50]);\n",
    "\n",
    "plt.legend(loc='lower left', bbox_to_anchor= (0.9, 0), ncol=1, frameon=False, handletextpad=0.05)\n",
    "plt.xlabel(r'$\\tau_{spiking}$ (ms)', fontsize=16);\n",
    "plt.ylabel(r'$\\tau_{ECoG}$ (ms)', fontsize=16); #plt.title(cond_query)\n",
    "plt.tight_layout();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "caption": "(**G**) ECoG-derived timescales are consistently correlated with (left), and ~10 times faster than (right), single-unit timescales across individual sessions. Hollow markers: individual sessions; shapes: animals; solid circles: grand average from (**F**).",
    "id": "fig2G",
    "label": "Figure 2G"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### compute and plot across-trial stats ###\n",
    "ecog_ts_mat = np.array([ecog_ts_avg[k] for k in cell_ts.keys()])\n",
    "cell_ts_mat = np.array([cell_ts_avg[k][0] for k in cell_ts.keys()])\n",
    "session_stats = np.array([stats.linregress(cell_ts_mat, ecog_ts_mat[:,i]) for i in range(ecog_ts_mat.shape[1])])\n",
    "session_rhos = np.array([stats.spearmanr(cell_ts_mat, ecog_ts_mat[:,i]) for i in range(ecog_ts_mat.shape[1])])\n",
    "\n",
    "plt.figure(figsize=(4,4))\n",
    "plt.subplot(1,2,1)\n",
    "for s_i, s in enumerate(sesh_mkrs): plt.plot(np.random.randn(1)/10, session_rhos[s_i,0],mec='k', mfc='w', ms=6, marker=s, alpha=0.9)\n",
    "plt.plot(0, rho,'ko', ms=10, alpha=0.6)\n",
    "plt.xlim([-0.5,1]); plt.ylim([0,1]);\n",
    "plt.xticks([]); plt.ylabel(r'Spearman $\\rho$')\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "for s_i, s in enumerate(sesh_mkrs): plt.plot(np.random.randn(1)/10, session_stats[s_i,0]*100, mec='k', mfc='w', ms=6, marker=s)\n",
    "plt.plot(0, m*100,'ko', ms=12, alpha=0.6)\n",
    "plt.xlim([-0.5,1]);\n",
    "plt.yticks([0, 10, 20], ['0%', '10%', '20%'])\n",
    "plt.xticks([]); plt.ylabel(r'$\\tau_{ECoG} : \\tau_{spiking}$ (%)', fontsize=16)\n",
    "plt.tight_layout(); "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Across the human cortex, timescales of fast electrophysiological dynamics (~10–50 ms) predominantly follow a rostrocaudal gradient ([Figure 2C](#fig2C), circles denote location of example data from 2A). Consistent with numerous accounts of a principal cortical axis spanning from primary sensory to association regions (@bib46; @bib64; @bib98), timescales are shorter in sensorimotor and early visual areas, and longer in association regions, especially cingulate, ventral/medial frontal, and medial temporal lobe (MTL) regions ([Figure 2—figure supplement 1G](#fig2s1) shows further pooling into 21 labeled macro-regions). We then compare the timescale gradient to the average T1w/T2w map from the Human Connectome Project, which captures gray matter myelination and indexes the proportion of feedforward vs. feedback connections between cortical regions, thus acting as a noninvasive proxy of connectivity-based anatomical hierarchy (@bib10; @bib39). We find that neuronal timescales are negatively correlated with T1w/T2w across the entire cortex ([Figure 2D](#fig2D), _ρ_ = −0.47, p&lt;0.001; corrected for spatial autocorrelation \\[SA], see Materials and methods and [Figure 2—figure supplement 2A–C](#fig2s2) for a comparison of correction methods), such that timescales are shorter in more heavily myelinated (i.e., lower-level, sensory) regions. Timescales are also positively correlated with cortical thickness ([Figure 2—figure supplement 3](#fig2s3), _ρ_ = 0.37, p=0.035)—another index of cortical hierarchy that is itself anti-correlated with T1w/T2w. Thus, we observe that neuronal timescales lengthen along the human cortical hierarchy, from sensorimotor to association regions.\n",
    "\n",
    "While surface ECoG recordings offer much broader spatial coverage than extracellular single-unit recordings, they are fundamentally different signals: ECoG and field potentials largely reflect integrated synaptic and other transmembrane currents across many neuronal and glial cells, rather than putative action potentials from single neurons (@bib12; [Figure 1A](#fig1A), yellow box). Considering this, we ask whether timescales measured from ECoG in this study (${\\displaystyle {\\tau }_{\\text{ECoG}}}$) are related to single-unit spiking timescales along the cortical hierarchy (${\\displaystyle {\\tau }_{\\text{spiking}}}$). To test this, we extract neuronal timescales from task-free ECoG recordings in macaques (@bib69) and compare them to a separate dataset of single-unit spiking timescales from a different group of macaques (@bib68) (see [Figure 2—figure supplement 4](#fig2s4) for electrode locations). Consistent with ${\\displaystyle {\\tau }_{\\text{spiking}}}$ estimates (@bib68; @bib99), ${\\displaystyle {\\tau }_{\\text{ECoG}}}$ also increase along the macaque cortical hierarchy. While there is a strong correspondence between spiking and ECoG timescales ([Figure 2F](#fig2F); _ρ_ = 0.96, p&lt;0.001)—measured from independent datasets—across the macaque cortex, ${\\displaystyle {\\tau }_{\\text{ECoG}}}$ are ~10 times faster than ${\\displaystyle {\\tau }_{\\text{spiking}}}$ and are conserved across individual sessions ([Figure 2G](#fig2G)). This suggests that neuronal spiking and transmembrane currents have distinct but related timescales of fluctuations, and that both are hierarchically organized along the primate cortex.\n",
    "\n",
    "## Synaptic and ion channel genes shape timescales of neuronal dynamics\n",
    "\n",
    "Next, we identify potential cellular and synaptic mechanisms underlying timescale variations across the human cortex. Theoretical accounts posit that NMDA-mediated recurrent excitation coupled with fast inhibition (@bib13; @bib95; @bib93), as well as cell-intrinsic properties (@bib23; @bib37; @bib59), are crucial for shaping neuronal timescales. While in vitro and in vivo studies in model organisms (@bib83; @bib97) can test these hypotheses at the single-neuron level, causal manipulations and large-scale recordings of neuronal networks embedded in the human brain are severely limited. Here, we apply an approach analogous to multimodal single-cell profiling (@bib5) and examine the transcriptomic basis of neuronal dynamics at the macroscale.\n",
    "\n",
    "Leveraging whole-cortex interpolation of the Allen Human Brain Atlas bulk mRNA expression (@bib42; @bib43), we project voxel-wise expression maps onto the HCP-MMP1.0 surface parcellation, and find that the neuronal timescale gradient overlaps with the dominant axis of gene expression (i.e., first principal component of 2429 brain-related genes) across the human cortex ([Figure 3A](#fig3A), _ρ_ = −0.60, p&lt;0.001; see [Figure 3—figure supplement 1](#fig3s1) for similar results with all 18,114 genes). Consistent with theoretical predictions ([Figure 3B](#fig3B)), timescales significantly correlate with the expression of genes encoding for NMDA (GRIN2B) and GABA-A (GABRA3) receptor subunits, voltage-gated sodium (SCN1A) and potassium (KCNA3) ion channel subunits, as well as inhibitory cell-type markers (parvalbumin, PVALB), and genes previously identified to be associated with single-neuron membrane time constants (PRR5) (@bib5) (all Spearman correlations corrected for SA in gradients)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "figure: Figure 3.\n",
    ":::\n",
    "\n",
    "## Timescale gradient is linked to expression of genes related to synaptic receptors and transmembrane ion channels across the human cortex.\n",
    "\n",
    ":::\n",
    "{#fig3}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "caption": "(**A**) Timescale gradient follows the dominant axis of gene expression variation across the cortex (left, z-scored PC1 of 2429 brain-specific genes, arbitrary direction). Middle: proportion of variance explained by first 10 PCs. Right: absolute Spearman correlation between timescale and first 10 PCs.",
    "execution": {
     "iopub.execute_input": "2021-04-13T22:37:45.234692Z",
     "iopub.status.busy": "2021-04-13T22:37:45.234378Z",
     "iopub.status.idle": "2021-04-13T22:37:51.283349Z",
     "shell.execute_reply": "2021-04-13T22:37:51.282171Z",
     "shell.execute_reply.started": "2021-04-13T22:37:45.234659Z"
    },
    "id": "fig3A",
    "label": "Figure 3A"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# perform PCA on brain genes\n",
    "from sklearn.decomposition import PCA\n",
    "brain_gene_symbols = pd.read_csv('./data/df_brain_genes_symbols.csv', index_col=0).values[:,1].tolist()\n",
    "df_genes = df_struct[brain_gene_symbols]\n",
    "\n",
    "n_pcs = 10\n",
    "gene_pca = PCA(n_pcs)\n",
    "gene_grad = gene_pca.fit_transform(df_genes.values)\n",
    "df_pc_weights = pd.DataFrame(gene_pca.components_.T, index=df_genes.columns, columns=['pc%i'%i for i in range(1, n_pcs+1)])\n",
    "\n",
    "# compute correlation\n",
    "x = stats.zscore(gene_grad[:,0])\n",
    "y = df_tau['log10 timescale (ms)']\n",
    "rho, pv, pv_perm, rho_null = compute_perm_corr(x,y.values,msr_nulls)\n",
    "m,b,_,_,_ = stats.linregress(x,y.values)\n",
    "\n",
    "plt.figure(figsize=(12,4))\n",
    "\n",
    "plt.subplot(1,3,1)\n",
    "plt.plot(x, y, 'o', color=C_ORD[0], alpha=0.5, ms=5)\n",
    "plt.xlim([-2.75, 2.75]); \n",
    "XL= np.array([-2.5, 2.5])\n",
    "plt.plot(XL,XL*m+b, '--', lw=2, color=C_ORD[0], alpha=0.8)\n",
    "plt.xlabel(r'gene expression PC1 ($\\sigma$)'); plt.ylabel('timescale (ms)');\n",
    "plt.yticks(np.log10(np.arange(10,60,10)), (np.arange(10, 60, 10)).astype(int))\n",
    "plt.tick_params('y', which='minor', left=False, labelleft=False)\n",
    "s = sig_str(rho, pv_perm, form='text')\n",
    "plt.annotate(s, xy=(0.05, 0.05), xycoords='axes fraction')\n",
    "\n",
    "plt.subplot(1,3,2)\n",
    "plt.bar(range(1,n_pcs+1), gene_pca.explained_variance_ratio_, fc='k', alpha=0.5)\n",
    "plt.xticks([1, 10], ['1', '10']);\n",
    "plt.xlabel('principal component'); plt.ylabel('proportion of var. explained');\n",
    "\n",
    "plt.subplot(1,3,3)\n",
    "all_pc_rhos = np.array([compute_perm_corr(x, y, msr_nulls)[:3] for x in gene_grad.T])\n",
    "plt.bar(range(1,n_pcs+1), np.abs(all_pc_rhos[:,0]), fc='k', alpha=0.5)\n",
    "for i in range(1,11):    \n",
    "    plt.annotate('*'*sum(all_pc_rhos[i-1,2]<[0.05, 0.01, 0.005, 0.001]), (i, abs(all_pc_rhos[i-1,0])+0.005), horizontalalignment='center')\n",
    "plt.xticks([1, 10], ['1', '10']);\n",
    "plt.xlabel('principal component'); plt.ylabel(r'absolute $\\rho$');\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "figure: Figure 3A.\n",
    ":::\n",
    "![](static_figs/fig_3A_inset.jpg)\n",
    ":::\n",
    "{#fig3A_inset}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "figure: Figure 3—figure supplement 1.\n",
    ":::\n",
    "![](elife-61277.xml.media/fig3-figsupp1.jpg)\n",
    "\n",
    "### Transcriptomic principal component analysis results.\n",
    "\n",
    "(**A**) Proportion of variance explained by the top 10 principal components (PCs) of brain-specific genes (top) and all AHBA genes (bottom). (**B**) Absolute Spearman correlation between timescale map and top 10 PCs from brain-specific or full gene dataset. Asterisks indicate resampled significance while accounting for spatial autocorrelation; \\*\\*\\*\\* indicate p_&lt;_0.001. Top PCs explain similar amounts of variance, while only PC1 in both cases is significantly correlated with timescale.\n",
    ":::\n",
    "{#fig3s1}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "caption": "(**B**) Timescale gradient is significantly correlated with expression of genes known to alter synaptic and neuronal membrane time constants, as well as inhibitory cell-type markers, but...",
    "execution": {
     "iopub.execute_input": "2021-04-13T22:26:19.570497Z",
     "iopub.status.busy": "2021-04-13T22:26:19.570252Z",
     "iopub.status.idle": "2021-04-13T22:26:22.759655Z",
     "shell.execute_reply": "2021-04-13T22:26:22.759023Z",
     "shell.execute_reply.started": "2021-04-13T22:26:19.570467Z"
    },
    "id": "fig3B",
    "label": "Figure 3B"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x432 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "xlb = ['GRIN2B', 'GABRA3','SCN1A', 'KCNA3','PVALB', 'PRR5']\n",
    "\n",
    "plt.figure(figsize=(4,6))\n",
    "y = df_tau['log10 timescale (ms)']\n",
    "xs = [df_struct[g] for g in xlb]\n",
    "\n",
    "for i_x, x in enumerate(xs):\n",
    "    rho, pv, pv_perm, rho_null = compute_perm_corr(x,y.values,msr_nulls)\n",
    "    m,b,_,_,_ = stats.linregress(x,y.values)\n",
    "\n",
    "    plt.subplot(3,2,i_x+1)\n",
    "    plt.plot(x, y, '.', color=C_ORD[0], alpha=0.5, ms=5);\n",
    "    XL= np.array([-2.5, 2.5])\n",
    "    plt.xlim([-2.75, 2.75]); \n",
    "    plt.plot(XL,XL*m+b, '--', lw=2, color=C_ORD[0], alpha=0.8)    \n",
    "    plt.gca().set_xticklabels([])\n",
    "    plt.gca().set_yticklabels([])\n",
    "    plt.annotate(sig_str(rho, pv_perm, form='*'), xy=(0.02,0.05), xycoords='axes fraction', fontsize=14);\n",
    "    plt.xlabel(xlb[i_x], fontsize=14, labelpad=0)\n",
    "    \n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-13T22:26:22.760957Z",
     "iopub.status.busy": "2021-04-13T22:26:22.760708Z",
     "iopub.status.idle": "2021-04-13T22:26:45.948318Z",
     "shell.execute_reply": "2021-04-13T22:26:45.947633Z",
     "shell.execute_reply.started": "2021-04-13T22:26:22.760928Z"
    }
   },
   "outputs": [],
   "source": [
    "geneset_nmda = ['GRIN1', 'GRINA', 'GRIN2A','GRIN2B','GRIN2C','GRIN2D','GRIN3A', 'GRIN3B'] # nmda receptor\n",
    "geneset_gabra = ['GABRA1','GABRA2','GABRA3','GABRA4','GABRA5','GABRA6'] # GABA-A alpha subchannels\n",
    "geneset_sodium = ['SCN1A',  'SCN2A',  'SCN3A', 'SCN4A',  'SCN5A', 'SCN7A', 'SCN8A', 'SCN9A', 'SCN10A']#,'SCN1B','SCN2B','SCN3B',SCN4B'] # sodium ion channels\n",
    "geneset_potassium = ['KCNA1','KCNA2','KCNA3','KCNA4','KCNA5','KCNA6'] # GABA-A alpha subchannels\n",
    "geneset_inh = ['CORT', 'CALB1', 'CALB2', 'SST', 'PVALB', 'CCK', 'NPY', 'PNOC', 'VIP'] # inhibitory markers\n",
    "geneset_sctau = ['CELF6', 'PRR5', 'FAM81A', 'LRRC4C','OXTR', 'CTXN1', 'ENC1', 'AKAIN1'] # single-cell membrane time constant\n",
    "gene_families = [geneset_nmda, geneset_gabra, geneset_sodium, geneset_potassium, geneset_inh, geneset_sctau]\n",
    "\n",
    "df_tau_gene_corrfam = pd.DataFrame([], index=sum(gene_families, []), columns=['rho', 'pv', 'pv_adj'])\n",
    "for i_g, g in df_tau_gene_corrfam.iterrows():\n",
    "    df_tau_gene_corrfam.loc[i_g] = compute_perm_corr(df_struct[i_g].values, y.values, msr_nulls)[0:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "caption": "(**C**) members within a gene family (e.g., NMDA receptor subunits) can be both positively and negatively associated with timescales, consistent with predictions from in vitro studies.",
    "execution": {
     "iopub.execute_input": "2021-04-13T22:26:45.949731Z",
     "iopub.status.busy": "2021-04-13T22:26:45.949505Z",
     "iopub.status.idle": "2021-04-13T22:26:46.760909Z",
     "shell.execute_reply": "2021-04-13T22:26:46.760227Z",
     "shell.execute_reply.started": "2021-04-13T22:26:45.949703Z"
    },
    "id": "fig3C",
    "label": "Figure 3C"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 216x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot all correlations\n",
    "plt.figure(figsize=(3,4))\n",
    "plt.axvline(0, color='k', lw=1)\n",
    "for i_g, gs in enumerate(gene_families):\n",
    "    set_x = df_tau_gene_corrfam.loc[gs]['rho']\n",
    "    set_y = np.random.randn(len(gs))/15+(5-i_g)\n",
    "\n",
    "    # color based on permutation significance\n",
    "    set_color = [C_ORD[0] if p<0.05 else 'w' for p in df_tau_gene_corrfam.loc[gs]['pv_adj']]\n",
    "    plt.axhline(5-i_g, lw=0.2, color='k')\n",
    "    plt.scatter(set_x, set_y, alpha=0.5, s=50, ec = C_ORD[0], c = set_color)\n",
    "    \n",
    "plt.xlabel(r'$\\rho$', labelpad=-15); plt.xticks([-0.5, 0.5])\n",
    "plt.yticks(np.arange(len(gene_families)), ['sc-tau','inhibitory','K+ chan.','Na+ chan.','GABAA', 'NMDA']); \n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "caption": "**Individual timescale-gene correlations magnitudes.** Correlation between timescale and expression of genes from [Figure 3C](#fig3), with gene symbols labeled and grouped into functional families for ease of interpretation.",
    "id": "fig3s2",
    "label": "Figure 3-figure supplement 2."
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# another view, plot families of correlations as hbar\n",
    "color = plt.cm.RdBu_r(np.linspace(0,1,200))\n",
    "plt.figure(figsize=(18,4))\n",
    "for i_g, g_subset in enumerate(gene_families):\n",
    "    plt.subplot(1,len(gene_families),i_g+1)\n",
    "    geneset_plot = df_tau_gene_corrfam.loc[g_subset]\n",
    "    for i_p in range(len(geneset_plot)):\n",
    "        rho, pv_perm = df_tau_gene_corrfam.loc[geneset_plot.iloc[i_p].name][['rho', 'pv_adj']]\n",
    "        plt.barh(i_p, rho, ec='k', fc=color[int((1+rho*1.5)*100)])\n",
    "        s = np.sum(pv_perm<=np.array([0.05, 0.01, 0.005, 0.001]))*'*'\n",
    "        plt.text(rho, i_p-0.4, s, fontsize=18, horizontalalignment='left' if rho>0 else 'right')\n",
    "\n",
    "    plt.plot([0,0], plt.ylim(), 'k')\n",
    "    plt.ylim([-0.5,len(geneset_plot)-0.5])\n",
    "    plt.yticks(range(len(geneset_plot)), geneset_plot.index.values, rotation=0, ha='right', va='center', rotation_mode='anchor', fontsize=14)\n",
    "    plt.tick_params(axis='y', which=u'both',length=0)\n",
    "    plt.xlim([-1,1]); plt.xlabel(r'$\\rho$', labelpad=-15); plt.xticks([-1,1])\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-04-13T22:26:46.762012Z",
     "iopub.status.busy": "2021-04-13T22:26:46.761713Z",
     "iopub.status.idle": "2021-04-13T22:26:56.665002Z",
     "shell.execute_reply": "2021-04-13T22:26:56.664245Z",
     "shell.execute_reply.started": "2021-04-13T22:26:46.761984Z"
    }
   },
   "outputs": [],
   "source": [
    "def collect_micro_macro_corr(df_micro_corr, df_macro_corr, col_names):\n",
    "    sc_feats, corr_metric,  prop_col, gene_col = col_names\n",
    "    micro_macro_corr = []\n",
    "    for i_f, feat in enumerate(sc_feats):\n",
    "        match_genes = []\n",
    "        for i_g, g in df_micro_corr[df_micro_corr[prop_col]==feat].iterrows():\n",
    "            if g[gene_col].upper() in df_macro_corr.index:\n",
    "                match_genes.append([i_g, g[gene_col].upper()])\n",
    "\n",
    "        match_genes = np.array(match_genes, dtype='object')\n",
    "        micro_macro_corr.append([match_genes[:,1], df_micro_corr.loc[match_genes[:,0]][corr_metric].values, df_macro_corr.loc[match_genes[:,1]]['rho'].values])\n",
    "\n",
    "    return np.hstack(micro_macro_corr), micro_macro_corr\n",
    "\n",
    "# collect micro/macro correlations of relevant genes\n",
    "df_bomkamp = pd.read_csv('./data/bomkamp_online_table1.txt', index_col=0)\n",
    "df_tripathy = pd.read_csv('./data/tripathy_tableS3.csv', index_col=0)\n",
    "\n",
    "df_genes = df_struct[df_struct.columns[2:]]\n",
    "df_tau_gene_corrall = pd.DataFrame([stats.spearmanr(g.values, y.values) for g_i, g in df_genes.iteritems()], columns=['rho','pv'], index=df_genes.columns)\n",
    "df_tau_gene_macro = df_tau_gene_corrall[df_tau_gene_corrall['pv']<0.05]\n",
    "#df_tau_gene_macro = df_tau_gene_corrall_rmvt1t2[df_tau_gene_corrall_rmvt1t2['pv']<0.05]\n",
    "\n",
    "col_names = [['tau', 'ri', 'cap'], 'beta_gene' , 'property', 'gene_symbol']\n",
    "micro_macro_bomkamp, mmc_bomkamp_split = collect_micro_macro_corr(df_bomkamp, df_tau_gene_macro, col_names)\n",
    "\n",
    "col_names = [['Tau', 'Rin', 'Cm'], 'DiscCorr', 'EphysProp', 'GeneSymbol']\n",
    "micro_macro_tripathy, mmc_tripathy_split = collect_micro_macro_corr(df_tripathy, df_tau_gene_macro, col_names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "caption": "(**D**) Macroscale timescale-transcriptomic correlation captures association between RNA-sequenced expression of the same genes and single-cell timescale properties fit to patch clamp data from two studies, and the correspondence improves for genes (separated by quintiles) that are more strongly correlated with timescale (solid: N = 170 [@bib81], dashed: N = 4168 genes [@bib5]; horizontal lines: correlation across all genes from the two studies, _ρ_ = 0.36 and 0.25, p_&lt;_0.001 for both).",
    "execution": {
     "iopub.execute_input": "2021-04-13T22:26:56.666341Z",
     "iopub.status.busy": "2021-04-13T22:26:56.666058Z",
     "iopub.status.idle": "2021-04-13T22:26:56.796605Z",
     "shell.execute_reply": "2021-04-13T22:26:56.795947Z",
     "shell.execute_reply.started": "2021-04-13T22:26:56.666309Z"
    },
    "id": "fig3D",
    "label": "Figure 3D"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 288x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(4,4))\n",
    "pct_bins = np.arange(0,100,20)\n",
    "lss = ['o-', '^--']\n",
    "labels=['Tripathy17', 'Bomkamp18']\n",
    "\n",
    "for i_m, micro_macro in enumerate([micro_macro_tripathy, micro_macro_bomkamp]):\n",
    "    meta_corr = []\n",
    "    micro_macro_abscorr = abs(micro_macro[2].astype(float))\n",
    "    bins = np.percentile(micro_macro_abscorr, pct_bins)\n",
    "    corr_quant_inds = np.digitize(micro_macro_abscorr, bins)\n",
    "    for i in np.unique(corr_quant_inds):\n",
    "        meta_corr.append(stats.pearsonr(micro_macro[1,corr_quant_inds==i],micro_macro[2,corr_quant_inds==i]))\n",
    "        \n",
    "    meta_corr=np.array(meta_corr)\n",
    "    r, pv = stats.pearsonr(micro_macro[1],micro_macro[2])\n",
    "    plt.plot(pct_bins+pct_bins[1], meta_corr[:,0], lss[i_m], color=C_ORD[0], ms=8, mfc='w', alpha=0.8, label=labels[i_m])\n",
    "    plt.plot(pct_bins[meta_corr[:,1]<0.05]+pct_bins[1], meta_corr[meta_corr[:,1]<0.05,0], lss[i_m][0], color=C_ORD[0], ms=8, alpha=0.8)\n",
    "    plt.plot(pct_bins[[0,-1]]+pct_bins[1], [r, r], ls=lss[i_m][1:], color='k', alpha=0.7, zorder=-20)\n",
    "\n",
    "plt.xticks([20,100],['1/5', '5/5']); \n",
    "plt.yticks(np.arange(-0.25, 1, 0.25))\n",
    "plt.legend(frameon=False, fontsize=14, loc=[0,0.8])\n",
    "plt.xlabel(r'macroscale absolute $\\rho$ quintile'); plt.ylabel('single-cell vs. macroscale\\nassociation correlation (r)')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "caption": "**Interactive scatterplot of timescale and gene across brain macroparcels.** Change `struct_query` to a different gene symbol, or 'T1T2'. Hover over dots to see area name.",
    "id": "fig3s3",
    "label": "Figure 3-figure supplement 3."
   },
   "outputs": [
    {
     "data": {
      "application/vnd.vegalite.v4+json": {
       "$schema": "https://vega.github.io/schema/vega-lite/v4.8.1.json",
       "config": {
        "axis": {
         "labelFontSize": 20,
         "titleFontSize": 20
        },
        "title": {
         "fontSize": 24
        },
        "view": {
         "continuousHeight": 300,
         "continuousWidth": 400
        }
       },
       "data": {
        "name": "data-0c24c940a531d49d4a553f18da809b0b"
       },
       "datasets": {
        "data-0c24c940a531d49d4a553f18da809b0b": [
         {
          "PVALB": -1.0739830000000004,
          "index": "ACC & mPFC",
          "timescale (ms)": 23.6646153617108
         },
         {
          "PVALB": -0.306959625,
          "index": "Auditory Association",
          "timescale (ms)": 21.1861655682381
         },
         {
          "PVALB": 0.9218425,
          "index": "Dorsal Visual",
          "timescale (ms)": 13.873876458342
         },
         {
          "PVALB": 0.5463709999999999,
          "index": "Early Auditory",
          "timescale (ms)": 20.428623012098498
         },
         {
          "PVALB": 1.41386,
          "index": "Early Visual",
          "timescale (ms)": 18.063889477225
         },
         {
          "PVALB": 0.46394075,
          "index": "Inferior Frontal",
          "timescale (ms)": 21.5522118232316
         },
         {
          "PVALB": 0.5275494999999999,
          "index": "Inferior Parietal",
          "timescale (ms)": 18.198201173955
         },
         {
          "PVALB": -1.343027461538462,
          "index": "Insular",
          "timescale (ms)": 23.6355651986536
         },
         {
          "PVALB": -0.8478840000000001,
          "index": "Lateral Temporal",
          "timescale (ms)": 23.9869071053532
         },
         {
          "PVALB": -1.8474807142857144,
          "index": "Medial Temporal",
          "timescale (ms)": 36.966767574418
         },
         {
          "PVALB": -0.5927243181818181,
          "index": "Orbital Frontal",
          "timescale (ms)": 29.97419085284
         },
         {
          "PVALB": 0.4159868125,
          "index": "Paracentral & Mid Cingulate",
          "timescale (ms)": 16.4674645323157
         },
         {
          "PVALB": 0.4980633333333333,
          "index": "Post Operculum",
          "timescale (ms)": 18.9387602397144
         },
         {
          "PVALB": 0.1066715821428571,
          "index": "Posterior Cingulate",
          "timescale (ms)": 21.0820832413214
         },
         {
          "PVALB": 0.8282357142857142,
          "index": "Premotor",
          "timescale (ms)": 18.0559188563814
         },
         {
          "PVALB": 0.963626,
          "index": "Sensorimotor",
          "timescale (ms)": 14.704584058618801
         },
         {
          "PVALB": 0.6980194999999999,
          "index": "Superior Parietal",
          "timescale (ms)": 17.4078740509136
         },
         {
          "PVALB": 0.3042686,
          "index": "Temporal-Parietal-Occipital Junction",
          "timescale (ms)": 18.6089681156172
         },
         {
          "PVALB": 0.3512427857142858,
          "index": "Ventral Visual",
          "timescale (ms)": 18.770335978593
         },
         {
          "PVALB": 0.7410799999999998,
          "index": "Visual (TPO)",
          "timescale (ms)": 18.9753226617018
         },
         {
          "PVALB": 0.3190209230769231,
          "index": "dlPFC",
          "timescale (ms)": 19.2918549709602
         }
        ]
       },
       "encoding": {
        "color": {
         "value": "black"
        },
        "tooltip": [
         {
          "field": "index",
          "type": "nominal"
         }
        ],
        "x": {
         "field": "PVALB",
         "scale": {
          "zero": false
         },
         "type": "quantitative"
        },
        "y": {
         "field": "timescale (ms)",
         "scale": {
          "domain": [
           10,
           40
          ],
          "zero": false
         },
         "type": "quantitative"
        }
       },
       "height": 600,
       "mark": {
        "size": 200,
        "type": "circle"
       },
       "selection": {
        "selector001": {
         "bind": "scales",
         "encodings": [
          "x",
          "y"
         ],
         "type": "interval"
        }
       },
       "width": 600
      },
      "text/plain": [
       "<VegaLite 4 object>\n",
       "\n",
       "If you see this message, it means the renderer has not been properly enabled\n",
       "for the frontend that you are using. For more information, see\n",
       "https://altair-viz.github.io/user_guide/troubleshooting.html\n"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# print('open to explore data!')\n",
    "struct_query = 'PVALB'\n",
    "\n",
    "# make temp dataframe because loading the whole thing in altair is slow af\n",
    "df_query = pd.concat([df_macro['index'], df_macro['timescale (ms)'], df_macro[struct_query]], axis=1)\n",
    "rho, pv = stats.spearmanr(df_macro['timescale (ms)'], df_macro[struct_query])\n",
    "\n",
    "alt.Chart(df_query).mark_circle(size=200).encode(\n",
    "    alt.X(struct_query, scale=alt.Scale(zero=False)),\n",
    "    alt.Y('timescale (ms)', scale=alt.Scale(zero=False, domain=(10, 40))),\n",
    "    color=alt.value('black'),\n",
    "    tooltip=['index']\n",
    ").properties(\n",
    "    width=600,\n",
    "    height=600\n",
    ").configure_axis(\n",
    "    labelFontSize=20,\n",
    "    titleFontSize=20\n",
    ").configure_title(fontSize=24).interactive()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "figure: Figure 3E-F.\n",
    ":::\n",
    "![](static_figs/fig_3EF.jpg)\n",
    "\n",
    "(**E**) T1w/T2w gradient is regressed out from timescale and gene expression gradients, and a partial least squares (PLS) model is fit to the residual maps. Genes with significant PLS weights (filled blue boxes) compared to spatial autocorrelation (SA)-preserved null distributions are submitted for gene ontology enrichment analysis (GOEA), returning a set of significant GO terms that represent functional gene clusters (filled green boxes). (**F**) Enriched genes are primarily linked to potassium and chloride transmembrane transporters, and GABA-ergic synapses; genes specifically with strong negative associations further over-represent transmembrane ion exchange mechanisms, especially voltage-gated potassium and cation transporters. Branches indicate GO items that share higher-level (parent) items, e.g., _voltage-gated cation channel activity_ is a child of _cation channel activity_ in the molecular functions (MF) ontology, and both are significantly associated with timescale. Color of lines indicate curated ontology (BP—biological process, CC—cellular components, or MF). Dotted, dashed, and solid lines correspond to analysis performed using all genes or only those with positive or negative PLS weights. Spatial correlation p-values in (**A–C**) are corrected for SA (see Materials and methods; asterisks in (**B,D**) indicate p&lt;0.05, 0.01, 0.005, and 0.001 respectively; filled markers in (**C,D**) indicate p&lt;0.05).\n",
    ":::\n",
    "\n",
    "{#fig3EF}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More specifically, in vitro electrophysiological studies have shown that, for example, increased expression of receptor subunit 2B extends the NMDA current time course (@bib27), while 2A expression shortens it (@bib66). Similarly, the GABA-A receptor time constant lengthens with increasing a3:a1 subunit ratio (@bib24). We show that these relationships are recapitulated at the macroscale, where neuronal timescales positively correlate with GRIN2B and GABRA3 expression and negatively correlate with GRIN2A and GABRA1 ([Figure 3C](#fig3C)). These results demonstrate that timescales of neural dynamics depend on specific receptor subunit combinations with different (de)activation timescales (@bib22; @bib37), in addition to broad excitation–inhibition interactions (@bib34; @bib98; @bib94). Notably, almost all genes related to voltage-gated sodium and potassium ion channel alpha-subunits—the main functional subunits—are correlated with timescale, while all inhibitory cell-type markers except parvalbumin have strong positive associations with timescale ([Figure 3C](#fig3C) and [Figure 3—figure supplement 2](#fig3s2)).\n",
    "\n",
    "We further test whether single-cell timescale-transcriptomic associations are captured at the macroscale as follows: for a given gene, we can measure how strongly its expression correlates with membrane time constant parameters at the single-cell level using patch-clamp and RNA sequencing (scRNASeq) data (@bib5; @bib81). Analogously, we can measure its macroscopic transcriptomic-timescale correlation using the cortical gradients above. If the association between the expression of this gene and neuronal timescale is preserved at both levels, then the correlation across cells at the microscale should be similar to the correlation across cortical regions at the macroscale. Comparing across these two scales for all previously identified timescale-related genes in two studies (N = 170 \\[@bib81] and 4168 \\[@bib5] genes), we find a significant correlation between the strength of association at the single-cell and macroscale levels ([Figure 3D](#fig3D), horizontal black lines; _ρ_ = 0.36 and 0.25 for the two datasets, p&lt;0.001 for both). Furthermore, genes with stronger associations to timescale tend to conserve this relationship across single-cell and macroscale levels ([Figure 3D](#fig3D), separated by macroscale correlation magnitude). Thus, the association between cellular variations in gene expression and cell-intrinsic temporal dynamics is captured at the macroscale, even though scRNAseq and microarray data represent entirely different measurements of gene expression.\n",
    "\n",
    "While we have shown associations between cortical timescales and genes suspected to influence neuronal dynamics, these data present an opportunity to discover additional novel genes that are functionally related to timescales through a data-driven approach. However, since transcriptomic variation and anatomical hierarchy overlap along a shared macroscopic gradient (@bib10; @bib49; @bib64), we cannot specify the role certain genes play based on their level of association with timescale alone: gene expression differences across the cortex first result in cell-type and connectivity differences, sculpting the hierarchical organization of cortical anatomy. Consequently, anatomy and cell-intrinsic properties jointly shape neuronal dynamics through connectivity differences (@bib13; @bib19) and expression of ion transport and receptor proteins with variable activation timescales, respectively. Therefore, we ask whether variation in gene expression still accounts for variation in timescale beyond the principal structural gradient, and if associated genes have known functional roles in biological processes (BP) (schematic in [Figure 3E](#fig3EF)). To do this, we first remove the contribution of anatomical hierarchy by linearly regressing out the T1w/T2w gradient from both timescale and individual gene expression gradients. We then fit partial least squares (PLS) models to simultaneously estimate regression weights for all genes (@bib101), submitting those with significant associations for gene ontology enrichment analysis (GOEA) (@bib58).\n",
    "\n",
    "We find that genes highly associated with neuronal timescales are preferentially related to transmembrane ion transporter complexes, as well as GABAergic synapses and chloride channels (see [Figure 3F](#fig3EF) and [Supplementary file 1](#supp1) for GOEA results with brain genes only, and [Supplementary file 2](#supp2) for all genes). When restricted to positively associated genes only (expression increases with timescales), one functional group related to phosphatidate phosphatase activity is uncovered, including the gene PLPPR1, which has been linked to neuronal plasticity (@bib78)—a much slower timescale physiological process. Conversely, genes that are negatively associated with timescale are related to numerous groups involved in the construction and functioning of transmembrane transporters and voltage-gated ion channels, especially potassium and other inorganic cation transporters. To further ensure that these genes specifically relate to neuronal timescale, we perform the same enrichment analysis with T1w/T2w vs. gene maps as a control. The control analysis yields no significant GO terms when restricted to brain-specific genes (in contrast to [Figure 3F](#fig3EF)), while repeating the analysis with all genes does yield significant GO terms related to ion channels and synapses, but are much less specific to those (see [Supplementary file 3](#supp3)), including a variety of other gene clusters associated with general metabolic processes, signaling pathways, and cellular components (CC). This further strengthens the point that removing the contribution of T1w/T2w aids in identifying genes that are more specifically associated with neurodynamics, suggesting that inhibition (@bib80)—mediated by GABA and chloride channels—and voltage-gated potassium channels have prominent roles in shaping neuronal timescale dynamics at the macroscale level, beyond what is expected based on the anatomical hierarchy alone.\n",
    "\n",
    "## Timescales lengthen in working memory and shorten in aging\n",
    "\n",
    "Finally, having shown that neuronal timescales are associated with stable anatomical and gene expression gradients across the human cortex, we turn to the final question of the study: are cortical timescales relatively static, or are they functionally dynamic and relevant for human cognition? While previous studies have shown hierarchical segregation of task-relevant information corresponding to intrinsic timescales of different cortical regions (@bib3; @bib15; @bib47; @bib76; @bib77; @bib99), as well as optimal adaptation of behavioral timescales to match the environment (@bib33; @bib71), evidence for functionally relevant changes in regional neuronal timescales is lacking. Here, we examine whether timescales undergo short- and long-term shifts during working memory maintenance and aging, respectively.\n",
    "\n",
    "We first analyze human ECoG recordings from parietal, frontal (PFC and orbitofrontal cortex \\[OFC]), and medial temporal lobe (MTL) regions of patients (N = 14) performing a visuospatial working memory task that requires a delayed cued response ([Figure 4A](#fig4A); @bib51). Neuronal timescales were extracted for pre-stimulus baseline and memory maintenance delay periods (900 ms, both stimulus-free). Replicating our previous result in [Figure 2—figure supplement 1G](#fig2s1), we observe that baseline neuronal timescales follow a hierarchical progression across association regions, where parietal cortex (PC), PFC, OFC, and MTL have gradually longer timescales (pairwise Mann–Whitney U-test, [Figure 4B](#fig4BC)). If neuronal timescales track the temporal persistence of information in a functional manner, then they should expand during delay periods. Consistent with our prediction, timescales in all regions are ~20% longer during delay periods ([Figure 4C](#fig4BC); Wilcoxon rank-sum test). Moreover, only timescale changes in the PFC are significantly correlated with behavior across participants, where longer delay-period timescales relative to baseline are associated with better working memory performance ([Figure 4D](#fig4D), _ρ_ = 0.75, p=0.003). No other spectral features in the recorded brain regions show consistent changes from baseline to delay periods while also significantly correlating with individual performance, including the 1/f-like spectral exponent, narrowband theta (3–8 Hz), and high-frequency (high gamma; 70–100 Hz) activity power ([Figure 4—figure supplement 1](#fig4s1))."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "figure: Figure 4.\n",
    ":::\n",
    "\n",
    "## Timescales expand during working memory maintenance while tracking performance, and task-free average timescales compress in older adults.\n",
    "\n",
    ":::\n",
    "{#fig4}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "figure: Figure 4A.\n",
    ":::\n",
    "![](static_figs/fig_4A.jpg)\n",
    "\n",
    "(**A**) Fourteen participants with overlapping intracranial coverage performed a visuospatial working memory task, with 900 ms of baseline (pre-stimulus) and delay period data analyzed (PC: parietal, PFC: prefrontal, OFC: orbitofrontal, MTL: medial temporal lobe; n denotes number of subjects with electrodes in that region).\n",
    ":::\n",
    "{#fig4}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "caption": "(**B**) Baseline timescales follow hierarchical organization within association regions (*: p&lt;0.05, Mann–Whitney U-test; small dots represent individual participants, large dots and error bar for mean ± s.e.m. across participants). (**C**) All regions show significant timescale increase during delay period compared to baseline (asterisks represent p&lt;0.05, 0.01, 0.005, 0.001, Wilcoxon signed-rank test).",
    "execution": {
     "iopub.execute_input": "2021-04-13T22:26:56.798336Z",
     "iopub.status.busy": "2021-04-13T22:26:56.798058Z",
     "iopub.status.idle": "2021-04-13T22:26:57.271439Z",
     "shell.execute_reply": "2021-04-13T22:26:57.270696Z",
     "shell.execute_reply.started": "2021-04-13T22:26:56.798305Z"
    },
    "id": "fig4BC",
    "label": "Figure 4B-C"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_patient_info = pd.read_csv('./data/fig4D_patientinfo.csv', index_col=0)\n",
    "plt.figure(figsize=(8,4))\n",
    "#### baseline timescale\n",
    "df_mean = pd.read_csv('./data/fig4B.csv', index_col=0)\n",
    "region_labels = ['Parietal', 'PFC', 'OFC', 'MTL']\n",
    "rl_short = ['PC', 'PFC', 'OFC', 'MTL']\n",
    "plt.subplot(1,2,1)\n",
    "for i_r, reg in enumerate(region_labels):\n",
    "    plt.plot([i_r]*len(df_mean)+np.random.randn(len(df_mean))/10, df_mean[reg].values, '.', ms=5, alpha=0.7, color=C_ORD[i_r])\n",
    "    plt.errorbar(i_r, df_mean[reg].mean(), df_mean[reg].sem(), color=C_ORD[i_r], fmt='o', ms=10, alpha=1)\n",
    "\n",
    "plt.xticks(range(len(rl_short)), rl_short)\n",
    "plt.yticks(np.arange(0.02,0.07, 0.01), (np.round(np.arange(0.02,0.07, 0.01)*1000)).astype(int))\n",
    "plt.ylabel('baseline timescale (ms)')\n",
    "plt.xlim([-0.5, 3.5])\n",
    "plt.tight_layout()\n",
    "\n",
    "# print('-----prestim-----')\n",
    "# print('PC-OFC: ', stats.mannwhitneyu(df_mean['Parietal'], df_mean['OFC'], alternative='two-sided'))\n",
    "# print('PC-PFC: ', stats.mannwhitneyu(df_mean['Parietal'], df_mean['PFC'], alternative='two-sided'))\n",
    "# print('PC-MTL: ', stats.mannwhitneyu(df_mean['Parietal'], df_mean['MTL'], alternative='two-sided'))\n",
    "# print('PFC-OFC: ', stats.mannwhitneyu(df_mean['PFC'], df_mean['OFC'], alternative='two-sided'))\n",
    "# print('PFC-MTL: ', stats.mannwhitneyu(df_mean['PFC'], df_mean['MTL'], alternative='two-sided'))\n",
    "# print('OFC-MTL: ', stats.mannwhitneyu(df_mean['OFC'], df_mean['MTL'], alternative='two-sided'))\n",
    "\n",
    "#### pre-post change in timescale\n",
    "# print('\\n-----pre:post change-----')\n",
    "df_mean = pd.read_csv('./data/fig4C_mean.csv', index_col=0)\n",
    "df_sem = pd.read_csv('./data/fig4C_sem.csv', index_col=0)\n",
    "plt.subplot(1,2,2)\n",
    "for i_r, reg in enumerate(region_labels):\n",
    "    plt.plot([i_r]*len(df_mean)+np.random.randn(len(df_mean))/10, df_mean[reg].values, '.', ms=5, alpha=0.7, color=C_ORD[i_r])\n",
    "    plt.errorbar(i_r, df_mean[reg].mean(), df_mean[reg].sem(), color=C_ORD[i_r], fmt='o', ms=10, alpha=1)\n",
    "    pv = stats.wilcoxon(df_mean[reg][~np.isnan(df_mean[reg])])[1]\n",
    "    # print(reg, pv)\n",
    "    s = sig_str(0, pv)\n",
    "    plt.annotate(s.split(' ')[-1], xy=(i_r, 0.145), horizontalalignment='center', fontsize=20)\n",
    "    for j_r, reg2 in enumerate(region_labels):\n",
    "        pass # print(reg, '-', reg2, stats.mannwhitneyu(df_mean[reg][~np.isnan(df_mean[reg])], df_mean[reg2][~np.isnan(df_mean[reg2])], alternative='two-sided'))\n",
    "        \n",
    "plt.xticks(range(len(rl_short)), rl_short)\n",
    "# change axis to show percent change\n",
    "yt = np.arange(1,1.41,0.2)\n",
    "plt.yticks(np.log10(yt), ['%i%%'%int(yt_*100) for yt_ in yt]); plt.ylim(np.log10([yt[0], yt[-1]]))\n",
    "plt.ylabel('delay period timescale\\n(as % of baseline)')\n",
    "plt.xlim([-0.5, 3.5])\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "caption": "(**D**) PFC timescale expansion during delay periods predicts average working memory accuracy across participants (dot represents individual participants, mean ± s.e.m. across PFC electrodes within participant); inset: correlation between working memory accuracy and timescale change for all regions.",
    "id": "fig4D",
    "label": "Figure 4D"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#### pre-post change per region\n",
    "plt.figure(figsize=(8,4))\n",
    "reg='PFC'\n",
    "df_mean = pd.read_csv('./data/fig4C_mean.csv', index_col=0)\n",
    "df_sem = pd.read_csv('./data/fig4C_sem.csv', index_col=0)\n",
    "y = df_mean[reg][~np.isnan(df_mean[reg])]\n",
    "y_err = df_sem[reg][~np.isnan(df_sem[reg])]\n",
    "x = df_patient_info[['acc_identity','acc_spatial','acc_temporal']].mean(1)\n",
    "rho, pv = stats.spearmanr(x[y.index].values,y.values)\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "plt.errorbar(x[y.index].values, y.values, y_err.values, fmt='o', ms=8, alpha=0.8, color=C_ORD[1])\n",
    "s = sig_str(rho, pv, form='text')\n",
    "plt.annotate(s, xy=(0.7, 0.05), xycoords='axes fraction');\n",
    "plt.yticks(np.log10(yt), ['%i%%'%int(yt_*100) for yt_ in yt]); plt.ylim(np.log10([yt[0], yt[-1]]))\n",
    "plt.xticks()\n",
    "plt.xlim(left=0.68);plt.ylim(bottom=-0.01);\n",
    "plt.xlabel('working memory accuracy'); plt.ylabel('PFC delay period timescale (%)')\n",
    "plt.tight_layout()\n",
    "\n",
    "# show all regions\n",
    "plt.subplot(1,2,2)\n",
    "for i_r, reg in enumerate(region_labels):\n",
    "    y = df_mean[reg][~np.isnan(df_mean[reg])]\n",
    "    rho, pv = stats.spearmanr(x[y.index].values,y.values)\n",
    "    # print(reg, rho, pv)\n",
    "    plt.barh(len(region_labels)-i_r, rho, fc=C_ORD[i_r], ec='k', lw=1, alpha=0.8)\n",
    "    s = sig_str(rho, pv)\n",
    "\n",
    "plt.xticks([-1,1]); plt.xlabel(r'$\\rho$', labelpad=-15)\n",
    "plt.yticks(range(1,5), region_labels[::-1]);\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "figure: Figure 4—figure supplement 1.\n",
    ":::\n",
    "![](elife-61277.xml.media/fig4-figsupp1.jpg)\n",
    "\n",
    "### Spectral correlates of working memory performance.\n",
    "\n",
    "(**A**) Difference between delay and baseline periods for 1/f-exponent, timescale (same as main [Figure 4C](#fig4BC) but absolute units on y-axis, instead of percentage), theta power, and high-frequency power. (**B**) Spearman correlation between spectral feature difference and working memory accuracy across participants, same features as in (**A**). \\*p&lt;0.05, \\*\\*p&lt;0.01, \\*\\*\\*p&lt;0.005 in (**A, B**). (**C**) Scatter plot of other significantly correlated spectral features from (**B**).\n",
    ":::\n",
    "{#fig4s1}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "caption": "(**E**) In the MNI-iEEG dataset, participant-average cortical timescales decrease (become faster) with age (n = 71 participants with at least 10 valid parcels, see [Figure 4—figure supplement 2B](#fig4s2)). (**F**) Most cortical parcels show a negative relationship between timescales and age, with the exception being parts of the visual cortex and the temporal poles (one-sample t-test, _t_ = −7.04, p&lt;0.001; n = 114 parcels where at least six participants have data, see [Figure 4—figure supplement 2C](#fig4s2)).",
    "execution": {
     "iopub.execute_input": "2021-04-13T22:26:57.272702Z",
     "iopub.status.busy": "2021-04-13T22:26:57.272463Z",
     "iopub.status.idle": "2021-04-13T22:26:57.505592Z",
     "shell.execute_reply": "2021-04-13T22:26:57.501893Z",
     "shell.execute_reply.started": "2021-04-13T22:26:57.272673Z"
    },
    "id": "fig4EF",
    "label": "Figure 4E-F"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "### normalized averages timescales over parcels per patient\n",
    "df_patient = pd.read_csv('./data/fig4E_patient.csv', index_col=0)\n",
    "tau_subj_normed = pd.read_csv('./data/fig4E.csv', index_col=0)\n",
    "n_parcel_thresh = 10\n",
    "n_main = n_parcel_thresh\n",
    "x = df_patient['age'][df_patient['coverage']>=n_parcel_thresh]\n",
    "y = tau_subj_normed[df_patient['coverage']>=n_parcel_thresh]\n",
    "rho, pv = stats.spearmanr(x,y)\n",
    "s = sig_str(rho, pv, form='text') +'\\nn = %i'%len(x)\n",
    "m,b,_,_,_ = stats.linregress(x.values, y['0'].values)\n",
    "\n",
    "plt.figure(figsize=(8,4))\n",
    "plt.subplot(1,2,1)\n",
    "plt.plot(x, y, 'ok', alpha=0.5, ms=5);\n",
    "XL= np.array(plt.xlim())\n",
    "plt.plot(XL,XL*m+b, 'k--', lw=2, alpha=0.8) \n",
    "plt.xlabel('age (years)'); plt.ylabel('parcel-normalized timescale');\n",
    "plt.xticks([10, 35, 60],None); plt.yticks([0,1])\n",
    "plt.annotate(s, xy=(0.05, 0.025), xycoords='axes fraction');\n",
    "\n",
    "### distribution of correlations\n",
    "parcel_corr = pd.read_csv('./data/fig4F.csv', index_col=0)\n",
    "x = parcel_corr.values\n",
    "# t-test on whether parcels have positive or negative correlation in aggregate\n",
    "tval, tt_pv = stats.ttest_1samp(x, 0, nan_policy='omit')\n",
    "plt.subplot(1,2,2)\n",
    "plt.hist(x[~np.isnan(x)], bins=np.arange(-1,1.1,0.1), color='k', alpha=0.5)\n",
    "plt.axvline(np.nanmean(x), color=C_ORD[3], ls='--')\n",
    "plt.xlabel(r'$\\rho$');plt.ylabel('parcels (%)', labelpad=-15);\n",
    "s = sig_str(np.nanmean(x), tt_pv, form='text', corr_letter=r'$\\bar{\\rho}$')+'\\nn = %i'%len(x[~np.isnan(x)])\n",
    "plt.annotate(s, xy=(0.45, 0.8), xycoords='axes fraction')\n",
    "plt.xticks([-1,0,1],None);plt.yticks(np.array([0,15])/100*sum(~np.isnan(x)), ['0%', '15%']);\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "figure: Figure 4F (inset).\n",
    ":::\n",
    "\n",
    "![](static_figs/fig_4F_inset.jpg)\n",
    "\n",
    "Timescale-age correlation across cortical parcels.\n",
    "\n",
    ":::\n",
    "{#fig4s2}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "figure: Figure 4—figure supplement 2.\n",
    ":::\n",
    "![](elife-61277.xml.media/fig4-figsupp2.jpg)\n",
    "\n",
    "### Parameter sensitivity for timescale-aging analysis.\n",
    "\n",
    "(**A**) Cortex-averaged timescale is independent of parcel coverage across participants. (**B**) Sensitivity analysis for the number of valid parcels a participant must have in order to be included in analysis for main [Figure 4E](#fig4EF) (red). As threshold increases (more stringent), fewer participants satisfy the criteria (right) but correlation between participant age and timescale remains robust (left). (**C**) Sensitivity analysis for the number of valid participants a parcel must have in order to be included in analysis for main [Figure 4F](#fig4EF). As threshold increases (more stringent), fewer parcels satisfy the criteria (right) but average correlation across all parcels remains robust (left, error bars denote s.e.m of distribution as in [Figure 4F](#fig4EF)).\n",
    ":::\n",
    "{#fig4s2}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "While timescales are consistent with the anatomical and gene expression hierarchy at a snapshot, brain structure itself is not static over time, undergoing many slower, neuroplastic changes during early development and throughout aging in older populations. In particular, aging is associated with a broad range of functional and structural changes, such as working memory impairments (@bib89; @bib96), as well as changes in neuronal dynamics (@bib89; @bib90; @bib96) and cortical structure (@bib18), such as the loss of slow-deactivating NMDA receptor subunits (@bib73). Since neuronal timescales may support working memory maintenance, we predict that timescales would shorten across the lifespan, in agreement with the observed cognitive and structural deteriorations. To this end, we leverage the wide age range in the MNI-iEEG dataset (13–62 years old) and probe average cortical timescales for each participant as a function of age. Since ECoG coverage is sparse and nonuniform across participants, simply averaging across parcels within individual participants confounds the effect of aging with the spatial effect of cortical hierarchy. Instead, we first normalize each parcel by its max value across all participants before averaging within participants, excluding those with fewer than 10 valid parcels (N = 71 of 106 subjects remaining; results hold for a large range of threshold values, [Figure 4—figure supplement 2B](#fig4s2)). We observe that older adults have faster neuronal timescales (_ρ_ = −0.31, p=0.010; [Figure 4E](#fig4EF)), and that timescales shorten with age in most areas across the cortex ([Figure 4F](#fig4EF), _t_ = −7.04, p&lt;0.001; 114 out of 189 parcels where at least six participants have data, see [Figure 4—figure supplement 2C](#fig4s2)). This timescale compression is especially prominent in sensorimotor, temporal, and medial frontal regions. These results support our hypothesis that neuronal timescales, estimated from transmembrane current fluctuations, can rapidly shift in a functionally relevant manner, as well as slowly—over decades—in healthy aging.\n",
    "\n",
    "# Discussion\n",
    "\n",
    "Theoretical accounts and converging empirical evidence predict a graded variation of neuronal timescales across the human cortex (@bib13; @bib49; @bib98), which reflects functional specialization and implements hierarchical temporal processing crucial for complex cognition (@bib57). This timescale gradient is thought to emerge as a consequence of cortical variations in cytoarchitecture, as well as both macroscale and microcircuit connectivity, thus serving as a bridge from brain structure to cognitive function (@bib55). In this work, we infer the timescale of non-rhythmic transmembrane current fluctuations from invasive human intracranial recordings and test those predictions explicitly. We discuss the implications and limitations of our findings below.\n",
    "\n",
    "## Multiple quantities for neuronal timescale and anatomical hierarchy\n",
    "\n",
    "We first find that neuronal timescales vary continuously across the human cortex and coincide with the anatomical hierarchy, with timescales increasing from primary sensory and motor to association regions. While we use the continuous T1w/T2w gradient as a surrogate measure for anatomical hierarchy, there are multiple related but distinct perspectives on what ‘cortical hierarchy’ means, including, for example, laminar connectivity patterns from tract tracing data (@bib26; @bib85), continuous (and latent-space) gradients of gene expression and microarchitectural features (@bib49), and network connectivity scales (see review of @bib46)—with most of these following a graded sensorimotor-to-association area progression. Similarly, it is important to note that there exist many different quantities that can be considered as characteristic neuronal timescales across several spatial scales, including membrane potential and synaptic current timescales (@bib22), single-unit spike-train timescales (@bib68), population code timescales (@bib76), and even large-scale circuit timescales measured from the fMRI BOLD signal (@bib100). We show here that timescales inferred from ECoG are consistently approximately 10 times faster than single-unit spiking timescales in macaques, corroborating the fact that field potential signals mainly reflect fast transmembrane and synaptic currents (@bib12), whose timescales are related to, but distinct from, single-unit timescales measured in previous studies (@bib21; @bib70; @bib99).\n",
    "\n",
    "Because field potential fluctuations are driven by currents from both locally generated and distal inputs, our results raise questions on how and when these timescales interact to shape downstream spiking dynamics. Furthermore, while we specifically investigate here the aperiodic timescale, which corresponds to the exponential decay timescale measured in previous studies, recent work has shown a similar gradient of oscillatory timescales (i.e., frequency) along the anterior–posterior axis of the human cortex (@bib63). Based on the similarity of these gradients and known mechanisms of asynchronous and oscillatory population dynamics (e.g., balance of excitation and inhibition in generating gamma oscillations and the asynchronous irregular state in cortical circuits \\[@bib8; @bib9]), we speculate that timescales of oscillatory and aperiodic neural dynamics may share (at least partially) circuit mechanisms at different spatial scales, analogous to the relationship between characteristic frequency and decay constant in a damped harmonic oscillator model.\n",
    "\n",
    "## Collinearity and surrogate nature of postmortem gene expression gradients\n",
    "\n",
    "Using postmortem gene expression data as a surrogate for protein density, transcriptomic analysis uncovers the potential roles that transmembrane ion transporters and synaptic receptors play in establishing the cortical gradient of neuronal timescales. The expression of voltage-gated potassium channel, chloride channel, and GABAergic receptor genes, in particular, are strongly associated with the spatial variation of neuronal timescale. Remarkably, we find that electrophysiology-transcriptomic relationships discovered at the single-cell level, through patch-clamp recordings and single-cell RNA sequencing, are recapitulated at the macroscale between bulk gene expression and timescales inferred from ECoG. That being said, it is impossible to make definitive causal claims with the data presented in this study, especially considering the fact that several microanatomical features, such as gray matter myelination and cortical thickness, follow similar gradients across the cortex (@bib10). To discover genes specifically associated with timescale while accounting for the contribution of the overlapping anatomical hierarchy, we linearly regress out the T1w/T2w gradient from both timescale and gene expression gradients. Although this procedure does not account for any nonlinear contributions from anatomy, gene enrichment control analysis using T1w/T2w instead of timescales further demonstrates that the discovered genes—transmembrane ion transporters and inhibitory synaptic receptors—are more specifically associated with the timescale gradient, over and above the level predicted by anatomical hierarchy alone. From these results, we infer that potassium and chloride ion channels, as well as GABAergic receptors, may play a mechanistic role in altering the timescale of transmembrane currents at the macroscopic level.\n",
    "\n",
    "However, this interpretation rests on the key assumption that mRNA expression level is a faithful representation of the amount of functional proteins in a given brain region. In general, gene expression levels are highly correlated with the percentage of cells expressing that gene within brain regions (@bib60). Therefore, on a population level, the regional density of a particular ion channel or receptor protein is high if bulk mRNA expression is high. Furthermore, recent works have shown that neurotransmitter receptor density measured via autoradiography in postmortem brains follows similar cortical gradients (@bib41), and that gene expression levels of neurotransmitter receptors (e.g., 5HT) are strongly correlated with ligand binding potential measured via PET (@bib42). Thus, as a first order approximation, receptor gene expression is an adequate surrogate for receptor protein density in the brain at the macroscale, though the relationship between mRNA expression and their transport and translation into channel proteins, the process of incorporating those proteins into membranes and synapses, and how these gene expression maps can be related to other overlapping macroscopic gradients are complex issues (see e.g., @bib28; @bib62). Thus, our analyses represent an initial data-mining process at the macroscopic level, which should motivate further studies in investigating the precise roles voltage-gated ion channels and synaptic inhibition play in shaping functional neuronal timescales through causal manipulations, complementary to existing lines of research focusing on NMDA activation and recurrent circuit motifs.\n",
    "\n",
    "## Structural constraints vs. behaviorally required flexibility in timescale\n",
    "\n",
    "Finally, we show that neuronal timescales are not static, but can change both in the short and long terms. Transmembrane current timescales across multiple association regions, including parietal, frontal, and medial temporal cortices, increase during the delay period of a working memory task, consistent with the emergence of persistent spiking during working memory delay. Working memory performance across individuals, however, is predicted by the extent of timescale increase in the PFC only. This further suggests that behaviorally relevant neural activity may be localized despite widespread task-related modulation (@bib74), even at the level of neuronal membrane fluctuations. In the long term, we find that neuronal timescale shortens with age in most cortical regions, linking age-related synaptic, cellular, and connectivity changes—particularly those that influence neuronal integration timescale—to the compensatory posterior-to-anterior shift of functional specialization in healthy aging (@bib17).\n",
    "\n",
    "These results raise further questions regarding contrasting, and potentially complementary, aspects of neuronal timescale: on the one hand, task-free timescales across the cortex are shaped by relatively static macro- and microarchitectural properties ([Figures 2](#fig2D) and [3](#fig3A)); on the other hand, timescales are dynamic and shift with behavioral demand ([Figure 4](#fig4D)). While long-term structural changes in the brain can explain shifts in neuronal timescales throughout the aging process, properties such as ion channel protein density probably do not change within seconds during a working memory task. We speculate that structural properties may constrain dynamical properties (such as timescale) to a possible range within a particular brain region and at different spatial scales, while task requirements, input statistics, short-term synaptic plasticity, and neuromodulation can then shift timescale within this range. We posit, then, that only shifts in dynamics within the area of relevance (i.e., PFC for working memory) are indicative of task performance, consistent with recent ideas of computation-through-dynamics (@bib91). Nevertheless, which neuromodulatory and circuit mechanisms are involved in shifting local timescales, and how timescales at different spatial scales (e.g., synaptic, neuronal, population) interact to influence each other remain open questions for future investigation (@bib6; @bib22; @bib31; @bib32; @bib37; @bib79).\n",
    "\n",
    "## Conclusion\n",
    "\n",
    "In summary, we identify consistent and converging patterns between transcriptomics, anatomy, dynamics, and function across multiple datasets of different modalities from different individuals and multiple species. As a result, evidence for these relationships can be supplemented by more targeted approaches such as imaging of receptor metabolism. Furthermore, the introduction and validation of an open-source toolbox (@bib20) for inferring timescales from macroscale electrophysiological recordings potentially allows for the noninvasive estimation of neuronal timescales, using widely accessible tools such as EEG and MEG. These results open up many avenues of research for discovering potential relationships between microscale gene expression and anatomy with the dynamics of neuronal population activity at the macroscale in humans."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Materials and methods\n",
    "\n",
    "## Inferring timescale from autocorrelation and PSD\n",
    "\n",
    "Consistent with previous studies, we define ‘neuronal timescale’ as the exponential decay time constant (_τ_) of the empirical ACF, or lagged correlation (@bib47; @bib68). _τ_ can be naively estimated to be the time it takes for the ACF to decrease by a factor of _e_ when there are no additional long-term, scale-free, or oscillatory processes, or by fitting a function of the form $f\\left(t\\right)={e}^{-\\frac{t}{\\tau }}$ and extracting the parameter _τ._ Equivalently, the PSD is the Fourier Transform of the ACF via Wiener–Khinchin theorem (@bib56) and follows a Lorentzian function of the form $L\\left(f\\right)=\\frac{A}{k+{f}^{\\chi }}$ for approximately exponential-decay processes, with $\\chi =2$ exactly when the ACF is solely composed of an exponential decay term, though it is often variable and in the range between 2 and 6 for neural time series (@bib20; @bib65; @bib75; @bib89). Timescale can be computed from the parameter _k_ as $\\tau =\\frac{1}{2\\pi {f}_{k}}$, where ${f}_{k}\\approx {k}^{1/\\chi }$ is approximated to be the ‘knee frequency’, at which a bend or knee in the power spectrum occurs, and equality holds when $\\chi =2$.\n",
    "\n",
    "## Computing PSD\n",
    "\n",
    "PSDs are estimated using a modified Welch’s method, where short-time windowed Fourier transforms (STFT) are computed from the time series, but the median is taken across time instead of the mean (in conventional Welch’s method) to minimize the effect of high-amplitude transients and artifacts (@bib50). Custom functions for this can be found in NeuroDSP (@bib16), a published and open-source digital signal processing toolbox for neural time series ([neurodsp.spectral.compute_spectrum](https://neurodsp-tools.github.io/neurodsp/generated/neurodsp.spectral.trim_spectrum.html)). For simulated data, Neurotycho macaque ECoG, and MNI-iEEG datasets, we use 1 s long Hamming windows with 0.5 s overlap. To estimate single-trial PSDs for the working memory ECoG dataset (CRCNS Johnson-ECoG @bib51; @bib52), we simply apply Hamming window to 900 ms long epoched time series and compute the squared magnitude of the windowed Fourier transform.\n",
    "\n",
    "## Spectral parametrization\n",
    "\n",
    "We apply spectral parameterization (@bib20) to extract timescales from PSDs. Briefly, we decompose log-power spectra into a summation of narrowband periodic components—modeled as Gaussians—and an aperiodic component—modeled as a generalized Lorentzian function centered at 0 Hz ($L\\left(f\\right)$ above). For inferring decay timescale, this formalism can be practically advantageous when a strong oscillatory or variable power-law (_χ_) component is present, as is often the case for neural signals. While oscillatory and power-law components can corrupt naive measurements of _τ_ as time for the ACF to reach 1/_e_, they can be easily accounted for and ignored in the frequency domain as narrowband peaks and 1/f-exponent fit. We discard the periodic components and infer timescale from the aperiodic component of the PSD. For a complete mathematical description of the model, see @bib20.\n",
    "\n",
    "## Simulation and validation\n",
    "\n",
    "We simulate the aperiodic background component of neural field potential recordings as autocorrelated stochastic processes by convolving Poisson population spikes with exponentially decaying synaptic kernels with predefined decay time constants (neurodsp.sim.sim_synaptic_current). PSDs of the simulated data are computed and parameterized as described above, and we compare the fitted timescales with their ground-truth values.\n",
    "\n",
    "## Macaque ECoG and single-unit timescales data\n",
    "\n",
    "Macaque single-unit timescales are taken directly from values reported in Figure 1c of @bib68. Whole-brain surface ECoG data (1000 Hz sampling rate) is taken from the Neurotycho repository (@bib69; @bib103), with eight sessions of 128-channel recordings from two animals (George and Chibi, four sessions each). Results reported in [Figure 2E–G](#fig2E) are from ~10 min eyes-open resting periods to match the pre-stimulus baseline condition of single-unit experiments. Timescales for individual ECoG channels are extracted and averaged over regions corresponding to single-unit recording areas from @bib68; [Figure 2F](#fig2F) inset and [Figure 2—figure supplement 3](#fig2s3), which are selected visually based on the overlapping cortical map and landmark sulci/gyri. Each region included between two and four electrodes (see [Figure 2—figure supplement 3B](#fig2s3) for selected ECoG channel indices for each region).\n",
    "\n",
    "## Statistical analysis for macaque ECoG and spiking timescale\n",
    "\n",
    "For each individual recording session, as well as the grand average, Spearman rank correlation was computed between spiking and ECoG timescales. Linear regression models were fit using the python package scipy (@bib86) (scipy.stats.linregress) and the linear slope was used to compute the scaling coefficient between spiking and ECoG timescales.\n",
    "\n",
    "## Variations in neuronal timescale, T1/T2 ratio, and mRNA expression across human cortex\n",
    "\n",
    "The following sections describe procedures for generating the average cortical gradient maps for neuronal timescale, MR-derived T1w/T2w ratio, and gene expression from the respective raw datasets. All maps were projected onto the 180 left hemisphere parcels of Human Connectome Project’s Multimodal Parcellation (@bib38) (HCP-MMP1.0) for comparison, described in the individual sections. Projection of T1w/T2w and gene expression maps from MNI volumetric coordinates to HCP-MMP1.0 can be found: <https://github.com/rudyvdbrink/Surface_projection> (@bib82).\n",
    "\n",
    "All spatial correlations are computed as Spearman rank correlations between maps. Procedure for computing statistical significance while accounting for SA is described in detail below under the sections 'Spatial statistics' and 'SA modeling'.\n",
    "\n",
    "## Neuronal timescale map\n",
    "\n",
    "The MNI Open iEEG dataset consists of 1 min of resting state data across 1772 channels from 106 epilepsy patients (13–62 years old, 58 males, and 48 females), recorded using either surface strip/grid or stereoEEG electrodes, and cleaned of visible artifacts (@bib29; @bib30). Neuronal timescales were extracted from PSDs of individual channels, and projected from MNI voxel coordinates onto HCP-MMP1.0 surface parcellation as follows.\n",
    "\n",
    "For each patient, timescale estimated from each electrode was extrapolated to the rest of the cortex in MNI coordinates using a Gaussian weighting function (confidence mask), $w\\left(r\\right)={e}^{-\\left({r}^{2}/{\\alpha }^{2}\\right)}$, where _r_ is the Euclidean distance between the electrode and a voxel, and _α_ is the distance scaling constant, chosen here such that a voxel 4 mm away has 50% weight (or confidence). Timescale at each voxel is computed as a weighted spatial average of timescales from all electrodes (i) of that patient:\n",
    "\n",
    "i.e., ${\\tau }_{voxel}=\\frac{{{\\displaystyle \\sum }}_{i}^{}w{\\left({r}_{i}\\right)}_{}{\\tau }_{i}}{{{\\displaystyle \\sum }}_{i}^{}w{\\left({r}_{i}\\right)}_{}}$.\n",
    "\n",
    "Similarly, each voxel is assigned a confidence rating that is the maximum of weights over all electrodes (_w~voxel~(r~min~), _of the closest electrode), i.e., a voxel right under an electrode has a confidence of 1, while a voxel 4 mm away from the closest electrode has a confidence of 0.5, etc.\n",
    "\n",
    "Timescales for each HCP-MMP parcel were then computed as the confidence-weighted arithmetic mean across all voxels that fall within the boundaries of that parcel. HCP-MMP boundary map is loaded and used for projection using NiBabel (@bib7). This results in a 180 parcels-by-106 patients timescale matrix. A per-parcel confidence matrix of the same dimensions was computed by taking the maximum confidence over all voxels for each parcel ([Figure 2—figure supplement 1A](#fig2s1)). The average cortical timescale map (gradient) is computed by taking the confidence-weighted average at each parcel across all participants. Note that this procedure for locally thresholded and weighted average is different from projection procedures used for the mRNA and T1w/T2w data due to region-constrained and heterogeneous ECoG electrode sites across participants. While coverage is sparse and idiosyncratic in individual participants, it does not vary as a function of age, and when pooling across the entire population, 178 of 180 parcels have at least one patient with an electrode within 4 mm, with the best coverage in sensorimotor, temporal, and frontal regions ([Figure 2—figure supplement 1](#fig2s1)).\n",
    "\n",
    "## T1w/T2w ratio and cortical thickness maps\n",
    "\n",
    "As a measure of structural cortical hierarchy, we used the ratio between T1- and T2-weighted structural MRI, referred to as T1w/T2w map in main text, or the myelin map (@bib10; @bib39). Since there is little variation in the myelin map across individuals, we used the group average myelin map of the WU-Minn HCP S1200 release (N = 1096, March 1, 2017 release) provided in HCP-MMP1.0 surface space. For correlation with other variables, we computed the median value per parcel, identical to the procedure for mRNA expression below. Cortical thickness map was similarly generated.\n",
    "\n",
    "## mRNA expression maps\n",
    "\n",
    "We used the Allen Human Brain Atlas (AHBA) gene expression dataset (@bib44; @bib43) that comprised postmortem samples of six donors (one female and five males) that underwent microarray transcriptional profiling. Spatial maps of mRNA expression were available in volumetric 2 mm isotropic MNI space, following improved nonlinear registration and whole-brain prediction using variogram modeling as implemented by @bib42. We used whole-brain maps available from @bib42 rather than the native sample-wise values in the AHBA database to prevent bias that could occur due to spatial inhomogeneity of the sampled locations. In total, 18,114 genes were included for analyses that related to the dominant axis of expression across the genome.\n",
    "\n",
    "We projected the volumetric mRNA expression data onto the HCP-MMP cortical surface using the HCP workbench software (v1.3.1 running on Windows OS 10) with the ‘enclosing’ method and custom MATLAB code ([github.com/rudyvdbrink/surface_projection](https://github.com/rudyvdbrink/surface_projection)) (@bib82). The enclosing method extracts for all vertices on the surface the value from enclosing voxels in the volumetric data. Alternative projection methods such as trilinear 3D linear interpolation of surrounding voxels, or ribbon mapping that constructs a polyhedron from each vertex's neighbors on the surface to compute a weighted mean for the respective vertices, yielded comparable values, but less complete cortical coverage. Moreover, the enclosing method ensured that no transformation of the data (nonlinear or otherwise) occurred during the projection process and thus the original values in the volumetric data were preserved.\n",
    "\n",
    "Next, for each parcel of the left hemisphere in HCP-MMP, we extracted the median vertex-wise value. We used the median rather than the mean because it reduced the contribution of outliers in expression values within parcels. Vertices that were not enclosed by voxels that contained data in volumetric space were not included in the parcel-wise median. This was the case for 539 vertices (1.81% of total vertices). Linear interpolation across empty vertices prior to computing median parcel-wise values yielded near-identical results (_r =_ 0.95 for reconstructed surfaces). Lastly, expression values were mean and variance normalized across parcels to facilitate visualization. Normalization had no effect on spatial correlation between gene expression and other variables since the spatial distribution of gene expression was left unaltered.\n",
    "\n",
    "## Selection of brain-specific genes\n",
    "\n",
    "Similar to @bib10; @bib25; @bib36, N = 2429 brain-specific genes were selected based on the criteria that expression in brain tissues were four times higher than the median expression across all tissue types, using Supplementary Dataset 1 of @bib25. PC1 result shown in [Figure 3A](#fig3A) is computed from brain-specific genes, though findings are similar when using all genes (_ρ_ = −0.56 with timescale map, [Figure 3—figure supplement 1](#fig3s1)).\n",
    "\n",
    "## Spatial statistics\n",
    "\n",
    "All correlations between spatial maps (timescale, T1w/T2w, gene principal component \\[PC], and individual gene expressions) were computed using Spearman rank correlation. As noted in @bib11; @bib10; @bib87, neural variables vary smoothly and continuously across the cortical surface, violating the assumption of independent samples. As a result, when correlating two variables, each with nontrivial SA, the naive p-value is artificially lowered since it is compared against an inappropriate null hypothesis, i.e., randomly distributed or shuffled values across space. Instead, a more appropriate null hypothesis introduces SA-preserving null maps, which destroys any potential correlation between two maps while respecting their SAs. For all spatial correlation analyses, we generated N = 1000 null maps of one variable (timescale map unless otherwise noted), and the test statistic, Spearman correlation (_ρ_), is computed against the other variable of interest to build the null distribution. Two-tailed significance is then computed as the proportion of the null distribution that is less extreme than the empirical correlation value. All regression lines were computed by fitting a linear regression to log-timescale and the structural feature maps.\n",
    "\n",
    "## SA modeling\n",
    "\n",
    "To generate SA-preserving null maps, we used Moran Spectral Randomization (MSR) (@bib92) from the python package BrainSpace (@bib87). Details of the algorithm can be found in the above references. Briefly, MSR performs eigendecomposition on a spatial weight matrix of choice, which is taken here to be the inverse average geodesic distance matrix between all pairs of parcels in HCP-MMP1.0. The eigenvectors of the weight matrix are then used to generate randomized null feature maps that preserves the autocorrelation of the empirical map. We used the singleton procedure for null map generation. All significance values reported ([Figures 2D](#fig2D)[ and 3A–C](#fig3A)) were adjusted using the above procedure.\n",
    "\n",
    "We also compare two other methods of generating null maps: spatial variogram fitting (VF) (@bib11) and spin permutation (@bib1). Null maps were generated for timescale using spatial VF, while for spin permutation they were generated for vertex-wise T1w/T2w and gene PC1 maps before parcellation, so as to preserve surface locations of the parcellation itself. All methods perform similarly, producing comparable SA in the null maps, assessed using spatial variogram, as well as null distribution of spatial correlation coefficients between timescale and T1w/T2w ([Figure 2—figure supplement 2](#fig2s2)).\n",
    "\n",
    "## Principal component analysis (PCA) of gene expression\n",
    "\n",
    "We used scikit-learn (@bib72) PCA (sklearn.decomposition.PCA) to identify the dominant axes of gene expression variation across the entire AHBA dataset, as well as for brain-specific genes. PCA was computed on the variance-normalized average gene expression maps, _X_, an N × P matrix where N = 18,114 (or N = 2429 brain-specific) genes, and P = 180 cortical parcels. Briefly, PCA factorizes _X_ such that _X = USV^T^_, where _U_ and _V_ are unitary matrices of dimensionality N × N and P × P, respectively. _S_ is the same dimensionality as _X_ and contains non-negative descending eigenvalues on its main diagonal (Λ). Columns of _V_ are defined as the PCs, and the dominant axis of gene expression is then defined as the first column of V, whose proportion of variance explained in the data is the first element of Λ divided by the sum over Λ. Results for PC1 and PC2-10 are shown in [Figure 3A](#fig3A) and [Figure 3—figure supplement 1](#fig3s1), respectively.\n",
    "\n",
    "## Comparison of timescale-transcriptomic association with single-cell timescale genes\n",
    "\n",
    "Single-cell timescale genes were selected based on data from Table S3 of @bib81 and Online Table 1 of @bib5. Using single-cell RNA sequencing data and patch-clamp recordings from transgenic mice cortical neurons, these studies identified genes whose expression significantly correlated with electrophysiological features derived from generalized linear integrate and fire (GLIF) model fits. We selected genes that were significantly correlated with membrane time constant (_tau_), input resistance (_Rin_ or _ri_), or capacitance (_Cm_ or _cap_) in the referenced data tables, and extracted the level of association between gene expression and those electrophysiological feature (correlation ‘DiscCorr’ in @bib81 and linear coefficient ‘beta_gene’ in @bib5).\n",
    "\n",
    "To compare timescale-gene expression association at the single-cell and macroscale level, we correlated the single-cell associations extracted above with the spatial correlation coefficient (macroscale _ρ_) between ECoG timescale and AHBA microarray expression data for those same genes, restricting to genes with p&lt;0.05 for macroscale correlation (results identical for non-restrictive gene set). Overall association for all genes, as well as split by quintiles of their absolute macroscale correlation coefficient, are shown in [Figure 3D](#fig3D). Example ‘single-cell timescale’ genes shown in [Figure 3B and C](#fig3B) are genes showing the highest correlations with those electrophysiology features reported in Table 2 of @bib5.\n",
    "\n",
    "## T1w/T2w-removed timescale and gene expression residual maps\n",
    "\n",
    "To remove anatomical hierarchy as a potential mediating variable in timescale–gene expression relationships, we linearly regress out the T1w/T2w map from the (log) timescale map and individual gene expression maps. T1w/T2w was linearly fit to log-timescale, and the error between T1w/T2w-predicted timescale and empirical timescale was extracted (residual); this identical procedure was applied to every gene expression map to retrieve the gene residuals. SA-preserving null timescale residual maps were similarly created using MSR.\n",
    "\n",
    "## PLS regression model\n",
    "\n",
    "Due to multicollinearity in the high-dimensional gene expression dataset (many more genes than parcels), we fit a PLS model to the timescale map with one output dimension (sklearn.cross_decomposition.PLSRegression) to estimate regression coefficient for all genes simultaneously, resulting in N = 18,114 (or N = 2429 brain-specific) PLS weights (@bib84; @bib101). To determine significantly associated (or ‘enriched’) genes, we repeated the above PLS-fitting procedure 1000 times but replaced the empirical timescale map (or residual map) with null timescale maps (or residual maps) that preserved its SA. Genes whose absolute empirical PLS weight was greater than 95% of its null weight distribution was deemed to be enriched, and submitted for GOEA.\n",
    "\n",
    "## Gene ontology enrichment analysis\n",
    "\n",
    "The Gene Ontology (GO) captures hierarchically structured relationships between GO items representing aspects of biological processes (BP), cellular components (CC), or molecular functions (MF). For example, ‘synaptic signaling’, ‘chemical synaptic transmission’, and ‘glutamatergic synaptic transmission’ are GO items with increasing specificity, with smaller subsets of genes associated with each function. Each GO item is annotated with a list of genes that have been linked to that particular process or function. GOEA examines the list of enriched genes from above to identify GO items that are more associated with those genes than expected by chance. We used GOATOOLS (@bib58) to perform GOEA programmatically in python.\n",
    "\n",
    "The list of unranked genes with significant empirical PLS weights was submitted for GOEA as the ‘study set’, while either the full ABHA list or brain-specific gene list was used as the ‘reference set’. The output of GOEA is a list of GO terms with annotated genes that are enriched or purified (i.e., preferentially appearing or missing in the study list, respectively) more often than by chance, determined by Fisher’s exact test.\n",
    "\n",
    "Enrichment ratio is defined as follows: given a reference set with _N_ total genes, and _n_ were found to be significantly associated with timescale (in the study set), for a single GO item with _B_ total genes annotated to it, where _b_ of them overlap with the study set, then. Statistical significance is adjusted for multiple comparisons following Benjamini–Hochberg procedure (false discovery rate q-value reported in [Figure 3F](#fig3EF)), and all significant GO items (q &lt; 0.05) are reported in [Figure 3F](#fig3EF), in addition to some example items that did not pass significance threshold. For a detailed exposition, see @bib4. [Figure 3F](#fig3EF) shows results using brain-specific genes. The GO items that are significantly associated are similar when using the full gene set, but typically with larger q-values ([Supplementary file 1](#supp1) and [2](#supp2)) due to a much larger set of (non-brain-specific) genes. Control analysis was conducted using T1w/T2w, with 1000 similarly generated null maps, instead of timescale.\n",
    "\n",
    "## Working memory ECoG data and analysis\n",
    "\n",
    "The CRCNS fcx-2 and fcx-3 datasets include 17 intracranial ECoG recordings in total from epilepsy patients (10 and 7, respectively) performing the same visuospatial working memory task (@bib54; @bib53; @bib51, @bib52). Subject 3 (s3) from fcx-2 was discarded due to poor data quality upon examination of trial-averaged PSDs (high noise floor near 20 Hz), while s5 and s7 from fcx-3 correspond to s5 and s8 in fcx-2 and were thus combined. Together, data from 14 unique participants (22–50 years old, five females) were analyzed, with variable and overlapping coverage in PC (n = 14), PFC (n = 13), OFC (n = 8), and MTL (n = 9). Each channel was annotated as belonging to one of the above macro regions.\n",
    "\n",
    "Experimental setup is described in @bib54; @bib53; @bib51, @bib52 in detail. Briefly, following a 1 s pre-trial fixation period (baseline), subjects were instructed to focus on one of two stimulus contexts (‘identity’ or ‘relation’ information). Then two shapes were presented in sequence for 200 ms each. After a 900 or 1150 ms jittered precue delay (delay1), the test cue appeared for 800 ms, followed by another post-cue delay period of the same length (delay2). Finally, the response period required participants to perform a 2-alternative forced choice test based on the test cue, which varied based on trial condition. For our analysis, we collapsed across the stimulus context conditions and compared neuronal timescales during the last 900 ms of baseline and delay periods from the epoched data, which were free of visual stimuli, in order to avoid stimulus-related event-related potential effects. Behavioral accuracy for each experimental condition was reported for each participant, and we average across both stimulus context conditions to produce a single working memory accuracy per participant.\n",
    "\n",
    "Single-trial power spectra were computed for each channel as the squared magnitude of the Hamming-windowed Fourier Transform. We used 900 ms of data in all three periods (pre-trial, delay1, and delay2). Timescales were estimated by applying spectral parameterization as above, and the two delay-period estimates were averaged to produce a single delay period value. For comparison, we computed single-trial theta (3–8 Hz) and high-frequency activity (high gamma \\[@bib67], 70–100 Hz) powers as the mean log-power within those frequency bins, as well as spectral exponent (χ). Single-trial timescale difference between delay and baseline was calculated as the difference of the log timescales due to the non-normal distribution of single-trial timescale estimates. All other neural features were computed by subtracting baseline from the delay period.\n",
    "\n",
    "All neural features were then averaged across channels within the same regions, then trials, for each participant, to produce per-participant region-wise estimates, and finally averaged across all participants for the regional average in [Figure 4B and C](#fig4BC). Two-sided Mann–Whitney U-tests were used to test for significant differences in baseline timescale between pairs of regions ([Figure 4B](#fig4BC)). Two-sided Wilcoxon rank-sum tests were used to determine the statistical significance of timescale change in each region ([Figure 4C](#fig4BC)), where the null hypothesis was no change between baseline and delay periods (i.e., delay is 100% of baseline). Spearman rank correlation was used to determine the relationship between neural activity (timescale; theta; high-frequency; χ) change and working memory accuracy across participants ([Figure 4D](#fig4D) and [Figure 4—figure supplement 1](#fig4s1)).\n",
    "\n",
    "## Per-subject average cortical timescale across age\n",
    "\n",
    "Since electrode coverage in the MNI-iEEG dataset is sparse and nonuniform across participants ([Figure 2—figure supplement 1](#fig2s1)), simply averaging across parcels within individuals to estimate an average cortical timescale per participant confounds the effect of age with the spatial effect of cortical hierarchy. Therefore, we instead first normalize each parcel by its max value across all participants before averaging within participants, excluding those with fewer than 10 valid parcels (71 of 106 subjects remaining; results hold for a range of threshold values; [Figure 4—figure supplement 2B](#fig4s2)). Spearman rank correlation was used to compute the association between age and average cortical timescale.\n",
    "\n",
    "## Age–timescale association for individual parcels\n",
    "\n",
    "Each cortical parcel had a variable number of participants with valid timescale estimates above the consistency threshold, so we compute Spearman correlation between age and timescale for each parcel, but including only those with at least five participants (114 of 180 parcels, result holds for a range of threshold values; [Figure 4—figure supplement 2C](#fig4s2)). Spatial effect of age-timescale variation is plotted in [Figure 4F](#fig4EF), where parcels that did not meet the threshold criteria are grayed out. Mean age–timescale correlation from individual parcels was significantly negative under one-sample t-test.\n",
    "\n",
    "## Data and materials’ availability\n",
    "\n",
    "All data analyzed in this manuscript are from open data sources. All code used for all analyses and plots are publicly available on GitHub at <https://github.com/rdgao/field-echos> (@bib35) and <https://github.com/rudyvdbrink/surface_projection> (@bib82). See [Tables 1](#table1) and [2](#table2) for details."
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