<html lang="en"> <head> <title>JROST Lightning Talk Demo</title> <meta charset="utf-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <meta http-equiv="X-UA-Compatible" content="ie=edge"> <link href="https://unpkg.com/@stencila/thema@2/dist/themes/stencila/styles.css" rel="stylesheet"> <script src="https://unpkg.com/@stencila/thema@2/dist/themes/stencila/index.js" type="text/javascript"></script> <script src="https://unpkg.com/@stencila/components@<=1/dist/stencila-components/stencila-components.esm.js" type="module"></script> <script src="https://unpkg.com/@stencila/components@<=1/dist/stencila-components/stencila-components.js" type="text/javascript" nomodule=""></script> </head> <body> <main role="main"> <article itemscope="" itemtype="http://schema.org/Article" data-itemscope="root"> <h1 itemprop="headline">JROST Lightning Talk Demo</h1> <meta itemprop="image" content="https://via.placeholder.com/1200x714/dbdbdb/4a4a4a.png?text=JROST%20Lightning%20Talk%20Demo"> <span itemscope="" itemtype="http://schema.org/Organization" itemprop="publisher"> <meta itemprop="name" content="Unknown"><span itemscope="" itemtype="http://schema.org/ImageObject" itemprop="logo"> <meta itemprop="url" content="https://via.placeholder.com/600x60/dbdbdb/4a4a4a.png?text=Unknown"> </span> </span> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">This Jupyter Notebook explores an important aspect of quantitative magnetic resonance imaging (qMRI): <strong itemscope="" itemtype="http://schema.stenci.la/Strong">validation</strong>. Focusing specifically on <strong itemscope="" itemtype="http://schema.stenci.la/Strong">myelin measures</strong>, we show the results of our <strong itemscope="" itemtype="http://schema.stenci.la/Strong">meta-analysis comparing quantitative MRI with histology</strong>. <a href="https://www.biorxiv.org/content/10.1101/2020.07.13.200972v4" itemscope="" itemtype="http://schema.stenci.la/Link">The full preprint is available on bioRxiv.</a> </p> <figure itemscope="" itemtype="http://schema.stenci.la/Figure" id="ea64578b-6b89-4ae4-a6c1-86a787ce97cf"> <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk" data-programminglanguage="r"> <pre class="language-r" itemscope="" itemtype="http://schema.stenci.la/CodeBlock" slot="text"><code>data <- read.csv('CatsOfZenodo.csv') dtrunc <- data dtrunc <- dtrunc[c(1,3,4)] dtrunc <- head(dtrunc) dtrunc</code></pre> <figure slot="outputs"> <div itemscope="" itemtype="http://schema.stenci.la/Datatable"> <table> <thead> <tr> <th>X</th> <th>P2</th> <th>P3</th> </tr> </thead> <tbody> <tr> <td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn"> indoor-lack-stimulation</td> <td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">2</td> <td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">-1</td> </tr> <tr> <td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn"> never-considered-wildlife</td> <td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">-6</td> <td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">-5</td> </tr> <tr> <td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn"> guilt-if-killed</td> <td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">2</td> <td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">1</td> </tr> <tr> <td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn"> used-to-indoors</td> <td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">0</td> <td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">5</td> </tr> <tr> <td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn"> hunting-no-bother</td> <td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">-2</td> <td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">-2</td> </tr> <tr> <td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn"> concern-bird-populations</td> <td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">2</td> <td itemscope="" itemtype="http://schema.stenci.la/DatatableColumn">3</td> </tr> </tbody> </table> </div> </figure> </stencila-code-chunk> <figcaption> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Why myelin? Myelin is a key component of the central nervous system. The myelin sheaths insulate axons with a triple effect: allowing fast electrical conduction, protecting the axon, and providing trophic support. The conduction velocity regulation has become an important research topic, with evidence of activity-dependent myelination as an additional mechanism of plasticity. Myelin is also relevant from a clinical perspective, given that demyelination is often observed in several neurological diseases such as multiple sclerosis.</p> </figcaption> </figure> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">A table that I just inserted </p> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph"><strong itemscope="" itemtype="http://schema.stenci.la/Strong">How are qMRI measures validated?</strong> Similarly to other qMRI biomarkers, MRI-based myelin measurements are noisy, indirect, and might be affected by other microstructural features. Assessing the accuracy of such measurements, as well as their sensitivity to change, is essential for their translation into clinical practice. That is why histological validation is necessary. The most common validation approach is based on acquiring MR data from <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">in vivo</em> or <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">ex vivo</em> tissue and then comparing those data with the related samples analysed using histological techniques.</p> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph"><strong itemscope="" itemtype="http://schema.stenci.la/Strong">Why a meta-analysis?</strong> So far, a long list of studies have looked at MRI-histology comparisons, each of them focusing on a specific pathology and a few MRI measures. Despite these numerous studies, there is still an ongoing debate on what MRI measure should be used to quantify myelin and as a consequence there is a constant methodological effort to propose new measures. We believe that this debate would benefit from a quantitative analysis of all the findings published so far, specifically addressing inter-study variations and prospects for future studies, something that is currently missing from the literature. </p> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">* * *</p> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph"><strong itemscope="" itemtype="http://schema.stenci.la/Strong">A bit more about this Jupyter Notebook</strong></p> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">The main idea for this Jupyter Notebook is to let you interactively explore our dataset. For this reason, in the next pages you will find brief descriptions of what has been done, but the figures (realized with <a href="https://plotly.com" itemscope="" itemtype="http://schema.stenci.la/Link">plotly</a>) are the main content. You will not find sections discussing or interpreting these results: for that, please check the <a href="https://www.biorxiv.org/content/10.1101/2020.07.13.200972v4" itemscope="" itemtype="http://schema.stenci.la/Link">preprint</a>.</p> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">* * *</p> <h3 itemscope="" itemtype="http://schema.stenci.la/Heading" id="selecting-the-studies"> Selecting the studies</h3> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">First, how were the studies selected? We used the <a href="https://pubmed.ncbi.nlm.nih.gov" itemscope="" itemtype="http://schema.stenci.la/Link">Medline database</a> and retrieved all the records mentioning (1) myelin, (2) MRI and (3) histology (or a related technique). The full list of keywords is provided in the preprint. The following Sankey diagram shows the screening process: you can hover with the mouse on each block and connection to see details about the number of studies and exclusion criteria.</p> <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk" data-execution_count="null" data-programminglanguage="python"> <pre class="language-python" itemscope="" itemtype="http://schema.stenci.la/CodeBlock" slot="text"><code># Run this cell in Google Colab to install the required packages and retrieve the data !wget https://raw.githubusercontent.com/matteomancini/myelin-meta-analysis/master/requirements.txt !wget https://github.com/matteomancini/myelin-meta-analysis/raw/master/database.xlsx !pip install -r requirements.txt !echo "install.packages(\"metafor\", repos=\"https://cran.rstudio.com\")" | R --no-save !echo "install.packages(\"multcomp\", repos=\"https://cran.rstudio.com\")" | R --no-save</code></pre> </stencila-code-chunk> <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk" data-execution_count="1" data-programminglanguage="python"> <pre class="language-python" itemscope="" itemtype="http://schema.stenci.la/CodeBlock" slot="text"><code>import numpy as np import pandas as pd import plotly.graph_objects as go import plotly.express as px import plotly.colors from plotly.subplots import make_subplots from rpy2.robjects.packages import importr import rpy2.robjects</code></pre> </stencila-code-chunk> <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk" data-execution_count="2" data-programminglanguage="python"> <pre class="language-python" itemscope="" itemtype="http://schema.stenci.la/CodeBlock" slot="text"><code>screening_info = ['Records obtained from the Medline database', 'Records obtained from previous reviews', """ Exclusion critera:<br> - work relying only on MRI;<br> - work relying only on histology or equivalent approach;<br> - work reporting only qualitative comparisons. """, 'Records selected for full-text evaluation', """ Exclusion criteria:<br> - studies using MRI-based measures in arbitrary units;<br> - studies using measures of variation in myelin content;<br> - studies using arbitrary assessment scales;<br> - studies comparing absolute measures of myelin with relative measures;<br> - studies reporting other quantitative measures than correlation or R^2 values;<br> - studies comparing histology from one dataset and MRI from a different one. """, 'Studies selected for literature overview', """ Exclusion criteria:<br> - not providing an indication of both number of subjects and number of ROIs. """] fig1 = go.Figure(data=[go.Sankey( arrangement = "freeform", node = dict( pad = 15, thickness = 20, line = dict(color = "black", width = 0.5), label = ["Main records identified (database searching)", "Additional records (reviews)", "Records screened", "Records excluded", "Full-text articles assessed for eligibility", "Full-text articles excluded", "Studied included in the literature overview", "Studies included in the meta-analysis"], x = [0, 0, 0.4, 0.6, 0.5, 0.8, 0.7, 1], y = [0, 0, 0.5, 0.8, 0.15, 0.05, 0.4, 0.6], hovertemplate = "%{label}<extra>%{value}</extra>", color = ["darkblue","darkblue","darkblue","darkred","darkgreen","darkred","darkgreen","darkgreen"] ), link = dict( source = [0, 1, 2, 2, 4, 4, 6], target = [2, 2, 3, 4, 5, 6, 7], value = [688, 1, 597, 92, 34, 58, 43], customdata = screening_info, hovertemplate = "%{customdata}", ))]) fig1.update_layout(title=dict(text='Figure 1: Review methodology',x=0.1), margin=dict(l=100), width=1000, height=500, font_size=12) fig1.show()</code></pre> <figure slot="outputs"> <pre><output></output></pre> <stencila-image-plotly> <picture> <script type="application/vnd.plotly.v1+json"> {"data":[{"link":{"value":[688,1,597,92,34,58,43],"source":[0,1,2,2,4,4,6],"target":[2,2,3,4,5,6,7],"customdata":["Records obtained from the Medline database","Records obtained from previous reviews","\n Exclusion critera:<br>\n - work relying only on MRI;<br>\n - work relying only on histology or equivalent approach;<br>\n - work reporting only qualitative comparisons.\n ","Records selected for full-text evaluation","\n Exclusion criteria:<br>\n - studies using MRI-based measures in arbitrary units;<br>\n - studies using measures of variation in myelin content;<br>\n - studies using arbitrary assessment scales;<br>\n - studies comparing absolute measures of myelin with relative measures;<br>\n - studies reporting other quantitative measures than correlation or R^2 values;<br>\n - studies comparing histology from one dataset and 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</script><img src="index.html.media/0" alt="" itemscope="" itemtype="http://schema.org/ImageObject"> </picture> </stencila-image-plotly> </figure> </stencila-code-chunk> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">We identified 58 studies reporting quantitative comparisons between MRI and histology: these included a variety of methodological choices and experimental conditions, in terms of tissue type (brain, spinal cord, peripheral nerve), condition (<em itemscope="" itemtype="http://schema.stenci.la/Emphasis">in vivo</em>, <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">ex vivo</em>, <em itemscope="" itemtype="http://schema.stenci.la/Emphasis">in situ</em>), species (human, animal), pathology model, and many more. A glimpse of these subdivisions is provided in the following treemap. You can click on each box to expand the related category, and for each study you can find out more details and the link to the original paper.</p> <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk" data-execution_count="3" data-programminglanguage="python"> <pre class="language-python" itemscope="" itemtype="http://schema.stenci.la/CodeBlock" slot="text"><code>info = pd.read_excel('database.xlsx', sheet_name='Details') year_str = info['Year'].astype(str) info['Study'] = info['First author'] + ' et al., ' + year_str info['Study'] = info.groupby('Study')['Study'].apply(lambda n: n+list(map(chr,np.arange(len(n))+97)) if len(n)>1 else n) info['Number of studies'] = np.ones((len(info),1)) info = info.sort_values('Study') info['Link'] = info['DOI'] info['Link'].replace('http',"""<a style='color:white' href='http""", inplace=True, regex=True) info['Link'] = info['Link'] + """'>->Go to the paper</a>""" fields = ['Approach', 'Magnetic field', 'MRI measure(s)', 'Histology/microscopy measure', 'Specific structure(s)'] info['Summary'] = info['Link'] + '<br><br>' for i in fields: info['Summary'] = info['Summary'] + i + ': ' + info[i].astype(str) + '<br><br>' args = dict(data_frame=info, values='Number of studies', color='Number of studies', hover_data='', path=['Focus', 'Tissue condition', 'Human/animal', 'Condition', 'Study'], color_continuous_scale='Viridis') args = px._core.build_dataframe(args, go.Treemap) treemap_df = px._core.process_dataframe_hierarchy(args)['data_frame'] fig2 = go.Figure(go.Treemap( ids=treemap_df['id'].tolist(), labels=treemap_df['labels'].tolist(), parents=treemap_df['parent'].tolist(), values=treemap_df['Number of studies'].tolist(), branchvalues='total', text=info['Summary'], hoverinfo='label', textfont=dict( size=15, ) ) ) fig2 = fig2.update_layout( title=dict(text='Figure 2: Literature survey overview',x=0.1), autosize=False, width=900, height=600, margin=dict( l=100, r=0, b=30, t=60, ) ) fig2.show()</code></pre> <figure slot="outputs"> <stencila-image-plotly> <picture> <script type="application/vnd.plotly.v1+json"> {"data":[{"ids":["Brain/Ex vivo - Fixed/Animal - Mouse/Optogenetic stimulation/Abe et al., 2019","Brain/In vivo/Animal - Rat/Hydrocephalus/Aojula et al., 2016","Brain/In vivo/Animal - Mouse/Demyelination - Cuprizone/Beckmann et al., 2018","Brain/Ex vivo - Fixed/Animal - Mouse/Demyelination - Knockout/Berman et al., 2018","Brain/In vivo/Animal - Mouse/Demyelination - Cuprizone/Chandran et al., 2012","Brain/Ex vivo - Fixed/Animal - Mouse/Healthy/Chang et al., 2017","Spinal cord/Ex vivo - Fixed/Animal - Rat/Injury - Dorsal columnar transection/Chen et al., 2017","Brain/In vivo/Animal - Mouse/Healthy/Duhamel et al., 2019","Brain/In vivo/Animal - Mouse/Ischemia - Induced hypoxia/Fatemi et al., 2011","Brain/In vivo/Animal - Mouse/Demyelination - Cuprizone/Fjaer et al., 2013","Brain/In vivo/Animal - Mouse/Demyelination - Autoimmune encephalomyelitis/Fjaer et al., 2015","Brain/In vivo/Animal - Mouse/Healthy/Guglielmetti et al., 2020","Brain/Ex vivo - Fixed/Animal - Rat/Healthy/Hakkarainen et al., 2016","Brain/In situ/Human/Vascular diseases/Hametner et al., 2018","Spinal cord/In vivo/Animal - Rat/Edema - Hexachlorophene/Harkins et al., 2013","Brain/Ex vivo - Fixed/Animal - Rat/Demyelination - Lipopolysaccharide/Janve et al., 2013","Brain/In vivo/Animal - Mouse/Demyelination - Cuprizone/Jelescu et al., 2016","Brain/In vivo/Animal - Mouse/Healthy/Jito et al., 2008","Brain/Ex vivo - Fixed/Animal - Mouse/Demyelination - Knockout/Kelm et al., 2016","Brain/In vivo/Animal - Mouse/Demyelination - Cuprizone/Khodanovic et al., 2017","Brain/In vivo/Animal - Mouse/Demyelination - Cuprizone/Khodanovic et al., 2019","Spinal cord/Ex vivo - Fixed/Animal - Rat/Injury - Dorsal columnar transection/Kozlowski et al., 2008","Spinal cord/In vivo/Animal - Rat/Injury - Dorsal columnar transection/Kozlowski et al., 2014","Brain/Ex vivo - Fixed/Human/Multiple sclerosis/Laule et al., 2006","Brain/Ex vivo - Fixed/Human/Multiple sclerosis/Laule et al., 2008","Brain/Ex vivo - Fixed/Human/Multiple sclerosis/Laule et al., 2011","Brain/In vivo/Animal - Rat/Demyelination - Lipopolysaccharide/Lehto et al., 2017a","Brain/Ex vivo - Unfixed/Animal - Rat/Traumatic brain injury/Lehto et al., 2017b","Brain/Ex vivo - Fixed/Human/Amyotrophic lateral sclerosis/Mollink et al., 2019","Peripheral nerve/Ex vivo - Unfixed/Animal - Rat/Demyelination - Tellurium/Odrobina et al., 2005","Brain/Ex vivo - Fixed/Human/Tuberous sclerosis complex/Peters et al., 2019","Brain/In vivo/Animal - Mouse/Healthy/Pol et al., 2019","Brain/In vivo/Animal - Mouse/Amyloidosis/Praet et al., 2018","Peripheral nerve/Ex vivo - Unfixed/Animal - Rat/Demyelination - Tellurium/Pun et al., 2005","Brain/Ex vivo - Fixed/Human/Epilepsy/Reeves et al., 2016","Brain/Ex vivo - Unfixed/Human/Multiple sclerosis/Schmierer et al., 2004","Brain/Ex vivo - Unfixed/Human/Multiple sclerosis/Schmierer et al., 2007a","Brain/Ex vivo - Unfixed/Human/Multiple sclerosis/Schmierer et al., 2007b","Brain/Ex vivo - Fixed/Human/Multiple sclerosis/Schmierer et al., 2008","Brain/Ex vivo - Fixed/Human/Multiple sclerosis/Schmierer et al., 2010","Brain/Ex vivo - Fixed/Human/Healthy/Seehaus et al., 2015","Brain/In situ/Animal - Mouse/Demyelination - Cuprizone/Soustelle et al., 2019","Peripheral nerve/Ex vivo - Unfixed/Animal - Rat/Degeneration - Contusive injury/Takagi et al., 2009","Brain/Ex vivo - Fixed/Human/Multiple sclerosis/Tardif et al., 2012","Brain/Ex vivo - Unfixed/Animal - Mouse/Demyelination - Cuprizone/Thiessen et al., 2013","Brain/In vivo/Animal - Rat/Traumatic brain injury/Tu et al., 2016","Brain/In vivo/Animal - Mouse/Demyelination - Cuprizone/Turati et al., 2015","Brain/In vivo/Animal - Mouse/Healthy/Underhill et al., 2011","Brain/In vivo/Animal - Rat/Ischemia - Induced hypoxia/Wang et al., 2009","Spinal cord/Ex vivo - Fixed/Animal - Mouse/Demyelination - Autoimmune encephalomyelitis/Wang et al., 2014","Spinal cord/Ex vivo - Fixed/Human/Multiple sclerosis/Wang et al., 2015","Brain/Ex vivo - Fixed/Animal - Mouse/Traumatic brain injury/Wendel et al., 2018","Brain/Ex vivo - Fixed/Animal - Mouse/Demyelination - Knockout/West et al., 2018","Brain/In vivo/Animal - Mouse/Demyelination - Cuprizone/Wu et al., 2008","Brain/Ex vivo - Fixed/Animal - Mouse/Demyelination - Cuprizone/Yano et al., 2018","Brain/In vivo/Animal - Mouse/Demyelination - Cuprizone/Zaaraoui et al., 2008","Spinal cord/Ex vivo - Fixed/Animal - Rat/Injury - Dorsal columnar transection/Zhang et al., 2009","Brain/Ex vivo - Fixed/Animal - Rat/White matter injury/van Tilborg et al., 2017","Brain/In vivo/Animal - Mouse/Amyloidosis","Brain/Ex vivo - Fixed/Human/Amyotrophic lateral sclerosis","Peripheral nerve/Ex vivo - Unfixed/Animal - Rat/Degeneration - Contusive injury","Spinal cord/Ex vivo - Fixed/Animal - Mouse/Demyelination - Autoimmune encephalomyelitis","Brain/In vivo/Animal - Mouse/Demyelination - Autoimmune encephalomyelitis","Brain/Ex vivo - Fixed/Animal - Mouse/Demyelination - Cuprizone","Brain/Ex vivo - Unfixed/Animal - Mouse/Demyelination - Cuprizone","Brain/In situ/Animal - Mouse/Demyelination - Cuprizone","Brain/In vivo/Animal - Mouse/Demyelination - Cuprizone","Brain/Ex vivo - Fixed/Animal - Mouse/Demyelination - Knockout","Brain/Ex vivo - Fixed/Animal - Rat/Demyelination - Lipopolysaccharide","Brain/In vivo/Animal - Rat/Demyelination - Lipopolysaccharide","Peripheral nerve/Ex vivo - Unfixed/Animal - Rat/Demyelination - Tellurium","Spinal cord/In vivo/Animal - Rat/Edema - Hexachlorophene","Brain/Ex vivo - Fixed/Human/Epilepsy","Brain/Ex vivo - Fixed/Animal - Mouse/Healthy","Brain/In vivo/Animal - Mouse/Healthy","Brain/Ex vivo - Fixed/Animal - Rat/Healthy","Brain/Ex vivo - Fixed/Human/Healthy","Brain/In vivo/Animal - Rat/Hydrocephalus","Spinal cord/Ex vivo - Fixed/Animal - Rat/Injury - Dorsal columnar transection","Spinal cord/In vivo/Animal - Rat/Injury - Dorsal columnar transection","Brain/In vivo/Animal - Mouse/Ischemia - Induced hypoxia","Brain/In vivo/Animal - Rat/Ischemia - Induced hypoxia","Brain/Ex vivo - Fixed/Human/Multiple sclerosis","Spinal cord/Ex vivo - Fixed/Human/Multiple sclerosis","Brain/Ex vivo - Unfixed/Human/Multiple sclerosis","Brain/Ex vivo - Fixed/Animal - Mouse/Optogenetic stimulation","Brain/Ex vivo - Fixed/Animal - Mouse/Traumatic brain injury","Brain/Ex vivo - Unfixed/Animal - Rat/Traumatic brain injury","Brain/In vivo/Animal - Rat/Traumatic brain injury","Brain/Ex vivo - Fixed/Human/Tuberous sclerosis complex","Brain/In situ/Human/Vascular diseases","Brain/Ex vivo - Fixed/Animal - Rat/White matter injury","Brain/Ex vivo - Fixed/Animal - Mouse","Spinal cord/Ex vivo - Fixed/Animal - Mouse","Brain/Ex vivo - Unfixed/Animal - Mouse","Brain/In situ/Animal - Mouse","Brain/In vivo/Animal - Mouse","Brain/Ex vivo - Fixed/Animal - Rat","Spinal cord/Ex vivo - Fixed/Animal - Rat","Brain/Ex vivo - Unfixed/Animal - Rat","Peripheral nerve/Ex vivo - Unfixed/Animal - Rat","Brain/In vivo/Animal - Rat","Spinal cord/In vivo/Animal - Rat","Brain/Ex vivo - Fixed/Human","Spinal cord/Ex vivo - Fixed/Human","Brain/Ex vivo - Unfixed/Human","Brain/In situ/Human","Brain/Ex vivo - Fixed","Spinal cord/Ex vivo - Fixed","Brain/Ex vivo - Unfixed","Peripheral nerve/Ex vivo - Unfixed","Brain/In situ","Brain/In vivo","Spinal cord/In vivo","Brain","Peripheral nerve","Spinal cord"],"text":["<a style='color:white' href='https://doi.org/10.1016/j.neuint.2019.02.017'>->Go to the paper</a><br><br>Approach: Diffusion<br><br>Magnetic field: 7.0<br><br>MRI measure(s): FA, RD, AD<br><br>Histology/microscopy measure: Microscopy - Myelin thickness<br><br>Specific structure(s): Putamen, Accumbens, Globus pallidus, Substantia nigra, Cortex<br><br>","<a style='color:white' href='https://doi.org/10.1186/s12987-016-0033-2'>->Go to the paper</a><br><br>Approach: Diffusion<br><br>Magnetic field: 7.0<br><br>MRI measure(s): FA, AD, RD, MD<br><br>Histology/microscopy measure: Immunohistochemistry - MBP<br><br>Specific structure(s): Corpus callosum<br><br>","<a style='color:white' href='https://doi.org/10.1186/s40478-018-0510-8'>->Go to the paper</a><br><br>Approach: Magnetization transfer<br><br>Magnetic field: 7.0<br><br>MRI measure(s): MTR<br><br>Histology/microscopy measure: Histology - LFB<br><br>Specific structure(s): Corpus callosum, External capsule<br><br>","<a style='color:white' href='https://doi.org/10.1016/j.neuroimage.2017.06.076'>->Go to the paper</a><br><br>Approach: Other<br><br>Magnetic field: 15.2<br><br>MRI measure(s): MTV<br><br>Histology/microscopy measure: EM - Myelin fraction<br><br>Specific structure(s): Corpus callosum<br><br>","<a style='color:white' href='https://doi.org/10.1016/j.neuroscience.2011.10.051'>->Go to the paper</a><br><br>Approach: Diffusion<br><br>Magnetic field: 7.0<br><br>MRI measure(s): FA, RD<br><br>Histology/microscopy measure: Immunohistochemistry - MBP<br><br>Specific structure(s): White matter<br><br>","<a style='color:white' href='https://doi.org/10.1016/j.dib.2016.12.018'>->Go to the paper</a><br><br>Approach: Diffusion<br><br>Magnetic field: 11.7<br><br>MRI measure(s): FA, AD, RD, MD<br><br>Histology/microscopy measure: Immunohistochemistry - MBP<br><br>Specific structure(s): Corpus callosum, Anterior commissure, Fimbria, Fornix, Stria medullaris, Fasciculus retroflexus, Mammilothalamic tract<br><br>","<a style='color:white' href='https://doi.org/10.1016/j.neuroimage.2017.03.065'>->Go to the paper</a><br><br>Approach: T2 relaxometry<br><br>Magnetic field: 7.0<br><br>MRI measure(s): MWF<br><br>Histology/microscopy measure: EM - Myelin fraction<br><br>Specific structure(s): Cervical sections<br><br>","<a style='color:white' href='https://doi.org/10.1016/j.neuroimage.2019.05.061'>->Go to the paper</a><br><br>Approach: Magnetization transfer<br><br>Magnetic field: 11.75<br><br>MRI measure(s): ihMTR, MTR<br><br>Histology/microscopy measure: Microscopy - Fluorescence<br><br>Specific structure(s): Corpus callosum, Internal capsule, Optic nerve, Thalamus, Cortex, Hippocampus, Inter-peduncular nuclues<br><br>","<a style='color:white' href='https://doi.org/10.1038/jcbfm.2011.68'>->Go to the paper</a><br><br>Approach: Magnetization transfer<br><br>Magnetic field: 9.4<br><br>MRI measure(s): MTR<br><br>Histology/microscopy measure: Immunohistochemistry - MBP<br><br>Specific structure(s): Corpus callosum, Internal capsule<br><br>","<a style='color:white' href='https://doi.org/10.1371/journal.pone.0084162'>->Go to the paper</a><br><br>Approach: Magnetization transfer<br><br>Magnetic field: 7.0<br><br>MRI measure(s): MTR<br><br>Histology/microscopy measure: Immunohistochemistry - PLP<br><br>Specific structure(s): Corpus callosum<br><br>","<a style='color:white' href='https://doi.org/10.1016/j.neuint.2015.02.006'>->Go to the paper</a><br><br>Approach: Magnetization transfer<br><br>Magnetic field: 7.0<br><br>MRI measure(s): MTR<br><br>Histology/microscopy measure: Immunohistochemistry - PLP<br><br>Specific structure(s): Corpus callosum<br><br>","<a style='color:white' href='https://doi.org/10.1016/j.neuroimage.2019.116415'>->Go to the paper</a><br><br>Approach: Magnetization transfer<br><br>Magnetic field: 7.0<br><br>MRI measure(s): MTR, MTR-UTE<br><br>Histology/microscopy measure: Immunohistochemistry - MBP<br><br>Specific structure(s): Corpus callosum, Internal capsule, Thalamus, Putamen, Hippocampus, Cortex<br><br>","<a style='color:white' href='https://doi.org/10.1002/mrm.25590'>->Go to the paper</a><br><br>Approach: Magnetization transfer, T1 relaxometry, T2 relaxometry, Other<br><br>Magnetic field: 9.4<br><br>MRI measure(s): T1, T2, MTR, T1p, T2p, RAFF<br><br>Histology/microscopy measure: Histology - Gold chloride<br><br>Specific structure(s): Corpus callosum, External capsule, Internal capsule, Cingulum, Fimbria, Optic tract, Dentate gyrus, Hippocampus, Amygdala, Somatosensory cortex, Thalamic nuclei<br><br>","<a style='color:white' href='https://doi.org/10.1016/j.neuroimage.2018.06.007'>->Go to the paper</a><br><br>Approach: T1 relaxometry, Other<br><br>Magnetic field: 7.0<br><br>MRI measure(s): R2*, T1, QSM<br><br>Histology/microscopy measure: Histology - LFB<br><br>Specific structure(s): White matter, Grey matter, Thalamus, Basal ganglia<br><br>","<a style='color:white' href='https://doi.org/10.1016/j.nicl.2013.06.007'>->Go to the paper</a><br><br>Approach: Magnetization transfer, T2 relaxometry<br><br>Magnetic field: 9.4<br><br>MRI measure(s): MWF, MPF<br><br>Histology/microscopy measure: Microscopy - Myelin fraction<br><br>Specific structure(s): Cervical sections<br><br>","<a style='color:white' href='https://doi.org/10.1016/j.neuroimage.2013.02.034'>->Go to the paper</a><br><br>Approach: Diffusion, Magnetization transfer<br><br>Magnetic field: 9.4<br><br>MRI measure(s): MPF, R1a, k_ba, FA, RD, MD, AD<br><br>Histology/microscopy measure: Histology - LFB<br><br>Specific structure(s): Corpus callosum<br><br>","<a style='color:white' href='https://doi.org/10.1016/j.neuroimage.2016.02.004'>->Go to the paper</a><br><br>Approach: Diffusion, Magnetization transfer, T2 relaxometry<br><br>Magnetic field: 7.0<br><br>MRI measure(s): RD, RK, AWF, Rde, T2, MTR<br><br>Histology/microscopy measure: EM - Myelin fraction<br><br>Specific structure(s): Corpus callosum<br><br>","<a style='color:white' href='https://doi.org/10.1002/jmri.21496'>->Go to the paper</a><br><br>Approach: Diffusion<br><br>Magnetic field: 7.0<br><br>MRI measure(s): FA<br><br>Histology/microscopy measure: Microscopy - Myelin sheath area<br><br>Specific structure(s): Corpus callosum<br><br>","<a style='color:white' href='https://doi.org/10.1016/j.neuroimage.2015.09.028'>->Go to the paper</a><br><br>Approach: Diffusion<br><br>Magnetic field: 15.2<br><br>MRI measure(s): MD, RD, MK, RK, AWF<br><br>Histology/microscopy measure: EM - Myelin fraction<br><br>Specific structure(s): Corpus callosum, External capsule, Internal capsule, Anterior commissure<br><br>","<a style='color:white' href='https://doi.org/10.1038/srep46686'>->Go to the paper</a><br><br>Approach: Magnetization transfer<br><br>Magnetic field: 11.7<br><br>MRI measure(s): MPF<br><br>Histology/microscopy measure: Histology - LFB<br><br>Specific structure(s): Corpus callosum, Internal capsule, Anterior commissure, Thalamus, Putamen, Cortex<br><br>","<a style='color:white' href='https://doi.org/10.3390/cells8101204'>->Go to the paper</a><br><br>Approach: Magnetization transfer<br><br>Magnetic field: 11.7<br><br>MRI measure(s): MPF<br><br>Histology/microscopy measure: Immunohistochemistry - MBP<br><br>Specific structure(s): Corpus callosum<br><br>","<a style='color:white' href='https://doi.org/10.1089/neu.2007.0462'>->Go to the paper</a><br><br>Approach: Diffusion, T2 relaxometry<br><br>Magnetic field: 7.0<br><br>MRI measure(s): MWF, FA, AD, RD, MD<br><br>Histology/microscopy measure: Immunohistochemistry - MBP<br><br>Specific structure(s): Cervical sections<br><br>","<a style='color:white' href='https://doi.org/10.1016/j.mri.2013.12.006'>->Go to the paper</a><br><br>Approach: T2 relaxometry<br><br>Magnetic field: 7.0<br><br>MRI measure(s): MWF<br><br>Histology/microscopy measure: Immunohistochemistry - MBP<br><br>Specific structure(s): Cervical sections<br><br>","<a style='color:white' href='https://doi.org/10.1177/1352458506070928'>->Go to the paper</a><br><br>Approach: T2 relaxometry<br><br>Magnetic field: 1.5<br><br>MRI measure(s): MWF<br><br>Histology/microscopy measure: Histology - LFB<br><br>Specific structure(s): White matter, Grey matter<br><br>","<a style='color:white' href='https://doi.org/10.1016/j.neuroimage.2007.12.008'>->Go to the paper</a><br><br>Approach: T2 relaxometry<br><br>Magnetic field: 7.0<br><br>MRI measure(s): MWF<br><br>Histology/microscopy measure: Histology - LFB<br><br>Specific structure(s): White matter, Grey matter<br><br>","<a style='color:white' href='https://doi.org/10.1177/1352458510384008'>->Go to the paper</a><br><br>Approach: T2 relaxometry<br><br>Magnetic field: 1.5<br><br>MRI measure(s): MWF<br><br>Histology/microscopy measure: Immunohistochemistry - MBP<br><br>Specific structure(s): White matter<br><br>","<a style='color:white' href='https://doi.org/10.3389/fnins.2017.00433'>->Go to the paper</a><br><br>Approach: Diffusion, Magnetization transfer, Other<br><br>Magnetic field: 7.0<br><br>MRI measure(s): RAFF, MTR, T1sat, FA, MD, AD, RD<br><br>Histology/microscopy measure: Histology - Gold chloride<br><br>Specific structure(s): Corpus callosum, Dorsal tegmental tract<br><br>","<a style='color:white' href='https://doi.org/10.1002/nbm.3678'>->Go to the paper</a><br><br>Approach: Magnetization transfer<br><br>Magnetic field: 9.4<br><br>MRI measure(s): MTR<br><br>Histology/microscopy measure: Histology - Gold chloride<br><br>Specific structure(s): Internal capsule, Globus pallidus, Striatum, Thalamus<br><br>","<a style='color:white' href='https://doi.org/10.1111/nan.12555'>->Go to the paper</a><br><br>Approach: Diffusion<br><br>Magnetic field: 11.7<br><br>MRI measure(s): FA<br><br>Histology/microscopy measure: Immunohistochemistry - MBP<br><br>Specific structure(s): Perforant pathway<br><br>","<a style='color:white' href='https://doi.org/10.1002/nbm.951'>->Go to the paper</a><br><br>Approach: Magnetization transfer, T1 relaxometry, T2 relaxometry<br><br>Magnetic field: 1.5<br><br>MRI measure(s): T1, T2, T2int, MWF, M0m, MTR<br><br>Histology/microscopy measure: Microscopy - Myelin fraction<br><br>Specific structure(s): Sciatic nerve<br><br>","<a style='color:white' href='https://doi.org/10.1002/acn3.793'>->Go to the paper</a><br><br>Approach: Diffusion<br><br>Magnetic field: 3.0<br><br>MRI measure(s): FA, MD<br><br>Histology/microscopy measure: Histology - LFB<br><br>Specific structure(s): White matter<br><br>","<a style='color:white' href='https://doi.org/10.1111/jon.12561'>->Go to the paper</a><br><br>Approach: Diffusion, Other<br><br>Magnetic field: 9.4<br><br>MRI measure(s): QSM, FA, MD<br><br>Histology/microscopy measure: Histology - Solochrome<br><br>Specific structure(s): Corpus callosum<br><br>","<a style='color:white' href='https://doi.org/10.1186/s13195-017-0329-8'>->Go to the paper</a><br><br>Approach: Diffusion<br><br>Magnetic field: 7.0<br><br>MRI measure(s): MK, RK, AK, FA, MD, RD, AD<br><br>Histology/microscopy measure: Immunohistochemistry - MBP<br><br>Specific structure(s): Motor cortex<br><br>","<a style='color:white' href='https://doi.org/10.1111/j.1085-9489.2005.10107.x'>->Go to the paper</a><br><br>Approach: T1 relaxometry, T2 relaxometry<br><br>Magnetic field: 1.5<br><br>MRI measure(s): T1, T2int, MWF<br><br>Histology/microscopy measure: Microscopy - Myelin fraction<br><br>Specific structure(s): Sciatic nerve<br><br>","<a style='color:white' href='https://doi.org/10.1111/bpa.12298'>->Go to the paper</a><br><br>Approach: T1 relaxometry, T2 relaxometry<br><br>Magnetic field: 9.4<br><br>MRI measure(s): T1, T2<br><br>Histology/microscopy measure: Immunohistochemistry - MBP<br><br>Specific structure(s): White matter, Grey matter<br><br>","<a style='color:white' href='https://doi.org/10.1002/ana.20202'>->Go to the paper</a><br><br>Approach: Magnetization transfer, T1 relaxometry<br><br>Magnetic field: 1.5<br><br>MRI measure(s): T1, MTR<br><br>Histology/microscopy measure: Histology - LFB<br><br>Specific structure(s): White matter, Lesions<br><br>","<a style='color:white' href='https://doi.org/10.1002/jmri.20984'>->Go to the paper</a><br><br>Approach: Magnetization transfer, T1 relaxometry<br><br>Magnetic field: 1.5<br><br>MRI measure(s): T1, MTR, MPF, T2m<br><br>Histology/microscopy measure: Histology - LFB<br><br>Specific structure(s): White matter, Lesions<br><br>","<a style='color:white' href='https://doi.org/10.1016/j.neuroimage.2006.12.010'>->Go to the paper</a><br><br>Approach: Diffusion<br><br>Magnetic field: 1.5<br><br>MRI measure(s): FA, MD<br><br>Histology/microscopy measure: Histology - LFB<br><br>Specific structure(s): White matter, Lesions<br><br>","<a style='color:white' href='https://doi.org/10.1002/mrm.21487'>->Go to the paper</a><br><br>Approach: Diffusion, Magnetization transfer, T1 relaxometry, T2 relaxometry<br><br>Magnetic field: 1.5<br><br>MRI measure(s): T1, T2, MTR, MPF, MD, FA, AD, RD<br><br>Histology/microscopy measure: Histology - LFB<br><br>Specific structure(s): White matter, Lesions<br><br>","<a style='color:white' href='https://doi.org/10.1093/brain/awp335'>->Go to the paper</a><br><br>Approach: Magnetization transfer, T2 relaxometry<br><br>Magnetic field: 9.4<br><br>MRI measure(s): MTR, T2<br><br>Histology/microscopy measure: Histology - LFB<br><br>Specific structure(s): Grey matter, Lesions<br><br>","<a style='color:white' href='https://doi.org/10.3389/fnana.2015.00098'>->Go to the paper</a><br><br>Approach: Diffusion<br><br>Magnetic field: 9.4<br><br>MRI measure(s): FA, RD, MD<br><br>Histology/microscopy measure: Histology - Silver<br><br>Specific structure(s): White matter, Grey matter<br><br>","<a style='color:white' href='https://doi.org/10.1002/nbm.4116'>->Go to the paper</a><br><br>Approach: Diffusion, Magnetization transfer, T2 relaxometry, Other<br><br>Magnetic field: 7.0<br><br>MRI measure(s): MPF, RD, MWF, rSPF<br><br>Histology/microscopy measure: Immunohistochemistry - MBP<br><br>Specific structure(s): Corpus callosum<br><br>","<a style='color:white' href='https://doi.org/10.1016/j.neuroimage.2008.09.022'>->Go to the paper</a><br><br>Approach: Diffusion<br><br>Magnetic field: 7.0<br><br>MRI measure(s): FA, AD<br><br>Histology/microscopy measure: EM - Myelin thickness<br><br>Specific structure(s): Sciatic nerve<br><br>","<a style='color:white' href='https://doi.org/10.1155/2012/742018'>->Go to the paper</a><br><br>Approach: Magnetization transfer, T1 relaxometry, T2 relaxometry, Other<br><br>Magnetic field: 3.0<br><br>MRI measure(s): T1, T2, MTR, PD<br><br>Histology/microscopy measure: Immunohistochemistry - MBP<br><br>Specific structure(s): White matter, Grey matter, Lesions<br><br>","<a style='color:white' href='https://doi.org/10.1002/nbm.2992'>->Go to the paper</a><br><br>Approach: Diffusion, Magnetization transfer, T1 relaxometry, T2 relaxometry<br><br>Magnetic field: 7.0<br><br>MRI measure(s): MPF, R1f, k_fm, k_mf, T2f, T2m, MD, RD, AD, FA, T1, T2<br><br>Histology/microscopy measure: EM - Myelin thickness<br><br>Specific structure(s): Corpus callosum<br><br>","<a style='color:white' href='https://doi.org/10.1002/ana.24641'>->Go to the paper</a><br><br>Approach: Diffusion, Magnetization transfer<br><br>Magnetic field: 7.0<br><br>MRI measure(s): FA, AD, RD, MD, MTR<br><br>Histology/microscopy measure: Immunohistochemistry - MBP<br><br>Specific structure(s): Corpus callosum<br><br>","<a style='color:white' href='https://doi.org/10.1002/nbm.3253'>->Go to the paper</a><br><br>Approach: Magnetization transfer<br><br>Magnetic field: 7.0<br><br>MRI measure(s): MPF<br><br>Histology/microscopy measure: Immunohistochemistry - MBP<br><br>Specific structure(s): Corpus callosum<br><br>","<a style='color:white' href='https://doi.org/10.1016/j.neuroimage.2010.10.065'>->Go to the paper</a><br><br>Approach: Magnetization transfer<br><br>Magnetic field: 3.0<br><br>MRI measure(s): MPF<br><br>Histology/microscopy measure: Histology - LFB<br><br>Specific structure(s): Putamen, Cerebellum, Cortex, Superior colliculus, Thalamus, Anterior commissure, Corpus callosum, Internal capsule, Hippocampal commissure<br><br>","<a style='color:white' href='https://doi.org/10.3174/ajnr.A1697'>->Go to the paper</a><br><br>Approach: Diffusion<br><br>Magnetic field: 7.0<br><br>MRI measure(s): FA, RD<br><br>Histology/microscopy measure: Histology - LFB<br><br>Specific structure(s): External capsule<br><br>","<a style='color:white' href='https://doi.org/10.1002/nbm.3129'>->Go to the paper</a><br><br>Approach: Diffusion<br><br>Magnetic field: 4.7<br><br>MRI measure(s): RD, RD-DBSI<br><br>Histology/microscopy measure: Immunohistochemistry - MBP<br><br>Specific structure(s): Cervical sections<br><br>","<a style='color:white' href='https://doi.org/10.1093/brain/awv046'>->Go to the paper</a><br><br>Approach: Diffusion<br><br>Magnetic field: 4.7<br><br>MRI measure(s): RD-DBSI<br><br>Histology/microscopy measure: Histology - LFB<br><br>Specific structure(s): Cervical sections<br><br>","<a style='color:white' href='https://doi.org/10.1089/neu.2018.5670'>->Go to the paper</a><br><br>Approach: Diffusion<br><br>Magnetic field: 9.4<br><br>MRI measure(s): FA, AD, RD, MD<br><br>Histology/microscopy measure: Immunohistochemistry - MBP<br><br>Specific structure(s): Corpus callosum<br><br>","<a style='color:white' href='https://doi.org/10.1016/j.neuroimage.2016.12.067'>->Go to the paper</a><br><br>Approach: Magnetization transfer, T2 relaxometry<br><br>Magnetic field: 15.2<br><br>MRI measure(s): MPF, MWF, MVF-T2, MVF-MT<br><br>Histology/microscopy measure: EM - Myelin fraction<br><br>Specific structure(s): Corpus callosum, Anterior commissure<br><br>","<a style='color:white' href='https://doi.org/10.1002/jmri.21111'>->Go to the paper</a><br><br>Approach: T2 relaxometry<br><br>Magnetic field: 4.7<br><br>MRI measure(s): T2<br><br>Histology/microscopy measure: Histology - LFB<br><br>Specific structure(s): Corpus callosum<br><br>","<a style='color:white' href='https://doi.org/10.1016/j.neuint.2017.10.004'>->Go to the paper</a><br><br>Approach: Diffusion<br><br>Magnetic field: 7.0<br><br>MRI measure(s): FA, RD, MD<br><br>Histology/microscopy measure: Immunohistochemistry - PLP<br><br>Specific structure(s): Corpus callosum<br><br>","<a style='color:white' href='https://doi.org/10.1007/s10334-008-0141-3'>->Go to the paper</a><br><br>Approach: Magnetization transfer<br><br>Magnetic field: 9.4<br><br>MRI measure(s): MTR<br><br>Histology/microscopy measure: Immunohistochemistry - MBP<br><br>Specific structure(s): Corpus callosum<br><br>","<a style='color:white' href='https://doi.org/10.1523/jneurosci.3941-08.2009'>->Go to the paper</a><br><br>Approach: Diffusion<br><br>Magnetic field: 11.7<br><br>MRI measure(s): RD<br><br>Histology/microscopy measure: Histology - LFB<br><br>Specific structure(s): Lumbar sections<br><br>","<a style='color:white' href='https://doi.org/10.1002/glia.23216'>->Go to the paper</a><br><br>Approach: Diffusion<br><br>Magnetic field: 9.4<br><br>MRI measure(s): FA<br><br>Histology/microscopy measure: Immunohistochemistry - MBP<br><br>Specific structure(s): Motor cortex<br><br>"],"type":"treemap","labels":["Abe et al., 2019","Aojula et al., 2016","Beckmann et al., 2018","Berman et al., 2018","Chandran et al., 2012","Chang et al., 2017","Chen et al., 2017","Duhamel et al., 2019","Fatemi et al., 2011","Fjaer et al., 2013","Fjaer et al., 2015","Guglielmetti et al., 2020","Hakkarainen et al., 2016","Hametner et al., 2018","Harkins et al., 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</script><img src="index.html.media/1" alt="" itemscope="" itemtype="http://schema.org/ImageObject"> </picture> </stencila-image-plotly> </figure> </stencila-code-chunk> <h3 itemscope="" itemtype="http://schema.stenci.la/Heading" id="a-closer-look">A closer look </h3> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Given the number of different variables influencing the results, we decided to focus only on brain studies. As we needed to take into account the sample size for quantitative comparisons, we also further selected only the studies that reported both the number of subjects and the number of ROIs (regions of interest) considered for correlation purposes. This further screening led us to 43 studies. For these studies we wanted to quantitatively evaluate the reported effect size taking into account the respective samples sizes: we chose the coefficient of determination R<sup itemscope="" itemtype="http://schema.stenci.la/Superscript">2</sup>, as it was the most common quantitative result we could obtain from these studies. To have a look at both sample size and effect size for each measure, we prepared an interactive bubble chart, where the size of each bubble is proportional to the sample size. You can hover on the bubbles to obtain additional details.</p> <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk" data-execution_count="4" data-programminglanguage="python"> <pre class="language-python" itemscope="" itemtype="http://schema.stenci.la/CodeBlock" slot="text"><code>df = pd.DataFrame() data = pd.read_excel('database.xlsx', sheet_name='R^2') measures = data.columns[1:] for _, row in data.iterrows(): measure_avail = {m:value for m, value in zip(measures, row.tolist()[1:]) if not np.isnan(value)} for m in measure_avail.keys(): df = df.append([[row.DOI, m, measure_avail[m], *info[info.DOI==row.DOI].values.tolist()[0][1:]]]) df.columns = ['DOI', 'Measure', 'R^2', *info.columns[1:]] df['ROI per subject'] = pd.to_numeric(df['ROI per subject'], errors='coerce') df['Subjects'] = pd.to_numeric(df['Subjects'], errors='coerce') df = df.dropna(subset=['ROI per subject', 'Subjects']) df = df[df['ROI per subject']<100] df['Sample points'] = df['ROI per subject'] * df['Subjects'] df=df.sort_values(by=['Measure']) filtered_df=df[df.Focus=='Brain'].copy() measure_type = {'Diffusion':['RD', 'AD', 'FA', 'MD', 'AWF', 'RK', 'RDe', 'MK'], 'Magnetization transfer':['MTR', 'ihMTR', 'MTR-UTE', 'MPF', 'MVF-MT', 'R1f', 'T2m', 'T2f', 'k_mf','k_fm'], 'T1 relaxometry':['T1'], 'T2 relaxometry':['T2', 'MWF', 'MVF-T2'], 'Other':['QSM', 'R2*', 'rSPF', 'MTV', 'T1p', 'T2p', 'RAFF', 'PD', 'T1sat']} color_dict = {m:plotly.colors.qualitative.Bold[n] for n,m in enumerate(measure_type.keys())} hover_text = [] bubble_size = [] for index, row in filtered_df.iterrows(): hover_text.append(('Measure: {measure}<br>'+ 'Number of subjects: {subjects}<br>'+ 'ROIs per subject: {rois}<br>'+ 'Total number of samples: {samples}').format(measure=row['Measure'], subjects=row['Subjects'], rois=row['ROI per subject'], samples=row['Sample points'])) bubble_size.append(2*np.sqrt(row['Sample points'])) filtered_df['Details'] = hover_text filtered_df['Size'] = bubble_size fig3 = go.Figure() for m in measure_type.keys(): df_m = filtered_df[filtered_df['Measure'].isin(measure_type[m])] fig3.add_trace(go.Scatter( x=df_m['Measure'], y=df_m['R^2'], text='Study: ' + df_m['Study']+ '<br>' + df_m['Details'], mode='markers', line = dict(color = 'rgba(0,0,0,0)'), marker = dict(color=color_dict[m]), marker_size = df_m['Size'], opacity=0.6, name=m )) fig3.update_layout( title=dict(text='Figure 3: R<sup>2</sup> between MRI and histology across measures',x=0.1), margin=dict(l=100), xaxis=dict(title='MRI measure'), yaxis=dict(title='R<sup>2</sup>'), autosize=False, width=900, height=600 ) fig3.show()</code></pre> <figure slot="outputs"> <stencila-image-plotly> <picture> <script type="application/vnd.plotly.v1+json"> {"data":[{"x":["AD","AD","AD","AD","AD","AD","AD","AD","AD","AWF","AWF","FA","FA","FA","FA","FA","FA","FA","FA","FA","FA","FA","FA","FA","FA","FA","FA","FA","MD","MD","MD","MD","MD","MD","MD","MD","MD","MD","MD","MD","MK","RD","RD","RD","RD","RD","RD","RD","RD","RD","RD","RD","RD","RD","RD","RD","RDe","RK","RK"],"y":[0.1296,0.64,0.4199,0.0121,0.1987,0.0064,0.1482,0.5625,0.0001,0.3364,0.3025,0.1989,0.0169,0.0729,0.2237,0.7056,0.5,0.5707,0.4637,0.1197,0.6889,0.334,0.2704,0.7569,0.6241000000000001,0.1024,0.7327,0.0576,0.0289,0.6561,0.0129,0.0784,0.3481,0.3492,0.4624000000000001,0.5329,0.6084,0.0564,0.245,0.1225,0.2304,0.7569,0.2097,0.34,0.2787,0.8281,0.0015,0.49,0.1681,0.5041,0.2401,0.2973,0.6561,0.2116,0.1369,0.038,0.25,0.0196,0.2401],"line":{"color":"rgba(0,0,0,0)"},"mode":"markers","name":"Diffusion","text":["Study: Aojula et al., 2016<br>Measure: AD<br>Number of subjects: 17.0<br>ROIs per subject: 1.0<br>Total number of samples: 17.0","Study: Schmierer et al., 2008<br>Measure: AD<br>Number of subjects: 15.0<br>ROIs per subject: 2.0<br>Total number of samples: 30.0","Study: Lehto et al., 2017a<br>Measure: AD<br>Number of subjects: 20.0<br>ROIs per subject: 4.0<br>Total number of samples: 80.0","Study: Abe et al., 2019<br>Measure: AD<br>Number of subjects: 5.0<br>ROIs per subject: 12.0<br>Total number of samples: 60.0","Study: Wendel et al., 2018<br>Measure: AD<br>Number of subjects: 12.0<br>ROIs per subject: 3.0<br>Total number of samples: 36.0","Study: Janve et al., 2013<br>Measure: AD<br>Number of subjects: 8.0<br>ROIs per subject: 6.0<br>Total number of samples: 48.0","Study: Chang et al., 2017<br>Measure: AD<br>Number of subjects: 4.0<br>ROIs per subject: 14.0<br>Total number of samples: 56.0","Study: Thiessen et al., 2013<br>Measure: AD<br>Number of subjects: 10.0<br>ROIs per subject: 1.0<br>Total number of samples: 10.0","Study: Tu et al., 2016<br>Measure: AD<br>Number of subjects: 25.0<br>ROIs per subject: 1.0<br>Total number of samples: 25.0","Study: Kelm et al., 2016<br>Measure: AWF<br>Number of subjects: 13.0<br>ROIs per subject: 6.0<br>Total number of samples: 78.0","Study: Jelescu et al., 2016<br>Measure: AWF<br>Number of subjects: 21.0<br>ROIs per subject: 1.0<br>Total number of samples: 21.0","Study: Chang et al., 2017<br>Measure: FA<br>Number of subjects: 4.0<br>ROIs per subject: 14.0<br>Total number of samples: 56.0","Study: Tu et al., 2016<br>Measure: FA<br>Number of subjects: 25.0<br>ROIs per subject: 1.0<br>Total number of samples: 25.0","Study: Janve et al., 2013<br>Measure: FA<br>Number of subjects: 8.0<br>ROIs per subject: 6.0<br>Total number of samples: 48.0","Study: Lehto et al., 2017a<br>Measure: FA<br>Number of subjects: 20.0<br>ROIs per subject: 4.0<br>Total number of samples: 80.0","Study: Thiessen et al., 2013<br>Measure: FA<br>Number of subjects: 10.0<br>ROIs per subject: 1.0<br>Total number of samples: 10.0","Study: Chandran et al., 2012<br>Measure: FA<br>Number of subjects: 20.0<br>ROIs per subject: 1.0<br>Total number of samples: 20.0","Study: Wendel et al., 2018<br>Measure: FA<br>Number of subjects: 12.0<br>ROIs per subject: 3.0<br>Total number of samples: 36.0","Study: Wang et al., 2009<br>Measure: FA<br>Number of subjects: 15.0<br>ROIs per subject: 1.0<br>Total number of samples: 15.0","Study: Aojula et al., 2016<br>Measure: FA<br>Number of subjects: 17.0<br>ROIs per subject: 1.0<br>Total number of samples: 17.0","Study: Schmierer et al., 2008<br>Measure: FA<br>Number of subjects: 15.0<br>ROIs per subject: 2.0<br>Total number of samples: 30.0","Study: van Tilborg et al., 2017<br>Measure: FA<br>Number of subjects: 12.0<br>ROIs per subject: 1.0<br>Total number of samples: 12.0","Study: Mollink et al., 2019<br>Measure: FA<br>Number of subjects: 18.0<br>ROIs per subject: 1.0<br>Total number of samples: 18.0","Study: Yano et al., 2018<br>Measure: FA<br>Number of subjects: 21.0<br>ROIs per subject: 1.0<br>Total number of samples: 21.0","Study: Schmierer et al., 2007b<br>Measure: FA<br>Number of subjects: 16.0<br>ROIs per subject: 3.0<br>Total number of samples: 48.0","Study: Abe et al., 2019<br>Measure: FA<br>Number of subjects: 5.0<br>ROIs per subject: 12.0<br>Total number of samples: 60.0","Study: Jito et al., 2008<br>Measure: FA<br>Number of subjects: 36.0<br>ROIs per subject: 1.0<br>Total number of samples: 36.0","Study: Pol et al., 2019<br>Measure: FA<br>Number of subjects: 11.0<br>ROIs per subject: 1.0<br>Total number of samples: 11.0","Study: Tu et al., 2016<br>Measure: MD<br>Number of subjects: 25.0<br>ROIs per subject: 1.0<br>Total number of samples: 25.0","Study: Thiessen et al., 2013<br>Measure: MD<br>Number of subjects: 10.0<br>ROIs per subject: 1.0<br>Total number of samples: 10.0","Study: Chang et al., 2017<br>Measure: MD<br>Number of subjects: 4.0<br>ROIs per subject: 14.0<br>Total number of samples: 56.0","Study: Janve et al., 2013<br>Measure: MD<br>Number of subjects: 8.0<br>ROIs per subject: 6.0<br>Total number of samples: 48.0","Study: Pol et al., 2019<br>Measure: MD<br>Number of subjects: 11.0<br>ROIs per subject: 1.0<br>Total number of samples: 11.0","Study: Lehto et al., 2017a<br>Measure: MD<br>Number of subjects: 20.0<br>ROIs per subject: 4.0<br>Total number of samples: 80.0","Study: Schmierer et al., 2007b<br>Measure: MD<br>Number of subjects: 16.0<br>ROIs per subject: 3.0<br>Total number of samples: 48.0","Study: Yano et al., 2018<br>Measure: MD<br>Number of subjects: 21.0<br>ROIs per subject: 1.0<br>Total number of samples: 21.0","Study: Schmierer et al., 2008<br>Measure: MD<br>Number of subjects: 15.0<br>ROIs per subject: 2.0<br>Total number of samples: 30.0","Study: Wendel et al., 2018<br>Measure: MD<br>Number of subjects: 12.0<br>ROIs per subject: 3.0<br>Total number of samples: 36.0","Study: Aojula et al., 2016<br>Measure: MD<br>Number of subjects: 17.0<br>ROIs per subject: 1.0<br>Total number of samples: 17.0","Study: Kelm et al., 2016<br>Measure: MD<br>Number of subjects: 13.0<br>ROIs per subject: 6.0<br>Total number of samples: 78.0","Study: Kelm et al., 2016<br>Measure: MK<br>Number of subjects: 13.0<br>ROIs per subject: 6.0<br>Total number of samples: 78.0","Study: Thiessen et al., 2013<br>Measure: RD<br>Number of subjects: 10.0<br>ROIs per subject: 1.0<br>Total number of samples: 10.0","Study: Aojula et al., 2016<br>Measure: RD<br>Number of subjects: 17.0<br>ROIs per subject: 1.0<br>Total number of samples: 17.0","Study: Chandran et al., 2012<br>Measure: RD<br>Number of subjects: 20.0<br>ROIs per subject: 1.0<br>Total number of samples: 20.0","Study: Wang et al., 2009<br>Measure: RD<br>Number of subjects: 15.0<br>ROIs per subject: 1.0<br>Total number of samples: 15.0","Study: Yano et al., 2018<br>Measure: RD<br>Number of subjects: 21.0<br>ROIs per subject: 1.0<br>Total number of samples: 21.0","Study: Lehto et al., 2017a<br>Measure: RD<br>Number of subjects: 20.0<br>ROIs per subject: 4.0<br>Total number of samples: 80.0","Study: Soustelle et al., 2019<br>Measure: RD<br>Number of subjects: 15.0<br>ROIs per subject: 1.0<br>Total number of samples: 15.0","Study: Abe et al., 2019<br>Measure: RD<br>Number of subjects: 5.0<br>ROIs per subject: 12.0<br>Total number of samples: 60.0","Study: Jelescu et al., 2016<br>Measure: RD<br>Number of subjects: 21.0<br>ROIs per subject: 1.0<br>Total number of samples: 21.0","Study: Janve et al., 2013<br>Measure: RD<br>Number of subjects: 8.0<br>ROIs per subject: 6.0<br>Total number of samples: 48.0","Study: Wendel et al., 2018<br>Measure: RD<br>Number of subjects: 12.0<br>ROIs per subject: 3.0<br>Total number of samples: 36.0","Study: Schmierer et al., 2008<br>Measure: RD<br>Number of subjects: 15.0<br>ROIs per subject: 2.0<br>Total number of samples: 30.0","Study: Tu et al., 2016<br>Measure: RD<br>Number of subjects: 25.0<br>ROIs per subject: 1.0<br>Total number of samples: 25.0","Study: Kelm et al., 2016<br>Measure: RD<br>Number of subjects: 13.0<br>ROIs per subject: 6.0<br>Total number of samples: 78.0","Study: Chang et al., 2017<br>Measure: RD<br>Number of subjects: 4.0<br>ROIs per subject: 14.0<br>Total number of samples: 56.0","Study: Jelescu et al., 2016<br>Measure: RDe<br>Number of subjects: 21.0<br>ROIs per subject: 1.0<br>Total number of samples: 21.0","Study: Jelescu et al., 2016<br>Measure: RK<br>Number of subjects: 21.0<br>ROIs per subject: 1.0<br>Total number of samples: 21.0","Study: Kelm et al., 2016<br>Measure: RK<br>Number of subjects: 13.0<br>ROIs per subject: 6.0<br>Total number of samples: 78.0"],"type":"scatter","marker":{"size":[8.246211251235321,10.954451150103322,17.88854381999832,15.491933384829668,12,13.856406460551018,14.966629547095765,6.324555320336759,10,17.663521732655695,9.16515138991168,14.966629547095765,10,13.856406460551018,17.88854381999832,6.324555320336759,8.94427190999916,12,7.745966692414834,8.246211251235321,10.954451150103322,6.928203230275509,8.48528137423857,9.16515138991168,13.856406460551018,15.491933384829668,12,6.6332495807108,10,6.324555320336759,14.966629547095765,13.856406460551018,6.6332495807108,17.88854381999832,13.856406460551018,9.16515138991168,10.954451150103322,12,8.246211251235321,17.663521732655695,17.663521732655695,6.324555320336759,8.246211251235321,8.94427190999916,7.745966692414834,9.16515138991168,17.88854381999832,7.745966692414834,15.491933384829668,9.16515138991168,13.856406460551018,12,10.954451150103322,10,17.663521732655695,14.966629547095765,9.16515138991168,9.16515138991168,17.663521732655695],"color":"rgb(127, 60, 141)"},"opacity":0.6},{"x":["MPF","MPF","MPF","MPF","MPF","MPF","MPF","MPF","MPF","MPF","MTR","MTR","MTR","MTR","MTR","MTR","MTR","MTR","MTR","MTR","MTR","MTR","MTR","MTR","MTR","MTR","MTR-UTE","MVF-MT","R1f","R1f","T2f","T2m","T2m","ihMTR","k_fm","k_mf","k_mf"],"y":[0.7225,0.779,0.7396,0.9801,0.6400000000000001,0.7,0.8649,0.7569,0.82,0.2872,0.7055999999999999,0.01,0.1024,0.4624,0.78,0.7055999999999999,0.5169,0.338,0.36,0.94,0.34,0.695,0.7668,0.6241000000000001,0.1444,0.46,0.76,0.7,0.0961,0.4356,0.36,0.1521,0.0001,0.96,0.1089,0.6724,0.0256],"line":{"color":"rgba(0,0,0,0)"},"mode":"markers","name":"Magnetization transfer","text":["Study: Janve et al., 2013<br>Measure: MPF<br>Number of subjects: 8.0<br>ROIs per subject: 6.0<br>Total number of samples: 48.0","Study: Khodanovic et al., 2017<br>Measure: MPF<br>Number of subjects: 14.0<br>ROIs per subject: 8.0<br>Total number of samples: 112.0","Study: Schmierer et al., 2008<br>Measure: MPF<br>Number of subjects: 15.0<br>ROIs per subject: 2.0<br>Total number of samples: 30.0","Study: Underhill et al., 2011<br>Measure: MPF<br>Number of subjects: 1.0<br>ROIs per subject: 9.0<br>Total number of samples: 9.0","Study: Schmierer et al., 2007a<br>Measure: MPF<br>Number of subjects: 37.0<br>ROIs per subject: 3.0<br>Total number of samples: 111.0","Study: West et al., 2018<br>Measure: MPF<br>Number of subjects: 15.0<br>ROIs per subject: 4.0<br>Total number of samples: 60.0","Study: Thiessen et al., 2013<br>Measure: MPF<br>Number of subjects: 10.0<br>ROIs per subject: 1.0<br>Total number of samples: 10.0","Study: Soustelle et al., 2019<br>Measure: MPF<br>Number of subjects: 15.0<br>ROIs per subject: 1.0<br>Total number of samples: 15.0","Study: Khodanovic et al., 2019<br>Measure: MPF<br>Number of subjects: 13.0<br>ROIs per subject: 1.0<br>Total number of samples: 13.0","Study: Turati et al., 2015<br>Measure: MPF<br>Number of subjects: 15.0<br>ROIs per subject: 1.0<br>Total number of samples: 15.0","Study: Schmierer et al., 2004<br>Measure: MTR<br>Number of subjects: 20.0<br>ROIs per subject: 3.0<br>Total number of samples: 60.0","Study: Fjaer et al., 2015<br>Measure: MTR<br>Number of subjects: 24.0<br>ROIs per subject: 1.0<br>Total number of samples: 24.0","Study: Jelescu et al., 2016<br>Measure: MTR<br>Number of subjects: 21.0<br>ROIs per subject: 1.0<br>Total number of samples: 21.0","Study: Schmierer et al., 2008<br>Measure: MTR<br>Number of subjects: 15.0<br>ROIs per subject: 2.0<br>Total number of samples: 30.0","Study: Duhamel et al., 2019<br>Measure: MTR<br>Number of subjects: 3.0<br>ROIs per subject: 7.0<br>Total number of samples: 21.0","Study: Schmierer et al., 2007a<br>Measure: MTR<br>Number of subjects: 37.0<br>ROIs per subject: 3.0<br>Total number of samples: 111.0","Study: Lehto et al., 2017a<br>Measure: MTR<br>Number of subjects: 20.0<br>ROIs per subject: 4.0<br>Total number of samples: 80.0","Study: Fjaer et al., 2013<br>Measure: MTR<br>Number of subjects: 54.0<br>ROIs per subject: 1.0<br>Total number of samples: 54.0","Study: Tardif et al., 2012<br>Measure: MTR<br>Number of subjects: 1.0<br>ROIs per subject: 18.0<br>Total number of samples: 18.0","Study: Lehto et al., 2017b<br>Measure: MTR<br>Number of subjects: 2.0<br>ROIs per subject: 16.0<br>Total number of samples: 32.0","Study: Hakkarainen et al., 2016<br>Measure: MTR<br>Number of subjects: 5.0<br>ROIs per subject: 12.0<br>Total number of samples: 60.0","Study: Fatemi et al., 2011<br>Measure: MTR<br>Number of subjects: 24.0<br>ROIs per subject: 25.0<br>Total number of samples: 600.0","Study: Beckmann et al., 2018<br>Measure: MTR<br>Number of subjects: 20.0<br>ROIs per subject: 1.0<br>Total number of samples: 20.0","Study: Zaaraoui et al., 2008<br>Measure: MTR<br>Number of subjects: 16.0<br>ROIs per subject: 1.0<br>Total number of samples: 16.0","Study: Tu et al., 2016<br>Measure: MTR<br>Number of subjects: 25.0<br>ROIs per subject: 1.0<br>Total number of samples: 25.0","Study: Guglielmetti et al., 2020<br>Measure: MTR<br>Number of subjects: 1.0<br>ROIs per subject: 9.0<br>Total number of samples: 9.0","Study: Guglielmetti et al., 2020<br>Measure: MTR-UTE<br>Number of subjects: 1.0<br>ROIs per subject: 9.0<br>Total number of samples: 9.0","Study: West et al., 2018<br>Measure: MVF-MT<br>Number of subjects: 15.0<br>ROIs per subject: 4.0<br>Total number of samples: 60.0","Study: Janve et al., 2013<br>Measure: R1f<br>Number of subjects: 8.0<br>ROIs per subject: 6.0<br>Total number of samples: 48.0","Study: Thiessen et al., 2013<br>Measure: R1f<br>Number of subjects: 10.0<br>ROIs per subject: 1.0<br>Total number of samples: 10.0","Study: Thiessen et al., 2013<br>Measure: T2f<br>Number of subjects: 10.0<br>ROIs per subject: 1.0<br>Total number of samples: 10.0","Study: Thiessen et al., 2013<br>Measure: T2m<br>Number of subjects: 10.0<br>ROIs per subject: 1.0<br>Total number of samples: 10.0","Study: Schmierer et al., 2007a<br>Measure: T2m<br>Number of subjects: 37.0<br>ROIs per subject: 3.0<br>Total number of samples: 111.0","Study: Duhamel et al., 2019<br>Measure: ihMTR<br>Number of subjects: 3.0<br>ROIs per subject: 7.0<br>Total number of samples: 21.0","Study: Thiessen et al., 2013<br>Measure: k_fm<br>Number of subjects: 10.0<br>ROIs per subject: 1.0<br>Total number of samples: 10.0","Study: Thiessen et al., 2013<br>Measure: k_mf<br>Number of subjects: 10.0<br>ROIs per subject: 1.0<br>Total number of samples: 10.0","Study: Janve et al., 2013<br>Measure: k_mf<br>Number of subjects: 8.0<br>ROIs per subject: 6.0<br>Total number of samples: 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</script><img src="index.html.media/2" alt="" itemscope="" itemtype="http://schema.org/ImageObject"> </picture> </stencila-image-plotly> </figure> </stencila-code-chunk> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">To provide a different way to explore sample size and effect size, we also prepared another treemap, where the studies are organised by measures. For each study, the area of its box is proportional to the sample size, while the color represents the related coefficient of determination.</p> <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk" data-execution_count="5" data-programminglanguage="python"> <pre class="language-python" itemscope="" itemtype="http://schema.stenci.la/CodeBlock" slot="text"><code>filtered_df=filtered_df.sort_values(by=['Study', 'Measure']) args = dict(data_frame=filtered_df, values='Sample points', color='R^2', hover_data='', path=['Measure', 'Study'], color_continuous_scale='Viridis') args = px._core.build_dataframe(args, go.Treemap) treemap_df = px._core.process_dataframe_hierarchy(args)['data_frame'] fig4 = go.Figure(go.Treemap( ids=treemap_df['id'].tolist(), labels=treemap_df['labels'].tolist(), parents=treemap_df['parent'].tolist(), values=treemap_df['Sample points'].tolist(), branchvalues='total', text='R<sup>2</sup>: ' + filtered_df['R^2'].astype(str) + '<br>' + filtered_df['Details'], hovertext=filtered_df['Study'] + '<br>R<sup>2</sup>: ' + filtered_df['R^2'].astype(str) + '<br>Number of samples: ' + filtered_df['Sample points'].astype(str), hoverinfo='text', textfont=dict( size=15, ), marker=dict( colors=filtered_df['R^2'], colorscale='Viridis', colorbar=dict(title='R<sup>2</sup>'), showscale=True ) ) ) fig4 = fig4.update_layout( autosize=False, width=900, height=600, margin=dict( l=100, r=0, b=30, t=60, ), title=dict(text='Figure 4: R<sup>2</sup> values across studies',x=0.1) ) fig4.show()</code></pre> <figure slot="outputs"> <stencila-image-plotly> <picture> <script type="application/vnd.plotly.v1+json"> {"data":[{"ids":["AD/Abe et al., 2019","FA/Abe et al., 2019","RD/Abe et al., 2019","AD/Aojula et al., 2016","FA/Aojula et al., 2016","MD/Aojula et al., 2016","RD/Aojula et al., 2016","MTR/Beckmann et al., 2018","MTV/Berman et al., 2018","FA/Chandran et al., 2012","RD/Chandran et al., 2012","AD/Chang et al., 2017","FA/Chang et al., 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2008","FA/van Tilborg et al., 2017","AD","AWF","FA","MD","MK","MPF","MTR","MTR-UTE","MTV","MVF-MT","MVF-T2","MWF","PD","QSM","R1f","R2*","RAFF","RD","RDe","RK","T1","T1p","T1sat","T2","T2f","T2m","T2p","ihMTR","k_fm","k_mf","rSPF"],"text":["R<sup>2</sup>: 0.0121<br>Measure: AD<br>Number of subjects: 5.0<br>ROIs per subject: 12.0<br>Total number of samples: 60.0","R<sup>2</sup>: 0.1024<br>Measure: FA<br>Number of subjects: 5.0<br>ROIs per subject: 12.0<br>Total number of samples: 60.0","R<sup>2</sup>: 0.1681<br>Measure: RD<br>Number of subjects: 5.0<br>ROIs per subject: 12.0<br>Total number of samples: 60.0","R<sup>2</sup>: 0.1296<br>Measure: AD<br>Number of subjects: 17.0<br>ROIs per subject: 1.0<br>Total number of samples: 17.0","R<sup>2</sup>: 0.1197<br>Measure: FA<br>Number of subjects: 17.0<br>ROIs per subject: 1.0<br>Total number of samples: 17.0","R<sup>2</sup>: 0.245<br>Measure: MD<br>Number of subjects: 17.0<br>ROIs per subject: 1.0<br>Total number of samples: 17.0","R<sup>2</sup>: 0.2097<br>Measure: RD<br>Number of subjects: 17.0<br>ROIs per subject: 1.0<br>Total number of samples: 17.0","R<sup>2</sup>: 0.7668<br>Measure: MTR<br>Number of subjects: 20.0<br>ROIs per subject: 1.0<br>Total number of samples: 20.0","R<sup>2</sup>: 0.74<br>Measure: MTV<br>Number of subjects: 4.0<br>ROIs per subject: 1.0<br>Total number of samples: 4.0","R<sup>2</sup>: 0.5<br>Measure: FA<br>Number of subjects: 20.0<br>ROIs per subject: 1.0<br>Total number of samples: 20.0","R<sup>2</sup>: 0.34<br>Measure: RD<br>Number of subjects: 20.0<br>ROIs per subject: 1.0<br>Total number of samples: 20.0","R<sup>2</sup>: 0.1482<br>Measure: AD<br>Number of subjects: 4.0<br>ROIs per subject: 14.0<br>Total number of samples: 56.0","R<sup>2</sup>: 0.1989<br>Measure: FA<br>Number of subjects: 4.0<br>ROIs per subject: 14.0<br>Total number of samples: 56.0","R<sup>2</sup>: 0.0129<br>Measure: MD<br>Number of subjects: 4.0<br>ROIs per subject: 14.0<br>Total number of samples: 56.0","R<sup>2</sup>: 0.038<br>Measure: RD<br>Number of subjects: 4.0<br>ROIs per subject: 14.0<br>Total number of samples: 56.0","R<sup>2</sup>: 0.78<br>Measure: MTR<br>Number of subjects: 3.0<br>ROIs per subject: 7.0<br>Total number of samples: 21.0","R<sup>2</sup>: 0.96<br>Measure: ihMTR<br>Number of subjects: 3.0<br>ROIs per subject: 7.0<br>Total number of samples: 21.0","R<sup>2</sup>: 0.695<br>Measure: MTR<br>Number of subjects: 24.0<br>ROIs per subject: 25.0<br>Total number of samples: 600.0","R<sup>2</sup>: 0.338<br>Measure: MTR<br>Number of subjects: 54.0<br>ROIs per subject: 1.0<br>Total number of samples: 54.0","R<sup>2</sup>: 0.01<br>Measure: MTR<br>Number of subjects: 24.0<br>ROIs per subject: 1.0<br>Total number of samples: 24.0","R<sup>2</sup>: 0.46<br>Measure: MTR<br>Number of subjects: 1.0<br>ROIs per subject: 9.0<br>Total number of samples: 9.0","R<sup>2</sup>: 0.76<br>Measure: MTR-UTE<br>Number of subjects: 1.0<br>ROIs per subject: 9.0<br>Total number of samples: 9.0","R<sup>2</sup>: 0.34<br>Measure: MTR<br>Number of subjects: 5.0<br>ROIs per subject: 12.0<br>Total number of samples: 60.0","R<sup>2</sup>: 0.84<br>Measure: RAFF<br>Number of subjects: 5.0<br>ROIs per subject: 12.0<br>Total number of samples: 60.0","R<sup>2</sup>: 0.77<br>Measure: T1<br>Number of subjects: 5.0<br>ROIs per subject: 12.0<br>Total number of samples: 60.0","R<sup>2</sup>: 0.63<br>Measure: T1p<br>Number of subjects: 5.0<br>ROIs per subject: 12.0<br>Total number of samples: 60.0","R<sup>2</sup>: 0.18<br>Measure: T2<br>Number of subjects: 5.0<br>ROIs per subject: 12.0<br>Total number of samples: 60.0","R<sup>2</sup>: 0.34<br>Measure: T2p<br>Number of subjects: 5.0<br>ROIs per subject: 12.0<br>Total number of samples: 60.0","R<sup>2</sup>: 0.1239<br>Measure: QSM<br>Number of subjects: 6.0<br>ROIs per subject: 79.0<br>Total number of samples: 474.0","R<sup>2</sup>: 0.0007<br>Measure: R2*<br>Number of subjects: 6.0<br>ROIs per subject: 79.0<br>Total number of samples: 474.0","R<sup>2</sup>: 0.3398<br>Measure: T1<br>Number of subjects: 6.0<br>ROIs per subject: 79.0<br>Total number of samples: 474.0","R<sup>2</sup>: 0.0064<br>Measure: AD<br>Number of subjects: 8.0<br>ROIs per subject: 6.0<br>Total number of samples: 48.0","R<sup>2</sup>: 0.0729<br>Measure: FA<br>Number of subjects: 8.0<br>ROIs per subject: 6.0<br>Total number of samples: 48.0","R<sup>2</sup>: 0.0784<br>Measure: MD<br>Number of subjects: 8.0<br>ROIs per subject: 6.0<br>Total number of samples: 48.0","R<sup>2</sup>: 0.7225<br>Measure: MPF<br>Number of subjects: 8.0<br>ROIs per subject: 6.0<br>Total number of samples: 48.0","R<sup>2</sup>: 0.0961<br>Measure: R1f<br>Number of subjects: 8.0<br>ROIs per subject: 6.0<br>Total number of samples: 48.0","R<sup>2</sup>: 0.2401<br>Measure: RD<br>Number of subjects: 8.0<br>ROIs per subject: 6.0<br>Total number of samples: 48.0","R<sup>2</sup>: 0.0256<br>Measure: k_mf<br>Number of subjects: 8.0<br>ROIs per subject: 6.0<br>Total number of samples: 48.0","R<sup>2</sup>: 0.3025<br>Measure: AWF<br>Number of subjects: 21.0<br>ROIs per subject: 1.0<br>Total number of samples: 21.0","R<sup>2</sup>: 0.1024<br>Measure: MTR<br>Number of subjects: 21.0<br>ROIs per subject: 1.0<br>Total number of samples: 21.0","R<sup>2</sup>: 0.5041<br>Measure: RD<br>Number of subjects: 21.0<br>ROIs per subject: 1.0<br>Total number of samples: 21.0","R<sup>2</sup>: 0.25<br>Measure: RDe<br>Number of subjects: 21.0<br>ROIs per subject: 1.0<br>Total number of samples: 21.0","R<sup>2</sup>: 0.0196<br>Measure: RK<br>Number of subjects: 21.0<br>ROIs per subject: 1.0<br>Total number of samples: 21.0","R<sup>2</sup>: 0.4096<br>Measure: T2<br>Number of subjects: 21.0<br>ROIs per subject: 1.0<br>Total number of samples: 21.0","R<sup>2</sup>: 0.7327<br>Measure: FA<br>Number of subjects: 36.0<br>ROIs per subject: 1.0<br>Total number of samples: 36.0","R<sup>2</sup>: 0.3364<br>Measure: AWF<br>Number of subjects: 13.0<br>ROIs per subject: 6.0<br>Total number of samples: 78.0","R<sup>2</sup>: 0.1225<br>Measure: MD<br>Number of subjects: 13.0<br>ROIs per subject: 6.0<br>Total number of samples: 78.0","R<sup>2</sup>: 0.2304<br>Measure: MK<br>Number of subjects: 13.0<br>ROIs per subject: 6.0<br>Total number of samples: 78.0","R<sup>2</sup>: 0.1369<br>Measure: RD<br>Number of subjects: 13.0<br>ROIs per subject: 6.0<br>Total number of samples: 78.0","R<sup>2</sup>: 0.2401<br>Measure: RK<br>Number of subjects: 13.0<br>ROIs per subject: 6.0<br>Total number of samples: 78.0","R<sup>2</sup>: 0.779<br>Measure: MPF<br>Number of subjects: 14.0<br>ROIs per subject: 8.0<br>Total number of samples: 112.0","R<sup>2</sup>: 0.82<br>Measure: MPF<br>Number of subjects: 13.0<br>ROIs per subject: 1.0<br>Total number of samples: 13.0","R<sup>2</sup>: 0.67<br>Measure: MWF<br>Number of subjects: 13.0<br>ROIs per subject: 44.0<br>Total number of samples: 572.0","R<sup>2</sup>: 0.78<br>Measure: MWF<br>Number of subjects: 3.0<br>ROIs per subject: 73.0<br>Total number of samples: 219.0","R<sup>2</sup>: 0.4199<br>Measure: AD<br>Number of subjects: 20.0<br>ROIs per subject: 4.0<br>Total number of samples: 80.0","R<sup>2</sup>: 0.2237<br>Measure: FA<br>Number of subjects: 20.0<br>ROIs per subject: 4.0<br>Total number of samples: 80.0","R<sup>2</sup>: 0.3492<br>Measure: MD<br>Number of subjects: 20.0<br>ROIs per subject: 4.0<br>Total number of samples: 80.0","R<sup>2</sup>: 0.5169<br>Measure: MTR<br>Number of subjects: 20.0<br>ROIs per subject: 4.0<br>Total number of samples: 80.0","R<sup>2</sup>: 0.552<br>Measure: RAFF<br>Number of subjects: 20.0<br>ROIs per subject: 4.0<br>Total number of samples: 80.0","R<sup>2</sup>: 0.0015<br>Measure: RD<br>Number of subjects: 20.0<br>ROIs per subject: 4.0<br>Total number of samples: 80.0","R<sup>2</sup>: 0.49<br>Measure: T1sat<br>Number of subjects: 20.0<br>ROIs per subject: 4.0<br>Total number of samples: 80.0","R<sup>2</sup>: 0.94<br>Measure: MTR<br>Number of subjects: 2.0<br>ROIs per subject: 16.0<br>Total number of samples: 32.0","R<sup>2</sup>: 0.2704<br>Measure: FA<br>Number of subjects: 18.0<br>ROIs per subject: 1.0<br>Total number of samples: 18.0","R<sup>2</sup>: 0.0576<br>Measure: FA<br>Number of subjects: 11.0<br>ROIs per subject: 1.0<br>Total number of samples: 11.0","R<sup>2</sup>: 0.3481<br>Measure: MD<br>Number of subjects: 11.0<br>ROIs per subject: 1.0<br>Total number of samples: 11.0","R<sup>2</sup>: 0.0361<br>Measure: QSM<br>Number of subjects: 11.0<br>ROIs per subject: 1.0<br>Total number of samples: 11.0","R<sup>2</sup>: 0.27<br>Measure: T1<br>Number of subjects: 13.0<br>ROIs per subject: 3.0<br>Total number of samples: 39.0","R<sup>2</sup>: 0.235<br>Measure: T2<br>Number of subjects: 13.0<br>ROIs per subject: 3.0<br>Total number of samples: 39.0","R<sup>2</sup>: 0.7055999999999999<br>Measure: MTR<br>Number of subjects: 20.0<br>ROIs per subject: 3.0<br>Total number of samples: 60.0","R<sup>2</sup>: 0.48999999999999994<br>Measure: T1<br>Number of subjects: 20.0<br>ROIs per subject: 3.0<br>Total number of samples: 60.0","R<sup>2</sup>: 0.6400000000000001<br>Measure: MPF<br>Number of subjects: 37.0<br>ROIs per subject: 3.0<br>Total number of samples: 111.0","R<sup>2</sup>: 0.7055999999999999<br>Measure: MTR<br>Number of subjects: 37.0<br>ROIs per subject: 3.0<br>Total number of samples: 111.0","R<sup>2</sup>: 0.4760999999999999<br>Measure: T1<br>Number of subjects: 37.0<br>ROIs per subject: 3.0<br>Total number of samples: 111.0","R<sup>2</sup>: 0.0001<br>Measure: T2m<br>Number of subjects: 37.0<br>ROIs per subject: 3.0<br>Total number of samples: 111.0","R<sup>2</sup>: 0.6241000000000001<br>Measure: FA<br>Number of subjects: 16.0<br>ROIs per subject: 3.0<br>Total number of samples: 48.0","R<sup>2</sup>: 0.4624000000000001<br>Measure: MD<br>Number of subjects: 16.0<br>ROIs per subject: 3.0<br>Total number of samples: 48.0","R<sup>2</sup>: 0.64<br>Measure: AD<br>Number of subjects: 15.0<br>ROIs per subject: 2.0<br>Total number of samples: 30.0","R<sup>2</sup>: 0.6889<br>Measure: FA<br>Number of subjects: 15.0<br>ROIs per subject: 2.0<br>Total number of samples: 30.0","R<sup>2</sup>: 0.6084<br>Measure: MD<br>Number of subjects: 15.0<br>ROIs per subject: 2.0<br>Total number of samples: 30.0","R<sup>2</sup>: 0.7396<br>Measure: MPF<br>Number of subjects: 15.0<br>ROIs per subject: 2.0<br>Total number of samples: 30.0","R<sup>2</sup>: 0.4624<br>Measure: MTR<br>Number of subjects: 15.0<br>ROIs per subject: 2.0<br>Total number of samples: 30.0","R<sup>2</sup>: 0.6561<br>Measure: RD<br>Number of subjects: 15.0<br>ROIs per subject: 2.0<br>Total number of samples: 30.0","R<sup>2</sup>: 0.7921<br>Measure: T1<br>Number of subjects: 15.0<br>ROIs per subject: 2.0<br>Total number of samples: 30.0","R<sup>2</sup>: 0.8464<br>Measure: T2<br>Number of subjects: 15.0<br>ROIs per subject: 2.0<br>Total number of samples: 30.0","R<sup>2</sup>: 0.7569<br>Measure: MPF<br>Number of subjects: 15.0<br>ROIs per subject: 1.0<br>Total number of samples: 15.0","R<sup>2</sup>: 0.4624<br>Measure: MWF<br>Number of subjects: 15.0<br>ROIs per subject: 1.0<br>Total number of samples: 15.0","R<sup>2</sup>: 0.49<br>Measure: RD<br>Number of subjects: 15.0<br>ROIs per subject: 1.0<br>Total number of samples: 15.0","R<sup>2</sup>: 0.7396<br>Measure: rSPF<br>Number of subjects: 15.0<br>ROIs per subject: 1.0<br>Total number of samples: 15.0","R<sup>2</sup>: 0.36<br>Measure: MTR<br>Number of subjects: 1.0<br>ROIs per subject: 18.0<br>Total number of samples: 18.0","R<sup>2</sup>: 0.3481<br>Measure: PD<br>Number of subjects: 1.0<br>ROIs per subject: 18.0<br>Total number of samples: 18.0","R<sup>2</sup>: 0.5929<br>Measure: T1<br>Number of subjects: 1.0<br>ROIs per subject: 18.0<br>Total number of samples: 18.0","R<sup>2</sup>: 0.4225<br>Measure: T2<br>Number of subjects: 1.0<br>ROIs per subject: 18.0<br>Total number of samples: 18.0","R<sup>2</sup>: 0.5625<br>Measure: AD<br>Number of subjects: 10.0<br>ROIs per subject: 1.0<br>Total number of samples: 10.0","R<sup>2</sup>: 0.7056<br>Measure: FA<br>Number of subjects: 10.0<br>ROIs per subject: 1.0<br>Total number of samples: 10.0","R<sup>2</sup>: 0.6561<br>Measure: MD<br>Number of subjects: 10.0<br>ROIs per subject: 1.0<br>Total number of samples: 10.0","R<sup>2</sup>: 0.8649<br>Measure: MPF<br>Number of subjects: 10.0<br>ROIs per subject: 1.0<br>Total number of samples: 10.0","R<sup>2</sup>: 0.4356<br>Measure: R1f<br>Number of subjects: 10.0<br>ROIs per subject: 1.0<br>Total number of samples: 10.0","R<sup>2</sup>: 0.7569<br>Measure: RD<br>Number of subjects: 10.0<br>ROIs per subject: 1.0<br>Total number of samples: 10.0","R<sup>2</sup>: 0.4356<br>Measure: T1<br>Number of subjects: 10.0<br>ROIs per subject: 1.0<br>Total number of samples: 10.0","R<sup>2</sup>: 0.0676<br>Measure: T2<br>Number of subjects: 10.0<br>ROIs per subject: 1.0<br>Total number of samples: 10.0","R<sup>2</sup>: 0.36<br>Measure: T2f<br>Number of subjects: 10.0<br>ROIs per subject: 1.0<br>Total number of samples: 10.0","R<sup>2</sup>: 0.1521<br>Measure: T2m<br>Number of subjects: 10.0<br>ROIs per subject: 1.0<br>Total number of samples: 10.0","R<sup>2</sup>: 0.1089<br>Measure: k_fm<br>Number of subjects: 10.0<br>ROIs per subject: 1.0<br>Total number of samples: 10.0","R<sup>2</sup>: 0.6724<br>Measure: k_mf<br>Number of subjects: 10.0<br>ROIs per subject: 1.0<br>Total number of samples: 10.0","R<sup>2</sup>: 0.0001<br>Measure: AD<br>Number of subjects: 25.0<br>ROIs per subject: 1.0<br>Total number of samples: 25.0","R<sup>2</sup>: 0.0169<br>Measure: FA<br>Number of subjects: 25.0<br>ROIs per subject: 1.0<br>Total number of samples: 25.0","R<sup>2</sup>: 0.0289<br>Measure: MD<br>Number of subjects: 25.0<br>ROIs per subject: 1.0<br>Total number of samples: 25.0","R<sup>2</sup>: 0.1444<br>Measure: MTR<br>Number of subjects: 25.0<br>ROIs per subject: 1.0<br>Total number of samples: 25.0","R<sup>2</sup>: 0.2116<br>Measure: RD<br>Number of subjects: 25.0<br>ROIs per subject: 1.0<br>Total number of samples: 25.0","R<sup>2</sup>: 0.2872<br>Measure: MPF<br>Number of subjects: 15.0<br>ROIs per subject: 1.0<br>Total number of samples: 15.0","R<sup>2</sup>: 0.9801<br>Measure: MPF<br>Number of subjects: 1.0<br>ROIs per subject: 9.0<br>Total number of samples: 9.0","R<sup>2</sup>: 0.4637<br>Measure: FA<br>Number of subjects: 15.0<br>ROIs per subject: 1.0<br>Total number of samples: 15.0","R<sup>2</sup>: 0.2787<br>Measure: RD<br>Number of subjects: 15.0<br>ROIs per subject: 1.0<br>Total number of samples: 15.0","R<sup>2</sup>: 0.1987<br>Measure: AD<br>Number of subjects: 12.0<br>ROIs per subject: 3.0<br>Total number of samples: 36.0","R<sup>2</sup>: 0.5707<br>Measure: FA<br>Number of subjects: 12.0<br>ROIs per subject: 3.0<br>Total number of samples: 36.0","R<sup>2</sup>: 0.0564<br>Measure: MD<br>Number of subjects: 12.0<br>ROIs per subject: 3.0<br>Total number of samples: 36.0","R<sup>2</sup>: 0.2973<br>Measure: RD<br>Number of subjects: 12.0<br>ROIs per subject: 3.0<br>Total number of samples: 36.0","R<sup>2</sup>: 0.7<br>Measure: MPF<br>Number of subjects: 15.0<br>ROIs per subject: 4.0<br>Total number of samples: 60.0","R<sup>2</sup>: 0.7<br>Measure: MVF-MT<br>Number of subjects: 15.0<br>ROIs per subject: 4.0<br>Total number of samples: 60.0","R<sup>2</sup>: 0.68<br>Measure: MVF-T2<br>Number of subjects: 15.0<br>ROIs per subject: 4.0<br>Total number of samples: 60.0","R<sup>2</sup>: 0.66<br>Measure: MWF<br>Number of subjects: 15.0<br>ROIs per subject: 4.0<br>Total number of samples: 60.0","R<sup>2</sup>: 0.5184<br>Measure: T2<br>Number of subjects: 8.0<br>ROIs per subject: 3.0<br>Total number of samples: 24.0","R<sup>2</sup>: 0.7569<br>Measure: FA<br>Number of subjects: 21.0<br>ROIs per subject: 1.0<br>Total number of samples: 21.0","R<sup>2</sup>: 0.5329<br>Measure: MD<br>Number of subjects: 21.0<br>ROIs per subject: 1.0<br>Total number of samples: 21.0","R<sup>2</sup>: 0.8281<br>Measure: RD<br>Number of subjects: 21.0<br>ROIs per subject: 1.0<br>Total number of samples: 21.0","R<sup>2</sup>: 0.6241000000000001<br>Measure: MTR<br>Number of subjects: 16.0<br>ROIs per subject: 1.0<br>Total number of samples: 16.0","R<sup>2</sup>: 0.334<br>Measure: FA<br>Number of subjects: 12.0<br>ROIs per subject: 1.0<br>Total number of samples: 12.0"],"type":"treemap","labels":["Abe et al., 2019","Abe et al., 2019","Abe et al., 2019","Aojula et al., 2016","Aojula et al., 2016","Aojula et al., 2016","Aojula et al., 2016","Beckmann et al., 2018","Berman et al., 2018","Chandran et al., 2012","Chandran et al., 2012","Chang et al., 2017","Chang et al., 2017","Chang et al., 2017","Chang et al., 2017","Duhamel et al., 2019","Duhamel et al., 2019","Fatemi et al., 2011","Fjaer et al., 2013","Fjaer et al., 2015","Guglielmetti et al., 2020","Guglielmetti et al., 2020","Hakkarainen et al., 2016","Hakkarainen et al., 2016","Hakkarainen et al., 2016","Hakkarainen et al., 2016","Hakkarainen et al., 2016","Hakkarainen et al., 2016","Hametner et al., 2018","Hametner et al., 2018","Hametner et al., 2018","Janve et al., 2013","Janve et al., 2013","Janve et al., 2013","Janve et al., 2013","Janve et al., 2013","Janve et al., 2013","Janve et al., 2013","Jelescu et al., 2016","Jelescu et al., 2016","Jelescu et al., 2016","Jelescu et al., 2016","Jelescu et al., 2016","Jelescu et al., 2016","Jito et al., 2008","Kelm et al., 2016","Kelm et al., 2016","Kelm et al., 2016","Kelm et al., 2016","Kelm et al., 2016","Khodanovic et al., 2017","Khodanovic et al., 2019","Laule et al., 2006","Laule et al., 2008","Lehto et al., 2017a","Lehto et al., 2017a","Lehto et al., 2017a","Lehto et al., 2017a","Lehto et al., 2017a","Lehto et al., 2017a","Lehto et al., 2017a","Lehto et al., 2017b","Mollink et al., 2019","Pol et al., 2019","Pol et al., 2019","Pol et al., 2019","Reeves et al., 2016","Reeves et al., 2016","Schmierer et al., 2004","Schmierer et al., 2004","Schmierer et al., 2007a","Schmierer et al., 2007a","Schmierer et al., 2007a","Schmierer et al., 2007a","Schmierer et al., 2007b","Schmierer et al., 2007b","Schmierer et al., 2008","Schmierer et al., 2008","Schmierer et al., 2008","Schmierer et al., 2008","Schmierer et al., 2008","Schmierer et al., 2008","Schmierer et al., 2008","Schmierer et al., 2008","Soustelle et al., 2019","Soustelle et al., 2019","Soustelle et al., 2019","Soustelle et al., 2019","Tardif et al., 2012","Tardif et al., 2012","Tardif et al., 2012","Tardif et al., 2012","Thiessen et al., 2013","Thiessen et al., 2013","Thiessen et al., 2013","Thiessen et al., 2013","Thiessen et al., 2013","Thiessen et al., 2013","Thiessen et al., 2013","Thiessen et al., 2013","Thiessen et al., 2013","Thiessen et al., 2013","Thiessen et al., 2013","Thiessen et al., 2013","Tu et al., 2016","Tu et al., 2016","Tu et al., 2016","Tu et al., 2016","Tu et al., 2016","Turati et al., 2015","Underhill et al., 2011","Wang et al., 2009","Wang et al., 2009","Wendel et al., 2018","Wendel et al., 2018","Wendel et al., 2018","Wendel et al., 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</script><img src="index.html.media/3" alt="" itemscope="" itemtype="http://schema.org/ImageObject"> </picture> </stencila-image-plotly> </figure> </stencila-code-chunk> <h3 itemscope="" itemtype="http://schema.stenci.la/Heading" id="using-meta-analysis-tools"> Using meta-analysis tools</h3> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Can we express quantitatively what we observed in the previous plots? This is where the meta-analysis tools come in: we used the R package <a href="http://www.metafor-project.org/doku.php" itemscope="" itemtype="http://schema.stenci.la/Link">metafor</a> to fit a mixed-effect (ME) model to the data reported for each measure. In this way, we can estimate an overall interval of R<sup itemscope="" itemtype="http://schema.stenci.la/Superscript">2</sup> values based on the effect sizes and the sample sizes. We can also estimate the interval of R<sup itemscope="" itemtype="http://schema.stenci.la/Superscript">2</sup> that we can expect in future studies (this is called prediction interval). A compact way to represent these results is given by forest plots: for each study, we represent the effect size and the related sample size using a square and a horizontal error bar; then for each measure, we represent the results from the ME model using a diamond and an additional error bar; finally to represent the prediction interval we use two hourglasses and a dotted line.</p> <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk" data-execution_count="6" data-programminglanguage="python"> <pre class="language-python" itemscope="" itemtype="http://schema.stenci.la/CodeBlock" slot="text"><code>filtered_df['Variance'] = (4*filtered_df['R^2'])*((1-filtered_df['R^2'])**2)/filtered_df['Sample points'] metafor = importr('metafor') stats = importr('stats') metastudy = {} for m in filtered_df.Measure.unique(): nstudies=len(filtered_df.Measure[filtered_df.Measure==m]) if nstudies > 2: df_m = filtered_df[filtered_df.Measure==m] df_m = df_m.sort_values(by=['Year']) r2 = rpy2.robjects.FloatVector(df_m['R^2']) var = rpy2.robjects.FloatVector(df_m['Variance']) fit = metafor.rma(r2, var, method="REML", test="knha") res = stats.predict(fit) results = dict(zip(res.names,list(res))) metastudy[m] = dict(pred=results['pred'][0], cilb=results['pred'][0]-results['ci.lb'][0], ciub=results['ci.ub'][0]-results['pred'][0], crub=results['cr.ub'][0], crlb=results['cr.lb'][0])</code></pre> </stencila-code-chunk> <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk" data-execution_count="7" data-programminglanguage="python"> <pre class="language-python" itemscope="" itemtype="http://schema.stenci.la/CodeBlock" slot="text"><code>measure_type_reverse={m:t for t,mlist in measure_type.items() for m in mlist} fig5 = make_subplots(rows=3, cols=3, start_cell="top-left", vertical_spacing=0.05, horizontal_spacing=0.2, x_title='R<sup>2</sup>', subplot_titles=sorted(metastudy.keys(), key=measure_type_reverse.get)) row=1 col=1 for m in sorted(metastudy.keys(), key=measure_type_reverse.get): fig5.add_trace(go.Scatter( x=[round(metastudy[m]['crlb'],2) if round(metastudy[m]['crlb'],2)>0 else 0, round(metastudy[m]['crub'],2) if round(metastudy[m]['crub'],2)<1 else 1], y=['Mixed model','Mixed model'], line=dict(color='black', width=2, dash='dot'), hovertemplate = 'Prediction boundary: %{x}<extra></extra>', marker_symbol = 'hourglass-open', marker_size = 8 ), row=row, col=col) fig5.add_trace(go.Scatter( x=[round(metastudy[m]['pred'],2)], y=['Mixed model'], mode='markers', marker = dict(color = 'black'), marker_symbol = 'diamond-wide', marker_size = 10, hovertemplate = 'R<sup>2</sup> estimate: %{x}<extra></extra>', error_x=dict( type='data', arrayminus=[round(metastudy[m]['cilb'],2) if round(metastudy[m]['cilb'],2)>0 else 0], array=[round(metastudy[m]['ciub'],2) if round(metastudy[m]['ciub'],2)<1 else 1]) ), row=row, col=col) df_m = filtered_df[filtered_df.Measure==m] df_m = df_m.sort_values(by=['Year'], ascending=False) fig5.add_trace(go.Scatter( x=df_m['R^2'], y=df_m['Study'], text=df_m['Sample points'], customdata=df_m['Histology/microscopy measure'], mode='markers', marker = dict(color = color_dict[measure_type_reverse[m]]), marker_symbol = 'square', marker_size = np.log(50/df_m['Variance']), hovertemplate = '%{y}<br>R<sup>2</sup>: %{x}<br>Number of samples: %{text}<br>' + 'Reference: %{customdata}<extra></extra>', error_x=dict( type='data', array=2*np.sqrt(df_m['Variance'])) ), row=row, col=col) if col == 3: col = 1 row += 1 else: col += 1 fig5.update_xaxes(range=[0, 1]) fig5.update_layout(showlegend=False, title=dict(text='Figure 5: Forest plots and mixed modelling results',x=0.1), margin=dict(l=200), width=1000, height=1400) fig5.show()</code></pre> <figure slot="outputs"> <stencila-image-plotly> <picture> <script type="application/vnd.plotly.v1+json"> {"data":[{"x":[0,0.75],"y":["Mixed model","Mixed 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What if we want to compare the R<sup itemscope="" itemtype="http://schema.stenci.la/Superscript">2</sup> estimates across measures? To do that, we pooled together all the measures from all the studies and computed first a repeated measures meta-regression and then all the possible pairwise comparisons (Tukey's test), correcting for multiple comparisons (Bonferroni correction). To visually represent these results, we used two heatmaps, one for the z-scores and one for the p-values: each element refers to the comparison between the measure on the x axis and the one on the y axis.</p> <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk" data-execution_count="8" data-programminglanguage="python"> <pre class="language-python" itemscope="" itemtype="http://schema.stenci.la/CodeBlock" slot="text"><code>multcomp = importr('multcomp') base = importr('base') thres = filtered_df.Measure.value_counts() > 2 df_thres = filtered_df[filtered_df.Measure.isin(filtered_df.Measure.value_counts()[thres].index)] r2 = rpy2.robjects.FloatVector(df_thres['R^2']) var = rpy2.robjects.FloatVector(df_thres['Variance']) measure_v = rpy2.robjects.StrVector(df_thres['Measure']) measure_f = rpy2.robjects.Formula('~ -1 + measure') env = measure_f.environment env['measure'] = measure_v study_v = rpy2.robjects.StrVector(df_thres['Study']) study_f = rpy2.robjects.Formula('~ 1 | study') env = study_f.environment env['study'] = study_v fit_mv = metafor.rma_mv(r2, var, method="REML", mods=measure_f, random=study_f) glht = multcomp.glht(fit_mv, base.cbind(multcomp.contrMat(base.rep(1,9), type="Tukey"))) mtests = multcomp.summary_glht(glht, test=multcomp.adjusted("bonferroni")) mtest_res = dict(zip(mtests.names, list(mtests))) stat_res = dict(zip(mtest_res['test'].names, list(mtest_res['test']))) pvals = stat_res['pvalues'] zvals = stat_res['tstat']</code></pre> </stencila-code-chunk> <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk" data-execution_count="9" data-programminglanguage="python"> <pre class="language-python" itemscope="" itemtype="http://schema.stenci.la/CodeBlock" slot="text"><code>measure_list = df_thres['Measure'].unique() measure_list.sort() n = len(measure_list) pvals_list = [] zvals_list = [] idx = 0 for i in range(n): pvals_i = [0] * i pvals_i.append(np.nan) pvals_i.extend(pvals[idx:idx+n-i-1]) zvals_i = [0] * i zvals_i.append(np.nan) zvals_i.extend(zvals[idx:idx+n-i-1]) idx = idx + n - i - 1 pvals_list.append(pvals_i) zvals_list.append(zvals_i) pvals_list=np.array(pvals_list) zvals_list=np.array(zvals_list) pvals_list=pvals_list+pvals_list.T zvals_list=zvals_list-zvals_list.T fig6 = make_subplots(rows=1, cols=2, horizontal_spacing=0.2, subplot_titles=['z-scores', 'p-values' ]) fig6.add_trace(go.Heatmap( z=zvals_list, x=measure_list, y=measure_list, customdata=pvals_list, hovertemplate = '%{x}-%{y}<br>z-score: %{z:.2f}<br>p-value: %{customdata:.5f}<extra></extra>', hoverongaps = False, colorscale='RdBu', colorbar=dict(title='z-score', x=0.42), showscale=True ), col=1, row=1) fig6.add_trace(go.Heatmap( z=pvals_list, x=measure_list, y=measure_list, customdata=zvals_list, hovertemplate = '%{x}-%{y}<br>z-score: %{customdata:.2f}<br>p-value: %{z:.5f}<extra></extra>', hoverongaps = False, colorscale='Purples', colorbar=dict(title='p-value'), zmin=0, zmax=0.05, showscale=True, reversescale=True ), col=2, row=1) fig6.update_layout( title='Figure 6: Statistical pairwise comparisons between R<sup>2</sup> estimates', height=500, width=950, paper_bgcolor='rgba(0,0,0,0)', plot_bgcolor='rgba(0,0,0,0)', ) fig6.show()</code></pre> <figure slot="outputs"> <stencila-image-plotly> <picture> <script type="application/vnd.plotly.v1+json"> 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</script><img src="index.html.media/5" alt="" itemscope="" itemtype="http://schema.org/ImageObject"> </picture> </stencila-image-plotly> </figure> </stencila-code-chunk> <h3 itemscope="" itemtype="http://schema.stenci.la/Heading" id="other-factors-to-consider"> Other factors to consider</h3> <p itemscope="" itemtype="http://schema.stenci.la/Paragraph">Can some of this variance be explained by the differences in methodological choices and experimental conditions we mention? The number of studies is limited for a quantitative evaluation, but we can get a qualitative idea using bar plots and scatter plots organized by each condition.</p> <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk" data-execution_count="10" data-programminglanguage="python"> <pre class="language-python" itemscope="" itemtype="http://schema.stenci.la/CodeBlock" slot="text"><code>structures={'Lesions':'Lesions', 'Substantia nigra':'Deep grey matter', 'Hippocampal commissure':'White matter', 'Putamen':'Deep grey matter', 'Motor cortex':'Grey matter', 'Globus pallidus':'Deep grey matter', 'Perforant pathway':'White matter', 'Mammilothalamic tract':'White matter', 'External capsule':'White matter', 'Inter-peduncular nuclues':'Deep grey matter', 'Hippocampus':'Deep grey matter', 'Thalamic nuclei':'Deep grey matter', 'Thalamus':'Deep grey matter', 'Cerebellum':'Grey matter', 'Amygdala':'Deep grey matter', 'Cingulum':'White matter', 'Striatum':'Deep grey matter', 'Accumbens':'Deep grey matter', 'Basal ganglia':'Deep grey matter', 'Anterior commissure':'White matter', 'Cortex':'Grey matter', 'Fimbria':'White matter', 'Somatosensory cortex':'Grey matter', 'Dorsal tegmental tract':'White matter', 'Superior colliculus':'Deep grey matter', 'Fasciculus retroflexus':'White matter', 'Optic nerve':'White matter', 'Dentate gyrus':'Grey matter', 'Corpus callosum':'White matter', 'Fornix':'White matter', 'White matter':'White matter', 'Grey matter':'Grey matter', 'Optic tract':'White matter', 'Internal capsule':'White matter', 'Stria medullaris':'White matter'} tissue_types=[] for s in filtered_df['Specific structure(s)']: t_list=[] for i in s.split(','): t_list.append(structures[i.strip()]) tissue_types.append('+'.join(list(set(t_list)))) filtered_df['Tissue types']=tissue_types</code></pre> </stencila-code-chunk> <stencila-code-chunk itemscope="" itemtype="http://schema.stenci.la/CodeChunk" data-execution_count="11" data-programminglanguage="python"> <pre class="language-python" itemscope="" itemtype="http://schema.stenci.la/CodeBlock" slot="text"><code>fig7 = make_subplots(rows=3, cols=1, start_cell="top-left", vertical_spacing=0.2, y_title='R<sup>2</sup>', subplot_titles=['R<sup>2</sup> values and reference techniques', 'R<sup>2</sup> values and pathology', 'R<sup>2</sup> values and tissue types']) references = ['Histology', 'Immunohistochemistry', 'Microscopy', 'EM'] for r in references: df_r=filtered_df[filtered_df['Histology/microscopy measure'].str.contains(r)] fig7.add_trace(go.Box( y=df_r['R^2'], x=df_r['Histology/microscopy measure'], boxpoints='all', text=df_r['Measure'] + ' - ' + df_r['Study'], name=r ), col=1, row=1) for t in filtered_df['Condition'].unique(): df_t=filtered_df[filtered_df['Condition']==t] fig7.add_trace(go.Box( y=df_t['R^2'], x=df_t['Condition'], boxpoints='all', text=df_t['Measure'] + ' - ' + df_t['Study'], name=t ), col=1, row=2) for t in filtered_df['Tissue types'].unique(): df_t=filtered_df[filtered_df['Tissue types']==t] fig7.add_trace(go.Box( y=df_t['R^2'], x=df_t['Tissue types'], boxpoints='all', text=df_t['Measure'] + ' - ' + df_t['Study'], name=t ), col=1, row=3) fig7.update_layout( title=dict( text='Figure 7: Experimental conditions and methodological choices influencing the R<sup>2</sup> values', x=0.1), margin=dict(l=100), showlegend=False, height=1200, width=900 ) fig7.show()</code></pre> <figure slot="outputs"> <stencila-image-plotly> <picture> <script type="application/vnd.plotly.v1+json"> {"data":[{"x":["Histology - LFB","Histology - Gold chloride","Histology - Gold chloride","Histology - Gold chloride","Histology - Gold chloride","Histology - Gold chloride","Histology - Gold chloride","Histology - LFB","Histology - LFB","Histology - LFB","Histology - LFB","Histology - LFB","Histology - LFB","Histology - LFB","Histology - LFB","Histology - LFB","Histology - LFB","Histology - LFB","Histology - LFB","Histology - LFB","Histology - Gold 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itemtype="http://schema.stenci.la/CodeBlock" slot="text"><code>fig8 = make_subplots(rows=2, cols=2, start_cell="top-left", vertical_spacing=0.15, y_title='R<sup>2</sup>', subplot_titles=['R<sup>2</sup> values and magnetic fields', 'R<sup>2</sup> values and tissue conditions', 'R<sup>2</sup> values and co-registration', 'R<sup>2</sup> values and human/animal tissue' ]) for m in measure_type.keys(): df_m=filtered_df[filtered_df["Measure"].isin(measure_type[m])] fig8.add_trace(go.Scatter(x=df_m['Magnetic field'], y=df_m['R^2'], text=df_m['Measure'] + ' - ' + df_m['Study'], marker=dict(color=color_dict[m]), name=m, mode='markers'), col=1, row=1) fig8.update_layout( xaxis=dict(title='Magnetic field [T]') ) for t in filtered_df['Tissue condition'].unique(): df_t=filtered_df[filtered_df['Tissue condition']==t] fig8.add_trace(go.Box( y=df_t['R^2'], x=df_t['Tissue condition'], boxpoints='all', text=df_t['Measure'] + ' - ' + df_t['Study'], name=t ), col=2, row=1) for t in filtered_df['Co-registration'].unique(): df_t=filtered_df[filtered_df['Co-registration']==t] fig8.add_trace(go.Box( y=df_t['R^2'], x=df_t['Co-registration'], boxpoints='all', text=df_t['Measure'] + ' - ' + df_t['Study'], name=t ), col=1, row=2) for t in filtered_df['Human/animal'].unique(): df_t=filtered_df[filtered_df['Human/animal']==t] fig8.add_trace(go.Box( y=df_t['R^2'], x=df_t['Human/animal'], boxpoints='all', text=df_t['Measure'] + ' - ' + df_t['Study'], name=t ), col=2, row=2) fig8.update_layout( title=dict(text='Figure 8: Other factors to consider when assessing R<sup>2</sup>', x=0.1), margin=dict(l=100), showlegend=False, height=900, width=900 ) fig8.show()</code></pre> <figure slot="outputs"> <stencila-image-plotly> <picture> <script type="application/vnd.plotly.v1+json"> 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