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Focusing specifically on ", "textStyle": { "bold": false, "italic": false, "underline": false, "strikethrough": false, "smallCaps": false, "backgroundColor": {}, "foregroundColor": { "color": { "rgbColor": { "red": 0.2, "green": 0.2, "blue": 0.2 } } }, "fontSize": { "magnitude": 11, "unit": "PT" }, "weightedFontFamily": { "fontFamily": "Lato", "weight": 400 }, "baselineOffset": "NONE" } } }, { "startIndex": 166, "endIndex": 181, "textRun": { "content": "myelin measures", "textStyle": { "bold": true, "italic": false, "underline": false, "strikethrough": false, "smallCaps": false, "backgroundColor": {}, "foregroundColor": { "color": { "rgbColor": { "red": 0.2, "green": 0.2, "blue": 0.2 } } }, "fontSize": { "magnitude": 11, "unit": "PT" }, "weightedFontFamily": { "fontFamily": "Lato", "weight": 400 }, "baselineOffset": "NONE" } } }, { "startIndex": 181, "endIndex": 210, "textRun": { "content": ", we show the results of our ", "textStyle": { "bold": false, "italic": false, "underline": false, "strikethrough": false, "smallCaps": false, "backgroundColor": {}, "foregroundColor": { "color": { "rgbColor": { "red": 0.2, "green": 0.2, "blue": 0.2 } } }, "fontSize": { "magnitude": 11, "unit": "PT" }, "weightedFontFamily": { "fontFamily": "Lato", "weight": 400 }, "baselineOffset": "NONE" } } }, { "startIndex": 210, "endIndex": 265, "textRun": { "content": "meta-analysis comparing quantitative MRI with histology", "textStyle": { "bold": true, "italic": false, "underline": false, "strikethrough": false, "smallCaps": false, "backgroundColor": {}, "foregroundColor": { "color": { "rgbColor": { "red": 0.2, "green": 0.2, "blue": 0.2 } } }, "fontSize": { "magnitude": 11, "unit": "PT" }, "weightedFontFamily": { "fontFamily": "Lato", "weight": 400 }, "baselineOffset": "NONE" } } }, { "startIndex": 265, "endIndex": 267, "textRun": { "content": ". 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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. 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true, "italic": false, "underline": false, "strikethrough": false, "smallCaps": false, "backgroundColor": {}, "foregroundColor": { "color": { "rgbColor": { "red": 0.2, "green": 0.2, "blue": 0.2 } } }, "fontSize": { "magnitude": 11, "unit": "PT" }, "weightedFontFamily": { "fontFamily": "Lato", "weight": 400 }, "baselineOffset": "NONE" } } }, { "startIndex": 909, "endIndex": 1319, "textRun": { "content": " 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 ", "textStyle": { "bold": false, "italic": false, "underline": false, "strikethrough": false, "smallCaps": false, "backgroundColor": {}, "foregroundColor": { "color": { "rgbColor": { "red": 0.2, "green": 0.2, "blue": 0.2 } } }, "fontSize": { "magnitude": 11, "unit": "PT" }, "weightedFontFamily": { "fontFamily": "Lato", "weight": 400 }, "baselineOffset": "NONE" } } }, { "startIndex": 1319, "endIndex": 1326, "textRun": { "content": "in vivo", "textStyle": { "bold": false, "italic": true, "underline": false, "strikethrough": false, "smallCaps": false, "backgroundColor": {}, "foregroundColor": { "color": { "rgbColor": { "red": 0.2, "green": 0.2, "blue": 0.2 } } }, "fontSize": { "magnitude": 11, "unit": "PT" }, "weightedFontFamily": { "fontFamily": "Lato", "weight": 400 }, "baselineOffset": "NONE" } } }, { "startIndex": 1326, "endIndex": 1330, "textRun": { "content": " or ", "textStyle": { "bold": false, "italic": false, "underline": false, "strikethrough": false, "smallCaps": false, "backgroundColor": {}, "foregroundColor": { "color": { "rgbColor": { "red": 0.2, "green": 0.2, "blue": 0.2 } } }, "fontSize": { "magnitude": 11, "unit": "PT" }, "weightedFontFamily": { "fontFamily": "Lato", "weight": 400 }, "baselineOffset": "NONE" } } }, { "startIndex": 1330, "endIndex": 1337, "textRun": { "content": "ex vivo", "textStyle": { "bold": false, "italic": true, "underline": false, "strikethrough": false, "smallCaps": false, "backgroundColor": {}, "foregroundColor": { "color": { "rgbColor": { "red": 0.2, "green": 0.2, "blue": 0.2 } } }, "fontSize": { "magnitude": 11, "unit": "PT" }, "weightedFontFamily": { "fontFamily": "Lato", "weight": 400 }, "baselineOffset": "NONE" } } }, { "startIndex": 1337, "endIndex": 1440, "textRun": { "content": " tissue and then comparing those data with the related samples analysed using histological techniques.\n", "textStyle": { "bold": false, "italic": false, "underline": false, "strikethrough": false, "smallCaps": false, "backgroundColor": {}, "foregroundColor": { "color": { "rgbColor": { "red": 0.2, "green": 0.2, "blue": 0.2 } } }, "fontSize": { "magnitude": 11, "unit": "PT" }, "weightedFontFamily": { "fontFamily": "Lato", "weight": 400 }, "baselineOffset": "NONE" } } } ], "paragraphStyle": { "namedStyleType": "NORMAL_TEXT", "alignment": "START", "lineSpacing": 110, "direction": "LEFT_TO_RIGHT", "spacingMode": "NEVER_COLLAPSE", "spaceAbove": { "magnitude": 9, "unit": "PT" }, "spaceBelow": { "magnitude": 9, "unit": "PT" }, "borderBetween": { "color": {}, "width": { "unit": "PT" }, "padding": { "unit": "PT" }, "dashStyle": "SOLID" }, "borderTop": { "color": {}, "width": { "unit": "PT" }, "padding": { "unit": "PT" }, "dashStyle": "SOLID" }, "borderBottom": { "color": {}, "width": { "unit": "PT" }, "padding": { "unit": "PT" }, "dashStyle": "SOLID" }, "borderLeft": { "color": {}, "width": { "unit": "PT" }, "padding": { "unit": "PT" }, "dashStyle": "SOLID" }, "borderRight": { "color": {}, "width": { "unit": "PT" }, "padding": { "unit": "PT" }, "dashStyle": "SOLID" }, "indentFirstLine": { "unit": "PT" }, "indentStart": { "unit": "PT" }, "indentEnd": { "unit": "PT" }, "keepLinesTogether": false, "keepWithNext": false, "avoidWidowAndOrphan": true, "shading": { "backgroundColor": {} } } } }, { "startIndex": 1440, "endIndex": 2059, "paragraph": { "elements": [ { "startIndex": 1440, "endIndex": 1460, "textRun": { "content": "Why a meta-analysis?", "textStyle": { "bold": true, "italic": false, "underline": false, "strikethrough": false, "smallCaps": false, "backgroundColor": {}, "foregroundColor": { "color": { "rgbColor": { "red": 0.2, "green": 0.2, "blue": 0.2 } } }, "fontSize": { "magnitude": 11, "unit": "PT" }, "weightedFontFamily": { "fontFamily": "Lato", "weight": 400 }, "baselineOffset": "NONE" } } }, { "startIndex": 1460, "endIndex": 2059, "textRun": { "content": " 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. 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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. 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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.\n", "textStyle": { "bold": false, "italic": false, "underline": false, "strikethrough": false, "smallCaps": false, "backgroundColor": {}, "foregroundColor": { "color": { "rgbColor": { "red": 0.2, "green": 0.2, "blue": 0.2 } } }, "fontSize": { "magnitude": 11, "unit": "PT" }, "weightedFontFamily": { "fontFamily": "Lato", "weight": 400 }, "baselineOffset": "NONE" } } } ], "paragraphStyle": { "namedStyleType": "NORMAL_TEXT", "alignment": "START", "lineSpacing": 110, "direction": "LEFT_TO_RIGHT", "spacingMode": "NEVER_COLLAPSE", "spaceAbove": { "magnitude": 9, "unit": "PT" }, "spaceBelow": { "magnitude": 9, "unit": "PT" }, "borderBetween": { "color": {}, "width": { "unit": "PT" }, "padding": { "unit": "PT" }, "dashStyle": "SOLID" }, "borderTop": { "color": {}, "width": { "unit": "PT" }, "padding": { "unit": "PT" }, "dashStyle": "SOLID" }, "borderBottom": { "color": {}, "width": { "unit": "PT" }, "padding": { "unit": "PT" }, 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This is where the meta-analysis tools come in: we used the R package ", "textStyle": { "bold": false, "italic": false, "underline": false, "strikethrough": false, "smallCaps": false, "backgroundColor": {}, "foregroundColor": { "color": { "rgbColor": { "red": 0.2, "green": 0.2, "blue": 0.2 } } }, "fontSize": { "magnitude": 11, "unit": "PT" }, "weightedFontFamily": { "fontFamily": "Lato", "weight": 400 }, "baselineOffset": "NONE" } } }, { "startIndex": 4795, "endIndex": 4802, "textRun": { "content": "metafor", "textStyle": { "bold": false, "italic": false, "underline": false, "strikethrough": false, "smallCaps": false, "backgroundColor": {}, "foregroundColor": { "color": { "rgbColor": { "red": 0.2, "green": 0.2, "blue": 0.2 } } }, "fontSize": { "magnitude": 11, "unit": "PT" }, "weightedFontFamily": { "fontFamily": "Lato", "weight": 400 }, "baselineOffset": "NONE", "link": { "url": "http://www.metafor-project.org/doku.php" } } } }, { "startIndex": 4802, "endIndex": 4928, "textRun": { "content": " 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", "textStyle": { "bold": false, "italic": false, "underline": false, "strikethrough": false, "smallCaps": false, "backgroundColor": {}, "foregroundColor": { "color": { "rgbColor": { "red": 0.2, "green": 0.2, "blue": 0.2 } } }, "fontSize": { "magnitude": 11, "unit": "PT" }, "weightedFontFamily": { "fontFamily": "Lato", "weight": 400 }, "baselineOffset": "NONE" } } }, { "startIndex": 4928, "endIndex": 4929, "textRun": { "content": "2", "textStyle": { "bold": false, "italic": false, "underline": false, "strikethrough": false, "smallCaps": false, "backgroundColor": {}, "foregroundColor": { "color": { "rgbColor": { "red": 0.2, "green": 0.2, "blue": 0.2 } } }, "fontSize": { "magnitude": 11, "unit": "PT" }, "weightedFontFamily": { "fontFamily": "Lato", "weight": 400 }, "baselineOffset": "SUPERSCRIPT" } } }, { "startIndex": 4929, "endIndex": 5023, "textRun": { "content": " values based on the effect sizes and the sample sizes. We can also estimate the interval of R", "textStyle": { "bold": false, "italic": false, "underline": false, "strikethrough": false, "smallCaps": false, "backgroundColor": {}, "foregroundColor": { "color": { "rgbColor": { "red": 0.2, "green": 0.2, "blue": 0.2 } } }, "fontSize": { "magnitude": 11, "unit": "PT" }, "weightedFontFamily": { "fontFamily": "Lato", "weight": 400 }, "baselineOffset": "NONE" } } }, { "startIndex": 5023, "endIndex": 5024, "textRun": { "content": "2", "textStyle": { "bold": false, "italic": false, "underline": false, "strikethrough": false, "smallCaps": false, "backgroundColor": {}, "foregroundColor": { "color": { "rgbColor": { "red": 0.2, "green": 0.2, "blue": 0.2 } } }, "fontSize": { "magnitude": 11, "unit": "PT" }, "weightedFontFamily": { "fontFamily": "Lato", "weight": 400 }, "baselineOffset": "SUPERSCRIPT" } } }, { "startIndex": 5024, "endIndex": 5481, "textRun": { "content": " 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.\n", "textStyle": { "bold": false, "italic": false, "underline": false, "strikethrough": false, "smallCaps": false, "backgroundColor": {}, "foregroundColor": { "color": { "rgbColor": { "red": 0.2, "green": 0.2, "blue": 0.2 } } }, "fontSize": { "magnitude": 11, "unit": "PT" }, "weightedFontFamily": { "fontFamily": "Lato", "weight": 400 }, "baselineOffset": "NONE" } } } ], "paragraphStyle": { "namedStyleType": "NORMAL_TEXT", "alignment": "START", "lineSpacing": 110, "direction": "LEFT_TO_RIGHT", "spacingMode": "NEVER_COLLAPSE", "spaceAbove": { "magnitude": 9, "unit": "PT" }, "spaceBelow": { "magnitude": 9, "unit": 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"{\"meta\":{\"execution_count\":10},\"text\":\"structures={'Lesions':'Lesions',\\n 'Substantia nigra':'Deep grey matter',\\n 'Hippocampal commissure':'White matter',\\n 'Putamen':'Deep grey matter',\\n 'Motor cortex':'Grey matter',\\n 'Globus pallidus':'Deep grey matter',\\n 'Perforant pathway':'White matter',\\n 'Mammilothalamic tract':'White matter',\\n 'External capsule':'White matter',\\n 'Inter-peduncular nuclues':'Deep grey matter',\\n 'Hippocampus':'Deep grey matter',\\n 'Thalamic nuclei':'Deep grey matter',\\n 'Thalamus':'Deep grey matter',\\n 'Cerebellum':'Grey matter',\\n 'Amygdala':'Deep grey matter',\\n 'Cingulum':'White matter',\\n 'Striatum':'Deep grey matter',\\n 'Accumbens':'Deep grey matter',\\n 'Basal ganglia':'Deep grey matter',\\n 'Anterior commissure':'White matter',\\n 'Cortex':'Grey matter',\\n 'Fimbria':'White matter',\\n 'Somatosensory cortex':'Grey matter',\\n 'Dorsal tegmental tract':'White matter',\\n 'Superior colliculus':'Deep grey matter',\\n 'Fasciculus retroflexus':'White matter',\\n 'Optic nerve':'White matter',\\n 'Dentate gyrus':'Grey matter',\\n 'Corpus callosum':'White matter',\\n 'Fornix':'White matter',\\n 'White matter':'White matter',\\n 'Grey matter':'Grey matter',\\n 'Optic tract':'White matter',\\n 'Internal capsule':'White matter',\\n 'Stria medullaris':'White matter'}\\n\\n\\ntissue_types=[]\\nfor s in filtered_df['Specific structure(s)']:\\n t_list=[]\\n for i in s.split(','):\\n t_list.append(structures[i.strip()])\\n tissue_types.append('+'.join(list(set(t_list))))\\n\\nfiltered_df['Tissue types']=tissue_types\",\"type\":\"CodeChunk\",\"programmingLanguage\":\"python\",\"id\":\"Au2MYtzNtXEwy7wABaaGXQvyakUFxu2L\"}", "embeddedObjectBorder": { "color": { "color": { "rgbColor": {} } }, "width": { "unit": "PT" }, "dashStyle": "SOLID", "propertyState": "NOT_RENDERED" }, "size": { "height": { "magnitude": 14, "unit": "PT" }, "width": { "magnitude": 20, "unit": "PT" } }, "marginTop": { "unit": "PT" }, "marginBottom": { 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fig7.add_trace(go.Box(\\n y=df_r['R^2'],\\n x=df_r['Histology/microscopy measure'],\\n boxpoints='all',\\n text=df_r['Measure'] + ' - ' + df_r['Study'],\\n name=r\\n ), col=1, row=1)\\n\\nfor t in filtered_df['Condition'].unique():\\n df_t=filtered_df[filtered_df['Condition']==t]\\n fig7.add_trace(go.Box(\\n y=df_t['R^2'],\\n x=df_t['Condition'],\\n boxpoints='all',\\n text=df_t['Measure'] + ' - ' + df_t['Study'],\\n name=t\\n ), col=1, row=2)\\n\\nfor t in filtered_df['Tissue types'].unique():\\n df_t=filtered_df[filtered_df['Tissue types']==t]\\n fig7.add_trace(go.Box(\\n y=df_t['R^2'],\\n x=df_t['Tissue types'],\\n boxpoints='all',\\n text=df_t['Measure'] + ' - ' + df_t['Study'],\\n name=t\\n ), col=1, row=3) \\n\\nfig7.update_layout(\\n title=dict(\\n text='Figure 7: Experimental conditions and methodological choices influencing the R<sup>2</sup> values',\\n x=0.1),\\n margin=dict(l=100),\\n showlegend=False,\\n height=1200,\\n width=900\\n)\\n\\nfig7.show()\",\"type\":\"CodeChunk\",\"outputs\":[],\"programmingLanguage\":\"python\",\"id\":\"57usinLDwJH2pzDFkUr6MDo9mALTvT9Z\"}", "embeddedObjectBorder": { "color": { "color": { "rgbColor": {} } }, "width": { "unit": "PT" }, "dashStyle": "SOLID", "propertyState": "NOT_RENDERED" }, "size": { "height": { "magnitude": 589.2412598425198, "unit": "PT" }, "width": { "magnitude": 451, "unit": "PT" } }, "marginTop": { "unit": "PT" }, "marginBottom": { "unit": "PT" }, "marginRight": { "magnitude": 9, "unit": "PT" }, "marginLeft": { "magnitude": 9, "unit": "PT" } } } }, "i.12": { "objectId": "i.12", "inlineObjectProperties": { "embeddedObject": { "imageProperties": { "contentUri": "https://lh3.googleusercontent.com/GmJrLR5BXHHWeOxYYEgTFIcCg0te3LsI9bLKe1zsUbFGcasKDOwI3pAEpHqT8u0lQ0Vjo8seuYHpMCo2wrW5vXMg5w7KwRgZeEKfkaAyPHFbQwnjNz4OaBh4W0skIi48kZwGLVTAyDmrB8d94UuqH5RwxL4", "cropProperties": {} }, "title": "CodeChunk", "description": "{\"meta\":{\"execution_count\":12},\"text\":\"fig8 = make_subplots(rows=2, cols=2, start_cell=\\\"top-left\\\", vertical_spacing=0.15, y_title='R<sup>2</sup>',\\n subplot_titles=['R<sup>2</sup> values and magnetic fields',\\n 'R<sup>2</sup> values and tissue conditions',\\n 'R<sup>2</sup> values and co-registration',\\n 'R<sup>2</sup> values and human/animal tissue'\\n ])\\n\\nfor m in measure_type.keys():\\n df_m=filtered_df[filtered_df[\\\"Measure\\\"].isin(measure_type[m])]\\n fig8.add_trace(go.Scatter(x=df_m['Magnetic field'],\\n y=df_m['R^2'],\\n text=df_m['Measure'] + ' - ' + df_m['Study'],\\n marker=dict(color=color_dict[m]),\\n name=m,\\n mode='markers'), col=1, row=1)\\n\\nfig8.update_layout(\\n xaxis=dict(title='Magnetic field [T]')\\n)\\n\\nfor t in filtered_df['Tissue condition'].unique():\\n df_t=filtered_df[filtered_df['Tissue condition']==t]\\n fig8.add_trace(go.Box(\\n y=df_t['R^2'],\\n x=df_t['Tissue condition'],\\n boxpoints='all',\\n text=df_t['Measure'] + ' - ' + df_t['Study'],\\n name=t\\n ), col=2, row=1)\\n \\nfor t in filtered_df['Co-registration'].unique():\\n df_t=filtered_df[filtered_df['Co-registration']==t]\\n fig8.add_trace(go.Box(\\n y=df_t['R^2'],\\n x=df_t['Co-registration'],\\n boxpoints='all',\\n text=df_t['Measure'] + ' - ' + df_t['Study'],\\n name=t\\n ), col=1, row=2)\\n \\nfor t in filtered_df['Human/animal'].unique():\\n df_t=filtered_df[filtered_df['Human/animal']==t]\\n fig8.add_trace(go.Box(\\n y=df_t['R^2'],\\n x=df_t['Human/animal'],\\n boxpoints='all',\\n text=df_t['Measure'] + ' - ' + df_t['Study'],\\n name=t\\n ), col=2, row=2)\\n\\nfig8.update_layout(\\n title=dict(text='Figure 8: Other factors to consider when assessing R<sup>2</sup>', x=0.1),\\n margin=dict(l=100),\\n showlegend=False,\\n height=900,\\n width=900\\n) \\n \\nfig8.show()\",\"type\":\"CodeChunk\",\"outputs\":[],\"programmingLanguage\":\"python\",\"id\":\"KoyoDz9anBUTHwwcvfdDWyGdtMKT8hsj\"}", "embeddedObjectBorder": { "color": { "color": { "rgbColor": 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histology or equivalent approach;<br>\\n - work reporting only qualitative comparisons.\\n \\\"\\\"\\\",\\n 'Records selected for full-text evaluation',\\n \\\"\\\"\\\"\\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 MRI from a different one.\\n \\\"\\\"\\\",\\n 'Studies selected for literature overview',\\n \\\"\\\"\\\"\\n Exclusion criteria:<br>\\n - not providing an indication of both number of subjects and number of ROIs.\\n \\\"\\\"\\\"]\\n\\nfig1 = go.Figure(data=[go.Sankey(\\n arrangement = \\\"freeform\\\",\\n node = dict(\\n pad = 15,\\n thickness = 20,\\n line = dict(color = \\\"black\\\", width = 0.5),\\n label = [\\\"Main records identified (database searching)\\\",\\n \\\"Additional records (reviews)\\\",\\n \\\"Records screened\\\",\\n \\\"Records excluded\\\",\\n \\\"Full-text articles assessed for eligibility\\\",\\n \\\"Full-text articles excluded\\\",\\n \\\"Studied included in the literature overview\\\",\\n \\\"Studies included in the meta-analysis\\\"],\\n x = [0, 0, 0.4, 0.6, 0.5, 0.8, 0.7, 1],\\n y = [0, 0, 0.5, 0.8, 0.15, 0.05, 0.4, 0.6],\\n hovertemplate = \\\"%{label}<extra>%{value}</extra>\\\",\\n color = [\\\"darkblue\\\",\\\"darkblue\\\",\\\"darkblue\\\",\\\"darkred\\\",\\\"darkgreen\\\",\\\"darkred\\\",\\\"darkgreen\\\",\\\"darkgreen\\\"]\\n ),\\n link = dict(\\n source = [0, 1, 2, 2, 4, 4, 6],\\n target = [2, 2, 3, 4, 5, 6, 7],\\n value = [688, 1, 597, 92, 34, 58, 43],\\n customdata = screening_info,\\n hovertemplate = \\\"%{customdata}\\\",\\n ))])\\n\\nfig1.update_layout(title=dict(text='Figure 1: Review methodology',x=0.1),\\n margin=dict(l=100),\\n width=1000,\\n height=500,\\n font_size=12)\\nfig1.show()\",\"type\":\"CodeChunk\",\"outputs\":[\"\"],\"programmingLanguage\":\"python\",\"id\":\"GScyyrARZUYPADbrqaHEUGNH7BwjpSGw\"}", "embeddedObjectBorder": { "color": { "color": { "rgbColor": {} } }, "width": { "unit": "PT" }, "dashStyle": "SOLID", "propertyState": "NOT_RENDERED" }, "size": { "height": { "magnitude": 233.01661417322833, "unit": "PT" }, "width": { "magnitude": 451, "unit": "PT" } }, "marginTop": { "unit": "PT" }, "marginBottom": { "unit": "PT" }, "marginRight": { "magnitude": 9, "unit": "PT" }, "marginLeft": { "magnitude": 9, "unit": "PT" } } } }, "i.3": { "objectId": "i.3", "inlineObjectProperties": { "embeddedObject": { "imageProperties": { "contentUri": "https://lh4.googleusercontent.com/ttMqD4WvgN4Qc2tfLC4xoepR3ppnprKOylsvgWDbcpp35SindR-5i8kY_Aej3O6iB2NXqT7nVdyhwrvhSNShY2ua4Y_yoLp-fp6-XxSu6xQuHD47jVf5sqnLm0Q4tErkARxouig_PoDB0DBzImNuGFI5GAU", "cropProperties": {} }, "title": "CodeChunk", "description": "{\"meta\":{\"execution_count\":3},\"text\":\"info = pd.read_excel('database.xlsx', sheet_name='Details')\\n\\nyear_str = info['Year'].astype(str)\\ninfo['Study'] = info['First author'] + ' et al., ' + year_str\\ninfo['Study'] = info.groupby('Study')['Study'].apply(lambda n: n+list(map(chr,np.arange(len(n))+97))\\n if len(n)>1 else n)\\ninfo['Number of studies'] = np.ones((len(info),1))\\ninfo = info.sort_values('Study')\\n\\ninfo['Link'] = info['DOI']\\ninfo['Link'].replace('http',\\\"\\\"\\\"<a style='color:white' href='http\\\"\\\"\\\",\\n inplace=True, regex=True)\\ninfo['Link'] = info['Link'] + \\\"\\\"\\\"'>->Go to the paper</a>\\\"\\\"\\\"\\n\\nfields = ['Approach', 'Magnetic field', 'MRI measure(s)',\\n 'Histology/microscopy measure', 'Specific structure(s)']\\ninfo['Summary'] = info['Link'] + '<br><br>'\\nfor i in fields:\\n info['Summary'] = info['Summary'] + i + ': ' + info[i].astype(str) + '<br><br>'\\n\\nargs = dict(data_frame=info, values='Number of studies',\\n color='Number of studies', hover_data='',\\n path=['Focus', 'Tissue condition', 'Human/animal', 'Condition', 'Study'],\\n color_continuous_scale='Viridis')\\nargs = px._core.build_dataframe(args, go.Treemap)\\ntreemap_df = px._core.process_dataframe_hierarchy(args)['data_frame']\\n\\nfig2 = go.Figure(go.Treemap(\\n ids=treemap_df['id'].tolist(),\\n labels=treemap_df['labels'].tolist(),\\n parents=treemap_df['parent'].tolist(),\\n values=treemap_df['Number of studies'].tolist(),\\n branchvalues='total',\\n text=info['Summary'],\\n hoverinfo='label',\\n textfont=dict(\\n size=15,\\n )\\n )\\n)\\n\\nfig2 = fig2.update_layout(\\n title=dict(text='Figure 2: Literature survey overview',x=0.1),\\n autosize=False,\\n width=900,\\n height=600,\\n margin=dict(\\n l=100,\\n r=0,\\n b=30,\\n t=60,\\n )\\n)\\n\\nfig2.show()\",\"type\":\"CodeChunk\",\"outputs\":[],\"programmingLanguage\":\"python\",\"id\":\"G7s8eoXXxHvE3PzgQmUPx2yv6TJHNng3\"}", "embeddedObjectBorder": { "color": { "color": { "rgbColor": {} } }, "width": { "unit": "PT" }, "dashStyle": "SOLID", "propertyState": "NOT_RENDERED" }, "size": { "height": { "magnitude": 295.1108661417323, "unit": "PT" }, "width": { "magnitude": 451, "unit": "PT" } }, "marginTop": { "unit": "PT" }, "marginBottom": { "unit": "PT" }, "marginRight": { "magnitude": 9, "unit": "PT" }, "marginLeft": { "magnitude": 9, "unit": "PT" } } } }, "i.4": { "objectId": "i.4", "inlineObjectProperties": { "embeddedObject": { "imageProperties": { "contentUri": "https://lh6.googleusercontent.com/h966XY8EzKSSFpJ3EZl-hOrGYBU-cVc1B_7BpPYoM4JEpv27ctJbMb5BAdLuUoTD4JkfsFOSDm3r8Q1JCKTyf1DPUHmpvpSQwrS88IKYn1wMIV4nUBD1ptbWM1dAKk-ANiUEMbeintQX_WeC6-YWQ3WpClE", "cropProperties": {} }, "title": "CodeChunk", "description": "{\"meta\":{\"execution_count\":4},\"text\":\"df = pd.DataFrame()\\ndata = pd.read_excel('database.xlsx', sheet_name='R^2')\\n\\nmeasures = data.columns[1:]\\nfor _, row in data.iterrows():\\n measure_avail = {m:value for m, value in zip(measures, row.tolist()[1:])\\n if not np.isnan(value)}\\n for m in measure_avail.keys():\\n df = df.append([[row.DOI, m, measure_avail[m],\\n *info[info.DOI==row.DOI].values.tolist()[0][1:]]])\\ndf.columns = ['DOI', 'Measure', 'R^2', *info.columns[1:]]\\n\\ndf['ROI per subject'] = pd.to_numeric(df['ROI per subject'], errors='coerce')\\ndf['Subjects'] = pd.to_numeric(df['Subjects'], errors='coerce')\\ndf = df.dropna(subset=['ROI per subject', 'Subjects'])\\ndf = df[df['ROI per subject']<100]\\ndf['Sample points'] = df['ROI per subject'] * df['Subjects']\\n\\ndf=df.sort_values(by=['Measure'])\\n\\nfiltered_df=df[df.Focus=='Brain'].copy()\\n\\nmeasure_type = {'Diffusion':['RD', 'AD', 'FA', 'MD',\\n 'AWF', 'RK', 'RDe', 'MK'],\\n 'Magnetization transfer':['MTR',\\n 'ihMTR', 'MTR-UTE', 'MPF', 'MVF-MT',\\n 'R1f', 'T2m', 'T2f', 'k_mf','k_fm'],\\n 'T1 relaxometry':['T1'], 'T2 relaxometry':['T2', 'MWF', 'MVF-T2'],\\n 'Other':['QSM', 'R2*', 'rSPF', 'MTV',\\n 'T1p', 'T2p', 'RAFF', 'PD', 'T1sat']}\\n\\ncolor_dict = {m:plotly.colors.qualitative.Bold[n]\\n for n,m in enumerate(measure_type.keys())}\\n\\nhover_text = []\\nbubble_size = []\\n\\nfor index, row in filtered_df.iterrows():\\n hover_text.append(('Measure: {measure}<br>'+\\n 'Number of subjects: {subjects}<br>'+\\n 'ROIs per subject: {rois}<br>'+\\n 'Total number of samples: {samples}').format(measure=row['Measure'],\\n subjects=row['Subjects'],\\n rois=row['ROI per subject'],\\n samples=row['Sample points']))\\n bubble_size.append(2*np.sqrt(row['Sample points']))\\n\\nfiltered_df['Details'] = hover_text\\nfiltered_df['Size'] = bubble_size\\n\\nfig3 = go.Figure()\\n\\nfor m in measure_type.keys():\\n df_m = filtered_df[filtered_df['Measure'].isin(measure_type[m])]\\n fig3.add_trace(go.Scatter(\\n x=df_m['Measure'],\\n y=df_m['R^2'],\\n text='Study: ' + \\n df_m['Study']+ '<br>' + df_m['Details'],\\n mode='markers',\\n line = dict(color = 'rgba(0,0,0,0)'),\\n marker = dict(color=color_dict[m]),\\n marker_size = df_m['Size'],\\n opacity=0.6,\\n name=m\\n ))\\n \\nfig3.update_layout(\\n title=dict(text='Figure 3: R<sup>2</sup> between MRI and histology across measures',x=0.1),\\n margin=dict(l=100),\\n xaxis=dict(title='MRI measure'),\\n yaxis=dict(title='R<sup>2</sup>'),\\n autosize=False,\\n width=900,\\n height=600\\n)\\n\\nfig3.show()\",\"type\":\"CodeChunk\",\"outputs\":[],\"programmingLanguage\":\"python\",\"id\":\"X6tg2LqqmCVyMpLS779hYY4hwtW56EVF\"}", "embeddedObjectBorder": { "color": { "color": { "rgbColor": {} } }, "width": { "unit": "PT" }, "dashStyle": "SOLID", "propertyState": "NOT_RENDERED" }, "size": { "height": { "magnitude": 295.1108661417323, "unit": "PT" }, "width": { "magnitude": 451, "unit": "PT" } }, "marginTop": { "unit": "PT" }, "marginBottom": { "unit": "PT" }, "marginRight": { "magnitude": 9, "unit": "PT" }, "marginLeft": { "magnitude": 9, "unit": "PT" } } } }, "i.5": { "objectId": "i.5", "inlineObjectProperties": { "embeddedObject": { "imageProperties": { "contentUri": "https://lh4.googleusercontent.com/BV-Yx-LIyygu5iDj8i2PJxRlk6d4dJ3cR83ZbZxiUVTk7UP7pyrSmiY-Qm_Ej79Urm65P7hEQHV8bDqAN8oO7BzjUR7gajSbTWxrCSIWYvGO_rnLQPCHFUoa3_mT0IixQZluTGueii75HjKLDTZnLyuCrTA", "cropProperties": {} }, "title": "CodeChunk", "description": "{\"meta\":{\"execution_count\":5},\"text\":\"filtered_df=filtered_df.sort_values(by=['Study', 'Measure'])\\n\\nargs = dict(data_frame=filtered_df, values='Sample points',\\n color='R^2', hover_data='',\\n path=['Measure', 'Study'],\\n color_continuous_scale='Viridis')\\nargs = px._core.build_dataframe(args, go.Treemap)\\ntreemap_df = px._core.process_dataframe_hierarchy(args)['data_frame']\\n\\nfig4 = go.Figure(go.Treemap(\\n ids=treemap_df['id'].tolist(),\\n labels=treemap_df['labels'].tolist(),\\n parents=treemap_df['parent'].tolist(),\\n values=treemap_df['Sample points'].tolist(),\\n branchvalues='total',\\n text='R<sup>2</sup>: ' + filtered_df['R^2'].astype(str) + '<br>' + filtered_df['Details'],\\n hovertext=filtered_df['Study'] + '<br>R<sup>2</sup>: ' + filtered_df['R^2'].astype(str) +\\n '<br>Number of samples: ' + filtered_df['Sample points'].astype(str),\\n hoverinfo='text',\\n textfont=dict(\\n size=15,\\n ),\\n marker=dict(\\n colors=filtered_df['R^2'],\\n colorscale='Viridis',\\n colorbar=dict(title='R<sup>2</sup>'),\\n showscale=True\\n )\\n )\\n)\\n\\nfig4 = fig4.update_layout(\\n autosize=False,\\n width=900,\\n height=600,\\n margin=dict(\\n l=100,\\n r=0,\\n b=30,\\n t=60,\\n ),\\n title=dict(text='Figure 4: R<sup>2</sup> values across studies',x=0.1)\\n)\\n\\nfig4.show()\",\"type\":\"CodeChunk\",\"outputs\":[],\"programmingLanguage\":\"python\",\"id\":\"UMjsq8cw2JHNmVVBNQgPzyPrMt7by9pF\"}", "embeddedObjectBorder": { "color": { "color": { "rgbColor": {} } }, "width": { "unit": "PT" }, "dashStyle": "SOLID", "propertyState": "NOT_RENDERED" }, "size": { "height": { "magnitude": 295.1108661417323, "unit": "PT" }, "width": { "magnitude": 451, "unit": "PT" } }, "marginTop": { "unit": "PT" }, "marginBottom": { "unit": "PT" }, "marginRight": { "magnitude": 9, "unit": "PT" }, "marginLeft": { "magnitude": 9, "unit": "PT" } } } }, "i.6": { "objectId": "i.6", "inlineObjectProperties": { "embeddedObject": { "imageProperties": { "contentUri": "https://lh5.googleusercontent.com/uqPDYGIirGkzI1yBB8aeRYEnQ4dCSZB-xHmuMLJnSOK4FZ2wb7gT1cYjRW9bztCBCp3oGCI8C9BtdTQ-0lZGqX8FISTnv1NKDu2TCpdjQIwCAKXekPSinC2rnV0oxErYO27jryJOwHBtg2v7SsSBuj1My14", "cropProperties": {} }, "title": "CodeChunk", "description": "{\"meta\":{\"execution_count\":6},\"text\":\"filtered_df['Variance'] = (4*filtered_df['R^2'])*((1-filtered_df['R^2'])**2)/filtered_df['Sample points']\\n\\nmetafor = importr('metafor')\\nstats = importr('stats')\\n\\nmetastudy = {}\\nfor m in filtered_df.Measure.unique():\\n nstudies=len(filtered_df.Measure[filtered_df.Measure==m])\\n if nstudies > 2:\\n df_m = filtered_df[filtered_df.Measure==m]\\n df_m = df_m.sort_values(by=['Year'])\\n \\n r2 = rpy2.robjects.FloatVector(df_m['R^2'])\\n var = rpy2.robjects.FloatVector(df_m['Variance'])\\n fit = metafor.rma(r2, var, method=\\\"REML\\\", test=\\\"knha\\\")\\n res = stats.predict(fit)\\n \\n results = dict(zip(res.names,list(res)))\\n \\n metastudy[m] = dict(pred=results['pred'][0], cilb=results['pred'][0]-results['ci.lb'][0],\\n ciub=results['ci.ub'][0]-results['pred'][0],\\n crub=results['cr.ub'][0], \\n crlb=results['cr.lb'][0])\",\"type\":\"CodeChunk\",\"programmingLanguage\":\"python\",\"id\":\"4YTXWkXejRXVXDKSdSkXCXezeAR8kBmD\"}", "embeddedObjectBorder": { "color": { "color": { "rgbColor": {} } }, "width": { "unit": "PT" }, "dashStyle": "SOLID", "propertyState": "NOT_RENDERED" }, "size": { "height": { "magnitude": 14, "unit": "PT" }, "width": { "magnitude": 20, "unit": "PT" } }, "marginTop": { "unit": "PT" }, "marginBottom": { "unit": "PT" }, "marginRight": { "magnitude": 9, "unit": "PT" }, "marginLeft": { "magnitude": 9, "unit": "PT" } } } }, "i.7": { "objectId": "i.7", 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line=dict(color='black', width=2, dash='dot'),\\n hovertemplate = 'Prediction boundary: %{x}<extra></extra>',\\n marker_symbol = 'hourglass-open', marker_size = 8\\n ), row=row, col=col)\\n \\n fig5.add_trace(go.Scatter(\\n x=[round(metastudy[m]['pred'],2)],\\n y=['Mixed model'],\\n mode='markers',\\n marker = dict(color = 'black'),\\n marker_symbol = 'diamond-wide',\\n marker_size = 10,\\n hovertemplate = 'R<sup>2</sup> estimate: %{x}<extra></extra>',\\n error_x=dict(\\n type='data',\\n arrayminus=[round(metastudy[m]['cilb'],2) if round(metastudy[m]['cilb'],2)>0 else 0],\\n array=[round(metastudy[m]['ciub'],2) if round(metastudy[m]['ciub'],2)<1 else 1])\\n ), row=row, col=col)\\n \\n df_m = filtered_df[filtered_df.Measure==m]\\n df_m = df_m.sort_values(by=['Year'], ascending=False)\\n fig5.add_trace(go.Scatter(\\n x=df_m['R^2'],\\n y=df_m['Study'],\\n text=df_m['Sample points'],\\n customdata=df_m['Histology/microscopy measure'],\\n mode='markers',\\n marker = dict(color = color_dict[measure_type_reverse[m]]),\\n marker_symbol = 'square',\\n marker_size = np.log(50/df_m['Variance']),\\n hovertemplate = '%{y}<br>R<sup>2</sup>: %{x}<br>Number of samples: %{text}<br>' +\\n 'Reference: %{customdata}<extra></extra>',\\n error_x=dict(\\n type='data',\\n array=2*np.sqrt(df_m['Variance']))\\n ), row=row, col=col)\\n\\n if col == 3:\\n col = 1\\n row += 1\\n else:\\n col += 1\\n\\nfig5.update_xaxes(range=[0, 1])\\n\\nfig5.update_layout(showlegend=False,\\n title=dict(text='Figure 5: Forest plots and mixed modelling results',x=0.1),\\n margin=dict(l=200),\\n width=1000,\\n height=1400)\\nfig5.show()\",\"type\":\"CodeChunk\",\"outputs\":[],\"programmingLanguage\":\"python\",\"id\":\"TanbUeWCTit6E35ci2S36nEYF7JBiC9W\"}", "embeddedObjectBorder": { "color": { "color": { "rgbColor": {} } }, "width": { "unit": "PT" }, "dashStyle": "SOLID", "propertyState": "NOT_RENDERED" }, "size": { "height": { "magnitude": 619.9038582677165, "unit": "PT" }, "width": { "magnitude": 451, "unit": "PT" } }, "marginTop": { "unit": "PT" }, "marginBottom": { "unit": "PT" }, "marginRight": { "magnitude": 9, "unit": "PT" }, "marginLeft": { "magnitude": 9, "unit": "PT" } } } }, "i.8": { "objectId": "i.8", "inlineObjectProperties": { "embeddedObject": { "imageProperties": { "contentUri": "https://lh4.googleusercontent.com/NkaCbWrTFUJsq7QxY4iKorNrjw55Xpa01vbmoti57a_w2tpPs0tQzy3wtrRfmlJkMSehIBWoHG13nfrOp0psNLhghIvAyFaL0HSucP5SpdKcosFFLowSaj3kSKTjFN3gmNJDpQbvFbfpwxRXhovcOjhDm3U", "cropProperties": {} }, "title": "CodeChunk", "description": "{\"meta\":{\"execution_count\":8},\"text\":\"multcomp = importr('multcomp')\\nbase = importr('base')\\n\\nthres = filtered_df.Measure.value_counts() > 2\\ndf_thres = filtered_df[filtered_df.Measure.isin(filtered_df.Measure.value_counts()[thres].index)]\\nr2 = rpy2.robjects.FloatVector(df_thres['R^2'])\\nvar = rpy2.robjects.FloatVector(df_thres['Variance'])\\nmeasure_v = rpy2.robjects.StrVector(df_thres['Measure'])\\nmeasure_f = rpy2.robjects.Formula('~ -1 + measure')\\nenv = measure_f.environment\\nenv['measure'] = measure_v\\nstudy_v = rpy2.robjects.StrVector(df_thres['Study'])\\nstudy_f = rpy2.robjects.Formula('~ 1 | study')\\nenv = study_f.environment\\nenv['study'] = study_v\\nfit_mv = metafor.rma_mv(r2, var, method=\\\"REML\\\", mods=measure_f, random=study_f)\\n\\nglht = multcomp.glht(fit_mv, base.cbind(multcomp.contrMat(base.rep(1,9), type=\\\"Tukey\\\")))\\nmtests = multcomp.summary_glht(glht, test=multcomp.adjusted(\\\"bonferroni\\\"))\\nmtest_res = dict(zip(mtests.names, list(mtests)))\\nstat_res = dict(zip(mtest_res['test'].names, list(mtest_res['test'])))\\npvals = stat_res['pvalues']\\nzvals = stat_res['tstat']\",\"type\":\"CodeChunk\",\"programmingLanguage\":\"python\",\"id\":\"LW4tTaGCsYJjd4AtHqpjgig53dLyjEnk\"}", "embeddedObjectBorder": { "color": { "color": { "rgbColor": {} } }, "width": { "unit": "PT" }, "dashStyle": "SOLID", "propertyState": "NOT_RENDERED" }, "size": { "height": { 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