# JROST Lightning Talk Demo

This Jupyter Notebook explores an important aspect of quantitative magnetic resonance imaging (qMRI): validation. Focusing specifically on myelin measures, we show the results of our meta-analysis comparing quantitative MRI with histology. The full preprint is available on bioRxiv.

    data <- read.csv('CatsOfZenodo.csv')
    dtrunc <- data
    dtrunc <- dtrunc[c(1,3,4)]
    dtrunc <- head(dtrunc)
    dtrunc

X | P2 | P3
--- | --- | ---
indoor-lack-stimulation | 2 | -1
never-considered-wildlife | -6 | -5
guilt-if-killed | 2 | 1
used-to-indoors | 0 | 5
hunting-no-bother | -2 | -2
concern-bird-populations | 2 | 3

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.

How are qMRI measures validated? 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 in vivo or ex vivo tissue and then comparing those data with the related samples analysed using histological techniques.

Why a meta-analysis? 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.

* * *

A bit more about this Jupyter Notebook

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 plotly) are the main content. You will not find sections discussing or interpreting these results: for that, please check the preprint.

* * *

### Selecting the studies

First, how were the studies selected? We used the Medline database 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.

    # 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

    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

    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()

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 (in vivo, ex vivo, in situ), 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.

    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()

### A closer look

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 R2, 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.

    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()

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.

    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()

### Using meta-analysis tools

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 metafor to fit a mixed-effect (ME) model to the data reported for each measure. In this way, we can estimate an overall interval of R2 values based on the effect sizes and the sample sizes. We can also estimate the interval of R2 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.

    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])

    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()

The forest plot offers a detailed summary for each measure. What if we want to compare the R2 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.

    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']

    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()

### Other factors to consider

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.

    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

    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()

    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()

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