Potential confounders¶
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.
Figure 7
Distributions of R2 values in relation to the reference techniques, pathology and tissue types are visualized using box plots. You can mouse over markers to see the studies they are drawn from.
import numpy as np
import pandas as pd
import plotly.graph_objects as go
from IPython.core.display import display, HTML
from plotly.offline import plot
import plotly.express as px
import plotly.colors
from plotly.subplots import make_subplots
from rpy2.robjects.packages import importr
import rpy2.robjects
import subprocess
subprocess.call('curl https://raw.githubusercontent.com/Notebook-Factory/brand/main/insertLogo.py --output /tmp/insertLogo.py', shell=True)
%run /tmp/insertLogo.py
Figure 7¶
config={'showLink': False, 'displayModeBar': False}
metafor = importr('metafor')
stats = importr('stats')
filtered_df = pd.read_pickle("filtered_df.pkl")
filtered_df['Variance'] = (4*filtered_df['R^2'])*((1-filtered_df['R^2'])**2)/filtered_df['Sample points']
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),
margin=dict(l=0),
showlegend=False,
height=1000,
width=600
)
fig7.update_xaxes(tickfont=dict(size=10))
plot(insertLogo(fig7,0.03,0.03,1,-0.149,-0.125,0.025), filename = 'fig7.html',config = config)
display(HTML('fig7.html'))
Figure 8
Other potential confounders include magnetic field strength, tissue conditions, co-registration and tissue type. You can mouse over markers in each panel to see the corresponding studies.
Figure 8¶
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())}
fig8 = make_subplots(rows=2, cols=2, start_cell="top-left", vertical_spacing=0.18, 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=600,
width=700
)
plot(insertLogo(fig8,0.04,0.04,1,-0.2,-0.12,0.048), filename = 'fig8.html',config = config)
display(HTML('fig8.html'))