Parkinson’s disease in the spinal cord: an exploratory study to establish T2*w, MTR and diffusion-weighted imaging metric values
import pandas as pd
import numpy as np
import plotly.graph_objects as go
from plotly.offline import plot
from plotly.subplots import make_subplots
import base64
import os
import plotly.io as pio
pio.renderers.default = "plotly_mimetype"
# List of metrics to include
DWI_metrics = ['FA', 'MD', 'AD', 'RD', 'ODI', 'FISO', 'FICVF', 'T2star', 'MTR']
# Labels and spinal levels
labels = ['spinal cord', 'white matter', 'gray matter', 'WM/GM', 'dorsal columns', 'ventral funiculi', 'lateral funiculi']
spinal_levels = ['2', '3', '4', '5']
# Load all datasets and compute WM/GM
datasets = {}
for metric in DWI_metrics:
# Load the data
df = pd.read_csv(f'../../data/parkinsons-spinalcord-mri-metrics/data/{metric}.csv')
# Compute WM/GM
data_WM = df[df['Label'] == 'white matter'].copy()
data_GM = df[df['Label'] == 'gray matter'].copy()
data_WMGM = data_WM.copy()
data_WMGM['Label'] = 'WM/GM'
data_WMGM['WA'] = data_WM['WA'].values / data_GM['WA'].values
df = pd.concat([df, data_WMGM], ignore_index=True)
datasets[metric] = df
# Create figure with subplots
fig = make_subplots(
rows=5, cols=8,
horizontal_spacing=0.035,
vertical_spacing=0.07
)
# Store trace visibility info per metric
trace_visibility_by_metric = {metric: [] for metric in DWI_metrics}
all_traces = []
# Add scatter traces for each metric
for metric_index, metric in enumerate(DWI_metrics):
df = datasets[metric]
for i, spinal_level in enumerate(spinal_levels):
for j, label in enumerate(labels):
row = i + 2 # Start at row 2 to leave the first row empty for the template image
col = j + 2 # Same for the columns
filtered = df[(df['Label'] == label) & (df['SpinalLevel'] == spinal_level)]
CTRL_data = filtered[filtered['CTRL_or_PD'].str.contains('CTRL', na=False)]
PD_data = filtered[filtered['CTRL_or_PD'].str.contains('PD', na=False)]
# CTRL scatter trace
trace1 = go.Scatter(
x=CTRL_data['Age'],
y=CTRL_data['WA'],
mode='markers',
marker=dict(color='#00517F', opacity=0.8, size=5),
name='HC',
showlegend=(row == 2 and col == 2), # Show the legend only once (first suplot)
legendgroup='HC', # Link all traces with the same legend group (so they are toggled together)
visible=(metric_index == 0)
)
fig.add_trace(trace1, row=row, col=col)
trace_visibility_by_metric[metric].append(len(all_traces))
all_traces.append(trace1)
# CTRL regression line
if len(CTRL_data) > 1:
coef = np.polyfit(CTRL_data['Age'], CTRL_data['WA'], 1)
x_range = np.linspace(CTRL_data['Age'].min(), CTRL_data['Age'].max(), 100)
y_fit = coef[0] * x_range + coef[1]
trace1_fit = go.Scatter(
x=x_range,
y=y_fit,
mode='lines',
line=dict(color='#00517F', width=2),
name='HC Trend',
showlegend=False,
legendgroup='HC',
visible=(metric_index == 0)
)
fig.add_trace(trace1_fit, row=row, col=col)
trace_visibility_by_metric[metric].append(len(all_traces))
all_traces.append(trace1_fit)
# PD trace
trace2 = go.Scatter(
x=PD_data['Age'],
y=PD_data['WA'],
mode='markers',
marker=dict(color='#B4464F', opacity=0.8, size=5),
name='PD',
showlegend=(row == 2 and col == 2), # Show the legend only once (first suplot)
legendgroup='PD', # Link all traces with the same legend group (so they are toggled together)
visible=(metric_index == 0)
)
fig.add_trace(trace2, row=row, col=col)
trace_visibility_by_metric[metric].append(len(all_traces))
all_traces.append(trace2)
# PD regression line
if len(PD_data) > 1:
coef = np.polyfit(PD_data['Age'], PD_data['WA'], 1)
x_range = np.linspace(PD_data['Age'].min(), PD_data['Age'].max(), 100)
y_fit = coef[0] * x_range + coef[1]
trace2_fit = go.Scatter(
x=x_range,
y=y_fit,
mode='lines',
line=dict(color='#B4464F', width=2),
name='HC Trend',
showlegend=False,
legendgroup='HC',
visible=(metric_index == 0)
)
fig.add_trace(trace2_fit, row=row, col=col)
trace_visibility_by_metric[metric].append(len(all_traces))
all_traces.append(trace2_fit)
# Axes titles and layout
fig.update_xaxes(tickfont=dict(size=11), title=dict(text='Age', font=dict(size=14, family='Arial')), row=i+2, col=j+2, title_standoff=0)
fig.update_yaxes(tickfont=dict(size=11), row=i+2, col=j+2, title_standoff=0, tickformat=".3f")
fig.update_yaxes(fixedrange=True)
fig.update_xaxes(fixedrange=True)
# Add dropdown menu
dropdown_buttons = []
for metric in DWI_metrics:
visibility = [False] * len(all_traces)
for trace_index in trace_visibility_by_metric[metric]:
visibility[trace_index] = True
button = dict(
label=metric,
method="update",
args=[{"visible": visibility}]
)
dropdown_buttons.append(button)
# Load static background image (from the "templates_for_figures" folder) and encode as base64
with open("../templates_for_figures/suppl_figures1and2_template.png", "rb") as image_file:
encoded_image = base64.b64encode(image_file.read()).decode()
fig.add_layout_image(
dict(
source="data:image/png;base64," + encoded_image,
xref="paper",
yref="paper",
x=-0.01, # Aligns the image to the left edge of the figure
y=1.02, # Aligns the image to the top edge of the figure
sizex=1.1, # Adjust the size relative to the figure
sizey=1.1, # Adjust the size relative to the figure
xanchor="left", # Anchors the image position to the left
yanchor="top", # Anchors the image position to the top
opacity=1,
layer="below"
)
)
# Update layout with dropdown
fig.update_layout(
updatemenus=[dict(
active=0,
buttons=dropdown_buttons,
x=0.01, xanchor='left',
y=1.0, yanchor='top',
font=dict(
size=16,
color='black',
family='Arial'
)
)],
height=800, width=1520,
legend=dict(
x=0.06, # Horizontal position (0 = left, 1 = right)
y=0.93, # Vertical position (0 = bottom, 1 = top)
xanchor='right', # Anchor relative to the x-position
yanchor='top', # Anchor relative to the y-position
bgcolor='rgba(255,255,255, 0)', # Transparent background
itemsizing='constant',
font=dict(size=16, family='Arial', color='white'),
)
)
fig.show()
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