Visualization of normative modeling outputs

The Normative Modeling Framework for Computational Psychiatry. Nature Protocols. https://www.nature.com/articles/s41596-022-00696-5.

Created by Saige Rutherford

We have also built an app for interactively viewing the evaluation metrics.

https://colab.research.google.com/assets/colab-badge.svg
../_images/blr_fig4.png

Brain space extreme deviation counts

Count the number of extreme (positive & negative) deviations at each brain region and visualize the count for each hemisphere.

! git clone https://github.com/predictive-clinical-neuroscience/PCNtoolkit-demo.git

import os
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns

os.chdir('/content/PCNtoolkit-demo')

Z_df = pd.read_csv('data/Z_long_format.csv')

# Change this threshold to view more or less extreme deviations.
# Discuss with your partner what you think is an appropriate threshold and adjust the below variables accordingly.
Z_positive = Z_df.query('value > 2')
Z_negative = Z_df.query('value < -2')

positive_left_z = Z_positive.query('hemi == "left"')
positive_right_z = Z_positive.query('hemi == "right"')
positive_sc_z = Z_positive.query('hemi == "subcortical"')
negative_left_z = Z_negative.query('hemi == "left"')
negative_right_z = Z_negative.query('hemi == "right"')
negative_sc_z = Z_negative.query('hemi == "subcortical"')

positive_left_z2 = positive_left_z['ROI_name'].value_counts().rename_axis('ROI').reset_index(name='counts')
positive_right_z2 = positive_right_z['ROI_name'].value_counts().rename_axis('ROI').reset_index(name='counts')
positive_sc_z2 = positive_sc_z['ROI_name'].value_counts().rename_axis('ROI').reset_index(name='counts')
negative_left_z2 = negative_left_z['ROI_name'].value_counts().rename_axis('ROI').reset_index(name='counts')
negative_right_z2 = negative_right_z['ROI_name'].value_counts().rename_axis('ROI').reset_index(name='counts')
negative_sc_z2 = negative_sc_z['ROI_name'].value_counts().rename_axis('ROI').reset_index(name='counts')

positive_left_z2.describe()
counts
count 74.000000
mean 24.432432
std 6.182346
min 13.000000
25% 20.000000
50% 23.000000
75% 28.000000
max 46.000000 
positive_right_z2.describe()
counts
count 74.000000
mean 24.027027
std 6.164354
min 11.000000
25% 20.250000
50% 23.000000
75% 27.750000
max 39.000000 
positive_sc_z2.describe()
counts
count 28.000000
mean 16.714286
std 5.449140
min 8.000000
25% 12.000000
50% 16.000000
75% 21.250000
max 27.000000 
negative_left_z2.describe()
counts
count 74.000000
mean 11.108108
std 5.193694
min 2.000000
25% 7.000000
50% 10.000000
75% 14.000000
max 27.000000 
negative_right_z2.describe()
counts
count 74.000000
mean 12.824324
std 4.603031
min 1.000000
25% 10.000000
50% 13.000000
75% 14.750000
max 33.000000 
negative_sc_z2.describe()
counts
count 28.000000
mean 9.142857
std 6.614878
min 1.000000
25% 4.000000
50% 7.000000
75% 12.000000
max 26.000000 
! pip install nilearn

from nilearn import plotting
import nibabel as nib
from nilearn import datasets

destrieux_atlas = datasets.fetch_atlas_surf_destrieux()
fsaverage = datasets.fetch_surf_fsaverage()

Dataset created in /root/nilearn_data/destrieux_surface

Downloading data from https://www.nitrc.org/frs/download.php/9343/lh.aparc.a2009s.annot ...

...done. (1 seconds, 0 min)

Downloading data from https://www.nitrc.org/frs/download.php/9342/rh.aparc.a2009s.annot ...

...done. (1 seconds, 0 min)

# The parcellation is already loaded into memory
parcellation_l = destrieux_atlas['map_left']
parcellation_r = destrieux_atlas['map_right']

nl = pd.read_csv('data/nilearn_order.csv')

atlas_r = destrieux_atlas['map_right']
atlas_l = destrieux_atlas['map_left']

nl_ROI = nl['ROI'].to_list()

Extreme positive deviation viz

nl_positive_left = pd.merge(nl, positive_left_z2, on='ROI', how='left')
nl_positive_right = pd.merge(nl, positive_right_z2, on='ROI', how='left')

nl_positive_left['counts'] = nl_positive_right['counts'].fillna(0)
nl_positive_right['counts'] = nl_positive_right['counts'].fillna(0)

nl_positive_left = nl_positive_left['counts'].to_numpy()
nl_positive_right = nl_positive_right['counts'].to_numpy()

a_list = list(range(1, 76))
parcellation_positive_l = atlas_l
for i, j in enumerate(a_list):
    parcellation_positive_l = np.where(parcellation_positive_l == j, nl_positive_left[i], parcellation_positive_l)

a_list = list(range(1, 76))
parcellation_positive_r = atlas_r
for i, j in enumerate(a_list):
    parcellation_positive_r = np.where(parcellation_positive_r == j, nl_positive_right[i], parcellation_positive_r)

# you can click around in 3D space on this visualization. Scroll in/out, move the brain around, etc. Have fun with it :)
view = plotting.view_surf(fsaverage.infl_right, parcellation_positive_r, threshold=None, symmetric_cmap=False, cmap='plasma', bg_map=fsaverage.sulc_right)

view

view = plotting.view_surf(fsaverage.infl_left, parcellation_positive_l, threshold=None, symmetric_cmap=False, cmap='plasma', bg_map=fsaverage.sulc_left)

view

Extreme negative deviation viz

nl_negative_left = pd.merge(nl, negative_left_z2, on='ROI', how='left')
nl_negative_right = pd.merge(nl, negative_right_z2, on='ROI', how='left')

nl_negative_left['counts'] = nl_negative_left['counts'].fillna(0)
nl_negative_right['counts'] = nl_negative_right['counts'].fillna(0)

nl_negative_left = nl_negative_left['counts'].to_numpy()
nl_negative_right = nl_negative_right['counts'].to_numpy()

a_list = list(range(1, 76))
parcellation_negative_l = atlas_l
for i, j in enumerate(a_list):
    parcellation_negative_l = np.where(parcellation_negative_l == j, nl_negative_left[i], parcellation_negative_l)

a_list = list(range(1, 76))
parcellation_negative_r = atlas_r
for i, j in enumerate(a_list):
    parcellation_negative_r = np.where(parcellation_negative_r == j, nl_negative_right[i], parcellation_negative_r)

view = plotting.view_surf(fsaverage.infl_right, parcellation_negative_r, threshold=None, symmetric_cmap=False, cmap='plasma', bg_map=fsaverage.sulc_right)

view

view = plotting.view_surf(fsaverage.infl_left, parcellation_negative_l, threshold=None, symmetric_cmap=False, cmap='plasma', bg_map=fsaverage.sulc_left)

view

Violin plots of extreme deviations

We will count the number of “extreme” deviations that each person has (both positive and negative) and summarize the distribution of extreme deviations for healthy controls and patients with schizophrenia.

Z_df = pd.read_csv('data/fcon1000_te_Z.csv')

deviation_counts = Z_df.loc[:, Z_df.columns.str.contains('Z_predict')]

deviation_counts['positive_count'] = deviation_counts[deviation_counts >= 2].count(axis=1)

deviation_counts['negative_count'] = deviation_counts[deviation_counts <= -2].count(axis=1)

deviation_counts['participant_id'] = Z_df['sub_id']
deviation_counts['group_ID'] = Z_df['group']
deviation_counts['site_ID'] = Z_df['site']

deviation_counts['all_counts'] = deviation_counts['positive_count'] + deviation_counts['negative_count']

fig, ax = plt.subplots(figsize=(6,6))
sns.violinplot(data=deviation_counts, y="all_counts", x="group_ID", inner='box', ax=ax);
plt.legend=False
../_images/visualizations_47_0.png

Centile visualization

The code used to visualize the centiles of variation can be found in this notebook.