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.

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