Univariate Regression

This example simulates data according to a very simple sketch of brain imaging data and applies a standard two-level univariate GLM to identify significant voxels.

Download pain dataset from neurovault

Here we fetch the pain dataset used in Chang et al., 2015. In this dataset there are 28 subjects with 3 separate beta images reflecting varying intensities of thermal pain (i.e., high, medium, low). The data will be downloaded to ~/nilearn_data, and automatically loaded as a Brain_Data() instance. The metadata will be stored in data.X.

from nltools.datasets import fetch_pain

data = fetch_pain()
metadata = data.X.copy()
subject_id = metadata['SubjectID']

Run Univariate Regression

We can loop over subjects and predict the intensity of each voxel from a simple model of pain intensity and an intercept. This is just for illustration purposes as there are only 3 observations per subject. We initialize an empty Brain_Data() instance and loop over all subjects running a univariate regression separately for each participant. We aggregate the beta estimates for pain intensity across subjects.

from nltools.data import Brain_Data
import numpy as np
import pandas as pd

all_sub = Brain_Data()
for s in subject_id.unique():
    sdat = data[np.where(metadata['SubjectID']==s)[0]]
    sdat.X = pd.DataFrame(data={'Intercept':np.ones(sdat.shape()[0]),'Pain':sdat.X['PainLevel']})
    stats = sdat.regress()
    all_sub = all_sub.append(stats['beta'][1])

We can now run a one-sample t-test at every voxel to test whether it is significantly different from zero across participants. We will threshold the results using FDR correction, q < 0.001.

t_stats = all_sub.ttest(threshold_dict={'fdr':.001})
t_stats['thr_t'].plot()
../../_images/sphx_glr_plot_univariate_regression_001.png

Out:

threshold is ignored for simple axial plots

Run Linear Contrast

Obviously, the univariate regression isn’t a great idea when there are only three observations per subject. As we predict a monotonic increase in pain across pain intensities, we can also calculate a linear contrast c=(-1,0,1). This is simple using matrix multiplication on the centered pain intensity values.

all_sub = Brain_Data()
for s in subject_id.unique():
    sdat = data[np.where(metadata['SubjectID']==s)[0]]
    sdat.X = pd.DataFrame(data={'Pain':sdat.X['PainLevel']})
    sdat.data = np.dot(sdat.data.T,sdat.X['Pain']-2)
    all_sub = all_sub.append(sdat)

We can again run a one-sample t-test at every voxel using an FDR threshold of q < 0.001.

t_stats = all_sub.ttest(threshold_dict={'fdr':.001})
t_stats['thr_t'].plot()
../../_images/sphx_glr_plot_univariate_regression_002.png

Out:

threshold is ignored for simple axial plots

Total running time of the script: ( 0 minutes 43.671 seconds)

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