Latent Growth Models of Longitudinal Changes in Functional Connectivity during Early Stage Psychosis.

Abstract

Resting state functional magnetic resonance imaging (fMRI) is a useful technique to characterize functional connectivity patterns between regions of the brain, based on the Fisher-transformed Pearson correlations in the BOLD signal. Pinpointing how connectivity patterns change in neuropathies like early-stage psychosis (ESP) can help understand the disorders and track progression. Using study data from 21 ESP subjects with complete data for three consecutive scans, we examined connectivity changes throughout the whole brain with a region of interest (ROI) to ROI-based approach for ROI defined by the Harvard-Oxford cortical and subcortical atlases, supplemented by the AAL atlas for the cerebellum, and by networks defined by the CONN toolbox independent component analysis of the Human Connectome Project. We applied latent growth modelling, which is a type of structural equation modelling, to these connectivity measurements across baseline and follow-up visits. The models use age, community functioning, and negative symptoms at baselines as the covariates for subject-specific slope and intercept of the longitudinal measurements. After stringent thresholding cutoffs of root mean square error of approximation, standardized root mean square residual, comparative fit index, and Benjamini-Hochberg corrected p-value, we found a subset of connectivity measurements with significant longitudinal slopes (N = 18 atlas, N = 6 network), and used the subject's slope estimates to stratify these subjects into three clusters based on how the ROI-to-ROI correlations of functional connectivity change over time. The connections with significant slopes include atlas level regions like the temporal lobe, fronto-parietal lobe, and cerebellum, and network level patterns like the DMN, FPN, and Salience Networks. The structural equation modelling approach identifies ROIs whose functional connectivity changes over time, indicating the ROIs most dynamic during ESP. This highlights the utility of latent growth models for the analysis of longitudinal functional connectivity measures across the whole brain with relatively small sample sizes.