Statelets: Capturing recurrent transient variations in dynamic functional network connectivity

Abstract

Dynamic functional network connectivity (dFNC) analysis is a widely used approach for capturing brain activation patterns, connectivity states, and network organization. However, a typical sliding window plus clustering (SWC) approach for analyzing dFNC models the system through a fixed sequence of connectivity states. SWC assumes connectivity patterns span throughout the brain, but they are relatively spatially constrained and temporally short‐lived in practice. Thus, SWC is neither designed to capture transient dynamic changes nor heterogeneity across subjects/time. We propose a state‐space time series summarization framework called “statelets” to address these shortcomings. It models functional connectivity dynamics at fine‐grained timescales, adapting time series motifs to changes in connectivity strength, and constructs a concise yet informative representation of the original data that conveys easily comprehensible information about the phenotypes. We leverage the earth mover distance in a nonstandard way to handle scale differences and utilize kernel density estimation to build a probability density profile for local motifs. We apply the framework to study dFNC of patients with schizophrenia (SZ) and healthy control (HC). Results demonstrate SZ subjects exhibit reduced modularity in their brain network organization relative to HC. Statelets in the HC group show an increased recurrence across the dFNC time‐course compared to the SZ. Analyzing the consistency of the connections across time reveals significant differences within visual, sensorimotor, and default mode regions where HC subjects show higher consistency than SZ. The introduced approach also enables handling dynamic information in cross‐modal and multimodal applications to study healthy and disordered brains.

Neuromark PET: A multivariate method for Estimating and comparing whole brain functional networks and connectomes from fMRI and PET data

“…ICA has been extensively utilized in biomedical domains to analyze the brain activity [27, 28, 29, 30, 31, 32, 33]. In fMRI data, ICA is typically applied on N × M data matrix X , where N and M represent the time points and voxels respectively.…”

dcSBM: A federated constrained source‐based morphometry approach for multivariate brain structure mapping

“…ICA is a widely used approach in the biomedical domain (Calhoun et al, 2009; Rahaman et al, 2022; D. K. Saha et al, 2021; R. Saha, Saha, et al, 2022). In SBM of structural MRI data, ICA is applied to factorize the multi‐subject volume matrix into a mixing matrix and a source matrix , such that and the rows of are statistically independent of one another.…”

“…ICA is a widely used approach in the biomedical domain (Calhoun et al, 2009;Rahaman et al, 2022;D. K. Saha et al, 2021;.…”

dcSBM: A federated constrained source-based morphometry approach for multivariate brain structure mapping

“…ICA is a widely used approach in biomedical domain [25, 26, 27, 28]. In structural MRI data, at first, ICA is imposed on subject volume matrix X , and decompose into a mixing matrix A , and a source matrix S .…”

“…ICA is a widely used approach in biomedical domain [25,26,27,28]. In structural MRI data, at first, ICA is imposed on subject volume matrix X, and decompose into a mixing matrix A, and a source matrix S. The mixing matrix represents the relationship between subjects and sources, and source matrix preserves the relationship between sources and voxels of brain.…”