Statelets: Capturing recurrent transient variations in dynamic functional network connectivity

• Published in: Human brain mapping Vol. 43; no. 8; pp. 2503 - 2518
• Main Authors: Rahaman, Md Abdur, Damaraju, Eswar, Saha, Debbrata K., Plis, Sergey M., Calhoun, Vince D.
• Format: Journal Article
• Language: English
• Published: Hoboken, USA John Wiley & Sons, Inc 01.06.2022
• Subjects: Brain; Brain - diagnostic imaging; Brain architecture; Brain Mapping - methods; Cluster Analysis; Clustering; Consistency; Density; dynamic functional network connectivity; Dynamical systems; earthmover distance; Heterogeneity; Humans; Investigations; kernel density estimator; Magnetic Resonance Imaging - methods; Mental disorders; Modularity; Neural networks; Phenotypes; resting‐state MRI; Schizophrenia; Schizophrenia - diagnostic imaging; Sensorimotor system; Time series; time series motifs summarization; Trends; time series motifs summarization; earthmover distance; resting-state MRI; kernel density estimator; schizophrenia; dynamic functional network connectivity
• ISSN: 1065-9471; 1097-0193; 1097-0193
• DOI: 10.1002/hbm.25799

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. We proposed a novel method for analyzing dynamic functional connectivity via extracting high‐frequency texture from the connectivity space. The analysis of those motifs enables measuring the characteristics of brain circuitry and network organization. The experiments don't he summary motifs facilitate the observation of distinguishing connectivity signatures and the interplay among the hubs to process information