A spatio-temporal decomposition framework for dynamic functional connectivity in the human brain.
Jinming Xiao1, Lucina Q Uddin2, Yao Meng1
1The Clinical Hospital of Chengdu Brain Science Institute, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China; High-Field Magnetic Resonance Brain Imaging Key Laboratory of Sichuan Province, MOE Key Lab for Neuro Information, University of Electronic Science and Technology of China, Chengdu 611731, China.
Neuroimage
|September 10, 2022
Summary
This study introduces a new computational method to analyze brain dynamics, revealing how functional connectivity changes across different brain states and identifying links to cognitive flexibility in schizophrenia.
Area of Science:
- Neuroscience
- Computational Biology
- Data Science
Background:
- Understanding brain dynamics is crucial for neuroscience.
- The rules governing spatio-temporal organization in functional connectivity across brain states are not well understood.
Purpose of the Study:
- To develop a novel computational approach to model dynamic functional connectivity.
- To investigate the rules constraining spatio-temporal organization in functional connectivity under different brain states.
- To explore the relationship between dynamic functional connectivity and cognitive function, particularly in schizophrenia.
Main Methods:
- Developed a dynamic decomposition model (DDM) using tensor decomposition and regularization.
- Represented dynamic functional connectivity as a linear combination of dynamic modules and time-varying weights.
- Applied DDM to resting-state fMRI data from healthy individuals and patients with schizophrenia.
Main Results:
- Identified eight whole-brain dynamic modules and five distinct brain states.
- Found that temporal variations in dynamic modules contribute to brain state transitions.
- Demonstrated that atypical flexibility of dynamic modules correlates with impaired cognitive flexibility in schizophrenia.
Conclusions:
- The DDM provides a quantitative framework for characterizing temporal variations in dynamic functional connectivity.
- This framework can help elucidate the neural underpinnings of cognitive function and dysfunction.
- Findings suggest potential biomarkers for cognitive impairments in conditions like schizophrenia.
