Related Experiment Videos
Estimating repetitive spatiotemporal patterns from many subjects' resting-state fMRIs
Yusuke Takeda1, Takashi Itahashi2, Masa-Aki Sato3
1Computational Brain Dynamics Team, RIKEN Center for Advanced Intelligence Project, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto, 619-0288, Japan; Department of Computational Brain Imaging, ATR Neural Information Analysis Laboratories, 2-2-2 Hikaridai, Seika-cho, Soraku-gun, Kyoto, 619-0288, Japan.
Neuroimage
|September 17, 2019
Summary
BigSTeP analyzes large resting-state fMRI datasets to identify common brain patterns. It found differences between autism spectrum disorder and typically developed groups, particularly when the default mode network is active.
Area of Science:
- Neuroscience
- Computational Neuroscience
- Brain Imaging Analysis
Background:
- Resting-state functional MRI (fMRI) data analysis is crucial for understanding brain function.
- Existing methods like SpatioTemporal Pattern estimation (STeP) can identify brain activity patterns but are limited with large datasets.
- Large-scale neuroimaging databases like ABIDE offer opportunities to study brain activity across many subjects.
Purpose of the Study:
- To extend the STeP method for analyzing large-scale resting-state fMRI datasets, termed BigSTeP.
- To identify common spatiotemporal patterns across subjects and subject-specific patterns.
- To investigate differences in brain activity patterns between individuals with autism spectrum disorder (ASD) and typically developed (TD) individuals.
Main Methods:
- Developed BigSTeP, an extension of STeP, to handle big data from resting-state fMRI.
- Applied BigSTeP to over 1,000 subjects' rsfMRI data from the ABIDE I database.
- Compared subject-specific spatiotemporal patterns between ASD and TD groups.
Main Results:
- Identified two common spatiotemporal patterns, including default mode (DMN), sensorimotor, auditory, and visual networks, suggesting temporal coordination.
- Revealed subject-specific spatiotemporal patterns for both common networks.
- Found significant differences between ASD and TD groups concentrated during specific periods of high DMN activity.
Conclusions:
- BigSTeP is effective for extracting common and subject-specific spatiotemporal patterns from large-scale resting-state fMRI datasets.
- The findings suggest context-dependent differences in brain activity between ASD and TD groups, particularly related to DMN activity.
- This data-driven approach facilitates hypothesis generation for understanding brain function and disorders.