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Longitudinally consistent estimates of intrinsic functional networks.

Qingyu Zhao1, Dongjin Kwon1,2, Eva M Müller-Oehring1,2

  • 1Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, California.

Human Brain Mapping
|February 27, 2019
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This study introduces a new longitudinal method for analyzing brain networks from resting-state fMRI data. It improves the accuracy of estimating individual brain networks and reveals developmental changes in adolescents.

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intrinsic functional networkslongitudinal analysisresting-state fMRI

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Area of Science:

  • Neuroimaging
  • Developmental Neuroscience
  • Functional Connectivity Analysis

Background:

  • Longitudinal resting-state functional MRI (rs-fMRI) is increasingly used to study brain architecture changes.
  • Current subject-level analysis of intrinsic functional networks (IFNs) using cross-sectional methods neglects intra-subject dependencies, leading to suboptimal network estimations.
  • Understanding developmental changes in brain networks is crucial for adolescent neurodevelopmental research.

Purpose of the Study:

  • To develop and validate a novel longitudinal approach for simultaneously extracting subject-specific IFNs across multiple visits.
  • To explicitly model functional brain development as a context for identifying changes in brain architecture.
  • To improve the accuracy of IFN estimation and identify significant developmental effects in adolescents.

Main Methods:

  • A novel longitudinal method was developed to extract subject-specific IFNs across multiple time points, modeling brain development.
  • The method's accuracy was evaluated using simulated rs-fMRI data based on real data.
  • Group analysis was performed on longitudinal estimates from 246 adolescents in the National Consortium on Alcohol and NeuroDevelopment in Adolescence (NCANDA) study.

Main Results:

  • The novel longitudinal approach demonstrated higher accuracy in estimating subject-specific IFNs compared to traditional cross-sectional methods.
  • Group analysis using longitudinally consistent estimates successfully identified significant developmental effects within IFNs in adolescents.
  • These developmental effects aligned with established concepts of adolescent neurodevelopment.

Conclusions:

  • The proposed longitudinal method offers a more accurate and robust way to analyze subject-specific intrinsic functional networks from rs-fMRI data.
  • This approach effectively captures developmental trajectories in brain functional architecture during adolescence.
  • The findings highlight the importance of longitudinal modeling for understanding neurodevelopmental changes.