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Related Experiment Video

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State-Dependent Effective Connectivity in Resting-State fMRI.

Hae-Jeong Park1,2,3,4,5, Jinseok Eo3,4, Chongwon Pae1,2,3,4

  • 1Department of Nuclear Medicine, Yonsei University College of Medicine, Seoul, South Korea.

Frontiers in Neural Circuits
|November 15, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces a novel method to analyze brain connectivity states in children with ADHD. The findings highlight differences in directed brain connections, emphasizing the excitation-inhibition balance in subtypes.

Keywords:
ADHDdynamic causal modeling (DCM)dynamic connectivityeffective connectivityresting state fMRI

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Area of Science:

  • Neuroscience
  • Computational Neuroscience
  • Psychiatry

Background:

  • The human brain exhibits dynamic transitions between metastable functional connectivity states during rest.
  • Understanding these state-specific dynamics is crucial for deciphering mental health disorders.
  • Existing functional connectivity measures lack information on directed causal influences (effective connectivity).

Purpose of the Study:

  • To develop and validate a framework for exploring state-dependent effective connectivity using dynamic causal modeling (DCM) on resting-state fMRI (rsfMRI).
  • To investigate the clinical applicability of this framework in characterizing subtypes of attention deficit hyperactivity disorder (ADHD).

Main Methods:

  • Utilized a hidden Markov model to identify brain states from rsfMRI data.
  • Employed spectral DCM and parametric empirical Bayes to model state-dependent effective connectivity.
  • Applied the method to a cohort of children with combined-type ADHD (ADHD-C) and typically developed children (TDC).

Main Results:

  • Demonstrated the plausibility of state-dependent effective connectivity analysis through simulations.
  • Characterized distinct state- and subtype-dependent effective connectivity patterns in the default mode network of ADHD-C vs. TDC.
  • Identified significant differences in self-inhibition, underscoring the role of excitation-inhibition balance in ADHD subtypes.

Conclusions:

  • The proposed DCM framework effectively decomposes state-dependent dynamic effective connectivity.
  • State-dependent analysis of directed brain coupling offers valuable insights into mental diseases like ADHD.
  • Findings highlight the importance of excitation-inhibition balance in understanding ADHD subtypes.