Abstract
The human brain at rest exhibits intrinsic dynamics transitioning among the multiple metastable states of the inter-regional functional connectivity. Accordingly, the demand for exploring the state-specific functional connectivity increases for a deeper understanding of mental diseases. Functional connectivity, however, lacks information about the directed causal influences among the brain regions, called effective connectivity. This study presents the dynamic causal modeling (DCM) framework to explore the state-dependent effective connectivity using spectral DCM for the resting-state functional MRI (rsfMRI). We established the sequence of brain states using the hidden Markov model with the multivariate autoregressive coefficients of rsfMRI, summarizing the functional connectivity. We decomposed the state-dependent effective connectivity using a parametric empirical Bayes scheme that models the effective connectivity of consecutive windows with the time course of the discrete states as regressors. We showed the plausibility of the state-dependent effective connectivity analysis in a simulation setting. To test the clinical applicability, we applied the proposed method to characterize the state- and subtype-dependent effective connectivity of the default mode network in children with combined-type attention deficit hyperactivity disorder (ADHD-C) compared with age-matched, typically developed children (TDC). All 88 children were subtyped according to the occupation times (i.e., dwell times) of the three dominant functional connectivity states, independently of clinical diagnosis. The state-dependent effective connectivity differences between ADHD-C and TDC according to the subtypes and those between the subtypes of ADHD-C were expressed mainly in self-inhibition, magnifying the importance of excitation inhibition balance in the subtyping. These findings provide a clear motivation for decomposing the state-dependent dynamic effective connectivity and state-dependent analysis of the directed coupling in exploring mental diseases.
Highlights
At rest, the human brain exhibits temporal changes in the regional brain activity and in the inter-regional coherence among distributed brain regions, called dynamic functional connectivity
We focused on the attention deficit hyperactivity disorder (ADHD)-combined type (ADHD-C), one of the most common subtypes in early ADHD, showing inattentiveness and hyperactivity/impulsivity
The procedures were composed of the following steps: (1) extraction of time series from the default mode network (DMN) and whole-brain; (2) identification of connectivity states and their transitions using Hidden Markov Modeling (HMM); (3) estimation of the first level spectral dynamic causal modeling (spDCM) for all the windows at each subject; (4) second-level state-dependent effective connectivity estimation using parametric empirical Bayes approach (PEB) with the states driven from the HMM; (5) group comparison using PEB
Summary
The human brain exhibits temporal changes in the regional brain activity and in the inter-regional coherence among distributed brain regions, called dynamic functional connectivity. The decomposition of the dynamic effective connectivity was achieved using the parametric empirical Bayes approach (PEB) (Friston et al, 2016) that models the effective connectivity of each window by incorporating random effects with a combination of multiple basis functions. This approach is an extension of a longitudinal study on recovery after thalamotomy in patients with essential tremors (Park et al, 2017). The slow changes in the effective connectivity (estimated by spDCM at consecutive windows) were modeled with a GLM using the occupation proportions of the brain states as regressors in the second-level PEB analysis. The third level PEB was applied to study the group characteristics of ADHD-C
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