Dataset Information

Large-scale DCMs for resting-state fMRI.

ABSTRACT: This paper considers the identification of large directed graphs for resting-state brain networks based on biophysical models of distributed neuronal activity, that is, effective connectivity. This identification can be contrasted with functional connectivity methods based on symmetric correlations that are ubiquitous in resting-state functional MRI (fMRI). We use spectral dynamic causal modeling (DCM) to invert large graphs comprising dozens of nodes or regions. The ensuing graphs are directed and weighted, hence providing a neurobiologically plausible characterization of connectivity in terms of excitatory and inhibitory coupling. Furthermore, we show that the use of to discover the most likely sparse graph (or model) from a parent (e.g., fully connected) graph eschews the arbitrary thresholding often applied to large symmetric (functional connectivity) graphs. Using empirical fMRI data, we show that spectral DCM furnishes connectivity estimates on large graphs that correlate strongly with the estimates provided by stochastic DCM. Furthermore, we increase the efficiency of model inversion using functional connectivity modes to place prior constraints on effective connectivity. In other words, we use a small number of modes to finesse the potentially redundant parameterization of large DCMs. We show that spectral DCM-with functional connectivity priors-is ideally suited for directed graph theoretic analyses of resting-state fMRI. We envision that directed graphs will prove useful in understanding the psychopathology and pathophysiology of neurodegenerative and neurodevelopmental disorders. We will demonstrate the utility of large directed graphs in clinical populations in subsequent reports, using the procedures described in this paper.

SUBMITTER: Razi A

PROVIDER: S-EPMC5796644 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

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Large-scale DCMs for resting-state fMRI.

Razi Adeel A Seghier Mohamed L ML Zhou Yuan Y McColgan Peter P Zeidman Peter P Park Hae-Jeong HJ Sporns Olaf O Rees Geraint G Friston Karl J KJ

Network neuroscience (Cambridge, Mass.) 20171027 3

This paper considers the identification of large directed graphs for resting-state brain networks based on biophysical models of distributed neuronal activity, that is, effective connectivity. This identification can be contrasted with functional connectivity methods based on symmetric correlations that are ubiquitous in resting-state functional MRI (fMRI). We use spectral dynamic causal modeling (DCM) to invert large graphs comprising dozens of nodes or regions. The ...[more]

PMID: 29400357

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Project description: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.

Project description:BackgroundIncreasing evidence suggests that heroin addiction may be related to the dysfunction among the triple brain network (default mode network [DMN], salience network [SN] and executive control network [ECN]). However, the characteristics of glucose metabolism and metabolic connectivity among core regions of the triple brain network remain unknown. Therefore, we hypothesized that individuals with heroin dependence would show abnormal glucose metabolism and accompanied abnormal metabolic connectivity within the triple brain network.MethodsIndividuals with heroin dependence and healthy controls matched for age and sex underwent integrated positron emission tomography/magnetic resonance imaging (PET/MRI). Differences in glucose metabolism and metabolic connectivity among the DMN, SN and ECN were analyzed based on 18F-fluorodeoxyglucose PET and resting-state fMRI data.ResultsWe included 36 individuals with heroin dependence and 30 matched healthy controls in our study. The heroin dependence group showed a significant reduction of glucose metabolism in the bilateral anterior insula (AI) and inferior parietal lobule (IPL), and a significantly decreased metabolic connectivity between the right AI and the left dorsolateral prefrontal cortex (DLPFC). The daily dose of methadone was negatively correlated with glucose metabolism of the right AI and right IPL.LimitationsThe results revealed the glucose metabolism alterations and metabolic connectivity only within the triple brain network in individuals with heroin dependence; additional brain networks should be investigated in future studies. Although methadone is an opioid with a similar neurophysiological mechanism as heroin, the specific chronic effects of methadone on cerebral metabolism and metabolic connectivity should also be investigated in future studies.ConclusionOur findings suggest that long-term opioid use might, to some extent, be associated with reduced synergistic ability between the SN and ECN, which may be associated with the dysfunction of cognitive control. In particular, the right AI, which showed hypometabolism and related reduction in SN-ECN metabolic connectivity, should receive increasing attention in future studies.

Dataset Information

Large-scale DCMs for resting-state fMRI.

Publications

Large-scale DCMs for resting-state fMRI.

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