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Periodic changes in fMRI connectivity.

ABSTRACT: The first two decades of brain research using fMRI have been dominated by studies that measure signal changes in response to a presented task. A rapidly increasing number of studies are showing that consistent activation maps appear by assessment of signal correlations during time periods in which the subjects were not directed to perform any specific task (i.e. "resting state correlations"). Even though neural interactions can happen on much shorter time scales, most "resting state" studies assess these temporal correlations over a period of about 5 to 10 min. Here we investigate how these temporal correlations change on a shorter time scale. We examine changes in brain correlations to the posterior cingulate cortex (PCC) across a 10-minute scan. We show: (1) fMRI correlations fluctuate over time, (2) these fluctuations can be periodic, and (3) correlations between the PCC and other brain regions fluctuate at distinct frequencies. While the precise frequencies of correlation fluctuations vary across subjects and runs, it is still possible to parse brain regions and combinations of brain regions based on fluctuation frequency differences. To evaluate the potential biological significance of these empirical observations, we then use synthetic time series data with identical amplitude spectra, but randomized phase to show that similar effects can still appear even if the timing relationships between voxels are randomized. This implies that observed correlation fluctuations could occur between regions with distinct amplitude spectra, whether or not there are dynamic changes in neural connectivity between such regions. As more studies of brain connectivity dynamics appear, particularly studies using correlation as a key metric, it is vital to better distinguish true neural connectivity dynamics from connectivity fluctuations that are inherently part of this method. Our results also highlight the rich information in the power spectra of fMRI data that can be used to parse brain regions.

SUBMITTER: Handwerker DA

PROVIDER: S-EPMC4180175 | biostudies-literature | 2012 Nov

REPOSITORIES: biostudies-literature

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Periodic changes in fMRI connectivity.

Handwerker Daniel A DA Roopchansingh Vinai V Gonzalez-Castillo Javier J Bandettini Peter A PA

NeuroImage 20120714 3

The first two decades of brain research using fMRI have been dominated by studies that measure signal changes in response to a presented task. A rapidly increasing number of studies are showing that consistent activation maps appear by assessment of signal correlations during time periods in which the subjects were not directed to perform any specific task (i.e. "resting state correlations"). Even though neural interactions can happen on much shorter time scales, most "resting state" studies ass ...[more]

PMID: 22796990

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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.

Dataset Information

Periodic changes in fMRI connectivity.

Publications

Periodic changes in fMRI connectivity.

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