Dataset Information

Resting-state fMRI detects the effects of learning in short term: A visual search training study.

ABSTRACT: Can resting-state functional connectivity (rs-FC) detect the impact of learning on the brain in the short term? To test this possibility, we have combined task-FC and rs-FC tested before and after a 30-min visual search training. Forty-two healthy adults (20 men) divided into no-contact control and trained groups completed the study. We studied the connectivity between four different regions of the brain involved in visual search: the primary visual area, the right posterior parietal cortex (rPPC), the right dorsolateral prefrontal cortex (rDLPFC), and the dorsal anterior cingulate cortex (dACC). Task-FC showed increased connectivity between the rPPC and rDLPFC and between the dACC and rDLPFC from pretraining to posttraining for both the control group and the trained group, suggesting that connectivity between these areas increased with task repetition. In rs-FC, we found enhanced connectivity between these regions in the trained group after training, especially in those with better learning. Whole brain independent component analyses did not reveal any change in main networks after training. These results imply that rs-FC may not only predict individual differences in task performance, but rs-FC might also serve to monitor the impact of learning on the brain after short periods of cognitive training, localizing them in brain areas specifically involved in training.

SUBMITTER: Bueicheku E

PROVIDER: S-EPMC6865379 | biostudies-literature | 2019 Jun

REPOSITORIES: biostudies-literature

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Resting-state fMRI detects the effects of learning in short term: A visual search training study.

Bueichekú Elisenda E Miró-Padilla Anna A Ávila César C

Human brain mapping 20190311 9

Can resting-state functional connectivity (rs-FC) detect the impact of learning on the brain in the short term? To test this possibility, we have combined task-FC and rs-FC tested before and after a 30-min visual search training. Forty-two healthy adults (20 men) divided into no-contact control and trained groups completed the study. We studied the connectivity between four different regions of the brain involved in visual search: the primary visual area, the right posterior parietal cortex (rPP ...[more]

PMID: 30859709

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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

Resting-state fMRI detects the effects of learning in short term: A visual search training study.

Publications

Resting-state fMRI detects the effects of learning in short term: A visual search training study.

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