Dataset Information

Biological Characteristics of Connection-Wise Resting-State Functional Connectivity Strength.

ABSTRACT: Functional connectivity is defined as the statistical dependency of neurophysiological activity between 2 separate brain areas. To investigate the biological characteristics of resting-state functional connectivity (rsFC)-and in particular the significance of connection-wise variation in time-series correlations-rsFC was compared with strychnine-based connectivity measured in the macaque. Strychnine neuronography is a historical technique that induces activity in cortical areas through means of local administration of the substance strychnine. Strychnine causes local disinhibition through GABA suppression and leads to subsequent activation of functional pathways. Multiple resting-state fMRI recordings were acquired in 4 macaques (examining in total 299 imaging runs) from which a group-averaged rsFC matrix was constructed. rsFC was observed to be higher (P < 0.0001) between region-pairs with a strychnine-based connection as compared with region-pairs with no strychnine-based connection present. In particular, higher resting-state connectivity was observed in connections that were relatively stronger (weak < moderate < strong; P < 0.01) and in connections that were bidirectional (P < 0.0001) instead of unidirectional in strychnine-based connectivity. Our results imply that the level of correlation between brain areas as extracted from resting-state fMRI relates to the strength of underlying interregional functional pathways.

SUBMITTER: Pijnenburg R

PROVIDER: S-EPMC7049309 | biostudies-literature | 2019 Dec

REPOSITORIES: biostudies-literature

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Biological Characteristics of Connection-Wise Resting-State Functional Connectivity Strength.

Pijnenburg Rory R Scholtens Lianne H LH Mantini Dante D Vanduffel Wim W Barrett Lisa Feldman LF van den Heuvel Martijn P MP

Cerebral cortex (New York, N.Y. : 1991) 20191201 11

Functional connectivity is defined as the statistical dependency of neurophysiological activity between 2 separate brain areas. To investigate the biological characteristics of resting-state functional connectivity (rsFC)-and in particular the significance of connection-wise variation in time-series correlations-rsFC was compared with strychnine-based connectivity measured in the macaque. Strychnine neuronography is a historical technique that induces activity in cortical areas through means of ...[more]

PMID: 30668705

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Project description:Estimation of functional connectivity (FC) has become an increasingly powerful tool for investigating healthy and abnormal brain function. Static connectivity, in particular, has played a large part in guiding conclusions from the majority of resting-state functional MRI studies. However, accumulating evidence points to the presence of temporal fluctuations in FC, leading to increasing interest in estimating FC as a dynamic quantity. One central issue that has arisen in this new view of connectivity is the dramatic increase in complexity caused by dynamic functional connectivity (dFC) estimation. To computationally handle this increased complexity, a limited set of dFC properties, primarily the mean and variance, have generally been considered. Additionally, it remains unclear how to integrate the increased information from dFC into pattern recognition techniques for subject-level prediction. In this study, we propose an approach to address these two issues based on a large number of previously unexplored temporal and spectral features of dynamic functional connectivity. A Generalized Autoregressive Conditional Heteroskedasticity (GARCH) model is used to estimate time-varying patterns of functional connectivity between resting-state networks. Time-frequency analysis is then performed on dFC estimates, and a large number of previously unexplored temporal and spectral features drawn from signal processing literature are extracted for dFC estimates. We apply the investigated features to two neurologic populations of interest, healthy controls and patients with temporal lobe epilepsy, and show that the proposed approach leads to substantial increases in predictive performance compared to both traditional estimates of static connectivity as well as current approaches to dFC. Variable importance is assessed and shows that there are several quantities that can be extracted from dFC signal which are more informative than the traditional mean or variance of dFC. This work illuminates many previously unexplored facets of the dynamic properties of functional connectivity between resting-state networks, and provides a platform for dynamic functional connectivity analysis that facilitates its usage as an investigative measure for healthy as well as abnormal brain function.

Dataset Information

Biological Characteristics of Connection-Wise Resting-State Functional Connectivity Strength.

Publications

Biological Characteristics of Connection-Wise Resting-State Functional Connectivity Strength.

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