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Dynamic functional connectivity of neurocognitive networks in children.

ABSTRACT: The human brain is highly dynamic, supporting a remarkable range of cognitive abilities that emerge over the course of development. While flexible and dynamic coordination between neural systems is firmly established for children, our understanding of brain functional organization in early life has been built largely on the implicit assumption that functional connectivity (FC) is static. Understanding the nature of dynamic neural interactions during development is a critical issue for cognitive neuroscience, with implications for neurodevelopmental pathologies that involve anomalies in brain connectivity. In this work, FC dynamics of neurocognitive networks in a sample of 146 youth from varied sociodemographic backgrounds were delineated. Independent component analysis, sliding time window correlation, and k-means clustering were applied to resting-state fMRI data. Results revealed six dynamic FC states that re-occur over time and that complement, but significantly extend, measures of static FC. Moreover, the occurrence and amount of time spent in specific FC states are related to the content of self-generated thought during the scan. Additionally, some connections are more variable over time than are others, including those between inferior parietal lobe and precuneus. These regions contribute to multiple networks and likely play a role in adaptive processes in childhood. Age-related increases in temporal variability of FC among neurocognitive networks were also found. Taken together, these findings lay the groundwork for understanding how variation in the developing chronnectome is related to risk for neurodevelopmental disorders. Understanding how brain systems reconfigure with development should provide insight into the ontogeny of complex, flexible cognitive processes. Hum Brain Mapp 38:97-108, 2017. © 2016 Wiley Periodicals, Inc.

SUBMITTER: Marusak HA

PROVIDER: S-EPMC5796541 | biostudies-literature |

REPOSITORIES: biostudies-literature

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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.

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Dynamic functional connectivity of neurocognitive networks in children.

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