Dataset Information

Aging Impacts the Overall Connectivity Strength of Regions Critical for Information Transfer Among Brain Networks.

ABSTRACT: Recent studies have demonstrated that connector hubs, regions considered critical for the flow of information across neural systems, are mostly involved in neurodegenerative dementia. Considering that aging can significantly affect the brain's intrinsic connectivity, identifying aging's impact on these regions' overall connection strength is important to differentiate changes associated with healthy aging from neurodegenerative disorders. Using resting state functional magnetic resonance imaging data from a carefully selected cohort of 175 healthy volunteers aging from 21 to 86 years old, we computed an intrinsic connectivity contrast (ICC) metric, which quantifies a region's overall connectivity strength, for whole brain, short-range, and long-range connections and examined age-related changes of this metric over the adult lifespan. We have identified a limited number of hub regions with ICC values that showed significant negative relationship with age. These include the medial precentral/midcingulate gyri and insula with both their short-range and long-range (and thus whole-brain) ICC values negatively associated with age, and the angular, middle frontal, and posterior cingulate gyri with their long-range ICC values mainly involved. Seed-based connectivity analyses further confirmed that these regions are connector hubs with connectivity profile that strongly overlapped with multiple large-scale brain networks. General cognitive performance was not associated with these hubs' ICC values. These findings suggest that even healthy aging could negatively impact the efficiency of regions critical for facilitating information transfer among different functional brain networks. The extent of the regions involved, however, was limited.

SUBMITTER: Bagarinao E

PROVIDER: S-EPMC7655963 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Aging Impacts the Overall Connectivity Strength of Regions Critical for Information Transfer Among Brain Networks.

Bagarinao Epifanio E Watanabe Hirohisa H Maesawa Satoshi S Mori Daisuke D Hara Kazuhiro K Kawabata Kazuya K Yoneyama Noritaka N Ohdake Reiko R Imai Kazunori K Masuda Michihito M Yokoi Takamasa T Ogura Aya A Taoka Toshiaki T Koyama Shuji S Tanabe Hiroki C HC Katsuno Masahisa M Wakabayashi Toshihiko T Kuzuya Masafumi M Hoshiyama Minoru M Isoda Haruo H Naganawa Shinji S Ozaki Norio N Sobue Gen G

Frontiers in aging neuroscience 20201028

Recent studies have demonstrated that connector hubs, regions considered critical for the flow of information across neural systems, are mostly involved in neurodegenerative dementia. Considering that aging can significantly affect the brain's intrinsic connectivity, identifying aging's impact on these regions' overall connection strength is important to differentiate changes associated with healthy aging from neurodegenerative disorders. Using resting state functional magnetic resonance imaging ...[more]

PMID: 33192489

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Project description:A resting-state functional connectivity (rsFC)-constructed functional network (FN) derived from functional magnetic resonance imaging (fMRI) data can effectively mine alterations in brain function during aging due to the non-invasive and effective advantages of fMRI. With global health research focusing on aging, several open fMRI datasets have been made available that combine deep learning with big data and are a new, promising trend and open issue for brain information detection in fMRI studies of brain aging. In this study, we proposed a new method based on deep learning from the perspective of big data, named Deep neural network (DNN) with Autoencoder (AE) pretrained Functional connectivity Analysis (DAFA), to deeply mine the important functional connectivity changes in fMRI during brain aging. First, using resting-state fMRI data from 421 subjects from the CamCAN dataset, functional connectivities were calculated using sliding window method, and the complex functional patterns were mined by an AE. Then, to increase the statistical power and reliability of the results, we used an AE-pretrained DNN to relabel the functional connectivities of each subject to classify them as belonging to the attributes of young or old individuals. A method called search-back analysis was performed to find alterations in brain function during aging according to the relabeled functional connectivities. Finally, behavioral data regarding fluid intelligence and response time were used to verify the revealed functional changes. Compared to traditional methods, DAFA revealed additional, important aged-related changes in FC patterns [e.g., FC connections within the default mode (DMN) and the sensorimotor and cingulo-opercular networks, as well as connections between the frontoparietal and cingulo-opercular networks, between the DMN and the frontoparietal/cingulo-opercular/sensorimotor/occipital/cerebellum networks, and between the sensorimotor and frontoparietal/cingulo-opercular networks], which were correlated to behavioral data. These findings demonstrated that the proposed DAFA method was superior to traditional FC-determining methods in discovering changes in brain functional connectivity during aging. In addition, it may be a promising method for exploring important information in other fMRI studies.

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

Aging Impacts the Overall Connectivity Strength of Regions Critical for Information Transfer Among Brain Networks.

Publications

Aging Impacts the Overall Connectivity Strength of Regions Critical for Information Transfer Among Brain Networks.

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