Lifespan changes in whole-brain functional brain networks using fMRI data from three HCP studies
Poster No:
991
Submission Type:
Abstract Submission
Authors:
Tingting Zhang1, Yaotian Wang2, Shuoran Li1, Jie He1, Lingyi Peng1, Qiaochu Wang1, Xu Zou1, Dana Tudorascu1, David Schaeffer1, Lauren Schaeffer1, Diego Szczupak1, Jung Eun Park1, Stacey Sukoff Rizzo1, Gregory Carter3, Afonso Silva4
Institutions:
1University of Pittsburgh, Pittsburgh, PA, 2Emory University, Atlanta, GA, 3The Jackson Laboratory, Bar Harbor, ME, 4University of Pittsburgh, Pittsburgh, PA 15213-2921, PA
First Author:
Co-Author(s):
Introduction:
Research has shown that the human brain network undergoes substantial changes in both functional connectivity (FC) and cognitive functions throughout a person's lifespan. This has sparked a keen interest in mapping and understanding the trajectories of age-related FC changes in the entire brain. The patterns of age-related FC changes vary across different brain regions, connections, and life stages. The complexity of age-related changes in FC has resulted in inconsistent findings across different studies. These discrepancies are partly due to the insufficient number of subjects analyzed in many of these studies, emphasizing the need for large datasets to accurately capture the heterogeneous patterns of age-related FC changes across different brain regions.
To obtain more accurate and comprehensive estimates of age-related FC changes in the entire brain, this study leveraged large datasets by analyzing resting-state functional magnetic resonance imaging (fMRI) data aggregated from three sources. Specifically, we analyzed data of 996 subjects from the Human Connectome Project Young Adults (HCP-YA), 621 subjects from the Lifespan Human Connectome Project in Development (HCP-D), and 714 subjects from the Lifespan Human Connectome Project in Aging (HCP-A).
We developed and applied a new clustering-enabled regression to FC data derived from resting-state fMRI data of 2,331 subjects across the three HCP studies.
To obtain more accurate and comprehensive estimates of age-related FC changes in the entire brain, this study leveraged large datasets by analyzing resting-state functional magnetic resonance imaging (fMRI) data aggregated from three sources. Specifically, we analyzed data of 996 subjects from the Human Connectome Project Young Adults (HCP-YA), 621 subjects from the Lifespan Human Connectome Project in Development (HCP-D), and 714 subjects from the Lifespan Human Connectome Project in Aging (HCP-A).
We developed and applied a new clustering-enabled regression to FC data derived from resting-state fMRI data of 2,331 subjects across the three HCP studies.
Methods:
We proposed a new clustering-enabled regression to evaluate lifespan changes in whole-brain functional networks. This method integrates clustering with regression models to analyze FC data, where FC measurements between pairs of brain regions are response variables, and age and sex are predictors. The clustering-enabled regression advances traditional independent regression by clustering brain regions that exhibit identical and substantial changes in FC with age, while also accounting for sex differences. As a result, this method not only identifies regions that share identical age-related FC changes but also uncovers distinct patterns of these changes across different clusters of regions. Overall, this new approach offers improved precision and comprehensiveness in mapping sex-specific trajectories of age-related FC changes across different regions.
Results:
1. Most brain connections show minimal yet significant FC changes with age, with only a few connections showing substantial age-related changes.
2. Regions with similar and significant age-related changes in FC typically belong to the same functional network, though even within the same network, patterns of FC changes can differ. 3. FC generally decreases over time between region clusters from the same functional network, while inter-network FC shows varied age-related patterns. 4. Our research reveals sex-specific FC trends: females exhibit higher FC primarily within the default mode network, while males show higher FC across multiple networks.
2. Regions with similar and significant age-related changes in FC typically belong to the same functional network, though even within the same network, patterns of FC changes can differ. 3. FC generally decreases over time between region clusters from the same functional network, while inter-network FC shows varied age-related patterns. 4. Our research reveals sex-specific FC trends: females exhibit higher FC primarily within the default mode network, while males show higher FC across multiple networks.
·Within-Network FC Trajectories
·Identified Region Clusters in Each Functional Network
Conclusions:
1. The decrease in FC within networks suggests reduced functional segregation due to brain aging. 2. We observed both increasing and inverted U-shaped between-network FC trajectories, potentially compensating for declining within-network connectivity. 3. Age-related FC changes vary by the functional networks and spatial proximity of region clusters. 4.Increasing sex-related FC differences with age are vital for understanding sex-specific behaviors and the progression of diseases like Alzheimer's.
Lifespan Development:
Aging
Early life, Adolescence, Aging 1
Modeling and Analysis Methods:
Bayesian Modeling
Connectivity (eg. functional, effective, structural) 2
fMRI Connectivity and Network Modeling
Keywords:
Data analysis
FUNCTIONAL MRI
Statistical Methods
1|2Indicates the priority used for review
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HCP-D, HCP-YA, and HCP-A Datasets. Under review.