Disrupted Dynamic Functional Network Connectivity Among Cognitive Control Networks in the Progression of Alzheimer's Disease
Year: 2021
Type: article
Study design: Observational Study
Abstract: Background Alzheimer’s disease (AD) is the most common age-related dementia that promotes a decline in memory, thinking, and social skills. The initial stages of dementia can be associated with mild symptoms, and symptom progression to a more severe state is heterogeneous across patients. Recent work has demonstrated the potential for functional network mapping to assist in the prediction of symptomatic progression. However, this work has primarily used static functional connectivity (sFC) from rs-fMRI. Recently, dynamic functional connectivity (dFC) has been recognized as a powerful advance in functional connectivity methodology to differentiate brain network dynamics between healthy and diseased populations. Methods Group independent component analysis was applied to extract 17 components within the cognitive control network (CCN) from 1385 individuals across varying stages of AD symptomology. We estimated dFC among 17 components within the CCN, followed by clustering the dFCs into 3 recurring brain states and then estimated a hidden Markov model and the occupancy rate for each subject. Finally, we investigated the link between CCN dFC connectivity features with AD progression. Results Progression of AD symptoms were associated with increases in connectivity within the middle frontal gyrus. Also, the AD with mild and severer symptoms showed less connectivity within the inferior parietal lobule and between this region with the rest of CCN. Finally, comparing with mild dementia, we found that the normal brain spends significantly more time in a state with lower within middle frontal gyrus connectivity and higher connectivity between the hippocampus and the rest of CCN, highlighting the importance of assessing the dynamics of brain connectivity in this disease. Conclusion Our results suggest that AD progress not only alters the CCN connectivity strength but also changes the temporal properties in this brain network. This suggests the temporal and spatial pattern of CCN as a biomarker that differentiates different stages of AD. Impact Statement By assuming that functional connectivity is static over time, many of previous studies have ignored the brain dynamic in Alzheimer’s disease progression. Here, a longitudinal resting-state functional magnetic resonance imaging data are used to explore the temporal changes of functional connectivity in the cognitive control network in Alzheimer’s disease progression. The result of this study would increase our understanding about the underlying mechanisms of Alzheimer’s Disease and help in finding future treatment of this neurological disorder.
Source: Brain Connectivity
Institutions: Georgia Institute of Technology, Emory University, Georgia State University, The Wallace H. Coulter Department of Biomedical Engineering, Center for Translational Research in Neuroimaging and Data Science, Shanxi University, Stanford Medicine, Stanford University, Harvard University, Massachusetts General Hospital
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
Awards: Unified multivariate data-driven solutions for static and dynamic brain connectivity, Mining the Genomewide Scan: Genetic Profiles of Structural Loss in Schizophrenia, Stanford Alzheimer's Disease Research Center, Neural Architecture of Social Emotional Processing and Regulation in Autism Spectrum Disorder: A Dynamic Connectivity Perspective, Dynamic imaging-genomic models for characterizing and predicting psychosis and mood disorders, DEVELOPMENTAL MULTIMODAL IMAGING OF NEUROCOGNITIVE DYNAMICS (DEV-MIND), A decentralized macro and micro gene-by-environment interaction analysis of substance use behavior and its brain biomarkers, Stanford Alzheimer's Disease Research Center, Integrating Information about Aging Surveys: Novel Integration of Contextual Data to Study Late-Life Cognition and Alzheimer’s Disease and Related Dementia and Dementia Care
OpenAlex ID: W3168608201