At rest, the brain's sensorimotor and higher cognitive systems engage in organized patterns of correlated activity forming resting-state networks. An important empirical question is how functional connectivity and structural connectivity within and between resting-state networks change with age. In this study we use network modeling techniques to identify significant changes in network organization across the human lifespan. The results of this study demonstrate that whole-brain functional and structural connectivity both exhibit reorganization with age. On average, functional connections within resting-state networks weaken in magnitude while connections between resting-state networks tend to increase. These changes can be localized to a small subset of functional connections that exhibit systematic changes across the lifespan. Collectively, changes in functional connectivity are also manifest at a system-wide level, as components of the control, default mode, saliency/ventral attention, dorsal attention, and visual networks become less functionally cohesive, as evidenced by decreased component modularity. Paralleling this functional reorganization is a decrease in the density and weight of anatomical white-matter connections. Hub regions are particularly affected by these changes, and the capacity of those regions to communicate with other regions exhibits a lifelong pattern of decline. Finally, the relationship between functional connectivity and structural connectivity also appears to change with age; functional connectivity along multi-step structural paths tends to be stronger in older subjects than in younger subjects. Overall, our analysis points to age-related changes in inter-regional communication unfolding within and between resting-state networks.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1016/j.neuroimage.2014.07.067 | DOI Listing |
Geroscience
January 2025
Department of Surgery, Immanuel Clinic Rüdersdorf, University Clinic of Brandenburg Medical School, Berlin, Germany.
Aging is a multi-organ disease, yet the traditional approach has been to study each organ in isolation. Such organ-specific studies have provided invaluable information regarding its pathomechanisms. However, an overall picture of the whole-body network (WBN) during aging is still incomplete.
View Article and Find Full Text PDFJ Imaging Inform Med
January 2025
Department of Chinese and Bilingual Studies, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong Special Administrative Region, China.
Deep learning models have shown promise in diagnosing neurodevelopmental disorders (NDD) like ASD and ADHD. However, many models either use graph neural networks (GNN) to construct single-level brain functional networks (BFNs) or employ spatial convolution filtering for local information extraction from rs-fMRI data, often neglecting high-order features crucial for NDD classification. We introduce a Multi-view High-order Network (MHNet) to capture hierarchical and high-order features from multi-view BFNs derived from rs-fMRI data for NDD prediction.
View Article and Find Full Text PDFBrain Imaging Behav
January 2025
Department of Radiology, Zhongnan Hospital of Wuhan University, Wuchang, Wuhan, Hubei, 430071, China.
This study investigates post-stroke cognitive impairment (PSCI) by utilizing spectral dynamic causal modeling (spDCM) to examine changes in effective connectivity (EC) within the default mode, executive control, dorsal attention, and salience networks. Forty-one PSCI patients and 41 demographically matched healthy controls underwent 3D-T1WI and resting-state functional magnetic resonance imaging on a 3.0T MRI.
View Article and Find Full Text PDFNeuroimage
January 2025
School of Biomedical Sciences and Engineering, South China University of Technology, Guangzhou International Campus, Guangzhou 511442, China; National Engineering Research Center for Tissue Restoration and Reconstruction, South China University of Technology, Guangzhou 510006, China; Department of Aging Research and Geriatric Medicine, Institute of Development, Aging and Cancer, Tohoku University, Sendai 980-8575, Japan. Electronic address:
The association between the human brain and gut microbiota, known as the "brain-gut-microbiota axis", is involved in the neuropathological mechanisms of schizophrenia (SZ); however, its association patterns and correlations with symptom severity and neurocognition are still largely unknown. In this study, 43 SZ patients and 55 normal controls (NCs) were included, and resting-state functional magnetic resonance imaging (rs-fMRI) and gut microbiota data were acquired for each participant. First, the brain features of brain images and functional brain networks were computed from rs-fMRI data; the gut features of gut microbiota abundance and the gut microbiota network were computed from gut microbiota data.
View Article and Find Full Text PDFBrain Imaging Behav
January 2025
Key Laboratory of Adolescent Cyberpsychology and Behavior (Ministry of Education), Wuhan, China.
Bipolar disorder (BD) is a complex psychiatric condition marked by significant mood fluctuations that deeply affect quality of life. Understanding the neural mechanisms underlying BD is critical for improving diagnostic accuracy and developing more effective treatments. This study utilized resting-state functional magnetic resonance imaging (rs-fMRI) to investigate functional connectivity within the ventral and dorsal attention networks in 52 patients with BD and 51 healthy controls.
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!