Despite their name, resting state networks (RSNs) provide a clear indication that the human brain may be hard-working. Unlike the cardiac and respiratory systems, which greatly reduce their rate of function during periods of inactivity, the human brain may have additional responsibilities during rest. One particularly intriguing function performed by the resting brain is the consolidation of recent learned information, which is known to take place over a period of several hours after learning. We recently reported that resting state brain activity is modulated by recent learning. We measured the brain activity using functional MRI during periods of rest that preceded and followed learning of a sensorimotor task, and found a network of brain areas that changed their resting activity. These areas are known to be involved in the acquisition and memory of such sensorimotor tasks. Furthermore, the changes were specific to a task that required learning, and were not found after motor performance without learning. Here we discuss the implications and possible extensions of this work and its relevance to the study of memory consolidation.
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http://dx.doi.org/10.4161/cib.2.6.9612 | 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 PDFSleep Med
January 2025
Peking University Sixth Hospital, Institute of Mental Health, Beijing, China; NHC Key Laboratory of Mental Health (Peking University), National Clinical Research Center for Mental Disorders (Peking University Sixth Hospital), Beijing, China. Electronic address:
Objectives: Children with attention-deficit/hyperactivity disorder often experience sleep problems, exacerbating symptoms, and cognitive deficits. However, the neurophysiological mechanisms underlying such deficits remained unclear. This study aims to use resting-state microstate analysis to investigate the neurophysiological characteristics in children with ADHD and sleep problems and explore whether neurophysiological abnormalities are associated with sleep problems.
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