The brain is highly dynamic, reorganizing its activity at different interacting spatial and temporal scales, including variation within and between brain networks. The chronnectome is a model of the brain in which nodal activity and connectivity patterns change in fundamental and recurring ways over time. Most literature assumes fixed spatial nodes/networks, ignoring the possibility that spatial nodes/networks may vary in time. Here, we introduce an approach to calculate a spatially fluid chronnectome (called the spatial chronnectome for clarity), which focuses on the variations of networks coupling at the voxel level, and identify a novel set of spatially dynamic features. Results reveal transient spatially fluid interactions between intra- and internetwork relationships in which brain networks transiently merge and separate, emphasizing dynamic segregation and integration. Brain networks also exhibit distinct spatial patterns with unique temporal characteristics, potentially explaining a broad spectrum of inconsistencies in previous studies that assumed static networks. Moreover, we show anticorrelative connections to brain networks are transient as opposed to constant across the entire scan. Preliminary assessments using a multi-site dataset reveal the ability of the approach to obtain new information and nuanced alterations that remain undetected during static analysis. Patients with schizophrenia (SZ) display transient decreases in voxel-wise network coupling within visual and auditory networks, and higher intradomain coupling variability. In summary, the spatial chronnectome represents a new direction of research enabling the study of functional networks which are transient at the voxel level, and the identification of mechanisms for within- and between-subject spatial variability.
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http://dx.doi.org/10.1002/hbm.24580 | DOI Listing |
Purpose: To examine associations between clinical measures (self-reported and clinician-administered) and subsequent injury rates in the year after concussion return to play (RTP) among adolescent athletes.
Methods: We performed a prospective, longitudinal study of adolescents ages 13-18 years. Each participant was initially assessed within 21 days of concussion and again within 5 days of receiving RTP clearance from their physician.
Sci Rep
December 2024
School of Electronic Information and Electrical Engineering, Yangtze University, Jingzhou, 434100, Hubei, China.
Emotions play a crucial role in human thoughts, cognitive processes, and decision-making. EEG has become a widely utilized tool in emotion recognition due to its high temporal resolution, real-time monitoring capabilities, portability, and cost-effectiveness. In this paper, we propose a novel end-to-end emotion recognition method from EEG signals, called MSDCGTNet, which is based on the Multi-Scale Dynamic 1D CNN and the Gated Transformer.
View Article and Find Full Text PDFJ Neurol
December 2024
Department of Neurosciences Rita Levi Montalcini, University of Turin, Turin, Italy.
Introduction: Non-motor symptoms (NMS) in Parkinson's disease (PD) can fluctuate daily, impacting patient quality of life. The Non-Motor Fluctuation Assessment (NoMoFA) Questionnaire, a recently validated tool, quantifies NMS fluctuations during ON- and OFF-medication states. Our study aimed to validate the Italian version of NoMoFA, comparing its results to the original validation and further exploring its clinimetric properties.
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December 2024
Department of Information and Computer Science, College of Computer Science and Engineering, University of Ha'il, Ha'il, 81481, Saudi Arabia.
Alzheimer's disease (AD) is a brain disorder that causes memory loss and behavioral and thinking problems. The symptoms of Alzheimer's are similar throughout its development stages, which makes it difficult to diagnose manually. Therefore, artificial intelligence (AI) techniques address the limitations of manual diagnosis.
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December 2024
Department of Neurosurgery, The Second Affiliated Hospital, Zhejiang University School of Medicine, Jiefang Road 88th, Hangzhou, 310009, China.
Chronic ischemia in moyamoya disease (MMD) impaired white matter microstructure and neural functional network. However, the coupling between cerebral blood flow (CBF) and functional connectivity and the association between structural and functional network are largely unknown. 38 MMD patients and 20 sex/age-matched healthy controls (HC) were included for T1-weighted imaging, arterial spin labeling imaging, resting-state functional MRI and diffusion tensor imaging.
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