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The Four-dimensional (spatiotemporal) Consistency of local Neural Activities (FOCA) metric was utilized to assess spontaneous whole-brain activity. Despite its application, the genetic underpinnings of FOCA alterations in Alzheimer's Disease (AD)-related Mild Cognitive Impairment (MCI) remain largely unexplored. To elucidate these changes, we analyzed group FOCA differences in 41 MCI patients and 46 controls from the Alzheimer's Disease Neuroimaging Initiative database. Integrating the Allen Human Brain Atlas, we performed transcriptome-neuroimaging spatial association analyses to pinpoint genes correlating with MCI-related FOCA changes. We observed heightened FOCA in the frontal-parietal system and diminished FOCA in the temporal lobe and medium cingulate gyrus among MCI patients. These FOCA alterations were spatially linked to the expression of 384 genes, which were enriched in crucial molecular functions, biological processes, and cellular components of the cerebral cortex, as well as related pathways. These genes were specifically expressed in brain tissue and corticothalamic neurons, particularly during late cortical development. They also connected to various behavioral domains. Furthermore, these genes could form a protein-protein interaction network, supported by 34 hub genes. Our results suggest that local spatiotemporal consistency of spontaneous brain activity in MCI may stem from the complex interplay of a broad spectrum of genes with diverse functional features.
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http://dx.doi.org/10.1093/cercor/bhaf045 | DOI Listing |
Neuroscience
September 2025
Department of Medicine, LSU Health Shreveport, Shreveport, LA, USA. Electronic address:
Early and accurate Alzheimer's disease (AD) diagnosis is critical for effective intervention, but it is still challenging due to neurodegeneration's slow and complex progression. Recent studies in brain imaging analysis have highlighted the crucial roles of deep learning techniques in computer-assisted interventions for diagnosing brain diseases. In this study, we propose AlzFormer, a novel deep learning framework based on a space-time attention mechanism, for multiclass classification of AD, MCI, and CN individuals using structural MRI scans.
View Article and Find Full Text PDFBackground Long COVID affects a substantial portion of the U.S. population, yet its spatiotemporal distribution remains poorly characterized.
View Article and Find Full Text PDFFront Public Health
September 2025
Department of Infection Control, Provincial Hospital of Shandong First Medical University, Jinan, Shandong, China.
Objective: The study aimed to investigate the relationship between global tracheobronchial lung cancer mortality rates and economic levels and assess the associated regional economic burden. Understanding these associations is crucial for global health resource allocation, informing cancer prevention and control strategies, and providing data to support the development of lung cancer and economic policies worldwide.
Methods: We analyzed respiratory cancer mortality data (International Classification of Diseases (ICD)-10 codes C33-C34) obtained from the World Health Organization (WHO) Mortality Database (2000-2019).
Ecotoxicol Environ Saf
September 2025
The Key Laboratory of Water and Air Pollution Control of Guangdong Province, South China Institute of Environmental Sciences, Ministry of Ecology and Environment, No. 18 Ruihe Road, Guangzhou 510530, China. Electronic address:
This study employed environmental DNA (eDNA) metabarcoding to investigate the differential responses of phytoplankton and zooplankton communities to combined tidal and urban stressors along the Dongjiang River, China. The results revealed distinct spatiotemporal patterns between phytoplankton and zooplankton groups: phytoplankton diversity showed significantly stronger seasonal variation (a 61.2 % increase in the wet season, P < 0.
View Article and Find Full Text PDFFront Physiol
August 2025
School of Mathematical Sciences, Zhejiang University, Hangzhou, Zhejiang, China.
Introduction: Segmentation of echocardiograms plays a crucial role in clinical diagnosis. Beyond accuracy, a major challenge of video echocardiogram analysis is the temporal consistency of consecutive frames. Stable and consistent segmentation of cardiac structures is essential for a reliable fully automatic echocardiogram interpretation.
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