Proceedings of the National Academy of Sciences of the United States of America
Jul 30, 2025
Past traumatic experiences shape neural responses to future stress, but the mechanisms underlying this dynamic interaction remain unclear. Here, we assessed how trauma-related brain networks respond to current acute stress in real time. Using a machi...
BACKGROUND: Rhegmatogenous retinal detachment (RRD) is known to induce functional alterations in the gray matter regions associated with vision. However, the impact of RRD on the white matter (WM) connectome remains largely unexplored.
European neuropsychopharmacology : the journal of the European College of Neuropsychopharmacology
Jun 17, 2025
Nearly 60 % of individuals with bipolar disorder (BD) are initially classified as major depressive disorder (MDD) patients, resulting in inappropriate drug treatment. Identifying reliable biomarkers for the differential diagnosis between MDD and BD p...
Suicide represents an egregious threat to society despite major advancements in medicine, in part due to limited knowledge of the biological mechanisms of suicidal behavior. We apply a connectome predictive modeling machine learning approach to ident...
The information-processing capability of the brain's cellular network depends on the physical wiring pattern between neurons and their molecular and functional characteristics. Mapping neurons and resolving their individual synaptic connections can b...
IEEE transactions on neural networks and learning systems
May 2, 2025
Spatiotemporal dynamics in the brain have been recognized as strongly related to the formation of perceived and cognitive diseases, such as delusions and hallucinations in Alzheimer's disease. However, two practical considerations are rarely mentione...
Computer methods and programs in biomedicine
Apr 23, 2025
BACKGROUND AND OBJECTIVE: Exploring the dependencies between multimodal brain networks and integrating node features to enhance brain disease diagnosis remains a significant challenge. Some work has examined only brain connectivity changes in patient...
Diffusion tensor imaging (DTI) is essential for assessing brain microstructure but requires long acquisition times, limiting clinical use. Recent deep learning (DL) approaches, such as SuperDTI or deepDTI, improve DTI metrics but demand large, high-q...
Multimodal neuroimaging data modeling has become a widely used approach but confronts considerable challenges due to their heterogeneity, which encompasses variability in data types, scales, and formats across modalities. This variability necessitate...
Machine learning may enhance clinical data analysis but requires large amounts of training data, which are scarce for rare pathologies. While generative neural network models can create realistic synthetic data such as 3D MRI volumes and, thus, augme...
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