AIMC Topic: Brain
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Aphasia severity prediction using a multi-modal machine learning approach.
NeuroImage
Jun 17, 2025
The present study examined an integrated multiple neuroimaging modality (T1 structural, Diffusion Tensor Imaging (DTI), and resting-state FMRI (rsFMRI)) to predict aphasia severity using Western Aphasia Battery-Revised Aphasia Quotient (WAB-R AQ) in ...
Differences in resting-state functional connectivity between depressed bipolar and major depressive disorder patients: A machine learning study.
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...
Identifying and predicting EEG microstates with sequence-to-sequence deep learning models for online applications.
Journal of neural engineering
Jun 17, 2025
Electroencephalographic (EEG) microstates, as a non-invasive and high-temporal-resolution tool for analyzing time-space features of brain activity, have been validated and applied in various research domains. However, current methods for EEG microsta...
Rate of brain aging associates with future executive function in Asian children and older adults.
eLife
Jun 16, 2025
Brain age has emerged as a powerful tool to understand neuroanatomical aging and its link to health outcomes like cognition. However, there remains a lack of studies investigating the rate of brain aging and its relationship to cognition. Furthermore...
MRI super-resolution reconstruction using efficient diffusion probabilistic model with residual shifting.
Physics in medicine and biology
Jun 16, 2025
Magnetic resonance imaging (MRI) is essential in clinical and research contexts, providing exceptional soft-tissue contrast. However, prolonged acquisition times often lead to patient discomfort and motion artifacts. Diffusion-based deep learning sup...
From neurotoxicity to neuroprotection: Rethinking GABAR-targeting anesthetics.
Cell biology and toxicology
Jun 14, 2025
The brain growth spurt (BGS) represents a pivotal window in neurodevelopment, defined by rapid neurogenesis, heightened synaptogenesis, and the dynamic establishment of neural networks. During this phase, heightened brain plasticity significantly enh...
Neur-Ally: a deep learning model for regulatory variant prediction based on genomic and epigenomic features in brain and its validation in certain neurological disorders.
NAR genomics and bioinformatics
Jun 13, 2025
Large-scale quantitative studies have identified significant genetic associations for various neurological disorders. Expression quantitative trait locus (eQTL) studies have shown the effect of single-nucleotide polymorphisms (SNPs) on the differenti...
NeuroEmo: A neuroimaging-based fMRI dataset to extract temporal affective brain dynamics for Indian movie video clips stimuli using dynamic functional connectivity approach with graph convolution neural network (DFC-GCNN).
Computers in biology and medicine
Jun 12, 2025
FMRI, a non-invasive neuroimaging technique, can detect emotional brain activation patterns. It allows researchers to observe functional changes in the brain, making it a valuable tool for emotion recognition. For improved emotion recognition systems...
Regional and whole-brain neurofunctional alterations during pain empathic processing of physical but not affective pain in migraine patients.
The journal of headache and pain
Jun 12, 2025
BACKGROUND: Accumulating evidence suggests that migraine patients present abnormal brain responses to salient sensory and emotional stimuli. However, it is still unclear whether this is a generalized or domain-specific phenomenon. Employing a well-va...
Providing context: Extracting non-linear and dynamic temporal motifs from brain activity.
PloS one
Jun 12, 2025
Approaches studying the dynamics of resting-state functional magnetic resonance imaging (rs-fMRI) activity often focus on time-resolved functional connectivity (tr-FC). While many tr-FC approaches have been proposed, most are linear approaches, e.g. ...
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