AIMC Topic: Brain

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Cognitive prediction using regional connectivities and network biomarkers in Alzheimer's disease.

Neuroscience
Achieving a deep understanding of brain mechanisms requires multi-scale perspectives to capture the architecture of complex networks. In this study, we focused on patients with cognitive impairment and constructed individual brain networks from neuro...

AlzFormer: Video-based space-time attention model for early diagnosis of Alzheimer's disease.

Neuroscience
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 r...

An Engineered Supramolecular Fluorescent Chemosensor for Multiscale Visualization of Glutamate Dynamics in Living Systems.

Journal of the American Chemical Society
Glutamate (Glu) plays a critical role in the brain, and the ability to directly measure glutamate activity is essential for understanding its physiological functions and pathological processes. Herein, we engineered a family of Glu sensors () based o...

Interpretable Artificial Intelligence Analysis of Functional Magnetic Resonance Imaging for Migraine Classification: Quantitative Study.

JMIR medical informatics
BACKGROUND: Deep learning has demonstrated significant potential in advancing computer-aided diagnosis for neuropsychiatric disorders, such as migraine, enabling patient-specific diagnosis at an individual level. However, despite the superior accurac...

Overcoming Site Variability in Multisite fMRI Studies: an Autoencoder Framework for Enhanced Generalizability of Machine Learning Models.

Neuroinformatics
Harmonizing multisite functional magnetic resonance imaging (fMRI) data is crucial for eliminating site-specific variability that hinders the generalizability of machine learning models. Traditional harmonization techniques, such as ComBat, depend on...

Functional Near-Infrared Spectroscopy in Schizophrenia Research: Progress, Challenges, and Future Directions.

Medical science monitor : international medical journal of experimental and clinical research
This review comprehensively examines the application and progress of functional near-infrared spectroscopy (fNIRS) in schizophrenia research. Schizophrenia is a complex neuropsychiatric disorder characterized by extensive dysfunction in the prefronta...

Using deep learning to predict internalizing problems from brain structure in youth.

Translational psychiatry
Internalizing problems (e.g., anxiety and depression) are associated with a wide range of adverse outcomes. While some predictors of internalizing problems are known (e.g., their frequent co-occurrence with neurodevelopmental (ND) conditions), the bi...

Identifying key brain pathology in bipolar and unipolar depression using a region-specific brain aging trajectories approach: Insights from the Taiwan Aging and Mental Illness Cohort.

Psychological medicine
BACKGROUND: Identifying key areas of brain dysfunction in mental illness is critical for developing precision diagnosis and treatment. This study aimed to develop region-specific brain aging trajectory prediction models using multimodal magnetic reso...

Neural correlates of metacognition in education: a machine learning approach.

Neuropsychologia
Metacognition, the ability to reflect and regulate one's cognitive processes, has been shown to play a role in various aspects of life, particularly in academic settings. While important steps have been made in uncovering the neural basis of metacogn...

Optimized AI-based neural decoding from BOLD fMRI signal for analyzing visual and semantic ROIs in the human visual system.

Journal of neural engineering
. AI-based neural decoding reconstructs visual perception by leveraging generative models to map brain activity measured through functional magnetic resonance imaging (fMRI) into the observed visual stimulus.. Traditionally, ridge linear models trans...