AIMC Topic: Brain
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Aberrant patterns of spontaneous brain activity in schizophrenia: A resting-state fMRI study and classification analysis.
Progress in neuro-psychopharmacology & biological psychiatry
Jun 18, 2024
BACKGROUND: Schizophrenia is a prevalent mental disorder, leading to severe disability. Currently, the absence of objective biomarkers hinders effective diagnosis. This study was conducted to explore the aberrant spontaneous brain activity and invest...
Bio-inspired deep learning-personalized ensemble Alzheimer's diagnosis model for mental well-being.
SLAS technology
Jun 18, 2024
Most classification models for Alzheimer's Diagnosis (AD) do not have specific strategies for individual input samples, leading to the problem of easily overlooking personalized differences between samples. This research introduces a customized dynam...
Association of Cardiovascular Health With Brain Age Estimated Using Machine Learning Methods in Middle-Aged and Older Adults.
Neurology
Jun 18, 2024
BACKGROUND AND OBJECTIVES: Cardiovascular health (CVH) has been associated with cognitive decline and dementia, but the extent to which CVH affects brain health remains unclear. We investigated the association of CVH, assessed using Life's Essential ...
Evaluation of T2W FLAIR MR image quality using artificial intelligence image reconstruction techniques in the pediatric brain.
Pediatric radiology
Jun 18, 2024
BACKGROUND: Artificial intelligence (AI) reconstruction techniques have the potential to improve image quality and decrease imaging time. However, these techniques must be assessed for safe and effective use in clinical practice.
Evolutionary Strategies AI Addresses Multiple Technical Challenges in Deep Learning Deployment: Proof-of-Principle Demonstration for Neuroblastoma Brain Metastasis Detection.
Journal of imaging informatics in medicine
Jun 17, 2024
Two significant obstacles hinder the advancement of Radiology AI. The first is the challenge of overfitting, where small training data sets can result in unreliable outcomes. The second challenge is the need for more generalizability, the lack of whi...
Deep learning based diagnosis of PTSD using 3D-CNN and resting-state fMRI data.
Psychiatry research. Neuroimaging
Jun 17, 2024
BACKGROUND: The incidence rate of Posttraumatic stress disorder (PTSD) is currently increasing due to wars, terrorism, and pandemic disease situations. Therefore, accurate detection of PTSD is crucial for the treatment of the patients, for this purpo...
An organic brain-inspired platform with neurotransmitter closed-loop control, actuation and reinforcement learning.
Materials horizons
Jun 17, 2024
Organic neuromorphic platforms have recently received growing interest for the implementation and integration of artificial and hybrid neuronal networks. Here, achieving closed-loop and learning/training processes as in the human brain is still a maj...
BrainLossNet: a fast, accurate and robust method to estimate brain volume loss from longitudinal MRI.
International journal of computer assisted radiology and surgery
Jun 16, 2024
PURPOSE: MRI-derived brain volume loss (BVL) is widely used as neurodegeneration marker. SIENA is state-of-the-art for BVL measurement, but limited by long computation time. Here we propose "BrainLossNet", a convolutional neural network (CNN)-based m...
EEG emotion recognition based on data-driven signal auto-segmentation and feature fusion.
Journal of affective disorders
Jun 15, 2024
Pattern recognition based on network connections has recently been applied to the brain-computer interface (BCI) research, offering new ideas for emotion recognition using Electroencephalogram (EEG) signal. However unified standards are currently lac...
Brain-computer interfaces inspired spiking neural network model for depression stage identification.
Journal of neuroscience methods
Jun 15, 2024
BACKGROUND: Depression is a global mental disorder, and traditional diagnostic methods mainly rely on scales and subjective evaluations by doctors, which cannot effectively identify symptoms and even carry the risk of misdiagnosis. Brain-Computer Int...
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