AIMC Topic: Brain
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Accurate Machine Learning-based Monitoring of Anesthesia Depth with EEG Recording.
Neuroscience bulletin
Sep 17, 2024
General anesthesia, pivotal for surgical procedures, requires precise depth monitoring to mitigate risks ranging from intraoperative awareness to postoperative cognitive impairments. Traditional assessment methods, relying on physiological indicators...
Using deep learning and pretreatment EEG to predict response to sertraline, bupropion, and placebo.
Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology
Sep 17, 2024
OBJECTIVE: Predicting an individual's response to antidepressant medication remains one of the most challenging tasks in the treatment of major depressive disorder (MDD). Our objective was to use the large EMBARC study database to develop an electroe...
Quantitative assessment of brain structural abnormalities in children with autism spectrum disorder based on artificial intelligence automatic brain segmentation technology and machine learning methods.
Psychiatry research. Neuroimaging
Sep 16, 2024
RATIONALE AND OBJECTIVES: To explore the characteristics of brain structure in Chinese children with autism spectrum disorder (ASD) using artificial intelligence automatic brain segmentation technique, and to diagnose children with ASD using machine ...
Automated classification of Alzheimer's disease, mild cognitive impairment, and cognitively normal patients using 3D convolutional neural network and radiomic features from T1-weighted brain MRI: A comparative study on detection accuracy.
Clinical imaging
Sep 16, 2024
OBJECTIVES: Alzheimer's disease (AD) is a common neurodegenerative disorder that primarily affects older individuals. Due to its high incidence, an accurate and efficient stratification system could greatly aid in the clinical diagnosis and prognosis...
Neuromorphic intermediate representation: A unified instruction set for interoperable brain-inspired computing.
Nature communications
Sep 16, 2024
Spiking neural networks and neuromorphic hardware platforms that simulate neuronal dynamics are getting wide attention and are being applied to many relevant problems using Machine Learning. Despite a well-established mathematical foundation for neur...
A Novel Method to Identify Mild Cognitive Impairment Using Dynamic Spatio-Temporal Graph Neural Network.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Sep 16, 2024
Resting-state functional magnetic resonance imaging (rs-fMRI) has been widely used in the identification of mild cognitive impairment (MCI) research, MCI patients are relatively at a higher risk of progression to Alzheimer's disease (AD). However, al...
Machine learning with multiple modalities of brain magnetic resonance imaging data to identify the presence of bipolar disorder.
Journal of affective disorders
Sep 14, 2024
BACKGROUND: Bipolar disorder (BD) is a chronic psychiatric mood disorder that is solely diagnosed based on clinical symptoms. These symptoms often overlap with other psychiatric disorders. Efforts to use machine learning (ML) to create predictive mod...
Prediction of fetal brain gestational age using multihead attention with Xception.
Computers in biology and medicine
Sep 14, 2024
Accurate gestational age (GA) prediction is crucial for monitoring fetal development and ensuring optimal prenatal care. Traditional methods often face challenges in terms of precision and prediction efficiency. In this context, leveraging modern dee...
DeepASD: a deep adversarial-regularized graph learning method for ASD diagnosis with multimodal data.
Translational psychiatry
Sep 14, 2024
Autism Spectrum Disorder (ASD) is a prevalent neurological condition with multiple co-occurring comorbidities that seriously affect mental health. Precisely diagnosis of ASD is crucial to intervention and rehabilitation. A single modality may not ful...
Accelerated CEST imaging through deep learning quantification from reduced frequency offsets.
Magnetic resonance in medicine
Sep 13, 2024
PURPOSE: To shorten CEST acquisition time by leveraging Z-spectrum undersampling combined with deep learning for CEST map construction from undersampled Z-spectra.
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