AIMC Topic: Brain
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Microstate-based brain network dynamics distinguishing temporal lobe epilepsy patients: A machine learning approach.
NeuroImage
Jun 14, 2024
Temporal lobe epilepsy (TLE) stands as the predominant adult focal epilepsy syndrome, characterized by dysfunctional intrinsic brain dynamics. However, the precise mechanisms underlying seizures in these patients remain elusive. Our study encompassed...
sMRI-ADNet: an interpretable deep learning framework integrating Euclidean-graph representations of Alzheimer's disease solely from structural MRI.
Magma (New York, N.Y.)
Jun 13, 2024
OBJECTIVE: To establish a multi-dimensional representation solely on structural MRI (sMRI) for early diagnosis of AD.
Autism spectrum disorders detection based on multi-task transformer neural network.
BMC neuroscience
Jun 13, 2024
Autism Spectrum Disorders (ASD) are neurodevelopmental disorders that cause people difficulties in social interaction and communication. Identifying ASD patients based on resting-state functional magnetic resonance imaging (rs-fMRI) data is a promisi...
An optimized EEGNet decoder for decoding motor image of four class fingers flexion.
Brain research
Jun 12, 2024
As a cutting-edge technology of connecting biological brain and external devices, brain-computer interface (BCI) exhibits promising applications on extensive fields such as medical and military. As for the disable individuals with four limbs losing t...
Discovering the gene-brain-behavior link in autism via generative machine learning.
Science advances
Jun 12, 2024
Autism is traditionally diagnosed behaviorally but has a strong genetic basis. A genetics-first approach could transform understanding and treatment of autism. However, isolating the gene-brain-behavior relationship from confounding sources of variab...
Visualization of incrementally learned projection trajectories for longitudinal data.
Scientific reports
Jun 12, 2024
Longitudinal studies that continuously generate data enable the capture of temporal variations in experimentally observed parameters, facilitating the interpretation of results in a time-aware manner. We propose IL-VIS (incrementally learned visualiz...
Individual Prediction of Electric Field Induced by Deep-Brain Magnetic Stimulation With CNN-Transformer.
IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Jun 12, 2024
Deep-brain Magnetic Stimulation (DMS) can improve the symptoms caused by Alzheimer's disease by inducing rhythmic electric field in the deep brain, and the induced electric field is rhythm-dependent. However, calculating the induced electric field re...
Deep learning-based correction for time truncation in cerebral computed tomography perfusion.
Radiological physics and technology
Jun 11, 2024
Cerebral computed tomography perfusion (CTP) imaging requires complete acquisition of contrast bolus inflow and washout in the brain parenchyma; however, time truncation undoubtedly occurs in clinical practice. To overcome this issue, we proposed a t...
Nose-to-Brain Drug Delivery and Physico-Chemical Properties of Nanosystems: Analysis and Correlation Studies of Data from Scientific Literature.
International journal of nanomedicine
Jun 11, 2024
BACKGROUND: In the last few decades, nose-to-brain delivery has been investigated as an alternative route to deliver molecules to the Central Nervous System (CNS), bypassing the Blood-Brain Barrier. The use of nanotechnological carriers to promote dr...
Machine Learning in Electroconvulsive Therapy: A Systematic Review.
The journal of ECT
Jun 10, 2024
Despite years of research, we are still not able to reliably predict who might benefit from electroconvulsive therapy (ECT) treatment. As we exhaust what is possible using traditional statistical analysis, ECT remains a good candidate for machine lea...
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