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Juvenile Myoclonic Epilepsy Imaging Endophenotypes and Relationship With Cognition and Resting-State EEG.

Human brain mapping
Structural neuroimaging studies of patients with Juvenile Myoclonic Epilepsy (JME) typically present two findings: 1-volume reduction of subcortical gray matter structures, and 2-abnormalities of cortical thickness. The general trend has been to obse...

MDD-SSTNet: detecting major depressive disorder by exploring spectral-spatial-temporal information on resting-state electroencephalography data based on deep neural network.

Cerebral cortex (New York, N.Y. : 1991)
Major depressive disorder (MDD) is a psychiatric disorder characterized by persistent lethargy that can lead to suicide in severe cases. Hence, timely and accurate diagnosis and treatment are crucial. Previous neuroscience studies have demonstrated t...

Acute Stress Disorder Detection using Machine Learning based on resting-state fMRI.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Early diagnosis of Acute Stress Disorder (ASD) is important, given its potential progression to post-traumatic system disorder (PTSD). The current diagnostic tool has some degree of subjectiveness in assessing emotional responses to trauma and the se...

Diagnosing Suicidal Ideation from Resting State EEG Data Using a Machine Learning Algorithm.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Suicide poses a global health crisis with significant social and economic impact. Prevention may be possible if objective quantitative methods are developed to supplement the often inaccurate interview-based risk assessments. Our research goal is to ...

Unsupervised Hybrid Deep Feature Encoder for Robust Feature Learning from Resting-State EEG Data.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
EEG classification is a challenging task due to the nonstationary nature of EEG data and the covariance shift induced by cross-subject variance. Recently, various machine learning and deep learning models have been developed to learn robust features ...

Association between Resting Heart Rate and Machine Learning-Based Brain Age in Middle- and Older-Age.

The journal of prevention of Alzheimer's disease
BACKGROUND: Resting heart rate (RHR), has been related to increased risk of dementia, but the relationship between RHR and brain age is unclear.

Classification of Mental Stress Levels using EEG Connectivity and Convolutional Neural Networks.

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference
Classifying mental stress is important as it helps in identifying the type and severity of stress, which can inform the most appropriate treatment or intervention. In this study, we propose utilizing electroencephalography (EEG) signals with convolut...

Predicting the fMRI Signal Fluctuation with Recurrent Neural Networks Trained on Vascular Network Dynamics.

Cerebral cortex (New York, N.Y. : 1991)
Resting-state functional MRI (rs-fMRI) studies have revealed specific low-frequency hemodynamic signal fluctuations (<0.1 Hz) in the brain, which could be related to neuronal oscillations through the neurovascular coupling mechanism. Given the vascul...

Brief Report: Can a Composite Heart Rate Variability Biomarker Shed New Insights About Autism Spectrum Disorder in School-Aged Children?

Journal of autism and developmental disorders
Several studies show altered heart rate variability (HRV) in autism spectrum disorder (ASD), but findings are neither universal nor specific to ASD. We apply a set of linear and nonlinear HRV measures-including phase rectified signal averaging-to seg...