Investigation of the Impact of Cross-Frequency Coupling on the Assessment of Depression Severity through the Analysis of Resting State EEG Signals

Medicine, Psychiatry, Neurology, Clinical Psychology, Medical Imaging,
cross-frequency coupling   resting EEG signals   major depressive disorder   depression severity classification   machine learning  

Background Depression, particularly Major Depressive Disorder (MDD), is a widespread and debilitating psychological disease often described as the “common cold” of mental health. Many people with MDD experience symptoms such as persistent sadness, hopelessness, cognitive impairment, and loss of motivation for daily activities, severely affecting pe...

Distinguishing Parkinsonian Rest Tremor from Voluntary Hand Movements through Subthalamic and Cortical Activity

Information Science, Artificial Intelligence, Medicine, Neurology, Medical Imaging, Life Sciences, Biophysics,
Parkinson’s disease   rest tremor   voluntary hand movements   brain activity   subthalamic nucleus   machine learning  

Parkinson’s disease (PD) is a common neurodegenerative disorder characterized mainly by resting tremor, bradykinesia, and rigidity. Deep Brain Stimulation (DBS) has been widely used to treat the motor symptoms of PD (Krauss et al., 2021). However, DBS treatment also has significant side effects, most of which are caused by the extension of stimulat...

The Role of EEG Microstates in Predicting Oxcarbazepine Treatment Outcomes in Patients with Newly-Diagnosed Focal Epilepsy

The Role of EEG Microstates in Predicting Oxcarbazepine Treatment Outcomes in Patients with Newly-Diagnosed Focal Epilepsy

Information Science, Computer Science, Artificial Intelligence, Medicine, Neurology, Medical Imaging,
EEG microstates   antiseizure medications   oxcarbazepine   machine learning   epilepsy  

The Role of EEG Microstates in Predicting the Therapeutic Outcomes of Oxcarbazepine in Newly Diagnosed Focal Epilepsy Patients Introduction Background Focal epilepsy is the most common type of epilepsy, accounting for about 60% of all epilepsy cases. The selection of antiepileptic drugs (AEDs) varies depending on the type of epilepsy. In the treatm...

Deep Learning-Based Assessment Model for Real-Time Identification of Visual Learners Using Raw EEG

Engineering, Electrical Engineering, Information Science, Computer Science, Artificial Intelligence, Medicine, Neurology, Life Sciences, Bioengineering,
deep learning   real-time identification   visual learners   EEG   machine learning   electroencephalogram   classification  

In the current educational environment, understanding students’ learning styles is crucial for improving their learning efficiency. Specifically, the identification of visual learning styles can help teachers and students adopt more effective strategies in the teaching and learning process. Currently, automatic identification of visual learning sty...

Magnetoencephalography-Derived Oscillatory Microstate Patterns Across Lifespan: The Cambridge Centre for Ageing and Neuroscience Cohort

Medicine, Psychiatry, Geriatrics, Neurology, Medical Imaging, Life Sciences, Biophysics,
machine learning   microstates   healthy aging   alpha  

Application of Magnetoencephalography (MEG) to Analyze Changes in Whole-Brain Oscillatory Microstate Patterns Across the Lifespan: Cambridge Centre for Aging and Neuroscience Cohort Study Research Background With the increasing seriousness of the aging population problem, understanding the neurophysiological changes during the aging process becomes...

Analysis of Cerebral CT Based on Supervised Machine Learning as a Predictor of Outcome After Out-of-Hospital Cardiac Arrest

Information Science, Artificial Intelligence, Medicine, Emergency Medicine, Neurology, Medical Imaging,
cerebral CT   machine learning   out-of-hospital cardiac arrest   prognosis   gray matter   neurological injury  

Brain CT Analysis as a Tool for Outcome Prediction after Out-of-Hospital Cardiac Arrest: A Supervised Machine Learning Analysis Research Background Out-of-Hospital Cardiac Arrest (OHCA) is one of the leading causes of death in the Western world, with extremely low survival rates, ranging from 3% to 16%. The neurological and overall outcomes after O...

Radiomics-based Prediction of Local Control in Patients with Brain Metastases Following Postoperative Stereotactic Radiotherapy

Information Science, Artificial Intelligence, Medicine, Neurology, Neurosurgery, Medical Imaging, Oncology,
radiomics   machine learning   local failure prediction   brain metastases   artificial intelligence  

Application of Radiomics in Predicting Local Control in Postoperative Stereotactic Radiotherapy for Brain Metastasis Patients Academic Background Brain Metastases (BMs) are the most common malignant brain tumors, far surpassing primary brain tumors like gliomas in incidence. Recent medical guidelines recommend surgical treatment for patients with s...

Raman-Based Machine Learning Platform Reveals Unique Metabolic Differences Between IDHmut and IDHwt Glioma

Information Science, Artificial Intelligence, Medicine, Medical Laboratory Science, Neurology, Medical Imaging, Oncology, Life Sciences, Cell Biology, Biochemistry,
Raman spectroscopy   glioma   machine learning   FFPE tissue   lipid metabolism  

Study on Metabolic Differences between IDH Mutant and Wild-type Glioma Cells Using Raman Spectroscopy and Machine Learning Platform Background Introduction In the diagnosis and treatment of gliomas, formalin-fixed, paraffin-embedded (FFPE) tissue sections are commonly used. However, due to background noise interference from the embedding medium, th...

A Novel CNN-Based Image Segmentation Pipeline for Individualized Feline Spinal Cord Stimulation Modeling

Information Science, Artificial Intelligence, Medicine, Neurology, Medical Imaging, Life Sciences, Bioengineering,
spinal cord stimulation   machine learning   automatic segmentation   axonal recruitment   convolutional neural network   artificial intelligence  

Automated Spinal Cord Segmentation Pipeline Based on Convolutional Neural Network (CNN) for Individualized Cat Spinal Cord Stimulation Modeling Background and Research Motivation Spinal cord stimulation (SCS) is a widely used treatment method for chronic pain management. In recent years, it has also been used to modulate neural activity, aiming to ...

Accelerating Ionizable Lipid Discovery for mRNA Delivery Using Machine Learning and Combinatorial Chemistry

Chemistry, Computational Chemistry, Information Science, Artificial Intelligence, Life Sciences, Biochemistry, Bioengineering,
mRNA delivery   machine learning   ionizable lipids   combinatorial chemistry   lipid nanoparticles  

Accelerating the Discovery of Ionizable Lipids for mRNA Delivery using Machine Learning and Combinatorial Chemistry Research Background To fully realize the potential of mRNA therapies, it is essential to expand the toolkit of lipid nanoparticles (LNPs). However, a key bottleneck in LNP development is identifying new ionizable lipids. Previous stud...