Neurology

Predicting Seizures Episodes and High-Risk Events in Autism Through Adverse Behavioral Patterns

To determine whether historical behavior data can predict the occurrence of high-risk behavioral or seizure events in individuals with profound Autism Spectrum Disorder (ASD), thereby facilitating early intervention and improved support. To our knowledge, this is the first work to integrate the prediction of seizures with behavioral data, highlighting the interplay between adverse behaviors and se...

Schizophrenia versus Healthy Controls Classification based on fMRI 4D Spatiotemporal Data

A wide array of machine learning approaches have been employed for differentiating patients with mental health disorders from healthy controls using neuroimaging data. However, almost all such methods have been applied on inputs based on connectivity matrices or features derived from the neuroimaging data. Only a few papers recently have considered such classification based on the original voxel-b...

Automatic segmentation of spinal cord lesions in MS: A robust tool for axial T2-weighted MRI scans

Deep learning models have achieved remarkable success in segmenting brain white matter lesions in multiple sclerosis (MS), becoming integral to both r...

Predicting Dementia in People with Parkinson’s Disease

Parkinson’s disease (PD) exhibits a variety of symptoms, with approximately 25% of patients experiencing mild cognitive impairment and 45% developing ...

Towards AI-based Precision Rehabilitation via Contextual Model-based Reinforcement Learning

Stroke is a condition marked by considerable variability in lesions, recovery trajectories, and responses to therapy. Consequently, precision medicine...

The genetics of TDP43-Type-C neurodegeneration: a whole genome sequencing study

Frontotemporal lobar degeneration-TDP Type C (TDP-C) is a unique neurodegenerative disease that starts by attacking the anterior temporal lobe leading...

Machine learning differentiation of Parkinson’s disease and normal pressure hydrocephalus using wearable sensors capturing gait impairments

Gait impairments in patients with Parkinson’s Disease (PD) and Normal Pressure Hydrocephalus (NPH) are diagnosed with visual clinical assessments. Des...

Data Extraction from Free-Text Stroke CT Reports Using GPT-4o and Llama-3.3-70B: The Impact of Annotation Guidelines

To evaluate the performance of LLMs in extracting data from stroke CT reports in the presence and absence of an annotation guideline. In this study, p...

VR-based Gamma Sensory Stimulation: A feasibility study

Alzheimer’s disease (AD) presents a critical global health challenge, with current therapies offering limited efficacy and safety in halting disease p...

WHITE-Net : White matter HyperIntensities Tissue Extraction using deep learning Network

Given the high prevalence of aging-associated cerebral small vessel disease in the general population, accurate detection of the related white matter ...

Comparison of Machine Learning Models in Predicting Mental Health Sequelae Following Concussion in Youth

Youth who experience concussions may be at greater risk for subsequent mental health challenges, making early detection crucial for timely interventio...

A Multimodal Sleep Foundation Model Developed with 500K Hours of Sleep Recordings for Disease Predictions

Sleep is a fundamental biological process with profound implications for physical and mental health, yet our understanding of its complex patterns and...

Evidence Based Gait Analysis Interpretation Tools (EB-GAIT)

Clinical gait analysis (CGA) has historically relied on clinician experience and judgment, leading to modest, stagnant, and unpredictable outcomes. Th...

Generalizable Prediction of Alzheimer Disease Pathologies with a Scalable Annotation Tool and an High-Accuracy Model

Characterizing the cardinal neuropathologies in Alzheimer disease (AD) can be laborious, time consuming, and susceptible to intra- and inter-observer ...

Miniaturization of Epileptic Abnormal Electrocorticogram Detector Using 3D Convolutional Neural Network

Epilepsy is a neurological disorder characterized by sudden and recurrent seizures caused by abnormal electrical activity in the brain. Responsive Neu...

Deep Learning-Driven EEG Analysis for Personalized Deep Brain Stimulation Programming in Parkinson’s Disease

Deep Brain Stimulation (DBS) is an invasive procedure used to alleviate motor symptoms in Parkinson’s Disease (PD) patients. While brain activity can ...

A Claims-Based Machine Learning Classifier of Modified Rankin Scale in Acute Ischemic Stroke

We developed a classifier to infer acute ischemic stroke (AIS) severity from Medicare claims using the Modified Rankin Scale (mRS) at discharge. The c...

Regional and whole-brain neurofunctional alterations during pain empathic processing of physical but not affective pain in migraine patients

Accumulating evidence suggests that migraine patients present abnormal brain responses to salient sensory and emotional stimuli. However, it is still ...

Explainable artificial intelligence for neuroimaging-based dementia diagnosis and prognosis

INTRODUCTION: Artificial intelligence and neuroimaging enable accurate dementia prediction, but ‘black box’ models can be difficult to trust. Explaina...

Quantifying Device Type and Handedness Biases in a Remote Parkinson’s Disease AI-Powered Assessment

Early detection of Parkinson’s Disease (PD) can enable early access to care, improving patient outcomes. We investigate the use of machine learning to...