Neurology

A Machine Learning Model for Post-Concussion Musculoskeletal Injury Risk in Collegiate Athletes

Emerging evidence indicates an elevated risk of post-concussion musculoskeletal (MSK) injuries in collegiate athletes; however, identifying athletes at highest risk remains to be elucidated. The purpose of this study was to model post-concussion MSK injury risk in collegiate athletes by integrating a comprehensive set of variables by machine learning. A risk model was developed and tested on a dat...

Short-Term Mortality After Opioid Initiation Among Opioid-Naïve and Non-Naïve Patients with Dementia: A Retrospective Cohort Study

Despite the ongoing opioid epidemic, the mortality risk of opioid initiation in patients with dementia or mild cognitive impairment (MCI) remains understudied despite their vulnerability. This study evaluates mortality risks associated with opioid use in patients diagnosed with dementia or MCI by comparing outcomes between new and consistent users. We conducted a retrospective cohort study using d...

Comparison of Large Language Models’ Performance on Neurosurgical Board Examination Questions

Multiple-choice board examinations are a primary objective measure of competency in medicine. Large language models (LLMs) have demonstrated rapid imp...

Ranking Pretrained Speech Embeddings in Parkinson’s Disease Detection: Does Wav2Vec 2.0 Outperform its 1.0 Version Across Speech Modes and Languages?

Speech and language technologies are effective tools for identifying the distinct speech changes associated with Parkinson’s disease (PD), enabling ea...

A Case for Automated Segmentation of MRI Data in Milder Neurodegenerative Diseases

Volumetric analysis and segmentation of magnetic resonance imaging (MRI) data is an important tool for evaluating neurological disease progression and...

At-Home Movement State Classification Using Totally Implantable Bidirectional Cortical-Basal Ganglia Neural Interface

Movement decoding from invasive human recordings typically relies on a distributed system employing advanced machine learning algorithms programmed in...

Transcriptomic analyses of human brains with Alzheimer’s disease identified dysregulated epilepsy-causing genes

Alzheimer’s Disease (AD) patients at multiple stages of disease progression have a high prevalence of seizures. However, whether AD and epilepsy share...

Language Model Applications for Early Diagnosis of Childhood Epilepsy

Accurate and timely epilepsy diagnosis is crucial to reduce delayed or unnecessary treatment. While language serves as an indispensable source of info...

Evaluation of Machine Learning and Traditional Statistical Models to Assess the Value of Stroke Genetic Liability for Prediction of Risk of Stroke within the UK Biobank

Stroke is one of the leading causes of mortality and long-term disability in adults over 18 years of age globally and its increasing incidence has bec...

The Clinical Value of ChatGPT for Epilepsy Presurgical Decision Making: Systematic Evaluation on Seizure Semiology Interpretation

For patients with drug-resistant focal epilepsy (DRE), surgical resection of the epileptogenic zone (EZ) is an effective treatment to control seizures...

DUNE: a versatile neuroimaging encoder captures brain complexity across three major diseases: cancer, dementia and schizophrenia

Magnetic resonance images (MRI) of the brain exhibit high dimensionality that pose significant challenges for computational analysis. While models pro...

Development and validation of a machine learning model to predict cognitive behavioral therapy outcome in obsessive-compulsive disorder using clinical and neuroimaging data

Cognitive behavioral therapy (CBT) is a first-line treatment for obsessive-compulsive disorder (OCD), but clinical response is difficult to predict. I...

Investigating the causal network of dementia by employing a causal discovery approach combined with natural language processing models

Comprehensively studying modifiable risk factors altogether to explore how they contribute to dementia mechanism is imperative for effective intervent...

Automatic classification of eeg signals, based on image interpretation of spatio-temporal information

Brain-Computer Interface (BCI) applications provide a direct way to map human brain activity onto the control of external devices, without a need for ...

Data-Driven Early Prediction of Cerebral Palsy Using AutoML and interpretable kinematic features

Early identification of cerebral palsy (CP) remains a major challenge due to the reliance on expert assessments that are time-intensive and not scalab...

Liquid-Dendrite Spiking Neural Network for Edge Devices: A 130 K-Parameter, 535 KB Model for Time-Domain Epileptic Seizure Detection

Epilepsy is a significant global health issue, requiring dependable diagnostic tools like scalp encephalogram (scalp-EEG), sub-scalp EEG, and intracra...

Multi-organ metabolome biological age implicates cardiometabolic conditions and mortality risk

Biological aging clocks across organs and omics data, including clinical phenotypes, neuroimaging, proteomics, and epigenetics, have proven instrument...

Leveraging functional annotations to map rare variants associated with Alzheimer’s disease with gruyere

The increasing availability of whole-genome sequencing (WGS) has begun to elucidate the contribution of rare variants (RVs), both coding and non-codin...

Comparative Medical Ecology of Gut Microbiomes in Major Neurodegenerative, Neurodevelopmental, and Psychiatric (NNP) Disorders

This study provides a comprehensive medical ecology analysis of gut microbiome alterations in four neuropsychiatric disorders: Alzheimer’s disease (AD...

AgeNet-SHAP: An explainable AI approach for optimally mapping multivariate regional brain age and clinical severity patterns in Alzheimer’s disease

Age is a significant risk factor for mild cognitive impairment (MCI) and Alzheimer’s disease (AD) and identifying brain age patterns is critical for c...