Neurology

GenBrain: A Generative Foundation Model of Multimodal Brain Imaging

Neuroimaging faces a reproducibility crisis, where studies on small, heterogeneous datasets produce unreliable brain-wide associations and AI models that fail to generalize. To address this, we introduce GenBrain, a generative foundation model pretrained on approximately 1.2 million 3D scans from over 44,000 individuals across 34 imaging modalities to learn a population prior of brain structure an...

Relationship of the Microbiome with Neurodegenerative Diseases: Development of AI Tools to Detect Alzheimer’s

This study aimed to develop an artificial intelligence (AI) algorithm capable of distinguishing Alzheimer’s disease (AD) from healthy patients using gut microbiome metagenomics data. To do so, 16S rRNA gene and Whole Genome Shotgun (WGS) datasets available in the literature were utilised. Data was pre-processed and filtered. Then, an initial analysis was done to study classical parameters of micro...

Predicting Motor Trajectories and Mapping Progression Subtypes in Parkinson’s Disease via Structure–Function Neural Field Encoding and Multi-View Representation

Parkinson’s disease (PD) is characterized by substantial heterogeneity in progression patterns, posing major challenges for individualized prognosis a...

Acoustic Analysis of Primary Care Patient-provider Conversations to Screen for Cognitive Impairment

Cognitive impairment (CI) is often under detected in primary care due to time and resource constraints. Passive analysis of clinical dialogue may offe...

A foundation model for mapping the phenomic and genetic landscape of cerebral small vessel disease biomarkers

Cerebral small vessel disease (CSVD) is a leading cause of age-related cognitive decline and neurological disorders, yet its precise characterization ...

Clustering high-cost patients in England using machine learning: a population-based cohort study

To identify clusters of high-cost patients in England based on diagnoses and sociodemographic characteristics to inform targeted population health man...

Exploring Machine Learning Models to Uncover Pathways in ALS Pathogenesis Using Immunohistochemical Features

Amyotrophic Lateral Sclerosis (ALS) is a degenerative disease of motor neurons that leads to muscle wasting, paralysis, and death, with an average lif...

DeepCRI: Real-time EEG-based Prognostication after Cardiac Arrest

Accurate prediction of neurological outcome after cardiac arrest is essential for guiding intensive care decisions. Electroencephalography (EEG) suppo...

A Comparison of Two Deep Learning Approaches to Distinguish Functional Dissociative from Epileptic Seizures Using Event Videos

Differentiating between motor functional dissociative seizures (FDS) and motor epileptic seizures (ES) is a common diagnostic challenge, requiring vid...

Dynamic Stroke Risk Stratification via Machine Learning: A Multi-Level Single-Center Study

Stroke is a leading global public health challenge and the second leading cause of death worldwide. In China, its burden continues to escalate amid po...

A preregistered, Open Pipeline for Early Cerebral Palsy Risk Assessment from Infant Videos

Cerebral Palsy (CP), affecting approximately 1 in 500 children due to abnormal brain development, impacts movement control. Early risk assessment via ...

A Pathway-Based Machine Learning Approach Identifies Region-Specific Markers and Patterns in Alzheimer’s Disease Patients Based on Spatial and Severity Metadata

Alzheimer’s disease (AD) exhibits profound spatial heterogeneity in its molecular and pathological features, yet the basis of this regional selectivit...

Cerebral Cortical Reorganization After Intracerebral Hemorrhage in Children

Structural changes following pediatric intracerebral hemorrhage (ICH) caused by ruptured brain vascular malformations remain poorly understood. We con...

Brain natural frequencies as physiologically meaningful biomarkers for machine-learning detection of Parkinson’s disease

In this study, we investigated whether individual brain maps of natural frequencies derived from EEG can serve as physiologically meaningful biomarker...

Machine Learning-Based Prediction of Cell-type Resolved Brain eQTLs Enhances Discovery of Variants Explaining Alzheimer’s Disease Heritability

The majority of causal genome-wide association studies (GWAS) variants for Alzheimer’s disease (AD) are believed to reside in noncoding regions of the...

Mapping heterogeneity in the neuroanatomical correlates of depression

Major depressive disorder (MDD) affects millions worldwide, yet its neurobiological underpinnings remain elusive. Neuroimaging studies have yielded in...

Multiscale-Multistage Temporal Convolutional Network for EEG-Based Mild Cognitive Impairment Detection

Mild cognitive impairment (MCI) is an intermediate stage between normal ageing and dementia, with affected individuals at a higher risk of progressing...

Personalized Data-Driven Robust Machine Learning Models to Differentiate Parkinson’s Disease Patients Using Heterogeneous Risk Factors

Parkinson’s Disease (PD) is the most prevalent neurodegenerative disorder after Alzheimer’s, yet its diagnosis largely relies on subjective clinical a...

A Federated Learning-based Optic Disc and Cup Segmentation Model for Glaucoma Monitoring In Color Fundus Photographs

Glaucoma, a leading cause of blindness worldwide, depends on accurate optic nerve head assessment, particularly optic disc and cup segmentation, for d...

Antidepressant Use at the Threshold: using electronic health records to characterise people prescribed antidepressants around the time of dementia diagnosis

Antidepressant use is common in people with dementia. Antidepressants may be started to manage symptoms of dementia, rather than depressive and anxiet...