Neurology

Graph Contrastive Learning for Connectome Classification

With recent advancements in non-invasive techniques for measuring brain activity, such as magnetic resonance imaging (MRI), the study of structural and functional brain networks through graph signal processing (GSP) has gained notable prominence. GSP stands as a key tool in unraveling the interplay between the brain's function and structure, enabling the analysis of graphs defined by the connect...

Explainable and externally validated machine learning for neuropsychiatric diagnosis via electrocardiograms

Electrocardiogram (ECG) analysis has emerged as a promising tool for identifying physiological changes associated with neuropsychiatric conditions. The relationship between cardiovascular health and neuropsychiatric disorders suggests that ECG abnormalities could serve as valuable biomarkers for more efficient detection, therapy monitoring, and risk stratification. However, the potential of the ...

Integrating Generative Artificial Intelligence in ADRD: A Framework for Streamlining Diagnosis and Care in Neurodegenerative Diseases

Healthcare systems are struggling to meet the growing demand for neurological care, with challenges particularly acute in Alzheimer's disease and re...

From Bedside to Desktop: A Data Protocol for Normative Intracranial EEG and Abnormality Mapping

Normative mapping is a framework used to map population-level features of health-related variables. It is widely used in neuroscience research, but ...

DECT: Harnessing LLM-assisted Fine-Grained Linguistic Knowledge and Label-Switched and Label-Preserved Data Generation for Diagnosis of Alzheimer's Disease

Alzheimer's Disease (AD) is an irreversible neurodegenerative disease affecting 50 million people worldwide. Low-cost, accurate identification of ke...

Leveraging Geolocation in Clinical Records to Improve Alzheimer's Disease Diagnosis Using DMV Framework

Alzheimer's Disease (AD) early detection is critical for enabling timely intervention and improving patient outcomes. This paper presents a DMV fram...

Revisiting convolutive blind source separation for identifying spiking motor neuron activity: From theory to practice

Objective: Identifying the activity of motor neurons (MNs) non-invasively is possible by decomposing signals from muscles, e.g., surface electromyog...

Multimodal Data-Driven Classification of Mental Disorders: A Comprehensive Approach to Diagnosing Depression, Anxiety, and Schizophrenia

This study investigates the potential of multimodal data integration, which combines electroencephalogram (EEG) data with sociodemographic character...

Unravelling Causal Genetic Biomarkers of Alzheimer's Disease via Neuron to Gene-token Backtracking in Neural Architecture: A Groundbreaking Reverse-Gene-Finder Approach

Alzheimer's Disease (AD) affects over 55 million people globally, yet the key genetic contributors remain poorly understood. Leveraging recent advan...

Fine-Tuning Strategies for Continual Online EEG Motor Imagery Decoding: Insights from a Large-Scale Longitudinal Study

This study investigates continual fine-tuning strategies for deep learning in online longitudinal electroencephalography (EEG) motor imagery (MI) de...

Diagnosis of Alzheimer's disease using FusionNet with improved secretary bird optimization algorithm for optimal MK-SVM based on imaging genetic data.

Alzheimer's disease is an irreversible central neurodegenerative disease, and early diagnosis of Alzheimer's disease is beneficial for its prevention ...

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

Major depressive disorder (MDD) is a psychiatric disorder characterized by persistent lethargy that can lead to suicide in severe cases. Hence, timely...

Dementia Classification Using Acoustic Speech and Feature Selection

Dementia is a general term for a group of syndromes that affect cognitive functions such as memory, thinking, reasoning, and the ability to perform ...

scBIT: Integrating Single-cell Transcriptomic Data into fMRI-based Prediction for Alzheimer's Disease Diagnosis

Functional MRI (fMRI) and single-cell transcriptomics are pivotal in Alzheimer's disease (AD) research, each providing unique insights into neural f...

VerteNet -- A Multi-Context Hybrid CNN Transformer for Accurate Vertebral Landmark Localization in Lateral Spine DXA Images

Lateral Spine Image (LSI) analysis is important for medical diagnosis, treatment planning, and detailed spinal health assessments. Although modaliti...

Human fields and their impact on brain waves A pilot study

During brain function, groups of neurons fire synchronously. When these groups are large enough, the resulting electrical signals can be measured on...

A Privacy-Preserving Domain Adversarial Federated learning for multi-site brain functional connectivity analysis

Resting-state functional magnetic resonance imaging (rs-fMRI) and its derived functional connectivity networks (FCNs) have become critical for under...

Heterogeneous Treatment Effect in Time-to-Event Outcomes: Harnessing Censored Data with Recursively Imputed Trees

Tailoring treatments to individual needs is a central goal in fields such as medicine. A key step toward this goal is estimating Heterogeneous Treat...

VisTA: Vision-Text Alignment Model with Contrastive Learning using Multimodal Data for Evidence-Driven, Reliable, and Explainable Alzheimer's Disease Diagnosis

Objective: Assessing Alzheimer's disease (AD) using high-dimensional radiology images is clinically important but challenging. Although Artificial I...

Deep generative computed perfusion-deficit mapping of ischaemic stroke

Focal deficits in ischaemic stroke result from impaired perfusion downstream of a critical vascular occlusion. While parenchymal lesions are traditi...