Neurology

Multimodal Fusion and Transfer Learning for the Detection of Degenerative Parkinsonisms with Dopamine Transporter SPECT Imaging.

Dopamine transporter (DAT) SPECT is a validated biomarker for Parkinson's disease (PD) and related degenerative parkinsonisms. Interpretation relies on visual assessment supported by striatal image features such as striatal binding ratios (SBRs). Deep learning can aid this process but often underuses complementary data, lacks robustness to heterogeneous inputs, and offers limited interpretability....

A Streamlined 4-item Wolf Motor Function Test for Efficient Assessment of Upper Extremity Motor Function in Chronic Stroke Survivors.

BackgroundThe Wolf Motor Function Test (WMFT) is a well-recognized measure for assessing upper extremity motor function in stroke rehabilitation. However, prolonged administration time limits the WMFT in clinical use.ObjectiveThis study aimed to reduce the number of WMFT tasks using machine learning and explore its measurement structure and psychometric properties, using data from 3 stroke rehabil...

Artificial intelligence for schizophrenia: from unimodal prediction to multimodal characterization.

PURPOSE OF REVIEW: Artificial intelligence is increasingly advancing both fundamental research and clinical applications in schizophrenia. This review...

Systemic Microvasculature Frailty: Brain frailty score predicts contrast-associated acute kidney injury after thrombectomy for stroke.

BACKGROUND: Contrast-associated acute kidney injury (CA-AKI) is a frequent complication after mechanical thrombectomy (MT). Cerebral small vessel dise...

Machine learning for triage of strokes with large vessel occlusion using photoplethysmography biomarkers.

Objective.Large vessel occlusion (LVO) stroke presents a major challenge in clinical practice due to the potential for poor outcomes with delayed trea...

Deep learning in stroke therapeutics: drug repurposing and beyond.

INTRODUCTION: Deep learning is reshaping stroke research by accelerating drug repurposing amid heterogeneous pathology, narrow therapeutic windows, an...

The Potential of Ensemble-Based Automated Sleep Staging on Single-Channel EEG Signal From a Wearable Device.

Machine-learning-based sleep staging models have achieved expert-level performance on standard polysomnographic (PSG) data. However, their application...

Spectral entropy variability of intraoperative electrocorticography predicts outcome after epilepsy surgery in people with focal cortical dysplasia.

OBJECTIVE: Epilepsy surgery in people with focal cortical dysplasia (FCD) requires accurate removal of all epileptogenic tissue, and outcome is diffic...

Deep Learning-Based Classification of Temporal Stages of AT8-Labeled Tau Pathology After Experimental Traumatic Brain Injury.

Tauopathies are characterised by a progressive accumulation of hyperphosphorylated tau. However, early and intermediate stages remain challenging to q...

Decoding Non-Neuronal Mechanisms and Therapeutic Targets in Huntington's Disease Through Integrative Transcriptomics and Machine Learning.

Huntington's disease (HD) is a rare, inherited neurodegenerative disorder caused by the expanded CAG repeats in the huntingtin gene. The HD domain sti...

Fluorescence from dual molecular rotors allows sensitive detection of widespread brain pathology in a mouse model of Alzheimer's disease.

Amyloid fibrils formed by the misfolding and aggregation of proteins are a pathological hallmark of many neurodegenerative conditions including Alzhei...

Resolution of generalized tonic seizures following focal ablative or resective surgery.

OBJECTIVE: Focal brain lesions may underlie generalized tonic seizures, as seen in Lennox-Gastaut syndrome, by engaging bilateral neural networks. How...

Development and validation of a deep survival model to predict time to seizure from routine electroencephalography.

OBJECTIVE: This study was undertaken to develop and validate a deep survival model (EEGSurvNet) that analyzes routine electroencephalography (EEG) to ...

EEGMoE: A Domain-Decoupled Mixture-of-Experts Model for Self-Supervised EEG Representation Learning.

Existing deep learning models for electroencephalogram (EEG) are typically tailored for specific tasks, datasets, or even subjects. This specializatio...

Classification of Alzheimer's Disease by Modeling Brain Networks as Signed Networks under Deep Learning Frameworks.

Alzheimer's disease (AD) is a progressive neurodegenerative disorder that remains a global challenge due to its complex pathology and the lack of defi...

Vascular cognitive impairment and dementia: Prevention, treatments, mechanisms and management options for the future.

Vascular Cognitive Impairment and Dementia (VCID), the second most common form of dementia, is becoming increasingly prevalent worldwide. However, cur...

EEG microstate features under visual feedback gain conditions exhibit high sensitivity in identifying early Parkinson's disease patients.

BACKGROUND: Early Parkinson's disease (PD) presents with subtle symptoms and lacks specific diagnostic methods. Clinical diagnosis primarily relies on...

Circulating inflammatory proteins predict dementia risk, are linked to structural brain changes and modifiable risk factors.

INTRODUCTION: Systemic inflammation has been identified as a key factor in neurodegeneration but the value of circulating inflammatory proteins in dem...

Unlocking the silent signals: Motor kinematics as a new frontier in early detection of mild cognitive impairment.

The increasing global prevalence of mild cognitive impairment (MCI) necessitates a paradigm shift in early detection strategies. Conventional neuropsy...

Multimodal diagnosis of Parkinson's disease with an internet-based collaborative agent architecture of medical language models.

Parkinson's disease (PD) remains one of the most prevalent neurodegenerative disorders, where delays in diagnosis compromise therapeutic outcomes and ...