Neurology

Effects of Combining Modified Response Elaboration Training, Semantic Feature Analysis, and Arm Ability Training on Cognitive, Language, and Motor Recovery in Poststroke Broca's Aphasia: A Randomized Controlled Trial.

PURPOSE: Stroke survivors often face problems in language, motor, and cognitive skills because the neural networks for these functions overlap. Consequently, the recovery is complex and requires a multimodal approach. This study examined the interactions among linguistic, motor, and cognitive processes. We also evaluated how integrating speech and language therapy (SLT) with arm ability training (...

AI-Enabled Wearables for Motor Function Assessment and Rehabilitation in Parkinson Disease: Scoping Review.

BACKGROUND: Artificial intelligence (AI)-enabled wearable devices are rapidly emerging in rehabilitation and motor function assessment for patients with Parkinson disease (PD). However, evidence remains fragmented, integration into nursing practice is limited, and comprehensive synthesis is lacking. OBJECTIVE: This study aimed to summarize studies on AI-enabled wearable devices for PD rehabilitati...

CPGPrompt: translating clinical guidelines into large language model-executable decision support.

OBJECTIVE: Clinical practice guidelines (CPGs) provide evidence-based recommendations for patient care; however, integrating them into artificial inte...

PDXNet: An eXplainable Hybrid Attention Convolutional Neural Network for Parkinson's Disease Monitoring and Evaluation.

Parkinson's disease (PD), the second most common neurodegenerative disorder, affects patients and caregivers worldwide. There is a growing need for te...

CapsFormer: A Dual-Stream Causal-Aware Capsule-Transformer Network for EMG Signal Representation Learning.

Electromyography (EMG) signals are widely applied in prosthetic control, rehabilitation training, and human-machine interaction. This places stringent...

Quantum Sensing for Precision Organ Aging Assessment.

Aging proceeds heterogeneously across organs, making chronological age an inadequate measure of physiological decline. The concept of organ biological...

From wrist data to lifespan: elucidating inflammation-driven biological aging via activity rhythms captured by wearable devices.

Systemic inflammation ("inflammaging") accelerates biological aging and drives cardiovascular, metabolic, and neurodegenerative disease. Circadian rhy...

Predicting rates of cognitive and functional decline in Alzheimer's disease and mild cognitive impairment.

BACKGROUND: The global population of People Living with Dementia (PLWD) is expected to grow rapidly in the coming decades, increasing the need for per...

Development and validation of interpretable multimodal clinical-radiomics models for predicting epileptogenic foci and surgical outcomes in tuberous sclerosis complex: A multicenter study.

Precise localization and resection of epileptogenic (epi) foci from multiple cortical foci determine surgical outcomes in the tuberous sclerosis compl...

AI-driven quality control of cell-based ATMPs: automated, accurate, and affordable.

BACKGROUND AIMS: The production of neuroepithelial stem (NES) cells, a promising therapeutic candidate for neurological conditions such as stroke and ...

Flicker-Suppressed Neuromorphic Unit for Dynamic Vision Processing.

Inspired by the dynamic visual perception of flying insects, rapid collision warning systems are crucial for advancing autonomous driving and machine ...

Early Detection of Traumatic Brain Hematoma Using Machine Learning-Powered Near-Infrared Spectroscopy: A Case Report in a Kabaddi Player.

Traumatic brain injury is a significant concern in contact sports, with concussions being the most common type. Kabaddi, a high-contact sport, carries...

How much EEG is needed for deep learning with convolutional neural networks? Predicting the benefit from additional data.

Objective.This study quantifies how the accuracy of convolutional neural networks for electroencephalogram (EEG) classification depends on the amount ...

Integrative computational and experimental identification of marine bacterial acetylcholinesterase inhibitors against alzheimer's disease.

Alzheimer's disease (AD) is a powerful neurodegenerative disease characterized by cholinergic deficiency, where the inhibition of acetylcholinesterase...

Assessment of the Modified Rankin Scale in Electronic Health Records With a Fine-Tuned Large Language Model: Development and Internal Validation.

BACKGROUND: The modified Rankin scale (mRS) is an important metric in stroke research, often used as a primary outcome in clinical trials and observat...

Machine learning integration of serial blood biomarkers enhances cognitive decline prediction in early Parkinson's disease.

Cognitive decline is a major non-motor complication in early Parkinson's disease (PD), but predicting its progression remains challenging. Using data ...

A neuroimaging functional connectivity signature of emotional conflict monitoring predicting cognitive decline in type 2 diabetes.

Type 2 diabetes (T2D) is associated with cognitive decline and neurodegenerative disorders. Changes in the connections between brain regions responsib...

Artificial Intelligence and CT Neuroimaging in Dementia and Psychotic Disorders: A Viewpoint.

Psychotic disorders are marked by heterogeneity in symptoms and treatment response, yet efforts to develop clinically useful predictive models through...

Leveraging infarct topography for early warning: a robust model for predicting malignant cerebral edema after endovascular treatment in acute ischemic stroke.

BACKGROUND: The early prediction of malignant cerebral edema (MCE) following endovascular therapy for acute ischemic stroke is of paramount importance...

Multi-scale kernel and electrode attention network for EEG-based epileptic seizure detection.

Epilepsy is a common chronic neurological disorder, and automated detection of epileptic seizures using multi-channel electroencephalography (EEG) is ...