Neurology

A Machine Learning Approach to Predict Objective and Subjective Low Back Fatigue Using Postural Control Features During Sustained Trunk Flexion.

OCCUPATIONAL APPLICATIONSThis study investigated whether tree-based machine learning algorithms can predict objective (EMG-based) and subjective (Borg CR10) low back fatigue during a sustained trunk flexion, with and without back-support exosuit assistance, using postural control features. The results revealed that all tree-based algorithms reasonably predicted objective and/or subjective fatigue ...

Resting-State Electroencephalogram and Heart Rate Variability Jointly Predicts Intentional Episodic Memory Control Using Machine Learning Fusion.

Deficits in intentional control over episodic memory constitute a risk factor for multiple psychiatric disorders. Guided by a body-brain dynamic systems perspective, we reasoned that intrinsic cortical dynamics and autonomic regulatory capacity may jointly shape individual differences in memory control. Accordingly, we tested whether resting-state electroencephalogram (EEG) and heart rate variabil...

The Deep Learning Revolution in Neuroimaging: Insights from a Bibliometric Analysis (2014-2024).

This paper presents a bibliometric analysis of the fast-growing area of deep learning in neuroimaging. Using data from the Scopus database, we analyze...

Explainable Convolutional Channel Ranking (ECCR) for EEG-Based Detection of Idiopathic Absence Seizures.

Accurate identification of EEG electrodes associated with epilepsy is essential for developing real-time diagnostic applications. This paper introduce...

Measuring Accuracy (Classification Probabilities, Positive, and Negative Predictive Values) of Executive Function Electroencephalogram Metrics in Attention-Deficit/Hyperactivity Disorder Diagnosis: Protocol for and Perspectives From the SINCRONIA Study.

BACKGROUND: Attention deficit/hyperactivity disorder (ADHD) is the most prevalent neurodevelopmental disorder worldwide, affecting approximately 5%-7%...

A novel interpretable classification of lumbar spinal stenosis using a cascade deep learning approach and T2-weighted MRI.

OBJECTIVE: Lumbar spinal stenosis (LSS) is a degenerative spinal condition characterized by the narrowing of the lumbar spinal canal, leading to back ...

Impact of an artificial intelligence-driven triage system on workflow and transfer efficiency: stratified analysis of 4548 admissions to four thrombectomy hubs receiving transfers from sixty spokes.

BACKGROUND: We aimed to evaluate the impact of implementing an artificial intelligence (AI)-enabled acute ischaemic stroke triage system on workflow e...

Parvalbumin Neuron-Targeted Loss of Alzheimer's Disease Risk Gene BIN1 Is Insufficient to Drive Cognitive or Network Excitability Changes.

Bridging integrator 1 (BIN1) is one of the strongest genetic risk factors for Alzheimer's disease (AD), yet its function in the brain and role in AD r...

An ultrasensitive electrochemical immunosensor for SLC2A3 detection enables prognostic prediction in acute ischemic stroke.

Acute ischemic stroke (AIS) presents significant challenges in biomarker discovery and detection. This study addresses these hurdles through an integr...

Syntaxin1A in synaptopathies: From molecular mechanisms to therapeutic implications in neurological disorders (Review).

Syntaxin1A (STX1A) is a presynaptic membrane protein that is abundantly expressed in the central nervous system. It is a key member of the soluble N-e...

Explainable MRI radiomics of the basal ganglia and ventral midbrain distinguishes Parkinson's disease, SWEDD, and healthy controls.

Distinguishing scans without evidence of dopaminergic deficit (SWEDD) from Parkinson's disease (PD) remains challenging on routine MRI. We extracted 1...

Advanced neuroimaging for central nervous system infections: when, where, how, and why?

PURPOSE OF REVIEW: Central nervous system (CNS) infections remain a major cause of morbidity and mortality worldwide, particularly in children, older ...

A machine learning approach using echocardiographic and hematological parameters to classify cerebral small vessel disease burden.

BACKGROUND: Cerebral small vessel disease (CSVD) is a leading cause of stroke and dementia and is associated with cardiac and hematological biomarkers...

Dual human-AI thematic analysis of a qualitative interview following cognitive rehabilitation in chronic severe traumatic brain injury.

Qualitative research offers important insight into lived experiences of individuals with traumatic brain injury (TBI), particularly in the chronic pha...

Impact of region-based shape alterations in MRI as indicators of Parkinson's Disease.

BACKGROUND: Parkinson's Disease (PD) is a neuro-degenerative condition that progressively impairs movement, resulting from the loss of dopamine-produc...

Screening for Alzheimer's disease in the community using an AI-driven screening platform: design of the PREDICTOM study.

BACKGROUND: Recent developments in physiological, imaging and digital biomarkers combined with the approval of new disease-modifying drugs against Alz...

Multimodal CT Perfusion-Based Deep Learning for Predicting Stroke Lesion Outcomes in Complete and No Recanalization Scenarios.

BACKGROUND AND PURPOSE: Predicting the final location and volume of lesions in acute ischemic stroke is crucial for clinical management. While CTP is ...

Deep learning-based precision phenotyping of spine curvature identifies novel genetic risk loci for scoliosis in the UK Biobank.

Scoliosis is the most common developmental spinal deformity, but its genetic underpinnings remain only partially understood. To identify scoliosis-rel...

3D ADHD-Net and DeepTrace: Decoding ADHD from EEG with neurophysiological insights.

EEG-based ADHD diagnosis models suffer from two persistent issues: data leakage and the lack of physiologically grounded interpretability, limiting cl...

Harnessing artificial intelligence: Revolutionizing clinical care for Parkinson's disease.

As the use of artificial intelligence (AI) in healthcare becomes more pervasive, its application in the clinical care for those with Parkinson's disea...