Neurology

Microglial pruning of glycinergic synapses disinhibits spinal PKCγ interneurons to drive pain hypersensitivity in mice.

Microglial activation is linked to neuroinflammation in neuropathic pain. Recently, microglia-mediated synaptic pruning has received mounting attention. However, the exact role of spinal microglia in modulating neuropathic pain-associated neural circuits remains unclear. To investigate this question, we used pharmacological, optogenetic, and genetic manipulations combined with behavioral tests, co...

Perspectives and Experiences of Family Caregivers Using Supportive Mobile Apps in Dementia Care: Meta-Synthesis of Qualitative Research.

BACKGROUND: Supportive mobile apps are effective tools for family caregivers of persons with dementia to obtain online information and psychological support. Nevertheless, details about the experiences of family caregivers of persons with dementia using mobile apps are limited.

Test-retest reliability of kinematic and EEG low-beta spectral features in a robot-based arm movement task.

Low-beta (L, 13-20 Hz) power plays a key role in upper-limb motor control and afferent processing, making it a strong candidate for a neurophysiologic...

Neural mechanisms underlying the improvement of gait disturbances in stroke patients through robot-assisted gait training based on QEEG and fNIRS: a randomized controlled study.

BACKGROUND: Robot-assisted gait training is more effective in improving lower limb function and walking ability in stroke patients compared to convent...

Identifying and predicting EEG microstates with sequence-to-sequence deep learning models for online applications.

Electroencephalographic (EEG) microstates, as a non-invasive and high-temporal-resolution tool for analyzing time-space features of brain activity, ha...

Impact of brain regions on attention deficit hyperactivity disorder (ADHD) electroencephalogram (EEG) signals: Comparison of machine learning algorithms with empirical mode decomposition and time domain analysis.

OBJECTIVE: This study emphasizes the importance of using proper combinations of brain area, extraction of features, and machine learning (ML) techniqu...

Traumatic brain injury management in the intensive care unit: standard of care and knowledge gaps.

Despite advances in the management of traumatic brain injury (TBI) in the intensive care unit (ICU), and the implementation of new diagnostic techniqu...

Development and Validation of an Interpretable Machine Learning Model for Predicting Tic Disorders and Severity in Children Based on Electroencephalogram Data.

Accurate diagnosis of Tic disorders (TD) and its severity based on electroencephalogram (EEG) data were of great clinical importance. This study analy...

A novel Swin transformer based framework for speech recognition for dysarthria.

Dysarthria frequently occurs in individuals with disorders such as stroke, Parkinson's disease, cerebral palsy, and other neurological disorders. Well...

Alzheimer's disease digital biomarkers multidimensional landscape and AI model scoping review.

As digital biomarkers gain traction in Alzheimer's disease (AD) diagnosis, understanding recent advancements is crucial. This review conducts a biblio...

Early outcome-prediction with an automated EEG background trend in hypothermia-treated newborns with encephalopathy.

BACKGROUND: Therapeutic hypothermia is an intervention that improves outcomes and alters early outcome-prediction in infants with moderate-severe hypo...

Development of artificial intelligence and multi-sensor-based dexterity assessment system: performance evaluation.

Manual dexterity tests are essential for diagnosing diseases and evaluating professional skills that require fine motor control. Traditional assessmen...

Contribution of α-synuclein cytopathologies to distinct seeding of misfolded α-synuclein.

Synucleinopathies are a group of neurodegenerative diseases characterized by the deposition of misfolded α-synuclein (αSyn), predominantly in oligoden...

Analysis of the neural mechanisms of social anxiety based on EEG features and machine learning and construction of a diagnostic model.

Social anxiety is a common psychological problem, and its accurate diagnosis and investigation of underlying neurophysiological mechanisms are of sign...

Effects of vascular endothelial cells on the neurite outgrowth of nerve cells.

The vascular and neuronal networks are structurally complex and highly branched. These networks play an important role in supplying oxygen and sending...

TCANet: a temporal convolutional attention network for motor imagery EEG decoding.

Decoding motor imagery electroencephalogram (MI-EEG) signals is fundamental to the development of brain-computer interface (BCI) systems. However, rob...

Optimizing stroke detection with genetic algorithm-based feature selection in deep learning models.

Brain stroke is a leading cause of disability and mortality worldwide, necessitating the development of accurate and efficient diagnostic models. In t...

Decoding Parkinson's Diagnosis: An OCT-Based Explainable AI with SHAP/LIME Transparency from the Persian Cohort Study.

BACKGROUND: Parkinson's disease (PD) diagnosis remains challenging due to subjective clinical assessments and late-stage symptom manifestation. Retina...

Discovery of Novel Anti-Acetylcholinesterase Peptides Using a Machine Learning and Molecular Docking Approach.

OBJECTIVE: Alzheimer's disease poses a significant threat to human health. Currenttherapeutic medicines, while alleviate symptoms, fail to reverse the...

Lumbar spinal stenosis: current concept of management.

Lumbar spinal stenosis (LSS) is a common degenerative spinal condition where spinal canal narrowing causes symptoms such as neurogenic claudication, r...