Neurology

Octopamine signaling from clock neurons plays dual roles in Drosophila long-term memory.

Circadian clock genes are best known for regulating circadian rhythms, but they also play crucial roles in memory processes. This suggests that memory is modulated by neural networks containing clock neurons, although the underlying mechanisms remain unclear. In Drosophila melanogaster, approximately 240 clock neurons are grouped into at least eight distinct clusters. Among them, the dorsal-latera...

Assessing the Impact of Sociodemographic Factors on Artificial Intelligence Models in Predicting Dementia: Retrospective Cohort Study.

BACKGROUND: Artificial intelligence (AI) is increasingly applied to health care, yet concerns about fairness persist, particularly in relation to sociodemographic disparities. Previous studies suggest that socioeconomic status (SES) and sex may influence AI model performance, potentially affecting groups that are historically underserved or understudied. OBJECTIVE: This study aimed to (1) assess a...

Prediction of Major Stroke (NIHSS > 5) Based on MR Perfusion Imaging Using a 3D ResNet.

PURPOSE: Prediction of stroke symptoms from perfusion patterns in ischemic stroke imaging would be desirable for decision making in relevant clinical ...

Germline Pathogenic Variant Prediction Model for Tumor-Only Sequencing Based on Japanese Clinicogenomic Database.

PURPOSE: Germline pathogenic variants (GPV) are frequently identified as secondary findings in cancer gene panel testing. Due to limited data on germl...

Physically reconfigurable synaptic plasticity and learning in stretchable neuromorphic systems.

Wearable electronics that use intrinsically stretchable organic neuromorphic devices offer a promising approach to achieve human-like on-device proces...

Early diagnosis of Alzheimer's Disease: Graph theoretical analysis of cerebellar network features based on 18F-AV45 PET.

Pathological and neuroimaging changes in the cerebellum of Alzheimer's disease (AD) patients have been well documented. However, the changes in cerebe...

GutMIND: A multi-cohort machine learning framework for integrative characteristics of the microbiota-gut-brain axis in neuropsychiatric disorders.

Emerging evidence underscores bidirectional communication along the microbiota-gut-brain axis in neuropsychiatric disorders. However, the field lacks ...

Bioelectronic interface for real-time brain biomarker analysis.

The brain is a highly complex organ, exhibiting a highly dynamic chemical environment, playing crucial role in coordinating pathophysiological process...

Discriminating between major depressive disorder and bipolar depression: Aberrant EEG microstate dynamics and machine learning classification.

BACKGROUND: Major depressive disorder (MDD) and bipolar depression (BD) are common mood disorders with overlapping clinical features, posing significa...

Noninvasive Assessment of the Ulnar Nerve in the Upper Extremity Using Machine Learning.

PURPOSE: The purpose of this study was to develop a machine learning algorithm trained on ultrasound images of the cubital tunnel that can be used to ...

Cognition beyond relapses: cognitive progression independent of relapse activity reflects early smoldering neurodegeneration in multiple sclerosis.

BACKGROUND: Progression independent of relapse activity (PIRA) is frequent in relapsing-remitting MS patients (RRMS). While often studied in terms of ...

Prioritising follow-up for people with suspected epilepsy using a digital EEG biomarker.

Lengthy waits for follow-up testing are common for people with suspected epilepsy. This delays diagnosis, prolongs uncertainty and increases seizure r...

Machine learning-based prediction of significant intraoperative blood loss to guide risk-adapted blood management decisions in spinal surgery.

INTRODUCTION: Significant blood loss (≥ 500 mL) during spinal surgery is common and linked to greater transfusion requirements and worse outcomes. Pre...

Environmentally sustainable UV spectrophotometric-chemometric approach for simultaneous determination of budesonide, glycopyrrolate, and formoterol.

This study introduces a sustainable UV-spectrophotometric platform for the simultaneous and individual determination of budesonide (BUD), glycopyrrola...

Development and validation of an explainable machine learning prediction model for futile recanalization after mechanical thrombectomy in acute large vessel occlusion stroke.

BACKGROUND: Mechanical thrombectomy (MT) is the primary treatment for acute ischemic stroke (AIS) caused by large vessel occlusion (LVO). However, the...

High-Resolution 3T MRI of the Membranous Labyrinth Using Deep Learning Reconstruction.

BACKGROUND AND PURPOSE: The labyrinth is a complex anatomic structure in the temporal bone. However, high-resolution imaging of its membranous portion...

Computational Drug Repurposing for Alzheimer's Disease via Sheaf Theoretic Population-Scale Analysis of snRNA-Seq Data.

Single-cell and single-nucleus RNA sequencing are used to reveal heterogeneity in cells, showing a growing potential for precision and personalized me...

Modalities and algorithms for generalized motor seizure detection and prediction: a scoping review.

INTRODUCTION: Generalized tonic - clonic seizures (GTCS) are among the most severe seizure types and a major cause of sudden unexpected death in epile...

In the artificial intelligence age, is Oswestry spinal risk index still useful? an external validation of Oswestry spinal risk index applied to radiotherapy patients and its relevance.

BACKGROUND AND STUDY AIMS: In 2013, Balain et al. proposed a scoring system, the Oswestry Spinal Risk Index (OSRI), to predict survival in patients wi...

A hybrid graph attention network with multi-dimensional features for enhanced EEG-based emotion recognition.

Emotion recognition using electroencephalogram (EEG) signals is a growing focus in affective computing due to its wide-ranging applications in human-c...