Neurology

Progressive Test Time Energy Adaptation for Medical Image Segmentation

We propose a model-agnostic, progressive test-time energy adaptation approach for medical image segmentation. Maintaining model performance across diverse medical datasets is challenging, as distribution shifts arise from inconsistent imaging protocols and patient variations. Unlike domain adaptation methods that require multiple passes through target data - impractical in clinical settings - ou...

Exploring Deep Learning Models for EEG Neural Decoding

Neural decoding is an important method in cognitive neuroscience that aims to decode brain representations from recorded neural activity using a multivariate machine learning model. The THINGS initiative provides a large EEG dataset of 46 subjects watching rapidly shown images. Here, we test the feasibility of using this method for decoding high-level object features using recent deep learning m...

Early Prediction of Alzheimer's and Related Dementias: A Machine Learning Approach Utilizing Social Determinants of Health Data

Alzheimer's disease and related dementias (AD/ADRD) represent a growing healthcare crisis affecting over 6 million Americans. While genetic factors ...

Fast Two-photon Microscopy by Neuroimaging with Oblong Random Acquisition (NORA)

Advances in neural imaging have enabled neuroscience to study how the joint activity of large neural populations conspire to produce perception, beh...

EEG-CLIP : Learning EEG representations from natural language descriptions

Deep networks for electroencephalogram (EEG) decoding are currently often trained to only solve a specific task like pathology or gender decoding. A...

PHGNN: A Novel Prompted Hypergraph Neural Network to Diagnose Alzheimer's Disease

The accurate diagnosis of Alzheimer's disease (AD) and prognosis of mild cognitive impairment (MCI) conversion are crucial for early intervention. H...

Sensory-driven microinterventions for improved health and wellbeing

The five senses are gateways to our wellbeing and their decline is considered a significant public health challenge which is linked to multiple cond...

Enhanced EEG Forecasting: A Probabilistic Deep Learning Approach.

Forecasting electroencephalography (EEG) signals, that is, estimating future values of the time series based on the past ones, is essential in many re...

U2AD: Uncertainty-based Unsupervised Anomaly Detection Framework for Detecting T2 Hyperintensity in MRI Spinal Cord

T2 hyperintensities in spinal cord MR images are crucial biomarkers for conditions such as degenerative cervical myelopathy. However, current clinic...

Combined impact of grey and superficial white matter abnormalities: implications for epilepsy surgery

Drug-resistant focal epilepsy is associated with abnormalities in the brain in both grey matter (GM) and superficial white matter (SWM). However, it...

Adaptive Transformer Attention and Multi-Scale Fusion for Spine 3D Segmentation

This study proposes a 3D semantic segmentation method for the spine based on the improved SwinUNETR to improve segmentation accuracy and robustness....

Oscillatory Signatures of Parkinson's Disease: Central and Parietal EEG Alterations Across Multiple Frequency Bands

This study investigates EEG as a potential early biomarker by applying deep learning techniques to resting-state EEG recordings from 31 subjects (15...

Impact of Data Patterns on Biotype identification Using Machine Learning

Background: Patient stratification in brain disorders remains a significant challenge, despite advances in machine learning and multimodal neuroimag...

Machine Learning-Based Model for Postoperative Stroke Prediction in Coronary Artery Disease

Coronary artery disease remains one of the leading causes of mortality globally. Despite advances in revascularization treatments like PCI and CABG,...

Alzheimer's Disease Classification Using Retinal OCT: TransnetOCT and Swin Transformer Models

Retinal optical coherence tomography (OCT) images are the biomarkers for neurodegenerative diseases, which are rising in prevalence. Early detection...

Adaptive Torque Control of Exoskeletons under Spasticity Conditions via Reinforcement Learning

Spasticity is a common movement disorder symptom in individuals with cerebral palsy, hereditary spastic paraplegia, spinal cord injury and stroke, b...

OPTIMUS: Predicting Multivariate Outcomes in Alzheimer's Disease Using Multi-modal Data amidst Missing Values

Alzheimer's disease, a neurodegenerative disorder, is associated with neural, genetic, and proteomic factors while affecting multiple cognitive and ...

AI and Deep Learning for Automated Segmentation and Quantitative Measurement of Spinal Structures in MRI

Background: Accurate spinal structure measurement is crucial for assessing spine health and diagnosing conditions like spondylosis, disc herniation,...

BioSerenity-E1: a self-supervised EEG model for medical applications

Electroencephalography (EEG) serves as an essential diagnostic tool in neurology; however, its accurate manual interpretation is a time-intensive pr...

Bilingual Dual-Head Deep Model for Parkinson's Disease Detection from Speech

This work aims to tackle the Parkinson's disease (PD) detection problem from the speech signal in a bilingual setting by proposing an ad-hoc dual-he...