Neurology

Automatic quality control in multi-centric fetal brain MRI super-resolution reconstruction

Quality control (QC) has long been considered essential to guarantee the reliability of neuroimaging studies. It is particularly important for fetal brain MRI, where acquisitions and image processing techniques are less standardized than in adult imaging. In this work, we focus on automated quality control of super-resolution reconstruction (SRR) volumes of fetal brain MRI, an important processi...

Reference-Free 3D Reconstruction of Brain Dissection Photographs with Machine Learning

Correlation of neuropathology with MRI has the potential to transfer microscopic signatures of pathology to invivo scans. Recently, a classical registration method has been proposed, to build these correlations from 3D reconstructed stacks of dissection photographs, which are routinely taken at brain banks. These photographs bypass the need for exvivo MRI, which is not widely accessible. However...

Discovering Influential Neuron Path in Vision Transformers

Vision Transformer models exhibit immense power yet remain opaque to human understanding, posing challenges and risks for practical applications. Wh...

A Hybrid Neural Network with Smart Skip Connections for High-Precision, Low-Latency EMG-Based Hand Gesture Recognition

Electromyography (EMG) is extensively used in key biomedical areas, such as prosthetics, and assistive and interactive technologies. This paper pres...

Is Limited Participant Diversity Impeding EEG-based Machine Learning?

The application of machine learning (ML) to electroencephalography (EEG) has great potential to advance both neuroscientific research and clinical a...

Identity Preserving Latent Diffusion for Brain Aging Modeling

Structural and appearance changes in brain imaging over time are crucial indicators of neurodevelopment and neurodegeneration. The rapid advancement...

The Detection of Saccadic Eye Movements and Per-Eye Comparisons using Virtual Reality Eye Tracking Devices

Eye tracking has been found to be useful in various tasks including diagnostic and screening tools. However, traditional eye trackers had a complica...

Event-Driven Implementation of a Physical Reservoir Computing Framework for superficial EMG-based Gesture Recognition

Wearable health devices have a strong demand in real-time biomedical signal processing. However traditional methods often require data transmission ...

NeuroChat: A Neuroadaptive AI Chatbot for Customizing Learning Experiences

Generative AI is transforming education by enabling personalized, on-demand learning experiences. However, AI tutors lack the ability to assess a le...

Machine learning for triage of strokes with large vessel occlusion using photoplethysmography biomarkers

Objective. Large vessel occlusion (LVO) stroke presents a major challenge in clinical practice due to the potential for poor outcomes with delayed t...

SKG-LLM: Developing a Mathematical Model for Stroke Knowledge Graph Construction Using Large Language Models

The purpose of this study is to introduce SKG-LLM. A knowledge graph (KG) is constructed from stroke-related articles using mathematical and large l...

Pathology-Guided AI System for Accurate Segmentation and Diagnosis of Cervical Spondylosis

Cervical spondylosis, a complex and prevalent condition, demands precise and efficient diagnostic techniques for accurate assessment. While MRI offe...

State-of-the-Art Stroke Lesion Segmentation at 1/1000th of Parameters

Efficient and accurate whole-brain lesion segmentation remains a challenge in medical image analysis. In this work, we revisit MeshNet, a parameter-...

Enhancing Alzheimer's Diagnosis: Leveraging Anatomical Landmarks in Graph Convolutional Neural Networks on Tetrahedral Meshes

Alzheimer's disease (AD) is a major neurodegenerative condition that affects millions around the world. As one of the main biomarkers in the AD diag...

BrainNet-MoE: Brain-Inspired Mixture-of-Experts Learning for Neurological Disease Identification

The Lewy body dementia (LBD) is the second most common neurodegenerative dementia after Alzheimer's disease (AD). Early differentiation between AD a...

AI-Enabled Conversational Journaling for Advancing Parkinson's Disease Symptom Tracking

Journaling plays a crucial role in managing chronic conditions by allowing patients to document symptoms and medication intake, providing essential ...

Federated Learning for Predicting Mild Cognitive Impairment to Dementia Conversion

Dementia is a progressive condition that impairs an individual's cognitive health and daily functioning, with mild cognitive impairment (MCI) often ...

Quantum-Inspired Privacy-Preserving Federated Learning Framework for Secure Dementia Classification

Dementia, a neurological disorder impacting millions globally, presents significant challenges in diagnosis and patient care. With the rise of priva...

Building 3D In-Context Learning Universal Model in Neuroimaging

In-context learning (ICL), a type of universal model, demonstrates exceptional generalization across a wide range of tasks without retraining by lev...

MindSimulator: Exploring Brain Concept Localization via Synthetic FMRI

Concept-selective regions within the human cerebral cortex exhibit significant activation in response to specific visual stimuli associated with par...