Neurology

scMamba: A Pre-Trained Model for Single-Nucleus RNA Sequencing Analysis in Neurodegenerative Disorders

Single-nucleus RNA sequencing (snRNA-seq) has significantly advanced our understanding of the disease etiology of neurodegenerative disorders. However, the low quality of specimens derived from postmortem brain tissues, combined with the high variability caused by disease heterogeneity, makes it challenging to integrate snRNA-seq data from multiple sources for precise analyses. To address these ...

EEG Artifact Detection and Correction with Deep Autoencoders

EEG signals convey important information about brain activity both in healthy and pathological conditions. However, they are inherently noisy, which poses significant challenges for accurate analysis and interpretation. Traditional EEG artifact removal methods, while effective, often require extensive expert intervention. This study presents LSTEEG, a novel LSTM-based autoencoder designed for th...

Enhanced LSTM by Attention Mechanism for Early Detection of Parkinson's Disease through Voice Signals

Parkinson's disease (PD) is a neurodegenerative condition characterized by notable motor and non-motor manifestations. The assessment tool known as ...

Rapid Whole Brain Motion-robust Mesoscale In-vivo MR Imaging using Multi-scale Implicit Neural Representation

High-resolution whole-brain in vivo MR imaging at mesoscale resolutions remains challenging due to long scan durations, motion artifacts, and limite...

Normative Cerebral Perfusion Across the Lifespan

Cerebral perfusion plays a crucial role in maintaining brain function and is tightly coupled with neuronal activity. While previous studies have exa...

Bridging Brain Signals and Language: A Deep Learning Approach to EEG-to-Text Decoding

Brain activity translation into human language delivers the capability to revolutionize machine-human interaction while providing communication supp...

Large Cognition Model: Towards Pretrained EEG Foundation Model

Electroencephalography provides a non-invasive window into brain activity, offering valuable insights for neurological research, brain-computer inte...

From Brainwaves to Brain Scans: A Robust Neural Network for EEG-to-fMRI Synthesis

While functional magnetic resonance imaging (fMRI) offers valuable insights into brain activity, it is limited by high operational costs and signifi...

The establishment of static digital humans and the integration with spinal models

Adolescent idiopathic scoliosis (AIS), a prevalent spinal deformity, significantly affects individuals' health and quality of life. Conventional ima...

From Thought to Action: How a Hierarchy of Neural Dynamics Supports Language Production

Humans effortlessly communicate their thoughts through intricate sequences of motor actions. Yet, the neural processes that coordinate language prod...

SincPD: An Explainable Method based on Sinc Filters to Diagnose Parkinson's Disease Severity by Gait Cycle Analysis

In this paper, an explainable deep learning-based classifier based on adaptive sinc filters for Parkinson's Disease diagnosis (PD) along with determ...

The Case for Cleaner Biosignals: High-fidelity Neural Compressor Enables Transfer from Cleaner iEEG to Noisier EEG

All data modalities are not created equal, even when the signal they measure comes from the same source. In the case of the brain, two of the most i...

Generalizable automated ischaemic stroke lesion segmentation with vision transformers

Ischaemic stroke, a leading cause of death and disability, critically relies on neuroimaging for characterising the anatomical pattern of injury. Di...

Can ChatGPT Diagnose Alzheimer's Disease?

Can ChatGPT diagnose Alzheimer's Disease (AD)? AD is a devastating neurodegenerative condition that affects approximately 1 in 9 individuals aged 65...

Diffusion Models for Computational Neuroimaging: A Survey

Computational neuroimaging involves analyzing brain images or signals to provide mechanistic insights and predictive tools for human cognition and b...

FEMBA: Efficient and Scalable EEG Analysis with a Bidirectional Mamba Foundation Model

Accurate and efficient electroencephalography (EEG) analysis is essential for detecting seizures and artifacts in long-term monitoring, with applica...

Uncertainty-Aware Adaptation of Large Language Models for Protein-Protein Interaction Analysis

Identification of protein-protein interactions (PPIs) helps derive cellular mechanistic understanding, particularly in the context of complex condit...

Neurophysiological correlates to the human brain complexity through $q$-statistical analysis of electroencephalogram

The prospects of assessing neural complexity (NC) by $q$-statistics of the systemic organization of different types and levels of brain activity wer...

Protecting Intellectual Property of EEG-based Neural Networks with Watermarking

EEG-based neural networks, pivotal in medical diagnosis and brain-computer interfaces, face significant intellectual property (IP) risks due to thei...

Image-Based Alzheimer's Disease Detection Using Pretrained Convolutional Neural Network Models

Alzheimer's disease is an untreatable, progressive brain disorder that slowly robs people of their memory, thinking abilities, and ultimately their ...