Neurology

Deep learning analysis of fMRI data for predicting Alzheimer's Disease: A focus on convolutional neural networks and model interpretability.

The early detection of Alzheimer's Disease (AD) is thought to be important for effective intervention and management. Here, we explore deep learning methods for the early detection of AD. We consider both genetic risk factors and functional magnetic resonance imaging (fMRI) data. However, we found that the genetic factors do not notably enhance the AD prediction by imaging. Thus, we focus on build...

A Machine learning classification framework using fused fractal property feature vectors for Alzheimer's disease diagnosis.

Alzheimer's disease (AD) profoundly affects brain tissue and network structures. Analyzing the topological properties of these networks helps to understand the progression of the disease. Most studies focus on single-scale brain networks, but few address multiscale brain networks. In this study, the renormalization group approach was applied to rescale the gray matter brain networks of AD patients...

Tensor dictionary-based heterogeneous transfer learning to study emotion-related gender differences in brain.

In practice, collecting auxiliary labeled data with same feature space from multiple domains is difficult. Thus, we focus on the heterogeneous transfe...

Transformer-based transfer learning on self-reported voice recordings for Parkinson's disease diagnosis.

Deep learning (DL) techniques are becoming more popular for diagnosing Parkinson's disease (PD) because they offer non-invasive and easily accessible ...

MSCNet-FS: development of intelligent epileptic seizure anticipation model by multi serial cascaded network with feature Specific using scalogram images of EEG signal.

The early stage of the Epileptic Seizure Anticipation (ESA) model plays a significant part in supplying accurate medical care. In this research work, ...

Portable, low-field magnetic resonance imaging for evaluation of Alzheimer's disease.

Portable, low-field magnetic resonance imaging (LF-MRI) of the brain may facilitate point-of-care assessment of patients with Alzheimer's disease (AD)...

Brain Network Classification for Accurate Detection of Alzheimer's Disease via Manifold Harmonic Discriminant Analysis.

Mounting evidence shows that Alzheimer's disease (AD) manifests the dysfunction of the brain network much earlier before the onset of clinical symptom...

A Bio-Inspired Spiking Attentional Neural Network for Attentional Selection in the Listening Brain.

Humans show a remarkable ability in solving the cocktail party problem. Decoding auditory attention from the brain signals is a major step toward the ...

Graph Neural Networks on SPD Manifolds for Motor Imagery Classification: A Perspective From the Time-Frequency Analysis.

The motor imagery (MI) classification has been a prominent research topic in brain-computer interfaces (BCIs) based on electroencephalography (EEG). O...

Hybrid Network Using Dynamic Graph Convolution and Temporal Self-Attention for EEG-Based Emotion Recognition.

The electroencephalogram (EEG) signal has become a highly effective decoding target for emotion recognition and has garnered significant attention fro...

Pathological Asymmetry-Guided Progressive Learning for Acute Ischemic Stroke Infarct Segmentation.

Quantitative infarct estimation is crucial for diagnosis, treatment and prognosis in acute ischemic stroke (AIS) patients. As the early changes of isc...

Toward automated detection of microbleeds with anatomical scale localization using deep learning.

Cerebral Microbleeds (CMBs) are chronic deposits of small blood products in the brain tissues, which have explicit relation to various cerebrovascular...

Estimating Ground Reaction Forces from Gait Kinematics in Cerebral Palsy: A Convolutional Neural Network Approach.

PURPOSE: While gait analysis is essential for assessing neuromotor disorders like cerebral palsy (CP), capturing accurate ground reaction force (GRF) ...

Psychosocial effects of a humanoid robot on informal caregivers of people with dementia: A randomised controlled trial with nested interviews.

BACKGROUND: Dementia rates are rising globally, impacting healthcare systems and society. The care of people with dementia is largely provided by info...

An adaptive session-incremental broad learning system for continuous motor imagery EEG classification.

Motor imagery electroencephalography (MI-EEG) is usually used as a driving signal in neuro-rehabilitation systems, and its feature space varies with t...

A Novel Real-time Phase Prediction Network in EEG Rhythm.

Closed-loop neuromodulation, especially using the phase of the electroencephalography (EEG) rhythm to assess the real-time brain state and optimize th...

Combining MRI radiomics and clinical features for early identification of drug-resistant epilepsy in people with newly diagnosed epilepsy.

OBJECTIVE: To identify newly diagnosed patients with drug-resistant epilepsy (DRE) based on radiomics and clinical features.

Retinal imaging based glaucoma detection using modified pelican optimization based extreme learning machine.

Glaucoma is defined as progressive optic neuropathy that damages the structural appearance of the optic nerve head and is characterized by permanent b...

Developing a prediction model for cognitive impairment in older adults following critical illness.

BACKGROUND: New or worsening cognitive impairment or dementia is common in older adults following an episode of critical illness, and screening post-d...

Comparison analysis between standard polysomnographic data and in-ear-electroencephalography signals: a preliminary study.

STUDY OBJECTIVES: Polysomnography (PSG) currently serves as the benchmark for evaluating sleep disorders. Its discomfort makes long-term monitoring un...