Neurology

Deep Learning and fMRI-Based Pipeline for Optimization of Deep Brain Stimulation During Parkinson's Disease Treatment: Toward Rapid Semi-Automated Stimulation Optimization.

OBJECTIVE: Optimized deep brain stimulation (DBS) is fast becoming a therapy of choice for the treatment of Parkinson's disease (PD). However, the post-operative optimization (aimed at maximizing patient clinical benefits and minimizing adverse effects) of all possible DBS parameter settings using the standard-of-care clinical protocol requires numerous clinical visits, which substantially increas...

PVTAD: ALZHEIMER'S DISEASE DIAGNOSIS USING PYRAMID VISION TRANSFORMER APPLIED TO WHITE MATTER OF T1-WEIGHTED STRUCTURAL MRI DATA.

Alzheimer's disease (AD) is a neurodegenerative disorder, and timely diagnosis is crucial for early interventions. AD is known to have disruptive local and global brain neural connections that may be instrumental in understanding and extracting specific biomarkers. Existing machine-learning approaches are mostly based on convolutional neural network (CNN) and standard vision transformer (ViT) mode...

Human hand gesture recognition using fast Fourier transform with coot optimization based on deep neural network.

Hand motion detection is particularly important for managing the movement of individuals who have limbs amputated. The existing algorithm is complex, ...

Establishment and validation of a risk stratification model for stroke risk within three years in patients with cerebral small vessel disease using a combined MRI and machine learning algorithm.

BACKGROUND: Cerebral small vessel disease (CSVD) is a major cause of stroke, particularly in the elderly population, leading to significant morbidity ...

Continual learning for seizure prediction via memory projection strategy.

Despite extensive algorithms for epilepsy prediction via machine learning, most models are tailored for offline scenarios and cannot handle actual sce...

An efficient ANN SoC for detecting Alzheimer's disease based on recurrent computing.

Alzheimer's Disease (AD) is an irreversible, degenerative condition that, while incurable, can have its progression slowed or impeded. While there are...

Benchmarking brain-computer interface algorithms: Riemannian approaches vs convolutional neural networks.

To date, a comprehensive comparison of Riemannian decoding methods with deep convolutional neural networks for EEG-based brain-computer interfaces rem...

A minimalistic approach to classifying Alzheimer's disease using simple and extremely small convolutional neural networks.

BACKGROUND: There is a broad interest in deploying deep learning-based classification algorithms to identify individuals with Alzheimer's disease (AD)...

A preliminary study on the effects of long-term robot suit exercise training on gait function and quality of life in patients with spinal and bulbar muscular atrophy.

Spinal and bulbar muscular atrophy (SBMA) progressively impairs gait function, resulting in the need for patients to use a wheelchair approximately 20...

Decoding myasthenia gravis: advanced diagnosis with infrared spectroscopy and machine learning.

Myasthenia Gravis (MG) is a rare neurological disease. Although there are intensive efforts, the underlying mechanism of MG still has not been fully e...

An Unsupervised Learning Tool for Plaque Tissue Characterization in Histopathological Images.

Stroke is the second leading cause of death and a major cause of disability around the world, and the development of atherosclerotic plaques in the ca...

Assessing Consciousness in Patients With Disorders of Consciousness Using a Musical Stimulation Paradigm and Verifiable Criteria.

Numerous studies have shown that musical stimulation can activate corresponding functional brain areas. Electroencephalogram (EEG) activity during mus...

Empowering High-Level Spinal Cord Injury Patients in Daily Tasks With a Hybrid Gaze and FEMG-Controlled Assistive Robotic System.

Individuals with high-level spinal cord injuries often face significant challenges in performing essential daily tasks due to their motor impairments....

An end-to-end deep learning pipeline to derive blood input with partial volume corrections for automated parametric brain PET mapping.

Dynamic 2-[18F] fluoro-2-deoxy-D-glucose positron emission tomography (dFDG-PET) for human brain imaging has considerable clinical potential, yet its ...

Machine learning quantification of Amyloid-β deposits in the temporal lobe of 131 brain bank cases.

Accurate and scalable quantification of amyloid-β (Aβ) pathology is crucial for deeper disease phenotyping and furthering research in Alzheimer Diseas...

Comparing the effectiveness of robotic plantarflexion resistance and biofeedback between overground and treadmill walking.

Individuals with diminished walking performance caused by neuromuscular impairments often lack plantar flexion muscle activity. Robotic devices have b...

Improved Dementia Prediction in Cerebral Small Vessel Disease Using Deep Learning-Derived Diffusion Scalar Maps From T1.

BACKGROUND: Cerebral small vessel disease is the most common pathology underlying vascular dementia. In small vessel disease, diffusion tensor imaging...

Artificial organic afferent nerves enable closed-loop tactile feedback for intelligent robot.

The emulation of tactile sensory nerves to achieve advanced sensory functions in robotics with artificial intelligence is of great interest. However, ...

Revolutionizing early Alzheimer's disease and mild cognitive impairment diagnosis: a deep learning MRI meta-analysis.

BACKGROUND:  The early diagnosis of Alzheimer's disease (AD) and mild cognitive impairment (MCI) remains a significant challenge in neurology, with co...

Attention-based CNN-BiLSTM for sleep state classification of spatiotemporal wide-field calcium imaging data.

BACKGROUND: Wide-field calcium imaging (WFCI) with genetically encoded calcium indicators allows for spatiotemporal recordings of neuronal activity in...