Neurology

rPTMDetermine: A Fully Automated Methodology for Endogenous Tyrosine Nitration Validation, Site-Localization, and Beyond.

We present herein rPTMDetermine, an adaptive and fully automated methodology for validation of the identification of rarely occurring post-translational modifications (PTMs), using a semisupervised approach with a linear discriminant analysis (LDA) algorithm. With this strategy, verification is enhanced through similarity scoring of tandem mass spectrometry (MS/MS) comparisons between modified pep...

Cell type prioritization in single-cell data.

We present Augur, a method to prioritize the cell types most responsive to biological perturbations in single-cell data. Augur employs a machine-learning framework to quantify the separability of perturbed and unperturbed cells within a high-dimensional space. We validate our method on single-cell RNA sequencing, chromatin accessibility and imaging transcriptomics datasets, and show that Augur out...

Computer Vision-Based Grasp Pattern Recognition With Application to Myoelectric Control of Dexterous Hand Prosthesis.

Artificial intelligence provides new feasibilities to the control of dexterous prostheses. To achieve suitable grasps over various objects, a novel co...

Spatial-Frequency Feature Learning and Classification of Motor Imagery EEG Based on Deep Convolution Neural Network.

EEG pattern recognition is an important part of motor imagery- (MI-) based brain computer interface (BCI) system. Traditional EEG pattern recognition ...

Integrating uncertainty in deep neural networks for MRI based stroke analysis.

At present, the majority of the proposed Deep Learning (DL) methods provide point predictions without quantifying the model's uncertainty. However, a ...

Feed-forward neural networks using cerebral MR spectroscopy and DTI might predict neurodevelopmental outcome in preterm neonates.

OBJECTIVES: We aimed to evaluate the ability of feed-forward neural networks (fNNs) to predict the neurodevelopmental outcome (NDO) of very preterm ne...

Neuro-fuzzy patch-wise R-CNN for multiple sclerosis segmentation.

The segmentation of the lesion plays a core role in diagnosis and monitoring of multiple sclerosis (MS). Magnetic resonance imaging (MRI) is the most ...

Exploring supervised machine learning approaches to predicting Veterans Health Administration chiropractic service utilization.

BACKGROUND: Chronic spinal pain conditions affect millions of US adults and carry a high healthcare cost burden, both direct and indirect. Conservativ...

Robot companion cats for people at home with dementia: A qualitative case study on companotics.

The use of robot companion pets for people in care homes has been extensively studied. The results are largely positive and suggest that they are valu...

Developing a neurally informed ontology of creativity measurement.

A central challenge for creativity research-as for all areas of experimental psychology and cognitive neuroscience-is to establish a mapping between c...

Multi-class motor imagery EEG classification using collaborative representation-based semi-supervised extreme learning machine.

Both labeled and unlabeled data have been widely used in electroencephalographic (EEG)-based brain-computer interface (BCI). However, labeled EEG samp...

Outcome prediction with resting-state functional connectivity after cardiac arrest.

Predicting outcome in comatose patients after successful cardiopulmonary resuscitation is challenging. Our primary aim was to assess the potential con...

A deep learning approach for magnetization transfer contrast MR fingerprinting and chemical exchange saturation transfer imaging.

Semisolid magnetization transfer contrast (MTC) and chemical exchange saturation transfer (CEST) MRI based on MT phenomenon have shown potential to ev...

Diagnosis of Alzheimer's disease using laser-induced breakdown spectroscopy and machine learning.

Alzheimer's disease (AD) is a progressive incurable neurodegenerative disease and a major health problem in aging population. We show that the combine...

Applications of machine learning to diagnosis and treatment of neurodegenerative diseases.

Globally, there is a huge unmet need for effective treatments for neurodegenerative diseases. The complexity of the molecular mechanisms underlying ne...

Towards subject-level cerebral infarction classification of CT scans using convolutional networks.

Automatic evaluation of 3D volumes is a topic of importance in order to speed up clinical decision making. We describe a method to classify computed t...

Predicting PET Cerebrovascular Reserve with Deep Learning by Using Baseline MRI: A Pilot Investigation of a Drug-Free Brain Stress Test.

Background Cerebrovascular reserve (CVR) may be measured by using an acetazolamide test to clinically evaluate patients with cerebrovascular disease. ...

Robotics-assisted visual-motor training influences arm position sense in three-dimensional space.

BACKGROUND: Performing activities of daily living depends, among other factors, on awareness of the position and movements of limbs. Neural injuries, ...

Robotic assessment of rapid motor decision making in children with perinatal stroke.

BACKGROUND: Activities of daily living frequently require children to make rapid decisions and execute desired motor actions while inhibiting unwanted...

Deep learning-based BCI for gait decoding from EEG with LSTM recurrent neural network.

OBJECTIVE: Mobile Brain/Body Imaging (MoBI) frameworks allowed the research community to find evidence of cortical involvement at walking initiation a...