Neurology

Implementation of deep neural networks to count dopamine neurons in substantia nigra.

Unbiased estimates of neuron numbers within substantia nigra are crucial for experimental Parkinson's disease models and gene-function studies. Unbiased stereological counting techniques with optical fractionation are successfully implemented, but are extremely laborious and time-consuming. The development of neural networks and deep learning has opened a new way to teach computers to count neuron...

Application of identity vectors for EEG classification.

BACKGROUND: Finding an optimal EEG subject verification algorithm is a long standing goal within the EEG community. For every advancement made, another feature set, classifier, or dataset is often introduced; tracking improvements in classification without a consistent benchmark, such as a classifier-feature pairing tested on a publicly available dataset, makes it difficult to understand how and w...

A Continuously Updated, Computationally Efficient Stress Recognition Framework Using Electroencephalogram (EEG) by Applying Online Multitask Learning Algorithms (OMTL).

Recognizing the factors that cause stress is a crucial step toward early detection of stressors. In this regard, several studies make an effort to rec...

Identifying Suitable Brain Regions and Trial Size Segmentation for Positive/Negative Emotion Recognition.

The development of suitable EEG-based emotion recognition systems has become a main target in the last decades for Brain Computer Interface applicatio...

Machine learning based on multi-parametric magnetic resonance imaging to differentiate glioblastoma multiforme from primary cerebral nervous system lymphoma.

PURPOSE: To evaluate the performance of a machine learning method based on texture features in multi-parametric magnetic resonance imaging (MRI) to di...

Comparison of logistic regression, support vector machines, and deep learning classifiers for predicting memory encoding success using human intracranial EEG recordings.

OBJECTIVE: We sought to test the performance of three strategies for binary classification (logistic regression, support vector machines, and deep lea...

A Novel Deep Learning Framework on Brain Functional Networks for Early MCI Diagnosis.

Although alternations of brain functional networks (BFNs) derived from resting-state functional magnetic resonance imaging (rs-fMRI) have been conside...

Temporal Correlation Structure Learning for MCI Conversion Prediction.

In Alzheimer's research, Mild Cognitive Impairment (MCI) is an important intermediate stage between normal aging and Alzheimer's. How to distinguish M...

Joint Classification and Regression via Deep Multi-Task Multi-Channel Learning for Alzheimer's Disease Diagnosis.

In the field of computer-aided Alzheimer's disease (AD) diagnosis, jointly identifying brain diseases and predicting clinical scores using magnetic re...

Sample Entropy on Multidistance Signal Level Difference for Epileptic EEG Classification.

Epilepsy is a disorder of the brain's nerves as a result of excessive brain cell activity. It is generally characterized by the recurrent unprovoked s...

A Novel Method of Segmentation and Classification for Meditation in Health Care Systems.

Meditation improves positivity in behavioral as well as psychological changes, which are brought elucidated by knowing neuro-physiological consequence...

Fibroblast growth factor23 is associated with axonal integrity and neural network architecture in the human frontal lobes.

Elevated levels of FGF23 in individuals with chronic kidney disease (CKD) are associated with adverse health outcomes, such as increased mortality, la...

Machine learning identified an Alzheimer's disease-related FDG-PET pattern which is also expressed in Lewy body dementia and Parkinson's disease dementia.

Utilizing the publicly available neuroimaging database enabled by Alzheimer's disease Neuroimaging Initiative (ADNI; http://adni.loni.usc.edu/ ), we h...

A Wearable Multi-Modal Bio-Sensing System Towards Real-World Applications.

Multi-modal bio-sensing has recently been used as effective research tools in affective computing, autism, clinical disorders, and virtual reality amo...

EEG-Based Automatic Sleep Staging Using Ontology and Weighting Feature Analysis.

Sleep staging is considered as an effective indicator for auxiliary diagnosis of sleep diseases and related psychiatric diseases, so it attracts a lot...

Parkinson's Disease Diagnosis via Joint Learning From Multiple Modalities and Relations.

Parkinson's disease (PD) is a neurodegenerative progressive disease that mainly affects the motor systems of patients. To slow this disease deteriorat...

Personalized prediction model for seizure-free epilepsy with levetiracetam therapy: a retrospective data analysis using support vector machine.

AIMS: To predict the probability of a seizure-free (SF) state in patients with epilepsy (PWEs) after treatment with levetiracetam and to identify the ...

A hierarchical multimodal system for motion analysis in patients with epilepsy.

During seizures, a myriad of clinical manifestations may occur. The analysis of these signs, known as seizure semiology, gives clues to the underlying...

Decision Support System for Seizure Onset Zone Localization Based on Channel Ranking and High-Frequency EEG Activity.

Interictal high-frequency oscillations (HFO) are a promising biomarker that can help define the seizure onset zone (SOZ) and predict the surgical outc...

Synthesis of Patient-Specific Transmission Data for PET Attenuation Correction for PET/MRI Neuroimaging Using a Convolutional Neural Network.

Attenuation correction is a notable challenge associated with simultaneous PET/MRI, particularly in neuroimaging, where sharp boundaries between air a...