AI Medical Compendium Journal:
Journal of neuroscience methods

Showing 111 to 120 of 161 articles

Automated dendritic spine detection using convolutional neural networks on maximum intensity projected microscopic volumes.

Journal of neuroscience methods
BACKGROUND: Dendritic spines are structural correlates of excitatory synapses in the brain. Their density and structure are shaped by experience, pointing to their role in memory encoding. Dendritic spine imaging, followed by manual analysis, is a pr...

Slow wave detection in sleeping mice: Comparison of traditional and machine learning methods.

Journal of neuroscience methods
BACKGROUND: During slow-wave sleep the electroencephalographic (EEG) and local field potential (LFP) recordings reveal the presence of large amplitude slow waves. Systematic extraction of individual slow waves is not trivial.

Robotic TMS mapping of motor cortex in the developing brain.

Journal of neuroscience methods
BACKGROUND: The human motor cortex can be mapped safely and painlessly with transcranial magnetic stimulation (TMS) to explore neurophysiology in health and disease. Human error likely contributes to heterogeneity of such TMS measures. Here, we aimed...

Automated detection of electroencephalography artifacts in human, rodent and canine subjects using machine learning.

Journal of neuroscience methods
BACKGROUND: Electroencephalography (EEG) invariably contains extra-cranial artifacts that are commonly dealt with based on qualitative and subjective criteria. Failure to account for EEG artifacts compromises data interpretation.

Pattern analysis of computer keystroke time series in healthy control and early-stage Parkinson's disease subjects using fuzzy recurrence and scalable recurrence network features.

Journal of neuroscience methods
BACKGROUND: Identifying patients with early stages of Parkinson's disease (PD) in a home environment is an important area of neurological disorder research, because it is of therapeutic and economic benefits to optimal intervention and management of ...

A fresh look at functional link neural network for motor imagery-based brain-computer interface.

Journal of neuroscience methods
BACKGROUND: Artificial neural networks (ANNs) are one of the widely used classifiers in the brain-computer interface (BCI) systems-based on noninvasive electroencephalography (EEG) signals. Among the different ANN architectures, the most commonly app...

An ensemble learning system for a 4-way classification of Alzheimer's disease and mild cognitive impairment.

Journal of neuroscience methods
Discriminating Alzheimer's disease (AD) from its prodromal form, mild cognitive impairment (MCI), is a significant clinical problem that may facilitate early diagnosis and intervention, in which a more challenging issue is to classify MCI subtypes, i...

Predication of different stages of Alzheimer's disease using neighborhood component analysis and ensemble decision tree.

Journal of neuroscience methods
BACKGROUND: There is a spectrum of the progression from healthy control (HC) to mild cognitive impairment (MCI) without conversion to Alzheimer's disease (AD), to MCI with conversion to AD (cMCI), and to AD. This study aims to predict the different d...

Clustering fMRI data with a robust unsupervised learning algorithm for neuroscience data mining.

Journal of neuroscience methods
BACKGROUND: Clustering approaches used in functional magnetic resonance imaging (fMRI) research use brain activity to divide the brain into various parcels with some degree of homogeneous characteristics, but choosing the appropriate clustering algor...