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Decoding of Human Movements Based on Deep Brain Local Field Potentials Using Ensemble Neural Networks.

Computational intelligence and neuroscience
Decoding neural activities related to voluntary and involuntary movements is fundamental to understanding human brain motor circuits and neuromotor disorders and can lead to the development of neuromotor prosthetic devices for neurorehabilitation. Th...

The characteristic patterns of neuronal avalanches in mice under anesthesia and at rest: An investigation using constrained artificial neural networks.

PloS one
Local perturbations within complex dynamical systems can trigger cascade-like events that spread across significant portions of the system. Cascades of this type have been observed across a broad range of scales in the brain. Studies of these cascade...

Machine Learning Analysis of Left Ventricular Function to Characterize Heart Failure With Preserved Ejection Fraction.

Circulation. Cardiovascular imaging
BACKGROUND: Current diagnosis of heart failure with preserved ejection fraction (HFpEF) is suboptimal. We tested the hypothesis that comprehensive machine learning (ML) of left ventricular function at rest and exercise objectively captures difference...

Patterns of thought: Population variation in the associations between large-scale network organisation and self-reported experiences at rest.

NeuroImage
Contemporary cognitive neuroscience recognises unconstrained processing varies across individuals, describing variation in meaningful attributes, such as intelligence. It may also have links to patterns of on-going experience. This study examined whe...

Disrupted functional connectivity within the default mode network and salience network in unmedicated bipolar II disorder.

Progress in neuro-psychopharmacology & biological psychiatry
BACKGROUND: Recent studies demonstrate that functional disruption in resting-state networks contributes to cognitive and affective symptoms of bipolar disorder (BD), however, the functional connectivity (FC) pattern underlying BD II depression within...

Abnormal Low-Frequency Oscillations Reflect Trait-Like Pain Ratings in Chronic Pain Patients Revealed through a Machine Learning Approach.

The Journal of neuroscience : the official journal of the Society for Neuroscience
Measures of moment-to-moment fluctuations in brain activity of an individual at rest have been shown to be a sensitive and reliable metric for studying pathological brain mechanisms across various chronic pain patient populations. However, the relati...

Machine learning multivariate pattern analysis predicts classification of posttraumatic stress disorder and its dissociative subtype: a multimodal neuroimaging approach.

Psychological medicine
BACKGROUND: The field of psychiatry would benefit significantly from developing objective biomarkers that could facilitate the early identification of heterogeneous subtypes of illness. Critically, although machine learning pattern recognition method...

Resting-state anticorrelated networks in Schizophrenia.

Psychiatry research. Neuroimaging
Converging evidences from different lines of research suggest abnormalities in functional brain connectivity in schizophrenia. While positively correlated brain networks have been well researched, anticorrelated functional connectivity remains under ...

Classification of needle-EMG resting potentials by machine learning.

Muscle & nerve
INTRODUCTION: The diagnostic importance of audio signal characteristics in needle electromyography (EMG) is well established. Given the recent advent of audio-sound identification by artificial intelligence, we hypothesized that the extraction of cha...

Serial electrocardiography to detect newly emerging or aggravating cardiac pathology: a deep-learning approach.

Biomedical engineering online
BACKGROUND: Serial electrocardiography aims to contribute to electrocardiogram (ECG) diagnosis by comparing the ECG under consideration with a previously made ECG in the same individual. Here, we present a novel algorithm to construct dedicated deep-...