Neuroimaging of Brain Oscillations During Human Sleep

Background: A large body of evidence suggested that disruption of neural rhythms and synchronization of brain oscillations are correlated with variety of cognitive and perceptual processes. Cognitive deficits are common features of psychiatric disorders that complicate treatment of the motivational, affective and emotional symptoms. Objective: Electrophysiological correlates of cognitive functions will contribute to understanding of neural circuits controlling cognition, the causes of their perturbation in psychiatric disorders and developing novel targets for treatment of cognitive impairments. Methods: This review includes description of brain oscillations in Alzheimer’s disease, bipolar disorder, attentiondeficit/hyperactivity disorder, major depression, obsessive compulsive disorders, anxiety disorders, schizophrenia and autism. Results: The review clearly shows that the reviewed neuropsychiatric diseases are associated with fundamental changes in both spectral power and coherence of EEG oscillations. Conclusion: In this article we examined nature of brain oscillations, association of brain rhythms with cognitive functions and relationship between EEG oscillations and neuropsychiatric diseases. Accordingly, EEG oscillations can most likely be used as biomarkers in psychiatric disorders.

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A Study of Human Sleep Staging Behavior Based on Polysomnography Using Machine Learning Techniques

Data Analytics in Bioinformatics ◽

10.1002/9781119785620.ch14 ◽

2021 ◽

pp. 339-358

Author(s):

Santosh Kumar Satapathy ◽

D. Loganathan

Keyword(s):

Machine Learning ◽

Machine Learning Techniques ◽

Sleep Staging ◽

Human Sleep ◽

Learning Techniques ◽

Behavior Based

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Resting-state brain oscillations predict cognitive function in psychiatric disorders: A transdiagnostic machine learning approach

NeuroImage Clinical ◽

10.1016/j.nicl.2021.102617 ◽

2021 ◽

Vol 30 ◽

pp. 102617

Author(s):

Kaia Sargent ◽

UnYoung Chavez-Baldini ◽

Sarah L. Master ◽

Karin J.H. Verweij ◽

Anja Lok ◽

...

Keyword(s):

Machine Learning ◽

Cognitive Function ◽

Psychiatric Disorders ◽

Resting State ◽

Learning Approach ◽

Brain Oscillations ◽

Machine Learning Approach

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Capturing the Development of Internal Representations in a High-Performing Deep Network for Sleep Stage Classification

SN Computer Science ◽

10.1007/s42979-021-00697-3 ◽

2021 ◽

Vol 2 (4) ◽

Author(s):

Sarun Paisarnsrisomsuk ◽

Carolina Ruiz ◽

Sergio A. Alvarez

Keyword(s):

Input Data ◽

Single Channel ◽

Sleep Stage ◽

Quantitative Measure ◽

Classification Performance ◽

Sleep Stages ◽

Layer Depth ◽

Human Sleep ◽

Stage Classification ◽

Sleep Stage Classification

AbstractDeep neural networks can provide accurate automated classification of human sleep signals into sleep stages that enables more effective diagnosis and treatment of sleep disorders. We develop a deep convolutional neural network (CNN) that attains state-of-the-art sleep stage classification performance on input data consisting of human sleep EEG and EOG signals. Nested cross-validation is used for optimal model selection and reliable estimation of out-of-sample classification performance. The resulting network attains a classification accuracy of $$84.50 \pm 0.13\%$$ 84.50 ± 0.13 % ; its performance exceeds human expert inter-scorer agreement, even on single-channel EEG input data, therefore providing more objective and consistent labeling than human experts demonstrate as a group. We focus on analyzing the learned internal data representations of our network, with the aim of understanding the development of class differentiation ability across the layers of processing units, as a function of layer depth. We approach this problem visually, using t-Stochastic Neighbor Embedding (t-SNE), and propose a pooling variant of Centered Kernel Alignment (CKA) that provides an objective quantitative measure of the development of sleep stage specialization and differentiation with layer depth. The results reveal a monotonic progression of both of these sleep stage modeling abilities as layer depth increases.

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