Neural models for detection and classification of brain states and transitions

Exploring natural or pharmacologically induced brain dynamics, such as sleep, wakefulness, or anesthesia, provides rich functional models for studying brain states. These models allow detailed examination of unique spatiotemporal neural activity patterns that reveal brain function. However, assessin...

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Bibliographic Details
Published in:Communications biology Vol. 8; no. 1; pp. 599 - 10
Main Authors: Marin-Llobet, Arnau, Manasanch, Arnau, Dalla Porta, Leonardo, Torao-Angosto, Melody, Sanchez-Vives, Maria V.
Format: Journal Article
Language:English
Published: London Nature Publishing Group UK 11.04.2025
Nature Publishing Group
Nature Portfolio
Subjects:
631/114/1305
631/378
9/30
Accuracy
Activity patterns
Algorithms
Anesthesia
Animals
Biomedical and Life Sciences
Brain - physiology
Cerebral cortex
Cerebral Cortex - physiology
Deep learning
Electrophysiological recording
Life Sciences
Male
Models, Neurological
Neural networks
Neural Networks, Computer
Oscillations
Rats
Sleep - physiology
Sleep and wakefulness
Wakefulness - physiology
ISSN:2399-3642, 2399-3642
Online Access:Get full text
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Summary:Exploring natural or pharmacologically induced brain dynamics, such as sleep, wakefulness, or anesthesia, provides rich functional models for studying brain states. These models allow detailed examination of unique spatiotemporal neural activity patterns that reveal brain function. However, assessing transitions between brain states remains computationally challenging. Here we introduce a pipeline to detect brain states and their transitions in the cerebral cortex using a dual-model Convolutional Neural Network (CNN) and a self-supervised autoencoder-based multimodal clustering algorithm. This approach distinguishes brain states such as slow oscillations, microarousals, and wakefulness with high confidence. Using chronic local field potential recordings from rats, our method achieved a global accuracy of 91%, with up to 96% accuracy for certain states. For the transitions, we report an average accuracy of 74%. Our models were trained using a leave-one-out methodology, allowing for broad applicability across subjects and pre-trained models for deployments. It also features a confidence parameter, ensuring that only highly certain cases are automatically classified, leaving ambiguous cases for the multimodal unsupervised classifier or further expert review. Our approach presents a reliable and efficient tool for brain state labeling and analysis, with applications in basic and clinical neuroscience. A deep learning self-supervised hybrid CNN-autoencoder model is used to detect brain states and transitions, like wakefulness, slow oscillations and microarousals, during the emergence from anesthesia in cortical local field potentials.
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ISSN:2399-3642
2399-3642
DOI:10.1038/s42003-025-07991-3