Neural models for detection and classification of brain states and transitions
Abstract 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,...
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| Format: | Article |
| Language: | English |
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Nature Portfolio
2025-04-01
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| Series: | Communications Biology |
| Online Access: | https://doi.org/10.1038/s42003-025-07991-3 |
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| author | Arnau Marin-Llobet Arnau Manasanch Leonardo Dalla Porta Melody Torao-Angosto Maria V. Sanchez-Vives |
| author_facet | Arnau Marin-Llobet Arnau Manasanch Leonardo Dalla Porta Melody Torao-Angosto Maria V. Sanchez-Vives |
| author_sort | Arnau Marin-Llobet |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-2020d03d42db406d9b9c8a31a0d518c3 |
| institution | DOAJ |
| issn | 2399-3642 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Communications Biology |
| spelling | doaj-art-2020d03d42db406d9b9c8a31a0d518c32025-08-20T03:06:50ZengNature PortfolioCommunications Biology2399-36422025-04-018111010.1038/s42003-025-07991-3Neural models for detection and classification of brain states and transitionsArnau Marin-Llobet0Arnau Manasanch1Leonardo Dalla Porta2Melody Torao-Angosto3Maria V. Sanchez-Vives4Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS)Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS)Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS)Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS)Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS)Abstract 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.https://doi.org/10.1038/s42003-025-07991-3 |
| spellingShingle | Arnau Marin-Llobet Arnau Manasanch Leonardo Dalla Porta Melody Torao-Angosto Maria V. Sanchez-Vives Neural models for detection and classification of brain states and transitions Communications Biology |
| title | Neural models for detection and classification of brain states and transitions |
| title_full | Neural models for detection and classification of brain states and transitions |
| title_fullStr | Neural models for detection and classification of brain states and transitions |
| title_full_unstemmed | Neural models for detection and classification of brain states and transitions |
| title_short | Neural models for detection and classification of brain states and transitions |
| title_sort | neural models for detection and classification of brain states and transitions |
| url | https://doi.org/10.1038/s42003-025-07991-3 |
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