Detection of Healthy and Unhealthy Brain States from Local Field Potentials Using Machine Learning

Marcos I. Fabietti; Mufti Mahmud; Ahmad Lotfi; Alessandro Leparulo; Roberto Fontana; Stefano Vassanelli; Cristina Fassolato

doi:10.1007/978-3-031-15037-1_3

Detection of Healthy and Unhealthy Brain States from Local Field Potentials Using Machine Learning

Marcos I. Fabietti^*
, Mufti Mahmud
, Ahmad Lotfi
, Alessandro Leparulo
, Roberto Fontana
, Stefano Vassanelli
, Cristina Fassolato

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

16 Scopus citations

Abstract

Neural signals are the recordings of the electrical activity individual or groups of neurons, and they are used for disease staging, brain-computer interface control and understanding the neural processes. When carrying out a functional connectivity study in rodents, processing must be done to eliminate disturbance in the data in order to have the most faithful representation of the neural activity. This step mainly includes filtering and artefact removal, where the latter can be approached by diverse methods. Furthermore, it is important to identify when the rodent is stressed, as the local field potentials can be coupled to theta oscillations. To this end, we set out to develop a machine learning-based model for the detection of stress in rodents with multi-modal recordings, namely local field potentials, respiration and electrocardiography. We explore subject-specific and cross-subject models, as well as employing an artefact detection model as a generic anomaly detector. Results show that subject-specific models can achieve a good performance, but the variability is significant across all three signals among rodents of the same age, gender and species.

Original language	English
Title of host publication	Brain Informatics - 15th International Conference, BI 2022, Proceedings
Editors	Mufti Mahmud, Jing He, Stefano Vassanelli, André van Zundert, Ning Zhong
Publisher	Springer Science and Business Media Deutschland GmbH
Pages	27-39
Number of pages	13
ISBN (Print)	9783031150364
DOIs	https://doi.org/10.1007/978-3-031-15037-1_3
State	Published - 2022
Externally published	Yes
Event	15th International Conference on Brain Informatics, BI 2022 - Virtual, Online Duration: 15 Jul 2022 → 17 Jul 2022

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	13406 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	15th International Conference on Brain Informatics, BI 2022
City	Virtual, Online
Period	15/07/22 → 17/07/22

Bibliographical note

Publisher Copyright:
© 2022, Springer Nature Switzerland AG.

Keywords

Computational neuroscience
Machine learning
Physiological signals

ASJC Scopus subject areas

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-031-15037-1_3

Cite this

Fabietti, M. I., Mahmud, M., Lotfi, A., Leparulo, A., Fontana, R., Vassanelli, S., & Fassolato, C. (2022). Detection of Healthy and Unhealthy Brain States from Local Field Potentials Using Machine Learning. In M. Mahmud, J. He, S. Vassanelli, A. van Zundert, & N. Zhong (Eds.), Brain Informatics - 15th International Conference, BI 2022, Proceedings (pp. 27-39). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13406 LNAI). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-15037-1_3

Fabietti, Marcos I. ; Mahmud, Mufti ; Lotfi, Ahmad et al. / Detection of Healthy and Unhealthy Brain States from Local Field Potentials Using Machine Learning. Brain Informatics - 15th International Conference, BI 2022, Proceedings. editor / Mufti Mahmud ; Jing He ; Stefano Vassanelli ; André van Zundert ; Ning Zhong. Springer Science and Business Media Deutschland GmbH, 2022. pp. 27-39 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "Neural signals are the recordings of the electrical activity individual or groups of neurons, and they are used for disease staging, brain-computer interface control and understanding the neural processes. When carrying out a functional connectivity study in rodents, processing must be done to eliminate disturbance in the data in order to have the most faithful representation of the neural activity. This step mainly includes filtering and artefact removal, where the latter can be approached by diverse methods. Furthermore, it is important to identify when the rodent is stressed, as the local field potentials can be coupled to theta oscillations. To this end, we set out to develop a machine learning-based model for the detection of stress in rodents with multi-modal recordings, namely local field potentials, respiration and electrocardiography. We explore subject-specific and cross-subject models, as well as employing an artefact detection model as a generic anomaly detector. Results show that subject-specific models can achieve a good performance, but the variability is significant across all three signals among rodents of the same age, gender and species.",

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Fabietti, MI, Mahmud, M, Lotfi, A, Leparulo, A, Fontana, R, Vassanelli, S & Fassolato, C 2022, Detection of Healthy and Unhealthy Brain States from Local Field Potentials Using Machine Learning. in M Mahmud, J He, S Vassanelli, A van Zundert & N Zhong (eds), Brain Informatics - 15th International Conference, BI 2022, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 13406 LNAI, Springer Science and Business Media Deutschland GmbH, pp. 27-39, 15th International Conference on Brain Informatics, BI 2022, Virtual, Online, 15/07/22. https://doi.org/10.1007/978-3-031-15037-1_3

Detection of Healthy and Unhealthy Brain States from Local Field Potentials Using Machine Learning. / Fabietti, Marcos I.; Mahmud, Mufti; Lotfi, Ahmad et al.
Brain Informatics - 15th International Conference, BI 2022, Proceedings. ed. / Mufti Mahmud; Jing He; Stefano Vassanelli; André van Zundert; Ning Zhong. Springer Science and Business Media Deutschland GmbH, 2022. p. 27-39 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 13406 LNAI).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Fabietti, Marcos I.

AU - Mahmud, Mufti

AU - Lotfi, Ahmad

AU - Leparulo, Alessandro

AU - Fontana, Roberto

AU - Vassanelli, Stefano

AU - Fassolato, Cristina

PY - 2022

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N2 - Neural signals are the recordings of the electrical activity individual or groups of neurons, and they are used for disease staging, brain-computer interface control and understanding the neural processes. When carrying out a functional connectivity study in rodents, processing must be done to eliminate disturbance in the data in order to have the most faithful representation of the neural activity. This step mainly includes filtering and artefact removal, where the latter can be approached by diverse methods. Furthermore, it is important to identify when the rodent is stressed, as the local field potentials can be coupled to theta oscillations. To this end, we set out to develop a machine learning-based model for the detection of stress in rodents with multi-modal recordings, namely local field potentials, respiration and electrocardiography. We explore subject-specific and cross-subject models, as well as employing an artefact detection model as a generic anomaly detector. Results show that subject-specific models can achieve a good performance, but the variability is significant across all three signals among rodents of the same age, gender and species.

AB - Neural signals are the recordings of the electrical activity individual or groups of neurons, and they are used for disease staging, brain-computer interface control and understanding the neural processes. When carrying out a functional connectivity study in rodents, processing must be done to eliminate disturbance in the data in order to have the most faithful representation of the neural activity. This step mainly includes filtering and artefact removal, where the latter can be approached by diverse methods. Furthermore, it is important to identify when the rodent is stressed, as the local field potentials can be coupled to theta oscillations. To this end, we set out to develop a machine learning-based model for the detection of stress in rodents with multi-modal recordings, namely local field potentials, respiration and electrocardiography. We explore subject-specific and cross-subject models, as well as employing an artefact detection model as a generic anomaly detector. Results show that subject-specific models can achieve a good performance, but the variability is significant across all three signals among rodents of the same age, gender and species.

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SN - 9783031150364

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

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Fabietti MI, Mahmud M, Lotfi A, Leparulo A, Fontana R, Vassanelli S et al. Detection of Healthy and Unhealthy Brain States from Local Field Potentials Using Machine Learning. In Mahmud M, He J, Vassanelli S, van Zundert A, Zhong N, editors, Brain Informatics - 15th International Conference, BI 2022, Proceedings. Springer Science and Business Media Deutschland GmbH. 2022. p. 27-39. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-031-15037-1_3