Automated sleep classification with chronic neural implants in freely behaving canines
Jazyk angličtina Země Anglie, Velká Británie Médium electronic
Typ dokumentu časopisecké články, Research Support, N.I.H., Extramural, práce podpořená grantem, Research Support, U.S. Gov't, Non-P.H.S.
Grantová podpora
R01 NS092882
NINDS NIH HHS - United States
U24 NS113637
NINDS NIH HHS - United States
UH2 NS095495
NINDS NIH HHS - United States
UH3 NS095495
NINDS NIH HHS - United States
PubMed
37536320
PubMed Central
PMC10480092
DOI
10.1088/1741-2552/aced21
Knihovny.cz E-zdroje
- Klíčová slova
- canine, implantable devices for sensing and stimulation, intracranial EEG, sleep classification,
- MeSH
- bdění fyziologie MeSH
- elektroencefalografie metody MeSH
- elektrokortikografie MeSH
- psi MeSH
- spánek REM fyziologie MeSH
- spánek * fyziologie MeSH
- stadia spánku * fyziologie MeSH
- zvířata MeSH
- Check Tag
- psi MeSH
- zvířata MeSH
- Publikační typ
- časopisecké články MeSH
- práce podpořená grantem MeSH
- Research Support, N.I.H., Extramural MeSH
- Research Support, U.S. Gov't, Non-P.H.S. MeSH
Objective.Long-term intracranial electroencephalography (iEEG) in freely behaving animals provides valuable electrophysiological information and when correlated with animal behavior is useful for investigating brain function.Approach.Here we develop and validate an automated iEEG-based sleep-wake classifier for canines using expert sleep labels derived from simultaneous video, accelerometry, scalp electroencephalography (EEG) and iEEG monitoring. The video, scalp EEG, and accelerometry recordings were manually scored by a board-certified sleep expert into sleep-wake state categories: awake, rapid-eye-movement (REM) sleep, and three non-REM sleep categories (NREM1, 2, 3). The expert labels were used to train, validate, and test a fully automated iEEG sleep-wake classifier in freely behaving canines.Main results. The iEEG-based classifier achieved an overall classification accuracy of 0.878 ± 0.055 and a Cohen's Kappa score of 0.786 ± 0.090. Subsequently, we used the automated iEEG-based classifier to investigate sleep over multiple weeks in freely behaving canines. The results show that the dogs spend a significant amount of the day sleeping, but the characteristics of daytime nap sleep differ from night-time sleep in three key characteristics: during the day, there are fewer NREM sleep cycles (10.81 ± 2.34 cycles per day vs. 22.39 ± 3.88 cycles per night;p< 0.001), shorter NREM cycle durations (13.83 ± 8.50 min per day vs. 15.09 ± 8.55 min per night;p< 0.001), and dogs spend a greater proportion of sleep time in NREM sleep and less time in REM sleep compared to night-time sleep (NREM 0.88 ± 0.09, REM 0.12 ± 0.09 per day vs. NREM 0.80 ± 0.08, REM 0.20 ± 0.08 per night;p< 0.001).Significance.These results support the feasibility and accuracy of automated iEEG sleep-wake classifiers for canine behavior investigations.
Department of Comparative Medicine Mayo Clinic Rochester MN United States of America
Department of Engineering Science Oxford University Oxford United Kingdom
Department of Neurologic Surgery Mayo Clinic Rochester MN United States of America
Department of Neuroradiology Mayo Clinic Rochester MN United States of America
Faculty of Biomedical Engineering Czech Technical University Prague Kladno Czech Republic
International Clinical Research Center St Anne's University Hospital Brno Czech Republic
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Heske L, Nødtvedt A, Jäderlund KH, Berendt M and Egenvall A 2014. A cohort study of epilepsy among 665,000 insured dogs: Incidence, mortality and survival after diagnosis Vet. J PubMed
Berendt M, Høgenhaven H, Flagstad A and Dam M 1999. Electroencephalography in dogs with epilepsy: Similarities between human and canine findings Acta Neurol. Scand PubMed
Bunford N, Andics A, Kis A, Miklósi Á and Gácsi M 2017. Canis familiaris As a Model for Non-Invasive Comparative Neuroscience Trends Neurosci PubMed
Hare B, Brown M, Williamson C and Tomasello M 2002. The domestication of social cognition in dogs Science (80-.) PubMed
Tang R, Noh H, Wang D, Sigurdsson S, Swofford R, Perloski M, Duxbury M, Patterson EE, Albright J, Castelhano M, Auton A, Boyko AR, Feng G, Lindblad-Toh K and Karlsson EK 2014. Candidate genes and functional noncoding variants identified in a canine model of obsessive-compulsive disorder Genome Biol 15 R25. PubMed PMC
Gregg NM, Sladky V, Nejedly P, Mivalt F, Kim I, Balzekas I, Sturges BK, Crowe C, Patterson EE, Van Gompel J J, Lundstrom BN, Leyde K, Denison TJ, Brinkmann BH, Kremen V and Worrell GA 2021. Thalamic deep brain stimulation modulates cycles of seizure risk in epilepsy Sci. Rep 11 24250. PubMed PMC
Sladky V, Nejedly P, Mivalt F, Brinkmann BH, Kim I, St. Louis EK, Gregg NM, Lundstrom BN, Crowe CM, Attia TP, Crepeau D, Balzekas I, Marks VS, Wheeler LP, Cimbalnik J, Cook M, Janca R, Sturges BK, Leyde K, Miller KJ, Van Gompel JJ, Denison T, Worrell GA and Kremen V 2022. Distributed brain co-processor for tracking spikes, seizures and behavior during electrical brain stimulation Brain Commun PubMed PMC
Brinkmann BH, Wagenaar J, Abbot D, Adkins P, Bosshard SC, Chen M, Tieng QM, He J, Muñoz-Almaraz FJ, Botella-Rocamora P, Pardo J, Zamora-Martinez F, Hills M, Wu W, Korshunova I, Cukierski W, Vite C, Patterson EE, Litt B and Worrell GA 2016. Crowdsourcing reproducible seizure forecasting in human and canine epilepsy Brain 139 1713–22 PubMed PMC
Davis KA, Sturges BK, Vite CH, Ruedebusch V, Worrell G, Gardner AB, Leyde K, Sheffield WD and Litt B 2011. A novel implanted device to wirelessly record and analyze continuous intracranial canine EEG Epilepsy Res 96 116–22 PubMed PMC
Baldassano SN, Brinkmann BH, Ung H, Blevins T, Conrad EC, Leyde K, Cook MJ, Khambhati AN, Wagenaar JB, Worrell GA and Litt B 2017. Crowdsourcing seizure detection: Algorithm development and validation on human implanted device recordings Brain 140 1680–91 PubMed PMC
Nejedly P, Kremen V, Sladky V, Nasseri M, Guragain H, Klimes P, Cimbalnik J, Varatharajah Y, Brinkmann BH and Worrell GA 2019. Deep-learning for seizure forecasting in canines with epilepsy J. Neural Eng 16 036031. PubMed
Pal Attia T, Crepeau D, Kremen V, Nasseri M, Guragain H, Steele SW, Sladky V, Nejedly P, Mivalt F, Herron JA, Stead M, Denison T, Worrell GA and Brinkmann BH 2021. Epilepsy Personal Assistant Device—A Mobile Platform for Brain State, Dense Behavioral and Physiology Tracking and Controlling Adaptive Stimulation Front. Neurol 12 PubMed PMC
Zamora M, Meller S, Kajin F, Sermon JJ, Toth R, Benjaber M, Dijk D-J, Bogacz R, Worrell GA, Valentin A, Duchet B, Volk HA and Denison T 2021. Case Report: Embedding “Digital Chronotherapy” Into Medical Devices—A Canine Validation for Controlling Status Epilepticus Through Multi-Scale Rhythmic Brain Stimulation Front. Neurosci 15 PubMed PMC
Nair DR, Laxer KD, Weber PB, Murro AM, Park YD, Barkley GL, Smith BJ, Gwinn RP, Doherty MJ, Noe KH, Zimmerman RS, Bergey GK, Anderson WS, Heck C, Liu CY, Lee RW, Sadler T, Duckrow RB, Hirsch LJ, Wharen RE, Tatum W, Srinivasan S, McKhann GM, Agostini MA, Alexopoulos AV., Jobst BC, Roberts DW, Salanova V, Witt TC, Cash SS, Cole AJ, Worrell GA, Lundstrom BN, Edwards JC, Halford JJ, Spencer DC, Ernst L, Skidmore CT, Sperling MR, Miller I, Geller EB, Berg MJ, Fessler AJ, Rutecki P, Goldman AM, Mizrahi EM, Gross RE, Shields DC, Schwartz TH, Labar DR, Fountain NB, Elias WJ, Olejniczak PW, Villemarette-Pittman NR, Eisenschenk S, Roper SN, Boggs JG, Courtney TA, Sun FT, Seale CG, Miller KL, Skarpaas TL and Morrell MJ 2020. Nine-year prospective efficacy and safety of brain-responsive neurostimulation for focal epilepsy Neurology 95 e1244–56 PubMed PMC
Fisher R, Salanova V, Witt T, Worth R, Henry T, Gross R, Oommen K, Osorio I, Nazzaro J, Labar D, Kaplitt M, Sperling M, Sandok E, Neal J, Handforth A, Stern J, DeSalles A, Chung S, Shetter A, Bergen D, Bakay R, Henderson J, French J, Baltuch G, Rosenfeld W, Youkilis A, Marks W, Garcia P, Barbaro N, Fountain N, Bazil C, Goodman R, McKhann G, Babu Krishnamurthy K, Papavassiliou S, Epstein C, Pollard J, Tonder L, Grebin J, Coffey R and Graves N 2010. Electrical stimulation of the anterior nucleus of thalamus for treatment of refractory epilepsy Epilepsia 51 899–908 PubMed
Salanova V, Sperling MR, Gross RE, Irwin CP, Vollhaber JA, Giftakis JE and Fisher RS 2021. The SANTÉ study at 10 years of follow-up: Effectiveness, safety, and sudden unexpected death in epilepsy Epilepsia 62 1306–17 PubMed
Morrell MJ 2011. Responsive cortical stimulation for the treatment of medically intractable partial epilepsy Neurology 77 1295–304 PubMed
Mivalt F, Kremen V, Sladky V, Balzekas I, Nejedly P, Gregg NM, Lundstrom BN, Lepkova K, Pridalova T, Brinkmann BH, Jurak P, Van Gompel J J, Miller K, Denison T, St. Louis EK and Worrell GA 2022. Electrical brain stimulation and continuous behavioral state tracking in ambulatory humans J. Neural Eng 19 016019 PubMed PMC
Gregg NM, Nasseri M, Kremen V, Patterson EE, Sturges BK, Denison TJ, Brinkmann BH and Worrell GA 2020. Circadian and multiday seizure periodicities, and seizure clusters in canine epilepsy Brain Commun 2 PubMed PMC
Dell KL, Payne DE, Kremen V, Maturana MI, Gerla V, Nejedly P, Worrell GA, Lenka L, Mivalt F, Boston RC, Brinkmann BH, D’Souza W, Burkitt AN, Grayden DB, Kuhlmann L, Freestone DR and Cook MJ 2021. Seizure likelihood varies with day-to-day variations in sleep duration in patients with refractory focal epilepsy: A longitudinal electroencephalography investigation EClinicalMedicine PubMed PMC
Hofstra W Ae and de Weerd AW 2009. The circadian rhythm and its interaction with human epilepsy: A review of literature Sleep Med. Rev PubMed
Durazzo TS, Spencer SS, Duckrow RB, Novotny EJ, Spencer DD and Zaveri HP 2008. Temporal distributions of seizure occurrence from various epileptogenic regions Neurology PubMed
Kremen V, Duque JJ, Brinkmann BH, Berry BM, Kucewicz MT, Khadjevand F, Van Gompel J, Stead M, St Louis E K and Worrell GA 2017. Behavioral state classification in epileptic brain using intracranial electrophysiology J. Neural Eng 14 026001. PubMed PMC
Kremen V, Brinkmann BH, Van Gompel J J, Stead M, St Louis E K and Worrell GA 2019. Automated unsupervised behavioral state classification using intracranial electrophysiology J. Neural Eng 16 026004. PubMed
Kremen V, Brinkmann BH, Kim I, Guragain H, Nasseri M, Magee AL, Pal Attia T, Nejedly P, Sladky V, Nelson N, Chang S-Y, Herron JA, Adamski T, Baldassano S, Cimbalnik J, Vasoli V, Fehrmann E, Chouinard T, Patterson EE, Litt B, Stead M, Van Gompel J, Sturges BK, Jo HJ, Crowe CM, Denison T and Worrell GA 2018. Integrating Brain Implants With Local and Distributed Computing Devices: A Next Generation Epilepsy Management System IEEE J. Transl. Eng. Heal. Med 6 1–12 PubMed PMC
Pal Attia T, Crepeau D, Kremen V, Nasseri M, Guragain H, Steele SW, Sladky V, Nejedly P, Mivalt F, Herron J, Stead M, Denison T, Worrell GA and Brinkmann BH 2021. Epilepsy Personal Assistant Device -A Mobile Platform for Brain State, Dense Behavioral and Physiology Tracking and Controlling Adaptive Stimulation Front. Neurol PubMed PMC
Mivalt F, Sladky V, Balzekas I, Pridalova T, Miller KJ, van Gompel J, Denison T, Brinkmann BH, Kremen V and Worrell GA 2022. Deep Generative Networks for Algorithm Development in Implantable Neural Technology 2022 IEEE International Conference on Systems, Man, and Cybernetics (SMC) (IEEE) pp 1736–41
Iber C, Ancoli-Israel S, Chesson A and Quan SF 2007. The AASM Manual for the Scoring of Sleep and Associated Events: Rules, Terminology and Technical Specification J. Clin. Sleep Med
Albus U 2012. Guide for the Care and Use of Laboratory Animals (8th edn) Lab. Anim
Grewal SS, Middlebrooks EH, Kaufmann TJ, Stead M, Lundstrom BN, Worrell GA, Lin C, Baydin S and Van Gompel J J 2018. Fast gray matter acquisition T1 inversion recovery MRI to delineate the mammillothalamic tract for preoperative direct targeting of the anterior nucleus of the thalamus for deep brain stimulation in epilepsy Neurosurg. Focus 45 E6 PubMed
Sudhyadhom A, Haq IU, Foote KD, Okun MS and Bova FJ 2009. A high resolution and high contrast MRI for differentiation of subcortical structures for DBS targeting: The Fast Gray Matter Acquisition T1 Inversion Recovery (FGATIR) Neuroimage PubMed
Brant-Zawadzki M, Gillan GD and Nitz WR 1992. MP RAGE: a three-dimensional, T1-weighted, gradient-echo sequence--initial experience in the brain. Radiology 182 769–75 PubMed
Silber MH 2012. Staging sleep Sleep Med. Clin 7 487–96
Wauquier A, Verheyen JL, Van Den Broeck WAE and Janssen PAJ 1979. Visual and computer-based analysis of 24 h sleep-waking patterns in the dog Electroencephalogr. Clin. Neurophysiol 46 33–48 PubMed
Kis A, Szakadát S, Kovács E, Gácsi M, Simor P, Gombos F, Topál J, Miklósi Á and Bódizs R 2014. Development of a non-invasive polysomnography technique for dogs (Canis familiaris) Physiol. Behav PubMed
Bódizs R, Kis A, Gácsi M and Topál J 2020. Sleep in the dog: comparative, behavioral and translational relevance Curr. Opin. Behav. Sci 33 25–33
Adams GJ and Johnson KG 1993. Sleep-wake cycles and other night-time behaviours of the domestic dog Canis familiaris Appl. Anim. Behav. Sci 36 233–48