{"doi":"10.1371/journal.pcbi.1009280","title":"A hidden Markov model reliably characterizes ketamine-induced spectral dynamics in macaque local field potentials and human electroencephalograms","abstract":"Ketamine is an NMDA receptor antagonist commonly used to maintain general anesthesia. At anesthetic doses, ketamine causes high power gamma (25-50 Hz) oscillations alternating with slow-delta (0.1-4 Hz) oscillations. These dynamics are readily observed in local field potentials (LFPs) of non-human primates (NHPs) and electroencephalogram (EEG) recordings from human subjects. However, a detailed statistical analysis of these dynamics has not been reported. We characterize ketamine's neural dynamics using a hidden Markov model (HMM). The HMM observations are sequences of spectral power in seven canonical frequency bands between 0 to 50 Hz, where power is averaged within each band and scaled between 0 and 1. We model the observations as realizations of multivariate beta probability distributions that depend on a discrete-valued latent state process whose state transitions obey Markov dynamics. Using an expectation-maximization algorithm, we fit this beta-HMM to LFP recordings from 2 NHPs, and separately, to EEG recordings from 9 human subjects who received anesthetic doses of ketamine. Our beta-HMM framework provides a useful tool for experimental data analysis. Together, the estimated beta-HMM parameters and optimal state trajectory revealed an alternating pattern of states characterized primarily by gamma and slow-delta activities. The mean duration of the gamma activity was 2.2s([1.7,2.8]s) and 1.2s([0.9,1.5]s) for the two NHPs, and 2.5s([1.7,3.6]s) for the human subjects. The mean duration of the slow-delta activity was 1.6s([1.2,2.0]s) and 1.0s([0.8,1.2]s) for the two NHPs, and 1.8s([1.3,2.4]s) for the human subjects. Our characterizations of the alternating gamma slow-delta activities revealed five sub-states that show regular sequential transitions. These quantitative insights can inform the development of rhythm-generating neuronal circuit models that give mechanistic insights into this phenomenon and how ketamine produces altered states of arousal.","journal":"PLoS Computational Biology","year":2021,"id":167462,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":32,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9553,"is_data_producer":false,"deposit_databanks":null,"is_oa":true,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2021-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":343783,"name":"S. Chakravarty","orcid":"0000-0002-3682-2060","position":1,"is_corresponding":false},{"id":694906,"name":"Jacob Donoghue","orcid":"0000-0002-8845-5098","position":2,"is_corresponding":false},{"id":694907,"name":"Meredith Mahnke","orcid":"0000-0002-7125-9031","position":3,"is_corresponding":false},{"id":694908,"name":"Pegah Kahali","orcid":"0000-0003-3959-4793","position":4,"is_corresponding":false},{"id":280806,"name":"Shubham Chamadia","orcid":"0000-0002-7055-8048","position":5,"is_corresponding":false},{"id":270289,"name":"Oluwaseun Akeju","orcid":"0000-0002-6740-1250","position":6,"is_corresponding":false},{"id":255651,"name":"Earl K. Miller","orcid":"0000-0002-0582-6958","position":7,"is_corresponding":false},{"id":343788,"name":"Emery N. Brown","orcid":"0000-0003-2668-7819","position":8,"is_corresponding":false},{"id":458704,"name":"Indie C. Garwood","orcid":"0000-0002-7578-7480","position":0,"is_corresponding":true}],"reference_count":83,"raw_metadata":null,"created_at":"2026-07-18T23:45:58.359801Z","pmid":"34407069","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}