D. Sinitsyn, A. Poydasheva, I. Bakulin, A. Zabirova, D. Lagoda, Natalia Suponeva, M. Piradov
{"title":"估计 N-Back 任务期间脑电图中最大 Theta-Gamma 耦合的频率:对方法和时间不稳定性的敏感性","authors":"D. Sinitsyn, A. Poydasheva, I. Bakulin, A. Zabirova, D. Lagoda, Natalia Suponeva, M. Piradov","doi":"10.3390/a16120540","DOIUrl":null,"url":null,"abstract":"Phase-amplitude coupling (PAC) of theta and gamma rhythms of the brain has been observed in animals and humans, with evidence of its involvement in cognitive functions and brain disorders. This motivates finding individual frequencies of maximal theta-gamma coupling (TGC) and using them to adjust brain stimulation. This use implies the stability of the frequencies at least during the investigation, which has not been sufficiently studied. Meanwhile, there is a range of available algorithms for PAC estimation in the literature. We explored several options at different steps of the calculation, applying the resulting algorithms to the EEG data of 16 healthy subjects performing the n-back working memory task, as well as a benchmark recording with previously reported strong PAC. By comparing the results for the two halves of each session, we estimated reproducibility at a time scale of a few minutes. For the benchmark data, the results were largely similar between the algorithms and stable over time. However, for the EEG, the results depended substantially on the algorithm, while also showing poor reproducibility, challenging the validity of using them for personalizing brain stimulation. Further research is needed on the PAC estimation algorithms, cognitive tasks, and other aspects to reliably determine and effectively use TGC parameters in neuromodulation.","PeriodicalId":7636,"journal":{"name":"Algorithms","volume":"26 1","pages":""},"PeriodicalIF":1.8000,"publicationDate":"2023-11-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Estimating the Frequencies of Maximal Theta-Gamma Coupling in EEG during the N-Back Task: Sensitivity to Methodology and Temporal Instability\",\"authors\":\"D. Sinitsyn, A. Poydasheva, I. Bakulin, A. Zabirova, D. Lagoda, Natalia Suponeva, M. Piradov\",\"doi\":\"10.3390/a16120540\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Phase-amplitude coupling (PAC) of theta and gamma rhythms of the brain has been observed in animals and humans, with evidence of its involvement in cognitive functions and brain disorders. This motivates finding individual frequencies of maximal theta-gamma coupling (TGC) and using them to adjust brain stimulation. This use implies the stability of the frequencies at least during the investigation, which has not been sufficiently studied. Meanwhile, there is a range of available algorithms for PAC estimation in the literature. We explored several options at different steps of the calculation, applying the resulting algorithms to the EEG data of 16 healthy subjects performing the n-back working memory task, as well as a benchmark recording with previously reported strong PAC. By comparing the results for the two halves of each session, we estimated reproducibility at a time scale of a few minutes. For the benchmark data, the results were largely similar between the algorithms and stable over time. However, for the EEG, the results depended substantially on the algorithm, while also showing poor reproducibility, challenging the validity of using them for personalizing brain stimulation. Further research is needed on the PAC estimation algorithms, cognitive tasks, and other aspects to reliably determine and effectively use TGC parameters in neuromodulation.\",\"PeriodicalId\":7636,\"journal\":{\"name\":\"Algorithms\",\"volume\":\"26 1\",\"pages\":\"\"},\"PeriodicalIF\":1.8000,\"publicationDate\":\"2023-11-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Algorithms\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.3390/a16120540\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Algorithms","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.3390/a16120540","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
Estimating the Frequencies of Maximal Theta-Gamma Coupling in EEG during the N-Back Task: Sensitivity to Methodology and Temporal Instability
Phase-amplitude coupling (PAC) of theta and gamma rhythms of the brain has been observed in animals and humans, with evidence of its involvement in cognitive functions and brain disorders. This motivates finding individual frequencies of maximal theta-gamma coupling (TGC) and using them to adjust brain stimulation. This use implies the stability of the frequencies at least during the investigation, which has not been sufficiently studied. Meanwhile, there is a range of available algorithms for PAC estimation in the literature. We explored several options at different steps of the calculation, applying the resulting algorithms to the EEG data of 16 healthy subjects performing the n-back working memory task, as well as a benchmark recording with previously reported strong PAC. By comparing the results for the two halves of each session, we estimated reproducibility at a time scale of a few minutes. For the benchmark data, the results were largely similar between the algorithms and stable over time. However, for the EEG, the results depended substantially on the algorithm, while also showing poor reproducibility, challenging the validity of using them for personalizing brain stimulation. Further research is needed on the PAC estimation algorithms, cognitive tasks, and other aspects to reliably determine and effectively use TGC parameters in neuromodulation.