Single-trial characterization of neural rhythms: Potential and challenges
The average power of rhythmic neural responses as captured by MEG/EEG/LFP recordings is a prevalent index of human brain function. Increasing evidence questions the utility of trial-/group averaged power estimates however, as seemingly sustained activity patterns may be brought about by time-varying...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2020-02-01
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| Series: | NeuroImage |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S105381191930922X |
| _version_ | 1818580119904583680 |
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| author | Julian Q. Kosciessa Thomas H. Grandy Douglas D. Garrett Markus Werkle-Bergner |
| author_facet | Julian Q. Kosciessa Thomas H. Grandy Douglas D. Garrett Markus Werkle-Bergner |
| author_sort | Julian Q. Kosciessa |
| collection | DOAJ |
| description | The average power of rhythmic neural responses as captured by MEG/EEG/LFP recordings is a prevalent index of human brain function. Increasing evidence questions the utility of trial-/group averaged power estimates however, as seemingly sustained activity patterns may be brought about by time-varying transient signals in each single trial. Hence, it is crucial to accurately describe the duration and power of rhythmic and arrhythmic neural responses on the single trial-level. However, it is less clear how well this can be achieved in empirical MEG/EEG/LFP recordings. Here, we extend an existing rhythm detection algorithm (extended Better OSCillation detection: “eBOSC”; cf. Whitten et al., 2011) to systematically investigate boundary conditions for estimating neural rhythms at the single-trial level. Using simulations as well as resting and task-based EEG recordings from a micro-longitudinal assessment, we show that alpha rhythms can be successfully captured in single trials with high specificity, but that the quality of single-trial estimates varies greatly between subjects. Despite those signal-to-noise-based limitations, we highlight the utility and potential of rhythm detection with multiple proof-of-concept examples, and discuss implications for single-trial analyses of neural rhythms in electrophysiological recordings. Using an applied example of working memory retention, rhythm detection indicated load-related increases in the duration of frontal theta and posterior alpha rhythms, in addition to a frequency decrease of frontal theta rhythms that was observed exclusively through amplification of rhythmic amplitudes. |
| first_indexed | 2024-12-16T07:12:32Z |
| format | Article |
| id | doaj.art-8574ebcdee4c4f93a9a4d6a4a2b161c6 |
| institution | Directory Open Access Journal |
| issn | 1095-9572 |
| language | English |
| last_indexed | 2024-12-16T07:12:32Z |
| publishDate | 2020-02-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj.art-8574ebcdee4c4f93a9a4d6a4a2b161c62022-12-21T22:39:52ZengElsevierNeuroImage1095-95722020-02-01206116331Single-trial characterization of neural rhythms: Potential and challengesJulian Q. Kosciessa0Thomas H. Grandy1Douglas D. Garrett2Markus Werkle-Bergner3Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Lentzeallee 94, 14195, Berlin, Germany; Center for Lifespan Psychology, Max Planck Institute for Human Development, Lentzeallee 94, 14195, Berlin, Germany; Department of Psychology, Humboldt-Universität zu Berlin, Rudower Chaussee 18, 12489, Berlin, Germany; Corresponding author. Center for Lifespan Psychology, Max Planck Institute for Human Development, Lentzeallee 94, 14195, Berlin, Germany.Center for Lifespan Psychology, Max Planck Institute for Human Development, Lentzeallee 94, 14195, Berlin, GermanyMax Planck UCL Centre for Computational Psychiatry and Ageing Research, Lentzeallee 94, 14195, Berlin, Germany; Center for Lifespan Psychology, Max Planck Institute for Human Development, Lentzeallee 94, 14195, Berlin, GermanyCenter for Lifespan Psychology, Max Planck Institute for Human Development, Lentzeallee 94, 14195, Berlin, Germany; Corresponding author.The average power of rhythmic neural responses as captured by MEG/EEG/LFP recordings is a prevalent index of human brain function. Increasing evidence questions the utility of trial-/group averaged power estimates however, as seemingly sustained activity patterns may be brought about by time-varying transient signals in each single trial. Hence, it is crucial to accurately describe the duration and power of rhythmic and arrhythmic neural responses on the single trial-level. However, it is less clear how well this can be achieved in empirical MEG/EEG/LFP recordings. Here, we extend an existing rhythm detection algorithm (extended Better OSCillation detection: “eBOSC”; cf. Whitten et al., 2011) to systematically investigate boundary conditions for estimating neural rhythms at the single-trial level. Using simulations as well as resting and task-based EEG recordings from a micro-longitudinal assessment, we show that alpha rhythms can be successfully captured in single trials with high specificity, but that the quality of single-trial estimates varies greatly between subjects. Despite those signal-to-noise-based limitations, we highlight the utility and potential of rhythm detection with multiple proof-of-concept examples, and discuss implications for single-trial analyses of neural rhythms in electrophysiological recordings. Using an applied example of working memory retention, rhythm detection indicated load-related increases in the duration of frontal theta and posterior alpha rhythms, in addition to a frequency decrease of frontal theta rhythms that was observed exclusively through amplification of rhythmic amplitudes.http://www.sciencedirect.com/science/article/pii/S105381191930922XRhythm detectionRhythmic durationRhythmic amplitudeInter-individual differencesSingle-trial rhythm estimates |
| spellingShingle | Julian Q. Kosciessa Thomas H. Grandy Douglas D. Garrett Markus Werkle-Bergner Single-trial characterization of neural rhythms: Potential and challenges NeuroImage Rhythm detection Rhythmic duration Rhythmic amplitude Inter-individual differences Single-trial rhythm estimates |
| title | Single-trial characterization of neural rhythms: Potential and challenges |
| title_full | Single-trial characterization of neural rhythms: Potential and challenges |
| title_fullStr | Single-trial characterization of neural rhythms: Potential and challenges |
| title_full_unstemmed | Single-trial characterization of neural rhythms: Potential and challenges |
| title_short | Single-trial characterization of neural rhythms: Potential and challenges |
| title_sort | single trial characterization of neural rhythms potential and challenges |
| topic | Rhythm detection Rhythmic duration Rhythmic amplitude Inter-individual differences Single-trial rhythm estimates |
| url | http://www.sciencedirect.com/science/article/pii/S105381191930922X |
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