Pre-stimulus brain state predicts auditory pattern identification accuracy

Hansen, Natalie; Harel, Assaf; Iyer, Nandini; Simpson, Brian D.; Wisniewski, Matthew G.

doi:10.1016/j.neuroimage.2019.05.054

Cited by 9 publications

(7 citation statements)

References 51 publications

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“…In the current study we show that phase modulates behavioral response choices. We did not find any effect of accuracy in contrast to previous articles (Hansen et al, 2019;Straub et al, 2015;Henry et al, 2016). It is still an open question what design choices lead to these differences.…”

Section: Limitations Of the Studycontrasting

confidence: 99%

Phase-Coded Oscillatory Ordering Promotes the Separation of Closely Matched Representations to Optimize Perceptual Discrimination

et al. 2020

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Low-frequency oscillations are proposed to be involved in separating neuronal representations belonging to different items. Although item-specific neuronal activity was found to cluster on different oscillatory phases, the influence of this mechanism on perception is unknown. Here, we investigated the perceptual consequences of neuronal item separation through oscillatory clustering. In an electroencephalographic experiment, participants categorized sounds parametrically varying in pitch, relative to an arbitrary pitch boundary. Pre-stimulus theta and alpha phase biased near-boundary sound categorization to one category or the other. Phase also modulated whether evoked neuronal responses contributed stronger to the fit of the sound envelope of one or another category. Intriguingly, participants with stronger oscillatory clustering (phase strongly biasing sound categorization) in the theta, but not alpha, range had steeper perceptual psychometric slopes (sharper sound category discrimination). These results indicate that neuronal sorting by phase directly influences subsequent perception and has a positive impact on discrimination performance.

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Section: Limitations Of the Studycontrasting

confidence: 99%

Phase-Coded Oscillatory Ordering Promotes the Separation of Closely Matched Representations to Optimize Perceptual Discrimination

et al. 2020

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“…The usefulness of visual information for aiding comprehension in adverse listening conditions could be investigated by presenting audio‐visual stimuli (e.g., Brungart et al., 2005) and measuring occipital alpha. Anticipatory attention could be examined by looking at prestimulus EEG activity (e.g., in the alpha band; Hansen et al., 2019; Klimesch, 2012). The possibilities are many.…”

Section: Discussionmentioning

confidence: 99%

EEG power spectral dynamics associated with listening in adverse conditions

et al. 2021

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“…It might also be that the phase of prestimulus tau rhythms at sound onsets should affect listening performance, with correct and incorrect trials exhibiting opposing phase angles at sound onsets (cf. Hansen et al, 2019). Still another possible prediction is that tau enhancement, rather than suppression, should relate to performance at moments when an ongoing auditory stimulus stream needs to be ignored (Hartmann et al, 2014).…”

Section: Why Use An Ica Decomposition Approach To Analysis Of Tau Rhy...mentioning

confidence: 99%

Finding tau rhythms in EEG: An independent component analysis approach

Wisniewski,

Joyner,

Zakrzewski

et al. 2024

Human Brain Mapping

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Tau rhythms are largely defined by sound responsive alpha band (~8–13 Hz) oscillations generated largely within auditory areas of the superior temporal gyri. Studies of tau have mostly employed magnetoencephalography or intracranial recording because of tau's elusiveness in the electroencephalogram. Here, we demonstrate that independent component analysis (ICA) decomposition can be an effective way to identify tau sources and study tau source activities in EEG recordings. Subjects (N = 18) were passively exposed to complex acoustic stimuli while the EEG was recorded from 68 electrodes across the scalp. Subjects' data were split into 60 parallel processing pipelines entailing use of five levels of high‐pass filtering (passbands of 0.1, 0.5, 1, 2, and 4 Hz), three levels of low‐pass filtering (25, 50, and 100 Hz), and four different ICA algorithms (fastICA, infomax, adaptive mixture ICA [AMICA], and multi‐model AMICA [mAMICA]). Tau‐related independent component (IC) processes were identified from this data as being localized near the superior temporal gyri with a spectral peak in the 8–13 Hz alpha band. These “tau ICs” showed alpha suppression during sound presentations that was not seen for other commonly observed IC clusters with spectral peaks in the alpha range (e.g., those associated with somatomotor mu, and parietal or occipital alpha). The choice of analysis parameters impacted the likelihood of obtaining tau ICs from an ICA decomposition. Lower cutoff frequencies for high‐pass filtering resulted in significantly fewer subjects showing a tau IC than more aggressive high‐pass filtering. Decomposition using the fastICA algorithm performed the poorest in this regard, while mAMICA performed best. The best combination of filters and ICA model choice was able to identify at least one tau IC in the data of ~94% of the sample. Altogether, the data reveal close similarities between tau EEG IC dynamics and tau dynamics observed in MEG and intracranial data. Use of relatively aggressive high‐pass filters and mAMICA decomposition should allow researchers to identify and characterize tau rhythms in a majority of their subjects. We believe adopting the ICA decomposition approach to EEG analysis can increase the rate and range of discoveries related to auditory responsive tau rhythms.

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Pre-stimulus brain state predicts auditory pattern identification accuracy

Cited by 9 publications

References 51 publications

Phase-Coded Oscillatory Ordering Promotes the Separation of Closely Matched Representations to Optimize Perceptual Discrimination

Phase-Coded Oscillatory Ordering Promotes the Separation of Closely Matched Representations to Optimize Perceptual Discrimination

EEG power spectral dynamics associated with listening in adverse conditions

Finding tau rhythms in EEG: An independent component analysis approach

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