Spontaneous alpha and theta oscillations are related to complementary aspects of cognitive control in younger and older adults

Clements, Grace M.; Bowie, Daniel C.; Low, Kathy A.; Fabiani, Monica; Gratton, Gabriele

doi:10.1101/2020.04.09.033811

Cited by 12 publications

(19 citation statements)

References 67 publications

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“…Finally, our results revealed significant and positive correlations between theta and alpha amplitude and the processing speed in the group of schizophrenia patients, both in MW and in episodes of attention to the task. This relationship is consistent with previous research [ 77 , 78 ] that linked processing speed with increases in the oscillatory power of alpha and theta frequencies. Furthermore, our results are consistent with those of a recent study with people with ADHD [ 79 ].…”

Section: Discussionsupporting

confidence: 93%

Local Oscillatory Brain Dynamics of Mind Wandering in Schizophrenia

et al. 2021

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A number of studies have focused on brain dynamics underlying mind wandering (MW) states in healthy people. However, there is limited understanding of how the oscillatory dynamics accompanying MW states and task-focused states are characterized in clinical populations. In this study, we explored EEG local synchrony of MW associated with schizophrenia, under the premise that changes in attention that arise during MW are associated with a different pattern of brain activity. To this end, we measured the power of EEG oscillations in different frequency bands, recorded while participants watched short video clips. In the group of participants diagnosed with schizophrenia, the power in MW states was significantly lower than during task-focused states, mainly in the frontal and posterior regions. However, in the group of healthy controls, the differences in power between the task-focused and MW states occurred exclusively in the posterior region. Furthermore, the power of the frequency bands during MW and during episodes of task-focused attention correlated with cognitive variables such as processing speed and working memory. These findings on dynamic changes of local synchronization in different frequency bands and areas of the cortex can improve our understanding of mental disorders, such as schizophrenia.

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Section: Discussionsupporting

confidence: 93%

Local Oscillatory Brain Dynamics of Mind Wandering in Schizophrenia

et al. 2021

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“…While broadband activity resulting from non-oscillatory sources is often referred to as 1/ f “noise,” it is likely to have functional significance for behavioral performance ( Clements et al, 2021 ; He et al, 2010 ; Miller et al, 2014 ; Ouyang et al, 2020 ). Nevertheless, we will use the terms “1/ f activity” and “1/ f noise” interchangeably throughout this manuscript to refer to broadband activity, simply because in studies of narrowband oscillatory activity, this feature is not typically of interest.…”

Section: Introductionmentioning

confidence: 99%

The impact of 1/f activity and baseline correction on the results and interpretation of time-frequency analyses of EEG/MEG data: A cautionary tale

et al. 2021

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Typically, time-frequency analysis (TFA) of electrophysiological data is aimed at isolating narrowband signals (oscillatory activity) from broadband non-oscillatory (1/ f ) activity, so that changes in oscillatory activity resulting from experimental manipulations can be assessed. A widely used method to do this is to convert the data to the decibel (dB) scale through baseline division and log transformation. This procedure assumes that, for each frequency, sources of power (i.e., oscillations and 1/ f activity) scale by the same factor relative to the baseline (multiplicative model). This assumption may be incorrect when signal and noise are independent contributors to the power spectrum (additive model). Using resting-state EEG data from 80 participants, we found that the level of 1/ f activity and alpha power are not positively correlated within participants, in line with the additive but not the multiplicative model. Then, to assess the effects of dB conversion on data that violate the multiplicativity assumption, we simulated a mixed design study with one between-subject (noise level, i.e., level of 1/ f activity) and one within-subject (signal amplitude, i.e., amplitude of oscillatory activity added onto the background 1/ f activity) factor. The effect size of the noise level × signal amplitude interaction was examined as a function of noise difference between groups, following dB conversion. Findings revealed that dB conversion led to the over- or under-estimation of the true interaction effect when groups differing in 1/ f levels were compared, and it also led to the emergence of illusory interactions when none were present. This is because signal amplitude was systematically underestimated in the noisier compared to the less noisy group. Hence, we recommend testing whether the level of 1/ f activity differs across groups or conditions and using multiple baseline correction strategies to validate results if it does. Such a situation may be particularly common in aging, developmental, or clinical studies.

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“…Notably, this design was chosen because the 1/f property of the power spectra of neural data has recently been shown to change with age (Clements et al, 2020;Dave et al, 2018;, clinical diagnosis (Robertson et al, 2019), and behavioral states (Miller et al, 2014;Podvalny et al, 2015). These findings make the modelled situation, whereby a within-subject change (e.g., the difference between one condition that purportedly engages a cognitive process and one that does not, such as the incongruent and the congruent conditions in a conflict task) is compared between two groups that differ in broadband activity (e.g., younger and older adults)…”

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confidence: 99%

“…As noted above, these findings are likely to be of high importance for researchers investigating between-group and individual differences, such as cross-sectional studies of development, aging, or psychiatric disorders. Neural variability as captured by characteristics of the 1/f slope of the power spectrum is likely to differ between various age groups (Clements et al, 2020;Dave et al, 2018; and healthy and clinical populations (e.g., in ADHD; Robertson et al, 2019). In such studies, it might be prudent to ensure that unexpected findings are not the result of pre-processing based on inappropriate assumptions, for example by investigating the correlation between levels of broadband and narrowband activity or by re-processing data using a different baseline correction method and reporting both in the Results section.…”

Section: Limitations and Suggestions For Future Researchmentioning

confidence: 99%

“…While broadband activity resulting from non-oscillatory sources is often referred to as 1/f "noise," it is likely to have functional significance for behavioral performance (Clements et al, 2020;He et al, 2010;Miller et al, 2014;Ouyang et al, 2020). Nevertheless, we will use the terms "1/f activity" and "1/f noise" interchangeably throughout this manuscript to refer to broadband activity, simply because in studies of narrowband oscillatory activity, this feature is not typically of interest.…”

Section: Introductionmentioning

confidence: 99%

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The impact of 1/factivity and baseline correction on the results and interpretation of time-frequency analyses of EEG/MEG data: A cautionary tale

Gyurkovics

Clements

Low

et al. 2020

Preprint

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Typically, time-frequency analysis (TFA) of electrophysiological data is aimed at isolating narrowband signals (oscillatory activity) from broadband non-oscillatory (1/f) activity, so that changes in oscillatory activity resulting from experimental manipulations can be assessed. A widely used method to do this is to convert the data to the decibel (dB) scale through baseline division and log transformation. This procedure assumes that, for each frequency, sources of power (i.e., oscillations and 1/f activity) scale by the same factor relative to the baseline (multiplicative model). This assumption may be incorrect when signal and noise are independent contributors to the power spectrum (additive model). Using resting-state EEG data from 80 participants, we found that the level of 1/f activity and alpha power are not positively correlated within participants, in line with the additive but not the multiplicative model. Then, to assess the effects of dB conversion on data that violate the multiplicativity assumption, we simulated a mixed design study with one between-subject (noise level, i.e., level of 1/f activity) and one within-subject (signal amplitude, i.e., amplitude of oscillatory activity added onto the background 1/f activity) factor. The effect size of the noise level × signal amplitude interaction was examined as a function of noise difference between groups, following dB conversion. Findings revealed that dB conversion led to the over- or under-estimation of the true interaction effect when groups differing in 1/f levels were compared, and it also led to the emergence of illusory interactions when none were present. This is because signal amplitude was systematically underestimated in the noisier compared to the less noisy group. Hence, we recommend testing whether the level of 1/f activity differs across groups or conditions and using multiple baseline correction strategies to validate results if it does. Such a situation may be particularly common in aging, developmental, or clinical studies.

show abstract

Spontaneous alpha and theta oscillations are related to complementary aspects of cognitive control in younger and older adults

Cited by 12 publications

References 67 publications

Local Oscillatory Brain Dynamics of Mind Wandering in Schizophrenia

Local Oscillatory Brain Dynamics of Mind Wandering in Schizophrenia

The impact of 1/f activity and baseline correction on the results and interpretation of time-frequency analyses of EEG/MEG data: A cautionary tale

The impact of 1/factivity and baseline correction on the results and interpretation of time-frequency analyses of EEG/MEG data: A cautionary tale

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