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Modality-specific tracking of attention and sensory statistics in the human
electrophysiological spectral exponent
Leonhard Waschke1,2§, Thomas Donoghue3, Lorenz Fiedler4, Sydney Smith5, Douglas D.
Garrett1,2, Bradley Voytek3,5,6,7#, & Jonas Obleser*8,9#
1 Max Planck UCL Centre for Computational Psychiatry and Ageing Research, Max
Planck Institute for Human Development, 14195 Berlin, Germany
2 Center for Lifespan Psychology, Max Planck Institute for Human Development, 14195
Berlin, Germany
3 Department of Cognitive Science, University of California, San Diego, 9500 Gilman Drive,
La Jolla, CA 92093, USA
4 Eriksholm Research Centre, Oticon A/S, Snekkersten, Denmark
5 Neurosciences Graduate Program, University of California, San Diego, 9500 Gilman Drive,
La Jolla, CA 92093, USA
6 Halıcıoğlu Data Science Institute, University of California, San Diego, La Jolla, CA 92093,
USA
7 Kavli Institute for Brain and Mind, University of California, San Diego, La Jolla, CA, USA
8 Department of Psychology, University of Lübeck, 23562 Lübeck, Germany
9 Center of Brain, Behavior, and Metabolism, University of Lübeck, 23562 Lübeck,
Germany
# Authors share senior authorship
§ Lead contact: Dr. Leonhard Waschke, Max Planck UCL Centre for Computational Psychiatry
and Ageing Research, Max Planck Institute for Human Development,14195 Berlin, Germany,
phone +49 30 82406 249, waschke@mpib-berlin.mpg.de
Conflict of interest: The authors declare no competing financial interests.
Author contributions: LW, JO, & BV designed research with contributions from LF & TD. LW,
TD, & SS recorded data. LW analysed data with contributions from JO, BV, DDG, TD, & LF.
LW wrote the manuscript with contributions from all authors.
Acknowledgements: LW and DDG are supported by an Emmy Noether Programme grant
from the German Research Foundation (to DDG), and by the Max Planck UCL Centre for
Computational Psychiatry and Ageing Research. BV is supported by the Whitehall Foundation
Grant 2017-12-73, the National Science Foundation Grant BCS-1736028 and the National
Institute of General Medical Sciences Grant R01GM134363-01. JO is supported by the
European Research Council (ERC-CoG-2014-646696).
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2
Abstract
A hallmark of electrophysiological brain activity is its 1/f-like spectrum – power decreases with
increasing frequency. The steepness of this “roll-off” is approximated by the spectral exponent,
which in invasively recorded neural populations reflects the balance of excitatory to inhibitory
neural activity (E:I balance). Here, we first demonstrate that the spectral exponent of non-
invasive electroencephalography (EEG) recordings is highly sensitive to general, anaesthesia-
driven as well as specific, attention-driven changes in E:I balance. We then present results
from an EEG experiment during which participants detected faint target stimuli in streams of
simultaneously presented auditory and visual noise. EEG spectral exponents over auditory
and visual sensory cortices tracked stimulus spectral exponents of the corresponding domain,
while evoked responses remained unchanged. Crucially, the degree of this stimulus–brain
spectral-exponent coupling was positively linked to behavioural performance. Our results
highlight the relevance of neural 1/f-like activity and enable the study of neural processes
previously thought to be inaccessible in non-invasive human recordings.
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3
Introduction
Non-invasive recordings of electrical brain activity represent the aggregated post-synaptic
activity of large cortical neuronal ensembles1. Frequency spectra of electrophysiological
recordings commonly display quasi-linearly decreasing power with increasing frequency (in
log/log space; see Fig 1; see refs2,3). In humans this decrease is super positioned by several
peaks, of which the most prominent is typically in the range of alpha oscillations (~8–12 Hz;
see ref4). The overall decrease in power as a function of frequency f, reflecting aperiodic as
opposed to oscillatory activity and the steepness of such 1/fχ spectra can be captured by the
spectral exponent χ, in which smaller values reflect flatter spectra.
Importantly, inter-individual differences in the steepness of human electroencephalography
power spectral densities (EEG PSD), estimated by the spectral exponent χ, are related to
chronological age, performance and display stable inter-individual differences2,5–8. Intra-
individual variations in EEG spectral exponents have also been reported as a function of
overall arousal level and activation9–11. Based on computational models and invasive
recordings of neural activity, it has been demonstrated that electrophysiological spectral
exponents capture the balance of excitatory and inhibitory neural activity (E:I), with lower
exponents indicating increased E:I balance12. Although unknown at present, it is plausible that
differences in non-invasive electrophysiological spectral exponents might also depict
variations in E:I balance.
The sensitivity of EEG spectral exponents to broad variations in E:I can be tested utilizing the
differential effects of distinct general anaesthetics on E:I. While propofol is known to result in
a net increase of inhibition, ketamine results in a relative increase of excitation13,14. In
accordance with models of single cell activity and invasive recordings12, propofol anaesthesia
should thus lead to an increase in the spectral exponent (steepening of the spectrum) and
ketamine anaesthesia to a decrease (flattening).
Even if EEG spectral exponents are sensitive to unspecific variations in E:I balance, it remains
unclear if specific, behaviourally relevant intra-individual E:I variations can be inferred in a
similar manner. For example, animal work has highlighted a topographically confined intra-
individual change in E:I balance during the selective allocation of attentional resources to one
sensory domain15–17. This work suggests that an attentional shift towards a given sensory
modality leads to desynchronized activity (i.e., reduced low-frequency oscillations and
increased high-frequency power) in cortical areas that process information from the currently
attended domain18,19. These shifts in desynchronization likely trace back to an attention-related
change in E:I balance towards excitation19,20, which is thought to manifest in a reduction of
spectral exponents12,21. If EEG spectral exponents indeed represent a sensitive approximation
of E:I balance, selective attention should also result in a topographically specific decrease of
exponents.
Importantly, 1/f-like processes not only take place in the human brain, but are ubiquitous in
nature22–25. Given that oscillatory brain activity has been shown to synchronize with oscillatory
sensory inputs in a behaviourally relevant manner26–29, this raises the question of whether a
similar link might exist between the 1/f profiles of neural responses and sensory inputs. Indeed,
even speech signals which are commonly linked to brain activity through correlations between
oscillatory power in speech stimuli and brain activity (i.e., entrainment in the broad sense30)
show a pronounced 1/f shape within their amplitude modulation (AM) spectrum (see e.g., ref31)
Hence, 1/f-like sensory inputs might represent an ecologically relevant signal beyond
oscillatory signals. However, it is unclear at present if the 1/f structure of sensory inputs is
tracked by 1/f-like neural activity in humans. Such a link between sensory inputs and brain
activity has been reported for non-human in vitro and in vivo experiments32–34, though it is
unclear at present whether this finding generalizes to humans and non-invasive recordings.
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Waschke et al.
4
Why would the 1/f of neural activity map the 1/f characteristics of sensory input? This might
trace back to the superposition of postsynaptic potentials, the main source of EEG signals1.
Postsynaptic potentials might temporally align and scale with the magnitude of sensory stimuli
and hence mimic their spectra, as has been suggested for steady state evoked potentials (e.g.,
ref35). Thus, beyond oscillatory inputs, it appears plausible that 1/f features of sensory input
may also be represented in the human brain and that this representation is essential to enable
behavioural performance. If the neural tracking of 1/f sensory features is non-invasively
detectable in humans, this would suggest non-oscillatory neural processes underlying
perception, while greatly extending the toolset of perceptual neuroscience to include a wide
range of naturalistic (1/f-like) stimuli.
Here, we test the conjecture that 1/f-like EEG activity captures changes in the E:I balance of
underlying neural populations. Such a non-invasive approximation of variations in human E:I
would be of great value, enabling investigations of processes previously inaccessible using
non-invasive imaging techniques. This includes the role of E:I in sensory processing and
perception36,37, selective attention19 and ageing38, and not least in disease39–41.
We here demonstrate the sensitivity of EEG spectral exponents to variations in E:I in two ways.
First, predicated on the differential effects of E:I imbalances in propofol and ketamine13,14, we
compare EEG spectra between quiet wakefulness, propofol, and ketamine anaesthesia42 and
demonstrate the potency of EEG spectral exponents to track broad intra-individual variations
in E:I balance. The second builds on the critical role of E:I balance in attention. Here, we
analysed data from human participants who performed a challenging multi-sensory detection
task during which they had to attend to one of two concurrently stimulated sensory modalities
(auditory vs. visual) in order to detect faint target stimuli. Our results demonstrate that despite
constant multisensory input, selective attention entails a modality-specific reduction of spectral
exponents (spectral flattening) that is in line with an increased E:I ratio. In addition to different
drivers of E:I balance, we investigated the link between environmental 1/f input and 1/f-like
brain activity. We tested the tracking of 1/f sensory features in 1/f EEG spectral exponents and
present evidence for behaviourally relevant neural tracking of 1/f inputs.
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Results
EEG spectral exponents track differential changes to E:I balance by Propofol and
Ketamine
To test the sensitivity of EEG spectral exponents to physiological changes in the balance of
excitatory and inhibitory neural activity (E:I balance), we contrasted spectral exponents of
human EEG recordings between quiet wakefulness and anaesthesia for two different general
anaesthetics: propofol and ketamine. While propofol is known to result in a net increase of
inhibition, ketamine results in a relative increase of excitation13,14. In accordance with models
of single cell activity and invasive recordings12, propofol anaesthesia should thus lead to an
increase in the spectral exponent (steepening of the spectrum) and ketamine anaesthesia to
a decrease (flattening). Based on previous results, the effect of anaesthesia on EEG spectral
exponents is expected to be highly consistent and display little topographical variation10. For
simplicity, we focused on a set of 5 central electrodes that receive contributions from many
cortical and subcortical sources (see Fig 1). Indeed, EEG spectral exponents increased under
propofol and decreased under ketamine anaesthesia in all participants (both ppermuted < .0009,
Fig 1). Thus, EEG spectral exponents are sensitive to broad changes of E:I balance in humans
and increase with net inhibition.
Behavioural performance in a multimodal detection task
Participants (N = 24) performed a challenging multisensory task during which they had to
detect regular (i.e., sinusoidal) amplitude variations in streams of amplitude modulated white
noise (Fig 2A). In detail, participants attended either auditory or visual noise stimuli which were
always presented simultaneously and displayed amplitude modulation spectra with spectral
exponents between 0 and 3 (Fig 2B). While training and adaptive adjustments of difficulty (see
Methods for details) ensured that the task was challenging but doable in both modalities
(average accuracy ≥ 70 %), participants performed better during visual compared to auditory
trials (t23 = 5.8, p = 7e-6, Cohen’s d = 1.18; Fig 2C).
Figure 1: EEG PSD exponents track anaesthesia-induced E:I changes. (A) Normalized EEG spectra
averaged across 4 subjects and 5 central electrodes (inset) displaying a contrast between rest and
propofol (left, lilac) and ketamine anaesthesia (right, green). While propofol entails a steepened
spectrum as compared to rest, ketamine is associated with spectral flattening. Note that oscillatory
signals (e.g., alpha power [8–12 Hz] or line noise at 50 Hz) were estimated separately and did not
contribute to spectral exponent estimates. (B) Pairwise scatter plots depicting subject-wise averaged
EEG PSD exponents during awake rest, propofol (left) and ketamine (right). Coloured dots represent
PSD exponents of 5 s snippets, black horizontal bars single subject means. Insets show 45° plots
comparing awake rest and one general anaesthetic.
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6
Modality-specific attention selectively reduces the EEG spectral exponent
To further specify the sensitivity of EEG spectral exponents to specific experimental
manipulations, we investigated changes in selective attention, which have been proposed to
coincide with a relative increase of excitatory neural activity in sensory cortices of the attended
domain. Average EEG spectral exponents for central and occipital regions of interest (see
insets in Fig 3) were controlled for neural alpha power and a set of additional nuisance
variables (see methods for details) and compared between auditory and visual attention using
a 2 x 2 repeated measures analysis of variance. In addition to a main effect of attentional focus
(F1,92 = 12.98, p = .0005, partial eta squared = .124), this analysis revealed an interaction
between attentional focus and ROI (F1,92 = 12.91, p = .0005, partial eta squared = .123).
Notably, EEG spectral exponents at occipital electrodes strongly decreased (spectra flattened)
under visual compared to auditory attention (t23 = 7.4, p = 1e–7, Cohen’s d = 1.52; see Fig 3B),
while this was the case to a much lesser extent at central electrodes (t23 = 2.6, p = .01, Cohen’s
d = .54). The topographical specificity of this attention-induced spectral flattening was
qualitatively confirmed by a cluster-based permutation test on the relative (z-scored) single-
subject EEG spectral exponent differences between auditory and visual attention, revealing a
central, negative cluster (p = .03) and an occipital positive cluster (p = .057). Thus, the selective
allocation of attentional resources to one modality results in a flattening of the EEG power
spectrum over electrodes typically associated with this modality, especially for occipital
electrodes during visual attention.
Figure 2: Task design and behavioural performance. (A) Participants were simultaneously presented
with auditory and visual amplitude modulated (AM) noise and had to detect sinusoidal AM (grey box) in
in the luminance variations of a visually presented disk (left) or in auditory presented white noise noise
(right) by pressing a button. (B) Frequency spectra for 4 sets of AM spectra (left) demonstrate the
identical flat spectra (white noise), further visualized by artificially offset spectra in the inset. AM spectra
displayed spectral exponents between 0 and 3 (right). (C) Auditory accuracy (70 %) was significantly
lower than visual accuracy (73 %).
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7
1/f-like stimulus properties are tracked by modality-specific changes in EEG spectral
exponents
In addition to attention-related variations in brain dynamics, we probed the link between
spectral exponents of sensory stimuli and EEG activity. Electrode-wise linear mixed effect
models of EEG spectral exponents were used to test the relationship between the amplitude
modulation (AM) spectral exponents of presented stimuli and recorded EEG activity (see
model details in table S1). A main effect of auditory stimulus exponent, capturing the trial-wise
positive linear relationship between auditory AM spectral exponents and EEG spectral
exponents was found at a set of four central electrodes (all z > 3.5, all pcorrected < .02; see Fig
4A). Similarly, a positive main effect of visual stimulus exponent was present at a set of three
occipital electrodes (all z > 3.7, all pcorrected < .01; see Fig 4A). Hence, EEG spectral exponents
displayed a topographically resolved tracking of stimulus exponents of both modalities.
Additionally, standardized single subject estimates of stimulus tracking at both electrode
clusters identified by the mixed model approach were extracted after controlling for covariates
also used in the final mixed model and revealed qualitatively similar results for both auditory
(t23 = 5.1, p = .00004, Cohen’s d = 1.03) and visual stimulus tracking (t23 = 3.9, p = .0008,
Cohen’s d = .79; see Fig 4A).
Stimulus tracking in EEG spectral exponents interacts with attentional focus
To further investigate the role of selective attention for the observed neural tracking of stimulus
spectral exponents, we tested for an interaction with attentional focus within the mixed model
framework. This approach revealed a positive central cluster for the interaction of auditory
stimulus exponents and attentional focus, as well as an occipital cluster for the interaction of
visual stimulus exponents and attentional focus (see Fig 4B). Note that t-values for the auditory
stimulus x attention interaction were inverted to remove the sign change caused by the zero-
centred effect coding of attentional focus. In this way, positive t-values represent evidence for
an increase in stimulus tracking if attention is directed towards this modality.
As can be discerned from the topographies in Figure 4B, selective attention likely improved
the tracking of stimulus spectral exponents over sensory-specific areas, separately for each
sensory domain. To extract single subject estimates of the stimulus x attention interaction, we
controlled for several covariates, focusing on EEG spectral exponents averaged within the
Figure 3: EEG PSD exponents track
the focus of selective attention. (A)
Average difference between EEG PSD
exponents during auditory and visual
attention. Note that exponents were
controlled for neural alpha power and
other confounding variables before
subject-wise differences were
calculated. A central negative and an
occipital positive cluster are clearly
visible. (B) Average central (lilac) and
occipital EEG PSD exponents (teal) for
auditory and visual attention (residuals
shown). Horizontal bars denote the
grand average. As indicated by the
cross-over of connecting lines, there
was a significant interaction of ROI and
attentional focus. (C) Grand average
spectra for auditory (blue) and visual
attention (red), shown for a central (left)
and an occipital ROI (right). Insets
display enlarged versions of spectra for
low and high frequencies, separately.
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Waschke et al.
8
clusters that displayed significant tracking (see above). Paired t-tests comparing standardized
regression coefficients that represent the strength of neural stimulus tracking revealed a
significant increase of auditory stimulus tracking under auditory attention (t23 = 2.4, p = .03,
Cohen’s d = .49). Visual stimulus tracking did not significantly improve under visual attention,
though the direction of the effect was the same (t23 = 1.0, p = .33, Cohen’s d = .20). Thus,
modality-specific attention was selectively associated with an improvement in the neural
tracking of auditory AM stimulus spectra.
Evoked responses do not index the spectral exponent of audiovisual stimuli
To test whether conventional estimates of sensory evoked EEG activity tracked the spectral
exponent of stimuli, we analysed evoked potentials (ERPs) on the single trial level. This
analysis did not reveal significant ERP clusters for either auditory (p > .3) or visual (p > .2)
spectral AM tracking (see Fig S2 for ERPs). The absence of such effects is visualized in
supplementary Figure 2 which displays ERPs time-courses for four bins of increasing auditory
as well as visual AM spectral exponents. In contrast to 1/f EEG exponents, conventional
metrics of sensory evoked EEG activity were thus insensitive to the AM spectral exponents of
presented stimuli.
The extent of modality-specific spectral-exponent tracking predicts behavioural
performance
Next, to investigate the link between individual levels of neural stimulus tracking and behavioral
performance, we computed between-subject correlations of standardized stimulus tracking
betas (auditory and visual) and common metrics of behavioural performance (accuracy and
Figure 4. EEG PSD exponents track stimulus PSD exponents. (A) Topographies depict t-values for the
main effect of stimulus PSD exponent, taken from a mixed model of EEG PSD exponents. White dots
represent electrodes with significant effects after Bonferroni correction. Auditory stimulus tracking (upper
row) clusters at central electrodes, visual stimulus tracking (lower row) at occipital electrodes. Line plots
show single subject tracking estimates (standardized betas), dark grey lines represent negative betas,
black lines the grand average. (B) Topographies show t-values for the interaction of attentional focus
and stimulus PSD exponent, taken from the same mixed model as in A. Positive clusters appear over
central (auditory) and occipital (visual) areas and represent improved tracking during selective attention
to the modality in question. Line plots visualize single subject effects of selective attention on stimulus
tracking for the clusters found in A (insets).
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Waschke et al.
9
response speed, separately for both modalities) using a multivariate partial least squares
analysis (PLS, see methods for details, McIntosh et al., 1996). This model revealed one
significant latent variable (p = .03) that captured the low-dimensional latent space of the
correlation between neural stimulus tracking and behavioural performance, which displayed a
clear fronto-central topography (Fig 5A). At the overall latent level, both auditory (rho = .49, p
= .016) and visual stimulus tracking (rho = .43, p = .035) were significantly correlated with
performance across subjects (Fig 5B). In detail, auditory stimulus tracking was positively
correlated with auditory response speed (rho = .30, CI = [.03, .64]) but negatively correlated
with visual accuracy (rho = –.28, CI = [-.62, -.06]) and response speed (rho = –.36, CI = [-.73,
-.17]). Analogously, visual stimulus tracking was positively linked with visual accuracy (rho =
.31, CI = [.15, .68]) and response speed (rho = .35, CI = [.11, .72]; see Fig 5A), but showed no
significant relationship with auditory performance. Taken together, participants who displayed
stronger neural tracking of AM stimulus spectra also performed better. However, the
behavioural benefit of stimulus tracking was modality-specific: performance in one sensory
domain (e.g., auditory) only benefited from tracking within that domain, but not in the other
(e.g., visual). Instead, visual detection performance was slower and less accurate in individuals
who displayed strong neural tracking of 1/f-like auditory stimulus features.
Figure 5. Stimulus tracking explains inter-individual differences in performance. (A) Results of a
multivariate neuro-behavioural correlation between stimulus tracking and performance using PLS. The
topography depicts bootstrap ratios (BSR) of the first latent variable and can be interpreted as z-values.
Within the smaller topography, BSRs are thresholded at a BSR of 2 (p < .05). Bar graphs represent the
correlation (Spearman rho) between auditory (blue) and visual stimulus tracking (red) with performance
(accuracy as % correct and response speed; RS in s–1) in both modalities. Vertical lines denote 95%
bootstrapped confidence intervals. (B) Scatter plots of latent correlations between latent auditory (upper
panel) and visual tracking (lower panel) with latent performance, respectively. Auditory stimulus tracking
was positively linked with auditory performance but negatively linked with visual performance. Visual
stimulus tracking was positively linked with visual performance. Headphones and eye symbolize
auditory and visual performance, respectively.
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Control analyses
To compare established metrics of early sensory processing, we contrasted evoked EEG
activity of different attention conditions. Event related potentials (ERPs) after noise onset were
compared between auditory and visual attention for electrodes Cz and Oz, separately. While
there was no significant attention-related ERP difference found at electrode Cz, early ERP
components at electrode Oz were increased during visual attention (~.08 – .15 s post-
stimulus, pcorrected < .05; see supplemental Fig S3). Additionally, we subjected single trial alpha
power (after partializing for EEG spectral exponents) from the ROIs shown in figure 3 to the
same ANOVA (Attention (2) x ROI (2)) as EEG spectral exponents. This analysis revealed a
main effect of attention (F = 20.3, p = .00002, partial eta squared = .180) as well as an
interaction of attention and ROI (F = 20.4, p = .00002, partial eta squared = .181). Hence,
alpha power increased from visual to auditory attention in a region-specific manner, with
stronger increases over occipital cortical areas (see spectra in Fig 3C). Importantly, this
attention-related change in alpha power took place over and above the observed attentional
modulation of EEG spectral exponents for two reasons. First, spectral parameterization
separates oscillatory and non-oscillatory components, dissecting alpha power from the
spectral exponent8. Second, and to account for shared variance between alpha power and
EEG spectral exponents that might occur due to fitting issues, we partialed each measure for
the linear contributions of the other. The presence of modality-specific, attention-dependent
changes despite these controls, strongly suggests that neural alpha power as well as EEG
spectral exponents track distinct changes in neural activity that accompany attentional shifts,
but they are not affected by the spectral content of the stimuli themselves.
Discussion
We have presented evidence for the sensitivity of the EEG spectral exponent to different
neurochemical, cognitive, and sensory influences. Jointly, the results underscore the
importance of 1/f brain activity for perception and behaviour. Specifically, we have shown
that the EEG spectral exponent (1) reflects systemic, anaesthesia-induced changes in brain
state closely linked to E:I balance, (2) captures focal attention-related changes in brain state,
and (3) tracks the spectral exponent of distinct, simultaneously presented sensory stimuli.
Furthermore, modality-specific stimulus tracking by EEG spectral exponents explained inter-
individual variance in behavioural performance, highlighting the functional relevance of 1/f
processes in human brain activity.
EEG spectral exponents as a non-invasive approximation of E:I balance
As hypothesized, propofol led to steeper spectra, while ketamine caused flattening (see Fig
1). The observed increase of spectral exponents (spectral steepening) during propofol
anaesthesia is in line with previous analyses of the same dataset9,21, as well as model-based
and invasive results12. Spectral steepening under propofol likely traces back to strengthened
inhibition via increased activity at gamma-Aminobutyric acid receptors (GABAergic activity)
and hence a reduced E:I balance as compared to quiet wakefulness13,14. Generally in line with
a previous analysis of the same data9, ketamine-induced spectral flattening likely depicts the
outcome of overall decreased inhibition that is caused by the blocking of excitatory N-methyl-
D-aspartate (NMDA) receptors and an associated decrease in GABA release, resulting in an
increased E:I balance14,43.
Combined, we present strong evidence for the suitability of EEG spectral exponents
as a non-invasive approximation of intra-individually changing E:I balance. While the EEG
spectral exponents cannot measure E:I directly, this non-invasive approximation enables
inference on neural processes previously only accessible in animals and using invasive
methods. Future studies should directly compare dose-response relationships between
GABA-A agonists or antagonists (e.g., Flumanezil) and the EEG spectral exponent in a larger
sample.
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EEG spectral exponents track modality-specific, attention-induced changes in E:I
While the EEG spectral exponent here proved indeed sensitive to systemic, exogenously
driven, drug-induced alterations in E:I, we also sought to test whether it can also be leveraged
to detect non-drug-induced but endogenous, attention-related variations in E:I balance.
Invasive animal work suggested previously that the allocation of attentional resources should
result in a topographically specific shift towards desynchronized activity and an increased E:I
ratio15,19.
Here, we observed a topographically-specific pattern of reduced EEG spectral
exponents through modality-specific attention. As indexed by a significant interaction
between region of interest and attentional focus, visual attention led to a flattening of EEG
spectra that was especially pronounced over occipital EEG channels (cf. Fig 3). The absence
of a comparable effect for auditory attention at central electrodes potentially traces back to
the varying sensitivity of EEG recordings to different cortical sources. While central electrodes
capture auditory cortical activity44,45 and hence are positioned far away from their dominant
source, occipital electrodes are sensitive to visual cortex activity and are directly positioned
above it46. This topographical difference in sensitivity is further exaggerated by the scaling of
volume conduction with distance, together resulting in a reduced signal to noise ratio for
auditory compared to visual cortex activity47.
Despite these important differences in the sensitivity of EEG signals, our results provide clear
evidence for a modality-specific flattening of EEG spectra through the selective allocation of
attentional resources. Importantly, these results cannot be explained by attention-dependent
differences in neural alpha power (8–12 Hz, Fig 3), commonly interpreted as a marker of top-
down guided sensory inhibition48. The methods we used not only separated oscillatory from
1/f-like signals, but all analyses additionally controlled for shared variance between single trial
EEG alpha power and spectral exponents. Hence, attention-dependent alterations in alpha
power and EEG spectral exponents occurred in parallel, but likely trace back to distinct
generating sources.
The observed topographically-specific flattening of EEG spectra likely depicts subtle
attention-dependent changes in E:I balance previously only accessible in invasive animal
studies. Extensive work in non-human animals suggests that the allocation of attentional
resources results in a thalamo-cortically induced shift of neural activity towards a
desynchronized state of reduced inhibition within cortical areas that process currently
attended sensory information18,19. Thus, EEG spectral exponents might represent a non-
invasive approximation of attention-dependent intra-individual changes of E:I balance.
However, future studies that combine a systemic manipulation of E:I (e.g., through GABAergic
agonists) with the investigation of selective attention in humans are needed to test the links
of E:I and EEG PSD exponents with greater detail. Furthermore, the separate contributions
and roles of attention-related changes in neural alpha power and spectral exponents invite an
exciting new line of experiments that target the neural dynamics of selective attention.
EEG spectral exponents track the 1/f features of sensory stimuli
For both modalities, the tracking of stimulus spectral exponents, based on single trials,
represented a strong effect, as indicated by standardized effect size estimates (Cohen’s d >
.79). This set of results is especially striking given that conventional sensory-evoked
responses showed no link with the spectral exponent of sensory stimuli (see Fig S2).
Furthermore, potential influences of single trial performance on stimulus tracking were ruled
out by a control analysis that accounted for single-trial behavioural performance and
replicated the results summarized above (see Fig S4).
The neural tracking of stimulus spectra displayed distinct central and occipital
topographies for auditory and visual stimuli, respectively (Fig 4). Of course, current source
density transforms of sensor-level EEG topographies as used here do not represent definitive
evidence for specific cortical sources. Yet, the spatial distinctiveness of both tracking patterns
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strongly suggests separate cortical origins. Furthermore, the topographies of stimulus
tracking strongly resemble those of sensory processing in auditory and visual sensory cortical
areas, respectively21,49. These results are conceptually in line with findings from extracellular
recordings in ferrets demonstrating the tracking of different AM spectra along the auditory
pathway50,51, and also extend previous work that analysed oscillatory human brain activity
during the presentation of 1/f stimuli52,53. Thus, by presenting first evidence in humans, our
results argue for a sensory-specific tracking of AM stimulus exponents within sensory cortical
areas at the level of single trials.
Of note, auditory stimulus tracking increased significantly when participants focused their
attention on auditory stimuli. In the auditory domain, this surfaced as an interaction of
attentional focus and AM stimulus exponents, while a weaker if not statistically significant
effect was discernible in the visual domain (Fig 4B). Hence, the selective allocation of
attentional resources yielded improved tracking of 1/f-like sensory features. Due to too short,
stimulus-free inter-trial intervals, we were unable to analyse if the degree of attention-induced
reduction in EEG spectral exponents directly reflected the magnitude of stimulus tracking.
Future research is needed to further investigate the precise link between individual averages
of EEG PSD exponents, their attention-related change, and the tracking of environmental 1/f
distributed inputs.
1/f stimulus tracking as a sign of non-oscillatory steady state potentials
What might constitute the mechanism that, at the level of sensory neural ensembles, gives
rise to the observed link between sensory stimuli and the spectral shape of the EEG? First, it
is important to emphasise that the representation of stimulus spectra in the EEG likely does
not trace back to an alignment of oscillatory neural activity and oscillatory stimulus features,
commonly referred to as “entrainment” in the strict sense30; the presented stimuli were
stochastic in nature and without clear sinusoidal signals. However, neurally tracking the
statistical properties of random noise time-series might emerge via a mechanism similar to
the one implied in the generation of steady-state visually evoked potentials (SSVEPs35). The
temporal sequence of transient sensory neural responses to periods of high energy in
presented stimuli ultimately may result in a spectrum of neural activity whose shape is similar
to the amplitude modulation spectrum of presented stimuli35,54. Although we do not want to
rule out that sensory input of different spectral exponents directly affects E:I balance in
sensory cortices, a possible underlying mechanism for such a link remains elusive at present
and the data reported in the present study do not allow the direct examination of such a
hypothesis. Hence, future experiments will have to determine the relationship between 1/f
neural tracking and E:I balance by combining direct manipulations of E:I (e.g., GABAergic or
glutamatergic drugs) with the presentation of 1/f like sensory stimuli.
Neural stimulus tracking explains inter-individual differences in performance
We used partial least squares to investigate the multivariate between-subject relationship of
neural stimulus tracking to behavioural performance. Importantly, the resulting positive
correlations (non-parametric) between stimulus tracking and performance cannot be
explained by potential outliers in a relatively small sample (see Fig 5). Importantly, this effect
was confined to each modality; while individuals who displayed high auditory tracking also
displayed fast responses in auditory trials, they exhibited slower and less accurate responses
on visual trials. Furthermore, participants who showed strong tracking of visual stimuli
performed especially fast and accurate on visual but not auditory trials. This specificity of
behavioural benefits through stimulus spectral tracking to each modality argues against the
idea of attention-dependent sensory filters that entail bi-directional effects (i.e., auditory
attention = visual ignoring30,55).
Of note, the topography of the between-subject neuro-behavioural correlations does not
display peaks at the central or occipital regions that were found to show significant stimulus
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Waschke et al.
13
tracking. However, a difference between both topographies is plausible for at least two
reasons. First, the sensory stimulus tracking topographies represent fixed effects, by
definition minimizing between-subject variance. In contrast, the between subject correlation
of stimulus tracking and performance seeks to maximise between-subject variance,
increasing the probability that a non-identical topography may be found. Second, although
stimulus exponents are significantly tracked at sensor locations that point to early sensory
cortices, our multisensory task required more abstract, high level representations of sensory
input for accurate performance. Indeed, the fronto-central topography of the between subject
correlation is suggestive of sources in frontal cortex, which have been shown to track multi-
sensory information56,57. Furthermore, prefrontal cortex activity that gives rise to highly similar
frontal topographies (Fig 5) has been found to represent information about the frequency
content of auditory, visual, and somatosensory stimuli58. Thus, the positive link between
neural stimulus tracking and performance at fronto-central electrodes points to the
behavioural relevance of higher-level stimulus features represented in a supramodal fashion.
Limitations and next steps
First, attention-dependent changes in EEG spectral exponents might trace back to altered
sensory-evoked responses. We argue that such a link is unlikely since differences in evoked
responses were limited to the visual domain, occipital electrodes, and an early time-window
(80–150 ms post noise onset, see Fig S2) that was well detached from the time-window used
to extract single trial EEG spectra (starting at 500 ms post noise onset). However, this does
not rule out entirely a remaining conflation of selective-attention effects and sensory-
processing signatures in the EEG spectral component as no trials without sensory input were
included. Although sensory input was comparable across different attention conditions
(auditory & visual stimuli simultaneously), future studies are needed to further specify the link
between modality-specific attention and EEG spectral exponents in the absence of sensory
input.
Second, one reason for the difference in attentional improvement of stimulus tracking
between modalities might lie in the difficulty of the task. Although auditory and visual difficulty
were closely matched, we found significantly lower performance for auditory compared to
visual trials (see Fig 2). Although we deem it unlikely that the observed difference of 3% in
accuracy (70% vs. 73%) might be indicative of a meaningful difference in performance, we
cannot rule out the possibility that participants needed more cognitive resources to perform
the auditory task and neurally track stimulus spectra. Due to these increased demands, the
effects of selective attention might have been able to amplify stimulus tracking more strongly
as compared to the potentially less demanding visual condition. Future studies should
investigate the role of parametric task demands for stimulus tracking, and attentional
improvements thereof, by additionally recording fluctuations in pupil size during constant light
conditions as a proxy measure of demand-related fluctuations in arousal59,60.
Conclusion
The present data represent strong evidence for the feasibility of the EEG spectral exponent
to represent a non-invasive approximation of intra-individual variations in states of E:I
balance, be they driven globally by central-acting anaesthetics or be they driven more focally
by re-allocation of selective attention. We highlight the sensitivity of EEG signals to aperiodic,
1/f-like stimulus features simultaneously in two sensory modalities and in relation to
behavioural outcomes. These findings pose a transfer of results from invasive non-human
animal physiology to the field of human cognitive neuroscience and set the stage for a new
line of experiments that investigate intra-individual variations in brain dynamics and their role
in sensory processing and behaviour using non-invasive approximations of aperiodic neural
activity.
Methods
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14
Pre-processing and analysis of EEG data under different anaesthetics
To test the effect of different central anaesthetics on 1/f EEG activity we analysed a previously
published openly available dataset42. Sarasso and colleagues recorded the EEG of healthy
individuals during quiet wakefulness and after the administration of different commonly used
central anaesthetics including propofol and ketamine. Details regarding the recording
protocol can be found in the original study and a recently published re-analysis9,42. We
analysed EEG (60 channels) recordings from eight participants who either received propofol
or ketamine infusion (4/4). EEG data were re-referenced to the average of all electrodes,
down-sampled to 1000 Hz, and filtered using an acausal finite impulse response bandpass
filter (.3–100 Hz, order 127). Next, to increase the number of samples per condition,
recordings were split up into 5 second epochs. Since the duration of recordings varied
between participants, this resulted in different numbers of epochs per anaesthetic and
participant (propofol: 108±96 epochs; ketamine: 76±27 epochs). The power spectrum of each
epoch and electrode between 1 and 100 Hz (0.25 Hz resolution) was estimated using the
Welch method (pwelch function). The spectral parameterization algorithm (version 1.0.0;
Donoghue et al., 2020) was used to parameterize neural power spectra. Settings for the
algorithm were set as: peak width limits: [1 – 8]; max number of peaks: 8; minimum peak
height: .05; peak threshold: 2.0; and aperiodic mode: ‘fixed’. Power spectra were
parameterized across the frequency range 3 to 55 Hz.
To statistically compare EEG spectral exponents between quiet wakefulness (resting state)
and anaesthesia despite the low number of participants (4 per anaesthesia condition), we
focused on five central electrodes (see inset in figure 1) and employed a permutation-based
approach. After comparing average spectral exponents of resting state and anaesthesia
recordings using two separate paired t-tests, we permuted condition labels (rest vs.
anaesthesia) and repeated the statistical comparison 1000 times. Hence, the percentage of
comparisons that exceed the observed t-value represents an empirically defined p-value.
Note that spectra were normalized (mean centered) before visualization (Fig 1) and non-
normalized power spectra can be found in the supplements (Fig S1).
EEG spectral exponents during a multisensory detection task
To investigate the dynamics of 1/f EEG activity during varying selective attention and the
processing of sensory stimuli with distinct 1/f features, we recorded EEG from 25 healthy
undergraduate students (21 ± 3 years old, 10 male) while they performed a challenging
multisensory detection task. All participants gave written informed consent, reported normal
hearing and had normal or corrected to normal vision. All experimental procedures were
approved by the institutional review board of the University of California, San Diego, Human
Research Protections Program. Due to below-chance level performance, one participant had
to be excluded from all further analyses.
Task design and experimental procedure
The novel multisensory design used in the current study required participants to focus their
attention to one modality of concurrently presented auditory and visual noise stimuli to detect
brief sinusoidal amplitude variations of the presented noise (Fig 2A). Participants were asked
to press the spacebar as fast and accurately as possible whenever they detected such a
sinusoidal amplitude modulation (target) in the currently attended sensory domain. The
experiment was divided into 12 blocks of 36 trials (432 trials total). At the beginning of each
block participants were instructed to detect targets embedded in either auditory or visual
noise stimuli. The to be attended modality alternated from block to block and was randomized
across participants for the first block. Prior to each trial, the central white fixation cross
changed its colour to green and back to white to indicate the start of the next trial. After 500
milliseconds (ms) the presentation of noise in both modalities started simultaneously. Trials
lasted between 4 and 4.5 seconds, ended with the central fixation cross reappearing on the
screen, and were separated by silent inter-trial intervals (uniformly sampled between 2–3.25
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Waschke et al.
15
s). After each experimental block, participants received feedback in the form of a percentage
correct score and were asked to take a break of at least one minute before continuing.
Participants were seated in a quiet room in front of a computer screen. The experiment,
including EEG preparation, lasted approximately 2.5 hours.
To ensure that the task was challenging but executable for all participants to a comparable
degree, we combined training with an adaptive tracking procedure. In detail, participants
performed 4 practice trials of each modality during which target stimuli were clearly
detectable. Subsequently, participants performed 12 blocks of 36 trials each during which
difficulty was adjusted by changing the modulation depth of presented targets to keep
performance constantly around 70% correct.
Stimulus generation
Auditory and visual stimuli of different AM spectra were built in three steps: First, 30 second
segments of white noise (sampling frequency 44.1 kHz) were generated and high-pass filtered
at 200 Hz. Second, four random time-series of the same duration but differing 1/fχ exponent
(χ = 0, 1, 2, or 3) were generated using an inverse Fourier transform and lowpass filtered at
100 Hz. Finally, separate multiplication of the white noise carrier with the modulators of
different spectral exponents resulted in four signals that only varied in their AM but not in their
long-term frequency spectra (see Fig 2B). The same noise was used for auditory and visual
stimuli after root mean square normalization (auditory) or down-sampling to 85 Hz and scaling
between 0.5 and 1 (visual). Noise stimuli presented during the experiment were cut out from
the 30 s long time-series. Importantly, the AM spectra of cut out noise snippets do not
necessarily overlap with the AM spectra of the longer time-series they were cut from. This
difference between global and local spectra resulted in a wide distribution of AM spectra that
were presented throughout the experiment. AM exponents were uncorrelated between
modalities across trials. Auditory noise was presented as amplitude modulated white noise
over headphones whereas visual noise was shown as luminance variations of a visually
presented disk. Targets consisted of short sinusoidal amplitude modulations (6–7.5 Hz, 400
ms) and modulation depth was varied throughout the experiment to keep performance around
70 % correct. All stimuli were generated using custom Matlab® code. Auditory stimuli were
presented over headphones (Sennheiser®) using a low-latency audio soundcard (Native
Instruments). Visual stimuli were presented on a computer screen (85 Hz refresh rate). Both
auditory and visual stimuli were presented using MATLAB® and Psychophysics toolbox
(Brainard, 1997). To later analyse the relationship between EEG activity, behaviour and the
AM spectra of presented stimuli, single trial stimulus spectra were extracted (1–30 Hz, .1 Hz
resolution, pwelch in MATLAB) and parameterized to fit 1/f exponents8. Settings for the
algorithm were set as: peak width limits: [0.5 – 12]; max number of peaks: infinite; minimum
peak height: 0; peak threshold: 2.0; and aperiodic mode: ‘fixed’. Power spectra were
parameterized across the frequency range 1 to 25 Hz.
EEG recording and pre-processing
64-channel EEG was recorded at a sampling rate of 1000 Hz using the brainamp and the
actichamp extension box (active electrodes; Brainproducts). Artifacts representing heartbeat,
movement, eye blinks or saccades and channel noise were removed using independent
component analysis based on functions from the fieldtrip and EEGlab toolboxes61,62.
Components were rejected based on power spectra, time-series, topography and dipole fit.
Continuous EEG signals were referenced to the average of all channels and filtered between
0.05 and 100 Hz (acausal FIR filter, order 207). Data was segmented into trials between –1
and 5 seconds relative to trial start (noise onset) and baseline corrected to the average of 1 s
prior to trial start. Trials containing artifacts were removed based on visual inspection (5 ± 7
trials rejected). EEG time-series were transformed to scalp current densities using default
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Waschke et al.
16
settings of the fieldtrip toolbox (ft_scalpcurrentdensity). Single trial power spectra between 1
and 100 Hz (0.5 Hz resolution) were calculated using the welch method63. To minimize the
impact of early sensory-evoked potentials, these spectra were based on the EEG signal
between 600 ms after noise stimulus onset and the appearance of a target sound. Trials
during which the target appeared within 500 ms after trial start were excluded (1.5% ± 0.3%
of trials). Furthermore, mirrored versions of single trial data were appended to the beginning
and end of each trial before calculating spectra, effectively tripling the number of samples
while not introducing new information (i.e., “mirror padding”). To approximate EEG activity in
a state of awake rest despite the absence of a dedicated resting state recording, we
calculated spectra based on the 500 ms before the fixation cross changed its colour,
signalling the start of the next trial (same settings as above). These single trial “resting-state”
spectra were averaged to reveal one resting state spectrum per participant and electrode. To
estimate 1/f spectral exponents of EEG activity as well as oscillatory activity, single trial and
average resting state spectra were fed into the spectral parameterization algorithm8 and
exponents were fit between 3 and 55 Hz using Python version 3.7. Trials where fits explained
less than 20% of variance in EEG spectra were excluded from all further analyses (0.4% ∓
0.6% of trials). Since we planned to control for the influence of alpha-oscillations (8–12 Hz),
we extracted single-trial, single-electrode power estimates from spectral parameterization
results if an oscillation was detected within the alpha frequency range. For trials where this
was not the case, spectral power was averaged between 8 and 12 Hz as a substitute.
Statistical analysis
Attention tracking
To test whether the allocation of attentional resources to one sensory domain is accompanied
by a selective flattening of the EEG power spectrum over related sensory areas, we followed
a two-step approach. First, we used multiple linear regression to control single trial, single
electrode EEG spectral exponents for a number of covariates. Specifically, and for every
participant, we controlled EEG spectral exponents for the influence of auditory stimulus
exponents, visual stimulus exponents, alpha power, and trial number. Next, we averaged the
residuals per electrode and attention condition (auditory vs. visual attention) across trials,
resulting in 2x64 EEG spectral exponent estimates per participant. The topographical pattern
of the average difference between auditory and visual attention EEG spectral exponents is
visualized in figure 3A. Following our hypothesis of sensory specific, attention-related
flattening of EEG spectra, we averaged EEG spectral exponent residuals across a set of
fronto-central (FC1, FC2, Fz, C1, C2, Cz) and parieto-occipital (P03, PO4, POz, O1, O2, Oz)
electrodes to contrast activity from auditory and visual sensory areas, respectively. We
modelled EEG spectral exponent as a function of ROI (central vs. occipital), attentional focus
(auditory vs. visual) and their interaction, including a random intercepts and random slopes
for all predictors within a linear mixed model (fitlme in MATLAB). Subsequently, analysis of
variance (ANOVA) was used to statistically evaluate the main effect of attentional focus as
well as the interaction of attentional focus and ROI.
As an additional control analysis, we tested the subject- and electrode-wise
differences of EEG spectral exponents between auditory and visual attention against zero
using a cluster-based permutation approach64. Finally, we used paired t-tests to compare
attention effects between ROIs and resolve the interaction effect.
Stimulus tracking
To test the link between EEG spectral exponents and AM spectral exponents in the auditory
and visual domain on the level of single trials, we used single-electrode linear mixed effect
models. Model fitting was performed iteratively and hypothesis-driven, starting with an
intercept only model and gradually increasing model complexity to find the best fitting
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17
model21,65. After every newly added fixed effect, model fits were compared using maximum
likelihood estimation. Once the final set of fixed effects was determined, a comparable
procedure was used for random effects. All continuous variables were z-scored across
participants (but within electrodes) before entering models, rendering the regression
coefficients â direct measures of effect size. The winning model included fixed effects for
auditory stimulus exponents, visual stimulus exponents, attentional focus, trial number and
resting state EEG spectral exponent (between subject factor) as well as a random intercept
for all predictors and random slopes for attentional focus (see table S1). To additionally test
the role of selective attention for the tracking of the presented sensory stimuli, we modelled
separate interactions between auditory as well as visual stimulus exponents with attention,
respectively. As models were fit for single electrodes (64 models), we corrected the resulting
p-values for multiple comparisons by adjusting the family-wise error rate using the Bonferroni-
Holm correction66,67.
To arrive at single subject estimates of stimulus tracking, we chose a stepwise
regression approach since including random slopes for auditory or visual stimulus exponents
within the final mixed models did not improve model fit but led to convergence issues due to
model complexity. Hence, on the level of single participants and electrodes, we regressed
EEG spectral exponents on attentional focus, trial number, and stimulus exponents of one
modality (e.g., visual). The z-scored residuals of this multiple regression were used in a
second step where they were regressed on the z-scored stimulus exponent of the remaining
sensory domain (e.g., auditory). The resulting beta coefficients were averaged across the
electrodes that showed significant stimulus tracking within the mixed model approach,
representing single subject estimates of stimulus tracking (see figure 3). By limiting data to
trials from one attention condition, single subject estimates of stimulus tracking for different
targets of selective attention were calculated following a similar approach.
Neuro-behavioural correlation
To investigate whether inter-individual differences in neural stimulus tracking relate to inter-
individual differences in performance, we analysed correlations between single subject
estimates of stimulus tracking and different metrics of behavioural performance using a
multivariate partial least square approach (PLS68,69). In brief, so-called “behavioral PLS”
begins by calculating a between-subject correlation matrix linking brain activity at each
electrode with behavioural measures of interest. The size of this rank correlation matrix is
determined by the number of electrodes, brain variables and behavioural variables [size =
(Nelectrodes × Nbrain variables) × Nbehavioral variables]. In the present study, we used two brain variables with
64 electrodes each (auditory and visual tracking betas) and 4 behavioural variables (auditory
and visual accuracy and response speed). Next, this correlation matrix is decomposed using
singular value decomposition (SVD), which results in Nbrainvar × Nbehavvar latent variables (8 in our
case).
This approach produces two crucial outputs: (1) A singular value for every latent
variable, representing the proportion of cross-block covariance accounted for by that latent
variable, and; (2) a pattern of weights (n = number of electrodes) or saliencies representing
the correlation strength between stimulus tracking and the used behavioural measures. The
multiplication (dot product) of these weights with electrode-wise tracking estimates yields so-
called “brain scores,” which here reflect the between-subject relationship of stimulus tracking
and performance where positive brain scores indicate that individuals with stronger tracking
display better performance. Statistical significance of brain scores and latent variables was
tested through permutations of behavioural measures across individuals (5000 permutations).
Additionally, the robustness of weights (saliencies) was estimated using a bootstrap
procedure (5000 bootstraps, with replacement). The division of each weight by the
corresponding bootstrapped standard error yields bootstrap ratios, which estimate the
robustness of observed effects on an electrode-wise basis. Bootstrap ratios can be
interpreted as a pseudo-Z metric. Crucially however, because multivariate PLS is run in a
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18
single mathematical step that includes (and weights the importance of) all elements of the
brain-behaviour matrix, multiple comparisons correction is neither typical nor required68.
Furthermore, bootstraps were used to estimate 95% confidence intervals (CIs) for observed
neuro-behavioural correlations.
Of note, behavioural performance metrics were calculated separately for auditory and visual
attention trials. Since difficulty was adaptively adjusted throughout the experiment, leading to
vastly different target modulation depths across participants, we controlled auditory and
visual accuracy for the final modulation depth of the respective domain and used the residuals
as a measure of performance. To furthermore exclude influences of learning and exhaustion
of response speed (reaction time-1) we controlled single trial response times for trial number
and used averaged residuals as subject-wise indicators of response speed. Note that such
an approach is especially warranted since we were specifically interested in between-subject
relationships and hence the association of correlation matrices between neural tracking and
performance. In accordance with such a reasoning, and to prevent outliers in our small sample
to obscure results, all PLS analysis were performed using spearman correlation.
Control analyses
To test the impact of attentional focus and AM stimulus spectra on sensory evoked activity,
which might potentially confound differences in EEG spectral exponents, we compared event-
related potentials (ERPs) after noise onset. First, we compared noise onset ERPs between
auditory and visual attention using a series of paired t-tests, separately for electrodes Cz and
Oz. We corrected for multiple comparisons by adjusting p-values for the false discovery rate70.
Next, on the level of subjects, voltage values were correlated with stimulus spectral exponents
(separately for auditory and visual stimuli) across trials, per electrode, time-point and
frequency (ft_statfun_correlationT in fieldtrip). On the second level, the resulting t-value time-
series were tested against zero using a cluster-based permutation approach64, separately for
auditory and visual stimuli. Finally, to rule out task difficulty as a potential confound of stimulus
tracking, we re-ran stimulus tracking mixed models including a main effect of single trial
performance (correct vs. incorrect) as well as interactions between single trial performance
and auditory and visual stimulus spectral exponents, respectively, to control stimulus tracking
for performance and difficulty.
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References
1. Buzsáki, G., Anastassiou, C. A. & Koch, C. The origin of extracellular fields and currents -
- EEG, ECoG, LFP and spikes. Nat Rev Neurosci 13, 407–420 (2012).
2. Voytek, B. et al. Age-Related Changes in 1/f Neural Electrophysiological Noise. The
Journal of Neuroscience 35, 13257–13265 (2015).
3. Miller, K. J., Sorensen, L. B., Ojemann, J. G. & Den Nijs, M. Power-law scaling in the brain
surface electric potential. PLoS Computational Biology 5, (2009).
4. Buzsáki, G., Logothetis, N. & Singer, W. Scaling Brain Size, Keeping Timing: Evolutionary
Preservation of Brain Rhythms. Neuron 80, 751–764 (2013).
5. Waschke, L., Wöstmann, M. & Obleser, J. States and traits of neural irregularity in the age-
varying human brain. Scientific Reports 7, 17381 (2017).
6. Dave, S., Brothers, T. A. & Swaab, T. Y. 1/ f neural noise and electrophysiological indices
of contextual prediction in aging. Brain Research 1691, 34–43 (2018).
7. Sheehan, T. C., Sreekumar, V., Inati, S. K. & Zaghloul, K. A. Signal Complexity of Human
Intracranial EEG Tracks Successful Associative-Memory Formation across Individuals.
The Journal of Neuroscience 38, 1744–1755 (2018).
8. Donoghue, T. Parameterizing neural power spectra into periodic and aperiodic
components. Nature Neuroscience 23, 24 (2020).
9. Colombo, M. A. et al. The spectral exponent of the resting EEG indexes the presence of
consciousness during unresponsiveness induced by propofol, xenon, and ketamine.
NeuroImage 189, 631–644 (2019).
10. Lendner, J. D. et al. An electrophysiological marker of arousal level in humans. eLife 9,
e55092 (2020).
11. Podvalny, E. et al. A unifying principle underlying the extracellular field potential spectral
responses in the human cortex. Journal of Neurophysiology 114, 505–519 (2015).
12. Gao, R., Peterson, E. J. & Voytek, B. Inferring synaptic excitation/inhibition balance from
field potentials. NeuroImage 158, 70–78 (2017).
13. Concas, A., Santoro, G., Serra, M., Sanna, E. & Biggio, G. Neurochemical action of the
general anaesthetic propofol on the chloride ion channel coupled with GABAA receptors.
Brain Research 542, 225–232 (1991).
14. Franks, N. P. General anaesthesia: from molecular targets to neuronal pathways of sleep
and arousal. Nat Rev Neurosci 9, 370–386 (2008).
15. Kanashiro, T., Ocker, G. K., Cohen, M. R. & Doiron, B. Attentional modulation of neuronal
variability in circuit models of cortex. eLife 6, e23978 (2017).
16. Ni, A. M., Ruff, D. A., Alberts, J. J., Symmonds, J. & Cohen, M. R. Learning and attention
reveal a general relationship between population activity and behavior. Science 359, 463–
465 (2018).
17. Ferguson, K. A. & Cardin, J. A. Mechanisms underlying gain modulation in the cortex. Nat
Rev Neurosci 21, 80–92 (2020).
18. Cohen, M. R. & Maunsell, J. H. R. Using Neuronal Populations to Study the Mechanisms
Underlying Spatial and Feature Attention. Neuron 70, 1192–1204 (2011).
19. Harris, K. D. & Thiele, A. Cortical state and attention. Nature Reviews Neuroscience 12,
509–523 (2011).
20. Zagha, E. & McCormick, D. A. Neural control of brain state. Current Opinion in
Neurobiology 29, 178–186 (2014).
21. Waschke, L., Tune, S. & Obleser, J. Local cortical desynchronization and pupil-linked
arousal differentially shape brain states for optimal sensory performance. eLife 8, e51501
(2019).
22. Keshner, M. S. 1/f noise. Proc. IEEE 70, 212–218 (1982).
23. Mandelbrot, B. B., Freeman, W. H., & Company. The Fractal Geometry of Nature. (Henry
Holt and Company, 1983).
24. Brown, J. H. et al. The fractal nature of nature: power laws, ecological complexity and
biodiversity. Phil. Trans. R. Soc. Lond. B 357, 619–626 (2002).
.CC-BY-ND 4.0 International licenseperpetuity. It is made available under a
preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in
The copyright holder for thisthis version posted January 14, 2021. ; https://doi.org/10.1101/2021.01.13.426522doi: bioRxiv preprint
Waschke et al.
20
25. Coensel, B. D., Botteldooren, D. & Muer, T. D. 1/f Noise in Rural and Urban Soundscapes.
ACTA ACUSTICA UNITED WITH ACUSTICA 89, 10 (2003).
26. Lakatos, P., Karmos, G., Mehta, A. D., Ulbert, I. & Schroeder, C. E. Entrainment of
neuronal oscillations as a mechanism of attentional selection. Science (New York, N.Y.)
320, 110–3 (2008).
27. Spaak, E., de Lange, F. P. & Jensen, O. Local Entrainment of Alpha Oscillations by Visual
Stimuli Causes Cyclic Modulation of Perception. Journal of Neuroscience 34, 3536–3544
(2014).
28. Henry, M. J., Herrmann, B. & Obleser, J. Entrained neural oscillations in multiple frequency
bands comodulate behavior. PNAS 111, 14935–14940 (2014).
29. Breska, A. & Deouell, L. Y. Neural mechanisms of rhythm-based temporal prediction: Delta
phase-locking reflects temporal predictability but not rhythmic entrainment. PLOS Biology
15, e2001665 (2017).
30. Obleser, J. & Kayser, C. Neural Entrainment and Attentional Selection in the Listening
Brain. Trends in Cognitive Sciences 23, 913–926 (2019).
31. Attias, H. & Schreiner, C. E. Temporal Low-Order Statistics of Natural Sounds. in NIPS
27–33 (MIT Press, 1997).
32. Qu, G., Fan, B., Fu, X. & Yu, Y. The Impact of Frequency Scale on the Response
Sensitivity and Reliability of Cortical Neurons to 1/fβ Input Signals. Front. Cell. Neurosci.
13, 311 (2019).
33. Yu, Y., Romero, R. & Lee, T. S. Preference of Sensory Neural Coding for 1 / f Signals.
Phys. Rev. Lett. 94, 108103 (2005).
34. Nozaki, D., Mar, D. J., Grigg, P. & Collins, J. J. Effects of Colored Noise on Stochastic
Resonance in Sensory Neurons. PHYSICAL REVIEW LETTERS 82, 4 (1999).
35. Norcia, A. M., Appelbaum, L. G., Ales, J. M., Cottereau, B. R. & Rossion, B. The steady-
state visual evoked potential in vision research: A review. Journal of Vision 15, 4 (2015).
36. Wehr, M. S. & Zador, A. M. Balanced inhibition underlies tuning and sharpens spike timing
in auditory cortex. Nature 426, 442–6 (2003).
37. Wörgötter, F. et al. State-dependent receptive-field restructuring in the visual cortex.
Nature 396, 165 (1998).
38. Luebke, J. I., Chang, Y.-M., Moore, T. L. & Rosene, D. L. Normal aging results in
decreased synaptic excitation and increased synaptic inhibition of layer 2/3 pyramidal cells
in the monkey prefrontal cortex. Neuroscience 125, 277–288 (2004).
39. Dani, V. S. et al. Reduced cortical activity due to a shift in the balance between excitation
and inhibition in a mouse model of Rett Syndrome. Proceedings of the National Academy
of Sciences 102, 12560–12565 (2005).
40. Cummings, D. M. et al. Alterations in Cortical Excitation and Inhibition in Genetic Mouse
Models of Huntington’s Disease. Journal of Neuroscience 29, 10371–10386 (2009).
41. Lisman, J. Excitation, inhibition, local oscillations, or large-scale loops: what causes the
symptoms of schizophrenia? Current Opinion in Neurobiology 22, 537–544 (2012).
42. Sarasso, S. et al. Consciousness and complexity during unresponsiveness induced by
propofol, xenon, and ketamine. Current Biology 25, 3099–3105 (2015).
43. Behrens, M. M. et al. Ketamine-Induced Loss of Phenotype of Fast-Spiking Interneurons
Is Mediated by NADPH-Oxidase. Science 318, 1645–1647 (2007).
44. Huotilainen, M. et al. Combined mapping of human auditory EEG and MEG responses.
Electroencephalography and Clinical Neurophysiology/Evoked Potentials Section 108,
370–379 (1998).
45. Stropahl, M., Bauer, A.-K. R., Debener, S. & Bleichner, M. G. Source-Modeling Auditory
Processes of EEG Data Using EEGLAB and Brainstorm. Front. Neurosci. 12, 309 (2018).
46. Hagler, D. J. et al. Source estimates for MEG/EEG visual evoked responses constrained
by multiple, retinotopically-mapped stimulus locations. Hum. Brain Mapp. 30, 1290–1309
(2009).
.CC-BY-ND 4.0 International licenseperpetuity. It is made available under a
preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in
The copyright holder for thisthis version posted January 14, 2021. ; https://doi.org/10.1101/2021.01.13.426522doi: bioRxiv preprint
Waschke et al.
21
47. Piastra, M. C. et al. A comprehensive study on electroencephalography and
magnetoencephalography sensitivity to cortical and subcortical sources. Hum Brain Mapp
hbm.25272 (2020) doi:10.1002/hbm.25272.
48. Jensen, O. & Mazaheri, A. Shaping Functional Architecture by Oscillatory Alpha Activity:
Gating by Inhibition. Frontiers in Human Neuroscience 4, 1–8 (2010).
49. Iemi, L. et al. Multiple mechanisms link prestimulus neural oscillations to sensory
responses. eLife 8, e43620 (2019).
50. Garcia-Lazaro, J. A., Ahmed, B. & Schnupp, J. W. H. Tuning to natural stimulus dynamics
in primary auditory cortex. Current Biology 16, 264–271 (2006).
51. Garcia-Lazaro, J. A., Ahmed, B. & Schnupp, J. W. H. Emergence of tuning to natural
stimulus statistics along the central auditory pathway. PLoS ONE 6, (2011).
52. Hermes, D., Miller, K. J., Wandell, B. A. & Winawer, J. Stimulus dependence of gamma
oscillations in human visual cortex. Cerebral Cortex 25, 2951–2959 (2015).
53. Teng, X., Tian, X., Doelling, K. & Poeppel, D. Theta band oscillations reflect more than
entrainment: behavioral and neural evidence demonstrates an active chunking process.
European Journal of Neuroscience 12, 3218–3221 (2017).
54. Galambos, R., Makeig, S. & Talmachoff, P. J. A 40-Hz auditory potential recorded from
the human scalp. Proceedings of the National Academy of Sciences 78, 2643–2647
(1981).
55. Lakatos, P., Schroeder, C. E., Leitman, D. I. & Javitt, D. C. Predictive Suppression of
Cortical Excitability and Its Deficit in Schizophrenia. Journal of Neuroscience 33, 11692–
11702 (2013).
56. Ghazanfar, A. & Schroeder, C. Is neocortex essentially multisensory? Trends in Cognitive
Sciences 10, 278–285 (2006).
57. Senkowski, D., Saint-Amour, D., Kelly, S. P. & Foxe, J. J. Multisensory processing of
naturalistic objects in motion: A high-density electrical mapping and source estimation
study. NeuroImage 36, 877–888 (2007).
58. Spitzer, B. & Blankenburg, F. Supramodal Parametric Working Memory Processing in
Humans. Journal of Neuroscience 32, 3287–3295 (2012).
59. Yerkes, R. M. & Dodson, J. D. The relation of strength of stimulus to rapidity of habit-
formation. Journal of Comparative Neurology and Psychology 18, 459–482 (1908).
60. Zekveld, A. A., Kramer, S. E. & Festen, J. M. Pupil Response as an Indication of Effortful
Listening: The Influence of Sentence Intelligibility: Ear and Hearing 31, 480–490 (2010).
61. Delorme, A. & Makeig, S. EEGLAB: an open source toolbox for analysis of single-trial EEG
dynamics including independent component analysis. Journal of Neuroscience Methods
134, 9–21 (2004).
62. Oostenveld, R., Fries, P., Maris, E. & Schoffelen, J.-M. FieldTrip: Open source software
for advanced analysis of MEG, EEG, and invasive electrophysiological data.
Computational intelligence and neuroscience 2011, 156869 (2011).
63. Welch, P. The use of fast Fourier transform for the estimation of power spectra: A method
based on time averaging over short, modified periodograms. IEEE Transactions on Audio
and Electroacoustics 15, 70–73 (1967).
64. Maris, E. & Oostenveld, R. Nonparametric statistical testing of EEG- and MEG-data.
Journal of Neuroscience Methods 164, 177–190 (2007).
65. Tune, S., Wöstmann, M. & Obleser, J. Probing the limits of alpha power lateralisation as a
neural marker of selective attention in middle-aged and older listeners. European Journal
of Neuroscience 48, 2537–2550 (2018).
66. Holm, S. A Simple Sequentially Rejective Multiple Test Procedure. Scandinavian Journal
of Statistics 6, 65–70 (1979).
67. Groppe, D., Urbach, T. & Kutas, M. Mass univariate analysis of event‐related brain
potentials/fields I: A critical tutorial review. Psychophysiology 48, 1711–25 (2011).
68. McIntosh, A. R., Bookstein, F. L., Haxby, J. V. & Grady, C. L. Spatial Pattern Analysis of
Functional Brain Images Using Partial Least Squares. NeuroImage 3, 143–157 (1996).
.CC-BY-ND 4.0 International licenseperpetuity. It is made available under a
preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in
The copyright holder for thisthis version posted January 14, 2021. ; https://doi.org/10.1101/2021.01.13.426522doi: bioRxiv preprint
Waschke et al.
22
69. Krishnan, A., Williams, L. J., McIntosh, A. R. & Abdi, H. Partial Least Squares (PLS)
methods for neuroimaging: A tutorial and review. NeuroImage 56, 455–475 (2011).
70. Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful
approach to multiple testing. Journal of the Royal Statistical Society B 57, 289–300 (1995).
.CC-BY-ND 4.0 International licenseperpetuity. It is made available under a
preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in
The copyright holder for thisthis version posted January 14, 2021. ; https://doi.org/10.1101/2021.01.13.426522doi: bioRxiv preprint
