States and traits of neural irregularity in the age-varying human brain

Waschke, Leonhard; Woestmann, Malte; Obleser, Jonas

doi:10.1101/103432

Cited by 9 publications

(19 citation statements)

References 94 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As we reported before in Waschke et al (2017), responses 1-3 (indicating higher pitch of S1) were relatively more frequent than responses 4-6 (indicating higher pitch of S2; t16 = 2.89; p = 0.01; r = 0.59; BF = 5.1). This indicates a general bias to judge the first of two identical tones as being higher in pitch.…”

Section: Resultssupporting

confidence: 75%

“…In the present study, we re-analysed data from a previously published experiment (Waschke et al, 2017). Below, we describe essential methodological aspects but refer to the original publication for further details.…”

Section: Methodsmentioning

confidence: 99%

“…Here, we re-analyse data from a forced-choice pitch discrimination task of two tones (Waschke et al, 2017), which entirely eliminated the presence of evidence and thus variations in evidence and decision accuracy from all trials of the experiment. Unbeknownst to participants, the two tones were physically identical on each trial and thus no evidence in the stimulus in favour of one decision was available.…”

mentioning

confidence: 99%

See 2 more Smart Citations

Prestimulus neural alpha power predicts confidence in discriminating identical tones

Woestmann

Waschke

Obleser

2018

Preprint

Self Cite

View full text Add to dashboard Cite

There is growing evidence that the power of prestimulus neural alpha oscillations (∼10 Hz) holds information on a perceiver’s bias or confidence in an ensuing perceptual decision, rather than perceptual sensitivity per se. Obviously, however, confidence also depends on the physical evidence available in the stimulus as well as on task performance. If prestimulus alpha power has a direct impact on decision confidence, this link should hold independent of variations in stimulus evidence and performance. We tested this assertion in a paradigm where human listeners (n = 17) rated their confidence in the discrimination of the pitch of two identical tones. Lower prestimulus alpha power in the electroencephalogram (EEG) was predictive of higher confidence ratings, but not of the decision outcome (i.e., judging the first or the second tone as being higher in pitch). Importantly, the link between prestimulus alpha power and decision confidence was not mediated by auditory evoked activity. Our findings demonstrate that the link between prestimulus alpha power and decision confidence does not hinge on physical evidence in the stimulus or task performance. Instead, these results speak to a model wherein low prestimulus alpha power increases neural baseline excitability, which is reflected in enhanced stimulus-evoked neural responses and higher confidence.Significance statementIn order to understand the mechanistic relevance of neural oscillations for perception, we here relate these directly to changes in human auditory decision confidence. Human subjects rated their confidence in the discrimination of the pitch of two tones, which were, unbeknownst to the listener, physically identical. In the absence of changing evidence in the physical stimulus or changes in task performance, we demonstrate that prestimulus alpha power negatively relates to decision confidence. Our results support a model of cortical alpha oscillations as a proxy for neural baseline excitability in which lower prestimulus alpha power does not lead to more precise but rather to overall amplified neural representations.

show abstract

Section: Resultssupporting

confidence: 75%

Section: Methodsmentioning

confidence: 99%

mentioning

confidence: 99%

See 1 more Smart Citation

Prestimulus neural alpha power predicts confidence in discriminating identical tones

Woestmann

Waschke

Obleser

2018

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…Another way in which our study helps to extend prior work is by explicitly linking the entropy of brain signals to the 1/ƒ χ component of power spectra (see Sheehan, Sreekumar, Inati, &Zaghloul, 2018 andWaschke et al, 2017). The PSD slope of large-scale field potentials has been proposed to be a measure of neural noise that reflects population spiking statistics .…”

Section: Entropy At Fine Time Scalesmentioning

confidence: 91%

“…An intriguing possibility, that we set out to test here, is that sleep-dependent changes in signal complexity and the power spectrum index a common phenomenon of neuronal noise that is driven by state shifts in cortical excitation and inhibition (Waschke, Wöstmann, & Obleser, 2017). Recent work demonstrates that specific features of the broadband power spectrum of neural field recordings (Gao, 2016;Gao, Peterson, & Voytek, 2017;Lombardi, Herrmann, & de Arcangelis, 2017) may be used to make inferences about synaptic excitatory-inhibitory (E:I) balance.…”

Section: Changes In Eeg Multiscale Entropy and Power-law Frequency mentioning

confidence: 99%

Changes in EEG multiscale entropy and power‐law frequency scaling during the human sleep cycle

Miskovic

MacDonald

Rhodes

et al. 2018

Human Brain Mapping

159

137

View full text Add to dashboard Cite

We explored changes in multiscale brain signal complexity and power‐law scaling exponents of electroencephalogram (EEG) frequency spectra across several distinct global states of consciousness induced in the natural physiological context of the human sleep cycle. We specifically aimed to link EEG complexity to a statistically unified representation of the neural power spectrum. Further, by utilizing surrogate‐based tests of nonlinearity we also examined whether any of the sleep stage‐dependent changes in entropy were separable from the linear stochastic effects contained in the power spectrum. Our results indicate that changes of brain signal entropy throughout the sleep cycle are strongly time‐scale dependent. Slow wave sleep was characterized by reduced entropy at short time scales and increased entropy at long time scales. Temporal signal complexity (at short time scales) and the slope of EEG power spectra appear, to a large extent, to capture a common phenomenon of neuronal noise, putatively reflecting cortical balance between excitation and inhibition. Nonlinear dynamical properties of brain signals accounted for a smaller portion of entropy changes, especially in stage 2 sleep.

show abstract

Signal complexity of human intracranial EEG tracks successful associative memory formation across individuals

Sheehan

Sreekumar

Inati

et al. 2017

Preprint

View full text Add to dashboard Cite

Memory performance is highly variable between individuals. Most studies examining human memory, however, have largely focused on the neural correlates of successful memory formation within individuals, rather than the differences between them. As such, what gives rise to this variability is poorly understood. Here, we examined intracranial EEG (iEEG) recordings captured from 43 participants (23 male) implanted with subdural electrodes for seizure monitoring as they performed a paired-associates verbal memory task. We identified three separate but related signatures of neural activity that tracked differences in successful memory formation across individuals. High performing individuals consistently exhibited less broadband power, flatter power spectral density (PSD) slopes, and greater complexity in their iEEG signals. Furthermore, within individuals across three separate time scales ranging from seconds to days, successful recall was positively associated with these same metrics. Our data therefore suggest that memory ability across individuals can be indexed by increased neural signal complexity.Significance StatementWe show that participants whose intracranial EEG exhibits less low frequency power, flatter power spectrums, and greater sample entropy overall are better able to memorize associations, and that the same metrics track fluctuations in memory performance across time within individuals. These metrics together signify greater neural signal complexity which may index the brain’s ability to flexibly engage with information and generate separable memory representations. Critically, the current set of results provide a unique window into the neural markers of individual differences in memory performance which have hitherto been underexplored.

show abstract

States and traits of neural irregularity in the age-varying human brain

Cited by 9 publications

References 94 publications

Prestimulus neural alpha power predicts confidence in discriminating identical tones

Prestimulus neural alpha power predicts confidence in discriminating identical tones

Changes in EEG multiscale entropy and power‐law frequency scaling during the human sleep cycle

Signal complexity of human intracranial EEG tracks successful associative memory formation across individuals

Contact Info

Product

Resources

About