Biology subjects

Waschke, L.

Publications and source records attributed to Waschke, L..

8 recordsLinked to original sources

bioRxiv2024

Evaluating and Comparing Measures of Aperiodic Neural Activity

Neuro-electrophysiological recordings contain prominent aperiodic activity - meaning irregular activity, with no characteristic frequency - which has variously been referred to as 1/f (or 1/f-like activity), fractal, or scale-free activity. Previous work has established that aperiodic features of neural activity is dynamic and variable, relating (between subjects) to healthy aging and to clinical diagnoses, and also (within subjects) tracking conscious states and behavioral performance. There are, however, a wide variety of conceptual frameworks and associated methods for the analyses and interpretation of aperiodic activity - for example, time domain measures such as the autocorrelation, fractal measures, and/or various complexity and entropy measures, as well as measures of the aperiodic exponent in the frequency domain. There is a lack of clear understanding of how these different measures relate to each other and to what extent they reflect the same or different properties of the data, which makes it difficult to synthesize results across approaches and complicates our overall understanding of the properties, biological significance, and demographic, clinical, and behavioral correlates of aperiodic neural activity. To address this problem, in this project we systematically survey the different approaches for measuring aperiodic neural activity, starting with an automated literature analysis to curate a collection of the most common methods. We then evaluate and compare these methods, using statistically representative time series simulations. In doing so, we establish consistent relationships between the measures, showing that much of what they capture reflects shared variance - though with some notable idiosyncrasies. Broadly, frequency domain methods are more specific to aperiodic features of the data, whereas time domain measures are more impacted by oscillatory activity. We extend this analysis by applying the measures to a series of empirical EEG and iEEG datasets, replicating the simulation results. We conclude by summarizing the relationships between the multiple methods, emphasizing opportunities for re-examining previous findings and for future work.

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bioRxiv2023

Single-neuron spiking variability in hippocampus dynamically tracks sensory content during memory formation in humans

During memory formation, the hippocampus is presumed to represent the "content" of stimuli, but how it does so is unknown. Using computational modelling and human single-neuron recordings, we show that the more precisely hippocampal spiking variability tracks the composite features of each individual stimulus, the better those stimuli are later remembered. We propose that moment-to-moment spiking variability may provide a new window into how the hippocampus constructs memories from the building blocks of our sensory world.

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bioRxiv2022

Dynamic regulation of neural variability during working memory reflects dopamine, functional integration, and decision-making

The regulation of moment-to-moment neural variability may permit effective responses to changing cognitive demands. However, the mechanisms that support variability regulation are unknown. In the context of working memory, we leverage the largest available PET and fMRI dataset to jointly consider three lenses through which neural variability regulation could be understood: dopamine capacity, network-level functional integration, and flexible decision processes. We show that with greater working memory load, upregulation of variability was associated with elevated dopamine capacity and heightened functional integration, effects dominantly expressed in the striato-thalamic system rather than cortex. Strikingly, behavioral modeling revealed that working memory load evoked substantial decision biases during evidence accumulation, and those who jointly expressed a more optimal decision bias and higher dopamine capacity were most likely to upregulate striato-thalamic variability under load. We argue that the ability to align striato-thalamic variability to level of demand may be a hallmark of a well-functioning brain.

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bioRxiv2021

A spatiotemporal complexity architecture of human brain activity

The human brain operates in large-scale functional networks. These networks are thought to arise from neural variability, yet the principles behind this link remain unknown. Here we report a mechanism by which the brains network architecture is tightly linked to critical episodes of neural regularity, visible as spontaneous complexity drops in functional MRI signals. These episodes support the formation of functional connections between brain regions, subserve the propagation of neural activity, and reflect inter-individual differences in age and behavior. Furthermore, complexity drops define neural states that dynamically shape the coupling strength, topological structure, and hierarchy of brain networks and comprehensively explain known structure-function relationships within the brain. These findings delineate a unifying complexity architecture of neural activity - a human complexome that underpins the brains functional network organization.

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bioRxiv2021

Modality-specific tracking of attention and sensory statistics in the human electrophysiological spectral exponent

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 establish that the spectral exponent of non-invasive electroencephalography (EEG) recordings is highly sensitive to general (i.e., anaesthesia-driven) changes in E:I balance. Building on the EEG spectral exponent as a viable marker of E:I, we then demonstrate its sensitivity to the focus of selective attention in an EEG experiment during which participants detected targets in simultaneous audio-visual noise. In addition to these endogenous changes in E:I balance, EEG spectral exponents over auditory and visual sensory cortices also tracked auditory and visual stimulus spectral exponents, respectively. Individuals degree of this selective stimulus-brain coupling in spectral exponents predicted behavioural performance. Our results highlight the rich information contained in 1/f-like neural activity, providing a window into diverse neural processes previously thought to be inaccessible in non-invasive human recordings.

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bioRxiv2019

Neural desynchronization and arousal differentially shape brain states for optimal sensory performance

Instantaneous brain states have consequences for our sensation, perception, and behaviour. Fluctuations in arousal and neural desynchronization likely pose perceptually relevant states. However, their relationship and their relative impact on perception is unclear. We here show that, at the single-trial level in humans, local desynchronization in sensory cortex (expressed as time-series entropy) versus pupil-linked arousal differentially impact perceptual processing. While we recorded electroencephalography (EEG) and pupillometry data, stimuli of a demanding auditory discrimination task were presented into states of high or low desynchronization of auditory cortex via a real-time closed-loop setup. Desynchronization and arousal distinctly influenced stimulus-evoked activity and shaped behaviour displaying an inverted u-shaped relationship: States of intermediate desynchronization elicited minimal response bias and fastest responses, while states of intermediate arousal gave rise to highest response sensitivity. Our results speak to a model in which independent states of local desynchronization and global arousal jointly optimise sensory processing and performance.

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bioRxiv2018

Prestimulus neural alpha power predicts confidence in discriminating identical tones

There is growing evidence that the power of prestimulus neural alpha oscillations ([~]10 Hz) holds information on a perceivers 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.\n\nSignificance 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.

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bioRxiv2017

Neural noise in the age-varying human brain predicts perceptual decisions

Sensory representations of the physical world and thus human percepts are susceptible to fluctuations in brain state or \"neural irregularity\". Furthermore, aging brains display altered levels of irregularity. We here show that a single, within-trial information-theoretic measure (weighted permutation entropy) captures neural irregularity in the human electroencephalogram as a proxy for both, trait-like differences between individuals of varying age, and state-like fluctuations that bias perceptual decisions. First, the overall level of neural irregularity increased with participants age, paralleled by a decrease in variability over time, likely indexing age-related disintegration on structural and functional levels of brain activity. Second, states of higher neural irregularity were associated with optimized sensory encoding and a subsequently increased probability of choosing the first of two physically identical stimuli. In sum, neural irregularity not only characterizes behaviorally relevant brain states, but also can identify trait-like changes that come with age.

neuroscience↗