Biology subjects

Voytek, B.

Publications and source records attributed to Voytek, B..

At least 19 recordsLinked to original sources

bioRxiv2026

Aperiodic dynamics track cortical state shifts during sleep K-complexes

Sleep microarchitecture is defined by brief electrophysiological events such as K-complexes(KCs), which are traditionally interpreted as discrete oscillatory phenomena involved in sleep maintenance, sensory gating, and cortical down-states. However, electroencephalography(EEG) also contains aperiodic, broadband activity that reflects non-oscillatory population dynamics and varies systematically across sleep stages. Because KCs are large, low-frequency transient waveforms, they also manifest across a broader frequency range, which creates a bidirectional measurement problem in which discrete events and aperiodic activity are difficult to disentangle. This is because aperiodic dynamics alone can generate fluctuations that resemble KCs, while conversely, the KC waveform itself can inflate the low-frequency end of the spectrum, steepening the fitted exponent even when the underlying aperiodic state is unchanged. These confounds blur what constitutes a real KC and a real aperiodic change in the presence of a KC. A KC-locked change in the aperiodic exponent could therefore reflect a genuine cortical state shift, a measurement artifact from the waveform biasing the spectral fit, or both. Here, we use time-resolved spectral parameterization of overnight sleep EEG to characterize aperiodic dynamics around expert-annotated KCs, with a series of controls designed to separate genuine aperiodic state changes from waveform artifacts. A component of the KC-locked aperiodic change survives these controls, suggesting that KCs are accompanied by a genuine shift toward a higher-exponent, inhibition-dominated cortical state. These findings establish aperiodic activity as a meaningful index of state dynamics during KCs and highlight measurement confounds that any event-locked spectral analysis of large transients must consider.

neuroscience↗

bioRxiv2026

Action potential waveforms are state-dependent

Action potentials are brief electrical impulses that form the mechanistic basis for how neurons communicate. While it is well known that the shape of action potentials can differ across neurons, one fundamental assumption is that the complex voltage waveforms of action potentials within a given neuron are reducible to binary spikes. This assumption has constrained our conception of possible neural codes to those amenable to binary signaling, such as rate, temporal, and population codes. Here, we show that action potential waveform variability is not random, but is, instead, state dependent. To show this, we parameterize action potential waveforms in a set of very high temporal resolution (200 kHz) intracellular action potential recordings. We show that an action potential is not a digital '1', but is instead a rich signal whose fine-scale features influence the shape and timing of the next action potential and whose waveform is systematically biased by input drive. We then show that intracellular action potential waveforms can vary as a function of the extracellular local field potential, but do so heterogeneously, as a function of the field potential amplitude and standard deviation. Our results have profound implications for systems and computational neuroscience, especially regarding the development of next-generation, biologically-inspired artificial neural networks that incorporate waveform dynamics. Non-binary action potentials point to a broader landscape of possible neural codes, whereby neurons communicate not just via binary spikes, but through their state-dependent waveform features.

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bioRxiv2026

Simulated field potentials capture multiple simultaneous processes underlying human temporal attention

Essential cognitive processes like attention, memory, and decision-making require the brain to perform many operations simultaneously. Disentangling the neural mechanisms underlying these processes is challenging because they often unfold concurrently with similar dynamics. In auditory temporal expectation, these mechanisms include the accumulation of incoming sensory information in the auditory cortex interacting with cortical signals biasing activity in anticipation of future stimuli. Here, we leverage invasive human field potential recordings from participants performing a subjective temporal expectation task. Rather than relying on traditional neuroimaging methods that average or transform neural responses, we instead simulate each trial's predicted field potential, based on known field potential physiology combined with components of established cognitive theories of the temporal expectation task: sensory input, top-down bias, and sensory anticipation. Our predicted field potentials strongly correlate with neural activity in auditory regions and in regions associated with auditory temporal perception, including the parietal, temporal, and prefrontal cortices. By differentiating individual cognitive mechanisms through simulation, we identify neural signatures of attentional bias and anticipatory sensory activity across multiple cortical regions that predict trial-by-trial perceptual accuracy and confidence.

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bioRxiv2026

Transcranial Random Aperiodic Stimulation Improves Working Memory Precision

Aperiodic neural activity, historically dismissed as noise, is independently modulated from neural oscillations during visual working memory maintenance. In the context of aging, flatter trait aperiodic activity is associated with poorer visual working memory performance. Here, we causally test the role of aperiodic neural activity in visual working memory in younger adults. We introduce a novel noninvasive neurostimulation method, transcranial random aperiodic stimulation (tRAS), to causally manipulate aperiodic activity to be either steeper or flatter. In a randomized, double-blind, placebo-controlled, crossover neurostimulation design (n = 30), we show that online noninvasive tRAS improves visual working memory precision when aperiodic activity is causally steepened. Our novel stimulation method paves the way for causal studies of non-oscillatory, aperiodic activity in human cognition, aging, and disease.

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bioRxiv2026

Aperiodic EEG activity is a domain-specific marker of attentional aging in neurotypical adults

Aperiodic neural activity has been reliably shown to be reduced among older adults, and these reductions are associated with age-related cognitive decline. These results suggest that aperiodic activity, as measured by the spectral exponent, might be a viable electrophysiological biomarker for cognitive aging. However, this has not been tested longitudinally, within participants, during normal aging. Here, we used a longitudinal cohort to test whether changes in aperiodic activity between two sessions, approximately 5 years apart, track behavioral change across two domains: sustained attention (Psychomotor Vigilance Test) and executive function (modified Simon task). We found that aperiodic activity declined within individuals after the interval, independent of age at baseline. This within-person change tracked concurrent changes in reaction times during visual attention but was not associated with changes in any Simon task measure. Within a resampling framework, the aperiodic-PVT association was stable across cohort resamples and independently fitted partitions, though it was not confirmed in a small, frozen held-out set. These findings suggest the longitudinal decline in aperiodic activity specifically tracks sustained-attention processing speed rather than general cognitive aging, supporting its potential as a domain-specific biomarker of attentional aging in neurotypical populations.

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bioRxiv2026

ScaleSurfer: multi-scale anatomical segmentation and parcellation of the human brain

Human brain magnetic resonance imaging (MRI) revolutionized our ability to non-invasively probe individual differences in neuroanatomy. These anatomical scans, in turn, also allow us to accurately localize functional MRI (fMRI) activity. However, extracting anatomical labels and structural characteristics, such as cortical surface area or thickness, is a computationally demanding task, taking on the order of hours per brain volume. This is an intrinsically multi-scale problem given that local image structure defines fine boundaries, whereas accurate assignments depend on broader anatomical context. Here, we introduce ScaleSurfer, a three-dimensional convolutional vision transformer model based on multi-scale learning. Convolution blocks capture local anatomical detail and a transformer bottleneck integrates the distributed spatial context. This approach provides rapid, whole-brain morphometric feature estimation, including volume, cortical thickness, surface area, and curvature. Importantly, ScaleSurfer accomplishes this nearly five orders of magnitude faster than current pipelines, taking 150-500 ms instead of 5 hours. We validated ScaleSurfer on multiple datasets, showing stable learning across heterogeneous MRI collections, and demonstrate feasibility by training an interpretable Alzheimers disease classifier that identifies reductions in primarily medial temporal lobe subregions compared to healthy controls. ScaleSurfer positions multi-scale representation learning as a practical route toward faster, anatomically faithful structural MRI processing, whose speed paves the way for nearly real-time anatomical quality control during scanning.

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bioRxiv2026

Revisiting post-stimulus theta activity: evidence for an aperiodic rather than oscillatory origin

The aperiodic, 1/f-like component of electrophysiological activity is increasingly recognized as a meaningful feature of neural function, rather than background noise. In parallel, many EEG studies report transient changes in oscillatory power following stimulus onset and interpret these effects as signatures of attention, salience, or cognitive control. However, such conclusions usually rely on baseline normalization procedures that assume aperiodic activity remains stable from pre-to post-stimulus periods. Using high-density EEG recordings from typically developing children, we tested this assumption in two paradigms: an audiovisual simple reaction-time task (n = 36) and a visual oddball task (n = 38). For each task, conventional spectral analyses were compared with analyses that explicitly modeled and removed the aperiodic component in both pre- and post-stimulus windows. Across tasks, stimulus onset was associated with robust increases in aperiodic exponent and offset, indicating systematic changes in the 1/f component of the spectrum. In the audiovisual task, these changes were modality-specific, with central, parieto-occipital, or combined topographies depending on stimulus type. These effects were reduced but remained significant after ERP removal, indicating that they were not fully explained by phase-locked activity. Critically, once aperiodic activity was accounted for, the apparent post-stimulus increase in theta power was largely abolished in both tasks, including the canonical fronto-central theta enhancement to infrequent targets in the oddball paradigm. The conventional method also overestimated the magnitude of beta desynchronization, particularly in the induced (ERP-removed) signal. The apparent gamma desynchronization detected by conventional analyses was reversed after aperiodic correction, revealing either synchronization or no change, indicating that it reflects a spurious consequence of spectral slope steepening rather than a true suppression of gamma oscillatory activity. In contrast, alpha desynchronization remained robust after aperiodic correction and was in fact enhanced, suggesting it reflects genuine oscillatory suppression. Together, these findings indicate that a substantial portion of conventional time-frequency effects, particularly apparent theta synchronization, may reflect changes in aperiodic activity in response to stimulation rather than genuine periodic oscillations, challenging core assumptions of conventional time-frequency analyses. O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=123 SRC="FIGDIR/small/732609v1_ufig1.gif" ALT="Figure 1"> View larger version (38K): org.highwire.dtl.DTLVardef@1d25f43org.highwire.dtl.DTLVardef@6c476aorg.highwire.dtl.DTLVardef@c4a02aorg.highwire.dtl.DTLVardef@ef417d_HPS_FORMAT_FIGEXP M_FIG O_FLOATNOGraphical abstractC_FLOATNO C_FIG

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bioRxiv2026

Cycle-by-cycle respiration waveforms are coupled with the shape of neural oscillations

Beyond sustaining life, breathing is a vital physiological rhythm that shapes cognition, perception, emotional regulation, and mental health. Breathing has a direct effect on neuronal excitability and is coupled to neural oscillations across a variety of brain regions. Notably, both respiration and neural oscillations are asymmetric and not perfectly rhythmic: for example, every breath has a different shape and duration, and is interspersed with variable pauses. Here, we examined the coupling between breathing and the brain by quantifying the nonsinusoidal features of each breath and comparing it to the shape of each corresponding neural oscillation cycle. By leveraging invasive human brain recordings from 16 participants, we found respiration-neural waveform coupling on a breath-by-breath, cycle-by-cycle basis across limbic and cortical forebrain regions. For decades, the dominant perspective on cognition and mental health have focused on the brain, but recent work is highlighting the importance of brain-body interactions. Our results show that the coupling between breathing and neural activity is much richer than previously appreciated, and our approach opens new avenues for studying these peripheral-to-central nervous system interactions in a more robust, temporally precise manner.

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bioRxiv2025

Estimating fMRI Timescale Maps

Brain activity unfolds over hierarchical timescales that reflect how brain regions integrate and process information, linking functional and structural organization. While timescale studies are prevalent, existing estimation methods rely on the restrictive assumption of exponentially decaying temporal autocorrelation and only provide point estimates without standard errors, limiting statistical inference. In this paper, we formalize and evaluate two methods for mapping timescales in resting-state fMRI: a time-domain fit of an autoregressive (AR1) model and an autocorrelation-domain fit of an exponential decay model. Rather than assuming exponential autocorrelation decay, we define timescales by projecting the fMRI time series onto these approximating models, requiring only stationarity and mixing conditions while incorporating robust standard errors to account for model misspecification. We introduce theoretical properties of timescale estimators and show parameter recovery in realistic simulations, as well as applications to fMRI from the Human Connectome Project. Comparatively, the time-domain method produces more accurate estimates under model misspecification, remains computationally efficient for high-dimensional fMRI data, and yields maps aligned with known functional brain organization. In this work, we show valid statistical inference on fMRI timescale maps, and provide Python implementations of all methods.

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bioRxiv2025

Aperiodic neural timescales in prefrontal cortex dilate with increased task abstraction

Navigating everyday environments requires that the brain perform information processing at multiple different timescales. For example, while watching a movie we use sensory information from every video frame to construct the current movie scene, which itself is continuously integrated into the narrative arc of the film. This critical function is supported by sensory inputs propagating from dynamic sensory cortices to association cortices, where neural activity remains more stable over time. The hierarchical organization of cortex is therefore reflected in a gradient of neural timescales. While this propagation of inputs up the cortical hierarchy is facilitated by both rhythmic (oscillatory) and non-rhythmic (aperiodic) neural activity, traditional measures of oscillations are often confounded by the influence of aperiodic signals. The reverse is also true: traditional measures of aperiodic neural timescales are influenced by oscillations. This makes it difficult to distinguish between oscillatory and timescale effects in cognition. Here, we analyzed electroencephalography (EEG) data from participants performing a cognitive control task that manipulated the amount of task-relevant contextual information, called task abstraction. Critically, we separated aperiodic neural timescales from the confounding influence of oscillatory power. We hypothesized that neural timescales would increase during the task, and more so in high-abstraction conditions. We found that task abstraction dilated the aperiodic neural timescale, as estimated from the autocorrelation function, over prefrontal cortical regions. Our findings suggests that neural timescales are a dynamic feature of the cerebral cortex that change to meet task demands. Significance StatementThough often thought of as indexing anatomical brain structure, neural timescales have recently come under the spotlight for their variability across different task conditions. Here, we investigated whether neural timescales dynamically change during cognitive tasks that require a greater level of contextual control. Previous work quantified neural timescales using the autocorrelation function, but this approach conflates oscillatory and aperiodic contributions to the timescale. Thus, we decomposed the neural timescale into oscillatory and aperiodic components, and found that aperiodic-derived timescale measures dynamically change with contextual control. Oscillatory power also exhibits change in response to task demands, reaffirming previous findings. These results together suggest that both established oscillatory and novel aperiodic measures of neural timescales can dynamically change to meet task demands.

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bioRxiv2025

Differential representations of spatial location by aperiodic and alpha oscillatory activity in working memory

Decades of research have shown working memory (WM) relies on sustained pre-frontal cortical activity and visual extrastriate activity, particularly in the alpha (8-12 Hz) frequency range. This alpha activity tracks the spatial location of WM items, even when spatial position is task-irrelevant and there is no stimulus currently being presented. Traditional analyses of putative oscillations using bandpass filters, however, conflate oscillations with non-oscillatory aperiodic activity. Here, we reanalyzed seven different human electroencephalography (EEG) visual WM datasets to test the hypothesis that aperiodic activity-which is thought to reflect the relative contributions of excitatory and inhibitory drive-plays a distinct role in visual WM from true alpha oscillations. To do this, we developed a novel, time-resolved spectral parameterization approach to disentangle oscillations from aperiodic activity during WM encoding and maintenance. Across all seven tasks, totaling 112 participants, we captured the representation of spatial location from total alpha power using an inverted encoding model (IEM), replicating traditional analyses. We then trained separate IEMs to estimate the strength of spatial location representation from aperiodic-adjusted alpha (reflecting just the oscillatory component) and aperiodic activity, and find that IEM performance improves for aperiodic-adjusted alpha compared to total alpha power that blends the two signals. We also discover a novel role for aperiodic activity, where IEM performance trained on aperiodic activity is highest during stimulus presentation, but not during the WM maintenance period. Our results emphasize the importance of controlling for aperiodic activity when studying neural oscillations while uncovering a novel functional role for aperiodic activity in the encoding of visual WM information. Significance statementWorking memory is a crucial component of cognition, yet its neural mechanisms are not fully understood. Research shows that alpha activity - presumed to reflect neural oscillations - tracks the location of items we hold in memory. However, these analyses assume that all alpha power is oscillatory, even though oscillations are mixed with non-oscillatory, aperiodic activity that may be physiologically and functionally distinct. Here, we use a novel analytical approach for separating alpha oscillations and aperiodic activity dynamically across time. Our results reveal distinct roles for each in human visual working memory: aperiodic activity encodes the spatial location of information whereas alpha oscillations maintain the location of that information.

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bioRxiv2024

Dissociating Contributions of Theta and Alpha Oscillations from Aperiodic Neural Activity in Human Visual Working Memory

While visual working memory (WM) is strongly associated with reductions in occipitoparietal alpha (8-12 Hz) power, the role of frontal midline theta (4-7 Hz) power is less clear, with both increases and decreases widely reported. Here, we test the hypothesis that this theta paradox can be explained by non-oscillatory, aperiodic neural activity dynamics. Because traditional time-frequency analyses of electroencephalography (EEG) data conflate oscillations and aperiodic activity, event-related changes in aperiodic activity can manifest as task-related changes in apparent oscillations, even when none are present. Reanalyzing EEG data from two visual WM experiments (n = 74, of either sex), and leveraging spectral parameterization, we found systematic changes in aperiodic activity with WM load, and we replicated classic alpha, but not theta, oscillatory effects after controlling for aperiodic changes. Aperiodic activity decreased during WM retention, and further flattened over the occipitoparietal cortex with an increase in WM load. After controlling for these dynamics, aperiodic-adjusted alpha power decreased with increasing WM load. In contrast, aperiodic-adjusted theta power appeared to increase during WM retention, but because aperiodic activity reduces more, it falsely appears as though theta "oscillatory" power (e.g., total band power) is reduced. Furthermore, only a minority of participants (31/74) had a detectable degree of theta oscillations. These results offer a potential resolution to the theta paradox where studies show contrasting power changes. Additionally, we have identified novel aperiodic dynamics during human visual WM. Significance statementWorking Memory (WM) is our ability to hold information in mind without it being present in our external environment. Years of research focused on oscillatory brain dynamics to discover the mechanisms of WM. Here, we specifically look at oscillatory and non-oscillatory, aperiodic activity as measured with scalp EEG to test their significance in supporting WM. We challenge earlier findings regarding theta oscillations with our analysis approach, while replicating alpha oscillation findings. Furthermore, aperiodic activity is found to be involved in WM, over frontal regions in a task-general manner, and over anterior regions this activity is reduced with an increase in the number of remembered items. Thus, we have identified novel aperiodic dynamics during human visual WM.

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bioRxiv2024

Functional connectivity is dominated by aperiodic, rather than oscillatory, coupling

Functional connectivity has attracted significant interest in the identification of specific circuits underlying brain (dys-)function. Classical analyses to estimate functional connectivity (i.e., filtering electrophysiological signals in canonical frequency bands and using connectivity metrics) assume that these reflect oscillatory networks. However, this approach conflates non-oscillatory, aperiodic neural activity with oscillations; raising the possibility that these functional networks may reflect aperiodic rather than oscillatory activity. Here, we provide the first study quantifying, in two different human electroencephalography (EEG) databases, the contribution of aperiodic activity on reconstructed oscillatory functional networks in resting state. We found that more than 99% of delta, theta, and gamma functional networks, more than 90% of beta functional networks and between 23 and 55% of alpha functional networks were actually driven by aperiodic activity. While there is no universal consensus on how to identify and quantify neural oscillations, our results demonstrate that oscillatory functional networks are drastically sparser than commonly assumed. These findings suggest that most functional connectivity studies focusing on resting state actually reflect aperiodic networks instead of oscillations-based networks. We highly recommend that oscillatory network analyses first check the presence of aperiodicity-unbiased neural oscillations before estimating their statistical coupling to strengthen the robustness, interpretability, and reproducibility of functional connectivity studies.

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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

Resting-state is not enough: alpha and mu rhythms change shape across development, but lack diagnostic sensitivity

In the human brain, the alpha rhythm in occipital cortex and the mu rhythm in sensorimotor cortex are among the most prominent rhythms, with both rhythms functionally implicated in gating modality-specific information. Separation of these rhythms is non-trivial due to the spatial mixing of these oscillations in sensor space. Using a computationally efficient processing pipeline requiring no manual data cleaning, we isolated alpha and/or mu rhythms from electroencephalography recordings performed on 1605 children aged 5-18. Using the extracted time series for each rhythm, we characterized the waveform shape on a cycle-by-cycle basis and examined whether and how the waveform shape differs across development. We demonstrate that alpha and mu rhythms both exhibit nonsinusoidal waveform shape that changes significantly across development, in addition to the known large changes in oscillatory frequency. This dataset also provided an opportunity to assess oscillatory measures for attention-deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD). We found no differences in the resting-state features of these alpha-band rhythms for either ADHD or ASD in comparison to typically developing participants in this dataset. While waveform shape is ignored by traditional Fourier spectral analyses, these nonsinusoidal properties may be informative for building more constrained generative models for different types of alpha-band rhythms, yielding more specific insight into their generation.

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bioRxiv2023

Changes in electrophysiological aperiodic activity during cognitive control in Parkinson's disease

Cognitive symptoms in Parkinsons disease (PD) are common and can significantly affect patients quality of life. Therefore, there is an urgent clinical need to identify a signature derived from behavioral and/or neuroimaging indicators that could predict which patients are at increased risk for early and rapid cognitive decline. Recently, converging evidence identified electroencephalogram (EEG) aperiodic activity as meaningful physiological information associated with age, development, cognitive and perceptual states or pathologies. In this study, we aimed to investigate aperiodic activity in PD during cognitive control and characterize its possible association with behavior. Here, we recorded high-density EEG (HD-EEG) in 30 healthy controls and 30 PD patients during a Simon task. We analyzed task-related behavioral data in the context of the activation-suppression model and extracted aperiodic parameters (offset, exponent) at both scalp and source levels. Our results showed behavioral alterations of cognitive control as well as higher offsets in patients in the parieto-occipital areas, suggesting increased excitability in PD. A small congruence effect on aperiodic parameters in pre- and post-central brain areas was also found, possibly associated with task execution. Significant differences in aperiodic parameters between the resting state, pre- and post-stimulus phases all across the scalp and cortex confirmed that the observed changes in aperiodic activity are linked to task execution. No correlation was found between aperiodic activity and behavior or clinical features. Our findings provide evidence that EEG aperiodic activity in PD is characterized by greater offsets, and that aperiodic parameters differ depending on arousal state. However, our results do not support the hypothesis that the behavior-related differences observed in PD are related to aperiodic changes. Overall, this study highlights the importance of considering aperiodic activity contributions in brain disorders and further investigating the relationship between aperiodic activity and behavior.

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bioRxiv2023

Neurophysiological signatures of cortical micro-architecture

Systematic spatial variation in micro-architecture is observed across the cortex. These micro-architectural gradients are reflected in neural activity, which can be captured by neurophysiological time-series. How spontaneous neurophysiological dynamics are organized across the cortex and how they arise from heterogeneous cortical micro-architecture remains unknown. Here we extensively profile regional neurophysiological dynamics across the human brain by estimating over 6 800 timeseries features from the resting state magnetoencephalography (MEG) signal. We then map regional time-series profiles to a comprehensive multi-modal, multi-scale atlas of cortical micro-architecture, including microstructure, metabolism, neurotransmitter receptors, cell types and laminar differentiation. We find that the dominant axis of neurophysiological dynamics reflects characteristics of power spectrum density and linear correlation structure of the signal, emphasizing the importance of conventional features of electromagnetic dynamics while identifying additional informative features that have traditionally received less attention. Moreover, spatial variation in neurophysiological dynamics is colocalized with multiple micro-architectural features, including genomic gradients, intracortical myelin, neurotransmitter receptors and transporters, and oxygen and glucose metabolism. Collectively, this work opens new avenues for studying the anatomical basis of neural activity.

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bioRxiv2022

Oscillations and aperiodic activity: Evidence for dynamic changes in both during memory encoding

Electrical recordings of human brain activity via electroencephalography (EEG) show prominent, rhythmic voltage fluctuations. These periodic oscillations have been linked to nearly every cognitive and perceptual process, as well numerous disease states. Recent methodological and theoretical advances, however, have given rise to evidence for a functional role for non-oscillatory, aperiodic neural activity. Physiologically, this aperiodic activity has been linked to the relative contributions of neuronal excitatory and inhibitory signaling. Most importantly, however, traditional data analysis methods often conflate oscillations and aperiodic activity, masking the potentially separate roles these processes play in perception, cognition, and disease. Here we present a reanalysis of intracranial human EEG recordings from Fellner et al., 2019, using new methods for separately parameterizing oscillations and aperiodic activity in a time-resolved manner. We find that human memory encoding is not related to just oscillations or aperiodic activity, but rather that both processes are rapidly co-modulated during memory encoding. These results provide strong evidence for event-related dynamics of aperiodic and oscillatory activity in human memory, paving the way for future investigations into the unique functional roles of these two independent, but linked, processes in human cognition.

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