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Voytek, B.

Publications and source records attributed to Voytek, B..

At least 37 records · Page 2Linked to original sources
bioRxiv2021

The timescale and magnitude of aperiodic activity decreases with cortical depth in humans, macaques and mice

Cortical dynamics obey a 1/f power law, exhibiting an exponential decay of spectral power with increasing frequency. The slope and offset of this 1/f decay reflect the timescale and magnitude of aperiodic neural activity, respectively. These properties are tightly linked to cellular and circuit mechanisms (e.g. excitation:inhibition balance and firing rates) as well as cognitive processes (e.g. perception, memory, and state). However, the physiology underlying the 1/f power law in cortical dynamics is not well understood. Here, we compared laminar recordings from human, macaque and mouse cortex to evaluate how 1/f aperiodic dynamics vary across cortical layers and species. We report that 1/f slope is steepest in superficial layers and flattest in deep layers in each species. Additionally, the magnitude of this 1/f decay is greatest in superficial cortex and decreases with depth. We could account for both of these findings with a simple model in which superficial cortical transmembrane currents had longer time constants and greater densities than those in deeper layers. Together, our results provide novel insight into the organization of cortical dynamics, suggesting that the amplitude and time constant of local currents control circuit processing as a function of laminar depth. This may represent a general mechanism to facilitate appropriate integration of fast sensory inputs (infragranular) with slow feedback-type inputs (supragranular) across cortical areas and species.

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bioRxiv2021

Costs and benefits of using rhythmic rate codes

Neural oscillations are observed ubiquitously in the mammalian nervous system, and the benefits of oscillatory coding have been the topic of frequent analysis. Many prior studies focused on communication between populations which were already oscillating and sought to understand how these rhythms and overall communication interact. We take a contrary view here. In this paper, we focus on measuring the costs of translating from an aperiodic code to a rhythmic one. We study two models. The first is simulated independent populations of neurons subjected to a theta-band (6 Hz) pacemaker using Linear-Nonlinear Poisson (LNP) sampling. The second is a model of beta-gamma oscillations using biophysical neurons with self-organized dynamics. We measure benefits and costs in both models using information theory. In both models oscillations can only benefit communications by increasing spiking by specific amounts, in effect, correcting for "undersampling" of the stimulus. This is mechanistically similar to theories for how deep brain stimulation can enhance cognition and is consistent with older studies of gamma entrainment. Yet this trend was not universal. No one guiding principle of dynamics determines the cost of a translation in the models we studied. In our models to predict the benefits or costs of an oscillatory translation we need to understand the exacting physical details of the intrinsic connections, the population size, and the external drive.

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bioRxiv2021

Enhancing oscillations in intracranial electrophysiological recordings with data-driven spatial filters

In invasive electrophysiological recordings, a variety of neuronal oscillations can be detected across the cortex, with overlap in space and time. This overlap complicates measurement of neuronal oscillations using standard referencing schemes, like common average or bipolar referencing. Here, we illustrate the effects of spatial mixing on measuring neuronal oscillations in invasive electrophysiological recordings and demonstrate the benefits of using data-driven referencing schemes in order to improve measurement of neuronal oscillations. We discuss referencing as the application of a spatial filter. Spatio-spectral decomposition is used to estimate data-driven spatial filters, a computationally fast method which specifically enhances signal-to-noise ratio for oscillations in a frequency band of interest. We show that application of these data-driven spatial filters has benefits for data exploration, investigation of temporal dynamics and assessment of peak frequencies of neuronal oscillations. We demonstrate multiple use cases, exploring between-participant variability in presence of oscillations, spatial spread and waveform shape of different rhythms as well as narrowband noise removal with the aid of spatial filters. We find high between-participant variability in the presence of neural oscillations, a large variation in spatial spread of individual rhythms and many non-sinusoidal rhythms across the cortex. Improved measurement of cortical rhythms will yield better conditions for establishing links between cortical activity and behavior, as well as bridging scales between the invasive intracranial measurements and noninvasive macroscale scalp measurements.

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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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bioRxiv2020

Longitudinal changes in aperiodic and periodic activity in electrophysiological recordings in the first seven months of life

Neuronal oscillations emerge in early human development. These periodic oscillations are thought to rapidly change in infancy and stabilize during maturity. Given their numerous connections to physiological and cognitive processes, as well as their pathological divergence, understanding the trajectory of oscillatory development is important for understanding healthy human brain development. This understanding is complicated by recent evidence that assessment of periodic neuronal oscillations is confounded by aperiodic neuronal activity, which is an inherent feature of electrophysiological neuronal recordings. Recent cross-sectional evidence shows that this aperiodic signal progressively shifts from childhood through early adulthood, and from early adulthood into later life. None of these studies, however, have been performed in infants, nor have they been examined longitudinally. Here, we analyzed non-invasive EEG data from 22 typically developing infants, across multiple time points, ranging between 38 and 203 days old. We show that the progressive flattening of the EEG power spectrum begins in very early development, continuing through the first several months of life. These results highlight the importance of separating the periodic and aperiodic neuronal signals, because the aperiodic signal can bias measurement of neuronal oscillations. Given the infrequent, bursting nature of oscillations in infants, we recommend the use of quantitative time domain approaches that isolate bursts and uncover changes in waveform properties of oscillatory bursts.

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bioRxiv2020

Local Field Potentials in a Pre-motor Region Predict Learned Vocal Sequences

Neuronal activity within the premotor region HVC is tightly synchronized to, and crucial for, the articulate production of learned song in birds. Characterizations of this neural activity typically focuses on patterns of sequential bursting in small carefully identified subsets of single neurons in the HVC population. Much less is known about population dynamics beyond the scale of individual neurons. There is a rich history of using local field potentials (LFP), to extract information about behavior that extends beyond the contribution of individual cells. These signals have the advantage of being stable over longer periods of time and have been used to study and decode complex motor behaviors, such as human speech. Here we characterize LFP signals in the putative HVC of freely behaving male zebra finches during song production, to determine if population activity may yield similar insights into the mechanisms underlying complex motor-vocal behavior. Following an initial observation that structured changes in the LFP were distinct to all vocalizations during song, we show that it is possible to extract time varying features from multiple frequency bands to decode the identity of specific vocalization elements (syllables) and to predict their temporal onsets within the motif. This demonstrates that LFP is a useful signal for studying motor control in songbirds. Surprisingly, the time frequency structure of putative HVC LFP is qualitatively similar to well established oscillations found in both human and non-human mammalian motor areas. This physiological similarity, despite distinct anatomical structures, may give insight to common computational principles for learning and/or generating complex motor-vocal behaviors. Author SummaryVocalizations, such as speech and song, are a motor process that requires the coordination of several muscle groups receiving instructions from specific brain regions. In songbirds, HVC is a premotor brain region required for singing and it is populated by a set of neurons that fire sparsely during song. How HVC enables song generation is not well understood. Here we describe network activity in putative HVC that precedes the initiation of each vocal element during singing. This network activity can be used to predict both the identity of each vocal element (syllable) and when it will occur during song. In addition, this network activity is similar to activity that has been documented in human, non-human primate, and mammalian premotor regions tied to muscle movements. These similarities add to a growing body of literature that finds parallels between songbirds and humans in respect to the motor control of vocal organs. Given the similarities of the songbird and human motor-vocal systems these results suggest that the songbird model could be leveraged to accelerate the development of clinically translatable speech prosthesis.

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bioRxiv2020

Neuronal timescales are functionally dynamic and shaped by cortical microarchitecture

Complex cognitive functions such as working memory and decision-making require information maintenance over many timescales, from transient sensory stimuli to long-term contextual cues. While theoretical accounts predict the emergence of a corresponding hierarchy of neuronal timescales, direct electrophysiological evidence across the human cortex is lacking. Here, we infer neuronal timescales from invasive intracranial recordings. Timescales increase along the principal sensorimotor-to-association axis across the entire human cortex, and scale with single-unit timescales within macaques. Cortex-wide transcriptomic analysis shows direct alignment between timescales and expression of excitation- and inhibition-related genes, as well as genes specific to voltage-gated transmembrane ion transporters. Finally, neuronal timescales are functionally dynamic: prefrontal cortex timescales expand during working memory maintenance and predict individual performance, while cortex-wide timescales compress with aging. Thus, neuronal timescales follow cytoarchitectonic gradients across the human cortex, and are relevant for cognition in both short- and long-terms, bridging microcircuit physiology with macroscale dynamics and behavior.

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bioRxiv2020

Electrophysiological Frequency Band Ratio Measures Conflate Periodic and Aperiodic Neural Activity

A common analysis measure for neuro-electrophysiological recordings is to compute the power ratio between two frequency bands. Applications of band ratio measures include investigations of cognitive processes as well as biomarkers for conditions such as attention-deficit hyperactivity disorder. Band ratio measures are typically interpreted as reflecting quantitative measures of periodic, or oscillatory, activity, which implicitly assumes that a ratio is measuring the relative powers of two distinct periodic components that are well captured by predefined frequency ranges. However, electrophysiological signals contain periodic components and a 1/f-like aperiodic component, which contributes power across all frequencies. In this work, we investigate whether band ratio measures reflect power differences between two oscillations, as intended. We examine to what extent ratios may instead reflect other periodic changes--such as in center frequency or bandwidth--and/or aperiodic activity. We test this first in simulation, exploring how band ratio measures relate to changes in multiple spectral features. In simulation, we show how multiple periodic and aperiodic features affect band ratio measures. We then validate these findings in a large electroencephalography (EEG) dataset, comparing band ratio measures to parameterizations of power spectral features. In EEG, we find that multiple disparate features influence ratio measures. For example, the commonly applied theta / beta ratio is most reflective of differences in aperiodic activity, and not oscillatory theta or beta power. Collectively, we show how periodic and aperiodic features can drive the same observed changes in band ratio measures. Our results demonstrate how ratio measures reflect different features in different contexts, inconsistent with their typical interpretations. We conclude that band ratio measures are non-specific, conflating multiple possible underlying spectral changes. Explicit parameterization of neural power spectra is better able to provide measurement specificity, elucidating which components of the data change in what ways, allowing for more appropriate physiological interpretations. Materials Descriptions & Availability StatementsO_ST_ABSProject RepositoryC_ST_ABSThis project is also made openly available through an online project repository in which the code and data are made available, with step-by-step guides through the analyses. Project Repository: http://github.com/voytekresearch/BandRatios DatasetsThis project uses simulated data, literature text mining data, and electroencephalography data. Simulated DataThe simulations used in this project are created with openly available software packages. Settings and code to re-generate simulated data is available with the open-access code for the project. Copies of the simulated data that were used in this investigation are available in the project repository. Literature DataLiterature data for this project was collected from the PubMed database. Exact search terms used to collect the data are available in the project repository. The exact data collected from the literature and meta-data about the collection are saved and available in the project repository. EEG DataThe EEG data used in this project is from the openly available dataset, the Multimodal Resource for Studying Information processing in the Developing Brain (MIPDB) database. This dataset is created and released by the Childmind Institute. This dataset was released and is re-used here under the terms of the Creative Commons-Attribution-Non-Commercial-Share-Alike License (CC-BY-NC-SA), and is described in (Langer et al., 2017). Child Mind Institute: https://childmind.org Data Portal: http://fcon_1000.projects.nitrc.org/indi/cmi_eeg/ SoftwareCode used and written for this project was written in the Python programming language. All the code used within this project is deposited in the project repository and is made openly available and licensed for re-use. As well as standard library Python, this project uses 3rd party software packages numpy and pandas for data management, scipy for data processing, matplotlib and seaborn for data visualization and MNE for managing and pre-processing data. This project also uses open-source Python packages developed and released by the authors: Simulations and spectral parameterization were done using the FOOOF toolbox. Code Repository: https://github.com/fooof-tools/fooof Literature collection and analyses were done using the LISC toolbox. Code Repository: https://github.com/lisc-tools/lisc

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bioRxiv2019

Co-Increasing Neuronal Noise and Beta Power in the Developing Brain

Accumulating evidence across species indicates that brain oscillations are superimposed upon an aperiodic 1/f - like power spectrum. Maturational changes in neuronal oscillations have not been assessed in tandem with this underlying aperiodic spectrum. The current study uncovers co-maturation of the aperiodic component alongside the periodic components (oscillations) in spontaneous magnetoencephalography (MEG) data. Beamformer-reconstructed MEG time-series allowed a direct comparison of power in the source domain between 24 children (8.0 {+/-} 2.5 years, 17 males) and 24 adults (40.6 {+/-} 17.4 years, 16 males). Our results suggest that the redistribution of oscillatory power from lower to higher frequencies that is observed in childhood does not hold once the age-related changes in the aperiodic signal are controlled for. When estimating both the periodic and aperiodic components, we found that power increases with age in the beta band only, and that the 1/f signal is flattened in adults compared to children. These results suggest a pattern of co-maturing beta oscillatory power with the aperiodic 1/f signal in typical childhood development.

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bioRxiv2019

Homeostatic mechanisms may shape the type and duration of oscillatory modulation.

Neural oscillations are observed ubiquitously in the mammalian brain, but their stability is known to be rather variable. Some oscillations are tonic and last for seconds or even minutes. Other oscillations appear as unstable bursts. Likewise, some oscillations rely on excitatory AMPAergic synapses, but others are GABAergic and inhibitory. Why this diversity exists is not clear. We hypothesized Ca 2+ -dependent homeostasis could be important in finding an explanation. We tested this hypothesis in a highly simplified model of hippocampal neurons. In this model homeostasis profoundly alters the modulatory effect of neural oscillations. Under homeostasis, tonic AMPAergic oscillations actually decrease excitability and desynchronize firing. Tonic oscillations that are synaptically GABAergic-like those in real hippocampus-dont provoke a homeostatic response, however. If our simple model is correct, homeostasis can explain why the theta rhythm in the hippocampus is synaptically inhibitory: GABA has little to no intrinsic homeostatic response, and so can preserve the pyramidal cells natural dynamic range. Based on these results we can also speculate that homeostasis may explain why AMPAergic oscillations in cortex, and hippocampus, often appear as bursts. Bursts do not interact with the slow homeostatic time constant, and so retain their normal excitatory effect. NO_SCPLOWEWC_SCPLOWO_SCPCAP C_SCPCAPO_SCPLOWANDC_SCPLOW NO_SCPLOWOTEWORTHYC_SCPLOWThe intricate interplay of neuromodulators, like acetylcholine, with homeostasis is well known. The interplay between oscillatory modulation and homeostasis is not. We studied oscillatory modulation and homeostasis for the first time using a simplified model of hippocampus. We report a paradoxical result: Ca-mediated homeostasis causes AMPAergic oscillations to become effectively inhibitory. This result, along with other new observations, means homeostasis might be just as complex and important for oscillations as it is for other neuromodulators.

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bioRxiv2019

Characteristics of Beta Waveform Shape in Parkinson's Disease Detected with Scalp Electroencephalography

Neural activity in the beta frequency range (13-30 Hz) is excessively synchronized in Parkinsons Disease (PD). Previous work using invasive intracranial recordings and non-invasive scalp electroencephalography (EEG) has shown that correlations between beta phase and broadband gamma amplitude (i.e., phase-amplitude coupling) are elevated in PD, perhaps a reflection of this synchrony. Recently, it has also been shown, in invasive human recordings, that nonsinusoidal features of beta oscillation shape also characterize PD. Here we show that these features of beta waveform shape also distinguish PD patients on and off medication using non-invasive recordings in a dataset of 15 PD patients with resting scalp EEG. Specifically, beta oscillations over sensorimotor electrodes in PD patients off medication had greater sharpness asymmetry and steepness asymmetry than on medication (sign rank, p=0.006, p=0.003 respectively). We also showed that beta oscillations over sensorimotor cortex most often had a canonical shape and that using this prototypical shape as an inclusion criterion increased the effect size of our findings. Together our findings suggest that novel ways of measuring beta synchrony that incorporate waveform shape could improve detection of PD pathophysiology in non-invasive recordings.

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bioRxiv2018

Hippocampal theta bursting and waveform shape reflect CA1 spiking patterns

Brain rhythms are nearly always analyzed in the spectral domain in terms of their power, phase, and frequency. While this conventional approach has uncovered spike-field coupling, as well as correlations to normal behaviors and pathological states, emerging work has highlighted the physiological and behavioral importance of multiple novel oscillation features. Oscillatory bursts, for example, uniquely index a variety of cognitive states, and the nonsinusoidal shape of oscillations relate to physiological changes, including Parkinsons disease. Open questions remain regarding how bursts and nonsinusoidal features relate to circuit-level processes, and how they interrelate. By analyzing unit and local field recordings in the rodent hippocampus, we uncover a number of significant relationships between oscillatory bursts, nonsinusoidal waveforms, and local inhibitory and excitatory spiking patterns. Bursts of theta oscillations are surprisingly related to a decrease in pyramidal neuron synchrony, and have no detectable effect on firing sequences, despite significant increases in neuronal firing rates during periods of theta bursting. Theta burst duration is predicted by the asymmetries of its first cycle, and cycle asymmetries relate to firing rate, synchrony, and sequences of pyramidal neurons and interneurons. These results provide compelling physiological evidence that time-domain features, of both nonsinusoidal hippocampal theta waveform and the theta bursting state, reflects local circuit properties. These results point to the possibility of inferring circuit states from local field potential features in the hippocampus and perhaps other brain regions with other rhythms.

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bioRxiv2018

Measuring the average power of neural oscillations

BackgroundNeural oscillations are often quantified as average power relative to a cognitive, perceptual, and/or behavioral task. This is commonly done using Fourier-based techniques, such as Welchs method for estimating the power spectral density, and/or by estimating narrowband oscillatory power across trials, conditions, and/or groups. The core assumption underlying these approaches is that the mean is an appropriate measure of central tendency. Despite the importance of this assumption, it has not been rigorously tested.\n\nNew methodWe introduce extensions of common approaches that are better suited for the physiological reality of how neural oscillations often manifest: as nonstationary, high-power bursts, rather than sustained rhythms. Log-transforming, or taking the median power, significantly reduces erroneously inflated power estimates.\n\nResultsAnalyzing 101 participants worth of human electrophysiology, totaling 3,560 channels and over 40 hours data, we show that, in all cases examined, spectral power is not Gaussian distributed. This is true even when oscillations are prominent and sustained, such as visual cortical alpha. Power across time, at every frequency, is characterized by a substantial long tail, which implies that estimates of average power are skewed toward large, infrequent high-power oscillatory bursts.\n\nComparison with existing methodsIn a simulated event-related experiment we show how introducing just a few high-power oscillatory bursts, as seen in real data, can, perhaps erroneously, cause significant differences between conditions using traditional methods. These erroneous effects are substantially reduced with our new methods.\n\nConclusionsThese results call into question the validity of common statistical practices in neural oscillation research.\n\nHighlightsO_LIAnalyses of oscillatory power often assume power is normally distributed.\nC_LIO_LIAnalyzing >40 hours of human M/EEG and ECoG, we show that in all cases it is not.\nC_LIO_LIThis effect is demonstrated in simple simulation of an event-related task.\nC_LIO_LIOverinflated power estimates are reduced via log-transformation or median power.\nC_LI

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bioRxiv2018

Nested oscillatory dynamics in cortical organoids model early human brain network development

Structural and transcriptional changes during early brain maturation follow fixed developmental programs defined by genetics. However, whether this is true for functional network activity remains unknown, primarily due to experimental inaccessibility of the initial stages of the living human brain. Here, we developed cortical organoids that spontaneously display periodic and regular oscillatory network events that are dependent on glutamatergic and GABAergic signaling. These nested oscillations exhibit cross-frequency coupling, proposed to coordinate neuronal computation and communication. As evidence of potential network maturation, oscillatory activity subsequently transitioned to more spatiotemporally irregular patterns, capturing features observed in preterm human electroencephalography (EEG). These results show that the development of structured network activity in the human neocortex may follow stable genetic programming, even in the absence of external or subcortical inputs. Our approach provides novel opportunities for investigating and manipulating the role of network activity in the developing human cortex.\n\nHIGHLIGHTSO_LIEarly development of human functional neural networks and oscillatory activity can be modeled in vitro.\nC_LIO_LICortical organoids exhibit phase-amplitude coupling between delta oscillation (2 Hz) and high-frequency activity (100-400 Hz) during network-synchronous events.\nC_LIO_LIDifferential role of glutamate and GABA in initiating and maintaining oscillatory network activity.\nC_LIO_LIDevelopmental impairment of MECP2-KO cortical organoids impacts the emergence of oscillatory activity.\nC_LIO_LICortical organoid network electrophysiological signatures correlate with human preterm neonatal EEG features.\nC_LI\n\neTOCBrain oscillations are a candidate mechanism for how neural populations are temporally organized to instantiate cognition and behavior. Cortical organoids initially exhibit periodic and highly regular nested oscillatory network events that eventually transition to more spatiotemporally complex activity, capturing features of late-stage preterm infant electroencephalography. Functional neural circuitry in cortical organoids exhibits emergence and development of oscillatory network dynamics similar to those found in the developing human brain.

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bioRxiv2018

The trade-off between neural computation and oscillatory coordination.

Neural oscillations can improve the fidelity of neural coding by grouping action potentials into synchronous windows of activity but this same effect can interfere with coding when action potentials become "over-synchronized". Diseases ranging from Parkinsons to epilepsy suggest such over-synchronization can lead to pathological outcomes, but the precise boundary separating healthy from pathological synchrony remains an open theoretical problem. In this paper, we focus on measuring the costs of translating from an aperiodic code to a rhythmic one and use the errors introduced in this translation to predict the rise of pathological results. We study a simple model of entrainment featuring a pacemaker population coupled to biophysical neurons. This model shows that "error" in individual cells computations can be traded for population-level synchronization of spike-times. But in this model error and synchronization are not traded linearly, but nonlinearly. The bulk of synchronization happens early with relatively low error. To predict this phenomenon we conceive of "voltage budget analysis", where small time windows of membrane voltage in single cells can be partitioned into "oscillatory" and "computational" terms. By comparing these terms we discover a set of inequalities that align with an inflection point in the curve of measured errors. In particular, when the entrainment and computational voltage terms are equal, the error curve plateaus. We show this point serves as a reliable natural boundary to define pathological synchrony in neurons. We also derive optimal algorithms for exchanging computational error with population synchrony. New and Noteworthy. We establish exact conditions for when rhythmic entrainment of precise spike-times in a neural population will improve or harm its ability to communicate.

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bioRxiv2018

Cycle-by-cycle analysis of neural oscillations

Neural oscillations are widely studied using methods based on the Fourier transform, which models data as sums of sinusoids. For decades these Fourier-based approaches have successfully uncovered links between oscillations and cognition or disease. However, because of the fundamental sinusoidal basis, these methods might not fully capture neural oscillatory dynamics, because neural data are both nonsinusoidal and non-stationary. Here, we present a new analysis framework, complementary to Fourier analysis, that quantifies cycle-by-cycle time-domain features. For each cycle, the amplitude, period, and waveform symmetry are measured, the latter of which is missed using conventional approaches. Additionally, oscillatory bursts are algorithmically identified, allowing us to investigate the variability of oscillatory features within and between bursts. This approach is validated on simulated noisy signals with oscillatory bursts and outperforms conventional metrics. Further, these methods are applied to real data--including hippocampal theta, motor cortical beta, and visual cortical alpha--and can differentiate behavioral conditions.

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bioRxiv2018

Parameterizing neural power spectra

Electrophysiological signals across species and recording scales exhibit both periodic and aperiodic features. Periodic oscillations have been widely studied and linked to numerous physiological, cognitive, behavioral, and disease states, while the aperiodic \"background\" 1/f component of neural power spectra has received far less attention. Most analyses of oscillations are conducted on a priori, canonically-defined frequency bands without consideration of the underlying aperiodic structure, or verification that a periodic signal even exists in addition to the aperiodic signal. This is problematic, as recent evidence shows that the aperiodic signal is dynamic, changing with age, task demands, and cognitive state. It has also been linked to the relative excitation/inhibition of the underlying neuronal population. This means that standard analytic approaches easily conflate changes in the periodic and aperiodic signals with one another because the aperiodic parameters--along with oscillation center frequency, power, and bandwidth--are all dynamic in physiologically meaningful, but likely different, ways. In order to overcome the limitations of traditional narrowband analyses and to reduce the potentially deleterious effects of conflating these features, we introduce a novel algorithm for automatic parameterization of neural power spectral densities (PSDs) as a combination of the aperiodic signal and putative periodic oscillations. Notably, this algorithm requires no a priori specification of band limits and accounts for potentially-overlapping oscillations while minimizing the degree to which they are confounded with one another. This algorithm is amenable to large-scale data exploration and analysis, providing researchers with a tool to quickly and accurately parameterize neural power spectra.

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bioRxiv2017

Alpha oscillations control cortical gain by modulating excitatory-inhibitory background activity.

The first recordings of human brain activity in 1929 revealed a striking 8-12 Hz oscillation in the visual cortex. During the intervening 90 years, these alpha oscillations have been linked to numerous physiological and cognitive processes. However, because of the vast and seemingly contradictory cognitive and physiological processes to which it has been related, the physiological function of alpha remains unclear. We identify a novel neural circuit mechanism--the modulation of both excitatory and inhibitory neurons in a balanced configuration--by which alpha can modulate gain. We find that this model naturally unifies the prior, highly diverse reports on alpha dynamics, while making the novel prediction that alpha rhythms have two functional roles: a sustained high-power mode that suppresses scortical gain and a weak, bursting mode that enhances gain.

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