Biology subjects

Voytek, B.

Publications and source records attributed to Voytek, B..

8 recordsLinked to original sources

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.

neuroscience↗

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

neuroscience↗

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.

neuroscience↗

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.

neuroscience↗

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.

neuroscience↗

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.

neuroscience↗

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.

neuroscience↗

bioRxiv2016

Field Potential Reflects the Balance of Synaptic Excitation and Inhibition

Neural circuits sit in a dynamic balance between excitation (E) and inhibition (I). Fluctuations in this E:I balance have been shown to influence neural computation, working memory, and information processing. While more drastic shifts and aberrant E:I patterns are implicated in numerous neurological and psychiatric disorders, current methods for measuring E:I dynamics require invasive procedures that are difficult to perform in behaving animals, and nearly impossible in humans. This has limited the ability to examine the full impact that E:I shifts have in neural computation and disease. In this study, we develop a computational model to show that E:I ratio can be estimated from the power law exponent (slope) of the electrophysiological power spectrum, and validate this relationship using previously published datasets from two species (rat local field potential and macaque electrocorticography). This simple method--one that can be applied retrospectively to existing data--removes a major hurdle in understanding a currently difficult to measure, yet fundamental, aspect of neural computation.

neuroscience↗