Biology subjects

Peterson, E. J.

Publications and source records attributed to Peterson, E. J..

6 recordsLinked to original sources

bioRxiv2018

In model-based fMRI significant is less than specific

By comparing computational model output to BOLD signal changes model-based fMRI has the potential to offer profound insight into what neural computations occur when. If this potential is to be fully realized, statistically significant outcomes must imply specific outcomes. That is, we must have a clear idea of how often a model not present in the BOLD signal but present in the predictor set will reach significance. We ran Monte Carlo simulations of reinforcement learning to examine this kind of specificity, focusing in on two aspects. One, to what degree can we tell related but theoretically distinct predictors apart. About 40% of the time the studied predictors were indistinguishable. Two, how well can we separate out different parameterizations of the same reinforcement learning terms. Nearly all parameter settings were indistinguishable. The lack of specificity between models and between parameters suggests a uncertain relation between significance and specificity. Follow up analyses suggest the temporally slow and prototyped nature of the haemodynamic response (HRF) can substantially increase correlations, ranging from -0.16 to 0.73 with an average of 0.27. Though we focused on a single case study, i.e., reinforcement learning, specificity concerns are potentially present in any design which does not account for the slow prototyped nature of the HRF. We suggest more specific conclusions can be reached by moving from null hypothesis testing approach to a model selection or model comparison framework.

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↗

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↗

bioRxiv2017

Pegivirus avoids immune recognition but does not attenuate acute-phase disease in a macaque model of HIV infection

Human pegivirus (HPgV) protects HIV+ people from HIV-associated disease, but the mechanism of this protective effect remains poorly understood. We sequentially infected cynomolgus macaques with simian pegivirus (SPgV) and simian immunodeficiency virus (SIV) to model HIV+HPgV co-infection. SPgV had no effect on acute-phase SIV pathogenesis - as measured by SIV viral load, CD4+ T cell destruction, and immune activation - suggesting that HPgVs protective effect is exerted primarily during the chronic phase of HIV infection. We also examined the immune response to SPgV in unprecedented detail, and found that this virus elicits virtually no activation of the immune system despite persistently high titers in the blood over long periods of time. Overall, this study expands our understanding of the pegiviruses - an understudied group of viruses with a high prevalence in the global human population - and suggests that the protective effect observed in HIV+HPgV co-infected people occurs primarily during the chronic phase of HIV infection.\n\nOne Sentence SummaryPegivirus avoids immune recognition but does not attenuate acute-phase disease in a macaque model of HIV infection.\n\nShort TitlePegivirus and AIDS-virus co-infection\n\nAccessible SummaryPeople infected with HIV live longer, healthier lives when they are co-infected with the human pegivirus (HPgV) - an understudied virus with a high prevalence in the global human population. To better understand how HPgV protects people with HIV from HIV-associated disease, we infected macaques with simian versions of these two viruses (SPgV and SIV). We found that SPgV had no impact on the incidence of SIV-associated disease early during the course of SIV infection - a time when SIV and HIV are known to cause irreversible damage to the immune system. Oddly, we found that the immune system did not recognize SPgV; a finding that warrants further investigation. Overall, this study greatly expands on our understanding of the pegiviruses and their interaction with the immune system.

immunology↗

bioRxiv2017

1/f neural noise is a better predictor of schizophrenia than neural oscillations

Diagnosis and symptom severity in schizophrenia are associated with irregularities across neural oscillatory frequency bands, including theta, alpha, beta, and gamma. However, electroencephalographic signals consist of both periodic and aperiodic activity characterized by the (1/fX) shape in the power spectrum. In this paper we investigated oscillatory and aperiodic activity differences between patients with schizophrenia and healthy controls during a target detection task. Separation into periodic and aperiodic components revealed that the steepness of the power spectrum better predicted group status than traditional band-limited oscillatory power in a classification analysis. Aperiodic activity also outperformed the predictions made using participants behavioral responses. Additionally, the differences in aperiodic activity were highly consistent across all electrodes. In sum, compared to oscillations the aperiodic activity appears to be a more accurate and more robust way to differentiate patients with schizophrenia from healthy controls. Significance statementUnderstanding the neurobiological origins of schizophrenia and identifying reliable and consistent biomarkers are of critical importance to improving treatment of that disease. Numerous studies have reported disruptions to neural oscillations in patients with schizophrenia. This has, in part, led to schizophrenia being characterized as a disease of disrupted neural coordination, reflected by changes in frequency band power. We report however that changes in the aperiodic signal can also predict clinical status. Unlike band-limited power though, aperiodic activity predicts status better than participants own behavioral performance and acts as a consistent predictor across all electrodes. Alterations in the aperiodic signal are consistent with well-established inhibitory neuron dysfunctions associated with schizophrenia, allowing for a direct link between noninvasive EEG and chronic, widespread, neurobiological deficits.

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↗