Biology subjects

Brown, E. N.

Publications and source records attributed to Brown, E. N..

30 records · Page 2Linked to original sources

bioRxiv2021

Deep brain stimulation in the subthalamic nucleus for Parkinson's disease can restore dynamics of striatal networks

Deep brain stimulation (DBS) of the subthalamic nucleus (STN) is highly effective in alleviating movement disability in patients with Parkinsons disease (PD). However, its therapeutic mechanism of action is unknown. The healthy striatum exhibits rich dynamics resulting from an interaction of beta, gamma and theta oscillations. These rhythms are at the heart of selection, initiation and execution of motor programs, and their loss or exaggeration due to dopamine (DA) depletion in PD is a major source of the behavioral deficits observed in PD patients. Interrupting abnormal rhythms and restoring the interaction of rhythms as observed in the healthy striatum may then be instrumental in the therapeutic action of DBS. We develop a biophysical networked model of a BG pathway to study how abnormal beta oscillations can emerge throughout the BG in PD, and how DBS can restore normal beta, gamma and theta striatal rhythms. Our model incorporates STN projections to the striatum, long known but understudied, that were recently shown to preferentially target fast spiking interneurons (FSI) in the striatum. We find that DBS in STN is able to normalize striatal medium spiny neuron (MSN) activity by recruiting FSI dynamics, and restoring the inhibitory potency of FSIs observed in normal condition. We also find that DBS allows the re-expression of gamma and theta rhythms, thought to be dependent on high DA levels and thus lost in PD, through cortical noise control. Our study shows how BG connectivity can amplify beta oscillations, and delineates the role of DBS in disrupting beta oscillations and providing corrective input to STN efferents to restore healthy striatal dynamics. It also suggests how gamma oscillations can be leveraged to enhance or supplement DBS treatment and improve its effectiveness.

neuroscience↗

bioRxiv2021

Elementary Integrate-and-Fire Process Underlies Pulse Amplitudes in Electrodermal Activity

Electrodermal activity (EDA) is a direct read-out of sweat-induced changes in the skins electrical conductance. Sympathetically-mediated pulsatile changes in skin sweat measured as EDA resemble an integrate-and-fire process, which yields an inverse Gaussian model as the inter-pulse interval distribution. We have previously showed that the inter-pulse intervals in EDA follow an inverse Gaussian distribution. However, the statistical structure of EDA pulse amplitudes has not yet been characterized based on the physiology. Expanding upon the integrate-and-fire nature of sweat glands, we hypothesized that the amplitude of an EDA pulse is proportional to the excess volume of sweat produced compared to what is required to just reach the surface of the skin. We modeled this as the difference of two inverse Gaussian models for each pulse, one which represents the time required to produce just enough sweat to rise to the surface of the skin and one which represents the time requires to produce the actual volume of sweat. We proposed and tested a series of four simplifications of our hypothesis, ranging from a single difference of inverse Gaussians to a single simple inverse Gaussian. We also tested four additional models for comparison, including the lognormal and gamma distributions. All models were tested on EDA data from two subject cohorts, 11 healthy volunteers during 1 hour of quiet wakefulness and a different set of 11 healthy volunteers during approximately 3 hours of controlled propofol sedation. All four models represent simplifications of our hypothesis outperformed other models across all 22 subjects, as measured by Akaikes Information Criterion (AIC), as well as mean and maximum distance from the diagonal on a quantile-quantile plot. Our broader model set of four simplifications offered a useful framework to enhance further statistical descriptions of EDA pulse amplitudes. Some of the simplifications prioritize fit near the mode of the distribution, while others prioritize fit near the tail. With this new insight, we can summarize the physiologically-relevant amplitude information in EDA with at most four parameters. Our findings establish that physiologically based probability models provide parsimonious and accurate description of temporal and amplitude characteristics in EDA. AUTHOR SUMMARYElectrodermal activity (EDA) is an indirect read-out of the bodys sympathetic nervous system, or fight-or-flight response, measured as sweat-induced changes in the electrical conductance properties of the skin. Interest is growing in using EDA to track physiological conditions such as stress levels, sleep quality, and emotional states. Our previous worked showed that the times in between EDA pulses obeyed a specific statistical distribution, the inverse Gaussian, that arises from the physiology of EDA production. In this work, we build on that insight to analyze the amplitudes of EDA pulses. In an analysis of EDA data recorded in 11 healthy volunteers during quiet wakefulness and 11 different healthy volunteers during controlled propofol sedation, we establish that the amplitudes of EDA pulses also have specific statistical structure, as the differences of inverse Gaussians, that arises from the physiology of sweat production. We capture that structure using a series of progressively simpler models that each prioritize different parts of the pulse amplitude distribution. Our findings show a physiologically-based statistical model provides a parsimonious and accurate description EDA. This enables increased reliability and robustness in analyzing EDA data collected under any circumstance.

neuroscience↗

bioRxiv2020

Quantitative assessment of the relationship between behavioral and autonomic dynamics during propofol-induced unconsciousness

During general anesthesia, both behavioral and autonomic changes are caused by the administration of anesthetics such as propofol. Propofol produces unconsciousness by creating highly structured oscillations in brain circuits. The anesthetic also has autonomic effects due to its actions as a vasodilator and myocardial depressant. Understanding how autonomic dynamics change in relation to propofol-induced unconsciousness is an important scientific and clinical question since anesthesiologists often infer changes in level of unconsciousness from changes in autonomic dynamics. Therefore, we present a framework combining physiology-based statistical models that have been developed specifically for heart rate variability and electrodermal activity with a robust statistical tool to compare behavioral and multimodal autonomic changes before, during, and after propofol-induced unconsciousness. We tested this framework on physiological data recorded from nine healthy volunteers during computer-controlled administration of propofol. We studied how autonomic dynamics related to behavioral markers of unconsciousness: 1) overall, 2) during the transitions of loss and recovery of consciousness, and 3) before and after anesthesia as a whole. Our results show a strong relationship between behavioral state of consciousness and autonomic dynamics. All of our prediction models showed areas under the curve greater than 0.75 despite the presence of non-monotonic relationships among the variables during the transition periods. Our analysis highlighted the specific roles played by fast versus slow changes, parasympathetic vs sympathetic activity, heart rate variability vs electrodermal activity, and even pulse rate vs pulse amplitude information within electrodermal activity. Further advancement upon this work can quantify the complex and subject-specific relationship between behavioral changes and autonomic dynamics before, during, and after anesthesia. However, this work demonstrates the potential of a multimodal, physiologically-informed, statistical approach to characterize autonomic dynamics.

neuroscience↗

bioRxiv2020

Analysis of Distributed Neural Synchrony through State-Space Coherence Analysis

Established methods to track the dynamics of neural representations focus at the level of individual neurons for spiking data, and individual or pair of channels for local field potentials. However, our understanding of neural function and computation has moved toward an integrative view, based upon coordinated activity of multiple neural populations across brain areas. To draw network-level inferences of brain function, we propose a new modeling framework that combines the state-space model and cross-spectral matrix estimates - this is called state-space coherence (SSCoh). We define elements of the SSCoh and derive system identification and approximate filter solution for multivariate space processes. We expand SCoh for mixed observation processes, where the observation includes different modalities of neural data including local filed potential and spiking activity. Finally, we show an application of the framework to study neural synchrony across different brain nodes of a task participant performing Stroop task under different distraction levels.

neuroscience↗

bioRxiv2020

Neural effects of propofol-induced unconsciousness and its reversal using thalamic stimulation

The specific circuit mechanisms through which anesthetics induce unconsciousness have not been completely characterized. We recorded neural activity from the frontal, parietal, and temporal cortices and thalamus while maintaining unconsciousness in non-human primates (NHPs) with the anesthetic propofol. Unconsciousness was marked by slow frequency (~1 Hz) oscillations in local field potentials, entrainment of local spiking to Up states alternating with Down states of little spiking, and decreased coherence in frequencies above 4 Hz. Thalamic stimulation "awakened" anesthetized NHPs and reversed the electrophysiologic features of unconsciousness. Unconsciousness is linked to cortical and thalamic slow frequency synchrony coupled with decreased spiking, and loss of higher-frequency dynamics. This may disrupt cortical communication/integration.

neuroscience↗

bioRxiv2020

A Model-Based Approach for Pulse Selection from Electrodermal Activity

ObjectiveThe goal of this work was to develop a physiology-based paradigm for pulse selection from electrodermal activity (EDA) data. MethodsWe aimed to use insight about the integrate-and-fire physiology of sweat gland bursts, which predicts inverse Gaussian inter-pulse interval structure. At the core of our paradigm is a subject-specific amplitude threshold selection process for pulses based on the statistical properties of four right-skewed models including the inverse Gaussian. These four models differ in their tail behavior, which reflects sweat gland physiology to varying degrees. By screening across thresholds and fitting all four models, we selected for heavier tails that reflect inverse Gaussian-like structure and verified the pulse selection with a goodness-of-fit analysis. ResultsWe tested our paradigm on two different subject cohorts recorded during different experimental conditions and using different equipment. In both cohorts, our method robustly and consistently recovered pulses that captured the inverse Gaussian-like structure predicted by physiology, despite large differences in noise level of the data. In contrast, an established EDA analysis paradigm, which assumes a constant amplitude threshold across all data, was unable to separate pulses from noise. ConclusionWe present a computationally efficient, statistically rigorous, and physiology-informed paradigm for pulse selection from EDA data that is robust across individuals and experimental conditions yet adaptable to changes in noise level. SignificanceThe robustness of our paradigm and its basis in physiology move EDA closer to serving as a clinical marker for sympathetic activity in diverse conditions such as pain, anxiety, depression, and sleep.

physiology↗

bioRxiv2020

Point Process Temporal Structure Characterizes Electrodermal Activity

Electrodermal activity (EDA) is a read-out of the bodys sympathetic nervous system measured as sweat-induced changes in the electrical conductance properties of the skin. There is growing interest in using EDA to track physiological conditions such as stress levels, sleep quality and emotional states. Standardized EDA data analysis methods are readily available. However, none considers two established physiological features of EDA: 1) sympathetically mediated pulsatile changes in skin sweat measured as EDA resemble an integrate-and-fire process; 2) inter-pulse interval times vary depending upon the local physiological state of the skin. Based on the anatomy and physiology that underlie feature 1, we postulate that inverse Gaussian probability models would accurately describe EDA inter-pulse intervals. Given feature 2, we postulate that under fluctuating local physiological states, the inter-pulse intervals would follow mixtures of inverse Gaussian models, that can be represented as lognormal models if the conditions favor longer intervals (heavy tails) or by gamma models if the conditions favor shorter intervals (light tails). To assess the validity of these probability models we recorded and analyzed EDA measurements in 11 healthy volunteers during 1 to 2 hours of quiet wakefulness. We assess the tail behavior of the probability models by computing their settling rates. All data series were accurately described by one or more of the models: two by inverse Gaussian models; five by lognormal models and three by gamma models. These probability models suggest a highly succinct point process framework for real-time tracking of sympathetically-mediated changes in physiological state.

neuroscience↗

bioRxiv2020

Robust point-process Granger causality analysis in presence of exogenous temporal modulations and trial-by-trial variability in spike trains.

Assessing directional influences between neurons is instrumental to understand how brain circuits process information. To this end, Granger causality, a technique originally developed for time-continuous signals, has been extended to discrete spike trains. A fundamental assumption of this technique is that the temporal evolution of neuronal responses must be due only to endogenous interactions between recorded units, including self-interactions. This assumption is however rarely met in neurophysiological studies, where the response of each neuron is modulated by other exogenous causes such as, for example, other unobserved units or slow adaptation processes. Here, we propose a novel point-process Granger causality technique that is robust with respect to the two most common exogenous modulations observed in real neuronal responses: within-trial temporal variations in spiking rate and between-trial variability in their magnitudes. This novel method works by explicitly including both types of modulations into the generalized linear model of the neuronal conditional intensity function (CIF). We then assess the causal influence of neuron i onto neuron j by measuring the relative reduction of neuron js point process likelihood obtained considering or removing neuron i. CIFs hyper-parameters are set on a per-neuron basis by minimizing Akaikes information criterion. In simulated data, the proposed method recovered with high accuracy the underlying ground-truth connectivity pattern. Application of presently available point-process Granger causality techniques produced instead a significant number of false positive connections. In real spiking responses recorded from neurons in the monkey pre-motor cortex (area F5), our method revealed many causal relationships between neurons as well as the temporal structure of their interactions. Given its robustness our method can be effectively applied to real neuronal data. Furthermore, its explicit estimate of the effects of unobserved causes on the recorded neuronal firing patterns can help decomposing their temporal variations into endogenous and exogenous components. Author summaryModern techniques in Neuroscience allow to investigate the brain at the network level by studying the flow of information between neurons. To this end, Granger causality has been extended to point process spike trains. A fundamental assumption of this technique is that there should be no unobserved causes of temporal variability in the recorded spike trains. This, however, greatly limits its applicability to real neuronal recordings as very often not all the sources of variability in neuronal responses can be concurrently recorded. We present here a robust point-process Granger causality technique that overcome this problem by explicitly incorporating unobserved sources of variability into the model of neuronal spiking responses. In synthetic data sets, our new technique correctly recovered the underlying ground-truth functional connectivity between simulated units with a great degree of accuracy. Furthermore, its application to real neuronal recordings revealed many causal relationships between neurons as well as the temporal structure of their interactions. Our results suggest that our novel Granger causality method is robust and it can be used to study the flow of information in the spiking patterns of simultaneously recorded neurons even in presence of unobserved causes of temporal variability.

neuroscience↗

bioRxiv2019

Neural signatures of loss of consciousness and its recovery by thalamic stimulation.

We know that general anesthesia produces unconsciousness but not quite how. We recorded neural activity from the frontal, parietal, and temporal cortices and thalamus while maintaining unconsciousness in non-human primates (NHPs) with propofol. Unconsciousness was marked by slow frequency ([~]1 Hz) oscillations in local field potentials, entraining local spiking to Up states alternating with Down states of little spiking, and decreased higher frequency (>4 Hz) coherence. The thalamus contributed to cortical rhythms. Its stimulation \"awakened\" anesthetized NHPs and reversed the electrophysiologic features of unconsciousness. Unconsciousness thus resulted from slow frequency hypersynchrony and loss of high-frequency dynamics, partly mediated by the thalamus, that disrupts cortical communication/integration.

neuroscience↗

bioRxiv2019

Broadband slow-wave modulation in posterior and anterior cortex tracks distinct states of propofol-induced unconsciousness

A controversy 5 has developed in recent years over the role that frontal and posterior cortices play in mediating consciousness and unconsciousness. One hypothesis proposes that posterior sensory and association cortices are the principal mediators of consciousness, citing evidence that strong slow-wave activity over posterior cortex during sleep disrupts the contents of dreaming. A competing hypothesis proposes that frontal-posterior interactions are critical to ignite a conscious percept, since activation of frontal cortex appears necessary for perception and can reverse unconsciousness under anesthesia. In both cases, EEG slow-waves (< 1 Hz) are considered evidence that up- and down-states are disrupting cortical activity necessary for consciousness. Here, we used anesthesia to study the interaction between the slow-wave and higher frequency activity in humans. If slow-waves are derived from underlying up and down-states, then they should modulate activity across a broad range of frequencies. We found that this broadband slow-wave modulation does occur: broadband slow-wave modulation occurs over posterior cortex when subjects initially become unconscious, but later encompasses both frontal and posterior cortex when subjects are more deeply anesthetized and likely unarousable. Based on these results, we argue that unconsciousness under anesthesia comprises several shifts in brain state that disrupt the sensory contents of consciousness distinct from arousal and awareness of those contents.\n\nSignificance StatementThe roles of frontal and posterior cortices in mediating consciousness and unconsciousness are controversial. Disruption of posterior cortex during sleep appears to suppress the contents of dreaming, yet activation of frontal cortex appears necessary for perception and can reverse unconsciousness under anesthesia. We studied the time course of regional cortical disruption, as mediated by slow-wave modulation of broadband activity, during anesthesia-induced unconsciousness in humans. We found that broadband slow-wave modulation covered posterior cortex when subjects initially became unconscious, but later encompassed both frontal and posterior cortex when subjects were deeply anesthetized and likely unarousable. This suggests that unconsciousness under anesthesia comprises several shifts in brain state that disrupt the contents of consciousness distinct from arousal and awareness of those contents.

neuroscience↗

bioRxiv2017

Left-Lateralized Contributions of Saccades to Cortical Activity during a One-Back Word Recognition Task

Saccadic eye movements are an inherent component of natural reading, yet their contribution to information processing at subsequent fixation remains elusive. Here we use anatomically-constrained magnetoencephalography (MEG) to examine cortical activity following saccades as healthy human subjects engaged in a one-back word recognition task. This activity was compared with activity following external visual stimulation that mimicked saccades. A combination of procedures were employed to eliminate saccadic ocular artifacts from the MEG signal. Both saccades and saccade-like external visual stimulation produced early-latency responses beginning ~70 ms after onset in occipital cortex and spreading through the ventral and dorsal visual streams to temporal, parietal and frontal cortices. Robust differential activity following the onset of saccades versus similar external visual stimulation emerged during 150-350 ms in a left-lateralized cortical network. This network included (i) left lateral occipitotemporal and nearby inferotemporal cortex, (ii) left posterior Sylvian fissure and nearby multimodal cortex, and (iii) medial parietooccipital, posterior cingulate and retrosplenial cortices. Moreover, this left-lateralized network colocalized with word repetition priming effects. Together, results suggest that central saccadic mechanisms influence a left-lateralized language network in occipitotemporal and temporal cortex above and beyond saccadic influences at preceding stages of information processing during visual word recognition.

neuroscience↗

bioRxiv2017

Electroencephalographic markers of brain development during sevoflurane anesthesia in children aged 0 to 3 years old

The general anesthetic sevoflurane acts on GABAergic-interneurons to generate stereotyped oscillations that relate fundamentally to neural circuit architecture. Each year, millions of children require general anesthesia, providing an experiment of nature that allows characterization of the developmental trajectory of GABAergic-inhibitory circuits in the human brain. We used multichannel electroencephalograph recordings in 91 children 0-40 months old. We mapped spatial power and coherence across the cortex. During sevoflurane exposure: (1) slow-delta oscillations are present in all ages; (2) theta and alpha oscillations emerge around 4 months; (3) alpha oscillations increase in power from 4 to 10 months; (4) frontal alpha oscillation predominance emerge at ~6 months; (5) frontal slow oscillations are coherent in the first 6 months of age only; and (6) frontal alpha oscillations become coherent around 10 months and persist in older ages. Our results suggest key developmental milestones are visible in the functional activity of sevoflurane-stimulated GABAergic circuits.

neuroscience↗