Probabilistic Encoding Models for Multivariate Neural Data

Basic Properties of Coordinated Neuronal Ensembles in the Auditory Thalamus

Coordinated neuronal activity has been identified to play an important role in information processing and transmission in the brain. However, current research predominantly focuses on understanding the properties and functions of neuronal coordination in hippocampal and cortical areas, leaving subcortical regions relatively unexplored. In this study, we use single-unit recordings in female Sprague Dawley rats to investigate the properties and functions of groups of neurons exhibiting coordinated activity in the auditory thalamus-the medial geniculate body (MGB). We reliably identify coordinated neuronal ensembles (cNEs), which are groups of neurons that fire synchronously, in the MGB. cNEs are shown not to be the result of false-positive detections or by-products of slow-state oscillations in anesthetized animals. We demonstrate that cNEs in the MGB have enhanced information-encoding properties over individual neurons. Their neuronal composition is stable between spontaneous and evoked activity, suggesting limited stimulus-induced ensemble dynamics. These MGB cNE properties are similar to what is observed in cNEs in the primary auditory cortex (A1), suggesting that ensembles serve as a ubiquitous mechanism for organizing local networks and play a fundamental role in sensory processing within the brain.

White Matter Organization and Cortical Thickness Differ Among Active Duty Service Members With Chronic Mild, Moderate, and Severe Traumatic Brain Injury

Abstract This study compared findings from whole-brain diffusion tensor imaging (DTI) and volumetric magnetic resonance imaging (MRI) among 90 Active Duty Service Members with chronic mild traumatic brain injury (TBI; n = 52), chronic moderate-to-severe TBI (n = 17), and TBI-negative controls (n = 21). Data were collected on a Philips Ingenia 3T MRI with DTI in 32 directions. Results demonstrated that history of TBI was associated with differences in white matter microstructure, white matter volume, and cortical thickness in both mild TBI and moderate-to-severe TBI groups relative to controls. However, the presence, pattern, and distribution of these findings varied substantially depending on the injury severity. Spatially-defined forms of DTI fractional anisotropy (FA) analyses identified altered white matter organization within the chronic moderate-to-severe TBI group, but they did not provide clear evidence of abnormalities within the chronic mild TBI group. In contrast, DTI FA "pothole" analyses identified widely distributed areas of decreased FA throughout the white matter in both the chronic mild TBI and chronic moderate-to-severe TBI groups. Additionally, decreased white matter volume was found in several brain regions for the chronic moderate-to-severe TBI group compared with the other groups. Greater number of DTI FA potholes and reduced cortical thickness were also related to greater severity of self-reported symptoms. In sum, this study expands upon a growing body of literature using advanced imaging techniques to identify potential effects of brain injury in military Service Members. These findings may differ from work in other TBI populations due to varying mechanisms and frequency of injury, as well as a potentially higher level of functioning in the current sample related to the ability to maintain continued Active Duty status after injury. In conclusion, this study provides DTI and volumetric MRI findings across the spectrum of TBI severity. These results provide support for the use of DTI and volumetric MRI to identify differences in white matter microstructure and volume related to TBI. In particular, DTI FA pothole analysis may provide greater sensitivity for detecting subtle forms of white matter injury than conventional DTI FA analyses.

Microglial Refinement of A-Fiber Projections in the Postnatal Spinal Cord Dorsal Horn Is Required for Normal Maturation of Dynamic Touch

Sensory systems are shaped in postnatal life by the refinement of synaptic connectivity. In the dorsal horn of the spinal cord, somatosensory circuits undergo postnatal activity-dependent reorganization, including the refinement of primary afferent A-fiber terminals from superficial to deeper spinal dorsal horn laminae which is accompanied by decreases in cutaneous sensitivity. Here, we show in the mouse that microglia, the resident immune cells in the CNS, phagocytose A-fiber terminals in superficial laminae in the first weeks of life. Genetic perturbation of microglial engulfment during the initial postnatal period in either sex prevents the normal process of A-fiber refinement and elimination, resulting in an altered sensitivity of dorsal horn cells to dynamic tactile cutaneous stimulation, and behavioral hypersensitivity to dynamic touch. Thus, functional microglia are necessary for the normal postnatal development of dorsal horn sensory circuits. In the absence of microglial engulfment, superfluous A-fiber projections remain in the dorsal horn, and the balance of sensory connectivity is disrupted, leading to lifelong hypersensitivity to dynamic touch.

Modeling the diverse effects of divisive normalization on noise correlations

Divisive normalization, a prominent descriptive model of neural activity, is employed by theories of neural coding across many different brain areas. Yet, the relationship between normalization and the statistics of neural responses beyond single neurons remains largely unexplored. Here we focus on noise correlations, a widely studied pairwise statistic, because its stimulus and state dependence plays a central role in neural coding. Existing models of covariability typically ignore normalization despite empirical evidence suggesting it affects correlation structure in neural populations. We therefore propose a pairwise stochastic divisive normalization model that accounts for the effects of normalization and other factors on covariability. We first show that normalization modulates noise correlations in qualitatively different ways depending on whether normalization is shared between neurons, and we discuss how to infer when normalization signals are shared. We then apply our model to calcium imaging data from mouse primary visual cortex (V1), and find that it accurately fits the data, often outperforming a popular alternative model of correlations. Our analysis indicates that normalization signals are often shared between V1 neurons in this dataset. Our model will enable quantifying the relation between normalization and covariability in a broad range of neural systems, which could provide new constraints on circuit mechanisms of normalization and their role in information transmission and representation.

Delaying the GABA Shift Indirectly Affects Membrane Properties in the Developing Hippocampus

During the first two postnatal weeks, intraneuronal chloride concentrations in rodents gradually decrease, causing a shift from depolarizing to hyperpolarizing GABA responses. The postnatal GABA shift is delayed in rodent models for neurodevelopmental disorders and in human patients, but the impact of a delayed GABA shift on the developing brain remains obscure. Here we examine the direct and indirect consequences of a delayed postnatal GABA shift on network development in organotypic hippocampal cultures made from 6- to 7-d-old mice by treating the cultures for 1 week with VU0463271, a specific inhibitor of the chloride exporter KCC2. We verified that VU treatment delayed the GABA shift and kept GABA signaling depolarizing until DIV9. We found that the structural and functional development of excitatory and inhibitory synapses at DIV9 was not affected after VU treatment. In line with previous studies, we observed that GABA signaling was already inhibitory in control and VU-treated postnatal slices. Surprisingly, 14 d after the VU treatment had ended (DIV21), we observed an increased frequency of spontaneous inhibitory postsynaptic currents in CA1 pyramidal cells, while excitatory currents were not changed. Synapse numbers and release probability were unaffected. We found that dendrite-targeting interneurons in the stratum radiatum had an elevated resting membrane potential, while pyramidal cells were less excitable compared with control slices. Our results show that depolarizing GABA signaling does not promote synapse formation after P7, and suggest that postnatal intracellular chloride levels indirectly affect membrane properties in a cell-specific manner.SIGNIFICANCE STATEMENT During brain development, the action of neurotransmitter GABA shifts from depolarizing to hyperpolarizing. This shift is a thought to play a critical role in synapse formation. A delayed shift is common in rodent models for neurodevelopmental disorders and in human patients, but its consequences for synaptic development remain obscure. Here, we delayed the GABA shift by 1 week in organotypic hippocampal cultures and carefully examined the consequences for circuit development. We find that delaying the shift has no direct effects on synaptic development, but instead leads to indirect, cell type-specific changes in membrane properties. Our data call for careful assessment of alterations in cellular excitability in neurodevelopmental disorders.

Nonlinear point-process estimation of neural spiking activity based on variational Bayesian inference

OBJECTIVE

Understanding neural encoding and decoding processes are crucial to the development of brain-machine interfaces (BMI). Higher decoding speed of neural signals is required for the large-scale neural data and the extremely low detection delay of closed-loop feedback experiment.

APPROACH

To achieve higher neural decoding speed, we proposed a novel adaptive higher-order nonlinear point-process filter based on the Variational Bayesian Inference (VBI) framework, called the HON-VBI. This algorithm avoids the complex Monte Carlo random sampling in the traditional method. Using the VBI method, it can quickly implement inferences of state posterior distribution and the tuning parameters.

MAIN RESULTS

Our result demonstrates the effectiveness and advantages of the HON-VBI by application for decoding the multichannel neural spike trains of the simulation data and real data. Compared with traditional methods, the HON-VBI greatly reduces the decoding time of large-scale neural spike trains. Through capturing the nonlinear evolution of system state and accurate estimating of time-varying tuning parameters, the decoding accuracy is improved.

SIGNIFICANCE

Our work can be applied to rapidly decode large-scale multichannel neural spike trains in brain-machine interfaces.

Inference of Multiplicative Factors Underlying Neural Variability in Calcium Imaging Data

Understanding brain function requires disentangling the high-dimensional activity of populations of neurons. Calcium imaging is an increasingly popular technique for monitoring such neural activity, but computational tools for interpreting extracted calcium signals are lacking. While there has been a substantial development of factor-analysis-type methods for neural spike train analysis, similar methods targeted at calcium imaging data are only beginning to emerge. Here we develop a flexible modeling framework that identifies low-dimensional latent factors in calcium imaging data with distinct additive and multiplicative modulatory effects. Our model includes spike-and-slab sparse priors that regularize additive factor activity and gaussian process priors that constrain multiplicative effects to vary only gradually, allowing for the identification of smooth and interpretable changes in multiplicative gain. These factors are estimated from the data using a variational expectation-maximization algorithm that requires a differentiable reparameterization of both continuous and discrete latent variables. After demonstrating our method on simulated data, we apply it to experimental data from the zebrafish optic tectum, uncovering low-dimensional fluctuations in multiplicative excitability that govern trial-to-trial variation in evoked responses.

Improved inference in coupling, encoding, and decoding models and its consequence for neuroscientific interpretation

BACKGROUND

A central goal of systems neuroscience is to understand the relationships amongst constituent units in neural populations, and their modulation by external factors, using high-dimensional and stochastic neural recordings. Parametric statistical models (e.g., coupling, encoding, and decoding models), play an instrumental role in accomplishing this goal. However, extracting conclusions from a parametric model requires that it is fit using an inference algorithm capable of selecting the correct parameters and properly estimating their values. Traditional approaches to parameter inference have been shown to suffer from failures in both selection and estimation. The recent development of algorithms that ameliorate these deficiencies raises the question of whether past work relying on such inference procedures have produced inaccurate systems neuroscience models, thereby impairing their interpretation.

NEW METHOD

We used algorithms based on Union of Intersections, a statistical inference framework based on stability principles, capable of improved selection and estimation.

COMPARISON

We fit functional coupling, encoding, and decoding models across a battery of neural datasets using both UoI and baseline inference procedures (e.g., ℓ₁-penalized GLMs), and compared the structure of their fitted parameters.

RESULTS

Across recording modality, brain region, and task, we found that UoI inferred models with increased sparsity, improved stability, and qualitatively different parameter distributions, while maintaining predictive performance. We obtained highly sparse functional coupling networks with substantially different community structure, more parsimonious encoding models, and decoding models that relied on fewer single-units.

CONCLUSIONS

Together, these results demonstrate that improved parameter inference, achieved via UoI, reshapes interpretation in diverse neuroscience contexts.

A Poisson generalized linear model application to disentangle the effects of various parameters on neurophysiological discharges

The protocol provides an extensive guide to apply the generalized linear model framework to neurophysiological recordings. This flexible technique can be adapted to test and quantify the contributions of many different parameters (e.g., kinematics, target position, choice, reward) on neural activity. To weight the influence of each parameter, we developed an intuitive metric (“w-value”) that can be used to build a “functional fingerprint” characteristic for each neuron. We also provide suggestions to extract complementary useful information from the method.

For complete details on the use and execution of this protocol, please refer to Diomedi et al. (2020).

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GLM applied to neuronal discharges reveals parameters that modulate their activity

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Applying regularizers helps to discard minority parameters and reduces noise

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Each neuron can be characterized by the weight assigned to each parameter tested

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Results are well suited for population analyses (i.e., clustering, correlation, etc)

Context-Dependent Multiplexing by Individual VTA Dopamine Neurons

Dopamine (DA) neurons of the VTA track cues and rewards to generate a reward prediction error signal during Pavlovian conditioning. Here we explored how these neurons respond to a self-paced, operant task in freely moving mice. The animal could trigger a reward-predicting cue by remaining in a specific location of an operant box for a brief time before moving to a spout for reward collection. VTA DA neurons were identified using DAT-Cre male mice that carried an optrode with minimal impact on the behavioral task. In vivo single-unit recordings revealed transient fast spiking responses to the cue and reward in correct trials, while for incorrect ones the activity paused, reflecting positive and negative error signals of a reward prediction. In parallel, a majority of VTA DA neurons simultaneously encoded multiple actions (e.g., movement velocity, acceleration, distance to goal, and licking) in sustained slow firing modulation. Applying a GLM, we show that such multiplexed encoding of rewarding and motor variables by individual DA neurons was only apparent while the mouse was engaged in the task. Downstream targets may exploit such goal-directed multiplexing of VTA DA neurons to adjust actions to optimize the task's outcome.SIGNIFICANCE STATEMENT VTA DA neurons code for multiple functions, including the reward prediction error but also motivation and locomotion. Here we show that about half of the recorded VTA DA neurons perform multiplexing: they exploit the phasic and tonic activity modes to encode, respectively, the cue/reward responses and motor parameters, most prominently when the mouse engages in a self-paced operand task. VTA non-DA neurons, by contrast, encode motor parameters regardless of task engagement.

Cognitive Fatigability Interventions in Neurological Conditions: A Systematic Review

INTRODUCTION

Although fatigue is a well-studied concept in neurological disease, cognitive fatigability (CF) is less understood. While most studies measure fatigue using subjective self-report, fewer have measured CF objectively. Given the negative impact of CF on quality-of-life, there is a need for targeted interventions. The objective of this review was to determine which procedural, behavioural and pharmacological treatments for objectively measured CF are available to people living with neurological conditions.

METHODS

In accordance with the PRISMA guidelines, systematic searches for randomized control trials (RCTs), case-controlled studies and case reports/series were conducted across the Ovid Medline, PsycInfo, EMBASE and Cochrane Library databases. English-language articles published between 1980 and February 2019 were considered for eligibility. Included were those that objectively measured CF in individuals with neurological disease/disorder/dysfunction between the ages of 18 and 65 years. Studies were reviewed using a modified Cochrane Data Extraction Template. Risk of bias was assessed using the Cochrane Risk of Bias tool. The review process was facilitated using Covidence software (www.covidence.org). Two authors reviewed articles independently, with a third resolving conflicts regarding article inclusion.

RESULTS

The search identified 450 records. After duplicates were removed and remaining titles/abstracts were screened for eligibility, 28 full-text articles were assessed, and two studies were included in the qualitative synthesis. Studies were a priori divided into those with pharmacological, procedural or behavioural interventions. Two studies met eligibility criteria; both of these included participants with multiple sclerosis. One study utilized a procedural intervention (i.e. transcranial direct current stimulation), while the other utilized a pharmacological intervention (i.e. fampridine-SR). Studies were evaluated for risk of bias, and evidence from both eligible studies was discussed.

CONCLUSION

Despite the positive results of the procedural intervention, the paucity of eligible studies and the nascent nature of the field suggests that more studies are required before firm conclusions can be drawn regarding the amenability of CF to treatment.

TRIAL REGISTRATION

The review was registered with PROSPERO (CRD42019118706).