A Granger Causality Measure for Point Process Models of Ensemble Neural Spiking Activity

The ability to identify directional interactions that occur among multiple neurons in the brain is crucial to an understanding of how groups of neurons cooperate in order to generate specific brain functions. However, an optimal method of assessing these interactions has not been established. Granger causality has proven to be an effective method for the analysis of the directional interactions between multiple sets of continuous-valued data, but cannot be applied to neural spike train recordings due to their discrete nature. This paper proposes a point process framework that enables Granger causality to be applied to point process data such as neural spike trains. The proposed framework uses the point process likelihood function to relate a neuron’s spiking probability to possible covariates, such as its own spiking history and the concurrent activity of simultaneously recorded neurons. Granger causality is assessed based on the relative reduction of the point process likelihood of one neuron obtained excluding one of its covariates compared to the likelihood obtained using all of its covariates. The method was tested on simulated data, and then applied to neural activity recorded from the primary motor cortex (MI) of a Felis catus subject. The interactions present in the simulated data were predicted with a high degree of accuracy, and when applied to the real neural data, the proposed method identified causal relationships between many of the recorded neurons. This paper proposes a novel method that successfully applies Granger causality to point process data, and has the potential to provide unique physiological insights when applied to neural spike trains.National Institutes of Health (U.S.) (Grant DP1-OD003646)National Institutes of Health (U.S.) (Grant R01-EB006385

Motor Cortical Networks for Skilled Movements Have Dynamic Properties That Are Related to Accurate Reaching

Neurons in the Primary Motor Cortex (MI) are known to form functional ensembles with one another in order to produce voluntary movement. Neural network changes during skill learning are thought to be involved in improved fluency and accuracy of motor tasks. Unforced errors during skilled tasks provide an avenue to study network connections related to motor learning. In order to investigate network activity in MI, microwires were implanted in the MI of cats trained to perform a reaching task. Spike trains from eight groups of simultaneously recorded cells (95 neurons in total) were acquired. A point process generalized linear model (GLM) was developed to assess simultaneously recorded cells for functional connectivity during reaching attempts where unforced errors or no errors were made. Whilst the same groups of neurons were often functionally connected regardless of trial success, functional connectivity between neurons was significantly different at fine time scales when the outcome of task performance changed. Furthermore, connections were shown to be significantly more robust across multiple latencies during successful trials of task performance. The results of this study indicate that reach-related neurons in MI form dynamic spiking dependencies whose temporal features are highly sensitive to unforced movement errors.National Science Foundation (U.S.) (Grant DP1-OD003646)National Science Foundation (U.S.) (R01- DA015644)Australian Neuromuscular Research Institut

A smartphone application for reporting symptoms in adults with cystic fibrosis: Protocol of a randomised controlled trial

Introduction: In people with cystic fibrosis (CF), exacerbations have been shown to have profound and prolonged negative effects such as reducing physical activity and health-related quality of life, increasing the rate of decline of lung function and healthcare costs, and ultimately increasing the risk of mortality. Delayed initiation of treatment following the signs of an exacerbation has been shown to be associated with failure to recover to baseline. Therefore, the late identification and treatment of an exacerbation due to delayed presentation will potentially worsen short-term and long-term outcomes. We have developed a smartphone application, containing questions which require yes or no responses relating to symptoms suggestive of a respiratory exacerbation. Its use is intended to facilitate the early identification of symptoms suggestive of a respiratory exacerbation, and allow the CF team to initiate treatment sooner, thereby potentially reducing the risk of severe exacerbations which require intravenous antibiotics (IVAB) and often a hospital admission. Methods: We will undertake a randomised controlled trial. 60 adults with CF will be recruited and randomised to either the intervention or control group. The intervention group will use the smartphone application weekly for 12 months, or earlier than the next weekly reporting time if they feel their symptoms have worsened. The control group will continue to receive usual care, involving regular (approximately 3 monthly) CF outpatient clinic appointments. The primary outcome measure will be courses and days of IVAB. Ethics and dissemination: Approval was obtained from the Sir Charles Gairdner Group Human Research Ethics Committee for WA Health (2015-030) and Curtin University Human Research Ethics Committee (HR212/2015), and has been registered with the Australian and New Zealand Clinical Trials Registry. Results: of this study will be presented at international conferences and published in peer-reviewed journals in accordance with the Consolidated Standards of Reporting Trials statement. Trial registration number ACTRN12615000599572

Statistical Inference for Assessing Functional Connectivity of Neuronal Ensembles With Sparse Spiking Data

The ability to accurately infer functional connectivity between ensemble neurons using experimentally acquired spike train data is currently an important research objective in computational neuroscience. Point process generalized linear models and maximum likelihood estimation have been proposed as effective methods for the identification of spiking dependency between neurons. However, unfavorable experimental conditions occasionally results in insufficient data collection due to factors such as low neuronal firing rates or brief recording periods, and in these cases, the standard maximum likelihood estimate becomes unreliable. The present studies compares the performance of different statistical inference procedures when applied to the estimation of functional connectivity in neuronal assemblies with sparse spiking data. Four inference methods were compared: maximum likelihood estimation, penalized maximum likelihood estimation, using either l2 or l1 regularization, and hierarchical Bayesian estimation based on a variational Bayes algorithm. Algorithmic performances were compared using well-established goodness-of-fit measures in benchmark simulation studies, and the hierarchical Bayesian approach performed favorably when compared with the other algorithms, and this approach was then successfully applied to real spiking data recorded from the cat motor cortex. The identification of spiking dependencies in physiologically acquired data was encouraging, since their sparse nature would have previously precluded them from successful analysis using traditional methods.National Institutes of Health (U.S.) (Grant DP1-OD003646)National Institutes of Health (U.S.) (Grant Grant R01-DA015644)National Institutes of Health (U.S.) (Grant Grant R01-HL08450

An efficient k.p method for calculation of total energy and electronic density of states

An efficient method for calculating the electronic structure in large systems with a fully converged BZ sampling is presented. The method is based on a k.p-like approximation developed in the framework of the density functional perturbation theory. The reliability and efficiency of the method are demostrated in test calculations on Ar and Si supercells

Motor Cortical Networks for Skilled Movements Have Dynamic Properties That Are Related to Accurate Reaching

Neurons in the Primary Motor Cortex (MI) are known to form functional ensembles with one another in order to produce voluntary movement. Neural network changes during skill learning are thought to be involved in improved fluency and accuracy of motor tasks. Unforced errors during skilled tasks provide an avenue to study network connections related to motor learning. In order to investigate network activity in MI, microwires were implanted in the MI of cats trained to perform a reaching task. Spike trains from eight groups of simultaneously recorded cells (95 neurons in total) were acquired. A point process generalized linear model (GLM) was developed to assess simultaneously recorded cells for functional connectivity during reaching attempts where unforced errors or no errors were made. Whilst the same groups of neurons were often functionally connected regardless of trial success, functional connectivity between neurons was significantly different at fine time scales when the outcome of task performance changed. Furthermore, connections were shown to be significantly more robust across multiple latencies during successful trials of task performance. The results of this study indicate that reach-related neurons in MI form dynamic spiking dependencies whose temporal features are highly sensitive to unforced movement errors

Photoelasticity of crystalline and amorphous silica from first principles

Based on density-functional perturbation theory we have computed from first principles the photoelastic tensor of few crystalline phases of silica at normal conditions and high pressure (quartz, $\alpha$-cristobalite, $\beta$-cristobalite) and of models of amorphous silica (containig up to 162 atoms), obtained by quenching from the melt in combined classical and Car-Parrinello molecular dynamics simulations. The computational framework has also been checked on the photoelastic tensor of crystalline silicon and MgO as prototypes of covalent and ionic systems. The agreement with available experimental data is good. A phenomenological model suitable to describe the photoelastic properties of different silica polymorphs is devised by fitting on the ab-initio data.Comment: ten figure

Differential Involvement of Excitatory and Inhibitory Neurons of Cat Motor Cortex in Coincident Spike Activity Related to Behavioral Context

To assess temporal associations in spike activity between pairs of neurons in the primary motor cortex (MI) related to different behaviors, we compared the incidence of coincident spiking activity of task-related (TR) and non-task-related (NTR) neurons during a skilled motor task and sitting quietly in adult cats (Felis domestica). Chronically implanted microwires were used to record spike activity of MI neurons in four animals (two male and two female) trained to perform a skilled reaching task or sit quietly. Neurons were identified as TR if spike activity was modulated during the task (and NTR if not). Based on spike characteristics, they were also classified as either regular-spiking (RS, putatively excitatory) or fast-spiking (FS, putatively inhibitory) neurons. Temporal associations in the activities of simultaneously recorded neurons were evaluated using shuffle-corrected cross-correlograms. Pairs of NTR and TR neurons showed associations in their firing patterns over wide areas of MI (representing forelimb and hindlimb movements) during quiet sitting, more commonly involving RS neurons. During skilled task performance, however, significantly coincident firing was seen almost exclusively between TR neurons in a smaller part of MI (representing forelimb movements), involving mainly FS neurons. The findings of this study show evidence for widespread interactions in MI when the animal sits quietly, which changes to a more specific and restricted pattern of interactions during task performance. Different populations of excitatory and inhibitory neurons appear to be synchronized during skilled movement and quiet sitting.National Institutes of Health (U.S.) (Grant DP1-OD003646)National Institutes of Health (U.S.) (Grant R01-DA015644)Australian Neuromuscular Research Institut