A Regularized Point Process Generalized Linear Model for Assessing the Functional Connectivity in the Cat Motor Cortex

Neuroscience Statistics Research Laboratory, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA.
Conference proceedings: ... Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Conference 01/2009; 2009:5006-9. DOI: 10.1109/IEMBS.2009.5334610
Source: PubMed


Identification of multiple simultaneously recorded neural spike train recordings is an important task in understanding neuronal dependency, functional connectivity, and temporal causality in neural systems. An assessment of the functional connectivity in a group of ensemble cells was performed using a regularized point process generalized linear model (GLM) that incorporates temporal smoothness or contiguity of the solution. An efficient convex optimization algorithm was then developed for the regularized solution. The point process model was applied to an ensemble of neurons recorded from the cat motor cortex during a skilled reaching task. The implications of this analysis to the coding of skilled movement in primary motor cortex is discussed.

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Available from: Zhe Chen, Aug 21, 2014
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    • "Further discussion regarding the choice of a smoothing operator for matrix Q is detailed in [37]. The regularization coefficient ρ was selected by leave-one-out cross-validation, and the optimization can be efficiently implemented by the conjugate gradient method [37]. Since the optimization problem is convex, the final estimate is globally optimal. "
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    ABSTRACT: 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.
    Neural Plasticity 10/2011; 2011(2090-5904):413543. DOI:10.1155/2011/413543 · 3.58 Impact Factor
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    • "Whereas in VB methods, we can adapt the priors to adjust the sparsity of the solution to avoid overfitting (when a uniform improper prior is used, the Bayesian solution reduces to the m.l.e.). Next, we study the functional connectivity among all 17 cells using a network likelihood model presented earlier [13], [5], where the covariates x consist of 28 Zernike polynomials plus the spike counts from all ensemble cells within a number of previous temporal windows. Here, we have limited the firing history up to past 100 ms in our analysis, with a constant history window length of 5 ms. "
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    ABSTRACT: Point process generalized linear models (GLMs) have been widely used for neural spike trains analysis. Statistical inference for GLMs include maximum likelihood and Bayesian estimation. Variational Bayesian (VB) methods provide a computationally appealing means to infer the posterior density of unknown parameters, in which conjugate priors are designed for the regression coefficients in logistic and Poisson regression. In this paper, we develop and apply VB inference for point process GLMs in neural spike train analysis. The hierarchical Bayesian framework allows us to tackle the variable selection problem. We assess and validate our methods with ensemble neuronal recordings from rat's hippocampal place cells and entorhinal cortical cells during foraging in an open field environment.
    Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2010, 14-19 March 2010, Sheraton Dallas Hotel, Dallas, Texas, USA; 01/2010
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    ABSTRACT: 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 l(2) or l(1) 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.
    IEEE transactions on neural systems and rehabilitation engineering: a publication of the IEEE Engineering in Medicine and Biology Society 10/2010; 19(2):121-35. DOI:10.1109/TNSRE.2010.2086079 · 3.19 Impact Factor
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