Real-time particle filtering and smoothing algorithms for detecting abrupt changes in neural ensemble spike activity

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Abstract

Sequential change-point detection from time series data is a common problem in many neuroscience applications, such as seizure detection, anomaly detection, and pain detection. In our previous work (Chen Z, Zhang Q, Tong AP, Manders TR, Wang J. J Neural Eng 14: 036023, 2017), we developed a latent state-space model, known as the Poisson linear dynamical system, for detecting abrupt changes in neuronal ensemble spike activity. In online brain-machine interface (BMI) applications, a recursive filtering algorithm is used to track the changes in the latent variable. However, previous methods have been restricted to Gaussian dynamical noise and have used Gaussian approximation for the Poisson likelihood. To improve the detection speed, we introduce non-Gaussian dynamical noise for modeling a stochastic jump process in the latent state space. To efficiently estimate the state posterior that accommodates non-Gaussian noise and non-Gaussian likelihood, we propose particle filtering and smoothing algorithms for the change-point detection problem. To speed up the computation, we implement the proposed particle filtering algorithms using advanced graphics processing unit computing technology. We validate our algorithms, using both computer simulations and experimental data for acute pain detection. Finally, we discuss several important practical issues in the context of real-time closed-loop BMI applications. NEW & NOTEWORTHY Sequential change-point detection is an important problem in closed-loop neuroscience experiments. This study proposes novel sequential Monte Carlo methods to quickly detect the onset and offset of a stochastic jump process that drives the population spike activity. This new approach is robust with respect to spike sorting noise and varying levels of signal-to-noise ratio. The GPU implementation of the computational algorithm allows for parallel processing in real time.

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Pain and emotion interactions in subregions of the cingulate gyrus.

Brent Vogt (2005)

Acute pain and emotion are processed in two forebrain networks, and the cingulate cortex is involved in both. Although Brodmann's cingulate gyrus had two divisions and was not based on any functional criteria, functional imaging studies still use this model. However, recent cytoarchitectural studies of the cingulate gyrus support a four-region model, with subregions, that is based on connections and qualitatively unique functions. Although the activity evoked by pain and emotion has been widely reported, some view them as emergent products of the brain rather than of small aggregates of neurons. Here, we assess pain and emotion in each cingulate subregion, and assess whether pain is co-localized with negative affect. Amazingly, these activation patterns do not simply overlap.