Sanggyun Kim

Large-scale spatiotemporal spike patterning consistent with wave propagation in motor cortex

Aggregate signals in cortex are known to be spatiotemporally organized as propagating waves across the cortical surface, but it remains unclear whether the same is true for spiking activity in individual neurons. Furthermore, the functional interactions between cortical neurons are well documented but their spatial arrangement on the cortical surface has been largely ignored. Here we use a functional network analysis to demonstrate that a subset of motor cortical neurons in non-human primates spatially coordinate their spiking activity in a manner that closely matches wave propagation measured in the beta oscillatory band of the local field potential. We also demonstrate that sequential spiking of pairs of neuron contains task-relevant information that peaks when the neurons are spatially oriented along the wave axis. We hypothesize that the spatial anisotropy of spike patterning may reflect the underlying organization of motor cortex and may be a general property shared by other cortical areas.

Dynamic and Succinct Statistical Analysis of Neuroscience Data

Modern neuroscientific recording technologies are increasingly generating rich, multimodal data that provide unique opportunities to investigate the intricacies of brain function. However, our ability to exploit the dynamic, interactive interplay among neural processes is limited by the lack of appropriate analysis methods. In this paper, some challenging issues in neuroscience data analysis are described, and some general-purpose approaches to address such challenges are proposed. Specifically, we discuss statistical methodologies with a theme of loss functions, and hierarchical Bayesian inference methodologies from the perspective of constructing optimal mappings. These approaches are demonstrated on both simulated and experimentally acquired neural data sets to assess causal influences and track time-varying interactions among neural processes on a fine time scale.

EEG Gamma Band Oscillations Differentiate the Planning of Spatially Directed Movements of the Arm Versus Eye: Multivariate Empirical Mode Decomposition Analysis

The neural dynamics underlying the coordination of spatially-directed limb and eye movements in humans is not well understood. Part of the difficulty has been a lack of signal processing tools suitable for the analysis of nonstationary electroencephalographic (EEG) signals. Here, we use multivariate empirical mode decomposition (MEMD), a data-driven approach that does not employ predefined basis functions. High-density EEG, and arm and eye movements were synchronously recorded in 10 subjects performing time-constrained reaching and/or eye movements. Subjects were allowed to move both the hand and the eyes, only the hand, or only the eyes following a 500-700 ms delay interval where the hand and gaze remained on a central fixation cross. An additional condition involved a nonspatially-directed “lift” movement of the hand. The neural activity during a 500 ms delay interval was decomposed into intrinsic mode functions (IMFs) using MEMD. Classification analysis revealed that gamma band (30 Hz <;) IMFs produced more classifiable features differentiating the EEG according to the different upcoming movements. A benchmark test using conventional algorithms demonstrated that MEMD was the best algorithm for extracting oscillatory bands from EEG, yielding the best classification of the different movement conditions. The gamma rhythm decomposed using MEMD showed a higher correlation with the eventual movement accuracy than any other band rhythm and than any other algorithm.

Efficient Bayesian inference methods via convex optimization and optimal transport

In this paper, we consider many problems in Bayesian inference - from drawing samples to posteriors, to calculating confidence intervals, to implementing posterior matching algorithms, by finding maps that push one distribution to another. We show that for a large class of problems (with log-concave likelihoods and log-concave priors), these problems can be efficiently solved using convex optimization. We provide example applications within the context of dynamic statistical signal processing.

Information transfer between neurons in the motor cortex triggered by visual cues

It was previously shown that beta oscillations of local field potentials in the arm area of the primary motor cortex (MI) of nonhuman primates propagate as travelling waves across MI of monkeys during movement preparation and execution and are believed to subserve cortical information transfer. To investigate the information transfer and its change over time at the single-cell level, we analyzed simultaneously recorded multiple MI neural spike trains of a monkey using a Granger causality measure for point process models before and after visual cues instructing the onset of reaching movements. In this analysis, we found that more pairs of neurons showed information transfer between them after appearances of upcoming movement targets than before, and the directions of the information transfer across neurons in MI were coincident with the directions of the propagating waves. These results suggest that the neuron pairs identified in the current study are the candidates of neurons that travel with spatiotemporal dynamics of beta oscillations in the MI.

A scalable framework to transform samples from one continuous distribution to another

We present a framework to transform a sample from one continuous distribution P to another ℚ. Our previous work considered the special case of Bayesian inference where P is the prior and ℚ is the posterior, showing that this can be solved with convex optimization under appropriate conditions. Here, our contribution is two fold: (i) we consider the more general case of arbitrary P and ℚ and show using optimal transport theory and KL divergence minimization that convexity holds provided that ℚ has a log-concave density; (ii) we develop a largescale distributed solver. With this general framework finding the optimal Bayesian map is done through a series of MAP estimation problems. Interesting applications are also presented.

Team decision theory and brain-machine interfaces

In this paper we present a general-purpose design methodology for designing policies for interaction between the user and external device for brain-machine interface (BMI). In short, we interpret a BMI as a system comprising two agents (the user and the external device) cooperating to achieve a common goal. Because of the inherent uncertainty in (a) the users intent, and (b) the noisy channel mapping desired commands to neural recordings, neither agent has a subset of the information of others. Nonetheless, we exploit recent research results to demonstrate how to design - for an arbitrary problem specification (e.g. cost function to minimize) - optimal policies that are easily implementable in BMIs across many modalities - including EEG and cortically controlled devices. The structural result we provide sheds light on the minimal amount of useful information that is required to provide perceptual feedback to the user.

An Information and Control Framework for Optimizing User-Compliant Human–Computer Interfaces

We consider a general framework for a human–computer interface whereby the humans knowledge is represented as a point in Euclidean space, the intention of the human is signaled to the computer over a noisy channel, and the computer queries the human in a manner that is amenable to human operation. With these constraints at hand, we demonstrate a class of systems that are nonetheless information-theoretically optimal in that the computer very rapidly hones in on the intent of the human. Much recent work on feedback information theory has been dedicated to the exploration of methods by which optimal feedback may be derived for the purpose of expediting the communication of a message point between an inanimate encoder and decoder. Our framework not only takes advantage of previous work to demonstrate its communication optimality from this perspective as well as from an information-theoretic perspective but also contributes two distinct advantages. First, our framework provides a simplified method based on optimal transport theory to generate optimal feedback signals between the computer and human in high dimension, while still preserving communication optimality. Second, our framework specifically lends itself to the integration of a human user by attempting to moderate the difficulty of the task presented to the user, while still preserving optimality. We demonstrate applications of our framework within the context of multi-agent brain-computer interfaces.

Granger causality analysis of state dependent functional connectivity of neurons in orofacial motor cortex during chewing and swallowing

Primate feeding behavior is characterized by a series of jaw movement cycles of different types making it ideal for investigating the role of motor cortex in controlling transitions between different kinematic states. We recorded spiking activity in populations of neurons in the orofacial portion of primary motor cortex (MIo) of a macaque monkey and, using a Granger causality model, estimated their functional connectivity during transitions between chewing cycles and from chewing to swallowing cycles. We found that during rhythmic chewing, the network was dominated by excitatory connections and exhibited a few “out degree” hub neurons, while during transitions from rhythmic chews to swallows, the numbers of excitatory and inhibitory connections became comparable, and more temporarily varying “in degree” hub neurons emerged. Furthermore, based on shared connections between neurons between different networks, networks from same state transitions were quantitatively shown to be more similar. These results suggest that networks of functionally connected neurons in MIo change their operative states with changes in kinematically defined behavioral states.

Granger causality analysis of functional connectivity of spiking neurons in orofacial motor cortex during chewing and swallowing

Primate feeding behavior is characterized by a series of jaw movement cycles of different types making it ideal for investigating the role of motor cortex in controlling transitions between different kinematic states. We recorded spiking activity in populations of neurons in the orofacial portion of primary motor cortex (MIo) of a macaque monkey and, using a Granger causality model, estimated their functional connectivity during transitions between chewing cycles and from chewing to swallowing cycles. We found that during rhythmic chewing, the network was dominated by excitatory connections and exhibited a few “out degree” hub neurons, while during transitions from rhythmic chews to swallows, the numbers of excitatory and inhibitory connections became comparable, and more “in degree” hub neurons emerged. These results suggest that networks of neurons in MIo change their operative states with changes in kinematically defined behavioral states.