Extracting neuronal functional network dynamics via adaptive Granger causality analysis

Abstract

Quantifying the functional relations between the nodes in a network based on local observations is a key challenge in studying complex systems. Most existing time series analysis techniques for this purpose provide static estimates of the network properties, pertain to stationary Gaussian data, or do not take into account the ubiquitous sparsity in the underlying functional networks. When applied to spike recordings from neuronal ensembles undergoing rapid task-dependent dynamics, they thus hinder a precise statistical characterization of the dynamic neuronal functional networks underlying adaptive behavior. We develop a dynamic estimation and inference paradigm for extracting functional neuronal network dynamics in the sense of Granger, by integrating techniques from adaptive filtering, compressed sensing, point process theory, and high-dimensional statistics. We demonstrate the utility of our proposed paradigm through theoretical analysis, algorithm development, and application to synthetic and real data. Application of our techniques to two-photon Ca imaging experiments from the mouse auditory cortex reveals unique features of the functional neuronal network structures underlying spontaneous activity at unprecedented spatiotemporal resolution. Our analysis of simultaneous recordings from the ferret auditory and prefrontal cortical areas suggests evidence for the role of rapid top-down and bottom-up functional dynamics across these areas involved in robust attentive behavior.

Laminar profile of task-related plasticity in ferret primary auditory cortex

“…Neurons in prefrontal cortex show greater selectivity than A1 for behaviorally meaningful sounds 15 , and stimulation of orbitofrontal cortex causes changes in A1 pure-tone frequency tuning 16,27 that resembles the task-related plasticity observed here and in previous studies [4][5][6][7] . Simultaneous recordings from frontal cortex and auditory cortex reveal behaviordependent changes in functional connectivity 13,15 . Figure 2g shows that the greatest target enhancement measured from L2/3 CSDs arose from an increase in the magnitude of the target-evoked CSD current sink (blue values) between 40-100 ms.…”

“…Converging lines of evidence from both anatomical and neurophysiological studies suggest that task-related plasticity in A1 may be greater in cortical layer 2/3 (L2/3) than layers 4-6 (L4-6), due to intracortical network activity within L2/3 that is believed to mediate top-down control of sensory processing [8][9][10][11][12][13] . The L2/3 intracortical network may provide a pathway for prefrontal cortex to bias A1 responsiveness in favor of behaviorally meaningful sounds [14][15][16] .…”

Laminar profile of task-related plasticity in ferret primary auditory cortex

“…Neurons in prefrontal cortex show greater selectivity than A1 for behaviorally meaningful sounds 15 , and stimulation of orbitofrontal cortex causes changes in A1 pure-tone frequency tuning 16 , 27 that resembles the task-related plasticity observed here and in previous studies 4 – 7 . Simultaneous recordings from frontal cortex and auditory cortex reveal behavior-dependent changes in functional connectivity 13 , 15 .…”

“…Converging lines of evidence from both anatomical and neurophysiological studies suggest that task-related plasticity in A1 may be greater in cortical layer 2/3 (L2/3) than layers 4–6 (L4–6), due to intracortical network activity within L2/3 that is believed to mediate top-down control of sensory processing 9 – 13 . The L2/3 intracortical network may provide a pathway for prefrontal cortex to bias A1 responsiveness in favor of behaviorally meaningful sounds 14 – 16 .…”

Electrophysiological Brain Connectivity: Theory and Implementation

“…The AR modeling allows an easy and straightforward implementation of Wiener-Granger causality under relatively wide assumptions, usually met by neuroimaging and neurophysiological data; it enables the estimation of the strength and the direction of the causal links as well as their statistical testing [6]. However, different implementations include nonlinear [274]- [276], non-parametric [7] and adaptive [277] modeling.…”