Cortico-cortical drive in a coupled premotor-primary motor cortex dynamical system.

In the conventional view of sensorimotor control, the premotor cortex (PM) plans actions that are executed by the primary motor cortex (M1). This notion arises in part from many experiments that have imposed a preparatory "planning" period, during which PM becomes active without M1. But during many natural movements, PM and M1 are co-activated, making it difficult to distinguish their functional roles. We leverage coupled dynamical systems models (cDSMs) to uncover interactions between PM and M1 during movements performed with no preparatory period. We build cDSMs using neural and behavioral data recorded from two non-human primates as they performed a reach-grasp-manipulate task. PM and M1 interact dynamically throughout these movements. Whereas PM drives the M1 in some situations, in other situations, M1 drives PM activity, contrary to the conventional assumption. Our DSM framework provides additional predictions differentiating the roles of PM and M1 in controlling movement.

Correlates of Attention in the Cingulate Cortex During Gambling in Humans.

People make decisions multiple times on a daily basis. However, some decisions are easier to make than others and perhaps require more attention to ensure a positive outcome. During gambling, one should attempt to compute the expected rewards and risks associated with decisions. Failing to allocate attention and neural resources to estimate these values can be costly, and in some cases can lead to bankruptcy. Alpha-band (8-12 Hz) oscillatory power in the brain is thought to reflect attention, but how this influences financial decision making is not well understood. Using local field potential recordings in nine human subjects performing a gambling task, we compared alpha-band power from the cingulate cortex (CC) during trials of low and high attention. We found that alpha-band power tended to be higher during a 2 second window after a fixation cue was shown in low attention trials.

Quantifying Interactions between Neural Populations during Behavior using Dynamical Systems Models.

There has been a recent surge of dynamical systems models (DSMs) constructed from brain activity to investigate how neural firing patterns evolve over time, and how such patterns in turn generate measured behavior. An advantage with DSMs are their ability to accurately reconstruct neural patterns and behavior simultaneously, capturing variability in data due to different tasks. In this paper, we demonstrate how to use a general DSM beyond reconstruction. In particular, we show that the general DSM can also be used to (i) quantify which states, i.e. which neural regions drive the dynamics of the observed neural activity throughout behavior, and (ii) whether interactions between populations are primarily within the same brain region (intra) or across brain regions (inter). We first begin with an intuitive dynamical system example - a coupled two-mass spring system, and then perform the same analyses for a DSM estimated from neural data collected from a nonhuman primate executing a reach-to-grasp motor task. These examples show that DSM is a modeling approach that not only can accurately reconstruct observed neural and behavioral activities, but can predict important dynamical neural drivers as well as interactions between different neural populations that cannot be gleaned from data alone.

Reconstructing Neural Activity and Kinematics Using a Systems-Level Model of Sensorimotor Control.

There are two popular and largely independent approaches to study the sensorimotor control system (SCS). One is to construct systems-level models of the SCS that characterize dynamics of motor regions in the brain, alpha motor neurons, and the musculoskeletal system to reconstruct motor behavior. These models view the brain as a feedforward and feedback controller that actuates the musculoskeletal system, and have been useful in understanding how the SCS generates movements. Another approach is to measure neural activity and movements simultaneously in primate and human subjects,and the nanalyze the data tounder standhow the brain encodes and controls movement. In this paper, we combine these two approaches by fitting parameters of a systems-level model of the SCS to neural activity and behavior measured from a nonhuman primate executing four types of reach-tograsp tasks. We applied a nonlinear least squares estimation to fit parameters of the model components that characterize cerebrocerebellar processing of movement error and muscles that are actuated by alpha motor neurons receiving commands from primary motor cortex (M1). Our fitted SCS model accurately reconstructs firing rate activity of six populations of M1 neurons and associated reaching trajectories. This study paves the way for the validation of systems-level models of the SCS using experimental data.

An input-output linear time invariant model captures neuronal firing responses to external and behavioral events.

Investigating how neurons in different motor regions respond to external stimuli and behavioral events provides insight into motor control. A recent approach to studying neuronal activity is to construct a zero-input linear time invariant (ZI-LTI) state-space model, wherein the state vector consists of firing rate signals for different populations of neurons across motor regions. This approach allows for the populations to influence each other in a dynamical manner given an initial firing rate condition, and the model can accurately reconstruct firing rates within a limited epoch in the motor task during which no event occurs. Here, we generalize this LTI modeling approach to characterize firing responses of neurons to two events (a go cue and movement onset) in a movement task with a non-zero input LTI state-space model, herein referred to as input-output LTI (IO-LTI). Specifically, responses from 196 neurons in the primary motor (M1), ventral premotor (PMv), and dorsal premotor cortex (PMd) were recorded and modeled in two nonhuman primates executing a reach-to-grasp task. We found that a single IO-LTI model can reconstruct neuronal firing rate patterns of six populations of these neurons across the three areas in the presence of multiple events (go cue, movement onset). This is the first step towards constructing generative models of neuronal firing rates in the presence of multiple events, which then can be used to construct better decoders for brain machine interactive control.