Task-related hemodynamic responses are modulated by reward and task engagement.

Hemodynamic recordings from visual cortex contain powerful endogenous task-related responses that may reflect task-related arousal, or "task engagement" distinct from attention. We tested this hypothesis with hemodynamic measurements (intrinsic-signal optical imaging) from monkey primary visual cortex (V1) while the animals' engagement in a periodic fixation task over several hours was varied through reward size and as animals took breaks. With higher rewards, animals appeared more task-engaged; task-related responses were more temporally precise at the task period (approximately 10-20 seconds) and modestly stronger. The 2-5 minute blocks of high-reward trials led to ramp-like decreases in mean local blood volume; these reversed with ramp-like increases during low reward. The blood volume increased even more sharply when the animal shut his eyes and disengaged completely from the task (5-10 minutes). We propose a mechanism that controls vascular tone, likely along with local neural responses in a manner that reflects task engagement over the full range of timescales tested.

Superior arm-movement decoding from cortex with a new, unsupervised-learning algorithm.

OBJECTIVE: The aim of this work is to improve the state of the art for motor-control with a brain-machine interface (BMI). BMIs use neurological recording devices and decoding algorithms to transform brain activity directly into real-time control of a machine, archetypically a robotic arm or a cursor. The standard procedure treats neural activity-vectors of spike counts in small temporal windows-as noisy observations of the kinematic state (position, velocity, acceleration) of the fingertip. Inferring the state from the observations then takes the form of a dynamical filter, typically some variant on Kalman's (KF). The KF, however, although fairly robust in practice, is optimal only when the relationships between variables are linear and the noise is Gaussian, conditions usually violated in practice. APPROACH: To overcome these limitations we introduce a new filter, the 'recurrent exponential-family harmonium' (rEFH), that models the spike counts explicitly as Poisson-distributed, and allows for arbitrary nonlinear dynamics and observation models. Furthermore, the model underlying the filter is acquired through unsupervised learning, which allows temporal correlations in spike counts to be explained by latent dynamics that do not necessarily correspond to the kinematic state of the fingertip. MAIN RESULTS: We test the rEFH on offline reconstruction of the kinematics of reaches in the plane. The rEFH outperforms the standard, as well as three other state-of-the-art, decoders, across three monkeys, two different tasks, most kinematic variables, and a range of bin widths, amounts of training data, and numbers of neurons. SIGNIFICANCE: Our algorithm establishes a new state of the art for offline decoding of reaches-in particular, for fingertip velocities, the variable used for control in most online decoders.

Simultaneously estimating the task-related and stimulus-evoked components of hemodynamic imaging measurements.

Task-related hemodynamic responses contribute prominently to functional magnetic resonance imaging (fMRI) recordings. They reflect behaviorally important brain states, such as arousal and attention, and can dominate stimulus-evoked responses, yet they remain poorly understood. To help characterize these responses, we present a method for parametrically estimating both stimulus-evoked and task-related components of hemodynamic responses from subjects engaged in temporally predictable tasks. The stimulus-evoked component is modeled by convolving a hemodynamic response function (HRF) kernel with spiking. The task-related component is modeled by convolving a Fourier-series task-related function (TRF) kernel with task timing. We fit this model with simultaneous electrode recordings and intrinsic-signal optical imaging from the primary visual cortex of alert, task-engaged monkeys. With high [Formula: see text], the model returns HRFs that are consistent across experiments and recording sites for a given animal and TRFs that entrain to task timing independent of stimulation or local spiking. When the task schedule conflicts with that of stimulation, the TRF remains locked to the task emphasizing its behavioral origins. The current approach is strikingly more robust to fluctuations than earlier ones and gives consistently, if modestly, better fits. This approach could help parse the distinct components of fMRI recordings made in the context of a task.

Stimulus-related neuroimaging in task-engaged subjects is best predicted by concurrent spiking.

The implicit goal of functional magnetic resonance imaging is to infer local neural activity. There is considerable debate, however, as to whether imaging correlates most linearly with local spiking or some local field potential (LFP) measurement. Through simultaneous neuroimaging (intrinsic-signal optical imaging) and electrode recordings from alert, task-engaged macaque monkeys, we showed previously that local electrophysiology correlates with only a specific stimulus-related imaging component. Here we show that this stimulus-related component--obtained by subtracting a substantial task-related component--is particularly linear with local spiking over a comprehensive range of response strengths. Matches to concurrent LFP measurements are, to varying degrees, poorer. As a control, we also tried matching the full imaging signal to local electrophysiology without subtracting task-related components. These control matches were consistently worse; they were, however, slightly better for gamma LFP than spiking, potentially resolving discrepancies between our findings and earlier reports favoring LFP.

The neuroimaging signal is a linear sum of neurally distinct stimulus- and task-related components.

Neuroimaging (for example, functional magnetic resonance imaging) signals are taken as a uniform proxy for local neural activity. By simultaneously recording electrode and neuroimaging (intrinsic optical imaging) signals in alert, task-engaged macaque visual cortex, we recently observed a large anticipatory trial-related neuroimaging signal that was poorly related to local spiking or field potentials. We used these same techniques to study the interactions of this trial-related signal with stimulus-evoked responses over the full range of stimulus intensities, including total darkness. We found that the two signals could be separated, and added linearly over this full range. The stimulus-evoked component was related linearly to local spiking and, consequently, could be used to obtain precise and reliable estimates of local neural activity. The trial-related signal likely has a distinct neural mechanism, however, and failure to account for it properly could lead to substantial errors when estimating local neural spiking from the neuroimaging signal.