Interfacing to the brain's motor decisions

Abstract

It has been long known that neural activity, recorded with electrophysiological methods, contains rich information about a subject's motor intentions, sensory experiences, allocation of attention, action planning, and even abstract thoughts. All these functions have been the subject of neurophysiological investigations, with the goal of understanding how neuronal activity represents behavioral parameters, sensory inputs, and cognitive functions. The field of brain-machine interfaces (BMIs) strives for a somewhat different goal: it endeavors to extract information from neural modulations to create a communication link between the brain and external devices. Although many remarkable successes have been already achieved in the BMI field, questions remain regarding the possibility of decoding high-order neural representations, such as decision making. Could BMIs be employed to decode the neural representations of decisions underlying goal-directed actions? In this review we lay out a framework that describes the computations underlying goal-directed actions as a multistep process performed by multiple cortical and subcortical areas. We then discuss how BMIs could connect to different decision-making steps and decode the neural processing ongoing before movements are initiated. Such decision-making BMIs could operate as a system with prediction that offers many advantages, such as shorter reaction time, better error processing, and improved unsupervised learning. To present the current state of the art, we review several recent BMIs incorporating decision-making components.

Keywords: action value; behavioral flexibility; brain-computer interface; brain-machine interface; decision making; reward; voluntary motor control.

Copyright © 2017 the American Physiological Society.

Figures

Fig. 1.

Schematics of a hypothetical decision-making BMI. A monkey is seated in front of a computer screen that displays a cursor and 2 targets. The monkey uses a rule (for example, “prefer circles to triangles”) to select the target and then places the cursor over the target to receive a reward. Initially, the monkey performs the task manually, using a handheld joystick to move the cursor. At the same time, neuronal ensemble activity is recorded in the monkey’s brain with the use of chronically implanted microelectrode arrays, and a decoding algorithm is trained to extract various components of the decision-making process from the neuronal activity. Depending on the brain areas implanted, the decoder could extract representation of the selected object, prepared movement direction, executed movement direction, and the decision to inhibit movement initiation. It is suggested that decoding these signals before movement onset can improve BMI accuracy and versatility.

Fig. 2.

Multistep decision model underlying the genesis of goal-directed actions. The model entails a set of decision processes leading to either the execution or the inhibition of an action according to their expected outcome. Clearly, the model does not have either a serial or a parallel structure, i.e., some processes must occur before others (e.g., the first decision aimed at evaluating whether acting is worthwhile must occur before goal selection), but other processes might occur in parallel (e.g., the monitoring system, whose role is to compute predictions about outcomes, is active during all the steps). CCBY 4.0, http://journal.frontiersin.org/article/10.3389/fnsys.2014.00206/full.

Fig. 3.

Temporal sequence of trials of stop signal task. All trials begin with the presentation of a central stimulus. Subjects are required to touch it for a variable time. Thereafter, the central stimulus disappears and simultaneously a target appears to the right (go-signal). In the no-stop trials, subjects have to perform a speeded reaching movement toward the peripheral target. Differently, in stop trials (33% of the total trials), after variable delays (known as stop-signal delays, SSDs), the central stimulus reappears. To perform correctly, subjects have to inhibit the pending movement by keeping the arm on the central stimulus (stop-success trial). Otherwise, if subjects execute the reaching movement despite the stop-signal presentation, the trial is scored as an error (stop-failure trial). CCBY 4.0, http://journal.frontiersin.org/article/10.3389/fneng.2012.00012/full.

Fig. 4.

Distribution of stop-event-related potentials (ERPs) in successful-stop (SS) trials. A: average stop-ERPs (solid red curves) of SS trials centered on stop-signal appearance corresponding to the selected channels for an example epileptic patient. Gray areas represent time intervals at which the stop-ERP was significantly different from 0 (Wilcoxon signed-rank test, P < 0.01). Brodmann's areas (BAs) over which electrodes were positioned are indicated. Colored areas identify electrodes placed over the primary motor cortex (red; BA4), premotor cortex (yellow; BA6), and dorsolateral prefrontal cortex (green; BA9). B: histogram of the stop-ERP sizes shown in A. Stop-ERP sizes were computed as the integral of absolute values of stop-ERP voltage deflections in the interval periods marked by gray areas within SSRT. Dashed line represents the threshold value for selecting channels with large enough stop-ERPs used for population analyses. C: number of channels showing large enough average stop-ERPs across 5 patients (n = 39) grouped by BA. Blue bar (others) represents those areas where channels were not selected more than twice across all patients. D: box plot of stop-ERP onsets measured with respect to the end of SSRT across all selected channels in all patients. Stop-ERP onset was defined as the first time that an electrode voltage was significantly different from 0. Average onset times are indicated by diamonds. Bars indicate the first and the third quartile. Vertical lines indicate the extreme time lags in the channel group. CCBY 4.0, http://journal.frontiersin.org/article/10.3389/fnsys.2014.00206/full.

Fig. 5.

Symbiotic BMI controller based on reward-related activity of rat nucleus accumbens (NAcc). The controller includes two components: 1) the actor, driven by M1 activity, and 2) the critic, based on the recordings from NAcc. The critic evaluates the actions of the actor by using the value estimator and adapts the actor’s parameters to increase the probability of reward. CCBY 4.0, http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0014760.

Fig. 6.

Decoding a change in motor decision. A: experimental setup where a monkey sits in front of a computer screen and uses a joystick to move a cursor. B: 4 potential locations of screen targets. C: task sequence. The cursor was first placed over the central target. An initial target then appeared on the boundary ring. This target persisted in one-fourth of trials, and it jumped to a new location in the remaining trials. D: time records for the joystick coordinates and target onsets and offsets. CCBY 4.0, http://journal.frontiersin.org/article/10.3389/fneng.2012.00016/full.