Brain-machine interface learning is facilitated by specific patterning of distributed cortical feedback.

Neuroprosthetics offer great hope for motor-impaired patients. One obstacle is that fine motor control requires near-instantaneous, rich somatosensory feedback. Such distributed feedback may be recreated in a brain-machine interface using distributed artificial stimulation across the cortical surface. Here, we hypothesized that neuronal stimulation must be contiguous in its spatiotemporal dynamics to be efficiently integrated by sensorimotor circuits. Using a closed-loop brain-machine interface, we trained head-fixed mice to control a virtual cursor by modulating the activity of motor cortex neurons. We provided artificial feedback in real time with distributed optogenetic stimulation patterns in the primary somatosensory cortex. Mice developed a specific motor strategy and succeeded to learn the task only when the optogenetic feedback pattern was spatially and temporally contiguous while it moved across the topography of the somatosensory cortex. These results reveal spatiotemporal properties of the sensorimotor cortical integration that set constraints on the design of neuroprosthetics.

Preserved electrophysiological markers of confidence in schizophrenia spectrum disorder.

A large number of behavioral studies suggest that confidence judgments are impaired in schizophrenia, motivating the search for neural correlates of an underlying metacognitive impairment. Electrophysiological studies suggest that a specific evoked response potential reflecting performance monitoring, namely the error-related negativity (ERN), is blunted in schizophrenia compared to healthy controls. However, attention has recently been drawn to a potential confound in the study of metacognition, namely that lower task-performance in schizophrenia compared to healthy controls involves a decreased index of metacognitive performance (where metacognitive performance is construed as the ability to calibrate one's confidence relative to response correctness), independently of metacognitive abilities among patients. Here, we assessed how this confound might also apply to ERN-blunting in schizophrenia. We used an adaptive staircase procedure to titrate task-performance on a motion discrimination task in which participants (N = 14 patients and 19 controls) had to report their confidence after each trial while we recorded high density EEG. Interestingly, not only metaperceptual abilities were preserved among patients at the behavioral level, but contrary to our hypothesis, we also found no electrophysiological evidence for altered EEG markers of performance monitoring. These results bring additional evidence suggesting an unaltered ability to monitor perceptual performance on a trial by trial basis in schizophrenia.

Learning in a closed-loop brain-machine interface with distributed optogenetic cortical feedback.

Objective.Distributed microstimulations at the cortical surface can efficiently deliver feedback to a subject during the manipulation of a prosthesis through a brain-machine interface (BMI). Such feedback can convey vast amounts of information to the prosthesis user and may be key to obtain an accurate control and embodiment of the prosthesis. However, so far little is known of the physiological constraints on the decoding of such patterns. Here, we aimed to test a rotary optogenetic feedback that was designed to encode efficiently the 360° movements of the robotic actuators used in prosthetics. We sought to assess its use by mice that controlled a prosthesis joint through a closed-loop BMI.Approach.We tested the ability of mice to optimize the trajectory of a virtual prosthesis joint in order to solve a rewarded reaching task. They could control the speed of the joint by modulating the activity of individual neurons in the primary motor cortex. During the task, the patterned optogenetic stimulation projected on the primary somatosensory cortex continuously delivered information to the mouse about the position of the joint.Main results.We showed that mice are able to exploit the continuous, rotating cortical feedback in the active behaving context of the task. Mice achieved better control than in the absence of feedback by detecting reward opportunities more often, and also by moving the joint faster towards the reward angular zone, and by maintaining it longer in the reward zone. Mice controlling acceleration rather than speed of the joint failed to improve motor control.Significance.These findings suggest that in the context of a closed-loop BMI, distributed cortical feedback with optimized shapes and topology can be exploited to control movement. Our study has direct applications on the closed-loop control of rotary joints that are frequently encountered in robotic prostheses.

Continuity within the somatosensory cortical map facilitates learning.

The topographic organization is a prominent feature of sensory cortices, but its functional role remains controversial. Particularly, it is not well determined how integration of activity within a cortical area depends on its topography during sensory-guided behavior. Here, we train mice expressing channelrhodopsin in excitatory neurons to track a photostimulation bar that rotated smoothly over the topographic whisker representation of the primary somatosensory cortex. Mice learn to discriminate angular positions of the light bar to obtain a reward. They fail not only when the spatiotemporal continuity of the photostimulation is disrupted in this area but also when cortical areas displaying map discontinuities, such as the trunk and legs, or areas without topographic map, such as the posterior parietal cortex, are photostimulated. In contrast, when cortical topographic continuity enables to predict future sensory activation, mice demonstrate anticipation of reward availability. These findings could be helpful for optimizing feedback while designing cortical neuroprostheses.

A fast intracortical brain-machine interface with patterned optogenetic feedback.

OBJECTIVE: The development of brain-machine interfaces (BMIs) brings new prospects to patients with a loss of autonomy. By combining online recordings of brain activity with a decoding algorithm, patients can learn to control a robotic arm in order to perform simple actions. However, in contrast to the vast amounts of somatosensory information channeled by limbs to the brain, current BMIs are devoid of touch and force sensors. Patients must therefore rely solely on vision and audition, which are maladapted to the control of a prosthesis. In contrast, in a healthy limb, somatosensory inputs alone can efficiently guide the handling of a fragile object, or ensure a smooth trajectory. We have developed a BMI in the mouse that includes a rich artificial somatosensory-like cortical feedback. APPROACH: Our setup includes online recordings of the activity of multiple neurons in the whisker primary motor cortex (vM1) and delivers feedback simultaneously via a low-latency, high-refresh-rate, spatially structured photo-stimulation of the whisker primary somatosensory cortex (vS1), based on a mapping obtained by intrinsic imaging. MAIN RESULTS: We demonstrate the operation of the loop and show that mice can detect the neuronal spiking in vS1 triggered by the photo-stimulations. Finally, we show that the mice can learn a behavioral task relying solely on the artificial inputs and outputs of the closed-loop BMI. SIGNIFICANCE: This is the first motor BMI that includes a short-latency, intracortical, somatosensory-like feedback. It will be a useful platform to discover efficient cortical feedback schemes towards future human BMI applications.