Neurophysiology. Decoding motor imagery from the posterior parietal cortex of a tetraplegic human

Abstract

Nonhuman primate and human studies have suggested that populations of neurons in the posterior parietal cortex (PPC) may represent high-level aspects of action planning that can be used to control external devices as part of a brain-machine interface. However, there is no direct neuron-recording evidence that human PPC is involved in action planning, and the suitability of these signals for neuroprosthetic control has not been tested. We recorded neural population activity with arrays of microelectrodes implanted in the PPC of a tetraplegic subject. Motor imagery could be decoded from these neural populations, including imagined goals, trajectories, and types of movement. These findings indicate that the PPC of humans represents high-level, cognitive aspects of action and that the PPC can be a rich source for cognitive control signals for neural prosthetics that assist paralyzed patients.

Copyright © 2015, American Association for the Advancement of Science.

Figures

Fig. 1. Goal and trajectory coding in the PPC

(A) The masked memory reach task was used to quantify goal and trajectory tuning in the PPC by dissociating their respective tuning in time. EGS imagined a continuous reaching movement to spatially cued targets after a delay period. Motion of the cursor was occluded from view by using a mask in interleaved trials. (B) Goal and trajectory fitting. Average neural response (±SE) of a sample neuron over the duration of a trial, along with a linear model reconstruction of the time course. The linear model included components for the transient early visual response, sustained goal tuning, and transient trajectory tuning. The significance of the fit coefficients was used to determine population tuning to goal and trajectory (see Fig. 2).

Fig. 2. Neurons in PPC encode both the goal and trajectory of movements

(A) The pie chart indicates the proportion of units that encode trajectory exclusively, goal exclusively, or mixed goal and trajectory. Insets show the activity (mean ± SE) for three example neurons. The lighter hue indicates response to the direction evoking maximal response; the darker hue indicates response for the opposite direction. Data taken from masked trials to avoid visual confounds (Fig. 1A). (B) Small populations of informative units allow accurate classification of motor goals from delay-period activity (when no visible target is present). Using a greedy algorithm, an optimized neural population for data combined across multiple days shows that >90% classification is possible with fewer than 30 units. (C) Temporal dynamics of goal representation. Offline analysis depicting accuracy of target classification through time [300-ms sliding window, 95% confidence interval (CI)]. Significant classification occurs within 190 ms of target presentation. (D) Similar to (B) but for trajectory reconstructions. All data taken from the MMR task (Fig. 1A).

Fig. 3. Goal decoding

(A) Direct goal classification (DGC) task. EGS was instructed to intend motion toward a cued target through a delay period after the target was removed from the screen. Neural activity from the final 500 ms of the delay period was used to decode the location of the spatial target. EGS was awarded points depending on the relative location of the decoded and cued target. The decoded target location was presented at the end of each trial. (B) Symbolic task. A target grid was presented along with a number indicating the current target. The cue was removed during the delay period. A series of tones was used to cue the start and end of movements. Multiple effectors were tested in interleaved blocks. Catch trials provided a means to ensure that EGS was, on average, engaged in the task. (C) Estimated classification accuracy (mean with 95% CI) for variable population sizes. Populations were constructed by using randomly sampled units from the recorded population for the MMR and DGC tasks. Chance based on number of potential targets (MMR: four targets; DGC: six targets). (D) Greedy dropping curves show that high classification accuracy is possible whether targets are cued directly (A) or symbolically (B). Best: best single day performance; Combo: performance when combining data across days.

Fig. 4. Effector specificity in PPC

(A) Unit showing preferential activation to imagined movements of the right arm. Each trace shows the neural firing rate (mean ± SE) for the movement direction evoking the maximal response for each effector. (B and C) Same as (A), but for left arm and saccade-preferring neurons. (D) Population analysis. The degree of effector specificity varied across the population. Effector specificity was quantified with a specificity index based on the normalized depth of modulation (DM) for reaches versus saccades (DMreach−DMsaccadeDMreach+DMsaccade). The specificity index for units that were spatially tuned to at least one effector is shown as a histogram. Colored bars indicate a significant preference for an effector. (E) Same as (D) but for imagined right arm versus left arm movements. (F) The effector used to perform the task could be decoded from the neural population (mean with 95% CI).