Two macaque monkeys were trained to perform center-out reaching movements using a robotic exoskeleton that constrained movements to the horizontal plane (i.e. Manual Control). Following implantation of microelectrodes, a small ensemble of neurons, typically from the contralateral M1, was randomly selected to be ‘directly linked’ to BMI control. The remaining neurons were recorded but not linked to the BMI (i.e. indirect neurons). The spiking activity of the ‘direct’ ensemble was transformed to motor commands with a linear decoder optimized to predict upper limb movements11,13,18,23,25
. The animals learned brain control using stable recordings of the direct ensemble across days and a decoder that was held constant after the initial training23,26–27
. Stability of recordings across days was assessed by stationarity of spike waveforms and the interspike interval (ISI) distribution23,28–31
. As an additional measure, we frequently monitored the directional modulation of each unit during manual control sessions.
The animals were trained to perform two tasks in brain control during separate experiments. Task 1 was structured to equate initial conditions for manual and brain control and to minimize changes in posture and workspace () 32–33
. The right upper limb remained in the exoskeleton under both conditions (). During manual control, the animal made physical movements to initiate and complete trials. During brain control, in contrast, the animal first made physical movements to the ‘Center’ target. After a variable ‘Hold’ period, a brain control trial started. During brain control of the computer cursor, the animal was required to hold its arm stationary with the hand in the center target. Arm kinematics were monitored continuously and the trial was aborted if any motion occurred. We also performed electromyogram (EMG) recordings to rule out muscle contractions during brain control (Supplementary Fig. 1
). We ensured that the trajectories were comparable using ‘guide’ lines (). If the cursor moved outside the lines, the trial was aborted. In contrast to Task 1, the second task was similar to past experiments11–13,19,23
, where the animal’s arm was taken out of the exoskeleton and restrained during brain control.
The animals typically developed proficient brain control over time (usually days ≥ 3 in each experiment, Supplementary Fig. 2
). It is important to note that while both of these animals had extensive experience with brain control, they required practice to achieve skilled control with a new set of neurons and a given decoder. Task performance during ‘late’ sessions (i.e. ≥ day 3 of practice) was 86 ± 2 % mean ± sem in Monkey P and 83 ± 2 % in Monkey R, with a mean time to target of 2.4 ± 0.3 s and 2.8 ± 0.25 s respectively in Monkey P (16 ‘late’ sessions from 4 experiments) and R (9 ‘late’ sessions from 3 experiments).
Modification of preferred directions
We first analyzed changes in the preferred direction of direct neurons during Task 1 (). We found that a significant proportion experienced a change in preferred direction during brain control in comparison to manual control (56 ± 8% mean ± sem with a significant change; 3 sessions with 10 neurons each from 1 experiment in Monkey P and 3 sessions with 15 neurons each from 1 experiment in Monkey P; bootstrap analysis with p<0.05 and a correction for multiple comparisons was used). When animals were further trained to rapidly switch between brain control and manual control on a single trial basis (Supplementary Fig. 3
), there was still a significant shift in preferred directions (11 of 20 neurons modified, 2 sessions in Monkey R). Moreover, consistent with past experiments12–14
, similar modifications were present during Task #2 (61 ± 5% mean ± sem, 8 sessions from 4 experiments, 10–45 neurons per session, p< 0.05 bootstrap analysis). Thus, changes in limb posture and workspace do not exclusively account for the changes in preferred direction after transition to brain control. For subsequent analysis, we combined the datasets from the two tasks.
We next analyzed the indirect neurons (). Interestingly, in both animals we found that indirect neurons also experienced a similar change in their preferred direction (Monkey P: n=6 sessions with 18–25 units per session, 60 ± 6% mean ± sem; Monkey R: n=4 sessions, 63 ± 10% mean ± sem with 10–18 units, p< 0.05 bootstrap analysis). To assess specific differences among population of neurons, we subdivided the indirect neurons (). Indirect neurons recorded on a BMI channel (i.e. microwire with a direct neuron) were labeled as ‘near’ (see Supplementary Fig. 4
). The remaining indirect neurons were labeled as ‘far’ (i.e. recorded on a microwire ~500–700µm from a BMI channel). We did not find a significant difference between the percentage and the extent of changes in the preferred direction of these two groups (p > 0.05, bootstrap analysis). Together, our results indicate that there were large scale changes in the preferred direction of both direct and indirect neurons after the transition to brain control.
While the analysis described above focused on individual units, we also examined changes at the population level. For the direct group, while a majority of the individual shifts in preferred direction (ΔPD) were significant, the sum of the positive and negative shifts resulted in a non-significant net shift (). There were no significant differences between the direct, near and far populations (, p > 0.05, bootstrap analysis). A similar finding was also evident when considering all neurons in both animals (Supplementary Fig. 5
). Thus, it appears that there is a relative remapping of the preferred directions without any significant systematic rotational shifts for each neural population.
Differential modification of modulation depths
We next examined for changes in modulation depth. For each neuron, we calculated the ratio of modulation depths between brain control and manual control (BC:MC MDratio
). We initially focused on sessions with proficient task performance (i.e. ≥ day 3 of practice defined as ‘Late’). During these brain control sessions, indirect neurons were less modulated than during manual control ( and ). Similar to past reports22–23,34
, there was some heterogeneity in the direct population responses. In contrast, both of the indirect populations experienced a consistent net relative reduction in MDratio
We compared population means across multiple experiments. The mean BC:MC1
was 1.2, 0.6, 0.5 respectively for the direct, near and far populations (). The median values were respectively 1.2, 0.5, and 0.5. Only the near and far groups demonstrated a significant decrease. Superimposed are the bootstrap distributions of each group. In addition, when we varied the time window for measurement of directional tuning, there were no significant changes in our conclusions (Supplementary Figs. 6 and 7
Across six experiments in both animals, we observed a consistent difference between the relative mean modulation depths of the direct and indirect neuronal populations (, BC:MC1 Late, 10 sessions from 6 experiments in Monkey P and R). Surprisingly, the units with close proximity to direct neurons behaved similarly to more distant neurons. These differences emerged upon stabilization of task performance (, BC:MC1 Early versus BC:MC1 Late, p < 0.05 for near and far populations, 9 sessions taken from 6 experiments in both Monkey P and R). Together, our results indicate that differential modulation of the neuronal populations was specifically present during proficient neuroprosthetic control and not during the initial learning period.
In addition, as evident in the examples illustrated in (also see Supplementary Fig. 4
), there were changes in the mean firing rate of individual neurons when comparing manual control to brain control. They appeared to be independent of the changes in modulation depth (e.g. compare near and far neurons in Supplementary Fig. 4
). While some neurons experienced a combined decrease in the mean firing rate and the modulation depth (e.g. left panel), other neurons experienced a change in the modulation depth while the mean firing rate remained unchanged (e.g. Supplementary Fig. 4
). At the population level, however, there were no significant systematic differences in the mean firing rate between manual control and brain control for either the direct or the indirect populations (n=6 experiments, p > 0.05 bootstrap analysis).
State-dependent modification of neural properties
As described above, the subjects performed manual control both before and after brain control. Thus, comparison of modulation depth during MC1
could assess for any lasting effects of the modifications during brain control. For example, studies of motor learning have documented the neural correlates of a ‘memory trace’ after motor learning4
. Interestingly, there was no significant difference between the direct, near and far groups for this comparison (p>0.05, bootstrap analysis, , MC1
). This indicates that the population modulation depth during manual control, both before and after the brain control, was very similar. Moreover, MC2
was significantly different from the BC:MC1
Late relationship for both the near and far neurons (p < 0.05, bootstrap analysis, 8 sessions taken from 6 experiments). This further implies that the population modulation depth reverts back to its original properties during the manual control task.
We subsequently assessed for differences at the level of individual units. The vast majority of units did not experience a significant change in preferred direction between MC1
(). Also shown is an example of a unit with a small, but significant, change. show respective examples of the distribution of individual changes in preferred direction and modulation depth (comparison of 64 neurons during a daily MC1
session in Monkey P). All three neural populations were combined as no significant differences were evident for each separate comparison. In general, we found that the vast majority of neurons reverted back to their task-related firing patterns during MC2
in comparison to MC1
(89 ± 5% mean ± std and 83 ± 4.5% mean ± std without significant changes in preferred direction and modulation depth respectively, n=6 experiments). We also did not find evidence of significant differences for manual control sessions associated with ‘early brain control’ (87 ± 8% mean ± std and 80 ± 3% mean ± std without changes in preferred direction and modulation depth, n=6 experiments). Moreover, the presence of a unimodal distribution of changes (i.e. ) perhaps suggests a small degree of instability of the neuron-behavior relationship during the two sessions7,28,35–36
. Alternatively, these changes could reflect subtle changes in task performance.
Stability of neural properties
What are the neural dynamics of switching (i.e. MC1 → brain control → MC2)? We measured the directional modulation relationship across sessions. For each transition, relatively rapid changes in preferred direction and modulation depth were evident for direct units (). Similar dynamics were evident for indirect neurons, albeit with a reduction of modulation (). Moreover, the properties of both direct and indirect neurons remained relatively stable during each state.
Stability of indirect neural properties across days
To further test the link between indirect units and brain control, we examined their properties across consecutive days of proficient brain control. For instance, if the properties of the indirect population remain constant across days of proficient brain control, it suggests that they play an active role. We selected a population of stable indirect neurons, all of which had stable waveform shapes, ISI distribution and preferred direction during manual control (). The activities of these neurons were compared across two consecutive days of brain control (Task Performance, Day 3: 97% and Day 4: 98%). There was no significant difference in either preferred direction or modulation depth for these examples (p > 0.05, bootstrap analysis). There were also neurons that were robustly modulated during manual control but consistently not modulated during each daily brain control session (). In general, individual indirect neurons maintained a relatively fixed neuron-behavior relationship for consecutive days of brain control (comparison of n=3 experiments, % of neurons with stable parameters: preferred direction 87 ± 4% and modulation depth 81 ± 2%, n= 16–20 indirect neurons). Strikingly, this was not significantly different from the neuron-behavior relationship for manual control described in the previous section (p > 0.05, bootstrap analysis).
We also compared the distribution of changes across days for both direct and indirect neurons at the population level (). Interestingly, the indirect neuron distribution was also not significantly different from that for direct neurons (indirect −19 ± 12° and direct −15 ± 9°, mean ± std, p > 0.05). Across multiple experiments we also found that the population dynamics were very similar across consecutive days (indirect 0.6 ± 11° and direct –4 ± 10°, mean ± std, n=3 experiments). This was also evident for the MDratio distributions (indirect 1.0 ± 0.14 and direct 1.07 ± 0.15, mean ± std, n=3 experiments). Together, this indicates that indirect neurons maintained a relatively fixed neuron-behavior relationship during brain control. The similarity with the direct neurons further suggests that the indirect population may play an active role during brain control.