Reversible large-scale modification of cortical networks during neuroprosthetic control

Abstract

Brain-machine interfaces (BMIs) provide a framework for studying cortical dynamics and the neural correlates of learning. Neuroprosthetic control has been associated with tuning changes in specific neurons directly projecting to the BMI (hereafter referred to as direct neurons). However, little is known about the larger network dynamics. By monitoring ensembles of neurons that were either causally linked to BMI control or indirectly involved, we found that proficient neuroprosthetic control is associated with large-scale modifications to the cortical network in macaque monkeys. Specifically, there were changes in the preferred direction of both direct and indirect neurons. Notably, with learning, there was a relative decrease in the net modulation of indirect neural activity in comparison with direct activity. These widespread differential changes in the direct and indirect population activity were markedly stable from one day to the next and readily coexisted with the long-standing cortical network for upper limb control. Thus, the process of learning BMI control is associated with differential modification of neural populations based on their specific relation to movement control.

Fig. 1. Modification of neural firing properties during brain control

a. For each daily session, subjects were required to serially perform a delayed center-out task in manual control (MC₁), brain control and then manual control (MC₂). In the brain control task shown, visual guides (i.e. lines shown in red) enforced straight trajectories. Trials were started by the animal physically moving to the center target. After a hold period, brain control (i.e. absence of any movements) was initiated. b. Changes in the preferred direction of a direct neuron. Solid lines are the cosine fit (R² is the percent of variance accounted for by the fit). Circles and bars (s.e.m.) show the directional modulation of the firing rate. Panels on the right show the waveform, crosscorrelograms (0.1% of spikes in a window < 1.5 ms) and the mean trajectories during manual control and brain control. Statistics performed with boostrap analysis. c. Changes in the preferred direction of indirect neurons. The directional modulation relationships are arranged similarly to b. Insets show waveforms of the respective indirect neurons.

Fig. 2. Differential modulation of neuronal populations during brain control

a. Distribution of shifts in preferred directions (ΔPD) between manual control and brain control. Each bar shows the number of neurons (i.e. ‘counts’) with a corresponding ΔPD. The labels above indicate the mean ΔPD for each population. Superimposed in gray is the bootstrap distribution. b. Distribution of changes in modulation depth ratio (MD_ratio) for BC:MC for the three neural populations. This panel is arranged similarly to a. c. Ratio of relative modulation depths. To compare multiple experiments and experimental conditions, we normalized each session to the mean MD_ratio for direct neurons. ‘Early’ and ‘Late’ represent brain control sessions respectively from days #1–2 and day ≥ 3 of training. MC₁:MC₂ is the ratio of modulation depths of the manual control sessions before and after brain control. Error bars show s.e.m.

Fig. 3. Stability of neural properties

a. Average directional modulation relationship during MC₁ (black) and MC₂ (gray) for three neurons. Neuron in lower panel experienced a significant change (bootstrap analysis, p<0.05). Error bars shown s.e.m. b. Actual (solid bars) and bootstrap (orange, mean ± std shown) distributions of changes in preferred direction during MC₁ and MC₂. All three neural populations were combined as they behaved similarly. c. Distributions of modulation depth changes. Arranged as in b.

Fig. 4. Stability of state-dependent changes in neural properties during a session

a. Traces show the preferred direction and modulation depth for a moving window of trials (window of 16 trials) for a direct unit. Each segment is color coded and labeled (MC₁, brain control or BC, MC₂). b. Average of multiple direct units from both animals. To illustrate the time course at the population level, the respective mean MC₁ value was subtracted from each individual trace and the absolute value was used for the average. n=number of units included in the average. Each plot shows the mean (thick line) ± 2 s.e.m. (thin line). c,d. Individual example and average responses of indirect units. Arranged as in a,b.

Fig. 5. Stability of neural properties across consecutive days of brain control

a. Average directional modulation relationship for a direct and near unit during manual control and brain control on two consecutive days. Partial lines above each tuning curve represent the respective preferred direction for each daily brain control (PD_BC) and manual control (PD_mC) session. Shaded region is the respective variance of the bootstrap distributions of PD_BC and PD_MC. Waveforms and interspike interval distributions from a direct (red) and near (blue) unit on consecutive days are also shown. b. Directional modulation of a far unit on two consecutive days. PD_BC could not be estimated because of a lack of modulation. c. Population distribution of preferred direction changes for indirect and direct neurons (PD_BC3–PD_BC4). For indirect units, the actual (grey bars) and bootstrap (black line) distributions are shown. The dark red line is the bootstrap distribution for direct units. Gray vertical line represents a ΔPD of 0.