Limb-state information encoded by peripheral and central somatosensory neurons: implications for an afferent interface

Abstract

A major issue to be addressed in the development of neural interfaces for prosthetic control is the need for somatosensory feedback. Here, we investigate two possible strategies: electrical stimulation of either dorsal root ganglia (DRG) or primary somatosensory cortex (S1). In each approach, we must determine a model that reflects the representation of limb state in terms of neural discharge. This model can then be used to design stimuli that artificially activate the nervous system to convey information about limb state to the subject. Electrically activating DRG neurons using naturalistic stimulus patterns, modeled on recordings made during passive limb movement, evoked activity in S1 that was similar to that of the original movement. We also found that S1 neural populations could accurately discriminate different patterns of DRG stimulation across a wide range of stimulus pulse-rates. In studying the neural coding in S1, we also decoded the kinematics of active limb movement using multi-electrode recordings in the monkey. Neurons having both proprioceptive and cutaneous receptive fields contributed equally to this decoding. Some neurons were most informative of limb state in the recent past, but many others appeared to signal upcoming movements suggesting that they also were modulated by an efference copy signal. Finally, we show that a monkey was able to detect stimulation through a large percentage of electrodes implanted in area 2. We discuss the design of appropriate stimulus paradigms for conveying time-varying limb state information, and the relative merits and limitations of central and peripheral approaches.

Fig. 1

Setup for PAMS experiments in cats. (A) Replay pattern experiments begin with passive movement trials. (B) The pattern of DRG neural activity from the movement trials is used to generate a pattern of stimulation pulses applied to the same 15 (cat 1) or 30 (cat 4) DRG electrodes during the replay PAMS trials performed with the foot position fixed. (C). Simple patterns of PAMS are fabricated by systematically varying the intensity, pulse-rate, and location of active (+) electrodes (cats 2 and 3).

Fig. 2

(A) Experimental setup for monkey experiments. (B) Hand trajectory during sequential random target task. Red and blue dots indicate firing of two neurons with PDs as indicated by colored arrows. Panels in (C) show the full tuning curves for two example neurons shown in (B) with PDs highlighted. (D) Differences in neuron preferred direction measured for pairs recorded from a single (blue) or across multiple (red) electrodes.

Fig. 3

Limb-state encoding properties of DRG neurons during a center-out movement of the cat’s foot. (A) Example of DRG neuron firing rate modeled as a linear function of foot position, velocity, and speed (i.e. combined model, R² = 0.61). (B) Cumulative distribution of R² values for tuning functions that include position, velocity, speed, or their combination (n = 142 neurons, cats 1, 2, and 3). The dotted vertical lines denote the 75th percentile for each model.

Fig. 4

Results from PAMS replay pattern trials in cats. (A) Example of single-unit activity in S1 during passive movement and replay PAMS. Stimulation was applied on 15 channels in the L7 DRG (cat 1) following the pattern shown in the raster plot. Each tick in the replay pattern plot denotes a 7 μA pulse applied to 1 of the 15 channels in the L7 DRG. The position and speed of the foot during the movement trials are shown at the bottom of panel A. (B) Accurate decoding of foot-speed from S1 activity is possible in both the movement and replay conditions. (C) Consistency of the S1 response across 28 repetitions of the motion and replay PAMS stimulation patterns (cat 4). During the motion trials, the leg was moved passively between near full extension and flexion.

Fig. 5

Discriminability of S1 neuronal responses to fabricated patterns of PAMS in cats. (A) S1 LFP response detection accuracy for PAMS of 5 different amplitudes (rows), applied to different DRG electrode locations represented by pairs of active electrodes (columns) and (B) classification accuracy of the effects of two stimulation locations (cat 3). (C) Classification accuracy between S1 firing rate responses to different stimulation pulse-rates (synchronous stimulation on all 16 DRG electrodes, amplitude = 6 μA; cat 2). The color scale for classification accuracy is shown at the bottom left. The inset shows examples of PSTH plots for one S1 channel for a range of stimulation pulse-rates; plots show the mean +/− 1 SEM firing rates. Asterisks indicate significance (p<0.001).

Fig. 6

Log-likelihood ratio for GLM predictions from area 2 neurons recorded from monkeys. (A) shows a curve representing the log-likelihood ratio as a function of lag for a representative neuron. (B) shows the distribution of the times of the peak of the log-likelihood ratio curve for all cells that had a peak at least twice the variance over the full range of lags (± 5 seconds). The dependent axis is configured such that negative lag implies neural activity before movement (motor-like) and positive lag implies neural activity after movement (sensory-like).

Fig. 7

Decoding of kinematics from neural activity recorded from area 2 in monkeys. Actual (solid) and predicted (dashed) horizontal position (A) and velocity (B) are shown for a representative ten second span from one of the sessions. (C) Goodness of fit for predictions of limb endpoint kinematics from each array. Shown are R² values position, velocity, and acceleration calculated on 10-fold cross-validated data presented as mean +/- standard deviation across folds. Predictions from monkey M’s array consistently underperformed the others for unknown reasons.

Fig. 8

Velocity prediction accuracy while dropping the most uniquely informative neurons for two monkeys. Steps on which a neuron with a proprioceptive RF was dropped are shown in gray, cutaneous in black. The intermingling of gray and black bars indicates that neurons with proprioceptive RFs were not more informative about kinematics than neurons with cutaneous RFs.

Fig. 9

Area 2 ICMS detection results for monkey P. (A) ICMS detection rates for single-electrode stimulation across a range of currents. (B) Response percentage for 6 different electrodes, each stimulated individually at 80 μA, in another experimental session (gray bars). Black bar indicates the condition in which no stimulation was delivered.