Inconvenient truths about neural processing in primary motor cortex

Abstract

Primary motor cortex (MI) plays an important role in voluntary motor behaviour, yet considerable debate remains on how neural processing within this brain region contributes to motor function. This article provides a brief review of the dominant conceptual frameworks used to interpret MI activity, notably servo-control during the 1970s and early 1980s, and sensorimotor transformations since that time. The former emphasized the use of feedback, but was abandoned because delays in sensory feedback could not permit sufficient feedback gains to generate observed patterns of limb movement. The latter framework focuses attention on identifying what coordinate frames, or representations, best describe neural processing in MI. However, studies have shown that MI activity correlates with a broad range of parameters of motor performance from spatial target location, hand or joint motion, joint torque and muscle activation patterns. Further, these representations can change across behaviours, such as from posture to movement. What do heterogeneous, labile neural representations mean and how do they help us understand how MI is involved in volitional motor control? Perhaps what is required is a new conceptual framework that re-focuses the experimental problem back on processes of control. Specifically, optimal feedback control has been proposed as a theory of the volitional motor system and it is argued here that it provides a rich, new perspective for addressing the role of MI and other brain regions in volitional motor control.

Figure 1. Servo-control as a model of wrist motor control

The difference between desired and present wrist position is first computed and then converted into motor commands that are sent to wrist muscles. Wrist movement is continuously sensed by muscle afferents and sent back to the central nervous system. This framework emphasizes the importance of feedback in motor control.

Figure 2. Putative sensorimotor transformations performed by the brain to generate limb movement

Spatial goals, such as to reach to a location in space, is converted into muscle activity through a sequential series of intermediary representations or coordinate frames. This concept emphasizes the open-loop aspects of motor control and the type of information conveyed in the discharge pattern of neurons in a given brain region.

Figure 3. Response of an exemplar neuron tested in seven directions, two distances and two instructed speeds

Each subpanel plots data for 1 target location. Grey traces plot mean hand velocity. Red and green traces plot mean firing rate for fast and slow reaches, respectively. Trace widths show ±s.e.m. Vertical calibration bars indicate 20 spikes s⁻¹. Reproduced from Churchland & Shenoy (2007) with permission of the American Physiological Society.

Figure 4. Load-related activity across posture and reaching periods with constant-magnitude load

A–C, vertical columns indicate exemplar neurons expressing load-related activity during both posture and reaching time periods, only during the reaching period, and only during the posture period. A, spike rasters and mean instantaneous firing rate under the nine load conditions in a muscle-torque coordinate frame, shoulder extensor (se) and flexor (SF) torques versus elbow extensor (EE) and flexor (FF) torques. 0, no muscular torque. Vertical lines indicate movement onset; shaded regions denote posture and movement epochs used for plane fits displayed in subsequent panels. B, plane fits of change in neural activity to change in muscle torque during the posture time period. Neuron's response gain and direction are indicated by strength and direction of the plane's colour gradient; arrows and plane coefficients clarify and denote significant plane fit (P < 0.01). C, plane fits of change in neural activity to change in muscle torque during reaching time period. Reproduced from Kurtzer et al. (2005).

Figure 5. Optimal feedback control

Feedback control laws convert state variables into motor commands. A key difference between servo-control and optimal feedback control is that all state variables related to the body (joint position, velocities, force, etc.) can influence motor commands to each muscle (multiple input, multiple output). Delays in sensory feedback from the periphery can be overcome by using efference copy of motor commands to help estimate present state variables. Reproduced from Scott (2002).