Griffin et al. (2008)

Do corticomotoneuronal cells predict target muscle EMG activity?.

DM Griffin, HM Hudson, A Belhaj-Saïf, BJ McKiernan, PD Cheney.

Data from two rhesus macaques were used to investigate the pattern of cortical cell activation during reach-to-grasp movements in relation to the corresponding activation pattern of the cell's facilitated target muscles. The presence of postspike facilitation (PSpF) in spike-triggered averages (SpTAs) of electromyographic (EMG) activity was used to identify cortical neurons with excitatory synaptic linkages with motoneurons. EMG activity from 22 to 24 muscles of the forelimb was recorded together with the activity of M1 cortical neurons. The extent of covariation was characterized by 1) identifying the task segment containing the cell and target muscle activity peaks, 2) quantifying the timing and overlap between corticomotoneuronal (CM) cell and EMG peaks, and 3) applying Pearson correlation analysis to plots of CM cell firing rate versus EMG activity of the cell's facilitated muscles. At least one firing rate peak, for nearly all (95%) CM cells tested, matched a corresponding peak in the EMG activity of the cell's target muscles. Although some individual CM cells had very strong correlations with target muscles, overall, substantial disparities were common. We also investigated correlations for ensembles of CM cells sharing the same target muscle. The ensemble population activity of even a small number of CM cells influencing the same target muscle produced a relatively good match (r >/= 0.8) to target muscle EMG activity. Our results provide evidence in support of the notion that corticomotoneuronal output from primary motor cortex encodes movement in a framework of muscle-based parameters, specifically muscle-activation patterns as reflected in EMG activity. - J Neurophysiol (2008) vol. 99 (3) pp. 1169-986

Krubitzer et al. (2004)

Organization of area 3a in macaque monkeys: contributions to the cortical phenotype.

L Krubitzer, KJ Huffman, E Disbrow, G Recanzone.

The detailed organization of somatosensory area 3a was examined in macaque monkeys using multiunit electrophysiological recording techniques. By examining topographic relationships, changes in receptive field size, and the type of stimulus that neurons responded to, functional boundaries of area 3a were determined and related to architectonic boundaries. One striking observation was that the location of area 3a varied with respect to the central sulcus. In one-half of the cases area 3a was on the rostral bank and fundus of the central sulcus and in the other half of the cases it was on the caudal bank and fundus of the central sulcus. In terms of topographic organization, we found that area 3a contains a complete representation of deep receptors and musculature of the contralateral body, and that the general organization of body part representations mirrors that of the primary somatosensory area, 3b. These results as well as results from studies of area 3a in ours and other laboratories indicate that area 3a is part of a network involved in proprioception, postural control, and the generation of coordinated movements. Further, comparative analysis of area 3a in a variety of species suggests that its construction is based, to a large extent, on the use of a particular body part rather than on innervation density. - J Comp Neurol (2004) vol. 471 (1) pp. 97-111

Stein et al. (2004)

Coding of position by simultaneously recorded sensory neurones in the cat dorsal root ganglion.

R Stein, DJ Weber, Y Aoyagi, A Prochazka, JB Wagenaar, S Shoham, RA Normann.

Muscle, cutaneous and joint afferents continuously signal information about the position and movement of individual joints. How does the nervous system extract more global information, for example about the position of the foot in space? To study this question we used microelectrode arrays to record impulses simultaneously from up to 100 discriminable nerve cells in the L6 and L7 dorsal root ganglia (DRG) of the anaesthetized cat. When the hindlimb was displaced passively with a random trajectory, the firing rate of the neurones could be predicted from a linear sum of positions and velocities in Cartesian (x, y), polar or joint angular coordinates. The process could also be reversed to predict the kinematics of the limb from the firing rates of the neurones with an accuracy of 1-2 cm. Predictions of position and velocity could be combined to give an improved fit to limb position. Decoders trained using random movements successfully predicted cyclic movements and movements in which the limb was displaced from a central point to various positions in the periphery. A small number of highly informative neurones (6-8) could account for over 80% of the variance in position and a similar result was obtained in a realistic limb model. In conclusion, this work illustrates how populations of sensory receptors may encode a sense of limb position and how the firing of even a small number of neurones can be used to decode the position of the limb in space. - J Physiol (Lond) (2004) vol. 560 (Pt 3) pp. 883-96