Modeling Reaching Impairment After Stroke Using a Population Vector Model of Movement Control That Incorporates Neural Firing-Rate Variability

Abstract

The directional control of reaching after stroke was simulated by including cell death and firing-rate noise in a population vector model of movement control. In this model, cortical activity was assumed to cause the hand to move in the direction of a population vector, defined by a summation of responses from neurons with cosine directional tuning. Two types of directional error were analyzed: the between-target variability, defined as the standard deviation of the directional error across a wide range of target directions, and the within-target variability, defined as the standard deviation of the directional error for many reaches to a single target. Both between- and within-target variability increased with increasing cell death. The increase in between-target variability arose because cell death caused a nonuniform distribution of preferred directions. The increase in within-target variability arose because the magnitude of the population vector decreased more quickly than its standard deviation for increasing cell death, provided appropriate levels of firing-rate noise were present. Comparisons to reaching data from 29 stroke subjects revealed similar increases in between- and within-target variability as clinical impairment severity increased. Relationships between simulated cell death and impairment severity were derived using the between- and within-target variability results. For both relationships, impairment severity increased similarly with decreasing percentage of surviving cells, consistent with results from previous imaging studies. These results demonstrate that a population vector model of movement control that incorporates cosine tuning, linear summation of unitary responses, firing-rate noise, and random cell death can account for some features of impaired arm movement after stroke.

Trajectory formation principles are the same after mild or moderate stroke

“…Moreover, we found that participants with stroke made 4 times larger errors than healthy at the end of the first sub-movement, which replicates the finding that directional errors increase with impairment after a stroke [32,45]. A computational model of cortical damage and its consequences on arm reaching movements revealed how the errors could be correlated with the loss of neurons [46,47]. In addition, participants with stroke are quickly confronted with fatigue [48], and fatigue contributes to increasing signal-dependent noise [49].…”

Queuing Network Modeling of Transcription Typing

“…For example, given that the population size (M) of the brain area corresponding to each finger [M =7300 on average, Reinkensmeyer et al 2003;Penfield and Rasmussen 1950] and the movement radius (RD) in typing (17.5cm on average, since the hands of the typist are moved to reach different keys with the wrist as an axis and the average distance from the wrist to the tip of fingers is 17.5cm [Armstrong 2004], the distribution of movement distance of each finger on average follows Equation (20). …”

Neuromotor recovery from stroke: computational models at central, functional, and muscle synergy level

“…Models of neuromotor recovery at the level of cortical circuitry (Goodall et al, 1997; Reinkensmeyer et al, 2003; Butz et al, 2009) address how focal cortical lesions elicit neural reorganization phenomena, and the way these lesions affect motor behavior.…”

“…Reinkensmeyer et al (2003) developed a model of cortical damage and its consequences on arm reaching movements. Different from the previous approaches, this model does not address intracortical connectivity and its reorganization.…”