Modulation of Neural Variability in Premotor, Motor, and Posterior Parietal Cortex during Change of Motor Intention

Abstract

The time course of neural variability was studied in three nodes of the parieto-frontal system: the dorsal premotor cortex (PMd, area 6), primary motor cortex (MI, area 4), and posterior parietal cortex (PPC, area 5) while monkeys made either direct reaches to visual targets or changed reach direction in response to an unexpected change of target location. These areas are crucial nodes in the distributed control of reaching and their lesion impairs trajectory formation and correction under different circumstances. During unperturbed reaches, neural variability declined before the onset of hand movement in both frontal and parietal cortex. When the original motor intention suddenly changed, neural variability displayed a complex and area-specific modulation because the perturbation of the motor state was signaled earlier in PMd than in MI and PPC. The comparison of perturbed versus unperturbed reaches revealed that, in the time between the onset of correction signal and trajectory change, identical hand movements were associated with different, therefore contextdependent, patterns of neural variability induced by the instruction to change hand movement direction. In PMd, neural variability was higher before the initiation of hand reach than before its correction, thus providing a neural underpinning to the phenomenon that it takes less time to correct than to initiate hand movement. Furthermore, neural variability was an excellent predictor of slow and fast reach corrections because it was lower during the latter than the former. We conclude that the analysis of neural variability can be an important tool for the study of complex forms of motor cognition.

Neural Code—Neural Self-information Theory on How Cell-Assembly Code Rises from Spike Time and Neuronal Variability

“…The second view is that neuronal variability is not entirely noise, rather it may also contain uncontrolled internal variables influenced by attention or intent, because the observed noise seems to be correlated within the recorded population (measured as noise correlation; Lee et al, 1998 ; Churchland et al, 2010 ; Marcos et al, 2013 ; Lin et al, 2015 ). A series of studies also suggested that neuronal variability can be beneficial for boosting weak signals (Stacey and Durand, 2001 ) or serving as modulatory signals (Lee et al, 1998 ; Boerlin and Deneve, 2011 ; Kohn et al, 2016 ; Saberi-Moghadam et al, 2016 ).…”

Methods for Measuring Swallowing Pressure Variability Using High-Resolution Manometry

“…Whenever a motor action is performed repeatedly, there is inherent variability in the kinematics and the outcome of each movement [25,26]. This variability can arise from noise or other subtle differences in neural and muscle activity [27][28][29][30][31][32][33] that can occur both during movement preparation [34][35][36][37] and execution [37][38][39]. Movement variability is sensitive to the sensorimotor and timing demands of a task.…”

Finding the balance between model complexity and performance: Using ventral striatal oscillations to classify feeding behavior in rats

“…For example, shell left high gamma power begins to approach its feeding level of activity before feeding initiates, stabilizes during feeding, and returns toward Not-Feeding levels after feeding stops. In other cases there is a pattern of decreased variance around feeding which has been seen in the literature in response to visual stimuli and rewards [52], as a marker of stimulus perception/detection [53,54], been correlated with behavioral performance [55,56] and motor preparation/initiation [57,58]. When the timing of the ramping events across the different LFP features is considered, it is apparent that they occur on different timescales.…”

“…Given that motor initiation and preparation are known to manifest as decreased neural variability [57,58] there is a possibility that the decrease in LFP signal variance around feeding was merely a byproduct of the approach behavior of the animal. It is also possible that the success of our pre-feeding models was really detecting approach behavior.…”