Movement and Performance Explain Widespread Cortical Activity in a Visual Detection Task

Abstract

Recent studies in mice reveal widespread cortical signals during task performance; however, the various task-related and task-independent processes underlying this activity are incompletely understood. Here, we recorded wide-field neural activity, as revealed by GCaMP6s, from dorsal cortex while simultaneously monitoring orofacial movements, walking, and arousal (pupil diameter) of head-fixed mice performing a Go/NoGo visual detection task and examined the ability of task performance and spontaneous or task-related movements to predict cortical activity. A linear model was able to explain a significant fraction (33–55% of variance) of widefield dorsal cortical activity, with the largest factors being movements (facial, walk, eye), response choice (hit, miss, false alarm), and arousal and indicate that a significant fraction of trial-to-trial variability arises from both spontaneous and task-related changes in state (e.g., movements, arousal). Importantly, secondary motor cortex was highly correlated with lick rate, critical for optimal task performance (high d′), and was the first region to significantly predict the lick response on target trials. These findings suggest that secondary motor cortex is critically involved in the decision and performance of learned movements and indicate that a significant fraction of trial-to-trial variation in cortical activity results from spontaneous and task-related movements and variations in behavioral/arousal state.

Sensory and Behavioral Components of Neocortical Signal Flow in Discrimination Tasks with Short-term Memory

“…This behavior-related activity likely reflects activity involved in movement execution and control, proprioceptive signals, and activity evoked by body parts touching external objects. This behavior-related component represents a substantial part of cortex-wide activity, in line with recent studies highlighting the strong influence of behavioral variables on cortical activity (Clancy et al, 2019;Musall et al, 2019;Salkoff et al, 2019;Stringer et al, 2019). Does higher activeness lead to better performance?…”

“…Several recent studies highlighted the strong influence of behavioral variables, i.e. movement patterns, on cortical dynamics (Clancy et al, 2019;Musall et al, 2019;Salkoff et al, 2019;Stringer et al, 2019). In our own study (Gilad et al, 2018), using a whisker-dependent go/no-go texture discrimination task with delayed response in mice, we found that the location of short-term memory strikingly depends on the movement behavior during sensation.…”

A distributed neocortical action map associated with reach-to-grasp

“…Our work expands upon this line of research, by providing supporting additional evidence for an involvement of sensorimotor areas during RtG and, more importantly, by directly observing all areas of the neocortex, hence providing an integrative framework not reported previously. The global communication scheme found in the present work is consistent with recent reports on other types of behavioral tasks (Goard et al, 2016; Kyriakatos et al, 2016; Wekselblatt et al, 2016; Allen et al, 2017; Makino et al, 2017; Salkoff et al, 2019; Stringer et al, 2019, Musall et al, 2019). However to our best knowledge, similar approaches were not yet applied to investigate neural dynamics underlying the control of unilateral forelimb movements requiring to locate, reach and grasp for an object.…”

“…We found that among the responsive areas, the secondary motor areas of both hemispheres displayed the most robust association between the neural and the movement kinematics. Previous work showed that these areas is encodes performance rate in a licking task (Salkoff et al, 2019). Our current results suggest that the secondary motor areas could encode for the forelimb movement duration, movement distance, and movement smoothness, encouraging future investigations toward decoding of movement kinematics from neural data.…”

“…Our current results suggest that the secondary motor areas could encode for the forelimb movement duration, movement distance, and movement smoothness, encouraging future investigations toward decoding of movement kinematics from neural data. Decoding approaches have been long and very well established for electrophysiological studies of motor control, and more recently applied to fluorescent imaging data of neural activity (Prsa et al, 2017; Li et al, 2019; Salkoff et al, 2019). Our findings extend these studies by providing evidence for correlation between large scale, area-level activity, and specific metrics of RtG movements.…”