Movement and performance predict 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.

Distinct Waking States for Strong Evoked Responses in Primary Visual Cortex and Optimal Visual Detection Performance

“…Cortical state fluctuations have dramatic consequences for the processing of sensory information and the execution of sensoryguided behavior. State fluctuations in mice have traditionally been studied in the context of stillness versus locomotion, though several recent studies have begun to uncover the functional properties of state changes that occur in the absence of locomotion (Reimer et al, 2014;McGinley et al, 2015a,b;Vinck et al, 2015;Salkoff et al, 2019;Stringer et al, 2019). Our results provide an account of how sensory neuronal responsiveness in mouse V1 and visual detection performance vary as a continuous function of arousal.…”

“…Mice were given ϳ2 h to recover from anesthesia before behavioral testing. We have previously shown that this volume of muscimol injection inactivates ϳ1 mm 2 of cerebral cortex, without inactivation of surrounding areas (Salkoff et al, 2019).…”

“…Sensory-evoked responses of cortical neurons are known to exhibit substantial variability (Tolhurst et al, 1983;Shadlen and Newsome, 1998), a portion of which can be accounted for by baseline cortical network state (Arieli et al, 1996;Fontanini and Katz, 2008;Goris et al, 2014). While the most prominent cortical state change occurs upon the transition from sleep to wakefulness (Steriade, 2000;Steriade et al, 2001), the waking period itself is characterized by frequent, and often underappreciated state changes (McGinley et al, 2015b;Musall et al, 2019;Salkoff et al, 2019;Stringer et al, 2019). These waking state fluctuations can profoundly impact sensory-evoked cortical responses and sensory-guided behaviors (Pinto et al, 2013;Sachidhanandam et al, 2013;Engel et al, 2016;Beaman et al, 2017;Shimaoka et al, 2019;Speed et al, 2019).…”

“…Despite the significant interest in uncovering the characteristics and mechanisms of state-dependent sensory processing in mice, only recently have studies investigated the consequences of waking state modulations on sensory-guided behaviors in headfixed mice that are free to locomote (Bennett et al, 2013;McGinley et al, 2015a;McBride et al, 2019;Salkoff et al, 2019). Whether there is a well delineated, optimal level of arousal for visual detection performance in mice is unknown.…”

Estimation of Current and Future Physiological States in Insular Cortex

“…In this case, task-evoked neural responses would occur on top of physiological statedependent patterns of ongoing activity (Arieli et al, 1996). Alternatively, ongoing activity could reflect other variables such as arousal or motor actions (Musall et al, 2019;Salkoff et al, 2019;Stringer et al, 2019).…”