Distributed coding of choice, action and engagement across the mouse brain

Abstract

Vision, choice, action, and behavioral engagement arise from neuronal activity that may be distributed across brain regions. Here we delineate the spatial distribution of neurons underlying these processes. We used Neuropixels probes 1,2 to record from ~30,000 neurons in 42 brain regions of mice performing a visual discrimination task 3. Neurons in nearly all regions responded non-specifically when the mouse initiated an action. By contrast, neurons encoding visual stimuli and upcoming choices occupied restricted regions in neocortex, basal ganglia, and midbrain. Choice signals were rare and emerged with indistinguishable timing across regions. Midbrain neurons were activated before contralateral choices and suppressed before ipsilateral choices, whereas forebrain neurons could prefer either side. Brain-wide pre-stimulus activity predicted engagement in individual trials and in the overall task, with enhanced subcortical but suppressed neocortical activity during engagement. These results reveal organizing principles for the distribution of neurons encoding behaviorally relevant variables across the mouse brain. Performing a perceptual decision involves processing sensory information, selecting actions that may lead to reward, and executing these actions. It is unknown how the neurons mediating these processes are distributed across brain regions, and whether they rely on circuits that are shared or distinct. Most studies of action selection (here referred to simply as choice) have focused on individual regions such as frontal, parietal, and motor cortex, basal ganglia, thalamus, cerebellum, and superior colliculus 4-11. However, neural correlates of movements, rewards, and other task variables have been observed in multiple brain regions, including in areas previously identified as purely sensory 12-24. It is therefore Users may view, print, copy, and download text and data-mine the content in such documents, for the purposes of academic research, subject always to the full Conditions of use:

Accurate localization of linear probe electrodes across multiple brains

“…Interpretation of neural activity in terms of neural circuits relies on accurate localization of individual electrodes and thereby the recorded neurons. Several workflows have been described (Allen et al, 2019;Siegle et al, 2019;Steinmetz et al, 2019), but groundtruth experiments assessing the accuracy of electrode localization are lacking. We took advantage of the ChR2-EYFP in bundles of axons and compared the locations of ChR2-EYFP expression as judged by fluorescence with light-evoked activity measured with electrodes that were registered to the CCF.…”

“…Specifically, Neuropixels probes (Jun et al, 2017a) have 960 recording electrodes distributed over 9.6 mm shanks. Because of their length, Neuropixels recordings in rodents naturally span multiple brain areas (Allen et al, 2019;Jun et al, 2017a;Steinmetz et al, 2019). Neuropixels probes do not have the electronic circuits to pass the large currents that are required to produce electrolytic lesions.…”

“…Localizing electrodes has been achieved by labeling electrodes with fluorescent dye and post hoc analysis of the recorded tissue using histological methods (DiCarlo et al, 1996;Guo et al, 2017;Jensen and Berg, 2016;Salatino et al, 2017), aided by identifying known electrophysiological features of specific anatomical locations (Allen et al, 2019;Jun et al, 2017a;Steinmetz et al, 2019). However, the accuracy of workflows for localizing electrodes need to be assessed using ground truth experiments.…”

Independent representations of reward-predicting cues and reward history in frontal cortical neurons

“…One of four cortical areas (dmFrC, dlFrC, M1, and S1b) was bilaterally photoinhibited in randomly chosen half of the trials. However, the effect of learning on the convergence of the cortex-wide activity could also be explained by the licking behavior because its trial-by-trial variability also decreased as the learning progressed ( Figure 1E), and such movements can have a large effect on the neuronal activity as a whole (Musall et al, 2019;Steinmetz et al, 2019;Stringer et al, 2019). Therefore, we compared the trial-by-trial correlation of the lick-rate and the trial-by-trial correlation of the neuronal trajectories in each learning stage and CS presentation, and found that these correlations showed a strong association ( Figure S2F).…”

Data architecture for a large-scale neuroscience collaboration

“…However, we have also provided ONE light (see Appendix 2), a backend that allows scientists to share data using ONE by simply uploading files to a web server, or to figshare . The ONE system is extensible, allowing users to add non-standard data sets using their own namespace, and, for example, has been used to share large-scale electrophysiology recordings collected in a different task via figshare file upload 13 . Full details of the ONE protocol are given in Appendix 1.…”