bioRxivNeurosciencePreprint
Brain-wide electrical spatiotemporal dynamics encode reward anticipation
Mai-Anh T. VuiD, Lisa K. David, Gwenaëlle E. Thomas, Meghana Kasturi VagwalaiD, Caley J. Burrus, Neil M. GallagheriD, Joyce WangiD, Cameron Blount, Dalton N. HughesiD, Elise Adamson, Nkemdilim Ndubuizu, Il Hwan Kim, Scott H. SoderlingiD, Stephen D. Mague, R. Alison AdcockiD, Kafui DzirasaiD
- Citations
- 2
Abstract
Anticipation of an upcoming stimulus induces neural activity across cortical and subcortical regions and influences subsequent behavior. Nevertheless, the network mechanism whereby the brain integrates this information to signal the anticipation of rewards remains relatively unexplored. Here we employ multi-circuit electrical recordings from six brain regions as mice perform a sample-to-match task in which reward anticipation is operationalized as their progress towards obtaining a potential reward. We then use machine learning to discover the naturally occurring network patterns that integrate this neural activity across timescales. Only one of the networks that we uncovered signals responses linked to reward anticipation, specifically relative proximity and reward magnitude. Activity in this Electome (electrical functional connectivity) network is dominated by theta oscillations leading from prelimbic cortex and striatum that converge on ventral tegmental area, and by beta oscillations leading from striatum that converge on prelimbic cortex. Network activity is also synchronized with brain-wide cellular firing. Critically, this network generalizes to new groups of healthy mice, as well as a mouse line that models aberrant neural circuitry observed in brain disorders that show altered reward anticipation. Thus, our findings reveal the network-level architecture whereby the brain integrates spatially distributed activity across timescales to signal reward anticipation.
- organism: mouse
- assay: electronic health records
- research focus: cell type annotation
- approach: machine learning (classical)
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