| First Author | Sylwestrak EL | Year | 2022 |
| Journal | Cell | Volume | 185 |
| Issue | 19 | Pages | 3568-3587.e27 |
| PubMed ID | 36113428 | Mgi Jnum | J:329336 |
| Mgi Id | MGI:7343450 | Doi | 10.1016/j.cell.2022.08.019 |
| Citation | Sylwestrak EL, et al. (2022) Cell-type-specific population dynamics of diverse reward computations. Cell 185(19):3568-3587.e27 |
| abstractText | Computational analysis of cellular activity has developed largely independently of modern transcriptomic cell typology, but integrating these approaches may be essential for full insight into cellular-level mechanisms underlying brain function and dysfunction. Applying this approach to the habenula (a structure with diverse, intermingled molecular, anatomical, and computational features), we identified encoding of reward-predictive cues and reward outcomes in distinct genetically defined neural populations, including TH(+) cells and Tac1(+) cells. Data from genetically targeted recordings were used to train an optimized nonlinear dynamical systems model and revealed activity dynamics consistent with a line attractor. High-density, cell-type-specific electrophysiological recordings and optogenetic perturbation provided supporting evidence for this model. Reverse-engineering predicted how Tac1(+) cells might integrate reward history, which was complemented by in vivo experimentation. This integrated approach describes a process by which data-driven computational models of population activity can generate and frame actionable hypotheses for cell-type-specific investigation in biological systems. |
