Identifier Results
| Field | Value |
|---|---|
| Identifier | nemo:dat-9acq8g2 |
| Dataset Name | Cell-type-specific population dynamics of diverse reward computations |
| Version | NA |
| Release Date | NA |
| DOI | NA |
| Source Data URL | https://data.nemoarchive.org/other/grant/u19_deisseroth/deisseroth/spatial_transcriptome/scell/starmap/ |
| Dataset Collection URL | NA |
| Description | 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. |
| Keywords | medial habenula; STARmap |
| Total Files in Collection | 0 |
| Total Size in Collection (in GB) | 0.0 |
| Authors | Emily L. Sylwestrak, YoungJu Jo, Sam Vesuna, Xiao Wang, Blake Holcomb, Rebecca H. Tien, Doo Kyung Kim, Lief Fenno, Charu Ramakrishnan, William E. Allen, Ritchie Chen, Krishna V. Shenoy, David Sussillo, Karl Deisseroth |
| Organization | Stanford University |
| Contact Person | Karl Deisseroth |
| Contact E-Mail | deissero@stanford.edu |
| External Identifier | https://doi.org/10.1016/j.cell.2022.08.019 |
| Grant Name | U19NS118284 |
| Consortium | BICCN |
| Data Repository | NeMO |
| Data Repository RRID | RRID:SCR_016152 |
| Data License | CC BY 4.0 |
| Data Access | https://biccn.org/terms-of-use |
| Community Standards | https://biccn.org/standards |
| Study Organism | mouse |
| Protocol ID | http://dx.doi.org/10.1126/science.aat5691 |
This identifier does not have sub-identifiers with "raw" data associated
This identifier does not have sub-identifiers with "analysis" data associated