The DOI matches its Crossref record; 98 of the 98 references it cites were found in a registry or an index.
Neuronal Dynamics Regulating Brain and Behavioral State Transitions
Open accessAbstract
Prolonged behavioral challenges can cause animals to switch from active to passive coping strategies, to manage effort-expenditure during stress; such normally-adaptive behavioral state transitions can become maladaptive in psychiatric disorders such as depression. The underlying neuronal dynamics and brainwide interactions important for passive coping have remained unclear. Here, we develop a paradigm to study these behavioral state transitions at cellular-resolution across the entire vertebrate brain. Using brainwide imaging in zebrafish, we observed that the transition to passive coping is manifested by progressive activation of neurons in the ventral (lateral) habenula. Activation of these ventral-habenula neurons suppressed downstream neurons in the serotonergic raphe nucleus and caused behavioral passivity, whereas inhibition of these neurons prevented passivity. Data-driven recurrent neural network modeling pointed to altered intra-habenula interactions as a contributory mechanism. These results demonstrate ongoing encoding of experience features in habenula, which guides recruitment of downstream networks and imposes a passive coping behavioral strategy.
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Scikit-learn: Machine Learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Müller, A., Nothman, J., Louppe, G., et al. (2012) · arXiv · doi:10.48550/arxiv.1201.0490 · Cited by 71,016
Matplotlib: a 2D graphics environment
Hunter, J. D. (2007) · Computing in Science & Engineering · doi:10.1109/mcse.2007.55 · Cited by 39,249
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.712192 · Cited by 27,225
Reinforcement Learning: An Introduction
(2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 24,496
The NumPy Array: A Structure for Efficient Numerical Computation
van der Walt, S., Colbert, S. C., & Varoquaux, G. (2011) · Computing in Science & Engineering · doi:10.1109/mcse.2011.37 · Cited by 9,381
scikit-image: image processing in Python
(2014) · doi:10.7717/peerj.453 · Cited by 6,794
Bradski, G. R. (2000) · Medical Entomology and Zoology · Cited by 5,994
Diagnostic and Statistical Manual of Mental Disorders ( DSM ‐ IV ‐ TR )
Segal, D. L. (2010) · The Corsini Encyclopedia of Psychology · doi:10.1002/9780470479216.corpsy0271 · Cited by 4,666
IPython: A System for Interactive Scientific Computing
Perez, F., & Granger, B. E. (2007) · Computing in Science & Engineering · doi:10.1109/mcse.2007.53 · Cited by 4,009
The tail suspension test: A new method for screening antidepressants in mice
Steru, L., Chermat, R., Thierry, B., & Simon, P. (1985) · Psychopharmacology · doi:10.1007/bf00428203 · Cited by 3,534
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Vyas, S., Golub, M. D., Sussillo, D., & Shenoy, K. V. (2020) · Annual Review of Neuroscience · doi:10.1146/annurev-neuro-092619-094115 · Cited by 671
Cerniauskas, I., Winterer, J., de Jong, J. W., Lukacsovich, D., Yang, H., Khan, F., Peck, J. R., Obayashi, S. K., Lilascharoen, V., Lim, B. K., et al. (2019) · Neuron · doi:10.1016/j.neuron.2019.09.005 · Cited by 187
Transcriptional and spatial resolution of cell types in the mammalian habenula
Hashikawa, Y., Hashikawa, K., Rossi, M. A., Basiri, M. L., Liu, Y., Johnston, N. L., Ahmad, O. R., & Stuber, G. D. (2020) · Neuron · doi:10.1016/j.neuron.2020.03.011 · Cited by 167
Cerebellar Neurodynamics Predict Decision Timing and Outcome on the Single-Trial Level
Lin, Q., Manley, J., Helmreich, M., Schlumm, F., Li, J. M., Robson, D. N., Engert, F., Schier, A., Nöbauer, T., & Vaziri, A. (2020) · Cell · doi:10.1016/j.cell.2019.12.018 · Cited by 122
Deep three-photon imaging of the brain in intact adult zebrafish
Chow, D. M., Sinefeld, D., Kolkman, K. E., Ouzounov, D. G., Akbari, N., Tatarsky, R., Bass, A., Xu, C., & Fetcho, J. R. (2020) · Nature Methods · doi:10.1038/s41592-020-0819-7 · Cited by 107
Metadata
- Authors
- Andalman, A. S., Burns, V. M., Lovett-Barron, M., Broxton, M., Poole, B., Yang, S. J., Grosenick, L., Lerner, T. N., Chen, R., Benster, T., Mourrain, P., Levoy, M., Rajan, K., & Deisseroth, K.
- Venue
- Cell
- Publisher
- Elsevier BV
- Published
- 2019-05
- Volume
- 177
- Issue
- 4
- Pages
- 970-985.e20
- ISSN
- 0092-8674 (ISSN-L)
- OpenAlex
- W2941652504
- Cited by
- 258
- References
- 98
- FWCI
- 14.29
- Type
- journal article
- Language
- en
- Open access
- bronze
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- Andalman, A. S., Burns, V. M., Lovett-Barron, M., Broxton, M., Poole, B., Yang, S. J., Grosenick, L., Lerner, T. N., Chen, R., Benster, T., Mourrain, P., Levoy, M., Rajan, K., & Deisseroth, K. (2019). Neuronal Dynamics Regulating Brain and Behavioral State Transitions. Cell. https://doi.org/10.1016/j.cell.2019.02.037
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