From connectome to effectome: learning the causal interaction map of the fly brain.
| العنوان: | From connectome to effectome: learning the causal interaction map of the fly brain. |
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| المؤلفون: | Pospisil DA; Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA., Aragon MJ; Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA., Dorkenwald S; Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA.; Computer Science Department, Princeton University, Princeton, NJ, USA., Matsliah A; Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA., Sterling AR; Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA., Schlegel P; Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK.; Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK., Yu SC; Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA., McKellar CE; Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA., Costa M; Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK., Eichler K; Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK., Jefferis GSXE; Neurobiology Division, MRC Laboratory of Molecular Biology, Cambridge, UK.; Drosophila Connectomics Group, Department of Zoology, University of Cambridge, Cambridge, UK., Murthy M; Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA., Pillow JW; Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA. |
| المصدر: | BioRxiv : the preprint server for biology [bioRxiv] 2024 Feb 09. Date of Electronic Publication: 2024 Feb 09. |
| نوع المنشور: | Preprint |
| اللغة: | English |
| بيانات الدورية: | Country of Publication: United States NLM ID: 101680187 Publication Model: Electronic Cited Medium: Internet NLM ISO Abbreviation: bioRxiv Subsets: PubMed not MEDLINE |
| مستخلص: | A long-standing goal of neuroscience is to obtain a causal model of the nervous system. This would allow neuroscientists to explain animal behavior in terms of the dynamic interactions between neurons. The recently reported whole-brain fly connectome [1-7] specifies the synaptic paths by which neurons can affect each other but not whether, or how, they do affect each other in vivo. To overcome this limitation, we introduce a novel combined experimental and statistical strategy for efficiently learning a causal model of the fly brain, which we refer to as the "effectome". Specifically, we propose an estimator for a dynamical systems model of the fly brain that uses stochastic optogenetic perturbation data to accurately estimate causal effects and the connectome as a prior to drastically improve estimation efficiency. We then analyze the connectome to propose circuits that have the greatest total effect on the dynamics of the fly nervous system. We discover that, fortunately, the dominant circuits significantly involve only relatively small populations of neurons-thus imaging, stimulation, and neuronal identification are feasible. Intriguingly, we find that this approach also re-discovers known circuits and generates testable hypotheses about their dynamics. Overall, our analyses of the connectome provide evidence that global dynamics of the fly brain are generated by a large collection of small and often anatomically localized circuits operating, largely, independently of each other. This in turn implies that a causal model of a brain, a principal goal of systems neuroscience, can be feasibly obtained in the fly. |
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| معلومات مُعتمدة: | RF1 DA056404 United States DA NIDA NIH HHS; U24 NS126935 United States NS NINDS NIH HHS; R01 NS104899 United States NS NINDS NIH HHS; RF1 MH117815 United States MH NIMH NIH HHS; R35 NS111580 United States NS NINDS NIH HHS |
| فهرسة مساهمة: | Keywords: causal inference; connectome; fly |
| تواريخ الأحداث: | Date Created: 20231114 Latest Revision: 20240227 |
| رمز التحديث: | 20240227 |
| مُعرف محوري في PubMed: | PMC10635032 |
| DOI: | 10.1101/2023.10.31.564922 |
| PMID: | 37961285 |
| قاعدة البيانات: | MEDLINE |
| DOI: | 10.1101/2023.10.31.564922 |
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