{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Pospisil DA"],"funding":["NIDA NIH HHS","NIMH NIH HHS","NINDS NIH HHS","Wellcome Trust"],"pagination":["201-209"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11446844"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["634(8032)"],"pubmed_abstract":["A goal of neuroscience is to obtain a causal model of the nervous system. The recently reported whole-brain fly connectome<sup>1-3</sup> specifies the synaptic paths by which neurons can affect each other, but not how strongly they do affect each other in vivo. To overcome this limitation, we introduce a 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 linear dynamical model of the fly brain that uses stochastic optogenetic perturbation data to estimate causal effects and the connectome as a prior to greatly improve estimation efficiency. We validate our estimator in connectome-based linear simulations and show that it recovers a linear approximation to "],"journal":["Nature"],"pubmed_title":["The fly connectome reveals a path to the effectome."],"pmcid":["PMC11446844"],"funding_grant_id":["203261/Z/16/Z","RF1 MH117815","R01 NS104899","RF1 DA056404","RF1 MH129268","U24 NS126935","R35 NS111580","220343/Z/20/Z"],"pubmed_authors":["Pospisil DA","Matsliah A","Dorkenwald S","Murthy M","Jefferis GSXE","McKellar CE","Yu SC","Pillow JW","Aragon MJ","Sterling AR","Eichler K","Costa M","Schlegel P"],"additional_accession":[]},"is_claimable":false,"name":"The fly connectome reveals a path to the effectome.","description":"A goal of neuroscience is to obtain a causal model of the nervous system. The recently reported whole-brain fly connectome<sup>1-3</sup> specifies the synaptic paths by which neurons can affect each other, but not how strongly they do affect each other in vivo. To overcome this limitation, we introduce a 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 linear dynamical model of the fly brain that uses stochastic optogenetic perturbation data to estimate causal effects and the connectome as a prior to greatly improve estimation efficiency. We validate our estimator in connectome-based linear simulations and show that it recovers a linear approximation to ","dates":{"release":"2024-01-01T00:00:00Z","publication":"2024 Oct","modification":"2026-07-15T15:33:52.489Z","creation":"2025-04-04T02:35:59.094Z"},"accession":"S-EPMC11446844","cross_references":{"pubmed":["39358526"],"doi":["10.1038/s41586-024-07982-0"]}}