biostudies-literaturePospisil DANIDA NIH HHSNIMH NIH HHSNINDS NIH HHSWellcome Trust201-209https://www.ebi.ac.uk/biostudies/studies/S-EPMC11446844biostudies-literatureUnknown634(8032)A goal of neuroscience is to obtain a causal model of the nervous system. The recently reported whole-brain fly connectome^1-3 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 NatureThe fly connectome reveals a path to the effectome.PMC11446844203261/Z/16/ZRF1 MH117815R01 NS104899RF1 DA056404RF1 MH129268U24 NS126935R35 NS111580220343/Z/20/ZPospisil DAMatsliah ADorkenwald SMurthy MJefferis GSXEMcKellar CEYu SCPillow JWAragon MJSterling AREichler KCosta MSchlegel PfalseThe fly connectome reveals a path to the effectome.A goal of neuroscience is to obtain a causal model of the nervous system. The recently reported whole-brain fly connectome^1-3 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 2024-01-01T00:00:00Z2024 Oct2026-07-15T15:33:52.489Z2025-04-04T02:35:59.094ZS-EPMC114468443935852610.1038/s41586-024-07982-0