Inferring causal connectivity from pairwise recordings and optogenetics.

Autor: Lepperød ME; Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway.; Simula Research Laboratory, Oslo, Norway., Stöber T; Simula Research Laboratory, Oslo, Norway.; Institute for Neural Computation, Faculty of Computer Science, Ruhr University Bochum, Bochum, Germany.; Epilepsy Center Frankfurt Rhine-Main, Department of Neurology, Goethe University, Frankfurt, Germany., Hafting T; Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway., Fyhn M; Simula Research Laboratory, Oslo, Norway.; Department of Biosciences, University of Oslo, Oslo, Norway., Kording KP; Department of Neuroscience, University of Pennsylvania, Pennsylvania, United States of America.
Jazyk: angličtina
Zdroj: PLoS computational biology [PLoS Comput Biol] 2023 Nov 07; Vol. 19 (11), pp. e1011574. Date of Electronic Publication: 2023 Nov 07 (Print Publication: 2023).
DOI: 10.1371/journal.pcbi.1011574
Abstrakt: To understand the neural mechanisms underlying brain function, neuroscientists aim to quantify causal interactions between neurons, for instance by perturbing the activity of neuron A and measuring the effect on neuron B. Recently, manipulating neuron activity using light-sensitive opsins, optogenetics, has increased the specificity of neural perturbation. However, using widefield optogenetic interventions, multiple neurons are usually perturbed, producing a confound-any of the stimulated neurons can have affected the postsynaptic neuron making it challenging to discern which neurons produced the causal effect. Here, we show how such confounds produce large biases in interpretations. We explain how confounding can be reduced by combining instrumental variables (IV) and difference in differences (DiD) techniques from econometrics. Combined, these methods can estimate (causal) effective connectivity by exploiting the weak, approximately random signal resulting from the interaction between stimulation and the absolute refractory period of the neuron. In simulated neural networks, we find that estimates using ideas from IV and DiD outperform naïve techniques suggesting that methods from causal inference can be useful to disentangle neural interactions in the brain.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2023 Lepperød et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze: MEDLINE
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