ArticlePLoS computational biology2023
Inferring causal connectivity from pairwise recordings and optogenetics.
Article in PLoS computational biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
10 citing papers in PubMed, 15 citations in OpenAlex.
- Linking functional and structural dendritic spine remodeling during fear learning and extinction in vivo.Science advances · 2026Article
- Nonparametric Causal Inference for Optogenetics: Sequential Excursion Effects for Dynamic Regimes.Journal of the American Statistical Association · 2026Article
- Constraining inference of across-region interactions using neural activity perturbations.bioRxiv : the preprint server for biology · 2025Article
- No Central Executive? Decision Formation Through Multi-Area Population Dynamics.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025Review
- Experience-dependent reorganization of inhibitory neuron synaptic connectivity.bioRxiv : the preprint server for biology · 2025Article
- The fly connectome reveals a path to the effectome.Nature · 2024Article
- Identification of interacting neural populations: methods and statistical considerations.Journal of neurophysiology · 2023Review
- From calcium imaging to graph topology.Network neuroscience (Cambridge, Mass.) · 2022Review
- Inferring thalamocortical monosynaptic connectivity in vivo.Journal of neurophysiology · 2021Article
- Quantifying causality in data science with quasi-experiments.Nature computational science · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 3 institutions in 3 countries.
Funding
Abstract
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.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.