@article{16525,
  keywords = {Neuroscience, causal inference, connectivity},
  author = {Mikkel Lepper{\o}d and Tristan St{\"o}ber and Torkel Hafting and Marianne Fyhn and Konrad Kording},
  title = {Inferring causal connectivity from pairwise recordings and optogenetics},
  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{\textemdash}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 na{\"\i}ve techniques suggesting that methods from causal inference can be useful to disentangle neural interactions in the brain.},
  year = {2023},
  journal = {PLOS Computational Biology},
  volume = {19},
  pages = {e1011574},
  publisher = {PLOS},
  url = {https://doi.org/10.1371/journal.pcbi.1011574},
  doi = {10.1371/journal.pcbi.1011574},
}