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Related Experiment Video

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Electrophysiological and Morphological Characterization of Neuronal Microcircuits in Acute Brain Slices Using Paired Patch-Clamp Recordings

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Published on: January 10, 2015

Inferring causal connectivity from pairwise recordings and optogenetics.

Mikkel Elle Lepperød^1,2, Tristan Stöber^2,3,4, Torkel Hafting¹

¹Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway.

Plos Computational Biology

|November 7, 2023

Summary

Neuroscientists can untangle neural interactions using econometrics. Combining instrumental variables and difference in differences methods reduces confounds in optogenetics, improving causal effective connectivity estimates.

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Area of Science:

Neuroscience
Computational Neuroscience
Econometrics

Background:

Understanding neural mechanisms requires quantifying causal interactions between neurons.
Optogenetics allows specific neural perturbation but widefield interventions cause confounds.
Discerning specific neuronal contributions is challenging due to simultaneous stimulation.

Purpose of the Study:

To address confounds in optogenetic interventions.
To introduce econometrics-based methods for estimating causal effective connectivity.
To improve the accuracy of neural interaction analysis.

Main Methods:

Combining instrumental variables (IV) and difference in differences (DiD) techniques.
Exploiting the interaction between stimulation and the absolute refractory period.
Applying methods to simulated neural networks.

Main Results:

Identified confounds in widefield optogenetics lead to biased interpretations.
Demonstrated that IV and DiD methods reduce confounding biases.
Showcased superior performance of IV/DiD over naive techniques in simulations.

Conclusions:

Econometric methods like IV and DiD can effectively disentangle neural interactions.
These causal inference techniques offer a powerful approach for analyzing brain connectivity.
The study highlights the utility of interdisciplinary methods in neuroscience research.