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Predicting perturbation effects from resting state activity using functional causal flow

View ORCID ProfileAmin Nejatbakhsh, Francesco Fumarola, Saleh Esteki, View ORCID ProfileTaro Toyoizumi, View ORCID ProfileRoozbeh Kiani, View ORCID ProfileLuca Mazzucato
doi: https://doi.org/10.1101/2020.11.23.394916
Amin Nejatbakhsh
1Center for Theoretical Neuroscience, Columbia University, New York, NY 10027, USA
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Francesco Fumarola
2Laboratory for Neural Computation and Adaptation, RIKEN Center for Brain Science, Wako, Saitama 351-0198, Japan
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Saleh Esteki
3Center for Neural Science, New York University, New York, NY 10003, USA
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Taro Toyoizumi
2Laboratory for Neural Computation and Adaptation, RIKEN Center for Brain Science, Wako, Saitama 351-0198, Japan
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Roozbeh Kiani
3Center for Neural Science, New York University, New York, NY 10003, USA
4Neuroscience Institute, NYU Langone Medical Center, New York, NY 10016, USA
5Department of Psychology, New York University, New York, NY 10003, USA
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  • For correspondence: roozbeh@nyu.edu lmazzuca@uoregon.edu
Luca Mazzucato
6Institute of Neuroscience and Departments of Biology, Mathematics and Physics, University of Oregon, Eugene, OR 97403, USA
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  • ORCID record for Luca Mazzucato
  • For correspondence: roozbeh@nyu.edu lmazzuca@uoregon.edu
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Abstract

Targeted manipulation of neural activity will be greatly facilitated by under-standing causal interactions within neural ensembles. Here, we introduce a novel statistical method to infer a network’s “functional causal flow” (FCF) from ensemble neural recordings. Using ground truth data from models of cortical circuits, we show that FCF captures functional hierarchies in the ensemble and reliably predicts the effects of perturbing individual neurons or neural clusters. Critically, FCF is robust to noise and can be inferred from the activity of even a small fraction of neurons in the circuit. It thereby permits accurate prediction of circuit perturbation effects with existing recording technologies for the primate brain. We confirm this prediction by recording changes in the prefrontal ensemble spiking activity of alert monkeys in response to single-electrode microstimulation. Our results provide a foundation for using targeted circuit manipulations to develop new brain-machine interfaces or ameliorate cognitive dysfunctions in the human brain.

Competing Interest Statement

The authors have declared no competing interest.

Footnotes

  • ↵† co-first authors

Copyright 
The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission.
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Posted November 24, 2020.
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Predicting perturbation effects from resting state activity using functional causal flow
Amin Nejatbakhsh, Francesco Fumarola, Saleh Esteki, Taro Toyoizumi, Roozbeh Kiani, Luca Mazzucato
bioRxiv 2020.11.23.394916; doi: https://doi.org/10.1101/2020.11.23.394916
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Predicting perturbation effects from resting state activity using functional causal flow
Amin Nejatbakhsh, Francesco Fumarola, Saleh Esteki, Taro Toyoizumi, Roozbeh Kiani, Luca Mazzucato
bioRxiv 2020.11.23.394916; doi: https://doi.org/10.1101/2020.11.23.394916

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