When Neural Activity Fails to Reveal Causal Contributions.

Kayson Fakhar, Shrey Dixit, Fatemeh Hadaeghi, Konrad P Kording, Claus C Hilgetag

Abstract readPreprint

In one paragraph

Article in bioRxiv : the preprint server for biology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed

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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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3 · Its place in the literature

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0 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Kayson FakharInstitute of Computational Neuroscience, University Medical Center Eppendorf, Hamburg University, Hamburg Center of Neuroscience, Germany.

Shrey DixitInstitute of Computational Neuroscience, University Medical Center Eppendorf, Hamburg University, Hamburg Center of Neuroscience, Germany.

Fatemeh HadaeghiInstitute of Computational Neuroscience, University Medical Center Eppendorf, Hamburg University, Hamburg Center of Neuroscience, Germany.

Konrad P KordingDepartments of Bioengineering and Neuroscience, University of Pennsylvania, Philadelphia, PA, USA.

Claus C HilgetagInstitute of Computational Neuroscience, University Medical Center Eppendorf, Hamburg University, Hamburg Center of Neuroscience, Germany.

Funding

Quantifying causality for neuroscienceR01EB028162 · NIBIB · UNIVERSITY OF PENNSYLVANIA · PI KORDING, KONRAD P. · 2019 to 2019

$1.1M

NIBIB NIH HHS R01 EB028162

6 · The paper itself

Abstract

Neuroscientists rely on distributed spatio-temporal patterns of neural activity to understand how neural units contribute to cognitive functions and behavior. However, the extent to which neural activity reliably indicates a unit's causal contribution to the behavior is not well understood. To address this issue, we provide a systematic multi-site perturbation framework that captures time-varying causal contributions of elements to a collectively produced outcome. Applying our framework to intuitive toy examples and artificial neuronal networks revealed that recorded activity patterns of neural elements may not be generally informative of their causal contribution due to activity transformations within a network. Overall, our findings emphasize the limitations of inferring causal mechanisms from neural activities and offer a rigorous lesioning framework for elucidating causal neural contributions.

Identifiers

PMID37333375

DOI10.1101/2023.06.06.543895

PMCPMC10274733

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LicenceCC BY-NC

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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.