Evidence map›Paper›PMID 38267481›Full record

ArticleScientific reports2024

Downstream network transformations dissociate neural activity from causal functional contributions.

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

Open access · goldAbstract read
In one paragraph

Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
2.7field-weighted citation impact, top 11% of its field
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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed, 8 citations in OpenAlex.

  1. No Central Executive? Decision Formation Through Multi-Area Population Dynamics.The Journal of neuroscience : the official journal of the Society for Neuroscience · 2025
    Review
  2. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors at 3 institutions in 3 countries.

Kayson FakharInstitute of Computational Neuroscience, University Medical Center Eppendorf, Hamburg University, Hamburg Center of Neuroscience, Hamburg, Germany. kayson.fakhar@gmail.com.
Shrey DixitInstitute of Computational Neuroscience, University Medical Center Eppendorf, Hamburg University, Hamburg Center of Neuroscience, Hamburg, Germany.
Fatemeh HadaeghiInstitute of Computational Neuroscience, University Medical Center Eppendorf, Hamburg University, Hamburg Center of Neuroscience, Hamburg, 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, Hamburg, Germany.
Universität Hamburg · DEUniversity Medical Center Hamburg-Eppendorf · DEUniversity of Pennsylvania · US

Funding

Quantifying causality for neuroscienceR01EB028162 · NIBIB · UNIVERSITY OF PENNSYLVANIA · PI KORDING, KONRAD P. · 2019 to 2019
$1.1M
NIBIB NIH HHS R01 EB028162NIH HHS 1R01EB028162-01
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 neural 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.

Indexed as

CognitionNeuronsCausalityIntuitionNeural Networks, Computer

Identifiers

PMID38267481
PMCPMC10808222
OpenAlexW4391176094

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.