Causal Discovery for fMRI data: Challenges, Solutions, and a Case Study.

Eric Rawls, Bryan Andrews, Kelvin Lim, Erich Kummerfeld

Abstract readPreprint

In one paragraph

Article in ArXiv, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it

0cells of the map it votes in

0citing papers in PubMed

–field-weighted citation impact

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

4 authors.

Eric RawlsPsychiatry & Behavioral Sciences, University of Minnesota, Minneapolis, Minnesota, USA.

Bryan AndrewsPsychiatry & Behavioral Sciences, University of Minnesota, Minneapolis, Minnesota, USA.

Kelvin LimPsychiatry & Behavioral Sciences, University of Minnesota, Minneapolis, Minnesota, USA.

Erich KummerfeldInstitute for Health Informatics, University of Minnesota, Minneapolis, Minnesota, USA.

Funding

University of Minnesota Clinical and Translational Science Institute (UMN CTSI)UL1TR002494 · NCATS · UNIVERSITY OF MINNESOTA · PI BLAZAR, BRUCE R, WEISDORF, DANIEL J · 2018 to 2022

$34.9M

Using a computational and network neuroscience framework to study pharmacological manipulations of state representation processes in early psychosisP50MH119569 · NIMH · UNIVERSITY OF MINNESOTA · PI A DAVID REDISH, Sophia Vinogradov · 2020 to 2026

$24.5M

Comorbidity: Substance Use Disorders and Other Psychiatric ConditionsT32DA037183 · NIDA · UNIVERSITY OF MINNESOTA · PI Anna Zilverstand · 2014 to 2026

$3.2M

NCATS NIH HHS UL1 TR002494NIDA NIH HHS T32 DA037183NIMH NIH HHS P50 MH119569

6 · The paper itself

Abstract

Designing studies that apply causal discovery requires navigating many researcher degrees of freedom. This complexity is exacerbated when the study involves fMRI data. In this paper we (i) describe nine challenges that occur when applying causal discovery to fMRI data, (ii) discuss the space of decisions that need to be made, (iii) review how a recent case study made those decisions, (iv) and identify existing gaps that could potentially be solved by the development of new methods. Overall, causal discovery is a promising approach for analyzing fMRI data, and multiple successful applications have indicated that it is superior to traditional fMRI functional connectivity methods, but current causal discovery methods for fMRI leave room for improvement.

Identifiers

PMID38196749

PMCPMC10775354

What OpenQuestion holds

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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.