Evidence map›Paper›PMID 38903114›Full record

ArticlebioRxiv : the preprint server for biology2024

In-Scanner Thoughts shape Resting-state Functional Connectivity: how participants "rest" matters.

J Gonzalez-Castillo, M A Spurney, K C Lam, I S Gephart, F Pereira, D A Handwerker, Jwy Kam, P A Bandettini

Abstract readPreprint
In one paragraph

Article in bioRxiv : the preprint server for biology, 2024. 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

5 · Who and what money

Authors and funding

8 authors.

J Gonzalez-CastilloSection on Functional Imaging Methods, NIMH, NIH, Bethesda, Maryland, USA.ORCID 0000-0002-6520-5125
M A SpurneySection on Functional Imaging Methods, NIMH, NIH, Bethesda, Maryland, USA.
K C LamMachine Learning Team, NIMH, NIH, Bethesda, Maryland, USA.
I S GephartSection on Functional Imaging Methods, NIMH, NIH, Bethesda, Maryland, USA.
F PereiraMachine Learning Team, NIMH, NIH, Bethesda, Maryland, USA.
D A HandwerkerSection on Functional Imaging Methods, NIMH, NIH, Bethesda, Maryland, USA.ORCID 0000-0001-7261-4042
Jwy KamDepartment of Psychology, University of Calgary, Calgary, Alberta, Canada.
P A BandettiniSection on Functional Imaging Methods, NIMH, NIH, Bethesda, Maryland, USA.ORCID 0000-0001-9038-4746

Funding

Functional MRI Method DevelopmentZIAMH002783 · NIMH · NATIONAL INSTITUTE OF MENTAL HEALTH · PI BANDETTINI, PETER · 2009 to 2025
$38.4M
Machine Learning TeamZICMH002968 · NIMH · NATIONAL INSTITUTE OF MENTAL HEALTH · PI PEREIRA, FRANCISCO · 2018 to 2025
$13.9M
Intramural NIH HHS ZIA MH002783Intramural NIH HHS ZIC MH002968
6 · The paper itself

Abstract

Resting-state fMRI (rs-fMRI) scans-namely those lacking experimentally-controlled stimuli or cognitive demands-are often used to identify aberrant patterns of functional connectivity (FC) in clinical populations. To minimize interpretational uncertainty, researchers control for across-cohort disparities in age, gender, co-morbidities, and head motion. Yet, studies rarely, if ever, consider the possibility that systematic differences in inner experience (i.e., what subjects think and feel during the scan) may directly affect FC measures. Here we demonstrate that is the case using a rs-fMRI dataset comprising 471 scans annotated with experiential data. Wide-spread significant differences in FC are observed between scans that systematically differ in terms of reported in-scanner experience. Additionally, we show that FC can successfully predict specific aspects of in-scanner experience in a manner similar to how it predicts demographics, cognitive abilities, clinical outcomes and labels. Together, these results highlight the key role of in-scanner experience in shaping rs-fMRI estimates of FC.

Identifiers

PMID38903114
PMCPMC11188111

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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.