Evidence map›Paper›PMID 38979212›Full record

ArticlebioRxiv : the preprint server for biology2024

A multimodal fMRI dataset unifying naturalistic processes with a rich array of experimental tasks.

Heejung Jung, Maryam Amini, Bethany J Hunt, Eilis I Murphy, Patrick Sadil, Yaroslav O Halchenko, Bogdan Petre, Zizhuang Miao, Philip A Kragel, Xiaochun Han and 5 more

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

15 authors.

Maryam Amini
Bethany J Hunt
Yaroslav O HalchenkoORCID 0000-0003-3456-2493
Xiaochun Han
Mickela O Heilicher
Michael Sun
Owen G Collins
Martin A LindquistORCID 0000-0003-2289-0828

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Cognitive neuroscience has advanced significantly due to the availability of openly shared datasets. Large sample sizes, large amounts of data per person, and diversity in tasks and data types are all desirable, but are difficult to achieve in a single dataset. Here, we present an open dataset with N = 101 participants and 6 hours of scanning per participant, with 6 multifaceted cognitive tasks including 2 hours of naturalistic movie viewing. This datasets' combination of ample sample size, extensive data per participant, more than 600 hours worth of data, and a wide range of experimental conditions - including cognitive, affective, social, and somatic/interoceptive tasks - positions it uniquely for probing important questions in cognitive neuroscience.

Identifiers

PMID38979212
PMCPMC11230225

What OpenQuestion holds

Textmetadata
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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.