Evidence map›Paper›PMID 37553449›Full record

ArticleBrain imaging and behavior2023

The abilities of movie-watching functional connectivity in individual identifications and individualized predictions.

Yun Guan, Hao Ma, Jiangcong Liu, Le Xu, Yang Zhang, Lixia Tian

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In one paragraph

Article in Brain imaging and behavior, 2023. 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
–field-weighted citation impact
1 · What the graph read from it

What it found

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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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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.

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4 · The record

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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

6 authors.

Yun GuanSchool of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044, China.ORCID http://orcid.org/0000-0002-3941-5030
Hao MaSchool of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044, China.
Jiangcong LiuSchool of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044, China.
Le XuSchool of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044, China.
Yang ZhangDepartment of Orthopedics, the Seventh Medical Center of Chinese PLA General Hospital, Beijing, 100700, China.
Lixia TianSchool of Computer and Information Technology, Beijing Jiaotong University, Beijing, 100044, China. tian.bme@gmail.com.ORCID http://orcid.org/0000-0002-8952-5158

Funding

National Natural Science Foundation of China 62276021
6 · The paper itself

Abstract

Quite a few studies have been performed based on movie-watching functional connectivity (FC). As compared to its resting-state counterpart, however, there is still much to know about its abilities in individual identifications and individualized predictions. To pave the way for appropriate usage of movie-watching FC, we systemically evaluated the minimum number of time points, as well as the exact functional networks, supporting individual identifications and individualized predictions of apparent traits based on it. We performed the study based on the 7T movie-watching fMRI data included in the HCP S1200 Release, and took IQ as the test case for the prediction analyses. The results indicate that movie-watching FC based on only 15 time points can support successful individual identifications (99.47%), and the connectivity contributed more to identifications were much associated with higher-order cognitive processes (the secondary visual network, the frontoparietal network and the posterior multimodal network). For individualized predictions of IQ, it was found that successful predictions necessitated 60 time points (predicted vs. actual IQ correlation significant at P < 0.05, based on 5,000 permutations), and the prediction accuracy increased logarithmically with the number of time points used for connectivity calculation. Furthermore, the connectivity that contributed more to individual identifications exhibited the strongest prediction ability. Collectively, our findings demonstrate that movie-watching FC can capture rich information about human brain function, and its ability in individualized predictions depends heavily on the length of fMRI scans.

Indexed as

BrainConnectomeHumansMagnetic Resonance ImagingMotion PicturesFingerprintingFunctional networkHuman connectome project (HCP)Naturalistic fMRIPartial least squares regression (PLSR)

Identifiers

PMID37553449

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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.