Evidence map›Paper›PMID 40567859›Full record

ArticleNeuroimage. Reports2021

The contributions of brain structural and functional variance in predicting age, sex and treatment.

Ning-Xuan Chen, Gui Fu, Xiao Chen, Le Li, Michael P Milham, Su Lui, Chao-Gan Yan

Abstract read
In one paragraph

Article in Neuroimage. Reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

7 authors.

Ning-Xuan ChenCAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China.
Gui FuDepartment of Radiology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, China.
Xiao ChenCAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China.
Le LiCenter for the Cognitive Science of Language, Beijing Language and Culture University, Beijing, 100083, China.
Michael P MilhamMATTER Lab, Child Mind Institute, New York, NY, 10022, USA.
Su LuiDepartment of Radiology, Huaxi MR Research Center (HMRRC), Functional and molecular imaging Key Laboratory of Sichuan Province, West China Hospital, Sichuan University, Chengdu 610041, China.
Chao-Gan YanCAS Key Laboratory of Behavioral Science, Institute of Psychology, Beijing, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Structural and functional neuroimaging have been widely used to track and predict demographic and clinical variables, including treatment outcomes. However, it is challenging to establish and compare the respective weights and contributions of brain structure and function in prediction studies. The present study aimed to directly investigate the respective roles of brain structural and functional indices, along with their contributions to the prediction of demographic variables (age/sex) and clinical changes in schizophrenia patients. The present study enrolled 492 healthy people from the Southwest University Adult Lifespan Dataset (SALD) for demographic variable analysis and 39 patients with schizophrenia from the West China Hospital for treatment analysis. We conducted a model fit test with two variables (one voxel-based structural metric and another voxel-based functional metric) and then performed variance partitioning on the voxels that could be predicted sufficiently. Permutation tests were applied to compare the difference in contribution between each pair of structural and functional measurements. We found that voxel-based structural indices had stronger predictive value for age and sex, while voxel-based functional metrics showed stronger predictive value for treatment. Therefore, through variance partitioning, we could clearly and directly explore and compare the voxel-based structural and functional indices with respect to particular variables. In sum, for the variables reflecting long-term changes (age) and constant biological features (sex), the voxel-based structural metrics would contribute more than voxel-based functional metrics, but for the variable reflecting short-term changes (schizophrenia treatment), the functional metrics could contribute more.

Indexed as

Functional metricsModel fitStructural metricsVariance partitioningVoxel-based analysis

Identifiers

PMID40567859
PMCPMC12172702

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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.