Evidence map›Paper›PMID 41648174›Full record

ArticlebioRxiv : the preprint server for biology2026

MULTI-MODAL AND MULTI-REGION DISTANCE MODEL FOR NEUROIMAGING: APPLICATION TO ABCD STUDY.

Xinyu Zhang, Simon Vandekar, Andrew A Chen, Kaidi Kang, Jakob Seidlitz, Aaron Alexander-Bloch, Jinyuan Liu

Abstract readPreprint
PubMed Full text at PMC Publisher
In one paragraph

Article in bioRxiv : the preprint server for biology, 2026. 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

7 authors.

Xinyu ZhangDepartment of Biostatistics, Vanderbilt University.
Simon VandekarDepartment of Biostatistics, Vanderbilt University.
Andrew A ChenDepartment of Public Health Sciences, Medical University of South Carolina.
Kaidi KangDepartment of Biostatistics and Data Science, Wake Forest University School of Medicine.
Jakob SeidlitzLifespan Brain Institute of CHOP and Penn Medicine.
Aaron Alexander-BlochLifespan Brain Institute of CHOP and Penn Medicine.
Jinyuan LiuDepartment of Biostatistics, Vanderbilt University.

Funding

Precision brain charts for imaging-genomics of schizophrenia and the psychosis spectrumR01MH133843 · NIMH · CHILDREN'S HOSP OF PHILADELPHIA · PI Aaron Felix Alexander-Bloch · 2023 to 2026
$3.2M
Semiparametric Inference for Psychiatric NeuroimagingR01MH123563 · NIMH · VANDERBILT UNIVERSITY MEDICAL CENTER · PI VANDEKAR, SIMON NEIL · 2020 to 2024
$2.1M
NIMH NIH HHS R01 MH123563NIMH NIH HHS R01 MH133843
6 · The paper itself

Abstract

Large-scale neuroimaging studies often collect multiple modalities, such as task and resting-state functional MRI, diffusion MRI, and structural MRI. Joint inference across these modalities uses shared variation to improve statistical efficiency, increase replicability, and provide a more integrated view of brain-phenotype associations. In practice, however, such analyses are limited because complex cross-modality covariance cannot be flexibly modeled, which makes the resulting joint effects difficult to interpret. A recent distance-based ANOVA extension allows multimodal analysis and increases power for detecting group differences, but it cannot easily distinguish location from scale effects in distance space, offers only an omnibus pseudo-

Indexed as

Generalized estimating equation (GEE)Multimodality regressionRepeated measurementSemiparametric robust inferenceU-statistics

Identifiers

PMID41648174
PMCPMC12871845

What Socratic holds

Textmetadata
LicenceCC BY-ND
Read underepoch 390

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 Socratic graph.