Evidence map›Paper›PMID 41195768›Full record

ArticleHuman brain mapping2025

Higher-Order Triadic Interactions: Insights Into the Multiscale Network Organization in Schizophrenia.

Qiang Li, Shujian Yu, Jesus Malo, Godfrey D Pearlson, Yu-Ping Wang, Vince D Calhoun

Abstract read
In one paragraph

Article in Human brain mapping, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. 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

6 authors.

Qiang LiTri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory University, Atlanta, Georgia, USA.
Shujian YuDepartment of Computer Science, Vrije Universiteit Amsterdam, Amsterdam, the Netherlands.
Jesus MaloImage Processing Laboratory, University of Valencia, Valencia, Spain.
Godfrey D PearlsonDepartments of Psychiatry and Neurobiology, Yale University, New Haven, Connecticut, USA.
Yu-Ping WangDepartment of Biomedical Engineering, Tulane University, New Orleans, Louisiana, USA.
Vince D CalhounTri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State, Georgia Tech, Emory University, Atlanta, Georgia, USA.

Funding

Data-driven solutions for temporal, spatial, and spatiotemporal dynamic functional connectivityR01MH123610 · NIMH · GEORGIA STATE UNIVERSITY · PI ADALI, TULAY, CALHOUN, VINCE D · 2021 to 2025
$3.1M
BBVA Foundations of Science program on Maths, Stats, Comp. Sci. and AI VIS4NNGeneralitat Valenciana CIPROM/2021/056National Science Foundation 2112455NIH HHS R01MH123610Spain and the European Union PID2023-152133NB-I00
6 · The paper itself

Abstract

Complex biological systems, like the brain, exhibit intricate multiway and multiscale interactions that drive emergent behaviors. In psychiatry, neural processes extend beyond pairwise connectivity, involving higher-order interactions critical for understanding mental disorders. Conventional brain network studies focus on pairwise links, offering insights into basic connectivity but failing to capture the complexity of neural dysfunction in psychiatric conditions. This study seeks to address this gap by utilizing a matrix-based entropy functional for estimating total correlation, which serves as a mathematical framework for capturing multivariate information. We apply this framework to fMRI-ICA-derived multiscale brain networks, enabling the investigation of multivariate interaction patterns within the human brain across multiple scales. Additionally, this approach holds significant promise for psychiatric research on schizophrenia, offering a novel framework for investigating higher-order triadic brain network interactions associated with the disorder. By examining both triple interactions and the latent factors underlying the triadic relationships among intrinsic brain connectivity networks through tensor decomposition, our study presents a novel approach to understanding changes in higher-order brain networks in schizophrenia. This framework not only advances our understanding of complex brain functions but also opens new avenues for investigating the pathophysiology of schizophrenia, potentially informing more targeted diagnostic and therapeutic strategies. Moreover, this method for analyzing multiway interactions is applicable across signal analysis domains. In this study, we apply this approach to neural signals in schizophrenia, demonstrating its ability to reveal complex multiway interaction patterns and provide new insights into brain connectivity beyond traditional pairwise analyses in the context of brain disorders.

Indexed as

BrainConnectomeNerve NetSchizophreniaAdultFemaleHumansMagnetic Resonance ImagingMaleYoung Adultbeyond pairwise relationshipsICAmatrix‐based entropy functionalmultiscale brain networkstensor decompositiontotal correlation

Identifiers

PMID41195768
PMCPMC12590578

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-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 OpenQuestion graph.