An interpretable cross-attentive multi-modal MRI fusion framework for schizophrenia identification.

Overview

Authors: Ziyu Zhou¹, Anton Orlichenko², Gang Qu², Zening Fu³, Zhengming Ding¹, Julia Stephen⁴, Tony Wilson⁵, Vince Calhoun³, Yu-Ping Wang²

ORCID iDs: Ziyu Zhou, Anton Orlichenko, Yu-Ping Wang

Tulane University, Department of Computer Science, 6823 St. Charles Ave, New Orleans, 70118, LA, USA
Tulane University, Department of Biomedical Engineering, 6823 St. Charles Ave, New Orleans, 70118, LA, USA
Georgia State University, Georgia Institute of Technology, Emory University, Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), 55 Park Pl NE, Atlanta, 30303, GA, USA
Mind Research Network, 1101 Yale Blvd NE, Albuquerque, 87106, NM, USA
Boys Town National Research Hospital, Institute for Human Neuroscience, 14000 Boys Town Hospital Rd, Boys Town, 68010, NE, USA

Institutions: Tulane University (United States); Georgia Institute of Technology (United States); Emory University (United States); Georgia State University (United States); Center for Translational Research in Neuroimaging and Data Science (United States); Mind Research Network (United States); Boys Town National Research Hospital (United States)

Journal: Neuroimage. Reports, volume 6, issue 2, article 100338

Dates: received 12 October 2025; accepted 16 March 2026; published online 24 March 2026

Type: Research article · Language: English

License: CC BY-NC-ND

Identifiers: DOI 10.1016/j.ynirp.2026.100338 · PMID 41947848 · PMCID PMC13052079 · OpenAlex W7140222405

Open access: gold, a free copy (OpenAlex)

Status: code on request (how the code is found and verified)

Categories: structural MRI / diffusion (modality), schizophrenia / psychosis (population), methods / tools (subfield)

Methods: Machine learning, fMRI & imaging

Keywords: Multi-modal MRI, Cross-modal attention, Transformer, Schizophrenia

Topic: Functional Brain Connectivity Studies (Cognitive Neuroscience, Neuroscience), according to OpenAlex

Funding: National Institute on Minority Health and Health Disparities (2U54MD007595); National Institute of General Medical Sciences (P20 GM103472, P20GM144641); National Institute of Biomedical Imaging and Bioengineering (R01 EB020407, R01EB006841); National Science Foundation (1539067, 2112455); National Institutes of Health (R01 MH104680, R01 MH107354, R01EB036247, R01 GM109068, R01MH121101, R56MH124925)

Citations: cited by 2 papers (Europe PMC); 54 references in the paper

Abstract

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Read it in the paper: doi.org/10.1016/j.ynirp.2026.100338.

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Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 9 authors, 4 keywords, 5 funders, 44 references.

Cite

This paper

Zhou, Z., Orlichenko, A., Qu, G., Fu, Z., Ding, Z., Stephen, J., Wilson, T., Calhoun, V., & Wang, Y.-P. (2026). An interpretable cross-attentive multi-modal MRI fusion framework for schizophrenia identification. Neuroimage. Reports, 6(2), 100338. https://doi.org/10.1016/j.ynirp.2026.100338

BibTeX

@article{zhou2026interpretable, author = {Zhou, Ziyu and Orlichenko, Anton and Qu, Gang and Fu, Zening and Ding, Zhengming and Stephen, Julia and Wilson, Tony and Calhoun, Vince and Wang, Yu-Ping}, title = {{An interpretable cross-attentive multi-modal MRI fusion framework for schizophrenia identification}}, journal = {Neuroimage. Reports}, year = {2026}, month = mar, volume = {6}, number = {2}, pages = {100338}, publisher = {Elsevier}, issn = {2666-9560}, doi = {10.1016/j.ynirp.2026.100338}, url = {https://doi.org/10.1016/j.ynirp.2026.100338}, pmid = {41947848}, pmcid = {PMC13052079} }

RIS

TY - JOUR AU - Zhou, Ziyu AU - Orlichenko, Anton AU - Qu, Gang AU - Fu, Zening AU - Ding, Zhengming AU - Stephen, Julia AU - Wilson, Tony AU - Calhoun, Vince AU - Wang, Yu-Ping TI - An interpretable cross-attentive multi-modal MRI fusion framework for schizophrenia identification T2 - Neuroimage. Reports J2 - Neuroimage Rep PY - 2026 DA - 2026/03/24 VL - 6 IS - 2 SP - 100338 SN - 2666-9560 PB - Elsevier DO - 10.1016/j.ynirp.2026.100338 UR - https://doi.org/10.1016/j.ynirp.2026.100338 LA - en ER -

CSL-JSON

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