Nonlinear KCCA in fMRI activation analysis: Self-supervised optimization and robust back-reconstruction.

Overview

1. Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, United States
2. Department of Psychology and Neuroscience, University of Colorado, Boulder, CO, United States

Abstract

Recent studies have extended nonlinear kernels to Kernel Canonical Correlation Analysis (KCCA), enabling more flexible modeling of complex relationships globally. Building on these developments, we propose three key enhancements to nonlinear KCCA. First, inspired by self-supervised learning in machine learning research, we refine the parameter optimization process by adopting a subject-wise criterion designed to mitigate overfitting. Second, we introduce an improved back-reconstruction (inverse mapping) method that achieves higher accuracy and robustness than existing voxel-importance estimation methods. Third, we further investigate the kernel selection strategy based on convergence behavior, and validate its effectiveness through activation accuracy, data augmentation robustness, and eigendecomposition. The proposed framework is evaluated on both simulated and task-based fMRI datasets, with results demonstrating consistent improvements across multiple performance metrics.

Reproduced under the paper's license (CC BY), from the paper cited above.

Code

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The paper's code and data availability statement is in the Data section.

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Data

Datasets cited

• humanconnectome.org/study/hcp-young-adult, at Human Connectome Project; found in “Data and Code Availability”

Data and Code Availability

The HCP data used in this study are publicly available and can be downloaded through the Human Connectome Project repository. Data access can be requested at https://www.humanconnectome.org/study/hcp-young-adult/data-releases. All the codes used in this study will be publicly available after acceptance at https://github.com/CCLRCBH-BIC.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

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Version 2, 28 September 2026

• Funding: added National Institute of General Medical Sciences: P20-GM109025

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, pages, dates, 4 authors, 7 keywords, 84 references.

Cite

This paper

Han, C., Yang, Z., Zhuang, X., & Cordes, D. (2026). Nonlinear KCCA in fMRI activation analysis: Self-supervised optimization and robust back-reconstruction. Imaging neuroscience (Cambridge, Mass.), 4, IMAG.a.1243. https://doi.org/10.1162/imag.a.1243

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