ORIGINAL RESEARCH article
Front. Neuroimaging
Sec. Brain Imaging Methods
Volume 4 - 2025 | doi: 10.3389/fnimg.2025.1649749
Nonlinear kernel-based fMRI activation detection
Provisionally accepted
- Lou Ruvo Center for Brain Health, Neurological Institute, Cleveland Clinic, Cleveland, United States
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Kernel Canonical Correlation Analysis (KCCA) is an effective method for globally detecting brain activation with reduced computational complexity. However, the current KCCA is limited to linear kernels, and the performance of more general types of kernels remains uncertain. This study aims to expand the current KCCA method to arbitrary nonlinear kernels. Our contributions are twofold: first, we proposed an inverse mapping algorithm which works for general types of nonlinear kernels. Second, we demonstrate that, nonlinear kernels yield improved performance, particularly when the true neural activation deviates from the hypothesized hemodynamic response function, due to the complex nature of neural responses. Our results, based on a simulated fMRI dataset and two task-based fMRI datasets, indicate that the nonlinear kernels outperform the linear kernel and effectively reduce activation in undesired regions.
Keywords: data analysis, fMRI, Task fMRI, activation, nonlinear kernel, CCA, KCCA
Received: 18 Jun 2025; Accepted: 12 Aug 2025.
Copyright: © 2025 Han, Yang, Zhuang and Cordes. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Dietmar Cordes, Lou Ruvo Center for Brain Health, Neurological Institute, Cleveland Clinic, Cleveland, United States
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