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Updated: Jun 5, 2026
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Simultaneous Data Collection of fMRI and fNIRS Measurements Using a Whole-Head Optode Array and Short-Distance Channels
Published on: October 20, 2023
Modeling Complex Effects and Individual Variability in Multi-Paradigm fMRI with Nonlinear Mixed Models
Xiaoxuan Li1, Gemeng Zhang2, Gang Qu1
1Department of Biomedical Engineering, Tulane University, New Orleans, LA, 70118, USA.
Biorxiv : the Preprint Server for Biology
|June 4, 2026
Summary
We developed a nonlinear mixed model (NMM) to analyze complex functional magnetic resonance imaging (fMRI) data, improving upon traditional linear models. NMM effectively captures individual brain variability and nonlinear relationships, offering better insights into brain function.
Area of Science:
- Neuroimaging
- Computational Neuroscience
- Biostatistics
Background:
- fMRI data present challenges due to high dimensionality, complex inter-regional dependencies, and individual variability.
- Traditional linear mixed models (LMMs) struggle to capture nonlinear relationships in neuroimaging data.
- Existing methods lack robust frameworks for modeling population-level effects alongside individual brain variability.
Purpose of the Study:
- Introduce a nonlinear mixed model (NMM) integrating neural networks to enhance fMRI analysis.
- Improve the modeling of complex fixed-effect relationships and individual differences in functional connectivity (FC).
- Provide a statistically rigorous and interpretable framework for analyzing large-scale brain organization.
Main Methods:
- Extended LMMs with neural networks to create the NMM framework.
- Utilized SHapley Additive exPlanations (SHAP) for post-hoc interpretability of nonlinear effects.
- Applied NMM to Philadelphia Neurodevelopmental Cohort (PNC) data across emotion, n-back, and resting-state paradigms.
Main Results:
- NMM identified robust, cross-paradigm functional connectivity (FC) patterns.
- SHAP analysis quantified contributions of age, sex, and paradigm to predicted FC across brain networks.
- Subject-specific random effects in NMM served as neural fingerprints, predicting cognitive scores and showing network variability.
Conclusions:
- NMM demonstrated superior model fit (lower MSE) compared to LMMs in predicting FC.
- The NMM framework offers a statistically sound and explainable method for modeling FC from covariates.
- NMM effectively separates population-level effects from stable individual variability in brain function.
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