Addressing the between-group comparison problem: Detecting differences between correlation matrix populations due to single-variable perturbations for resting state fMRI
4 affiliations
- 1Department of Statistics and Data Science, Hebrew University of Jerusalem, Israel
- 2Department of Psychology, University of Pennsylvania, PA, USA
- 3Department of Medical Neurobiology, Hebrew University of Jerusalem, Israel; Department of Neurology, Hadassah Medical Center, Jerusalem, Israel
- 4Department of Statistics and Data Science, Hebrew University of Jerusalem, Israel; Corresponding author.
Abstract
Resting-state fMRI (RS-fMRI) has been known for decades as a promising method for evaluating cognitive and mental states, both in health and especially in disease, due to its ease of implementation as a short, standard MRI protocol. In clinical settings, a group of patients with a given disorder is typically compared to a group of healthy controls. This poses an inherent challenge of between-group comparison. We propose a new efficient model for characterizing changes to the temporal synchronization of brain activity measured using RS-fMRI between groups, summarized as individual correlation matrices. Our model posits that the between-group differences are the product of single-region effects describing the increase or decay of synchronization with the rest of the brain. This parsimonious model pools the correlation coefficients of each region with all others, and therefore can detect differences between groups even in small samples. Inference for this model accounts for the variability in individual correlation matrices, the within-group differences across individuals, and for the approximation error of the single-region model. This results in per-region estimates and confidence intervals for the parameters governing the difference between groups. In simulations, our model shows increased power to detect model-aligned alternatives compared with competing approaches. To demonstrate feasibility of the method in a clinical application, we use the model to analyze RS-fMRI correlation matrices in patients with transient global amnesia and healthy controls. Our model detects significant decreases in synchronization for the patient population in the amygdala after multiplicity correction as well as borderline decreases in memory-related brain regions that were not detected using mass-univariate tests without prior knowledge, suggesting its usefulness in the application of RS-fMRI in clinical settings.
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BibTeX
@article{faran2026addressing,
title = {{Addressing the between-group comparison problem: Detecting differences between correlation matrix populations due to single-variable perturbations for resting state fMRI}},
author = {Itamar Faran and Michael Peer and Shahar Arzy and Yuval Benjamini},
journal = {NeuroImage},
year = {2026},
month = oct,
doi = {10.1016/j.neuroimage.2026.122239},
url = {https://doi.org/10.1016/j.neuroimage.2026.122239},
note = {CC BY},
}
RIS
TY - JOUR
TI - Addressing the between-group comparison problem: Detecting differences between correlation matrix populations due to single-variable perturbations for resting state fMRI
AU - Itamar Faran
AU - Michael Peer
AU - Shahar Arzy
AU - Yuval Benjamini
PY - 2026
DA - 2026/10/30
JO - NeuroImage
DO - 10.1016/j.neuroimage.2026.122239
UR - https://doi.org/10.1016/j.neuroimage.2026.122239
AB - Resting-state fMRI (RS-fMRI) has been known for decades as a promising method for evaluating cognitive and mental states, both in health and especially in disease, due to its ease of implementation as a short, standard MRI protocol. In clinical settings, a group of patients with a given disorder is typically compared to a group of healthy controls. This poses an inherent challenge of between-group comparison. We propose a new efficient model for characterizing changes to the temporal synchronization of brain activity measured using RS-fMRI between groups, summarized as individual correlation matrices. Our model posits that the between-group differences are the product of single-region effects describing the increase or decay of synchronization with the rest of the brain. This parsimonious model pools the correlation coefficients of each region with all others, and therefore can detect differences between groups even in small samples. Inference for this model accounts for the variability in individual correlation matrices, the within-group differences across individuals, and for the approximation error of the single-region model. This results in per-region estimates and confidence intervals for the parameters governing the difference between groups. In simulations, our model shows increased power to detect model-aligned alternatives compared with competing approaches. To demonstrate feasibility of the method in a clinical application, we use the model to analyze RS-fMRI correlation matrices in patients with transient global amnesia and healthy controls. Our model detects significant decreases in synchronization for the patient population in the amygdala after multiplicity correction as well as borderline decreases in memory-related brain regions that were not detected using mass-univariate tests without prior knowledge, suggesting its usefulness in the application of RS-fMRI in clinical settings.
SN - 1095-9572
SN - 1053-8119
ER -