Abstract
ABSTRACT
Functional magnetic resonance imaging (fMRI) time series are known to exhibit long‐range temporal dependencies that challenge traditional modeling approaches. In this study, we propose a novel computational pipeline to characterize and interpret these dependencies using a long‐memory (LM) framework, which captures the slow, power‐law decay of autocorrelation in resting‐state fMRI (rs‐fMRI) signals. The pipeline involves voxelwise estimation of LM parameters via a wavelet‐based Bayesian method, yielding spatial maps that reflect temporal dependence across the brain. These maps are then projected onto a lower‐dimensional space via a composite basis and are then related to individual‐level covariates through group‐level regression. We applied this framework to the ADHD‐200 dataset. Analysis identified a significant positive association between age and the long‐memory parameter in the hippocampus after adjusting for ADHD symptom severity and medication status. After accounting for acquisition site, the estimated effect sizes were attenuated and no longer reached statistical significance, although the spatial pattern was largely preserved, with the strongest estimated effects remaining localized to the same anatomical regions. These findings suggest a relationship between long‐range temporal dependence and developmental changes in memory‐related brain regions while highlighting the influence of acquisition‐site variability on effect magnitude and statistical significance. Overall, the proposed methodology enables detailed mapping of intrinsic temporal dynamics in rs‐fMRI and offers new insights into the relationship between functional signal memory and brain development.