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In this study, we propose a novel computational pipeline to characterize and interpret these dependencies using a long\u2010memory (LM) framework, which captures the slow, power\u2010law decay of autocorrelation in resting\u2010state fMRI (rs\u2010fMRI) signals. The pipeline involves voxelwise estimation of LM parameters via a wavelet\u2010based Bayesian method, yielding spatial maps that reflect temporal dependence across the brain. These maps are then projected onto a lower\u2010dimensional space via a composite basis and are then related to individual\u2010level covariates through group\u2010level regression. We applied this framework to the ADHD\u2010200 dataset. Analysis identified a significant positive association between age and the long\u2010memory 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\u2010range temporal dependence and developmental changes in memory\u2010related brain regions while highlighting the influence of acquisition\u2010site variability on effect magnitude and statistical significance. Overall, the proposed methodology enables detailed mapping of intrinsic temporal dynamics in rs\u2010fMRI and offers new insights into the relationship between functional signal memory and brain development.<\/jats:p>","DOI":"10.1002\/hbm.70641","type":"journal-article","created":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T03:40:54Z","timestamp":1789702854000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Wavelet\u2010Based Framework for Mapping Long Memory in Resting\u2010State\n                    <scp>fMRI<\/scp>\n                    : Age\u2010Related Changes in the Hippocampus From the\n                    <scp>ADHD<\/scp>\n                    \u2010200 Dataset"],"prefix":"10.1002","volume":"47","author":[{"ORCID":"https:\/\/orcid.org\/0009-0001-4061-3181","authenticated-orcid":false,"given":"Yasaman","family":"Shahhosseini","sequence":"first","affiliation":[{"name":"Department of Mathematics and Statistics University of Victoria  Victoria British Columbia Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6050-5313","authenticated-orcid":false,"given":"C\u00e9dric","family":"Beaulac","sequence":"additional","affiliation":[{"name":"Department of Mathematics Universit\u00e9 du Qu\u00e9bec \u00e0 Montr\u00e9al  Montr\u00e9al Quebec Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Farouk S.","family":"Nathoo","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Statistics University of Victoria  Victoria British Columbia Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2673-4307","authenticated-orcid":false,"given":"Michelle F.","family":"Miranda","sequence":"additional","affiliation":[{"name":"Department of Mathematics and Statistics University of Victoria  Victoria British Columbia Canada"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,9,17]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1006\/nimg.1997.0264"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.3389\/fphys.2018.01378"},{"key":"e_1_2_9_4_1","doi-asserted-by":"publisher","DOI":"10.1103\/PhysRevLett.59.381"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2016.06.034"},{"key":"e_1_2_9_6_1","volume-title":"Introduction to Resting State fMRI Functional Connectivity","author":"Bijsterbosch J.","year":"2017"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1006\/nimg.2001.0873"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.3109\/15622975.2015.1112034"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2004.07.012"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1002\/1097\u20100193(200102)12:2<61::AID\u2010HBM1004>3.0.CO;2\u2010W"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.3389\/fphys.2021.809943"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.1002\/hbm.25801"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.3389\/fphys.2012.00186"},{"key":"e_1_2_9_14_1","doi-asserted-by":"publisher","DOI":"10.1137\/070710111"},{"key":"e_1_2_9_15_1","volume-title":"Conners' Parent Rating Scale\u2010Revised: Long Version","author":"Conners C. 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