{"id":4413,"date":"2026-05-06T15:57:00","date_gmt":"2026-05-06T20:57:00","guid":{"rendered":"https:\/\/my.vanderbilt.edu\/masi\/?p=4413"},"modified":"2026-05-06T15:58:24","modified_gmt":"2026-05-06T20:58:24","slug":"analytic-bounds-on-gamlss-model-variability-of-normative-white-matter-brain-charts","status":"publish","type":"post","link":"https:\/\/my.vanderbilt.edu\/masi\/2026\/05\/analytic-bounds-on-gamlss-model-variability-of-normative-white-matter-brain-charts\/","title":{"rendered":"Analytic Bounds on GAMLSS Model Variability of Normative White Matter Brain Charts"},"content":{"rendered":"

Michael E. Kim<\/strong>, Gaurav Rudravaram, Adam Saunders, Chenyu Gao, Karthik Ramadass, Nancy R. Newlin, Praitayini Kanakaraj, Sam Bogdanov, Derek Archer, Timothy J. Hohman, Angela L. Jefferson, Victoria L. Morgan, Alexandra Roche, Dario J. Englot, Susan M. Resnick, Lori L. Beason Held, Murat Bilgel, Laurie E. Cutting, Laura A. Barquero, Micah A. D\u2019Archangel, Tin Q. Nguyen, Kathryn L. Humphreys, Yanbin Niu, Sophia Vinci-Booher, Carissa J. Cascio, Kimberly R. Pechman, Niranjana Shashikumar, The HABS-HD Study Team, Alzheimer\u2019s Disease Neuroimaging Initiative, The BIOCARD Study Team, Zhiyuan Li, Simon N. Vandekar, Panpan Zhang, John C. Gore, Yihao Liu, Lianrui Zuo, Kurt G. Schilling, Daniel C. Moyer, Bennett A. Landman. \u201cAnalytic Bounds on GAMLSS Model Variability of Normative White Matter Brain Charts.\u201d SPIE Medical Imaging: Image Processing (2026)<\/p>\n

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Figure 4. (Left) Across the lifespan, the feature exhibiting the largest analytic and empirical COV is predominantly macrostructural for nearly all tracts. This is true of both the analytic and empirical methods, with this agreement suggesting that the analytic method is appropriate to assess model variability. Volume commonly emerges as the feature with the highest cumulative COV, followed by surface area. (Right) Conversely, the feature with the smallest analytic COV is typically microstructural, with most tracts having AD as the model with the smallest COV, followed by MD. However, for empirical COV, average length is frequently the feature with the smallest COV, followed by AD. Tracts are colored by the respective features with the largest and smallest average COV.<\/figcaption><\/figure>\n

 <\/p>\n

Purpose:<\/strong> Brain charts, or normative models of quantitative neuroimaging measures, can identify trajectories of brain development and abnormalities in groups and individuals by leveraging large populations. Recent work has extended these brain charts to model microstructural and macrostructural features of white matter. Assessments of variance for these brain charts are necessary to determine whether the models being used for these data are stable.<\/p>\n

Approach:<\/strong> We implement an analytic approach to characterize variability of the parameters in previously released brain charts created using the generalized additive models for location, scale, and shape (GAMLSS) framework. Additionally, we empirically validate the accuracy of each analytic model through a comparison to a bootstrapping approach from 0.2 to 90 years of age.<\/p>\n

Results:<\/strong> We find that across all models, the analytic coefficient of variation (COV) remains below 5% for ages greater than 0.25 years, with the maximum empirical observed COV reaching 7% at 0.2 years of age. Further, the empirical assessment shows high agreement with the analytic assessment, with COV estimates averaged across the lifespan for all models having a Pearson correlation coefficient of 0.776 and a mean difference of . Both methods exhibit volume and surface area as the features with the largest average COV for the majority of tracts. However, the analytic assessment yields axial diffusivity as the feature most frequently having the smallest COV, whereas the corresponding feature for the empirical assessment is average length.<\/p>\n

Conclusion:<\/strong> These results suggest that the analytic approach overestimates model stability for WM brain charts when the COV is low and that the validation method is suitable for assessing whether GAMLSS models are unstable.<\/p>\n","protected":false},"excerpt":{"rendered":"

Michael E. Kim, Gaurav Rudravaram, Adam Saunders, Chenyu Gao, Karthik Ramadass, Nancy R. Newlin, Praitayini Kanakaraj, Sam Bogdanov, Derek Archer, Timothy J. Hohman, Angela L. Jefferson, Victoria L. Morgan, Alexandra Roche, Dario J. Englot, Susan M. Resnick, Lori L. Beason Held, Murat Bilgel, Laurie E. Cutting, Laura A. Barquero, Micah A. D\u2019Archangel, Tin Q. Nguyen,…<\/p>\n","protected":false},"author":9556,"featured_media":4414,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[211,27,201,9,132,8,4,213],"tags":[],"class_list":["post-4413","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-ai-ready","category-big-data","category-characterization","category-diffusion-weighted-mri","category-harmonization","category-informatics-big-data","category-neuroimaging","category-normative-modeling"],"_links":{"self":[{"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/posts\/4413","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/users\/9556"}],"replies":[{"embeddable":true,"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/comments?post=4413"}],"version-history":[{"count":3,"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/posts\/4413\/revisions"}],"predecessor-version":[{"id":4417,"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/posts\/4413\/revisions\/4417"}],"wp:featuredmedia":[{"embeddable":true,"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/media\/4414"}],"wp:attachment":[{"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/media?parent=4413"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/categories?post=4413"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/my.vanderbilt.edu\/masi\/wp-json\/wp\/v2\/tags?post=4413"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}