*Result*: Leveraging Longitudinal Data to Improve BrainChart Calibration for Small Study Sample Sizes.
Title:
Leveraging Longitudinal Data to Improve BrainChart Calibration for Small Study Sample Sizes.
Authors:
Adamson C; National Centre for Healthy Ageing, Frankston, Victoria, Australia.; Peninsula Clinical School, Central Clinical School, Monash University, Frankston, Victoria, Australia.; Developmental Imaging Group, Murdoch Children's Research Institute, Royal Children's Hospital, Parkville, Victoria, Australia., Moran C; National Centre for Healthy Ageing, Frankston, Victoria, Australia.; School of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.; Department of Home, Acute and Community, Alfred Health, Caulfield, Victoria, Australia., Brown A; Diabetes Research Group, Murdoch Children's Research Institute, Royal Children's Hospital, Parkville, Victoria, Australia., Collyer TA; National Centre for Healthy Ageing, Frankston, Victoria, Australia.; Peninsula Clinical School, Central Clinical School, Monash University, Frankston, Victoria, Australia., Sakowski SA; Department of Neurology, University of Michigan, Ann Arbor, Michigan, USA., Srikanth V; National Centre for Healthy Ageing, Frankston, Victoria, Australia.; Peninsula Clinical School, Central Clinical School, Monash University, Frankston, Victoria, Australia.; Department of Geriatric Medicine, Peninsula Health, Mornington, Victoria, Australia., Northam EA; Diabetes Research Group, Murdoch Children's Research Institute, Royal Children's Hospital, Parkville, Victoria, Australia.; Department of Paediatrics, University of Melbourne, Parkville, Victoria, Australia.; Department of Endocrinology and Diabetes, Royal Children's Hospital, Parkville, Victoria, Australia., Feldman EL; Department of Neurology, University of Michigan, Ann Arbor, Michigan, USA., Cameron FJ; Diabetes Research Group, Murdoch Children's Research Institute, Royal Children's Hospital, Parkville, Victoria, Australia.; Department of Paediatrics, University of Melbourne, Parkville, Victoria, Australia.; Department of Endocrinology and Diabetes, Royal Children's Hospital, Parkville, Victoria, Australia., Beare R; National Centre for Healthy Ageing, Frankston, Victoria, Australia.; Peninsula Clinical School, Central Clinical School, Monash University, Frankston, Victoria, Australia.; Developmental Imaging Group, Murdoch Children's Research Institute, Royal Children's Hospital, Parkville, Victoria, Australia.
Source:
Human brain mapping [Hum Brain Mapp] 2026 Feb 15; Vol. 47 (3), pp. e70476.
Publication Type:
Journal Article
Language:
English
Journal Info:
Publisher: Wiley Country of Publication: United States NLM ID: 9419065 Publication Model: Print Cited Medium: Internet ISSN: 1097-0193 (Electronic) Linking ISSN: 10659471 NLM ISO Abbreviation: Hum Brain Mapp Subsets: MEDLINE
Imprint Name(s):
Publication: New York : Wiley
Original Publication: New York : Wiley-Liss, c1993-
Original Publication: New York : Wiley-Liss, c1993-
MeSH Terms:
Brain*/diagnostic imaging , Brain*/anatomy & histology , Magnetic Resonance Imaging*/methods , Magnetic Resonance Imaging*/standards , Neuroimaging*/methods , Neuroimaging*/standards , Image Processing, Computer-Assisted*/methods , Image Processing, Computer-Assisted*/standards , Software*, Humans ; Longitudinal Studies ; Sample Size ; Calibration ; Reproducibility of Results ; Male ; Female ; Computer Simulation ; Adult
References:
Alexander, L. M., J. Escalera, L. Ai, et al. 2017. “An Open Resource for Transdiagnostic Research in Pediatric Mental Health and Learning Disorders.” Scientific Data 4: 170181. https://doi.org/10.1038/sdata.2017.181.
Bethlehem, R. A. I., J. Seidlitz, S. R. White, et al. 2022. “Brain Charts for the Human Lifespan.” Nature 604: 525–533. https://doi.org/10.1038/s41586‐022‐04554‐y.
Brown, A., S. A. Sakowski, C. Moran, et al. 2025. “Identifying Lifelong Factors That Impact Brain Health and Functional Outcomes in Adults With Childhood‐Onset Type 1 Diabetes: The Cognition and Longitudinal Assessments of Risk Factors Over 30 Years (CLARiFY)—Diabetes Complications Study Protocol.” BMJ Open 15: e105887. https://doi.org/10.1136/bmjopen‐2025‐105887.
Callisaya, M. L., R. Beare, C. Moran, T. Phan, W. Wang, and V. K. Srikanth. 2019. “Type 2 Diabetes Mellitus, Brain Atrophy and Cognitive Decline in Older People: A Longitudinal Study.” Diabetologia 62: 448–458. https://doi.org/10.1007/s00125‐018‐4778‐9.
Casey, B. J., T. Cannonier, M. I. Conley, et al. 2018. “The Adolescent Brain Cognitive Development (ABCD) Study: Imaging Acquisition Across 21 Sites.” Developmental Cognitive Neuroscience 32: 43–54. https://doi.org/10.1016/j.dcn.2018.03.001.
Chen, H., C. Kelly, F. Xavier Castellanos, Y. He, X.‐N. Zuo, and P. T. Reiss. 2015. “Quantile Rank Maps: A New Tool for Understanding Individual Brain Development.” NeuroImage 111: 454–463. https://doi.org/10.1016/j.neuroimage.2014.12.082.
Di Martino, A., C. G. Yan, Q. Li, et al. 2014. “The Autism Brain Imaging Data Exchange: Towards a Large‐Scale Evaluation of the Intrinsic Brain Architecture in Autism.” Molecular Psychiatry 19: 659–667. https://doi.org/10.1038/mp.2013.78.
Dong, H.‐M., F. X. Castellanos, N. Yang, et al. 2020. “Charting Brain Growth in Tandem With Brain Templates at School Age.” Science Bulletin 65: 1924–1934. https://doi.org/10.1016/j.scib.2020.07.027.
Fischl, B. 2012. “FreeSurfer.” NeuroImage 62: 774–781. https://doi.org/10.1016/j.neuroimage.2012.01.021.
Frangou, S., A. Modabbernia, S. C. R. Williams, et al. 2022. “Cortical Thickness Across the Lifespan: Data From 17,075 Healthy Individuals Aged 3–90 Years.” Human Brain Mapping 43: 431–451. https://doi.org/10.1002/hbm.25364.
Ge, R., Y. Yu, Y. X. Qi, et al. 2024. “Normative Modelling of Brain Morphometry Across the Lifespan With CentileBrain: Algorithm Benchmarking and Model Optimisation.” Lancet Digital Health 6: e211–e221. https://doi.org/10.1016/S2589‐7500(23)00250‐9.
Han, X., J. Jovicich, D. Salat, et al. 2006. “Reliability of MRI‐Derived Measurements of Human Cerebral Cortical Thickness: The Effects of Field Strength, Scanner Upgrade and Manufacturer.” NeuroImage 32: 180–194. https://doi.org/10.1016/j.neuroimage.2006.02.051.
Hawco, C., J. D. Viviano, S. Chavez, et al. 2018. “A Longitudinal Human Phantom Reliability Study of Multi‐Center T1‐Weighted, DTI, and Resting State fMRI Data.” Psychiatry Research: Neuroimaging 282: 134–142. https://doi.org/10.1016/j.pscychresns.2018.06.004.
Jack, C. R., M. A. Bernstein, N. C. Fox, et al. 2008. “The Alzheimer's Disease Neuroimaging Initiative (ADNI): MRI Methods.” Journal of Magnetic Resonance Imaging 27: 685–691. https://doi.org/10.1002/jmri.21049.
Northam, E. A., D. Rankins, A. Lin, et al. 2009. “Central Nervous System Function in Youth With Type 1 Diabetes 12 Years After Disease Onset.” Diabetes Care 32: 445–450. https://doi.org/10.2337/dc08‐1657.
Rehák Bučková, B., C. Fraza, R. Rehák, et al. 2025. “Using Normative Models Pre‐Trained on Cross‐Sectional Data to Evaluate Intra‐Individual Longitudinal Changes in Neuroimaging Data.” eLife 13: RP95823. https://doi.org/10.7554/eLife.95823.
Sudlow, C., J. Gallacher, N. Allen, et al. 2015. “UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age.” PLoS Medicine 12: e1001779. https://doi.org/10.1371/journal.pmed.1001779.
Zhu, A. H., T. M. Nir, S. Javid, et al. 2025. “Lifespan Reference Curves for Harmonizing Multi‐Site Regional Brain White Matter Metrics From Diffusion MRI.” Scientific Data 12: 748. https://doi.org/10.1038/s41597‐025‐05028‐2.
Bethlehem, R. A. I., J. Seidlitz, S. R. White, et al. 2022. “Brain Charts for the Human Lifespan.” Nature 604: 525–533. https://doi.org/10.1038/s41586‐022‐04554‐y.
Brown, A., S. A. Sakowski, C. Moran, et al. 2025. “Identifying Lifelong Factors That Impact Brain Health and Functional Outcomes in Adults With Childhood‐Onset Type 1 Diabetes: The Cognition and Longitudinal Assessments of Risk Factors Over 30 Years (CLARiFY)—Diabetes Complications Study Protocol.” BMJ Open 15: e105887. https://doi.org/10.1136/bmjopen‐2025‐105887.
Callisaya, M. L., R. Beare, C. Moran, T. Phan, W. Wang, and V. K. Srikanth. 2019. “Type 2 Diabetes Mellitus, Brain Atrophy and Cognitive Decline in Older People: A Longitudinal Study.” Diabetologia 62: 448–458. https://doi.org/10.1007/s00125‐018‐4778‐9.
Casey, B. J., T. Cannonier, M. I. Conley, et al. 2018. “The Adolescent Brain Cognitive Development (ABCD) Study: Imaging Acquisition Across 21 Sites.” Developmental Cognitive Neuroscience 32: 43–54. https://doi.org/10.1016/j.dcn.2018.03.001.
Chen, H., C. Kelly, F. Xavier Castellanos, Y. He, X.‐N. Zuo, and P. T. Reiss. 2015. “Quantile Rank Maps: A New Tool for Understanding Individual Brain Development.” NeuroImage 111: 454–463. https://doi.org/10.1016/j.neuroimage.2014.12.082.
Di Martino, A., C. G. Yan, Q. Li, et al. 2014. “The Autism Brain Imaging Data Exchange: Towards a Large‐Scale Evaluation of the Intrinsic Brain Architecture in Autism.” Molecular Psychiatry 19: 659–667. https://doi.org/10.1038/mp.2013.78.
Dong, H.‐M., F. X. Castellanos, N. Yang, et al. 2020. “Charting Brain Growth in Tandem With Brain Templates at School Age.” Science Bulletin 65: 1924–1934. https://doi.org/10.1016/j.scib.2020.07.027.
Fischl, B. 2012. “FreeSurfer.” NeuroImage 62: 774–781. https://doi.org/10.1016/j.neuroimage.2012.01.021.
Frangou, S., A. Modabbernia, S. C. R. Williams, et al. 2022. “Cortical Thickness Across the Lifespan: Data From 17,075 Healthy Individuals Aged 3–90 Years.” Human Brain Mapping 43: 431–451. https://doi.org/10.1002/hbm.25364.
Ge, R., Y. Yu, Y. X. Qi, et al. 2024. “Normative Modelling of Brain Morphometry Across the Lifespan With CentileBrain: Algorithm Benchmarking and Model Optimisation.” Lancet Digital Health 6: e211–e221. https://doi.org/10.1016/S2589‐7500(23)00250‐9.
Han, X., J. Jovicich, D. Salat, et al. 2006. “Reliability of MRI‐Derived Measurements of Human Cerebral Cortical Thickness: The Effects of Field Strength, Scanner Upgrade and Manufacturer.” NeuroImage 32: 180–194. https://doi.org/10.1016/j.neuroimage.2006.02.051.
Hawco, C., J. D. Viviano, S. Chavez, et al. 2018. “A Longitudinal Human Phantom Reliability Study of Multi‐Center T1‐Weighted, DTI, and Resting State fMRI Data.” Psychiatry Research: Neuroimaging 282: 134–142. https://doi.org/10.1016/j.pscychresns.2018.06.004.
Jack, C. R., M. A. Bernstein, N. C. Fox, et al. 2008. “The Alzheimer's Disease Neuroimaging Initiative (ADNI): MRI Methods.” Journal of Magnetic Resonance Imaging 27: 685–691. https://doi.org/10.1002/jmri.21049.
Northam, E. A., D. Rankins, A. Lin, et al. 2009. “Central Nervous System Function in Youth With Type 1 Diabetes 12 Years After Disease Onset.” Diabetes Care 32: 445–450. https://doi.org/10.2337/dc08‐1657.
Rehák Bučková, B., C. Fraza, R. Rehák, et al. 2025. “Using Normative Models Pre‐Trained on Cross‐Sectional Data to Evaluate Intra‐Individual Longitudinal Changes in Neuroimaging Data.” eLife 13: RP95823. https://doi.org/10.7554/eLife.95823.
Sudlow, C., J. Gallacher, N. Allen, et al. 2015. “UK Biobank: An Open Access Resource for Identifying the Causes of a Wide Range of Complex Diseases of Middle and Old Age.” PLoS Medicine 12: e1001779. https://doi.org/10.1371/journal.pmed.1001779.
Zhu, A. H., T. M. Nir, S. Javid, et al. 2025. “Lifespan Reference Curves for Harmonizing Multi‐Site Regional Brain White Matter Metrics From Diffusion MRI.” Scientific Data 12: 748. https://doi.org/10.1038/s41597‐025‐05028‐2.
Grant Information:
R01 DK129320 United States NH NIH HHS
Contributed Indexing:
Keywords: BrainChart; FreeSurfer; MR imaging; brain structure; calibration; longitudinal studies; neuroimaging
Entry Date(s):
Date Created: 20260218 Date Completed: 20260218 Latest Revision: 20260221
Update Code:
20260221
PubMed Central ID:
PMC12914350
DOI:
10.1002/hbm.70476
PMID:
41705288
Database:
MEDLINE
*Further Information*
*Longitudinal brain imaging studies offer valuable insight into trajectories of brain structures over time; however, changes in acquisition scanners and protocols can introduce biases in the resulting measures. Although BrainChart provides a framework for calibrating FreeSurfer-derived brain structure measurements between studies or time points, population samples with small subject numbers ≤ 100 are known to give unstable sample effect parameter estimates. Under the assumption that centiles of control subjects are stable across time points, we present a method to improve reliability of population sample effect parameter estimation for time points in longitudinal studies that have small numbers of control participants but include a nested sample with repeat scans of some control participants at multiple time points. We verify the accuracy of this method using both simulated and real datasets, which demonstrate comparable estimates close to the ground truth and improved confidence intervals at smaller sample sizes than the original method. Additionally, our approach confirms accuracy across both time points and scanners. The software, which is available on GitHub, offers a means to extend the value of brain imaging data acquired using variable scanners and/or protocols for longitudinal studies, thus maximizing the value of brain imaging data in established cohorts.
(© 2026 The Author(s). Human Brain Mapping published by Wiley Periodicals LLC.)*