Normative modelling of brain morphometry across the lifespan with CentileBrain: algorithm benchmarking and model optimisation.

Journal: The Lancet. Digital health

Volume: 6

Issue: 3

Year of Publication: 2024

Affiliated Institutions:  Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada. Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA. Netherlands Twin Register, Department of Biological Psychology, Vrije Universiteit, Amsterdam, Netherlands. Centre for Healthy Brain Ageing, University of New South Wales, Sydney, NSW, Australia. Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Vrije Universiteit, Amsterdam, Netherlands. Center for Brain Science, Harvard University, Cambridge, MA, USA. Centre for Neuropsychiatric Genetics and Genomics, Cardiff University, Cardiff, Wales, UK. Groupe d'Imagerie Neurofonctionnelle-Institut des Maladies Neurodégénératives, Université de Bordeaux, CNRS UMR , Bordeaux, France. Erasmus School of Social and Behavioural Sciences, Erasmus University Rotterdam, Rotterdam, Netherlands. Division of Mind and Brain Research, Department of Psychiatry and Psychotherapy, Charité-Universitätsmedizin Berlin, Berlin, Germany. Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, Netherlands. Departments of Human Genetics, Psychiatry and Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, Netherlands. Department of Psychiatry and Behavioral Sciences, Boston Children's Hospital, Boston, MA, USA. Department of Psychiatry and Psychotherapy, University of Münster, Münster, Germany. Section for Experimental Psychopathology and Neuroimaging, Department of General Psychiatry, Heidelberg University, Heidelberg, Germany. Department of Experimental Psychology, Helmholtz Institute, Utrecht University, Utrecht, Netherlands. Centre for Population Neuroscience and Stratified Medicine, Institute for Science and Technology of Brain-inspired Intelligence, Fudan University, Shanghai, China; PONS Centre, Department of Psychiatry and Clinical Neuroscience, CCM, Charite Universitätsmedizin Berlin, Berlin, Germany. Department of Psychology, University of Oslo, Oslo, Norway. Brain and Development Research Center, Leiden University, Leiden, Netherlands. Mark and Mary Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Marina del Rey, CA, USA. Djavad Mowafaghian Centre for Brain Health, University of British Columbia, Vancouver, BC, Canada; Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA. Electronic address: sophia.frangou@gmail.com.

Abstract summary 

The value of normative models in research and clinical practice relies on their robustness and a systematic comparison of different modelling algorithms and parameters; however, this has not been done to date. We aimed to identify the optimal approach for normative modelling of brain morphometric data through systematic empirical benchmarking, by quantifying the accuracy of different algorithms and identifying parameters that optimised model performance. We developed this framework with regional morphometric data from 37 407 healthy individuals (53% female and 47% male; aged 3-90 years) from 87 datasets from Europe, Australia, the USA, South Africa, and east Asia following a comparative evaluation of eight algorithms and multiple covariate combinations pertaining to image acquisition and quality, parcellation software versions, global neuroimaging measures, and longitudinal stability. The multivariate fractional polynomial regression (MFPR) emerged as the preferred algorithm, optimised with non-linear polynomials for age and linear effects of global measures as covariates. The MFPR models showed excellent accuracy across the lifespan and within distinct age-bins and longitudinal stability over a 2-year period. The performance of all MFPR models plateaued at sample sizes exceeding 3000 study participants. This model can inform about the biological and behavioural implications of deviations from typical age-related neuroanatomical changes and support future study designs. The model and scripts described here are freely available through CentileBrain.

Authors & Co-authors:  Ge Ruiyang R Yu Yuetong Y Qi Yi Xuan YX Fan Yu-Nan YN Chen Shiyu S Gao Chuntong C Haas Shalaila S SS New Faye F Boomsma Dorret I DI Brodaty Henry H Brouwer Rachel M RM Buckner Randy R Caseras Xavier X Crivello Fabrice F Crone Eveline A EA Erk Susanne S Fisher Simon E SE Franke Barbara B Glahn David C DC Dannlowski Udo U Grotegerd Dominik D Gruber Oliver O Hulshoff Pol Hilleke E HE Schumann Gunter G Tamnes Christian K CK Walter Henrik H Wierenga Lara M LM Jahanshad Neda N Thompson Paul M PM Frangou Sophia S

Study Outcome 

Source Link: Visit source

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Citations :  Bethlehem RAI, Seidlitz J, White SR, et al. Brain charts for the human lifespan. Nature 2022; 604: 525–33.
Authors :  31
Identifiers
Doi : 10.1016/S2589-7500(23)00250-9
SSN : 2589-7500
Study Population
Female
Mesh Terms
Humans
Other Terms
Study Design
Longitudinal Study,Cross Sectional Study
Study Approach
Systemic Review
Country of Study
South Africa
Publication Country
England