Charting the velocity of brain growth and development
Presented During: Poster Session 3
Friday, June 27, 2025: 01:45 PM - 03:45 PM
Friday, June 27, 2025: 01:45 PM - 03:45 PM
Presented During: Poster Session 4
Saturday, June 28, 2025: 01:45 PM - 03:45 PM
Saturday, June 28, 2025: 01:45 PM - 03:45 PM
Presented During:
Saturday, June 28, 2025: 11:30 AM - 12:45 PM
Brisbane Convention & Exhibition Centre
Room:
P2 (Plaza Level)
Poster No:
1497
Submission Type:
Abstract Submission
Authors:
Johanna Bayer1, Augustijn de Boer1, Charlotte Fraza1, Andre Marquand1
Institutions:
1Donders Institute for Brain, Cognition and Behaviour, Nijmegen, Gelderland
First Author:
Co-Author(s):
Introduction:
Normative modelling (NM) is an emerging method for parsing heterogeneity of brain imaging phenotypes[1–4]. Until now, however, NM has focused on the estimation centiles derived from cross-sectional data ('distance centiles'). While distance centiles quantify individual's deviation from the median, they cannot quantify longitudinal change, and of movements across centiles across time. To estimate the significance of such centile crossings, velocity centiles are needed. These map the rate of change and require a fundamentally different approach. They can also only be estimated from longitudinal data.
Further, 'thrive lines'[5] can be derived from estimates of the correlation between two successive measurements.[6] These are defined as a +/- 1.96 SD rate of change. Translated to neuroimaging, a change outside of a projected thrive line ('failure to thrive') would signify a change more extreme than 97.5% of the population between those two measurements.
Here, we present three fundamental novelties: we update our large scale pre-trained normative models[7] using an advanced non-Gaussian model[8], we augment our previous cross sectional data set[7] with 23264 longitudinally processed scans from 10812 subjects and we estimate velocity centiles and thrive lines for 148 cortical[9] and 37 subcortical[10] regions of interest (ROIs). To our knowledge, this provides the first method that enables statistical quantification of change in brain imaging derived features at the level of the individual.
Further, 'thrive lines'[5] can be derived from estimates of the correlation between two successive measurements.[6] These are defined as a +/- 1.96 SD rate of change. Translated to neuroimaging, a change outside of a projected thrive line ('failure to thrive') would signify a change more extreme than 97.5% of the population between those two measurements.
Here, we present three fundamental novelties: we update our large scale pre-trained normative models[7] using an advanced non-Gaussian model[8], we augment our previous cross sectional data set[7] with 23264 longitudinally processed scans from 10812 subjects and we estimate velocity centiles and thrive lines for 148 cortical[9] and 37 subcortical[10] regions of interest (ROIs). To our knowledge, this provides the first method that enables statistical quantification of change in brain imaging derived features at the level of the individual.
Methods:
Our sample contains scans from 55995 healthy subjects (104 sites). 45174 subjects with one scan each, processed with Freesurfer[11]. The remaining 10821 subjects have longitudinal measurements for up to 9 follow up points, processed using longitudinal Freesurfer[11].
Training and set sets contain 25567 and 29834 subjects respectively. All longitudinally processed subjects are part of the test set, as well as 196 longitudinal subjects from the OASIS[12,13] cohort diagnosed with mild cognitive impairment and dementia.
NM was described elsewhere[8]. In brief, we estimated three models per ROI, one having a normal likelihood and two with a sin-arcsinh likelihood (SHASHo, aSHASHb_1)[8]. The SHASH models show good performance for non-Gaussian and heteroscedastic distributions. Covariates include age, sex and a random site intercept. Distance centiles were generated based on the training set, and z-scores were estimated for each subject in the test set.
We calculate z-scores for all longitudinal data points to estimate the correlation between consecutive measurements,[6] allowing us to overcome dependencies on age and the time period6. Consequently, velocity centiles originating in a starting point Z_i to the next point Z_(i+1) can be calculated iteratively by:
Z_(i+1) = r_i Z_i+ z_α (√(1-r_i^2 ))
Where r_i is the correlation between time points i and i+1 and Z_α is the velocity offset. In the case of thrive lines, Z_α is +/-1.96.
Further, significance of a change between time points is given by:
Z_t= (Z_(i+1)–rZ_i)/√(1-r^2 )
Training and set sets contain 25567 and 29834 subjects respectively. All longitudinally processed subjects are part of the test set, as well as 196 longitudinal subjects from the OASIS[12,13] cohort diagnosed with mild cognitive impairment and dementia.
NM was described elsewhere[8]. In brief, we estimated three models per ROI, one having a normal likelihood and two with a sin-arcsinh likelihood (SHASHo, aSHASHb_1)[8]. The SHASH models show good performance for non-Gaussian and heteroscedastic distributions. Covariates include age, sex and a random site intercept. Distance centiles were generated based on the training set, and z-scores were estimated for each subject in the test set.
We calculate z-scores for all longitudinal data points to estimate the correlation between consecutive measurements,[6] allowing us to overcome dependencies on age and the time period6. Consequently, velocity centiles originating in a starting point Z_i to the next point Z_(i+1) can be calculated iteratively by:
Z_(i+1) = r_i Z_i+ z_α (√(1-r_i^2 ))
Where r_i is the correlation between time points i and i+1 and Z_α is the velocity offset. In the case of thrive lines, Z_α is +/-1.96.
Further, significance of a change between time points is given by:
Z_t= (Z_(i+1)–rZ_i)/√(1-r^2 )
Results:
As expected[8], sin arcsinh models outperform Gaussian models (Fig1a-c).
Further, we uncover substantial longitudinal variability underneath cross-sectional distance centiles, evident in varying lengths of velocity centiles and thrive lines for a fixed period (eg 2 years [Fig1e] or 5 years [Fig1f]). Velocity varies between regions (Fig1e&f) and time period. Last, the thrive lines for the Left-Lateral-Ventricle against a subject diagnosed with Alzheimer's disease (AD) shows that "failure to thrive" indexes structural deterioration related to AD
Further, we uncover substantial longitudinal variability underneath cross-sectional distance centiles, evident in varying lengths of velocity centiles and thrive lines for a fixed period (eg 2 years [Fig1e] or 5 years [Fig1f]). Velocity varies between regions (Fig1e&f) and time period. Last, the thrive lines for the Left-Lateral-Ventricle against a subject diagnosed with Alzheimer's disease (AD) shows that "failure to thrive" indexes structural deterioration related to AD
Conclusions:
This is the first application of velocity centiles and thrive lines to neuroimaging phenotypes. These provide an estimate of the expected rate of change for a given period. The comparison of "failure to thrive" predicted trajectories to trajectories of individuals from clinical populations provides the opportunity to detect, validate and intervene on structural changes related to illness
Lifespan Development:
Normal Brain Development: Fetus to Adolescence 2
Modeling and Analysis Methods:
Bayesian Modeling
Methods Development 1
Other Methods
Novel Imaging Acquisition Methods:
Anatomical MRI
Keywords:
Data analysis
Modeling
NORMAL HUMAN
STRUCTURAL MRI
Other - normative modelling; centiles
1|2Indicates the priority used for review
Abstract Information
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Were any human subjects research approved by the relevant Institutional Review Board or ethics panel? NOTE: Any human subjects studies without IRB approval will be automatically rejected.
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Provide references using APA citation style.
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3. Marquand, A. F., Rezek, I., Buitelaar, J. & Beckmann, C. F. Understanding Heterogeneity in Clinical Cohorts Using Normative Models: Beyond Case-Control Studies. Biol. Psychiatry 80, 552–561 (2016).
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