Performance Scaling for Structural MRI Surface Parcellations: A Machine Learning Analysis in the ABCD Study

BioRxiv : the Preprint Server for Biology
Sage HahnN. Allgaier

Abstract

The use of pre-defined parcellations on surface-based representations of the brain as a method for data reduction is common across neuroimaging studies. In particular, prediction-based studies typically employ parcellation-driven summaries of brain measures as input to predictive algorithms, but the choice of parcellation and its influence on performance is often ignored. Here we employed pre-processed structural magnetic resonance imaging data (sMRI) from the ABCD Study to examine the relationship between 220 parcellations and out-of-sample predictive performance across 45 phenotypic measures in a large sample of 9-10-year-old children (N=9,432). Choice of Machine Learning (ML) pipeline and use of alternative multiple parcellation-based strategies were also assessed. Relative parcellation performance was dependent on the spatial resolution of the parcellation, with larger numbers of parcels (up to ~4000) outperforming coarser parcellations, according to a power-law scaling of between 1/4 and 1/3. Performance was further influenced by the type of parcellation, ML pipeline, and general strategy, with existing literature-based parcellations, a support vector based pipeline, and ensembling across multiple parcellations, respective...Continue Reading

Software Mentioned

Brain Predictability toolbox
Light Gradient Boosting Machine ( LGBM )
Nevergrad
Freesurfer
statsmodel
FS LR
LGBM
Human Connectome Project Workbench
Elastic
learn

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