Abstract
INTRODUCTION
Tau positron emission tomography (PET) is a reliable neuroimaging technique for assessing regional load of tau pathology in the brain, but its routine clinical use is limited by cost and accessibility barriers.
METHODS
We thoroughly investigated the ability of various machine learning models to predict clinically useful tau-PET composites (load and laterality index) from low-cost and non-invasive features, for example, clinical variables, plasma biomarkers, and structural magnetic resonance imaging (MRI).
RESULTS
Models including plasma biomarkers yielded the most accurate predictions of tau-PET burden (best model: R-squared = 0.66–0.69), with especially high contribution from plasma phosphorylated tau-217 (p-tau217). MRI variables were the best predictors of asymmetric tau load between the two hemispheres (best model: R-squared = 0.28–0.42). The models showed high generalizability to external test cohorts with data collected at multiple sites. Through a proof-of-concept two-step classification workflow, we also demonstrated possible model translations to a clinical setting.
DISCUSSION
This study highlights the promising and limiting aspects of using machine learning to predict tau-PET from scalable cost-effective variables, with findings relevant for clinical settings and future research.
Highlights
Accessible variables showed potential in estimating tau tangle load and distribution.
Plasma phosphorylated tau-217 (p-tau217) and magnetic resonance imaging (MRI) were the best predictors of different tau-PET (positron emission tomography) composites.
Machine learning models demonstrated high generalizability across AD cohorts.
If you do not see content above, kindly GO TO SOURCE.
Not all publishers encode content in a way that enables republishing at Neuro.vip.
This post is Copyright: Linda Karlsson,
Jacob Vogel,
Ida Arvidsson,
Kalle Åström,
Olof Strandberg,
Jakob Seidlitz,
Richard A. I. Bethlehem,
Erik Stomrud,
Rik Ossenkoppele,
Nicholas J. Ashton,
Henrik Zetterberg,
Kaj Blennow,
Sebastian Palmqvist,
Ruben Smith,
Shorena Janelidze,
Renaud La Joie,
Gil D. Rabinovici,
Alexa Pichet Binette,
Niklas Mattsson‐Carlgren,
Oskar Hansson | February 22, 2025