Description: Brain-age prediction: a systematic comparison of machine learning workflows

This title appears in the Scientific Report : 2022

Brain-age prediction: a systematic comparison of machine learning workflows

Prediction of age using anatomical brain MRI, i.e., brain age, is proving valuable in exploring accelerated aging (brain age delta) as a proxy for aging-related diseases and crucial future health outcomes [1]. While various data representations and machine learning (ML) algorithms have been used for...

Personal Name(s):	More, Shammi (Corresponding author)
	Antonoupolous, Georgios / Hoffstaedter, Felix / Caspers, Julian / Eickhoff, Simon / Patil, Kaustubh
Contributing Institute:	Gehirn & Verhalten; INM-7
Imprint:	2022
Conference:	Organisation for Human Brain Mapping, Glasgow, Scotland (UK), 2022-06-19 - 2022-06-23
Document Type:	Conference Presentation
Research Program:	Neuroscientific Data Analytics and AI Multilevel Brain Organization and Variability
	Publikationsportal JuSER

Prediction of age using anatomical brain MRI, i.e., brain age, is proving valuable in exploring accelerated aging (brain age delta) as a proxy for aging-related diseases and crucial future health outcomes [1]. While various data representations and machine learning (ML) algorithms have been used for brain-age prediction [2,3], the impact of these choices on prediction accuracy remains uncharacterized. Moreover, several methodological challenges remain before a predictive model can be deployed in the real world; (1) robust within-site performance, (2) accurate cross-site prediction and, (3) consistent prediction for the same individual. To fill this gap, we systematically evaluated 70 workflows consisting of ten feature spaces derived from grey matter (GM) images and seven ML algorithms with diverse inductive biases to establish guidelines for designing brain-age prediction workflows.