Functional Connectivity-Based Prediction of Autism on Site Harmonized ABIDE Dataset.

ABSTRACT:

Objective

The larger sample sizes available from multi-site publicly available neuroimaging data repositories makes machine-learning based diagnostic classification of mental disorders more feasible by alleviating the curse of dimensionality. However, since multi-site data are aggregated post-hoc, i.e. they were acquired from different scanners with different acquisition parameters, non-neural inter-site variability may mask inter-group differences that are at least in part neural in origin. Hence, the advantages gained by the larger sample size in the context of machine-learning based diagnostic classification may not be realized.

Methods

We address this issue using harmonization of multi-site neuroimaging data using the ComBat technique, which is based on an empirical Baye

SUBMITTER: Ingalhalikar M 

PROVIDER: S-EPMC8696194 | biostudies-literature | 2021 Dec

REPOSITORIES: biostudies-literature