Effects of upgrading acquisition-techniques and harmonization methods: A multi-modal MRI study with implications for longitudinal designs

Abstract

A downside of upgrading MRI acquisition sequences is the discontinuity of technological homogeneity of the MRI data. It hampers combining new and old datasets, especially in a longitudinal design. Characterizing upgrading effects on multiple brain parameters and examining the efficacy of harmonization methods are essential. This study investigated the upgrading effects on three structural parameters, including cortical thickness (CT), surface area (SA), cortical volume (CV), and resting-state functional connectivity (rs-FC) collected from 64 healthy volunteers. We used two evaluation metrics, Cohen’s d and classification accuracy, to quantify the effects. In classification analyses, we built classifiers for differentiating the protocols from brain parameters. We investigated the efficacy of three harmonization methods, including traveling subject (TS), TS-ComBat, and ComBat methods, and the sufficient number of participants for eliminating the effects on the evaluation metrics. Finally, we performed age prediction as an example to confirm that harmonization methods retained biological information. The results without harmonization methods revealed small to large mean Cohen’s d values on brain parameters (CT:0.85, SA:0.66, CV:0.68, and rs-FC:0.24) with better classification accuracy (>92% accuracy). With harmonization methods, Cohen’s d values approached zero. Classification performance reached the chance level with TS-based techniques when data from less than 26 participants were used for estimating the effects, while the Combat method required more participants. Furthermore, harmonization methods improved age prediction performance, except for the ComBat method. These results suggest that acquiring TS data is essential to preserve the continuity of MRI data.

Effects of MRI scanner manufacturers in classification tasks with deep learning models

“…We aim to evaluate the effectiveness of the standard ComBat and one of the modified ComBat-based harmonization approaches for structural imaging using four large multi-center longitudinal MRI datasets involving three major scanner manufacturers. Existing research shows that ComBat is highly successful in neuroimaging data harmonization, focusing on removing scanner effects from a set of imaging features such as cortical thickness, surface area, and subcortical volumes 17 – 20 . Pomponio et al 21 applied a modified ComBat method to 145 anatomical ROI volumes to eliminate location and scale effects for each ROI.…”