Abstract
Neuromelanin-sensitive magnetic resonance imaging (NM-MRI) is a validated measure of neuromelanin concentration in the substantia nigra-ventral tegmental area (SN-VTA) complex and is a proxy measure of dopaminergic function with potential as a noninvasive biomarker. The development of generalizable biomarkers requires large-scale samples necessitating harmonization approaches to combine data collected across sites. To develop a method to harmonize NM-MRI across scanners and sites. Prospective. A total of 128 healthy subjects (18-73 years old; 45% female) from three sites and five MRI scanners. 3.0 T; NM-MRI two-dimensional gradient-recalled echo with magnetization-transfer pulse and three-dimensional T1-weighted images. NM-MRI contrast (contrast-to-noise ratio [CNR]) maps were calculated and CNR values within the SN-VTA (defined previously by manual tracing on a standardized NM-MRI template) were determined before harmonization (raw CNR) and after ComBat harmonization (harmonized CNR). Scanner differences were assessed by calculating the classification accuracy of a support vector machine (SVM). To assess the effect of harmonization on biological variability, support vector regression (SVR) was used to predict age and the difference in goodness-of-fit (Δr) was calculated as the correlation (between actual and predicted ages) for the harmonized CNR minus the correlation for the raw CNR. Permutation tests were used to determine if SVM classification accuracy was above chance level and if SVR Δr was significant. A P-value <0.05 was considered significant. In the raw CNR, SVM MRI scanner classification was above chance level (accuracy=86.5%). In the harmonized CNR, the accuracy of the SVM was at chance level (accuracy=29.5%; P=0.8542). There was no significant difference in age prediction using the raw or harmonized CNR (Δr=-0.06; P=0.7304). ComBat harmonization removes differences in SN-VTA CNR across scanners while preserving biologically meaningful variability associated with age. 2 TECHNICAL EFFICACY: 1.
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