Cross‐Scanner Harmonization of Neuromelanin‐Sensitive MRI for Multisite Studies. Issue 4 (6th May 2021)

• Record Type: Journal Article
• Authors: Wengler, Kenneth; Cassidy, Clifford; van der Pluijm, Marieke; Weinstein, Jodi J.; Abi‐Dargham, Anissa; van de Giessen, Elsmarieke; Horga, Guillermo
• Is Part Of: Journal of magnetic resonance imaging. Volume 54:Issue 4(2021)
• Page Start: 1189
• Page End: 1199
• Publication Date: 2021-05-06
• Subjects: neuromelanin -- harmonization -- ComBat -- neuromelanin‐sensitive magnetic resonance imaging -- dopamine -- neurodegeneration; Magnetic resonance imaging -- Periodicals; 616
• DOI: 10.1002/jmri.27679
• Languages: English
• ISSNs: 1053-1807

Abstract

Abstract : Background: 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. Purpose: To develop a method to harmonize NM‐MRI across scanners and sites. Study Type: Prospective. Population: A total of 128 healthy subjects (18–73 years old; 45% female) from three sites and five MRI scanners. Field Strength/Sequence: 3.0 T; NM‐MRI two‐dimensional gradient‐recalled echo with magnetization‐transfer pulse and three‐dimensional T1‐weighted images. Assessment: 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 (Δ r ) was calculated as the correlation (between actual and predicted ages) for the harmonized CNR minus the correlation for the raw CNR. Statistical Tests: Permutation tests were used to determine if SVM classification accuracy was above chance level and if SVR Δ r was significant. A P ‐value <0.05 was considered significant. Results: 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 (Δ r = −0.06; P = 0.7304). Data Conclusion: ComBat harmonization removes differences in SN–VTA CNR across scanners while preserving biologically meaningful variability associated with age. Level of Evidence: 2 Technical Efficacy: 1