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Cross‐Scanner Harmonization of Neuromelanin‐Sensitive MRI for Multisite Studies
Author(s) -
Wengler Kenneth,
Cassidy Clifford,
Pluijm Marieke,
Weinstein Jodi J.,
AbiDargham Anissa,
Giessen Elsmarieke,
Horga Guillermo
Publication year - 2021
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.27679
Subject(s) - support vector machine , magnetic resonance imaging , nuclear medicine , scanner , neuromelanin , pattern recognition (psychology) , artificial intelligence , computer science , medicine , nuclear magnetic resonance , radiology , physics , substantia nigra , dopaminergic , dopamine
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