Cross‐Scanner Harmonization of Neuromelanin‐Sensitive MRI for Multisite Studies
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 gen...
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Veröffentlicht in: | Journal of magnetic resonance imaging 2021-10, Vol.54 (4), p.1189-1199 |
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Sprache: | eng |
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Zusammenfassung: | 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 (Δr) 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 Δr was significant. A P‐value |
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ISSN: | 1053-1807 1522-2586 |
DOI: | 10.1002/jmri.27679 |