Background Neuromelanin‐sensitive MRI (NM‐MRI) of the substantia nigra provides a noninvasive way to acquire an indirect measure of dopamine functioning. Despite the potential of NM‐MRI as a candidate biomarker for dopaminergic pathology, studies about its reproducibility are sparse. Purpose To assess the test–retest reproducibility of three commonly used NM‐MRI sequences and evaluate three analysis methods. Study Type Prospective study. Population A total of 11 healthy participants age between 20–27 years. Field Strength/Sequence 3.0T; NM‐MRI gradient recalled echo (GRE) with magnetization transfer (MT) pulse; NM‐MRI turbo spin echo (TSE) with MT pulse; NM‐MRI TSE without MT pulse. Assessment Participants were scanned twice with a 3‐week interval. Manual analysis, threshold analysis, and voxelwise analysis were performed for volume and contrast ratio (CR) measurements. Statistical Tests Intraclass correlation coefficients (ICCs) were calculated for test–retest and inter‐ and intrarater variability. Results The GRE sequence achieved the highest contrast and lowest variability (4.9–5.7%) and showed substantial to almost perfect test–retest ICC (0.72–0.90) for CR measurements. For volume measurements, the manual analysis showed a higher variability (10.7–17.9%) and scored lower test–retest ICCs (–0.13–0.73) than the other analysis methods. The threshold analysis showed higher test–retest ICC (0.77) than the manual analysis for the volume measurements. Data Conclusion NM‐MRI is a highly reproducible measure, especially when using the GRE sequence and CR measurements. Volume measurements appear to be more sensitive to inter/intrarater variability and variability in placement and orientation of the NM‐MRI slab. The threshold analysis appears to be the best alternative for volume analysis. Level of Evidence 2 Technical Efficacy Stage 1

BackgroundMultiple pathway models of ADHD suggest that multiple, separable biological pathways may lead to symptoms of the disorder. If this is the case, it should be possible to identify subgroups of children with ADHD based on distinct patterns of brain activity. Previous studies have used latent class analysis (LCA) to define subgroups at the behavioral and cognitive level and to then test whether they differ at the neurobiological level. In this proof of concept study, we took a reverse approach. We applied LCA to functional imaging data from two previously published studies to explore whether we could identify subgroups of children with ADHD symptoms at the neurobiological level with a meaningful relation to behavior or neuropsychology.MethodsFifty-six children with symptoms of ADHD (27 children with ADHD and 29 children with ASD and ADHD symptoms) and 31 typically developing children performed two neuropsychological tasks assessing reward sensitivity and temporal expectancy during functional magnetic resonance imaging. LCA was used to identify subgroups with similar patterns of brain activity separately for children with ADHD-symptoms and typically developing children. Behavioral and neuropsychological differences between subgroups were subsequently investigated.ResultsFor typically developing children, a one-subgroup model gave the most parsimonious fit, whereas for children with ADHD-symptoms a two-subgroup model best fits the data. The first ADHD subgroup (n = 49) showed attenuated brain activity compared to the second subgroup (n = 7) and to typically developing children (n = 31). Notably, the ADHD subgroup with attenuated brain activity showed less behavioral problems in everyday life.ConclusionsIn this proof of concept study, we showed that we could identify distinct subgroups of children with ADHD-symptoms based on their brain activity profiles. Generalizability was limited due to the small sample size, but ultimately such neurobiological profiles could improve insight in individual prognosis and treatment options.

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 <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 (Δr = −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