Reliability and Reproducibility of Neuromelanin-Sensitive Imaging of the Substantia Nigra: A Comparison of Three Different Sequences.

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.

Associations between psychotropic drugs and rsEEG connectivity and network characteristics: a cross-sectional study in hospital-admitted psychiatric patients.

Introduction: Resting-state EEG (rsEEG) characteristics, such as functional connectivity and network topology, are studied as potential biomarkers in psychiatric research. However, the presence of psychopharmacological treatment in study participants poses a potential confounding factor in biomarker research. To address this concern, our study aims to explore the impact of both single and multi-class psychotropic treatments on aforementioned rsEEG characteristics in a psychiatric population. Methods: RsEEG was analyzed in a real-world cross-sectional sample of 900 hospital-admitted psychiatric patients. Patients were clustered into eight psychopharmacological groups: unmedicated, single-class treatment with antipsychotics (AP), antidepressants (AD) or benzodiazepines (BDZ), and multi-class combinations of these treatments. To assess the associations between psychotropic treatments and the macroscale rsEEG characteristics mentioned above, we employed a general linear model with post-hoc tests. Additionally, Spearman's rank correlation analyses were performed to explore potential dosage effects. Results: Compared to unmedicated patients, single-class use of AD was associated with lower functional connectivity in the delta band, while AP was associated with lower functional connectivity in both the delta and alpha bands. Single-class use of BDZ was associated with widespread rsEEG differences, including lower functional connectivity across frequency bands and a different network topology within the beta band relative to unmedicated patients. All of the multi-class groups showed associations with functional connectivity or topology measures, but effects were most pronounced for concomitant use of all three classes of psychotropics. Differences were not only observed in comparison with unmedicated patients, but were also evident in comparisons between single-class, multi-class, and single/multi-class groups. Importantly, multi-class associations with rsEEG characteristics were found even in the absence of single-class associations, suggesting potential cumulative or interaction effects of different classes of psychotropics. Dosage correlations were only found for antipsychotics. Conclusion: Our exploratory, cross-sectional study suggests small but significant associations between single and multi-class use of antidepressants, antipsychotics and benzodiazepines and macroscale rsEEG functional connectivity and network topology characteristics. These findings highlight the importance of considering the effects of specific psychotropics, as well as their interactions, when investigating rsEEG biomarkers in a medicated psychiatric population.