Serotonin transporter binding in major depressive disorder: impact of serotonin system anatomy.

Serotonin transporter (5-HTT) binding deficits are reported in major depressive disorder (MDD). However, most studies have not considered serotonin system anatomy when parcellating brain regions of interest (ROIs). We now investigate 5-HTT binding in MDD in two novel ways: (1) use of a 5-HTT tract-based analysis examining binding along serotonergic axons; and (2) using the Copenhagen University Hospital Neurobiology Research Unit (NRU) 5-HT Atlas, based on brain-wide binding patterns of multiple serotonin receptor types. [11C]DASB 5-HTT PET scans were obtained in 60 unmedicated participants with MDD in a current depressive episode and 31 healthy volunteers (HVs). Binding potential (BPP) was quantified with empirical Bayesian estimation in graphical analysis (EBEGA). Within the [11C]DASB tract, the MDD group showed significantly lower BPP compared with HVs (p = 0.02). This BPP diagnosis difference also significantly varied by tract location (p = 0.02), with the strongest MDD binding deficit most proximal to brainstem raphe nuclei. NRU 5-HT Atlas ROIs showed a BPP diagnosis difference that varied by region (p < 0.001). BPP was lower in MDD in 3/10 regions (p-values < 0.05). Neither [11C]DASB tract or NRU 5-HT Atlas BPP correlated with depression severity, suicidal ideation, suicide attempt history, or antidepressant medication exposure. Future studies are needed to determine the causes of this deficit in 5-HTT binding being more pronounced in proximal axon segments and in only a subset of ROIs for the pathogenesis of MDD. Such regional specificity may have implications for targeting antidepressant treatment, and may extend to other serotonin-related disorders.

Permutation-Based Inference for Function-on-Scalar Regression With an Application in PET Brain Imaging.

The density of various proteins throughout the human brain can be studied through the use of positron emission tomography (PET) imaging. We report here on data from a study of serotonin transporter (5-HTT) binding. While PET imaging data analysis is most commonly performed on data that are aggregated into several discrete a priori regions of interest, in this study, primary interest is on measures of 5-HTT binding potential that are made at many locations along a continuous anatomically defined tract, one that was chosen to follow serotonergic axons. Our goal is to characterize the binding patterns along this tract and also to determine how such patterns differ between control subjects and depressed patients. Due to the nature of our data, we utilize function-on-scalar regression modeling to make optimal use of our data. Inference on both main effects (position along the tract; diagnostic group) and their interactions is made using permutation testing strategies that do not require distributional assumptions. Also, to investigate the question of homogeneity we implement a permutation testing strategy, which adapts a "block bootstrapping" approach from time series analysis to the functional data setting.

Nonlinear Mixed-Effects Modeling Approach for Simplified Reference Tissue Model.

OBJECTIVE: The conventional approach to the analysis of dynamic PET data can be described as a two-stage approach. In Stage 1, each individual's kinetic parameter estimates are obtained by modeling their PET data. Then in Stage 2, those parameter estimates are treated as though they are the observed data and compared across subjects and groups using standard statistical analyses. In this context, we explore the application of nonlinear mixed-effects (NLME) model under the assumptions of simplified reference tissue model. METHODS: In the NLME framework, all subject's PET data are modeled simultaneously and the estimation of kinetic parameters and statistical inference across subjects are performed jointly. RESULTS: In simulated [ 11C]WAY100635 data, this NLME approach shows improved power (6-27% increase) for detecting group differences and greater consistency of population (1.13-1.44 times greater) and individual-level parameter estimation compared to the two-stage approach applying simplified reference tissue model for pharmacokinetic modeling of PET data. We applied our NLME approach to clinical PET data and observed shrinkage of individual-level parameters that is inherent in this modeling structure. CONCLUSION: The proposed approach is more powerful and accurate than the two-stage approach under the assumptions of simplified reference tissue model in PET data. SIGNIFICANCE: The stability of the NLME approach not only improves the efficiency of collected data, but also comes with no additional financial cost and negligible computation cost.