Attenuation Correction Approaches for Serotonin Transporter Quantification With PET/MRI.

BACKGROUND: Several MR-based attenuation correction (AC) approaches were developed to conquer the challenging AC in hybrid PET/MR imaging. These AC methods are commonly evaluated on standardized uptake values or tissue concentration. However, in neurotransmitter system studies absolute quantification is more favorable due to its accuracy. Therefore, our aim was to investigate the accuracy of segmentation- and atlas-based MR AC approaches on serotonin transporter (SERT) distribution volumes and occupancy after a drug challenge. METHODS: 18 healthy subjects (7 male) underwent two [11C]DASB PET/MRI measurements in a double-blinded, placebo controlled, cross-over design. After 70 min the selective serotonin reuptake inhibitor (SSRI) citalopram or a placebo was infused. The parameters total and specific volume of distribution (VT, VS = BPP) and occupancy were quantified. All subjects underwent a low-dose CT scan as reference AC method. Besides the standard AC approaches DIXON and UTE, a T1-weighted structural image was recorded to estimate a pseudo-CT based on an MR/CT database (pseudoCT). Another evaluated AC approach superimposed a bone model on AC DIXON. Lastly, an approach optimizing the segmentation of UTE images was analyzed (RESOLUTE). PET emission data were reconstructed with all 6 AC methods. The accuracy of the AC approaches was evaluated on a region of interest-basis for the parameters VT, BPP, and occupancy with respect to the results of AC CT. RESULTS: Variations for VT and BPP were found with all AC methods with bias ranging from -15 to 17%. The smallest relative errors for all regions were found with AC pseudoCT (<|5%|). Although the bias between BPP SSRI and BPP placebo varied markedly with AC DIXON (<|12%|) and AC UTE (<|9%|), a high correlation to AC CT was obtained (r 2∼1). The relative difference of the occupancy for all tested AC methods was small for SERT high binding regions (<|4%|). CONCLUSION: The high correlation might offer a rescaling from the biased parameters VT and BPP to the true values. Overall, the pseudoCT approach yielded smallest errors and the best agreement with AC CT. For SERT occupancy, all AC methods showed little bias in high binding regions, indicating that errors may cancel out in longitudinal assessments.

Modeling the acute pharmacological response to selective serotonin reuptake inhibitors in human brain using simultaneous PET/MR imaging.

Pharmacological imaging of the effects of selective serotonin reuptake inhibitors (SSRI) may aid the clarification of their mechanism of action and influence treatment of highly prevalent neuropsychiatric conditions if the detected effects could be related to patient outcomes. In a randomized double-blind design, 38 healthy participants received a constant infusion of 8 mg citalopram or saline during either their first or second of two PET/MR scans. Resting-state functional MRI (fMRI) was acquired simultaneously with PET data on the binding of serotonin transporters (5-HTT) using [11C]DASB. Three different approaches for modeling of pharmacological fMRI response were tested separately. These relied on the use of regressors corresponding to (1) the drug infusion paradigm, (2) time courses of citalopram plasma concentrations and (3) changes in 5-HTT binding measured in each individual, respectively. Furthermore, the replication of results of a widely used model-free analysis method was attempted which assesses the deviation of signal in discrete time bins of fMRI data acquired after start of drug infusion. Following drug challenge, average 5-HTT occupancy was 69±7% and peak citalopram plasma levels were 111.8 ±â€¯21.1 ng/ml. None of the applied methods could detect significant differences in the pharmacological response between SSRI and placebo scans. The failed replication of SSRI effects reported in the literature despite a threefold larger sample size highlights the importance of appropriate correction for family-wise error in order to avoid spurious results in pharmacological imaging. This calls for the development of analysis methods which take regional specialization and the dynamics of brain activity into account.

Spatial analysis and high resolution mapping of the human whole-brain transcriptome for integrative analysis in neuroimaging.

The quantification of big pools of diverse molecules provides important insights on brain function, but is often restricted to a limited number of observations, which impairs integration with other modalities. To resolve this issue, a method allowing for the prediction of mRNA expression in the entire brain based on microarray data provided in the Allen Human Brain Atlas was developed. Microarray data of 3702 samples from 6 brain donors was registered to MNI and cortical surface space using FreeSurfer. For each of 18,686 genes, spatial dependence of transcription was assessed using variogram modelling. Variogram models were employed in Gaussian process regression to calculate best linear unbiased predictions for gene expression at all locations represented in well-established imaging atlases for cortex, subcortical structures and cerebellum. For validation, predicted whole-brain transcription of the HTR1A gene was correlated with [carbonyl-11C]WAY-100635 positron emission tomography data collected from 30 healthy subjects. Prediction results showed minimal bias ranging within ±0.016 (cortical surface), ±0.12 (subcortical regions) and ±0.14 (cerebellum) in units of log2 expression intensity for all genes. Across genes, the correlation of predicted and observed mRNA expression in leave-one-out cross-validation correlated with the strength of spatial dependence (cortical surface: r = 0.91, subcortical regions: r = 0.85, cerebellum: r = 0.84). 816 out of 18,686 genes exhibited a high spatial dependence accounting for more than 50% of variance in the difference of gene expression on the cortical surface. In subcortical regions and cerebellum, different sets of genes were implicated by high spatially structured variability. For the serotonin 1A receptor, correlation between PET binding potentials and predicted comprehensive mRNA expression was markedly higher (Spearman ρ = 0.72 for cortical surface, ρ = 0.84 for subcortical regions) than correlation of PET and discrete samples only (ρ = 0.55 and ρ = 0.63, respectively). Prediction of mRNA expression in the entire human brain allows for intuitive visualization of gene transcription and seamless integration in multimodal analysis without bias arising from non-uniform distribution of available samples. Extension of this methodology promises to facilitate translation of omics research and enable investigation of human brain function at a systems level.