Joint explorative analysis of neuroreceptor subsystems in the human brain: application to receptor?transporter correlation using PET data

Dopamine D1-Receptor Organization Contributes to Functional Brain Architecture

Recent work has recognized a gradient-like organization in cortical function, spanning from primary sensory to transmodal cortices. It has been suggested that this axis is aligned with regional differences in neurotransmitter expression. Given the abundance of dopamine D1-receptors (D1DR), and its importance for modulation and neural gain, we tested the hypothesis that D1DR organization is aligned with functional architecture, and that inter-regional relationships in D1DR co-expression modulate functional cross talk. Using the world's largest dopamine D1DR-PET and MRI database (N = 180%, 50% female), we demonstrate that D1DR organization follows a unimodal-transmodal hierarchy, expressing a high spatial correspondence to the principal gradient of functional connectivity. We also demonstrate that individual differences in D1DR density between unimodal and transmodal regions are associated with functional differentiation of the apices in the cortical hierarchy. Finally, we show that spatial co-expression of D1DR primarily modulates couplings within, but not between, functional networks. Together, our results show that D1DR co-expression provides a biomolecular layer to the functional organization of the brain.

Covariance statistics and network analysis of brain PET imaging studies

The analysis of structural and functional neuroimaging data using graph theory has increasingly become a popular approach for visualising and understanding anatomical and functional relationships between different cerebral areas. In this work we applied a network-based approach for brain PET studies using population-based covariance matrices, with the aim to explore topological tracer kinetic differences in cross-sectional investigations. Simulations, test-retest studies and applications to cross-sectional datasets from three different tracers ([18F]FDG, [18F]FDOPA and [11C]SB217045) and more than 400 PET scans were investigated to assess the applicability of the methodology in healthy controls and patients. A validation of statistics, including the assessment of false positive differences in parametric versus permutation testing, was also performed. Results showed good reproducibility and general applicability of the method within the range of experimental settings typical of PET neuroimaging studies, with permutation being the method of choice for the statistical analysis. The use of graph theory for the quantification of [18F]FDG brain PET covariance, including the definition of an entropy metric, proved to be particularly relevant for Alzheimer's disease, showing an association with the progression of the pathology. This study shows that covariance statistics can be applied to PET neuroimaging data to investigate the topological characteristics of the tracer kinetics and its related targets, although sensitivity to experimental variables, group inhomogeneities and image resolution need to be considered when the method is applied to cross-sectional studies.

Imaging the availability of serotonin transporter in rat brain with 123I-ADAM and small-animal SPECT

BACKGROUND

Imaging serotonin transporters during antidepressant treatment in small animals is a useful tool for preclinical study during drug development. In this work, we aimed to demonstrate the feasibility of using 123I-ADAM and small-animal SPECT to monitor serotonin transporter availabilities in rat brains prior to and after administration of a selective serotonin re-uptake inhibitor.

METHODS

Male Sprague-Dawley rats with and without administration of citalopram (4 mg x kg body weight) were examined in this study. During the process rat brains were scanned using a double-headed microSPECT system equipped with pinhole collimators. SPECT tomographic images and X-ray computed tomography (CT) were acquired after introducing 123I-ADAM via the tail vein. The 123I-ADAM specific binding was assessed by SPECT/CT fused image to draw regions of interest in the midbrain and cerebellum. Ex-vivo autoradiography was carried out as a parallel investigation to validate the SPECT technique.

RESULTS

SPECT images displayed specific binding ratio in midbrain to be 0.91+/-0.30 averaged from three rats. Drug occupancies (95.47+/-1.56)% were shown after administration of citalopram in a dosage of 4 mg x kg.

CONCLUSION

This study demonstrated that the serotonin transporter availability during antidepressant treatment in small animals can be assessed semi-quantitatively by using 123I-ADAM and SPECT.

A comparison of recent parametric neuroreceptor mapping approaches based on measurements with the high affinity PET radioligands [11C]FLB 457 and [11C]WAY 100635

In positron emission tomography (PET) studies, the detailed mapping of neuroreceptor binding is a trade-off between parametric accuracy and spatial precision. Logan's graphical approach is a straightforward way to quickly obtain binding potential values at the voxel level but it has been shown to have a noise-dependent negative bias. More recently suggested approaches claim to improve parametric accuracy with retained spatial resolution. In the present study, we used PET measurements on regional D2 dopamine and 5-HT1A serotonin receptor binding in man to compare binding potential (BP) estimates of six different parametric imaging approaches to the traditional Logan ROI-based approach which was used as a "gold standard". The parametric imaging approaches included Logan's reference tissue graphical analysis (PILogan), its version recently modified by Varga and Szabo (PIVarga), two versions of the wavelet-based approach, Gunn's basis function method (BFM) and Gunn et al.'s recent compartmental theory-based approach employing basis pursuit strategy for kinetic modeling (called DEPICT). Applicability for practical purposes in basic and clinical research was also considered. The results indicate that the PILogan and PIVarga approaches fail to recover the correct values, the wavelet-based approaches overcome the noise susceptibility of the Logan fit with generally good recovery of BP values, and BFM and DEPICT seem to produce values with a bias dependent on receptor density. Further investigations on this bias and other phenomena revealed fundamental issues regarding the use of BFM and DEPICT on noisy voxel-wise data. In conclusion, the wavelet-based approaches seem to provide the most valid and reliable estimates across regions with a wide range of receptor densities. Furthermore, the results support the use of receptor parametric imaging in applied studies in basic or clinical research.