CB1 receptors (CB1R) mediate the psychoactive and therapeutic effects of cannabinoids including ∆9-tetrahydrocannabinol (THC), the main psychoactive constituent in marijuana. However, therapeutic use is limited by side effects and tolerance and dependence with chronic administration. Tolerance to cannabinoid-mediated effects is associated with CB1R adaptations, including desensitization (receptor-G-protein uncoupling) and downregulation (receptor degradation). The objectives of this thesis are to investigate the regional-specificity in CB1R function and regulation. Previous studies have investigated CB1Rs in a subset of regions involved in cannabinoid effects, but an inclusive regional comparison of the relative efficacies of different classes of cannabinoids to activate G-proteins has not been conducted. A novel unbiased whole-brain analysis was developed based on Statistical Parametric Mapping (SPM) for 3D-reconstructed mouse brain images derived from agonist-stimulated [35S]GTPgS autoradiography, which has not been described before. SPM demonstrated regional differences in the relative efficacies of cannabinoid agonists methanandamide (M-AEA), CP55,940 (CP), and WIN55,212-2 (WIN) in mouse brains. To assess potential contribution of novel sites, CB1R knockout (KO) mice were used. SPM analysis revealed that WIN, but not CP or M-AEA, stimulated [35S]GTPgS binding in regions that partially overlapped with the expression of CB1Rs. We then examined the role of the regulatory protein Beta-arrestin-2 (βarr2) in CB1R adaptations to chronic THC treatment. Deletion of βarr2 reduced CB1R desensitization/downregulation in the cerebellum, caudal periaqueductal gray (PAG), and spinal cord. However in hippocampus, amygdala and rostral PAG, similar desensitization was present in both genotypes. Interestingly, enhanced desensitization was found in the hypothalamus and cortex in βarr2 KO animals. Intra-regional differences in the magnitude of desensitization were noted in the caudal hippocampus, where βarr2 KO animals exhibited greater desensitization compared to WT. Regional differences in βarr2-mediated CB1R adaptation were associated with differential effects on tolerance, where THC-mediated antinociception, but not catalepsy or hypothermia, was attenuated in βarr2 KO mice. Overall, studies using SPM revealed intra- and inter-regional specificity in the function and regulation of CB1Rs and underscores an advantage of using a whole-brain unbiased approach. Understanding the regulation of CB1R signaling within different anatomical contexts represents an important fundamental prerequisite in the therapeutic exploitation of the cannabinoid system.

In typical statistical parametric mapping (SPM) of fMRI data, the functional data are pre-smoothed using a Gaussian kernel to reduce noise at the cost of losing spatial specificity. Wavelet approaches have been incorporated in such analysis by enabling an efficient representation of the underlying brain activity through spatial transformation of the original, un-smoothed data; a successful framework is the wavelet-based statistical parametric mapping (WSPM) which enables integrated wavelet processing and spatial statistical testing. However, in using the conventional wavelets, the functional data are considered to lie on a regular Euclidean space, which is far from reality, since the underlying signal lies within the complex, non rectangular domain of the cerebral cortex. Thus, using wavelets that function on more complex domains such as a graph holds promise. The aim of the current project has been to integrate a recently developed spectral graph wavelet transform as an advanced transformation for fMRI brain data into the WSPM framework. We introduce the design of suitable weighted and un-weighted graphs which are defined based on the convoluted structure of the cerebral cortex. An optimal design of spatially localized spectral graph wavelet frames suitable for the designed large scale graphs is introduced. We have evaluated the proposed graph approach for fMRI analysis on both simulated as well as real data. The results show a superior performance in detecting fine structured, spatially localized activation maps compared to the use of conventional wavelets, as well as normal SPM. The approach is implemented in an SPM compatible manner, and is included as an extension to the WSPM toolbox for SPM.

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京都大学 / 0048 / 新制・課程博士 / 博士(医学) / 甲第21669号 / 医博第4475号 / 新制||医||1035(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 村井 俊哉, 教授 古川 壽亮, 教授 宮本 享 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DFAM

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