Browsing by Subject "Positron Emission Tomography"

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    Assessment of the dopamine system in addiction using positron emission tomography
    (2014) Albrecht, Daniel Strakis; Hutchins, Gary D.; Saykin, Andrew J.; Kareken, David A.; Yoder, Karmen K.; Grahame, Nicholas J.
    Drug addiction is a behavioral disorder characterized by impulsive behavior and continued intake of drug in the face of adverse consequences. Millions of people suffer the financial and social consequences of addiction, and yet many of the current therapies for addiction treatment have limited efficacy. Therefore, there is a critical need to characterize the neurobiological substrates of addiction in order to formulate better treatment options. In the first chapter, the striatal dopamine system is interrogated with [11C]raclopride PET to assess differences between chronic cannabis users and healthy controls. The results of this chapter indicate that chronic cannabis use is not associated with a reduction in striatal D2/D3 receptor availability, unlike many other drugs of abuse. Additionally, recent cannabis consumption in chronic users was negatively correlated with D2/D3 receptor availability. Chapter 2 describes a retrospective analysis in which striatal D2/D3 receptor availability is compared between three groups of alcohol-drinking and tobacco-smoking subjects: nontreatment-seeking alcoholic smokers, social-drinking smokers, and social-drinking non-smokers. Results showed that smokers had reduced D2/D3 receptor availability throughout the striatum, independent of drinking status. The results of the first two chapters suggest that some combustion product of marijuana and tobacco smoke may have an effect on striatal dopamine concentration. Furthermore, they serve to highlight the effectiveness of using baseline PET imaging to characterize dopamine dysfunction in addictions. The final chapter explores the use of [18F]fallypride PET in a proof-of-concept study to determine whether changes in cortical dopamine can be detected during a response inhibition task. We were able to detect several cortical regions of significant dopamine changes in response to the task, and the amount of change in three regions was significantly associated with task performance. Overall, the results of Chapter 3 validate the use of [18F]fallypride PET to detect cortical dopamine changes during a impulse control task. In summary, the results reported in the current document demonstrate the effectiveness of PET imaging as a tool for probing resting and activated dopamine systems in addiction. Future studies will expand on these results, and incorporate additional methods to further elucidate the neurobiology of addiction.
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    Neurodegenerative Patterns of Cognitive Clusters of Early-Onset Alzheimer's Disease Subjects: Evidence for Disease Heterogeneity
    (Karger, 2019) Phillips, Meredith L.; Stage, Eddie C., Jr.; Lane, Kathleen A.; Gao, Sujuan; Risacher, Shannon L.; Goukasian, Naira; Saykin, Andrew J.; Carrillo, Maria C.; Dickerson, Bradford C.; Rabinovici, Gil D.; Apostolova, Liana G.; Epidemiology, School of Public Health
    Background/aims: Alzheimer's disease (AD) with onset before 65 (early-onset AD [EOAD]) occurs in approximately 6% of cases and can affect nonmemory domains. Here, we analyze patterns of impairment in amnestic EOAD individuals using data-driven statistical analyses. Methods: Cognitive data of 146 EOAD subjects were Z-normalized to 395 cognitively normal (CN) individuals. Domain-averaged Z-scores were adjusted for age, sex, and education followed by Wald cluster analysis of residuals. Magnetic resonance imaging and positron emission tomography comparisons of EOAD clusters to age-matched CN were done using Statistic Parametric Mapping 8. Cluster-level-family-wise error (p < 0.05) correction was applied. Mixed-effect models were used to compute longitudinal change across clusters. Results: Scree plot using the pseudo-T-squared suggested a 4-cluster solution. Cluster 1 (memory-predominant impairment) showed atrophy/hypometabolism in medial/lateral temporal, lateral parietal, and posterior cingulate regions. Cluster 2 (memory/visuospatial-predominant) showed atrophy/hypometabolism of medial temporal, temporoparietal, and frontal cortices. Cluster 3 (memory, language, and executive function) and Cluster 4 (globally impaired) manifested atrophy and hypometabolism throughout the brain. Longitudinally between-cluster differences in the visuospatial and language/executive domains were significant, suggesting phenotypic variation. Conclusion: We observed significant heterogeneity in cognitive presentation among amnestic EOAD subjects and patterns of atrophy/hypometabolism in each cluster in agreement with the observed cognitive phenotype.
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    Tissue-specific network-based genome wide study of amygdala imaging phenotypes to identify functional interaction modules
    (Oxford University Press, 2017-10-15) Yao, Xiaohui; Yan, Jingwen; Liu, Kefei; Kim, Sungeun; Nho, Kwangsik; Risacher, Shannon L.; Greene, Casey S.; Moore, Jason H.; Saykin, Andrew J.; Shen, Li; Alzheimer’s Disease Neuroimaging Initiative; BioHealth Informatics, School of Informatics and Computing
    Motivation: Network-based genome-wide association studies (GWAS) aim to identify functional modules from biological networks that are enriched by top GWAS findings. Although gene functions are relevant to tissue context, most existing methods analyze tissue-free networks without reflecting phenotypic specificity. Results: We propose a novel module identification framework for imaging genetic studies using the tissue-specific functional interaction network. Our method includes three steps: (i) re-prioritize imaging GWAS findings by applying machine learning methods to incorporate network topological information and enhance the connectivity among top genes; (ii) detect densely connected modules based on interactions among top re-prioritized genes; and (iii) identify phenotype-relevant modules enriched by top GWAS findings. We demonstrate our method on the GWAS of [18F]FDG-PET measures in the amygdala region using the imaging genetic data from the Alzheimer's Disease Neuroimaging Initiative, and map the GWAS results onto the amygdala-specific functional interaction network. The proposed network-based GWAS method can effectively detect densely connected modules enriched by top GWAS findings. Tissue-specific functional network can provide precise context to help explore the collective effects of genes with biologically meaningful interactions specific to the studied phenotype.