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Browsing by Author "Risacher, Shannon Leigh"
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Item Association Between Anticholinergic Medication Use and Cognition, Brain Metabolism, and Brain Atrophy in Cognitively Normal Older Adults(American Medical Association, 2016-06-01) Risacher, Shannon Leigh; McDonald, Brenna C.; Tallman, Eileen F.; West, John D.; Farlow, Martin R.; Unverzagt, Fredrick W.; Gao, Sujuan; Boustani, Malaz; Crane, Paul K.; Petersen, Ronald C.; Jack, Clifford R.; Jagust, William J.; Aisen, Paul S.; Weiner, Michael W.; Saykin, Andrew J.; Department of Radiology and Imaging Sciences, School of MedicineIMPORTANCE: The use of anticholinergic (AC) medication is linked to cognitive impairment and an increased risk of dementia. To our knowledge, this is the first study to investigate the association between AC medication use and neuroimaging biomarkers of brain metabolism and atrophy as a proxy for understanding the underlying biology of the clinical effects of AC medications. OBJECTIVE: To assess the association between AC medication use and cognition, glucose metabolism, and brain atrophy in cognitively normal older adults from the Alzheimer's Disease Neuroimaging Initiative (ADNI) and the Indiana Memory and Aging Study (IMAS). DESIGN, SETTING, AND PARTICIPANTS: The ADNI and IMAS are longitudinal studies with cognitive, neuroimaging, and other data collected at regular intervals in clinical and academic research settings. For the participants in the ADNI, visits are repeated 3, 6, and 12 months after the baseline visit and then annually. For the participants in the IMAS, visits are repeated every 18 months after the baseline visit (402 cognitively normal older adults in the ADNI and 49 cognitively normal older adults in the IMAS were included in the present analysis). Participants were either taking (hereafter referred to as the AC+ participants [52 from the ADNI and 8 from the IMAS]) or not taking (hereafter referred to as the AC- participants [350 from the ADNI and 41 from the IMAS]) at least 1 medication with medium or high AC activity. Data analysis for this study was performed in November 2015. MAIN OUTCOMES AND MEASURES: Cognitive scores, mean fludeoxyglucose F 18 standardized uptake value ratio (participants from the ADNI only), and brain atrophy measures from structural magnetic resonance imaging were compared between AC+ participants and AC- participants after adjusting for potential confounders. The total AC burden score was calculated and was related to target measures. The association of AC use and longitudinal clinical decline (mean [SD] follow-up period, 32.1 [24.7] months [range, 6-108 months]) was examined using Cox regression. RESULTS: The 52 AC+ participants (mean [SD] age, 73.3 [6.6] years) from the ADNI showed lower mean scores on Weschler Memory Scale-Revised Logical Memory Immediate Recall (raw mean scores: 13.27 for AC+ participants and 14.16 for AC- participants; P = .04) and the Trail Making Test Part B (raw mean scores: 97.85 seconds for AC+ participants and 82.61 seconds for AC- participants; P = .04) and a lower executive function composite score (raw mean scores: 0.58 for AC+ participants and 0.78 for AC- participants; P = .04) than the 350 AC- participants (mean [SD] age, 73.3 [5.8] years) from the ADNI. Reduced total cortical volume and temporal lobe cortical thickness and greater lateral ventricle and inferior lateral ventricle volumes were seen in the AC+ participants relative to the AC- participants. CONCLUSIONS AND RELEVANCE: The use of AC medication was associated with increased brain atrophy and dysfunction and clinical decline. Thus, use of AC medication among older adults should likely be discouraged if alternative therapies are available.Item MRI Measures of Neurodegeneration as Biomarkers of Alzheimer's Disease(2012-03-19) Risacher, Shannon Leigh; Shen, Li; Farlow, Martin R.; Gao, Sujuan; McDonald, Brenna C.; Saykin, Andrew J.; Yoder, Karmen K.Alzheimer’s disease (AD) is the most common age-related neurodegenerative disease. Many researchers believe that an effective AD treatment will prevent the development of disease rather than treat the disease after a diagnosis. Therefore, the development of tools to detect AD-related pathology in early stages is an important goal. In this report, MRI-based markers of neurodegeneration are explored as biomarkers of AD. In the first chapter, the sensitivity of cross-sectional MRI biomarkers to neurodegenerative changes is evaluated in AD patients and in patients with a diagnosis of mild cognitive impairment (MCI), a prodromal stage of AD. The results in Chapter 1 suggest that cross-sectional MRI biomarkers effectively measure neurodegeneration in AD and MCI patients and are sensitive to atrophic changes in patients who convert from MCI to AD up to 1 year before clinical conversion. Chapter 2 investigates longitudinal MRI-based measures of neurodegeneration as biomarkers of AD. In Chapter 2a, measures of brain atrophy rate in a cohort of AD and MCI patients are evaluated; whereas in Chapter 2b, these measures are assessed in a pre-MCI stage, namely older adults with cognitive complaints (CC) but no significant deficits. The results from Chapter 2 suggest that dynamic MRI-based measures of neurodegeneration are sensitive biomarkers for measuring progressive atrophy associated with the development of AD. In the final chapter, a novel biomarker for AD, visual contrast sensitivity, was evaluated. The results demonstrated contrast sensitivity impairments in AD and MCI patients, as well as slightly in CC participants. Impaired contrast sensitivity was also shown to be significantly associated with known markers of AD, including cognitive impairments and temporal lobe atrophy on MRI-based measures. The results of Chapter 3 support contrast sensitivity as a potential novel biomarker for AD and suggest that future studies are warranted. Overall, the results of this report support MRI-based measures of neurodegeneration as effective biomarkers for AD, even in early clinical and preclinical disease stages. Future therapeutic trials may consider utilizing these measures to evaluate potential treatment efficacy and mechanism of action, as well as for sample enrichment with patients most likely to rapidly progress towards AD.Item Network-based analysis of genetic variants associated with hippocampal volume in Alzheimer's disease: a study of ADNI cohorts(BioMed Central, 2016) Song, Ailin; Yan, Jingwen; Kim, Sungeun; Risacher, Shannon Leigh; Wong, Aaron K.; Saykin, Andrew J.; Shen, Li; Greene, Casey S.; Department of Radiology and Imaging Sciences, IU School of MedicineBACKGROUND: Alzheimer's disease (AD) is a neurodegenerative disease that causes dementia. While molecular basis of AD is not fully understood, genetic factors are expected to participate in the development and progression of the disease. Our goal was to uncover novel genetic underpinnings of Alzheimer's disease with a bioinformatics approach that accounts for tissue specificity. FINDINGS: We performed genome-wide association studies (GWAS) for hippocampal volume in two Alzheimer's Disease Neuroimaging Initiative (ADNI) cohorts. We used these GWAS in a subsequent tissue-specific network-wide association study (NetWAS), which applied nominally significant associations in the initial GWAS to identify disease relevant patterns in a functional network for the hippocampus. We compared prioritized gene lists from NetWAS and GWAS with literature curated AD-associated genes from the Online Mendelian Inheritance in Man (OMIM) database. In the ADNI-1 GWAS, where we also observed an enrichment of low p-values, NetWAS prioritized disease-gene associations in accordance with OMIM annotations. This was not observed in the ADNI-2 dataset. We provide source code to replicate these analyses as well as complete results under permissive licenses. CONCLUSIONS: We performed the first analysis of hippocampal volume using NetWAS, which uses machine learning algorithms applied to tissue-specific functional interaction network to prioritize GWAS results. Our findings support the idea that tissue-specific networks may provide helpful context for understanding the etiology of common human diseases and reveal challenges that network-based approaches encounter in some datasets. Our source code and intermediate results files can facilitate the development of methods to address these challenges.