There are many active research projects accessing and applying shared ADNI data. Use the search above to find specific research focuses on the active ADNI investigations. This information is requested annually as a requirement for data access.
| Principal Investigator | |
| Principal Investigator's Name: | Jennifer Yokoyama |
| Institution: | University of California, San Francisco |
| Department: | Neurology |
| Country: | |
| Proposed Analysis: | Analysis Proposal: Effect of Klotho genotype on the healthy aging brain. Investigators: Jennifer S. Yokoyama, PhD Dena B. Dubal, MD, PhD Background: Klotho was serendipitously identified in mice as a potent anti-aging factor [1], and genetic variation in the human analogue, KLOTHO (KL), has been associated with longevity [2] and resistance to cardiovascular disease [3]. Given the finding that mice deficient in klotho appear to have hastened CNS aging [4], we seek to determine whether carrying 1 copy of the protective ‘KL-VS’ haplotype (composed of F352V and C370S, which are in perfect linkage disequilibrium) affects brain structure in healthy aging adults. Research Question: Is heterozygosity of the protective KL haplotype, KL-VS, associated with increased brain volume in cognitively normal older adults from the ADNI research cohort? Predictor variable: KL-VS heterozygosity as measured by SNP genotype at F352V (rs9536314) Outcome variable: brain volume (voxel intensity) from structural MRI Covariates: age at time of scan, gender, total intracranial volume Analysis: KL-VS heterozygosity will be determined via genotype of the SNP rs9536314, which has already been genotyped via the Illumina Human 610-Quad BeadChip [6]. To assess the effect of KL-VS heterozygosity (scored as absent or present) on grey and white matter volume, we will perform VBM on 1.5T MRI from ADNI using previously described methods [7, 8]. Briefly, T1-weighted structural MR images will be segmented in SPM5 running under Matlab. Grey matter, white matter and CSF segmented images will next be preprocessed with DARTEL [9]. Finally, DARTEL-processed grey matter, white matter and CSF images will be re-merged to create combined gray-white matter images and smoothed at 8mm x 8mm x 8mm kernel for VBM. For VBM analysis, a glm will be fit at each voxel to model the dependence of tissue volume on KL-VS heterozygosity for both gray and white matter. Nuisance variables include age at time of scan, total intracranial volume, and gender. We will also assess the role of APOE4 genotype in modifying this effect. The T-threshold for family wise error (FWE) correction at a pFWE≤0.05 level will be established by performing 1000 permutations of the error in each analytic model and identifying the T-value at p≤0.05 on the error distribution [10]. We will assess both positive (heterozygosity associated with increased volume) and negative (heterozygosity associated with reduced volume) glm relationships. Thank you for considering our proposal. References 1. Kuro-o, M., et al., Mutation of the mouse klotho gene leads to a syndrome resembling ageing. Nature, 1997. 390: p. 45-51. 2. Arking, D.E., et al., Association of human aging with a functional variant of klotho. PNAS, 2002. 99(2): p. 856-861. 3. Arking, D.E., et al., Association between a functional variant of the KLOTHO gene and high-density lipoprotein cholesterol, blood pressure, stroke, and longevity. Circ Res, 2005. 96(4): p. 412-8. 4. Shiozaki, M., et al., Morphological and biochemical signs of age-related neurodegenerative changes in klotho mutant mice. Neuroscience, 2008. 152(4): p. 924-41. 5. Yokoyama, J.S., et al., Klotho genetic variants in brain aging. In preparation. 6. Shen, L., et al., Whole genome association study of brain-wide imaging phenotypes for identifying quantitative trait loci in MCI and AD: A study of the ADNI cohort. Neuroimage, 2010. 53(3): p. 1051-63. 7. Ashburner, J. and K.J. Friston, Voxel-based morphometry--the methods. Neuroimage, 2000. 11(6 Pt 1): p. 805-21. 8. Wilson, S.M., et al., The neural basis of surface dyslexia in semantic dementia. Brain, 2009. 132(Pt 1): p. 71-86. 9. Ashburner, J., A fast diffeomorphic image registration algorithm. Neuroimage, 2007. 38(1): p. 95-113. 10. Kimberg, D.Y., H.B. Coslett, and M.F. Schwartz, Power in Voxel-based lesion-symptom mapping. J Cogn Neurosci, 2007. 19(7): p. 1067-80. |
| Additional Investigators | |
| Investigator's Name: | Luke Bonham |
| Proposed Analysis: | We have developed a longitudinal VBM analytical pipeline in SPM12 which we will be using to analyze ADNI AD, MCI, and control longitudinal data to assess the effects of KL-VS genotype on brain structure over time. |
| Investigator's Name: | Ethan Geier |
| Proposed Analysis: | Ethan will look at longitudinal CSF markers. |
| Investigator's Name: | William Mantyh |
| Proposed Analysis: | Analysis Proposal: Effect of Klotho genotype on the healthy aging brain. Investigators: Jennifer S. Yokoyama, PhD Dena B. Dubal, MD, PhD Background: Klotho was serendipitously identified in mice as a potent anti-aging factor [1], and genetic variation in the human analogue, KLOTHO (KL), has been associated with longevity [2] and resistance to cardiovascular disease [3]. Given the finding that mice deficient in klotho appear to have hastened CNS aging [4], we seek to determine whether carrying 1 copy of the protective ‘KL-VS’ haplotype (composed of F352V and C370S, which are in perfect linkage disequilibrium) affects brain structure in healthy aging adults. Research Question: Is heterozygosity of the protective KL haplotype, KL-VS, associated with increased brain volume in cognitively normal older adults from the ADNI research cohort? Predictor variable: KL-VS heterozygosity as measured by SNP genotype at F352V (rs9536314) Outcome variable: brain volume (voxel intensity) from structural MRI Covariates: age at time of scan, gender, total intracranial volume Analysis: KL-VS heterozygosity will be determined via genotype of the SNP rs9536314, which has already been genotyped via the Illumina Human 610-Quad BeadChip [6]. To assess the effect of KL-VS heterozygosity (scored as absent or present) on grey and white matter volume, we will perform VBM on 1.5T MRI from ADNI using previously described methods [7, 8]. Briefly, T1-weighted structural MR images will be segmented in SPM5 running under Matlab. Grey matter, white matter and CSF segmented images will next be preprocessed with DARTEL [9]. Finally, DARTEL-processed grey matter, white matter and CSF images will be re-merged to create combined gray-white matter images and smoothed at 8mm x 8mm x 8mm kernel for VBM. For VBM analysis, a glm will be fit at each voxel to model the dependence of tissue volume on KL-VS heterozygosity for both gray and white matter. Nuisance variables include age at time of scan, total intracranial volume, and gender. We will also assess the role of APOE4 genotype in modifying this effect. The T-threshold for family wise error (FWE) correction at a pFWE≤0.05 level will be established by performing 1000 permutations of the error in each analytic model and identifying the T-value at p≤0.05 on the error distribution [10]. We will assess both positive (heterozygosity associated with increased volume) and negative (heterozygosity associated with reduced volume) glm relationships. Thank you for considering our proposal. References 1. Kuro-o, M., et al., Mutation of the mouse klotho gene leads to a syndrome resembling ageing. Nature, 1997. 390: p. 45-51. 2. Arking, D.E., et al., Association of human aging with a functional variant of klotho. PNAS, 2002. 99(2): p. 856-861. 3. Arking, D.E., et al., Association between a functional variant of the KLOTHO gene and high-density lipoprotein cholesterol, blood pressure, stroke, and longevity. Circ Res, 2005. 96(4): p. 412-8. 4. Shiozaki, M., et al., Morphological and biochemical signs of age-related neurodegenerative changes in klotho mutant mice. Neuroscience, 2008. 152(4): p. 924-41. 5. Yokoyama, J.S., et al., Klotho genetic variants in brain aging. In preparation. 6. Shen, L., et al., Whole genome association study of brain-wide imaging phenotypes for identifying quantitative trait loci in MCI and AD: A study of the ADNI cohort. Neuroimage, 2010. 53(3): p. 1051-63. 7. Ashburner, J. and K.J. Friston, Voxel-based morphometry--the methods. Neuroimage, 2000. 11(6 Pt 1): p. 805-21. 8. Wilson, S.M., et al., The neural basis of surface dyslexia in semantic dementia. Brain, 2009. 132(Pt 1): p. 71-86. 9. Ashburner, J., A fast diffeomorphic image registration algorithm. Neuroimage, 2007. 38(1): p. 95-113. 10. Kimberg, D.Y., H.B. Coslett, and M.F. Schwartz, Power in Voxel-based lesion-symptom mapping. J Cogn Neurosci, 2007. 19(7): p. 1067-80. |
| Investigator's Name: | Caroline Warly Solsberg |
| Proposed Analysis: | Carloline will be conducting GWAS in the ADNI cohort. |
| Investigator's Name: | Alexis Oddi |
| Proposed Analysis: | Alexis will be using multimodal imaging and genetic data to identify early markers of AD progression. |
| Investigator's Name: | Taylor Johnson |
| Proposed Analysis: | Taylor will be analyzing markers of angiogenesis and their role in memory function. |
| Investigator's Name: | Michael Corley |
| Proposed Analysis: | Dr Corley will explore methylation patterns and their relationship with Alzheimer's disease biomarkers. |
| Investigator's Name: | Zizheng Li |
| Proposed Analysis: | Zizheng will work with Caroline to conduct GWAS in the ADNI cohort. |
