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: | Michele Cavallari |
| Institution: | Brigham and Women's Hospital, Harvard Medical School |
| Department: | Radiology |
| Country: | |
| Proposed Analysis: | Assessing the association between perivascular dysfunction and beta-amyloid burden in subjects with or at risk for dementia Summary Tissue deposits of misaggregated proteins or peptides are consistently associated with neurodegenerative disorders. The deposits may reflect excessive protein aggregation, decreased degradation and depletion, or both. While the contribution of the different mechanisms associated with the accumulation of misfolded protein is uncertain, recent experimental data have emphasized the role of removal of soluble waste in the neurodegeneration (1). Among the different mechanisms that mediate the clearance of beta-amyloid, experimental findings have recently stressed the potential relevance of the glymphatic pathway (2), particularly in animal models of Alzheimer’s disease (3). An anatomical component of the glymphatic pathway is the perivascular space of penetrating arteries. The perivascular space drains fluid in and out of the brain, and is thought to exert a central role in the clearance by driving interstitial metabolic waste products from the periarterial towards the perivenous spaces (4). PVS are likely to gain size under conditions of altered function, and enlarged perivascular spaces (ePVS) have emerged as potential biomarkers of neurovascular dysfunction (5,6) and impaired clearance (7). By evaluating the association between ePVS and beta-amyloid load in neurocognitive disorders, the proposed project will contribute to the debate on the role of the glymphatic clearance in Alzheimer’s disease (8). We propose to investigate the association between ePVS and beta-amyloid load through analysis of magnetic resonance imaging (MRI) and positron emission tomography data from the Alzheimer's Disease Neuroimaging Initiative (ADNI, adni.loni.usc.edu). Specific Aim To estimate the association between MRI-evident ePVS and PET-derived measures of beta-amyloid load in cross-sectional and longitudinal analyses, under the hypothesis that perivascular dysfunction contributes to the accrual of beta-amyloid in subjects with or at risk for sporadic Alzheimer’s Disease and related dementia. Subjects Subjects who underwent both MRI and amyloid PET will be included in the study. MRI Analysis of ePVS On MRI images, ePVS appear as ovoid or linear structures with signal intensity similar to the cerebrospinal fluid (CSF), located along the course of perforating vessels, usually <3mm in size (8). The ePVS scoring will be performed according to the STandards for Reporting Vascular Changes on NEuroimaging (STRIVE) criteria (9, 10), using axial T2-weighted images as reference. FLAIR images will be used to differentiate between ePVS (iso-/hypo-intense on FLAIR) and white matter hyperintensities (WMH, hyperintense on FLAIR). The ePVS scoring will be performed at four locations where ePVS are more likely to be detected on MRI: the centrum semiovale, basal ganglia, hippocampus, and brainstem. Amyloid PET Analysis Beta-amyloid load will be measured in Standardized Uptake Value Ratio (SUVR), using the cerebellum as reference (11). SUVR of the supratentorial brain regions will be used for the global analysis. For the regional analysis, regional SUVR of the centrum semiovale, hippocampus, and basal ganglia region will be taken into account and compared with the regional ePVS burden at the corresponding location. VolBrain (http://volbrain.upv.es) and FreeSurfer (http://freesurfer.net) will be used to extract segmentation and parcellation labels of the different brain areas from MRI images. Specifically, Volbrain will be used to segment the cerebellum and basal ganglia; FreeSurfer will provide the segmentation and parcellation of cortical brain regions. The labels obtained from VolBrain and FreeSurfer will be aligned to the PET images using nonlinear registration. The mean signal intensity within the regions of interest defined by the aligned labels will be computed to measure SUVR values. Statistical Analysis We will estimate the association between ePVS and PET-derived measures of beta-amyloid load using general linear modeling and mixed-effect modeling in cross-sectional and longitudinal analyses, respectively. In addition to age and sex, WMH load will be factored into the analysis to estimate the effect of cerebrovascular pathology on the relationship between perivascular dysfunction and amyloid load. References 1. Kress BT, Iliff JJ, Xia M, Wang M, Wei Bs HS, Zeppenfeld D, et al. Impairment of paravascular clearance pathways in the aging brain. Ann Neurol. 2014;76(6):845–61. 2. Iliff JJ, Wang M, Liao Y, Plogg B a, Peng W, Gundersen G a, et al. A Paravascular Pathway Facilitates CSF Flow Through the Brain Parenchyma and the Clearance of Interstitial Solutes, Including Amyloid β. Sci Transl Med. 2012;4(147). 3. Tarasoff-Conway JM, Carare RO, Osorio RS, Glodzik L, Butler T, Fieremans E, et al. Clearance systems in the brain—implications for Alzheimer disease. Nat Rev Neurol. Nature Publishing Group; 2015;11(8):457–70. 4. Plog BA, Nedergaard M. The Glymphatic System in Central Nervous System Health and Disease: Past, Present, and Future. Annu Rev Pathol Mech Dis. 2018;13(1):annurev-pathol-051217-111018. 5. Ramirez J, Berezuk C, McNeely AA, Gao F, McLaurin J, Black SE. Imaging the Perivascular Space as a Potential Biomarker of Neurovascular and Neurodegenerative Diseases. Cell Mol Neurobiol. 2016 Mar 18. 6. Wardlaw JM, Smith C, Dichgans M. Mechanisms of sporadic cerebral small vessel disease: insights from neuroimaging. Lancet Neurol. Elsevier Ltd; 2013 May;12(5):483–97. 7. Veluw SJ Van, Biessels GJ, Bouvy WH, Spliet WGM, Zwanenburg JJM, Luijten PR, et al. Cerebral amyloid angiopathy severity is linked to dilation of juxtacortical perivascular spaces. 2016;3–7. 8. Kyrtsos CR, Baras JS. Modeling the role of the glymphatic pathway and cerebral blood vessel properties in Alzheimer’s disease pathogenesis. PLoS One. 2015;10(10):1–20. 8. Adachi M, Hosoya T, Haku T, Yamaguchi K. Dilated Virchow-Robin spaces: MRI pathological study. Neuroradiology. 1998 Jan;40(1):27-31. 9. Wardlaw JM et al. Neuroimaging standards for research into small vessel disease and its contribution to ageing and neurodegeneration. Lancet Neurol. 2013 Aug;12(8):822-38. 10. Potter GM, Chappell FM, Morris Z, Wardlaw JM. Cerebral perivascular spaces visible on magnetic resonance imaging: development of a qualitative rating scale and its observer reliability. Cerebrovasc Dis. 2015;39(3-4):224-31. 11. Bullich S, Seibyl J, Catafau AM, Jovalekic A, Koglin N, Barthel H, Sabri O, De Santi S. Optimized classification of 18F-Florbetaben PET scans as positive and negative using an SUVR quantitative approach and comparison to visual assessment. Neuroimage Clin. 2017 May 13;15:325-332. |
| Additional Investigators | |
| Investigator's Name: | Fernanda Troili |
| Proposed Analysis: | Co-Investigator on the project investigating perivascular clearance of beta-amyloid in ADNI |
| Investigator's Name: | Alfredo Morales Pinzon |
| Proposed Analysis: | Computer scientist. He will aid with the analysis for the ongoing BrightFocus Foundation Project on the association between enlarged perivascular spaces and beta-amyloid deposition, as well as for the SAGES/ADNI study investigating overlapping and distinct features of the brain substrates of vulnerability to delirium and AD. |
