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: | Frédéric St-Onge |
| Institution: | McGill University |
| Department: | Psychiatry |
| Country: | |
| Proposed Analysis: | Objectives. Despite differences in clinical and pathological features between individuals [1], neuroimaging studies on Alzheimer's disease (AD) rely heavily on group-wise analyses. Functional connectome fingerprinting (FP) is a promising tool for the characterization of these differences. FP extracts functional magnetic resonance imaging (fMRI) connectivity patterns unique to the individual to identify subjects within a large dataset of fMRI scans, across time and scanning modality, with high accuracy.[2,3] It uses a simple correlation (FP coefficient) between connectivity patterns of the same individual at time 1 and time 2 to compare similarity between self and others. However, whether the FP coefficient is stable despite neurodegenerative disease processes, is unknown. The main objective of this study is to explore FP in participants on the AD spectrum (cognitively normal to AD dementia diagnosis). Specifically, 1) stability of FP in asymptomatic older adults and participants with mild cognitive impairment (MCI) or AD dementia will be described and associations between FP stability and potential covariates will be tested; and 2) associations between the stability of FP and AD-related pathology (amyloid and tau) will be explored. Preliminary results from our laboratory suggest that FP is stable across ages and is only slightly higher in adults over 70 (in preparation, CAMCAN data, n ≈ 450). We hypothesize that 1) FP will be more stable in healthy older adults than in participants with MCI or AD dementia but will not be associated to other covariates. We expect that 2) FP stability will decrease over time with higher pathology, even in asymptomatic subjects. Research approach. Participants. For both objectives, we will rely on two cohorts. First, we will rely on the PREVENT-AD cohort (Douglas, Montreal), an ongoing longitudinal study of ~350 individuals at risk of AD due to family history. Participants undergo yearly cognitive testing and MRI scans, including fMRI. They are cognitively normal at entry into the study. A subset undergoes positron-emission tomography scans to quantify amyloid and tau accumulation. We will combine the PREVENT-AD with the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset, a longitudinal study of 480 cognitively normal, 900 MCI and 450 AD dementia participants, using similar measures as those described above. Subjects with at least two fMRI scans will be included. Procedure. Connectivity matrices, containing the correlations between the signal produced by each brain region across the course of the scan, will be generated from each fMRI scan using the Schaefer Atlas.[4] FP will be calculated as the correlation coefficient between the fMRI connectivity matrix of a subject at time 1 and at time 2. If the matrix correlates best within the same subject over time, the participant is recognized [2]. Stability of the FP will be calculated between each follow-up visit available. Analyses. 1) We will describe the FP stability across diagnostic categories (cognitively normal, MCI or AD) and over follow-up visits. We will use linear mixed effect models to test whether diagnostic categories or other relevant predictors (e.g., sex, ApoE4) are associated with the FP coefficient over time. 2) We will use significant predictors from objective 1 as covariates in linear mixed effect models, to test the association between tau/amyloid and FP coefficients. Feasibility. All the data from PREVENT-AD has already been collected and pre-processed. Data from the ADNI is openly available. The role of the applicant will consist of processing the remaining neuroimaging data and running the analyses. This is highly feasible as our laboratory possesses the expertise and infrastructure. Expected outcomes. AD affects more than 43 million people worldwide [5], and there is still no cure for the disease. Understanding inter-individual differences in AD could help us better understand patients’ trajectories of disease, and in turn, help us better target whom is more suitable for specific clinical trials. This makes studying FP (and particularly its associations with AD-related pathology) a crucial preliminary step in this direction. References. 1. Ferreira D, et al. Biological subtypes of Alzheimer disease: A systematic review and meta-analysis. Neurology. 2020;94(10) 2. Finn ES, et al. Functional connectome fingerprinting: Identifying individuals using patterns of brain connectivity. Nat Neurosci. 2015;18(11). 3. Horien C, et al. The individual functional connectome is unique and stable over months to years. Neuroimage. 2019;189. 4. Schaefer A, et al. Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI. Cereb Cortex. 2018;28(9). 5. Nichols E, et al. Global, regional, and national burden of Alzheimer’s disease and other dementias, 1990–2016: a systematic analysis for the Global Burden of Disease Study 2016. Lancet Neurol. 2019;18(1). |
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