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Principal Investigator  
Principal Investigator's Name: Breton Asken
Institution: Brown University
Department: Psychiatry and Human Behavior
Proposed Analysis: Aim 1A: Examine rates of clinical conversion from cognitively normal to MCI and from MCI to dementia using a novel, dimensional cognitive risk score. Aim 1B: Analyze the accuracy of a novel, dimensional cognitive risk score at predicting Alzheimer’s-related biomarker status Aim 2: Explore discordance between cognitive profiles (e.g., memory-predominant cognitive changes) and biomarker status (e.g., amyloid-negative) Prior research has used principles of multivariate base rates to develop more robust criteria for defining mild cognitive impairment (MCI). For example, rather than reliance on a single test score below a certain cutoff, defining impairment based on the pattern of low scores observed both within and between domains improves diagnostic stability and prognosis. However, these approaches are still limited by dichotomizing individuals into [+] and [-] categories prior to examining relationships with outcomes of interest. We propose methodology for calculating a novel, dimensional cognitive risk score that is first validated as a continuous variable against clinical and biologic outcomes, and then dichotomized at data-driven cutoff scores. For Aims 1A and 1B, we will use neuropsychological test scores collected during the Screening/Baseline visits for ADNI-1 to calculate a Total Cognitive Risk Score. Neuropsychological tests will be grouped into 3 domains (Memory, Language, and Executive Function) with 2 tests per domain. In brief, domain-specific risk scores range from 0-5 and are summed for a Total Cognitive Risk score (range 0-15). A maximum score of “5” is assigned when both scores within a domain are below z=-2.0, while a score of “0” is assigned if both scores within a domain are above z=-1.0. Scores of 1-4 are assigned based on the pattern of scores below cutoffs of z=-1.0, z=-1.5, and z=-2.0. The goal of the cognitive risk score is not to redefine MCI psychometrically per se, but rather to produce a continuum of confidences that an observed cognitive profile represents presence of absence of true decline. However, it is possible that a specific cutoff score will ultimately demonstrate improved prediction of clinical conversion (e.g, from MCI to dementia), reduced instances of reversion to “normal,” and better associations with biomarkers of neuropathology. Clinicians and researchers may use different cutoff scores that best suit their specific question (e.g, emphasizing NPV vs. PPV, or sensitivity vs. specificity). We have both clinical and biologic outcomes of interest. The clinical outcome is incident conversion to MCI or dementia. Binary logistic regression with area under the receiver operating characteristic curve (AUC/ROC) will be used with Total Cognitive Risk Score as the primary independent variable and conversion status (converter vs. non-converter) as the dependent variable. Pending these results, various cutoff scores may be used to dichotomize cognitive risk groups and then apply this categorical variable to a Cox proportional hazard regression analysis with incident conversion as the outcome. Incident reversion to “clinically normal” will also be explored. The primary biologic outcome of interest is PET-derived amyloid burden (global and regional SUVr, and A+/- designation). We will use linear regression analyses to examine relationships between cognitive risk scores and PET-amyloid SUVr. We will then again use binary logistic regression with AUC/ROC to analyze Total Cognitive Risk Score as a predictor of A+/A- status. Analyses will be repeated in a subset of participants who also have CSF tau measurements so that true Alzheimer’s disease (A+/T+) can be examined as a dependent variable. Longitudinal change in biomarker status will not be examined as part of this study as the focus is on using cognitive outcomes to predict clinical rather than biologic trajectories. Lastly, we will explore factors associated with clinical-biomarker discordance based on individuals presenting with a cognitive profile suggesting Alzheimer’s pathology (e.g., Memory score < Language, Executive Function scores) who are amyloid-negative. Requested Variables: --Neuropsychological test scores (all components): Auditory Verbal Learning Test, Logical Memory, Trail Making Test, Clock Drawing, Digit Span, Category Fluency, Boston Naming Test, Digit-Symbol, American National Reading Test (ANART) --Time Points: Screening/Baseline --Other clinical outcomes: Diagnostic Summary (e.g., cognitively normal, MCI, dementia), CDR Sum of Boxes --Time Points: All --Biologic outcomes: PET amyloid (global and regional SUVr), CSF total tau, CSF p-tau --Time Points: Screening/Baseline (closest in time to the neuropsychological assessment) --Other covariates: age/date of birth, sex, race, date(s) of assessment(s), years of education, primary language, handedness, APOE status, Hachinski score, Geriatric Depression Scale score, hippocampal volume (MRI)
Additional Investigators