Development and assessment of a composite score for memory in the Alzheimer’s Disease Neuroimaging Initiative (ADNI)
ABSTRACT We sought to develop and evaluate a composite memory score from the neuropsychological battery used in the Alzheimer's Disease (AD) Neuroimaging Initiative (ADNI). We used modern psychometric approaches to analyze longitudinal Rey Auditory Verbal Learning Test (RAVLT, 2 versions), AD Assessment Schedule - Cognition (ADAS-Cog, 3 versions), Mini-Mental State Examination (MMSE), and Logical Memory data to develop ADNI-Mem, a composite memory score. We compared RAVLT and ADAS-Cog versions, and compared ADNI-Mem to RAVLT recall sum scores, four ADAS-Cog-derived scores, the MMSE, and the Clinical Dementia Rating Sum of Boxes. We evaluated rates of decline in normal cognition, mild cognitive impairment (MCI), and AD, ability to predict conversion from MCI to AD, strength of association with selected imaging parameters, and ability to differentiate rates of decline between participants with and without AD cerebrospinal fluid (CSF) signatures. The second version of the RAVLT was harder than the first. The ADAS-Cog versions were of similar difficulty. ADNI-Mem was slightly better at detecting change than total RAVLT recall scores. It was as good as or better than all of the other scores at predicting conversion from MCI to AD. It was associated with all our selected imaging parameters for people with MCI and AD. Participants with MCI with an AD CSF signature had somewhat more rapid decline than did those without. This paper illustrates appropriate methods for addressing the different versions of word lists, and demonstrates the additional power to be gleaned with a psychometrically sound composite memory score.
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ABSTRACT: A rare heterozygous TREM2 variant p.R47H (rs75932628) has been associated with an increased risk for Alzheimer's disease (AD). We aimed to investigate the clinical presentation, neuropsychological profile, and regional pattern of gray matter and white matter loss associated with the TREM2 variant p.R47H, and to establish which regions best differentiate p.R47H carriers from noncarriers in 2 sample sets (Spanish and Alzheimer's Disease Neuroimaging Initiative, ADNI1). This was a cross-sectional study including a total number of 16 TREM2 p.R47H carriers diagnosed with AD or mild cognitive impairment, 75 AD p.R47H noncarriers and 75 cognitively intact TREM2 p.R47H noncarriers. Spanish AD TREM2 p.R47H carriers showed apraxia (9 of 9) and psychiatric symptoms such as personality changes, anxiety, paranoia, or fears more frequently than in AD noncarriers (corrected p = 0.039). For gray matter and white matter volumetric brain magnetic resonance imaging voxelwise analyses, we used statistical parametric mapping (SPM8) based on the General Linear Model. We used 3 different design matrices with a full factorial design. Voxel-based morphometry analyses were performed separately in the 2 sample sets. The absence of interset statistical differences allowed us to perform joint and conjunction analyses. Independent voxel-based morphometry analysis of the Spanish set as well as conjunction and joint analyses revealed substantial gray matter loss in orbitofrontal cortex and anterior cingulate cortex with relative preservation of parietal lobes in AD and/or mild cognitive impairment TREM2 p.R47H carriers, suggesting that TREM2 p.R47H variant is associated with certain clinical and neuroimaging AD features in addition to the increased TREM2 p.R47H atrophy in temporal lobes as described previously. The high frequency of pathologic behavioral symptoms, combined with a preferential frontobasal gray matter cortical loss, suggests that frontobasal and temporal regions could be more susceptible to the deleterious biological effects of the TREM2 variant p.R47H.Neurobiology of Aging 12/2014; DOI:10.1016/j.neurobiolaging.2014.06.007 · 4.85 Impact Factor
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ABSTRACT: Background It is unknown which commonly used Alzheimer disease (AD) biomarker values—baseline or progression—best predict longitudinal cognitive decline. Methods 526 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI). ADNI composite memory and executive scores were the primary outcomes. Individual-specific slope of the longitudinal trajectory of each biomarker was first estimated. These estimates and observed baseline biomarker values were used as predictors of cognitive declines. Variability in cognitive declines explained by baseline biomarker values was compared with variability explained by biomarker progression values. Results About 40% of variability in memory and executive function declines was explained by ventricular volume progression among mild cognitive impairment patients. A total of 84% of memory and 65% of executive function declines were explained by fluorodeoxyglucose positron emission tomography (FDG-PET) score progression and ventricular volume progression, respectively, among AD patients. Conclusions For most biomarkers, biomarker progressions explained higher variability in cognitive decline than biomarker baseline values. This has important implications for clinical trials targeted to modify AD biomarkers.Alzheimer's and Dementia 11/2014; 10(6). DOI:10.1016/j.jalz.2014.04.513 · 17.47 Impact Factor
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ABSTRACT: Identifying predictors of mild cognitive impairment (MCI) and Alzheimer׳s disease (AD) can lead to more accurate diagnosis and facilitate clinical trial participation. We identified 320 participants (93 cognitively normal or CN, 162 MCI, 65 AD) with baseline magnetic resonance imaging (MRI) data, cerebrospinal fluid biomarkers, and cognition data in the Alzheimer׳s Disease Neuroimaging Initiative database. We used independent component analysis (ICA) on structural MR images to derive 30 matter covariance patterns (ICs) across all participants. These ICs were used in iterative and stepwise discriminant classifier analyses to predict diagnostic classification at 24 months for CN vs. MCI, CN vs. AD, MCI vs. AD, and stable MCI (MCI-S) vs. MCI progression to AD (MCI-P). Models were cross-validated with a "leave-10-out" procedure. For CN vs. MCI, 84.7% accuracy was achieved based on cognitive performance measures, ICs, p-tau181p, and ApoE ε4 status. For CN vs. AD, 94.8% accuracy was achieved based on cognitive performance measures, ICs, and p-tau181p. For MCI vs. AD and MCI-S vs. MCI-P, models achieved 83.1% and 80.3% accuracy, respectively, based on cognitive performance measures, ICs, and p-tau181p. ICA-derived MRI biomarkers achieve excellent diagnostic accuracy for MCI conversion, which is little improved by CSF biomarkers and ApoE ε4 status.Psychiatry Research Neuroimaging 08/2014; 224(2). DOI:10.1016/j.pscychresns.2014.08.005 · 2.83 Impact Factor