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.
"Thus, the tests selected were the following (the number of subjects who underwent this test is reflected in brackets): the CDR sum of boxes (CDR-SOB) scale as a measure of cognitive and functional impairment (Morris, 1993; n ¼ 36), the MMSE (Folstein et al., 1975), the Alzheimer's disease assessment scale Word list (Mohs et al., 1997; n ¼ 36), Geriatric Depression Rating Scale short form (Yesavage et al., 1982; n ¼ 36), semantic verbal fluency (list of animals ) (Parkin and Java, 1999; n ¼ 27), Trail Making Test-A and B (Tombaugh, 2004; n ¼ 27), and the 30-item Boston Naming Test (Williams et al., 1989; n ¼ 27). Two composite scores for memory and executive function (ADNI-MEM and ADNI-EF) were also recorded (Crane et al., 2012; Gibbons et al., 2012; n ¼ 34 and 33; respectively). More details Table 1 Comparison of demographic and clinical data between TREM2 p.R47H carriers and noncarriers from both sets "
[Show abstract][Hide abstract] 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 · 5.01 Impact Factor
") of cognitive domain measurements examined are ADNI-memory (ADNI-Mem) and ADNIexecutive function (ADNI-Exe)  .The scores are psychometrically optimized composite scores of memory and executive function, respectively, derived from items from ADNI NP tests. These measurements were validated previously, robust, and have external validity    "
[Show abstract][Hide abstract] ABSTRACT: Background
It is unknown which commonly used Alzheimer disease (AD) biomarker values—baseline or progression—best predict longitudinal cognitive decline.
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.
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.
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 · 12.41 Impact Factor
"showed that cognitive measures were better predictors of MCI conversion to AD than most biomarkers . However , we advise caution because the cognitive performance measures we used were derived , in part , from the same ADNI participants to whom we applied diagnostic classification . While other reports have also used these factors in classifiers ( Crane et al . , 2012 ; Habeck et al . , 2012 ; Nir et al . , 2013 ) , we would like to caution readers to this source of potential bias and the possibility of inflated classification accuracy based on using these cognitive factors . Nonetheless , our findings suggest that there is added accuracy gained by incorporating MRI metrics to predict diagnostic clas"
[Show abstract][Hide abstract] 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.
Data provided are for informational purposes only. Although carefully collected, accuracy cannot be guaranteed. The impact factor represents a rough estimation of the journal's impact factor and does not reflect the actual current impact factor. Publisher conditions are provided by RoMEO. Differing provisions from the publisher's actual policy or licence agreement may be applicable.