Harvard Aging Brain Study: Dataset and accessibility.

The Harvard Aging Brain Study is sharing its data with the global research community. The longitudinal dataset consists of a 284-subject cohort with the following modalities acquired: demographics, clinical assessment, comprehensive neuropsychological testing, clinical biomarkers, and neuroimaging. To promote more extensive analyses, imaging data was designed to be compatible with other publicly available datasets. A cloud-based system enables access to interested researchers with blinded data available contingent upon completion of a data usage agreement and administrative approval. Data collection is ongoing and currently in its fifth year.

Odor identification and Alzheimer disease biomarkers in clinically normal elderly.

OBJECTIVES: Our objective was to investigate cross-sectional associations between odor identification ability and imaging biomarkers of neurodegeneration and amyloid deposition in clinically normal (CN) elderly individuals, specifically testing the hypothesis that there may be an interaction between amyloid deposition and neurodegeneration in predicting odor identification dysfunction. METHODS: Data were collected on 215 CN participants from the Harvard Aging Brain Study. Measurements included the 40-item University of Pennsylvania Smell Identification Test and neuropsychological testing, hippocampal volume (HV) and entorhinal cortex (EC) thickness from MRI, and amyloid burden using Pittsburgh compound B (PiB) PET. A linear regression model with backward elimination (p < 0.05 retention) evaluated the cross-sectional association between the University of Pennsylvania Smell Identification Test and amyloid burden, HV, and EC thickness, assessing for effect modification by PiB status. Covariates included age, sex, premorbid intelligence, APOE ε4 carrier status, and Boston Naming Test. RESULTS: In unadjusted univariate analyses, worse olfaction was associated with decreased HV (p < 0.001), thinner EC (p = 0.003), worse episodic memory (p = 0.03), and marginally associated with greater amyloid burden (binary PiB status, p = 0.06). In the multivariate model, thinner EC in PiB-positive individuals (interaction term) was associated with worse olfaction (p = 0.02). CONCLUSIONS: In CN elderly, worse odor identification was associated with markers of neurodegeneration. Furthermore, individuals with elevated cortical amyloid and thinner EC exhibited worse odor identification, elucidating the potential contribution of olfactory testing to detect preclinical AD in CN individuals.

Depressive Symptoms and Biomarkers of Alzheimer's Disease in Cognitively Normal Older Adults.

Even low levels of depressive symptoms are associated with an increased risk of cognitive decline in older adults without overt cognitive impairment (CN). Our objective was to examine whether very low, "subthreshold symptoms of depression" are associated with Alzheimer's disease (AD) biomarkers of neurodegeneration in CN adults and whether these associations are specific to particular depressive symptoms. We analyzed data from 248 community-dwelling CN older adults, including measurements of cortical amyloid burden, neurodegeneration markers of hippocampal volume (HV) and cerebral 18F-fluorodeoxyglucose (FDG) metabolism in a composite of AD-related regions and the 30-item Geriatric Depression Scale (GDS). Participants with GDS >10 were excluded. General linear regression models evaluated the cross-sectional relations of GDS to HV or FDG in separate backward elimination models. Predictors included GDS total score, age, gender, premorbid intelligence, a binary amyloid variable and its interaction with GDS. Principal component analyses of GDS item scores revealed three factors (the Dysphoria, Apathy-Anhedonia, and Anxiety-Concentration Factors). In secondary analyses, GDS total score was replaced with the three factor scores in repeated models. Higher GDS score (p = 0.03) was significantly associated with lower HV and was marginally related (p = 0.06) to FDG hypometabolism. In secondary models, higher Dysphoria (p = 0.02) and Apathy-Anhedonia (p = 0.05) were related to lower HV while higher Apathy-Anhedonia (p = 0.003) was the sole factor related to FDG hypometabolism. Amyloid was not a significant predictor in any model. In conclusion, very low-level dysphoria, apathy and anhedonia may point to neurodegeneration in AD-related regions but this association appears to be independent of amyloid burden.

Statistical label fusion with hierarchical performance models.

Label fusion is a critical step in many image segmentation frameworks (e.g., multi-atlas segmentation) as it provides a mechanism for generalizing a collection of labeled examples into a single estimate of the underlying segmentation. In the multi-label case, typical label fusion algorithms treat all labels equally - fully neglecting the known, yet complex, anatomical relationships exhibited in the data. To address this problem, we propose a generalized statistical fusion framework using hierarchical models of rater performance. Building on the seminal work in statistical fusion, we reformulate the traditional rater performance model from a multi-tiered hierarchical perspective. This new approach provides a natural framework for leveraging known anatomical relationships and accurately modeling the types of errors that raters (or atlases) make within a hierarchically consistent formulation. Herein, we describe several contributions. First, we derive a theoretical advancement to the statistical fusion framework that enables the simultaneous estimation of multiple (hierarchical) performance models within the statistical fusion context. Second, we demonstrate that the proposed hierarchical formulation is highly amenable to the state-of-the-art advancements that have been made to the statistical fusion framework. Lastly, in an empirical whole-brain segmentation task we demonstrate substantial qualitative and significant quantitative improvement in overall segmentation accuracy.