David Wolk

Psychometric tests predict conversion of mild cognitive impairment (MCI) to probable Alzheimer's disease (AD). Because the definition of clinical AD relies on those same psychometric tests, the ability of these tests to identify underlying AD pathology remains unclear. To determine the degree to which psychometric testing predicts molecular evidence of AD amyloid pathology, as indicated by cerebrospinal fluid (CSF) amyloid-β (Aβ)1-42, in patients with MCI, as compared to neuroimaging biomarkers. We identified 408 MCI subjects with CSF Aβ levels, psychometric test data, FDG-PET scans, and acceptable volumetric MR scans from the Alzheimer's Disease Neuroimaging Initiative (ADNI). We used psychometric tests and imaging biomarkers in univariate and multivariate models to predict Aβ status. The 30-min delayed recall score of the Rey Auditory Verbal Learning Test was the best predictor of Aβ status among the psychometric tests, achieving an AUC of 0.67 ± 0.02 and odds ratio of 2.5...

We present a new framework for prior-constrained sparse decomposition of matrices derived from the neuroimaging data and apply this method to functional network analysis of a clinically relevant population. Matrix decomposition methods are powerful dimensionality reduction tools that have found widespread use in neuroimaging. However, the unconstrained nature of these totally data-driven techniques makes it difficult to interpret the results in a domain where network-specific hypotheses may exist. We propose a novel approach, Prior Based Eigenanatomy (p-Eigen), which seeks to identify a data-driven matrix decomposition but at the same time constrains the individual components by spatial anatomical priors (probabilistic ROIs). We formulate our novel solution in terms of prior-constrained ℓ1 penalized (sparse) principal component analysis. p-Eigen starts with a common functional parcellation for all the subjects and refines it with subject-specific information. This enables modeling o...

Short-term memory (STM) has generally been thought to be independent of the medial temporal lobe (MTL) in contrast to long-term memory (LTM). Prodromal Alzheimer's disease (AD) is a condition in which the MTL is a major early focus of pathology and LTM is thought disproportionately affected relative to STM. However, recent studies have suggested a role for the MTL in STM, particularly hippocampus, when binding of different elements is required. Other work has suggested involvement of extrahippocampal MTL structures, particularly in STM tasks that involve item-level memory. We examined STM for individual objects, locations, and object-location conjunctions in amnestic mild cognitive impairment (MCI), often associated with prodromal AD. Relative to age-matched, cognitively normal controls, MCI patients not only displayed impairment on object-location conjunctions but were similarly impaired for non-bound objects and locations. Moreover, across all participants, these conditions di...

Two neuroanatomically dissociable, large-scale cortical memory networks, referred to as the anterior and posterior medial temporal lobe (MTL) networks have recently been described in young adults using resting-state blood oxygen level-dependent (BOLD) functional magnetic resonance imaging (fMRI)-based functional connectivity (fc-BOLD). They have been hypothesized to subserve distinct mnemonic and non-memory cognitive functions and are thought to be associated with differential vulnerability in neurological disorders. In this article, we demonstrate the existence of these functional networks in an older adult population and in a cohort of patients diagnosed with amnestic mild cognitive impairment (aMCI). Anatomic subregions of interest in the MTL were defined using high-resolution T2-weighted MRI and used as seeds for defining the putative networks using fc-BOLD. Although the literature has suggested that the posterior MTL network is particularly vulnerable to early Alzheimer's disease, we show that both the networks are affected in MCI, to varying degrees, compared with the control group. Furthermore, cortical thickness in the brain regions defined by these networks was reduced in MCI.

C-reactive protein (CRP) participates in the systemic response to inflammation. Previous studies report inconsistent findings regarding the relationship between plasma CRP and Alzheimer's disease (AD). We measured plasma CRP in 203 subjects with AD, 58 subjects with mild cognitive impairment (MCI) and 117 normal aging subjects and administered annual Mini-Mental State Examinations (MMSE) during a 3-year follow-up period to investigate CRP's relationship with diagnosis and progression of cognitive decline. Adjusted for age, sex, and education, subjects with AD had significantly lower levels of plasma CRP than subjects with MCI and normal aging. However, there was no significant association between plasma CRP at baseline and subsequent cognitive decline as assessed by longitudinal changes in MMSE score. Our results support previous reports of reduced levels of plasma CRP in AD and indicate its potential utility as a biomarker for the diagnosis of AD.

Vascular disease was once considered the principal cause of aging-related dementia. More recently, however, research emphasis has shifted to studies of progressive neurodegenerative disease processes, such as those giving rise to neuritic plaques, neurofibrillary tangles, and Lewy bodies. Although these studies have led to critical insights and potential therapeutic strategies, interest in the role of systemic and cerebrovascular disease mechanisms waned and has received relatively less attention and research support. Recent studies suggest that vascular disease mechanisms play an important role in the risk for aging-related cognitive decline and disorders. Vascular disease frequently coexists with cognitive decline in aging individuals, shares many risk factors with dementias considered to be of the "Alzheimer type," and is observed more frequently than expected in postmortem material from individuals manifesting "specific" disease stigmata, such as abundant plaques and tangles. Considerable difficulties have emerged in attempting to classify dementias as being related to vascular versus neurodegenerative causes, and several systems of criteria have been used. Despite multiple attempts, a lack of consensus remains regarding the optimal means of incorporating vascular disease into clinical diagnostic, neurocognitive, or neuropathologic classification schemes for dementias. We propose here an integrative, rather than a strictly taxonomic, approach to the study and elucidation of how vascular disease mechanisms contribute to the development of dementias. We argue that, instead of discriminating between, for example, "Alzheimer's disease," "vascular dementia," and other diseases, there is a greater need to focus clinical and research efforts on elucidating specific pathophysiologic mechanisms that contribute to dementia phenotypes and neuropathologic outcomes. We outline a multitiered strategy, beginning with clinical and public health interventions that can be implemented immediately, enhancements to ongoing longitudinal studies to increase…

Neurodegenerative diseases (NDs) are defined by the accumulation of abnormal protein deposits in the central nervous system (CNS), and only neuropathological examination enables a definitive diagnosis. Brain banks and their associated scientific programs have shaped the actual knowledge of NDs, identifying and characterizing the CNS deposits that define new diseases, formulating staging schemes, and establishing correlations between neuropathological changes and clinical features. However, brain banks have evolved to accommodate the banking of biofluids as well as DNA and RNA samples. Moreover, the value of biobanks is greatly enhanced if they link all the multidimensional clinical and laboratory information of each case, which is accomplished, optimally, using systematic and standardized operating procedures, and in the framework of multidisciplinary teams with the support of a flexible and user-friendly database system that facilitates the sharing of information of all the teams in the network. We describe a biobanking system that is a platform for discovery research at the Center for Neurodegenerative Disease Research at the University of Pennsylvania.

We evaluate a fully automatic technique for labeling hippocampal subfields and cortical subregions in the medial temporal lobe in in vivo 3 Tesla MRI. The method performs segmentation on a T2-weighted MRI scan with 0.4 × 0.4 × 2.0 mm(3) resolution, partial brain coverage, and oblique orientation. Hippocampal subfields, entorhinal cortex, and perirhinal cortex are labeled using a pipeline that combines multi-atlas label fusion and learning-based error correction. In contrast to earlier work on automatic subfield segmentation in T2-weighted MRI [Yushkevich et al., 2010], our approach requires no manual initialization, labels hippocampal subfields over a greater anterior-posterior extent, and labels the perirhinal cortex, which is further subdivided into Brodmann areas 35 and 36. The accuracy of the automatic segmentation relative to manual segmentation is measured using cross-validation in 29 subjects from a study of amnestic mild cognitive impairment (aMCI) and is highest for the dentate gyrus (Dice coefficient is 0.823), CA1 (0.803), perirhinal cortex (0.797), and entorhinal cortex (0.786) labels. A larger cohort of 83 subjects is used to examine the effects of aMCI in the hippocampal region using both subfield volume and regional subfield thickness maps. Most significant differences between aMCI and healthy aging are observed bilaterally in the CA1 subfield and in the left Brodmann area 35. Thickness analysis results are consistent with volumetry, but provide additional regional specificity and suggest nonuniformity in the effects of aMCI on hippocampal subfields and MTL cortical subregions.