Faculty Bibliography

Microvascular rarefaction, or the decrease in vascular density, has been described in the cerebrovasculature of aging humans, rats, and, more recently, mice in the presence and absence of age-dependent diseases. Given the wide use of mice in modeling age-dependent human diseases of the cerebrovasculature, visualization, and quantification of the global murine cerebrovasculature is necessary for establishing the baseline changes that occur with aging. To provide in vivo whole-brain imaging of the cerebrovasculature in aging C57BL/6 mice longitudinally, contrast-enhanced magnetic resonance angiography (CE-MRA) was employed using a house-made gadolinium-bearing micellar blood pool agent. Enhancement in the vascular space permitted quantification of the detectable, or apparent, cerebral blood volume (aCBV), which was analyzed over 2 years of aging and compared to histological analysis of the cerebrovascular density. A significant loss in the aCBV was detected by CE-MRA over the aging period. Histological analysis via vessel-probing immunohistochemistry confirmed a significant loss in the cerebrovascular density over the same 2-year aging period, validating the CE-MRA findings. While these techniques use widely different methods of assessment and spatial resolutions, their comparable findings in detected vascular loss corroborate the growing body of literature describing vascular rarefaction aging. These findings suggest that such age-dependent changes can contribute to cerebrovascular and neurodegenerative diseases, which are modeled using wild-type and transgenic laboratory rodents.

PrP^Sc is an infectious and disease-specific conformer of the prion protein, which accumulation in the CNS underlies the pathology of prion diseases. PrP^Sc replicates by binding to the cellular conformer of the prion protein (PrP^C) expressed by host cells and rendering its secondary structure a likeness of itself. PrP^C is a plasma membrane anchored protein, which constitutively recirculates between the cell surface and the endocytic compartment. Since PrP^Sc engages PrP^C along this trafficking pathway, its replication process is often referred to as "recycling propagation." Certain monoclonal antibodies (mAbs) directed against prion protein can abrogate the presence of PrP^Sc from prion-infected cells. However, the precise mechanism(s) underlying their therapeutic propensities remains obscure. Using N2A murine neuroblastoma cell line stably infected with 22L mouse-adapted scrapie strain (N2A/22L), we investigated here the modus operandi of the 6D11 clone, which was raised against the PrP^Sc conformer and has been shown to permanently clear prion-infected cells from PrP^Sc presence. We determined that 6D11 mAb engages and sequesters PrP^C and PrP^Sc at the cell surface. PrP^C/6D11 and PrP^Sc/6D11 complexes are then endocytosed from the plasma membrane and are directed to lysosomes, therefore precluding recirculation of nascent PrP^Sc back to the cell surface. Targeting PrP^Sc by 6D11 mAb to the lysosomal compartment facilitates its proteolysis and eventually shifts the balance between PrP^Sc formation and degradation. Ongoing translation of PrP^C allows maintaining the steady-state level of prion protein within the cells, which was not depleted under 6D11 mAb treatment. Our findings demonstrate that through disrupting recycling propagation of PrP^Sc and promoting its degradation, 6D11 mAb restores cellular proteostasis of prion protein.

With the rapid growth of modern technology, many biomedical studies are being conducted to collect massive datasets with volumes of multi-modality imaging, genetic, neurocognitive, and clinical information from increasingly large cohorts. Simultaneously extracting and integrating rich and diverse heterogeneous information in neuroimaging and/or genomics from these big datasets could transform our understanding of how genetic variants impact brain structure and function, cognitive function, and brain-related disease risk across the lifespan. Such understanding is critical for diagnosis, prevention, and treatment of numerous complex brain-related disorders (e.g., schizophrenia and Alzheimer's disease). However, the development of analytical methods for the joint analysis of both high-dimensional imaging phenotypes and high-dimensional genetic data, a big data squared (BD2) problem, presents major computational and theoretical challenges for existing analytical methods. Besides the high-dimensional nature of BD2, various neuroimaging measures often exhibit strong spatial smoothness and dependence and genetic markers may have a natural dependence structure arising from linkage disequilibrium. We review some recent developments of various statistical techniques for imaging genetics, including massive univariate and voxel-wise approaches, reduced rank regression, mixture models, and group sparse multi-task regression. By doing so, we hope that this review may encourage others in the statistical community to enter into this new and exciting field of research.

Alzheimer's disease (AD) is the most common type of dementia in the elderly with no effective treatment currently. Recent studies of noninvasive neuroimaging, resting-state functional magnetic resonance imaging (rs-fMRI) with graph theoretical analysis have shown that patients with AD and mild cognitive impairment (MCI) exhibit disrupted topological organization in large-scale brain networks. In previous work, it is a common practice to threshold such networks. However, it is not only difficult to make a principled choice of threshold values, but also worse is the discard of potential important information. To address this issue, we propose a threshold-free feature by integrating a prior persistent homology-based topological feature (the zeroth Betti number) and a newly defined connected component aggregation cost feature to model brain networks over all possible scales. We show that the induced topological feature (Integrated Persistent Feature) follows a monotonically decreasing convergence function and further propose to use its slope as a concise and persistent brain network topological measure. We apply this measure to study rs-fMRI data from the Alzheimer's Disease Neuroimaging Initiative and compare our approach with five other widely used graph measures across five parcellation schemes ranging from 90 to 1,024 region-of-interests. The experimental results demonstrate that the proposed network measure shows more statistical power and stronger robustness in group difference studies in that the absolute values of the proposed measure of AD are lower than MCI and much lower than normal controls, providing empirical evidence for decreased functional integration in AD dementia and MCI.

The entorhinal-hippocampal circuit is a strategic hub for cognition and the first site affected by Alzheimer's disease (AD). We investigated magnetic resonance imaging patterns of brain atrophy and functional connectivity in an Alzheimer's Disease Neuroimaging Initiative data set that included healthy controls, mild cognitive impairment (MCI), and patients with AD. Individuals with MCI were clinically evaluated 24Â months after the first magnetic resonance imaging scan, and the cohort subdivided into sets of individuals who either did or did not convert to AD. The MCI group was also divided into patients who did show or not the presence of AD-related alterations in the cerebrospinal fluid. Patients with AD exhibited the collapse of the long-range hippocampal/entorhinal connectivity, pronounced cortical/subcortical atrophy, and a dramatic decline in cognitive performances. Patients with MCI who converted to AD or patients with MCI who showed the presence of AD-related alterations in the cerebrospinal fluid showed memory deficits, entorhinal/hippocampal hypoconnectivity, and concomitant atrophy of the two regions. Patients with MCI who did not convert to AD or patients with MCI who did not show the presence of AD-related alterations in the cerebrospinal fluid had no atrophy but showed hippocampal/entorhinal hyperconnectivity with selected neocortical/subcortical regions involved in memory processing and brain metastability. This hyperconnectivity may represent a compensatory strategy against the progression of cognitive impairment.

The International Genomics of Alzheimer's Project (IGAP) is a consortium for characterizing the genetic landscape of Alzheimer's disease (AD). The identified and/or confirmed 19 single-nucleotide polymorphisms (SNPs) associated with AD are located on non-coding DNA regions, and their functional impacts on AD are as yet poorly understood. We evaluated the roles of the IGAP SNPs by integrating data from many resources, based on whether the IGAP SNP was (1) a proxy for a coding SNP or (2) associated with altered mRNA transcript levels. For (1), we confirmed that 12 AD-associated coding common SNPs and five nonsynonymous rare variants are in linkage disequilibrium with the IGAP SNPs. For (2), the IGAP SNPs in CELF1 and MS4A6A were associated with expression of their neighboring genes, MYBPC3 and MS4A6A, respectively, in blood. The IGAP SNP in DSG2 was an expression quantitative trait loci (eQTL) for DLGAP1 and NETO1 in the human frontal cortex. The IGAP SNPs in ABCA7, CD2AP, and CD33 each acted as eQTL for AD-associated genes in brain. Our approach for identifying proxies and examining eQTL highlighted potentially impactful, novel gene regulatory phenomena pertinent to the AD phenotype.

INTRODUCTION:Bile acids (BAs) are the end products of cholesterol metabolism produced by human and gut microbiome co-metabolism. Recent evidence suggests gut microbiota influence pathological features of Alzheimer's disease (AD) including neuroinflammation and amyloid-β deposition. METHOD:F]FDG PET). RESULTS:("A") and three with CSF p-tau181 ("T") (corrected P < .05). Furthermore, three, twelve, and fourteen BA signatures were associated with CSF t-tau, glucose metabolism, and atrophy ("N"), respectively (corrected P < .05). DISCUSSION:This is the first study to show serum-based BA metabolites are associated with "A/T/N" AD biomarkers, providing further support for a role of BA pathways in AD pathophysiology. Prospective clinical observations and validation in model systems are needed to assess causality and specific mechanisms underlying this association.

BACKGROUND:Little is known with respect to behavioral markers of subjective cognitive decline (SCD), a condition initially described in association with Global Deterioration Scale (GDS) stage 2. OBJECTIVE:Two-year interval behavioral markers were investigated herein. METHODS:Subjects from a published 7-year outcome study of GDS stage 2 subjects were selected. This study had demonstrated a hazard ratio of 4.5 for progression of GDS stage 2, in comparison with GDS stage 1 (no subjective or objective cognitive decline) subjects, after controlling for demographic and temporal variables. Because GDS 2 subjects have previously demonstrated impairment in comparison with healthy persons free of complaints, we herein suggest the terminology "SCD(I)" for these persons. 98 SCD(I) persons, 63 women and 35 men, mean baseline age, 67.12±8.75 years, with a mean educational background of 15.55±2.60 years, and mean baseline MMSE scores of 28.9±1.24 were followed for 2.13±0.30 years. RESULTS:Observed annual decline on the GDS was 6.701% per annum, very close to a 1986 published estimate. At follow up, the MMSE, and 7 of 8 psychometric tests did not decline significantly. Of 21 Hamilton Depression Scale items, 2 improved and the remainder were unchanged. Anxieties declined from multiple perspectives. The Brief Cognitive Rating Scale (BCRS) declined significantly (p < 0.001), with component declines in Remote memory (p < 0.01), and Functioning/self-care (p = 0.01). CONCLUSION/CONCLUSIONS:SCD(I) persons decline at an annual rate of approximately 6.7% /year from several recent studies. The BCRS assessments and the Digit Symbol Substitution Test can be sensitive measures for future studies of progression mitigation.

Resting-state functional magnetic resonance imaging (rs-fMRI) based on the blood-oxygen-level-dependent (BOLD) signal has been widely used in healthy individuals and patients to investigate brain functions when the subjects are in a resting or task-negative state. Head motion considerably confounds the interpretation of rs-fMRI data. Nuisance regression is commonly used to reduce motion-related artifacts with six motion parameters estimated from rigid-body realignment as regressors. To further compensate for the effect of head movement, the first-order temporal derivatives of motion parameters and squared motion parameters were proposed previously as possible motion regressors. However, these additional regressors may not be sufficient to model the impact of head motion because of the complexity of motion artifacts. In addition, while using more motion-related regressors could explain more variance in the data, the neural signal may also be removed with increasing number of motion regressors. To better model how in-scanner motion affects rs-fMRI data, a robust and automated convolutional neural network (CNN) model is developed in this study to obtain optimal motion regressors. The CNN network consists of two temporal convolutional layers and the output from the network are the derived motion regressors used in the following nuisance regression. The temporal convolutional layer in the network can non-parametrically model the prolonged effect of head motion. The set of regressors derived from the neural network is compared with the same number of regressors used in a traditional nuisance regression approach. It is demonstrated that the CNN-derived regressors can more effectively reduce motion-related artifacts.