Faculty Bibliography

BACKGROUND: APOE genotype is the foremost genetic factor modulating beta-amyloid (Abeta) deposition and risk of sporadic Alzheimer's disease (AD). Here we investigated how APOE genotype influences response to anti-Abeta immunotherapy. METHODS: APPSW/PS1dE9 (APP) transgenic mice with targeted replacement of the murine Apoe gene for human APOE alleles received 10D5 anti-Abeta or TY11-15 isotype control antibodies between the ages of 12 and 15 months. RESULTS: Anti-Abeta immunization decreased both the load of fibrillar plaques and the load of Abeta immunopositive plaques in mice of all APOE backgrounds. Although the relative reduction in parenchymal Abeta plaque load was comparable across all APOE genotypes, APP/epsilon4 mice showed the greatest reduction in the absolute Abeta plaque load values, given their highest baseline. The immunization stimulated phagocytic activation of microglia, which magnitude adjusted for the post-treatment plaque load was the greatest in APP/epsilon4 mice implying association between the epsilon4 allele and impaired Abeta phagocytosis. Perivascular hemosiderin deposits reflecting ensued microhemorrhages were associated with vascular Abeta (VAbeta) and ubiquitously present in control mice of all APOE genotypes, although in APP/epsilon3 mice their incidence was the lowest. Anti-Abeta immunization significantly reduced VAbeta burden but increased the number of hemosiderin deposits across all APOE genotypes with the strongest and the weakest effect in APP/epsilon2 and APP/epsilon3 mice, respectively. CONCLUSIONS: Our studies indicate that APOE genotype differentially modulates microglia activation and Abeta plaque load reduction during anti-Abeta immunotherapy. The APOE epsilon3 allele shows strong protective effect against immunotherapy associated microhemorrhages; while, conversely, the APOE epsilon2 allele increases risk thereof.

Background: Ongoing research is focusing on the identification of those individuals with mild cognitive impairment (MCI) who are most likely to convert to Alzheimer's disease (AD). We investigated whether recognition memory tasks in combination with delayed recall measure of episodic memory and CSF biomarkers can predict MCI to AD conversion at 24-month follow-up. Methods: A total of 397 amnestic-MCI subjects from Alzheimer's disease Neuroimaging Initiative were included. Logistic regression modeling was done to assess the predictive value of all RAVLT measures, risk factors such as age, sex, education, APOE genotype, and CSF biomarkers for progression to AD. Estimating adjusted odds ratios was used to determine which variables would produce an optimal predictive model, and whether adding tests of interaction between the RAVLT Delayed Recall and recognition measures (traditional score and d-prime) would improve prediction of the conversion from a-MCI to AD. Results: 112 (28.2%) subjects developed dementia and 285 (71.8%) subjects did not. Of the all included variables, CSF Aβ1-42 levels, RAVLT Delayed Recall, and the combination of RAVLT Delayed Recall and d-prime were predictive of progression to AD (χ2 = 38.23, df = 14, p < 0.001). Conclusions: The combination of RAVLT Delayed Recall and d-prime measures may be predictor of conversion from MCI to AD in the ADNI cohort, especially in combination with amyloid biomarkers. A predictive model to help identify individuals at-risk for dementia should include not only traditional episodic memory measures (delayed recall or recognition), but also additional variables (d-prime) that allow the homogenization of the assessment procedures in the diagnosis of MCI.

Alzheimer's Disease (AD) is the most common neurodegenerative disease in elderly people, and current drugs, unfortunately, do not represent yet a cure but only slow down its progression. This is explained, at least in part, because the understanding of the neurodegenerative process is still incomplete, being sometimes mistaken, particularly at the first steps of the illness, with the natural aging process. A better identification of how the functional activity deteriorates is thus crucial to develop new and more effective treatments. Sparse inverse covariance estimates (SICE) have been recently employed for deriving functional connectivity patterns from Positron Emission Tomography (PET) of brains affected by Alzheimer's Disease. SICE, unlike the traditional covariance methods, allows to analyze the interdependencies between brain regions factoring out the influence of others. To analyze the effects of the illness, connectivity patterns of brains affected by AD are compared with those obtained for control groups. These comparisons are, however, carried out for binary (undirected and unweighted) adjacency matrices with the same number of arcs. Additionally, the effect of the number of subjects employed or the validity of the regularization parameter used to compute the SICE have been not hitherto analyzed. In this paper, we delve into the construction of connectivity patterns from PET using SICE. In particular, we describe the effect that the number of subjects employed has on the results and identify, based on the reconstruction error of linear regression systems, a range of valid values for the regularization parameter. The amount of arcs is also proved as a discriminant value, and we show that it is possible to pass from unweighted (binary) to weighted adjacency matrices, where the weight of a connection corresponding to the existence of a relationship between two brain areas can be correlated to the persistence of this relationship when computed for different values of the regularization parameter and sets of subjects. Finally, network measures are computed for the connectivity patterns confirming that SICE may be particularly apt for assessing the efficiency of drugs, since it produces reliable brain connectivity models with small sample sizes, and that connectivity patterns affected by AD seem much less segregated, reducing the small-worldness.

We used a data-driven Bayesian model to automatically identify distinct latent factors of overlapping atrophy patterns from voxelwise structural MRIs of late-onset Alzheimer's disease (AD) dementia patients. Our approach estimated the extent to which multiple distinct atrophy patterns were expressed within each participant rather than assuming that each participant expressed a single atrophy factor. The model revealed a temporal atrophy factor (medial temporal cortex, hippocampus, and amygdala), a subcortical atrophy factor (striatum, thalamus, and cerebellum), and a cortical atrophy factor (frontal, parietal, lateral temporal, and lateral occipital cortices). To explore the influence of each factor in early AD, atrophy factor compositions were inferred in beta-amyloid-positive (Aβ+) mild cognitively impaired (MCI) and cognitively normal (CN) participants. All three factors were associated with memory decline across the entire clinical spectrum, whereas the cortical factor was associated with executive function decline in Aβ+ MCI participants and AD dementia patients. Direct comparison between factors revealed that the temporal factor showed the strongest association with memory, whereas the cortical factor showed the strongest association with executive function. The subcortical factor was associated with the slowest decline for both memory and executive function compared with temporal and cortical factors. These results suggest that distinct patterns of atrophy influence decline across different cognitive domains. Quantification of this heterogeneity may enable the computation of individual-level predictions relevant for disease monitoring and customized therapies. Factor compositions of participants and code used in this article are publicly available for future research.

Complex physiological and behavioral traits, including neurological and psychiatric disorders, often associate with distributed anatomical variation. This paper introduces a global metric, called morphometricity, as a measure of the anatomical signature of different traits. Morphometricity is defined as the proportion of phenotypic variation that can be explained by macroscopic brain morphology. We estimate morphometricity via a linear mixed-effects model that uses an anatomical similarity matrix computed based on measurements derived from structural brain MRI scans. We examined over 3,800 unique MRI scans from nine large-scale studies to estimate the morphometricity of a range of phenotypes, including clinical diagnoses such as Alzheimer's disease, and nonclinical traits such as measures of cognition. Our results demonstrate that morphometricity can provide novel insights about the neuroanatomical correlates of a diverse set of traits, revealing associations that might not be detectable through traditional statistical techniques.

Introduction Abnormal changes in the neurovascular architecture are associated with numerous conditions including tumors, Alzheimer's disease, and diabetes. Pre-clinical mouse models are invaluable in our understanding, diagnosis and treatment of such conditions, but we have yet to see a longitudinal assessment of neurovascular changes in wild type (WT) control mice. Contrast enhanced-magnetic resonance angiography (CE-MRA) utilizes an exogenous contrast agent to study neurovasculature clinically and pre-clinically with little hemodynamic-dependence. Here, we implemented 3D in vivo CE-MRA to longitudinally study the neurovasculature of aging WT mice. This study provides insight into the normal aging process in WT mice and could serve as a baseline for future studies of neurovascular disease models. Materials and Methods Gadolinium (Gd)-bound micelles were synthesized to serve as a blood pool agent via a previously described thin-film method¹ combining Gd-DPTA, polyethylene glycol, and Rhodamine B-bound lipids. Assessment of size, relaxivity, and plasma half-life confirmed the imaging potential of this compound. Gd-micelles were administered via femoral injection into female WT C57BL/6 mice; the most widely used inbred strain for models of human disease². Twentyseven micelle-administered mice were imaged between ages 2-to-26 months (mo). A subset of mice was aged and imaged at 2-4mo, 14-16mo, and 24-26mo to assess variability in neurovascular changes of individual mice. Angiograms were acquired on a 7-Tesla Bruker micro-MRI system with an 87-minute (100mum) ³ isotropic resolution scan. Neurovascular analysis was applied to anatomically identifiable regions following brain alignment with software by the Mouse Imaging Center (Toronto, Canada)3 and tools developed by the Montreal Neurological Institute (Montreal, Canada). Neurovascular changes were quantified using intensity-based vascular thresholding and segmentation (see figure). Results Quantification of the whole brain showed a significant decrease in detectable neurovasculature between the 2-4mo and 14-16mo groups (p<0.01, one-way ANOVA plus Bonferroni test). A reduction was again seen in the second year of aging. We also quantified the neurovascular changes of the cortex, circle of willis, and sagittal midline and found a significant reduction during the first year of aging and further reduction in the second year. However, the hippocampus showed no significant neurovascular changes. Conclusion Gd micelle-enhanced MRA allowed for the detection of an overall decline in the neurovascular volume of aging C57BL/6 mice. To our knowledge, this is the first report of an age-dependent neurovascular reduction in longitudinally monitored WT animals. These unexpected results stress the need to establish a baseline using control animals of the same background when studying transgenic models of neurovascular diseases. Such reductions may also explain age-dependent changes in cerebral blood flow and function. (Figure Presented)

Depressive symptoms are frequently seen in patients with dementia and mild cognitive impairment (MCI). Evidence suggests that there may be a link between current depressive symptoms and Alzheimer disease (AD)-associated pathological changes, such as an increase in cortical amyloid-β (Aβ). However, limited in vivo studies have explored the relationship between current depressive symptoms and cortical Aβ in patients with MCI and AD. Our study, using a large sample of 455 patients with MCI and 153 patients with AD from the Alzheimer's disease Neuroimaging Initiatives, investigated whether current depressive symptoms are related to cortical Aβ deposition. Depressive symptoms were assessed using the Geriatric Depression Scale and Neuropsychiatric Inventory-depression/dysphoria. Cortical Aβ was quantified using positron emission tomography with the Aβ probe(18)F-florbetapir (AV-45).(18)F-florbetapir standardized uptake value ratio (AV-45 SUVR) from the frontal, cingulate, parietal, and temporal regions was estimated. A global AV-45 SUVR, defined as the average of frontal, cingulate, precuneus, and parietal cortex, was also used. We observed that current depressive symptoms were not related to cortical Aβ, after controlling for potential confounds, including history of major depression. We also observed that there was no difference in cortical Aβ between matched participants with high and low depressive symptoms, as well as no difference between matched participants with the presence and absence of depressive symptoms. The association between depression and cortical Aβ deposition does not exist, but the relationship is highly influenced by stressful events in the past, such as previous depressive episodes, and complex interactions of different pathways underlying both depression and dementia.

PURPOSE/OBJECTIVE:Posterior cingulate cortex (PCC) hypometabolism as measured by FDG PET is an indicator of Alzheimer's disease (AD) in prodromal stages, such as in mild cognitive impairment (MCI), and has been found to be closely associated with hippocampus atrophy in AD dementia. We studied the effects of local and remote atrophy and of local amyloid load on the PCC metabolic signal in patients with different preclinical and clinical stages of AD. METHODS:We determined the volume of the hippocampus and PCC grey matter based on volumetric MRI scans, PCC amyloid load based on AV45 PET, and PCC metabolism based on FDG PET in 667 subjects participating in the Alzheimer's Disease Neuroimaging Initiative spanning the range from cognitively normal ageing through prodromal AD to AD dementia. RESULTS:In cognitively normal individuals and those with early MCI, PCC hypometabolism was exclusively associated with hippocampus atrophy, whereas in subjects with late MCI it was associated with both local and remote effects of atrophy as well as local amyloid load. In subjects with AD dementia, PCC hypometabolism was exclusively related to local atrophy. CONCLUSION/CONCLUSIONS:Our findings suggest that the effects of remote pathology on PCC hypometabolism decrease and the effects of local pathology increase from preclinical to clinical stages of AD, consistent with a progressive disconnection of the PCC from downstream cortical and subcortical brain regions.

The immune response is highly active in Alzheimer's disease (AD). Identification of genetic risk contributed by immune genes to AD may provide essential insight for the prognosis, diagnosis, and treatment of this neurodegenerative disease. In this study, we performed a genetic screening for AD-related top immune genes identified in Europeans in a Chinese cohort, followed by a multiple-stage study focusing on Complement Factor H (CFH) gene. Effects of the risk SNPs on AD-related neuroimaging endophenotypes were evaluated through magnetic resonance imaging scan, and the effects on AD cerebrospinal fluid biomarkers (CSF) and CFH expression changes were measured in aged and AD brain tissues and AD cellular models. Our results showed that the AD-associated top immune genes reported in Europeans (CR1, CD33, CLU, and TREML2) have weak effects in Chinese, whereas CFH showed strong effects. In particular, rs1061170 (P(meta)=5.0 × 10(-4)) and rs800292 (P(meta)=1.3 × 10(-5)) showed robust associations with AD, which were confirmed in multiple world-wide sample sets (4317 cases and 16 795 controls). Rs1061170 (P=2.5 × 10(-3)) and rs800292 (P=4.7 × 10(-4)) risk-allele carriers have an increased entorhinal thickness in their young age and a higher atrophy rate as the disease progresses. Rs800292 risk-allele carriers have higher CSF tau and Aβ levels and severe cognitive decline. CFH expression level, which was affected by the risk-alleles, was increased in AD brains and cellular models. These comprehensive analyses suggested that CFH is an important immune factor in AD and affects multiple pathological changes in early life and during disease progress.