Andrea Lessa Benedet

Tracking neuroinflammation in Alzheimer’s disease: the role of positron emission tomography imaging

Alzheimer’s disease (AD) has been reconceptualized as a dynamic pathophysiological process, where the accumulation of amyloid-beta (Aβ) is thought to trigger a cascade of neurodegenerative events resulting in cognitive impairment and, eventually, dementia. In addition to Aβ pathology, various lines of research have implicated neuroinflammation as an important participant in AD pathophysiology. Currently, neuroinflammation can be measured in vivo using positron emission tomography (PET) with ligands targeting diverse biological processes such as microglial activation, reactive astrocytes and phospholipase A2 activity. In terms of therapeutic strategies, despite a strong rationale and epidemiological studies suggesting that the use of non-steroidal anti-inflammatory drugs (NSAIDs) may reduce the prevalence of AD, clinical trials conducted to date have proven inconclusive. In this respect, it has been hypothesized that NSAIDs may only prove protective if administered early on in the disease course, prior to the accumulation of significant AD pathology. In order to test various hypotheses pertaining to the exact role of neuroinflammation in AD, studies in asymptomatic carriers of mutations deterministic for early-onset familial AD may prove of use. In this respect, PET ligands for neuroinflammation may act as surrogate markers of disease progression, allowing for the development of more integrative models of AD, as well as for the measuring of target engagement in the context of clinical trials using NSAIDs. In this review, we address the biological basis of neuroinflammatory changes in AD, underscore therapeutic strategies using anti-inflammatory compounds, and shed light on the possibility of tracking neuroinflammation in vivo using PET imaging ligands.

Amyloid-β and hyperphosphorylated tau synergy drives metabolic decline in preclinical Alzheimer’s disease

This study was designed to test the interaction between amyloid-β and tau proteins as a determinant of metabolic decline in preclinical Alzheimer’s disease (AD). We assessed 120 cognitively normal individuals with [18F]florbetapir positron emission tomography (PET) and cerebrospinal fluid (CSF) measurements at baseline, as well as [18F]fluorodeoxyglucose ([18F]FDG) PET at baseline and at 24 months. A voxel-based interaction model was built to test the associations between continuous measurements of CSF biomarkers, [18F]florbetapir and [18F]FDG standardized uptake value ratios (SUVR). We found that the synergistic interaction between [18F]florbetapir SUVR and CSF phosphorylated tau (p-tau) measurements, rather than the sum of their independent effects, was associated with a 24-month metabolic decline in basal and mesial temporal, orbitofrontal, and anterior and posterior cingulate cortices (P<0.001). In contrast, interactions using CSF amyloid-β1–42 and total tau biomarkers did not associate with metabolic decline over a time frame of 24 months. The interaction found in this study further support the framework that amyloid-β and hyperphosphorylated tau aggregates synergistically interact to cause downstream AD neurodegeneration. In fact, the regions displaying the metabolic decline reported here were confined to brain networks affected early by amyloid-β plaques and neurofibrillary tangles. Preventive clinical trials may benefit from using a combination of amyloid-β PET and p-tau biomarkers to enrich study populations of cognitively normal subjects with a high probability of disease progression in studies, using [18F]FDG as a biomarker of efficacy.

Neuropsychiatric symptoms predict hypometabolism in preclinical Alzheimer disease

Objective: To identify regional brain metabolic dysfunctions associated with neuropsychiatric symptoms (NPS) in preclinical Alzheimer disease (AD). Methods: We stratified 115 cognitively normal individuals into preclinical AD (both amyloid and tau pathologies present), asymptomatic at risk for AD (either amyloid or tau pathology present), or healthy controls (no amyloid or tau pathology present) using [18F]florbetapir PET and CSF phosphorylated tau biomarkers. Regression and voxel-based regression models evaluated the relationships between baseline NPS measured by the Neuropsychiatric Inventory (NPI) and baseline and 2-year change in metabolism measured by [18F]fluorodeoxyglucose (FDG) PET. Results: Individuals with preclinical AD with higher NPI scores had higher [18F]FDG uptake in the posterior cingulate cortex (PCC), ventromedial prefrontal cortex, and right anterior insula at baseline. High NPI scores predicted subsequent hypometabolism in the PCC over 2 years only in individuals with preclinical AD. Sleep/nighttime behavior disorders and irritability and lability were the components of the NPI that drove this metabolic dysfunction. Conclusions: The magnitude of NPS in preclinical cases, driven by sleep behavior and irritability domains, is linked to transitory metabolic dysfunctions within limbic networks vulnerable to the AD process and predicts subsequent PCC hypometabolism. These findings support an emerging conceptual framework in which NPS constitute an early clinical manifestation of AD pathophysiology.

Synergistic interaction between amyloid and tau predicts the progression to dementia

Recent literature proposes that amyloid β (Aβ) and phosphorylated tau (p‐tau) synergism accelerates biomarker abnormalities in controls. Yet, it remains to be answered whether this synergism is the driving force behind Alzheimer disease (AD) dementia.

VoxelStats: A MATLAB Package for Multi-Modal Voxel-Wise Brain Image Analysis

In healthy individuals, behavioral outcomes are highly associated with the variability on brain regional structure or neurochemical phenotypes. Similarly, in the context of neurodegenerative conditions, neuroimaging reveals that cognitive decline is linked to the magnitude of atrophy, neurochemical declines, or concentrations of abnormal protein aggregates across brain regions. However, modeling the effects of multiple regional abnormalities as determinants of cognitive decline at the voxel level remains largely unexplored by multimodal imaging research, given the high computational cost of estimating regression models for every single voxel from various imaging modalities. VoxelStats is a voxel-wise computational framework to overcome these computational limitations and to perform statistical operations on multiple scalar variables and imaging modalities at the voxel level. VoxelStats package has been developed in Matlab® and supports imaging formats such as Nifti-1, ANALYZE, and MINC v2. Prebuilt functions in VoxelStats enable the user to perform voxel-wise general and generalized linear models and mixed effect models with multiple volumetric covariates. Importantly, VoxelStats can recognize scalar values or image volumes as response variables and can accommodate volumetric statistical covariates as well as their interaction effects with other variables. Furthermore, this package includes built-in functionality to perform voxel-wise receiver operating characteristic analysis and paired and unpaired group contrast analysis. Validation of VoxelStats was conducted by comparing the linear regression functionality with existing toolboxes such as glim_image and RMINC. The validation results were identical to existing methods and the additional functionality was demonstrated by generating feature case assessments (t-statistics, odds ratio, and true positive rate maps). In summary, VoxelStats expands the current methods for multimodal imaging analysis by allowing the estimation of advanced regional association metrics at the voxel level.

Cytokine gene polymorphisms and Alzheimer's disease in Brazil.

Background: Single-nucleotide polymorphisms in genes encoding immunological mediators can affect the biological activity of these molecules by regulating transcription, translation, or secretion, modulating the genetic risk of inflammatory damage in Alzheimers disease (AD). Nonetheless, the Brazilian contingent is highly admixed, and few association trials performed herein with AD patients have considered genetic ancestry estimates as co-variables when investigating markers for this complex trait. Methods: We analyzed polymorphisms in 10 inflammatory genes and compared the genotype distribution across outpatients with late-onset AD and noncognitively impaired subjects from Midwest Brazil under a strict criterion, and controlling for ancestry heritage and ApoE genotype. Results: Our findings show an almost 40% lower chance of AD (p = 0.004) among homozygotes of the IL10 -1082A allele (rs1800896). Dichotomization to ApoE and mean ancestry levels did not affect protection, except among those with greater European or minor African heritage. Conclusion: The IL10 locus seems to affect the onset of AD in a context sensitive to the genetic ancestry of Brazilian older adults.

Epistasis analysis links immune cascades and cerebral amyloidosis.

BackgroundSeveral lines of evidence suggest the involvement of neuroinflammatory changes in Alzheimer’s disease (AD) pathophysiology such as amyloidosis and neurodegeneration. In fact, genome-wide association studies (GWAS) have shown a link between genes involved in neuroinflammation and AD. In order to further investigate whether interactions between candidate genetic variances coding for neuroinflammatory molecules are associated with brain amyloid β (Aβ) fibrillary accumulation, we conducted an epistasis analysis on a pool of genes associated with molecular mediators of inflammation.Methods[18F]Florbetapir positron emission tomography (PET) imaging was employed to assess brain Aβ levels in 417 participants from ADNI-GO/2 and posteriorly 174 from ADNI-1. IL-1β, IL4, IL6, IL6r, IL10, IL12, IL18, C5, and C9 genes were chosen based on previous studies conducted in AD patients. Using the [18F]florbetapir standardized uptake value ratio (SUVR) as a quantitative measure of fibrillary Aβ, epistasis analyses were performed between two sets of markers of immune-related genes using gender, diagnosis, and apolipoprotein E (APOE) as covariates. Voxel-based analyses were also conducted. The results were corrected for multiple comparison tests. Cerebrospinal fluid (CSF) Aβ1-42/phosphorylated tau (p-tau) ratio concentrations were used to confirm such associations.ResultsEpistasis analysis unveiled two significant single nucleotide polymorphism (SNP)-SNP interactions (false discovery rate (FDR) threshold 0.1), both interactions between C9 gene (rs261752) and IL6r gene (rs4240872, rs7514452). In a combined sample, the interactions were confirmed (p ≤ 10–5) and associated with amyloid accumulation within cognitively normal and AD spectrum groups. Voxel-based analysis corroborated initial findings. CSF biomarker (Aβ1-42/p-tau) confirmed the genetic interaction. Additionally, rs4240872 and rs7514452 SNPs were shown to be associated with CSF and plasma concentrations of IL6r protein.ConclusionsCertain allele combinations involving IL6r and C9 genes are associated with Aβ burden in the brain. Hypothesis-driven search for epistasis is a valuable strategy for investigating imaging endophenotypes in complex neurodegenerative diseases.

Imaging Alzheimer's disease pathophysiology with PET

ABSTRACT Alzheimers disease (AD) has been reconceptualised as a dynamic pathophysiological process characterized by preclinical, mild cognitive impairment (MCI), and dementia stages. Positron emission tomography (PET) associated with various molecular imaging agents reveals numerous aspects of dementia pathophysiology, such as brain amyloidosis, tau accumulation, neuroreceptor changes, metabolism abnormalities and neuroinflammation in dementia patients. In the context of a growing shift toward presymptomatic early diagnosis and disease-modifying interventions, PET molecular imaging agents provide an unprecedented means of quantifying the AD pathophysiological process, monitoring disease progression, ascertaining whether therapies engage their respective brain molecular targets, as well as quantifying pharmacological responses. In the present study, we highlight the most important contributions of PET in describing brain molecular abnormalities in AD.

Anosognosia predicts default mode network hypometabolism and clinical progression to dementia

Objective To identify the pathophysiologic mechanisms and clinical significance of anosognosia for cognitive decline in mild cognitive impairment. Methods We stratified 468 patients with amnestic mild cognitive impairment into intact and impaired awareness groups, determined by the discrepancy between the patient and the informant score on the Everyday Cognition questionnaire. Voxel-based linear regression models evaluated the associations between self-awareness status and baseline β-amyloid load, measured by [18F]florbetapir, and the relationships between awareness status and regional brain glucose metabolism measured by [18F]fluorodeoxyglucose at baseline and at 24-month follow-up. Multivariate logistic regression tested the association of awareness status with conversion from amnestic mild cognitive impairment to dementia. Results We found that participants with impaired awareness had lower [18F]fluorodeoxyglucose uptake and increased [18F]florbetapir uptake in the posterior cingulate cortex at baseline. In addition, impaired awareness in mild cognitive impairment predicted [18F]fluorodeoxyglucose hypometabolism in the posterior cingulate cortex, left basal forebrain, bilateral medial temporal lobes, and right lateral temporal lobe over 24 months. Furthermore, participants with impaired awareness had a nearly 3-fold increase in likelihood of conversion to dementia within a 2-year time frame. Conclusions Our results suggest that anosognosia is linked to Alzheimer disease pathophysiology in vulnerable structures, and predicts subsequent hypometabolism in the default mode network, accompanied by an increased risk of progression to dementia. This highlights the importance of assessing awareness of cognitive decline in the clinical evaluation and management of individuals with amnestic mild cognitive impairment.

Principal component of explained variance: An efficient and optimal data dimension reduction framework for association studies.

The genomics era has led to an increase in the dimensionality of data collected in the investigation of biological questions. In this context, dimension-reduction techniques can be used to summarise high-dimensional signals into low-dimensional ones, to further test for association with one or more covariates of interest. This paper revisits one such approach, previously known as principal component of heritability and renamed here as principal component of explained variance (PCEV). As its name suggests, the PCEV seeks a linear combination of outcomes in an optimal manner, by maximising the proportion of variance explained by one or several covariates of interest. By construction, this method optimises power; however, due to its computational complexity, it has unfortunately received little attention in the past. Here, we propose a general analytical PCEV framework that builds on the assets of the original method, i.e. conceptually simple and free of tuning parameters. Moreover, our framework extends the range of applications of the original procedure by providing a computationally simple strategy for high-dimensional outcomes, along with exact and asymptotic testing procedures that drastically reduce its computational cost. We investigate the merits of the PCEV using an extensive set of simulations. Furthermore, the use of the PCEV approach is illustrated using three examples taken from the fields of epigenetics and brain imaging.