Spontaneous low frequency BOLD signal variations from resting-state fMRI are decreased in Alzheimer disease

Previous studies have demonstrated altered brain activity in Alzheimer\u27s disease using task based functional MRI (fMRI), network based resting-state fMRI, and glucose metabolism from 18 F fluorodeoxyglucose-PET (FDG-PET). Our goal was to define a novel indicator of neuronal activity based on a first-order textural feature of the resting state functional MRI (RS-fMRI) signal. Furthermore, we examined the association between this neuronal activity metric and glucose metabolism from F-18 FDG-PET. We studied 15 normal elderly controls (NEC) and 15 probable Alzheimer disease (AD) subjects from the AD Neuroimaging Initiative. An independent component analysis was applied to the RS-fMRI, followed by template matching to identify neuronal components (NC). A regional brain activity measurement was constructed based on the variation of the RS-fMRI signal of these NC. The standardized glucose uptake values of several brain regions relative to the cerebellum (SUVR) were measured from partial volume corrected FDG-PET images. Comparing the AD and NEC groups, the mean brain activity metric was significantly lower in the accumbens, while the glucose SUVR was significantly lower in the amygdala and hippocampus. The RS-fMRI brain activity metric was positively correlated with cognitive measures and amyloid beta 1-42 cerebral spinal fluid levels; however, these did not remain significant following Bonferroni correction. There was a significant linear correlation between the brain activity metric and the glucose SUVR measurements. This proof of concept study demonstrates that this novel and easy to implement RS-fMRI brain activity metric can differentiate a group of healthy elderly controls from a group of people with AD

Genome-Wide Association Study for Variants That Modulate Relationships Between Cerebrospinal Fluid Amyloid-Beta 42, Tau, and P-Tau Levels

Background: A relationship quantitative trait locus exists when the correlation between multiple traits varies by genotype for that locus. Relationship quantitative trait loci (rQTL) are often involved in gene-by-gene (G×G) interactions or gene-by-environmental interactions, making them a powerful tool for detecting G×G. Methods: We performed genome-wide association studies to identify rQTL between tau and Aβ42 and ptau and Aβ42 with over 3000 individuals using age, gender, series, APOE ε2, APOE ε4, and two principal components for population structure as covariates. Each significant rQTL was separately screened for interactions with other loci for each trait in the rQTL model. Parametric bootstrapping was used to assess significance. Results: We found four significant tau/Aβ42 rQTL from three unique locations and six ptau/Aβ42 rQTL from five unique locations. G×G screens with these rQTL produced four significant G×G interactions (one Aβ42, two ptau, and one tau) with four rQTL where each second locus was from a unique location. On follow-up, rs1036819 and rs74025622 were associated with Alzheimer’s disease (AD) case/control status; rs15205 and rs79099429 were associated with rate of decline. Conclusions: The two most significant rQTL (rs8027714 and rs1036819) for ptau/Aβ42 are on different chromosomes and both are strong hits for pelvic organ prolapse. While diseases of the nervous system can cause pelvic organ prolapse, it is unlikely related to the ptau/Aβ42 relationship but may suggest that these two loci share a pathway. In addition to a ptau/Aβ42 rQTL and association with AD case/control status, rs1036819 is a strong rQTL for case/control status/Aβ42 and for tau/Aβ42. It resides in the ZFAT gene, which is related to autoimmune thyroid disease. For tau, rs9817620 interacts with the tau/Aβ42 rQTL rs74025622. It is in the CHL1 gene, which is a neural cell adhesion molecule and may be involved in signal transduction pathways. CHL1 is related to BACE1, which is a β-secretase enzyme that initiates production of the β-amyloid peptide involved in AD and is a primary drug target. Overall, there are numerous loci that affect the relationship between these important AD endophenotypes and some are due to interactions with other loci. Some affect the risk of AD and/or rate of progression

Exercise prevents obesity-induced cognitive decline and white matter damage in mice.

Obesity in the western world has reached epidemic proportions, and yet the long-term effects on brain health are not well understood. To address this, we performed transcriptional profiling of brain regions from a mouse model of western diet (WD)-induced obesity. Both the cortex and hippocampus from C57BL/6J (B6) mice fed either a WD or a control diet from 2 months of age to 12 months of age (equivalent to midlife in a human population) were profiled. Gene set enrichment analyses predicted that genes involved in myelin generation, inflammation, and cerebrovascular health were differentially expressed in brains from WD-fed compared to control diet-fed mice. White matter damage and cerebrovascular decline were evident in brains from WD-fed mice using immunofluorescence and electron microscopy. At the cellular level, the WD caused an increase in the numbers of oligodendrocytes and myeloid cells suggesting that a WD is perturbing myelin turnover. Encouragingly, cerebrovascular damage and white matter damage were prevented by exercising WD-fed mice despite mice still gaining a significant amount of weight. Collectively, these data show that chronic consumption of a WD in B6 mice causes obesity, neuroinflammation, and cerebrovascular and white matter damage, but these potentially damaging effects can be prevented by modifiable risk factors such as exercise

Ferritin Levels in the Cerebrospinal Fluid Predict Alzheimer\u27s Disease Outcomes and Are Regulated by APOE

Brain iron elevation is implicated in Alzheimer\u27s disease (AD) pathogenesis, but the impact of iron on disease outcomes has not been previously explored in a longitudinal study. Ferritin is the major iron storage protein of the body; by using cerebrospinal fluid (CSF) levels of ferritin as an index, we explored whether brain iron status impacts longitudinal outcomes in the Alzheimer\u27s Disease Neuroimaging Initiative (ADNI) cohort. We show that baseline CSF ferritin levels were negatively associated with cognitive performance over 7 years in 91 cognitively normal, 144 mild cognitive impairment (MCI) and 67 AD subjects, and predicted MCI conversion to AD. Ferritin was strongly associated with CSF apolipoprotein E levels and was elevated by the Alzheimer\u27s risk allele, APOE-ɛ4. These findings reveal that elevated brain iron adversely impacts on AD progression, and introduce brain iron elevation as a possible mechanism for APOE-ɛ4 being the major genetic risk factor for AD

rPOP: Robust PET-Only Processing of Community Acquired Heterogeneous Amyloid-PET Data

The reference standard for amyloid-PET quantification requires structural MRI (sMRI) for preprocessing in both multi-site research studies and clinical trials. Here we describe rPOP (robust PET-Only Processing), a MATLAB-based MRI-free pipeline implementing non-linear warping and differential smoothing of amyloid-PET scans performed with any of the FDA-approved radiotracers (18F-florbetapir/FBP, 18F-florbetaben/FBB or 18F-flutemetamol/FLUTE). Each image undergoes spatial normalization based on weighted PET templates and data-driven differential smoothing, then allowing users to perform their quantification of choice. Prior to normalization, users can choose whether to automatically reset the origin of the image to the center of mass or proceed with the pipeline with the image as it is. We validate rPOP with n = 740 (514 FBP, 182 FBB, 44 FLUTE) amyloid-PET scans from the Imaging Dementia—Evidence for Amyloid Scanning – Brain Health Registry sub-study (IDEAS-BHR) and n = 1,518 scans from the Alzheimer\u27s Disease Neuroimaging Initiative (n = 1,249 FBP, n = 269 FBB), including heterogeneous acquisition and reconstruction protocols. After running rPOP, a standard quantification to extract Standardized Uptake Value ratios and the respective Centiloids conversion was performed. rPOP-based amyloid status (using an independent pathology-based threshold of ≥24.4 Centiloid units) was compared with either local visual reads (IDEAS-BHR, n = 663 with complete valid data and reads available) or with amyloid status derived from an MRI-based PET processing pipeline (ADNI, thresholds of \u3e20/\u3e18 Centiloids for FBP/FBB). Finally, within the ADNI dataset, we tested the linear associations between rPOP- and MRI-based Centiloid values. rPOP achieved accurate warping for N = 2,233/2,258 (98.9%) in the first pass. Of the N = 25 warping failures, 24 were rescued with manual reorientation and origin reset prior to warping. We observed high concordance between rPOP-based amyloid status and both visual reads (IDEAS-BHR, Cohen\u27s k = 0.72 [0.7–0.74], ∼86% concordance) or MRI-pipeline based amyloid status (ADNI, k = 0.88 [0.87–0.89], ∼94% concordance). rPOP- and MRI-pipeline based Centiloids were strongly linearly related (R2:0.95, p\u3c0.001), with this association being significantly modulated by estimated PET resolution (β= -0.016, p\u3c0.001). rPOP provides reliable MRI-free amyloid-PET warping and quantification, leveraging widely available software and only requiring an attenuation-corrected amyloid-PET image as input. The rPOP pipeline enables the comparison and merging of heterogeneous datasets and is publicly available at https://github.com/leoiacca/rPO

Select Atrophied Regions in Alzheimer disease (SARA): An improved volumetric model for identifying Alzheimer disease dementia

INTRODUCTION: Volumetric biomarkers for Alzheimer disease (AD) are attractive due to their wide availability and ease of administration, but have traditionally shown lower diagnostic accuracy than measures of neuropathological contributors to AD. Our purpose was to optimize the diagnostic specificity of structural MRIs for AD using quantitative, data-driven techniques. METHODS: This retrospective study assembled several non-overlapping cohorts (total n = 1287) with publicly available data and clinical patients from Barnes-Jewish Hospital (data gathered 1990-2018). The Normal Aging Cohort (n = 383) contained amyloid biomarker negative, cognitively normal (CN) participants, and provided a basis for determining age-related atrophy in other cohorts. The Training (n = 216) and Test (n = 109) Cohorts contained participants with symptomatic AD and CN controls. Classification models were developed in the Training Cohort and compared in the Test Cohort using the receiver operating characteristics areas under curve (AUCs). Additional model comparisons were done in the Clinical Cohort (n = 579), which contained patients who were diagnosed with dementia due to various etiologies in a tertiary care outpatient memory clinic. RESULTS: While the Normal Aging Cohort showed regional age-related atrophy, classification models were not improved by including age as a predictor or by using volumetrics adjusted for age-related atrophy. The optimal model used multiple regions (hippocampal volume, inferior lateral ventricle volume, amygdala volume, entorhinal thickness, and inferior parietal thickness) and was able to separate AD and CN controls in the Test Cohort with an AUC of 0.961. In the Clinical Cohort, this model separated AD from non-AD diagnoses with an AUC 0.820, an incrementally greater separation of the cohort than by hippocampal volume alone (AUC of 0.801, p = 0.06). Greatest separation was seen for AD vs. frontotemporal dementia and for AD vs. non-neurodegenerative diagnoses. CONCLUSIONS: Volumetric biomarkers distinguished individuals with symptomatic AD from CN controls and other dementia types but were not improved by controlling for normal aging

The Trend of Disruption in the Functional Brain Network Topology of Alzheimer’s Disease

Alzheimer’s disease (AD) is a progressive disorder associated with cognitive dysfunction that alters the brain’s functional connectivity. Assessing these alterations has become a topic of increasing interest. However, a few studies have examined different stages of AD from a complex network perspective that cover different topological scales. This study used resting state fMRI data to analyze the trend of functional connectivity alterations from a cognitively normal (CN) state through early and late mild cognitive impairment (EMCI and LMCI) and to Alzheimer’s disease. The analyses had been done at the local (hubs and activated links and areas), meso (clustering, assortativity, and rich-club), and global (small-world, small-worldness, and efficiency) topological scales. The results showed that the trends of changes in the topological architecture of the functional brain network were not entirely proportional to the AD progression. There were network characteristics that have changed non-linearly regarding the disease progression, especially at the earliest stage of the disease, i.e., EMCI. Further, it has been indicated that the diseased groups engaged somatomotor, frontoparietal, and default mode modules compared to the CN group. The diseased groups also shifted the functional network towards more random architecture. In the end, the methods introduced in this paper enable us to gain an extensive understanding of the pathological changes of the AD process

Meta-analysis of genome-wide DNA methylation identifies shared associations across neurodegenerative disorders

Background People with neurodegenerative disorders show diverse clinical syndromes, genetic heterogeneity, and distinct brain pathological changes, but studies report overlap between these features. DNA methylation (DNAm) provides a way to explore this overlap and heterogeneity as it is determined by the combined effects of genetic variation and the environment. In this study, we aim to identify shared blood DNAm differences between controls and people with Alzheimer’s disease, amyotrophic lateral sclerosis, and Parkinson’s disease. Results We use a mixed-linear model method (MOMENT) that accounts for the effect of (un)known confounders, to test for the association of each DNAm site with each disorder. While only three probes are found to be genome-wide significant in each MOMENT association analysis of amyotrophic lateral sclerosis and Parkinson’s disease (and none with Alzheimer’s disease), a fixed-effects meta-analysis of the three disorders results in 12 genome-wide significant differentially methylated positions. Predicted immune cell-type proportions are disrupted across all neurodegenerative disorders. Protein inflammatory markers are correlated with profile sum-scores derived from disease-associated immune cell-type proportions in a healthy aging cohort. In contrast, they are not correlated with MOMENT DNAm-derived profile sum-scores, calculated using effect sizes of the 12 differentially methylated positions as weights. Conclusions We identify shared differentially methylated positions in whole blood between neurodegenerative disorders that point to shared pathogenic mechanisms. These shared differentially methylated positions may reflect causes or consequences of disease, but they are unlikely to reflect cell-type proportion differences

The effect of ApoE ε4 on longitudinal brain region-specific glucose metabolism in patients with mild cognitive impairment: a FDG-PET study

While the ApoE ε4 allele is a known risk factor for mild cognitive impairment (MCI) and Alzheimer's disease, brain region specific effects remain elusive. In this study, we investigate whether the ApoE ε4 allele exhibits brain region specific effects in longitudinal glucose uptake among patients with MCI from the Alzheimer's Disease Neuroimaging Initiative (ADNI). Preprocessed FDG PET images, MRIs, and demographic information were downloaded from the ADNI database. An iterative reblurred Van Cittertiteration method was used for partial volume correction (PVC) on all PET images. Structural MRIs were used for PET spatial normalization and region of interest (ROI) definition in standard space. Longitudinal changes in ROI FDG standardized uptake value ratio (SUVR) relative to cerebellum in 24 ApoE ε4 carriers and 24 age-matched ApoE ε4 non-carriers were measured for up to 84-months (median 72 months, SD = 11.2 months) and compared using a generalized linear mixed effects model controlling for gender, education, baseline age, and follow-up period. Additionally, voxelwise analysis was performed by implementing a paired t-test comparing matched baseline and 72 month FDG SUVR images in ApoE carriers and non-carriers separately. Results with PVC were compared with ones from non-PVC based analysis. After applying PVC, the superior fontal, parietal, lateral temporal, medial temporal, caudate, thalamus, and post-cingulate, and amygdala regions had greater longitudinal decreases in FDG uptake in ApoE ε4 carriers with MCI compared to non-carriers with MCI. Similar forebrain and limbic clusters were found through voxelwise analysis. Compared to the PVC based analysis, fewer significant ApoE-associated regions and clusters were found in the non-PVC based PET analysis. Our findings suggest that the ApoE ε4 genotype is associated with a longitudinal decline in glucose uptake in 8 forebrain and limbic brain regions in the context of MCI. In conclusion, this 84-months longitudinal FDG PET study demonstrates a novel ApoE ε4-associated brain-region specific glucose metabolism pattern in patients with MCI. Partial volume correction improved FDG PET quantification. Keywords: Alzheimer's disease, Mild cognitive impairment, ApoE ε4, FDG PET, Longitudinal, Partial volume correctio