Chronic psychological stress has been implicated as a risk factor for Alzheimer’s disease (AD), potentially through cortisol-mediated acceleration of disease progression. However, the molecular pathways underlying this relationship remain poorly understood. Epigenetic regulation of the glucocorticoid and mineralocorticoid receptor genes (NR3C1 and NR3C2), which encode receptors for cortisol, may play an important role, but has not been examined in relation to AD progression. Therefore, this study investigated associations between DNA methylation of NR3C1/NR3C2 and AD-related phenotypes, including cognition, brain amyloid-β (Aβ) burden, and regional brain volumes. These associations were examined in two independent cohorts of cognitively unimpaired individuals with accumulating brain Aβ (n = 89–298 across outcomes) using linear regression and meta-analyses. The study also explored whether DNA methylation within NR3C1 and NR3C2 interacted with depression symptoms to influence relationships with AD-related phenotypes. While only nominal associations were observed in direct analyses, stronger associations emerged in interaction with depressive symptoms. Interaction analyses showed that relationships between DNA methylation and AD-related phenotypes (cognition, hippocampal volume and ventricular expansion) differed depending on the presence of depression symptoms. Consistent patterns across cohorts were observed, with associations primarily evident among individuals with clinically relevant depressive symptoms. One site (NR3C1 cg24052866) was associated with cognitive decline, one (NR3C1 cg08845721) with cross-sectional hippocampal volume, and eight (NR3C1 cg21979215, cg16594263; NR3C2 cg27460943, cg17253842, cg04867484, cg10993059, cg25672354, cg27234800) with ventricular expansion. These exploratory findings suggest epigenetic variation within cortisol receptor genes may influence AD-related neurodegeneration in a depression-dependent manner.

White matter microstructural changes play a crucial role in cognitive decline in aging and neurodegenerative disorders including Alzheimer’s disease (AD). However, the processes underlying white matter microstructural changes and the molecular pathways leading to these changes in AD remain largely unknown. AD involves cortical and juxtacortical microstructural changes, with free water fraction (FWF) as a potential imaging marker. We measured FWF using diffusion magnetic resonance imaging in 68 juxtacortical regions of 153 cognitively normal controls and 194 patients with AD as evidenced by elevated amyloid PET. We estimated the expression of 15,633 genes in the same regions using transcriptomic data from the Allen Human Brain Atlas. The biological processes and cell types associated with the linked genes were evaluated. Mediation analysis was used to examine whether FWF mediates the association between APOE ε4 status and cognitive performance. Gene ontological analyses revealed that these genes were enriched for biological processes relating to lipid metabolic process, ensheathment of neurons, and synaptic signaling and were predominantly expressed in oligodendrocytes, GABAergic neurons, and pyramidal neurons from the hippocampus CA region. These ontological enrichment results were replicated in two additional datasets. Furthermore, mediation analyses revealed a domain-specific role of FWF in the association between APOE ε4 status and cognitive performance. Our findings provide mechanistic insights into regional juxtacortical microstructural changes in AD, particularly the processes involving lipid metabolism, offering potential therapeutic targets.

Brain network dynamics have been extensively explored in patients with subjective cognitive decline (SCD). However, these studies are susceptible to individual differences, scanning parameters, and other confounding factors. Therefore, how to reveal subtle SCD-related subtle changes remains unclear. Cross-sectional and longitudinal resting-state functional magnetic resonance imaging data from both Chinese and Western populations were analyzed. We proposed a framework of dynamic proportional loss of functional connectivity (DPLFC). After its stability was validated, the optimal parameters were applied for the clinical diagnosis of SCD. DPLFC yielded a relatively high intraclass correlation coefficient. In particular, the DPLFC of the left superior frontal gyrus (SFG) progressively decreased along the Alzheimer’s disease (AD) continuum. Compared with the traditional index, the DPLFC had better classification performance between cognitively normal controls and patients with SCD. Furthermore, DPLFC was related to Aβ deposition and scale scores. Patients with lower DPLFC values had a greater risk of cognitive decline. Decreased DPLFC in the left SFG may be a potential AD-related neuroimaging biomarker at an early stage.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

In real-world networks, information from source to destination does not only flow along the shortest path connecting them, but can flow along any alternative route. Communicability is a network metric that accounts for this issue and, especially in diffusion-like processes, provides a reliable measure of the ease of communication between node pairs. Accordingly, communicability appears to be promising for highlighting the disruption of connectivity among brain regions, caused by the white matter degeneration due to Alzheimer's disease (AD). Such a degeneration can be captured by digital imaging techniques, in particular diffusion tensor imaging (DTI), which allow to build the brain connectivity network through tractography algorithms and studying its complexity through graph theory. In this study, a cohort of 122 DTI scans, composed by 52 healthy control (HC) subjects, 40 AD patients and 30 mild cognitive impairment (MCI) converter subjects, from Alzheimer's Disease Neuroimaging Initiative (ADNI) database, has been employed to study the suitability of communicability to serve as discriminant factor for AD. We developed a two-fold investigation. On one hand, a statistical analysis has been carried out to ascertain the information content provided by communicability to detect the brain regions mostly affected by the disease: node pairs with statistical significant different communicability have been found, corresponding to some well-known AD-related brain regions. On the other hand, heterogeneous groups of network features (which include/not include communicability) were input to a support vector machine, to assess the impact of communicability on the classification performances in the HC/AD and the HC/AD/MCI discrimination. The best performances, i.e., AUC = 0.82 in the HC/AD case and multiclass AUC = 0.77 in the HC/AD/MCI task, were obtained by using the values of communicability, outperforming the performance obtained with the other network metrics. In summary, this article suggests that communicability can be promising for an automatized AD diagnosis.

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. The Canadian Journal of Statistics 47: 108-131; 2019 (c) 2019 Statistical Society of Canada

INTRODUCTION: TBI (traumatic brain injury) is associated with an increased risk of late neurodegeneration in chronic TBI survivors. The underlying pathophysiology of trauma-related neurodegeneration is hypothesized to involve a tauopathy, with p-tau deposited in beta-pleated sheets. Current research focuses on identifying strategies to detect trauma-related neurodegeneration in-Vivo. [F-18]AV-1451, a tau-specific PET radiotracer, may detect hyper-phosphorylated tau deposits in living patients. METHODS: Participants with a history of TBI >6 mo prior with concern for cognitive decline with age-matched controls were recruited. Subjects were classified into three groups: few (=3 TBI exposures), intermediate (4-10 exposures), and numerous (>10 exposures). Participants underwent PET imaging with [F-18]AV-1451, and qualitative and semi-quantitative (SUVR) analyses of radiotracer retention were performed. Visual classification of tau positivity (+/−) was performed with absence of established positivity thresholds for [F-18]AV-1451 SUVR values. All subjects underwent neuropsychological evaluation, including measures of processing speed, executive function, and memory. RESULTS: Twenty-seven TBI subjects and 7 controls were enrolled. A total of 9 participants were categorized as few, 2 as intermediate, 7 as numerous. All TBI subjects demonstrated impairment on at least one neurocognitive measure, while control subjects had normal neuropsychological test results. Analysis of [F-18]AV-1451 uptake patterns demonstrated evidence of tauopathy in 3 subjects, based on visual reads. Significantly increased [F-18]AV-1451 retention was noted in occipital gray matter, posterior cingulate gyrus, and parietal cortex in these 3 tau (+) TBI subjects compared to 24 TBI subjects visually classified as tau (−) and also normal controls. CONCLUSION: Evidence of tauopathy, indicative of trauma-related neurodegeneration, was noted in 3 chronic TBI subjects, all of whom were categorized as numerous (>10) TBI exposures and cognitive deficits on neuropsychological testing. No tau PET [F-18]AV-1451 uptake was noted in control participants or in participants categorized as few or intermediate. The data represent a possible [F-18]AV-1451 PET uptake pattern associated with a clinical neurodegeneration syndrome in repetitive TBI.

BACKGROUND:Memory assessment is a key factor for the diagnosis of cognitive impairment. However, memory performance over time may be quite heterogeneous within diagnostic groups.METHOD:To identify latent trajectories in memory performance and their associated risk factors, we analyzed data from Alzheimer's Disease Neuroimaging Initiative (ADNI) participants who were classified either as cognitively normal or as Mild Cognitive Impairment (MCI) at baseline and were administered the Rey Auditory Verbal Learning test (RAVLT) for up to 9 years. Group-based trajectory modeling on the 30-minute RAVLT delayed recall score was applied separately to the two baseline diagnostic groups.RESULTS:There were 219 normal subjects with mean age 75.9 (range from 59.9 to 89.6) and 52.5% male participants, and 372 MCI subjects with mean age 74.8 (range from 55.1 to 89.3) and 63.7% male participants included in the analysis. For normal subjects, six trajectories were identified. Trajectories were classified into three types, determined by the shape, each of which may comprise more than one trajectory: stable (~30% of subjects), curvilinear decline (~ 28%), and linear decline (~ 42%). Notably, none of the normal subjects assigned to the stable stratum progressed to dementia during the study period. In contrast, all trajectories identified for the MCI group tended to decline, although some participants were later re-diagnosed with normal cognition. Age, sex, and education were significantly associated with trajectory membership for both diagnostic groups, while APOE ɛ4 was only significantly associated with trajectories among MCI participants.CONCLUSION:Memory trajectory is a strong indicator of dementia risk. If likely trajectory of memory performance can be identified early, such work may allow clinicians to monitor or predict progression of individual patient cognition. This work also shows the importance of longitudinal cognitive testing and monitoring.

We present an algorithm for creating high resolution anatomically plausible images consistent with acquired clinical brain MRI scans with large inter-slice spacing. Although large data sets of clinical images contain a wealth of information, time constraints during acquisition result in sparse scans that fail to capture much of the anatomy. These characteristics often render computational analysis impractical as many image analysis algorithms tend to fail when applied to such images. Highly specialized algorithms that explicitly handle sparse slice spacing do not generalize well across problem domains. In contrast, we aim to enable application of existing algorithms that were originally developed for high resolution research scans to significantly undersampled scans. We introduce a generative model that captures fine-scale anatomical structure across subjects in clinical image collections and derive an algorithm for filling in the missing data in scans with large inter-slice spacing. Our experimental results demonstrate that the resulting method outperforms state-of-the-art upsampling super-resolution techniques, and promises to facilitate subsequent analysis not previously possible with scans of this quality. Our implementation is freely available at https://github.com/adalca/papago.

AbstractIntroductionCentiloid standardization was developed to establish a quantitative outcome measure of amyloid burden that could accommodate the integration of different amyloid positron emission tomography radiotracers or different methods of quantifying the same tracer. The goal of this study was to examine the use of Centiloids for establishing amyloid classification cutoffs for differing region‐of‐interest (ROI) delineation schemes.MethodsUsing ROIs from hand‐drawn delineation in native space as the gold standard, we compared standard uptake value ratios obtained from the 6 hand‐drawn ROIs that determine amyloid‐positivity classification with standard uptake value ratio obtained from 3 different automated techniques (FreeSurfer, Statistical Parametric Mapping, and superimposed hand‐drawn ROIs in Pittsburgh Compound B template space). We tested between‐methods reliability using repeated measures models and intraclass correlation coefficients.ResultsWe found high reliability between the hand‐drawn standard method and other methods for almost all the regions considered. However, small differences in standard uptake value ratio were found to lead to unreliable classifications when the hand‐drawn native space‐derived cutoffs were used across other ROI delineation methods.DiscussionThe use of Centiloid standardization greatly improved the agreement of Pittsburgh Compound B classification across methods and may serve as an alternative method for applying cutoffs across methodologically different outcomes.

Traditional neuroimaging analysis, such as clustering the data collected for the Alzheimer's disease (AD), usually relies on the data from one single imaging modality. However, recent technology and equipment advancements provide with us opportunities to better analyze diseases, where we could collect and employ the data from different image and genetic modalities that may potentially enhance the predictive performance. To perform better clustering in AD analysis, in this paper we conduct a new study to make use of the data from different modalities/views. To achieve this goal, we propose a simple yet efficient method based on Non-negative Matrix Factorization (NMF) which can not only achieve better prediction performance but also deal with some data missing in some views. Experimental results on the ADNI dataset demonstrate the effectiveness of our proposed method.

Many existing studies on complex brain disorders, such as Alzheimer's Disease, usually employed regression analysis to associate the neuroimaging measures to cognitive status. However, whether these measures in multiple modalities have the predictive power to infer the trajectory of cognitive performance over time still remain under-explored. In this paper, we propose a high-order multi-modal multi-mask feature learning model to uncover temporal relationship between the longitudinal neuroimaging measures and progressive cognitive output scores. The regularizations through sparsity-induced norms implemented in the proposed learning model enable the selection of only a small number of imaging features over time and capture modality structures for multi-modal imaging markers. The promising experimental results in extensive empirical studies performed on the ADNI cohort have validated the effectiveness of the proposed method.

Exploratory Clustering is a novel general purpose clustering tool which is especially appropriate for medical domains in which we need to identify subpopulations that are similar in two different data layers. The tool implements the multi-layer clustering algorithm in a framework that enables iterative experiments by the user in his search for relevant patient subpopulations. A unique property of the tool is integration of clustering and feature selection algorithms. Differences in values of most relevant attributes are used to demonstrate decisive properties of constructed clusters. Usefulness of the tool is illustrated on a task of discovering groups of patients with similar cognitive impairment.

Objective: To evaluate alterations in cerebral blood flow (CBF) using arterial spin-labeled MRI in autosomal dominant Alzheimer disease (ADAD) mutation carriers (MCs) in relation to cerebral amyloid and compared with age-matched healthy controls. Background: Recent work has identified alterations in CBF in elderly subjects with mild cognitive impairment and Alzheimer dementia using MRI. However, similar studies are lacking in ADAD. Subjects with ADAD are generally free of significant vascular disease and offer the opportunity to measure CBF early in the pathologic process before significant symptom onset when unique markers might be identified. Methods: Fourteen MCs (presenilin-1 and amyloid beta precursor protein) (Clinical Dementia Rating [CDR] 0 = 9, CDR 0.5 = 4, CDR 1 = 1) and 50 controls underwent 3-tesla pulsed arterial spin-labeled MRI. SPM8 was used to test the effect of MC status at the voxel level on CBF before and after controlling for age and CDR. Results: MCs had decreased perfusion in the caudate and inferior striatum bilaterally even after controlling for age and CDR. In MCs, separate areas of decreased CBF were associated with increasing cerebral amyloid and to decreased performance of attention and executive function. Conclusions: Early CBF changes were identified in asymptomatic and mildly symptomatic subjects with ADAD, particularly in the anterior striatum. Furthermore, amyloid deposition was associated with decreased CBF in a number of regions including anterior and posterior cortical areas. Both amyloid and decreased CBF were associated with declines primarily in executive cognitive function.

OBJECTIVE: To examine the association between amyloid-β (Aβ) deposition in 191 non-demented elderly subjects (mean age 85) and incident dementia two years later.

With multiple radiopharmaceuticals and analysis techniques available for amyloid PET imaging, it is difficult to compare or pool results among studies and provide quantitative values for clinicians to apply to their patients. To overcome these problems, a method for standardized expression of quantitative amyloid imaging data has been developed by a multinational coalition of academic and commercial amyloid PET researchers. In line with the metric system, the method provides a measure that is independent of tracer or analysis technique, that ranges from zero (no amyloid) to 100 (the mean of patients with sporadic AD). The units are called centiloids. A standard method to convert 11C-PiB SUVR to centiloids was devised. Scans from young normals aged < 45 and sporadic AD patients (minus outliers) were analyzed in standard space with a single large cortical ROI defined by subtracting the mean normal scan from the mean AD scan. Several reference regions are under evaluation. This dataset and ROI will be freely available to download. To convert any current or new analysis method output to centiloids, one simply downloads the standard data set and ROI (freely available to download) and creates a linear equation to transform the new method results on the standard dataset to match the standard method centiloid results. For new tracers, collection of PiB and new tracer studies in the same individuals is required in normals aged <45 and across a range of positive individuals. The acquired scans are analysed with the centiloid method converting the PiB results to centiloids and a linear equation is calculated to convert the new tracer results to matching centiloids. These datasets and the correction equation are then made freely available to download. Other sites can use these data to validate their analysis methods and apply the tracer conversion equation to their scans. The centiloid conversion method has been applied to several F-18 amyloid tracers using available data sets. Expected benefits include the ability to pool large datasets irrespective of the amyloid tracer employed, better prognostic information based on standardized, quantitative results and meaningful comparison of the effectiveness of anti-amyloid therapies.

Down syndrome (DS) occurs in approximately 1 in 800 live births. I ndividuals with DS are at high risk for A lzheimer's D isorder (AD) due to the presence of an extra copy of chromosome 21, which codes for the A b precursor protein gene. AD occurs in over 40% for DS individuals between 50 and 59 years of age (yrs). This study examines amyloid deposition (with PiB PET) and neuropsychological functioning in 47 non- demented adults with DS >30 yrs. Standardized uptake tissue ratios (SUVR) were calculated for cortical regions-of-interest (ROI), using cerebellum as reference. PiB(+) subjects were defined as regionally positive when exceeding the SUVR threshold (defined by sparse k-means clustering) in any one of six ROI's or as globally PiB(+) by exceeding the SUVR threshold in the global cortical region (GBL6). N europsychological assessment of memory, att ention and language was also performed. Three of 25 subjects 30–39 yrs. were PiB(+) while 16 of 22 subjects >40 yrs. were PiB(+). There was a significant association between age and PiB status (p-value<.015). Of the 19 PiB(+) subjects, 18 were positive in the AVS region, 11 were positive in ACT region and 10 were positive in both the FRC and LTC regions. Nine subjects were positive on GBL6. The neuropsychological assessments revealed significantly more errors on a test of visual memory (p =.02) for PiB(+) than PiB(-) subjects. There was a trend for PiB(+) adults to perform more poorly verbal learning and working memory tasks (p = .09) and to exhibit more repetitions on a task involving verbal fluency (p = .10) than PiB(-) adults.

Postmortem studies of amyloid deposition have shown variations in amyloid load at different levels of severity. In vivo amyloid imaging opens an opportunity to follow the deposition of amyloid within individuals over time. Our goal is to determine the change in amyloid deposition over time in normal controls (NC), Mild Cognitive Impairment (MCI) and Alzheimer's disease (AD). 22 subjects (6-AD, 7-NC, 9-MCI) received two PiB-PET scans (15mCi, 40-60min; ECAT HR+) within 28 days to determine test-retest variability. The changes in these subjects were compared to that in 55 NC [24 (44%) PiB(+)], 27 MCI [20 (74%) PiB(+)] and 15 AD patients [100% PiB(+)]imaged 1 or 2 years after baseline. Tissue ratios were calculated for regions-of-interest (ROIs) in anterior cingulate, frontal, lateral temporal, parietal, and precuneus cortex and a global ROI composed of these 5 ROIs. Data was normalized to cerebellum after atrophy correction (SUVR). The delta-SUVR (follow-up minus baseline) was calculated. Test-retest variability was -0.001 ± 0.14 SUVR units. An increase above 1.645 standard deviations (0.14×1.645 = 0.23 SUVR units) in any ROI was defined as a significant increase (p < 0.05). Subjects were divided into those PiB(+) (at their most recent scan) and those “stably” PiB(-) during the study. Of the PiB(+) group, 14/24 (58%) NC; 11/20 (55%) MCI; and 9/15 (60%) AD subjects showed significant increases in PiB retention. Of the stably PiB(-) group, 4/31 (13%) NC and 2/7 (29%) MCI increased [no AD was PiB(-)]. 8 NC and 4 MCI converted from PiB(-) to PiB(+) during the study. No subject reverted from PiB(+) to PiB(-). About 2/3 of all subjects who showed a significant increase in PiB retention in any ROI also showed a global increase. The mean increase in global SUVR across all subjects over 1-2 years was 0.09 ± 0.16 (p = 0.014) for NC, 0.15 ± 0.21 (p = 0.005) for MCI and 0.14 ± 0.18 (p = 0.02) for AD. A progressive accumulation of Aβ deposits was detectable with PiB-PET in 58% of PiB(+), but only 16% of stably PiB(-) subjects. The frequency and degree of increased PiB retention was similar across diagnoses. Information regarding the natural history of amyloid deposition is critical to the interpretation of the effects of anti-amyloid therapies.