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

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.

Brain imaging with diffusion-weighted MRI (dMRI) is sensitive to microstructural white matter (WM) changes associated with brain aging and neurodegeneration. In its third phase, the Alzheimer's Disease Neuroimaging Initiative (ADNI3) is collecting data across multiple sites and scanners using different dMRI acquisition protocols, to better understand disease effects. It is vital to understand when data can be pooled across scanners, and how the choice of dMRI protocol affects the sensitivity of extracted measures to differences in clinical impairment. Here, we analyzed ADNI3 data from 317 participants (mean age: 75.4 ± 7.9 years; 143 men/174 women), who were each scanned at one of 47 sites with one of six dMRI protocols using scanners from three different manufacturers. We computed four standard diffusion tensor imaging (DTI) indices including fractional anisotropy (FADTI) and mean, radial, and axial diffusivity, and one FA index based on the tensor distribution function (FATDF), in 24 bilaterally averaged WM regions of interest. We found that protocol differences significantly affected dMRI indices, in particular FADTI. We ranked the diffusion indices for their strength of association with four clinical assessments. In addition to diagnosis, we evaluated cognitive impairment as indexed by three commonly used screening tools for detecting dementia and AD: the AD Assessment Scale (ADAS-cog), the Mini-Mental State Examination (MMSE), and the Clinical Dementia Rating scale sum-of-boxes (CDR-sob). Using a nested random-effects regression model to account for protocol and site, we found that across all dMRI indices and clinical measures, the hippocampal-cingulum and fornix (crus)/stria terminalis regions most consistently showed strong associations with clinical impairment. Overall, the greatest effect sizes were detected in the hippocampal-cingulum (CGH) and uncinate fasciculus (UNC) for associations between axial or mean diffusivity and CDR-sob. FATDF detected robust widespread associations with clinical measures, while FADTI was the weakest of the five indices for detecting associations. Ultimately, we were able to successfully pool dMRI data from multiple acquisition protocols from ADNI3 and detect consistent and robust associations with clinical impairment and age.

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.

Diffusion-weighted MRI (dMRI) offers a range of measures that are sensitive to brain aging and neurodegeneration. Here we analyzed data from 318 participants (mean age: 75.4±7.9 years; 143 men/175 women) from the third phase of the Alzheimer’s Disease Neuroimaging Initiative (ADNI3), who were each scanned with one of six different diffusion MRI protocols using scanners from three different manufacturers. We computed 4 standard diffusion tensor imaging (DTI) anisotropy and diffusivity indices, and one advanced anisotropy index based on the tensor distribution function (TDF), in 24 white matter regions of interest. Modeling protocol effects, we ranked the diffusion indices for their strength of correlation with 3 standard clinical measures of cognitive impairment: the ADAS-Cog, MMSE, and sum-of-boxes Clinical Dementia Rating. Across all dMRI indices and cognitive measures, the cingulum-hippocampal region and the uncinate showed some of the strongest associations with cognitive impairment; largest effect sizes were detected with axial diffusivity (AxDDTI). While fractional anisotropy (FA) derived from the DTI model was the weakest in detecting associations with cognitive measures, FA derived from the TDF detected widespread, robust associations. Protocol differences affected dMRI indices; however by modeling protocol effects, we were able to pool dMRI data from multiple acquisition protocols and detect consistent associations with cognitive impairment and age. dMRI indices computed from the upgraded scanning protocols in ADNI3 were sensitive to cognitive impairment in brain aging, offering a benchmark to compare to future multi-shell or multi-compartment diffusion indices.

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: Investigate connectivity in Alzheimer's Disease Neuroimaging Initiative (ADNI).

A research-focused scientific journal serves two main purposes. The first is to provide a forum for investigators to publish their research findings. The second is to disseminate these findings to enhance further research, and to aid others who wish to apply the findings for the benefit of society. These purposes motivate scientific journals in general and Medical Physics in particular, where the audience for the latter is principally medical physicists worldwide who are engaged in research or in the application of research results to improve patient care. Every journal is challenged by the need to know how well it is satisfying its purposes. Measures of success include the international stature of the journal, the reputation and productivity of scientists who publish in the journal, the frequency of article downloads, the number of manuscripts submitted to the journal, and the rate at which articles are accepted or rejected. While these are useful indicators, they do not provide a single quantitative index of how well the journal is meeting its purposes. In 1975 the Institute for Scientific Information (now known as Thomson–Reuters or Thomson ISI) began offering journal citation reports (JCR) as part of its publication known as the science citation index [1]. The JCR’s intent is to provide quantitative tools for ranking, evaluating, categorizing, and comparing journals. The major tool used by the JCR is the impact factor (IF), which is a measure of the frequency with which an “average article” in a journal is cited during a particular period. Most often the 2-year IF is used for journal ranking. As an example, the 2-year IF of a journal for 2010 is computed from data in the JCR repository as follows:

Diffusion weighted magnetic resonance imaging (DW-MRI) are now widely used to assess brain integrity in clinical populations. The growing interest in mapping brain connectivity has made it vital to consider what scanning parameters affect the accuracy, stability, and signal-to-noise of diffusion measures. Trade-offs between scan parameters can only be optimized if their effects on various commonly-derived measures are better understood. To explore angular versus spatial resolution trade-offs in standard tensor-derived measures, and in measures that use the full angular information in diffusion signal, we scanned eight subjects twice, 2 weeks apart, using three protocols that took the same amount of time (7 min). Scans with 3.0, 2.7, 2.5 mm isotropic voxels were collected using 48, 41, and 37 diffusion-sensitized gradients to equalize scan times. A specially designed DTI phantom was also scanned with the same protocols, and different b-values. We assessed how several diffusion measures including fractional anisotropy (FA), mean diffusivity (MD), and the full 3D orientation distribution function (ODF) depended on the spatial/angular resolution and the SNR. We also created maps of stability over time in the FA, MD, ODF, skeleton FA of 14 TBSS-derived ROIs, and an information uncertainty index derived from the tensor distribution function, which models the signal using a continuous mixture of tensors. In scans of the same duration, higher angular resolution and larger voxels boosted SNR and improved stability over time. The increased partial voluming in large voxels also led to bias in estimating FA, but this was partially addressed by using "beyond-tensor" models of diffusion.

Objective To empirically assess the concept that Alzheimer disease (AD) biomarkers significantly depart from normality in a temporally ordered manner. Design Validation sample. Setting Multisite, referral centers. Participants A total of 401 elderly participants in the Alzheimer's Disease Neuroimaging Initiative who were cognitively normal, who had mild cognitive impairment, or who had AD dementia. We compared the proportions of 3 AD biomarker values (the Aβ42 level in cerebrospinal fluid [CSF], the total tau level in CSF, and the hippocampal volume adjusted for intracranial volume [hereafter referred to as the adjusted hippocampal volume]) that were abnormal as cognitive impairment worsened. Cut points demarcating normal vs abnormal for each biomarker were established by maximizing diagnostic accuracy in independent autopsy samples. Main Outcome Measures Three AD biomarkers (ie, the CSF Aβ42 level, the CSF total tau level, and the adjusted hippocampal volume). Results Within each clinical group of the entire sample (n = 401), the CSF Aβ42 level was abnormal more often than was the CSF total tau level or the adjusted hippocampal volume. Among the 298 participants with both baseline and 12-month data, the proportion of participants with an abnormal Aβ42 level did not change from baseline to 12 months in any group. The proportion of participants with an abnormal total tau level increased from baseline to 12 months in cognitively normal participants (P = .05) but not in participants with mild cognitive impairment or AD dementia. For 209 participants with an abnormal CSF Aβ42 level at baseline, the percentage with an abnormal adjusted hippocampal volume but normal CSF total tau level increased from baseline to 12 months in participants with mild cognitive impairment. No change in the percentage of MCI participants with an abnormal total tau level was seen between baseline and 12 months. Conclusions A reduction in the CSF Aβ42 level denotes a pathophysiological process that significantly departs from normality (ie, becomes dynamic) early, whereas the CSF total tau level and the adjusted hippocampal volume are biomarkers of downstream pathophysiological processes. The CSF total tau level becomes dynamic before the adjusted hippocampal volume, but the hippocampal volume is more dynamic in the clinically symptomatic mild cognitive impairment and AD dementia phases of the disease than is the CSF total tau level.

Cross-sectional studies in Alzheimer's disease (AD) patients have demonstrated brain volume reductions on magnetic resonance imaging (MRI), with medial temporal structures preferentially affected. Longitudinal studies have shown greater annualized declines in medial temporal lobe structures and in whole brain volume than in healthy aging. Objective: To investigate six-month declines in the regionally distributed pattern of gray matter atrophy in AD patients and healthy controls from the Alzheimer's Disease Neuroimaging Initiative (ADNI; www.loni.ucla.edu/ADNI), we used voxel-based morphometry (VBM) with multivariate network analysis. Subjects included 39 AD patients and 50 healthy controls, matched in age and gender. Longitudinal VBM processing was performed with statistical parametric mapping (SPM5) for volumetric T1 MPRAGE MRIs obtained at baseline and after a six month follow up visit to produce smoothed gray matter maps. Multivariate Scaled Subprofile Model (SSM) analysis was performed to identify a regional pattern of gray matter reductions that distinguished the groups and to test for a group × time interaction in pattern expression over the six month scan interval. SSM analysis for the two groups combined, with network subject scores averaged over the two scan time-points, identified a linear combination of component patterns that distinguished the AD patients from controls (R2=0.56, p<=0.0001). This combined pattern, reflecting the group main effect, showed bilateral reductions in medial temporal regions, as well as in bilateral perisylvian, lateral temporal, left parietal, and thalamic regions with relative preservation mainly in cerebellum. Using SSM network subject scores derived for each subject's baseline and six month follow up scan, we observed a group × time interaction (p<=0.017) with the AD patients showing greater expression of the AD-related pattern from baseline to six months (p<=0.001) consistent with a decline in regional gray matter that was not observed in the healthy controls (p=ns). These findings support a regionally distributed MRI gray matter pattern in which AD patients show a greater progression of atrophy over 6 months than in healthy elderly. The application of multivariate SSM network analysis with MRI longitudinal VBM may aid in tracking the progression of AD and potentially assist in evaluating treatments and prevention therapies.