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.

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.

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.

The goal of this study was to determine if the apolipoprotein ε gene, which is a well-established susceptibility factor for Alzheimer disease (AD) pathology in typical amnestic dementias, may also represent a risk factor in the language-based dementia, primary progressive aphasia (PPA). Apolipoprotein E genotyping was obtained from 149 patients with a clinical diagnosis of PPA, 330 cognitively healthy individuals (NC), and 179 patients with a clinical diagnosis of probable Alzheimer's disease (PrAD). Allele frequencies were compared among the groups. Analyses were also completed by sex and in 2 subsets of PPA patients: 1 in which the patients were classified by subtype (logopenic, agrammatic, and semantic) and another in which pathologic data were available. The allele frequencies for the PPA group (ε2:5%, ε3:79.5%, and ε4:15.4%) showed a distribution similar to the NC group, but significantly different from the PrAD group. The presence of an ε4 allele did not influence the age of symptom onset or aid in the prediction of AD pathology in PPA. These data show that ε4 polymorphism, which is a well-known risk factor for AD pathology in typical amnestic dementias, has no similar relationship to the clinical syndrome of PPA or its association with AD pathology.

Gerard Schellenberg and colleagues report a genome-wide association study of late-onset Alzheimer's disease (LOAD), as part of the Alzheimer Disease Genetics Consortium. They identify common variants in MS4A4/MS4A6E, CD2AP, CD33 and EPHA1 associated with LOAD. The Alzheimer Disease Genetics Consortium (ADGC) performed a genome-wide association study of late-onset Alzheimer disease using a three-stage design consisting of a discovery stage (stage 1) and two replication stages (stages 2 and 3). Both joint analysis and meta-analysis approaches were used. We obtained genome-wide significant results at MS4A4A (rs4938933; stages 1 and 2, meta-analysis P (PM) = 1.7 × 10−9, joint analysis P (PJ) = 1.7 × 10−9; stages 1, 2 and 3, PM = 8.2 × 10−12), CD2AP (rs9349407; stages 1, 2 and 3, PM = 8.6 × 10−9), EPHA1 (rs11767557; stages 1, 2 and 3, PM = 6.0 × 10−10) and CD33 (rs3865444; stages 1, 2 and 3, PM = 1.6 × 10−9). We also replicated previous associations at CR1 (rs6701713; PM = 4.6 × 10−10, PJ = 5.2 × 10−11), CLU (rs1532278; PM = 8.3 × 10−8, PJ = 1.9 × 10−8), BIN1 (rs7561528; PM = 4.0 × 10−14, PJ = 5.2 × 10−14) and PICALM (rs561655; PM = 7.0 × 10−11, PJ = 1.0 × 10−10), but not at EXOC3L2, to late-onset Alzheimer's disease susceptibility1,2,3.

Johnson, Nancy A. PhD; Rademaker, Alfred PhD; Weintraub, Sandra PhD; Gitelman, Darren MD; Wienecke, Christina BS; Mesulam, Marsel MD Author Information

The clinical syndrome of primary progressive aphasia (PPA) can be associated with a variety of neuropathologic diagnoses at autopsy. Thirty percent of cases have Alzheimer disease (AD) pathology, most often in the usual distribution, which defies principles of brain-behavior organization, in that aphasia is not symptomatic of limbic disease. The present study investigated whether concomitant TDP-43 pathology could resolve the lack of clinico-anatomic concordance. In this paper, 16 cases of clinical PPA and 10 cases of primarily non-aphasic frontotemporal dementia (FTD), all with AD pathology, were investigated to determine whether their atypical clinical phenotypes reflected the presence of additional TDP-43 pathology. A comparison group consisted of 27 cases of pathologic AD with the typical amnestic clinical phenotype of probable AD. Concomitant TDP-43 pathology was discovered in only three of the FTD and PPA but in more than half of the typical amnestic clinical phenotypes. Hippocampal sclerosis (HS) was closely associated with TDP-43 pathology when all groups were combined for analysis. Therefore, the clinical phenotypes of PPA and FTD in cases with pathologic AD are only rarely associated with TDP-43 proteinopathy. Furthermore, medial temporal TDP-43 pathology is more tightly linked to HS than to clinical phenotype. These findings challenge the current notions about clinicopathologic correlation, especially about the role of multiple pathologies.

Robust norms developed on longitudinally stable normal individuals improve identification of future cognitive decline in healthy individuals. The neuropsychological battery from the Uniform Data Set (UDS) of the NIA Alzheimer's Disease Centers (ADC) is used to assess cognitive performance by all ADCs. Robust norms for this test battery would provide more sensitive reference data for identifying cognitive impairment in the ADC population. We developed Robust norms for the UDS cognitive battery using longitudinally stable normal individuals and determined the utility of these norms for identifying decline to MCI or AD. We retrospectively examined UDS data from 375 normal individuals from Northwestern University (n = 91), Washington University (n = 150) and NYU (n = 131) who were normal at the first administration of the UDS cognitive battery and had at least 2 administrations of this examination. Subjects had a mean age of 72.9 yrs, had 16.2 yrs of education, and were predominately Caucasian (88%). 340 individuals remained normal at follow-up and were randomly divided into Robust norm and test groups. The remaining 35 subjects declined to MCI (n = 23) or AD (n = 12) (NL-DEC). Norms were developed on the Robust group using linear regression models including education, gender, age, race and number of test administrations . Any significant two way interactions were added into the equation in a stepwise fashion. Z scores were generated for the test group by subtracting the predicted value (via the linear regression model) from the individual test score and dividing by the standard deviation of the residuals. At the first UDS time point, a stepwise logistic regression model determined the UDS z score combination that best predicted group membership (NL-NL vs NL-DEC). The final model included the cross sectional MMSE, Trails B time, BNT and Logical Memory II, and resulted in an overall accuracy of 85% (specificity = 85%, sensitivity = 87%). Excessive z score change over time for any variable did not increment the cross sectional model. Robust UDS norms can serve to determine cognitive impairment in ADC participants and may be useful for identifying individuals at-risk for future decline. Such individuals would be candidates for early interventions.

Pathogenic mutations in the gene encoding TDP-43, TARDBP, have been reported in familial amyotrophic lateral sclerosis (FALS) and, more recently, in families with a heterogeneous clinical phenotype including both ALS and frontotemporal lobar degeneration (FTLD). In our previous study, sequencing analyses identified one variant in the 3′-untranslated region (3′-UTR) of the TARDBP gene in two affected members of one family with bvFTD and ALS and in one unrelated clinically assessed case of FALS. Since that study, brain tissue has become available and provides autopsy confirmation of FTLD-TDP in the proband and ALS in the brother of the bvFTD-ALS family and the neuropathology of those two cases is reported here. The 3′-UTR variant was not found in 982 control subjects (1,964 alleles). To determine the functional significance of this variant, we undertook quantitative gene expression analysis. Allele-specific amplification showed a significant increase of 22% ( P < 0.05) in disease-specific allele expression with a twofold increase in total TARDBP mRNA. The segregation of this variant in a family with clinical bvFTD and ALS adds to the spectrum of clinical phenotypes previously associated with TARDBP variants. In summary, TARDBP variants may result in clinically and neuropathologically heterogeneous phenotypes linked by a common molecular pathology called TDP-43 proteinopathy.