Gray and White Matter Changes in Subjective Cognitive Impairment,Amnestic Mild Cognitive Impairment and Alzheimer's Disease: A Voxel-Based Analysis Study

Subjective cognitive impairment may be a very early at-risk period of the continuum of dementia. However, it is difficult to discriminate at-risk states from normal aging. Thus, detection of the early pathological changes in the subjective cognitive impairment period is needed. To elucidate these changes, we employed diffusion tensor imaging and volumetry analysis, and compared subjective cognitive impairment with normal, mild cognitive impairment and Alzheimer''s disease. The subjects in this study were 39 Alzheimer''s disease, 43 mild cognitive impairment, 28 subjective cognitive impairment and 41 normal controls. There were no statistically significant differences between the normal control and subjective cognitive impairment groups in all measures. Alzheimer''s disease and mild cognitive impairment had the same extent of brain atrophy and diffusion changes. These results are consistent with the hypothetical model of the dynamic biomarkers of Alzheimer''s disease.     

Cerebral microinfarct is emergency consequence of Alzheimer's disease: a new insight into development of neurodegenerative diseases

Imbalance of Aβ and tau protein production and clearance are the key factors among many causes of Alzheimer''s disease that leading to neurons degeneration and cognitive disorders. As a novel approach, glymphatic system quickly clear metabolic waste (especially Aβ and tau) from cerebral environment, and dysfunction of glymphatic system may relate to occurrence of Alzheimer''s disease. Microinfarct is a common histopathologic situation occurring in aging brain and leads to dramatic increase the generation of metabolic by-product after neuronal injury, hindering the operation of glymphatic system and suppress cerebral spinal fluid (CSF) and cerebral interstitial fluid (interstitial fluid, ISF) exchange. Microinfarcts destruct the integrity of microvascular and microstructural tissue, result in Aβ deposition and tau phosphorylation that form neurofibrillary tangles and associated with the cause of Alzheimer''s disease. Currently, it has been found that glymphatic system is involved in the pathological process of Alzheimer''s disease. Improving the function of glymphatic system after cerebral microinfarcts could be developed as a new approach for Alzheimer''s disease prevention and treatment. In this review, we will provide in-depth discussion on functional changes of glymphatic system after cerebral microinfarcts, further reveal pathogenesis of Alzheimer''s disease and provide a potentially more effective method for treatment of Alzheimer''s disease.     

Predicting Progression from Mild Cognitive Impairment to Alzheimer's Dementia Using Clinical,MRI, and Plasma Biomarkers via Probabilistic Pattern Classification

Background

Individuals with mild cognitive impairment (MCI) have a substantially increased risk of developing dementia due to Alzheimer''s disease (AD). In this study, we developed a multivariate prognostic model for predicting MCI-to-dementia progression at the individual patient level.

Methods

Using baseline data from 259 MCI patients and a probabilistic, kernel-based pattern classification approach, we trained a classifier to distinguish between patients who progressed to AD-type dementia (n = 139) and those who did not (n = 120) during a three-year follow-up period. More than 750 variables across four data sources were considered as potential predictors of progression. These data sources included risk factors, cognitive and functional assessments, structural magnetic resonance imaging (MRI) data, and plasma proteomic data. Predictive utility was assessed using a rigorous cross-validation framework.

Results

Cognitive and functional markers were most predictive of progression, while plasma proteomic markers had limited predictive utility. The best performing model incorporated a combination of cognitive/functional markers and morphometric MRI measures and predicted progression with 80% accuracy (83% sensitivity, 76% specificity, AUC = 0.87). Predictors of progression included scores on the Alzheimer''s Disease Assessment Scale, Rey Auditory Verbal Learning Test, and Functional Activities Questionnaire, as well as volume/cortical thickness of three brain regions (left hippocampus, middle temporal gyrus, and inferior parietal cortex). Calibration analysis revealed that the model is capable of generating probabilistic predictions that reliably reflect the actual risk of progression. Finally, we found that the predictive accuracy of the model varied with patient demographic, genetic, and clinical characteristics and could be further improved by taking into account the confidence of the predictions.

Conclusions

We developed an accurate prognostic model for predicting MCI-to-dementia progression over a three-year period. The model utilizes widely available, cost-effective, non-invasive markers and can be used to improve patient selection in clinical trials and identify high-risk MCI patients for early treatment.     

Inference for low‐ and high‐dimensional multigroup repeated measures designs with unequal covariance matrices

We propose tests for main and simple treatment effects, time effects, as well as treatment by time interactions in possibly high‐dimensional multigroup repeated measures designs. The proposed inference procedures extend the work by Brunner et al. (2012) from two to several treatment groups and remain valid for unbalanced data and under unequal covariance matrices. In addition to showing consistency when sample size and dimension tend to infinity at the same rate, we provide finite sample approximations and evaluate their performance in a simulation study, demonstrating better maintenance of the nominal α‐level than the popular Box‐Greenhouse–Geisser and Huynh–Feldt methods, and a gain in power for informatively increasing dimension. Application is illustrated using electroencephalography (EEG) data from a neurological study involving patients with Alzheimer's disease and other cognitive impairments.     

Estimating equations for a latent transition model with multiple discrete indicators

This paper proposes a two-part model for studying transitions between health states over time when multiple, discrete health indicators are available. The includes a measurement model positing underlying latent health states and a transition model between latent health states over time. Full maximum likelihood estimation procedures are computationally complex in this latent variable framework, making only a limited class of models feasible and estimation of standard errors problematic. For this reason, an estimating equations analogue of the pseudo-likelihood method for the parameters of interest, namely the transition model parameters, is considered. The finite sample properties of the proposed procedure are investigated through a simulation study and the importance of choosing strong indicators of the latent variable is demonstrated. The applicability of the methodology is illustrated with health survey data measuring disability in the elderly from the Longitudinal Study of Aging.     

Biochemical Assessment of Serotonergic and Cholinergic Dysfunction and Cerebral Atrophy in Alzheimer's Disease

Abstract: Markers of serotonin synapses in entire temporal lobe and frontal and temporal neocortex were examined for changes in Alzheimer's disease by use of both neurosurgical and autopsy samples. Uptake of [³H]sero-tonin, binding of [³H]imipramine, and content of indola-mines were all significantly reduced, indicating that serotonin nerve terminals are affected. Binding of [³H]serotonin was also reduced, whereas that of [³H]qui-nuclidinyl benzilate, [³H]muscimol, and [³H]dihydroal-prenolol were unaltered. When the Alzheimer's samples were subdivided according to age, the reduction in [³H]serotonin binding was a feature of only autopsy samples from younger patients. In contrast, presynaptic cholinergic activity was reduced in all groups of Alzheimer's samples, including neurosurgical specimens. Five markers, thought to reflect cerebral atrophy, cytoplasm, nerve cell membrane, and neuronal perikarya were measured in the entire temporal lobe. In Alzheimer's disease the reductions (mean 25%, range 20–35%) were thought to be too large to be due only to loss of structures associated with the presumed cholinergic perikarya in the basal forebrain and monoamine neurones in the brain stem.     

Dynamic Model for Multivariate Markers of Fecundability

Summary : Dynamic latent class models provide a flexible framework for studying biologic processes that evolve over time. Motivated by studies of markers of the fertile days of the menstrual cycle, we propose a discrete‐time dynamic latent class framework, allowing change points to depend on time, fixed predictors, and random effects. Observed data consist of multivariate categorical indicators, which change dynamically in a flexible manner according to latent class status. Given the flexibility of the framework, which incorporates semi‐parametric components using mixtures of betas, identifiability constraints are needed to define the latent classes. Such constraints are most appropriately based on the known biology of the process. The Bayesian method is developed particularly for analyzing mucus symptom data from a study of women using natural family planning.     

Neurochemical characteristics of early and late onset types of Alzheimer's disease.

Brains of 49 patients who had died with Alzheimer''s disease and 54 controls were examined. The Alzheimer group exhibited noticeably reduced activity of the cholinergic marker enzyme choline acetyltransferase in the cerebral cortex, but cortical concentrations of noradrenaline, gamma-aminobutyric acid, and somatostatin were also significantly reduced. Analysis of the results according to age at death showed that the older patients, dying in their 9th and 10th decades, had a relatively pure cholinergic deficit confined to temporal lobe and hippocampus, together with a reduced concentration of somatostatin confined to temporal cortex. By contrast, the younger patients, dying in their 7th and 8th decades, had a widespread and severe cholinergic deficit together with the abnormalities of noradrenaline, gamma-aminobutyric acid, and somatostatin, and the younger patients accounted for most of the abnormalities in these systems observed in the overall group. Comparison of the young subjects with Alzheimer''s disease with the older controls did not support the concept of Alzheimer''s disease representing an acceleration of the aging process. These results suggest that Alzheimer''s disease in people aged under 80 may represent a distinct form of presenile dementia which differs in important respects from the dementia of old age.     

A composite likelihood approach to latent multivariate Gaussian modeling of SNP data with application to genetic association testing

Many statistical tests have been proposed for case-control data to detect disease association with multiple single nucleotide polymorphisms (SNPs) in linkage disequilibrium. The main reason for the existence of so many tests is that each test aims to detect one or two aspects of many possible distributional differences between cases and controls, largely due to the lack of a general and yet simple model for discrete genotype data. Here we propose a latent variable model to represent SNP data: the observed SNP data are assumed to be obtained by discretizing a latent multivariate Gaussian variate. Because the latent variate is multivariate Gaussian, its distribution is completely characterized by its mean vector and covariance matrix, in contrast to much more complex forms of a general distribution for discrete multivariate SNP data. We propose a composite likelihood approach for parameter estimation. A direct application of this latent variable model is to association testing with multiple SNPs in a candidate gene or region. In contrast to many existing tests that aim to detect only one or two aspects of many possible distributional differences of discrete SNP data, we can exclusively focus on testing the mean and covariance parameters of the latent Gaussian distributions for cases and controls. Our simulation results demonstrate potential power gains of the proposed approach over some existing methods.     

A Model for Adjusting for Nonignorable Verification Bias in Estimation of the ROC Curve and Its Area with Likelihood‐Based Approach

Summary In estimation of the ROC curve, when the true disease status is subject to nonignorable missingness, the observed likelihood involves the missing mechanism given by a selection model. In this article, we proposed a likelihood‐based approach to estimate the ROC curve and the area under the ROC curve when the verification bias is nonignorable. We specified a parametric disease model in order to make the nonignorable selection model identifiable. With the estimated verification and disease probabilities, we constructed four types of empirical estimates of the ROC curve and its area based on imputation and reweighting methods. In practice, a reasonably large sample size is required to estimate the nonignorable selection model in our settings. Simulation studies showed that all four estimators of ROC area performed well, and imputation estimators were generally more efficient than the other estimators proposed. We applied the proposed method to a data set from research in Alzheimer's disease.     

Maximum likelihood analysis of a general latent variable model with hierarchically mixed data

A general two-level latent variable model is developed to provide a comprehensive framework for model comparison of various submodels. Nonlinear relationships among the latent variables in the structural equations at both levels, as well as the effects of fixed covariates in the measurement and structural equations at both levels, can be analyzed within the framework. Moreover, the methodology can be applied to hierarchically mixed continuous, dichotomous, and polytomous data. A Monte Carlo EM algorithm is implemented to produce the maximum likelihood estimate. The E-step is completed by approximating the conditional expectations through observations that are simulated by Markov chain Monte Carlo methods, while the M-step is completed by conditional maximization. A procedure is proposed for computing the complicated observed-data log likelihood and the BIC for model comparison. The methods are illustrated by using a real data set.     

Generation of a neuro-specific microarray reveals novel differentially expressed noncoding RNAs in mouse models for neurodegenerative diseases

We have generated a novel, neuro-specific ncRNA microarray, covering 1472 ncRNA species, to investigate their expression in different mouse models for central nervous system diseases. Thereby, we analyzed ncRNA expression in two mouse models with impaired calcium channel activity, implicated in Epilepsy or Parkinson''s disease, respectively, as well as in a mouse model mimicking pathophysiological aspects of Alzheimer''s disease. We identified well over a hundred differentially expressed ncRNAs, either from known classes of ncRNAs, such as miRNAs or snoRNAs or which represented entirely novel ncRNA species. Several differentially expressed ncRNAs in the calcium channel mouse models were assigned as miRNAs and target genes involved in calcium signaling, thus suggesting feedback regulation of miRNAs by calcium signaling. In the Alzheimer mouse model, we identified two snoRNAs, whose expression was deregulated prior to amyloid plaque formation. Interestingly, the presence of snoRNAs could be detected in cerebral spine fluid samples in humans, thus potentially serving as early diagnostic markers for Alzheimer''s disease. In addition to known ncRNAs species, we also identified 63 differentially expressed, entirely novel ncRNA candidates, located in intronic or intergenic regions of the mouse genome, genomic locations, which previously have been shown to harbor the majority of functional ncRNAs.     

Abnormal cortical lysosomal β-hexosaminidase and β-galactosidase activity at post-synaptic sites during Alzheimer's disease progression

A critical role of endosomal–lysosomal system alteration in neurodegeneration is supported by several studies. Dysfunction of the lysosomal compartment is a common feature also in Alzheimer's disease. Altered expression of lysosomal glycohydrolases has been demonstrated not only in the brain and peripheral tissues of Alzheimer's disease patients, but also in presymptomatic subjects before degenerative phenomenon becomes evident. Moreover, the presence of glycohydrolases associated to the plasma membrane have been widely demonstrated and their alteration in pathological conditions has been documented. In particular, lipid microdomains-associated glycohydrolases can be functional to the maintenance of the proper glycosphingolipids pattern, especially at cell surface level, where they are crucial for the function of cell types such as neurons. In this study we investigated the localization of β-hexosaminidase and β-galactosidase glycohydrolases, both involved in step by step degradation of the GM1 to GM3 gangliosides, in lipid microdomains from the cortex of both an early and advanced TgCRND8 mouse model of Alzheimer's disease. Throughout immunoprecipitation experiments of purified cortical lipid microdomains, we demonstrated for the first time that β-hexosaminidase and β-galactosidase are associated with post-synaptic vesicles and that their activities are increased at both the early and the advanced stage of Alzheimer's disease. The early increase of lipid microdomain-associated β-hexosaminidase and β-galactosidase activities could have relevant implications for the pathophysiology of the disease since their possible pharmacological manipulation could shed light on new reliable targets and biological markers of Alzheimer's disease.     

Joint regression analysis of correlated data using Gaussian copulas

Summary .  This article concerns a new joint modeling approach for correlated data analysis. Utilizing Gaussian copulas, we present a unified and flexible machinery to integrate separate one-dimensional generalized linear models (GLMs) into a joint regression analysis of continuous, discrete, and mixed correlated outcomes. This essentially leads to a multivariate analogue of the univariate GLM theory and hence an efficiency gain in the estimation of regression coefficients. The availability of joint probability models enables us to develop a full maximum likelihood inference. Numerical illustrations are focused on regression models for discrete correlated data, including multidimensional logistic regression models and a joint model for mixed normal and binary outcomes. In the simulation studies, the proposed copula-based joint model is compared to the popular generalized estimating equations, which is a moment-based estimating equation method to join univariate GLMs. Two real-world data examples are used in the illustration.     

CSF and Brain Structural Imaging Markers of the Alzheimer's Pathological Cascade

Cerebral spinal fluid (CSF) and structural imaging markers are suggested as biomarkers amended to existing diagnostic criteria of mild cognitive impairment (MCI) and Alzheimer''s disease (AD). But there is no clear instruction on which markers should be used at which stage of dementia. This study aimed to first investigate associations of the CSF markers as well as volumes and shapes of the hippocampus and lateral ventricles with MCI and AD at the baseline and secondly apply these baseline markers to predict MCI conversion in a two-year time using the Alzheimer''s Disease Neuroimaging Initiative (ADNI) cohort. Our results suggested that the CSF markers, including Aβ42, t-tau, and p-tau, distinguished MCI or AD from NC, while the Aβ42 CSF marker contributed to the differentiation between MCI and AD. The hippocampal shapes performed better than the hippocampal volumes in classifying NC and MCI, NC and AD, as well as MCI and AD. Interestingly, the ventricular volumes were better than the ventricular shapes to distinguish MCI or AD from NC, while the ventricular shapes showed better accuracy than the ventricular volumes in classifying MCI and AD. As the CSF markers and the structural markers are complementary, the combination of them showed great improvements in the classification accuracies of MCI and AD. Moreover, the combination of these markers showed high sensitivity but low specificity for predicting conversion from MCI to AD in two years. Hence, it is feasible to employ a cross-sectional sample to investigate dynamic associations of the CSF and imaging markers with MCI and AD and to predict future MCI conversion. In particular, the volumetric information may be good for the early stage of AD, while morphological shapes should be considered as markers in the prediction of MCI conversion to AD together with the CSF markers.     

Reduced dopamine-beta-hydroxylase activity in Alzheimer's disease

The activity of the noradrenergic marker enzyme dopamine-beta-hydroxylase was measured in brains removed postmortem from control patients and patients with Alzheimer''s disease. Enzyme activity was decreased in the frontal and temporal cortices and hippocampus in patients with Alzheimer''s disease, but was within the normal range in patients with depression, multiinfarct dementia, and terminal coma.The decrease in enzyme activity in Alzheimer''s disease may reflect an abnormality of cortical noradrenergic fibres in some patients with the disease.     

Plasma Based Markers of [11C] PiB-PET Brain Amyloid Burden

Changes in brain amyloid burden have been shown to relate to Alzheimer''s disease pathology, and are believed to precede the development of cognitive decline. There is thus a need for inexpensive and non-invasive screening methods that are able to accurately estimate brain amyloid burden as a marker of Alzheimer''s disease. One potential method would involve using demographic information and measurements on plasma samples to establish biomarkers of brain amyloid burden; in this study data from the Alzheimer''s Disease Neuroimaging Initiative was used to explore this possibility. Sixteen of the analytes on the Rules Based Medicine Human Discovery Multi-Analyte Profile 1.0 panel were found to associate with [¹¹C]-PiB PET measurements. Some of these markers of brain amyloid burden were also found to associate with other AD related phenotypes. Thirteen of these markers of brain amyloid burden – c-peptide, fibrinogen, alpha-1-antitrypsin, pancreatic polypeptide, complement C3, vitronectin, cortisol, AXL receptor kinase, interleukin-3, interleukin-13, matrix metalloproteinase-9 total, apolipoprotein E and immunoglobulin E – were used along with co-variates in multiple linear regression, and were shown by cross-validation to explain >30% of the variance of brain amyloid burden. When a threshold was used to classify subjects as PiB positive, the regression model was found to predict actual PiB positive individuals with a sensitivity of 0.918 and a specificity of 0.545. The number of APOE ϵ 4 alleles and plasma apolipoprotein E level were found to contribute most to this model, and the relationship between these variables and brain amyloid burden was explored.     

Luteinizing hormone as a key player in the cognitive decline of Alzheimer's disease

This article is part of a Special Issue "SBN 2014".Alzheimer's disease is one of the most prevalent and costly neurological diseases in the world. Although decades of research have focused on understanding Alzheimer's disease pathology and progression, there is still a great lack of clinical treatments for those who suffer from it. One of the factors most commonly associated with the onset of Alzheimer's disease is a decrease in levels of gonadal hormones, such as estrogens and androgens. Despite the correlational and experimental data which support the role of these hormones in the etiology of Alzheimer's disease, clinical trials involving their reintroduction through hormone therapy have had varied results and these gonadal hormones often have accompanying health risks. More recently, investigation has turned toward other hormones in the hypothalamic–pituitary–gonadal axis that are disrupted by age-related decreases in gonadal hormones. Specifically, luteinizing hormone, which is increased with age in both men and women (in response to removal of negative feedback), has surfaced as a potentially powerful player in the risk and onset of Alzheimer's disease. Mounting evidence in basic research and epidemiological studies supports the role of elevated luteinizing hormone in exacerbating age-related cognitive decline in both males and females. This review summarizes the recent developments involving luteinizing hormone in increasing the cognitive deficits and molecular pathology characteristic of Alzheimer's disease.     

Speech signal analysis of alzheimer’s diseases in farsi using auditory model system

In recent years, extensive studies have been conducted on the diagnosis of Alzheimer''s disease (AD) using the non-invasive speech signal recognition method. In this study, Farsi speech signals were analyzed using the auditory model system (AMS) in order to recognize AD. For this purpose, after the pre-processing of the speech signals and utilizing AMS, 4D outputs as function of time, frequency, rate, and scale range were obtained. The AMS outcomes were averaged in term of time to analyze the rate-frequency-scale for both groups, Alzheimer''s and healthy control subjects. Thereafter, the maximum of spectral and temporal modulation and frequency were extracted to classify by the support vector machine (SVM). The SVM achieves higher promising recognition accuracy with compare to prevalent approaches in the field of speech processing. The acceptable results demonstrate the applicability of the proposed algorithm in non-invasive and low-cost recognizing Alzheimer''s only using the few extracted features of the speech signal.