Quantifying the pattern of beta/A4 amyloid protein distribution in Alzheimer's disease by image analysis

Mathematical models on Alzheimer's disease and its treatment: A review

Alzheimer's disease is a gradually advancing neurodegenerative disease. According to the report by "World Health Organization (WHO)", there are over 55 million individuals currently living with Alzheimer's disease and other dementia globally, and the number of sufferers is increasing every day. In absence of effective cures and preventive measures, this number is predicted to triple by 2050. The disease's origin is still unclear, and also no such treatment is available for eradicating the disease. Based on the crucial factors that are connected to the disease's progression, the authors developed several types of mathematical models. We review such mathematical models that are utilized to better understand the pathophysiology of Alzheimer's disease. Section-wise, we categorize the mathematical models in terms of different components that might be responsible for Alzheimer's disease. We explain the mathematical models with their descriptions and respective conclusions. In addition to mathematical models, we concentrate on biological aspects of the disease and possible therapeutic targets. We explore the disease's biological basis primarily to understand how proteins, glial cells, cytokines, genes, calcium signaling and oxidative stress contribute to the disease. We go through several treatment targets that might stop the progression of the disease or at least slow it down. We present a table that summarizes the mathematical models in terms of their formalisms, highlighting key components and important remarks.

Application of Haralick texture features in brain [18F]-florbetapir positron emission tomography without reference region normalization

Objectives

Semi-quantitative image analysis methods in Alzheimer’s Disease (AD) require normalization of positron emission tomography (PET) images. However, recent studies have found variabilities associated with reference region selection of amyloid PET images. Haralick features (HFs) generated from the Gray Level Co-occurrence Matrix (GLCM) quantify spatial characteristics of amyloid PET radiotracer uptake without the need for intensity normalization. The objective of this study is to calculate several HFs in different diagnostic groups and determine the group differences.

Methods

All image and metadata were acquired through the Alzheimer’s Disease Neuroimaging Initiative database. Subjects were grouped in three ways: by clinical diagnosis, by APOE e4 allele, and by Alzheimer’s Disease Assessment Scale-cognitive subscale (ADAS-Cog) score. Several GLCM matrices were calculated for different direction and distances (1–4 mm) from multiple regions on PET images. The HFs, contrast, correlation, dissimilarity, energy, entropy, and homogeneity, were calculated from these GLCMs. Wilcoxon tests and Student t-tests were performed on Haralick features and standardized uptake value ratio (SUVR) values, respectively, to determine group differences. In addition to statistical testing, receiver operating characteristic (ROC) curves were generated to determine the discrimination performance of the selected regional HFs and the SUVR values.

Results

Preliminary results from statistical testing indicate that HFs were capable of distinguishing groups at baseline and follow-up (false discovery rate corrected p<0.05) in particular regions at much higher occurrences than SUVR (81 of 252). Conversely, we observed nearly no significant differences between all groups within ROIs at baseline or follow-up utilizing SUVR. From the ROC analysis, we found that the Energy and Entropy offered the best performance to distinguish Normal versus mild cognitive impairment and ADAS-Cog negative versus ADAS-Cog positive groups.

Conclusion

These results suggest that this technique could improve subject stratification in AD drug trials and help to evaluate the disease progression and treatment effects longitudinally without the disadvantages associated with intensity normalization.

Clinicians' ability to diagnose dementia with Lewy bodies is not affected by β-amyloid load

OBJECTIVE

To investigate whether an increasing load of β-amyloid and/or neuritic plaques influences the phenotype, and thus the clinical diagnostic accuracy, of dementia with Lewy bodies (DLB).

METHODS

A series of 64 subjects with autopsy-proven DLB was studied. Last diagnosis before death was used to determine the clinical diagnostic accuracy of DLB in relation to Lewy body distribution and extent of Alzheimer β-amyloid and/or neuritic pathology. DLB pathologic diagnosis was made according to consensus criteria, using α-synuclein immunostaining for Lewy body identification. β-Amyloid immunostaining was used for quantifying β-amyloid deposits. The Consortium to Establish a Registry for Alzheimer's Disease criteria and Braak stage were applied for semiquantitative grading of neuritic plaque and neurofibrillary tangle pathology.

RESULTS

Overall clinical diagnostic accuracy for the entire DLB cohort was high (80%), reflecting the high prevalence of core clinical features (fluctuations [81%], parkinsonism [77%], visual hallucinations [70%]). Lower frequencies of core clinical features of DLB, resulting in lower accuracy of its clinical diagnosis, were associated with decreasing Lewy body distribution (p < 0.0001) and with increasing neuritic plaque pathology (p = 0.035), but not with the number of β-amyloid plaque deposits.

CONCLUSIONS

The likelihood of occurrence of the DLB clinical syndrome is positively related to the extent of Lewy body pathology and negatively related to the severity of Alzheimer neuritic pathology, while β-amyloid load has no effect.

Analysis of beta-amyloid (Abeta) deposition in the temporal lobe in Alzheimer's disease using Fourier (spectral) analysis

AIM

To determine the spatial pattern of beta-amyloid (Abeta) deposition throughout the temporal lobe in Alzheimer's disease (AD).

METHODS

Sections of the complete temporal lobe from six cases of sporadic AD were immunolabelled with antibody against Abeta. Fourier (spectral) analysis was used to identify sinusoidal patterns in the fluctuation of Abeta deposition in a direction parallel to the pia mater or alveus.

RESULTS

Significant sinusoidal fluctuations in density were evident in 81/99 (82%) analyses. In 64% of analyses, two frequency components were present with density peaks of Abeta deposits repeating every 500-1000 microm and at distances greater than 1000 microm. In 25% of analyses, three or more frequency components were present. The estimated period or wavelength (number of sample units to complete one full cycle) of the first and second frequency components did not vary significantly between gyri of the temporal lobe, but there was evidence that the fluctuations of the classic deposits had longer periods than the diffuse and primitive deposits.

CONCLUSIONS

(i) Abeta deposits exhibit complex sinusoidal fluctuations in density in the temporal lobe in AD; (ii) fluctuations in Abeta deposition may reflect the formation of Abeta deposits in relation to the modular and vascular structure of the cortex; and (iii) Fourier analysis may be a useful statistical method for studying the patterns of Abeta deposition both in AD and in transgenic models of disease.

Exploring a mathematical model for the kinetics of beta-amyloid molecular imaging probes through a critical analysis of plaque pathology

Amyloid plaques are highly heterogeneous in content, size, density, and macromolecular crowding, as they are composed of masses of fibrils and other cellular material. Given this target architecture, the aggregated microenvironment offers a unique imaging target for ligands and positron emission tomography (PET) molecular imaging probes (MIPs). In this work, we address how the heterogeneous microenvironment of a plaque and its evolution may affect the kinetic rate constant of PET MIPs. We argue that macromolecular crowding will result in anomalous diffusion within plaque regions. To account for anomalous diffusion within plaques, we propose a diffusion-limited ligand-receptor compartmental model. Given the current state of knowledge about the pathological progression of Alzheimer's disease (AD), the model's parameters may be a function of the pathological progression of AD, which could result in biased estimates of the true amyloid load. The bias may be partially overcome through evaluation in conjunction with other measures of AD progression including cerebral glucose metabolism rate, neuronal cell loss, and activated inflammatory presence.

Imaging beta-amyloid fibrils in Alzheimer's disease: a critical analysis through simulation of amyloid fibril polymerization

The polymerization of beta-amyloid (A beta) peptides into fibrillary plaques is implicated, in part, in the pathogenesis of Alzheimer's disease. A beta molecular imaging probes (A beta-MIPs) have been introduced in an effort to quantify amyloid burden or load, in subjects afflicted with AD by invoking the classic PET receptor model for the quantitation of neuronal receptor density. In this communication, we explore conceptual differences between imaging the density of amyloid fibril polymers and neuronal receptors. We formulate a mathematical model for the polymerization of A beta with parameters that are mapped to biological modulators of fibrillogenesis and introduce a universal measure for amyloid load to accommodate various interactions of A beta-MIPs with fibrils. Subsequently, we hypothesize four A beta-MIPs and utilize the fibrillogenesis model to simulate PET tissue time activity curves (TACs). Given the unique nature of polymer growth and resulting PET TAC, the four probes report differing amyloid burdens for a given brain pathology, thus complicating the interpretation of PET images. In addition, we introduce the notion of an MIP's resolution, apparent maximal binding site concentration, optimal kinetic topology and its resolving power in characterizing the pathological progression of AD and the effectiveness of drug therapy. The concepts introduced in this work call for a new paradigm that goes beyond the classic parameters B(max) and K(D) to include binding characteristics to polymeric peptide aggregates such as amyloid fibrils, neurofibrillary tangles and prions.

Quantifying the pathology of neurodegenerative disorders: quantitative measurements, sampling strategies and data analysis

The use of quantitative methods has become increasingly important in the study of neurodegenerative disease. Disorders such as Alzheimer's disease (AD) are characterized by the formation of discrete, microscopic, pathological lesions which play an important role in pathological diagnosis. This article reviews the advantages and limitations of the different methods of quantifying the abundance of pathological lesions in histological sections, including estimates of density, frequency, coverage, and the use of semiquantitative scores. The major sampling methods by which these quantitative measures can be obtained from histological sections, including plot or quadrat sampling, transect sampling, and point-quarter sampling, are also described. In addition, the data analysis methods commonly used to analyse quantitative data in neuropathology, including analyses of variance (anova) and principal components analysis (PCA), are discussed. These methods are illustrated with reference to particular problems in the pathological diagnosis of AD and dementia with Lewy bodies (DLB).

The load and distribution of beta-amyloid in brain tissue of patients with Alzheimer's disease

Recent findings have underlined the significance of beta-amyloid protein (betaA4) in the etiology of Alzheimer's disease (AD). In 60 patients with AD, the amount of betaA4 deposition, estimated applying immunohistochemical techniques, was shown to be significantly influenced by apolipoprotein E (ApoE) genotype (epsilon4/epsilon4 > epsilon4/x > x/x), by the age at onset (presenile > senile), by the age at death (younger > older patients) and by the duration of the disease (long > short). Morphometric analysis revealed that the betaA4 load was highest in the superficial layer of the cortex and a significant influence on the vertical distribution was seen in females but not in males, in familial but not in sporadic cases and in senile but not in presenile cases. Our findings indicate that not only the load but also the vertical distribution of betaA4 within cortex is influenced by risk factors such as ApoE genotype and gender.

Analysis of spatial patterns in histological sections of brain tissue using a method based on regression

A method of determining the spatial pattern of any histological feature in sections of brain tissue which can be measured quantitatively is described and compared with a previously described method. A measurement of a histological feature such as density, area, amount or load is obtained for a series of contiguous sample fields. The regression coefficient (beta) is calculated from the measurements taken in pairs, first in pairs of adjacent samples and then in pairs of samples taken at increasing degrees of separation between them, i.e. separated by 2, 3, 4,..., n units. A plot of beta versus the degree of separation between the pairs of sample fields reveals whether the histological feature is distributed randomly, uniformly or in clusters. If the feature is clustered, the analysis determines whether the clusters are randomly or regularly distributed, the mean size of the clusters and the spacing of the clusters. The method is simple to apply and interpret and is illustrated using simulated data and studies of the spatial patterns of blood vessels in the cerebral cortex of normal brain, the degree of vacuolation of the cortex in patients with Creutzfeldt-Jacob disease (CJD) and the characteristic lesions present in Alzheimer's disease (AD).

Analysis of spatial patterns in histological sections of brain tissue

A method is described which enables the spatial pattern of discrete objects in histological sections of brain tissue to be determined. The method can be applied to cell bodies, sections of blood vessels or the characteristic lesions which develop in the brain of patients with neurodegenerative disorders. The density of the histological feature under study is measured in a series of contiguous sample fields arranged in a grid or transect. Data from adjacent sample fields are added together to provide density data for larger field sizes. A plot of the variance/mean ratio (V/M) of the data versus field size reveals whether the objects are distributed randomly, uniformly or in clusters. If the objects are clustered, the analysis determines whether the clusters are randomly or regularly distributed and the mean size of the clusters. In addition, if two different histological features are clustered, the analysis can determine whether their clusters are in phase, out of phase or unrelated to each other. To illustrate the method, the spatial patterns of senile plaques and neurofibrillary tangles were studied in histological sections of brain tissue from patients with Alzheimer's disease.

Multiscale detection and analysis of the senile plaques of Alzheimer's disease

Senile plaques (SP) are one of the characteristic neuropathologic lesions of Alzheimer's Disease (AD), and studies of SP cortical distribution, density (number of SP/mm2), and morphology are expected to lead to new information about the mechanism and pathogenesis of AD. We describe a digital image analysis procedure to detect SP, and to measure SP size, shape, and total fractional area in digital micrographs of silver-stained tissue sections. This histology is nonspecific so the program detects all the significant stained objects and a classifier sorts the SP from other tissue elements. SP vary greatly in size and form, and detection is based on multiscale template correlation. Three independent comparisons of computed versus expert-determined SP densities produced correlation coefficients greater than 0.8. The program found 94,000 SP in 2800 digital images of tissue sections from 42 postmortem cases including healthy aged controls and severely demented subjects.

Computed detection and quantitative morphometry of Alzheimer senile plaques

Senile plaques (SP) are the most characteristic neuropathologic lesions of Alzheimer's disease (AD) and studies of plaque cortical distribution, density, and morphology may lead to new information about the origin and pathogenesis of this disease. We have developed an automated computer image analysis program to detect SP (including diffuse and mature forms) and to measure SP size, shape, and fractional area or load in digital micrographs of silver-stained tissue sections. The plaques are detected with adaptive thresholding, requiring no user interaction. Measures of SP size, morphology, and load are readily calculated from the pixel values in the detected SP features. These measurements are achieved accurately and exhaustively, and this method offers an alternative to manual SP counting. We demonstrate its application to 4 cases spanning the full range of the severity of the disease.

Analysis of staining methods for different cortical plaques in Alzheimer's disease

This study evaluated current methods for demonstrating and categorizing cortical plaques, with the aim of establishing objective methodology for future diagnostic evaluation. Analysis of four methods of tissue processing revealed that the highest numbers of plaques were identified in formalin-fixed, paraffin-embedded tissue regardless of the stain used. Analysis of three silver stains and four immunohistochemical dilutions of an antibody to beta A4 protein revealed that the recent silver method published by Garvey et al. [(1990) J Histotechnol 14: 39-42] was equivalent to beta A4 immunohistochemistry in demonstrating the highest number of plaques. Plaque differentiation was easier and more reliable in silver compared to beta A4-stained sections, although the number of identifiable small compact plaques was significantly reduced in silver-stained sections. These studies show that plaque differentiation may be compromised by tissue processing and staining protocols. The establishment of superior methods may provide better diagnostic resolution for patients with Alzheimer's disease.

Differential pattern of beta-amyloid protein deposition within cortical sulci and gyri in Alzheimer's disease

We have quantified the pattern of beta-amyloid protein (beta AP) deposition in the sulci and gyri of the frontal cortex in Alzheimer's disease. The distribution pattern in the frontal cortex gyri is unequivocally different from the pattern found in the frontal sulci. These findings, in conjunction with our previous studies, reflect a differential vulnerability between the sulci and the gyri with respect to Alzheimer's disease pathology, sulci being affected to a greater extent. It is probable that the different patterns of deposition relate to the underlying cytoarchitecture of the cortex.