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).

Statnote 28:Canonical Variate Analysis (CVA)

The pattern of correlation between two sets of variables can be tested using canonical variate analysis (CVA). CVA, like principal components analysis (PCA) and factor analysis (FA) (Statnote 27, Hilton & Armstrong, 2011b), is a multivariate analysis Essentially, as in PCA/FA, the objective is to determine whether the correlations between two sets of variables can be explained by a smaller number of ‘axes of correlation’ or ‘canonical roots’.

Quantitative neuropathology:data collection and statistical analysis

The use of quantitative methods has become increasingly important in the study of neuropathology and especially in neurodegenerative disease. Disorders such as Alzheimer's disease (AD) and the frontotemporal dementias (FTD) are characterized by the formation of discrete, microscopic, pathological lesions which play an important role in pathological diagnosis. This chapter reviews the advantages and limitations of the different methods of quantifying pathological lesions in histological sections including estimates of density, frequency, coverage, and the use of semi-quantitative scores. The sampling strategies by which these quantitative measures can be obtained from histological sections, including plot or quadrat sampling, transect sampling, and point-quarter sampling, are described. In addition, data analysis methods commonly used to analysis quantitative data in neuropathology, including analysis of variance (ANOVA), polynomial curve fitting, multiple regression, classification trees, and principal components analysis (PCA), are discussed. These methods are illustrated with reference to quantitative studies of a variety of neurodegenerative disorders.

A quantitative study of the pathological lesions in the neocortex and hippocampus of twelve patients with corticobasal degeneration

The density of ballooned neurons (BN), tau-positive neurons with inclusion bodies (tau+ neurons), and tau-positive plaques (tau+ plaques) was determined in sections of the frontal, parietal, and temporal lobe in 12 patients with corticobasal degeneration (CBD). No significant differences in the mean density of BN and tau+ neurons were observed between neocortical regions. In the hippocampus, the densities of BN were significantly lower than in the neocortex, and densities of tau+ neurons were greater in sectors CA1 and CA2, compared with CA3 and CA4. Tau+ plaques were present in one or more brain regions in six patients. Significantly more BN were recorded in the lower (laminae V/VI) compared with the upper cortex (laminae I/II/III) but tau+ neurons were equally frequent in the upper and lower cortex. No significant correlations were observed between the densities of BN and tau+ neurons, but the densities of BN in the superior temporal gyrus and tau+ plaques in the frontal cortex were positively correlated with age. A principal components analysis (PCA) suggested that differences in the density of tau+ neurons in the frontal and motor cortex were the most important sources of variation between patients. In addition, one patient with a particularly high density of tau+ neurons in the hippocampus appeared to be atypical of the patient group studied. The data support the hypothesis that, although clinically heterogeneous, CBD is a pathologically distinct disorder. (C) 2000 Academic Press.

Quantitative variations in the pathology of 11 cases of variant Creutzfeldt-Jakob disease (vCJD)

Quantitative variations in the density and distribution of the vacuolation ('spongiform change'), surviving neurons, and prion protein (PrP) deposits were studied in eight brain regions from 11 cases of variant Creutzfeldt-Jakob disease (vCJD). Principal components analysis (PCA) was used to study the similarities and differences between cases and to identify the neuropathological variables which could best account for these variations. Two principal components (PC) were extracted from the data accounting in total for 93.4% of the variance; the majority of the variance (90%) being associated with PC1. Some clustering of the 11 cases in relation to PC1 and PC2 was evident. The densities of the vacuolation in the occipital cortex and the molecular layer of the cerebellum were positively and negatively correlated, respectively, with PC1. No significant variation between cases was associated with PrP deposition. These data suggest that vCJD cases have a consistent neuropathological profile characterised by the presence of vacuolation, neuronal loss and PrP deposition in the form of florid and non-florid deposits. However, there are quantitative variations between cases in the development of the vacuolation especially affecting the occipital cortex and cerebellum. © 2002 Elsevier Science Ireland Ltd. All rights reserved.

Quantification of pathological lesions in the frontal and temporal lobe of ten patients diagnosed with Pick's disease

The densities of Pick bodies (PB), Pick cells (PC), senile plaques (SP) and neurofibrillary tangles (NFT) in the frontal and temporal lobe were determined in ten patients diagnosed with Pick's disease (PD). The density of PB was significantly higher in the dentate gyrus granule cells compared with the cortex and the CA sectors of the hippocampus. Within the hippocampus, the highest densities of PB were observed in sector CA1. PC were absent in the dentate gyrus and no significant differences in PC density were observed in the remaining brain regions. With the exception of two patients, the densities of SP and NFT were low with no significant differences in mean densities between cortical regions. In the hippocampus, the density of NFT was greatest in sector CA1. PB and PC densities were positively correlated in the frontal cortex but no correlations were observed between the PD and AD lesions. A principal components analysis (PCA) of the neuropathological variables suggested that variations in the densities of SP in the frontal cortex, temporal cortex and hippocampus were the most important sources of heterogeneity within the patient group. Variations in the densities of PB and NFT in the temporal cortex and hippocampus were of secondary importance. In addition, the PCA suggested that two of the ten patients were atypical. One patient had a higher than average density of SP and one familial patient had a higher density of NFT but few SP.

Is neuropathological heterogeneity in Alzheimer’s disease related to apolipoprotein genotype?

The abundance of senile plaques (SP) and neurofibrillary tangles (NFT) was studied in cortical and subcortical regions from 30 patients with Alzheimer’s disease (AD) expressing different apolipoprotein E (apoE) genotypes. Principal components analysis (PCA) was used to identify the most important neuropathological variations between individual patients and to determine whether these variations were related to apoE genotype. The first two principal components (PC) accounted for 60% and 40% of the total variance of the SP and NFT data respectively. The abundance of SP in the frontal and occipital cortex and NFT in the frontal cortex, amygdala and substantia nigra were positively correlated with the first principal component (PC1). Analysis of the SP data revealed that the apoE score of the patient (the sum of the two alleles) was positively correlated with PC1 while analysis of the NFT data revealed no significant correlations between apoE score and the PC. The data suggest that apoE genotype was more closely related to variations in the distribution and abundance of SP than of NFT. In addition, a more rapid spread of SP into the frontal and occipital cortex may occur in patients with a high apoE score.

Relationships between morphological types of plaque in Alzheimer’s disease as revealed in silver and immunostained preparations

The objective of this study was to determine the possible relationships between the morphological types of plaque revealed in silver and immunostained sections of Alzheimer’s disease (AD) tissue. The density of cored and uncored senile plaques in Glees and Marsland preparations, and of diffuse, primitive, classic and compact beta/A4 deposits in immunostained preparations were estimated. A principal components analysis (PCA) of the data suggested that three uncorrelated principal components accounted for 80% of the variation in lesion density in the tissues. This suggested that thee processes lead independently to the formation of: (1) the uncored Glees plaques; (2) the primitive beta/A4 deposits and most of the classic beta/A4 deposits and (3) the compact beta/A4 deposits and the remaining classic deposits. Hence, the uncored plaques revealed by the Glees stain and the primitive beta/A4 deposits represented distinct plaque populations. In addition, the classic beta/A4 deposits did not appear to represent a uniform plaque population but to originate from at least two pathological processes. The uncored Glees plaques appeared to the only plaque population closely related to the diffuse beta/A4 deposits.

TDP-43-Immunoreactive pathology in frontotemporal lobar degeneration with TDP proteinopathy (FTLD-TDP) with and without associated motor neuron disease

A proportion of patients with motor neuron disease (MND) exhibit frontotemporal dementia (FTD) and some patients with FTD develop the clinical features of MND. Frontotemporal lobar degeneration (FTLD) is the pathological substrate of FTD and some forms of this disease (referred to as FTLD-U) share with MND the common feature of ubiquitin-immunoreactive, tau-negative cellular inclusions in the cerebral cortex and hippocampus. Recently, the transactive response (TAR) DNA-binding protein of 43 kDa (TDP-43) has been found to be a major protein of the inclusions of FTLD-U with or without MND and these cases are referred to as FTLD with TDP-43 proteinopathy (FTLD-TDP). To clarify the relationship between MND and FTLD-TDP, TDP-43 pathology was studied in nine cases of FTLD-MND and compared with cases of familial and sporadic FTLD–TDP without associated MND. A principal components analysis (PCA) of the nine FTLD-MND cases suggested that variations in the density of surviving neurons in the frontal cortex and neuronal cytoplasmic inclusions (NCI) in the dentate gyrus (DG) were the major histological differences between cases. The density of surviving neurons in FTLD-MND was significantly less than in FTLD-TDP cases without MND, and there were greater densities of NCI but fewer neuronal intranuclear inclusions (NII) in some brain regions in FTLD-MND. A PCA of all FTLD-TDP cases, based on TDP-43 pathology alone, suggested that neuropathological heterogeneity was essentially continuously distributed. The FTLD-MND cases exhibited consistently high loadings on PC2 and overlapped with subtypes 2 and 3 of FTLD-TDP. The data suggest: (1) FTLD-MND cases have a consistent pathology, variations in the density of NCI in the DG being the major TDP-43-immunoreactive difference between cases, (2) there are considerable similarities in the neuropathology of FTLD-TDP with and without MND, but with greater neuronal loss in FTLD-MND, and (3) FTLD-MND cases are part of the FTLD-TDP ‘continuum’ overlapping with FTLD-TDP disease subtypes 2 and 3.

Quantitative pathological changes in the cerebellum of multiple system atrophy

Multiple system atrophy (MSA) is a rare neurodegenerative disorder associated with parkinsonism, ataxia, and autonomic dysfunction. Its pathology is primarily subcortical comprising vacuolation, neuronal loss, gliosis, and α-synuclein-immunoreactive glial cytoplasmic inclusions (GO). To quantify cerebellar pathology in MSA, the density and spatial pattern of the pathological changes were studied in α-synuclein-immunolabelled sections of the cerebellar hemisphere in 10 MSA and 10 control cases. In MSA, densities of Purkinje cells (PC) were decreased and vacuoles in the granule cell layer (GL) increased compared with controls. In six MSA cases, GCI were present in cerebellar white matter. In the molecular layer (ML) and GL of MSA, vacuoles were clustered, the clusters exhibiting a regular distribution parallel to the edge of the folia. Purkinje cells were randomly or regularly distributed with large gaps between surviving cells. Densities of glial cells and surviving neurons in the ML and surviving cells and vacuoles in the GL were negatively correlated consistent with gliosis and vacuolation in response to neuronal loss. Principal components analysis (PCA) suggested vacuole densities in the ML and vacuole density and cell losses in the GL were the main source of neuropathological variation among cases. The data suggest that: (1) cell losses and vacuolation of the GCL and loss of PC were the most significant pathological changes in the cases studied, (2) pathological changes were topographically distributed, and (3) cerebellar pathology could influence cerebral function in MSA via the cerebello-dentato-thalamic tract.

TDP-43-immunoreactive pathology in frontotemporal lobar degeneration with TDP proteinopathy (FTLD-TDP) with and without associated motor neuron disease (MND)

A proportion of patients with motor neuron disease (MND) exhibit frontotemporal dementia (FTD) and some patients with FTD develop the clinical features of MND. Frontotemporal lobar degeneration (FTLD) is the pathological substrate of FTD and some forms of this disease (referred to as FTLD-U) share with MND the common feature of ubiquitin-immunoreactive, tau-negative cellular inclusions in the cerebral cortex and hippocampus. Recently, the transactive response (TAR) DNA-binding protein of 43 kDa (TDP-43) has been found to be a major protein of the inclusions of FTLD-U with or without MND and these cases are referred to as FTLD with TDP-43 proteinopathy (FTLD-TDP). To clarify the relationship between MND and FTLD-TDP, TDP-43 pathology was studied in nine cases of FTLD-MND and compared with cases of familial and sporadic FTLD-TDP without associated MND. A principal components analysis (PCA) of the nine FTLD-MND cases suggested that variations in the density of surviving neurons in the frontal cortex and neuronal cytoplasmic inclusions (NCI) in the dentate gyrus (DG) were the major histological differences between cases. The density of surviving neurons in FTLD-MND was significantly less than in FTLD-TDP cases without MND, and there were greater densities of NCI but fewer neuronal intranuclear inclusions (NII) in some brain regions in FTLD-MND. A PCA of all FTLD-TDP cases, based on TDP-43 pathology alone, suggested that neuropathological heterogeneity was essentially continuously distributed. The FTLD-MND cases exhibited consistently high loadings on PC2 and overlapped with subtypes 2 and 3 of FTLD-TDP. The data suggest: (1) FTLD-MND cases have a consistent pathology, variations in the density of NCI in the DG being the major TDP-43-immunoreactive difference between cases, (2) there are considerable similarities in the neuropathology of FTLD-TDP with and without MND, but with greater neuronal loss in FTLD-MND, and (3) FTLD-MND cases are part of the FTLD-TDP 'continuum' overlapping with FTLD-TDP disease subtypes 2 and 3. © 2012 Nova Science Publishers, Inc. All rights reserved.

A quantitative study of tau pathology in eleven cases of chronic traumatic encephalopathy

AIMS: To quantify tau pathology of chronic traumatic encephalopathy (CTE) and investigate influence of dot-like lesions (DL), brain region, co-morbidity, and sporting career length. METHODS: Densities of neurofibrillary tangles (NFT), astrocytic tangles (AT), DL, oligodendroglial inclusions (GI), neuropil threads (NT), vacuoles, neurons, and enlarged neurons (EN) were measured in tau-immunoreactive sections of upper cortical laminae of frontal and temporal lobe, hippocampus (HC), amygdala, and substantia nigra (SN) of eleven cases of CTE. RESULTS: DL were a consistent finding in CTE. Densities of NFT, NT and DL were greatest in sectors CA1 and CA2 of the HC. Densities of AT were lower than NFT, small numbers of GI were recorded in temporal lobe, and low densities of vacuoles and EN were consistently present. β-amyloid containing neuritic plaques (NP) also occurred at low density. Densities of NFT, NT, DL, and AT were greater in sulci than gyri while vacuole density was greater in gyri. Principal components analysis (PCA) suggested that sporting career length and densities of NFT in entorhinal cortex, NT in CA2 and SN, and vacuolation in the DG were significant sources of variation among cases. CONCLUSION: DL are frequent in CTE suggesting affinity with argyrophilic grain disease (AGD) and Parkinson's disease dementia (PD-Dem). Densities of AT in all regions and NT/DL in sectors CA2/4 were consistent features of CTE. The eleven cases are neuropathologically heterogeneous which may result from genetic diversity, and variation in anatomical pathways subjected to trauma. This article is protected by copyright. All rights reserved.

Mixtures of probabilistic principal component analysers

Principal component analysis (PCA) is one of the most popular techniques for processing, compressing and visualising data, although its effectiveness is limited by its global linearity. While nonlinear variants of PCA have been proposed, an alternative paradigm is to capture data complexity by a combination of local linear PCA projections. However, conventional PCA does not correspond to a probability density, and so there is no unique way to combine PCA models. Previous attempts to formulate mixture models for PCA have therefore to some extent been ad hoc. In this paper, PCA is formulated within a maximum-likelihood framework, based on a specific form of Gaussian latent variable model. This leads to a well-defined mixture model for probabilistic principal component analysers, whose parameters can be determined using an EM algorithm. We discuss the advantages of this model in the context of clustering, density modelling and local dimensionality reduction, and we demonstrate its application to image compression and handwritten digit recognition.

Exploratory data analysis with non-linear and missing data in geochemistry

Exploratory analysis of data seeks to find common patterns to gain insights into the structure and distribution of the data. In geochemistry it is a valuable means to gain insights into the complicated processes making up a petroleum system. Typically linear visualisation methods like principal components analysis, linked plots, or brushing are used. These methods can not directly be employed when dealing with missing data and they struggle to capture global non-linear structures in the data, however they can do so locally. This thesis discusses a complementary approach based on a non-linear probabilistic model. The generative topographic mapping (GTM) enables the visualisation of the effects of very many variables on a single plot, which is able to incorporate more structure than a two dimensional principal components plot. The model can deal with uncertainty, missing data and allows for the exploration of the non-linear structure in the data. In this thesis a novel approach to initialise the GTM with arbitrary projections is developed. This makes it possible to combine GTM with algorithms like Isomap and fit complex non-linear structure like the Swiss-roll. Another novel extension is the incorporation of prior knowledge about the structure of the covariance matrix. This extension greatly enhances the modelling capabilities of the algorithm resulting in better fit to the data and better imputation capabilities for missing data. Additionally an extensive benchmark study of the missing data imputation capabilities of GTM is performed. Further a novel approach, based on missing data, will be introduced to benchmark the fit of probabilistic visualisation algorithms on unlabelled data. Finally the work is complemented by evaluating the algorithms on real-life datasets from geochemical projects.

Understanding U.S. regional linguistic variation with Twitter data analysis

We analyze a Big Data set of geo-tagged tweets for a year (Oct. 2013–Oct. 2014) to understand the regional linguistic variation in the U.S. Prior work on regional linguistic variations usually took a long time to collect data and focused on either rural or urban areas. Geo-tagged Twitter data offers an unprecedented database with rich linguistic representation of fine spatiotemporal resolution and continuity. From the one-year Twitter corpus, we extract lexical characteristics for twitter users by summarizing the frequencies of a set of lexical alternations that each user has used. We spatially aggregate and smooth each lexical characteristic to derive county-based linguistic variables, from which orthogonal dimensions are extracted using the principal component analysis (PCA). Finally a regionalization method is used to discover hierarchical dialect regions using the PCA components. The regionalization results reveal interesting linguistic regional variations in the U.S. The discovered regions not only confirm past research findings in the literature but also provide new insights and a more detailed understanding of very recent linguistic patterns in the U.S.