Current Alzheimer Research - Volume 13, Issue 5, 2016

There are a number of clinical questions for which there are no easy answers, even for welltrained doctors. The diagnostic tool commonly used to assess cognitive impairment in neurodegenerative diseases is based on established clinical criteria. However, the differential diagnosis between disorders can be difficult, especially in early phases or atypical variants. This takes on particular importance when it is still possible to use an appropriate treatment. To solve this problem, physicians need to have access to an arsenal of diagnostic tests, such as neurofunctional imaging, that allow higher specificity in clinical assessment. However, the reliability of diagnostic tests may vary from one to the next, so the diagnostic validity of a given investigation must be estimated by comparing the results obtained from “true” criteria to the “gold standard” or reference test. While pathological analysis is considered to be the gold standard in a wide spectrum of diseases, it cannot be applied to neurological processes. Other approaches could provide solutions, including clinical patient follow-up, creation of a data bank or use of computer-aided diagnostic algorithms. In this article, we discuss the development of different methodological procedures related to analysis of diagnostic validity and present an example from our own experience based on the use of I-123-ioflupane-SPECT in the study of patients with movement disorders. The aim of this chapter is to approach the problem of diagnosis from the point of view of the clinician, taking into account specific aspects of neurodegenerative disease.

Late life depression (LLD) even in subsyndromal stages shows high conversion rates from cognitively normal (CN) to mild cognitive impairment (MCI). Results of [18F]-fluorodesoxyglucose positron-emission-tomography (FDG-PET) were inconsistent in LLD patients, whereas atrophy was repeatedly described. Therefore, we set out to investigate FDG metabolism and the effect of atrophy correction (PVEC) in geriatric CN patients with depressive symptoms. 21 CN subjects with positive item for the depression category (DEP) in the Neuropsychiatric-Inventory-Questionnaire and 29 CN subjects with an absent depression item (NON-DEP) were selected from the ADNI cohort. FDG-PETs were analyzed in individual PET space using volumes-of-interest (VOI) and statistical-parametric-mapping (SPM) approaches. VOI- and MRI-based PVEC were applied to PET data. DEP subjects showed significant hypometabolism in fronto-temporal cortices and the posterior cingulate cortex (PCC) when contrasted against NON-DEP in uncorrected data. Both in VOI- and SPM-based approaches PVEC eliminated significance in PCC, while fronto-temporal regions remained significant or even attained significance such as in case of the left amygdala. Subsyndromally depressed CN subjects had decreased FDG metabolism in mood-related brain regions, which may be relevant to their elevated risk for conversion from CN to MCI. Methodological advances in PET analyses should be considered in future studies as PVEC relevantly changed results of FDG-PET for detecting apparent metabolic differences between DEP and NON-DEP subjects. Furthermore, VOI-based analyses in individual PET space will allow a more accurate consideration of variability in anatomy, especially in subcortical regions.

Alzheimer's Disease (AD) is an invalidating neurodegenerative disorders frequently affecting the aging population. In view of the increase of elderlies, not only in western countries, the related growing societal problems urge for identifying clinical biomarkers in view of potential treatments interfering or blocking the disease course. Among the plenty of anatomo-functional in vivo imaging techniques to inspect brain circuits and physiology, the Magnetic Resonance Imaging (MRI), the functional MRI (fMRI), the Electroencephalography (EEG) and Magnetoencephalography (MEG), have been extensively used for the study of AD, with different achievements and limitations. Eventually, the methodologies summoned by brain connectomics further strengthen the expectations in this field, as shown by recent results obtained with [18F]2-fluoro-2-deoxyglucose 18FDG-PET and fMRI in the prediction of the AD in early stages. However, the inherent complexity of the pathophysiology of the AD suggests that only integrative approaches combining different techniques and methodologies of brain scanning could produce significant breakthroughs in the study of AD. This review proposes a formal framework able to combine brain connectomic data from multimodal acquisitions by means of different in vivo neuroimaging techniques, briefly reporting their different advantages and drawbacks. Indeed, a specialized complex multiplex network, where nodes interact in layers linking the same pair of nodes and each layer reflects a distinct type of brain acquisition, can model the plurality of connectomes recommended in this framework.

The analysis of positron emission tomography (PET) scan image is challenging due to a high level of noise and a low resolution and also because differences between healthy and demented are very subtle. High dimensional classification methods based on PET have been proposed to automatically discriminate between normal control group (NC) patients and patients with Alzheimer’s disease (AD), with mild cognitive impairment (MCI), and mild cognitive impairment converting to Alzheimer’s disease (MCIAD ) (a group of patients that clearly degrades to AD). We developed a voxelbased method for volumetric image analysis. We performed 3 classification experiments AD vs CG, AD vs MCI, MCIAD vs MCI. We will also give a small demonstration of the presented method on a set of face images. This method is capable to extract information about the location of metabolic changes induced by Alzheimer’s disease that directly relies statistical features and brain regions of interest (ROIs). We produce “maps” to visualize the most informative regions of the brain and compare them with voxel-wise statistics. Using the mean intensity of about 2000 6 × 6 × 6mm patches, selected by the extracted map, as input for a classifier we obtain a classification rate of 95.5%.

The emergence of Alzheimer’s Disease (AD) as a consequence of increasing aging population makes urgent the availability of methods for the early and accurate diagnosis. Magnetic Resonance Imaging (MRI) could be used as in vivo, non invasive tool to identify sensitive and specific markers of very early AD progression. In recent years, multivariate pattern analysis (MVPA) and machine- learning algorithms have attracted strong interest within the neuroimaging community, as they allow automatic classification of imaging data with higher performance than univariate statistical analysis. An exhaustive search of PubMed, Web of Science and Medline records was performed in this work, in order to retrieve studies focused on the potential role of MRI in aiding the clinician in early diagnosis of AD by using Support Vector Machines (SVMs) as MVPA automated classification method. A total of 30 studies emerged, published from 2008 to date. This review aims to give a state-of-the-art overview about SVM for the early and differential diagnosis of AD-related pathologies by means of MRI data, starting from preliminary steps such as image pre-processing, feature extraction and feature selection, and ending with classification, validation strategies and extraction of MRI-related biomarkers. The main advantages and drawbacks of the different techniques were explored. Results obtained by the reviewed studies were reported in terms of classification performance and biomarker outcomes, in order to shed light on the parameters that accompany normal and pathological aging. Unresolved issues and possible future directions were finally pointed out.

Quantitative MRI provides important information about tissue properties in brain both in normal ageing and in degenerative disorders. Although it is well known that those with Alzheimer’s disease (AD) show a specific pattern and faster rate of atrophy than controls, the precise spatial and temporal patterns of quantitative MRI in AD are unknown. We aimed to investigate neuroimaging correlates of AD using serial quantitative MRI. In our study, twenty-one subjects with AD and thirty-two similar-aged healthy controls underwent two serial MRI scans at baseline and 12 months. Tissue characteristics were captured using two quantitative MRI parameters: longitudinal relaxation time (qT1) and transverse relaxation time (qT2). The two groups (AD and controls) were statistically compared using a voxel based quantification (VBQ) method based on Matlab and SPM8. At baseline, subjects with AD showed a significant reduction of qT1 and qT2 compared to controls in bilateral temporal and parietal lobes, hippocampus, and basal ganglia. This pattern was also observed at follow-up. Longitudinally, in AD we found a significant increase rather than further reduction of qT1 and qT2 from the baseline in bilateral hippocampus, thalamus and right caudate nucleus. In addition, the longitudinal change of qT1 in left hippocampus was negatively correlated with cognitive decline in AD over the 1-year period, and the general disease severity significantly predicted the amount of increase of qT1 in bilateral hippocampus over 12 months. The longitudinal change of qT2 in left parahippocampus correlated with change in neuropsychiatric features over time. In summary, quantitative MRI parameters were reduced in AD cross-sectionally, but increased over time, showing distinct spatiotemporal patterns from the atrophy in AD. We also showed the clinical relevance of quantitative MRI parameters, indicating their potential promise as new imaging markers in AD.

Alzheimer’s disease (AD) is a chronic neurodegenerative disease of the central nervous system that has no cure and leads to death. One of the most prevalent tools for AD diagnosis is magnetic resonance imaging (MRI), because of its capability to visualize brain anatomical structures. There is a variety of classification methods for automatic diagnosis of AD, such as support vector machines, genetic algorithms, Bayes classifiers, neural networks, random forests, etc., but none of them provides robust information about the stage of the AD, they can just reveal the presence of disease. In this paper, a new approach for classification of MRI images using a fuzzy inference system is proposed. Two statistical moments (mean and standard deviation) of 116 anatomical regions of interests (ROIs) are used as input features for the classification system. A t-test feature selection method is used to identify the most discriminative ROIs. In order to evaluate the proposed system, MRI images from a database consisting of 818 subjects (229 normal, 401 mild cognitive impairment and 188 AD subjects) collected from the Alzheimer’s disease neuroimaging initiative (ADNI) is analyzed. The receiver operating characteristics (ROC) curve and the area under the curve (AUC) of the proposed fuzzy inference system fed by statistical input features are employed as the evaluation criteria with k-fold cross validation. The proposed system yields promising results in normal vs. AD classification with AUC of 0.99 on the training set and 0.8622±0.0033 on the testing set.

Background: Late Onset Bipolar Disorder (LOBD) is the arousal of Bipolar Disorder (BD) at old age (>60) without any previous history of disorders. LOBD is often difficult to distinguish from degenerative dementias, such as Alzheimer Disease (AD), due to comorbidities and common cognitive symptoms. Moreover, LOBD prevalence is increasing due to population aging. Biomarkers extracted from blood plasma are not discriminant because both pathologies share pathophysiological features related to neuroinflammation, therefore we look for anatomical features highly correlated with blood biomarkers that allow accurate diagnosis prediction. This may shed some light on the basic biological mechanisms leading to one or another disease. Moreover, accurate diagnosis is needed to select the best personalized treatment. Objective: We look for white matter features which are correlated with blood plasma biomarkers (inflammatory and neurotrophic) discriminating LOBD from AD. Materials: A sample of healthy controls (HC) (n=19), AD patients (n=35), and BD patients (n=24) has been recruited at the Alava University Hospital. Plasma biomarkers have been obtained at recruitment time. Diffusion weighted (DWI) magnetic resonance imaging (MRI) are obtained for each subject. Methods: DWI is preprocessed to obtain diffusion tensor imaging (DTI) data, which is reduced to fractional anisotropy (FA) data. In the selection phase, eigenanatomy finds FA eigenvolumes maximally correlated with plasma biomarkers by partial sparse canonical correlation analysis (PSCCAN). In the analysis phase, we take the eigenvolume projection coefficients as the classification features, carrying out cross-validation of support vector machine (SVM) to obtain discrimination power of each biomarker effects. The John Hopkins Universtiy white matter atlas is used to provide anatomical localizations of the detected feature clusters. Results: Classification results show that one specific biomarker of oxidative stress (malondialdehyde MDA) gives the best classification performance ( accuracy 85%, F-score 86%, sensitivity, and specificity 87%, ) in the discrimination of AD and LOBD. Discriminating features appear to be localized in the posterior limb of the internal capsule and superior corona radiata. Conclusion: It is feasible to support contrast diagnosis among LOBD and AD by means of predictive classifiers based on eigenanatomy features computed from FA imaging correlated to plasma biomarkers. In addition, white matter eigenanatomy localizations offer some new avenues to assess the differential pathophysiology of LOBD and AD.

Although measurement of total hippocampal volume is considered as an important hallmark of Alzheimer’s disease (AD), recent evidence demonstrated that atrophies of hippocampal subregions might be more sensitive in predicting this neurodegenerative disease. The vast majority of neuroimaging papers investigating this topic are focused on the difference between AD and patients with mild cognitive impairment (MCI), not considering the impact of MCI patients who will or not convert in AD. For this reason, the aim of this study was to determine if measurements of hippocampal subfields provide advantages over total hippocampal volume for discriminating these groups. Hippocampal subfields volumetry was extracted in 55 AD, 32 converted and 89 not-converted MCI (c/nc-MCI) and 47 healthy controls, using an atlas-based automatic algorithm based on Markov random fields embedded in the Freesurfer framework. To evaluate the impact of hippocampal atrophy in discriminating the insurgence of AD-like phenotypes we used three classification methods: Support Vector Machine, Naïve Bayesian Classifier and Neural Networks Classifier. Taking into account only the total hippocampal volume, all classification models, reached a sensitivity of about 66% in discriminating between c-MCI and nc-MCI. Otherwise, classification analysis considering all segmenting subfields increased accuracy to diagnose c-MCI from 68% to 72%. This effect resulted to be strongly dependent upon atrophies of the subiculum and presubiculum. Our multivariate analysis revealed that the magnitude of the difference considering hippocampal subfield volumetry, as segmented by the considered atlas-based automatic algorithm, offers an advantage over hippocampal volume in distinguishing early AD from nc-MCI.

Magnetic Resonance Imaging (MRI) is of fundamental importance in neuroscience, providing good contrast and resolution, as well as not being considered invasive. Despite the development of newer techniques involving radiopharmaceuticals, it is still a recommended tool in Alzheimer’s Disease (AD) neurological practice to assess neurodegeneration, and recent research suggests that it could reveal changes in the brain even before the symptomatology appears. In this paper we propose a method that performs a Spherical Brain Mapping, using different measures to project the three-dimensional MR brain images onto two-dimensional maps revealing statistical characteristics of the tissue. The resulting maps could be assessed visually, but also perform a significant feature reduction that will allow further supervised or unsupervised processing, reducing the computational load while maintaining a large amount of the original information. We have tested our methodology against a MRI database comprising 180 AD affected patients and 180 normal controls, where some of the mappings have revealed as an optimum strategy for the automatic processing and characterization of AD patterns, achieving up to a 90.9% of accuracy, as well as significantly reducing the computational load. Additionally, our maps allow the visual analysis and interpretation of the images, which can be of great help in the diagnosis of this and other types of dementia.

Alzheimer’s disease (AD) is a clinically, anatomically and biologically heterogeneous disorder encompassing a wide spectrum of cognitive profiles, ranging from the typical amnestic syndrome to visuospatial changes in posterior cortical atrophy, language deficits in primary progressive aphasia and behavioural/executive dysfunctions in anterior variants. With the emergence of functional imaging and neural network analysis using graph theory for instance, some authors have hypothesized that this phenotypic variability is produced by the differential involvement of large-scale neural networks – a model called ‘molecular nexopathy’. At the moment, however, the hypothesized mechanisms underlying AD’s divergent network degeneration remain speculative and mostly involve selective premorbid network vulnerability. Herein we present an overview of AD’s clinicoanatomical variability, outline functional imaging and graph theory contributions to our understanding of the disease and discuss ongoing debates regarding the biological roots of its heterogeneity. We finally discuss the clinical promises of statistical signal processing disciplines (graph theory and information theory) in predicting the trajectory of AD variants. This paper aims to raise awareness about AD clinicoanatomical heterogeneity and outline how statistical signal processing methods could lead to a better understanding, diagnosis and treatment of AD variants in the future.

Introduction: Magnet resonance image (MRI)-based segmentations are widely used for clinical brain research, especially in conjunction with positron-emission-tomography (PET). Although artifacts due to segmentation errors arise commonly, the impact of these artifacts on PET quantitation has not yet been investigated systematically. Therefore, the aim of this study was to assess the effect of segmentation errors on [18F]-AV45 and [18F]-FDG PET quantitation, with and without correction for partial volume effects (PVE). Material and Methods: 119 subjects with both [18F]-AV45, and [18F]-FDG PET as well as T1-weighted MRI at baseline and at two-year follow-up were selected from the ADNI cohort, and their MRI brain images were segmented using PMOD 3.5. MRIs with segmentation artifacts were masked with the corresponding [18F]-FDG PET standard-uptake-value (SUV) images to elucidate and quantify the impact of artifacts on PET analyses for six defined volumes-of-interest (VOI). Artifact volumes were calculated for each VOI, together with error-[%] and root-mean-square-errors (RMSE) in uncorrected and PVE corrected SUV results for the two PET tracers. We also assessed the bias in longitudinal PET data. Results: Artifacts occurred most frequently in the parietal cortex VOI. For [18F]-AV45 and [18F]-FDG PET, the percentage-errors were dependent on artifact volumes. PVEC SUVs were consequently more distorted than were their uncorrected counterparts. In static and longitudinal assessment, a small subgroup of subjects with large artifacts (≥1500 voxels; ≙5.06 cm³) accounted for much of the PET quantitation bias. Conclusion: Large segmentation artifacts need to be detected and resolved as they considerably bias PET quantitation, especially when PVEC is applied to PET data.