Exploratory graphical models of functional and structural connectivity patterns for Alzheimer's Disease diagnosis.

Alzheimer's Disease (AD) is the most common neurodegenerative disease in elderly people. Its development has been shown to be closely related to changes in the brain connectivity network and in the brain activation patterns along with structural changes caused by the neurodegenerative process. Methods to infer dependence between brain regions are usually derived from the analysis of covariance between activation levels in the different areas. However, these covariance-based methods are not able to estimate conditional independence between variables to factor out the influence of other regions. Conversely, models based on the inverse covariance, or precision matrix, such as Sparse Gaussian Graphical Models allow revealing conditional independence between regions by estimating the covariance between two variables given the rest as constant. This paper uses Sparse Inverse Covariance Estimation (SICE) methods to learn undirected graphs in order to derive functional and structural connectivity patterns from Fludeoxyglucose (18F-FDG) Position Emission Tomography (PET) data and segmented Magnetic Resonance images (MRI), drawn from the ADNI database, for Control, MCI (Mild Cognitive Impairment Subjects), and AD subjects. Sparse computation fits perfectly here as brain regions usually only interact with a few other areas. The models clearly show different metabolic covariation patters between subject groups, revealing the loss of strong connections in AD and MCI subjects when compared to Controls. Similarly, the variance between GM (Gray Matter) densities of different regions reveals different structural covariation patterns between the different groups. Thus, the different connectivity patterns for controls and AD are used in this paper to select regions of interest in PET and GM images with discriminative power for early AD diagnosis. Finally, functional an structural models are combined to leverage the classification accuracy. The results obtained in this work show the usefulness of the Sparse Gaussian Graphical models to reveal functional and structural connectivity patterns. This information provided by the sparse inverse covariance matrices is not only used in an exploratory way but we also propose a method to use it in a discriminative way. Regression coefficients are used to compute reconstruction errors for the different classes that are then introduced in a SVM for classification. Classification experiments performed using 68 Controls, 70 AD, and 111 MCI images and assessed by cross-validation show the effectiveness of the proposed method.

Statistical significance in the selection of the regions of interest for Parkinson brain image processing.

This paper presents the analysis of the statistical significance in the selection of the ROI for the discriminant analysis of brain images to identify Parkinson patients or subjects without any pathology. The particular features and brain functional patterns of the Parkinson's disease cause that there are regions that conveniently reveal the presence of the pathology, in this case mainly the striatum region. The selection of the brain mask makes incidence in two main aspects: the selection of the region of interest (striatum and surrounding area) for the analysis, but also the selection of the region without significance, which is the reference area for the intensity normalization, previous to the analysis. This work studies the statistical significance in the selection of ROIs in 3D brain images for Parkinson, depending on the objective to be achieved in the posterior analysis process.

Projecting MRI brain images for the detection of Alzheimer's Disease.

Recent advances in the process of diagnosis of neurodegenerative diseases, such as Alzheimer's Disease, rely on the use of molecular imaging that allow the interpretation of different metabolic biomarkers in the brain. However these procedures are considered of invasive nature, as they involve the injection of radioactive markers. On the other hand, Magnetic Resonance Imaging (MRI) is perhaps the most widely used and less invasive medical imaging technique, although its ability to detect Alzheimer's Disease has revealed limited. In this paper, a new method that simplifies the process of analysing 3D MRI brain images using a two dimensional projection is proposed. Our system outperforms other methods that use MRI, achieving up to a 86% of accuracy and significantly reducing the computational load. Additionally, it allows the visual analysis and interpretation of the images, which can be of great help in the diagnosis of this and other types of dementia.