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

• Main Authors: Andres eOrtiz, Jorge eMunilla, Ignacio Alvarez Illan, Juan Manuel Gorriz, Javier eRamírez
• Format: Article
• Language: English
• Published: Frontiers Media S.A. 2015-11-01
• Series: Frontiers in Computational Neuroscience
• Subjects: Alzheimer's disease; multiple regression; Gaussian graphical models; Brain connectivity network; Sparse Inverse Covariance
• Online Access: http://journal.frontiersin.org/Journal/10.3389/fncom.2015.00132/full

Alzheimer’s Disease (AD) is the most common neurodegenerative disease in elderly people. Itsdevelopment has been shown to be closely related to changes in the brain connectivity networkand in the brain activation patterns along with structural changes caused by the neurodegenerativeprocess.Methods to infer dependence between brain regions are usually derived from the analysis ofcovariance between activation levels in the different areas. However, these covariance-basedmethods are not able to estimate conditional independence between variables to factor out theinfluence of other regions. Conversely, models based on the inverse covariance, or precisionmatrix, such as Sparse Gaussian Graphical Models allow revealing conditional independencebetween regions by estimating the covariance between two variables given the rest as constant.This paper uses Sparse Inverse Covariance Estimation (SICE) methods to learn undirectedgraphs in order to derive functional and structural connectivity patterns from Fludeoxyglucose(18F-FDG) Position Emission Tomography (PET) data and segmented Magnetic Resonanceimages (MRI), drawn from the ADNI database, for Control, MCI (Mild Cognitive ImpairmentSubjects) and AD subjects. Sparse computation fits perfectly here as brain regions usually onlyinteract with a few other areas.The models clearly show different metabolic covariation patters between subject groups, revealingthe loss of strong connections in AD and MCI subjects when compared to Controls. Similarly,the variance between GM (Grey Matter) densities of different regions reveals different structuralcovariation patterns between the different groups. Thus, the different connectivity patterns forcontrols and AD are used in this paper to select regions of interest in PET and GM images withdiscriminative power for early AD diagnosis. Finally, functional an structural models are combinedto 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 sparseinverse covariance matrices is not only used in an exploratory way but we also propose a methodto use it in a discriminative way. Regression coefficients are used to compute reconstructionerrors for the different classes that are then introduced in a SVM for classification.