The current research focuses on a relatively simple method for formulating “query” vectors from groups of PET scans and then evaluating the utility of these vectors for retrieving relevant scans (i.e., for making diagnoses or predictions on the subjects who contributed the scans). Fig. 1 summarizes the residual vector analysis method, the first step of which is mathematically identical to computing the ordinary least squares approximation of the solution to a system of linear equations. Geometrically,

the ordinary least squares approximation is the projection of one vector (www.selleckchem.com/products/Staurosporine.html composed of the values Inhibitors,research,lifescience,medical of the dependent variable) onto a space defined by other vectors (the matrix of independent variables). This projection is the linear combination of vectors from the matrix column Inhibitors,research,lifescience,medical space that is closest to the original vector. Subtraction of this projection vector from the original vector yields a residual vector that is orthogonal to all of the vectors in the matrix column space. Thus, when similarity Inhibitors,research,lifescience,medical is quantified in terms of the cosine of the angle between two vectors (i.e., zero for perpendicular vectors, one for parallel vectors), the residual vector will have zero similarity with all of the column vectors in the matrix. Because the residual

vector is a component of the original vector, it will maintain some cosine similarity with it (except in the unlikely event that a perfect solution is found, in which case the residual will be the zero vector). Figure Inhibitors,research,lifescience,medical 1 Geometric interpretation of ordinary least squares regression. A vector N (representing

the PET scan of an MCI nonconverter) is projected onto a space, C, which is composed of PET scans from MCI patients who converted to AD within 2 years of being scanned. … The goal of this project was to determine whether residual vectors computed Inhibitors,research,lifescience,medical in this manner have any utility as query vectors when used to search a database of PET scans that were not used in computation of the residual vector itself. The specific questions being posed were: (1) Do cosine similarity scores derived from the residual vectors Bay 11-7085 make a significant contribution to variance in logistic regression models using AD diagnostic status or MCI conversion status as the dependent variable? (2) Can cosine similarity scores predict functional decline? (3) How do these logistic regression models fare when used as classifiers of cases not used in the model computation? METHODS Alzheimer’s disease neuroimaging initiative (ADNI) participants Data used in the preparation of this article were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.ucla.edu).