Towards the identification of imaging biomarkers in schizophrenia, using multivariate pattern classification at a single-subject level

Abstract

Standard univariate analyses of brain imaging data have revealed a host of structural and functional brain alterations in schizophrenia. However, these analyses typically involve examining each voxel separately and making inferences at group-level, thus limiting clinical translation of their findings. Taking into account the fact that brain alterations in schizophrenia expand over a widely distributed network of brain regions, univariate analysis methods may not be the most suited choice for imaging data analysis. To address these limitations, the neuroimaging community has turned to machine learning methods both because of their ability to examine voxels jointly and their potential for making inferences at a single-subject level. This article provides a critical overview of the current and foreseeable applications of machine learning, in identifying imaging-based biomarkers that could be used for the diagnosis, early detection and treatment response of schizophrenia, and could, thus, be of high clinical relevance. We discuss promising future research directions and the main difficulties facing machine learning researchers as far as their potential translation into clinical practice is concerned.

Fig. 1

Representation of a linear, binary SVM classifier. The optimal separating hyperplane is the one with the largest margin of separation between the two groups and is described as a function of f(x) = w ∗ x + b, where w is a weight vector that is normal to the hyperplane, b is an offset and b/||w|| is the distance from the hyperplane to the origin. Points in the dashed lines represent the support vectors. During the training phase, the SVM classifier computes the optimal decision function f(x) and in the testing phase, this decision boundary is applied to new data instances.

Fig. 2

Representation of LDA for a two-class classification problem based on synthetic two-dimensional data representing measurements in feature 1 and feature 2. As observed, classification is more accurate if the data are projected onto the X dimension, as opposed to the Y dimension where there is substantial overlap between the classes, as shown in the histograms. Once the projection of data instances onto the dimension that fulfills Fisher's criteria is specified, new data instances can be classified based on a threshold (for example, if X_i < 4 classify as class 1, otherwise class 2) or a specified metric (e.g. Euclidean distance from the mean of a class).