The role of neuroimaging in diagnosis and personalized medicine--current position and likely future directions

Abstract

The main aim of this article is to discuss the current state of in vivo brain imaging methods in the context of the diagnosis and treatment of mental illness. The background to current practice is discussed, and the new methods introduced which may have the capacity to increase the relevance of magnetic resonance imaging, particularly functional magnetic resonance imaging, for clinical application. The main focus will be on magnetic resonance imaging, but many of the comments have a general relevance across imaging modalities.

Figure 1.. Data flow in a simple 2-task functional magnetic resonance imaging (fMRI) experiment (alternating blocks of each task) through traditional univariate analysis with general linear modeling (GLM) and support vector machine (SVM) analysis. The univariate approach analyses individual voxels using time points when task 1 is being performed and time points where task 2 is being performed, contrasting the two using a simple statistical test (eg, a f test) with a correction for the number of voxels analyzed. The output is a map of regions where the responses to the two tasks are significantly different. SVM-based analysis takes whole-brain volumes when task 1 is being performed and whole-brain volumes when task 2 is being performed and “trains” a computer program to associate patterns of fMRI response with each task. The outputs are a map of the regions which discriminate between the two tasks and a measure of how well the two tasks are discriminated on the basis of the whole brain data. After training, the task being performed can be predicted purely from the fMRI data. For group separation, tasks 1 and 2 can be replaced by groups 1 and 2 ( eg, patients/controls) performing a given task or structural MRI data from the two groups.