Multivoxel pattern analysis of functional brain imaging data for the prediction of the development and maintenance of alcohol use disorders

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Previous neuroimaging studies aimed at predicting clinical outcome in alcohol addiction have provided encouraging results. However, conventional univariate statistical approaches suffer from the problem that the predictive value of single surrogate markers, such as activity in a given brain region, is usually low despite the presence of a statistically significant relationship. Multivoxel pattern analysis (MVPA) can dramatically increase predictive power by accumulating information across multiple voxels, i.e., to use the information contained in a distributed pattern of brain activity rather than a single brain region. MVPA has been successfully applied both at the individual subject level and for diagnostic or prognostic classification between subjects. Functional and structural magnetic resonance imaging (MRI) data from Project 1 will be used to predict pathological drinking patterns in young adults. Treatment outcomes in alcohol dependent patients will be predicted on the basis of MRI data from Project 2. Crucially, MVPA will be used to identify those features, or patterns of features, in functional and structural MRI scans that reliably predict future clinical outcomes. This knowledge will inform pathogenetic models for neurobiological mechanisms underlying pathological drinking and help tailor therapeutic interventions in accord with an individual¿s neurobiological risk profile.