The present invention relates to a method and a system for an adaptive pattern recognition for psychosis risk modeling with at least the following steps and features: automatically generating a first risk quantification or classification system on the basis of brain images and data mining automatically generating a second risk quantification or classification system on the basis of genomic and/or metabolomic information and data mining and further processing the first and second risk quantification or classification systems by data mining computing so as to create a meta-level risk quantification data to automatically quantify psychosis risk at the single-subject level. Preferably the first and/or second risk quantification or classification system(s) extract specific surrogate markers by multi-modal data acquisition and/or the surrogate markers are categorized and/or quantified by a multi-axial scoring system. Data can be controlled and outliers can be detected and eliminated preferably by determining cut-off thresholds. More preferably an outlier detection method transfers the brain image into a calibrated image, a segmented image and/or a registered image. Uni-modal data can be further generated and optionally optimized on the basis of the data acquired and one or more similarity and/or dissimilarity between the multi-modal data and the uni-modal data can be quantified.
