@phdthesis{Hahn2010,
  author    = {Hahn, Tim},
  title     = {Integrating neurobiological markers of depression: an fMRI-based pattern classification approach},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-49962},
  school      = {Universit{\"a}t W{\"u}rzburg},
  year      = {2010},
  abstract  = {While depressive disorders are, to date, diagnosed based on behavioral symptoms and course of illness, the interest in neurobiological markers of psychiatric disorders has grown substantially in recent years. However, current classification approaches are mainly based on data from a single biomarker, making it difficult to predict diseases such as depression which are characterized by a complex pattern of symptoms. Accordingly, none of the previously investigated single biomarkers has shown sufficient predictive power for practical application. In this work, we therefore propose an algorithm which integrates neuroimaging data associated with multiple, symptom-related neural processes relevant in depression to improve classification accuracy. First, we identified the core-symptoms of depression from standard classification systems. Then, we designed and conducted three experimental paradigms probing psychological processes known to be related to these symptoms using functional Magnetic Resonance Imaging. In order to integrate the resulting 12 high-dimensional biomarkers, we developed a multi-source pattern recognition algorithm based on a combination of Gaussian Process Classifiers and decision trees. Applying this approach to a group of 30 healthy controls and 30 depressive in-patients who were on a variety of medications and displayed varying degrees of symptom-severity allowed for high-accuracy single-subject classification. Specifically, integrating biomarkers yielded an accuracy of 83\% while the best of the 12 single biomarkers alone classified a significantly lower number of subjects (72\%) correctly. Thus, integrated biomarker-based classification of a heterogeneous, real-life sample resulted in accuracy comparable to the highest ever achieved in previous single biomarker research. Furthermore, investigation of the final prediction model revealed that neural activation during the processing of neutral facial expressions, large rewards, and safety cues is most relevant for over-all classification. We conclude that combining brain activation related to the core-symptoms of depression using the multi-source pattern classification approach developed in this work substantially increases classification accuracy while providing a sparse relational biomarker-model for future prediction.},
  subject      = {Patientenklassifikation},
  language  = {en}
}