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Guggenmos, Matthias; Schmack, Katharina; Veer, Ilya M.; Lett, Tristram; Sekutowicz, Maria; Sebold, Miriam; Garbusow, Maria; Sommer, Christian; Wittchen, Hans-Ulrich; Zimmermann, Ulrich S.; Smolka, Michael N.; Walter, Henrik; Heinz, Andreas; Sterzer, Philipp (2020): A multimodal neuroimaging classifier for alcohol dependence. In: Scientific Reports, Vol. 10, No. 1, 298
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With progress in magnetic resonance imaging technology and a broader dissemination of state-of-the-art imaging facilities, the acquisition of multiple neuroimaging modalities is becoming increasingly feasible. One particular hope associated with multimodal neuroimaging is the development of reliable data-driven diagnostic classifiers for psychiatric disorders, yet previous studies have often failed to find a benefit of combining multiple modalities. As a psychiatric disorder with established neurobiological effects at several levels of description, alcohol dependence is particularly well-suited for multimodal classification. To this aim, we developed a multimodal classification scheme and applied it to a rich neuroimaging battery (structural, functional task-based and functional resting-state data) collected in a matched sample of alcohol-dependent patients (N = 119) and controls (N = 97). We found that our classification scheme yielded 79.3% diagnostic accuracy, which outperformed the strongest individual modality - grey-matter density - by 2.7%. We found that this moderate benefit of multimodal classification depended on a number of critical design choices: a procedure to select optimal modality-specific classifiers, a fine-grained ensemble prediction based on cross-modal weight matrices and continuous classifier decision values. We conclude that the combination of multiple neuroimaging modalities is able to moderately improve the accuracy of machine-learning-based diagnostic classification in alcohol dependence.