Journal
SCIENTIFIC REPORTS
Volume 10, Issue 1, Pages -Publisher
NATURE PORTFOLIO
DOI: 10.1038/s41598-019-56923-9
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Funding
- German Research Foundation (Deutsche Forschungsgemeinschaft, DFG) [FOR 1617, STE 1430/6-1, STE 1430/6-2, SCHM 3209/1-2, ZI 1119/3-1, ZI 1119/3-2, HE 2597/14-1, HE 2597/14-2, WA 1539/7-1, WI 709/10-1, WI 709/10-2, GU 1845/1-1]
- Federal Ministry of Education and Research (BMBF grants) [01ZX1311H, 01ZX1311D/1611D, 01ZX1311E/1611E, 01EE1406A, 01EE1406B]
- Leopoldina - Research Fellowship of Leopoldina-German National Academy of Sciences [LPDS 2018-03]
- German Research Foundation (Deutsche Forschungsgemeinschaft, DFG, Excellence Cluster) [Exc 257]
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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.
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