We found a match
Your institution may have access to this item. Find your institution then sign in to continue.
- Title
A multimodal neuroimaging classifier for alcohol dependence.
- Authors
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
- Abstract
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.
- Subjects
PSYCHIATRIC diagnosis; BRAIN imaging; GRAY matter (Nerve tissue); ALCOHOL Dependence Scale; NEUROBIOLOGY
- Publication
Scientific Reports, 2020, Vol 10, Issue 1, p1
- ISSN
2045-2322
- Publication type
Article
- DOI
10.1038/s41598-019-56923-9