A multimodal neuroimaging classifier for alcohol dependence.
Sci Rep
; 10(1): 298, 2020 01 15.
Article
em En
| MEDLINE
| ID: mdl-31941972
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.
Texto completo:
1
Coleções:
01-internacional
Contexto em Saúde:
2_ODS3
/
8_ODS3_consumo_sustancias_psicoactivas
Base de dados:
MEDLINE
Assunto principal:
Encéfalo
/
Imageamento por Ressonância Magnética
/
Alcoolismo
Tipo de estudo:
Observational_studies
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Prognostic_studies
/
Risk_factors_studies
Limite:
Adult
/
Aged
/
Female
/
Humans
/
Male
/
Middle aged
Idioma:
En
Revista:
Sci Rep
Ano de publicação:
2020
Tipo de documento:
Article