Resting-state connectome-based support-vector-machine predictive modeling of internet gaming disorder.
Addict Biol
; 26(4): e12969, 2021 07.
Article
em En
| MEDLINE
| ID: mdl-33047425
ABSTRACT
Internet gaming disorder (IGD), a worldwide mental health issue, has been widely studied using neuroimaging techniques during the last decade. Although dysfunctions in resting-state functional connectivity have been reported in IGD, mapping relationships from abnormal connectivity patterns to behavioral measures have not been fully investigated. Connectome-based predictive modeling (CPM)-a recently developed machine-learning approach-has been used to examine potential neural mechanisms in addictions and other psychiatric disorders. To identify the resting-state connections associated with IGD, we modified the CPM approach by replacing its core learning algorithm with a support vector machine. Resting-state functional magnetic resonance imaging (fMRI) data were acquired in 72 individuals with IGD and 41 healthy comparison participants. The modified CPM was conducted with respect to classification and regression. A comparison of whole-brain and network-based analyses showed that the default-mode network (DMN) is the most informative network in predicting IGD both in classification (individual identification accuracy = 78.76%) and regression (correspondence between predicted and actual psychometric scale score r = 0.44, P < 0.001). To facilitate the characterization of the aberrant resting-state activity in the DMN, the identified networks have been mapped into a three-subsystem division of the DMN. Results suggest that individual differences in DMN function at rest could advance our understanding of IGD and variability in disorder etiology and intervention outcomes.
Palavras-chave
Texto completo:
1
Base de dados:
MEDLINE
Assunto principal:
Comportamento Aditivo
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Jogos de Vídeo
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Máquina de Vetores de Suporte
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Conectoma
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Transtorno de Adição à Internet
Tipo de estudo:
Prognostic_studies
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Risk_factors_studies
Limite:
Adult
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Humans
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Male
Idioma:
En
Ano de publicação:
2021
Tipo de documento:
Article