Using machine learning to determine a functional classifier of reward responsiveness and its association with adolescent psychiatric symptomatology.

ABSTRACT:

Background

Machine learning (ML) has developed classifiers differentiating patient groups despite concerns regarding diagnostic reliability. An alternative strategy, used here, is to develop a functional classifier (hyperplane) (e.g. distinguishing the neural responses to received reward v. received punishment in typically developing (TD) adolescents) and then determine the functional integrity of the response (reward response distance from the hyperplane) in adolescents with externalizing and internalizing conditions and its associations with symptom clusters.

Methods

Two hundred and ninety nine adolescents (mean age = 15.07 ± 2.30 years, 117 females) were divided into three groups: a training sample of TD adolescents where the Support Vector Machine (SVM) algorithm

SUBMITTER: Blair RJR 

PROVIDER: S-EPMC11650163 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature