Development of a classifier for gambling disorder based on functional connections between brain regions.

ABSTRACT:

Aim

Recently, a machine-learning (ML) technique has been used to create generalizable classifiers for psychiatric disorders based on information of functional connections (FCs) between brain regions at resting state. These classifiers predict diagnostic labels by a weighted linear sum (WLS) of the correlation values of a small number of selected FCs. We aimed to develop a generalizable classifier for gambling disorder (GD) from the information of FCs using the ML technique and examine relationships between WLS and clinical data.

Methods

As a training dataset for ML, data from 71 GD patients and 90 healthy controls (HCs) were obtained from two magnetic resonance imaging sites. We used an ML algorithm consisting of a cascade of an L1-regularized sparse canonical correlation an

SUBMITTER: Takeuchi H 

PROVIDER: S-EPMC9322453 | biostudies-literature | 2022 Jun

REPOSITORIES: biostudies-literature