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

Hideaki Takeuchi, Noriaki Yahata, Giuseppe Lisi, Kosuke Tsurumi, Yujiro Yoshihara, Ryosaku Kawada, Takuro Murao, Hiroto Mizuta, Tatsunori Yokomoto, Takashi Miyagi, Yukako Nakagami, Toshinori Yoshioka, Junichiro Yoshimoto, Mitsuo Kawato, Toshiya Murai, Jun Morimoto, Hidehiko Takahashi

Research output: Contribution to journalArticlepeer-review

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 analysis and a sparse logistic regression to create the classifier. The generalizability of the classifier was verified using an external dataset. This external dataset consisted of six GD patients and 14 HCs, and was collected at a different site from the sites of the training dataset. Correlations between WLS and South Oaks Gambling Screen (SOGS) and duration of illness were examined. Results: The classifier distinguished between the GD patients and HCs with high accuracy in leave-one-out cross-validation (area under curve (AUC = 0.89)). This performance was confirmed in the external dataset (AUC = 0.81). There was no correlation between WLS, and SOGS and duration of illness in the GD patients. Conclusion: We developed a generalizable classifier for GD based on information of functional connections between brain regions at resting state.

Original languageEnglish
Pages (from-to)260-267
Number of pages8
JournalPsychiatry and clinical neurosciences
Volume76
Issue number6
DOIs
Publication statusPublished - 06-2022
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Neuroscience(all)
  • Neurology
  • Clinical Neurology
  • Psychiatry and Mental health

Fingerprint

Dive into the research topics of 'Development of a classifier for gambling disorder based on functional connections between brain regions'. Together they form a unique fingerprint.

Cite this