Development of a classifier for gambling disorder based on functional connections between brain regions. Issue 6 (14th April 2022)

• Record Type: Journal Article
• Authors: Takeuchi, Hideaki; Yahata, Noriaki; Lisi, Giuseppe; Tsurumi, Kosuke; Yoshihara, Yujiro; Kawada, Ryosaku; Murao, Takuro; Mizuta, Hiroto; Yokomoto, Tatsunori; Miyagi, Takashi; Nakagami, Yukako; Yoshioka, Toshinori; Yoshimoto, Junichiro; Kawato, Mitsuo; Murai, Toshiya; Morimoto, Jun; Takahashi, Hidehiko
• Is Part Of: Psychiatry and clinical neurosciences. Volume 76:Issue 6(2022)
• Page Start: 260
• Page End: 267
• Publication Date: 2022-04-14
• Subjects: functional connection -- gambling disorder -- generalizable classifier -- machine learning; Psychiatry -- Periodicals; Neurology -- Periodicals; 616.89
• DOI: 10.1111/pcn.13350
• Languages: English
• ISSNs: 1323-1316

Abstract

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.