Differentiation of schizophrenia using structural MRI with consideration of scanner differences: A real‐world multisite study

• Published in: Psychiatry and clinical neurosciences Vol. 74; no. 1; pp. 56 - 63
• Main Authors: Nemoto, Kiyotaka, Shimokawa, Tetsuya, Fukunaga, Masaki, Yamashita, Fumio, Tamura, Masashi, Yamamori, Hidenaga, Yasuda, Yuka, Azechi, Hirotsugu, Kudo, Noriko, Watanabe, Yoshiyuki, Kido, Mikio, Takahashi, Tsutomu, Koike, Shinsuke, Okada, Naohiro, Hirano, Yoji, Onitsuka, Toshiaki, Yamasue, Hidenori, Suzuki, Michio, Kasai, Kiyoto, Hashimoto, Ryota, Arai, Tetsuaki
• Format: Journal Article
• Language: English
• Published: Melbourne John Wiley & Sons Australia, Ltd 01.01.2020; Wiley Subscription Services, Inc
• Subjects: classification; Datasets; Diagnosis; Magnetic resonance imaging; Medical imaging; Mental disorders; Morphometry; multisite study; Neuroimaging; Regular; Scanners; Schizophrenia; structural MRI; Substantia grisea; voxel‐based morphometry; voxel-based morphometry; structural MRI; schizophrenia; classification; multisite study
• ISSN: 1323-1316; 1440-1819; 1440-1819
• DOI: 10.1111/pcn.12934

Aim Neuroimaging studies have revealed that patients with schizophrenia exhibit reduced gray matter volume in various regions. With these findings, various studies have indicated that structural MRI can be useful for the diagnosis of schizophrenia. However, multisite studies are limited. Here, we evaluated a simple model that could be used to differentiate schizophrenia from control subjects considering MRI scanner differences employing voxel‐based morphometry. Methods Subjects were 541 patients with schizophrenia and 1252 healthy volunteers. Among them, 95 patients and 95 controls (Dataset A) were used for the generation of regions of interest (ROI), and the rest (Dataset B) were used to evaluate our method. The two datasets were comprised of different subjects. Three‐dimensional T1‐weighted MRI scans were taken for all subjects and gray‐matter images were extracted. To differentiate schizophrenia, we generated ROI for schizophrenia from Dataset A. Then, we determined volume within the ROI for each subject from Dataset B. Using the extracted volume data, we calculated a differentiation feature considering age, sex, and intracranial volume for each MRI scanner. Receiver–operator curve analyses were performed to evaluate the differentiation feature. Results The area under the curve ranged from 0.74 to 0.84, with accuracy from 69% to 76%. Receiver–operator curve analysis with all samples revealed an area under the curve of 0.76 and an accuracy of 73%. Conclusion We moderately successfully differentiated schizophrenia from control using structural MRI from differing scanners from multiple sites. This could be useful for applying neuroimaging techniques to clinical settings for the accurate diagnosis of schizophrenia.