Jiayue Chen,1,2,* Hongjun Tian,2,* Xinxin Zhang,3,* Yingchao Song,4 Yanmin Peng,5 Guotao Yin,6 Qianchen Li,7 Xiaoxiao Xiao,5 Yu Zhang,8 Jun Chen,1 Chuanjun Zhuo9 

1Clinical Research Center, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China; 2Department of Psychiatry, Tianjin Fourth Center Hospital, The Fourth Central Clinical College, Tianjin Medical University, Nankai University Affiliated Tianjin Fourth Center Hospital, Tianjin, People’s Republic of China; 3Department of Radiology, Tianjin Children’s Hospital, Children’s Hospital, Tianjin University, Tianjin Key Laboratory of BirthDefects for Prevention and Treatment, Tianjin, People’s Republic of China; 4Department of Pediatric Nephrology and Rheumatism and Immunology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, People’s Republic of China; 5School of Medical Technology & School of Medical Imaging & Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University, Tianjin, People’s Republic of China; 6Department of Radiology, Qilu Hospital of Shandong University, Jinan, Shandong, People’s Republic of China; 7Department of Pharmacy, Hebei General Hospital, Shijiazhuang, People’s Republic of China; 8Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, People’s Republic of China; 9Laboratory of Psychiatric-Neuroimaging-Genetic and Co-Morbidity (Pngc_lab), Tianjin Anding Hospital, Nankai University Affiliated Tianjin Anding Hospital, Tianjin Medical University Affiliated Tianjin Anding Hospital, Tianjin Medical University Affiliated Tianjin Mental Health Center, Tianjin, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Jun Chen, Clinical Research Center, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, No. 600 Wanping South Road, Xuhui District, Shanghai, 200030, People’s Republic of China, Email doctorcj2010@gmail.com Chuanjun Zhuo, Laboratory of Psychiatric-Neuroimaging-Genetic and Co-morbidity (PNGC_Lab), Tianjin Anding Hospital, Nankai University Affiliated Tianjin Anding Hospital, Tianjin Medical University Affiliated Tianjin Anding Hospital, Tianjin Medical University Affiliated Tianjin Mental Health Center, No. 13 Liulin Road, Hexi District, Tianjin, 300222, People’s Republic of China, Email chuanjunzhuotjmh@163.com

Purpose: The deformation-based morphometry (DBM) method could precisely detect the brain morphological changes, which has rarely been explored in bipolar disorder (BD) patients. This study utilized DBM to identify the structure of grey matter (GM) and white matter in BD patients, and compared DBM with traditional voxel-based morphometry (VBM) on decoding the abnormal changes within the brain, to provide new insights for the pathophysiological mechanism of BD.
Patients and Methods: Brain structural changes in 67 BD patients and 70 healthy controls (HC) were analyzed using DBM and VBM. The spatial correlations of the two indicators in both hemispheres were calculated, regions with significant differences between BD and HC were analyzed by correlating with clinical variables. Furthermore, support vector machine classification algorithm was utilized to detect the capability of VBM, DBM, and fusing two indicators in diagnosing BD patients.
Results: DBM showed increased volumes in GM region in the insula and pregenual anterior cingulate cortex in BD patients. VBM showed reduction of the grey matter volume (GMV) from the inferior temporal gyrus, hippocampus, inferior frontal gyrus, olfactory cortex, fusiform gyrus, middle temporal gyrus, superior temporal gyrus, middle frontal gyrus, middle cingulate and paracingulate gyri, inferior occipital gyrus, Heschl’s gyrus, and dorsolateral superior frontal gyrus. The white matter volume (WMV) from the thalamus, inferior frontal gyrus, pallidum, and anterior cingulum were decreased in BD patients. The spatial correlations of the two indicators in both hemispheres were moderately correlated. Furthermore, the highest classification accuracy of DBM-GM and GMV were 69.34% and 72.42%, respectively, which was further increased to 73.72% after fusing two indicators, indicating fusion as the superior strategy.
Conclusion: Our findings indicated structural abnormalities in multiple brain regions in BD patients using VBM and DBM, with different information obtained. Fusing DBM-GM and GMV significantly improved the classification accuracy, suggesting their potential as neuroimaging markers to assist the diagnosis of BD.

Keywords: bipolar disorder, voxel-based morphometry, deformation-based morphometry, machine learning, multivariate pattern analysis