title: Abnormal hubs of white matter networks in the frontal-parieto circuit contribute to depression discrimination via pattern classification.
abstract: Previous studies had explored the diagnostic and prognostic value of the structural neuroimaging data of MDD and treated the whole brain voxels, the fractional anisotropy and the structural connectivity as classification features. To our best knowledge, no study examined the potential diagnostic value of the hubs of anatomical brain networks in MDD. The purpose of the current study was to provide an exploratory examination of the potential diagnostic and prognostic values of hubs of white matter brain networks in MDD discrimination and the corresponding impaired hub pattern via a multi-pattern analysis. We constructed white matter brain networks from 29 depressions and 30 healthy controls based on diffusion tensor imaging data, calculated nodal measures and identified hubs. Using these measures as features, two types of feature architectures were established, one only included hubs (HUB) and the other contained both hubs and non hubs. The support vector machine classifiers with Gaussian radial basis kernel were used after the feature selection. Moreover, the relative contribution of the features was estimated by means of the consensus features. Our results presented that the hubs (including the bilateral dorsolateral part of superior frontal gyrus, the left middle frontal gyrus, the bilateral middle temporal gyrus, and the bilateral inferior temporal gyrus) played an important role in distinguishing the depressions from healthy controls with the best accuracy of 83.05%. Moreover, most of the HUB consensus features located in the frontal-parieto circuit. These findings provided evidence that the hubs could be served as valuable potential diagnostic measure for MDD, and the hub-concentrated lesion distribution of MDD was primarily anchored within the frontal-parieto circuit.
authors.affiliation: Key Laboratory of Child Development and Learning Science (Ministry of education), Research Centre for Learning Science, Southeast University, Nanjing 210096, China.
authors.forename: Jiaolong
authors.initials: J
authors.lastname: Qin
authors.affiliation: Key Laboratory of Child Development and Learning Science (Ministry of education), Research Centre for Learning Science, Southeast University, Nanjing 210096, China.
authors.forename: Maobin
authors.initials: M
authors.lastname: Wei
authors.affiliation: Department of Psychiatry, Affiliated Nanjing Brain Hospital of Nanjing Medical University, Nanjing 210029, China.
authors.forename: Haiyan
authors.initials: H
authors.lastname: Liu
authors.affiliation: Department of Psychiatry, Affiliated Nanjing Brain Hospital of Nanjing Medical University, Nanjing 210029, China.
authors.forename: Jianhuai
authors.initials: J
authors.lastname: Chen
authors.affiliation: Department of Psychiatry, Affiliated Nanjing Brain Hospital of Nanjing Medical University, Nanjing 210029, China.
authors.forename: Rui
authors.initials: R
authors.lastname: Yan
authors.affiliation: Medical School, Nanjing University, 22 Hankou Road, Nanjing 210093, China.
authors.forename: Lingling
authors.initials: L
authors.lastname: Hua
authors.affiliation: Department of Psychiatry, Affiliated Nanjing Brain Hospital of Nanjing Medical University, Nanjing 210029, China.
authors.forename: Ke
authors.initials: K
authors.lastname: Zhao
authors.affiliation: Department of Psychiatry, Affiliated Nanjing Brain Hospital of Nanjing Medical University, Nanjing 210029, China; Medical School, Nanjing University, 22 Hankou Road, Nanjing 210093, China. Electronic address: zjyao@njmu.edu.cn.
authors.forename: Zhijian
authors.initials: Z
authors.lastname: Yao
authors.affiliation: Key Laboratory of Child Development and Learning Science (Ministry of education), Research Centre for Learning Science, Southeast University, Nanjing 210096, China. Electronic address: luq@seu.edu.cn.
authors.forename: Qing
authors.initials: Q
authors.lastname: Lu
mesh_terms: D000328:Adult; D000465:Algorithms; D016880:Anisotropy; D016477:Artifacts; D001921:Brain / Q000473:pathology*; D001931:Brain Mapping / Q000379:methods*; D003863:Depression / Q000175:diagnosis*; D056324:Diffusion Tensor Imaging / Q000379:methods*; D005260:Female; D005625:Frontal Lobe / Q000473:pathology*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D009038:Motion; D009415:Nerve Net; D016011:Normal Distribution; D010296:Parietal Lobe / Q000473:pathology*; D010363:Pattern Recognition, Automated; D015203:Reproducibility of Results; D060388:Support Vector Machine; D013702:Temporal Lobe / Q000473:pathology; D066127:White Matter / Q000473:pathology*; D055815:Young Adult
keywords: Diffusion tensor imaging; Frontal-parieto circuit; Hubs; Major depressive disorder; Support vector machine