Combining multi-modality data for searching biomarkers in schizophrenia

• Published in: PloS one 2018-02, Vol.13 (2), p.e0191202-e0191202
• Main Authors: Guo, Shuixia, Huang, Chu-Chung, Zhao, Wei, Yang, Albert C, Lin, Ching-Po, Nichols, Thomas, Tsai, Shih-Jen
• Format: Article
• Language: English
• Subjects: Adult; Biological markers; Biology and Life Sciences; Biomarkers; Biomarkers - analysis; Brain Mapping - methods; Brain research; Case-Control Studies; Computer science; Data integration; Diagnosis; Diagnostic systems; Diffusion Tensor Imaging - methods; Female; Functional magnetic resonance imaging; Genomes; Genomics; Humans; Image Interpretation, Computer-Assisted - methods; Information processing; Laboratories; Magnetic resonance imaging; Magnetic Resonance Imaging - methods; Male; Mathematical analysis; Medical diagnosis; Medical imaging; Medical research; Medicine and Health Sciences; Mental disorders; Middle Aged; Multimodal Imaging - methods; Multisensor fusion; Multivariate analysis; Neuroimaging; Neurosciences; NMR; Nuclear magnetic resonance; Patients; Psychiatry; Research and Analysis Methods; Schizophrenia; Schizophrenia - diagnosis; Sensory integration; Structure-function relationships; Variance analysis
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0191202

Identification of imaging biomarkers for schizophrenia is an important but still challenging problem. Even though considerable efforts have been made over the past decades, quantitative alterations between patients and healthy subjects have not yet provided a diagnostic measure with sufficient high sensitivity and specificity. One of the most important reasons is the lack of consistent findings, which is in part due to single-mode study, which only detects single dimensional information by each modality, and thus misses the most crucial differences between groups. Here, we hypothesize that multimodal integration of functional MRI (fMRI), structural MRI (sMRI), and diffusion tensor imaging (DTI) might yield more power for the diagnosis of schizophrenia. A novel multivariate data fusion method for combining these modalities is introduced without reducing the dimension or using the priors from 161 schizophrenia patients and 168 matched healthy controls. The multi-index feature for each ROI is constructed and summarized with Wilk's lambda by performing multivariate analysis of variance to calculate the significant difference between different groups. Our results show that, among these modalities, fMRI has the most significant featureby calculating the Jaccard similarity coefficient (0.7416) and Kappa index (0.4833). Furthermore, fusion of these modalities provides the most plentiful information and the highest predictive accuracy of 86.52%. This work indicates that multimodal integration can improve the ability of distinguishing differences between groups and might be assisting in further diagnosis of schizophrenia.