Combining multi-modality data for searching biomarkers in schizophrenia

Abstract

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.

Fig 1. The flowchart of data fusion in this paper.

Fig 2

(A) Results of four kinds of measures with all components of fMRI. For visualization, the y label is -log₁₀(p) rather than the p value. (B) 16 ROIs have the smallest p value for fMRI measure. (C) 9 ROIs have the smallest p value for sMRI measure. (D) 10 ROIs have the smallest p value for DTI measure. (E)55 ROIs have the smallest p value for fusion measure.

Fig 3

(A) Results of four kinds of measures with the 5 principle components of fMRI. For visualization, the y label is -log₁₀(p) rather than the p value. (B) 10 ROIs have the smallest p value for fMRI measure. (C) 4 ROIs have the smallest p value for sMRI measure. (D) 0 ROIs have the smallest p value for DTI measure. (E) 76 ROIs have the smallest p value for fusion measure.

Fig 4

ROC curves of different modalities, for all components of fMRI (A) and for the first 5 components of fMRI (B). The Jaccard similarity coefficient (C) and Kappa index (D) for six different comparison.

Fig 5

Goodness of fit for different measures with all components in fMRI (A) and the first 5 principle components in fMRI (C). The contribution of fMRI, sMRI, DTI to fusion data with all components in fMRI (B) and the first 5 components in fMRI (D).