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

Abstract

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.

Keywords: classification; multisite study; schizophrenia; structural MRI; voxel-based morphometry.

Figure 1

Scheme of feature extraction. Segmentation of 3‐D T1‐weighted image was performed to extract gray matter image and intracranial volume (ICV), followed by anatomical normalization with diffeomorphic anatomical registration through an exponentiated lie algebra (DARTEL) and smoothing. Then the within‐region‐of‐interest (ROI) volume was calculated. Using this volume as a dependent variable Y, a general linear model was fitted considering age, sex, ICV, and a constant that could reflect scanner differences for each MRI scanner. Then the residual ε for each subject was calculated and this value was treated as a differentiation feature.

Figure 2

Regions of interest (ROI) for this study. ROI are defined from group comparisons between 95 patients with schizophrenia and age‐ and sex‐matched controls. Statistical threshold was set to a family‐wise‐error‐corrected P‐value of <0.01 with an extent threshold of 100 voxels. These regions include the bilateral superior temporal gyri, the middle frontal gyri, the medial portion of the superior frontal gyri, and hippocampi.

Figure 3

Receiver–operator curve (ROC) analysis. The area under the curve (AUC) ranged from 0.74 to 0.84, and accuracy from 69% to 76%. ROC analysis with all samples revealed an AUC of 0.76 and an accuracy of 73%.