Improving Classification Rate of Schizophrenia Using a Multimodal Multi-Layer Perceptron Model with Structural and Functional MR
This work addresses the challenge of accurate schizophrenia diagnosis for medical professionals by enhancing classification rates with multimodal neuroimaging data, representing an incremental improvement over existing methods.
The paper tackled the problem of improving schizophrenia classification by combining structural and functional MRI data using a multimodal multi-layer perceptron model, achieving an average AUC of 0.850±0.051 compared to 0.741±0.075 for sMRI and 0.833±0.050 for fMRI.
The wide variety of brain imaging technologies allows us to exploit information inherent to different data modalities. The richness of multimodal datasets may increase predictive power and reveal latent variables that otherwise would have not been found. However, the analysis of multimodal data is often conducted by assuming linear interactions which impact the accuracy of the results. We propose the use of a multimodal multi-layer perceptron model to enhance the predictive power of structural and functional magnetic resonance imaging (sMRI and fMRI) combined. We also use a synthetic data generator to pre-train each modality input layers, alleviating the effects of the small sample size that is often the case for brain imaging modalities. The proposed model improved the average and uncertainty of the area under the ROC curve to 0.850+-0.051 compared to the best results on individual modalities (0.741+-0.075 for sMRI, and 0.833+-0.050 for fMRI).