Early and precise diagnosis of schizophrenia disorder (SZ) has an essential role in the quality of a patient’s life and future treatments. Structural and functional neuroimaging provides robust biomarkers for understanding the anatomical and functional changes associated with SZ. Each of the neuroimaging techniques shows only a different perspective on the functional or structural of the brain, while multi-modal fusion can reveal latent connections in the brain. In this paper, we propose an approach for the fusion of structural and functional brain data with a deep learning-based model to take advantage of data fusion and increase the accuracy of schizophrenia disorder diagnosis. The proposed method consists of an architecture of 3D convolutional neural networks (CNNs) that applied to magnetic resonance imaging (MRI), functional magnetic resonance imaging (fMRI), and diffusion tensor imaging (DTI) extracted features. We use 3D MRI patches, fMRI spatial independent component analysis (ICA) map, and DTI fractional anisotropy (FA) as model inputs. Our method is validated on the COBRE dataset, and an average accuracy of 99.35% is obtained. The proposed method demonstrates promising classification performance and can be applied to real data.

中文翻译：

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通过3D卷积神经网络架构进行多模式神经影像特征融合以用于精神分裂症诊断

精神分裂症（SZ）的早期和精确诊断在患者的生活质量和未来治疗中起着至关重要的作用。结构和功能神经影像学提供了强大的生物标记，可用于了解与SZ相关的解剖和功能变化。每种神经成像技术仅对大脑的功能或结构显示不同的观点，而多峰融合可以揭示大脑中的潜在连接。在本文中，我们提出了一种使用基于深度学习的模型融合结构和功能性大脑数据的方法，以利用数据融合的优势并提高精神分裂症诊断的准确性。所提出的方法由应用于磁共振成像（MRI）的3D卷积神经网络（CNN）架构组成，功能磁共振成像（fMRI）和扩散张量成像（DTI）提取的功能。我们使用3D MRI补丁，fMRI空间独立成分分析（ICA）图和DTI分数各向异性（FA）作为模型输入。我们的方法在COBRE数据集上得到了验证，平均准确度为99.35％。所提出的方法证明了有希望的分类性能，并且可以应用于真实数据。