■精神分裂症(SZ)是一种精神病,对个人的认知产生不利影响,情感,和行为方面。SZ的病因,尽管进行了广泛的研究,还不清楚,因为多种因素共同为其发展做出贡献。有一致的证据证明SZ患者大脑中存在结构和功能偏差。此外,SZ的遗传方面得到了基因组学标记的显著参与的支持。因此,需要从多模态角度研究SZ,并开发改进检测的方法。
■我们提出的方法采用了结合结构磁共振成像(sMRI)特征的深度学习框架,功能磁共振成像(fMRI),和遗传标记如单核苷酸多态性(SNP)。对于核磁共振成像,我们使用预训练的DenseNet来提取形态特征。为了确定功能磁共振成像和与SZ相关的SNP中最相关的功能连接,我们应用了一维卷积神经网络(CNN),然后是分层相关传播(LRP)。最后,我们将这些获得的特征跨模态连接在一起,并将它们提供给基于极端梯度提升(XGBoost)树的分类器,以将SZ与健康对照(HC)分类.
■对临床数据集的实验评估表明,与从每种模式单独获得的结果相比,我们提出的多模式方法从HC中对SZ个体进行了分类,准确率提高了79.01%.
■我们提出了一种基于深度学习的框架,该框架选择多模态(sMRI,fMRI和遗传)特征有效地融合它们以获得改进的分类分数。此外,通过使用可解释AI(XAI),我们能够查明并验证对SZ分类贡献最大的重要功能网络连接和SNP,为我们的发现提供必要的解释。
UNASSIGNED: Schizophrenia (SZ) is a psychiatric condition that adversely affects an individual\'s cognitive, emotional, and behavioral aspects. The etiology of SZ, although extensively studied, remains unclear, as multiple factors come together to contribute toward its development. There is a consistent body of evidence documenting the presence of structural and functional deviations in the brains of individuals with SZ. Moreover, the hereditary aspect of SZ is supported by the significant involvement of genomics markers. Therefore, the need to investigate SZ from a multi-modal perspective and develop approaches for improved detection arises.
UNASSIGNED: Our proposed method employed a deep learning framework combining features from structural magnetic resonance imaging (sMRI), functional magnetic resonance imaging (fMRI), and genetic markers such as single nucleotide polymorphism (SNP). For sMRI, we used a pre-trained DenseNet to extract the morphological features. To identify the most relevant functional connections in fMRI and SNPs linked to SZ, we applied a 1-dimensional convolutional neural network (CNN) followed by layerwise relevance propagation (LRP). Finally, we concatenated these obtained features across modalities and fed them to the extreme gradient boosting (XGBoost) tree-based classifier to classify SZ from healthy control (HC).
UNASSIGNED: Experimental evaluation on clinical dataset demonstrated that, compared to the outcomes obtained from each modality individually, our proposed multi-modal approach performed classification of SZ individuals from HC with an improved accuracy of 79.01%.
UNASSIGNED: We proposed a deep learning based framework that selects multi-modal (sMRI, fMRI and genetic) features efficiently and fuse them to obtain improved classification scores. Additionally, by using Explainable AI (XAI), we were able to pinpoint and validate significant functional network connections and SNPs that contributed the most toward SZ classification, providing necessary interpretation behind our findings.