Abstract：
BACKGROUND: The absence of clinically-validated biomarkers or objective protocols hinders effective major depressive disorder (MDD) diagnosis. Compared to healthy control (HC), MDD exhibits anomalies in plasma protein levels and neuroimaging presentations. Despite extensive machine learning studies in psychiatric diagnosis, a reliable tool integrating multi-modality data is still lacking.
METHODS: In this study, blood samples from 100 MDD and 100 HC were analyzed, along with MRI images from 46 MDD and 49 HC. Here, we devised a novel algorithm, integrating graph neural networks and attention modules, for MDD diagnosis based on inflammatory cytokines, neurotrophic factors, and Orexin A levels in the blood samples. Model performance was assessed via accuracy and F1 value in 3-fold cross-validation, comparing with 9 traditional algorithms. We then applied our algorithm to a dataset containing both the aforementioned protein quantifications and neuroimages, evaluating if integrating neuroimages into the model improves performance.
RESULTS: Compared to HC, MDD showed significant alterations in plasma protein levels and gray matter volume revealed by MRI. Our new algorithm exhibited superior performance, achieving an F1 value and accuracy of 0.9436 and 94.08 %, respectively. Integration of neuroimaging data enhanced our novel algorithm\'s performance, resulting in an improved F1 value and accuracy, reaching 0.9543 and 95.06 %.
CONCLUSIONS: This single-center study with a small sample size requires future evaluations on a larger test set for improved reliability.
CONCLUSIONS: In comparison to traditional machine learning models, our newly developed MDD diagnostic model exhibited superior performance and showed promising potential for inclusion in routine clinical diagnosis for MDD.
METHODS: In this study, blood samples from 100 MDD and 100 HC were analyzed, along with MRI images from 46 MDD and 49 HC. Here, we devised a novel algorithm, integrating graph neural networks and attention modules, for MDD diagnosis based on inflammatory cytokines, neurotrophic factors, and Orexin A levels in the blood samples. Model performance was assessed via accuracy and F1 value in 3-fold cross-validation, comparing with 9 traditional algorithms. We then applied our algorithm to a dataset containing both the aforementioned protein quantifications and neuroimages, evaluating if integrating neuroimages into the model improves performance.
RESULTS: Compared to HC, MDD showed significant alterations in plasma protein levels and gray matter volume revealed by MRI. Our new algorithm exhibited superior performance, achieving an F1 value and accuracy of 0.9436 and 94.08 %, respectively. Integration of neuroimaging data enhanced our novel algorithm\'s performance, resulting in an improved F1 value and accuracy, reaching 0.9543 and 95.06 %.
CONCLUSIONS: This single-center study with a small sample size requires future evaluations on a larger test set for improved reliability.
CONCLUSIONS: In comparison to traditional machine learning models, our newly developed MDD diagnostic model exhibited superior performance and showed promising potential for inclusion in routine clinical diagnosis for MDD.
摘要：
背景：缺乏临床验证的生物标志物或客观方案阻碍了有效的重度抑郁症（MDD）诊断。与健康对照（HC）相比，MDD显示血浆蛋白水平和神经影像学表现异常。尽管在精神病学诊断中进行了广泛的机器学习研究，仍然缺乏集成多模态数据的可靠工具。
方法：在本研究中，分析了来自100MDD和100HC的血液样本，以及来自46个MDD和49个HC的MRI图像。这里,我们设计了一个新的算法，集成图神经网络和注意力模块，用于基于炎性细胞因子的MDD诊断，神经营养因子，和血样中的食欲素A水平.通过3倍交叉验证的准确性和F1值评估模型性能，与9种传统算法进行比较。然后，我们将我们的算法应用于包含上述蛋白质定量和神经图像的数据集，评估将神经图像集成到模型中是否会提高性能。
结果：与HC相比，MDD显示MRI显示的血浆蛋白水平和灰质体积显着变化。我们的新算法表现出优越的性能，达到0.9436和94.08%的F1值和精度，分别。神经影像数据的整合增强了我们新算法的性能，导致改进的F1值和精度，达到0.9543和95.06%。
结论：这项样本量较小的单中心研究需要在更大的测试集上进行未来评估，以提高可靠性。
结论：与传统机器学习模型相比，我们新开发的MDD诊断模型表现出优异的性能,并在MDD的常规临床诊断中显示出有希望的纳入潜力.
方法：在本研究中，分析了来自100MDD和100HC的血液样本，以及来自46个MDD和49个HC的MRI图像。这里,我们设计了一个新的算法，集成图神经网络和注意力模块，用于基于炎性细胞因子的MDD诊断，神经营养因子，和血样中的食欲素A水平.通过3倍交叉验证的准确性和F1值评估模型性能，与9种传统算法进行比较。然后，我们将我们的算法应用于包含上述蛋白质定量和神经图像的数据集，评估将神经图像集成到模型中是否会提高性能。
结果：与HC相比，MDD显示MRI显示的血浆蛋白水平和灰质体积显着变化。我们的新算法表现出优越的性能，达到0.9436和94.08%的F1值和精度，分别。神经影像数据的整合增强了我们新算法的性能，导致改进的F1值和精度，达到0.9543和95.06%。
结论：这项样本量较小的单中心研究需要在更大的测试集上进行未来评估，以提高可靠性。
结论：与传统机器学习模型相比，我们新开发的MDD诊断模型表现出优异的性能,并在MDD的常规临床诊断中显示出有希望的纳入潜力.
