GIRUS-net: A Multimodal Deep Learning Model Identifying Imaging and Genetic Biomarkers Linked to Alzheimer's Disease Severity.
GIRUS-net: A Multimodal Deep Learning Model Identifying Imaging and Genetic Biomarkers Linked to Alzheimer's Disease Severity.
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GIRUS-net：一种多模式深度学习模型，可识别与阿尔茨海默病严重程度相关的影像和遗传生物标志物。
DOI：
10.1109/embc40787.2023.10341000
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发表时间：
2023
期刊：
影响因子：
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通讯作者：
Ghosal,Sayan
中科院分区：
文献类型：
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作者：
Wu,Sarah;Venkataraman,Archana;Ghosal,Sayan
我们介绍了一种可解释的深层神经结构，它结合了大脑结构和遗传影响，以改善阿尔茨海默病的疾病严重程度预测。我们的框架由编码器、解码器和秩一致性有序回归模块组成。编码器将神经成像和遗传学数据投影到由解码器规则化的低维潜在空间中。有序回归模块指导特征嵌入过程，以找到代表疾病严重程度的判别模式。我们还添加了一个可学习的退出层，该层学习特征重要性并从数据中提取可解释的生物标记物。我们使用结构磁共振成像(SMRI)和阿尔茨海默病神经成像计划(ADNI)数据库提供的单核苷酸多态(SNP)数据来评估我们的模型。在2级严重性分类比较中，我们的模型的中位F分数为0.86(基线中位F分数范围为0.57-0.81)。在三级分类比较中，我们的模型的中位数F分数为0.50(基线范围：0.17-0.41)。在4级分类比较中，我们的模型的中位数F-分数为0.40(基线范围：0.14-0.39)。我们证明，我们的模型提供了改进的疾病诊断以及稀疏和临床相关的生物标记物。临床相关性-这项研究提供了一个深度学习模型，可以预测阿尔茨海默病的严重程度，同时确定一致的和临床相关的生物标记物。
We introduce an explainable deep neural architecture that combines brain structure with genetic influence to improve disease severity prediction in Alzheimer’s disease. Our framework consists of an encoder, a decoder, and a rank-consistent ordinal regression module. The encoder projects neural imaging and genetics data into a low-dimensional latent space regularized by the decoder. The ordinal regression module guides the feature embedding process to find discriminative patterns representative of disease severity. We also add a learnable dropout layer that learns feature importance and extracts explainable biomarkers from the data. We evaluate our model using structural MRI (sMRI) and Single Nucleotide Polymorphism (SNP) data provided by the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database. In 2-class severity classification comparison, our model has a median F-score of 0.86 (baseline median F-score range: 0.57-0.81). In 3-class classification comparison, our model’s median F-score is 0.50 (baseline range: 0.17 - 0.41). In 4-class classification comparison, our model’s median F-score is 0.40 (baseline range: 0.14 - 0.39). We demonstrate that our model provides improved disease diagnosis alongside sparse and clinically relevant biomarkers.Clinical relevance—This study provides a deep-learning model that can predict Alzheimer’s disease severity levels while identifying consistent and clinically relevant biomarkers.