Morphological fingerprinting: Identifying patients with first-episode schizophrenia using auto-encoded morphological patterns.
Morphological fingerprinting: Identifying patients with first-episode schizophrenia using auto-encoded morphological patterns.
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DOI：
10.1002/hbm.26098
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发表时间：
2023-02-01
影响因子：
4.8
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中科院分区：
文献类型：
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尽管已经进行了大量病例对照统计和机器学习研究来调查精神分裂症的大脑结构变化，但如何最好地测量和表征结构异常以用于分类算法仍然是一个悬而未决的问题。在当前的研究中，构建了专门为离散体积设计的卷积 3D 自动编码器，并使用来自 477 名健康个体的分段大脑进行训练。将包含 158 名首发精神分裂症患者和 166 名匹配对照者的队列输入训练有素的自动编码器中，以生成自动编码的形态模式。通过自动机器学习，使用该队列中 80% 的样本建立了一个区分精神分裂症患者和健康对照的分类器，并在剩余 20% 的样本上进行了验证，并且该分类器在另一个包含 77 名首发精神分裂症患者和以不同分辨率获取的 58 名匹配对照的独立队列中得到了进一步验证。这种专门设计的自动编码器可以令人满意地恢复输入。在相同的特征维度下，使用自编码特征训练的分类器比使用传统形态学特征训练的分类器高出约 10%，在内部验证集上实现了 73.44% 的准确率和 0.8 AUC，在外部验证集上实现了 71.85% 的准确率和 0.77 AUC。使用从分段大脑自动学习的特征可以更好地从健康对照中识别精神分裂症患者，但仍需要进一步改进以建立临床诊断标记。然而，由于样本量有限，当前研究中提出的方法深入了解了深度学习在精神疾病中的应用。提出了一种基于深度自动编码器的新型特征提取方法，基于此类特征训练的分类器优于基于经典大脑特征的分类器。
Although a large number of case–control statistical and machine learning studies have been conducted to investigate structural brain changes in schizophrenia, how best to measure and characterize structural abnormalities for use in classification algorithms remains an open question. In the current study, a convolutional 3D autoencoder specifically designed for discretized volumes was constructed and trained with segmented brains from 477 healthy individuals. A cohort containing 158 first‐episode schizophrenia patients and 166 matched controls was fed into the trained autoencoder to generate auto‐encoded morphological patterns. A classifier discriminating schizophrenia patients from healthy controls was built using 80% of the samples in this cohort by automated machine learning and validated on the remaining 20% of the samples, and this classifier was further validated on another independent cohort containing 77 first‐episode schizophrenia patients and 58 matched controls acquired at a different resolution. This specially designed autoencoder allowed a satisfactory recovery of the input. With the same feature dimension, the classifier trained with autoencoded features outperformed the classifier trained with conventional morphological features by about 10% points, achieving 73.44% accuracy and 0.8 AUC on the internal validation set and 71.85% accuracy and 0.77 AUC on the external validation set. The use of features automatically learned from the segmented brain can better identify schizophrenia patients from healthy controls, but there is still a need for further improvements to establish a clinical diagnostic marker. However, with a limited sample size, the method proposed in the current study shed insight into the application of deep learning in psychiatric disorders. A novel feature extraction method based deep autoencoder was proposed, classifier trained on such features outperformed classifier built on classical brain features.
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