基于功能连接的自闭症在 ABIDE 数据集上的预测。
Functional Connectivity-Based Prediction of Autism on Site Harmonized ABIDE Dataset.
出版信息
IEEE Trans Biomed Eng. 2021 Dec;68(12):3628-3637. doi: 10.1109/TBME.2021.3080259. Epub 2021 Nov 19.
OBJECTIVE
The larger sample sizes available from multi-site publicly available neuroimaging data repositories makes machine-learning based diagnostic classification of mental disorders more feasible by alleviating the curse of dimensionality. However, since multi-site data are aggregated post-hoc, i.e. they were acquired from different scanners with different acquisition parameters, non-neural inter-site variability may mask inter-group differences that are at least in part neural in origin. Hence, the advantages gained by the larger sample size in the context of machine-learning based diagnostic classification may not be realized.
METHODS
We address this issue using harmonization of multi-site neuroimaging data using the ComBat technique, which is based on an empirical Bayes formulation to remove inter-site differences in data distributions, to improve diagnostic classification accuracy. Specifically, we demonstrate this using ABIDE (Autism Brain Imaging Data Exchange) multi-site data for classifying individuals with Autism from healthy controls using resting state fMRI-based functional connectivity data.
RESULTS
Our results show that higher classification accuracies across multiple classification models can be obtained (especially for models based on artificial neural networks) from multi-site data post harmonization with the ComBat technique as compared to without harmonization, outperforming earlier results from existing studies using ABIDE. Furthermore, our network ablation analysis facilitated important insights into autism spectrum disorder pathology and the connectivity in networks shown to be important for classification covaried with verbal communication impairments in Autism.
CONCLUSION
Multi-site data harmonization using ComBat improves neuroimaging-based diagnostic classification of mental disorders.
SIGNIFICANCE
ComBat has the potential to make AI-based clinical decision-support systems more feasible in psychiatry.
目的
多站点公开可用神经影像学数据存储库中提供的更大样本量通过缓解维度诅咒使得基于机器学习的精神障碍诊断分类更加可行。然而，由于多站点数据是事后聚合的，即它们是从具有不同采集参数的不同扫描仪中获取的，因此非神经站点间的可变性可能会掩盖至少部分源于神经的组间差异。因此，基于机器学习的诊断分类中较大样本量带来的优势可能无法实现。
方法
我们使用基于经验贝叶斯公式的 ComBat 技术来解决这个问题，该技术用于协调多站点神经影像学数据，以消除数据分布中的站点间差异，从而提高诊断分类准确性。具体来说，我们使用 ABIDE（自闭症脑成像数据交换）多站点数据来演示这一点，使用静息态 fMRI 功能连接数据对自闭症个体与健康对照进行分类。
结果
我们的结果表明，与没有协调相比，使用 ComBat 技术对多站点数据进行协调后，可以从多个分类模型中获得更高的分类准确性（特别是对于基于人工神经网络的模型），优于使用 ABIDE 的现有研究中的早期结果。此外，我们的网络消融分析为自闭症谱系障碍病理学和对分类重要的网络连通性提供了重要的见解，这些连通性与自闭症中的语言交流障碍相关。
结论
使用 ComBat 进行多站点数据协调可提高基于神经影像学的精神障碍诊断分类。
意义
ComBat 有可能使基于人工智能的临床决策支持系统在精神病学中更可行。
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