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首页> 外文会议>IEEE International Symposium on Biomedical Imaging >Elucidating brain connectivity networks in major depressive disorder using classification-based scoring
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Elucidating brain connectivity networks in major depressive disorder using classification-based scoring

机译：使用基于分类的评分阐明重度抑郁症的大脑连接网络

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Graph theory is increasingly used in the field of neuroscience to understand the large-scale network structure of the human brain. There is also considerable interest in applying machine learning techniques in clinical settings, for example, to make diagnoses or predict treatment outcomes. Here we used support-vector machines (SVMs), in conjunction with whole-brain tractography, to identify graph metrics that best differentiate individuals with Major Depressive Disorder (MDD) from nondepressed controls. To do this, we applied a novel feature-scoring procedure that incorporates iterative classifier performance to assess feature robustness. We found that small-worldness, a measure of the balance between global integration and local specialization, most reliably differentiated MDD from nondepressed individuals. Post-hoc regional analyses suggested that heightened connectivity of the subcallosal cingulate gyrus (SCG) in MDDs contributes to these differences. The current study provides a novel way to assess the robustness of classification features and reveals anomalies in large-scale neural networks in MDD.
机译：图论在神经科学领域中越来越多地用于了解人脑的大规模网络结构。在临床环境中应用机器学习技术，例如进行诊断或预测治疗结果，也引起了极大的兴趣。在这里，我们使用支持向量机（SVM）结合全脑束线描记术，来确定能最好地区分患有重性抑郁症（MDD）的人与未抑郁的对照者的图形指标。为此，我们应用了一种新颖的特征评分程序，该程序结合了迭代分类器的性能来评估特征的鲁棒性。我们发现，小世界是衡量全球整合与本地专业化之间平衡的一种方式，可以最可靠地将MDD与非抑郁者区分开。事后区域分析表明，MDD中call骨扣带回（SCG）的增强连通性造成了这些差异。当前的研究提供了一种新颖的方法来评估分类特征的鲁棒性，并揭示了MDD中大型神经网络中的异常情况。

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