Support vector machine classification of major depressive disorder using diffusion-weighted neuroimaging and graph theory.
Support vector machine classification of major depressive disorder using diffusion-weighted neuroimaging and graph theory.
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DOI：
10.3389/fpsyt.2015.00021
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发表时间：
2015
影响因子：
4.7
通讯作者：
Gotlib IH
中科院分区：
文献类型：
--
作者：
Sacchet MD;Prasad G;Foland-Ross LC;Thompson PM;Gotlib IH
最近，有相当大的兴趣，了解大脑网络在重度抑郁症（MDD）。神经通路可以使用弥散加权成像（DWI）在活体大脑中进行跟踪;然后可以使用图论来研究所产生的纤维网络的属性。到目前为止，在MDD中基于追踪图的图形度量中尚未报告全局异常，因此我们使用了基于“支持向量机”的机器学习方法，以基于多个大脑网络属性区分抑郁症患者和健康人群。我们还评估了特定的图形指标对于这种差异化的重要性。最后，我们进行了局部图分析，以识别网络中特定节点的异常连通性。我们能够使用全脑图指标对抑郁症进行分类。小世界性是最有用的分类图度量。MDD患者右侧眶部、右侧顶下皮层和左侧扣带前扣带喙部的网络连接均异常。这是第一次使用结构性全局图度量对抑郁个体进行分类。这些发现强调了未来研究的重要性，以了解抑郁症的网络特性，改善分类结果，并将网络改变与精神症状，药物治疗和合并症联系起来。
Recently, there has been considerable interest in understanding brain networks in major depressive disorder (MDD). Neural pathways can be tracked in the living brain using diffusion-weighted imaging (DWI); graph theory can then be used to study properties of the resulting fiber networks. To date, global abnormalities have not been reported in tractography-based graph metrics in MDD, so we used a machine learning approach based on “support vector machines” to differentiate depressed from healthy individuals based on multiple brain network properties. We also assessed how important specific graph metrics were for this differentiation. Finally, we conducted a local graph analysis to identify abnormal connectivity at specific nodes of the network. We were able to classify depression using whole-brain graph metrics. Small-worldness was the most useful graph metric for classification. The right pars orbitalis, right inferior parietal cortex, and left rostral anterior cingulate all showed abnormal network connectivity in MDD. This is the first use of structural global graph metrics to classify depressed individuals. These findings highlight the importance of future research to understand network properties in depression across imaging modalities, improve classification results, and relate network alterations to psychiatric symptoms, medication, and comorbidities.
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影响因子：
3.7
作者：
Humphries MD;Gurney K
通讯作者：
Gurney K
影响因子：
10.6
作者：
Frodl, Thomas;Bokde, Arun L. W.;Meisenzahl, Eva
通讯作者：
Meisenzahl, Eva
影响因子：
5.7
作者：
Jovicich, Jorge;Marizzoni, Moira;Frisoni, Giovanni B.
通讯作者：
Frisoni, Giovanni B.
影响因子：
7.2
作者：
Ajilore, Olusola;Lamar, Melissa;Kumar, Anand
通讯作者：
Kumar, Anand
DOI：
10.1111/j.2517-6161.1995.tb02031.x
发表时间：
1995-01-01
影响因子：
5.8
作者：
BENJAMINI, Y;HOCHBERG, Y
通讯作者：
HOCHBERG, Y