Multivariate Classification of Major Depressive Disorder Using the Effective Connectivity and Functional Connectivity.
Multivariate Classification of Major Depressive Disorder Using the Effective Connectivity and Functional Connectivity.
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使用有效连接和功能连接对重度抑郁症进行多元分类
DOI：
10.3389/fnins.2018.00038
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发表时间：
2018
影响因子：
4.3
通讯作者：
Shi Y
中科院分区：
文献类型：
--
作者：
Geng X;Xu J;Liu B;Shi Y
重度抑郁症（MDD）是一种精神障碍，其特征是至少2周的情绪低落，这在大多数情况下都存在。静息态功能磁共振成像（fMRI）数据具有高维、小样本、噪声和个体差异等特点，使得利用fMRI诊断MDD面临诸多挑战。据我们所知，没有研究旨在MDD患者和健康对照之间的有效连接和功能连接措施的分类。在这项研究中，我们进行了数据驱动的分类分析，使用全脑连接的措施，其中包括从两个大脑模板的功能连接和有效的连接措施创建的默认模式网络（DMN），背侧注意网络（DAN），额顶叶网络（FPN），沉默网络（SN）。有效的连接措施提取光谱动态因果模型（spDCM），并转化为向量特征空间。采用线性支持向量机（linear SVM）、非线性支持向量机（non-linear SVM）、k-最近邻（k-Nearest Neighbor，KNN）和逻辑回归（Logistic Regression，LR）作为分类器，识别MDD患者和健康对照之间的差异。我们的结果表明，当使用19个有效连接时，准确率达到91.67%（p < 0.0001），当使用6，650个功能连接时，准确率达到89.36%。高分辨力的功能连接主要位于整个脑静息态网络内或跨脑静息态网络，而分辨力高的有效连接则位于后扣带皮层（PCC）、腹内侧前额叶皮层（vmPFC）、背侧扣带皮层（dACC）和顶叶下叶（IPL）等特定区域。为了进一步比较功能连接和有效连接的区分能力，仅使用来自这四个网络的功能连接进行分类分析，最高准确率达到78.33%（p < 0.0001）。我们的研究表明，有效的连接措施可能发挥更重要的作用，比功能连接在探索患者和健康对照之间的变化，并提供了更好的机制解释。此外，我们的研究结果显示了有效连接的诊断潜力，用于诊断MDD患者，具有较高的准确性，可以进行早期预防或干预。
Major depressive disorder (MDD) is a mental disorder characterized by at least 2 weeks of low mood, which is present across most situations. Diagnosis of MDD using rest-state functional magnetic resonance imaging (fMRI) data faces many challenges due to the high dimensionality, small samples, noisy and individual variability. To our best knowledge, no studies aim at classification with effective connectivity and functional connectivity measures between MDD patients and healthy controls. In this study, we performed a data-driving classification analysis using the whole brain connectivity measures which included the functional connectivity from two brain templates and effective connectivity measures created by the default mode network (DMN), dorsal attention network (DAN), frontal-parietal network (FPN), and silence network (SN). Effective connectivity measures were extracted using spectral Dynamic Causal Modeling (spDCM) and transformed into a vectorial feature space. Linear Support Vector Machine (linear SVM), non-linear SVM, k-Nearest Neighbor (KNN), and Logistic Regression (LR) were used as the classifiers to identify the differences between MDD patients and healthy controls. Our results showed that the highest accuracy achieved 91.67% (p < 0.0001) when using 19 effective connections and 89.36% when using 6,650 functional connections. The functional connections with high discriminative power were mainly located within or across the whole brain resting-state networks while the discriminative effective connections located in several specific regions, such as posterior cingulate cortex (PCC), ventromedial prefrontal cortex (vmPFC), dorsal cingulate cortex (dACC), and inferior parietal lobes (IPL). To further compare the discriminative power of functional connections and effective connections, a classification analysis only using the functional connections from those four networks was conducted and the highest accuracy achieved 78.33% (p < 0.0001). Our study demonstrated that the effective connectivity measures might play a more important role than functional connectivity in exploring the alterations between patients and health controls and afford a better mechanistic interpretability. Moreover, our results showed a diagnostic potential of the effective connectivity for the diagnosis of MDD patients with high accuracies allowing for earlier prevention or intervention.
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人脑网络组图谱：基于连接架构的新大脑图谱
DOI：
10.1093/cercor/bhw157
发表时间：
2016-08
期刊：
Cerebral cortex (New York, N.Y. : 1991)
影响因子：
--
作者：
Fan L;Li H;Zhuo J;Zhang Y;Wang J;Chen L;Yang Z;Chu C;Xie S;Laird AR;Fox PT;Eickhoff SB;Yu C;Jiang T
通讯作者：
Jiang T
影响因子：
82.9
作者：
Drysdale AT;Grosenick L;Downar J;Dunlop K;Mansouri F;Meng Y;Fetcho RN;Zebley B;Oathes DJ;Etkin A;Schatzberg AF;Sudheimer K;Keller J;Mayberg HS;Gunning FM;Alexopoulos GS;Fox MD;Pascual-Leone A;Voss HU;Casey BJ;Dubin MJ;Liston C
通讯作者：
Liston C
影响因子：
48
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Craddock, R. Cameron;Jbabdi, Saad;Yan, Chao-Gan;Vogelstein, Joshua T.;Castellanos, F. Xavier;Di Martino, Adriana;Kelly, Clare;Heberlein, Keith;Colcombe, Stan;Milham, Michael P.
通讯作者：
Milham, Michael P.
影响因子：
17.7
作者：
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通讯作者：
Post, RM
影响因子：
4.8
作者：
Buchanan, Angel;Wang, Xue;Gollan, Jackie K.
通讯作者：
Gollan, Jackie K.