Discriminative sparse connectivity patterns for classification of fMRI Data.
Discriminative sparse connectivity patterns for classification of fMRI Data.
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DOI：
10.1007/978-3-319-10443-0_25
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发表时间：
2014
期刊：
影响因子：
--
通讯作者：
Davatzikos, Christos
中科院分区：
文献类型：
--
作者：
Eavani, Harini;Satterthwaite, Theodore D.;Gur, Raquel E.;Gur, Ruben C.;Davatzikos, Christos
静息状态功能磁共振成像的功能连接已成为了解正常大脑功能以及大脑发育和各种大脑疾病过程中发生的变化的重要研究工具。大多数先前的工作都是使用多变量分类方法（如支持向量机（SVM））检查成对功能连接值的变化。虽然支持向量机很强大，但它会产生一组密集的高维权重向量作为输出，这很难解释，并且需要额外的后处理才能与已知的功能网络相关联。在本文中，我们提出了一个结合网络识别和分类的联合框架，从而产生一组网络，或称为稀疏连接模式（scp），这些网络在功能上可解释，并且对两组具有高度的区分性。将该方法应用于儿童与成人的正常发育分类研究中，准确率为76%(AUC= 0.85)，与支持向量机（79%，AUC=0.87）相当，但特征数量明显减少（50个特征，而支持向量机为34716）。更重要的是，这导致了神经科学可解释性的巨大改进，这在这种研究中特别有利，因为群体差异在整个大脑中广泛存在。最高级别的辨别性scp反映了成人额叶区和后扣带区之间远程连通性的增加。相比之下，成人的双侧海马旁回之间的连通性比儿童低。
Functional connectivity using resting-state fMRI has emerged as an important research tool for understanding normal brain function as well as changes occurring during brain development and in various brain disorders. Most prior work has examined changes in pair-wise functional connectivity values using a multi-variate classification approach, such as Support Vector Machines (SVM).While it is powerful, SVMs produce a dense set of high-dimensional weight vectors as output, which are difficult to interpret, and require additional post-processing to relate to known functional networks. In this paper, we propose a joint framework that combines network identification and classification, resulting in a set of networks, or Sparse Connectivity Patterns (SCPs) which are functionally interpretable as well as highly discriminative of the two groups. Applied to a study of normal development classifying children vs. adults, the proposed method provided accuracy of 76%(AUC= 0.85), comparable to SVM (79%,AUC=0.87), but with dramatically fewer number of features (50 features vs. 34716 for the SVM). More importantly, this leads to a tremendous improvement in neuro-scientific interpretability, which is specially advantageous in such a study where the group differences are wide-spread throughout the brain. Highest-ranked discriminative SCPs reflect increases in long-range connectivity in adults between the frontal areas and posterior cingulate regions. In contrast, connectivity between the bilateral parahippocampal gyri was decreased in adults compared to children.
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DOI：
10.1523/jneurosci.2612-10.2010
发表时间：
2010-11-10
期刊：
The Journal of neuroscience : the official journal of the Society for Neuroscience
影响因子：
--
作者：
Zuo XN;Kelly C;Di Martino A;Mennes M;Margulies DS;Bangaru S;Grzadzinski R;Evans AC;Zang YF;Castellanos FX;Milham MP
通讯作者：
Milham MP
影响因子：
10.6
作者：
Batmanghelich NK;Taskar B;Davatzikos C
通讯作者：
Davatzikos C
BrainNet Viewer：人脑连接组学的网络可视化工具
影响因子：
3.7
作者：
Xia M;Wang J;He Y
通讯作者：
He Y
影响因子：
5.7
作者：
Satterthwaite, Theodore D.;Wolf, Daniel H.;Ruparel, Kosha;Erus, Guray;Elliott, Mark A.;Eickhoff, Simon B.;Gennatas, Efstathios D.;Jackson, Chad;Prabhakaran, Karthik;Smith, Alex;Hakonarson, Hakon;Verna, Ragini;Davatzikos, Christos;Gur, Raquel E.;Gur, Ruben C.
通讯作者：
Gur, Ruben C.
影响因子：
5.7
作者：
Van Dijk, Koene R. A.;Sabuncu, Mert R.;Buckner, Randy L.
通讯作者：
Buckner, Randy L.