Pattern classification of large-scale functional brain networks: identification of informative neuroimaging markers for epilepsy.
Pattern classification of large-scale functional brain networks: identification of informative neuroimaging markers for epilepsy.
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大规模功能性大脑网络的模式分类：癫痫信息性神经影像标记物的识别
DOI：
10.1371/journal.pone.0036733
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发表时间：
2012
期刊：
影响因子：
3.7
通讯作者：
Feng J
中科院分区：
文献类型：
--
作者：
Zhang J;Cheng W;Wang Z;Zhang Z;Lu W;Lu G;Feng J
利用静息态功能磁共振成像（fMRI）准确预测一般神经精神疾病，在个体的基础上，是一个具有重大临床意义的具有挑战性的任务。尽管在组水平上绘制健康对照组和患者之间的差异方面取得了进展，但个体之间功能性大脑网络的模式分类仍然不太发达。在本文中，我们确定了两种新的神经影像学措施，证明是强有力的预测神经影像学标志物之间的模式分类健康对照组和一般癫痫患者。这些措施表征功能性脑网络的两个重要方面的定量方式：（i）空间分布的大脑区域之间的协调操作，和（ii）双边同源脑区域的不对称性，在其功能连接的全球模式。第二种测量方法提供了对网络层面大脑不对称性的独特理解，据我们所知，之前尚未用于功能性大脑网络的模式分类。使用稀疏回归和支持向量机等现代模式识别方法，我们在由180名健康对照和癫痫患者组成的大型数据集中实现了83.9%的交叉验证分类准确率（特异性：82.5%;灵敏度：85%）。我们确定了癫痫患者认知功能受损的病理生理机制的基础上显着改变的功能通路和子网络。特别是，我们发现癫痫患者的大脑手术的不对称性在颞叶和边缘系统，与健康人相比，显着增强。本研究表明，与专门设计的信息神经影像学标记，静息态功能磁共振成像可以作为一个最有前途的工具，用于临床诊断，也揭示了复杂的神经精神疾病背后的生理。我们在这里提出的系统的方法，预计将有更广泛的应用在一般的神经精神疾病。
The accurate prediction of general neuropsychiatric disorders, on an individual basis, using resting-state functional magnetic resonance imaging (fMRI) is a challenging task of great clinical significance. Despite the progress to chart the differences between the healthy controls and patients at the group level, the pattern classification of functional brain networks across individuals is still less developed. In this paper we identify two novel neuroimaging measures that prove to be strongly predictive neuroimaging markers in pattern classification between healthy controls and general epileptic patients. These measures characterize two important aspects of the functional brain network in a quantitative manner: (i) coordinated operation among spatially distributed brain regions, and (ii) the asymmetry of bilaterally homologous brain regions, in terms of their global patterns of functional connectivity. This second measure offers a unique understanding of brain asymmetry at the network level, and, to the best of our knowledge, has not been previously used in pattern classification of functional brain networks. Using modern pattern-recognition approaches like sparse regression and support vector machine, we have achieved a cross-validated classification accuracy of 83.9% (specificity: 82.5%; sensitivity: 85%) across individuals from a large dataset consisting of 180 healthy controls and epileptic patients. We identified significantly changed functional pathways and subnetworks in epileptic patients that underlie the pathophysiological mechanism of the impaired cognitive functions. Specifically, we find that the asymmetry of brain operation for epileptic patients is markedly enhanced in temporal lobe and limbic system, in comparison with healthy individuals. The present study indicates that with specifically designed informative neuroimaging markers, resting-state fMRI can serve as a most promising tool for clinical diagnosis, and also shed light onto the physiology behind complex neuropsychiatric disorders. The systematic approaches we present here are expected to have wider applications in general neuropsychiatric disorders.
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影响因子：
5.7
作者：
FRISTON, KJ;HOLMES, AP;TURNER, R
通讯作者：
TURNER, R
DOI：
10.1146/annurev-clinpsy-040510-143934
发表时间：
2011-01-01
影响因子：
18.4
作者：
Bullmore, Edward T.;Bassett, Danielle S.
通讯作者：
Bassett, Danielle S.
影响因子：
5.7
作者：
Kawasaki, Yasuhiro;Suzuki, Michio;Kurachi, Masayoshi
通讯作者：
Kurachi, Masayoshi
DOI：
10.1073/pnas.0908073106
发表时间：
2009-12-01
影响因子：
11.1
作者：
Liu, Hesheng;Stufflebeam, Steven M.;Buckner, Randy L.
通讯作者：
Buckner, Randy L.
影响因子：
5.6
作者：
Bonelli SB;Powell R;Yogarajah M;Thompson PJ;Symms MR;Koepp MJ;Duncan JS
通讯作者：
Duncan JS