Determination of vascular dementia brain in distinct frequency bands with whole brain functional connectivity patterns.
Determination of vascular dementia brain in distinct frequency bands with whole brain functional connectivity patterns.
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用全脑功能连接模式确定不同频段的血管性痴呆脑
DOI：
10.1371/journal.pone.0054512
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发表时间：
2013
期刊：
影响因子：
3.7
通讯作者：
Huang R
中科院分区：
文献类型：
--
作者：
Zhang D;Liu B;Chen J;Peng X;Liu X;Fan Y;Liu M;Huang R
最近的研究表明，多变量模式分析（MVPA）可以用于区分大脑疾病的类别。这样的分析可以极大地丰富和促进临床诊断。利用MPVA方法，全脑功能网络，特别是使用不同频率窗的全脑功能网络，可以用于检测大脑状态。我们使用静息状态BOLD-fMRI （rsfMRI）数据从三个频段(慢-5 （0.01 ~ 0.027 Hz）、慢-4 （0.027 ~ 0.073 Hz）和全频段（0.01 ~ 0.073 Hz）为血管性痴呆（VaD）患者组和对照组构建了全脑功能网络。然后我们使用支持向量机（SVM），一种MVPA分类器，来确定功能连接的模式。我们的研究结果表明，这三个频段的rsfMRI数据（19例VaD患者和20例对照）得出的脑功能网络似乎反映了VaD患者的神经生物学变化。这种差异可以用来区分VaD患者和健康人的大脑状态。我们还发现，人类大脑在三个频段的功能连接模式不同，他们区分大脑状态的能力也不同。具体来说，功能连接模式区分VaD大脑与健康大脑的能力在慢-5 （0.01 ~ 0.027 Hz）频段比在其他两个频段更有效。我们的研究结果表明，MVPA方法可用于检测VaD患者在不同频段的功能连接异常。识别这些异常可能有助于我们对VaD发病机制的理解。
Recent studies have shown that multivariate pattern analysis (MVPA) can be useful for distinguishing brain disorders into categories. Such analyses can substantially enrich and facilitate clinical diagnoses. Using MPVA methods, whole brain functional networks, especially those derived using different frequency windows, can be applied to detect brain states. We constructed whole brain functional networks for groups of vascular dementia (VaD) patients and controls using resting state BOLD-fMRI (rsfMRI) data from three frequency bands - slow-5 (0.01∼0.027 Hz), slow-4 (0.027∼0.073 Hz), and whole-band (0.01∼0.073 Hz). Then we used the support vector machine (SVM), a type of MVPA classifier, to determine the patterns of functional connectivity. Our results showed that the brain functional networks derived from rsfMRI data (19 VaD patients and 20 controls) in these three frequency bands appear to reflect neurobiological changes in VaD patients. Such differences could be used to differentiate the brain states of VaD patients from those of healthy individuals. We also found that the functional connectivity patterns of the human brain in the three frequency bands differed, as did their ability to differentiate brain states. Specifically, the ability of the functional connectivity pattern to differentiate VaD brains from healthy ones was more efficient in the slow-5 (0.01∼0.027 Hz) band than in the other two frequency bands. Our findings suggest that the MVPA approach could be used to detect abnormalities in the functional connectivity of VaD patients in distinct frequency bands. Identifying such abnormalities may contribute to our understanding of the pathogenesis of VaD.
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DOI：
10.1523/jneurosci.1984-11.2011
发表时间：
2011-09-28
期刊：
The Journal of neuroscience : the official journal of the Society for Neuroscience
影响因子：
--
作者：
Baliki MN;Baria AT;Apkarian AV
通讯作者：
Apkarian AV
影响因子：
3.2
作者：
Grady, CL;Springer, MV;Winocur, G
通讯作者：
Winocur, G
影响因子：
3.7
作者：
Davis, Simon W.;Dennis, Nancy A.;Cabeza, Roberto
通讯作者：
Cabeza, Roberto
DOI：
10.1073/pnas.0308627101
发表时间：
2004-03-30
影响因子：
11.1
作者：
Greicius, MD;Srivastava, G;Menon, V
通讯作者：
Menon, V
影响因子：
3.7
作者：
Deshpande G;Li Z;Santhanam P;Coles CD;Lynch ME;Hamann S;Hu X
通讯作者：
Hu X