The effect of preprocessing in dynamic functional network connectivity used to classify mild traumatic brain injury.
The effect of preprocessing in dynamic functional network connectivity used to classify mild traumatic brain injury.
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DOI：
10.1002/brb3.809
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发表时间：
2017-10
影响因子：
3.1
通讯作者：
Calhoun VD
中科院分区：
文献类型：
--
作者：
Vergara VM;Mayer AR;Damaraju E;Calhoun VD
动态功能网络连接（dFNC）源于磁共振成像（fMRI），是寻找轻度创伤性脑损伤（mTBI）等脑疾病生物标志物的重要技术。在个体层面上，mTBI可以影响认知功能和改变人格特征。先前的研究旨在检测mTBI受试者dFNC的显著变化。然而，dFNC分析中主要关注的问题之一是用于纠正受试者运动的方法的适当性。在这项工作中，我们重点研究了在利用mTBI数据的dFNC分析中，在处理管道的不同点重新安排运动校正的效果。样本队列包括50名mTBI患者和匹配的健康对照。每个参与者完成5分钟的静息状态跑步。使用不同的管道方法对数据进行预处理，这些方法随移除运动相关方差的位置而变化。在所有管道中，分别进行组无关成分分析（gICA）和dFNC分析。对时间尖峰的检测、gICA组分的数量和滑动窗口的大小进行了额外的测试。使用线性支持向量机测试各管道对分类精度的影响。结果表明，在空间平滑之前对运动方差进行校正，而在gICA之后对尖峰时间过程进行校正，可以获得最佳的平均分类性能。gICA成分的数量和滑动窗口大小也是决定分类性能的重要因素。尖峰校正的方差对某些管道的影响大于其他管道，但与其他参数相比差异较小。预处理步骤依次为运动回归、平滑、gICA和去噪，产生了最适合区分mTBI和健康受试者的数据。然而，最优预处理参数的选择对最终结果有很大影响。
Dynamic functional network connectivity (dFNC), derived from magnetic resonance imaging (fMRI), is an important technique in the search for biomarkers of brain diseases such as mild traumatic brain injury (mTBI). At the individual level, mTBI can affect cognitive functions and change personality traits. Previous research aimed at detecting significant changes in the dFNC of mTBI subjects. However, one of the main concerns in dFNC analysis is the appropriateness of methods used to correct for subject movement. In this work, we focus on the effect that rearranging movement correction at different points of the processing pipeline has in dFNC analysis utilizing mTBI data. The sample cohort consists of 50 mTBI patients and matched healthy controls. A 5‐min resting‐state run was completed by each participant. Data were preprocessed using different pipeline alternatives varying with the place where motion‐related variance was removed. In all pipelines, group‐independent component analysis (gICA) followed by dFNC analysis was performed. Additional tests were performed varying the detection of temporal spikes, the number of gICA components, and the sliding‐window size. A linear support vector machine was used to test how each pipeline affects classification accuracy. Results suggest that correction for motion variance before spatial smoothing, but leaving correction for spiky time courses after gICA produced the best mean classification performance. The number of gICA components and the sliding‐window size were also important in determining classification performance. Variance in spikes correction affected some pipelines more than others with fewer significant differences than the other parameters. The sequence of preprocessing steps motion regression, smoothing, gICA, and despiking produced data most suitable for differentiating mTBI from healthy subjects. However, the selection of optimal preprocessing parameters strongly affected the final results.
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轻度创伤性脑损伤后，丘脑在丘脑的高度连接性。
影响因子：
3.2
作者：
Sours C;George EO;Zhuo J;Roys S;Gullapalli RP
通讯作者：
Gullapalli RP
DOI：
10.1109/tbme.2011.2167149
发表时间：
2011-12
期刊：
IEEE transactions on bio-medical engineering
影响因子：
--
作者：
Ma S;Correa NM;Li XL;Eichele T;Calhoun VD;Adalı T
通讯作者：
Adalı T
影响因子：
3.7
作者：
Shirer, W. R.;Ryali, S.;Greicius, M. D.
通讯作者：
Greicius, M. D.
影响因子：
5.3
作者：
Bonnelle, Valerie;Leech, Robert;Sharp, David J.
通讯作者：
Sharp, David J.
影响因子：
14.5
作者：
Grouiller, Frederic;Thornton, Rachel C.;Vulliemoz, Serge
通讯作者：
Vulliemoz, Serge