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The effects of connection reconstruction method on the interregional connectivity of brain networks via diffusion tractography

机译:连接重建方法通过扩散束摄影术对脑网络区域间连接的影响

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Estimating the interregional structural connections of the brain via diffusion tractography is a complex procedure and the parameters chosen can affect the outcome of the connectivity matrix. Here, we investigated the influence of different connection reconstruction methods on brain connectivity networks. Specifically, we applied three connection reconstruction methods to the same set of diffusion MRI data, initiating tracking from deep white matter (method #1, M1), from the gray matter/white matter interface (M2), and from the gray/white matter interface with thresholded tract volume rather than the connection probability as the connectivity index (M3). Small-world properties, hub identification, and hemispheric asymmetry in connectivity patterns were then calculated and compared across methods. Despite moderate to high correlations in the graph-theoretic measures across different methods, significant differences were observed in small-world indices, identified hubs, and hemispheric asymmetries, highlighting the importance of reconstruction method on network parameters. Consistent with the prior reports, the left precuneus was identified as a hub region in all three methods, suggesting it has a prominent role in brain networks.
机译:通过扩散束线照相术估计大脑的区域间结构连接是一个复杂的过程,选择的参数可能会影响连接矩阵的结果。在这里,我们研究了不同的连接重建方法对大脑连接网络的影响。具体来说,我们将三种连接重建方法应用于同一组扩散MRI数据,从深白质(方法#1,M1),灰质/白质界面(M2)以及灰/白质开始跟踪与阈值管道体积进行交互,而不是将连接概率作为连接性指标(M3)。然后计算了小世界属性,中心识别和连接模式中的半球不对称性,并比较了各种方法。尽管在不同方法之间的图论度量之间存在中等至高度的相关性,但在小世界指数,已识别的中心和半球不对称性方面仍观察到显着差异,从而突出了重构方法对网络参数的重要性。与先前的报告一致,在所有这三种方法中,左前突神经都被确定为中枢区域,表明左前突神经在脑网络中具有重要作用。

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