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Mapping individual voxel-wise morphological connectivity using wavelet transform of voxel-based morphology

机译：使用基于体素的形态学的小波变换映射单个体素的形态学连通性

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摘要

Mapping individual brain networks has drawn significant research interest in recent years. Most individual brain networks developed to date have been based on fMRI or diffusion MRI. Given recent concerns regarding confounding artifacts, various preprocessing steps are generally included in functional or structural brain networks. Notably, voxel-based morphometry (VBM) derived from anatomical MRI exhibits high signal-to-noise ratios and has been applied to individual interregional morphological networks. To the best of our knowledge, individual voxel-wise morphological networks remain unexplored. The goal of this research is twofold: to build novel metrics for individual voxel-wise morphological networks and to test the reliability of the proposed morphological connectivity. To this end, anatomical scans of a cohort of healthy subjects were obtained from a public database. The anatomical datasets were preprocessed and normalized to the standard brain space. For each individual, wavelet-transform was applied on the VBM measures to obtain voxel-wise hierarchical features. The voxel-wise morphological connectivity was computed based on the wavelet features. Reliable brain hubs were detected by the z-scores of degree centrality. High reliability was discovered by test-retest analysis. No effects of wavelet scale, network threshold or network type were found on hubs of group-level degree centrality. However, significant effects of wavelet scale, network threshold and network type were found on individual-level degree centrality. Significant effects of network threshold and network type were found on reliability of degree centrality. The results suggested that the voxel-wise morphological connectivity was reliable and exhibited a hub structure. Moreover, the voxel-wise morphological connectivity could reflect individual differences. In summary, individual voxel-wise wavelet-based features can probe morphological connectivity and may be beneficial for investigating the brain morphological connectomes.
机译：映射个人的大脑网络近年来引起了巨大的研究兴趣。迄今为止，开发的大多数个体脑网络都基于fMRI或扩散MRI。考虑到最近对混杂的伪影的担忧，各种预处理步骤通常包含在功能或结构性大脑网络中。值得注意的是，源自解剖学MRI的基于体素的形态学（VBM）表现出较高的信噪比，并已应用于各个区域间形态学网络。据我们所知，单个体素形态网络尚未得到开发。这项研究的目的是双重的：为各个体素形态网络建立新的度量标准，并测试所提出的形态连通性的可靠性。为此，从公共数据库中获得了一组健康受试者的解剖扫描。解剖数​​据集已经过预处理，并标准化为标准的大脑空间。对于每个个体，将小波变换应用于VBM度量以获得体素级层次特征。基于小波特征计算体素方向的形态连通性。通过度数中心度的z分数可以检测到可靠的大脑中枢。通过重测分析发现了高可靠性。没有发现小波规模，网络阈值或网络类型对组级别度中心的影响。然而，发现小波尺度，网络阈值和网络类型对个人级别的中心度有显着影响。发现网络阈值和网络类型对度中心性的可靠性有显着影响。结果表明体素方向形态连接是可靠的，并表现出毂结构。而且，体素方向的形态连通性可以反映个体差异。总而言之，基于个体素的基于小波的特征可以探测形态学连通性，并且可能对于研究脑部形态学连接基因组是有益的。

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  • 作者

    Xun-Heng Wang; Yun Jiao; Lihua Li;

  • 作者单位
  • 年(卷),期 -1(13),7
  • 年度 -1
  • 页码 e0201243
  • 总页数 16
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