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A unified framework for mapping individual interregional high-order morphological connectivity based on regional cortical features from anatomical MRI.
Magnetic Resonance Imaging ( IF 2.5 ) Pub Date : 2019-11-05 , DOI: 10.1016/j.mri.2019.11.003
Xun-Heng Wang 1 , Yun Jiao 2 , Lihua Li 1
Affiliation  

Building individual brain networks form the single volume of anatomical MRI is a challenging task. Furthermore, the high-order connectivity of morphological networks remains unexplored. This paper aimed to investigate the individual high-order morphological connectivity from anatomical MRI. Towards this goal, a unified framework based on six feature distances (euclidean, seuclidean, mahalanobis, cityblock, minkowski, and chebychev) was proposed to derive high-order interregional morphological features. The test-retest datasets and the healthy aging datasets were applied to analyze the reliability and the inter-subject variability of the novel features. In addition, the predictive models based on these novel features were established for age estimation. The proposed six neuroanatomical features exhibited significant high-to-excellent reliability. Certain connections were significantly correlated to biological age based on the six novel metrics (p < .05, FDR corrected). Moreover, the predicted age were significantly correlated to the original age in each regression task (r > 0.5, p < 10-6). The results suggested that the novel high-order metrics were reliable and could reflect individual differences, which could be beneficial for current methods of individual brain connectomes.

中文翻译:

一个基于解剖MRI区域皮层特征绘制单个区域间高阶形态学连通性的统一框架。

从解剖学MRI的单个体积中构建个人的大脑网络是一项艰巨的任务。此外,形态网络的高阶连通性仍未开发。本文旨在研究解剖MRI的个体高阶形态学连通性。为了实现这一目标,提出了一个基于六个特征距离(欧几里得,半胱氨酸,马哈拉诺比斯,城市街区,明可夫斯基和契比雪夫)的统一框架,以推导高阶区域间形态特征。应用重测数据集和健康衰老数据集来分析新颖特征的可靠性和受试者间变异性。另外,建立了基于这些新颖特征的预测模型用于年龄估计。拟议的六个神经解剖学特征表现出显着的高至出色的可靠性。根据六个新指标,某些联系与生物学年龄显着相关(p <.05,FDR校正)。此外,在每个回归任务中,预测年龄与原始年龄显着相关(r> 0.5,p <10-6)。结果表明,新颖的高阶度量是可靠的,并且可以反映个体差异,这可能对当前的个体脑连接组方法有益。
更新日期:2019-11-05
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