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Modelling brain development to detect white matter injury in term and preterm born neonates.
Brain ( IF 14.5 ) Pub Date : 2020-01-16 , DOI: 10.1093/brain/awz412
Jonathan O'Muircheartaigh 1, 2, 3 , Emma C Robinson 2, 4 , Maximillian Pietsch 2 , Thomas Wolfers 5, 6 , Paul Aljabar 2 , Lucilio Cordero Grande 2 , Rui P A G Teixeira 2 , Jelena Bozek 7 , Andreas Schuh 8 , Antonios Makropoulos 8 , Dafnis Batalle 1, 2 , Jana Hutter 2 , Katy Vecchiato 2 , Johannes K Steinweg 2 , Sean Fitzgibbon 9 , Emer Hughes 2 , Anthony N Price 2 , Andre Marquand 5, 6, 10 , Daniel Reuckert 8 , Mary Rutherford 2 , Joseph V Hajnal 2 , Serena J Counsell 2 , A David Edwards 2, 3
Affiliation  

Premature birth occurs during a period of rapid brain growth. In this context, interpreting clinical neuroimaging can be complicated by the typical changes in brain contrast, size and gyrification occurring in the background to any pathology. To model and describe this evolving background in brain shape and contrast, we used a Bayesian regression technique, Gaussian process regression, adapted to multiple correlated outputs. Using MRI, we simultaneously estimated brain tissue intensity on T1- and T2-weighted scans as well as local tissue shape in a large cohort of 408 neonates scanned cross-sectionally across the perinatal period. The resulting model provided a continuous estimate of brain shape and intensity, appropriate to age at scan, degree of prematurity and sex. Next, we investigated the clinical utility of this model to detect focal white matter injury. In individual neonates, we calculated deviations of a neonate’s observed MRI from that predicted by the model to detect punctate white matter lesions with very good accuracy (area under the curve > 0.95). To investigate longitudinal consistency of the model, we calculated model deviations in 46 neonates who were scanned on a second occasion. These infants’ voxelwise deviations from the model could be used to identify them from the other 408 images in 83% (T2-weighted) and 76% (T1-weighted) of cases, indicating an anatomical fingerprint. Our approach provides accurate estimates of non-linear changes in brain tissue intensity and shape with clear potential for radiological use.

中文翻译:

对大脑发育进行建模以检测足月和早产新生儿的白质损伤。

早产发生在大脑快速成长的时期。在这种情况下,对临床神经影像的解释可能会因任何病理背景中发生的脑部对比度,大小和回旋的典型变化而变得复杂。为了建模和描述这种不断发展的大脑形状和对比度背景,我们使用了贝叶斯回归技术,即高斯过程回归,适用于多个相关输出。使用MRI,我们同时估算了T 1-和T 2上的脑组织强度在整个围产期期间进行横断面扫描的408个新生儿的大队列中,进行加权加权扫描以及局部组织形状。生成的模型提供了对大脑形状和强度的连续估计,适合于扫描时的年龄,早产程度和性别。接下来,我们研究了该模型检测局灶性白质损伤的临床实用性。在个别新生儿中,我们计算了新生儿观察到的MRI与模型预测的MRI偏差,从而可以非常准确地检测点状白质病变(曲线下面积> 0.95)。为了研究模型的纵向一致性,我们计算了第二次扫描的46名新生儿的模型偏差。这些婴儿与模型的体素偏差可用于从其他408张图像中识别出83%(T 2)和76%(T 1加权)的病例,表明有解剖指纹。我们的方法可以准确估计脑组织强度和形状的非线性变化,并具有明显的放射学应用潜力。
更新日期:2020-02-10
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