A mechanical model predicts morphological abnormalities in the developing human brain.
A mechanical model predicts morphological abnormalities in the developing human brain.
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DOI：
10.1038/srep05644
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发表时间：
2014-07-10
影响因子：
4.6
通讯作者：
Kuhl E
中科院分区：
文献类型：
--
作者：
Budday S;Raybaud C;Kuhl E
发育中的人脑仍然是为数不多的未解之谜之一。发育生物学、神经科学和医学成像的进步使我们比以往任何时候都更接近了解健康和疾病中的大脑发育。然而，机制在整个过程中的确切作用仍然被低估，人们对此知之甚少。在这里，我们展示了机械拉伸在大脑发育中起着至关重要的作用。利用力学的非线性场论和有限增长理论，我们将人脑建模为一个具有形态发生生长的外表面和拉伸驱动的生长核心的生命系统。这种方法无缝地整合了两种流行但相互竞争的皮质折叠假说：轴突张力和差异生长。我们使用早产儿的磁共振图像来校准我们的模型。我们的模型预测，皮质生长和厚度的偏差会导致形态异常。利用脑回指数，即总表面积与裸露表面积的比值，我们证明这些异常符合无脑和多小脑回的经典病理。了解发育中人脑皮质折叠的机制对癫痫、精神分裂症和自闭症等神经疾病的诊断和治疗具有直接意义。
The developing human brain remains one of the few unsolved mysteries of science. Advancements in developmental biology, neuroscience, and medical imaging have brought us closer than ever to understand brain development in health and disease. However, the precise role of mechanics throughout this process remains underestimated and poorly understood. Here we show that mechanical stretch plays a crucial role in brain development. Using the nonlinear field theories of mechanics supplemented by the theory of finite growth, we model the human brain as a living system with a morphogenetically growing outer surface and a stretch-driven growing inner core. This approach seamlessly integrates the two popular but competing hypotheses for cortical folding: axonal tension and differential growth. We calibrate our model using magnetic resonance images from very preterm neonates. Our model predicts that deviations in cortical growth and thickness induce morphological abnormalities. Using the gyrification index, the ratio between the total and exposed surface area, we demonstrate that these abnormalities agree with the classical pathologies of lissencephaly and polymicrogyria. Understanding the mechanisms of cortical folding in the developing human brain has direct implications in the diagnostics and treatment of neurological disorders, including epilepsy, schizophrenia, and autism.
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影响因子：
2.9
作者：
MARINPADILLA, M
通讯作者：
MARINPADILLA, M
DOI：
10.1073/pnas.1217391110
发表时间：
2013-06-25
影响因子：
11.1
作者：
Ben Amar, Martine;Jia, Fei
通讯作者：
Jia, Fei
影响因子：
5.3
作者：
Dunlop, J. W. C.;Fischer, F. D.;Fratzl, P.
通讯作者：
Fratzl, P.
影响因子：
3.5
作者：
Kuhl, E.;Maas, R.;Menzel, A.
通讯作者：
Menzel, A.
影响因子：
16.6
作者：
Harrington, Matthew J.;Razghandi, Khashayar;Burgert, Ingo
通讯作者：
Burgert, Ingo