Modelling white matter in gyral blades as a continuous vector field.

Modelling white matter in gyral blades as a continuous vector field.
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DOI:
10.1016/j.neuroimage.2020.117693
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发表时间:
2021-02-15
期刊:
影响因子:
5.7
通讯作者:
Jbabdi S
Jbabdi S
中科院分区:
医学1区
文献类型:
--
作者:
Cottaar M;Bastiani M;Boddu N;Glasser MF;Haber S;van Essen DC;Sotiropoulos SN;Jbabdi S

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许多脑成像研究的目的是用弥散纤维束成像测量结构连接。然而,纤维束成像数据的偏差,特别是在白色物质和皮质灰质之间的边界附近,可能会限制这种研究的准确性。当从白色物质播种时,流线倾向于平行于回旋的皮质表面行进,在很大程度上避开沟底,并优先终止于脑回冠。当从皮质灰质播种时,流线通常在皮质表面附近运行,直到到达深层白色物质。这些所谓的“脑回偏差”限制了通过纤维束成像算法估计的皮质结构连接性轮廓的准确性和有效分辨率,并且它们不反映在侵入性示踪剂研究或有髓纤维染色中看到的轴突密度的预期分布。我们提出了一种算法,同时模型的纤维密度和方向,使用一个无发散的矢量场内回叶片,鼓励解剖合理的流线密度分布沿着皮质白色/灰质边界,同时保持与扩散MRI估计的纤维方向对齐。使用人体连接组项目的体内数据,我们表明,该算法减少了纤维束成像的偏见。我们比较结构连接体的功能连接体从静息态功能磁共振成像,表明我们的模型提高了跨模态协议。最后,我们发现,包裹后的结构连接体的变化是非常轻微的略改善半球间的连接(即,更多的同伦连接)和略差的半球内连接相比,示踪剂。
Many brain imaging studies aim to measure structural connectivity with diffusion tractography. However, biases in tractography data, particularly near the boundary between white matter and cortical grey matter can limit the accuracy of such studies. When seeding from the white matter, streamlines tend to travel parallel to the convoluted cortical surface, largely avoiding sulcal fundi and terminating preferentially on gyral crowns. When seeding from the cortical grey matter, streamlines generally run near the cortical surface until reaching deep white matter. These so-called “gyral biases” limit the accuracy and effective resolution of cortical structural connectivity profiles estimated by tractography algorithms, and they do not reflect the expected distributions of axonal densities seen in invasive tracer studies or stains of myelinated fibres. We propose an algorithm that concurrently models fibre density and orientation using a divergence-free vector field within gyral blades to encourage an anatomically-justified streamline density distribution along the cortical white/grey-matter boundary while maintaining alignment with the diffusion MRI estimated fibre orientations. Using in vivo data from the Human Connectome Project, we show that this algorithm reduces tractography biases. We compare the structural connectomes to functional connectomes from resting-state fMRI, showing that our model improves cross-modal agreement. Finally, we find that after parcellation the changes in the structural connectome are very minor with slightly improved interhemispheric connections (i.e, more homotopic connectivity) and slightly worse intrahemi-spheric connections when compared to tracers.
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