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An MRI-Based, Data-Driven Model of Cortical Laminar Connectivity.
Neuroinformatics ( IF 2.7 ) Pub Date : 2020-09-19 , DOI: 10.1007/s12021-020-09491-7
Ittai Shamir 1 , Yaniv Assaf 1, 2
Affiliation  

Over the past two centuries, great scientific efforts have been spent on deciphering the structure and function of the cerebral cortex using a wide variety of methods. Since the advent of MRI neuroimaging, significant progress has been made in imaging of global white matter connectivity (connectomics), followed by promising new studies regarding imaging of grey matter laminar compartments. Despite progress in both fields, there still lacks mesoscale information regarding cortical laminar connectivity that could potentially bridge the gap between the current resolution of connectomics and the relatively higher resolution of cortical laminar imaging. Here, we systematically review a sample of prominent published articles regarding cortical laminar connectivity, in order to offer a simplified data-driven model that integrates white and grey matter MRI datasets into a novel way of exploring whole-brain tissue-level connectivity. Although it has been widely accepted that the cortex is exceptionally organized and interconnected, studies on the subject display a variety of approaches towards its structural building blocks. Our model addresses three principal cortical building blocks: cortical layer definitions (laminar grouping), vertical connections (intraregional, within the cortical microcircuit and subcortex) and horizontal connections (interregional, including connections within and between the hemispheres). While cortical partitioning into layers is more widely accepted as common knowledge, certain aspects of others such as cortical columns or microcircuits are still being debated. This study offers a broad and simplified view of histological and microscopical knowledge in laminar research that is applicable to the limitations of MRI methodologies, primarily regarding specificity and resolution.



中文翻译:

基于MRI的皮质层连接的数据驱动模型。

在过去的两个世纪中,人们花费了大量的科学努力来使用多种方法来解密大脑皮层的结构和功能。自MRI神经影像学问世以来,全球白质连通性成像(连接组学)取得了显着进展,随后有希望进行有关灰质层状腔室成像的新研究。尽管在这两个领域都取得了进展,但仍然缺乏有关皮质层流连通性的中尺度信息,这可能会弥合当前的组学分辨率和相对较高的皮质层流成像分辨率之间的差距。在这里,我们系统地回顾了有关皮质层流连接的著名文章的样本,为了提供简化的数据驱动模型,该模型将白和灰质MRI数据集整合为探索全脑组织级连通性的新颖方法。尽管人们公认皮质的组织性和相互连接性非常好,但是有关该主题的研究显示出了多种方法来构造其皮质。我们的模型解决了三个主要的皮质构建基块:皮质层定义(层组),垂直连接(区域内,皮质微电路和皮层内)和水平连接(区域间,包括半球内和半球之间的连接)。尽管将皮层划分为层已被广泛接受为常识,但其他方面的某些方面(例如皮层色谱柱或微电路)仍在争论中。

更新日期:2020-09-20
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