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大脑图谱框架:癫痫动力学的启示。

A framework For brain atlases: Lessons from seizure dynamics.

机构信息

Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA.

Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Medical Scientist Training Program, University of California, San Francisco, CA 94143, USA.

出版信息

Neuroimage. 2022 Jul 1;254:118986. doi: 10.1016/j.neuroimage.2022.118986. Epub 2022 Mar 23.

Abstract

Brain maps, or atlases, are essential tools for studying brain function and organization. The abundance of available atlases used across the neuroscience literature, however, creates an implicit challenge that may alter the hypotheses and predictions we make about neurological function and pathophysiology. Here, we demonstrate how parcellation scale, shape, anatomical coverage, and other atlas features may impact our prediction of the brain's function from its underlying structure. We show how network topology, structure-function correlation (SFC), and the power to test specific hypotheses about epilepsy pathophysiology may change as a result of atlas choice and atlas features. Through the lens of our disease system, we propose a general framework and algorithm for atlas selection. This framework aims to maximize the descriptive, explanatory, and predictive validity of an atlas. Broadly, our framework strives to provide empirical guidance to neuroscience research utilizing the various atlases published over the last century.

摘要

脑图谱,或图谱,是研究大脑功能和组织的重要工具。然而,神经科学文献中可用的图谱数量众多,这就带来了一个潜在的挑战,可能会改变我们对神经功能和病理生理学的假设和预测。在这里,我们展示了分割尺度、形状、解剖覆盖范围和其他图谱特征如何影响我们根据大脑的结构预测其功能。我们展示了网络拓扑、结构-功能相关性(SFC)以及测试癫痫病理生理学特定假设的能力如何因图谱选择和图谱特征而改变。通过我们的疾病系统的视角,我们提出了一个用于图谱选择的通用框架和算法。该框架旨在最大限度地提高图谱的描述性、解释性和预测性有效性。广义而言,我们的框架旨在为利用上个世纪发表的各种图谱进行的神经科学研究提供经验指导。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/49da/9342687/7098ffb1f49b/nihms-1810856-f0001.jpg

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