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运用计算建模和光遗传学 fMRI 技术解决大脑回路的功能和障碍问题。

Solving brain circuit function and dysfunction with computational modeling and optogenetic fMRI.

机构信息

Department of Neurology and Neurological Sciences, Stanford University, Stanford, CA 94305, USA.

Department of Bioengineering, Stanford University, Stanford, CA 94305, USA.

出版信息

Science. 2022 Nov 4;378(6619):493-499. doi: 10.1126/science.abq3868. Epub 2022 Nov 3.

Abstract

Can we construct a model of brain function that enables an understanding of whole-brain circuit mechanisms underlying neurological disease and use it to predict the outcome of therapeutic interventions? How are pathologies in neurological disease, some of which are observed to have spatial spreading mechanisms, associated with circuits and brain function? In this review, we discuss approaches that have been used to date and future directions that can be explored to answer these questions. By combining optogenetic functional magnetic resonance imaging (fMRI) with computational modeling, cell type-specific, large-scale brain circuit function and dysfunction are beginning to be quantitatively parameterized. We envision that these developments will pave the path for future therapeutics developments based on a systems engineering approach aimed at directly restoring brain function.

摘要

我们能否构建一个大脑功能模型,从而理解神经疾病的全脑回路机制,并利用该模型预测治疗干预的效果?在神经疾病中,有些病变观察到具有空间扩散机制,这些病变与回路和大脑功能有何关联?在这篇综述中,我们讨论了迄今为止所采用的方法以及未来可以探索的方向,以回答这些问题。通过将光遗传学功能磁共振成像(fMRI)与计算模型相结合,开始对细胞类型特异性的大规模脑回路功能和功能障碍进行定量参数化。我们设想,这些发展将为未来的治疗发展铺平道路,这些治疗方法基于旨在直接恢复大脑功能的系统工程方法。

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