Biophysical Modeling of Large-Scale Brain Dynamics and Applications for Computational Psychiatry.
Biophysical Modeling of Large-Scale Brain Dynamics and Applications for Computational Psychiatry.
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DOI：
10.1016/j.bpsc.2018.07.004
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发表时间：
2018-09
期刊：
影响因子：
--
通讯作者：
Anticevic A
中科院分区：
文献类型：
--
作者：
Murray JD;Demirtaş M;Anticevic A
非侵入性神经成像已经彻底改变了人类大脑的组织，以及它的结构和功能是如何在精神障碍中改变的研究。一个关键的解释差距在于我们对系统水平的神经成像生物标记物如何从与疾病状态相关的潜在突触水平的扰动中出现的机械性理解。我们描述了一种新兴的计算精神病学方法，利用基于生物物理学的大规模脑动力学计算模型，以及它们与临床和药物神经成像的潜在集成。特别是，我们关注神经电路模型，它描述了在静息状态功能磁共振成像中观察到的功能连接模式是如何从神经动力学中出现的，这些神经动力学由区域间的相互作用通过定义长期投影的潜在结构连接来塑造。我们强调了局部电路生理动力学与结构连通性相结合在形成新出现的功能连通性方面的重要性。此外，影响大规模动力学的跨脑区域局部电路属性的异质性，可能对建模全脑现象以及精神障碍和药物操作中的变化至关重要。最后，我们讨论了未来模型开发和生物物理扩展的重要方向，这将扩大它们将临床神经成像与神经生物学机制联系起来的效用。
Noninvasive neuroimaging has revolutionized the study of the organization of the human brain and how its structure and function are altered in psychiatric disorders. A critical explanatory gap lies in our mechanistic understanding of how systems-level neuroimaging biomarkers emerge from underlying synaptic-level perturbations associated with a disease state. We describe an emerging computational psychiatry approach leveraging biophysically based computational models of large-scale brain dynamics and their potential integration with clinical and pharmacological neuroimaging. In particular, we focus on neural circuit models, which describe how patterns of functional connectivity observed in resting-state functional magnetic resonance imaging emerge from neural dynamics shaped by inter-areal interactions through underlying structural connectivity defining long-range projections. We highlight the importance of local circuit physiological dynamics, in combination with structural connectivity, in shaping the emergent functional connectivity. Furthermore, heterogeneity of local circuit properties across brain areas, which impacts large-scale dynamics, may be critical for modeling whole-brain phenomena and alterations in psychiatric disorders and pharmacological manipulation. Finally, we discuss important directions for future model development and biophysical extensions, which will expand their utility to link clinical neuroimaging to neurobiological mechanisms.
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