Tagamets M A, Horwitz B
Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD 21228, USA.
Brain Res Bull. 2001 Feb;54(3):267-73. doi: 10.1016/s0361-9230(00)00435-4.
Human brain imaging methods such as postiron emission tomography and functional magnetic resonance imaging have recently achieved widespread use in the study of both normal cognitive processes and neurological disorders. While many of these studies have begun to yield important insights into human brain function, the relationship between these measurements and the underlying neuronal activity is still not well understood. One open question is how neuronal inhibition is reflected in these imaging results. In this paper, we describe how large-scale modeling can be used to address this question. Specifically, we identify three factors that may play a role in how inhibition affects imaging results: (1) local connectivity; (2) context; and (3) type of inhibitory connection. Simulation results are presented that show how the interaction among these three factors can explain seemingly contradictory experimental results. The modeling suggests that neuronal inhibition can raise brain imaging measures if there is either low local excitatory recurrence or if the region is not otherwise being driven by excitation. Conversely, with high recurrence or actively driven excitation, inhibition can lower observed values.
诸如正电子发射断层扫描和功能磁共振成像等人类脑成像方法最近在正常认知过程和神经疾病的研究中得到了广泛应用。虽然其中许多研究已开始对人类脑功能产生重要见解,但这些测量与潜在神经元活动之间的关系仍未得到充分理解。一个悬而未决的问题是神经元抑制如何在这些成像结果中体现。在本文中,我们描述了如何使用大规模建模来解决这个问题。具体而言,我们确定了三个可能在抑制如何影响成像结果方面发挥作用的因素:(1)局部连接性;(2)背景;(3)抑制性连接的类型。给出的模拟结果表明了这三个因素之间的相互作用如何能够解释看似矛盾的实验结果。该建模表明,如果局部兴奋性递归较低或者该区域没有受到其他兴奋驱动,神经元抑制会提高脑成像测量值。相反,在高递归或活跃驱动的兴奋情况下,抑制会降低观测值。