Department of Experimental Psychology, University of Oxford, Anna Watts Building, Radcliffe Observatory Quarter, Woodstock Rd, Oxford, OX2 6HG, UK.
Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Canada.
Brain Struct Funct. 2022 Dec;227(9):3027-3041. doi: 10.1007/s00429-022-02570-2. Epub 2022 Oct 7.
Lesion research classically maps behavioral effects of focal damage to the directly injured brain region. However, such damage can also have distant effects that can be assessed with modern imaging methods. Furthermore, the combination and comparison of imaging methods in a lesion model may shed light on the biological basis of structural and functional networks in the healthy brain. We characterized network organization assessed with multiple MRI imaging modalities in 13 patients with chronic focal damage affecting either superior or inferior frontal gyrus (SFG, IFG) and 18 demographically matched healthy Controls. We first defined structural and functional network parameters in Controls and then investigated grey matter (GM) and white matter (WM) differences between patients and Controls. Finally, we examined the differences in functional coupling to large-scale resting state networks (RSNs). The results suggest lesions are associated with widespread within-network GM loss at distal sites, yet leave WM and RSNs relatively preserved. Lesions to either prefrontal region also had a similar relative level of impact on structural and functional networks. The findings provide initial evidence for causal contributions of specific prefrontal regions to brain networks in humans that will ultimately help to refine models of the human brain.
病灶研究经典地将局灶性损伤对直接受损脑区的行为效应映射出来。然而，这种损伤也可能产生远距离的影响，可以用现代成像方法来评估。此外，在病灶模型中结合和比较成像方法可以揭示健康大脑中结构和功能网络的生物学基础。我们使用多种 MRI 成像方式来描述 13 名慢性局灶性损伤患者（影响额上回或额下回）和 18 名年龄匹配的健康对照组的网络组织。我们首先在对照组中定义了结构和功能网络参数，然后研究了患者和对照组之间的灰质（GM）和白质（WM）差异。最后，我们研究了与大尺度静息态网络（RSNs）的功能耦合的差异。结果表明，病灶与远端部位的广泛内网络 GM 损失有关，但 WM 和 RSN 相对保留。前额叶区域的损伤对结构和功能网络也有类似的相对影响。这些发现为特定前额叶区域对人类大脑网络的因果贡献提供了初步证据，最终有助于完善人类大脑模型。
