Hughes Nicholas J, Hunt Jonathan J, Cloherty Shaun L, Ibbotson Michael R, Sengpiel Frank, Goodhill Geoffrey J
Queensland Brain Institute, The University of Queensland, St Lucia, Queensland 4072, Australia.
National Vision Research Institute, Australian College of Optometry, Carlton, Victoria 3053, Australia; Department of Optometry and Vision Science, University of Melbourne, Parkville, Victoria 3010, Australia.
Neuroimage. 2014 Jul 15;95:305-19. doi: 10.1016/j.neuroimage.2014.03.031. Epub 2014 Mar 20.
An important example of brain plasticity is the change in the structure of the orientation map in mammalian primary visual cortex in response to a visual environment consisting of stripes of one orientation. In principle there are many different ways in which the structure of a normal map could change to accommodate increased preference for one orientation. However, until now these changes have been characterised only by the relative sizes of the areas of primary visual cortex representing different orientations. Here we extend to the stripe-reared case a recently proposed Bayesian method for reconstructing orientation maps from intrinsic signal optical imaging data. We first formulated a suitable prior for the stripe-reared case, and developed an efficient method for maximising the marginal likelihood of the model in order to determine the optimal parameters. We then applied this to a set of orientation maps from normal and stripe-reared cats. This analysis revealed that several parameters of overall map structure, specifically the difference between wavelength, scaling and mean of the two vector components of maps, changed in response to stripe-rearing, which together give a more nuanced assessment of the effect of rearing condition on map structure than previous measures. Overall this work expands our understanding of the effects of the environment on brain structure.
大脑可塑性的一个重要例子是，哺乳动物初级视觉皮层中定向图谱的结构会因由单一方向条纹组成的视觉环境而发生变化。原则上，正常图谱的结构有许多不同的变化方式，以适应对单一方向增加的偏好。然而，到目前为止，这些变化仅通过代表不同方向的初级视觉皮层区域的相对大小来表征。在这里，我们将一种最近提出的用于从内在信号光学成像数据重建定向图谱的贝叶斯方法扩展到条纹饲养的情况。我们首先为条纹饲养的情况制定了合适的先验，并开发了一种有效的方法来最大化模型的边际似然性，以确定最佳参数。然后我们将此应用于一组来自正常猫和条纹饲养猫的定向图谱。该分析表明，总体图谱结构的几个参数，特别是图谱两个向量分量的波长、缩放和均值之间的差异，因条纹饲养而发生变化，这比以前的测量方法更细致入微地评估了饲养条件对图谱结构的影响。总体而言，这项工作扩展了我们对环境对大脑结构影响的理解。
