Imaging retinotopic maps in the human brain.

Imaging retinotopic maps in the human brain.
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DOI:
10.1016/j.visres.2010.08.004
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发表时间:
2011-04-13
期刊:
影响因子:
1.8
通讯作者:
Winawer, Jonathan
Winawer, Jonathan
中科院分区:
心理学3区
文献类型:
--
作者:
Wandell, Brian A.;Winawer, Jonathan

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25年前,视觉神经学家对人类视觉皮质中视网膜定位图的数量和组织知之甚少。功能磁共振成像(MRI)是一种测量大脑活动的非侵入性、空间分辨率的技术,它的出现为人类视网膜定位图提供了丰富的数据。就像非人类灵长类地图之间存在差异一样,人类地图也有自己独特的属性。在不到一小时的实验过程中,许多人体地图可以在单个受试者身上可靠地测量出来。测量的效率和视觉皮质中相对较大的功能MRI信号的幅度使得在个别受试者的特定地图内开发功能反应的量化模型成为可能。在过去的四分之一个世纪里,在测量一系列长度和时间尺度的人脑属性方面也取得了重大进展,包括白质通路、灰质和白质的宏观属性以及细胞和分子组织属性。我们希望在接下来的25年里,将有大量的工作旨在通过对视觉信号网络进行建模来整合这些数据。我们不知道这样的理论会是什么样子,但过去25年人类视网膜定位图的特征很可能是未来关于视觉计算的想法的重要组成部分。
A quarter-century ago visual neuroscientists had little information about the number and organization of retinotopic maps in human visual cortex. The advent of functional magnetic resonance imaging (MRI), a non-invasive, spatially-resolved technique for measuring brain activity, provided a wealth of data about human retinotopic maps. Just as there are differences amongst nonhuman primate maps, the human maps have their own unique properties. Many human maps can be measured reliably in individual subjects during experimental sessions lasting less than an hour. The efficiency of the measurements and the relatively large amplitude of functional MRI signals in visual cortex make it possible to develop quantitative models of functional responses within specific maps in individual subjects. During this last quarter century, there has also been significant progress in measuring properties of the human brain at a range of length and time scales, including white matter pathways, macroscopic properties of gray and white matter, and cellular and molecular tissue properties. We hope the next twenty-five years will see a great deal of work that aims to integrate these data by modeling the network of visual signals. We don’t know what such theories will look like, but the characterization of human retinotopic maps from the last twenty-five years is likely to be an important part of future ideas about visual computations.
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