five

Layer-Specific fMRI Reflects Different Neuronal Computations at Different Depths in Human V1

收藏
Figshare2016-01-19 更新2026-04-29 收录
下载链接:
https://figshare.com/articles/dataset/Layer_Specific_fMRI_Reflects_Different_Neuronal_Computations_at_Different_Depths_in_Human_V1/127415
下载链接
链接失效反馈
官方服务:
资源简介:
Recent work has established that cerebral blood flow is regulated at a spatial scale that can be resolved by high field fMRI to show cortical columns in humans. While cortical columns represent a cluster of neurons with similar response properties (spanning from the pial surface to the white matter), important information regarding neuronal interactions and computational processes is also contained within a single column, distributed across the six cortical lamina. A basic understanding of underlying neuronal circuitry or computations may be revealed through investigations of the distribution of neural responses at different cortical depths. In this study, we used T2-weighted imaging with 0.7 mm (isotropic) resolution to measure fMRI responses at different depths in the gray matter while human subjects observed images with either recognizable or scrambled (physically impossible) objects. Intact and scrambled images were partially occluded, resulting in clusters of activity distributed across primary visual cortex. A subset of the identified clusters of voxels showed a preference for scrambled objects over intact; in these clusters, the fMRI response in middle layers was stronger during the presentation of scrambled objects than during the presentation of intact objects. A second experiment, using stimuli targeted at either the magnocellular or the parvocellular visual pathway, shows that laminar profiles in response to parvocellular-targeted stimuli peak in more superficial layers. These findings provide new evidence for the differential sensitivity of high-field fMRI to modulations of the neural responses at different cortical depths.
创建时间:
2016-01-19
5,000+
优质数据集
54 个
任务类型
进入经典数据集
二维码
社区交流群

面向社区/商业的数据集话题

二维码
科研交流群

面向高校/科研机构的开源数据集话题

数据驱动未来

携手共赢发展

商业合作