Individual-specific features of brain systems identified with resting state functional correlations.
Individual-specific features of brain systems identified with resting state functional correlations.
复制标题
DOI：
10.1016/j.neuroimage.2016.08.032
复制
发表时间：
2017-02-01
期刊：
影响因子：
5.7
通讯作者：
Petersen SE
中科院分区：
文献类型：
--
作者：
Gordon EM;Laumann TO;Adeyemo B;Gilmore AW;Nelson SM;Dosenbach NUF;Petersen SE
最近的研究在描述人类皮层的大规模系统级组织方面取得了重要进展，通过分析跨组受试者的平均功能磁共振成像（fMRI）数据。然而，新的发现表明，个体的皮层系统在拓扑结构上是复杂的，包含了在群体平均数据集中无法观察到的小而可靠的特征，部分原因是这些特征在皮层片上的位置存在可变性。之前的工作只报道了这些特定于个体的系统特征的具体例子；到目前为止，这些特征还没有得到全面的描述。在这里，我们使用fMRI在三个大型跨学科数据集和一个高度采样的主题数据集中识别个体受试者的皮质系统特征。我们观察到以前没有被表征的系统特征，但是1)在单个个体的许多扫描会话中可靠地检测到，2)可以在许多个体中匹配。总的来说，我们确定了43个与组平均系统不匹配的系统特征，但它们在三个独立的数据集中复制。我们描述了每个非类群特征的大小和空间分布。我们进一步观察到，一些个体缺少特定的系统特征，这表明皮质区域的系统成员存在个体差异。最后，我们发现个体特定的系统特征可以用来增加主题与主题之间的相似性。总之，这项工作确定了人类大脑系统的个体特异性特征，从而提供了以前未被观察到的大脑系统特征的目录，并为详细检查个体大脑连接奠定了基础。
Recent work has made important advances in describing the large-scale systems-level organization of human cortex by analyzing functional magnetic resonance imaging (fMRI) data averaged across groups of subjects. However, new findings have emerged suggesting that individuals’ cortical systems are topologically complex, containing small but reliable features that cannot be observed in group-averaged datasets, due in part to variability in the position of such features along the cortical sheet. This previous work has reported only specific examples of these individual-specific system features; to date, such features have not been comprehensively described. Here we used fMRI to identify cortical system features in individual subjects within three large cross-subject datasets and one highly sampled within-subject dataset. We observed system features that have not been previously characterized, but 1) were reliably detected across many scanning sessions within a single individual, and 2) could be matched across many individuals. In total, we identified forty-three system features that did not match group-average systems, but that replicated across three independent datasets. We described the size and spatial distribution of each non-group feature. We further observed that some individuals were missing specific system features, suggesting individual differences in the system membership of cortical regions. Finally, we found that individual-specific system features could be used to increase subject-to-subject similarity. Together, this work identifies individual-specific features of human brain systems, thus providing a catalog of previously unobserved brain system features and laying the foundation for detailed examinations of brain connectivity in individuals.