Park Hyunjin, Park Yeong-Hun, Cha Jungho, Seo Sang Won, Na Duk L, Lee Jong-Min
School of Electronic and Electrical Engineering, Sungkyunkwan University, Suwon, Korea.
Center for Neuroscience Imaging Research, Institute for Basic Science, Suwon, Korea.
PLoS One. 2017 Mar 22;12(3):e0171803. doi: 10.1371/journal.pone.0171803. eCollection 2017.
Parcellation of the human cortex has important implications in neuroscience. Parcellation is often a crucial requirement before meaningful regional analysis can occur. The human cortex can be parcellated into distinct regions based on structural features, such as gyri and sulci. Brain network patterns in a given region with respect to its neighbors, known as connectional fingerprints, can be used to parcellate the cortex. Distinct imaging modalities might provide complementary information for brain parcellation. Here, we established functional connectivity with time series data from functional MRI (fMRI) combined with a correlation map of cortical thickness obtained from T1-weighted MRI. We aimed to extend the previous study, which parcellated the medial frontal cortex (MFC) using functional connectivity, and to test the value of additional information regarding cortical thickness. Two types of network information were used to parcellate the MFC into two sub-regions with spectral and Ward's clustering approaches. The MFC region was defined using manual delineation based on in-house data (n = 12). Parcellation was applied to independent large-scale data obtained from the Human Connectome Project (HCP, n = 248). Agreement between parcellation using fMRI- and thickness-driven connectivity yielded dice coefficient overlaps of 0.74 (Ward's clustering) and 0.54 (spectral clustering). We also explored whole brain connectivity using the MFC sub-regions as seed regions based on these two types of information. The results of whole brain connectivity analyses were also consistent for both types of information. We observed that an inter-regional correlation map derived from cortical thickness strongly reflected the underlying functional connectivity of MFC region.
人类大脑皮层的分区在神经科学中具有重要意义。分区通常是进行有意义的区域分析之前的关键要求。人类大脑皮层可以根据诸如脑回和脑沟等结构特征被划分为不同的区域。给定区域相对于其相邻区域的脑网络模式,即所谓的连接指纹,可用于对皮层进行分区。不同的成像方式可能为大脑分区提供补充信息。在这里,我们利用功能磁共振成像(fMRI)的时间序列数据建立功能连接,并结合从T1加权磁共振成像获得的皮质厚度相关图。我们旨在扩展先前使用功能连接对内侧额叶皮层(MFC)进行分区的研究,并测试有关皮质厚度的额外信息的价值。使用两种类型的网络信息,通过光谱聚类和沃德聚类方法将MFC划分为两个子区域。MFC区域是根据内部数据(n = 12)通过手动描绘来定义的。分区应用于从人类连接体项目(HCP,n = 248)获得的独立大规模数据。使用fMRI和厚度驱动的连接进行分区之间的一致性产生了0.74(沃德聚类)和0.54(光谱聚类)的骰子系数重叠。我们还基于这两种类型的信息,以MFC子区域作为种子区域探索了全脑连接性。全脑连接性分析的结果对于这两种类型的信息也是一致的。我们观察到,从皮质厚度得出的区域间相关图强烈反映了MFC区域潜在的功能连接性。