Jaspers Ellen, Balsters Joshua H, Kassraian Fard Pegah, Mantini Dante, Wenderoth Nicole
Department of Health Sciences and Technology, Neural Control of Movement Lab, ETH Zurich, Switzerland.
Department of Kinesiology, Movement Control & Neuroplasticity Research Group, KU Leuven, Belgium.
Hum Brain Mapp. 2017 Mar;38(3):1478-1491. doi: 10.1002/hbm.23466. Epub 2016 Nov 12.
Over the last decade, structure-function relationships have begun to encompass networks of brain areas rather than individual structures. For example, corticostriatal circuits have been associated with sensorimotor, limbic, and cognitive information processing, and damage to these circuits has been shown to produce unique behavioral outcomes in Autism, Parkinson's Disease, Schizophrenia and healthy ageing. However, it remains an open question how abnormal or absent connectivity can be detected at the individual level. Here, we provide a method for clustering gross morphological structures into subregions with unique functional connectivity fingerprints, and generate network probability maps usable as a baseline to compare individual cases against. We used connectivity metrics derived from resting-state fMRI (N = 100), in conjunction with hierarchical clustering methods, to parcellate the striatum into functionally distinct clusters. We identified three highly reproducible striatal subregions, across both hemispheres and in an independent replication dataset (N = 100) (dice-similarity values 0.40-1.00). Each striatal seed region resulted in a highly reproducible distinct connectivity fingerprint: the putamen showed predominant connectivity with cortical and cerebellar sensorimotor and language processing areas; the ventromedial striatum cluster had a distinct limbic connectivity pattern; the caudate showed predominant connectivity with the thalamus, frontal and occipital areas, and the cerebellum. Our corticostriatal probability maps agree with existing connectivity data in humans and non-human primates, and showed a high degree of replication. We believe that these maps offer an efficient tool to further advance hypothesis driven research and provide important guidance when investigating deviant connectivity in neurological patient populations suffering from e.g., stroke or cerebral palsy. Hum Brain Mapp 38:1478-1491, 2017. © 2016 Wiley Periodicals, Inc.
在过去十年中，结构-功能关系已开始涵盖脑区网络而非单个结构。例如，皮质-纹状体回路已被证明与感觉运动、边缘系统和认知信息处理相关，并且这些回路受损已被证实会在自闭症、帕金森病、精神分裂症以及正常衰老过程中产生独特的行为结果。然而，如何在个体层面检测到异常或缺失的连接性仍是一个悬而未决的问题。在此，我们提供了一种方法，可将大体形态结构聚类为具有独特功能连接指纹的子区域，并生成可用作基线的网络概率图，以便与个体病例进行比较。我们使用从静息态功能磁共振成像（N = 100）得出的连接性指标，结合层次聚类方法，将纹状体分割为功能上不同的簇。我们在两个半球以及一个独立的复制数据集（N = 100）中识别出三个高度可重复的纹状体子区域（骰子相似性值为0.40 - 1.00）。每个纹状体种子区域都产生了高度可重复的独特连接指纹：壳核与皮质和小脑感觉运动及语言处理区域显示出主要连接；腹内侧纹状体簇具有独特的边缘系统连接模式；尾状核与丘脑、额叶和枕叶区域以及小脑显示出主要连接。我们的皮质-纹状体概率图与人类和非人类灵长类动物现有的连接性数据一致，并显示出高度的重复性。我们相信，这些图谱为进一步推进假设驱动的研究提供了一个有效的工具，并在研究患有例如中风或脑瘫等神经系统疾病的患者群体中的异常连接性时提供重要指导。《人类大脑图谱》38:1478 - 1491, 2017。© 2016威利期刊公司
