Spectral partitioning identifies individual heterogeneity in the functional network topography of ventral and anterior medial prefrontal cortex
Spectral partitioning identifies individual heterogeneity in the functional network topography of ventral and anterior medial prefrontal cortex
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DOI：
10.1016/j.neuroimage.2019.116305
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发表时间：
2019-05
期刊：
影响因子：
5.7
通讯作者：
Claudio A. Toro-Serey;S. Tobyne;Joseph T. McGuire
中科院分区：
文献类型：
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作者：
Claudio A. Toro-Serey;S. Tobyne;Joseph T. McGuire
人类内侧前额叶皮层（mPFC）和后扣带皮层（PCC）的区域是默认网络（DN）的一部分，并且还涉及从自传体记忆到主观评价的各种认知功能。我们解释任务相关效应与DN区域的明显共定位的能力受到对mPFC/PCC功能组织中个体水平异质性的有限理解的限制。在这里，我们使用皮质表面为基础的荟萃分析，以确定一个包裹在人类PCC，更强烈地与DN比估值的影响。然后，我们使用静息状态的功能磁共振成像数据和数据驱动的网络分析算法，频谱分割，分区mPFC和PCC为“DN”和“非DN”的细分在个人参与者（n = 100从人类连接组项目）。谱分割算法确定了个体水平的皮质细分，个体之间差异显着，特别是在mPFC，是可靠的测试/重测数据集。我们的研究结果指向新的策略，以评估不同的认知功能是否从事共同或不同的mPFC子区域在个人层面上。
Regions of human medial prefrontal cortex (mPFC) and posterior cingulate cortex (PCC) are part of the default network (DN), and additionally are implicated in diverse cognitive functions ranging from autobiographical memory to subjective valuation. Our ability to interpret the apparent co-localization of task-related effects with DN-regions is constrained by a limited understanding of the individual-level heterogeneity in mPFC/PCC functional organization. Here we used cortical surface-based meta-analysis to identify a parcel in human PCC that was more strongly associated with the DN than with valuation effects. We then used resting-state fMRI data and a data-driven network analysis algorithm, spectral partitioning, to partition mPFC and PCC into “DN” and “non-DN” subdivisions in individual participants (n = 100 from the Human Connectome Project). The spectral partitioning algorithm identified individual-level cortical subdivisions that varied markedly across individuals, especially in mPFC, and were reliable across test/retest datasets. Our results point toward new strategies for assessing whether distinct cognitive functions engage common or distinct mPFC subregions at the individual level.