The parietal cortex (PC) is implicated in a confusing myriad of different cognitive processes/tasks. Consequently, understanding the nature and organization of the core underlying neurocomputations is challenging. According to the Parietal Unified Connectivity-biased Computation model, two properties underpin PC function and organization. Firstly, PC is a multidomain, context-dependent buffer of time- and space-varying input, the function of which, over time, becomes sensitive to the statistical temporal/spatial structure of events. Secondly, over and above this core buffering computation, differences in long-range connectivity will generate graded variations in task engagement across subregions. The current study tested these hypotheses using a group independent component analysis technique with two independent functional magnetic resonance imaging datasets (task and resting state data). Three functional organizational principles were revealed: Factor 1, inferior PC was sensitive to the statistical structure of sequences for all stimulus types (pictures, sentences, numbers); Factor 2, a dorsal–ventral variation in generally task-positive versus task-negative (variable) engagement; and Factor 3, an anterior–posterior dimension in inferior PC reflecting different engagement in verbal versus visual tasks, respectively. Together, the data suggest that the core neurocomputation implemented by PC is common across domains, with graded task engagement across regions reflecting variations in the connectivity of task-specific networks that interact with PC.

中文翻译：

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下顶叶皮层功能组织的总体原则和维度。

顶叶皮层 (PC) 涉及无数令人困惑的不同认知过程/任务。因此，了解核心基础神经计算的性质和组织具有挑战性。根据 Parietal Unified Connectivity-biased Computation 模型，有两个属性支持 PC 功能和组织。首先，PC 是一个多域、上下文相关的时空变化输入缓冲区，其功能随着时间的推移变得对事件的统计时间/空间结构敏感。其次，除了这个核心缓冲计算之外，远程连接的差异将产生跨子区域的任务参与的分级变化。当前的研究使用组独立成分分析技术和两个独立的功能磁共振成像数据集（任务和静息状态数据）测试了这些假设。揭示了三个功能性组织原则：因素 1，劣等 PC 对所有刺激类型（图片、句子、数字）的序列统计结构敏感；因素 2，通常任务积极与任务消极（可变）参与的背腹变化；和因素 3，下层 PC 的前后维度，分别反映了对语言和视觉任务的不同参与。总之，数据表明 PC 实现的核心神经计算在跨域中是通用的，