Overarching Principles and Dimensions of the Functional Organization in the Inferior Parietal Cortex

Abstract

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.

The convergence and divergence of episodic and semantic functions across lateral parietal cortex

“…By exploring both the variation in functional response and connectivity profiles across the LPC, the current study found evidence in favour of the neurocomputational principles embraced by the PUCC model (1,10,35). A key notion is that the expressed functions of a cortical region will reflect the combination of the local computation and its pattern of connectivity.…”

“…In contrast the anterior vLPC (PGa) showed structural connectivity with temporal lobe language processing areas (5,30,31). Indeed, the same region has been shown to be positively engaged by tasks that involve sentence-level processing (1,35,36). Whereas, the posterior vLPC (posterior PGp) connected with the occipital lobe and medial parietal areas associated with visual attention (2,34), and this same region has been shown to be positively engaged by a picture-sequence task (35).…”

“…Indeed, the same region has been shown to be positively engaged by tasks that involve sentence-level processing (1,35,36). Whereas, the posterior vLPC (posterior PGp) connected with the occipital lobe and medial parietal areas associated with visual attention (2,34), and this same region has been shown to be positively engaged by a picture-sequence task (35). Together these results fit with the PUCC model that suggests a graded change in the functional engagement of vLPC based on graded changes in the underlying structurally connectivity of the network (see Figure 4 for a schematic model).…”

“…When engaged in a goal-oriented task, on-going automatic information accumulation in VPC subregions would presumably be unhelpful/disruptive unless this input is necessary for task performance. In this case, activation in the irrelevant VPC subregions would be suppressed/deactivated (35). This would be especially needed with increasing demands on task performance, thereby explaining the common observed pattern of anticorrelated activation modulated by task difficulty (18).…”

“…Different functions across the VPC itself should emerge given that there are graded differences in connectivity of VPC subregions to distinct neural networks (Fig. 4.D) (1,35,61,62).…”