Correspondent Functional Topography of the Human Left Inferior Parietal Lobule at Rest and Under Task Revealed Using Resting‐State fMRI and Coactivation Based Parcellation

Abstract

The human left inferior parietal lobule (LIPL) plays a pivotal role in many cognitive functions and is an important node in the default mode network (DMN). Although many previous studies have proposed different parcellation schemes for the LIPL, the detailed functional organization of the LIPL and the exact correspondence between the DMN and LIPL subregions remain unclear. Mounting evidence indicates that spontaneous fluctuations in the brain are strongly associated with cognitive performance at the behavioral level. However, whether a consistent functional topographic organization of the LIPL during rest and under task can be revealed remains unknown. Here, they used resting-state functional connectivity (RSFC) and task-related coactivation patterns separately to parcellate the LIPL and identified seven subregions. Four subregions were located in the supramarginal gyrus (SMG) and three subregions were located in the angular gyrus (AG). The subregion-specific networks and functional characterization revealed that the four anterior subregions were found to be primarily involved in sensorimotor processing, movement imagination and inhibitory control, audition perception and speech processing, and social cognition, whereas the three posterior subregions were mainly involved in episodic memory, semantic processing, and spatial cognition. The results revealed a detailed functional organization of the LIPL and suggested that the LIPL is a functionally heterogeneous area. In addition, the present study demonstrated that the functional architecture of the LIPL during rest corresponds with that found in task processing. Hum Brain Mapp 38:1659-1675, 2017. © 2017 Wiley Periodicals, Inc.

Specifying the brain anatomy underlying temporo-parietal junction activations for theory of mind: A review using probabilistic atlases from different imaging modalities

“…With respect to connectivity, studies showed that single‐subject connectivity patterns from diffusion weighted imaging [Osher et al, ; Saygin et al, ] and resting‐state fMRI [Tavor et al, ] predict if an area is activated during task‐based fMRI on a voxel‐by‐voxel level; this finding that was replicated across several cognitive (task) domains. Connectivity‐based parcellations have been found recently not only within the TPJ [Bzdok et al, ; Mars et al, ], but also within several other areas of the social brain, such as the medial prefrontal cortex [Bzdok et al, ; Eickhoff et al, ; Neubert et al, ; Sallet et al, ], posterior medial cortex/precuneus [Bzdok et al, ; Margulies et al, ], and the inferior parietal lobule [Bzdok et al, ; Wang, et al, ].…”

Functional Connectivity Between Anterior Insula and Key Nodes of Frontoparietal Executive Control and Salience Networks Distinguish Bipolar Depression From Unipolar Depression and Healthy Control Subjects

“…The left IPL has been identified as an important node in an integrative multi-network system and plays a major role in emotion-cognition integration (58-60). A recent meta-analysis identified functional roles in executive control, reasoning, working memory, and behavioral response inhibition for a left IPL region overlapping with the a priori ROI in our study (58).…”

“…Similarly, Crichtley et al (64) found significantly greater engagement of the left IPL when participants simultaneously monitored their own heartbeat (interoception) and judged the asynchronous timing of an audio tone (a cognitive task). In healthy subjects, this left IPL region shares strong functional connections to AI, VLPFC, and dmPFC regions of the SN, and plays an important role in regulating the endogenous stimuli-driven ventral attention network (58, 62). Our data highlight the connectivity of the left IPL with the AI, a primary node for interoceptive information processing.…”

Specific Functional Connectivity Patterns of Middle Temporal Gyrus Subregions in Children and Adults with Autism Spectrum Disorder

“…To determine the final cluster numbers, we used the percentage of voxels not grouped with the parent as an index. This measure is related to the hierarchy index [Kahnt, Chang, Park, Heinzle, & Haynes, ; Wang, Xie, et al, ] and corresponds to the percentage of voxels that are not present in the hierarchy, k , compared to the previous k – 1 solution. Optimal solutions for a given k cluster parcellation were those wherein the percentage of lost voxels was below the median across all possible solutions ( k = 2, 3, …, 9 in our present study), where the respective clustering step resulted in a local minimum and/or the following clustering‐step featured a maximum in the percentage of lost (hierarchically inconsistent) voxels.…”