| Literature DB >> 25462801 |
Danilo Bzdok1, Adrian Heeger2, Robert Langner1, Angela R Laird3, Peter T Fox4, Nicola Palomero-Gallagher5, Brent A Vogt6, Karl Zilles7, Simon B Eickhoff8.
Abstract
The posterior medial cortex (PMC) is particularly poorly understood. Its neural activity changes have been related to highly disparate mental processes. We therefore investigated PMC properties with a data-driven exploratory approach. First, we subdivided the PMC by whole-brain coactivation profiles. Second, functional connectivity of the ensuing PMC regions was compared by task-constrained meta-analytic coactivation mapping (MACM) and task-unconstrained resting-state correlations (RSFC). Third, PMC regions were functionally described by forward/reverse functional inference. A precuneal cluster was mostly connected to the intraparietal sulcus, frontal eye fields, and right temporo-parietal junction; associated with attention and motor tasks. A ventral posterior cingulate cortex (PCC) cluster was mostly connected to the ventromedial prefrontal cortex and middle left inferior parietal cortex (IPC); associated with facial appraisal and language tasks. A dorsal PCC cluster was mostly connected to the dorsomedial prefrontal cortex, anterior/posterior IPC, posterior midcingulate cortex, and left dorsolateral prefrontal cortex; associated with delay discounting. A cluster in the retrosplenial cortex was mostly connected to the anterior thalamus and hippocampus. Furthermore, all PMC clusters were congruently coupled with the default mode network according to task-unconstrained but not task-constrained connectivity. We thus identified distinct regions in the PMC and characterized their neural networks and functional implications.Entities:
Keywords: Connectivity-based parcellation; Default mode network; Functional decoding; Parietal lobe; Posterior cingulate cortex; Retrosplenial cortex; Statistical learning
Mesh:
Year: 2014 PMID: 25462801 PMCID: PMC4780672 DOI: 10.1016/j.neuroimage.2014.11.009
Source DB: PubMed Journal: Neuroimage ISSN: 1053-8119 Impact factor: 6.556