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The ability to jointly consider several structured mental representations, or relations, is fundamental to human cognition. Prior studies have consistently linked this capacity for relational integration to rostrolateral prefrontal cortex (RLPFC). Here, we sought to test two competing hypotheses: (1) RLPFC processes relations in a domain-general manner, interacting with different brain regions as a function of the type of lower-level relations that must be integrated; or (2) A dorsal-ventral gradient exists within RLPFC, such that relational integration in the visuospatial domain involves relatively more dorsal RLPFC than integration in the semantic domain. To this end, we examined patterns of fMRI activation and functional connectivity during performance of visuospatial and semantic variants of a relational matching task. Across the two task variants, the regions that were most strongly engaged during relational comparison were left RLPFC and left intraparietal sulcus (IPS). Within left RLPFC, there was considerable overlap in activation for the semantic and visuospatial tasks. However, visuospatial task activation peaks were located dorsally to the semantic task peaks. In addition, RLPFC exhibited differential functional connectivity on the two tasks, interacting with different brain regions as a function of the type of relations being compared. While neurons throughout RLPFC may share the function of integrating diverse inputs, individual RLPFC neurons may have privileged access to particular representations depending on their anatomical inputs, organized along a dorsal-ventral gradient. Thus, RLPFC is well-positioned as a locus of abstraction from concrete, domain-specific details to the general principles and rules that enable higher-level cognition.
Pubmed ID: 21834102
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A toolbox for SPM which provides routines for region of interest analysis. Features include region of interest definition, combination of regions of interest with simple algebra, extraction of data for regions with and without SPM preprocessing (scaling, filtering), and statistical analyses of ROI data using the SPM statistics machinery.
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