Publication details for Dr Jason ConnollyConnolly, J.D., Kentridge, R.W. & Cavina-Pratesi, C. (2016). Coding of attention across the human intraparietal sulcus. Experimental Brain Research 234(3): 917-930.
- Publication type: Journal Article
- ISSN/ISBN: 0014-4819, 1432-1106
- DOI: 10.1007/s00221-015-4507-2
- Keywords: Intention, Attention, Functional magnetic resonance imaging, Posterior parietal cortex, Reaching, Eye movements.
- Further publication details on publisher web site
- Durham Research Online (DRO) - may include full text
Author(s) from Durham
There has been concentrated debate over four decades as to whether or not the nonhuman primate parietal cortex codes for intention or attention. In nonhuman primates, certain studies report results consistent with an intentional role, whereas others provide support for coding of visual-spatial attention. Until now, no one has yet directly contrasted an established motor “intention” paradigm with a verified “attention” paradigm within the same protocol. This debate has continued in both the nonhuman primate and healthy human brain and is subsequently timely. We incorporated both paradigms across two distinct temporal epochs within a whole-parietal slow event-related human functional magnetic resonance imaging experiment. This enabled us to examine whether or not one paradigm proves more effective at driving the neural response across three intraparietal areas. As participants performed saccadic eye and/or pointing tasks, discrete event-related components with dissociable responses were elicited in distinct sub-regions of human parietal cortex. Critically, the posterior intraparietal area showed robust activity consistent with attention (no intention planning). The most contentious area in the literature, the middle intraparietal area produced activation patterns that further reinforce attention coding in human parietal cortex. Finally, the anterior intraparietal area showed the same pattern. Therefore, distributed coding of attention is relatively more pronounced across the two computations within human parietal cortex.