Journal Article
Research Support, Non-U.S. Gov't
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Brain morphometry predicts individual creative potential and the ability to combine remote ideas.

For complex mental functions such as creative thinking, inter-individual variability is useful to better understand the underlying cognitive components and brain anatomy. Associative theories propose that creative individuals have flexible semantic associations, which allows remote elements to be formed into new combinations. However, the structural brain variability associated with the ability to combine remote associates has not been explored. To address this question, we performed a voxel-based morphometry (VBM) study and explored the anatomical connectivity of significant regions. We developed a Remote Combination Association Task adapted from Mednick's test, in which subjects had to find a solution word related to three cue words presented to them. In our adaptation of the task, we used free association norms to quantify the associative distance between the cue words and solution words, and we varied this distance. The tendency to solve the task with insight and the ability to evaluate the appropriateness of a proposed solution were also analysed. Fifty-four healthy volunteers performed this task and underwent a structural MRI. Structure-function relationships were analysed using regression models between grey matter (GM) volume and task performance. Significant clusters were mapped onto an atlas of white matter (WM) tracts. The ability to solve the task, which depended on the associative distance of the solution word, was associated with structural variation in the left rostrolateral prefrontal and posterior parietal regions; the left rostral prefrontal region was connected to distant regions through long-range pathways. By using a creative combination task in which the semantic distance between words varied, we revealed a brain network centred on the left frontal pole that appears to support the ability to combine information in new ways by bridging the semantic distance between pieces of information.

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