46
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: not found
      • Article: not found

      Robust prediction of individual creative ability from brain functional connectivity

      Read this article at

      ScienceOpenPublisherPMC
      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          <p id="d10155045e317">People’s capacity to generate creative ideas is central to technological and cultural progress. Despite advances in the neuroscience of creativity, the field lacks clarity on whether a specific neural architecture distinguishes the highly creative brain. Using methods in network neuroscience, we modeled individual creative thinking ability as a function of variation in whole-brain functional connectivity. We identified a brain network associated with creative ability comprised of regions within default, salience, and executive systems—neural circuits that often work in opposition. Across four independent datasets, we show that a person’s capacity to generate original ideas can be reliably predicted from the strength of functional connectivity within this network, indicating that creative thinking ability is characterized by a distinct brain connectivity profile. </p><p class="first" id="d10155045e320">People’s ability to think creatively is a primary means of technological and cultural progress, yet the neural architecture of the highly creative brain remains largely undefined. Here, we employed a recently developed method in functional brain imaging analysis—connectome-based predictive modeling—to identify a brain network associated with high-creative ability, using functional magnetic resonance imaging (fMRI) data acquired from 163 participants engaged in a classic divergent thinking task. At the behavioral level, we found a strong correlation between creative thinking ability and self-reported creative behavior and accomplishment in the arts and sciences ( <i>r</i> = 0.54). At the neural level, we found a pattern of functional brain connectivity related to high-creative thinking ability consisting of frontal and parietal regions within default, salience, and executive brain systems. In a leave-one-out cross-validation analysis, we show that this neural model can reliably predict the creative quality of ideas generated by novel participants within the sample. Furthermore, in a series of external validation analyses using data from two independent task fMRI samples and a large task-free resting-state fMRI sample, we demonstrate robust prediction of individual creative thinking ability from the same pattern of brain connectivity. The findings thus reveal a whole-brain network associated with high-creative ability comprised of cortical hubs within default, salience, and executive systems—intrinsic functional networks that tend to work in opposition—suggesting that highly creative people are characterized by the ability to simultaneously engage these large-scale brain networks. </p>

          Related collections

          Most cited references23

          • Record: found
          • Abstract: found
          • Article: not found

          Groupwise whole-brain parcellation from resting-state fMRI data for network node identification.

          In this paper, we present a groupwise graph-theory-based parcellation approach to define nodes for network analysis. The application of network-theory-based analysis to extend the utility of functional MRI has recently received increased attention. Such analyses require first and foremost a reasonable definition of a set of nodes as input to the network analysis. To date many applications have used existing atlases based on cytoarchitecture, task-based fMRI activations, or anatomic delineations. A potential pitfall in using such atlases is that the mean timecourse of a node may not represent any of the constituent timecourses if different functional areas are included within a single node. The proposed approach involves a groupwise optimization that ensures functional homogeneity within each subunit and that these definitions are consistent at the group level. Parcellation reproducibility of each subunit is computed across multiple groups of healthy volunteers and is demonstrated to be high. Issues related to the selection of appropriate number of nodes in the brain are considered. Within typical parameters of fMRI resolution, parcellation results are shown for a total of 100, 200, and 300 subunits. Such parcellations may ultimately serve as a functional atlas for fMRI and as such three atlases at the 100-, 200- and 300-parcellation levels derived from 79 healthy normal volunteers are made freely available online along with tools to interface this atlas with SPM, BioImage Suite and other analysis packages. Copyright © 2013 Elsevier Inc. All rights reserved.
            Bookmark
            • Record: found
            • Abstract: found
            • Article: not found

            A neuromarker of sustained attention from whole-brain functional connectivity

            Although attention plays a ubiquitous role in perception and cognition, researchers lack a simple way to measure a person’s overall attentional abilities. Because behavioral measures are diverse and difficult to standardize, we pursued a neuromarker of an important aspect of attention, sustained attention, using functional magnetic resonance imaging. To this end, we identified functional brain networks whose strength during a sustained attention task predicted individual differences in performance. Models based on these networks generalized to previously unseen individuals, even predicting performance from resting-state connectivity alone. Furthermore, these same models predicted a clinical measure of attention—symptoms of attention deficit hyperactivity disorder—from resting-state connectivity in an independent sample of children and adolescents. These results demonstrate that whole-brain functional network strength provides a broadly applicable neuromarker of sustained attention.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Using connectome-based predictive modeling to predict individual behavior from brain connectivity

              This protocol describes how to develop linear models to predict individual behavior from brain connectivity data with proper cross-validation, and how to use an online tool to visualize the most predictive features of the models.
                Bookmark

                Author and article information

                Journal
                Proceedings of the National Academy of Sciences
                Proc Natl Acad Sci USA
                Proceedings of the National Academy of Sciences
                0027-8424
                1091-6490
                January 30 2018
                January 30 2018
                : 115
                : 5
                : 1087-1092
                Article
                10.1073/pnas.1713532115
                5798342
                29339474
                834cc333-2be7-47b4-bdf1-9cffaaa46071
                © 2018

                http://www.pnas.org/site/misc/userlicense.xhtml

                History

                Comments

                Comment on this article