47
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      Trait paranoia shapes inter-subject synchrony in brain activity during an ambiguous social narrative

      research-article

      Read this article at

      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

          Individuals often interpret the same event in different ways. How do personality traits modulate brain activity evoked by a complex stimulus? Here we report results from a naturalistic paradigm designed to draw out both neural and behavioral variation along a specific dimension of interest, namely paranoia. Participants listen to a narrative during functional MRI describing an ambiguous social scenario, written such that some individuals would find it highly suspicious, while others less so. Using inter-subject correlation analysis, we identify several brain areas that are differentially synchronized during listening between participants with high and low trait-level paranoia, including theory-of-mind regions. Follow-up analyses indicate that these regions are more active to mentalizing events in high-paranoia individuals. Analyzing participants’ speech as they freely recall the narrative reveals semantic and syntactic features that also scale with paranoia. Results indicate that a personality trait can act as an intrinsic “prime,” yielding different neural and behavioral responses to the same stimulus across individuals.

          Abstract

          Reactions to the same event can vary vastly based on multiple factors. Here the authors show that people with high trait-level paranoia process ambiguous information in a narrative differently and this can be attributed to greater activity in mentalizing brain regions during the moments of ambiguity.

          Related collections

          Most cited references64

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

          Research domain criteria (RDoC): toward a new classification framework for research on mental disorders.

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

            Large-scale automated synthesis of human functional neuroimaging data

            The explosive growth of the human neuroimaging literature has led to major advances in understanding of human brain function, but has also made aggregation and synthesis of neuroimaging findings increasingly difficult. Here we describe and validate an automated brain mapping framework that uses text mining, meta-analysis and machine learning techniques to generate a large database of mappings between neural and cognitive states. We demonstrate the capacity of our approach to automatically conduct large-scale, high-quality neuroimaging meta-analyses, address long-standing inferential problems in the neuroimaging literature, and support accurate ‘decoding’ of broad cognitive states from brain activity in both entire studies and individual human subjects. Collectively, our results validate a powerful and generative framework for synthesizing human neuroimaging data on an unprecedented scale.
              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Functional connectome fingerprinting: Identifying individuals based on patterns of brain connectivity

              While fMRI studies typically collapse data from many subjects, brain functional organization varies between individuals. Here, we establish that this individual variability is both robust and reliable, using data from the Human Connectome Project to demonstrate that functional connectivity profiles act as a “fingerprint” that can accurately identify subjects from a large group. Identification was successful across scan sessions and even between task and rest conditions, indicating that an individual’s connectivity profile is intrinsic, and can be used to distinguish that individual regardless of how the brain is engaged during imaging. Characteristic connectivity patterns were distributed throughout the brain, but notably, the frontoparietal network emerged as most distinctive. Furthermore, we show that connectivity profiles predict levels of fluid intelligence; the same networks that were most discriminating of individuals were also most predictive of cognitive behavior. Results indicate the potential to draw inferences about single subjects based on functional connectivity fMRI.
                Bookmark

                Author and article information

                Contributors
                emily.finn@nih.gov
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                23 May 2018
                23 May 2018
                2018
                : 9
                : 2043
                Affiliations
                [1 ]ISNI 0000 0004 0464 0574, GRID grid.416868.5, Section on Functional Imaging Methods, Laboratory of Brain and Cognition, , National Institute of Mental Health, ; Bethesda, MD 20892-9663 USA
                [2 ]ISNI 0000000419368710, GRID grid.47100.32, Department of Psychiatry, , Yale School of Medicine, ; New Haven, CT 06511-6662 USA
                [3 ]ISNI 0000 0004 0464 0574, GRID grid.416868.5, Scientific and Statistical Computing Core, , National Institute of Mental Health, ; Bethesda, MD 20892-9663 USA
                [4 ]ISNI 0000000419368710, GRID grid.47100.32, Department of Radiology and Biomedical Imaging, , Yale School of Medicine, ; New Haven, CT 06520-8042 USA
                Author information
                http://orcid.org/0000-0001-8591-3068
                http://orcid.org/0000-0001-5661-9521
                Article
                4387
                10.1038/s41467-018-04387-2
                5966466
                29795116
                19d3db3e-1c42-41db-aed0-e4c8da71b488
                © The Author(s) 2018

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 8 December 2017
                : 26 April 2018
                Categories
                Article
                Custom metadata
                © The Author(s) 2018

                Uncategorized
                Uncategorized

                Comments

                Comment on this article