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      Task-induced brain state manipulation improves prediction of individual traits

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          Abstract

          Recent work has begun to relate individual differences in brain functional organization to human behaviors and cognition, but the best brain state to reveal such relationships remains an open question. In two large, independent data sets, we here show that cognitive tasks amplify trait-relevant individual differences in patterns of functional connectivity, such that predictive models built from task fMRI data outperform models built from resting-state fMRI data. Further, certain tasks consistently yield better predictions of fluid intelligence than others, and the task that generates the best-performing models varies by sex. By considering task-induced brain state and sex, the best-performing model explains over 20% of the variance in fluid intelligence scores, as compared to <6% of variance explained by rest-based models. This suggests that identifying and inducing the right brain state in a given group can better reveal brain-behavior relationships, motivating a paradigm shift from rest- to task-based functional connectivity analyses.

          Abstract

          Decoding or predicting cognitive traits from brain activity is an exciting prospect. Here, the authors show that task-based functional connectivity better predicts intelligence-related measures than rest-based connectivity, suggesting that cognitive tasks amplify individual differences in trait-relevant circuitry.

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          Most cited references44

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          Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing

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            A whole brain fMRI atlas generated via spatially constrained spectral clustering.

            Connectivity analyses and computational modeling of human brain function from fMRI data frequently require the specification of regions of interests (ROIs). Several analyses have relied on atlases derived from anatomical or cyto-architectonic boundaries to specify these ROIs, yet the suitability of atlases for resting state functional connectivity (FC) studies has yet to be established. This article introduces a data-driven method for generating an ROI atlas by parcellating whole brain resting-state fMRI data into spatially coherent regions of homogeneous FC. Several clustering statistics are used to compare methodological trade-offs as well as determine an adequate number of clusters. Additionally, we evaluate the suitability of the parcellation atlas against four ROI atlases (Talairach and Tournoux, Harvard-Oxford, Eickoff-Zilles, and Automatic Anatomical Labeling) and a random parcellation approach. The evaluated anatomical atlases exhibit poor ROI homogeneity and do not accurately reproduce FC patterns present at the voxel scale. In general, the proposed functional and random parcellations perform equivalently for most of the metrics evaluated. ROI size and hence the number of ROIs in a parcellation had the greatest impact on their suitability for FC analysis. With 200 or fewer ROIs, the resulting parcellations consist of ROIs with anatomic homology, and thus offer increased interpretability. Parcellation results containing higher numbers of ROIs (600 or 1,000) most accurately represent FC patterns present at the voxel scale and are preferable when interpretability can be sacrificed for accuracy. The resulting atlases and clustering software have been made publicly available at: http://www.nitrc.org/projects/cluster_roi/. Copyright © 2011 Wiley Periodicals, Inc.
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              Choosing Prediction Over Explanation in Psychology: Lessons From Machine Learning.

              Psychology has historically been concerned, first and foremost, with explaining the causal mechanisms that give rise to behavior. Randomized, tightly controlled experiments are enshrined as the gold standard of psychological research, and there are endless investigations of the various mediating and moderating variables that govern various behaviors. We argue that psychology's near-total focus on explaining the causes of behavior has led much of the field to be populated by research programs that provide intricate theories of psychological mechanism but that have little (or unknown) ability to predict future behaviors with any appreciable accuracy. We propose that principles and techniques from the field of machine learning can help psychology become a more predictive science. We review some of the fundamental concepts and tools of machine learning and point out examples where these concepts have been used to conduct interesting and important psychological research that focuses on predictive research questions. We suggest that an increased focus on prediction, rather than explanation, can ultimately lead us to greater understanding of behavior.
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                Author and article information

                Contributors
                abigail.greene@yale.edu
                Journal
                Nat Commun
                Nat Commun
                Nature Communications
                Nature Publishing Group UK (London )
                2041-1723
                18 July 2018
                18 July 2018
                2018
                : 9
                : 2807
                Affiliations
                [1 ]ISNI 0000000419368710, GRID grid.47100.32, Interdepartmental Neuroscience Program, , Yale School of Medicine, ; New Haven, 06520 CT USA
                [2 ]Department of Biomedical Engineering, Yale School of Engineering and Applied Science, New Haven, 06520 CT USA
                [3 ]ISNI 0000000419368710, GRID grid.47100.32, Department of Radiology and Biomedical Imaging, , Yale School of Medicine, ; New Haven, 06520 CT USA
                [4 ]ISNI 0000000419368710, GRID grid.47100.32, Department of Neurosurgery, , Yale School of Medicine, ; New Haven, 06520 CT USA
                Author information
                http://orcid.org/0000-0001-6011-7903
                http://orcid.org/0000-0002-8925-7042
                http://orcid.org/0000-0001-5661-9521
                Article
                4920
                10.1038/s41467-018-04920-3
                6052101
                30022026
                f42ce69b-3947-4ae6-b8ea-3ecf4b5e7733
                © 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
                : 2 October 2017
                : 1 June 2018
                Funding
                Funded by: FundRef https://doi.org/10.13039/100000057, U.S. Department of Health & Human Services | NIH | National Institute of General Medical Sciences (NIGMS);
                Award ID: T32GM007205
                Award Recipient :
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