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      Distinct neural substrates of visuospatial and verbal-analytic reasoning as assessed by Raven’s Advanced Progressive Matrices

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      Scientific Reports
      Nature Publishing Group UK

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          Abstract

          Recent studies revealed spontaneous neural activity to be associated with fluid intelligence (gF) which is commonly assessed by Raven’s Advanced Progressive Matrices, and embeds two types of reasoning: visuospatial and verbal-analytic reasoning. With resting-state fMRI data, using global brain connectivity (GBC) analysis which averages functional connectivity of a voxel in relation to all other voxels in the brain, distinct neural correlates of these two reasoning types were found. For visuospatial reasoning, negative correlations were observed in both the primary visual cortex (PVC) and the precuneus, and positive correlations were observed in the temporal lobe. For verbal-analytic reasoning, negative correlations were observed in the right inferior frontal gyrus (rIFG), dorsal anterior cingulate cortex and temporoparietal junction, and positive correlations were observed in the angular gyrus. Furthermore, an interaction between GBC value and type of reasoning was found in the PVC, rIFG and the temporal lobe. These findings suggest that visuospatial reasoning benefits more from elaborate perception to stimulus features, whereas verbal-analytic reasoning benefits more from feature integration and hypothesis testing. In sum, the present study offers, for different types of reasoning in gF, first empirical evidence of separate neural substrates in the resting brain.

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          Separating respiratory-variation-related fluctuations from neuronal-activity-related fluctuations in fMRI.

          Subtle changes in a subject's breathing rate or depth, which occur naturally during rest at low frequencies (<0.1 Hz), have been shown to be significantly correlated with fMRI signal changes throughout gray matter and near large vessels. The goal of this study was to investigate the impact of these low-frequency respiration variations on both task activation fMRI studies and resting-state functional connectivity analysis. Unlike MR signal changes correlated with the breathing motion ( approximately 0.3 Hz), BOLD signal changes correlated with across-breath variations in respiratory volume ( approximately 0.03 Hz) appear localized to blood vessels and regions with high blood volume, such as gray matter, similar to changes seen in response to a breath-hold challenge. In addition, the respiration-variation-induced signal changes were found to coincide with many of the areas identified as part of the 'default mode' network, a set of brain regions hypothesized to be more active at rest. Regions could therefore be classified as being part of a resting network based on their similar respiration-induced changes rather than their synchronized neuronal activity. Monitoring and removing these respiration variations led to a significant improvement in the identification of task-related activation and deactivation and only slight differences in regions correlated with the posterior cingulate at rest. Regressing out global signal changes or cueing the subject to breathe at a constant rate and depth resulted in an improved spatial overlap between deactivations and resting-state correlations among areas that showed deactivation.
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            A positive-negative mode of population covariation links brain connectivity, demographics and behavior

            We investigated the relationship between individual subjects’ functional connectomes and 280 behavioral and demographic measures, in a single holistic multivariate analysis relating imaging to non-imaging data from 461 subjects in the Human Connectome Project. We identified one strong mode of population co-variation; subjects were predominantly spread along a single “positive-negative” axis, linking lifestyle, demographic and psychometric measures to each other and to a specific pattern of brain connectivity.
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              What one intelligence test measures: a theoretical account of the processing in the Raven Progressive Matrices Test.

              The cognitive processes in a widely used, nonverbal test of analytic intelligence, the Raven Progressive Matrices Test (Raven, 1962), are analyzed in terms of which processes distinguish between higher scoring and lower scoring subjects and which processes are common to all subjects and all items on the test. The analysis is based on detailed performance characteristics, such as verbal protocols, eye-fixation patterns, and errors. The theory is expressed as a pair of computer simulation models that perform like the median or best college students in the sample. The processing characteristic common to all subjects is an incremental, reiterative strategy for encoding and inducing the regularities in each problem. The processes that distinguish among individuals are primarily the ability to induce abstract relations and the ability to dynamically manage a large set of problem-solving goals in working memory.
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                Author and article information

                Contributors
                liujia@bnu.edu.cn
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                24 November 2017
                24 November 2017
                2017
                : 7
                : 16230
                Affiliations
                [1 ]ISNI 0000 0004 1798 0690, GRID grid.411868.2, Department of Psychology, , Jiangxi University of Traditional Chinese Medicine, ; Nanchang, China
                [2 ]ISNI 0000000122931605, GRID grid.5590.9, Institute for Management Research, Radboud University, ; Nijmegen, The Netherlands
                [3 ]ISNI 0000 0001 2069 7798, GRID grid.5342.0, Department of Personnel Management, , Work and Organizational Psychology, Ghent University, ; Ghent, Belgium
                [4 ]ISNI 0000 0001 1431 9176, GRID grid.24695.3c, School of Life Sciences, Beijing University of Chinese Medicine, ; Beijing, China
                [5 ]ISNI 0000 0004 1789 9964, GRID grid.20513.35, Beijing Key Laboratory of Applied Experimental Psychology, School of Psychology, Beijing Normal University, ; Beijing, China
                Article
                16437
                10.1038/s41598-017-16437-8
                5701148
                29176725
                54b76b24-4f3e-4124-a597-e9465929afec
                © The Author(s) 2017

                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/.

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                : 5 May 2017
                : 13 November 2017
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