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      Tracking perceptual decision mechanisms through changes in interhemispheric functional connectivity in human visual cortex

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          Abstract

          The role of long-range integration mechanisms underlying visual perceptual binding and their link to interhemispheric functional connectivity, as measured by fMRI, remains elusive. Only inferences on anatomical organization from resting state data paradigms not requiring coherent binding have been achieved. Here, we used a paradigm that allowed us to study such relation between perceptual interpretation and functional connectivity under bistable interhemispheric binding vs. non-binding of visual surfaces. Binding occurs by long-range perceptual integration of motion into a single object across hemifields and non-binding reflects opponent segregation of distinct moving surfaces into each hemifield. We hypothesized that perceptual integration vs. segregation of surface motion, which is achieved in visual area hMT+, is modulated by changes in interhemispheric connectivity in this region. Using 7T fMRI, we found that perceptual long-range integration of bistable motion can be tracked by changes in interhemispheric functional connectivity between left/right hMT+. Increased connectivity was tightly related with long-range perceptual integration. Our results indicate that hMT+ interhemispheric functional connectivity reflects perceptual decision, suggesting its pivotal role on long-range disambiguation of bistable physically constant surface motion. We reveal for the first time, at the scale of fMRI, a relation between interhemispheric functional connectivity and decision based perceptual binding.

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          Can sliding-window correlations reveal dynamic functional connectivity in resting-state fMRI?

          During the last several years, the focus of research on resting-state functional magnetic resonance imaging (fMRI) has shifted from the analysis of functional connectivity averaged over the duration of scanning sessions to the analysis of changes of functional connectivity within sessions. Although several studies have reported the presence of dynamic functional connectivity (dFC), statistical assessment of the results is not always carried out in a sound way and, in some studies, is even omitted. In this study, we explain why appropriate statistical tests are needed to detect dFC, we describe how they can be carried out and how to assess the performance of dFC measures, and we illustrate the methodology using spontaneous blood-oxygen level-dependent (BOLD) fMRI recordings of macaque monkeys under general anesthesia and in human subjects under resting-state conditions. We mainly focus on sliding-window correlations since these are most widely used in assessing dFC, but also consider a recently proposed non-linear measure. The simulations and methodology, however, are general and can be applied to any measure. The results are twofold. First, through simulations, we show that in typical resting-state sessions of 10 min, it is almost impossible to detect dFC using sliding-window correlations. This prediction is validated by both the macaque and the human data: in none of the individual recording sessions was evidence for dFC found. Second, detection power can be considerably increased by session- or subject-averaging of the measures. In doing so, we found that most of the functional connections are in fact dynamic. With this study, we hope to raise awareness of the statistical pitfalls in the assessment of dFC and how they can be avoided by using appropriate statistical methods.
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            Multistable phenomena: changing views in perception.

            Traditional explanations of multistable visual phenomena (e.g. ambiguous figures, perceptual rivalry) suggest that the basis for spontaneous reversals in perception lies in antagonistic connectivity within the visual system. In this review, we suggest an alternative, albeit speculative, explanation for visual multistability - that spontaneous alternations reflect responses to active, programmed events initiated by brain areas that integrate sensory and non-sensory information to coordinate a diversity of behaviors. Much evidence suggests that perceptual reversals are themselves more closely related to the expression of a behavior than to passive sensory responses: (1) they are initiated spontaneously, often voluntarily, and are influenced by subjective variables such as attention and mood; (2) the alternation process is greatly facilitated with practice and compromised by lesions in non-visual cortical areas; (3) the alternation process has temporal dynamics similar to those of spontaneously initiated behaviors; (4) functional imaging reveals that brain areas associated with a variety of cognitive behaviors are specifically activated when vision becomes unstable. In this scheme, reorganizations of activity throughout the visual cortex, concurrent with perceptual reversals, are initiated by higher, largely non-sensory brain centers. Such direct intervention in the processing of the sensory input by brain structures associated with planning and motor programming might serve an important role in perceptual organization, particularly in aspects related to selective attention.
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              Comparing the Pearson and Spearman correlation coefficients across distributions and sample sizes: A tutorial using simulations and empirical data.

              The Pearson product–moment correlation coefficient (rp) and the Spearman rank correlation coefficient (rs) are widely used in psychological research. We compare rp and rs on 3 criteria: variability, bias with respect to the population value, and robustness to an outlier. Using simulations across low (N = 5) to high (N = 1,000) sample sizes we show that, for normally distributed variables, rp and rs have similar expected values but rs is more variable, especially when the correlation is strong. However, when the variables have high kurtosis, rp is more variable than rs. Next, we conducted a sampling study of a psychometric dataset featuring symmetrically distributed data with light tails, and of 2 Likert-type survey datasets, 1 with light-tailed and the other with heavy-tailed distributions. Consistent with the simulations, rp had lower variability than rs in the psychometric dataset. In the survey datasets with heavy-tailed variables in particular, rs had lower variability than rp, and often corresponded more accurately to the population Pearson correlation coefficient (Rp) than rp did. The simulations and the sampling studies showed that variability in terms of standard deviations can be reduced by about 20% by choosing rs instead of rp. In comparison, increasing the sample size by a factor of 2 results in a 41% reduction of the standard deviations of rs and rp. In conclusion, rp is suitable for light-tailed distributions, whereas rs is preferable when variables feature heavy-tailed distributions or when outliers are present, as is often the case in psychological research.
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                Author and article information

                Contributors
                mcbranco@fmed.uc.pt
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                4 February 2019
                4 February 2019
                2019
                : 9
                : 1242
                Affiliations
                [1 ]ISNI 0000 0000 9511 4342, GRID grid.8051.c, Coimbra Institute for Biomedical Imaging and Translational Research (CIBIT), , University of Coimbra, ; Coimbra, Portugal
                [2 ]ISNI 0000 0000 9511 4342, GRID grid.8051.c, Institute of Nuclear Sciences Applied to Health (ICNAS), , University of Coimbra, ; Coimbra, Portugal
                [3 ]ISNI 0000 0000 9511 4342, GRID grid.8051.c, Institute for Biomedical Imaging and Life Sciences (CNC.IBILI), Faculty of Medicine, , University of Coimbra, ; Coimbra, Portugal
                [4 ]ISNI 0000 0001 0481 6099, GRID grid.5012.6, Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, , University of Maastricht, ; Maastricht, Netherlands
                [5 ]ISNI 0000 0001 2171 8263, GRID grid.419918.c, Department of Neuroimaging and Neuromodeling, , Netherlands Institute for Neuroscience, Royal Netherlands Academy of Arts and Sciences (KNAW), ; Amsterdam, Netherlands
                Author information
                http://orcid.org/0000-0003-2652-3152
                http://orcid.org/0000-0001-8586-9554
                http://orcid.org/0000-0001-7184-185X
                http://orcid.org/0000-0003-4364-6373
                Article
                37822
                10.1038/s41598-018-37822-x
                6362201
                30718636
                a8026963-344b-423b-b8af-0cd0c7e9240a
                © The Author(s) 2019

                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
                : 26 February 2018
                : 15 December 2018
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