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      Brain entropy and human intelligence: A resting-state fMRI study

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      PLoS ONE
      Public Library of Science

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          Abstract

          Human intelligence comprises comprehension of and reasoning about an infinitely variable external environment. A brain capable of large variability in neural configurations, or states, will more easily understand and predict variable external events. Entropy measures the variety of configurations possible within a system, and recently the concept of brain entropy has been defined as the number of neural states a given brain can access. This study investigates the relationship between human intelligence and brain entropy, to determine whether neural variability as reflected in neuroimaging signals carries information about intellectual ability. We hypothesize that intelligence will be positively associated with entropy in a sample of 892 healthy adults, using resting-state fMRI. Intelligence is measured with the Shipley Vocabulary and WASI Matrix Reasoning tests. Brain entropy was positively associated with intelligence. This relation was most strongly observed in the prefrontal cortex, inferior temporal lobes, and cerebellum. This relationship between high brain entropy and high intelligence indicates an essential role for entropy in brain functioning. It demonstrates that access to variable neural states predicts complex behavioral performance, and specifically shows that entropy derived from neuroimaging signals at rest carries information about intellectual capacity. Future work in this area may elucidate the links between brain entropy in both resting and active states and various forms of intelligence. This insight has the potential to provide predictive information about adaptive behavior and to delineate the subdivisions and nature of intelligence based on entropic patterns.

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          Rhythms of the Brain

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            Can parametric statistical methods be trusted for fMRI based group studies?

            The most widely used task fMRI analyses use parametric methods that depend on a variety of assumptions. While individual aspects of these fMRI models have been evaluated, they have not been evaluated in a comprehensive manner with empirical data. In this work, a total of 2 million random task fMRI group analyses have been performed using resting state fMRI data, to compute empirical familywise error rates for the software packages SPM, FSL and AFNI, as well as a standard non-parametric permutation method. While there is some variation, for a nominal familywise error rate of 5% the parametric statistical methods are shown to be conservative for voxel-wise inference and invalid for cluster-wise inference; in particular, cluster size inference with a cluster defining threshold of p = 0.01 generates familywise error rates up to 60%. We conduct a number of follow up analyses and investigations that suggest the cause of the invalid cluster inferences is spatial auto correlation functions that do not follow the assumed Gaussian shape. By comparison, the non-parametric permutation test, which is based on a small number of assumptions, is found to produce valid results for voxel as well as cluster wise inference. Using real task data, we compare the results between one parametric method and the permutation test, and find stark differences in the conclusions drawn between the two using cluster inference. These findings speak to the need of validating the statistical methods being used in the neuroimaging field.
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              A Fast Algorithm for the Minimum Covariance Determinant Estimator

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                Author and article information

                Contributors
                Role: ConceptualizationRole: InvestigationRole: MethodologyRole: SupervisionRole: Writing – original draftRole: Writing – review & editing
                Role: ConceptualizationRole: Data curationRole: Formal analysisRole: InvestigationRole: MethodologyRole: ValidationRole: VisualizationRole: Writing – original draft
                Role: Formal analysisRole: InvestigationRole: MethodologyRole: Project administrationRole: Writing – review & editing
                Role: Editor
                Journal
                PLoS One
                PLoS ONE
                plos
                plosone
                PLoS ONE
                Public Library of Science (San Francisco, CA USA )
                1932-6203
                12 February 2018
                2018
                : 13
                : 2
                : e0191582
                Affiliations
                [001]Department of Child and Adolescent Psychiatry, New York University School of Medicine, New York, New York, United States of America
                University of Cambridge, UNITED KINGDOM
                Author notes

                Competing Interests: The authors have declared that no competing interests exist.

                Author information
                http://orcid.org/0000-0002-4756-1169
                Article
                PONE-D-17-20866
                10.1371/journal.pone.0191582
                5809019
                29432427
                4c5cfc88-94ee-4c35-a123-b30c8cef834a
                © 2018 Saxe et al

                This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

                History
                : 31 May 2017
                : 8 January 2018
                Page count
                Figures: 9, Tables: 5, Pages: 21
                Funding
                The authors received no specific funding for this work.
                Categories
                Research Article
                Physical Sciences
                Physics
                Thermodynamics
                Entropy
                Biology and Life Sciences
                Neuroscience
                Cognitive Science
                Cognitive Psychology
                Intelligence
                Biology and Life Sciences
                Psychology
                Cognitive Psychology
                Intelligence
                Social Sciences
                Psychology
                Cognitive Psychology
                Intelligence
                Biology and Life Sciences
                Neuroscience
                Brain Mapping
                Functional Magnetic Resonance Imaging
                Medicine and Health Sciences
                Diagnostic Medicine
                Diagnostic Radiology
                Magnetic Resonance Imaging
                Functional Magnetic Resonance Imaging
                Research and Analysis Methods
                Imaging Techniques
                Diagnostic Radiology
                Magnetic Resonance Imaging
                Functional Magnetic Resonance Imaging
                Medicine and Health Sciences
                Radiology and Imaging
                Diagnostic Radiology
                Magnetic Resonance Imaging
                Functional Magnetic Resonance Imaging
                Research and Analysis Methods
                Imaging Techniques
                Neuroimaging
                Functional Magnetic Resonance Imaging
                Biology and Life Sciences
                Neuroscience
                Neuroimaging
                Functional Magnetic Resonance Imaging
                Biology and Life Sciences
                Neuroscience
                Cognitive Science
                Cognitive Psychology
                Reasoning
                Biology and Life Sciences
                Psychology
                Cognitive Psychology
                Reasoning
                Social Sciences
                Psychology
                Cognitive Psychology
                Reasoning
                Research and Analysis Methods
                Mathematical and Statistical Techniques
                Statistical Methods
                Regression Analysis
                Physical Sciences
                Mathematics
                Statistics (Mathematics)
                Statistical Methods
                Regression Analysis
                Biology and Life Sciences
                Anatomy
                Brain
                Cerebral Cortex
                Cerebellum
                Medicine and Health Sciences
                Anatomy
                Brain
                Cerebral Cortex
                Cerebellum
                Biology and Life Sciences
                Neuroscience
                Cognitive Science
                Cognitive Psychology
                Intelligence
                Human Intelligence
                Biology and Life Sciences
                Psychology
                Cognitive Psychology
                Intelligence
                Human Intelligence
                Social Sciences
                Psychology
                Cognitive Psychology
                Intelligence
                Human Intelligence
                Biology and Life Sciences
                Anatomy
                Brain
                Cerebral Cortex
                Temporal Lobe
                Medicine and Health Sciences
                Anatomy
                Brain
                Cerebral Cortex
                Temporal Lobe
                Custom metadata
                The data used in this study were provided by the Brain Genomics Superstruct Project of Harvard University and Massachusetts General Hospital (PIs: R. Buckner, J. Roffman, and J. Smoller) and is available to download via Harvard University’s online data repository ( https://dataverse.harvard.edu/dataverse/GSP). Informed consent was obtained for initial data collection (see: “Brain Genomics Superstruct Project initial data release with structural, functional, and behavioral measures”). The Brain Entropy Mapping Toolbox (Z. Wang et al.) was used for entropy calculation and is available to download at https://cfn.upenn.edu/~zewang/BENtbx.php. Our analysis of this open-access data set was approved by the NYU Langone Medical Center IRB.

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