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      Age-related increases in long-range connectivity in fetal functional neural connectivity networks in utero

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          Highlights

          • We examined patterns of connectivity in human fetal brain networks.

          • The fetal brain demonstrates cerebral-cerebellar and cortical-subcortical connectivity.

          • Many forms of cerebral connectivity are present by the third trimester.

          • Default mode network connections were evident in fetuses older than 35 weeks.

          • Long-range functional connectivity is more prominent in older fetuses.

          Abstract

          Formation of operational neural networks is one of the most significant accomplishments of human fetal brain growth. Recent advances in functional magnetic resonance imaging (fMRI) have made it possible to obtain information about brain function during fetal development. Specifically, resting-state fMRI and novel signal covariation approaches have opened up a new avenue for non-invasive assessment of neural functional connectivity (FC) before birth. Early studies in this area have unearthed new insights about principles of prenatal brain function. However, very little is known about the emergence and maturation of neural networks during fetal life. Here, we obtained cross-sectional rs-fMRI data from 39 fetuses between 24 and 38 weeks postconceptual age to examine patterns of connectivity across ten neural FC networks. We identified primitive forms of motor, visual, default mode, thalamic, and temporal networks in the human fetal brain. We discovered the first evidence of increased long-range, cerebral-cerebellar, cortical-subcortical, and intra-hemispheric FC with advancing fetal age. Continued aggregation of data about fundamental neural connectivity systems in utero is essential to establishing principles of connectomics at the beginning of human life. Normative data provides a vital context against which to compare instances of abnormal neurobiological development.

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          Most cited references22

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          Conn: a functional connectivity toolbox for correlated and anticorrelated brain networks.

          Resting state functional connectivity reveals intrinsic, spontaneous networks that elucidate the functional architecture of the human brain. However, valid statistical analysis used to identify such networks must address sources of noise in order to avoid possible confounds such as spurious correlations based on non-neuronal sources. We have developed a functional connectivity toolbox Conn ( www.nitrc.org/projects/conn ) that implements the component-based noise correction method (CompCor) strategy for physiological and other noise source reduction, additional removal of movement, and temporal covariates, temporal filtering and windowing of the residual blood oxygen level-dependent (BOLD) contrast signal, first-level estimation of multiple standard functional connectivity magnetic resonance imaging (fcMRI) measures, and second-level random-effect analysis for resting state as well as task-related data. Compared to methods that rely on global signal regression, the CompCor noise reduction method allows for interpretation of anticorrelations as there is no regression of the global signal. The toolbox implements fcMRI measures, such as estimation of seed-to-voxel and region of interest (ROI)-to-ROI functional correlations, as well as semipartial correlation and bivariate/multivariate regression analysis for multiple ROI sources, graph theoretical analysis, and novel voxel-to-voxel analysis of functional connectivity. We describe the methods implemented in the Conn toolbox for the analysis of fcMRI data, together with examples of use and interscan reliability estimates of all the implemented fcMRI measures. The results indicate that the CompCor method increases the sensitivity and selectivity of fcMRI analysis, and show a high degree of interscan reliability for many fcMRI measures.
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            Correlations and anticorrelations in resting-state functional connectivity MRI: a quantitative comparison of preprocessing strategies.

            Resting-state data sets contain coherent fluctuations unrelated to neural processes originating from residual motion artefacts, respiration and cardiac action. Such confounding effects may introduce correlations and cause an overestimation of functional connectivity strengths. In this study we applied several multidimensional linear regression approaches to remove artificial coherencies and examined the impact of preprocessing on sensitivity and specificity of functional connectivity results in simulated data and resting-state data sets from 40 subjects. Furthermore, we aimed at clarifying possible causes of anticorrelations and test the hypothesis that anticorrelations are introduced via certain preprocessing approaches, with particular focus on the effects of regression against the global signal. Our results show that preprocessing in general greatly increased connection specificity, in particular correction for global signal fluctuations almost doubled connection specificity. However, widespread anticorrelated networks were only found when regression against the global signal was applied. Results in simulated data sets compared with result of human data strongly suggest that anticorrelations are indeed introduced by global signal regression and should therefore be interpreted very carefully. In addition, global signal regression may also reduce the sensitivity for detecting true correlations, i.e. increase the number of false negatives. Concluding from our results we suggest that is highly recommended to apply correction against realignment parameters, white matter and ventricular time courses, as well as the global signal to maximize the specificity of positive resting-state correlations.
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              • Article: not found

              Synaptic activity and the construction of cortical circuits.

              Vision is critical for the functional and structural maturation of connections in the mammalian visual system. Visual experience, however, is a subset of a more general requirement for neural activity in transforming immature circuits into the organized connections that subserve adult brain function. Early in development, internally generated spontaneous activity sculpts circuits on the basis of the brain's "best guess" at the initial configuration of connections necessary for function and survival. With maturation of the sense organs, the developing brain relies less on spontaneous activity and increasingly on sensory experience. The sequential combination of spontaneously generated and experience-dependent neural activity endows the brain with an ongoing ability to accommodate to dynamically changing inputs during development and throughout life.
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                Author and article information

                Contributors
                Journal
                Dev Cogn Neurosci
                Dev Cogn Neurosci
                Developmental Cognitive Neuroscience
                Elsevier
                1878-9293
                1878-9307
                27 September 2014
                February 2015
                27 September 2014
                : 11
                : 96-104
                Affiliations
                [a ]Merrill Palmer Skillman Institute for Child and Family Development, Wayne State University, Detroit, MI 48202, USA
                [b ]Department of Pediatrics, Wayne State University School of Medicine, Detroit, MI 48202, USA
                [c ]Perinatology Research Branch, NICHD/NIH/DHHS, Bethesda, MD, USA
                [d ]Perinatology Research Branch, NICHD/NIH/DHHS, Detroit, MI 48202, USA
                [e ]Michigan State University School of Medicine, East Lansing, MI, 48824 USA
                [f ]Department of Radiology, Wayne State University School of Medicine, Detroit, MI 48202, USA
                [g ]University of Michigan School of Dentistry, Ann Arbor, MI 48109, USA
                [h ]Department of Obstetrics and Gynecology, Wayne State University School of Medicine, Detroit, MI 48202, USA
                Author notes
                [* ]Corresponding author at: Merrill Palmer Skillman Institute, 71 E. Ferry Street, Detroit, MI 48202, USA. Tel.: +1 313 664 2517; fax: +1 313 664 2555 moriah@ 123456wayne.edu
                Article
                S1878-9293(14)00064-4
                10.1016/j.dcn.2014.09.001
                4532276
                25284273
                39252e79-c1ce-4941-b03f-661e1e19b72f
                © 2014 The Authors

                This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/3.0/).

                History
                : 30 April 2014
                : 28 July 2014
                : 1 September 2014
                Categories
                Original Research

                Neurosciences
                fetus,fmri,human,prenatal,resting-state,connectome
                Neurosciences
                fetus, fmri, human, prenatal, resting-state, connectome

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