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      Altered functional connectivity of the amygdaloid input nuclei in adolescents and young adults with autism spectrum disorder: a resting state fMRI study

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          Abstract

          Background

          Amygdala dysfunction is hypothesized to underlie the social deficits observed in autism spectrum disorders (ASD). However, the neurobiological basis of this hypothesis is underspecified because it is unknown whether ASD relates to abnormalities of the amygdaloid input or output nuclei. Here, we investigated the functional connectivity of the amygdaloid social-perceptual input nuclei and emotion-regulation output nuclei in ASD versus controls.

          Methods

          We collected resting state functional magnetic resonance imaging (fMRI) data, tailored to provide optimal sensitivity in the amygdala as well as the neocortex, in 20 adolescents and young adults with ASD and 25 matched controls. We performed a regular correlation analysis between the entire amygdala (EA) and the whole brain and used a partial correlation analysis to investigate whole-brain functional connectivity uniquely related to each of the amygdaloid subregions.

          Results

          Between-group comparison of regular EA correlations showed significantly reduced connectivity in visuospatial and superior parietal areas in ASD compared to controls. Partial correlation analysis revealed that this effect was driven by the left superficial and right laterobasal input subregions, but not the centromedial output nuclei.

          Conclusions

          These results indicate reduced connectivity of specifically the amygdaloid sensory input channels in ASD, suggesting that abnormal amygdalo-cortical connectivity can be traced down to the socio-perceptual pathways.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s13229-015-0060-x) contains supplementary material, which is available to authorized users.

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          Most cited references51

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          Diagnostic and statistical manual of mental disorders.

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            Network modelling methods for FMRI.

            There is great interest in estimating brain "networks" from FMRI data. This is often attempted by identifying a set of functional "nodes" (e.g., spatial ROIs or ICA maps) and then conducting a connectivity analysis between the nodes, based on the FMRI timeseries associated with the nodes. Analysis methods range from very simple measures that consider just two nodes at a time (e.g., correlation between two nodes' timeseries) to sophisticated approaches that consider all nodes simultaneously and estimate one global network model (e.g., Bayes net models). Many different methods are being used in the literature, but almost none has been carefully validated or compared for use on FMRI timeseries data. In this work we generate rich, realistic simulated FMRI data for a wide range of underlying networks, experimental protocols and problematic confounds in the data, in order to compare different connectivity estimation approaches. Our results show that in general correlation-based approaches can be quite successful, methods based on higher-order statistics are less sensitive, and lag-based approaches perform very poorly. More specifically: there are several methods that can give high sensitivity to network connection detection on good quality FMRI data, in particular, partial correlation, regularised inverse covariance estimation and several Bayes net methods; however, accurate estimation of connection directionality is more difficult to achieve, though Patel's τ can be reasonably successful. With respect to the various confounds added to the data, the most striking result was that the use of functionally inaccurate ROIs (when defining the network nodes and extracting their associated timeseries) is extremely damaging to network estimation; hence, results derived from inappropriate ROI definition (such as via structural atlases) should be regarded with great caution. Copyright © 2010 Elsevier Inc. All rights reserved.
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              Neurobiology of emotion perception I: The neural basis of normal emotion perception.

              There is at present limited understanding of the neurobiological basis of the different processes underlying emotion perception. We have aimed to identify potential neural correlates of three processes suggested by appraisalist theories as important for emotion perception: 1) the identification of the emotional significance of a stimulus; 2) the production of an affective state in response to 1; and 3) the regulation of the affective state. In a critical review, we have examined findings from recent animal, human lesion, and functional neuroimaging studies. Findings from these studies indicate that these processes may be dependent upon the functioning of two neural systems: a ventral system, including the amygdala, insula, ventral striatum, and ventral regions of the anterior cingulate gyrus and prefrontal cortex, predominantly important for processes 1 and 2 and automatic regulation of emotional responses; and a dorsal system, including the hippocampus and dorsal regions of anterior cingulate gyrus and prefrontal cortex, predominantly important for process 3. We suggest that the extent to which a stimulus is identified as emotive and is associated with the production of an affective state may be dependent upon levels of activity within these two neural systems.
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                Author and article information

                Contributors
                +31(0)24 68487 , a.rausch@psych.ru.nl
                w.zhang@donders.ru.nl
                k.haak@donders.ru.nl
                maarten.mennes@fcdonders.ru.nl
                erno.hermans@donders.ru.nl
                e.vanoort@fcdonders.ru.nl
                guidovanwingen@gmail.com
                c.beckmann@donders.ru.nl
                Jan.Buitelaar@radboudumc.nl
                w.groen@karakter.com
                Journal
                Mol Autism
                Mol Autism
                Molecular Autism
                BioMed Central (London )
                2040-2392
                28 January 2016
                28 January 2016
                2016
                : 7
                : 13
                Affiliations
                [ ]Department of Cognitive Neuroscience, Radboud University Medical Center Nijmegen, P.O. Box 9101, 6500 HB Nijmegen, The Netherlands
                [ ]Donders Institute for Brain, Cognition and Behaviour, Centre for Cognitive Neuroimaging, Radboud University, Nijmegen, The Netherlands
                [ ]MIRA Institute, University of Twente, Enschede, The Netherlands
                [ ]Department of Psychiatry, Academic Medical Center, University of Amsterdam, Amsterdam, The Netherlands
                [ ]Centre for Functional MRI of the Brain (FMRIB), University of Oxford, Oxford, United Kingdom
                [ ]Karakter Child and Adolescent Psychiatry University Centre, Nijmegen, The Netherlands
                Article
                60
                10.1186/s13229-015-0060-x
                4730628
                26823966
                6ff4ed2f-5f4e-4db2-877c-7153ce0312c9
                © Rausch et al. 2016

                Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License ( http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided 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 Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

                History
                : 10 April 2015
                : 7 December 2015
                Categories
                Research
                Custom metadata
                © The Author(s) 2016

                Neurosciences
                amygdala,autism spectrum disorder,centromedial,connectivity,input-output,laterobasal,nuclei,social perception,superficial

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