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      Childhood Trauma and Functional Connectivity between Amygdala and Medial Prefrontal Cortex: A Dynamic Functional Connectivity and Large-Scale Network Perspective

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          Abstract

          Altered functional connectivity (FC) between the medial prefrontal cortex (mPFC) and amygdala is widely implicated as a neural mechanism explaining risk for psychopathology among those exposed to early life trauma. Nonetheless, contemporary neuroimaging research has shifted toward large-scale network models of brain function, and it is not clear how this common bi-nodal finding fits into larger-scale network models. Here, using dynamic functional connectivity (DFC) approaches combined with large-scale network analyses, the larger role of bi-nodal FC between mPFC and amygdala among a sample of adolescent girls is investigated. The sample was comprised of 30 healthy control girls and 26 girls exposed to either physical or sexual assault who underwent a resting-state scan during 3T MRI. DFC using a sliding window approach was used to create weighted, undirected, graphs from the resting-state data following parcellation with a 215 regions-of-interest (ROI) atlas. Using a priori ROI, the predicted finding of lessor FC between mPFC and amygdala as a function of early life trauma was replicated in this sample. By contrast, early life trauma was associated with greater large-scale network modularity. Using a dynamic FC approach, it is also demonstrated that within-subject variability in this bi-nodal FC closely tracks within-subject fluctuations in large-scale network patterns, including connectivity between a limbic and default mode network (in which the amygdala and mPFC nodes belong, respectively) as well as overall modular organization. These results suggest that bi-nodal FC, such as amygdala-mPFC FC, may generally reflect larger-scale network patterns. Future research is necessary to understand whether these associations between nodal FC and large-scale network organization better reflect top-down processes (larger-scale network organization drives bi-nodal FC) or bottom-up processes (bi-nodal FC drives larger-scale network organization) and the related impact of early life trauma.

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          Most cited references18

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          Modularity and community structure in networks

          M. Newman (2006)
          Many networks of interest in the sciences, including a variety of social and biological networks, are found to divide naturally into communities or modules. The problem of detecting and characterizing this community structure has attracted considerable recent attention. One of the most sensitive detection methods is optimization of the quality function known as "modularity" over the possible divisions of a network, but direct application of this method using, for instance, simulated annealing is computationally costly. Here we show that the modularity can be reformulated in terms of the eigenvectors of a new characteristic matrix for the network, which we call the modularity matrix, and that this reformulation leads to a spectral algorithm for community detection that returns results of better quality than competing methods in noticeably shorter running times. We demonstrate the algorithm with applications to several network data sets.
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            A whole brain fMRI atlas generated via spatially constrained spectral clustering.

            Connectivity analyses and computational modeling of human brain function from fMRI data frequently require the specification of regions of interests (ROIs). Several analyses have relied on atlases derived from anatomical or cyto-architectonic boundaries to specify these ROIs, yet the suitability of atlases for resting state functional connectivity (FC) studies has yet to be established. This article introduces a data-driven method for generating an ROI atlas by parcellating whole brain resting-state fMRI data into spatially coherent regions of homogeneous FC. Several clustering statistics are used to compare methodological trade-offs as well as determine an adequate number of clusters. Additionally, we evaluate the suitability of the parcellation atlas against four ROI atlases (Talairach and Tournoux, Harvard-Oxford, Eickoff-Zilles, and Automatic Anatomical Labeling) and a random parcellation approach. The evaluated anatomical atlases exhibit poor ROI homogeneity and do not accurately reproduce FC patterns present at the voxel scale. In general, the proposed functional and random parcellations perform equivalently for most of the metrics evaluated. ROI size and hence the number of ROIs in a parcellation had the greatest impact on their suitability for FC analysis. With 200 or fewer ROIs, the resulting parcellations consist of ROIs with anatomic homology, and thus offer increased interpretability. Parcellation results containing higher numbers of ROIs (600 or 1,000) most accurately represent FC patterns present at the voxel scale and are preferable when interpretability can be sacrificed for accuracy. The resulting atlases and clustering software have been made publicly available at: http://www.nitrc.org/projects/cluster_roi/. Copyright © 2011 Wiley Periodicals, Inc.
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              Neurocircuitry models of posttraumatic stress disorder and extinction: human neuroimaging research--past, present, and future.

              The prevailing neurocircuitry models of anxiety disorders have been amygdalocentric in form. The bases for such models have progressed from theoretical considerations, extrapolated from research in animals, to in vivo human imaging data. For example, one current model of posttraumatic stress disorder (PTSD) has been highly influenced by knowledge from rodent fear conditioning research. Given the phenomenological parallels between fear conditioning and the pathogenesis of PTSD, we have proposed that PTSD is characterized by exaggerated amygdala responses (subserving exaggerated acquisition of fear associations and expression of fear responses) and deficient frontal cortical function (mediating deficits in extinction and the capacity to suppress attention/response to trauma-related stimuli), as well as deficient hippocampal function (mediating deficits in appreciation of safe contexts and explicit learning/memory). Neuroimaging studies have yielded convergent findings in support of this model. However, to date, neuroimaging investigations of PTSD have not principally employed conditioning and extinction paradigms per se. The recent development of such imaging probes now sets the stage for directly testing hypotheses regarding the neural substrates of fear conditioning and extinction abnormalities in PTSD.
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                Author and article information

                Contributors
                Journal
                Front Syst Neurosci
                Front Syst Neurosci
                Front. Syst. Neurosci.
                Frontiers in Systems Neuroscience
                Frontiers Media S.A.
                1662-5137
                11 May 2017
                2017
                : 11
                : 29
                Affiliations
                [1]Department of Psychiatry, University of Wisconsin–Madison, Madison WI, USA
                Author notes

                Edited by: Avishek Adhikari, Stanford University, USA

                Reviewed by: Andrea Brovelli, Centre National de la Recherche Scientifique, France; Lei Wang, Northwestern University, USA

                *Correspondence: Josh M. Cisler, jcisler2@ 123456wisc.edu
                Article
                10.3389/fnsys.2017.00029
                5425605
                28553208
                0c2de5a1-8329-4103-8201-bbe0d9909f2f
                Copyright © 2017 Cisler.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 17 November 2016
                : 26 April 2017
                Page count
                Figures: 6, Tables: 1, Equations: 0, References: 42, Pages: 11, Words: 0
                Funding
                Funded by: National Institute of Mental Health 10.13039/100000025
                Award ID: MH108753
                Award ID: MH106860
                Categories
                Neuroscience
                Original Research

                Neurosciences
                early life trauma,amygdala,functional connectivity,dynamic functional connectivity

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