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      Electroconvulsive therapy and structural neuroplasticity in neocortical, limbic and paralimbic cortex

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          Abstract

          Electroconvulsive therapy (ECT) is a highly effective and rapidly acting treatment for severe depression. To understand the biological bases of therapeutic response, we examined variations in cortical thickness from magnetic resonance imaging (MRI) data in 29 patients scanned at three time points during an ECT treatment index series and in 29 controls at two time points. Changes in thickness across time and with symptom improvement were evaluated at high spatial resolution across the cortex and within discrete cortical regions of interest. Patients showed increased thickness over the course of ECT in the bilateral anterior cingulate cortex (ACC), inferior and superior temporal, parahippocampal, entorhinal and fusiform cortex and in distributed prefrontal areas. No changes across time occurred in controls. In temporal and fusiform regions showing significant ECT effects, thickness differed between patients and controls at baseline and change in thickness related to therapeutic response in patients. In the ACC, these relationships occurred in treatment responders only, and thickness measured soon after treatment initiation predicted the overall ECT response. ECT leads to widespread neuroplasticity in neocortical, limbic and paralimbic regions and changes relate to the extent of antidepressant response. Variations in ACC thickness, which discriminate treatment responders and predict response early in the course of ECT, may represent a biomarker of overall clinical outcome. Because post-mortem studies show focal reductions in glial density and neuronal size in patients with severe depression, ECT-related increases in thickness may be attributable to neuroplastic processes affecting the size and/or density of neurons and glia and their connections.

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          Most cited references49

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          Conflict monitoring and cognitive control.

          A neglected question regarding cognitive control is how control processes might detect situations calling for their involvement. The authors propose here that the demand for control may be evaluated in part by monitoring for conflicts in information processing. This hypothesis is supported by data concerning the anterior cingulate cortex, a brain area involved in cognitive control, which also appears to respond to the occurrence of conflict. The present article reports two computational modeling studies, serving to articulate the conflict monitoring hypothesis and examine its implications. The first study tests the sufficiency of the hypothesis to account for brain activation data, applying a measure of conflict to existing models of tasks shown to engage the anterior cingulate. The second study implements a feedback loop connecting conflict monitoring to cognitive control, using this to simulate a number of important behavioral phenomena.
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            A neurotrophic model for stress-related mood disorders.

            There is a growing body of evidence demonstrating that stress decreases the expression of brain-derived neurotrophic factor (BDNF) in limbic structures that control mood and that antidepressant treatment reverses or blocks the effects of stress. Decreased levels of BDNF, as well as other neurotrophic factors, could contribute to the atrophy of certain limbic structures, including the hippocampus and prefrontal cortex that has been observed in depressed subjects. Conversely, the neurotrophic actions of antidepressants could reverse neuronal atrophy and cell loss and thereby contribute to the therapeutic actions of these treatments. This review provides a critical examination of the neurotrophic hypothesis of depression that has evolved from this work, including analysis of preclinical cellular (adult neurogenesis) and behavioral models of depression and antidepressant actions, as well as clinical neuroimaging and postmortem studies. Although there are some limitations, the results of these studies are consistent with the hypothesis that decreased expression of BDNF and possibly other growth factors contributes to depression and that upregulation of BDNF plays a role in the actions of antidepressant treatment.
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              Cortical thickness or grey matter volume? The importance of selecting the phenotype for imaging genetics studies.

              Choosing the appropriate neuroimaging phenotype is critical to successfully identify genes that influence brain structure or function. While neuroimaging methods provide numerous potential phenotypes, their role for imaging genetics studies is unclear. Here we examine the relationship between brain volume, grey matter volume, cortical thickness and surface area, from a genetic standpoint. Four hundred and eighty-six individuals from randomly ascertained extended pedigrees with high-quality T1-weighted neuroanatomic MRI images participated in the study. Surface-based and voxel-based representations of brain structure were derived, using automated methods, and these measurements were analysed using a variance-components method to identify the heritability of these traits and their genetic correlations. All neuroanatomic traits were significantly influenced by genetic factors. Cortical thickness and surface area measurements were found to be genetically and phenotypically independent. While both thickness and area influenced volume measurements of cortical grey matter, volume was more closely related to surface area than cortical thickness. This trend was observed for both the volume-based and surface-based techniques. The results suggest that surface area and cortical thickness measurements should be considered separately and preferred over gray matter volumes for imaging genetic studies. Copyright 2009 Elsevier Inc. All rights reserved.
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                Author and article information

                Journal
                Transl Psychiatry
                Transl Psychiatry
                Translational Psychiatry
                Nature Publishing Group
                2158-3188
                June 2016
                07 June 2016
                1 June 2016
                : 6
                : 6
                : e832
                Affiliations
                [1 ]Department of Neurology, Ahamason-Lovelace Brain Mapping Center, University of California Los Angeles , Los Angeles, CA, USA
                [2 ]Department of Psychiatry and Biobehavioral Sciences, University of California Los Angeles , Los Angeles, CA, USA
                Author notes
                [* ]Department of Neurology, Ahamason-Lovelace Brain Mapping Center, University of California Los Angeles , 935 Charles E Young Dr S #225, Los Angeles, CA 90095, USA. E-mail: narr@ 123456ucla.edu
                Author information
                http://orcid.org/0000-0003-2895-4212
                Article
                tp2016102
                10.1038/tp.2016.102
                4931600
                27271858
                1094e2b6-12bb-4033-8d8c-2949a4d9208a
                Copyright © 2016 Macmillan Publishers Limited

                This work is licensed under a Creative Commons Attribution 4.0 International License. The images or other third party material in this article are included in the article's Creative Commons license, unless indicated otherwise in the credit line; if the material is not included under the Creative Commons license, users will need to obtain permission from the license holder to reproduce the material. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                History
                : 09 November 2015
                : 05 April 2016
                : 24 April 2016
                Categories
                Original Article

                Clinical Psychology & Psychiatry
                Clinical Psychology & Psychiatry

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