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      Resting state functional connectivity patterns as biomarkers of treatment response to escitalopram in patients with major depressive disorder

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          Abstract

          Rational

          With no available response biomarkers, matching an appropriate antidepressant to an individual can be a lengthy process. Improving understanding of processes underlying treatment responsivity in depression is crucial for facilitating work on response biomarkers.

          Objectives

          To identify differences in patterns of pre-treatment resting-state functional connectivity (rsFC) that may underlie response to antidepressant treatment.

          Methods

          After a baseline MRI scan, thirty-four drug-free patients with depression were treated with an SSRI escitalopram 10 mg daily for 6 weeks; response was defined as ≥ 50% decrease in Hamilton Depression Rating Scale (HAMD) score. Thirty-one healthy controls had a baseline clinical assessment and scan. Healthy participants did not receive treatment.

          Results

          Twenty-one (62%) of patients responded to escitalopram. Treatment responsivity was associated with enhanced rsFC of the right fronto-parietal network (FPN)—with the posterior DMN, somatomotor network (SMN) and somatosensory association cortex. The lack of treatment response was characterized by reduced rsFC: of the bilateral FPN with the contralateral SMN, of the right FPN with the posterior DMN, and of the extended sensorimotor auditory area with the inferior parietal lobule (IPL) and posterior DMN. Reduced rsFC of the posterior DMN with IPL was seen in treatment responders, although only when compared with HC.

          Conclusions

          The study supports the role of resting-state networks in response to antidepressant treatment, and in particular the central role of the frontoparietal and default mode networks.

          Supplementary Information

          The online version contains supplementary material available at 10.1007/s00213-021-05915-7.

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          Most cited references57

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          A RATING SCALE FOR DEPRESSION

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            The organization of the human cerebral cortex estimated by intrinsic functional connectivity.

            Information processing in the cerebral cortex involves interactions among distributed areas. Anatomical connectivity suggests that certain areas form local hierarchical relations such as within the visual system. Other connectivity patterns, particularly among association areas, suggest the presence of large-scale circuits without clear hierarchical relations. In this study the organization of networks in the human cerebrum was explored using resting-state functional connectivity MRI. Data from 1,000 subjects were registered using surface-based alignment. A clustering approach was employed to identify and replicate networks of functionally coupled regions across the cerebral cortex. The results revealed local networks confined to sensory and motor cortices as well as distributed networks of association regions. Within the sensory and motor cortices, functional connectivity followed topographic representations across adjacent areas. In association cortex, the connectivity patterns often showed abrupt transitions between network boundaries. Focused analyses were performed to better understand properties of network connectivity. A canonical sensory-motor pathway involving primary visual area, putative middle temporal area complex (MT+), lateral intraparietal area, and frontal eye field was analyzed to explore how interactions might arise within and between networks. Results showed that adjacent regions of the MT+ complex demonstrate differential connectivity consistent with a hierarchical pathway that spans networks. The functional connectivity of parietal and prefrontal association cortices was next explored. Distinct connectivity profiles of neighboring regions suggest they participate in distributed networks that, while showing evidence for interactions, are embedded within largely parallel, interdigitated circuits. We conclude by discussing the organization of these large-scale cerebral networks in relation to monkey anatomy and their potential evolutionary expansion in humans to support cognition.
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              An inventory for measuring depression.

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                Author and article information

                Contributors
                beata.godlewska@psych.ox.ac.uk
                Journal
                Psychopharmacology (Berl)
                Psychopharmacology (Berl)
                Psychopharmacology
                Springer Berlin Heidelberg (Berlin/Heidelberg )
                0033-3158
                1432-2072
                3 September 2021
                3 September 2021
                2022
                : 239
                : 11
                : 3447-3460
                Affiliations
                [1 ]GRID grid.4991.5, ISNI 0000 0004 1936 8948, Department of Psychiatry, , University of Oxford, ; Oxford, UK
                [2 ]GRID grid.4991.5, ISNI 0000 0004 1936 8948, Wellcome Center for Integrative Neuroimaging, University of Oxford, ; OX3 9DU Oxford, UK
                [3 ]GRID grid.4991.5, ISNI 0000 0004 1936 8948, Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry, , University of Oxford, ; Oxford, UK
                [4 ]GRID grid.451190.8, ISNI 0000 0004 0573 576X, Oxford Health NHS Foundation Trust, ; Oxford, UK
                Article
                5915
                10.1007/s00213-021-05915-7
                9584978
                34477887
                13b20b3a-dd61-43f2-8c4a-bbd74fcf8660
                © The Author(s) 2021, corrected publication 2022

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 16 December 2020
                : 28 June 2021
                Funding
                Funded by: Medical Research Council (UK)
                Award ID: MR/K022202
                Categories
                Original Investigation
                Custom metadata
                © Springer-Verlag GmbH Germany, part of Springer Nature 2022

                Pharmacology & Pharmaceutical medicine
                major depressive disorder,treatment biomarkers,treatment response,escitalopram,resting-state fmri,resting-state networks,independent component analysis

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