Resting state functional connectivity predictors of treatment response to electroconvulsive therapy in depression View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2019-12

AUTHORS

M. Moreno-Ortega, J. Prudic, S. Rowny, G. H. Patel, A. Kangarlu, S. Lee, J. Grinband, T. Palomo, T. Perera, M. F. Glasser, D. C. Javitt

ABSTRACT

There is increasing focus on use of resting-state functional connectivity (RSFC) analyses to subtype depression and to predict treatment response. To date, identification of RSFC patterns associated with response to electroconvulsive therapy (ECT) remain limited, and focused on interactions between dorsal prefrontal and regions of the limbic or default-mode networks. Deficits in visual processing are reported in depression, however, RSFC with or within the visual network have not been explored in recent models of depression. Here, we support prior studies showing in a sample of 18 patients with depression that connectivity between dorsal prefrontal and regions of the limbic and default-mode networks serves as a significant predictor. In addition, however, we demonstrate that including visual connectivity measures greatly increases predictive power of the RSFC algorithm (>80% accuracy of remission). These exploratory results encourage further investigation into visual dysfunction in depression, and use of RSFC algorithms incorporating the visual network in prediction of response to both ECT and transcranial magnetic stimulation (TMS), offering a new framework for the development of RSFC-guided TMS interventions in depression. More... »

PAGES

5071

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1038/s41598-019-41175-4

    DOI

    http://dx.doi.org/10.1038/s41598-019-41175-4

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1112971491

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/30911075


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    RDF/XML is a standard XML format for linked data.

    curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/s41598-019-41175-4'


     

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