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http://doi.org/10.25358/openscience-7189
Autoren: | Gonzalez-Escamilla, Gabriel Muthuraman, Muthuraman Reich, Martin Koirala, Nabin Riedel, Christian Glaser, Martin Lange, Florian Deuschl, Günther Volkmann, Jens Groppa, Sergiu |
Titel: | Cortical network fingerprints predict deep brain stimulation outcome in dystonia |
Online-Publikationsdatum: | 21-Jun-2022 |
Erscheinungsdatum: | 2019 |
Sprache des Dokuments: | Englisch |
Zusammenfassung/Abstract: | BACKGROUND Deep brain stimulation (DBS) is an effective evidence-based therapy for dystonia. However, no unequivocal predictors of therapy responses exist. We investigated whether patients optimally responding to DBS present distinct brain network organization and structural patterns. METHODS From a German multicenter cohort of 82 dystonia patients with segmental and generalized dystonia who received DBS implantation in the globus pallidus internus, we classified patients based on the clinical response 3 years after DBS. Patients were assigned to the superior-outcome group or moderate-outcome group, depending on whether they had above or below 70% motor improvement, respectively. Fifty-one patients met MRI-quality and treatment response requirements (mean age, 51.3 ± 13.2 years; 25 female) and were included in further analysis. From preoperative MRI we assessed cortical thickness and structural covariance, which were then fed into network analysis using graph theory. We designed a support vector machine to classify subjects for the clinical response based on individual gray-matter fingerprints. RESULTS The moderate-outcome group showed cortical atrophy mainly in the sensorimotor and visuomotor areas and disturbed network topology in these regions. The structural integrity of the cortical mantle explained about 45% of the DBS stimulation amplitude for optimal response in individual subjects. Classification analyses achieved up to 88% of accuracy using individual gray-matter atrophy patterns to predict DBS outcomes. CONCLUSIONS The analysis of cortical integrity, informed by group-level network properties, could be developed into independent predictors to identify dystonia patients who benefit from DBS. |
DDC-Sachgruppe: | 610 Medizin 610 Medical sciences |
Veröffentlichende Institution: | Johannes Gutenberg-Universität Mainz |
Organisationseinheit: | FB 04 Medizin |
Veröffentlichungsort: | Mainz |
ROR: | https://ror.org/023b0x485 |
DOI: | http://doi.org/10.25358/openscience-7189 |
Version: | Published version |
Publikationstyp: | Zeitschriftenaufsatz |
Nutzungsrechte: | CC BY |
Informationen zu den Nutzungsrechten: | https://creativecommons.org/licenses/by/4.0/ |
Zeitschrift: | Movement disorders 34 10 |
Seitenzahl oder Artikelnummer: | 1536 1545 |
Verlag: | Wiley |
Verlagsort: | New York, NY |
Erscheinungsdatum: | 2019 |
ISSN: | 1531-8257 |
DOI der Originalveröffentlichung: | 10.1002/mds.27808 |
Enthalten in den Sammlungen: | JGU-Publikationen |
Dateien zu dieser Ressource:
Datei | Beschreibung | Größe | Format | ||
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cortical_network_fingerprints-20220621123408398.pdf | 1.11 MB | Adobe PDF | Öffnen/Anzeigen |