Gutenberg Open Science: Cortical network fingerprints predict deep brain stimulation outcome in dystonia

Please use this identifier to cite or link to this item: http://doi.org/10.25358/openscience-7189

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DC Field	Value	Language
dc.contributor.author	Gonzalez-Escamilla, Gabriel	-
dc.contributor.author	Muthuraman, Muthuraman	-
dc.contributor.author	Reich, Martin	-
dc.contributor.author	Koirala, Nabin	-
dc.contributor.author	Riedel, Christian	-
dc.contributor.author	Glaser, Martin	-
dc.contributor.author	Lange, Florian	-
dc.contributor.author	Deuschl, Günther	-
dc.contributor.author	Volkmann, Jens	-
dc.contributor.author	Groppa, Sergiu	-
dc.date.accessioned	2022-06-21T10:33:33Z	-
dc.date.available	2022-06-21T10:33:33Z	-
dc.date.issued	2019	-
dc.identifier.uri	https://openscience.ub.uni-mainz.de/handle/20.500.12030/7203	-
dc.description.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.	en_GB
dc.language.iso	eng	de
dc.rights	CC BY	*
dc.rights.uri	https://creativecommons.org/licenses/by/4.0/	*
dc.subject.ddc	610 Medizin	de_DE
dc.subject.ddc	610 Medical sciences	en_GB
dc.title	Cortical network fingerprints predict deep brain stimulation outcome in dystonia	en_GB
dc.type	Zeitschriftenaufsatz	de
dc.identifier.doi	http://doi.org/10.25358/openscience-7189	-
jgu.type.dinitype	article	en_GB
jgu.type.version	Published version	de
jgu.type.resource	Text	de
jgu.organisation.department	FB 04 Medizin	de
jgu.organisation.number	2700	-
jgu.organisation.name	Johannes Gutenberg-Universität Mainz	-
jgu.rights.accessrights	openAccess	-
jgu.journal.title	Movement disorders	de
jgu.journal.volume	34	de
jgu.journal.issue	10	de
jgu.pages.start	1536	de
jgu.pages.end	1545	de
jgu.publisher.year	2019	-
jgu.publisher.name	Wiley	de
jgu.publisher.place	New York, NY	de
jgu.publisher.issn	1531-8257	de
jgu.organisation.place	Mainz	-
jgu.subject.ddccode	610	de
jgu.publisher.doi	10.1002/mds.27808	de
jgu.organisation.ror	https://ror.org/023b0x485
Appears in collections:	JGU-Publikationen

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