PT  - JOURNAL ARTICLE
AU  - Gabriel Gonzalez-Escamilla
AU  - Muthuraman Muthuraman
AU  - Martin M Reich
AU  - Nabin Koirala
AU  - Christian Riedel
AU  - Martin Glaser
AU  - Florian Lange
AU  - Günther Deuschl
AU  - Jens Volkmann
AU  - Sergiu Groppa
TI  - Cortical network fingerprints predict deep brain stimulation outcome in dystonia
AID  - 10.1101/470526
DP  - 2018 Jan 01
TA  - bioRxiv
PG  - 470526
4099  - http://biorxiv.org/content/early/2018/11/15/470526.short
4100  - http://biorxiv.org/content/early/2018/11/15/470526.full
AB  - Background Deep brain stimulation (DBS) is an effective evidence-based therapy for dystonia. However, no unequivocal predictors of therapy responses exist. We investigate whether patients optimally responding to DBS present distinct brain network organization and structural patterns.Methods Based on a German multicentre cohort of eighty-two dystonia patients with segmental and generalized dystonia, who received DBS implantation in the globus pallidus internus patients were classified based on the clinical response 36 months after DBS, as superior-outcome group or moderate-outcome group, as 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 into 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 group network properties and individual grey matter fingerprints.Results The moderate-outcome group showed cortical atrophy mainly in the sensorimotor and visuomotor areas and disturbed network topology in these regions. From all the structural integrity of the cortical mantle explained about 45% of the stimulation amplitude. Classification analyses achieved 88% of accuracy using individual grey matter atrophy patterns to predict DBS outcome.Conclusions The analysis of cortical integrity and network properties could be developed into independent predictors to identify dystonia patients who benefit from DBS.Author ContributionsG.G.E., M.M., and S.G. designed the study, validated the extracted data, supervised the study, made a critical revision of the manuscript for intellectual content, and approved the final version of the manuscript. M.M.R., J.K., and S.G. acquired and extracted the data. G.G.E., M.M., and N.K. extracted the data, and performed the MRI-based volumetric analysis. G.G.E. performed the statistical analysis, and drafted the manuscript for intellectual content. S.G. and M.M. made a critical revision of the manuscript for intellectual content and results interpretation. S.G. had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. All authors contributed to the writing of the manuscript for intellectual content, revised and approved the final version of the manuscript.The authors would like to thank Cheryl Ernest for proofreading the manuscript.