@article{Gonzalez‐EscamillaMuthuramanReichetal.2019,
  author    = {Gonzalez-Escamilla, Gabriel and Muthuraman, Muthuraman and Reich, Martin M. and Koirala, Nabin and Riedel, Christian and Glaser, Martin and Lange, Florian and Deuschl, G{\"u}nther and Volkmann, Jens and Groppa, Sergiu},
  title     = {Cortical network fingerprints predict deep brain stimulation outcome in dystonia},
  series = {Movement Disorders},
  volume    = {34},
  journal   = {Movement Disorders},
  number    = {10},
  doi       = {10.1002/mds.27808},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-213532},
  pages     = {1536 -- 1545},
  year      = {2019},
  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.},
  language  = {en}
}