@article{DeebGiordanoRossietal.2016,
  author    = {Deeb, Wissam and Giordano, James J. and Rossi, Peter J. and Mogilner, Alon Y. and Gunduz, Aysegul and Judy, Jack W. and Klassen, Bryan T. and Butson, Christopher R. and Van Horne, Craig and Deny, Damiaan and Dougherty, Darin D. and Rowell, David and Gerhardt, Greg A. and Smith, Gwenn S. and Ponce, Francisco A. and Walker, Harrison C. and Bronte-Stewart, Helen M. and Mayberg, Helen S. and Chizeck, Howard J. and Langevin, Jean-Philippe and Volkmann, Jens and Ostrem, Jill L. and Shute, Jonathan B. and Jimenez-Shahed, Joohi and Foote, Kelly D. and Wagle Shukla, Aparna and Rossi, Marvin A. and Oh, Michael and Pourfar, Michael and Rosenberg, Paul B. and Silburn, Peter A. and de Hemptine, Coralie and Starr, Philip A. and Denison, Timothy and Akbar, Umer and Grill, Warren M. and Okun, Michael S.},
  title     = {Proceedings of the Fourth Annual Deep Brain Stimulation Think Tank: A Review of Emerging Issues and Technologies},
  series = {Frontiers in Integrative Neuroscience},
  volume    = {10},
  journal   = {Frontiers in Integrative Neuroscience},
  number    = {38},
  doi       = {10.3389/fnint.2016.00038},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-168493},
  year      = {2016},
  abstract  = {This paper provides an overview of current progress in the technological advances and the use of deep brain stimulation (DBS) to treat neurological and neuropsychiatric disorders, as presented by participants of the Fourth Annual DBS Think Tank, which was convened in March 2016 in conjunction with the Center for Movement Disorders and Neurorestoration at the University of Florida, Gainesveille FL, USA. The Think Tank discussions first focused on policy and advocacy in DBS research and clinical practice, formation of registries, and issues involving the use of DBS in the treatment of Tourette Syndrome. Next, advances in the use of neuroimaging and electrochemical markers to enhance DBS specificity were addressed. Updates on ongoing use and developments of DBS for the treatment of Parkinson's disease, essential tremor, Alzheimer's disease, depression, post-traumatic stress disorder, obesity, addiction were presented, and progress toward innovation(s) in closed-loop applications were discussed. Each section of these proceedings provides updates and highlights of new information as presented at this year's international Think Tank, with a view toward current and near future advancement of the field.},
  language  = {en}
}
@article{SmithBrayHoffmanetal.2015,
  author    = {Smith, Craig J. and Bray, Benjamin D. and Hoffman, Alex and Meisel, Andreas and Heuschmann, Peter U. and Wolfe, Charles D. A. and Tyrrell, Pippa J. and Rudd, Anthony G.},
  title     = {Can a novel clinical risk score improve pneumonia prediction in acute stroke care? A UK multicenter cohort study},
  series = {Journal of the American Heart Association},
  volume    = {4},
  journal   = {Journal of the American Heart Association},
  number    = {1},
  doi       = {10.1161/JAHA.114.001307},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-144602},
  pages     = {e001307},
  year      = {2015},
  abstract  = {Background Pneumonia frequently complicates stroke and has amajor impact on outcome. We derived and internally validated a simple clinical risk score for predicting stroke-associated pneumonia (SAP), and compared the performance with an existing score (A\(^{2}\)DS\(^{2}\)). Methods and Results We extracted data for patients with ischemic stroke or intracerebral hemorrhage from the Sentinel Stroke National Audit Programme multicenter UK registry. The data were randomly allocated into derivation (n=11 551) and validation (n=11 648) samples. A multivariable logistic regression model was fitted to the derivation data to predict SAP in the first 7 days of admission. The characteristics of the score were evaluated using receiver operating characteristics (discrimination) and by plotting predicted versus observed SAP frequency in deciles of risk (calibration). Prevalence of SAP was 6.7\% overall. The final 22-point score (ISAN: prestroke Independence [modified Rankin scale], Sex, Age, National Institutes of Health Stroke Scale) exhibited good discrimination in the ischemic stroke derivation (C-statistic 0.79; 95\% CI 0.77 to 0.81) and validation (C-statistic 0.78; 95\% CI 0.76 to 0.80) samples. It was well calibrated in ischemic stroke and was further classified into meaningful risk groups (low 0 to 5, medium6 to 10, high 11 to 14, and very high >= 15) associated with SAP frequencies of 1.6\%, 4.9\%, 12.6\%, and 26.4\%, respectively, in the validation sample. Discrimination for both scores was similar, although they performed less well in the intracerebral hemorrhage patients with an apparent ceiling effect. Conclusions The ISAN score is a simple tool for predicting SAP in clinical practice. External validation is required in ischemic and hemorrhagic stroke cohorts.},
  language  = {en}
}
@article{BreuerMattheisenFranketal.2018,
  author    = {Breuer, Ren{\´e} and Mattheisen, Manuel and Frank, Josef and Krumm, Bertram and Treutlein, Jens and Kassem, Layla and Strohmaier, Jana and Herms, Stefan and M{\"u}hleisen, Thomas W. and Degenhardt, Franziska and Cichon, Sven and N{\"o}then, Markus M. and Karypis, George and Kelsoe, John and Greenwood, Tiffany and Nievergelt, Caroline and Shilling, Paul and Shekhtman, Tatyana and Edenberg, Howard and Craig, David and Szelinger, Szabolcs and Nurnberger, John and Gershon, Elliot and Alliey-Rodriguez, Ney and Zandi, Peter and Goes, Fernando and Schork, Nicholas and Smith, Erin and Koller, Daniel and Zhang, Peng and Badner, Judith and Berrettini, Wade and Bloss, Cinnamon and Byerley, William and Coryell, William and Foroud, Tatiana and Guo, Yirin and Hipolito, Maria and Keating, Brendan and Lawson, William and Liu, Chunyu and Mahon, Pamela and McInnis, Melvin and Murray, Sarah and Nwulia, Evaristus and Potash, James and Rice, John and Scheftner, William and Z{\"o}llner, Sebastian and McMahon, Francis J. and Rietschel, Marcella and Schulze, Thomas G.},
  title     = {Detecting significant genotype-phenotype association rules in bipolar disorder: market research meets complex genetics},
  series = {International Journal of Bipolar Disorders},
  volume    = {6},
  journal   = {International Journal of Bipolar Disorders},
  doi       = {10.1186/s40345-018-0132-x},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-220509},
  year      = {2018},
  abstract  = {Background Disentangling the etiology of common, complex diseases is a major challenge in genetic research. For bipolar disorder (BD), several genome-wide association studies (GWAS) have been performed. Similar to other complex disorders, major breakthroughs in explaining the high heritability of BD through GWAS have remained elusive. To overcome this dilemma, genetic research into BD, has embraced a variety of strategies such as the formation of large consortia to increase sample size and sequencing approaches. Here we advocate a complementary approach making use of already existing GWAS data: a novel data mining procedure to identify yet undetected genotype-phenotype relationships. We adapted association rule mining, a data mining technique traditionally used in retail market research, to identify frequent and characteristic genotype patterns showing strong associations to phenotype clusters. We applied this strategy to three independent GWAS datasets from 2835 phenotypically characterized patients with BD. In a discovery step, 20,882 candidate association rules were extracted. Results Two of these rules—one associated with eating disorder and the other with anxiety—remained significant in an independent dataset after robust correction for multiple testing. Both showed considerable effect sizes (odds ratio ~ 3.4 and 3.0, respectively) and support previously reported molecular biological findings. Conclusion Our approach detected novel specific genotype-phenotype relationships in BD that were missed by standard analyses like GWAS. While we developed and applied our method within the context of BD gene discovery, it may facilitate identifying highly specific genotype-phenotype relationships in subsets of genome-wide data sets of other complex phenotype with similar epidemiological properties and challenges to gene discovery efforts.},
  language  = {en}
}