TY  - JOUR
A1  - Deeb, Wissam
A1  - Giordano, James J.
A1  - Rossi, Peter J.
A1  - Mogilner, Alon Y.
A1  - Gunduz, Aysegul
A1  - Judy, Jack W.
A1  - Klassen, Bryan T.
A1  - Butson, Christopher R.
A1  - Van Horne, Craig
A1  - Deny, Damiaan
A1  - Dougherty, Darin D.
A1  - Rowell, David
A1  - Gerhardt, Greg A.
A1  - Smith, Gwenn S.
A1  - Ponce, Francisco A.
A1  - Walker, Harrison C.
A1  - Bronte-Stewart, Helen M.
A1  - Mayberg, Helen S.
A1  - Chizeck, Howard J.
A1  - Langevin, Jean-Philippe
A1  - Volkmann, Jens
A1  - Ostrem, Jill L.
A1  - Shute, Jonathan B.
A1  - Jimenez-Shahed, Joohi
A1  - Foote, Kelly D.
A1  - Wagle Shukla, Aparna
A1  - Rossi, Marvin A.
A1  - Oh, Michael
A1  - Pourfar, Michael
A1  - Rosenberg, Paul B.
A1  - Silburn, Peter A.
A1  - de Hemptine, Coralie
A1  - Starr, Philip A.
A1  - Denison, Timothy
A1  - Akbar, Umer
A1  - Grill, Warren M.
A1  - Okun, Michael S.
T1  - Proceedings of the Fourth Annual Deep Brain Stimulation Think Tank: A Review of Emerging Issues and Technologies
JF  - Frontiers in Integrative Neuroscience
N2  - 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.
KW  - deep brain stimulation
KW  - Parkinson’s disease
KW  - Alzheimer’s disease
KW  - closed-loop
KW  - depression
KW  - post-traumatic stress disorder
KW  - Tourette syndrome
KW  - DARPA
Y1  - 2016
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-168493
VL  - 10
IS  - 38
ER  - 
TY  - JOUR
A1  - Smith, Craig J.
A1  - Bray, Benjamin D.
A1  - Hoffman, Alex
A1  - Meisel, Andreas
A1  - Heuschmann, Peter U.
A1  - Wolfe, Charles D. A.
A1  - Tyrrell, Pippa J.
A1  - Rudd, Anthony G.
T1  - Can a novel clinical risk score improve pneumonia prediction in acute stroke care? A UK multicenter cohort study
JF  - Journal of the American Heart Association
N2  - 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.
KW  - acute ischemic stroke
KW  - medical complications
KW  - infection
KW  - diagnosis
KW  - stroke-associated pneumonia
KW  - clinical risk score
KW  - pneumonia
KW  - stroke, acute
KW  - metaanalysis
KW  - reliability
KW  - dysphagia
KW  - scale
KW  - mortality
KW  - intracerebral hemorrhage
Y1  - 2015
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-144602
VL  - 4
IS  - 1
ER  - 
TY  - JOUR
A1  - Breuer, René
A1  - Mattheisen, Manuel
A1  - Frank, Josef
A1  - Krumm, Bertram
A1  - Treutlein, Jens
A1  - Kassem, Layla
A1  - Strohmaier, Jana
A1  - Herms, Stefan
A1  - Mühleisen, Thomas W.
A1  - Degenhardt, Franziska
A1  - Cichon, Sven
A1  - Nöthen, Markus M.
A1  - Karypis, George
A1  - Kelsoe, John
A1  - Greenwood, Tiffany
A1  - Nievergelt, Caroline
A1  - Shilling, Paul
A1  - Shekhtman, Tatyana
A1  - Edenberg, Howard
A1  - Craig, David
A1  - Szelinger, Szabolcs
A1  - Nurnberger, John
A1  - Gershon, Elliot
A1  - Alliey-Rodriguez, Ney
A1  - Zandi, Peter
A1  - Goes, Fernando
A1  - Schork, Nicholas
A1  - Smith, Erin
A1  - Koller, Daniel
A1  - Zhang, Peng
A1  - Badner, Judith
A1  - Berrettini, Wade
A1  - Bloss, Cinnamon
A1  - Byerley, William
A1  - Coryell, William
A1  - Foroud, Tatiana
A1  - Guo, Yirin
A1  - Hipolito, Maria
A1  - Keating, Brendan
A1  - Lawson, William
A1  - Liu, Chunyu
A1  - Mahon, Pamela
A1  - McInnis, Melvin
A1  - Murray, Sarah
A1  - Nwulia, Evaristus
A1  - Potash, James
A1  - Rice, John
A1  - Scheftner, William
A1  - Zöllner, Sebastian
A1  - McMahon, Francis J.
A1  - Rietschel, Marcella
A1  - Schulze, Thomas G.
T1  - Detecting significant genotype–phenotype association rules in bipolar disorder: market research meets complex genetics
JF  - International Journal of Bipolar Disorders
N2  - 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.
KW  - bipolar disorder
KW  - subphenotypes
KW  - rule discovery
KW  - data mining
KW  - genotype-phenotype patterns
Y1  - 2018
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:20-opus-220509
VL  - 6
ER  -