@article{DavisYuKeenanetal.2013,
  author    = {Davis, Lea K. and Yu, Dongmei and Keenan, Clare L. and Gamazon, Eric R. and Konkashbaev, Anuar I. and Derks, Eske M. and Neale, Benjamin M. and Yang, Jian and Lee, S. Hong and Evans, Patrick and Barr, Cathy L. and Bellodi, Laura and Benarroch, Fortu and Berrio, Gabriel Bedoya and Bienvenu, Oscar J. and Bloch, Michael H. and Blom, Rianne M. and Bruun, Ruth D. and Budman, Cathy L. and Camarena, Beatriz and Campbell, Desmond and Cappi, Carolina and Cardona Silgado, Julio C. and Cath, Danielle C. and Cavallini, Maria C. and Chavira, Denise A. and Chouinard, Sylvian and Conti, David V. and Cook, Edwin H. and Coric, Vladimir and Cullen, Bernadette A. and Deforce, Dieter and Delorme, Richard and Dion, Yves and Edlund, Christopher K. and Egberts, Karin and Falkai, Peter and Fernandez, Thomas V. and Gallagher, Patience J. and Garrido, Helena and Geller, Daniel and Girard, Simon L. and Grabe, Hans J. and Grados, Marco A. and Greenberg, Benjamin D. and Gross-Tsur, Varda and Haddad, Stephen and Heiman, Gary A. and Hemmings, Sian M. J. and Hounie, Ana G. and Illmann, Cornelia and Jankovic, Joseph and Jenike, Micheal A. and Kennedy, James L. and King, Robert A. and Kremeyer, Barbara and Kurlan, Roger and Lanzagorta, Nuria and Leboyer, Marion and Leckman, James F. and Lennertz, Leonhard and Liu, Chunyu and Lochner, Christine and Lowe, Thomas L. and Macciardi, Fabio and McCracken, James T. and McGrath, Lauren M. and Restrepo, Sandra C. Mesa and Moessner, Rainald and Morgan, Jubel and Muller, Heike and Murphy, Dennis L. and Naarden, Allan L. and Ochoa, William Cornejo and Ophoff, Roel A. and Osiecki, Lisa and Pakstis, Andrew J. and Pato, Michele T. and Pato, Carlos N. and Piacentini, John and Pittenger, Christopher and Pollak, Yehunda and Rauch, Scott L. and Renner, Tobias J. and Reus, Victor I. and Richter, Margaret A. and Riddle, Mark A. and Robertson, Mary M. and Romero, Roxana and Ros{\`a}rio, Maria C. and Rosenberg, David and Rouleau, Guy A. and Ruhrmann, Stephan and Ruiz-Linares, Andreas and Sampaio, Aline S. and Samuels, Jack and Sandor, Paul and Sheppard, Broke and Singer, Harvey S. and Smit, Jan H. and Stein, Dan J. and Strengman, E. and Tischfield, Jay A. and Valencia Duarte, Ana V. and Vallada, Homero and Van Nieuwerburgh, Flip and Veenstra-VanderWeele, Jeremy and Walitza, Susanne and Wang, Ying and Wendland, Jens R. and Westenberg, Herman G. M. and Shugart, Yin Yao and Miguel, Euripedes C. and McMahon, William and Wagner, Michael and Nicolini, Humberto and Posthuma, Danielle and Hanna, Gregory L. and Heutink, Peter and Denys, Damiaan and Arnold, Paul D. and Oostra, Ben A. and Nestadt, Gerald and Freimer, Nelson B. and Pauls, David L. and Wray, Naomi R. and Stewart, S. Evelyn and Mathews, Carol A. and Knowles, James A. and Cox, Nancy J. and Scharf, Jeremiah M.},
  title     = {Partitioning the Heritability of Tourette Syndrome and Obsessive Compulsive Disorder Reveals Differences in Genetic Architecture},
  series = {PLoS Genetics},
  volume    = {9},
  journal   = {PLoS Genetics},
  number    = {10},
  issn      = {1553-7390},
  doi       = {10.1371/journal.pgen.1003864},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-127377},
  pages     = {e1003864},
  year      = {2013},
  abstract  = {The direct estimation of heritability from genome-wide common variant data as implemented in the program Genome-wide Complex Trait Analysis (GCTA) has provided a means to quantify heritability attributable to all interrogated variants. We have quantified the variance in liability to disease explained by all SNPs for two phenotypically-related neurobehavioral disorders, obsessive-compulsive disorder (OCD) and Tourette Syndrome (TS), using GCTA. Our analysis yielded a heritability point estimate of 0.58 (se = 0.09, p = 5.64e-12) for TS, and 0.37 (se = 0.07, p = 1.5e-07) for OCD. In addition, we conducted multiple genomic partitioning analyses to identify genomic elements that concentrate this heritability. We examined genomic architectures of TS and OCD by chromosome, MAF bin, and functional annotations. In addition, we assessed heritability for early onset and adult onset OCD. Among other notable results, we found that SNPs with a minor allele frequency of less than 5\% accounted for 21\% of the TS heritability and 0\% of the OCD heritability. Additionally, we identified a significant contribution to TS and OCD heritability by variants significantly associated with gene expression in two regions of the brain (parietal cortex and cerebellum) for which we had available expression quantitative trait loci (eQTLs). Finally we analyzed the genetic correlation between TS and OCD, revealing a genetic correlation of 0.41 (se = 0.15, p = 0.002). These results are very close to previous heritability estimates for TS and OCD based on twin and family studies, suggesting that very little, if any, heritability is truly missing (i.e., unassayed) from TS and OCD GWAS studies of common variation. The results also indicate that there is some genetic overlap between these two phenotypically-related neuropsychiatric disorders, but suggest that the two disorders have distinct genetic architectures.},
  language  = {en}
}
@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{ArltBiehlTayloretal.2011,
  author    = {Arlt, Wiebke and Biehl, Michael and Taylor, Angela E. and Hahner, Stefanie and Lib{\´e}, Rossella and Hughes, Beverly A. and Schneider, Petra and Smith, David J. and Stiekema, Han and Krone, Nils and Porfiri, Emilio and Opocher, Giuseppe and Bertherat, Jer{\^o}me and Mantero, Franco and Allolio, Bruno and Terzolo, Massimo and Nightingale, Peter and Shackleton, Cedric H. L. and Bertagna, Xavier and Fassnacht, Martin and Stewart, Paul M.},
  title     = {Urine Steroid Metabolomics as a Biomarker Tool for Detecting Malignancy in Adrenal Tumors},
  series = {The Journal of Clinical Endocrinology \& Metabolism},
  volume    = {96},
  journal   = {The Journal of Clinical Endocrinology \& Metabolism},
  number    = {12},
  doi       = {10.1210/jc.2011-1565},
  url       = {http://nbn-resolving.de/urn:nbn:de:bvb:20-opus-154682},
  pages     = {3775 -- 3784},
  year      = {2011},
  abstract  = {Context: Adrenal tumors have a prevalence of around 2\% in the general population. Adrenocortical carcinoma (ACC) is rare but accounts for 2-11\% of incidentally discovered adrenal masses. Differentiating ACC from adrenocortical adenoma (ACA) represents a diagnostic challenge in patients with adrenal incidentalomas, with tumor size, imaging, and even histology all providing unsatisfactory predictive values. Objective: Here we developed a novel steroid metabolomic approach, mass spectrometry-based steroid profiling followed by machine learning analysis, and examined its diagnostic value for the detection of adrenal malignancy. Design: Quantification of 32 distinct adrenal derived steroids was carried out by gas chromatography/mass spectrometry in 24-h urine samples from 102 ACA patients (age range 19-84 yr) and 45 ACC patients (20-80 yr). Underlying diagnosis was ascertained by histology and metastasis in ACC and by clinical follow-up [median duration 52 (range 26-201) months] without evidence of metastasis in ACA. Steroid excretion data were subjected to generalized matrix learning vector quantization (GMLVQ) to identify the most discriminative steroids. Results: Steroid profiling revealed a pattern of predominantly immature, early-stage steroidogenesis in ACC. GMLVQ analysis identified a subset of nine steroids that performed best in differentiating ACA from ACC. Receiver-operating characteristics analysis of GMLVQ results demonstrated sensitivity = specificity = 90\% (area under the curve = 0.97) employing all 32 steroids and sensitivity = specificity = 88\% (area under the curve = 0.96) when using only the nine most differentiating markers. Conclusions: Urine steroid metabolomics is a novel, highly sensitive, and specific biomarker tool for discriminating benign from malignant adrenal tumors, with obvious promise for the diagnostic work-up of patients with adrenal incidentalomas.},
  language  = {en}
}