Kornhuber, Johannes

[["dc.bibliographiccitation.artnumber","e13950"],["dc.bibliographiccitation.issue","11"],["dc.bibliographiccitation.journal","PLoS ONE"],["dc.bibliographiccitation.volume","5"],["dc.contributor.author","Jones, Lesley"],["dc.contributor.author","Holmans, Peter A."],["dc.contributor.author","Hamshere, Marian L."],["dc.contributor.author","Harold, Denise"],["dc.contributor.author","Moskvina, Valentina"],["dc.contributor.author","Ivanov, Dobril"],["dc.contributor.author","Pocklington, Andrew"],["dc.contributor.author","Abraham, Richard"],["dc.contributor.author","Hollingworth, Paul"],["dc.contributor.author","Sims, Rebecca"],["dc.contributor.author","Gerrish, Amy"],["dc.contributor.author","Pahwa, Jaspreet Singh"],["dc.contributor.author","Jones, Nicola"],["dc.contributor.author","Stretton, Alexandra"],["dc.contributor.author","Morgan, Angharad R."],["dc.contributor.author","Lovestone, Simon"],["dc.contributor.author","Powell, John"],["dc.contributor.author","Proitsi, Petroula"],["dc.contributor.author","Lupton, Michelle K."],["dc.contributor.author","Brayne, Carol"],["dc.contributor.author","Rubinsztein, David C."],["dc.contributor.author","Gill, Michael"],["dc.contributor.author","Lawlor, Brian"],["dc.contributor.author","Lynch, Aoibhinn"],["dc.contributor.author","Morgan, Kevin"],["dc.contributor.author","Brown, Kristelle S."],["dc.contributor.author","Passmore, Peter A."],["dc.contributor.author","Craig, David"],["dc.contributor.author","McGuinness, Bernadette"],["dc.contributor.author","Todd, Stephen"],["dc.contributor.author","Holmes, Clive"],["dc.contributor.author","Mann, David"],["dc.contributor.author","Smith, A. David"],["dc.contributor.author","Love, Seth"],["dc.contributor.author","Kehoe, Patrick G."],["dc.contributor.author","Mead, Simon"],["dc.contributor.author","Fox, Nick"],["dc.contributor.author","Rossor, Martin"],["dc.contributor.author","Collinge, John"],["dc.contributor.author","Maier, Wolfgang"],["dc.contributor.author","Jessen, Frank"],["dc.contributor.author","Schürmann, Britta"],["dc.contributor.author","van den Bussche, Hendrik"],["dc.contributor.author","Heuser, Isabella"],["dc.contributor.author","Peters, Oliver"],["dc.contributor.author","Kornhuber, Johannes"],["dc.contributor.author","Wiltfang, Jens"],["dc.contributor.author","Dichgans, Martin"],["dc.contributor.author","Frölich, Lutz"],["dc.contributor.author","Hampel, Harald"],["dc.contributor.author","Hüll, Michael"],["dc.contributor.author","Rujescu, Dan"],["dc.contributor.author","Goate, Alison M."],["dc.contributor.author","Kauwe, John S. K."],["dc.contributor.author","Cruchaga, Carlos"],["dc.contributor.author","Nowotny, Petra"],["dc.contributor.author","Morris, John C."],["dc.contributor.author","Mayo, Kevin"],["dc.contributor.author","Livingston, Gill"],["dc.contributor.author","Bass, Nicholas J."],["dc.contributor.author","Gurling, Hugh"],["dc.contributor.author","McQuillin, Andrew"],["dc.contributor.author","Gwilliam, Rhian"],["dc.contributor.author","Deloukas, Panos"],["dc.contributor.author","Al-Chalabi, Ammar"],["dc.contributor.author","Shaw, Christopher E."],["dc.contributor.author","Singleton, Andrew B."],["dc.contributor.author","Guerreiro, Rita"],["dc.contributor.author","Mühleisen, Thomas W."],["dc.contributor.author","Nöthen, Markus M."],["dc.contributor.author","Moebus, Susanne"],["dc.contributor.author","Jöckel, Karl-Heinz"],["dc.contributor.author","Klopp, Norman"],["dc.contributor.author","Wichmann, H.-Erich"],["dc.contributor.author","Rüther, Eckhard"],["dc.contributor.author","Carrasquillo, Minerva M."],["dc.contributor.author","Pankratz, V. Shane"],["dc.contributor.author","Younkin, Steven G."],["dc.contributor.author","Hardy, John"],["dc.contributor.author","O'Donovan, Michael C."],["dc.contributor.author","Owen, Michael J."],["dc.contributor.author","Williams, Julie"],["dc.date.accessioned","2019-07-09T11:54:00Z"],["dc.date.available","2019-07-09T11:54:00Z"],["dc.date.issued","2010-11-15"],["dc.description.abstract","Background: Late Onset Alzheimer’s disease (LOAD) is the leading cause of dementia. Recent large genome-wide association studies (GWAS) identified the first strongly supported LOAD susceptibility genes since the discovery of the involvement of APOE in the early 1990s. We have now exploited these GWAS datasets to uncover key LOAD pathophysiological processes. Methodology: We applied a recently developed tool for mining GWAS data for biologically meaningful information to a LOAD GWAS dataset. The principal findings were then tested in an independent GWAS dataset. Principal Findings: We found a significant overrepresentation of association signals in pathways related to cholesterol metabolism and the immune response in both of the two largest genome-wide association studies for LOAD. Significance: Processes related to cholesterol metabolism and the innate immune response have previously been implicated by pathological and epidemiological studies of Alzheimer’s disease, but it has been unclear whether those findings reflected primary aetiological events or consequences of the disease process. Our independent evidence from two large studies now demonstrates that these processes are aetiologically relevant, and suggests that they may be suitable targets for novel and existing therapeutic approaches."],["dc.format.extent","11"],["dc.identifier.doi","10.1371/journal.pone.0013950"],["dc.identifier.pmid","21085570"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/8307"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/60547"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.relation.issn","1932-6203"],["dc.rights","CC BY 2.5"],["dc.rights.uri","https://creativecommons.org/licenses/by/2.5"],["dc.title","Genetic Evidence Implicates the Immune System and Cholesterol Metabolism in the Aetiology of Alzheimer's Disease"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","656"],["dc.bibliographiccitation.journal","NeuroImage. Clinical"],["dc.bibliographiccitation.lastpage","662"],["dc.bibliographiccitation.volume","14"],["dc.contributor.author","Meyer, Sebastian"],["dc.contributor.author","Mueller, Karsten"],["dc.contributor.author","Stuke, Katharina"],["dc.contributor.author","Bisenius, Sandrine"],["dc.contributor.author","Diehl-Schmid, Janine"],["dc.contributor.author","Jessen, Frank"],["dc.contributor.author","Kassubek, Jan"],["dc.contributor.author","Kornhuber, Johannes"],["dc.contributor.author","Ludolph, Albert C."],["dc.contributor.author","Prudlo, Johannes"],["dc.contributor.author","Schneider, Anja"],["dc.contributor.author","Schuemberg, Katharina"],["dc.contributor.author","Yakushev, Igor"],["dc.contributor.author","Otto, Markus"],["dc.contributor.author","Schroeter, Matthias L."],["dc.date.accessioned","2019-07-09T11:44:53Z"],["dc.date.available","2019-07-09T11:44:53Z"],["dc.date.issued","2017"],["dc.description.abstract","PURPOSE: Frontotemporal lobar degeneration (FTLD) is a common cause of early onset dementia. Behavioral variant frontotemporal dementia (bvFTD), its most common subtype, is characterized by deep alterations in behavior and personality. In 2011, new diagnostic criteria were suggested that incorporate imaging criteria into diagnostic algorithms. The study aimed at validating the potential of imaging criteria to individually predict diagnosis with machine learning algorithms. MATERIALS & METHODS: Brain atrophy was measured with structural magnetic resonance imaging (MRI) at 3 Tesla in a multi-centric cohort of 52 bvFTD patients and 52 healthy control subjects from the German FTLD Consortium's Study. Beside group comparisons, diagnosis bvFTD vs. controls was individually predicted in each subject with support vector machine classification in MRI data across the whole brain or in frontotemporal, insular regions, and basal ganglia known to be mainly affected based on recent meta-analyses. Multi-center effects were controlled for with a new method, \"leave one center out\" conjunction analyses, i.e. repeatedly excluding subjects from each center from the analysis. RESULTS: Group comparisons revealed atrophy in, most consistently, the frontal lobe in bvFTD beside alterations in the insula, basal ganglia and temporal lobe. Most remarkably, support vector machine classification enabled predicting diagnosis in single patients with a high accuracy of up to 84.6%, where accuracy was highest in a region-of-interest approach focusing on frontotemporal, insular regions, and basal ganglia in comparison with the whole brain approach. CONCLUSION: Our study demonstrates that MRI, a widespread imaging technology, can individually identify bvFTD with high accuracy in multi-center imaging data, paving the road to personalized diagnostic approaches in the future."],["dc.identifier.doi","10.1016/j.nicl.2017.02.001"],["dc.identifier.pmid","28348957"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/14946"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/59118"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.relation.issn","2213-1582"],["dc.rights","CC BY-NC-ND 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by-nc-nd/4.0"],["dc.subject.ddc","610"],["dc.subject.mesh","Aged"],["dc.subject.mesh","Atrophy"],["dc.subject.mesh","Brain"],["dc.subject.mesh","Brain Mapping"],["dc.subject.mesh","Cohort Studies"],["dc.subject.mesh","Female"],["dc.subject.mesh","Frontotemporal Dementia"],["dc.subject.mesh","Humans"],["dc.subject.mesh","Image Processing, Computer-Assisted"],["dc.subject.mesh","Magnetic Resonance Imaging"],["dc.subject.mesh","Male"],["dc.subject.mesh","Middle Aged"],["dc.subject.mesh","Predictive Value of Tests"],["dc.subject.mesh","Support Vector Machine"],["dc.title","Predicting behavioral variant frontotemporal dementia with pattern classification in multi-center structural MRI data."],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","e100812"],["dc.bibliographiccitation.issue","7"],["dc.bibliographiccitation.journal","PLoS ONE"],["dc.bibliographiccitation.volume","9"],["dc.contributor.author","Wolfsgruber, Steffen"],["dc.contributor.author","Wagner, Michael"],["dc.contributor.author","Schmidtke, Klaus"],["dc.contributor.author","Froelich, Lutz"],["dc.contributor.author","Kurz, Alexander"],["dc.contributor.author","Schulz, Stefanie"],["dc.contributor.author","Hampel, Harald"],["dc.contributor.author","Heuser, Isabella"],["dc.contributor.author","Peters, Oliver"],["dc.contributor.author","Reischies, Friedel M."],["dc.contributor.author","Jahn, Holger"],["dc.contributor.author","Luckhaus, Christian"],["dc.contributor.author","Huell, Michael"],["dc.contributor.author","Gertz, Hermann-Josef"],["dc.contributor.author","Schroeder, Johannes"],["dc.contributor.author","Pantel, Johannes"],["dc.contributor.author","Rienhoff, Otto"],["dc.contributor.author","Ruether, Eckart"],["dc.contributor.author","Henn, Fritz A."],["dc.contributor.author","Wiltfang, Jens"],["dc.contributor.author","Maier, Wolfgang"],["dc.contributor.author","Kornhuber, Johannes"],["dc.contributor.author","Jessen, Frank"],["dc.date.accessioned","2018-11-07T09:37:42Z"],["dc.date.available","2018-11-07T09:37:42Z"],["dc.date.issued","2014"],["dc.description.abstract","Background: Concerns about worsening memory (\"memory concerns\"; MC) and impairment in memory performance are both predictors of Alzheimer's dementia (AD). The relationship of both in dementia prediction at the pre-dementia disease stage, however, is not well explored. Refined understanding of the contribution of both MC and memory performance in dementia prediction is crucial for defining at-risk populations. We examined the risk of incident AD by MC and memory performance in patients with mild cognitive impairment (MCI). Methods: We analyzed data of 417 MCI patients from a longitudinal multicenter observational study. Patients were classified based on presence (n = 305) vs. absence (n = 112) of MC. Risk of incident AD was estimated with Cox Proportional-Hazards regression models. Results: Risk of incident AD was increased by MC (HR = 2.55, 95% CI: 1.33-4.89), lower memory performance (HR = 0.63, 95% CI: 0.56-0.71) and ApoE4-genotype (HR = 1.89, 95% CI: 1.18-3.02). An interaction effect between MC and memory performance was observed. The predictive power of MC was greatest for patients with very mild memory impairment and decreased with increasing memory impairment. Conclusions: Our data suggest that the power of MC as a predictor of future dementia at the MCI stage varies with the patients' level of cognitive impairment. While MC are predictive at early stage MCI, their predictive value at more advanced stages of MCI is reduced. This suggests that loss of insight related to AD may occur at the late stage of MCI."],["dc.identifier.doi","10.1371/journal.pone.0100812"],["dc.identifier.isi","000339618600007"],["dc.identifier.pmid","25019225"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/10482"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/32899"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Public Library Science"],["dc.relation.issn","1932-6203"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Memory Concerns, Memory Performance and Risk of Dementia in Patients with Mild Cognitive Impairment"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","404"],["dc.bibliographiccitation.issue","5"],["dc.bibliographiccitation.journal","Dementia and Geriatric Cognitive Disorders"],["dc.bibliographiccitation.lastpage","417"],["dc.bibliographiccitation.volume","27"],["dc.contributor.author","Kornhuber, Johannes"],["dc.contributor.author","Schmidtke, Klaus"],["dc.contributor.author","Froelich, Lutz"],["dc.contributor.author","Perneczky, Robert"],["dc.contributor.author","Wolf, Stefanie"],["dc.contributor.author","Hampel, Harald"],["dc.contributor.author","Jessen, Frank"],["dc.contributor.author","Heuser, Isabella"],["dc.contributor.author","Peters, Oliver"],["dc.contributor.author","Weih, Markus"],["dc.contributor.author","Jahn, Holger"],["dc.contributor.author","Luckhaus, Christian"],["dc.contributor.author","Huell, Michael"],["dc.contributor.author","Gertz, Hermann-Josef"],["dc.contributor.author","Schroeder, Johannes"],["dc.contributor.author","Pantel, Johannes"],["dc.contributor.author","Rienhoff, Otto"],["dc.contributor.author","Seuchter, Susanne A."],["dc.contributor.author","Ruether, Eckart"],["dc.contributor.author","Henn, Fritz A."],["dc.contributor.author","Maier, Wolfgang"],["dc.contributor.author","Wiltfang, Jens"],["dc.date.accessioned","2018-11-07T08:34:22Z"],["dc.date.available","2018-11-07T08:34:22Z"],["dc.date.issued","2009"],["dc.description.abstract","Background: The German Dementia Competence Network (DCN) has established procedures for standardized multicenter acquisition of clinical, biological and imaging data, for centralized data management, and for the evaluation of new treatments. Methods: A longitudinal cohort study was set up for patients with mild cognitive impairment (MCI), patients with mild dementia and control subjects. The aims were to establish the diagnostic, differential diagnostic and prognostic power of a range of clinical, laboratory and imaging methods. Furthermore, 2 clinical trials were conducted with patients suffering from MCI and mild to moderate Alzheimer's Disease (AD). These trials aimed at evaluating the efficacy and safety of the combination of galantamine and memantine versus galantamine alone. Results: Here, we report on the scope and projects of the DCN, the methods that were employed, the composition and flow within the diverse groups of patients and control persons and on the clinical and neuropsychological baseline characteristics of the group of 2,113 subjects who participated in the observational and clinical trials. Conclusion: These data have an impact on the procedures for the early and differential clinical diagnosis of dementias, the current standard treatment of AD as well as on future clinical trials in AD. Copyright (C) 2009 S. Karger AG, Basel"],["dc.identifier.doi","10.1159/000210388"],["dc.identifier.isi","000266098300002"],["dc.identifier.pmid","19339779"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/9317"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/17792"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Karger"],["dc.relation.issn","1420-8008"],["dc.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.title","Early and Differential Diagnosis of Dementia and Mild Cognitive Impairment Design and Cohort Baseline Characteristics of the German Dementia Competence Network"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","1"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Human Mutation"],["dc.bibliographiccitation.lastpage","13"],["dc.bibliographiccitation.volume","41"],["dc.contributor.author","Karsak, Meliha"],["dc.contributor.author","Glebov, Konstantin"],["dc.contributor.author","Scheffold, Marina"],["dc.contributor.author","Bajaj, Thomas"],["dc.contributor.author","Kawalia, Amit"],["dc.contributor.author","Karaca, Ilker"],["dc.contributor.author","Rading, Sebastian"],["dc.contributor.author","Kornhuber, Johannes"],["dc.contributor.author","Peters, Oliver"],["dc.contributor.author","Diez‐Fairen, Monica"],["dc.contributor.author","Frölich, Lutz"],["dc.contributor.author","Hüll, Michael"],["dc.contributor.author","Wiltfang, Jens"],["dc.contributor.author","Scherer, Martin"],["dc.contributor.author","Riedel‐Heller, Steffi"],["dc.contributor.author","Schneider, Anja"],["dc.contributor.author","Heneka, Michael T."],["dc.contributor.author","Fliessbach, Klaus"],["dc.contributor.author","Sharaf, Ahmed"],["dc.contributor.author","Thiele, Holger"],["dc.contributor.author","Lennarz, Martina"],["dc.contributor.author","Jessen, Frank"],["dc.contributor.author","Maier, Wolfgang"],["dc.contributor.author","Kubisch, Christian"],["dc.contributor.author","Ignatova, Zoya"],["dc.contributor.author","Nürnberg, Peter"],["dc.contributor.author","Pastor, Pau"],["dc.contributor.author","Walter, Jochen"],["dc.contributor.author","Ramirez, Alfredo"],["dc.date.accessioned","2019-12-02T14:18:33Z"],["dc.date.accessioned","2021-10-27T13:21:45Z"],["dc.date.available","2019-12-02T14:18:33Z"],["dc.date.available","2021-10-27T13:21:45Z"],["dc.date.issued","2019"],["dc.description.abstract","Rare coding variants in the triggering receptor expressed on myeloid cells-2 (TREM2) gene have been associated with Alzheimer disease (AD) and homozygous TREM2 loss-of-function variants have been reported in families with monogenic frontotemporal-like dementia with/without bone abnormalities. In a whole-exome sequencing study of a family with probable AD-type dementia without pathogenic variants in known autosomal dominant dementia disease genes and negative for the apolipoprotein E (APOE) ε4 allele, we identified an extremely rare TREM2 coding variant, that is, a glycine-to-tryptophan substitution at amino acid position 145 (NM_018965.3:c.433G>T/p.[Gly145Trp]). This alteration is found in only 1 of 251,150 control alleles in gnomAD. It was present in both severely affected as well as in another putatively affected and one 61 years old as yet unaffected family member suggesting incomplete penetrance and/or a variable age of onset. Gly145 maps to an intrinsically disordered region (IDR) of TREM2 between the immunoglobulin-like and transmembrane domain. Subsequent cellular studies showed that the variant led to IDR shortening and structural changes of the mutant protein resulting in an impairment of cellular responses upon receptor activation. Our results, suggest that a p.(Gly145Trp)-induced structural disturbance and functional impairment of TREM2 may contribute to the pathogenesis of an AD-like form of dementia."],["dc.identifier.doi","10.1002/humu.23904"],["dc.identifier.eissn","1098-1004"],["dc.identifier.isbn","31464095"],["dc.identifier.issn","1059-7794"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/16802"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/92042"],["dc.language.iso","en"],["dc.notes.intern","Migrated from goescholar"],["dc.relation.eissn","1098-1004"],["dc.relation.issn","1098-1004"],["dc.relation.issn","1059-7794"],["dc.relation.orgunit","Universitätsmedizin Göttingen"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.subject.ddc","610"],["dc.title","A rare heterozygous TREM2 coding variant identified in familial clustering of dementia affects an intrinsically disordered protein region and function of TREM2"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","334"],["dc.bibliographiccitation.journal","NeuroImage. Clinical"],["dc.bibliographiccitation.lastpage","343"],["dc.bibliographiccitation.volume","14"],["dc.contributor.author","Bisenius, Sandrine"],["dc.contributor.author","Mueller, Karsten"],["dc.contributor.author","Diehl-Schmid, Janine"],["dc.contributor.author","Fassbender, Klaus"],["dc.contributor.author","Grimmer, Timo"],["dc.contributor.author","Jessen, Frank"],["dc.contributor.author","Kassubek, Jan"],["dc.contributor.author","Kornhuber, Johannes"],["dc.contributor.author","Landwehrmeyer, Bernhard"],["dc.contributor.author","Ludolph, Albert"],["dc.contributor.author","Schneider, Anja"],["dc.contributor.author","Anderl-Straub, Sarah"],["dc.contributor.author","Stuke, Katharina"],["dc.contributor.author","Danek, Adrian"],["dc.contributor.author","Otto, Markus"],["dc.contributor.author","Schroeter, Matthias L."],["dc.date.accessioned","2019-07-09T11:44:53Z"],["dc.date.available","2019-07-09T11:44:53Z"],["dc.date.issued","2017"],["dc.description.abstract","Primary progressive aphasia (PPA) encompasses the three subtypes nonfluent/agrammatic variant PPA, semantic variant PPA, and the logopenic variant PPA, which are characterized by distinct patterns of language difficulties and regional brain atrophy. To validate the potential of structural magnetic resonance imaging data for early individual diagnosis, we used support vector machine classification on grey matter density maps obtained by voxel-based morphometry analysis to discriminate PPA subtypes (44 patients: 16 nonfluent/agrammatic variant PPA, 17 semantic variant PPA, 11 logopenic variant PPA) from 20 healthy controls (matched for sample size, age, and gender) in the cohort of the multi-center study of the German consortium for frontotemporal lobar degeneration. Here, we compared a whole-brain with a meta-analysis-based disease-specific regions-of-interest approach for support vector machine classification. We also used support vector machine classification to discriminate the three PPA subtypes from each other. Whole brain support vector machine classification enabled a very high accuracy between 91 and 97% for identifying specific PPA subtypes vs. healthy controls, and 78/95% for the discrimination between semantic variant vs. nonfluent/agrammatic or logopenic PPA variants. Only for the discrimination between nonfluent/agrammatic and logopenic PPA variants accuracy was low with 55%. Interestingly, the regions that contributed the most to the support vector machine classification of patients corresponded largely to the regions that were atrophic in these patients as revealed by group comparisons. Although the whole brain approach took also into account regions that were not covered in the regions-of-interest approach, both approaches showed similar accuracies due to the disease-specificity of the selected networks. Conclusion, support vector machine classification of multi-center structural magnetic resonance imaging data enables prediction of PPA subtypes with a very high accuracy paving the road for its application in clinical settings."],["dc.identifier.doi","10.1016/j.nicl.2017.02.003"],["dc.identifier.pmid","28229040"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/14947"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/59119"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.relation.issn","2213-1582"],["dc.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.subject.ddc","610"],["dc.subject.mesh","Aged"],["dc.subject.mesh","Aphasia, Primary Progressive"],["dc.subject.mesh","Brain"],["dc.subject.mesh","Female"],["dc.subject.mesh","Humans"],["dc.subject.mesh","Image Processing, Computer-Assisted"],["dc.subject.mesh","Magnetic Resonance Imaging"],["dc.subject.mesh","Male"],["dc.subject.mesh","Middle Aged"],["dc.subject.mesh","Predictive Value of Tests"],["dc.subject.mesh","Support Vector Machine"],["dc.title","Predicting primary progressive aphasias with support vector machine approaches in structural MRI data."],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.artnumber","84"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","Alzheimer's Research & Therapy"],["dc.bibliographiccitation.volume","9"],["dc.contributor.author","Frölich, Lutz"],["dc.contributor.author","Peters, Oliver"],["dc.contributor.author","Lewczuk, Piotr"],["dc.contributor.author","Gruber, Oliver"],["dc.contributor.author","Teipel, Stefan J."],["dc.contributor.author","Gertz, Hermann J."],["dc.contributor.author","Jahn, Holger"],["dc.contributor.author","Jessen, Frank"],["dc.contributor.author","Kurz, Alexander"],["dc.contributor.author","Luckhaus, Christian"],["dc.contributor.author","Hüll, Michael"],["dc.contributor.author","Pantel, Johannes"],["dc.contributor.author","Reischies, Friedel M."],["dc.contributor.author","Schröder, Johannes"],["dc.contributor.author","Wagner, Michael"],["dc.contributor.author","Rienhoff, Otto"],["dc.contributor.author","Wolf, Stefanie"],["dc.contributor.author","Bauer, Chris"],["dc.contributor.author","Schuchhardt, Johannes"],["dc.contributor.author","Heuser, Isabella"],["dc.contributor.author","Rüther, Eckart"],["dc.contributor.author","Henn, Fritz"],["dc.contributor.author","Maier, Wolfgang"],["dc.contributor.author","Wiltfang, Jens"],["dc.contributor.author","Kornhuber, Johannes"],["dc.date.accessioned","2020-12-10T18:39:07Z"],["dc.date.available","2020-12-10T18:39:07Z"],["dc.date.issued","2017"],["dc.description.abstract","Abstract Background The progression of mild cognitive impairment (MCI) to Alzheimer’s disease (AD) dementia can be predicted by cognitive, neuroimaging, and cerebrospinal fluid (CSF) markers. Since most biomarkers reveal complementary information, a combination of biomarkers may increase the predictive power. We investigated which combination of the Mini-Mental State Examination (MMSE), Clinical Dementia Rating (CDR)-sum-of-boxes, the word list delayed free recall from the Consortium to Establish a Registry of Dementia (CERAD) test battery, hippocampal volume (HCV), amyloid-beta1–42 (Aβ42), amyloid-beta1–40 (Aβ40) levels, the ratio of Aβ42/Aβ40, phosphorylated tau, and total tau (t-Tau) levels in the CSF best predicted a short-term conversion from MCI to AD dementia. Methods We used 115 complete datasets from MCI patients of the “Dementia Competence Network”, a German multicenter cohort study with annual follow-up up to 3 years. MCI was broadly defined to include amnestic and nonamnestic syndromes. Variables known to predict progression in MCI patients were selected a priori. Nine individual predictors were compared by receiver operating characteristic (ROC) curve analysis. ROC curves of the five best two-, three-, and four-parameter combinations were analyzed for significant superiority by a bootstrapping wrapper around a support vector machine with linear kernel. The incremental value of combinations was tested for statistical significance by comparing the specificities of the different classifiers at a given sensitivity of 85%. Results Out of 115 subjects, 28 (24.3%) with MCI progressed to AD dementia within a mean follow-up period of 25.5 months. At baseline, MCI-AD patients were no different from stable MCI in age and gender distribution, but had lower educational attainment. All single biomarkers were significantly different between the two groups at baseline. ROC curves of the individual predictors gave areas under the curve (AUC) between 0.66 and 0.77, and all single predictors were statistically superior to Aβ40. The AUC of the two-parameter combinations ranged from 0.77 to 0.81. The three-parameter combinations ranged from AUC 0.80–0.83, and the four-parameter combination from AUC 0.81–0.82. None of the predictor combinations was significantly superior to the two best single predictors (HCV and t-Tau). When maximizing the AUC differences by fixing sensitivity at 85%, the two- to four-parameter combinations were superior to HCV alone. Conclusion A combination of two biomarkers of neurodegeneration (e.g., HCV and t-Tau) is not superior over the single parameters in identifying patients with MCI who are most likely to progress to AD dementia, although there is a gradual increase in the statistical measures across increasing biomarker combinations. This may have implications for clinical diagnosis and for selecting subjects for participation in clinical trials."],["dc.identifier.doi","10.1186/s13195-017-0301-7"],["dc.identifier.eissn","1758-9193"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/15155"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/77546"],["dc.language.iso","en"],["dc.notes.intern","DOI Import GROB-354"],["dc.notes.intern","Merged from goescholar"],["dc.notes.intern","In goescholar not merged with http://resolver.sub.uni-goettingen.de/purl?gs-1/16989 but duplicate"],["dc.publisher","BioMed Central"],["dc.rights","CC BY 4.0"],["dc.rights.holder","The Author(s)."],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.title","Incremental value of biomarker combinations to predict progression of mild cognitive impairment to Alzheimer’s dementia"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]