Otto, Markus

[["dc.bibliographiccitation.artnumber","e7624"],["dc.bibliographiccitation.issue","10"],["dc.bibliographiccitation.journal","PLoS One"],["dc.bibliographiccitation.volume","4"],["dc.contributor.author","Linker, Ralf A."],["dc.contributor.author","Brechlin, Peter"],["dc.contributor.author","Jesse, Sarah"],["dc.contributor.author","Steinacker, Petra"],["dc.contributor.author","Lee, D. H."],["dc.contributor.author","Asif, Abdul R."],["dc.contributor.author","Jahn, Olaf"],["dc.contributor.author","Tumani, Hayrettin"],["dc.contributor.author","Gold, Ralf"],["dc.contributor.author","Otto, Markus"],["dc.date.accessioned","2019-07-09T11:52:40Z"],["dc.date.available","2019-07-09T11:52:40Z"],["dc.date.issued","2009-10-28"],["dc.description.abstract","The identification of new biomarkers is of high interest for the prediction of the disease course and also for the identification of pathomechanisms in multiple sclerosis (MS). To specify markers of the chronic disease phase, we performed proteome profiling during the later phase of myelin oligodendrocyte glycoprotein induced experimental autoimmune encephalomyelitis (MOG-EAE, day 35 after immunization) as a model disease mimicking many aspects of secondary progressive MS. In comparison to healthy controls, high resolution 2 dimensional gel electrophoresis revealed a number of regulated proteins, among them glial fibrilary acidic protein (GFAP). Phase specific up-regulation of GFAP in chronic EAE was confirmed by western blotting and immunohistochemistry. Protein levels of GFAP were also increased in the cerebrospinal fluid of MS patients with specificity for the secondary progressive disease phase. In a next step, proteome profiling of an EAE model with enhanced degenerative mechanisms revealed regulation of alpha-internexin, syntaxin binding protein 1, annexin V and glutamate decarboxylase in the ciliary neurotrophic factor (CNTF) knockout mouse. The identification of these proteins implicate an increased apoptosis and enhanced axonal disintegration and correlate well the described pattern of tissue injury in CNTF -/- mice which involve oligodendrocyte (OL) apoptosis and axonal injury.In summary, our findings underscore the value of proteome analyses as screening method for stage specific biomarkers and for the identification of new culprits for tissue damage in chronic autoimmune demyelination."],["dc.format.extent","9"],["dc.identifier.doi","10.1371/journal.pone.0007624"],["dc.identifier.fs","544326"],["dc.identifier.pmid","19865482"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/5819"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/60250"],["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.subject.ddc","610"],["dc.subject.mesh","Animals"],["dc.subject.mesh","Apoptosis"],["dc.subject.mesh","Axons"],["dc.subject.mesh","Disease Models, Animal"],["dc.subject.mesh","Encephalomyelitis, Autoimmune, Experimental"],["dc.subject.mesh","Gene Expression Profiling"],["dc.subject.mesh","Gene Expression Regulation"],["dc.subject.mesh","Mice"],["dc.subject.mesh","Mice, Inbred C57BL"],["dc.subject.mesh","Mice, Transgenic"],["dc.subject.mesh","Multiple Sclerosis"],["dc.subject.mesh","Oligodendroglia"],["dc.subject.mesh","Proteome"],["dc.subject.mesh","Proteomics"],["dc.subject.mesh","Time Factors"],["dc.title","Proteome profiling in murine models of multiple sclerosis: identification of stage specific markers and culprits for tissue damage."],["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.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.journal","International journal of Alzheimer's disease"],["dc.bibliographiccitation.volume","2010"],["dc.contributor.author","Bibl, Mirko"],["dc.contributor.author","Esselmann, Hermann"],["dc.contributor.author","Lewczuk, Piotr"],["dc.contributor.author","Trenkwalder, Claudia"],["dc.contributor.author","Otto, Markus"],["dc.contributor.author","Kornhuber, Johannes"],["dc.contributor.author","Wiltfang, Jens"],["dc.contributor.author","Mollenhauer, Brit"],["dc.date.accessioned","2019-07-09T11:53:09Z"],["dc.date.available","2019-07-09T11:53:09Z"],["dc.date.issued","2010"],["dc.description.abstract","We studied the diagnostic value of CSF Aβ42/tau versus low Aβ1-42% and high Aβ1-40(ox)% levels for differential diagnosis of Alzheimer's disease (AD) and dementia with Lewy bodies (DLB), respectively. CSF of 45 patients with AD, 15 with DLB, 21 with Parkinson's disease dementia (PDD), and 40 nondemented disease controls (NDC) was analyzed by Aβ-SDS-PAGE/immunoblot and ELISAs (Aβ42 and tau). Aβ42/tau lacked specificity in discriminating AD from DLB and PDD. Best discriminating biomarkers were Aβ1-42% and Aβ1-40(ox)% for AD and DLB, respectively. AD and DLB could be differentiated by both Aβ1-42% and Aβ1-40(ox)% with an accuracy of 80% at minimum. Thus, we consider Aβ1-42% and Aβ1-40(ox)% to be useful biomarkers for AD and DLB, respectively. We propose further studies on the integration of Aβ1-42% and Aβ1-40(ox)% into conventional assay formats. Moreover, future studies should investigate the combination of Aβ1-40(ox)% and CSF alpha-synuclein for the diagnosis of DLB."],["dc.identifier.doi","10.4061/2010/761571"],["dc.identifier.fs","575758"],["dc.identifier.pmid","20862375"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/6918"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/60350"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.relation.issn","2090-0252"],["dc.rights","Goescholar"],["dc.rights.uri","https://goescholar.uni-goettingen.de/licenses"],["dc.subject.ddc","610"],["dc.title","Combined Analysis of CSF Tau, Aβ42, Aβ1-42% and Aβ1-40% in Alzheimer's Disease, Dementia with Lewy Bodies and Parkinson's Disease Dementia."],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]