Otto, Markus

[["dc.bibliographiccitation.firstpage","124"],["dc.bibliographiccitation.journal","Cortex"],["dc.bibliographiccitation.lastpage","135"],["dc.bibliographiccitation.volume","83"],["dc.contributor.author","Teipel, Stefan J."],["dc.contributor.author","Raiser, Theresa"],["dc.contributor.author","Riedl, Lina"],["dc.contributor.author","Riederer, Isabelle"],["dc.contributor.author","Schroeter, Matthias L."],["dc.contributor.author","Bisenius, Sandrine"],["dc.contributor.author","Schneider, Anja"],["dc.contributor.author","Kornhuber, Johannes"],["dc.contributor.author","Fliessbach, Klaus"],["dc.contributor.author","Spottke, Annika"],["dc.contributor.author","Grothe, Michel J."],["dc.contributor.author","Prudlo, Johannes"],["dc.contributor.author","Kassubek, Jan"],["dc.contributor.author","Ludolph, Albert C."],["dc.contributor.author","Landwehrmeyer, Bernhard G."],["dc.contributor.author","Anderl-Straub, Sarah"],["dc.contributor.author","Otto, Markus"],["dc.contributor.author","Danek, Adrian"],["dc.date.accessioned","2018-11-07T10:07:28Z"],["dc.date.available","2018-11-07T10:07:28Z"],["dc.date.issued","2016"],["dc.description.abstract","Primary progressive aphasia (PPA) is characterized by profound destruction of cortical language areas. Anatomical studies suggest an involvement of cholinergic basal forebrain (BF) in PPA syndromes, particularly in the area of the nucleus subputaminalis (NSP). Here we aimed to determine the pattern of atrophy and structural covariance as a proxy of structural connectivity of BF nuclei in PPA variants. We studied 62 prospectively recruited cases with the clinical diagnosis of PPA and 31 healthy older control participants from the cohort study of the German consortium for frontotemporal lobar. degeneration (FTLD). We determined cortical and BF atrophy based on high-resolution magnetic resonance imaging (MRI) scans. Patterns of structural covariance of BF with cortical regions were determined using voxel-based partial least square analysis. We found significant atrophy of total BF and BF subregions in PPA patients compared with controls [F(1, 82) = 20.2, p < .001]. Atrophy was most pronounced in the NSP and the posterior BF, and most severe in the semantic variant and the nonfluent variant of PPA. Structural covariance analysis in healthy controls revealed associations of the BF nuclei, particularly the NSP, with left hemispheric predominant prefrontal, lateral temporal, and parietal cortical areas, including Broca's speech area (p < .001, permutation test). In contrast, the PPA patients showed preserved structural covariance of the BF nuclei mostly with right but not with left hemispheric cortical areas (p < .001, permutation test). Our findings agree with the neuroanatomically proposed involvement of the cholinergic BF, particularly the NSP, in PPA syndromes. We found a shift from a structural covariance of the BF with left hemispheric cortical areas in healthy aging towards right hemispheric cortical areas in PPA, possibly reflecting a consequence of the profound and early destruction of cortical language areas in PPA. (C) 2016 The Author(s). Published by Elsevier Ltd."],["dc.identifier.doi","10.1016/j.cortex.2016.07.004"],["dc.identifier.isi","000385599300011"],["dc.identifier.pmid","27509365"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/14211"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/39287"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.relation.eissn","0010-9452"],["dc.relation.issn","1973-8102"],["dc.rights","CC BY-NC-ND 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by-nc-nd/4.0"],["dc.title","Atrophy and structural covariance of the cholinergic basal forebrain in primary progressive aphasia"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.journal","Journal of Neurochemistry"],["dc.bibliographiccitation.volume","138"],["dc.contributor.author","Bisenius, Sandrine"],["dc.contributor.author","Mueller, K."],["dc.contributor.author","Diehl-Schmid, Janine"],["dc.contributor.author","Fassbender, Klaus"],["dc.contributor.author","Jessen, Frank"],["dc.contributor.author","Kassubek, Jan"],["dc.contributor.author","Kornhuber, Johannes"],["dc.contributor.author","Schneider, Anja"],["dc.contributor.author","Stuke, K."],["dc.contributor.author","Danek, A."],["dc.contributor.author","Otto, Markus"],["dc.contributor.author","Schroeter, M. L."],["dc.date.accessioned","2018-11-07T10:10:48Z"],["dc.date.available","2018-11-07T10:10:48Z"],["dc.date.issued","2016"],["dc.format.extent","350"],["dc.identifier.isi","000382568400332"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/39930"],["dc.notes.status","zu prüfen"],["dc.notes.submitter","Najko"],["dc.publisher","Wiley-blackwell"],["dc.publisher.place","Hoboken"],["dc.relation.conference","10th International Conference on Frontotemporal Dementias"],["dc.relation.eventlocation","Munich, GERMANY"],["dc.relation.issn","1471-4159"],["dc.relation.issn","0022-3042"],["dc.title","Classifying primary progressive aphasias individually with support vector machine approaches in MRI data"],["dc.type","conference_abstract"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.status","published"],["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.journal","Frontiers in Aging Neuroscience"],["dc.bibliographiccitation.volume","10"],["dc.contributor.author","Schroeter, Matthias L."],["dc.contributor.author","Pawelke, Sarah"],["dc.contributor.author","Bisenius, Sandrine"],["dc.contributor.author","Kynast, Jana"],["dc.contributor.author","Schuemberg, Katharina"],["dc.contributor.author","Polyakova, Maryna"],["dc.contributor.author","Anderl-Straub, Sarah"],["dc.contributor.author","Danek, Adrian"],["dc.contributor.author","Fassbender, Klaus"],["dc.contributor.author","Jahn, Holger"],["dc.contributor.author","Jessen, Frank"],["dc.contributor.author","Kornhuber, Johannes"],["dc.contributor.author","Lauer, Martin"],["dc.contributor.author","Prudlo, Johannes"],["dc.contributor.author","Schneider, Anja"],["dc.contributor.author","Uttner, Ingo"],["dc.contributor.author","Thöne-Otto, Angelika"],["dc.contributor.author","Otto, Markus"],["dc.contributor.author","Diehl-Schmid, Janine"],["dc.date.accessioned","2020-12-10T18:44:29Z"],["dc.date.available","2020-12-10T18:44:29Z"],["dc.date.issued","2018"],["dc.identifier.doi","10.3389/fnagi.2018.00011"],["dc.identifier.eissn","1663-4365"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/78473"],["dc.notes.intern","DOI Import GROB-354"],["dc.title","A Modified Reading the Mind in the Eyes Test Predicts Behavioral Variant Frontotemporal Dementia Better Than Executive Function Tests"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["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"]]