Focke, Niels K.

[["dc.bibliographiccitation.artnumber","e0190480"],["dc.bibliographiccitation.issue","1"],["dc.bibliographiccitation.journal","PlOS ONE"],["dc.bibliographiccitation.volume","13"],["dc.contributor.author","Sahib, Ashish Kaul"],["dc.contributor.author","Erb, Michael"],["dc.contributor.author","Marquetand, Justus"],["dc.contributor.author","Martin, Pascal"],["dc.contributor.author","Elshahabi, Adham"],["dc.contributor.author","Klamer, Silke"],["dc.contributor.author","Vulliemoz, Serge"],["dc.contributor.author","Scheffler, Klaus"],["dc.contributor.author","Ethofer, Thomas"],["dc.contributor.author","Focke, Niels K"],["dc.date.accessioned","2019-07-09T11:45:07Z"],["dc.date.available","2019-07-09T11:45:07Z"],["dc.date.issued","2018"],["dc.description.abstract","The human brain is known to contain several functional networks that interact dynamically. Therefore, it is desirable to analyze the temporal features of these networks by dynamic functional connectivity (dFC). A sliding window approach was used in an event-related fMRI (visual stimulation using checkerboards) to assess the impact of repetition time (TR) and window size on the temporal features of BOLD dFC. In addition, we also examined the spatial distribution of dFC and tested the feasibility of this approach for the analysis of interictal epileptiforme discharges. 15 healthy controls (visual stimulation paradigm) and three patients with epilepsy (EEG-fMRI) were measured with EPI-fMRI. We calculated the functional connectivity degree (FCD) by determining the total number of connections of a given voxel above a predefined threshold based on Pearson correlation. FCD could capture hemodynamic changes relative to stimulus onset in controls. A significant effect of TR and window size was observed on FCD estimates. At a conventional TR of 2.6 s, FCD values were marginal compared to FCD values using sub-seconds TRs achievable with multiband (MB) fMRI. Concerning window sizes, a specific maximum of FCD values (inverted u-shape behavior) was found for each TR, indicating a limit to the possible gain in FCD for increasing window size. In patients, a dynamic FCD change was found relative to the onset of epileptiform EEG patterns, which was compatible with their clinical semiology. Our findings indicate that dynamic FCD transients are better detectable with sub-second TR than conventional TR. This approach was capable of capturing neuronal connectivity across various regions of the brain, indicating a potential to study the temporal characteristics of interictal epileptiform discharges and seizures in epilepsy patients or other brain diseases with brief events."],["dc.identifier.doi","10.1371/journal.pone.0190480"],["dc.identifier.pmid","29357371"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/15034"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/59160"],["dc.language.iso","en"],["dc.notes.intern","Merged from goescholar"],["dc.relation.issn","1932-6203"],["dc.rights","CC BY 4.0"],["dc.rights.uri","https://creativecommons.org/licenses/by/4.0"],["dc.subject.ddc","610"],["dc.subject.mesh","Adult"],["dc.subject.mesh","Brain"],["dc.subject.mesh","Brain Mapping"],["dc.subject.mesh","Case-Control Studies"],["dc.subject.mesh","Electroencephalography"],["dc.subject.mesh","Epilepsy"],["dc.subject.mesh","Humans"],["dc.subject.mesh","Magnetic Resonance Imaging"],["dc.subject.mesh","Middle Aged"],["dc.subject.mesh","Young Adult"],["dc.title","Evaluating the impact of fast-fMRI on dynamic functional connectivity in an event-based paradigm"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","5042"],["dc.bibliographiccitation.issue","17"],["dc.bibliographiccitation.journal","Human Brain Mapping"],["dc.bibliographiccitation.lastpage","5055"],["dc.bibliographiccitation.volume","40"],["dc.contributor.author","Kotikalapudi, Raviteja"],["dc.contributor.author","Martin, Pascal"],["dc.contributor.author","Erb, Michael"],["dc.contributor.author","Scheffler, Klaus"],["dc.contributor.author","Marquetand, Justus"],["dc.contributor.author","Bender, Benjamin"],["dc.contributor.author","Focke, Niels K."],["dc.date.accessioned","2019-11-14T14:53:04Z"],["dc.date.accessioned","2021-10-27T13:21:28Z"],["dc.date.available","2019-11-14T14:53:04Z"],["dc.date.available","2021-10-27T13:21:28Z"],["dc.date.issued","2019"],["dc.description.abstract","We assessed the applicability of MP2RAGE for voxel-based morphometry. To this end, we analyzed its brain tissue segmentation characteristics in healthy subjects and the potential for detecting focal epileptogenic lesions (previously visible and nonvisible). Automated results and expert visual interpretations were compared with conventional VBM variants (i.e., T1 and T1 + FLAIR). Thirty-one healthy controls and 21 patients with focal epilepsy were recruited. 3D T1-, T2-FLAIR, and MP2RAGE images (consisting of INV1, INV2, and MP2 maps) were acquired on a 3T MRI. The effects of brain tissue segmentation and lesion detection rates were analyzed among single- and multispectral VBM variants. MP2-single-contrast gave better delineation of deep, subcortical nuclei but was prone to misclassification of dura/vessels as gray matter, even more than conventional-T1. The addition of multispectral combinations (INV1, INV2, or FLAIR) could markedly reduce such misclassifications. MP2 + INV1 yielded generally clearer gray matter segmentation allowing better differentiation of white matter and neighboring gyri. Different models detected known lesions with a sensitivity between 60 and 100%. In non lesional cases, MP2 + INV1 was found to be best with a concordant rate of 37.5%, specificity of 51.6% and concordant to discordant ratio of 0.60. In summary, we show that multispectral MP2RAGE VBM (e.g., MP2 + INV1, MP2 + INV2) can improve brain tissue segmentation and lesion detection in epilepsy."],["dc.identifier.doi","10.1002/hbm.24756"],["dc.identifier.eissn","1097-0193"],["dc.identifier.issn","1065-9471"],["dc.identifier.pmid","31403244"],["dc.identifier.purl","https://resolver.sub.uni-goettingen.de/purl?gs-1/16674"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/92024"],["dc.language.iso","en"],["dc.notes.intern","Migrated from goescholar"],["dc.relation.eissn","1097-0193"],["dc.relation.issn","1097-0193"],["dc.relation.issn","1065-9471"],["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","MP2RAGE multispectral voxel‐based morphometry in focal epilepsy"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.peerReviewed","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]