Multi-scale image analysis and prediction of visual field defects after selective amygdalohippocampectomy
Abstract Selective amygdalohippocampectomy is an effective treatment for patients with therapy-refractory temporal lobe epilepsy but may cause visual field defect (VFD). Here, we aimed to describe tissue-specific pre- and postoperative imaging correlates of the VFD severity using whole-brain analyse...
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Nature Portfolio
2021
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oai:doaj.org-article:98b3ccc5d64e4e48ac29f980874fefa72021-12-02T14:12:46ZMulti-scale image analysis and prediction of visual field defects after selective amygdalohippocampectomy10.1038/s41598-020-80751-x2045-2322https://doaj.org/article/98b3ccc5d64e4e48ac29f980874fefa72021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-020-80751-xhttps://doaj.org/toc/2045-2322Abstract Selective amygdalohippocampectomy is an effective treatment for patients with therapy-refractory temporal lobe epilepsy but may cause visual field defect (VFD). Here, we aimed to describe tissue-specific pre- and postoperative imaging correlates of the VFD severity using whole-brain analyses from voxel- to network-level. Twenty-eight patients with temporal lobe epilepsy underwent pre- and postoperative MRI (T1-MPRAGE and Diffusion Tensor Imaging) as well as kinetic perimetry according to Goldmann standard. We probed for whole-brain gray matter (GM) and white matter (WM) correlates of VFD using voxel-based morphometry and tract-based spatial statistics, respectively. We furthermore reconstructed individual structural connectomes and conducted local and global network analyses. Two clusters in the bihemispheric middle temporal gyri indicated a postsurgical GM volume decrease with increasing VFD severity (FWE-corrected p < 0.05). A single WM cluster showed a fractional anisotropy decrease with increasing severity of VFD in the ipsilesional optic radiation (FWE-corrected p < 0.05). Furthermore, patients with (vs. without) VFD showed a higher number of postoperative local connectivity changes. Neither in the GM, WM, nor in network metrics we found preoperative correlates of VFD severity. Still, in an explorative analysis, an artificial neural network meta-classifier could predict the occurrence of VFD based on presurgical connectomes above chance level.Bastian DavidJasmine EberleDaniel DelevJennifer GaubatzConrad C. PrillwitzJan WagnerJan-Christoph Schoene-BakeGuido LuechtersAlexander RadbruchBettina WabbelsJohannes SchrammBernd WeberRainer SurgesChristian E. ElgerTheodor RüberNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-14 (2021) |
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Medicine R Science Q |
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Medicine R Science Q Bastian David Jasmine Eberle Daniel Delev Jennifer Gaubatz Conrad C. Prillwitz Jan Wagner Jan-Christoph Schoene-Bake Guido Luechters Alexander Radbruch Bettina Wabbels Johannes Schramm Bernd Weber Rainer Surges Christian E. Elger Theodor Rüber Multi-scale image analysis and prediction of visual field defects after selective amygdalohippocampectomy |
| description |
Abstract Selective amygdalohippocampectomy is an effective treatment for patients with therapy-refractory temporal lobe epilepsy but may cause visual field defect (VFD). Here, we aimed to describe tissue-specific pre- and postoperative imaging correlates of the VFD severity using whole-brain analyses from voxel- to network-level. Twenty-eight patients with temporal lobe epilepsy underwent pre- and postoperative MRI (T1-MPRAGE and Diffusion Tensor Imaging) as well as kinetic perimetry according to Goldmann standard. We probed for whole-brain gray matter (GM) and white matter (WM) correlates of VFD using voxel-based morphometry and tract-based spatial statistics, respectively. We furthermore reconstructed individual structural connectomes and conducted local and global network analyses. Two clusters in the bihemispheric middle temporal gyri indicated a postsurgical GM volume decrease with increasing VFD severity (FWE-corrected p < 0.05). A single WM cluster showed a fractional anisotropy decrease with increasing severity of VFD in the ipsilesional optic radiation (FWE-corrected p < 0.05). Furthermore, patients with (vs. without) VFD showed a higher number of postoperative local connectivity changes. Neither in the GM, WM, nor in network metrics we found preoperative correlates of VFD severity. Still, in an explorative analysis, an artificial neural network meta-classifier could predict the occurrence of VFD based on presurgical connectomes above chance level. |
| format |
article |
| author |
Bastian David Jasmine Eberle Daniel Delev Jennifer Gaubatz Conrad C. Prillwitz Jan Wagner Jan-Christoph Schoene-Bake Guido Luechters Alexander Radbruch Bettina Wabbels Johannes Schramm Bernd Weber Rainer Surges Christian E. Elger Theodor Rüber |
| author_facet |
Bastian David Jasmine Eberle Daniel Delev Jennifer Gaubatz Conrad C. Prillwitz Jan Wagner Jan-Christoph Schoene-Bake Guido Luechters Alexander Radbruch Bettina Wabbels Johannes Schramm Bernd Weber Rainer Surges Christian E. Elger Theodor Rüber |
| author_sort |
Bastian David |
| title |
Multi-scale image analysis and prediction of visual field defects after selective amygdalohippocampectomy |
| title_short |
Multi-scale image analysis and prediction of visual field defects after selective amygdalohippocampectomy |
| title_full |
Multi-scale image analysis and prediction of visual field defects after selective amygdalohippocampectomy |
| title_fullStr |
Multi-scale image analysis and prediction of visual field defects after selective amygdalohippocampectomy |
| title_full_unstemmed |
Multi-scale image analysis and prediction of visual field defects after selective amygdalohippocampectomy |
| title_sort |
multi-scale image analysis and prediction of visual field defects after selective amygdalohippocampectomy |
| publisher |
Nature Portfolio |
| publishDate |
2021 |
| url |
https://doaj.org/article/98b3ccc5d64e4e48ac29f980874fefa7 |
| work_keys_str_mv |
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