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      Hemispheric Regional Based Analysis of Diffusion Tensor Imaging and Diffusion Tensor Tractography in Patients with Temporal Lobe Epilepsy and Correlation with Patient outcomes

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          Abstract

          Imaging in the field of epilepsy surgery remains an essential tool in terms of its ability to identify regions where the seizure focus might present as a resectable area. However, in many instances, an obvious structural abnormality is not visualized. This has created the opportunity for new approaches and imaging innovation in the field of epilepsy, such as with Diffusion Tensor Imaging (DTI) and Diffusion Tensor Tractography (DTT). In this study, we aim to evaluate the use of DTI and DTT as a predictive model in the field of epilepsy, specifically Temporal Lobe Epilepsy (TLE), and correlate their clinical significance with respect to postsurgical outcomes. A hemispheric based analysis was used to compare the tract density, as well as DTI indices of the specific regions of interest from the pathologic hemisphere to the healthy hemisphere in TLE patients. A total of 22 patients with TLE (12 males, 10 females, 22–57 age range) underwent either a craniotomy, Anterior Temporal Lobectomy (ATL), or a less invasive method of Selective Laser Amygdalohippocampectomy (SLAH) and were imaged using 3.0 T Philips Achieva MR scanner. Of the participants, 12 underwent SLAH while 10 underwent ATL. The study was approved by the institutional review board of Thomas Jefferson University Hospital. Informed consent was obtained from all patients. All patients had a diagnosis of TLE according to standard clinical criteria. DTI images were acquired axially in the same anatomical location prescribed for the T1-weighted images. The raw data set consisting of diffusion volumes were first corrected for eddy current distortions and motion artifacts. Various DTI indices such as Fractional Anisotropy (FA), Mean Diffusivity (MD), Radial Diffusivity (RD) and Axial Diffusivity (AD) were estimated and co-registered to the brain parcellation map obtained by freesurfer. 16 consolidated cortical and subcortical regions were selected as regions of interest (ROIs) by a functional neurosurgeon and DTI values for each ROI were calculated and compared with the corresponding ROI in the opposite hemisphere. Also, track density imaging (TDI) of 68 white matter parcels were generated using fiber orientation distribution (FOD) based deterministic fiber tracking and compared with contralateral side of the brain in each epileptic group: left mesial temporal sclerosis (LMTS) and right MTS (RMTS)). In patients with LMTS, MD and RD values of the left hippocampus decreased significantly using two-tailed t-test (p = 0.03 and p = 0.01 respectively) compared to the right hippocampus. Also, RD showed a marginally significant decrease in left amygdala (p = 0.05). DTT analysis in LMTS shows a marginally significant decrease in the left white matter supramarginal parcel (p = 0.05). In patients with RMTS, FA showed a significant decrease in the ipsilateral mesial temporal lobe (p = 0.02), parahippocampal area (p = 0.03) and thalamus (p = 0.006). RD showed a marginally significant increase in the ipsilateral hippocampus (p = 0.05) and a significant increase in the ipsilateral parahippocampal area (p = 0.03). Also, tract density of the ipsilateral white matter inferior parietal parcel showed a marginally significant increase compared to the contralateral side (p = 0.05). With respect to postsurgical outcomes, we found an association between residual seizures and tract density in five white matter segments including ipsilateral lingual (p = 0.04), ipsilateral temporal pole (p = 0.007), ipsilateral pars opercularis (p = 0.03), ipsilateral inferior parietal (p = 0.04) and contralateral frontal pole (p = 0.04). These results may have the potential to be developed into imaging prognostic markers of postoperative outcomes and provide new insights for why some patients with TLE continue to experience postoperative seizures if pathological/clinical correlates are further confirmed.

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          Robust determination of the fibre orientation distribution in diffusion MRI: non-negativity constrained super-resolved spherical deconvolution.

          Diffusion-weighted (DW) MR images contain information about the orientation of brain white matter fibres that potentially can be used to study human brain connectivity in vivo using tractography techniques. Currently, the diffusion tensor model is widely used to extract fibre directions from DW-MRI data, but fails in regions containing multiple fibre orientations. The spherical deconvolution technique has recently been proposed to address this limitation. It provides an estimate of the fibre orientation distribution (FOD) by assuming the DW signal measured from any fibre bundle is adequately described by a single response function. However, the deconvolution is ill-conditioned and susceptible to noise contamination. This tends to introduce artefactual negative regions in the FOD, which are clearly physically impossible. In this study, the introduction of a constraint on such negative regions is proposed to improve the conditioning of the spherical deconvolution. This approach is shown to provide FOD estimates that are robust to noise whilst preserving angular resolution. The approach also permits the use of super-resolution, whereby more FOD parameters are estimated than were actually measured, improving the angular resolution of the results. The method provides much better defined fibre orientation estimates, and allows orientations to be resolved that are separated by smaller angles than previously possible. This should allow tractography algorithms to be designed that are able to track reliably through crossing fibre regions.
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            MRtrix: Diffusion tractography in crossing fiber regions

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              Voxel-based diffusion tensor imaging in patients with mesial temporal lobe epilepsy and hippocampal sclerosis.

              Mesial temporal lobe epilepsy (mTLE) with hippocampus sclerosis (HS) is an important cause for focal epilepsy. In this study, we explored the integrity of connecting networks using diffusion tensor imaging (DTI) and two whole-brain voxel-based methods: statistical parametric mapping (SPM) and tract-based spatial statistics (TBSS). Thirty-three consecutive patients with mTLE and HS undergoing presurgical evaluation were scanned at 3 T, a DTI data set was acquired and parametric maps of fractional anisotropy (FA) and mean diffusivity (MD) were calculated. Twenty-one patients had left hippocampal sclerosis (LHS) and 12 patients had right HS (RHS). These groups were compared to 37 normal control subjects using both SPM5 and TBSS. The ipsilateral temporal lobe showed widespread FA reduction in both groups. The limbic system was clearly abnormal in the LHS group, also involving the arcuate fasciculus. In RHS, changes were more restricted but also showed involvement of the contralateral temporal and inferior frontal lobe. Increased MD was found in the ipsilateral hippocampus by SPM that was only marginally detected by TBSS. In white matter regions, however, TBSS was more sensitive to changes than SPM. DTI detects extensive changes in mTLE with HS. The affected networks were principally in the ipsilateral temporal lobe and the limbic system but also the arcuate fasciculus. SPM and TBSS gave complementary information with higher sensitivity to FA changes using TBSS.
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                Author and article information

                Contributors
                mahdi.alizadeh.2@jefferson.edu
                Journal
                Sci Rep
                Sci Rep
                Scientific Reports
                Nature Publishing Group UK (London )
                2045-2322
                18 January 2019
                18 January 2019
                2019
                : 9
                : 215
                Affiliations
                [1 ]ISNI 0000 0001 2166 5843, GRID grid.265008.9, Department of Neurosurgery, , Thomas Jefferson University, ; Philadelphia, PA USA
                [2 ]ISNI 0000 0001 2166 5843, GRID grid.265008.9, Jefferson Integrated Magnetic Resonance Imaging Center, Department of Radiology, , Thomas Jefferson University, ; Philadelphia, PA USA
                [3 ]ISNI 0000 0001 2166 5843, GRID grid.265008.9, Sidney Kimmel Medical College, , Thomas Jefferson University, ; Philadelphia, PA USA
                Article
                36818
                10.1038/s41598-018-36818-x
                6338779
                30659215
                f0ac4e0d-89dd-44b0-9ba2-32c3b86ced2f
                © The Author(s) 2019

                Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.

                History
                : 30 May 2018
                : 16 November 2018
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