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Bu J, Yin H, Ren N, Zhu H, Xu H, Zhang R, Zhang S. Structural and functional changes in the default mode network in drug-resistant epilepsy. Epilepsy Behav 2024; 151:109593. [PMID: 38157823 DOI: 10.1016/j.yebeh.2023.109593] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/07/2023] [Revised: 11/25/2023] [Accepted: 12/07/2023] [Indexed: 01/03/2024]
Abstract
PURPOSE To investigate brain network properties and connectivity abnormalities of the default mode network (DMN) in drug-resistant epilepsy (DRE). The study was based on probabilistic fiber tracking and functional connectivity (FC) analysis, to explore the structural and functional connectivity patterns change between frontal lobe epilepsy (FLE) and temporal lobe epilepsy (TLE). METHODS A total of 33 DRE patients (18 TLE and 15 FLE) and 30 healthy controls (HCs) were recruited. The volume fraction of the septal brain region of the DMN in DRE was calculated using FreeSurfer. The FC analysis was performed using Data Processing and Analysis for Brain Imaging in MATLAB. The structural connections between brain regions of the DMN were calculated based on probabilistic fiber tracking. RESULTS The left precuneus (PCUN) volumes in epilepsy groups were lower than that in HCs. Compared with FLE, TLE showed reduced FC between the left hippocampus (HIP) and PCUN/medial frontal gyrus, and between the right inferior parietal lobule (IPL) and right superior temporal gyrus. Compared with HCs, FLE showed increased FCs between the right IPL and occipital lobe, and between the left superior frontal gyrus (SFG) and bilateral superior temporal gyrus. In terms of structural connectivity, TLE exhibited increased connectivity strength between the left SFG and left PCUN, and showed reduced connection strength between the left HIP and left posterior cingulate gyrus/left PCUN, when compared with the FLE. CONCLUSIONS TLE and FLE patients showed structural and functional changes in the DMN. Compared with FLE patients, the TLE patients showed reduced structural and functional connection strengths between the left HIP and PCUN. These alterations in connection strengths holds promise for the identification of TLE and FLE.
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Affiliation(s)
- Jinxin Bu
- Department of Functional Neurosurgery, Affiliated Nanjing Brain Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, China
| | - Hangxing Yin
- Department of Neurology, Affiliated Nanjing Brain Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, China
| | - Nanxiao Ren
- Department of Functional Neurosurgery, Affiliated Nanjing Brain Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, China
| | - Haitao Zhu
- Department of Functional Neurosurgery, Affiliated Nanjing Brain Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, China
| | - Honghao Xu
- Department of Functional Neurosurgery, Affiliated Nanjing Brain Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, China
| | - Rui Zhang
- Department of Functional Neurosurgery, Affiliated Nanjing Brain Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, China.
| | - Shugang Zhang
- Department of Neurology, Affiliated Nanjing Brain Hospital of Nanjing Medical University, Nanjing 210029, Jiangsu, China.
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Shapey J, Vos SB, Mancini L, Sanders B, Thornton JS, Tournier JD, Saeed SR, Kitchen N, Khalil S, Grover P, Bradford R, Dorent R, Sparks R, Vercauteren T, Yousry T, Bisdas S, Ourselin S. Diffusion MRI of the facial-vestibulocochlear nerve complex: a prospective clinical validation study. Eur Radiol 2023; 33:8067-8076. [PMID: 37328641 PMCID: PMC10598116 DOI: 10.1007/s00330-023-09736-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2022] [Revised: 02/08/2023] [Accepted: 03/12/2023] [Indexed: 06/18/2023]
Abstract
OBJECTIVES Surgical planning of vestibular schwannoma surgery would benefit greatly from a robust method of delineating the facial-vestibulocochlear nerve complex with respect to the tumour. This study aimed to optimise a multi-shell readout-segmented diffusion-weighted imaging (rs-DWI) protocol and develop a novel post-processing pipeline to delineate the facial-vestibulocochlear complex within the skull base region, evaluating its accuracy intraoperatively using neuronavigation and tracked electrophysiological recordings. METHODS In a prospective study of five healthy volunteers and five patients who underwent vestibular schwannoma surgery, rs-DWI was performed and colour tissue maps (CTM) and probabilistic tractography of the cranial nerves were generated. In patients, the average symmetric surface distance (ASSD) and 95% Hausdorff distance (HD-95) were calculated with reference to the neuroradiologist-approved facial nerve segmentation. The accuracy of patient results was assessed intraoperatively using neuronavigation and tracked electrophysiological recordings. RESULTS Using CTM alone, the facial-vestibulocochlear complex of healthy volunteer subjects was visualised on 9/10 sides. CTM were generated in all 5 patients with vestibular schwannoma enabling the facial nerve to be accurately identified preoperatively. The mean ASSD between the annotators' two segmentations was 1.11 mm (SD 0.40) and the mean HD-95 was 4.62 mm (SD 1.78). The median distance from the nerve segmentation to a positive stimulation point was 1.21 mm (IQR 0.81-3.27 mm) and 2.03 mm (IQR 0.99-3.84 mm) for the two annotators, respectively. CONCLUSIONS rs-DWI may be used to acquire dMRI data of the cranial nerves within the posterior fossa. CLINICAL RELEVANCE STATEMENT Readout-segmented diffusion-weighted imaging and colour tissue mapping provide 1-2 mm spatially accurate imaging of the facial-vestibulocochlear nerve complex, enabling accurate preoperative localisation of the facial nerve. This study evaluated the technique in 5 healthy volunteers and 5 patients with vestibular schwannoma. KEY POINTS • Readout-segmented diffusion-weighted imaging (rs-DWI) with colour tissue mapping (CTM) visualised the facial-vestibulocochlear nerve complex on 9/10 sides in 5 healthy volunteer subjects. • Using rs-DWI and CTM, the facial nerve was visualised in all 5 patients with vestibular schwannoma and within 1.21-2.03 mm of the nerve's true intraoperative location. • Reproducible results were obtained on different scanners.
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Affiliation(s)
- Jonathan Shapey
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
- Department of Neurosurgery, King's College Hospital, London, UK.
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, UK.
| | - Sjoerd B Vos
- Centre for Medical Image Computing, University College London, London, UK
- Neuroradiological Academic Unit, UCL Institute of Neurology, London, UK
| | - Laura Mancini
- Neuroradiological Academic Unit, UCL Institute of Neurology, London, UK
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, Queen Square, London, UK
| | - Brett Sanders
- Department of Neurophysiology, National Hospital for Neurology and Neurosurgery, Queen Square, London, UK
| | - John S Thornton
- Neuroradiological Academic Unit, UCL Institute of Neurology, London, UK
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, Queen Square, London, UK
| | | | - Shakeel R Saeed
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, Queen Square, London, UK
- The Ear Institute, University College London, London, UK
- The Royal National Throat, Nose and Ear Hospital, London, UK
| | - Neil Kitchen
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, Queen Square, London, UK
| | - Sherif Khalil
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, Queen Square, London, UK
- The Royal National Throat, Nose and Ear Hospital, London, UK
| | - Patrick Grover
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, Queen Square, London, UK
| | - Robert Bradford
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, Queen Square, London, UK
| | - Reuben Dorent
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Rachel Sparks
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Tom Vercauteren
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Tarek Yousry
- Neuroradiological Academic Unit, UCL Institute of Neurology, London, UK
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, Queen Square, London, UK
| | - Sotirios Bisdas
- Neuroradiological Academic Unit, UCL Institute of Neurology, London, UK
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, Queen Square, London, UK
| | - Sebastien Ourselin
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
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Carrozzi A, Gramegna LL, Sighinolfi G, Zoli M, Mazzatenta D, Testa C, Lodi R, Tonon C, Manners DN. Methods of diffusion MRI tractography for localization of the anterior optic pathway: A systematic review of validated methods. Neuroimage Clin 2023; 39:103494. [PMID: 37651845 PMCID: PMC10477810 DOI: 10.1016/j.nicl.2023.103494] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2023] [Revised: 06/21/2023] [Accepted: 08/07/2023] [Indexed: 09/02/2023]
Abstract
The anterior optic pathway (AOP) is a system of three structures (optic nerves, optic chiasma, and optic tracts) that convey visual stimuli from the retina to the lateral geniculate nuclei. A successful reconstruction of the AOP using tractography could be helpful in several clinical scenarios, from presurgical planning and neuronavigation of sellar and parasellar surgery to monitoring the stage of fiber degeneration both in acute (e.g., traumatic optic neuropathy) or chronic conditions that affect AOP structures (e.g., amblyopia, glaucoma, demyelinating disorders or genetic optic nerve atrophies). However, its peculiar anatomy and course, as well as its surroundings, pose a serious challenge to obtaining successful tractographic reconstructions. Several AOP tractography strategies have been adopted but no standard procedure has been agreed upon. We performed a systematic review of the literature according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) 2020 guidelines in order to find the combinations of acquisition and reconstruction parameters that have been performed previously and have provided the highest rate of successful reconstruction of the AOP, in order to promote their routine implementation in clinical practice. For this purpose, we reviewed data regarding how the process of anatomical validation of the tractographies was performed. The Cochrane Handbook for Systematic Reviews of Interventions was used to assess the risk of bias and thus the study quality We identified thirty-nine studies that met our inclusion criteria, and only five were considered at low risk of bias and achieved over 80% of successful reconstructions. We found a high degree of heterogeneity in the acquisition and analysis parameters used to perform AOP tractography and different combinations of them can achieve satisfactory levels of anterior optic tractographic reconstruction both in real-life research and clinical scenarios. One thousand s/mm2 was the most frequently used b value, while both deterministic and probabilistic tractography algorithms performed morphological reconstruction of the tract satisfactorily, although probabilistic algorithms estimated a more realistic percentage of crossing fibers (45.6%) in healthy subjects. A wide heterogeneity was also found regarding the method used to assess the anatomical fidelity of the AOP reconstructions. Three main strategies can be found: direct visual direct visual assessment of the tractography superimposed to a conventional MR image, surgical evaluation, and computational methods. Because the latter is less dependent on a priori knowledge of the anatomy by the operator, computational methods of validation of the anatomy should be considered whenever possible.
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Affiliation(s)
- Alessandro Carrozzi
- Department of Biomedical and Neuromotor Sciences (DIBINEM), University of Bologna, Bologna, Italy
| | - Laura Ludovica Gramegna
- Department of Biomedical and Neuromotor Sciences (DIBINEM), University of Bologna, Bologna, Italy; IRCCS Istituto delle Scienze Neurologiche di Bologna, Functional and Molecular Neuroimaging Unit, Bologna, Italy.
| | - Giovanni Sighinolfi
- Department of Biomedical and Neuromotor Sciences (DIBINEM), University of Bologna, Bologna, Italy
| | - Matteo Zoli
- Department of Biomedical and Neuromotor Sciences (DIBINEM), University of Bologna, Bologna, Italy; IRCCS Istituto delle Scienze Neurologiche di Bologna, Pituitary Unit, Bologna, Italy
| | - Diego Mazzatenta
- Department of Biomedical and Neuromotor Sciences (DIBINEM), University of Bologna, Bologna, Italy; IRCCS Istituto delle Scienze Neurologiche di Bologna, Pituitary Unit, Bologna, Italy
| | - Claudia Testa
- Department of Physics and Astronomy, University of Bologna, Bologna, Italy
| | - Raffaele Lodi
- Department of Biomedical and Neuromotor Sciences (DIBINEM), University of Bologna, Bologna, Italy; IRCCS Istituto delle Scienze Neurologiche di Bologna, Bologna, Italy
| | - Caterina Tonon
- Department of Biomedical and Neuromotor Sciences (DIBINEM), University of Bologna, Bologna, Italy; IRCCS Istituto delle Scienze Neurologiche di Bologna, Functional and Molecular Neuroimaging Unit, Bologna, Italy
| | - David Neil Manners
- IRCCS Istituto delle Scienze Neurologiche di Bologna, Functional and Molecular Neuroimaging Unit, Bologna, Italy; Department for Life Quality Studies (QUVI), University of Bologna, Bologna, Italy
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4
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Jin R, Cai Y, Zhang S, Yang T, Feng H, Jiang H, Zhang X, Hu Y, Liu J. Computational approaches for the reconstruction of optic nerve fibers along the visual pathway from medical images: a comprehensive review. Front Neurosci 2023; 17:1191999. [PMID: 37304011 PMCID: PMC10250625 DOI: 10.3389/fnins.2023.1191999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 05/09/2023] [Indexed: 06/13/2023] Open
Abstract
Optic never fibers in the visual pathway play significant roles in vision formation. Damages of optic nerve fibers are biomarkers for the diagnosis of various ophthalmological and neurological diseases; also, there is a need to prevent the optic nerve fibers from getting damaged in neurosurgery and radiation therapy. Reconstruction of optic nerve fibers from medical images can facilitate all these clinical applications. Although many computational methods are developed for the reconstruction of optic nerve fibers, a comprehensive review of these methods is still lacking. This paper described both the two strategies for optic nerve fiber reconstruction applied in existing studies, i.e., image segmentation and fiber tracking. In comparison to image segmentation, fiber tracking can delineate more detailed structures of optic nerve fibers. For each strategy, both conventional and AI-based approaches were introduced, and the latter usually demonstrates better performance than the former. From the review, we concluded that AI-based methods are the trend for optic nerve fiber reconstruction and some new techniques like generative AI can help address the current challenges in optic nerve fiber reconstruction.
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Affiliation(s)
- Richu Jin
- Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology, Shenzhen, China
- Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Yongning Cai
- Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology, Shenzhen, China
| | - Shiyang Zhang
- Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Ting Yang
- Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Haibo Feng
- Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Hongyang Jiang
- Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Xiaoqing Zhang
- Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Yan Hu
- Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Jiang Liu
- Research Institute of Trustworthy Autonomous Systems, Southern University of Science and Technology, Shenzhen, China
- Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China
- Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation, Department of Computer Science and Engineering, Southern University of Science and Technology, Shenzhen, China
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5
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Xie L, Huang J, Yu J, Zeng Q, Hu Q, Chen Z, Xie G, Feng Y. CNTSeg: A multimodal deep-learning-based network for cranial nerves tract segmentation. Med Image Anal 2023; 86:102766. [PMID: 36812693 DOI: 10.1016/j.media.2023.102766] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2022] [Revised: 09/21/2022] [Accepted: 02/08/2023] [Indexed: 02/12/2023]
Abstract
The segmentation of cranial nerves (CNs) tracts based on diffusion magnetic resonance imaging (dMRI) provides a valuable quantitative tool for the analysis of the morphology and course of individual CNs. Tractography-based approaches can describe and analyze the anatomical area of CNs by selecting the reference streamlines in combination with ROIs-based (regions-of-interests) or clustering-based. However, due to the slender structure of CNs and the complex anatomical environment, single-modality data based on dMRI cannot provide a complete and accurate description, resulting in low accuracy or even failure of current algorithms in performing individualized CNs segmentation. In this work, we propose a novel multimodal deep-learning-based multi-class network for automated cranial nerves tract segmentation without using tractography, ROI placement or clustering, called CNTSeg. Specifically, we introduced T1w images, fractional anisotropy (FA) images, and fiber orientation distribution function (fODF) peaks into the training data set, and design the back-end fusion module which uses the complementary information of the interphase feature fusion to improve the segmentation performance. CNTSeg has achieved the segmentation of 5 pairs of CNs (i.e. optic nerve CN II, oculomotor nerve CN III, trigeminal nerve CN V, and facial-vestibulocochlear nerve CN VII/VIII). Extensive comparisons and ablation experiments show promising results and are anatomically convincing even for difficult tracts. The code will be openly available at https://github.com/IPIS-XieLei/CNTSeg.
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Affiliation(s)
- Lei Xie
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China.
| | - Jiahao Huang
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Jiangli Yu
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Qingrun Zeng
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Qiming Hu
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Zan Chen
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China; Zhejiang Provincial United Key Laboratory of Embedded Systems, Hangzhou 310023, China
| | - Guoqiang Xie
- Nuclear Industry 215 Hospital of Shaanxi Province, Xianyang, 712000, China.
| | - Yuanjing Feng
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China; Zhejiang Provincial United Key Laboratory of Embedded Systems, Hangzhou 310023, China.
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6
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Huang J, Li M, Zeng Q, Xie L, He J, Chen G, Liang J, Li M, Feng Y. Automatic oculomotor nerve identification based on
data‐driven
fiber clustering. Hum Brain Mapp 2022; 43:2164-2180. [PMID: 35092135 PMCID: PMC8996358 DOI: 10.1002/hbm.25779] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 12/09/2021] [Accepted: 12/26/2021] [Indexed: 11/10/2022] Open
Abstract
The oculomotor nerve (OCN) is the main motor nerve innervating eye muscles and can be involved in multiple flammatory, compressive, or pathologies. The diffusion magnetic resonance imaging (dMRI) tractography is now widely used to describe the trajectory of the OCN. However, the complex cranial structure leads to difficulties in fiber orientation distribution (FOD) modeling, fiber tracking, and region of interest (ROI) selection. Currently, the identification of OCN relies on expert manual operation, resulting in challenges, such as the carries high clinical, time‐consuming, and labor costs. Thus, we propose a method that can automatically identify OCN from dMRI tractography. First, we choose the multi‐shell multi‐tissue constraint spherical deconvolution (MSMT‐CSD) FOD estimation model and deterministic tractography to describe the 3D trajectory of the OCN. Then, we rely on the well‐established computational pipeline and anatomical expertise to create a data‐driven OCN tractography atlas from 40 HCP data. We identify six clusters belonging to the OCN from the atlas, including the structures of three kinds of positional relationships (pass between, pass through, and go around) with the red nuclei and two kinds of positional relationships with medial longitudinal fasciculus. Finally, we apply the proposed OCN atlas to identify the OCN automatically from 40 new HCP subjects and two patients with brainstem cavernous malformation. In terms of spatial overlap and visualization, experiment results show that the automatically and manually identified OCN fibers are consistent. Our proposed OCN atlas provides an effective tool for identifying OCN by avoiding the traditional selection strategy of ROIs.
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Affiliation(s)
- Jiahao Huang
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology Hangzhou China
- Zhejiang Provincial United Key Laboratory of Embedded Systems Hangzhou China
| | - Mengjun Li
- Department of Radiology, Second Xiangya Hospital Central South University Hunan China
- Department of Neurosurgery Capital Medical University Xuanwu Hospital Beijing China
| | - Qingrun Zeng
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology Hangzhou China
- Zhejiang Provincial United Key Laboratory of Embedded Systems Hangzhou China
| | - Lei Xie
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology Hangzhou China
- Zhejiang Provincial United Key Laboratory of Embedded Systems Hangzhou China
| | - Jianzhong He
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology Hangzhou China
- Zhejiang Provincial United Key Laboratory of Embedded Systems Hangzhou China
| | - Ge Chen
- Department of Neurosurgery Capital Medical University Xuanwu Hospital Beijing China
| | - Jiantao Liang
- Department of Neurosurgery Capital Medical University Xuanwu Hospital Beijing China
| | - Mingchu Li
- Department of Neurosurgery Capital Medical University Xuanwu Hospital Beijing China
| | - Yuanjing Feng
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology Hangzhou China
- Zhejiang Provincial United Key Laboratory of Embedded Systems Hangzhou China
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7
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Gumus M, Mack ML, Green R, Khodadadi M, Wennberg R, Crawley A, Colella B, Tarazi A, Mikulis DJ, Tator CH, Tartaglia MC. Brain Connectivity Changes in Post-Concussion Syndrome as the Neural Substrate of a Heterogeneous Syndrome. Brain Connect 2022; 12:711-724. [PMID: 35018791 DOI: 10.1089/brain.2021.0127] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND Post-concussion syndrome (PCS) or persistent symptoms of concussion refers to a constellation of symptoms that persist for weeks and months after a concussion. To better capture the heterogeneity of the symptoms of patients with post-concussion syndrome, we aimed to separate patients into clinical subtypes based on brain connectivity changes. METHODS Subject-specific structural and functional connectomes were created based on Diffusion Weighted and Resting State Functional Magnetic Resonance Imaging, respectively. Following an informed dimensionality reduction, a gaussian mixture model was used on patient specific structural and functional connectivity matrices to find potential patient clusters. For validation, the resulting patient subtypes were compared in terms of cognitive, neuropsychiatric, and post-concussive symptom differences. RESULTS Multimodal analyses of brain connectivity were predictive of behavioural outcomes. Our modelling revealed 2 patient subtypes; mild and severe. The severe group showed significantly higher levels of depression, anxiety, aggression, and a greater number of symptoms than the mild patient subgroup. CONCLUSION This study suggests that structural and functional connectivity changes together can help us better understand the symptom severity and neuropsychiatric profiles of patients with post-concussion syndrome. This work allows us to move towards precision medicine in concussions and provides a novel machine learning approach that can be applicable to other heterogeneous conditions.
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Affiliation(s)
- Melisa Gumus
- University of Toronto, 7938, 60 Leonard Avenue, Krembil Discovery Tower, Toronto, Toronto, Ontario, Canada, M5S 1A1;
| | | | - Robin Green
- University of Toronto, 7938, Toronto, Ontario, Canada;
| | | | | | | | - Brenda Colella
- University Health Network, 7989, Toronto, Ontario, Canada;
| | - Apameh Tarazi
- University Health Network, 7989, Toronto, Ontario, Canada;
| | - David J Mikulis
- Toronto Western Hospital, 26625, Joint Department of Medical Imaging, 399 Bathurst St., Toronto, Ontario, Canada, m5t2s8;
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8
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Zeng Q, Li M, Yuan S, He J, Wang J, Chen Z, Zhao C, Chen G, Liang J, Li M, Feng Y. Automated facial-vestibulocochlear nerve complex identification based on data-driven tractography clustering. NMR IN BIOMEDICINE 2021; 34:e4607. [PMID: 34486766 DOI: 10.1002/nbm.4607] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Revised: 08/04/2021] [Accepted: 08/07/2021] [Indexed: 06/13/2023]
Abstract
Small size and intricate anatomical environment are the main difficulties facing tractography of the facial-vestibulocochlear nerve complex (FVN), and lead to challenges in fiber orientation distribution (FOD) modeling, fiber tracking, region-of-interest selection, and fiber filtering. Experts need rich experience in anatomy and tractography, as well as substantial labor costs, to identify the FVN. Thus, we present a pipeline to identify the FVN automatically, in what we believe is the first study of the automated identification of the FVN. First, we created an FVN template. Forty high-resolution multishell data were used to perform data-driven fiber clustering based on the multishell multitissue constraint spherical deconvolution FOD model and deterministic tractography. We selected the brainstem and cerebellum (BS-CB) region as the seed region and removed the fibers that reach other brain regions. We then performed spectral fiber clustering twice. The first clustering was to create a BS-CB atlas and separate the fibers that pass through the cerebellopontine angle, and the other one was to extract the FVN. Second, we registered the subject-specific fibers in the space of the FVN template and assigned each fiber to the closest cluster to identify the FVN automatically by spectral embedding. We applied the proposed method to different acquirement sites, including two different healthy datasets and two tumor patient datasets. Experimental results showed that our automatic identification results have ideal colocalization with expert manual identification in terms of spatial overlap and visualization. Importantly, we successfully applied our method to tumor patient data. The FVNs identified by the proposed method were in agreement with intraoperative findings.
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Affiliation(s)
- Qingrun Zeng
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
- Zhejiang Provincial United Key Laboratory of Embedded Systems, Hangzhou, China
| | - Mengjun Li
- Department of Radiology, Second Xiangya Hospital, Central South University, Changsha, China
- Department of Neurosurgery, Capital Medical University Xuanwu Hospital, Beijing, China
| | - Shaonan Yuan
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
- Zhejiang Provincial United Key Laboratory of Embedded Systems, Hangzhou, China
| | - Jianzhong He
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
- Zhejiang Provincial United Key Laboratory of Embedded Systems, Hangzhou, China
| | - Jingqiang Wang
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
- Zhejiang Provincial United Key Laboratory of Embedded Systems, Hangzhou, China
| | - Zan Chen
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
- Zhejiang Provincial United Key Laboratory of Embedded Systems, Hangzhou, China
| | - Changchen Zhao
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
- Zhejiang Provincial United Key Laboratory of Embedded Systems, Hangzhou, China
| | - Ge Chen
- Department of Neurosurgery, Capital Medical University Xuanwu Hospital, Beijing, China
| | - Jiantao Liang
- Department of Neurosurgery, Capital Medical University Xuanwu Hospital, Beijing, China
| | - Mingchu Li
- Department of Neurosurgery, Capital Medical University Xuanwu Hospital, Beijing, China
| | - Yuanjing Feng
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
- Zhejiang Provincial United Key Laboratory of Embedded Systems, Hangzhou, China
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Seow P, Hernowo AT, Narayanan V, Wong JHD, Bahuri NFA, Cham CY, Abdullah NA, Kadir KAA, Rahmat K, Ramli N. Neural Fiber Integrity in High- Versus Low-Grade Glioma using Probabilistic Fiber Tracking. Acad Radiol 2021; 28:1721-1732. [PMID: 33023809 DOI: 10.1016/j.acra.2020.09.007] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2020] [Revised: 09/05/2020] [Accepted: 09/07/2020] [Indexed: 02/02/2023]
Abstract
RATIONALE AND OBJECTIVES Gliomatous tumors are known to affect neural fiber integrity, either by displacement or destruction. The aim of this study is to investigate the integrity and distribution of the white matter tracts within and around the glioma regions using probabilistic fiber tracking. MATERIAL AND METHODS Forty-two glioma patients were subjected to MRI using a standard tumor protocol with diffusion tensor imaging (DTI). The tumor and peritumor regions were delineated using snake model with reference to structural and diffusion MRI. A preprocessing pipeline of the structural MRI image, DTI data, and tumor regions was implemented. Tractography was performed to delineate the white matter (WM) tracts in the selected tumor regions via probabilistic fiber tracking. DTI indices were investigated through comparative mapping of WM tracts and tumor regions in low-grade gliomas (LGG) and high-grade gliomas (HGG). RESULTS Significant differences were seen in the planar tensor (Cp) in peritumor regions; mean diffusivity, axial diffusivity and pure isotropic diffusion in solid-enhancing tumor regions; and fractional anisotropy, axial diffusivity, pure anisotropic diffusion (q), total magnitude of diffusion tensor (L), relative anisotropy, Cp and spherical tensor (Cs) in solid nonenhancing tumor regions for affected WM tracts. In most cases of HGG, the WM tracts were not completely destroyed, but found intact inside the tumor. DISCUSSION Probabilistic fiber tracking revealed the existence and distribution of WM tracts inside tumor core for both LGG and HGG groups. There were more DTI indices in the solid nonenhancing tumor region, which showed significant differences between LGG and HGG.
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10
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He J, Zhang F, Xie G, Yao S, Feng Y, Bastos DCA, Rathi Y, Makris N, Kikinis R, Golby AJ, O'Donnell LJ. Comparison of multiple tractography methods for reconstruction of the retinogeniculate visual pathway using diffusion MRI. Hum Brain Mapp 2021; 42:3887-3904. [PMID: 33978265 PMCID: PMC8288095 DOI: 10.1002/hbm.25472] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 04/24/2021] [Accepted: 04/25/2021] [Indexed: 12/31/2022] Open
Abstract
The retinogeniculate visual pathway (RGVP) conveys visual information from the retina to the lateral geniculate nucleus. The RGVP has four subdivisions, including two decussating and two nondecussating pathways that cannot be identified on conventional structural magnetic resonance imaging (MRI). Diffusion MRI tractography has the potential to trace these subdivisions and is increasingly used to study the RGVP. However, it is not yet known which fiber tracking strategy is most suitable for RGVP reconstruction. In this study, four tractography methods are compared, including constrained spherical deconvolution (CSD) based probabilistic (iFOD1) and deterministic (SD‐Stream) methods, and multi‐fiber (UKF‐2T) and single‐fiber (UKF‐1T) unscented Kalman filter (UKF) methods. Experiments use diffusion MRI data from 57 subjects in the Human Connectome Project. The RGVP is identified using regions of interest created by two clinical experts. Quantitative anatomical measurements and expert anatomical judgment are used to assess the advantages and limitations of the four tractography methods. Overall, we conclude that UKF‐2T and iFOD1 produce the best RGVP reconstruction results. The iFOD1 method can better quantitatively estimate the percentage of decussating fibers, while the UKF‐2T method produces reconstructed RGVPs that are judged to better correspond to the known anatomy and have the highest spatial overlap across subjects. Overall, we find that it is challenging for current tractography methods to both accurately track RGVP fibers that correspond to known anatomy and produce an approximately correct percentage of decussating fibers. We suggest that future algorithm development for RGVP tractography should take consideration of both of these two points.
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Affiliation(s)
- Jianzhong He
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou, China.,Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Fan Zhang
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Guoqiang Xie
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.,Department of Neurosurgery, Nuclear Industry 215 Hospital of Shaanxi Province, Xianyang, China
| | - Shun Yao
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.,Center for Pituitary Tumor Surgery, Department of Neurosurgery, The First Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Yuanjing Feng
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Dhiego C A Bastos
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Yogesh Rathi
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.,Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Nikos Makris
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.,Departments of Psychiatry, Neurology and Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Ron Kikinis
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Alexandra J Golby
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.,Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Lauren J O'Donnell
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
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11
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Abstract
Magnetic resonance (MR) imaging is a crucial tool for evaluation of the skull base, enabling characterization of complex anatomy by utilizing multiple image contrasts. Recent technical MR advances have greatly enhanced radiologists' capability to diagnose skull base pathology and help direct management. In this paper, we will summarize cutting-edge clinical and emerging research MR techniques for the skull base, including high-resolution, phase-contrast, diffusion, perfusion, vascular, zero echo-time, elastography, spectroscopy, chemical exchange saturation transfer, PET/MR, ultra-high-field, and 3D visualization. For each imaging technique, we provide a high-level summary of underlying technical principles accompanied by relevant literature review and clinical imaging examples.
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Affiliation(s)
- Claudia F Kirsch
- Division Chief, Neuroradiology, Professor of Neuroradiology and Otolaryngology, Department of Radiology, Northwell Health, Zucker Hofstra School of Medicine at Northwell, North Shore University Hospital, Manhasset, NY
| | - Mai-Lan Ho
- Associate Professor of Radiology, Director of Research, Department of Radiology, Director, Advanced Neuroimaging Core, Chair, Asian Pacific American Network, Secretary, Association for Staff and Faculty Women, Nationwide Children's Hospital and The Ohio State University, Columbus, OH; Division Chief, Neuroradiology, Professor of Neuroradiology and Otolaryngology, Department of Radiology, Northwell Health, Zucker Hofstra School of Medicine at Northwell, North Shore University Hospital, Manhasset, NY.
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12
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Chen Z, Liu P, Zhang C, Yu Z, Feng T. Neural markers of procrastination in white matter microstructures and networks. Psychophysiology 2021; 58:e13782. [PMID: 33586198 DOI: 10.1111/psyp.13782] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2020] [Revised: 01/13/2021] [Accepted: 01/13/2021] [Indexed: 01/20/2023]
Abstract
More than 15% of adults suffer from pathological procrastination, which leads to substantial harm to their mental and psychiatric health. Our previous work demonstrated the role of three neuroanatomical networks as neural substrates of procrastination, but their potential interaction remains unknown. Three large-scale independent samples (total n = 901) were recruited. In sample A, tract-based spatial statistics (TBSS) and connectome-based graph-theoretical analysis was conducted to probe association between topological properties of white matter (WM) network and procrastination. In sample B, the above analysis was reproduced to demonstrate replicability. In sample C, machine learning models were built to predict individual procrastination. TBSS results showed a negative association between procrastination and WM integrity of limbic-prefrontal connection, and a positive relationship between intra-connection within the limbic system and procrastination. Also, both the efficiency and integrity of limbic WM network were found to be linked to procrastination. The above findings were all confirmed to replicate in an independent sample; prediction models demonstrated that these WM features can predict procrastination accurately in sample C. In conclusion, this study moves forward our understanding of procrastination by clarifying the role of interplay of self-control and emotional regulation with it.
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Affiliation(s)
- Zhiyi Chen
- Faculty of Psychology, Southwest University, Chongqing, China.,Key Laboratory of Cognition and Personality, Ministry of Education, Chongqing, China
| | - Peiwei Liu
- Department of Psychology, University of Florida, Gainesville, FL, USA
| | - Chenyan Zhang
- Cognitive Psychology Unit, Faculty of Social and Behavioural Sciences, The Institute of Psychology, Leiden University, Leiden, Netherlands
| | - Zeyuan Yu
- Teacher College, Southwest University, Chongqing, China
| | - Tingyong Feng
- Faculty of Psychology, Southwest University, Chongqing, China.,Key Laboratory of Cognition and Personality, Ministry of Education, Chongqing, China
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13
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Diffusion Tensor Imaging Tractography Detecting Isolated Oculomotor Paralysis Caused by Pituitary Apoplexy. Neurologist 2020; 25:157-161. [PMID: 33181723 DOI: 10.1097/nrl.0000000000000290] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
OBJECTIVES Pituitary apoplexy (PA)-induced oculomotor palsy, although rare, can be caused by compression on the lateral wall of the cavernous sinus. This study aimed to visualize PA-induced oculomotor nerve damage using diffusion tensor imaging (DTI) tractography. MATERIALS AND METHODS We enrolled 5 patients with PA-induced isolated oculomotor palsy (patient group) and 10 healthy participants (control group); all underwent DTI tractography preoperatively. Fractional anisotropy (FA) and mean diffusion (MD) values of the cisternal portion of the bilateral oculomotor nerve were measured. DTI tractography was repeated after the recovery of oculomotor palsy. RESULTS While no statistical difference was observed in FA and MD values of the bilateral oculomotor nerve in the control group (P>0.05), the oculomotor nerve on the affected side was disrupted in the patient group, with a statistical difference in FA and MD values of the bilateral oculomotor nerve (P<0.01). After the recovery of oculomotor palsy, the FA value of the oculomotor nerve on the affected side increased, whereas the MD value decreased (P<0.01). Meanwhile, no significant difference was observed in FA and MD values of the bilateral oculomotor nerve (P>0.05). DTI tractography of the oculomotor nerve on the affected side revealed restoration of integrity. Furthermore, the symptoms of oculomotor palsy improved in all patients 7 days postoperatively. CONCLUSION DTI tractography could be a helpful adjunct to the standard clinical and paraclinical ophthalmoplegia examinations in patients with PA; thus, this study establishes the feasibility of DTI tractography in this specific clinical setting.
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14
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Zhang F, Xie G, Leung L, Mooney MA, Epprecht L, Norton I, Rathi Y, Kikinis R, Al-Mefty O, Makris N, Golby AJ, O'Donnell LJ. Creation of a novel trigeminal tractography atlas for automated trigeminal nerve identification. Neuroimage 2020; 220:117063. [PMID: 32574805 PMCID: PMC7572753 DOI: 10.1016/j.neuroimage.2020.117063] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 06/07/2020] [Accepted: 06/14/2020] [Indexed: 12/29/2022] Open
Abstract
Diffusion MRI (dMRI) tractography has been successfully used to study the trigeminal nerves (TGNs) in many clinical and research applications. Currently, identification of the TGN in tractography data requires expert nerve selection using manually drawn regions of interest (ROIs), which is prone to inter-observer variability, time-consuming and carries high clinical and labor costs. To overcome these issues, we propose to create a novel anatomically curated TGN tractography atlas that enables automated identification of the TGN from dMRI tractography. In this paper, we first illustrate the creation of a trigeminal tractography atlas. Leveraging a well-established computational pipeline and expert neuroanatomical knowledge, we generate a data-driven TGN fiber clustering atlas using tractography data from 50 subjects from the Human Connectome Project. Then, we demonstrate the application of the proposed atlas for automated TGN identification in new subjects, without relying on expert ROI placement. Quantitative and visual experiments are performed with comparison to expert TGN identification using dMRI data from two different acquisition sites. We show highly comparable results between the automatically and manually identified TGNs in terms of spatial overlap and visualization, while our proposed method has several advantages. First, our method performs automated TGN identification, and thus it provides an efficient tool to reduce expert labor costs and inter-operator bias relative to expert manual selection. Second, our method is robust to potential imaging artifacts and/or noise that can prevent successful manual ROI placement for TGN selection and hence yields a higher successful TGN identification rate.
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Affiliation(s)
- Fan Zhang
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA.
| | - Guoqiang Xie
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; Department of Neurosurgery, Nuclear Industry 215 Hospital of Shaanxi Province, Xianyang, China
| | - Laura Leung
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China
| | - Michael A Mooney
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Lorenz Epprecht
- Department of Otolaryngology, Head and Neck Surgery, University Hospital Zurich, Zurich, Switzerland
| | - Isaiah Norton
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Yogesh Rathi
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Ron Kikinis
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Ossama Al-Mefty
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Nikos Makris
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; Departments of Psychiatry, Neurology and Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, USA
| | - Alexandra J Golby
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Lauren J O'Donnell
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
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15
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Adachi K, Hasegawa M, Hirose Y. Prediction of trigeminal nerve position based on the main feeding artery in petroclival meningioma. Neurosurg Rev 2020; 44:1173-1181. [PMID: 32424648 DOI: 10.1007/s10143-020-01313-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2020] [Revised: 04/09/2020] [Accepted: 04/29/2020] [Indexed: 10/24/2022]
Abstract
The trigeminal nerve is often displaced by petroclival meningioma (PCM) compression, making it difficult to locate during PCM surgery. This study investigated whether the deviated position of the trigeminal nerve could be easily predicted using the main tumor feeding artery. We retrospectively examined 32 patients who underwent surgery for primary PCM. The deviation of the trigeminal nerve was classified as either Type 1 (displacement toward the back of the cerebellar tentorium), Type 2 (toward the back of the superior petrosal sinus), Type 3 (toward the back of the petrous apex dura), Type 4 (toward the inferior aspect of the tumor), or Type 5 (toward the surface of the brain stem). The main feeding artery was determined by preoperative angiography. The trigeminal nerve was classified as Type 2 in 60% of cases where the proximal tentorial artery (TA) was the main feeding vessel. The nerve was Type 5 where the distal portion of the TA was the main feeding vessel (60% of the cases). The nerves were Type 3 and Type 4 where the proximal inferior lateral trunk (ILT) (60%) and distal ILT (75%), respectively, were the main feeding vessels. In 66.7% of the cases where the dorsal meningeal artery was the main feeding vessel, the nerve was Type 3. Type 1 classification applied in all cases where the ascending pharyngeal artery was the main feeding artery. The main feeding artery can be used to predict trigeminal nerve transposition during PCM surgery.
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Affiliation(s)
- Kazuhide Adachi
- Department of Neurosurgery, School of Medicine, Fujita Health University, 1-98, Kutsugake Dengakugakubo, Toyoake City, Aichi, 470-1192, Japan.
| | - Mituhiro Hasegawa
- Department of Neurosurgery, School of Medicine, Fujita Health University, 1-98, Kutsugake Dengakugakubo, Toyoake City, Aichi, 470-1192, Japan
| | - Yuichi Hirose
- Department of Neurosurgery, School of Medicine, Fujita Health University, 1-98, Kutsugake Dengakugakubo, Toyoake City, Aichi, 470-1192, Japan
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16
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Wende T, Hoffmann KT, Meixensberger J. Tractography in Neurosurgery: A Systematic Review of Current Applications. J Neurol Surg A Cent Eur Neurosurg 2020; 81:442-455. [PMID: 32176926 DOI: 10.1055/s-0039-1691823] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
The ability to visualize the brain's fiber connections noninvasively in vivo is relatively young compared with other possibilities of functional magnetic resonance imaging. Although many studies showed tractography to be of promising value for neurosurgical care, the implications remain inconclusive. An overview of current applications is presented in this systematic review. A search was conducted for (("tractography" or "fiber tracking" or "fibre tracking") and "neurosurgery") that produced 751 results. We identified 260 relevant articles and added 20 more from other sources. Most publications concerned surgical planning for resection of tumors (n = 193) and vascular lesions (n = 15). Preoperative use of transcranial magnetic stimulation was discussed in 22 of these articles. Tractography in skull base surgery presents a special challenge (n = 29). Fewer publications evaluated traumatic brain injury (TBI) (n = 25) and spontaneous intracranial bleeding (n = 22). Twenty-three articles focused on tractography in pediatric neurosurgery. Most authors found tractography to be a valuable addition in neurosurgical care. The accuracy of the technique has increased over time. There are articles suggesting that tractography improves patient outcome after tumor resection. However, no reliable biomarkers have yet been described. The better rehabilitation potential after TBI and spontaneous intracranial bleeding compared with brain tumors offers an insight into the process of neurorehabilitation. Tractography and diffusion measurements in some studies showed a correlation with patient outcome that might help uncover the neuroanatomical principles of rehabilitation itself. Alternative corticofugal and cortico-cortical networks have been implicated in motor recovery after ischemic stroke, suggesting more complex mechanisms in neurorehabilitation that go beyond current models. Hence tractography may potentially be able to predict clinical deficits and rehabilitation potential, as well as finding possible explanations for neurologic disorders in retrospect. However, large variations of the results indicate a lack of data to establish robust diagnostical concepts at this point. Therefore, in vivo tractography should still be interpreted with caution and by experienced surgeons.
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Affiliation(s)
- Tim Wende
- Department of Neurosurgery, University Hospital Leipzig, Leipzig, Germany
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17
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Xie G, Zhang F, Leung L, Mooney MA, Epprecht L, Norton I, Rathi Y, Kikinis R, Al-Mefty O, Makris N, Golby AJ, O'Donnell LJ. Anatomical assessment of trigeminal nerve tractography using diffusion MRI: A comparison of acquisition b-values and single- and multi-fiber tracking strategies. NEUROIMAGE-CLINICAL 2020; 25:102160. [PMID: 31954337 PMCID: PMC6962690 DOI: 10.1016/j.nicl.2019.102160] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/19/2019] [Revised: 12/26/2019] [Accepted: 12/28/2019] [Indexed: 12/14/2022]
Abstract
Investigation of the performance of multiple dMRI acquisitions and fiber models for trigeminal nerve (TGN) identification. Expert rating study of over 1000 TGN visualizations using seven proposed expert rating anatomical criteria. The two-tensor tractography method had better performance on identifying true positive structures, while generating more false positive streamlines in comparison to the single-tensor tractography method. TGN tracking performance was significantly different across the three b-values for almost all structures studied.
Background The trigeminal nerve (TGN) is the largest cranial nerve and can be involved in multiple inflammatory, compressive, ischemic or other pathologies. Currently, imaging-based approaches to identify the TGN mostly rely on T2-weighted magnetic resonance imaging (MRI), which provides localization of the cisternal portion of the TGN where the contrast between nerve and cerebrospinal fluid (CSF) is high enough to allow differentiation. The course of the TGN within the brainstem as well as anterior to the cisternal portion, however, is more difficult to display on traditional imaging sequences. An advanced imaging technique, diffusion MRI (dMRI), enables tracking of the trajectory of TGN fibers and has the potential to visualize anatomical regions of the TGN not seen on T2-weighted imaging. This may allow a more comprehensive assessment of the nerve in the context of pathology. To date, most work in TGN tracking has used clinical dMRI acquisitions with a b-value of 1000 s/mm2 and conventional diffusion tensor MRI (DTI) tractography methods. Though higher b-value acquisitions and multi-tensor tractography methods are known to be beneficial for tracking brain white matter fiber tracts, there have been no studies conducted to evaluate the performance of these advanced approaches on nerve tracking of the TGN, in particular on tracking different anatomical regions of the TGN. Objective We compare TGN tracking performance using dMRI data with different b-values, in combination with both single- and multi-tensor tractography methods. Our goal is to assess the advantages and limitations of these different strategies for identifying the anatomical regions of the TGN. Methods We proposed seven anatomical rating criteria including true and false positive structures, and we performed an expert rating study of over 1000 TGN visualizations, as follows. We tracked the TGN using high-quality dMRI data from 100 healthy adult subjects from the Human Connectome Project (HCP). TGN tracking performance was compared across dMRI acquisitions with b = 1000 s/mm2, b = 2000 s/mm2 and b = 3000 s/mm2, using single-tensor (1T) and two-tensor (2T) unscented Kalman filter (UKF) tractography. This resulted in a total of six tracking strategies. The TGN was identified using an anatomical region-of-interest (ROI) selection approach. First, in a subset of the dataset we identified ROIs that provided good TGN tracking performance across all tracking strategies. Using these ROIs, the TGN was then tracked in all subjects using the six tracking strategies. An expert rater (GX) visually assessed and scored each TGN based on seven anatomical judgment criteria. These criteria included the presence of multiple expected anatomical segments of the TGN (true positive structures), specifically branch-like structures, cisternal portion, mesencephalic trigeminal tract, and spinal cord tract of the TGN. False positive criteria included the presence of any fibers entering the temporal lobe, the inferior cerebellar peduncle, or the middle cerebellar peduncle. Expert rating scores were analyzed to compare TGN tracking performance across the six tracking strategies. Intra- and inter-rater validation was performed to assess the reliability of the expert TGN rating result. Results The TGN was selected using two anatomical ROIs (Meckel's Cave and cisternal portion of the TGN). The two-tensor tractography method had significantly better performance on identifying true positive structures, while generating more false positive streamlines in comparison to the single-tensor tractography method. TGN tracking performance was significantly different across the three b-values for almost all structures studied. Tracking performance was reported in terms of the percentage of subjects achieving each anatomical rating criterion. Tracking of the cisternal portion and branching structure of the TGN was generally successful, with the highest performance of over 98% using two-tensor tractography and b = 1000 or b = 2000. However, tracking the smaller mesencephalic and spinal cord tracts of the TGN was quite challenging (highest performance of 37.5% and 57.07%, using two-tensor tractography with b = 1000 and b = 2000, respectively). False positive connections to the temporal lobe (over 38% of subjects for all strategies) and cerebellar peduncles (100% of subjects for all strategies) were prevalent. High joint probability of agreement was obtained in the inter-rater (on average 83%) and intra-rater validation (on average 90%), showing a highly reliable expert rating result. Conclusions Overall, the results of the study suggest that researchers and clinicians may benefit from tailoring their acquisition and tracking methodology to the specific anatomical portion of the TGN that is of the greatest interest. For example, tracking of branching structures and TGN-T2 overlap can be best achieved with a two-tensor model and an acquisition using b = 1000 or b = 2000. In general, b = 1000 and b = 2000 acquisitions provided the best-rated tracking results. Further research is needed to improve both sensitivity and specificity of the depiction of the TGN anatomy using dMRI.
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Affiliation(s)
- Guoqiang Xie
- Department of Neurosurgery, Nuclear Industry 215 Hospital of Shaanxi Province, Xianyang, China; Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Fan Zhang
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA.
| | - Laura Leung
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China
| | - Michael A Mooney
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Lorenz Epprecht
- Department of Otolaryngology, Head and Neck Surgery, University Hospital Zurich, Zurich, Switzerland
| | - Isaiah Norton
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Yogesh Rathi
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Ron Kikinis
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Ossama Al-Mefty
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Nikos Makris
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; Departments of Psychiatry, Neurology and Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, USA
| | - Alexandra J Golby
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Lauren J O'Donnell
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
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18
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Touska P, Connor SEJ. Recent advances in MRI of the head and neck, skull base and cranial nerves: new and evolving sequences, analyses and clinical applications. Br J Radiol 2019; 92:20190513. [PMID: 31529977 PMCID: PMC6913354 DOI: 10.1259/bjr.20190513] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2019] [Revised: 09/09/2019] [Accepted: 09/12/2019] [Indexed: 12/14/2022] Open
Abstract
MRI is an invaluable diagnostic tool in the investigation and management of patients with pathology of the head and neck. However, numerous technical challenges exist, owing to a combination of fine anatomical detail, complex geometry (that is subject to frequent motion) and susceptibility effects from both endogenous structures and exogenous implants. Over recent years, there have been rapid developments in several aspects of head and neck imaging including higher resolution, isotropic 3D sequences, diffusion-weighted and diffusion-tensor imaging as well as permeability and perfusion imaging. These have led to improvements in anatomic, dynamic and functional imaging. Further developments using contrast-enhanced 3D FLAIR for the delineation of endolymphatic structures and black bone imaging for osseous structures are opening new diagnostic avenues. Furthermore, technical advances in compressed sensing and metal artefact reduction have the capacity to improve imaging speed and quality, respectively. This review explores novel and evolving MRI sequences that can be employed to evaluate diseases of the head and neck, including the skull base.
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Affiliation(s)
- Philip Touska
- Department of Radiology, Guy’s and St. Thomas’ NHS Foundation Trust, Guy’s Hospital, Great Maze Pond, London, SE1 9RT, United Kingdom
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19
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Mishra VR, Sreenivasan KR, Zhuang X, Yang Z, Cordes D, Banks SJ, Bernick C. Understanding white matter structural connectivity differences between cognitively impaired and nonimpaired active professional fighters. Hum Brain Mapp 2019; 40:5108-5122. [PMID: 31403734 DOI: 10.1002/hbm.24761] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2019] [Revised: 07/20/2019] [Accepted: 07/31/2019] [Indexed: 11/06/2022] Open
Abstract
Long-term traumatic brain injury due to repeated head impacts (RHI) has been shown to be a risk factor for neurodegenerative disorders, characterized by a loss in cognitive performance. Establishing the correlation between changes in the white matter (WM) structural connectivity measures and neuropsychological test scores might help to identify the neural correlates of the scores that are used in daily clinical setting to investigate deficits due to repeated head blows. Hence, in this study, we utilized high angular diffusion MRI (dMRI) of 69 cognitively impaired and 70 nonimpaired active professional fighters from the Professional Fighters Brain Health Study, and constructed structural connectomes to understand: (a) whether there is a difference in the topological WM organization between cognitively impaired and nonimpaired active professional fighters, and (b) whether graph-theoretical measures exhibit correlations with neuropsychological scores in these groups. A dMRI derived structural connectome was constructed for every participant using brain regions defined in AAL atlas as nodes, and the product of fiber number and average fractional anisotropy of the tracts connecting the nodes as edges. Our study identified a topological WM reorganization due to RHI in fighters prone to cognitive decline that was correlated with neuropsychological scores. Furthermore, graph-theoretical measures were correlated differentially with neuropsychological scores between groups. We also found differentiated WM connectivity involving regions of hippocampus, precuneus, and insula within our cohort of cognitively impaired fighters suggesting that there is a discernible WM topological reorganization in fighters prone to cognitive decline.
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Affiliation(s)
- Virendra R Mishra
- Lou Ruvo Center for Brain Health, Cleveland Clinic Foundation, Las Vegas, Nevada
| | | | - Xiaowei Zhuang
- Lou Ruvo Center for Brain Health, Cleveland Clinic Foundation, Las Vegas, Nevada
| | - Zhengshi Yang
- Lou Ruvo Center for Brain Health, Cleveland Clinic Foundation, Las Vegas, Nevada
| | - Dietmar Cordes
- Lou Ruvo Center for Brain Health, Cleveland Clinic Foundation, Las Vegas, Nevada.,Department of Psychology and Neuroscience, University of Colorado, Boulder, Colorado
| | - Sarah J Banks
- Department of Neurosciences, University of California at San Diego, San Diego, California
| | - Charles Bernick
- Lou Ruvo Center for Brain Health, Cleveland Clinic Foundation, Las Vegas, Nevada
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Jacquesson T, Yeh FC, Panesar S, Barrios J, Attyé A, Frindel C, Cotton F, Gardner P, Jouanneau E, Fernandez-Miranda JC. Full tractography for detecting the position of cranial nerves in preoperative planning for skull base surgery: technical note. J Neurosurg 2019; 132:1642-1652. [PMID: 31003214 DOI: 10.3171/2019.1.jns182638] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/11/2018] [Accepted: 01/28/2019] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Diffusion imaging tractography has allowed the in vivo description of brain white matter. One of its applications is preoperative planning for brain tumor resection. Due to a limited spatial and angular resolution, it is difficult for fiber tracking to delineate fiber crossing areas and small-scale structures, in particular brainstem tracts and cranial nerves. New methods are being developed but these involve extensive multistep tractography pipelines including the patient-specific design of multiple regions of interest (ROIs). The authors propose a new practical full tractography method that could be implemented in routine presurgical planning for skull base surgery. METHODS A Philips MRI machine provided diffusion-weighted and anatomical sequences for 2 healthy volunteers and 2 skull base tumor patients. Tractography of the full brainstem, the cerebellum, and cranial nerves was performed using the software DSI Studio, generalized-q-sampling reconstruction, orientation distribution function (ODF) of fibers, and a quantitative anisotropy-based generalized deterministic algorithm. No ROI or extensive manual filtering of spurious fibers was used. Tractography rendering was displayed in a tridimensional space with directional color code. This approach was also tested on diffusion data from the Human Connectome Project (HCP) database. RESULTS The brainstem, the cerebellum, and the cisternal segments of most cranial nerves were depicted in all participants. In cases of skull base tumors, the tridimensional rendering permitted the visualization of the whole anatomical environment and cranial nerve displacement, thus helping the surgical strategy. CONCLUSIONS As opposed to classical ROI-based methods, this novel full tractography approach could enable routine enhanced surgical planning or brain imaging for skull base tumors.
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Affiliation(s)
- Timothee Jacquesson
- 1Department of Neurological Surgery, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania.,2Skull Base Multi-Disciplinary Unit, Department of Neurosurgery B, Neurological Hospital Pierre Wertheimer, Hospices Civils de Lyon.,3CREATIS Laboratory CNRS UMR5220, Inserm U1206, INSA-Lyon, University of Lyon 1
| | - Fang-Chang Yeh
- 1Department of Neurological Surgery, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
| | - Sandip Panesar
- 4Department of Neurosurgery, Stanford University Medical Center, Stanford, California
| | - Jessica Barrios
- 1Department of Neurological Surgery, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
| | - Arnaud Attyé
- 5Department of Neuroradiology and MRI, Grenoble University Hospital, Grenoble, France; and
| | - Carole Frindel
- 3CREATIS Laboratory CNRS UMR5220, Inserm U1206, INSA-Lyon, University of Lyon 1
| | - Francois Cotton
- 3CREATIS Laboratory CNRS UMR5220, Inserm U1206, INSA-Lyon, University of Lyon 1.,6Department of Radiology, Lyon Sud Hospital, Hospices Civils de Lyon, Lyon
| | - Paul Gardner
- 1Department of Neurological Surgery, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
| | - Emmanuel Jouanneau
- 2Skull Base Multi-Disciplinary Unit, Department of Neurosurgery B, Neurological Hospital Pierre Wertheimer, Hospices Civils de Lyon
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Shapey J, Vos SB, Vercauteren T, Bradford R, Saeed SR, Bisdas S, Ourselin S. Clinical Applications for Diffusion MRI and Tractography of Cranial Nerves Within the Posterior Fossa: A Systematic Review. Front Neurosci 2019; 13:23. [PMID: 30809109 PMCID: PMC6380197 DOI: 10.3389/fnins.2019.00023] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2018] [Accepted: 01/11/2019] [Indexed: 12/21/2022] Open
Abstract
Objective: This paper presents a systematic review of diffusion MRI (dMRI) and tractography of cranial nerves within the posterior fossa. We assess the effectiveness of the diffusion imaging methods used and examine their clinical applications. Methods: The Pubmed, Web of Science and EMBASE databases were searched from January 1st 1997 to December 11th 2017 to identify relevant publications. Any study reporting the use of diffusion imaging and/or tractography in patients with confirmed cranial nerve pathology was eligible for selection. Study quality was assessed using the Methodological Index for Non-Randomized Studies (MINORS) tool. Results: We included 41 studies comprising 16 studies of patients with trigeminal neuralgia (TN), 22 studies of patients with a posterior fossa tumor and three studies of patients with other pathologies. Most acquisition protocols used single-shot echo planar imaging (88%) with a single b-value of 1,000 s/mm2 (78%) but there was significant variation in the number of gradient directions, in-plane resolution, and slice thickness between studies. dMRI of the trigeminal nerve generated interpretable data in all cases. Analysis of diffusivity measurements found significantly lower fractional anisotropy (FA) values within the root entry zone of nerves affected by TN and FA values were significantly lower in patients with multiple sclerosis. Diffusivity values within the trigeminal nerve correlate with the effectiveness of surgical treatment and there is some evidence that pre-operative measurements may be predictive of treatment outcome. Fiber tractography was performed in 30 studies (73%). Most studies evaluating fiber tractography involved patients with a vestibular schwannoma (82%) and focused on generating tractography of the facial nerve to assist with surgical planning. Deterministic tractography using diffusion tensor imaging was performed in 93% of cases but the reported success rate and accuracy of generating fiber tracts from the acquired diffusion data varied considerably. Conclusions: dMRI has the potential to inform our understanding of the microstructural changes that occur within the cranial nerves in various pathologies. Cranial nerve tractography is a promising technique but new avenues of using dMRI should be explored to optimize and improve its reliability.
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Affiliation(s)
- Jonathan Shapey
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, United Kingdom.,Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, United Kingdom.,School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Sjoerd B Vos
- Wellcome/EPSRC Centre for Interventional and Surgical Sciences, University College London, London, United Kingdom.,Translational Imaging Group-Centre for Medical Image Computing, University College London, London, United Kingdom.,Epilepsy Society MRI Unit, Chalfont St Peter, United Kingdom
| | - Tom Vercauteren
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Robert Bradford
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, United Kingdom
| | - Shakeel R Saeed
- Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, London, United Kingdom.,The Ear Institute, University College London, London, United Kingdom.,The Royal National Throat, Nose and Ear Hospital, London, United Kingdom
| | | | - Sebastien Ourselin
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
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Jacquesson T, Frindel C, Kocevar G, Berhouma M, Jouanneau E, Attyé A, Cotton F. Overcoming Challenges of Cranial Nerve Tractography: A Targeted Review. Neurosurgery 2018; 84:313-325. [PMID: 30010992 DOI: 10.1093/neuros/nyy229] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2017] [Accepted: 05/01/2018] [Indexed: 11/14/2022] Open
Affiliation(s)
- Timothée Jacquesson
- Skull Base Multi-disciplinary Unit, Department of Neurosurgery B, Neurological Hospital Pierre Wertheimer, Hospices Civils de Lyon, Lyon, France
- Department of Anatomy, University of Lyon 1, Lyon, France
- CREATIS Laboratory CNRS UMR5220, Inserm U1206, INSA-Lyon, University of Lyon 1, Lyon, France
| | - Carole Frindel
- CREATIS Laboratory CNRS UMR5220, Inserm U1206, INSA-Lyon, University of Lyon 1, Lyon, France
| | - Gabriel Kocevar
- CREATIS Laboratory CNRS UMR5220, Inserm U1206, INSA-Lyon, University of Lyon 1, Lyon, France
| | - Moncef Berhouma
- Skull Base Multi-disciplinary Unit, Department of Neurosurgery B, Neurological Hospital Pierre Wertheimer, Hospices Civils de Lyon, Lyon, France
- CREATIS Laboratory CNRS UMR5220, Inserm U1206, INSA-Lyon, University of Lyon 1, Lyon, France
| | - Emmanuel Jouanneau
- Skull Base Multi-disciplinary Unit, Department of Neurosurgery B, Neurological Hospital Pierre Wertheimer, Hospices Civils de Lyon, Lyon, France
| | - Arnaud Attyé
- Department of Radiology, Grenoble University Hospital, Grenoble, France
| | - Francois Cotton
- CREATIS Laboratory CNRS UMR5220, Inserm U1206, INSA-Lyon, University of Lyon 1, Lyon, France
- Department of Radiology, Lyon Sud Hospital, Hospices Civils de Lyon, Lyon, France
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Savardekar AR, Patra DP, Thakur JD, Narayan V, Mohammed N, Bollam P, Nanda A. Preoperative diffusion tensor imaging–fiber tracking for facial nerve identification in vestibular schwannoma: a systematic review on its evolution and current status with a pooled data analysis of surgical concordance rates. Neurosurg Focus 2018; 44:E5. [DOI: 10.3171/2017.12.focus17672] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
OBJECTIVETotal tumor excision with the preservation of neurological function and quality of life is the goal of modern-day vestibular schwannoma (VS) surgery. Postoperative facial nerve (FN) paralysis is a devastating complication of VS surgery. Determining the course of the FN in relation to a VS preoperatively is invaluable to the neurosurgeon and is likely to enhance surgical safety with respect to FN function. Diffusion tensor imaging–fiber tracking (DTI-FT) technology is slowly gaining traction as a viable tool for preoperative FN visualization in patients with VS.METHODSA systematic review of the literature in the PubMed, Cochrane Library, and Web of Science databases was performed in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, and those studies that preoperatively localized the FN in relation to a VS using the DTI-FT technique and verified those preoperative FN tracking results by using microscopic observation and electrophysiological monitoring during microsurgery were included. A pooled analysis of studies was performed to calculate the surgical concordance rate (accuracy) of DTI-FT technology for FN localization.RESULTSFourteen studies included 234 VS patients (male/female ratio 1:1.4, age range 17–75 years) who had undergone preoperative DTI-FT for FN identification. The mean tumor size among the studies ranged from 29 to 41.3 mm. Preoperative DTI-FT could not visualize the FN tract in 8 patients (3.4%) and its findings could not be verified in 3 patients (1.2%), were verified but discordant in 18 patients (7.6%), and were verified and concordant in 205 patients (87.1%).CONCLUSIONSPreoperative DTI-FT for FN identification is a useful adjunct in the surgical planning for large VSs (> 2.5 cm). A pooled analysis showed that DTI-FT successfully identifies the complete FN course in 96.6% of VSs (226 of 234 cases) and that FN identification by DTI-FT is accurate in 90.6% of cases (205 of 226 cases). Larger studies with DTI-FT–integrated neuronavigation are required to look at the direct benefit offered by this specific technique in preserving postoperative FN function.
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Zolal A, Juratli TA, Podlesek D, Rieger B, Kitzler HH, Linn J, Schackert G, Sobottka SB. Probabilistic Tractography of the Cranial Nerves in Vestibular Schwannoma. World Neurosurg 2017; 107:47-53. [DOI: 10.1016/j.wneu.2017.07.102] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2017] [Revised: 07/16/2017] [Accepted: 07/17/2017] [Indexed: 12/23/2022]
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