1
|
Trimmel K, Vos SB, Binding L, Caciagli L, Xiao F, van Graan LA, Koepp MJ, Thompson PJ, Duncan JS. Naming fMRI-guided white matter language tract volumes influence naming decline after temporal lobe resection. J Neurol 2024; 271:4158-4167. [PMID: 38583105 PMCID: PMC11233363 DOI: 10.1007/s00415-024-12315-2] [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: 12/16/2023] [Revised: 03/08/2024] [Accepted: 03/08/2024] [Indexed: 04/08/2024]
Abstract
OBJECTIVE The aim of this study was to explore the relation of language functional MRI (fMRI)-guided tractography with postsurgical naming decline in people with temporal lobe epilepsy (TLE). METHODS Twenty patients with unilateral TLE (9 left) were studied with auditory and picture naming functional MRI tasks. Activation maxima in the left posterobasal temporal lobe were used as seed regions for whole-brain fibre tractography. Clinical naming performance was assessed preoperatively, 4 months, and 12 months following temporal lobe resection. Volumes of white matter language tracts in both hemispheres as well as tract volume laterality indices were explored as moderators of postoperative naming decline using Pearson correlations and multiple linear regression with other clinical variables. RESULTS Larger volumes of white matter language tracts derived from auditory and picture naming maxima in the hemisphere of subsequent surgery as well as stronger lateralization of picture naming tract volumes to the side of surgery correlated with greater language decline, which was independent of fMRI lateralization status. Multiple regression for picture naming tract volumes was associated with a significant decline of naming function with 100% sensitivity and 93% specificity at both short-term and long-term follow-up. INTERPRETATION Naming fMRI-guided white matter language tract volumes relate to postoperative naming decline after temporal lobe resection in people with TLE. This can assist stratification of surgical outcome and minimize risk of postoperative language deficits in TLE.
Collapse
Affiliation(s)
- Karin Trimmel
- Department of Neurology, Medical University of Vienna, Vienna, Austria.
- Epilepsy Society MRI Unit, Epilepsy Society, Chalfont St Peter, UK.
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, London, UK.
| | - Sjoerd B Vos
- Epilepsy Society MRI Unit, Epilepsy Society, Chalfont St Peter, UK
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
- Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, London, UK
- Centre for Microscopy Characterisation and Analysis, University of Western Australia, Nedlands, Australia
| | - Lawrence Binding
- Epilepsy Society MRI Unit, Epilepsy Society, Chalfont St Peter, UK
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, London, UK
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Lorenzo Caciagli
- Epilepsy Society MRI Unit, Epilepsy Society, Chalfont St Peter, UK
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, London, UK
- Department of Neurology, Inselspital, Sleep-Wake-Epilepsy-Center, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Fenglai Xiao
- Epilepsy Society MRI Unit, Epilepsy Society, Chalfont St Peter, UK
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, London, UK
| | - Louis A van Graan
- Epilepsy Society MRI Unit, Epilepsy Society, Chalfont St Peter, UK
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, London, UK
| | - Matthias J Koepp
- Epilepsy Society MRI Unit, Epilepsy Society, Chalfont St Peter, UK
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, London, UK
| | - Pamela J Thompson
- Epilepsy Society MRI Unit, Epilepsy Society, Chalfont St Peter, UK
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, London, UK
| | - John S Duncan
- Epilepsy Society MRI Unit, Epilepsy Society, Chalfont St Peter, UK
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, London, UK
| |
Collapse
|
2
|
Zhong Y, Liu H, Tan C, Liu X, Chen L. More than grey matter: a case of spinocerebellar ataxia type 48. Acta Neurol Belg 2024; 124:653-656. [PMID: 37566366 DOI: 10.1007/s13760-023-02355-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 07/25/2023] [Indexed: 08/12/2023]
Affiliation(s)
- Yuke Zhong
- Department of Neurology, The Second Affiliated Hospital of Chongqing Medical University, 74 Linjiang Road, Yuzhong District, Chongqing, 400010, China
| | - Hang Liu
- Department of Neurology, The Second Affiliated Hospital of Chongqing Medical University, 74 Linjiang Road, Yuzhong District, Chongqing, 400010, China
| | - Changhong Tan
- Department of Neurology, The Second Affiliated Hospital of Chongqing Medical University, 74 Linjiang Road, Yuzhong District, Chongqing, 400010, China
| | - Xi Liu
- Department of Neurology, The Second Affiliated Hospital of Chongqing Medical University, 74 Linjiang Road, Yuzhong District, Chongqing, 400010, China.
| | - Lifen Chen
- Department of Neurology, The Second Affiliated Hospital of Chongqing Medical University, 74 Linjiang Road, Yuzhong District, Chongqing, 400010, China.
| |
Collapse
|
3
|
Iwami K, Fujii M, Watanabe T, Osuka K. Exo- and endoscopic lateral orbital wall approach for the medial temporal lobe glioma: how I do it. Acta Neurochir (Wien) 2024; 166:110. [PMID: 38409616 DOI: 10.1007/s00701-024-06011-3] [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: 11/16/2023] [Accepted: 02/20/2024] [Indexed: 02/28/2024]
Abstract
BACKGROUND The endoscopic transorbital approach provides a direct access to the medial temporal lobe (MTL). However, when excising a highly vascular tumour, a wider access route that enables the concurrent use of standard neurosurgical instruments with both hands is preferable. METHOD We described the concept and technique of the lateral orbital wall approach (LOWA), which comprises orbitotomy and mini-craniotomy to treat MTL lesions using an exoscope and endoscope. CONCLUSION The LOWA provides a safe and natural surgical corridor to the MTL and enables 2- or 3-hand surgery. Hence, LOWA can potentially improve safety and efficiency to treat MTL lesions.
Collapse
Affiliation(s)
- Kenichiro Iwami
- Department of Neurosurgery, Aichi Medical University, 1-1 Yazakokarimata, Nagakute City, Aichi Prefecture, 480-1195, Japan.
- Skull Base Surgery Center, Aichi Medical University Hospital, 1-1 Yazakokarimata, Nagakute City, Prefecture Aichi, 480-1195, Japan.
| | - Masazumi Fujii
- Department of Neurosurgery, Fukushima Medical University, Fukushima, Japan
| | - Tadashi Watanabe
- Department of Neurosurgery, Aichi Medical University, 1-1 Yazakokarimata, Nagakute City, Aichi Prefecture, 480-1195, Japan
| | - Koji Osuka
- Department of Neurosurgery, Aichi Medical University, 1-1 Yazakokarimata, Nagakute City, Aichi Prefecture, 480-1195, Japan
| |
Collapse
|
4
|
Kamagata K, Andica C, Uchida W, Takabayashi K, Saito Y, Lukies M, Hagiwara A, Fujita S, Akashi T, Wada A, Hori M, Kamiya K, Zalesky A, Aoki S. Advancements in Diffusion MRI Tractography for Neurosurgery. Invest Radiol 2024; 59:13-25. [PMID: 37707839 DOI: 10.1097/rli.0000000000001015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/15/2023]
Abstract
ABSTRACT Diffusion magnetic resonance imaging tractography is a noninvasive technique that enables the visualization and quantification of white matter tracts within the brain. It is extensively used in preoperative planning for brain tumors, epilepsy, and functional neurosurgical procedures such as deep brain stimulation. Over the past 25 years, significant advancements have been made in imaging acquisition, fiber direction estimation, and tracking methods, resulting in considerable improvements in tractography accuracy. The technique enables the mapping of functionally critical pathways around surgical sites to avoid permanent functional disability. When the limitations are adequately acknowledged and considered, tractography can serve as a valuable tool to safeguard critical white matter tracts and provides insight regarding changes in normal white matter and structural connectivity of the whole brain beyond local lesions. In functional neurosurgical procedures such as deep brain stimulation, it plays a significant role in optimizing stimulation sites and parameters to maximize therapeutic efficacy and can be used as a direct target for therapy. These insights can aid in patient risk stratification and prognosis. This article aims to discuss state-of-the-art tractography methodologies and their applications in preoperative planning and highlight the challenges and new prospects for the use of tractography in daily clinical practice.
Collapse
Affiliation(s)
- Koji Kamagata
- From the Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan (K.K., C.A., W.U., K.T., Y.S., A.H., S.F., T.A., A.W., S.A.); Faculty of Health Data Science, Juntendo University, Chiba, Japan (C.A., S.A.); Department of Radiology, Alfred Health, Melbourne, Victoria, Australia (M.L.); Department of Radiology, University of Tokyo, Tokyo, Japan (S.F.); Department of Radiology, Toho University Omori Medical Center, Tokyo, Japan (M.H., K.K.); Melbourne Neuropsychiatry Center, Department of Psychiatry, The University of Melbourne and Melbourne Health, Parkville, Victoria, Australia (A.Z.); and Melbourne School of Engineering, University of Melbourne, Melbourne, Victoria, Australia (A.Z.)
| | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
5
|
Baxendale S. What are we really predicting with fMRI in epilepsy surgery? Epilepsy Behav 2023; 145:109298. [PMID: 37356225 DOI: 10.1016/j.yebeh.2023.109298] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 05/30/2023] [Accepted: 06/01/2023] [Indexed: 06/27/2023]
Abstract
While memory and language functional magnetic resonance imaging (fMRI) paradigms are becoming evermore refined, the measures of outcome they predict following epilepsy surgery tend to remain single scores on pencil and paper tests that were developed decades ago and have been repeatedly shown to bear little relation to patients' subjective reports of memory problems in the real world. The growing imbalance between the increasing sophistication of the predictive paradigms on the one hand and the vintage measures of the outcome on the other in the fMRI epilepsy surgery literature threatens the clinical relevance of studies employing these technologies. This paper examines some of the core principles of assessing neuropsychological outcomes following epilepsy surgery and explores how these may be adapted and applied in fMRI study designs to maximize the clinical relevance of these studies.
Collapse
Affiliation(s)
- Sallie Baxendale
- Department of Clinical and Experimental Epilepsy, Institute of Neurology, UCL, UK; University College Hospital, London, UK.
| |
Collapse
|
6
|
Peter Binding L, Neal Taylor P, O'Keeffe AG, Giampiccolo D, Fleury M, Xiao F, Caciagli L, de Tisi J, Winston GP, Miserocchi A, McEvoy A, Duncan JS, Vos SB. The impact of temporal lobe epilepsy surgery on picture naming and its relationship to network metric change. Neuroimage Clin 2023; 38:103444. [PMID: 37300974 PMCID: PMC10300575 DOI: 10.1016/j.nicl.2023.103444] [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: 03/08/2023] [Revised: 05/04/2023] [Accepted: 05/22/2023] [Indexed: 06/12/2023]
Abstract
BACKGROUND Anterior temporal lobe resection (ATLR) is a successful treatment for medically-refractory temporal lobe epilepsy (TLE). In the language-dominant hemisphere, 30%- 50% of individuals experience a naming decline which can impact upon daily life. Measures of structural networks are associated with language performance pre-operatively. It is unclear if analysis of network measures may predict post-operative decline. METHODS White matter fibre tractography was performed on preoperative diffusion MRI of 44 left lateralised and left resection individuals with TLE to reconstruct the preoperative structural network. Resection masks, drawn on co-registered pre- and post-operative T1-weighted MRI scans, were used as exclusion regions on pre-operative tractography to estimate the post-operative network. Changes in graph theory metrics, cortical strength, betweenness centrality, and clustering coefficient were generated by comparing the estimated pre- and post-operative networks. These were thresholded based on the presence of the connection in each patient, ranging from 75% to 100% in steps of 5%. The average graph theory metric across thresholds was taken. We incorporated leave-one-out cross-validation with smoothly clipped absolute deviation (SCAD) least absolute shrinkage and selection operator (LASSO) feature selection and a support vector classifier to assess graph theory metrics on picture naming decline. Picture naming was assessed via the Graded Naming Test preoperatively and at 3 and 12 months post-operatively and the outcome was classified using the reliable change index (RCI) to identify clinically significant decline. The best feature combination and model was selected using the area under the curve (AUC). The sensitivity, specificity and F1-score were also reported. Permutation testing was performed to assess the machine learning model and selected regions difference significance. RESULTS A combination of clinical and graph theory metrics were able to classify outcome of picture naming at 3 months with an AUC of 0.84. At 12 months, change in strength to cortical regions was best able to correctly classify outcome with an AUC of 0.86. Longitudinal analysis revealed that betweenness centrality was the best metric to identify patients who declined at 3 months, who will then continue to experience decline from 3 to 12 months. Both models were significantly higher AUC values than a random classifier. CONCLUSION Our results suggest that inferred changes of network integrity were able to correctly classify picture naming decline after ATLR. These measures may be used to prospectively to identify patients who are at risk of picture naming decline after surgery and could potentially be utilised to assist tailoring the resection in order to prevent this decline.
Collapse
Affiliation(s)
- Lawrence Peter Binding
- Centre for Medical Image Computing, Department of Computer Science, UCL, London, United Kingdom; Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, United Kingdom.
| | - Peter Neal Taylor
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, United Kingdom; CNNP lab, Interdisciplinary Computing and Complex BioSystems Group, School of Computing Science, Newcastle University, United Kingdom
| | - Aidan G O'Keeffe
- School of Mathematical Sciences, University of Nottingham, United Kingdom; Institute of Epidemiology and Healthcare, UCL, London WC1E 6BT, United Kingdom
| | - Davide Giampiccolo
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, United Kingdom; Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, Queen Square, London, United Kingdom; Department of Neurosurgery, Institute of Neurosciences, Cleveland Clinic London, United Kingdom
| | - Marine Fleury
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Fenglai Xiao
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, United Kingdom; MRI Unit, Chalfont Centre for Epilepsy, Chalfont St Peter, United Kingdom
| | - Lorenzo Caciagli
- MRI Unit, Chalfont Centre for Epilepsy, Chalfont St Peter, United Kingdom; Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Jane de Tisi
- MRI Unit, Chalfont Centre for Epilepsy, Chalfont St Peter, United Kingdom
| | - Gavin P Winston
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, United Kingdom; MRI Unit, Chalfont Centre for Epilepsy, Chalfont St Peter, United Kingdom; Department of Medicine, Division of Neurology, Queens University, Kingston, Canada
| | - Anna Miserocchi
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, United Kingdom; Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, Queen Square, London, United Kingdom
| | - Andrew McEvoy
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, United Kingdom; Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, Queen Square, London, United Kingdom
| | - John S Duncan
- Department of Clinical and Experimental Epilepsy, UCL Queen Square Institute of Neurology, London, United Kingdom; MRI Unit, Chalfont Centre for Epilepsy, Chalfont St Peter, United Kingdom
| | - Sjoerd B Vos
- Centre for Medical Image Computing, Department of Computer Science, UCL, London, United Kingdom; Neuroradiological Academic Unit, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom; Centre for Microscopy, Characterisation, and Analysis, The University of Western Australia, Nedlands, Australia
| |
Collapse
|