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Lucena O, Lavrador JP, Irzan H, Semedo C, Borges P, Vergani F, Granados A, Sparks R, Ashkan K, Ourselin S. Assessing informative tract segmentation and nTMS for pre-operative planning. J Neurosci Methods 2023; 396:109933. [PMID: 37524245 PMCID: PMC10861808 DOI: 10.1016/j.jneumeth.2023.109933] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Revised: 07/15/2023] [Accepted: 07/28/2023] [Indexed: 08/02/2023]
Abstract
BACKGROUND Deep learning-based (DL) methods are the best-performing methods for white matter tract segmentation in anatomically healthy subjects. However, tract annotations are variable or absent in clinical data and manual annotations are especially difficult in patients with tumors where normal anatomy may be distorted. Direct cortical and subcortical stimulation is the gold standard ground truth to determine the cortical and sub-cortical lo- cation of motor-eloquent areas intra-operatively. Nonetheless, this technique is invasive, prolongs the surgical procedure, and may cause patient fatigue. Navigated Transcranial Magnetic Stimulation (nTMS) has a well-established correlation to direct cortical stimulation for motor mapping and the added advantage of being able to be acquired pre-operatively. NEW METHOD In this work, we evaluate the feasibility of using nTMS motor responses as a method to assess corticospinal tract (CST) binary masks and estimated uncertainty generated by a DL-based tract segmentation in patients with diffuse gliomas. RESULTS Our results show CST binary masks have a high overlap coefficient (OC) with nTMS response masks. A strong negative correlation is found between estimated uncertainty and nTMS response mask distance to the CST binary mask. COMPARISON WITH EXISTING METHODS We compare our approach (UncSeg) with the state-of-the-art TractSeg in terms of OC between the CST binary masks and nTMS response masks. CONCLUSIONS In this study, we demonstrate that estimated uncertainty from UncSeg is a good measure of the agreement between the CST binary masks and nTMS response masks distance to the CST binary mask boundary.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Keyoumars Ashkan
- King's College London, London, UK; King's College Hospital Foundation Trust, London, UK
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Hazem SR, Awan M, Lavrador JP, Patel S, Wren HM, Lucena O, Semedo C, Irzan H, Melbourne A, Ourselin S, Shapey J, Kailaya-Vasan A, Gullan R, Ashkan K, Bhangoo R, Vergani F. Middle Frontal Gyrus and Area 55b: Perioperative Mapping and Language Outcomes. Front Neurol 2021; 12:646075. [PMID: 33776898 PMCID: PMC7988187 DOI: 10.3389/fneur.2021.646075] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2020] [Accepted: 01/29/2021] [Indexed: 12/20/2022] Open
Abstract
Background: The simplistic approaches to language circuits are continuously challenged by new findings in brain structure and connectivity. The posterior middle frontal gyrus and area 55b (pFMG/area55b), in particular, has gained a renewed interest in the overall language network. Methods: This is a retrospective single-center cohort study of patients who have undergone awake craniotomy for tumor resection. Navigated transcranial magnetic simulation (nTMS), tractography, and intraoperative findings were correlated with language outcomes. Results: Sixty-five awake craniotomies were performed between 2012 and 2020, and 24 patients were included. nTMS elicited 42 positive responses, 76.2% in the inferior frontal gyrus (IFG), and hesitation was the most common error (71.4%). In the pMFG/area55b, there were seven positive errors (five hesitations and two phonemic errors). This area had the highest positive predictive value (43.0%), negative predictive value (98.3%), sensitivity (50.0%), and specificity (99.0%) among all the frontal gyri. Intraoperatively, there were 33 cortical positive responses—two (6.0%) in the superior frontal gyrus (SFG), 15 (45.5%) in the MFG, and 16 (48.5%) in the IFG. A total of 29 subcortical positive responses were elicited−21 in the deep IFG–MFG gyri and eight in the deep SFG–MFG gyri. The most common errors identified were speech arrest at the cortical level (20 responses−13 in the IFG and seven in the MFG) and anomia at the subcortical level (nine patients—eight in the deep IFG–MFG and one in the deep MFG–SFG). Moreover, 83.3% of patients had a transitory deterioration of language after surgery, mainly in the expressive component (p = 0.03). An increased number of gyri with intraoperative positive responses were related with better preoperative (p = 0.037) and worse postoperative (p = 0.029) outcomes. The involvement of the SFG–MFG subcortical area was related with worse language outcomes (p = 0.037). Positive nTMS mapping in the IFG was associated with a better preoperative language outcome (p = 0.017), relating to a better performance in the expressive component, while positive mapping in the MFG was related to a worse preoperative receptive component of language (p = 0.031). Conclusion: This case series suggests that the posterior middle frontal gyrus, including area 55b, is an important integration cortical hub for both dorsal and ventral streams of language.
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Affiliation(s)
- Sally Rosario Hazem
- Department of Neurosurgery, King's College Hospital National Health Service Foundation Trust, London, United Kingdom.,King's Neuro Lab, Department of Neurosurgery, King's College Hospital National Health Service Foundation Trust, London, United Kingdom
| | - Mariam Awan
- Department of Neurosurgery, King's College Hospital National Health Service Foundation Trust, London, United Kingdom.,King's Neuro Lab, Department of Neurosurgery, King's College Hospital National Health Service Foundation Trust, London, United Kingdom
| | - Jose Pedro Lavrador
- Department of Neurosurgery, King's College Hospital National Health Service Foundation Trust, London, United Kingdom.,King's Neuro Lab, Department of Neurosurgery, King's College Hospital National Health Service Foundation Trust, London, United Kingdom
| | - Sabina Patel
- Department of Neurosurgery, King's College Hospital National Health Service Foundation Trust, London, United Kingdom.,King's Neuro Lab, Department of Neurosurgery, King's College Hospital National Health Service Foundation Trust, London, United Kingdom
| | - Hilary Margaret Wren
- Department of Neurosurgery, King's College Hospital National Health Service Foundation Trust, London, United Kingdom
| | - Oeslle Lucena
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Carla Semedo
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.,Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Hassna Irzan
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.,Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Andrew Melbourne
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom.,Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Sebastien Ourselin
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Jonathan Shapey
- Department of Neurosurgery, King's College Hospital National Health Service Foundation Trust, London, United Kingdom.,King's Neuro Lab, Department of Neurosurgery, King's College Hospital National Health Service Foundation Trust, London, United Kingdom.,School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Ahilan Kailaya-Vasan
- Department of Neurosurgery, King's College Hospital National Health Service Foundation Trust, London, United Kingdom.,King's Neuro Lab, Department of Neurosurgery, King's College Hospital National Health Service Foundation Trust, London, United Kingdom
| | - Richard Gullan
- Department of Neurosurgery, King's College Hospital National Health Service Foundation Trust, London, United Kingdom
| | - Keyoumars Ashkan
- Department of Neurosurgery, King's College Hospital National Health Service Foundation Trust, London, United Kingdom.,King's Neuro Lab, Department of Neurosurgery, King's College Hospital National Health Service Foundation Trust, London, United Kingdom
| | - Ranjeev Bhangoo
- Department of Neurosurgery, King's College Hospital National Health Service Foundation Trust, London, United Kingdom.,King's Neuro Lab, Department of Neurosurgery, King's College Hospital National Health Service Foundation Trust, London, United Kingdom
| | - Francesco Vergani
- Department of Neurosurgery, King's College Hospital National Health Service Foundation Trust, London, United Kingdom.,King's Neuro Lab, Department of Neurosurgery, King's College Hospital National Health Service Foundation Trust, London, United Kingdom
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Mata A, Ferreira JP, Semedo C, Serra T, Duarte CMM, Bronze MR. Contribution to the characterization of Opuntia spp. juices by LC-DAD-ESI-MS/MS. Food Chem 2016; 210:558-65. [PMID: 27211682 DOI: 10.1016/j.foodchem.2016.04.033] [Citation(s) in RCA: 53] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2015] [Revised: 04/01/2016] [Accepted: 04/12/2016] [Indexed: 12/11/2022]
Abstract
Opuntia spp. fruits are considered as health promoting foods due to the diversity of bioactive molecules found in these fruits. The composition in organic acids, flavonols and betalains in the Opuntia ficus-indica juice from a region of Portugal was accomplished for the first time by liquid chromatography and tandem mass spectrometry using an electrospray ionization source operating in negative and positive mode. The methodology used allowed the detection of 44 compounds, from which 32 were identified. Isorhamnetin derivatives were the dominant flavonol glycosides. A total of 9 betalains including 6 betaxanthins and 3 betacyanin were also detected in the fruit juice samples and indicaxanthin, betanin and isobetanin were the major pigments. Phenolic acid and phenylpyruvic acid derivatives were also identified. To our knowledge, it is the first time derivative compounds from piscidic acid, phenolic compounds and betalains are characterized in cactus pear juice using a single LC-DAD-ESI-MS/MS method.
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Affiliation(s)
- A Mata
- Faculdade de Farmácia da Universidade de Lisboa, Av. Prof. Gama Pinto, 1649-019 Lisboa, Portugal
| | - J P Ferreira
- Faculdade de Farmácia da Universidade de Lisboa, Av. Prof. Gama Pinto, 1649-019 Lisboa, Portugal
| | - C Semedo
- Faculdade de Farmácia da Universidade de Lisboa, Av. Prof. Gama Pinto, 1649-019 Lisboa, Portugal
| | - T Serra
- Instituto de Biologia Experimental e Tecnológica, Apartado 12, 2780-901 Oeiras, Portugal; Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Av. da República, 2780-157 Oeiras, Portugal
| | - C M M Duarte
- Instituto de Biologia Experimental e Tecnológica, Apartado 12, 2780-901 Oeiras, Portugal; Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Av. da República, 2780-157 Oeiras, Portugal
| | - M R Bronze
- Faculdade de Farmácia da Universidade de Lisboa, Av. Prof. Gama Pinto, 1649-019 Lisboa, Portugal; Instituto de Biologia Experimental e Tecnológica, Apartado 12, 2780-901 Oeiras, Portugal; Instituto de Tecnologia Química e Biológica António Xavier, Universidade Nova de Lisboa, Av. da República, 2780-157 Oeiras, Portugal.
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Dingwall N, Chalk A, Martin TI, Scott CJ, Semedo C, Le Q, Orasanu E, Cardoso JM, Melbourne A, Marlow N, Ourselin S. T2 relaxometry in the extremely-preterm brain at adolescence. Magn Reson Imaging 2015; 34:508-14. [PMID: 26723846 PMCID: PMC4819563 DOI: 10.1016/j.mri.2015.12.020] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2015] [Accepted: 12/14/2015] [Indexed: 11/13/2022]
Abstract
Survival following very preterm birth is associated with cognitive and behavioral sequelae, which may have identifiable neural correlates. Many survivors of modern neonatal care in the 1990s are now young adults and the evolution of MRI findings into adult life has rarely been evaluated. We have investigated a cohort of 19-year-old adolescents without severe impairments born between 22 and 26 weeks of gestation in 1995 (extremely preterm: EP). Using T2 data derived from magnetic resonance imaging we investigate differences between the brains of 46 EP participants (n = 46) and the brains of a group of term-born controls (n = 20). Despite EP adolescents having significantly reduced gray and white matter volumes, the composition of these tissues, assessed by both single and multi-component relaxometry, appears to be unrelated to either preterm status or gender. This may represent either insensitivity of the imaging technique or reflect that there are only subtle differences between EP subjects and their term-born peers.
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Affiliation(s)
| | - Alan Chalk
- Department of Computer Science, University College London, UK
| | - Teresa I Martin
- Department of Computer Science, University College London, UK
| | - Catherine J Scott
- Centre for Medical Image Computing (CMIC), University College London, UK
| | - Carla Semedo
- Centre for Medical Image Computing (CMIC), University College London, UK
| | - Quan Le
- Department of Computer Science, University College London, UK
| | - Eliza Orasanu
- Centre for Medical Image Computing (CMIC), University College London, UK
| | - Jorge M Cardoso
- Centre for Medical Image Computing (CMIC), University College London, UK
| | - Andrew Melbourne
- Centre for Medical Image Computing (CMIC), University College London, UK.
| | - Neil Marlow
- Academic Neonatology, EGA UCL Institute for Women's Health, London, UK
| | - Sebastien Ourselin
- Centre for Medical Image Computing (CMIC), University College London, UK
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