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Vikner T, Garpebring A, Björnfot C, Nyberg L, Malm J, Eklund A, Wåhlin A. Blood-brain barrier integrity is linked to cognitive function, but not to cerebral arterial pulsatility, among elderly. Sci Rep 2024; 14:15338. [PMID: 38961135 PMCID: PMC11222381 DOI: 10.1038/s41598-024-65944-y] [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: 12/09/2023] [Accepted: 06/24/2024] [Indexed: 07/05/2024] Open
Abstract
Blood-brain barrier (BBB) disruption may contribute to cognitive decline, but questions remain whether this association is more pronounced for certain brain regions, such as the hippocampus, or represents a whole-brain mechanism. Further, whether human BBB leakage is triggered by excessive vascular pulsatility, as suggested by animal studies, remains unknown. In a prospective cohort (N = 50; 68-84 years), we used contrast-enhanced MRI to estimate the permeability-surface area product (PS) and fractional plasma volume ( v p ), and 4D flow MRI to assess cerebral arterial pulsatility. Cognition was assessed by the Montreal Cognitive Assessment (MoCA) score. We hypothesized that high PS would be associated with high arterial pulsatility, and that links to cognition would be specific to hippocampal PS. For 15 brain regions, PS ranged from 0.38 to 0.85 (·10-3 min-1) and v p from 0.79 to 1.78%. Cognition was related to PS (·10-3 min-1) in hippocampus (β = - 2.9; p = 0.006), basal ganglia (β = - 2.3; p = 0.04), white matter (β = - 2.6; p = 0.04), whole-brain (β = - 2.7; p = 0.04) and borderline-related for cortex (β = - 2.7; p = 0.076). Pulsatility was unrelated to PS for all regions (p > 0.19). Our findings suggest PS-cognition links mainly reflect a whole-brain phenomenon with only slightly more pronounced links for the hippocampus, and provide no evidence of excessive pulsatility as a trigger of BBB disruption.
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Affiliation(s)
- Tomas Vikner
- Department of Diagnostics and Intervention, Umeå University, 90187, Umeå, Sweden.
- Department of Applied Physics and Electronics, Umeå University, 90187, Umeå, Sweden.
- Department of Medical Physics, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, 53792, USA.
| | - Anders Garpebring
- Department of Diagnostics and Intervention, Umeå University, 90187, Umeå, Sweden
| | - Cecilia Björnfot
- Department of Diagnostics and Intervention, Umeå University, 90187, Umeå, Sweden
| | - Lars Nyberg
- Department of Diagnostics and Intervention, Umeå University, 90187, Umeå, Sweden
- Umeå Center for Functional Brain Imaging (UFBI), Umeå University, 90187, Umeå, Sweden
- Department of Medical and Translational Biology, Umeå University, 90187, Umeå, Sweden
| | - Jan Malm
- Department of Clinical Science, Neurosciences, Umeå University, 90187, Umeå, Sweden
| | - Anders Eklund
- Department of Diagnostics and Intervention, Umeå University, 90187, Umeå, Sweden
- Department of Applied Physics and Electronics, Umeå University, 90187, Umeå, Sweden
- Umeå Center for Functional Brain Imaging (UFBI), Umeå University, 90187, Umeå, Sweden
| | - Anders Wåhlin
- Department of Diagnostics and Intervention, Umeå University, 90187, Umeå, Sweden.
- Department of Applied Physics and Electronics, Umeå University, 90187, Umeå, Sweden.
- Umeå Center for Functional Brain Imaging (UFBI), Umeå University, 90187, Umeå, Sweden.
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Lewis D, Li KL, Waqar M, Coope DJ, Pathmanaban ON, King AT, Djoukhadar I, Zhao S, Cootes TF, Jackson A, Zhu X. Low-dose GBCA administration for brain tumour dynamic contrast enhanced MRI: a feasibility study. Sci Rep 2024; 14:4905. [PMID: 38418818 PMCID: PMC10902320 DOI: 10.1038/s41598-024-53871-x] [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: 06/14/2023] [Accepted: 02/06/2024] [Indexed: 03/02/2024] Open
Abstract
A key limitation of current dynamic contrast enhanced (DCE) MRI techniques is the requirement for full-dose gadolinium-based contrast agent (GBCA) administration. The purpose of this feasibility study was to develop and assess a new low GBCA dose protocol for deriving high-spatial resolution kinetic parameters from brain DCE-MRI. Nineteen patients with intracranial skull base tumours were prospectively imaged at 1.5 T using a single-injection, fixed-volume low GBCA dose, dual temporal resolution interleaved DCE-MRI acquisition. The accuracy of kinetic parameters (ve, Ktrans, vp) derived using this new low GBCA dose technique was evaluated through both Monte-Carlo simulations (mean percent deviation, PD, of measured from true values) and an in vivo study incorporating comparison with a conventional full-dose GBCA protocol and correlation with histopathological data. The mean PD of data from the interleaved high-temporal-high-spatial resolution approach outperformed use of high-spatial, low temporal resolution datasets alone (p < 0.0001, t-test). Kinetic parameters derived using the low-dose interleaved protocol correlated significantly with parameters derived from a full-dose acquisition (p < 0.001) and demonstrated a significant association with tissue markers of microvessel density (p < 0.05). Our results suggest accurate high-spatial resolution kinetic parameter mapping is feasible with significantly reduced GBCA dose.
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Affiliation(s)
- Daniel Lewis
- Department of Neurosurgery, Manchester Centre for Clinical Neurosciences, Salford Royal NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK.
- Geoffrey Jefferson Brain Research Centre, University of Manchester, Manchester, UK.
- Division of Neuroscience and Experimental Psychology, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.
- Department of Neurosurgery, Manchester Centre for Clinical Neurosciences, Salford Royal NHS Foundation Trust, Stott Lane, Salford, Greater Manchester, M6 8HD, UK.
| | - Ka-Loh Li
- Geoffrey Jefferson Brain Research Centre, University of Manchester, Manchester, UK
- Division of Informatics, Imaging and Data Sciences, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Mueez Waqar
- Department of Neurosurgery, Manchester Centre for Clinical Neurosciences, Salford Royal NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
- Geoffrey Jefferson Brain Research Centre, University of Manchester, Manchester, UK
- Division of Cancer Sciences, School of Medical Sciences, Faculty of Biology Medicine and Health, University of Manchester, Manchester, UK
| | - David J Coope
- Department of Neurosurgery, Manchester Centre for Clinical Neurosciences, Salford Royal NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
- Geoffrey Jefferson Brain Research Centre, University of Manchester, Manchester, UK
- Division of Neuroscience and Experimental Psychology, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Omar N Pathmanaban
- Department of Neurosurgery, Manchester Centre for Clinical Neurosciences, Salford Royal NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
- Geoffrey Jefferson Brain Research Centre, University of Manchester, Manchester, UK
- Division of Cell Matrix Biology and Regenerative Medicine, School of Biological Sciences, Faculty of Biology Medicine and Health, University of Manchester, Manchester, UK
| | - Andrew T King
- Department of Neurosurgery, Manchester Centre for Clinical Neurosciences, Salford Royal NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
- Geoffrey Jefferson Brain Research Centre, University of Manchester, Manchester, UK
- Division of Cardiovascular Sciences, School of Medical Sciences, Faculty of Biology Medicine and Health, University of Manchester, Manchester, UK
| | - Ibrahim Djoukhadar
- Department of Neuroradiology, Manchester Centre for Clinical Neurosciences, Salford Royal NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK
| | - Sha Zhao
- Division of Informatics, Imaging and Data Sciences, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Timothy F Cootes
- Division of Informatics, Imaging and Data Sciences, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Alan Jackson
- Division of Informatics, Imaging and Data Sciences, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Xiaoping Zhu
- Division of Informatics, Imaging and Data Sciences, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
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Chang H, Kobzarenko V, Mitra D. Inverse radon transform with deep learning: an application in cardiac motion correction. Phys Med Biol 2024; 69:035010. [PMID: 37988757 DOI: 10.1088/1361-6560/ad0eb5] [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: 05/26/2023] [Accepted: 11/21/2023] [Indexed: 11/23/2023]
Abstract
Objective. This paper addresses performing inverse radon transform (IRT) with artificial neural network (ANN) or deep learning, simultaneously with cardiac motion correction (MC). The suggested application domain is cardiac image reconstruction in emission or transmission tomography where IRT is relevant. Our main contribution is in proposing an ANN architecture that is particularly suitable for this purpose.Approach. We validate our approach with two types of datasets. First, we use an abstract object that looks like a heart to simulate motion-blurred radon transform. With the known ground truth in hand, we then train our proposed ANN architecture and validate its effectiveness in MC. Second, we used human cardiac gated datasets for training and validation of our approach. The gating mechanism bins data over time using the electro-cardiogram (ECG) signals for cardiac motion correction.Main results. We have shown that trained ANNs can perform motion-corrected image reconstruction directly from a motion-corrupted sinogram. We have compared our model against two other known ANN-based approaches.Significance. Our method paves the way for eliminating any need for hardware gating in medical imaging.
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Affiliation(s)
- Haoran Chang
- Department of Electrical Engineering and Computer Science, Florida Institute of Technology, Melbourne, FL 32901, United States of America
| | - Valerie Kobzarenko
- Department of Electrical Engineering and Computer Science, Florida Institute of Technology, Melbourne, FL 32901, United States of America
| | - Debasis Mitra
- Department of Electrical Engineering and Computer Science, Florida Institute of Technology, Melbourne, FL 32901, United States of America
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Li KL, Lewis D, Zhu X, Coope DJ, Djoukhadar I, King AT, Cootes T, Jackson A. A Novel Multi-Model High Spatial Resolution Method for Analysis of DCE MRI Data: Insights from Vestibular Schwannoma Responses to Antiangiogenic Therapy in Type II Neurofibromatosis. Pharmaceuticals (Basel) 2023; 16:1282. [PMID: 37765090 PMCID: PMC10534691 DOI: 10.3390/ph16091282] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 09/01/2023] [Accepted: 09/05/2023] [Indexed: 09/29/2023] Open
Abstract
This study aimed to develop and evaluate a new DCE-MRI processing technique that combines LEGATOS, a dual-temporal resolution DCE-MRI technique, with multi-kinetic models. This technique enables high spatial resolution interrogation of flow and permeability effects, which is currently challenging to achieve. Twelve patients with neurofibromatosis type II-related vestibular schwannoma (20 tumours) undergoing bevacizumab therapy were imaged at 1.5 T both before and at 90 days following treatment. Using the new technique, whole-brain, high spatial resolution images of the contrast transfer coefficient (Ktrans), vascular fraction (vp), extravascular extracellular fraction (ve), capillary plasma flow (Fp), and the capillary permeability-surface area product (PS) could be obtained, and their predictive value was examined. Of the five microvascular parameters derived using the new method, baseline PS exhibited the strongest correlation with the baseline tumour volume (p = 0.03). Baseline ve showed the strongest correlation with the change in tumour volume, particularly the percentage tumour volume change at 90 days after treatment (p < 0.001), and PS demonstrated a larger reduction at 90 days after treatment (p = 0.0001) when compared to Ktrans or Fp alone. Both the capillary permeability-surface area product (PS) and the extravascular extracellular fraction (ve) significantly differentiated the 'responder' and 'non-responder' tumour groups at 90 days (p < 0.05 and p < 0.001, respectively). These results highlight that this novel DCE-MRI analysis approach can be used to evaluate tumour microvascular changes during treatment and the need for future larger clinical studies investigating its role in predicting antiangiogenic therapy response.
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Affiliation(s)
- Ka-Loh Li
- Division of Informatics, Imaging and Data Sciences, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PL, UK; (K.-L.L.); (T.C.); (A.J.)
- Geoffrey Jefferson Brain Research Centre, University of Manchester, Manchester M13 9PL, UK; (D.L.); (D.J.C.); (A.T.K.)
| | - Daniel Lewis
- Geoffrey Jefferson Brain Research Centre, University of Manchester, Manchester M13 9PL, UK; (D.L.); (D.J.C.); (A.T.K.)
- Department of Neurosurgery, Manchester Centre for Clinical Neurosciences, Salford Royal NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester M13 9NT, UK
- Division of Neuroscience and Experimental Psychology, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PL, UK
| | - Xiaoping Zhu
- Division of Informatics, Imaging and Data Sciences, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PL, UK; (K.-L.L.); (T.C.); (A.J.)
- Wolfson Molecular Imaging Centre, University of Manchester, 27 Palatine Road, Manchester M20 3LJ, UK
| | - David J. Coope
- Geoffrey Jefferson Brain Research Centre, University of Manchester, Manchester M13 9PL, UK; (D.L.); (D.J.C.); (A.T.K.)
- Department of Neurosurgery, Manchester Centre for Clinical Neurosciences, Salford Royal NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester M13 9NT, UK
- Division of Neuroscience and Experimental Psychology, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PL, UK
| | - Ibrahim Djoukhadar
- Department of Neuroradiology, Manchester Centre for Clinical Neurosciences, Salford Royal NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester M13 9NT, UK;
| | - Andrew T. King
- Geoffrey Jefferson Brain Research Centre, University of Manchester, Manchester M13 9PL, UK; (D.L.); (D.J.C.); (A.T.K.)
- Department of Neurosurgery, Manchester Centre for Clinical Neurosciences, Salford Royal NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester M13 9NT, UK
- Division of Cardiovascular Sciences, School of Medical Sciences, Faculty of Biology Medicine and Health, University of Manchester, Manchester M13 9PL, UK
| | - Timothy Cootes
- Division of Informatics, Imaging and Data Sciences, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PL, UK; (K.-L.L.); (T.C.); (A.J.)
| | - Alan Jackson
- Division of Informatics, Imaging and Data Sciences, School of Health Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester M13 9PL, UK; (K.-L.L.); (T.C.); (A.J.)
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Cui E, Kersche G, Grubic N, Hétu MF, Pang SC, Sillesen H, Johri AM. Effect of pharmacologic anti-atherosclerotic therapy on carotid intraplaque neovascularization: A systematic review. J Clin Lipidol 2023; 17:315-326. [PMID: 37173161 DOI: 10.1016/j.jacl.2023.04.009] [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: 12/26/2022] [Revised: 04/07/2023] [Accepted: 04/23/2023] [Indexed: 05/15/2023]
Abstract
Intraplaque neovascularization (IPN), a key feature of vulnerable carotid plaque, is associated with adverse cardiovascular (CV) events. Statin therapy has been shown to diminish and stabilize atherosclerotic plaque, but its effect on IPN is uncertain. This review investigated the effects of common pharmacologic anti-atherosclerotic therapies on carotid IPN. Electronic databases (MEDLINE, EMBASE and Cochrane Library) were searched from inception until July 13, 2022. Studies evaluating the effect of anti-atherosclerotic therapy on carotid IPN among adults with carotid atherosclerosis were included. Sixteen studies were eligible for inclusion. Contrast-enhanced ultrasound (CEUS) was the most common IPN assessment modality (n=8), followed by dynamic contrast-enhanced MRI (DCE-MRI) (n=4), excised plaque histology (n=3) and superb microvascular imaging (n=2). In fifteen studies, statins were the therapy of interest and one study assessed PCSK9 inhibitors. Among CEUS studies, baseline statin use was associated with a lower frequency of carotid IPN (median OR = 0.45). Prospective studies showed regression of IPN after 6-12 months of lipid-lowering therapy, with more regression observed in treated participants compared to untreated controls. Our findings suggest that lipid-lowering therapy with statins or PCSK9 inhibitors is associated with IPN regression. However, there was no correlation between change in IPN parameters and change in serum lipids and inflammatory markers in statin-treated participants, so it is unclear whether these factors are mediators in the observed IPN changes. Lastly, this review was limited by study heterogeneity and small sample sizes, so larger trials are needed to validate findings.
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Affiliation(s)
- Edward Cui
- Department of Medicine, Cardiovascular Imaging Network at Queen's (CINQ), Queen's University, Kingston, Canada (Drs Cui, Kersche, Grubic, Hétu, Johri)
| | - Georgia Kersche
- Department of Medicine, Cardiovascular Imaging Network at Queen's (CINQ), Queen's University, Kingston, Canada (Drs Cui, Kersche, Grubic, Hétu, Johri)
| | - Nicholas Grubic
- Department of Medicine, Cardiovascular Imaging Network at Queen's (CINQ), Queen's University, Kingston, Canada (Drs Cui, Kersche, Grubic, Hétu, Johri)
| | - Marie-France Hétu
- Department of Medicine, Cardiovascular Imaging Network at Queen's (CINQ), Queen's University, Kingston, Canada (Drs Cui, Kersche, Grubic, Hétu, Johri)
| | - Stephen C Pang
- Department of Biomedical and Molecular Sciences, Queen's University, Kingston, Canada (Dr Pang)
| | - Henrik Sillesen
- Department of Vascular Surgery, Copenhagen University Hospital, Rigshospitalet, Copenhagen, Denmark (Dr Sillesen); Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark (Dr Sillesen)
| | - Amer M Johri
- Department of Medicine, Cardiovascular Imaging Network at Queen's (CINQ), Queen's University, Kingston, Canada (Drs Cui, Kersche, Grubic, Hétu, Johri).
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Bressler I, Ben Bashat D, Buchsweiler Y, Aizenstein O, Limon D, Bokestein F, Blumenthal TD, Nevo U, Artzi M. Model-free dynamic contrast-enhanced MRI analysis: differentiation between active tumor and necrotic tissue in patients with glioblastoma. MAGMA (NEW YORK, N.Y.) 2023; 36:33-42. [PMID: 36287282 DOI: 10.1007/s10334-022-01045-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Revised: 10/09/2022] [Accepted: 10/13/2022] [Indexed: 11/07/2022]
Abstract
OBJECTIVE Treatment response assessment in patients with high-grade gliomas (HGG) is heavily dependent on changes in lesion size on MRI. However, in conventional MRI, treatment-related changes can appear as enhancing tissue, with similar presentation to that of active tumor tissue. We propose a model-free data-driven method for differentiation between these tissues, based on dynamic contrast-enhanced (DCE) MRI. MATERIALS AND METHODS The study included a total of 66 scans of patients with glioblastoma. Of these, 48 were acquired from 1 MRI vendor and 18 scans were acquired from a different MRI vendor and used as test data. Of the 48, 24 scans had biopsy results. Analysis included semi-automatic arterial input function (AIF) extraction, direct DCE pharmacokinetic-like feature extraction, and unsupervised clustering of the two tissue types. Validation was performed via (a) comparison to biopsy result (b) correlation to literature-based DCE curves for each tissue type, and (c) comparison to clinical outcome. RESULTS Consistency between the model prediction and biopsy results was found in 20/24 cases. An average correlation of 82% for active tumor and 90% for treatment-related changes was found between the predicted component and population-based templates. An agreement between the predicted results and radiologist's assessment, based on RANO criteria, was found in 11/12 cases. CONCLUSION The proposed method could serve as a non-invasive method for differentiation between lesion tissue and treatment-related changes.
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Affiliation(s)
- Idan Bressler
- Sagol Brain Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.,The Iby and Aladar Fleischman Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel
| | - Dafna Ben Bashat
- Sagol Brain Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.,Sackler Faculty of Medicine, Tel Aviv University, 6 Weizmann St, 64239, Tel-Aviv, Israel.,Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Yuval Buchsweiler
- Sagol Brain Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.,The Iby and Aladar Fleischman Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel
| | - Orna Aizenstein
- Sackler Faculty of Medicine, Tel Aviv University, 6 Weizmann St, 64239, Tel-Aviv, Israel.,Division of Radiology, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Dror Limon
- Sackler Faculty of Medicine, Tel Aviv University, 6 Weizmann St, 64239, Tel-Aviv, Israel.,Division of Oncology, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Felix Bokestein
- Sackler Faculty of Medicine, Tel Aviv University, 6 Weizmann St, 64239, Tel-Aviv, Israel.,Neuro-Oncology Service, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - T Deborah Blumenthal
- Sackler Faculty of Medicine, Tel Aviv University, 6 Weizmann St, 64239, Tel-Aviv, Israel.,Neuro-Oncology Service, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
| | - Uri Nevo
- The Iby and Aladar Fleischman Faculty of Engineering, Tel Aviv University, Tel Aviv, Israel.,Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - Moran Artzi
- Sagol Brain Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel. .,Sackler Faculty of Medicine, Tel Aviv University, 6 Weizmann St, 64239, Tel-Aviv, Israel. .,Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.
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