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Piluso S, Souedet N, Jan C, Hérard AS, Clouchoux C, Delzescaux T. giRAff: an automated atlas segmentation tool adapted to single histological slices. Front Neurosci 2024; 17:1230814. [PMID: 38274499 PMCID: PMC10808556 DOI: 10.3389/fnins.2023.1230814] [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: 05/29/2023] [Accepted: 10/31/2023] [Indexed: 01/27/2024] Open
Abstract
Conventional histology of the brain remains the gold standard in the analysis of animal models. In most biological studies, standard protocols usually involve producing a limited number of histological slices to be analyzed. These slices are often selected into a specific anatomical region of interest or around a specific pathological lesion. Due to the lack of automated solutions to analyze such single slices, neurobiologists perform the segmentation of anatomical regions manually most of the time. Because the task is long, tedious, and operator-dependent, we propose an automated atlas segmentation method called giRAff, which combines rigid and affine registrations and is suitable for conventional histological protocols involving any number of single slices from a given mouse brain. In particular, the method has been tested on several routine experimental protocols involving different anatomical regions of different sizes and for several brains. For a given set of single slices, the method can automatically identify the corresponding slices in the mouse Allen atlas template with good accuracy and segmentations comparable to those of an expert. This versatile and generic method allows the segmentation of any single slice without additional anatomical context in about 1 min. Basically, our proposed giRAff method is an easy-to-use, rapid, and automated atlas segmentation tool compliant with a wide variety of standard histological protocols.
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Affiliation(s)
- Sébastien Piluso
- Université Paris-Saclay, CEA, CNRS, MIRCen, Laboratoire des Maladies Neurodégénératives, Fontenay-aux-Roses, France
- WITSEE, Paris, France
| | - Nicolas Souedet
- Université Paris-Saclay, CEA, CNRS, MIRCen, Laboratoire des Maladies Neurodégénératives, Fontenay-aux-Roses, France
| | - Caroline Jan
- Université Paris-Saclay, CEA, CNRS, MIRCen, Laboratoire des Maladies Neurodégénératives, Fontenay-aux-Roses, France
| | - Anne-Sophie Hérard
- Université Paris-Saclay, CEA, CNRS, MIRCen, Laboratoire des Maladies Neurodégénératives, Fontenay-aux-Roses, France
| | | | - Thierry Delzescaux
- Université Paris-Saclay, CEA, CNRS, MIRCen, Laboratoire des Maladies Neurodégénératives, Fontenay-aux-Roses, France
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Ortega-Cruz D, Bress KS, Gazula H, Rabano A, Iglesias JE, Strange BA. Three-dimensional histology reveals dissociable human hippocampal long axis gradients of Alzheimer's pathology. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.12.05.570038. [PMID: 38105985 PMCID: PMC10723286 DOI: 10.1101/2023.12.05.570038] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/19/2023]
Abstract
INTRODUCTION Three-dimensional (3D) histology analyses are essential to overcome sampling variability and understand pathological differences beyond the dissection axis. We present Path2MR, the first pipeline allowing 3D reconstruction of sparse human histology without an MRI reference. We implemented Path2MR with post-mortem hippocampal sections to explore pathology gradients in Alzheimer's Disease. METHODS Blockface photographs of brain hemisphere slices are used for 3D reconstruction, from which an MRI-like image is generated using machine learning. Histology sections are aligned to the reconstructed hemisphere and subsequently to an atlas in standard space. RESULTS Path2MR successfully registered histological sections to their anatomical position along the hippocampal longitudinal axis. Combined with histopathology quantification, we found an expected peak of tau pathology at the anterior end of the hippocampus, while amyloid-β displayed a quadratic anterior-posterior distribution. CONCLUSION Path2MR, which enables 3D histology using any brain bank dataset, revealed significant differences along the hippocampus between tau and amyloid-β.
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Affiliation(s)
- Diana Ortega-Cruz
- Laboratory for Clinical Neuroscience, Center for Biomedical Technology, Universidad Politécnica de Madrid, IdISSC, 28223, Madrid, Spain
- Alzheimer's Disease Research Unit, CIEN Foundation, Queen Sofia Foundation Alzheimer Center, 28031, Madrid, Spain
| | - Kimberly S Bress
- Alzheimer's Disease Research Unit, CIEN Foundation, Queen Sofia Foundation Alzheimer Center, 28031, Madrid, Spain
- Current address: Vanderbilt University School of Medicine, 37232, Nashville, TN, USA
| | - Harshvardhan Gazula
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, 02129, Boston, MA, USA
| | - Alberto Rabano
- Alzheimer's Disease Research Unit, CIEN Foundation, Queen Sofia Foundation Alzheimer Center, 28031, Madrid, Spain
| | - Juan Eugenio Iglesias
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, 02129, Boston, MA, USA
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, 02139, Boston, MA, USA
- Centre for Medical Image Computing, University College London, WC1V 6LJ, London, United Kingdom
| | - Bryan A Strange
- Laboratory for Clinical Neuroscience, Center for Biomedical Technology, Universidad Politécnica de Madrid, IdISSC, 28223, Madrid, Spain
- Alzheimer's Disease Research Unit, CIEN Foundation, Queen Sofia Foundation Alzheimer Center, 28031, Madrid, Spain
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Kumarasami R, Verma R, Pandurangan K, Ramesh JJ, Pandidurai S, Savoia S, Jayakumar J, Bota M, Mitra P, Joseph J, Sivaprakasam M. A technology platform for standardized cryoprotection and freezing of large-volume brain tissues for high-resolution histology. Front Neuroanat 2023; 17:1292655. [PMID: 38020211 PMCID: PMC10651725 DOI: 10.3389/fnana.2023.1292655] [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: 09/11/2023] [Accepted: 10/10/2023] [Indexed: 12/01/2023] Open
Abstract
Understanding and mapping the human connectome is a long-standing endeavor of neuroscience, yet the significant challenges associated with the large size of the human brain during cryosectioning remain unsolved. While smaller brains, such as rodents and marmosets, have been the focus of previous connectomics projects, the processing of the larger human brain requires significant technological advancements. This study addresses the problem of freezing large brains in aligned neuroanatomical coordinates with minimal tissue damage, facilitating large-scale distortion-free cryosectioning. We report the most effective and stable freezing technique utilizing an appropriate choice of cryoprotection and leveraging engineering tools such as brain master patterns, custom-designed molds, and a continuous temperature monitoring system. This standardized approach to freezing enables high-quality, distortion-free histology, allowing researchers worldwide to explore the complexities of the human brain at a cellular level. Our approach combines neuroscience and engineering technologies to address this long-standing challenge with limited resources, enhancing accessibility of large-scale scientific endeavors beyond developed countries, promoting diverse approaches, and fostering collaborations.
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Affiliation(s)
- Ramdayalan Kumarasami
- Department of Electrical Engineering, Indian Institute of Technology Madras, Chennai, India
- Healthcare Technology Innovation Centre, Indian Institute of Technology Madras, Chennai, India
| | - Richa Verma
- Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai, India
| | - Karthika Pandurangan
- Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai, India
| | - Jivitha Jyothi Ramesh
- Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai, India
| | - Sathish Pandidurai
- Healthcare Technology Innovation Centre, Indian Institute of Technology Madras, Chennai, India
- Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai, India
| | - Stephen Savoia
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, NY, United States
| | - Jaikishan Jayakumar
- Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai, India
- Center for Computational Brain Research, Indian Institute of Technology Madras, Chennai, India
| | - Mihail Bota
- Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai, India
| | - Partha Mitra
- Cold Spring Harbor Laboratory, Cold Spring Harbor, New York, NY, United States
| | - Jayaraj Joseph
- Department of Electrical Engineering, Indian Institute of Technology Madras, Chennai, India
- Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai, India
| | - Mohanasankar Sivaprakasam
- Department of Electrical Engineering, Indian Institute of Technology Madras, Chennai, India
- Healthcare Technology Innovation Centre, Indian Institute of Technology Madras, Chennai, India
- Sudha Gopalakrishnan Brain Centre, Indian Institute of Technology Madras, Chennai, India
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Karthik S, Joseph J, Jayakumar J, Manoj R, Shetty M, Bota M, Verma R, Mitra P, Sivaprakasam M. Wide field block face imaging using deep ultraviolet induced autofluorescence of the human brain. J Neurosci Methods 2023; 397:109921. [PMID: 37459898 DOI: 10.1016/j.jneumeth.2023.109921] [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: 03/24/2023] [Revised: 06/26/2023] [Accepted: 07/13/2023] [Indexed: 08/22/2023]
Abstract
BACKGROUND Imaging large volume human brains at cellular resolution involve histological methods that cause structural changes. A reference point prior to sectioning is needed to quantify these changes and is achieved by serial block face imaging (BFI) methods that have been applied to small volume tissue (∼1 cm3). NEW METHOD We have developed a BFI uniquely designed for large volume tissues (∼1300 cm3) with a very large field of view (20 × 20 cm) at a resolution of 70 µm/pixel under deep ultraviolet (UV-C) illumination which highlights key features. RESULTS The UV-C imaging ensures high contrast imaging of the brain tissue and highlights salient features of the brain. The system is designed to provide uniform and stable illumination across the entire surface area of the tissue and to work at low temperatures, which are required during cryosectioning. Most importantly, it has been designed to maintain its optical focus over the large depth of tissue and over long periods of time, without readjustments. The BFI was installed within a cryomacrotome, and was used to image a large cryoblock of an adult human cerebellum and brainstem (∼6 cm depth resulting in 2995 serial images) with precise optical focus and no loss during continuous serial acquisition. COMPARISON WITH EXISTING METHOD(S) The deep UV-C induced BFI highlights several large fibre tracts within the brain including the cerebellar peduncles, and the corticospinal tract providing important advantage over white light BFI. CONCLUSIONS The 3D reconstructed serial BFI images can assist in the registration and alignment of the microscopic high-resolution histological tissue sections.
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Affiliation(s)
- Srinivasa Karthik
- Healthcare Technology Innovation Centre, No. 1, 5th Floor, 'C' Block, Phase-II, IIT Madras Research Park, Kanagam Road, Taramani, Chennai 600113, India; Department of Electrical Engineering, Indian Institute of Technology Madras, IIT P.O., Chennai 600036, India.
| | - Jayaraj Joseph
- Department of Electrical Engineering, Indian Institute of Technology Madras, IIT P.O., Chennai 600036, India
| | - Jaikishan Jayakumar
- Sudha Gopalakrishnan Brain Centre (SGBC), Indian Institute of Technology Madras, NAC Building 1, Stilt Floor, IIT P.O., Chennai 600036, India; Center for Computational Brain Research, Indian Institute of Technology Madras, IIT P.O., Chennai 600036, India
| | - Rahul Manoj
- Healthcare Technology Innovation Centre, No. 1, 5th Floor, 'C' Block, Phase-II, IIT Madras Research Park, Kanagam Road, Taramani, Chennai 600113, India; Department of Electrical Engineering, Indian Institute of Technology Madras, IIT P.O., Chennai 600036, India
| | - Mahesh Shetty
- Sudha Gopalakrishnan Brain Centre (SGBC), Indian Institute of Technology Madras, NAC Building 1, Stilt Floor, IIT P.O., Chennai 600036, India
| | - Mihail Bota
- Sudha Gopalakrishnan Brain Centre (SGBC), Indian Institute of Technology Madras, NAC Building 1, Stilt Floor, IIT P.O., Chennai 600036, India
| | - Richa Verma
- Sudha Gopalakrishnan Brain Centre (SGBC), Indian Institute of Technology Madras, NAC Building 1, Stilt Floor, IIT P.O., Chennai 600036, India
| | - Partha Mitra
- Center for Computational Brain Research, Indian Institute of Technology Madras, IIT P.O., Chennai 600036, India; Cold Spring Harbor Laboratory, 1, Bungtown Road, Cold Spring Harbor, New York 11724, United States
| | - Mohanasankar Sivaprakasam
- Healthcare Technology Innovation Centre, No. 1, 5th Floor, 'C' Block, Phase-II, IIT Madras Research Park, Kanagam Road, Taramani, Chennai 600113, India; Department of Electrical Engineering, Indian Institute of Technology Madras, IIT P.O., Chennai 600036, India; Sudha Gopalakrishnan Brain Centre (SGBC), Indian Institute of Technology Madras, NAC Building 1, Stilt Floor, IIT P.O., Chennai 600036, India
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Ghose S, Ju Y, McDonough E, Ho J, Karunamurthy A, Chadwick C, Cho S, Rose R, Corwin A, Surrette C, Martinez J, Williams E, Sood A, Al-Kofahi Y, Falo LD, Börner K, Ginty F. 3D reconstruction of skin and spatial mapping of immune cell density, vascular distance and effects of sun exposure and aging. Commun Biol 2023; 6:718. [PMID: 37468758 PMCID: PMC10356782 DOI: 10.1038/s42003-023-04991-z] [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: 04/28/2022] [Accepted: 05/11/2023] [Indexed: 07/21/2023] Open
Abstract
Mapping the human body at single cell resolution in three dimensions (3D) is important for understanding cellular interactions in context of tissue and organ organization. 2D spatial cell analysis in a single tissue section may be limited by cell numbers and histology. Here we show a workflow for 3D reconstruction of multiplexed sequential tissue sections: MATRICS-A (Multiplexed Image Three-D Reconstruction and Integrated Cell Spatial - Analysis). We demonstrate MATRICS-A in 26 serial sections of fixed skin (stained with 18 biomarkers) from 12 donors aged between 32-72 years. Comparing the 3D reconstructed cellular data with the 2D data, we show significantly shorter distances between immune cells and vascular endothelial cells (56 µm in 3D vs 108 µm in 2D). We also show 10-70% more T cells (total) within 30 µm of a neighboring T helper cell in 3D vs 2D. Distances of p53, DDB2 and Ki67 positive cells to the skin surface were consistent across all ages/sun exposure and largely localized to the lower stratum basale layer of the epidermis. MATRICS-A provides a framework for analysis of 3D spatial cell relationships in healthy and aging organs and could be further extended to diseased organs.
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Affiliation(s)
- Soumya Ghose
- GE Research Center, 1 Research Circle, Niskayuna, NY, 12309, USA
| | - Yingnan Ju
- Indiana University, 107 South Indiana Ave, Bloomington, IN, 47405, USA
| | | | - Jonhan Ho
- University of Pittsburgh School of Medicine, 3550 Terrace St, Pittsburgh, PA, 15213, USA
| | | | | | - Sanghee Cho
- GE Research Center, 1 Research Circle, Niskayuna, NY, 12309, USA
| | - Rachel Rose
- GE Research Center, 1 Research Circle, Niskayuna, NY, 12309, USA
| | - Alex Corwin
- GE Research Center, 1 Research Circle, Niskayuna, NY, 12309, USA
| | | | - Jessica Martinez
- GE Research Center, 1 Research Circle, Niskayuna, NY, 12309, USA
| | - Eric Williams
- GE Research Center, 1 Research Circle, Niskayuna, NY, 12309, USA
| | - Anup Sood
- GE Research Center, 1 Research Circle, Niskayuna, NY, 12309, USA
| | - Yousef Al-Kofahi
- GE Research Center, 1 Research Circle, Niskayuna, NY, 12309, USA
| | - Louis D Falo
- University of Pittsburgh School of Medicine, 3550 Terrace St, Pittsburgh, PA, 15213, USA
| | - Katy Börner
- Indiana University, 107 South Indiana Ave, Bloomington, IN, 47405, USA.
| | - Fiona Ginty
- GE Research Center, 1 Research Circle, Niskayuna, NY, 12309, USA.
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Sithambaram P, Kumarasami R, Pandidurai S, Sekar S, Vasan JK, Sivaprakasam M, Joseph J. Automation of slide staining for large tissue sections. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2023; 2023:1-4. [PMID: 38083015 DOI: 10.1109/embc40787.2023.10339963] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2023]
Abstract
Staining is a critical step in tissue analysis as it enhances the visibility and contrast of tissue structures for microscopic examination. Large tissue sections such as the human brain, heart, and liver are becoming increasingly important in studying complex tissue structures, providing critical information about the tissue's normal or abnormal development, function, and disease processes. Manual staining methods are still widely used and are prone to inconsistencies and inaccuracies, leading to unreliable results. Commercially available automated staining systems offer a more efficient alternative, but currently, these systems are only available for smaller 1" x 3" slides which are ill-suited for examining larger tissue sections. To address this challenge, we present a custom-designed Large format Automated Slide Stainer that can handle various glass slides, from the standard 1" x 3" slides to the custom-sized 2" x 3", 5" x 7", and 6" x 8" glass slides. The system uses a Cartesian robotic arm to stain the slides and has a user-friendly and intuitive interface for creating and modifying custom staining protocols. Safety features include chemical isolation, a ventilation system, an emergency shutdown, and a protective shield to minimize hazards from handling chemicals and biological materials. The automated stainer showed little variability in positioning with a mean offset error of 1.65 ± 0.65 mm and 1.73 ± 0.76 mm in the X and Y axes, respectively. In addition, the automated staining process showed better uniformity than manual staining. A pairwise distance was used to evaluate how well image histograms matched within a batch. The automated staining had a mean pairwise distance of 0.0070 ± 0.0017 (Nissl) and 0.0060 ± 0.0003 (Hematoxylin and Eosin(H&E)), which were far superior to the manual staining distances (Nissl: 0.0173 ± 0.0107 and H&E: 0.0185 ± 0.0067). This system represents a substantial advancement in tissue staining and has the potential to improve the reliability of tissue analysis significantly.Clinical relevance - Automated system for providing accurate, reproducible, and high-throughput staining of large tissue sections for use in histopathology and research.
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Nolte P, Dullin C, Svetlove A, Brettmacher M, Rußmann C, Schilling AF, Alves F, Stock B. Current Approaches for Image Fusion of Histological Data with Computed Tomography and Magnetic Resonance Imaging. Radiol Res Pract 2022; 2022:6765895. [PMID: 36408297 PMCID: PMC9668453 DOI: 10.1155/2022/6765895] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2022] [Accepted: 08/17/2022] [Indexed: 10/30/2023] Open
Abstract
Classical analysis of biological samples requires the destruction of the tissue's integrity by cutting or grinding it down to thin slices for (Immuno)-histochemical staining and microscopic analysis. Despite high specificity, encoded in the stained 2D section of the whole tissue, the structural information, especially 3D information, is limited. Computed tomography (CT) or magnetic resonance imaging (MRI) scans performed prior to sectioning in combination with image registration algorithms provide an opportunity to regain access to morphological characteristics as well as to relate histological findings to the 3D structure of the local tissue environment. This review provides a summary of prevalent literature addressing the problem of multimodal coregistration of hard- and soft-tissue in microscopy and tomography. Grouped according to the complexity of the dimensions, including image-to-volume (2D ⟶ 3D), image-to-image (2D ⟶ 2D), and volume-to-volume (3D ⟶ 3D), selected currently applied approaches are investigated by comparing the method accuracy with respect to the limiting resolution of the tomography. Correlation of multimodal imaging could position itself as a useful tool allowing for precise histological diagnostic and allow the a priori planning of tissue extraction like biopsies.
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Affiliation(s)
- Philipp Nolte
- Faculty of Engineering and Health, University of Applied Sciences and Arts, Goettingen 37085, Germany
- Institute for Diagnostic and Interventional Radiology, University Medical Center Goettingen, Goettingen 37075, Germany
- Department of Trauma Surgery, Orthopedics and Plastic Surgery, University Medical Center Goettingen, Gottingen 37075, Germany
| | - Christian Dullin
- Institute for Diagnostic and Interventional Radiology, University Medical Center Goettingen, Goettingen 37075, Germany
- Translational Molecular Imaging, Max-Planck Institute for Multidisciplinary Sciences, City Campus, 37075 Goettingen, Germany
- Department for Diagnostic and Interventional Radiology, University Hospital Heidelberg, Heidelberg 69120, Germany
| | - Angelika Svetlove
- Institute for Diagnostic and Interventional Radiology, University Medical Center Goettingen, Goettingen 37075, Germany
- Translational Molecular Imaging, Max-Planck Institute for Multidisciplinary Sciences, City Campus, 37075 Goettingen, Germany
| | - Marcel Brettmacher
- Faculty of Engineering and Health, University of Applied Sciences and Arts, Goettingen 37085, Germany
| | - Christoph Rußmann
- Faculty of Engineering and Health, University of Applied Sciences and Arts, Goettingen 37085, Germany
- Brigham and Women's Hospital, Harvard Medical School, Boston 02155, MA, USA
| | - Arndt F. Schilling
- Department of Trauma Surgery, Orthopedics and Plastic Surgery, University Medical Center Goettingen, Gottingen 37075, Germany
| | - Frauke Alves
- Institute for Diagnostic and Interventional Radiology, University Medical Center Goettingen, Goettingen 37075, Germany
- Translational Molecular Imaging, Max-Planck Institute for Multidisciplinary Sciences, City Campus, 37075 Goettingen, Germany
| | - Bernd Stock
- Faculty of Engineering and Health, University of Applied Sciences and Arts, Goettingen 37085, Germany
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Casamitjana A, Iglesias JE. High-resolution atlasing and segmentation of the subcortex: Review and perspective on challenges and opportunities created by machine learning. Neuroimage 2022; 263:119616. [PMID: 36084858 DOI: 10.1016/j.neuroimage.2022.119616] [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: 03/29/2022] [Revised: 08/30/2022] [Accepted: 09/05/2022] [Indexed: 11/17/2022] Open
Abstract
This paper reviews almost three decades of work on atlasing and segmentation methods for subcortical structures in human brain MRI. In writing this survey, we have three distinct aims. First, to document the evolution of digital subcortical atlases of the human brain, from the early MRI templates published in the nineties, to the complex multi-modal atlases at the subregion level that are available today. Second, to provide a detailed record of related efforts in the automated segmentation front, from earlier atlas-based methods to modern machine learning approaches. And third, to present a perspective on the future of high-resolution atlasing and segmentation of subcortical structures in in vivo human brain MRI, including open challenges and opportunities created by recent developments in machine learning.
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Affiliation(s)
- Adrià Casamitjana
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, UK.
| | - Juan Eugenio Iglesias
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, UK; Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Boston, USA
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Tan J, Labrinidis A, Williams R, Mian M, Anderson PJ, Ranjitkar S. Micro-CT-Based Bone Microarchitecture Analysis of the Murine Skull. METHODS IN MOLECULAR BIOLOGY (CLIFTON, N.J.) 2022; 2403:129-145. [PMID: 34913121 DOI: 10.1007/978-1-0716-1847-9_10] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
X-ray micro-computed tomography (micro-CT) imaging has important applications in microarchitecture analysis of cortical and trabecular bone structure. While standardized protocols exist for micro-CT-based microarchitecture assessment of long bones, specific protocols need to be developed for different types of skull bones taking into account differences in embryogenesis, organization, development, and growth compared to the rest of the body. This chapter describes the general principles of bone microarchitecture analysis of murine craniofacial skeleton to accommodate for morphological variations in different regions of interest.
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Affiliation(s)
- Jenny Tan
- Adelaide Dental School, The University of Adelaide, Adelaide, SA, Australia
| | - Agatha Labrinidis
- Adelaide Microscopy, The University of Adelaide, Adelaide, SA, Australia
| | - Ruth Williams
- Adelaide Microscopy, The University of Adelaide, Adelaide, SA, Australia
| | - Mustafa Mian
- Adelaide Dental School, The University of Adelaide, Adelaide, SA, Australia
| | - Peter J Anderson
- Adelaide Dental School, The University of Adelaide, Adelaide, SA, Australia.,Australian Craniofacial Unit, Women's and Children's Hospital, North Adelaide, SA, Australia.,South Australian Health and Medical Research Institute (SAHMRI), Adelaide, SA, Australia
| | - Sarbin Ranjitkar
- Adelaide Dental School, The University of Adelaide, Adelaide, SA, Australia. .,Department of Dentistry and Oral Health, La Trobe Rural Health School, La Trobe University, Bendigo, VIC, Australia.
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Newmaster KT, Kronman FA, Wu YT, Kim Y. Seeing the Forest and Its Trees Together: Implementing 3D Light Microscopy Pipelines for Cell Type Mapping in the Mouse Brain. Front Neuroanat 2022; 15:787601. [PMID: 35095432 PMCID: PMC8794814 DOI: 10.3389/fnana.2021.787601] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 12/02/2021] [Indexed: 12/14/2022] Open
Abstract
The brain is composed of diverse neuronal and non-neuronal cell types with complex regional connectivity patterns that create the anatomical infrastructure underlying cognition. Remarkable advances in neuroscience techniques enable labeling and imaging of these individual cell types and their interactions throughout intact mammalian brains at a cellular resolution allowing neuroscientists to examine microscopic details in macroscopic brain circuits. Nevertheless, implementing these tools is fraught with many technical and analytical challenges with a need for high-level data analysis. Here we review key technical considerations for implementing a brain mapping pipeline using the mouse brain as a primary model system. Specifically, we provide practical details for choosing methods including cell type specific labeling, sample preparation (e.g., tissue clearing), microscopy modalities, image processing, and data analysis (e.g., image registration to standard atlases). We also highlight the need to develop better 3D atlases with standardized anatomical labels and nomenclature across species and developmental time points to extend the mapping to other species including humans and to facilitate data sharing, confederation, and integrative analysis. In summary, this review provides key elements and currently available resources to consider while developing and implementing high-resolution mapping methods.
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Affiliation(s)
- Kyra T Newmaster
- Department of Neural and Behavioral Sciences, The Pennsylvania State University, Hershey, PA, United States
| | - Fae A Kronman
- Department of Neural and Behavioral Sciences, The Pennsylvania State University, Hershey, PA, United States
| | - Yuan-Ting Wu
- Department of Neural and Behavioral Sciences, The Pennsylvania State University, Hershey, PA, United States
| | - Yongsoo Kim
- Department of Neural and Behavioral Sciences, The Pennsylvania State University, Hershey, PA, United States
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Casamitjana A, Lorenzi M, Ferraris S, Peter L, Modat M, Stevens A, Fischl B, Vercauteren T, Iglesias JE. Robust joint registration of multiple stains and MRI for multimodal 3D histology reconstruction: Application to the Allen human brain atlas. Med Image Anal 2022; 75:102265. [PMID: 34741894 PMCID: PMC8678374 DOI: 10.1016/j.media.2021.102265] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 08/07/2021] [Accepted: 10/07/2021] [Indexed: 02/04/2023]
Abstract
Joint registration of a stack of 2D histological sections to recover 3D structure ("3D histology reconstruction") finds application in areas such as atlas building and validation of in vivo imaging. Straightforward pairwise registration of neighbouring sections yields smooth reconstructions but has well-known problems such as "banana effect" (straightening of curved structures) and "z-shift" (drift). While these problems can be alleviated with an external, linearly aligned reference (e.g., Magnetic Resonance (MR) images), registration is often inaccurate due to contrast differences and the strong nonlinear distortion of the tissue, including artefacts such as folds and tears. In this paper, we present a probabilistic model of spatial deformation that yields reconstructions for multiple histological stains that that are jointly smooth, robust to outliers, and follow the reference shape. The model relies on a spanning tree of latent transforms connecting all the sections and slices of the reference volume, and assumes that the registration between any pair of images can be see as a noisy version of the composition of (possibly inverted) latent transforms connecting the two images. Bayesian inference is used to compute the most likely latent transforms given a set of pairwise registrations between image pairs within and across modalities. We consider two likelihood models: Gaussian (ℓ2 norm, which can be minimised in closed form) and Laplacian (ℓ1 norm, minimised with linear programming). Results on synthetic deformations on multiple MR modalities, show that our method can accurately and robustly register multiple contrasts even in the presence of outliers. The framework is used for accurate 3D reconstruction of two stains (Nissl and parvalbumin) from the Allen human brain atlas, showing its benefits on real data with severe distortions. Moreover, we also provide the registration of the reconstructed volume to MNI space, bridging the gaps between two of the most widely used atlases in histology and MRI. The 3D reconstructed volumes and atlas registration can be downloaded from https://openneuro.org/datasets/ds003590. The code is freely available at https://github.com/acasamitjana/3dhirest.
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Affiliation(s)
| | - Marco Lorenzi
- Universitë Côte dÁzur, Inria, Epione Team, 06902 Sophia Antipolis, France
| | | | - Loïc Peter
- Center for Medical Image Computing, University College London, UK
| | - Marc Modat
- School of Biomedical Engineering & Imaging Sciences, King's College London, UK
| | - Allison Stevens
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, USA
| | - Bruce Fischl
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, USA; Program in Health Sciences and Technology, Massachusetts Institute of Technology, USA
| | - Tom Vercauteren
- School of Biomedical Engineering & Imaging Sciences, King's College London, UK
| | - Juan Eugenio Iglesias
- Center for Medical Image Computing, University College London, UK; Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, USA
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12
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Zimmerman BE, Johnson SL, Odéen HA, Shea JE, Factor RE, Joshi SC, Payne AH. Histology to 3D in vivo MR registration for volumetric evaluation of MRgFUS treatment assessment biomarkers. Sci Rep 2021; 11:18923. [PMID: 34556678 PMCID: PMC8460731 DOI: 10.1038/s41598-021-97309-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2020] [Accepted: 08/24/2021] [Indexed: 11/09/2022] Open
Abstract
Advances in imaging and early cancer detection have increased interest in magnetic resonance (MR) guided focused ultrasound (MRgFUS) technologies for cancer treatment. MRgFUS ablation treatments could reduce surgical risks, preserve organ tissue and function, and improve patient quality of life. However, surgical resection and histological analysis remain the gold standard to assess cancer treatment response. For non-invasive ablation therapies such as MRgFUS, the treatment response must be determined through MR imaging biomarkers. However, current MR biomarkers are inconclusive and have not been rigorously evaluated against histology via accurate registration. Existing registration methods rely on anatomical features to directly register in vivo MR and histology. For MRgFUS applications in anatomies such as liver, kidney, or breast, anatomical features that are not caused by the treatment are often insufficient to drive direct registration. We present a novel MR to histology registration workflow that utilizes intermediate imaging and does not rely on anatomical MR features being visible in histology. The presented workflow yields an overall registration accuracy of 1.00 ± 0.13 mm. The developed registration pipeline is used to evaluate a common MRgFUS treatment assessment biomarker against histology. Evaluating MR biomarkers against histology using this registration pipeline will facilitate validating novel MRgFUS biomarkers to improve treatment assessment without surgical intervention. While the presented registration technique has been evaluated in a MRgFUS ablation treatment model, this technique could be potentially applied in any tissue to evaluate a variety of therapeutic options.
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Affiliation(s)
- Blake E Zimmerman
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA. .,Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA.
| | - Sara L Johnson
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA.,Utah Center for Advanced Imaging Research, University of Utah, Salt Lake City, UT, USA
| | - Henrik A Odéen
- Utah Center for Advanced Imaging Research, University of Utah, Salt Lake City, UT, USA
| | - Jill E Shea
- Department of Surgery, University of Utah, Salt Lake City, UT, USA
| | - Rachel E Factor
- Department of Pathology, University of Utah, Salt Lake City, UT, USA
| | - Sarang C Joshi
- Department of Biomedical Engineering, University of Utah, Salt Lake City, UT, USA.,Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA
| | - Allison H Payne
- Utah Center for Advanced Imaging Research, University of Utah, Salt Lake City, UT, USA
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13
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Bobić-Rasonja M, Pogledić I, Mitter C, Štajduhar A, Milković-Periša M, Trnski S, Bettelheim D, Hainfellner JA, Judaš M, Prayer D, Jovanov-Milošević N. Developmental Differences Between the Limbic and Neocortical Telencephalic Wall: An Intrasubject Slice-Matched 3 T MRI-Histological Correlative Study in Humans. Cereb Cortex 2021; 31:3536-3550. [PMID: 33704445 DOI: 10.1093/cercor/bhab030] [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: 12/05/2020] [Revised: 01/19/2021] [Accepted: 01/26/2021] [Indexed: 12/20/2022] Open
Abstract
The purpose of the study was to investigate the interrelation of the signal intensities and thicknesses of the transient developmental zones in the cingulate and neocortical telencephalic wall, using T2-weighted 3 T-magnetic resonance imaging (MRI) and histological scans from the same brain hemisphere. The study encompassed 24 postmortem fetal brains (15-35 postconceptional weeks, PCW). The measurements were performed using Fiji and NDP.view2. We found that T2w MR signal-intensity curves show a specific regional and developmental stage profile already at 15 PCW. The MRI-histological correlation reveals that the subventricular-intermediate zone (SVZ-IZ) contributes the most to the regional differences in the MRI-profile and zone thicknesses, growing by a factor of 2.01 in the cingulate, and 1.78 in the neocortical wall. The interrelations of zone or wall thicknesses, obtained by both methods, disclose a different rate and extent of shrinkage per region (highest in neocortical subplate and SVZ-IZ) and stage (highest in the early second half of fetal development), distorting the zones' proportion in histological sections. This intrasubject, slice-matched, 3 T correlative MRI-histological study provides important information about regional development of the cortical wall, critical for the design of MRI criteria for prenatal brain monitoring and early detection of cortical or other brain pathologies in human fetuses.
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Affiliation(s)
- Mihaela Bobić-Rasonja
- Croatian Institute for Brain Research, School of Medicine University of Zagreb, 10000 Zagreb, Croatia.,Department of Biology, School of Medicine, University of Zagreb, 10000 Zagreb, Croatia
| | - Ivana Pogledić
- Division of Neuroradiology and Musculoskeletal Radiology, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
| | - Christian Mitter
- Division of Neuroradiology and Musculoskeletal Radiology, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
| | - Andrija Štajduhar
- Croatian Institute for Brain Research, School of Medicine University of Zagreb, 10000 Zagreb, Croatia.,Andrija Štampar School of Public Health, School of Medicine, University of Zagreb, 10000 Zagreb, Croatia
| | - Marija Milković-Periša
- University Hospital Centre Zagreb, Department of Pathology and Cytology, 10000 Zagreb, Croatia
| | - Sara Trnski
- Croatian Institute for Brain Research, School of Medicine University of Zagreb, 10000 Zagreb, Croatia
| | - Dieter Bettelheim
- Division of Obstetrics and Feto-Maternal Medicine, Department of Obstetrics and Gynecology, Medical University of Vienna, 1090 Vienna, Austria
| | - Johannes A Hainfellner
- Division of Neuropathology and Neurochemistry, Department of Neurology, Medical University of Vienna, 1090 Vienna, Austria
| | - Miloš Judaš
- Croatian Institute for Brain Research, School of Medicine University of Zagreb, 10000 Zagreb, Croatia
| | - Daniela Prayer
- Division of Neuroradiology and Musculoskeletal Radiology, Department of Biomedical Imaging and Image-Guided Therapy, Medical University of Vienna, 1090 Vienna, Austria
| | - Nataša Jovanov-Milošević
- Croatian Institute for Brain Research, School of Medicine University of Zagreb, 10000 Zagreb, Croatia.,Department of Biology, School of Medicine, University of Zagreb, 10000 Zagreb, Croatia
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14
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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: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [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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