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Ciceri T, Casartelli L, Montano F, Conte S, Squarcina L, Bertoldo A, Agarwal N, Brambilla P, Peruzzo D. Fetal brain MRI atlases and datasets: A review. Neuroimage 2024; 292:120603. [PMID: 38588833 DOI: 10.1016/j.neuroimage.2024.120603] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Revised: 03/28/2024] [Accepted: 04/05/2024] [Indexed: 04/10/2024] Open
Abstract
Fetal brain development is a complex process involving different stages of growth and organization which are crucial for the development of brain circuits and neural connections. Fetal atlases and labeled datasets are promising tools to investigate prenatal brain development. They support the identification of atypical brain patterns, providing insights into potential early signs of clinical conditions. In a nutshell, prenatal brain imaging and post-processing via modern tools are a cutting-edge field that will significantly contribute to the advancement of our understanding of fetal development. In this work, we first provide terminological clarification for specific terms (i.e., "brain template" and "brain atlas"), highlighting potentially misleading interpretations related to inconsistent use of terms in the literature. We discuss the major structures and neurodevelopmental milestones characterizing fetal brain ontogenesis. Our main contribution is the systematic review of 18 prenatal brain atlases and 3 datasets. We also tangentially focus on clinical, research, and ethical implications of prenatal neuroimaging.
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Affiliation(s)
- Tommaso Ciceri
- NeuroImaging Lab, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy; Department of Information Engineering, University of Padua, Padua, Italy
| | - Luca Casartelli
- Theoretical and Cognitive Neuroscience Unit, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy
| | - Florian Montano
- Diagnostic Imaging and Neuroradiology Unit, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy
| | - Stefania Conte
- Psychology Department, State University of New York at Binghamton, New York, USA
| | - Letizia Squarcina
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy
| | - Alessandra Bertoldo
- Department of Information Engineering, University of Padua, Padua, Italy; Padova Neuroscience Center, University of Padua, Padua, Italy
| | - Nivedita Agarwal
- Diagnostic Imaging and Neuroradiology Unit, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy
| | - Paolo Brambilla
- Department of Pathophysiology and Transplantation, University of Milan, Milan, Italy; Department of Neurosciences and Mental Health, Fondazione IRCCS Ca' Granda Ospedale Maggiore Policlinico, Milan, Italy.
| | - Denis Peruzzo
- NeuroImaging Lab, Scientific Institute IRCCS Eugenio Medea, Bosisio Parini, Italy
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Shen Y, Shao M, Hao ZZ, Huang M, Xu N, Liu S. Multimodal Nature of the Single-cell Primate Brain Atlas: Morphology, Transcriptome, Electrophysiology, and Connectivity. Neurosci Bull 2024; 40:517-532. [PMID: 38194157 PMCID: PMC11003949 DOI: 10.1007/s12264-023-01160-4] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2023] [Accepted: 09/23/2023] [Indexed: 01/10/2024] Open
Abstract
Primates exhibit complex brain structures that augment cognitive function. The neocortex fulfills high-cognitive functions through billions of connected neurons. These neurons have distinct transcriptomic, morphological, and electrophysiological properties, and their connectivity principles vary. These features endow the primate brain atlas with a multimodal nature. The recent integration of next-generation sequencing with modified patch-clamp techniques is revolutionizing the way to census the primate neocortex, enabling a multimodal neuronal atlas to be established in great detail: (1) single-cell/single-nucleus RNA-seq technology establishes high-throughput transcriptomic references, covering all major transcriptomic cell types; (2) patch-seq links the morphological and electrophysiological features to the transcriptomic reference; (3) multicell patch-clamp delineates the principles of local connectivity. Here, we review the applications of these technologies in the primate neocortex and discuss the current advances and tentative gaps for a comprehensive understanding of the primate neocortex.
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Affiliation(s)
- Yuhui Shen
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, 510060, China
| | - Mingting Shao
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, 510060, China
| | - Zhao-Zhe Hao
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, 510060, China
| | - Mengyao Huang
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, 510060, China
| | - Nana Xu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, 510060, China
| | - Sheng Liu
- State Key Laboratory of Ophthalmology, Zhongshan Ophthalmic Center, Sun Yat-sen University, Guangdong Provincial Key Laboratory of Ophthalmology and Visual Science, Guangzhou, 510060, China.
- Guangdong Province Key Laboratory of Brain Function and Disease, Guangzhou, 510080, China.
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3
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Lu Y, Cui Y, Cao L, Dong Z, Cheng L, Wu W, Wang C, Liu X, Liu Y, Zhang B, Li D, Zhao B, Wang H, Li K, Ma L, Shi W, Li W, Ma Y, Du Z, Zhang J, Xiong H, Luo N, Liu Y, Hou X, Han J, Sun H, Cai T, Peng Q, Feng L, Wang J, Paxinos G, Yang Z, Fan L, Jiang T. Macaque Brainnetome Atlas: A multifaceted brain map with parcellation, connection, and histology. Sci Bull (Beijing) 2024:S2095-9273(24)00187-7. [PMID: 38580551 DOI: 10.1016/j.scib.2024.03.031] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Revised: 01/18/2024] [Accepted: 03/11/2024] [Indexed: 04/07/2024]
Abstract
The rhesus macaque (Macaca mulatta) is a crucial experimental animal that shares many genetic, brain organizational, and behavioral characteristics with humans. A macaque brain atlas is fundamental to biomedical and evolutionary research. However, even though connectivity is vital for understanding brain functions, a connectivity-based whole-brain atlas of the macaque has not previously been made. In this study, we created a new whole-brain map, the Macaque Brainnetome Atlas (MacBNA), based on the anatomical connectivity profiles provided by high angular and spatial resolution ex vivo diffusion MRI data. The new atlas consists of 248 cortical and 56 subcortical regions as well as their structural and functional connections. The parcellation and the diffusion-based tractography were evaluated with invasive neuronal-tracing and Nissl-stained images. As a demonstrative application, the structural connectivity divergence between macaque and human brains was mapped using the Brainnetome atlases of those two species to uncover the genetic underpinnings of the evolutionary changes in brain structure. The resulting resource includes: (1) the thoroughly delineated Macaque Brainnetome Atlas (MacBNA), (2) regional connectivity profiles, (3) the postmortem high-resolution macaque diffusion and T2-weighted MRI dataset (Brainnetome-8), and (4) multi-contrast MRI, neuronal-tracing, and histological images collected from a single macaque. MacBNA can serve as a common reference frame for mapping multifaceted features across modalities and spatial scales and for integrative investigation and characterization of brain organization and function. Therefore, it will enrich the collaborative resource platform for nonhuman primates and facilitate translational and comparative neuroscience research.
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Affiliation(s)
- Yuheng Lu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yue Cui
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Long Cao
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China; Key Laboratory for NeuroInformation of the Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Zhenwei Dong
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Luqi Cheng
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, China; Research Center for Augmented Intelligence, Zhejiang Lab, Hangzhou 311100, China
| | - Wen Wu
- Research Center for Augmented Intelligence, Zhejiang Lab, Hangzhou 311100, China
| | - Changshuo Wang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China; Sino-Danish College, University of Chinese Academy of Science, Beijing 100049, China
| | - Xinyi Liu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Youtong Liu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Baogui Zhang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Deying Li
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Bokai Zhao
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Haiyan Wang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Kaixin Li
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Mechanical and Power Engineering, Harbin University of Science and Technology, Harbin 150080, China
| | - Liang Ma
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Weiyang Shi
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Wen Li
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yawei Ma
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China; Sino-Danish College, University of Chinese Academy of Science, Beijing 100049, China
| | - Zongchang Du
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jiaqi Zhang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Hui Xiong
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Na Luo
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Yanyan Liu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Xiaoxiao Hou
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Jinglu Han
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China; Sino-Danish College, University of Chinese Academy of Science, Beijing 100049, China
| | - Hongji Sun
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Tao Cai
- Research Center for Augmented Intelligence, Zhejiang Lab, Hangzhou 311100, China
| | - Qiang Peng
- Research Center for Augmented Intelligence, Zhejiang Lab, Hangzhou 311100, China
| | - Linqing Feng
- Research Center for Augmented Intelligence, Zhejiang Lab, Hangzhou 311100, China
| | - Jiaojian Wang
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming 650500, China
| | - George Paxinos
- Neuroscience Research Australia and The University of New South Wales, Sydney NSW 2031, Australia
| | - Zhengyi Yang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; Xiaoxiang Institute for Brain Health and Yongzhou Central Hospital, Yongzhou 425000, China.
| | - Lingzhong Fan
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China.
| | - Tianzi Jiang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China; Research Center for Augmented Intelligence, Zhejiang Lab, Hangzhou 311100, China; Xiaoxiang Institute for Brain Health and Yongzhou Central Hospital, Yongzhou 425000, China.
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4
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Cecyn MN, Abrahao KP. Where do you measure the Bregma for rodent stereotaxic surgery? IBRO Neurosci Rep 2023; 15:143-148. [PMID: 38204571 PMCID: PMC10776314 DOI: 10.1016/j.ibneur.2023.07.003] [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] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2023] [Accepted: 07/26/2023] [Indexed: 01/12/2024] Open
Abstract
The advent of the stereotaxic apparatus developed by Clarke and Horsley revolutionized neuroscience research, enabling precise 3D navigation along the skull mediolateral, anteroposterior, and dorsoventral axes. In rodents, the Bregma is widely used as the origin reference point for the stereotaxic coordinates, but the specific procedure for its measurement varies among different laboratories. Notably, the renowned brain atlas developed by Paxinos and Franklin lacks explicit instructions on the Bregma determination. Recent studies have found discrepancies in skull and brain landmark measurements. This review describes the commonly used brain atlases and highlights the limitations in accurately measuring the stereotaxic coordinates. In addition, we propose alternative and more reliable approaches to measure the Bregma. It is imperative to address the misconceptions about the accuracy of stereotaxic surgeries, as it can significantly impact a substantial portion of neuroscience research.
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Affiliation(s)
- Marianna Nogueira Cecyn
- Departamento de Psicobiologia, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, SP, Brazil
| | - Karina Possa Abrahao
- Departamento de Psicobiologia, Escola Paulista de Medicina, Universidade Federal de São Paulo, São Paulo, SP, Brazil
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5
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Zachlod D, Palomero-Gallagher N, Dickscheid T, Amunts K. Mapping Cytoarchitectonics and Receptor Architectonics to Understand Brain Function and Connectivity. Biol Psychiatry 2023; 93:471-479. [PMID: 36567226 DOI: 10.1016/j.biopsych.2022.09.014] [Citation(s) in RCA: 4] [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] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/24/2022] [Revised: 08/18/2022] [Accepted: 09/10/2022] [Indexed: 02/04/2023]
Abstract
This review focuses on cytoarchitectonics and receptor architectonics as biological correlates of function and connectivity. It introduces the 3-dimensional cytoarchitectonic probabilistic maps of cortical areas and nuclei of the Julich-Brain Atlas, available at EBRAINS, to study structure-function relationships. The maps are linked to the BigBrain as microanatomical reference model and template space. The siibra software tool suite enables programmatic access to the maps and to receptor architectonic data that are anchored to brain areas. Such cellular and molecular data are tools for studying magnetic resonance connectivity including modeling and simulation. At the end, we highlight perspectives of the Julich-Brain as well as methodological considerations. Thus, microstructural maps as part of a multimodal atlas help elucidate the biological correlates of large-scale networks and brain function with a high level of anatomical detail, which provides a basis to study brains of patients with psychiatric disorders.
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Affiliation(s)
- Daniel Zachlod
- Institute of Neurosciences and Medicine, Research Centre Jülich, Jülich, Germany.
| | - Nicola Palomero-Gallagher
- Institute of Neurosciences and Medicine, Research Centre Jülich, Jülich, Germany; C. & O. Vogt Institute for Brain Research, Medical Faculty, University Hospital Düsseldorf, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany; Department of Psychiatry, Psychotherapy, Psychosomatics, Medical Faculty, RWTH Aachen, Jülich Aachen Research Alliance-Translational Brain Medicine, Aachen, Germany
| | - Timo Dickscheid
- Institute of Neurosciences and Medicine, Research Centre Jülich, Jülich, Germany; Helmholtz AI, Research Centre Jülich, Jülich, Germany; Department of Computer Science, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany
| | - Katrin Amunts
- Institute of Neurosciences and Medicine, Research Centre Jülich, Jülich, Germany; C. & O. Vogt Institute for Brain Research, Medical Faculty, University Hospital Düsseldorf, Heinrich-Heine-Universität Düsseldorf, Düsseldorf, Germany
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6
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Perens J, Salinas CG, Roostalu U, Skytte JL, Gundlach C, Hecksher-Sørensen J, Dahl AB, Dyrby TB. Multimodal 3D Mouse Brain Atlas Framework with the Skull-Derived Coordinate System. Neuroinformatics 2023; 21:269-286. [PMID: 36809643 DOI: 10.1007/s12021-023-09623-9] [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] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/01/2023] [Indexed: 02/23/2023]
Abstract
Magnetic resonance imaging (MRI) and light-sheet fluorescence microscopy (LSFM) are technologies that enable non-disruptive 3-dimensional imaging of whole mouse brains. A combination of complementary information from both modalities is desirable for studying neuroscience in general, disease progression and drug efficacy. Although both technologies rely on atlas mapping for quantitative analyses, the translation of LSFM recorded data to MRI templates has been complicated by the morphological changes inflicted by tissue clearing and the enormous size of the raw data sets. Consequently, there is an unmet need for tools that will facilitate fast and accurate translation of LSFM recorded brains to in vivo, non-distorted templates. In this study, we have developed a bidirectional multimodal atlas framework that includes brain templates based on both imaging modalities, region delineations from the Allen's Common Coordinate Framework, and a skull-derived stereotaxic coordinate system. The framework also provides algorithms for bidirectional transformation of results obtained using either MR or LSFM (iDISCO cleared) mouse brain imaging while the coordinate system enables users to easily assign in vivo coordinates across the different brain templates.
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Affiliation(s)
- Johanna Perens
- Gubra ApS, Hørsholm, Denmark.,Section for Visual Computing, Department of Applied Mathematics and Computer Science, Technical University Denmark, Kongens Lyngby, Denmark.,Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Copenhagen, Denmark
| | | | | | | | - Carsten Gundlach
- Neutrons and X-rays for Materials Physics, Department of Physics, Technical University Denmark, Kongens Lyngby, Denmark
| | | | - Anders Bjorholm Dahl
- Section for Visual Computing, Department of Applied Mathematics and Computer Science, Technical University Denmark, Kongens Lyngby, Denmark
| | - Tim B Dyrby
- Section for Visual Computing, Department of Applied Mathematics and Computer Science, Technical University Denmark, Kongens Lyngby, Denmark.,Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Copenhagen, Denmark
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7
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Jedynak M, Boyer A, Chanteloup-Forêt B, Bhattacharjee M, Saubat C, Tadel F, Kahane P, David O. Variability of Single Pulse Electrical Stimulation Responses Recorded with Intracranial Electroencephalography in Epileptic Patients. Brain Topogr 2023; 36:119-127. [PMID: 36520342 PMCID: PMC9834344 DOI: 10.1007/s10548-022-00928-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2022] [Accepted: 11/09/2022] [Indexed: 12/23/2022]
Abstract
Cohort studies of brain stimulations performed with stereo-electroencephalographic (SEEG) electrodes in epileptic patients allow to derive large scale functional connectivity. It is known, however, that brain responses to electrical or magnetic stimulation techniques are not always reproducible. Here, we study variability of responses to single pulse SEEG electrical stimulation. We introduce a second-order probability analysis, i.e. we extend estimation of connection probabilities, defined as the proportion of responses trespassing a statistical threshold (determined in terms of Z-score with respect to spontaneous neuronal activity before stimulation) over all responses and derived from a number of individual measurements, to an analysis of pairs of measurements.Data from 445 patients were processed. We found that variability between two equivalent measurements is substantial in particular conditions. For long ( > ~ 90 mm) distances between stimulating and recording sites, and threshold value Z = 3, correlation between measurements drops almost to zero. In general, it remains below 0.5 when the threshold is smaller than Z = 4 or the stimulating current intensity is 1 mA. It grows with an increase of either of these factors. Variability is independent of interictal spiking rates in the stimulating and recording sites.We conclude that responses to SEEG stimulation in the human brain are variable, i.e. in a subject at rest, two stimulation trains performed at the same electrode contacts and with the same protocol can give discrepant results. Our findings highlight an advantage of probabilistic interpretation of such results even in the context of a single individual.
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Affiliation(s)
- Maciej Jedynak
- Grenoble Institut Neurosciences, Université Grenoble Alpes, Inserm, U1216, 38000, Grenoble, France.
- Aix Marseille Université, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France.
| | - Anthony Boyer
- Grenoble Institut Neurosciences, Université Grenoble Alpes, Inserm, U1216, 38000, Grenoble, France
- Aix Marseille Université, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France
| | | | - Manik Bhattacharjee
- Grenoble Institut Neurosciences, Université Grenoble Alpes, Inserm, U1216, 38000, Grenoble, France
- Aix Marseille Université, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France
| | - Carole Saubat
- Grenoble Institut Neurosciences, Université Grenoble Alpes, Inserm, U1216, 38000, Grenoble, France
| | - François Tadel
- Grenoble Institut Neurosciences, Université Grenoble Alpes, Inserm, U1216, 38000, Grenoble, France
- Signal and Image Processing Institute, University of Southern California, Los Angeles, USA
| | - Philippe Kahane
- Grenoble Institut Neurosciences, Université Grenoble Alpes, Inserm, U1216, 38000, Grenoble, France
- Neurology Department, CHU Grenoble Alpes, Grenoble, France
| | - Olivier David
- Grenoble Institut Neurosciences, Université Grenoble Alpes, Inserm, U1216, 38000, Grenoble, France
- Aix Marseille Université, Inserm, INS, Institut de Neurosciences des Systèmes, Marseille, France
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8
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Joshi AA, Choi S, Liu Y, Chong M, Sonkar G, Gonzalez-Martinez J, Nair D, Wisnowski JL, Haldar JP, Shattuck DW, Damasio H, Leahy RM. A hybrid high-resolution anatomical MRI atlas with sub-parcellation of cortical gyri using resting fMRI. J Neurosci Methods 2022; 374:109566. [PMID: 35306036 PMCID: PMC9302382 DOI: 10.1016/j.jneumeth.2022.109566] [Citation(s) in RCA: 15] [Impact Index Per Article: 7.5] [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: 06/18/2021] [Revised: 12/23/2021] [Accepted: 03/13/2022] [Indexed: 11/17/2022]
Abstract
We present a new high-quality, single-subject atlas with sub-millimeter voxel resolution, high SNR, and excellent gray-white tissue contrast to resolve fine anatomical details. The atlas is labeled into two parcellation schemes: 1) the anatomical BCI-DNI atlas, which is manually labeled based on known morphological and anatomical features, and 2) the hybrid USCBrain atlas, which incorporates functional information to guide the sub-parcellation of cerebral cortex. In both cases, we provide consistent volumetric and cortical surface-based parcellation and labeling. The intended use of the atlas is as a reference template for structural coregistration and labeling of individual brains. A single-subject T1-weighted image was acquired five times at a resolution of 0.547 mm × 0.547 mm × 0.800 mm and averaged. Images were processed by an expert neuroanatomist using semi-automated methods in BrainSuite to extract the brain, classify tissue-types, and render anatomical surfaces. Sixty-six cortical and 29 noncortical regions were manually labeled to generate the BCI-DNI atlas. The cortical regions were further sub-parcellated into 130 cortical regions based on multi-subject connectivity analysis using resting fMRI (rfMRI) data from the Human Connectome Project (HCP) database to produce the USCBrain atlas. In addition, we provide a delineation between sulcal valleys and gyral crowns, which offer an additional set of 26 sulcal subregions per hemisphere. Lastly, a probabilistic map is provided to give users a quantitative measure of reliability for each gyral subdivision. Utility of the atlas was assessed by computing Adjusted Rand Indices (ARIs) between individual sub-parcellations obtained through structural-only coregistration to the USCBrain atlas and sub-parcellations obtained directly from each subject's resting fMRI data. Both atlas parcellations can be used with the BrainSuite, FreeSurfer, and FSL software packages.
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Affiliation(s)
- Anand A. Joshi
- Signal and Image Processing Institute, University of Southern California, Los Angeles, USA,Correspondence to: Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, 3740 McClintock Avenue, EEB 426, Los Angeles, CA 90089-2560. (A.A. Joshi)
| | - Soyoung Choi
- Signal and Image Processing Institute, University of Southern California, Los Angeles, USA,Neuroscience Graduate Program, University of Southern California, Los Angeles, USA
| | - Yijun Liu
- Signal and Image Processing Institute, University of Southern California, Los Angeles, USA
| | - Minqi Chong
- Signal and Image Processing Institute, University of Southern California, Los Angeles, USA
| | - Gaurav Sonkar
- Dept. of Computer Science, National Institute of Technology Warangal, India
| | | | - Dileep Nair
- Epilepsy Center, Cleveland Clinic Foundation, Cleveland, OH, USA
| | - Jessica L. Wisnowski
- Dornsife Cognitive Neuroscience Imaging Center, University of Southern California, Los Angles, USA
| | - Justin P. Haldar
- Signal and Image Processing Institute, University of Southern California, Los Angeles, USA
| | - David W. Shattuck
- Ahmanson-Lovelace Brain Mapping Center, University of California, Los Angeles, CA, USA
| | - Hanna Damasio
- Dornsife Cognitive Neuroscience Imaging Center, University of Southern California, Los Angles, USA
| | - Richard M. Leahy
- Signal and Image Processing Institute, University of Southern California, Los Angeles, USA
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9
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Revell AY, Silva AB, Arnold TC, Stein JM, Das SR, Shinohara RT, Bassett DS, Litt B, Davis KA. A framework For brain atlases: Lessons from seizure dynamics. Neuroimage 2022; 254:118986. [PMID: 35339683 PMCID: PMC9342687 DOI: 10.1016/j.neuroimage.2022.118986] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [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: 07/26/2021] [Revised: 01/13/2022] [Accepted: 02/07/2022] [Indexed: 01/03/2023] Open
Abstract
Brain maps, or atlases, are essential tools for studying brain function and organization. The abundance of available atlases used across the neuroscience literature, however, creates an implicit challenge that may alter the hypotheses and predictions we make about neurological function and pathophysiology. Here, we demonstrate how parcellation scale, shape, anatomical coverage, and other atlas features may impact our prediction of the brain’s function from its underlying structure. We show how network topology, structure–function correlation (SFC), and the power to test specific hypotheses about epilepsy pathophysiology may change as a result of atlas choice and atlas features. Through the lens of our disease system, we propose a general framework and algorithm for atlas selection. This framework aims to maximize the descriptive, explanatory, and predictive validity of an atlas. Broadly, our framework strives to provide empirical guidance to neuroscience research utilizing the various atlases published over the last century.
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Affiliation(s)
- Andrew Y Revell
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA.
| | - Alexander B Silva
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Medical Scientist Training Program, University of California, San Francisco, CA 94143, USA
| | - T Campbell Arnold
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Joel M Stein
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sandhitsu R Das
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Russell T Shinohara
- Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Penn Statistics in Imaging and Visualization Endeavor, Perelman school of Medicine, University of Pennsylvania, PA 19104, USA
| | - Dani S Bassett
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Biomedical Image Computing and Analytics, Perelman School of Medicine, University of Pennsylvania, PA 19104, USA; Department of Electrical and Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Physics and Astronomy, College of Arts and Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Santa Fe Institute, Santa Fe, NM 87501, USA
| | - Brian Litt
- Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Kathryn A Davis
- Department of Neuroscience, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Neuroengineering and Therapeutics, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
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Chen L, Wu Z, Hu D, Wang Y, Zhao F, Zhong T, Lin W, Wang L, Li G. A 4D infant brain volumetric atlas based on the UNC/UMN baby connectome project (BCP) cohort. Neuroimage 2022; 253:119097. [PMID: 35301130 PMCID: PMC9155180 DOI: 10.1016/j.neuroimage.2022.119097] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/22/2021] [Revised: 03/06/2022] [Accepted: 03/11/2022] [Indexed: 12/16/2022] Open
Abstract
Spatiotemporal (four-dimensional) infant-dedicated brain atlases are essential for neuroimaging analysis of early dynamic brain development. However, due to the substantial technical challenges in the acquisition and processing of infant brain MR images, 4D atlases densely covering the dynamic brain development during infancy are still scarce. Few existing ones generally have fuzzy tissue contrast and low spatiotemporal resolution, leading to degraded accuracy of atlas-based normalization and subsequent analyses. To address this issue, in this paper, we construct a 4D structural MRI atlas for infant brains based on the UNC/UMN Baby Connectome Project (BCP) dataset, which features a high spatial resolution, extensive age-range coverage, and densely sampled time points. Specifically, 542 longitudinal T1w and T2w scans from 240 typically developing infants up to 26-month of age were utilized for our atlas construction. To improve the co-registration accuracy of the infant brain images, which typically exhibit dynamic appearance with low tissue contrast, we employed the state-of-the-art registration method and leveraged our generated reliable brain tissue probability maps in addition to the intensity images to improve the alignment of individual images. To achieve consistent region labeling on both infant and adult brain images for facilitating region-based analysis across ages, we mapped the widely used Desikan cortical parcellation onto our atlas by following an age-decreasing mapping manner. Meanwhile, the typical subcortical structures were manually delineated to facilitate the studies related to the subcortex. Compared with the existing infant brain atlases, our 4D atlas has much higher spatiotemporal resolution and preserves more structural details, and thus can boost accuracy in neurodevelopmental analysis during infancy.
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Affiliation(s)
- Liangjun Chen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA and for UNC/UMN Baby Connectome Project Consortium.
| | - Zhengwang Wu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA and for UNC/UMN Baby Connectome Project Consortium
| | - Dan Hu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA and for UNC/UMN Baby Connectome Project Consortium
| | - Ya Wang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA and for UNC/UMN Baby Connectome Project Consortium
| | - Fenqiang Zhao
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA and for UNC/UMN Baby Connectome Project Consortium
| | - Tao Zhong
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA and for UNC/UMN Baby Connectome Project Consortium
| | - Weili Lin
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA and for UNC/UMN Baby Connectome Project Consortium
| | - Li Wang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA and for UNC/UMN Baby Connectome Project Consortium
| | - Gang Li
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, USA and for UNC/UMN Baby Connectome Project Consortium.
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11
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Nowinski WL. NOWinBRAIN: a Large, Systematic, and Extendable Repository of 3D Reconstructed Images of a Living Human Brain Cum Head and Neck. J Digit Imaging 2022. [PMID: 35013825 DOI: 10.1007/s10278-021-00528-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2021] [Revised: 09/23/2021] [Accepted: 10/15/2021] [Indexed: 10/19/2022] Open
Abstract
Despite the tremendous development of various brain-related resources, a large, systematic, comprehensive, extendable, and beautiful repository of 3D reconstructed images of a living human brain expanded to the head and neck is not yet available. I have created such a novel repository and populated it with images derived from a 3D atlas constructed from 3/7 Tesla MRI and high-resolution CT scans. This web-based repository contains 6 galleries hierarchically organized in 444 albums and sub-albums with 5,156 images. Its original features include a systematic design in terms of multiple standard views, modes of presentation, and spatially co-registered image sequences; multi-tissue class galleries constructed from 26 primary tissue classes and 199 sub-classes; and a unique image naming syntax enabling image searching based solely on the image name. Anatomic structures are displayed in 6 standard views (anterior, left, posterior, right, superior, inferior), all views having the same brain size, and optionally with additional arbitrary views. In each view, the images are shown as sequences in three standard modes of presentation, non-parcellated unlabeled, parcellated unlabeled, and parcellated labeled. There are two types of spatially co-registered image sequences (imitating image layers and enabling animation creation), the appearance image sequence (for standard views) and the context image sequence (with a growing number of tissue classes). Color-coded neuroanatomic content makes the brain beautiful and facilitates its learning and understanding. This unique repository is freely available and easily accessible online at www.nowinbrain.org for a wide spectrum of users in medicine and beyond. Its future extensions are in progress.
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12
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Costa-Gertrudes R, Simão D, Franco A, Morgado C, Peralta AR, Pimentel J, Gonçalves-Ferreira A, Bentes C, Campos AR. Anterior Nucleus of Thalamus Deep Brain Stimulation: A Clinical-Based Analysis of the Ideal Target in Drug-Resistant Epilepsy. Stereotact Funct Neurosurg 2021; 100:108-120. [PMID: 34915532 DOI: 10.1159/000519917] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [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: 04/18/2021] [Accepted: 09/27/2021] [Indexed: 11/19/2022]
Abstract
INTRODUCTION Deep brain stimulation of the anterior nucleus of thalamus (ANT-DBS) is an approved procedure for drug-resistant epilepsy. However, the preferred location inside ANT is not well known. In this study, we investigated the relationship between stereotactical coordinates of stimulated contacts and clinical improvement, in order to define the ideal target for ANT-DBS. METHODS Individual contact's coordinates were obtained in the Montreal Neurological Institute (MNI) 152 space, with the utilization of advanced normalization tools and co-registration of pre- and postoperative MRI and CT images in open-source toolbox lead-DBS with the "Atlas of the Human Thalamus." Each contact's pair was either classified as a responder (≥50% seizure reduction and absence of intolerable adverse effects) or nonresponder, with a minimum follow-up of 11 continuous months of stimulation. RESULTS A total of 19 contacts' pairs were tested in 14 patients. The responder rate was 9 out of 14 patients (64.3%). In 4 patients, a change in contacts' pairs was needed to achieve this result. A highly encouraging location inside ANT (HELIA) was delimited in MNI space, corresponding to an area in the anterior and inferior portion of the anteroventral (AV) nucleus, medially to the endpoint of the mammillothalamic tract (ANT-mtt junction) (x [3.8; 5.85], y [-2.1; -6.35] and z [6.2; 10.1] in MNI space). Statistically significant difference was observed between responders and nonresponders, in terms of the number of coordinates inside this volume. Seven responders and two nonresponders had at least 5 of 6 coordinates (2 electrodes) inside HELIA (77.8% sensitivity and 80% specificity). In 3 patients, changing to contacts that were better placed inside HELIA changed the status from nonresponder to responder. CONCLUSIONS A relationship between stimulated contacts' coordinates and responder status was observed in drug-resistant epilepsy. The possibility to target different locations inside HELIA may help surpass anatomical variations and eventually obtain increased clinical benefit.
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Affiliation(s)
| | - Diogo Simão
- Department of Neurosciences and Mental Health, Department of Neurosurgery, Hospital de Santa Maria, Centro Hospitalar Universitário de Lisboa Norte, Lisbon, Portugal.,Centro de Referência Para Epilepsias Refractárias from EpiCare Network (European Reference Network for Rare and Complex Epilepsies), Hospital de Santa Maria, CHULN, Lisbon, Portugal
| | - Ana Franco
- Centro de Referência Para Epilepsias Refractárias from EpiCare Network (European Reference Network for Rare and Complex Epilepsies), Hospital de Santa Maria, CHULN, Lisbon, Portugal.,EEG/Sleep Lab and Neurophysiology Monitoring Unit, Department of Neurosciences and Mental Health (Neurology), Hospital de Santa Maria, Centro Hospitalar Universitário de Lisboa Norte, Lisboa, Portugal
| | - Carlos Morgado
- Faculdade de Medicina da Universidade de Lisboa, Lisbon, Portugal.,Centro de Referência Para Epilepsias Refractárias from EpiCare Network (European Reference Network for Rare and Complex Epilepsies), Hospital de Santa Maria, CHULN, Lisbon, Portugal.,Department of Neuroradiology, Hospital de Santa Maria, Centro Hospitalar Universitário de Lisboa Norte, Lisbon, Portugal
| | - Ana Rita Peralta
- Faculdade de Medicina da Universidade de Lisboa, Lisbon, Portugal.,Centro de Referência Para Epilepsias Refractárias from EpiCare Network (European Reference Network for Rare and Complex Epilepsies), Hospital de Santa Maria, CHULN, Lisbon, Portugal.,EEG/Sleep Lab and Neurophysiology Monitoring Unit, Department of Neurosciences and Mental Health (Neurology), Hospital de Santa Maria, Centro Hospitalar Universitário de Lisboa Norte, Lisboa, Portugal
| | - José Pimentel
- Faculdade de Medicina da Universidade de Lisboa, Lisbon, Portugal.,Centro de Referência Para Epilepsias Refractárias from EpiCare Network (European Reference Network for Rare and Complex Epilepsies), Hospital de Santa Maria, CHULN, Lisbon, Portugal
| | - António Gonçalves-Ferreira
- Faculdade de Medicina da Universidade de Lisboa, Lisbon, Portugal.,Department of Neurosciences and Mental Health, Department of Neurosurgery, Hospital de Santa Maria, Centro Hospitalar Universitário de Lisboa Norte, Lisbon, Portugal.,Centro de Referência Para Epilepsias Refractárias from EpiCare Network (European Reference Network for Rare and Complex Epilepsies), Hospital de Santa Maria, CHULN, Lisbon, Portugal
| | - Carla Bentes
- Faculdade de Medicina da Universidade de Lisboa, Lisbon, Portugal.,Centro de Referência Para Epilepsias Refractárias from EpiCare Network (European Reference Network for Rare and Complex Epilepsies), Hospital de Santa Maria, CHULN, Lisbon, Portugal.,EEG/Sleep Lab and Neurophysiology Monitoring Unit, Department of Neurosciences and Mental Health (Neurology), Hospital de Santa Maria, Centro Hospitalar Universitário de Lisboa Norte, Lisboa, Portugal
| | - Alexandre Rainha Campos
- Faculdade de Medicina da Universidade de Lisboa, Lisbon, Portugal.,Department of Neurosciences and Mental Health, Department of Neurosurgery, Hospital de Santa Maria, Centro Hospitalar Universitário de Lisboa Norte, Lisbon, Portugal.,Centro de Referência Para Epilepsias Refractárias from EpiCare Network (European Reference Network for Rare and Complex Epilepsies), Hospital de Santa Maria, CHULN, Lisbon, Portugal
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13
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Perens J, Salinas CG, Skytte JL, Roostalu U, Dahl AB, Dyrby TB, Wichern F, Barkholt P, Vrang N, Jelsing J, Hecksher-Sørensen J. An Optimized Mouse Brain Atlas for Automated Mapping and Quantification of Neuronal Activity Using iDISCO+ and Light Sheet Fluorescence Microscopy. Neuroinformatics 2021; 19:433-446. [PMID: 33063286 PMCID: PMC8233272 DOI: 10.1007/s12021-020-09490-8] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [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] [Indexed: 12/21/2022]
Abstract
In recent years, the combination of whole-brain immunolabelling, light sheet fluorescence microscopy (LSFM) and subsequent registration of data with a common reference atlas, has enabled 3D visualization and quantification of fluorescent markers or tracers in the adult mouse brain. Today, the common coordinate framework version 3 developed by the Allen’s Institute of Brain Science (AIBS CCFv3), is widely used as the standard brain atlas for registration of LSFM data. However, the AIBS CCFv3 is based on histological processing and imaging modalities different from those used for LSFM imaging and consequently, the data differ in both tissue contrast and morphology. To improve the accuracy and speed by which LSFM-imaged whole-brain data can be registered and quantified, we have created an optimized digital mouse brain atlas based on immunolabelled and solvent-cleared brains. Compared to the AIBS CCFv3 atlas, our atlas resulted in faster and more accurate mapping of neuronal activity as measured by c-Fos expression, especially in the hindbrain. We further demonstrated utility of the LSFM atlas by comparing whole-brain quantitative changes in c-Fos expression following acute administration of semaglutide in lean and diet-induced obese mice. In combination with an improved algorithm for c-Fos detection, the LSFM atlas enables unbiased and computationally efficient characterization of drug effects on whole-brain neuronal activity patterns. In conclusion, we established an optimized reference atlas for more precise mapping of fluorescent markers, including c-Fos, in mouse brains processed for LSFM.
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Affiliation(s)
- Johanna Perens
- Gubra ApS, 2970, Hørsholm, Denmark.,Department of Applied Mathematics and Computer Science, Technical University Denmark, 2800, Kongens Lyngby, Denmark
| | | | | | | | - Anders Bjorholm Dahl
- Department of Applied Mathematics and Computer Science, Technical University Denmark, 2800, Kongens Lyngby, Denmark
| | - Tim B Dyrby
- Department of Applied Mathematics and Computer Science, Technical University Denmark, 2800, Kongens Lyngby, Denmark.,Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, 2650, Hvidovre, Denmark
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14
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Perens J, Salinas CG, Skytte JL, Roostalu U, Dahl AB, Dyrby TB, Wichern F, Barkholt P, Vrang N, Jelsing J, Hecksher-Sørensen J. An Optimized Mouse Brain Atlas for Automated Mapping and Quantification of Neuronal Activity Using iDISCO+ and Light Sheet Fluorescence Microscopy. Neuroinformatics 2021. [PMID: 33063286 DOI: 10.1007/s12021-020-09490-8/figures/5] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/23/2023]
Abstract
In recent years, the combination of whole-brain immunolabelling, light sheet fluorescence microscopy (LSFM) and subsequent registration of data with a common reference atlas, has enabled 3D visualization and quantification of fluorescent markers or tracers in the adult mouse brain. Today, the common coordinate framework version 3 developed by the Allen's Institute of Brain Science (AIBS CCFv3), is widely used as the standard brain atlas for registration of LSFM data. However, the AIBS CCFv3 is based on histological processing and imaging modalities different from those used for LSFM imaging and consequently, the data differ in both tissue contrast and morphology. To improve the accuracy and speed by which LSFM-imaged whole-brain data can be registered and quantified, we have created an optimized digital mouse brain atlas based on immunolabelled and solvent-cleared brains. Compared to the AIBS CCFv3 atlas, our atlas resulted in faster and more accurate mapping of neuronal activity as measured by c-Fos expression, especially in the hindbrain. We further demonstrated utility of the LSFM atlas by comparing whole-brain quantitative changes in c-Fos expression following acute administration of semaglutide in lean and diet-induced obese mice. In combination with an improved algorithm for c-Fos detection, the LSFM atlas enables unbiased and computationally efficient characterization of drug effects on whole-brain neuronal activity patterns. In conclusion, we established an optimized reference atlas for more precise mapping of fluorescent markers, including c-Fos, in mouse brains processed for LSFM.
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Affiliation(s)
- Johanna Perens
- Gubra ApS, 2970, Hørsholm, Denmark
- Department of Applied Mathematics and Computer Science, Technical University Denmark, 2800, Kongens Lyngby, Denmark
| | | | | | | | - Anders Bjorholm Dahl
- Department of Applied Mathematics and Computer Science, Technical University Denmark, 2800, Kongens Lyngby, Denmark
| | - Tim B Dyrby
- Department of Applied Mathematics and Computer Science, Technical University Denmark, 2800, Kongens Lyngby, Denmark
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, 2650, Hvidovre, Denmark
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15
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Pijnenburg R, Scholtens LH, Ardesch DJ, de Lange SC, Wei Y, van den Heuvel MP. Myelo- and cytoarchitectonic microstructural and functional human cortical atlases reconstructed in common MRI space. Neuroimage 2021; 239:118274. [PMID: 34146709 DOI: 10.1016/j.neuroimage.2021.118274] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2021] [Revised: 05/14/2021] [Accepted: 06/15/2021] [Indexed: 11/23/2022] Open
Abstract
The parcellation of the brain's cortical surface into anatomically and/or functionally distinct areas is a topic of ongoing investigation and interest. We provide digital versions of six classical human brain atlases in common MRI space. The cortical atlases represent a range of modalities, including cyto- and myeloarchitecture (Campbell, Smith, Brodmann and Von Economo), myelogenesis (Flechsig), and mappings of symptomatic information in relation to the spatial location of brain lesions (Kleist). Digital reconstructions of these important cortical atlases widen the range of modalities for which cortex-wide imaging atlases are currently available and offer the opportunity to compare and combine microstructural and lesion-based functional atlases with in-vivo imaging-based atlases.
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16
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Khan AM, D'Arcy CE, Olimpo JT. A historical perspective on training students to create standardized maps of novel brain structure: Newly-uncovered resonances between past and present research-based neuroanatomy curricula. Neurosci Lett 2021; 759:136052. [PMID: 34139317 DOI: 10.1016/j.neulet.2021.136052] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [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: 08/18/2020] [Revised: 02/08/2021] [Accepted: 06/01/2021] [Indexed: 11/25/2022]
Abstract
Recent efforts to reform postsecondary STEM education in the U.S. have resulted in the creation of course-based undergraduate research experiences (CUREs), which, among other outcomes, have successfully retained freshmen in their chosen STEM majors and provided them with a greater sense of identity as scientists by enabling them to experience how research is conducted in a laboratory setting. In 2014, we launched our own laboratory-based CURE, Brain Mapping & Connectomics (BMC). Now in its seventh year, BMC trains University of Texas at El Paso (UTEP) undergraduates to identify and label neuron populations in the rat brain, analyze their cytoarchitecture, and draw their detailed chemoarchitecture onto standardized rat brain atlas maps in stereotaxic space. Significantly, some BMC students produce atlas drawings derived from their coursework or from further independent study after the course that are being presented and/or published in the scientific literature. These maps should prove useful to neuroscientists seeking to experimentally target elusive neuron populations. Here, we review the procedures taught in BMC that have empowered students to learn about the scientific process. We contextualize our efforts with those similarly carried out over a century ago to reform U.S. medical education. Notably, we have uncovered historical records that highlight interesting resonances between our curriculum and that created at the Johns Hopkins University Medical School (JHUMS) in the 1890s. Although the two programs are over a century apart and were created for students of differing career levels, many aspects between them are strikingly similar, including the unique atlas-based brain mapping methods they encouraged students to learn. A notable example of these efforts was the brain atlas maps published by Florence Sabin, a JHUMS student who later became the first woman to be elected to the U.S. National Academy of Sciences. We conclude by discussing how the revitalization of century-old methods and their dissemination to the next generation of scientists in BMC not only provides student benefit and academic development, but also acts to preserve what are increasingly becoming "lost arts" critical for advancing neuroscience - brain histology, cytoarchitectonics, and atlas-based mapping of novel brain structure.
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Affiliation(s)
- Arshad M Khan
- UTEP Systems Neuroscience Laboratory, The University of Texas at El Paso, El Paso, TX 79968, USA; Department of Biological Sciences, The University of Texas at El Paso, El Paso, TX 79968, USA; Border Biomedical Research Center, The University of Texas at El Paso, El Paso, TX 79968, USA; UTEP PERSIST Brain Mapping and Connectomics Teaching Laboratory, The University of Texas at El Paso, El Paso, TX 79968, USA; BUILDing SCHOLARS Program, The University of Texas at El Paso, El Paso, TX 79968, USA; UTEP RISE Program, The University of Texas at El Paso, El Paso, TX 79968, USA; UTEP Neuroscience Bachelor of Science Degree Program, The University of Texas at El Paso, El Paso, TX 79968, USA.
| | - Christina E D'Arcy
- UTEP Systems Neuroscience Laboratory, The University of Texas at El Paso, El Paso, TX 79968, USA; Biology Education Research Group, The University of Texas at El Paso, El Paso, TX 79968, USA; Department of Biological Sciences, The University of Texas at El Paso, El Paso, TX 79968, USA; Border Biomedical Research Center, The University of Texas at El Paso, El Paso, TX 79968, USA; UTEP PERSIST Brain Mapping and Connectomics Teaching Laboratory, The University of Texas at El Paso, El Paso, TX 79968, USA; BUILDing SCHOLARS Program, The University of Texas at El Paso, El Paso, TX 79968, USA.
| | - Jeffrey T Olimpo
- Biology Education Research Group, The University of Texas at El Paso, El Paso, TX 79968, USA; Department of Biological Sciences, The University of Texas at El Paso, El Paso, TX 79968, USA; Border Biomedical Research Center, The University of Texas at El Paso, El Paso, TX 79968, USA; BUILDing SCHOLARS Program, The University of Texas at El Paso, El Paso, TX 79968, USA
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17
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Bingham CS, Parent M, McIntyre CC. Histology-driven model of the macaque motor hyperdirect pathway. Brain Struct Funct 2021; 226:2087-2097. [PMID: 34091730 DOI: 10.1007/s00429-021-02307-7] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [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: 11/25/2020] [Accepted: 05/22/2021] [Indexed: 11/28/2022]
Abstract
Emerging appreciation for the hyperdirect pathway (HDP) as an important cortical glutamatergic input to the subthalamic nucleus (STN) has motivated a wide range of recent investigations on its role in motor control, as well as the mechanisms of subthalamic deep brain stimulation (DBS). However, the pathway anatomy and terminal arbor morphometry by which the HDP links cortical and subthalamic activity are incompletely understood. One critical hindrance to advancing understanding is the lack of anatomically detailed population models which can help explain how HDP pathway anatomy and neuronal biophysics give rise to spatiotemporal patterns of stimulus-response activity observed in vivo. Therefore, the goal of this study was to establish a population model of motor HDP axons through application of generative algorithms constrained by recent histology and imaging data. The products of this effort include a de novo macaque brain atlas, detailed statistical analysis of histological reconstructions of macaque motor HDP axons, and the generation of 10,000 morphometrically constrained synthetic motor HDP axons. The synthetic HDP axons exhibited a 3.8% mean error with respect to parametric distributions of the fiber target volume, total length, number of bifurcations, bifurcation angles, meander angles, and segment lengths measured in BDA-labeled HDP axon reconstructions. As such, this large population of synthetic motor HDP axons represents an anatomically based foundation for biophysical simulations that can be coupled to electrophysiological and/or behavioral measurements, with the goal of better understanding the role of the HDP in motor system activity.
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Affiliation(s)
- Clayton S Bingham
- Department of Biomedical Engineering, Case Western Reserve University, 2103 Cornell Road, Rm 6224, Cleveland, OH, 44106, USA
| | - Martin Parent
- CERVO Brain Research Center, Department of Psychiatry and Neuroscience, Faculty of Medicine, University of Laval, Quebec, Canada
| | - Cameron C McIntyre
- Department of Biomedical Engineering, Case Western Reserve University, 2103 Cornell Road, Rm 6224, Cleveland, OH, 44106, USA.
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Popovych OV, Jung K, Manos T, Diaz-Pier S, Hoffstaedter F, Schreiber J, Yeo BTT, Eickhoff SB. Inter-subject and inter-parcellation variability of resting-state whole-brain dynamical modeling. Neuroimage 2021; 236:118201. [PMID: 34033913 PMCID: PMC8271096 DOI: 10.1016/j.neuroimage.2021.118201] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [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: 03/01/2021] [Revised: 05/21/2021] [Accepted: 05/22/2021] [Indexed: 11/05/2022] Open
Abstract
Modern approaches to investigate complex brain dynamics suggest to represent the brain as a functional network of brain regions defined by a brain atlas, while edges represent the structural or functional connectivity among them. This approach is also utilized for mathematical modeling of the resting-state brain dynamics, where the applied brain parcellation plays an essential role in deriving the model network and governing the modeling results. There is however no consensus and empirical evidence on how a given brain atlas affects the model outcome, and the choice of parcellation is still rather arbitrary. Accordingly, we explore the impact of brain parcellation on inter-subject and inter-parcellation variability of model fitting to empirical data. Our objective is to provide a comprehensive empirical evidence of potential influences of parcellation choice on resting-state whole-brain dynamical modeling. We show that brain atlases strongly influence the quality of model validation and propose several variables calculated from empirical data to account for the observed variability. A few classes of such data variables can be distinguished depending on their inter-subject and inter-parcellation explanatory power.
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Affiliation(s)
- Oleksandr V Popovych
- Institute of Neuroscience and Medicine (INM-7), Research Centre Juelich, Juelich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine, University Duesseldorf, Duesseldorf, Germany.
| | - Kyesam Jung
- Institute of Neuroscience and Medicine (INM-7), Research Centre Juelich, Juelich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine, University Duesseldorf, Duesseldorf, Germany
| | - Thanos Manos
- Institute of Neuroscience and Medicine (INM-7), Research Centre Juelich, Juelich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine, University Duesseldorf, Duesseldorf, Germany; Laboratoire de Physique Théorique et Modélisation, CY Cergy Paris Université, CNRS, UMR 8089, Cergy-Pontoise cedex 95302, France
| | - Sandra Diaz-Pier
- Institute for Advanced Simulation, Juelich Supercomputing Centre (JSC), Research Centre Juelich, Juelich, Germany
| | - Felix Hoffstaedter
- Institute of Neuroscience and Medicine (INM-7), Research Centre Juelich, Juelich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine, University Duesseldorf, Duesseldorf, Germany
| | - Jan Schreiber
- Institute of Neuroscience and Medicine (INM-1), Research Centre Juelich, Juelich, Germany
| | - B T Thomas Yeo
- Centre for Sleep and Cognition, Centre for Translational MR Research & N.1 Institute for Health, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA; Integrative Sciences and Engineering Programme (ISEP), Singapore
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine (INM-7), Research Centre Juelich, Juelich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine, University Duesseldorf, Duesseldorf, Germany
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Reijonen J, Könönen M, Tuunanen P, Määttä S, Julkunen P. Atlas-informed computational processing pipeline for individual targeting of brain areas for therapeutic navigated transcranial magnetic stimulation. Clin Neurophysiol 2021; 132:1612-1621. [PMID: 34030058 DOI: 10.1016/j.clinph.2021.01.037] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [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: 10/12/2020] [Revised: 01/06/2021] [Accepted: 01/29/2021] [Indexed: 01/19/2023]
Abstract
OBJECTIVE Navigated transcranial magnetic stimulation (nTMS) is targeted at different cortical sites for diagnostic, therapeutic, and neuroscientific purposes. Correct identification of the cortical target areas is important for achieving desired effects, but it is challenging when no direct responses arise upon target area stimulation. We aimed at utilizing atlas-based marking of cortical areas for nTMS targeting to present a convenient, rater-independent method for overlaying the individual target sites with brain anatomy. METHODS We developed a pipeline, which fits a brain atlas to the individual brain and enables visualization of the target areas during the nTMS session. We applied the pipeline to our previous nTMS data, focusing on depression and schizophrenia patients. Furthermore, we included examples of Tourette syndrome and tinnitus therapies, as well as neurosurgical and motor mappings. RESULTS In depression and schizophrenia patients, the visually selected dorsolateral prefrontal cortex (DLPFC) targets were close to the border between atlas areas A9/46 and A8. In the other areas, the atlas-based areas were in agreement with the treatment targets. CONCLUSIONS The atlas-based target areas agreed well with the cortical targets selected by experts during the treatments. SIGNIFICANCE Overlaying atlas information over the navigation view is a convenient and useful add-on for improving nTMS targeting.
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Affiliation(s)
- Jusa Reijonen
- Department of Clinical Neurophysiology, Kuopio University Hospital, Kuopio, Finland; Department of Applied Physics, University of Eastern Finland, Kuopio, Finland.
| | - Mervi Könönen
- Department of Clinical Radiology, Kuopio University Hospital, Kuopio, Finland
| | - Pasi Tuunanen
- Department of Clinical Neurophysiology, Kuopio University Hospital, Kuopio, Finland
| | - Sara Määttä
- Department of Clinical Neurophysiology, Kuopio University Hospital, Kuopio, Finland
| | - Petro Julkunen
- Department of Clinical Neurophysiology, Kuopio University Hospital, Kuopio, Finland; Department of Applied Physics, University of Eastern Finland, Kuopio, Finland
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Fil JE, Joung S, Zimmerman BJ, Sutton BP, Dilger RN. High-resolution magnetic resonance imaging-based atlases for the young and adolescent domesticated pig (Sus scrofa). J Neurosci Methods 2021; 354:109107. [PMID: 33675840 DOI: 10.1016/j.jneumeth.2021.109107] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2020] [Revised: 02/22/2021] [Accepted: 02/25/2021] [Indexed: 12/15/2022]
Abstract
BACKGROUND Neurodevelopmental studies utilize the pig as a translational animal model due to anatomical and morphological similarities between the pig and human brain. However, neuroimaging resources are not as well developed for the pig as they are for humans and other animal models. We established a magnetic resonance imaging-based brain atlas at two different ages for biomedical studies utilizing the pig as a preclinical model. NEW METHOD Twenty artificially-reared domesticated male pigs (Sus scrofa) and thirteen sow-reared adolescent domesticated male pigs (Sus scrofa) underwent a series of scans measuring brain macrostructure, microstructure, and arterial cerebral blood volume. RESULTS An atlas for the 4-week-old and 12-week-old pig were created along with twenty-six regions of interest. Normative data for brain measures were obtained and detailed descriptions of the data processing pipelines were provided. COMPARISON WITH EXISTING METHOD Atlases at the two different ages were created for the pig utilizing newer imaging technology and software. This facilitates the performance of longitudinal studies and enables more precise volume measurements in pigs of various ages by appropriately representing the neuroanatomical features of younger and older pigs and accommodating the proportion differences of the brain over time. CONCLUSION Two high-resolution MRI brain atlases specific to the domesticated young and adolescent pig were created using defined image acquisition and data processing methods to facilitate the generation of high-quality normative data for neurodevelopmental research.
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Affiliation(s)
- Joanne E Fil
- Piglet Nutrition & Cognition Laboratory, University of Illinois, Urbana, IL, 61801, USA; Neuroscience Program, University of Illinois, Urbana, IL, 61801, USA
| | - Sangyun Joung
- Piglet Nutrition & Cognition Laboratory, University of Illinois, Urbana, IL, 61801, USA; Neuroscience Program, University of Illinois, Urbana, IL, 61801, USA
| | - Benjamin J Zimmerman
- Neuroscience Program, University of Illinois, Urbana, IL, 61801, USA; Beckman Institute for Advances Science & Technology, University of Illinois, Urbana, IL, 61801, USA
| | - Bradley P Sutton
- Neuroscience Program, University of Illinois, Urbana, IL, 61801, USA; Department of Bioengineering, University of Illinois, Urbana, IL, 61801, USA; Beckman Institute for Advances Science & Technology, University of Illinois, Urbana, IL, 61801, USA
| | - Ryan N Dilger
- Piglet Nutrition & Cognition Laboratory, University of Illinois, Urbana, IL, 61801, USA; Neuroscience Program, University of Illinois, Urbana, IL, 61801, USA; Department of Animal Sciences, University of Illinois, Urbana, IL, 61801, USA; Division of Nutritional Sciences, University of Illinois, Urbana, IL, 61801, USA.
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21
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Abstract
Due to the complexity and limited availability of human brain tissues, for decades, pathologists have sought to maximize information gained from individual samples, based on which (patho)physiological processes could be inferred. Recently, new understandings of chemical and physical properties of biological tissues and multiple chemical profiling have given rise to the development of scalable tissue clearing methods allowing superior optical clearing of across-the-scale samples. In the past decade, tissue clearing techniques, molecular labeling methods, advanced laser scanning microscopes, and data visualization and analysis have become commonplace. Combined, they have made 3D visualization of brain tissues with unprecedented resolution and depth widely accessible. To facilitate further advancements and applications, here we provide a critical appraisal of these techniques. We propose a classification system of current tissue clearing and expansion methods that allows users to judge the applicability of individual ones to their questions, followed by a review of the current progress in molecular labeling, optical imaging, and data processing to demonstrate the whole 3D imaging pipeline based on tissue clearing and downstream techniques for visualizing the brain. We also raise the path forward of tissue-clearing-based imaging technology, that is, integrating with state-of-the-art techniques, such as multiplexing protein imaging, in situ signal amplification, RNA detection and sequencing, super-resolution imaging techniques, multiomics studies, and deep learning, for drawing the complete atlas of the human brain and building a 3D pathology platform for central nervous system disorders.
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Affiliation(s)
- Jiajia Zhao
- Department of Neurosurgery, The National Key Clinical Specialty, The Engineering Technology Research Center of Education Ministry of China, Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, Zhujiang Hospital, Southern Medical University, Guangzhou 510515, China
- The Second Clinical Medical College, Southern Medical University, Guangzhou 510515, China
| | - Hei Ming Lai
- Department of Psychiatry, Faculty of Medicine, The Chinese University of Hong Kong, Shatin, NT, Hong Kong SAR, China
| | - Yuwei Qi
- Department of Neurosurgery, The National Key Clinical Specialty, The Engineering Technology Research Center of Education Ministry of China, Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, Zhujiang Hospital, Southern Medical University, Guangzhou 510515, China
- The Second Clinical Medical College, Southern Medical University, Guangzhou 510515, China
| | - Dian He
- Department of Neurosurgery, The National Key Clinical Specialty, The Engineering Technology Research Center of Education Ministry of China, Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, Zhujiang Hospital, Southern Medical University, Guangzhou 510515, China
- The Second Clinical Medical College, Southern Medical University, Guangzhou 510515, China
| | - Haitao Sun
- Department of Neurosurgery, The National Key Clinical Specialty, The Engineering Technology Research Center of Education Ministry of China, Guangdong Provincial Key Laboratory on Brain Function Repair and Regeneration, Zhujiang Hospital, Southern Medical University, Guangzhou 510515, China
- The Second Clinical Medical College, Southern Medical University, Guangzhou 510515, China
- Microbiome Medicine Center, Department of Laboratory Medicine, Clinical Biobank Center, Zhujiang Hospital, Southern Medical University, Guangzhou 510282, China
- Key Laboratory of Mental Health of the Ministry of Education, Guangdong-Hong Kong-Macao Greater Bay Area Center for Brain Science and Brain-Inspired Intelligence, Southern Medical University, Guangzhou 510515, China
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Abstract
Tissue optical clearing technology has been developing rapidly in the past decade due to advances in microscopy equipment and various labeling techniques. Consistent modification of primary methods for optical tissue transparency has allowed observation of the whole mouse body at single-cell resolution or thick tissue slices at the nanoscale level, with the final aim to make intact primate and human brains or thick human brain tissues optically transparent. Optical clearance combined with flexible large-volume tissue labeling technology can not only preserve the anatomical structure but also visualize multiple molecular information from intact samples in situ. It also provides a new strategy for studying complex tissues, which is of great significance for deciphering the functional structure of healthy brains and the mechanisms of neurological pathologies. In this review, we briefly introduce the existing optical clearing technology and discuss its application in deciphering connection and structure, brain development, and brain diseases. Besides, we discuss the standard computational analysis tools for large-scale imaging dataset processing and information extraction. In general, we hope that this review will provide a valuable reference for researchers who intend to use optical clearing technology in studying the brain.
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Affiliation(s)
- Xiaohan Liang
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, 430074, Wuhan, Hubei, China
- MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, 430074, Wuhan, Hubei, China
| | - Haiming Luo
- Britton Chance Center for Biomedical Photonics, Wuhan National Laboratory for Optoelectronics-Huazhong University of Science and Technology, 430074, Wuhan, Hubei, China
- MoE Key Laboratory for Biomedical Photonics, Collaborative Innovation Center for Biomedical Engineering, School of Engineering Sciences, Huazhong University of Science and Technology, 430074, Wuhan, Hubei, China
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23
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Xu X, Guan Y, Gong H, Feng Z, Shi W, Li A, Ren M, Yuan J, Luo Q. Automated Brain Region Segmentation for Single Cell Resolution Histological Images Based on Markov Random Field. Neuroinformatics 2020; 18:181-97. [PMID: 31376002 DOI: 10.1007/s12021-019-09432-z] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
Abstract
The brain consists of massive regions with different functions and the precise delineation of brain region boundaries is important for brain region identification and atlas illustration. In this paper we propose a hierarchical Markov random field (MRF) model for brain region segmentation, where a MRF is applied to the downsampled low-resolution images and the result is used to initialize another MRF for the original high-resolution images. A fractional differential feature and a gray level co-occurrence matrix are extracted as the observed vector for the MRF and a new potential energy function, which can capture the spatial characteristic of brain regions, is proposed as well. A fuzzy entropy criterion is used to fine-tune the boundary from the hierarchical MRF model. We test the model both on synthetic images and real histological mouse brain images. The result suggests that the model can accurately identify target regions and even the whole mouse brain outline as a special case. An interesting observation is that the model cannot only segment regions with different cell density but also can segment regions with similar cell density and different cell morphology texture. Thus this model shows great potential for building the high-resolution 3D brain atlas.
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Polanski WH, Zolal A, Sitoci-Ficici KH, Hiepe P, Schackert G, Sobottka SB. Comparison of Automatic Segmentation Algorithms for the Subthalamic Nucleus. Stereotact Funct Neurosurg 2020; 98:256-262. [PMID: 32369819 DOI: 10.1159/000507028] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [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: 11/12/2019] [Accepted: 02/13/2020] [Indexed: 12/30/2022]
Abstract
INTRODUCTION Various automatic segmentation algorithms for the subthalamic nucleus (STN) have been published recently. However, most of the available software tools are not approved for clinical use. OBJECTIVE The aim of this study is to evaluate a clinically available automatic segmentation tool of the navigation planning software Brainlab Elements (BL-E) by comparing the output to manual segmentation and a nonclinically approved research method using the DISTAL atlas (DA) and the Horn electrophysiological atlas (HEA). METHODS Preoperative MRI data of 30 patients with idiopathic Parkinson's disease were used, resulting in 60 STN segmentations. The segmentations were created manually by two clinical experts. Automatic segmentations of the STN were obtained from BL-E and Advanced Normalization Tools using DA and HEA. Differences between manual and automatic segmentations were quantified by Dice and Jaccard coefficient, target overlap, and false negative/positive value (FNV/FPV) measurements. Statistical differences between similarity measures were assessed using the Wilcoxon signed-rank test with continuity correction, and comparison with interrater results was performed using the Mann-Whitney U test. RESULTS For manual segmentation, the mean size of the segmented STN was 133 ± 24 mm3. The mean size of the STN was 121 ± 18 mm3 for BL-E, 162 ± 21 mm3 for DA, and 130 ± 17 mm3 for HEA. The Dice coefficient for the interrater comparison was 0.63 and 0.54 ± 0.12, 0.59 ± 0.13, and 0.52 ± 0.14 for BL-E, DA, and HEA, respectively. Significant differences between similarity measures were found for Dice and Jaccard coefficient, target overlap and FNV between BL-E and DA; and FPV between BL-E and HEA. However, none of the differences were significant compared to interrater variability. The analysis of the center of gravity of the segmentations revealed that the BL-E STN ROI was located more medially, superior and posterior compared to other segmentations. Regarding the target overlap for beta power within the STN ROI included with the HEA, the BL-E segmentation showed a significantly higher value compared to manual segmentation. CONCLUSION Automatic image segmentation by means of the clinically approved software BL-E provides STN segmentations with similar accuracy like research tools, and differences are in the range of observed interrater variability. Further studies are required to investigate the clinical validity, for example, by comparing segmentation results of BL-E with electrophysiological data.
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Affiliation(s)
- Witold H Polanski
- Department of Neurosurgery, University Hospital Carl-Gustav-Carus, Technical University of Dresden, Dresden, Germany,
| | - Amir Zolal
- Department of Neurosurgery, University Hospital Carl-Gustav-Carus, Technical University of Dresden, Dresden, Germany.,Department of Spine Surgery and Neurotraumatology, SRH Wald-Klinikum Gera, Gera, Germany
| | - Kerim Hakan Sitoci-Ficici
- Department of Neurosurgery, University Hospital Carl-Gustav-Carus, Technical University of Dresden, Dresden, Germany
| | | | - Gabriele Schackert
- Department of Neurosurgery, University Hospital Carl-Gustav-Carus, Technical University of Dresden, Dresden, Germany
| | - Stephan B Sobottka
- Department of Neurosurgery, University Hospital Carl-Gustav-Carus, Technical University of Dresden, Dresden, Germany
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Jiang Y, Li Z, Zhao Y, Xiao X, Zhang W, Sun P, Yang Y, Zhu C. Targeting brain functions from the scalp: Transcranial brain atlas based on large-scale fMRI data synthesis. Neuroimage 2020; 210:116550. [PMID: 31981781 DOI: 10.1016/j.neuroimage.2020.116550] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [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: 04/16/2019] [Revised: 12/16/2019] [Accepted: 01/14/2020] [Indexed: 12/28/2022] Open
Abstract
Transcranial brain mapping techniques, such as functional near-infrared spectroscopy (fNIRS) and transcranial magnetic stimulation (TMS), have been playing an increasingly important role in studies of human brain functions. Given a brain function of interest, fNIRS probes and TMS coils should be properly placed on the scalp to ensure that the function is effectively measured or modulated. However, since brain activity is inside the skull and invisible to the researcher during placement, this blind targeting may cause the device to partially or completely miss the functional target, resulting in inconsistent experimental results and divergent clinical outcomes, especially when participants' structural MRI data are not available. To address this issue, we propose here a framework for targeting a designated function directly from the scalp. First, a functional brain atlas for the targeted brain function is constructed via a meta-analysis of large-scale functional magnetic resonance imaging datasets. Second, the functional brain atlas is presented on the scalp surface by using a transcranial mapping previously established from an structural MRI dataset (n = 114), resulting in a novel functional transcranial brain atlas (fTBA). Finally, a low-cost, portable scalp-navigation system is used to localize the transcranial device on the individual's scalp with the guidance of the fTBA. To demonstrate the feasibility of the targeting framework, both fNIRS and TMS mapping experiments were conducted. The results show that fTBA-guided fNIRS positioning can detect functional activity with high sensitivity and specificity for working memory and motor systems; Moreover, compared with traditional TMS targeting approaches (e.g. the International 10-20 System and the conventional 5-cm rule), the fTBA suggested motor stimulation site is closesr to both the motor hotspot and the center of gravity of motor evoked potentials (MEP-COG). In summary, the proposed method unblinds the transcranial function targeting process using prior information, providing an effective and straightforward approach to transcranial brain mapping studies, especially those without participants' structural MRI data.
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Affiliation(s)
- Yihan Jiang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Zheng Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, China
| | - Yang Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Xiang Xiao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Wei Zhang
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Peipei Sun
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China
| | - Yihong Yang
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD, USA
| | - Chaozhe Zhu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; Center for Collaboration and Innovation in Brain and Learning Sciences, Beijing Normal University, Beijing, China.
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26
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Jermakowicz WJ, Wu C, Neal E, Cajigas I, D'Haese PF, Donahue DJ, Sharan AD, Vale FL, Jagid JR. Clinically Significant Visual Deficits after Laser Interstitial Thermal Therapy for Mesiotemporal Epilepsy. Stereotact Funct Neurosurg 2020; 97:347-355. [PMID: 31935727 DOI: 10.1159/000504856] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [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: 02/10/2019] [Accepted: 11/18/2019] [Indexed: 12/22/2022]
Abstract
BACKGROUND Laser interstitial thermal therapy (LITT) has recently gained popularity as a minimally invasive surgical option for the treatment of mesiotemporal epilepsy (mTLE). Similar to traditional open procedures for epilepsy, the most frequent neurological complications of LITT are visual deficits; however, a critical analysis of these injuries is lacking. OBJECTIVES To evaluate the visual deficits that occur after LITT for mTLE and their etiology. METHOD We surveyed five academic epilepsy centers that regularly perform LITT for cases of self-reported postoperative visual deficits. For these patients all pre-, intra- and postoperative MRIs were co-registered with an anatomic atlas derived from 7T MRI data. This was used to estimate thermal injury to early visual pathways and measure imaging variables relevant to the LITT procedure. Using logistic regression, we then compared 14 variables derived from demographics, mesiotemporal anatomy, and the surgical procedure for the patients with visual deficits to a normal cohort comprised of the first 30 patients to undergo this procedure at a single institution. RESULTS Of 90 patients that underwent LITT for mTLE, 6 (6.7%) reported a postoperative visual deficit. These included 2 homonymous hemianopsias (HHs), 2 quadrantanopsias, and 2 cranial nerve (CN) IV palsies. These deficits localized to the posterior aspect of the ablation, corresponding to the hippocampal body and tail, and tended to have greater laser energy delivered in that region than the normal cohort. The patients with HH had insult localized to the lateral geniculate nucleus, which was -associated with young age and low choroidal fissure CSF volume. Quadrantanopsia, likely from injury to the optic radiation in Meyer's loop, was correlated with a lateral trajectory and excessive energy delivered at the tail end of the ablation. Patients with CN IV injury had extension of contrast to the tentorial edge associated with a mesial laser trajectory. CONCLUSIONS LITT for epilepsy may be complicated by various classes of visual deficit, each with distinct etiology and clinical significance. It is our hope that by better understanding these injuries and their mechanisms we can eventually reduce their occurrence by identifying at-risk patients and trajectories and appropriately tailoring the ablation procedure.
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Affiliation(s)
| | - Chengyuan Wu
- Department of Neurological Surgery, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Elliot Neal
- Department of Neurosurgery and Brain Repair, University of South Florida, Tampa, Florida, USA
| | - Iahn Cajigas
- Department of Neurological Surgery, University of Miami, Miami, Florida, USA
| | - Pierre-François D'Haese
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, Tennessee, USA
| | - David J Donahue
- Department of Neurological Surgery, Cook Children's Medical Center, Fort Worth, Texas, USA
| | - Ashwini D Sharan
- Department of Neurological Surgery, Thomas Jefferson University, Philadelphia, Pennsylvania, USA
| | - Fernando L Vale
- Department of Neurosurgery and Brain Repair, University of South Florida, Tampa, Florida, USA
| | - Jonathan R Jagid
- Department of Neurological Surgery, University of Miami, Miami, Florida, USA,
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Abstract
Brain atlases play a key role in modern neuroimaging analysis of brain structure and function. We review available atlas databases for humans and animals and illustrate common state-of-the-art workflows in neuroimaging research based on image registration. Advances in noninvasive imaging methods, 3D ex vivo microscopy, and image processing are summarized which will eventually close the current resolution gap between brain atlases based on conventional 2D histology and those based on 3D in vivo imaging.
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Affiliation(s)
- Andreas Hess
- Institute for Experimental Pharmacology, Friedrich Alexander University Erlangen Nuremberg, Fahrstraße 17, 91054, Erlangen, Germany.
| | - Rukun Hinz
- Bio-Imaging Lab, University of Antwerp, Antwerp, Belgium
| | | | - Philipp Boehm-Sturm
- Department of Experimental Neurology and Center for Stroke Research Berlin, Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Charitéplatz 1, 10117, Berlin, Germany. .,NeuroCure Cluster of Excellence and Charité Core Facility 7T Experimental MRIs, Charité - Universitätsmedizin Berlin, Berlin, Germany.
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28
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Nie B, Wu D, Liang S, Liu H, Sun X, Li P, Huang Q, Zhang T, Feng T, Ye S, Zhang Z, Shan B. A stereotaxic MRI template set of mouse brain with fine sub-anatomical delineations: Application to MEMRI studies of 5XFAD mice. Magn Reson Imaging 2018; 57:83-94. [PMID: 30359719 DOI: 10.1016/j.mri.2018.10.014] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [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: 06/28/2018] [Revised: 10/16/2018] [Accepted: 10/18/2018] [Indexed: 01/22/2023]
Abstract
PURPOSE Manganese-enhanced magnetic resonance imaging (MEMRI) can help us trace the active neurons and neuronal pathway in transgenic mouse AD model. 5XFAD has been widespread accepted as a valuable model system for studying brain dysfunction progresses in the courses of AD. To further understand the development of AD at early stages, an effective and objective data analysis platform for MEMRI studies should be constructed. MATERIALS AND METHODS A set of stereotaxic templates of mouse brain in Paxinos and Franklin space, "the Institute of High Energy Physics Mouse Template", or IMT for short, was constructed by iteratively registration and averaging. An atlas image was reconstructed from the Paxinos and Franklin atlas figures and each sub-anatomical segmentation was assigning a unique integer. An analysis SPM plug-in toolbox was further created, that automates and standardizes the time-consuming processes of brain extraction, tissue segmentation, and statistical analysis for MEMRI scans. RESULTS The IMT comprised a T2WI template image, a MEMRI template image, intracranial tissue segmentations, and accompany with a digital mouse brain atlas image, in which 707 sub-anatomical brain regions are delineated. Data analyses were performed on groups of developing 5XFAD mice to demonstrate the usage of IMT, and the results shows that abnormal neuronal activity occurs at early stage in 5XFAD mice. CONCLUSION We have constructed a stereotaxic template set of mouse brain named IMT with fine delineations of sub-anatomical structures, which is compatible with SPM. It will give a widely range of researchers a standardized coordinate system for localization of any mouse brain related data.
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Affiliation(s)
- Binbin Nie
- Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China; School of Nuclear Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai 200031, China
| | - Di Wu
- Department of Neurology, Affiliated ZhongDa Hospital, Neuropsychiatric Institute, School of Medicine, Southeast University, Nanjing 210009, China
| | - Shengxiang Liang
- Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China; School of Nuclear Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, China; College of Rehabilitation Medicine, Fujian University of Traditional Chinese Medicine, Fuzhou 350122, China; Physical Science and Technology College, Zhengzhou University, Zhengzhou 450001, China
| | - Hua Liu
- Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China; School of Nuclear Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xi Sun
- Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China; School of Nuclear Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, China; Physical Science and Technology College, Zhengzhou University, Zhengzhou 450001, China
| | - Panlong Li
- Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China; School of Nuclear Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, China; Physical Science and Technology College, Zhengzhou University, Zhengzhou 450001, China
| | - Qi Huang
- Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China; School of Nuclear Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Tianhao Zhang
- Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China; School of Nuclear Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Ting Feng
- Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China; School of Nuclear Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, China; Physical Science and Technology College, Zhengzhou University, Zhengzhou 450001, China
| | - Songtao Ye
- College of Information Engineering, Xiangtan University, Xiangtan 411105, China
| | - Zhijun Zhang
- Department of Neurology, Affiliated ZhongDa Hospital, Neuropsychiatric Institute, School of Medicine, Southeast University, Nanjing 210009, China.
| | - Baoci Shan
- Beijing Engineering Research Center of Radiographic Techniques and Equipment, Institute of High Energy Physics, Chinese Academy of Sciences, Beijing 100049, China; School of Nuclear Science and Technology, University of Chinese Academy of Sciences, Beijing 100049, China; CAS Center for Excellence in Brain Science and Intelligence Technology, Shanghai 200031, China.
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Phan TV, Smeets D, Talcott JB, Vandermosten M. Processing of structural neuroimaging data in young children: Bridging the gap between current practice and state-of-the-art methods. Dev Cogn Neurosci 2018; 33:206-223. [PMID: 29033222 PMCID: PMC6969273 DOI: 10.1016/j.dcn.2017.08.009] [Citation(s) in RCA: 37] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2016] [Revised: 07/28/2017] [Accepted: 08/17/2017] [Indexed: 11/25/2022] Open
Abstract
The structure of the brain is subject to very rapid developmental changes during early childhood. Pediatric studies based on Magnetic Resonance Imaging (MRI) over this age range have recently become more frequent, with the advantage of providing in vivo and non-invasive high-resolution images of the developing brain, toward understanding typical and atypical trajectories. However, it has also been demonstrated that application of currently standard MRI processing methods that have been developed with datasets from adults may not be appropriate for use with pediatric datasets. In this review, we examine the approaches currently used in MRI studies involving young children, including an overview of the rationale for new MRI processing methods that have been designed specifically for pediatric investigations. These methods are mainly related to the use of age-specific or 4D brain atlases, improved methods for quantifying and optimizing image quality, and provision for registration of developmental data obtained with longitudinal designs. The overall goal is to raise awareness of the existence of these methods and the possibilities for implementing them in developmental neuroimaging studies.
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Affiliation(s)
- Thanh Vân Phan
- Experimental Oto-rhino-laryngology, Department Neurosciences, KU Leuven, Leuven, Belgium; icometrix, Research and Development, Leuven, Belgium.
| | - Dirk Smeets
- icometrix, Research and Development, Leuven, Belgium
| | - Joel B Talcott
- Aston Brain Centre, School of Life and Health Sciences, Aston University, Birmingham, United Kingdom
| | - Maaike Vandermosten
- Experimental Oto-rhino-laryngology, Department Neurosciences, KU Leuven, Leuven, Belgium
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30
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Shiffman S, Basak S, Kozlowski C, Fuji RN. An automated mapping method for Nissl-stained mouse brain histologic sections. J Neurosci Methods 2018; 308:219-227. [PMID: 30096343 DOI: 10.1016/j.jneumeth.2018.08.005] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [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: 05/29/2018] [Revised: 08/01/2018] [Accepted: 08/06/2018] [Indexed: 02/09/2023]
Abstract
BACKGROUND Histologic evaluation of the central nervous system is often a critical endpoint in in vivo efficacy studies, and is considered the essential component of neurotoxicity assessment in safety studies. Automated image analysis is a powerful tool that can radically reduce the workload associated with evaluating brain histologic sections. NEW METHOD We developed an automated brain mapping method that identifies neuroanatomic structures in mouse histologic coronal brain sections. The method utilizes the publicly available Allen Brain Atlas to map brain regions on digitized Nissl-stained sections. RESULTS The method's accuracy was first assessed by comparing the mapping results to structure delineations from the Franklin and Paxinos (FP) mouse brain atlas. Brain regions mapped from FP Nissl-stained sections and calculated volumes were similar to structure delineations and volumes derived from corresponding FP illustrations. We subsequently applied our method to mouse brain sections from an in vivo study where the hippocampus was the structure of interest. Nissl-stained sections were mapped and hippocampal boundaries transferred to adjacent immunohistochemically stained sections. Optical density quantification results were comparable to those from time-consuming, manually drawn hippocampal delineations on the IHC-stained sections. COMPARISON WITH EXISTING METHODS Compared to other published methods, our method requires less manual input, and has been validated comprehensively using a secondary atlas, as well as manually annotated brain IHC sections from 68 study mice. CONCLUSIONS We propose that our automated brain mapping method enables greater efficiency and consistency in mouse neuropathologic assessments.
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Affiliation(s)
- Smadar Shiffman
- Safety Assessment Pathology, Genentech, Inc., 1 DNA Way, South San Francisco, CA94080, USA
| | - Sayantani Basak
- Safety Assessment Pathology, Genentech, Inc., 1 DNA Way, South San Francisco, CA94080, USA
| | - Cleopatra Kozlowski
- Safety Assessment Pathology, Genentech, Inc., 1 DNA Way, South San Francisco, CA94080, USA.
| | - Reina N Fuji
- Safety Assessment Pathology, Genentech, Inc., 1 DNA Way, South San Francisco, CA94080, USA.
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31
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Trebaul L, Deman P, Tuyisenge V, Jedynak M, Hugues E, Rudrauf D, Bhattacharjee M, Tadel F, Chanteloup-Foret B, Saubat C, Reyes Mejia GC, Adam C, Nica A, Pail M, Dubeau F, Rheims S, Trébuchon A, Wang H, Liu S, Blauwblomme T, Garcés M, De Palma L, Valentin A, Metsähonkala EL, Petrescu AM, Landré E, Szurhaj W, Hirsch E, Valton L, Rocamora R, Schulze-Bonhage A, Mindruta I, Francione S, Maillard L, Taussig D, Kahane P, David O. Probabilistic functional tractography of the human cortex revisited. Neuroimage 2018; 181:414-429. [PMID: 30025851 PMCID: PMC6150949 DOI: 10.1016/j.neuroimage.2018.07.039] [Citation(s) in RCA: 69] [Impact Index Per Article: 11.5] [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: 01/29/2018] [Revised: 06/21/2018] [Accepted: 07/15/2018] [Indexed: 12/13/2022] Open
Abstract
In patients with pharmaco-resistant focal epilepsies investigated with intracranial electroencephalography (iEEG), direct electrical stimulations of a cortical region induce cortico-cortical evoked potentials (CCEP) in distant cerebral cortex, which properties can be used to infer large scale brain connectivity. In 2013, we proposed a new probabilistic functional tractography methodology to study human brain connectivity. We have now been revisiting this method in the F-TRACT project (f-tract.eu) by developing a large multicenter CCEP database of several thousand stimulation runs performed in several hundred patients, and associated processing tools to create a probabilistic atlas of human cortico-cortical connections. Here, we wish to present a snapshot of the methods and data of F-TRACT using a pool of 213 epilepsy patients, all studied by stereo-encephalography with intracerebral depth electrodes. The CCEPs were processed using an automated pipeline with the following consecutive steps: detection of each stimulation run from stimulation artifacts in raw intracranial EEG (iEEG) files, bad channels detection with a machine learning approach, model-based stimulation artifact correction, robust averaging over stimulation pulses. Effective connectivity between the stimulated and recording areas is then inferred from the properties of the first CCEP component, i.e. onset and peak latency, amplitude, duration and integral of the significant part. Finally, group statistics of CCEP features are implemented for each brain parcel explored by iEEG electrodes. The localization (coordinates, white/gray matter relative positioning) of electrode contacts were obtained from imaging data (anatomical MRI or CT scans before and after electrodes implantation). The iEEG contacts were repositioned in different brain parcellations from the segmentation of patients' anatomical MRI or from templates in the MNI coordinate system. The F-TRACT database using the first pool of 213 patients provided connectivity probability values for 95% of possible intrahemispheric and 56% of interhemispheric connections and CCEP features for 78% of intrahemisheric and 14% of interhemispheric connections. In this report, we show some examples of anatomo-functional connectivity matrices, and associated directional maps. We also indicate how CCEP features, especially latencies, are related to spatial distances, and allow estimating the velocity distribution of neuronal signals at a large scale. Finally, we describe the impact on the estimated connectivity of the stimulation charge and of the contact localization according to the white or gray matter. The most relevant maps for the scientific community are available for download on f-tract. eu (David et al., 2017) and will be regularly updated during the following months with the addition of more data in the F-TRACT database. This will provide an unprecedented knowledge on the dynamical properties of large fiber tracts in human.
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Affiliation(s)
- Lena Trebaul
- Inserm, U1216, Grenoble, F-38000, France; Univ. Grenoble Alpes, Grenoble Institut des Neurosciences, GIN, Grenoble, F-38000, France
| | - Pierre Deman
- Inserm, U1216, Grenoble, F-38000, France; Univ. Grenoble Alpes, Grenoble Institut des Neurosciences, GIN, Grenoble, F-38000, France
| | - Viateur Tuyisenge
- Inserm, U1216, Grenoble, F-38000, France; Univ. Grenoble Alpes, Grenoble Institut des Neurosciences, GIN, Grenoble, F-38000, France
| | - Maciej Jedynak
- Inserm, U1216, Grenoble, F-38000, France; Univ. Grenoble Alpes, Grenoble Institut des Neurosciences, GIN, Grenoble, F-38000, France
| | - Etienne Hugues
- Inserm, U1216, Grenoble, F-38000, France; Univ. Grenoble Alpes, Grenoble Institut des Neurosciences, GIN, Grenoble, F-38000, France
| | - David Rudrauf
- Inserm, U1216, Grenoble, F-38000, France; Univ. Grenoble Alpes, Grenoble Institut des Neurosciences, GIN, Grenoble, F-38000, France
| | - Manik Bhattacharjee
- Inserm, U1216, Grenoble, F-38000, France; Univ. Grenoble Alpes, Grenoble Institut des Neurosciences, GIN, Grenoble, F-38000, France
| | - François Tadel
- Inserm, U1216, Grenoble, F-38000, France; Univ. Grenoble Alpes, Grenoble Institut des Neurosciences, GIN, Grenoble, F-38000, France
| | - Blandine Chanteloup-Foret
- Inserm, U1216, Grenoble, F-38000, France; Univ. Grenoble Alpes, Grenoble Institut des Neurosciences, GIN, Grenoble, F-38000, France
| | - Carole Saubat
- Inserm, U1216, Grenoble, F-38000, France; Univ. Grenoble Alpes, Grenoble Institut des Neurosciences, GIN, Grenoble, F-38000, France
| | - Gina Catalina Reyes Mejia
- Inserm, U1216, Grenoble, F-38000, France; Univ. Grenoble Alpes, Grenoble Institut des Neurosciences, GIN, Grenoble, F-38000, France
| | - Claude Adam
- Epilepsy Unit, Dept of Neurology, Pitié-Salpêtrière Hospital, APHP, Paris, France
| | - Anca Nica
- Neurology Department, CHU, Rennes, France
| | - Martin Pail
- Brno Epilepsy Center, Department of Neurology, St. Anne's University Hospital and Medical Faculty of Masaryk University, Brno, Czech Republic
| | - François Dubeau
- Montreal Neurological Institute and Hospital, Montreal, Canada
| | - Sylvain Rheims
- Department of Functional Neurology and Epileptology, Hospices Civils de Lyon and University of Lyon, Lyon, France
| | - Agnès Trébuchon
- Service de Neurophysiologie Clinique, APHM, Hôpitaux de la Timone, Marseille, France
| | - Haixiang Wang
- Yuquan Hospital Epilepsy Center, Tsinghua University, Beijing, China
| | - Sinclair Liu
- Canton Sanjiu Brain Hospital Epilepsy Center, Jinan University, Guangzhou, China
| | - Thomas Blauwblomme
- Department of Pediatric Neurosurgery, Hôpital Necker-Enfants Malades, Université Paris V Descartes, Sorbonne Paris Cité, Paris, France
| | - Mercedes Garcés
- Multidisciplinary Epilepsy Unit, Hospital Universitario y Politécnico La Fe, Valencia, Spain
| | - Luca De Palma
- Department of Neuroscience, Bambino Gesù Children's Hospital, IRRCS, Rome, Italy
| | - Antonio Valentin
- Department of Basic and Clinical Neuroscience, Institute of Psychiatry, Psychology & Neuroscience (IoPPN), London, UK
| | | | | | | | - William Szurhaj
- Epilepsy Unit, Department of Clinical Neurophysiology, Lille University Medical Center, Lille, France
| | - Edouard Hirsch
- University Hospital, Department of Neurology, Strasbourg, France
| | - Luc Valton
- University Hospital, Department of Neurology, Toulouse, France
| | - Rodrigo Rocamora
- Epilepsy Monitoring Unit, Department of Neurology, Hospital del Mar-IMIM, Barcelona, Spain
| | - Andreas Schulze-Bonhage
- Epilepsy Center, Medical Center - University of Freiburg, Faculty of Medicine, University of Freiburg, Germany
| | - Ioana Mindruta
- Neurology Department, University Emergency Hospital, Bucharest, Romania
| | | | - Louis Maillard
- Centre Hospitalier Universitaire de Nancy, Nancy, France
| | - Delphine Taussig
- Service de neurochirurgie pédiatrique, Fondation Rothschild, Paris, France
| | - Philippe Kahane
- Inserm, U1216, Grenoble, F-38000, France; Univ. Grenoble Alpes, Grenoble Institut des Neurosciences, GIN, Grenoble, F-38000, France; CHU Grenoble Alpes, Neurology Department, Grenoble, France
| | - Olivier David
- Inserm, U1216, Grenoble, F-38000, France; Univ. Grenoble Alpes, Grenoble Institut des Neurosciences, GIN, Grenoble, F-38000, France.
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Santarelli AJ, Khan AM, Poulos AM. Contextual fear retrieval-induced Fos expression across early development in the rat: An analysis using established nervous system nomenclature ontology. Neurobiol Learn Mem 2018; 155:42-49. [PMID: 29807127 DOI: 10.1016/j.nlm.2018.05.015] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [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: 01/12/2018] [Revised: 05/07/2018] [Accepted: 05/19/2018] [Indexed: 02/07/2023]
Abstract
The neural circuits underlying the acquisition, retention and retrieval of contextual fear conditioning have been well characterized in the adult animal. A growing body of work in younger rodents indicates that context-mediated fear expression may vary across development. However, it remains unclear how this expression may be defined across the full range of key developmental ages. Nor is it fully clear whether the structure of the adult context fear network generalizes to earlier ages. In this study, we compared context fear retrieval-induced behavior and neuroanatomically constrained immediate early-gene expression across infant (P19), early and late juvenile (P24 and P35), and adult (P90) male Long-Evans rats. We focused our analysis on neuroanatomically defined subregions and nuclei of the basolateral complex of the amygdala (BLA complex), dorsal and ventral portions of the hippocampus and the subregions of the medial prefrontal cortex as defined by the nomenclature of the Swanson (2004) adult rat brain atlas. Relative to controls and across all ages tested, there were greater numbers of Fos immunoreactive (Fos-ir) neurons in the posterior part of the basolateral amygdalar nuclei (BLAp) following context fear retrieval that correlated statistically with the expression of freezing. However, Fos-ir within regions having known connections with the BLA complex was differentially constrained by developmental age: early juvenile, but not adult rats exhibited an increase of context fear-dependent Fos-ir neurons in prelimbic and infralimbic areas, while adult, but not juvenile rats displayed increases in Fos-ir neurons within the ventral CA1 hippocampus. These results suggest that juvenile and adult rodents may recruit developmentally unique pathways in the acquisition and retrieval of contextual fear. This study extends prior work by providing a broader set of developmental ages and a rigorously defined neuroanatomical ontology within which the contextual fear network can be studied further.
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Affiliation(s)
- Anthony J Santarelli
- Department of Psychology, Center for Neuroscience, State University of New York, University at Albany, Albany, NY 12222, USA
| | - Arshad M Khan
- UTEP Systems Neuroscience Laboratory, Department of Biological Sciences and Border Biomedical Research Center, University of Texas at El Paso, El Paso, TX 79968, USA
| | - Andrew M Poulos
- Department of Psychology, Center for Neuroscience, State University of New York, University at Albany, Albany, NY 12222, USA.
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Abstract
The baby brain is constantly changing due to its active neurodevelopment, and research into the baby brain is one of the frontiers in neuroscience. To help guide neuroscientists and clinicians in their investigation of this frontier, maps of the baby brain, which contain a priori knowledge about neurodevelopment and anatomy, are essential. "Brain atlas" in this review refers to a 3D-brain image with a set of reference labels, such as a parcellation map, as the anatomical reference that guides the mapping of the brain. Recent advancements in scanners, sequences, and motion control methodologies enable the creation of various types of high-resolution baby brain atlases. What is becoming clear is that one atlas is not sufficient to characterize the existing knowledge about the anatomical variations, disease-related anatomical alterations, and the variations in time-dependent changes. In this review, the types and roles of the human baby brain MRI atlases that are currently available are described and discussed, and future directions in the field of developmental neuroscience and its clinical applications are proposed. The potential use of disease-based atlases to characterize clinically relevant information, such as clinical labels, in addition to conventional anatomical labels, is also discussed.
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Affiliation(s)
- Kenichi Oishi
- Department of Radiology, Johns Hopkins University School of Medicine, Baltimore, MD, USA.
| | - Linda Chang
- Departments of Diagnostic Radiology and Nuclear Medicine, and Neurology, University of Maryland School of Medicine, Baltimore, MD, USA; Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD, USA; Department of Medicine, John A. Burns School of Medicine, University of Hawaii at Manoa, Honolulu, HI, USA
| | - Hao Huang
- Department of Radiology, University of Pennsylvania School of Medicine, Philadelphia, PA, USA; Department of Radiology, Children's Hospital of Philadelphia, Philadelphia, PA, USA
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Li G, Wang L, Yap PT, Wang F, Wu Z, Meng Y, Dong P, Kim J, Shi F, Rekik I, Lin W, Shen D. Computational neuroanatomy of baby brains: A review. Neuroimage 2019; 185:906-25. [PMID: 29574033 DOI: 10.1016/j.neuroimage.2018.03.042] [Citation(s) in RCA: 95] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2017] [Revised: 02/23/2018] [Accepted: 03/19/2018] [Indexed: 12/12/2022] Open
Abstract
The first postnatal years are an exceptionally dynamic and critical period of structural, functional and connectivity development of the human brain. The increasing availability of non-invasive infant brain MR images provides unprecedented opportunities for accurate and reliable charting of dynamic early brain developmental trajectories in understanding normative and aberrant growth. However, infant brain MR images typically exhibit reduced tissue contrast (especially around 6 months of age), large within-tissue intensity variations, and regionally-heterogeneous, dynamic changes, in comparison with adult brain MR images. Consequently, the existing computational tools developed typically for adult brains are not suitable for infant brain MR image processing. To address these challenges, many infant-tailored computational methods have been proposed for computational neuroanatomy of infant brains. In this review paper, we provide a comprehensive review of the state-of-the-art computational methods for infant brain MRI processing and analysis, which have advanced our understanding of early postnatal brain development. We also summarize publically available infant-dedicated resources, including MRI datasets, computational tools, grand challenges, and brain atlases. Finally, we discuss the limitations in current research and suggest potential future research directions.
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35
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Kosaka T, Kosaka K. Calcium-binding protein, secretagogin, specifies the microcellular tegmental nucleus and intermediate and ventral parts of the cuneiform nucleus of the mouse and rat. Neurosci Res 2018; 134:30-38. [PMID: 29366872 DOI: 10.1016/j.neures.2018.01.005] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [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: 12/23/2017] [Revised: 01/15/2018] [Accepted: 01/17/2018] [Indexed: 11/30/2022]
Abstract
Secretagogin (SCGN) is a recently discovered calcium binding protein of the EF hand family, cloned from β cells of pancreatic island of Langerhans and endocrine cells of the gastrointestinal gland. SCGN characterizes some particular neuron groups in various regions of the nervous system and is considered as one of the useful neuron subpopulation markers. In the present study we reported that SCGN specifically labelled a particular neuronal cluster in the brainstem of the mice and rats. The comparison of the SCGN immunostaining with the choline acetyltransferase immunostaining and acetylcholinesterase staining clearly indicated that the particular cluster of SCGN positive neurons corresponded to the microcellular tegmental nucleus (MiTg) and the ventral portion of the cuneiform nucleus (CnF), both of which are components of the isthmus. The analyses in mice indicated that SCGN positive neurons in the MiTg and CnF were homogeneous in size and shape, appearing to compose a single complex: their somata were small comparing with the adjacent cholinergic neurons in the pedunculotegmantal nucleus, 10.5 vs 16.0 μm in diameter, and extended 2-3 slender smooth processes. SCGN might be one of significant markers to reconsider the delineations of the structures of the mouse, and presumably rat, brainstem.
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Affiliation(s)
- Toshio Kosaka
- Department of Medical Science Technology, Faculty of Health and Welfare Sciences in Fukuoka, International University of Health and Welfare, 137-1 Enokizu, Okawa City, Fukuoka 831-8501, Japan.
| | - Katsuko Kosaka
- Department of Medical Science Technology, Faculty of Health and Welfare Sciences in Fukuoka, International University of Health and Welfare, 137-1 Enokizu, Okawa City, Fukuoka 831-8501, Japan
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36
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Khan AM, Grant AH, Martinez A, Burns GAPC, Thatcher BS, Anekonda VT, Thompson BW, Roberts ZS, Moralejo DH, Blevins JE. Mapping Molecular Datasets Back to the Brain Regions They are Extracted from: Remembering the Native Countries of Hypothalamic Expatriates and Refugees. Adv Neurobiol 2018; 21:101-193. [PMID: 30334222 PMCID: PMC6310046 DOI: 10.1007/978-3-319-94593-4_6] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
This article focuses on approaches to link transcriptomic, proteomic, and peptidomic datasets mined from brain tissue to the original locations within the brain that they are derived from using digital atlas mapping techniques. We use, as an example, the transcriptomic, proteomic and peptidomic analyses conducted in the mammalian hypothalamus. Following a brief historical overview, we highlight studies that have mined biochemical and molecular information from the hypothalamus and then lay out a strategy for how these data can be linked spatially to the mapped locations in a canonical brain atlas where the data come from, thereby allowing researchers to integrate these data with other datasets across multiple scales. A key methodology that enables atlas-based mapping of extracted datasets-laser-capture microdissection-is discussed in detail, with a view of how this technology is a bridge between systems biology and systems neuroscience.
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Affiliation(s)
- Arshad M Khan
- UTEP Systems Neuroscience Laboratory, University of Texas at El Paso, El Paso, TX, USA.
- Department of Biological Sciences, University of Texas at El Paso, El Paso, TX, USA.
- Border Biomedical Research Center, University of Texas at El Paso, El Paso, TX, USA.
| | - Alice H Grant
- UTEP Systems Neuroscience Laboratory, University of Texas at El Paso, El Paso, TX, USA
- Department of Biological Sciences, University of Texas at El Paso, El Paso, TX, USA
- Graduate Program in Pathobiology, University of Texas at El Paso, El Paso, TX, USA
| | - Anais Martinez
- UTEP Systems Neuroscience Laboratory, University of Texas at El Paso, El Paso, TX, USA
- Department of Biological Sciences, University of Texas at El Paso, El Paso, TX, USA
- Graduate Program in Pathobiology, University of Texas at El Paso, El Paso, TX, USA
| | - Gully A P C Burns
- Information Sciences Institute, Viterbi School of Engineering, University of Southern California, Marina del Rey, CA, USA
| | - Brendan S Thatcher
- VA Puget Sound Health Care System, Office of Research and Development Medical Research Service, Department of Veterans Affairs Medical Center, Seattle, WA, USA
| | - Vishwanath T Anekonda
- VA Puget Sound Health Care System, Office of Research and Development Medical Research Service, Department of Veterans Affairs Medical Center, Seattle, WA, USA
| | - Benjamin W Thompson
- VA Puget Sound Health Care System, Office of Research and Development Medical Research Service, Department of Veterans Affairs Medical Center, Seattle, WA, USA
| | - Zachary S Roberts
- VA Puget Sound Health Care System, Office of Research and Development Medical Research Service, Department of Veterans Affairs Medical Center, Seattle, WA, USA
| | - Daniel H Moralejo
- Division of Neonatology, Department of Pediatrics, University of Washington School of Medicine, Seattle, WA, USA
| | - James E Blevins
- VA Puget Sound Health Care System, Office of Research and Development Medical Research Service, Department of Veterans Affairs Medical Center, Seattle, WA, USA
- Division of Metabolism, Endocrinology, and Nutrition, Department of Medicine, University of Washington School of Medicine, Seattle, WA, USA
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Majka P, Chlodzinska N, Turlejski K, Banasik T, Djavadian RL, Węglarz WP, Wójcik DK. A three-dimensional stereotaxic atlas of the gray short-tailed opossum (Monodelphis domestica) brain. Brain Struct Funct 2017; 223:1779-1795. [PMID: 29214509 PMCID: PMC5884921 DOI: 10.1007/s00429-017-1540-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2016] [Accepted: 10/15/2017] [Indexed: 12/22/2022]
Abstract
The gray short-tailed opossum (Monodelphis domestica) is a small marsupial gaining recognition as a laboratory animal in biomedical research. Despite numerous studies on opossum neuroanatomy, a consistent and comprehensive neuroanatomical reference for this species is still missing. Here we present the first three-dimensional, multimodal atlas of the Monodelphis opossum brain. It is based on four complementary imaging modalities: high resolution ex vivo magnetic resonance images, micro-computed tomography scans of the cranium, images of the face of the cutting block, and series of sections stained with the Nissl method and for myelinated fibers. Individual imaging modalities were reconstructed into a three-dimensional form and then registered to the MR image by means of affine and deformable registration routines. Based on a superimposition of the 3D images, 113 anatomical structures were demarcated and the volumes of individual regions were measured. The stereotaxic coordinate system was defined using a set of cranial landmarks: interaural line, bregma, and lambda, which allows for easy expression of any location within the brain with respect to the skull. The atlas is released under the Creative Commons license and available through various digital atlasing web services.
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Affiliation(s)
- Piotr Majka
- Laboratory of Neuroinformatics, Department of Neurophysiology, Nencki Institute of Experimental Biology of Polish Academy of Sciences, 3 Pasteur Street, 02-093, Warsaw, Poland.
| | - Natalia Chlodzinska
- Laboratory of Neurobiology of Development and Evolution, Nencki Institute of Experimental Biology of Polish Academy of Sciences, 3 Pasteur Street, 02-093, Warsaw, Poland
| | - Krzysztof Turlejski
- Department of Biology and Environmental Science, Cardinal Stefan Wyszynski University, 1/3 Woycicki Street, 01-938, Warsaw, Poland
| | - Tomasz Banasik
- H. Niewodniczański Institute of Nuclear Physics of Polish Academy of Sciences, Radzikowskiego 152, 31-342, Kraków, Poland
| | - Ruzanna L Djavadian
- Department of Molecular and Cellular Neurobiology, Nencki Institute of Experimental Biology of Polish Academy of Sciences, 3 Pasteur Street, 02-093, Warsaw, Poland
| | - Władysław P Węglarz
- H. Niewodniczański Institute of Nuclear Physics of Polish Academy of Sciences, Radzikowskiego 152, 31-342, Kraków, Poland
| | - Daniel K Wójcik
- Laboratory of Neuroinformatics, Department of Neurophysiology, Nencki Institute of Experimental Biology of Polish Academy of Sciences, 3 Pasteur Street, 02-093, Warsaw, Poland
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Kozák LR, van Graan LA, Chaudhary UJ, Szabó ÁG, Lemieux L. ICN_Atlas: Automated description and quantification of functional MRI activation patterns in the framework of intrinsic connectivity networks. Neuroimage 2017; 163:319-341. [PMID: 28899742 PMCID: PMC5725313 DOI: 10.1016/j.neuroimage.2017.09.014] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2017] [Revised: 08/30/2017] [Accepted: 09/06/2017] [Indexed: 12/29/2022] Open
Abstract
Generally, the interpretation of functional MRI (fMRI) activation maps continues to rely on assessing their relationship to anatomical structures, mostly in a qualitative and often subjective way. Recently, the existence of persistent and stable brain networks of functional nature has been revealed; in particular these so-called intrinsic connectivity networks (ICNs) appear to link patterns of resting state and task-related state connectivity. These networks provide an opportunity of functionally-derived description and interpretation of fMRI maps, that may be especially important in cases where the maps are predominantly task-unrelated, such as studies of spontaneous brain activity e.g. in the case of seizure-related fMRI maps in epilepsy patients or sleep states. Here we present a new toolbox (ICN_Atlas) aimed at facilitating the interpretation of fMRI data in the context of ICN. More specifically, the new methodology was designed to describe fMRI maps in function-oriented, objective and quantitative way using a set of 15 metrics conceived to quantify the degree of 'engagement' of ICNs for any given fMRI-derived statistical map of interest. We demonstrate that the proposed framework provides a highly reliable quantification of fMRI activation maps using a publicly available longitudinal (test-retest) resting-state fMRI dataset. The utility of the ICN_Atlas is also illustrated on a parametric task-modulation fMRI dataset, and on a dataset of a patient who had repeated seizures during resting-state fMRI, confirmed on simultaneously recorded EEG. The proposed ICN_Atlas toolbox is freely available for download at http://icnatlas.com and at http://www.nitrc.org for researchers to use in their fMRI investigations.
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Affiliation(s)
- Lajos R Kozák
- MR Research Center, Semmelweis University, 1085, Budapest, Hungary.
| | - Louis André van Graan
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, University College London, WC1N 3BG, London, UK; Epilepsy Society, SL9 0RJ Chalfont St. Peter, Buckinghamshire, UK.
| | - Umair J Chaudhary
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, University College London, WC1N 3BG, London, UK; Epilepsy Society, SL9 0RJ Chalfont St. Peter, Buckinghamshire, UK.
| | | | - Louis Lemieux
- Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, University College London, WC1N 3BG, London, UK; Epilepsy Society, SL9 0RJ Chalfont St. Peter, Buckinghamshire, UK.
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Rondina JM, Ferreira LK, de Souza Duran FL, Kubo R, Ono CR, Leite CC, Smid J, Nitrini R, Buchpiguel CA, Busatto GF. Selecting the most relevant brain regions to discriminate Alzheimer's disease patients from healthy controls using multiple kernel learning: A comparison across functional and structural imaging modalities and atlases. Neuroimage Clin 2017; 17:628-641. [PMID: 29234599 PMCID: PMC5716956 DOI: 10.1016/j.nicl.2017.10.026] [Citation(s) in RCA: 33] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2016] [Revised: 10/12/2017] [Accepted: 10/24/2017] [Indexed: 12/11/2022]
Abstract
BACKGROUND Machine learning techniques such as support vector machine (SVM) have been applied recently in order to accurately classify individuals with neuropsychiatric disorders such as Alzheimer's disease (AD) based on neuroimaging data. However, the multivariate nature of the SVM approach often precludes the identification of the brain regions that contribute most to classification accuracy. Multiple kernel learning (MKL) is a sparse machine learning method that allows the identification of the most relevant sources for the classification. By parcelating the brain into regions of interest (ROI) it is possible to use each ROI as a source to MKL (ROI-MKL). METHODS We applied MKL to multimodal neuroimaging data in order to: 1) compare the diagnostic performance of ROI-MKL and whole-brain SVM in discriminating patients with AD from demographically matched healthy controls and 2) identify the most relevant brain regions to the classification. We used two atlases (AAL and Brodmann's) to parcelate the brain into ROIs and applied ROI-MKL to structural (T1) MRI, 18F-FDG-PET and regional cerebral blood flow SPECT (rCBF-SPECT) data acquired from the same subjects (20 patients with early AD and 18 controls). In ROI-MKL, each ROI received a weight (ROI-weight) that indicated the region's relevance to the classification. For each ROI, we also calculated whether there was a predominance of voxels indicating decreased or increased regional activity (for 18F-FDG-PET and rCBF-SPECT) or volume (for T1-MRI) in AD patients. RESULTS Compared to whole-brain SVM, the ROI-MKL approach resulted in better accuracies (with either atlas) for classification using 18F-FDG-PET (92.5% accuracy for ROI-MKL versus 84% for whole-brain), but not when using rCBF-SPECT or T1-MRI. Although several cortical and subcortical regions contributed to discrimination, high ROI-weights and predominance of hypometabolism and atrophy were identified specially in medial parietal and temporo-limbic cortical regions. Also, the weight of discrimination due to a pattern of increased voxel-weight values in AD individuals was surprisingly high (ranging from approximately 20% to 40% depending on the imaging modality), located mainly in primary sensorimotor and visual cortices and subcortical nuclei. CONCLUSION The MKL-ROI approach highlights the high discriminative weight of a subset of brain regions of known relevance to AD, the selection of which contributes to increased classification accuracy when applied to 18F-FDG-PET data. Moreover, the MKL-ROI approach demonstrates that brain regions typically spared in mild stages of AD also contribute substantially in the individual discrimination of AD patients from controls.
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Key Words
- 18F-FDG-PET, 18F-Fluorodeoxyglucose-Positron Emission Tomography
- AAL, Automated Anatomical Labeling (atlas)
- AD, Alzheimer's Disease
- Alzheimer's Disease
- BA, Brodmann's Area
- Brain atlas
- GM, Gray Matter
- MKL, Multiple Kernel Learning
- MKL-ROI, MKL based on regions of interest
- ML, Machine Learning
- MRI
- Multiple kernel learning
- NF, number of features
- NSR, Number of Selected Regions
- PET
- PVE, Partial Volume Effects
- ROI, Region of Interest
- SPECT
- SVM, Support Vector Machine
- T1-MRI, T1-weighted Magnetic Resonance Imaging
- TN, True Negative (specificity - proportion of healthy controls correctly classified)
- TP, True Positive (sensitivity - proportion of patients correctly classified)
- rAUC, Ratio between negative and positive Area Under Curve
- rCBF-SPECT, Regional Cerebral Blood Flow
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Affiliation(s)
- Jane Maryam Rondina
- Laboratory of Psychiatric Neuroimaging (LIM 21), Department of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil; Sobell Department of Motor Neuroscience and Movement Disorders, Institute of Neurology, University College London, London, UK.
| | - Luiz Kobuti Ferreira
- Laboratory of Psychiatric Neuroimaging (LIM 21), Department of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil; Núcleo de Apoio à Pesquisa em Neurociência Aplicada (NAPNA), University of São Paulo, São Paulo, Brazil
| | - Fabio Luis de Souza Duran
- Laboratory of Psychiatric Neuroimaging (LIM 21), Department of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil
| | - Rodrigo Kubo
- Department of Radiology and Oncology, University of São Paulo Medical School, São Paulo, Brazil
| | - Carla Rachel Ono
- Department of Radiology and Oncology, University of São Paulo Medical School, São Paulo, Brazil
| | - Claudia Costa Leite
- Department of Radiology and Oncology, University of São Paulo Medical School, São Paulo, Brazil; Department of Radiology, University of North Carolina at Chapel Hill, NC, USA
| | - Jerusa Smid
- Department of Neurology and Cognitive Disorders Reference Center (CEREDIC), University of São Paulo, São Paulo, Brazil
| | - Ricardo Nitrini
- Department of Neurology and Cognitive Disorders Reference Center (CEREDIC), University of São Paulo, São Paulo, Brazil
| | | | - Geraldo F Busatto
- Laboratory of Psychiatric Neuroimaging (LIM 21), Department of Psychiatry, Faculty of Medicine, University of São Paulo, São Paulo, Brazil; Núcleo de Apoio à Pesquisa em Neurociência Aplicada (NAPNA), University of São Paulo, São Paulo, Brazil; Department and Institute of Psychiatry, University of São Paulo, São Paulo, Brazil
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Aswendt M, Schwarz M, Abdelmoula WM, Dijkstra J, Dedeurwaerdere S. Whole-Brain Microscopy Meets In Vivo Neuroimaging: Techniques, Benefits, and Limitations. Mol Imaging Biol 2017; 19:1-9. [PMID: 27590493 DOI: 10.1007/s11307-016-0988-z] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/22/2023]
Abstract
Magnetic resonance imaging, positron emission tomography, and optical imaging have emerged as key tools to understand brain function and neurological disorders in preclinical mouse models. They offer the unique advantage of monitoring individual structural and functional changes over time. What remained unsolved until recently was to generate whole-brain microscopy data which can be correlated to the 3D in vivo neuroimaging data. Conventional histological sections are inappropriate especially for neuronal tracing or the unbiased screening for molecular targets through the whole brain. As part of the European Society for Molecular Imaging (ESMI) meeting 2016 in Utrecht, the Netherlands, we addressed this issue in the Molecular Neuroimaging study group meeting. Presentations covered new brain clearing methods, light sheet microscopes for large samples, and automatic registration of microscopy to in vivo imaging data. In this article, we summarize the discussion; give an overview of the novel techniques; and discuss the practical needs, benefits, and limitations.
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Azimi N, Yadollahikhales G, Argenti JP, Cunningham MG. Discrepancies in stereotaxic coordinate publications and improving precision using an animal-specific atlas. J Neurosci Methods 2017; 284:15-20. [PMID: 28392415 DOI: 10.1016/j.jneumeth.2017.03.019] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [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: 01/18/2017] [Revised: 03/24/2017] [Accepted: 03/27/2017] [Indexed: 10/19/2022]
Abstract
Rodent brain atlases have traditionally been used to identify brain structures in three-dimensional space for a variety of stereotaxic procedures. As neuroscience becomes increasingly sophisticated, higher levels of precision and consistency are needed. Observations of various atlases currently in use across labs reveal numerous coordinate discrepancies. Here we provide examples of inconsistencies by comparing the coordinates of the boundaries of various brain structures across six atlas publications. We conclude that the coordinates determined by any particular atlas should be considered as only a first approximation of the actual target coordinates for the experimental animal for a particular study. Furthermore, the coordinates determined by one research team cannot be assumed to be universally applicable and accurate in other experimental settings. To optimize precision, we describe a simple protocol for the construction of a customized atlas that is specific to the surgical approach and to the species, gender, and age of the animal used in any given study.
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Affiliation(s)
- Nima Azimi
- Laboratory for Neural Reconstruction, McLean Hospital Department of Psychiatry, 115 Mill Street Belmont, MA 02478, United States
| | - Golnaz Yadollahikhales
- Laboratory for Neural Reconstruction, McLean Hospital Department of Psychiatry, 115 Mill Street Belmont, MA 02478, United States
| | - John Paul Argenti
- Laboratory for Neural Reconstruction, McLean Hospital Department of Psychiatry, 115 Mill Street Belmont, MA 02478, United States
| | - Miles G Cunningham
- Laboratory for Neural Reconstruction, McLean Hospital Department of Psychiatry, 115 Mill Street Belmont, MA 02478, United States.
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Zhang J, Zhang L, Xiang L, Shao Y, Wu G, Zhou X, Shen D, Wang Q. Brain Atlas Fusion from High-Thickness Diagnostic Magnetic Resonance Images by Learning-Based Super-Resolution. Pattern Recognit 2017; 63:531-541. [PMID: 29062159 PMCID: PMC5650249 DOI: 10.1016/j.patcog.2016.09.019] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
It is fundamentally important to fuse the brain atlas from magnetic resonance (MR) images for many imaging-based studies. Most existing works focus on fusing the atlases from high-quality MR images. However, for low-quality diagnostic images (i.e., with high inter-slice thickness), the problem of atlas fusion has not been addressed yet. In this paper, we intend to fuse the brain atlas from the high-thickness diagnostic MR images that are prevalent for clinical routines. The main idea of our works is to extend the conventional groupwise registration by incorporating a novel super-resolution strategy. The contribution of the proposed super-resolution framework is two-fold. First, each high-thickness subject image is reconstructed to be isotropic by the patch-based sparsity learning. Then, the reconstructed isotropic image is enhanced for better quality through the random-forest-based regression model. In this way, the images obtained by the super-resolution strategy can be fused together by applying the groupwise registration method to construct the required atlas. Our experiments have shown that the proposed framework can effectively solve the problem of atlas fusion from the low-quality brain MR images.
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Affiliation(s)
- Jinpeng Zhang
- Med-X Research Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Lichi Zhang
- Med-X Research Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States
| | - Lei Xiang
- Med-X Research Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
| | - Yeqin Shao
- Nantong University, Nantong, Jiangsu 226019, China
| | - Guorong Wu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States
| | - Xiaodong Zhou
- Shanghai United Imaging Healthcare Co., Ltd., Shanghai 201815, China
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, United States
- Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea
| | - Qian Wang
- Med-X Research Institute, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
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Bjarkam CR, Glud AN, Orlowski D, Sørensen JCH, Palomero-Gallagher N. The telencephalon of the Göttingen minipig, cytoarchitecture and cortical surface anatomy. Brain Struct Funct 2017; 222:2093-114. [PMID: 27778106 DOI: 10.1007/s00429-016-1327-5] [Citation(s) in RCA: 31] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2016] [Accepted: 10/15/2016] [Indexed: 12/19/2022]
Abstract
During the last 20 years pigs have become increasingly popular in large animal translational neuroscience research as an economical and ethical feasible substitute to non-human primates. The anatomy of the pig telencephalon is, however, not well known. We present, accordingly, a detailed description of the surface anatomy and cytoarchitecture of the Göttingen minipig telencephalon based on macrophotos and consecutive high-power microphotographs of 15 μm thick paraffin embedded Nissl-stained coronal sections. In 1-year-old specimens the formalin perfused brain measures approximately 55 × 47 × 36 mm (length, width, height) and weighs around 69 g. The telencephalic part of the Göttingen minipig cerebrum covers a large surface area, which can be divided into a neocortical gyrencephalic part located dorsal to the rhinal fissure, and a ventral subrhinal part dominated by olfactory, amygdaloid, septal, and hippocampal structures. This part of the telencephalon is named the subrhinal lobe, and based on cytoarchitectural and sulcal anatomy, can be discerned from the remaining dorsally located neocortical perirhinal/insular, pericallosal, frontal, parietal, temporal, and occipital lobes. The inner subcortical structure of the minipig telencephalon is dominated by a prominent ventricular system and large basal ganglia, wherein the putamen and the caudate nucleus posterior and dorsally are separated into two entities by the internal capsule, whereas both structures ventrally fuse into a large accumbens nucleus. The presented anatomical data is accompanied by surface renderings and high-power macrophotographs illustrating the telencephalic sulcal pattern, and the localization of the identified lobes and cytoarchitectonic areas. Additionally, 24 representative Nissl-stained telencephalic coronal sections are presented as supplementary material in atlas form on http://www.cense.dk/minipig_atlas/index.html and referred to as S1-S24 throughout the manuscript.
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Abstract
Techniques based on imaging serial sections of brain tissue provide insight into brain structure and function. However, to compare or combine them with results from three dimensional imaging methods, reconstruction into a volumetric form is required. Currently, there are no tools for performing such a task in a streamlined way. Here we propose the Possum volumetric reconstruction framework which provides a selection of 2D to 3D image reconstruction routines allowing one to build workflows tailored to one's specific requirements. The main components include routines for reconstruction with or without using external reference and solutions for typical issues encountered during the reconstruction process, such as propagation of the registration errors due to distorted sections. We validate the implementation using synthetic datasets and actual experimental imaging data derived from publicly available resources. We also evaluate efficiency of a subset of the algorithms implemented. The Possum framework is distributed under MIT license and it provides researchers with a possibility of building reconstruction workflows from existing components, without the need for low-level implementation. As a consequence, it also facilitates sharing and data exchange between researchers and laboratories.
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Affiliation(s)
- Piotr Majka
- />Nencki Institute of Experimental Biology, 3 Pasteur Street, 02-093 Warsaw, Poland
- />Department of Physiology, Monash University, Clayton, Victoria 3800 Australia
| | - Daniel K. Wójcik
- />Nencki Institute of Experimental Biology, 3 Pasteur Street, 02-093 Warsaw, Poland
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Abstract
Human brain atlases, although prevalent in medical education and stereotactic and functional neurosurgery, are not yet applied practically in neuroradiology. In a step towards introducing brain atlases to neuroradiology, we discuss nine different situations of potential atlas use: (1) to support interpretation of brain scans with clearly visible structures (to increase confidence of non-neuroradiologists); (2) to delineate and label scans of low anatomical content (with indiscernible or poorly visible anatomy); (3) to assist in generating the structured report; (4) to assist in interpreting small deep lesions, since an atlas's anatomical parcellation is higher than that of the interpreted scan; (5) to approximate distorted due to pathology (and unknown to the interpreter) anatomy and label it; (6) to cope with data explosion; (7) to assist in the interpretation of functional scans (to label the activation foci with the underlying anatomy and Brodmann's areas); (8) to support ischemic stroke image handling by means of atlases of anatomy and blood supply territories; and (9) to communicate image interpretation results (diagnosis) to others. The usefulness of the atlas for automatic structure identification, localisation, delineation, labelling and quantification, as well as for reporting and communication, potentially increases the interpreter's efficiency and confidence, as well as expedites image interpretation.
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Affiliation(s)
- Wieslaw L Nowinski
- John Paul II Center for Virtual Anatomy and Surgical Simulation, Cardinal Stefan Wyszynski University, Poland
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Miller LE, Urban JE, Stitzel JD. Development and validation of an atlas-based finite element brain model. Biomech Model Mechanobiol 2016; 15:1201-14. [PMID: 26762217 DOI: 10.1007/s10237-015-0754-1] [Citation(s) in RCA: 66] [Impact Index Per Article: 8.3] [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: 07/13/2015] [Accepted: 12/21/2015] [Indexed: 11/30/2022]
Abstract
Traumatic brain injury is a leading cause of disability and injury-related death. To enhance our ability to prevent such injuries, brain response can be studied using validated finite element (FE) models. In the current study, a high-resolution, anatomically accurate FE model was developed from the International Consortium for Brain Mapping brain atlas. Due to wide variation in published brain material parameters, optimal brain properties were identified using a technique called Latin hypercube sampling, which optimized material properties against three experimental cadaver tests to achieve ideal biomechanics. Additionally, falx pretension and thickness were varied in a lateral impact variation. The atlas-based brain model (ABM) was subjected to the boundary conditions from three high-rate experimental cadaver tests with different material parameter combinations. Local displacements, determined experimentally using neutral density targets, were compared to displacements predicted by the ABM at the same locations. Error between the observed and predicted displacements was quantified using CORrelation and Analysis (CORA), an objective signal rating method that evaluates the correlation of two curves. An average CORA score was computed for each variation and maximized to identify the optimal combination of parameters. The strongest relationships between CORA and material parameters were observed for the shear parameters. Using properties obtained through the described multiobjective optimization, the ABM was validated in three impact configurations and shows good agreement with experimental data. The final model developed in this study consists of optimized brain material properties and was validated in three cadaver impacts against local brain displacement data.
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Affiliation(s)
- Logan E Miller
- Wake Forest Center for Injury Biomechanics, 575 Patterson Ave., Suite 120, Winston-Salem, NC, 27101, USA
| | - Jillian E Urban
- Wake Forest Center for Injury Biomechanics, 575 Patterson Ave., Suite 120, Winston-Salem, NC, 27101, USA
| | - Joel D Stitzel
- Wake Forest Center for Injury Biomechanics, 575 Patterson Ave., Suite 120, Winston-Salem, NC, 27101, USA
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Schubert R, Frank F, Nagelmann N, Liebsch L, Schuldenzucker V, Schramke S, Wirsig M, Johnson H, Kim EY, Ott S, Hölzner E, Demokritov SO, Motlik J, Faber C, Reilmann R. Neuroimaging of a minipig model of Huntington's disease: Feasibility of volumetric, diffusion-weighted and spectroscopic assessments. J Neurosci Methods 2015; 265:46-55. [PMID: 26658298 DOI: 10.1016/j.jneumeth.2015.11.017] [Citation(s) in RCA: 16] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2015] [Revised: 11/19/2015] [Accepted: 11/20/2015] [Indexed: 12/17/2022]
Abstract
BACKGROUND As novel treatment approaches for Huntington's disease (HD) evolve, the use of transgenic (tg) large animal models has been considered for preclinical safety and efficacy assessments. It is hoped that large animal models may provide higher reliability in translating preclinical findings to humans, e.g., by using similar endpoints and biomarkers. NEW METHOD We here investigated the feasibility to conduct MRI assessments in a recently developed tgHD model in the Libechov minipig. The model is characterized by high genetic homology to humans and a similar body mass and compartments. The minipig brain provides anatomical features that are attractive for imaging studies and could be used as endpoints for disease modifying preclinical studies similar to human HD. RESULTS We demonstrate that complex MRI protocols can be successfully acquired with tgHD and wild type (wt) Libechov minipigs. We show that acquisition of anatomical images applicable for volumetric assessments is feasible and outline the development of a segmented MRI brain atlas. Similarly diffusion-weighted imaging (DWI) including fiber tractography is presented. We also demonstrate the feasibility to conduct in vivo metabolic assessments using MR spectroscopy. COMPARISON WITH EXISTING METHODS In human HD, these MRI methods are already validated and used as reliable biomarker of disease progression even before the onset of a clinical motor phenotype. CONCLUSIONS The results show that the minipig brain is well suited for MRI assessments in preclinical studies. We conclude that further characterization of phenotypical differences between tg and wt animals in sufficiently powered cross-sectional and longitudinal studies is warranted.
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Affiliation(s)
- Robin Schubert
- George-Huntington-Institute, Technology Park, Johann-Krane-Weg 27, 48149 Muenster, Germany
| | - Frauke Frank
- George-Huntington-Institute, Technology Park, Johann-Krane-Weg 27, 48149 Muenster, Germany; Dept of Radiology, University of Muenster, Albert-Schweitzer Campus 1, 48149 Muenster, Germany
| | - Nina Nagelmann
- Dept of Radiology, University of Muenster, Albert-Schweitzer Campus 1, 48149 Muenster, Germany
| | - Lennart Liebsch
- Dept of Radiology, University of Muenster, Albert-Schweitzer Campus 1, 48149 Muenster, Germany
| | - Verena Schuldenzucker
- George-Huntington-Institute, Technology Park, Johann-Krane-Weg 27, 48149 Muenster, Germany
| | - Sarah Schramke
- George-Huntington-Institute, Technology Park, Johann-Krane-Weg 27, 48149 Muenster, Germany; Institute for Animal Hygiene, Animal Welfare and Farm Animal Behaviour, University of Veterinary Medicine Hannover, Bischofsholer Damm 15, 30173 Hannover, Germany
| | - Maike Wirsig
- George-Huntington-Institute, Technology Park, Johann-Krane-Weg 27, 48149 Muenster, Germany
| | - Hans Johnson
- Dept of Psychiatry, University of Iowa, IowaCity, IA, USA; Electrical and Computer Engineering, University of Iowa, IowaCity, IA, USA
| | - Eun Young Kim
- Dept of Psychiatry, University of Iowa, IowaCity, IA, USA
| | - Stefanie Ott
- George-Huntington-Institute, Technology Park, Johann-Krane-Weg 27, 48149 Muenster, Germany
| | - Eva Hölzner
- George-Huntington-Institute, Technology Park, Johann-Krane-Weg 27, 48149 Muenster, Germany
| | - Sergej O Demokritov
- Department of Physics and Center for Nonlinear Science, University of Muenster, Germany
| | - Jan Motlik
- Laboratory of Cell Regeneration and Plasticity, Institute of Animal Physiology and Genetics, v.v.i., AS CR, Libechov, Czech Republic
| | - Cornelius Faber
- Dept of Radiology, University of Muenster, Albert-Schweitzer Campus 1, 48149 Muenster, Germany
| | - Ralf Reilmann
- George-Huntington-Institute, Technology Park, Johann-Krane-Weg 27, 48149 Muenster, Germany; Dept of Radiology, University of Muenster, Albert-Schweitzer Campus 1, 48149 Muenster, Germany; Department of Neurology, University of Munster, Germany; Department of Neurodegenerative Diseases and Hertie-Institute for Clinical Brain Research, University of Tuebingen, Hoppe-Seyler Str. 3, 72076 Tuebingen, Germany.
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Calabrese E, Badea A, Coe CL, Lubach GR, Shi Y, Styner MA, Johnson GA. A diffusion tensor MRI atlas of the postmortem rhesus macaque brain. Neuroimage 2015; 117:408-16. [PMID: 26037056 DOI: 10.1016/j.neuroimage.2015.05.072] [Citation(s) in RCA: 119] [Impact Index Per Article: 13.2] [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: 02/09/2015] [Revised: 04/22/2015] [Accepted: 05/24/2015] [Indexed: 12/27/2022] Open
Abstract
The rhesus macaque (Macaca mulatta) is the most widely used nonhuman primate for modeling the structure and function of the brain. Brain atlases, and particularly those based on magnetic resonance imaging (MRI), have become important tools for understanding normal brain structure, and for identifying structural abnormalities resulting from disease states, exposures, and/or aging. Diffusion tensor imaging (DTI)-based MRI brain atlases are widely used in both human and macaque brain imaging studies because of the unique contrasts, quantitative diffusion metrics, and diffusion tractography that they can provide. Previous MRI and DTI atlases of the rhesus brain have been limited by low contrast and/or low spatial resolution imaging. Here we present a microscopic resolution MRI/DTI atlas of the rhesus brain based on 10 postmortem brain specimens. The atlas includes both structural MRI and DTI image data, a detailed three-dimensional segmentation of 241 anatomic structures, diffusion tractography, cortical thickness estimates, and maps of anatomic variability among atlas specimens. This atlas incorporates many useful features from previous work, including anatomic label nomenclature and ontology, data orientation, and stereotaxic reference frame, and further extends prior analyses with the inclusion of high-resolution multi-contrast image data.
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Affiliation(s)
- Evan Calabrese
- Center for In Vivo Microscopy, Department of Radiology, Duke University Medical Center, Durham, NC 27710, USA
| | - Alexandra Badea
- Center for In Vivo Microscopy, Department of Radiology, Duke University Medical Center, Durham, NC 27710, USA
| | - Christopher L Coe
- Harlow Center for Biological Psychology, University of Wisconsin, Madison, WI 53715, USA
| | - Gabriele R Lubach
- Harlow Center for Biological Psychology, University of Wisconsin, Madison, WI 53715, USA
| | - Yundi Shi
- Department of Computer Science, Department of Psychiatry, University of North Carolina, Chapel Hill, NC 27599, USA
| | - Martin A Styner
- Department of Computer Science, Department of Psychiatry, University of North Carolina, Chapel Hill, NC 27599, USA
| | - G Allan Johnson
- Center for In Vivo Microscopy, Department of Radiology, Duke University Medical Center, Durham, NC 27710, USA.
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Dinh C, Strohmeier D, Luessi M, Güllmar D, Baumgarten D, Haueisen J, Hämäläinen MS. Real-Time MEG Source Localization Using Regional Clustering. Brain Topogr 2015; 28:771-84. [PMID: 25782980 DOI: 10.1007/s10548-015-0431-9] [Citation(s) in RCA: 10] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2014] [Accepted: 03/09/2015] [Indexed: 10/23/2022]
Abstract
With its millisecond temporal resolution, Magnetoencephalography (MEG) is well suited for real-time monitoring of brain activity. Real-time feedback allows the adaption of the experiment to the subject's reaction and increases time efficiency by shortening acquisition and off-line analysis. Two formidable challenges exist in real-time analysis: the low signal-to-noise ratio (SNR) and the limited time available for computations. Since the low SNR reduces the number of distinguishable sources, we propose an approach which downsizes the source space based on a cortical atlas and allows to discern the sources in the presence of noise. Each cortical region is represented by a small set of dipoles, which is obtained by a clustering algorithm. Using this approach, we adapted dynamic statistical parametric mapping for real-time source localization. In terms of point spread and crosstalk between regions the proposed clustering technique performs better than selecting spatially evenly distributed dipoles. We conducted real-time source localization on MEG data from an auditory experiment. The results demonstrate that the proposed real-time method localizes sources reliably in the superior temporal gyrus. We conclude that real-time source estimation based on MEG is a feasible, useful addition to the standard on-line processing methods, and enables feedback based on neural activity during the measurements.
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Nowinski WL. Toward the holistic, reference, and extendable atlas of the human brain, head, and neck. Brain Inform 2015; 2:65-76. [PMID: 27747483 DOI: 10.1007/s40708-015-0012-4] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2015] [Accepted: 01/29/2015] [Indexed: 12/16/2022] Open
Abstract
Despite numerous efforts, a fairly complete (holistic) anatomical model of the whole, normal, adult human brain, which is required as the reference in brain studies and clinical applications, has not yet been constructed. Our ultimate objective is to build this kind of atlas from advanced in vivo imaging. This work presents the taxonomy of our currently developed brain atlases and addresses the design, content, functionality, and current results in the holistic atlas development as well as atlas usefulness and future directions. We have developed to date 35 commercial brain atlases (along with numerous research prototypes), licensed to 63 companies and institutions, and made available to medical societies, organizations, medical schools, and individuals. These atlases have been applied in education, research, and clinical applications. Hundreds of thousands of patients have been treated by using our atlases. Based on this experience, the first version of the holistic and reference atlas of the brain, head, and neck has been developed and made available. The atlas has been created from multispectral 3 and 7 Tesla and high-resolution CT in vivo scans. It is fully 3D, scalable, interactive, and highly detailed with about 3,000 labeled components. This atlas forms a foundation for the development of a multi-level molecular, cellular, anatomical, physiological, and behavioral brain atlas platform.
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