1
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Edlow BL, Olchanyi M, Freeman HJ, Li J, Maffei C, Snider SB, Zöllei L, Iglesias JE, Augustinack J, Bodien YG, Haynes RL, Greve DN, Diamond BR, Stevens A, Giacino JT, Destrieux C, van der Kouwe A, Brown EN, Folkerth RD, Fischl B, Kinney HC. Multimodal MRI reveals brainstem connections that sustain wakefulness in human consciousness. Sci Transl Med 2024; 16:eadj4303. [PMID: 38691619 DOI: 10.1126/scitranslmed.adj4303] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2023] [Accepted: 04/10/2024] [Indexed: 05/03/2024]
Abstract
Consciousness is composed of arousal (i.e., wakefulness) and awareness. Substantial progress has been made in mapping the cortical networks that underlie awareness in the human brain, but knowledge about the subcortical networks that sustain arousal in humans is incomplete. Here, we aimed to map the connectivity of a proposed subcortical arousal network that sustains wakefulness in the human brain, analogous to the cortical default mode network (DMN) that has been shown to contribute to awareness. We integrated data from ex vivo diffusion magnetic resonance imaging (MRI) of three human brains, obtained at autopsy from neurologically normal individuals, with immunohistochemical staining of subcortical brain sections. We identified nodes of the proposed default ascending arousal network (dAAN) in the brainstem, hypothalamus, thalamus, and basal forebrain. Deterministic and probabilistic tractography analyses of the ex vivo diffusion MRI data revealed projection, association, and commissural pathways linking dAAN nodes with one another and with DMN nodes. Complementary analyses of in vivo 7-tesla resting-state functional MRI data from the Human Connectome Project identified the dopaminergic ventral tegmental area in the midbrain as a widely connected hub node at the nexus of the subcortical arousal and cortical awareness networks. Our network-based autopsy methods and connectivity data provide a putative neuroanatomic architecture for the integration of arousal and awareness in human consciousness.
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Affiliation(s)
- Brian L Edlow
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA
| | - Mark Olchanyi
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - Holly J Freeman
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA
| | - Jian Li
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA
| | - Chiara Maffei
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA
| | - Samuel B Snider
- Department of Neurology, Brigham and Women's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Lilla Zöllei
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA
| | - J Eugenio Iglesias
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA
| | - Jean Augustinack
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA
| | - Yelena G Bodien
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital and Harvard Medical School, Charlestown, MA 02129, USA
| | - Robin L Haynes
- Department of Pathology, Boston Children's Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Douglas N Greve
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA
| | - Bram R Diamond
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA
| | - Allison Stevens
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA
| | - Joseph T Giacino
- Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital and Harvard Medical School, Charlestown, MA 02129, USA
| | - Christophe Destrieux
- UMR 1253, iBrain, Université de Tours, Inserm, 10 Boulevard Tonnellé, 37032, Tours, France
- CHRU de Tours, 2 Boulevard Tonnellé, Tours, France
| | - Andre van der Kouwe
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA
| | - Emery N Brown
- Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | | | - Bruce Fischl
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA 02129, USA
| | - Hannah C Kinney
- Department of Pathology, Boston Children's Hospital and Harvard Medical School, Boston, MA 02115, USA
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2
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Bryant KL, Manger PR, Bertelsen MF, Khrapitchev AA, Sallet J, Benn RA, Mars RB. A map of white matter tracts in a lesser ape, the lar gibbon. Brain Struct Funct 2023:10.1007/s00429-023-02709-9. [PMID: 37904002 DOI: 10.1007/s00429-023-02709-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2023] [Accepted: 09/01/2023] [Indexed: 11/01/2023]
Abstract
The recent development of methods for constructing directly comparable white matter atlases in primate brains from diffusion MRI allows us to probe specializations unique to humans, great apes, and other primate taxa. Here, we constructed the first white matter atlas of a lesser ape using an ex vivo diffusion-weighted scan of a brain from a young adult (5.5 years) male lar gibbon. We find that white matter architecture of the gibbon temporal lobe suggests specializations that are reminiscent of those previously reported for great apes, specifically, the expansion of the arcuate fasciculus and the inferior longitudinal fasciculus in the temporal lobe. Our findings suggest these white matter expansions into the temporal lobe were present in the last common ancestor to hominoids approximately 16 million years ago and were further modified in the great ape and human lineages. White matter atlases provide a useful resource for identifying neuroanatomical differences and similarities between humans and other primate species and provide insight into the evolutionary variation and stasis of brain organization.
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Affiliation(s)
- Katherine L Bryant
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK.
- Laboratoire de Psychologie Cognitive, Aix-Marseille Université, Marseille, France.
| | - Paul R Manger
- School of Anatomical Sciences, Faculty of Health Sciences, University of Witwatersrand, Johannesburg, South Africa
| | - Mads F Bertelsen
- Centre for Zoo and Wild Animal Health, Copenhagen Zoo, Frederiksberg, Denmark
| | | | - Jérôme Sallet
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
- Stem Cell and Brain Research Institute, Université Lyon 1, Inserm, Bron, France
| | - R Austin Benn
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
- Integrative Neuroscience and Cognition Center, Université de Paris, CNRS, Paris, France
| | - Rogier B Mars
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
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3
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Girard G, Rafael-Patiño J, Truffet R, Aydogan DB, Adluru N, Nair VA, Prabhakaran V, Bendlin BB, Alexander AL, Bosticardo S, Gabusi I, Ocampo-Pineda M, Battocchio M, Piskorova Z, Bontempi P, Schiavi S, Daducci A, Stafiej A, Ciupek D, Bogusz F, Pieciak T, Frigo M, Sedlar S, Deslauriers-Gauthier S, Kojčić I, Zucchelli M, Laghrissi H, Ji Y, Deriche R, Schilling KG, Landman BA, Cacciola A, Basile GA, Bertino S, Newlin N, Kanakaraj P, Rheault F, Filipiak P, Shepherd TM, Lin YC, Placantonakis DG, Boada FE, Baete SH, Hernández-Gutiérrez E, Ramírez-Manzanares A, Coronado-Leija R, Stack-Sánchez P, Concha L, Descoteaux M, Mansour L S, Seguin C, Zalesky A, Marshall K, Canales-Rodríguez EJ, Wu Y, Ahmad S, Yap PT, Théberge A, Gagnon F, Massi F, Fischi-Gomez E, Gardier R, Haro JLV, Pizzolato M, Caruyer E, Thiran JP. Tractography passes the test: Results from the diffusion-simulated connectivity (disco) challenge. Neuroimage 2023; 277:120231. [PMID: 37330025 PMCID: PMC10771037 DOI: 10.1016/j.neuroimage.2023.120231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 05/12/2023] [Accepted: 06/14/2023] [Indexed: 06/19/2023] Open
Abstract
Estimating structural connectivity from diffusion-weighted magnetic resonance imaging is a challenging task, partly due to the presence of false-positive connections and the misestimation of connection weights. Building on previous efforts, the MICCAI-CDMRI Diffusion-Simulated Connectivity (DiSCo) challenge was carried out to evaluate state-of-the-art connectivity methods using novel large-scale numerical phantoms. The diffusion signal for the phantoms was obtained from Monte Carlo simulations. The results of the challenge suggest that methods selected by the 14 teams participating in the challenge can provide high correlations between estimated and ground-truth connectivity weights, in complex numerical environments. Additionally, the methods used by the participating teams were able to accurately identify the binary connectivity of the numerical dataset. However, specific false positive and false negative connections were consistently estimated across all methods. Although the challenge dataset doesn't capture the complexity of a real brain, it provided unique data with known macrostructure and microstructure ground-truth properties to facilitate the development of connectivity estimation methods.
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Affiliation(s)
- Gabriel Girard
- CIBM Center for Biomedical Imaging, Switzerland; Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland; Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
| | - Jonathan Rafael-Patiño
- Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland; Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
| | - Raphaël Truffet
- Univ Rennes, Inria, CNRS, Inserm, IRISA UMR 6074, Empenn ERL U-1228, Rennes, France
| | - Dogu Baran Aydogan
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland; Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland; Department of Psychiatry, Helsinki University Hospital, Helsinki, Finland
| | - Nagesh Adluru
- Waisman Center, University of Wisconsin-Madison, Madison, WI, United States; Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States
| | - Veena A Nair
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States
| | - Vivek Prabhakaran
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States
| | - Barbara B Bendlin
- Department of Medicine, University of Wisconsin-Madison, Madison, WI, United States
| | - Andrew L Alexander
- Waisman Center, University of Wisconsin-Madison, Madison, WI, United States; Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, United States; Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, United States
| | - Sara Bosticardo
- Diffusion Imaging and Connectivity Estimation (DICE) Lab, Department of Computer Science, University of Verona, Verona, Italy; Translational Imaging in Neurology (ThINk), Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Ilaria Gabusi
- Diffusion Imaging and Connectivity Estimation (DICE) Lab, Department of Computer Science, University of Verona, Verona, Italy; Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
| | - Mario Ocampo-Pineda
- Diffusion Imaging and Connectivity Estimation (DICE) Lab, Department of Computer Science, University of Verona, Verona, Italy
| | - Matteo Battocchio
- Diffusion Imaging and Connectivity Estimation (DICE) Lab, Department of Computer Science, University of Verona, Verona, Italy; Sherbrooke Connectivity Imaging Laboratory (SCIL), Department of Computer Science, University of Sherbrooke, Sherbrooke, QC, Canada
| | - Zuzana Piskorova
- Diffusion Imaging and Connectivity Estimation (DICE) Lab, Department of Computer Science, University of Verona, Verona, Italy; Brno Faculty of Electrical Engineering and Communication, Department of mathematics, University of Technology, Brno, Czech Republic
| | - Pietro Bontempi
- Diffusion Imaging and Connectivity Estimation (DICE) Lab, Department of Computer Science, University of Verona, Verona, Italy
| | - Simona Schiavi
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy
| | - Alessandro Daducci
- Diffusion Imaging and Connectivity Estimation (DICE) Lab, Department of Computer Science, University of Verona, Verona, Italy
| | | | - Dominika Ciupek
- Sano Centre for Computational Personalised Medicine, Kraków, Poland
| | - Fabian Bogusz
- AGH University of Science and Technology, Kraków, Poland
| | - Tomasz Pieciak
- AGH University of Science and Technology, Kraków, Poland; Laboratorio de Procesado de Imagen (LPI), ETSI Telecomunicación, Universidad de Valladolid, Valladolid, Spain
| | - Matteo Frigo
- Athena Project Team, Centre Inria d'Université Côte d'Azur, France
| | - Sara Sedlar
- Athena Project Team, Centre Inria d'Université Côte d'Azur, France
| | | | - Ivana Kojčić
- Athena Project Team, Centre Inria d'Université Côte d'Azur, France
| | - Mauro Zucchelli
- Athena Project Team, Centre Inria d'Université Côte d'Azur, France
| | - Hiba Laghrissi
- Athena Project Team, Centre Inria d'Université Côte d'Azur, France; Institut de Biologie de Valrose, Université Côte d'Azur, Nice, France
| | - Yang Ji
- Athena Project Team, Centre Inria d'Université Côte d'Azur, France
| | - Rachid Deriche
- Athena Project Team, Centre Inria d'Université Côte d'Azur, France
| | - Kurt G Schilling
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Bennett A Landman
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States; Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, United States
| | - Alberto Cacciola
- Brain Mapping Lab, Department of Biomedical, Dental Sciences and Morphological and Functional Images, University of Messina, Messina, Italy; Center for Complex Network Intelligence (CCNI), Tsinghua Laboratory of Brain and Intelligence (THBI), Tsinghua University, Beijing, China; Department of Biomedical Engineering, Tsinghua University, Beijing, China
| | - Gianpaolo Antonio Basile
- Brain Mapping Lab, Department of Biomedical, Dental Sciences and Morphological and Functional Images, University of Messina, Messina, Italy
| | - Salvatore Bertino
- Brain Mapping Lab, Department of Biomedical, Dental Sciences and Morphological and Functional Images, University of Messina, Messina, Italy
| | - Nancy Newlin
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, United States
| | - Praitayini Kanakaraj
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, United States
| | - Francois Rheault
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, United States
| | - Patryk Filipiak
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, NYU Langone Health, New York, NY, United States
| | - Timothy M Shepherd
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, NYU Langone Health, New York, NY, United States
| | - Ying-Chia Lin
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, NYU Langone Health, New York, NY, United States
| | - Dimitris G Placantonakis
- Department of Neurosurgery, Perlmutter Cancer Center, Neuroscience Institute, Kimmel Center for Stem Cell Biology, NYU Langone Health, New York, NY, United States
| | - Fernando E Boada
- Department of Radiology, Stanford University, Stanford, CA, United States
| | - Steven H Baete
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, NYU Langone Health, New York, NY, United States
| | - Erick Hernández-Gutiérrez
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Department of Computer Science, University of Sherbrooke, Sherbrooke, QC, Canada
| | | | - Ricardo Coronado-Leija
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, NYU Langone Health, New York, NY, United States
| | - Pablo Stack-Sánchez
- Computer Science Department, Centro de Investigación en Matemáticas A.C, Guanajuato, México
| | - Luis Concha
- Instituto de Neurobiología, Universidad Nacional Autónoma de México, Juriquilla, Querétaro, México
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Department of Computer Science, University of Sherbrooke, Sherbrooke, QC, Canada
| | - Sina Mansour L
- Department of Biomedical Engineering, The University of Melbourne, Parkville, Victoria, Australia; Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Parkville, Victoria, Australia
| | - Caio Seguin
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Parkville, Victoria, Australia; School of Biomedical Engineering, The University of Sydney, Sydney, Australia; Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, United States
| | - Andrew Zalesky
- Department of Biomedical Engineering, The University of Melbourne, Parkville, Victoria, Australia; Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Parkville, Victoria, Australia
| | - Kenji Marshall
- Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; McGill University, Montréal, QC, Canada
| | - Erick J Canales-Rodríguez
- Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Ye Wu
- Department of Radiology and Biomedical Research Imaging Center (BRIC), The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States; School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China
| | - Sahar Ahmad
- Department of Radiology and Biomedical Research Imaging Center (BRIC), The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Pew-Thian Yap
- Department of Radiology and Biomedical Research Imaging Center (BRIC), The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Antoine Théberge
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Department of Computer Science, University of Sherbrooke, Sherbrooke, QC, Canada
| | - Florence Gagnon
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Department of Computer Science, University of Sherbrooke, Sherbrooke, QC, Canada
| | - Frédéric Massi
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Department of Computer Science, University of Sherbrooke, Sherbrooke, QC, Canada
| | - Elda Fischi-Gomez
- CIBM Center for Biomedical Imaging, Switzerland; Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland; Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Rémy Gardier
- Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Juan Luis Villarreal Haro
- Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Marco Pizzolato
- Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Emmanuel Caruyer
- Univ Rennes, Inria, CNRS, Inserm, IRISA UMR 6074, Empenn ERL U-1228, Rennes, France
| | - Jean-Philippe Thiran
- CIBM Center for Biomedical Imaging, Switzerland; Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland; Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
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4
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Edlow BL, Olchanyi M, Freeman HJ, Li J, Maffei C, Snider SB, Zöllei L, Iglesias JE, Augustinack J, Bodien YG, Haynes RL, Greve DN, Diamond BR, Stevens A, Giacino JT, Destrieux C, van der Kouwe A, Brown EN, Folkerth RD, Fischl B, Kinney HC. Sustaining wakefulness: Brainstem connectivity in human consciousness. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.13.548265. [PMID: 37502983 PMCID: PMC10369992 DOI: 10.1101/2023.07.13.548265] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Consciousness is comprised of arousal (i.e., wakefulness) and awareness. Substantial progress has been made in mapping the cortical networks that modulate awareness in the human brain, but knowledge about the subcortical networks that sustain arousal is lacking. We integrated data from ex vivo diffusion MRI, immunohistochemistry, and in vivo 7 Tesla functional MRI to map the connectivity of a subcortical arousal network that we postulate sustains wakefulness in the resting, conscious human brain, analogous to the cortical default mode network (DMN) that is believed to sustain self-awareness. We identified nodes of the proposed default ascending arousal network (dAAN) in the brainstem, hypothalamus, thalamus, and basal forebrain by correlating ex vivo diffusion MRI with immunohistochemistry in three human brain specimens from neurologically normal individuals scanned at 600-750 μm resolution. We performed deterministic and probabilistic tractography analyses of the diffusion MRI data to map dAAN intra-network connections and dAAN-DMN internetwork connections. Using a newly developed network-based autopsy of the human brain that integrates ex vivo MRI and histopathology, we identified projection, association, and commissural pathways linking dAAN nodes with one another and with cortical DMN nodes, providing a structural architecture for the integration of arousal and awareness in human consciousness. We release the ex vivo diffusion MRI data, corresponding immunohistochemistry data, network-based autopsy methods, and a new brainstem dAAN atlas to support efforts to map the connectivity of human consciousness.
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Affiliation(s)
- Brian L. Edlow
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown MA 02129, USA
| | - Mark Olchanyi
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
| | - Holly J. Freeman
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown MA 02129, USA
| | - Jian Li
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown MA 02129, USA
| | - Chiara Maffei
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown MA 02129, USA
| | - Samuel B. Snider
- Department of Neurology, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Lilla Zöllei
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown MA 02129, USA
| | - J. Eugenio Iglesias
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown MA 02129, USA
| | - Jean Augustinack
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown MA 02129, USA
| | - Yelena G. Bodien
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital and Harvard Medical School, Charlestown, MA 02129 USA
| | - Robin L. Haynes
- Department of Pathology, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Douglas N. Greve
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown MA 02129, USA
| | - Bram R. Diamond
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02114, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown MA 02129, USA
| | - Allison Stevens
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown MA 02129, USA
| | - Joseph T. Giacino
- Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital and Harvard Medical School, Charlestown, MA 02129 USA
| | - Christophe Destrieux
- UMR 1253, iBrain, Université de Tours, Inserm, 10 Boulevard Tonnellé, 37032, Tours, France
- CHRU de Tours, 2 Boulevard Tonnellé, Tours, France
| | - Andre van der Kouwe
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown MA 02129, USA
| | - Emery N. Brown
- Department of Anesthesia, Critical Care, and Pain Medicine, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA
| | | | - Bruce Fischl
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown MA 02129, USA
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Hannah C. Kinney
- Department of Pathology, Boston Children’s Hospital and Harvard Medical School, Boston, MA 02115, USA
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5
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Liang Z, Arefin TM, Lee CH, Zhang J. Using mesoscopic tract-tracing data to guide the estimation of fiber orientation distributions in the mouse brain from diffusion MRI. Neuroimage 2023; 270:119999. [PMID: 36871795 PMCID: PMC10052941 DOI: 10.1016/j.neuroimage.2023.119999] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 02/21/2023] [Accepted: 02/28/2023] [Indexed: 03/07/2023] Open
Abstract
Diffusion MRI (dMRI) tractography is the only tool for non-invasive mapping of macroscopic structural connectivity over the entire brain. Although it has been successfully used to reconstruct large white matter tracts in the human and animal brains, the sensitivity and specificity of dMRI tractography remained limited. In particular, the fiber orientation distributions (FODs) estimated from dMRI signals, key to tractography, may deviate from histologically measured fiber orientation in crossing fibers and gray matter regions. In this study, we demonstrated that a deep learning network, trained using mesoscopic tract-tracing data from the Allen Mouse Brain Connectivity Atlas, was able to improve the estimation of FODs from mouse brain dMRI data. Tractography results based on the network generated FODs showed improved specificity while maintaining sensitivity comparable to results based on FOD estimated using a conventional spherical deconvolution method. Our result is a proof-of-concept of how mesoscale tract-tracing data can guide dMRI tractography and enhance our ability to characterize brain connectivity.
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Affiliation(s)
- Zifei Liang
- Department of Radiology, Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, 660 First Ave, New York, NY 10016, USA
| | - Tanzil Mahmud Arefin
- Department of Radiology, Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, 660 First Ave, New York, NY 10016, USA
| | - Choong H Lee
- Department of Radiology, Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, 660 First Ave, New York, NY 10016, USA
| | - Jiangyang Zhang
- Department of Radiology, Bernard and Irene Schwartz Center for Biomedical Imaging, New York University School of Medicine, 660 First Ave, New York, NY 10016, USA.
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6
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Insights from the IronTract challenge: Optimal methods for mapping brain pathways from multi-shell diffusion MRI. Neuroimage 2022; 257:119327. [PMID: 35636227 PMCID: PMC9453851 DOI: 10.1016/j.neuroimage.2022.119327] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/11/2022] [Revised: 05/06/2022] [Accepted: 05/19/2022] [Indexed: 01/25/2023] Open
Abstract
Limitations in the accuracy of brain pathways reconstructed by diffusion MRI (dMRI) tractography have received considerable attention. While the technical advances spearheaded by the Human Connectome Project (HCP) led to significant improvements in dMRI data quality, it remains unclear how these data should be analyzed to maximize tractography accuracy. Over a period of two years, we have engaged the dMRI community in the IronTract Challenge, which aims to answer this question by leveraging a unique dataset. Macaque brains that have received both tracer injections and ex vivo dMRI at high spatial and angular resolution allow a comprehensive, quantitative assessment of tractography accuracy on state-of-the-art dMRI acquisition schemes. We find that, when analysis methods are carefully optimized, the HCP scheme can achieve similar accuracy as a more time-consuming, Cartesian-grid scheme. Importantly, we show that simple pre- and post-processing strategies can improve the accuracy and robustness of many tractography methods. Finally, we find that fiber configurations that go beyond crossing (e.g., fanning, branching) are the most challenging for tractography. The IronTract Challenge remains open and we hope that it can serve as a valuable validation tool for both users and developers of dMRI analysis methods.
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7
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Yendiki A, Aggarwal M, Axer M, Howard AF, van Cappellen van Walsum AM, Haber SN. Post mortem mapping of connectional anatomy for the validation of diffusion MRI. Neuroimage 2022; 256:119146. [PMID: 35346838 PMCID: PMC9832921 DOI: 10.1016/j.neuroimage.2022.119146] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 03/02/2022] [Accepted: 03/23/2022] [Indexed: 01/13/2023] Open
Abstract
Diffusion MRI (dMRI) is a unique tool for the study of brain circuitry, as it allows us to image both the macroscopic trajectories and the microstructural properties of axon bundles in vivo. The Human Connectome Project ushered in an era of impressive advances in dMRI acquisition and analysis. As a result of these efforts, the quality of dMRI data that could be acquired in vivo improved substantially, and large collections of such data became widely available. Despite this progress, the main limitation of dMRI remains: it does not image axons directly, but only provides indirect measurements based on the diffusion of water molecules. Thus, it must be validated by methods that allow direct visualization of axons but that can only be performed in post mortem brain tissue. In this review, we discuss methods for validating the various features of connectional anatomy that are extracted from dMRI, both at the macro-scale (trajectories of axon bundles), and at micro-scale (axonal orientations and other microstructural properties). We present a range of validation tools, including anatomic tracer studies, Klingler's dissection, myelin stains, label-free optical imaging techniques, and others. We provide an overview of the basic principles of each technique, its limitations, and what it has taught us so far about the accuracy of different dMRI acquisition and analysis approaches.
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Affiliation(s)
- Anastasia Yendiki
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, United States,Corresponding author (A. Yendiki)
| | - Manisha Aggarwal
- Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Markus Axer
- Forschungszentrum Jülich, Institute of Neuroscience and Medicine, Jülich, Germany,Department of Physics, University of Wuppertal Germany
| | - Amy F.D. Howard
- Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Anne-Marie van Cappellen van Walsum
- Department of Medical Imaging, Anatomy, Radboud University Medical Center, Nijmegen, the Netherland,Cognition and Behaviour, Donders Institute for Brain, Nijmegen, the Netherland
| | - Suzanne N. Haber
- Department of Pharmacology and Physiology, University of Rochester, Rochester, NY, United States,McLean Hospital, Belmont, MA, United States
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8
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Grier MD, Yacoub E, Adriany G, Lagore RL, Harel N, Zhang RY, Lenglet C, Uğurbil K, Zimmermann J, Heilbronner SR. Ultra-high field (10.5T) diffusion-weighted MRI of the macaque brain. Neuroimage 2022; 255:119200. [PMID: 35427769 PMCID: PMC9446284 DOI: 10.1016/j.neuroimage.2022.119200] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 03/08/2022] [Accepted: 04/07/2022] [Indexed: 11/26/2022] Open
Abstract
Diffu0sion-weighted magnetic resonance imaging (dMRI) is a non-invasive imaging technique that provides information about the barriers to the diffusion of water molecules in tissue. In the brain, this information can be used in several important ways, including to examine tissue abnormalities associated with brain disorders and to infer anatomical connectivity and the organization of white matter bundles through the use of tractography algorithms. However, dMRI also presents certain challenges. For example, historically, the biological validation of tractography models has shown only moderate correlations with anatomical connectivity as determined through invasive tract-tracing studies. Some of the factors contributing to such issues are low spatial resolution, low signal-to-noise ratios, and long scan times required for high-quality data, along with modeling challenges like complex fiber crossing patterns. Leveraging the capabilities provided by an ultra-high field scanner combined with denoising, we have acquired whole-brain, 0.58 mm isotropic resolution dMRI with a 2D-single shot echo planar imaging sequence on a 10.5 Tesla scanner in anesthetized macaques. These data produced high-quality tractograms and maps of scalar diffusion metrics in white matter. This work demonstrates the feasibility and motivation for in-vivo dMRI studies seeking to benefit from ultra-high fields.
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Affiliation(s)
- Mark D Grier
- Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, United States
| | - Essa Yacoub
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN 55455, United States; Center for Neuroengineering, University of Minnesota, Minneapolis, MN 55455, United States
| | - Gregor Adriany
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN 55455, United States; Center for Neuroengineering, University of Minnesota, Minneapolis, MN 55455, United States
| | - Russell L Lagore
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN 55455, United States
| | - Noam Harel
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN 55455, United States; Department of Neurosurgery, University of Minnesota, Minneapolis, MN 55455, United States
| | - Ru-Yuan Zhang
- Institute of Psychology and Behavioral Science, Shanghai Jiao Tong University, Shanghai 200030, P.R. China; Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, Shanghai 200030, P.R. China; Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN 55455, United States
| | - Christophe Lenglet
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN 55455, United States
| | - Kâmil Uğurbil
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN 55455, United States; Center for Neuroengineering, University of Minnesota, Minneapolis, MN 55455, United States
| | - Jan Zimmermann
- Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, United States; Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN 55455, United States; Center for Neuroengineering, University of Minnesota, Minneapolis, MN 55455, United States; Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455, United States
| | - Sarah R Heilbronner
- Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, United States; Center for Neuroengineering, University of Minnesota, Minneapolis, MN 55455, United States.
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9
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Comparing human and chimpanzee temporal lobe neuroanatomy reveals modifications to human language hubs beyond the frontotemporal arcuate fasciculus. Proc Natl Acad Sci U S A 2022; 119:e2118295119. [PMID: 35787056 PMCID: PMC9282369 DOI: 10.1073/pnas.2118295119] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023] Open
Abstract
The biological foundation for the language-ready brain in the human lineage remains a debated subject. In humans, the arcuate fasciculus (AF) white matter and the posterior portions of the middle temporal gyrus are crucial for language. Compared with other primates, the human AF has been shown to dramatically extend into the posterior temporal lobe, which forms the basis of a number of models of the structural connectivity basis of language. Recent advances in both language research and comparative neuroimaging invite a reassessment of the anatomical differences in language streams between humans and our closest relatives. Here, we show that posterior temporal connectivity via the AF in humans compared with chimpanzees is expanded in terms of its connectivity not just to the ventral frontal cortex but also to the parietal cortex. At the same time, posterior temporal regions connect more strongly to the ventral white matter in chimpanzees as opposed to humans. This pattern is present in both brain hemispheres. Additionally, we show that the anterior temporal lobe harbors a combination of connections present in both species through the inferior fronto-occipital fascicle and human-unique expansions through the uncinate and middle and inferior longitudinal fascicles. These findings elucidate structural changes that are unique to humans and may underlie the anatomical foundations for full-fledged language capacity.
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10
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Tang-Wright K, Smith JET, Bridge H, Miller KL, Dyrby TB, Ahmed B, Reislev NL, Sallet J, Parker AJ, Krug K. Intra-Areal Visual Topography in Primate Brains Mapped with Probabilistic Tractography of Diffusion-Weighted Imaging. Cereb Cortex 2022; 32:2555-2574. [PMID: 34730185 PMCID: PMC9201591 DOI: 10.1093/cercor/bhab364] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 08/28/2021] [Accepted: 08/29/2021] [Indexed: 11/24/2022] Open
Abstract
Noninvasive diffusion-weighted magnetic resonance imaging (dMRI) can be used to map the neural connectivity between distinct areas in the intact brain, but the standard resolution achieved fundamentally limits the sensitivity of such maps. We investigated the sensitivity and specificity of high-resolution postmortem dMRI and probabilistic tractography in rhesus macaque brains to produce retinotopic maps of the lateral geniculate nucleus (LGN) and extrastriate cortical visual area V5/MT based on their topographic connections with the previously established functional retinotopic map of primary visual cortex (V1). We also replicated the differential connectivity of magnocellular and parvocellular LGN compartments with V1 across visual field positions. Predicted topographic maps based on dMRI data largely matched the established retinotopy of both LGN and V5/MT. Furthermore, tractography based on in vivo dMRI data from the same macaque brains acquired at standard field strength (3T) yielded comparable topographic maps in many cases. We conclude that tractography based on dMRI is sensitive enough to reveal the intrinsic organization of ordered connections between topographically organized neural structures and their resultant functional organization.
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Affiliation(s)
- K Tang-Wright
- Department of Physiology Anatomy and Genetics, University of Oxford, Oxford, OX1 3PT, UK
| | - J E T Smith
- Department of Physiology Anatomy and Genetics, University of Oxford, Oxford, OX1 3PT, UK
- Ernst Strüngmann Institute (ESI) for Neuroscience in cooperation with Max Planck Society, 60528 Frankfurt, Germany
| | - H Bridge
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, UK
| | - K L Miller
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford OX3 9DU, UK
| | - T B Dyrby
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital, Amager & Hvidovre, 2650 Hvidovre, Denmark
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, 2800 Kongens Lyngby, Denmark
| | - B Ahmed
- Department of Physiology Anatomy and Genetics, University of Oxford, Oxford, OX1 3PT, UK
| | - N L Reislev
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital, Amager & Hvidovre, 2650 Hvidovre, Denmark
| | - J Sallet
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford OX1 3UD, UK
- Université Lyon 1, INSERM, Stem Cell and Brain Research Institute U1208, 69500 Bron, France
| | - A J Parker
- Department of Physiology Anatomy and Genetics, University of Oxford, Oxford, OX1 3PT, UK
- Institute of Biology, Otto-von-Guericke-University Magdeburg, 39120 Magdeburg, Germany
- Leibniz Institute for Neurobiology, 39118 Magdeburg, Germany
| | - K Krug
- Department of Physiology Anatomy and Genetics, University of Oxford, Oxford, OX1 3PT, UK
- Institute of Biology, Otto-von-Guericke-University Magdeburg, 39120 Magdeburg, Germany
- Leibniz Institute for Neurobiology, 39118 Magdeburg, Germany
- Centre for Behavioral Brain Sciences, Otto-von-Guericke-University Magdeburg, 39106 Magdeburg, Germany
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11
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Boyne P, DiFrancesco M, Awosika OO, Williamson B, Vannest J. Mapping the human corticoreticular pathway with multimodal delineation of the gigantocellular reticular nucleus and high-resolution diffusion tractography. J Neurol Sci 2022; 434:120091. [PMID: 34979371 PMCID: PMC8957549 DOI: 10.1016/j.jns.2021.120091] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2021] [Revised: 11/17/2021] [Accepted: 12/10/2021] [Indexed: 11/29/2022]
Abstract
The corticoreticular pathway (CRP) is a major motor tract that transmits cortical input to the reticular formation motor nuclei and may be an important mediator of motor recovery after central nervous system damage. However, its cortical origins, trajectory and laterality are incompletely understood in humans. This study aimed to map the human CRP and generate an average CRP template in standard MRI space. Following recently established guidelines, we manually delineated the primary reticular formation motor nucleus (gigantocellular reticular nucleus [GRN]) using several group-mean MRI contrasts from the Human Connectome Project (HCP). CRP tractography was then performed with HCP diffusion-weighted MRI data (N = 1065) by selecting diffusion streamlines that reached both the cortex and GRN. Corticospinal tract (CST) tractography was also performed for comparison. Results suggest that the human CRP has widespread origins, which overlap with the CST across most of the motor cortex and include additional exclusive inputs from the medial and anterior prefrontal cortices. The estimated CRP projected through the anterior and posterior limbs of the internal capsule before partially decussating in the midbrain tegmentum and converging bilaterally on the pontomedullary reticular formation. Thus, the CRP trajectory appears to partially overlap the CST, while being more distributed and anteromedial to the CST in the cerebrum before moving posterior to the CST in the brainstem. These findings have important implications for neurophysiologic testing, cortical stimulation and movement recovery after brain lesions. We expect that our GRN and tract maps will also facilitate future CRP research.
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Affiliation(s)
- Pierce Boyne
- Department of Rehabilitation, Exercise and Nutrition Sciences, College of Allied Health Sciences, University of Cincinnati, Cincinnati, OH 45267, USA.
| | - Mark DiFrancesco
- Department of Radiology and Pediatric Neuroimaging Research Consortium, Cincinnati Children's Hospital Medical Center, Cincinnati, OH 45267, USA
| | - Oluwole O Awosika
- Department of Neurology and Rehabilitation Medicine, College of Medicine, University of Cincinnati, Cincinnati, OH 45267, USA
| | - Brady Williamson
- Department of Radiology, College of Medicine, University of Cincinnati, Cincinnati, OH 45267, USA
| | - Jennifer Vannest
- Department of Communication Sciences and Disorders, College of Allied Health Sciences, University of Cincinnati, Cincinnati, OH 45267, USA
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12
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Haber SN, Liu H, Seidlitz J, Bullmore E. Prefrontal connectomics: from anatomy to human imaging. Neuropsychopharmacology 2022; 47:20-40. [PMID: 34584210 PMCID: PMC8617085 DOI: 10.1038/s41386-021-01156-6] [Citation(s) in RCA: 31] [Impact Index Per Article: 15.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Revised: 07/23/2021] [Accepted: 08/02/2021] [Indexed: 12/22/2022]
Abstract
The fundamental importance of prefrontal cortical connectivity to information processing and, therefore, disorders of cognition, emotion, and behavior has been recognized for decades. Anatomic tracing studies in animals have formed the basis for delineating the direct monosynaptic connectivity, from cells of origin, through axon trajectories, to synaptic terminals. Advances in neuroimaging combined with network science have taken the lead in developing complex wiring diagrams or connectomes of the human brain. A key question is how well these magnetic resonance imaging (MRI)-derived networks and hubs reflect the anatomic "hard wiring" first proposed to underlie the distribution of information for large-scale network interactions. In this review, we address this challenge by focusing on what is known about monosynaptic prefrontal cortical connections in non-human primates and how this compares to MRI-derived measurements of network organization in humans. First, we outline the anatomic cortical connections and pathways for each prefrontal cortex (PFC) region. We then review the available MRI-based techniques for indirectly measuring structural and functional connectivity, and introduce graph theoretical methods for analysis of hubs, modules, and topologically integrative features of the connectome. Finally, we bring these two approaches together, using specific examples, to demonstrate how monosynaptic connections, demonstrated by tract-tracing studies, can directly inform understanding of the composition of PFC nodes and hubs, and the edges or pathways that connect PFC to cortical and subcortical areas.
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Affiliation(s)
- Suzanne N. Haber
- grid.412750.50000 0004 1936 9166Department of Pharmacology and Physiology, University of Rochester School of Medicine & Dentistry, Rochester, NY 14642 USA ,grid.38142.3c000000041936754XDepartment of Psychiatry, McLean Hospital, Harvard Medical School, Belmont, MA 02478 USA
| | - Hesheng Liu
- grid.259828.c0000 0001 2189 3475Department of Neuroscience, Medical University of South Carolina, Charleston, SC USA ,grid.38142.3c000000041936754XDepartment of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA USA
| | - Jakob Seidlitz
- grid.25879.310000 0004 1936 8972Department of Psychiatry, University of Pennsylvania, Philadelphia, USA
| | - Ed Bullmore
- grid.5335.00000000121885934Department of Psychiatry, University of Cambridge, Herchel Smith Building for Brain and Mind Sciences, Cambridge Biomedical Campus, Cambridge, CB2 0SZ UK
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13
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Sarwar T, Ramamohanarao K, Zalesky A. A critical review of connectome validation studies. NMR IN BIOMEDICINE 2021; 34:e4605. [PMID: 34516016 DOI: 10.1002/nbm.4605] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 07/22/2021] [Accepted: 08/01/2021] [Indexed: 06/13/2023]
Abstract
Diffusion MRI tractography is the most widely used macroscale method for mapping connectomes in vivo. However, tractography is prone to various errors and biases, and thus tractography-derived connectomes require careful validation. Here, we critically review studies that have developed or utilized phantoms and tracer maps to validate tractography-derived connectomes, either quantitatively or qualitatively. We identify key factors impacting connectome reconstruction accuracy, including streamline seeding, propagation and filtering methods, and consider the strengths and limitations of state-of-the-art connectome phantoms and associated validation studies. These studies demonstrate the inherent limitations of current fiber orientation models and tractography algorithms and their impact on connectome reconstruction accuracy. Reconstructing connectomes with both high sensitivity and high specificity is challenging, given that some tractography methods can generate an abundance of spurious connections, while others can overlook genuine fiber bundles. We argue that streamline filtering can minimize spurious connections and potentially improve the biological plausibility of connectomes derived from tractography. We find that algorithmic choices such as the tractography seeding methodology, angular threshold, and streamline propagation method can substantially impact connectome reconstruction accuracy. Hence, careful application of tractography is necessary to reconstruct accurate connectomes. Improvements in diffusion MRI acquisition techniques will not necessarily overcome current tractography limitations without accompanying modeling and algorithmic advances.
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Affiliation(s)
- Tabinda Sarwar
- School of Computing Technologies, RMIT University, Melbourne, Victoria, Australia
| | - Kotagiri Ramamohanarao
- Department of Computing and Information Systems, The University of Melbourne, Melbourne, Victoria, Australia
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, The University of Melbourne, Melbourne, Victoria, Australia
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, Victoria, Australia
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14
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Boyne P, Awosika OO, Luo Y. Mapping the corticoreticular pathway from cortex-wide anterograde axonal tracing in the mouse. J Neurosci Res 2021; 99:3392-3405. [PMID: 34676909 DOI: 10.1002/jnr.24975] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2021] [Revised: 08/31/2021] [Accepted: 09/21/2021] [Indexed: 11/09/2022]
Abstract
The corticoreticular pathway (CRP) has been implicated as an important mediator of motor recovery and rehabilitation after central nervous system damage. However, its origins, trajectory and laterality are not well understood. This study mapped the mouse CRP in comparison with the corticospinal tract (CST). We systematically searched the Allen Mouse Brain Connectivity Atlas (© 2011 Allen Institute for Brain Science) for experiments that used anterograde tracer injections into the right isocortex in mice. For each eligible experiment (N = 607), CRP and CST projection strength were quantified by the tracer volume reaching the reticular formation motor nuclei (RFmotor ) and pyramids, respectively. Tracer density in each brain voxel was also correlated with RFmotor versus pyramids projection strength to explore the relative trajectories of the CRP and CST. We found significant CRP projections originating from the primary and secondary motor cortices, anterior cingulate, primary somatosensory cortex, and medial prefrontal cortex. Compared with the CST, the CRP had stronger projections from each region except the primary somatosensory cortex. Ipsilateral projections were stronger than contralateral for both tracts (above the pyramidal decussation), but the CRP projected more bilaterally than the CST. The estimated CRP trajectory was anteromedial to the CST in the internal capsule and dorsal to the CST in the brainstem. Our findings reveal a widespread distribution of CRP origins and confirm strong bilateral CRP projections, theoretically increasing the potential for partial sparing after brain lesions and contralesional compensation after unilateral injury.
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Affiliation(s)
- Pierce Boyne
- Department of Rehabilitation, Exercise and Nutrition Sciences, College of Allied Health Sciences, University of Cincinnati, Cincinnati, Ohio, USA
| | - Oluwole O Awosika
- Department of Neurology and Rehabilitation Medicine, College of Medicine, University of Cincinnati, Cincinnati, Ohio, USA
| | - Yu Luo
- Department of Molecular Genetics, Biochemistry and Microbiology, College of Medicine, University of Cincinnati, Cincinnati, Ohio, USA
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15
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Moody JF, Adluru N, Alexander AL, Field AS. The Connectomes: Methods of White Matter Tractography and Contributions of Resting State fMRI. Semin Ultrasound CT MR 2021; 42:507-522. [PMID: 34537118 DOI: 10.1053/j.sult.2021.07.007] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
A comprehensive mapping of the structural and functional circuitry of the brain is a major unresolved problem in contemporary neuroimaging research. Diffusion-weighted and functional MRI have provided investigators with the capability to assess structural and functional connectivity in-vivo, driven primarily by methods of white matter tractography and resting-state fMRI, respectively. These techniques have paved the way for the construction of the functional and structural connectomes, which are quantitative representations of brain architecture as neural networks, comprised of nodes and edges. The connectomes, typically depicted as matrices or graphs, possess topological properties that inherently characterize the strength, efficiency, and organization of the connections between distinct brain regions. Graph theory, a general mathematical framework for analyzing networks, can be implemented to derive metrics from the connectomes that are sensitive to changes in brain connectivity associated with age, sex, cognitive function, and disease. These quantities can be assessed at either the global (whole brain) or local levels, allowing for the identification of distinct regional connectivity hubs and associated localized brain networks, which together serve crucial roles in establishing the structural and functional architecture of the brain. As a result, structural and functional connectomes have each been employed to study the brain circuitry underlying early brain development, neuroplasticity, developmental disorders, psychopathology, epilepsy, aging, neurodegenerative disorders, and traumatic brain injury. While these studies have yielded important insights into brain structure, function, and pathology, a precise description of the innate relationship between functional and structural networks across the brain remains unachieved. To date, connectome research has merely scratched the surface of potential clinical applications and related characterizations of brain-wide connectivity. Continued advances in diffusion and functional MRI acquisition, the delineation of functional and structural networks, and the quantification of neural network properties in specific brain regions, will be invaluable to future progress in neuroimaging science.
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Affiliation(s)
- Jason F Moody
- Department of Medical Physics, University of Wisconsin-Madison, Madison, WI; Waisman Center, University of Wisconsin-Madison, Madison, WI
| | - Nagesh Adluru
- Waisman Center, University of Wisconsin-Madison, Madison, WI; Department of Radiology, University of Wisconsin-Madison, Madison, WI
| | - Andrew L Alexander
- Department of Medical Physics, University of Wisconsin-Madison, Madison, WI; Department of Psychiatry, University of Wisconsin-Madison, Madison, WI; Waisman Center, University of Wisconsin-Madison, Madison, WI
| | - Aaron S Field
- Department of Radiology, University of Wisconsin-Madison, Madison, WI.
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16
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The Complex Hodological Architecture of the Macaque Dorsal Intraparietal Areas as Emerging from Neural Tracers and DW-MRI Tractography. eNeuro 2021; 8:ENEURO.0102-21.2021. [PMID: 34039649 PMCID: PMC8266221 DOI: 10.1523/eneuro.0102-21.2021] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2021] [Revised: 04/21/2021] [Accepted: 05/01/2021] [Indexed: 11/21/2022] Open
Abstract
In macaque monkeys, dorsal intraparietal areas are involved in several daily visuomotor actions. However, their border and sources of cortical afferents remain loosely defined. Combining retrograde histologic tracing and MRI diffusion-based tractography, we found a complex hodology of the dorsal bank of the intraparietal sulcus (db-IPS), which can be subdivided into a rostral intraparietal area PEip, projecting to the spinal cord, and a caudal medial intraparietal area MIP lacking such projections. Both include an anterior and a posterior sector, emerging from their ipsilateral, gradient-like connectivity profiles. As tractography estimations, we used the cross-sectional area of the white matter bundles connecting each area with other parietal and frontal regions, after selecting regions of interest (ROIs) corresponding to the injection sites of neural tracers. For most connections, we found a significant correlation between the proportions of cells projecting to all sectors of PEip and MIP along the continuum of the db-IPS and tractography. The latter also revealed “false positive” but plausible connections awaiting histologic validation.
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Bryant KL, Li L, Eichert N, Mars RB. A comprehensive atlas of white matter tracts in the chimpanzee. PLoS Biol 2020; 18:e3000971. [PMID: 33383575 PMCID: PMC7806129 DOI: 10.1371/journal.pbio.3000971] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2020] [Revised: 01/13/2021] [Accepted: 12/09/2020] [Indexed: 12/26/2022] Open
Abstract
Chimpanzees (Pan troglodytes) are, along with bonobos, humans’ closest living relatives. The advent of diffusion MRI tractography in recent years has allowed a resurgence of comparative neuroanatomical studies in humans and other primate species. Here we offer, in comparative perspective, the first chimpanzee white matter atlas, constructed from in vivo chimpanzee diffusion-weighted scans. Comparative white matter atlases provide a useful tool for identifying neuroanatomical differences and similarities between humans and other primate species. Until now, comprehensive fascicular atlases have been created for humans (Homo sapiens), rhesus macaques (Macaca mulatta), and several other nonhuman primate species, but never in a nonhuman ape. Information on chimpanzee neuroanatomy is essential for understanding the anatomical specializations of white matter organization that are unique to the human lineage. Diffusion MRI tractography reveals the first complete atlas of white matter of the chimpanzee, with the potential to help understand differences between the organization of human and chimpanzee brains.
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Affiliation(s)
- Katherine L. Bryant
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
- * E-mail:
| | - Longchuan Li
- Marcus Autism Center, Children’s Healthcare of Atlanta, Emory University, Atlanta, Georgia, United States of America
| | - Nicole Eichert
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
| | - Rogier B. Mars
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom
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18
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Howells H, Simone L, Borra E, Fornia L, Cerri G, Luppino G. Reproducing macaque lateral grasping and oculomotor networks using resting state functional connectivity and diffusion tractography. Brain Struct Funct 2020; 225:2533-2551. [PMID: 32936342 PMCID: PMC7544728 DOI: 10.1007/s00429-020-02142-2] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2019] [Accepted: 09/02/2020] [Indexed: 12/31/2022]
Abstract
Cortico-cortical networks involved in motor control have been well defined in the macaque using a range of invasive techniques. The advent of neuroimaging has enabled non-invasive study of these large-scale functionally specialized networks in the human brain; however, assessing its accuracy in reproducing genuine anatomy is more challenging. We set out to assess the similarities and differences between connections of macaque motor control networks defined using axonal tracing and those reproduced using structural and functional connectivity techniques. We processed a cohort of macaques scanned in vivo that were made available by the open access PRIME-DE resource, to evaluate connectivity using diffusion imaging tractography and resting state functional connectivity (rs-FC). Sectors of the lateral grasping and exploratory oculomotor networks were defined anatomically on structural images, and connections were reproduced using different structural and functional approaches (probabilistic and deterministic whole-brain and seed-based tractography; group template and native space functional connectivity analysis). The results showed that parieto-frontal connections were best reproduced using both structural and functional connectivity techniques. Tractography showed lower sensitivity but better specificity in reproducing connections identified by tracer data. Functional connectivity analysis performed in native space had higher sensitivity but lower specificity and was better at identifying connections between intrasulcal ROIs than group-level analysis. Connections of AIP were most consistently reproduced, although those connected with prefrontal sectors were not identified. We finally compared diffusion MR modelling with histology based on an injection in AIP and speculate on anatomical bases for the observed false negatives. Our results highlight the utility of precise ex vivo techniques to support the accuracy of neuroimaging in reproducing connections, which is relevant also for human studies.
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Affiliation(s)
- Henrietta Howells
- MoCA Laboratory, Department of Medical Biotechnology and Translational Medicine, University of Milan, Milan, Italy.
| | - Luciano Simone
- MoCA Laboratory, Department of Medical Biotechnology and Translational Medicine, University of Milan, Milan, Italy.
| | - Elena Borra
- Department of Medicine and Surgery, Neuroscience Unit, University of Parma, Parma, Italy
| | - Luca Fornia
- MoCA Laboratory, Department of Medical Biotechnology and Translational Medicine, University of Milan, Milan, Italy
| | - Gabriella Cerri
- MoCA Laboratory, Department of Medical Biotechnology and Translational Medicine, University of Milan, Milan, Italy
| | - Giuseppe Luppino
- Department of Medicine and Surgery, Neuroscience Unit, University of Parma, Parma, Italy
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19
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He B, Cao L, Xia X, Zhang B, Zhang D, You B, Fan L, Jiang T. Fine-Grained Topography and Modularity of the Macaque Frontal Pole Cortex Revealed by Anatomical Connectivity Profiles. Neurosci Bull 2020; 36:1454-1473. [PMID: 33108588 PMCID: PMC7719154 DOI: 10.1007/s12264-020-00589-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2020] [Accepted: 07/30/2020] [Indexed: 11/25/2022] Open
Abstract
The frontal pole cortex (FPC) plays key roles in various higher-order functions and is highly developed in non-human primates. An essential missing piece of information is the detailed anatomical connections for finer parcellation of the macaque FPC than provided by the previous tracer results. This is important for understanding the functional architecture of the cerebral cortex. Here, combining cross-validation and principal component analysis, we formed a tractography-based parcellation scheme that applied a machine learning algorithm to divide the macaque FPC (2 males and 6 females) into eight subareas using high-resolution diffusion magnetic resonance imaging with the 9.4T Bruker system, and then revealed their subregional connections. Furthermore, we applied improved hierarchical clustering to the obtained parcels to probe the modular structure of the subregions, and found that the dorsolateral FPC, which contains an extension to the medial FPC, was mainly connected to regions of the default-mode network. The ventral FPC was mainly involved in the social-interaction network and the dorsal FPC in the metacognitive network. These results enhance our understanding of the anatomy and circuitry of the macaque brain, and contribute to FPC-related clinical research.
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Affiliation(s)
- Bin He
- School of Mechanical and Power Engineering, Harbin University of Science and Technology, Harbin, 150080, China.,Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences (CAS), Beijing, 100190, China
| | - Long Cao
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, 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
| | - Xiaoluan Xia
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,College of Information and Computer, Taiyuan University of Technology, Taiyuan, 030600, China
| | - Baogui Zhang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences (CAS), Beijing, 100190, China
| | - Dan Zhang
- Core Facility, Center of Biomedical Analysis, Tsinghua University, Beijing, 100084, China
| | - Bo You
- School of Mechanical and Power Engineering, Harbin University of Science and Technology, Harbin, 150080, China.
| | - Lingzhong Fan
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China. .,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences (CAS), Beijing, 100190, China. .,Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, CAS, Beijing, 100190, China. .,University of CAS, Beijing, 100049, China.
| | - Tianzi Jiang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, 100190, China. .,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences (CAS), Beijing, 100190, China. .,Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, CAS, Beijing, 100190, 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. .,The Queensland Brain Institute, University of Queensland, Brisbane, QLD, 4072, Australia. .,University of CAS, Beijing, 100049, China. .,Chinese Institute for Brain Research, Beijing, 102206, China.
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20
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Gerbella M, Pinardi C, Di Cesare G, Rizzolatti G, Caruana F. Two Neural Networks for Laughter: A Tractography Study. Cereb Cortex 2020; 31:899-916. [DOI: 10.1093/cercor/bhaa264] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Revised: 07/14/2020] [Accepted: 08/14/2020] [Indexed: 02/07/2023] Open
Abstract
Abstract
Laughter is a complex motor behavior occurring in both emotional and nonemotional contexts. Here, we investigated whether the different functions of laughter are mediated by distinct networks and, if this is the case, which are the white matter tracts sustaining them. We performed a multifiber tractography investigation placing seeds in regions involved in laughter production, as identified by previous intracerebral electrical stimulation studies in humans: the pregenual anterior cingulate (pACC), ventral temporal pole (TPv), frontal operculum (FO), presupplementary motor cortex, and ventral striatum/nucleus accumbens (VS/NAcc). The primary motor cortex (M1) and two subcortical territories were also studied to trace the descending projections. Results provided evidence for the existence of two relatively distinct networks. A first network, including pACC, TPv, and VS/NAcc, is interconnected through the anterior cingulate bundle, the accumbofrontal tract, and the uncinate fasciculus, reaching the brainstem throughout the mamillo-tegmental tract. This network is likely involved in the production of emotional laughter. A second network, anchored to FO and M1, projects to the brainstem motor nuclei through the internal capsule. It is most likely the neural basis of nonemotional and conversational laughter. The two networks interact throughout the pre-SMA that is connected to both pACC and FO.
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Affiliation(s)
- M Gerbella
- Department of Medicine and Surgery, University of Parma, Parma 43125, Italy
| | - C Pinardi
- Neuroradiology Department, Fondazione I.R.C.C.S. Istituto Neurologico Carlo Besta, Milan 20133, Italy
| | - G Di Cesare
- Cognitive Architecture for Collaborative Technologies Unit, Italian Institute of Technology, Genova 16163, Italy
| | - G Rizzolatti
- Department of Medicine and Surgery, University of Parma, Parma 43125, Italy
- Institute of Neuroscience, Italian National Research Council (CNR), Parma 43125, Italy
| | - F Caruana
- Institute of Neuroscience, Italian National Research Council (CNR), Parma 43125, Italy
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21
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Grier MD, Zimmermann J, Heilbronner SR. Estimating Brain Connectivity With Diffusion-Weighted Magnetic Resonance Imaging: Promise and Peril. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2020; 5:846-854. [PMID: 32513555 PMCID: PMC7483308 DOI: 10.1016/j.bpsc.2020.04.009] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/23/2020] [Revised: 03/20/2020] [Accepted: 04/18/2020] [Indexed: 01/22/2023]
Abstract
Diffusion-weighted magnetic resonance imaging (dMRI) is a popular tool for noninvasively assessing properties of white matter in the brain. Among other uses, dMRI data can be used to produce estimates of anatomical connectivity on the basis of tractography. However, direct comparisons of anatomical connectivity as estimated through invasive neural tract-tracing experiments and dMRI-derived connectivity have shown only a moderate relationship in nonhuman primate (particularly macaque) studies. Tractography is plagued by known problems associated with resolution, crossing fibers, and curving fibers, among others. These problems lead to deficits in both sensitivity and specificity, which trade off with each other in multiple datasets. Although not yet examined quantitatively, there is reason to believe that some large white matter bundles, those with more topographic organization, may produce more accurate results than others. Moving forward, sophisticated analytical approaches and anatomical constraints may improve tractography accuracy. However, broadly speaking, dMRI-derived estimates of brain connectivity should be approached with caution.
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Affiliation(s)
- Mark D Grier
- Department of Neuroscience, University of Minnesota, Minneapolis, Minnesota
| | - Jan Zimmermann
- Department of Neuroscience, University of Minnesota, Minneapolis, Minnesota; Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, Minnesota
| | - Sarah R Heilbronner
- Department of Neuroscience, University of Minnesota, Minneapolis, Minnesota.
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22
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Takemura H, Palomero-Gallagher N, Axer M, Gräßel D, Jorgensen MJ, Woods R, Zilles K. Anatomy of nerve fiber bundles at micrometer-resolution in the vervet monkey visual system. eLife 2020; 9:e55444. [PMID: 32844747 PMCID: PMC7532002 DOI: 10.7554/elife.55444] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2020] [Accepted: 08/22/2020] [Indexed: 12/11/2022] Open
Abstract
Although the primate visual system has been extensively studied, detailed spatial organization of white matter fiber tracts carrying visual information between areas has not been fully established. This is mainly due to the large gap between tracer studies and diffusion-weighted MRI studies, which focus on specific axonal connections and macroscale organization of fiber tracts, respectively. Here we used 3D polarization light imaging (3D-PLI), which enables direct visualization of fiber tracts at micrometer resolution, to identify and visualize fiber tracts of the visual system, such as stratum sagittale, inferior longitudinal fascicle, vertical occipital fascicle, tapetum and dorsal occipital bundle in vervet monkey brains. Moreover, 3D-PLI data provide detailed information on cortical projections of these tracts, distinction between neighboring tracts, and novel short-range pathways. This work provides essential information for interpretation of functional and diffusion-weighted MRI data, as well as revision of wiring diagrams based upon observations in the vervet visual system.
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Affiliation(s)
- Hiromasa Takemura
- Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology, and Osaka UniversityOsakaJapan
- Graduate School of Frontier Biosciences, Osaka UniversityOsakaJapan
| | - Nicola Palomero-Gallagher
- Institute of Neuroscience and Medicine INM-1, Research Centre JülichJülichGermany
- Department of Psychiatry, Psychotherapy and Psychosomatics, Medical Faculty, RWTH AachenAachenGermany
- C. & O. Vogt Institute for Brain Research, Heinrich-Heine-UniversityDüsseldorfGermany
| | - Markus Axer
- Institute of Neuroscience and Medicine INM-1, Research Centre JülichJülichGermany
| | - David Gräßel
- Institute of Neuroscience and Medicine INM-1, Research Centre JülichJülichGermany
| | - Matthew J Jorgensen
- Department of Pathology, Section on Comparative Medicine, Wake Forest School of MedicineWinston-SalemUnited States
| | - Roger Woods
- Ahmanson-Lovelace Brain Mapping Center, Departments of Neurology and of Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, UCLALos AngelesUnited States
| | - Karl Zilles
- Institute of Neuroscience and Medicine INM-1, Research Centre JülichJülichGermany
- JARA - Translational Brain MedicineAachenGermany
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23
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Intact tactile detection yet biased tactile localization in a hand-centered frame of reference: Evidence from a dissociation. Neuropsychologia 2020; 147:107585. [PMID: 32841632 DOI: 10.1016/j.neuropsychologia.2020.107585] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2019] [Revised: 04/20/2020] [Accepted: 08/10/2020] [Indexed: 11/21/2022]
Abstract
We examined the performance of an individual with subcortical damage, but an intact somatosensory thalamocortical pathway, to examine the functional architecture of tactile detection and tactile localization processes. Consistent with the intact somatosensory thalamocortical pathway, tactile detection on the contralesional hand was well within the normal range. Despite intact detection, the individual demonstrated substantial localization biases. Across all localization experiments, he consistently localized tactile stimuli to the left side in space relative to the long axis of his hand. This was observed when the contralesional hand was palm up, palm down, rotated 90° relative to the trunk, and when making verbal responses. Furthermore, control experiments demonstrated that this response pattern was unlikely a motor response error. These findings indicate that tactile localization on the body is influenced by proprioceptive information specifically in a hand-centered frame of reference. Furthermore, this also provides evidence that aspects of tactile localization are mediated by pathways outside of the primary somatosensory thalamocortical pathway.
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24
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Brain connections derived from diffusion MRI tractography can be highly anatomically accurate-if we know where white matter pathways start, where they end, and where they do not go. Brain Struct Funct 2020; 225:2387-2402. [PMID: 32816112 DOI: 10.1007/s00429-020-02129-z] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Accepted: 08/07/2020] [Indexed: 12/20/2022]
Abstract
MR Tractography, which is based on MRI measures of water diffusivity, is currently the only method available for noninvasive reconstruction of fiber pathways in the brain. However, it has several fundamental limitations that call into question its accuracy in many applications. Therefore, there has been intense interest in defining and mitigating the intrinsic limitations of the method. Recent studies have reported that tractography is inherently limited in its ability to accurately reconstruct the connections of the brain, when based on voxel-averaged estimates of local fiber orientation alone. Several validation studies have confirmed that tractography techniques are plagued by both false-positive and false-negative connections. However, these validation studies which quantify sensitivity and specificity, particularly in animal models, have not utilized prior anatomical knowledge, as is done in the human literature, for virtual dissection of white matter pathways, instead assessing tractography implemented in a relatively unconstrained manner. Thus, they represent a worse-case scenario for bundle-segmentation techniques and may not be indicative of the anatomical accuracy in the process of bundle segmentation, where streamline filtering using inclusion and exclusion regions-of-interest is common. With this in mind, the aim of the current study is to investigate and quantify the upper bounds of accuracy using current tractography methods. Making use of the same dataset utilized in two seminal validation papers, we show that prior anatomical knowledge in the form of manually placed or template-driven constraints can significantly improve the anatomical accuracy of estimated brain connections. Thus, we show that it is possible to achieve a high sensitivity and high specificity simultaneously, and conclude that current tractography algorithms, in combination with anatomically driven constraints, can result in reconstructions which very accurately reflect the ground truth white matter connections.
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25
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Chen Y, Zhang ZK, He Y, Zhou C. A Large-Scale High-Density Weighted Structural Connectome of the Macaque Brain Acquired by Predicting Missing Links. Cereb Cortex 2020; 30:4771-4789. [PMID: 32313935 PMCID: PMC7391281 DOI: 10.1093/cercor/bhaa060] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2019] [Revised: 02/20/2020] [Accepted: 02/24/2020] [Indexed: 01/21/2023] Open
Abstract
As a substrate for function, large-scale brain structural networks are crucial for fundamental and systems-level understanding of primate brains. However, it is challenging to acquire a complete primate whole-brain structural connectome using track tracing techniques. Here, we acquired a weighted brain structural network across 91 cortical regions of a whole macaque brain hemisphere with a connectivity density of 59% by predicting missing links from the CoCoMac-based binary network with a low density of 26.3%. The prediction model combines three factors, including spatial proximity, topological similarity, and cytoarchitectural similarity-to predict missing links and assign connection weights. The model was tested on a recently obtained high connectivity density yet partial-coverage experimental weighted network connecting 91 sources to 29 target regions; the model showed a prediction sensitivity of 74.1% in the predicted network. This predicted macaque hemisphere-wide weighted network has module segregation closely matching functional domains. Interestingly, the areas that act as integrators linking the segregated modules are mainly distributed in the frontoparietal network and correspond to the regions with large wiring costs in the predicted weighted network. This predicted weighted network provides a high-density structural dataset for further exploration of relationships between structure, function, and metabolism in the primate brain.
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Affiliation(s)
- Yuhan Chen
- National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China
- Department of Physics, Centre for Nonlinear Studies, and Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems (Hong Kong), Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Hong Kong
| | - Zi-Ke Zhang
- College of Media and International Culture, Zhejiang University, Hangzhou 310058, China
- Alibaba Research Center for Complex Sciences, Hangzhou Normal University, Hangzhou 311121, China
| | - Yong He
- National Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China
| | - Changsong Zhou
- Department of Physics, Centre for Nonlinear Studies, and Beijing-Hong Kong-Singapore Joint Centre for Nonlinear and Complex Systems (Hong Kong), Institute of Computational and Theoretical Studies, Hong Kong Baptist University, Hong Kong
- Department of Physics, Zhejiang University, Hangzhou 310027, China
- Research Centre, HKBU Institute of Research and Continuing Education, Shenzhen 518000, China
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26
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Girard G, Caminiti R, Battaglia-Mayer A, St-Onge E, Ambrosen KS, Eskildsen SF, Krug K, Dyrby TB, Descoteaux M, Thiran JP, Innocenti GM. On the cortical connectivity in the macaque brain: A comparison of diffusion tractography and histological tracing data. Neuroimage 2020; 221:117201. [PMID: 32739552 DOI: 10.1016/j.neuroimage.2020.117201] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Revised: 07/17/2020] [Accepted: 07/22/2020] [Indexed: 12/22/2022] Open
Abstract
Diffusion-weighted magnetic resonance imaging (DW-MRI) tractography is a non-invasive tool to probe neural connections and the structure of the white matter. It has been applied successfully in studies of neurological disorders and normal connectivity. Recent work has revealed that tractography produces a high incidence of false-positive connections, often from "bottleneck" white matter configurations. The rich literature in histological connectivity analysis studies in the macaque monkey enables quantitative evaluation of the performance of tractography algorithms. In this study, we use the intricate connections of frontal, cingulate, and parietal areas, well established by the anatomical literature, to derive a symmetrical histological connectivity matrix composed of 59 cortical areas. We evaluate the performance of fifteen diffusion tractography algorithms, including global, deterministic, and probabilistic state-of-the-art methods for the connectivity predictions of 1711 distinct pairs of areas, among which 680 are reported connected by the literature. The diffusion connectivity analysis was performed on a different ex-vivo macaque brain, acquired using multi-shell DW-MRI protocol, at high spatial and angular resolutions. Across all tested algorithms, the true-positive and true-negative connections were dominant over false-positive and false-negative connections, respectively. Moreover, three-quarters of streamlines had endpoints location in agreement with histological data, on average. Furthermore, probabilistic streamline tractography algorithms show the best performances in predicting which areas are connected. Altogether, we propose a method for quantitative evaluation of tractography algorithms, which aims at improving the sensitivity and the specificity of diffusion-based connectivity analysis. Overall, those results confirm the usefulness of tractography in predicting connectivity, although errors are produced. Many of the errors result from bottleneck white matter configurations near the cortical grey matter and should be the target of future implementation of methods.
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Affiliation(s)
- Gabriel Girard
- Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland; Center for BioMedical Imaging, Lausanne, Switzerland; Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
| | - Roberto Caminiti
- Neuroscience and Behavior Laboratory, Istituto Italiano di Tecnologia, Rome, Italy
| | | | - Etienne St-Onge
- Sherbrooke Connectivity Imaging Lab, Computer Science Department, Faculty of Science, Université de Sherbrooke, Sherbrooke, Canada
| | - Karen S Ambrosen
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark; Danish Research Centre for Magnetic Resonance, Center for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark
| | - Simon F Eskildsen
- Center of Functionally Integrative Neuroscience, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Kristine Krug
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom; Institute of Biology, Otto-von-Guericke-Universität Magdeburg, Magdeburg, Germany; Leibniz-Insitute for Neurobiology, Magdeburg, Germany
| | - Tim B Dyrby
- Danish Research Centre for Magnetic Resonance, Center for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark; Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Lab, Computer Science Department, Faculty of Science, Université de Sherbrooke, Sherbrooke, Canada
| | - Jean-Philippe Thiran
- Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland; Center for BioMedical Imaging, Lausanne, Switzerland; Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Giorgio M Innocenti
- Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden; Brain and Mind Institute, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
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27
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Abstract
The development and persistence of laterality is a key feature of human motor behavior, with the asymmetry of hand use being the most prominent. The idea that asymmetrical functions of the hands reflect asymmetries in terms of structural and functional brain organization has been tested many times. However, despite advances in laterality research and increased understanding of this population-level bias, the neural basis of handedness remains elusive. Recent developments in diffusion magnetic resonance imaging enabled the exploration of lateralized motor behavior also in terms of white matter and connectional neuroanatomy. Despite incomplete and partly inconsistent evidence, structural connectivity of both intrahemispheric and interhemispheric white matter seems to differ between left and right-handers. Handedness was related to asymmetry of intrahemispheric pathways important for visuomotor and visuospatial processing (superior longitudinal fasciculus), but not to projection tracts supporting motor execution (corticospinal tract). Moreover, the interindividual variability of the main commissural pathway corpus callosum seems to be associated with handedness. The review highlights the importance of exploring new avenues for the study of handedness and presents the latest state of knowledge that can be used to guide future neuroscientific and genetic research.
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Affiliation(s)
- Sanja Budisavljevic
- Department of General Psychology, University of Padova, Padova, Italy.,The School of Medicine, University of St. Andrews, St. Andrews, UK
| | - Umberto Castiello
- Department of General Psychology, University of Padova, Padova, Italy
| | - Chiara Begliomini
- Department of General Psychology, University of Padova, Padova, Italy
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28
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Di Cesare G, Pinardi C, Carapelli C, Caruana F, Marchi M, Gerbella M, Rizzolatti G. Insula Connections With the Parieto-Frontal Circuit for Generating Arm Actions in Humans and Macaque Monkeys. Cereb Cortex 2020; 29:2140-2147. [PMID: 29741595 DOI: 10.1093/cercor/bhy095] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2017] [Revised: 03/08/2018] [Indexed: 11/12/2022] Open
Abstract
It has been recently found that the human dorso-central insular cortex contributes to the execution and recognition of the affective component of hand actions, most likely through modulation of the activity of the parieto-frontal circuits. While the anatomical connections between the hand representation of the insula and, the parietal and frontal regions controlling reaching/grasping actions is well assessed in the monkey, it is unknown the existence of a homolog circuit in humans. In the present study, we performed a multifiber tractography investigation to trace the tracts possibly connecting the insula to the parieto-frontal circuits by locating seeds in the parietal, premotor, and prefrontal nodes of the reaching/grasping network, in both humans and monkeys. Results showed that, in both species, the insula is connected with the cortical action execution/recognition circuit by similar white matter tracts, running in parallel to the third branch of the superior longitudinal fasciculus and the anterior segment of the arcuate fasciculus.
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Affiliation(s)
- G Di Cesare
- Department of Robotics, Brain and Cognitive Sciences (RBCS), Istituto Italiano di Tecnologia (IIT), Genova, Italy
| | - C Pinardi
- Department of Medicine and Surgery, Neuroscience Unit, University of Parma, Parma, Italy
| | - C Carapelli
- Department of Medicine and Surgery, Neuroscience Unit, University of Parma, Parma, Italy
| | - F Caruana
- Department of Medicine and Surgery, Neuroscience Unit, University of Parma, Parma, Italy
| | - M Marchi
- Department of Computer Science, University of Milan, Milan, Italy
| | - M Gerbella
- Istituto Italiano di Tecnologia (IIT), Center for Biomolecular Nanotechnologies (CBN), Via Barsanti, Arnesano, Italy
| | - G Rizzolatti
- Department of Medicine and Surgery, Neuroscience Unit, University of Parma, Parma, Italy.,Consiglio nazionale delle Ricerche, Istituto di Neuroscienze, Parma, Italy
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29
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Rushmore RJ, Bouix S, Kubicki M, Rathi Y, Yeterian EH, Makris N. How Human Is Human Connectional Neuroanatomy? Front Neuroanat 2020; 14:18. [PMID: 32351367 PMCID: PMC7176274 DOI: 10.3389/fnana.2020.00018] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2019] [Accepted: 03/23/2020] [Indexed: 01/16/2023] Open
Abstract
The structure of the human brain has been studied extensively. Despite all the knowledge accrued, direct information about connections, from origin to termination, in the human brain is extremely limited. Yet there is a widespread misperception that human connectional neuroanatomy is well-established and validated. In this article, we consider what is known directly about human structural and connectional neuroanatomy. Information on neuroanatomical connections in the human brain is derived largely from studies in non-human experimental models in which the entire connectional pathway, including origins, course, and terminations, is directly visualized. Techniques to examine structural connectivity in the human brain are progressing rapidly; nevertheless, our present understanding of such connectivity is limited largely to data derived from homological comparisons, particularly with non-human primates. We take the position that an in-depth and more precise understanding of human connectional neuroanatomy will be obtained by a systematic application of this homological approach.
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Affiliation(s)
- R Jarrett Rushmore
- Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA, United States.,Psychiatric Neuroimaging Laboratory, Harvard Medical School, Brigham and Women's Hospital, Boston, MA, United States.,Center for Morphometric Analysis, Department of Psychiatry and Neurology, A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Sylvain Bouix
- Psychiatric Neuroimaging Laboratory, Harvard Medical School, Brigham and Women's Hospital, Boston, MA, United States
| | - Marek Kubicki
- Psychiatric Neuroimaging Laboratory, Harvard Medical School, Brigham and Women's Hospital, Boston, MA, United States.,Center for Morphometric Analysis, Department of Psychiatry and Neurology, A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Yogesh Rathi
- Psychiatric Neuroimaging Laboratory, Harvard Medical School, Brigham and Women's Hospital, Boston, MA, United States.,Center for Morphometric Analysis, Department of Psychiatry and Neurology, A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Edward H Yeterian
- Psychiatric Neuroimaging Laboratory, Harvard Medical School, Brigham and Women's Hospital, Boston, MA, United States.,Center for Morphometric Analysis, Department of Psychiatry and Neurology, A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States.,Department of Psychology, Colby College, Waterville, ME, United States
| | - Nikos Makris
- Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA, United States.,Psychiatric Neuroimaging Laboratory, Harvard Medical School, Brigham and Women's Hospital, Boston, MA, United States.,Center for Morphometric Analysis, Department of Psychiatry and Neurology, A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
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30
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Movahedian Attar F, Kirilina E, Haenelt D, Pine KJ, Trampel R, Edwards LJ, Weiskopf N. Mapping Short Association Fibers in the Early Cortical Visual Processing Stream Using In Vivo Diffusion Tractography. Cereb Cortex 2020; 30:4496-4514. [PMID: 32297628 PMCID: PMC7325803 DOI: 10.1093/cercor/bhaa049] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Short association fibers (U-fibers) connect proximal cortical areas and constitute the majority of white matter connections in the human brain. U-fibers play an important role in brain development, function, and pathology but are underrepresented in current descriptions of the human brain connectome, primarily due to methodological challenges in diffusion magnetic resonance imaging (dMRI) of these fibers. High spatial resolution and dedicated fiber and tractography models are required to reliably map the U-fibers. Moreover, limited quantitative knowledge of their geometry and distribution makes validation of U-fiber tractography challenging. Submillimeter resolution diffusion MRI—facilitated by a cutting-edge MRI scanner with 300 mT/m maximum gradient amplitude—was used to map U-fiber connectivity between primary and secondary visual cortical areas (V1 and V2, respectively) in vivo. V1 and V2 retinotopic maps were obtained using functional MRI at 7T. The mapped V1–V2 connectivity was retinotopically organized, demonstrating higher connectivity for retinotopically corresponding areas in V1 and V2 as expected. The results were highly reproducible, as demonstrated by repeated measurements in the same participants and by an independent replication group study. This study demonstrates a robust U-fiber connectivity mapping in vivo and is an important step toward construction of a more complete human brain connectome.
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Affiliation(s)
- Fakhereh Movahedian Attar
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany
| | - Evgeniya Kirilina
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany.,Department of Education and Psychology, Center for Cognitive Neuroscience Berlin, Free University Berlin, 14195 Berlin, Germany
| | - Daniel Haenelt
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany
| | - Kerrin J Pine
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany
| | - Robert Trampel
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany
| | - Luke J Edwards
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany
| | - Nikolaus Weiskopf
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany.,Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth Sciences, Leipzig University, 04109 Leipzig, Germany
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31
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Nath V, Schilling KG, Parvathaneni P, Huo Y, Blaber JA, Hainline AE, Barakovic M, Romascano D, Rafael-Patino J, Frigo M, Girard G, Thiran JP, Daducci A, Rowe M, Rodrigues P, Prchkovska V, Aydogan DB, Sun W, Shi Y, Parker WA, Ould Ismail AA, Verma R, Cabeen RP, Toga AW, Newton AT, Wasserthal J, Neher P, Maier-Hein K, Savini G, Palesi F, Kaden E, Wu Y, He J, Feng Y, Paquette M, Rheault F, Sidhu J, Lebel C, Leemans A, Descoteaux M, Dyrby TB, Kang H, Landman BA. Tractography reproducibility challenge with empirical data (TraCED): The 2017 ISMRM diffusion study group challenge. J Magn Reson Imaging 2020; 51:234-249. [PMID: 31179595 PMCID: PMC6900461 DOI: 10.1002/jmri.26794] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2019] [Accepted: 05/06/2019] [Indexed: 12/30/2022] Open
Abstract
BACKGROUND Fiber tracking with diffusion-weighted MRI has become an essential tool for estimating in vivo brain white matter architecture. Fiber tracking results are sensitive to the choice of processing method and tracking criteria. PURPOSE To assess the variability for an algorithm in group studies reproducibility is of critical context. However, reproducibility does not assess the validity of the brain connections. Phantom studies provide concrete quantitative comparisons of methods relative to absolute ground truths, yet do no capture variabilities because of in vivo physiological factors. The ISMRM 2017 TraCED challenge was created to fulfill the gap. STUDY TYPE A systematic review of algorithms and tract reproducibility studies. SUBJECTS Single healthy volunteers. FIELD STRENGTH/SEQUENCE 3.0T, two different scanners by the same manufacturer. The multishell acquisition included b-values of 1000, 2000, and 3000 s/mm2 with 20, 45, and 64 diffusion gradient directions per shell, respectively. ASSESSMENT Nine international groups submitted 46 tractography algorithm entries each consisting 16 tracts per scan. The algorithms were assessed using intraclass correlation (ICC) and the Dice similarity measure. STATISTICAL TESTS Containment analysis was performed to assess if the submitted algorithms had containment within tracts of larger volume submissions. This also serves the purpose to detect if spurious submissions had been made. RESULTS The top five submissions had high ICC and Dice >0.88. Reproducibility was high within the top five submissions when assessed across sessions or across scanners: 0.87-0.97. Containment analysis shows that the top five submissions are contained within larger volume submissions. From the total of 16 tracts as an outcome relatively the number of tracts with high, moderate, and low reproducibility were 8, 4, and 4. DATA CONCLUSION The different methods clearly result in fundamentally different tract structures at the more conservative specificity choices. Data and challenge infrastructure remain available for continued analysis and provide a platform for comparison. LEVEL OF EVIDENCE 5 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2020;51:234-249.
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Affiliation(s)
- Vishwesh Nath
- Computer Science, Vanderbilt University, Nashville, TN, USA
| | | | | | - Yuankai Huo
- Electrical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Justin A. Blaber
- Electrical Engineering, Vanderbilt University, Nashville, TN, USA
| | | | | | | | | | | | | | | | | | | | | | | | - Dogu B. Aydogan
- Keck School of Medicine, University of Southern California (NICR), Los Angeles CA, USA
| | - Wei Sun
- Keck School of Medicine, University of Southern California (NICR), Los Angeles CA, USA
| | - Yonggang Shi
- Keck School of Medicine, University of Southern California (NICR), Los Angeles CA, USA
| | - William A. Parker
- Center for Biomedical Image Computing and Analytics, Dept of Radiology, Perelman School of Medicine, University of Pennsylvania (UPENN)
| | - Abdol A. Ould Ismail
- Center for Biomedical Image Computing and Analytics, Dept of Radiology, Perelman School of Medicine, University of Pennsylvania (UPENN)
| | - Ragini Verma
- Center for Biomedical Image Computing and Analytics, Dept of Radiology, Perelman School of Medicine, University of Pennsylvania (UPENN)
| | - Ryan P. Cabeen
- Laboratory of Neuro Imaging (LONI), USC Stevens Neuroimaging and Informatics Institute
| | - Arthur W. Toga
- Laboratory of Neuro Imaging (LONI), USC Stevens Neuroimaging and Informatics Institute
| | - Allen T. Newton
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN
| | - Jakob Wasserthal
- Medical Image Computing Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Peter Neher
- Medical Image Computing Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Klaus Maier-Hein
- Medical Image Computing Group, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | - Fulvia Palesi
- Brain Connectivity Center, C. Mondino National Neurological Institute (EFG), Pavia, Italy
| | - Enrico Kaden
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
| | - Ye Wu
- Institution of Information Processing and Automation, Zhejiang University of Technology (ZUT), Hangzhou, China
| | - Jianzhong He
- Institution of Information Processing and Automation, Zhejiang University of Technology (ZUT), Hangzhou, China
| | - Yuanjing Feng
- Institution of Information Processing and Automation, Zhejiang University of Technology (ZUT), Hangzhou, China
| | - Michael Paquette
- Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Université de Sherbrooke, 2500 Boul. Université, J1K 2R1, Sherbrooke, Canada
| | - Francois Rheault
- Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Université de Sherbrooke, 2500 Boul. Université, J1K 2R1, Sherbrooke, Canada
| | - Jasmeen Sidhu
- Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Université de Sherbrooke, 2500 Boul. Université, J1K 2R1, Sherbrooke, Canada
| | | | - Alexander Leemans
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Université de Sherbrooke, 2500 Boul. Université, J1K 2R1, Sherbrooke, Canada
| | - Tim B. Dyrby
- Danish Research Centre for Magnetic Resonance, Copenhagen University Hospital, Hvidovre, Denmark
| | - Hakmook Kang
- Biostatistics, Vanderbilt University, Nashville, TN, USA
| | - Bennett A. Landman
- Computer Science, Vanderbilt University, Nashville, TN, USA
- Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
- Electrical Engineering, Vanderbilt University, Nashville, TN, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN
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32
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Ambrosen KS, Eskildsen SF, Hinne M, Krug K, Lundell H, Schmidt MN, van Gerven MAJ, Mørup M, Dyrby TB. Validation of structural brain connectivity networks: The impact of scanning parameters. Neuroimage 2019; 204:116207. [PMID: 31539592 DOI: 10.1016/j.neuroimage.2019.116207] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2019] [Revised: 08/20/2019] [Accepted: 09/17/2019] [Indexed: 12/20/2022] Open
Abstract
Evaluation of the structural connectivity (SC) of the brain based on tractography has mainly focused on the choice of diffusion model, tractography algorithm, and their respective parameter settings. Here, we systematically validate SC derived from a post mortem monkey brain, while varying key acquisition parameters such as the b-value, gradient angular resolution and image resolution. As gold standard we use the connectivity matrix obtained invasively with histological tracers by Markov et al. (2014). As performance metric, we use cross entropy as a measure that enables comparison of the relative tracer labeled neuron counts to the streamline counts from tractography. We find that high angular resolution and high signal-to-noise ratio are important to estimate SC, and that SC derived from low image resolution (1.03 mm3) are in better agreement with the tracer network, than those derived from high image resolution (0.53 mm3) or at an even lower image resolution (2.03 mm3). In contradiction, sensitivity and specificity analyses suggest that if the angular resolution is sufficient, the balanced compromise in which sensitivity and specificity are identical remains 60-64% regardless of the other scanning parameters. Interestingly, the tracer graph is assumed to be the gold standard but by thresholding, the balanced compromise increases to 70-75%. Hence, by using performance metrics based on binarized tracer graphs, one risks losing important information, changing the performance of SC graphs derived by tractography and their dependence of different scanning parameters.
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Affiliation(s)
- Karen S Ambrosen
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark; Danish Research Centre for Magnetic Resonance, Center for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark
| | - Simon F Eskildsen
- Center of Functionally Integrative Neuroscience (CFIN), Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Max Hinne
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - Kristine Krug
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, UK; Institute of Biology, Otto-von-Guericke-Universität Magdeburg, Magdeburg, Germany; Leibniz-Insitute for Neurobiology, Magdeburg, Germany
| | - Henrik Lundell
- Danish Research Centre for Magnetic Resonance, Center for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark
| | - Mikkel N Schmidt
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Marcel A J van Gerven
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - Morten Mørup
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Tim B Dyrby
- Danish Research Centre for Magnetic Resonance, Center for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark; Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark.
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33
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Takemura H, Pestilli F, Weiner KS. Comparative neuroanatomy: Integrating classic and modern methods to understand association fibers connecting dorsal and ventral visual cortex. Neurosci Res 2019; 146:1-12. [PMID: 30389574 PMCID: PMC6491271 DOI: 10.1016/j.neures.2018.10.011] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2018] [Revised: 10/19/2018] [Accepted: 10/25/2018] [Indexed: 12/13/2022]
Abstract
Comparative neuroanatomy studies improve understanding of brain structure and function and provide insight regarding brain development, evolution, and also what features of the brain are uniquely human. With modern methods such as diffusion MRI (dMRI) and quantitative MRI (qMRI), we are able to measure structural features of the brain with the same methods across human and non-human primates. In this review article, we discuss how recent dMRI measurements of vertical occipital connections in humans and macaques can be compared with previous findings from invasive anatomical studies that examined connectivity, including relatively forgotten classic strychnine neuronography studies. We then review recent progress in understanding the neuroanatomy of vertical connections within the occipitotemporal cortex by combining modern quantitative MRI and classical histological measurements in human and macaque. Finally, we a) discuss current limitations of dMRI and tractography and b) consider potential paths for future investigations using dMRI and tractography for comparative neuroanatomical studies of white matter tracts between species. While we focus on vertical association connections in visual cortex in the present paper, this same approach can be applied to other white matter tracts. Similar efforts are likely to continue to advance our understanding of the neuroanatomical features of the brain that are shared across species, as well as to distinguish the features that are uniquely human.
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Affiliation(s)
- Hiromasa Takemura
- Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology, and Osaka University, Suita, Japan; Graduate School of Frontier Biosciences, Osaka University, Suita, Japan.
| | - Franco Pestilli
- Departments of Psychological and Brain Sciences, Computer Science and Intelligent Systems Engineering, Programs in Neuroscience and Cognitive Science, School of Optometry, Indiana University, Bloomington, IN, USA
| | - Kevin S Weiner
- Department of Psychology, University of California, Berkeley, CA, USA; Helen Wills Neuroscience Institute, University of California, Berkeley, CA, USA
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34
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Johnson GA, Wang N, Anderson RJ, Chen M, Cofer GP, Gee JC, Pratson F, Tustison N, White LE. Whole mouse brain connectomics. J Comp Neurol 2019; 527:2146-2157. [PMID: 30328104 PMCID: PMC6467764 DOI: 10.1002/cne.24560] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2018] [Revised: 10/01/2018] [Accepted: 10/02/2018] [Indexed: 12/22/2022]
Abstract
Methods have been developed to allow quantitative connectivity of the whole fixed mouse brain by means of magnetic resonance imaging (MRI). We have translated what we have learned in clinical imaging to the very special domain of the mouse brain. Diffusion tensor imaging (DTI) of perfusion fixed specimens can now be performed with spatial resolution of 45 μm3 , that is, voxels that are 21,000 times smaller than the human connectome protocol. Specimen preparation has been optimized through an active staining protocol using a Gd chelate. Compressed sensing has been integrated into high performance reconstruction and post processing pipelines allowing acquisition of a whole mouse brain connectome in <12 hr. The methods have been validated against retroviral tracer studies. False positive tracts, which are especially problematic in clinical studies, have been reduced substantially to ~28%. The methods have been streamlined to provide high-fidelity, whole mouse brain connectomes as a routine study. The data package provides holistic insight into the mouse brain with anatomic definition at the meso-scale, quantitative volumes of subfields, scalar DTI metrics, and quantitative tractography.
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Affiliation(s)
- G. Allan Johnson
- Duke Center for In Vivo Microscopy Department of Radiology, Duke Medical Center Durham, NC 27710
- Biomedical Engineering Duke University Durham, NC 27710
| | - Nian Wang
- Duke Center for In Vivo Microscopy Department of Radiology, Duke Medical Center Durham, NC 27710
| | - Robert J. Anderson
- Duke Center for In Vivo Microscopy Department of Radiology, Duke Medical Center Durham, NC 27710
| | - Min Chen
- Penn Image Computing Lab University of Pennsylvania Philadelphia, PA 19104-6116
| | - Gary P. Cofer
- Duke Center for In Vivo Microscopy Department of Radiology, Duke Medical Center Durham, NC 27710
| | - James C. Gee
- Penn Image Computing Lab University of Pennsylvania Philadelphia, PA 19104-6116
| | - Forrest Pratson
- Duke Center for In Vivo Microscopy Department of Radiology, Duke Medical Center Durham, NC 27710
| | - Nicholas Tustison
- Department of Radiology and Medical Imaging University of Virginia Charlottesville, VA 22903
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35
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Bryant KL, Glasser MF, Li L, Jae-Cheol Bae J, Jacquez NJ, Alarcón L, Fields A, Preuss TM. Organization of extrastriate and temporal cortex in chimpanzees compared to humans and macaques. Cortex 2019; 118:223-243. [PMID: 30910223 PMCID: PMC6697630 DOI: 10.1016/j.cortex.2019.02.010] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2018] [Revised: 08/31/2018] [Accepted: 02/13/2019] [Indexed: 01/11/2023]
Abstract
There is evidence for enlargement of association cortex in humans compared to other primate species. Expansion of temporal association cortex appears to have displaced extrastriate cortex posteriorly and inferiorly in humans compared to macaques. However, the details of the organization of these recently expanded areas are still being uncovered. Here, we used diffusion tractography to examine the organization of extrastriate and temporal association cortex in chimpanzees, humans, and macaques. Our goal was to characterize the organization of visual and auditory association areas with respect to their corresponding primary areas (primary visual cortex and auditory core) in humans and chimpanzees. We report three results: (1) Humans, chimpanzees, and macaques show expected retinotopic organization of primary visual cortex (V1) connectivity to V2 and to areas immediately anterior to V2; (2) In contrast to macaques, chimpanzee and human V1 shows apparent connectivity with lateral, inferior, and anterior temporal regions, beyond the retinotopically organized extrastriate areas; (3) Also in contrast to macaques, chimpanzee and human auditory core shows apparent connectivity with temporal association areas, with some important differences between humans and chimpanzees. Diffusion tractography reconstructs diffusion patterns that reflect white matter organization, but does not definitively represent direct anatomical connectivity. Therefore, it is important to recognize that our findings are suggestive of species differences in long-distance white matter organization rather than demonstrations of direct connections. Our data support the conclusion that expansion of temporal association cortex, and the resulting posterior displacement of extrastriate cortex, occurred in the human lineage after its separation from the chimpanzee lineage. It is possible, however, that some expansion of the temporal lobe occurred prior to the separation of humans and chimpanzees, reflected in the reorganization of long white matter tracts in the temporal lobe that connect occipital areas to the fusiform gyrus, middle temporal gyrus, and anterior temporal lobe.
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Affiliation(s)
- Katherine L Bryant
- Division of Neuropharmacology and Neurologic Diseases, Yerkes National Primate Research Center, Emory University, Atlanta, GA, USA; Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, the Netherlands
| | - Matthew F Glasser
- Departments of Radiology and Neuroscience, Washington University Medical School, St. Louis, MO, USA
| | - Longchuan Li
- Marcus Autism Center, Children's Healthcare of Atlanta, Emory University, Atlanta, GA, USA
| | - Jason Jae-Cheol Bae
- Neuroscience and Behavioral Biology, Emory University, Atlanta, GA, USA; College of Medicine, American University of Antigua, USA
| | - Nadine J Jacquez
- Neuroscience and Behavioral Biology, Emory University, Atlanta, GA, USA
| | - Laura Alarcón
- Neuroscience and Behavioral Biology, Emory University, Atlanta, GA, USA
| | - Archie Fields
- Department of Philosophy, University of Calgary, Calgary, Alberta, Canada
| | - Todd M Preuss
- Division of Neuropharmacology and Neurologic Diseases, Yerkes National Primate Research Center, Emory University, Atlanta, GA, USA; Center for Translational Social Neuroscience, Emory University, Atlanta, GA, USA; Department of Pathology & Laboratory Medicine, Emory University School of Medicine, Atlanta, GA, USA.
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36
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Delettre C, Messé A, Dell LA, Foubet O, Heuer K, Larrat B, Meriaux S, Mangin JF, Reillo I, de Juan Romero C, Borrell V, Toro R, Hilgetag CC. Comparison between diffusion MRI tractography and histological tract-tracing of cortico-cortical structural connectivity in the ferret brain. Netw Neurosci 2019; 3:1038-1050. [PMID: 31637337 PMCID: PMC6777980 DOI: 10.1162/netn_a_00098] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2019] [Accepted: 05/23/2019] [Indexed: 12/20/2022] Open
Abstract
The anatomical wiring of the brain is a central focus in network neuroscience. Diffusion MRI tractography offers the unique opportunity to investigate the brain fiber architecture in vivo and noninvasively. However, its reliability is still highly debated. Here, we explored the ability of diffusion MRI tractography to match invasive anatomical tract-tracing connectivity data of the ferret brain. We also investigated the influence of several state-of-the-art tractography algorithms on this match to ground truth connectivity data. Tract-tracing connectivity data were obtained from retrograde tracer injections into the occipital, parietal, and temporal cortices of adult ferrets. We found that the relative densities of projections identified from the anatomical experiments were highly correlated with the estimates from all the studied diffusion tractography algorithms (Spearman's rho ranging from 0.67 to 0.91), while only small, nonsignificant variations appeared across the tractography algorithms. These results are comparable to findings reported in mouse and monkey, increasing the confidence in diffusion MRI tractography results. Moreover, our results provide insights into the variations of sensitivity and specificity of the tractography algorithms, and hence into the influence of choosing one algorithm over another.
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Affiliation(s)
- Céline Delettre
- Unité de Génétique Humaine et Fonctions Cognitives, Institut Pasteur, UMR 3571, CNRS, Paris, France
- Institute of Computational Neuroscience, University Medical Center Eppendorf, Hamburg University, Hamburg, Germany
- Université Paris Diderot, Sorbonne Paris Cité, Paris, France
| | - Arnaud Messé
- Institute of Computational Neuroscience, University Medical Center Eppendorf, Hamburg University, Hamburg, Germany
| | - Leigh-Anne Dell
- Institute of Computational Neuroscience, University Medical Center Eppendorf, Hamburg University, Hamburg, Germany
| | - Ophélie Foubet
- Unité de Génétique Humaine et Fonctions Cognitives, Institut Pasteur, UMR 3571, CNRS, Paris, France
| | - Katja Heuer
- Unité de Génétique Humaine et Fonctions Cognitives, Institut Pasteur, UMR 3571, CNRS, Paris, France
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Benoit Larrat
- NeuroSpin, CEA, Paris-Saclay University, Gif-sur-Yvette, France
| | | | | | - Isabel Reillo
- Developmental Neurobiology Unit, Instituto de Neurociencias, Consejo Superior de Investigaciones Científicas, Universidad Miguel Hernández, Sant Joan d’Alacant, Spain
| | - Camino de Juan Romero
- Developmental Neurobiology Unit, Instituto de Neurociencias, Consejo Superior de Investigaciones Científicas, Universidad Miguel Hernández, Sant Joan d’Alacant, Spain
| | - Victor Borrell
- Developmental Neurobiology Unit, Instituto de Neurociencias, Consejo Superior de Investigaciones Científicas, Universidad Miguel Hernández, Sant Joan d’Alacant, Spain
| | - Roberto Toro
- Unité de Génétique Humaine et Fonctions Cognitives, Institut Pasteur, UMR 3571, CNRS, Paris, France
- Center for Research and Interdisciplinarity (CRI), Université Paris Descartes, Paris, France
| | - Claus C. Hilgetag
- Institute of Computational Neuroscience, University Medical Center Eppendorf, Hamburg University, Hamburg, Germany
- Department of Health Sciences, Boston University, Boston, MA, USA
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Shen K, Bezgin G, Schirner M, Ritter P, Everling S, McIntosh AR. A macaque connectome for large-scale network simulations in TheVirtualBrain. Sci Data 2019; 6:123. [PMID: 31316116 PMCID: PMC6637142 DOI: 10.1038/s41597-019-0129-z] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/18/2018] [Accepted: 06/18/2019] [Indexed: 12/15/2022] Open
Abstract
Models of large-scale brain networks that are informed by the underlying anatomical connectivity contribute to our understanding of the mapping between the structure of the brain and its dynamical function. Connectome-based modelling is a promising approach to a more comprehensive understanding of brain function across spatial and temporal scales, but it must be constrained by multi-scale empirical data from animal models. Here we describe the construction of a macaque (Macaca mulatta and Macaca fascicularis) connectome for whole-cortex simulations in TheVirtualBrain, an open-source simulation platform. We take advantage of available axonal tract-tracing datasets and enhance the existing connectome data using diffusion-based tractography in macaques. We illustrate the utility of the connectome as an extension of TheVirtualBrain by simulating resting-state BOLD-fMRI data and fitting it to empirical resting-state data.
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Affiliation(s)
- Kelly Shen
- Rotman Research Institute, Baycrest, Toronto, Ontario, Canada.
| | - Gleb Bezgin
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Michael Schirner
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Department of Neurology, Berlin, Germany
- Berlin Institute of Health (BIH), Berlin, Germany
- Bernstein Center for Computational Neuroscience, Berlin, Germany
| | - Petra Ritter
- Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, Department of Neurology, Berlin, Germany
- Berlin Institute of Health (BIH), Berlin, Germany
- Bernstein Center for Computational Neuroscience, Berlin, Germany
| | - Stefan Everling
- Robarts Research Institute, University of Western Ontario, London, Ontario, Canada
- Department of Physiology and Pharmacology, University of Western Ontario, London, Ontario, Canada
| | - Anthony R McIntosh
- Rotman Research Institute, Baycrest, Toronto, Ontario, Canada
- Department of Psychology, University of Toronto, Toronto, Ontario, Canada
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The Known and Missing Links Between the Cerebellum, Basal Ganglia, and Cerebral Cortex. THE CEREBELLUM 2019; 16:753-755. [PMID: 28215041 DOI: 10.1007/s12311-017-0850-0] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
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Bridge H, Bell AH, Ainsworth M, Sallet J, Premereur E, Ahmed B, Mitchell AS, Schüffelgen U, Buckley M, Tendler BC, Miller KL, Mars RB, Parker AJ, Krug K. Preserved extrastriate visual network in a monkey with substantial, naturally occurring damage to primary visual cortex. eLife 2019; 8:e42325. [PMID: 31120417 PMCID: PMC6533062 DOI: 10.7554/elife.42325] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/02/2018] [Accepted: 04/27/2019] [Indexed: 12/13/2022] Open
Abstract
Lesions of primary visual cortex (V1) lead to loss of conscious visual perception with significant impact on human patients. Understanding the neural consequences of such damage may aid the development of rehabilitation methods. In this rare case of a Rhesus macaque (monkey S), likely born without V1, the animal's in-group behaviour was unremarkable, but visual task training was impaired. With multi-modal magnetic resonance imaging, visual structures outside of the lesion appeared normal. Visual stimulation under anaesthesia with checkerboards activated lateral geniculate nucleus of monkey S, while full-field moving dots activated pulvinar. Visual cortical activation was sparse but included face patches. Consistently across lesion and control monkeys, functional connectivity analysis revealed an intact network of bilateral dorsal visual areas temporally correlated with V5/MT activation, even without V1. Despite robust subcortical responses to visual stimulation, we found little evidence for strengthened subcortical input to V5/MT supporting residual visual function or blindsight-like phenomena.
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Affiliation(s)
- Holly Bridge
- Wellcome Centre for Integrative Neuroimaging, FMRIBOxford UniversityOxfordUnited Kingdom
- Nuffield Department of Clinical NeurosciencesOxford UniversityOxfordUnited Kingdom
| | - Andrew H Bell
- Wellcome Centre for Integrative Neuroimaging, FMRIBOxford UniversityOxfordUnited Kingdom
- Department of Experimental PsychologyOxford UniversityOxfordUnited Kingdom
- MRC Cognition and Brain Sciences UnitCambridgeUnited Kingdom
| | - Matthew Ainsworth
- Department of Experimental PsychologyOxford UniversityOxfordUnited Kingdom
- MRC Cognition and Brain Sciences UnitCambridgeUnited Kingdom
| | - Jerome Sallet
- Wellcome Centre for Integrative Neuroimaging, FMRIBOxford UniversityOxfordUnited Kingdom
- Department of Experimental PsychologyOxford UniversityOxfordUnited Kingdom
| | - Elsie Premereur
- Laboratory for Neuro- and PsychophysiologyKU LeuvenLeuvenBelgium
| | - Bashir Ahmed
- Department of Physiology, Anatomy and GeneticsOxford UniversityOxfordUnited Kingdom
| | - Anna S Mitchell
- Department of Experimental PsychologyOxford UniversityOxfordUnited Kingdom
| | - Urs Schüffelgen
- Wellcome Centre for Integrative Neuroimaging, FMRIBOxford UniversityOxfordUnited Kingdom
- Department of Experimental PsychologyOxford UniversityOxfordUnited Kingdom
| | - Mark Buckley
- Department of Experimental PsychologyOxford UniversityOxfordUnited Kingdom
| | - Benjamin C Tendler
- Wellcome Centre for Integrative Neuroimaging, FMRIBOxford UniversityOxfordUnited Kingdom
- Nuffield Department of Clinical NeurosciencesOxford UniversityOxfordUnited Kingdom
| | - Karla L Miller
- Wellcome Centre for Integrative Neuroimaging, FMRIBOxford UniversityOxfordUnited Kingdom
- Nuffield Department of Clinical NeurosciencesOxford UniversityOxfordUnited Kingdom
| | - Rogier B Mars
- Wellcome Centre for Integrative Neuroimaging, FMRIBOxford UniversityOxfordUnited Kingdom
- Nuffield Department of Clinical NeurosciencesOxford UniversityOxfordUnited Kingdom
- Donders Institute for Brain, Cognition and BehaviourRadboud University NijmegenNijmegenNetherlands
| | - Andrew J Parker
- Department of Physiology, Anatomy and GeneticsOxford UniversityOxfordUnited Kingdom
| | - Kristine Krug
- Department of Physiology, Anatomy and GeneticsOxford UniversityOxfordUnited Kingdom
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Circadian circuits in humans: White matter microstructure predicts daytime sleepiness. Cortex 2019; 122:97-107. [PMID: 31097190 DOI: 10.1016/j.cortex.2019.01.011] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2018] [Revised: 10/31/2018] [Accepted: 01/14/2019] [Indexed: 11/22/2022]
Abstract
The suprachiasmatic nucleus of the hypothalamus is the chief circadian pacemaker in the brain, and is entrained to day-night cycles by visual afferents from melanopsin containing retinal ganglion cells via the inferior accessory optic tract. Tracer studies have demonstrated efferents from the suprachiasmatic nucleus projecting to the paraventricular nucleus of the hypothalamus, which in turn project to first-order sympathetic neurons in the intermedio-lateral grey of the spinal cord. Sympathetic projections to the pineal gland trigger the secretion of the sleep inducing hormone melatonin. The current study reports the first demonstration of potential sympathopetal hypothalamic projections involved in circadian regulation in humans with in vivo virtual white matter dissections using probabilistic diffusion tensor imaging (DTI) tractography. Additionally, our data shows a correlation between individual differences in white matter microstructure (measured with fractional anisotropy) and increased daytime sleepiness [measured with the Epworth Sleepiness Scale (ESS, Johns, 1991)]. Sympathopetal connections with the hypothalamus were virtually dissected using designated masks on the optic chiasm, which served as an anatomical landmark for retinal fibres projecting to the suprachiasmatic nucleus, and a waypoint mask on the lateral medulla, where hypothalamic projections to the sympathetic nervous system traverse in humans. Sympathopetal projections were demonstrated in each hemisphere in twenty-six subjects. The tract passed through the suprachiasmatic nucleus of the hypothalamus and its trajectory corresponds to the dorsal longitudinal fasciculus traversing the periaqueductal region and the lateral medulla. White matter microstructure (FA) in the left hemisphere correlated with high scores on the ESS, suggesting an association between circadian pathway white matter microstructure, and increased daytime sleepiness.
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Wang Y, Xu C, Park JH, Lee S, Stern Y, Yoo S, Kim JH, Kim HS, Cha J. Diagnosis and prognosis of Alzheimer's disease using brain morphometry and white matter connectomes. NEUROIMAGE-CLINICAL 2019; 23:101859. [PMID: 31150957 PMCID: PMC6541902 DOI: 10.1016/j.nicl.2019.101859] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/02/2018] [Revised: 05/02/2019] [Accepted: 05/11/2019] [Indexed: 01/05/2023]
Abstract
Accurate, reliable prediction of risk for Alzheimer's disease (AD) is essential for early, disease-modifying therapeutics. Multimodal MRI, such as structural and diffusion MRI, is likely to contain complementary information of neurodegenerative processes in AD. Here we tested the utility of the multimodal MRI (T1-weighted structure and diffusion MRI), combined with high-throughput brain phenotyping—morphometry and structural connectomics—and machine learning, as a diagnostic tool for AD. We used, firstly, a clinical cohort at a dementia clinic (National Health Insurance Service-Ilsan Hospital [NHIS-IH]; N = 211; 110 AD, 64 mild cognitive impairment [MCI], and 37 cognitively normal with subjective memory complaints [SMC]) to test the diagnostic models; and, secondly, Alzheimer's Disease Neuroimaging Initiative (ADNI)-2 to test the generalizability. Our machine learning models trained on the morphometric and connectome estimates (number of features = 34,646) showed optimal classification accuracy (AD/SMC: 97% accuracy, MCI/SMC: 83% accuracy; AD/MCI: 97% accuracy) in NHIS-IH cohort, outperforming a benchmark model (FLAIR-based white matter hyperintensity volumes). In ADNI-2 data, the combined connectome and morphometry model showed similar or superior accuracies (AD/HC: 96%; MCI/HC: 70%; AD/MCI: 75% accuracy) compared with the CSF biomarker model (t-tau, p-tau, and Amyloid β, and ratios). In predicting MCI to AD progression in a smaller cohort of ADNI-2 (n = 60), the morphometry model showed similar performance with 69% accuracy compared with CSF biomarker model with 70% accuracy. Our comparisons of the classifiers trained on structural MRI, diffusion MRI, FLAIR, and CSF biomarkers showed the promising utility of the white matter structural connectomes in classifying AD and MCI in addition to the widely used structural MRI-based morphometry, when combined with machine learning. This study tests the utility of multimodal brain MRI and machine learning in diagnosis and prognosis of AD. Models trained on connectomes and morphometry beast classify elders with AD or MCI from cognitively normal elders. Models trained on morphometry best predict MCI to AD progression.
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Affiliation(s)
- Yun Wang
- Department of Psychiatry, Columbia University Medical Center, New York, NY, USA
| | - Chenxiao Xu
- Department of Applied Mathematics, Stony Brook University, Stony Brook, NY, USA
| | - Ji-Hwan Park
- Department of Applied Mathematics, Stony Brook University, Stony Brook, NY, USA
| | - Seonjoo Lee
- Department of Biostatistics, School of Public Health, Columbia University Medical Center, New York, NY, USA
| | - Yaakov Stern
- Department of Neurology, Columbia University Medical Center, New York, NY, USA
| | - Shinjae Yoo
- Computational Science Initiative, Brookhaven National Laboratory, Upton, NY, USA
| | - Jong Hun Kim
- Department of Neurology, National Health Insurance Service Ilsan Hospital, Goyang, Republic of Korea
| | - Hyoung Seop Kim
- Department of Physical Medicine and Rehabilitation, National Health Insurance Service Ilsan Hospital, Goyang, Republic of Korea.
| | - Jiook Cha
- Department of Psychiatry, Columbia University Medical Center, New York, NY, USA; Data Science Institute, Columbia University, New York, NY, USA.
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Sotiropoulos SN, Zalesky A. Building connectomes using diffusion MRI: why, how and but. NMR IN BIOMEDICINE 2019; 32:e3752. [PMID: 28654718 PMCID: PMC6491971 DOI: 10.1002/nbm.3752] [Citation(s) in RCA: 149] [Impact Index Per Article: 29.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2016] [Revised: 04/05/2017] [Accepted: 05/03/2017] [Indexed: 05/14/2023]
Abstract
Why has diffusion MRI become a principal modality for mapping connectomes in vivo? How do different image acquisition parameters, fiber tracking algorithms and other methodological choices affect connectome estimation? What are the main factors that dictate the success and failure of connectome reconstruction? These are some of the key questions that we aim to address in this review. We provide an overview of the key methods that can be used to estimate the nodes and edges of macroscale connectomes, and we discuss open problems and inherent limitations. We argue that diffusion MRI-based connectome mapping methods are still in their infancy and caution against blind application of deep white matter tractography due to the challenges inherent to connectome reconstruction. We review a number of studies that provide evidence of useful microstructural and network properties that can be extracted in various independent and biologically relevant contexts. Finally, we highlight some of the key deficiencies of current macroscale connectome mapping methodologies and motivate future developments.
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Affiliation(s)
- Stamatios N. Sotiropoulos
- Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUK
- Sir Peter Mansfield Imaging Centre, School of MedicineUniversity of NottinghamNottinghamUK
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre and Melbourne School of EngineeringUniversity of MelbourneVictoriaAustralia
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Shen K, Goulas A, Grayson DS, Eusebio J, Gati JS, Menon RS, McIntosh AR, Everling S. Exploring the limits of network topology estimation using diffusion-based tractography and tracer studies in the macaque cortex. Neuroimage 2019; 191:81-92. [PMID: 30739059 DOI: 10.1016/j.neuroimage.2019.02.018] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2018] [Revised: 01/24/2019] [Accepted: 02/06/2019] [Indexed: 12/31/2022] Open
Abstract
Reconstructing the anatomical pathways of the brain to study the human connectome has become an important endeavour for understanding brain function and dynamics. Reconstruction of the cortico-cortical connectivity matrix in vivo often relies on noninvasive diffusion-weighted imaging (DWI) techniques but the extent to which they can accurately represent the topological characteristics of structural connectomes remains unknown. We addressed this question by constructing connectomes using DWI data collected from macaque monkeys in vivo and with data from published invasive tracer studies. We found the strength of fiber tracts was well estimated from DWI and topological properties like degree and modularity were captured by tractography-based connectomes. Rich-club/core-periphery type architecture could also be detected but the classification of hubs using betweenness centrality, participation coefficient and core-periphery identification techniques was inaccurate. Our findings indicate that certain aspects of cortical topology can be faithfully represented in noninvasively-obtained connectomes while other network analytic measures warrant cautionary interpretations.
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Affiliation(s)
- Kelly Shen
- Rotman Research Institute, Baycrest, Toronto, Ontario, Canada.
| | - Alexandros Goulas
- Department of Computational Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg University, Hamburg, Germany
| | | | - John Eusebio
- Rotman Research Institute, Baycrest, Toronto, Ontario, Canada
| | - Joseph S Gati
- The Centre for Functional and Metabolic Mapping, Department of Physiology and Pharmacology, University of Western Ontario, London, Ontario, Canada
| | - Ravi S Menon
- The Centre for Functional and Metabolic Mapping, Department of Physiology and Pharmacology, University of Western Ontario, London, Ontario, Canada; Department of Physiology and Pharmacology, University of Western Ontario, London, Ontario, Canada
| | - Anthony R McIntosh
- Rotman Research Institute, Baycrest, Toronto, Ontario, Canada; Department of Psychology, University of Toronto, Toronto, Ontario, Canada
| | - Stefan Everling
- The Centre for Functional and Metabolic Mapping, Department of Physiology and Pharmacology, University of Western Ontario, London, Ontario, Canada; Department of Physiology and Pharmacology, University of Western Ontario, London, Ontario, Canada
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Waugh JL, Kuster JK, Makhlouf ML, Levenstein JM, Multhaupt-Buell TJ, Warfield SK, Sharma N, Blood AJ. A registration method for improving quantitative assessment in probabilistic diffusion tractography. Neuroimage 2019; 189:288-306. [PMID: 30611874 DOI: 10.1016/j.neuroimage.2018.12.057] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2017] [Revised: 12/26/2018] [Accepted: 12/28/2018] [Indexed: 01/07/2023] Open
Abstract
Diffusion MRI-based probabilistic tractography is a powerful tool for non-invasively investigating normal brain architecture and alterations in structural connectivity associated with disease states. Both voxelwise and region-of-interest methods of analysis are capable of integrating population differences in tract amplitude (streamline count or density), given proper alignment of the tracts of interest. However, quantification of tract differences (between groups, or longitudinally within individuals) has been hampered by two related features of white matter. First, it is unknown to what extent healthy individuals differ in the precise location of white matter tracts, and to what extent experimental factors influence perceived tract location. Second, white matter lacks the gross neuroanatomical features (e.g., gyri, histological subtyping) that make parcellation of grey matter plausible - determining where tracts "should" lie within larger white matter structures is difficult. Accurately quantifying tractographic connectivity between individuals is thus inherently linked to the difficulty of identifying and aligning precise tract location. Tractography is often utilized to study neurological diseases in which the precise structural and connectivity abnormalities are unknown, underscoring the importance of accounting for individual differences in tract location when evaluating the strength of structural connectivity. We set out to quantify spatial variance in tracts aligned through a standard, whole-brain registration method, and to assess the impact of location mismatch on groupwise assessments of tract amplitude. We then developed a method for tract alignment that enhances the existing standard whole brain registration, and then tested whether this method improved the reliability of groupwise contrasts. Specifically, we conducted seed-based probabilistic diffusion tractography from primary motor, supplementary motor, and visual cortices, projecting through the corpus callosum. Streamline counts decreased rapidly with movement from the tract center (-35% per millimeter); tract misalignment of a few millimeters caused substantial compromise of amplitude comparisons. Alignment of tracts "peak-to-peak" is essential for accurate amplitude comparisons. However, for all transcallosal tracts registered through the whole-brain method, the mean separation distance between an individual subject's tract and the average tract (3.2 mm) precluded accurate comparison: at this separation, tract amplitudes were reduced by 74% from peak value. In contrast, alignment of subcortical tracts (thalamo-putaminal, pallido-rubral) was substantially better than alignment for cortical tracts; whole-brain registration was sufficient for these subcortical tracts. We demonstrated that location mismatches in cortical tractography were sufficient to produce false positive and false negative amplitude estimates in both groupwise and longitudinal comparisons. We then showed that our new tract alignment method substantially reduced location mismatch and improved both reliability and statistical power of subsequent quantitative comparisons.
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Affiliation(s)
- J L Waugh
- Mood and Motor Control Laboratory, Massachusetts General Hospital, Charlestown, MA, United States; Dept. of Neurology, Massachusetts General Hospital, Boston, MA, United States; Division of Child Neurology, Boston Children's Hospital, United States; Harvard Medical School, Boston, MA, United States; Martinos Center for Biomedical Imaging, MGH, Charlestown, MA, United States.
| | - J K Kuster
- Mood and Motor Control Laboratory, Massachusetts General Hospital, Charlestown, MA, United States; Dept. Psychiatry, Massachusetts General Hospital, Boston, MA, United States; Martinos Center for Biomedical Imaging, MGH, Charlestown, MA, United States.
| | - M L Makhlouf
- Mood and Motor Control Laboratory, Massachusetts General Hospital, Charlestown, MA, United States; Dept. Psychiatry, Massachusetts General Hospital, Boston, MA, United States; Harvard-MIT HST Program, United States; Martinos Center for Biomedical Imaging, MGH, Charlestown, MA, United States.
| | - J M Levenstein
- Mood and Motor Control Laboratory, Massachusetts General Hospital, Charlestown, MA, United States; Dept. Psychiatry, Massachusetts General Hospital, Boston, MA, United States; Martinos Center for Biomedical Imaging, MGH, Charlestown, MA, United States.
| | - T J Multhaupt-Buell
- Dept. of Neurology, Massachusetts General Hospital, Boston, MA, United States.
| | - S K Warfield
- Department of Radiology, Boston Children's Hospital, United States; Harvard Medical School, Boston, MA, United States.
| | - N Sharma
- Dept. of Neurology, Massachusetts General Hospital, Boston, MA, United States; Department of Neurology, Brigham and Women's Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States.
| | - A J Blood
- Mood and Motor Control Laboratory, Massachusetts General Hospital, Charlestown, MA, United States; Laboratory of Neuroimaging and Genetics, Massachusetts General Hospital, Charlestown, MA, United States; Dept. Psychiatry, Massachusetts General Hospital, Boston, MA, United States; Harvard Medical School, Boston, MA, United States; Martinos Center for Biomedical Imaging, MGH, Charlestown, MA, United States.
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Sébille SB, Rolland AS, Welter ML, Bardinet E, Santin MD. Post mortem high resolution diffusion MRI for large specimen imaging at 11.7 T with 3D segmented echo-planar imaging. J Neurosci Methods 2019; 311:222-234. [PMID: 30321565 DOI: 10.1016/j.jneumeth.2018.10.010] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2018] [Revised: 10/09/2018] [Accepted: 10/10/2018] [Indexed: 11/25/2022]
Abstract
BACKGROUND Diffusion weighted imaging (DWI) is the only in vivo technique allowing for the mapping of tissue fiber architecture. Post mortem DWI is an increasingly popular method, since longer acquisition times (compared to in vivo) allow higher spatial and angular resolutions to be achieved. However, DWI protocols must be adapted to post mortem tissue (e.g., tuning acquisition parameters to account for changes in T1/T2). New method: In this work, we developed a framework to obtain high quality diffusion weighted images on post mortem large samples by using a combination of fast imaging with 3D diffusion-weighted segmented EPI (3D-DW seg-EPI), Gadolinium soaking and data denoising. Analyses including tractography were used to check the quality of the acquired data, including a comparison with 3D-DW SE acquisitions. Comparison with existing method: Effects on diffusion data of each of the components of the framework were tested: 3D-DW seg-EPI versus 3D-DW SE EPI; with and without data denoising; with and without Gd-soaking. CONCLUSIONS Our study demonstrated the feasibility of analysing anatomical connectivity using diffusion imaging of a post mortem macaque brain with a 3D-DW seg-EPI sequence acquired at ultra-high field. The combination of high angular and spatial resolution DWI with Gd-soaking and denoising provided data allowing us to perform diffusion tractography with results very similar to those obtained with a 3D-DW SE acquisition (with shorter acquisition times: 222 h versus 37 h for 3D-DW seg-EPI).
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Affiliation(s)
- Sophie Bernadette Sébille
- Sorbonne Universités, UPMC Univ Paris 06, CNRS, INSERM, APHP GH Pitié-Salpêtrière, Institut du cerveau et de la moelle épinière (ICM), F-75013 Paris, France; Centre de Neuro-Imagerie de Recherche (CENIR), Paris, France
| | - Anne-Sophie Rolland
- Sorbonne Universités, UPMC Univ Paris 06, CNRS, INSERM, APHP GH Pitié-Salpêtrière, Institut du cerveau et de la moelle épinière (ICM), F-75013 Paris, France
| | - Marie-Laure Welter
- Sorbonne Universités, UPMC Univ Paris 06, CNRS, INSERM, APHP GH Pitié-Salpêtrière, Institut du cerveau et de la moelle épinière (ICM), F-75013 Paris, France
| | - Eric Bardinet
- Sorbonne Universités, UPMC Univ Paris 06, CNRS, INSERM, APHP GH Pitié-Salpêtrière, Institut du cerveau et de la moelle épinière (ICM), F-75013 Paris, France; Centre de Neuro-Imagerie de Recherche (CENIR), Paris, France
| | - Mathieu David Santin
- Sorbonne Universités, UPMC Univ Paris 06, CNRS, INSERM, APHP GH Pitié-Salpêtrière, Institut du cerveau et de la moelle épinière (ICM), F-75013 Paris, France; Centre de Neuro-Imagerie de Recherche (CENIR), Paris, France.
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Sampaio-Baptista C, Diosi K, Johansen-Berg H. Magnetic Resonance Techniques for Imaging White Matter. Methods Mol Biol 2019; 1936:397-407. [PMID: 30820911 DOI: 10.1007/978-1-4939-9072-6_22] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
The white matter is a complex network of brain fibers connecting different information processing regions in the brain. In recent years, the investigation of white matter in humans and in animal models has greatly benefitted from the introduction of in vivo noninvasive magnetic resonance imaging (MRI) techniques. MRI allows for multiple in vivo time-point whole-brain acquisition in the same subject, thus it can be used longitudinally to monitor white matter brain change, intervention effects, as well as disease progression. However, MRI has low spatial resolution compared to gold standard cellular techniques and MRI measures are sensitive to a number of tissue properties resulting in a lack of specificity.The following chapter describes in simple technical terms to non-imaging experts some common MRI techniques that can be used to investigate white matter structure noninvasively, covering some of the advantages and pitfalls of each technique.
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Affiliation(s)
- Cassandra Sampaio-Baptista
- NDCN Department, Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, University of Oxford, Oxford, UK.
| | - Kata Diosi
- NDCN Department, Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, University of Oxford, Oxford, UK
| | - Heidi Johansen-Berg
- NDCN Department, Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, University of Oxford, Oxford, UK
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Schilling KG, Nath V, Hansen C, Parvathaneni P, Blaber J, Gao Y, Neher P, Aydogan DB, Shi Y, Ocampo-Pineda M, Schiavi S, Daducci A, Girard G, Barakovic M, Rafael-Patino J, Romascano D, Rensonnet G, Pizzolato M, Bates A, Fischi E, Thiran JP, Canales-Rodríguez EJ, Huang C, Zhu H, Zhong L, Cabeen R, Toga AW, Rheault F, Theaud G, Houde JC, Sidhu J, Chamberland M, Westin CF, Dyrby TB, Verma R, Rathi Y, Irfanoglu MO, Thomas C, Pierpaoli C, Descoteaux M, Anderson AW, Landman BA. Limits to anatomical accuracy of diffusion tractography using modern approaches. Neuroimage 2018; 185:1-11. [PMID: 30317017 DOI: 10.1016/j.neuroimage.2018.10.029] [Citation(s) in RCA: 141] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2018] [Revised: 09/14/2018] [Accepted: 10/09/2018] [Indexed: 12/12/2022] Open
Abstract
Diffusion MRI fiber tractography is widely used to probe the structural connectivity of the brain, with a range of applications in both clinical and basic neuroscience. Despite widespread use, tractography has well-known pitfalls that limits the anatomical accuracy of this technique. Numerous modern methods have been developed to address these shortcomings through advances in acquisition, modeling, and computation. To test whether these advances improve tractography accuracy, we organized the 3-D Validation of Tractography with Experimental MRI (3D-VoTEM) challenge at the ISBI 2018 conference. We made available three unique independent tractography validation datasets - a physical phantom and two ex vivo brain specimens - resulting in 176 distinct submissions from 9 research groups. By comparing results over a wide range of fiber complexities and algorithmic strategies, this challenge provides a more comprehensive assessment of tractography's inherent limitations than has been reported previously. The central results were consistent across all sub-challenges in that, despite advances in tractography methods, the anatomical accuracy of tractography has not dramatically improved in recent years. Taken together, our results independently confirm findings from decades of tractography validation studies, demonstrate inherent limitations in reconstructing white matter pathways using diffusion MRI data alone, and highlight the need for alternative or combinatorial strategies to accurately map the fiber pathways of the brain.
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Affiliation(s)
- Kurt G Schilling
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA.
| | - Vishwesh Nath
- Electrical Engineering & Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Colin Hansen
- Department of Electrical Engineering, Vanderbilt University, Nashville, TN, USA
| | | | - Justin Blaber
- Department of Electrical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Yurui Gao
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
| | - Peter Neher
- Division of Medical Image Computing, German Cancer Research Center, Heidelberg, Germany
| | - Dogu Baran Aydogan
- Laboratory of Neuro Imaging (LONI), USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Yonggang Shi
- Laboratory of Neuro Imaging (LONI), USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | | | - Simona Schiavi
- Computer Science Department, University of Verona, Verona, Italy
| | | | - Gabriel Girard
- Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Muhamed Barakovic
- Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Jonathan Rafael-Patino
- Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - David Romascano
- Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Gaëtan Rensonnet
- Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Marco Pizzolato
- Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Alice Bates
- Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Elda Fischi
- Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Jean-Philippe Thiran
- Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland
| | - Erick J Canales-Rodríguez
- Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland
| | - Chao Huang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Liming Zhong
- Guangdong Provincial Key Laboratory of Medical Image Processing, School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Ryan Cabeen
- Laboratory of Neuro Imaging (LONI), USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Arthur W Toga
- Laboratory of Neuro Imaging (LONI), USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Francois Rheault
- Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Université de Sherbrooke, Canada
| | - Guillaume Theaud
- Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Université de Sherbrooke, Canada
| | - Jean-Christophe Houde
- Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Université de Sherbrooke, Canada
| | - Jasmeen Sidhu
- Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Université de Sherbrooke, Canada
| | - Maxime Chamberland
- Cardiff University, Brain Research Imaging Centre, School of Psychology, Cardiff, UK
| | | | - Tim B Dyrby
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark; Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Ragini Verma
- Center for Biomedical Image Computing and Analytics, University of Pennsylvania, Philadelphia, PA, USA
| | - Yogesh Rathi
- Brigham and Women's Hospital, Harvard Medical School, USA
| | - M Okan Irfanoglu
- National Institute of Biomedical Imaging and Bioengineering, NIH, Bethesda, MD, USA
| | - Cibu Thomas
- Section on Learning and Plasticity, Laboratory of Brain and Cognition, NIMH, Bethesda, MD, USA
| | - Carlo Pierpaoli
- National Institute of Biomedical Imaging and Bioengineering, NIH, Bethesda, MD, USA
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Université de Sherbrooke, Canada
| | - Adam W Anderson
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Bennett A Landman
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Department of Electrical Engineering, Vanderbilt University, Nashville, TN, USA
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Sarwar T, Ramamohanarao K, Zalesky A. Mapping connectomes with diffusion MRI: deterministic or probabilistic tractography? Magn Reson Med 2018; 81:1368-1384. [PMID: 30303550 DOI: 10.1002/mrm.27471] [Citation(s) in RCA: 107] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2018] [Revised: 06/11/2018] [Accepted: 07/09/2018] [Indexed: 12/21/2022]
Abstract
PURPOSE Human connectomics necessitates high-throughput, whole-brain reconstruction of multiple white matter fiber bundles. Scaling up tractography to meet these high-throughput demands yields new fiber tracking challenges, such as minimizing spurious connections and controlling for gyral biases. The aim of this study is to determine which of the two broadest classes of tractography algorithms-deterministic or probabilistic-is most suited to mapping connectomes. METHODS This study develops numerical connectome phantoms that feature realistic network topologies and that are matched to the fiber complexity of in vivo diffusion MRI (dMRI) data. The phantoms are utilized to evaluate the performance of tensor-based and multi-fiber implementations of deterministic and probabilistic tractography. RESULTS For connectome phantoms that are representative of the fiber complexity of in vivo dMRI, multi-fiber deterministic tractography yields the most accurate connectome reconstructions (F-measure = 0.35). Probabilistic algorithms are hampered by an abundance of false-positive connections, leading to lower specificity (F = 0.19). While omitting connections with the fewest number of streamlines (thresholding) improves the performance of probabilistic algorithms (F = 0.38), multi-fiber deterministic tractography remains optimal when it benefits from thresholding (F = 0.42). CONCLUSIONS Multi-fiber deterministic tractography is well suited to connectome mapping, while connectome thresholding is essential when using probabilistic algorithms.
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Affiliation(s)
- Tabinda Sarwar
- School of Computing and Information Systems, The University of Melbourne, Parkville, Victoria, Australia
| | - Kotagiri Ramamohanarao
- School of Computing and Information Systems, The University of Melbourne, Parkville, Victoria, Australia
| | - Andrew Zalesky
- Department of Biomedical Engineering, The University of Melbourne, Parkville, Victoria, Australia
- Melbourne Neuropsychiatry Centre, The University of Melbourne, Parkville, Victoria, Australia
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Schilling KG, Gao Y, Stepniewska I, Janve V, Landman BA, Anderson AW. Anatomical accuracy of standard-practice tractography algorithms in the motor system - A histological validation in the squirrel monkey brain. Magn Reson Imaging 2018; 55:7-25. [PMID: 30213755 DOI: 10.1016/j.mri.2018.09.004] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/11/2018] [Revised: 08/06/2018] [Accepted: 09/06/2018] [Indexed: 01/15/2023]
Abstract
For two decades diffusion fiber tractography has been used to probe both the spatial extent of white matter pathways and the region to region connectivity of the brain. In both cases, anatomical accuracy of tractography is critical for sound scientific conclusions. Here we assess and validate the algorithms and tractography implementations that have been most widely used - often because of ease of use, algorithm simplicity, or availability offered in open source software. Comparing forty tractography results to a ground truth defined by histological tracers in the primary motor cortex on the same squirrel monkey brains, we assess tract fidelity on the scale of voxels as well as over larger spatial domains or regional connectivity. No algorithms are successful in all metrics, and, in fact, some implementations fail to reconstruct large portions of pathways or identify major points of connectivity. The accuracy is most dependent on reconstruction method and tracking algorithm, as well as the seed region and how this region is utilized. We also note a tremendous variability in the results, even though the same MR images act as inputs to all algorithms. In addition, anatomical accuracy is significantly decreased at increased distances from the seed. An analysis of the spatial errors in tractography reveals that many techniques have trouble properly leaving the gray matter, and many only reveal connectivity to adjacent regions of interest. These results show that the most commonly implemented algorithms have several shortcomings and limitations, and choices in implementations lead to very different results. This study should provide guidance for algorithm choices based on study requirements for sensitivity, specificity, or the need to identify particular connections, and should serve as a heuristic for future developments in tractography.
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Affiliation(s)
- Kurt G Schilling
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA.
| | - Yurui Gao
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | | | - Vaibhav Janve
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Bennett A Landman
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN, USA; Department of Electrical Engineering, Vanderbilt University, Nashville, TN, USA
| | - Adam W Anderson
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA; Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN, USA
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50
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Rizzo G, Milardi D, Bertino S, Basile GA, Di Mauro D, Calamuneri A, Chillemi G, Silvestri G, Anastasi G, Bramanti A, Cacciola A. The Limbic and Sensorimotor Pathways of the Human Amygdala: A Structural Connectivity Study. Neuroscience 2018; 385:166-180. [DOI: 10.1016/j.neuroscience.2018.05.051] [Citation(s) in RCA: 31] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2018] [Revised: 05/28/2018] [Accepted: 05/31/2018] [Indexed: 12/21/2022]
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