1
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Vladimirov N, Voigt FF, Naert T, Araujo GR, Cai R, Reuss AM, Zhao S, Schmid P, Hildebrand S, Schaettin M, Groos D, Mateos JM, Bethge P, Yamamoto T, Aerne V, Roebroeck A, Ertürk A, Aguzzi A, Ziegler U, Stoeckli E, Baudis L, Lienkamp SS, Helmchen F. Benchtop mesoSPIM: a next-generation open-source light-sheet microscope for cleared samples. Nat Commun 2024; 15:2679. [PMID: 38538644 PMCID: PMC10973490 DOI: 10.1038/s41467-024-46770-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2023] [Accepted: 03/08/2024] [Indexed: 04/04/2024] Open
Abstract
In 2015, we launched the mesoSPIM initiative, an open-source project for making light-sheet microscopy of large cleared tissues more accessible. Meanwhile, the demand for imaging larger samples at higher speed and resolution has increased, requiring major improvements in the capabilities of such microscopes. Here, we introduce the next-generation mesoSPIM ("Benchtop") with a significantly increased field of view, improved resolution, higher throughput, more affordable cost, and simpler assembly compared to the original version. We develop an optical method for testing detection objectives that enables us to select objectives optimal for light-sheet imaging with large-sensor cameras. The improved mesoSPIM achieves high spatial resolution (1.5 µm laterally, 3.3 µm axially) across the entire field of view, magnification up to 20×, and supports sample sizes ranging from sub-mm up to several centimeters while being compatible with multiple clearing techniques. The microscope serves a broad range of applications in neuroscience, developmental biology, pathology, and even physics.
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Grants
- U01 NS090475 NINDS NIH HHS
- This work was supported by the University Research Priority Program (URPP) “Adaptive Brain Circuits in Development and Learning (AdaBD)” of the University of Zurich (N.V., E.S. and F.H.). Additionally, F.F.V. is supported by an HFSP fellowship (LT00687), T.N. received funding from H2020 Marie Skłodowska-Curie Actions (xenCAKUT - 891127), A.R. and S.H. were supported by a Dutch Science Foundation VIDI Grant (14637), and A.R. was supported by an ERC Starting Grant (MULTICONNECT, 639938). Further funding support came from the Swiss National Science Foundation (SNF grant nos. 31003B-170269, 310030_192617 and CRSII5-18O316 to F.H., 310030_189102 to S.S.L., 200020_204950 to L.B., G.R.A, and V.A.); from an ERC Starting Grant by the European Union’s Horizon 2020 Research and Innovation Programme (grant agreement no. 804474, DiRECT, S.S.L); and the US Brain Initiative (1U01NS090475-01, F.H.).
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Affiliation(s)
- Nikita Vladimirov
- Brain Research Institute, University of Zurich, Zurich, Switzerland.
- University Research Priority Program (URPP), Adaptive Brain Circuits in Development and Learning, University of Zurich, Zurich, Switzerland.
- Center for Microscopy and Image Analysis (ZMB), University of Zurich, Zurich, Switzerland.
| | - Fabian F Voigt
- Brain Research Institute, University of Zurich, Zurich, Switzerland
- Neuroscience Center Zurich (ZNZ), University of Zurich, Zurich, Switzerland
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA
| | - Thomas Naert
- Institute of Anatomy and Zurich Kidney Center (ZKC), University of Zurich, Zurich, Switzerland
| | | | - Ruiyao Cai
- Institute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Center Munich, Neuherberg, Germany
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians University Munich, Munich, Germany
- Department of Biology, Stanford University, Stanford, CA, USA
| | - Anna Maria Reuss
- Neuroscience Center Zurich (ZNZ), University of Zurich, Zurich, Switzerland
- Institute of Neuropathology, University Hospital Zurich, Zurich, Switzerland
| | - Shan Zhao
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Patricia Schmid
- Institute of Anatomy and Zurich Kidney Center (ZKC), University of Zurich, Zurich, Switzerland
| | - Sven Hildebrand
- Department of Cognitive Neuroscience, Faculty of Psychology & Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Martina Schaettin
- Neuroscience Center Zurich (ZNZ), University of Zurich, Zurich, Switzerland
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
| | - Dominik Groos
- Brain Research Institute, University of Zurich, Zurich, Switzerland
| | - José María Mateos
- Center for Microscopy and Image Analysis (ZMB), University of Zurich, Zurich, Switzerland
| | - Philipp Bethge
- Brain Research Institute, University of Zurich, Zurich, Switzerland
- Neuroscience Center Zurich (ZNZ), University of Zurich, Zurich, Switzerland
| | - Taiyo Yamamoto
- Institute of Anatomy and Zurich Kidney Center (ZKC), University of Zurich, Zurich, Switzerland
| | - Valentino Aerne
- Department of Physics, University of Zurich, Zurich, Switzerland
| | - Alard Roebroeck
- Department of Cognitive Neuroscience, Faculty of Psychology & Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Ali Ertürk
- Institute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Center Munich, Neuherberg, Germany
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians University Munich, Munich, Germany
| | - Adriano Aguzzi
- Neuroscience Center Zurich (ZNZ), University of Zurich, Zurich, Switzerland
- Institute of Neuropathology, University Hospital Zurich, Zurich, Switzerland
| | - Urs Ziegler
- Center for Microscopy and Image Analysis (ZMB), University of Zurich, Zurich, Switzerland
| | - Esther Stoeckli
- University Research Priority Program (URPP), Adaptive Brain Circuits in Development and Learning, University of Zurich, Zurich, Switzerland
- Neuroscience Center Zurich (ZNZ), University of Zurich, Zurich, Switzerland
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
| | - Laura Baudis
- Department of Physics, University of Zurich, Zurich, Switzerland
| | - Soeren S Lienkamp
- Institute of Anatomy and Zurich Kidney Center (ZKC), University of Zurich, Zurich, Switzerland
| | - Fritjof Helmchen
- Brain Research Institute, University of Zurich, Zurich, Switzerland.
- University Research Priority Program (URPP), Adaptive Brain Circuits in Development and Learning, University of Zurich, Zurich, Switzerland.
- Neuroscience Center Zurich (ZNZ), University of Zurich, Zurich, Switzerland.
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Harms RL, Fritz FJ, Schoenmakers S, Roebroeck A. Fast and robust quantification of uncertainty in non-linear diffusion MRI models. Neuroimage 2024; 285:120496. [PMID: 38101495 DOI: 10.1016/j.neuroimage.2023.120496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2023] [Revised: 12/08/2023] [Accepted: 12/10/2023] [Indexed: 12/17/2023] Open
Abstract
Diffusion MRI (dMRI) allows for non-invasive investigation of brain tissue microstructure. By fitting a model to the dMRI signal, various quantitative measures can be derived from the data, such as fractional anisotropy, neurite density and axonal radii maps. We investigate the Fisher Information Matrix (FIM) and uncertainty propagation as a generally applicable method for quantifying the parameter uncertainties in linear and non-linear diffusion MRI models. In direct comparison with Markov Chain Monte Carlo (MCMC) sampling, the FIM produces similar uncertainty estimates at much lower computational cost. Using acquired and simulated data, we then list several characteristics that influence the parameter variances, including data complexity and signal-to-noise ratio. For practical purposes we investigate a possible use of uncertainty estimates in decreasing intra-group variance in group statistics by uncertainty-weighted group estimates. This has potential use cases for detection and suppression of imaging artifacts.
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Affiliation(s)
- R L Harms
- Department of Cognitive Neuroscience, Faculty of Psychology & Neuroscience, Maastricht University, The Netherlands.
| | - F J Fritz
- Department of Cognitive Neuroscience, Faculty of Psychology & Neuroscience, Maastricht University, The Netherlands
| | - S Schoenmakers
- Department of Cognitive Neuroscience, Faculty of Psychology & Neuroscience, Maastricht University, The Netherlands
| | - A Roebroeck
- Department of Cognitive Neuroscience, Faculty of Psychology & Neuroscience, Maastricht University, The Netherlands.
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3
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Voigt FF, Reuss AM, Naert T, Hildebrand S, Schaettin M, Hotz AL, Whitehead L, Bahl A, Neuhauss SCF, Roebroeck A, Stoeckli ET, Lienkamp SS, Aguzzi A, Helmchen F. Reflective multi-immersion microscope objectives inspired by the Schmidt telescope. Nat Biotechnol 2024; 42:65-71. [PMID: 36997681 PMCID: PMC10791577 DOI: 10.1038/s41587-023-01717-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Accepted: 02/20/2023] [Indexed: 04/03/2023]
Abstract
Imaging large, cleared samples requires microscope objectives that combine a large field of view (FOV) with a long working distance (WD) and a high numerical aperture (NA). Ideally, such objectives should be compatible with a wide range of immersion media, which is challenging to achieve with conventional lens-based objective designs. Here we introduce the multi-immersion 'Schmidt objective' consisting of a spherical mirror and an aspherical correction plate as a solution to this problem. We demonstrate that a multi-photon variant of the Schmidt objective is compatible with all homogeneous immersion media and achieves an NA of 1.08 at a refractive index of 1.56, 1.1-mm FOV and 11-mm WD. We highlight its versatility by imaging cleared samples in various media ranging from air and water to benzyl alcohol/benzyl benzoate, dibenzyl ether and ethyl cinnamate and by imaging of neuronal activity in larval zebrafish in vivo. In principle, the concept can be extended to any imaging modality, including wide-field, confocal and light-sheet microscopy.
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Affiliation(s)
- Fabian F Voigt
- Brain Research Institute, University of Zurich, Zurich, Switzerland.
- Neuroscience Center Zurich, University of Zurich, Zurich, Switzerland.
- Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA.
| | - Anna Maria Reuss
- Neuroscience Center Zurich, University of Zurich, Zurich, Switzerland
- Institute of Neuropathology, University Hospital Zurich, Zurich, Switzerland
| | - Thomas Naert
- Institute of Anatomy, University of Zurich, Zurich, Switzerland
| | - Sven Hildebrand
- Department of Cognitive Neuroscience, Faculty of Psychology & Neuroscience, Maastricht University, Maastricht, the Netherlands
| | - Martina Schaettin
- Neuroscience Center Zurich, University of Zurich, Zurich, Switzerland
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
| | - Adriana L Hotz
- Neuroscience Center Zurich, University of Zurich, Zurich, Switzerland
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
| | - Lachlan Whitehead
- Walter and Eliza Hall Institute of Medical Research, Melbourne, Victoria, Australia
- Department of Medical Biology, University of Melbourne, Melbourne, Victoria, Australia
| | - Armin Bahl
- Centre for the Advanced Study of Collective Behaviour, University of Konstanz, Konstanz, Germany
| | - Stephan C F Neuhauss
- Neuroscience Center Zurich, University of Zurich, Zurich, Switzerland
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
| | - Alard Roebroeck
- Department of Cognitive Neuroscience, Faculty of Psychology & Neuroscience, Maastricht University, Maastricht, the Netherlands
| | - Esther T Stoeckli
- Neuroscience Center Zurich, University of Zurich, Zurich, Switzerland
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
- University Research Priority Program (URPP), Adaptive Brain Circuits in Development and Learning (AdaBD), University of Zürich, Zurich, Switzerland
| | | | - Adriano Aguzzi
- Neuroscience Center Zurich, University of Zurich, Zurich, Switzerland
- Institute of Neuropathology, University Hospital Zurich, Zurich, Switzerland
| | - Fritjof Helmchen
- Brain Research Institute, University of Zurich, Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich, Zurich, Switzerland
- University Research Priority Program (URPP), Adaptive Brain Circuits in Development and Learning (AdaBD), University of Zürich, Zurich, Switzerland
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4
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Vladimirov N, Voigt FF, Naert T, Araujo GR, Cai R, Reuss AM, Zhao S, Schmid P, Hildebrand S, Schaettin M, Groos D, Mateos JM, Bethge P, Yamamoto T, Aerne V, Roebroeck A, Ertürk A, Aguzzi A, Ziegler U, Stoeckli E, Baudis L, Lienkamp SS, Helmchen F. The Benchtop mesoSPIM: a next-generation open-source light-sheet microscope for large cleared samples. bioRxiv 2023:2023.06.16.545256. [PMID: 38168219 PMCID: PMC10760166 DOI: 10.1101/2023.06.16.545256] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/05/2024]
Abstract
In 2015, we launched the mesoSPIM initiative (www.mesospim.org), an open-source project for making light-sheet microscopy of large cleared tissues more accessible. Meanwhile, the demand for imaging larger samples at higher speed and resolution has increased, requiring major improvements in the capabilities of light-sheet microscopy. Here, we introduce the next-generation mesoSPIM ("Benchtop") with significantly increased field of view, improved resolution, higher throughput, more affordable cost and simpler assembly compared to the original version. We developed a new method for testing objectives, enabling us to select detection objectives optimal for light-sheet imaging with large-sensor sCMOS cameras. The new mesoSPIM achieves high spatial resolution (1.5 μm laterally, 3.3 μm axially) across the entire field of view, a magnification up to 20x, and supports sample sizes ranging from sub-mm up to several centimetres, while being compatible with multiple clearing techniques. The new microscope serves a broad range of applications in neuroscience, developmental biology, and even physics.
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Affiliation(s)
- Nikita Vladimirov
- Brain Research Institute, University of Zurich, Zurich, Switzerland
- University Research Priority Program (URPP) Adaptive Brain Circuits in Development and Learning (AdaBD), University of Zurich, Zurich, Switzerland
- Center for Microscopy and Image Analysis (ZMB), University of Zurich, Zurich, Switzerland
| | - Fabian F. Voigt
- Brain Research Institute, University of Zurich, Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich, Zurich, Switzerland
- Present address: Department of Molecular and Cellular Biology, Harvard University, Cambridge, MA, USA
| | - Thomas Naert
- Institute of Anatomy and Zurich Kidney Center (ZKC), University of Zurich, Zurich, Switzerland
| | | | - Ruiyao Cai
- Present address: Department of Biology, Stanford University, Stanford, CA, USA
- Institute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Center Munich, Neuherberg, Germany
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians University Munich, Munich, German
| | - Anna Maria Reuss
- Neuroscience Center Zurich, University of Zurich, Zurich, Switzerland
- Institute of Neuropathology, University Hospital Zurich, Zurich, Switzerland
| | - Shan Zhao
- Department of Quantitative Biomedicine, University of Zurich, Zurich, Switzerland
| | - Patricia Schmid
- Institute of Anatomy and Zurich Kidney Center (ZKC), University of Zurich, Zurich, Switzerland
| | - Sven Hildebrand
- Department of Cognitive Neuroscience, Faculty of Psychology & Neuroscience, Maastricht University, Maastricht, the Netherlands
| | - Martina Schaettin
- Neuroscience Center Zurich, University of Zurich, Zurich, Switzerland
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
| | - Dominik Groos
- Brain Research Institute, University of Zurich, Zurich, Switzerland
| | - José María Mateos
- Center for Microscopy and Image Analysis (ZMB), University of Zurich, Zurich, Switzerland
| | - Philipp Bethge
- Brain Research Institute, University of Zurich, Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich, Zurich, Switzerland
| | - Taiyo Yamamoto
- Institute of Anatomy and Zurich Kidney Center (ZKC), University of Zurich, Zurich, Switzerland
| | - Valentino Aerne
- Department of Physics, University of Zurich, Zurich, Switzerland
| | - Alard Roebroeck
- Department of Cognitive Neuroscience, Faculty of Psychology & Neuroscience, Maastricht University, Maastricht, the Netherlands
| | - Ali Ertürk
- Institute for Tissue Engineering and Regenerative Medicine (iTERM), Helmholtz Center Munich, Neuherberg, Germany
- Institute for Stroke and Dementia Research, Klinikum der Universität München, Ludwig-Maximilians University Munich, Munich, German
| | - Adriano Aguzzi
- Neuroscience Center Zurich, University of Zurich, Zurich, Switzerland
- Institute of Neuropathology, University Hospital Zurich, Zurich, Switzerland
| | - Urs Ziegler
- Center for Microscopy and Image Analysis (ZMB), University of Zurich, Zurich, Switzerland
| | - Esther Stoeckli
- University Research Priority Program (URPP) Adaptive Brain Circuits in Development and Learning (AdaBD), University of Zurich, Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich, Zurich, Switzerland
- Department of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
| | - Laura Baudis
- Department of Physics, University of Zurich, Zurich, Switzerland
| | - Soeren S. Lienkamp
- Institute of Anatomy and Zurich Kidney Center (ZKC), University of Zurich, Zurich, Switzerland
| | - Fritjof Helmchen
- Brain Research Institute, University of Zurich, Zurich, Switzerland
- University Research Priority Program (URPP) Adaptive Brain Circuits in Development and Learning (AdaBD), University of Zurich, Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich, Zurich, Switzerland
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5
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Pimpini L, Kochs S, Frannsen S, van den Hurk J, Valente G, Roebroeck A, Jansen A, Roefs A. Corrigendum to "More complex than you might think: Neural representations of food reward value in obesity" [Appetite 178 (2022) 106164]. Appetite 2023:107079. [PMID: 37858485 DOI: 10.1016/j.appet.2023.107079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2023]
Affiliation(s)
- Leonardo Pimpini
- Department of Clinical Psychological Science, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands.
| | - Sarah Kochs
- Department of Clinical Psychological Science, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Sieske Frannsen
- Department of Clinical Psychological Science, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands; Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Netherlands
| | - Job van den Hurk
- Scannexus, Maastricht, Netherlands; Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Netherlands
| | - Giancarlo Valente
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Netherlands
| | - Alard Roebroeck
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Netherlands
| | - Anita Jansen
- Department of Clinical Psychological Science, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Anne Roefs
- Department of Clinical Psychological Science, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
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Ivanov D, De Martino F, Formisano E, Fritz FJ, Goebel R, Huber L, Kashyap S, Kemper VG, Kurban D, Roebroeck A, Sengupta S, Sorger B, Tse DHY, Uludağ K, Wiggins CJ, Poser BA. Magnetic resonance imaging at 9.4 T: the Maastricht journey. MAGMA 2023; 36:159-173. [PMID: 37081247 PMCID: PMC10140139 DOI: 10.1007/s10334-023-01080-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Revised: 03/18/2023] [Accepted: 03/20/2023] [Indexed: 04/22/2023]
Abstract
The 9.4 T scanner in Maastricht is a whole-body magnet with head gradients and parallel RF transmit capability. At the time of the design, it was conceptualized to be one of the best fMRI scanners in the world, but it has also been used for anatomical and diffusion imaging. 9.4 T offers increases in sensitivity and contrast, but the technical ultra-high field (UHF) challenges, such as field inhomogeneities and constraints set by RF power deposition, are exacerbated compared to 7 T. This article reviews some of the 9.4 T work done in Maastricht. Functional imaging experiments included blood oxygenation level-dependent (BOLD) and blood-volume weighted (VASO) fMRI using different readouts. BOLD benefits from shorter T2* at 9.4 T while VASO from longer T1. We show examples of both ex vivo and in vivo anatomical imaging. For many applications, pTx and optimized coils are essential to harness the full potential of 9.4 T. Our experience shows that, while considerable effort was required compared to our 7 T scanner, we could obtain high-quality anatomical and functional data, which illustrates the potential of MR acquisitions at even higher field strengths. The practical challenges of working with a relatively unique system are also discussed.
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Affiliation(s)
- Dimo Ivanov
- Faculty of Psychology and Neuroscience, Maastricht University, Universiteitssingel 40, 6229 ER, Maastricht, The Netherlands.
| | - Federico De Martino
- Faculty of Psychology and Neuroscience, Maastricht University, Universiteitssingel 40, 6229 ER, Maastricht, The Netherlands
| | - Elia Formisano
- Faculty of Psychology and Neuroscience, Maastricht University, Universiteitssingel 40, 6229 ER, Maastricht, The Netherlands
| | - Francisco J Fritz
- Institute of Systems Neuroscience, Center for Experimental Medicine, University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
| | - Rainer Goebel
- Faculty of Psychology and Neuroscience, Maastricht University, Universiteitssingel 40, 6229 ER, Maastricht, The Netherlands
| | - Laurentius Huber
- Faculty of Psychology and Neuroscience, Maastricht University, Universiteitssingel 40, 6229 ER, Maastricht, The Netherlands
| | - Sriranga Kashyap
- Krembil Brain Institute, University Health Network, Toronto, ON, Canada
| | - Valentin G Kemper
- Faculty of Psychology and Neuroscience, Maastricht University, Universiteitssingel 40, 6229 ER, Maastricht, The Netherlands
| | - Denizhan Kurban
- Faculty of Psychology and Neuroscience, Maastricht University, Universiteitssingel 40, 6229 ER, Maastricht, The Netherlands
| | - Alard Roebroeck
- Faculty of Psychology and Neuroscience, Maastricht University, Universiteitssingel 40, 6229 ER, Maastricht, The Netherlands
| | | | - Bettina Sorger
- Faculty of Psychology and Neuroscience, Maastricht University, Universiteitssingel 40, 6229 ER, Maastricht, The Netherlands
| | - Desmond H Y Tse
- Scannexus BV, Oxfordlaan 55, 6229 EV, Maastricht, The Netherlands
| | - Kâmil Uludağ
- Krembil Brain Institute, Koerner Scientist in MR Imaging, University Health Network Toronto, Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
- Center for Neuroscience Imaging Research, Institute for Basic Science and Department of Biomedical Engineering, Sungkyunkwan University, Suwon, Republic of Korea
| | - Christopher J Wiggins
- Imaging Core Facility (INM-ICF), Institut für Neurowissenschaften und Medizin, Forschungszentrum Jülich GmbH, 52425, Jülich, Germany
| | - Benedikt A Poser
- Faculty of Psychology and Neuroscience, Maastricht University, Universiteitssingel 40, 6229 ER, Maastricht, The Netherlands
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7
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Kochs S, Franssen S, Pimpini L, van den Hurk J, Valente G, Roebroeck A, Jansen A, Roefs A. IT IS A MATTER OF PERSPECTIVE: ATTENTIONAL FOCUS RATHER THAN DIETARY RESTRAINT DRIVES BRAIN RESPONSES TO FOOD STIMULI. Neuroimage 2023; 273:120076. [PMID: 37004828 DOI: 10.1016/j.neuroimage.2023.120076] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 03/16/2023] [Accepted: 03/30/2023] [Indexed: 04/03/2023] Open
Abstract
Brain responses to food are thought to reflect food's rewarding value and to fluctuate with dietary restraint. We propose that brain responses to food are dynamic and depend on attentional focus. Food pictures (high-caloric/low-caloric, palatable/unpalatable) were presented during fMRI-scanning, while attentional focus (hedonic/health/neutral) was induced in 52 female participants varying in dietary restraint. The level of brain activity was hardly different between palatable versus unpalatable foods or high-caloric versus low-caloric foods. Activity in several brain regions was higher in hedonic than in health or neutral attentional focus (p < 0.05, FWE-corrected). Palatability and calorie content could be decoded from multi-voxel activity patterns (p < 0.05, FDR-corrected). Dietary restraint did not significantly influence brain responses to food. So, level of brain activity in response to food stimuli depends on attentional focus, and may reflect salience, not reward value. Palatability and calorie content are reflected in patterns of brain activity.
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Pimpini L, Kochs S, Franssen S, van den Hurk J, Valente G, Roebroeck A, Jansen A, Roefs A. More complex than you might think: Neural representations of food reward value in obesity. Appetite 2022; 178:106164. [PMID: 35863505 DOI: 10.1016/j.appet.2022.106164] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Revised: 07/01/2022] [Accepted: 07/08/2022] [Indexed: 01/22/2023]
Abstract
Obesity reached pandemic proportions and weight-loss treatments are mostly ineffective. The level of brain activity in the reward circuitry is proposed to be proportionate to the reward value of food stimuli, and stronger in people with obesity. However, empirical evidence is inconsistent. This may be due to the double-sided nature of high caloric palatable foods: at once highly palatable and high in calories (unhealthy). This study hypothesizes that, viewing high caloric palatable foods, a hedonic attentional focus compared to a health and a neutral attentional focus elicits more activity in reward-related brain regions, mostly in people with obesity. Moreover, caloric content and food palatability can be decoded from multivoxel patterns of activity most accurately in people with obesity and in the corresponding attentional focus. During one fMRI-session, attentional focus (hedonic, health, neutral) was manipulated using a one-back task with individually tailored food stimuli in 32 healthy-weight people and 29 people with obesity. Univariate analyses (p < 0.05, FWE-corrected) showed that brain activity was not different for palatable vs. unpalatable foods, nor for high vs. low caloric foods. Instead, this was higher in the hedonic compared to the health and neutral attentional focus. Multivariate analyses (MVPA) (p < 0.05, FDR-corrected) showed that palatability and caloric content could be decoded above chance level, independently of either BMI or attentional focus. Thus, brain activity to visual food stimuli is neither proportionate to the reward value (palatability and/or caloric content), nor significantly moderated by BMI. Instead, it depends on people's attentional focus, and may reflect motivational salience. Furthermore, food palatability and caloric content are represented as patterns of brain activity, independently of BMI and attentional focus. So, food reward value is reflected in patterns, not levels, of brain activity.
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Affiliation(s)
- Leonardo Pimpini
- Department of Clinical Psychological Science, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands.
| | - Sarah Kochs
- Department of Clinical Psychological Science, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Sieske Franssen
- Department of Clinical Psychological Science, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands; Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Netherlands
| | - Job van den Hurk
- Scannexus, Maastricht, Netherlands; Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Netherlands
| | - Giancarlo Valente
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Netherlands
| | - Alard Roebroeck
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Netherlands
| | - Anita Jansen
- Department of Clinical Psychological Science, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Anne Roefs
- Department of Clinical Psychological Science, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
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9
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Wang Y, Zhan M, Roebroeck A, De Weerd P, Kashyap S, Roberts MJ. Inconsistencies in atlas-based volumetric measures of the human nucleus basalis of Meynert: A need for high-resolution alternatives. Neuroimage 2022; 259:119421. [PMID: 35779763 DOI: 10.1016/j.neuroimage.2022.119421] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2021] [Revised: 06/10/2022] [Accepted: 06/28/2022] [Indexed: 10/17/2022] Open
Abstract
The nucleus basalis of Meynert (nbM) is the major source of cortical acetylcholine (ACh) and has been related to cognitive processes and to neurological disorders. However, spatially delineating the human nbM in MRI studies remains challenging. Due to the absence of a functional localiser for the human nbM, studies to date have localised it using nearby neuroanatomical landmarks or using probabilistic atlases. To understand the feasibility of MRI of the nbM we set our four goals; our first goal was to review current human nbM region-of-interest (ROI) selection protocols used in MRI studies, which we found have reported highly variable nbM volume estimates. Our next goal was to quantify and discuss the limitations of existing atlas-based volumetry of nbM. We found that the identified ROI volume depends heavily on the atlas used and on the probabilistic threshold set. In addition, we found large disparities even for data/studies using the same atlas and threshold. To test whether spatial resolution contributes to volume variability, as our third goal, we developed a novel nbM mask based on the normalized BigBrain dataset. We found that as long as the spatial resolution of the target data was 1.3 mm isotropic or above, our novel nbM mask offered realistic and stable volume estimates. Finally, as our last goal we tried to discern nbM using publicly available and novel high resolution structural MRI ex vivo MRI datasets. We find that, using an optimised 9.4T quantitative T2⁎ ex vivo dataset, the nbM can be visualised using MRI. We conclude caution is needed when applying the current methods of mapping nbM, especially for high resolution MRI data. Direct imaging of the nbM appears feasible and would eliminate the problems we identify, although further development is required to allow such imaging using standard (f)MRI scanning.
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Affiliation(s)
- Yawen Wang
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands.
| | - Minye Zhan
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands; U992 (Cognitive neuroimaging unit), NeuroSpin, INSERM-CEA, Gif sur Yvette, France
| | - Alard Roebroeck
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Peter De Weerd
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Sriranga Kashyap
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands; Techna Institute, University Health Network, Toronto, ON, Canada
| | - Mark J Roberts
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands.
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10
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So S, Park HW, Kim B, Fritz FJ, Poser BA, Roebroeck A, Bilgic B. BUDA-MESMERISE: Rapid acquisition and unsupervised parameter estimation for T 1 , T 2 , M 0 , B 0 , and B 1 maps. Magn Reson Med 2022; 88:292-308. [PMID: 35344611 DOI: 10.1002/mrm.29228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2021] [Revised: 02/21/2022] [Accepted: 02/22/2022] [Indexed: 11/10/2022]
Abstract
PURPOSE Rapid acquisition scheme and parameter estimation method are proposed to acquire distortion-free spin- and stimulated-echo signals and combine the signals with a physics-driven unsupervised network to estimate T1 , T2 , and proton density (M0 ) parameter maps, along with B0 and B1 information from the acquired signals. THEORY AND METHODS An imaging sequence with three 90° RF pulses is utilized to acquire spin- and stimulated-echo signals. We utilize blip-up/-down acquisition to eliminate geometric distortion incurred by the effects of B0 inhomogeneity on rapid EPI acquisitions. For multislice imaging, echo-shifting is applied to utilize dead time between the second and third RF pulses to encode information from additional slice positions. To estimate parameter maps from the spin- and stimulated-echo signals with high fidelity, 2 estimation methods, analytic fitting and a novel unsupervised deep neural network method, are developed. RESULTS The proposed acquisition provided distortion-free T1 , T2 , relative proton density (M0), B0 , and B1 maps with high fidelity both in phantom and in vivo brain experiments. From the rapidly acquired spin- and stimulated-echo signals, analytic fitting and the network-based method were able to estimate T1 , T2 , M0 , B0 , and B1 maps with high accuracy. Network estimates demonstrated noise robustness owing to the fact that the convolutional layers take information into account from spatially adjacent voxels. CONCLUSION The proposed acquisition/reconstruction technique enabled whole-brain acquisition of coregistered, distortion-free, T1 , T2 , M0 , B0 , and B1 maps at 1 × 1 × 5 mm3 resolution in 50 s. The proposed unsupervised neural network provided noise-robust parameter estimates from this rapid acquisition.
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Affiliation(s)
- Seohee So
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - Hyun Wook Park
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - Byungjai Kim
- School of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea
| | - Francisco J Fritz
- Institute of Systems Neuroscience, Center for Experimental Medicine, University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany
| | - Benedikt A Poser
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Alard Roebroeck
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.,Department of Radiology, Harvard Medical School, Charlestown, Massachusetts, USA.,Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, USA
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11
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Franssen S, Jansen A, van den Hurk J, Adam T, Geyskens K, Roebroeck A, Roefs A. Effects of mindset on hormonal responding, neural representations, subjective experience and intake. Physiol Behav 2022; 249:113746. [PMID: 35182553 DOI: 10.1016/j.physbeh.2022.113746] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2021] [Revised: 02/10/2022] [Accepted: 02/14/2022] [Indexed: 10/19/2022]
Abstract
A person can alternate between food-related mindsets, which in turn may depend on one's emotional state or situation. Being in a certain mindset can influence food-related thoughts, but interestingly it might also affect eating-related physiological responses. The current study investigates the influence of an induced 'loss of control' mindset as compared to an 'in control' mindset on hormonal, neural and behavioural responses to chocolate stimuli. Mindsets were induced by having female chocolate lovers view a short movie during two sessions in a within-subjects design. Neural responses to visual chocolate stimuli were measured using an ultra-high field (7T) scanner. Momentary ghrelin and glucagon-like peptide 1 (GLP-1) levels were determined on five moments and were simultaneously assessed with self-reports on perceptions of chocolate craving, hunger and feelings of control. Furthermore, chocolate intake was measured using a bogus chocolate taste test. It was hypothesized that the loss of control mindset would lead to hormonal, neural and behavioural responses that prepare for ongoing food intake, even after eating, while the control mindset would lead to responses reflecting satiety. Results show that neural activity in the mesocorticolimbic system was stronger for chocolate stimuli than for neutral stimuli and that ghrelin and GLP-1 levels responded to food intake, irrespective of mindset. Self-reported craving and actual chocolate intake were affected by mindset, in that cravings and intake were higher with a loss of control mindset than with a control mindset. Interestingly, these findings suggest that physiology on the one hand (hormonal and neural responses) and behavior and subjective experience (food intake and craving) on the other hand are not in sync, are not equally affected by mindset.
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Affiliation(s)
- Sieske Franssen
- Department of Clinical Psychological Science, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands.
| | - Anita Jansen
- Department of Clinical Psychological Science, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | | | - Tanja Adam
- Department of school of Nutrition and Translational Research in Metabolism (NUTRIM), Faculty of Health, Medicine and Life Sciences, Maastricht UMC+, Maastricht University, Maastricht, The Netherlands
| | - Kelly Geyskens
- Department of Marketing and Supply Chain Management, School of Business and Economics, Maastricht University, Maastricht, The Netherlands
| | - Alard Roebroeck
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht
| | - Anne Roefs
- Department of Clinical Psychological Science, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
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12
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Gooijers J, Chalavi S, Koster LK, Roebroeck A, Kaas A, Swinnen SP. Representational Similarity Scores of Digits in the Sensorimotor Cortex Are Associated with Behavioral Performance. Cereb Cortex 2022; 32:3848-3863. [PMID: 35029640 DOI: 10.1093/cercor/bhab452] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 11/08/2021] [Accepted: 11/09/2021] [Indexed: 02/06/2023] Open
Abstract
Previous studies aimed to unravel a digit-specific somatotopy in the primary sensorimotor (SM1) cortex. However, it remains unknown whether digit somatotopy is associated with motor preparation and/or motor execution during different types of tasks. We adopted multivariate representational similarity analysis to explore digit activation patterns in response to a finger tapping task (FTT). Sixteen healthy young adults underwent magnetic resonance imaging, and additionally performed an out-of-scanner choice reaction time task (CRTT) to assess digit selection performance. During both the FTT and CRTT, force data of all digits were acquired using force transducers. This allowed us to assess execution-related interference (i.e., digit enslavement; obtained from FTT & CRTT), as well as planning-related interference (i.e., digit selection deficit; obtained from CRTT) and determine their correlation with digit representational similarity scores of SM1. Findings revealed that digit enslavement during FTT was associated with contralateral SM1 representational similarity scores. During the CRTT, digit enslavement of both hands was also associated with representational similarity scores of the contralateral SM1. In addition, right hand digit selection performance was associated with representational similarity scores of left S1. In conclusion, we demonstrate a cortical origin of digit enslavement, and uniquely reveal that digit selection is associated with digit representations in primary somatosensory cortex (S1). Significance statement In current systems neuroscience, it is of critical importance to understand the relationship between brain function and behavioral outcome. With the present work, we contribute significantly to this understanding by uniquely assessing how digit representations in the sensorimotor cortex are associated with planning- and execution-related digit interference during a continuous finger tapping and a choice reaction time task. We observe that digit enslavement (i.e., execution-related interference) finds its origin in contralateral digit representations of SM1, and that deficits in digit selection (i.e., planning-related interference) in the right hand during a choice reaction time task are associated with more overlapping digit representations in left S1. This knowledge sheds new light on the functional contribution of the sensorimotor cortex to everyday motor skills.
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Affiliation(s)
- J Gooijers
- Movement Control and Neuroplasticity Research Group, Department of Movement Sciences, KU Leuven, Leuven 3000, Belgium
- LBI-KU Leuven Brain Institute, Leuven 3000, Belgium
| | - S Chalavi
- Movement Control and Neuroplasticity Research Group, Department of Movement Sciences, KU Leuven, Leuven 3000, Belgium
- LBI-KU Leuven Brain Institute, Leuven 3000, Belgium
| | - L K Koster
- Movement Control and Neuroplasticity Research Group, Department of Movement Sciences, KU Leuven, Leuven 3000, Belgium
| | - A Roebroeck
- Department of Cognitive Neuroscience, Faculty of Psychology & Neuroscience, Maastricht University, Maastricht 6229 EV, the Netherlands
| | - A Kaas
- Department of Cognitive Neuroscience, Faculty of Psychology & Neuroscience, Maastricht University, Maastricht 6229 EV, the Netherlands
| | - S P Swinnen
- Movement Control and Neuroplasticity Research Group, Department of Movement Sciences, KU Leuven, Leuven 3000, Belgium
- LBI-KU Leuven Brain Institute, Leuven 3000, Belgium
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13
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van Lanen RHGJ, Wiggins CJ, Colon AJ, Backes WH, Jansen JFA, Uher D, Drenthen GS, Roebroeck A, Ivanov D, Poser BA, Hoeberigs MC, van Kuijk SMJ, Hoogland G, Rijkers K, Wagner GL, Beckervordersandforth J, Delev D, Clusmann H, Wolking S, Klinkenberg S, Rouhl RPW, Hofman PAM, Schijns OEMG. Value of ultra-high field MRI in patients with suspected focal epilepsy and negative 3 T MRI (EpiUltraStudy): protocol for a prospective, longitudinal therapeutic study. Neuroradiology 2022; 64:753-764. [PMID: 34984522 PMCID: PMC8907090 DOI: 10.1007/s00234-021-02884-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 12/09/2021] [Indexed: 10/30/2022]
Abstract
PURPOSE Resective epilepsy surgery is a well-established, evidence-based treatment option in patients with drug-resistant focal epilepsy. A major predictive factor of good surgical outcome is visualization and delineation of a potential epileptogenic lesion by MRI. However, frequently, these lesions are subtle and may escape detection by conventional MRI (≤ 3 T). METHODS We present the EpiUltraStudy protocol to address the hypothesis that application of ultra-high field (UHF) MRI increases the rate of detection of structural lesions and functional brain aberrances in patients with drug-resistant focal epilepsy who are candidates for resective epilepsy surgery. Additionally, therapeutic gain will be addressed, testing whether increased lesion detection and tailored resections result in higher rates of seizure freedom 1 year after epilepsy surgery. Sixty patients enroll the study according to the following inclusion criteria: aged ≥ 12 years, diagnosed with drug-resistant focal epilepsy with a suspected epileptogenic focus, negative conventional 3 T MRI during pre-surgical work-up. RESULTS All patients will be evaluated by 7 T MRI; ten patients will undergo an additional 9.4 T MRI exam. Images will be evaluated independently by two neuroradiologists and a neurologist or neurosurgeon. Clinical and UHF MRI will be discussed in the multidisciplinary epilepsy surgery conference. Demographic and epilepsy characteristics, along with postoperative seizure outcome and histopathological evaluation, will be recorded. CONCLUSION This protocol was reviewed and approved by the local Institutional Review Board and complies with the Declaration of Helsinki and principles of Good Clinical Practice. Results will be submitted to international peer-reviewed journals and presented at international conferences. TRIAL REGISTRATION NUMBER www.trialregister.nl : NTR7536.
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Affiliation(s)
- R H G J van Lanen
- Department of Neurosurgery, Maastricht University Medical Center, Maastricht, the Netherlands. .,School for Mental Health and Neuroscience (MHeNs), Maastricht University, Maastricht, the Netherlands.
| | - C J Wiggins
- Scannexus, Ultra-High Field MRI Research Center, Maastricht, the Netherlands
| | - A J Colon
- Academic Centre for Epileptology, Kempenhaeghe/Maastricht University Medical Center, Heeze/Maastricht, the Netherlands
| | - W H Backes
- School for Mental Health and Neuroscience (MHeNs), Maastricht University, Maastricht, the Netherlands.,Department of Radiology, Maastricht University Medical Center, Maastricht, the Netherlands
| | - J F A Jansen
- School for Mental Health and Neuroscience (MHeNs), Maastricht University, Maastricht, the Netherlands.,Academic Centre for Epileptology, Kempenhaeghe/Maastricht University Medical Center, Heeze/Maastricht, the Netherlands.,Department of Radiology, Maastricht University Medical Center, Maastricht, the Netherlands.,Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, the Netherlands
| | - D Uher
- School for Mental Health and Neuroscience (MHeNs), Maastricht University, Maastricht, the Netherlands.,Department of Radiology, Maastricht University Medical Center, Maastricht, the Netherlands
| | - G S Drenthen
- School for Mental Health and Neuroscience (MHeNs), Maastricht University, Maastricht, the Netherlands.,Department of Radiology, Maastricht University Medical Center, Maastricht, the Netherlands
| | - A Roebroeck
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, the Netherlands
| | - D Ivanov
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, the Netherlands
| | - B A Poser
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, the Netherlands
| | - M C Hoeberigs
- School for Mental Health and Neuroscience (MHeNs), Maastricht University, Maastricht, the Netherlands.,Academic Centre for Epileptology, Kempenhaeghe/Maastricht University Medical Center, Heeze/Maastricht, the Netherlands.,Department of Radiology, Maastricht University Medical Center, Maastricht, the Netherlands
| | - S M J van Kuijk
- Department of Clinical Epidemiology and Medical Technology Assessment, Maastricht University Medical Center, Maastricht, the Netherlands
| | - G Hoogland
- Department of Neurosurgery, Maastricht University Medical Center, Maastricht, the Netherlands.,School for Mental Health and Neuroscience (MHeNs), Maastricht University, Maastricht, the Netherlands.,Academic Centre for Epileptology, Kempenhaeghe/Maastricht University Medical Center, Heeze/Maastricht, the Netherlands
| | - K Rijkers
- Department of Neurosurgery, Maastricht University Medical Center, Maastricht, the Netherlands.,School for Mental Health and Neuroscience (MHeNs), Maastricht University, Maastricht, the Netherlands.,Academic Centre for Epileptology, Kempenhaeghe/Maastricht University Medical Center, Heeze/Maastricht, the Netherlands
| | - G L Wagner
- Academic Centre for Epileptology, Kempenhaeghe/Maastricht University Medical Center, Heeze/Maastricht, the Netherlands
| | | | - D Delev
- Department of Neurosurgery, RWTH Aachen University Hospital, Aachen, Germany
| | - H Clusmann
- Department of Neurosurgery, RWTH Aachen University Hospital, Aachen, Germany
| | - S Wolking
- Department of Epileptology and Neurology, RWTH Aachen University Hospital, Aachen, Germany
| | - S Klinkenberg
- School for Mental Health and Neuroscience (MHeNs), Maastricht University, Maastricht, the Netherlands.,Academic Centre for Epileptology, Kempenhaeghe/Maastricht University Medical Center, Heeze/Maastricht, the Netherlands.,Department of Neurology, Maastricht University Medical Center, Maastricht, the Netherlands
| | - R P W Rouhl
- School for Mental Health and Neuroscience (MHeNs), Maastricht University, Maastricht, the Netherlands.,Academic Centre for Epileptology, Kempenhaeghe/Maastricht University Medical Center, Heeze/Maastricht, the Netherlands.,Department of Neurology, Maastricht University Medical Center, Maastricht, the Netherlands
| | - P A M Hofman
- Academic Centre for Epileptology, Kempenhaeghe/Maastricht University Medical Center, Heeze/Maastricht, the Netherlands.,Department of Radiology, Maastricht University Medical Center, Maastricht, the Netherlands
| | - O E M G Schijns
- Department of Neurosurgery, Maastricht University Medical Center, Maastricht, the Netherlands.,School for Mental Health and Neuroscience (MHeNs), Maastricht University, Maastricht, the Netherlands.,Academic Centre for Epileptology, Kempenhaeghe/Maastricht University Medical Center, Heeze/Maastricht, the Netherlands
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14
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Gooijers J, De Luca A, Zivari Adab H, Leemans A, Roebroeck A, Swinnen SP. Indices of callosal axonal density and radius from diffusion MRI relate to upper and lower limb motor performance. Neuroimage 2021; 241:118433. [PMID: 34324975 DOI: 10.1016/j.neuroimage.2021.118433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Revised: 07/15/2021] [Accepted: 07/26/2021] [Indexed: 11/16/2022] Open
Abstract
Understanding the relationship between human brain structure and functional outcome is of critical importance in systems neuroscience. Diffusion MRI (dMRI) studies show that fractional anisotropy (FA) is predictive of motor control, underscoring the importance of white matter (WM). However, as FA is a surrogate marker of WM, we aim to shed new light on the structural underpinnings of this relationship by applying a multi-compartment microstructure model providing axonal density/radius indices. Sixteen young adults (7 males / 9 females), performed a hand/foot tapping task and a Multi Limb Reaction Time task. Furthermore, diffusion (STEAM &HARDI) and fMRI (localizer hand/foot activations) data were obtained. Sphere ROIs were placed on activation clusters with highest t value to guide interhemispheric WM tractography. Axonal radius/density indices of callosal parts intersecting with tractography were calculated from STEAM, using the diffusion-time dependent AxCaliber model, and correlated with behavior. Results indicated a possible association between larger apparent axonal radii of callosal motor fibers of the hand and higher tapping scores of both hands, and faster selection-related processing (normalized reaction) times (RTs) on diagonal limb combinations. Additionally, a trend was present for faster selection-related processing (normalized reaction) times for lower limbs being related with higher axonal density of callosal foot motor fibers, and for higher FA values of callosal motor fibers in general being related with better tapping and faster selection-related processing (normalized reaction) times. Whereas FA is sensitive in demonstrating associations with motor behavior, axon radius/density (i.e., fiber geometry) measures are promising to explain the physiological source behind the observed FA changes, contributing to deeper insights into brain-behavior interactions.
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Affiliation(s)
- J Gooijers
- Movement Control and Neuroplasticity Research Group, Department of Movement Sciences, KU Leuven, Leuven (3000), Belgium; LBI-KU Leuven Brain Institute, Leuven (3000), Belgium.
| | - A De Luca
- PROVIDI Lab, Image Sciences Institute, University Medical Center Utrecht, Utrecht 3584 CX, Netherlands; Neurology Department, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht 3584 CX, Netherlands
| | - H Zivari Adab
- Movement Control and Neuroplasticity Research Group, Department of Movement Sciences, KU Leuven, Leuven (3000), Belgium; LBI-KU Leuven Brain Institute, Leuven (3000), Belgium
| | - A Leemans
- PROVIDI Lab, Image Sciences Institute, University Medical Center Utrecht, Utrecht 3584 CX, Netherlands
| | - A Roebroeck
- Department of Cognitive Neuroscience, Faculty of Psychology & Neuroscience, Maastricht University, Maastricht 6229 EV, Netherlands
| | - S P Swinnen
- Movement Control and Neuroplasticity Research Group, Department of Movement Sciences, KU Leuven, Leuven (3000), Belgium; LBI-KU Leuven Brain Institute, Leuven (3000), Belgium
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15
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Fritz FJ, Poser BA, Roebroeck A. MESMERISED: Super-accelerating T 1 relaxometry and diffusion MRI with STEAM at 7 T for quantitative multi-contrast and diffusion imaging. Neuroimage 2021; 239:118285. [PMID: 34147632 DOI: 10.1016/j.neuroimage.2021.118285] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2020] [Revised: 06/14/2021] [Accepted: 06/16/2021] [Indexed: 12/17/2022] Open
Abstract
There is an increasing interest in quantitative imaging of T1, T2 and diffusion contrast in the brain due to greater robustness against bias fields and artifacts, as well as better biophysical interpretability in terms of microstructure. However, acquisition time constraints are a challenge, particularly when multiple quantitative contrasts are desired and when extensive sampling of diffusion directions, high b-values or long diffusion times are needed for multi-compartment microstructure modeling. Although ultra-high fields of 7 T and above have desirable properties for many MR modalities, the shortening T2 and the high specific absorption rate (SAR) of inversion and refocusing pulses bring great challenges to quantitative T1, T2 and diffusion imaging. Here, we present the MESMERISED sequence (Multiplexed Echo Shifted Multiband Excited and Recalled Imaging of STEAM Encoded Diffusion). MESMERISED removes the dead time in Stimulated Echo Acquisition Mode (STEAM) imaging by an echo-shifting mechanism. The echo-shift (ES) factor is independent of multiband (MB) acceleration and allows for very high multiplicative (ESxMB) acceleration factors, particularly under moderate and long mixing times. This results in super-acceleration and high time efficiency at 7 T for quantitative T1 and diffusion imaging, while also retaining the capacity to perform quantitative T2 and B1 mapping. We demonstrate the super-acceleration of MESMERISED for whole-brain T1 relaxometry with total acceleration factors up to 36 at 1.8 mm isotropic resolution, and up to 54 at 1.25 mm resolution qT1 imaging, corresponding to a 6x and 9x speedup, respectively, compared to MB-only accelerated acquisitions. We then demonstrate highly efficient diffusion MRI with high b-values and long diffusion times in two separate cases. First, we show that super-accelerated multi-shell diffusion acquisitions with 370 whole-brain diffusion volumes over 8 b-value shells up to b = 7000 s/mm2 can be generated at 2 mm isotropic in under 8 minutes, a data rate of almost a volume per second, or at 1.8 mm isotropic in under 11 minutes, achieving up to 3.4x speedup compared to MB-only. A comparison of b = 7000 s/mm2 MESMERISED against standard MB pulsed gradient spin echo (PGSE) diffusion imaging shows 70% higher SNR efficiency and greater effectiveness in supporting complex diffusion signal modeling. Second, we demonstrate time-efficient sampling of different diffusion times with 1.8 mm isotropic diffusion data acquired at four diffusion times up to 290 ms, which supports both Diffusion Tensor Imaging (DTI) and Diffusion Kurtosis Imaging (DKI) at each diffusion time. Finally, we demonstrate how adding quantitative T2 and B1+ mapping to super-accelerated qT1 and diffusion imaging enables efficient quantitative multi-contrast mapping with the same MESMERISED sequence and the same readout train. MESMERISED extends possibilities to efficiently probe T1, T2 and diffusion contrast for multi-component modeling of tissue microstructure.
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Affiliation(s)
- F J Fritz
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, the Netherlands; Institut für Systemische Neurowissenschaften, Zentrum für Experimentelle Medizin, Universitätklinikum Hamburg-Eppendorf (UKE), Hamburg, Deutschland
| | - B A Poser
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, the Netherlands
| | - A Roebroeck
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, the Netherlands.
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16
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Boonstra JT, Michielse S, Roebroeck A, Temel Y, Jahanshahi A. Dedicated container for postmortem human brain ultra-high field magnetic resonance imaging. Neuroimage 2021; 235:118010. [PMID: 33819610 DOI: 10.1016/j.neuroimage.2021.118010] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/06/2020] [Revised: 03/14/2021] [Accepted: 03/23/2021] [Indexed: 11/28/2022] Open
Abstract
BACKGROUND The emerging field of ultra-high field MRI (UHF-MRI, 7 Tesla and higher) provides the opportunity to image human brains at a higher resolution and with higher signal-to-noise ratios compared to the more widely available 1.5 and 3T scanners. Scanning postmortem tissue additionally allows for greatly increased scan times and fewer movement issues leading to improvements in image quality. However, typical postmortem neuroimaging routines involve placing the tissue within plastic bags that leave room for susceptibility artifacts from tissue-air interfaces, inadequate submersion, and leakage issues. To address these challenges in postmortem imaging, a custom-built nonferromagnetic container was developed that allows whole brain hemispheres to be scanned at sub-millimeter resolution within typical head-coils. METHOD The custom-built polymethylmethacrylaat container consists of a cylinder with a hemispheric side and a lid with valves on the adjacent side. This shape fits within common MR head-coils and allows whole hemispheres to be submerged and vacuum sealed within it reducing imaging artifacts that would otherwise arise at air-tissue boundaries. Two hemisphere samples were scanned on a Siemens 9.4T Magnetom MRI scanner. High resolution T2* weighted data was obtained with a custom 3D gradient echo (GRE) sequence and diffusion-weighted imaging (DWI) scans were obtained with a 3D kT-dSTEAM sequence along 48 directions. RESULTS The custom-built container proved to submerge and contain tissue samples effectively and showed no interferences with MR scanning acquisition. The 3D GRE sequence provided high resolution isotropic T2* weighted data at 250 μm which showed a clear visualization of gray and white matter structures. DWI scans allowed for dense reconstruction of structural white matter connections via tractography. CONCLUSION Using this custom-built container worked towards achieving high quality MR images of postmortem brain material. This procedure can have advantages over traditional schemes including utilization of a standardized protocol and the reduced likelihood of leakage. This methodology could be adjusted and used to improve typical postmortem imaging routines.
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Affiliation(s)
- Jackson Tyler Boonstra
- Department of Neurosurgery, Maastricht University Medical Center, P. Debyelaan 25, Maastricht, 6202 AZ, the Netherlands; School for Mental Health and Neuroscience (MHeNS), Maastricht University Medical Center, P. Debyelaan 25, Maastricht, 6200 MD, the Netherlands.
| | - Stijn Michielse
- Department of Neurosurgery, Maastricht University Medical Center, P. Debyelaan 25, Maastricht, 6202 AZ, the Netherlands; School for Mental Health and Neuroscience (MHeNS), Maastricht University Medical Center, P. Debyelaan 25, Maastricht, 6200 MD, the Netherlands
| | - Alard Roebroeck
- Department of Cognitive Neuroscience, Faculty of Psychology & Neuroscience, Maastricht University, the Netherlands
| | - Yasin Temel
- Department of Neurosurgery, Maastricht University Medical Center, P. Debyelaan 25, Maastricht, 6202 AZ, the Netherlands; School for Mental Health and Neuroscience (MHeNS), Maastricht University Medical Center, P. Debyelaan 25, Maastricht, 6200 MD, the Netherlands
| | - Ali Jahanshahi
- Department of Neurosurgery, Maastricht University Medical Center, P. Debyelaan 25, Maastricht, 6202 AZ, the Netherlands; School for Mental Health and Neuroscience (MHeNS), Maastricht University Medical Center, P. Debyelaan 25, Maastricht, 6200 MD, the Netherlands
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17
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van Lanen RHGJ, Colon AJ, Wiggins CJ, Hoeberigs MC, Hoogland G, Roebroeck A, Ivanov D, Poser BA, Rouhl RPW, Hofman PAM, Jansen JFA, Backes W, Rijkers K, Schijns OEMG. Ultra-high field magnetic resonance imaging in human epilepsy: A systematic review. Neuroimage Clin 2021; 30:102602. [PMID: 33652376 PMCID: PMC7921009 DOI: 10.1016/j.nicl.2021.102602] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/05/2020] [Revised: 02/12/2021] [Accepted: 02/13/2021] [Indexed: 12/15/2022]
Abstract
RATIONALE Resective epilepsy surgery is an evidence-based curative treatment option for patients with drug-resistant focal epilepsy. The major preoperative predictor of a good surgical outcome is detection of an epileptogenic lesion by magnetic resonance imaging (MRI). Application of ultra-high field (UHF) MRI, i.e. field strengths ≥ 7 Tesla (T), may increase the sensitivity to detect such a lesion. METHODS A keyword search strategy was submitted to Pubmed, EMBASE, Cochrane Database and clinicaltrials.gov to select studies on UHF MRI in patients with epilepsy. Follow-up study selection and data extraction were performed following PRISMA guidelines. We focused on I) diagnostic gain of UHF- over conventional MRI, II) concordance of MRI-detected lesion, seizure onset zone and surgical decision-making, and III) postoperative histopathological diagnosis and seizure outcome. RESULTS Sixteen observational cohort studies, all using 7T MRI were included. Diagnostic gain of 7T over conventional MRI ranged from 8% to 67%, with a pooled gain of 31%. Novel techniques to visualize pathological processes in epilepsy and lesion detection are discussed. Seizure freedom was achieved in 73% of operated patients; no seizure outcome comparison was made between 7T MRI positive, 7T negative and 3T positive patients. 7T could influence surgical decision-making, with high concordance of lesion and seizure onset zone. Focal cortical dysplasia (54%), hippocampal sclerosis (12%) and gliosis (8.1%) were the most frequently diagnosed histopathological entities. SIGNIFICANCE UHF MRI increases, yet variably, the sensitivity to detect an epileptogenic lesion, showing potential for use in clinical practice. It remains to be established whether this results in improved seizure outcome after surgical treatment. Prospective studies with larger cohorts of epilepsy patients, uniform scan and sequence protocols, and innovative post-processing technology are equally important as further increasing field strengths. Besides technical ameliorations, improved correlation of imaging features with clinical semiology, histopathology and clinical outcome has to be established.
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Affiliation(s)
- R H G J van Lanen
- Department of Neurosurgery, Maastricht University Medical Center, Maastricht, the Netherlands; School for Mental Health and Neuroscience (MHeNs), Maastricht University, Maastricht, The Netherlands.
| | - A J Colon
- Academic Center for Epileptology, Kempenhaeghe/Maastricht University Medical Center, Heeze/Maastricht, The Netherlands
| | - C J Wiggins
- Scannexus, Ultra High Field MRI Research Center, Maastricht, The Netherlands
| | - M C Hoeberigs
- Department of Radiology, Maastricht University Medical Center, Maastricht, The Netherlands
| | - G Hoogland
- Department of Neurosurgery, Maastricht University Medical Center, Maastricht, the Netherlands; School for Mental Health and Neuroscience (MHeNs), Maastricht University, Maastricht, The Netherlands; Academic Center for Epileptology, Kempenhaeghe/Maastricht University Medical Center, Heeze/Maastricht, The Netherlands
| | - A Roebroeck
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - D Ivanov
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - B A Poser
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - R P W Rouhl
- School for Mental Health and Neuroscience (MHeNs), Maastricht University, Maastricht, The Netherlands; Academic Center for Epileptology, Kempenhaeghe/Maastricht University Medical Center, Heeze/Maastricht, The Netherlands; Department of Neurology, Maastricht University Medical Center, Maastricht, The Netherlands
| | - P A M Hofman
- School for Mental Health and Neuroscience (MHeNs), Maastricht University, Maastricht, The Netherlands; Department of Radiology, Maastricht University Medical Center, Maastricht, The Netherlands
| | - J F A Jansen
- School for Mental Health and Neuroscience (MHeNs), Maastricht University, Maastricht, The Netherlands; Department of Radiology, Maastricht University Medical Center, Maastricht, The Netherlands; Department of Electrical Engineering, Eindhoven University of Technology, Eindhoven, The Netherlands
| | - W Backes
- School for Mental Health and Neuroscience (MHeNs), Maastricht University, Maastricht, The Netherlands; Department of Radiology, Maastricht University Medical Center, Maastricht, The Netherlands
| | - K Rijkers
- Department of Neurosurgery, Maastricht University Medical Center, Maastricht, the Netherlands; School for Mental Health and Neuroscience (MHeNs), Maastricht University, Maastricht, The Netherlands; Academic Center for Epileptology, Kempenhaeghe/Maastricht University Medical Center, Heeze/Maastricht, The Netherlands
| | - O E M G Schijns
- Department of Neurosurgery, Maastricht University Medical Center, Maastricht, the Netherlands; School for Mental Health and Neuroscience (MHeNs), Maastricht University, Maastricht, The Netherlands; Academic Center for Epileptology, Kempenhaeghe/Maastricht University Medical Center, Heeze/Maastricht, The Netherlands
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18
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Franssen S, Jansen A, Van Den Hurk J, Roebroeck A, Roefs A. Power of mind: attentional focus rather than palatability dominates neural responding to visual food stimuli in females with overweight. Appetite 2021. [DOI: 10.1016/j.appet.2020.104947] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
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19
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Špiclin Ž, McClelland J, Kybic J, Goksel O, Pyles J, Eck J, Bastiani M, Roebroeck A, Ashburner J, Goebel R. An Image Registration-Based Method for EPI Distortion Correction Based on Opposite Phase Encoding (COPE). Biomedical Image Registration 2020; 12120. [PMCID: PMC7279930 DOI: 10.1007/978-3-030-50120-4_12] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Surprisingly, estimated voxel displacement maps (VDMs), based on image registration, seem to work just as well to correct geometrical distortion in functional MRI data (EPI) as VDMs based on actual information about the magnetic field. In this article, we compare our new image registration-based distortion correction method ‘COPE’ to an implementation of the pixelshift method. Our approach builds on existing image registration-based techniques using opposite phase encoding, extending these by local cost aggregation. Comparison of these methods with 3T and 7T spin-echo (SE) and gradient-echo (GE) data show that the image registration-based method is a good alternative to the fieldmap-based EPI distortion correction method.
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Affiliation(s)
- Žiga Špiclin
- Faculty of Electrical Engineering, University of Ljubljana, Ljubljana, Slovenia
| | - Jamie McClelland
- Centre for Medical Image Computing, University College London, London, UK
| | - Jan Kybic
- Faculty of Electrical Engineering, Czech Technical University in Prague, Prague, Czech Republic
| | - Orcun Goksel
- Computer Vision Lab, ETH Zurich, Zurich, Switzerland
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20
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Gonzalez-Escamilla G, Ciolac D, De Santis S, Radetz A, Fleischer V, Droby A, Roebroeck A, Meuth SG, Muthuraman M, Groppa S. Gray matter network reorganization in multiple sclerosis from 7-Tesla and 3-Tesla MRI data. Ann Clin Transl Neurol 2020; 7:543-553. [PMID: 32255566 PMCID: PMC7187719 DOI: 10.1002/acn3.51029] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Revised: 03/13/2020] [Accepted: 03/13/2020] [Indexed: 12/21/2022] Open
Abstract
Objective The objective of this study was to determine the ability of 7T‐MRI for characterizing brain tissue integrity in early relapsing‐remitting MS patients compared to conventional 3T‐MRI and to investigate whether 7T‐MRI improves the performance for detecting cortical gray matter neurodegeneration and its associated network reorganization dynamics. Methods Seven early relapsing‐remitting MS patients and seven healthy individuals received MRI at 7T and 3T, whereas 30 and 40 healthy controls underwent separate 3T‐ and 7T‐MRI sessions, respectively. Surface‐based cortical thickness (CT) and gray‐to‐white contrast (GWc) measures were used to model morphometric networks, analyzed with graph theory by means of modularity, clustering coefficient, path length, and small‐worldness. Results 7T‐MRI had lower CT and higher GWc compared to 3T‐MRI in MS. CT and GWc measures robustly differentiated MS from controls at 3T‐MRI. 7T‐ and 3T‐MRI showed high regional correspondence for CT (r = 0.72, P = 2e‐78) and GWc (r = 0.83, P = 5.5e‐121) in MS patients. MS CT and GWc morphometric networks at 7T‐MRI showed higher modularity, clustering coefficient, and small‐worldness than 3T, also compared to controls. Interpretation 7T‐MRI allows to more precisely quantify morphometric alterations across the cortical mantle and captures more sensitively MS‐related network reorganization. Our findings open new avenues to design more accurate studies quantifying brain tissue loss and test treatment effects on tissue repair.
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Affiliation(s)
- Gabriel Gonzalez-Escamilla
- Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine-Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Dumitru Ciolac
- Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine-Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | | | - Angela Radetz
- Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine-Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Vinzenz Fleischer
- Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine-Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Amgad Droby
- Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine-Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Alard Roebroeck
- Department of Cognitive Neuroscience, Faculty of Psychology & Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Sven G Meuth
- Department of Neurology with Institute of Translational Neurology, University of Münster, Münster, Germany
| | - Muthuraman Muthuraman
- Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine-Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Sergiu Groppa
- Department of Neurology, Focus Program Translational Neuroscience (FTN), Rhine-Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
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21
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Franssen S, Jansen A, van den Hurk J, Roebroeck A, Roefs A. Power of mind: Attentional focus rather than palatability dominates neural responding to visual food stimuli in females with overweight. Appetite 2020; 148:104609. [PMID: 31954729 DOI: 10.1016/j.appet.2020.104609] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2019] [Revised: 01/10/2020] [Accepted: 01/10/2020] [Indexed: 11/26/2022]
Abstract
Research investigating neural responses to visual food stimuli has produced inconsistent results. Crucially, high-caloric palatable foods have a double-sided nature - they are often craved but are also considered unhealthy - which may have contributed to the inconsistency in the literature. Taking this double-sided nature into account in the current study, neural responses to individually tailored palatable and unpalatable high caloric food stimuli were measured, while participants' (females with overweight: n = 23) attentional focus was manipulated to be either hedonic or neutral. Notably, results showed that the level of neural activity was not significantly different for palatable than for unpalatable food stimuli. Instead, independent of food palatability, several brain regions (including regions in the mesocorticolimbic system) responded more strongly when attentional focus was hedonic than when neutral (p < 0.05, cluster-based FWE corrected). Multivariate analyses showed that food palatability could be decoded from multi-voxel patterns of neural activity (p < 0.05, FDR corrected), mostly with a hedonic attentional focus. These findings illustrate that the level of neural activity might not be proportionate to the palatability of foods, but that food palatability can be decoded from multi-voxel patterns of neural activity. Moreover, they underline the importance of considering attentional focus when measuring food-related neural responses.
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Affiliation(s)
- Sieske Franssen
- Department of Clinical Psychological Science, Faculty of Psychology and Neuroscience, Maastricht University, 6200 MD, Maastricht, the Netherlands.
| | - Anita Jansen
- Department of Clinical Psychological Science, Faculty of Psychology and Neuroscience, Maastricht University, 6200 MD, Maastricht, the Netherlands
| | - Job van den Hurk
- Scannexus, 6229 EV, Maastricht, the Netherlands; Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, 6200 MD, Maastricht, the Netherlands
| | - Alard Roebroeck
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, 6200 MD, Maastricht, the Netherlands
| | - Anne Roefs
- Department of Clinical Psychological Science, Faculty of Psychology and Neuroscience, Maastricht University, 6200 MD, Maastricht, the Netherlands
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22
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Voigt FF, Kirschenbaum D, Platonova E, Pagès S, Campbell RAA, Kastli R, Schaettin M, Egolf L, van der Bourg A, Bethge P, Haenraets K, Frézel N, Topilko T, Perin P, Hillier D, Hildebrand S, Schueth A, Roebroeck A, Roska B, Stoeckli ET, Pizzala R, Renier N, Zeilhofer HU, Karayannis T, Ziegler U, Batti L, Holtmaat A, Lüscher C, Aguzzi A, Helmchen F. The mesoSPIM initiative: open-source light-sheet microscopes for imaging cleared tissue. Nat Methods 2019; 16:1105-1108. [PMID: 31527839 PMCID: PMC6824906 DOI: 10.1038/s41592-019-0554-0] [Citation(s) in RCA: 107] [Impact Index Per Article: 21.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Revised: 07/31/2019] [Accepted: 08/06/2019] [Indexed: 11/09/2022]
Abstract
Light-sheet microscopy is an ideal technique for imaging large cleared samples; however, the community is still lacking instruments capable of producing volumetric images of centimeter-sized cleared samples with near-isotropic resolution within minutes. Here, we introduce the mesoscale selective plane-illumination microscopy initiative, an open-hardware project for building and operating a light-sheet microscope that addresses these challenges and is compatible with any type of cleared or expanded sample ( www.mesospim.org ).
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Affiliation(s)
- Fabian F Voigt
- Brain Research Institute, University of Zurich, Zurich, Switzerland.
- Neuroscience Center Zurich, University of Zurich, ETH Zurich, Zurich, Switzerland.
| | | | - Evgenia Platonova
- Center for Microscopy and Image Analysis, University of Zurich, Zurich, Switzerland
| | - Stéphane Pagès
- Wyss Center for Bio- and Neuroengineering, Geneva, Switzerland
- Department of Basic Neurosciences, Geneva Neuroscience Center, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | | | - Rahel Kastli
- Brain Research Institute, University of Zurich, Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich, ETH Zurich, Zurich, Switzerland
| | - Martina Schaettin
- Neuroscience Center Zurich, University of Zurich, ETH Zurich, Zurich, Switzerland
- Institute of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
| | - Ladan Egolf
- Brain Research Institute, University of Zurich, Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich, ETH Zurich, Zurich, Switzerland
| | - Alexander van der Bourg
- Brain Research Institute, University of Zurich, Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich, ETH Zurich, Zurich, Switzerland
| | - Philipp Bethge
- Brain Research Institute, University of Zurich, Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich, ETH Zurich, Zurich, Switzerland
| | - Karen Haenraets
- Neuroscience Center Zurich, University of Zurich, ETH Zurich, Zurich, Switzerland
- Institute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland
| | - Noémie Frézel
- Neuroscience Center Zurich, University of Zurich, ETH Zurich, Zurich, Switzerland
- Institute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland
| | | | - Paola Perin
- Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
| | - Daniel Hillier
- Hungarian Academy of Sciences Research Centre for Natural Sciences, Budapest, Hungary
- Faculty of Information Technology and Bionics, Pazmany Peter Catholic University, Budapest, Hungary
| | - Sven Hildebrand
- Faculty of Psychology & Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Anna Schueth
- Faculty of Psychology & Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Alard Roebroeck
- Faculty of Psychology & Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Botond Roska
- Friedrich Miescher Institute Basel, Basel, Switzerland
- Institute of Molecular and Clinical Ophthalmology Basel, Basel, Switzerland
| | - Esther T Stoeckli
- Neuroscience Center Zurich, University of Zurich, ETH Zurich, Zurich, Switzerland
- Institute of Molecular Life Sciences, University of Zurich, Zurich, Switzerland
| | - Roberto Pizzala
- Department of Molecular Medicine, University of Pavia, Pavia, Italy
| | - Nicolas Renier
- Institute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland
| | - Hanns Ulrich Zeilhofer
- Neuroscience Center Zurich, University of Zurich, ETH Zurich, Zurich, Switzerland
- Institute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland
| | - Theofanis Karayannis
- Brain Research Institute, University of Zurich, Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich, ETH Zurich, Zurich, Switzerland
| | - Urs Ziegler
- Center for Microscopy and Image Analysis, University of Zurich, Zurich, Switzerland
| | - Laura Batti
- Wyss Center for Bio- and Neuroengineering, Geneva, Switzerland
| | - Anthony Holtmaat
- Department of Basic Neurosciences, Geneva Neuroscience Center, Faculty of Medicine, University of Geneva, Geneva, Switzerland
| | - Christian Lüscher
- Department of Basic Neurosciences, Geneva Neuroscience Center, Faculty of Medicine, University of Geneva, Geneva, Switzerland
- Clinic of Neurology, Department of Clinical Neurosciences, Geneva University Hospital, Geneva, Switzerland
| | | | - Fritjof Helmchen
- Brain Research Institute, University of Zurich, Zurich, Switzerland
- Neuroscience Center Zurich, University of Zurich, ETH Zurich, Zurich, Switzerland
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Hildebrand S, Schueth A, Herrler A, Galuske R, Roebroeck A. Scalable Labeling for Cytoarchitectonic Characterization of Large Optically Cleared Human Neocortex Samples. Sci Rep 2019; 9:10880. [PMID: 31350519 PMCID: PMC6659684 DOI: 10.1038/s41598-019-47336-9] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2019] [Accepted: 07/12/2019] [Indexed: 01/27/2023] Open
Abstract
Optical clearing techniques and light sheet microscopy have transformed fluorescent imaging of rodent brains, and have provided a crucial alternative to traditional confocal or bright field techniques for thin sections. However, clearing and labeling human brain tissue through all cortical layers and significant portions of a cortical area, has so far remained extremely challenging, especially for formalin fixed adult cortical tissue. Here, we present MASH (Multiscale Architectonic Staining of Human cortex): a simple, fast and low-cost cytoarchitectonic labeling approach for optically cleared human cortex samples, which can be applied to large (up to 5 mm thick) formalin fixed adult brain samples. A suite of small-molecule fluorescent nuclear and cytoplasmic dye protocols in combination with new refractive index matching solutions allows deep volume imaging. This greatly reduces time and cost of imaging cytoarchitecture in thick samples and enables classification of cytoarchitectonic layers over the full cortical depth. We demonstrate application of MASH to large archival samples of human visual areas, characterizing cortical architecture in 3D from the scale of cortical areas to that of single cells. In combination with scalable light sheet imaging and data analysis, MASH could open the door to investigation of large human cortical systems at cellular resolution and in the context of their complex 3-dimensional geometry.
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Affiliation(s)
- Sven Hildebrand
- Department of Cognitive Neuroscience, Faculty of Psychology & Neuroscience, Maastricht, The Netherlands
| | - Anna Schueth
- Department of Cognitive Neuroscience, Faculty of Psychology & Neuroscience, Maastricht, The Netherlands
| | - Andreas Herrler
- Department of Anatomy & Embryology, Faculty of Health, Medicine & Life Science, Maastricht University, Maastricht, The Netherlands
| | - Ralf Galuske
- Systems Neurophysiology, Department of Biology, Technische Universität Darmstadt, Darmstadt, Germany
| | - Alard Roebroeck
- Department of Cognitive Neuroscience, Faculty of Psychology & Neuroscience, Maastricht, The Netherlands.
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24
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Roebroeck A, Miller KL, Aggarwal M. Ex vivo diffusion MRI of the human brain: Technical challenges and recent advances. NMR Biomed 2019; 32:e3941. [PMID: 29863793 PMCID: PMC6492287 DOI: 10.1002/nbm.3941] [Citation(s) in RCA: 70] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/15/2016] [Revised: 04/17/2018] [Accepted: 04/17/2018] [Indexed: 05/23/2023]
Abstract
This review discusses ex vivo diffusion magnetic resonance imaging (dMRI) as an important research tool for neuroanatomical investigations and the validation of in vivo dMRI techniques, with a focus on the human brain. We review the challenges posed by the properties of post-mortem tissue, and discuss state-of-the-art tissue preparation methods and recent advances in pulse sequences and acquisition techniques to tackle these. We then review recent ex vivo dMRI studies of the human brain, highlighting the validation of white matter orientation estimates and the atlasing and mapping of large subcortical structures. We also give particular emphasis to the delineation of layered gray matter structure with ex vivo dMRI, as this application illustrates the strength of its mesoscale resolution over large fields of view. We end with a discussion and outlook on future and potential directions of the field.
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Affiliation(s)
- Alard Roebroeck
- Department of Cognitive Neuroscience, Faculty of Psychology & NeuroscienceMaastricht UniversityMaastrichtthe Netherlands
| | | | - Manisha Aggarwal
- Department of RadiologyJohns Hopkins University School of MedicineBaltimoreMDUSA
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25
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Harms RL, Roebroeck A. Robust and Fast Markov Chain Monte Carlo Sampling of Diffusion MRI Microstructure Models. Front Neuroinform 2018; 12:97. [PMID: 30618702 PMCID: PMC6305549 DOI: 10.3389/fninf.2018.00097] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2018] [Accepted: 11/28/2018] [Indexed: 11/29/2022] Open
Abstract
In diffusion MRI analysis, advances in biophysical multi-compartment modeling have gained popularity over the conventional Diffusion Tensor Imaging (DTI), because they can obtain a greater specificity in relating the dMRI signal to underlying cellular microstructure. Biophysical multi-compartment models require a parameter estimation, typically performed using either the Maximum Likelihood Estimation (MLE) or the Markov Chain Monte Carlo (MCMC) sampling. Whereas, the MLE provides only a point estimate of the fitted model parameters, the MCMC recovers the entire posterior distribution of the model parameters given in the data, providing additional information such as parameter uncertainty and correlations. MCMC sampling is currently not routinely applied in dMRI microstructure modeling, as it requires adjustment and tuning, specific to each model, particularly in the choice of proposal distributions, burn-in length, thinning, and the number of samples to store. In addition, sampling often takes at least an order of magnitude, more time than non-linear optimization. Here we investigate the performance of the MCMC algorithm variations over multiple popular diffusion microstructure models, to examine whether a single, well performing variation could be applied efficiently and robustly to many models. Using an efficient GPU-based implementation, we showed that run times can be removed as a prohibitive constraint for the sampling of diffusion multi-compartment models. Using this implementation, we investigated the effectiveness of different adaptive MCMC algorithms, burn-in, initialization, and thinning. Finally we applied the theory of the Effective Sample Size, to the diffusion multi-compartment models, as a way of determining a relatively general target for the number of samples needed to characterize parameter distributions for different models and data sets. We conclude that adaptive Metropolis methods increase MCMC performance and select the Adaptive Metropolis-Within-Gibbs (AMWG) algorithm as the primary method. We furthermore advise to initialize the sampling with an MLE point estimate, in which case 100 to 200 samples are sufficient as a burn-in. Finally, we advise against thinning in most use-cases and as a relatively general target for the number of samples, we recommend a multivariate Effective Sample Size of 2,200.
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Affiliation(s)
- Robbert L. Harms
- Department of Cognitive Neuroscience, Faculty of Psychology & Neuroscience, Maastricht University, Maastricht, Netherlands
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Tobisch A, Stirnberg R, Harms RL, Schultz T, Roebroeck A, Breteler MMB, Stöcker T. Compressed Sensing Diffusion Spectrum Imaging for Accelerated Diffusion Microstructure MRI in Long-Term Population Imaging. Front Neurosci 2018; 12:650. [PMID: 30319336 PMCID: PMC6165908 DOI: 10.3389/fnins.2018.00650] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2018] [Accepted: 08/30/2018] [Indexed: 11/23/2022] Open
Abstract
Mapping non-invasively the complex microstructural architecture of the living human brain, diffusion magnetic resonance imaging (dMRI) is one of the core imaging modalities in current population studies. For the application in longitudinal population imaging, the dMRI protocol should deliver reliable data with maximum potential for future analysis. With the recent introduction of novel MRI hardware, advanced dMRI acquisition strategies can be applied within reasonable scan time. In this work we conducted a pilot study based on the requirements for high resolution dMRI in a long-term and high throughput population study. The key question was: can diffusion spectrum imaging accelerated by compressed sensing theory (CS-DSI) be used as an advanced imaging protocol for microstructure dMRI in a long-term population imaging study? As a minimum requirement we expected a high level of agreement of several diffusion metrics derived from both CS-DSI and a 3-shell high angular resolution diffusion imaging (HARDI) acquisition, an established imaging strategy used in other population studies. A wide spectrum of state-of-the-art diffusion processing and analysis techniques was applied to the pilot study data including quantitative diffusion and microstructural parameter mapping, fiber orientation estimation and white matter fiber tracking. When considering diffusion weighted images up to the same maximum diffusion weighting for both protocols, group analysis across 20 subjects indicates that CS-DSI performs comparable to 3-shell HARDI in the estimation of diffusion and microstructural parameters. Further, both protocols provide similar results in the estimation of fiber orientations and for local fiber tracking. CS-DSI provides high radial resolution while maintaining high angular resolution and it is well-suited for analysis strategies that require high b-value acquisitions, such as CHARMED modeling and biomarkers from the diffusion propagator.
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Affiliation(s)
- Alexandra Tobisch
- German Center for Neurodegenerative Diseases, Bonn, Germany.,Department of Computer Science, University of Bonn, Bonn, Germany
| | | | - Robbert L Harms
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Thomas Schultz
- Department of Computer Science, University of Bonn, Bonn, Germany.,Bonn-Aachen International Center for Information Technology, University of Bonn, Bonn, Germany
| | - Alard Roebroeck
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Monique M B Breteler
- German Center for Neurodegenerative Diseases, Bonn, Germany.,Faculty of Medicine, Institute for Medical Biometry, Informatics and Epidemiology, University of Bonn, Bonn, Germany
| | - Tony Stöcker
- German Center for Neurodegenerative Diseases, Bonn, Germany.,Department of Physics and Astronomy, University of Bonn, Bonn, Germany
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Havlicek M, Ivanov D, Roebroeck A, Uludağ K. Determining Excitatory and Inhibitory Neuronal Activity from Multimodal fMRI Data Using a Generative Hemodynamic Model. Front Neurosci 2017; 11:616. [PMID: 29249925 PMCID: PMC5715391 DOI: 10.3389/fnins.2017.00616] [Citation(s) in RCA: 30] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2017] [Accepted: 10/23/2017] [Indexed: 12/12/2022] Open
Abstract
Hemodynamic responses, in general, and the blood oxygenation level-dependent (BOLD) fMRI signal, in particular, provide an indirect measure of neuronal activity. There is strong evidence that the BOLD response correlates well with post-synaptic changes, induced by changes in the excitatory and inhibitory (E-I) balance between active neuronal populations. Typical BOLD responses exhibit transients, such as the early-overshoot and post-stimulus undershoot, that can be linked to transients in neuronal activity, but they can also result from vascular uncoupling between cerebral blood flow (CBF) and venous cerebral blood volume (venous CBV). Recently, we have proposed a novel generative hemodynamic model of the BOLD signal within the dynamic causal modeling framework, inspired by physiological observations, called P-DCM (Havlicek et al., 2015). We demonstrated the generative model's ability to more accurately model commonly observed neuronal and vascular transients in single regions but also effective connectivity between multiple brain areas (Havlicek et al., 2017b). In this paper, we additionally demonstrate the versatility of the generative model to jointly explain dynamic relationships between neuronal and hemodynamic physiological variables underlying the BOLD signal using multi-modal data. For this purpose, we utilized three distinct data-sets of experimentally induced responses in the primary visual areas measured in human, cat, and monkey brain, respectively: (1) CBF and BOLD responses; (2) CBF, total CBV, and BOLD responses (Jin and Kim, 2008); and (3) positive and negative neuronal and BOLD responses (Shmuel et al., 2006). By fitting the generative model to the three multi-modal experimental data-sets, we showed that the presence or absence of dynamic features in the BOLD signal is not an unambiguous indication of presence or absence of those features on the neuronal level. Nevertheless, the generative model that takes into account the dynamics of the physiological mechanisms underlying the BOLD response allowed dissociating neuronal from vascular transients and deducing excitatory and inhibitory neuronal activity time-courses from BOLD data alone and from multi-modal data.
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Affiliation(s)
- Martin Havlicek
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Dimo Ivanov
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Alard Roebroeck
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Kamil Uludağ
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
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28
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Pracht ED, Feiweier T, Ehses P, Brenner D, Roebroeck A, Weber B, Stöcker T. SAR and scan-time optimized 3D whole-brain double inversion recovery imaging at 7T. Magn Reson Med 2017; 79:2620-2628. [PMID: 28905416 DOI: 10.1002/mrm.26913] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2017] [Revised: 07/28/2017] [Accepted: 08/18/2017] [Indexed: 11/10/2022]
Abstract
PURPOSE The aim of this project was to implement an ultra-high field (UHF) optimized double inversion recovery (DIR) sequence for gray matter (GM) imaging, enabling whole brain coverage in short acquisition times ( ≈5 min, image resolution 1 mm3 ). METHODS A 3D variable flip angle DIR turbo spin echo (TSE) sequence was optimized for UHF application. We implemented an improved, fast, and specific absorption rate (SAR) efficient TSE imaging module, utilizing improved reordering. The DIR preparation was tailored to UHF application. Additionally, fat artifacts were minimized by employing water excitation instead of fat saturation. RESULTS GM images, covering the whole brain, were acquired in 7 min scan time at 1 mm isotropic resolution. SAR issues were overcome by using a dedicated flip angle calculation considering SAR and SNR efficiency. Furthermore, UHF related artifacts were minimized. CONCLUSION The suggested sequence is suitable to generate GM images with whole-brain coverage at UHF. Due to the short total acquisition times and overall robustness, this approach can potentially enable DIR application in a routine setting and enhance lesion detection in neurological diseases. Magn Reson Med 79:2620-2628, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
| | | | - Philipp Ehses
- Max Planck Institute for Biological Cybernetics, Tübingen, Germany
| | - Daniel Brenner
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - Alard Roebroeck
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht, Maastricht University, The Netherlands
| | - Bernd Weber
- Department of Epileptology, University Hospital Bonn, Bonn, Germany
| | - Tony Stöcker
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany.,Department of Physics and Astronomy, University of Bonn, Bonn, Germany
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Havlicek M, Roebroeck A, Friston KJ, Gardumi A, Ivanov D, Uludag K. On the importance of modeling fMRI transients when estimating effective connectivity: A dynamic causal modeling study using ASL data. Neuroimage 2017; 155:217-233. [DOI: 10.1016/j.neuroimage.2017.03.017] [Citation(s) in RCA: 18] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2016] [Revised: 03/02/2017] [Accepted: 03/08/2017] [Indexed: 01/28/2023] Open
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Harms RL, Fritz FJ, Tobisch A, Goebel R, Roebroeck A. Robust and fast nonlinear optimization of diffusion MRI microstructure models. Neuroimage 2017; 155:82-96. [PMID: 28457975 PMCID: PMC5518773 DOI: 10.1016/j.neuroimage.2017.04.064] [Citation(s) in RCA: 71] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2016] [Revised: 04/07/2017] [Accepted: 04/09/2017] [Indexed: 02/07/2023] Open
Abstract
Advances in biophysical multi-compartment modeling for diffusion MRI (dMRI) have gained popularity because of greater specificity than DTI in relating the dMRI signal to underlying cellular microstructure. A large range of these diffusion microstructure models have been developed and each of the popular models comes with its own, often different, optimization algorithm, noise model and initialization strategy to estimate its parameter maps. Since data fit, accuracy and precision is hard to verify, this creates additional challenges to comparability and generalization of results from diffusion microstructure models. In addition, non-linear optimization is computationally expensive leading to very long run times, which can be prohibitive in large group or population studies. In this technical note we investigate the performance of several optimization algorithms and initialization strategies over a few of the most popular diffusion microstructure models, including NODDI and CHARMED. We evaluate whether a single well performing optimization approach exists that could be applied to many models and would equate both run time and fit aspects. All models, algorithms and strategies were implemented on the Graphics Processing Unit (GPU) to remove run time constraints, with which we achieve whole brain dataset fits in seconds to minutes. We then evaluated fit, accuracy, precision and run time for different models of differing complexity against three common optimization algorithms and three parameter initialization strategies. Variability of the achieved quality of fit in actual data was evaluated on ten subjects of each of two population studies with a different acquisition protocol. We find that optimization algorithms and multi-step optimization approaches have a considerable influence on performance and stability over subjects and over acquisition protocols. The gradient-free Powell conjugate-direction algorithm was found to outperform other common algorithms in terms of run time, fit, accuracy and precision. Parameter initialization approaches were found to be relevant especially for more complex models, such as those involving several fiber orientations per voxel. For these, a fitting cascade initializing or fixing parameter values in a later optimization step from simpler models in an earlier optimization step further improved run time, fit, accuracy and precision compared to a single step fit. This establishes and makes available standards by which robust fit and accuracy can be achieved in shorter run times. This is especially relevant for the use of diffusion microstructure modeling in large group or population studies and in combining microstructure parameter maps with tractography results. Evaluate robustness of fit, accuracy, precision for diffusion microstructure models Test three optimization algorithms and three parameter initialization strategies GPU implementation removes run time constraints; whole brain fit within minutes Powell conjugate-direction algorithm has superior fit, accuracy, precision Initialization approaches are important for crossing fiber microstructure models
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Affiliation(s)
- R L Harms
- Dept. of Cognitive Neuroscience, Faculty of Psychology & Neuroscience, Maastricht University, The Netherlands; Brain Innovation B.V., Maastricht, The Netherlands.
| | - F J Fritz
- Dept. of Cognitive Neuroscience, Faculty of Psychology & Neuroscience, Maastricht University, The Netherlands
| | - A Tobisch
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - R Goebel
- Dept. of Cognitive Neuroscience, Faculty of Psychology & Neuroscience, Maastricht University, The Netherlands
| | - A Roebroeck
- Dept. of Cognitive Neuroscience, Faculty of Psychology & Neuroscience, Maastricht University, The Netherlands
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Kolber P, Droby A, Roebroeck A, Goebel R, Fleischer V, Groppa S, Zipp F. A “kissing lesion”: In-vivo 7T evidence of meningeal inflammation in early multiple sclerosis. Mult Scler 2017; 23:1167-1169. [DOI: 10.1177/1352458516683267] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Background: The role of cortical lesions (CLs) in disease progression and clinical deficits is increasingly recognized in multiple sclerosis (MS); however the origin of CLs in MS still remains unclear. Objective: Here, we report a para-sulcal CL detected two years after diagnosis in a relapsing-remitting MS (RRMS) patient without manifestation of clinical deficit. Methods: Ultra-high field (7T) MR imaging using magnetization-prepared 2 rapid acquisition gradient echoes (MP2RAGE) sequence was performed. Results: A para-sulcal CL was detected which showed hypointense rim and iso- to hyperintense core. This was detected in the proximity of the leptomeninges in the left precentral gyrus extending to the adjacent postcentral gyrus. Conclusion: This finding indicates that inflammatory infiltration into the cortex through the meninges underlies cortical pathology already in the early stage of disease and in mild disease course.
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Affiliation(s)
- Pierre Kolber
- Focus Program Translational Neurosciences (FTN), Rhine Main Neuroscience Network (rmn2), Department of Neurology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Amgad Droby
- Focus Program Translational Neurosciences (FTN), Rhine Main Neuroscience Network (rmn2), Department of Neurology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Alard Roebroeck
- Maastricht Brain Imaging Center, Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Rainer Goebel
- Maastricht Brain Imaging Center, Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Vinzenz Fleischer
- Focus Program Translational Neurosciences (FTN), Rhine Main Neuroscience Network (rmn2), Department of Neurology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Sergiu Groppa
- Focus Program Translational Neurosciences (FTN), Rhine Main Neuroscience Network (rmn2), Department of Neurology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
| | - Frauke Zipp
- Focus Program Translational Neurosciences (FTN), Rhine Main Neuroscience Network (rmn2), Department of Neurology, University Medical Center of the Johannes Gutenberg University Mainz, Mainz, Germany
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Sengupta S, Fritz FJ, Harms RL, Hildebrand S, Tse DHY, Poser BA, Goebel R, Roebroeck A. High resolution anatomical and quantitative MRI of the entire human occipital lobe ex vivo at 9.4T. Neuroimage 2017; 168:162-171. [PMID: 28336427 PMCID: PMC5862655 DOI: 10.1016/j.neuroimage.2017.03.039] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2016] [Revised: 03/17/2017] [Accepted: 03/19/2017] [Indexed: 11/06/2022] Open
Abstract
Several magnetic resonance imaging (MRI) contrasts are sensitive to myelin content in gray matter in vivo which has ignited ambitions of MRI-based in vivo cortical histology. Ultra-high field (UHF) MRI, at fields of 7 T and beyond, is crucial to provide the resolution and contrast needed to sample contrasts over the depth of the cortex and get closer to layer resolved imaging. Ex vivo MRI of human post mortem samples is an important stepping stone to investigate MRI contrast in the cortex, validate it against histology techniques applied in situ to the same tissue, and investigate the resolutions needed to translate ex vivo findings to in vivo UHF MRI. Here, we investigate key technology to extend such UHF studies to large human brain samples while maintaining high resolution, which allows investigation of the layered architecture of several cortical areas over their entire 3D extent and their complete borders where architecture changes. A 16 channel cylindrical phased array radiofrequency (RF) receive coil was constructed to image a large post mortem occipital lobe sample (~80×80×80 mm3) in a wide-bore 9.4 T human scanner with the aim of achieving high-resolution anatomical and quantitative MR images. Compared with a human head coil at 9.4 T, the maximum Signal-to-Noise ratio (SNR) was increased by a factor of about five in the peripheral cortex. Although the transmit profile with a circularly polarized transmit mode at 9.4 T is relatively inhomogeneous over the large sample, this challenge was successfully resolved with parallel transmit using the kT-points method. Using this setup, we achieved 60μm anatomical images for the entire occipital lobe showing increased spatial definition of cortical details compared to lower resolutions. In addition, we were able to achieve sufficient control over SNR, B0 and B1 homogeneity and multi-contrast sampling to perform quantitative T2* mapping over the same volume at 200 μm. Markov Chain Monte Carlo sampling provided maximum posterior estimates of quantitative T2* and their uncertainty, allowing delineation of the stria of Gennari over the entire length and width of the calcarine sulcus. We discuss how custom RF receive coil arrays built to specific large post mortem sample sizes can provide a platform for UHF cortical layer-specific quantitative MRI over large fields of view. Custom-built 16 channel 9.4 T RF-coil to image large post mortem samples at high resolution. Parallel transmit techniques allow homogenization of B1+ for 3D GRE imaging at UHF. 60 μm anatomical MRI of the entire human occipital lobe. 200 μm isotropic quantitative T2* mapping of the entire human occipital lobe. A platform for future UHF cortical layer specific qMRI over large FoVs.
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Affiliation(s)
- S Sengupta
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands.
| | - F J Fritz
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - R L Harms
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - S Hildebrand
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - D H Y Tse
- Department of Neuropsychology and Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - B A Poser
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - R Goebel
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - A Roebroeck
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
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Domen P, Peeters S, Michielse S, Gronenschild E, Viechtbauer W, Roebroeck A, Os JV, Marcelis M. Differential Time Course of Microstructural White Matter in Patients With Psychotic Disorder and Individuals at Risk: A 3-Year Follow-up Study. Schizophr Bull 2017; 43:160-170. [PMID: 27190279 PMCID: PMC5216846 DOI: 10.1093/schbul/sbw061] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
BACKGROUND Although widespread reduced white matter (WM) integrity is a consistent finding in cross-sectional diffusion tensor imaging (DTI) studies of schizophrenia, little is known about the course of these alterations. This study examined to what degree microstructural WM alterations display differential trajectories over time as a function of level of psychosis liability. METHODS Two DTI scans with a 3-year time interval were acquired from 159 participants (55 patients with a psychotic disorder, 55 nonpsychotic siblings and 49 healthy controls) and processed with tract-based spatial statistics. The mean fractional anisotropy (FA) change over time was calculated. Main effects of group, as well as group × region interactions in the model of FA change were examined with multilevel (mixed-effects) models. RESULTS Siblings revealed a significant mean FA decrease over time compared to controls (B = -0.004, P = .04), resulting in a significant sibling-control difference at follow-up (B = -0.007, P = .03). Patients did not show a significant change over time, but their mean FA was lower than controls both at baseline and at follow-up. A significant group × region interaction (χ2 = 105.4, P = .01) revealed group differences in FA change in the right cingulum, left posterior thalamic radiation, right retrolenticular part of the internal capsule, and the right posterior corona radiata. CONCLUSION Whole brain mean FA remained stable over a 3-year period in patients with psychotic disorder and declined over time in nonaffected siblings, so that at follow-up both groups had lower FA with respect to controls. The results suggest that liability for psychosis may involve a process of WM alterations.
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Affiliation(s)
- Patrick Domen
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands;
| | - Sanne Peeters
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
- Faculty of Psychology and Educational Sciences, Open University of the Netherlands, Heerlen, The Netherlands
| | - Stijn Michielse
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Ed Gronenschild
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Wolfgang Viechtbauer
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Alard Roebroeck
- Department of Cognitive Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Jim van Os
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
- King's College London, King's Health Partners, Department of Psychosis Studies, Institute of Psychiatry, London, UK
| | - Machteld Marcelis
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht, The Netherlands
- Institute for Mental Health Care Eindhoven (GGzE), Eindhoven, The Netherlands
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Timmers I, Roebroeck A, Bastiani M, Jansma B, Rubio-Gozalbo E, Zhang H. Assessing Microstructural Substrates of White Matter Abnormalities: A Comparative Study Using DTI and NODDI. PLoS One 2016; 11:e0167884. [PMID: 28002426 PMCID: PMC5176300 DOI: 10.1371/journal.pone.0167884] [Citation(s) in RCA: 62] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2016] [Accepted: 11/22/2016] [Indexed: 11/23/2022] Open
Abstract
Neurite orientation dispersion and density imaging (NODDI) enables more specific characterization of tissue microstructure by estimating neurite density (NDI) and orientation dispersion (ODI), two key contributors to fractional anisotropy (FA). The present work compared NODDI- with diffusion tensor imaging (DTI)-derived indices for investigating white matter abnormalities in a clinical sample. We assessed the added value of NODDI parameters over FA, by contrasting group differences identified by both models. Diffusion-weighted images with multiple shells were acquired in a group of 8 healthy controls and 8 patients with an inherited metabolic disease. Both standard DTI and NODDI analyses were performed. Tract based spatial statistics (TBSS) was used for group inferences, after which overlap and unique contributions across different parameters were evaluated. Results showed that group differences in NDI and ODI were complementary, and together could explain much of the FA results. Further, compared to FA analysis, NDI and ODI gave a pattern of results that was more regionally specific and were able to capture additional discriminative voxels that FA failed to identify. Finally, ODI from single-shell NODDI analysis, but not NDI, was found to reproduce the group differences from the multi-shell analysis. To conclude, by using a clinically feasible acquisition and analysis protocol, we demonstrated that NODDI is of added value to standard DTI, by revealing specific microstructural substrates to white matter changes detected with FA. As the (simpler) DTI model was more sensitive in identifying group differences, NODDI is recommended to be used complementary to DTI, thereby adding greater specificity regarding microstructural underpinnings of the differences. The finding that ODI abnormalities can be identified reliably using single-shell data may allow the retrospective analysis of standard DTI with NODDI.
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Affiliation(s)
- Inge Timmers
- Department of Cognitive Neuroscience, Maastricht University, Maastricht, the Netherlands
- Department of Rehabilitation Medicine, Maastricht University, Maastricht, the Netherlands
- * E-mail:
| | - Alard Roebroeck
- Department of Cognitive Neuroscience, Maastricht University, Maastricht, the Netherlands
- Maastricht Brain Imaging Center (M-BIC), Maastricht, the Netherlands
| | - Matteo Bastiani
- Oxford Centre for Functional MRI of the Brain (FMRIB Centre), University of Oxford, Headington, Oxford, United Kingdom
| | - Bernadette Jansma
- Department of Cognitive Neuroscience, Maastricht University, Maastricht, the Netherlands
- Maastricht Brain Imaging Center (M-BIC), Maastricht, the Netherlands
| | - Estela Rubio-Gozalbo
- Department of Pediatrics and Laboratory Genetic Metabolic Diseases, Maastricht University Medical Center, Maastricht, the Netherlands
| | - Hui Zhang
- Department of Computer Science and Centre for Medical Image Computing, University College London, London, United Kingdom
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Sengupta S, Roebroeck A, Kemper VG, Poser BA, Zimmermann J, Goebel R, Adriany G. A Specialized Multi-Transmit Head Coil for High Resolution fMRI of the Human Visual Cortex at 7T. PLoS One 2016; 11:e0165418. [PMID: 27911950 PMCID: PMC5135047 DOI: 10.1371/journal.pone.0165418] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/19/2016] [Accepted: 10/11/2016] [Indexed: 11/19/2022] Open
Abstract
PURPOSE To design, construct and validate radiofrequency (RF) transmit and receive phased array coils for high-resolution visual cortex imaging at 7 Tesla. METHODS A 4 channel transmit and 16 channel receive array was constructed on a conformal polycarbonate former. Transmit field efficiency and homogeneity were simulated and validated, along with the Specific Absorption Rate, using [Formula: see text] mapping techniques and electromagnetic simulations. Receiver signal-to-noise ratio (SNR), temporal SNR (tSNR) across EPI time series, g-factors for accelerated imaging and noise correlations were evaluated and compared with a commercial 32 channel whole head coil. The performance of the coil was further evaluated with human subjects through functional MRI (fMRI) studies at standard and submillimeter resolutions of upto 0.8mm isotropic. RESULTS The transmit and receive sections were characterized using bench tests and showed good interelement decoupling, preamplifier decoupling and sample loading. SNR for the 16 channel coil was ∼ 1.5 times that of the commercial coil in the human occipital lobe, and showed better g-factor values for accelerated imaging. fMRI tests conducted showed better response to Blood Oxygen Level Dependent (BOLD) activation, at resolutions of 1.2mm and 0.8mm isotropic. CONCLUSION The 4 channel phased array transmit coil provides homogeneous excitation across the visual cortex, which, in combination with the dual row 16 channel receive array, makes for a valuable research tool for high resolution anatomical and functional imaging of the visual cortex at 7T.
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Affiliation(s)
- Shubharthi Sengupta
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
- Maastricht Brain Imaging Center, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
- * E-mail:
| | - Alard Roebroeck
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
- Maastricht Brain Imaging Center, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Valentin G. Kemper
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
- Maastricht Brain Imaging Center, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Benedikt A. Poser
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
- Maastricht Brain Imaging Center, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Jan Zimmermann
- Center for Neural Science, New York University, NY, United States of America
| | - Rainer Goebel
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
- Maastricht Brain Imaging Center, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Gregor Adriany
- Department of Radiology, Center for Magnetic Resonance Research, University of Minnesota Medical School, Minneapolis, MN, United States of America
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Bastiani M, Oros-Peusquens AM, Seehaus A, Brenner D, Möllenhoff K, Celik A, Felder J, Bratzke H, Shah NJ, Galuske R, Goebel R, Roebroeck A. Automatic Segmentation of Human Cortical Layer-Complexes and Architectural Areas Using Ex vivo Diffusion MRI and Its Validation. Front Neurosci 2016; 10:487. [PMID: 27891069 PMCID: PMC5102896 DOI: 10.3389/fnins.2016.00487] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2016] [Accepted: 10/11/2016] [Indexed: 11/14/2022] Open
Abstract
Recently, several magnetic resonance imaging contrast mechanisms have been shown to distinguish cortical substructure corresponding to selected cortical layers. Here, we investigate cortical layer and area differentiation by automatized unsupervised clustering of high-resolution diffusion MRI data. Several groups of adjacent layers could be distinguished in human primary motor and premotor cortex. We then used the signature of diffusion MRI signals along cortical depth as a criterion to detect area boundaries and find borders at which the signature changes abruptly. We validate our clustering results by histological analysis of the same tissue. These results confirm earlier studies which show that diffusion MRI can probe layer-specific intracortical fiber organization and, moreover, suggests that it contains enough information to automatically classify architecturally distinct cortical areas. We discuss the strengths and weaknesses of the automatic clustering approach and its appeal for MR-based cortical histology.
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Affiliation(s)
- Matteo Bastiani
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht UniversityMaastricht, Netherlands; Research Centre Jülich, Institute of Neuroscience and Medicine (INM-4)Jülich, Germany
| | | | - Arne Seehaus
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht UniversityMaastricht, Netherlands; Department of Biology, TU DarmstadtDarmstadt, Germany
| | - Daniel Brenner
- Research Centre Jülich, Institute of Neuroscience and Medicine (INM-4) Jülich, Germany
| | - Klaus Möllenhoff
- Research Centre Jülich, Institute of Neuroscience and Medicine (INM-4) Jülich, Germany
| | - Avdo Celik
- Research Centre Jülich, Institute of Neuroscience and Medicine (INM-4) Jülich, Germany
| | - Jörg Felder
- Research Centre Jülich, Institute of Neuroscience and Medicine (INM-4) Jülich, Germany
| | - Hansjürgen Bratzke
- Department of Forensic Medicine, Faculty of Medicine, Goethe University Frankfurt Frankfurt, Germany
| | - Nadim J Shah
- Research Centre Jülich, Institute of Neuroscience and Medicine (INM-4)Jülich, Germany; Department of Neurology, Faculty of Medicine, Jülich Aachen Research Alliance, RWTH Aachen UniversityAachen, Germany
| | - Ralf Galuske
- Department of Biology, TU Darmstadt Darmstadt, Germany
| | - Rainer Goebel
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht UniversityMaastricht, Netherlands; Department of Neuroimaging and Neuromodeling, Netherlands Institute for Neuroscience - KNAWAmsterdam, Netherlands
| | - Alard Roebroeck
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University Maastricht, Netherlands
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Plantinga B, Temel Y, Duchin Y, Uludag K, Roebroeck A, Kuijf M, Jahanshahi A, Haar Romenij BT, Vitek J, Harel N. SP 9. Individualized identification of the motor part of the subthalamic nucleus in Parkinson’s disease. Clin Neurophysiol 2016. [DOI: 10.1016/j.clinph.2016.05.195] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
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De Santis S, Assaf Y, Jeurissen B, Jones DK, Roebroeck A. T1 relaxometry of crossing fibres in the human brain. Neuroimage 2016; 141:133-142. [PMID: 27444568 PMCID: PMC5035137 DOI: 10.1016/j.neuroimage.2016.07.037] [Citation(s) in RCA: 37] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2016] [Revised: 07/13/2016] [Accepted: 07/15/2016] [Indexed: 12/13/2022] Open
Abstract
A comprehensive tract-based characterisation of white matter should include the ability to quantify myelin and axonal attributes irrespective of the complexity of fibre organisation within the voxel. Recently, a new experimental framework that combines inversion recovery and diffusion MRI, called inversion recovery diffusion tensor imaging (IR-DTI), was introduced and applied in an animal study. IR-DTI provides the ability to assign to each unique fibre population within a voxel a specific value of the longitudinal relaxation time, T1, which is a proxy for myelin content. Here, we apply the IR-DTI approach to the human brain in vivo on 7 healthy subjects for the first time. We demonstrate that the approach is able to measure differential tract properties in crossing fibre areas, reflecting the different myelination of tracts. We also show that tract-specific T1 has less inter-subject variability compared to conventional T1 in areas of crossing fibres, suggesting increased specificity to distinct fibre populations. Finally we show in simulations that changes in myelination selectively affecting one fibre bundle in crossing fibre areas can potentially be detected earlier using IR-DTI. We apply the inversion recovery DTI approach to the human brain in vivo for the first time. We demonstrate that IR-DTI can measure tract-specific T1 in crossing fibres. IR-DTI T1 has less inter-subject variability compared to conventional T1 in crossing fibres. Changes in myelination affecting one fibre in crossing fibres can be detected earlier using IR-DTI.
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Affiliation(s)
- Silvia De Santis
- CUBRIC, School of Psychology, Cardiff University, Cardiff CF24 4HQ,UK; Maastricht University, Maastricht, The Netherlands.
| | - Yaniv Assaf
- Department of Neurobiology, Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978, Israel
| | - Ben Jeurissen
- iMinds-Vision Lab, Dept. of Physics, University of Antwerp, Antwerp, Belgium
| | - Derek K Jones
- CUBRIC, School of Psychology, Cardiff University, Cardiff CF24 4HQ,UK; Neuroscience & Mental Health Research Institute, Cardiff University, CF10 3AT,UK
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Plantinga BR, Roebroeck A, Kemper VG, Uludağ K, Melse M, Mai J, Kuijf ML, Herrler A, Jahanshahi A, Ter Haar Romeny BM, Temel Y. Ultra-High Field MRI Post Mortem Structural Connectivity of the Human Subthalamic Nucleus, Substantia Nigra, and Globus Pallidus. Front Neuroanat 2016; 10:66. [PMID: 27378864 PMCID: PMC4909758 DOI: 10.3389/fnana.2016.00066] [Citation(s) in RCA: 35] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2016] [Accepted: 06/01/2016] [Indexed: 01/14/2023] Open
Abstract
Introduction: The subthalamic nucleus, substantia nigra, and globus pallidus, three nuclei of the human basal ganglia, play an important role in motor, associative, and limbic processing. The network of the basal ganglia is generally characterized by a direct, indirect, and hyperdirect pathway. This study aims to investigate the mesoscopic nature of these connections between the subthalamic nucleus, substantia nigra, and globus pallidus and their surrounding structures. Methods: A human post mortem brain specimen including the substantia nigra, subthalamic nucleus, and globus pallidus was scanned on a 7 T MRI scanner. High resolution diffusion weighted images were used to reconstruct the fibers intersecting the substantia nigra, subthalamic nucleus, and globus pallidus. The course and density of these tracks was analyzed. Results: Most of the commonly established projections of the subthalamic nucleus, substantia nigra, and globus pallidus were successfully reconstructed. However, some of the reconstructed fiber tracks such as the connections of the substantia nigra pars compacta to the other included nuclei and the connections with the anterior commissure have not been shown previously. In addition, the quantitative tractography approach showed a typical degree of connectivity previously not documented. An example is the relatively larger projections of the subthalamic nucleus to the substantia nigra pars reticulata when compared to the projections to the globus pallidus internus. Discussion: This study shows that ultra-high field post mortem tractography allows for detailed 3D reconstruction of the projections of deep brain structures in humans. Although the results should be interpreted carefully, the newly identified connections contribute to our understanding of the basal ganglia.
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Affiliation(s)
- Birgit R Plantinga
- Department of Biomedical Image Analysis, Eindhoven University of TechnologyEindhoven, Netherlands; Department of Translational Neuroscience, Maastricht UniversityMaastricht, Netherlands
| | - Alard Roebroeck
- Department of Cognitive Neuroscience, Maastricht University Maastricht, Netherlands
| | - Valentin G Kemper
- Department of Cognitive Neuroscience, Maastricht University Maastricht, Netherlands
| | - Kâmil Uludağ
- Department of Cognitive Neuroscience, Maastricht University Maastricht, Netherlands
| | - Maartje Melse
- Department of Translational Neuroscience, Maastricht University Maastricht, Netherlands
| | - Jürgen Mai
- Department of Neuroanatomy, Heinrich-Heine-University Düsseldorf Düsseldorf, Germany
| | - Mark L Kuijf
- Department of Neurology, Maastricht University Medical Center Maastricht, Netherlands
| | - Andreas Herrler
- Department of Anatomy and Embryology, Maastricht University Maastricht, Netherlands
| | - Ali Jahanshahi
- Department of Neurosurgery, Maastricht University Medical Center Maastricht, Netherlands
| | - Bart M Ter Haar Romeny
- Department of Biomedical Image Analysis, Eindhoven University of Technology Eindhoven, Netherlands
| | - Yasin Temel
- Department of Translational Neuroscience, Maastricht UniversityMaastricht, Netherlands; Department of Neurosurgery, Maastricht University Medical CenterMaastricht, Netherlands
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De Santis S, Jones DK, Roebroeck A. Including diffusion time dependence in the extra-axonal space improves in vivo estimates of axonal diameter and density in human white matter. Neuroimage 2016; 130:91-103. [PMID: 26826514 PMCID: PMC4819719 DOI: 10.1016/j.neuroimage.2016.01.047] [Citation(s) in RCA: 70] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2015] [Revised: 01/14/2016] [Accepted: 01/20/2016] [Indexed: 12/01/2022] Open
Abstract
Axonal density and diameter are two fundamental properties of brain white matter. Recently, advanced diffusion MRI techniques have made these two parameters accessible in vivo. However, the techniques available to estimate such parameters are still under development. For example, current methods to map axonal diameters capture relative trends over different structures, but consistently over-estimate absolute diameters. Axonal density estimates are more accessible experimentally, but different modeling approaches exist and the impact of the experimental parameters has not been thoroughly quantified, potentially leading to incompatibility of results obtained in different studies using different techniques. Here, we characterise the impact of diffusion time on axonal density and diameter estimates using Monte Carlo simulations and STEAM diffusion MRI at 7 T on 9 healthy volunteers. We show that axonal density and diameter estimates strongly depend on diffusion time, with diameters almost invariably overestimated and density both over and underestimated for some commonly used models. Crucially, we also demonstrate that these biases are reduced when the model accounts for diffusion time dependency in the extra-axonal space. For axonal density estimates, both upward and downward bias in different situations are removed by modeling extra-axonal time-dependence, showing increased accuracy in these estimates. For axonal diameter estimates, we report increased accuracy in ground truth simulations and axonal diameter estimates decreased away from high values given by earlier models and towards known values in the human corpus callosum when modeling extra-axonal time-dependence. Axonal diameter feasibility under both advanced and clinical settings is discussed in the light of the proposed advances.
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Affiliation(s)
- Silvia De Santis
- CUBRIC, School of Psychology, Cardiff University, Cardiff CF10 3AT, UK; Faculty of Psychology & Neuroscience, Maastricht University, Maastricht, The Netherlands.
| | - Derek K Jones
- CUBRIC, School of Psychology, Cardiff University, Cardiff CF10 3AT, UK; Neuroscience & Mental Health Research Institute, Cardiff University, CF10 3AT, UK
| | - Alard Roebroeck
- Faculty of Psychology & Neuroscience, Maastricht University, Maastricht, The Netherlands
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Havlicek M, Roebroeck A, Friston K, Gardumi A, Ivanov D, Uludag K. Physiologically informed dynamic causal modeling of fMRI data. Neuroimage 2015; 122:355-72. [DOI: 10.1016/j.neuroimage.2015.07.078] [Citation(s) in RCA: 57] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2015] [Revised: 07/27/2015] [Accepted: 07/28/2015] [Indexed: 12/15/2022] Open
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42
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Seehaus A, Roebroeck A, Bastiani M, Fonseca L, Bratzke H, Lori N, Vilanova A, Goebel R, Galuske R. Histological validation of high-resolution DTI in human post mortem tissue. Front Neuroanat 2015; 9:98. [PMID: 26257612 PMCID: PMC4511840 DOI: 10.3389/fnana.2015.00098] [Citation(s) in RCA: 88] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2015] [Accepted: 07/10/2015] [Indexed: 11/13/2022] Open
Abstract
Diffusion tensor imaging (DTI) is amongst the simplest mathematical models available for diffusion magnetic resonance imaging, yet still by far the most used one. Despite the success of DTI as an imaging tool for white matter fibers, its anatomical underpinnings on a microstructural basis remain unclear. In this study, we used 65 myelin-stained sections of human premotor cortex to validate modeled fiber orientations and oft used microstructure-sensitive scalar measures of DTI on the level of individual voxels. We performed this validation on high spatial resolution diffusion MRI acquisitions investigating both white and gray matter. We found a very good agreement between DTI and myelin orientations with the majority of voxels showing angular differences less than 10°. The agreement was strongest in white matter, particularly in unidirectional fiber pathways. In gray matter, the agreement was good in the deeper layers highlighting radial fiber directions even at lower fractional anisotropy (FA) compared to white matter. This result has potentially important implications for tractography algorithms applied to high resolution diffusion MRI data if the aim is to move across the gray/white matter boundary. We found strong relationships between myelin microstructure and DTI-based microstructure-sensitive measures. High FA values were linked to high myelin density and a sharply tuned histological orientation profile. Conversely, high values of mean diffusivity (MD) were linked to bimodal or diffuse orientation distributions and low myelin density. At high spatial resolution, DTI-based measures can be highly sensitive to white and gray matter microstructure despite being relatively unspecific to concrete microarchitectural aspects.
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Affiliation(s)
- Arne Seehaus
- Faculty of Psychology and Neuroscience, Maastricht University Maastricht, Netherlands ; Systems Neurophysiology, Technische Universität Darmstadt Darmstadt, Germany
| | - Alard Roebroeck
- Faculty of Psychology and Neuroscience, Maastricht University Maastricht, Netherlands
| | - Matteo Bastiani
- Faculty of Psychology and Neuroscience, Maastricht University Maastricht, Netherlands ; Jülich Research Centre, Institute of Neuroscience and Medicine (INM-4) Jülich, Germany
| | - Lúcia Fonseca
- Department of Biomedical Engineering, Eindhoven University of Technology Eindhoven, Netherlands
| | | | - Nicolás Lori
- Visual Neuroscience Laboratory, Institute for Biomedical Imaging and Life Sciences (IBILI), Faculty of Medicine, University of Coimbra Coimbra, Portugal
| | - Anna Vilanova
- Department of Biomedical Engineering, Eindhoven University of Technology Eindhoven, Netherlands
| | - Rainer Goebel
- Faculty of Psychology and Neuroscience, Maastricht University Maastricht, Netherlands
| | - Ralf Galuske
- Systems Neurophysiology, Technische Universität Darmstadt Darmstadt, Germany
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Bastiani M, Roebroeck A. Unraveling the multiscale structural organization and connectivity of the human brain: the role of diffusion MRI. Front Neuroanat 2015; 9:77. [PMID: 26106304 PMCID: PMC4460430 DOI: 10.3389/fnana.2015.00077] [Citation(s) in RCA: 25] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2014] [Accepted: 05/21/2015] [Indexed: 01/31/2023] Open
Abstract
The structural architecture and the anatomical connectivity of the human brain show different organizational principles at distinct spatial scales. Histological staining and light microscopy techniques have been widely used in classical neuroanatomical studies to unravel brain organization. Using such techniques is a laborious task performed on 2-dimensional histological sections by skilled anatomists possibly aided by semi-automated algorithms. With the recent advent of modern magnetic resonance imaging (MRI) contrast mechanisms, cortical layers and columns can now be reliably identified and their structural properties quantified post-mortem. These developments are allowing the investigation of neuroanatomical features of the brain at a spatial resolution that could be interfaced with that of histology. Diffusion MRI and tractography techniques, in particular, have been used to probe the architecture of both white and gray matter in three dimensions. Combined with mathematical network analysis, these techniques are increasingly influential in the investigation of the macro-, meso-, and microscopic organization of brain connectivity and anatomy, both in vivo and ex vivo. Diffusion MRI-based techniques in combination with histology approaches can therefore support the endeavor of creating multimodal atlases that take into account the different spatial scales or levels on which the brain is organized. The aim of this review is to illustrate and discuss the structural architecture and the anatomical connectivity of the human brain at different spatial scales and how recently developed diffusion MRI techniques can help investigate these.
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Affiliation(s)
- Matteo Bastiani
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University Maastricht, Netherlands
| | - Alard Roebroeck
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University Maastricht, Netherlands
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Timmers I, Zhang H, Bastiani M, Jansma BM, Roebroeck A, Rubio-Gozalbo ME. White matter microstructure pathology in classic galactosemia revealed by neurite orientation dispersion and density imaging. J Inherit Metab Dis 2015; 38:295-304. [PMID: 25344151 PMCID: PMC4341012 DOI: 10.1007/s10545-014-9780-x] [Citation(s) in RCA: 39] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/12/2014] [Revised: 10/02/2014] [Accepted: 10/06/2014] [Indexed: 12/31/2022]
Abstract
White matter abnormalities have been observed in patients with classic galactosemia, an inborn error of galactose metabolism. However, magnetic resonance imaging (MRI) data collected in the past were generally qualitative in nature. Our objective was to investigate white matter microstructure pathology and examine correlations with outcome and behaviour in this disease, by using multi-shell diffusion weighted imaging. In addition to standard diffusion tensor imaging (DTI), neurite orientation dispersion and density imaging (NODDI) was used to estimate density and orientation dispersion of neurites in a group of eight patients (aged 16-21 years) and eight healthy controls (aged 15-20 years). Extensive white matter abnormalities were found: neurite density index (NDI) was lower in the patient group in bilateral anterior areas, and orientation dispersion index (ODI) was increased mainly in the left hemisphere. These specific regional profiles are in agreement with the cognitive profile observed in galactosemia, showing higher order cognitive impairments, and language and motor impairments, respectively. Less favourable white matter properties correlated positively with age and age at onset of diet, and negatively with behavioural outcome (e.g. visual working memory). To conclude, this study provides evidence of white matter pathology regarding density and dispersion of neurites in these patients. The results are discussed in light of suggested pathophysiological mechanisms.
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Affiliation(s)
- Inge Timmers
- Department of Cognitive Neuroscience, Maastricht University, PO Box 616, 6200 MD, Maastricht, The Netherlands,
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Frankort A, Roefs A, Siep N, Roebroeck A, Havermans R, Jansen A. Neural predictors of chocolate intake following chocolate exposure. Appetite 2014; 87:98-107. [PMID: 25528694 DOI: 10.1016/j.appet.2014.12.204] [Citation(s) in RCA: 17] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2014] [Revised: 11/23/2014] [Accepted: 12/13/2014] [Indexed: 01/22/2023]
Abstract
Previous studies have shown that one's brain response to high-calorie food cues can predict long-term weight gain or weight loss. The neural correlates that predict food intake in the short term have, however, hardly been investigated. This study examined which brain regions' activation predicts chocolate intake after participants had been either exposed to real chocolate or to control stimuli during approximately one hour, with interruptions for fMRI measurements. Further we investigated whether the variance in chocolate intake could be better explained by activated brain regions than by self-reported craving. In total, five brain regions correlated with subsequent chocolate intake. The activation of two reward regions (the right caudate and the left frontopolar cortex) correlated positively with intake in the exposure group. The activation of two regions associated with cognitive control (the left dorsolateral and left mid-dorsolateral PFC) correlated negatively with intake in the control group. When the regression analysis was conducted with the exposure and the control group together, an additional region's activation (the right anterior PFC) correlated positively with chocolate intake. In all analyses, the intake variance explained by neural correlates was above and beyond the variance explained by self-reported craving. These results are in line with neuroimaging research showing that brain responses are a better predictor of subsequent intake than self-reported craving. Therefore, our findings might provide for a missing link by associating brain activation, previously shown to predict weight change, with short-term intake.
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Affiliation(s)
- Astrid Frankort
- Department of Clinical Psychological Science, Faculty of Psychology and Neuroscience, Maastricht University, P.O. Box 616, Maastricht 6200 MD, The Netherlands
| | - Anne Roefs
- Department of Clinical Psychological Science, Faculty of Psychology and Neuroscience, Maastricht University, P.O. Box 616, Maastricht 6200 MD, The Netherlands.
| | - Nicolette Siep
- Department of Clinical Psychological Science, Faculty of Psychology and Neuroscience, Maastricht University, P.O. Box 616, Maastricht 6200 MD, The Netherlands
| | - Alard Roebroeck
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, The Netherlands
| | - Remco Havermans
- Department of Clinical Psychological Science, Faculty of Psychology and Neuroscience, Maastricht University, P.O. Box 616, Maastricht 6200 MD, The Netherlands
| | - Anita Jansen
- Department of Clinical Psychological Science, Faculty of Psychology and Neuroscience, Maastricht University, P.O. Box 616, Maastricht 6200 MD, The Netherlands
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Plantinga BR, Temel Y, Roebroeck A, Uludağ K, Ivanov D, Kuijf ML, Ter Haar Romenij BM. Ultra-high field magnetic resonance imaging of the basal ganglia and related structures. Front Hum Neurosci 2014; 8:876. [PMID: 25414656 PMCID: PMC4220687 DOI: 10.3389/fnhum.2014.00876] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2014] [Accepted: 10/10/2014] [Indexed: 12/13/2022] Open
Abstract
Deep brain stimulation is a treatment for Parkinson's disease and other related disorders, involving the surgical placement of electrodes in the deeply situated basal ganglia or thalamic structures. Good clinical outcome requires accurate targeting. However, due to limited visibility of the target structures on routine clinical MR images, direct targeting of structures can be challenging. Non-clinical MR scanners with ultra-high magnetic field (7T or higher) have the potential to improve the quality of these images. This technology report provides an overview of the current possibilities of visualizing deep brain stimulation targets and their related structures with the aid of ultra-high field MRI. Reviewed studies showed improved resolution, contrast- and signal-to-noise ratios at ultra-high field. Sequences sensitive to magnetic susceptibility such as T2* and susceptibility weighted imaging and their maps in general showed the best visualization of target structures, including a separation between the subthalamic nucleus and the substantia nigra, the lamina pallidi medialis and lamina pallidi incompleta within the globus pallidus and substructures of the thalamus, including the ventral intermediate nucleus (Vim). This shows that the visibility, identification, and even subdivision of the small deep brain stimulation targets benefit from increased field strength. Although ultra-high field MR imaging is associated with increased risk of geometrical distortions, it has been shown that these distortions can be avoided or corrected to the extent where the effects are limited. The availability of ultra-high field MR scanners for humans seems to provide opportunities for a more accurate targeting for deep brain stimulation in patients with Parkinson's disease and related disorders.
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Affiliation(s)
- Birgit R Plantinga
- Biomedical Image Analysis, Eindhoven University of Technology Eindhoven, Netherlands ; Department of Neuroscience, Maastricht University Maastricht, Netherlands
| | - Yasin Temel
- Department of Neuroscience, Maastricht University Maastricht, Netherlands ; Department of Neurology, Maastricht University Medical Center Maastricht, Netherlands
| | - Alard Roebroeck
- Department of Neurosurgery, Maastricht University Medical Center Maastricht, Netherlands
| | - Kâmil Uludağ
- Department of Neurosurgery, Maastricht University Medical Center Maastricht, Netherlands
| | - Dimo Ivanov
- Department of Neurosurgery, Maastricht University Medical Center Maastricht, Netherlands
| | - Mark L Kuijf
- Department of Cognitive Neuroscience, Maastricht University Maastricht, Netherlands
| | - Bart M Ter Haar Romenij
- Biomedical Image Analysis, Eindhoven University of Technology Eindhoven, Netherlands ; Department of Biomedical and Information Engineering, Northeastern University Shenyang, China
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Uludağ K, Roebroeck A. General overview on the merits of multimodal neuroimaging data fusion. Neuroimage 2014; 102 Pt 1:3-10. [PMID: 24845622 DOI: 10.1016/j.neuroimage.2014.05.018] [Citation(s) in RCA: 70] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2013] [Revised: 04/28/2014] [Accepted: 05/08/2014] [Indexed: 10/25/2022] Open
Abstract
Multimodal neuroimaging has become a mainstay of basic and cognitive neuroscience in humans and animals, despite challenges to consider when acquiring and combining non-redundant imaging data. Multimodal data integration can yield important insights into brain processes and structures in addition to spatiotemporal resolution complementarity, including: a comprehensive physiological view on brain processes and structures, quantification, generalization and normalization, and availability of biomarkers. In this review, we discuss data acquisition and fusion in multimodal neuroimaging in the context of each of these potential merits. However, limitations - due to differences in the neuronal and structural underpinnings of each method - have to be taken into account when modeling and interpreting multimodal data using generative models. We conclude that when these challenges are adequately met, multimodal data fusion can create substantial added value for neuroscience applications making it an indispensable approach for studying the brain.
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Affiliation(s)
- Kâmil Uludağ
- Department of Cognitive Neuroscience, Maastricht Brain Imaging Centre (MBIC), Faculty of Psychology & Neuroscience, Maastricht University, PO Box 616, 6200MD, Maastricht, The Netherlands.
| | - Alard Roebroeck
- Department of Cognitive Neuroscience, Maastricht Brain Imaging Centre (MBIC), Faculty of Psychology & Neuroscience, Maastricht University, PO Box 616, 6200MD, Maastricht, The Netherlands.
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Bittner RA, Linden DEJ, Roebroeck A, Härtling F, Rotarska-Jagiela A, Maurer K, Goebel R, Singer W, Haenschel C. The When and Where of Working Memory Dysfunction in Early-Onset Schizophrenia-A Functional Magnetic Resonance Imaging Study. Cereb Cortex 2014; 25:2494-506. [PMID: 24675869 DOI: 10.1093/cercor/bhu050] [Citation(s) in RCA: 39] [Impact Index Per Article: 3.9] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
Abstract
Behavioral evidence indicates that working memory (WM) in schizophrenia is already impaired at the encoding stage. However, the neurophysiological basis of this primary deficit remains poorly understood. Using event-related fMRI, we assessed differences in brain activation and functional connectivity during the encoding, maintenance and retrieval stages of a visual WM task with 3 levels of memory load in 17 adolescents with early-onset schizophrenia (EOS) and 17 matched controls. The amount of information patients could store in WM was reduced at all memory load levels. During encoding, activation in left ventrolateral prefrontal cortex (VLPFC) and extrastriate visual cortex, which in controls positively correlated with the amount of stored information, was reduced in patients. Additionally, patients showed disturbed functional connectivity between prefrontal and visual areas. During retrieval, right inferior VLPFC hyperactivation was correlated with hypoactivation of left VLPFC in patients during encoding. Visual WM encoding is disturbed by a failure to adequately engage a visual-prefrontal network critical for the transfer of perceptual information into WM. Prefrontal hyperactivation appears to be a secondary consequence of this primary deficit. Isolating the component processes of WM can lead to more specific neurophysiological markers for translational efforts seeking to improve the treatment of cognitive dysfunction in schizophrenia.
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Affiliation(s)
- Robert A Bittner
- Laboratory for Neurophysiology and Neuroimaging, Department of Psychiatry, Psychosomatic Medicine and Psychotherapy and Brain Imaging Center, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany Department of Neurophysiology, Max-Planck-Institute for Brain Research, Frankfurt am Main, Germany Ernst Strüngmann Institute for Neuroscience (ESI) in Cooperation with Max Planck Society, Frankfurt am Main, Germany
| | - David E J Linden
- MRC Centre for Neuropsychiatric Genetics and Genomics, Institute of Psychological Medicine and Clinical Neurosciences, School of Medicine School of Psychology, Cardiff University, Cardiff, UK
| | - Alard Roebroeck
- Department of Neurocognition, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Fabian Härtling
- Department of Child and Adolescent Psychiatry, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany
| | - Anna Rotarska-Jagiela
- Laboratory for Neurophysiology and Neuroimaging, Department of Psychiatry, Psychosomatic Medicine and Psychotherapy and Brain Imaging Center, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany Department of Neurophysiology, Max-Planck-Institute for Brain Research, Frankfurt am Main, Germany
| | - Konrad Maurer
- Laboratory for Neurophysiology and Neuroimaging, Department of Psychiatry, Psychosomatic Medicine and Psychotherapy and Brain Imaging Center, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany
| | - Rainer Goebel
- Department of Neurocognition, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - Wolf Singer
- Department of Neurophysiology, Max-Planck-Institute for Brain Research, Frankfurt am Main, Germany Ernst Strüngmann Institute for Neuroscience (ESI) in Cooperation with Max Planck Society, Frankfurt am Main, Germany
| | - Corinna Haenschel
- Laboratory for Neurophysiology and Neuroimaging, Department of Psychiatry, Psychosomatic Medicine and Psychotherapy and Brain Imaging Center, University Hospital Frankfurt, Goethe University, Frankfurt am Main, Germany School of Psychology, City University, London, UK
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Jeurissen D, Sack AT, Roebroeck A, Russ BE, Pascual-Leone A. TMS affects moral judgment, showing the role of DLPFC and TPJ in cognitive and emotional processing. Front Neurosci 2014; 8:18. [PMID: 24592204 PMCID: PMC3923146 DOI: 10.3389/fnins.2014.00018] [Citation(s) in RCA: 44] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2013] [Accepted: 01/23/2014] [Indexed: 11/13/2022] Open
Abstract
Decision-making involves a complex interplay of emotional responses and reasoning processes. In this study, we use TMS to explore the neurobiological substrates of moral decisions in humans. To examining the effects of TMS on the outcome of a moral-decision, we compare the decision outcome of moral-personal and moral-impersonal dilemmas to each other and examine the differential effects of applying TMS over the right DLPFC or right TPJ. In this comparison, we find that the TMS-induced disruption of the DLPFC during the decision process, affects the outcome of the moral-personal judgment, while TMS-induced disruption of TPJ affects only moral-impersonal conditions. In other words, we find a double-dissociation between DLPFC and TPJ in the outcome of a moral decision. Furthermore, we find that TMS-induced disruption of the DLPFC during non-moral, moral-impersonal, and moral-personal decisions lead to lower ratings of regret about the decision. Our results are in line with the dual-process theory and suggest a role for both the emotional response and cognitive reasoning process in moral judgment. Both the emotional and cognitive processes were shown to be involved in the decision outcome.
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Affiliation(s)
- Danique Jeurissen
- Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center, Harvard Medical School Boston, MA, USA ; Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University Maastricht, Netherlands ; Department of Vision and Cognition, Netherlands Institute for Neuroscience, Royal Netherlands Academy of Arts and Sciences Amsterdam, Netherlands
| | - Alexander T Sack
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University Maastricht, Netherlands ; Maastricht Brain Imaging Center Maastricht, Netherlands
| | - Alard Roebroeck
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University Maastricht, Netherlands ; Maastricht Brain Imaging Center Maastricht, Netherlands
| | - Brian E Russ
- Department of Psychology, Harvard University Cambridge, MA, USA
| | - Alvaro Pascual-Leone
- Berenson-Allen Center for Noninvasive Brain Stimulation, Beth Israel Deaconess Medical Center, Harvard Medical School Boston, MA, USA
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Jansma H, Roebroeck A, Münte TF. A network analysis of audiovisual affective speech perception. Neuroscience 2013; 256:230-41. [PMID: 24184115 DOI: 10.1016/j.neuroscience.2013.10.047] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2013] [Revised: 09/12/2013] [Accepted: 10/22/2013] [Indexed: 11/25/2022]
Abstract
In this study we were interested in the neural system supporting the audiovisual (AV) integration of emotional expression and emotional prosody. To this end normal participants were exposed to short videos of a computer-animated face voicing emotionally positive or negative words with the appropriate prosody. Facial expression of the face was either neutral or emotionally appropriate. To reveal the neural network involved in affective AV integration, standard univariate analysis of functional magnetic resonance (fMRI) data was followed by a random-effects Granger causality mapping (RFX-GCM). The regions that distinguished emotional from neutral facial expressions in the univariate analysis were taken as seed regions. In trials showing emotional expressions compared to neutral trials univariate analysis showed activation primarily in bilateral amygdala, fusiform gyrus, middle temporal gyrus/superior temporal sulcus and inferior occipital gyrus. When employing either the left amygdala or the right amygdala as a seed region in RFX-GCM we found connectivity with the right hemispheric fusiform gyrus, with the indication that the fusiform gyrus sends information to the Amygdala. These results led to a working model for face perception in general and for AV-affective integration in particular which is an elaborated adaptation of existing models.
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Affiliation(s)
- H Jansma
- Department of Neurology, University of Lübeck, Lübeck, Germany; Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - A Roebroeck
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, The Netherlands
| | - T F Münte
- Department of Neurology, University of Lübeck, Lübeck, Germany.
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