1
|
de Jong JJA, Jansen JFA, Vergoossen LWM, Schram MT, Stehouwer CDA, Wildberger JE, Linden DEJ, Backes WH. Effect of Magnetic Resonance Image Quality on Structural and Functional Brain Connectivity: The Maastricht Study. Brain Sci 2024; 14:62. [PMID: 38248277 PMCID: PMC10813868 DOI: 10.3390/brainsci14010062] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Revised: 12/27/2023] [Accepted: 01/04/2024] [Indexed: 01/23/2024] Open
Abstract
In population-based cohort studies, magnetic resonance imaging (MRI) is vital for examining brain structure and function. Advanced MRI techniques, such as diffusion-weighted MRI (dMRI) and resting-state functional MRI (rs-fMRI), provide insights into brain connectivity. However, biases in MRI data acquisition and processing can impact brain connectivity measures and their associations with demographic and clinical variables. This study, conducted with 5110 participants from The Maastricht Study, explored the relationship between brain connectivity and various image quality metrics (e.g., signal-to-noise ratio, head motion, and atlas-template mismatches) that were obtained from dMRI and rs-fMRI scans. Results revealed that in particular increased head motion (R2 up to 0.169, p < 0.001) and reduced signal-to-noise ratio (R2 up to 0.013, p < 0.001) negatively impacted structural and functional brain connectivity, respectively. These image quality metrics significantly affected associations of overall brain connectivity with age (up to -59%), sex (up to -25%), and body mass index (BMI) (up to +14%). Associations with diabetes status, educational level, history of cardiovascular disease, and white matter hyperintensities were generally less affected. This emphasizes the potential confounding effects of image quality in large population-based neuroimaging studies on brain connectivity and underscores the importance of accounting for it.
Collapse
Affiliation(s)
- Joost J. A. de Jong
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre, 6202 AZ Maastricht, The Netherlands
- School for Mental Health and Neurosciences (MHeNs), Maastricht University, 6200 MD Maastricht, The Netherlands
| | - Jacobus F. A. Jansen
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre, 6202 AZ Maastricht, The Netherlands
- School for Mental Health and Neurosciences (MHeNs), Maastricht University, 6200 MD Maastricht, The Netherlands
| | - Laura W. M. Vergoossen
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre, 6202 AZ Maastricht, The Netherlands
- School for Mental Health and Neurosciences (MHeNs), Maastricht University, 6200 MD Maastricht, The Netherlands
| | - Miranda T. Schram
- School for Mental Health and Neurosciences (MHeNs), Maastricht University, 6200 MD Maastricht, The Netherlands
- Department of Internal Medicine, Maastricht University Medical Centre, 6202 AZ Maastricht, The Netherlands
- School for Cardiovascular Disease (CARIM), Maastricht University, 6200 MD Maastricht, The Netherlands
- Heart and Vascular Centre, Maastricht University Medical Centre, 6202 AZ Maastricht, The Netherlands
| | - Coen D. A. Stehouwer
- School for Mental Health and Neurosciences (MHeNs), Maastricht University, 6200 MD Maastricht, The Netherlands
- Department of Internal Medicine, Maastricht University Medical Centre, 6202 AZ Maastricht, The Netherlands
- School for Cardiovascular Disease (CARIM), Maastricht University, 6200 MD Maastricht, The Netherlands
- Heart and Vascular Centre, Maastricht University Medical Centre, 6202 AZ Maastricht, The Netherlands
| | - Joachim E. Wildberger
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre, 6202 AZ Maastricht, The Netherlands
- School for Cardiovascular Disease (CARIM), Maastricht University, 6200 MD Maastricht, The Netherlands
| | - David E. J. Linden
- School for Mental Health and Neurosciences (MHeNs), Maastricht University, 6200 MD Maastricht, The Netherlands
| | - Walter H. Backes
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre, 6202 AZ Maastricht, The Netherlands
- School for Mental Health and Neurosciences (MHeNs), Maastricht University, 6200 MD Maastricht, The Netherlands
| |
Collapse
|
2
|
Raven EP, Veraart J, Kievit RA, Genc S, Ward IL, Hall J, Cunningham A, Doherty J, van den Bree MBM, Jones DK. In vivo evidence of microstructural hypo-connectivity of brain white matter in 22q11.2 deletion syndrome. Mol Psychiatry 2023; 28:4342-4352. [PMID: 37495890 PMCID: PMC7615578 DOI: 10.1038/s41380-023-02178-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/18/2020] [Revised: 06/26/2023] [Accepted: 07/03/2023] [Indexed: 07/28/2023]
Abstract
22q11.2 deletion syndrome, or 22q11.2DS, is a genetic syndrome associated with high rates of schizophrenia and autism spectrum disorders, in addition to widespread structural and functional abnormalities throughout the brain. Experimental animal models have identified neuronal connectivity deficits, e.g., decreased axonal length and complexity of axonal branching, as a primary mechanism underlying atypical brain development in 22q11.2DS. However, it is still unclear whether deficits in axonal morphology can also be observed in people with 22q11.2DS. Here, we provide an unparalleled in vivo characterization of white matter microstructure in participants with 22q11.2DS (12-15 years) and those undergoing typical development (8-18 years) using a customized magnetic resonance imaging scanner which is sensitive to axonal morphology. A rich array of diffusion MRI metrics are extracted to present microstructural profiles of typical and atypical white matter development, and provide new evidence of connectivity differences in individuals with 22q11.2DS. A recent, large-scale consortium study of 22q11.2DS identified higher diffusion anisotropy and reduced overall diffusion mobility of water as hallmark microstructural alterations of white matter in individuals across a wide age range (6-52 years). We observed similar findings across the white matter tracts included in this study, in addition to identifying deficits in axonal morphology. This, in combination with reduced tract volume measurements, supports the hypothesis that abnormal microstructural connectivity in 22q11.2DS may be mediated by densely packed axons with disproportionately small diameters. Our findings provide insight into the in vivo white matter phenotype of 22q11.2DS, and promote the continued investigation of shared features in neurodevelopmental and psychiatric disorders.
Collapse
Affiliation(s)
- Erika P Raven
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK.
- Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA.
| | - Jelle Veraart
- Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
| | - Rogier A Kievit
- Medical Research Council Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
- Cognitive Neuroscience Department, Donders Institute for Brain, Cognition and Behavior, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Sila Genc
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
- Neuroscience Advanced Clinical Imaging Service (NACIS), Department of Neurosurgery, The Royal Children's Hospital, Parkville, VIC, Australia
| | - Isobel L Ward
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
| | - Jessica Hall
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Adam Cunningham
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Joanne Doherty
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
| | - Marianne B M van den Bree
- Centre for Neuropsychiatric Genetics and Genomics, Division of Psychological Medicine and Clinical Neurosciences, Cardiff University, Cardiff, UK
- Neuroscience and Mental Health Innovation Institute, Cardiff University, Cardiff, UK
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
| |
Collapse
|
3
|
Rasooli A, Adab HZ, Van Ruitenbeek P, Weerasekera A, Chalavi S, Cuypers K, Levin O, Dhollander T, Peeters R, Sunaert S, Mantini D, Swinnen SP. White matter and neurochemical mechanisms underlying age-related differences in motor processing speed. iScience 2023; 26:106794. [PMID: 37255665 PMCID: PMC10225899 DOI: 10.1016/j.isci.2023.106794] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 01/11/2023] [Accepted: 04/27/2023] [Indexed: 06/01/2023] Open
Abstract
Aging is associated with changes in the central nervous system and leads to reduced life quality. Here, we investigated the age-related differences in the CNS underlying motor performance deficits using magnetic resonance spectroscopy and diffusion MRI. MRS measured N-acetyl aspartate (NAA), choline (Cho), and creatine (Cr) concentrations in the sensorimotor and occipital cortex, whereas dMRI quantified apparent fiber density (FD) in the same voxels to evaluate white matter microstructural organization. We found that aging was associated with increased reaction time and reduced FD and NAA concentration in the sensorimotor voxel. Both FD and NAA mediated the association between age and reaction time. The NAA concentration was found to mediate the association between age and FD in the sensorimotor voxel. We propose that the age-related decrease in NAA concentration may result in reduced axonal fiber density in the sensorimotor cortex which may ultimately account for the response slowness of older participants.
Collapse
Affiliation(s)
- Amirhossein Rasooli
- Movement Control & Neuroplasticity Research Group, Group Biomedical Sciences, KU Leuven, Leuven, Belgium
- KU Leuven Brain Institute, KU Leuven, Leuven, Belgium
| | - Hamed Zivari Adab
- Movement Control & Neuroplasticity Research Group, Group Biomedical Sciences, KU Leuven, Leuven, Belgium
- KU Leuven Brain Institute, KU Leuven, Leuven, Belgium
| | - Peter Van Ruitenbeek
- Movement Control & Neuroplasticity Research Group, Group Biomedical Sciences, KU Leuven, Leuven, Belgium
- Department of Neuropsychology and Psychopharmacology, Faculty of Psychology and Neuroscience, Maastricht University, 6200 MD Maastricht, the Netherlands
| | - Akila Weerasekera
- Movement Control & Neuroplasticity Research Group, Group Biomedical Sciences, KU Leuven, Leuven, Belgium
- KU Leuven Brain Institute, KU Leuven, Leuven, Belgium
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Sima Chalavi
- Movement Control & Neuroplasticity Research Group, Group Biomedical Sciences, KU Leuven, Leuven, Belgium
- KU Leuven Brain Institute, KU Leuven, Leuven, Belgium
| | - Koen Cuypers
- Movement Control & Neuroplasticity Research Group, Group Biomedical Sciences, KU Leuven, Leuven, Belgium
- KU Leuven Brain Institute, KU Leuven, Leuven, Belgium
- REVAL Rehabilitation Research Center, Hasselt University, Diepenbeek, Belgium
| | - Oron Levin
- Movement Control & Neuroplasticity Research Group, Group Biomedical Sciences, KU Leuven, Leuven, Belgium
- KU Leuven Brain Institute, KU Leuven, Leuven, Belgium
| | - Thijs Dhollander
- Murdoch Children’s Research Institute, Melbourne, VIC, Australia
| | - Ronald Peeters
- KU Leuven, Department of Imaging and Pathology, Group Biomedical Sciences, KU Leuven, Leuven, Belgium
| | - Stefan Sunaert
- KU Leuven, Department of Imaging and Pathology, Group Biomedical Sciences, KU Leuven, Leuven, Belgium
| | - Dante Mantini
- Movement Control & Neuroplasticity Research Group, Group Biomedical Sciences, KU Leuven, Leuven, Belgium
- KU Leuven Brain Institute, KU Leuven, Leuven, Belgium
| | - Stephan P. Swinnen
- Movement Control & Neuroplasticity Research Group, Group Biomedical Sciences, KU Leuven, Leuven, Belgium
- KU Leuven Brain Institute, KU Leuven, Leuven, Belgium
| |
Collapse
|
4
|
Lin CW, Ellegood J, Tamada K, Miura I, Konda M, Takeshita K, Atarashi K, Lerch JP, Wakana S, McHugh TJ, Takumi T. An old model with new insights: endogenous retroviruses drive the evolvement toward ASD susceptibility and hijack transcription machinery during development. Mol Psychiatry 2023; 28:1932-1945. [PMID: 36882500 PMCID: PMC10575786 DOI: 10.1038/s41380-023-01999-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Revised: 02/08/2023] [Accepted: 02/10/2023] [Indexed: 03/09/2023]
Abstract
The BTBR T+Itpr3tf/J (BTBR/J) strain is one of the most valid models of idiopathic autism, serving as a potent forward genetics tool to dissect the complexity of autism. We found that a sister strain with an intact corpus callosum, BTBR TF/ArtRbrc (BTBR/R), showed more prominent autism core symptoms but moderate ultrasonic communication/normal hippocampus-dependent memory, which may mimic autism in the high functioning spectrum. Intriguingly, disturbed epigenetic silencing mechanism leads to hyperactive endogenous retrovirus (ERV), a mobile genetic element of ancient retroviral infection, which increases de novo copy number variation (CNV) formation in the two BTBR strains. This feature makes the BTBR strain a still evolving multiple-loci model toward higher ASD susceptibility. Furthermore, active ERV, analogous to virus infection, evades the integrated stress response (ISR) of host defense and hijacks the transcriptional machinery during embryonic development in the BTBR strains. These results suggest dual roles of ERV in the pathogenesis of ASD, driving host genome evolution at a long-term scale and managing cellular pathways in response to viral infection, which has immediate effects on embryonic development. The wild-type Draxin expression in BTBR/R also makes this substrain a more precise model to investigate the core etiology of autism without the interference of impaired forebrain bundles as in BTBR/J.
Collapse
Affiliation(s)
- Chia-Wen Lin
- Laboratory for Mental Biology, RIKEN Brain Science Institute, Wako, 351-0198, Saitama, Japan
- Laboratory for Circuit and Behavioral Physiology, RIKEN Center for Brain Science, Wako, 351-0198, Saitama, Japan
- Department of Physiology and Cell Biology, Kobe University School of Medicine, Chuo, 650-0017, Kobe, Japan
| | - Jacob Ellegood
- Mouse Imaging Centre, Hospital for Sick Children, Toronto, Ontario, M5T 3H7, Canada
| | - Kota Tamada
- Laboratory for Mental Biology, RIKEN Brain Science Institute, Wako, 351-0198, Saitama, Japan
- Department of Physiology and Cell Biology, Kobe University School of Medicine, Chuo, 650-0017, Kobe, Japan
| | - Ikuo Miura
- Technology and Development Team for Mouse Phenotype Analysis, Japan Mouse Clinic, RIKEN BioResource Research Center, Tsukuba, Ibaraki, 305-0074, Japan
| | - Mikiko Konda
- Department of Microbiology and Immunology, Keio University School of Medicine, Shinjuku, 160-8582, Tokyo, Japan
| | - Kozue Takeshita
- Department of Microbiology and Immunology, Keio University School of Medicine, Shinjuku, 160-8582, Tokyo, Japan
| | - Koji Atarashi
- Department of Microbiology and Immunology, Keio University School of Medicine, Shinjuku, 160-8582, Tokyo, Japan
- RIKEN Center for Integrative Medical Sciences, Tsurumi, 230-0045, Yokohama, Japan
| | - Jason P Lerch
- Mouse Imaging Centre, Hospital for Sick Children, Toronto, Ontario, M5T 3H7, Canada
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, Oxfordshire, OX39DU, UK
| | - Shigeharu Wakana
- Technology and Development Team for Mouse Phenotype Analysis, Japan Mouse Clinic, RIKEN BioResource Research Center, Tsukuba, Ibaraki, 305-0074, Japan
| | - Thomas J McHugh
- Laboratory for Circuit and Behavioral Physiology, RIKEN Center for Brain Science, Wako, 351-0198, Saitama, Japan
| | - Toru Takumi
- Laboratory for Mental Biology, RIKEN Brain Science Institute, Wako, 351-0198, Saitama, Japan.
- Department of Physiology and Cell Biology, Kobe University School of Medicine, Chuo, 650-0017, Kobe, Japan.
- RIKEN Center for Biosystems Dynamics Research, Chuo, 650-0047, Kobe, Japan.
| |
Collapse
|
5
|
Orset T, Royo J, Santin MD, Pouget P, Thiebaut de Schotten M. A new open, high-resolution, multishell, diffusion-weighted imaging dataset of the living squirrel monkey. Sci Data 2023; 10:224. [PMID: 37081025 PMCID: PMC10119165 DOI: 10.1038/s41597-023-02126-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2023] [Accepted: 03/31/2023] [Indexed: 04/22/2023] Open
Abstract
Although very well adapted to brain study, Magnetic Resonance Imaging (MRI) remains limited by the facilities and capabilities required to acquire data, especially for non-human primates. Addressing the data gaps resulting from these limitations requires making data more accessible and open. In contempt of the regular use of Saimiri sciureus in neuroscience research, in vivo diffusion has yet to be openly available for this species. Here we built and made openly available a unique new resource consisting of a high-resolution, multishell diffusion-weighted dataset in the anesthetized Saimiri sciureus. The data were acquired on 11 individuals with an 11.7 T MRI scanner (isotropic resolution of 400 µm3). This paper presents an overview of our dataset and illustrates some of its possible use through example analyses. To assess the quality of our data, we analyzed long-range connections (whole-brain tractography), microstructure (Neurite Orientation Dispersion and Density Imaging), and axon diameter in the corpus callosum (ActiveAx). Constituting an essential new resource for primate evolution studies, all data are openly available.
Collapse
Affiliation(s)
- Thomas Orset
- Brain Connectivity and Behaviour Laboratory, Sorbonne University, Paris, France.
- Sorbonne University, Inserm U1127, CNRS UMR7225, UM75, ICM, Movement Investigation and Therapeutics Team, Paris, France.
| | - Julie Royo
- Brain Connectivity and Behaviour Laboratory, Sorbonne University, Paris, France
- Sorbonne University, Inserm U1127, CNRS UMR7225, UM75, ICM, Movement Investigation and Therapeutics Team, Paris, France
| | | | - Pierre Pouget
- Brain Connectivity and Behaviour Laboratory, Sorbonne University, Paris, France
- Sorbonne University, Inserm U1127, CNRS UMR7225, UM75, ICM, Movement Investigation and Therapeutics Team, Paris, France
| | - Michel Thiebaut de Schotten
- Brain Connectivity and Behaviour Laboratory, Sorbonne University, Paris, France
- Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives-UMR 5293, CNRS, CEA University of Bordeaux, Bordeaux, France
| |
Collapse
|
6
|
Yendiki A, Aggarwal M, Axer M, Howard AF, van Cappellen van Walsum AM, Haber SN. Post mortem mapping of connectional anatomy for the validation of diffusion MRI. Neuroimage 2022; 256:119146. [PMID: 35346838 PMCID: PMC9832921 DOI: 10.1016/j.neuroimage.2022.119146] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 03/02/2022] [Accepted: 03/23/2022] [Indexed: 01/13/2023] Open
Abstract
Diffusion MRI (dMRI) is a unique tool for the study of brain circuitry, as it allows us to image both the macroscopic trajectories and the microstructural properties of axon bundles in vivo. The Human Connectome Project ushered in an era of impressive advances in dMRI acquisition and analysis. As a result of these efforts, the quality of dMRI data that could be acquired in vivo improved substantially, and large collections of such data became widely available. Despite this progress, the main limitation of dMRI remains: it does not image axons directly, but only provides indirect measurements based on the diffusion of water molecules. Thus, it must be validated by methods that allow direct visualization of axons but that can only be performed in post mortem brain tissue. In this review, we discuss methods for validating the various features of connectional anatomy that are extracted from dMRI, both at the macro-scale (trajectories of axon bundles), and at micro-scale (axonal orientations and other microstructural properties). We present a range of validation tools, including anatomic tracer studies, Klingler's dissection, myelin stains, label-free optical imaging techniques, and others. We provide an overview of the basic principles of each technique, its limitations, and what it has taught us so far about the accuracy of different dMRI acquisition and analysis approaches.
Collapse
Affiliation(s)
- Anastasia Yendiki
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, United States,Corresponding author (A. Yendiki)
| | - Manisha Aggarwal
- Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Markus Axer
- Forschungszentrum Jülich, Institute of Neuroscience and Medicine, Jülich, Germany,Department of Physics, University of Wuppertal Germany
| | - Amy F.D. Howard
- Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Anne-Marie van Cappellen van Walsum
- Department of Medical Imaging, Anatomy, Radboud University Medical Center, Nijmegen, the Netherland,Cognition and Behaviour, Donders Institute for Brain, Nijmegen, the Netherland
| | - Suzanne N. Haber
- Department of Pharmacology and Physiology, University of Rochester, Rochester, NY, United States,McLean Hospital, Belmont, MA, United States
| |
Collapse
|
7
|
Liberato de Matos SNF, Ladeia-Rocha G, Neto JAC, de Oliveira CJV, Neto CA, Passos L, Oliveira-Filho J, Carvalho EM. Diffusion tensor imaging metrics in diagnosis of
HTLV‐1‐associated
myelopathy. Ann Clin Transl Neurol 2022; 9:488-496. [PMID: 35263043 PMCID: PMC8994983 DOI: 10.1002/acn3.51521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 01/31/2022] [Accepted: 01/31/2022] [Indexed: 11/25/2022] Open
Affiliation(s)
- Sheila N F Liberato de Matos
- Immunology Service, Professor Edgard Santos University Hospital, Federal University of Bahia, Salvador.,UniFTC, Salvador, Bahia, Brazil
| | | | - José Abraão Carneiro Neto
- Immunology Service, Professor Edgard Santos University Hospital, Federal University of Bahia, Salvador
| | - Cassius J V de Oliveira
- Immunology Service, Professor Edgard Santos University Hospital, Federal University of Bahia, Salvador
| | | | - Lúcia Passos
- Immunology Service, Professor Edgard Santos University Hospital, Federal University of Bahia, Salvador
| | - Jamary Oliveira-Filho
- Neurology Service, Professor Edgard Santos University Hospital, Salvador, Bahia, Brazil.,Instituto de Ciências da Saúde, Federal University of Bahia, Salvador, Bahia, Brazil
| | - Edgar M Carvalho
- Immunology Service, Professor Edgard Santos University Hospital, Federal University of Bahia, Salvador.,Laboratório de Pesquisas Clínicas (LAPEC), Instituto Gonçalo Moniz, FIOCRUZ, Salvador, Bahia, Brazil.,National Institute of Science and Technology in Tropical Diseases (INCT-DT), CNPq, Brazil
| |
Collapse
|
8
|
Betti S, Fedele M, Castiello U, Sartori L, Budisavljević S. Corticospinal excitability and conductivity are related to the anatomy of the corticospinal tract. Brain Struct Funct 2021; 227:1155-1164. [PMID: 34698904 DOI: 10.1007/s00429-021-02410-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Accepted: 10/08/2021] [Indexed: 11/30/2022]
Abstract
Probing the brain structure-function relationship is at the heart of modern neuroscientific explorations, enabled by recent advances in brain mapping techniques. This study aimed to explore the anatomical blueprint of corticospinal excitability and shed light on the structure-function relationship within the human motor system. Using diffusion magnetic resonance imaging tractography, based on the spherical deconvolution approach, and transcranial magnetic stimulation (TMS), we show that anatomical inter-individual variability of the corticospinal tract (CST) modulates the corticospinal excitability and conductivity. Our findings show for the first time the relationship between increased corticospinal excitability and conductivity in individuals with a bigger CST (i.e., number of streamlines), as well as increased corticospinal microstructural organization (i.e., fractional anisotropy). These findings can have important implications for the understanding of the neuroanatomical basis of TMS as well as the study of the human motor system in both health and disease.
Collapse
Affiliation(s)
- Sonia Betti
- Department of General Psychology, University of Padova, Padova, Italy.
| | - Marta Fedele
- Faculty of Psychology and Educational Sciences, KU Leuven Kulak, Kortrijk, Belgium
| | - Umberto Castiello
- Department of General Psychology, University of Padova, Padova, Italy
| | - Luisa Sartori
- Department of General Psychology, University of Padova, Padova, Italy.,Padova Neuroscience Center, University of Padova, Padova, Italy
| | - Sanja Budisavljević
- Department of General Psychology, University of Padova, Padova, Italy.,School of Medicine, University of St Andrews, St Andrews, UK
| |
Collapse
|
9
|
Yang JYM, Yeh CH, Poupon C, Calamante F. Diffusion MRI tractography for neurosurgery: the basics, current state, technical reliability and challenges. Phys Med Biol 2021; 66. [PMID: 34157706 DOI: 10.1088/1361-6560/ac0d90] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Accepted: 06/22/2021] [Indexed: 01/20/2023]
Abstract
Diffusion magnetic resonance imaging (dMRI) tractography is currently the only imaging technique that allows for non-invasive delineation and visualisation of white matter (WM) tractsin vivo,prompting rapid advances in related fields of brain MRI research in recent years. One of its major clinical applications is for pre-surgical planning and intraoperative image guidance in neurosurgery, where knowledge about the location of WM tracts nearby the surgical target can be helpful to guide surgical resection and optimise post-surgical outcomes. Surgical injuries to these WM tracts can lead to permanent neurological and functional deficits, making the accuracy of tractography reconstructions paramount. The quality of dMRI tractography is influenced by many modifiable factors, ranging from MRI data acquisition through to the post-processing of tractography output, with the potential of error propagation based on decisions made at each and subsequent processing steps. Research over the last 25 years has significantly improved the anatomical accuracy of tractography. An updated review about tractography methodology in the context of neurosurgery is now timely given the thriving research activities in dMRI, to ensure more appropriate applications in the clinical neurosurgical realm. This article aims to review the dMRI physics, and tractography methodologies, highlighting recent advances to provide the key concepts of tractography-informed neurosurgery, with a focus on the general considerations, the current state of practice, technical challenges, potential advances, and future demands to this field.
Collapse
Affiliation(s)
- Joseph Yuan-Mou Yang
- Department of Neurosurgery, The Royal Children's Hospital, Melbourne, Australia.,Neuroscience Research, Murdoch Children's Research Institute, Melbourne, Australia.,Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Australia.,Department of Paediatrics, The University of Melbourne, Melbourne, Australia
| | - Chun-Hung Yeh
- Institute for Radiological Research, Chang Gung University and Chang Gung Memorial Hospital, Taoyuan, Taiwan.,Department of Child and Adolescent Psychiatry, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan, Taiwan
| | - Cyril Poupon
- NeuroSpin, Frédéric Joliot Life Sciences Institute, CEA, CNRS, Paris-Saclay University, Gif-sur-Yvette, France
| | - Fernando Calamante
- The University of Sydney, Sydney Imaging, Sydney, Australia.,The University of Sydney, School of Biomedical Engineering, Sydney, Australia
| |
Collapse
|
10
|
Gyori NG, Clark CA, Alexander DC, Kaden E. On the potential for mapping apparent neural soma density via a clinically viable diffusion MRI protocol. Neuroimage 2021; 239:118303. [PMID: 34174390 PMCID: PMC8363942 DOI: 10.1016/j.neuroimage.2021.118303] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Revised: 06/16/2021] [Accepted: 06/22/2021] [Indexed: 12/14/2022] Open
Abstract
B-tensor encoding enables estimation of spherical cellular structures in the brain. Spherical compartments may provide markers for apparent neural soma density. Model parameters can be estimated in a fast and robust way using deep learning. Practical acquisition times are achievable on widely available clinical scanners.
Diffusion MRI is a valuable tool for probing tissue microstructure in the brain noninvasively. Today, model-based techniques are widely available and used for white matter characterisation where their development is relatively mature. Conversely, tissue modelling in grey matter is more challenging, and no generally accepted models exist. With advances in measurement technology and modelling efforts, a clinically viable technique that reveals salient features of grey matter microstructure, such as the density of quasi-spherical cell bodies and quasi-cylindrical cell projections, is an exciting prospect. As a step towards capturing the microscopic architecture of grey matter in clinically feasible settings, this work uses a biophysical model that is designed to disentangle the diffusion signatures of spherical and cylindrical structures in the presence of orientation heterogeneity, and takes advantage of B-tensor encoding measurements, which provide additional sensitivity compared to standard single diffusion encoding sequences. For the fast and robust estimation of microstructural parameters, we leverage recent advances in machine learning and replace conventional fitting techniques with an artificial neural network that fits complex biophysical models within seconds. Our results demonstrate apparent markers of spherical and cylindrical geometries in healthy human subjects, and in particular an increased volume fraction of spherical compartments in grey matter compared to white matter. We evaluate the extent to which spherical and cylindrical geometries may be interpreted as correlates of neural soma and neural projections, respectively, and quantify parameter estimation errors in the presence of various departures from the modelling assumptions. While further work is necessary to translate the ideas presented in this work to the clinic, we suggest that biomarkers focussing on quasi-spherical cellular geometries may be valuable for the enhanced assessment of neurodevelopmental disorders and neurodegenerative diseases.
Collapse
Affiliation(s)
- Noemi G Gyori
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom; Great Ormond Street Institute of Child Health, University College London, London, United Kingdom.
| | - Christopher A Clark
- Great Ormond Street Institute of Child Health, University College London, London, United Kingdom
| | - Daniel C Alexander
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
| | - Enrico Kaden
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom; Great Ormond Street Institute of Child Health, University College London, London, United Kingdom
| |
Collapse
|
11
|
Hickmott RA, Bosakhar A, Quezada S, Barresi M, Walker DW, Ryan AL, Quigley A, Tolcos M. The One-Stop Gyrification Station - Challenges and New Technologies. Prog Neurobiol 2021; 204:102111. [PMID: 34166774 DOI: 10.1016/j.pneurobio.2021.102111] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2021] [Revised: 05/31/2021] [Accepted: 06/18/2021] [Indexed: 12/12/2022]
Abstract
The evolution of the folded cortical surface is an iconic feature of the human brain shared by a subset of mammals and considered pivotal for the emergence of higher-order cognitive functions. While our understanding of the neurodevelopmental processes involved in corticogenesis has greatly advanced over the past 70 years of brain research, the fundamental mechanisms that result in gyrification, along with its originating cytoarchitectural location, remain largely unknown. This review brings together numerous approaches to this basic neurodevelopmental problem, constructing a narrative of how various models, techniques and tools have been applied to the study of gyrification thus far. After a brief discussion of core concepts and challenges within the field, we provide an analysis of the significant discoveries derived from the parallel use of model organisms such as the mouse, ferret, sheep and non-human primates, particularly with regard to how they have shaped our understanding of cortical folding. We then focus on the latest developments in the field and the complementary application of newly emerging technologies, such as cerebral organoids, advanced neuroimaging techniques, and atomic force microscopy. Particular emphasis is placed upon the use of novel computational and physical models in regard to the interplay of biological and physical forces in cortical folding.
Collapse
Affiliation(s)
- Ryan A Hickmott
- School of Health and Biomedical Sciences, RMIT University, Bundoora, VIC, 3083, Australia; BioFab3D@ACMD, St Vincent's Hospital Melbourne, Fitzroy, VIC, 3065, Australia
| | - Abdulhameed Bosakhar
- School of Health and Biomedical Sciences, RMIT University, Bundoora, VIC, 3083, Australia
| | - Sebastian Quezada
- School of Health and Biomedical Sciences, RMIT University, Bundoora, VIC, 3083, Australia
| | - Mikaela Barresi
- School of Health and Biomedical Sciences, RMIT University, Bundoora, VIC, 3083, Australia
| | - David W Walker
- School of Health and Biomedical Sciences, RMIT University, Bundoora, VIC, 3083, Australia
| | - Amy L Ryan
- Hastings Centre for Pulmonary Research, Department of Pulmonary, Critical Care and Sleep Medicine, USC Keck School of Medicine, University of Southern California, CA, USA and Department of Stem Cell and Regenerative Medicine, University of Southern California, CA, 90033, USA
| | - Anita Quigley
- School of Health and Biomedical Sciences, RMIT University, Bundoora, VIC, 3083, Australia; BioFab3D@ACMD, St Vincent's Hospital Melbourne, Fitzroy, VIC, 3065, Australia; School of Engineering, RMIT University, Melbourne, VIC, 3000, Australia; Department of Medicine, University of Melbourne, St Vincent's Hospital, Fitzroy, VIC, 3065, Australia; ARC Centre of Excellence in Electromaterials Science, University of Wollongong, Wollongong, NSW, 2522, Australia
| | - Mary Tolcos
- School of Health and Biomedical Sciences, RMIT University, Bundoora, VIC, 3083, Australia.
| |
Collapse
|
12
|
Zhang X, Li CX, Yan Y, Nair G, Rilling JK, Herndon JG, Preuss TM, Hu X, Li L. In-vivo diffusion MRI protocol optimization for the chimpanzee brain and examination of aging effects on the primate optic nerve at 3T. Magn Reson Imaging 2020; 77:194-203. [PMID: 33359631 DOI: 10.1016/j.mri.2020.12.015] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2020] [Revised: 10/30/2020] [Accepted: 12/20/2020] [Indexed: 12/13/2022]
Abstract
BACKGROUND Diffusion MRI (dMRI) data acquisition protocols are well-established on modern high-field clinical scanners for human studies. However, these protocols are not suitable for the chimpanzee (or other large-brained mammals) because of its substantial difference in head geometry and brain volume compared with humans. Therefore, an optimal dMRI data acquisition protocol dedicated to chimpanzee neuroimaging is needed. METHODS A multi-shot (4 segments) double spin-echo echo-planar imaging (MS-EPI) sequence and a single-shot double spin-echo EPI (SS-EPI) sequence were optimized separately for in vivo dMRI data acquisition of chimpanzees using a clinical 3T scanner. Correction for severe susceptibility-induced image distortion and signal drop-off of the chimpanzee brain was performed and evaluated using FSL software. DTI indices in different brain regions and probabilistic tractography were compared. A separate DTI data set from n=34 chimpanzees (13 to 56 years old) was collected using the optimal protocol. Age-related changes in diffusivity indices of optic nerve fibers were evaluated. RESULTS The SS-EPI sequence acquired dMRI data of the chimpanzee brain with approximately doubled the SNR as the MS-EPI sequence given the same scan time. The quality of white matter fiber tracking from the SS-EPI data was much higher than that from MS-EPI data. However, quantitative analysis of DTI indices showed no difference in most ROIs between the SS-EPI and MS-EPI sequences. The progressive evolution of diffusivity indices of optic nerves indicated mild changes in fiber bundles of chimpanzees aged 40 years and above. CONCLUSION The single-shot EPI-based acquisition protocol provided better image quality of dMRI for chimpanzee brains and is recommended for in vivo dMRI study or clinical diagnosis of chimpanzees (or other large animals) using a clinical scanner. Also, the tendency of FA decrease or diffusivity increase in the optic nerve of aged chimpanzees was seen but did not show significant age-related changes, suggesting aging may have less impact on optic nerve fiber integrity of chimpanzees, in contrast to previous results for both macaque monkeys and humans.
Collapse
Affiliation(s)
- Xiaodong Zhang
- Yerkes Imaging Center, Yerkes National Primate Research Center, Emory University, Atlanta, GA, United States of America; Division of Neuropharmacology and Neurologic Diseases, Yerkes National Primate Research Center, Emory University, Atlanta, GA, United States of America.
| | - Chun-Xia Li
- Yerkes Imaging Center, Yerkes National Primate Research Center, Emory University, Atlanta, GA, United States of America
| | - Yumei Yan
- Yerkes Imaging Center, Yerkes National Primate Research Center, Emory University, Atlanta, GA, United States of America
| | - Govind Nair
- qMRI Core Facility, NINDS, NIH, Bethesda, MD 20892, United States of America
| | - James K Rilling
- Department of Anthropology, Emory University, Atlanta, GA, United States of America; Yerkes National Primate Research Center, Emory University, Atlanta, GA, United States of America
| | - James G Herndon
- Division of Neuropharmacology and Neurologic Diseases, Yerkes National Primate Research Center, Emory University, Atlanta, GA, United States of America
| | - Todd M Preuss
- Division of Neuropharmacology and Neurologic Diseases, Yerkes National Primate Research Center, Emory University, Atlanta, GA, United States of America
| | - Xiaoping Hu
- Dept of Bioengineering, University of California, Riverside, CA, United States of America
| | - Longchuan Li
- Marcus Autism Center, Children's Healthcare of Atlanta, Emory University, Atlanta, GA, United States of America.
| |
Collapse
|
13
|
De Luca A, Guo F, Froeling M, Leemans A. Spherical deconvolution with tissue-specific response functions and multi-shell diffusion MRI to estimate multiple fiber orientation distributions (mFODs). Neuroimage 2020; 222:117206. [DOI: 10.1016/j.neuroimage.2020.117206] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/26/2020] [Revised: 07/20/2020] [Accepted: 07/23/2020] [Indexed: 12/18/2022] Open
|
14
|
Ikuta T, Stansberry TE, Lowe RO. Sleep duration is associated with auditory radiation microstructure. Neurol Res 2020; 42:739-743. [DOI: 10.1080/01616412.2020.1773603] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Affiliation(s)
- Toshikazu Ikuta
- Department of Communication Sciences and Disorders, School of Applied Sciences, University of Mississippi, Oxford, MS, USA
| | - Taylor E. Stansberry
- Department of Communication Sciences and Disorders, School of Applied Sciences, University of Mississippi, Oxford, MS, USA
| | - Rebecca O. Lowe
- Department of Communication Sciences and Disorders, School of Applied Sciences, University of Mississippi, Oxford, MS, USA
| |
Collapse
|
15
|
|
16
|
Girard G, Caminiti R, Battaglia-Mayer A, St-Onge E, Ambrosen KS, Eskildsen SF, Krug K, Dyrby TB, Descoteaux M, Thiran JP, Innocenti GM. On the cortical connectivity in the macaque brain: A comparison of diffusion tractography and histological tracing data. Neuroimage 2020; 221:117201. [PMID: 32739552 DOI: 10.1016/j.neuroimage.2020.117201] [Citation(s) in RCA: 39] [Impact Index Per Article: 9.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2020] [Revised: 07/17/2020] [Accepted: 07/22/2020] [Indexed: 12/22/2022] Open
Abstract
Diffusion-weighted magnetic resonance imaging (DW-MRI) tractography is a non-invasive tool to probe neural connections and the structure of the white matter. It has been applied successfully in studies of neurological disorders and normal connectivity. Recent work has revealed that tractography produces a high incidence of false-positive connections, often from "bottleneck" white matter configurations. The rich literature in histological connectivity analysis studies in the macaque monkey enables quantitative evaluation of the performance of tractography algorithms. In this study, we use the intricate connections of frontal, cingulate, and parietal areas, well established by the anatomical literature, to derive a symmetrical histological connectivity matrix composed of 59 cortical areas. We evaluate the performance of fifteen diffusion tractography algorithms, including global, deterministic, and probabilistic state-of-the-art methods for the connectivity predictions of 1711 distinct pairs of areas, among which 680 are reported connected by the literature. The diffusion connectivity analysis was performed on a different ex-vivo macaque brain, acquired using multi-shell DW-MRI protocol, at high spatial and angular resolutions. Across all tested algorithms, the true-positive and true-negative connections were dominant over false-positive and false-negative connections, respectively. Moreover, three-quarters of streamlines had endpoints location in agreement with histological data, on average. Furthermore, probabilistic streamline tractography algorithms show the best performances in predicting which areas are connected. Altogether, we propose a method for quantitative evaluation of tractography algorithms, which aims at improving the sensitivity and the specificity of diffusion-based connectivity analysis. Overall, those results confirm the usefulness of tractography in predicting connectivity, although errors are produced. Many of the errors result from bottleneck white matter configurations near the cortical grey matter and should be the target of future implementation of methods.
Collapse
Affiliation(s)
- Gabriel Girard
- Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland; Center for BioMedical Imaging, Lausanne, Switzerland; Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
| | - Roberto Caminiti
- Neuroscience and Behavior Laboratory, Istituto Italiano di Tecnologia, Rome, Italy
| | | | - Etienne St-Onge
- Sherbrooke Connectivity Imaging Lab, Computer Science Department, Faculty of Science, Université de Sherbrooke, Sherbrooke, Canada
| | - Karen S Ambrosen
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark; Danish Research Centre for Magnetic Resonance, Center for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark
| | - Simon F Eskildsen
- Center of Functionally Integrative Neuroscience, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Kristine Krug
- Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom; Institute of Biology, Otto-von-Guericke-Universität Magdeburg, Magdeburg, Germany; Leibniz-Insitute for Neurobiology, Magdeburg, Germany
| | - Tim B Dyrby
- Danish Research Centre for Magnetic Resonance, Center for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark; Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Lab, Computer Science Department, Faculty of Science, Université de Sherbrooke, Sherbrooke, Canada
| | - Jean-Philippe Thiran
- Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland; Center for BioMedical Imaging, Lausanne, Switzerland; Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Giorgio M Innocenti
- Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden; Brain and Mind Institute, École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| |
Collapse
|
17
|
Tan ET, Wilmes LJ, Joe BN, Onishi N, Arasu VA, Hylton NM, Marinelli L, Newitt DC. Denoising and Multiple Tissue Compartment Visualization of Multi-b-Valued Breast Diffusion MRI. J Magn Reson Imaging 2020; 53:271-282. [PMID: 32614125 DOI: 10.1002/jmri.27268] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/13/2019] [Revised: 06/10/2020] [Accepted: 06/11/2020] [Indexed: 02/01/2023] Open
Abstract
BACKGROUND Multi-b-valued/multi-shell diffusion provides potentially valuable metrics in breast MRI but suffers from low signal-to-noise ratio and has potentially long scan times. PURPOSE To investigate the effects of model-based denoising with no loss of spatial resolution on multi-shell breast diffusion MRI; to determine the effects of downsampling on multi-shell diffusion; and to quantify these effects in multi-b-valued (three directions per b-value) acquisitions. STUDY TYPE Prospective ("fully-sampled" multi-shell) and retrospective longitudinal (multi-b). SUBJECTS One normal subject (multi-shell) and 10 breast cancer subjects imaging at four timepoints (multi-b). FIELD STRENGTH/SEQUENCE 3T multi-shell acquisition and 1.5T multi-b acquisition. ASSESSMENT The "fully-sampled" multi-shell acquisition was retrospectively downsampled to determine the bias and error from downsampling. Mean, axial/parallel, radial diffusivity, and fractional anisotropy (FA) were analyzed. Denoising was applied retrospectively to the multi-b-valued breast cancer subject dataset and assessed subjectively for image noise level and tumor conspicuity. STATISTICAL TESTS Parametric paired t-test (P < 0.05 considered statistically significant) on mean and coefficient of variation of each metric-the apparent diffusion coefficient (ADC) from all b-values, fast ADC, slow ADC, and perfusion fraction. Paired and two-sample t-tests for each metric comparing normal and tumor tissue. RESULTS In the multi-shell data, denoising effectively suppressed FA (-45% to -78%), with small biases in mean diffusivity (-5% in normal, +23% in tumor, and -4% in vascular compartments). In the multi-b data, denoising resulted in small biases to the ADC metrics in tumor and normal contralateral tissue (by -3% to +11%), but greatly reduced the coefficient of variation for every metric (by -1% to -24%). Denoising improved differentiation of tumor and normal tissue regions in most metrics and timepoints; subjectively, image noise level and tumor conspicuity were improved in the fast ADC maps. DATA CONCLUSION Model-based denoising effectively suppressed erroneously high FA and improved the accuracy of diffusivity metrics. EVIDENCE LEVEL 3 TECHNICAL EFFICACY STAGE: 1.
Collapse
Affiliation(s)
- Ek T Tan
- GE Global Research, Niskayuna, New York, USA.,Department of Radiology and Imaging, Hospital for Special Surgery, New York, New York, USA
| | - Lisa J Wilmes
- Department of Radiology, University of California, San Francisco, California, USA
| | - Bonnie N Joe
- Department of Radiology, University of California, San Francisco, California, USA
| | - Natsuko Onishi
- Department of Radiology, University of California, San Francisco, California, USA
| | - Vignesh A Arasu
- Department of Radiology, University of California, San Francisco, California, USA.,Department of Radiology, Kaiser Permanente Medical Center, Vallejo, California, USA.,Division of Research, Kaiser Permanente Northern California, Oakland, California, USA
| | - Nola M Hylton
- Department of Radiology, University of California, San Francisco, California, USA
| | | | - David C Newitt
- Department of Radiology, University of California, San Francisco, California, USA
| |
Collapse
|
18
|
Network-Based Imaging and Connectomics. Stereotact Funct Neurosurg 2020. [DOI: 10.1007/978-3-030-34906-6_7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
|
19
|
Lee Y, Kettinger AO, Wilm BJ, Deichmann R, Weiskopf N, Lambert C, Pruessmann KP, Nagy Z. A comprehensive approach for correcting voxel-wise b-value errors in diffusion MRI. Magn Reson Med 2019; 83:2173-2184. [PMID: 31840300 PMCID: PMC7065087 DOI: 10.1002/mrm.28078] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2019] [Revised: 09/23/2019] [Accepted: 10/22/2019] [Indexed: 01/29/2023]
Abstract
PURPOSE In diffusion MRI, the actual b-value played out on the scanner may deviate from the nominal value due to magnetic field imperfections. A simple image-based correction method for this problem is presented. METHODS The apparent diffusion constant (ADC) of a water phantom was measured voxel-wise along 64 diffusion directions at b = 1000 s/mm2 . The true diffusion constant of water was estimated, considering the phantom temperature. A voxel-wise correction factor, providing an effective b-value including any magnetic field deviations, was determined for each diffusion direction by relating the measured ADC to the true diffusion constant. To test the method, the measured b-value map was used to calculate the corrected voxel-wise ADC for additionally acquired diffusion data sets on the same water phantom and data sets acquired on a small water phantom at three different positions. Diffusion tensor was estimated by applying the measured b-value map to phantom and in vivo data sets. RESULTS The b-value-corrected ADC maps of the phantom showed the expected spatial uniformity as well as a marked improvement in consistency across diffusion directions. The b-value correction for the brain data resulted in a 5.8% and 5.5% decrease in mean diffusivity and angular differences of the primary diffusion direction of 2.71° and 0.73° inside gray and white matter, respectively. CONCLUSION The actual b-value deviates significantly from its nominal setting, leading to a spatially variable error in the common diffusion outcome measures. The suggested method measures and corrects these artifacts.
Collapse
Affiliation(s)
- Yoojin Lee
- Laboratory for Social and Neural Systems Research (SNS Lab), University of Zurich, Zurich, Switzerland.,Institute for Biomedical Engineering, ETH Zurich and University of Zurich, Zurich, Switzerland
| | - Adam O Kettinger
- Brain Imaging Centre, Research Centre for Natural Sciences, Budapest, Hungary.,Department of Nuclear Techniques, Budapest University of Technology and Economics, Budapest, Hungary
| | - Bertram Jakob Wilm
- Institute for Biomedical Engineering, ETH Zurich and University of Zurich, Zurich, Switzerland
| | - Ralf Deichmann
- Brain Imaging Centre, Goethe University, Frankfurt, Germany.,Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Nikolaus Weiskopf
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, United Kingdom.,Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Christian Lambert
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, United Kingdom
| | - Klaas Paul Pruessmann
- Institute for Biomedical Engineering, ETH Zurich and University of Zurich, Zurich, Switzerland
| | - Zoltan Nagy
- Laboratory for Social and Neural Systems Research (SNS Lab), University of Zurich, Zurich, Switzerland.,Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, United Kingdom
| |
Collapse
|
20
|
Calamante F. The Seven Deadly Sins of Measuring Brain Structural Connectivity Using Diffusion MRI Streamlines Fibre-Tracking. Diagnostics (Basel) 2019; 9:diagnostics9030115. [PMID: 31500098 PMCID: PMC6787694 DOI: 10.3390/diagnostics9030115] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2019] [Revised: 08/13/2019] [Accepted: 09/04/2019] [Indexed: 12/13/2022] Open
Abstract
There is great interest in the study of brain structural connectivity, as white matter abnormalities have been implicated in many disease states. Diffusion magnetic resonance imaging (MRI) provides a powerful means to characterise structural connectivity non-invasively, by using a fibre-tracking algorithm. The most widely used fibre-tracking strategy is based on the step-wise generation of streamlines. Despite their popularity and widespread use, there are a number of practical considerations that must be taken into account in order to increase the robustness of streamlines tracking results, particularly when these methods are used to study brain structural connectivity, and the connectome. This review article describes what we consider the ‘seven deadly sins’ of mapping structural connections using diffusion MRI streamlines fibre-tracking, with particular emphasis on ‘sins’ that can be practically avoided and they can have an important impact in the results. It is shown that there are important ‘deadly sins’ to be avoided at every step of the pipeline, such as during data acquisition, during data modelling to estimate local fibre architecture, during the fibre-tracking process itself, and during quantification of the tracking results. The recommendations here are intended to inform users on potential important shortcomings of their current tracking protocols, as well as to guide future users on some of the key issues and decisions that must be faced when designing their processing pipelines.
Collapse
Affiliation(s)
- Fernando Calamante
- Sydney Imaging, The University of Sydney, Sydney, New South Wales 2050, Australia.
- School of Aerospace, Mechanical and Mechatronic Engineering, The University of Sydney, Sydney, New South Wales 2006, Australia.
- Florey Department of Neuroscience and Mental Health, University of Melbourne, Melbourne, Victoria 3052, Australia.
- Brain and Mind Centre, The University of Sydney, 94 Mallett Street, Camperdown, NSW 2050, Australia.
| |
Collapse
|
21
|
Das A, Takahashi E. Characterization of White Matter Tracts by Diffusion MR Tractography in Cat and Ferret that Have Similar Gyral Patterns. Cereb Cortex 2019; 28:1338-1347. [PMID: 28334159 DOI: 10.1093/cercor/bhx048] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/12/2016] [Indexed: 01/15/2023] Open
Abstract
The developmental relationships between gyral structures and white matter tracts have long been debated, but it is still difficult to discern whether they influence each other's development or are causally related. To explore this topic, this study used cats and ferrets as models for species that share similar gyral folding patterns and imaged with diffusion magnetic resonance imaging to compare white matter innervations in homologous gyri and other brain regions. Adult cat and ferret brains were analyzed via diffusion spectrum imaging tractography and homologous regions of interest were compared. Although similar genetic lineage and gyral structures would suggest analogous white matter tracts, tractography reveals significantly differing white matter connectivity in both the visual and auditory cortices. Similarities in connectivity were concentrated primarily in the highly conserved cerebellar region. These results correlate well with existing histological and functional studies of both species. Our results indicate that, while the 2 species may share similar gyral structures, they utilize different white matter connectivity; suggesting that while species may share similar gyral structures, they can develop different underlying white matter connectivity.
Collapse
Affiliation(s)
- Avilash Das
- Medical Sciences in the College of Arts and Sciences, Boston University, Boston, MA, USA.,Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.,Fetal-Neonatal Brain Imaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Emi Takahashi
- Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.,Fetal-Neonatal Brain Imaging and Developmental Science Center, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.,Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| |
Collapse
|
22
|
Tournier JD. Diffusion MRI in the brain - Theory and concepts. PROGRESS IN NUCLEAR MAGNETIC RESONANCE SPECTROSCOPY 2019; 112-113:1-16. [PMID: 31481155 DOI: 10.1016/j.pnmrs.2019.03.001] [Citation(s) in RCA: 29] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/18/2019] [Revised: 03/05/2019] [Accepted: 03/07/2019] [Indexed: 06/10/2023]
Abstract
Over the past two decades, diffusion MRI has become an essential tool in neuroimaging investigations. This is due to its sensitivity to the motion of water molecules as they diffuse through the microstructural environment, allowing diffusion MRI to be used as a 'probe' of tissue microstructure. Furthermore, this sensitivity is strongly direction-dependent, notably in brain white matter, due to the alignment of structures that restrict or hinder the motion of water molecules, notably axonal membranes. This provides a means of inferring the orientation of fibres in vivo, and by use of appropriate fibre-tracking algorithms, of delineating the path of white matter tracts in the brain. The ability to perform so-called tractography in humans in vivo non-invasively is unique to diffusion MRI, and is now used in applications such as neurosurgery planning and more broadly within investigations of brain connectomics. This review describes the theory and concepts of diffusion MRI and describes its most important areas of application in the brain, with a strong focus on tractography.
Collapse
Affiliation(s)
- J-Donald Tournier
- Department of Biomedical Engineering, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, St. Thomas' Hospital, London SE1 7EH, UK; Centre for the Developing Brain, School of Biomedical Engineering and Imaging Sciences, King's College London, King's Health Partners, St. Thomas' Hospital, London SE1 7EH, UK.
| |
Collapse
|
23
|
Gotink RA, Vernooij MW, Ikram MA, Niessen WJ, Krestin GP, Hofman A, Tiemeier H, Hunink MGM. Meditation and yoga practice are associated with smaller right amygdala volume: the Rotterdam study. Brain Imaging Behav 2019; 12:1631-1639. [PMID: 29417491 PMCID: PMC6302143 DOI: 10.1007/s11682-018-9826-z] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
To determine the association between meditation and yoga practice, experienced stress, and amygdala and hippocampal volume in a large population-based study. This study was embedded within the population-based Rotterdam Study and included 3742 participants for cross-sectional association. Participants filled out a questionnaire assessing meditation practice, yoga practice, and experienced stress, and underwent a magnetic resonance scan of the brain. 2397 participants underwent multiple brain scans, and were assessed for structural change over time. Amygdala and hippocampal volumes were regions of interest, as these are structures that may be affected by meditation. Multivariable linear regression analysis and mixed linear models were performed adjusted for age, sex, educational level, intracranial volume, cardiovascular risk, anxiety, depression and stress. 15.7% of individuals participated in at least one form of practice. Those who performed meditation and yoga practices reported significantly more stress (mean difference 0.2 on a 1–5 scale, p < .001) and more depressive symptoms (mean difference 1.03 on CESD, p = .015). Partaking in meditation and yoga practices was associated with a significantly lower right amygdala volume (β = − 31.8 mm3, p = .005), and lower left hippocampus volume (β = − 75.3 mm3, p = .025). Repeated measurements using linear mixed models showed a significant effect over time on the right amygdala of practicing meditation and yoga (β = − 24.4 mm3, SE 11.3, p = .031). Partaking in meditation and yoga practice is associated with more experienced stress while it also helps cope with stress, and is associated with smaller right amygdala volume.
Collapse
Affiliation(s)
- Rinske A Gotink
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands.,Department of Radiology, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Meike W Vernooij
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands.,Department of Radiology, Erasmus Medical Center, Rotterdam, the Netherlands
| | - M Arfan Ikram
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Wiro J Niessen
- Department of Radiology, Erasmus Medical Center, Rotterdam, the Netherlands.,Department of Medical Informatics, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Gabriel P Krestin
- Department of Radiology, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Albert Hofman
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands.,Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Henning Tiemeier
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands.,Department of Psychiatry, Erasmus Medical Center, Rotterdam, the Netherlands.,Department of Social and Behavioral Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - M G Myriam Hunink
- Department of Epidemiology, Erasmus Medical Center, Rotterdam, the Netherlands. .,Department of Radiology, Erasmus Medical Center, Rotterdam, the Netherlands. .,Center for Health Decision Sciences, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
| |
Collapse
|
24
|
Assaf Y, Johansen-Berg H, Thiebaut de Schotten M. The role of diffusion MRI in neuroscience. NMR IN BIOMEDICINE 2019; 32:e3762. [PMID: 28696013 DOI: 10.1002/nbm.3762] [Citation(s) in RCA: 69] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2016] [Revised: 04/25/2017] [Accepted: 05/17/2017] [Indexed: 05/05/2023]
Abstract
Diffusion-weighted imaging has pushed the boundaries of neuroscience by allowing us to examine the white matter microstructure of the living human brain. By doing so, it has provided answers to fundamental neuroscientific questions, launching a new field of research that had been largely inaccessible. We briefly summarize key questions that have historically been raised in neuroscience concerning the brain's white matter. We then expand on the benefits of diffusion-weighted imaging and its contribution to the fields of brain anatomy, functional models and plasticity. In doing so, this review highlights the invaluable contribution of diffusion-weighted imaging in neuroscience, presents its limitations and proposes new challenges for future generations who may wish to exploit this powerful technology to gain novel insights.
Collapse
Affiliation(s)
- Yaniv Assaf
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
- Department of Neurobiology, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel
| | - Heidi Johansen-Berg
- FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - Michel Thiebaut de Schotten
- Brain Connectivity and Behaviour Group, Frontlab, Brain and Spine Institute, Paris, France
- Sorbonne Universités, UPMC Université Paris 06, Inserm, CNRS, Institut du cerveau et la moelle (ICM) - Hôpital Pitié-Salpêtrière, Boulevard de l'hôpital, Paris, France
- Centre de Neuroimagerie de Recherche CENIR, Groupe Hospitalier Pitié-Salpêtrière, Paris, France
| |
Collapse
|
25
|
Dell'Acqua F, Tournier J. Modelling white matter with spherical deconvolution: How and why? NMR IN BIOMEDICINE 2019; 32:e3945. [PMID: 30113753 PMCID: PMC6585735 DOI: 10.1002/nbm.3945] [Citation(s) in RCA: 68] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/16/2017] [Revised: 04/18/2018] [Accepted: 04/24/2018] [Indexed: 05/30/2023]
Abstract
Since the realization that diffusion MRI can probe the microstructural organization and orientation of biological tissue in vivo and non-invasively, a multitude of diffusion imaging methods have been developed and applied to study the living human brain. Diffusion tensor imaging was the first model to be widely adopted in clinical and neuroscience research, but it was also clear from the beginning that it suffered from limitations when mapping complex configurations, such as crossing fibres. In this review, we highlight the main steps that have led the field of diffusion imaging to move from the tensor model to the adoption of diffusion and fibre orientation density functions as a more effective way to describe the complexity of white matter organization within each brain voxel. Among several techniques, spherical deconvolution has emerged today as one of the main approaches to model multiple fibre orientations and for tractography applications. Here we illustrate the main concepts and the reasoning behind this technique, as well as the latest developments in the field. The final part of this review provides practical guidelines and recommendations on how to set up processing and acquisition protocols suitable for spherical deconvolution.
Collapse
Affiliation(s)
- Flavio Dell'Acqua
- Institute of Psychiatry Psychology and Neuroscience, King's College LondonDepartment of NeuroimagingUK
- Sackler Institute for Translational Neurodevelopment, Institute of Psychiatry Psychology and Neuroscience, King's College LondonDepartment of Forensic and Neurodevelopmental SciencesUK
| | - J.‐Donald Tournier
- King's College LondonDivision of Imaging Sciences and Biomedical EngineeringUK
| |
Collapse
|
26
|
Drakesmith M, Parker GD, Smith J, Linden SC, Rees E, Williams N, Owen MJ, van den Bree M, Hall J, Jones DK, Linden DEJ. Genetic risk for schizophrenia and developmental delay is associated with shape and microstructure of midline white-matter structures. Transl Psychiatry 2019; 9:102. [PMID: 30804328 PMCID: PMC6389944 DOI: 10.1038/s41398-019-0440-7] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/28/2019] [Accepted: 02/13/2019] [Indexed: 11/22/2022] Open
Abstract
Genomic copy number variants (CNVs) are amongst the most highly penetrant genetic risk factors for neuropsychiatric disorders. The scarcity of carriers of individual CNVs and their phenotypical heterogeneity limits investigations of the associated neural mechanisms and endophenotypes. We applied a novel design based on CNV penetrance for schizophrenia (Sz) and developmental delay (DD) that allows us to identify structural sequelae that are most relevant to neuropsychiatric disorders. Our focus on brain structural abnormalities was based on the hypothesis that convergent mechanisms contributing to neurodevelopmental disorders would likely manifest in the macro- and microstructure of white matter and cortical and subcortical grey matter. Twenty one adult participants carrying neuropsychiatric risk CNVs (including those located at 22q11.2, 15q11.2, 1q21.1, 16p11.2 and 17q12) and 15 age- and gender-matched controls underwent T1-weighted structural, diffusion and relaxometry MRI. The macro- and microstructural properties of the cingulum bundles were associated with penetrance for both developmental delay and schizophrenia, in particular curvature along the anterior-posterior axis (Sz: pcorr = 0.026; DD: pcorr = 0.035) and intracellular volume fraction (Sz: pcorr = 0.019; DD: pcorr = 0.064). Further principal component analysis showed alterations in the interrelationships between the volumes of several midline white-matter structures (Sz: pcorr = 0.055; DD: pcorr = 0.027). In particular, the ratio of volumes in the splenium and body of the corpus callosum was significantly associated with both penetrance scores (Sz: p = 0.037; DD; p = 0.006). Our results are consistent with the notion that a significant alteration in developmental trajectories of midline white-matter structures constitutes a common neurodevelopmental aberration contributing to risk for schizophrenia and intellectual disability.
Collapse
Affiliation(s)
- Mark Drakesmith
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, United Kingdom.
- Neuroscience and Mental Health Research Institute (NMHRI), Cardiff University, Maindy Road, Cardiff, CF24 4HQ, United Kingdom.
| | - Greg D Parker
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, United Kingdom
- Neuroscience and Mental Health Research Institute (NMHRI), Cardiff University, Maindy Road, Cardiff, CF24 4HQ, United Kingdom
- Experimental MRI Centre (EMRIC), School of Biosciences, Cardiff University, Sir Martin Evans Building, Museum Avenue, CF10 3AX, Cardiff, United Kingdom
| | - Jacqueline Smith
- Neuroscience and Mental Health Research Institute (NMHRI), Cardiff University, Maindy Road, Cardiff, CF24 4HQ, United Kingdom
- MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, United Kingdom
| | - Stefanie C Linden
- Neuroscience and Mental Health Research Institute (NMHRI), Cardiff University, Maindy Road, Cardiff, CF24 4HQ, United Kingdom
- MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, United Kingdom
| | - Elliott Rees
- Neuroscience and Mental Health Research Institute (NMHRI), Cardiff University, Maindy Road, Cardiff, CF24 4HQ, United Kingdom
- MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, United Kingdom
| | - Nigel Williams
- Neuroscience and Mental Health Research Institute (NMHRI), Cardiff University, Maindy Road, Cardiff, CF24 4HQ, United Kingdom
- MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, United Kingdom
| | - Michael J Owen
- Neuroscience and Mental Health Research Institute (NMHRI), Cardiff University, Maindy Road, Cardiff, CF24 4HQ, United Kingdom
- MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, United Kingdom
| | - Marianne van den Bree
- Neuroscience and Mental Health Research Institute (NMHRI), Cardiff University, Maindy Road, Cardiff, CF24 4HQ, United Kingdom
- MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, United Kingdom
| | - Jeremy Hall
- Neuroscience and Mental Health Research Institute (NMHRI), Cardiff University, Maindy Road, Cardiff, CF24 4HQ, United Kingdom
- MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, United Kingdom
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, United Kingdom
- Neuroscience and Mental Health Research Institute (NMHRI), Cardiff University, Maindy Road, Cardiff, CF24 4HQ, United Kingdom
- School of Psychology, Faculty of Health Sciences, Australian Catholic University, Melbourne, VIC, 3065, Australia
| | - David E J Linden
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, United Kingdom
- Neuroscience and Mental Health Research Institute (NMHRI), Cardiff University, Maindy Road, Cardiff, CF24 4HQ, United Kingdom
- MRC Centre for Neuropsychiatric Genetics and Genomics, School of Medicine, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, United Kingdom
- School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
| |
Collapse
|
27
|
Foulon C, Cerliani L, Kinkingnéhun S, Levy R, Rosso C, Urbanski M, Volle E, Thiebaut de Schotten M. Advanced lesion symptom mapping analyses and implementation as BCBtoolkit. Gigascience 2018; 7:1-17. [PMID: 29432527 PMCID: PMC5863218 DOI: 10.1093/gigascience/giy004] [Citation(s) in RCA: 185] [Impact Index Per Article: 30.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2017] [Accepted: 01/23/2018] [Indexed: 01/04/2023] Open
Abstract
Background Patients with brain lesions provide a unique opportunity to understand the functioning of the human mind. However, even when focal, brain lesions have local and remote effects that impact functionally and structurally connected circuits. Similarly, function emerges from the interaction between brain areas rather than their sole activity. For instance, category fluency requires the associations between executive, semantic, and language production functions. Findings Here, we provide, for the first time, a set of complementary solutions for measuring the impact of a given lesion on the neuronal circuits. Our methods, which were applied to 37 patients with a focal frontal brain lesions, revealed a large set of directly and indirectly disconnected brain regions that had significantly impacted category fluency performance. The directly disconnected regions corresponded to areas that are classically considered as functionally engaged in verbal fluency and categorization tasks. These regions were also organized into larger directly and indirectly disconnected functional networks, including the left ventral fronto-parietal network, whose cortical thickness correlated with performance on category fluency. Conclusions The combination of structural and functional connectivity together with cortical thickness estimates reveal the remote effects of brain lesions, provide for the identification of the affected networks, and strengthen our understanding of their relationship with cognitive and behavioral measures. The methods presented are available and freely accessible in the BCBtoolkit as supplementary software [1].
Collapse
Affiliation(s)
- Chris Foulon
- Brain Connectivity and Behaviour Group, Sorbonne Universities, Paris France.,Frontlab, Institut du Cerveau et de la Moelle épinière (ICM), UPMC UMRS 1127, Inserm U 1127, CNRS UMR 7225, Paris, France.,Centre de Neuroimagerie de Recherche CENIR, Groupe Hospitalier Pitié-Salpêtrière, Paris, France
| | - Leonardo Cerliani
- Brain Connectivity and Behaviour Group, Sorbonne Universities, Paris France.,Frontlab, Institut du Cerveau et de la Moelle épinière (ICM), UPMC UMRS 1127, Inserm U 1127, CNRS UMR 7225, Paris, France.,Centre de Neuroimagerie de Recherche CENIR, Groupe Hospitalier Pitié-Salpêtrière, Paris, France
| | - Serge Kinkingnéhun
- Brain Connectivity and Behaviour Group, Sorbonne Universities, Paris France
| | - Richard Levy
- Frontlab, Institut du Cerveau et de la Moelle épinière (ICM), UPMC UMRS 1127, Inserm U 1127, CNRS UMR 7225, Paris, France
| | - Charlotte Rosso
- Centre de Neuroimagerie de Recherche CENIR, Groupe Hospitalier Pitié-Salpêtrière, Paris, France.,Abnormal Movements and Basal Ganglia team, Inserm U 1127, CNRS UMR 7225, Sorbonne Universities, UPMC Univ Paris 06, Institut du Cerveau et de la Moelle épinière, ICM, Paris, France.,APHP, Urgences Cérébro-Vasculaires, Groupe Hospitalier Pitié-Salpêtrière, Paris, France
| | - Marika Urbanski
- Brain Connectivity and Behaviour Group, Sorbonne Universities, Paris France.,Frontlab, Institut du Cerveau et de la Moelle épinière (ICM), UPMC UMRS 1127, Inserm U 1127, CNRS UMR 7225, Paris, France.,Medicine and Rehabilitation Department, Hôpitaux de Saint-Maurice, Saint-Maurice, France
| | - Emmanuelle Volle
- Brain Connectivity and Behaviour Group, Sorbonne Universities, Paris France.,Frontlab, Institut du Cerveau et de la Moelle épinière (ICM), UPMC UMRS 1127, Inserm U 1127, CNRS UMR 7225, Paris, France.,Centre de Neuroimagerie de Recherche CENIR, Groupe Hospitalier Pitié-Salpêtrière, Paris, France
| | - Michel Thiebaut de Schotten
- Brain Connectivity and Behaviour Group, Sorbonne Universities, Paris France.,Frontlab, Institut du Cerveau et de la Moelle épinière (ICM), UPMC UMRS 1127, Inserm U 1127, CNRS UMR 7225, Paris, France.,Centre de Neuroimagerie de Recherche CENIR, Groupe Hospitalier Pitié-Salpêtrière, Paris, France
| |
Collapse
|
28
|
Aydogan DB, Shi Y. Tracking and validation techniques for topographically organized tractography. Neuroimage 2018; 181:64-84. [PMID: 29986834 PMCID: PMC6139055 DOI: 10.1016/j.neuroimage.2018.06.071] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2018] [Revised: 05/18/2018] [Accepted: 06/26/2018] [Indexed: 12/22/2022] Open
Abstract
Topographic regularity of axonal connections is commonly understood as the preservation of spatial relationships between nearby neurons and is a fundamental structural property of the brain. In particular the retinotopic mapping of the visual pathway can even be quantitatively computed. Inspired from this previously untapped anatomical knowledge, we propose a novel tractography method that preserves both topographic and geometric regularity. We make use of parameterized curves with Frenet-Serret frame and introduce a highly flexible mechanism for controlling geometric regularity. At the same time, we incorporate a novel local data support term in order to account for topographic organization. Unifying geometry with topographic regularity, we develop a Bayesian framework for generating highly organized streamlines that accurately follow neuroanatomy. We additionally propose two novel validation techniques to quantify topographic regularity. In our experiments, we studied the results of our approach with respect to connectivity, reproducibility and topographic regularity aspects. We present both qualitative and quantitative comparisons of our technique against three algorithms from MRtrix3. We show that our method successfully generates highly organized fiber tracks while capturing bundle anatomy that are geometrically challenging for other approaches.
Collapse
Affiliation(s)
- Dogu Baran Aydogan
- Laboratory of Neuro Imaging (LONI), USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Yonggang Shi
- Laboratory of Neuro Imaging (LONI), USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA.
| |
Collapse
|
29
|
Tsai PT, Rudolph S, Guo C, Ellegood J, Gibson JM, Schaeffer SM, Mogavero J, Lerch JP, Regehr W, Sahin M. Sensitive Periods for Cerebellar-Mediated Autistic-like Behaviors. Cell Rep 2018; 25:357-367.e4. [PMID: 30304677 PMCID: PMC6226056 DOI: 10.1016/j.celrep.2018.09.039] [Citation(s) in RCA: 35] [Impact Index Per Article: 5.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2018] [Revised: 07/06/2018] [Accepted: 09/12/2018] [Indexed: 02/06/2023] Open
Abstract
Despite a prevalence exceeding 1%, mechanisms underlying autism spectrum disorders (ASDs) are poorly understood, and targeted therapies and guiding parameters are urgently needed. We recently demonstrated that cerebellar dysfunction is sufficient to generate autistic-like behaviors in a mouse model of tuberous sclerosis complex (TSC). Here, using the mechanistic target of rapamycin (mTOR)-specific inhibitor rapamycin, we define distinct sensitive periods for treatment of autistic-like behaviors with sensitive periods extending into adulthood for social behaviors. We identify cellular and electrophysiological parameters that may contribute to behavioral rescue, with rescue of Purkinje cell survival and excitability corresponding to social behavioral rescue. In addition, using anatomic and diffusion-based MRI, we identify structural changes in cerebellar domains implicated in ASD that correlate with sensitive periods of specific autism-like behaviors. These findings thus not only define treatment parameters into adulthood, but also support a mechanistic basis for the targeted rescue of autism-related behaviors.
Collapse
Affiliation(s)
- Peter T Tsai
- F.M. Kirby Neurobiology Center, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; Department of Neurology and Neurotherapeutics, UT Southwestern Medical Center, Dallas, TX, USA.
| | | | - Chong Guo
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Jacob Ellegood
- Mouse Imaging Centre, Hospital for Sick Kids, Toronto, ON, Canada
| | - Jennifer M Gibson
- Department of Neurology and Neurotherapeutics, UT Southwestern Medical Center, Dallas, TX, USA
| | - Samantha M Schaeffer
- F.M. Kirby Neurobiology Center, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Jazmin Mogavero
- F.M. Kirby Neurobiology Center, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Jason P Lerch
- Mouse Imaging Centre, Hospital for Sick Kids, Toronto, ON, Canada; Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Wade Regehr
- Department of Neurobiology, Harvard Medical School, Boston, MA, USA
| | - Mustafa Sahin
- F.M. Kirby Neurobiology Center, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
| |
Collapse
|
30
|
Sokolov AA, Zeidman P, Erb M, Ryvlin P, Pavlova MA, Friston KJ. Linking structural and effective brain connectivity: structurally informed Parametric Empirical Bayes (si-PEB). Brain Struct Funct 2018; 224:205-217. [PMID: 30302538 PMCID: PMC6373362 DOI: 10.1007/s00429-018-1760-8] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2018] [Accepted: 09/21/2018] [Indexed: 12/13/2022]
Abstract
Despite the potential for better understanding functional neuroanatomy, the complex relationship between neuroimaging measures of brain structure and function has confounded integrative, multimodal analyses of brain connectivity. This is particularly true for task-related effective connectivity, which describes the causal influences between neuronal populations. Here, we assess whether measures of structural connectivity may usefully inform estimates of effective connectivity in larger scale brain networks. To this end, we introduce an integrative approach, capitalising on two recent statistical advances: Parametric Empirical Bayes, which provides group-level estimates of effective connectivity, and Bayesian model reduction, which enables rapid comparison of competing models. Crucially, we show that structural priors derived from high angular resolution diffusion imaging on a dynamic causal model of a 12-region network-based on functional MRI data from the same subjects-substantially improve model evidence (posterior probability 1.00). This provides definitive evidence that structural and effective connectivity depend upon each other in mediating distributed, large-scale interactions in the brain. Furthermore, this work offers novel perspectives for understanding normal brain architecture and its disintegration in clinical conditions.
Collapse
Affiliation(s)
- Arseny A Sokolov
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London (UCL), London, WC1N 3BG, UK. .,Service de Neurologie, Département des Neurosciences Cliniques, Centre Hospitalier Universitaire Vaudois (CHUV), 1011, Lausanne, Switzerland.
| | - Peter Zeidman
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London (UCL), London, WC1N 3BG, UK
| | - Michael Erb
- Department of Biomedical Magnetic Resonance, Department of Radiology, University of Tübingen Medical School, 72076, Tübingen, Germany
| | - Philippe Ryvlin
- Service de Neurologie, Département des Neurosciences Cliniques, Centre Hospitalier Universitaire Vaudois (CHUV), 1011, Lausanne, Switzerland
| | - Marina A Pavlova
- Department of Psychiatry and Psychotherapy, University of Tübingen Medical School, 72076, Tübingen, Germany
| | - Karl J Friston
- Wellcome Centre for Human Neuroimaging, Institute of Neurology, University College London (UCL), London, WC1N 3BG, UK
| |
Collapse
|
31
|
Chan KS, Norris DG, Marques JP. Structure tensor informed fibre tractography at 3T. Hum Brain Mapp 2018; 39:4440-4451. [PMID: 30030945 DOI: 10.1002/hbm.24283] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2018] [Revised: 05/14/2018] [Accepted: 06/12/2018] [Indexed: 12/21/2022] Open
Abstract
Structure tensor informed fibre tractography (STIFT) based on informing tractography for diffusion-weighted images at 3T and by utilising the structure tensor obtained from gradient-recalled echo (GRE) images at 7T is able to delineate fibres when seed voxels are placed close to the fibre boundaries. However, incorporating data from two different field strengths limits the applicability of STIFT. In this study, STIFT was implemented with both diffusion-weighted images and GRE images acquired at 3T. Instead of using the magnitude GRE data directly for STIFT as in the previous work, the utility of T2 * maps and quantitative susceptibility maps derived from complex-valued GRE data to improve fibre delineation was explored. Single-seed tractography was performed and the results show that the optic radiation reconstructed with STIFT is more distinguishable from the inferior longitudinal fasciculus/inferior fronto-occipital fasciculus complex when compared to standard diffusion-weighted imaging tractography. We further investigated the quantitative effects of STIFT in a group of five healthy volunteers and evaluated its impact on measures of structural connectivity. The framework was extended to evaluate implementations of STIFT based on T2 *-weighted and quantitative susceptibility-weighted images in a whole-brain connectivity study. In terms of connectivity, no systematic differences were found between STIFT and diffusion-weighted imaging tractography, suggesting that local improvements in tractography are not translated to the atlas-based structural connectivity analysis. Nevertheless, the reduction in the number of statistically significant connections in the STIFT connectivity matrix suggests that STIFT can potentially reduce the false-positive connections in fibre tractography.
Collapse
Affiliation(s)
- Kwok-Shing Chan
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - David G Norris
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands.,Erwin L. Hahn Institute for Magnetic Resonance Imaging, University of Duisburg-Essen, Essen, Germany.,MIRA Institute for Biomedical Technology and Technical Medicine, University of Twente, Enschede, The Netherlands
| | - José P Marques
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| |
Collapse
|
32
|
Nouls JC, Badea A, Anderson RB, Cofer GP, Johnson GA. Diffusion tensor imaging using multiple coils for mouse brain connectomics. NMR IN BIOMEDICINE 2018; 31:e3921. [PMID: 29675882 PMCID: PMC5980786 DOI: 10.1002/nbm.3921] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/09/2017] [Revised: 02/15/2018] [Accepted: 02/20/2018] [Indexed: 06/08/2023]
Abstract
The correlation between brain connectivity and psychiatric or neurological diseases has intensified efforts to develop brain connectivity mapping techniques on mouse models of human disease. The neural architecture of mouse brain specimens can be shown non-destructively and three-dimensionally by diffusion tensor imaging, which enables tractography, the establishment of a connectivity matrix and connectomics. However, experiments on cohorts of animals can be prohibitively long. To improve throughput in a 7-T preclinical scanner, we present a novel two-coil system in which each coil is shielded, placed off-isocenter along the axis of the magnet and connected to a receiver circuit of the scanner. Preservation of the quality factor of each coil is essential to signal-to-noise ratio (SNR) performance and throughput, because mouse brain specimen imaging at 7 T takes place in the coil-dominated noise regime. In that regime, we show a shielding configuration causing no SNR degradation in the two-coil system. To acquire data from several coils simultaneously, the coils are placed in the magnet bore, around the isocenter, in which gradient field distortions can bias diffusion tensor imaging metrics, affect tractography and contaminate measurements of the connectivity matrix. We quantified the experimental alterations in fractional anisotropy and eigenvector direction occurring in each coil. We showed that, when the coils were placed 12 mm away from the isocenter, measurements of the brain connectivity matrix appeared to be minimally altered by gradient field distortions. Simultaneous measurements on two mouse brain specimens demonstrated a full doubling of the diffusion tensor imaging throughput in practice. Each coil produced images devoid of shading or artifact. To further improve the throughput of mouse brain connectomics, we suggested a future expansion of the system to four coils. To better understand acceptable trade-offs between imaging throughput and connectivity matrix integrity, studies may seek to clarify how measurement variability, post-processing techniques and biological variability impact mouse brain connectomics.
Collapse
Affiliation(s)
- John C. Nouls
- Center for In Vivo Microscopy, Radiology, Duke University Medical Center, Durham, NC, USA
- Radiology, Duke University, Durham, NC, USA
| | - Alexandra Badea
- Center for In Vivo Microscopy, Radiology, Duke University Medical Center, Durham, NC, USA
- Radiology, Duke University, Durham, NC, USA
| | - Robert B.J. Anderson
- Center for In Vivo Microscopy, Radiology, Duke University Medical Center, Durham, NC, USA
- Radiology, Duke University, Durham, NC, USA
| | - Gary P. Cofer
- Center for In Vivo Microscopy, Radiology, Duke University Medical Center, Durham, NC, USA
- Radiology, Duke University, Durham, NC, USA
| | - G. Allan Johnson
- Center for In Vivo Microscopy, Radiology, Duke University Medical Center, Durham, NC, USA
- Radiology, Duke University, Durham, NC, USA
| |
Collapse
|
33
|
Rafique SA, Richards JR, Steeves JKE. Altered white matter connectivity associated with visual hallucinations following occipital stroke. Brain Behav 2018; 8:e01010. [PMID: 29781583 PMCID: PMC5991596 DOI: 10.1002/brb3.1010] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/03/2017] [Revised: 04/11/2018] [Accepted: 04/20/2018] [Indexed: 11/05/2022] Open
Abstract
INTRODUCTION Visual hallucinations that arise following vision loss stem from aberrant functional activity in visual cortices and an imbalance of activity across associated cortical and subcortical networks subsequent to visual pathway damage. We sought to determine if structural changes in white matter connectivity play a role in cases of chronic visual hallucinations associated with visual cortical damage. METHODS We performed diffusion tensor imaging (DTI) and probabilistic fiber tractography to assess white matter connectivity in a patient suffering from continuous and disruptive phosphene (simple) visual hallucinations for more than 2 years following right occipital stroke. We compared these data to that of healthy age-matched controls. RESULTS Probabilistic tractography to reconstruct white matter tracts suggests regeneration of terminal fibers of the ipsilesional optic radiations in the patient. However, arrangement of the converse reconstruction of these tracts, which were seeded from the ipsilesional visual cortex to the intrahemispheric lateral geniculate body, remained disrupted. We further observed compromised structural characteristics, and changes in diffusion (measured using diffusion tensor indices) of white matter tracts in the patient connecting the visual cortex with frontal and temporal regions, and also in interhemispheric connectivity between visual cortices. CONCLUSIONS Cortical remapping and the disruption of communication between visual cortices and remote regions are consistent with our previous functional magnetic resonance imaging (fMRI) data showing imbalanced functional activity of the same regions in this patient (Rafique et al, 2016, Neurology, 87, 1493-1500). Long-term adaptive and disruptive changes in white matter connectivity may account for the rare nature of cases presenting with chronic and continuous visual hallucinations.
Collapse
Affiliation(s)
- Sara A Rafique
- Department of Psychology, Centre for Vision Research, York University, Toronto, ON, Canada
| | - John R Richards
- Department of Emergency Medicine, University of California, Davis, Medical Center, Sacramento, California
| | - Jennifer K E Steeves
- Department of Psychology, Centre for Vision Research, York University, Toronto, ON, Canada
| |
Collapse
|
34
|
Ciullo V, Vecchio D, Gili T, Spalletta G, Piras F. Segregation of Brain Structural Networks Supports Spatio-Temporal Predictive Processing. Front Hum Neurosci 2018; 12:212. [PMID: 29881338 PMCID: PMC5978278 DOI: 10.3389/fnhum.2018.00212] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2017] [Accepted: 05/08/2018] [Indexed: 01/21/2023] Open
Abstract
The ability to generate probabilistic expectancies regarding when and where sensory stimuli will occur, is critical to derive timely and accurate inferences about updating contexts. However, the existence of specialized neural networks for inferring predictive relationships between events is still debated. Using graph theoretical analysis applied to structural connectivity data, we tested the extent of brain connectivity properties associated with spatio-temporal predictive performance across 29 healthy subjects. Participants detected visual targets appearing at one out of three locations after one out of three intervals; expectations about stimulus location (spatial condition) or onset (temporal condition) were induced by valid or invalid symbolic cues. Connectivity matrices and centrality/segregation measures, expressing the relative importance of, and the local interactions among specific cerebral areas respect to the behavior under investigation, were calculated from whole-brain tractography and cortico-subcortical parcellation. Results: Response preparedness to cued stimuli relied on different structural connectivity networks for the temporal and spatial domains. Significant covariance was observed between centrality measures of regions within a subcortical-fronto-parietal-occipital network -comprising the left putamen, the right caudate nucleus, the left frontal operculum, the right inferior parietal cortex, the right paracentral lobule and the right superior occipital cortex-, and the ability to respond after a short cue-target delay suggesting that the local connectedness of such nodes plays a central role when the source of temporal expectation is explicit. When the potential for functional segregation was tested, we found highly clustered structural connectivity across the right superior, the left middle inferior frontal gyrus and the left caudate nucleus as related to explicit temporal orienting. Conversely, when the interaction between explicit and implicit temporal orienting processes was considered at the long interval, we found that explicit processes were related to centrality measures of the bilateral inferior parietal lobule. Degree centrality of the same region in the left hemisphere covaried with behavioral measures indexing the process of attentional re-orienting. These results represent a crucial step forward the ordinary predictive processing description, as we identified the patterns of connectivity characterizing the brain organization associated with the ability to generate and update temporal expectancies in case of contextual violations.
Collapse
Affiliation(s)
- Valentina Ciullo
- Neuropsychiatry Laboratory, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, Rome, Italy
- Neurosciences, Psychology, Drug Research and Child Health (NEUROFARBA), University of Florence, Florence, Italy
| | - Daniela Vecchio
- Neuropsychiatry Laboratory, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, Rome, Italy
- Department of Psychology, Sapienza University of Rome, Rome, Italy
| | - Tommaso Gili
- Neuropsychiatry Laboratory, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, Rome, Italy
- IMT School for Advanced Studies, Lucca, Italy
| | - Gianfranco Spalletta
- Neuropsychiatry Laboratory, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, Rome, Italy
- Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, United States
| | - Federica Piras
- Neuropsychiatry Laboratory, Department of Clinical and Behavioral Neurology, IRCCS Santa Lucia Foundation, Rome, Italy
| |
Collapse
|
35
|
Sydnor VJ, Rivas-Grajales AM, Lyall AE, Zhang F, Bouix S, Karmacharya S, Shenton ME, Westin CF, Makris N, Wassermann D, O'Donnell LJ, Kubicki M. A comparison of three fiber tract delineation methods and their impact on white matter analysis. Neuroimage 2018; 178:318-331. [PMID: 29787865 DOI: 10.1016/j.neuroimage.2018.05.044] [Citation(s) in RCA: 26] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2018] [Revised: 04/09/2018] [Accepted: 05/18/2018] [Indexed: 12/20/2022] Open
Abstract
Diffusion magnetic resonance imaging (dMRI) is an important method for studying white matter connectivity in the brain in vivo in both healthy and clinical populations. Improvements in dMRI tractography algorithms, which reconstruct macroscopic three-dimensional white matter fiber pathways, have allowed for methodological advances in the study of white matter; however, insufficient attention has been paid to comparing post-tractography methods that extract white matter fiber tracts of interest from whole-brain tractography. Here we conduct a comparison of three representative and conceptually distinct approaches to fiber tract delineation: 1) a manual multiple region of interest-based approach, 2) an atlas-based approach, and 3) a groupwise fiber clustering approach, by employing methods that exemplify these approaches to delineate the arcuate fasciculus, the middle longitudinal fasciculus, and the uncinate fasciculus in 10 healthy male subjects. We enable qualitative comparisons across methods, conduct quantitative evaluations of tract volume, tract length, mean fractional anisotropy, and true positive and true negative rates, and report measures of intra-method and inter-method agreement. We discuss methodological similarities and differences between the three approaches and the major advantages and drawbacks of each, and review research and clinical contexts for which each method may be most apposite. Emphasis is given to the means by which different white matter fiber tract delineation approaches may systematically produce variable results, despite utilizing the same input tractography and reliance on similar anatomical knowledge.
Collapse
Affiliation(s)
- Valerie J Sydnor
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Ana María Rivas-Grajales
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA; Menninger Department of Psychiatry and Behavioral Sciences, Baylor College of Medicine, Houston, TX, USA
| | - Amanda E Lyall
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Fan Zhang
- Laboratory for Mathematics in Imaging, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Sylvain Bouix
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Sarina Karmacharya
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Martha E Shenton
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA; Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; VA Boston Healthcare System, Brockton Division, Brockton, MA, USA
| | - Carl-Fredrik Westin
- Laboratory for Mathematics in Imaging, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Nikos Makris
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA
| | - Demian Wassermann
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Athena, Université Cote d'Azur, Inria, France; Parietal, CEA, Université Paris-Saclay, INRIA Saclay Île-de-France, France
| | - Lauren J O'Donnell
- Laboratory for Mathematics in Imaging, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Marek Kubicki
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA, USA; Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
| |
Collapse
|
36
|
De Benedictis A, Nocerino E, Menna F, Remondino F, Barbareschi M, Rozzanigo U, Corsini F, Olivetti E, Marras CE, Chioffi F, Avesani P, Sarubbo S. Photogrammetry of the Human Brain: A Novel Method for Three-Dimensional Quantitative Exploration of the Structural Connectivity in Neurosurgery and Neurosciences. World Neurosurg 2018; 115:e279-e291. [PMID: 29660551 DOI: 10.1016/j.wneu.2018.04.036] [Citation(s) in RCA: 36] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2018] [Accepted: 04/05/2018] [Indexed: 01/23/2023]
Abstract
BACKGROUND Anatomic awareness of the structural connectivity of the brain is mandatory for neurosurgeons, to select the most effective approaches for brain resections. Although standard microdissection is a validated technique to investigate the different white matter (WM) pathways and to verify the results of tractography, the possibility of interactive exploration of the specimens and reliable acquisition of quantitative information has not been described. Photogrammetry is a well-established technique allowing an accurate metrology on highly defined three-dimensional (3D) models. The aim of this work is to propose the application of the photogrammetric technique for supporting the 3D exploration and the quantitative analysis on the cerebral WM connectivity. METHODS The main perisylvian pathways, including the superior longitudinal fascicle and the arcuate fascicle were exposed using the Klingler technique. The photogrammetric acquisition followed each dissection step. The point clouds were registered to a reference magnetic resonance image of the specimen. All the acquisitions were coregistered into an open-source model. RESULTS We analyzed 5 steps, including the cortical surface, the short intergyral fibers, the indirect posterior and anterior superior longitudinal fascicle, and the arcuate fascicle. The coregistration between the magnetic resonance imaging mesh and the point clouds models was highly accurate. Multiple measures of distances between specific cortical landmarks and WM tracts were collected on the photogrammetric model. CONCLUSIONS Photogrammetry allows an accurate 3D reproduction of WM anatomy and the acquisition of unlimited quantitative data directly on the real specimen during the postdissection analysis. These results open many new promising neuroscientific and educational perspectives and also optimize the quality of neurosurgical treatments.
Collapse
Affiliation(s)
- Alessandro De Benedictis
- Neurosurgery Unit, Department of Neuroscience and Neurorehabilitation, Bambino Gesù Children's Hospital, IRCCS, Roma, Italy.
| | - Erica Nocerino
- Theoretical Physics ETH Zürich, Zurich, Switzerland; LSIS Laboratory-Laboratoire des Sciences de l'Information et des Systèmes, I&M Team, Images & Models AMU, Aix-Marseille Université POLYTECH, Marseille, France
| | - Fabio Menna
- 3D Optical Metrology (3DOM) Unit, Bruno Kessler Foundation (FBK), Trento, Italy
| | - Fabio Remondino
- 3D Optical Metrology (3DOM) Unit, Bruno Kessler Foundation (FBK), Trento, Italy
| | | | - Umberto Rozzanigo
- Department of Radiology, Neuroradiology Unit, "S. Chiara" Hospital, Trento APSS, Italy
| | - Francesco Corsini
- Division of Neurosurgery, Structural and Functional Connectivity (SFC) Lab Project, "S. Chiara" Hospital, Trento APSS, Italy
| | - Emanuele Olivetti
- Neuroinformatics Laboratory (NILab), Bruno Kessler Foundation, Trento, Italy; Center for Mind/Brain Science (CIMeC), University of Trento, Mattarello (TN), Italy
| | - Carlo Efisio Marras
- Neurosurgery Unit, Department of Neuroscience and Neurorehabilitation, Bambino Gesù Children's Hospital, IRCCS, Roma, Italy
| | - Franco Chioffi
- Division of Neurosurgery, Structural and Functional Connectivity (SFC) Lab Project, "S. Chiara" Hospital, Trento APSS, Italy
| | - Paolo Avesani
- Neuroinformatics Laboratory (NILab), Bruno Kessler Foundation, Trento, Italy; Center for Mind/Brain Science (CIMeC), University of Trento, Mattarello (TN), Italy
| | - Silvio Sarubbo
- Division of Neurosurgery, Structural and Functional Connectivity (SFC) Lab Project, "S. Chiara" Hospital, Trento APSS, Italy
| |
Collapse
|
37
|
Ouyang M, Dubois J, Yu Q, Mukherjee P, Huang H. Delineation of early brain development from fetuses to infants with diffusion MRI and beyond. Neuroimage 2018; 185:836-850. [PMID: 29655938 DOI: 10.1016/j.neuroimage.2018.04.017] [Citation(s) in RCA: 133] [Impact Index Per Article: 22.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2017] [Revised: 04/01/2018] [Accepted: 04/08/2018] [Indexed: 02/08/2023] Open
Abstract
Dynamic macrostructural and microstructural changes take place from the mid-fetal stage to 2 years after birth. Delineating structural changes of the brain during early development provides new insights into the complicated processes of both typical development and the pathological mechanisms underlying various psychiatric and neurological disorders including autism, attention deficit hyperactivity disorder and schizophrenia. Decades of histological studies have identified strong spatial and functional maturation gradients in human brain gray and white matter. The recent improvements in magnetic resonance imaging (MRI) techniques, especially diffusion MRI (dMRI), relaxometry imaging, and magnetization transfer imaging (MTI) have provided unprecedented opportunities to non-invasively quantify and map the early developmental changes at whole brain and regional levels. Here, we review the recent advances in understanding early brain structural development during the second half of gestation and the first two postnatal years using modern MR techniques. Specifically, we review studies that delineate the emergence and microstructural maturation of white matter tracts, as well as dynamic mapping of inhomogeneous cortical microstructural organization unique to fetuses and infants. These imaging studies converge into maturational curves of MRI measurements that are distinctive across different white matter tracts and cortical regions. Furthermore, contemporary models offering biophysical interpretations of the dMRI-derived measurements are illustrated to infer the underlying microstructural changes. Collectively, this review summarizes findings that contribute to charting spatiotemporally heterogeneous gray and white matter structural development, offering MRI-based biomarkers of typical brain development and setting the stage for understanding aberrant brain development in neurodevelopmental disorders.
Collapse
Affiliation(s)
- Minhui Ouyang
- Radiology Research, Children's Hospital of Philadelphia, PA, United States
| | - Jessica Dubois
- INSERM, UMR992, CEA, NeuroSpin Center, University Paris Saclay, Gif-sur-Yvette, France
| | - Qinlin Yu
- Radiology Research, Children's Hospital of Philadelphia, PA, United States
| | - Pratik Mukherjee
- Department of Radiology & Biomedical Imaging, University of California, San Francisco, CA, United States
| | - Hao Huang
- Radiology Research, Children's Hospital of Philadelphia, PA, United States; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, PA, United States.
| |
Collapse
|
38
|
Kang DW, Kim D, Chang LH, Kim YH, Takahashi E, Cain MS, Watanabe T, Sasaki Y. Structural and Functional Connectivity Changes Beyond Visual Cortex in a Later Phase of Visual Perceptual Learning. Sci Rep 2018; 8:5186. [PMID: 29581455 PMCID: PMC5979999 DOI: 10.1038/s41598-018-23487-z] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2017] [Accepted: 03/13/2018] [Indexed: 11/09/2022] Open
Abstract
The neural mechanisms of visual perceptual learning (VPL) remain unclear. Previously we found that activation in the primary visual cortex (V1) increased in the early encoding phase of training, but returned to baseline levels in the later retention phase. To examine neural changes during the retention phase, we measured structural and functional connectivity changes using MRI. After weeks of training on a texture discrimination task, the fractional anisotropy of the inferior longitudinal fasciculus, a major tract connecting visual and anterior areas, was increased, as well as the functional connectivity between V1 and anterior regions mediated by the ILF. These changes were strongly correlated with behavioral performance improvements. These results suggest a two-phase model of VPL in which localized functional changes in V1 in the encoding phase of training are followed by changes in both structural and functional connectivity in ventral visual processing, perhaps leading to the long-term stabilization of VPL.
Collapse
Affiliation(s)
- Dong-Wha Kang
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, South Korea
| | - Dongho Kim
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, South Korea
- Department of Cognitive, Linguistic and Psychological Sciences, Brown University, 190 Thayer Street - BOX 1821, Providence, RI, 02912, USA
| | - Li-Hung Chang
- Department of Cognitive, Linguistic and Psychological Sciences, Brown University, 190 Thayer Street - BOX 1821, Providence, RI, 02912, USA
- Education Center for Humanities and Social Sciences and Institute of Neuroscience, National Yang-Ming University, No. 155, Sec. 2, Linong St, Taipei City, 112, Taiwan
| | - Yong-Hwan Kim
- Department of Neurology, Asan Medical Center, University of Ulsan College of Medicine, 88 Olympic-ro 43-gil, Songpa-gu, Seoul, 05505, South Korea
| | - Emi Takahashi
- Division of Newborn Medicine, Department of Medicine, Boston Children's Hospital, 1 Autumn st. AU 453, Boston, MA, 02215, USA
| | - Matthew S Cain
- Department of Cognitive, Linguistic and Psychological Sciences, Brown University, 190 Thayer Street - BOX 1821, Providence, RI, 02912, USA
- U.S. Army Natick Soldier Research, Development, and Engineering Center, Natick, MA, 01760, USA
| | - Takeo Watanabe
- Department of Cognitive, Linguistic and Psychological Sciences, Brown University, 190 Thayer Street - BOX 1821, Providence, RI, 02912, USA
| | - Yuka Sasaki
- Department of Cognitive, Linguistic and Psychological Sciences, Brown University, 190 Thayer Street - BOX 1821, Providence, RI, 02912, USA.
| |
Collapse
|
39
|
Andreotti J, Dierks T, Wahlund LO, Grieder M. Diverging Progression of Network Disruption and Atrophy in Alzheimer's Disease and Semantic Dementia. J Alzheimers Dis 2018; 55:981-993. [PMID: 27802229 PMCID: PMC5147505 DOI: 10.3233/jad-160571] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022]
Abstract
The progression of cognitive deficits in Alzheimer's disease and semantic dementia is accompanied by grey matter atrophy and white matter deterioration. The impact of neuronal loss on the structural network connectivity in these dementia subtypes is, however, not well understood. In order to gain a more refined knowledge of the topological organization of white matter alterations in dementia, we used a network-based approach to analyze the brain's structural connectivity network. Diffusion-weighted and anatomical MRI images of groups with eighteen Alzheimer's disease and six semantic dementia patients, as well as twenty-one healthy controls were recorded to reconstruct individual connectivity networks. Additionally, voxel-based morphometry, using grey and white matter volume, served to relate atrophy to altered structural connectivity. The analyses showed that Alzheimer's disease is characterized by decreased connectivity strength in various cortical regions. An overlap with grey matter loss was found only in the inferior frontal and superior temporal areas. In semantic dementia, significantly reduced network strength was found in the temporal lobes, which converged with grey and white matter atrophy. Therefore, this study demonstrated that the structural disconnection in early Alzheimer's disease goes beyond grey matter atrophy and is independent of white matter volume loss, an observation that was not found in semantic dementia.
Collapse
Affiliation(s)
- Jennifer Andreotti
- Translational Research Center, University Hospital of Psychiatry, University of Bern, Bern, Switzerland
| | - Thomas Dierks
- Translational Research Center, University Hospital of Psychiatry, University of Bern, Bern, Switzerland
| | - Lars-Olof Wahlund
- Karolinska Institute, Department of Neurobiology, Care Sciences and Society (NVS), Division of Clinical Geriatrics, Stockholm, Sweden
| | - Matthias Grieder
- Translational Research Center, University Hospital of Psychiatry, University of Bern, Bern, Switzerland
| |
Collapse
|
40
|
Akram H, Dayal V, Mahlknecht P, Georgiev D, Hyam J, Foltynie T, Limousin P, De Vita E, Jahanshahi M, Ashburner J, Behrens T, Hariz M, Zrinzo L. Connectivity derived thalamic segmentation in deep brain stimulation for tremor. Neuroimage Clin 2018; 18:130-142. [PMID: 29387530 PMCID: PMC5790021 DOI: 10.1016/j.nicl.2018.01.008] [Citation(s) in RCA: 113] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/23/2017] [Revised: 12/23/2017] [Accepted: 01/13/2018] [Indexed: 02/02/2023]
Abstract
The ventral intermediate nucleus (VIM) of the thalamus is an established surgical target for stereotactic ablation and deep brain stimulation (DBS) in the treatment of tremor in Parkinson's disease (PD) and essential tremor (ET). It is centrally placed on a cerebello-thalamo-cortical network connecting the primary motor cortex, to the dentate nucleus of the contralateral cerebellum through the dentato-rubro-thalamic tract (DRT). The VIM is not readily visible on conventional MR imaging, so identifying the surgical target traditionally involved indirect targeting that relies on atlas-defined coordinates. Unfortunately, this approach does not fully account for individual variability and requires surgery to be performed with the patient awake to allow for intraoperative targeting confirmation. The aim of this study is to identify the VIM and the DRT using probabilistic tractography in patients that will undergo thalamic DBS for tremor. Four male patients with tremor dominant PD and five patients (three female) with ET underwent high angular resolution diffusion imaging (HARDI) (128 diffusion directions, 1.5 mm isotropic voxels and b value = 1500) preoperatively. Patients received VIM-DBS using an MR image guided and MR image verified approach with indirect targeting. Postoperatively, using parallel Graphical Processing Unit (GPU) processing, thalamic areas with the highest diffusion connectivity to the primary motor area (M1), supplementary motor area (SMA), primary sensory area (S1) and contralateral dentate nucleus were identified. Additionally, volume of tissue activation (VTA) corresponding to active DBS contacts were modelled. Response to treatment was defined as 40% reduction in the total Fahn-Tolosa-Martin Tremor Rating Score (FTMTRS) with DBS-ON, one year from surgery. Three out of nine patients had a suboptimal, long-term response to treatment. The segmented thalamic areas corresponded well to anatomically known counterparts in the ventrolateral (VL) and ventroposterior (VP) thalamus. The dentate-thalamic area, lay within the M1-thalamic area in a ventral and lateral location. Streamlines corresponding to the DRT connected M1 to the contralateral dentate nucleus via the dentate-thalamic area, clearly crossing the midline in the mesencephalon. Good response was seen when the active contact VTA was in the thalamic area with highest connectivity to the contralateral dentate nucleus. Non-responders had active contact VTAs outside the dentate-thalamic area. We conclude that probabilistic tractography techniques can be used to segment the VL and VP thalamus based on cortical and cerebellar connectivity. The thalamic area, best representing the VIM, is connected to the contralateral dentate cerebellar nucleus. Connectivity based segmentation of the VIM can be achieved in individual patients in a clinically feasible timescale, using HARDI and high performance computing with parallel GPU processing. This same technique can map out the DRT tract with clear mesencephalic crossing.
Collapse
Key Words
- AC, anterior commissure
- BEDPOSTX, Bayesian estimation of diffusion parameters obtained using sampling techniques X
- BET, brain extraction tool
- CI, confidence interval
- CON, connectivity
- Connectivity
- DBS
- DBS, deep brain stimulation
- DF, degrees of freedom
- DICOM, digital imaging and communications in medicine
- DRT
- DWI
- DWI, diffusion weighted imaging
- Deep brain stimulation
- Dentate nucleus
- Dentato-rubro-thalamic tract
- Diffusion weighted imaging
- EV, explanatory variable
- FLIRT, FMRIB's linear image registration tool
- FMRIB, Oxford centre for functional MRI of the brain
- FNIRT, FMRIB's non-linear image registration tool
- FSL, FMRIB's software library
- FoV, field of view
- GLM, general linear model
- HARDI, high angular resolution diffusion imaging
- HFS, high frequency stimulation
- IPG, implantable pulse generator
- LC, Levodopa challenge
- LEDD, l-DOPA equivalent daily dose
- M1, primary motor cortex
- MMS, mini-mental score
- MNI, Montreal neurological institute
- MPRAGE, magnetization-prepared rapid gradient-echo
- MPTP, 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine
- NHNN, National Hospital for Neurology and Neurosurgery
- NIfTI, neuroimaging informatics technology initiative
- PC, posterior commissure
- PD
- PFC, prefrontal cortex
- PMC, premotor cortex
- Parkinson's disease
- S1, primary sensory cortex
- SAR, specific absorption rate
- SD, standard deviation
- SE, standard error
- SMA, supplementary motor area
- SNR, signal-to-noise ratio
- SSEPI, single-shot echo planar imaging
- STN, subthalamic nucleus
- TFCE, threshold-free cluster enhancement
- TMS, transcranial magnetic stimulation
- Tremor
- UPDRS, unified Parkinson's disease rating scale
- VBM, voxel based morphometry
- VIM
- VL
- VL, ventral lateral
- VP, ventral posterior
- VTA, volume of tissue activated
- Ventrointermedialis
- Ventrolateral nucleus
- cZI, caudal zona incerta
Collapse
Affiliation(s)
- Harith Akram
- Unit of Functional Neurosurgery, Sobell Department of Motor Neuroscience and Movement Disorders, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK; Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, Queen Square, London WC1N 3BG, UK.
| | - Viswas Dayal
- Unit of Functional Neurosurgery, Sobell Department of Motor Neuroscience and Movement Disorders, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK
| | - Philipp Mahlknecht
- Unit of Functional Neurosurgery, Sobell Department of Motor Neuroscience and Movement Disorders, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK; Department of Neurology, Innsbruck Medical University, Innsbruck, Austria
| | - Dejan Georgiev
- Unit of Functional Neurosurgery, Sobell Department of Motor Neuroscience and Movement Disorders, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK
| | - Jonathan Hyam
- Unit of Functional Neurosurgery, Sobell Department of Motor Neuroscience and Movement Disorders, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK; Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, Queen Square, London WC1N 3BG, UK
| | - Thomas Foltynie
- Unit of Functional Neurosurgery, Sobell Department of Motor Neuroscience and Movement Disorders, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK
| | - Patricia Limousin
- Unit of Functional Neurosurgery, Sobell Department of Motor Neuroscience and Movement Disorders, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK
| | - Enrico De Vita
- Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK; Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, University College London NHS Foundation Trust, London, UK
| | - Marjan Jahanshahi
- Unit of Functional Neurosurgery, Sobell Department of Motor Neuroscience and Movement Disorders, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK
| | - John Ashburner
- Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK
| | - Tim Behrens
- Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK; Centre for Functional MRI of the Brain (FMRIB), John Radcliffe Hospital, Oxford OX3 9DU, UK
| | - Marwan Hariz
- Unit of Functional Neurosurgery, Sobell Department of Motor Neuroscience and Movement Disorders, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK; Department of Clinical Neuroscience, Umeå University, Umeå, Sweden
| | - Ludvic Zrinzo
- Unit of Functional Neurosurgery, Sobell Department of Motor Neuroscience and Movement Disorders, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK; Victor Horsley Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, Queen Square, London WC1N 3BG, UK
| |
Collapse
|
41
|
Yousaf T, Dervenoulas G, Politis M. Advances in MRI Methodology. INTERNATIONAL REVIEW OF NEUROBIOLOGY 2018; 141:31-76. [DOI: 10.1016/bs.irn.2018.08.008] [Citation(s) in RCA: 63] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/17/2022]
|
42
|
Tamnes CK, Roalf DR, Goddings AL, Lebel C. Diffusion MRI of white matter microstructure development in childhood and adolescence: Methods, challenges and progress. Dev Cogn Neurosci 2017; 33:161-175. [PMID: 29229299 PMCID: PMC6969268 DOI: 10.1016/j.dcn.2017.12.002] [Citation(s) in RCA: 92] [Impact Index Per Article: 13.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2017] [Revised: 05/18/2017] [Accepted: 12/04/2017] [Indexed: 12/13/2022] Open
Abstract
Diffusion magnetic resonance imaging (dMRI) continues to grow in popularity as a useful neuroimaging method to study brain development, and longitudinal studies that track the same individuals over time are emerging. Over the last decade, seminal work using dMRI has provided new insights into the development of brain white matter (WM) microstructure, connections and networks throughout childhood and adolescence. This review provides an introduction to dMRI, both diffusion tensor imaging (DTI) and other dMRI models, as well as common acquisition and analysis approaches. We highlight the difficulties associated with ascribing these imaging measurements and their changes over time to specific underlying cellular and molecular events. We also discuss selected methodological challenges that are of particular relevance for studies of development, including critical choices related to image acquisition, image analysis, quality control assessment, and the within-subject and longitudinal reliability of dMRI measurements. Next, we review the exciting progress in the characterization and understanding of brain development that has resulted from dMRI studies in childhood and adolescence, including brief overviews and discussions of studies focusing on sex and individual differences. Finally, we outline future directions that will be beneficial to the field.
Collapse
Affiliation(s)
| | - David R Roalf
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | | | - Catherine Lebel
- Department of Radiology, Cumming School of Medicine, and Alberta Children's Hospital Research Institute, University of Calgary, Calgary, Alberta, Canada
| |
Collapse
|
43
|
de Haan B, Karnath HO. 'Whose atlas I use, his song I sing?' - The impact of anatomical atlases on fiber tract contributions to cognitive deficits after stroke. Neuroimage 2017; 163:301-309. [PMID: 28958880 DOI: 10.1016/j.neuroimage.2017.09.051] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2017] [Revised: 09/21/2017] [Accepted: 09/23/2017] [Indexed: 11/26/2022] Open
Abstract
Nowadays, different anatomical atlases exist for the anatomical interpretation of the results from neuroimaging and lesion analysis studies that investigate the contribution of white matter fiber tract integrity to cognitive (dys)function. A major problem with the use of different atlases in different studies, however, is that the anatomical interpretation of neuroimaging and lesion analysis results might vary as a function of the atlas used. This issue might be particularly prominent in studies that investigate the contribution of white matter fiber tract integrity to cognitive (dys)function. We used a single large-sample dataset of right brain damaged stroke patients with and without cognitive deficit (here: spatial neglect) to systematically compare the influence of three different, widely-used white matter fiber tract atlases (1 histology-based atlas and 2 DTI tractography-based atlases) on conclusions concerning the involvement of white matter fiber tracts in the pathogenesis of cognitive dysfunction. We both calculated the overlap between the statistical lesion analysis results and each long association fiber tract (topological analyses) and performed logistic regressions on the extent of fiber tract damage in each individual for each long association white matter fiber tract (hodological analyses). For the topological analyses, our results suggest that studies that use tractography-based atlases are more likely to conclude that white matter integrity is critical for a cognitive (dys)function than studies that use a histology-based atlas. The DTI tractography-based atlases classified approximately 10 times as many voxels of the statistical map as being located in a long association white matter fiber tract than the histology-based atlas. For hodological analyses on the other hand, we observed that the conclusions concerning the overall importance of long association fiber tract integrity to cognitive function do not necessarily depend on the white matter atlas used, but conclusions may vary as a function of atlas used at the level of individual fiber tracts. Moreover, these analyses revealed that hodological studies that express the individual extent of injury to each fiber tract as a binomial variable are more likely to conclude that white matter integrity is critical for a cognitive function than studies that express the individual extent of injury to each fiber tract as a continuous variable.
Collapse
Affiliation(s)
- Bianca de Haan
- Division of Neuropsychology, Center of Neurology, Hertie-Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany.
| | - Hans-Otto Karnath
- Division of Neuropsychology, Center of Neurology, Hertie-Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany; Department of Psychology, University of South Carolina, Columbia, USA
| |
Collapse
|
44
|
Analysis of correlation between white matter changes and functional responses in thalamic stroke: a DTI & EEG study. Brain Imaging Behav 2017; 10:424-36. [PMID: 25957181 DOI: 10.1007/s11682-015-9397-1] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Diffusion tensor imaging (DTI) allows in vivo structural brain mapping and detection of microstructural disruption of white matter (WM). One of the commonly used parameters for grading the anisotropic diffusivity in WM is fractional anisotropy (FA). FA value helps to quantify the directionality of the local tract bundle. Therefore, FA images are being used in voxelwise statistical analyses (VSA). The present study used Tract-Based Spatial Statistics (TBSS) of FA images across subjects, and computes the mean skeleton map to detect voxelwise knowledge of the tracts yielding to groupwise comparison. The skeleton image illustrates WM structure and shows any changes caused by brain damage. The microstructure of WM in thalamic stroke is investigated, and the VSA results of healthy control and thalamic stroke patients are reported. It has been shown that several skeleton regions were affected subject to the presence of thalamic stroke (FWE, p < 0.05). Furthermore the correlation of quantitative EEG (qEEG) scores and neurophysiological tests with the FA skeleton for the entire test group is also investigated. We compared measurements that are related to the same fibers across subjects, and discussed implications for VSA of WM in thalamic stroke cases, for the relationship between behavioral tests and FA skeletons, and for the correlation between the FA maps and qEEG scores.Results obtained through the regression analyses did not exceed the corrected statistical threshold values for multiple comparisons (uncorrected, p < 0.05). However, in the regression analysis of FA values and the theta band activity of EEG, cingulum bundle and corpus callosum were found to be related. These areas are parts of the Default Mode Network (DMN) where DMN is known to be involved in resting state EEG theta activity. The relation between the EEG alpha band power values and FA values of the skeleton was found to support the cortico-thalamocortical cycles for both subject groups. Further, the neurophysiological tests including Benton Face Recognition (BFR), Digit Span test (DST), Warrington Topographic Memory test (WTMT), California Verbal Learning test (CVLT) has been regressed with the FA skeleton maps for both subject groups. Our results corresponding to DST task were found to be similar with previously reported findings for working memory and episodic memory tasks. For the WTMT, FA values of the cingulum (right) that plays a role in memory process was found to be related with the behavioral responses. Splenium of corpus callosum was found to be correlated for both subject groups for the BFR.
Collapse
|
45
|
Liao R, Ning L, Chen Z, Rigolo L, Gong S, Pasternak O, Golby AJ, Rathi Y, O'Donnell LJ. Performance of unscented Kalman filter tractography in edema: Analysis of the two-tensor model. NEUROIMAGE-CLINICAL 2017; 15:819-831. [PMID: 28725549 PMCID: PMC5506885 DOI: 10.1016/j.nicl.2017.06.027] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/10/2016] [Revised: 06/01/2017] [Accepted: 06/19/2017] [Indexed: 11/30/2022]
Abstract
Diffusion MRI tractography is increasingly used in pre-operative neurosurgical planning to visualize critical fiber tracts. However, a major challenge for conventional tractography, especially in patients with brain tumors, is tracing fiber tracts that are affected by vasogenic edema, which increases water content in the tissue and lowers diffusion anisotropy. One strategy for improving fiber tracking is to use a tractography method that is more sensitive than the traditional single-tensor streamline tractography. We performed experiments to assess the performance of two-tensor unscented Kalman filter (UKF) tractography in edema. UKF tractography fits a diffusion model to the data during fiber tracking, taking advantage of prior information from the previous step along the fiber. We studied UKF performance in a synthetic diffusion MRI digital phantom with simulated edema and in retrospective data from two neurosurgical patients with edema affecting the arcuate fasciculus and corticospinal tracts. We compared the performance of several tractography methods including traditional streamline, UKF single-tensor, and UKF two-tensor. To provide practical guidance on how the UKF method could be employed, we evaluated the impact of using various seed regions both inside and outside the edematous regions, as well as the impact of parameter settings on the tractography sensitivity. We quantified the sensitivity of different methods by measuring the percentage of the patient-specific fMRI activation that was reached by the tractography. We expected that diffusion anisotropy threshold parameters, as well as the inclusion of a free water model, would significantly influence the reconstruction of edematous WM fiber tracts, because edema increases water content in the tissue and lowers anisotropy. Contrary to our initial expectations, varying the fractional anisotropy threshold and including a free water model did not affect the UKF two-tensor tractography output appreciably in these two patient datasets. The most effective parameter for increasing tracking sensitivity was the generalized anisotropy (GA) threshold, which increased the length of tracked fibers when reduced to 0.075. In addition, the most effective seeding strategy was seeding in the whole brain or in a large region outside of the edema. Overall, the main contribution of this study is to provide insight into how UKF tractography can work, using a two-tensor model, to begin to address the challenge of fiber tract reconstruction in edematous regions near brain tumors. Reconstruction of edematous white matter from diffusion MRI is investigated. The performance of two–tensor unscented Kalman filter (UKF) tractography is assessed. The two–tensor model in UKF is analyzed in phantom and patient data experiments. Practical guidance on employing the UKF method in neurosurgical patients is provided
Collapse
Affiliation(s)
- Ruizhi Liao
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Lipeng Ning
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Zhenrui Chen
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Laura Rigolo
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Shun Gong
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Shanghai Changzheng Hospital, Shanghai, China
| | - Ofer Pasternak
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Alexandra J Golby
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Yogesh Rathi
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | | |
Collapse
|
46
|
Evaluation of Visual-Evoked Cerebral Metabolic Rate of Oxygen as a Diagnostic Marker in Multiple Sclerosis. Brain Sci 2017; 7:brainsci7060064. [PMID: 28604606 PMCID: PMC5483637 DOI: 10.3390/brainsci7060064] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/31/2017] [Revised: 06/03/2017] [Accepted: 06/05/2017] [Indexed: 11/25/2022] Open
Abstract
A multiple sclerosis (MS) diagnosis often relies upon clinical presentation and qualitative analysis of standard, magnetic resonance brain images. However, the accuracy of MS diagnoses can be improved by utilizing advanced brain imaging methods. We assessed the accuracy of a new neuroimaging marker, visual-evoked cerebral metabolic rate of oxygen (veCMRO2), in classifying MS patients and closely age- and sex-matched healthy control (HC) participants. MS patients and HCs underwent calibrated functional magnetic resonance imaging (cfMRI) during a visual stimulation task, diffusion tensor imaging, T1- and T2-weighted imaging, neuropsychological testing, and completed self-report questionnaires. Using resampling techniques to avoid bias and increase the generalizability of the results, we assessed the accuracy of veCMRO2 in classifying MS patients and HCs. veCMRO2 classification accuracy was also examined in the context of other evoked visuofunctional measures, white matter microstructural integrity, lesion-based measures from T2-weighted imaging, atrophy measures from T1-weighted imaging, neuropsychological tests, and self-report assays of clinical symptomology. veCMRO2 was significant and within the top 16% of measures (43 total) in classifying MS status using both within-sample (82% accuracy) and out-of-sample (77% accuracy) observations. High accuracy of veCMRO2 in classifying MS demonstrated an encouraging first step toward establishing veCMRO2 as a neurodiagnostic marker of MS.
Collapse
|
47
|
A diffusion-weighted imaging informed continuum model of the rabbit triceps surae complex. Biomech Model Mechanobiol 2017; 16:1729-1741. [PMID: 28516387 DOI: 10.1007/s10237-017-0916-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2016] [Accepted: 05/02/2017] [Indexed: 10/19/2022]
Abstract
The NZ white rabbit is the animal of choice for much experimental work due to its muscular frame and similar response to human diseases, and is one of the few mammals that have had their genome sequenced. However, continuum-level computational models of rabbit muscle detailing fibre architecture are limited in the literature, especially the triceps surae complex (gastrocnemius, plantaris and soleus), which has similar biomechanics and translatable findings to the human. This study presents a geometrical model of the rabbit triceps surae informed with diffusion-weighted imaging (DWI)-based fibres. Passive rabbit-specific material properties are estimated using known muscle deformation inferred from magnetic resonance imaging data and dorsiflexion force measured with a custom-built rabbit rig and transducer. Muscle shape prediction is evaluated against a second rabbit. This study revealed that the triceps surae steady-state force post-rigor is close to post-mortem for small deformations but increases by a fixed ratio as the deformation increases and can be used to evaluate the passive behaviour of muscle. DWI fibre orientation significantly influences shape and mechanics during simulated computational muscle contraction. The presented triceps surae force and material properties may be used to inform the constitutive behaviour of continuum rabbit muscle models used to investigate pathology and musculotendon treatments that may be translated to the human condition.
Collapse
|
48
|
Denoise diffusion-weighted images using higher-order singular value decomposition. Neuroimage 2017; 156:128-145. [PMID: 28416450 DOI: 10.1016/j.neuroimage.2017.04.017] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2016] [Revised: 02/22/2017] [Accepted: 04/06/2017] [Indexed: 11/21/2022] Open
Abstract
Noise usually affects the reliability of quantitative analysis in diffusion-weighted (DW) magnetic resonance imaging (MRI), especially at high b-values and/or high spatial resolution. Higher-order singular value decomposition (HOSVD) has recently emerged as a simple, effective, and adaptive transform to exploit sparseness within multidimensional data. In particular, the patch-based HOSVD denoising has demonstrated superb performance when applied to T1-, T2-, and proton density-weighted MRI data. In this study, we aim to investigate the feasibility of denoising DW data using the HOSVD transform. With the low signal-to-noise ratio in typical DW data, the patch-based HOSVD denoising suffers from stripe artifacts in homogeneous regions because of the HOSVD bases learned from the noisy patches. To address this problem, we propose a novel denoising method. It first introduces a global HOSVD-based denoising as a prefiltering stage to guide the subsequent patch-based HOSVD denoising stage. The HOSVD bases from the patch groups in prefiltered images are then used to transform the noisy patch groups in original DW data. Experiments were performed using simulated and in vivo DW data. Results show that the proposed method significantly reduces stripe artifacts compared with conventional patch-based HOSVD denoising methods, and outperforms two state-of-the-art denoising methods in terms of denoising quality and diffusion parameters estimation.
Collapse
|
49
|
Leuze C, Aswendt M, Ferenczi E, Liu CW, Hsueh B, Goubran M, Tian Q, Steinberg G, Zeineh MM, Deisseroth K, McNab JA. The separate effects of lipids and proteins on brain MRI contrast revealed through tissue clearing. Neuroimage 2017; 156:412-422. [PMID: 28411157 DOI: 10.1016/j.neuroimage.2017.04.021] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2016] [Revised: 03/20/2017] [Accepted: 04/08/2017] [Indexed: 01/01/2023] Open
Abstract
Despite the widespread use of magnetic resonance imaging (MRI) of the brain, the relative contribution of different biological components (e.g. lipids and proteins) to structural MRI contrasts (e.g., T1, T2, T2*, proton density, diffusion) remains incompletely understood. This limitation can undermine the interpretation of clinical MRI and hinder the development of new contrast mechanisms. Here, we determine the respective contribution of lipids and proteins to MRI contrast by removing lipids and preserving proteins in mouse brains using CLARITY. We monitor the temporal dynamics of tissue clearance via NMR spectroscopy, protein assays and optical emission spectroscopy. MRI of cleared brain tissue showed: 1) minimal contrast on standard MRI sequences; 2) increased relaxation times; and 3) diffusion rates close to free water. We conclude that lipids, present in myelin and membranes, are a dominant source of MRI contrast in brain tissue.
Collapse
Affiliation(s)
- Christoph Leuze
- Department of Radiology, Stanford University, Stanford, CA, USA.
| | - Markus Aswendt
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
| | - Emily Ferenczi
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Corey W Liu
- Stanford Magnetic Resonance Laboratory, Stanford University, Stanford, CA, USA
| | - Brian Hsueh
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA
| | - Maged Goubran
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Qiyuan Tian
- Department of Radiology, Stanford University, Stanford, CA, USA
| | - Gary Steinberg
- Department of Neurosurgery, Stanford University, Stanford, CA, USA
| | | | - Karl Deisseroth
- Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA; Department of Bioengineering, Stanford University, Stanford, CA, USA; Howard Hughes Medical Institute, Stanford University, Stanford, CA, USA
| | | |
Collapse
|
50
|
Yin XX, Zhang Y, Cao J, Wu JL, Hadjiloucas S. Exploring the complementarity of THz pulse imaging and DCE-MRIs: Toward a unified multi-channel classification and a deep learning framework. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2016; 137:87-114. [PMID: 28110743 DOI: 10.1016/j.cmpb.2016.08.026] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2015] [Revised: 07/23/2016] [Accepted: 08/31/2016] [Indexed: 06/06/2023]
Abstract
We provide a comprehensive account of recent advances in biomedical image analysis and classification from two complementary imaging modalities: terahertz (THz) pulse imaging and dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI). The work aims to highlight underlining commonalities in both data structures so that a common multi-channel data fusion framework can be developed. Signal pre-processing in both datasets is discussed briefly taking into consideration advances in multi-resolution analysis and model based fractional order calculus system identification. Developments in statistical signal processing using principal component and independent component analysis are also considered. These algorithms have been developed independently by the THz-pulse imaging and DCE-MRI communities, and there is scope to place them in a common multi-channel framework to provide better software standardization at the pre-processing de-noising stage. A comprehensive discussion of feature selection strategies is also provided and the importance of preserving textural information is highlighted. Feature extraction and classification methods taking into consideration recent advances in support vector machine (SVM) and extreme learning machine (ELM) classifiers and their complex extensions are presented. An outlook on Clifford algebra classifiers and deep learning techniques suitable to both types of datasets is also provided. The work points toward the direction of developing a new unified multi-channel signal processing framework for biomedical image analysis that will explore synergies from both sensing modalities for inferring disease proliferation.
Collapse
Affiliation(s)
- X-X Yin
- Centre of Applied Informatics, College of Engineering and Science, Victoria University, Melbourne, VIC 8001, Australia.
| | - Y Zhang
- Centre of Applied Informatics, College of Engineering and Science, Victoria University, Melbourne, VIC 8001, Australia; School of Computer Science, Fudan University, Shanghai, China.
| | - J Cao
- Nanjing University of Finance and Economics school of Computer Science, Nanjing, China
| | - J-L Wu
- Department of Radiology, Affiliated Zhongshan Hospital of Dalian University, Dalian, Liaoning, China.
| | - S Hadjiloucas
- School of Biological Sciences and Department of Bioengineering, University of Reading, Reading RG6 6AY, UK.
| |
Collapse
|