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Aguayo-González JF, Ehrlich-Lopez H, Concha L, Rivera M. Light-weight neural network for intra-voxel structure analysis. Front Neuroinform 2024; 18:1277050. [PMID: 39315001 PMCID: PMC11417038 DOI: 10.3389/fninf.2024.1277050] [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: 08/13/2023] [Accepted: 08/16/2024] [Indexed: 09/25/2024] Open
Abstract
We present a novel neural network-based method for analyzing intra-voxel structures, addressing critical challenges in diffusion-weighted MRI analysis for brain connectivity and development studies. The network architecture, called the Local Neighborhood Neural Network, is designed to use the spatial correlations of neighboring voxels for an enhanced inference while reducing parameter overhead. Our model exploits these relationships to improve the analysis of complex structures and noisy data environments. We adopt a self-supervised approach to address the lack of ground truth data, generating signals of voxel neighborhoods to integrate the training set. This eliminates the need for manual annotations and facilitates training under realistic conditions. Comparative analyses show that our method outperforms the constrained spherical deconvolution (CSD) method in quantitative and qualitative validations. Using phantom images that mimic in vivo data, our approach improves angular error, volume fraction estimation accuracy, and success rate. Furthermore, a qualitative comparison of the results in actual data shows a better spatial consistency of the proposed method in areas of real brain images. This approach demonstrates enhanced intra-voxel structure analysis capabilities and holds promise for broader application in various imaging scenarios.
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Affiliation(s)
| | | | - Luis Concha
- Department of Behavioral and Cognitive Neurobiology, Institute of Neurobiology, National Autonomous University of Mexico, Queretaro, Mexico
| | - Mariano Rivera
- Centro de Investigacion en Matematicas, Guanajuato, Mexico
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2
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Kebiri H, Gholipour A, Lin R, Vasung L, Calixto C, Krsnik Ž, Karimi D, Bach Cuadra M. Deep learning microstructure estimation of developing brains from diffusion MRI: A newborn and fetal study. Med Image Anal 2024; 95:103186. [PMID: 38701657 DOI: 10.1016/j.media.2024.103186] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2023] [Revised: 02/06/2024] [Accepted: 04/22/2024] [Indexed: 05/05/2024]
Abstract
Diffusion-weighted magnetic resonance imaging (dMRI) is widely used to assess the brain white matter. Fiber orientation distribution functions (FODs) are a common way of representing the orientation and density of white matter fibers. However, with standard FOD computation methods, accurate estimation requires a large number of measurements that usually cannot be acquired for newborns and fetuses. We propose to overcome this limitation by using a deep learning method to map as few as six diffusion-weighted measurements to the target FOD. To train the model, we use the FODs computed using multi-shell high angular resolution measurements as target. Extensive quantitative evaluations show that the new deep learning method, using significantly fewer measurements, achieves comparable or superior results than standard methods such as Constrained Spherical Deconvolution and two state-of-the-art deep learning methods. For voxels with one and two fibers, respectively, our method shows an agreement rate in terms of the number of fibers of 77.5% and 22.2%, which is 3% and 5.4% higher than other deep learning methods, and an angular error of 10° and 20°, which is 6° and 5° lower than other deep learning methods. To determine baselines for assessing the performance of our method, we compute agreement metrics using densely sampled newborn data. Moreover, we demonstrate the generalizability of the new deep learning method across scanners, acquisition protocols, and anatomy on two clinical external datasets of newborns and fetuses. We validate fetal FODs, successfully estimated for the first time with deep learning, using post-mortem histological data. Our results show the advantage of deep learning in computing the fiber orientation density for the developing brain from in-vivo dMRI measurements that are often very limited due to constrained acquisition times. Our findings also highlight the intrinsic limitations of dMRI for probing the developing brain microstructure.
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Affiliation(s)
- Hamza Kebiri
- CIBM Center for Biomedical Imaging, Switzerland; Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland; Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA.
| | - Ali Gholipour
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - Rizhong Lin
- Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland; Signal Processing Laboratory 5 (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Lana Vasung
- Department of Pediatrics, Boston Children's Hospital, and Harvard Medical School, Boston, MA, USA
| | - Camilo Calixto
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - Željka Krsnik
- Croatian Institute for Brain Research, School of Medicine, University of Zagreb, Zagreb, Croatia
| | - Davood Karimi
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - Meritxell Bach Cuadra
- CIBM Center for Biomedical Imaging, Switzerland; Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
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3
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Takemura H, Kruper JA, Miyata T, Rokem A. Tractometry of Human Visual White Matter Pathways in Health and Disease. Magn Reson Med Sci 2024; 23:316-340. [PMID: 38866532 PMCID: PMC11234945 DOI: 10.2463/mrms.rev.2024-0007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2024] Open
Abstract
Diffusion-weighted MRI (dMRI) provides a unique non-invasive view of human brain tissue properties. The present review article focuses on tractometry analysis methods that use dMRI to assess the properties of brain tissue within the long-range connections comprising brain networks. We focus specifically on the major white matter tracts that convey visual information. These connections are particularly important because vision provides rich information from the environment that supports a large range of daily life activities. Many of the diseases of the visual system are associated with advanced aging, and tractometry of the visual system is particularly important in the modern aging society. We provide an overview of the tractometry analysis pipeline, which includes a primer on dMRI data acquisition, voxelwise model fitting, tractography, recognition of white matter tracts, and calculation of tract tissue property profiles. We then review dMRI-based methods for analyzing visual white matter tracts: the optic nerve, optic tract, optic radiation, forceps major, and vertical occipital fasciculus. For each tract, we review background anatomical knowledge together with recent findings in tractometry studies on these tracts and their properties in relation to visual function and disease. Overall, we find that measurements of the brain's visual white matter are sensitive to a range of disorders and correlate with perceptual abilities. We highlight new and promising analysis methods, as well as some of the current barriers to progress toward integration of these methods into clinical practice. These barriers, such as variability in measurements between protocols and instruments, are targets for future development.
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Affiliation(s)
- Hiromasa Takemura
- Division of Sensory and Cognitive Brain Mapping, Department of System Neuroscience, National Institute for Physiological Sciences, Okazaki, Aichi, Japan
- Graduate Institute for Advanced Studies, SOKENDAI, Hayama, Kanagawa, Japan
- Center for Information and Neural Networks (CiNet), Advanced ICT Research Institute, National Institute of Information and Communications Technology, Suita, Osaka, Japan
| | - John A Kruper
- Department of Psychology and eScience Institute, University of Washington, Seattle, WA, USA
| | - Toshikazu Miyata
- Division of Sensory and Cognitive Brain Mapping, Department of System Neuroscience, National Institute for Physiological Sciences, Okazaki, Aichi, Japan
- Center for Information and Neural Networks (CiNet), Advanced ICT Research Institute, National Institute of Information and Communications Technology, Suita, Osaka, Japan
| | - Ariel Rokem
- Department of Psychology and eScience Institute, University of Washington, Seattle, WA, USA
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Razban RM, Antal BB, Dill KA, Mujica-Parodi LR. Brain signaling becomes less integrated and more segregated with age. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.11.17.567376. [PMID: 38014139 PMCID: PMC10680817 DOI: 10.1101/2023.11.17.567376] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/29/2023]
Abstract
The integration-segregation framework is a popular first step to understand brain dynamics because it simplifies brain dynamics into two states based on global vs. local signaling patterns. However, there is no consensus for how to best define what the two states look like. Here, we map integration and segregation to order and disorder states from the Ising model in physics to calculate state probabilities, P int and P seg , from functional MRI data. We find that integration/segregation decreases/increases with age across three databases, and changes are consistent with weakened connection strength among regions rather than topological connectivity based on structural and diffusion MRI data.
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Affiliation(s)
- Rostam M Razban
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA
| | - Botond B Antal
- Dept. of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Ken A Dill
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA
- Dept. of Physics and Astronomy, Stony Brook University, Stony Brook, NY, USA
- Dept. of Chemistry, Stony Brook University, Stony Brook, NY, USA
| | - Lilianne R Mujica-Parodi
- Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA
- Dept. of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA
- Program in Neuroscience, Stony Brook University, Stony Brook, NY, USA
- Dept. of Physics and Astronomy, Stony Brook University, Stony Brook, NY, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
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5
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Kruper J, Richie-Halford A, Benson NC, Caffarra S, Owen J, Wu Y, Egan C, Lee AY, Lee CS, Yeatman JD, Rokem A. Convolutional neural network-based classification of glaucoma using optic radiation tissue properties. COMMUNICATIONS MEDICINE 2024; 4:72. [PMID: 38605245 PMCID: PMC11009254 DOI: 10.1038/s43856-024-00496-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2023] [Accepted: 03/28/2024] [Indexed: 04/13/2024] Open
Abstract
BACKGROUND Sensory changes due to aging or disease can impact brain tissue. This study aims to investigate the link between glaucoma, a leading cause of blindness, and alterations in brain connections. METHODS We analyzed diffusion MRI measurements of white matter tissue in a large group, consisting of 905 glaucoma patients (aged 49-80) and 5292 healthy individuals (aged 45-80) from the UK Biobank. Confounds due to group differences were mitigated by matching a sub-sample of controls to glaucoma subjects. We compared classification of glaucoma using convolutional neural networks (CNNs) focusing on the optic radiations, which are the primary visual connection to the cortex, against those analyzing non-visual brain connections. As a control, we evaluated the performance of regularized linear regression models. RESULTS We showed that CNNs using information from the optic radiations exhibited higher accuracy in classifying subjects with glaucoma when contrasted with CNNs relying on information from non-visual brain connections. Regularized linear regression models were also tested, and showed significantly weaker classification performance. Additionally, the CNN was unable to generalize to the classification of age-group or of age-related macular degeneration. CONCLUSIONS Our findings indicate a distinct and potentially non-linear signature of glaucoma in the tissue properties of optic radiations. This study enhances our understanding of how glaucoma affects brain tissue and opens avenues for further research into how diseases that affect sensory input may also affect brain aging.
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Affiliation(s)
- John Kruper
- Department of Psychology, University of Washington, Seattle, WA, USA
- eScience Institute, University of Washington, Seattle, WA, USA
| | - Adam Richie-Halford
- Graduate School of Education and Division of Developmental Behavioral Pediatrics, Stanford University, Stanford, CA, USA
| | - Noah C Benson
- eScience Institute, University of Washington, Seattle, WA, USA
| | - Sendy Caffarra
- Graduate School of Education and Division of Developmental Behavioral Pediatrics, Stanford University, Stanford, CA, USA
- University of Modena and Reggio Emilia, Modena, Italy
| | - Julia Owen
- Department of Ophthalmology, University of Washington, Seattle, WA, USA
- Roger and Angie Karalis Johnson Retina Center, Seattle, WA, USA
| | - Yue Wu
- Department of Ophthalmology, University of Washington, Seattle, WA, USA
- Roger and Angie Karalis Johnson Retina Center, Seattle, WA, USA
| | | | - Aaron Y Lee
- Department of Ophthalmology, University of Washington, Seattle, WA, USA
- Roger and Angie Karalis Johnson Retina Center, Seattle, WA, USA
| | - Cecilia S Lee
- Department of Ophthalmology, University of Washington, Seattle, WA, USA
- Roger and Angie Karalis Johnson Retina Center, Seattle, WA, USA
| | - Jason D Yeatman
- Graduate School of Education and Division of Developmental Behavioral Pediatrics, Stanford University, Stanford, CA, USA
| | - Ariel Rokem
- Department of Psychology, University of Washington, Seattle, WA, USA.
- eScience Institute, University of Washington, Seattle, WA, USA.
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Fokkinga E, Hernandez-Tamames JA, Ianus A, Nilsson M, Tax CMW, Perez-Lopez R, Grussu F. Advanced Diffusion-Weighted MRI for Cancer Microstructure Assessment in Body Imaging, and Its Relationship With Histology. J Magn Reson Imaging 2023. [PMID: 38032021 DOI: 10.1002/jmri.29144] [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: 08/11/2023] [Revised: 10/30/2023] [Accepted: 10/31/2023] [Indexed: 12/01/2023] Open
Abstract
Diffusion-weighted magnetic resonance imaging (DW-MRI) aims to disentangle multiple biological signal sources in each imaging voxel, enabling the computation of innovative maps of tissue microstructure. DW-MRI model development has been dominated by brain applications. More recently, advanced methods with high fidelity to histology are gaining momentum in other contexts, for example, in oncological applications of body imaging, where new biomarkers are urgently needed. The objective of this article is to review the state-of-the-art of DW-MRI in body imaging (ie, not including the nervous system) in oncology, and to analyze its value as compared to reference colocalized histology measurements, given that demonstrating the histological validity of any new DW-MRI method is essential. In this article, we review the current landscape of DW-MRI techniques that extend standard apparent diffusion coefficient (ADC), describing their acquisition protocols, signal models, fitting settings, microstructural parameters, and relationship with histology. Preclinical, clinical, and in/ex vivo studies were included. The most used techniques were intravoxel incoherent motion (IVIM; 36.3% of used techniques), diffusion kurtosis imaging (DKI; 16.7%), vascular, extracellular, and restricted diffusion for cytometry in tumors (VERDICT; 13.3%), and imaging microstructural parameters using limited spectrally edited diffusion (IMPULSED; 11.7%). Another notable category of techniques relates to innovative b-tensor diffusion encoding or joint diffusion-relaxometry. The reviewed approaches provide histologically meaningful indices of cancer microstructure (eg, vascularization/cellularity) which, while not necessarily accurate numerically, may still provide useful sensitivity to microscopic pathological processes. Future work of the community should focus on improving the inter-/intra-scanner robustness, and on assessing histological validity in broader contexts. LEVEL OF EVIDENCE: NA TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Ella Fokkinga
- Biomedical Engineering, Track Medical Physics, Delft University of Technology, Delft, The Netherlands
- Radiomics Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Juan A Hernandez-Tamames
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
- Department of Imaging Physics, Delft University of Technology, Delft, The Netherlands
| | - Andrada Ianus
- Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal
| | - Markus Nilsson
- Department of Diagnostic Radiology, Clinical Sciences Lund, Lund, Sweden
| | - Chantal M W Tax
- Cardiff University Brain Research Imaging Center (CUBRIC), School of Physics and Astronomy, Cardiff University, Cardiff, United Kingdom
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Raquel Perez-Lopez
- Radiomics Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Francesco Grussu
- Radiomics Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
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Grotheer M, Bloom D, Kruper J, Richie-Halford A, Zika S, Aguilera González VA, Yeatman JD, Grill-Spector K, Rokem A. Human white matter myelinates faster in utero than ex utero. Proc Natl Acad Sci U S A 2023; 120:e2303491120. [PMID: 37549280 PMCID: PMC10438384 DOI: 10.1073/pnas.2303491120] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 06/27/2023] [Indexed: 08/09/2023] Open
Abstract
The formation of myelin, the fatty sheath that insulates nerve fibers, is critical for healthy brain function. A fundamental open question is what impact being born has on myelin growth. To address this, we evaluated a large (n = 300) cross-sectional sample of newborns from the Developing Human Connectome Project (dHCP). First, we developed software for the automated identification of 20 white matter bundles in individual newborns that is well suited for large samples. Next, we fit linear models that quantify how T1w/T2w (a myelin-sensitive imaging contrast) changes over time at each point along the bundles. We found faster growth of T1w/T2w along the lengths of all bundles before birth than right after birth. Further, in a separate longitudinal sample of preterm infants (N = 34), we found lower T1w/T2w than in full-term peers measured at the same age. By applying the linear models fit on the cross-section sample to the longitudinal sample of preterm infants, we find that their delay in T1w/T2w growth is well explained by the amount of time they spent developing in utero and ex utero. These results suggest that white matter myelinates faster in utero than ex utero. The reduced rate of myelin growth after birth, in turn, explains lower myelin content in individuals born preterm and could account for long-term cognitive, neurological, and developmental consequences of preterm birth. We hypothesize that closely matching the environment of infants born preterm to what they would have experienced in the womb may reduce delays in myelin growth and hence improve developmental outcomes.
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Affiliation(s)
- Mareike Grotheer
- Department of Psychology, Philipps-Universität Marburg, Marburg35039, Germany
- Center for Mind, Brain and Behavior, Philipps-Universität Marburg and Justus-Liebig-Universität Giessen, Marburg35039, Germany
| | - David Bloom
- Department of Psychology, University of Washington, Seattle, WA98105
- eScience Institute, University of Washington, Seattle, WA98105
| | - John Kruper
- Department of Psychology, University of Washington, Seattle, WA98105
- eScience Institute, University of Washington, Seattle, WA98105
| | - Adam Richie-Halford
- Department of Psychology, University of Washington, Seattle, WA98105
- eScience Institute, University of Washington, Seattle, WA98105
| | - Stephanie Zika
- Department of Psychology, Philipps-Universität Marburg, Marburg35039, Germany
- Center for Mind, Brain and Behavior, Philipps-Universität Marburg and Justus-Liebig-Universität Giessen, Marburg35039, Germany
| | - Vicente A. Aguilera González
- Department of Psychology, Philipps-Universität Marburg, Marburg35039, Germany
- Center for Mind, Brain and Behavior, Philipps-Universität Marburg and Justus-Liebig-Universität Giessen, Marburg35039, Germany
| | - Jason D. Yeatman
- Department of Psychology, Stanford University, Stanford, CA94305
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA94305
- Graduate School of Education, Stanford University, Stanford, CA94305
- Division of Developmental-Behavioral Pediatrics, Stanford University School of Medicine, Stanford, CA94305
| | - Kalanit Grill-Spector
- Department of Psychology, Stanford University, Stanford, CA94305
- Wu Tsai Neurosciences Institute, Stanford University, Stanford, CA94305
| | - Ariel Rokem
- Department of Psychology, University of Washington, Seattle, WA98105
- eScience Institute, University of Washington, Seattle, WA98105
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Liu R, Li M, Dunson DB. PPA: Principal parcellation analysis for brain connectomes and multiple traits. Neuroimage 2023; 276:120214. [PMID: 37286151 DOI: 10.1016/j.neuroimage.2023.120214] [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] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 05/31/2023] [Indexed: 06/09/2023] Open
Abstract
Our understanding of the structure of the brain and its relationships with human traits is largely determined by how we represent the structural connectome. Standard practice divides the brain into regions of interest (ROIs) and represents the connectome as an adjacency matrix having cells measuring connectivity between pairs of ROIs. Statistical analyses are then heavily driven by the (largely arbitrary) choice of ROIs. In this article, we propose a human trait prediction framework utilizing a tractography-based representation of the brain connectome, which clusters fiber endpoints to define a data-driven white matter parcellation targeted to explain variation among individuals and predict human traits. This leads to Principal Parcellation Analysis (PPA), representing individual brain connectomes by compositional vectors building on a basis system of fiber bundles that captures the connectivity at the population level. PPA eliminates the need to choose atlases and ROIs a priori, and provides a simpler, vector-valued representation that facilitates easier statistical analysis compared to the complex graph structures encountered in classical connectome analyses. We illustrate the proposed approach through applications to data from the Human Connectome Project (HCP) and show that PPA connectomes improve power in predicting human traits over state-of-the-art methods based on classical connectomes, while dramatically improving parsimony and maintaining interpretability. Our PPA package is publicly available on GitHub, and can be implemented routinely for diffusion image data.
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Affiliation(s)
- Rongjie Liu
- Department of Statistics, Florida State University, Tallahassee, FL, USA
| | - Meng Li
- Department of Statistics, Rice University, Houston, TX, USA.
| | - David B Dunson
- Department of Statistical Science, Duke University, Durham, NC, USA
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Kebiri H, Gholipour A, Vasung L, Krsnik Ž, Karimi D, Cuadra MB. Deep learning microstructure estimation of developing brains from diffusion MRI: a newborn and fetal study. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.01.547351. [PMID: 37425859 PMCID: PMC10327173 DOI: 10.1101/2023.07.01.547351] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/11/2023]
Abstract
Diffusion-weighted magnetic resonance imaging (dMRI) is widely used to assess the brain white matter. Fiber orientation distribution functions (FODs) are a common way of representing the orientation and density of white matter fibers. However, with standard FOD computation methods, accurate estimation of FODs requires a large number of measurements that usually cannot be acquired for newborns and fetuses. We propose to overcome this limitation by using a deep learning method to map as few as six diffusion-weighted measurements to the target FOD. To train the model, we use the FODs computed using multi-shell high angular resolution measurements as target. Extensive quantitative evaluations show that the new deep learning method, using significantly fewer measurements, achieves comparable or superior results to standard methods such as Constrained Spherical Deconvolution. We demonstrate the generalizability of the new deep learning method across scanners, acquisition protocols, and anatomy on two clinical datasets of newborns and fetuses. Additionally, we compute agreement metrics within the HARDI newborn dataset, and validate fetal FODs with post-mortem histological data. The results of this study show the advantage of deep learning in inferring the microstructure of the developing brain from in-vivo dMRI measurements that are often very limited due to subject motion and limited acquisition times, but also highlight the intrinsic limitations of dMRI in the analysis of the developing brain microstructure. These findings, therefore, advocate for the need for improved methods that are tailored to studying the early development of human brain.
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Affiliation(s)
- Hamza Kebiri
- CIBM Center for Biomedical Imaging, Switzerland
- Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Ali Gholipour
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Lana Vasung
- Department of Pediatrics, Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | - Željka Krsnik
- Croatian Institute for Brain Research, School of Medicine, University of Zagreb, Zagreb, Croatia
| | - Davood Karimi
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Meritxell Bach Cuadra
- CIBM Center for Biomedical Imaging, Switzerland
- Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
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10
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Villaseñor PJ, Cortés-Servín D, Pérez-Moriel A, Aquiles A, Luna-Munguía H, Ramirez-Manzanares A, Coronado-Leija R, Larriva-Sahd J, Concha L. Multi-tensor diffusion abnormalities of gray matter in an animal model of cortical dysplasia. Front Neurol 2023; 14:1124282. [PMID: 37342776 PMCID: PMC10278582 DOI: 10.3389/fneur.2023.1124282] [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: 12/15/2022] [Accepted: 04/18/2023] [Indexed: 06/23/2023] Open
Abstract
Focal cortical dysplasias are a type of malformations of cortical development that are a common cause of drug-resistant focal epilepsy. Surgical treatment is a viable option for some of these patients, with their outcome being highly related to complete surgical resection of lesions visible in magnetic resonance imaging (MRI). However, subtle lesions often go undetected on conventional imaging. Several methods to analyze MRI have been proposed, with the common goal of rendering subtle cortical lesions visible. However, most image-processing methods are targeted to detect the macroscopic characteristics of cortical dysplasias, which do not always correspond to the microstructural disarrangement of these cortical malformations. Quantitative analysis of diffusion-weighted MRI (dMRI) enables the inference of tissue characteristics, and novel methods provide valuable microstructural features of complex tissue, including gray matter. We investigated the ability of advanced dMRI descriptors to detect diffusion abnormalities in an animal model of cortical dysplasia. For this purpose, we induced cortical dysplasia in 18 animals that were scanned at 30 postnatal days (along with 19 control animals). We obtained multi-shell dMRI, to which we fitted single and multi-tensor representations. Quantitative dMRI parameters derived from these methods were queried using a curvilinear coordinate system to sample the cortical mantle, providing inter-subject anatomical correspondence. We found region- and layer-specific diffusion abnormalities in experimental animals. Moreover, we were able to distinguish diffusion abnormalities related to altered intra-cortical tangential fibers from those associated with radial cortical fibers. Histological examinations revealed myelo-architectural abnormalities that explain the alterations observed through dMRI. The methods for dMRI acquisition and analysis used here are available in clinical settings and our work shows their clinical relevance to detect subtle cortical dysplasias through analysis of their microstructural properties.
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Affiliation(s)
- Paulina J. Villaseñor
- Instituto de Neurobiología, Universidad Nacional Autónoma de México Campus Juriquilla, Querétaro, Mexico
| | - David Cortés-Servín
- Instituto de Neurobiología, Universidad Nacional Autónoma de México Campus Juriquilla, Querétaro, Mexico
| | | | - Ana Aquiles
- Instituto de Neurobiología, Universidad Nacional Autónoma de México Campus Juriquilla, Querétaro, Mexico
| | - Hiram Luna-Munguía
- Instituto de Neurobiología, Universidad Nacional Autónoma de México Campus Juriquilla, Querétaro, Mexico
| | | | - Ricardo Coronado-Leija
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, United States
| | - Jorge Larriva-Sahd
- Instituto de Neurobiología, Universidad Nacional Autónoma de México Campus Juriquilla, Querétaro, Mexico
| | - Luis Concha
- Instituto de Neurobiología, Universidad Nacional Autónoma de México Campus Juriquilla, Querétaro, Mexico
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Vaish A, Gupta A, Rajwade A. CSR-PERT: Joint framework for MRI and HARDI data reconstruction using perturbed radial trajectory estimated from compressively sensed measurements. Comput Biol Med 2022; 150:106117. [PMID: 36208594 DOI: 10.1016/j.compbiomed.2022.106117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2022] [Revised: 08/20/2022] [Accepted: 09/17/2022] [Indexed: 11/16/2022]
Abstract
Radial sampling pattern is an important signal acquisition strategy in magnetic resonance imaging (MRI) owing to better immunity to motion-induced artifacts and less pronounced aliasing due to undersampling compared to the Cartesian sampling. These advantages of radial sampling can be exploited in acquisition of multidimensional signals such as High Angular Resolution Diffusion Imaging (HARDI), with tremendous scope of acceleration. Despite such benefits, gradient delays lead to samples being acquired from unknown miscentered radial trajectories, severely degrading the image reconstruction quality. In the present work, we propose Csr-Pert that is a joint framework, wherein these perturbed radial trajectories are estimated and utilized for image reconstruction from the compressively sensed measurements of (i) MRI data and (ii) HARDI data. The proposed Csr-Pert method is tested on one real MRI dataset with trajectory deviations and is observed to perform better than the existing state-of-the-art method at high acceleration factors up to 8. To the best of our knowledge, this is the first work to address the problem of estimating perturbed trajectories using the compressively sensed MRI and HARDI data. The method is also tested for varying combinations of trajectory deviations and sampling proportions. It is observed to yield very good quality HARDI reconstruction for a wide variety of scenarios. We have also demonstrated the robustness of the proposed method on real datasets in clinical settings assuming perturbed as well as noisy trajectories.
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12
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Abstract
We describe a collection of T1-, diffusion- and functional T2*-weighted magnetic resonance imaging data from human individuals with albinism and achiasma. This repository can be used as a test-bed to develop and validate tractography methods like diffusion-signal modeling and fiber tracking as well as to investigate the properties of the human visual system in individuals with congenital abnormalities. The MRI data is provided together with tools and files allowing for its preprocessing and analysis, along with the data derivatives such as manually curated masks and regions of interest for performing tractography.
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13
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De Luca A, Ianus A, Leemans A, Palombo M, Shemesh N, Zhang H, Alexander DC, Nilsson M, Froeling M, Biessels GJ, Zucchelli M, Frigo M, Albay E, Sedlar S, Alimi A, Deslauriers-Gauthier S, Deriche R, Fick R, Afzali M, Pieciak T, Bogusz F, Aja-Fernández S, Özarslan E, Jones DK, Chen H, Jin M, Zhang Z, Wang F, Nath V, Parvathaneni P, Morez J, Sijbers J, Jeurissen B, Fadnavis S, Endres S, Rokem A, Garyfallidis E, Sanchez I, Prchkovska V, Rodrigues P, Landman BA, Schilling KG. On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types: Chronicles of the MEMENTO challenge. Neuroimage 2021; 240:118367. [PMID: 34237442 PMCID: PMC7615259 DOI: 10.1016/j.neuroimage.2021.118367] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Revised: 06/09/2021] [Accepted: 07/04/2021] [Indexed: 12/29/2022] Open
Abstract
Diffusion MRI (dMRI) has become an invaluable tool to assess the microstructural organization of brain tissue. Depending on the specific acquisition settings, the dMRI signal encodes specific properties of the underlying diffusion process. In the last two decades, several signal representations have been proposed to fit the dMRI signal and decode such properties. Most methods, however, are tested and developed on a limited amount of data, and their applicability to other acquisition schemes remains unknown. With this work, we aimed to shed light on the generalizability of existing dMRI signal representations to different diffusion encoding parameters and brain tissue types. To this end, we organized a community challenge - named MEMENTO, making available the same datasets for fair comparisons across algorithms and techniques. We considered two state-of-the-art diffusion datasets, including single-diffusion-encoding (SDE) spin-echo data from a human brain with over 3820 unique diffusion weightings (the MASSIVE dataset), and double (oscillating) diffusion encoding data (DDE/DODE) of a mouse brain including over 2520 unique data points. A subset of the data sampled in 5 different voxels was openly distributed, and the challenge participants were asked to predict the remaining part of the data. After one year, eight participant teams submitted a total of 80 signal fits. For each submission, we evaluated the mean squared error, the variance of the prediction error and the Bayesian information criteria. The received submissions predicted either multi-shell SDE data (37%) or DODE data (22%), followed by cartesian SDE data (19%) and DDE (18%). Most submissions predicted the signals measured with SDE remarkably well, with the exception of low and very strong diffusion weightings. The prediction of DDE and DODE data seemed more challenging, likely because none of the submissions explicitly accounted for diffusion time and frequency. Next to the choice of the model, decisions on fit procedure and hyperparameters play a major role in the prediction performance, highlighting the importance of optimizing and reporting such choices. This work is a community effort to highlight strength and limitations of the field at representing dMRI acquired with trending encoding schemes, gaining insights into how different models generalize to different tissue types and fiber configurations over a large range of diffusion encodings.
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Affiliation(s)
- Alberto De Luca
- PROVIDI Lab, Image Sciences Institute, University Medical Center Utrecht, Utrecht, the Netherlands; Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands.
| | - Andrada Ianus
- Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Alexander Leemans
- PROVIDI Lab, Image Sciences Institute, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Marco Palombo
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
| | - Noam Shemesh
- Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Hui Zhang
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
| | - Daniel C Alexander
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
| | - Markus Nilsson
- Clinical Sciences Lund, Radiology, Lund University, Lund, Sweden
| | - Martijn Froeling
- Department of Radiology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Geert-Jan Biessels
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Mauro Zucchelli
- Inria Sophia Antipolis - Méditerranée, Université Côte d'Azur, Sophia Antipolis, France
| | - Matteo Frigo
- Inria Sophia Antipolis - Méditerranée, Université Côte d'Azur, Sophia Antipolis, France
| | - Enes Albay
- Inria Sophia Antipolis - Méditerranée, Université Côte d'Azur, Sophia Antipolis, France; Istanbul Technical University, Istanbul, Turkey
| | - Sara Sedlar
- Inria Sophia Antipolis - Méditerranée, Université Côte d'Azur, Sophia Antipolis, France
| | - Abib Alimi
- Inria Sophia Antipolis - Méditerranée, Université Côte d'Azur, Sophia Antipolis, France
| | | | - Rachid Deriche
- Inria Sophia Antipolis - Méditerranée, Université Côte d'Azur, Sophia Antipolis, France
| | | | - Maryam Afzali
- Cardiff University Brain Research, Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Tomasz Pieciak
- AGH University of Science and Technology, Kraków, Poland; LPI, ETSI Telecomunicación, Universidad de Valladolid, Valladolid, Spain
| | - Fabian Bogusz
- AGH University of Science and Technology, Kraków, Poland
| | | | - Evren Özarslan
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden; Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden
| | - Derek K Jones
- Cardiff University Brain Research, Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Haoze Chen
- School of Instruments and Electronics, North University of China, Taiyuan, China
| | - Mingwu Jin
- Department of Physics, University of Texas at Arlington, Arlington, USA
| | - Zhijie Zhang
- School of Instruments and Electronics, North University of China, Taiyuan, China
| | - Fengxiang Wang
- School of Instruments and Electronics, North University of China, Taiyuan, China
| | | | | | - Jan Morez
- Imec-Vision lab, Department of Physics, University of Antwerp, Antwerp, Belgium
| | - Jan Sijbers
- Imec-Vision lab, Department of Physics, University of Antwerp, Antwerp, Belgium
| | - Ben Jeurissen
- Imec-Vision lab, Department of Physics, University of Antwerp, Antwerp, Belgium
| | - Shreyas Fadnavis
- Intelligent Systems Engineering, Indiana University Bloomington, Indiana, USA
| | - Stefan Endres
- Leibniz Institute for Materials Engineering - IWT, Faculty of Production Engineering, University of Bremen, Bremen, Germany
| | - Ariel Rokem
- Department of Psychology and the eScience Institute, University of Washington, Seattle, WA USA
| | | | | | | | | | - Bennet A Landman
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, USA
| | - Kurt G Schilling
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, USA; Department of Radiology and Radiological Science, Vanderbilt University Medical Center, Nashville, USA
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14
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Karimi D, Vasung L, Jaimes C, Machado-Rivas F, Warfield SK, Gholipour A. Learning to estimate the fiber orientation distribution function from diffusion-weighted MRI. Neuroimage 2021; 239:118316. [PMID: 34182101 PMCID: PMC8385546 DOI: 10.1016/j.neuroimage.2021.118316] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2020] [Revised: 05/20/2021] [Accepted: 06/25/2021] [Indexed: 02/06/2023] Open
Abstract
Estimation of white matter fiber orientation distribution function (fODF) is the essential first step for reliable brain tractography and connectivity analysis. Most of the existing fODF estimation methods rely on sub-optimal physical models of the diffusion signal or mathematical simplifications, which can impact the estimation accuracy. In this paper, we propose a data-driven method that avoids some of these pitfalls. Our proposed method is based on a multilayer perceptron that learns to map the diffusion-weighted measurements, interpolated onto a fixed spherical grid in the q space, to the target fODF. Importantly, we also propose methods for synthesizing reliable simulated training data. We show that the model can be effectively trained with simulated or real training data. Our phantom experiments show that the proposed method results in more accurate fODF estimation and tractography than several competing methods including the multi-tensor model, Bayesian estimation, spherical deconvolution, and two other machine learning techniques. On real data, we compare our method with other techniques in terms of accuracy of estimating the ground-truth fODF. The results show that our method is more accurate than other methods, and that it performs better than the competing methods when applied to under-sampled diffusion measurements. We also compare our method with the Sparse Fascicle Model in terms of expert ratings of the accuracy of reconstruction of several commissural, projection, association, and cerebellar tracts. The results show that the tracts reconstructed with the proposed method are rated significantly higher by three independent experts. Our study demonstrates the potential of data-driven methods for improving the accuracy and robustness of fODF estimation.
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Affiliation(s)
- Davood Karimi
- Computational Radiology Laboratory (CRL), Department of Radiology, Boston Children's Hospital, and Harvard Medical School, USA.
| | - Lana Vasung
- Department of Pediatrics, Boston Children's Hospital, and Harvard Medical School, Boston, MA, USA
| | - Camilo Jaimes
- Computational Radiology Laboratory (CRL), Department of Radiology, Boston Children's Hospital, and Harvard Medical School, USA
| | - Fedel Machado-Rivas
- Computational Radiology Laboratory (CRL), Department of Radiology, Boston Children's Hospital, and Harvard Medical School, USA
| | - Simon K Warfield
- Computational Radiology Laboratory (CRL), Department of Radiology, Boston Children's Hospital, and Harvard Medical School, USA
| | - Ali Gholipour
- Computational Radiology Laboratory (CRL), Department of Radiology, Boston Children's Hospital, and Harvard Medical School, USA
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15
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Karimi D, Vasung L, Jaimes C, Machado-Rivas F, Khan S, Warfield SK, Gholipour A. A machine learning-based method for estimating the number and orientations of major fascicles in diffusion-weighted magnetic resonance imaging. Med Image Anal 2021; 72:102129. [PMID: 34182203 PMCID: PMC8320341 DOI: 10.1016/j.media.2021.102129] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2020] [Revised: 05/26/2021] [Accepted: 05/28/2021] [Indexed: 12/29/2022]
Abstract
Accurate modeling of diffusion-weighted magnetic resonance imaging measurements is necessary for accurate brain connectivity analysis. Existing methods for estimating the number and orientations of fascicles in an imaging voxel either depend on non-convex optimization techniques that are sensitive to initialization and measurement noise, or are prone to predicting spurious fascicles. In this paper, we propose a machine learning-based technique that can accurately estimate the number and orientations of fascicles in a voxel. Our method can be trained with either simulated or real diffusion-weighted imaging data. Our method estimates the angle to the closest fascicle for each direction in a set of discrete directions uniformly spread on the unit sphere. This information is then processed to extract the number and orientations of fascicles in a voxel. On realistic simulated phantom data with known ground truth, our method predicts the number and orientations of crossing fascicles more accurately than several classical and machine learning methods. It also leads to more accurate tractography. On real data, our method is better than or compares favorably with other methods in terms of robustness to measurement down-sampling and also in terms of expert quality assessment of tractography results.
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Affiliation(s)
- Davood Karimi
- Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA.
| | - Lana Vasung
- Department of Pediatrics at Boston Children's Hospital, and Harvard Medical School, Boston, Massachusetts, USA
| | - Camilo Jaimes
- Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Fedel Machado-Rivas
- Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Shadab Khan
- Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Simon K Warfield
- Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
| | - Ali Gholipour
- Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA
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16
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Henriques RN, Correia MM, Marrale M, Huber E, Kruper J, Koudoro S, Yeatman JD, Garyfallidis E, Rokem A. Diffusional Kurtosis Imaging in the Diffusion Imaging in Python Project. Front Hum Neurosci 2021; 15:675433. [PMID: 34349631 PMCID: PMC8327208 DOI: 10.3389/fnhum.2021.675433] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Accepted: 06/17/2021] [Indexed: 12/28/2022] Open
Abstract
Diffusion-weighted magnetic resonance imaging (dMRI) measurements and models provide information about brain connectivity and are sensitive to the physical properties of tissue microstructure. Diffusional Kurtosis Imaging (DKI) quantifies the degree of non-Gaussian diffusion in biological tissue from dMRI. These estimates are of interest because they were shown to be more sensitive to microstructural alterations in health and diseases than measures based on the total anisotropy of diffusion which are highly confounded by tissue dispersion and fiber crossings. In this work, we implemented DKI in the Diffusion in Python (DIPY) project-a large collaborative open-source project which aims to provide well-tested, well-documented and comprehensive implementation of different dMRI techniques. We demonstrate the functionality of our methods in numerical simulations with known ground truth parameters and in openly available datasets. A particular strength of our DKI implementations is that it pursues several extensions of the model that connect it explicitly with microstructural models and the reconstruction of 3D white matter fiber bundles (tractography). For instance, our implementations include DKI-based microstructural models that allow the estimation of biophysical parameters, such as axonal water fraction. Moreover, we illustrate how DKI provides more general characterization of non-Gaussian diffusion compatible with complex white matter fiber architectures and gray matter, and we include a novel mean kurtosis index that is invariant to the confounding effects due to tissue dispersion. In summary, DKI in DIPY provides a well-tested, well-documented and comprehensive reference implementation for DKI. It provides a platform for wider use of DKI in research on brain disorders and in cognitive neuroscience.
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Affiliation(s)
| | - Marta M. Correia
- Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom
| | - Maurizio Marrale
- Department of Physics and Chemistry “Emilio Segrè”, University of Palermo, Palermo, Italy
- National Institute for Nuclear Physics (INFN), Catania Division, Catania, Italy
| | - Elizabeth Huber
- Department of Speech and Hearing, Institute for Learning and Brain Science, University of Washington, Seattle, WA, United States
| | - John Kruper
- Department of Psychology and eScience Institute, The University of Washington, Seattle, WA, United States
| | - Serge Koudoro
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computer Science and Engineering, Indiana University, Bloomington, IN, United States
| | - Jason D. Yeatman
- Department of Speech and Hearing, Institute for Learning and Brain Science, University of Washington, Seattle, WA, United States
- Department of Pediatrics, Graduate School of Education, Stanford University, Stanford, CA, United States
| | - Eleftherios Garyfallidis
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computer Science and Engineering, Indiana University, Bloomington, IN, United States
| | - Ariel Rokem
- Department of Psychology and eScience Institute, The University of Washington, Seattle, WA, United States
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17
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Mushtaha FN, Kuehn TK, El-Deeb O, Rohani SA, Helpard LW, Moore J, Ladak H, Moehring A, Baron CA, Khan AR. Design and characterization of a 3D-printed axon-mimetic phantom for diffusion MRI. Magn Reson Med 2021; 86:2482-2496. [PMID: 34196049 PMCID: PMC8596689 DOI: 10.1002/mrm.28886] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Revised: 05/17/2021] [Accepted: 05/18/2021] [Indexed: 01/05/2023]
Abstract
PURPOSE To introduce and characterize inexpensive and easily produced 3D-printed axon-mimetic diffusion MRI phantoms in terms of pore geometry and diffusion kurtosis imaging metrics. METHODS Phantoms were 3D-printed with a composite printing material that, after the dissolution of the polyvinyl alcohol, exhibits microscopic fibrous pores. Confocal microscopy and synchrotron phase-contrast micro-CT imaging were performed to visualize and assess the pore sizes. Diffusion MRI scans of four identical phantoms and phantoms with varying print parameters in water were performed at 9.4 T. Diffusion kurtosis imaging was fit to both data sets and used to assess the reproducibility between phantoms and effects of print parameters on diffusion kurtosis imaging metrics. Identical scans were performed 25 and 76 days later, to test their stability. RESULTS Segmentation of pores in three microscopy images yielded a mean, median, and SD of equivalent pore diameters of 7.57 μm, 3.51 μm, and 12.13 μm, respectively. Phantoms had T1 /T2 = 2 seconds/180 ms, and those with identical parameters showed a low coefficient of variation (~10%) in mean diffusivity (1.38 × 10-3 mm2 /s) and kurtosis (0.52) metrics and radial diffusivity (1.01 × 10-3 mm2 /s) and kurtosis (1.13) metrics. Printing temperature and speed had a small effect on diffusion kurtosis imaging metrics (< 16%), whereas infill density had a larger and more variable effect (> 16%). The stability analysis showed small changes over 2.5 months (< 7%). CONCLUSION Three-dimension-printed axon-mimetic phantoms can mimic the fibrous structure of axon bundles on a microscopic scale, serving as complex, anisotropic diffusion MRI phantoms.
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Affiliation(s)
- Farah N Mushtaha
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, Western University, London, Canada
| | - Tristan K Kuehn
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, Western University, London, Canada.,School of Biomedical Engineering, Western University, London, Canada
| | - Omar El-Deeb
- Department of Biology, Western University, London, Canada
| | - Seyed A Rohani
- School of Biomedical Engineering, Western University, London, Canada
| | - Luke W Helpard
- School of Biomedical Engineering, Western University, London, Canada
| | - John Moore
- Imaging Research Laboratories, Robarts Research Institute, Western University, London, Canada
| | - Hanif Ladak
- School of Biomedical Engineering, Western University, London, Canada.,Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Canada.,Department of Electrical and Computer Engineering, Western University, London, Canada
| | | | - Corey A Baron
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, Western University, London, Canada.,School of Biomedical Engineering, Western University, London, Canada.,Imaging Research Laboratories, Robarts Research Institute, Western University, London, Canada.,Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Canada.,The Brain and Mind Institute, Western University, London, Canada
| | - Ali R Khan
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, Western University, London, Canada.,Department of Biology, Western University, London, Canada.,Imaging Research Laboratories, Robarts Research Institute, Western University, London, Canada.,Department of Medical Biophysics, Schulich School of Medicine and Dentistry, Western University, London, Canada.,The Brain and Mind Institute, Western University, London, Canada
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18
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Kelley S, Plass J, Bender AR, Polk TA. Age-Related Differences in White Matter: Understanding Tensor-Based Results Using Fixel-Based Analysis. Cereb Cortex 2021; 31:3881-3898. [PMID: 33791797 DOI: 10.1093/cercor/bhab056] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2020] [Revised: 01/19/2021] [Accepted: 02/16/2021] [Indexed: 12/13/2022] Open
Abstract
Aging is associated with widespread alterations in cerebral white matter (WM). Most prior studies of age differences in WM have used diffusion tensor imaging (DTI), but typical DTI metrics (e.g., fractional anisotropy; FA) can reflect multiple neurobiological features, making interpretation challenging. Here, we used fixel-based analysis (FBA) to investigate age-related WM differences observed using DTI in a sample of 45 older and 25 younger healthy adults. Age-related FA differences were widespread but were strongly associated with differences in multi-fiber complexity (CX), suggesting that they reflected differences in crossing fibers in addition to structural differences in individual fiber segments. FBA also revealed a frontolimbic locus of age-related effects and provided insights into distinct microstructural changes underlying them. Specifically, age differences in fiber density were prominent in fornix, bilateral anterior internal capsule, forceps minor, body of the corpus callosum, and corticospinal tract, while age differences in fiber cross section were largest in cingulum bundle and forceps minor. These results provide novel insights into specific structural differences underlying major WM differences associated with aging.
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Affiliation(s)
- Shannon Kelley
- Department of Psychology, University of Michigan, Ann Arbor, MI 48109, USA
| | - John Plass
- Department of Psychology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Andrew R Bender
- Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI 48824, USA
| | - Thad A Polk
- Department of Psychology, University of Michigan, Ann Arbor, MI 48109, USA
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19
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Kruper J, Yeatman JD, Richie-Halford A, Bloom D, Grotheer M, Caffarra S, Kiar G, Karipidis II, Roy E, Chandio BQ, Garyfallidis E, Rokem A. Evaluating the Reliability of Human Brain White Matter Tractometry. APERTURE NEURO 2021; 1:10.52294/e6198273-b8e3-4b63-babb-6e6b0da10669. [PMID: 35079748 PMCID: PMC8785971 DOI: 10.52294/e6198273-b8e3-4b63-babb-6e6b0da10669] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
The validity of research results depends on the reliability of analysis methods. In recent years, there have been concerns about the validity of research that uses diffusion-weighted MRI (dMRI) to understand human brain white matter connections in vivo, in part based on the reliability of analysis methods used in this field. We defined and assessed three dimensions of reliability in dMRI-based tractometry, an analysis technique that assesses the physical properties of white matter pathways: (1) reproducibility, (2) test-retest reliability, and (3) robustness. To facilitate reproducibility, we provide software that automates tractometry (https://yeatmanlab.github.io/pyAFQ). In measurements from the Human Connectome Project, as well as clinical-grade measurements, we find that tractometry has high test-retest reliability that is comparable to most standardized clinical assessment tools. We find that tractometry is also robust: showing high reliability with different choices of analysis algorithms. Taken together, our results suggest that tractometry is a reliable approach to analysis of white matter connections. The overall approach taken here both demonstrates the specific trustworthiness of tractometry analysis and outlines what researchers can do to establish the reliability of computational analysis pipelines in neuroimaging.
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Affiliation(s)
- John Kruper
- Department of Psychology, University of Washington, Seattle, WA, 98195, USA
- eScience Institute, University of Washington, Seattle, WA, 98195, USA
| | - Jason D Yeatman
- Graduate School of Education, Stanford University, Stanford, CA, 94305, USA
- Division of Developmental-Behavioral Pediatrics, Stanford University School of Medicine, Stanford, CA, 94305, USA
| | | | - David Bloom
- Department of Psychology, University of Washington, Seattle, WA, 98195, USA
- eScience Institute, University of Washington, Seattle, WA, 98195, USA
| | - Mareike Grotheer
- Center for Mind, Brain and Behavior - CMBB, Hans-Meerwein-Straße 6, Marburg 35032, Germany
- Department of Psychology, University of Marburg, Marburg 35039, Germany
| | - Sendy Caffarra
- Graduate School of Education, Stanford University, Stanford, CA, 94305, USA
- Division of Developmental-Behavioral Pediatrics, Stanford University School of Medicine, Stanford, CA, 94305, USA
- Basque Center on Cognition, Brain and Language, BCBL, 20009, Spain
| | - Gregory Kiar
- Department of Biomedical Engineering, McGill University, Montreal, H3A 0E9, Canada
| | - Iliana I Karipidis
- Center for Interdisciplinary Brain Sciences Research, Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine,Stanford, CA, 94305, USA
| | - Ethan Roy
- Graduate School of Education, Stanford University, Stanford, CA, 94305, USA
| | - Bramsh Q Chandio
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing and Engineering, Indiana University Bloomington, Bloomington, IN, 47408, USA
| | - Eleftherios Garyfallidis
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computing and Engineering, Indiana University Bloomington, Bloomington, IN, 47408, USA
| | - Ariel Rokem
- Department of Psychology, University of Washington, Seattle, WA, 98195, USA
- eScience Institute, University of Washington, Seattle, WA, 98195, USA
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20
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Dubner SE, Rose J, Bruckert L, Feldman HM, Travis KE. Neonatal white matter tract microstructure and 2-year language outcomes after preterm birth. NEUROIMAGE-CLINICAL 2020; 28:102446. [PMID: 33035964 PMCID: PMC7554644 DOI: 10.1016/j.nicl.2020.102446] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/25/2020] [Revised: 09/18/2020] [Accepted: 09/20/2020] [Indexed: 01/04/2023]
Abstract
Preterm infant white matter tracts uniquely predict later toddler language. Neonatal medical history moderates posterior corpus callosum–language relations. Different associations by tract may relate to brain maturation and medical history.
Aim To determine whether variability in diffusion MRI (dMRI) white matter tract metrics, obtained in a cohort of preterm infants prior to neonatal hospital discharge, would be associated with language outcomes at age 2 years, after consideration of age at scan and number of major neonatal complications. Method 30 children, gestational age 28.9 (2.4) weeks, underwent dMRI at mean post menstrual age 36.4 (1.4) weeks and language assessment with the Bayley Scales of Infant Development–III at mean age 22.2 (1.7) months chronological age. Mean fractional anisotropy (FA) and mean diffusivity (MD) were calculated for 5 white matter tracts. Hierarchical linear regression assessed associations between tract FA, moderating variables, and language outcomes. Results FA of the left inferior longitudinal fasciculus accounted for 17% (p = 0.03) of the variance in composite language and FA of the posterior corpus callosum accounted for 19% (p = 0.02) of the variance in composite language, beyond that accounted for by post-menstrual age at scan and neonatal medical complications. The number of neonatal medical complications moderated the relationship between language and posterior corpus callosum FA but did not moderate the association in the other tract. Conclusion Language at age 2 is associated with white matter metrics in early infancy in preterm children. The different pattern of associations by fiber group may relate to the stage of brain maturation and/or the nature and timing of medical complications related to preterm birth. Future studies should replicate these findings with a larger sample size to assure reliability of the findings.
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Affiliation(s)
- Sarah E Dubner
- Division of Developmental-Behavioral Pediatrics, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA.
| | - Jessica Rose
- Division of Pediatric Orthopaedics, Stanford University School of Medicine, Stanford, CA, USA
| | - Lisa Bruckert
- Division of Developmental-Behavioral Pediatrics, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Heidi M Feldman
- Division of Developmental-Behavioral Pediatrics, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - Katherine E Travis
- Division of Developmental-Behavioral Pediatrics, Department of Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
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21
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Saliani A, Zaimi A, Nami H, Duval T, Stikov N, Cohen-Adad J. Construction of a rat spinal cord atlas of axon morphometry. Neuroimage 2019; 202:116156. [PMID: 31491525 DOI: 10.1016/j.neuroimage.2019.116156] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2019] [Revised: 08/26/2019] [Accepted: 09/02/2019] [Indexed: 12/27/2022] Open
Abstract
Atlases of the central nervous system are essential for understanding the pathophysiology of neurological diseases, which remains one of the greatest challenges in neuroscience research today. These atlases provide insight into the underlying white matter microstructure and have been created from a variety of animal models, including rats. Although existing atlases of the rat spinal cord provide some details of axon microstructure, there is currently no histological dataset that quantifies axon morphometry exhaustively in the entire spinal cord. In this study, we created the first comprehensive rat spinal cord atlas of the white matter microstructure with quantifiable axon and myelin morphometrics. Using full-slice scanning electron microscopy images and state-of-the-art segmentation algorithms, we generated an atlas of microstructural metrics such as axon diameter, axonal density and g-ratio. After registering the Watson spinal cord white matter atlas to our template, we computed statistics across metrics, spinal levels and tracts. We notably found that g-ratio is relatively constant, whereas axon diameter showed the greatest variation. The atlas, data and full analysis code are freely available at: https://github.com/neuropoly/atlas-rat.
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Affiliation(s)
- Ariane Saliani
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada.
| | - Aldo Zaimi
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
| | - Harris Nami
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
| | - Tanguy Duval
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
| | - Nikola Stikov
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada; Montreal Heart Institute, Montreal, QC, Canada
| | - Julien Cohen-Adad
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada; Functional Neuroimaging Unit, CRIUGM, Université de Montréal, Montréal, QC, Canada.
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22
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Puzniak RJ, Ahmadi K, Kaufmann J, Gouws A, Morland AB, Pestilli F, Hoffmann MB. Quantifying nerve decussation abnormalities in the optic chiasm. NEUROIMAGE-CLINICAL 2019; 24:102055. [PMID: 31722288 PMCID: PMC6849426 DOI: 10.1016/j.nicl.2019.102055] [Citation(s) in RCA: 14] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/16/2019] [Revised: 10/14/2019] [Accepted: 10/22/2019] [Indexed: 12/26/2022]
Abstract
Diffusion MRI is capable of detecting structural abnormalities of the optic chiasm. Quantification of crossing strength in optic chiasm is of promise for albinism diagnostics. Optic chiasm is a powerful test model for neuroimaging methods resolving crossing fibers.
Objective The human optic chiasm comprises partially crossing optic nerve fibers. Here we used diffusion MRI (dMRI) for the in-vivo identification of the abnormally high proportion of crossing fibers found in the optic chiasm of people with albinism. Methods In 9 individuals with albinism and 8 controls high-resolution 3T dMRI data was acquired and analyzed with a set of methods for signal modeling [Diffusion Tensor (DT) and Constrained Spherical Deconvolution (CSD)], tractography, and streamline filtering (LiFE, COMMIT, and SIFT2). The number of crossing and non-crossing streamlines and their weights after filtering entered ROC-analyses to compare the discriminative power of the methods based on the area under the curve (AUC). The dMRI results were cross-validated with fMRI estimates of misrouting in a subset of 6 albinotic individuals. Results We detected significant group differences in chiasmal crossing for both unfiltered DT (p = 0.014) and CSD tractograms (p = 0.0009) also reflected by AUC measures (for DT and CSD: 0.61 and 0.75, respectively), underlining the discriminative power of the approach. Estimates of crossing strengths obtained with dMRI and fMRI were significantly correlated for CSD (R2 = 0.83, p = 0.012). The results show that streamline filtering methods in combination with probabilistic tracking, both optimized for the data at hand, can improve the detection of crossing in the human optic chiasm. Conclusions Especially CSD-based tractography provides an efficient approach to detect structural abnormalities in the optic chiasm. The most realistic results were obtained with filtering methods with parameters optimized for the data at hand. Significance Our findings demonstrate a novel anatomy-driven approach for the individualized diagnostics of optic chiasm abnormalities.
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Affiliation(s)
- Robert J Puzniak
- Department of Ophthalmology, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany
| | - Khazar Ahmadi
- Department of Ophthalmology, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany
| | - Jörn Kaufmann
- Department of Neurology, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany
| | - Andre Gouws
- York Neuroimaging Centre, Department of Psychology, University of York, York, United Kingdom
| | - Antony B Morland
- York Neuroimaging Centre, Department of Psychology, University of York, York, United Kingdom; York Biomedical Research Institute, University of York, York, United Kingdom
| | - Franco Pestilli
- Department of Psychological and Brain Sciences, Program in Neuroscience and Program in Cognitive Science, Indiana University, Bloomington, USA
| | - Michael B Hoffmann
- Department of Ophthalmology, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany; Center for Behavioral Brain Sciences, Magdeburg, Germany.
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23
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Associative white matter connecting the dorsal and ventral posterior human cortex. Brain Struct Funct 2019; 224:2631-2660. [DOI: 10.1007/s00429-019-01907-8] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2018] [Accepted: 06/07/2019] [Indexed: 02/05/2023]
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24
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Blecher T, Miron S, Schneider GG, Achiron A, Ben-Shachar M. Association Between White Matter Microstructure and Verbal Fluency in Patients With Multiple Sclerosis. Front Psychol 2019; 10:1607. [PMID: 31379663 PMCID: PMC6657651 DOI: 10.3389/fpsyg.2019.01607] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2018] [Accepted: 06/26/2019] [Indexed: 12/14/2022] Open
Abstract
Verbal fluency refers to the ability to generate words quickly and efficiently according to predefined phonological or semantic criteria. Deficits in verbal fluency limit patients' ability to communicate effectively and to function well in social setups. Multiple sclerosis (MS) patients suffer from various cognitive impairments, and some of them experience language deficits as well. The goal of this study is to examine the contribution of the dorsal and ventral language pathways to verbal fluency in MS patients. All patients (N = 33) underwent diffusion MRI (dMRI) and fluency measurements. Diffusion parameters were calculated along dorsal and ventral language-related pathways and their right-hemispheric homologs, identified individually in each patient. Significant correlations were found between fluency measures and mean fractional anisotropy (FA) in several pathways, including the left fronto-temporal arcuate fasciculus (AFft), bilateral inferior fronto-occipital fasciculus (IFOF), and bilateral frontal aslant tract. Along-tract correlations revealed a more selective pattern of associations: letter-based fluency was associated with FA in a segment of the left AFft (dorsal pathway), while category-based fluency was associated with FA in a segment of the right IFOF (ventral pathway). The observed pattern of associations, mapping letter-based fluency to the dorsal stream and category-based fluency to the ventral stream, fits well within the dual stream framework of language processing. Further studies will be necessary to assess whether these associations generalize to the typical adult population or whether they are tied to the clinical state.
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Affiliation(s)
- Tal Blecher
- Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat Gan, Israel
| | - Shmuel Miron
- Multiple Sclerosis Center, Sheba Medical Center, Tel Hashomer, Israel
| | | | - Anat Achiron
- Multiple Sclerosis Center, Sheba Medical Center, Tel Hashomer, Israel
- Sackler School of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Michal Ben-Shachar
- Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat Gan, Israel
- Department of English Literature and Linguistics, Bar-Ilan University, Ramat Gan, Israel
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25
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Avesani P, McPherson B, Hayashi S, Caiafa CF, Henschel R, Garyfallidis E, Kitchell L, Bullock D, Patterson A, Olivetti E, Sporns O, Saykin AJ, Wang L, Dinov I, Hancock D, Caron B, Qian Y, Pestilli F. The open diffusion data derivatives, brain data upcycling via integrated publishing of derivatives and reproducible open cloud services. Sci Data 2019; 6:69. [PMID: 31123325 PMCID: PMC6533280 DOI: 10.1038/s41597-019-0073-y] [Citation(s) in RCA: 45] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2018] [Accepted: 04/11/2019] [Indexed: 12/31/2022] Open
Abstract
We describe the Open Diffusion Data Derivatives (O3D) repository: an integrated collection of preserved brain data derivatives and processing pipelines, published together using a single digital-object-identifier. The data derivatives were generated using modern diffusion-weighted magnetic resonance imaging data (dMRI) with diverse properties of resolution and signal-to-noise ratio. In addition to the data, we publish all processing pipelines (also referred to as open cloud services). The pipelines utilize modern methods for neuroimaging data processing (diffusion-signal modelling, fiber tracking, tractography evaluation, white matter segmentation, and structural connectome construction). The O3D open services can allow cognitive and clinical neuroscientists to run the connectome mapping algorithms on new, user-uploaded, data. Open source code implementing all O3D services is also provided to allow computational and computer scientists to reuse and extend the processing methods. Publishing both data-derivatives and integrated processing pipeline promotes practices for scientific reproducibility and data upcycling by providing open access to the research assets for utilization by multiple scientific communities.
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Affiliation(s)
- Paolo Avesani
- Neuroinformatics Laboratory, Center for Information Technology, Fondazione Bruno Kessler, via Sommarive 18, 38123, Trento, Italy
- Center for Mind/Brain Sciences (CIMeC), University of Trento, via Delle Regole 101, 38123, Trento, Italy
| | - Brent McPherson
- Pestilli Lab. Department of Psychological and Brain Sciences, Program in Cognitive Science, Indiana University Bloomington, 1101 E 10th Street, Bloomington, Indiana, 47405, USA
| | - Soichi Hayashi
- Department of Psychological and Brain Sciences and Pervasive Technology Institute, University Information Technology Services, Indiana University, 1101 E 10th Street, Bloomington, IN, 47405, USA
| | - Cesar F Caiafa
- Pestilli Lab. Department of Psychological and Brain Sciences, Indiana University Bloomington, 1101 E 10th Street, Bloomington, Indiana, 47405, USA
- Instituto Argentino de Radioastronomía (CCT-La Plata, CONICET; CICPBA), CC5 V, Elisa, 1894, Argentina
- Facultad de Ingeniería, Universidad de Buenos Aires, Buenos Aires, C1063ACV, Argentina
| | - Robert Henschel
- Pervasive Technology Institute, Indiana University Bloomington, 2709 E 10th Street, Bloomington, IN, 47408, USA
| | - Eleftherios Garyfallidis
- Department of Intelligent Systems Engineering, Programs in Neuroscience and Cognitive Science, Indiana University Bloomington, 700N Woodlawn Ave, Bloomington, Indiana, 47408, USA
| | - Lindsey Kitchell
- Pestilli Lab. Department of Psychological and Brain Sciences, Program in Cognitive Science, Indiana University Bloomington, 1101 E 10th Street, Bloomington, Indiana, 47405, USA
| | - Daniel Bullock
- Pestilli Lab. Department of Psychological and Brain Sciences, Program in Neuroscience, Indiana University Bloomington, 1101 E 10th Street, Bloomington, Indiana, 47405, USA
| | - Andrew Patterson
- Pestilli Lab. Department of Psychological and Brain Sciences, Program in Neuroscience, Indiana University Bloomington, 1101 E 10th Street, Bloomington, Indiana, 47405, USA
| | - Emanuele Olivetti
- Neuroinformatics Laboratory, Center for Information Technology, Fondazione Bruno Kessler, via Sommarive 18, 38123, Trento, Italy
- Center for Mind/Brain Sciences (CIMeC), University of Trento, via Delle Regole 101, 38123, Trento, Italy
| | - Olaf Sporns
- Department of Psychological and Brain Sciences, Programs in Neuroscience and Cognitive Science, and Indiana Network Science Institute, Indiana University Bloomington, 1101 E 10th Street, Bloomington, Indiana, 47405, USA
| | - Andrew J Saykin
- Indiana University School of Medicine, Departments of Radiology and Imaging Sciences and Medical and Molecular Genetics, and the Indiana Alzheimer Disease Center, Indiana University, 355 W 16th St., Indianapolis, Indiana, 46202, USA
| | - Lei Wang
- Departments of Psychiatry and Behavioral Sciences and Radiology, Northwestern University Feinberg School of Medicine, 710N. Lake Shore Drive, Abbott Hall 1322, Chicago, IL, 60611, USA
| | - Ivo Dinov
- Statistics Online Computational Resource (SOCR), Center for Complexity of Self-Management in Chronic Disease (CSCD), Health Behavior and Biological Sciences, Michigan Institute for Data Science (MIDAS), University of Michigan, Ann Arbor, MI, 49109, USA
| | - David Hancock
- Pervasive Technology Institute, Indiana University Bloomington, 2709 E 10th Street, Bloomington, IN, 47408, USA
| | - Bradley Caron
- Pestilli Lab. Indiana University School of Optometry and Program in Neuroscience, Indiana University Bloomington, 1101 E 10th Street, Bloomington, Indiana, USA
| | - Yiming Qian
- Pestilli Lab. Department of Psychological and Brain Sciences, Indiana University Bloomington, 1101 E 10th Street, Bloomington, Indiana, 47405, USA
| | - Franco Pestilli
- Pestilli Lab. Department of Psychological and Brain Sciences, Engineering, Computer Science, Programs in Neuroscience and Cognitive Science, School of Optometry, and Indiana Network Science Institute, Indiana University Bloomington, 1101 E 10th Street, Bloomington, Indiana, 47405, USA.
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26
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Bain JS, Yeatman JD, Schurr R, Rokem A, Mezer AA. Evaluating arcuate fasciculus laterality measurements across dataset and tractography pipelines. Hum Brain Mapp 2019; 40:3695-3711. [PMID: 31106944 DOI: 10.1002/hbm.24626] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2019] [Revised: 04/18/2019] [Accepted: 05/01/2019] [Indexed: 11/11/2022] Open
Abstract
The arcuate fasciculi are white-matter pathways that connect frontal and temporal lobes in each hemisphere. The arcuate plays a key role in the language network and is believed to be left-lateralized, in line with left hemisphere dominance for language. Measuring the arcuate in vivo requires diffusion magnetic resonance imaging-based tractography, but asymmetry of the in vivo arcuate is not always reliably detected in previous studies. It is unknown how the choice of tractography algorithm, with each method's freedoms, constraints, and vulnerabilities to false-positive and -negative errors, impacts findings of arcuate asymmetry. Here, we identify the arcuate in two independent datasets using a number of tractography strategies and methodological constraints, and assess their impact on estimates of arcuate laterality. We test three tractography methods: a deterministic, a probabilistic, and a tractography-evaluation (LiFE) algorithm. We extract the arcuate from the whole-brain tractogram, and compare it to an arcuate bundle constrained even further by selecting only those streamlines that connect to anatomically relevant cortical regions. We test arcuate macrostructure laterality, and also evaluate microstructure profiles for properties such as fractional anisotropy and quantitative R1. We find that both tractography choice and implementing the cortical constraints substantially impact estimates of all indices of arcuate laterality. Together, these results emphasize the effect of the tractography pipeline on estimates of arcuate laterality in both macrostructure and microstructure.
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Affiliation(s)
- Jonathan S Bain
- The Edmond & Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Jason D Yeatman
- Institute for Learning & Brain Sciences and Department of Speech and Hearing Science, The University of Washington, Seattle, Washington, USA
| | - Roey Schurr
- The Edmond & Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Ariel Rokem
- The University of Washington eScience Institute, The University of Washington, Seattle, Washington, USA
| | - Aviv A Mezer
- The Edmond & Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
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27
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Ather S, Proudlock FA, Welton T, Morgan PS, Sheth V, Gottlob I, Dineen RA. Aberrant visual pathway development in albinism: From retina to cortex. Hum Brain Mapp 2019; 40:777-788. [PMID: 30511784 PMCID: PMC6865554 DOI: 10.1002/hbm.24411] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/19/2018] [Revised: 09/08/2018] [Accepted: 09/18/2018] [Indexed: 12/27/2022] Open
Abstract
Albinism refers to a group of genetic abnormalities in melanogenesis that are associated neuronal misrouting through the optic chiasm. We perform quantitative assessment of visual pathway structure and function in 23 persons with albinism (PWA) and 20 matched controls using optical coherence tomography (OCT), volumetric magnetic resonance imaging (MRI), diffusion tensor imaging and visual evoked potentials (VEP). PWA had a higher streamline decussation index (percentage of total tractography streamlines decussating at the chiasm) compared with controls (Z = -2.24, p = .025), and streamline decussation index correlated weakly with inter-hemispheric asymmetry measured using VEP (r = .484, p = .042). For PWA, a significant correlation was found between foveal development index and total number of streamlines (r = .662, p < .001). Significant positive correlations were found between peri-papillary retinal nerve fibre layer thickness and optic nerve (r = .642, p < .001) and tract (r = .663, p < .001) width. Occipital pole cortical thickness was 6.88% higher (Z = -4.10, p < .001) in PWA and was related to anterior visual pathway structures including foveal retinal pigment epithelium complex thickness (r = -.579, p = .005), optic disc (r = .478, p = .021) and rim areas (r = .597, p = .003). We were unable to demonstrate a significant relationship between OCT-derived foveal or optic nerve measures and MRI-derived chiasm size or streamline decussation index. Our novel tractographic demonstration of altered chiasmatic decussation in PWA corresponds to VEP measured cortical asymmetry and is consistent with chiasmatic misrouting in albinism. We also demonstrate a significant relationship between retinal pigment epithelium and visual cortex thickness indicating that retinal pigmentation defects in albinism lead to downstream structural reorganisation of the visual cortex.
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Affiliation(s)
- Sarim Ather
- Nuffield Department of Surgical SciencesUniversity of OxfordOxfordUnited Kingdom
| | - Frank Anthony Proudlock
- University of Leicester Ulverscroft Eye UnitRobert Kilpatrick Clinical Sciences BuildingLeicesterUnited Kingdom
| | - Thomas Welton
- Radiological Sciences, Division of Clinical NeuroscienceUniversity of Nottingham, Queen's Medical CentreNottinghamUnited Kingdom
- Sir Peter Mansfield Imaging Centre, University of NottinghamQueen's Medical CentreNottinghamUnited Kingdom
| | - Paul S. Morgan
- Sir Peter Mansfield Imaging Centre, University of NottinghamQueen's Medical CentreNottinghamUnited Kingdom
- Medical Physics and Clinical Engineering, Nottingham University Hospitals NHS TrustQueen's Medical CentreNottinghamUnited Kingdom
| | - Viral Sheth
- University of Leicester Ulverscroft Eye UnitRobert Kilpatrick Clinical Sciences BuildingLeicesterUnited Kingdom
| | - Irene Gottlob
- University of Leicester Ulverscroft Eye UnitRobert Kilpatrick Clinical Sciences BuildingLeicesterUnited Kingdom
| | - Rob A. Dineen
- Radiological Sciences, Division of Clinical NeuroscienceUniversity of Nottingham, Queen's Medical CentreNottinghamUnited Kingdom
- Sir Peter Mansfield Imaging Centre, University of NottinghamQueen's Medical CentreNottinghamUnited Kingdom
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28
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Nguyen VD, Jansson J, Tran HTA, Hoffman J, Li JR. Diffusion MRI simulation in thin-layer and thin-tube media using a discretization on manifolds. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2019; 299:176-187. [PMID: 30641268 DOI: 10.1016/j.jmr.2019.01.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/14/2018] [Revised: 12/16/2018] [Accepted: 01/07/2019] [Indexed: 06/09/2023]
Abstract
The Bloch-Torrey partial differential equation can be used to describe the evolution of the transverse magnetization of the imaged sample under the influence of diffusion-encoding magnetic field gradients inside the MRI scanner. The integral of the magnetization inside a voxel gives the simulated diffusion MRI signal. This paper proposes a finite element discretization on manifolds in order to efficiently simulate the diffusion MRI signal in domains that have a thin layer or a thin tube geometrical structure. The variable thickness of the three-dimensional domains is included in the weak formulation established on the manifolds. We conducted a numerical study of the proposed approach by simulating the diffusion MRI signals from the extracellular space (a thin layer medium) and from neurons (a thin tube medium), comparing the results with the reference signals obtained using a standard three-dimensional finite element discretization. We show good agreements between the simulated signals using our proposed method and the reference signals for a wide range of diffusion MRI parameters. The approximation becomes better as the diffusion time increases. The method helps to significantly reduce the required simulation time, computational memory, and difficulties associated with mesh generation, thus opening the possibilities to simulating complicated structures at low cost for a better understanding of diffusion MRI in the brain.
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Affiliation(s)
- Van-Dang Nguyen
- Department of Computational Science and Technology, KTH Royal Institute of Technology, Sweden.
| | - Johan Jansson
- Department of Computational Science and Technology, KTH Royal Institute of Technology, Sweden.
| | - Hoang Trong An Tran
- CMAP - Center for Applied Mathematics, Ecole Polytechnique, Palaiseau, France
| | - Johan Hoffman
- Department of Computational Science and Technology, KTH Royal Institute of Technology, Sweden.
| | - Jing-Rebecca Li
- CMAP - Center for Applied Mathematics, Ecole Polytechnique, Palaiseau, France.
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29
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Schurr R, Duan Y, Norcia AM, Ogawa S, Yeatman JD, Mezer AA. Tractography optimization using quantitative T1 mapping in the human optic radiation. Neuroimage 2018; 181:645-658. [DOI: 10.1016/j.neuroimage.2018.06.060] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2018] [Revised: 06/03/2018] [Accepted: 06/20/2018] [Indexed: 12/31/2022] Open
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30
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Spees WM, Lin TH, Sun P, Song C, George A, Gary SE, Yang HC, Song SK. MRI-based assessment of function and dysfunction in myelinated axons. Proc Natl Acad Sci U S A 2018; 115:E10225-E10234. [PMID: 30297414 PMCID: PMC6205472 DOI: 10.1073/pnas.1801788115] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022] Open
Abstract
Repetitive electrical activity produces microstructural alteration in myelinated axons, which may afford the opportunity to noninvasively monitor function of myelinated fibers in peripheral nervous system (PNS)/CNS pathways. Microstructural changes were assessed via two different magnetic-resonance-based approaches: diffusion fMRI and dynamic T2 spectroscopy in the ex vivo perfused bullfrog sciatic nerves. Using this robust, classical model as a platform for testing, we demonstrate that noninvasive diffusion fMRI, based on standard diffusion tensor imaging (DTI), can clearly localize the sites of axonal conduction blockage as might be encountered in neurotrauma or other lesion types. It is also shown that the diffusion fMRI response is graded in proportion to the total number of electrical impulses carried through a given locus. Dynamic T2 spectroscopy of the perfused frog nerves point to an electrical-activity-induced redistribution of tissue water and myelin structural changes. Diffusion basis spectrum imaging (DBSI) reveals a reversible shift of tissue water into a restricted isotropic diffusion signal component. Submyelinic vacuoles are observed in electron-microscopy images of tissue fixed during electrical stimulation. A slowing of the compound action potential conduction velocity accompanies repetitive electrical activity. Correlations between electrophysiology and MRI parameters during and immediately after stimulation are presented. Potential mechanisms and interpretations of these results are discussed.
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Affiliation(s)
- William M Spees
- Biomedical MR Laboratory, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110;
- Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO 63110
| | - Tsen-Hsuan Lin
- Biomedical MR Laboratory, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110
| | - Peng Sun
- Biomedical MR Laboratory, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110
| | - Chunyu Song
- Department of Biomedical Engineering, Washington University, St. Louis, MO 63110
| | - Ajit George
- Biomedical MR Laboratory, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110
| | - Sam E Gary
- Biomedical MR Laboratory, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110
| | - Hsin-Chieh Yang
- Biomedical MR Laboratory, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110
| | - Sheng-Kwei Song
- Biomedical MR Laboratory, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110
- Hope Center for Neurological Disorders, Washington University School of Medicine, St. Louis, MO 63110
- Department of Biomedical Engineering, Washington University, St. Louis, MO 63110
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31
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Yoshimine S, Ogawa S, Horiguchi H, Terao M, Miyazaki A, Matsumoto K, Tsuneoka H, Nakano T, Masuda Y, Pestilli F. Age-related macular degeneration affects the optic radiation white matter projecting to locations of retinal damage. Brain Struct Funct 2018; 223:3889-3900. [PMID: 29951918 DOI: 10.1007/s00429-018-1702-5] [Citation(s) in RCA: 27] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2017] [Accepted: 06/17/2018] [Indexed: 12/16/2022]
Abstract
We investigated the impact of age-related macular degeneration (AMD) on visual acuity and the visual white matter. We combined an adaptive cortical atlas and diffusion-weighted magnetic resonance imaging (dMRI) and tractography to separate optic radiation (OR) projections to different retinal eccentricities in human primary visual cortex. We exploited the known anatomical organization of the OR and clinically relevant data to segment the OR into three primary components projecting to fovea, mid- and far-periphery. We measured white matter tissue properties-fractional anisotropy, linearity, planarity, sphericity-along the aforementioned three components of the optic radiation to compare AMD patients and controls. We found differences in white matter properties specific to OR white matter fascicles projecting to primary visual cortex locations corresponding to the location of retinal damage (fovea). Additionally, we show that the magnitude of white matter properties in AMD patients' correlates with visual acuity. In sum, we demonstrate a specific relation between visual loss, anatomical location of retinal damage and white matter damage in AMD patients. Importantly, we demonstrate that these changes are so profound that can be detected using magnetic resonance imaging data with clinical resolution. The conserved mapping between retinal and white matter damage suggests that retinal neurodegeneration might be a primary cause of white matter degeneration in AMD patients. The results highlight the impact of eye disease on brain tissue, a process that may become an important target to monitor during the course of treatment.
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Affiliation(s)
- Shoyo Yoshimine
- Department of Ophthalmology, The Jikei University School of Medicine, 3-25-8 Nishi-Shinbashi, Minato-ku, Tokyo, 105-8461, Japan.
| | - Shumpei Ogawa
- Department of Ophthalmology, The Jikei University School of Medicine, 3-25-8 Nishi-Shinbashi, Minato-ku, Tokyo, 105-8461, Japan.,Department of Ophthalmology, Atsugi City Hospital, Kanagawa, Japan
| | - Hiroshi Horiguchi
- Department of Ophthalmology, The Jikei University School of Medicine, 3-25-8 Nishi-Shinbashi, Minato-ku, Tokyo, 105-8461, Japan
| | - Masahiko Terao
- Research Institute for Time Studies, Yamaguchi University, Yamaguchi, Japan
| | | | - Kenji Matsumoto
- Tamagawa University Brain Science Institute, Machida, Tokyo, Japan
| | - Hiroshi Tsuneoka
- Department of Ophthalmology, The Jikei University School of Medicine, 3-25-8 Nishi-Shinbashi, Minato-ku, Tokyo, 105-8461, Japan
| | - Tadashi Nakano
- Department of Ophthalmology, The Jikei University School of Medicine, 3-25-8 Nishi-Shinbashi, Minato-ku, Tokyo, 105-8461, Japan
| | - Yoichiro Masuda
- Department of Ophthalmology, The Jikei University School of Medicine, 3-25-8 Nishi-Shinbashi, Minato-ku, Tokyo, 105-8461, Japan
| | - Franco Pestilli
- Department of Psychological and Brain Sciences, Indiana Network Science Institute, Indiana University, Bloomington, IN, 47405, USA. .,Department of Computer Science, Indiana University, Bloomington, USA. .,Department of Intelligent Systems Engineering, Indiana University, Bloomington, USA. .,Program in Neuroscience, Indiana University, Bloomington, USA. .,Program in Cognitive Science, Indiana University, Bloomington, USA. .,School of Optometry, Indiana University, Bloomington, USA.
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Kronfeld-Duenias V, Civier O, Amir O, Ezrati-Vinacour R, Ben-Shachar M. White matter pathways in persistent developmental stuttering: Lessons from tractography. JOURNAL OF FLUENCY DISORDERS 2018; 55:68-83. [PMID: 29050641 DOI: 10.1016/j.jfludis.2017.09.002] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2016] [Revised: 04/18/2017] [Accepted: 09/05/2017] [Indexed: 06/07/2023]
Abstract
PURPOSE Fluent speech production relies on the coordinated processing of multiple brain regions. This highlights the role of neural pathways that connect distinct brain regions in producing fluent speech. Here, we aim to investigate the role of the white matter pathways in persistent developmental stuttering (PDS), where speech fluency is disrupted. METHODS We use diffusion weighted imaging and tractography to compare the white matter properties between adults who do and do not stutter. We compare the diffusion properties along 18 major cerebral white matter pathways. We complement the analysis with an overview of the methodology and a roadmap of the pathways implicated in PDS according to the existing literature. RESULTS We report differences in the microstructural properties of the anterior callosum, the right inferior longitudinal fasciculus and the right cingulum in people who stutter compared with fluent controls. CONCLUSIONS Persistent developmental stuttering is consistently associated with differences in bilateral distributed networks. We review evidence showing that PDS involves differences in bilateral dorsal fronto-temporal and fronto-parietal pathways, in callosal pathways, in several motor pathways and in basal ganglia connections. This entails an important role for long range white matter pathways in this disorder. Using a wide-lens analysis, we demonstrate differences in additional, right hemispheric pathways, which go beyond the replicable findings in the literature. This suggests that the affected circuits may extend beyond the known language and motor pathways.
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Affiliation(s)
- Vered Kronfeld-Duenias
- The Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat-Gan, Israel.
| | - Oren Civier
- The Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat-Gan, Israel
| | - Ofer Amir
- The Department of Communication Disorders, Sackler Faculty of Medicine, Tel-Aviv University, Israel
| | - Ruth Ezrati-Vinacour
- The Department of Communication Disorders, Sackler Faculty of Medicine, Tel-Aviv University, Israel
| | - Michal Ben-Shachar
- The Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat-Gan, Israel; The Department of English Literature and Linguistics, Bar-Ilan University, Ramat-Gan, Israel.
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Ajina S, Bridge H. Subcortical pathways to extrastriate visual cortex underlie residual vision following bilateral damage to V1. Neuropsychologia 2018; 128:140-149. [PMID: 29320715 PMCID: PMC6562274 DOI: 10.1016/j.neuropsychologia.2018.01.007] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2017] [Revised: 12/18/2017] [Accepted: 01/05/2018] [Indexed: 11/29/2022]
Abstract
Residual vision, or blindsight, following damage to the primary visual cortex (V1) has been investigated for almost half a century. While there have been many studies of patients with unilateral damage to V1, far fewer have examined bilateral damage, mainly due to the rarity of such patients. Here we re-examine the residual visual function and underlying pathways of previously studied patient SBR who, as a young adult, suffered bilateral damage restricted to V1 which rendered him cortically blind. While earlier work compared his visual cortex to healthy, sighted participants, here we consider how his visual responses and connections compare to patients with unilateral damage to V1 in addition to sighted participants. Detection of drifting Gabor patches of different contrasts (1%, 5%, 10%, 50% and 100%) was tested in SBR and a group of eight patients with unilateral damage to V1. Performance was compared to the neural activation in motion area hMT+ measured using functional magnetic resonance imaging. Diffusion tractography was also used to determine the white matter microstructure of the visual pathways in all participants. Like the patients with unilateral damage, patient SBR showed increased % BOLD signal change to the high contrast stimuli that he could detect compared to the lower contrast stimuli that were not detectable. Diffusion tractography suggests this information is conveyed by a direct pathway between the lateral geniculate nucleus (LGN) and hMT+ since this pathway had microstructure that was comparable to the healthy control group. In contrast, the pathway between LGN and V1 had reduced integrity compared to controls. A further finding of note was that, unlike control participants, SBR showed similar patterns of contralateral and ipsilateral activity in hMT+, in addition to healthy white matter microstructure in the tract connecting hMT+ between the two hemispheres. This raises the possibility of increased connectivity between the two hemispheres in the absence of V1 input. In conclusion, the pattern of visual function and anatomy in bilateral cortical damage is comparable to that seen in a group of patients with unilateral damage. Thus, while the intact hemisphere may play a role in residual vision in patients with unilateral damage, its influence is not evident with the methodology employed here. Bilaterally hemianopic patient SBR has neural patterns like unilateral patients. hMT+ activity increases with stimulus contrast and better stimulus detection. Like in unilateral patients, the pathway between LGN and hMT+ is intact in SBR.
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Affiliation(s)
- Sara Ajina
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Holly Bridge
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK.
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Uesaki M, Takemura H, Ashida H. Computational neuroanatomy of human stratum proprium of interparietal sulcus. Brain Struct Funct 2018; 223:489-507. [PMID: 28871500 PMCID: PMC5772143 DOI: 10.1007/s00429-017-1492-1] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2017] [Accepted: 08/04/2017] [Indexed: 11/19/2022]
Abstract
Recent advances in diffusion-weighted MRI (dMRI) and tractography have enabled identification of major long-range white matter tracts in the human brain. Yet, our understanding of shorter tracts, such as those within the parietal lobe, remains limited. Over a century ago, a tract connecting the superior and inferior parts of the parietal cortex was identified in a post-mortem study: stratum proprium of interparietal sulcus (SIPS; Sachs, Das hemisphärenmark des menschlichen grosshirns. Verlag von georg thieme, Leipzig, 1892). The tract has since been replicated in another fibre dissection study (Vergani et al., Cortex 56:145-156, 2014), however, it has not been fully investigated in the living human brain and its precise anatomical properties are yet to be described. We used dMRI and tractography to identify and characterise SIPS in vivo, and explored its spatial proximity to the cortical areas associated with optic-flow processing using fMRI. SIPS was identified bilaterally in all subjects, and its anatomical position and trajectory are consistent with previous post-mortem studies. Subsequent evaluation of the tractography results using the linear fascicle evaluation and virtual lesion analysis yielded strong statistical evidence for SIPS. We also found that the SIPS endpoints are adjacent to the optic-flow selective areas. In sum, we show that SIPS is a short-range tract connecting the superior and inferior parts of the parietal cortex, wrapping around the intraparietal sulcus, and that it may be a crucial anatomy underlying optic-flow processing. In vivo identification and characterisation of SIPS will facilitate further research on SIPS in relation to cortical functions, their development, and diseases that affect them.
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Affiliation(s)
- Maiko Uesaki
- Department of Psychology, Graduate School of Letters, Kyoto University, Kyoto, Japan.
- Japan Society for the Promotion of Science, Tokyo, Japan.
- Open Innovation and Collaboration Research Organization, Ritsumeikan University, Osaka, Japan.
| | - Hiromasa Takemura
- Japan Society for the Promotion of Science, Tokyo, Japan.
- Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology, and Osaka University, Suita, Japan.
- Graduate School of Frontier Biosciences, Osaka University, Suita, Japan.
| | - Hiroshi Ashida
- Department of Psychology, Graduate School of Letters, Kyoto University, Kyoto, Japan
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Chang KH, Thomas JM, Boynton GM, Fine I. Reconstructing Tone Sequences from Functional Magnetic Resonance Imaging Blood-Oxygen Level Dependent Responses within Human Primary Auditory Cortex. Front Psychol 2017; 8:1983. [PMID: 29184522 PMCID: PMC5694557 DOI: 10.3389/fpsyg.2017.01983] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2017] [Accepted: 10/30/2017] [Indexed: 01/12/2023] Open
Abstract
Here we show that, using functional magnetic resonance imaging (fMRI) blood-oxygen level dependent (BOLD) responses in human primary auditory cortex, it is possible to reconstruct the sequence of tones that a person has been listening to over time. First, we characterized the tonotopic organization of each subject’s auditory cortex by measuring auditory responses to randomized pure tone stimuli and modeling the frequency tuning of each fMRI voxel as a Gaussian in log frequency space. Then, we tested our model by examining its ability to work in reverse. Auditory responses were re-collected in the same subjects, except this time they listened to sequences of frequencies taken from simple songs (e.g., “Somewhere Over the Rainbow”). By finding the frequency that minimized the difference between the model’s prediction of BOLD responses and actual BOLD responses, we were able to reconstruct tone sequences, with mean frequency estimation errors of half an octave or less, and little evidence of systematic biases.
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Affiliation(s)
- Kelly H Chang
- Department of Psychology, University of Washington, Seattle, WA, United States
| | - Jessica M Thomas
- Department of Psychology, University of Washington, Seattle, WA, United States
| | - Geoffrey M Boynton
- Department of Psychology, University of Washington, Seattle, WA, United States
| | - Ione Fine
- Department of Psychology, University of Washington, Seattle, WA, United States
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36
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Rae CL, Davies G, Garfinkel SN, Gabel MC, Dowell NG, Cercignani M, Seth AK, Greenwood KE, Medford N, Critchley HD. Deficits in Neurite Density Underlie White Matter Structure Abnormalities in First-Episode Psychosis. Biol Psychiatry 2017; 82:716-725. [PMID: 28359565 DOI: 10.1016/j.biopsych.2017.02.008] [Citation(s) in RCA: 46] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/03/2016] [Revised: 01/30/2017] [Accepted: 02/08/2017] [Indexed: 10/20/2022]
Abstract
BACKGROUND Structural abnormalities across multiple white matter tracts are recognized in people with early psychosis, consistent with dysconnectivity as a neuropathological account of symptom expression. We applied advanced neuroimaging techniques to characterize microstructural white matter abnormalities for a deeper understanding of the developmental etiology of psychosis. METHODS Thirty-five first-episode psychosis patients, and 19 healthy controls, participated in a quantitative neuroimaging study using neurite orientation dispersion and density imaging, a multishell diffusion-weighted magnetic resonance imaging technique that distinguishes white matter fiber arrangement and geometry from changes in neurite density. Fractional anisotropy (FA) and mean diffusivity images were also derived. Tract-based spatial statistics compared white matter structure between patients and control subjects and tested associations with age, symptom severity, and medication. RESULTS Patients with first-episode psychosis had lower regional FA in multiple commissural, corticospinal, and association tracts. These abnormalities predominantly colocalized with regions of reduced neurite density, rather than aberrant fiber bundle arrangement (orientation dispersion index). There was no direct relationship with active symptoms. FA decreased and orientation dispersion index increased with age in patients, but not control subjects, suggesting accelerated effects of white matter geometry change. CONCLUSIONS Deficits in neurite density appear fundamental to abnormalities in white matter integrity in early psychosis. In the first application of neurite orientation dispersion and density imaging in psychosis, we found that processes compromising axonal fiber number, density, and myelination, rather than processes leading to spatial disruption of fiber organization, are implicated in the etiology of psychosis. This accords with a neurodevelopmental origin of aberrant brain-wide structural connectivity predisposing individuals to psychosis.
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Affiliation(s)
- Charlotte L Rae
- Sackler Centre for Consciousness Science, University of Sussex, Falmer, Brighton; Division of Neuroscience, University of Sussex, Falmer, Brighton.
| | - Geoff Davies
- Brighton & Sussex Medical School, School of Psychology, University of Sussex, Falmer, Brighton; Sussex Partnership National Health Service Foundation Trust, United Kingdom
| | - Sarah N Garfinkel
- Sackler Centre for Consciousness Science, University of Sussex, Falmer, Brighton; Division of Neuroscience, University of Sussex, Falmer, Brighton
| | - Matt C Gabel
- Division of Neuroscience, University of Sussex, Falmer, Brighton
| | | | - Mara Cercignani
- Division of Neuroscience, University of Sussex, Falmer, Brighton
| | - Anil K Seth
- Sackler Centre for Consciousness Science, University of Sussex, Falmer, Brighton; School of Engineering & Informatics, University of Sussex, Falmer, Brighton
| | - Kathryn E Greenwood
- Brighton & Sussex Medical School, School of Psychology, University of Sussex, Falmer, Brighton; Sussex Partnership National Health Service Foundation Trust, United Kingdom
| | - Nick Medford
- Sackler Centre for Consciousness Science, University of Sussex, Falmer, Brighton; Division of Neuroscience, University of Sussex, Falmer, Brighton; Sussex Partnership National Health Service Foundation Trust, United Kingdom
| | - Hugo D Critchley
- Sackler Centre for Consciousness Science, University of Sussex, Falmer, Brighton; Division of Neuroscience, University of Sussex, Falmer, Brighton; Sussex Partnership National Health Service Foundation Trust, United Kingdom
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Abstract
The ability to map brain networks in living individuals is fundamental in efforts to chart the relation between human behavior, health and disease. Advances in network neuroscience may benefit from developing new frameworks for mapping brain connectomes. We present a framework to encode structural brain connectomes and diffusion-weighted magnetic resonance (dMRI) data using multidimensional arrays. The framework integrates the relation between connectome nodes, edges, white matter fascicles and diffusion data. We demonstrate the utility of the framework for in vivo white matter mapping and anatomical computing by evaluating 1,490 connectomes, thirteen tractography methods, and three data sets. The framework dramatically reduces storage requirements for connectome evaluation methods, with up to 40x compression factors. Evaluation of multiple, diverse datasets demonstrates the importance of spatial resolution in dMRI. We measured large increases in connectome resolution as function of data spatial resolution (up to 52%). Moreover, we demonstrate that the framework allows performing anatomical manipulations on white matter tracts for statistical inference and to study the white matter geometrical organization. Finally, we provide open-source software implementing the method and data to reproduce the results.
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Affiliation(s)
- Cesar F Caiafa
- Department of Psychological and, Brain Sciences Indiana University Bloomington, IN, 47405, USA
- Instituto Argentino de Radioastronomía (IAR), CONICET CCT, La Plata Villa Elisa, 1894, Argentina
- Facultad de Ingeniería - Departamento de Computación, UBA Buenos Aires, C1063ACV, Argentina
| | - Franco Pestilli
- Department of Psychological and, Brain Sciences Indiana University Bloomington, IN, 47405, USA.
- Department of Intelligent Systems, Engineering Indiana University Bloomington, IN, 47405, USA.
- Department of Computer Science, Indiana University Bloomington, IN, 47405, USA.
- Program in Neuroscience Indiana University Bloomington, IN, 47405, USA.
- Program in Cognitive Science Indiana University Bloomington, IN, 47405, USA.
- School of Optometry Indiana University Bloomington, IN, 47405, USA.
- Indiana Network Science Institute Indiana University Bloomington, IN, 47405, USA.
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Ferizi U, Scherrer B, Schneider T, Alipoor M, Eufracio O, Fick RH, Deriche R, Nilsson M, Loya‐Olivas AK, Rivera M, Poot DH, Ramirez‐Manzanares A, Marroquin JL, Rokem A, Pötter C, Dougherty RF, Sakaie K, Wheeler‐Kingshott C, Warfield SK, Witzel T, Wald LL, Raya JG, Alexander DC. Diffusion MRI microstructure models with in vivo human brain Connectome data: results from a multi-group comparison. NMR IN BIOMEDICINE 2017; 30:e3734. [PMID: 28643354 PMCID: PMC5563694 DOI: 10.1002/nbm.3734] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/06/2016] [Revised: 03/01/2017] [Accepted: 03/27/2017] [Indexed: 05/16/2023]
Abstract
A large number of mathematical models have been proposed to describe the measured signal in diffusion-weighted (DW) magnetic resonance imaging (MRI). However, model comparison to date focuses only on specific subclasses, e.g. compartment models or signal models, and little or no information is available in the literature on how performance varies among the different types of models. To address this deficiency, we organized the 'White Matter Modeling Challenge' during the International Symposium on Biomedical Imaging (ISBI) 2015 conference. This competition aimed to compare a range of different kinds of models in their ability to explain a large range of measurable in vivo DW human brain data. Specifically, we assessed the ability of models to predict the DW signal accurately for new diffusion gradients and b values. We did not evaluate the accuracy of estimated model parameters, as a ground truth is hard to obtain. We used the Connectome scanner at the Massachusetts General Hospital, using gradient strengths of up to 300 mT/m and a broad set of diffusion times. We focused on assessing the DW signal prediction in two regions: the genu in the corpus callosum, where the fibres are relatively straight and parallel, and the fornix, where the configuration of fibres is more complex. The challenge participants had access to three-quarters of the dataset and their models were ranked on their ability to predict the remaining unseen quarter of the data. The challenge provided a unique opportunity for a quantitative comparison of diverse methods from multiple groups worldwide. The comparison of the challenge entries reveals interesting trends that could potentially influence the next generation of diffusion-based quantitative MRI techniques. The first is that signal models do not necessarily outperform tissue models; in fact, of those tested, tissue models rank highest on average. The second is that assuming a non-Gaussian (rather than purely Gaussian) noise model provides little improvement in prediction of unseen data, although it is possible that this may still have a beneficial effect on estimated parameter values. The third is that preprocessing the training data, here by omitting signal outliers, and using signal-predicting strategies, such as bootstrapping or cross-validation, could benefit the model fitting. The analysis in this study provides a benchmark for other models and the data remain available to build up a more complete comparison in the future.
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Affiliation(s)
- Uran Ferizi
- Centre for Medical Image ComputingDepartment of Computer Science, University College LondonUK
- Department of RadiologyNew York University School of MedicineUSA
- Department of Neuroinflammation, Institute of NeurologyUniversity College LondonUK
| | - Benoit Scherrer
- Computational Radiology Laboratory, Boston Children's Hosp.Harvard UniversityUSA
| | - Torben Schneider
- Department of Neuroinflammation, Institute of NeurologyUniversity College LondonUK
- Philips HealthcareGuildfordSurreyUK
| | | | - Odin Eufracio
- Centro de Investigacion en Matematicas ACGuanajuatoMexico
| | | | - Rachid Deriche
- Athena Project‐TeamINRIA Sophia Antipolis ‐ MéditerranéeFrance
| | | | | | - Mariano Rivera
- Centro de Investigacion en Matematicas ACGuanajuatoMexico
| | - Dirk H.J. Poot
- Erasmus Medical Center and Delft University of Technologythe Netherlands
| | | | | | - Ariel Rokem
- eScience InstituteUniversity of WashingtonUSA
- Center for Cognitive and Neurobiological ImagingStanford UniversityUSA
| | - Christian Pötter
- Center for Cognitive and Neurobiological ImagingStanford UniversityUSA
| | | | - Ken Sakaie
- Imaging InstituteThe Cleveland ClinicClevelandUSA
| | | | - Simon K. Warfield
- Computational Radiology Laboratory, Boston Children's Hosp.Harvard UniversityUSA
| | - Thomas Witzel
- A.A. Martinos Center for Biomedical Imaging, MGHHarvard UniversityUSA
| | - Lawrence L. Wald
- A.A. Martinos Center for Biomedical Imaging, MGHHarvard UniversityUSA
| | - José G. Raya
- Department of RadiologyNew York University School of MedicineUSA
| | - Daniel C. Alexander
- Centre for Medical Image ComputingDepartment of Computer Science, University College LondonUK
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Campbell JSW, Leppert IR, Narayanan S, Boudreau M, Duval T, Cohen-Adad J, Pike GB, Stikov N. Promise and pitfalls of g-ratio estimation with MRI. Neuroimage 2017; 182:80-96. [PMID: 28822750 DOI: 10.1016/j.neuroimage.2017.08.038] [Citation(s) in RCA: 82] [Impact Index Per Article: 11.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2017] [Revised: 07/28/2017] [Accepted: 08/12/2017] [Indexed: 12/13/2022] Open
Abstract
The fiber g-ratio is the ratio of the inner to the outer diameter of the myelin sheath of a myelinated axon. It has a limited dynamic range in healthy white matter, as it is optimized for speed of signal conduction, cellular energetics, and spatial constraints. In vivo imaging of the g-ratio in health and disease would greatly increase our knowledge of the nervous system and our ability to diagnose, monitor, and treat disease. MRI based g-ratio imaging was first conceived in 2011, and expanded to be feasible in full brain white matter with preliminary results in 2013. This manuscript reviews the growing g-ratio imaging literature and speculates on future applications. It details the methodology for imaging the g-ratio with MRI, and describes the known pitfalls and challenges in doing so.
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Affiliation(s)
- Jennifer S W Campbell
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada; NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montréal, Montréal, QC, Canada.
| | - Ilana R Leppert
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Sridar Narayanan
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Mathieu Boudreau
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Tanguy Duval
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montréal, Montréal, QC, Canada
| | - Julien Cohen-Adad
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montréal, Montréal, QC, Canada; Functional Neuroimaging Unit, CRIUGM, Université de Montréal, Montréal, QC, Canada
| | | | - Nikola Stikov
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montréal, Montréal, QC, Canada; Montreal Heart Institute, Université de Montréal, Montréal, QC, Canada
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Ware JB, Hart T, Whyte J, Rabinowitz A, Detre JA, Kim J. Inter-Subject Variability of Axonal Injury in Diffuse Traumatic Brain Injury. J Neurotrauma 2017; 34:2243-2253. [PMID: 28314375 DOI: 10.1089/neu.2016.4817] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023] Open
Abstract
Traumatic brain injury (TBI) is a leading cause of cognitive morbidity worldwide for which reliable biomarkers are needed. Diffusion tensor imaging (DTI) is a promising biomarker of traumatic axonal injury (TAI); however, existing studies have been limited by a primary reliance on group-level analytic methods not well suited to account for inter-subject variability. In this study, 42 adults with TBI of at least moderate severity were examined 3 months following injury and compared with 35 healthy controls. DTI data were used for both traditional group-level comparison and subject-specific analysis using the distribution-corrected Z-score (DisCo-Z) approach. Inter-subject variation in TAI was assessed in a threshold-invariant manner using a threshold-weighted overlap map derived from subject-specific analysis. Receiver operator curve analysis was used to examine the ability of subject-specific DTI analysis to identify TBI subjects with significantly impaired processing speed in comparison with region of interest-based fractional anisotropy (FA) measurements and clinical characteristics. Traditional group-wise analysis demonstrated widespread reductions of white matter FA within the TBI group (voxel-wise p < 0.05, corrected), despite relatively low consistency of subject-level effects secondary to widespread variation in the spatial distribution of TAI. Subject-specific mapping of TAI with the DisCo-Z approach was the best predictor of impaired processing speed, achieving high classification accuracy (area under the curve [AUC] = 0.94). In moderate-to-severe TBI, there is substantial inter-subject variation in TAI, with extent strongly correlated to post-traumatic deficits in processing speed. Significant group-level effects do not necessarily represent consistent effects at the individual level. Better accounting for inter-subject variability in neurobiological manifestations of TBI may substantially improve the ability to detect and classify patterns of injury.
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Affiliation(s)
- Jeffrey B Ware
- 1 Department of Radiology, Hospital of the University of Pennsylvania , Philadelphia, Pennsylvania
| | - Tessa Hart
- 2 Moss Rehabilitation Research Institute , Philadelphia, Pennsylvania
| | - John Whyte
- 2 Moss Rehabilitation Research Institute , Philadelphia, Pennsylvania
| | - Amanda Rabinowitz
- 2 Moss Rehabilitation Research Institute , Philadelphia, Pennsylvania
| | - John A Detre
- 3 Department of Neurology, Hospital of the University of Pennsylvania , Philadelphia, Pennsylvania
| | - Junghoon Kim
- 2 Moss Rehabilitation Research Institute , Philadelphia, Pennsylvania.,4 Department of Physiology, Pharmacology, and Neuroscience, City University of New York School of Medicine , New York, New York
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Nir TM, Jahanshad N, Villalon-Reina JE, Isaev D, Zavaliangos-Petropulu A, Zhan L, Leow AD, Jack CR, Weiner MW, Thompson PM. Fractional anisotropy derived from the diffusion tensor distribution function boosts power to detect Alzheimer's disease deficits. Magn Reson Med 2017; 78:2322-2333. [PMID: 28266059 DOI: 10.1002/mrm.26623] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2016] [Revised: 11/21/2016] [Accepted: 01/08/2017] [Indexed: 12/30/2022]
Abstract
PURPOSE In diffusion MRI (dMRI), fractional anisotropy derived from the single-tensor model (FADTI ) is the most widely used metric to characterize white matter (WM) microarchitecture, despite known limitations in regions with crossing fibers. Due to time constraints when scanning patients in clinical settings, high angular resolution diffusion imaging acquisition protocols, often used to overcome these limitations, are still rare in clinical population studies. However, the tensor distribution function (TDF) may be used to model multiple underlying fibers by representing the diffusion profile as a probabilistic mixture of tensors. METHODS We compared the ability of standard FADTI and TDF-derived FA (FATDF ), calculated from a range of dMRI angular resolutions (41, 30, 15, and 7 gradient directions), to profile WM deficits in 251 individuals from the Alzheimer's Disease Neuroimaging Initiative and to detect associations with 1) Alzheimer's disease diagnosis, 2) Clinical Dementia Rating scores, and 3) average hippocampal volume. RESULTS Across angular resolutions and statistical tests, FATDF showed larger effect sizes than FADTI , particularly in regions preferentially affected by Alzheimer's disease, and was less susceptible to crossing fiber anomalies. CONCLUSION The TDF "corrected" form of FA may be a more sensitive and accurate alternative to the commonly used FADTI , even in clinical quality dMRI data. Magn Reson Med 78:2322-2333, 2017. © 2017 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Talia M Nir
- Imaging Genetics Center, University of Southern California, Marina del Rey, California, USA
| | - Neda Jahanshad
- Imaging Genetics Center, University of Southern California, Marina del Rey, California, USA
| | - Julio E Villalon-Reina
- Imaging Genetics Center, University of Southern California, Marina del Rey, California, USA
| | - Dmitry Isaev
- Imaging Genetics Center, University of Southern California, Marina del Rey, California, USA
| | | | - Liang Zhan
- Computer Engineering Program, University of Wisconsin-Stout, Menomonie, Wisconsin, USA
| | - Alex D Leow
- Department of Psychiatry and Bioengineering, University of Illinois, Chicago, Illinois, USA
| | - Clifford R Jack
- Department of Radiology, Mayo Clinic and Foundation, Rochester, Minnesota, USA
| | - Michael W Weiner
- Department of Radiology, University of California San Francisco School of Medicine, San Francisco, California, USA
| | - Paul M Thompson
- Imaging Genetics Center, University of Southern California, Marina del Rey, California, USA
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Rokem A, Takemura H, Bock AS, Scherf KS, Behrmann M, Wandell BA, Fine I, Bridge H, Pestilli F. The visual white matter: The application of diffusion MRI and fiber tractography to vision science. J Vis 2017; 17:4. [PMID: 28196374 PMCID: PMC5317208 DOI: 10.1167/17.2.4] [Citation(s) in RCA: 60] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2016] [Accepted: 12/12/2016] [Indexed: 12/19/2022] Open
Abstract
Visual neuroscience has traditionally focused much of its attention on understanding the response properties of single neurons or neuronal ensembles. The visual white matter and the long-range neuronal connections it supports are fundamental in establishing such neuronal response properties and visual function. This review article provides an introduction to measurements and methods to study the human visual white matter using diffusion MRI. These methods allow us to measure the microstructural and macrostructural properties of the white matter in living human individuals; they allow us to trace long-range connections between neurons in different parts of the visual system and to measure the biophysical properties of these connections. We also review a range of findings from recent studies on connections between different visual field maps, the effects of visual impairment on the white matter, and the properties underlying networks that process visual information supporting visual face recognition. Finally, we discuss a few promising directions for future studies. These include new methods for analysis of MRI data, open datasets that are becoming available to study brain connectivity and white matter properties, and open source software for the analysis of these data.
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Affiliation(s)
- Ariel Rokem
- The University of Washington eScience Institute, Seattle, WA, ://arokem.org
| | - Hiromasa Takemura
- Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology, and Osaka University, Suita-shi, JapanGraduate School of Frontier Biosciences, Osaka University, Suita-shi,
| | | | | | | | | | - Ione Fine
- University of Washington, Seattle, WA,
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Xu T, Feng Y, Wu Y, Zeng Q, Zhang J, He J, Zhuge Q. A Novel Richardson-Lucy Model with Dictionary Basis and Spatial Regularization for Isolating Isotropic Signals. PLoS One 2017; 12:e0168864. [PMID: 28081561 PMCID: PMC5233428 DOI: 10.1371/journal.pone.0168864] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2016] [Accepted: 12/07/2016] [Indexed: 11/27/2022] Open
Abstract
Diffusion-weighted magnetic resonance imaging is a non-invasive imaging method that has been increasingly used in neuroscience imaging over the last decade. Partial volume effects (PVEs) exist in sampling signal for many physical and actual reasons, which lead to inaccurate fiber imaging. We overcome the influence of PVEs by separating isotropic signal from diffusion-weighted signal, which can provide more accurate estimation of fiber orientations. In this work, we use a novel response function (RF) and the correspondent fiber orientation distribution function (fODF) to construct different signal models, in which case the fODF is represented using dictionary basis function. We then put forward a new index Piso, which is a part of fODF to quantify white and gray matter. The classic Richardson-Lucy (RL) model is usually used in the field of digital image processing to solve the problem of spherical deconvolution caused by highly ill-posed least-squares algorithm. In this case, we propose an innovative model integrating RL model with spatial regularization to settle the suggested double-models, which improve noise resistance and accuracy of imaging. Experimental results of simulated and real data show that the proposal method, which we call iRL, can robustly reconstruct a more accurate fODF and the quantitative index Piso performs better than fractional anisotropy and general fractional anisotropy.
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Affiliation(s)
- Tiantian Xu
- Institute of Information Processing and Automation, Zhejiang University of Technology, Hangzhou, Zhejiang, China
| | - Yuanjing Feng
- Institute of Information Processing and Automation, Zhejiang University of Technology, Hangzhou, Zhejiang, China
| | - Ye Wu
- Institute of Information Processing and Automation, Zhejiang University of Technology, Hangzhou, Zhejiang, China
| | - Qingrun Zeng
- Institute of Information Processing and Automation, Zhejiang University of Technology, Hangzhou, Zhejiang, China
| | - Jun Zhang
- Institute of Information Processing and Automation, Zhejiang University of Technology, Hangzhou, Zhejiang, China
| | - Jianzhong He
- Institute of Information Processing and Automation, Zhejiang University of Technology, Hangzhou, Zhejiang, China
| | - Qichuan Zhuge
- Zhejiang Provincial Key Laboratory of Aging and Neurological Disorder Research, Wenzhou Medical University, Wenzhou, Zhejiang, China
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Raffelt DA, Tournier JD, Smith RE, Vaughan DN, Jackson G, Ridgway GR, Connelly A. Investigating white matter fibre density and morphology using fixel-based analysis. Neuroimage 2016; 144:58-73. [PMID: 27639350 PMCID: PMC5182031 DOI: 10.1016/j.neuroimage.2016.09.029] [Citation(s) in RCA: 387] [Impact Index Per Article: 48.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2016] [Revised: 09/05/2016] [Accepted: 09/13/2016] [Indexed: 12/13/2022] Open
Abstract
Voxel-based analysis of diffusion MRI data is increasingly popular. However, most white matter voxels contain contributions from multiple fibre populations (often referred to as crossing fibres), and therefore voxel-averaged quantitative measures (e.g. fractional anisotropy) are not fibre-specific and have poor interpretability. Using higher-order diffusion models, parameters related to fibre density can be extracted for individual fibre populations within each voxel ('fixels'), and recent advances in statistics enable the multi-subject analysis of such data. However, investigating within-voxel microscopic fibre density alone does not account for macroscopic differences in the white matter morphology (e.g. the calibre of a fibre bundle). In this work, we introduce a novel method to investigate the latter, which we call fixel-based morphometry (FBM). To obtain a more complete measure related to the total number of white matter axons, information from both within-voxel microscopic fibre density and macroscopic morphology must be combined. We therefore present the FBM method as an integral piece within a comprehensive fixel-based analysis framework to investigate measures of fibre density, fibre-bundle morphology (cross-section), and a combined measure of fibre density and cross-section. We performed simulations to demonstrate the proposed measures using various transformations of a numerical fibre bundle phantom. Finally, we provide an example of such an analysis by comparing a clinical patient group to a healthy control group, which demonstrates that all three measures provide distinct and complementary information. By capturing information from both sources, the combined fibre density and cross-section measure is likely to be more sensitive to certain pathologies and more directly interpretable.
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Affiliation(s)
- David A Raffelt
- Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia.
| | - J-Donald Tournier
- Department of Biomedical Engineering, Division of Imaging Sciences & Biomedical Engineering, King's College London, London, UK; Centre for the Developing Brain, King's College London, London, UK
| | - Robert E Smith
- Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia
| | - David N Vaughan
- Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia; Florey Department of Neuroscience and Mental Health, University of Melbourne, Melbourne, Victoria, Australia; Department of Neurology, Austin Health and Northern Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Graeme Jackson
- Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia; Florey Department of Neuroscience and Mental Health, University of Melbourne, Melbourne, Victoria, Australia; Department of Medicine, Austin Health and Northern Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Gerard R Ridgway
- FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, UK; Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, London, UK
| | - Alan Connelly
- Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia; Florey Department of Neuroscience and Mental Health, University of Melbourne, Melbourne, Victoria, Australia; Department of Neurology, Austin Health and Northern Health, University of Melbourne, Melbourne, Victoria, Australia
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Takemura H, Rokem A, Winawer J, Yeatman JD, Wandell BA, Pestilli F. A Major Human White Matter Pathway Between Dorsal and Ventral Visual Cortex. Cereb Cortex 2016; 26:2205-2214. [PMID: 25828567 PMCID: PMC4830295 DOI: 10.1093/cercor/bhv064] [Citation(s) in RCA: 107] [Impact Index Per Article: 13.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/28/2023] Open
Abstract
Human visual cortex comprises many visual field maps organized into clusters. A standard organization separates visual maps into 2 distinct clusters within ventral and dorsal cortex. We combined fMRI, diffusion MRI, and fiber tractography to identify a major white matter pathway, the vertical occipital fasciculus (VOF), connecting maps within the dorsal and ventral visual cortex. We use a model-based method to assess the statistical evidence supporting several aspects of the VOF wiring pattern. There is strong evidence supporting the hypothesis that dorsal and ventral visual maps communicate through the VOF. The cortical projection zones of the VOF suggest that human ventral (hV4/VO-1) and dorsal (V3A/B) maps exchange substantial information. The VOF appears to be crucial for transmitting signals between regions that encode object properties including form, identity, and color and regions that map spatial information.
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Affiliation(s)
| | - Ariel Rokem
- Department of Psychology, Stanford University, Stanford, CA, USA
| | - Jonathan Winawer
- Department of Psychology, New York University, New York, NY, USA
| | - Jason D. Yeatman
- Department of Psychology, Stanford University, Stanford, CA, USA
- Institute for Learning and Brain Science (ILABS), University of Washington, Seattle, WA, USA
| | - Brian A. Wandell
- Department of Psychology, Stanford University, Stanford, CA, USA
| | - Franco Pestilli
- Department of Psychology, Stanford University, Stanford, CA, USA
- Department of Psychological and Brain Sciences, Programs in Neuroscience and Cognitive Science, Indiana University, Bloomington, IN, USA
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Abstract
Progress in magnetic resonance imaging (MRI) now makes it possible to identify the major white matter tracts in the living human brain. These tracts are important because they carry many of the signals communicated between different brain regions. MRI methods coupled with biophysical modeling can measure the tissue properties and structural features of the tracts that impact our ability to think, feel, and perceive. This review describes the fundamental ideas of the MRI methods used to identify the major white matter tracts in the living human brain.
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Affiliation(s)
- Brian A Wandell
- Department of Psychology and Stanford Neurosciences Institute, Stanford University, Stanford, California 94305;
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Takemura H, Caiafa CF, Wandell BA, Pestilli F. Ensemble Tractography. PLoS Comput Biol 2016; 12:e1004692. [PMID: 26845558 PMCID: PMC4742469 DOI: 10.1371/journal.pcbi.1004692] [Citation(s) in RCA: 76] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2015] [Accepted: 12/03/2015] [Indexed: 01/02/2023] Open
Abstract
Tractography uses diffusion MRI to estimate the trajectory and cortical projection zones of white matter fascicles in the living human brain. There are many different tractography algorithms and each requires the user to set several parameters, such as curvature threshold. Choosing a single algorithm with specific parameters poses two challenges. First, different algorithms and parameter values produce different results. Second, the optimal choice of algorithm and parameter value may differ between different white matter regions or different fascicles, subjects, and acquisition parameters. We propose using ensemble methods to reduce algorithm and parameter dependencies. To do so we separate the processes of fascicle generation and evaluation. Specifically, we analyze the value of creating optimized connectomes by systematically combining candidate streamlines from an ensemble of algorithms (deterministic and probabilistic) and systematically varying parameters (curvature and stopping criterion). The ensemble approach leads to optimized connectomes that provide better cross-validated prediction error of the diffusion MRI data than optimized connectomes generated using a single-algorithm or parameter set. Furthermore, the ensemble approach produces connectomes that contain both short- and long-range fascicles, whereas single-parameter connectomes are biased towards one or the other. In summary, a systematic ensemble tractography approach can produce connectomes that are superior to standard single parameter estimates both for predicting the diffusion measurements and estimating white matter fascicles. Diffusion MRI and tractography opened a new avenue for studying white matter fascicles and their tissue properties in the living human brain. There are many different tractography methods, and each requires the user to set several parameters. A limitation of tractography is that the results depend on the selection of algorithms and parameters. Here, we analyze an ensemble method, Ensemble Tractography (ET), that reduces the effect of algorithm and parameter selection. ET creates a large set of candidate streamlines using an ensemble of algorithms and parameter values and then selects the streamlines with strong support from the data using a global fascicle evaluation method. Compared to single parameter connectomes, ET connectomes predict diffusion MRI signals better and cover a wider range of white matter volume. Importantly, ET connectomes include both short- and long-association fascicles, which are not typically found together in single-parameter connectomes.
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Affiliation(s)
- Hiromasa Takemura
- Center for Information and Neural Networks (CiNet), National Institute of Information and Communications Technology, and Osaka University, Suita, Japan
- The Japan Society for the Promotion of Science, Tokyo, Japan
- Graduate School of Frontier Biosciences, Osaka University, Suita, Japan
- Department of Psychology, Stanford University, Stanford, California, United States of America
- * E-mail: (HT); (FP)
| | - Cesar F. Caiafa
- Instituto Argentino de Radioastronomía (IAR)—CCT La Plata—CONICET, Villa Elisa, Buenos Aires, Argentina
| | - Brian A. Wandell
- Department of Psychology, Stanford University, Stanford, California, United States of America
| | - Franco Pestilli
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, Indiana, United States of America
- Programs in Neuroscience and Cognitive Science, Indiana University Network Science Institute, Indiana University, Bloomington, Indiana, United States of America
- * E-mail: (HT); (FP)
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Ajina S, Pestilli F, Rokem A, Kennard C, Bridge H. Human blindsight is mediated by an intact geniculo-extrastriate pathway. eLife 2015; 4. [PMID: 26485034 PMCID: PMC4641435 DOI: 10.7554/elife.08935] [Citation(s) in RCA: 90] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2015] [Accepted: 10/20/2015] [Indexed: 11/30/2022] Open
Abstract
Although damage to the primary visual cortex (V1) causes hemianopia, many patients retain some residual vision; known as blindsight. We show that blindsight may be facilitated by an intact white-matter pathway between the lateral geniculate nucleus and motion area hMT+. Visual psychophysics, diffusion-weighted magnetic resonance imaging and fibre tractography were applied in 17 patients with V1 damage acquired during adulthood and 9 age-matched controls. Individuals with V1 damage were subdivided into blindsight positive (preserved residual vision) and negative (no residual vision) according to psychophysical performance. All blindsight positive individuals showed intact geniculo-hMT+ pathways, while this pathway was significantly impaired or not measurable in blindsight negative individuals. Two white matter pathways previously implicated in blindsight: (i) superior colliculus to hMT+ and (ii) between hMT+ in each hemisphere were not consistently present in blindsight positive cases. Understanding the visual pathways crucial for residual vision may direct future rehabilitation strategies for hemianopia patients. DOI:http://dx.doi.org/10.7554/eLife.08935.001 Visual information from our eyes projects to a region at the back of the brain called the primary visual cortex, which is where the information is processed to allow us to see the world around us. If a person suffers a stroke that affects this primary visual cortex, he or she can become blind on one side. However, some people can still detect images within this ‘blind’ area, even if they are not consciously aware of it. This phenomenon is known as ‘blindsight’, but it remains unclear which pathways and structures in the brain might allow this information to be detected. Ajina et al. have now examined the brains of a large group of patients with damage to the visual cortex. The results for the patients with blindsight were compared to those without, and to a group of sighted control participants. This analysis identified a pathway that seems to underlie blindsight. This pathway (which runs between an area of the brain called the lateral geniculate nucleus and another called the motion area hMT+) was present in all patients with blindsight, but was missing or disrupted in those patients without blindsight. Ajina et al. then examined other pathways that had previously been suggested to support blindsight and revealed that they were unlikely to do so. This is because the suggested connections were not identifiable in all patients with blindsight, and were often intact in those patients without blindsight. So far, this work has addressed the structure of the pathways rather than their activity. Future work will attempt to determine whether it is possible to strengthen such pathways to improve visual ability. DOI:http://dx.doi.org/10.7554/eLife.08935.002
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Affiliation(s)
- Sara Ajina
- Oxford Centre for Functional MRI of the Brain, University of Oxford, Oxford, United Kingdom.,Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Franco Pestilli
- Department of Psychological and Brain Sciences, Programs in Neuroscience and Cognitive Science, Indiana University Network Science Institute, Indiana University, Bloomington, United States
| | - Ariel Rokem
- Department of Psychology, Stanford University, Stanford, United States.,eScience Institute, University of Washington, Seattle, United States
| | - Christopher Kennard
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Holly Bridge
- Oxford Centre for Functional MRI of the Brain, University of Oxford, Oxford, United Kingdom.,Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
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