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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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2
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Xue T, Zhang F, Zekelman LR, Zhang C, Chen Y, Cetin-Karayumak S, Pieper S, Wells WM, Rathi Y, Makris N, Cai W, O'Donnell LJ. TractoSCR: a novel supervised contrastive regression framework for prediction of neurocognitive measures using multi-site harmonized diffusion MRI tractography. Front Neurosci 2024; 18:1411797. [PMID: 38988766 PMCID: PMC11233814 DOI: 10.3389/fnins.2024.1411797] [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: 04/03/2024] [Accepted: 06/10/2024] [Indexed: 07/12/2024] Open
Abstract
Neuroimaging-based prediction of neurocognitive measures is valuable for studying how the brain's structure relates to cognitive function. However, the accuracy of prediction using popular linear regression models is relatively low. We propose a novel deep regression method, namely TractoSCR, that allows full supervision for contrastive learning in regression tasks using diffusion MRI tractography. TractoSCR performs supervised contrastive learning by using the absolute difference between continuous regression labels (i.e., neurocognitive scores) to determine positive and negative pairs. We apply TractoSCR to analyze a large-scale dataset including multi-site harmonized diffusion MRI and neurocognitive data from 8,735 participants in the Adolescent Brain Cognitive Development (ABCD) Study. We extract white matter microstructural measures using a fine parcellation of white matter tractography into fiber clusters. Using these measures, we predict three scores related to domains of higher-order cognition (general cognitive ability, executive function, and learning/memory). To identify important fiber clusters for prediction of these neurocognitive scores, we propose a permutation feature importance method for high-dimensional data. We find that TractoSCR obtains significantly higher accuracy of neurocognitive score prediction compared to other state-of-the-art methods. We find that the most predictive fiber clusters are predominantly located within the superficial white matter and projection tracts, particularly the superficial frontal white matter and striato-frontal connections. Overall, our results demonstrate the utility of contrastive representation learning methods for regression, and in particular for improving neuroimaging-based prediction of higher-order cognitive abilities. Our code will be available at: https://github.com/SlicerDMRI/TractoSCR.
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Affiliation(s)
- Tengfei Xue
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
- School of Computer Science, University of Sydney, Sydney, NSW, Australia
| | - Fan Zhang
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Leo R. Zekelman
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Chaoyi Zhang
- School of Computer Science, University of Sydney, Sydney, NSW, Australia
| | - Yuqian Chen
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | | | - Steve Pieper
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - William M. Wells
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Yogesh Rathi
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Nikos Makris
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Weidong Cai
- School of Computer Science, University of Sydney, Sydney, NSW, Australia
| | - Lauren J. O'Donnell
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
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3
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Kruper J, Hagen MP, Rheault F, Crane I, Gilmore A, Narayan M, Motwani K, Lila E, Rorden C, Yeatman JD, Rokem A. Tractometry of the Human Connectome Project: resources and insights. Front Neurosci 2024; 18:1389680. [PMID: 38933816 PMCID: PMC11199395 DOI: 10.3389/fnins.2024.1389680] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2024] [Accepted: 05/15/2024] [Indexed: 06/28/2024] Open
Abstract
Introduction The Human Connectome Project (HCP) has become a keystone dataset in human neuroscience, with a plethora of important applications in advancing brain imaging methods and an understanding of the human brain. We focused on tractometry of HCP diffusion-weighted MRI (dMRI) data. Methods We used an open-source software library (pyAFQ; https://yeatmanlab.github.io/pyAFQ) to perform probabilistic tractography and delineate the major white matter pathways in the HCP subjects that have a complete dMRI acquisition (n = 1,041). We used diffusion kurtosis imaging (DKI) to model white matter microstructure in each voxel of the white matter, and extracted tract profiles of DKI-derived tissue properties along the length of the tracts. We explored the empirical properties of the data: first, we assessed the heritability of DKI tissue properties using the known genetic linkage of the large number of twin pairs sampled in HCP. Second, we tested the ability of tractometry to serve as the basis for predictive models of individual characteristics (e.g., age, crystallized/fluid intelligence, reading ability, etc.), compared to local connectome features. To facilitate the exploration of the dataset we created a new web-based visualization tool and use this tool to visualize the data in the HCP tractometry dataset. Finally, we used the HCP dataset as a test-bed for a new technological innovation: the TRX file-format for representation of dMRI-based streamlines. Results We released the processing outputs and tract profiles as a publicly available data resource through the AWS Open Data program's Open Neurodata repository. We found heritability as high as 0.9 for DKI-based metrics in some brain pathways. We also found that tractometry extracts as much useful information about individual differences as the local connectome method. We released a new web-based visualization tool for tractometry-"Tractoscope" (https://nrdg.github.io/tractoscope). We found that the TRX files require considerably less disk space-a crucial attribute for large datasets like HCP. In addition, TRX incorporates a specification for grouping streamlines, further simplifying tractometry analysis.
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Affiliation(s)
- John Kruper
- Department of Psychology, University of Washington, Seattle, WA, United States
| | - McKenzie P. Hagen
- Department of Psychology, University of Washington, Seattle, WA, United States
| | - François Rheault
- Department of Computer Science, Universitè de Sherbrooke, Sherbrooke, QC, Canada
| | - Isaac Crane
- Department of Psychology, University of Chicago, Chicago, IL, United States
| | - Asa Gilmore
- Department of Psychology, University of Washington, Seattle, WA, United States
| | - Manjari Narayan
- Graduate School of Education, Stanford University, Stanford, CA, United States
| | - Keshav Motwani
- Department of Biostatistics, University of Washington, Seattle, WA, United States
| | - Eardi Lila
- Department of Biostatistics, University of Washington, Seattle, WA, United States
| | - Chris Rorden
- Department of Psychology, University of South Carolina, Columbia, SC, United States
| | - Jason D. Yeatman
- Graduate School of Education, Stanford University, Stanford, CA, United States
| | - Ariel Rokem
- Department of Psychology, University of Washington, Seattle, WA, United States
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4
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Roy E, Van Rinsveld A, Nedelec P, Richie-Halford A, Rauschecker AM, Sugrue LP, Rokem A, McCandliss BD, Yeatman JD. Differences in educational opportunity predict white matter development. Dev Cogn Neurosci 2024; 67:101386. [PMID: 38676989 DOI: 10.1016/j.dcn.2024.101386] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2023] [Revised: 02/05/2024] [Accepted: 04/15/2024] [Indexed: 04/29/2024] Open
Abstract
Coarse measures of socioeconomic status, such as parental income or parental education, have been linked to differences in white matter development. However, these measures do not provide insight into specific aspects of an individual's environment and how they relate to brain development. On the other hand, educational intervention studies have shown that changes in an individual's educational context can drive measurable changes in their white matter. These studies, however, rarely consider socioeconomic factors in their results. In the present study, we examined the unique relationship between educational opportunity and white matter development, when controlling other known socioeconomic factors. To explore this question, we leveraged the rich demographic and neuroimaging data available in the ABCD study, as well the unique data-crosswalk between ABCD and the Stanford Education Data Archive (SEDA). We find that educational opportunity is related to accelerated white matter development, even when accounting for other socioeconomic factors, and that this relationship is most pronounced in white matter tracts associated with academic skills. These results suggest that the school a child attends has a measurable relationship with brain development for years to come.
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Affiliation(s)
- Ethan Roy
- Graduate School of Education, Stanford University, Stanford, CA, USA.
| | | | - Pierre Nedelec
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Adam Richie-Halford
- Graduate School of Education, Stanford University, Stanford, CA, USA; Division of Developmental-Behavioral Pediatrics, Stanford University, Stanford, CA, USA
| | - Andreas M Rauschecker
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Leo P Sugrue
- Department of Radiology and Biomedical Imaging, University of California San Francisco, San Francisco, CA, USA
| | - Ariel Rokem
- Department of Psychology and eScience Institute, University of Washington, Seattle, WA, USA
| | | | - Jason D Yeatman
- Graduate School of Education, Stanford University, Stanford, CA, USA; Division of Developmental-Behavioral Pediatrics, Stanford University, Stanford, CA, USA
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5
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Tayebi M, Kwon E, Maller J, McGeown J, Scadeng M, Qiao M, Wang A, Nielsen P, Fernandez J, Holdsworth S, Shim V. Integration of diffusion tensor imaging parameters with mesh morphing for in-depth analysis of brain white matter fibre tracts. Brain Commun 2024; 6:fcae027. [PMID: 38638147 PMCID: PMC11024816 DOI: 10.1093/braincomms/fcae027] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/07/2023] [Revised: 10/06/2023] [Accepted: 02/07/2024] [Indexed: 04/20/2024] Open
Abstract
Averaging is commonly used for data reduction/aggregation to analyse high-dimensional MRI data, but this often leads to information loss. To address this issue, we developed a novel technique that integrates diffusion tensor metrics along the whole volume of the fibre bundle using a 3D mesh-morphing technique coupled with principal component analysis for delineating case and control groups. Brain diffusion tensor MRI scans of high school rugby union players (n = 30, age 16-18) were acquired on a 3 T MRI before and after the sports season. A non-contact sport athlete cohort with matching demographics (n = 12) was also scanned. The utility of the new method in detecting differences in diffusion tensor metrics of the right corticospinal tract between contact and non-contact sport athletes was explored. The first step was to run automated tractography on each subject's native space. A template model of the right corticospinal tract was generated and morphed into each subject's native shape and space, matching individual geometry and diffusion metric distributions with minimal information loss. The common dimension of the 20 480 diffusion metrics allowed further data aggregation using principal component analysis to cluster the case and control groups as well as visualization of diffusion metric statistics (mean, ±2 SD). Our approach of analysing the whole volume of white matter tracts led to a clear delineation between the rugby and control cohort, which was not possible with the traditional averaging method. Moreover, our approach accounts for the individual subject's variations in diffusion tensor metrics to visualize group differences in quantitative MR data. This approach may benefit future prediction models based on other quantitative MRI methods.
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Affiliation(s)
- Maryam Tayebi
- Auckland Bioengineering Institute, The University of Auckland, Auckland, 1010, New Zealand
- Mātai Medical Research Institute, Gisborne, 4010, New Zealand
| | - Eryn Kwon
- Auckland Bioengineering Institute, The University of Auckland, Auckland, 1010, New Zealand
- Mātai Medical Research Institute, Gisborne, 4010, New Zealand
| | | | - Josh McGeown
- Mātai Medical Research Institute, Gisborne, 4010, New Zealand
| | - Miriam Scadeng
- Mātai Medical Research Institute, Gisborne, 4010, New Zealand
- Faculty of Medical and Health Sciences, The University of Auckland, Auckland, 1023, New Zealand
| | - Miao Qiao
- Department of Computer Science, The University of Auckland, Auckland, 1010, New Zealand
| | - Alan Wang
- Auckland Bioengineering Institute, The University of Auckland, Auckland, 1010, New Zealand
- Faculty of Medical and Health Sciences, The University of Auckland, Auckland, 1023, New Zealand
| | - Poul Nielsen
- Auckland Bioengineering Institute, The University of Auckland, Auckland, 1010, New Zealand
- Department of Engineering Science, The University of Auckland, Auckland, 1010, New Zealand
| | - Justin Fernandez
- Auckland Bioengineering Institute, The University of Auckland, Auckland, 1010, New Zealand
- Mātai Medical Research Institute, Gisborne, 4010, New Zealand
- Department of Engineering Science, The University of Auckland, Auckland, 1010, New Zealand
| | - Samantha Holdsworth
- Mātai Medical Research Institute, Gisborne, 4010, New Zealand
- Faculty of Medical and Health Sciences, The University of Auckland, Auckland, 1023, New Zealand
| | - Vickie Shim
- Auckland Bioengineering Institute, The University of Auckland, Auckland, 1010, New Zealand
- Mātai Medical Research Institute, Gisborne, 4010, New Zealand
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6
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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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7
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Richie-Halford A, Cieslak M, Ai L, Caffarra S, Covitz S, Franco AR, Karipidis II, Kruper J, Milham M, Avelar-Pereira B, Roy E, Sydnor VJ, Yeatman JD, Satterthwaite TD, Rokem A. An analysis-ready and quality controlled resource for pediatric brain white-matter research. Sci Data 2022; 9:616. [PMID: 36224186 PMCID: PMC9556519 DOI: 10.1038/s41597-022-01695-7] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2022] [Accepted: 09/12/2022] [Indexed: 11/08/2022] Open
Abstract
We created a set of resources to enable research based on openly-available diffusion MRI (dMRI) data from the Healthy Brain Network (HBN) study. First, we curated the HBN dMRI data (N = 2747) into the Brain Imaging Data Structure and preprocessed it according to best-practices, including denoising and correcting for motion effects, susceptibility-related distortions, and eddy currents. Preprocessed, analysis-ready data was made openly available. Data quality plays a key role in the analysis of dMRI. To optimize QC and scale it to this large dataset, we trained a neural network through the combination of a small data subset scored by experts and a larger set scored by community scientists. The network performs QC highly concordant with that of experts on a held out set (ROC-AUC = 0.947). A further analysis of the neural network demonstrates that it relies on image features with relevance to QC. Altogether, this work both delivers resources to advance transdiagnostic research in brain connectivity and pediatric mental health, and establishes a novel paradigm for automated QC of large datasets.
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Affiliation(s)
- Adam Richie-Halford
- Stanford University, Division of Developmental and Behavioral Pediatrics, Stanford, California, 94305, USA.
- Stanford University, Graduate School of Education, Stanford, California, 94305, USA.
| | - Matthew Cieslak
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA.
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, Pennsylvania, 19104, USA.
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA.
| | - Lei Ai
- Child Mind Institute, Center for the Developing Brain, New York City, New York, 10022, USA
| | - Sendy Caffarra
- Stanford University, Division of Developmental and Behavioral Pediatrics, Stanford, California, 94305, USA
- Stanford University, Graduate School of Education, Stanford, California, 94305, USA
- University of Modena and Reggio Emilia, Department of Biomedical, Metabolic and Neural Sciences, 41125, Modena, Italy
| | - Sydney Covitz
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, Pennsylvania, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
| | - Alexandre R Franco
- Child Mind Institute, Center for the Developing Brain, New York City, New York, 10022, USA
- Nathan Kline Institute for Psychiatric Research, Center for Biomedical Imaging and Neuromodulation, Orangeburg, New York, 10962, USA
| | - Iliana I Karipidis
- Stanford University, Graduate School of Education, Stanford, California, 94305, USA
- Stanford University, Department of Psychiatry and Behavioral Sciences, School of Medicine, Stanford, California, 94305, USA
- University of Zurich, Department of Child and Adolescent Psychiatry and Psychotherapy, University Hospital of Psychiatry Zurich, Zurich, 8032, Switzerland
- Neuroscience Center Zurich, University of Zurich and ETH Zurich, Zurich, 8057, Switzerland
| | - John Kruper
- University of Washington, Department of Psychology, Seattle, Washington, 98195, USA
| | - Michael Milham
- Child Mind Institute, Center for the Developing Brain, New York City, New York, 10022, USA
- Nathan Kline Institute for Psychiatric Research, Center for Biomedical Imaging and Neuromodulation, Orangeburg, New York, 10962, USA
| | - Bárbara Avelar-Pereira
- Stanford University, Department of Psychiatry and Behavioral Sciences, School of Medicine, Stanford, California, 94305, USA
| | - Ethan Roy
- Stanford University, Graduate School of Education, Stanford, California, 94305, USA
| | - Valerie J Sydnor
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, Pennsylvania, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
| | - Jason D Yeatman
- Stanford University, Division of Developmental and Behavioral Pediatrics, Stanford, California, 94305, USA
- Stanford University, Graduate School of Education, Stanford, California, 94305, USA
| | - Theodore D Satterthwaite
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, Pennsylvania, 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, 19104, USA
| | - Ariel Rokem
- University of Washington, Department of Psychology, Seattle, Washington, 98195, USA
- University of Washington, eScience Institute, Seattle, Washington, 98195, USA
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8
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Shams B, Wang Z, Roine T, Aydogan DB, Vajkoczy P, Lippert C, Picht T, Fekonja LS. Machine learning-based prediction of motor status in glioma patients using diffusion MRI metrics along the corticospinal tract. Brain Commun 2022; 4:fcac141. [PMID: 35694146 PMCID: PMC9175193 DOI: 10.1093/braincomms/fcac141] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/16/2021] [Revised: 03/01/2022] [Accepted: 05/24/2022] [Indexed: 12/03/2022] Open
Abstract
Along tract statistics enables white matter characterization using various diffusion MRI metrics. These diffusion models reveal detailed insights into white matter microstructural changes with development, pathology and function. Here, we aim at assessing the clinical utility of diffusion MRI metrics along the corticospinal tract, investigating whether motor glioma patients can be classified with respect to their motor status. We retrospectively included 116 brain tumour patients suffering from either left or right supratentorial, unilateral World Health Organization Grades II, III and IV gliomas with a mean age of 53.51 ± 16.32 years. Around 37% of patients presented with preoperative motor function deficits according to the Medical Research Council scale. At group level comparison, the highest non-overlapping diffusion MRI differences were detected in the superior portion of the tracts’ profiles. Fractional anisotropy and fibre density decrease, apparent diffusion coefficient axial diffusivity and radial diffusivity increase. To predict motor deficits, we developed a method based on a support vector machine using histogram-based features of diffusion MRI tract profiles (e.g. mean, standard deviation, kurtosis and skewness), following a recursive feature elimination method. Our model achieved high performance (74% sensitivity, 75% specificity, 74% overall accuracy and 77% area under the curve). We found that apparent diffusion coefficient, fractional anisotropy and radial diffusivity contributed more than other features to the model. Incorporating the patient demographics and clinical features such as age, tumour World Health Organization grade, tumour location, gender and resting motor threshold did not affect the model’s performance, revealing that these features were not as effective as microstructural measures. These results shed light on the potential patterns of tumour-related microstructural white matter changes in the prediction of functional deficits.
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Affiliation(s)
- Boshra Shams
- Department of Neurosurgery, Charité - Universitätsmedizin Berlin, Klinik für Neurochirurgie mit Arbeitsbereich Pädiatrische Neurochirurgie, Campus Charité Mitte , Charitéplatz 1, 10117 Berlin, Germany
- Cluster of Excellence: ‘Matters of Activity. Image Space Material’, Humboldt University Berlin , Berlin, Germany
| | - Ziqian Wang
- Department of Neurosurgery, Charité - Universitätsmedizin Berlin, Klinik für Neurochirurgie mit Arbeitsbereich Pädiatrische Neurochirurgie, Campus Charité Mitte , Charitéplatz 1, 10117 Berlin, Germany
| | - Timo Roine
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science , Espoo, Finland
- Turku Brain and Mind Center, University of Turku , Turku, Finland
| | - Dogu Baran Aydogan
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science , Espoo, Finland
- Department of Psychiatry, Helsinki University and Helsinki University Hospital , Helsinki, Finland
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland , Kuopio, Finland
| | - Peter Vajkoczy
- Department of Neurosurgery, Charité - Universitätsmedizin Berlin, Klinik für Neurochirurgie mit Arbeitsbereich Pädiatrische Neurochirurgie, Campus Charité Mitte , Charitéplatz 1, 10117 Berlin, Germany
| | - Christoph Lippert
- Digital Health - Machine Learning, Hasso Plattner Institute, University of Potsdam , Potsdam, Germany
- Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai , New York, NY, USA
| | - Thomas Picht
- Department of Neurosurgery, Charité - Universitätsmedizin Berlin, Klinik für Neurochirurgie mit Arbeitsbereich Pädiatrische Neurochirurgie, Campus Charité Mitte , Charitéplatz 1, 10117 Berlin, Germany
- Cluster of Excellence: ‘Matters of Activity. Image Space Material’, Humboldt University Berlin , Berlin, Germany
| | - Lucius S. Fekonja
- Department of Neurosurgery, Charité - Universitätsmedizin Berlin, Klinik für Neurochirurgie mit Arbeitsbereich Pädiatrische Neurochirurgie, Campus Charité Mitte , Charitéplatz 1, 10117 Berlin, Germany
- Cluster of Excellence: ‘Matters of Activity. Image Space Material’, Humboldt University Berlin , Berlin, Germany
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9
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Ettehadi N, Kashyap P, Zhang X, Wang Y, Semanek D, Desai K, Guo J, Posner J, Laine AF. Automated Multiclass Artifact Detection in Diffusion MRI Volumes via 3D Residual Squeeze-and-Excitation Convolutional Neural Networks. Front Hum Neurosci 2022; 16:877326. [PMID: 35431841 PMCID: PMC9005752 DOI: 10.3389/fnhum.2022.877326] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/16/2022] [Accepted: 03/07/2022] [Indexed: 12/14/2022] Open
Abstract
Diffusion MRI (dMRI) is widely used to investigate neuronal and structural development of brain. dMRI data is often contaminated with various types of artifacts. Hence, artifact type identification in dMRI volumes is an essential pre-processing step prior to carrying out any further analysis. Manual artifact identification amongst a large pool of dMRI data is a highly labor-intensive task. Previous attempts at automating this process are often limited to a binary classification ("poor" vs. "good" quality) of the dMRI volumes or focus on detecting a single type of artifact (e.g., motion, Eddy currents, etc.). In this work, we propose a deep learning-based automated multiclass artifact classifier for dMRI volumes. Our proposed framework operates in 2 steps. In the first step, the model predicts labels associated with 3D mutually exclusive collectively exhaustive (MECE) sub-volumes or "slabs" extracted from whole dMRI volumes. In the second step, through a voting process, the model outputs the artifact class present in the whole volume under investigation. We used two different datasets for training and evaluating our model. Specifically, we utilized 2,494 poor-quality dMRI volumes from the Adolescent Brain Cognitive Development (ABCD) and 4,226 from the Healthy Brain Network (HBN) dataset. Our results demonstrate accurate multiclass volume-level main artifact type prediction with 96.61 and 97.52% average accuracies on the ABCD and HBN test sets, respectively. Finally, in order to demonstrate the effectiveness of the proposed framework in dMRI pre-processing pipelines, we conducted a proof-of-concept dMRI analysis exploring the relationship between whole-brain fractional anisotropy (FA) and participant age, to test whether the use of our model improves the brain-age association.
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Affiliation(s)
- Nabil Ettehadi
- Heffner Biomedical Imaging Laboratory, Department of Biomedical Engineering, Columbia University, New York, NY, United States
| | - Pratik Kashyap
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, United States
| | - Xuzhe Zhang
- Heffner Biomedical Imaging Laboratory, Department of Biomedical Engineering, Columbia University, New York, NY, United States
| | - Yun Wang
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, United States
| | - David Semanek
- Department of Psychiatry, Columbia University Medical Center, New York, NY, United States
| | - Karan Desai
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, United States
| | - Jia Guo
- Department of Psychiatry, Columbia University Medical Center, New York, NY, United States
- Zuckerman Institute, Columbia University, New York, NY, United States
| | - Jonathan Posner
- Department of Psychiatry and Behavioral Sciences, Duke University, Durham, NC, United States
| | - Andrew F. Laine
- Heffner Biomedical Imaging Laboratory, Department of Biomedical Engineering, Columbia University, New York, NY, United States
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10
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Rokem A. Detect-ing brain anomalies with autoencoders. NATURE COMPUTATIONAL SCIENCE 2021; 1:569-570. [PMID: 38217132 DOI: 10.1038/s43588-021-00128-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/15/2024]
Affiliation(s)
- Ariel Rokem
- Department of Psychology and eScience Institute, University of Washington, Seattle, WA, USA.
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11
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Erramuzpe A, Schurr R, Yeatman JD, Gotlib IH, Sacchet MD, Travis KE, Feldman HM, Mezer AA. A Comparison of Quantitative R1 and Cortical Thickness in Identifying Age, Lifespan Dynamics, and Disease States of the Human Cortex. Cereb Cortex 2021; 31:1211-1226. [PMID: 33095854 PMCID: PMC8485079 DOI: 10.1093/cercor/bhaa288] [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: 05/15/2020] [Revised: 08/25/2020] [Accepted: 09/03/2020] [Indexed: 07/22/2023] Open
Abstract
Brain development and aging are complex processes that unfold in multiple brain regions simultaneously. Recently, models of brain age prediction have aroused great interest, as these models can potentially help to understand neurological diseases and elucidate basic neurobiological mechanisms. We test whether quantitative magnetic resonance imaging can contribute to such age prediction models. Using R1, the longitudinal rate of relaxation, we explore lifespan dynamics in cortical gray matter. We compare R1 with cortical thickness, a well-established biomarker of brain development and aging. Using 160 healthy individuals (6-81 years old), we found that R1 and cortical thickness predicted age similarly, but the regions contributing to the prediction differed. Next, we characterized R1 development and aging dynamics. Compared with anterior regions, in posterior regions we found an earlier R1 peak but a steeper postpeak decline. We replicate these findings: firstly, we tested a subset (N = 10) of the original dataset for whom we had additional scans at a lower resolution; and second, we verified the results on an independent dataset (N = 34). Finally, we compared the age prediction models on a subset of 10 patients with multiple sclerosis. The patients are predicted older than their chronological age using R1 but not with cortical thickness.
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Affiliation(s)
| | - R Schurr
- The Hebrew University of Jerusalem, The Edmond and Lily Safra Center for Brain Sciences, Jerusalem, Israel
| | - J D Yeatman
- Graduate School of Education, Stanford University, Stanford, CA, USA
- Division of Developmental-Behavioral Pediatrics, Stanford University School of Medicine, Stanford, CA, USA
| | - I H Gotlib
- Psychology, Stanford University, Stanford, CA, USA
| | - M D Sacchet
- Harvard Medical School, Center for Depression, Anxiety, and Stress Research, McLean Hospital, Belmont, MA, USA
| | - K E Travis
- Pediatrics, Stanford University, Stanford, CA, USA
| | - H M Feldman
- Development and Behavior Unit, Stanford University, Stanford, CA, USA
| | - A A Mezer
- The Hebrew University of Jerusalem, The Edmond and Lily Safra Center for Brain Sciences, Jerusalem, Israel
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12
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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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