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Kim Y, Joshi AA, Choi S, Joshi SH, Bhushan C, Varadarajan D, Haldar JP, Leahy RM, Shattuck DW. BrainSuite BIDS App: Containerized Workflows for MRI Analysis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.03.14.532686. [PMID: 36993283 PMCID: PMC10055125 DOI: 10.1101/2023.03.14.532686] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 04/29/2023]
Abstract
There has been a concerted effort by the neuroimaging community to establish standards for computational methods for data analysis that promote reproducibility and portability. In particular, the Brain Imaging Data Structure (BIDS) specifies a standard for storing imaging data, and the related BIDS App methodology provides a standard for implementing containerized processing environments that include all necessary dependencies to process BIDS datasets using image processing workflows. We present the BrainSuite BIDS App, which encapsulates the core MRI processing functionality of BrainSuite within the BIDS App framework. Specifically, the BrainSuite BIDS App implements a participant-level workflow comprising three pipelines and a corresponding set of group-level analysis workflows for processing the participant-level outputs. The BrainSuite Anatomical Pipeline (BAP) extracts cortical surface models from a T1-weighted (T1w) MRI. It then performs surface-constrained volumetric registration to align the T1w MRI to a labeled anatomical atlas, which is used to delineate anatomical regions of interest in the MRI brain volume and on the cortical surface models. The BrainSuite Diffusion Pipeline (BDP) processes diffusion-weighted imaging (DWI) data, with steps that include coregistering the DWI data to the T1w scan, correcting for geometric image distortion, and fitting diffusion models to the DWI data. The BrainSuite Functional Pipeline (BFP) performs fMRI processing using a combination of FSL, AFNI, and BrainSuite tools. BFP coregisters the fMRI data to the T1w image, then transforms the data to the anatomical atlas space and to the Human Connectome Project's grayordinate space. Each of these outputs can then be processed during group-level analysis. The outputs of BAP and BDP are analyzed using the BrainSuite Statistics in R (bssr) toolbox, which provides functionality for hypothesis testing and statistical modeling. The outputs of BFP can be analyzed using atlas-based or atlas-free statistical methods during group-level processing. These analyses include the application of BrainSync, which synchronizes the time-series data temporally and enables comparison of resting-state or task-based fMRI data across scans. We also present the BrainSuite Dashboard quality control system, which provides a browser-based interface for reviewing the outputs of individual modules of the participant-level pipelines across a study in real-time as they are generated. BrainSuite Dashboard facilitates rapid review of intermediate results, enabling users to identify processing errors and make adjustments to processing parameters if necessary. The comprehensive functionality included in the BrainSuite BIDS App provides a mechanism for rapidly deploying the BrainSuite workflows into new environments to perform large-scale studies. We demonstrate the capabilities of the BrainSuite BIDS App using structural, diffusion, and functional MRI data from the Amsterdam Open MRI Collection's Population Imaging of Psychology dataset.
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Affiliation(s)
- Yeun Kim
- Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Anand A. Joshi
- Signal and Image Processing Institute, Department of Electrical Engineering – Systems, University of Southern California, Los Angeles, CA, USA
| | - Soyoung Choi
- Signal and Image Processing Institute, Department of Electrical Engineering – Systems, University of Southern California, Los Angeles, CA, USA
- Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Shantanu H. Joshi
- Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
| | - Chitresh Bhushan
- Signal and Image Processing Institute, Department of Electrical Engineering – Systems, University of Southern California, Los Angeles, CA, USA
- GE Research, Schenectady, NY, USA
| | - Divya Varadarajan
- Signal and Image Processing Institute, Department of Electrical Engineering – Systems, University of Southern California, Los Angeles, CA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - Justin P. Haldar
- Signal and Image Processing Institute, Department of Electrical Engineering – Systems, University of Southern California, Los Angeles, CA, USA
| | - Richard M. Leahy
- Signal and Image Processing Institute, Department of Electrical Engineering – Systems, University of Southern California, Los Angeles, CA, USA
| | - David W. Shattuck
- Ahmanson-Lovelace Brain Mapping Center, Department of Neurology, David Geffen School of Medicine at UCLA, Los Angeles, CA, USA
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Fan Y, Wang G, Dong Q, Liu Y, Leporé N, Wang Y. Tetrahedral spectral feature-Based bayesian manifold learning for grey matter morphometry: Findings from the Alzheimer's disease neuroimaging initiative. Med Image Anal 2021; 72:102123. [PMID: 34214958 PMCID: PMC8316398 DOI: 10.1016/j.media.2021.102123] [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: 10/13/2020] [Revised: 03/30/2021] [Accepted: 05/26/2021] [Indexed: 11/17/2022]
Abstract
Structural and anatomical analyses of magnetic resonance imaging (MRI) data often require a reconstruction of the three-dimensional anatomy to a statistical shape model. Our prior work demonstrated the usefulness of tetrahedral spectral features for grey matter morphometry. However, most of the current methods provide a large number of descriptive shape features, but lack an unsupervised scheme to automatically extract a concise set of features with clear biological interpretations and that also carries strong statistical power. Here we introduce a new tetrahedral spectral feature-based Bayesian manifold learning framework for effective statistical analysis of grey matter morphology. We start by solving the technical issue of generating tetrahedral meshes which preserve the details of the grey matter geometry. We then derive explicit weak-form tetrahedral discretizations of the Hamiltonian operator (HO) and the Laplace-Beltrami operator (LBO). Next, the Schrödinger's equation is solved for constructing the scale-invariant wave kernel signature (SIWKS) as the shape descriptor. By solving the heat equation and utilizing the SIWKS, we design a morphometric Gaussian process (M-GP) regression framework and an active learning strategy to select landmarks as concrete shape descriptors. We evaluate the proposed system on publicly available data from the Alzheimers Disease Neuroimaging Initiative (ADNI), using subjects structural MRI covering the range from cognitively unimpaired (CU) to full blown Alzheimer's disease (AD). Our analyses suggest that the SIWKS and M-GP compare favorably with seven other baseline algorithms to obtain grey matter morphometry-based diagnoses. Our work may inspire more tetrahedral spectral feature-based Bayesian learning research in medical image analysis.
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Affiliation(s)
- Yonghui Fan
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Gang Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA; School of Information and Electrical Engineering, Ludong University, Yantai, China
| | - Qunxi Dong
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Yuxiang Liu
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA
| | - Natasha Leporé
- CIBORG Lab, Department of Radiology Children's Hospital Los Angeles, Los Angeles, CA, USA
| | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA.
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Mostapha M, Kim SH, Evans AC, Dager SR, Estes AM, McKinstry RC, Botteron KN, Gerig G, Pizer SM, Schultz RT, Hazlett HC, Piven J, Girault JB, Shen MD, Styner MA. A Novel Method for High-Dimensional Anatomical Mapping of Extra-Axial Cerebrospinal Fluid: Application to the Infant Brain. Front Neurosci 2020; 14:561556. [PMID: 33132824 PMCID: PMC7561674 DOI: 10.3389/fnins.2020.561556] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Accepted: 08/21/2020] [Indexed: 12/21/2022] Open
Abstract
Cerebrospinal fluid (CSF) plays an essential role in early postnatal brain development. Extra-axial CSF (EA-CSF) volume, which is characterized by CSF in the subarachnoid space surrounding the brain, is a promising marker in the early detection of young children at risk for neurodevelopmental disorders. Previous studies have focused on global EA-CSF volume across the entire dorsal extent of the brain, and not regionally-specific EA-CSF measurements, because no tools were previously available for extracting local EA-CSF measures suitable for localized cortical surface analysis. In this paper, we propose a novel framework for the localized, cortical surface-based analysis of EA-CSF. The proposed processing framework combines probabilistic brain tissue segmentation, cortical surface reconstruction, and streamline-based local EA-CSF quantification. The quantitative analysis of local EA-CSF was applied to a dataset of typically developing infants with longitudinal MRI scans from 6 to 24 months of age. There was a high degree of consistency in the spatial patterns of local EA-CSF across age using the proposed methods. Statistical analysis of local EA-CSF revealed several novel findings: several regions of the cerebral cortex showed reductions in EA-CSF from 6 to 24 months of age, and specific regions showed higher local EA-CSF in males compared to females. These age-, sex-, and anatomically-specific patterns of local EA-CSF would not have been observed if only a global EA-CSF measure were utilized. The proposed methods are integrated into a freely available, open-source, cross-platform, user-friendly software tool, allowing neuroimaging labs to quantify local extra-axial CSF in their neuroimaging studies to investigate its role in typical and atypical brain development.
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Affiliation(s)
- Mahmoud Mostapha
- Department of Computer Science, University of North Carolina, Chapel Hill, NC, United States
| | - Sun Hyung Kim
- Department of Psychiatry, UNC School of Medicine, University of North Carolina, Chapel Hill, NC, United States
| | - Alan C Evans
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Stephen R Dager
- Department of Radiology, University of Washington, Seattle, WA, United States
| | - Annette M Estes
- Department of Speech and Hearing Sciences, University of Washington, Seattle, WA, United States
| | - Robert C McKinstry
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, United States
| | - Kelly N Botteron
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St Louis, MO, United States.,Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, United States
| | - Guido Gerig
- Department of Computer Science and Engineering, New York University, New York, NY, United States
| | - Stephen M Pizer
- Department of Computer Science, University of North Carolina, Chapel Hill, NC, United States
| | - Robert T Schultz
- Department of Pediatrics, Center for Autism Research, Children's Hospital of Philadelphia, University of Pennsylvania, Philadelphia, PA, United States
| | - Heather C Hazlett
- Department of Psychiatry, UNC School of Medicine, University of North Carolina, Chapel Hill, NC, United States.,Carolina Institute for Developmental Disabilities, UNC School of Medicine, University of North Carolina-Chapel Hill, Chapel Hill, NC, United States
| | - Joseph Piven
- Department of Psychiatry, UNC School of Medicine, University of North Carolina, Chapel Hill, NC, United States.,Carolina Institute for Developmental Disabilities, UNC School of Medicine, University of North Carolina-Chapel Hill, Chapel Hill, NC, United States
| | - Jessica B Girault
- Department of Psychiatry, UNC School of Medicine, University of North Carolina, Chapel Hill, NC, United States.,Carolina Institute for Developmental Disabilities, UNC School of Medicine, University of North Carolina-Chapel Hill, Chapel Hill, NC, United States
| | - Mark D Shen
- Department of Psychiatry, UNC School of Medicine, University of North Carolina, Chapel Hill, NC, United States.,Carolina Institute for Developmental Disabilities, UNC School of Medicine, University of North Carolina-Chapel Hill, Chapel Hill, NC, United States.,UNC Neuroscience Center, University of North Carolina-Chapel Hill, Chapel Hill, NC, United States
| | - Martin A Styner
- Department of Computer Science, University of North Carolina, Chapel Hill, NC, United States.,Department of Psychiatry, UNC School of Medicine, University of North Carolina, Chapel Hill, NC, United States
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Fan Y, Leporé N, Wang Y. Morphometric Gaussian Process for Landmarking on Grey Matter Tetrahedral Models. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2020; 11330:113300H. [PMID: 33981127 PMCID: PMC8112202 DOI: 10.1117/12.2542492] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/22/2022]
Abstract
High-dimensional manifold modeling increases the precision and performance of cortical morphometry analysis by densely sampling on the grey matters. But this also brings redundant information and increased computational burden. Gaussian process regression has been used to tackle this problem by learning a mapping to a low-dimensional subspace. However, current methods may not take relevant morphometric properties, usually measured by geometric features, into account, and as a result, may generate morphometrically insignificant selections. In this paper, we propose a morphometric Gaussian process (M-GP) as a novel Bayesian model on the gray matter tetrahedral meshes. We also implement an M-GP regression landmarking algorithm as a manifold learning method for non-linear dimensionality reduction. The definition of M-GP involves a scale-invariant wave kernel signature distance map measuring the local similarities of geometric features, and a heat flow entropy which implicitly embeds the global curvature flow. With such a design, the prior knowledge fully encodes the geometric information so that a posterior predictive inference is morphometrically significant. In experiments, we use 518 grey matter tetrahedral meshes generated from structural magnetic resonance images of a publicly available Alzheimer's disease imaging cohort to empirically and numerically evaluate our method. The results verify that our method is theoretically and experimentally valid in selecting a representative subset from the original massive data. Our work may benefit any studies involving large-scale or iterative computations on extensive manifold-valued data, including morphometry analyses and general medical data processing.
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Affiliation(s)
- Yonghui Fan
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, 699 S Mill Ave, Tempe, USA
| | - Natasha Leporé
- CIBORG Lab, Department of Radiology, Children’s Hospital Los Angeles, 4650 Sunset Blvd. MS #81, Los Angeles, USA
| | - Yalin Wang
- School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, 699 S Mill Ave, Tempe, USA
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Joshi AA, Li J, Akrami H, Leahy RM. Predicting Cognitive Scores from Resting fMRI Data and Geometric Features of the Brain. PROCEEDINGS OF SPIE--THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING 2019; 10949. [PMID: 34305256 DOI: 10.1117/12.2512063] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Abstract
Anatomical T1 weighted Magnetic Resonance Imaging (MRI) and functional magnetic resonance imaging collected during resting (rfMRI) are promising markers that offer insight into the structure and function of the human brain. The objective of this work is to explore the use of a deep learning neural network to predict cognitive performance scores for a population of normal controls and subjects with Attention Deficit Hyperactivity Disorder (ADHD). Specifically, we predict verbal and performance IQs and ADHD index from features derived from T1 and rfMRI imaging data. First, we processed the rfMRI and MRI data of subjects using the BrainSuite fMRI Processing (BFP) pipeline to perform anatomical and functional preprocessing. This produces for each subject fMRI and geometric (anatomical) features represented in a standardized grayordinate system. The geometric and functional cortical data corresponding to the two hemispheres were then transformed to 128×128 multichannel images and input to a convolutional component of the neural network. Subcortical data were presented in a standard vector form and inputted to a input layer of the network. The neural network was implemented in Python using the Keras library with a TensorFlow backend. Training was performed on 168 images with 90 images used for testing. We observed a high correlation between predicted and actual values of the indices tested: Performance IQ: 0.47; Verbal IQ: 0.41, ADHD: 0.57. Comparing these values to those from network trained on functional-only and structural-only data, we saw that rfMRI is more informative than MRI, but the two modalities are highly complementary in terms of predicting these indices.
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Affiliation(s)
- Anand A Joshi
- Signal and Image Processing Institute, University of Southern California, Los Angeles, CA 90089
| | - Jian Li
- Signal and Image Processing Institute, University of Southern California, Los Angeles, CA 90089
| | - Haleh Akrami
- Signal and Image Processing Institute, University of Southern California, Los Angeles, CA 90089
| | - Richard M Leahy
- Signal and Image Processing Institute, University of Southern California, Los Angeles, CA 90089
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