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Newlin NR, Kim ME, Kanakaraj P, Yao T, Hohman T, Pechman KR, Beason-Held LL, Resnick SM, Archer D, Jefferson A, Landman BA, Moyer D. MidRISH: Unbiased harmonization of rotationally invariant harmonics of the diffusion signal. Magn Reson Imaging 2024; 111:113-119. [PMID: 38537892 PMCID: PMC11283839 DOI: 10.1016/j.mri.2024.03.033] [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: 08/09/2023] [Revised: 03/09/2024] [Accepted: 03/20/2024] [Indexed: 04/09/2024]
Abstract
Data harmonization is necessary for removing confounding effects in multi-site diffusion image analysis. One such harmonization method, LinearRISH, scales rotationally invariant spherical harmonic (RISH) features from one site ("target") to the second ("reference") to reduce confounding scanner effects. However, reference and target site designations are not arbitrary and resultant diffusion metrics (fractional anisotropy, mean diffusivity) are biased by this choice. In this work we propose MidRISH: rather than scaling reference RISH features to target RISH features, we project both sites to a mid-space. We validate MidRISH with the following experiments: harmonizing scanner differences from 37 matched patients free of cognitive impairment, and harmonizing acquisition and study differences on 117 matched patients free of cognitive impairment. We find that MidRISH reduces bias of reference selection while preserving harmonization efficacy of LinearRISH. Users should be cautious when performing LinearRISH harmonization. To select a reference site is to choose diffusion metric effect-size. Our proposed method eliminates the bias-inducing site selection step.
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Affiliation(s)
- Nancy R Newlin
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA.
| | - Michael E Kim
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | | | - Tianyuan Yao
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
| | - Timothy Hohman
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA; Vanderbilt Genetics Institute, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Kimberly R Pechman
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Lori L Beason-Held
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Derek Archer
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA; Vanderbilt Genetics Institute, Vanderbilt University School of Medicine, Nashville, TN, USA
| | - Angela Jefferson
- Vanderbilt Memory and Alzheimer's Center, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA; Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Bennett A Landman
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA; Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA
| | - Daniel Moyer
- Department of Computer Science, Vanderbilt University, Nashville, TN, USA
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Wu Z, Weng X, Shen J, Hong M. Voxel-Wise Fusion of 3T and 7T Diffusion MRI Data to Extract more Accurate Fiber Orientations. Brain Topogr 2024; 37:684-698. [PMID: 38568279 DOI: 10.1007/s10548-024-01046-2] [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: 06/19/2023] [Accepted: 03/12/2024] [Indexed: 09/14/2024]
Abstract
While 7T diffusion magnetic resonance imaging (dMRI) has high spatial resolution, its diffusion imaging quality is usually affected by signal loss due to B1 inhomogeneity, T2 decay, susceptibility, and chemical shift. In contrast, 3T dMRI has relative higher diffusion angular resolution, but lower spatial resolution. Combination of 3T and 7T dMRI, thus, may provide more detailed and accurate information about the voxel-wise fiber orientations to better understand the structural brain connectivity. However, this topic has not yet been thoroughly explored until now. In this study, we explored the feasibility of fusing 3T and 7T dMRI data to extract voxel-wise quantitative parameters at higher spatial resolution. After 3T and 7T dMRI data was preprocessed, respectively, 3T dMRI volumes were coregistered into 7T dMRI space. Then, 7T dMRI data was harmonized to the coregistered 3T dMRI B0 (b = 0) images. Last, harmonized 7T dMRI data was fused with 3T dMRI data according to four fusion rules proposed in this study. We employed high-quality 3T and 7T dMRI datasets (N = 24) from the Human Connectome Project to test our algorithms. The diffusion tensors (DTs) and orientation distribution functions (ODFs) estimated from the 3T-7T fused dMRI volumes were statistically analyzed. More voxels containing multiple fiber populations were found from the fused dMRI data than from 7T dMRI data set. Moreover, extra fiber directions were extracted in temporal brain regions from the fused dMRI data at Otsu's thresholds of quantitative anisotropy, but could not be extracted from 7T dMRI dataset. This study provides novel algorithms to fuse intra-subject 3T and 7T dMRI data for extracting more detailed information of voxel-wise quantitative parameters, and a new perspective to build more accurate structural brain networks.
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Affiliation(s)
- Zhanxiong Wu
- School of Electronic Information, Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, China
| | - Xinmeng Weng
- School of Electronic Information, Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, China
| | - Jian Shen
- Neurosurgery Department, The First Affiliated Hospital of Zhejiang University School of Medicine, Zhejiang University, Hangzhou, 310003, Zhejiang, China
| | - Ming Hong
- School of Electronic Information, Hangzhou Dianzi University, Hangzhou, Zhejiang, 310018, China.
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Hajianfar G, Hosseini SA, Bagherieh S, Oveisi M, Shiri I, Zaidi H. Impact of harmonization on the reproducibility of MRI radiomic features when using different scanners, acquisition parameters, and image pre-processing techniques: a phantom study. Med Biol Eng Comput 2024; 62:2319-2332. [PMID: 38536580 DOI: 10.1007/s11517-024-03071-6] [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: 10/07/2023] [Accepted: 03/05/2024] [Indexed: 07/31/2024]
Abstract
This study investigated the impact of ComBat harmonization on the reproducibility of radiomic features extracted from magnetic resonance images (MRI) acquired on different scanners, using various data acquisition parameters and multiple image pre-processing techniques using a dedicated MRI phantom. Four scanners were used to acquire an MRI of a nonanatomic phantom as part of the TCIA RIDER database. In fast spin-echo inversion recovery (IR) sequences, several inversion durations were employed, including 50, 100, 250, 500, 750, 1000, 1500, 2000, 2500, and 3000 ms. In addition, a 3D fast spoiled gradient recalled echo (FSPGR) sequence was used to investigate several flip angles (FA): 2, 5, 10, 15, 20, 25, and 30 degrees. Nineteen phantom compartments were manually segmented. Different approaches were used to pre-process each image: Bin discretization, Wavelet filter, Laplacian of Gaussian, logarithm, square, square root, and gradient. Overall, 92 first-, second-, and higher-order statistical radiomic features were extracted. ComBat harmonization was also applied to the extracted radiomic features. Finally, the Intraclass Correlation Coefficient (ICC) and Kruskal-Wallis's (KW) tests were implemented to assess the robustness of radiomic features. The number of non-significant features in the KW test ranged between 0-5 and 29-74 for various scanners, 31-91 and 37-92 for three times tests, 0-33 to 34-90 for FAs, and 3-68 to 65-89 for IRs before and after ComBat harmonization, with different image pre-processing techniques, respectively. The number of features with ICC over 90% ranged between 0-8 and 6-60 for various scanners, 11-75 and 17-80 for three times tests, 3-83 to 9-84 for FAs, and 3-49 to 3-63 for IRs before and after ComBat harmonization, with different image pre-processing techniques, respectively. The use of various scanners, IRs, and FAs has a great impact on radiomic features. However, the majority of scanner-robust features is also robust to IR and FA. Among the effective parameters in MR images, several tests in one scanner have a negligible impact on radiomic features. Different scanners and acquisition parameters using various image pre-processing might affect radiomic features to a large extent. ComBat harmonization might significantly impact the reproducibility of MRI radiomic features.
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Affiliation(s)
- Ghasem Hajianfar
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
| | - Seyyed Ali Hosseini
- Translational Neuroimaging Laboratory, McGill University Research Centre for Studies in Aging, Douglas Hospital, McGill University, Montréal, Québec, Canada
- Department of Neurology and Neurosurgery, Faculty of Medicine, McGill University, Montréal, Québec, Canada
| | - Sara Bagherieh
- School of Medicine, Isfahan University of Medical Sciences, Isfahan, Iran
| | - Mehrdad Oveisi
- Department of Computer Science, University of British Columbia, Vancouver, BC, Canada
| | - Isaac Shiri
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland
- Department of Cardiology, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
| | - Habib Zaidi
- Division of Nuclear Medicine and Molecular Imaging, Geneva University Hospital, CH-1211, Geneva, Switzerland.
- Department of Nuclear Medicine and Molecular Imaging, University of Groningen, University Medical Center Groningen, Groningen, Netherlands.
- Department of Nuclear Medicine, University of Southern Denmark, Odense, Denmark.
- University Research and Innovation Center, Óbuda University, Budapest, Hungary.
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Schramm S, Börner C, Reichert M, Hoffmann G, Kaczmarz S, Griessmair M, Jung K, Berndt MT, Zimmer C, Baum T, Heinen F, Bonfert MV, Sollmann N. Perfusion imaging by arterial spin labeling in migraine: A literature review. J Cereb Blood Flow Metab 2024; 44:1253-1270. [PMID: 38483125 PMCID: PMC11342727 DOI: 10.1177/0271678x241237733] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/26/2023] [Revised: 01/19/2024] [Accepted: 01/23/2024] [Indexed: 08/15/2024]
Abstract
Arterial spin labeling (ASL) is a non-invasive magnetic resonance imaging (MRI) method for the assessment of cerebral blood flow (CBF). This review summarizes recent ASL-based investigations in adult and pediatric patients with migraine with aura, migraine without aura, and chronic migraine. A systematic search according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines was conducted within PubMed and reference sections of articles identified from April 2014 to November 2022. Out of 236 initial articles, 20 remained after filtering, encompassing data from 1155 subjects in total. Cross-sectional studies in adults showed inconsistent results, while longitudinal studies demonstrated that cerebral perfusion changes over the migraine cycle can be tracked using ASL. The most consistent findings were observed in ictal states among pediatric migraine patients, where studies showed hypoperfusion matching aura symptoms during early imaging followed by hyperperfusion. Overall, ASL is a useful but currently underutilized modality for evaluating cerebral perfusion in patients with migraine. The generalizability of results is currently limited by heterogeneities regarding study design and documentation of clinical variables (e.g., relation of attacks to scanning timepoint, migraine subtypes). Future MRI studies should consider augmenting imaging protocols with ASL to further elucidate perfusion dynamics in migraine.
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Affiliation(s)
- Severin Schramm
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Corinna Börner
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- LMU Hospital, Dr. von Hauner Children’s Hospital, Department of Pediatric Neurology and Developmental Medicine, Munich, Germany
- LMU Center for Children with Medical Complexity – iSPZ Hauner, Ludwig Maximilian University, Munich, Germany
| | - Miriam Reichert
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Gabriel Hoffmann
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Stephan Kaczmarz
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- Philips GmbH Market DACH, Hamburg, Germany
| | - Michael Griessmair
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Kirsten Jung
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Maria T Berndt
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Claus Zimmer
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Thomas Baum
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Florian Heinen
- LMU Hospital, Dr. von Hauner Children’s Hospital, Department of Pediatric Neurology and Developmental Medicine, Munich, Germany
| | - Michaela V Bonfert
- LMU Hospital, Dr. von Hauner Children’s Hospital, Department of Pediatric Neurology and Developmental Medicine, Munich, Germany
- LMU Center for Children with Medical Complexity – iSPZ Hauner, Ludwig Maximilian University, Munich, Germany
| | - Nico Sollmann
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
- Department of Diagnostic and Interventional Radiology, University Hospital Ulm, Ulm, Germany
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5
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Jacobs GR, Coleman MJ, Lewandowski KE, Pasternak O, Cetin-Karayumak S, Mesholam-Gately RI, Wojcik J, Kennedy L, Knyazhanskaya E, Reid B, Swago S, Lyons MG, Rizzoni E, John O, Carrington H, Kim N, Kotler E, Veale S, Haidar A, Prunier N, Haaf M, Levitt JJ, Seitz-Holland J, Rathi Y, Kubicki M, Keshavan MS, Holt DJ, Seidman LJ, Öngür D, Breier A, Bouix S, Shenton ME. An Introduction to the Human Connectome Project for Early Psychosis. Schizophr Bull 2024:sbae123. [PMID: 39036958 DOI: 10.1093/schbul/sbae123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/23/2024]
Abstract
BACKGROUND The time following a recent onset of psychosis is a critical period during which intervention may be maximally effective. Studying individuals in this period also offers an opportunity to investigate putative brain biomarkers of illness prior to the long-term effects of chronicity and medication. The Human Connectome Project for Early Psychosis (HCP-EP) was funded by the National Institutes of Mental Health (NIMH) as an extension of the original Human Connectome Project's approach to understanding the human brain and its structural and functional connections. DESIGN The HCP-EP data were collected at 3 sites in Massachusetts (Beth Israel Deaconess Medical Center, McLean Hospital, and Massachusetts General Hospital), and one site in Indiana (Indiana University). Brigham and Women's Hospital served as the data coordination center and as an imaging site. RESULTS The HCP-EP dataset includes high-quality clinical, cognitive, functional, neuroimaging, and blood specimen data acquired from 303 individuals between the ages of 16-35 years old with affective psychosis (n = 75), non-affective psychosis (n = 148), and healthy controls (n = 80). Participants with early psychosis were within 5 years of illness onset (mean duration = 1.9 years, standard deviation = 1.4 years). All data and novel or modified analytic tools developed as part of the study are publicly available to the research community through the NIMH Data Archive (NDA) or GitHub (https://github.com/pnlbwh). CONCLUSIONS This paper provides an overview of the specific HCP-EP procedures, assessments, and protocols, as well as a brief characterization of the study participants to make it easier for researchers to use this rich dataset. Although we focus here on discussing and comparing affective and non-affective psychosis groups, the HCP-EP dataset also provides sufficient information for investigators to group participants differently.
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Affiliation(s)
- Grace R Jacobs
- Department of Psychiatry, Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Michael J Coleman
- Department of Psychiatry, Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Ofer Pasternak
- Department of Psychiatry, Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Suheyla Cetin-Karayumak
- Department of Psychiatry, Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Raquelle I Mesholam-Gately
- Massachusetts Mental Health Center Public Psychiatry Division, Department of Psychiatry, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Joanne Wojcik
- Massachusetts Mental Health Center Public Psychiatry Division, Department of Psychiatry, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Leda Kennedy
- Department of Psychiatry, Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Evdokiya Knyazhanskaya
- Department of Psychiatry, Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Benjamin Reid
- Department of Psychiatry, Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Sophia Swago
- Department of Psychiatry, Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Monica G Lyons
- Department of Psychiatry, Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Elizabeth Rizzoni
- Department of Psychiatry, Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Omar John
- Department of Psychiatry, Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Holly Carrington
- Department of Psychiatry, Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Nicholas Kim
- Department of Psychiatry, Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Elana Kotler
- Department of Psychiatry, Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Simone Veale
- Department of Psychiatry, Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Anastasia Haidar
- Department of Psychiatry, Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Nicholas Prunier
- Department of Psychiatry, Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Moritz Haaf
- Department of Psychiatry, Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry and Psychotherapy, Psychiatry Neuroimaging Branch (PNB), University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - James J Levitt
- Department of Psychiatry, Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, VA Boston Healthcare System, Brockton Division, Brockton, MA, United States
| | - Johanna Seitz-Holland
- Department of Psychiatry, Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Yogesh Rathi
- Department of Psychiatry, Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Marek Kubicki
- Department of Psychiatry, Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Matcheri S Keshavan
- Massachusetts Mental Health Center Public Psychiatry Division, Department of Psychiatry, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Daphne J Holt
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Larry J Seidman
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Massachusetts Mental Health Center Public Psychiatry Division, Department of Psychiatry, Harvard Medical School, Beth Israel Deaconess Medical Center, Boston, MA, USA
| | - Dost Öngür
- Department of Psychiatry, McLean Hospital, Harvard Medical School, Belmont, MA, USA
| | - Alan Breier
- Department of Psychiatry, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Sylvain Bouix
- Department of Psychiatry, Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Software Engineering and Information Technology, École de Technologie Supérieure, Université du Québec, Montréal, QC, Canada
| | - Martha E Shenton
- Department of Psychiatry, Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
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Li Y, Zhang W, Wu Y, Yin L, Zhu C, Chen Y, Cetin-Karayumak S, Cho KIK, Zekelman LR, Rushmore J, Rathi Y, Makris N, O'Donnell LJ, Zhang F. A diffusion MRI tractography atlas for concurrent white matter mapping across Eastern and Western populations. Sci Data 2024; 11:787. [PMID: 39019877 PMCID: PMC11255335 DOI: 10.1038/s41597-024-03624-2] [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: 04/09/2024] [Accepted: 07/08/2024] [Indexed: 07/19/2024] Open
Abstract
The study of brain differences across Eastern and Western populations provides vital insights for understanding potential cultural and genetic influences on cognition and mental health. Diffusion MRI (dMRI) tractography is an important tool in assessing white matter (WM) connectivity and brain tissue microstructure across different populations. However, a comprehensive investigation into WM fiber tracts between Eastern and Western populations is challenged due to the lack of a cross-population WM atlas and the large site-specific variability of dMRI data. This study presents a dMRI tractography atlas, namely the East-West WM Atlas, for concurrent WM mapping between Eastern and Western populations and creates a large, harmonized dMRI dataset (n=306) based on the Human Connectome Project and the Chinese Human Connectome Project. The curated WM atlas, as well as subject-specific data including the harmonized dMRI data, the whole brain tractography data, and parcellated WM fiber tracts and their diffusion measures, are publicly released. This resource is a valuable addition to facilitating the exploration of brain commonalities and differences across diverse cultural backgrounds.
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Affiliation(s)
- Yijie Li
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Wei Zhang
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Ye Wu
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China
| | - Li Yin
- West China Hospital of Medical Science, Sichuan University, Chengdu, China
| | - Ce Zhu
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Yuqian Chen
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | | | - Kang Ik K Cho
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Leo R Zekelman
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Jarrett Rushmore
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
- Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, USA
| | - Yogesh Rathi
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Nikos Makris
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Lauren J O'Donnell
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA.
| | - Fan Zhang
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China.
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7
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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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8
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Liu Q, Ning L, Shaik IA, Liao C, Gagoski B, Bilgic B, Grissom W, Nielsen JF, Zaitsev M, Rathi Y. Reduced cross-scanner variability using vendor-agnostic sequences for single-shell diffusion MRI. Magn Reson Med 2024; 92:246-256. [PMID: 38469671 PMCID: PMC11055665 DOI: 10.1002/mrm.30062] [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: 12/07/2023] [Revised: 01/31/2024] [Accepted: 02/05/2024] [Indexed: 03/13/2024]
Abstract
PURPOSE To reduce the inter-scanner variability of diffusion MRI (dMRI) measures between scanners from different vendors by developing a vendor-neutral dMRI pulse sequence using the open-source vendor-agnostic Pulseq platform. METHODS We implemented a standard EPI based dMRI sequence in Pulseq. We tested it on two clinical scanners from different vendors (Siemens Prisma and GE Premier), systematically evaluating and comparing the within- and inter-scanner variability across the vendors, using both the vendor-provided and Pulseq dMRI sequences. Assessments covered both a diffusion phantom and three human subjects, using standard error (SE) and Lin's concordance correlation to measure the repeatability and reproducibility of standard DTI metrics including fractional anisotropy (FA) and mean diffusivity (MD). RESULTS Identical dMRI sequences were executed on both scanners using Pulseq. On the phantom, the Pulseq sequence showed more than a 2.5× reduction in SE (variability) across Siemens and GE scanners. Furthermore, Pulseq sequences exhibited markedly reduced SE in-vivo, maintaining scan-rescan repeatability while delivering lower variability in FA and MD (more than 50% reduction in cortical/subcortical regions) compared to vendor-provided sequences. CONCLUSION The Pulseq diffusion sequence reduces the cross-scanner variability for both phantom and in-vivo data, which will benefit multi-center neuroimaging studies and improve the reproducibility of neuroimaging studies.
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Affiliation(s)
- Qiang Liu
- Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Lipeng Ning
- Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Imam Ahmed Shaik
- Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
| | - Congyu Liao
- Department of Radiology, Stanford University, Stanford, CA, United States
| | - Borjan Gagoski
- Department of Radiology, Harvard Medical School, Boston, MA, United States
- Fetal-Neonatal Neuroimaging & Developmental Science Center, Boston Children’s Hospital, Boston, MA, United States
| | - Berkin Bilgic
- Department of Radiology, Harvard Medical School, Boston, MA, United States
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Boston, MA, United States
- Harvard/MIT Health Sciences and Technology, Cambridge, MA, United States
| | - William Grissom
- Department of Biomedical Engineering, Case School of Medicine, Case Western Reserve University, Cleveland, OH, United States
| | - Jon-Fredrik Nielsen
- fMRI Laboratory and Biomedical Engineering, University of Michigan, Ann Arbor, MI, United States
| | - Maxim Zaitsev
- Division of Medical Physics, Department of Radiology, University Medical Center Freiburg, Freiburg, Germany
| | - Yogesh Rathi
- Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, United States
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9
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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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10
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Joo SW, Jo YT, Choi W, Kim SM, Yoo SY, Joe S, Lee J. Topological abnormalities of the morphometric similarity network of the cerebral cortex in schizophrenia. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2024; 10:57. [PMID: 38886369 PMCID: PMC11183129 DOI: 10.1038/s41537-024-00477-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/23/2024] [Accepted: 05/30/2024] [Indexed: 06/20/2024]
Abstract
A morphometric similarity (MS) network can be constructed using multiple magnetic resonance imaging parameters of each cortical region. An MS network can be used to assess the similarity between cortical regions. Although MS networks can detect microstructural alterations and capture connections between histologically similar cortical areas, the influence of schizophrenia on the topological characteristics of MS networks remains unclear. We obtained T1- and diffusion-weighted images of 239 healthy controls and 190 individuals with schizophrenia to construct the MS network. Group comparisons of the mean MS of the cortical regions and subnetworks were performed. The strengths of the connections between the cortical regions and the global and nodal network indices were compared between the groups. Clinical associations with the network indices were tested using Spearman's rho. Compared with healthy controls, individuals with schizophrenia had significant group differences in the mean MS of several cortical regions and subnetworks. Individuals with schizophrenia had both superior and inferior strengths of connections between cortical regions compared with those of healthy controls. We observed regional abnormalities of the MS network in individuals with schizophrenia regarding lower centrality values of the pars opercularis, superior frontal, and superior temporal areas. Specific nodal network measures of the right pars opercularis and left superior temporal areas were associated with illness duration in individuals with schizophrenia. We identified regional abnormalities of the MS network in schizophrenia with the left superior temporal area possibly being a key region in topological organization and cortical connections.
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Affiliation(s)
- Sung Woo Joo
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Young Tak Jo
- Department of Psychiatry, Kangdong Sacred Heart Hospital, Hallym University College of Medicine, Seoul, Republic of Korea
| | - Woohyeok Choi
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Sun Min Kim
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - So Young Yoo
- Department of Psychiatry, SMG-SNU Boramae Medical Center, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Soohyun Joe
- Brain Laboratory, Department of Psychiatry, University of California San Diego, School of Medicine, San Diego, California, USA
| | - Jungsun Lee
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
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11
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Weerasekera A, Ion-Mărgineanu A, Nolan GP, Mody M. Subcortical-cortical white matter connectivity in adults with autism spectrum disorder and schizophrenia patients. Psychiatry Res Neuroimaging 2024; 340:111806. [PMID: 38508025 DOI: 10.1016/j.pscychresns.2024.111806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/01/2023] [Revised: 11/20/2023] [Accepted: 02/29/2024] [Indexed: 03/22/2024]
Abstract
Autism spectrum disorder (ASD) and schizophrenia (SZ) are neuropsychiatric disorders that overlap in symptoms associated with social-cognitive impairment. Alterations of the cingulate cortex, subcortical, medial-temporal, and orbitofrontal structures are frequently reported in both disorders. In this study, we examined white-matter connectivity between these structures in adults with ASD and SZ patients compared with their respective neurotypical controls and indirectly with each other, using probabilistic and local DTI tractography. This exploratory study utilized publicly available neuroimaging databases, of adults with ASD (ABIDE II; n = 28) and SZ (COBRE; n = 38), age-gender matched neurotypicals (NT) and associated phenotypic data. Tractography was performed using Freesurfer and MRtrix software, and diffusion metrics of white-matter tracts between cingulate-, orbitofrontal- cortices, subcortical structures, parahippocampal, entorhinal cortex were assessed. In ASD, atypical diffusivity parameters were found in the isthmus cingulate and parahippocampal connectivity to subcortical and rostral-anterior cingulate, which were also associated with IQ and social skills (SRS). In contrast, atypical diffusivity parameters were observed between the medial-orbitofrontal cortex and subcortical structures in SZ, and were associated with executive function (i.e., IQ, processing speed) and emotional regulation. Overall, the results suggest that defects in the isthmus cingulate, medial-orbitofrontal, and striato-limbic white matter connectivity may help unravel the neural underpinnings of executive and social-emotional dysfunction at the core of neuropsychiatric disorders.
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Affiliation(s)
- Akila Weerasekera
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02115, USA.
| | - Adrian Ion-Mărgineanu
- ESAT - STADIUS, KU Leuven, Leuven. Belgium; Biomed Artificial Intelligence LLC, Bucharest, Romania
| | - Garry P Nolan
- Department of Microbiology & Immunology, Stanford University School of Medicine, United States
| | - Maria Mody
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA 02115, USA
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12
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Yuan W, Sun J, Li Q, Zheng R, Guan B, Chen Z, Ding J, Sun Q, Fu R, Wang W, Fan Y, Kang Y, Sun C, Li A, Wu D, Wang D, Qi L, Chen L, Feng S, Zhou H. Protocol for the Chinese Real-World Evidence for Acute Spinal Cord Injury (ChiRES) study: a prospective, observational, multicentre cohort study of acute spinal cord injury. BMJ Open 2024; 14:e080358. [PMID: 38749680 PMCID: PMC11097860 DOI: 10.1136/bmjopen-2023-080358] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/28/2023] [Accepted: 04/30/2024] [Indexed: 05/18/2024] Open
Abstract
INTRODUCTION Spinal cord injury (SCI) is a catastrophic event with devastating physical, social and occupational consequences for patients and their families. The number of patients with acute SCI in China continues to grow rapidly, but there have been no large prospective cohort studies of patients with acute SCI. This proposed study aims to establish a multicentre, extensive sample cohort of clinical data and biological samples of patients in China, which would aid the systematisation and standardisation of clinical research and treatment of acute SCI, thus reducing the heavy burden of acute SCI on patients and society. METHODS AND ANALYSIS The Chinese Real-World Evidence for Acute Spinal Cord Injury (ChiRES) study is an observational, multicentre cohort study of patients with acute SCI admitted to the Qilu Hospital of Shandong University and other participating centres with prospective collection of their clinical data and biological samples. We aim to recruit 2097 patients in this study. Demographics, disease history, emergency intervention information, motor and sensory examinations, surgical information, medication information and rehabilitation evaluation will be recorded. This will facilitate the development of a prediction model for complications and prognosis of patients with acute SCI and an evaluation of the current management of acute SCI. Among these variables, detailed information on surgical treatment will also be used to assess procedures for acute SCI treatment. Outcome measurements, including the International Standard for Neurological Classification of Spinal Cord Injury examinations, the occurrence of complications and death, will be performed repeatedly during follow-up. We will analyse imaging data and blood samples to develop SCI imaging markers and biomarkers. ETHICS AND DISSEMINATION This study protocol has been approved by the Medical Ethics Committee of the Qilu Hospital of Shandong University and all other participating centres. The findings will be disseminated in peer-reviewed journals and academic conferences.
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Affiliation(s)
- Wenjian Yuan
- Department of Orthopaedics, Qilu Hospital of Shandong University, Shandong University Centre for Orthopaedics, Advanced Medical Research Institute, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
| | - Jiuxiao Sun
- Department of Orthopaedics, Qilu Hospital of Shandong University, Shandong University Centre for Orthopaedics, Advanced Medical Research Institute, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
| | - Qingyang Li
- Department of Orthopaedics, Qilu Hospital of Shandong University, Shandong University Centre for Orthopaedics, Advanced Medical Research Institute, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
| | - Ruiyuan Zheng
- Department of Orthopaedics, Qilu Hospital of Shandong University, Shandong University Centre for Orthopaedics, Advanced Medical Research Institute, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
| | - Bin Guan
- Department of Orthopaedics, Qilu Hospital of Shandong University, Shandong University Centre for Orthopaedics, Advanced Medical Research Institute, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
| | - Zhuo Chen
- Department of Orthopaedics, Qilu Hospital of Shandong University, Shandong University Centre for Orthopaedics, Advanced Medical Research Institute, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
| | - Jiaming Ding
- Department of Orthopaedics, Qilu Hospital of Shandong University, Shandong University Centre for Orthopaedics, Advanced Medical Research Institute, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
| | - Qingyu Sun
- Department of Orthopaedics, Qilu Hospital of Shandong University, Shandong University Centre for Orthopaedics, Advanced Medical Research Institute, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
| | - Runhan Fu
- Department of Orthopaedics, Qilu Hospital of Shandong University, Shandong University Centre for Orthopaedics, Advanced Medical Research Institute, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
| | - Wei Wang
- Department of Orthopaedics, Qilu Hospital of Shandong University, Shandong University Centre for Orthopaedics, Advanced Medical Research Institute, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
| | - Yuxuan Fan
- Department of Orthopaedics, Tianjin Medical University General Hospital, Tianjin Medical University, International Science and Technology Cooperation Base of Spinal Cord Injury, Tianjin Key Laboratory of Spine and Spinal Cord, Tianjin, China
| | - Yi Kang
- Department of Orthopaedics, Tianjin Medical University General Hospital, Tianjin Medical University, International Science and Technology Cooperation Base of Spinal Cord Injury, Tianjin Key Laboratory of Spine and Spinal Cord, Tianjin, China
| | - Chao Sun
- Department of Orthopaedics, Tianjin Medical University General Hospital, Tianjin Medical University, International Science and Technology Cooperation Base of Spinal Cord Injury, Tianjin Key Laboratory of Spine and Spinal Cord, Tianjin, China
| | - Ang Li
- Department of Orthopaedics, Henan Provincial People's Hospital, Zhengzhou, Henan, China
| | - Dongjin Wu
- The Second Hospital of Shandong University, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
| | - Dachuan Wang
- The Second Hospital of Shandong University, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
| | - Lei Qi
- The Second Hospital of Shandong University, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
| | - Lingxiao Chen
- Department of Orthopaedics, Qilu Hospital of Shandong University, Shandong University Centre for Orthopaedics, Advanced Medical Research Institute, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
- Department of Biostatistics, School of Public Health, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
- Sydney Musculoskeletal Health, The Kolling Institute, Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
| | - Shiqing Feng
- Department of Orthopaedics, Qilu Hospital of Shandong University, Shandong University Centre for Orthopaedics, Advanced Medical Research Institute, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
- Department of Orthopaedics, Tianjin Medical University General Hospital, Tianjin Medical University, International Science and Technology Cooperation Base of Spinal Cord Injury, Tianjin Key Laboratory of Spine and Spinal Cord, Tianjin, China
- The Second Hospital of Shandong University, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
| | - Hengxing Zhou
- Department of Orthopaedics, Qilu Hospital of Shandong University, Shandong University Centre for Orthopaedics, Advanced Medical Research Institute, Cheeloo College of Medicine, Shandong University, Jinan, Shandong, China
- Center for Reproductive Medicine, Shandong University, Jinan, Shandong, China
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13
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Wannan CMJ, Nelson B, Addington J, Allott K, Anticevic A, Arango C, Baker JT, Bearden CE, Billah T, Bouix S, Broome MR, Buccilli K, Cadenhead KS, Calkins ME, Cannon TD, Cecci G, Chen EYH, Cho KIK, Choi J, Clark SR, Coleman MJ, Conus P, Corcoran CM, Cornblatt BA, Diaz-Caneja CM, Dwyer D, Ebdrup BH, Ellman LM, Fusar-Poli P, Galindo L, Gaspar PA, Gerber C, Glenthøj LB, Glynn R, Harms MP, Horton LE, Kahn RS, Kambeitz J, Kambeitz-Ilankovic L, Kane JM, Kapur T, Keshavan MS, Kim SW, Koutsouleris N, Kubicki M, Kwon JS, Langbein K, Lewandowski KE, Light GA, Mamah D, Marcy PJ, Mathalon DH, McGorry PD, Mittal VA, Nordentoft M, Nunez A, Pasternak O, Pearlson GD, Perez J, Perkins DO, Powers AR, Roalf DR, Sabb FW, Schiffman J, Shah JL, Smesny S, Spark J, Stone WS, Strauss GP, Tamayo Z, Torous J, Upthegrove R, Vangel M, Verma S, Wang J, Rossum IWV, Wolf DH, Wolff P, Wood SJ, Yung AR, Agurto C, Alvarez-Jimenez M, Amminger P, Armando M, Asgari-Targhi A, Cahill J, Carrión RE, Castro E, Cetin-Karayumak S, Mallar Chakravarty M, Cho YT, Cotter D, D’Alfonso S, Ennis M, Fadnavis S, Fonteneau C, Gao C, Gupta T, Gur RE, Gur RC, Hamilton HK, Hoftman GD, Jacobs GR, Jarcho J, Ji JL, Kohler CG, Lalousis PA, Lavoie S, Lepage M, Liebenthal E, Mervis J, Murty V, Nicholas SC, Ning L, Penzel N, Poldrack R, Polosecki P, Pratt DN, Rabin R, Rahimi Eichi H, Rathi Y, Reichenberg A, Reinen J, Rogers J, Ruiz-Yu B, Scott I, Seitz-Holland J, Srihari VH, Srivastava A, Thompson A, Turetsky BI, Walsh BC, Whitford T, Wigman JTW, Yao B, Yuen HP, Ahmed U, Byun A(JS, Chung Y, Do K, Hendricks L, Huynh K, Jeffries C, Lane E, Langholm C, Lin E, Mantua V, Santorelli G, Ruparel K, Zoupou E, Adasme T, Addamo L, Adery L, Ali M, Auther A, Aversa S, Baek SH, Bates K, Bathery A, Bayer JMM, Beedham R, Bilgrami Z, Birch S, Bonoldi I, Borders O, Borgatti R, Brown L, Bruna A, Carrington H, Castillo-Passi RI, Chen J, Cheng N, Ching AE, Clifford C, Colton BL, Contreras P, Corral S, Damiani S, Done M, Estradé A, Etuka BA, Formica M, Furlan R, Geljic M, Germano C, Getachew R, Goncalves M, Haidar A, Hartmann J, Jo A, John O, Kerins S, Kerr M, Kesselring I, Kim H, Kim N, Kinney K, Krcmar M, Kotler E, Lafanechere M, Lee C, Llerena J, Markiewicz C, Matnejl P, Maturana A, Mavambu A, Mayol-Troncoso R, McDonnell A, McGowan A, McLaughlin D, McIlhenny R, McQueen B, Mebrahtu Y, Mensi M, Hui CLM, Suen YN, Wong SMY, Morrell N, Omar M, Partridge A, Phassouliotis C, Pichiecchio A, Politi P, Porter C, Provenzani U, Prunier N, Raj J, Ray S, Rayner V, Reyes M, Reynolds K, Rush S, Salinas C, Shetty J, Snowball C, Tod S, Turra-Fariña G, Valle D, Veale S, Whitson S, Wickham A, Youn S, Zamorano F, Zavaglia E, Zinberg J, Woods SW, Shenton ME. Accelerating Medicines Partnership® Schizophrenia (AMP® SCZ): Rationale and Study Design of the Largest Global Prospective Cohort Study of Clinical High Risk for Psychosis. Schizophr Bull 2024; 50:496-512. [PMID: 38451304 PMCID: PMC11059785 DOI: 10.1093/schbul/sbae011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/08/2024]
Abstract
This article describes the rationale, aims, and methodology of the Accelerating Medicines Partnership® Schizophrenia (AMP® SCZ). This is the largest international collaboration to date that will develop algorithms to predict trajectories and outcomes of individuals at clinical high risk (CHR) for psychosis and to advance the development and use of novel pharmacological interventions for CHR individuals. We present a description of the participating research networks and the data processing analysis and coordination center, their processes for data harmonization across 43 sites from 13 participating countries (recruitment across North America, Australia, Europe, Asia, and South America), data flow and quality assessment processes, data analyses, and the transfer of data to the National Institute of Mental Health (NIMH) Data Archive (NDA) for use by the research community. In an expected sample of approximately 2000 CHR individuals and 640 matched healthy controls, AMP SCZ will collect clinical, environmental, and cognitive data along with multimodal biomarkers, including neuroimaging, electrophysiology, fluid biospecimens, speech and facial expression samples, novel measures derived from digital health technologies including smartphone-based daily surveys, and passive sensing as well as actigraphy. The study will investigate a range of clinical outcomes over a 2-year period, including transition to psychosis, remission or persistence of CHR status, attenuated positive symptoms, persistent negative symptoms, mood and anxiety symptoms, and psychosocial functioning. The global reach of AMP SCZ and its harmonized innovative methods promise to catalyze the development of new treatments to address critical unmet clinical and public health needs in CHR individuals.
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Affiliation(s)
- Cassandra M J Wannan
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Barnaby Nelson
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Jean Addington
- Department of Psychiatry, Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Kelly Allott
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Alan Anticevic
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Connecticut Mental Health Center, New Haven, CT, USA
| | - Celso Arango
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, IiSGM, CIBERSAM, Instituto de Salud Carlos III, School of Medicine, Universidad Complutense, Madrid, Spain
| | - Justin T Baker
- Department of Psychiatry, McLean Hospital and Harvard Medical School, Boston, MA, USA
| | - Carrie E Bearden
- Department of Psychiatry, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
- Department Biobehavioral Sciences and Psychology, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
| | - Tashrif Billah
- Department of Psychiatry, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Sylvain Bouix
- Department of Software Engineering and Information Technology, École de technologie supérieure, Montréal, Canada
| | - Matthew R Broome
- School of Psychology, Institute for Mental Health, University of Birmingham, Birmingham, UK
- Early Intervention for Psychosis Services, Birmingham Women’s and Children’s NHS Foundation Trust, Birmingham, UK
| | - Kate Buccilli
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | | | - Monica E Calkins
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Tyrone D Cannon
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Department of Psychology, Yale University, New Haven, CT, USA
| | | | - Eric Yu Hai Chen
- Department of Psychiatry, University of Hong Kong, Pok Fu Lam, Hong Kong
| | - Kang Ik K Cho
- Department Biobehavioral Sciences and Psychology, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
| | - Jimmy Choi
- Olin Neuropsychiatry Research Center, Hartford Hospital, Hartford, CT, USA
| | - Scott R Clark
- Discipline of Psychiatry, University of Adelaide, Adelaide, SA, Australia
- Basil Hetzel Institute, Woodville, SA, Australia
| | - Michael J Coleman
- Department Biobehavioral Sciences and Psychology, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
| | - Philippe Conus
- General Psychiatry Service, Treatment and Early Intervention in Psychosis Program (TIPP–Lausanne), Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | - Cheryl M Corcoran
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Barbara A Cornblatt
- Department of Psychiatry, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
| | - Covadonga M Diaz-Caneja
- Department of Child and Adolescent Psychiatry, Institute of Psychiatry and Mental Health, Hospital General Universitario Gregorio Marañón, IiSGM, CIBERSAM, Instituto de Salud Carlos III, School of Medicine, Universidad Complutense, Madrid, Spain
| | - Dominic Dwyer
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Bjørn H Ebdrup
- Centre for Neuropsychiatric Schizophrenia Research, CNSR Mental Health Centre, Glostrup, Copenhagen, Denmark
| | - Lauren M Ellman
- Department of Psychology and Neuroscience, Temple University, Philadelphia, PA, USA
| | - Paolo Fusar-Poli
- Department of Psychosis Studies, King’s College London, London, UK
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Liliana Galindo
- Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Pablo A Gaspar
- Department of Psychiatry, IMHAY, University of Chile, Santiago, Chile
| | - Carla Gerber
- Behavioral Health Services, PeaceHealth Medical Group, Eugene, OR, USA
| | - Louise Birkedal Glenthøj
- Copenhagen Research Centre for Mental Health, Mental Health Copenhagen, University of Copenhagen, Denmark
- Department of Psychology, University of Copenhagen, Copenhagen, Denmark
| | - Robert Glynn
- Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Biostatistics, Harvard T.H. Chan School of Public Health and Harvard Medical School, Boston, MA, USA
| | - Michael P Harms
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO, USA
| | - Leslie E Horton
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - René S Kahn
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Joseph Kambeitz
- Department of Psychiatry, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - Lana Kambeitz-Ilankovic
- Department of Psychiatry, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany
| | - John M Kane
- Department of Psychiatry, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
| | - Tina Kapur
- Department of Radiology, Brigham and Women’s Hospital, Boston, MA, USA
| | - Matcheri S Keshavan
- Department of Psychiatry, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Sung-Wan Kim
- Department of Psychiatry, Chonnam National University Medical School, Gwangju, Korea
- Mindlink, Gwangju Bukgu Mental Health Center, Gwangju, Korea
| | - Nikolaos Koutsouleris
- Department of Psychiatry and Psychotherapy, Ludwig Maximilian University of Munich, Munich, Germany
| | - Marek Kubicki
- Department Biobehavioral Sciences and Psychology, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Jun Soo Kwon
- Department of Psychiatry, Seoul National University College of Medicine, Seoul, Korea
- Department of Neuropsychiatry, Seoul National University Hospital, Seoul, Korea
| | - Kerstin Langbein
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
| | - Kathryn E Lewandowski
- Department of Psychiatry, McLean Hospital and Harvard Medical School, Boston, MA, USA
| | - Gregory A Light
- Department of Psychiatry, University of California, San Diego, CA, USA
- Veterans Affairs San Diego Health Care System, San Diego, CA, USA
| | - Daniel Mamah
- Department of Psychiatry, Washington University Medical School, St. Louis, MO, USA
| | | | - Daniel H Mathalon
- Department of Psychiatry and Behavioral Sciences and Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, CA, USA
- Mental Health Service 116D, Veterans Affairs San Francisco Health Care System, San Francisco, CA, USA
| | - Patrick D McGorry
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Vijay A Mittal
- Department of Psychology, Northwestern University, Evanston, IL, USA
| | - Merete Nordentoft
- Copenhagen Research Centre for Mental Health, Mental Health Copenhagen, University of Copenhagen, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Angela Nunez
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Connecticut Mental Health Center, New Haven, CT, USA
| | - Ofer Pasternak
- Department Biobehavioral Sciences and Psychology, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Godfrey D Pearlson
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Olin Neuropsychiatry Research Center, Hartford Hospital, Hartford, CT, USA
| | - Jesus Perez
- CAMEO, Early Intervention in Psychosis Service, Cambridgeshire and Peterborough NHS Foundation Trust, Cambridge, UK
- Department of Medicine, Institute of Biomedical Research (IBSAL), Universidad de Salamanca, Salamanca, Spain
| | - Diana O Perkins
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Albert R Powers
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Connecticut Mental Health Center, New Haven, CT, USA
| | - David R Roalf
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Fred W Sabb
- Prevention Science Institute, University of Oregon, Eugene, OR, USA
| | - Jason Schiffman
- Department of Psychological Science, University of California, Irvine, Irvine, CA, USA
| | - Jai L Shah
- PEPP-Montreal, Douglas Research Centre, Montreal, Quebec, Canada
- Department of Psychiatry, McGill University, Montreal, Quebec, Canada
| | - Stefan Smesny
- Department of Psychiatry and Psychotherapy, Jena University Hospital, Jena, Germany
| | - Jessica Spark
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - William S Stone
- Department of Psychiatry, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | | | - Zailyn Tamayo
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Connecticut Mental Health Center, New Haven, CT, USA
| | - John Torous
- Department of Psychiatry, Chonnam National University Medical School, Gwangju, Korea
| | - Rachel Upthegrove
- Department of Software Engineering and Information Technology, École de technologie supérieure, Montréal, Canada
- Birmingham Womens and Childrens, NHS Foundation Trust, Birmingham, UK
| | - Mark Vangel
- Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Swapna Verma
- Department of Psychosis, Institute of Mental Health, Singapore
| | - Jijun Wang
- Shanghai Mental Health Center, Shanghai Jiaotong University School of Medicine, Shanghai, China
| | - Inge Winter-van Rossum
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
| | - Daniel H Wolf
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Phillip Wolff
- Department of Psychology, Emory University, Atlanta, GA, USA
| | - Stephen J Wood
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
- School of Psychology, University of Birmingham, Edgbaston, UK
| | - Alison R Yung
- Institute of Mental and Physical Health and Clinical Translation (IMPACT), Deakin University, Geelong, VIC, Australia
- School of Health Sciences, University of Manchester, Manchester, UK
| | - Carla Agurto
- IBM T.J. Watson Research Center, Yorktown Heights, NY, USA
| | - Mario Alvarez-Jimenez
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Paul Amminger
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Marco Armando
- Youth Early Detection/Intervention in Psychosis Platform (Plateforme ERA), Service of Child and Adolescent Psychiatry, Department of Psychiatry, Lausanne University Hospital and The University of Lausanne, Lausanne, Switzerland
| | | | - John Cahill
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Connecticut Mental Health Center, New Haven, CT, USA
| | - Ricardo E Carrión
- Department of Psychiatry, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
| | - Eduardo Castro
- IBM T.J. Watson Research Center, Yorktown Heights, NY, USA
| | - Suheyla Cetin-Karayumak
- Department Biobehavioral Sciences and Psychology, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
| | | | - Youngsun T Cho
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Connecticut Mental Health Center, New Haven, CT, USA
| | - David Cotter
- Department Psychiatry, Beaumont Hospital, Dublin 9, Ireland
- Department of Psychiatry, Royal College of Surgeons in Ireland, Dublin 2, Ireland
| | - Simon D’Alfonso
- School of Computing and Information Systems, The University of Melbourne, Parkville, VIC, Australia
| | - Michaela Ennis
- Department of Psychiatry, McLean Hospital and Harvard Medical School, Boston, MA, USA
| | - Shreyas Fadnavis
- Department Biobehavioral Sciences and Psychology, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
| | - Clara Fonteneau
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Connecticut Mental Health Center, New Haven, CT, USA
| | - Caroline Gao
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Tina Gupta
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Raquel E Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ruben C Gur
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Holly K Hamilton
- Department of Psychiatry, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Gil D Hoftman
- Department of Psychiatry, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
- Department Biobehavioral Sciences and Psychology, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
| | - Grace R Jacobs
- Department of Psychiatry, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Johanna Jarcho
- Department of Psychology and Neuroscience, Temple University, Philadelphia, PA, USA
| | - Jie Lisa Ji
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Connecticut Mental Health Center, New Haven, CT, USA
| | - Christian G Kohler
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Paris Alexandros Lalousis
- School of Psychology, Institute for Mental Health, University of Birmingham, Birmingham, UK
- Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, UK
| | - Suzie Lavoie
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Martin Lepage
- Department of Psychiatry, McGill University, Montreal, Quebec, Canada
| | - Einat Liebenthal
- Program for Specialized Treatment Early in Psychosis (STEP), CMHC, New Haven, CT, USA
| | - Josh Mervis
- Department of Psychology and Neuroscience, Temple University, Philadelphia, PA, USA
| | - Vishnu Murty
- Department of Psychology and Neuroscience, Temple University, Philadelphia, PA, USA
| | - Spero C Nicholas
- Department of Psychiatry, Washington University Medical School, St. Louis, MO, USA
| | - Lipeng Ning
- Department Biobehavioral Sciences and Psychology, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Radiology, Brigham and Women’s Hospital, Boston, MA, USA
| | - Nora Penzel
- Department Biobehavioral Sciences and Psychology, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
| | - Russell Poldrack
- Department of Psychology, Stanford University, Stanford, CA, USA
| | | | - Danielle N Pratt
- Department of Psychology, Northwestern University, Evanston, IL, USA
| | - Rachel Rabin
- PEPP-Montreal, Douglas Research Centre, Montreal, Quebec, Canada
| | | | - Yogesh Rathi
- Department of Psychiatry, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women’s Hospital, Boston, MA, USA
| | - Avraham Reichenberg
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jenna Reinen
- IBM T.J. Watson Research Center, Yorktown Heights, NY, USA
| | - Jack Rogers
- Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, UK
| | - Bernalyn Ruiz-Yu
- Department of Psychiatry, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
| | - Isabelle Scott
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Johanna Seitz-Holland
- Department of Psychiatry, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Vinod H Srihari
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Program for Specialized Treatment Early in Psychosis (STEP), CMHC, New Haven, CT, USA
| | - Agrima Srivastava
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Andrew Thompson
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Bruce I Turetsky
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Barbara C Walsh
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Connecticut Mental Health Center, New Haven, CT, USA
| | - Thomas Whitford
- Orygen, Parkville, VIC, Australia
- School of Psychology, University of New South Wales (UNSW), Kensington, NSW, Australia
| | - Johanna T W Wigman
- Department of Psychiatry, Interdisciplinary Center Psychopathology and Emotion Regulation, University of Groningen, University Medical Center,Groningen, Netherlands
| | - Beier Yao
- Department of Psychiatry, McLean Hospital and Harvard Medical School, Boston, MA, USA
| | - Hok Pan Yuen
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | | | - Andrew (Jin Soo) Byun
- Division of Digital Psychiatry, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
- John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Yoonho Chung
- Department of Psychiatry, McLean Hospital and Harvard Medical School, Boston, MA, USA
| | - Kim Do
- Department of Psychiatry, Center for Psychiatric Neuroscience, Lausanne University Hospital and University of Lausanne (CHUV-UNIL), Lausanne, Switzerland
- Department of Psychological Medicine, Institute of Psychiatry, Psychology and Neuroscience King’s College London, London, UK
| | - Larry Hendricks
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Kevin Huynh
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Clark Jeffries
- Renaissance Computing Institute, University of North Carolina, Chapel Hill, NC, USA
| | - Erlend Lane
- Department of Psychiatry, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Carsten Langholm
- Department of Psychiatry, Beth Israel Deaconess Medical Center and Harvard Medical School, Boston, MA, USA
| | - Eric Lin
- Department of Psychiatry, McLean Hospital and Harvard Medical School, Boston, MA, USA
- Medical Informatics Fellowship, Veteran Affairs Boston Healthcare System, Boston, MA, USA
- Food and Drug Administration, Silver Spring, MD, USA
| | - Valentina Mantua
- Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA
| | - Gennarina Santorelli
- Department of Psychiatry, Chonnam National University Medical School, Gwangju, Korea
| | - Kosha Ruparel
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Eirini Zoupou
- Department of Psychology, Yale University, New Haven, CT, USA
| | - Tatiana Adasme
- Department of Psychiatry, IMHAY, University of Chile, Santiago, Chile
| | - Lauren Addamo
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Laura Adery
- Department of Psychiatry, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
- Department Biobehavioral Sciences and Psychology, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
| | - Munaza Ali
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Connecticut Mental Health Center, New Haven, CT, USA
| | - Andrea Auther
- Department of Psychiatry, Donald and Barbara Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
| | - Samantha Aversa
- PEPP-Montreal, Douglas Research Centre, Montreal, Quebec, Canada
| | - Seon-Hwa Baek
- Department of Psychiatry, Chonnam National University Medical School, Gwangju, Korea
- Mindlink, Gwangju Bukgu Mental Health Center, Gwangju, Korea
| | - Kelly Bates
- Department of Psychology, Northwestern University, Evanston, IL, USA
| | - Alyssa Bathery
- Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA
| | - Johanna M M Bayer
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Rebecca Beedham
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Zarina Bilgrami
- Department of Psychology, Emory University, Atlanta, GA, USA
| | - Sonia Birch
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Ilaria Bonoldi
- Department of Psychosis Studies, King’s College London, London, UK
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Owen Borders
- Department of Psychiatry, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Renato Borgatti
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- IRCCS Mondino Foundation, Pavia, Italy
| | - Lisa Brown
- Department of Radiology, Brigham and Women’s Hospital, Boston, MA, USA
| | - Alejandro Bruna
- Department of Psychiatry, IMHAY, University of Chile, Santiago, Chile
| | - Holly Carrington
- Department of Psychiatry, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Rolando I Castillo-Passi
- Department of Psychiatry, IMHAY, University of Chile, Santiago, Chile
- Department of Neurology and Psychiatry, Clínica Alemana—Universidad del Desarrollo, Santiago, Chile
| | - Justine Chen
- Department of Psychiatry, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Nicholas Cheng
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Ann Ee Ching
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Chloe Clifford
- School of Psychology, Institute for Mental Health, University of Birmingham, Birmingham, UK
| | - Beau-Luke Colton
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Pamela Contreras
- Department of Psychiatry, IMHAY, University of Chile, Santiago, Chile
| | - Sebastián Corral
- Department of Psychiatry, IMHAY, University of Chile, Santiago, Chile
| | - Stefano Damiani
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Monica Done
- Department of Psychiatry, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
- Department Biobehavioral Sciences and Psychology, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
| | - Andrés Estradé
- Early Psychosis Detection and Clinical Intervention (EPIC) Lab, Department of Psychosis Studies, King’s College London, London, UK
| | - Brandon Asika Etuka
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Melanie Formica
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Rachel Furlan
- Department of Psychology and Neuroscience, Temple University, Philadelphia, PA, USA
| | - Mia Geljic
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Carmela Germano
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Ruth Getachew
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | | | - Anastasia Haidar
- Department of Psychiatry, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Jessica Hartmann
- Department of Public Mental Health, Central Institute of Mental Health, Heidelberg Univeristy, Mannheim, Germany
| | - Anna Jo
- Department of Psychiatry, Chonnam National University Medical School, Gwangju, Korea
| | - Omar John
- Department of Psychiatry, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Sarah Kerins
- Early Psychosis Detection and Clinical Intervention (EPIC) Lab, Department of Psychosis Studies, King’s College London, London, UK
| | - Melissa Kerr
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Irena Kesselring
- Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA
| | - Honey Kim
- Department of Psychiatry, Chonnam National University Medical School, Gwangju, Korea
| | - Nicholas Kim
- Department of Psychiatry, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Kyle Kinney
- Department of Psychology and Neuroscience, Temple University, Philadelphia, PA, USA
| | - Marija Krcmar
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Elana Kotler
- Department of Psychiatry, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Melanie Lafanechere
- School of Psychology, University of Birmingham, Edgbaston, UK
- Centre for Human Brain Health, School of Psychology, University of Birmingham, Birmingham, UK
| | - Clarice Lee
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Joshua Llerena
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | | | | | | | - Aissata Mavambu
- School of Psychology, Institute for Mental Health, University of Birmingham, Birmingham, UK
| | | | - Amelia McDonnell
- Department of Psychology and Neuroscience, Temple University, Philadelphia, PA, USA
| | - Alessia McGowan
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | | | - Rebecca McIlhenny
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Brittany McQueen
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Yohannes Mebrahtu
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Martina Mensi
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- IRCCS Mondino Foundation, Pavia, Italy
| | | | - Yi Nam Suen
- Department of Psychiatry, University of Hong Kong, Pok Fu Lam, Hong Kong
| | | | - Neal Morrell
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Mariam Omar
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Alice Partridge
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Christina Phassouliotis
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Anna Pichiecchio
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- Neuroradiology Department, IRCCS Mondino Foundation, Pavia, Italy
| | - Pierluigi Politi
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Christian Porter
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA, USA
| | - Umberto Provenzani
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Nicholas Prunier
- Department of Psychiatry, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Jasmine Raj
- Department of Psychology, Northwestern University, Evanston, IL, USA
| | - Susan Ray
- Northwell Health, Glen Oaks, NY, USA
| | - Victoria Rayner
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Manuel Reyes
- Department of Psychiatry, IMHAY, University of Chile, Santiago, Chile
- Department of Neurology and Psychiatry, Clínica Alemana—Universidad del Desarrollo, Santiago, Chile
| | - Kate Reynolds
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Sage Rush
- Department of Psychiatry, Neurodevelopment & Psychosis Section, University of Pennsylvania, Philadelphia, PA, USA
| | - Cesar Salinas
- Department of Psychiatry, IMHAY, University of Chile, Santiago, Chile
| | - Jashmina Shetty
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Callum Snowball
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Sophie Tod
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | | | - Daniela Valle
- Department of Psychiatry, IMHAY, University of Chile, Santiago, Chile
| | - Simone Veale
- Department of Psychiatry, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Sarah Whitson
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Alana Wickham
- Department of Psychiatry, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
| | - Sarah Youn
- Centre for Youth Mental Health, The University of Melbourne, Parkville, VIC, Australia
- Orygen, Parkville, VIC, Australia
| | - Francisco Zamorano
- Unidad de imágenes cuantitativas avanzadas, departamento de imágenes, clínica alemana, universidad del Desarrollo, Santiago, Chile
- Facultad de ciencias para el cuidado de la salud, Universidad San Sebastián, Campus Los Leones, Santiago, Chile
| | - Elissa Zavaglia
- PEPP-Montreal, Douglas Research Centre, Montreal, Quebec, Canada
| | - Jamie Zinberg
- Department of Psychiatry, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
- Department Biobehavioral Sciences and Psychology, Semel Institute for Neuroscience and Human Behavior, University of California, Los Angeles, Los Angeles, CA, USA
| | - Scott W Woods
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
- Connecticut Mental Health Center, New Haven, CT, USA
| | - Martha E Shenton
- Department of Psychiatry, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women’s Hospital, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
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14
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Xia Y, Shi Y. Diffusion MRI harmonization via personalized template mapping. Hum Brain Mapp 2024; 45:e26661. [PMID: 38520363 PMCID: PMC10960558 DOI: 10.1002/hbm.26661] [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: 07/07/2023] [Revised: 11/17/2023] [Accepted: 03/07/2024] [Indexed: 03/25/2024] Open
Abstract
One fundamental challenge in diffusion magnetic resonance imaging (dMRI) harmonization is to disentangle the contributions of scanner-related effects from the variable brain anatomy for the observed imaging signals. Conventional harmonization methods rely on establishing an atlas space to resolve anatomical variability and generate a unified inter-site mapping function. However, this approach is limited in accounting for the misalignment of neuroanatomy that still widely persists even after registration, especially in regions close to cortical boundaries. To overcome this challenge, we propose a personalized framework in this paper to more effectively address the confounding from the misalignment of neuroanatomy in dMRI harmonization. Instead of using a common template representing site-effects for all subjects, the main novelty of our method is the adaptive computation of personalized templates for both source and target scanning sites to estimate the inter-site mapping function. We integrate our method with the rotation invariant spherical harmonics (RISH) features to achieve the harmonization of dMRI signals between sites. In our experiments, the proposed approach is applied to harmonize the dMRI data acquired from two scanning platforms: Siemens Prisma and GE MR750 from the Adolescent Brain Cognitive Development dataset and compared with a state-of-the-art method based on RISH features. Our results indicate that the proposed harmonization framework achieves superior performance not only in reducing inter-site variations due to scanner differences but also in preserving sex-related biological variability in original cohorts. Moreover, we assess the impact of harmonization on the estimation of fiber orientation distributions and show the robustness of the personalized harmonization procedure in preserving the fiber orientation of original dMRI signals.
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Affiliation(s)
- Yihao Xia
- USC Stevens Neuroimaging and Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
- Department of Electrical and Computer Engineering, Viterbi School of EngineeringUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Yonggang Shi
- USC Stevens Neuroimaging and Informatics Institute, Keck School of MedicineUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
- Department of Electrical and Computer Engineering, Viterbi School of EngineeringUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
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15
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Betz AK, Cetin-Karayumak S, Bonke EM, Seitz-Holland J, Zhang F, Pieper S, O'Donnell LJ, Tripodis Y, Rathi Y, Shenton ME, Koerte IK. Executive functioning, behavior, and white matter microstructure in the chronic phase after pediatric mild traumatic brain injury: results from the adolescent brain cognitive development study. Psychol Med 2024:1-11. [PMID: 38497117 DOI: 10.1017/s0033291724000229] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/19/2024]
Abstract
BACKGROUND Mild traumatic brain injury (mTBI) is common in children. Long-term cognitive and behavioral outcomes as well as underlying structural brain alterations following pediatric mTBI have yet to be determined. In addition, the effect of age-at-injury on long-term outcomes is largely unknown. METHODS Children with a history of mTBI (n = 406; Mage = 10 years, SDage = 0.63 years) who participated in the Adolescent Brain Cognitive Development (ABCD) study were matched (1:2 ratio) with typically developing children (TDC; n = 812) and orthopedic injury (OI) controls (n = 812). Task-based executive functioning, parent-rated executive functioning and emotion-regulation, and self-reported impulsivity were assessed cross-sectionally. Regression models were used to examine the effect of mTBI on these domains. The effect of age-at-injury was assessed by comparing children with their first mTBI at either 0-3, 4-7, or 8-10 years to the respective matched TDC controls. Fractional anisotropy (FA) and mean diffusivity (MD), both MRI-based measures of white matter microstructure, were compared between children with mTBI and controls. RESULTS Children with a history of mTBI displayed higher parent-rated executive dysfunction, higher impulsivity, and poorer self-regulation compared to both control groups. At closer investigation, these differences to TDC were only present in one respective age-at-injury group. No alterations were found in task-based executive functioning or white matter microstructure. CONCLUSIONS Findings suggest that everyday executive function, impulsivity, and emotion-regulation are affected years after pediatric mTBI. Outcomes were specific to the age at which the injury occurred, suggesting that functioning is differently affected by pediatric mTBI during vulnerable periods. Groups did not differ in white matter microstructure.
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Affiliation(s)
- Anja K Betz
- cBRAIN, Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Ludwig-Maximilians-Universität, Munich, Germany
| | - Suheyla Cetin-Karayumak
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Elena M Bonke
- cBRAIN, Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Ludwig-Maximilians-Universität, Munich, Germany
- Graduate School of Systemic Neurosciences, Ludwig-Maximilians-Universität, Munich, Germany
| | - Johanna Seitz-Holland
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Fan Zhang
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Lauren J O'Donnell
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Yorghos Tripodis
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Yogesh Rathi
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Martha E Shenton
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Inga K Koerte
- cBRAIN, Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, Ludwig-Maximilians-Universität, Munich, Germany
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Graduate School of Systemic Neurosciences, Ludwig-Maximilians-Universität, Munich, Germany
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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16
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Kim ME, Gao C, Cai LY, Yang Q, Newlin NR, Ramadass K, Jefferson A, Archer D, Shashikumar N, Pechman KR, Gifford KA, Hohman TJ, Beason-Held LL, Resnick SM, Winzeck S, Schilling KG, Zhang P, Moyer D, Landman BA. Empirical assessment of the assumptions of ComBat with diffusion tensor imaging. J Med Imaging (Bellingham) 2024; 11:024011. [PMID: 38655188 PMCID: PMC11034156 DOI: 10.1117/1.jmi.11.2.024011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2023] [Revised: 02/28/2024] [Accepted: 03/25/2024] [Indexed: 04/26/2024] Open
Abstract
Purpose Diffusion tensor imaging (DTI) is a magnetic resonance imaging technique that provides unique information about white matter microstructure in the brain but is susceptible to confounding effects introduced by scanner or acquisition differences. ComBat is a leading approach for addressing these site biases. However, despite its frequent use for harmonization, ComBat's robustness toward site dissimilarities and overall cohort size have not yet been evaluated in terms of DTI. Approach As a baseline, we match N = 358 participants from two sites to create a "silver standard" that simulates a cohort for multi-site harmonization. Across sites, we harmonize mean fractional anisotropy and mean diffusivity, calculated using participant DTI data, for the regions of interest defined by the JHU EVE-Type III atlas. We bootstrap 10 iterations at 19 levels of total sample size, 10 levels of sample size imbalance between sites, and 6 levels of mean age difference between sites to quantify (i) β AGE , the linear regression coefficient of the relationship between FA and age; (ii) γ ^ s f * , the ComBat-estimated site-shift; and (iii) δ ^ s f * , the ComBat-estimated site-scaling. We characterize the reliability of ComBat by evaluating the root mean squared error in these three metrics and examine if there is a correlation between the reliability of ComBat and a violation of assumptions. Results ComBat remains well behaved for β AGE when N > 162 and when the mean age difference is less than 4 years. The assumptions of the ComBat model regarding the normality of residual distributions are not violated as the model becomes unstable. Conclusion Prior to harmonization of DTI data with ComBat, the input cohort should be examined for size and covariate distributions of each site. Direct assessment of residual distributions is less informative on stability than bootstrap analysis. We caution use ComBat of in situations that do not conform to the above thresholds.
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Affiliation(s)
- Michael E. Kim
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
| | - Chenyu Gao
- Vanderbilt University, Department of Electrical Engineering, Nashville, Tennessee, United States
| | - Leon Y. Cai
- Vanderbilt University, Department of Biomedical Engineering, Nashville, Tennessee, United States
- Vanderbilt University, Medical Scientist Training Program, Nashville, Tennessee, United States
| | - Qi Yang
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
| | - Nancy R. Newlin
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
| | - Karthik Ramadass
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
- Vanderbilt University, Department of Electrical Engineering, Nashville, Tennessee, United States
| | - Angela Jefferson
- Vanderbilt University Medical Center, Vanderbilt Memory and Alzheimer’s Center, Nashville, Tennessee, United States
- Vanderbilt University Medical Center, Department of Medicine, Nashville, Tennessee, United States
- Vanderbilt University Medical Center, Department of Neurology, Nashville, Tennessee, United States
| | - Derek Archer
- Vanderbilt University Medical Center, Vanderbilt Memory and Alzheimer’s Center, Nashville, Tennessee, United States
- Vanderbilt University Medical Center, Vanderbilt Genetics Institute, Nashville, Tennessee, United States
| | - Niranjana Shashikumar
- Vanderbilt University Medical Center, Vanderbilt Memory and Alzheimer’s Center, Nashville, Tennessee, United States
| | - Kimberly R. Pechman
- Vanderbilt University Medical Center, Vanderbilt Memory and Alzheimer’s Center, Nashville, Tennessee, United States
| | - Katherine A. Gifford
- Vanderbilt University Medical Center, Vanderbilt Memory and Alzheimer’s Center, Nashville, Tennessee, United States
| | - Timothy J. Hohman
- Vanderbilt University Medical Center, Vanderbilt Memory and Alzheimer’s Center, Nashville, Tennessee, United States
- Vanderbilt University Medical Center, Vanderbilt Genetics Institute, Nashville, Tennessee, United States
| | - Lori L. Beason-Held
- National Institutes of Health, National Institute on Aging, Laboratory of Behavioral Neuroscience, Baltimore, Maryland, United States
| | - Susan M. Resnick
- National Institutes of Health, National Institute on Aging, Laboratory of Behavioral Neuroscience, Baltimore, Maryland, United States
| | - Stefan Winzeck
- Imperial College London, Department of Computing, BioMedIA Group, London, United Kingdom
| | - Kurt G. Schilling
- Vanderbilt University Medical Center, Department of Radiology, Nashville, Tennessee, United States
| | - Panpan Zhang
- Vanderbilt University Medical Center, Vanderbilt Memory and Alzheimer’s Center, Nashville, Tennessee, United States
- Vanderbilt University Medical Center, Department of Biostatistics, Nashville, Tennessee, United States
| | - Daniel Moyer
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
| | - Bennett A. Landman
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
- Vanderbilt University, Department of Electrical Engineering, Nashville, Tennessee, United States
- Vanderbilt University, Department of Biomedical Engineering, Nashville, Tennessee, United States
- Vanderbilt University Medical Center, Department of Biostatistics, Nashville, Tennessee, United States
- Vanderbilt University Institute of Imaging Science, Nashville, Tennessee, United States
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17
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Cetin-Karayumak S, Zhang F, Zurrin R, Billah T, Zekelman L, Makris N, Pieper S, O'Donnell LJ, Rathi Y. Harmonized diffusion MRI data and white matter measures from the Adolescent Brain Cognitive Development Study. Sci Data 2024; 11:249. [PMID: 38413633 PMCID: PMC10899197 DOI: 10.1038/s41597-024-03058-w] [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: 05/25/2023] [Accepted: 02/12/2024] [Indexed: 02/29/2024] Open
Abstract
The Adolescent Brain Cognitive Development (ABCD) Study® has collected data from over 10,000 children across 21 sites, providing insights into adolescent brain development. However, site-specific scanner variability has made it challenging to use diffusion MRI (dMRI) data from this study. To address this, a dataset of harmonized and processed ABCD dMRI data (from release 3) has been created, comprising quality-controlled imaging data from 9,345 subjects, focusing exclusively on the baseline session, i.e., the first time point of the study. This resource required substantial computational time (approx. 50,000 CPU hours) for harmonization, whole-brain tractography, and white matter parcellation. The dataset includes harmonized dMRI data, 800 white matter clusters, 73 anatomically labeled white matter tracts in full and low resolution, and 804 different dMRI-derived measures per subject (72.3 TB total size). Accessible via the NIMH Data Archive, it offers a large-scale dMRI dataset for studying structural connectivity in child and adolescent neurodevelopment. Additionally, several post-harmonization experiments were conducted to demonstrate the success of the harmonization process on the ABCD dataset.
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Affiliation(s)
- Suheyla Cetin-Karayumak
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA.
| | - Fan Zhang
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Ryan Zurrin
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Tashrif Billah
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Leo Zekelman
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Program in Speech and Hearing Bioscience and Technology, Division of Medical Sciences, Harvard University, Boston, Massachusetts, USA
| | - Nikos Makris
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | | | - Lauren J O'Donnell
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
| | - Yogesh Rathi
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
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18
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Li S, Zhang W, Yao S, He J, Zhu C, Gao J, Xue T, Xie G, Chen Y, Torio EF, Feng Y, Bastos DC, Rathi Y, Makris N, Kikinis R, Bi WL, Golby AJ, O'Donnell LJ, Zhang F. Tractography-based automated identification of the retinogeniculate visual pathway with novel microstructure-informed supervised contrastive learning. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.03.574115. [PMID: 38260369 PMCID: PMC10802389 DOI: 10.1101/2024.01.03.574115] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
The retinogeniculate visual pathway (RGVP) is responsible for carrying visual information from the retina to the lateral geniculate nucleus. Identification and visualization of the RGVP are important in studying the anatomy of the visual system and can inform the treatment of related brain diseases. Diffusion MRI (dMRI) tractography is an advanced imaging method that uniquely enables in vivo mapping of the 3D trajectory of the RGVP. Currently, identification of the RGVP from tractography data relies on expert (manual) selection of tractography streamlines, which is time-consuming, has high clinical and expert labor costs, and is affected by inter-observer variability. In this paper, we present a novel deep learning framework, DeepRGVP , to enable fast and accurate identification of the RGVP from dMRI tractography data. We design a novel microstructure-informed supervised contrastive learning method that leverages both streamline label and tissue microstructure information to determine positive and negative pairs. We propose a simple and successful streamline-level data augmentation method to address highly imbalanced training data, where the number of RGVP streamlines is much lower than that of non-RGVP streamlines. We perform comparisons with several state-of-the-art deep learning methods that were designed for tractography parcellation, and we show superior RGVP identification results using DeepRGVP. In addition, we demonstrate a good generalizability of DeepRGVP to dMRI tractography data from neurosurgical patients with pituitary tumors and we show DeepRGVP can successfully identify RGVPs despite the effect of lesions affecting the RGVPs. Overall, our study shows the high potential of using deep learning to automatically identify the RGVP.
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19
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Lin R, Gholipour A, Thiran JP, Karimi D, Kebiri H, Cuadra MB. CROSS-AGE AND CROSS-SITE DOMAIN SHIFT IMPACTS ON DEEP LEARNING-BASED WHITE MATTER FIBER ESTIMATION IN NEWBORN AND BABY BRAINS. ARXIV 2023:arXiv:2312.14773v1. [PMID: 38196752 PMCID: PMC10775359] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 01/11/2024]
Abstract
Deep learning models have shown great promise in estimating tissue microstructure from limited diffusion magnetic resonance imaging data. However, these models face domain shift challenges when test and train data are from different scanners and protocols, or when the models are applied to data with inherent variations such as the developing brains of infants and children scanned at various ages. Several techniques have been proposed to address some of these challenges, such as data harmonization or domain adaptation in the adult brain. However, those techniques remain unexplored for the estimation of fiber orientation distribution functions in the rapidly developing brains of infants. In this work, we extensively investigate the age effect and domain shift within and across two different cohorts of 201 newborns and 165 babies using the Method of Moments and fine-tuning strategies. Our results show that reduced variations in the microstructural development of babies in comparison to newborns directly impact the deep learning models' cross-age performance. We also demonstrate that a small number of target domain samples can significantly mitigate domain shift problems.
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Affiliation(s)
- Rizhong Lin
- Signal Processing Laboratory 5 (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
- College of Electronic and Information Engineering, Tongji University, Shanghai, China
| | - Ali Gholipour
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - Jean-Philippe Thiran
- Signal Processing Laboratory 5 (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
- CIBM Center for Biomedical Imaging, Switzerland
| | - Davood Karimi
- Computational Radiology Laboratory, Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, MA, USA
| | - Hamza Kebiri
- Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
- CIBM Center for Biomedical Imaging, Switzerland
| | - 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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20
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Gherardini L, Zajdel A, Pini L, Crimi A. Prediction of misfolded proteins spreading in Alzheimer's disease using machine learning and spreading models. Cereb Cortex 2023; 33:11471-11485. [PMID: 37833822 PMCID: PMC10724880 DOI: 10.1093/cercor/bhad380] [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: 08/08/2023] [Revised: 09/23/2023] [Accepted: 09/23/2023] [Indexed: 10/15/2023] Open
Abstract
The pervasive impact of Alzheimer's disease on aging society represents one of the main challenges at this time. Current investigations highlight 2 specific misfolded proteins in its development: Amyloid-$\beta$ and tau. Previous studies focused on spreading for misfolded proteins exploited simulations, which required several parameters to be empirically estimated. Here, we provide an alternative view based on 2 machine learning approaches which we compare with known simulation models. The first approach applies an autoregressive model constrained by structural connectivity, while the second is based on graph convolutional networks. The aim is to predict concentrations of Amyloid-$\beta$ 2 yr after a provided baseline. We also evaluate its real-world effectiveness and suitability by providing a web service for physicians and researchers. In experiments, the autoregressive model generally outperformed state-of-the-art models resulting in lower prediction errors. While it is important to note that a comprehensive prognostic plan cannot solely rely on amyloid beta concentrations, their prediction, achieved by the discussed approaches, can be valuable for planning therapies and other cures, especially when dealing with asymptomatic patients for whom novel therapies could prove effective.
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Affiliation(s)
- Luca Gherardini
- Computer Vision Data Science Group, Sano centre for computational medicine, Czarnowiejska 36, Krakow 30-054, Poland
| | - Aleksandra Zajdel
- Computer Vision Data Science Group, Sano centre for computational medicine, Czarnowiejska 36, Krakow 30-054, Poland
| | - Lorenzo Pini
- Padua Neuroscience Center, University of Padua, Via 8 Febbraio, 2, Padua 35122, Italy
| | - Alessandro Crimi
- Computer Vision Data Science Group, Sano centre for computational medicine, Czarnowiejska 36, Krakow 30-054, Poland
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21
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Joo SW, Jo YT, Ahn S, Choi YJ, Choi W, Kim SK, Joe S, Lee J. Structural impairment in superficial and deep white matter in schizophrenia. Acta Neuropsychiatr 2023:1-10. [PMID: 37620164 DOI: 10.1017/neu.2023.44] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 08/26/2023]
Abstract
OBJECTIVE Although disconnectivity among brain regions has been one of the main hypotheses for schizophrenia, the superficial white matter (SWM) has received less attention in schizophrenia research than the deep white matter (DWM) owing to the challenge of consistent reconstruction across subjects. METHODS We obtained the diffusion magnetic resonance imaging (dMRI) data of 223 healthy controls and 143 patients with schizophrenia. After harmonising the raw dMRIs from three different studies, we performed whole-brain two-tensor tractography and fibre clustering on the tractography data. We compared the fractional anisotropy (FA) of white matter tracts between healthy controls and patients with schizophrenia. Spearman's rho was adopted for the associations with clinical symptoms measured by the Positive and Negative Syndrome Scale (PANSS). The Bonferroni correction was used to adjust multiple testing. RESULTS Among the 33 DWM and 8 SWM tracts, patients with schizophrenia had a lower FA in 14 DWM and 4 SWM tracts than healthy controls, with small effect sizes. In the patient group, the FA deviations of the corticospinal and superficial-occipital tracts were negatively correlated with the PANSS negative score; however, this correlation was not evident after adjusting for multiple testing. CONCLUSION We observed the structural impairments of both the DWM and SWM tracts in patients with schizophrenia. The SWM could be a potential target of interest in future research on neural biomarkers for schizophrenia.
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Affiliation(s)
- Sung Woo Joo
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Young Tak Jo
- Department of Psychiatry, Kangdong Sacred Heart Hospital, Hallym University College of Medicine, Seoul, Republic of Korea
| | - Soojin Ahn
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Young Jae Choi
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Woohyeok Choi
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Sang Kyoung Kim
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Soohyun Joe
- Brain Laboratory, Department of Psychiatry, University of California San Diego, School of Medicine, San Diego, CA, USA
| | - Jungsun Lee
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
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22
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Newlin NR, Kim ME, Kanakaraj P, Yao T, Hohman T, Pechman KR, Beason-Held LL, Resnick SM, Archer D, Jefferson A, Landman BA, Moyer D. MidRISH: Unbiased harmonization of rotationally invariant harmonics of the diffusion signal. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.08.12.553099. [PMID: 37645973 PMCID: PMC10462069 DOI: 10.1101/2023.08.12.553099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/31/2023]
Abstract
Objective Data harmonization is necessary for removing confounding effects in multi-site diffusion image analysis. One such harmonization method, LinearRISH, scales rotationally invariant spherical harmonic (RISH) features from one site ("target") to the second ("reference") to reduce confounding scanner effects. However, reference and target site designations are not arbitrary and resultant diffusion metrics (fractional anisotropy, mean diffusivity) are biased by this choice. In this work we propose MidRISH: rather than scaling reference RISH features to target RISH features, we project both sites to a mid-space. Methods We validate MidRISH with the following experiments: harmonizing scanner differences from 37 matched patients free of cognitive impairment, and harmonizing acquisition and study differences on 117 matched patients free of cognitive impairment. Conclusion MidRISH reduces bias of reference selection while preserving harmonization efficacy of LinearRISH. Significance Users should be cautious when performing LinearRISH harmonization. To select a reference site is to choose diffusion metric effect-size. Our proposed method eliminates the bias-inducing site selection step.
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Affiliation(s)
- Nancy R Newlin
- Department of Computer Science at Vanderbilt University, Nashville, TN, USA
| | - Michael E Kim
- Department of Computer Science at Vanderbilt University, Nashville, TN, USA
| | | | - Tianyuan Yao
- Department of Computer Science at Vanderbilt University, Nashville, TN, USA
| | - Timothy Hohman
- VMAC, VUMC, Nashville, TN, USA and Vanderbilt University, Nashville, TN, USA
| | | | - Lori L Beason-Held
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Susan M Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, USA
| | - Derek Archer
- VMAC, VUMC, Nashville, TN, USA and Vanderbilt University, Nashville, TN, USA
| | - Angela Jefferson
- VMAC, VUMC, Nashville, TN, USA and Vanderbilt University, Nashville, TN, USA
| | - Bennett A Landman
- Department of Computer Science at Vanderbilt University, Nashville, TN, USA
| | - Daniel Moyer
- Department of Computer Science at Vanderbilt University, Nashville, TN, USA
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23
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Borrelli P, Savini G, Cavaliere C, Palesi F, Grazia Bruzzone M, Aquino D, Biagi L, Bosco P, Carne I, Ferraro S, Giulietti G, Napolitano A, Nigri A, Pavone L, Pirastru A, Redolfi A, Tagliavini F, Tosetti M, Salvatore M, Gandini Wheeler-Kingshott CAM, Aiello M. Normative values of the topological metrics of the structural connectome: A multi-site reproducibility study across the Italian Neuroscience network. Phys Med 2023; 112:102610. [PMID: 37331082 DOI: 10.1016/j.ejmp.2023.102610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Revised: 03/20/2023] [Accepted: 05/30/2023] [Indexed: 06/20/2023] Open
Abstract
PURPOSE The use of topological metrics to derive quantitative descriptors from structural connectomes is receiving increasing attention but deserves specific studies to investigate their reproducibility and variability in the clinical context. This work exploits the harmonization of diffusion-weighted acquisition for neuroimaging data performed by the Italian Neuroscience and Neurorehabilitation Network initiative to obtain normative values of topological metrics and to investigate their reproducibility and variability across centers. METHODS Different topological metrics, at global and local level, were calculated on multishell diffusion-weighted data acquired at high-field (e.g. 3 T) Magnetic Resonance Imaging scanners in 13 different centers, following the harmonization of the acquisition protocol, on young and healthy adults. A "traveling brains" dataset acquired on a subgroup of subjects at 3 different centers was also analyzed as reference data. All data were processed following a common processing pipeline that includes data pre-processing, tractography, generation of structural connectomes and calculation of graph-based metrics. The results were evaluated both with statistical analysis of variability and consistency among sites with the traveling brains range. In addition, inter-site reproducibility was assessed in terms of intra-class correlation variability. RESULTS The results show an inter-center and inter-subject variability of <10%, except for "clustering coefficient" (variability of 30%). Statistical analysis identifies significant differences among sites, as expected given the wide range of scanners' hardware. CONCLUSIONS The results show low variability of connectivity topological metrics across sites running a harmonised protocol.
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Affiliation(s)
| | | | | | - Fulvia Palesi
- Department of Brain and Behavioral Sciences, Università degli Studi di Pavia, Pavia, Italy
| | - Maria Grazia Bruzzone
- Neuroradiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Domenico Aquino
- Neuroradiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Laura Biagi
- Laboratory of Medical Physics and Magnetic Resonance, IRCCS Stella Maris Foundation, Pisa, Italy
| | - Paolo Bosco
- Laboratory of Medical Physics and Magnetic Resonance, IRCCS Stella Maris Foundation, Pisa, Italy
| | - Irene Carne
- Neuroradiology Unit, IRCCS Istituti Clinici Scientifici Maugeri, Pavia, Italy
| | - Stefania Ferraro
- Neuroradiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Giovanni Giulietti
- Neuroimaging Laboratory, IRCCS Santa Lucia Foundation, Rome, Italy; SAIMLAL Department, Sapienza University of Rome, Rome, Italy
| | - Antonio Napolitano
- Medical Physics, IRCCS Istituto Ospedale Pediatrico Bambino Gesù, Rome, Italy
| | - Anna Nigri
- Neuroradiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | | | | | - Alberto Redolfi
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Fabrizio Tagliavini
- Scientific Direction, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Michela Tosetti
- Laboratory of Medical Physics and Magnetic Resonance, IRCCS Stella Maris Foundation, Pisa, Italy
| | | | - Claudia A M Gandini Wheeler-Kingshott
- Department of Brain and Behavioral Sciences, Università degli Studi di Pavia, Pavia, Italy; NMR Research Unit, Department of Neuroinflammation, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
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Zhang R, Oliver LD, Voineskos AN, Park JY. RELIEF: A structured multivariate approach for removal of latent inter-scanner effects. IMAGING NEUROSCIENCE (CAMBRIDGE, MASS.) 2023; 1:1-16. [PMID: 37719839 PMCID: PMC10503485 DOI: 10.1162/imag_a_00011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 08/02/2023] [Indexed: 09/19/2023]
Abstract
Combining data collected from multiple study sites is becoming common and is advantageous to researchers to increase the generalizability and replicability of scientific discoveries. However, at the same time, unwanted inter-scanner biases are commonly observed across neuroimaging data collected from multiple study sites or scanners, rendering difficulties in integrating such data to obtain reliable findings. While several methods for handling such unwanted variations have been proposed, most of them use univariate approaches that could be too simple to capture all sources of scanner-specific variations. To address these challenges, we propose a novel multivariate harmonization method called RELIEF (REmoval of Latent Inter-scanner Effects through Factorization) for estimating and removing both explicit and latent scanner effects. Our method is the first approach to introduce the simultaneous dimension reduction and factorization of interlinked matrices to a data harmonization context, which provides a new direction in methodological research for correcting inter-scanner biases. Analyzing diffusion tensor imaging (DTI) data from the Social Processes Initiative in Neurobiology of the Schizophrenia (SPINS) study and conducting extensive simulation studies, we show that RELIEF outperforms existing harmonization methods in mitigating inter-scanner biases and retaining biological associations of interest to increase statistical power. RELIEF is publicly available as an R package.
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Affiliation(s)
- Rongqian Zhang
- Department of Statistical Sciences, University of Toronto, Toronto, Canada
| | | | - Aristotle N. Voineskos
- Centre for Addiction and Mental Health, Toronto, Canada
- Department of Psychiatry, University of Toronto, Toronto, Canada
| | - Jun Young Park
- Department of Statistical Sciences, University of Toronto, Toronto, Canada
- Department of Psychology, University of Toronto, Toronto, Canada
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25
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Hu F, Chen AA, Horng H, Bashyam V, Davatzikos C, Alexander-Bloch A, Li M, Shou H, Satterthwaite TD, Yu M, Shinohara RT. Image harmonization: A review of statistical and deep learning methods for removing batch effects and evaluation metrics for effective harmonization. Neuroimage 2023; 274:120125. [PMID: 37084926 PMCID: PMC10257347 DOI: 10.1016/j.neuroimage.2023.120125] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 04/12/2023] [Accepted: 04/19/2023] [Indexed: 04/23/2023] Open
Abstract
Magnetic resonance imaging and computed tomography from multiple batches (e.g. sites, scanners, datasets, etc.) are increasingly used alongside complex downstream analyses to obtain new insights into the human brain. However, significant confounding due to batch-related technical variation, called batch effects, is present in this data; direct application of downstream analyses to the data may lead to biased results. Image harmonization methods seek to remove these batch effects and enable increased generalizability and reproducibility of downstream results. In this review, we describe and categorize current approaches in statistical and deep learning harmonization methods. We also describe current evaluation metrics used to assess harmonization methods and provide a standardized framework to evaluate newly-proposed methods for effective harmonization and preservation of biological information. Finally, we provide recommendations to end-users to advocate for more effective use of current methods and to methodologists to direct future efforts and accelerate development of the field.
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Affiliation(s)
- Fengling Hu
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, United States.
| | - Andrew A Chen
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, United States
| | - Hannah Horng
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, United States
| | - Vishnu Bashyam
- Center for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, United States
| | - Christos Davatzikos
- Center for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, United States
| | - Aaron Alexander-Bloch
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, United States; Penn-CHOP Lifespan Brain Institute, United States; Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, United States
| | - Mingyao Li
- Statistical Center for Single-Cell and Spatial Genomics, Perelman School of Medicine, University of Pennsylvania, United States
| | - Haochang Shou
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, United States; Center for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, United States
| | - Theodore D Satterthwaite
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, United States; Penn-CHOP Lifespan Brain Institute, United States; The Penn Lifespan Informatics and Neuroimaging Center, Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, United States
| | - Meichen Yu
- Indiana Alzheimer's Disease Research Center, Indiana University School of Medicine, United States
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Endeavor (PennSIVE), Department of Biostatistics, Epidemiology, and Informatics, Perelman School of Medicine, University of Pennsylvania, 423 Guardian Dr, Philadelphia, PA 19104, United States; Center for Biomedical Image Computing and Analytics (CBICA), Perelman School of Medicine, United States
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26
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Levitt JJ, Zhang F, Vangel M, Nestor PG, Rathi Y, Cetin-Karayumak S, Kubicki M, Coleman MJ, Lewandowski KE, Holt DJ, Keshavan M, Bouix S, Öngür D, Breier A, Shenton ME, O'Donnell LJ. The organization of frontostriatal brain wiring in non-affective early psychosis compared with healthy subjects using a novel diffusion imaging fiber cluster analysis. Mol Psychiatry 2023; 28:2301-2311. [PMID: 37173451 DOI: 10.1038/s41380-023-02031-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 02/13/2023] [Accepted: 03/08/2023] [Indexed: 05/15/2023]
Abstract
BACKGROUND Alterations in brain connectivity may underlie neuropsychiatric conditions such as schizophrenia. We here assessed the degree of convergence of frontostriatal fiber projections in 56 young adult healthy controls (HCs) and 108 matched Early Psychosis-Non-Affective patients (EP-NAs) using our novel fiber cluster analysis of whole brain diffusion magnetic resonance imaging tractography. METHODS Using whole brain tractography and our fiber clustering methodology on harmonized diffusion magnetic resonance imaging data from the Human Connectome Project for Early Psychosis we identified 17 white matter fiber clusters that connect frontal cortex (FCtx) and caudate (Cd) per hemisphere in each group. To quantify the degree of convergence and, hence, topographical relationship of these fiber clusters, we measured the inter-cluster mean distances between the endpoints of the fiber clusters at the level of the FCtx and of the Cd, respectively. RESULTS We found (1) in both groups, bilaterally, a non-linear relationship, yielding convex curves, between FCtx and Cd distances for FCtx-Cd connecting fiber clusters, driven by a cluster projecting from inferior frontal gyrus; however, in the right hemisphere, the convex curve was more flattened in EP-NAs; (2) that cluster pairs in the right (p = 0.03), but not left (p = 0.13), hemisphere were significantly more convergent in HCs vs EP-NAs; (3) in both groups, bilaterally, similar clusters projected significantly convergently to the Cd; and, (4) a significant group by fiber cluster pair interaction for 2 right hemisphere fiber clusters (numbers 5, 11; p = .00023; p = .00023) originating in selective PFC subregions. CONCLUSIONS In both groups, we found the FCtx-Cd wiring pattern deviated from a strictly topographic relationship and that similar clusters projected significantly more convergently to the Cd. Interestingly, we also found a significantly more convergent pattern of connectivity in HCs in the right hemisphere and that 2 clusters from PFC subregions in the right hemisphere significantly differed in their pattern of connectivity between groups.
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Affiliation(s)
- J J Levitt
- Department of Psychiatry, VA Boston Healthcare System, Brockton Division, Brockton, MA, 02301, USA.
- Harvard Medical School, Boston, MA, 02115, USA.
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA.
| | - F Zhang
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - M Vangel
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
| | - P G Nestor
- Department of Psychiatry, VA Boston Healthcare System, Brockton Division, Brockton, MA, 02301, USA
- Harvard Medical School, Boston, MA, 02115, USA
- Department of Psychology, University of Massachusetts, Boston, MA, 02125, USA
| | - Y Rathi
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
| | - S Cetin-Karayumak
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - M Kubicki
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
| | - M J Coleman
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - K E Lewandowski
- McLean Hospital, Harvard Medical School, Belmont, MA, 02478, USA
| | - D J Holt
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
| | - M Keshavan
- Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, 02215, USA
| | - S Bouix
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Department of Software Engineering and Information Technology, École de technologie supérieure, Université du Québec, Montréal, QC, H3C 1K3, Canada
| | - D Öngür
- McLean Hospital, Harvard Medical School, Belmont, MA, 02478, USA
| | - A Breier
- Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - M E Shenton
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
| | - L J O'Donnell
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
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27
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Cetin-Karayumak S, Zhang F, Billah T, Zekelman L, Makris N, Pieper S, O’Donnell LJ, Rathi Y. Harmonized diffusion MRI data and white matter measures from the Adolescent Brain Cognitive Development Study. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.04.04.535587. [PMID: 37066186 PMCID: PMC10104063 DOI: 10.1101/2023.04.04.535587] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/18/2023]
Abstract
The Adolescent Brain Cognitive Development (ABCD) study has collected data from over 10,000 children across 21 sites, providing valuable insights into adolescent brain development. However, site-specific scanner variability has made it challenging to use diffusion MRI (dMRI) data from this study. To address this, a database of harmonized and processed ABCD dMRI data has been created, comprising quality-controlled imaging data from 9345 subjects. This resource required significant computational effort, taking ~50,000 CPU hours to harmonize the data, perform white matter parcellation, and run whole brain tractography. The database includes harmonized dMRI data, 800 white matter clusters, 73 anatomically labeled white matter tracts both in full-resolution (for analysis) and low-resolution (for visualization), and 804 different dMRI-derived measures per subject. It is available via the NIMH Data Archive and offers tremendous potential for scientific discoveries in structural connectivity studies of neurodevelopment in children and adolescents. Additionally, several post-harmonization experiments were conducted to demonstrate the success of the harmonization process on the ABCD dataset.
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Affiliation(s)
- Suheyla Cetin-Karayumak
- Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Fan Zhang
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Tashrif Billah
- Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Leo Zekelman
- Department of Neurosurgery, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Program in Speech and Hearing Bioscience and Technology, Division of Medical Sciences, Harvard University, Boston, Massachusetts, USA
| | - Nikos Makris
- Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | | | - Lauren J. O’Donnell
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Yogesh Rathi
- Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, Massachusetts, USA
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28
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Cetin-Karayumak S, Lyall AE, Di Biase MA, Seitz-Holland J, Zhang F, Kelly S, Elad D, Pearlson G, Tamminga CA, Sweeney JA, Clementz BA, Schretlen D, Stegmayer K, Walther S, Lee J, Crow T, James A, Voineskos A, Buchanan RW, Szeszko PR, Malhotra AK, Keshavan M, Shenton ME, Rathi Y, Pasternak O, Kubicki M. Characterization of the extracellular free water signal in schizophrenia using multi-site diffusion MRI harmonization. Mol Psychiatry 2023; 28:2030-2038. [PMID: 37095352 PMCID: PMC11146151 DOI: 10.1038/s41380-023-02068-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Revised: 03/06/2023] [Accepted: 04/05/2023] [Indexed: 04/26/2023]
Abstract
Studies applying Free Water Imaging have consistently reported significant global increases in extracellular free water (FW) in populations of individuals with early psychosis. However, these published studies focused on homogenous clinical participant groups (e.g., only first episode or chronic), thereby limiting our understanding of the time course of free water elevations across illness stages. Moreover, the relationship between FW and duration of illness has yet to be directly tested. Leveraging our multi-site diffusion magnetic resonance imaging(dMRI) harmonization approach, we analyzed dMRI scans collected by 12 international sites from 441 healthy controls and 434 individuals diagnosed with schizophrenia-spectrum disorders at different illness stages and ages (15-58 years). We characterized the pattern of age-related FW changes by assessing whole brain white matter in individuals with schizophrenia and healthy controls. In individuals with schizophrenia, average whole brain FW was higher than in controls across all ages, with the greatest FW values observed from 15 to 23 years (effect size range = [0.70-0.87]). Following this peak, FW exhibited a monotonic decrease until reaching a minima at the age of 39 years. After 39 years, an attenuated monotonic increase in FW was observed, but with markedly smaller effect sizes when compared to younger patients (effect size range = [0.32-0.43]). Importantly, FW was found to be negatively associated with duration of illness in schizophrenia (p = 0.006), independent of the effects of other clinical and demographic data. In summary, our study finds in a large, age-diverse sample that participants with schizophrenia with a shorter duration of illness showed higher FW values compared to participants with more prolonged illness. Our findings provide further evidence that elevations in the FW are present in individuals with schizophrenia, with the greatest differences in the FW being observed in those at the early stages of the disorder, which might suggest acute extracellular processes.
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Affiliation(s)
- Suheyla Cetin-Karayumak
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
| | - Amanda E Lyall
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Maria A Di Biase
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, VIC, Australia
| | - Johanna Seitz-Holland
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Fan Zhang
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Sinead Kelly
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Beth Israel Deaconess Medical Centre, Harvard Medical School, Boston, MA, USA
| | - Doron Elad
- Ruth and Bruce Rappaport Faculty of Medicine, Technion-Israel Institute of Technology, Haifa, Israel
| | | | - Carol A Tamminga
- Department of Psychiatry, UT Southwestern Medical Center, Dallas, TX, USA
| | - John A Sweeney
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH, USA
| | - Brett A Clementz
- Departments of Psychology and Neuroscience, Bio-Imaging Research Center, University of Georgia, Athens, GA, USA
| | - David Schretlen
- Department of Psychiatry and Behavioral Sciences, Morgan Department of Radiology and Radiological Science, Johns Hopkins Medical Institutions, Baltimore, MD, USA
| | - Katharina Stegmayer
- University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Sebastian Walther
- University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Jungsun Lee
- Department of Psychiatry, University of Ulsan College of Medicine, Asan Medical Center, Seoul, Korea
| | - Tim Crow
- Department of Psychiatry, SANE POWIC, Warneford Hospital, University of Oxford, Oxford, UK
| | - Anthony James
- Department of Psychiatry, SANE POWIC, Warneford Hospital, University of Oxford, Oxford, UK
| | | | - Robert W Buchanan
- Maryland Psychiatric Research Center, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Philip R Szeszko
- Departments of Psychiatry and Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mental Illness Research, Education, and Clinical Center, James J. Peters Veterans Affairs Medical Center, Bronx, NY, USA
| | - Anil K Malhotra
- The Feinstein Institutes for Medical Research and Zucker Hillside Hospital, Glen Oaks, NY, USA
| | - Matcheri Keshavan
- Department of Psychiatry, Beth Israel Deaconess Medical Centre, Harvard Medical School, Boston, MA, USA
| | - Martha E Shenton
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Yogesh Rathi
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Ofer Pasternak
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Marek Kubicki
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
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29
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Clemente A, Attyé A, Renard F, Calamante F, Burmester A, Imms P, Deutscher E, Akhlaghi H, Beech P, Wilson PH, Poudel G, Domínguez D JF, Caeyenberghs K. Individualised profiling of white matter organisation in moderate-to-severe traumatic brain injury patients. Brain Res 2023; 1806:148289. [PMID: 36813064 DOI: 10.1016/j.brainres.2023.148289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2022] [Revised: 12/22/2022] [Accepted: 02/15/2023] [Indexed: 02/22/2023]
Abstract
BACKGROUND AND PURPOSE Approximately 65% of moderate-to-severe traumatic brain injury (m-sTBI) patients present with poor long-term behavioural outcomes, which can significantly impair activities of daily living. Numerous diffusion-weighted MRI studies have linked these poor outcomes to decreased white matter integrity of several commissural tracts, association fibres and projection fibres in the brain. However, most studies have focused on group-based analyses, which are unable to deal with the substantial between-patient heterogeneity in m-sTBI. As a result, there is increasing interest and need in conducting individualised neuroimaging analyses. MATERIALS AND METHODS Here, we generated a detailed subject-specific characterisation of microstructural organisation of white matter tracts in 5 chronic patients with m-sTBI (29 - 49y, 2 females), presented as a proof-of-concept. We developed an imaging analysis framework using fixel-based analysis and TractLearn to determine whether the values of fibre density of white matter tracts at the individual patient level deviate from the healthy control group (n = 12, 8F, Mage = 35.7y, age range 25 - 64y). RESULTS Our individualised analysis revealed unique white matter profiles, confirming the heterogenous nature of m-sTBI and the need of individualised profiles to properly characterise the extent of injury. Future studies incorporating clinical data, as well as utilising larger reference samples and examining the test-retest reliability of the fixel-wise metrics are warranted. CONCLUSIONS Individualised profiles may assist clinicians in tracking recovery and planning personalised training programs for chronic m-sTBI patients, which is necessary to achieve optimal behavioural outcomes and improved quality of life.
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Affiliation(s)
- Adam Clemente
- Neuroscience of Addiction and Mental Health Program, Healthy Brain and Mind Research Centre, School of Behavioural, Health and Human Sciences, Faculty of Health Sciences, Australian Catholic University, Melbourne, Victoria, Australia.
| | - Arnaud Attyé
- CNRS LPNC UMR 5105, University of Grenoble Alpes, Grenoble, France; School of Biomedical Engineering, The University of Sydney, Sydney, New South Wales 2006, Australia
| | - Félix Renard
- CNRS LPNC UMR 5105, University of Grenoble Alpes, Grenoble, France
| | - Fernando Calamante
- School of Biomedical Engineering, The University of Sydney, Sydney, New South Wales 2006, Australia; Sydney Imaging - The University of Sydney, Sydney, Australia
| | - Alex Burmester
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, Victoria, Australia
| | - Phoebe Imms
- Leonard Davis School of Gerontology, University of Southern California, Australia
| | - Evelyn Deutscher
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, Victoria, Australia
| | - Hamed Akhlaghi
- Emergency Department, St. Vincent's Hospital, University of Melbourne, Melbourne, Victoria, Australia; Department of Psychology, Faculty of Health, Deakin University, Australia
| | - Paul Beech
- Department of Radiology and Nuclear Medicine, The Alfred Hospital, Melbourne, Victoria, Australia
| | - Peter H Wilson
- Development and Disability over the Lifespan Program, Healthy Brain and Mind Research Centre, School of Behavioural, Health and Human Sciences, Faculty of Health Sciences, Australian Catholic University, Melbourne, Victoria, Australia
| | - Govinda Poudel
- Mary MacKillop Institute for Health Research, Faculty of Health Sciences, Australian Catholic University, Melbourne, Victoria, Australia
| | - Juan F Domínguez D
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, Victoria, Australia
| | - Karen Caeyenberghs
- Cognitive Neuroscience Unit, School of Psychology, Deakin University, Geelong, Victoria, Australia
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Diffusion-Weighted Imaging in Mild Traumatic Brain Injury: A Systematic Review of the Literature. Neuropsychol Rev 2023; 33:42-121. [PMID: 33721207 DOI: 10.1007/s11065-021-09485-5] [Citation(s) in RCA: 10] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2019] [Accepted: 02/09/2021] [Indexed: 12/14/2022]
Abstract
There is evidence that diffusion-weighted imaging (DWI) is able to detect tissue alterations following mild traumatic brain injury (mTBI) that may not be observed on conventional neuroimaging; however, findings are often inconsistent between studies. This systematic review assesses patterns of differences in DWI metrics between those with and without a history of mTBI. A PubMed literature search was performed using relevant indexing terms for articles published prior to May 14, 2020. Findings were limited to human studies using DWI in mTBI. Articles were excluded if they were not full-length, did not contain original data, if they were case studies, pertained to military populations, had inadequate injury severity classification, or did not report post-injury interval. Findings were reported independently for four subgroups: acute/subacute pediatric mTBI, acute/subacute adult mTBI, chronic adult mTBI, and sport-related concussion, and all DWI acquisition and analysis methods used were included. Patterns of findings between studies were reported, along with strengths and weaknesses of the current state of the literature. Although heterogeneity of sample characteristics and study methods limited the consistency of findings, alterations in DWI metrics were most commonly reported in the corpus callosum, corona radiata, internal capsule, and long association pathways. Many acute/subacute pediatric studies reported higher FA and lower ADC or MD in various regions. In contrast, acute/subacute adult studies most commonly indicate lower FA within the context of higher MD and RD. In the chronic phase of recovery, FA may remain low, possibly indicating overall demyelination or Wallerian degeneration over time. Longitudinal studies, though limited, generally indicate at least a partial normalization of DWI metrics over time, which is often associated with functional improvement. We conclude that DWI is able to detect structural mTBI-related abnormalities that may persist over time, although future DWI research will benefit from larger samples, improved data analysis methods, standardized reporting, and increasing transparency.
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Dennis EL, Keleher F, Tate DF, Wilde EA. The Role of Neuroimaging in Evolving TBI Research and Clinical Practice. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.02.24.23286258. [PMID: 36865222 PMCID: PMC9980266 DOI: 10.1101/2023.02.24.23286258] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/28/2023]
Abstract
Neuroimaging technologies such as computed tomography (CT) and magnetic resonance imaging (MRI) have been widely adopted in the clinical diagnosis and management of traumatic brain injury (TBI), particularly at the more acute and severe levels of injury. Additionally, a number of advanced applications of MRI have been employed in TBI-related clinical research with great promise, and researchers have used these techniques to better understand underlying mechanisms, progression of secondary injury and tissue perturbation over time, and relation of focal and diffuse injury to later outcome. However, the acquisition and analysis time, the cost of these and other imaging modalities, and the need for specialized expertise have represented historical barriers in extending these tools in clinical practice. While group studies are important in detecting patterns, heterogeneity among patient presentation and limited sample sizes from which to compare individual level data to well-developed normative data have also played a role in the limited translatability of imaging to wider clinical application. Fortunately, the field of TBI has benefitted from increased public and scientific awareness of the prevalence and impact of TBI, particularly in head injury related to recent military conflicts and sport-related concussion. This awareness parallels an increase in federal funding in the United States and other countries allocated to investigation in these areas. In this article we summarize funding and publication trends since the mainstream adoption of imaging in TBI to elucidate evolving trends and priorities in the application of different techniques and patient populations. We also review recent and ongoing efforts to advance the field through promoting reproducibility, data sharing, big data analytic methods, and team science. Finally, we discuss international collaborative efforts to combine and harmonize neuroimaging, cognitive, and clinical data, both prospectively and retrospectively. Each of these represent unique, but related, efforts that facilitate closing gaps between the use of advanced imaging solely as a research tool and the use of it in clinical diagnosis, prognosis, and treatment planning and monitoring.
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Affiliation(s)
- Emily L Dennis
- Traumatic Brain Injury and Concussion Center, Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT
- George E. Wahlen Veterans Affairs Medical Center, Salt Lake City, UT
| | - Finian Keleher
- Traumatic Brain Injury and Concussion Center, Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT
| | - David F Tate
- Traumatic Brain Injury and Concussion Center, Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT
- George E. Wahlen Veterans Affairs Medical Center, Salt Lake City, UT
| | - Elisabeth A Wilde
- Traumatic Brain Injury and Concussion Center, Department of Neurology, University of Utah School of Medicine, Salt Lake City, UT
- George E. Wahlen Veterans Affairs Medical Center, Salt Lake City, UT
- Baylor College of Medicine, Houston, TX
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Lawrence KE, Abaryan Z, Laltoo E, Hernandez LM, Gandal MJ, McCracken JT, Thompson PM. White matter microstructure shows sex differences in late childhood: Evidence from 6797 children. Hum Brain Mapp 2023; 44:535-548. [PMID: 36177528 PMCID: PMC9842921 DOI: 10.1002/hbm.26079] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 07/29/2022] [Accepted: 08/19/2022] [Indexed: 02/01/2023] Open
Abstract
Sex differences in white matter microstructure have been robustly demonstrated in the adult brain using both conventional and advanced diffusion-weighted magnetic resonance imaging approaches. However, sex differences in white matter microstructure prior to adulthood remain poorly understood; previous developmental work focused on conventional microstructure metrics and yielded mixed results. Here, we rigorously characterized sex differences in white matter microstructure among over 6000 children from the Adolescent Brain Cognitive Development study who were between 9 and 10 years old. Microstructure was quantified using both the conventional model-diffusion tensor imaging (DTI)-and an advanced model, restriction spectrum imaging (RSI). DTI metrics included fractional anisotropy (FA) and mean, axial, and radial diffusivity (MD, AD, RD). RSI metrics included normalized isotropic, directional, and total intracellular diffusion (N0, ND, NT). We found significant and replicable sex differences in DTI or RSI microstructure metrics in every white matter region examined across the brain. Sex differences in FA were regionally specific. Across white matter regions, boys exhibited greater MD, AD, and RD than girls, on average. Girls displayed increased N0, ND, and NT compared to boys, on average, suggesting greater cell and neurite density in girls. Together, these robust and replicable findings provide an important foundation for understanding sex differences in health and disease.
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Affiliation(s)
- Katherine E. Lawrence
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics InstituteUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Zvart Abaryan
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics InstituteUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Emily Laltoo
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics InstituteUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
| | - Leanna M. Hernandez
- Department of Psychiatry and Biobehavioral SciencesUniversity of California Los AngelesLos AngelesCaliforniaUSA
| | - Michael J. Gandal
- Department of Psychiatry and Biobehavioral SciencesUniversity of California Los AngelesLos AngelesCaliforniaUSA
- Department of Neurology, Center for Autism Research and Treatment, Semel Institute, David Geffen School of MedicineUniversity of California Los AngelesLos AngelesCaliforniaUSA
- Department of Human Genetics, David Geffen School of MedicineUniversity of California Los AngelesLos AngelesCaliforniaUSA
| | - James T. McCracken
- Department of Psychiatry and Biobehavioral SciencesUniversity of California Los AngelesLos AngelesCaliforniaUSA
| | - Paul M. Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging and Informatics InstituteUniversity of Southern CaliforniaLos AngelesCaliforniaUSA
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33
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Schramm S, Börner C, Reichert M, Baum T, Zimmer C, Heinen F, Bonfert MV, Sollmann N. Functional magnetic resonance imaging in migraine: A systematic review. Cephalalgia 2023; 43:3331024221128278. [PMID: 36751858 DOI: 10.1177/03331024221128278] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/09/2023]
Abstract
BACKGROUND Migraine is a highly prevalent primary headache disorder. Despite a high burden of disease, key disease mechanisms are not entirely understood. Functional magnetic resonance imaging is an imaging method using the blood-oxygen-level-dependent signal, which has been increasingly used in migraine research over recent years. This systematic review summarizes recent findings employing functional magnetic resonance imaging for the investigation of migraine. METHODS We conducted a systematic search and selection of functional magnetic resonance imaging applications in migraine from April 2014 to December 2021 (PubMed and references of identified articles according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines). Methodological details and main findings were extracted and synthesized. RESULTS Out of 224 articles identified, 114 were included after selection. Repeatedly emerging structures of interest included the insula, brainstem, limbic system, hypothalamus, thalamus, and functional networks. Assessment of functional brain changes in response to treatment is emerging, and machine learning has been used to investigate potential functional magnetic resonance imaging-based markers of migraine. CONCLUSIONS A wide variety of functional magnetic resonance imaging-based metrics were found altered across the brain for heterogeneous migraine cohorts, partially correlating with clinical parameters and supporting the concept to conceive migraine as a brain state. However, a majority of findings from previous studies have not been replicated, and studies varied considerably regarding image acquisition and analyses techniques. Thus, while functional magnetic resonance imaging appears to have the potential to advance our understanding of migraine pathophysiology, replication of findings in large representative datasets and precise, standardized reporting of clinical data would likely benefit the field and further increase the value of observations.
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Affiliation(s)
- Severin Schramm
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Corinna Börner
- LMU Hospital, Dr. von Hauner Children's Hospital, Department of Pediatric Neurology and Developmental Medicine, Munich, Germany.,LMU Center for Children with Medical Complexity, iSPZ Hauner, Ludwig Maximilian University, Munich, Germany
| | - Miriam Reichert
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Thomas Baum
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Claus Zimmer
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.,TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany
| | - Florian Heinen
- LMU Hospital, Dr. von Hauner Children's Hospital, Department of Pediatric Neurology and Developmental Medicine, Munich, Germany
| | - Michaela V Bonfert
- LMU Hospital, Dr. von Hauner Children's Hospital, Department of Pediatric Neurology and Developmental Medicine, Munich, Germany.,LMU Center for Children with Medical Complexity, iSPZ Hauner, Ludwig Maximilian University, Munich, Germany
| | - Nico Sollmann
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.,TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, Munich, Germany.,Department of Diagnostic and Interventional Radiology, University Hospital Ulm, Ulm, Germany
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Marcolini S, Rojczyk P, Seitz-Holland J, Koerte IK, Alosco ML, Bouix S. Posttraumatic Stress and Traumatic Brain Injury: Cognition, Behavior, and Neuroimaging Markers in Vietnam Veterans. J Alzheimers Dis 2023; 95:1427-1448. [PMID: 37694363 PMCID: PMC10578246 DOI: 10.3233/jad-221304] [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] [Accepted: 07/24/2023] [Indexed: 09/12/2023]
Abstract
BACKGROUND Posttraumatic stress disorder (PTSD) and traumatic brain injury (TBI) are common in Veterans and linked to behavioral disturbances, increased risk of cognitive decline, and Alzheimer's disease. OBJECTIVE We studied the synergistic effects of PTSD and TBI on behavioral, cognitive, and neuroimaging measures in Vietnam war Veterans. METHODS Data were acquired at baseline and after about one-year from male Veterans categorized into: PTSD, TBI, PTSD+TBI, and Veteran controls without PTSD or TBI. We applied manual tractography to examine white matter microstructure of three fiber tracts: uncinate fasciculus (N = 91), cingulum (N = 87), and inferior longitudinal fasciculus (N = 95). ANCOVAs were used to compare Veterans' baseline behavioral and cognitive functioning (N = 285), white matter microstructure, amyloid-β (N = 230), and tau PET (N = 120). Additional ANCOVAs examined scores' differences from baseline to follow-up. RESULTS Veterans with PTSD and PTSD+TBI, but not Veterans with TBI only, exhibited poorer behavioral and cognitive functioning at baseline than controls. The groups did not differ in baseline white matter, amyloid-β, or tau, nor in behavioral and cognitive functioning, and tau accumulation change. Progression of white matter abnormalities of the uncinate fasciculus in Veterans with PTSD compared to controls was observed; analyses in TBI and PTSD+TBI were not run due to insufficient sample size. CONCLUSIONS PTSD and PTSD+TBI negatively affect behavioral and cognitive functioning, while TBI does not contribute independently. Whether progressive decline in uncinate fasciculus microstructure in Veterans with PTSD might account for cognitive decline should be further studied. Findings did not support an association between PTSD, TBI, and Alzheimer's disease pathology based on amyloid and tau PET.
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Affiliation(s)
- Sofia Marcolini
- Department of Neurology and Alzheimer Center, University Medical Center Groningen, Groningen, The Netherlands
| | - Philine Rojczyk
- Department of Psychiatry, Psychiatry Neuroimaging Laboratory, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- cBRAIN, Department of Child and Adolescent Psychiatry, Psychosomatics, and Psychotherapy, University Hospital, Ludwig Maximilian University Munich, Germany
| | - Johanna Seitz-Holland
- Department of Psychiatry, Psychiatry Neuroimaging Laboratory, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Inga K. Koerte
- Department of Psychiatry, Psychiatry Neuroimaging Laboratory, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- cBRAIN, Department of Child and Adolescent Psychiatry, Psychosomatics, and Psychotherapy, University Hospital, Ludwig Maximilian University Munich, Germany
| | - Michael L. Alosco
- Department of Neurology, Boston University Alzheimer’s Disease Research Center, Boston University CTE Center, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Sylvain Bouix
- Department of Psychiatry, Psychiatry Neuroimaging Laboratory, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Software Engineering and Information Technology, École de Technologie Supe´rieure, Montre´al, Canada
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35
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Dinsdale NK, Bluemke E, Sundaresan V, Jenkinson M, Smith SM, Namburete AIL. Challenges for machine learning in clinical translation of big data imaging studies. Neuron 2022; 110:3866-3881. [PMID: 36220099 DOI: 10.1016/j.neuron.2022.09.012] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2021] [Revised: 08/27/2021] [Accepted: 09/08/2022] [Indexed: 12/15/2022]
Abstract
Combining deep learning image analysis methods and large-scale imaging datasets offers many opportunities to neuroscience imaging and epidemiology. However, despite these opportunities and the success of deep learning when applied to a range of neuroimaging tasks and domains, significant barriers continue to limit the impact of large-scale datasets and analysis tools. Here, we examine the main challenges and the approaches that have been explored to overcome them. We focus on issues relating to data availability, interpretability, evaluation, and logistical challenges and discuss the problems that still need to be tackled to enable the success of "big data" deep learning approaches beyond research.
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Affiliation(s)
- Nicola K Dinsdale
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Oxford Machine Learning in NeuroImaging Lab, OMNI, Department of Computer Science, University of Oxford, Oxford, UK.
| | - Emma Bluemke
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Vaanathi Sundaresan
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA
| | - Mark Jenkinson
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Australian Institute for Machine Learning (AIML), School of Computer Science, University of Adelaide, Adelaide, SA, Australia; South Australian Health and Medical Research Institute (SAHMRI), North Terrace, Adelaide, SA, Australia
| | - Stephen M Smith
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Ana I L Namburete
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Oxford Machine Learning in NeuroImaging Lab, OMNI, Department of Computer Science, University of Oxford, Oxford, UK
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36
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de Brito Robalo BM, de Luca A, Chen C, Dewenter A, Duering M, Hilal S, Koek HL, Kopczak A, Lam BYK, Leemans A, Mok V, Onkenhout LP, van den Brink H, Biessels GJ. Improved sensitivity and precision in multicentre diffusion MRI network analysis using thresholding and harmonization. Neuroimage Clin 2022; 36:103217. [PMID: 36240537 PMCID: PMC9668636 DOI: 10.1016/j.nicl.2022.103217] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2022] [Revised: 08/22/2022] [Accepted: 10/01/2022] [Indexed: 11/06/2022]
Abstract
PURPOSE To investigate if network thresholding and raw data harmonization improve consistency of diffusion MRI (dMRI)-based brain networks while also increasing precision and sensitivity to detect disease effects in multicentre datasets. METHODS Brain networks were reconstructed from dMRI of five samples with cerebral small vessel disease (SVD; 629 patients, 166 controls), as a clinically relevant exemplar condition for studies on network integrity. We evaluated consistency of network architecture in age-matched controls, by calculating cross-site differences in connection probability and fractional anisotropy (FA). Subsequently we evaluated precision and sensitivity to disease effects by identifying connections with low FA in sporadic SVD patients relative to controls, using more severely affected patients with a pure form of genetically defined SVD as reference. RESULTS In controls, thresholding and harmonization improved consistency of network architecture, minimizing cross-site differences in connection probability and FA. In patients relative to controls, thresholding improved precision to detect disrupted connections by removing false positive connections (precision, before: 0.09-0.19; after: 0.38-0.70). Before harmonization, sensitivity was low within individual sites, with few connections surviving multiple testing correction (k = 0-25 connections). Harmonization and pooling improved sensitivity (k = 38), while also achieving higher precision when combined with thresholding (0.97). CONCLUSION We demonstrated that network consistency, precision and sensitivity to detect disease effects in SVD are improved by thresholding and harmonization. We recommend introducing these techniques to leverage large existing multicentre datasets to better understand the impact of disease on brain networks.
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Affiliation(s)
- Bruno M. de Brito Robalo
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands,Image Sciences Institute, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Alberto de Luca
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands,Image Sciences Institute, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Christopher Chen
- Memory, Aging and Cognition Center, Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Anna Dewenter
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Germany
| | - Marco Duering
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Germany,Medical Image Analysis Center (MIAC AG) and Department of Biomedical Engineering, University of Basel, Basel, Switzerland
| | - Saima Hilal
- Memory, Aging and Cognition Center, Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore,Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore
| | - Huiberdina L. Koek
- Department of Geriatric Medicine, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Anna Kopczak
- Institute for Stroke and Dementia Research (ISD), University Hospital, LMU Munich, Germany
| | - Bonnie Yin Ka Lam
- Division of Neurology, Department of Medicine and Therapeutics, Gerald Choa Neuroscience Centre, Faculty of Medicine, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region
| | - Alexander Leemans
- Image Sciences Institute, University Medical Center Utrecht, Utrecht University, Utrecht, the Netherlands
| | - Vincent Mok
- Division of Neurology, Department of Medicine and Therapeutics, Gerald Choa Neuroscience Centre, Faculty of Medicine, Prince of Wales Hospital, The Chinese University of Hong Kong, Shatin, Hong Kong Special Administrative Region
| | - Laurien P. Onkenhout
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Hilde van den Brink
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Geert Jan Biessels
- Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands,Corresponding author at: Department of Neurology and Neurosurgery, UMC Utrecht Brain Center, University Medical Center Utrecht, P.O. Box 85500, 3508 GA Utrecht, the Netherlands.
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37
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Singh NM, Harrod JB, Subramanian S, Robinson M, Chang K, Cetin-Karayumak S, Dalca AV, Eickhoff S, Fox M, Franke L, Golland P, Haehn D, Iglesias JE, O'Donnell LJ, Ou Y, Rathi Y, Siddiqi SH, Sun H, Westover MB, Whitfield-Gabrieli S, Gollub RL. How Machine Learning is Powering Neuroimaging to Improve Brain Health. Neuroinformatics 2022; 20:943-964. [PMID: 35347570 PMCID: PMC9515245 DOI: 10.1007/s12021-022-09572-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/07/2022] [Indexed: 12/31/2022]
Abstract
This report presents an overview of how machine learning is rapidly advancing clinical translational imaging in ways that will aid in the early detection, prediction, and treatment of diseases that threaten brain health. Towards this goal, we aresharing the information presented at a symposium, "Neuroimaging Indicators of Brain Structure and Function - Closing the Gap Between Research and Clinical Application", co-hosted by the McCance Center for Brain Health at Mass General Hospital and the MIT HST Neuroimaging Training Program on February 12, 2021. The symposium focused on the potential for machine learning approaches, applied to increasingly large-scale neuroimaging datasets, to transform healthcare delivery and change the trajectory of brain health by addressing brain care earlier in the lifespan. While not exhaustive, this overview uniquely addresses many of the technical challenges from image formation, to analysis and visualization, to synthesis and incorporation into the clinical workflow. Some of the ethical challenges inherent to this work are also explored, as are some of the regulatory requirements for implementation. We seek to educate, motivate, and inspire graduate students, postdoctoral fellows, and early career investigators to contribute to a future where neuroimaging meaningfully contributes to the maintenance of brain health.
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Affiliation(s)
- Nalini M Singh
- Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Jordan B Harrod
- Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Sandya Subramanian
- Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Mitchell Robinson
- Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Ken Chang
- Harvard-MIT Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Suheyla Cetin-Karayumak
- Department of Psychiatry, Brigham and Women's Hospital and Harvard Medical School, Boston, 02115, USA
| | | | - Simon Eickhoff
- Institute of Systems Neuroscience, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7) Research Centre Jülich, Jülich, Germany
| | - Michael Fox
- Center for Brain Circuit Therapeutics, Department of Neurology, Psychiatry, and Radiology, Brigham and Women's Hospital and Harvard Medical School, 02115, Boston, USA
| | - Loraine Franke
- University of Massachusetts Boston, Boston, MA, 02125, USA
| | - Polina Golland
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Daniel Haehn
- University of Massachusetts Boston, Boston, MA, 02125, USA
| | - Juan Eugenio Iglesias
- Centre for Medical Image Computing, University College London, London, UK
- Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, 02114, USA
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, 02139, USA
| | - Lauren J O'Donnell
- Department of Radiology, Brigham and Women's Hospital and Harvard Medical School, MA, 02115, Boston, USA
| | - Yangming Ou
- Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - Yogesh Rathi
- Department of Psychiatry, Brigham and Women's Hospital and Harvard Medical School, Boston, 02115, USA
| | - Shan H Siddiqi
- Department of Psychiatry, Brigham and Women's Hospital and Harvard Medical School, Boston, 02115, USA
| | - Haoqi Sun
- Department of Neurology and McCance Center for Brain Health / Harvard Medical School, Massachusetts General Hospital, Boston, 02114, USA
| | - M Brandon Westover
- Department of Neurology and McCance Center for Brain Health / Harvard Medical School, Massachusetts General Hospital, Boston, 02114, USA
| | | | - Randy L Gollub
- Department of Psychiatry and Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, 02114, USA.
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38
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Palacios EM, Yuh EL, Mac Donald CL, Bourla I, Wren-Jarvis J, Sun X, Vassar MJ, Diaz-Arrastia R, Giacino JT, Okonkwo DO, Robertson CS, Stein MB, Temkin N, McCrea MA, Levin HS, Markowitz AJ, Jain S, Manley GT, Mukherjee P. Diffusion Tensor Imaging Reveals Elevated Diffusivity of White Matter Microstructure that Is Independently Associated with Long-Term Outcome after Mild Traumatic Brain Injury: A TRACK-TBI Study. J Neurotrauma 2022; 39:1318-1328. [PMID: 35579949 PMCID: PMC9529303 DOI: 10.1089/neu.2021.0408] [Citation(s) in RCA: 18] [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] [Indexed: 02/01/2023] Open
Abstract
Diffusion tensor imaging (DTI) literature on single-center studies contains conflicting results regarding acute effects of mild traumatic brain injury (mTBI) on white matter (WM) microstructure and the prognostic significance. This larger-scale multi-center DTI study aimed to determine how acute mTBI affects WM microstructure over time and how early WM changes affect long-term outcome. From Transforming Research and Clinical Knowledge in Traumatic Brain Injury (TRACK-TBI), a cohort study at 11 United States level 1 trauma centers, a total of 391 patients with acute mTBI ages 17 to 60 years were included and studied at two weeks and six months post-injury. Demographically matched friends or family of the participants were the control group (n = 148). Axial diffusivity (AD), fractional anisotropy (FA), mean diffusivity (MD), and radial diffusivity (RD) were the measures of WM microstructure. The primary outcome was the Glasgow Outcome Scale Extended (GOSE) score of injury-related functional limitations across broad life domains at six months post-injury. The AD, MD, and RD were higher and FA was lower in mTBI versus friend control (FC) at both two weeks and six months post-injury throughout most major WM tracts of the cerebral hemispheres. In the mTBI group, AD and, to a lesser extent, MD decreased in WM from two weeks to six months post-injury. At two weeks post-injury, global WM AD and MD were both independently associated with six-month incomplete recovery (GOSE <8 vs = 8) even after accounting for demographic, clinical, and other imaging factors. DTI provides reliable imaging biomarkers of dynamic WM microstructural changes after mTBI that have utility for patient selection and treatment response in clinical trials. Continued technological advances in the sensitivity, specificity, and precision of diffusion magnetic resonance imaging hold promise for routine clinical application in mTBI.
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Affiliation(s)
- Eva M. Palacios
- Department of Radiology and Biomedical Imaging, UCSF, San Francisco, California, USA
| | - Esther L. Yuh
- Department of Radiology and Biomedical Imaging, UCSF, San Francisco, California, USA
- Brain and Spinal Cord Injury Center, Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, California, USA
| | | | - Ioanna Bourla
- Department of Radiology and Biomedical Imaging, UCSF, San Francisco, California, USA
| | - Jamie Wren-Jarvis
- Department of Radiology and Biomedical Imaging, UCSF, San Francisco, California, USA
| | - Xiaoying Sun
- Biostatistics Research Center, Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, La Jolla, California, USA
| | - Mary J. Vassar
- Brain and Spinal Cord Injury Center, Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, California, USA
- Department of Neurological Surgery, UCSF, San Francisco, California, USA
| | - Ramon Diaz-Arrastia
- Department of Neurology, University of Pennsylvania, Philadelphia, Pennsylvania, USA
| | - Joseph T. Giacino
- Department of Physical Medicine and Rehabilitation, Spaulding Rehabilitation Hospital, Charlestown, Massachusetts, USA
- Department of Physical Medicine and Rehabilitation, Harvard Medical School, Boston, Massachusetts, USA
| | - David O. Okonkwo
- Department of Neurological Surgery, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA
| | | | - Murray B. Stein
- Biostatistics Research Center, Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, La Jolla, California, USA
- Department of Psychiatry, University of California, San Diego, La Jolla, California, USA
| | - Nancy Temkin
- Department of Neurological Surgery, University of Washington, Seattle, Washington, USA
| | - Michael A. McCrea
- Department of Neurosurgery and Neurology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Harvey S. Levin
- Department of Neurology, Baylor College of Medicine, Houston, Texas, USA
| | - Amy J. Markowitz
- Department of Neurological Surgery, University of Washington, Seattle, Washington, USA
- Biostatistics Research Center, Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, La Jolla, California, USA
| | - Sonia Jain
- Biostatistics Research Center, Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, La Jolla, California, USA
| | - Geoffrey T. Manley
- Department of Neurological Surgery, University of Washington, Seattle, Washington, USA
- Biostatistics Research Center, Herbert Wertheim School of Public Health and Human Longevity Science, University of California, San Diego, La Jolla, California, USA
| | - Pratik Mukherjee
- Department of Radiology and Biomedical Imaging, UCSF, San Francisco, California, USA
- Brain and Spinal Cord Injury Center, Zuckerberg San Francisco General Hospital and Trauma Center, San Francisco, California, USA
- Department of Bioengineering and Therapeutic Sciences, UCSF, San Francisco, California, USA
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Seitz-Holland J, Wojcik JD, Cetin-Karayumak S, Lyall AE, Pasternak O, Rathi Y, Vangel M, Pearlson G, Tamminga C, Sweeney JA, Clementz BA, Schretlen DA, Viher PV, Stegmayer K, Walther S, Lee J, Crow T, James A, Voineskos A, Buchanan RW, Szeszko PR, Malhotra AK, Kelly S, Shenton ME, Keshavan MS, Mesholam-Gately RI, Kubicki M. Cognitive deficits, clinical variables, and white matter microstructure in schizophrenia: a multisite harmonization study. Mol Psychiatry 2022; 27:3719-3730. [PMID: 35982257 PMCID: PMC10538303 DOI: 10.1038/s41380-022-01731-3] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/11/2022] [Revised: 07/18/2022] [Accepted: 07/29/2022] [Indexed: 02/08/2023]
Abstract
Cognitive deficits are among the best predictors of real-world functioning in schizophrenia. However, our understanding of how cognitive deficits relate to neuropathology and clinical presentation over the disease lifespan is limited. Here, we combine multi-site, harmonized cognitive, imaging, demographic, and clinical data from over 900 individuals to characterize a) cognitive deficits across the schizophrenia lifespan and b) the association between cognitive deficits, clinical presentation, and white matter (WM) microstructure. Multimodal harmonization was accomplished using T-scores for cognitive data, previously reported standardization methods for demographic and clinical data, and an established harmonization method for imaging data. We applied t-tests and correlation analysis to describe cognitive deficits in individuals with schizophrenia. We then calculated whole-brain WM fractional anisotropy (FA) and utilized regression-mediation analyses to model the association between diagnosis, FA, and cognitive deficits. We observed pronounced cognitive deficits in individuals with schizophrenia (p < 0.006), associated with more positive symptoms and medication dosage. Regression-mediation analyses showed that WM microstructure mediated the association between schizophrenia and language/processing speed/working memory/non-verbal memory. In addition, processing speed mediated the influence of diagnosis and WM microstructure on the other cognitive domains. Our study highlights the critical role of cognitive deficits in schizophrenia. We further show that WM is crucial when trying to understand the role of cognitive deficits, given that it explains the association between schizophrenia and cognitive deficits (directly and via processing speed).
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Affiliation(s)
- Johanna Seitz-Holland
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
| | - Joanne D Wojcik
- Department of Psychiatry, Beth Israel Deaconess Medical Center and Massachusetts Mental Health Center, Harvard Medical School, Boston, MA, USA
| | - Suheyla Cetin-Karayumak
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Amanda E Lyall
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Ofer Pasternak
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Yogesh Rathi
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Mark Vangel
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | | | - Carol Tamminga
- Department of Psychiatry, UT Southwestern Medical Center, Dallas, TX, USA
| | - John A Sweeney
- Department of Psychiatry and Behavioral Neuroscience, University of Cincinnati, Cincinnati, OH, USA
| | - Brett A Clementz
- Department of Psychology and Neuroscience, Bio-Imaging Research Center, University of Georgia, Athens, GA, USA
| | - David A Schretlen
- Department of Psychiatry and Behavioral Sciences, Morgan Department of Radiology and Radiological Science, Johns Hopkins Medical Institutions, Baltimore, MD, USA
| | - Petra Verena Viher
- University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Katharina Stegmayer
- University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Sebastian Walther
- University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Jungsun Lee
- Department of Psychiatry, University of Ulsan College of Medicine, Asan Medical Center, Seoul, South Korea
| | - Tim Crow
- Department of Psychiatry, SANE POWIC, Warneford Hospital, University of Oxford, Oxford, UK
| | - Anthony James
- Department of Psychiatry, SANE POWIC, Warneford Hospital, University of Oxford, Oxford, UK
| | - Aristotle Voineskos
- Center for Addiction and Mental Health, Department of Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Robert W Buchanan
- Maryland Psychiatric Research Center, Department of Psychiatry, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Philip R Szeszko
- Department of Psychiatry, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Mental Illness Research, Education and Clinical Center, James J. Peters VA Medical Center, Bronx, New York, NY, USA
| | - Anil K Malhotra
- The Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY, USA
| | - Sinead Kelly
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Beth Israel Deaconess Medical Center and Massachusetts Mental Health Center, Harvard Medical School, Boston, MA, USA
| | - Martha E Shenton
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Matcheri S Keshavan
- Department of Psychiatry, Beth Israel Deaconess Medical Center and Massachusetts Mental Health Center, Harvard Medical School, Boston, MA, USA
| | - Raquelle I Mesholam-Gately
- Department of Psychiatry, Beth Israel Deaconess Medical Center and Massachusetts Mental Health Center, Harvard Medical School, Boston, MA, USA
| | - Marek Kubicki
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
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Xia Y, Shi Y. Personalized dMRI Harmonization on Cortical Surface. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2022; 13436:717-725. [PMID: 38500664 PMCID: PMC10948117 DOI: 10.1007/978-3-031-16446-0_68] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/20/2024]
Abstract
The inter-site variability of diffusion magnetic resonance imaging (dMRI) hinders the aggregation of dMRI data from multiple centers. This necessitates dMRI harmonization for removing non-biological site-effects. Recently, the emergence of high-resolution dMRI data across various connectome imaging studies allows the large-scale analysis of cortical micro-structure. Existing harmonization methods, however, perform poorly in the harmonization of dMRI data in cortical areas because they rely on image registration methods to factor out anatomical variations, which have known difficulty in aligning cortical folding patterns. To overcome this fundamental challenge in dMRI harmonization, we propose a framework of personalized dMRI harmonization on the cortical surface to improve the dMRI harmonization of gray matter by adaptively estimating the inter-site harmonization mappings. In our experiments, we demonstrate the effectiveness of the proposed method by applying it to harmonize dMRI across the Human Connectome Project (HCP) and the Lifespan Human Connectome Projects in Development (HCPD) studies and achieved much better performance in comparison with conventional methods based on image registration.
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Affiliation(s)
- Yihao Xia
- Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California (USC), Los Angeles, CA 90033, USA
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California (USC), Los Angeles, CA 90089, USA
| | - Yonggang Shi
- Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California (USC), Los Angeles, CA 90033, USA
- Ming Hsieh Department of Electrical and Computer Engineering, Viterbi School of Engineering, University of Southern California (USC), Los Angeles, CA 90089, USA
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Oladosu O, Liu WQ, Brown L, Pike BG, Metz LM, Zhang Y. Advanced diffusion MRI and image texture analysis detect widespread brain structural differences between relapsing-remitting and secondary progressive multiple sclerosis. Front Hum Neurosci 2022; 16:944908. [PMID: 36034111 PMCID: PMC9413838 DOI: 10.3389/fnhum.2022.944908] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2022] [Accepted: 07/18/2022] [Indexed: 11/29/2022] Open
Abstract
Introduction Disease development in multiple sclerosis (MS) causes dramatic structural changes, but the exact changing patterns are unclear. Our objective is to investigate the differences in brain structure locally and spatially between relapsing-remitting MS (RRMS) and its advanced form, secondary progressive MS (SPMS), through advanced analysis of diffusion magnetic resonance imaging (MRI) and image texture. Methods A total of 20 patients with RRMS and nine patients with SPMS from two datasets underwent 3T anatomical and diffusion tensor imaging (DTI). The DTI was harmonized, augmented, and then modeled, which generated six voxel- and sub-voxel-scale measures. Texture analysis focused on T2 and FLAIR MRI, which produced two phase-based measures, namely, phase congruency and weighted mean phase. Data analysis was 3-fold, i.e., histogram analysis of whole-brain normal appearing white matter (NAWM); region of interest (ROI) analysis of NAWM and lesions within three critical white matter tracts, namely, corpus callosum, corticospinal tract, and optic radiation; and along-tract statistics. Furthermore, by calculating the z-score of core-rim pathology within lesions based on diffusion measures, we developed a novel method to define chronic active lesions and compared them between cohorts. Results Histogram features from diffusion and all but one texture measure differentiated between RRMS and SPMS. Within-tract ROI analysis detected cohort differences in both NAWM and lesions of the corpus callosum body in three measures of neurite orientation and anisotropy. Along-tract statistics detected cohort differences from multiple measures, particularly lesion extent, which increased significantly in SPMS in posterior corpus callosum and optic radiations. The number of chronic active lesions were also significantly higher (by 5-20% over z-scores 0.5 and 1.0) in SPMS than RRMS based on diffusion anisotropy, neurite content, and diameter. Conclusion Advanced diffusion MRI and texture analysis may be promising approaches for thorough understanding of brain structural changes from RRMS to SPMS, thereby providing new insight into disease development mechanisms in MS.
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Affiliation(s)
- Olayinka Oladosu
- Department of Neuroscience, Faculty of Graduate Studies, University of Calgary, Calgary, AB, Canada
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
| | - Wei-Qiao Liu
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Lenora Brown
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Bruce G. Pike
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Luanne M. Metz
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Yunyan Zhang
- Hotchkiss Brain Institute, University of Calgary, Calgary, AB, Canada
- Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
- Department of Radiology, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
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Hau J, Baker A, Chaaban C, Kohli JS, Jao Keehn RJ, Linke AC, Mash LE, Wilkinson M, Kinnear MK, Müller RA, Carper RA. Reduced asymmetry of the hand knob area and decreased sensorimotor u-fiber connectivity in middle-aged adults with autism. Cortex 2022; 153:110-125. [PMID: 35640320 PMCID: PMC9988270 DOI: 10.1016/j.cortex.2022.04.004] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 12/07/2021] [Accepted: 04/06/2022] [Indexed: 01/27/2023]
Abstract
Individuals with autism spectrum disorder (ASD) frequently present with impairments in motor skills (e.g., limb coordination, handwriting and balance), which are observed across the lifespan but remain largely untreated. Many adults with ASD may thus experience adverse motor outcomes in aging, when physical decline naturally occurs. The 'hand knob' of the sensorimotor cortex is an area that is critical for motor control of the fingers and hands. However, this region has received little attention in ASD research, especially in adults after midlife. The hand knob area of the precentral (PrChand) and postcentral (PoChand) gyri was semi-manually delineated in 49 right-handed adults (25 ASD, 24 typical comparison [TC] participants, aged 41-70 years). Using multimodal (T1-weighted, diffusion-weighted, and resting-state functional) MRI, we examined the morphology, ipsilateral connectivity and laterality of these regions. We also explored correlations between hand knob measures with motor skills and autism symptoms, and between structural and functional connectivity measures. Bayesian analyses indicated moderate evidence of group effects with greater right PrChand volume and reduced leftward laterality of PrChand and PoChand volume in the ASD relative to TC group. Furthermore, the right PoC-PrChand u-fibers showed increased mean diffusivity in the ASD group. In the ASD group, right u-fiber volume positively correlated with corresponding functional connectivity but did not survive multiple comparisons correction. Correlations of hand knob measures and behavior were observed in the ASD group but did not survive multiple comparisons correction. Our findings suggest that morphological laterality and u-fiber connectivity of the sensorimotor network, putatively involved in hand motor/premotor function, may be diminished in middle-aged adults with ASD, perhaps rendering them more vulnerable to motor decline in old age. The altered morphology may relate to atypical functional motor asymmetries found in ASD earlier in life, possibly reflecting altered functional asymmetries over time.
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Affiliation(s)
- Janice Hau
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, CA, USA
| | - Ashley Baker
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, CA, USA
| | - Chantal Chaaban
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, CA, USA
| | - Jiwandeep S Kohli
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, CA, USA
| | - R Joanne Jao Keehn
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, CA, USA
| | - Annika C Linke
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, CA, USA
| | - Lisa E Mash
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, CA, USA
| | - Molly Wilkinson
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, CA, USA
| | - Mikaela K Kinnear
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, CA, USA
| | - Ralph-Axel Müller
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, CA, USA
| | - Ruth A Carper
- Brain Development Imaging Laboratories, Department of Psychology, San Diego State University, San Diego, CA, USA.
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De Luca A, Karayumak SC, Leemans A, Rathi Y, Swinnen S, Gooijers J, Clauwaert A, Bahr R, Sandmo SB, Sochen N, Kaufmann D, Muehlmann M, Biessels GJ, Koerte I, Pasternak O. Cross-site harmonization of multi-shell diffusion MRI measures based on rotational invariant spherical harmonics (RISH). Neuroimage 2022; 259:119439. [PMID: 35788044 DOI: 10.1016/j.neuroimage.2022.119439] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2022] [Revised: 06/23/2022] [Accepted: 06/30/2022] [Indexed: 11/25/2022] Open
Abstract
Quantification methods based on the acquisition of diffusion magnetic resonance imaging (dMRI) with multiple diffusion weightings (e.g., multi-shell) are becoming increasingly applied to study the in-vivo brain. Compared to single-shell data for diffusion tensor imaging (DTI), multi-shell data allows to apply more complex models such as diffusion kurtosis imaging (DKI), which attempts to capture both diffusion hindrance and restriction effects, or biophysical models such as NODDI, which attempt to increase specificity by separating biophysical components. Because of the strong dependence of the dMRI signal on the measurement hardware, DKI and NODDI metrics show scanner and site differences, much like other dMRI metrics. These effects limit the implementation of multi-shell approaches in multicenter studies, which are needed to collect large sample sizes for robust analyses. Recently, a post-processing technique based on rotation invariant spherical harmonics (RISH) features was introduced to mitigate cross-scanner differences in DTI metrics. Unlike statistical harmonization methods, which require repeated application to every dMRI metric of choice, RISH harmonization is applied once on the raw data, and can be followed by any analysis. RISH features harmonization has been tested on DTI features but not its generalizability to harmonize multi-shell dMRI. In this work, we investigated whether performing the RISH features harmonization of multi-shell dMRI data removes cross-site differences in DKI and NODDI metrics while retaining longitudinal effects. To this end, 46 subjects underwent a longitudinal (up to 3 time points) two-shell dMRI protocol at 3 imaging sites. DKI and NODDI metrics were derived before and after harmonization and compared both at the whole brain level and at the voxel level. Then, the harmonization effects on cross-sectional and on longitudinal group differences were evaluated. RISH features averaged for each of the 3 sites exhibited prominent between-site differences in the frontal and posterior part of the brain. Statistically significant differences in fractional anisotropy, mean diffusivity and mean kurtosis were observed both at the whole brain and voxel level between all the acquisition sites before harmonization, but not after. The RISH method also proved effective to harmonize NODDI metrics, particularly in white matter. The RISH based harmonization maintained the magnitude and variance of longitudinal changes as compared to the non-harmonized data of all considered metrics. In conclusion, the application of RISH feature based harmonization to multi-shell dMRI data can be used to remove cross-site differences in DKI metrics and NODDI analyses, while retaining inherent relations between longitudinal acquisitions.
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Affiliation(s)
- Alberto De Luca
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands; PROVIDI Lab, Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands; Institute of Neuroimmunology, Slovak Academy of Sciences, Bratislava, Slovakia.
| | | | - Alexander Leemans
- PROVIDI Lab, Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Yogesh Rathi
- Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Stephan Swinnen
- Movement Control and Neuroplasticity Research Group, KU Leuven, Leuven, Belgium; KU Leuven Brain Institute (LBI), Leuven, Belgium
| | - Jolien Gooijers
- Movement Control and Neuroplasticity Research Group, KU Leuven, Leuven, Belgium; KU Leuven Brain Institute (LBI), Leuven, Belgium
| | - Amanda Clauwaert
- Movement Control and Neuroplasticity Research Group, KU Leuven, Leuven, Belgium; KU Leuven Brain Institute (LBI), Leuven, Belgium
| | - Roald Bahr
- Oslo Sports Trauma Research Center, Norwegian School of Sport Sciences, Oslo, Norway
| | - Stian Bahr Sandmo
- Oslo Sports Trauma Research Center, Norwegian School of Sport Sciences, Oslo, Norway
| | - Nir Sochen
- Department of Applied Mathematics, Tel Aviv University, Tel Aviv, Israel; Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
| | - David Kaufmann
- Radiology Department, Charite University Hospital, Berlin, Germany
| | - Marc Muehlmann
- Department of Radiology, Ludwig-Maximilians-Universität, Munich, Germany
| | - Geert-Jan Biessels
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Inga Koerte
- Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA; cBRAIN, Department of Child and Adolescent Psychiatry, Ludwig-Maximilians-Universität, Munich, Germany
| | - Ofer Pasternak
- Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
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Nan Y, Ser JD, Walsh S, Schönlieb C, Roberts M, Selby I, Howard K, Owen J, Neville J, Guiot J, Ernst B, Pastor A, Alberich-Bayarri A, Menzel MI, Walsh S, Vos W, Flerin N, Charbonnier JP, van Rikxoort E, Chatterjee A, Woodruff H, Lambin P, Cerdá-Alberich L, Martí-Bonmatí L, Herrera F, Yang G. Data harmonisation for information fusion in digital healthcare: A state-of-the-art systematic review, meta-analysis and future research directions. AN INTERNATIONAL JOURNAL ON INFORMATION FUSION 2022; 82:99-122. [PMID: 35664012 PMCID: PMC8878813 DOI: 10.1016/j.inffus.2022.01.001] [Citation(s) in RCA: 40] [Impact Index Per Article: 20.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/24/2021] [Revised: 12/22/2021] [Accepted: 01/07/2022] [Indexed: 05/13/2023]
Abstract
Removing the bias and variance of multicentre data has always been a challenge in large scale digital healthcare studies, which requires the ability to integrate clinical features extracted from data acquired by different scanners and protocols to improve stability and robustness. Previous studies have described various computational approaches to fuse single modality multicentre datasets. However, these surveys rarely focused on evaluation metrics and lacked a checklist for computational data harmonisation studies. In this systematic review, we summarise the computational data harmonisation approaches for multi-modality data in the digital healthcare field, including harmonisation strategies and evaluation metrics based on different theories. In addition, a comprehensive checklist that summarises common practices for data harmonisation studies is proposed to guide researchers to report their research findings more effectively. Last but not least, flowcharts presenting possible ways for methodology and metric selection are proposed and the limitations of different methods have been surveyed for future research.
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Affiliation(s)
- Yang Nan
- National Heart and Lung Institute, Imperial College London, London, Northern Ireland UK
| | - Javier Del Ser
- Department of Communications Engineering, University of the Basque Country UPV/EHU, Bilbao 48013, Spain
- TECNALIA, Basque Research and Technology Alliance (BRTA), Derio 48160, Spain
| | - Simon Walsh
- National Heart and Lung Institute, Imperial College London, London, Northern Ireland UK
| | - Carola Schönlieb
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, Northern Ireland UK
| | - Michael Roberts
- Department of Applied Mathematics and Theoretical Physics, University of Cambridge, Cambridge, Northern Ireland UK
- Oncology R&D, AstraZeneca, Cambridge, Northern Ireland UK
| | - Ian Selby
- Department of Radiology, University of Cambridge, Cambridge, Northern Ireland UK
| | - Kit Howard
- Clinical Data Interchange Standards Consortium, Austin, TX, United States of America
| | - John Owen
- Clinical Data Interchange Standards Consortium, Austin, TX, United States of America
| | - Jon Neville
- Clinical Data Interchange Standards Consortium, Austin, TX, United States of America
| | - Julien Guiot
- University Hospital of Liège (CHU Liège), Respiratory medicine department, Liège, Belgium
- University of Liege, Department of clinical sciences, Pneumology-Allergology, Liège, Belgium
| | - Benoit Ernst
- University Hospital of Liège (CHU Liège), Respiratory medicine department, Liège, Belgium
- University of Liege, Department of clinical sciences, Pneumology-Allergology, Liège, Belgium
| | | | | | - Marion I. Menzel
- Technische Hochschule Ingolstadt, Ingolstadt, Germany
- GE Healthcare GmbH, Munich, Germany
| | - Sean Walsh
- Radiomics (Oncoradiomics SA), Liège, Belgium
| | - Wim Vos
- Radiomics (Oncoradiomics SA), Liège, Belgium
| | - Nina Flerin
- Radiomics (Oncoradiomics SA), Liège, Belgium
| | | | | | - Avishek Chatterjee
- Department of Precision Medicine, Maastricht University, Maastricht, The Netherlands
| | - Henry Woodruff
- Department of Precision Medicine, Maastricht University, Maastricht, The Netherlands
| | - Philippe Lambin
- Department of Precision Medicine, Maastricht University, Maastricht, The Netherlands
| | - Leonor Cerdá-Alberich
- Medical Imaging Department, Hospital Universitari i Politècnic La Fe, Valencia, Spain
| | - Luis Martí-Bonmatí
- Medical Imaging Department, Hospital Universitari i Politècnic La Fe, Valencia, Spain
| | - Francisco Herrera
- Department of Computer Sciences and Artificial Intelligence, Andalusian Research Institute in Data Science and Computational Intelligence (DaSCI) University of Granada, Granada, Spain
- Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah 21589, Saudi Arabia
| | - Guang Yang
- National Heart and Lung Institute, Imperial College London, London, Northern Ireland UK
- Cardiovascular Research Centre, Royal Brompton Hospital, London, Northern Ireland UK
- School of Biomedical Engineering & Imaging Sciences, King's College London, London, Northern Ireland UK
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Kornaropoulos EN, Winzeck S, Rumetshofer T, Wikstrom A, Knutsson L, Correia MM, Sundgren PC, Nilsson M. Sensitivity of Diffusion MRI to White Matter Pathology: Influence of Diffusion Protocol, Magnetic Field Strength, and Processing Pipeline in Systemic Lupus Erythematosus. Front Neurol 2022; 13:837385. [PMID: 35557624 PMCID: PMC9087851 DOI: 10.3389/fneur.2022.837385] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 03/16/2022] [Indexed: 11/13/2022] Open
Abstract
There are many ways to acquire and process diffusion MRI (dMRI) data for group studies, but it is unknown which maximizes the sensitivity to white matter (WM) pathology. Inspired by this question, we analyzed data acquired for diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) at 3T (3T-DTI and 3T-DKI) and DTI at 7T in patients with systemic lupus erythematosus (SLE) and healthy controls (HC). Parameter estimates in 72 WM tracts were obtained using TractSeg. The impact on the sensitivity to WM pathology was evaluated for the diffusion protocol, the magnetic field strength, and the processing pipeline. Sensitivity was quantified in terms of Cohen's d for group comparison. Results showed that the choice of diffusion protocol had the largest impact on the effect size. The effect size in fractional anisotropy (FA) across all WM tracts was 0.26 higher when derived by DTI than by DKI and 0.20 higher in 3T compared with 7T. The difference due to the diffusion protocol was larger than the difference due to magnetic field strength for the majority of diffusion parameters. In contrast, the difference between including or excluding different processing steps was near negligible, except for the correction of distortions from eddy currents and motion which had a clearly positive impact. For example, effect sizes increased on average by 0.07 by including motion and eddy correction for FA derived from 3T-DTI. Effect sizes were slightly reduced by the incorporation of denoising and Gibbs-ringing removal (on average by 0.011 and 0.005, respectively). Smoothing prior to diffusion model fitting generally reduced effect sizes. In summary, 3T-DTI in combination with eddy current and motion correction yielded the highest sensitivity to WM pathology in patients with SLE. However, our results also indicated that the 3T-DKI and 7T-DTI protocols used here may be adjusted to increase effect sizes.
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Affiliation(s)
- Evgenios N. Kornaropoulos
- Clinical Sciences, Diagnostic Radiology, Lund University, Lund, Sweden
- Division of Anaesthesia, University of Cambridge, Cambridge, United Kingdom
| | - Stefan Winzeck
- Division of Anaesthesia, University of Cambridge, Cambridge, United Kingdom
- BioMedIA Group, Department of Computing, Imperial College London, London, United Kingdom
| | | | - Anna Wikstrom
- Clinical Sciences, Diagnostic Radiology, Lund University, Lund, Sweden
| | - Linda Knutsson
- Department of Medical Radiation Physics, Lund University, Lund, Sweden
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University, School of Medicine, Baltimore, MD, United States
- F.M. Kirby Research Center, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Marta M. Correia
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom
| | - Pia C. Sundgren
- Clinical Sciences, Diagnostic Radiology, Lund University, Lund, Sweden
- Lund University BioImaging Center, Lund University, Lund, Sweden
- Department of Medical Imaging and Physiology, Skåne University Hospital, Lund, Sweden
| | - Markus Nilsson
- Clinical Sciences, Diagnostic Radiology, Lund University, Lund, Sweden
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46
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Oliver LD, Hawco C, Viviano JD, Voineskos AN. From the Group to the Individual in Schizophrenia Spectrum Disorders: Biomarkers of Social Cognitive Impairments and Therapeutic Translation. Biol Psychiatry 2022; 91:699-708. [PMID: 34799097 DOI: 10.1016/j.biopsych.2021.09.007] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/01/2021] [Revised: 08/11/2021] [Accepted: 09/11/2021] [Indexed: 12/23/2022]
Abstract
People with schizophrenia spectrum disorders (SSDs) often experience persistent social cognitive impairments, associated with poor functional outcome. There are currently no approved treatment options for these debilitating symptoms, highlighting the need for novel therapeutic strategies. Work to date has elucidated differential social processes and underlying neural circuitry affected in SSDs, which may be amenable to modulation using neurostimulation. Further, advances in functional connectivity mapping and electric field modeling may be used to identify individualized treatment targets to maximize the impact of brain stimulation on social cognitive networks. Here, we review literature supporting a roadmap for translating functional connectivity biomarker discovery to individualized treatment development for social cognitive impairments in SSDs. First, we outline the relevance of social cognitive impairments in SSDs. We review machine learning approaches for dimensional brain-behavior biomarker discovery, emphasizing the importance of individual differences. We synthesize research showing that brain stimulation techniques, such as repetitive transcranial magnetic stimulation, can be used to target relevant networks. Further, functional connectivity-based individualized targeting may enhance treatment response. We then outline recent approaches to account for neuroanatomical variability and optimize coil positioning to individually maximize target engagement. Overall, the synthesized literature provides support for the utility and feasibility of this translational approach to precision treatment. The proposed roadmap to translate biomarkers of social cognitive impairments to individualized treatment is currently under evaluation in precision-guided trials. Such a translational approach may also be applicable across conditions and generalizable for the development of individualized neurostimulation targeting other behavioral deficits.
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Affiliation(s)
- Lindsay D Oliver
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Colin Hawco
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Joseph D Viviano
- Mila-Quebec Artificial Intelligence Institute, Montreal, Quebec, Canada
| | - Aristotle N Voineskos
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada.
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47
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The SACT Template: A Human Brain Diffusion Tensor Template for School-age Children. Neurosci Bull 2022; 38:607-621. [PMID: 35092576 DOI: 10.1007/s12264-022-00820-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 10/22/2021] [Indexed: 10/19/2022] Open
Abstract
School-age children are in a specific development stage corresponding to juvenility, when the white matter of the brain experiences ongoing maturation. Diffusion-weighted magnetic resonance imaging (DWI), especially diffusion tensor imaging (DTI), is extensively used to characterize the maturation by assessing white matter properties in vivo. In the analysis of DWI data, spatial normalization is crucial for conducting inter-subject analyses or linking the individual space with the reference space. Using tensor-based registration with an appropriate diffusion tensor template presents high accuracy regarding spatial normalization. However, there is a lack of a standardized diffusion tensor template dedicated to school-age children with ongoing brain development. Here, we established the school-age children diffusion tensor (SACT) template by optimizing tensor reorientation on high-quality DTI data from a large sample of cognitively normal participants aged 6-12 years. With an age-balanced design, the SACT template represented the entire age range well by showing high similarity to the age-specific templates. Compared with the tensor template of adults, the SACT template revealed significantly higher spatial normalization accuracy and inter-subject coherence upon evaluation of subjects in two different datasets of school-age children. A practical application regarding the age associations with the normalized DTI-derived data was conducted to further compare the SACT template and the adult template. Although similar spatial patterns were found, the SACT template showed significant effects on the distributions of the statistical results, which may be related to the performance of spatial normalization. Looking forward, the SACT template could contribute to future studies of white matter development in both healthy and clinical populations. The SACT template is publicly available now ( https://figshare.com/articles/dataset/SACT_template/14071283 ).
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de Luca A, Biessels GJ. Towards multicentre diffusion MRI studies in cerebral small vessel disease. J Neurol Neurosurg Psychiatry 2022; 93:5. [PMID: 34663625 DOI: 10.1136/jnnp-2021-326993] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/12/2021] [Accepted: 10/04/2021] [Indexed: 11/03/2022]
Affiliation(s)
- Alberto de Luca
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands.,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
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49
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Joo SW, Kim H, Jo YT, Ahn S, Choi YJ, Park S, Kang Y, Lee J. White matter impairments in patients with schizophrenia: A multisite diffusion MRI study. Prog Neuropsychopharmacol Biol Psychiatry 2021; 111:110381. [PMID: 34111494 DOI: 10.1016/j.pnpbp.2021.110381] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/09/2020] [Revised: 05/31/2021] [Accepted: 06/04/2021] [Indexed: 10/21/2022]
Abstract
There is a lack of convincing and replicative findings regarding white matter abnormalities in schizophrenia. Several multisite diffusion magnetic resonance imaging (dMRI) studies have been conducted to increase statistical power and reveal subtle white matter changes. Data pooling methods are crucial in joint analysis to compensate for the use of different scanners and image acquisition parameters. A harmonization method using raw dMRI data was developed to overcome the limited generalizability of previous data pooling methods. We obtained dMRI data of 242 healthy controls and 190 patients with schizophrenia from four different study sites. After applying the harmonization method to the raw dMRI data, a two-tensor whole-brain tractography was performed, and diffusion measures were compared between the two groups. The correlation of fractional anisotropy (FA) with the positive and negative symptoms was evaluated, and the interaction effect of diagnosis-by-age, age-squared, and sex was examined. The following white matter tracts showed significant group differences in the FA: the right superior longitudinal fascicle (SLF), the left-to-right lateral orbitofrontal commissural tract, pars orbitalis (pOr-pOr) commissural tract, and pars triangularis (pTr-pTr) commissural tract. The FA of the right SLF and pTr-pTr commissural tract were significantly associated with the Positive and Negative Syndrome Scale (PANSS) positive and negative scores. No significant interaction effect was observed. These findings add to the evidence on structural brain abnormalities in schizophrenia and can aid in obtaining a better understanding of the biological foundations of schizophrenia.
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Affiliation(s)
- Sung Woo Joo
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Harin Kim
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Young Tak Jo
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Soojin Ahn
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Young Jae Choi
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Soyeon Park
- Department of Psychiatry, Medical Foundation Yongin Mental Hospital, Yongin, Republic of Korea
| | - Yuree Kang
- Department of Psychiatry, Medical Foundation Yongin Mental Hospital, Yongin, Republic of Korea
| | - Jungsun Lee
- Department of Psychiatry, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea.
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50
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Eshaghzadeh Torbati M, Minhas DS, Ahmad G, O'Connor EE, Muschelli J, Laymon CM, Yang Z, Cohen AD, Aizenstein HJ, Klunk WE, Christian BT, Hwang SJ, Crainiceanu CM, Tudorascu DL. A multi-scanner neuroimaging data harmonization using RAVEL and ComBat. Neuroimage 2021; 245:118703. [PMID: 34736996 PMCID: PMC8820090 DOI: 10.1016/j.neuroimage.2021.118703] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Revised: 10/07/2021] [Accepted: 10/28/2021] [Indexed: 11/27/2022] Open
Abstract
Modern neuroimaging studies frequently combine data collected from multiple scanners and experimental conditions. Such data often contain substantial technical variability associated with image intensity scale (image intensity scales are not the same in different images) and scanner effects (images obtained from different scanners contain substantial technical biases). Here we evaluate and compare results of data analysis methods without any data transformation (RAW), with intensity normalization using RAVEL, with regional harmonization methods using ComBat, and a combination of RAVEL and ComBat. Methods are evaluated on a unique sample of 16 study participants who were scanned on both 1.5T and 3T scanners a few months apart. Neuroradiological evaluation was conducted for 7 different regions of interest (ROI's) pertinent to Alzheimer's disease (AD). Cortical measures and results indicate that: (1) RAVEL substantially improved the reproducibility of image intensities; (2) ComBat is preferred over RAVEL and the RAVEL-ComBat combination in terms of regional level harmonization due to more consistent harmonization across subjects and image-derived measures; (3) RAVEL and ComBat substantially reduced bias compared to analysis of RAW images, but RAVEL also resulted in larger variance; and (4) the larger root mean square deviation (RMSD) of RAVEL compared to ComBat is due mainly to its larger variance.
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Affiliation(s)
- Mahbaneh Eshaghzadeh Torbati
- Intelligent System Program, University of Pittsburgh School of Computing and Information, Pittsburgh, PA 15213, USA
| | - Davneet S Minhas
- Department of Radiology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - Ghasan Ahmad
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Erin E O'Connor
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - John Muschelli
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Charles M Laymon
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Zixi Yang
- Department of Diagnostic Radiology and Nuclear Medicine, University of Maryland School of Medicine, Baltimore, MD 21201, USA
| | - Ann D Cohen
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - Howard J Aizenstein
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - William E Klunk
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA
| | - Bradley T Christian
- Department of Medical Physics, University of Wisconsin-Madison, Madison, WI 53705, USA
| | - Seong Jae Hwang
- Intelligent System Program, University of Pittsburgh School of Computing and Information, Pittsburgh, PA 15213, USA; Department of Computer Science, University of Pittsburgh School of Computing and Information, Pittsburgh, PA 15213, USA
| | - Ciprian M Crainiceanu
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Dana L Tudorascu
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA; Department of Biostatistics, University of Pittsburgh, Pittsburgh, PA 15213, USA.
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