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Callewaert B, Gsell W, Lox M, Backes WH, Jones EAV, Himmelreich U. Intravoxel incoherent motion as a surrogate marker of perfused vascular density in rat brain. NMR IN BIOMEDICINE 2024; 37:e5148. [PMID: 38556903 DOI: 10.1002/nbm.5148] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/13/2023] [Revised: 02/21/2024] [Accepted: 02/22/2024] [Indexed: 04/02/2024]
Abstract
Intravoxel incoherent motion (IVIM) MRI has emerged as a valuable technique for the assessment of tissue characteristics and perfusion. However, there is limited knowledge about the relationship between IVIM-derived measures and changes at the level of the vascular network. In this study, we investigated the potential use of IVIM MRI as a noninvasive tool for measuring changes in cerebral vascular density. Variations in quantitative immunohistochemical measurements of the vascular density across different regions in the rat brain (cortex, corpus callosum, hippocampus, thalamus, and hypothalamus) were related to the pseudo-diffusion coefficient D* and the flowing blood fraction f in healthy Wistar rats. We assessed whether region-wise differences in the vascular density are reflected by variations in the IVIM measurements and found a significant positive relationship with the pseudo-diffusion coefficient (p < 0.05, β = 0.24). The effect of cerebrovascular alterations, such as blood-brain barrier (BBB) disruption on the perfusion-related IVIM parameters, is not well understood. Therefore, we investigated the effect of BBB disruption on the IVIM measures in a rat model of metabolic and vascular comorbidities (ZSF1 obese rat) and assessed whether this affects the relationship between the cerebral vascular density and the noninvasive IVIM measurements. We observed increased vascular permeability without detecting any differences in diffusivity, suggesting that BBB leakage is present before changes in the tissue integrity. We observed no significant difference in the relationship between cerebral vascular density and the IVIM measurements in our model of comorbidities compared with healthy normotensive rats.
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Affiliation(s)
- Bram Callewaert
- Department of Cardiovascular Sciences, Center for Molecular and Vascular Biology (CMVB), KU Leuven, Leuven, Belgium
- Department of Imaging and Pathology, Biomedical MRI Unit, KU Leuven, Leuven, Belgium
| | - Willy Gsell
- Department of Imaging and Pathology, Biomedical MRI Unit, KU Leuven, Leuven, Belgium
| | - Marleen Lox
- Department of Cardiovascular Sciences, Center for Molecular and Vascular Biology (CMVB), KU Leuven, Leuven, Belgium
| | - Walter H Backes
- Departments of Neurology and Radiology and Nuclear Medicine, Institute for Cardiovascular Diseases (CARIM), Maastricht University Medical Center, Maastricht, The Netherlands
- Department of Radiology and Nuclear Medicine, Institute for Mental Health & Neuroscience (MHeNs), Maastricht University Medical Center, Maastricht, The Netherlands
| | - Elizabeth A V Jones
- Department of Cardiovascular Sciences, Center for Molecular and Vascular Biology (CMVB), KU Leuven, Leuven, Belgium
- Department of Cardiology, Institute for Cardiovascular Diseases (CARIM), Maastricht University, Maastricht, The Netherlands
| | - Uwe Himmelreich
- Department of Imaging and Pathology, Biomedical MRI Unit, KU Leuven, Leuven, Belgium
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2
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Singh S, Sutkus L, Li Z, Baker S, Bear J, Dilger RN, Miller DJ. Standardization of a silver stain to reveal mesoscale myelin in histological preparations of the mammalian brain. J Neurosci Methods 2024; 407:110139. [PMID: 38626852 DOI: 10.1016/j.jneumeth.2024.110139] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Revised: 03/26/2024] [Accepted: 04/12/2024] [Indexed: 04/29/2024]
Abstract
BACKGROUND The brain is built of neurons supported by myelin, a fatty substance that improves cellular communication. Noninvasive magnetic resonance imaging (MRI) is now able to measure brain structure like myelin and requires histological validation. NEW METHOD Here we present work in small and large biomedical model mammals to standardize a silver impregnation method as a high-throughput histological myelin visualization procedure. Specifically, we built a new staining well plate to increase batch size, and then systematically varied the staining and clearing cycles to describe the staining response curve across taxa and conditions. We compared tissues fixed by immersion or perfusion, mounted versus free-floating, and cut as thicker or thinner slices, with two-weeks of post-fixation. RESULTS The staining response curves show optimal staining with a single exposure across taxa when incubation and clearing epochs are held to within 3-9 min. We show that clearing was slower in mounted vs free-floating tissue, and that staining was faster and caused fracturing earlier in thinner sliced and smaller volumes of tissue. COMPARISON WITH EXISTING METHODS We developed a batch processing approach to increase throughput while ensuring reproducibility and demonstrate the optimal conditions for fine myelinated fiber morphology visualization with short cycles (<9 minutes). CONCLUSIONS We present our optimized protocol to reveal mesoscale neuroanatomical myelin content in histology across mammals. This standard staining procedure will facilitate multiscale analyses of myelin content across development as well as in the presence of injury or disease.
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Affiliation(s)
- S Singh
- Department of Evolution, Ecology, and Behavior, at the University of Illinois at Urbana-Champaign, 505 South Goodwin Ave, Urbana, IL 61801, United States of America
| | - L Sutkus
- Neuroscience Program, at the University of Illinois at Urbana-Champaign, 505 South Goodwin Ave, Urbana, IL 61801, United States of America
| | - Z Li
- Neuroscience Program, at the University of Illinois at Urbana-Champaign, 505 South Goodwin Ave, Urbana, IL 61801, United States of America
| | - S Baker
- Machine Shop, at the University of Illinois at Urbana-Champaign, 505 South Goodwin Ave, Urbana, IL 61801, United States of America
| | - J Bear
- Machine Shop, at the University of Illinois at Urbana-Champaign, 505 South Goodwin Ave, Urbana, IL 61801, United States of America
| | - R N Dilger
- Department of Animal Sciences, at the University of Illinois at Urbana-Champaign, 505 South Goodwin Ave, Urbana, IL 61801, United States of America; Neuroscience Program, at the University of Illinois at Urbana-Champaign, 505 South Goodwin Ave, Urbana, IL 61801, United States of America; Beckman Institute for Advanced Science and Technology, at the University of Illinois at Urbana-Champaign, 505 South Goodwin Ave, Urbana, IL 61801, United States of America
| | - D J Miller
- Department of Evolution, Ecology, and Behavior, at the University of Illinois at Urbana-Champaign, 505 South Goodwin Ave, Urbana, IL 61801, United States of America; Neuroscience Program, at the University of Illinois at Urbana-Champaign, 505 South Goodwin Ave, Urbana, IL 61801, United States of America; Beckman Institute for Advanced Science and Technology, at the University of Illinois at Urbana-Champaign, 505 South Goodwin Ave, Urbana, IL 61801, United States of America.
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3
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Noche JA, Radhakrishnan H, Ubele MF, Boaz K, Mefford JL, Jones ED, van Rooyen HY, Perpich JA, McCarty K, Meacham B, Smiley J, Bembenek Bailey SA, Puskás LG, Powell DK, Sordo L, Phelan MJ, Norris CM, Head E, Stark CEL. Age-Related Brain Atrophy and the Positive Effects of Behavioral Enrichment in Middle-Aged Beagles. J Neurosci 2024; 44:e2366232024. [PMID: 38561226 PMCID: PMC11097262 DOI: 10.1523/jneurosci.2366-23.2024] [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: 12/15/2023] [Revised: 02/08/2024] [Accepted: 02/28/2024] [Indexed: 04/04/2024] Open
Abstract
Aging dogs serve as a valuable preclinical model for Alzheimer's disease (AD) due to their natural age-related development of β-amyloid (Aβ) plaques, human-like metabolism, and large brains that are ideal for studying structural brain aging trajectories from serial neuroimaging. Here we examined the effects of chronic treatment with the calcineurin inhibitor (CNI) tacrolimus or the nuclear factor of activated T cells (NFAT)-inhibiting compound Q134R on age-related canine brain atrophy from a longitudinal study in middle-aged beagles (36 females, 7 males) undergoing behavioral enrichment. Annual MRI was analyzed using modern, automated techniques for region-of-interest-based and voxel-based volumetric assessments. We found that the frontal lobe showed accelerated atrophy with age, while the caudate nucleus remained relatively stable. Remarkably, the hippocampus increased in volume in all dogs. None of these changes were influenced by tacrolimus or Q134R treatment. Our results suggest that behavioral enrichment can prevent atrophy and increase the volume of the hippocampus but does not prevent aging-associated prefrontal cortex atrophy.
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Affiliation(s)
| | - Hamsanandini Radhakrishnan
- University of California, Irvine, California 92697
- University of Pennsylvania, Philadelphia, Pennsylvania 19104
| | | | - Kathy Boaz
- University of Kentucky, Lexington, Kentucky 40506
| | | | - Erin D Jones
- University of Kentucky, Lexington, Kentucky 40506
| | | | | | | | | | | | | | | | | | - Lorena Sordo
- University of California, Irvine, California 92697
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4
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Gilani N, Mikheev A, Brinkmann IM, Kumbella M, Babb JS, Basukala D, Wetscherek A, Benkert T, Chandarana H, Sigmund EE. Spatial profiling of in vivo diffusion-weighted MRI parameters in the healthy human kidney. MAGMA (NEW YORK, N.Y.) 2024:10.1007/s10334-024-01159-6. [PMID: 38703246 DOI: 10.1007/s10334-024-01159-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Revised: 01/17/2024] [Accepted: 03/26/2024] [Indexed: 05/06/2024]
Abstract
OBJECTIVE Diffusion-weighted MRI is a technique that can infer microstructural and microcirculatory features from biological tissue, with particular application to renal tissue. There is extensive literature on diffusion tensor imaging (DTI) of anisotropy in the renal medulla, intravoxel incoherent motion (IVIM) measurements separating microstructural from microcirculation effects, and combinations of the two. However, interpretation of these features and adaptation of more specific models remains an ongoing challenge. One input to this process is a whole organ distillation of corticomedullary contrast of diffusion metrics, as has been explored for other renal biomarkers. MATERIALS AND METHODS In this work, we probe the spatial dependence of diffusion MRI metrics with concentrically layered segmentation in 11 healthy kidneys at 3 T. The metrics include those from DTI, IVIM, a combined approach titled "REnal Flow and Microstructure AnisotroPy (REFMAP)", and a multiply encoded model titled "FC-IVIM" providing estimates of fluid velocity and branching length. RESULTS Fractional anisotropy decreased from the inner kidney to the outer kidney with the strongest layer correlation in both parenchyma (including cortex and medulla) and medulla with Spearman correlation coefficients and p-values (r, p) of (0.42, <0.001) and (0.37, <0.001), respectively. Also, dynamic parameters derived from the three models significantly decreased with a high correlation from the inner to the outer parenchyma or medulla with (r, p) ranges of (0.46-0.55, <0.001). CONCLUSIONS These spatial trends might find implications for indirect assessments of kidney physiology and microstructure using diffusion MRI.
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Affiliation(s)
- Nima Gilani
- Department of Radiology, Center for Advanced Imaging Innovation and Research (CAI2R), Center for Biomedical Imaging, NYU Langone Health, New York, USA.
| | - Artem Mikheev
- Department of Radiology, Center for Advanced Imaging Innovation and Research (CAI2R), Center for Biomedical Imaging, NYU Langone Health, New York, USA
| | | | - Malika Kumbella
- Department of Radiology, Center for Advanced Imaging Innovation and Research (CAI2R), Center for Biomedical Imaging, NYU Langone Health, New York, USA
| | - James S Babb
- Department of Radiology, Center for Advanced Imaging Innovation and Research (CAI2R), Center for Biomedical Imaging, NYU Langone Health, New York, USA
| | - Dibash Basukala
- Department of Radiology, Center for Advanced Imaging Innovation and Research (CAI2R), Center for Biomedical Imaging, NYU Langone Health, New York, USA
| | - Andreas Wetscherek
- Joint Department of Physics, The Institute of Cancer Research and The Royal Marsden NHS Foundation Trust, London, UK
| | - Thomas Benkert
- MR Application Predevelopment, Siemens Healthcare GmbH, Erlangen, Germany
| | - Hersh Chandarana
- Department of Radiology, Center for Advanced Imaging Innovation and Research (CAI2R), Center for Biomedical Imaging, NYU Langone Health, New York, USA
| | - Eric E Sigmund
- Department of Radiology, Center for Advanced Imaging Innovation and Research (CAI2R), Center for Biomedical Imaging, NYU Langone Health, New York, USA
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5
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Kamagata K, Saito Y, Andica C, Uchida W, Takabayashi K, Yoshida S, Hagiwara A, Fujita S, Nakaya M, Akashi T, Wada A, Kamiya K, Hori M, Aoki S. Noninvasive Magnetic Resonance Imaging Measures of Glymphatic System Activity. J Magn Reson Imaging 2024; 59:1476-1493. [PMID: 37655849 DOI: 10.1002/jmri.28977] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2023] [Revised: 08/09/2023] [Accepted: 08/09/2023] [Indexed: 09/02/2023] Open
Abstract
The comprehension of the glymphatic system, a postulated mechanism responsible for the removal of interstitial solutes within the central nervous system (CNS), has witnessed substantial progress recently. While direct measurement techniques involving fluorescence and contrast agent tracers have demonstrated success in animal studies, their application in humans is invasive and presents challenges. Hence, exploring alternative noninvasive approaches that enable glymphatic research in humans is imperative. This review primarily focuses on several noninvasive magnetic resonance imaging (MRI) techniques, encompassing perivascular space (PVS) imaging, diffusion tensor image analysis along the PVS, arterial spin labeling, chemical exchange saturation transfer, and intravoxel incoherent motion. These methodologies provide valuable insights into the dynamics of interstitial fluid, water permeability across the blood-brain barrier, and cerebrospinal fluid flow within the cerebral parenchyma. Furthermore, the review elucidates the underlying concept and clinical applications of these noninvasive MRI techniques, highlighting their strengths and limitations. It addresses concerns about the relationship between glymphatic system activity and pathological alterations, emphasizing the necessity for further studies to establish correlations between noninvasive MRI measurements and pathological findings. Additionally, the challenges associated with conducting multisite studies, such as variability in MRI systems and acquisition parameters, are addressed, with a suggestion for the use of harmonization methods, such as the combined association test (COMBAT), to enhance standardization and statistical power. Current research gaps and future directions in noninvasive MRI techniques for assessing the glymphatic system are discussed, emphasizing the need for larger sample sizes, harmonization studies, and combined approaches. In conclusion, this review provides invaluable insights into the application of noninvasive MRI methods for monitoring glymphatic system activity in the CNS. It highlights their potential in advancing our understanding of the glymphatic system, facilitating clinical applications, and paving the way for future research endeavors in this field. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 5.
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Affiliation(s)
- Koji Kamagata
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Yuya Saito
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Christina Andica
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Faculty of Health Data Science, Juntendo University, Chiba, Japan
| | - Wataru Uchida
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Kaito Takabayashi
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Seina Yoshida
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Radiological Sciences, Graduate School of Human Health Sciences, Tokyo Metropolitan University, Tokyo, Japan
| | - Akifumi Hagiwara
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Shohei Fujita
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Radiology, The University of Tokyo, Tokyo, Japan
| | - Moto Nakaya
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Radiology, The University of Tokyo, Tokyo, Japan
| | - Toshiaki Akashi
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Akihiko Wada
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Kouhei Kamiya
- Department of Radiology, Toho University Omori Medical Center, Tokyo, Japan
| | - Masaaki Hori
- Department of Radiology, Toho University Omori Medical Center, Tokyo, Japan
| | - Shigeki Aoki
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Faculty of Health Data Science, Juntendo University, Chiba, Japan
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6
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Radhakrishnan H, Zhao C, Sydnor VJ, Baller EB, Cook PA, Fair DA, Giesbrecht B, Larsen B, Murtha K, Roalf DR, Rush‐Goebel S, Shinohara RT, Shou H, Tisdall MD, Vettel JM, Grafton ST, Cieslak M, Satterthwaite TD. A practical evaluation of measures derived from compressed sensing diffusion spectrum imaging. Hum Brain Mapp 2024; 45:e26580. [PMID: 38520359 PMCID: PMC10960521 DOI: 10.1002/hbm.26580] [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: 03/22/2023] [Revised: 12/13/2023] [Accepted: 12/20/2023] [Indexed: 03/25/2024] Open
Abstract
Diffusion Spectrum Imaging (DSI) using dense Cartesian sampling of q-space has been shown to provide important advantages for modeling complex white matter architecture. However, its adoption has been limited by the lengthy acquisition time required. Sparser sampling of q-space combined with compressed sensing (CS) reconstruction techniques has been proposed as a way to reduce the scan time of DSI acquisitions. However prior studies have mainly evaluated CS-DSI in post-mortem or non-human data. At present, the capacity for CS-DSI to provide accurate and reliable measures of white matter anatomy and microstructure in the living human brain remains unclear. We evaluated the accuracy and inter-scan reliability of 6 different CS-DSI schemes that provided up to 80% reductions in scan time compared to a full DSI scheme. We capitalized on a dataset of 26 participants who were scanned over eight independent sessions using a full DSI scheme. From this full DSI scheme, we subsampled images to create a range of CS-DSI images. This allowed us to compare the accuracy and inter-scan reliability of derived measures of white matter structure (bundle segmentation, voxel-wise scalar maps) produced by the CS-DSI and the full DSI schemes. We found that CS-DSI estimates of both bundle segmentations and voxel-wise scalars were nearly as accurate and reliable as those generated by the full DSI scheme. Moreover, we found that the accuracy and reliability of CS-DSI was higher in white matter bundles that were more reliably segmented by the full DSI scheme. As a final step, we replicated the accuracy of CS-DSI in a prospectively acquired dataset (n = 20, scanned once). Together, these results illustrate the utility of CS-DSI for reliably delineating in vivo white matter architecture in a fraction of the scan time, underscoring its promise for both clinical and research applications.
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Affiliation(s)
- Hamsanandini Radhakrishnan
- Lifespan Informatics and Neuroimaging CenterUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Psychiatry, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Chenying Zhao
- Lifespan Informatics and Neuroimaging CenterUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Psychiatry, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Lifespan Brain Institute, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Bioengineering, School of Engineering and Applied ScienceUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Valerie J. Sydnor
- Lifespan Informatics and Neuroimaging CenterUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Psychiatry, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Erica B. Baller
- Lifespan Informatics and Neuroimaging CenterUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Psychiatry, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Philip A. Cook
- Department of Radiology, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Damien A. Fair
- Masonic Institute for the Developing BrainUniversity of MinnesotaMinneapolisMinnesotaUSA
| | - Barry Giesbrecht
- Department of Psychological and Brain SciencesUniversity of CaliforniaSanta BarbaraCaliforniaUSA
| | - Bart Larsen
- Lifespan Informatics and Neuroimaging CenterUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Psychiatry, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Kristin Murtha
- Lifespan Informatics and Neuroimaging CenterUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Psychiatry, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - David R. Roalf
- Department of Psychiatry, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Lifespan Brain Institute, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Sage Rush‐Goebel
- Lifespan Informatics and Neuroimaging CenterUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Psychiatry, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Russell T. Shinohara
- Department of Biostatistics, Epidemiology and InformaticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Center for Biomedical Image Computing & AnalyticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Haochang Shou
- Department of Biostatistics, Epidemiology and InformaticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Center for Biomedical Image Computing & AnalyticsUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - M. Dylan Tisdall
- Department of Radiology, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Jean M. Vettel
- Department of Psychological and Brain SciencesUniversity of CaliforniaSanta BarbaraCaliforniaUSA
- U.S. Army Research LaboratoryAberdeen Proving GroundAberdeenMarylandUSA
| | - Scott T. Grafton
- Department of Psychological and Brain SciencesUniversity of CaliforniaSanta BarbaraCaliforniaUSA
| | - Matthew Cieslak
- Lifespan Informatics and Neuroimaging CenterUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Psychiatry, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
| | - Theodore D. Satterthwaite
- Lifespan Informatics and Neuroimaging CenterUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of Psychiatry, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
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7
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Spagnolo F, Gobbi S, Zsoldos E, Edde M, Weigel M, Granziera C, Descoteaux M, Barakovic M, Magon S. Down-sampling in diffusion MRI: a bundle-specific DTI and NODDI study. FRONTIERS IN NEUROIMAGING 2024; 3:1359589. [PMID: 38606197 PMCID: PMC11007093 DOI: 10.3389/fnimg.2024.1359589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Accepted: 03/13/2024] [Indexed: 04/13/2024]
Abstract
Introduction Multi-shell diffusion Magnetic Resonance Imaging (dMRI) data has been widely used to characterise white matter microstructure in several neurodegenerative diseases. The lack of standardised dMRI protocols often implies the acquisition of redundant measurements, resulting in prolonged acquisition times. In this study, we investigate the impact of the number of gradient directions on Diffusion Tensor Imaging (DTI) and on Neurite Orientation Dispersion and Density Imaging (NODDI) metrics. Methods Data from 124 healthy controls collected in three different longitudinal studies were included. Using an in-house algorithm, we reduced the number of gradient directions in each data shell. We estimated DTI and NODDI measures on six white matter bundles clinically relevant for neurodegenerative diseases. Results Fractional Anisotropy (FA) measures on bundles where data were sampled at the 30% rate, showed a median L1 distance of up to 3.92% and a 95% CI of (1.74, 8.97)% when compared to those obtained at reference sampling. Mean Diffusivity (MD) reached up to 4.31% and a 95% CI of (1.60, 16.98)% on the same premises. At a sampling rate of 50%, we obtained a median of 3.90% and a 95% CI of (1.99, 16.65)% in FA, and 5.49% with a 95% CI of (2.14, 21.68)% in MD. The Intra-Cellular volume fraction (ICvf) median L1 distance was up to 2.83% with a 95% CI of (1.98, 4.82)% at a 30% sampling rate and 3.95% with a 95% CI of (2.39, 7.81)% at a 50% sampling rate. The volume difference of the reconstructed white matter at reference and 50% sampling reached a maximum of (2.09 ± 0.81)%. Discussion In conclusion, DTI and NODDI measures reported at reference sampling were comparable to those obtained when the number of dMRI volumes was reduced by up to 30%. Close to reference DTI and NODDI metrics were estimated with a significant reduction in acquisition time using three shells, respectively with: 4 directions at a b value of 700 s/mm2, 14 at 1000 s/mm2, and 32 at 2000 s/mm2. The study revealed aspects that can be important for large-scale clinical studies on bundle-specific diffusion MRI.
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Affiliation(s)
- Federico Spagnolo
- Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center, Basel, Switzerland
| | - Susanna Gobbi
- Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center, Basel, Switzerland
| | - Enikő Zsoldos
- Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center, Basel, Switzerland
| | - Manon Edde
- Sherbrooke Connectivity Imaging Lab (SCIL), Université de Sherbrooke, Sherbrooke, QC, Canada
- Imeka Solutions Inc, Sherbrooke, QC, Canada
| | - Matthias Weigel
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland
- Department of Neurology, University Hospital Basel, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
- Division of Radiological Physics, Department of Radiology, University Hospital Basel, Basel, Switzerland
| | - Cristina Granziera
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland
- Department of Neurology, University Hospital Basel, Basel, Switzerland
- Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Lab (SCIL), Université de Sherbrooke, Sherbrooke, QC, Canada
- Imeka Solutions Inc, Sherbrooke, QC, Canada
| | - Muhamed Barakovic
- Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center, Basel, Switzerland
| | - Stefano Magon
- Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center, Basel, Switzerland
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8
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Pérot JB, Brouillet E, Flament J. The contribution of preclinical magnetic resonance imaging and spectroscopy to Huntington's disease. Front Aging Neurosci 2024; 16:1306312. [PMID: 38414634 PMCID: PMC10896846 DOI: 10.3389/fnagi.2024.1306312] [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: 10/03/2023] [Accepted: 01/24/2024] [Indexed: 02/29/2024] Open
Abstract
Huntington's disease is an inherited disorder characterized by psychiatric, cognitive, and motor symptoms due to degeneration of medium spiny neurons in the striatum. A prodromal phase precedes the onset, lasting decades. Current biomarkers include clinical score and striatal atrophy using Magnetic Resonance Imaging (MRI). These markers lack sensitivity for subtle cellular changes during the prodromal phase. MRI and MR spectroscopy offer different contrasts for assessing metabolic, microstructural, functional, or vascular alterations in the disease. They have been used in patients and mouse models. Mouse models can be of great interest to study a specific mechanism of the degenerative process, allow better understanding of the pathogenesis from the prodromal to the symptomatic phase, and to evaluate therapeutic efficacy. Mouse models can be divided into three different constructions: transgenic mice expressing exon-1 of human huntingtin (HTT), mice with an artificial chromosome expressing full-length human HTT, and knock-in mouse models with CAG expansion inserted in the murine htt gene. Several studies have used MRI/S to characterized these models. However, the multiplicity of modalities and mouse models available complicates the understanding of this rich corpus. The present review aims at giving an overview of results obtained using MRI/S for each mouse model of HD, to provide a useful resource for the conception of neuroimaging studies using mouse models of HD. Finally, despite difficulties in translating preclinical protocols to clinical applications, many biomarkers identified in preclinical models have already been evaluated in patients. This review also aims to cover this aspect to demonstrate the importance of MRI/S for studying HD.
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Affiliation(s)
- Jean-Baptiste Pérot
- Laboratoire des Maladies Neurodégénératives, Molecular Imaging Research Center, Commissariat à l’Energie Atomique et aux Energies Alternatives, Centre National de la Recherche Scientifique, Université Paris-Saclay, Fontenay-aux-Roses, France
- Institut du Cerveau – Paris Brain Institute – ICM, Sorbonne Université, Paris, France
| | - Emmanuel Brouillet
- Laboratoire des Maladies Neurodégénératives, Molecular Imaging Research Center, Commissariat à l’Energie Atomique et aux Energies Alternatives, Centre National de la Recherche Scientifique, Université Paris-Saclay, Fontenay-aux-Roses, France
| | - Julien Flament
- Laboratoire des Maladies Neurodégénératives, Molecular Imaging Research Center, Commissariat à l’Energie Atomique et aux Energies Alternatives, Centre National de la Recherche Scientifique, Université Paris-Saclay, Fontenay-aux-Roses, France
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9
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Yao T, Rheault F, Cai LY, Nath V, Asad Z, Newlin N, Cui C, Deng R, Ramadass K, Shafer A, Resnick S, Schilling K, Landman BA, Huo Y. Robust fiber orientation distribution function estimation using deep constrained spherical deconvolution for diffusion-weighted magnetic resonance imaging. J Med Imaging (Bellingham) 2024; 11:014005. [PMID: 38188934 PMCID: PMC10768686 DOI: 10.1117/1.jmi.11.1.014005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2023] [Revised: 11/04/2023] [Accepted: 12/14/2023] [Indexed: 01/09/2024] Open
Abstract
Purpose Diffusion-weighted magnetic resonance imaging (DW-MRI) is a critical imaging method for capturing and modeling tissue microarchitecture at a millimeter scale. A common practice to model the measured DW-MRI signal is via fiber orientation distribution function (fODF). This function is the essential first step for the downstream tractography and connectivity analyses. With recent advantages in data sharing, large-scale multisite DW-MRI datasets are being made available for multisite studies. However, measurement variabilities (e.g., inter- and intrasite variability, hardware performance, and sequence design) are inevitable during the acquisition of DW-MRI. Most existing model-based methods [e.g., constrained spherical deconvolution (CSD)] and learning-based methods (e.g., deep learning) do not explicitly consider such variabilities in fODF modeling, which consequently leads to inferior performance on multisite and/or longitudinal diffusion studies. Approach In this paper, we propose a data-driven deep CSD method to explicitly constrain the scan-rescan variabilities for a more reproducible and robust estimation of brain microstructure from repeated DW-MRI scans. Specifically, the proposed method introduces a three-dimensional volumetric scanner-invariant regularization scheme during the fODF estimation. We study the Human Connectome Project (HCP) young adults test-retest group as well as the MASiVar dataset (with inter- and intrasite scan/rescan data). The Baltimore Longitudinal Study of Aging dataset is employed for external validation. Results From the experimental results, the proposed data-driven framework outperforms the existing benchmarks in repeated fODF estimation. By introducing the contrastive loss with scan/rescan data, the proposed method achieved a higher consistency while maintaining higher angular correlation coefficients with the CSD modeling. The proposed method is assessing the downstream connectivity analysis and shows increased performance in distinguishing subjects with different biomarkers. Conclusion We propose a deep CSD method to explicitly reduce the scan-rescan variabilities, so as to model a more reproducible and robust brain microstructure from repeated DW-MRI scans. The plug-and-play design of the proposed approach is potentially applicable to a wider range of data harmonization problems in neuroimaging.
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Affiliation(s)
- Tianyuan Yao
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
| | - Francois Rheault
- Université de Sherbrooke, Department of Computer Science, Sherbrooke, Québec, Canada
| | - Leon Y. Cai
- Vanderbilt University, Department of Biomedical Engineering, Nashville, Tennessee, United States
| | - Vishwesh Nath
- NVIDIA Corporation, Bethesda, Maryland, United States
| | - Zuhayr Asad
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
| | - Nancy Newlin
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
| | - Can Cui
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
| | - Ruining Deng
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
| | - Karthik Ramadass
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
| | - Andrea Shafer
- National Institute on Aging, Laboratory of Behavioral Neuroscience, Baltimore, Maryland, United States
| | - Susan Resnick
- National Institute on Aging, Laboratory of Behavioral Neuroscience, Baltimore, Maryland, United States
| | - Kurt Schilling
- Vanderbilt University, Department of Biomedical Engineering, Nashville, Tennessee, United States
| | - Bennett A. Landman
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
- Vanderbilt University, Department of Biomedical Engineering, Nashville, Tennessee, United States
- Vanderbilt University, Department of Electrical and Computer Engineering, Nashville, Tennessee, United States
| | - Yuankai Huo
- Vanderbilt University, Department of Computer Science, Nashville, Tennessee, United States
- Vanderbilt University, Department of Electrical and Computer Engineering, Nashville, Tennessee, United States
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10
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Wang B, Wang Y, Wang J, Jin C, Zhou R, Guo J, Zhang H, Wang M. Multiparametric Magnetic Resonance Investigations on Acute and Long-Term Kidney Injury. J Magn Reson Imaging 2024; 59:43-57. [PMID: 37246343 DOI: 10.1002/jmri.28784] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/26/2022] [Revised: 04/29/2023] [Accepted: 05/01/2023] [Indexed: 05/30/2023] Open
Abstract
Acute kidney injury (AKI) is a frequent complication of critical illness and carries a significant risk of short- and long-term mortality. The prediction of the progression of AKI to long-term injury has been difficult for renal disease treatment. Radiologists are keen for the early detection of transition from AKI to long-term kidney injury, which would help in the preventive measures. The lack of established methods for early detection of long-term kidney injury underscores the pressing needs of advanced imaging technology that reveals microscopic tissue alterations during the progression of AKI. Fueled by recent advances in data acquisition and post-processing methods of magnetic resonance imaging (MRI), multiparametric MRI is showing great potential as a diagnostic tool for many kidney diseases. Multiparametric MRI studies offer a precious opportunity for real-time noninvasive monitoring of pathological development and progression of AKI to long-term injury. It provides insight into renal vasculature and function (arterial spin labeling, intravoxel incoherent motion), tissue oxygenation (blood oxygen level-dependent), tissue injury and fibrosis (diffusion tensor imaging, diffusion kurtosis imaging, T1 and T2 mapping, quantitative susceptibility mapping). The multiparametric MRI approach is highly promising but the longitudinal investigation on the transition of AKI to irreversible long-term impairment is largely ignored. Further optimization and implementation of renal MR methods in clinical practice will enhance our comprehension of not only AKI but chronic kidney diseases. Novel imaging biomarkers for microscopic renal tissue alterations could be discovered and benefit the preventative interventions. This review explores recent MRI applications on acute and long-term kidney injury while addressing lingering challenges, with emphasis on the potential value of the development of multiparametric MRI for renal imaging on clinical systems. EVIDENCE LEVEL: 1 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Bin Wang
- Department of Medical Imaging, First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
- Department of Medical Imaging, Shanxi Medical University, Taiyuan, Shanxi, China
| | - Yongfang Wang
- Department of Medical Imaging, First Hospital of Shanxi Medical University, Taiyuan, Shanxi, China
| | - Jing Wang
- Department of Nuclear Medicine and PET Center, The Second Hospital of Zhejiang University School of Medicine, Hangzhou, China
- Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, China
| | - Chentao Jin
- Department of Nuclear Medicine and PET Center, The Second Hospital of Zhejiang University School of Medicine, Hangzhou, China
- Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, China
| | - Rui Zhou
- Department of Nuclear Medicine and PET Center, The Second Hospital of Zhejiang University School of Medicine, Hangzhou, China
- Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, China
| | - Jinxia Guo
- GE Healthcare, MR Research China, Beijing, China
| | - Hong Zhang
- Department of Nuclear Medicine and PET Center, The Second Hospital of Zhejiang University School of Medicine, Hangzhou, China
- Key Laboratory of Medical Molecular Imaging of Zhejiang Province, Hangzhou, China
- Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, China
- College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
| | - Min Wang
- Key Laboratory for Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, China
- College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, China
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11
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Ibrahim K, Bennett IJ. Hippocampal microstructure, but not macrostructure, mediates age differences in episodic memory. Front Aging Neurosci 2023; 15:1285375. [PMID: 38053843 PMCID: PMC10694359 DOI: 10.3389/fnagi.2023.1285375] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/29/2023] [Accepted: 11/07/2023] [Indexed: 12/07/2023] Open
Abstract
Introduction Separate unimodal magnetic resonance imaging (MRI) literatures have shown that hippocampal gray matter macrostructure (volume) and microstructure (diffusion) decline with age and relate to episodic memory performance, with multimodal MRI studies reporting that episodic memory may be better explained by a combination of these metrics. However, these effects are often assessed independent of age or only within older adults and therefore do not address whether these distinct modalities explain variance in (i.e, mediate) the effect of age on episodic memory. Methods Here, we simultaneously examined the unique and joint contribution of hippocampal volume and diffusion to age-related differences in episodic memory in 83 younger and 61 older adults who underwent a T1- and diffusion-weighted MRI and completed the Rey Auditory Verbal Learning Test. Results As expected, older age was significantly related to smaller volume and higher diffusion (intracellular, dispersion, and free) in bilateral hippocampus and to worse episodic memory performance (immediate and delayed free recall, recognition). Structural equation modelling revealed that the age-memory relationship was significantly mediated by hippocampal diffusion, but not volume. A non-significant influential indirect effect further revealed that the structural metrics did not jointly mediate the age-memory relationship. Discussion Together, these findings indicate that hippocampal microstructure uniquely contributes to age-related differences in episodic memory and suggest that volume and diffusion capture distinct neurobiological properties of hippocampal gray matter.
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Affiliation(s)
| | - Ilana J. Bennett
- Department of Psychology, University of California, Riverside, Riverside, CA, United States
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12
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Wang Y, Guo L, Wang R, Wang Y, Duan F, Zhan Y, Cheng J, Sun X, Tang Z. Abnormal Topological Organization of White Matter Structural Networks in Normal Tension Glaucoma Revealed via Diffusion Tensor Tractography. Brain Sci 2023; 13:1597. [PMID: 38002558 PMCID: PMC10669977 DOI: 10.3390/brainsci13111597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Revised: 11/10/2023] [Accepted: 11/15/2023] [Indexed: 11/26/2023] Open
Abstract
BACKGROUND Normal tension glaucoma (NTG) is considered a neurodegenerative disease with glaucomatous damage extending to diffuse brain areas. Therefore, this study aims to explore the abnormalities in the NTG structural network to help in the early diagnosis and course evaluation of NTG. METHODS The structural networks of 46 NTG patients and 19 age- and sex-matched healthy controls were constructed using diffusion tensor imaging, followed by graph theory analysis and correlation analysis of small-world properties with glaucoma clinical indicators. In addition, the network-based statistical analysis (NBS) method was used to compare structural network connectivity differences between NTG patients and healthy controls. RESULTS Structural brain networks in both NTG and NC groups exhibited small-world properties. However, the small-world index in the severe NTG group was reduced and correlated with a mean deviation of the visual field (MDVF) and retinal nerve fiber layer (RNFL) thickness. When compared to healthy controls, degree centrality and nodal efficiency in visual brain areas were significantly decreased, and betweenness centrality and nodal local efficiency in both visual and nonvisual brain areas were also significantly altered in NTG patients (all p < 0.05, FDR corrected). Furthermore, NTG patients exhibited increased structural connectivity in the occipitotemporal area, with the left fusiform gyrus (FFG.L) as the hub (p < 0.05). CONCLUSIONS NTG exhibited altered global properties and local properties of visual and cognitive-emotional brain areas, with enhanced structural connections within the occipitotemporal area. Moreover, the disrupted small-world properties of white matter might be imaging biomarkers for assessing NTG progression.
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Affiliation(s)
- Yin Wang
- Department of Radiology, Eye & ENT Hospital of Fudan University, Fudan University, Shanghai 200031, China (F.D.)
| | - Linying Guo
- Department of Radiology, Eye & ENT Hospital of Fudan University, Fudan University, Shanghai 200031, China (F.D.)
| | - Rong Wang
- Department of Radiology, Huashan Hospital of Fudan University, Fudan University, Shanghai 200040, China
| | - Yuzhe Wang
- Department of Radiology, Zhongshan Hospital of Fudan University, Fudan University, Shanghai 200032, China; (Y.W.)
| | - Fei Duan
- Department of Radiology, Eye & ENT Hospital of Fudan University, Fudan University, Shanghai 200031, China (F.D.)
| | - Yang Zhan
- Department of Radiology, Zhongshan Hospital of Fudan University, Fudan University, Shanghai 200032, China; (Y.W.)
| | - Jingfeng Cheng
- Department of Radiology, Eye & ENT Hospital of Fudan University, Fudan University, Shanghai 200031, China (F.D.)
| | - Xinghuai Sun
- Department of Ophthalmology & Visual Science, Eye & ENT Hospital of Fudan University, Fudan University, Shanghai 200031, China;
| | - Zuohua Tang
- Department of Radiology, Eye & ENT Hospital of Fudan University, Fudan University, Shanghai 200031, China (F.D.)
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Saito Y, Kamagata K, Andica C, Maikusa N, Uchida W, Takabayashi K, Yoshida S, Hagiwara A, Fujita S, Akashi T, Wada A, Irie R, Shimoji K, Hori M, Kamiya K, Koike S, Hayashi T, Aoki S. Traveling Subject-Informed Harmonization Increases Reliability of Brain Diffusion Tensor and Neurite Mapping. Aging Dis 2023:AD.2023.1020. [PMID: 38029401 DOI: 10.14336/ad.2023.1020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2023] [Accepted: 10/20/2023] [Indexed: 12/01/2023] Open
Abstract
Diffusion-weighted magnetic resonance imaging (dMRI) of brain has helped elucidate the microstructural changes of psychiatric and neurodegenerative disorders. Inconsistency between MRI models has hampered clinical application of dMRI-based metrics. Using harmonized dMRI data of 300 scans from 69 traveling subjects (TS) scanning the same individuals at multiple conditions with 13 MRI models and 2 protocols, the widely-used metrics such as diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI) were evaluated before and after harmonization with a combined association test (ComBat) or TS-based general linear model (TS-GLM). Results showed that both ComBat and TS-GLM significantly reduced the effects of the MRI site, model, and protocol for diffusion metrics while maintaining the intersubject biological effects. The harmonization power of TS-GLM based on TS data model is more powerful than that of ComBat. In conclusion, our research demonstrated that although ComBat and TS-GLM harmonization approaches were effective at reducing the scanner effects of the site, model, and protocol for DTI and NODDI metrics in WM, they exhibited high retainability of biological effects. Therefore, we suggest that, after harmonizing DTI and NODDI metrics, a multisite study with large cohorts can accurately detect small pathological changes by retaining pathological effects.
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Affiliation(s)
- Yuya Saito
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo Japan
| | - Koji Kamagata
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo Japan
| | - Christina Andica
- Faculty of Health Data Science, Juntendo University, Chiba, Japan
| | - Norihide Maikusa
- Center for Evolutionary Cognitive Sciences, Graduate School of Art and Sciences, The University of Tokyo, Tokyo, Japan
| | - Wataru Uchida
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo Japan
| | - Kaito Takabayashi
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo Japan
| | - Seina Yoshida
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo Japan
- Department of Radiological Sciences, Graduate School of Human Health Sciences, Tokyo Metropolitan University, Tokyo, Japan
| | - Akifumi Hagiwara
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo Japan
| | - Shohei Fujita
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo Japan
- Department of Radiology, The University of Tokyo, Tokyo, Japan
| | - Toshiaki Akashi
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo Japan
| | - Akihiko Wada
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo Japan
| | - Ryusuke Irie
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo Japan
| | - Keigo Shimoji
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo Japan
- Faculty of Health Data Science, Juntendo University, Chiba, Japan
| | - Masaaki Hori
- Department of Radiology, Toho University Omori Medical Center, Tokyo Japan
| | - Kouhei Kamiya
- Department of Radiology, Toho University Omori Medical Center, Tokyo Japan
| | - Shinsuke Koike
- Center for Evolutionary Cognitive Sciences, Graduate School of Art and Sciences, The University of Tokyo, Tokyo, Japan
| | - Takuya Hayashi
- Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, Japan
- Department of Brain Connectomics, Kyoto University Graduate School of Medicine
| | - Shigeki Aoki
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo Japan
- Faculty of Health Data Science, Juntendo University, Chiba, Japan
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Falangola MF, Dhiman S, Voltin J, Jensen JH. Quantitative microglia morphological features correlate with diffusion MRI in 2-month-old 3xTg-AD mice. Magn Reson Imaging 2023; 103:8-17. [PMID: 37392805 PMCID: PMC10528126 DOI: 10.1016/j.mri.2023.06.017] [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: 05/12/2023] [Accepted: 06/28/2023] [Indexed: 07/03/2023]
Abstract
Microglia (MØ) morphologies are closely related to their functional state and have a central role in the maintenance of brain homeostasis. It is well known that inflammation contributes to neurodegeneration at later stages of Alzheimer's Disease, but it is not clear which role MØ-mediated inflammation may play earlier in the disease pathogenesis. We have previously reported that diffusion MRI (dMRI) is able to detect early myelin abnormalities present in 2-month-old 3xTg-AD (TG) mice; since MØ actively participate in regulating myelination, the goal of this study was to assess quantitatively MØ morphological characteristics and its association with dMRI metrics patterns in 2-month-old 3xTg-AD mice. Our results show that, even at this young age (2-month-old), TG mice have statistically significantly more MØ cells, which are overall smaller and more complex, compared with age-matched normal control mice (NC). Our results also confirm that myelin basic protein is reduced in TG mice, particularly in fimbria (Fi) and cortex. Additionally, MØ morphological characteristics, in both groups, correlate with several dMRI metrics, depending on the brain region examined. For example, the increase in MØ number correlated with higher radial diffusivity (r = 0.59, p = 0.008), lower fractional anisotropy (FA) (r = -0.47, p = 0.03), and lower kurtosis fractional anisotropy (KFA) (r = -0.55, p = 0.01) in the CC. Furthermore, smaller MØ cells correlate with higher axial diffusivity) in the HV (r = 0.49, p = 0.03) and Sub (r = 0.57, p = 0.01). Our findings demonstrate, for the first time, that MØ proliferation/activation are a common and widespread feature in 2-month-old 3xTg-AD mice and suggest that dMRI measures are sensitive to these MØ alterations, which are associated in this model with myelin dysfunction and microstructural integrity abnormalities.
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Affiliation(s)
- Maria Fatima Falangola
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC, USA; Center for Biomedical Imaging, Medical University of South Carolina, Charleston, SC, USA.
| | - Siddhartha Dhiman
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC, USA
| | - Joshua Voltin
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC, USA; Center for Biomedical Imaging, Medical University of South Carolina, Charleston, SC, USA
| | - Jens H Jensen
- Department of Neuroscience, Medical University of South Carolina, Charleston, SC, USA; Center for Biomedical Imaging, Medical University of South Carolina, Charleston, SC, USA; Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, SC, USA
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15
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DiPiero M, Cordash H, Prigge MB, King CK, Morgan J, Guerrero-Gonzalez J, Adluru N, King JB, Lange N, Bigler ED, Zielinski BA, Alexander AL, Lainhart JE, Dean DC. Tract- and gray matter- based spatial statistics show white matter and gray matter microstructural differences in autistic males. Front Neurosci 2023; 17:1231719. [PMID: 37829720 PMCID: PMC10565827 DOI: 10.3389/fnins.2023.1231719] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Accepted: 09/07/2023] [Indexed: 10/14/2023] Open
Abstract
Background Autism spectrum disorder (ASD) is a neurodevelopmental condition commonly studied in the context of early childhood. As ASD is a life-long condition, understanding the characteristics of brain microstructure from adolescence into adulthood and associations to clinical features is critical for improving outcomes across the lifespan. In the current work, we utilized Tract Based Spatial Statistics (TBSS) and Gray Matter Based Spatial Statistics (GBSS) to examine the white matter (WM) and gray matter (GM) microstructure in neurotypical (NT) and autistic males. Methods Multi-shell diffusion MRI was acquired from 78 autistic and 81 NT males (12-to-46-years) and fit to the DTI and NODDI diffusion models. TBSS and GBSS were performed to analyze WM and GM microstructure, respectively. General linear models were used to investigate group and age-related group differences. Within the ASD group, relationships between WM and GM microstructure and measures of autistic symptoms were investigated. Results All dMRI measures were significantly associated with age across WM and GM. Significant group differences were observed across WM and GM. No significant age-by-group interactions were detected. Within the ASD group, positive relationships with WM microstructure were observed with ADOS-2 Calibrated Severity Scores. Conclusion Using TBSS and GBSS our findings provide new insights into group differences of WM and GM microstructure in autistic males from adolescence into adulthood. Detection of microstructural differences across the lifespan as well as their relationship to the level of autistic symptoms will deepen to our understanding of brain-behavior relationships of ASD and may aid in the improvement of intervention options for autistic adults.
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Affiliation(s)
- Marissa DiPiero
- Neuroscience Training Program, University of Wisconsin-Madison, Madison, WI, United States
- Waisman Center, University of Wisconsin-Madison, Madison, WI, United States
| | - Hassan Cordash
- Waisman Center, University of Wisconsin-Madison, Madison, WI, United States
| | - Molly B. Prigge
- Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, UT, United States
| | - Carolyn K. King
- Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, UT, United States
| | - Jubel Morgan
- Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, UT, United States
| | | | - Nagesh Adluru
- Waisman Center, University of Wisconsin-Madison, Madison, WI, United States
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States
| | - Jace B. King
- Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, UT, United States
| | - Nicholas Lange
- Department of Psychiatry, Harvard School of Medicine, Boston, MA, United States
| | - Erin D. Bigler
- Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, UT, United States
- Department of Neurology, University of Utah, Salt Lake City, UT, United States
- Department of Psychiatry, University of Utah, Salt Lake City, UT, United States
- Department of Psychology and Neuroscience Center, Brigham Young University, Provo, UT, United States
- Department of Neurology, University of California, Davis, Davis, CA, United States
| | - Brandon A. Zielinski
- Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, UT, United States
- Department of Neurology, University of Utah, Salt Lake City, UT, United States
- Department of Pediatrics, University of Utah, Salt Lake City, UT, United States
- Departments of Pediatrics and Neurology, University of Florida, Gainesville, FL, United States
- McKnight Brain Institute, University of Florida, Gainesville, FL, United States
| | - Andrew L. Alexander
- Waisman Center, University of Wisconsin-Madison, Madison, WI, United States
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, United States
- Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, United States
| | - Janet E. Lainhart
- Waisman Center, University of Wisconsin-Madison, Madison, WI, United States
- Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, United States
| | - Douglas C. Dean
- Waisman Center, University of Wisconsin-Madison, Madison, WI, United States
- Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, United States
- Department of Pediatrics, University of Wisconsin-Madison, Madison, WI, United States
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16
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Manivannan A, Foley LM, Hitchens TK, Rattray I, Bates GP, Modo M. Ex vivo 100 μm isotropic diffusion MRI-based tractography of connectivity changes in the end-stage R6/2 mouse model of Huntington's disease. NEUROPROTECTION 2023; 1:66-83. [PMID: 37745674 PMCID: PMC10516267 DOI: 10.1002/nep3.14] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 11/08/2022] [Indexed: 09/26/2023]
Abstract
Background Huntington's disease is a progressive neurodegenerative disorder. Brain atrophy, as measured by volumetric magnetic resonance imaging (MRI), is a downstream consequence of neurodegeneration, but microstructural changes within brain tissue are expected to precede this volumetric decline. The tissue microstructure can be assayed non-invasively using diffusion MRI, which also allows a tractographic analysis of brain connectivity. Methods We here used ex vivo diffusion MRI (11.7 T) to measure microstructural changes in different brain regions of end-stage (14 weeks of age) wild type and R6/2 mice (male and female) modeling Huntington's disease. To probe the microstructure of different brain regions, reduce partial volume effects and measure connectivity between different regions, a 100 μm isotropic voxel resolution was acquired. Results Although fractional anisotropy did not reveal any difference between wild-type controls and R6/2 mice, mean, axial, and radial diffusivity were increased in female R6/2 mice and decreased in male R6/2 mice. Whole brain streamlines were only reduced in male R6/2 mice, but streamline density was increased. Region-to-region tractography indicated reductions in connectivity between the cortex, hippocampus, and thalamus with the striatum, as well as within the basal ganglia (striatum-globus pallidus-subthalamic nucleus-substantia nigra-thalamus). Conclusions Biological sex and left/right hemisphere affected tractographic results, potentially reflecting different stages of disease progression. This proof-of-principle study indicates that diffusion MRI and tractography potentially provide novel biomarkers that connect volumetric changes across different brain regions. In a translation setting, these measurements constitute a novel tool to assess the therapeutic impact of interventions such as neuroprotective agents in transgenic models, as well as patients with Huntington's disease.
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Affiliation(s)
- Ashwinee Manivannan
- Department of Neuroscience, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Lesley M. Foley
- Animal Imaging Center, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - T. Kevin Hitchens
- Animal Imaging Center, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
- Department of Neurobiology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
| | - Ivan Rattray
- Department of Neurodegenerative Disease, Queen Square Institute of Neurology, Huntington’s Disease Centre and UK Dementia Research Institute at UCL, University College London, London, UK
| | - Gillian P. Bates
- Department of Neurodegenerative Disease, Queen Square Institute of Neurology, Huntington’s Disease Centre and UK Dementia Research Institute at UCL, University College London, London, UK
| | - Michel Modo
- Department of Radiology, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
- Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
- Center for the Neural Basis of Cognition, University of Pittsburgh, Pittsburgh, Pennsylvania, USA
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Feizollah S, Tardif CL. High-resolution diffusion-weighted imaging at 7 Tesla: single-shot readout trajectories and their impact on signal-to-noise ratio, spatial resolution and accuracy. Neuroimage 2023; 274:120159. [PMID: 37150332 DOI: 10.1016/j.neuroimage.2023.120159] [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: 02/03/2023] [Revised: 03/31/2023] [Accepted: 05/04/2023] [Indexed: 05/09/2023] Open
Abstract
Diffusion MRI (dMRI) is a valuable imaging technique to study the connectivity and microstructure of the brain in vivo. However, the resolution of dMRI is limited by the low signal-to-noise ratio (SNR) of this technique. Various multi-shot acquisition strategies have been developed to achieve sub-millimeter resolution, but they require long scan times which can be restricting for patient scans. Alternatively, the SNR of single-shot acquisitions can be increased by using a spiral readout trajectory to minimize the sequence echo time. Imaging at ultra-high fields (UHF) could further increase the SNR of single-shot dMRI; however, the shorter T2* of brain tissue and the greater field non-uniformities at UHFs will degrade image quality, causing image blurring, distortions, and signal loss. In this study, we investigated the trade-off between the SNR and resolution of different k-space trajectories, including echo planar imaging (EPI), partial Fourier EPI, and spiral trajectories, over a range of dMRI resolutions at 7T. The effective resolution, spatial specificity and sharpening effect were measured from the point spread function (PSF) of the simulated diffusion sequences for a nominal resolution range of 0.6-1.8 mm. In-vivo partial brain scans at a nominal resolution of 1.5 mm isotropic were acquired using the three readout trajectories to validate the simulation results. Field probes were used to measure dynamic magnetic fields offline up to the 3rd order of spherical harmonics. Image reconstruction was performed using static ΔB0 field maps and the measured trajectories to correct image distortions and artifacts, leaving T2* effects as the primary source of blurring. The effective resolution was examined in fractional anisotropy (FA) maps calculated from a multi-shell dataset with b-values of 300, 1000, and 2000 s/mm2 in 5, 16, and 48 directions, respectively. In-vivo scans at nominal resolutions of 1, 1.2, and 1.5 mm were acquired and the SNR of the different trajectories calculated using the multiple replica method to investigate the SNR. Finally, in-vivo whole brain scans with an effective resolution of 1.5 mm isotropic were acquired to explore the SNR and efficiency of different trajectories at a matching effective resolution. FA and intra-cellular volume fraction (ICVF) maps calculated using neurite orientation dispersion and density imaging (NODDI) were used for the comparison. The simulations and in vivo imaging results showed that for matching nominal resolutions, EPI trajectories had the highest specificity and effective resolution with maximum image sharpening effect. However, spirals have a significantly higher SNR, in particular at higher resolutions and even when the effective image resolutions are matched. Overall, this work shows that the higher SNR of single-shot spiral trajectories at 7T allows us to achieve higher effective resolutions compared to EPI and PF-EPI to map the microstructure and connectivity of small brain structures.
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Affiliation(s)
- Sajjad Feizollah
- Department of Neurology and Neurosurgery, Faculty of Medicine and Health Sciences, McGill University, 3801 Rue University, Montreal, QC, Canada; McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, 3801 Rue University, Montreal, QC, Canada.
| | - Christine L Tardif
- Department of Neurology and Neurosurgery, Faculty of Medicine and Health Sciences, McGill University, 3801 Rue University, Montreal, QC, Canada; McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, 3801 Rue University, Montreal, QC, Canada; Department of Biomedical Engineering, Faculty of Medicine and Health Sciences, McGill University, Duff Medical Building, 3775 Rue University, Suite 316, Montreal, QC, Canada.
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18
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Faiyaz A, Doyley MM, Schifitto G, Uddin MN. Artificial intelligence for diffusion MRI-based tissue microstructure estimation in the human brain: an overview. Front Neurol 2023; 14:1168833. [PMID: 37153663 PMCID: PMC10160660 DOI: 10.3389/fneur.2023.1168833] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2023] [Accepted: 03/27/2023] [Indexed: 05/10/2023] Open
Abstract
Artificial intelligence (AI) has made significant advances in the field of diffusion magnetic resonance imaging (dMRI) and other neuroimaging modalities. These techniques have been applied to various areas such as image reconstruction, denoising, detecting and removing artifacts, segmentation, tissue microstructure modeling, brain connectivity analysis, and diagnosis support. State-of-the-art AI algorithms have the potential to leverage optimization techniques in dMRI to advance sensitivity and inference through biophysical models. While the use of AI in brain microstructures has the potential to revolutionize the way we study the brain and understand brain disorders, we need to be aware of the pitfalls and emerging best practices that can further advance this field. Additionally, since dMRI scans rely on sampling of the q-space geometry, it leaves room for creativity in data engineering in such a way that it maximizes the prior inference. Utilization of the inherent geometry has been shown to improve general inference quality and might be more reliable in identifying pathological differences. We acknowledge and classify AI-based approaches for dMRI using these unifying characteristics. This article also highlighted and reviewed general practices and pitfalls involving tissue microstructure estimation through data-driven techniques and provided directions for building on them.
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Affiliation(s)
- Abrar Faiyaz
- Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY, United States
| | - Marvin M. Doyley
- Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY, United States
- Department of Imaging Sciences, University of Rochester, Rochester, NY, United States
- Department of Biomedical Engineering, University of Rochester, Rochester, NY, United States
| | - Giovanni Schifitto
- Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY, United States
- Department of Imaging Sciences, University of Rochester, Rochester, NY, United States
- Department of Neurology, University of Rochester, Rochester, NY, United States
| | - Md Nasir Uddin
- Department of Biomedical Engineering, University of Rochester, Rochester, NY, United States
- Department of Neurology, University of Rochester, Rochester, NY, United States
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19
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Ward IL, Raven EP, de la Rosa S, Jones DK, Teufel C, von dem Hagen E. White matter microstructure in face and body networks predicts facial expression and body posture perception across development. Hum Brain Mapp 2023; 44:2307-2322. [PMID: 36661194 PMCID: PMC10028674 DOI: 10.1002/hbm.26211] [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: 06/02/2022] [Revised: 12/05/2022] [Accepted: 01/07/2023] [Indexed: 01/21/2023] Open
Abstract
Facial expression and body posture recognition have protracted developmental trajectories. Interactions between face and body perception, such as the influence of body posture on facial expression perception, also change with development. While the brain regions underpinning face and body processing are well-defined, little is known about how white-matter tracts linking these regions relate to perceptual development. Here, we obtained complementary diffusion magnetic resonance imaging (MRI) measures (fractional anisotropy [FA], spherical mean Ṧμ ), and a quantitative MRI myelin-proxy measure (R1), within white-matter tracts of face- and body-selective networks in children and adolescents and related these to perceptual development. In tracts linking occipital and fusiform face areas, facial expression perception was predicted by age-related maturation, as measured by Ṧμ and R1, as well as age-independent individual differences in microstructure, captured by FA and R1. Tract microstructure measures linking posterior superior temporal sulcus body region with anterior temporal lobe (ATL) were related to the influence of body on facial expression perception, supporting ATL as a site of face and body network convergence. Overall, our results highlight age-dependent and age-independent constraints that white-matter microstructure poses on perceptual abilities during development and the importance of complementary microstructural measures in linking brain structure and behaviour.
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Affiliation(s)
- Isobel L. Ward
- Cardiff University Brain Research Imaging Centre, School of PsychologyCardiff UniversityCardiffUK
| | - Erika P. Raven
- Cardiff University Brain Research Imaging Centre, School of PsychologyCardiff UniversityCardiffUK
- Center for Biomedical Imaging, Department of RadiologyNew York University Grossman School of MedicineNew YorkNew YorkUSA
| | | | - Derek K. Jones
- Cardiff University Brain Research Imaging Centre, School of PsychologyCardiff UniversityCardiffUK
| | - Christoph Teufel
- Cardiff University Brain Research Imaging Centre, School of PsychologyCardiff UniversityCardiffUK
| | - Elisabeth von dem Hagen
- Cardiff University Brain Research Imaging Centre, School of PsychologyCardiff UniversityCardiffUK
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20
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Gangolli M, Wang WT, Gai ND, Pham DL, Butman JA. Simultaneous Acquisition of Diffusion Tensor and Dynamic Diffusion MRI. J Magn Reson Imaging 2023; 57:1079-1092. [PMID: 36056625 PMCID: PMC9981815 DOI: 10.1002/jmri.28407] [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: 09/16/2021] [Revised: 08/08/2022] [Accepted: 08/09/2022] [Indexed: 11/12/2022] Open
Abstract
BACKGROUND Dynamic diffusion magnetic resonance imaging (ddMRI) metrics can assess transient microstructural alterations in tissue diffusivity but requires additional scan time hindering its clinical application. PURPOSE To determine whether a diffusion gradient table can simultaneously acquire data to estimate dynamic and diffusion tensor imaging (DTI) metrics. STUDY TYPE Prospective. SUBJECTS Seven healthy subjects, 39 epilepsy patients (15 female, 31 male, age ± 15). FIELD STRENGTH/SEQUENCE Two-dimensional diffusion MRI (b = 1000 s/mm2 ) at a field strength of 3 T. Sessions in healthy subjects-standard ddMRI (30 directions), standard DTI (15 and 30 directions), and nested cubes scans (15 and 30 directions). Sessions in epilepsy patients-two 30 direction (standard ddMRI, 10 nested cubes) or two 15 direction scans (standard DTI, 5 nested cubes). ASSESSMENT Fifteen direction DTI was repeated twice for within-session test-retest measurements in healthy subjects. Bland-Altman analysis computed bias and limits of agreement for DTI metrics using test-retest scans and standard 15 direction vs. 5 nested cubes scans. Intraclass correlation (ICC) analysis compared tensor metrics between 15 direction DTI scans (standard vs. 5 nested cubes) and the coefficients of variation (CoV) of trace and apparent diffusion coefficient (ADC) between 30 direction ddMRI scans (standard vs. 10 nested cubes). STATISTICAL TESTS Bland-Altman and ICC analysis using a P-value of 0.05 for statistical significance. RESULTS Correlations of mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) were strong and significant in gray (ICC > 0.95) and white matter (ICC > 0.95) between standard vs. nested cubes DTI acquisitions. Correlation of white matter fractional anisotropy was also strong (ICC > 0.95) and significant. ICCs of the CoV of dynamic ADC measured using repeated cubes and nested cubes acquisitions were modest (ICC >0.60), but significant in gray matter. CONCLUSION A nested cubes diffusion gradient table produces tensor-based and dynamic diffusion measurements in a single acquisition. LEVEL OF EVIDENCE 2 TECHNICAL EFFICACY: Stage 1.
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Affiliation(s)
- Mihika Gangolli
- Center for Neuroscience and Regenerative Medicine
- The Henry M. Jackson Foundation for the Advancement of Military Medicine, Inc
| | - Wen-Tung Wang
- National Institutes of Health, Radiology and Imaging Sciences
| | - Neville D. Gai
- National Institutes of Health, National Heart Lung and Blood Institute
| | - Dzung L. Pham
- Center for Neuroscience and Regenerative Medicine
- Uniformed Services University, Radiology and Radiological Sciences
| | - John A. Butman
- Center for Neuroscience and Regenerative Medicine
- National Institutes of Health, Radiology and Imaging Sciences
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21
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Gilli F, Ceccarelli A. Magnetic resonance imaging approaches for studying mouse models of multiple sclerosis: A mini review. J Neurosci Res 2023. [DOI: 10.1002/jnr.25193] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 01/30/2023] [Accepted: 03/10/2023] [Indexed: 04/03/2023]
Affiliation(s)
- Francesca Gilli
- Department of Neurology, Dartmouth Hitchcock Medical Center Geisel School of Medicine at Dartmouth Lebanon New Hampshire USA
| | - Antonia Ceccarelli
- Department of Neurology EpiCURA Centre Hospitalier Ath Belgium
- Hearthrhythmanagement, UZB Brussels Belgium
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22
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Ferizi U, Müller-Oehring EM, Peterson ET, Pohl KM. The distortions of the free water model for diffusion MRI data when assuming single compartment relaxometry and proton density. Phys Med Biol 2023; 68:10.1088/1361-6560/acb30b. [PMID: 36638532 PMCID: PMC10100575 DOI: 10.1088/1361-6560/acb30b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2022] [Accepted: 01/13/2023] [Indexed: 01/15/2023]
Abstract
Objective.To document the bias of thesimplifiedfree water model of diffusion MRI (dMRI) signal vis-à-vis aspecificmodel which, in addition to diffusion, incorporates compartment-specific proton density (PD), T1 recovery during repetition time (TR), and T2 decay during echo time (TE).Approach.Both models assume that volume fractionfof the total signal in any voxel arises from the free water compartment (fw) such as cerebrospinal fluid or edema, and the remainder (1-f) from hindered water (hw) which is constrained by cellular structures such as white matter (WM). Thespecificandsimplifiedmodels are compared on a synthetic dataset, using a range of PD, T1 and T2 values. We then fit the models to anin vivohealthy brain dMRI dataset. For bothsyntheticandin vivodata we use experimentally feasible TR, TE, signal-to-noise ratio (SNR) and physiologically plausible diffusion profiles.Main results.From the simulations we see that the difference between the estimatedsimplified fandspecific fis largest for mid-range ground-truthf, and it increases as SNR increases. The estimation of volume fractionfis sensitive to the choice of model,simplifiedorspecific, but the estimated diffusion parameters are robust to small perturbations in the simulation.Specific fis more accurate and precise thansimplified f. In the white matter (WM) regions of thein vivoimages,specific fis lower thansimplified f.Significance.In dMRI models for free water, accounting for compartment specific PD, T1 and T2, in addition to diffusion, improves the estimation of model parameters. This extra model specification attenuates the estimation bias of compartmental volume fraction without affecting the estimation of other diffusion parameters.
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Affiliation(s)
- Uran Ferizi
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, United States of America
| | - Eva M Müller-Oehring
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, United States of America
| | - Eric T Peterson
- Center for Health Sciences, SRI International, Menlo Park, CA, United States of America
| | - Kilian M Pohl
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA, United States of America
- Center for Health Sciences, SRI International, Menlo Park, CA, United States of America
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23
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Singh AP, Jain VS, Yu JPJ. Diffusion radiomics for subtyping and clustering in autism spectrum disorder: A preclinical study. Magn Reson Imaging 2023; 96:116-125. [PMID: 36496097 PMCID: PMC9815912 DOI: 10.1016/j.mri.2022.12.003] [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: 09/16/2022] [Revised: 10/24/2022] [Accepted: 12/01/2022] [Indexed: 12/12/2022]
Abstract
Autism spectrum disorder (ASD) is a highly prevalent, heterogenous neurodevelopmental disorder. Neuroimaging methods such as functional, structural, and diffusion MRI have been used to identify candidate imaging biomarkers for ASD, but current findings remain non-specific and likely arise from the heterogeneity present in ASD. To account for this, efforts to subtype ASD have emerged as a potential strategy for both the study of ASD and advancement of tailored behavioral therapies and therapeutics. Towards these ends, to improve upon current neuroimaging methods, we propose combining biologically sensitive neurite orientation dispersion and density index (NODDI) diffusion MR imaging with radiomics image processing to create a new methodological approach that, we hypothesize, can sensitively and specifically capture neurobiology. We demonstrate this method can sensitively distinguish differences between four genetically distinct rat models of ASD (Fmr1, Pten, Nrxn1, Disc1). Further, we demonstrate diffusion radiomic analyses hold promise for subtyping in ASD as we show unsupervised clustering of NODDI radiomic data generates clusters specific to the underlying genetic differences between the animal models. Taken together, our findings suggest the unique application of radiomic analysis on NODDI diffusion MRI may have the capacity to sensitively and specifically disambiguate the neurobiological heterogeneity present in the ASD population.
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Affiliation(s)
- Ajay P Singh
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI 53705, USA.; Medical Scientist Training Program, University of Wisconsin School of Medicine and Public Health, Madison, WI 53705, USA; Graduate Program in Cellular and Molecular Biology, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Vansh S Jain
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI 53705, USA
| | - John-Paul J Yu
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI 53705, USA.; Graduate Program in Cellular and Molecular Biology, University of Wisconsin-Madison, Madison, WI 53706, USA; Neuroscience Training Program, Wisconsin Institutes for Medical Research, University of Wisconsin-Madison, Madison, WI 53705, USA; Department of Biomedical Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA; Department of Psychiatry, University of Wisconsin School of Medicine and Public Health, Madison, WI 53705, USA.
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24
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Ni HC, Chao YP, Tseng RY, Wu CT, Cocchi L, Chou TL, Chen RS, Gau SSF, Yeh CH, Lin HY. Lack of effects of four-week theta burst stimulation on white matter macro/microstructure in children and adolescents with autism. Neuroimage Clin 2023; 37:103324. [PMID: 36638598 PMCID: PMC9852693 DOI: 10.1016/j.nicl.2023.103324] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2022] [Revised: 12/18/2022] [Accepted: 01/05/2023] [Indexed: 01/09/2023]
Abstract
Following the published behavioral and cognitive results of this single-blind parallel sham-controlled randomized clinical trial, the current study aimed to explore the impact of intermittent theta burst stimulation (iTBS), a variant of excitatory transcranial magnetic stimulation, over the bilateral posterior superior temporal sulci (pSTS) on white matter macro/microstructure in intellectually able children and adolescents with autism. Participants were randomized and blindly received active or sham iTBS for 4 weeks (the single-blind sham-controlled phase). Then, all participants continued to receive active iTBS for another 4 weeks (the open-label phase). The clinical results were published elsewhere. Here, we present diffusion magnetic resonance imaging data on potential changes in white matter measures after iTBS. Twenty-two participants in Active-Active group and 27 participants in Sham-Active group underwent multi-shell high angular resolution diffusion imaging (64-direction for b = 2000 & 1000 s/mm2, respectively) at baseline, week 4, and week 8. With longitudinal fixel-based analysis, we found no white matter changes following iTBS from baseline to week 4 (a null treatment by time interaction and a null within-group paired comparison in the Active-Active group), nor from baseline to week 8 (null within-group paired comparisons in both Active-Active and Sham-Active groups). As for the brain-symptoms relationship, we did not find baseline white matter metrics associated with symptom changes at week 4 in either group. Our results raise the question of what the minimal cumulative stimulation dose required to induce the white matter plasticity is.
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Affiliation(s)
- Hsing-Chang Ni
- Department of Psychiatry, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
| | - Yi-Ping Chao
- Deparment of Computer Science and Information Engineering, Chang Gung University, Taoyuan, Taiwan; Department of Otorhinolaryngology-Head and Neck Surgery, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
| | - Rung-Yu Tseng
- Department of Psychiatry, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan; Institute for Radiological Research, Chang Gung University, Taoyuan, Taiwan
| | - Chen-Te Wu
- Department of Medical Imaging and Intervention, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
| | - Luca Cocchi
- Clinical Brain Networks Group, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia
| | - Tai-Li Chou
- Department of Psychology, National Taiwan University, Taipei, Taiwan; Graduate Institute of Brain and Mind Sciences, College of Medicine, National Taiwan University, Taipei, Taiwan; Neurobiology and Cognitive Science Center, National Taiwan University, Taipei, Taiwan
| | - Rou-Shayn Chen
- Department of Neurology, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
| | - Susan Shur-Fen Gau
- Graduate Institute of Brain and Mind Sciences, College of Medicine, National Taiwan University, Taipei, Taiwan; Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan
| | - Chun-Hung Yeh
- Department of Psychiatry, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan; Institute for Radiological Research, Chang Gung University, Taoyuan, Taiwan.
| | - Hsiang-Yuan Lin
- Azrieli Adult Neurodevelopmental Centre, Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
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25
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Chen X, Schädelin S, Lu PJ, Ocampo-Pineda M, Weigel M, Barakovic M, Ruberte E, Cagol A, Marechal B, Kober T, Kuhle J, Kappos L, Melie-Garcia L, Granziera C. Personalized maps of T1 relaxometry abnormalities provide correlates of disability in multiple sclerosis patients. Neuroimage Clin 2023; 37:103349. [PMID: 36801600 PMCID: PMC9958406 DOI: 10.1016/j.nicl.2023.103349] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2022] [Revised: 02/08/2023] [Accepted: 02/10/2023] [Indexed: 02/16/2023]
Abstract
OBJECTIVES AND AIMS Quantitative MRI (qMRI) has greatly improved the sensitivity and specificity of microstructural brain pathology in multiple sclerosis (MS) when compared to conventional MRI (cMRI). More than cMRI, qMRI also provides means to assess pathology within the normal-appearing and lesion tissue. In this work, we further developed a method providing personalized quantitative T1 (qT1) abnormality maps in individual MS patients by modeling the age dependence of qT1 alterations. In addition, we assessed the relationship between qT1 abnormality maps and patients' disability, in order to evaluate the potential value of this measurement in clinical practice. METHODS We included 119 MS patients (64 relapsing-remitting MS (RRMS), 34 secondary progressive MS (SPMS), 21 primary progressive MS (PPMS)), and 98 Healthy Controls (HC). All individuals underwent 3T MRI examinations, including Magnetization Prepared 2 Rapid Acquisition Gradient Echoes (MP2RAGE) for qT1 maps and High-Resolution 3D Fluid Attenuated Inversion Recovery (FLAIR) imaging. To calculate personalized qT1 abnormality maps, we compared qT1 in each brain voxel in MS patients to the average qT1 obtained in the same tissue (grey/white matter) and region of interest (ROI) in healthy controls, hereby providing individual voxel-based Z-score maps. The age dependence of qT1 in HC was modeled using linear polynomial regression. We computed the average qT1 Z-scores in white matter lesions (WMLs), normal-appearing white matter (NAWM), cortical grey matter lesions (GMcLs) and normal-appearing cortical grey matter (NAcGM). Lastly, a multiple linear regression (MLR) model with the backward selection including age, sex, disease duration, phenotype, lesion number, lesion volume and average Z-score (NAWM/NAcGM/WMLs/GMcLs) was used to assess the relationship between qT1 measures and clinical disability (evaluated with EDSS). RESULTS The average qT1 Z-score was higher in WMLs than in NAWM. (WMLs: 1.366 ± 0.409, NAWM: -0.133 ± 0.288, [mean ± SD], p < 0.001). The average Z-score in NAWM in RRMS patients was significantly lower than in PPMS patients (p = 0.010). The MLR model showed a strong association between average qT1 Z-scores in white matter lesions (WMLs) and EDSS (R2 = 0.549, β = 0.178, 97.5 % CI = 0.030 to 0.326, p = 0.019). Specifically, we measured a 26.9 % increase in EDSS per unit of qT1 Z-score in WMLs in RRMS patients (R2 = 0.099, β = 0.269, 97.5 % CI = 0.078 to 0.461, p = 0.007). CONCLUSIONS We showed that personalized qT1 abnormality maps in MS patients provide measures related to clinical disability, supporting the use of those maps in clinical practice.
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Affiliation(s)
- Xinjie Chen
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Sabine Schädelin
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Po-Jui Lu
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Mario Ocampo-Pineda
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Matthias Weigel
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland; Division of Radiological Physics, Department of Radiology, University Hospital Basel, Basel, Switzerland
| | - Muhamed Barakovic
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Esther Ruberte
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Alessandro Cagol
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Benedicte Marechal
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland
| | - Tobias Kober
- Advanced Clinical Imaging Technology, Siemens Healthineers International AG, Lausanne, Switzerland
| | - Jens Kuhle
- Department of Neurology, University Hospital Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Ludwig Kappos
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Lester Melie-Garcia
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland
| | - Cristina Granziera
- Translational Imaging in Neurology (ThINK) Basel, Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel, Basel, Switzerland; Department of Neurology, University Hospital Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Basel, Switzerland.
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DiPiero MA, Surgent OJ, Travers BG, Alexander AL, Lainhart JE, Dean Iii DC. Gray matter microstructure differences in autistic males: A gray matter based spatial statistics study. Neuroimage Clin 2022; 37:103306. [PMID: 36587584 PMCID: PMC9817031 DOI: 10.1016/j.nicl.2022.103306] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2022] [Revised: 11/29/2022] [Accepted: 12/24/2022] [Indexed: 12/27/2022]
Abstract
BACKGROUND Autism spectrum disorder (ASD) is a complex neurodevelopmental condition. Understanding the brain's microstructure and its relationship to clinical characteristics is important to advance our understanding of the neural supports underlying ASD. In the current work, we implemented Gray-Matter Based Spatial Statistics (GBSS) to examine and characterize cortical microstructure and assess differences between typically developing (TD) and autistic males. METHODS A multi-shell diffusion MRI (dMRI) protocol was acquired from 83 TD and 70 autistic males (5-to-21-years) and fit to the DTI and NODDI models. GBSS was performed for voxelwise analysis of cortical gray matter (GM). General linear models were used to investigate group differences, while age-by-group interactions assessed age-related differences between groups. Within the ASD group, relationships between cortical microstructure and measures of autistic symptoms were investigated. RESULTS All dMRI measures were significantly associated with age across the GM skeleton. Group differences and age-by-group interactions are reported. Group-wise increases in neurite density in autistic individuals were observed across frontal, temporal, and occipital regions of the right hemisphere. Significant age-by-group interactions of neurite density were observed within the middle frontal gyrus, precentral gyrus, and frontal pole. Negative relationships between neurite dispersion and the ADOS-2 Calibrated Severity Scores (CSS) were observed within the ASD group. DISCUSSION Findings demonstrate group and age-related differences between groups in neurite density in ASD across right-hemisphere brain regions supporting cognitive processes. Results provide evidence of altered neurodevelopmental processes affecting GM microstructure in autistic males with implications for the role of cortical microstructure in the level of autistic symptoms. CONCLUSION Using dMRI and GBSS, our findings provide new insights into group and age-related differences of the GM microstructure in autistic males. Defining where and when these cortical GM differences arise will contribute to our understanding of brain-behavior relationships of ASD and may aid in the development and monitoring of targeted and individualized interventions.
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Affiliation(s)
- Marissa A DiPiero
- Waisman Center, University of Wisconsin-Madison, Madison, WI, USA; Neuroscience Training Program, University of Wisconsin-Madison, Madison, WI, USA
| | - Olivia J Surgent
- Waisman Center, University of Wisconsin-Madison, Madison, WI, USA; Neuroscience Training Program, University of Wisconsin-Madison, Madison, WI, USA
| | - Brittany G Travers
- Waisman Center, University of Wisconsin-Madison, Madison, WI, USA; Occupational Therapy Program, University of Wisconsin-Madison, Madison, WI, USA; Department of Kinesiology, University of Wisconsin-Madison, Madison, WI, USA
| | - Andrew L Alexander
- Waisman Center, University of Wisconsin-Madison, Madison, WI, USA; Department of Psychiatry, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, USA
| | - Janet E Lainhart
- Waisman Center, University of Wisconsin-Madison, Madison, WI, USA; Department of Psychiatry, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Douglas C Dean Iii
- Waisman Center, University of Wisconsin-Madison, Madison, WI, USA; Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, USA; Department of Pediatrics, University of Wisconsin-Madison, Madison, WI, USA.
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Avram AV, Saleem KS, Basser PJ. COnstrained Reference frame diffusion TEnsor Correlation Spectroscopic (CORTECS) MRI: A practical framework for high-resolution diffusion tensor distribution imaging. Front Neurosci 2022; 16:1054509. [PMID: 36590291 PMCID: PMC9798222 DOI: 10.3389/fnins.2022.1054509] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 11/25/2022] [Indexed: 12/23/2022] Open
Abstract
High-resolution imaging studies have consistently shown that in cortical tissue water diffuses preferentially along radial and tangential orientations with respect to the cortical surface, in agreement with histology. These dominant orientations do not change significantly even if the relative contributions from microscopic water pools to the net voxel signal vary across experiments that use different diffusion times, b-values, TEs, and TRs. With this in mind, we propose a practical new framework for imaging non-parametric diffusion tensor distributions (DTDs) by constraining the microscopic diffusion tensors of the DTD to be diagonalized using the same orthonormal reference frame of the mesoscopic voxel. In each voxel, the constrained DTD (cDTD) is completely determined by the correlation spectrum of the microscopic principal diffusivities associated with the axes of the voxel reference frame. Consequently, all cDTDs are inherently limited to the domain of positive definite tensors and can be reconstructed efficiently using Inverse Laplace Transform methods. Moreover, the cDTD reconstruction can be performed using only data acquired efficiently with single diffusion encoding, although it also supports datasets with multiple diffusion encoding. In tissues with a well-defined architecture, such as the cortex, we can further constrain the cDTD to contain only cylindrically symmetric diffusion tensors and measure the 2D correlation spectra of principal diffusivities along the radial and tangential orientation with respect to the cortical surface. To demonstrate this framework, we perform numerical simulations and analyze high-resolution dMRI data from a fixed macaque monkey brain. We estimate 2D cDTDs in the cortex and derive, in each voxel, the marginal distributions of the microscopic principal diffusivities, the corresponding distributions of the microscopic fractional anisotropies and mean diffusivities along with their 2D correlation spectra to quantify the cDTD shape-size characteristics. Signal components corresponding to specific bands in these cDTD-derived spectra show high specificity to cortical laminar structures observed with histology. Our framework drastically simplifies the measurement of non-parametric DTDs in high-resolution datasets with mesoscopic voxel sizes much smaller than the radius of curvature of the underlying anatomy, e.g., cortical surface, and can be applied retrospectively to analyze existing diffusion MRI data from fixed cortical tissues.
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Affiliation(s)
- Alexandru V. Avram
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, United States
- Center for Neuroscience and Regenerative Medicine, Bethesda, MD, United States
- Henry M. Jackson Foundation for the Advancement of Military Medicine Inc., Bethesda, MD, United States
| | - Kadharbatcha S. Saleem
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, United States
- Center for Neuroscience and Regenerative Medicine, Bethesda, MD, United States
- Henry M. Jackson Foundation for the Advancement of Military Medicine Inc., Bethesda, MD, United States
| | - Peter J. Basser
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health, Bethesda, MD, United States
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Avram AV, Saleem KS, Komlosh ME, Yen CC, Ye FQ, Basser PJ. High-resolution cortical MAP-MRI reveals areal borders and laminar substructures observed with histological staining. Neuroimage 2022; 264:119653. [PMID: 36257490 DOI: 10.1016/j.neuroimage.2022.119653] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2022] [Revised: 09/11/2022] [Accepted: 09/26/2022] [Indexed: 11/06/2022] Open
Abstract
The variations in cellular composition and tissue architecture measured with histology provide the biological basis for partitioning the brain into distinct cytoarchitectonic areas and for characterizing neuropathological tissue alterations. Clearly, there is an urgent need to develop whole-brain neuroradiological methods that can assess cortical cyto- and myeloarchitectonic features non-invasively. Mean apparent propagator (MAP) MRI is a clinically feasible diffusion MRI method that quantifies efficiently and comprehensively the net microscopic displacements of water molecules diffusing in tissues. We investigate the sensitivity of high-resolution MAP-MRI to detecting areal and laminar variations in cortical cytoarchitecture and compare our results with observations from corresponding histological sections in the entire brain of a rhesus macaque monkey. High-resolution images of MAP-derived parameters, in particular the propagator anisotropy (PA), non-gaussianity (NG), and the return-to-axis probability (RTAP) reveal cortical area-specific lamination patterns in good agreement with the corresponding histological stained sections. In a few regions, the MAP parameters provide superior contrast to the five histological stains used in this study, delineating more clearly boundaries and transition regions between cortical areas and laminar substructures. Throughout the cortex, various MAP parameters can be used to delineate transition regions between specific cortical areas observed with histology and to refine areal boundaries estimated using atlas registration-based cortical parcellation. Using surface-based analysis of MAP parameters we quantify the cortical depth dependence of diffusion propagators in multiple regions-of-interest in a consistent and rigorous manner that is largely independent of the cortical folding geometry. The ability to assess cortical cytoarchitectonic features efficiently and non-invasively, its clinical feasibility, and translatability make high-resolution MAP-MRI a promising 3D imaging tool for studying whole-brain cortical organization, characterizing abnormal cortical development, improving early diagnosis of neurodegenerative diseases, identifying targets for biopsies, and complementing neuropathological investigations.
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Affiliation(s)
- Alexandru V Avram
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health,9000 Rockville Pike,Bethesda 20892, MD, USA; Center for Neuroscience and Regenerative Medicine, 4301 Jones Bridge Road,Bethesda, 20814,MD, USA; Henry M. Jackson Foundation for the Advancement of Military Medicine Inc., 6720A Rockledge Drive, Bethesda, 20814, MD, USA.
| | - Kadharbatcha S Saleem
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health,9000 Rockville Pike,Bethesda 20892, MD, USA; Center for Neuroscience and Regenerative Medicine, 4301 Jones Bridge Road,Bethesda, 20814,MD, USA; Henry M. Jackson Foundation for the Advancement of Military Medicine Inc., 6720A Rockledge Drive, Bethesda, 20814, MD, USA
| | - Michal E Komlosh
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health,9000 Rockville Pike,Bethesda 20892, MD, USA; Center for Neuroscience and Regenerative Medicine, 4301 Jones Bridge Road,Bethesda, 20814,MD, USA; Henry M. Jackson Foundation for the Advancement of Military Medicine Inc., 6720A Rockledge Drive, Bethesda, 20814, MD, USA
| | - Cecil C Yen
- National Institute of Neurological Disorders and Stroke, National Institutes of Health, 9000 Rockville Pike, Bethesda, 20892, MD, USA
| | - Frank Q Ye
- National Institute of Mental Health, National Institutes of Health, 9000 Rockville Pike, Bethesda, 20892,MD, USA
| | - Peter J Basser
- Eunice Kennedy Shriver National Institute of Child Health and Human Development, National Institutes of Health,9000 Rockville Pike,Bethesda 20892, MD, USA
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Campillo BW, Galguera D, Cerdan S, López-Larrubia P, Lizarbe B. Short-term high-fat diet alters the mouse brain magnetic resonance imaging parameters consistently with neuroinflammation on males and metabolic rearrangements on females. A pre-clinical study with an optimized selection of linear mixed-effects models. Front Neurosci 2022; 16:1025108. [PMID: 36507349 PMCID: PMC9729798 DOI: 10.3389/fnins.2022.1025108] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Accepted: 10/20/2022] [Indexed: 11/25/2022] Open
Abstract
Introduction High-fat diet (HFD) consumption is known to trigger an inflammatory response in the brain that prompts the dysregulation of energy balance, leads to insulin and leptin resistance, and ultimately obesity. Obesity, at the same, has been related to cerebral magnetic resonance imaging (MRI) alterations, but the onset of HFD-induced neuroinflammation, however, has been principally reported on male rodents and by ex vivo methods, with the effects on females and the origin of MRI changes remaining unassessed. Methods We characterized the onset and evolution of obesity on male and female mice during standard or HFD administration by physiological markers and multiparametric MRI on four cerebral regions involved in appetite regulation and energy homeostasis. We investigated the effects of diet, time under diet, brain region and sex by identifying their significant contributions to sequential linear mixed-effects models, and obtained their regional neurochemical profiles by high-resolution magic angle spinning spectroscopy. Results Male mice developed an obese phenotype paralleled by fast increases in magnetization transfer ratio values, while females delayed the obesity progress and showed no MRI-signs of cerebral inflammation, but larger metabolic rearrangements on the neurochemical profile. Discussion Our study reveals early MRI-detectable changes compatible with the development of HFD-induced cerebral cytotoxic inflammation on males but suggest the existence of compensatory metabolic adaptations on females that preclude the corresponding detection of MRI alterations.
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Affiliation(s)
- Basilio Willem Campillo
- Instituto de Investigaciones Biomédicas Alberto Sols (IIBm), Consejo Superior de Investigaciones Científicas-Universidad Autónoma de Madrid (CSIC-UAM), Madrid, Spain
| | - David Galguera
- Instituto de Investigaciones Biomédicas Alberto Sols (IIBm), Consejo Superior de Investigaciones Científicas-Universidad Autónoma de Madrid (CSIC-UAM), Madrid, Spain
| | - Sebastian Cerdan
- Instituto de Investigaciones Biomédicas Alberto Sols (IIBm), Consejo Superior de Investigaciones Científicas-Universidad Autónoma de Madrid (CSIC-UAM), Madrid, Spain
| | - Pilar López-Larrubia
- Instituto de Investigaciones Biomédicas Alberto Sols (IIBm), Consejo Superior de Investigaciones Científicas-Universidad Autónoma de Madrid (CSIC-UAM), Madrid, Spain,Pilar López-Larrubia,
| | - Blanca Lizarbe
- Instituto de Investigaciones Biomédicas Alberto Sols (IIBm), Consejo Superior de Investigaciones Científicas-Universidad Autónoma de Madrid (CSIC-UAM), Madrid, Spain,Departamento de Bioquímica, Universidad Autónoma de Madrid (UAM), Instituto de Investigaciones Biomédicas Alberto Sols (CSIC-UAM), Madrid, Spain,*Correspondence: Blanca Lizarbe,
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30
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Baxi M, Cetin-Karayumak S, Papadimitriou G, Makris N, van der Kouwe A, Jenkins B, Moore TL, Rosene DL, Kubicki M, Rathi Y. Investigating the contribution of cytoarchitecture to diffusion MRI measures in gray matter using histology. FRONTIERS IN NEUROIMAGING 2022; 1:947526. [PMID: 37555179 PMCID: PMC10406256 DOI: 10.3389/fnimg.2022.947526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 07/19/2022] [Indexed: 08/10/2023]
Abstract
Postmortem studies are currently considered a gold standard for investigating brain structure at the cellular level. To investigate cellular changes in the context of human development, aging, or disease treatment, non-invasive in-vivo imaging methods such as diffusion MRI (dMRI) are needed. However, dMRI measures are only indirect measures and require validation in gray matter (GM) in the context of their sensitivity to the underlying cytoarchitecture, which has been lacking. Therefore, in this study we conducted direct comparisons between in-vivo dMRI measures and histology acquired from the same four rhesus monkeys. Average and heterogeneity of fractional anisotropy and trace from diffusion tensor imaging and mean squared displacement (MSD) and return-to-origin-probability from biexponential model were calculated in nine cytoarchitectonically different GM regions using dMRI data. DMRI measures were compared with corresponding histology measures of regional average and heterogeneity in cell area density. Results show that both average and heterogeneity in trace and MSD measures are sensitive to the underlying cytoarchitecture (cell area density) and capture different aspects of cell composition and organization. Trace and MSD thus would prove valuable as non-invasive imaging biomarkers in future studies investigating GM cytoarchitectural changes related to development and aging as well as abnormal cellular pathologies in clinical studies.
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Affiliation(s)
- Madhura Baxi
- Graduate Program for Neuroscience, Boston University, Boston, MA, United States
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Suheyla Cetin-Karayumak
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - George Papadimitriou
- Center for Morphometric Analysis, Massachusetts General Hospital, Charlestown, MA, United States
| | - Nikos Makris
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Andre van der Kouwe
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
| | - Bruce Jenkins
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
| | - Tara L. Moore
- Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA, United States
- Center for Systems Neuroscience, Boston, MA, United States
| | - Douglas L. Rosene
- Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA, United States
- Center for Systems Neuroscience, Boston, MA, United States
| | - Marek Kubicki
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
| | - Yogesh Rathi
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
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31
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Hubbard Cristinacce PL, Keaveney S, Aboagye EO, Hall MG, Little RA, O'Connor JPB, Parker GJM, Waterton JC, Winfield JM, Jauregui-Osoro M. Clinical translation of quantitative magnetic resonance imaging biomarkers - An overview and gap analysis of current practice. Phys Med 2022; 101:165-182. [PMID: 36055125 DOI: 10.1016/j.ejmp.2022.08.015] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2022] [Revised: 08/05/2022] [Accepted: 08/17/2022] [Indexed: 10/14/2022] Open
Abstract
PURPOSE This overview of the current landscape of quantitative magnetic resonance imaging biomarkers (qMR IBs) aims to support the standardisation of academic IBs to assist their translation to clinical practice. METHODS We used three complementary approaches to investigate qMR IB use and quality management practices within the UK: 1) a literature search of qMR and quality management terms during 2011-2015 and 2016-2020; 2) a database search for clinical research studies using qMR IBs during 2016-2020; and 3) a survey to ascertain the current availability and quality management practices for clinical MRI scanners and associated equipment at research institutions across the UK. RESULTS The analysis showed increased use of all qMR methods between the periods 2011-2015 and 2016-2020 and diffusion-tensor MRI and volumetry to be popular methods. However, the "translation ratio" of journal articles to clinical research studies was higher for qMR methods that have evidence of clinical translation via a commercial route, such as fat fraction and T2 mapping. The number of journal articles citing quality management terms doubled between the periods 2011-2015 and 2016-2020; although, its proportion relative to all journal articles only increased by 3.0%. The survey suggested that quality assurance (QA) and quality control (QC) of data acquisition procedures are under-reported in the literature and that QA/QC of acquired data/data analysis are under-developed and lack consistency between institutions. CONCLUSIONS We summarise current attempts to standardise and translate qMR IBs, and conclude by outlining the ideal quality management practices and providing a gap analysis between current practice and a metrological standard.
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Affiliation(s)
| | - Sam Keaveney
- MRI Unit, Royal Marsden NHS Foundation Trust, Downs Road, Sutton, Surrey SM2 5PT, UK; Division of Radiotherapy and Imaging, The Institute of Cancer Research, 123 Old Brompton Road, London SW7 3RP, UK
| | - Eric O Aboagye
- Department of Surgery & Cancer, Division of Cancer, Imperial College London, W12 0NN London, UK
| | - Matt G Hall
- National Physical Laboratory, Hampton Road, Teddington TW11 0LW, UK
| | - Ross A Little
- Division of Cancer Sciences, The University of Manchester, Manchester M13 9PT, UK
| | - James P B O'Connor
- Division of Cancer Sciences, The University of Manchester, Manchester M13 9PT, UK; Division of Radiotherapy and Imaging, The Institute of Cancer Research, 123 Old Brompton Road, London SW7 3RP, UK
| | - Geoff J M Parker
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, 90 High Holborn, London WC1V 6LJ, UK; Bioxydyn Ltd, Manchester M15 6SZ, UK
| | - John C Waterton
- Bioxydyn Ltd, Manchester M15 6SZ, UK; Division of Informatics, Imaging and Data Sciences, The University of Manchester, Manchester M13 9PT, UK
| | - Jessica M Winfield
- MRI Unit, Royal Marsden NHS Foundation Trust, Downs Road, Sutton, Surrey SM2 5PT, UK; Division of Radiotherapy and Imaging, The Institute of Cancer Research, 123 Old Brompton Road, London SW7 3RP, UK
| | - Maite Jauregui-Osoro
- Department of Surgery & Cancer, Division of Cancer, Imperial College London, W12 0NN London, UK
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32
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Fan Q, Eichner C, Afzali M, Mueller L, Tax CMW, Davids M, Mahmutovic M, Keil B, Bilgic B, Setsompop K, Lee HH, Tian Q, Maffei C, Ramos-Llordén G, Nummenmaa A, Witzel T, Yendiki A, Song YQ, Huang CC, Lin CP, Weiskopf N, Anwander A, Jones DK, Rosen BR, Wald LL, Huang SY. Mapping the human connectome using diffusion MRI at 300 mT/m gradient strength: Methodological advances and scientific impact. Neuroimage 2022; 254:118958. [PMID: 35217204 PMCID: PMC9121330 DOI: 10.1016/j.neuroimage.2022.118958] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2021] [Revised: 01/27/2022] [Accepted: 01/31/2022] [Indexed: 12/20/2022] Open
Abstract
Tremendous efforts have been made in the last decade to advance cutting-edge MRI technology in pursuit of mapping structural connectivity in the living human brain with unprecedented sensitivity and speed. The first Connectom 3T MRI scanner equipped with a 300 mT/m whole-body gradient system was installed at the Massachusetts General Hospital in 2011 and was specifically constructed as part of the Human Connectome Project. Since that time, numerous technological advances have been made to enable the broader use of the Connectom high gradient system for diffusion tractography and tissue microstructure studies and leverage its unique advantages and sensitivity to resolving macroscopic and microscopic structural information in neural tissue for clinical and neuroscientific studies. The goal of this review article is to summarize the technical developments that have emerged in the last decade to support and promote large-scale and scientific studies of the human brain using the Connectom scanner. We provide a brief historical perspective on the development of Connectom gradient technology and the efforts that led to the installation of three other Connectom 3T MRI scanners worldwide - one in the United Kingdom in Cardiff, Wales, another in continental Europe in Leipzig, Germany, and the latest in Asia in Shanghai, China. We summarize the key developments in gradient hardware and image acquisition technology that have formed the backbone of Connectom-related research efforts, including the rich array of high-sensitivity receiver coils, pulse sequences, image artifact correction strategies and data preprocessing methods needed to optimize the quality of high-gradient strength diffusion MRI data for subsequent analyses. Finally, we review the scientific impact of the Connectom MRI scanner, including advances in diffusion tractography, tissue microstructural imaging, ex vivo validation, and clinical investigations that have been enabled by Connectom technology. We conclude with brief insights into the unique value of strong gradients for diffusion MRI and where the field is headed in the coming years.
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Affiliation(s)
- Qiuyun Fan
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Cornelius Eichner
- Max Planck Institute for Human Cognitive and Brain Sciences, Department of Neuropsychology, Leipzig, Germany
| | - Maryam Afzali
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, Wales, UK; Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds LS2 9JT, UK
| | - Lars Mueller
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds LS2 9JT, UK
| | - Chantal M W Tax
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, Wales, UK; Image Sciences Institute, University Medical Center (UMC) Utrecht, Utrecht, the Netherlands
| | - Mathias Davids
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA; Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Mirsad Mahmutovic
- Institute of Medical Physics and Radiation Protection (IMPS), TH-Mittelhessen University of Applied Sciences (THM), Giessen, Germany
| | - Boris Keil
- Institute of Medical Physics and Radiation Protection (IMPS), TH-Mittelhessen University of Applied Sciences (THM), Giessen, Germany
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Kawin Setsompop
- Department of Radiology, Stanford University, Stanford, CA, USA; Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Hong-Hsi Lee
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Chiara Maffei
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Gabriel Ramos-Llordén
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Aapo Nummenmaa
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA
| | | | - Anastasia Yendiki
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Yi-Qiao Song
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA USA
| | - Chu-Chung Huang
- Key Laboratory of Brain Functional Genomics (MOE & STCSM), Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai, China; Shanghai Changning Mental Health Center, Shanghai, China
| | - Ching-Po Lin
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, Taiwan; Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Nikolaus Weiskopf
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth Sciences, Leipzig University, Leipzig, Germany
| | - Alfred Anwander
- Max Planck Institute for Human Cognitive and Brain Sciences, Department of Neuropsychology, Leipzig, Germany
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, Wales, UK
| | - Bruce R Rosen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Lawrence L Wald
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Susie Y Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA.
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Syed Nasser N, Rajan S, Venugopal VK, Lasič S, Mahajan V, Mahajan H. A review on investigation of the basic contrast mechanism underlying multidimensional diffusion MRI in assessment of neurological disorders. J Clin Neurosci 2022; 102:26-35. [PMID: 35696817 DOI: 10.1016/j.jocn.2022.05.027] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 05/20/2022] [Accepted: 05/30/2022] [Indexed: 12/26/2022]
Abstract
INTRODUCTION Multidimensional diffusion MRI (MDD MRI) is a novel diffusion technique that uses advanced gradient waveforms for microstructural tissue characterization to provide information about average rate, anisotropy and orientation of the diffusion and to disentangle the signal fraction from specific cell types i.e., elongated cells, isotropic cells and free water. AIM To review the diagnostic potential of MDD MRI in the clinical setting for microstructural tissue characterization in patients with neurological disorders to aid in patient care and treatment. METHOD A scoping review on the clinical applications of MDD MRI was conducted from original articles published in PubMed and Scopus from 2015 to 2021 using the keywords "Multidimensional diffusion MRI" OR "diffusion tensor distribution" OR "Tensor-Valued Diffusion" OR "b-tensor encoding" OR "microscopic diffusion anisotropy" OR "microscopic anisotropy" OR "microscopic fractional anisotropy" OR "double diffusion encoding" OR "triple diffusion encoding" OR "double pulsed field gradients" OR "double wave vector" OR "correlation tensor imaging" AND "brain" OR "axons". RESULTS Initially 145 articles were screened and after applying inclusion and exclusion criteria, nine articles were included in the final analysis. In most of these studies, microscopic diffusion anisotropy within the lesion showed deviation from the normal-appearing tissue. CONCLUSION Multidimensional diffusion MRI can provide better quantification and visualization of tissue microstructure than conventional diffusion MRI and can be used in the clinical setting for diagnosis of neurological disorders.
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Affiliation(s)
| | - Sriram Rajan
- Department of Radiology, Mahajan Imaging, New Delhi, India
| | | | | | | | - Harsh Mahajan
- CARPL.ai, New Delhi, India; Department of Radiology, Mahajan Imaging, New Delhi, India
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Berry SC, Lawrence AD, Lancaster TM, Casella C, Aggleton JP, Postans M. Subiculum-BNST structural connectivity in humans and macaques. Neuroimage 2022; 253:119096. [PMID: 35304264 PMCID: PMC9227740 DOI: 10.1016/j.neuroimage.2022.119096] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Revised: 02/21/2022] [Accepted: 03/11/2022] [Indexed: 11/27/2022] Open
Abstract
Invasive tract-tracing studies in rodents implicate a direct connection between the subiculum and bed nucleus of the stria terminalis (BNST) as a key component of neural pathways mediating hippocampal regulation of the Hypothalamic-Pituitary-Adrenal (HPA) axis. A clear characterisation of the connections linking the subiculum and BNST in humans and non-human primates is lacking. To address this, we first delineated the projections from the subiculum to the BNST using anterograde tracers injected into macaque monkeys, revealing evidence for a monosynaptic subiculum-BNST projection involving the fornix. Second, we used in vivo diffusion MRI tractography in macaques and humans to demonstrate substantial subiculum complex connectivity to the BNST in both species. This connection was primarily carried by the fornix, with additional connectivity via the amygdala, consistent with rodent anatomy. Third, utilising the twin-based nature of our human sample, we found that microstructural properties of these tracts were moderately heritable (h2 ∼ 0.5). In a final analysis, we found no evidence of any significant association between subiculum complex-BNST tract microstructure and indices of perceived stress/dispositional negativity and alcohol use, derived from principal component analysis decomposition of self-report data. Our findings address a key translational gap in our knowledge of the neurocircuitry regulating stress.
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Affiliation(s)
- Samuel C Berry
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK.
| | - Andrew D Lawrence
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
| | | | - Chiara Casella
- Department of Perinatal Imaging and Health, School of Biomedical Engineering & Imaging Sciences, Kings College London, London, UK
| | - John P Aggleton
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
| | - Mark Postans
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
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Yeh CH, Tseng RY, Ni HC, Cocchi L, Chang JC, Hsu MY, Tu EN, Wu YY, Chou TL, Gau SSF, Lin HY. White matter microstructural and morphometric alterations in autism: implications for intellectual capabilities. Mol Autism 2022; 13:21. [PMID: 35585645 PMCID: PMC9118608 DOI: 10.1186/s13229-022-00499-1] [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: 12/24/2021] [Accepted: 04/30/2022] [Indexed: 12/13/2022] Open
Abstract
Background Neuroimage literature of autism spectrum disorder (ASD) has a moderate-to-high risk of bias, partially because those combined with intellectual impairment (II) and/or minimally verbal (MV) status are generally ignored. We aimed to provide more comprehensive insights into white matter alterations of ASD, inclusive of individuals with II (ASD-II-Only) or MV expression (ASD-MV). Methods Sixty-five participants with ASD (ASD-Whole; 16.6 ± 5.9 years; comprising 34 intellectually able youth, ASD-IA, and 31 intellectually impaired youth, ASD-II, including 24 ASD-II-Only plus 7 ASD-MV) and 38 demographic-matched typically developing controls (TDC; 17.3 ± 5.6 years) were scanned in accelerated diffusion-weighted MRI. Fixel-based analysis was undertaken to investigate the categorical differences in fiber density (FD), fiber cross section (FC), and a combined index (FDC), and brain symptom/cognition associations. Results ASD-Whole had reduced FD in the anterior and posterior corpus callosum and left cerebellum Crus I, and smaller FDC in right cerebellum Crus II, compared to TDC. ASD-IA, relative to TDC, had no significant discrepancies, while ASD-II showed almost identical alterations to those from ASD-Whole vs. TDC. ASD-II-Only had greater FD/FDC in the isthmus splenium of callosum than ASD-MV. Autistic severity negatively correlated with FC in right Crus I. Nonverbal full-scale IQ positively correlated with FC/FDC in cerebellum VI. FD/FDC of the right dorsolateral prefrontal cortex showed a diagnosis-by-executive function interaction. Limitations We could not preclude the potential effects of age and sex from the ASD cohort, although statistical tests suggested that these factors were not influential. Our results could be confounded by variable psychiatric comorbidities and psychotropic medication uses in our ASD participants recruited from outpatient clinics, which is nevertheless closer to a real-world presentation of ASD. The outcomes related to ASD-MV were considered preliminaries due to the small sample size within this subgroup. Finally, our study design did not include intellectual impairment-only participants without ASD to disentangle the mixture of autistic and intellectual symptoms. Conclusions ASD-associated white matter alterations appear driven by individuals with II and potentially further by MV. Results suggest that changes in the corpus callosum and cerebellum are key for psychopathology and cognition associated with ASD. Our work highlights an essential to include understudied subpopulations on the spectrum in research. Supplementary Information The online version contains supplementary material available at 10.1186/s13229-022-00499-1.
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Affiliation(s)
- Chun-Hung Yeh
- Institute for Radiological Research, Chang Gung University, No. 259, Wenhua 1st Road, Guishan District, 333, Taoyuan City, Taiwan. .,Department of Psychiatry, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan.
| | - Rung-Yu Tseng
- Institute for Radiological Research, Chang Gung University, No. 259, Wenhua 1st Road, Guishan District, 333, Taoyuan City, Taiwan.,Department of Psychiatry, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
| | - Hsing-Chang Ni
- Department of Psychiatry, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
| | - Luca Cocchi
- Clinical Brain Networks Group, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Jung-Chi Chang
- Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan
| | | | - En-Nien Tu
- Department of Psychiatry, University of Oxford, Oxford, UK.,Department of Psychiatry, Keelung Chang Gung Memorial Hospital, Keelung, Taiwan
| | | | - Tai-Li Chou
- Department of Psychology, National Taiwan University, Taipei, Taiwan
| | - Susan Shur-Fen Gau
- Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan
| | - Hsiang-Yuan Lin
- Department of Psychiatry, National Taiwan University Hospital and College of Medicine, Taipei, Taiwan. .,Azrieli Adult Neurodevelopmental Centre, Campbell Family Mental Health Research Institute, and Adult Neurodevelopmental and Geriatric Psychiatry Division, Centre for Addiction and Mental Health, 1025 Queen St W - 3314, Toronto, ON, M6J 1H4, Canada. .,Department of Psychiatry and Institute of Medical Science, Temerty Faculty of Medicine, University of Toronto, Toronto, ON, Canada.
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Bogusz F, Pieciak T, Afzali M, Pizzolato M. Diffusion-relaxation scattered MR signal representation in a multi-parametric sequence. Magn Reson Imaging 2022; 91:52-61. [PMID: 35561868 DOI: 10.1016/j.mri.2022.05.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2021] [Revised: 05/03/2022] [Accepted: 05/05/2022] [Indexed: 11/26/2022]
Abstract
This work focuses on obtaining a more general diffusion magnetic resonance imaging (MRI) signal representation that accounts for a longitudinal T1 and transverse T2⋆ relaxations while at the same time integrating directional diffusion in the context of scattered multi-parametric acquisitions, where only a few diffusion gradient directions and b-values are available for each pair of echo and inversion times. The method is based on the three-dimensional simple harmonic oscillator-based reconstruction and estimation (SHORE) representation of the diffusion signal, which enables the estimation of the orientation distribution function and the retrieval of various quantitative indices such as the generalized fractional anisotropy or the return-to-the-origin probability while simultaneously resolving for T1 and T2⋆ relaxation times. Our technique, the Relax-SHORE, has been tested on both in silico and in vivo diffusion-relaxation scattered MR data. The results show that Relax-SHORE is accurate in the context of scattered acquisitions while guaranteeing flexibility in the diffusion signal representation from multi-parametric sequences.
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Affiliation(s)
- Fabian Bogusz
- AGH University of Science and Technology, Kraków, Poland.
| | - Tomasz Pieciak
- AGH University of Science and Technology, Kraków, Poland; LPI, ETSI Telecomunicación, Universidad de Valladolid, Valladolid, Spain
| | - Maryam Afzali
- Leeds Institute of Cardiovascular and Metabolic Medicine (LICAMM), Leeds, United Kingdom; Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom
| | - Marco Pizzolato
- Department of applied mathematics and computer science, Technical University of Denmark, Kongens Lyngby, Denmark; Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland.
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Bergamino M, Schiavi S, Daducci A, Walsh RR, Stokes AM. Analysis of Brain Structural Connectivity Networks and White Matter Integrity in Patients With Mild Cognitive Impairment. Front Aging Neurosci 2022; 14:793991. [PMID: 35173605 PMCID: PMC8842680 DOI: 10.3389/fnagi.2022.793991] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 01/06/2022] [Indexed: 11/13/2022] Open
Abstract
White matter integrity and structural connectivity may be altered in mild cognitive impairment (MCI), and these changes may closely reflect decline in specific cognitive domains. Multi-shell diffusion data in healthy control (HC, n = 31) and mild cognitive impairment (MCI, n = 19) cohorts were downloaded from the ADNI3 database. The data were analyzed using an advanced approach to assess both white matter microstructural integrity and structural connectivity. Compared with HC, lower intracellular compartment (IC) and higher isotropic (ISO) values were found in MCI. Additionally, significant correlations were found between IC and Montreal Cognitive Assessment (MoCA) scores in the MCI cohort. Network analysis detected structural connectivity differences between the two groups, with lower connectivity in MCI. Additionally, significant differences between HC and MCI were observed for global network efficiency. Our results demonstrate the potential of advanced diffusion MRI biomarkers for understanding brain changes in MCI.
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Affiliation(s)
- Maurizio Bergamino
- Barrow Neuroimaging Innovation Center, Barrow Neurological Institute, Phoenix, AZ, United States
| | - Simona Schiavi
- Department of Computer Science, University of Verona, Verona, Italy
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy
| | | | - Ryan R. Walsh
- Muhammad Ali Parkinson Center, Barrow Neurological Institute, Phoenix, AZ, United States
| | - Ashley M. Stokes
- Barrow Neuroimaging Innovation Center, Barrow Neurological Institute, Phoenix, AZ, United States
- *Correspondence: Ashley M. Stokes,
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38
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de Ruiter MB, Reneman L, Kieffer JM, Oldenburg HSA, Schagen SB. Brain White Matter Microstructure as a Risk Factor for Cognitive Decline After Chemotherapy for Breast Cancer. J Clin Oncol 2021; 39:3908-3917. [PMID: 34591652 DOI: 10.1200/jco.21.00627] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
PURPOSE Cognitive decline is frequently observed after chemotherapy. As chemotherapy is associated with changes in brain white matter microstructure, we investigated whether white matter microstructure before chemotherapy is a risk factor for cognitive decline after chemotherapy. METHODS Neuropsychologic tests were administered before and 6 months (n = 49), 2 years (n = 32), and 3 years (n = 32) after chemotherapy in patients with breast cancer receiving anthracycline-based chemotherapy (BC + CT group), at matched intervals to patients with BC who did not receive systemic therapy (BC - CT group: n = 39, 23, and 19, respectively) and to no-cancer controls (NC group: n = 37, 29, and 28, respectively). Using multivariate normative comparison, we evaluated to what extent the cognitive profiles of patients deviated from those of controls. Fractional anisotropy (FA), derived from magnetic resonance diffusion tensor imaging, was used to measure white matter microstructure before treatment. FA was evaluated as a risk factor for cognitive decline, in addition to baseline age, fatigue, cognitive complaints, and premorbid intelligence quotient. We subsequently ran voxel-wise diffusion tensor imaging analyses to investigate white matter microstructure in specific nerve tracts. RESULTS Low FA independently predicted cognitive decline early (6 months, P = .013) and late (3 years, P < .001) after chemotherapy. FA did not predict cognitive decline in the BC - CT and NC groups. Voxel-wise analysis indicated involvement of white matter tracts essential for cognitive functioning. CONCLUSION Low FA may reflect low white matter reserve. This may be a risk factor for cognitive decline after chemotherapy for BC. If validated in future trials, identification of patients with low white matter reserve could improve patient care, for example, by facilitating targeted, early interventions or even by influencing choices of patients and doctors for receiving chemotherapy.
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Affiliation(s)
- Michiel B de Ruiter
- Division of Psychosocial Research and Epidemiology, Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Liesbeth Reneman
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Centers, Location AMC, University of Amsterdam, Amsterdam, the Netherlands
| | - Jacobien M Kieffer
- Division of Psychosocial Research and Epidemiology, Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Hester S A Oldenburg
- Department of Surgical Oncology, Netherlands Cancer Institute, Amsterdam, the Netherlands
| | - Sanne B Schagen
- Division of Psychosocial Research and Epidemiology, Netherlands Cancer Institute, Amsterdam, the Netherlands.,Brain and Cognition, Department of Psychology, University of Amsterdam, Amsterdam, the Netherlands
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White matter variability, cognition, and disorders: a systematic review. Brain Struct Funct 2021; 227:529-544. [PMID: 34731328 PMCID: PMC8844174 DOI: 10.1007/s00429-021-02382-w] [Citation(s) in RCA: 26] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2021] [Accepted: 09/03/2021] [Indexed: 11/23/2022]
Abstract
Inter-individual differences can inform treatment procedures and—if accounted for—have the potential to significantly improve patient outcomes. However, when studying brain anatomy, these inter-individual variations are commonly unaccounted for, despite reports of differences in gross anatomical features, cross-sectional, and connectional anatomy. Brain connections are essential to facilitate functional organization and, when severed, cause impairments or complete loss of function. Hence, the study of cerebral white matter may be an ideal compromise to capture inter-individual variability in structure and function. We reviewed the wealth of studies that associate cognitive functions and clinical symptoms with individual tracts using diffusion tractography. Our systematic review indicates that tractography has proven to be a sensitive method in neurology, psychiatry, and healthy populations to identify variability and its functional correlates. However, the literature may be biased, as the most commonly studied tracts are not necessarily those with the highest sensitivity to cognitive functions and pathologies. Additionally, the hemisphere of the studied tract is often unreported, thus neglecting functional laterality and asymmetries. Finally, we demonstrate that tracts, as we define them, are not correlated with one, but multiple cognitive domains or pathologies. While our systematic review identified some methodological caveats, it also suggests that tract–function correlations might still be a promising tool in identifying biomarkers for precision medicine. They can characterize variations in brain anatomy, differences in functional organization, and predicts resilience and recovery in patients.
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40
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Cheng H, Calhoun V. Exploring microstructure with diffusion-weighted imaging: From acquisition to modeling. J Neurosci Methods 2021; 363:109335. [PMID: 34450220 DOI: 10.1016/j.jneumeth.2021.109335] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Affiliation(s)
- Hu Cheng
- Imaging Research Facility, Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA.
| | - Vince Calhoun
- Tri-institutional center for Translational Research in Neuroimaging and Data Science (TReNDS) Center, Georgia Institute of Technology, Emory University, Georgia State University, Atlanta 30303, GA, USA.
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41
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Afzali M, Nilsson M, Palombo M, Jones DK. SPHERIOUSLY? The challenges of estimating sphere radius non-invasively in the human brain from diffusion MRI. Neuroimage 2021; 237:118183. [PMID: 34020013 PMCID: PMC8285594 DOI: 10.1016/j.neuroimage.2021.118183] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 04/25/2021] [Accepted: 05/16/2021] [Indexed: 11/16/2022] Open
Abstract
The Soma and Neurite Density Imaging (SANDI) three-compartment model was recently proposed to disentangle cylindrical and spherical geometries, attributed to neurite and soma compartments, respectively, in brain tissue. There are some recent advances in diffusion-weighted MRI signal encoding and analysis (including the use of multiple so-called 'b-tensor' encodings and analysing the signal in the frequency-domain) that have not yet been applied in the context of SANDI. In this work, using: (i) ultra-strong gradients; (ii) a combination of linear, planar, and spherical b-tensor encodings; and (iii) analysing the signal in the frequency domain, three main challenges to robust estimation of sphere size were identified: First, the Rician noise floor in magnitude-reconstructed data biases estimates of sphere properties in a non-uniform fashion. It may cause overestimation or underestimation of the spherical compartment size and density. This can be partly ameliorated by accounting for the noise floor in the estimation routine. Second, even when using the strongest diffusion-encoding gradient strengths available for human MRI, there is an empirical lower bound on the spherical signal fraction and radius that can be detected and estimated robustly. For the experimental setup used here, the lower bound on the sphere signal fraction was approximately 10%. We employed two different ways of establishing the lower bound for spherical radius estimates in white matter. The first, examining power-law relationships between the DW-signal and diffusion weighting in empirical data, yielded a lower bound of 7μm, while the second, pure Monte Carlo simulations, yielded a lower limit of 3μm and in this low radii domain, there is little differentiation in signal attenuation. Third, if there is sensitivity to the transverse intra-cellular diffusivity in cylindrical structures, e.g., axons and cellular projections, then trying to disentangle two diffusion-time-dependencies using one experimental parameter (i.e., change in frequency-content of the encoding waveform) makes spherical radii estimates particularly challenging. We conclude that due to the aforementioned challenges spherical radii estimates may be biased when the corresponding sphere signal fraction is low, which must be considered.
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Affiliation(s)
- Maryam Afzali
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom.
| | - Markus Nilsson
- Clinical Sciences Lund, Radiology, Lund University, Lund, Sweden.
| | - Marco Palombo
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom.
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom.
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Abstract
Quasi-diffusion imaging (QDI) is a novel quantitative diffusion magnetic resonance imaging (dMRI) technique that enables high quality tissue microstructural imaging in a clinically feasible acquisition time. QDI is derived from a special case of the continuous time random walk (CTRW) model of diffusion dynamics and assumes water diffusion is locally Gaussian within tissue microstructure. By assuming a Gaussian scaling relationship between temporal (α) and spatial (β) fractional exponents, the dMRI signal attenuation is expressed according to a diffusion coefficient, D (in mm2 s−1), and a fractional exponent, α. Here we investigate the mathematical properties of the QDI signal and its interpretation within the quasi-diffusion model. Firstly, the QDI equation is derived and its power law behaviour described. Secondly, we derive a probability distribution of underlying Fickian diffusion coefficients via the inverse Laplace transform. We then describe the functional form of the quasi-diffusion propagator, and apply this to dMRI of the human brain to perform mean apparent propagator imaging. QDI is currently unique in tissue microstructural imaging as it provides a simple form for the inverse Laplace transform and diffusion propagator directly from its representation of the dMRI signal. This study shows the potential of QDI as a promising new model-based dMRI technique with significant scope for further development.
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Yang JYM, Yeh CH, Poupon C, Calamante F. Diffusion MRI tractography for neurosurgery: the basics, current state, technical reliability and challenges. Phys Med Biol 2021; 66. [PMID: 34157706 DOI: 10.1088/1361-6560/ac0d90] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Accepted: 06/22/2021] [Indexed: 01/20/2023]
Abstract
Diffusion magnetic resonance imaging (dMRI) tractography is currently the only imaging technique that allows for non-invasive delineation and visualisation of white matter (WM) tractsin vivo,prompting rapid advances in related fields of brain MRI research in recent years. One of its major clinical applications is for pre-surgical planning and intraoperative image guidance in neurosurgery, where knowledge about the location of WM tracts nearby the surgical target can be helpful to guide surgical resection and optimise post-surgical outcomes. Surgical injuries to these WM tracts can lead to permanent neurological and functional deficits, making the accuracy of tractography reconstructions paramount. The quality of dMRI tractography is influenced by many modifiable factors, ranging from MRI data acquisition through to the post-processing of tractography output, with the potential of error propagation based on decisions made at each and subsequent processing steps. Research over the last 25 years has significantly improved the anatomical accuracy of tractography. An updated review about tractography methodology in the context of neurosurgery is now timely given the thriving research activities in dMRI, to ensure more appropriate applications in the clinical neurosurgical realm. This article aims to review the dMRI physics, and tractography methodologies, highlighting recent advances to provide the key concepts of tractography-informed neurosurgery, with a focus on the general considerations, the current state of practice, technical challenges, potential advances, and future demands to this field.
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Affiliation(s)
- Joseph Yuan-Mou Yang
- Department of Neurosurgery, The Royal Children's Hospital, Melbourne, Australia.,Neuroscience Research, Murdoch Children's Research Institute, Melbourne, Australia.,Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Australia.,Department of Paediatrics, The University of Melbourne, Melbourne, Australia
| | - Chun-Hung Yeh
- Institute for Radiological Research, Chang Gung University and Chang Gung Memorial Hospital, Taoyuan, Taiwan.,Department of Child and Adolescent Psychiatry, Chang Gung Memorial Hospital, Linkou Medical Center, Taoyuan, Taiwan
| | - Cyril Poupon
- NeuroSpin, Frédéric Joliot Life Sciences Institute, CEA, CNRS, Paris-Saclay University, Gif-sur-Yvette, France
| | - Fernando Calamante
- The University of Sydney, Sydney Imaging, Sydney, Australia.,The University of Sydney, School of Biomedical Engineering, Sydney, Australia
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Hasan KM, Yamada K. Overview of Diffusion Tensor, Diffusion Kurtosis, and Q-space Imaging and Software Tools. Magn Reson Imaging Clin N Am 2021; 29:263-268. [PMID: 33902908 DOI: 10.1016/j.mric.2021.02.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
This work offers a short up-to-date review of diffusion-weighted MR imaging (dMRI) and software tools that are used widely to process and analyze clinical dMRI. A consolidated dMRI protocol for clinical applications that enables the mapping of tissue microstructural attributes is presented.
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Affiliation(s)
- Khader M Hasan
- Department of Diagnostic and Interventional Radiology, The University of Texas Health Science Center, McGovern Medical School, Houston, Texas, USA.
| | - Kei Yamada
- Department of Radiology, Kyoto Prefectural University of Medicine, 465 Kajii-cho, Kamigyo-ku, Kyoto 6028566, Japan
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Girardi-Schappo M, Fadaie F, Lee HM, Caldairou B, Sziklas V, Crane J, Bernhardt BC, Bernasconi A, Bernasconi N. Altered communication dynamics reflect cognitive deficits in temporal lobe epilepsy. Epilepsia 2021; 62:1022-1033. [PMID: 33705572 DOI: 10.1111/epi.16864] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/22/2020] [Revised: 02/15/2021] [Accepted: 02/15/2021] [Indexed: 12/23/2022]
Abstract
OBJECTIVE Although temporal lobe epilepsy (TLE) is recognized as a system-level disorder, little work has investigated pathoconnectomics from a dynamic perspective. By leveraging computational simulations that quantify patterns of information flow across the connectome, we tested the hypothesis that network communication is abnormal in this condition, studied the interplay between hippocampal- and network-level disease effects, and assessed associations with cognition. METHODS We simulated signal spreading via a linear threshold model that temporally evolves on a structural graph derived from diffusion-weighted magnetic resonance imaging (MRI), comparing a homogeneous group of 31 patients with histologically proven hippocampal sclerosis to 31 age- and sex-matched healthy controls. We evaluated the modulatory effects of structural alterations of the neocortex and hippocampus on network dynamics. Furthermore, multivariate statistics addressed the relationship with cognitive parameters. RESULTS We observed a slowing of in- and out-spreading times across multiple areas bilaterally, indexing delayed information flow, with the strongest effects in ipsilateral frontotemporal regions, thalamus, and hippocampus. Effects were markedly reduced when controlling for hippocampal volume but not cortical thickness, underscoring the central role of the hippocampus in whole-brain disease expression. Multivariate analysis associated slower spreading time in frontoparietal, limbic, default mode, and subcortical networks with impairment across tasks tapping into sensorimotor, executive, memory, and verbal abilities. SIGNIFICANCE Moving beyond descriptions of static topology toward the formulation of brain dynamics, our work provides novel insight into structurally mediated network dysfunction and demonstrates that altered whole-brain communication dynamics contribute to common cognitive difficulties in TLE.
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Affiliation(s)
- Mauricio Girardi-Schappo
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Center, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Fatemeh Fadaie
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Center, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Hyo Min Lee
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Center, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Benoit Caldairou
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Center, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Viviane Sziklas
- Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Joelle Crane
- Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Boris C Bernhardt
- Multimodal Imaging and Connectome Analysis Lab, McConnell Brain Imaging Center, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Andrea Bernasconi
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Center, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Neda Bernasconi
- Neuroimaging of Epilepsy Laboratory, McConnell Brain Imaging Center, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
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