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Parker KJ, Kabir IE, Doyley MM, Faiyaz A, Uddin MN, Flores G, Schifitto G. Brain elastography in aging relates to fluid/solid trendlines. Phys Med Biol 2024; 69:115037. [PMID: 38670141 DOI: 10.1088/1361-6560/ad4446] [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: 01/23/2024] [Accepted: 04/26/2024] [Indexed: 04/28/2024]
Abstract
The relatively new tools of brain elastography have established a general trendline for healthy, aging adult humans, whereby the brain's viscoelastic properties 'soften' over many decades. Earlier studies of the aging brain have demonstrated a wide spectrum of changes in morphology and composition towards the later decades of lifespan. This leads to a major question of causal mechanisms: of the many changes documented in structure and composition of the aging brain, which ones drive the long term trendline for viscoelastic properties of grey matter and white matter? The issue is important for illuminating which factors brain elastography is sensitive to, defining its unique role for study of the brain and clinical diagnoses of neurological disease and injury. We address these issues by examining trendlines in aging from our elastography data, also utilizing data from an earlier landmark study of brain composition, and from a biophysics model that captures the multiscale biphasic (fluid/solid) structure of the brain. Taken together, these imply that long term changes in extracellular water in the glymphatic system of the brain along with a decline in the extracellular matrix have a profound effect on the measured viscoelastic properties. Specifically, the trendlines indicate that water tends to replace solid fraction as a function of age, then grey matter stiffness decreases inversely as water fraction squared, whereas white matter stiffness declines inversely as water fraction to the 2/3 power, a behavior consistent with the cylindrical shape of the axons. These unique behaviors point to elastography of the brain as an important macroscopic measure of underlying microscopic structural change, with direct implications for clinical studies of aging, disease, and injury.
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Affiliation(s)
- Kevin J Parker
- Department of Electrical and Computer Engineering, University of Rochester, 724 Computer Studies Building, Box 270231, Rochester, NY 14627, United States of America
- Department of Biomedical Engineering, University of Rochester, 204 Goergen Hall, Box 270168, Rochester, NY 14627, United States of America
- Department of Imaging Sciences, University of Rochester Medical Center, 601 Elmwood Ave, Box 648, Rochester, NY 14642, United States of America
| | - Irteza Enan Kabir
- Department of Electrical and Computer Engineering, University of Rochester, 724 Computer Studies Building, Box 270231, Rochester, NY 14627, United States of America
| | - Marvin M Doyley
- Department of Electrical and Computer Engineering, University of Rochester, 724 Computer Studies Building, Box 270231, Rochester, NY 14627, United States of America
- Department of Biomedical Engineering, University of Rochester, 204 Goergen Hall, Box 270168, Rochester, NY 14627, United States of America
- Department of Imaging Sciences, University of Rochester Medical Center, 601 Elmwood Ave, Box 648, Rochester, NY 14642, United States of America
| | - Abrar Faiyaz
- Department of Electrical and Computer Engineering, University of Rochester, 724 Computer Studies Building, Box 270231, Rochester, NY 14627, United States of America
| | - Md Nasir Uddin
- Department of Biomedical Engineering, University of Rochester, 204 Goergen Hall, Box 270168, Rochester, NY 14627, United States of America
- Department of Neurology, University of Rochester Medical Center, 601 Elmwood Ave, Box 673, Rochester, NY 14642, United States of America
| | - Gilmer Flores
- Department of Biomedical Engineering, University of Rochester, 204 Goergen Hall, Box 270168, Rochester, NY 14627, United States of America
| | - Giovanni Schifitto
- Department of Electrical and Computer Engineering, University of Rochester, 724 Computer Studies Building, Box 270231, Rochester, NY 14627, United States of America
- Department of Imaging Sciences, University of Rochester Medical Center, 601 Elmwood Ave, Box 648, Rochester, NY 14642, United States of America
- Department of Neurology, University of Rochester Medical Center, 601 Elmwood Ave, Box 673, Rochester, NY 14642, United States of America
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He J, Wang Y. Superficial white matter microstructural imaging method based on time-space fractional-order diffusion. Phys Med Biol 2024; 69:065010. [PMID: 38394673 DOI: 10.1088/1361-6560/ad2ca1] [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: 09/10/2023] [Accepted: 02/23/2024] [Indexed: 02/25/2024]
Abstract
Objective. Microstructure imaging based on diffusion magnetic resonance signal is an advanced imaging technique that enablesin vivomapping of the brain's microstructure. Superficial white matter (SWM) plays an important role in brain development, maturation, and aging, while fewer microstructure imaging methods address the SWM due to its complexity. Therefore, this study aims to develop a diffusion propagation model to investigate the microstructural characteristics of the SWM region.Approach. In this paper, we hypothesize that the effect of cell membrane permeability and the water exchange between soma and dendrites cannot be neglected for typical clinical diffusion times (20 ms
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Affiliation(s)
- Jianglin He
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, People's Republic of China
| | - Yuanjun Wang
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, People's Republic of China
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Thurston LT, Skorska MN, Lobaugh NJ, Zucker KJ, Chakravarty MM, Lai MC, Chavez S, VanderLaan DP. White matter microstructure in transmasculine and cisgender adolescents: A multiparametric and multivariate study. PLoS One 2024; 19:e0300139. [PMID: 38470896 DOI: 10.1371/journal.pone.0300139] [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: 02/21/2024] [Indexed: 03/14/2024] Open
Abstract
Adolescence is a sensitive developmental period for neural sex/gender differentiation. The present study used multiparametric mapping to better characterize adolescent white matter (WM) microstructure. WM microstructure was investigated using diffusion tensor indices (fractional anisotropy; mean, radial, and axial diffusivity [AD]) and quantitative T1 relaxometry (T1) in hormone therapy naïve adolescent cisgender girls, cisgender boys, and transgender boys (i.e., assigned female at birth and diagnosed with gender dysphoria). Diffusion indices were first analyzed for group differences using tract-based spatial statistics, which revealed a group difference in AD. Thus, two multiparametric and multivariate analyses assessed AD in conjunction with T1 relaxation time, and with respect to developmental proxy variables (i.e., age, serum estradiol, pubertal development, sexual attraction) thought to be relevant to adolescent brain development. The multivariate analyses showed a shared pattern between AD and T1 such that higher AD was associated with longer T1, and AD and T1 strongly related to all five developmental variables in cisgender boys (10 significant correlations, r range: 0.21-0.73). There were fewer significant correlations between the brain and developmental variables in cisgender girls (three correlations, r range: -0.54-0.54) and transgender boys (two correlations, r range: -0.59-0.77). Specifically, AD related to direction of sexual attraction (i.e., gynephilia, androphilia) in all groups, and T1 related to estradiol inversely in cisgender boys compared with transgender boys. These brain patterns may be indicative of less myelination and tissue density in cisgender boys, which corroborates other reports of protracted WM development in cisgender boys. Further, these findings highlight the importance of considering developmental trajectory when assessing the subtleties of neural structure associated with variations in sex, gender, and sexual attraction.
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Affiliation(s)
- Lindsey T Thurston
- Department of Psychology, University of Toronto Mississauga, Mississauga, Ontario, Canada
| | - Malvina N Skorska
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Nancy J Lobaugh
- Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
- Division of Neurology, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Kenneth J Zucker
- Department of Psychiatry, Temerty Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - M Mallar Chakravarty
- Cerebral Imaging Centre, Douglas Mental Health University Institute, Montreal, Ontario, Canada
| | - Meng-Chuan Lai
- 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
- Department of Psychiatry, The Hospital for Sick Children, Toronto, Ontario, Canada
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, United Kingdom
| | - Sofia Chavez
- Brain Health Imaging Centre, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - Doug P VanderLaan
- Department of Psychology, University of Toronto Mississauga, Mississauga, Ontario, Canada
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, Ontario, Canada
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Wei X, Wang S, Zhang M, Yan Y, Wang Z, Wei W, Tuo H, Wang Z. Gait impairment-related axonal degeneration in Parkinson's disease by neurite orientation dispersion and density imaging. NPJ Parkinsons Dis 2024; 10:45. [PMID: 38413647 PMCID: PMC10899173 DOI: 10.1038/s41531-024-00654-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2023] [Accepted: 02/06/2024] [Indexed: 02/29/2024] Open
Abstract
Microstructural alterations in the brain networks of Parkinson's disease (PD) patients are correlated with gait impairments. Evaluate microstructural alterations in the white matter (WM) fiber bundle tracts using neurite orientation dispersion and density imaging (NODDI) technique in PD versus healthy controls (HC). In this study, 24 PD patients and 29 HC were recruited. NODDI and high-resolution 3D structural images were acquired for each participant. The NODDI indicators, including the intracellular neurite density index (NDI), orientation dispersion index (ODI), and isotropic volume fraction (ISO), were compared between the two groups. Diffusion-weighted (DW) images were preprocessed using MRtrix 3.0 software and the orientation distribution function to trace the main nerve fiber tracts in PD patients. Quantitative gait and clinical assessment scales were used to compare the medication "ON" and "OFF" states of PD patients. The NDI, ODI, and ISO values of the WM fiber bundles were significantly higher in PD patients compared to HC. Fiber bundles, including the anterior thalamic radiation, corticospinal tract, superior longitudinal fasciculus, forceps major, cingulum, and inferior longitudinal fasciculus, were found to be significantly affected in PD. The NDI changes of PD patients were well correlated with stride lengths in the "ON" state; ODI changes were correlated with the stride time in the "ON" and "OFF" states and ISO changes were correlated with the stride time and cadence in the "ON" state. In conclusion, combination of NODDI technique and gait parameters can help detect gait impairment in PD patients early and accurately.
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Grants
- 82202097 National Natural Science Foundation of China (National Science Foundation of China)
- 82071257 National Natural Science Foundation of China (National Science Foundation of China)
- Beijing Scholars Program is the highest-level talent development program approved by the Beijing Municipal People’s Government. It aims to cultivate a group of scientists, engineers, and renowned experts who are at the forefront of global science and technology, possess innovative capabilities, and have international advanced levels. The program provides intellectual support for the construction of a globally influential science and technology innovation center.
- Beijing Hospitals Authority’ Youth Programme is one of the three major talent development programs, namely "Qingmiao, Dengfeng, Shiming," launched by the Beijing Hospital Management Center in 2015. This program aims to support and cultivate young talents and provide a development platform for the growth of young talents in municipal hospitals through various training initiatives. Training Fund for Open Projects at Clinical Institutes and Departments of Capital Medical University is a research support fund program for young doctors opened by Capital Medical University, targeting different specialties, colleges, and departments.
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Affiliation(s)
- Xuan Wei
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Shiya Wang
- Department of Neurology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Mingkai Zhang
- Department of Neurology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Ying Yan
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Zheng Wang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Wei Wei
- Division of Science and Technology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Houzhen Tuo
- Department of Neurology, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
| | - Zhenchang Wang
- Department of Radiology, Beijing Friendship Hospital, Capital Medical University, Beijing, China.
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Uddin MN, Tivarus ME, Schifitto G, Rudko DA. Editorial: Neuroimaging of neuroinflammation in neurological disorders. Front Neurol 2023; 14:1328511. [PMID: 38020608 PMCID: PMC10668016 DOI: 10.3389/fneur.2023.1328511] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/26/2023] [Accepted: 10/31/2023] [Indexed: 12/01/2023] Open
Affiliation(s)
- Md Nasir Uddin
- Department of Neurology, University of Rochester, Rochester, NY, United States
- Department of Biomedical Engineering, University of Rochester, Rochester, NY, United States
| | - Madalina E. Tivarus
- Department of Imaging Sciences, University of Rochester, Rochester, NY, United States
- Department of Neuroscience, University of Rochester, Rochester, NY, United States
| | - Giovanni Schifitto
- Department of Neurology, University of Rochester, Rochester, NY, United States
- Department of Imaging Sciences, University of Rochester, Rochester, NY, United States
- Department of Electrical and Computer Engineering, University of Rochester, Rochester, NY, United States
| | - David A. Rudko
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- Department of Biomedical Engineering, McGill University, Montreal, QC, Canada
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Alsameen MH, Gong Z, Qian W, Kiely M, Triebswetter C, Bergeron CM, Cortina LE, Faulkner ME, Laporte JP, Bouhrara M. C-NODDI: a constrained NODDI model for axonal density and orientation determinations in cerebral white matter. Front Neurol 2023; 14:1205426. [PMID: 37602266 PMCID: PMC10435293 DOI: 10.3389/fneur.2023.1205426] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2023] [Accepted: 07/14/2023] [Indexed: 08/22/2023] Open
Abstract
Purpose Neurite orientation dispersion and density imaging (NODDI) provides measures of neurite density and dispersion through computation of the neurite density index (NDI) and the orientation dispersion index (ODI). However, NODDI overestimates the cerebrospinal fluid water fraction in white matter (WM) and provides physiologically unrealistic high NDI values. Furthermore, derived NDI values are echo-time (TE)-dependent. In this work, we propose a modification of NODDI, named constrained NODDI (C-NODDI), for NDI and ODI mapping in WM. Methods Using NODDI and C-NODDI, we investigated age-related alterations in WM in a cohort of 58 cognitively unimpaired adults. Further, NDI values derived using NODDI or C-NODDI were correlated with the neurofilament light chain (NfL) concentration levels, a plasma biomarker of axonal degeneration. Finally, we investigated the TE dependence of NODDI or C-NODDI derived NDI and ODI. Results ODI derived values using both approaches were virtually identical, exhibiting constant trends with age. Further, our results indicated a quadratic relationship between NDI and age suggesting that axonal maturation continues until middle age followed by a decrease. This quadratic association was notably significant in several WM regions using C-NODDI, while limited to a few regions using NODDI. Further, C-NODDI-NDI values exhibited a stronger correlation with NfL concentration levels as compared to NODDI-NDI, with lower NDI values corresponding to higher levels of NfL. Finally, we confirmed the previous finding that NDI estimation using NODDI was dependent on TE, while NDI derived values using C-NODDI exhibited lower sensitivity to TE in WM. Conclusion C-NODDI provides a complementary method to NODDI for determination of NDI in white matter.
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Affiliation(s)
| | | | | | | | | | | | | | | | | | - Mustapha Bouhrara
- Laboratory of Clinical Investigation, National Institute on Aging, National Institutes of Health, Baltimore, MD, United States
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Aja-Fernández S, Martín-Martín C, Planchuelo-Gómez Á, Faiyaz A, Uddin MN, Schifitto G, Tiwari A, Shigwan SJ, Kumar Singh R, Zheng T, Cao Z, Wu D, Blumberg SB, Sen S, Goodwin-Allcock T, Slator PJ, Yigit Avci M, Li Z, Bilgic B, Tian Q, Wang X, Tang Z, Cabezas M, Rauland A, Merhof D, Manzano Maria R, Campos VP, Santini T, da Costa Vieira MA, HashemizadehKolowri S, DiBella E, Peng C, Shen Z, Chen Z, Ullah I, Mani M, Abdolmotalleby H, Eckstrom S, Baete SH, Filipiak P, Dong T, Fan Q, de Luis-García R, Tristán-Vega A, Pieciak T. Validation of deep learning techniques for quality augmentation in diffusion MRI for clinical studies. Neuroimage Clin 2023; 39:103483. [PMID: 37572514 PMCID: PMC10440596 DOI: 10.1016/j.nicl.2023.103483] [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: 03/02/2023] [Revised: 07/24/2023] [Accepted: 07/25/2023] [Indexed: 08/14/2023]
Abstract
The objective of this study is to evaluate the efficacy of deep learning (DL) techniques in improving the quality of diffusion MRI (dMRI) data in clinical applications. The study aims to determine whether the use of artificial intelligence (AI) methods in medical images may result in the loss of critical clinical information and/or the appearance of false information. To assess this, the focus was on the angular resolution of dMRI and a clinical trial was conducted on migraine, specifically between episodic and chronic migraine patients. The number of gradient directions had an impact on white matter analysis results, with statistically significant differences between groups being drastically reduced when using 21 gradient directions instead of the original 61. Fourteen teams from different institutions were tasked to use DL to enhance three diffusion metrics (FA, AD and MD) calculated from data acquired with 21 gradient directions and a b-value of 1000 s/mm2. The goal was to produce results that were comparable to those calculated from 61 gradient directions. The results were evaluated using both standard image quality metrics and Tract-Based Spatial Statistics (TBSS) to compare episodic and chronic migraine patients. The study results suggest that while most DL techniques improved the ability to detect statistical differences between groups, they also led to an increase in false positive. The results showed that there was a constant growth rate of false positives linearly proportional to the new true positives, which highlights the risk of generalization of AI-based tasks when assessing diverse clinical cohorts and training using data from a single group. The methods also showed divergent performance when replicating the original distribution of the data and some exhibited significant bias. In conclusion, extreme caution should be exercised when using AI methods for harmonization or synthesis in clinical studies when processing heterogeneous data in clinical studies, as important information may be altered, even when global metrics such as structural similarity or peak signal-to-noise ratio appear to suggest otherwise.
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Affiliation(s)
- Santiago Aja-Fernández
- Laboratorio de Procesado de Imagen (LPI), ETSI Telecomunicación, Universidad de Valladolid, Spain.
| | - Carmen Martín-Martín
- Laboratorio de Procesado de Imagen (LPI), ETSI Telecomunicación, Universidad de Valladolid, Spain
| | - Álvaro Planchuelo-Gómez
- Laboratorio de Procesado de Imagen (LPI), ETSI Telecomunicación, Universidad de Valladolid, Spain; Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
| | | | | | | | | | | | | | | | | | - Dan Wu
- Zhejiang University, China
| | | | | | | | | | | | - Zihan Li
- Athinoula A. Martinos Center for Biomedical Imaging, USA
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, USA
| | - Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, USA
| | | | | | | | | | | | | | | | | | | | | | | | | | | | - Zan Chen
- Zhejiang University of Technology, China
| | | | | | | | | | | | | | | | | | - Rodrigo de Luis-García
- Laboratorio de Procesado de Imagen (LPI), ETSI Telecomunicación, Universidad de Valladolid, Spain
| | - Antonio Tristán-Vega
- Laboratorio de Procesado de Imagen (LPI), ETSI Telecomunicación, Universidad de Valladolid, Spain
| | - Tomasz Pieciak
- Laboratorio de Procesado de Imagen (LPI), ETSI Telecomunicación, Universidad de Valladolid, Spain
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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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Figley CR, Uddin MN, Wong K, Kornelsen J, Puig J, Figley TD. Potential Pitfalls of Using Fractional Anisotropy, Axial Diffusivity, and Radial Diffusivity as Biomarkers of Cerebral White Matter Microstructure. Front Neurosci 2022; 15:799576. [PMID: 35095400 PMCID: PMC8795606 DOI: 10.3389/fnins.2021.799576] [Citation(s) in RCA: 68] [Impact Index Per Article: 34.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/21/2021] [Accepted: 12/17/2021] [Indexed: 01/31/2023] Open
Abstract
Fractional anisotropy (FA), axial diffusivity (AD), and radial diffusivity (RD) are commonly used as MRI biomarkers of white matter microstructure in diffusion MRI studies of neurodevelopment, brain aging, and neurologic injury/disease. Some of the more frequent practices include performing voxel-wise or region-based analyses of these measures to cross-sectionally compare individuals or groups, longitudinally assess individuals or groups, and/or correlate with demographic, behavioral or clinical variables. However, it is now widely recognized that the majority of cerebral white matter voxels contain multiple fiber populations with different trajectories, which renders these metrics highly sensitive to the relative volume fractions of the various fiber populations, the microstructural integrity of each constituent fiber population, and the interaction between these factors. Many diffusion imaging experts are aware of these limitations and now generally avoid using FA, AD or RD (at least in isolation) to draw strong reverse inferences about white matter microstructure, but based on the continued application and interpretation of these metrics in the broader biomedical/neuroscience literature, it appears that this has perhaps not yet become common knowledge among diffusion imaging end-users. Therefore, this paper will briefly discuss the complex biophysical underpinnings of these measures in the context of crossing fibers, provide some intuitive “thought experiments” to highlight how conventional interpretations can lead to incorrect conclusions, and suggest that future studies refrain from using (over-interpreting) FA, AD, and RD values as standalone biomarkers of cerebral white matter microstructure.
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Affiliation(s)
- Chase R. Figley
- Department of Radiology, University of Manitoba, Winnipeg, MB, Canada
- Division of Diagnostic Imaging, Health Sciences Centre, Winnipeg, MB, Canada
- Neuroscience Research Program, Kleysen Institute for Advanced Medicine, Winnipeg, MB, Canada
- Department of Physiology & Pathophysiology, University of Manitoba, Winnipeg, MB, Canada
- *Correspondence: Chase R. Figley,
| | - Md Nasir Uddin
- Department of Radiology, University of Manitoba, Winnipeg, MB, Canada
- Department of Neurology, University of Rochester, Rochester, NY, United States
| | - Kaihim Wong
- Department of Radiology, University of Manitoba, Winnipeg, MB, Canada
- Neuroscience Research Program, Kleysen Institute for Advanced Medicine, Winnipeg, MB, Canada
| | - Jennifer Kornelsen
- Department of Radiology, University of Manitoba, Winnipeg, MB, Canada
- Division of Diagnostic Imaging, Health Sciences Centre, Winnipeg, MB, Canada
- Neuroscience Research Program, Kleysen Institute for Advanced Medicine, Winnipeg, MB, Canada
- Department of Physiology & Pathophysiology, University of Manitoba, Winnipeg, MB, Canada
| | - Josep Puig
- Department of Radiology, University of Manitoba, Winnipeg, MB, Canada
- Division of Diagnostic Imaging, Health Sciences Centre, Winnipeg, MB, Canada
- Girona Biomedical Research Institute (IDIBGI), Hospital Universitari de Girona Dr. Josep Trueta, Girona, Spain
| | - Teresa D. Figley
- Department of Radiology, University of Manitoba, Winnipeg, MB, Canada
- Division of Diagnostic Imaging, Health Sciences Centre, Winnipeg, MB, Canada
- Neuroscience Research Program, Kleysen Institute for Advanced Medicine, Winnipeg, MB, Canada
- Department of Physiology & Pathophysiology, University of Manitoba, Winnipeg, MB, Canada
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