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Ye X, Ma X, Pan Z, Zhang Z, Guo H, Uğurbil K, Wu X. Denoising complex-valued diffusion MR images using a two-step, nonlocal principal component analysis approach. Magn Reson Med 2025; 93:2473-2487. [PMID: 40079233 PMCID: PMC11980993 DOI: 10.1002/mrm.30502] [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: 11/23/2024] [Revised: 01/17/2025] [Accepted: 02/25/2025] [Indexed: 03/14/2025]
Abstract
PURPOSE To propose a two-step, nonlocal principal component analysis (PCA) method and demonstrate its utility for denoising complex diffusion MR images with a few diffusion directions. METHODS A two-step denoising pipeline was implemented to ensure accurate patch selection even with high noise levels and was coupled with data preprocessing for g-factor normalization and phase stabilization before data denoising with a nonlocal PCA algorithm. At the heart of our proposed pipeline was the use of a data-driven optimal shrinkage algorithm to manipulate the singular values in a way that would optimally estimate the noise-free signal. Our approach's denoising performances were evaluated using simulation and in vivo human data experiments. The results were compared with those obtained with existing local PCA-based methods. RESULTS In both simulation and human data experiments, our approach substantially enhanced image quality relative to the noisy counterpart, yielding improved performances for estimation of relevant diffusion tensor imaging metrics. It also outperformed existing local PCA-based methods in reducing noise while preserving anatomic details. It also led to improved whole-brain tractography relative to the noisy counterpart. CONCLUSION The proposed denoising method has the utility for improving image quality for diffusion MRI with a few diffusion directions and is believed to benefit many applications, especially those aiming to achieve high-quality parametric mapping using only a few image volumes.
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Affiliation(s)
- Xinyu Ye
- Center for Biomedical Imaging Research, School of Biomedical Engineering, Tsinghua University, Beijing, China
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford
| | - Xiaodong Ma
- Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah, United States
| | - Ziyi Pan
- Center for Biomedical Imaging Research, School of Biomedical Engineering, Tsinghua University, Beijing, China
| | - Zhe Zhang
- Tiantan Neuroimaging Center of Excellence, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Hua Guo
- Center for Biomedical Imaging Research, School of Biomedical Engineering, Tsinghua University, Beijing, China
| | - Kamil Uğurbil
- Center for Magnetic Resonance Research, Radiology, Medical School, University of Minnesota, Minneapolis, Minnesota
| | - Xiaoping Wu
- Center for Magnetic Resonance Research, Radiology, Medical School, University of Minnesota, Minneapolis, Minnesota
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Xu J, Devan SP, Shi D, Pamulaparthi A, Yan N, Zu Z, Smith DS, Harkins KD, Gore JC, Jiang X. MATI: A GPU-accelerated toolbox for microstructural diffusion MRI simulation and data fitting with a graphical user interface. Magn Reson Imaging 2025:110428. [PMID: 40419173 DOI: 10.1016/j.mri.2025.110428] [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: 05/04/2025] [Accepted: 05/20/2025] [Indexed: 05/28/2025]
Abstract
PURPOSE To introduce MATI (Microstructural Analysis Toolbox for Imaging), a versatile MATLAB-based toolbox that combines both simulation and data fitting capabilities for microstructural dMRI research. METHODS MATI provides a user-friendly, graphical user interface that enables researchers, including those without much programming experience, to perform advanced simulations and data analyses for microstructural MRI research. For simulation, MATI supports arbitrary microstructural tissues and pulse sequences. For data fitting, MATI supports a range of fitting methods, including traditional non-linear least squares, Bayesian approaches, machine learning, and dictionary matching methods, allowing users to tailor analyses based on specific research needs. RESULTS Optimized with vectorized matrix operations and high-performance numerical libraries, MATI achieves high computational efficiency, enabling rapid simulations and data fitting on CPU and GPU hardware. While designed for microstructural dMRI, MATI's generalized framework can be extended to other imaging methods, making it a flexible and scalable tool for quantitative MRI research. CONCLUSION MATI offers a significant step toward translating advanced microstructural MRI techniques into clinical applications.
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Affiliation(s)
- Junzhong Xu
- Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States of America; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States of America; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States of America; Department of Physics and Astronomy, Vanderbilt University, Nashville, TN, United States of America.
| | - Sean P Devan
- Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States of America
| | | | - Adithya Pamulaparthi
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, United States of America
| | - Nicholas Yan
- Farragut High School, Farragut, TN, United States of America
| | - Zhongliang Zu
- Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States of America; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States of America
| | - David S Smith
- Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States of America; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States of America; Department of Physics and Astronomy, Vanderbilt University, Nashville, TN, United States of America; Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, United States of America
| | - Kevin D Harkins
- Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States of America; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States of America
| | - John C Gore
- Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States of America; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States of America; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States of America; Department of Physics and Astronomy, Vanderbilt University, Nashville, TN, United States of America
| | - Xiaoyu Jiang
- Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States of America; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States of America
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3
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Wang S, Wang L, Cao Y, Deng Z, Ye C, Wang R, Zhu Y, Wei H. Self-supervised arbitrary-scale super-angular resolution diffusion MRI reconstruction. Med Phys 2025; 52:2976-2998. [PMID: 39976309 DOI: 10.1002/mp.17691] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2024] [Revised: 01/03/2025] [Accepted: 01/07/2025] [Indexed: 02/21/2025] Open
Abstract
BACKGROUND Diffusion magnetic resonance imaging (dMRI) is currently the unique noninvasive imaging technique to investigate the microstructure of in vivo tissues. To fully explore the complex tissue microstructure at sub-voxel scale, diffusion weighted (DW) images along many diffusion gradient directions are usually acquired, this is undoubtedly time consuming and inhibits their clinical applications. How to estimate the tissue microstructure only from DW images acquired with few diffusion directions remains a challenge. PURPOSE To address this challenge, we propose a self-supervised arbitrary scale super-angular resolution diffusion MRI reconstruction network (SARDI-nn), which can generate DW images along any directions from few acquisitions, allowing to overcome the limits of diffusion direction number on exploring the tissue microstructure. METHODS SARDI-nn is mainly composed of a diffusion direction-specific DW image feature extraction (DWFE) module and a physics-driven implicit expression and reconstruction (IRR) module. During training, dual downsampling operations are implemented. The first downsampling is used to produce the low-angular resolution (LAR) DW images; the second downsampling is for constructing input and learning target of SARDI-nn. The input LAR DW images pass through a DWFE module (composed of several residual blocks) to extract the feature representations of DW images along input directions, and then these features and the difference between the any querying diffusion direction and the input directions are input into a IRR module to derive the implicit representation and DW image along this query direction. Finally, based on the principle of dMRI, an adaptive weighting method is used to refine the DW image quality. During testing, given any diffusion directions, we can simply infer the corresponding DW images along these directions, accordingly, SARDI-nn can realize arbitrary scale angular super resolution. To test the effectiveness of the proposed method, we compare it with several existing methods in terms of peak signal to noise ratio (PSNR), structural similarity index measure (SSIM), and root mean square error (RMSE) of DW image and microstructure metrics derived from diffusion kurtosis imaging (DKI) and neurite orientation dispersion and density imaging (NODDI) models at different upsampling scales on Human Connectome Project (HCP) and several in-house datasets. RESULTS The comparison results demonstrate that our method achieves almost the best performance at all scales, with SSIM of reconstructed DW images improved by 10.04% at the upscale of 3 and 5.9% at the upscale of 15. Regarding the microstructures derived from DKI and NODDI models, when the upscale is not larger than 6, our method outperforms the best supervised learning method. In addition, the test results on external datasets show the well generality of our method. CONCLUSIONS SARDI-nn is currently the only method that can reconstruct high-angular resolution DW images with any upscales, which allows the variation of both input diffusion direction number and upscales, therefore, it can be easily extended to any unseen test datasets, not requiring to retrain the model. SARDI-nn provides a promising means for exploring the tissue microstructures from DW images along few diffusion gradient directions.
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Affiliation(s)
- Shuangxing Wang
- Key Laboratory of Advanced Medical Imaging and Intelligent Computing of Guizhou Province, Engineering Research Center of Text Computing & Cognitive Intelligence, Ministry of Education, State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, China
| | - Lihui Wang
- Key Laboratory of Advanced Medical Imaging and Intelligent Computing of Guizhou Province, Engineering Research Center of Text Computing & Cognitive Intelligence, Ministry of Education, State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, China
| | - Ying Cao
- Key Laboratory of Advanced Medical Imaging and Intelligent Computing of Guizhou Province, Engineering Research Center of Text Computing & Cognitive Intelligence, Ministry of Education, State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, China
| | - Zeyu Deng
- Key Laboratory of Advanced Medical Imaging and Intelligent Computing of Guizhou Province, Engineering Research Center of Text Computing & Cognitive Intelligence, Ministry of Education, State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, China
| | - Chen Ye
- Key Laboratory of Advanced Medical Imaging and Intelligent Computing of Guizhou Province, Engineering Research Center of Text Computing & Cognitive Intelligence, Ministry of Education, State Key Laboratory of Public Big Data, College of Computer Science and Technology, Guizhou University, Guiyang, China
| | - Rongpin Wang
- Key Laboratory of Advanced Medical Imaging and Intelligent Computing of Guizhou Province, Department of Radiology, Guizhou Provincial People's Hospital, Guiyang, China
| | - Yuemin Zhu
- University Lyon, INSA Lyon, CNRS, Inserm, IRP Metislab CREATIS UMR5220, U1206, Lyon, France
| | - Hongjiang Wei
- Institute for Medical Imaging Technology, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, China
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Park T, Choi Y, Kwon HJ, Lee MB, Rhee HY, Park S, Ryu CW, Jahng GH. Exploring the relationship between larmor-frequency electrical conductivity, diffusivity, and tissue volume in the aging brain. Quant Imaging Med Surg 2025; 15:4669-4688. [PMID: 40384663 PMCID: PMC12082610 DOI: 10.21037/qims-24-2145] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2024] [Accepted: 03/12/2025] [Indexed: 05/20/2025]
Abstract
Background The aging brain undergoes various microstructural changes that influence its electrical properties. Conductivity, a measure of ion mobility, is particularly sensitive to these changes and can be assessed non-invasively using magnetic resonance electrical properties tomography (MREPT). Despite advancements in imaging techniques, the relationship between brain conductivity, diffusivity, and tissue volume in the context of aging and neurodegeneration remains incompletely understood. This study explores the relationships between electrical conductivity, diffusivity, and brain tissue volume in the aging brain, which is crucial for early diagnosis and monitoring of neurodegenerative diseases such as Alzheimer's, where these parameters could serve as potential biomarkers for disease progression. Methods In this cross-sectional, prospective study, 77 patients were assessed brain MREPT and diffusion tensor imaging with multiple shells and gradient directions (b=0, 800, and 2,000 s/mm2). High-frequency conductivity (HFC) was calculated and separated into extra-neurite (EC) and intra-neurite conductivities (IC). We analyzed correlations between these conductivity indices and other magnetic resonance imaging (MRI) metrics, controlling for age, and explored the relationship between conductivity, diffusion, and Mini-Mental State Examination (MMSE) scores using multiple regression analysis. Results EC within the insular region negatively correlated with MMSE scores (r=-0.3027, P=0.0079). HFC in the hippocampus was positively associated with mean diffusivity (MD; β=192.4, P=0.008) and radial diffusivity (RD; β=207.6, P=0.004). HFC in the insula was positively associated with axial diffusivity (AxD; β=356.9, P=0.0004), MD (β=314.4, P=0.004), RD (β=275.5, P=0.012). EC in the hippocampus was positively associated with AxD (β=309.3, P=0.0001), MD (β=333.7, P<0.001), RD (β=341.8, P<0.001). EC in the insular was positively associated with AxD (β=324.1, P=0.0009) and MD (β=270.4, P=0.01). IC was positively correlated with intra-neurite diffusivity (ID) in the amygdala, thalamus, and insula. Conclusions These findings suggest that increased conductivity is associated with altered diffusivity and reduced cognitive performance, suggesting the use of MREPT to differentiate between conductivity changes due to ion mobility versus proton density, and how this approach contributes to understanding the aging brain and neurodegeneration. MREPT-derived measurements primarily reflect ion mobility and caution that clinical interpretations should consider the direct relationships between conductivity and diffusion changes.
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Affiliation(s)
- Taejun Park
- Department of Radiology, Kyung Hee University Hospital at Gangdong, Seoul, Republic of Korea
| | - Yunjeong Choi
- Department of Biomedical Engineering, Undergraduate School, College of Electronics and Information, Kyung Hee University, Yongin-si, Republic of Korea
| | - Hyeok-Jae Kwon
- Department of Chemistry, College of Basic Science, Yonsei University, Seoul, Republic of Korea
| | - Mun Bae Lee
- Department of Mathematics, College of Basic Science, Konkuk University, Seoul, Republic of Korea
| | - Hak Young Rhee
- Department of Neurology, Kyung Hee University Hospital at Gangdong, Seoul, Republic of Korea
- Department of Medicine, Kyung Hee University College of Medicine, Seoul, Republic of Korea
| | - Soonchan Park
- Department of Radiology, Kyung Hee University Hospital at Gangdong, Seoul, Republic of Korea
- Department of Medicine, Kyung Hee University College of Medicine, Seoul, Republic of Korea
| | - Chang-Woo Ryu
- Department of Radiology, Kyung Hee University Hospital at Gangdong, Seoul, Republic of Korea
- Department of Medicine, Kyung Hee University College of Medicine, Seoul, Republic of Korea
| | - Geon-Ho Jahng
- Department of Radiology, Kyung Hee University Hospital at Gangdong, Seoul, Republic of Korea
- Department of Medicine, Kyung Hee University College of Medicine, Seoul, Republic of Korea
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5
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Maximov II, Westlye LT. Comparison of different neurite density metrics with brain asymmetry evaluation. Z Med Phys 2025; 35:177-192. [PMID: 37562999 DOI: 10.1016/j.zemedi.2023.07.003] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 07/05/2023] [Accepted: 07/13/2023] [Indexed: 08/12/2023]
Abstract
The standard diffusion MRI model with intra- and extra-axonal water pools offers a set of microstructural parameters describing brain white matter architecture. However, non-linearities in the standard model and diffusion data contamination by noise and imaging artefacts make estimation of diffusion metrics challenging. In order to develop reliable diffusion approaches and to avoid computational model degeneracy, additional theoretical assumptions allowing stable numerical implementations are required. Advanced diffusion approaches allow for estimation of intra-axonal water fraction (AWF), describing a key structural characteristic of brain tissue. AWF can be interpreted as an indirect measure or proxy of neurite density and has a potential as useful clinical biomarker. Established diffusion approaches such as white matter tract integrity, neurite orientation dispersion and density imaging (NODDI), and spherical mean technique provide estimates of AWF within their respective theoretical frameworks. In the present study, we estimated AWF metrics using different diffusion approaches and compared measures of brain asymmetry between the different metrics in a sub-sample of 182 subjects from the UK Biobank. Multivariate decomposition by mean of linked independent component analysis revealed that the various AWF proxies derived from the different diffusion approaches reflect partly non-overlapping variance of independent components, with distinct anatomical distributions and sensitivity to age. Further, voxel-wise analysis revealed age-related differences in AWF-based brain asymmetry, indicating less apparent left-right hemisphere difference with higher age. Finally, we demonstrated that NODDI metrics suffer from a quite strong dependence on used numerical algorithms and post-processing pipeline. The analysis based on AWF metrics strongly depends on the used diffusion approach and leads to poorly reproducible results.
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Affiliation(s)
- Ivan I Maximov
- Department of Psychology, University of Oslo, Oslo, Norway; Norwegian Centre for Mental Disorders Research (NORMENT), Department of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway.
| | - Lars T Westlye
- Department of Psychology, University of Oslo, Oslo, Norway; Norwegian Centre for Mental Disorders Research (NORMENT), Department of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; KG Jensen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
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Korbmacher M, Tranfa M, Pontillo G, van der Meer D, Wang MY, Andreassen OA, Westlye LT, Maximov II. White matter microstructure links with brain, bodily and genetic attributes in adolescence, mid- and late life. Neuroimage 2025; 310:121132. [PMID: 40096952 DOI: 10.1016/j.neuroimage.2025.121132] [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: 11/27/2024] [Revised: 03/02/2025] [Accepted: 03/07/2025] [Indexed: 03/19/2025] Open
Abstract
Advanced diffusion magnetic resonance imaging (dMRI) allows one to probe and assess brain white matter (WM) organisation and microstructure in vivo. Various dMRI models with different theoretical and practical assumptions have been developed, representing partly overlapping characteristics of the underlying brain biology with potentially complementary value in the cognitive and clinical neurosciences. To which degree the different dMRI metrics relate to clinically relevant geno- and phenotypes is still debated. Hence, we investigate how tract-based and whole WM skeleton parameters from different dMRI approaches associate with clinically relevant and white matter-related phenotypes (sex, age, pulse pressure (PP), body-mass-index (BMI), brain asymmetry) and genetic markers in the UK Biobank (UKB, n=52,140) and the Adolescent Brain Cognitive Development (ABCD) Study (n=5,844). In general, none of the imaging approaches could explain all examined phenotypes, though the approaches were overall similar in explaining variability of the examined phenotypes. Nevertheless, particular diffusion parameters of the used dMRI approaches stood out in explaining some important phenotypes known to correlate with general human health outcomes. A multi-compartment Bayesian dMRI approach provided the strongest WM associations with age, and together with diffusion tensor imaging, the largest accuracy for sex-classifications. We find a similar pattern of metric and tract-dependent asymmetries across datasets, with stronger asymmetries in ABCD data. The magnitude of WM associations with polygenic scores as well as PP depended more on the sample, and likely age, than dMRI metrics. However, kurtosis was most indicative of BMI and potentially of bipolar disorder polygenic scores. We conclude that WM microstructure is differentially associated with clinically relevant pheno- and genotypes at different points in life.
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Affiliation(s)
- Max Korbmacher
- Neuro-SysMed Center of Excellence for Clinical Research in Neurological Diseases, Department of Neurology, Haukeland University Hospital, Bergen, Norway; Mohn Medical Imaging and Visualization Centre (MMIV),Department of Radiology, Haukeland University Hospital, Bergen, Norway; Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway.
| | - Mario Tranfa
- Department of Advanced Biomedical Sciences, University "Federico II", Naples, Italy; Department of Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam,Amsterdam UMC location VUMC, Amsterdam, The Netherlands
| | - Giuseppe Pontillo
- Department of Advanced Biomedical Sciences, University "Federico II", Naples, Italy; Department of Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam,Amsterdam UMC location VUMC, Amsterdam, The Netherlands; Department of Brain Repair & Rehabilitation, UCL Queen Square Institute of Neurology,University College London, London, United Kingdom
| | - Dennis van der Meer
- Center for Precision Psychiatry, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Meng-Yun Wang
- Max Planck Institute for Psycholinguistics, Nijmegen, Netherlands
| | - Ole A Andreassen
- Center for Precision Psychiatry, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Lars T Westlye
- Department of Psychology, University of Oslo, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Ivan I Maximov
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
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7
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Korbmacher M, Vidal‐Pineiro D, Wang M, van der Meer D, Wolfers T, Nakua H, Eikefjord E, Andreassen OA, Westlye LT, Maximov II. Cross-Sectional Brain Age Assessments Are Limited in Predicting Future Brain Change. Hum Brain Mapp 2025; 46:e70203. [PMID: 40235434 PMCID: PMC12000824 DOI: 10.1002/hbm.70203] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2024] [Revised: 03/05/2025] [Accepted: 03/17/2025] [Indexed: 04/17/2025] Open
Abstract
The concept of brain age (BA) describes an integrative imaging marker of brain health, often suggested to reflect aging processes. However, the degree to which cross-sectional MRI features, including BA, reflect past, ongoing, and future brain changes across different tissue types from macro- to microstructure remains controversial. Here, we use multimodal imaging data of 39,325 UK Biobank participants, aged 44-82 years at baseline and 2,520 follow-ups within 1.12-6.90 years to examine BA changes and their relationship to anatomical brain changes. We find insufficient evidence to conclude that BA reflects the rate of brain aging. However, modality-specific differences in brain ages reflect the state of the brain, highlighting diffusion and multimodal MRI brain age as potentially useful cross-sectional markers.
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Affiliation(s)
- Max Korbmacher
- Department of Health and FunctioningWestern Norway University of Applied SciencesBergenNorway
- Department of NeurologyNeuro‐SysMed Center of Excellence for Clinical Research in Neurological Diseases, Haukeland University HospitalBergenNorway
- Mohn Medical Imaging and Visualization Centre (MMIV)BergenNorway
| | - Didac Vidal‐Pineiro
- Center for Lifespan Changes in Brain and Cognition, Department of PsychologyUniversity of OsloOsloNorway
| | - Meng‐Yun Wang
- Max Planck Institute for PsycholinguisticsNijmegenthe Netherlands
| | - Dennis van der Meer
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical MedicineUniversity of OsloOsloNorway
| | - Thomas Wolfers
- Department of Psychiatry and Psychotherapy, Tübingen Center for Mental HealthUniversity of TübingenTübingenGermany
| | - Hajer Nakua
- Columbia University Irving Medical CentreColumbia UniversityNew York CityUSA
| | - Eli Eikefjord
- Department of Health and FunctioningWestern Norway University of Applied SciencesBergenNorway
- Department of NeurologyNeuro‐SysMed Center of Excellence for Clinical Research in Neurological Diseases, Haukeland University HospitalBergenNorway
| | - Ole A. Andreassen
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical MedicineUniversity of OsloOsloNorway
- KG Jebsen Centre for Neurodevelopmental DisordersUniversity of OsloOsloNorway
| | - Lars T. Westlye
- Centre for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical MedicineUniversity of OsloOsloNorway
- KG Jebsen Centre for Neurodevelopmental DisordersUniversity of OsloOsloNorway
- Department of PsychologyUniversity of OsloOsloNorway
| | - Ivan I. Maximov
- Department of Health and FunctioningWestern Norway University of Applied SciencesBergenNorway
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Lopez-Soley E, Martinez-Heras E, Vivo F, Calvi A, Alba-Arbalat S, Romero-Pinel L, Martínez-Yélamos S, Ramo-Tello C, Presas-Rodríguez S, Munteis E, Martínez-Rodríguez JE, Sastre-Garriga J, Anglada E, Meza-Murillo ER, Arévalo MJ, Sánchez-Carrión R, Pelayo R, Bernabeu M, Sola-Valls N, Hervas M, Sepulveda M, Saiz A, Blanco Y, Solana E, Llufriu S. Efficacy of cognitive rehabilitation in cognition and brain networks: A randomised clinical trial in patients with multiple sclerosis. Neuroimage Clin 2025; 46:103775. [PMID: 40184878 PMCID: PMC11999584 DOI: 10.1016/j.nicl.2025.103775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2024] [Revised: 03/19/2025] [Accepted: 03/26/2025] [Indexed: 04/07/2025]
Abstract
This study evaluated the efficacy of the computerised Guttmann, NeuroPersonalTrainer® (GNPT) cognitive rehabilitation (CR) and characterised the induced changes in cerebral networks in patients with multiple sclerosis (MS). This multicentre, double-blind, randomised clinical trial compared upward intensity training (active treatment) to low-intensity static training (static treatment). Cognition was assessed using the Brief Repeatable battery before and after 12 weeks of training and at 10-months follow-up, and patients were classified as having a mild or severe cognitive impairment (CI). Brain MRI pre- and post-CR were analysed using an advanced tractography algorithm, based on multishell diffusion MRI, to obtain node-based graph metrics (local efficiency and strength) from microscopic fractional anisotropy. Seventy MS patients completed the study (age 48.9 ± 8.8, disease duration 16.8 ± 9.0 years); active treatment: 36, static treatment: 34. Verbal memory improved significantly post-CR in both groups (55 % active; 34 % static treatment), accompanied by increases in local efficiency and strength in multimodal regions. At follow-up, verbal memory declined in both groups but remained above the pre-CR assessment (-25 % and -17 %, respectively). Patients with severe-CI (n = 36) showed improvement only with active treatment, while those with mild-CI (n = 34) improved regardless of intensity treatment. Network changes were more pronounced in patients in active treatment and in those with severe-CI. Quality of life did not change at post-CR, and cognitive improvement was influenced by cognitive reserve (p = 0.011). In MS, GNPT temporarily improves verbal memory and increases network connectivity, reinforcing the CR as a valuable tool for enhancing cognitive skills and promoting neuronal plasticity.
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Affiliation(s)
- E Lopez-Soley
- Neuroimmunology and Multiple Sclerosis Unit. Hospital Clinic Barcelona, Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona, Spain
| | - E Martinez-Heras
- Neuroimmunology and Multiple Sclerosis Unit. Hospital Clinic Barcelona, Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona, Spain
| | - F Vivo
- Neuroimmunology and Multiple Sclerosis Unit. Hospital Clinic Barcelona, Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona, Spain
| | - A Calvi
- Neuroimmunology and Multiple Sclerosis Unit. Hospital Clinic Barcelona, Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona, Spain
| | - S Alba-Arbalat
- Neuroimmunology and Multiple Sclerosis Unit. Hospital Clinic Barcelona, Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona, Spain
| | - L Romero-Pinel
- Multiple Sclerosis Unit, Department of Neurology, Hospital Universitari de Bellvitge. Neurology and Neurogenetics Group. Neuroscience Program, Institut d'Investigació Biomèdica de Bellvitge (IDIBELL), Spain
| | - S Martínez-Yélamos
- Multiple Sclerosis Unit, Department of Neurology, Hospital Universitari de Bellvitge. Neurology and Neurogenetics Group. Neuroscience Program, Institut d'Investigació Biomèdica de Bellvitge (IDIBELL), Spain; Department of Clinical Sciences, School of Medicine, Universitat de Barcelona (UB), L'Hospitalet de Llobregat, Spain
| | - C Ramo-Tello
- Multiple Sclerosis Unit, Department of Neurosciences, Hospital Universitari Germans Trias i Pujol, Spain
| | - S Presas-Rodríguez
- Multiple Sclerosis Unit, Department of Neurosciences, Hospital Universitari Germans Trias i Pujol, Spain
| | - E Munteis
- Neurology Department, Hospital del Mar Medical Research Institute (IMIM), Spain
| | | | - J Sastre-Garriga
- Centre d'Esclerosi Múltiple de Catalunya (CEMcat), Hospital Universitari Vall d'Hebron, Spain
| | - E Anglada
- Centre d'Esclerosi Múltiple de Catalunya (CEMcat), Hospital Universitari Vall d'Hebron, Spain
| | - E R Meza-Murillo
- Centre d'Esclerosi Múltiple de Catalunya (CEMcat), Hospital Universitari Vall d'Hebron, Spain
| | - M J Arévalo
- Centre d'Esclerosi Múltiple de Catalunya (CEMcat), Hospital Universitari Vall d'Hebron, Spain
| | - R Sánchez-Carrión
- Institut Guttmann, Institut Universitari de Neurorehabilitació Affiliated to the Universitat Autònoma de Barcelona and Fundació Institut d'Investigació en Ciències de la Salut Germans Trias i Pujol, Spain
| | - R Pelayo
- Institut Guttmann, Institut Universitari de Neurorehabilitació Affiliated to the Universitat Autònoma de Barcelona and Fundació Institut d'Investigació en Ciències de la Salut Germans Trias i Pujol, Spain
| | - M Bernabeu
- Institut Guttmann, Institut Universitari de Neurorehabilitació Affiliated to the Universitat Autònoma de Barcelona and Fundació Institut d'Investigació en Ciències de la Salut Germans Trias i Pujol, Spain
| | - N Sola-Valls
- Neurology Department, Hospital Universitari Sant Joan de Reus, Clinical and Epidemiological Neuroscience Group (NeuroÈpia), Institut d'Investigació Sanitària Pere Virgili (IISPV), Spain
| | - M Hervas
- Hospital de Sabadell Parc Taulí, Spain
| | - M Sepulveda
- Neuroimmunology and Multiple Sclerosis Unit. Hospital Clinic Barcelona, Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona, Spain
| | - A Saiz
- Neuroimmunology and Multiple Sclerosis Unit. Hospital Clinic Barcelona, Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona, Spain
| | - Y Blanco
- Neuroimmunology and Multiple Sclerosis Unit. Hospital Clinic Barcelona, Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona, Spain
| | - E Solana
- Neuroimmunology and Multiple Sclerosis Unit. Hospital Clinic Barcelona, Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona, Spain.
| | - S Llufriu
- Neuroimmunology and Multiple Sclerosis Unit. Hospital Clinic Barcelona, Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona, Spain.
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9
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Kjer HM, Andersson M, He Y, Pacureanu A, Daducci A, Pizzolato M, Salditt T, Robisch AL, Eckermann M, Töpperwien M, Bjorholm Dahl A, Elkjær ML, Illes Z, Ptito M, Andersen Dahl V, Dyrby TB. Bridging the 3D geometrical organisation of white matter pathways across anatomical length scales and species. eLife 2025; 13:RP94917. [PMID: 40019134 PMCID: PMC11870653 DOI: 10.7554/elife.94917] [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] [Indexed: 03/01/2025] Open
Abstract
We used diffusion MRI and x-ray synchrotron imaging on monkey and mice brains to examine the organisation of fibre pathways in white matter across anatomical scales. We compared the structure in the corpus callosum and crossing fibre regions and investigated the differences in cuprizone-induced demyelination in mouse brains versus healthy controls. Our findings revealed common principles of fibre organisation that apply despite the varying patterns observed across species; small axonal fasciculi and major bundles formed laminar structures with varying angles, according to the characteristics of major pathways. Fasciculi exhibited non-straight paths around obstacles like blood vessels, comparable across the samples of varying fibre complexity and demyelination. Quantifications of fibre orientation distributions were consistent across anatomical length scales and modalities, whereas tissue anisotropy had a more complex relationship, both dependent on the field-of-view. Our study emphasises the need to balance field-of-view and voxel size when characterising white matter features across length scales.
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Affiliation(s)
- Hans Martin Kjer
- Danish Research Centre for Magnetic Resonance, Center for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and HvidovreHvidovreDenmark
- Department of Applied Mathematics and Computer Science, Technical University of DenmarkKongens LyngbyDenmark
| | - Mariam Andersson
- Danish Research Centre for Magnetic Resonance, Center for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and HvidovreHvidovreDenmark
- Department of Applied Mathematics and Computer Science, Technical University of DenmarkKongens LyngbyDenmark
| | - Yi He
- Danish Research Centre for Magnetic Resonance, Center for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and HvidovreHvidovreDenmark
- Guangdong Provincial Engineering Research Center of Molecular Imaging, The Fifth Affiliated Hospital, Sun Yat-sen UniversityZhuhaiChina
| | | | | | - Marco Pizzolato
- Department of Applied Mathematics and Computer Science, Technical University of DenmarkKongens LyngbyDenmark
| | - Tim Salditt
- Institut für Röntgenphysik, Universität Göttingen, Friedrich-Hund-PlatzGöttingenGermany
| | - Anna-Lena Robisch
- Institut für Röntgenphysik, Universität Göttingen, Friedrich-Hund-PlatzGöttingenGermany
| | - Marina Eckermann
- ESRF - The European SynchrotronGrenobleFrance
- Institut für Röntgenphysik, Universität Göttingen, Friedrich-Hund-PlatzGöttingenGermany
| | - Mareike Töpperwien
- Institut für Röntgenphysik, Universität Göttingen, Friedrich-Hund-PlatzGöttingenGermany
| | - Anders Bjorholm Dahl
- Department of Applied Mathematics and Computer Science, Technical University of DenmarkKongens LyngbyDenmark
| | - Maria Louise Elkjær
- Department of Neurology, Odense University HospitalOdenseDenmark
- Institute of Molecular Medicine, University of Southern DenmarkOdenseDenmark
| | - Zsolt Illes
- Department of Neurology, Odense University HospitalOdenseDenmark
- Institute of Molecular Medicine, University of Southern DenmarkOdenseDenmark
- BRIDGE—Brain Research—Inter-Disciplinary Guided Excellence, Department of Clinical Research, University of Southern DenmarkOdenseDenmark
- Rheumatology Research Unit, Odense University HospitalOdenseDenmark
| | - Maurice Ptito
- Department of Applied Mathematics and Computer Science, Technical University of DenmarkKongens LyngbyDenmark
- School of Optometry, University of MontrealMontrealCanada
| | - Vedrana Andersen Dahl
- Department of Applied Mathematics and Computer Science, Technical University of DenmarkKongens LyngbyDenmark
| | - Tim B Dyrby
- Danish Research Centre for Magnetic Resonance, Center for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and HvidovreHvidovreDenmark
- Department of Applied Mathematics and Computer Science, Technical University of DenmarkKongens LyngbyDenmark
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10
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Schilling KG, Palombo M, Witt AA, O'Grady KP, Pizzolato M, Landman BA, Smith SA. Characterization of neurite and soma organization in the brain and spinal cord with diffusion MRI. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2025:2025.02.19.638936. [PMID: 40027805 PMCID: PMC11870568 DOI: 10.1101/2025.02.19.638936] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/05/2025]
Abstract
The central nervous system (CNS), comprised of both the brain and spinal cord, and is a complex network of white and gray matter responsible for sensory, motor, and cognitive functions. Advanced diffusion MRI (dMRI) techniques offer a promising mechanism to non-invasively characterize CNS architecture, however, most studies focus on the brain or spinal cord in isolation. Here, we implemented a clinically feasible dMRI protocol on a 3T scanner to simultaneously characterize neurite and soma microstructure of both the brain and spinal cord. The protocol enabled the use of Diffusion Tensor Imaging (DTI), Standard Model Imaging (SMI), and Soma and Neurite Density Imaging (SANDI), representing the first time SMI and SANDI have been evaluated in the cord, and in the cord and brain simultaneously. Our results demonstrate high image quality even at high diffusion weightings, reproducibility of SMI and SANDI derived metrics similar to those of DTI with few exceptions, and biologically feasible contrasts between and within white and gray matter regions. Reproducibility and contrasts were decreased in the cord compared to that of the brain, revealing challenges due to partial volume effects and image preprocessing. This study establishes a harmonized approach for brain and cord microstructural imaging, and the opportunity to study CNS pathologies and biomarkers of structural integrity across the neuroaxis.
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11
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Toubasi AA, Cutter G, Gheen C, Vinarsky T, Yoon K, AshShareef S, Adapa P, Gruder O, Taylor S, Eaton JE, Xu J, Bagnato F. Improving the Assessment of Axonal Injury in Early Multiple Sclerosis. Acad Radiol 2025; 32:1002-1014. [PMID: 39277455 DOI: 10.1016/j.acra.2024.08.048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2024] [Revised: 06/28/2024] [Accepted: 08/22/2024] [Indexed: 09/17/2024]
Abstract
RATIONALE AND OBJECTIVES Several quantitative magnetic resonance imaging (MRI) methods are available to measure tissue injury in multiple sclerosis (MS), but their pathological specificity remains limited. The multi-compartment diffusion imaging using the spherical mean technique (SMT) overcomes several technical limitations of the diffusion-weighted image signal, thus delivering metrics with increased pathological specificity. Given these premises, here we assess whether the SMT-derived apparent axonal volume (Vax) provides a better tissue classifier than the diffusion tensor imaging (DTI)-derived axial diffusivity (AD) in the white matter (WM) of MS brains. METHODS Forty-three treatment-naïve people with newly diagnosed MS, clinically isolated syndrome, or radiologically isolated syndrome and 18 healthy controls (HCs) underwent a 3.0 Tesla MRI inclusive of T1-weighted (T1-w) and T2-w fluid-attenuated inversion recovery (FLAIR) sequences, and multi-b shell diffusion-weighted imaging. In patients only, pre- and post-gadolinium diethylenetriamine penta-acetic acid T1-w sequences were obtained for the evaluation of contrast-active lesions (CELs). Vax and AD were calculated in T2-lesions, chronic black holes (cBHs), and normal appearing (NAWM) in patients and normal WM (NWM) in HCs. Vax and AD values were compared across all the possible combinations of these regions. CELs were excluded from the analyses. RESULTS Vax differed in all comparisons (p ≤ 0.047 by paired t-test); AD differed in most comparisons (p < 0.001) except between NAWM and NWM, and between cBHs and T2-lesions. Vax had higher accuracy (p ≤ 0.029 by DeLong test) and larger effect size (p ≤ 0.038 by paired t-test) than AD in differentiating areas with even minimal tissue injury. CONCLUSIONS Vax provides a better radiological quantitative discriminator of different degrees of axonal-mediated tissue injury even between areas with expected minimal pathology. Our data support further studies to assess the readiness of Vax as a measure of outcome for clinical trials on neuroprotection in MS.
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Affiliation(s)
- Ahmad A Toubasi
- Neuroimaging Unit, Neuroimmunology Division, Department of Neurology, Vanderbilt University Medical Center (VUMC), Nashville, TN (A.A.T., C.G., T.V., K.Y., S.A., P.A., F.B.)
| | - Gary Cutter
- Department of Biostatistics, School of Public Health, The University of Alabama at Birmingham, Birmingham, AL (G.C.)
| | - Caroline Gheen
- Neuroimaging Unit, Neuroimmunology Division, Department of Neurology, Vanderbilt University Medical Center (VUMC), Nashville, TN (A.A.T., C.G., T.V., K.Y., S.A., P.A., F.B.)
| | - Taegan Vinarsky
- Neuroimaging Unit, Neuroimmunology Division, Department of Neurology, Vanderbilt University Medical Center (VUMC), Nashville, TN (A.A.T., C.G., T.V., K.Y., S.A., P.A., F.B.)
| | - Keejin Yoon
- Neuroimaging Unit, Neuroimmunology Division, Department of Neurology, Vanderbilt University Medical Center (VUMC), Nashville, TN (A.A.T., C.G., T.V., K.Y., S.A., P.A., F.B.); University of Central Florida, College of Medicine, Orlando, FL (K.Y.)
| | - Salma AshShareef
- Neuroimaging Unit, Neuroimmunology Division, Department of Neurology, Vanderbilt University Medical Center (VUMC), Nashville, TN (A.A.T., C.G., T.V., K.Y., S.A., P.A., F.B.); Department of Life and Physical Sciences, Fisk University, Nashville, TN (S.A.)
| | - Pragnya Adapa
- Neuroimaging Unit, Neuroimmunology Division, Department of Neurology, Vanderbilt University Medical Center (VUMC), Nashville, TN (A.A.T., C.G., T.V., K.Y., S.A., P.A., F.B.); College of Arts and Sciences, Vanderbilt University, Nashville, TN (P.A.)
| | - Olivia Gruder
- Neuroimmunology Division, Department of Neurology, VUMC, Nashville, TN (O.G., S.T., J.E.E.)
| | - Stephanie Taylor
- Neuroimmunology Division, Department of Neurology, VUMC, Nashville, TN (O.G., S.T., J.E.E.)
| | - James E Eaton
- Neuroimmunology Division, Department of Neurology, VUMC, Nashville, TN (O.G., S.T., J.E.E.); Cognitive Division, Department of Neurology, VUMC, Nashville, TN (J.E.E.)
| | - Junzhong Xu
- Vanderbilt University Institute of Imaging Sciences, Departments of Radiology and Radiological Sciences, VUMC, Nashville, TN (J.X.)
| | - Francesca Bagnato
- Neuroimaging Unit, Neuroimmunology Division, Department of Neurology, Vanderbilt University Medical Center (VUMC), Nashville, TN (A.A.T., C.G., T.V., K.Y., S.A., P.A., F.B.); Department of Neurology, VA Medical Center, TN Valley Healthcare System, Nashville, TN (F.B.).
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12
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Shamir I, Assaf Y. Tutorial: a guide to diffusion MRI and structural connectomics. Nat Protoc 2025; 20:317-335. [PMID: 39232202 DOI: 10.1038/s41596-024-01052-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 07/09/2024] [Indexed: 09/06/2024]
Abstract
Diffusion magnetic resonance imaging (dMRI) is a versatile imaging technique that has gained popularity thanks to its sensitive ability to measure displacement of water molecules within a living tissue on a micrometer scale. Although dMRI has been around since the early 1990s, its applications are constantly evolving, primarily regarding the inference of structural connectomics from nerve fiber trajectories. However, these applications require expertise in image processing and statistics, and it can be difficult for a newcomer to choose an appropriate pipeline to fit their research needs, not least because dMRI is such a flexible methodology that dozens of acquisition and analysis pipelines have been developed over the years. This introductory guide is designed for graduate students and researchers in the neuroscience community who are interested in integrating this new methodology regardless of their background in neuroimaging and computational tools. The guide provides a brief overview of the basic dMRI methodologies but focuses on its applications in neuroplasticity and connectomics. The guide starts with dMRI experimental designs and a complete step-by-step pipeline for structural connectomics. The following section covers the basics of dMRI, including parameters and clinical applications (apparent diffusion coefficient, mean diffusivity, fractional anisotropy and microscopic fractional anisotropy), as well as different approaches and models. The final section focuses on structural connectomics, covering subjects from fiber tracking (techniques, evaluation and limitations) to structural networks (constructing, analyzing and visualizing a network).
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Affiliation(s)
- Ittai Shamir
- Department of Neurobiology, Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel
| | - Yaniv Assaf
- Department of Neurobiology, Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel.
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.
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13
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Tendler BC. Investigating time-independent and time-dependent diffusion phenomena using steady-state diffusion MRI. Sci Rep 2025; 15:3580. [PMID: 39875547 PMCID: PMC11775203 DOI: 10.1038/s41598-025-87377-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2024] [Accepted: 01/20/2025] [Indexed: 01/30/2025] Open
Abstract
Diffusion MRI is a leading method to non-invasively characterise brain tissue microstructure across multiple domains and scales. Diffusion-weighted steady-state free precession (DW-SSFP) is an established imaging sequence for post-mortem MRI, addressing the challenging imaging environment of fixed tissue with short T2 and low diffusivities. However, a current limitation of DW-SSFP is signal interpretation: it is not clear what diffusion 'regime' the sequence probes and therefore its potential to characterise tissue microstructure. Building on Extended Phase Graphs (EPG), I establish two alternative representations of the DW-SSFP signal in terms of (1) conventional b-values (time-independent diffusion) and (2) encoding power-spectra (time-dependent diffusion). The proposed representations provide insights into how different parameter regimes and gradient waveforms impact the diffusion sensitivity of DW-SSFP. I subsequently introduce an approach to incorporate existing biophysical models into DW-SSFP without the requirement of extensive derivations, with time dependence estimated via a Gaussian phase approximation representation of the DW-SSFP signal. Investigations incorporating free-diffusion and tissue-relevant microscopic restrictions (cylinder of varying radius) give excellent agreement to complementary analytical models and Monte Carlo simulations. Experimentally, the time-independent representation is used to derive Tensor and proof-of-principle NODDI estimates in a whole human post-mortem brain. A final SNR-efficiency investigation demonstrates the theoretical potential of DW-SSFP for ultra-high field microstructural imaging.
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Affiliation(s)
- Benjamin C Tendler
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
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14
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Hakhu S, Hu LS, Beeman S, Sadleir RJ. Comparison of modelled diffusion-derived electrical conductivities found using magnetic resonance imaging. FRONTIERS IN RADIOLOGY 2025; 5:1492479. [PMID: 39917284 PMCID: PMC11794185 DOI: 10.3389/fradi.2025.1492479] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/07/2024] [Accepted: 01/02/2025] [Indexed: 02/09/2025]
Abstract
Introduction Magnetic resonance-based electrical conductivity imaging offers a promising new contrast mechanism to enhance disease diagnosis. Conductivity tensor imaging (CTI) combines data from MR diffusion microstructure imaging to reconstruct electrodeless low-frequency conductivity images. However, different microstructure imaging methods rely on varying diffusion models and parameters, leading to divergent tissue conductivity estimates. This study investigates the variability in conductivity predictions across different microstructure models and evaluates their alignment with experimental observations. Methods We used publicly available diffusion databases from neurotypical adults to extract microstructure parameters for three diffusion-based brain models: Neurite Orientation Dispersion and Density Imaging (NODDI), Soma and Neurite Density Imaging (SANDI), and Spherical Mean technique (SMT) conductivity predictions were calculated for gray matter (GM) and white matter (WM) tissues using each model. Comparative analyses were performed to assess the range of predicted conductivities and the consistency between bilateral tissue conductivities for each method. Results Significant variability in conductivity estimates was observed across the three models. Each method predicted distinct conductivity values for GM and WM tissues, with notable differences in the range of conductivities observed for specific tissue examples. Despite the variability, many WM and GM tissues exhibited symmetric bilateral conductivities within each microstructure model. SMT yielded conductivity estimates closer to values reported in experimental studies, while none of the methods aligned with spectroscopic models of tissue conductivity. Discussion and conclusion Our findings highlight substantial discrepancies in tissue conductivity estimates generated by different diffusion models, underscoring the challenge of selecting an appropriate model for low-frequency electrical conductivity imaging. SMT demonstrated better alignment with experimental results. However other microstructure models may produce better tissue discrimination.
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Affiliation(s)
- Sasha Hakhu
- School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ, United States
| | - Leland S. Hu
- Department of Radiology, Mayo Clinic Arizona, Phoenix, AZ, United States
| | - Scott Beeman
- School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ, United States
| | - Rosalind J. Sadleir
- School of Biological and Health Systems Engineering, Arizona State University, Tempe, AZ, United States
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15
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Sedlák V, Němý M, Májovský M, Bubeníková A, Nordin LE, Moravec T, Engelová J, Sila D, Konečná D, Belšan T, Westman E, Netuka D. IDH Status in Brain Gliomas Can Be Predicted by the Spherical Mean MRI Technique. AJNR Am J Neuroradiol 2025; 46:121-128. [PMID: 39779292 PMCID: PMC11735434 DOI: 10.3174/ajnr.a8432] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2024] [Accepted: 07/10/2024] [Indexed: 01/11/2025]
Abstract
BACKGROUND AND PURPOSE Diffuse gliomas, a heterogeneous group of primary brain tumors, have traditionally been stratified by histology, but recent insights into their molecular features, especially the IDH mutation status, have fundamentally changed their classification and prognosis. Current diagnostic methods, still predominantly relying on invasive biopsy, necessitate the exploration of noninvasive imaging alternatives for glioma characterization. MATERIALS AND METHODS In this prospective study, we investigated the utility of the spherical mean technique (SMT) in predicting the IDH status and histologic grade of adult-type diffuse gliomas. Patients with histologically confirmed adult-type diffuse glioma underwent a multiparametric MRI examination using a 3T system, which included a multishell diffusion sequence. Advanced diffusion parameters were obtained using SMT, diffusional kurtosis imaging, and ADC modeling. The diagnostic performance of studied parameters was evaluated by plotting receiver operating characteristic curves with associated area under curve, specificity, and sensitivity values. RESULTS A total of 80 patients with a mean age of 48 (SD, 16) years were included in the study. SMT metrics, particularly microscopic fractional anisotropy (μFA), intraneurite voxel fraction, and μFA to the third power (μFA3), demonstrated strong diagnostic performance (all AUC = 0.905, 95% CI, 0.835-0.976; P < .001) in determining IDH status and compared favorably with diffusional kurtosis imaging and ADC models. These parameters also showed a strong predictive capability for tumor grade, with intraneurite voxel fraction and μFA achieving the highest diagnostic accuracy (AUC = 0.937, 95% CI, 0.880-0.993; P < .001). Control analyses on normal-appearing brain tissue confirmed the specificity of these metrics for tumor tissue. CONCLUSIONS Our study highlights the potential of SMT for noninvasive characterization of adult-type diffuse gliomas, with a potential to predict IDH status and tumor grade more accurately than traditional ADC metrics. SMT offers a promising addition to the current diagnostic toolkit, enabling more precise preoperative assessments and contributing to personalized treatment planning.
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Affiliation(s)
- Vojtěch Sedlák
- From the Department of Radiology (V.S., T.B.), Military University Hospital, Prague, Czech Republic
| | - Milan Němý
- Division of Clinical Geriatrics (M.N., L.E.N., E.W.), Department of Neurobiology, Care Sciences and Society, Center for Alzheimer Research, Karolinska Institute, Stockholm, Sweden
- Department of Biomedical Engineering and Assistive Technology (M.N.), Czech Institute of Informatics, Robotics and Cybernetics, Czech Technical University in Prague, Prague, Czech Republic
| | - Martin Májovský
- Department of Neurosurgery and Neurooncology (M.M., A.B., T.M., D.K., D.N.), First Faculty of Medicine, Charles University and Military University Hospital, Prague, Czech Republic
| | - Adéla Bubeníková
- Department of Neurosurgery and Neurooncology (M.M., A.B., T.M., D.K., D.N.), First Faculty of Medicine, Charles University and Military University Hospital, Prague, Czech Republic
| | - Love Engstrom Nordin
- Division of Clinical Geriatrics (M.N., L.E.N., E.W.), Department of Neurobiology, Care Sciences and Society, Center for Alzheimer Research, Karolinska Institute, Stockholm, Sweden
- Department of Diagnostic Medical Physics (L.E.N.), Karolinska University Hospital Solna, Stockholm, Sweden
| | - Tomáš Moravec
- Department of Neurosurgery and Neurooncology (M.M., A.B., T.M., D.K., D.N.), First Faculty of Medicine, Charles University and Military University Hospital, Prague, Czech Republic
| | - Jana Engelová
- Radiodiagnostic Department (J.E.), Proton Therapy Center Czech Ltd, Prague, Czech Republic
| | - Dalibor Sila
- Department of Neurosurgery and Spine Surgery (D.S.), Arberlandklinik Viechtach, Germany
| | - Dora Konečná
- Department of Neurosurgery and Neurooncology (M.M., A.B., T.M., D.K., D.N.), First Faculty of Medicine, Charles University and Military University Hospital, Prague, Czech Republic
| | - Tomáš Belšan
- From the Department of Radiology (V.S., T.B.), Military University Hospital, Prague, Czech Republic
| | - Eric Westman
- Division of Clinical Geriatrics (M.N., L.E.N., E.W.), Department of Neurobiology, Care Sciences and Society, Center for Alzheimer Research, Karolinska Institute, Stockholm, Sweden
- Department of Neuroimaging (E.W.), Centre for Neuroimaging Science, Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, UK
| | - David Netuka
- Department of Neurosurgery and Neurooncology (M.M., A.B., T.M., D.K., D.N.), First Faculty of Medicine, Charles University and Military University Hospital, Prague, Czech Republic
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16
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Geints AA, Dobrynina LA, Egerev IM, Kremneva EI, Shamtieva KV, Belousov VO. [Animal experimental models in the study of age-dependent cerebral microangiopathy]. Zh Nevrol Psikhiatr Im S S Korsakova 2025; 125:57-68. [PMID: 40123139 DOI: 10.17116/jnevro202512503257] [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] [Indexed: 03/25/2025]
Abstract
The modeling of age-dependent cerebral microangiopathy (CMA) is highly relevant due to its high prevalence and the heavy burden of clinical manifestations - strokes and cognitive disorders in the elderly, as well as the lack of effective pathogenetic treatment. Experimental modeling of CMA is a promising area of preclinical scientific research that contributes to the study of the disease pathogenesis at the genetic, molecular, and cellular levels and the search for optimal methods of its treatment and prevention. This review aimed to analyze, systematize, and compare data on current experimental models of CMA. The review analyzed the results of various studies on experimental models published in journals indexed in the PubMed, Scopus, and eLibrary databases. Available CMA models reflect different CMA attributes and mechanisms. The choice of research model should be based on the experiment's objectives. Understanding available models, combining them, and developing new models should be aimed at choosing the most relevant ones, reproducing the features of modern CMA, characterized by the control of classical risk factors, to assess pathological mechanisms and develop pathogenetic treatment.
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Affiliation(s)
- A A Geints
- Research Centre of Neurology, Moscow, Russia
| | | | - I M Egerev
- Lomonosov Moscow State University, Moscow, Russia
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17
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Chan KS, Ma Y, Lee H, Marques JP, Olesen J, Coelho S, Novikov DS, Jespersen S, Huang SY, Lee HH. In vivo human neurite exchange imaging (NEXI) at 500 mT/m diffusion gradients. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.12.13.628450. [PMID: 39763747 PMCID: PMC11702555 DOI: 10.1101/2024.12.13.628450] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 01/19/2025]
Abstract
Evaluating tissue microstructure and membrane integrity in the living human brain through diffusion-water exchange imaging is challenging due to requirements for a high signal-to-noise ratio and short diffusion times dictated by relatively fast exchange processes. The goal of this work was to demonstrate the feasibility of in vivo imaging of tissue micro-geometries and water exchange within the brain gray matter using the state-of-the-art Connectome 2.0 scanner equipped with an ultra-high-performance gradient system (maximum gradient strength=500 mT/m, maximum slew rate=600 T/m/s). We performed diffusion MRI measurements in 15 healthy volunteers at multiple diffusion times (13-30 ms) and b -values up to 17.5 ms/μm2. The anisotropic Kärger model was applied to estimate the exchange time between intra-neurite and extracellular water in gray matter. The estimated exchange time across the cortical ribbon was around (median±interquartile range) 13±8 ms on Connectome 2.0, substantially faster than that measured using an imaging protocol compatible with Connectome 1.0-alike systems on the same cohort. Our investigation suggested that the NEXI exchange time estimation using a Connectome 1.0 compatible protocol was more prone to residual noise floor biases due to the small time-dependent signal contrasts across diffusion times when the exchange is fast (≤20 ms). Furthermore, spatial variation of exchange time was observed across the cortex, where the motor cortex, somatosensory cortex and visual cortex exhibit longer exchange times compared to other cortical regions. Non-linear fitting for the anisotropic Kärger model was accelerated 100 times using a GPU-based pipeline compared to the conventional CPU-based approach. This study highlighted the importance of the chosen diffusion times and measures to address Rician noise in dMRI data, which can have a substantial impact on the estimated NEXI exchange time and require extra attention when comparing NEXI results between various hardware setups.
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Affiliation(s)
- Kwok-Shing Chan
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Yixin Ma
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Hansol Lee
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - José P. Marques
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Jonas Olesen
- Center of Functionally Integrative Neuroscience (CFIN) and MINDLab, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Department of Physics and Astronomy, Aarhus University, Aarhus, Denmark
| | - Santiago Coelho
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY 10016, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University School of Medicine, New York, NY 10016, USA
| | - Dmitry S Novikov
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY 10016, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University School of Medicine, New York, NY 10016, USA
| | - Sune Jespersen
- Center of Functionally Integrative Neuroscience (CFIN) and MINDLab, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
- Department of Physics and Astronomy, Aarhus University, Aarhus, Denmark
| | - Susie Y. Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
| | - Hong-Hsi Lee
- Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA, United States
- Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, United States
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Brendstrup‐Brix K, Ulv Larsen SM, Lee H, Knudsen GM. Perivascular space diffusivity and brain microstructural measures are associated with circadian time and sleep quality. J Sleep Res 2024; 33:e14226. [PMID: 38676409 PMCID: PMC11512690 DOI: 10.1111/jsr.14226] [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: 10/18/2023] [Revised: 04/04/2024] [Accepted: 04/12/2024] [Indexed: 04/28/2024]
Abstract
The glymphatic system is centred around brain cerebrospinal fluid flow and is enhanced during sleep, and the synaptic homeostasis hypothesis proposes that sleep acts on brain microstructure by selective synaptic downscaling. While so far primarily studied in animals, we here examine in humans if brain diffusivity and microstructure is related to time of day, sleep quality and cognitive performance. We use diffusion weighted images from 916 young healthy individuals, aged between 22 and 37 years, collected as part of the Human Connectome Project to assess diffusion tensor image analysis along the perivascular space index, white matter fractional anisotropy, intra-neurite volume fraction and extra-neurite mean diffusivity. Next, we examine if these measures are associated with circadian time of acquisition, the Pittsburgh Sleep Quality Index (high scores correspond to low sleep quality) and age-adjusted cognitive function total composite score. Consistent with expectations, we find that diffusion tensor image analysis along the perivascular space index and orbitofrontal grey matter extra-neurite mean diffusivity are negatively and white matter fractional anisotropy positively correlated with circadian time. Further, we find that grey matter intra-neurite volume fraction correlates positively with Pittsburgh Sleep Quality Index, and that this correlation is driven by sleep duration. Finally, we find positive correlations between grey matter intra-neurite volume fraction and cognitive function total composite score, as well as negative interaction effects between cognitive function total composite score and Pittsburgh Sleep Quality Index on grey matter intra-neurite volume fraction. Our findings propose that perivascular flow is under circadian control and that sleep downregulates the intra-neurite volume in healthy adults with positive impact on cognitive function.
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Affiliation(s)
- Kristoffer Brendstrup‐Brix
- Neurobiology Research UnitCopenhagen University Hospital RigshospitaletCopenhagenDenmark
- Faculty of Health and Medical SciencesUniversity of CopenhagenCopenhagenDenmark
| | - Sara Marie Ulv Larsen
- Neurobiology Research UnitCopenhagen University Hospital RigshospitaletCopenhagenDenmark
- Faculty of Health and Medical SciencesUniversity of CopenhagenCopenhagenDenmark
| | - Hong‐Hsi Lee
- Athinoula A. Martinos Center for Biomedical Imaging, Department of RadiologyMassachusetts General HospitalCharlestownMassachusettsUSA
- Harvard Medical SchoolBostonMassachusettsUSA
| | - Gitte Moos Knudsen
- Neurobiology Research UnitCopenhagen University Hospital RigshospitaletCopenhagenDenmark
- Faculty of Health and Medical SciencesUniversity of CopenhagenCopenhagenDenmark
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Lee M, Jahng GH, Kwon OI. Reconstruction of intra- and extra-neurite conductivity tensors via conductivity at Larmor frequency and DWI data patterns. Neuroimage 2024; 302:120900. [PMID: 39486495 DOI: 10.1016/j.neuroimage.2024.120900] [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: 07/15/2024] [Revised: 09/24/2024] [Accepted: 10/22/2024] [Indexed: 11/04/2024] Open
Abstract
The developed magnetic resonance electrical properties tomography (MREPT) techniques visualize the internal conductivity distribution at Larmor frequency by measuring the B1 transceive phase data. In biological tissues, electrical conductivity is influenced by ion concentrations and mobility. To visualize the anisotropic conductivity tensor of biological tissues, we use the Einstein-Smoluchowski equation, which links the diffusion coefficient to particle mobility. By assuming a correlation between ion mobility and water diffusivity, we aim to decompose the internal isotropic conductivity at Larmor frequency into anisotropic conductivity tensors within the intra- and extra-neurite compartments. The multi-compartment spherical mean technique (MC-SMT), utilizing both high and low b-value diffusion-weighted imaging (DWI) data, characterizes the diffusion of water molecules within and across the intra- and extra-neurite compartments by analyzing the microstructural intricacies and the foundational architectural arrangement of the brain's tissues. By analyzing the relationships between the measured DWI data, the microscopic diffusion signal, and the fiber orientation distribution function, we predict the DWI data for the intra- and extra-neurite compartments using spherical harmonic decomposition. Using the predicted DWI data for the intra- and extra-neurite compartments, we develop a conductivity tensor imaging method that operates specifically within the separated compartments. Human brain experiments, involving four healthy volunteers and a brain tumor patient, were performed to assess and confirm the reliability of the proposed method.
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Affiliation(s)
- Munbae Lee
- Department of Mathematics, Konkuk University, Seoul, 05029, Republic of Korea.
| | - Geon-Ho Jahng
- Department of Radiology, Kyung Hee University Hospital at Gangdong, Seoul, 05278, Republic of Korea.
| | - Oh-In Kwon
- Department of Mathematics, Konkuk University, Seoul, 05029, Republic of Korea.
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20
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Cagol A, Ocampo-Pineda M, Lu PJ, Weigel M, Barakovic M, Melie-Garcia L, Chen X, Lutti A, Calabrese P, Kuhle J, Kappos L, Sormani MP, Granziera C. Advanced Quantitative MRI Unveils Microstructural Thalamic Changes Reflecting Disease Progression in Multiple Sclerosis. NEUROLOGY(R) NEUROIMMUNOLOGY & NEUROINFLAMMATION 2024; 11:e200299. [PMID: 39270143 PMCID: PMC11409727 DOI: 10.1212/nxi.0000000000200299] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/15/2024]
Abstract
BACKGROUND AND OBJECTIVES In patients with multiple sclerosis (PwMS), thalamic atrophy occurs during the disease course. However, there is little understanding of the mechanisms leading to volume loss and of the relationship between microstructural thalamic pathology and disease progression. This cross-sectional and longitudinal study aimed to comprehensively characterize in vivo pathologic changes within thalamic microstructure in PwMS using advanced multiparametric quantitative MRI (qMRI). METHODS Thalamic microstructural integrity was evaluated using quantitative T1, magnetization transfer saturation, multishell diffusion, and quantitative susceptibility mapping (QSM) in 183 PwMS and 105 healthy controls (HCs). The same qMRI protocol was available for 127 PwMS and 73 HCs after a 2-year follow-up period. Inclusion criteria for PwMS encompassed either an active relapsing-remitting MS (RRMS) or inactive progressive MS (PMS) disease course. Thalamic alterations were compared between PwMS and HCs and among disease phenotypes. In addition, the study investigated the relationship between thalamic damage and clinical and conventional MRI measures of disease severity. RESULTS Compared with HCs, PwMS exhibited substantial thalamic alterations, indicative of microstructural and macrostructural damage, demyelination, and disruption in iron homeostasis. These alterations extended beyond focal thalamic lesions, affecting normal-appearing thalamic tissue diffusely. Over the follow-up period, PwMS displayed an accelerated decrease in myelin volume fraction [mean difference in annualized percentage change (MD-ApC) = -1.50; p = 0.041] and increase in quantitative T1 (MD-ApC = 0.92; p < 0.0001) values, indicating heightened demyelinating and neurodegenerative processes. The observed differences between PwMS and HCs were substantially driven by the subgroup with PMS, wherein thalamic degeneration was significantly accelerated, even in comparison with patients with RRMS. Thalamic qMRI alterations showed extensive correlations with conventional MRI, clinical, and cognitive disease burden measures. Disability progression over follow-up was associated with accelerated thalamic degeneration, as reflected by enhanced diffusion (β = -0.067; p = 0.039) and QSM (β = -0.077; p = 0.027) changes. Thalamic qMRI metrics emerged as significant predictors of neurologic and cognitive disability even when accounting for other established markers including white matter lesion load and brain and thalamic atrophy. DISCUSSION These findings offer deeper insights into thalamic pathology in PwMS, emphasizing the clinical relevance of thalamic damage and its link to disease progression. Advanced qMRI biomarkers show promising potential in guiding interventions aimed at mitigating thalamic neurodegenerative processes.
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Affiliation(s)
- Alessandro Cagol
- From the Translational Imaging in Neurology (ThINk) Basel (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., L.K., C.G.), Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel; Department of Neurology (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., J.K., L.K., C.G.), University Hospital Basel; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB) (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., J.K., L.K., C.G.), University Hospital Basel and University of Basel, Switzerland; Dipartimento di Scienze della Salute, (A.C., M.P.S.), Università degli Studi di Genova, Italy; Division of Radiological Physics (M.W.), Department of Radiology, University Hospital Basel; Laboratory for Research in Neuroimaging (A.L.), Department of Clinical Neuroscience, Lausanne University Hospital and University of Lausanne; Neuropsychology and Behavioral Neurology Unit (P.C.), Division of Cognitive and Molecular Neuroscience, University of Basel, Switzerland; and IRCCS Ospedale Policlinico San Martino (M.P.S.), Genova, Italy
| | - Mario Ocampo-Pineda
- From the Translational Imaging in Neurology (ThINk) Basel (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., L.K., C.G.), Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel; Department of Neurology (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., J.K., L.K., C.G.), University Hospital Basel; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB) (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., J.K., L.K., C.G.), University Hospital Basel and University of Basel, Switzerland; Dipartimento di Scienze della Salute, (A.C., M.P.S.), Università degli Studi di Genova, Italy; Division of Radiological Physics (M.W.), Department of Radiology, University Hospital Basel; Laboratory for Research in Neuroimaging (A.L.), Department of Clinical Neuroscience, Lausanne University Hospital and University of Lausanne; Neuropsychology and Behavioral Neurology Unit (P.C.), Division of Cognitive and Molecular Neuroscience, University of Basel, Switzerland; and IRCCS Ospedale Policlinico San Martino (M.P.S.), Genova, Italy
| | - Po-Jui Lu
- From the Translational Imaging in Neurology (ThINk) Basel (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., L.K., C.G.), Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel; Department of Neurology (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., J.K., L.K., C.G.), University Hospital Basel; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB) (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., J.K., L.K., C.G.), University Hospital Basel and University of Basel, Switzerland; Dipartimento di Scienze della Salute, (A.C., M.P.S.), Università degli Studi di Genova, Italy; Division of Radiological Physics (M.W.), Department of Radiology, University Hospital Basel; Laboratory for Research in Neuroimaging (A.L.), Department of Clinical Neuroscience, Lausanne University Hospital and University of Lausanne; Neuropsychology and Behavioral Neurology Unit (P.C.), Division of Cognitive and Molecular Neuroscience, University of Basel, Switzerland; and IRCCS Ospedale Policlinico San Martino (M.P.S.), Genova, Italy
| | - Matthias Weigel
- From the Translational Imaging in Neurology (ThINk) Basel (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., L.K., C.G.), Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel; Department of Neurology (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., J.K., L.K., C.G.), University Hospital Basel; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB) (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., J.K., L.K., C.G.), University Hospital Basel and University of Basel, Switzerland; Dipartimento di Scienze della Salute, (A.C., M.P.S.), Università degli Studi di Genova, Italy; Division of Radiological Physics (M.W.), Department of Radiology, University Hospital Basel; Laboratory for Research in Neuroimaging (A.L.), Department of Clinical Neuroscience, Lausanne University Hospital and University of Lausanne; Neuropsychology and Behavioral Neurology Unit (P.C.), Division of Cognitive and Molecular Neuroscience, University of Basel, Switzerland; and IRCCS Ospedale Policlinico San Martino (M.P.S.), Genova, Italy
| | - Muhamed Barakovic
- From the Translational Imaging in Neurology (ThINk) Basel (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., L.K., C.G.), Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel; Department of Neurology (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., J.K., L.K., C.G.), University Hospital Basel; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB) (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., J.K., L.K., C.G.), University Hospital Basel and University of Basel, Switzerland; Dipartimento di Scienze della Salute, (A.C., M.P.S.), Università degli Studi di Genova, Italy; Division of Radiological Physics (M.W.), Department of Radiology, University Hospital Basel; Laboratory for Research in Neuroimaging (A.L.), Department of Clinical Neuroscience, Lausanne University Hospital and University of Lausanne; Neuropsychology and Behavioral Neurology Unit (P.C.), Division of Cognitive and Molecular Neuroscience, University of Basel, Switzerland; and IRCCS Ospedale Policlinico San Martino (M.P.S.), Genova, Italy
| | - Lester Melie-Garcia
- From the Translational Imaging in Neurology (ThINk) Basel (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., L.K., C.G.), Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel; Department of Neurology (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., J.K., L.K., C.G.), University Hospital Basel; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB) (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., J.K., L.K., C.G.), University Hospital Basel and University of Basel, Switzerland; Dipartimento di Scienze della Salute, (A.C., M.P.S.), Università degli Studi di Genova, Italy; Division of Radiological Physics (M.W.), Department of Radiology, University Hospital Basel; Laboratory for Research in Neuroimaging (A.L.), Department of Clinical Neuroscience, Lausanne University Hospital and University of Lausanne; Neuropsychology and Behavioral Neurology Unit (P.C.), Division of Cognitive and Molecular Neuroscience, University of Basel, Switzerland; and IRCCS Ospedale Policlinico San Martino (M.P.S.), Genova, Italy
| | - Xinjie Chen
- From the Translational Imaging in Neurology (ThINk) Basel (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., L.K., C.G.), Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel; Department of Neurology (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., J.K., L.K., C.G.), University Hospital Basel; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB) (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., J.K., L.K., C.G.), University Hospital Basel and University of Basel, Switzerland; Dipartimento di Scienze della Salute, (A.C., M.P.S.), Università degli Studi di Genova, Italy; Division of Radiological Physics (M.W.), Department of Radiology, University Hospital Basel; Laboratory for Research in Neuroimaging (A.L.), Department of Clinical Neuroscience, Lausanne University Hospital and University of Lausanne; Neuropsychology and Behavioral Neurology Unit (P.C.), Division of Cognitive and Molecular Neuroscience, University of Basel, Switzerland; and IRCCS Ospedale Policlinico San Martino (M.P.S.), Genova, Italy
| | - Antoine Lutti
- From the Translational Imaging in Neurology (ThINk) Basel (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., L.K., C.G.), Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel; Department of Neurology (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., J.K., L.K., C.G.), University Hospital Basel; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB) (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., J.K., L.K., C.G.), University Hospital Basel and University of Basel, Switzerland; Dipartimento di Scienze della Salute, (A.C., M.P.S.), Università degli Studi di Genova, Italy; Division of Radiological Physics (M.W.), Department of Radiology, University Hospital Basel; Laboratory for Research in Neuroimaging (A.L.), Department of Clinical Neuroscience, Lausanne University Hospital and University of Lausanne; Neuropsychology and Behavioral Neurology Unit (P.C.), Division of Cognitive and Molecular Neuroscience, University of Basel, Switzerland; and IRCCS Ospedale Policlinico San Martino (M.P.S.), Genova, Italy
| | - Pasquale Calabrese
- From the Translational Imaging in Neurology (ThINk) Basel (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., L.K., C.G.), Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel; Department of Neurology (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., J.K., L.K., C.G.), University Hospital Basel; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB) (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., J.K., L.K., C.G.), University Hospital Basel and University of Basel, Switzerland; Dipartimento di Scienze della Salute, (A.C., M.P.S.), Università degli Studi di Genova, Italy; Division of Radiological Physics (M.W.), Department of Radiology, University Hospital Basel; Laboratory for Research in Neuroimaging (A.L.), Department of Clinical Neuroscience, Lausanne University Hospital and University of Lausanne; Neuropsychology and Behavioral Neurology Unit (P.C.), Division of Cognitive and Molecular Neuroscience, University of Basel, Switzerland; and IRCCS Ospedale Policlinico San Martino (M.P.S.), Genova, Italy
| | - Jens Kuhle
- From the Translational Imaging in Neurology (ThINk) Basel (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., L.K., C.G.), Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel; Department of Neurology (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., J.K., L.K., C.G.), University Hospital Basel; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB) (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., J.K., L.K., C.G.), University Hospital Basel and University of Basel, Switzerland; Dipartimento di Scienze della Salute, (A.C., M.P.S.), Università degli Studi di Genova, Italy; Division of Radiological Physics (M.W.), Department of Radiology, University Hospital Basel; Laboratory for Research in Neuroimaging (A.L.), Department of Clinical Neuroscience, Lausanne University Hospital and University of Lausanne; Neuropsychology and Behavioral Neurology Unit (P.C.), Division of Cognitive and Molecular Neuroscience, University of Basel, Switzerland; and IRCCS Ospedale Policlinico San Martino (M.P.S.), Genova, Italy
| | - Ludwig Kappos
- From the Translational Imaging in Neurology (ThINk) Basel (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., L.K., C.G.), Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel; Department of Neurology (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., J.K., L.K., C.G.), University Hospital Basel; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB) (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., J.K., L.K., C.G.), University Hospital Basel and University of Basel, Switzerland; Dipartimento di Scienze della Salute, (A.C., M.P.S.), Università degli Studi di Genova, Italy; Division of Radiological Physics (M.W.), Department of Radiology, University Hospital Basel; Laboratory for Research in Neuroimaging (A.L.), Department of Clinical Neuroscience, Lausanne University Hospital and University of Lausanne; Neuropsychology and Behavioral Neurology Unit (P.C.), Division of Cognitive and Molecular Neuroscience, University of Basel, Switzerland; and IRCCS Ospedale Policlinico San Martino (M.P.S.), Genova, Italy
| | - Maria Pia Sormani
- From the Translational Imaging in Neurology (ThINk) Basel (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., L.K., C.G.), Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel; Department of Neurology (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., J.K., L.K., C.G.), University Hospital Basel; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB) (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., J.K., L.K., C.G.), University Hospital Basel and University of Basel, Switzerland; Dipartimento di Scienze della Salute, (A.C., M.P.S.), Università degli Studi di Genova, Italy; Division of Radiological Physics (M.W.), Department of Radiology, University Hospital Basel; Laboratory for Research in Neuroimaging (A.L.), Department of Clinical Neuroscience, Lausanne University Hospital and University of Lausanne; Neuropsychology and Behavioral Neurology Unit (P.C.), Division of Cognitive and Molecular Neuroscience, University of Basel, Switzerland; and IRCCS Ospedale Policlinico San Martino (M.P.S.), Genova, Italy
| | - Cristina Granziera
- From the Translational Imaging in Neurology (ThINk) Basel (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., L.K., C.G.), Department of Biomedical Engineering, Faculty of Medicine, University Hospital Basel and University of Basel; Department of Neurology (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., J.K., L.K., C.G.), University Hospital Basel; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB) (A.C., M.O.-P., P.-J.L., M.W., M.B., L.M.-G., X.C., J.K., L.K., C.G.), University Hospital Basel and University of Basel, Switzerland; Dipartimento di Scienze della Salute, (A.C., M.P.S.), Università degli Studi di Genova, Italy; Division of Radiological Physics (M.W.), Department of Radiology, University Hospital Basel; Laboratory for Research in Neuroimaging (A.L.), Department of Clinical Neuroscience, Lausanne University Hospital and University of Lausanne; Neuropsychology and Behavioral Neurology Unit (P.C.), Division of Cognitive and Molecular Neuroscience, University of Basel, Switzerland; and IRCCS Ospedale Policlinico San Martino (M.P.S.), Genova, Italy
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Ye X, Ma X, Pan Z, Zhang Z, Guo H, Uğurbil K, Wu X. Denoising complex-valued diffusion MR images using a two-step non-local principal component analysis approach. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.10.30.621081. [PMID: 39553996 PMCID: PMC11565869 DOI: 10.1101/2024.10.30.621081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 11/19/2024]
Abstract
Purpose to propose a two-step non-local principal component analysis (PCA) method and demonstrate its utility for denoising diffusion tensor MRI (DTI) with a few diffusion directions. Methods A two-step denoising pipeline was implemented to ensure accurate patch selection even with high noise levels and was coupled with data preprocessing for g-factor normalization and phase stabilization before data denoising with a non-local PCA algorithm. At the heart of our proposed pipeline was the use of a data-driven optimal shrinkage algorithm to manipulate the singular values in a way that would optimally estimate the noise-free signal. Our approach's denoising performances were evaluated using simulation and in-vivo human data experiments. The results were compared to those obtained with existing local-PCA-based methods. Results In both simulation and human data experiments, our approach substantially enhanced image quality relative to the noisy counterpart, yielding improved performances for estimation of relevant DTI metrics. It also outperformed existing local-PCA-based methods in reducing noise while preserving anatomic details. It also led to improved whole-brain tractography relative to the noisy counterpart. Conclusion The proposed denoising method has the utility for improving image quality for DTI with reduced diffusion directions and is believed to benefit many applications especially those aiming to achieve quality parametric mapping using only a few image volumes.
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Affiliation(s)
- Xinyu Ye
- Center for Biomedical Imaging Research, School of Biomedical Engineering, Tsinghua University, Beijing, China
| | - Xiaodong Ma
- Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah, United States
| | - Ziyi Pan
- Center for Biomedical Imaging Research, School of Biomedical Engineering, Tsinghua University, Beijing, China
| | - Zhe Zhang
- Tiantan Neuroimaging Center of Excellence, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Hua Guo
- Center for Biomedical Imaging Research, School of Biomedical Engineering, Tsinghua University, Beijing, China
| | - Kâmil Uğurbil
- Center for Magnetic Resonance Research, Radiology, Medical School, University of Minnesota, Minneapolis, Minnesota
| | - Xiaoping Wu
- Center for Magnetic Resonance Research, Radiology, Medical School, University of Minnesota, Minneapolis, Minnesota
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22
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Li Y, Zhuo Z, Liu C, Duan Y, Shi Y, Wang T, Li R, Wang Y, Jiang J, Xu J, Tian D, Zhang X, Shi F, Zhang X, Carass A, Barkhof F, Prince JL, Ye C, Liu Y. Deep learning enables accurate brain tissue microstructure analysis based on clinically feasible diffusion magnetic resonance imaging. Neuroimage 2024; 300:120858. [PMID: 39317273 DOI: 10.1016/j.neuroimage.2024.120858] [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: 08/05/2024] [Revised: 09/14/2024] [Accepted: 09/17/2024] [Indexed: 09/26/2024] Open
Abstract
Diffusion magnetic resonance imaging (dMRI) allows non-invasive assessment of brain tissue microstructure. Current model-based tissue microstructure reconstruction techniques require a large number of diffusion gradients, which is not clinically feasible due to imaging time constraints, and this has limited the use of tissue microstructure information in clinical settings. Recently, approaches based on deep learning (DL) have achieved promising tissue microstructure reconstruction results using clinically feasible dMRI. However, it remains unclear whether the subtle tissue changes associated with disease or age are properly preserved with DL approaches and whether DL reconstruction results can benefit clinical applications. Here, we provide the first evidence that DL approaches to tissue microstructure reconstruction yield reliable brain tissue microstructure analysis based on clinically feasible dMRI scans. Specifically, we reconstructed tissue microstructure from four different brain dMRI datasets with only 12 diffusion gradients, a clinically feasible protocol, and the neurite orientation dispersion and density imaging (NODDI) and spherical mean technique (SMT) models were considered. With these results we show that disease-related and age-dependent alterations of brain tissue were accurately identified. These findings demonstrate that DL tissue microstructure reconstruction can accurately quantify microstructural alterations in the brain based on clinically feasible dMRI.
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Affiliation(s)
- Yuxing Li
- School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing, China
| | - Zhizheng Zhuo
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Chenghao Liu
- School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing, China
| | - Yunyun Duan
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Yulu Shi
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Tingting Wang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Runzhi Li
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Beijing, China
| | - Yanli Wang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Beijing, China
| | - Jiwei Jiang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Beijing, China
| | - Jun Xu
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Decai Tian
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Beijing, China; Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China
| | - Xinghu Zhang
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China
| | - Fudong Shi
- Department of Neurology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China; China National Clinical Research Center for Neurological Diseases, Beijing Tiantan Hospital, Beijing, China; Department of Neurology and Tianjin Neurological Institute, Tianjin Medical University General Hospital, Tianjin, China
| | - Xiaofeng Zhang
- School of Information and Electronics, Beijing Institute of Technology, Zhuhai, China
| | - Aaron Carass
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, USA
| | - Frederik Barkhof
- Department of Radiology and Nuclear Medicine, Amsterdam University Medical Center, Amsterdam, 1081 HV, the Netherlands
| | - Jerry L Prince
- Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, USA
| | - Chuyang Ye
- School of Integrated Circuits and Electronics, Beijing Institute of Technology, Beijing, China.
| | - Yaou Liu
- Department of Radiology, Beijing Tiantan Hospital, Capital Medical University, Beijing, China.
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23
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Shi D, Liu F, Li S, Chen L, Jiang X, Gore JC, Zheng Q, Guo H, Xu J. Restriction-induced time-dependent transcytolemmal water exchange: Revisiting the Kӓrger exchange model. JOURNAL OF MAGNETIC RESONANCE (SAN DIEGO, CALIF. : 1997) 2024; 367:107760. [PMID: 39241283 DOI: 10.1016/j.jmr.2024.107760] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/11/2024] [Revised: 08/21/2024] [Accepted: 08/26/2024] [Indexed: 09/09/2024]
Abstract
The Kӓrger model and its derivatives have been widely used to incorporate transcytolemmal water exchange rate, an essential characteristic of living cells, into analyses of diffusion MRI (dMRI) signals from tissues. The Kӓrger model consists of two homogeneous exchanging components coupled by an exchange rate constant and assumes measurements are made with sufficiently long diffusion time and slow water exchange. Despite successful applications, it remains unclear whether these assumptions are generally valid for practical dMRI sequences and biological tissues. In particular, barrier-induced restrictions to diffusion produce inhomogeneous magnetization distributions in relatively large-sized compartments such as cancer cells, violating the above assumptions. The effects of this inhomogeneity are usually overlooked. We performed computer simulations to quantify how restriction effects, which in images produce edge enhancements at compartment boundaries, influence different variants of the Kӓrger-model. The results show that the edge enhancement effect will produce larger, time-dependent estimates of exchange rates in e.g., tumors with relatively large cell sizes (>10 μm), resulting in overestimations of water exchange as previously reported. Moreover, stronger diffusion gradients, longer diffusion gradient durations, and larger cell sizes, all cause more pronounced edge enhancement effects. This helps us to better understand the feasibility of the Kärger model in estimating water exchange in different tissue types and provides useful guidance on signal acquisition methods that may mitigate the edge enhancement effect. This work also indicates the need to correct the overestimated transcytolemmal water exchange rates obtained assuming the Kärger-model.
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Affiliation(s)
- Diwei Shi
- Center for Nano and Micro Mechanics, Department of Engineering Mechanics, Tsinghua University, Beijing, China
| | - Fan Liu
- Center for Biomedical Imaging Research, School of Biomedical Engineering, Tsinghua University, Beijing, China
| | - Sisi Li
- Center for Biomedical Imaging Research, School of Biomedical Engineering, Tsinghua University, Beijing, China
| | - Li Chen
- Center for Nano and Micro Mechanics, Department of Engineering Mechanics, Tsinghua University, Beijing, China
| | - Xiaoyu Jiang
- Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States
| | - John C Gore
- Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States; Department of Physics and Astronomy, Vanderbilt University, Nashville, TN, United States
| | - Quanshui Zheng
- Center for Nano and Micro Mechanics, Department of Engineering Mechanics, Tsinghua University, Beijing, China
| | - Hua Guo
- Center for Biomedical Imaging Research, School of Biomedical Engineering, Tsinghua University, Beijing, China
| | - Junzhong Xu
- Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States; Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States; Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States; Department of Physics and Astronomy, Vanderbilt University, Nashville, TN, United States.
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24
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Toubasi AA, Xu J, Eisma JJ, AshShareef S, Gheen C, Vinarsky T, Adapa P, Shah S, Eaton J, Dortch RD, Donahue MJ, Bagnato F. Watershed regions are more susceptible to tissue microstructural injury in multiple sclerosis. Brain Commun 2024; 6:fcae299. [PMID: 39372138 PMCID: PMC11452773 DOI: 10.1093/braincomms/fcae299] [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: 03/06/2024] [Revised: 07/22/2024] [Accepted: 09/02/2024] [Indexed: 10/08/2024] Open
Abstract
Histopathologic studies report higher concentrations of multiple sclerosis white matter lesions in watershed areas of the brain, suggesting that areas with relatively lower oxygen levels may be more vulnerable to disease. However, it is unknown at what point in the disease course lesion predilection for watershed territories begins. Accordingly, we studied a cohort of people with newly diagnosed disease and asked whether (1) white matter lesions disproportionally localize to watershed-regions and (2) the degree of microstructural injury in watershed-lesions is more severe. Fifty-four participants, i.e. 38 newly diagnosed people with multiple sclerosis, clinically isolated syndrome or radiologically isolated syndrome, and 16 age- and sex-matched healthy controls underwent brain magnetic resonance imaging. T1-weighted and T2-weighted fluid-attenuated inversion recovery sequences, selective inversion recovery quantitative magnetisation transfer images, and the multi-compartment diffusion imaging with the spherical mean technique were acquired. We computed the macromolecular-to-free pool size ratio, and the apparent axonal volume fraction maps to indirectly estimate myelin and axonal integrity, respectively. We produced a flow territory atlas in each subject's native T2-weighted fluid-attenuated inversion recovery images using a T1-weighted magnetic resonance imaging template in the Montreal Neurological Institute 152 space. Lesion location relative to the watershed, non-watershed and mixed brain vascular territories was annotated. The same process was performed on the T2-weighted fluid-attenuated inversion recovery images of the healthy controls using 294 regions of interest. Generalized linear mixed models for continuous outcomes were used to assess differences in size, pool size ratio and axonal volume fraction between lesions/regions of interests (in healthy controls) situated in different vascular territories. In patients, we assessed 758 T2-lesions and 356 chronic black holes (cBHs). The watershed-territories had higher relative and absolute concentrations of T2-lesions (P≤0.041) and cBHs (P≤0.036) compared to either non-watershed- or mixed-zones. T2-lesions in watershed-areas also had lower pool size ratio relative to T2-lesions in either non-watershed- or mixed-zones (P = 0.039). These results retained significance in the sub-cohort of people without vascular comorbidities and when accounting for periventricular lesions. In healthy controls, axonal volume fraction was higher only in mixed-areas regions of interest compared to non-watershed-ones (P = 0.008). No differences in pool size ratio were seen. We provide in vivo evidence that there is an association between arterial vascularisation of the brain and multiple sclerosis-induced tissue injury as early as the time of disease diagnosis. Our findings underline the importance of oxygen delivery and healthy arterial vascularisation to prevent lesion formation and foster a better outcome in multiple sclerosis.
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Affiliation(s)
- Ahmad A Toubasi
- Neuorimaging Unit, Neuroimmunology Division, Department of Neurology, Vanderbilt University Medical Center (VUMC), Nashville, TN 37232, USA
| | - Junzhong Xu
- Department of Radiology and Radiological Sciences, Vanderbilt University Institute of Imaging Sciences, VUMC, Nashville, TN 37232, USA
| | - Jarrod J Eisma
- Neuorimaging Unit, Neuroimmunology Division, Department of Neurology, Vanderbilt University Medical Center (VUMC), Nashville, TN 37232, USA
| | - Salma AshShareef
- Neuorimaging Unit, Neuroimmunology Division, Department of Neurology, Vanderbilt University Medical Center (VUMC), Nashville, TN 37232, USA
- Department of Life and Physical Sciences, Fisk University, Nashville, TN 37208, USA
| | - Caroline Gheen
- Neuorimaging Unit, Neuroimmunology Division, Department of Neurology, Vanderbilt University Medical Center (VUMC), Nashville, TN 37232, USA
| | - Taegan Vinarsky
- Neuorimaging Unit, Neuroimmunology Division, Department of Neurology, Vanderbilt University Medical Center (VUMC), Nashville, TN 37232, USA
| | - Pragnya Adapa
- Neuorimaging Unit, Neuroimmunology Division, Department of Neurology, Vanderbilt University Medical Center (VUMC), Nashville, TN 37232, USA
- College of Arts and Sciences, Vanderbilt University, Nashville, TN 37240, USA
| | - Shailee Shah
- Neuroimmunology Division, Department of Neurology, VUMC, Nashville, TN 37232, USA
| | - James Eaton
- Neuroimmunology Division, Department of Neurology, VUMC, Nashville, TN 37232, USA
- Cognitive Division, Department of Neurology, VUMC, Nashville, TN 37232, USA
| | - Richard D Dortch
- Department of Translational Neuroscience, Barrow Neurological Institute, Phoenix, AZ 85013, USA
| | - Manus J Donahue
- Cognitive Division, Department of Neurology, VUMC, Nashville, TN 37232, USA
- Department of Psychiatry and Behavioral Science, VUMC, Nashville, TN 37232, USA
| | - Francesca Bagnato
- Neuorimaging Unit, Neuroimmunology Division, Department of Neurology, Vanderbilt University Medical Center (VUMC), Nashville, TN 37232, USA
- Department of Neurology, TN Valley Healthcare System, Nashville Veterans Affairs Medical Center, Nashville, TN 37212, USA
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25
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Dobrynina LA, Kremneva EI, Shamtieva KV, Geints AA, Filatov AS, Gadzhieva ZS, Gnedovskaya EV, Krotenkova MV, Maximov II. Cognitive Impairment in Cerebral Small Vessel Disease Is Associated with Corpus Callosum Microstructure Changes Based on Diffusion MRI. Diagnostics (Basel) 2024; 14:1838. [PMID: 39202326 PMCID: PMC11353603 DOI: 10.3390/diagnostics14161838] [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: 08/01/2024] [Revised: 08/19/2024] [Accepted: 08/19/2024] [Indexed: 09/03/2024] Open
Abstract
The cerebral small vessel disease (cSVD) is one of the main causes of vascular and mixed cognitive impairment (CI), and it is associated, in particular, with brain ageing. An understanding of structural tissue changes in an intact cerebral white matter in cSVD might allow one to develop the sensitive biomarkers for early diagnosis and monitoring of disease progression. PURPOSE OF THE STUDY to evaluate microstructural changes in the corpus callosum (CC) using diffusion MRI (D-MRI) approaches in cSVD patients with different severity of CI and reveal the most sensitive correlations of diffusion metrics with CI. METHODS the study included 166 cSVD patients (51.8% women; 60.4 ± 7.6 years) and 44 healthy volunteers (65.9% women; 59.6 ± 6.8 years). All subjects underwent D-MRI (3T) with signal (diffusion tensor and kurtosis) and biophysical (neurite orientation dispersion and density imaging, NODDI, white matter tract integrity, WMTI, multicompartment spherical mean technique, MC-SMT) modeling in three CC segments as well as a neuropsychological assessment. RESULTS in cSVD patients, microstructural changes were found in all CC segments already at the subjective CI stage, which was found to worsen into mild CI and dementia. More pronounced changes were observed in the forceps minor. Among the signal models FA, MD, MK, RD, and RK, as well as among the biophysical models, MC-SMT (EMD, ETR) and WMTI (AWF) metrics exhibited the largest area under the curve (>0.85), characterizing the loss of microstructural integrity, the severity of potential demyelination, and the proportion of intra-axonal water, respectively. Conclusion: the study reveals the relevance of advanced D-MRI approaches for the assessment of brain tissue changes in cSVD. The identified diffusion biomarkers could be used for the clarification and observation of CI progression.
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Affiliation(s)
- Larisa A. Dobrynina
- Research Center of Neurology, 125367 Moscow, Russia; (L.A.D.); (A.A.G.); (A.S.F.); (E.V.G.); (M.V.K.)
| | - Elena I. Kremneva
- Research Center of Neurology, 125367 Moscow, Russia; (L.A.D.); (A.A.G.); (A.S.F.); (E.V.G.); (M.V.K.)
| | - Kamila V. Shamtieva
- Research Center of Neurology, 125367 Moscow, Russia; (L.A.D.); (A.A.G.); (A.S.F.); (E.V.G.); (M.V.K.)
| | - Anastasia A. Geints
- Research Center of Neurology, 125367 Moscow, Russia; (L.A.D.); (A.A.G.); (A.S.F.); (E.V.G.); (M.V.K.)
| | - Alexey S. Filatov
- Research Center of Neurology, 125367 Moscow, Russia; (L.A.D.); (A.A.G.); (A.S.F.); (E.V.G.); (M.V.K.)
| | - Zukhra Sh. Gadzhieva
- Research Center of Neurology, 125367 Moscow, Russia; (L.A.D.); (A.A.G.); (A.S.F.); (E.V.G.); (M.V.K.)
| | - Elena V. Gnedovskaya
- Research Center of Neurology, 125367 Moscow, Russia; (L.A.D.); (A.A.G.); (A.S.F.); (E.V.G.); (M.V.K.)
| | - Marina V. Krotenkova
- Research Center of Neurology, 125367 Moscow, Russia; (L.A.D.); (A.A.G.); (A.S.F.); (E.V.G.); (M.V.K.)
| | - Ivan I. Maximov
- Department of Health and Functioning, Western Norway University of Applied Sciences (HVL), 5063 Bergen, Norway;
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26
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Drabek-Maunder ER, Mankad K, Aquilina K, Dean JA, Nisbet A, Clark CA. Using diffusion MRI to understand white matter damage and the link between brain microstructure and cognitive deficits in paediatric medulloblastoma patients. Eur J Radiol 2024; 177:111562. [PMID: 38901074 DOI: 10.1016/j.ejrad.2024.111562] [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: 03/01/2024] [Revised: 05/09/2024] [Accepted: 06/10/2024] [Indexed: 06/22/2024]
Abstract
PURPOSE Survivors of medulloblastoma face a range of challenges after treatment, involving behavioural, cognitive, language and motor skills. Post-treatment outcomes are associated with structural changes within the brain resulting from both the tumour and the treatment. Diffusion magnetic resonance imaging (MRI) has been used to investigate the microstructure of the brain. In this review, we aim to summarise the literature on diffusion MRI in patients treated for medulloblastoma and discuss future directions on how diffusion imaging can be used to improve patient quality. METHOD This review summarises the current literature on medulloblastoma in children, focusing on the impact of both the tumour and its treatment on brain microstructure. We review studies where diffusion MRI has been correlated with either treatment characteristics or cognitive outcomes. We discuss the role diffusion MRI has taken in understanding the relationship between microstructural damage and cognitive and behavioural deficits. RESULTS We identified 35 studies that analysed diffusion MRI changes in patients treated for medulloblastoma. The majority of these studies found significant group differences in measures of brain microstructure between patients and controls, and some of these studies showed associations between microstructure and neurocognitive outcomes, which could be influenced by patient characteristics (e.g. age), treatment, radiation dose and treatment type. CONCLUSIONS In future, studies would benefit from being able to separate microstructural white matter damage caused by the tumour, tumour-related complications and treatment. Additionally, advanced diffusion modelling methods can be explored to understand and describe microstructural changes to white matter.
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Affiliation(s)
- Emily R Drabek-Maunder
- UCL Great Ormond Street Institute of Child Health, 30 Guildford Street, London WC1N 1EH, UK; UCL Dept of Medical Physics and Biomedical Engineering, Malet Place, Gower St, London WC1E 6BT, UK; Great Ormond Street Hospital for Children, Great Ormond St, London WC1N 3JH, UK.
| | - Kshitij Mankad
- UCL Great Ormond Street Institute of Child Health, 30 Guildford Street, London WC1N 1EH, UK; Great Ormond Street Hospital for Children, Great Ormond St, London WC1N 3JH, UK
| | - Kristian Aquilina
- UCL Great Ormond Street Institute of Child Health, 30 Guildford Street, London WC1N 1EH, UK; Great Ormond Street Hospital for Children, Great Ormond St, London WC1N 3JH, UK
| | - Jamie A Dean
- UCL Dept of Medical Physics and Biomedical Engineering, Malet Place, Gower St, London WC1E 6BT, UK
| | - Andrew Nisbet
- UCL Dept of Medical Physics and Biomedical Engineering, Malet Place, Gower St, London WC1E 6BT, UK
| | - Chris A Clark
- UCL Great Ormond Street Institute of Child Health, 30 Guildford Street, London WC1N 1EH, UK; Great Ormond Street Hospital for Children, Great Ormond St, London WC1N 3JH, UK
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27
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Crestol A, de Lange AMG, Schindler L, Subramaniapillai S, Nerland S, Oppenheimer H, Westlye LT, Andreassen OA, Agartz I, Tamnes CK, Barth C. Linking menopause-related factors, history of depression, APOE ε4, and proxies of biological aging in the UK biobank cohort. Horm Behav 2024; 164:105596. [PMID: 38944998 PMCID: PMC11372440 DOI: 10.1016/j.yhbeh.2024.105596] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Revised: 06/14/2024] [Accepted: 06/18/2024] [Indexed: 07/02/2024]
Abstract
In a subset of females, postmenopausal status has been linked to accelerated aging and neurological decline. A complex interplay between reproductive-related factors, mental disorders, and genetics may influence brain function and accelerate the rate of aging in the postmenopausal phase. Using multiple regressions corrected for age, in this preregistered study we investigated the associations between menopause-related factors (i.e., menopausal status, menopause type, age at menopause, and reproductive span) and proxies of cellular aging (leukocyte telomere length, LTL) and brain aging (white and gray matter brain age gap, BAG) in 13,780 females from the UK Biobank (age range 39-82). We then determined how these proxies of aging were associated with each other, and evaluated the effects of menopause-related factors, history of depression (= lifetime broad depression), and APOE ε4 genotype on BAG and LTL, examining both additive and interactive relationships. We found that postmenopausal status and older age at natural menopause were linked to longer LTL and lower BAG. Surgical menopause and longer natural reproductive span were also associated with longer LTL. BAG and LTL were not significantly associated with each other. The greatest variance in each proxy of biological aging was most consistently explained by models with the addition of both lifetime broad depression and APOE ε4 genotype. Overall, this study demonstrates a complex interplay between menopause-related factors, lifetime broad depression, APOE ε4 genotype, and proxies of biological aging. However, results are potentially influenced by a disproportionate number of healthier participants among postmenopausal females. Future longitudinal studies incorporating heterogeneous samples are an essential step towards advancing female health.
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Affiliation(s)
- Arielle Crestol
- Division of Mental Health and Substance Abuse, Diakonhjemmet Hospital, Oslo, Norway; Center for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway.
| | - Ann-Marie G de Lange
- Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland; Department of Psychology, University of Oslo, Oslo, Norway; Department of Psychiatry, University of Oxford, Oxford, UK
| | - Louise Schindler
- Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland; Department of Psychology, University of Oslo, Oslo, Norway; Department of Psychiatry, University of Oxford, Oxford, UK
| | - Sivaniya Subramaniapillai
- Centre for Research in Neurosciences, Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland; Department of Psychology, University of Oslo, Oslo, Norway
| | - Stener Nerland
- Division of Mental Health and Substance Abuse, Diakonhjemmet Hospital, Oslo, Norway; Division of Mental Health and Addiction, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Hannah Oppenheimer
- Division of Mental Health and Substance Abuse, Diakonhjemmet Hospital, Oslo, Norway; Department of Psychology, University of Oslo, Oslo, Norway
| | - Lars T Westlye
- Center for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychology, University of Oslo, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo & Oslo University Hospital, Oslo, Norway
| | - Ole A Andreassen
- Center for Precision Psychiatry, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo & Oslo University Hospital, Oslo, Norway
| | - Ingrid Agartz
- Division of Mental Health and Substance Abuse, Diakonhjemmet Hospital, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo & Oslo University Hospital, Oslo, Norway; Department of Clinical Neuroscience, Centre for Psychiatry Research, Stockholm Health Care Services, Karolinska Institute, Stockholm County Council, Stockholm, Sweden; Division of Mental Health and Addiction, Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Christian K Tamnes
- Division of Mental Health and Substance Abuse, Diakonhjemmet Hospital, Oslo, Norway; PROMENTA Research Center, Department of Psychology, University of Oslo, Oslo, Norway
| | - Claudia Barth
- Division of Mental Health and Substance Abuse, Diakonhjemmet Hospital, Oslo, Norway.
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28
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Cagol A, Tsagkas C, Granziera C. Advanced Brain Imaging in Central Nervous System Demyelinating Diseases. Neuroimaging Clin N Am 2024; 34:335-357. [PMID: 38942520 DOI: 10.1016/j.nic.2024.03.003] [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] [Indexed: 06/30/2024]
Abstract
In recent decades, advances in neuroimaging have profoundly transformed our comprehension of central nervous system demyelinating diseases. Remarkable technological progress has enabled the integration of cutting-edge acquisition and postprocessing techniques, proving instrumental in characterizing subtle focal changes, diffuse microstructural alterations, and macroscopic pathologic processes. This review delves into state-of-the-art modalities applied to multiple sclerosis, neuromyelitis optica spectrum disorders, and myelin oligodendrocyte glycoprotein antibody-associated disease. Furthermore, it explores how this dynamic landscape holds significant promise for the development of effective and personalized clinical management strategies, encompassing support for differential diagnosis, prognosis, monitoring treatment response, and patient stratification.
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Affiliation(s)
- Alessandro Cagol
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Hegenheimermattweg 167b, 4123 Allschwil, Switzerland; Department of Neurology, University Hospital Basel, Petersgraben 4, 4031 Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Spitalstrasse 2, 4031 Basel, Switzerland; Department of Health Sciences, University of Genova, Via A. Pastore, 1 16132 Genova, Italy. https://twitter.com/CagolAlessandr0
| | - Charidimos Tsagkas
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Hegenheimermattweg 167b, 4123 Allschwil, Switzerland; Translational Neuroradiology Section, National Institute of Neurological Disorders and Stroke, National Institutes of Health (NIH), 10 Center Drive, Bethesda, MD 20892, USA
| | - Cristina Granziera
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Hegenheimermattweg 167b, 4123 Allschwil, Switzerland; Department of Neurology, University Hospital Basel, Petersgraben 4, 4031 Basel, Switzerland; Research Center for Clinical Neuroimmunology and Neuroscience Basel (RC2NB), University Hospital Basel and University of Basel, Spitalstrasse 2, 4031 Basel, Switzerland.
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29
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Aranda S, Jiménez E, Canales-Rodríguez EJ, Verdolini N, Alonso S, Sepúlveda E, Julià A, Marsal S, Bobes J, Sáiz PA, García-Portilla P, Menchón JM, Crespo JM, González-Pinto A, Pérez V, Arango C, Sierra P, Sanjuán J, Pomarol-Clotet E, Vieta E, Vilella E. Processing speed mediates the relationship between DDR1 and psychosocial functioning in euthymic patients with bipolar disorder presenting psychotic symptoms. Mol Psychiatry 2024; 29:2050-2058. [PMID: 38374360 DOI: 10.1038/s41380-024-02480-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/28/2023] [Revised: 02/01/2024] [Accepted: 02/05/2024] [Indexed: 02/21/2024]
Abstract
The DDR1 locus is associated with the diagnosis of schizophrenia and with processing speed in patients with schizophrenia and first-episode psychosis. Here, we investigated whether DDR1 variants are associated with bipolar disorder (BD) features. First, we performed a case‒control association study comparing DDR1 variants between patients with BD and healthy controls. Second, we performed linear regression analyses to assess the associations of DDR1 variants with neurocognitive domains and psychosocial functioning. Third, we conducted a mediation analysis to explore whether neurocognitive impairment mediated the association between DDR1 variants and psychosocial functioning in patients with BD. Finally, we studied the association between DDR1 variants and white matter microstructure. We did not find any statistically significant associations in the case‒control association study; however, we found that the combined genotypes rs1264323AA-rs2267641AC/CC were associated with worse neurocognitive performance in patients with BD with psychotic symptoms. In addition, the combined genotypes rs1264323AA-rs2267641AC/CC were associated with worse psychosocial functioning through processing speed. We did not find correlations between white matter microstructure abnormalities and the neurocognitive domains associated with the combined genotypes rs1264323AA-rs2267641AC/CC. Overall, the results suggest that DDR1 may be a marker of worse neurocognitive performance and psychosocial functioning in patients with BD, specifically those with psychotic symptoms.
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Affiliation(s)
- Selena Aranda
- Institut d'Investigació Sanitària Pere Virgili-CERCA, Reus, Spain
- Hospital Universitari Institut Pere Mata, Reus, Spain
- Universitat Rovira i Virgili, Reus, Spain
- Centro de Investigación Biomédica en Red en Salud Mental (CIBERSAM)-Instituto de Salud Carlos III, Madrid, Spain
| | - Esther Jiménez
- Centro de Investigación Biomédica en Red en Salud Mental (CIBERSAM)-Instituto de Salud Carlos III, Madrid, Spain
- Bipolar and Depressive Disorders Unit, Hospital Clinic, Institute of Neurosciences, University of Barcelona, IDIBAPS, Barcelona, Spain
- Department of Psychiatry, University of the Basque Country (UPV-EHU), Vitoria-Gasteiz, Spain
| | - Erick J Canales-Rodríguez
- Centro de Investigación Biomédica en Red en Salud Mental (CIBERSAM)-Instituto de Salud Carlos III, Madrid, Spain
- FIDMAG Germanes Hospitalàries Research Foundation, Sant Boi de Llobregat, Barcelona, Spain
- Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Norma Verdolini
- Centro de Investigación Biomédica en Red en Salud Mental (CIBERSAM)-Instituto de Salud Carlos III, Madrid, Spain
- Bipolar and Depressive Disorders Unit, Hospital Clinic, Institute of Neurosciences, University of Barcelona, IDIBAPS, Barcelona, Spain
- FIDMAG Germanes Hospitalàries Research Foundation, Sant Boi de Llobregat, Barcelona, Spain
| | - Silvia Alonso
- Centro de Investigación Biomédica en Red en Salud Mental (CIBERSAM)-Instituto de Salud Carlos III, Madrid, Spain
- Bipolar and Depressive Disorders Unit, Hospital Clinic, Institute of Neurosciences, University of Barcelona, IDIBAPS, Barcelona, Spain
| | - Esteban Sepúlveda
- Institut d'Investigació Sanitària Pere Virgili-CERCA, Reus, Spain
- Hospital Universitari Institut Pere Mata, Reus, Spain
- Universitat Rovira i Virgili, Reus, Spain
- Centro de Investigación Biomédica en Red en Salud Mental (CIBERSAM)-Instituto de Salud Carlos III, Madrid, Spain
| | - Antonio Julià
- Rheumatology Research Group, Vall d'Hebron Research Institute, Barcelona, Spain
| | - Sara Marsal
- Rheumatology Research Group, Vall d'Hebron Research Institute, Barcelona, Spain
| | - Julio Bobes
- Centro de Investigación Biomédica en Red en Salud Mental (CIBERSAM)-Instituto de Salud Carlos III, Madrid, Spain
- Department of Psychiatry, Universidad de Oviedo, Oviedo, Spain
- nstituto de Investigación Sanitaria del Principado de Asturias (ISPA), Oviedo, Spain
- Instituto Universitario de Neurociencias del Principado de Asturias (INEUROPA), Oviedo, Spain
- Servicio de Salud del Principado de Asturias (SESPA) Oviedo, Oviedo, Spain
| | - Pilar A Sáiz
- Centro de Investigación Biomédica en Red en Salud Mental (CIBERSAM)-Instituto de Salud Carlos III, Madrid, Spain
- Department of Psychiatry, Universidad de Oviedo, Oviedo, Spain
- nstituto de Investigación Sanitaria del Principado de Asturias (ISPA), Oviedo, Spain
- Instituto Universitario de Neurociencias del Principado de Asturias (INEUROPA), Oviedo, Spain
- Servicio de Salud del Principado de Asturias (SESPA) Oviedo, Oviedo, Spain
| | - Paz García-Portilla
- Centro de Investigación Biomédica en Red en Salud Mental (CIBERSAM)-Instituto de Salud Carlos III, Madrid, Spain
- Department of Psychiatry, Universidad de Oviedo, Oviedo, Spain
- nstituto de Investigación Sanitaria del Principado de Asturias (ISPA), Oviedo, Spain
- Instituto Universitario de Neurociencias del Principado de Asturias (INEUROPA), Oviedo, Spain
- Servicio de Salud del Principado de Asturias (SESPA) Oviedo, Oviedo, Spain
| | - Jose M Menchón
- Centro de Investigación Biomédica en Red en Salud Mental (CIBERSAM)-Instituto de Salud Carlos III, Madrid, Spain
- Bellvitge Biomedical Research Institute-IDIBELL, Bellvitge University Hospital, Barcelona, Spain
- Department of Clinical Sciences, University of Barcelona, Barcelona, Spain
| | - José M Crespo
- Centro de Investigación Biomédica en Red en Salud Mental (CIBERSAM)-Instituto de Salud Carlos III, Madrid, Spain
- Bellvitge Biomedical Research Institute-IDIBELL, Bellvitge University Hospital, Barcelona, Spain
- Department of Clinical Sciences, University of Barcelona, Barcelona, Spain
| | - Ana González-Pinto
- Centro de Investigación Biomédica en Red en Salud Mental (CIBERSAM)-Instituto de Salud Carlos III, Madrid, Spain
- Department of Psychiatry, University of the Basque Country (UPV-EHU), Vitoria-Gasteiz, Spain
- Araba University Hospital, Bioaraba Research Institute, UPV/EHU, Vitoria-Gasteiz, Spain
| | - Víctor Pérez
- Centro de Investigación Biomédica en Red en Salud Mental (CIBERSAM)-Instituto de Salud Carlos III, Madrid, Spain
- Hospital de Mar. Mental Health Institute, Barcelona, Spain
- Neurosciences Research Unit, Hospital del Mar d'Investigacions Mèdiques, Barcelona, Spain
- Department of Experimental and Health Sciences, Pompeu Fabra University, Barcelona, Spain
| | - Celso Arango
- Centro de Investigación Biomédica en Red en Salud Mental (CIBERSAM)-Instituto de Salud Carlos III, Madrid, Spain
- Institute of Psychiatry and Mental Health, Madrid, Spain
- Hospital General Universitario Gregorio Marañón, Madrid, Spain
- Universidad Complutense, Madrid, Spain
| | - Pilar Sierra
- La Fe University and Polytechnic Hospital, Valencia, Spain
- Department of Psychiatry, School of Medicine, University of Valencia, Valencia, Spain
| | - Julio Sanjuán
- Centro de Investigación Biomédica en Red en Salud Mental (CIBERSAM)-Instituto de Salud Carlos III, Madrid, Spain
- Department of Psychiatry, School of Medicine, University of Valencia, Valencia, Spain
| | - Edith Pomarol-Clotet
- Centro de Investigación Biomédica en Red en Salud Mental (CIBERSAM)-Instituto de Salud Carlos III, Madrid, Spain
- FIDMAG Germanes Hospitalàries Research Foundation, Sant Boi de Llobregat, Barcelona, Spain
| | - Eduard Vieta
- Centro de Investigación Biomédica en Red en Salud Mental (CIBERSAM)-Instituto de Salud Carlos III, Madrid, Spain
- Bipolar and Depressive Disorders Unit, Hospital Clinic, Institute of Neurosciences, University of Barcelona, IDIBAPS, Barcelona, Spain
| | - Elisabet Vilella
- Institut d'Investigació Sanitària Pere Virgili-CERCA, Reus, Spain.
- Hospital Universitari Institut Pere Mata, Reus, Spain.
- Universitat Rovira i Virgili, Reus, Spain.
- Centro de Investigación Biomédica en Red en Salud Mental (CIBERSAM)-Instituto de Salud Carlos III, Madrid, Spain.
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Korbmacher M, van der Meer D, Beck D, Askeland-Gjerde DE, Eikefjord E, Lundervold A, Andreassen OA, Westlye LT, Maximov II. Distinct Longitudinal Brain White Matter Microstructure Changes and Associated Polygenic Risk of Common Psychiatric Disorders and Alzheimer's Disease in the UK Biobank. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2024; 4:100323. [PMID: 39132576 PMCID: PMC11313202 DOI: 10.1016/j.bpsgos.2024.100323] [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: 03/21/2024] [Revised: 03/24/2024] [Accepted: 04/16/2024] [Indexed: 08/13/2024] Open
Abstract
Background During the course of adulthood and aging, white matter (WM) structure and organization are characterized by slow degradation processes such as demyelination and shrinkage. An acceleration of such aging processes has been linked to the development of a range of diseases. Thus, an accurate description of healthy brain maturation, particularly in terms of WM features, is fundamental to the understanding of aging. Methods We used longitudinal diffusion magnetic resonance imaging to provide an overview of WM changes at different spatial and temporal scales in the UK Biobank (UKB) (n = 2678; agescan 1 = 62.38 ± 7.23 years; agescan 2 = 64.81 ± 7.1 years). To examine the genetic overlap between WM structure and common clinical conditions, we tested the associations between WM structure and polygenic risk scores for the most common neurodegenerative disorder, Alzheimer's disease, and common psychiatric disorders (unipolar and bipolar depression, anxiety, obsessive-compulsive disorder, autism, schizophrenia, attention-deficit/hyperactivity disorder) in longitudinal (n = 2329) and cross-sectional (n = 31,056) UKB validation data. Results Our findings indicate spatially distributed WM changes across the brain, as well as distributed associations of polygenic risk scores with WM. Importantly, brain longitudinal changes reflected genetic risk for disorder development better than the utilized cross-sectional measures, with regional differences giving more specific insights into gene-brain change associations than global averages. Conclusions We extend recent findings by providing a detailed overview of WM microstructure degeneration on different spatial levels, helping to understand fundamental brain aging processes. Further longitudinal research is warranted to examine aging-related gene-brain associations.
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Affiliation(s)
- Max Korbmacher
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- Mohn Medical Imaging and Visualization Centre, Bergen, Norway
| | - Dennis van der Meer
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, The Netherlands
| | - Dani Beck
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Daniel E. Askeland-Gjerde
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
| | - Eli Eikefjord
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
- Mohn Medical Imaging and Visualization Centre, Bergen, Norway
| | - Arvid Lundervold
- Mohn Medical Imaging and Visualization Centre, Bergen, Norway
- Department of Biomedicine, University of Bergen, Bergen, Norway
| | - Ole A. Andreassen
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Lars T. Westlye
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Ivan I. Maximov
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
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31
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Takemura H, Kruper JA, Miyata T, Rokem A. Tractometry of Human Visual White Matter Pathways in Health and Disease. Magn Reson Med Sci 2024; 23:316-340. [PMID: 38866532 PMCID: PMC11234945 DOI: 10.2463/mrms.rev.2024-0007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 06/14/2024] Open
Abstract
Diffusion-weighted MRI (dMRI) provides a unique non-invasive view of human brain tissue properties. The present review article focuses on tractometry analysis methods that use dMRI to assess the properties of brain tissue within the long-range connections comprising brain networks. We focus specifically on the major white matter tracts that convey visual information. These connections are particularly important because vision provides rich information from the environment that supports a large range of daily life activities. Many of the diseases of the visual system are associated with advanced aging, and tractometry of the visual system is particularly important in the modern aging society. We provide an overview of the tractometry analysis pipeline, which includes a primer on dMRI data acquisition, voxelwise model fitting, tractography, recognition of white matter tracts, and calculation of tract tissue property profiles. We then review dMRI-based methods for analyzing visual white matter tracts: the optic nerve, optic tract, optic radiation, forceps major, and vertical occipital fasciculus. For each tract, we review background anatomical knowledge together with recent findings in tractometry studies on these tracts and their properties in relation to visual function and disease. Overall, we find that measurements of the brain's visual white matter are sensitive to a range of disorders and correlate with perceptual abilities. We highlight new and promising analysis methods, as well as some of the current barriers to progress toward integration of these methods into clinical practice. These barriers, such as variability in measurements between protocols and instruments, are targets for future development.
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Affiliation(s)
- Hiromasa Takemura
- Division of Sensory and Cognitive Brain Mapping, Department of System Neuroscience, National Institute for Physiological Sciences, Okazaki, Aichi, Japan
- Graduate Institute for Advanced Studies, SOKENDAI, Hayama, Kanagawa, Japan
- Center for Information and Neural Networks (CiNet), Advanced ICT Research Institute, National Institute of Information and Communications Technology, Suita, Osaka, Japan
| | - John A Kruper
- Department of Psychology and eScience Institute, University of Washington, Seattle, WA, USA
| | - Toshikazu Miyata
- Division of Sensory and Cognitive Brain Mapping, Department of System Neuroscience, National Institute for Physiological Sciences, Okazaki, Aichi, Japan
- Center for Information and Neural Networks (CiNet), Advanced ICT Research Institute, National Institute of Information and Communications Technology, Suita, Osaka, Japan
| | - Ariel Rokem
- Department of Psychology and eScience Institute, University of Washington, Seattle, WA, USA
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Vivó F, Solana E, Calvi A, Lopez‐Soley E, Reid LB, Pascual‐Diaz S, Garrido C, Planas‐Tardido L, Cabrera‐Maqueda JM, Alba‐Arbalat S, Sepulveda M, Blanco Y, Kanber B, Prados F, Saiz A, Llufriu S, Martinez‐Heras E. Microscopic fractional anisotropy outperforms multiple sclerosis lesion assessment and clinical outcome associations over standard fractional anisotropy tensor. Hum Brain Mapp 2024; 45:e26706. [PMID: 38867646 PMCID: PMC11170024 DOI: 10.1002/hbm.26706] [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: 12/11/2023] [Revised: 04/19/2024] [Accepted: 04/25/2024] [Indexed: 06/14/2024] Open
Abstract
We aimed to compare the ability of diffusion tensor imaging and multi-compartment spherical mean technique to detect focal tissue damage and in distinguishing between different connectivity patterns associated with varying clinical outcomes in multiple sclerosis (MS). Seventy-six people diagnosed with MS were scanned using a SIEMENS Prisma Fit 3T magnetic resonance imaging (MRI), employing both conventional (T1w and fluid-attenuated inversion recovery) and advanced diffusion MRI sequences from which fractional anisotropy (FA) and microscopic FA (μFA) maps were generated. Using automated fiber quantification (AFQ), we assessed diffusion profiles across multiple white matter (WM) pathways to measure the sensitivity of anisotropy diffusion metrics in detecting localized tissue damage. In parallel, we analyzed structural brain connectivity in a specific patient cohort to fully grasp its relationships with cognitive and physical clinical outcomes. This evaluation comprehensively considered different patient categories, including cognitively preserved (CP), mild cognitive deficits (MCD), and cognitively impaired (CI) for cognitive assessment, as well as groups distinguished by physical impact: those with mild disability (Expanded Disability Status Scale [EDSS] <=3) and those with moderate-severe disability (EDSS >3). In our initial objective, we employed Ridge regression to forecast the presence of focal MS lesions, comparing the performance of μFA and FA. μFA exhibited a stronger association with tissue damage and a higher predictive precision for focal MS lesions across the tracts, achieving an R-squared value of .57, significantly outperforming the R-squared value of .24 for FA (p-value <.001). In structural connectivity, μFA exhibited more pronounced differences than FA in response to alteration in both cognitive and physical clinical scores in terms of effect size and number of connections. Regarding cognitive groups, FA differences between CP and MCD groups were limited to 0.5% of connections, mainly around the thalamus, while μFA revealed changes in 2.5% of connections. In the CP and CI group comparison, which have noticeable cognitive differences, the disparity was 5.6% for FA values and 32.5% for μFA. Similarly, μFA outperformed FA in detecting WM changes between the MCD and CI groups, with 5% versus 0.3% of connections, respectively. When analyzing structural connectivity between physical disability groups, μFA still demonstrated superior performance over FA, disclosing a 2.1% difference in connectivity between regions closely associated with physical disability in MS. In contrast, FA spotted a few regions, comprising only 0.6% of total connections. In summary, μFA emerged as a more effective tool than FA in predicting MS lesions and identifying structural changes across patients with different degrees of cognitive and global disability, offering deeper insights into the complexities of MS-related impairments.
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Affiliation(s)
- F. Vivó
- Neuroimmunology and Multiple Sclerosis Unit Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clinic BarcelonaFundació de Recerca Clínic Barcelona‐Institut d'Investigacions Biomèdiques August Pi i Sunyer and Universitat de BarcelonaBarcelonaSpain
| | - E. Solana
- Neuroimmunology and Multiple Sclerosis Unit Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clinic BarcelonaFundació de Recerca Clínic Barcelona‐Institut d'Investigacions Biomèdiques August Pi i Sunyer and Universitat de BarcelonaBarcelonaSpain
| | - A. Calvi
- Neuroimmunology and Multiple Sclerosis Unit Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clinic BarcelonaFundació de Recerca Clínic Barcelona‐Institut d'Investigacions Biomèdiques August Pi i Sunyer and Universitat de BarcelonaBarcelonaSpain
| | - E. Lopez‐Soley
- Neuroimmunology and Multiple Sclerosis Unit Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clinic BarcelonaFundació de Recerca Clínic Barcelona‐Institut d'Investigacions Biomèdiques August Pi i Sunyer and Universitat de BarcelonaBarcelonaSpain
| | - L. B. Reid
- Department of PsychiatryUniversity of California San FranciscoSan FranciscoCaliforniaUSA
- Department of RadiologyUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - S. Pascual‐Diaz
- Institute of Neurosciences, Department of Medicine, School of Medicine and Health SciencesUniversity of BarcelonaBarcelonaSpain
| | - C. Garrido
- Magnetic Resonance Imaging Core FacilityInstitut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS)BarcelonaSpain
| | - L. Planas‐Tardido
- Neuroimmunology and Multiple Sclerosis Unit Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clinic BarcelonaFundació de Recerca Clínic Barcelona‐Institut d'Investigacions Biomèdiques August Pi i Sunyer and Universitat de BarcelonaBarcelonaSpain
| | - J. M. Cabrera‐Maqueda
- Neuroimmunology and Multiple Sclerosis Unit Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clinic BarcelonaFundació de Recerca Clínic Barcelona‐Institut d'Investigacions Biomèdiques August Pi i Sunyer and Universitat de BarcelonaBarcelonaSpain
| | - S. Alba‐Arbalat
- Neuroimmunology and Multiple Sclerosis Unit Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clinic BarcelonaFundació de Recerca Clínic Barcelona‐Institut d'Investigacions Biomèdiques August Pi i Sunyer and Universitat de BarcelonaBarcelonaSpain
| | - M. Sepulveda
- Neuroimmunology and Multiple Sclerosis Unit Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clinic BarcelonaFundació de Recerca Clínic Barcelona‐Institut d'Investigacions Biomèdiques August Pi i Sunyer and Universitat de BarcelonaBarcelonaSpain
| | - Y. Blanco
- Neuroimmunology and Multiple Sclerosis Unit Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clinic BarcelonaFundació de Recerca Clínic Barcelona‐Institut d'Investigacions Biomèdiques August Pi i Sunyer and Universitat de BarcelonaBarcelonaSpain
| | - B. Kanber
- Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain ScienceUniversity College of LondonLondonUK
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and BioengineeringUniversity College LondonLondonUK
| | - F. Prados
- Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain ScienceUniversity College of LondonLondonUK
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and BioengineeringUniversity College LondonLondonUK
- E‐Health CenterUniversitat Oberta de CatalunyaBarcelonaSpain
| | - A. Saiz
- Neuroimmunology and Multiple Sclerosis Unit Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clinic BarcelonaFundació de Recerca Clínic Barcelona‐Institut d'Investigacions Biomèdiques August Pi i Sunyer and Universitat de BarcelonaBarcelonaSpain
| | - S. Llufriu
- Neuroimmunology and Multiple Sclerosis Unit Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clinic BarcelonaFundació de Recerca Clínic Barcelona‐Institut d'Investigacions Biomèdiques August Pi i Sunyer and Universitat de BarcelonaBarcelonaSpain
| | - E. Martinez‐Heras
- Neuroimmunology and Multiple Sclerosis Unit Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clinic BarcelonaFundació de Recerca Clínic Barcelona‐Institut d'Investigacions Biomèdiques August Pi i Sunyer and Universitat de BarcelonaBarcelonaSpain
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Canales-Rodríguez EJ, Pizzolato M, Zhou FL, Barakovic M, Thiran JP, Jones DK, Parker GJM, Dyrby TB. Pore size estimation in axon-mimicking microfibers with diffusion-relaxation MRI. Magn Reson Med 2024; 91:2579-2596. [PMID: 38192108 PMCID: PMC7617479 DOI: 10.1002/mrm.29991] [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: 10/17/2023] [Revised: 12/04/2023] [Accepted: 12/12/2023] [Indexed: 01/10/2024]
Abstract
PURPOSE This study aims to evaluate two distinct approaches for fiber radius estimation using diffusion-relaxation MRI data acquired in biomimetic microfiber phantoms that mimic hollow axons. The methods considered are the spherical mean power-law approach and a T2-based pore size estimation technique. THEORY AND METHODS A general diffusion-relaxation theoretical model for the spherical mean signal from water molecules within a distribution of cylinders with varying radii was introduced, encompassing the evaluated models as particular cases. Additionally, a new numerical approach was presented for estimating effective radii (i.e., MRI-visible mean radii) from the ground truth radii distributions, not reliant on previous theoretical approximations and adaptable to various acquisition sequences. The ground truth radii were obtained from scanning electron microscope images. RESULTS Both methods show a linear relationship between effective radii estimated from MRI data and ground-truth radii distributions, although some discrepancies were observed. The spherical mean power-law method overestimated fiber radii. Conversely, the T2-based method exhibited higher sensitivity to smaller fiber radii, but faced limitations in accurately estimating the radius in one particular phantom, possibly because of material-specific relaxation changes. CONCLUSION The study demonstrates the feasibility of both techniques to predict pore sizes of hollow microfibers. The T2-based technique, unlike the spherical mean power-law method, does not demand ultra-high diffusion gradients, but requires calibration with known radius distributions. This research contributes to the ongoing development and evaluation of neuroimaging techniques for fiber radius estimation, highlights the advantages and limitations of both methods, and provides datasets for reproducible research.
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Affiliation(s)
- Erick J Canales-Rodríguez
- Signal Processing Laboratory 5 (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Danish Research Centre for Magnetic Resonance (DRCMR), Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Copenhagen, Denmark
| | - Marco Pizzolato
- Danish Research Centre for Magnetic Resonance (DRCMR), Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Copenhagen, Denmark
- Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU), Kongens Lyngby, Denmark
| | - Feng-Lei Zhou
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London (UCL), London, UK
- MicroPhantoms Limited, Cambridge, UK
| | - Muhamed Barakovic
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Jean-Philippe Thiran
- Signal Processing Laboratory 5 (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland
- Centre d'Imagerie Biomédicale (CIBM), EPFL, Lausanne, Switzerland
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, UK
| | - Geoffrey J M Parker
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London (UCL), London, UK
- Department of Neuroinflammation, Queen Square Institute of Neurology, University College London (UCL), London, UK
- Bioxydyn Limited, Manchester, UK
| | - Tim B Dyrby
- Danish Research Centre for Magnetic Resonance (DRCMR), Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Copenhagen, Denmark
- Department of Applied Mathematics and Computer Science, Technical University of Denmark (DTU), Kongens Lyngby, Denmark
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Hong S, Choi Y, Lee MB, Rhee HY, Park S, Ryu CW, Cho AR, Kwon OI, Jahng GH. Increased extra-neurite conductivity of brain in patients with Alzheimer's disease: A pilot study. Psychiatry Res Neuroimaging 2024; 340:111807. [PMID: 38520873 DOI: 10.1016/j.pscychresns.2024.111807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 01/31/2024] [Accepted: 03/03/2024] [Indexed: 03/25/2024]
Abstract
The objectives of this study were to investigate how the extra-neurite conductivity (EC) and intra-neurite conductivity (IC) were reflected in Alzheimer's disease (AD) patients compared with old cognitively normal (CN) people and patients with amnestic mild cognitive impairment (MCI) and to evaluate the association between those conductivity values and cognitive decline. To do this, high-frequency conductivity (HFC) at the Larmor frequency was obtained using MRI-based electrical property tomography (MREPT) and was decomposed into EC and IC using information of multi-shell multi-gradient direction diffusion tensor images. This prospective single-center study included 20 patients with mild or moderate AD, 25 patients with amnestic MCI, and 21 old CN participants. After decomposing EC and IC from HFC for all participants, we performed voxel-based and regions-of-interest analyses to compare conductivity between the three participant groups and to evaluate the association with either age or the Mini-Mental State Examination (MMSE) scores. We found increased EC in AD compared to CN and MCI. EC was significantly negatively associated with MMSE scores in the insula, and middle temporal gyrus. EC might be used as an imaging biomarker for helping to monitor cognitive function.
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Affiliation(s)
- Seowon Hong
- Department of Radiology, Kyung Hee University Hospital at Gangdong, 892 Dongnam-ro, Gangdong-Gu, Seoul 05278, Republic of Korea
| | - Yunjeong Choi
- Department of Biomedical Engineering, Undergraduate School, College of Electronics and Information, Kyung Hee University, 1732 Deogyeong-daero, Giheung-gu, Yongin-si, Gyeonggi-do 17104, Republic of Korea
| | - Mun Bae Lee
- Department of Mathematics, College of Basic Science, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Republic of Korea
| | - Hak Young Rhee
- Department of Neurology, Kyung Hee University Hospital at Gangdong, 892 Dongnam-ro, Gangdong-Gu, Seoul 05278, Republic of Korea; Department of Medicine, Kyung Hee University College of Medicine, 26 Kyung Hee Dae-ro, Dongdaemun-gu, Seoul 02447, Republic of Korea
| | - Soonchan Park
- Department of Radiology, Kyung Hee University Hospital at Gangdong, 892 Dongnam-ro, Gangdong-Gu, Seoul 05278, Republic of Korea; Department of Medicine, Kyung Hee University College of Medicine, 26 Kyung Hee Dae-ro, Dongdaemun-gu, Seoul 02447, Republic of Korea
| | - Chang-Woo Ryu
- Department of Radiology, Kyung Hee University Hospital at Gangdong, 892 Dongnam-ro, Gangdong-Gu, Seoul 05278, Republic of Korea; Department of Medicine, Kyung Hee University College of Medicine, 26 Kyung Hee Dae-ro, Dongdaemun-gu, Seoul 02447, Republic of Korea
| | - Ah Rang Cho
- Department of Medicine, Kyung Hee University College of Medicine, 26 Kyung Hee Dae-ro, Dongdaemun-gu, Seoul 02447, Republic of Korea; Department of Psychiatry, Kyung Hee University Hospital at Gangdong, 892 Dongnam-ro, Gangdong-Gu, Seoul 05278, Republic of Korea
| | - Oh In Kwon
- Department of Mathematics, College of Basic Science, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul 05029, Republic of Korea.
| | - Geon-Ho Jahng
- Department of Radiology, Kyung Hee University Hospital at Gangdong, 892 Dongnam-ro, Gangdong-Gu, Seoul 05278, Republic of Korea; Department of Medicine, Kyung Hee University College of Medicine, 26 Kyung Hee Dae-ro, Dongdaemun-gu, Seoul 02447, Republic of Korea.
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35
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Fuchs C, Dessain Q, Delinte N, Dausort M, Macq B. Sparse Blind Spherical Deconvolution of diffusion weighted MRI. Front Neurosci 2024; 18:1385975. [PMID: 38846718 PMCID: PMC11155299 DOI: 10.3389/fnins.2024.1385975] [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: 02/14/2024] [Accepted: 04/19/2024] [Indexed: 06/09/2024] Open
Abstract
Diffusion-weighted magnetic resonance imaging provides invaluable insights into in-vivo neurological pathways. However, accurate and robust characterization of white matter fibers microstructure remains challenging. Widely used spherical deconvolution algorithms retrieve the fiber Orientation Distribution Function (ODF) by using an estimation of a response function, i.e., the signal arising from individual fascicles within a voxel. In this paper, an algorithm of blind spherical deconvolution is proposed, which only assumes the axial symmetry of the response function instead of its exact knowledge. This algorithm provides a method for estimating the peaks of the ODF in a voxel without any explicit response function, as well as a method for estimating signals associated with the peaks of the ODF, regardless of how those peaks were obtained. The two stages of the algorithm are tested on Monte Carlo simulations, as well as compared to state-of-the-art methods on real in-vivo data for the orientation retrieval task. Although the proposed algorithm was shown to attain lower angular errors than the state-of-the-art constrained spherical deconvolution algorithm on synthetic data, it was outperformed by state-of-the-art spherical deconvolution algorithms on in-vivo data. In conjunction with state-of-the art methods for axon bundles direction estimation, the proposed method showed its potential for the derivation of per-voxel per-direction metrics on synthetic as well as in-vivo data.
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Affiliation(s)
- Clément Fuchs
- Institute of Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), UCLouvain, Louvain-la-Neuve, Belgium
| | - Quentin Dessain
- Institute of Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), UCLouvain, Louvain-la-Neuve, Belgium
- Institute of NeuroScience, UCLouvain, Brussels, Belgium
| | - Nicolas Delinte
- Institute of Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), UCLouvain, Louvain-la-Neuve, Belgium
- Institute of NeuroScience, UCLouvain, Brussels, Belgium
| | - Manon Dausort
- Institute of Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), UCLouvain, Louvain-la-Neuve, Belgium
| | - Benoît Macq
- Institute of Information and Communication Technologies, Electronics and Applied Mathematics (ICTEAM), UCLouvain, Louvain-la-Neuve, Belgium
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Beck D, de Lange AG, Gurholt TP, Voldsbekk I, Maximov II, Subramaniapillai S, Schindler L, Hindley G, Leonardsen EH, Rahman Z, van der Meer D, Korbmacher M, Linge J, Leinhard OD, Kalleberg KT, Engvig A, Sønderby I, Andreassen OA, Westlye LT. Dissecting unique and common variance across body and brain health indicators using age prediction. Hum Brain Mapp 2024; 45:e26685. [PMID: 38647042 PMCID: PMC11034003 DOI: 10.1002/hbm.26685] [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: 12/29/2023] [Revised: 03/21/2024] [Accepted: 04/04/2024] [Indexed: 04/25/2024] Open
Abstract
Ageing is a heterogeneous multisystem process involving different rates of decline in physiological integrity across biological systems. The current study dissects the unique and common variance across body and brain health indicators and parses inter-individual heterogeneity in the multisystem ageing process. Using machine-learning regression models on the UK Biobank data set (N = 32,593, age range 44.6-82.3, mean age 64.1 years), we first estimated tissue-specific brain age for white and gray matter based on diffusion and T1-weighted magnetic resonance imaging (MRI) data, respectively. Next, bodily health traits, including cardiometabolic, anthropometric, and body composition measures of adipose and muscle tissue from bioimpedance and body MRI, were combined to predict 'body age'. The results showed that the body age model demonstrated comparable age prediction accuracy to models trained solely on brain MRI data. The correlation between body age and brain age predictions was 0.62 for the T1 and 0.64 for the diffusion-based model, indicating a degree of unique variance in brain and bodily ageing processes. Bayesian multilevel modelling carried out to quantify the associations between health traits and predicted age discrepancies showed that higher systolic blood pressure and higher muscle-fat infiltration were related to older-appearing body age compared to brain age. Conversely, higher hand-grip strength and muscle volume were related to a younger-appearing body age. Our findings corroborate the common notion of a close connection between somatic and brain health. However, they also suggest that health traits may differentially influence age predictions beyond what is captured by the brain imaging data, potentially contributing to heterogeneous ageing rates across biological systems and individuals.
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Affiliation(s)
- Dani Beck
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
- Department of Mental Health and Substance AbuseDiakonhjemmet HospitalOsloNorway
- Department of PsychologyUniversity of OsloOsloNorway
| | - Ann‐Marie G. de Lange
- Department of PsychologyUniversity of OsloOsloNorway
- LREN, Centre for Research in Neurosciences, Department of Clinical NeurosciencesCHUV and University of LausanneLausanneSwitzerland
- Department of PsychiatryUniversity of OxfordOxfordUK
| | - Tiril P. Gurholt
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
| | - Irene Voldsbekk
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
- Department of PsychologyUniversity of OsloOsloNorway
| | - Ivan I. Maximov
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
- Department of Health and FunctioningWestern Norway University of Applied SciencesBergenNorway
| | - Sivaniya Subramaniapillai
- Department of PsychologyUniversity of OsloOsloNorway
- LREN, Centre for Research in Neurosciences, Department of Clinical NeurosciencesCHUV and University of LausanneLausanneSwitzerland
| | - Louise Schindler
- Department of PsychologyUniversity of OsloOsloNorway
- LREN, Centre for Research in Neurosciences, Department of Clinical NeurosciencesCHUV and University of LausanneLausanneSwitzerland
| | - Guy Hindley
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
| | - Esten H. Leonardsen
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
- Department of PsychologyUniversity of OsloOsloNorway
| | - Zillur Rahman
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
| | - Dennis van der Meer
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
- School of Mental Health and Neuroscience, Faculty of Health, Medicine and Life SciencesMaastricht UniversityMaastrichtThe Netherlands
| | - Max Korbmacher
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
- Department of Health and FunctioningWestern Norway University of Applied SciencesBergenNorway
| | - Jennifer Linge
- AMRA Medical ABLinköpingSweden
- Division of Diagnostics and Specialist Medicine, Department of Health, Medicine and Caring SciencesLinköping UniversityLinköpingSweden
| | - Olof D. Leinhard
- AMRA Medical ABLinköpingSweden
- Division of Diagnostics and Specialist Medicine, Department of Health, Medicine and Caring SciencesLinköping UniversityLinköpingSweden
| | | | - Andreas Engvig
- Department of Endocrinology, Obesity and Preventive Medicine, Section of Preventive CardiologyOslo University HospitalOsloNorway
| | - Ida Sønderby
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
- Department of Medical GeneticsOslo University HospitalOsloNorway
- KG Jebsen Centre for Neurodevelopmental DisordersUniversity of Oslo
| | - Ole A. Andreassen
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
- KG Jebsen Centre for Neurodevelopmental DisordersUniversity of Oslo
| | - Lars T. Westlye
- NORMENT, Division of Mental Health and AddictionOslo University Hospital & Institute of Clinical Medicine, University of OsloOsloNorway
- Department of PsychologyUniversity of OsloOsloNorway
- KG Jebsen Centre for Neurodevelopmental DisordersUniversity of Oslo
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Lee HH, Tian Q, Sheft M, Coronado-Leija R, Ramos-Llorden G, Abdollahzadeh A, Fieremans E, Novikov DS, Huang SY. The effects of axonal beading and undulation on axonal diameter estimation from diffusion MRI: Insights from simulations in human axons segmented from three-dimensional electron microscopy. NMR IN BIOMEDICINE 2024; 37:e5087. [PMID: 38168082 PMCID: PMC10942763 DOI: 10.1002/nbm.5087] [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: 08/16/2023] [Revised: 11/19/2023] [Accepted: 11/21/2023] [Indexed: 01/05/2024]
Abstract
The increasing availability of high-performance gradient systems in human MRI scanners has generated great interest in diffusion microstructural imaging applications such as axonal diameter mapping. Practically, sensitivity to axon diameter in diffusion MRI is attained at strong diffusion weightings b , where the deviation from the expected 1 / b scaling in white matter yields a finite transverse diffusivity, which is then translated into an axon diameter estimate. While axons are usually modeled as perfectly straight, impermeable cylinders, local variations in diameter (caliber variation or beading) and direction (undulation) are known to influence axonal diameter estimates and have been observed in microscopy data of human axons. In this study, we performed Monte Carlo simulations of diffusion in axons reconstructed from three-dimensional electron microscopy of a human temporal lobe specimen using simulated sequence parameters matched to the maximal gradient strength of the next-generation Connectome 2.0 human MRI scanner ( ≲ 500 mT/m). We show that axon diameter estimation is accurate for nonbeaded, nonundulating fibers; however, in fibers with caliber variations and undulations, the axon diameter is heavily underestimated due to caliber variations, and this effect overshadows the known overestimation of the axon diameter due to undulations. This unexpected underestimation may originate from variations in the coarse-grained axial diffusivity due to caliber variations. Given that increased axonal beading and undulations have been observed in pathological tissues, such as traumatic brain injury and ischemia, the interpretation of axon diameter alterations in pathology may be significantly confounded.
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Affiliation(s)
- Hong-Hsi Lee
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Maxina Sheft
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Harvard–MIT Health Sciences and Technology, Cambridge, Massachusetts, USA
| | - Ricardo Coronado-Leija
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, New York, USA
| | - Gabriel Ramos-Llorden
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
| | - Ali Abdollahzadeh
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, New York, USA
| | - Els Fieremans
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, New York, USA
| | - Dmitry S. Novikov
- Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, New York, USA
- Center for Advanced Imaging Innovation and Research (CAI2R), New York University School of Medicine, New York, New York, USA
| | - Susie Y. Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, USA
- Harvard Medical School, Boston, Massachusetts, USA
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38
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Lyu W, Wu Y, Huynh KM, Ahmad S, Yap PT. A multimodal submillimeter MRI atlas of the human cerebellum. Sci Rep 2024; 14:5622. [PMID: 38453991 PMCID: PMC10920891 DOI: 10.1038/s41598-024-55412-y] [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: 11/17/2023] [Accepted: 02/23/2024] [Indexed: 03/09/2024] Open
Abstract
The human cerebellum is engaged in a broad array of tasks related to motor coordination, cognition, language, attention, memory, and emotional regulation. A detailed cerebellar atlas can facilitate the investigation of the structural and functional organization of the cerebellum. However, existing cerebellar atlases are typically limited to a single imaging modality with insufficient characterization of tissue properties. Here, we introduce a multifaceted cerebellar atlas based on high-resolution multimodal MRI, facilitating the understanding of the neurodevelopment and neurodegeneration of the cerebellum based on cortical morphology, tissue microstructure, and intra-cerebellar and cerebello-cerebral connectivity.
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Affiliation(s)
- Wenjiao Lyu
- Department of Radiology, University of North Carolina, Chapel Hill, NC, USA
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA
| | - Ye Wu
- Department of Radiology, University of North Carolina, Chapel Hill, NC, USA
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA
| | - Khoi Minh Huynh
- Department of Radiology, University of North Carolina, Chapel Hill, NC, USA
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA
| | - Sahar Ahmad
- Department of Radiology, University of North Carolina, Chapel Hill, NC, USA
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA
| | - Pew-Thian Yap
- Department of Radiology, University of North Carolina, Chapel Hill, NC, USA.
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA.
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Coelho S, Liao Y, Szczepankiewicz F, Veraart J, Chung S, Lui YW, Novikov DS, Fieremans E. Assessment of Precision and Accuracy of Brain White Matter Microstructure using Combined Diffusion MRI and Relaxometry. ARXIV 2024:arXiv:2402.17175v1. [PMID: 38463511 PMCID: PMC10925389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Joint modeling of diffusion and relaxation has seen growing interest due to its potential to provide complementary information about tissue microstructure. For brain white matter, we designed an optimal diffusion-relaxometry MRI protocol that samples multiple b-values, B-tensor shapes, and echo times (TE). This variable-TE protocol (27 min) has as subsets a fixed-TE protocol (15 min) and a 2-shell dMRI protocol (7 min), both characterizing diffusion only. We assessed the sensitivity, specificity and reproducibility of these protocols with synthetic experiments and in six healthy volunteers. Compared with the fixed-TE protocol, the variable-TE protocol enables estimation of free water fractions while also capturing compartmental T 2 relaxation times. Jointly measuring diffusion and relaxation offers increased sensitivity and specificity to microstructure parameters in brain white matter with voxelwise coefficients of variation below 10%.
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Affiliation(s)
- Santiago Coelho
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
| | - Ying Liao
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
| | | | - Jelle Veraart
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
| | - Sohae Chung
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
| | - Yvonne W Lui
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
| | - Dmitry S Novikov
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
| | - Els Fieremans
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
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40
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Tazza F, Schiavi S, Leveraro E, Cellerino M, Boffa G, Ballerini S, Dighero M, Uccelli A, Sbragia E, Aluan K, Inglese M, Lapucci C. Clinical and radiological correlates of apathy in multiple sclerosis. Mult Scler 2024; 30:247-256. [PMID: 38095151 DOI: 10.1177/13524585231217918] [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] [Indexed: 12/21/2023]
Abstract
BACKGROUND Although apathy has been associated with fronto-striatal dysfunction in several neurological disorders, its clinical and magnetic resonance imaging (MRI) correlates have been poorly investigated in people with multiple sclerosis (PwMS). OBJECTIVES To evaluate clinical variables and investigate microstructural integrity of fronto-striatal grey matter (GM) and white matter (WM) structures using diffusion tensor imaging (DTI). METHODS A total of 123 PwMS (age: 40.25 ± 11.5; female: 60.9%; relapsing-remitting multiple sclerosis: 75.6%) were prospectively enrolled and underwent neurological and neuropsychological evaluation, including Expanded Disability Status Scale (EDSS), Apathy Evaluation Scale (AES-S), Hospital Anxiety and Depression Scale (HADS), Modified Fatigue Impact Scale (MFIS) and brain 3T-MRI volumes of whole brain, frontal/prefrontal cortex (PFC) and subcortical regions were calculated. DTI-derived metrics were evaluated in the same GM regions and in connecting WM tracts. RESULTS Apathetic PwMS (32.5%) showed lower education levels, higher HADS, MFIS scores and WM lesions volume than nonapathetic PwMS. Significant differences in DTI metrics were found in middle frontal, anterior cingulate and superior frontal PFC subregions and in caudate nuclei. Significant alterations were found in the right cingulum and left striatal-frontorbital tracts. CONCLUSIONS Apathy in PwMS is associated with higher levels of physical disability, depression, anxiety and fatigue together with lower educational backgrounds. Microstructural damage within frontal cortex, caudate and fronto-striatal WM bundles is a significant pathological substrate of apathy in multiple sclerosis (MS).
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Affiliation(s)
- Francesco Tazza
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Simona Schiavi
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Elisa Leveraro
- Department of Neurology, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Maria Cellerino
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Giacomo Boffa
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Stefania Ballerini
- Department of Neuroradiology, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Mara Dighero
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Antonio Uccelli
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
- Department of Neurology, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Elvira Sbragia
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Kenda Aluan
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Matilde Inglese
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy
- IRCCS Ospedale Policlinico San Martino, Genoa, Italy
- Department of Neurology, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
| | - Caterina Lapucci
- Department of Neurology, IRCCS Ospedale Policlinico San Martino, Genoa, Italy
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Korbmacher M, van der Meer D, Beck D, de Lange AMG, Eikefjord E, Lundervold A, Andreassen OA, Westlye LT, Maximov II. Brain asymmetries from mid- to late life and hemispheric brain age. Nat Commun 2024; 15:956. [PMID: 38302499 PMCID: PMC10834516 DOI: 10.1038/s41467-024-45282-3] [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: 08/22/2023] [Accepted: 01/19/2024] [Indexed: 02/03/2024] Open
Abstract
The human brain demonstrates structural and functional asymmetries which have implications for ageing and mental and neurological disease development. We used a set of magnetic resonance imaging (MRI) metrics derived from structural and diffusion MRI data in N=48,040 UK Biobank participants to evaluate age-related differences in brain asymmetry. Most regional grey and white matter metrics presented asymmetry, which were higher later in life. Informed by these results, we conducted hemispheric brain age (HBA) predictions from left/right multimodal MRI metrics. HBA was concordant to conventional brain age predictions, using metrics from both hemispheres, but offers a supplemental general marker of brain asymmetry when setting left/right HBA into relationship with each other. In contrast to WM brain asymmetries, left/right discrepancies in HBA are lower at higher ages. Our findings outline various sex-specific differences, particularly important for brain age estimates, and the value of further investigating the role of brain asymmetries in brain ageing and disease development.
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Affiliation(s)
- Max Korbmacher
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway.
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway.
- Mohn Medical Imaging and Visualization Centre (MMIV), Bergen, Norway.
| | - Dennis van der Meer
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- Faculty of Health, Medicine and Life Sciences, Maastricht University, Maastricht, Netherlands
| | - Dani Beck
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
| | - Ann-Marie G de Lange
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- Department of Clinical Neurosciences, Lausanne University Hospital (CHUV) and University of Lausanne, Lausanne, Switzerland
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Eli Eikefjord
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
- Mohn Medical Imaging and Visualization Centre (MMIV), Bergen, Norway
| | - Arvid Lundervold
- Mohn Medical Imaging and Visualization Centre (MMIV), Bergen, Norway
- Department of Biomedicine, University of Bergen, Bergen, Norway
| | - Ole A Andreassen
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Lars T Westlye
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway
- Department of Psychology, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| | - Ivan I Maximov
- Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway.
- NORMENT Centre for Psychosis Research, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway.
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Montejo L, Sole B, Fortea L, Jimenez E, Martinez-Aran A, Martinez-Heras E, Sanchez-Moreno J, Ortuño M, Pariente J, Solanes A, Torrent C, Vilajosana E, De Prisco M, Vieta E, Radua J. Study protocol - elucidating the neural correlates of functional remediation for older adults with bipolar disorder. Front Psychiatry 2024; 14:1302255. [PMID: 38298927 PMCID: PMC10827946 DOI: 10.3389/fpsyt.2023.1302255] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Accepted: 12/22/2023] [Indexed: 02/02/2024] Open
Abstract
Introduction Beyond mood abnormalities, bipolar disorder (BD) includes cognitive impairments that worsen psychosocial functioning and quality of life. These deficits are especially severe in older adults with BD (OABD), a condition expected to represent most individuals with BD in the upcoming years. Restoring the psychosocial functioning of this population will thus soon represent a public health priority. To help tackle the problem, the Bipolar and Depressive Disorders Unit at the Hospital Clínic of Barcelona has recently adapted its Functional Remediation (FR) program to that population, calling it FROA-BD. However, while scarce previous studies localize the neural mechanisms of cognitive remediation interventions in the dorsal prefrontal cortex, the specific mechanisms are seldom unknown. In the present project, we will investigate the neural correlates of FR-OABD to understand its mechanisms better and inform for potential optimization. The aim is to investigate the brain features and changes associated with FROA-BD efficacy. Methods Thirty-two individuals with OABD in full or partial remission will undergo a magnetic resonance imaging (MRI) session before receiving FR-OABD. After completing the FR-OABD intervention, they will undergo another MRI session. The MRI sessions will include structural, diffusion-weighted imaging (DWI), functional MRI (fMRI) with working memory (n-back) and verbal learning tasks, and frontal spectroscopy. We will correlate the pre-post change in dorsolateral and dorsomedial prefrontal cortices activation during the n-back task with the change in psychosocial functioning [measured with the Functioning Assessment Short Test (FAST)]. We will also conduct exploratory whole-brain correlation analyses between baseline or pre-post changes in MRI data and other clinical and cognitive outcomes to provide more insights into the mechanisms and explore potential brain markers that may predict a better treatment response. We will also conduct separate analyses by sex. Discussion The results of this study may provide insights into how FROA-BD and other cognitive remediations modulate brain function and thus could optimize these interventions.
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Affiliation(s)
- Laura Montejo
- Bipolar and Depressive Disorders Unit, Hospital Clinic de Barcelona, Barcelona, Spain
- Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Institute of Neurosciences (UBNeuro), Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
| | - Brisa Sole
- Bipolar and Depressive Disorders Unit, Hospital Clinic de Barcelona, Barcelona, Spain
- Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Institute of Neurosciences (UBNeuro), Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
| | - Lydia Fortea
- Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Departament de Medicina, Facultat de Medicina i Ciències de la Salut, Universitat de Barcelona, Barcelona, Spain
| | - Esther Jimenez
- Bipolar and Depressive Disorders Unit, Hospital Clinic de Barcelona, Barcelona, Spain
- Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Institute of Neurosciences (UBNeuro), Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
| | - Anabel Martinez-Aran
- Bipolar and Depressive Disorders Unit, Hospital Clinic de Barcelona, Barcelona, Spain
- Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Institute of Neurosciences (UBNeuro), Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Departament de Psicologia Clínica i Psicobiologia, Facultat de Medicina i Ciències de la Salut, Universitat de Barcelona, Barcelona, Spain
| | - Eloy Martinez-Heras
- Neuroimmunology and Multiple Sclerosis Unit and Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clinic Barcelona, Fundació de Recerca Clínic Barcelona-IDIBAPS, Barcelona, Spain
| | - Jose Sanchez-Moreno
- Bipolar and Depressive Disorders Unit, Hospital Clinic de Barcelona, Barcelona, Spain
- Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Institute of Neurosciences (UBNeuro), Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
| | - Maria Ortuño
- Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Departament de Medicina, Facultat de Medicina i Ciències de la Salut, Universitat de Barcelona, Barcelona, Spain
| | - Jose Pariente
- Magnetic Resonance Image Core Facility, Institut d'Investigacions Biomediques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Aleix Solanes
- Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Carla Torrent
- Bipolar and Depressive Disorders Unit, Hospital Clinic de Barcelona, Barcelona, Spain
- Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Institute of Neurosciences (UBNeuro), Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
| | - Enric Vilajosana
- Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
| | - Michele De Prisco
- Bipolar and Depressive Disorders Unit, Hospital Clinic de Barcelona, Barcelona, Spain
- Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Institute of Neurosciences (UBNeuro), Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
| | - Eduard Vieta
- Bipolar and Depressive Disorders Unit, Hospital Clinic de Barcelona, Barcelona, Spain
- Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Institute of Neurosciences (UBNeuro), Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Departament de Medicina, Facultat de Medicina i Ciències de la Salut, Universitat de Barcelona, Barcelona, Spain
| | - Joaquim Radua
- Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain
- Centro de Investigación Biomédica en Red de Salud Mental (CIBERSAM), Instituto de Salud Carlos III, Madrid, Spain
- Departament de Medicina, Facultat de Medicina i Ciències de la Salut, Universitat de Barcelona, Barcelona, Spain
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Lapidaire W, Clayden JD, Fewtrell MS, Clark CA. Increased white matter fibre dispersion and lower IQ scores in adults born preterm. Hum Brain Mapp 2024; 45:e26545. [PMID: 38070181 PMCID: PMC10789207 DOI: 10.1002/hbm.26545] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/22/2023] [Revised: 11/08/2023] [Accepted: 11/13/2023] [Indexed: 01/16/2024] Open
Abstract
Preterm birth has been associated with altered microstructural properties of the white matter and lower cognitive ability in childhood and adulthood. Due to methodological limitations of the diffusion tensor model, it is not clear whether alterations in myelination or variation in fibre orientation are driving these differences. Novel models applied to multi-shell diffusion imaging have been used to disentangle these effects, but to date this has not been used to study the preterm brain in adulthood. This study investigated whether novel advanced diffusion MRI metrics such as microscopic anisotropy and orientation dispersion are altered in adults born preterm, and whether this was associated with cognitive performance. Seventy-two preterm born participants (<37 weeks gestational age) were recruited from a 1982-1984 cohort (33 males, mean age 33.5 ± 1.0 years). Seventy-two term born (>37 weeks gestational age) controls (34 males, mean age 30.9 ± 4.0 years) were recruited from the general population. Tensor FA was calculated with FSL, while microscopic FA and orientation dispersion entropy (ODE) were estimated using the Spherical Mean Technique (SMT). Estimated Full Scale IQ (FSIQ), Verbal Comprehension Index (VCI) and Perceptual Reasoning Index (PRI) were obtained from the WASI-II (abbreviated) IQ test. Voxel-wise comparisons using FSL's tract-based spatial statistics were performed to test between-group differences in diffusion MRI metrics as well as within-group associations of diffusion MRI metrics and IQ outcomes. The preterm group had significantly lower FSIQ, VCI and PRI scores. Preterm subjects demonstrated widespread decreases in ODE reflecting increased fibre dispersion, but no differences in microscopic FA. Tensor FA was increased in a small area in the anterior corona radiata. Lower FA values in the preterm population were associated with lower FSIQ and PRI scores. An increase in fibre dispersion in white matter and lower IQ scores after preterm birth exist in adulthood. Advanced diffusion MRI metrics such as the orientation dispersion entropy can be used to monitor white matter alterations across the lifespan in preterm born individuals. Although not significantly different between preterm and term groups, tensor FA values in the preterm group were associated with cognitive outcome.
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Affiliation(s)
- Winok Lapidaire
- UCL Great Ormond Street Institute of Child HealthUniversity College LondonLondonUK
- Oxford Cardiovascular Clinical Research Facility, Division of Cardiovascular Medicine, Radcliffe Department of MedicineUniversity of Oxford, John Radcliffe HospitalOxfordUK
| | - Jonathan D. Clayden
- UCL Great Ormond Street Institute of Child HealthUniversity College LondonLondonUK
| | - Mary S. Fewtrell
- UCL Great Ormond Street Institute of Child HealthUniversity College LondonLondonUK
| | - Christopher A. Clark
- UCL Great Ormond Street Institute of Child HealthUniversity College LondonLondonUK
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44
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Lampinen B, Szczepankiewicz F, Lätt J, Knutsson L, Mårtensson J, Björkman-Burtscher IM, van Westen D, Sundgren PC, Ståhlberg F, Nilsson M. Probing brain tissue microstructure with MRI: principles, challenges, and the role of multidimensional diffusion-relaxation encoding. Neuroimage 2023; 282:120338. [PMID: 37598814 DOI: 10.1016/j.neuroimage.2023.120338] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 06/30/2023] [Accepted: 08/17/2023] [Indexed: 08/22/2023] Open
Abstract
Diffusion MRI uses the random displacement of water molecules to sensitize the signal to brain microstructure and to properties such as the density and shape of cells. Microstructure modeling techniques aim to estimate these properties from acquired data by separating the signal between virtual tissue 'compartments' such as the intra-neurite and the extra-cellular space. A key challenge is that the diffusion MRI signal is relatively featureless compared with the complexity of brain tissue. Another challenge is that the tissue microstructure is wildly different within the gray and white matter of the brain. In this review, we use results from multidimensional diffusion encoding techniques to discuss these challenges and their tentative solutions. Multidimensional encoding increases the information content of the data by varying not only the b-value and the encoding direction but also additional experimental parameters such as the shape of the b-tensor and the echo time. Three main insights have emerged from such encoding. First, multidimensional data contradict common model assumptions on diffusion and T2 relaxation, and illustrates how the use of these assumptions cause erroneous interpretations in both healthy brain and pathology. Second, many model assumptions can be dispensed with if data are acquired with multidimensional encoding. The necessary data can be easily acquired in vivo using protocols optimized to minimize Cramér-Rao lower bounds. Third, microscopic diffusion anisotropy reflects the presence of axons but not dendrites. This insight stands in contrast to current 'neurite models' of brain tissue, which assume that axons in white matter and dendrites in gray matter feature highly similar diffusion. Nevertheless, as an axon-based contrast, microscopic anisotropy can differentiate gray and white matter when myelin alterations confound conventional MRI contrasts.
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Affiliation(s)
- Björn Lampinen
- Clinical Sciences Lund, Diagnostic Radiology, Lund University, Lund, Sweden.
| | | | - Jimmy Lätt
- Department of Medical Imaging and Physiology, Skåne University Hospital Lund, Lund, Sweden
| | - Linda Knutsson
- Clinical Sciences Lund, Medical Radiation Physics, Lund University, Lund, Sweden; Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Johan Mårtensson
- Clinical Sciences Lund, Logopedics, Phoniatrics and Audiology, Lund University, Lund, Sweden
| | - Isabella M Björkman-Burtscher
- Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg and Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Danielle van Westen
- Clinical Sciences Lund, Diagnostic Radiology, Lund University, Lund, Sweden; Department of Medical Imaging and Physiology, Skåne University Hospital Lund, Lund, Sweden
| | - Pia C Sundgren
- Clinical Sciences Lund, Diagnostic Radiology, Lund University, Lund, Sweden; Department of Medical Imaging and Physiology, Skåne University Hospital Lund, Lund, Sweden; Lund University BioImaging Centre (LBIC), Lund University, Lund, Sweden
| | - Freddy Ståhlberg
- Clinical Sciences Lund, Diagnostic Radiology, Lund University, Lund, Sweden; Clinical Sciences Lund, Medical Radiation Physics, Lund University, Lund, Sweden
| | - Markus Nilsson
- Clinical Sciences Lund, Diagnostic Radiology, Lund University, Lund, Sweden
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Wu Y, Liu X, Zhang X, Huynh KM, Ahmad S, Yap PT. Relaxation-Diffusion Spectrum Imaging for Probing Tissue Microarchitecture. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2023; 14227:152-162. [PMID: 39184022 PMCID: PMC11340880 DOI: 10.1007/978-3-031-43993-3_15] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/27/2024]
Abstract
Brain tissue microarchitecture is characterized by heterogeneous degrees of diffusivity and rates of transverse relaxation. Unlike standard diffusion MRI with a single echo time (TE), which provides information primarily on diffusivity, relaxation-diffusion MRI involves multiple TEs and multiple diffusion-weighting strengths for probing tissue-specific coupling between relaxation and diffusivity. Here, we introduce a relaxation-diffusion model that characterizes tissue apparent relaxation coefficients for a spectrum of diffusion length scales and at the same time factors out the effects of intra-voxel orientation heterogeneity. We examined the model with an in vivo dataset, acquired using a clinical scanner, involving different health conditions. Experimental results indicate that our model caters to heterogeneous tissue microstructure and can distinguish fiber bundles with similar diffusivities but different relaxation rates. Code with sample data is available at https://github.com/dryewu/RDSI.
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Affiliation(s)
- Ye Wu
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China
| | - Xiaoming Liu
- Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Xinyuan Zhang
- School of Biomedical Engineering, Southern Medical University, Guangzhou, China
| | - Khoi Minh Huynh
- Department of Radiology, University of North Carolina, Chapel Hill, NC, USA
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA
| | - Sahar Ahmad
- Department of Radiology, University of North Carolina, Chapel Hill, NC, USA
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA
| | - Pew-Thian Yap
- Department of Radiology, University of North Carolina, Chapel Hill, NC, USA
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, NC, USA
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46
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Huynh KM, Wu Y, Ahmad S, Yap PT. Microstructure Fingerprinting for Heterogeneously Oriented Tissue Microenvironments. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2023; 14227:131-141. [PMID: 39129859 PMCID: PMC11315459 DOI: 10.1007/978-3-031-43993-3_13] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 08/13/2024]
Abstract
Most diffusion biophysical models capture basic properties of tissue microstructure, such as diffusivity and anisotropy. More realistic models that relate the diffusion-weighted signal to cell size and membrane permeability often require simplifying assumptions such as short gradient pulse and Gaussian phase distribution, leading to tissue features that are not necessarily quantitative. Here, we propose a method to quantify tissue microstructure without jeopardizing accuracy owing to unrealistic assumptions. Our method utilizes realistic signals simulated from the geometries of cellular microenvironments as fingerprints, which are then employed in a spherical mean estimation framework to disentangle the effects of orientation dispersion from microscopic tissue properties. We demonstrate the efficacy of microstructure fingerprinting in estimating intra-cellular, extra-cellular, and intra-soma volume fractions as well as axon radius, soma radius, and membrane permeability.
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Affiliation(s)
- Khoi Minh Huynh
- Department of Radiology, University of North Carolina, Chapel Hill, USA
- Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, USA
| | - Ye Wu
- Department of Radiology, University of North Carolina, Chapel Hill, USA
- Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, USA
| | - Sahar Ahmad
- Department of Radiology, University of North Carolina, Chapel Hill, USA
- Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, USA
| | - Pew-Thian Yap
- Department of Radiology, University of North Carolina, Chapel Hill, USA
- Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, USA
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Pieciak T, París G, Beck D, Maximov II, Tristán-Vega A, de Luis-García R, Westlye LT, Aja-Fernández S. Spherical means-based free-water volume fraction from diffusion MRI increases non-linearly with age in the white matter of the healthy human brain. Neuroimage 2023; 279:120324. [PMID: 37574122 DOI: 10.1016/j.neuroimage.2023.120324] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Revised: 08/08/2023] [Accepted: 08/10/2023] [Indexed: 08/15/2023] Open
Abstract
The term free-water volume fraction (FWVF) refers to the signal fraction that could be found as the cerebrospinal fluid of the brain, which has been demonstrated as a sensitive measure that correlates with cognitive performance and various neuropathological processes. It can be quantified by properly fitting the isotropic component of the magnetic resonance (MR) signal in diffusion-sensitized sequences. Using N=287 healthy subjects (178F/109M) aged 25-94, this study examines in detail the evolution of the FWVF obtained with the spherical means technique from multi-shell acquisitions in the human brain white matter across the adult lifespan, which has been previously reported to exhibit a positive trend when estimated from single-shell data using the bi-tensor signal representation. We found evidence of a noticeably non-linear gain after the sixth decade of life, with a region-specific variate and varying change rate of the spherical means-based multi-shell FWVF parameter with age, at the same time, a heteroskedastic pattern across the adult lifespan is suggested. On the other hand, the FW corrected diffusion tensor imaging (DTI) leads to a region-dependent flattened age-related evolution of the mean diffusivity (MD) and fractional anisotropy (FA), along with a considerable reduction in their variability, as compared to the studies conducted over the standard (single-component) DTI. This way, our study provides a new perspective on the trajectory-based assessment of the brain and explains the conceivable reason for the variations observed in FA and MD parameters across the lifespan with previous studies under the standard diffusion tensor imaging.
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Affiliation(s)
- Tomasz Pieciak
- Laboratorio de Procesado de Imagen (LPI), ETSI Telecomunicación, Universidad de Valladolid, Valladolid, Spain.
| | - Guillem París
- Laboratorio de Procesado de Imagen (LPI), ETSI Telecomunicación, Universidad de Valladolid, Valladolid, Spain
| | - Dani Beck
- Department of Psychology, University of Oslo, Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Psychiatric Research, Diakonhjemmet Hospital, Oslo, Norway. https://twitter.com/_DaniBeck
| | - Ivan I Maximov
- NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
| | - Antonio Tristán-Vega
- Laboratorio de Procesado de Imagen (LPI), ETSI Telecomunicación, Universidad de Valladolid, Valladolid, Spain
| | - Rodrigo de Luis-García
- Laboratorio de Procesado de Imagen (LPI), ETSI Telecomunicación, Universidad de Valladolid, Valladolid, Spain
| | - Lars T Westlye
- Department of Psychology, University of Oslo, Oslo, Norway; NORMENT, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway; KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway. https://twitter.com/larswestlye
| | - Santiago Aja-Fernández
- Laboratorio de Procesado de Imagen (LPI), ETSI Telecomunicación, Universidad de Valladolid, Valladolid, Spain. https://twitter.com/SantiagoAjaFer1
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Seyedmirzaei H, Nabizadeh F, Aarabi MH, Pini L. Neurite Orientation Dispersion and Density Imaging in Multiple Sclerosis: A Systematic Review. J Magn Reson Imaging 2023; 58:1011-1029. [PMID: 37042392 DOI: 10.1002/jmri.28727] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2023] [Revised: 03/27/2023] [Accepted: 03/28/2023] [Indexed: 04/13/2023] Open
Abstract
Diffusion-weighted imaging has been applied to investigate alterations in multiple sclerosis (MS). In the last years, advanced diffusion models were used to identify subtle changes and early lesions in MS. Among these models, neurite orientation dispersion and density imaging (NODDI) is an emerging approach, quantifying specific neurite morphology in both grey (GM) and white matter (WM) tissue and increasing the specificity of diffusion imaging. In this systematic review, we summarized the NODDI findings in MS. A search was conducted on PubMed, Scopus, and Embase, which yielded a total number of 24 eligible studies. Compared to healthy tissue, these studies identified consistent alterations in NODDI metrics involving WM (neurite density index), and GM lesions (neurite density index), or normal-appearing WM tissue (isotropic volume fraction and neurite density index). Despite some limitations, we pointed out the potential of NODDI in MS to unravel microstructural alterations. These results might pave the way to a deeper understanding of the pathophysiological mechanism of MS. EVIDENCE LEVEL: 2. TECHNICAL EFFICACY: Stage 3.
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Affiliation(s)
| | | | | | - Lorenzo Pini
- Padova Neuroscience Center (PNC), University of Padova, Padova, Italy
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49
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Schiavi S, Palombo M, Zacà D, Tazza F, Lapucci C, Castellan L, Costagli M, Inglese M. Mapping tissue microstructure across the human brain on a clinical scanner with soma and neurite density image metrics. Hum Brain Mapp 2023; 44:4792-4811. [PMID: 37461286 PMCID: PMC10400787 DOI: 10.1002/hbm.26416] [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: 11/01/2022] [Revised: 05/02/2023] [Accepted: 06/23/2023] [Indexed: 08/05/2023] Open
Abstract
Soma and neurite density image (SANDI) is an advanced diffusion magnetic resonance imaging biophysical signal model devised to probe in vivo microstructural information in the gray matter (GM). This model requires acquisitions that include b values that are at least six times higher than those used in clinical practice. Such high b values are required to disentangle the signal contribution of water diffusing in soma from that diffusing in neurites and extracellular space, while keeping the diffusion time as short as possible to minimize potential bias due to water exchange. These requirements have limited the use of SANDI only to preclinical or cutting-edge human scanners. Here, we investigate the potential impact of neglecting water exchange in the SANDI model and present a 10-min acquisition protocol that enables to characterize both GM and white matter (WM) on 3 T scanners. We implemented analytical simulations to (i) evaluate the stability of the fitting of SANDI parameters when diminishing the number of shells; (ii) estimate the bias due to potential exchange between neurites and extracellular space in such reduced acquisition scheme, comparing it with the bias due to experimental noise. Then, we demonstrated the feasibility and assessed the repeatability and reproducibility of our approach by computing microstructural metrics of SANDI with AMICO toolbox and other state-of-the-art models on five healthy subjects. Finally, we applied our protocol to five multiple sclerosis patients. Results suggest that SANDI is a practical method to characterize WM and GM tissues in vivo on performant clinical scanners.
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Affiliation(s)
- Simona Schiavi
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI)University of GenoaGenoaItaly
| | - Marco Palombo
- CUBRIC, School of PsychologyCardiff UniversityCardiffUK
- School of Computer Science and InformaticsCardiff UniversityCardiffUK
| | | | - Francesco Tazza
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI)University of GenoaGenoaItaly
| | - Caterina Lapucci
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI)University of GenoaGenoaItaly
- HNSR, IRRCS Ospedale Policlinico San MartinoGenoaItaly
| | - Lucio Castellan
- Department of NeuroradiologyIRCCS Ospedale Policlinico San MartinoGenoaItaly
| | - Mauro Costagli
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI)University of GenoaGenoaItaly
- Laboratory of Medical Physics and Magnetic ResonanceIRCCS Stella MarisPisaItaly
| | - Matilde Inglese
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI)University of GenoaGenoaItaly
- IRCCS Ospedale Policlinico San MartinoGenoaItaly
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50
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Barakovic M, Pizzolato M, Tax CMW, Rudrapatna U, Magon S, Dyrby TB, Granziera C, Thiran JP, Jones DK, Canales-Rodríguez EJ. Estimating axon radius using diffusion-relaxation MRI: calibrating a surface-based relaxation model with histology. Front Neurosci 2023; 17:1209521. [PMID: 37638307 PMCID: PMC10457121 DOI: 10.3389/fnins.2023.1209521] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/20/2023] [Accepted: 07/24/2023] [Indexed: 08/29/2023] Open
Abstract
Axon radius is a potential biomarker for brain diseases and a crucial tissue microstructure parameter that determines the speed of action potentials. Diffusion MRI (dMRI) allows non-invasive estimation of axon radius, but accurately estimating the radius of axons in the human brain is challenging. Most axons in the brain have a radius below one micrometer, which falls below the sensitivity limit of dMRI signals even when using the most advanced human MRI scanners. Therefore, new MRI methods that are sensitive to small axon radii are needed. In this proof-of-concept investigation, we examine whether a surface-based axonal relaxation process could mediate a relationship between intra-axonal T2 and T1 times and inner axon radius, as measured using postmortem histology. A unique in vivo human diffusion-T1-T2 relaxation dataset was acquired on a 3T MRI scanner with ultra-strong diffusion gradients, using a strong diffusion-weighting (i.e., b = 6,000 s/mm2) and multiple inversion and echo times. A second reduced diffusion-T2 dataset was collected at various echo times to evaluate the model further. The intra-axonal relaxation times were estimated by fitting a diffusion-relaxation model to the orientation-averaged spherical mean signals. Our analysis revealed that the proposed surface-based relaxation model effectively explains the relationship between the estimated relaxation times and the histological axon radius measured in various corpus callosum regions. Using these histological values, we developed a novel calibration approach to predict axon radius in other areas of the corpus callosum. Notably, the predicted radii and those determined from histological measurements were in close agreement.
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Affiliation(s)
- Muhamed Barakovic
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
- Department of Neurology, University Hospital Basel, Basel, Switzerland
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, Wales, United Kingdom
- Signal Processing Laboratory 5 (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center, Basel, Switzerland
| | - Marco Pizzolato
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Chantal M. W. Tax
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, Wales, United Kingdom
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands
| | - Umesh Rudrapatna
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, Wales, United Kingdom
| | - Stefano Magon
- Roche Pharma Research and Early Development, Neuroscience and Rare Diseases, Roche Innovation Center, Basel, Switzerland
| | - Tim B. Dyrby
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark
- Danish Research Centre for Magnetic Resonance (DRCMR), Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Copenhagen, Denmark
| | - Cristina Granziera
- Translational Imaging in Neurology (ThINk) Basel, Department of Biomedical Engineering, 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
| | - Jean-Philippe Thiran
- Signal Processing Laboratory 5 (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
- Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland
- Centre d’Imagerie Biomédicale (CIBM), EPFL, Lausanne, Switzerland
| | - Derek K. Jones
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, Wales, United Kingdom
| | - Erick J. Canales-Rodríguez
- Signal Processing Laboratory 5 (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
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