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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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da S Senra Filho AC, Murta Junior LO, Monteiro Paschoal A. Assessing biological self-organization patterns using statistical complexity characteristics: a tool for diffusion tensor imaging analysis. MAGMA (NEW YORK, N.Y.) 2024:10.1007/s10334-024-01185-4. [PMID: 39068635 DOI: 10.1007/s10334-024-01185-4] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2024] [Revised: 06/24/2024] [Accepted: 06/28/2024] [Indexed: 07/30/2024]
Abstract
OBJECT Diffusion-weighted imaging (DWI) and diffusion tensor imaging (DTI) are well-known and powerful imaging techniques for MRI. Although DTI evaluation has evolved continually in recent years, there are still struggles regarding quantitative measurements that can benefit brain areas that are consistently difficult to measure via diffusion-based methods, e.g., gray matter (GM). The present study proposes a new image processing technique based on diffusion distribution evaluation of López-Ruiz, Mancini and Calbet (LMC) complexity called diffusion complexity (DC). MATERIALS AND METHODS The OASIS-3 and TractoInferno open-science databases for healthy individuals were used, and all the codes are provided as open-source materials. RESULTS The DC map showed relevant signal characterization in brain tissues and structures, achieving contrast-to-noise ratio (CNR) gains of approximately 39% and 93%, respectively, compared to those of the FA and ADC maps. DISCUSSION In the special case of GM tissue, the DC map obtains its maximum signal level, showing the possibility of studying cortical and subcortical structures challenging for classical DTI quantitative formalism. The ability to apply the DC technique, which requires the same imaging acquisition for DTI and its potential to provide complementary information to study the brain's GM structures, can be a rich source of information for further neuroscience research and clinical practice.
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Brunaud C, Valable S, Ropars G, Dwiri FA, Naveau M, Toutain J, Bernaudin M, Freret T, Léger M, Touzani O, Pérès EA. Deformation-based morphometry: a sensitive imaging approach to detect radiation-induced brain injury? Cancer Imaging 2024; 24:95. [PMID: 39026377 PMCID: PMC11256482 DOI: 10.1186/s40644-024-00736-1] [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: 02/26/2024] [Accepted: 06/27/2024] [Indexed: 07/20/2024] Open
Abstract
BACKGROUND Radiotherapy is a major therapeutic approach in patients with brain tumors. However, it leads to cognitive impairments. To improve the management of radiation-induced brain sequalae, deformation-based morphometry (DBM) could be relevant. Here, we analyzed the significance of DBM using Jacobian determinants (JD) obtained by non-linear registration of MRI images to detect local vulnerability of healthy cerebral tissue in an animal model of brain irradiation. METHODS Rats were exposed to fractionated whole-brain irradiation (WBI, 30 Gy). A multiparametric MRI (anatomical, diffusion and vascular) study was conducted longitudinally from 1 month up to 6 months after WBI. From the registration of MRI images, macroscopic changes were analyzed by DBM and microscopic changes at the cellular and vascular levels were evaluated by quantification of cerebral blood volume (CBV) and diffusion metrics including mean diffusivity (MD). Voxel-wise comparisons were performed on the entire brain and in specific brain areas identified by DBM. Immunohistology analyses were undertaken to visualize the vessels and astrocytes. RESULTS DBM analysis evidenced time-course of local macrostructural changes; some of which were transient and some were long lasting after WBI. DBM revealed two vulnerable brain areas, namely the corpus callosum and the cortex. DBM changes were spatially associated to microstructural alterations as revealed by both diffusion metrics and CBV changes, and confirmed by immunohistology analyses. Finally, matrix correlations demonstrated correlations between JD/MD in the early phase after WBI and JD/CBV in the late phase both in the corpus callosum and the cortex. CONCLUSIONS Brain irradiation induces local macrostructural changes detected by DBM which could be relevant to identify brain structures prone to radiation-induced tissue changes. The translation of these data in patients could represent an added value in imaging studies on brain radiotoxicity.
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Affiliation(s)
- Carole Brunaud
- Université de Caen Normandie, CNRS, Normandie Université, ISTCT UMR6030, GIP Cyceron, Caen, F-14000, France
| | - Samuel Valable
- Université de Caen Normandie, CNRS, Normandie Université, ISTCT UMR6030, GIP Cyceron, Caen, F-14000, France
| | - Gwenn Ropars
- Université de Caen Normandie, CNRS, Normandie Université, ISTCT UMR6030, GIP Cyceron, Caen, F-14000, France
| | - Fatima-Azzahra Dwiri
- Université de Caen Normandie, CNRS, Normandie Université, ISTCT UMR6030, GIP Cyceron, Caen, F-14000, France
| | - Mikaël Naveau
- Université de Caen Normandie, CNRS, INSERM, Normandie Université, UAR 3408/US50, GIP Cyceron, Caen, F-14000, France
| | - Jérôme Toutain
- Université de Caen Normandie, CNRS, Normandie Université, ISTCT UMR6030, GIP Cyceron, Caen, F-14000, France
| | - Myriam Bernaudin
- Université de Caen Normandie, CNRS, Normandie Université, ISTCT UMR6030, GIP Cyceron, Caen, F-14000, France
| | - Thomas Freret
- Université de Caen Normandie, INSERM, Normandie Université, COMETE UMR-S 1075, GIP Cyceron, Caen, F-14000, France
| | - Marianne Léger
- Université de Caen Normandie, INSERM, Normandie Université, COMETE UMR-S 1075, GIP Cyceron, Caen, F-14000, France
| | - Omar Touzani
- Université de Caen Normandie, CNRS, Normandie Université, ISTCT UMR6030, GIP Cyceron, Caen, F-14000, France
| | - Elodie A Pérès
- Université de Caen Normandie, CNRS, Normandie Université, ISTCT UMR6030, GIP Cyceron, Caen, F-14000, France.
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Zhang Y, Cheng Z, Peng H, Ma W, Zhang R, Ma J, Gao S, Li W, Xu Y. Factors influencing diffusion tensor imaging of knee cartilage in children ages 6-12 years: a prospective study. Pediatr Radiol 2024; 54:1284-1293. [PMID: 38910223 DOI: 10.1007/s00247-024-05965-x] [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: 01/14/2024] [Revised: 05/26/2024] [Accepted: 05/28/2024] [Indexed: 06/25/2024]
Abstract
BACKGROUND Magnetic resonance diffusion tensor imaging (DTI) has recently been used to evaluate the developing cartilage of children, but the influencing factors have not been well studied. OBJECTIVE The objective of this study was to investigate the influence of the diffusion gradient strength (b value), diffusion gradient direction, age and sex on knee cartilage DTI in healthy children aged 6-12 years. MATERIALS AND METHODS A total of 30 healthy child volunteers, with an average age of 8.9 ± 1.6 (mean ± standard deviation) years, were enrolled in this study. They were categorized into three groups according to their age range: 6-8 years, 8-10 years and 10-12 years, ensuring equal sex distribution in each group (5 boys and 5 girls). These volunteers underwent routine left knee joint magnetic resonance imaging (MRI) and serial DTI scans. DTI parameters were altered as follows: when b value = 600 s/mm2, diffusion gradient direction was set to 6, 15, 25, 35 and 45; and when diffusion gradient direction = 25, b value was set to 300, 600, 900 and 1200 s/mm2. The values of fractional anisotropy (FA) and apparent diffusion coefficient (ADC) were separately acquired using image post-processing techniques. The correlation between various b values, diffusion gradient directions, age and sex on the one hand and FA and ADC values on the other, was investigated. RESULTS (1) When diffusion gradient direction was fixed and the b value was varied, both FA and ADC exhibited a decreasing trend as the b value increased (P < 0.001). (2) When the b value was fixed and diffusion gradient direction was varied, the FA of knee cartilage showed a decreasing trend with increasing diffusion gradient direction (P < 0.001). (3) The FA value increased with age (P < 0.05). CONCLUSION The b value, diffusion gradient direction value and age exert a significant impact on both FA and ADC values in MR DTI of knee cartilage in children aged 6-12 years. In order to obtain a stable DTI, it is recommended to select a b value ≥ 600 s/mm2 and a diffusion gradient direction ≥ 25 during scanning.
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Affiliation(s)
- Yilu Zhang
- Department of Radiology Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing Key Laboratory of Pediatric Metabolism and Inflammatory Diseases, 136 Zhongshan Er Lu, Yuzhong District, Chongqing, 400000, China
| | - Zhuo Cheng
- Department of Radiology Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing Key Laboratory of Pediatric Metabolism and Inflammatory Diseases, 136 Zhongshan Er Lu, Yuzhong District, Chongqing, 400000, China
| | - Hailun Peng
- Department of Radiology Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing Key Laboratory of Pediatric Metabolism and Inflammatory Diseases, 136 Zhongshan Er Lu, Yuzhong District, Chongqing, 400000, China
| | - Wei Ma
- Department of Radiology, The People's Hospital of Yubei District of Chongqing City, Yubei District, Chongqing, China
| | - Rui Zhang
- Department of Radiology Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing Key Laboratory of Pediatric Metabolism and Inflammatory Diseases, 136 Zhongshan Er Lu, Yuzhong District, Chongqing, 400000, China
| | - Junya Ma
- Department of Radiology Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing Key Laboratory of Pediatric Metabolism and Inflammatory Diseases, 136 Zhongshan Er Lu, Yuzhong District, Chongqing, 400000, China
| | - Sijie Gao
- Department of Radiology Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing Key Laboratory of Pediatric Metabolism and Inflammatory Diseases, 136 Zhongshan Er Lu, Yuzhong District, Chongqing, 400000, China
| | - Wei Li
- Department of Radiology Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing Key Laboratory of Pediatric Metabolism and Inflammatory Diseases, 136 Zhongshan Er Lu, Yuzhong District, Chongqing, 400000, China
| | - Ye Xu
- Department of Radiology Children's Hospital of Chongqing Medical University, National Clinical Research Center for Child Health and Disorders, Ministry of Education Key Laboratory of Child Development and Disorders, China International Science and Technology Cooperation Base of Child Development and Critical Disorders, Chongqing Key Laboratory of Pediatric Metabolism and Inflammatory Diseases, 136 Zhongshan Er Lu, Yuzhong District, Chongqing, 400000, China.
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Osorio-Londoño D, Morales-Guadarrama A, Olayo-González R, Roldan-Valadez E. Quantitative Magnetic Resonance Identifies Recovery from Spinal Cord Injury after Bioactive Implants. Arch Med Res 2024; 55:103012. [PMID: 38851049 DOI: 10.1016/j.arcmed.2024.103012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2024] [Revised: 05/07/2024] [Accepted: 05/22/2024] [Indexed: 06/10/2024]
Affiliation(s)
| | - Axayácatl Morales-Guadarrama
- Medical Imaging and Instrumentation Research National Center, Universidad Autónoma Metropolitana, Mexico City, Mexico
| | | | - Ernesto Roldan-Valadez
- Division of Research, Instituto Nacional de Rehabilitación, Luis Guillermo Ibarra Ibarra, Mexico City, Mexico; I.M. Sechenov First Moscow State Medical University, Sechenov University, Department of Radiology, Moscow, Russia.
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Bottenhorn KL, Sukumaran K, Cardenas-Iniguez C, Habre R, Schwartz J, Chen JC, Herting MM. Air pollution from biomass burning disrupts early adolescent cortical microarchitecture development. ENVIRONMENT INTERNATIONAL 2024; 189:108769. [PMID: 38823157 DOI: 10.1016/j.envint.2024.108769] [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: 11/20/2023] [Revised: 05/08/2024] [Accepted: 05/21/2024] [Indexed: 06/03/2024]
Abstract
Exposure to outdoor particulate matter (PM2.5) represents a ubiquitous threat to human health, and particularly the neurotoxic effects of PM2.5 from multiple sources may disrupt neurodevelopment. Studies addressing neurodevelopmental implications of PM exposure have been limited by small, geographically limited samples and largely focus either on macroscale cortical morphology or postmortem histological staining and total PM mass. Here, we leverage residentially assigned exposure to six, data-driven sources of PM2.5 and neuroimaging data from the longitudinal Adolescent Brain Cognitive Development Study (ABCD Study®), collected from 21 different recruitment sites across the United States. To contribute an interpretable and actionable assessment of the role of air pollution in the developing brain, we identified alterations in cortical microstructure development associated with exposure to specific sources of PM2.5 using multivariate, partial least squares analyses. Specifically, average annual exposure (i.e., at ages 8-10 years) to PM2.5 from biomass burning was related to differences in neurite development across the cortex between 9 and 13 years of age.
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Affiliation(s)
- Katherine L Bottenhorn
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA; Department of Psychology, Florida International University, Miami, FL, USA.
| | - Kirthana Sukumaran
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA
| | - Carlos Cardenas-Iniguez
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA
| | - Rima Habre
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA; Spatial Sciences Institute, University of Southern California, Los Angeles, CA, USA
| | - Joel Schwartz
- Department of Environmental Health, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Jiu-Chiuan Chen
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA; Department of Neurology, Keck School of Medicine of University of Southern California, Los Angeles, CA, USA
| | - Megan M Herting
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles, CA, USA.
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Al-Sharif NB, Zavaliangos-Petropulu A, Narr KL. A review of diffusion MRI in mood disorders: mechanisms and predictors of treatment response. Neuropsychopharmacology 2024:10.1038/s41386-024-01894-3. [PMID: 38902355 DOI: 10.1038/s41386-024-01894-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/08/2024] [Revised: 05/15/2024] [Accepted: 05/21/2024] [Indexed: 06/22/2024]
Abstract
By measuring the molecular diffusion of water molecules in brain tissue, diffusion MRI (dMRI) provides unique insight into the microstructure and structural connections of the brain in living subjects. Since its inception, the application of dMRI in clinical research has expanded our understanding of the possible biological bases of psychiatric disorders and successful responses to different therapeutic interventions. Here, we review the past decade of diffusion imaging-based investigations with a specific focus on studies examining the mechanisms and predictors of therapeutic response in people with mood disorders. We present a brief overview of the general application of dMRI and key methodological developments in the field that afford increasingly detailed information concerning the macro- and micro-structural properties and connectivity patterns of white matter (WM) pathways and their perturbation over time in patients followed prospectively while undergoing treatment. This is followed by a more in-depth summary of particular studies using dMRI approaches to examine mechanisms and predictors of clinical outcomes in patients with unipolar or bipolar depression receiving pharmacological, neurostimulation, or behavioral treatments. Limitations associated with dMRI research in general and with treatment studies in mood disorders specifically are discussed, as are directions for future research. Despite limitations and the associated discrepancies in findings across individual studies, evolving research strongly indicates that the field is on the precipice of identifying and validating dMRI biomarkers that could lead to more successful personalized treatment approaches and could serve as targets for evaluating the neural effects of novel treatments.
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Affiliation(s)
- Noor B Al-Sharif
- Departments of Neurology and Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA.
| | - Artemis Zavaliangos-Petropulu
- Departments of Neurology and Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
| | - Katherine L Narr
- Departments of Neurology and Psychiatry and Biobehavioral Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, CA, USA
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Li T, Guo Y, Jin X, Liu T, Wu G, Huang W, Chen F. Dynamic monitoring of radiation-induced white matter microstructure injury in nasopharyngeal carcinoma via high-angular resolution diffusion imaging. Brain Res 2024; 1833:148851. [PMID: 38479491 DOI: 10.1016/j.brainres.2024.148851] [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: 11/23/2023] [Revised: 02/22/2024] [Accepted: 03/05/2024] [Indexed: 03/24/2024]
Abstract
PURPOSE To investigate white matter microstructural abnormalities caused by radiotherapy in nasopharyngeal carcinoma (NPC) patients using MRI high-angular resolution diffusion imaging (HARDI). METHODS We included 127 patients with pathologically confirmed NPC: 36 in the pre-radiotherapy group, 29 in the acute response period (post-RT-AP), 23 in the early delayed period (post-RT-ED) group, and 39 in the late-delayed period (post-RT-LD) group. HARDI data were acquired for each patient, and dispersion parameters were calculated to compare the differences in specific fibre bundles among the groups. The Montreal Neurocognitive Assessment (MoCA) was used to evaluate neurocognitive function, and the correlations between dispersion parameters and MoCA were analysed. RESULTS In the right cingulum frontal parietal bundles, the fractional anisotropy value decreased to the lowest level post-RT-AP and then reversed and increased post-RT-ED and post-RT-LD. The mean, axial, and radial diffusivity were significantly increased in the post-RT-AP (p < 0.05) and decreased in the post-RT-ED and post-RT-LD groups to varying degrees. MoCA scores were decreased post-radiotherapy than those before radiotherapy (p = 0.005). MoCA and mean diffusivity exhibited a mild correlation in the left cingulum frontal parahippocampal bundle. CONCLUSIONS White matter tract changes detected by HARDI are potential biomarkers for monitoring radiotherapy-related brain damage in NPC patients.
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Affiliation(s)
- Tiansheng Li
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), NO. 19, Xiuhua St, Xiuying Dic, Haikou, Hainan, 570311, PR China
| | - Yihao Guo
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), NO. 19, Xiuhua St, Xiuying Dic, Haikou, Hainan, 570311, PR China
| | - Xin Jin
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), NO. 19, Xiuhua St, Xiuying Dic, Haikou, Hainan, 570311, PR China
| | - Tao Liu
- Department of Geriatric Center, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), NO. 19, Xiuhua St, Xiuying Dic, Haikou, Hainan, 570311, PR China
| | - Gang Wu
- Department of Radiotherapy, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), NO. 19, Xiuhua St, Xiuying Dic, Haikou, Hainan, 570311, PR China
| | - Weiyuan Huang
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), NO. 19, Xiuhua St, Xiuying Dic, Haikou, Hainan, 570311, PR China.
| | - Feng Chen
- Department of Radiology, Hainan General Hospital (Hainan Affiliated Hospital of Hainan Medical University), NO. 19, Xiuhua St, Xiuying Dic, Haikou, Hainan, 570311, PR China.
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9
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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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10
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Hoffman LJ, Foley JM, Leong JK, Sullivan-Toole H, Elliott BL, Olson IR. An in vivo Dissection, and Analysis of Socio-Affective Symptoms related to Cerebellum-Midbrain Reward Circuitry in Humans. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.09.29.560239. [PMID: 38798382 PMCID: PMC11118266 DOI: 10.1101/2023.09.29.560239] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Emerging research in non-human animals implicates cerebellar projections to the ventral tegmental area (VTA) in appetitive behaviors, but these circuits have not been characterized in humans. Here, we mapped cerebello-VTA white-matter connectivity in humans using probabilistic tractography on diffusion imaging data from the Human Connectome Project. We uncovered the topographical organization of these connections by separately tracking from parcels of cerebellar lobule VI, crus I/II, vermis, paravermis, and cerebrocerebellum. Results revealed that connections from the cerebellum to the VTA predominantly originate in the right hemisphere, interposed nucleus, and paravermal cortex, and terminate mostly ipsilaterally. Paravermal crus I sends the most connections to the VTA compared to other lobules. We discovered a medial-to-lateral gradient of connectivity, such that the medial cerebellum has the highest connectivity with the VTA. Individual differences in microstructure were associated with measures of negative affect and social functioning. By splitting the tracts into quarters, we found that the socio-affective effects were driven by the third quarter of the tract, corresponding to the point at which the fibers leave the deep nuclei. Taken together, we produced detailed maps of cerebello-VTA structural connectivity for the first time in humans and established their relevance for trait differences in socio-affective regulation.
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Affiliation(s)
- Linda J. Hoffman
- Temple University, Department of Psychology and Neuroscience, Philadelphia, PA, USA
| | - Julia M. Foley
- Temple University, Department of Psychology and Neuroscience, Philadelphia, PA, USA
| | - Josiah K. Leong
- University of Arkansas, Department of Psychological Science, Fayetteville, AR, USA
| | - Holly Sullivan-Toole
- Temple University, Department of Psychology and Neuroscience, Philadelphia, PA, USA
| | - Blake L. Elliott
- Temple University, Department of Psychology and Neuroscience, Philadelphia, PA, USA
| | - Ingrid R. Olson
- Temple University, Department of Psychology and Neuroscience, Philadelphia, PA, USA
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Bottenhorn KL, Sukumaran K, Cardenas-Iniguez C, Habre R, Schwartz J, Chen JC, Herting MM. Air pollution from biomass burning disrupts early adolescent cortical microarchitecture development. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2023.10.21.563430. [PMID: 38798573 PMCID: PMC11118378 DOI: 10.1101/2023.10.21.563430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
Exposure to outdoor particulate matter (PM 2.5 ) represents a ubiquitous threat to human health, and particularly the neurotoxic effects of PM 2.5 from multiple sources may disrupt neurodevelopment. Studies addressing neurodevelopmental implications of PM exposure have been limited by small, geographically limited samples and largely focus either on macroscale cortical morphology or postmortem histological staining and total PM mass. Here, we leverage residentially assigned exposure to six, data-driven sources of PM 2.5 and neuroimaging data from the longitudinal Adolescent Brain Cognitive Development Study (ABCD Study®), collected from 21 different recruitment sites across the United States. To contribute an interpretable and actionable assessment of the role of air pollution in the developing brain, we identified alterations in cortical microstructure development associated with exposure to specific sources of PM 2.5 using multivariate, partial least squares analyses. Specifically, average annual exposure (i.e., at ages 8-10 years) to PM 2.5 from biomass burning was related to differences in neurite development across the cortex between 9 and 13 years of age.
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12
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Mishra S, Bapuraj J, Srinivasan A. Multiple Sclerosis Part 2: Advanced Imaging and Emerging Techniques. Magn Reson Imaging Clin N Am 2024; 32:221-231. [PMID: 38555138 DOI: 10.1016/j.mric.2024.01.002] [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: 04/02/2024]
Abstract
Multiple advanced imaging methods for multiple sclerosis (MS) have been in investigation to identify new imaging biomarkers for early disease detection, predicting disease prognosis, and clinical trial endpoints. Multiple techniques probing different aspects of tissue microstructure (ie, advanced diffusion imaging, magnetization transfer, myelin water imaging, magnetic resonance spectroscopy, glymphatic imaging, and perfusion) support the notion that MS is a global disease with microstructural changes evident in normal-appearing white and gray matter. These global changes are likely better predictors of disability compared with lesion load alone. Emerging techniques in glymphatic and molecular imaging may improve understanding of pathophysiology and emerging treatments.
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Affiliation(s)
- Shruti Mishra
- Department of Radiology, University of Michigan, 1500 East Medical Center Drive, UH B2A209, Ann Arbor, MI 48109-5030, USA.
| | - Jayapalli Bapuraj
- Department of Radiology, University of Michigan, 1500 East Medical Center Drive, UH B2A209, Ann Arbor, MI 48109-5030, USA
| | - Ashok Srinivasan
- Department of Radiology, University of Michigan, 1500 East Medical Center Drive, UH B2A209, Ann Arbor, MI 48109-5030, USA
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Du L, Roy S, Wang P, Li Z, Qiu X, Zhang Y, Yuan J, Guo B. Unveiling the future: Advancements in MRI imaging for neurodegenerative disorders. Ageing Res Rev 2024; 95:102230. [PMID: 38364912 DOI: 10.1016/j.arr.2024.102230] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2024] [Revised: 02/11/2024] [Accepted: 02/11/2024] [Indexed: 02/18/2024]
Abstract
Neurodegenerative disorders represent a significant and growing global health challenge, necessitating continuous advancements in diagnostic tools for accurate and early detection. This work explores the recent progress in Magnetic Resonance Imaging (MRI) techniques and their application in the realm of neurodegenerative disorders. The introductory section provides a comprehensive overview of the study's background, significance, and objectives. Recognizing the current challenges associated with conventional MRI, the manuscript delves into advanced imaging techniques such as high-resolution structural imaging (HR-MRI), functional MRI (fMRI), diffusion tensor imaging (DTI), and positron emission tomography-MRI (PET-MRI) fusion. Each technique is critically examined regarding its potential to address theranostic limitations and contribute to a more nuanced understanding of the underlying pathology. A substantial portion of the work is dedicated to exploring the applications of advanced MRI in specific neurodegenerative disorders, including Parkinson's disease, Alzheimer's disease, Huntington's disease, and Amyotrophic Lateral Sclerosis (ALS). In addressing the future landscape, the manuscript examines technological advances, including the integration of machine learning and artificial intelligence in neuroimaging. The conclusion summarizes key findings, outlines implications for future research, and underscores the importance of these advancements in reshaping our understanding and approach to neurodegenerative disorders.
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Affiliation(s)
- Lixin Du
- Department of Medical Imaging, Shenzhen Longhua District Central Hospital, Shenzhen Longhua District Key Laboratory of Neuroimaging, Shenzhen 518110, China.
| | - Shubham Roy
- School of Science, Shenzhen Key Laboratory of Flexible Printed Electronics Technology, Shenzhen Key Laboratory of Advanced Functional Carbon Materials Research and Comprehensive Application, Harbin Institute of Technology, Shenzhen 518055, China
| | - Pan Wang
- Department of Medical Imaging, Shenzhen Longhua District Central Hospital, Shenzhen Longhua District Key Laboratory of Neuroimaging, Shenzhen 518110, China
| | - Zhigang Li
- Department of Medical Imaging, Shenzhen Longhua District Central Hospital, Shenzhen Longhua District Key Laboratory of Neuroimaging, Shenzhen 518110, China
| | - Xiaoting Qiu
- Department of Medical Imaging, Shenzhen Longhua District Central Hospital, Shenzhen Longhua District Key Laboratory of Neuroimaging, Shenzhen 518110, China
| | - Yinghe Zhang
- School of Science, Shenzhen Key Laboratory of Flexible Printed Electronics Technology, Shenzhen Key Laboratory of Advanced Functional Carbon Materials Research and Comprehensive Application, Harbin Institute of Technology, Shenzhen 518055, China
| | - Jianpeng Yuan
- Department of Radiology, The Seventh Affiliated Hospital, Sun Yat-sen University, Shenzhen 518107, China.
| | - Bing Guo
- School of Science, Shenzhen Key Laboratory of Flexible Printed Electronics Technology, Shenzhen Key Laboratory of Advanced Functional Carbon Materials Research and Comprehensive Application, Harbin Institute of Technology, Shenzhen 518055, China.
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Xiao J, Sun C, Chen R, Zhao Z, Wang G, Wu D. Reproducibility of Diffusion MRI-Based Tractography in the Fetal Brain. J Magn Reson Imaging 2024. [PMID: 38284561 DOI: 10.1002/jmri.29253] [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: 08/29/2023] [Revised: 01/09/2024] [Accepted: 01/11/2024] [Indexed: 01/30/2024] Open
Abstract
BACKGROUND Tractography based on diffusion MRI (dMRI) is a useful tool to study white matter of the developing brain. However, its application in fetal brains is limited due to motion artifacts and low resolution of in utero dMRI, leading to reduced reliability, which was scarcely investigated in previous studies. PURPOSE To identify reliably traceable fibers in fetal brains and assess whether reproducibility varies with gestational age (GA) and varies between brain regions. STUDY TYPE Prospective cohort study. SUBJECTS A total of 44 healthy fetuses with GAs between 25 and 37 (31 ± 6). FIELD STRENGTH/SEQUENCE 3-T, diffusion-weighted echo-planar imaging sequence (2-5 repeated dMRI scans within the same session per subject). ASSESSMENT We fitted dMRI with constrained spherical deconvolution model and conducted tractography on eight fibers. We extracted volume, fractional anisotropy, and fiber count for each fiber and assessed the reproducibility of these metrics between repeated scans within each subject. Data were divided into two age-based subgroups (≤30 weeks, N = 28, and >30 weeks, N = 16) for further tests. STATISTICAL TESTS The reproducibility were compared between fibers by analysis of variance and two-sample t tests. Multiple comparisons were corrected by the false discovery rate (5% was accepted). RESULTS The reproducibility of the anterior thalamic radiation, inferior longitudinal fasciculus (ILF), genu of the corpus callosum (GCC), and body of the corpus callosum (BCC) significantly decreased with advancing GA (correlation coefficient = 0.525-0.823), as confirmed by group comparisons between fetuses in early GA (≤30 weeks) and late GA (>30 weeks) groups. Corticospinal tract, inferior fronto-occipital fasciculus, and GCC showed high reproducibility for fiber count (weighted dice average = 0.846 vs. 0.814), while BCC and ILF exhibited the lowest reproducibility in both age groups. DATA CONCLUSION The study indicates that the reliability of fetal brain tractography depends on GA and varies among different fibers. LEVEL OF EVIDENCE 2 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Jiaxin Xiao
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Cong Sun
- Department of Radiology, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, China
| | - Ruike Chen
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Zhiyong Zhao
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
| | - Guangbin Wang
- Department of Radiology, Shandong Provincial Hospital Affiliated to Shandong First Medical University, Jinan, China
| | - Dan Wu
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou, China
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Porcu M, Cocco L, Cau R, Suri JS, Mannelli L, Manchia M, Puig J, Qi Y, Saba L. Correlation of Cognitive Reappraisal and the Microstructural Properties of the Forceps Minor: A Deductive Exploratory Diffusion Tensor Imaging Study. Brain Topogr 2024; 37:63-74. [PMID: 38062326 DOI: 10.1007/s10548-023-01020-4] [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: 07/15/2022] [Accepted: 10/29/2023] [Indexed: 01/07/2024]
Abstract
Cognitive reappraisal (CR) is a mechanism for emotion regulation, and the prefrontal cortex (PFC) plays a central role in the regulation of emotions. We tested the hypothesis of an association between CR function and microstructural properties of forceps minor (a commissural bundle within the PFC) in healthy subjects (HS). We analyzed a population of 65 young HS of a public dataset. The diffusion tensor imaging (DTI) sequence of every subject was analyzed to extract the derived shape (diameter and volume) and DTI metrics in terms of fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD) of the forceps minor. The CR subscale of the German version of the Emotion Regulation Questionnaire (ERQ) was used for CR assessment. The Shapiro-Wilk test was applied to test the assumption of normality in all these parameters, adopting a statistical threshold at p < 0.05. Whenever appropriate a non-parametric two-tailed partial correlation analysis was applied to test for correlations between the CR ERQ score and the derived shape and DTI metrics, including age and sex as confounders, adopting a statistical threshold at p < 0.05. The non-parametric two-tailed partial correlation analysis revealed a mildly significant correlation with FA (ρ = 0.303; p = 0.016), a weakly significant negative correlation with MD (ρ = - 0.269; p = 0.033), and a mildly significant negative correlation with RD (ρ = - 0.305; p = 0.015). These findings suggest a correlation between DTI microstructural properties of forceps minor and CR.
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Affiliation(s)
- Michele Porcu
- Department of Radiology, AOU Cagliari, University of Cagliari, Cagliari, Italy.
- Department of Medical Imaging, Azienda Ospedaliera Universitaria di Cagliari, S.S: 554, Km 4,500, Monserrato, 09042, Cagliari, Italy.
| | - Luigi Cocco
- Department of Radiology, AOU Cagliari, University of Cagliari, Cagliari, Italy
| | - Riccardo Cau
- Department of Radiology, AOU Cagliari, University of Cagliari, Cagliari, Italy
| | - Jasjit S Suri
- Stroke Monitoring and Diagnostic Division, AtheroPoint™, Roseville, CA, USA
| | | | - Mirko Manchia
- Unit of Psychiatry, Department of Medical Sciences and Public Health, University of Cagliari, Cagliari, Italy
- Unit of Clinical Psychiatry, University Hospital Agency of Cagliari, Cagliari, Italy
- Department of Pharmacology, Dalhousie University, Halifax, NS, Canada
| | - Josep Puig
- Department of Radiology (IDI) and Girona Biomedical Research Institute (IDIBGI), Hospital Universitari de Girona Dr Josep Trueta, Girona, Spain
| | - Yang Qi
- Xuanwu Hospital, Capital Medical University, No.45 Changchun Street, Xicheng District, Beijing, China
| | - Luca Saba
- Department of Radiology, AOU Cagliari, University of Cagliari, Cagliari, Italy
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16
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Strobel J, Müller HP, Ludolph AC, Beer AJ, Sollmann N, Kassubek J. New Perspectives in Radiological and Radiopharmaceutical Hybrid Imaging in Progressive Supranuclear Palsy: A Systematic Review. Cells 2023; 12:2776. [PMID: 38132096 PMCID: PMC10742083 DOI: 10.3390/cells12242776] [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: 10/30/2023] [Revised: 11/28/2023] [Accepted: 12/04/2023] [Indexed: 12/23/2023] Open
Abstract
Progressive supranuclear palsy (PSP) is a neurodegenerative disease characterized by four-repeat tau deposition in various cell types and anatomical regions, and can manifest as several clinical phenotypes, including the most common phenotype, Richardson's syndrome. The limited availability of biomarkers for PSP relates to the overlap of clinical features with other neurodegenerative disorders, but identification of a growing number of biomarkers from imaging is underway. One way to increase the reliability of imaging biomarkers is to combine different modalities for multimodal imaging. This review aimed to provide an overview of the current state of PSP hybrid imaging by combinations of positron emission tomography (PET) and magnetic resonance imaging (MRI). Specifically, combined PET and MRI studies in PSP highlight the potential of [18F]AV-1451 to detect tau, but also the challenge in differentiating PSP from other neurodegenerative diseases. Studies over the last years showed a reduced synaptic density in [11C]UCB-J PET, linked [11C]PK11195 and [18F]AV-1451 markers to disease progression, and suggested the potential role of [18F]RO948 PET for identifying tau pathology in subcortical regions. The integration of quantitative global and regional gray matter analysis by MRI may further guide the assessment of reduced cortical thickness or volume alterations, and diffusion MRI could provide insight into microstructural changes and structural connectivity in PSP. Challenges in radiopharmaceutical biomarkers and hybrid imaging require further research targeting markers for comprehensive PSP diagnosis.
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Affiliation(s)
- Joachim Strobel
- Department of Nuclear Medicine, University Hospital Ulm, 89081 Ulm, Germany;
| | - Hans-Peter Müller
- Department of Neurology, University Hospital Ulm, 89081 Ulm, Germany; (H.-P.M.); (A.C.L.); (J.K.)
| | - Albert C. Ludolph
- Department of Neurology, University Hospital Ulm, 89081 Ulm, Germany; (H.-P.M.); (A.C.L.); (J.K.)
- German Center for Neurodegenerative Diseases (DZNE), Ulm University, 89081 Ulm, Germany
| | - Ambros J. Beer
- Department of Nuclear Medicine, University Hospital Ulm, 89081 Ulm, Germany;
| | - Nico Sollmann
- Department of Diagnostic and Interventional Radiology, University Hospital Ulm, 89081 Ulm, Germany;
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, 81675 Munich, Germany
- TUM-Neuroimaging Center, Klinikum rechts der Isar, Technical University of Munich, 81675 Munich, Germany
| | - Jan Kassubek
- Department of Neurology, University Hospital Ulm, 89081 Ulm, Germany; (H.-P.M.); (A.C.L.); (J.K.)
- German Center for Neurodegenerative Diseases (DZNE), Ulm University, 89081 Ulm, Germany
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17
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Yang WT, Tang FQ. Isolated bilateral fornix anterior columns infarction with acute amnesia and fiber tracts damage, a case report. Neurocase 2023; 29:186-190. [PMID: 38700142 DOI: 10.1080/13554794.2024.2348762] [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: 07/13/2023] [Accepted: 04/22/2024] [Indexed: 05/05/2024]
Abstract
Isolated fornix anterior column infarction has rarely been described and is difficult to assess accurately using conventional magnetic resonance imaging (MRI). We report the case of a 75-year-old female who experienced acute anterograde amnesia. MRI performed within 24 h after amnesia onset showed an isolated infarction of the bilateral anterior columns of the fornix on diffusion-weighted imaging (DWI). Her symptoms persisted for up to 50 days, and diffusion tensor imaging (DTI) showed disruption of the fiber tracts of the fornix. when acute amnesia syndrome onset, fornix anterior column infarction should be considered, and optimized DWI and DTI methods are needed to study the fornix in vivo in future research.
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Affiliation(s)
- Wen-Ting Yang
- Department of Radiology, Jinhua Hospital of Zhejiang University (Jinhua Municipal Central Hospital), Jinhua, China
| | - Fu-Qiang Tang
- Department of Radiology, Jinhua Hospital of Zhejiang University (Jinhua Municipal Central Hospital), Jinhua, China
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18
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Gayathiri E, Prakash P, Kumaravel P, Jayaprakash J, Ragunathan MG, Sankar S, Pandiaraj S, Thirumalaivasan N, Thiruvengadam M, Govindasamy R. Computational approaches for modeling and structural design of biological systems: A comprehensive review. PROGRESS IN BIOPHYSICS AND MOLECULAR BIOLOGY 2023; 185:17-32. [PMID: 37821048 DOI: 10.1016/j.pbiomolbio.2023.08.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/30/2023] [Revised: 08/14/2023] [Accepted: 08/27/2023] [Indexed: 10/13/2023]
Abstract
The convergence of biology and computational science has ushered in a revolutionary era, revolutionizing our understanding of biological systems and providing novel solutions to global problems. The field of genetic engineering has facilitated the manipulation of genetic codes, thus providing opportunities for the advancement of innovative disease therapies and environmental enhancements. The emergence of bio-molecular simulation represents a significant advancement in this particular field, as it offers the ability to gain microscopic insights into molecular-level biological processes over extended periods. Biomolecular simulation plays a crucial role in advancing our comprehension of organismal mechanisms by establishing connections between molecular structures, interactions, and biological functions. The field of computational biology has demonstrated its significance in deciphering intricate biological enigmas through the utilization of mathematical models and algorithms. The process of decoding the human genome has resulted in the advancement of therapies for a wide range of genetic disorders, while the simulation of biological systems contributes to the identification of novel pharmaceutical compounds. The potential of biomolecular simulation and computational biology is vast and limitless. As the exploration of the underlying principles that govern living organisms progresses, the potential impact of this understanding on cancer treatment, environmental restoration, and other domains is anticipated to be transformative. This review examines the notable advancements achieved in the field of computational biology, emphasizing its potential to revolutionize the comprehension and enhancement of biological systems.
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Affiliation(s)
- Ekambaram Gayathiri
- Department of Plant Biology and Plant Biotechnology, Guru Nanak College (Autonomous), Chennai, 42, Tamil Nadu, India
| | - Palanisamy Prakash
- Department of Botany, Periyar University, Periyar Palkalai Nagar, Salem, 636011, Tamil Nadu, India
| | - Priya Kumaravel
- Department of Biotechnology, St. Joseph College (Arts & Science), Kovur, Chennai, Tamil Nadu, India
| | - Jayanthi Jayaprakash
- Department of Advanced Zoology and Biotechnology, Guru Nanak College, Chennai, Tamil Nadu, India
| | | | - Sharmila Sankar
- Department of Advanced Zoology and Biotechnology, Guru Nanak College, Chennai, Tamil Nadu, India
| | - Saravanan Pandiaraj
- Department of Self-Development Skills, King Saud University, P.O. Box 2455, Riyadh, 11451, Saudi Arabia
| | - Natesan Thirumalaivasan
- Department of Periodontics, Saveetha Dental College, and Hospitals, Saveetha Institute of Medical and Technical Sciences (SIMTAS), Chennai, 600077, Tamil Nadu, India
| | - Muthu Thiruvengadam
- Department of Applied Bioscience, College of Life and Environmental Sciences, Konkuk University, Seoul, 05029, South Korea
| | - Rajakumar Govindasamy
- Department of Orthodontics, Saveetha Dental College and Hospitals, Saveetha University, Chennai, India.
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Fokkinga E, Hernandez-Tamames JA, Ianus A, Nilsson M, Tax CMW, Perez-Lopez R, Grussu F. Advanced Diffusion-Weighted MRI for Cancer Microstructure Assessment in Body Imaging, and Its Relationship With Histology. J Magn Reson Imaging 2023. [PMID: 38032021 DOI: 10.1002/jmri.29144] [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: 08/11/2023] [Revised: 10/30/2023] [Accepted: 10/31/2023] [Indexed: 12/01/2023] Open
Abstract
Diffusion-weighted magnetic resonance imaging (DW-MRI) aims to disentangle multiple biological signal sources in each imaging voxel, enabling the computation of innovative maps of tissue microstructure. DW-MRI model development has been dominated by brain applications. More recently, advanced methods with high fidelity to histology are gaining momentum in other contexts, for example, in oncological applications of body imaging, where new biomarkers are urgently needed. The objective of this article is to review the state-of-the-art of DW-MRI in body imaging (ie, not including the nervous system) in oncology, and to analyze its value as compared to reference colocalized histology measurements, given that demonstrating the histological validity of any new DW-MRI method is essential. In this article, we review the current landscape of DW-MRI techniques that extend standard apparent diffusion coefficient (ADC), describing their acquisition protocols, signal models, fitting settings, microstructural parameters, and relationship with histology. Preclinical, clinical, and in/ex vivo studies were included. The most used techniques were intravoxel incoherent motion (IVIM; 36.3% of used techniques), diffusion kurtosis imaging (DKI; 16.7%), vascular, extracellular, and restricted diffusion for cytometry in tumors (VERDICT; 13.3%), and imaging microstructural parameters using limited spectrally edited diffusion (IMPULSED; 11.7%). Another notable category of techniques relates to innovative b-tensor diffusion encoding or joint diffusion-relaxometry. The reviewed approaches provide histologically meaningful indices of cancer microstructure (eg, vascularization/cellularity) which, while not necessarily accurate numerically, may still provide useful sensitivity to microscopic pathological processes. Future work of the community should focus on improving the inter-/intra-scanner robustness, and on assessing histological validity in broader contexts. LEVEL OF EVIDENCE: NA TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Ella Fokkinga
- Biomedical Engineering, Track Medical Physics, Delft University of Technology, Delft, The Netherlands
- Radiomics Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Juan A Hernandez-Tamames
- Department of Radiology and Nuclear Medicine, Erasmus MC, Rotterdam, The Netherlands
- Department of Imaging Physics, Delft University of Technology, Delft, The Netherlands
| | - Andrada Ianus
- Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal
| | - Markus Nilsson
- Department of Diagnostic Radiology, Clinical Sciences Lund, Lund, Sweden
| | - Chantal M W Tax
- Cardiff University Brain Research Imaging Center (CUBRIC), School of Physics and Astronomy, Cardiff University, Cardiff, United Kingdom
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Raquel Perez-Lopez
- Radiomics Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Francesco Grussu
- Radiomics Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
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Stabinska J, Wittsack HJ, Lerman LO, Ljimani A, Sigmund EE. Probing Renal Microstructure and Function with Advanced Diffusion MRI: Concepts, Applications, Challenges, and Future Directions. J Magn Reson Imaging 2023:10.1002/jmri.29127. [PMID: 37991093 PMCID: PMC11117411 DOI: 10.1002/jmri.29127] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2023] [Revised: 10/26/2023] [Accepted: 10/27/2023] [Indexed: 11/23/2023] Open
Abstract
Diffusion measurements in the kidney are affected not only by renal microstructure but also by physiological processes (i.e., glomerular filtration, water reabsorption, and urine formation). Because of the superposition of passive tissue diffusion, blood perfusion, and tubular pre-urine flow, the limitations of the monoexponential apparent diffusion coefficient (ADC) model in assessing pathophysiological changes in renal tissue are becoming apparent and motivate the development of more advanced diffusion-weighted imaging (DWI) variants. These approaches take advantage of the fact that the length scale probed in DWI measurements can be adjusted by experimental parameters, including diffusion-weighting, diffusion gradient directions and diffusion time. This forms the basis by which advanced DWI models can be used to capture not only passive diffusion effects, but also microcirculation, compartmentalization, tissue anisotropy. In this review, we provide a comprehensive overview of the recent advancements in the field of renal DWI. Following a short introduction on renal structure and physiology, we present the key methodological approaches for the acquisition and analysis of renal DWI data, including intravoxel incoherent motion (IVIM), diffusion tensor imaging (DTI), non-Gaussian diffusion, and hybrid IVIM-DTI. We then briefly summarize the applications of these methods in chronic kidney disease and renal allograft dysfunction. Finally, we discuss the challenges and potential avenues for further development of renal DWI. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 2.
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Affiliation(s)
- Julia Stabinska
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Hans-Jörg Wittsack
- Department of Diagnostic and Interventional Radiology, Medical Faculty, Heinrich Heine University Düsseldorf, Dusseldorf, Germany
| | - Lilach O. Lerman
- Division of Nephrology and Hypertension and Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Alexandra Ljimani
- Department of Diagnostic and Interventional Radiology, Medical Faculty, Heinrich Heine University Düsseldorf, Dusseldorf, Germany
| | - Eric E. Sigmund
- Bernard and Irene Schwartz Center for Biomedical Imaging Center for Advanced Imaging Innovation and Research (CAI2R), New York University Langone Health, New York City, New York, USA
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21
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Martinez-Heras E, Solana E, Vivó F, Lopez-Soley E, Calvi A, Alba-Arbalat S, Schoonheim MM, Strijbis EM, Vrenken H, Barkhof F, Rocca MA, Filippi M, Pagani E, Groppa S, Fleischer V, Dineen RA, Bellenberg B, Lukas C, Pareto D, Rovira A, Sastre-Garriga J, Collorone S, Prados F, Toosy A, Ciccarelli O, Saiz A, Blanco Y, Llufriu S. Diffusion-based structural connectivity patterns of multiple sclerosis phenotypes. J Neurol Neurosurg Psychiatry 2023; 94:916-923. [PMID: 37321841 DOI: 10.1136/jnnp-2023-331531] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 05/30/2023] [Indexed: 06/17/2023]
Abstract
BACKGROUND We aimed to describe the severity of the changes in brain diffusion-based connectivity as multiple sclerosis (MS) progresses and the microstructural characteristics of these networks that are associated with distinct MS phenotypes. METHODS Clinical information and brain MRIs were collected from 221 healthy individuals and 823 people with MS at 8 MAGNIMS centres. The patients were divided into four clinical phenotypes: clinically isolated syndrome, relapsing-remitting, secondary progressive and primary progressive. Advanced tractography methods were used to obtain connectivity matrices. Then, differences in whole-brain and nodal graph-derived measures, and in the fractional anisotropy of connections between groups were analysed. Support vector machine algorithms were used to classify groups. RESULTS Clinically isolated syndrome and relapsing-remitting patients shared similar network changes relative to controls. However, most global and local network properties differed in secondary progressive patients compared with the other groups, with lower fractional anisotropy in most connections. Primary progressive participants had fewer differences in global and local graph measures compared with clinically isolated syndrome and relapsing-remitting patients, and reductions in fractional anisotropy were only evident for a few connections. The accuracy of support vector machine to discriminate patients from healthy controls based on connection was 81%, and ranged between 64% and 74% in distinguishing among the clinical phenotypes. CONCLUSIONS In conclusion, brain connectivity is disrupted in MS and has differential patterns according to the phenotype. Secondary progressive is associated with more widespread changes in connectivity. Additionally, classification tasks can distinguish between MS types, with subcortical connections being the most important factor.
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Affiliation(s)
- Eloy Martinez-Heras
- Neuroimmunology and Multiple Sclerosis Unit and Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clinic and Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), University of Barcelona, Barcelona, Spain
| | - Elisabeth Solana
- Neuroimmunology and Multiple Sclerosis Unit and Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clinic and Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), University of Barcelona, Barcelona, Spain
| | - Francesc Vivó
- Neuroimmunology and Multiple Sclerosis Unit and Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clinic and Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), University of Barcelona, Barcelona, Spain
| | - Elisabet Lopez-Soley
- Neuroimmunology and Multiple Sclerosis Unit and Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clinic and Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), University of Barcelona, Barcelona, Spain
| | - Alberto Calvi
- Neuroimmunology and Multiple Sclerosis Unit and Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clinic and Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), University of Barcelona, Barcelona, Spain
| | - Salut Alba-Arbalat
- Neuroimmunology and Multiple Sclerosis Unit and Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clinic and Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), University of Barcelona, Barcelona, Spain
| | - Menno M Schoonheim
- MS Center Amsterdam, Anatomy and Neurosciences, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
| | - Eva M Strijbis
- MS Center Amsterdam, Neurology, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
| | - Hugo Vrenken
- Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
| | - Frederik Barkhof
- Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam Neuroscience, Amsterdam UMC location VUmc, Amsterdam, The Netherlands
- Queen Square Institute of Neurology and Centre for Medical Image Computing, University College London, London, UK
| | - Maria A Rocca
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milano, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milano, Italy
- Vita-Salute San Raffaele University, Milano, Italy
| | - Massimo Filippi
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milano, Italy
- Neurology Unit, IRCCS San Raffaele Scientific Institute, Milano, Italy
- Vita-Salute San Raffaele University, Milano, Italy
- Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milano, Italy
| | - Elisabetta Pagani
- Neuroimaging Research Unit, Institute of Experimental Neurology, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milano, Italy
| | - Sergiu Groppa
- Department of Neurology, Neurostimulation and Neuroimaging, Focus Program Translational Neuroscience (FTN), Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University, Mainz, Germany
| | - Vinzenz Fleischer
- Department of Neurology, Neurostimulation and Neuroimaging, Focus Program Translational Neuroscience (FTN), Rhine Main Neuroscience Network (rmn2), University Medical Center of the Johannes Gutenberg University, Mainz, Germany
| | - Robert A Dineen
- Mental Health and Clinical Neuroscience, School of Medicine, University of Nottingham, Nottingham, UK; and NIHR Nottingham Biomedical Research Centre, Nottingham, UK
| | - Barbara Bellenberg
- Institute of Neuroradiology, St. Josef Hospital, Ruhr-University Bochum, Bochum, Germany
| | - Carsten Lukas
- Institute of Neuroradiology, St. Josef Hospital, Ruhr-University Bochum, Bochum, Germany
| | - Deborah Pareto
- Section of Neuroradiology, Department of Radiology, Vall d'Hebron University Hospital and Research Institute (VHIR), Barcelona, Spain
| | - Alex Rovira
- Section of Neuroradiology, Department of Radiology, Vall d'Hebron University Hospital and Research Institute (VHIR), Barcelona, Spain
| | - Jaume Sastre-Garriga
- Neurology-Neuroimmunology Department, Centre d'Esclerosi Múltiple de Catalunya (Cemcat), Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
| | - Sara Collorone
- Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Science, University College of London, London, UK
| | - Ferran Prados
- Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Science, University College of London, London, UK
- Centre for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK
- E-health Centre, Universitat Oberta de Catalunya, Barcelona, Spain
| | - Ahmed Toosy
- Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Science, University College of London, London, UK
| | - Olga Ciccarelli
- Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Science, University College of London, London, UK
| | - Albert Saiz
- Neuroimmunology and Multiple Sclerosis Unit and Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clinic and Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), University of Barcelona, Barcelona, Spain
| | - Yolanda Blanco
- Neuroimmunology and Multiple Sclerosis Unit and Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clinic and Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), University of Barcelona, Barcelona, Spain
| | - Sara Llufriu
- Neuroimmunology and Multiple Sclerosis Unit and Laboratory of Advanced Imaging in Neuroimmunological Diseases (ImaginEM), Hospital Clinic and Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), University of Barcelona, Barcelona, Spain
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22
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Hong TY, Yang CJ, Cheng LK, Li WC, Tseng WYI, Yeh TC, Yu HY, Chen LF, Hsieh JC. Enhanced white matter fiber tract of the cortical visual system in visual artists: implications for creativity. Front Neurosci 2023; 17:1248266. [PMID: 37946727 PMCID: PMC10631786 DOI: 10.3389/fnins.2023.1248266] [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: 06/26/2023] [Accepted: 10/05/2023] [Indexed: 11/12/2023] Open
Abstract
Introduction This study aimed to examine the white matter characteristics of visual artists (VAs) in terms of visual creativity and the structural connectivity within the cortical visual system. Methods Diffusion spectrum imaging was utilized to examine the changes in white matter within the cortical visual system of a group of VAs (n = 25) in comparison to a group of healthy controls matched for age and education (n = 24). To assess the integrity of white matter and its relationship with visual creativity, we conducted a comprehensive analysis using region-based and track-specific tractographic examinations. Results Our study uncovered that VAs demonstrated increased normalized quantitative anisotropy in specific brain regions, including the right inferior temporal gyrus and right lateral occipital gyrus, along with the corresponding white matter fiber tracts connecting these regions. These enhancements within the cortical visual system were also found to be correlated with measures of visual creativity obtained through psychological assessments. Discussion The noted enhancement in the white matter within the cortical visual system of VAs, along with its association with visual creativity, is consistent with earlier research demonstrating heightened functional connectivity in the same system among VAs. Our study's findings suggest a link between the visual creativity of VAs and structural alterations within the brain's visual system.
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Affiliation(s)
- Tzu-Yi Hong
- Institute of Brain Science, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Integrated Brain Research Unit, Division of Clinical Research, Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan
- Center for Intelligent Drug Systems and Smart Bio-devices (IDS2B), National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Ching-Ju Yang
- Institute of Brain Science, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Integrated Brain Research Unit, Division of Clinical Research, Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan
- Center for Intelligent Drug Systems and Smart Bio-devices (IDS2B), National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Li-Kai Cheng
- Institute of Brain Science, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Integrated Brain Research Unit, Division of Clinical Research, Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Wei-Chi Li
- Institute of Brain Science, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Integrated Brain Research Unit, Division of Clinical Research, Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan
- Department of Biological Science and Technology, College of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
| | - Wen-Yih Isaac Tseng
- Institute of Medical Device and Imaging, College of Medicine, National Taiwan University, Taipei, Taiwan
| | - Tzu-Chen Yeh
- Institute of Brain Science, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Integrated Brain Research Unit, Division of Clinical Research, Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan
- Department of Radiology, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Hsin-Yen Yu
- Graduate Institute of Arts and Humanities Education, Taipei National University of the Arts, Taipei, Taiwan
| | - Li-Fen Chen
- Institute of Brain Science, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Integrated Brain Research Unit, Division of Clinical Research, Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan
- Institute of Biomedical Informatics, College of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
| | - Jen-Chuen Hsieh
- Integrated Brain Research Unit, Division of Clinical Research, Department of Medical Research, Taipei Veterans General Hospital, Taipei, Taiwan
- Center for Intelligent Drug Systems and Smart Bio-devices (IDS2B), National Yang Ming Chiao Tung University, Hsinchu, Taiwan
- Department of Biological Science and Technology, College of Biological Science and Technology, National Yang Ming Chiao Tung University, Hsinchu, Taiwan
- Brain Research Center, National Yang Ming Chiao Tung University, Taipei, Taiwan
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23
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Li J, Zhou H, Lu X, Wang Y, Pang H, Cesar D, Liu A, Zhou P. Preoperative prediction of cervical cancer survival using a high-resolution MRI-based radiomics nomogram. BMC Med Imaging 2023; 23:153. [PMID: 37821840 PMCID: PMC10568765 DOI: 10.1186/s12880-023-01111-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 09/25/2023] [Indexed: 10/13/2023] Open
Abstract
BACKGROUND Cervical cancer patients receiving radiotherapy and chemotherapy require accurate survival prediction methods. The objective of this study was to develop a prognostic analysis model based on a radiomics score to predict overall survival (OS) in cervical cancer patients. METHODS Predictive models were developed using data from 62 cervical cancer patients who underwent radical hysterectomy between June 2020 and June 2021. Radiological features were extracted from T2-weighted (T2W), T1-weighted (T1W), and diffusion-weighted (DW) magnetic resonance images prior to treatment. We obtained the radiomics score (rad-score) using least absolute shrinkage and selection operator (LASSO) regression and Cox's proportional hazard model. We divided the patients into low- and high-risk groups according to the critical rad-score value, and generated a nomogram incorporating radiological features. We evaluated the model's prediction performance using area under the receiver operating characteristic (ROC) curve (AUC) and classified the participants into high- and low-risk groups based on radiological characteristics. RESULTS The 62 patients were divided into high-risk (n = 43) and low-risk (n = 19) groups based on the rad-score. Four feature parameters were selected via dimensionality reduction, and the scores were calculated after modeling. The AUC values of ROC curves for prediction of 3- and 5-year OS using the model were 0.84 and 0.93, respectively. CONCLUSION Our nomogram incorporating a combination of radiological features demonstrated good performance in predicting cervical cancer OS. This study highlights the potential of radiomics analysis in improving survival prediction for cervical cancer patients. However, further studies on a larger scale and external validation cohorts are necessary to validate its potential clinical utility.
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Affiliation(s)
- Jia Li
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Hao Zhou
- Department of Cardiology, West China Hospital, Sichuan University/West China School of Nursing, Sichuan University, Chengdu, China
| | - Xiaofei Lu
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Yiren Wang
- School of Nursing, Southwest Medical University, Luzhou, China
| | - Haowen Pang
- Department of Oncology, The Affiliated Hospital of Southwest Medical University, Luzhou, China
| | - Daniel Cesar
- Department of Gynecology Oncology, National Cancer Institute, Rio de Janeiro, Brazil
| | - Aiai Liu
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, China.
| | - Ping Zhou
- Department of Radiology, The Affiliated Hospital of Southwest Medical University, Luzhou, China.
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24
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Martucci A, Di Giuliano F, Minosse S, Pocobelli G, Nucci C, Garaci F. MRI and Clinical Biomarkers Overlap between Glaucoma and Alzheimer's Disease. Int J Mol Sci 2023; 24:14932. [PMID: 37834380 PMCID: PMC10573932 DOI: 10.3390/ijms241914932] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2023] [Accepted: 09/22/2023] [Indexed: 10/15/2023] Open
Abstract
Glaucoma is the leading cause of blindness worldwide. It is classically associated with structural and functional changes in the optic nerve head and retinal nerve fiber layer, but the damage is not limited to the eye. The involvement of the central visual pathways and disruption of brain network organization have been reported using advanced neuroimaging techniques. The brain structural changes at the level of the areas implied in processing visual information could justify the discrepancy between signs and symptoms and underlie the analogy of this disease with neurodegenerative dementias, such as Alzheimer's disease, and with the complex group of pathologies commonly referred to as "disconnection syndromes." This review aims to summarize the current state of the art on the use of advanced neuroimaging techniques in glaucoma and Alzheimer's disease, highlighting the emerging biomarkers shared by both diseases.
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Affiliation(s)
- Alessio Martucci
- Ophthalmology Unit, Department of Experimental Medicine, University of Rome “Tor Vergata”, 00133 Rome, Italy; (A.M.); (G.P.)
| | - Francesca Di Giuliano
- Neuroradiology Unit, Department of Biomedicine and Prevention, University of Rome “Tor Vergata”, 00133 Rome, Italy;
| | - Silvia Minosse
- Diagnostic Imaging Unit, Department of Biomedicine and Prevention, University of Rome “Tor Vergata”, 00133 Rome, Italy; (S.M.); (F.G.)
| | - Giulio Pocobelli
- Ophthalmology Unit, Department of Experimental Medicine, University of Rome “Tor Vergata”, 00133 Rome, Italy; (A.M.); (G.P.)
| | - Carlo Nucci
- Ophthalmology Unit, Department of Experimental Medicine, University of Rome “Tor Vergata”, 00133 Rome, Italy; (A.M.); (G.P.)
| | - Francesco Garaci
- Diagnostic Imaging Unit, Department of Biomedicine and Prevention, University of Rome “Tor Vergata”, 00133 Rome, Italy; (S.M.); (F.G.)
- San Raffaele Cassino, 03043 Frosinone, Italy
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25
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Lima Santos JP, Jia-Richards M, Kontos AP, Collins MW, Versace A. Emotional Regulation and Adolescent Concussion: Overview and Role of Neuroimaging. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:6274. [PMID: 37444121 PMCID: PMC10341732 DOI: 10.3390/ijerph20136274] [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: 04/27/2023] [Revised: 06/16/2023] [Accepted: 06/29/2023] [Indexed: 07/15/2023]
Abstract
Emotional dysregulation symptoms following a concussion are associated with an increased risk for emotional dysregulation disorders (e.g., depression and anxiety), especially in adolescents. However, predicting the emergence or worsening of emotional dysregulation symptoms after concussion and the extent to which this predates the onset of subsequent psychiatric morbidity after injury remains challenging. Although advanced neuroimaging techniques, such as functional magnetic resonance imaging and diffusion magnetic resonance imaging, have been used to detect and monitor concussion-related brain abnormalities in research settings, their clinical utility remains limited. In this narrative review, we have performed a comprehensive search of the available literature regarding emotional regulation, adolescent concussion, and advanced neuroimaging techniques in electronic databases (PubMed, Scopus, and Google Scholar). We highlight clinical evidence showing the heightened susceptibility of adolescents to experiencing emotional dysregulation symptoms following a concussion. Furthermore, we describe and provide empirical support for widely used magnetic resonance imaging modalities (i.e., functional and diffusion imaging), which are utilized to detect abnormalities in circuits responsible for emotional regulation. Additionally, we assess how these abnormalities relate to the emotional dysregulation symptoms often reported by adolescents post-injury. Yet, it remains to be determined if a progression of concussion-related abnormalities exists, especially in brain regions that undergo significant developmental changes during adolescence. We conclude that neuroimaging techniques hold potential as clinically useful tools for predicting and, ultimately, monitoring the treatment response to emotional dysregulation in adolescents following a concussion.
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Affiliation(s)
- João Paulo Lima Santos
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA; (M.J.-R.); (A.V.)
| | - Meilin Jia-Richards
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA; (M.J.-R.); (A.V.)
| | - Anthony P. Kontos
- Department of Orthopaedic Surgery, UPMC Sports Concussion Program, University of Pittsburgh, Pittsburgh, PA 15213, USA; (A.P.K.); (M.W.C.)
| | - Michael W. Collins
- Department of Orthopaedic Surgery, UPMC Sports Concussion Program, University of Pittsburgh, Pittsburgh, PA 15213, USA; (A.P.K.); (M.W.C.)
| | - Amelia Versace
- Department of Psychiatry, University of Pittsburgh School of Medicine, Pittsburgh, PA 15213, USA; (M.J.-R.); (A.V.)
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26
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Lopez-Rueda A, Puig J, Thió-Henestrosa S, Moreno-Negrete JL, Zwanzger C, Pujol T, Aldecoa I, Pineda E, Valduvieco I, González JJ, Oleaga L. Texture Analysis of the Apparent Diffusion Coefficient Focused on Contrast-Enhancing Lesions in Predicting Survival for Bevacizumab-Treated Patients with Recurrent Glioblastoma. Cancers (Basel) 2023; 15:cancers15113026. [PMID: 37296988 DOI: 10.3390/cancers15113026] [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: 01/23/2023] [Revised: 04/19/2023] [Accepted: 05/24/2023] [Indexed: 06/12/2023] Open
Abstract
PURPOSE Glioblastoma often recurs after treatment. Bevacizumab increases progression-free survival in some patients with recurrent glioblastoma. Identifying pretreatment predictors of survival can help clinical decision making. Magnetic resonance texture analysis (MRTA) quantifies macroscopic tissue heterogeneity indirectly linked to microscopic tissue properties. We investigated the usefulness of MRTA in predicting survival in patients with recurrent glioblastoma treated with bevacizumab. METHODS We evaluated retrospective longitudinal data from 33 patients (20 men; mean age 56 ± 13 years) who received bevacizumab on the first recurrence of glioblastoma. Volumes of contrast-enhancing lesions segmented on postcontrast T1-weighted sequences were co-registered on apparent diffusion coefficient maps to extract 107 radiomic features. To assess the performance of textural parameters in predicting progression-free survival and overall survival, we used receiver operating characteristic curves, univariate and multivariate regression analysis, and Kaplan-Meier plots. RESULTS Longer progression-free survival (>6 months) and overall survival (>1 year) were associated with lower values of major axis length (MAL), a lower maximum 2D diameter row (m2Ddr), and higher skewness values. Longer progression-free survival was also associated with higher kurtosis, and longer overall survival with higher elongation values. The model combining MAL, m2Ddr, and skewness best predicted progression-free survival at 6 months (AUC 0.886, 100% sensitivity, 77.8% specificity, 50% PPV, 100% NPV), and the model combining m2Ddr, elongation, and skewness best predicted overall survival (AUC 0.895, 83.3% sensitivity, 85.2% specificity, 55.6% PPV, 95.8% NPV). CONCLUSIONS Our preliminary analyses suggest that in patients with recurrent glioblastoma pretreatment, MRTA helps to predict survival after bevacizumab treatment.
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Affiliation(s)
- Antonio Lopez-Rueda
- Department of Radiology (CDI), Hospital Clínic de Barcelona, 08036 Barcelona, Spain
| | - Josep Puig
- Department of Radiology (IDI) and IDIBGI Hospital Universitari de Girona Doctor Josep Trueta, 17190 Girona, Spain
| | - Santiago Thió-Henestrosa
- Department of Computer Science Applied Mathmatics and Statistics, University of Girona, 17003 Girona, Spain
| | | | | | - Teresa Pujol
- Department of Radiology (CDI), Hospital Clínic de Barcelona, 08036 Barcelona, Spain
| | - Iban Aldecoa
- Department of Anatomical Pathology, Hospital Clínic de Barcelona, 08036 Barcelona, Spain
| | - Estela Pineda
- Translational Genomics and Targeted Therapeutics in Solid Tumors Group, Medical Oncology Department, Hospital Clínic de Barcelona, IDIBAPS, University of Barcelona, 08036 Barcelona, Spain
| | - Izaskun Valduvieco
- Radiotherapy Oncology Service, Hospital Clínic de Barcelona, 08036 Barcelona, Spain
| | - José Juan González
- Department of Neurosurgery, Laboratory of Experimental Oncological Neurosurgery, Hospital Clínic de Barcelona, 08036 Barcelona, Spain
| | - Laura Oleaga
- Department of Radiology (CDI), Hospital Clínic de Barcelona, 08036 Barcelona, Spain
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27
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Wielenga VH, Dijkhuizen RM, Van der Toorn A. Post-mortem Magnetic Resonance Imaging of Degenerating and Reorganizing White Matter in Post-stroke Rodent Brain. Methods Mol Biol 2023; 2616:153-168. [PMID: 36715933 DOI: 10.1007/978-1-0716-2926-0_12] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
Magnetic resonance imaging (MRI) allows noninvasive and non-destructive imaging of brain tissue. More specifically, the status of white matter fibers can be measured with diffusion-weighted MRI, enabling assessment of structural degeneration or remodeling of white matter tracts in diseased brain. Here, we describe the preparation of post-stroke rodent brain samples for post-mortem high-resolution 3D diffusion-weighted MR imaging, accompanied with guidelines for acquiring and processing the images.
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Affiliation(s)
- Vera H Wielenga
- Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht & Utrecht University, Utrecht, The Netherlands
| | - Rick M Dijkhuizen
- Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht & Utrecht University, Utrecht, The Netherlands
| | - Annette Van der Toorn
- Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht & Utrecht University, Utrecht, The Netherlands.
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28
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Wang J, Zhang Y, Meng X, Liu G. Application of diffusion tensor imaging technology in glaucoma diagnosis. Front Neurosci 2023; 17:1125638. [PMID: 36816120 PMCID: PMC9932933 DOI: 10.3389/fnins.2023.1125638] [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: 12/16/2022] [Accepted: 01/11/2023] [Indexed: 02/05/2023] Open
Abstract
Glaucoma is the first major category of irreversible blinding eye illnesses worldwide. Its leading cause is the death of retinal ganglion cells and their axons, which results in the loss of vision. Research indicates that glaucoma affects the optic nerve and the whole visual pathway. It also reveals that degenerative lesions caused by glaucoma can be found outside the visual pathway. Diffusion tensor imaging (DTI) is a magnetic resonance imaging (MRI) technique that can investigate the complete visual system, including alterations in the optic nerve, optic chiasm, optic tract, lateral geniculate nuclear, and optic radiation. In order to provide a more solid foundation for the degenerative characteristics of glaucoma, this paper will discuss the standard diagnostic techniques for glaucoma through a review of the literature, describe the use of DTI technology in glaucoma in humans and animal models, and introduce these techniques. With the advancement of DTI technology and its coupling with artificial intelligence, DTI represents a potential future for MRI technology in glaucoma research.
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Singh AP, Jain VS, Yu JPJ. Diffusion radiomics for subtyping and clustering in autism spectrum disorder: A preclinical study. Magn Reson Imaging 2023; 96:116-125. [PMID: 36496097 PMCID: PMC9815912 DOI: 10.1016/j.mri.2022.12.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2022] [Revised: 10/24/2022] [Accepted: 12/01/2022] [Indexed: 12/12/2022]
Abstract
Autism spectrum disorder (ASD) is a highly prevalent, heterogenous neurodevelopmental disorder. Neuroimaging methods such as functional, structural, and diffusion MRI have been used to identify candidate imaging biomarkers for ASD, but current findings remain non-specific and likely arise from the heterogeneity present in ASD. To account for this, efforts to subtype ASD have emerged as a potential strategy for both the study of ASD and advancement of tailored behavioral therapies and therapeutics. Towards these ends, to improve upon current neuroimaging methods, we propose combining biologically sensitive neurite orientation dispersion and density index (NODDI) diffusion MR imaging with radiomics image processing to create a new methodological approach that, we hypothesize, can sensitively and specifically capture neurobiology. We demonstrate this method can sensitively distinguish differences between four genetically distinct rat models of ASD (Fmr1, Pten, Nrxn1, Disc1). Further, we demonstrate diffusion radiomic analyses hold promise for subtyping in ASD as we show unsupervised clustering of NODDI radiomic data generates clusters specific to the underlying genetic differences between the animal models. Taken together, our findings suggest the unique application of radiomic analysis on NODDI diffusion MRI may have the capacity to sensitively and specifically disambiguate the neurobiological heterogeneity present in the ASD population.
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Affiliation(s)
- Ajay P. Singh
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI 53705, USA.,Medical Scientist Training Program, University of Wisconsin School of Medicine and Public Health, Madison, WI 53705, USA,Graduate Program in Cellular and Molecular Biology, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Vansh S. Jain
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI 53705, USA
| | - John-Paul J. Yu
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, WI 53705, USA.,Graduate Program in Cellular and Molecular Biology, University of Wisconsin-Madison, Madison, WI 53706, USA.,Neuroscience Training Program, Wisconsin Institutes for Medical Research, University of Wisconsin–Madison, Madison, WI 53705, USA.,Department of Biomedical Engineering, University of Wisconsin–Madison, Madison, WI 53706, USA.,Department of Psychiatry, University of Wisconsin School of Medicine and Public Health, Madison, WI 53705, USA.,Corresponding Author: John-Paul J. Yu, MD, PhD, Departments of Radiology, Psychiatry, and Biomedical Engineering, University of Wisconsin School of Medicine and Public Health, 600 Highland Avenue, Madison, WI 53792,
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30
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Dobrynina LA, Kremneva EI, Shamtieva KV, Geints AA, Filatov AS, Trubitsyna VV, Bitsieva ET, Byrochkina AA, Akhmetshina YI, Maksimov II, Krotenkova MV. [Disruption of corpus callosum microstructural integrity by diffusion MRI as a predictor of progression of cerebral microangiopathy]. Zh Nevrol Psikhiatr Im S S Korsakova 2023; 123:95-104. [PMID: 37994894 DOI: 10.17116/jnevro202312311195] [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: 11/24/2023]
Abstract
OBJECTIVE To assess the microstructural integrity of the corpus callosum in patients with cerebral small vessel disease (cSVD) using signal and biophysical diffusion MRI models and to identify the most sensitive markers of disease progression. MATERIAL AND METHODS Diffusion MRI (3 Tesla) was performed in 166 patients (51.8% women; mean age 60.4±7.6) with cSVD and cognitive impairment of varying severity and in 44 healthy volunteers (65.9% women; mean age 59.6±6.8), followed by calculation of signal (diffusion tensor and diffusion kurtosis) and biophysical (WMTI, NODDI, MC-SMT) models, from which profiles of three corpus callosum segments were constructed. RESULTS The best results were obtained for metrics in the forceps minor and body of the corpus callosum. Among the metrics of the signal models in the forceps minor, fraction anisotropy (FA) and mean diffusion (MD), which characterize the overall loss of microstructural integrity and increase in extra-axonal water, as well as indirect markers of demyelination when considering transverse diffusion parameters (radial diffusion and radial kurtosis), had the larger area under the curve according to the ROC analysis. Among the metrics of the biophysical models in the forceps minor, a larger area under the curve was found in the MC-SMT model for extra-axonal transverse diffusion (ETR), mean diffusion (EMD), and intra-axonal water fraction (INTRA), and in the WMTI model for intra-axonal water fraction (AWF). ETR had high inverse correlations with INTRA and AWF, while INTRA and AWF had high direct intercorrelations. CONCLUSION Metrics of signaling (FA, MD, RD, RK) and biophysical patterns (ETR, EMD, INTRA, AWF) in the forceps minor and the corpus callosum body can be considered as indicators of cSVD progression. They indicate disease progression, mainly by an increase in extra-axonal water with the development of demyelination and tissue degeneration in the corpus callosum.
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Affiliation(s)
| | | | | | - A A Geints
- Lomonosov Moscow State University, Moscow, Russia
| | - A S Filatov
- Research Center of Neurology, Moscow, Russia
| | | | | | | | | | - I I Maksimov
- West Norwegian University of Applied Sciences (HVL), Bergen, Norway
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31
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Jain HV, Norton KA, Prado BB, Jackson TL. SMoRe ParS: A novel methodology for bridging modeling modalities and experimental data applied to 3D vascular tumor growth. Front Mol Biosci 2022; 9:1056461. [PMID: 36619168 PMCID: PMC9816661 DOI: 10.3389/fmolb.2022.1056461] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Accepted: 12/09/2022] [Indexed: 12/24/2022] Open
Abstract
Multiscale systems biology is having an increasingly powerful impact on our understanding of the interconnected molecular, cellular, and microenvironmental drivers of tumor growth and the effects of novel drugs and drug combinations for cancer therapy. Agent-based models (ABMs) that treat cells as autonomous decision-makers, each with their own intrinsic characteristics, are a natural platform for capturing intratumoral heterogeneity. Agent-based models are also useful for integrating the multiple time and spatial scales associated with vascular tumor growth and response to treatment. Despite all their benefits, the computational costs of solving agent-based models escalate and become prohibitive when simulating millions of cells, making parameter exploration and model parameterization from experimental data very challenging. Moreover, such data are typically limited, coarse-grained and may lack any spatial resolution, compounding these challenges. We address these issues by developing a first-of-its-kind method that leverages explicitly formulated surrogate models (SMs) to bridge the current computational divide between agent-based models and experimental data. In our approach, Surrogate Modeling for Reconstructing Parameter Surfaces (SMoRe ParS), we quantify the uncertainty in the relationship between agent-based model inputs and surrogate model parameters, and between surrogate model parameters and experimental data. In this way, surrogate model parameters serve as intermediaries between agent-based model input and data, making it possible to use them for calibration and uncertainty quantification of agent-based model parameters that map directly onto an experimental data set. We illustrate the functionality and novelty of Surrogate Modeling for Reconstructing Parameter Surfaces by applying it to an agent-based model of 3D vascular tumor growth, and experimental data in the form of tumor volume time-courses. Our method is broadly applicable to situations where preserving underlying mechanistic information is of interest, and where computational complexity and sparse, noisy calibration data hinder model parameterization.
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Affiliation(s)
- Harsh Vardhan Jain
- Department of Mathematics and Statistics, University of Minnesota Duluth, Duluth, MN, United States
| | - Kerri-Ann Norton
- Reem and Kayden Center for Science and Computation, Computational Biology Laboratory, Computer Science Program, Bard College, Annandale-on-Hudson, NY, United States
| | | | - Trachette L. Jackson
- Department of Mathematics, University of Michigan, Ann Arbor, MI, United States,*Correspondence: Trachette L. Jackson,
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Brain Microstructural Changes in Patients with Amnestic mild Cognitive Impairment. Clin Neuroradiol 2022; 33:445-453. [DOI: 10.1007/s00062-022-01226-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Accepted: 10/10/2022] [Indexed: 12/02/2022]
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Pasquini J, Firbank MJ, Ceravolo R, Silani V, Pavese N. Diffusion Magnetic Resonance Imaging Microstructural Abnormalities in Multiple System Atrophy: A Comprehensive Review. Mov Disord 2022; 37:1963-1984. [PMID: 36036378 DOI: 10.1002/mds.29195] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Revised: 07/22/2022] [Accepted: 08/01/2022] [Indexed: 01/07/2023] Open
Abstract
Multiple system atrophy (MSA) is a neurodegenerative disease characterized by autonomic failure, ataxia, and/or parkinsonism. Its prominent pathological alterations can be investigated using diffusion magnetic resonance imaging (dMRI), a technique that exploits the characteristics of water random motion inside brain tissue. The aim of this report was to review currently available literature on the application of dMRI in MSA and to describe microstructural abnormalities, diagnostic applications, and pathophysiological correlates. Sixty-four published studies involving microstructural investigation using dMRI in MSA were included. Widespread microstructural abnormalities of white matter were described, especially in the middle cerebellar peduncle, corticospinal tract, and hemispheric fibers. Gray matter degeneration was identified as well, with diffuse involvement of subcortical structures, especially in the putamina. Diagnostic applications of dMRI were mostly explored for the differential diagnosis between MSA parkinsonism and Parkinson's disease. Recently, machine learning algorithms for image processing and disease classification have demonstrated high diagnostic accuracy, showing potential for translation into clinical practice. To a lesser extent, clinical correlates of microstructural abnormalities have also been investigated, and abnormalities related to motor, ocular, and cognitive impairments were described. dMRI in MSA has contributed to in vivo identification of known pathological abnormalities. Translation into clinical practice of the latest advancements for the differential diagnosis between MSA and other forms of parkinsonism seems feasible. Current limitations involve the possibility of correctly diagnosing MSA in the very early stages, when the clinical diagnosis is most uncertain. Furthermore, pathophysiological correlates of microstructural abnormalities remain understudied. © 2022 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Jacopo Pasquini
- Clinical Ageing Research Unit, Newcastle University, Newcastle upon Tyne, United Kingdom.,Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy
| | - Michael J Firbank
- Positron Emission Tomography Centre, Newcastle University, Newcastle upon Tyne, United Kingdom
| | - Roberto Ceravolo
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy.,Neurodegenerative Diseases Center, Azienda Ospedaliero Universitaria Pisana, Pisa, Italy
| | - Vincenzo Silani
- Department of Neurology and Laboratory of Neuroscience, Istituto Auxologico Italiano IRCCS, Milan, Italy.,Department of Pathophysiology and Transplantation, Dino Ferrari Center, Università degli Studi di Milano, Milan, Italy
| | - Nicola Pavese
- Clinical Ageing Research Unit, Newcastle University, Newcastle upon Tyne, United Kingdom.,Department of Nuclear Medicine and PET Centre, Aarhus University Hospital, Aarhus, Denmark
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Medrano-Escalada Y, Plaza-Manzano G, Fernández-de-las-Peñas C, Valera-Calero JA. Structural, Functional and Neurochemical Cortical Brain Changes Associated with Chronic Low Back Pain. Tomography 2022; 8:2153-2163. [PMID: 36136876 PMCID: PMC9498382 DOI: 10.3390/tomography8050180] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2022] [Revised: 08/16/2022] [Accepted: 08/22/2022] [Indexed: 12/19/2022] Open
Abstract
Chronic low back pain (CLBP) is one of the most prevalent musculoskeletal disorders, being one of the leading contributors to disability worldwide and involving an important economic and social burden. Up to 90% of CLBP is non-specific (not associated with specific injuries), with a chronicity expectation estimated at 10%. Currently, motivational and emotional central circuits are being investigated due to their role in CLBP persistency and chronification. Therefore, this narrative review aimed to summarize the evidence regarding the cortical brain changes described for proposing novel multidisciplinary approaches. Novel advances in neuroimaging techniques demonstrated structural (e.g., decrease in the grey matter located at the dorsolateral prefrontal cortex), functional (e.g., connectivity impairments in those areas involved in pain processing), and neurochemical changes (e.g., decrease in cerebral metabolites). In addition, significant changes were found in the primary somatosensory and motor cortex, contributing to the alteration of low back muscles activation and function.
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Affiliation(s)
| | - Gustavo Plaza-Manzano
- Department of Radiology, Rehabilitation and Physiotherapy, Universidad Complutense de Madrid, 28040 Madrid, Spain
- Grupo InPhysio, Instituto de Investigación Sanitaria del Hospital Clínico San Carlos (IdISSC), 28040 Madrid, Spain
- Correspondence: ; Tel.: +34-913-941-545
| | - César Fernández-de-las-Peñas
- Department of Physical Therapy, Occupational Therapy, Rehabilitation and Physical Medicine, Universidad Rey Juan Carlos, 28922 Alcorcón, Spain
- Clínica e Investigación en Fisioterapia, Terapia Manual, Punción Seca y Ejercicio Terapéutico, Universidad Rey Juan Carlos, 28922 Alcorcón, Spain
| | - Juan Antonio Valera-Calero
- VALTRADOFI Research Group, Department of Physiotherapy, Faculty of Health, Universidad Camilo José Cela, Villanueva de la Cañada, 28692 Madrid, Spain
- Department of Physiotherapy, Faculty of Health, Universidad Camilo José Cela, Villanueva de la Cañada, 28692 Madrid, Spain
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Liu X, Huang X, Han T, Li S, Xue C, Deng J, Zhou Q, Sun Q, Zhou J. Discrimination between microcystic meningioma and atypical meningioma using whole-lesion apparent diffusion coefficient histogram analysis. Clin Radiol 2022; 77:864-869. [PMID: 36030110 DOI: 10.1016/j.crad.2022.07.004] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/02/2021] [Revised: 07/06/2022] [Accepted: 07/06/2022] [Indexed: 11/03/2022]
Abstract
AIM To explore the value of whole-lesion apparent diffusion coefficient (ADC) histogram analysis in discriminating microcystic meningioma (MCM) from atypical meningioma (AM). MATERIALS AND METHODS Clinical and preoperative MRI data of 20 patients with MCM and 26 patients with AM were analysed retrospectively. Whole-lesion apparent diffusion coefficient (ADC) histogram analysis was performed on each patient's lesion to obtain histogram parameters, including mean, variance, skewness, kurtosis, the 1st (ADCp1), 10th (ADCp10), 50th (ADCp50), 90th (ADCp90), and 99th (ADCp99) percentiles of ADC. The differences between the ADC histogram parameters of the two tumours were compared, and the receiver operating characteristic (ROC) curve was used to assess the diagnostic performance of statistically significant parameters in distinguishing the two tumours. RESULTS The mean, ADCp1, ADCp10, ADCp50, and ADCp90 of MCM were greater than those of AM, and significant differences were observed in these parameters between MCM and AM (all p<0.05). ROC analysis showed that the mean had the highest area under the curve value (AUC) in distinguishing the two tumours (AUC = 0.852), when using 120.46 × 10-6 mm2/s as the optimal threshold, the sensitivity, specificity, accuracy, positive predictive value, and negative predictive value for discriminating the two groups were 84.6%, 75%, 80.4%, 81.5%, and 78.9%, respectively. CONCLUSION Histogram analysis based on whole-lesion ADC maps was useful for discriminating between MCM from AM preoperatively, with the mean being the most promising potential parameter.
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Affiliation(s)
- X Liu
- Radiology of Department, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou 730030, People's Republic of China; Second Clinical School, Lanzhou University, Lanzhou, People's Republic of China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, People's Republic of China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, People's Republic of China
| | - X Huang
- Radiology of Department, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou 730030, People's Republic of China; Second Clinical School, Lanzhou University, Lanzhou, People's Republic of China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, People's Republic of China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, People's Republic of China
| | - T Han
- Radiology of Department, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou 730030, People's Republic of China; Second Clinical School, Lanzhou University, Lanzhou, People's Republic of China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, People's Republic of China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, People's Republic of China
| | - S Li
- Radiology of Department, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou 730030, People's Republic of China; Second Clinical School, Lanzhou University, Lanzhou, People's Republic of China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, People's Republic of China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, People's Republic of China
| | - C Xue
- Radiology of Department, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou 730030, People's Republic of China; Second Clinical School, Lanzhou University, Lanzhou, People's Republic of China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, People's Republic of China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, People's Republic of China
| | - J Deng
- Radiology of Department, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou 730030, People's Republic of China; Second Clinical School, Lanzhou University, Lanzhou, People's Republic of China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, People's Republic of China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, People's Republic of China
| | - Q Zhou
- Radiology of Department, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou 730030, People's Republic of China; Second Clinical School, Lanzhou University, Lanzhou, People's Republic of China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, People's Republic of China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, People's Republic of China
| | - Q Sun
- Radiology of Department, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou 730030, People's Republic of China; Second Clinical School, Lanzhou University, Lanzhou, People's Republic of China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, People's Republic of China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, People's Republic of China
| | - J Zhou
- Radiology of Department, Lanzhou University Second Hospital, Cuiyingmen No.82, Chengguan District, Lanzhou 730030, People's Republic of China; Second Clinical School, Lanzhou University, Lanzhou, People's Republic of China; Key Laboratory of Medical Imaging of Gansu Province, Lanzhou, People's Republic of China; Gansu International Scientific and Technological Cooperation Base of Medical Imaging Artificial Intelligence, Lanzhou, People's Republic of China.
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Apparent Diffusion Coefficient (ADC) Histogram Analysis in Parotid Gland Tumors: Evaluating a Novel Approach for Differentiation between Benign and Malignant Parotid Lesions Based on Full Histogram Distributions. Diagnostics (Basel) 2022; 12:diagnostics12081860. [PMID: 36010211 PMCID: PMC9406314 DOI: 10.3390/diagnostics12081860] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Revised: 07/24/2022] [Accepted: 07/25/2022] [Indexed: 11/16/2022] Open
Abstract
The aim of this study was to assess the diagnostic value of ADC distribution curves for differentiation between benign and malignant parotid gland tumors and to compare with mean ADC values. 73 patients with parotid gland tumors underwent head-and-neck MRI on a 1.5 Tesla scanner prior to surgery and histograms of ADC values were extracted. Histopathological results served as a reference standard for further analysis. ADC histograms were evaluated by comparing their similarity to a reference distribution using Chi2-test-statistics. The assumed reference distribution for benign and malignant parotid gland lesions was calculated after pooling the entire ADC data. In addition, mean ADC values were determined. For both methods, we calculated and compared the sensitivity and specificity between benign and malignant parotid gland tumors and three subgroups (pleomorphic adenoma, Warthin tumor, and malignant lesions), respectively. Moreover, we performed cross-validation (CV) techniques to estimate the predictive performance between ADC distributions and mean values. Histopathological results revealed 30 pleomorphic adenomas, 22 Warthin tumors, and 21 malignant tumors. ADC histogram distribution yielded a better specificity for detection of benign parotid gland lesions (ADChistogram: 75.0% vs. ADCmean: 71.2%), but mean ADC values provided a higher sensitivity (ADCmean: 71.4% vs. ADChistogram: 61.9%). The discrepancies are most pronounced in the differentiation between malignant and Warthin tumors (sensitivity ADCmean: 76.2% vs. ADChistogram: 61.9%; specificity ADChistogram: 81.8% vs. ADCmean: 68.2%). Using CV techniques, ADC distribution revealed consistently better accuracy to differentiate benign from malignant lesions (“leave-one-out CV” accuracy ADChistogram: 71.2% vs. ADCmean: 67.1%). ADC histogram analysis using full distribution curves is a promising new approach for differentiation between primary benign and malignant parotid gland tumors, especially with respect to the advantage in predictive performance based on CV techniques.
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