1
|
Jelescu IO, Grussu F, Ianus A, Hansen B, Barrett RLC, Aggarwal M, Michielse S, Nasrallah F, Syeda W, Wang N, Veraart J, Roebroeck A, Bagdasarian AF, Eichner C, Sepehrband F, Zimmermann J, Soustelle L, Bowman C, Tendler BC, Hertanu A, Jeurissen B, Verhoye M, Frydman L, van de Looij Y, Hike D, Dunn JF, Miller K, Landman BA, Shemesh N, Anderson A, McKinnon E, Farquharson S, Dell'Acqua F, Pierpaoli C, Drobnjak I, Leemans A, Harkins KD, Descoteaux M, Xu D, Huang H, Santin MD, Grant SC, Obenaus A, Kim GS, Wu D, Le Bihan D, Blackband SJ, Ciobanu L, Fieremans E, Bai R, Leergaard TB, Zhang J, Dyrby TB, Johnson GA, Cohen‐Adad J, Budde MD, Schilling KG. Considerations and recommendations from the ISMRM diffusion study group for preclinical diffusion MRI: Part 1: In vivo small-animal imaging. Magn Reson Med 2025; 93:2507-2534. [PMID: 40008568 PMCID: PMC11971505 DOI: 10.1002/mrm.30429] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2024] [Revised: 12/19/2024] [Accepted: 12/26/2024] [Indexed: 02/27/2025]
Abstract
Small-animal diffusion MRI (dMRI) has been used for methodological development and validation, characterizing the biological basis of diffusion phenomena, and comparative anatomy. The steps from animal setup and monitoring, to acquisition, analysis, and interpretation are complex, with many decisions that may ultimately affect what questions can be answered using the resultant data. This work aims to present selected considerations and recommendations from the diffusion community on best practices for preclinical dMRI of in vivo animals. We describe the general considerations and foundational knowledge that must be considered when designing experiments. We briefly describe differences in animal species and disease models and discuss why some may be more or less appropriate for different studies. We, then, give recommendations for in vivo acquisition protocols, including decisions on hardware, animal preparation, and imaging sequences, followed by advice for data processing including preprocessing, model-fitting, and tractography. Finally, we provide an online resource that lists publicly available preclinical dMRI datasets and software packages to promote responsible and reproducible research. In each section, we attempt to provide guides and recommendations, but also highlight areas for which no guidelines exist (and why), and where future work should focus. Although we mainly cover the central nervous system (on which most preclinical dMRI studies are focused), we also provide, where possible and applicable, recommendations for other organs of interest. An overarching goal is to enhance the rigor and reproducibility of small animal dMRI acquisitions and analyses, and thereby advance biomedical knowledge.
Collapse
Affiliation(s)
- Ileana O. Jelescu
- Department of RadiologyLausanne University Hospital and University of LausanneLausanneSwitzerland
- CIBM Center for Biomedical ImagingEcole Polytechnique Fédérale de LausanneLausanneSwitzerland
| | - Francesco Grussu
- Radiomics GroupVall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital CampusBarcelonaSpain
- Queen Square MS Centre, Queen Square Institute of Neurology, Faculty of Brain SciencesUniversity College LondonLondonUK
| | - Andrada Ianus
- Champalimaud ResearchChampalimaud FoundationLisbonPortugal
- School of Biomedical Engineering and Imaging SciencesKing's College LondonLondonUK
| | - Brian Hansen
- Center of Functionally Integrative NeuroscienceAarhus UniversityAarhusDenmark
| | - Rachel L. C. Barrett
- Department of NeuroimagingInstitute of Psychiatry, Psychology and Neuroscience, King's College LondonLondonUK
- NatBrainLab, Department of Forensics and Neurodevelopmental SciencesInstitute of Psychiatry, Psychology and Neuroscience, King's College LondonLondonUK
| | - Manisha Aggarwal
- Russell H. Morgan Department of Radiology and Radiological ScienceJohns Hopkins University School of MedicineBaltimoreMarylandUSA
| | - Stijn Michielse
- Department of Neurosurgery, School for Mental Health and Neuroscience (MHeNS)Maastricht University Medical CenterMaastrichtThe Netherlands
| | - Fatima Nasrallah
- The Queensland Brain InstituteThe University of QueenslandSt LuciaQueenslandAustralia
| | - Warda Syeda
- Melbourne Neuropsychiatry CentreThe University of MelbourneParkvilleVictoriaAustralia
| | - Nian Wang
- Department of Radiology and Imaging SciencesIndiana UniversityBloomingtonIndianaUSA
- Stark Neurosciences Research InstituteIndiana University School of MedicineBloomingtonIndianaUSA
| | - Jelle Veraart
- Center for Biomedical ImagingNYU Grossman School of MedicineNew YorkNew YorkUSA
| | - Alard Roebroeck
- Faculty of psychology and NeuroscienceMaastricht UniversityMaastrichtThe Netherlands
| | - Andrew F. Bagdasarian
- Department of Chemical and Biomedical Engineering, FAMU‐FSU College of EngineeringFlorida State UniversityTallahasseeFloridaUSA
- Center for Interdisciplinary Magnetic ResonanceNational HIgh Magnetic Field LaboratoryTallahasseeFloridaUSA
| | - Cornelius Eichner
- Department of NeuropsychologyMax Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Farshid Sepehrband
- USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USCUniversity of Southern CaliforniaCaliforniaLos AngelesUSA
| | - Jan Zimmermann
- Department of Neuroscience, Center for Magnetic Resonance ResearchUniversity of MinnesotaMinneapolisMinnesotaUSA
| | | | - Christien Bowman
- Bio‐Imaging Lab, Faculty of Pharmaceutical, Biomedical and Veterinary SciencesUniversity of AntwerpAntwerpBelgium
- μNEURO Research Centre of ExcellenceUniversity of AntwerpAntwerpBelgium
| | - Benjamin C. Tendler
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUK
| | - Andreea Hertanu
- Department of RadiologyLausanne University Hospital and University of LausanneLausanneSwitzerland
| | - Ben Jeurissen
- imec Vision Lab, Department of PhysicsUniversity of AntwerpAntwerpenBelgium
- Lab for Equilibrium Investigations and Aerospace, Department of PhysicsUniversity of AntwerpAntwerpenBelgium
| | - Marleen Verhoye
- Bio‐Imaging Lab, Faculty of Pharmaceutical, Biomedical and Veterinary SciencesUniversity of AntwerpAntwerpBelgium
- μNEURO Research Centre of ExcellenceUniversity of AntwerpAntwerpBelgium
| | - Lucio Frydman
- Department of Chemical and Biological PhysicsWeizmann Institute of ScienceRehovotIsrael
| | - Yohan van de Looij
- Division of Child Development and Growth, Department of Pediatrics, Gynaecology and Obstetrics, School of MedicineUniversité de GenèveGenèveSwitzerland
| | - David Hike
- Department of Chemical and Biomedical Engineering, FAMU‐FSU College of EngineeringFlorida State UniversityTallahasseeFloridaUSA
- Center for Interdisciplinary Magnetic ResonanceNational HIgh Magnetic Field LaboratoryTallahasseeFloridaUSA
| | - Jeff F. Dunn
- Department of Radiology, Cumming School of MedicineUniversity of CalgaryCalgaryAlbertaCanada
- Hotchkiss Brain Institute, Cumming School of MedicineUniversity of CalgaryCalgaryAlbertaCanada
- Alberta Children's Hospital Research Institute, Cumming School of MedicineUniversity of CalgaryCalgaryAlbertaCanada
| | - Karla Miller
- FMRIB Centre, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUK
| | - Bennett A. Landman
- Department of Electrical and Computer EngineeringVanderbilt UniversityNashvilleTennesseeUSA
| | - Noam Shemesh
- Champalimaud ResearchChampalimaud FoundationLisbonPortugal
| | - Adam Anderson
- Vanderbilt University Institute of Imaging ScienceVanderbilt UniversityNashvilleTennesseeUSA
- Department of Radiology and Radiological SciencesVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Emilie McKinnon
- Medical University of South CarolinaCharlestonSouth CarolinaUSA
| | - Shawna Farquharson
- National Imaging FacilityThe University of QueenslandBrisbaneQueenslandAustralia
| | - Flavio Dell'Acqua
- Department of Forensic and Neurodevelopmental SciencesKing's College LondonLondonUK
| | - Carlo Pierpaoli
- Laboratory on Quantitative Medical imaging, NIBIBNational Institutes of HealthBethesdaMarylandUSA
| | - Ivana Drobnjak
- Department of Computer ScienceUniversity College LondonLondonUK
| | - Alexander Leemans
- PROVIDI Lab, Image Sciences InstituteUniversity Medical Center UtrechtUtrechtThe Netherlands
| | - Kevin D. Harkins
- Vanderbilt University Institute of Imaging ScienceVanderbilt UniversityNashvilleTennesseeUSA
- Radiology and Radiological SciencesVanderbilt University Medical CenterNashvilleTennesseeUSA
- Biomedical EngineeringVanderbilt UniversityNashvilleTennesseeUSA
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaing Lab (SCIL), Computer Science DepartmentUniversité de SherbrookeSherbrookeQuebecCanada
- Imeka SolutionsSherbrookeQuebecCanada
| | - Duan Xu
- Department of Radiology and Biomedical ImagingUniversity of California San FranciscoSan FranciscoCaliforniaUSA
| | - Hao Huang
- Department of Radiology, Perelman School of MedicineUniversity of PennsylvaniaPhiladelphiaPennsylvaniaUSA
- Department of RadiologyChildren's Hospital of PhiladelphiaPhiladelphiaPennsylvaniaUSA
| | - Mathieu D. Santin
- Centre for NeuroImaging Research (CENIR), Inserm U 1127, CNRS UMR 7225Sorbonne UniversitéParisFrance
- Paris Brain InstituteParisFrance
| | - Samuel C. Grant
- Department of Chemical and Biomedical Engineering, FAMU‐FSU College of EngineeringFlorida State UniversityTallahasseeFloridaUSA
- Center for Interdisciplinary Magnetic ResonanceNational HIgh Magnetic Field LaboratoryTallahasseeFloridaUSA
| | - Andre Obenaus
- Division of Biomedical SciencesUniversity of California RiversideRiversideCaliforniaUSA
- Preclinical and Translational Imaging CenterUniversity of California IrvineIrvineCaliforniaUSA
| | - Gene S. Kim
- Department of RadiologyWeill Cornell Medical CollegeNew YorkNew YorkUSA
| | - Dan Wu
- Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrument ScienceZhejiang UniversityHangzhouChina
| | - Denis Le Bihan
- CEA, DRF, JOLIOT, NeuroSpinGif‐sur‐YvetteFrance
- Université Paris‐SaclayGif‐sur‐YvetteFrance
| | - Stephen J. Blackband
- Department of NeuroscienceUniversity of FloridaGainesvilleFloridaUSA
- McKnight Brain InstituteUniversity of FloridaGainesvilleFloridaUSA
- National High Magnetic Field LaboratoryTallahasseeFloridaUSA
| | - Luisa Ciobanu
- NeuroSpin, UMR CEA/CNRS 9027Paris‐Saclay UniversityGif‐sur‐YvetteFrance
| | - Els Fieremans
- Department of RadiologyNew York University Grossman School of MedicineNew YorkNew YorkUSA
| | - Ruiliang Bai
- Interdisciplinary Institute of Neuroscience and Technology, School of MedicineZhejiang UniversityHangzhouChina
- Frontier Center of Brain Science and Brain‐Machine IntegrationZhejiang UniversityZhejiangChina
| | - Trygve B. Leergaard
- Department of Molecular Biology, Institute of Basic Medical SciencesUniversity of OsloOsloNorway
| | - Jiangyang Zhang
- Department of RadiologyNew York University School of MedicineNew YorkNew YorkUSA
| | - Tim B. Dyrby
- Danish Research Centre for Magnetic ResonanceCentre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and HvidovreHvidovreDenmark
- Department of Applied Mathematics and Computer ScienceTechnical University of DenmarkKongens LyngbyDenmark
| | - G. Allan Johnson
- Duke Center for In Vivo Microscopy, Department of RadiologyDuke UniversityDurhamNorth CarolinaUSA
- Department of Biomedical EngineeringDuke UniversityDurhamNorth CarolinaUSA
| | - Julien Cohen‐Adad
- NeuroPoly Lab, Institute of Biomedical EngineeringPolytechnique MontrealMontrealQuebecCanada
- Functional Neuroimaging Unit, CRIUGMUniversity of MontrealMontrealQuebecCanada
- Mila ‐ Quebec AI InstituteMontrealQuebecCanada
| | - Matthew D. Budde
- Department of NeurosurgeryMedical College of WisconsinMilwaukeeWisconsinUSA
- Clement J Zablocki VA Medical CenterMilwaukeeWisconsinUSA
| | - Kurt G. Schilling
- Vanderbilt University Institute of Imaging ScienceVanderbilt UniversityNashvilleTennesseeUSA
- Radiology and Radiological SciencesVanderbilt University Medical CenterNashvilleTennesseeUSA
| |
Collapse
|
2
|
Yang L, Wang Y. Noise reduction in magnitude diffusion-weighted images using spatial similarity and diffusion redundancy. Magn Reson Imaging 2025; 118:110344. [PMID: 39892480 DOI: 10.1016/j.mri.2025.110344] [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: 11/07/2024] [Revised: 01/15/2025] [Accepted: 01/29/2025] [Indexed: 02/03/2025]
Abstract
PURPOSE Diffusion-weighted imaging (DWI) has significant value in clinical application, which however suffers from a serious low signal-to-noise ratio (SNR) problem, especially at high spatial resolution and/or high diffusion sensitivity factor. METHODS Here, we propose a denoising method for magnitude DWI. The method consists of two modules: pre-denoising and post-filtering, the former mines the diffusion redundancy by local kernel principal component analysis, and the latter fully mines the non-local self-similarity using patch-based non-local mean. RESULTS Validated by simulation and in vivo datasets, the experiment results show that the proposed method is capable of improving the SNR of the whole brain, thus enhancing the performance for diffusion metrics estimation, crossing fiber discrimination, and human brain fiber tractography tracking compared with the different three state-of-the-art comparison methods. More importantly, the proposed method consistently exhibits superior performance to comparison methods when used for denoising diffusion data acquired with sensitivity encoding (SENSE). CONCLUSION The proposed denoising method is expected to show significant practicability in acquiring high-quality whole-brain diffusion data, which is crucial for many neuroscience studies.
Collapse
Affiliation(s)
- Liming Yang
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China
| | - Yuanjun Wang
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
| |
Collapse
|
3
|
Yang L, Wang Y. New method for diffusion-weighted images denoising based on patch-matching with higher-order singular value decomposition. JOURNAL OF X-RAY SCIENCE AND TECHNOLOGY 2025; 33:526-539. [PMID: 40343883 DOI: 10.1177/08953996241313321] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2025]
Abstract
BackgroundDiffusion-weighted imaging (DWI) is an important technique to study brain microstructure. However, diffusion-weighted (DW) images suffer from severe low signal-to-noise ratio (SNR) problem, affecting subsequent diffusion analysis.ObjectiveThe goal of this paper is to develop advanced DWI denoising technique to effectively reduce noise while improving the accuracy and reliability of subsequent diffusion model fitting and diffusion analysis, thereby facilitating the research and analysis of brain science.MethodsWe propose a new method for denoising DW images based on patch-matching with higher-order singular value decomposition (HOSVD) by combined with the variance-stabilizing transformation technique. It starts with introducing a novel non-local mean algorithm as a prefiltering stage, and then denoises the noisy data using a local HOSVD algorithm based on the HOSVD bases learned from prefiltered images.ResultsExperiments are performed on simulation, HCP and in vivo brain DWI datasets. Results show that the proposed method significantly reduces spatially invariant and variant noise, improving the most reliable diffusion analysis compared with the different denoising methods.ConclusionsThe proposed method achieves state-of-the-art performance which can improve image quality and enable accurate diffusion analysis.
Collapse
Affiliation(s)
- Liming Yang
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| | - Yuanjun Wang
- School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai, China
| |
Collapse
|
4
|
Alharshan F, Aloufi A, Rowe F, Meyer G. Tracking microstructural adaptations in primary visual areas: A diffusion kurtosis imaging study on visuomotor learning. Neuroimage 2025; 310:121138. [PMID: 40081515 DOI: 10.1016/j.neuroimage.2025.121138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2024] [Revised: 02/17/2025] [Accepted: 03/10/2025] [Indexed: 03/16/2025] Open
Abstract
This study investigates the potential of Diffusion Kurtosis Imaging (DKI) to detect microstructural changes induced by visuomotor training and its added value over Diffusion Tensor Imaging (DTI). Fourteen healthy participants completed a six-week home-based eye movement training intervention. Pre-and post-training DKI scans were analysed. Descriptive analysis, including the Coefficient of Variation (CV) and Bland-Altman (BA) metrics, was used to assess training effects. Results revealed significant reductions in kurtosis (MK, RK, AK) and diffusivity (MD) in task-relevant areas, particularly the cuneus, with overlapping findings between DKI and DTI-derived measures. In contrast, the pericalcarine area showed reductions only in MK and AK, suggesting that kurtosis metrics were more sensitive in this region. Increases in KA and FA post-training were not significant. BA analysis confirmed systematic training-related changes in the visual target area, while the transverse temporal gyrus, used as a control, remained stable, providing evidence for the specificity of these effects. These findings highlight DKI's ability to capture training-induced microstructural changes, complementing DTI. Among the metrics, AK emerged as a stable, sensitive marker, while MK and RK provided additional insights with greater variability. This study underscores the role of the cuneus in visuomotor adaptation and the potential of DKI to measure microstructural change cognitive training and neurorehabilitation.
Collapse
Affiliation(s)
- Fahad Alharshan
- Department of Radiological Sciences, College of Applied Medical Scieneces, King Saud bin Abdulaziz University for Health Sciences, Alahsa, Saudi Arabia; Faculty of Health and Life Sciences, University of Liverpool, Liverpool, United Kingdom
| | - Abdulrahman Aloufi
- Department of Radiologic Technology, College of Applied Medical Sciences, Qassim University, Buraydah 51452, Saudi Arabia
| | - Fiona Rowe
- Institute of Population Health, University of Liverpool, Liverpool, United Kingdom
| | - Georg Meyer
- Digital Innovation Facility, University of Liverpool, Liverpool, United Kingdom; Hanse Wissenschaftskolleg, Delmenhorst, Germany.
| |
Collapse
|
5
|
Tahedl M, Tournier JD, Smith RE. Structural connectome construction using constrained spherical deconvolution in multi-shell diffusion-weighted magnetic resonance imaging. Nat Protoc 2025:10.1038/s41596-024-01129-1. [PMID: 39953164 DOI: 10.1038/s41596-024-01129-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2024] [Accepted: 12/05/2024] [Indexed: 02/17/2025]
Abstract
Connectional neuroanatomical maps can be generated in vivo by using diffusion-weighted magnetic resonance imaging (dMRI) data, and their representation as structural connectome (SC) atlases adopts network-based brain analysis methods. We explain the generation of high-quality SCs of brain connectivity by using recent advances for reconstructing long-range white matter connections such as local fiber orientation estimation on multi-shell dMRI data with constrained spherical deconvolution, which yields both increased sensitivity to detecting crossing fibers compared with competing methods and the ability to separate signal contributions from different macroscopic tissues, and improvements to streamline tractography such as anatomically constrained tractography and spherical-deconvolution informed filtering of tractograms, which have increased the biological accuracy of SC creation. Here, we provide step-by-step instructions to creating SCs by using these methods. In addition, intermediate steps of our procedure can be adapted for related analyses, including region of interest-based tractography and quantification of local white matter properties. The associated software MRtrix3 implements the relevant tools for easy application of the protocol, with specific processing tasks deferred to components of the FSL software. The protocol is suitable for users with expertise in dMRI and neuroscience and requires between 2 h and 13 h to complete, depending on the available computational system.
Collapse
Affiliation(s)
- Marlene Tahedl
- Department of Neuroradiology, School of Medicine and Health, Technical University of Munich, Munich, Germany.
| | - J-Donald Tournier
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
- Department of Biomedical Engineering, School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK
| | - Robert E Smith
- Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia
- Florey Department of Neuroscience and Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| |
Collapse
|
6
|
Lebret A, Frese S, Lévy S, Curt A, Callot V, Freund P, Seif M. Spinal Cord Blood Perfusion Deficit is Associated with Clinical Impairment after Spinal Cord Injury. J Neurotrauma 2025; 42:280-291. [PMID: 39323313 DOI: 10.1089/neu.2024.0267] [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: 09/27/2024] Open
Abstract
Spinal cord injury (SCI) results in intramedullary microvasculature disruption and blood perfusion deficit at and remote from the injury site. However, the relationship between remote vascular impairment and functional recovery remains understudied. We characterized perfusion impairment in vivo, rostral to the injury, using magnetic resonance imaging (MRI), and investigated its association with lesion extent and impairment following SCI. Twenty-one patients with chronic cervical SCI and 39 healthy controls (HC) underwent a high-resolution MRI protocol, including intravoxel incoherent motion (IVIM) and T2*-weighted MRI covering C1-C3 cervical levels, as well as T2-weighted MRI to determine lesion volumes. IVIM matrices (i.e., blood volume fraction, velocity, flow indices, and diffusion) and cord structural characteristics were calculated to assess perfusion changes and cervical cord atrophy, respectively. Patients with SCI additionally underwent a standard clinical examination protocol to assess functional impairment. Correlation analysis was used to investigate associations between IVIM parameters with lesion volume and sensorimotor dysfunction. Cervical cord white and gray matter were atrophied (27.60% and 21.10%, p < 0.0001, respectively) above the cervical cord injury, accompanied by a lower blood volume fraction (-22.05%, p < 0.001) and a higher blood velocity-related index (+38.72%, p < 0.0001) in patients with SCI compared with HC. Crucially, gray matter remote perfusion deficit correlated with larger lesion volumes and clinical impairment. This study shows clinically eloquent perfusion deficit rostral to a SCI, its magnitude driven by injury severity. These findings indicate trauma-induced widespread microvascular alterations beyond the injury site. Perfusion MRI matrices in the spinal cord hold promise as biomarkers for monitoring treatment effects and dynamic changes in microvasculature integrity following SCI.
Collapse
Affiliation(s)
- Anna Lebret
- Spinal Cord Injury Center, Balgrist University Hospital, Zurich, Switzerland
| | - Sabina Frese
- Spinal Cord Injury Center, Balgrist University Hospital, Zurich, Switzerland
- High Field MR Center, Medical University of Vienna, Vienna, Austria
| | - Simon Lévy
- Aix-Marseille Univ, CNRS, CRMBM, Marseille, France
- APHM, Hôpital Universitaire Timone, CEMEREM, Marseille, France
- MR Research Collaborations, Siemens Healthcare Pty Ltd, Melbourne, Australia
| | - Armin Curt
- Spinal Cord Injury Center, Balgrist University Hospital, Zurich, Switzerland
| | - Virginie Callot
- Aix-Marseille Univ, CNRS, CRMBM, Marseille, France
- APHM, Hôpital Universitaire Timone, CEMEREM, Marseille, France
| | - Patrick Freund
- Spinal Cord Injury Center, Balgrist University Hospital, Zurich, Switzerland
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- Department of Brain Repair and Rehabilitation, Wellcome Trust Center for Neuroimaging, Institute of Neurology, University College London, United Kingdom
| | - Maryam Seif
- Spinal Cord Injury Center, Balgrist University Hospital, Zurich, Switzerland
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| |
Collapse
|
7
|
Alwashmi K, Rowe F, Meyer G. Multimodal MRI analysis of microstructural and functional connectivity brain changes following systematic audio-visual training in a virtual environment. Neuroimage 2025; 305:120983. [PMID: 39732221 DOI: 10.1016/j.neuroimage.2024.120983] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2024] [Revised: 12/06/2024] [Accepted: 12/18/2024] [Indexed: 12/30/2024] Open
Abstract
Recent work has shown rapid microstructural brain changes in response to learning new tasks. These cognitive tasks tend to draw on multiple brain regions connected by white matter (WM) tracts. Therefore, behavioural performance change is likely to be the result of microstructural, functional activation, and connectivity changes in extended neural networks. Here we show for the first time that learning-induced microstructural change in WM tracts, quantified with diffusion tensor and kurtosis imaging (DTI, DKI) is linked to functional connectivity changes in brain areas that use these tracts to communicate. Twenty healthy participants engaged in a month of virtual reality (VR) systematic audiovisual (AV) training. DTI analysis using repeated-measures ANOVA unveiled a decrease in mean diffusivity (MD) in the SLF II, alongside a significant increase in fractional anisotropy (FA) in optic radiations post-training, persisting in the follow-up (FU) assessment (post: MD t(76) = 6.13, p < 0.001, FA t(76) = 3.68, p < 0.01, FU: MD t(76) = 4.51, p < 0.001, FA t(76) = 2.989, p < 0.05). The MD reduction across participants was significantly correlated with the observed behavioural performance gains. A functional connectivity (FC) analysis showed significantly enhanced functional activity correlation between primary visual and auditory cortices post-training, which was evident by the DKI microstructural changes found within these two regions as well as in the sagittal stratum including WM tracts connecting occipital and temporal lobes (mean kurtosis (MK): cuneus t(19)=2.3 p < 0.05, transverse temporal t(19)=2.6 p < 0.05, radial kurtosis (RK): sagittal stratum t(19)=2.3 p < 0.05). DTI and DKI show complementary data, both of which are consistent with the task-relevant brain networks. The results demonstrate the utility of multimodal imaging analysis to provide complementary evidence for brain changes at the level of networks. In summary, our study shows the complex relationship between microstructural adaptations and functional connectivity, unveiling the potential of multisensory integration within immersive VR training. These findings have implications for learning and rehabilitation strategies, facilitating more effective interventions within virtual environments.
Collapse
Affiliation(s)
- Kholoud Alwashmi
- Faculty of Health and Life Sciences, University of Liverpool, United Kingdom; Department of Radiology, Princess Nourah bint Abdulrahman University, Saudi Arabia.
| | - Fiona Rowe
- IDEAS, University of Liverpool, United Kingdom.
| | - Georg Meyer
- Institute of Population Health, University of Liverpool, United Kingdom; Hanse Wissenschaftskolleg, Delmenhorst, Germany.
| |
Collapse
|
8
|
Satoer D, Dulyan L, Forkel S. Oncology: Brain asymmetries in language-relevant brain tumors. HANDBOOK OF CLINICAL NEUROLOGY 2025; 208:65-87. [PMID: 40074418 DOI: 10.1016/b978-0-443-15646-5.00041-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/14/2025]
Abstract
Brain tumors are classified as rare diseases, with an annual occurrence of 300,000 cases and account for an annual loss of 241,000 lives, highlighting their devastating nature. Recent advancements in diagnosis and treatment have significantly improved the management and care of brain tumors. This chapter provides an overview of the common types of primary brain tumors affecting language functions-gliomas and meningiomas. Techniques for identifying and mapping critical language areas, including the white matter language system, such as awake brain tumor surgery and diffusion-weighted tractography, are pivotal for understanding language localization and informing personalized treatment approaches. Numerous studies have demonstrated that gliomas in the dominant hemisphere can lead to (often subtle) impairments across various cognitive domains, with a particular emphasis on language. Recently, increased attention has been directed toward (nonverbal) cognitive deficits in patients with gliomas in the nondominant hemisphere, as well as cognitive outcomes in patients with meningiomas, a group historically overlooked. A patient-tailored approach to language and cognitive functions across the pre-, intra-, and postoperative phases is mandatory for brain tumor patients to preserve quality of life. Continued follow-up studies, in conjunction with advanced imaging techniques, are crucial for understanding the brain's potential for neuroplasticity and optimizing patient outcomes.
Collapse
Affiliation(s)
- Djaina Satoer
- Department of Neurosurgery, Erasmus MC University Medical Center Rotterdam, The Netherlands.
| | - Lilit Dulyan
- Donders Institute for Brain Cognition Behaviour, Radboud University, Nijmegen, The Netherlands; Brain Connectivity and Behaviour Laboratory, Sorbonne Universities, Paris, France
| | - Stephanie Forkel
- Donders Institute for Brain Cognition Behaviour, Radboud University, Nijmegen, The Netherlands; Brain Connectivity and Behaviour Laboratory, Sorbonne Universities, Paris, France; Centre for Neuroimaging Sciences, Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, United Kingdom
| |
Collapse
|
9
|
Autio JA, Uematsu A, Ikeda T, Ose T, Hou Y, Magrou L, Kimura I, Ohno M, Murata K, Coalson T, Kennedy H, Glasser MF, Van Essen DC, Hayashi T. Charting cortical-layer specific area boundaries using Gibbs' ringing attenuated T1w/T2w-FLAIR myelin MRI. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.09.27.615294. [PMID: 39386722 PMCID: PMC11463467 DOI: 10.1101/2024.09.27.615294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 10/12/2024]
Abstract
Cortical areas have traditionally been defined by their distinctive layer cyto- and/or myelo- architecture using postmortem histology. Recent studies have delineated many areas by measuring overall cortical myelin content and its spatial gradients using the T1w/T2w ratio MRI in living primates, including humans. While T1w/T2w studies of areal transitions might benefit from using the layer profile of this myelin-related contrast, a significant confound is Gibbs' ringing artefact, which produces signal fluctuations resembling cortical layers. Here, we address these issues with a novel approach using cortical layer thickness-adjusted T1w/T2w-FLAIR imaging, which effectively cancels out Gibbs' ringing artefacts while enhancing intra-cortical myelin contrast. Whole-brain MRI measures were mapped onto twelve equivolumetric layers, and layer-specific sharp myeloarchitectonic transitions were identified using spatial gradients resulting in a putative 182 area/subarea partition of the macaque cerebral cortex. The myelin maps exhibit notably high homology with those in humans, suggesting cortical myelin shares a similar developmental program across species. Comparison with histological Gallyas myelin stains explains over 80% of the variance in the laminar T1w/T2w-FLAIR profiles, substantiating the validity of the method. Altogether, our approach provides a novel, noninvasive means for precision mapping layer myeloarchitecture in the primate cerebral cortex, advancing the pioneering work of classical neuroanatomists.
Collapse
Affiliation(s)
- Joonas A Autio
- Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan
| | - Akiko Uematsu
- Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan
| | - Takuro Ikeda
- Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan
| | - Takayuki Ose
- Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan
| | - Yujie Hou
- Université Lyon, Université Claude Bernard Lyon 1, Inserm, Stem Cell and Brain Research Institute U1208, 69500, Bron, France
| | - Loïc Magrou
- Université Lyon, Université Claude Bernard Lyon 1, Inserm, Stem Cell and Brain Research Institute U1208, 69500, Bron, France
- Center for Neural Science, New York University, New York, NY, United States
| | - Ikko Kimura
- Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan
| | - Masahiro Ohno
- Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan
| | | | - Tim Coalson
- Department of Neuroscience, Washington University School of Medicine, St Louis, Missouri USA
| | - Henry Kennedy
- Université Lyon, Université Claude Bernard Lyon 1, Inserm, Stem Cell and Brain Research Institute U1208, 69500, Bron, France
- Institute of Neuroscience, State Key Laboratory of Neuroscience, Chinese Academy of Sciences (CAS) Key Laboratory of Primate Neurobiology, Shanghai 200031, China
| | - Matthew F Glasser
- Department of Neuroscience, Washington University School of Medicine, St Louis, Missouri USA
- Department of Radiology, Washington University School of Medicine, St Louis, Missouri, USA
| | - David C Van Essen
- Department of Neuroscience, Washington University School of Medicine, St Louis, Missouri USA
| | - Takuya Hayashi
- Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, Kobe, Japan
| |
Collapse
|
10
|
Gaughan C, Nasa A, Roman E, Cullinane D, Kelly L, Riaz S, Brady C, Browne C, Sooknarine V, Mosley O, Almulla A, Alsehli A, Kelliher A, Murphy C, O'Hanlon E, Cannon M, Roddy DW. A Pilot Study of Adolescents with Psychotic Experiences: Potential Cerebellar Circuitry Disruption Early Along the Psychosis Spectrum. CEREBELLUM (LONDON, ENGLAND) 2024; 23:1772-1782. [PMID: 37351730 PMCID: PMC11489369 DOI: 10.1007/s12311-023-01579-5] [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] [Accepted: 06/13/2023] [Indexed: 06/24/2023]
Abstract
A berrant connectivity in the cerebellum has been found in psychotic conditions such as schizophrenia corresponding with cognitive and motor deficits found in these conditions. Diffusion differences in the superior cerebellar peduncles, the white matter connecting the cerebellar circuitry to the rest of the brain, have also been found in schizophrenia and high-risk states. However, white matter diffusivity in the peduncles in individuals with sub-threshold psychotic experiences (PEs) but not reaching the threshold for a definitive diagnosis remains unstudied. This study investigates the cerebellar peduncles in adolescents with PEs but no formal psychiatric diagnosis.Sixteen adolescents with PEs and 17 age-matched controls recruited from schools underwent High-Angular-Resolution-Diffusion neuroimaging. Following constrained spherical deconvolution whole-brain tractography, the superior, inferior and middle peduncles were isolated and virtually dissected out using ExploreDTI. Differences for macroscopic and microscopic tract metrics were calculated using one-way between-group analyses of covariance controlling for age, sex and estimated Total Intracranial Volume (eTIV). Multiple comparisons were corrected using Bonferroni correction.A decrease in fractional anisotropy was identified in the right (p = 0.045) and left (p = 0.058) superior cerebellar peduncle; however, this did not survive strict Bonferroni multiple comparison correction. There were no differences in volumes or other diffusion metrics in either the middle or inferior peduncles.Our trend level changes in the superior cerebellar peduncle in a non-clinical sample exhibiting psychotic experiences complement similar but more profound changes previously found in ultra-high-risk individuals and those with psychotic disorders. This suggests that superior cerebellar peduncle circuitry perturbations may occur early along in the psychosis spectrum.
Collapse
Affiliation(s)
- Caoimhe Gaughan
- Department of Psychiatry, Royal College of Surgeons in Ireland, Education and Research Centre, Beaumont Hospital, Dublin 9, Ireland
| | - Anurag Nasa
- Department of Psychiatry, Royal College of Surgeons in Ireland, Education and Research Centre, Beaumont Hospital, Dublin 9, Ireland
| | - Elena Roman
- Department of Psychiatry, Royal College of Surgeons in Ireland, Education and Research Centre, Beaumont Hospital, Dublin 9, Ireland
| | - Dearbhla Cullinane
- Department of Psychiatry, Royal College of Surgeons in Ireland, Education and Research Centre, Beaumont Hospital, Dublin 9, Ireland
| | - Linda Kelly
- Department of Psychiatry, Royal College of Surgeons in Ireland, Education and Research Centre, Beaumont Hospital, Dublin 9, Ireland
| | - Sahar Riaz
- Department of Psychiatry, Royal College of Surgeons in Ireland, Education and Research Centre, Beaumont Hospital, Dublin 9, Ireland
| | - Conan Brady
- Department of Psychiatry, Royal College of Surgeons in Ireland, Education and Research Centre, Beaumont Hospital, Dublin 9, Ireland
| | - Ciaran Browne
- Department of Psychiatry, Royal College of Surgeons in Ireland, Education and Research Centre, Beaumont Hospital, Dublin 9, Ireland
| | - Vitallia Sooknarine
- Department of Psychiatry, Royal College of Surgeons in Ireland, Education and Research Centre, Beaumont Hospital, Dublin 9, Ireland
| | - Olivia Mosley
- Department of Psychiatry, Royal College of Surgeons in Ireland, Education and Research Centre, Beaumont Hospital, Dublin 9, Ireland
| | - Ahmad Almulla
- Department of Psychiatry, Royal College of Surgeons in Ireland, Education and Research Centre, Beaumont Hospital, Dublin 9, Ireland
| | - Assael Alsehli
- Department of Psychiatry, Royal College of Surgeons in Ireland, Education and Research Centre, Beaumont Hospital, Dublin 9, Ireland
| | - Allison Kelliher
- Department of Psychiatry, Royal College of Surgeons in Ireland, Education and Research Centre, Beaumont Hospital, Dublin 9, Ireland
| | - Cian Murphy
- Department of Psychiatry, Royal College of Surgeons in Ireland, Education and Research Centre, Beaumont Hospital, Dublin 9, Ireland
| | - Erik O'Hanlon
- Department of Psychiatry, Royal College of Surgeons in Ireland, Education and Research Centre, Beaumont Hospital, Dublin 9, Ireland
| | - Mary Cannon
- Department of Psychiatry, Royal College of Surgeons in Ireland, Education and Research Centre, Beaumont Hospital, Dublin 9, Ireland
| | - Darren William Roddy
- Department of Psychiatry, Royal College of Surgeons in Ireland, Education and Research Centre, Beaumont Hospital, Dublin 9, Ireland.
| |
Collapse
|
11
|
Gao C, Yang Q, Kim ME, Khairi NM, Cai LY, Newlin NR, Kanakaraj P, Remedios LW, Krishnan AR, Yu X, Yao T, Zhang P, Schilling KG, Moyer D, Archer DB, Resnick SM, Landman BA, Alzheimer’s Disease Neuroimaging Initiative, BIOCARD Study team. Characterizing patterns of diffusion tensor imaging variance in aging brains. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.08.22.23294381. [PMID: 37662348 PMCID: PMC10473788 DOI: 10.1101/2023.08.22.23294381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Purpose As large analyses merge data across sites, a deeper understanding of variance in statistical assessment across the sources of data becomes critical for valid analyses. Diffusion tensor imaging (DTI) exhibits spatially varying and correlated noise, so care must be taken with distributional assumptions. Here we characterize the role of physiology, subject compliance, and the interaction of subject with the scanner in the understanding of DTI variability, as modeled in spatial variance of derived metrics in homogeneous regions. Approach We analyze DTI data from 1035 subjects in the Baltimore Longitudinal Study of Aging (BLSA), with ages ranging from 22.4 to 103 years old. For each subject, up to 12 longitudinal sessions were conducted. We assess variance of DTI scalars within regions of interest (ROIs) defined by four segmentation methods and investigate the relationships between the variance and covariates, including baseline age, time from the baseline (referred to as "interval"), motion, sex, and whether it is the first scan or the second scan in the session. Results Covariate effects are heterogeneous and bilaterally symmetric across ROIs. Inter-session interval is positively related ( p ≪ 0.001 ) to FA variance in the cuneus and occipital gyrus, but negatively ( p ≪ 0.001 ) in the caudate nucleus. Males show significantly ( p ≪ 0.001 ) higher FA variance in the right putamen, thalamus, body of the corpus callosum, and cingulate gyrus. In 62 out of 176 ROIs defined by the Eve type-1 atlas, an increase in motion is associated ( p < 0.05 ) with a decrease in FA variance. Head motion increases during the rescan of DTI ( Δ μ = 0.045 millimeters per volume). Conclusions The effects of each covariate on DTI variance, and their relationships across ROIs are complex. Ultimately, we encourage researchers to include estimates of variance when sharing data and consider models of heteroscedasticity in analysis. This work provides a foundation for study planning to account for regional variations in metric variance.
Collapse
Affiliation(s)
- Chenyu Gao
- Vanderbilt University, Department of Electrical and Computer Engineering, Nashville, United States
| | - Qi Yang
- Vanderbilt University, Department of Computer Science, Nashville, United States
| | - Michael E. Kim
- Vanderbilt University, Department of Computer Science, Nashville, United States
| | - Nazirah Mohd Khairi
- Vanderbilt University, Department of Electrical and Computer Engineering, Nashville, United States
| | - Leon Y. Cai
- Vanderbilt University, Department of Biomedical Engineering, Nashville, United States
| | - Nancy R. Newlin
- Vanderbilt University, Department of Computer Science, Nashville, United States
| | | | - Lucas W. Remedios
- Vanderbilt University, Department of Computer Science, Nashville, United States
| | - Aravind R. Krishnan
- Vanderbilt University, Department of Electrical and Computer Engineering, Nashville, United States
| | - Xin Yu
- Vanderbilt University, Department of Computer Science, Nashville, United States
| | - Tianyuan Yao
- Vanderbilt University, Department of Computer Science, Nashville, United States
| | - Panpan Zhang
- Vanderbilt University Medical Center, Department of Biostatistics, Nashville, United States
| | - Kurt G. Schilling
- Vanderbilt University Medical Center, Department of Radiology and Radiological Sciences, Nashville, USA
- Vanderbilt University, Vanderbilt University Institute of Imaging Science, Nashville, USA
| | - Daniel Moyer
- Vanderbilt University, Department of Computer Science, Nashville, United States
| | - Derek B. Archer
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University School of Medicine, Nashville, USA
- Vanderbilt University Medical Center, Vanderbilt Genetics Institute, Nashville, USA
| | - Susan M. Resnick
- National Institute on Aging, Laboratory of Behavioral Neuroscience, Baltimore, United States
| | - Bennett A. Landman
- Vanderbilt University, Department of Electrical and Computer Engineering, Nashville, United States
- Vanderbilt University, Department of Computer Science, Nashville, United States
- Vanderbilt University, Department of Biomedical Engineering, Nashville, United States
- Vanderbilt University Medical Center, Department of Radiology and Radiological Sciences, Nashville, USA
- Vanderbilt University, Vanderbilt University Institute of Imaging Science, Nashville, USA
| | | | | |
Collapse
|
12
|
Mazur-Rosmus W, Krzyżak AT. The effect of elimination of gibbs ringing, noise and systematic errors on the DTI metrics and tractography in a rat brain. Sci Rep 2024; 14:15010. [PMID: 38951163 PMCID: PMC11217413 DOI: 10.1038/s41598-024-66076-z] [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: 02/01/2024] [Accepted: 06/26/2024] [Indexed: 07/03/2024] Open
Abstract
Diffusion tensor imaging (DTI) metrics and tractography can be biased due to low signal-to-noise ratio (SNR) and systematic errors resulting from image artifacts and imperfections in magnetic field gradients. The imperfections include non-uniformity and nonlinearity, effects caused by eddy currents, and the influence of background and imaging gradients. We investigated the impact of systematic errors on DTI metrics of an isotropic phantom and DTI metrics and tractography of a rat brain measured at high resolution. We tested denoising and Gibbs ringing removal methods combined with the B matrix spatial distribution (BSD) method for magnetic field gradient calibration. The results showed that the performance of the BSD method depends on whether Gibbs ringing is removed and the effectiveness of stochastic error removal. Region of interest (ROI)-based analysis of the DTI metrics showed that, depending on the size of the ROI and its location in space, correction methods can remove systematic bias to varying degrees. The preprocessing pipeline proposed and dedicated to this type of data together with the BSD method resulted in an even - 90% decrease in fractional anisotropy (FA) (globally and locally) in the isotropic phantom and - 45% in the rat brain. The largest global changes in the rat brain tractogram compared to the standard method without preprocessing (sDTI) were noticed after denoising. The direction of the first eigenvector obtained from DTI after denoising, Gibbs ringing removal and BSD differed by an average of 56 and 10 degrees in the ROI from sDTI and from sDTI after denoising and Gibbs ringing removal, respectively. The latter can be identified with the amount of improvement in tractography due to the elimination of systematic errors related to imperfect magnetic field gradients. Based on the results, the systematic bias for high resolution data mainly depended on SNR, but the influence of non-uniform gradients could also be seen. After denoising, the BSD method was able to further correct both the metrics and tractography of the diffusion tensor in the rat brain by taking into account the actual distribution of magnetic field gradients independent of the examined object and uniquely dependent on the scanner and sequence. This means that in vivo studies are also subject to this type of errors, which should be taken into account when processing such data.
Collapse
Affiliation(s)
| | - Artur T Krzyżak
- AGH University of Krakow, Al. Mickiewicza 30, 30-059, Krakow, Poland.
| |
Collapse
|
13
|
Richmond SB, Seidler RD, Iliff JJ, Schwartz DL, Luther M, Silbert LC, Wood SJ, Bloomberg JJ, Mulder E, Lee JK, De Luca A, Piantino J. Dynamic changes in perivascular space morphology predict signs of spaceflight-associated neuro-ocular syndrome in bed rest. NPJ Microgravity 2024; 10:24. [PMID: 38429289 PMCID: PMC10907584 DOI: 10.1038/s41526-024-00368-6] [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: 08/03/2023] [Accepted: 02/15/2024] [Indexed: 03/03/2024] Open
Abstract
During long-duration spaceflight, astronauts experience headward fluid shifts and expansion of the cerebral perivascular spaces (PVS). A major limitation to our understanding of the changes in brain structure and physiology induced by spaceflight stems from the logistical difficulties of studying astronauts. The current study aimed to determine whether PVS changes also occur on Earth with the spaceflight analog head-down tilt bed rest (HDBR). We examined how the number and morphology of magnetic resonance imaging-visible PVS (MV-PVS) are affected by HDBR with and without elevated carbon dioxide (CO2). These environments mimic the headward fluid shifts, body unloading, and elevated CO2 observed aboard the International Space Station. Additionally, we sought to understand how changes in MV-PVS are associated with signs of Spaceflight Associated Neuro-ocular Syndrome (SANS), ocular structural alterations that can occur with spaceflight. Participants were separated into two bed rest campaigns: HDBR (60 days) and HDBR + CO2 (30 days with elevated ambient CO2). Both groups completed multiple magnetic resonance image acquisitions before, during, and post-bed rest. We found that at the group level, neither spaceflight analog affected MV-PVS quantity or morphology. However, when taking into account SANS status, persons exhibiting signs of SANS showed little or no MV-PVS changes, whereas their No-SANS counterparts showed MV-PVS morphological changes during the HDBR + CO2 campaign. These findings highlight spaceflight analogs as models for inducing changes in MV-PVS and implicate MV-PVS dynamic compliance as a mechanism underlying SANS. These findings may lead to countermeasures to mitigate health risks associated with human spaceflight.
Collapse
Affiliation(s)
- Sutton B Richmond
- Department of Applied Physiology and Kinesiology, University of Florida, 1864, Stadium Rd., Gainesville, FL, USA
| | - Rachael D Seidler
- Department of Applied Physiology and Kinesiology, University of Florida, 1864, Stadium Rd., Gainesville, FL, USA
- Norman Fixel Institute for Neurological Diseases, University of Florida, Gainesville, FL, USA
| | - Jeffrey J Iliff
- Department of Psychiatry and Behavioral Sciences, University of Washington School of Medicine, Seattle, WA, USA
- Department of Neurology, University of Washington School of Medicine, Seattle, WA, USA
- VISN 20 Mental Illness Research, Education and Clinical Center (MIRECC), VA Puget Sound Health Care System, Seattle, WA, USA
| | - Daniel L Schwartz
- Layton-NIA Oregon Aging and Alzheimer's Disease Research Center, Department of Neurology, Oregon Health & Science University, Portland, OR, USA
- Advanced Imaging Research Center, Oregon Health & Science University, Portland, OR, USA
| | - Madison Luther
- Department of Pediatrics, Division of Child Neurology, Doernbecher Children's Hospital, Oregon Health and Science University, Portland, OR, USA
| | - Lisa C Silbert
- Layton-NIA Oregon Aging and Alzheimer's Disease Research Center, Department of Neurology, Oregon Health & Science University, Portland, OR, USA
- Veteran's Affairs Portland Health Care System, Neurology, Portland, OR, USA
| | | | | | | | - Jessica K Lee
- Department of Applied Physiology and Kinesiology, University of Florida, 1864, Stadium Rd., Gainesville, FL, USA
- German Aerospace Center (DLR), Cologne, Germany
| | - Alberto De Luca
- Department of Radiology, University Medical Center Utrecht, Utrecht, Netherlands
| | - Juan Piantino
- Department of Pediatrics, Division of Child Neurology, Doernbecher Children's Hospital, Oregon Health and Science University, Portland, OR, USA.
| |
Collapse
|
14
|
Gonzalez Alam TRJ, Cruz Arias J, Jefferies E, Smallwood J, Leemans A, Marino Davolos J. Ventral and dorsal aspects of the inferior frontal-occipital fasciculus support verbal semantic access and visually-guided behavioural control. Brain Struct Funct 2024; 229:207-221. [PMID: 38070006 PMCID: PMC10827863 DOI: 10.1007/s00429-023-02729-5] [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: 08/17/2022] [Accepted: 11/03/2023] [Indexed: 01/31/2024]
Abstract
The Inferior Frontal Occipital Fasciculus (IFOF) is a major anterior-to-posterior white matter pathway in the ventral human brain that connects parietal, temporal and occipital regions to frontal cortex. It has been implicated in a range of functions, including language, semantics, inhibition and the control of action. The recent research shows that the IFOF can be sub-divided into a ventral and dorsal branch, but the functional relevance of this distinction, as well as any potential hemispheric differences, are poorly understood. Using DTI tractography, we investigated the involvement of dorsal and ventral subdivisions of the IFOF in the left and right hemisphere in a response inhibition task (Go/No-Go), where the decision to respond or to withhold a prepotent response was made on the basis of semantic or non-semantic aspects of visual inputs. The task also varied the presentation modality (whether concepts were presented as written words or images). The results showed that the integrity of both dorsal and ventral IFOF in the left hemisphere were associated with participants' inhibition performance when the signal to stop was meaningful and presented in the verbal modality. This effect was absent in the right hemisphere. The integrity of dorsal IFOF was also associated with participants' inhibition efficiency in difficult perceptually guided decisions. This pattern of results indicates that left dorsal IFOF is implicated in the domain-general control of visually-guided behaviour, while the left ventral branch might interface with the semantic system to support the control of action when the inhibitory signal is based on meaning.
Collapse
Affiliation(s)
- Tirso R J Gonzalez Alam
- Department of Psychology and York Neuroimaging Centre, University of York, York, UK.
- School of Psychology, Bangor University, Bangor, UK.
| | | | - Elizabeth Jefferies
- Department of Psychology and York Neuroimaging Centre, University of York, York, UK
| | | | - Alexander Leemans
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
| | | |
Collapse
|
15
|
Lebret A, Lévy S, Pfender N, Farshad M, Altorfer FCS, Callot V, Curt A, Freund P, Seif M. Investigation of perfusion impairment in degenerative cervical myelopathy beyond the site of cord compression. Sci Rep 2023; 13:22660. [PMID: 38114733 PMCID: PMC10730822 DOI: 10.1038/s41598-023-49896-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/09/2023] [Accepted: 12/13/2023] [Indexed: 12/21/2023] Open
Abstract
The aim of this study was to determine tissue-specific blood perfusion impairment of the cervical cord above the compression site in patients with degenerative cervical myelopathy (DCM) using intravoxel incoherent motion (IVIM) imaging. A quantitative MRI protocol, including structural and IVIM imaging, was conducted in healthy controls and patients. In patients, T2-weighted scans were acquired to quantify intramedullary signal changes, the maximal canal compromise, and the maximal cord compression. T2*-weighted MRI and IVIM were applied in all participants in the cervical cord (covering C1-C3 levels) to determine white matter (WM) and grey matter (GM) cross-sectional areas (as a marker of atrophy), and tissue-specific perfusion indices, respectively. IVIM imaging resulted in microvascular volume fraction ([Formula: see text]), blood velocity ([Formula: see text]), and blood flow ([Formula: see text]) indices. DCM patients additionally underwent a standard neurological clinical assessment. Regression analysis assessed associations between perfusion parameters, clinical outcome measures, and remote spinal cord atrophy. Twenty-nine DCM patients and 30 healthy controls were enrolled in the study. At the level of stenosis, 11 patients showed focal radiological evidence of cervical myelopathy. Above the stenosis level, cord atrophy was observed in the WM (- 9.3%; p = 0.005) and GM (- 6.3%; p = 0.008) in patients compared to healthy controls. Blood velocity (BV) and blood flow (BF) indices were decreased in the ventral horns of the GM (BV: - 20.1%, p = 0.0009; BF: - 28.2%, p = 0.0008), in the ventral funiculi (BV: - 18.2%, p = 0.01; BF: - 21.5%, p = 0.04) and lateral funiculi (BV: - 8.5%, p = 0.03; BF: - 16.5%, p = 0.03) of the WM, across C1-C3 levels. A decrease in microvascular volume fraction was associated with GM atrophy (R = 0.46, p = 0.02). This study demonstrates tissue-specific cervical perfusion impairment rostral to the compression site in DCM patients. IVIM indices are sensitive to remote perfusion changes in the cervical cord in DCM and may serve as neuroimaging biomarkers of hemodynamic impairment in future studies. The association between perfusion impairment and cervical cord atrophy indicates that changes in hemodynamics caused by compression may contribute to the neurodegenerative processes in DCM.
Collapse
Affiliation(s)
- Anna Lebret
- Spinal Cord Injury Center, Balgrist University Hospital, Zürich, Switzerland
| | - Simon Lévy
- CNRS, CRMBM, Aix-Marseille University, Marseille, France
- APHM, CEMEREM, Hôpital Universitaire Timone, Marseille, France
- MR Research Collaborations, Siemens Healthcare Pty Ltd, Melbourne, Australia
| | - Nikolai Pfender
- Spinal Cord Injury Center, Balgrist University Hospital, Zürich, Switzerland
| | - Mazda Farshad
- Department of Orthopedic Surgery, Balgrist University Hospital, Zürich, Switzerland
| | | | - Virginie Callot
- CNRS, CRMBM, Aix-Marseille University, Marseille, France
- APHM, CEMEREM, Hôpital Universitaire Timone, Marseille, France
| | - Armin Curt
- Spinal Cord Injury Center, Balgrist University Hospital, Zürich, Switzerland
| | - Patrick Freund
- Spinal Cord Injury Center, Balgrist University Hospital, Zürich, Switzerland
- Department of Brain Repair and Rehabilitation, Wellcome Trust Center for Neuroimaging, Institute of Neurology, University College London, London, UK
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Maryam Seif
- Spinal Cord Injury Center, Balgrist University Hospital, Zürich, Switzerland.
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
| |
Collapse
|
16
|
Chen Q, Fang S, Yuchen Y, Li R, Deng R, Chen Y, Ma D, Lin H, Yan F. Clinical feasibility of deep learning reconstruction in liver diffusion-weighted imaging: Improvement of image quality and impact on apparent diffusion coefficient value. Eur J Radiol 2023; 168:111149. [PMID: 37862927 DOI: 10.1016/j.ejrad.2023.111149] [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: 06/05/2023] [Revised: 09/26/2023] [Accepted: 10/10/2023] [Indexed: 10/22/2023]
Abstract
PURPOSE Diffusion-weighted imaging (DWI) of the liver suffers from low resolution, noise, and artifacts. This study aimed to investigate the effect of deep learning reconstruction (DLR) on image quality and apparent diffusion coefficient (ADC) quantification of liver DWI at 3 Tesla. METHOD In this prospective study, images of the liver obtained at DWI with b-values of 0 (DWI0), 50 (DWI50) and 800 s/mm2 (DWI800) from consecutive patients with liver lesions from February 2022 to February 2023 were reconstructed with and without DLR (non-DLR). Image quality was assessed qualitatively using Likert scoring system and quantitatively using signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR) and liver/parenchyma boundary sharpness from region-of-interest (ROI) analysis. ADC value of lesion were measured. Phantom experiment was also performed to investigate the factors that determine the effect of DLR on ADC value. Qualitative score, SNR, CNR, boundary sharpness, and apparent diffusion coefficients (ADCs) for DWI were compared using paired t-test and Wilcoxon signed rank test. P < 0.05 was considered statistically significant. RESULTS A total of 85 patients with 170 lesions were included. DLR group showed a higher qualitative score than the non-DLR group. for example, with DWI800 the score was 4.77 ± 0.52 versus 4.30 ± 0.63 (P < 0.001). DLR group also showed higher SNRs, CNRs and boundary sharpness than the non-DLR group. DLR reduced the ADC of malignant tumors (1.105[0.904, 1.340] versus 1.114[0.904, 1.320]) (P < 0.001), but there was no significant difference in the diagnostic value of malignancy for DLR and non-DLR groups (P = 57.3). The phantom study confirmed a reduction of ADC in images with low resolution, and a stronger reduction of ADC in heterogeneous structures than in homogeneous ones (P < 0.001). CONCLUSIONS DLR improved image quality of liver DWI. DLR reduced the ADC value of lesions, but did not affect the diagnostic performance of ADC in distinguishing malignant tumors on a 3.0-T MRI system.
Collapse
Affiliation(s)
- Qian Chen
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, NO. 197 Ruijin Er Road, Shanghai 200025, China; Department of Radiology, Key Laboratory of Cancer Prevention and Therapy, Tianjin Medical University Cancer Institute & Hospital, National Clinical Research Center for Cancer, Huan-Hu-Xi Road, Ti-Yuan-Bei, He Xi District, Tianjin 300060, China
| | - Shu Fang
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, NO. 197 Ruijin Er Road, Shanghai 200025, China
| | - Yang Yuchen
- Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University School Of Medicine, NO. 197 Ruijin Er Road, Shanghai 200025, China
| | - Ruokun Li
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, NO. 197 Ruijin Er Road, Shanghai 200025, China
| | - Rong Deng
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, NO. 197 Ruijin Er Road, Shanghai 200025, China
| | - Yongjun Chen
- Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University School Of Medicine, NO. 197 Ruijin Er Road, Shanghai 200025, China
| | - Di Ma
- Department of General Surgery, Ruijin Hospital, Shanghai Jiao Tong University School Of Medicine, NO. 197 Ruijin Er Road, Shanghai 200025, China
| | - Huimin Lin
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, NO. 197 Ruijin Er Road, Shanghai 200025, China.
| | - Fuhua Yan
- Department of Radiology, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, NO. 197 Ruijin Er Road, Shanghai 200025, China; College of Health Science and Technology, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
| |
Collapse
|
17
|
Neophytou K, Wiley R, Litovsky C, Tsapkini K, Rapp B. The right hemisphere's capacity for language: evidence from primary progressive aphasia. Cereb Cortex 2023; 33:9971-9985. [PMID: 37522277 PMCID: PMC10502784 DOI: 10.1093/cercor/bhad258] [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: 01/06/2023] [Revised: 06/21/2023] [Accepted: 06/22/2023] [Indexed: 08/01/2023] Open
Abstract
The role of the right hemisphere (RH) in core language processes is still a matter of intense debate. Most of the relevant evidence has come from studies of gray matter, with relatively little research on RH white matter (WM) connectivity. Using Diffusion Tensor Imaging-based tractography, the current work examined the role of the two hemispheres in language processing in 33 individuals with Primary Progressive Aphasia (PPA), aiming to better characterize the contribution of the RH to language processing in the context of left hemisphere (LH) damage. The findings confirm the impact of PPA on the integrity of the WM language tracts in the LH. Additionally, an examination of the relationship between tract integrity and language behaviors provides robust evidence of the involvement of the WM language tracts of both hemispheres in language processing in PPA. Importantly, this study provides novel evidence of a unique contribution of the RH to language processing (i.e. a contribution independent from that of the language-dominant LH). Finally, we provide evidence that the RH contribution is specific to language processing rather than being domain general. These findings allow us to better characterize the role of RH in language processing, particularly in the context of LH damage.
Collapse
Affiliation(s)
- Kyriaki Neophytou
- Department of Neurology, Johns Hopkins Medicine, Baltimore, MD, United States
| | - Robert Wiley
- Department of Psychology, University of North Carolina at Greensboro, Greensboro, NC, United States
- Department of Cognitive Science, Johns Hopkins University, Baltimore, MD, United States
| | - Celia Litovsky
- Department of Cognitive Science, Johns Hopkins University, Baltimore, MD, United States
| | - Kyrana Tsapkini
- Department of Neurology, Johns Hopkins Medicine, Baltimore, MD, United States
- Department of Cognitive Science, Johns Hopkins University, Baltimore, MD, United States
| | - Brenda Rapp
- Department of Cognitive Science, Johns Hopkins University, Baltimore, MD, United States
| |
Collapse
|
18
|
Aho L, Sairanen V, Lönnberg P, Wolford E, Lano A, Metsäranta M. Visual alertness and brain diffusion tensor imaging at term age predict neurocognitive development at preschool age in extremely preterm-born children. Brain Behav 2023; 13:e3048. [PMID: 37165734 PMCID: PMC10338808 DOI: 10.1002/brb3.3048] [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: 12/15/2022] [Revised: 04/12/2023] [Accepted: 04/19/2023] [Indexed: 05/12/2023] Open
Abstract
INTRODUCTION Cognitive development is characterized by the structural and functional maturation of the brain. Diffusion-weighted magnetic resonance imaging (dMRI) provides methods of investigating the brain structure and connectivity and their correlations with the neurocognitive outcome. Our aim was to examine the relationship between early visual abilities, brain white matter structures, and the later neurocognitive outcome. METHODS This study included 20 infants who were born before 28 gestational weeks and followed until the age of 6.5 years. At term age, visual alertness was evaluated and dMRI was used to investigate the brain white matter structure using fractional anisotropy (FA) in tract-based spatial statistics analysis. The JHU DTI white matter atlas was used to locate the findings. The neuropsychological assessment was used to assess neurocognitive performance at 6.5 years. RESULTS Optimal visual alertness at term age was significantly associated with better visuospatial processing (p < .05), sensorimotor functioning (p < .05), and social perception (p < .05) at 6.5 years of age. Optimal visual alertness related to higher FA values, and further, the FA values positively correlated with the neurocognitive outcome. The tract-based spatial differences in FA values were detected between children with optimal and nonoptimal visual alertness according to performance at 6.5 years. CONCLUSION We provide neurobiological evidence for the global and tract-based spatial differences in the white matter maturation between extremely preterm children with optimal and nonoptimal visual alertness at term age and a link between white matter maturation, visual alertness and the neurocognitive outcome at 6.5 years proposing that early visual function is a building block for the later neurocognitive development.
Collapse
Affiliation(s)
- Leena Aho
- New Children's Hospital, Pediatric Research CenterUniversity of Helsinki and Helsinki University HospitalHelsinkiFinland
| | - Viljami Sairanen
- BABA Center, Pediatric Research Center, Department of Clinical NeurophysiologyChildren's HospitalHelsinki University Hospital and University of HelsinkiHelsinkiFinland
| | - Piia Lönnberg
- New Children's Hospital, Pediatric Research CenterUniversity of Helsinki and Helsinki University HospitalHelsinkiFinland
| | - Elina Wolford
- Department of Psychology and LogopedicsUniversity of HelsinkiHelsinkiFinland
| | - Aulikki Lano
- New Children's Hospital, Pediatric Research CenterUniversity of Helsinki and Helsinki University HospitalHelsinkiFinland
| | - Marjo Metsäranta
- New Children's Hospital, Pediatric Research CenterUniversity of Helsinki and Helsinki University HospitalHelsinkiFinland
| |
Collapse
|
19
|
Christiaanse E, Wyss PO, Scheel‐Sailer A, Frotzler A, Lehnick D, Verma RK, Berger MF, Leemans A, De Luca A. Mean kurtosis-Curve (MK-Curve) correction improves the test-retest reproducibility of diffusion kurtosis imaging at 3 T. NMR IN BIOMEDICINE 2023; 36:e4856. [PMID: 36285630 PMCID: PMC10078439 DOI: 10.1002/nbm.4856] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/07/2022] [Revised: 09/25/2022] [Accepted: 10/17/2022] [Indexed: 06/16/2023]
Abstract
Diffusion kurtosis imaging (DKI) is applied to gain insights into the microstructural organization of brain tissues. However, the reproducibility of DKI outside brain white matter, particularly in combination with advanced estimation to remedy its noise sensitivity, remains poorly characterized. Therefore, in this study, we investigated the variability and reliability of DKI metrics while correcting implausible values with a fit method called mean kurtosis (MK)-Curve. A total of 10 volunteers (four women; age: 41.4 ± 9.6 years) were included and underwent two MRI examinations of the brain. The images were acquired on a clinical 3-T scanner and included a T1-weighted image and a diffusion sequence with multiple diffusion weightings suitable for DKI. Region of interest analysis of common kurtosis and tensor metrics derived with the MK-Curve DKI fit was performed, including intraclass correlation (ICC) and Bland-Altman (BA) plot statistics. A p value of less than 0.05 was considered statistically significant. The analyses showed good to excellent agreement of both kurtosis tensor- and diffusion tensor-derived MK-Curve-corrected metrics (ICC values: 0.77-0.98 and 0.87-0.98, respectively), with the exception of two DKI-derived metrics (axial kurtosis in the cortex: ICC = 0.68, and radial kurtosis in deep gray matter: ICC = 0.544). Non-MK-Curve-corrected kurtosis tensor-derived metrics ranged from 0.01 to 0.52 and diffusion tensor-derived metrics from 0.06 to 0.66, indicating poor to moderate reliability. No structural bias was observed in the BA plots for any of the diffusion metrics. In conclusion, MK-Curve-corrected DKI metrics of the human brain can be reliably acquired in white and gray matter at 3 T and DKI metrics have good to excellent agreement in a test-retest setting.
Collapse
Affiliation(s)
- Ernst Christiaanse
- Department of RadiologySwiss Paraplegic CentreNottwilSwitzerland
- Image Sciences Institute, Division Imaging & OncologyUniversity Medical Center UtrechtUtrechtthe Netherlands
| | - Patrik O. Wyss
- Department of RadiologySwiss Paraplegic CentreNottwilSwitzerland
| | - Anke Scheel‐Sailer
- Rehabilitation and Quality ManagementSwiss Paraplegic CentreNottwilSwitzerland
- Department of Health Sciences and MedicineUniversity of LucerneLucerneSwitzerland
| | - Angela Frotzler
- Clinical Trial UnitSwiss Paraplegic CentreNottwilSwitzerland
| | - Dirk Lehnick
- Department of Health Sciences and Medicine, Biostatistics and MethodologyUniversity LucerneLucerneSwitzerland
| | - Rajeev K. Verma
- Department of RadiologySwiss Paraplegic CentreNottwilSwitzerland
| | - Markus F. Berger
- Department of RadiologySwiss Paraplegic CentreNottwilSwitzerland
| | - Alexander Leemans
- Image Sciences Institute, Division Imaging & OncologyUniversity Medical Center UtrechtUtrechtthe Netherlands
| | - Alberto De Luca
- Image Sciences Institute, Division Imaging & OncologyUniversity Medical Center UtrechtUtrechtthe Netherlands
- Neurology Department, UMC Utrecht Brain CenterUniversity Medical Center UtrechtUtrechtthe Netherlands
| |
Collapse
|
20
|
Morita Y, Kamagata K, Andica C, Takabayashi K, Kikuta J, Fujita S, Samoyeau T, Uchida W, Saito Y, Tabata H, Naito H, Someya Y, Kaga H, Tamura Y, Miyata M, Akashi T, Wada A, Taoka T, Naganawa S, Watada H, Kawamori R, Abe O, Aoki S. Glymphatic system impairment in nonathlete older male adults who played contact sports in their youth associated with cognitive decline: A diffusion tensor image analysis along the perivascular space study. Front Neurol 2023; 14:1100736. [PMID: 36873446 PMCID: PMC9977161 DOI: 10.3389/fneur.2023.1100736] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2022] [Accepted: 01/09/2023] [Indexed: 02/17/2023] Open
Abstract
Background and purpose Exposure to contact sports in youth causes brain health problems later in life. For instance, the repetitive head impacts in contact sports might contribute to glymphatic clearance impairment and cognitive decline. This study aimed to assess the effect of contact sports participation in youth on glymphatic function in old age and the relationship between glymphatic function and cognitive status using the analysis along the perivascular space (ALPS) index. Materials and methods A total of 52 Japanese older male subjects were included in the study, including 12 who played heavy-contact sports (mean age, 71.2 years), 15 who played semicontact sports (mean age, 73.1 years), and 25 who played noncontact sports (mean age, 71.3 years) in their youth. All brain diffusion-weighted images (DWIs) of the subjects were acquired using a 3T MRI scanner. The ALPS indices were calculated using a validated semiautomated pipeline. The ALPS indices from the left and right hemispheres were compared between groups using a general linear model, including age and years of education. Furthermore, partial Spearman's rank correlation tests were performed to assess the correlation between the ALPS indices and cognitive scores (Mini-Mental State Examination and the Japanese version of the Montreal Cognitive Assessment [MoCA-J]) after adjusting for age years of education and HbA1c. Results The left ALPS index was significantly lower in the heavy-contact and semicontact groups than that in the noncontact group. Although no significant differences were observed in the left ALPS index between the heavy-contact and semicontact groups and in the right ALPS index among groups, a trend toward lower was found in the right ALPS index in individuals with semicontact and heavy-contact compared to the noncontact group. Both sides' ALPS indices were significantly positively correlated with the MoCA-J scores. Conclusion The findings indicated the potential adverse effect of contact sports experience in youth on the glymphatic system function in old age associated with cognitive decline.
Collapse
Affiliation(s)
- Yuichi Morita
- Department of Radiology, Graduate School of Medicine, Juntendo University, Tokyo, Japan.,Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Koji Kamagata
- Department of Radiology, Graduate School of Medicine, Juntendo University, Tokyo, Japan
| | - Christina Andica
- Department of Radiology, Graduate School of Medicine, Juntendo University, Tokyo, Japan.,Faculty of Health Data Science, Juntendo University, Chiba, Japan
| | - Kaito Takabayashi
- Department of Radiology, Graduate School of Medicine, Juntendo University, Tokyo, Japan
| | - Junko Kikuta
- Department of Radiology, Graduate School of Medicine, Juntendo University, Tokyo, Japan
| | - Shohei Fujita
- Department of Radiology, Graduate School of Medicine, Juntendo University, Tokyo, Japan.,Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Thomas Samoyeau
- Department of Radiology, Necker Hospital, Paris University, Paris, France
| | - Wataru Uchida
- Department of Radiology, Graduate School of Medicine, Juntendo University, Tokyo, Japan
| | - Yuya Saito
- Department of Radiology, Graduate School of Medicine, Juntendo University, Tokyo, Japan
| | - Hiroki Tabata
- Sportology Center, Graduate School of Medicine, Juntendo University, Tokyo, Japan
| | - Hitoshi Naito
- Department of Metabolism and Endocrinology, Graduate School of Medicine, Juntendo University, Tokyo, Japan
| | - Yuki Someya
- Sportology Center, Graduate School of Medicine, Juntendo University, Tokyo, Japan.,Graduate School of Health and Sports Science, Juntendo University, Inzai, Chiba, Japan
| | - Hideyoshi Kaga
- Department of Metabolism and Endocrinology, Graduate School of Medicine, Juntendo University, Tokyo, Japan
| | - Yoshifumi Tamura
- Sportology Center, Graduate School of Medicine, Juntendo University, Tokyo, Japan.,Department of Metabolism and Endocrinology, Graduate School of Medicine, Juntendo University, Tokyo, Japan
| | - Mari Miyata
- Department of Functional Brain Imaging, National Institutes for Quantum and Radiological Science and Technology, Chiba, Japan
| | - Toshiaki Akashi
- Department of Radiology, Graduate School of Medicine, Juntendo University, Tokyo, Japan
| | - Akihiko Wada
- Department of Radiology, Graduate School of Medicine, Juntendo University, Tokyo, Japan
| | - Toshiaki Taoka
- Department of Innovative Biomedical Visualization, Graduate School of Medicine, Nagoya University, Nagoya, Japan
| | - Shinji Naganawa
- Department of Radiology, Graduate School of Medicine, Nagoya University, Nagoya, Japan
| | - Hirotaka Watada
- Sportology Center, Graduate School of Medicine, Juntendo University, Tokyo, Japan.,Department of Metabolism and Endocrinology, Graduate School of Medicine, Juntendo University, Tokyo, Japan
| | - Ryuzo Kawamori
- Sportology Center, Graduate School of Medicine, Juntendo University, Tokyo, Japan.,Department of Metabolism and Endocrinology, Graduate School of Medicine, Juntendo University, Tokyo, Japan
| | - Osamu Abe
- Department of Radiology, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan
| | - Shigeki Aoki
- Department of Radiology, Graduate School of Medicine, Juntendo University, Tokyo, Japan
| |
Collapse
|
21
|
Constrained spherical deconvolution -based tractography of major language tracts reveals post-stroke bilateral white matter changes correlated to aphasia. Magn Reson Imaging 2023; 95:19-26. [PMID: 36252694 DOI: 10.1016/j.mri.2022.10.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2021] [Revised: 10/04/2022] [Accepted: 10/11/2022] [Indexed: 11/19/2022]
Abstract
PURPOSE Using constrained spherical deconvolution (CSD)-based tractography, we aimed to obtain conjoint analysis of diffusion measures of major language white matter (WM) tracts in post-stroke aphasic patients bilaterally, and to correlate the measures of each tract to the different language deficits. MATERIAL AND METHODS 17 aphasic patients with left hemispheric stroke, at the subacute stage, and ten age- matched controls underwent diffusion MRI examination. CSD-based tractography was performed. Diffusion measures [fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), axial diffusivity (AD)] were extracted after dissection of major language tracts bilaterally. Aphasia was assessed using language subset of hemispheric stroke scale. Comparisons of diffusion measures, for all tracts, between the two groups were performed. Partial correlations between the diffusion measures and different language components were obtained. RESULTS In the left hemisphere, significant lower FA and or higher MD with higher RD of patients' WM tracts compared to the control group. Significant differences of diffusion measures were also evident in the right hemisphere yet, less prominent. All changes reflected damage of the tracts' integrity. Significant correlations were found between comprehension and FA of the left arcuate fasciculus (AF) and left inferior longitudinal fasciculus. Additionally, a significant correlation was found between MD of the right AF and repetition. CONCLUSION Conjoint analysis of diffusion measures, based on CSD tractography, can provide important markers for the underlying WM changes bilaterally. Moreover, our findings emphasize that language processing can be mediated by both ventral and dorsal streams and further highlight the contribution of the right AF in repetition.
Collapse
|
22
|
De Luca A, Kuijf H, Exalto L, Thiebaut de Schotten M, Biessels GJ. Multimodal tract-based MRI metrics outperform whole brain markers in determining cognitive impact of small vessel disease-related brain injury. Brain Struct Funct 2022; 227:2553-2567. [PMID: 35994115 PMCID: PMC9418106 DOI: 10.1007/s00429-022-02546-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2022] [Accepted: 07/27/2022] [Indexed: 01/04/2023]
Abstract
In cerebral small vessel disease (cSVD), whole brain MRI markers of cSVD-related brain injury explain limited variance to support individualized prediction. Here, we investigate whether considering abnormalities in brain tracts by integrating multimodal metrics from diffusion MRI (dMRI) and structural MRI (sMRI), can better capture cognitive performance in cSVD patients than established approaches based on whole brain markers. We selected 102 patients (73.7 ± 10.2 years old, 59 males) with MRI-visible SVD lesions and both sMRI and dMRI. Conventional linear models using demographics and established whole brain markers were used as benchmark of predicting individual cognitive scores. Multi-modal metrics of 73 major brain tracts were derived from dMRI and sMRI, and used together with established markers as input of a feed-forward artificial neural network (ANN) to predict individual cognitive scores. A feature selection strategy was implemented to reduce the risk of overfitting. Prediction was performed with leave-one-out cross-validation and evaluated with the R2 of the correlation between measured and predicted cognitive scores. Linear models predicted memory and processing speed with R2 = 0.26 and R2 = 0.38, respectively. With ANN, feature selection resulted in 13 tract-specific metrics and 5 whole brain markers for predicting processing speed, and 28 tract-specific metrics and 4 whole brain markers for predicting memory. Leave-one-out ANN prediction with the selected features achieved R2 = 0.49 and R2 = 0.40 for processing speed and memory, respectively. Our results show proof-of-concept that combining tract-specific multimodal MRI metrics can improve the prediction of cognitive performance in cSVD by leveraging tract-specific multi-modal metrics.
Collapse
Affiliation(s)
- Alberto De Luca
- VCI Group, Neurology Department, UMC Utrecht Brain Center, UMC Utrecht, Utrecht, The Netherlands.
- Image Sciences Institute, Division Imaging and Oncology, UMC Utrecht, Utrecht, The Netherlands.
| | - Hugo Kuijf
- Image Sciences Institute, Division Imaging and Oncology, UMC Utrecht, Utrecht, The Netherlands
| | - Lieza Exalto
- VCI Group, Neurology Department, UMC Utrecht Brain Center, UMC Utrecht, Utrecht, The Netherlands
| | - Michel Thiebaut de Schotten
- Brain Connectivity and Behaviour Lab, Sorbonne University, Paris, France
- Institut des Maladies Neurodégénératives, Neurofunctional Imaging Group, University of Bordeaux, Bordeaux, France
| | - Geert-Jan Biessels
- VCI Group, Neurology Department, UMC Utrecht Brain Center, UMC Utrecht, Utrecht, The Netherlands
| |
Collapse
|
23
|
Grier MD, Yacoub E, Adriany G, Lagore RL, Harel N, Zhang RY, Lenglet C, Uğurbil K, Zimmermann J, Heilbronner SR. Ultra-high field (10.5T) diffusion-weighted MRI of the macaque brain. Neuroimage 2022; 255:119200. [PMID: 35427769 PMCID: PMC9446284 DOI: 10.1016/j.neuroimage.2022.119200] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 03/08/2022] [Accepted: 04/07/2022] [Indexed: 11/26/2022] Open
Abstract
Diffu0sion-weighted magnetic resonance imaging (dMRI) is a non-invasive imaging technique that provides information about the barriers to the diffusion of water molecules in tissue. In the brain, this information can be used in several important ways, including to examine tissue abnormalities associated with brain disorders and to infer anatomical connectivity and the organization of white matter bundles through the use of tractography algorithms. However, dMRI also presents certain challenges. For example, historically, the biological validation of tractography models has shown only moderate correlations with anatomical connectivity as determined through invasive tract-tracing studies. Some of the factors contributing to such issues are low spatial resolution, low signal-to-noise ratios, and long scan times required for high-quality data, along with modeling challenges like complex fiber crossing patterns. Leveraging the capabilities provided by an ultra-high field scanner combined with denoising, we have acquired whole-brain, 0.58 mm isotropic resolution dMRI with a 2D-single shot echo planar imaging sequence on a 10.5 Tesla scanner in anesthetized macaques. These data produced high-quality tractograms and maps of scalar diffusion metrics in white matter. This work demonstrates the feasibility and motivation for in-vivo dMRI studies seeking to benefit from ultra-high fields.
Collapse
Affiliation(s)
- Mark D Grier
- Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, United States
| | - Essa Yacoub
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN 55455, United States; Center for Neuroengineering, University of Minnesota, Minneapolis, MN 55455, United States
| | - Gregor Adriany
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN 55455, United States; Center for Neuroengineering, University of Minnesota, Minneapolis, MN 55455, United States
| | - Russell L Lagore
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN 55455, United States
| | - Noam Harel
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN 55455, United States; Department of Neurosurgery, University of Minnesota, Minneapolis, MN 55455, United States
| | - Ru-Yuan Zhang
- Institute of Psychology and Behavioral Science, Shanghai Jiao Tong University, Shanghai 200030, P.R. China; Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai Jiao Tong University, Shanghai 200030, P.R. China; Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN 55455, United States
| | - Christophe Lenglet
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN 55455, United States
| | - Kâmil Uğurbil
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN 55455, United States; Center for Neuroengineering, University of Minnesota, Minneapolis, MN 55455, United States
| | - Jan Zimmermann
- Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, United States; Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN 55455, United States; Center for Neuroengineering, University of Minnesota, Minneapolis, MN 55455, United States; Department of Biomedical Engineering, University of Minnesota, Minneapolis, MN 55455, United States
| | - Sarah R Heilbronner
- Department of Neuroscience, University of Minnesota, Minneapolis, MN 55455, United States; Center for Neuroengineering, University of Minnesota, Minneapolis, MN 55455, United States.
| |
Collapse
|
24
|
De Stefano N, Battaglini M, Pareto D, Cortese R, Zhang J, Oesingmann N, Prados F, Rocca MA, Valsasina P, Vrenken H, Gandini Wheeler-Kingshott CAM, Filippi M, Barkhof F, Rovira À. MAGNIMS recommendations for harmonization of MRI data in MS multicenter studies. Neuroimage Clin 2022; 34:102972. [PMID: 35245791 PMCID: PMC8892169 DOI: 10.1016/j.nicl.2022.102972] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2021] [Revised: 02/22/2022] [Accepted: 02/23/2022] [Indexed: 11/24/2022]
Abstract
Sharing data from cooperative studies is essential to develop new biomarkers in MS. Differences in MRI acquisition, analysis, storage represent a substantial constraint. We review the state of the art and developments in the harmonization of MRI. We provide recommendations to harmonize large MRI datasets in the MS field.
There is an increasing need of sharing harmonized data from large, cooperative studies as this is essential to develop new diagnostic and prognostic biomarkers. In the field of multiple sclerosis (MS), the issue has become of paramount importance due to the need to translate into the clinical setting some of the most recent MRI achievements. However, differences in MRI acquisition parameters, image analysis and data storage across sites, with their potential bias, represent a substantial constraint. This review focuses on the state of the art, recent technical advances, and desirable future developments of the harmonization of acquisition, analysis and storage of large-scale multicentre MRI data of MS cohorts. Huge efforts are currently being made to achieve all the requirements needed to provide harmonized MRI datasets in the MS field, as proper management of large imaging datasets is one of our greatest opportunities and challenges in the coming years. Recommendations based on these achievements will be provided here. Despite the advances that have been made, the complexity of these tasks requires further research by specialized academical centres, with dedicated technical and human resources. Such collective efforts involving different professional figures are of crucial importance to offer to MS patients a personalised management while minimizing consumption of resources.
Collapse
Affiliation(s)
- Nicola De Stefano
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy.
| | - Marco Battaglini
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Deborah Pareto
- Section of Neuroradiology, Department of Radiology, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| | - Rosa Cortese
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | - Jian Zhang
- Department of Medicine, Surgery and Neuroscience, University of Siena, Siena, Italy
| | | | - Ferran Prados
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom; Center for Medical Imaging Computing, Medical Physics and Biomedical Engineering, UCL, London, WC1V 6LJ, United Kingdom; e-Health Center, Universitat Oberta de Catalunya, Barcelona, Spain
| | - Maria A Rocca
- Neuroimaging Research Unit, Division of Neuroscience, and Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy; Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy
| | - Paola Valsasina
- Neuroimaging Research Unit, Division of Neuroscience, and Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Hugo Vrenken
- Amsterdam Neuroscience, MS Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Amsterdam, Netherlands
| | - Claudia A M Gandini Wheeler-Kingshott
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom; Brain MRI 3T Research Center, C. Mondino National Neurological Institute, Pavia, Italy; Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy
| | - Massimo Filippi
- Neuroimaging Research Unit, Division of Neuroscience, and Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy; Neurology Unit, IRCCS San Raffaele Scientific Institute, Milan, Italy; Vita-Salute San Raffaele University, Milan, Italy; Neurorehabilitation Unit, and Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy; Neurophysiology Service, IRCCS San Raffaele Scientific Institute, Milan, Italy
| | - Frederik Barkhof
- Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom; Center for Medical Imaging Computing, Medical Physics and Biomedical Engineering, UCL, London, WC1V 6LJ, United Kingdom; Amsterdam Neuroscience, MS Center Amsterdam, Department of Radiology and Nuclear Medicine, Amsterdam UMC, Amsterdam, Netherlands
| | - Àlex Rovira
- Section of Neuroradiology, Department of Radiology, Hospital Universitari Vall d'Hebron, Universitat Autònoma de Barcelona, Barcelona, Spain
| |
Collapse
|
25
|
Tax CMW, Bastiani M, Veraart J, Garyfallidis E, Okan Irfanoglu M. What's new and what's next in diffusion MRI preprocessing. Neuroimage 2022; 249:118830. [PMID: 34965454 PMCID: PMC9379864 DOI: 10.1016/j.neuroimage.2021.118830] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 10/26/2021] [Accepted: 12/15/2021] [Indexed: 02/07/2023] Open
Abstract
Diffusion MRI (dMRI) provides invaluable information for the study of tissue microstructure and brain connectivity, but suffers from a range of imaging artifacts that greatly challenge the analysis of results and their interpretability if not appropriately accounted for. This review will cover dMRI artifacts and preprocessing steps, some of which have not typically been considered in existing pipelines or reviews, or have only gained attention in recent years: brain/skull extraction, B-matrix incompatibilities w.r.t the imaging data, signal drift, Gibbs ringing, noise distribution bias, denoising, between- and within-volumes motion, eddy currents, outliers, susceptibility distortions, EPI Nyquist ghosts, gradient deviations, B1 bias fields, and spatial normalization. The focus will be on "what's new" since the notable advances prior to and brought by the Human Connectome Project (HCP), as presented in the predecessing issue on "Mapping the Connectome" in 2013. In addition to the development of novel strategies for dMRI preprocessing, exciting progress has been made in the availability of open source tools and reproducible pipelines, databases and simulation tools for the evaluation of preprocessing steps, and automated quality control frameworks, amongst others. Finally, this review will consider practical considerations and our view on "what's next" in dMRI preprocessing.
Collapse
Affiliation(s)
- Chantal M W Tax
- Image Sciences Institute, University Medical Center Utrecht, The Netherlands; Cardiff University Brain Research Imaging Centre, School of Physics and Astronomy, Cardiff University, UK.
| | - Matteo Bastiani
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, UK; Wellcome Centre for Integrative Neuroimaging (WIN), Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, UK
| | - Jelle Veraart
- Center for Biomedical Imaging, New York University Grossman School of Medicine, NY, USA
| | | | - M Okan Irfanoglu
- Quantitative Medical Imaging Section, National Institute of Biomedical Imaging and Bioengineering, National Institutes of Health, Bethesda, MD, USA
| |
Collapse
|
26
|
Manso Jimeno M, Ravi KS, Jin Z, Oyekunle D, Ogbole G, Geethanath S. ArtifactID: Identifying artifacts in low-field MRI of the brain using deep learning. Magn Reson Imaging 2022; 89:42-48. [PMID: 35176447 DOI: 10.1016/j.mri.2022.02.002] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2021] [Revised: 02/08/2022] [Accepted: 02/10/2022] [Indexed: 01/14/2023]
Abstract
Low-field MR scanners are more accessible in resource-constrained settings where skilled personnel are scarce. Images acquired in such scenarios are prone to artifacts such as wrap-around and Gibbs ringing. Such artifacts negatively affect the diagnostic quality and may be confused with pathology or reduce the region of interest visibility. As a first step solution, ArtifactID identifies wrap-around and Gibbs ringing in low-field brain MRI. We utilized two datasets: 179 T1-weighted pathological brain images from a 0.36 T scanner and 581 publicly available T1-weighted brain images. Individual binary classification models were trained to identify through-plane wrap-around, in-plane wrap-around, and Gibbs ringing. Visual explanations obtained via the GradCAM method helped develop trust in the wrap-around model. The mean precision and recall metrics across the four implemented models were 97.6% and 92.83% respectively. Agreement analysis of the models and the radiologists' labels returned Cohen's kappa values of 0.768 ± 0.062, 1.00 ± 0.000, 0.89 ± 0.085, and 0.878 ± 0.103 for the through-plane wrap-around, in-plane wrap-around, and Gibbs ringing models, respectively.
Collapse
Affiliation(s)
- Marina Manso Jimeno
- Department of Biomedical Engineering, Columbia University in the City of New York, New York, NY 10027, USA; Columbia University Magnetic Resonance Research Center, Columbia University in the City of New York, New York, NY 10027, USA
| | - Keerthi Sravan Ravi
- Department of Biomedical Engineering, Columbia University in the City of New York, New York, NY 10027, USA; Columbia University Magnetic Resonance Research Center, Columbia University in the City of New York, New York, NY 10027, USA
| | - Zhezhen Jin
- Mailman School of Public Health, Columbia University in the City of New York, New York, NY 10027, USA
| | - Dotun Oyekunle
- Department of Radiology, University College Hospital, Ibadan 200285, Nigeria
| | - Godwin Ogbole
- Department of Radiology, University College Hospital, Ibadan 200285, Nigeria
| | - Sairam Geethanath
- Columbia University Magnetic Resonance Research Center, Columbia University in the City of New York, New York, NY 10027, USA.
| |
Collapse
|
27
|
Yeh FC, Irimia A, Bastos DCDA, Golby AJ. Tractography methods and findings in brain tumors and traumatic brain injury. Neuroimage 2021; 245:118651. [PMID: 34673247 PMCID: PMC8859988 DOI: 10.1016/j.neuroimage.2021.118651] [Citation(s) in RCA: 40] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 10/05/2021] [Accepted: 10/11/2021] [Indexed: 12/31/2022] Open
Abstract
White matter fiber tracking using diffusion magnetic resonance imaging (dMRI) provides a noninvasive approach to map brain connections, but improving anatomical accuracy has been a significant challenge since the birth of tractography methods. Utilizing tractography in brain studies therefore requires understanding of its technical limitations to avoid shortcomings and pitfalls. This review explores tractography limitations and how different white matter pathways pose different challenges to fiber tracking methodologies. We summarize the pros and cons of commonly-used methods, aiming to inform how tractography and its related analysis may lead to questionable results. Extending these experiences, we review the clinical utilization of tractography in patients with brain tumors and traumatic brain injury, starting from tensor-based tractography to more advanced methods. We discuss current limitations and highlight novel approaches in the context of these two conditions to inform future tractography developments.
Collapse
Affiliation(s)
- Fang-Cheng Yeh
- Department of Neurological Surgery, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania, USA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, Pennsylvania, USA.
| | - Andrei Irimia
- Ethel Percy Andrus Gerontology Center, Leonard Davis School of Gerontology, University of Southern California, Los Angeles, California, USA; Corwin D. Denney Research Center, Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, Los Angeles, California, USA
| | | | - Alexandra J Golby
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA; Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA
| |
Collapse
|
28
|
Sairanen V, Ocampo-Pineda M, Granziera C, Schiavi S, Daducci A. Incorporating outlier information into diffusion-weighted MRI modeling for robust microstructural imaging and structural brain connectivity analyses. Neuroimage 2021; 247:118802. [PMID: 34896584 DOI: 10.1016/j.neuroimage.2021.118802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/10/2021] [Revised: 11/01/2021] [Accepted: 12/09/2021] [Indexed: 11/28/2022] Open
Abstract
The white matter structures of the human brain can be represented using diffusion-weighted MRI tractography. Unfortunately, tractography is prone to find false-positive streamlines causing a severe decline in its specificity and limiting its feasibility in accurate structural brain connectivity analyses. Filtering algorithms have been proposed to reduce the number of invalid streamlines but the currently available filtering algorithms are not suitable to process data that contains motion artefacts which are typical in clinical research. We augmented the Convex Optimization Modelling for Microstructure Informed Tractography (COMMIT) algorithm to adjust for these signals drop-out motion artefacts. We demonstrate with comprehensive Monte-Carlo whole brain simulations and in vivo infant data that our robust algorithm is capable of properly filtering tractography reconstructions despite these artefacts. We evaluated the results using parametric and non-parametric statistics and our results demonstrate that if not accounted for, motion artefacts can have severe adverse effects in human brain structural connectivity analyses as well as in microstructural property mappings. In conclusion, the usage of robust filtering methods to mitigate motion related errors in tractogram filtering is highly beneficial, especially in clinical studies with uncooperative patient groups such as infants. With our presented robust augmentation and open-source implementation, robust tractogram filtering is readily available.
Collapse
Affiliation(s)
- Viljami Sairanen
- Department of Computer Science, University of Verona, Verona, Italy; Translational Imaging in Neurology, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Neurologic Clinic and Policlinic, Basel, Switzerland; BABA Center, Pediatric Research Center, Department of Clinical Neurophysiology, Children's Hospital, Helsinki University Hospital and University of Helsinki, Helsinki, Finland.
| | | | - Cristina Granziera
- Translational Imaging in Neurology, Department of Medicine and Biomedical Engineering, University Hospital Basel and University of Basel, Neurologic Clinic and Policlinic, Basel, Switzerland
| | - Simona Schiavi
- Department of Computer Science, University of Verona, Verona, Italy
| | | |
Collapse
|
29
|
Farrher E, Chiang CW, Cho KH, Grinberg F, Buschbeck RP, Chen MJ, Wu KJ, Wang Y, Huang SM, Abbas Z, Choi CH, Shah NJ, Kuo LW. Spatiotemporal characterisation of ischaemic lesions in transient stroke animal models using diffusion free water elimination and mapping MRI with echo time dependence. Neuroimage 2021; 244:118605. [PMID: 34592438 DOI: 10.1016/j.neuroimage.2021.118605] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Revised: 09/14/2021] [Accepted: 09/19/2021] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND AND PURPOSE The excess fluid as a result of vasogenic oedema and the subsequent tissue cavitation obscure the microstructural characterisation of ischaemic tissue by conventional diffusion and relaxometry MRI. They lead to a pseudo-normalisation of the water diffusivity and transverse relaxation time maps in the subacute and chronic phases of stroke. Within the context of diffusion MRI, the free water elimination and mapping method (FWE) with echo time dependence has been proposed as a promising approach to measure the amount of free fluid in brain tissue robustly and to eliminate its biasing effect on other biomarkers. In this longitudinal study of transient middle cerebral artery occlusion (MCAo) in the rat brain, we investigated the use of FWE MRI with echo time dependence for the characterisation of the tissue microstructure and explored the potential of the free water fraction as a novel biomarker of ischaemic tissue condition. METHODS Adult rats received a transient MCAo. Diffusion- and transverse relaxation-weighted MRI experiments were performed longitudinally, pre-occlusion and on days 1, 3, 4, 5, 6, 7 and 10 after MCAo on four rats. Histology was performed for non-stroke and 1, 3 and 10 days after MCAo on three different rats at each time point. RESULTS The free water fraction was homogeneously increased in the ischaemic cortex one day after stroke. Between three and ten days after stroke, the core of the ischaemic tissue showed a progressive normalisation in the amount of free water, whereas the inner and outer border zones of the ischaemic cortex depicted a large, monotonous increase with time. The specific lesions in brain sections were verified by H&E and immunostaining. The tissue-specific diffusion and relaxometry MRI metrics in the ischaemic cortex were significantly different compared to their conventional counterpart. CONCLUSIONS Our results demonstrate that the free water fraction in FWE MRI with echo time dependence is a valuable biomarker, sensitive to the progressive degeneration in ischaemic tissue. We showed that part of the heterogeneity previously observed in conventional parameter maps can be accounted for by a heterogeneous distribution of free water in the tissue. Our results suggest that the temporal evolution of the free fluid fraction map at the core and inner border zone can be associated with the pathological changes linked to the evolution of vasogenic oedema. Namely, the homogeneous increase in free water one day after stroke and its tendency to normalise in the core of the ischaemic cortex starting three days after stroke, followed by a progressive increase in free water at the inner border zone from three to ten days after stroke. Finally, the monotonous increase in free fluid in the outer border zone of the cortex reflects the formation of fluid-filled cysts.
Collapse
Affiliation(s)
- Ezequiel Farrher
- Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, Germany.
| | - Chia-Wen Chiang
- Institute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, Miaoli, Taiwan
| | - Kuan-Hung Cho
- Institute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, Miaoli, Taiwan
| | - Farida Grinberg
- Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, Germany
| | - Richard P Buschbeck
- Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, Germany
| | - Ming-Jye Chen
- Institute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, Miaoli, Taiwan
| | - Kuo-Jen Wu
- Center for Neuropsychiatric Research, National Health Research Institutes, Miaoli, Taiwan
| | - Yun Wang
- Center for Neuropsychiatric Research, National Health Research Institutes, Miaoli, Taiwan
| | - Sheng-Min Huang
- Institute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, Miaoli, Taiwan
| | - Zaheer Abbas
- Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, Germany
| | - Chang-Hoon Choi
- Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, Germany
| | - N Jon Shah
- Institute of Neuroscience and Medicine 4, INM-4, Forschungszentrum Jülich, Germany; Department of Neurology, RWTH Aachen University, Aachen, Germany; JARA - BRAIN - Translational Medicine, Aachen, Germany; Institute of Neuroscience and Medicine 11, INM-11, JARA, Forschungszentrum Jülich, Germany
| | - Li-Wei Kuo
- Institute of Biomedical Engineering and Nanomedicine, National Health Research Institutes, Miaoli, Taiwan; Institute of Medical Device and Imaging, National Taiwan University College of Medicine, Taipei, Taiwan.
| |
Collapse
|
30
|
Koirala N, Kleinman D, Perdue MV, Su X, Villa M, Grigorenko EL, Landi N. Widespread effects of dMRI data quality on diffusion measures in children. Hum Brain Mapp 2021; 43:1326-1341. [PMID: 34799957 PMCID: PMC8837592 DOI: 10.1002/hbm.25724] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/07/2021] [Revised: 11/02/2021] [Accepted: 11/11/2021] [Indexed: 12/12/2022] Open
Abstract
Diffusion magnetic resonance imaging (dMRI) datasets are susceptible to several confounding factors related to data quality, which is especially true in studies involving young children. With the recent trend of large‐scale multicenter studies, it is more critical to be aware of the varied impacts of data quality on measures of interest. Here, we investigated data quality and its effect on different diffusion measures using a multicenter dataset. dMRI data were obtained from 691 participants (5–17 years of age) from six different centers. Six data quality metrics—contrast to noise ratio, outlier slices, and motion (absolute, relative, translation, and rotational)—and four diffusion measures—fractional anisotropy, mean diffusivity, tract density, and length—were computed for each of 36 major fiber tracts for all participants. The results indicated that four out of six data quality metrics (all except absolute and translation motion) differed significantly between centers. Associations between these data quality metrics and the diffusion measures differed significantly across the tracts and centers. Moreover, these effects remained significant after applying recently proposed harmonization algorithms that purport to remove unwanted between‐site variation in diffusion data. These results demonstrate the widespread impact of dMRI data quality on diffusion measures. These tracts and measures have been routinely associated with individual differences as well as group‐wide differences between neurotypical populations and individuals with neurological or developmental disorders. Accordingly, for analyses of individual differences or group effects (particularly in multisite dataset), we encourage the inclusion of data quality metrics in dMRI analysis.
Collapse
Affiliation(s)
| | | | - Meaghan V Perdue
- Haskins Laboratories, New Haven, Connecticut, USA.,Department of Psychological Sciences, University of Connecticut, Connecticut, USA
| | - Xing Su
- Haskins Laboratories, New Haven, Connecticut, USA
| | - Martina Villa
- Haskins Laboratories, New Haven, Connecticut, USA.,Department of Psychological Sciences, University of Connecticut, Connecticut, USA
| | - Elena L Grigorenko
- Haskins Laboratories, New Haven, Connecticut, USA.,Department of Psychology, University of Houston, Houston, Texas, USA
| | - Nicole Landi
- Haskins Laboratories, New Haven, Connecticut, USA.,Department of Psychological Sciences, University of Connecticut, Connecticut, USA
| |
Collapse
|
31
|
Changes in white matter microstructure and MRI-derived cerebral blood flow after 1-week of exercise training. Sci Rep 2021; 11:22061. [PMID: 34764358 PMCID: PMC8586229 DOI: 10.1038/s41598-021-01630-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2020] [Accepted: 05/31/2021] [Indexed: 11/23/2022] Open
Abstract
Exercise is beneficial for brain health, inducing neuroplasticity and vascular plasticity in the hippocampus, which is possibly mediated by brain-derived neurotrophic factor (BDNF) levels. Here we investigated the short-term effects of exercise, to determine if a 1-week intervention is sufficient to induce brain changes. Fifteen healthy young males completed five supervised exercise training sessions over seven days. This was preceded and followed by a multi-modal magnetic resonance imaging (MRI) scan (diffusion-weighted MRI, perfusion-weighted MRI, dual-calibrated functional MRI) acquired 1 week apart, and blood sampling for BDNF. A diffusion tractography analysis showed, after exercise, a significant reduction relative to baseline in restricted fraction-an axon-specific metric-in the corpus callosum, uncinate fasciculus, and parahippocampal cingulum. A voxel-based approach found an increase in fractional anisotropy and reduction in radial diffusivity symmetrically, in voxels predominantly localised in the corpus callosum. A selective increase in hippocampal blood flow was found following exercise, with no change in vascular reactivity. BDNF levels were not altered. Thus, we demonstrate that 1 week of exercise is sufficient to induce microstructural and vascular brain changes on a group level, independent of BDNF, providing new insight into the temporal dynamics of plasticity, necessary to exploit the therapeutic potential of exercise.
Collapse
|
32
|
Drobnjak I, Neher P, Poupon C, Sarwar T. Physical and digital phantoms for validating tractography and assessing artifacts. Neuroimage 2021; 245:118704. [PMID: 34748954 DOI: 10.1016/j.neuroimage.2021.118704] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 10/01/2021] [Accepted: 11/01/2021] [Indexed: 11/17/2022] Open
Abstract
Fiber tractography is widely used to non-invasively map white-matter bundles in vivo using diffusion-weighted magnetic resonance imaging (dMRI). As it is the case for all scientific methods, proper validation is a key prerequisite for the successful application of fiber tractography, be it in the area of basic neuroscience or in a clinical setting. It is well-known that the indirect estimation of the fiber tracts from the local diffusion signal is highly ambiguous and extremely challenging. Furthermore, the validation of fiber tractography methods is hampered by the lack of a real ground truth, which is caused by the extremely complex brain microstructure that is not directly observable non-invasively and that is the basis of the huge network of long-range fiber connections in the brain that are the actual target of fiber tractography methods. As a substitute for in vivo data with a real ground truth that could be used for validation, a widely and successfully employed approach is the use of synthetic phantoms. In this work, we are providing an overview of the state-of-the-art in the area of physical and digital phantoms, answering the following guiding questions: "What are dMRI phantoms and what are they good for?", "What would the ideal phantom for validation fiber tractography look like?" and "What phantoms, phantom datasets and tools used for their creation are available to the research community?". We will further discuss the limitations and opportunities that come with the use of dMRI phantoms, and what future direction this field of research might take.
Collapse
Affiliation(s)
- Ivana Drobnjak
- Center for Medical Image Computing, Department of Computer Science, University College London, UK.
| | - Peter Neher
- Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Cyril Poupon
- BAOBAB, NeuroSpin, Commissariat à l'Energie Atomique, Institut des Sciences du Vivant Frédéric Joliot, Gif-sur-Yvette, France
| | - Tabinda Sarwar
- School of Computing Technologies, RMIT University, Australia
| |
Collapse
|
33
|
Same Brain, Different Look?-The Impact of Scanner, Sequence and Preprocessing on Diffusion Imaging Outcome Parameters. J Clin Med 2021; 10:jcm10214987. [PMID: 34768507 PMCID: PMC8584364 DOI: 10.3390/jcm10214987] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2021] [Revised: 10/21/2021] [Accepted: 10/23/2021] [Indexed: 11/17/2022] Open
Abstract
In clinical diagnostics and longitudinal studies, the reproducibility of MRI assessments is of high importance in order to detect pathological changes, but developments in MRI hard- and software often outrun extended periods of data acquisition and analysis. This could potentially introduce artefactual changes or mask pathological alterations. However, if and how changes of MRI hardware, scanning protocols or preprocessing software affect complex neuroimaging outcomes from, e.g., diffusion weighted imaging (DWI) remains largely understudied. We therefore compared DWI outcomes and artefact severity of 121 healthy participants (age range 19–54 years) who underwent two matched DWI protocols (Siemens product and Center for Magnetic Resonance Research sequence) at two sites (Siemens 3T Magnetom Verio and Skyrafit). After different preprocessing steps, fractional anisotropy (FA) and mean diffusivity (MD) maps, obtained by tensor fitting, were processed with tract-based spatial statistics (TBSS). Inter-scanner and inter-sequence variability of skeletonised FA values reached up to 5% and differed largely in magnitude and direction across the brain. Skeletonised MD values differed up to 14% between scanners. We here demonstrate that DTI outcome measures strongly depend on imaging site and software, and that these biases vary between brain regions. These regionally inhomogeneous biases may exceed and considerably confound physiological effects such as ageing, highlighting the need to harmonise data acquisition and analysis. Future studies thus need to implement novel strategies to augment neuroimaging data reliability and replicability.
Collapse
|
34
|
Effects of tDCS on Language Recovery in Post-Stroke Aphasia: A Pilot Study Investigating Clinical Parameters and White Matter Change with Diffusion Imaging. Brain Sci 2021; 11:brainsci11101277. [PMID: 34679342 PMCID: PMC8534035 DOI: 10.3390/brainsci11101277] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 09/21/2021] [Accepted: 09/22/2021] [Indexed: 11/16/2022] Open
Abstract
Objectives: In this pilot study we investigated the effects of transcranial direct current stimulation (tDCS) on language recovery in the subacute stage of post-stroke aphasia using clinical parameters and diffusion imaging with constrained spherical deconvolution-based tractography. Methods: The study included 21 patients with subacute post-stroke aphasia. Patients were randomly classified into two groups with a ratio of 2:1 to receive real tDCS or sham tDCS as placebo control. Patients received 10 sessions (5/week) bi-hemispheric tDCS treatments over the left affected Broca's area (anodal electrode) and over the right unaffected Broca's area (cathodal stimulation). Aphasia score was assessed clinically using the language section of the Hemispheric Stroke Scale (HSS) before and after treatment sessions. Diffusion imaging and tractography were performed for seven patients of the real group, both before and after the 10th session. Dissection of language-related white matter tracts was achieved, and diffusion measures were extracted. A paired Student's t-test was used to compare the clinical recovery and diffusion measures of the dissected tracts both pre- and post- treatment. The partial correlation between changes in diffusion measures and the language improvements was calculated. Results: At baseline assessment, there were no significant differences between groups in demographic and clinical HSS language score. No significant clinical recovery in HSS was evident in the sham group. However, significant improvements in the different components of HSS were only observed in patients receiving real tDCS. Associated significant increase in the fractional anisotropy of the right uncinate fasciculus and a significant reduction in the mean diffusivity of the right frontal aslant tract were reported. A significant positive correlation was found between the changes in the right uncinate fasciculus and fluency improvement. Conclusions: Aphasia recovery after bi-hemispheric transcranial direct current stimulation was associated with contralesional right-sided white matter changes at the subacute stage. These changes probably reflect neuroplasticity that could contribute to the recovery. Both the right uncinate fasciculus and right frontal aslant tract seem to be involved in aphasia recovery.
Collapse
|
35
|
Henriques RN, Jespersen SN, Jones DK, Veraart J. Toward more robust and reproducible diffusion kurtosis imaging. Magn Reson Med 2021; 86:1600-1613. [PMID: 33829542 PMCID: PMC8199974 DOI: 10.1002/mrm.28730] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2020] [Revised: 01/20/2021] [Accepted: 01/24/2021] [Indexed: 12/21/2022]
Abstract
PURPOSE The general utility of diffusion kurtosis imaging (DKI) is challenged by its poor robustness to imaging artifacts and thermal noise that often lead to implausible kurtosis values. THEORY AND METHODS A robust scalar kurtosis index can be estimated from powder-averaged diffusion-weighted data. We introduce a novel DKI estimator that uses this scalar kurtosis index as a proxy for the mean kurtosis to regularize the fit. RESULTS The regularized DKI estimator improves the robustness and reproducibility of the kurtosis metrics and results in parameter maps with enhanced quality and contrast. CONCLUSION Our novel DKI estimator promotes the wider use of DKI in clinical research and potentially diagnostics by improving the reproducibility and precision of DKI fitting and, as such, enabling enhanced visual, quantitative, and statistical analyses of DKI parameters.
Collapse
Affiliation(s)
| | - Sune N. Jespersen
- Center of Functionally Integrative Neuroscience (CFIN) and MINDLabDepartment of Clinical MedicineAarhus UniversityAarhusDenmark
- Department of Physics and AstronomyAarhus UniversityAarhusDenmark
| | - Derek K. Jones
- CUBRICSchool of PsychologyCardiff UniversityCardiffUK
- Mary MacKillop Institute for Health ResearchAustralian Catholic UniversityMelbourneVictoriaAustralia
| | - Jelle Veraart
- Center for Biomedical ImagingNew York University Grossman School of MedicineNew YorkNYUSA
| |
Collapse
|
36
|
Corrêa DG, Tijms BM, Dicks E, Rêgo C, Alves‐Leon SV, Marcondes J, Gasparetto EL, van Duinkerken E. Effects of seizure burden on structural global brain networks in patients with unilateral hippocampal sclerosis. Brain Behav 2021; 11:e2237. [PMID: 34105906 PMCID: PMC8413824 DOI: 10.1002/brb3.2237] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/17/2020] [Revised: 05/08/2021] [Accepted: 05/23/2021] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND AND PURPOSE Temporal lobe epilepsy secondary to hippocampal sclerosis is related to epileptogenic networks rather than a focal epileptogenic source. Graph-theoretical gray and white matter networks may help to identify alterations within these epileptogenic networks. METHODS Twenty-seven patients with hippocampal sclerosis and 14 controls underwent magnetic resonance imaging, including 3D-T1, fluid-attenuated inversion recovery, and diffusion tensor imaging. Subject-specific structural gray and white matter network properties (normalized path length, clustering, and small-worldness) were reconstructed. Group differences and differences between those with higher and lower seizure burden (<4 vs. ≥4 average monthly seizures in the last year) in network parameters were evaluated. Additionally, correlations between network properties and disease-related variables were calculated. RESULTS All patients with hippocampal sclerosis as one group did not have altered gray or white matter network properties (all p > .05). Patients with lower seizure burden had significantly lower gray matter small-worldness and normalized clustering compared to controls and those with higher seizure burden (all p < .04). A higher number of monthly seizures was significantly associated with increased gray and white matter small-worldness, indicating a more rigid network. CONCLUSION Overall, there were no differences in network properties in this group of patients with hippocampal sclerosis. However, patients with lower seizure burden had significantly lower gray matter network indices, indicating a more random organization. The correlation between higher monthly seizures and a more rigid network is driven by those with higher seizure burden, who presented a more rigid network compared to those with a lower seizure burden.
Collapse
Affiliation(s)
- Diogo Goulart Corrêa
- Clínica de Diagnóstico por Imagem (CDPI)/DASAAvenida das Américas, Barra da TijucaRio de JaneiroBrazil
- Instituto Estadual do Cérebro Paulo NiemeyerRio de JaneiroBrazil
| | - Betty M. Tijms
- Department of NeurologyAlzheimer Center, Amsterdam University Medical Centers, Vrije UniversiteitAmsterdamThe Netherlands
| | - Ellen Dicks
- Department of NeurologyAlzheimer Center, Amsterdam University Medical Centers, Vrije UniversiteitAmsterdamThe Netherlands
| | - Cláudia Rêgo
- Department of NeurologyEpilepsy CenterHospital Universitário Clementino Fraga Filho, Federal University of Rio de JaneiroCidade Universitária, Ilha do FundãoRio de JaneiroBrazil
| | - Soniza Vieira Alves‐Leon
- Department of NeurologyEpilepsy CenterHospital Universitário Clementino Fraga Filho, Federal University of Rio de JaneiroCidade Universitária, Ilha do FundãoRio de JaneiroBrazil
| | - Jorge Marcondes
- Department of NeurologyEpilepsy CenterHospital Universitário Clementino Fraga Filho, Federal University of Rio de JaneiroCidade Universitária, Ilha do FundãoRio de JaneiroBrazil
| | - Emerson Leandro Gasparetto
- Clínica de Diagnóstico por Imagem (CDPI)/DASAAvenida das Américas, Barra da TijucaRio de JaneiroBrazil
- Instituto Estadual do Cérebro Paulo NiemeyerRio de JaneiroBrazil
| | - Eelco van Duinkerken
- Instituto Estadual do Cérebro Paulo NiemeyerRio de JaneiroBrazil
- Department of Medical PsychologyAmsterdam University Medical Centers, Vrije UniversiteitAmsterdamThe Netherlands
- Department of Internal MedicineAmsterdam Diabetes CenterAmsterdam University Medical Centers, Vrije UniversiteitAmsterdamThe Netherlands
- Post‐Graduate Program in NeurologyHospital Universitário Gaffrée e Guinle, Universidade Federal do Estado do Rio de JaneiroRio de JaneiroBrazil
| |
Collapse
|
37
|
White matter alterations in Parkinson's disease with levodopa-induced dyskinesia. Parkinsonism Relat Disord 2021; 90:8-14. [PMID: 34325387 DOI: 10.1016/j.parkreldis.2021.07.021] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 06/18/2021] [Accepted: 07/20/2021] [Indexed: 11/20/2022]
Abstract
INTRODUCTION Levodopa-induced dyskinesia is a complication of levodopa therapy and negatively impacts the quality of life of patients. We aimed to elucidate white matter alterations in Parkinson's disease with levodopa-induced dyskinesia using advanced diffusion magnetic resonance imaging techniques. METHODS The enrolled subjects included 26 clinically confirmed Parkinson's disease patients without levodopa-induced dyskinesia, 25 Parkinson's disease patients with levodopa-induced dyskinesia, and 23 healthy controls. Subjects were imaged using a 3-T magnetic resonance scanner. Diffusion tensor imaging, diffusion kurtosis imaging, and neurite orientation dispersion and density imaging findings were compared between groups with a group-wise whole brain approach and a region-of-interest analysis for each white matter tract. Additionally, logistic regression analysis was used to calculate odds ratios for levodopa-induced dyskinesia. RESULTS Group-wise tract-based spatial statistical analysis revealed significant white matter differences in isotropic diffusion, complexity, or heterogeneity, and neurite density between healthy controls and Parkinson's disease patients without levodopa-induced dyskinesia and between patients with and without levodopa-induced dyskinesia. Region-of-interest analysis revealed similar alterations using a group-wise whole-brain approach in the external capsule, inferior fronto-occipital fasciculus, inferior longitudinal fasciculus, and uncinate fasciculus. These tracts had an odds ratio of approximately 2.3 for the presence of levodopa-induced dyskinesia. CONCLUSIONS Our findings suggest that Parkinson's disease with levodopa-induced dyskinesia produces less white matter microstructural disruption, especially in temporal lobe fibers, than Parkinson's disease without levodopa-induced dyskinesia. These fibers has a more than 2-fold odds ratio for the presence of levodopa-induced dyskinesia and might be associated with the pathogenesis of the sequela.
Collapse
|
38
|
Henriques RN, Correia MM, Marrale M, Huber E, Kruper J, Koudoro S, Yeatman JD, Garyfallidis E, Rokem A. Diffusional Kurtosis Imaging in the Diffusion Imaging in Python Project. Front Hum Neurosci 2021; 15:675433. [PMID: 34349631 PMCID: PMC8327208 DOI: 10.3389/fnhum.2021.675433] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Accepted: 06/17/2021] [Indexed: 12/28/2022] Open
Abstract
Diffusion-weighted magnetic resonance imaging (dMRI) measurements and models provide information about brain connectivity and are sensitive to the physical properties of tissue microstructure. Diffusional Kurtosis Imaging (DKI) quantifies the degree of non-Gaussian diffusion in biological tissue from dMRI. These estimates are of interest because they were shown to be more sensitive to microstructural alterations in health and diseases than measures based on the total anisotropy of diffusion which are highly confounded by tissue dispersion and fiber crossings. In this work, we implemented DKI in the Diffusion in Python (DIPY) project-a large collaborative open-source project which aims to provide well-tested, well-documented and comprehensive implementation of different dMRI techniques. We demonstrate the functionality of our methods in numerical simulations with known ground truth parameters and in openly available datasets. A particular strength of our DKI implementations is that it pursues several extensions of the model that connect it explicitly with microstructural models and the reconstruction of 3D white matter fiber bundles (tractography). For instance, our implementations include DKI-based microstructural models that allow the estimation of biophysical parameters, such as axonal water fraction. Moreover, we illustrate how DKI provides more general characterization of non-Gaussian diffusion compatible with complex white matter fiber architectures and gray matter, and we include a novel mean kurtosis index that is invariant to the confounding effects due to tissue dispersion. In summary, DKI in DIPY provides a well-tested, well-documented and comprehensive reference implementation for DKI. It provides a platform for wider use of DKI in research on brain disorders and in cognitive neuroscience.
Collapse
Affiliation(s)
| | - Marta M. Correia
- Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom
| | - Maurizio Marrale
- Department of Physics and Chemistry “Emilio Segrè”, University of Palermo, Palermo, Italy
- National Institute for Nuclear Physics (INFN), Catania Division, Catania, Italy
| | - Elizabeth Huber
- Department of Speech and Hearing, Institute for Learning and Brain Science, University of Washington, Seattle, WA, United States
| | - John Kruper
- Department of Psychology and eScience Institute, The University of Washington, Seattle, WA, United States
| | - Serge Koudoro
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computer Science and Engineering, Indiana University, Bloomington, IN, United States
| | - Jason D. Yeatman
- Department of Speech and Hearing, Institute for Learning and Brain Science, University of Washington, Seattle, WA, United States
- Department of Pediatrics, Graduate School of Education, Stanford University, Stanford, CA, United States
| | - Eleftherios Garyfallidis
- Department of Intelligent Systems Engineering, Luddy School of Informatics, Computer Science and Engineering, Indiana University, Bloomington, IN, United States
| | - Ariel Rokem
- Department of Psychology and eScience Institute, The University of Washington, Seattle, WA, United States
| |
Collapse
|
39
|
Lee HH, Novikov DS, Fieremans E. Removal of partial Fourier-induced Gibbs (RPG) ringing artifacts in MRI. Magn Reson Med 2021; 86:2733-2750. [PMID: 34227142 DOI: 10.1002/mrm.28830] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2020] [Revised: 03/29/2021] [Accepted: 04/16/2021] [Indexed: 01/03/2023]
Abstract
PURPOSE To investigate and remove Gibbs-ringing artifacts caused by partial Fourier (PF) acquisition and zero filling interpolation in MRI data. THEORY AND METHODS Gibbs ringing of fully sampled data, leading to oscillations around tissue boundaries, is caused by the symmetric truncation of k-space. Such ringing can be removed by conventional methods, with the local subvoxel shifts method being the state-of-the-art. However, the asymmetric truncation of k-space in routinely used PF acquisitions leads to additional ringings of wider intervals in the PF sampling dimension that cannot be corrected solely based on magnitude images reconstructed via zero filling. Here, we develop a pipeline for the Removal of PF-induced Gibbs ringing (RPG) to remove ringing patterns of different periods by applying the conventional method twice. The proposed pipeline is validated on numerical phantoms, demonstrated on in vivo diffusion MRI measurements, and compared with the conventional method and neural network-based approach. RESULTS For PF = 7/8 and 6/8, Gibbs-ringings and subsequent bias in diffusion metrics induced by PF acquisition and zero filling are robustly removed by using the proposed RPG pipeline. For PF = 5/8, however, ringing removal via RPG leads to excessive image blurring due to the interplay of image phase and convolution kernel. CONCLUSIONS RPG corrects Gibbs-ringing artifacts in magnitude images of PF acquired data and reduces the bias in quantitative MR metrics. Considering the benefit of PF acquisition and the feasibility of ringing removal, we suggest applying PF = 6/8 when PF acquisition is necessary.
Collapse
Affiliation(s)
- Hong-Hsi Lee
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University School of Medicine, New York, NY, USA
| | - Dmitry S Novikov
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University School of Medicine, New York, NY, USA
| | - Els Fieremans
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, New York University School of Medicine, New York, NY, USA
| |
Collapse
|
40
|
Eradath MK, Pinsk MA, Kastner S. A causal role for the pulvinar in coordinating task-independent cortico-cortical interactions. J Comp Neurol 2021; 529:3772-3784. [PMID: 34013540 DOI: 10.1002/cne.25193] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2021] [Revised: 05/11/2021] [Accepted: 05/17/2021] [Indexed: 01/01/2023]
Abstract
The pulvinar is the largest nucleus in the primate thalamus and has topographically organized connections with multiple cortical areas, thereby forming extensive cortico-pulvino-cortical input-output loops. Neurophysiological studies have suggested a role for these transthalamic pathways in regulating information transmission between cortical areas. However, evidence for a causal role of the pulvinar in regulating cortico-cortical interactions is sparse and it is not known whether pulvinar's influences on cortical networks are task-dependent or, alternatively, reflect more basic large-scale network properties that maintain functional connectivity across networks regardless of active task demands. In the current study, under passive viewing conditions, we conducted simultaneous electrophysiological recordings from ventral (area V4) and dorsal (lateral intraparietal area [LIP]) nodes of macaque visual system, while reversibly inactivating the dorsal part of the lateral pulvinar (dPL), which shares common anatomical connectivity with V4 and LIP, to probe a causal role of the pulvinar. Our results show a significant reduction in local field potential phase coherence between LIP and V4 in low frequencies (4-15 Hz) following muscimol injection into dPL. At the local level, no significant changes in firing rates or LFP power were observed in LIP or in V4 following dPL inactivation. Synchronization between pulvinar spikes and cortical LFP phase decreased in low frequencies (4-15 Hz) both in LIP and V4, while the low frequency synchronization between LIP spikes and pulvinar phase increased. These results indicate a causal role for pulvinar in synchronizing neural activity between interconnected cortical nodes of a large-scale network, even in the absence of an active task state.
Collapse
Affiliation(s)
- Manoj K Eradath
- Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, USA
| | - Mark A Pinsk
- Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, USA
| | - Sabine Kastner
- Princeton Neuroscience Institute, Princeton University, Princeton, New Jersey, USA.,Department of Psychology, Princeton University, Princeton, New Jersey, USA
| |
Collapse
|
41
|
Bertrand E, van Duinkerken E, Laks J, Dourado MCN, Bernardes G, Landeira-Fernandez J, Mograbi DC. Structural Gray and White Matter Correlates of Awareness in Alzheimer's Disease. J Alzheimers Dis 2021; 81:1321-1330. [PMID: 33935073 DOI: 10.3233/jad-201246] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
BACKGROUND Unawareness of disease is a common feature of Alzheimer's disease (AD), but few studies explored its neural correlates. Additionally, neural correlates according to the object of awareness are unexplored. OBJECTIVE To investigate structural brain correlates in relation to different objects of awareness. METHODS 27 people with AD underwent MRI scanning on a 3T Siemens Prisma. T1-MPRAGE was used to investigate cortical thickness and white matter microstructure was defined by DTI as fractional anisotropy, mean, axial, and radial diffusivity. Preprocessing used FreeSurfer6.0, ExploreDTI, and FSL-TBSS. Awareness of disease, cognitive deficits, emotional state, relationships, and functional capacity were assessed with the short version of the Assessment Scale of Psychosocial Impact of the Diagnosis of Dementia. Voxel-wise correlations between brain structure and awareness were determined by FSL-PALM. Analyses were corrected for multiple comparisons using Threshold Free Cluster Enhancement and FWE. RESULTS Lower left hemisphere cortical thickness was related to poorer disease awareness uncorrected and corrected for age, sex, and MMSE. In the uncorrected model, mainly right-sided, but also left temporal lower cortical thickness was related to decreased awareness of cognitive deficits. Correcting for age, sex, and MMSE eliminated correlations for the right hemisphere, but extensive correlations in the left hemisphere remained. For white matter integrity, higher right hemisphere MD was related to lower cognitive awareness deficits, and lower FA was related to lower functional capacity awareness. CONCLUSION Findings suggest that extensive regions of the brain are linked to self-awareness, with particular frontal and temporal alterations leading to unawareness, in agreement with theoretical models indicating executive and mnemonic forms of anosognosia in AD.
Collapse
Affiliation(s)
- Elodie Bertrand
- MC2Lab (URP 7536), Institut de Psychologie, Université de Paris, Paris, France.,Department of Psychology, Pontifícia Universidade Católica-Rio (PUC-Rio), Rio de Janeiro, Brazil
| | - Eelco van Duinkerken
- Department of Medical Psychology, Amsterdam University Medical Centers, Vrije Universiteit, Amsterdam, The Netherlands.,Center for Epilepsy, Instituto Estadual do Cérebro Paulo Niemeyer, Rio de Janeiro, Brazil.,Postgraduate Program in Neurology, Hospital Universitário Gaffrée e Guinle -UNIRIO, Rio de Janeiro, Brazil
| | - Jerson Laks
- Institute of Psychiatry, Federal University of Rio de Janeiro, Rio de Janeiro, Brazil.,Department of Psychology, Universidade do Grande Rio (Unigranrio), Duque de Caxias, Brazil
| | | | - Gabriel Bernardes
- Department of Psychology, Pontifícia Universidade Católica-Rio (PUC-Rio), Rio de Janeiro, Brazil
| | - Jesus Landeira-Fernandez
- Department of Psychology, Pontifícia Universidade Católica-Rio (PUC-Rio), Rio de Janeiro, Brazil
| | - Daniel C Mograbi
- Department of Psychology, Pontifícia Universidade Católica-Rio (PUC-Rio), Rio de Janeiro, Brazil.,Institute of Psychiatry, Psychology & Neuroscience, King's College London, London, UK
| |
Collapse
|
42
|
Maximov II, van der Meer D, de Lange AMG, Kaufmann T, Shadrin A, Frei O, Wolfers T, Westlye LT. Fast qualitY conTrol meThod foR derIved diffUsion Metrics (YTTRIUM) in big data analysis: U.K. Biobank 18,608 example. Hum Brain Mapp 2021; 42:3141-3155. [PMID: 33788350 PMCID: PMC8193531 DOI: 10.1002/hbm.25424] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2020] [Revised: 03/10/2021] [Accepted: 03/13/2021] [Indexed: 12/12/2022] Open
Abstract
Deriving reliable information about the structural and functional architecture of the brain in vivo is critical for the clinical and basic neurosciences. In the new era of large population‐based datasets, when multiple brain imaging modalities and contrasts are combined in order to reveal latent brain structural patterns and associations with genetic, demographic and clinical information, automated and stringent quality control (QC) procedures are important. Diffusion magnetic resonance imaging (dMRI) is a fertile imaging technique for probing and visualising brain tissue microstructure in vivo, and has been included in most standard imaging protocols in large‐scale studies. Due to its sensitivity to subject motion and technical artefacts, automated QC procedures prior to scalar diffusion metrics estimation are required in order to minimise the influence of noise and artefacts. However, the QC procedures performed on raw diffusion data cannot guarantee an absence of distorted maps among the derived diffusion metrics. Thus, robust and efficient QC methods for diffusion scalar metrics are needed. Here, we introduce Fast qualitY conTrol meThod foR derIved diffUsion Metrics (YTTRIUM), a computationally efficient QC method utilising structural similarity to evaluate diffusion map quality and mean diffusion metrics. As an example, we applied YTTRIUM in the context of tract‐based spatial statistics to assess associations between age and kurtosis imaging and white matter tract integrity maps in U.K. Biobank data (n = 18,608). To assess the influence of outliers on results obtained using machine learning (ML) approaches, we tested the effects of applying YTTRIUM on brain age prediction. We demonstrated that the proposed QC pipeline represents an efficient approach for identifying poor quality datasets and artefacts and increases the accuracy of ML based brain age prediction.
Collapse
Affiliation(s)
- Ivan I Maximov
- Department of Psychology, University of Oslo, Oslo, Norway.,Norwegian Centre for Mental Disorders Research (NORMENT), Department of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway
| | - Dennis van der Meer
- Norwegian Centre for Mental Disorders Research (NORMENT), Department of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,School of Mental Health and Neuroscience, Faculty of Health Medicine and Life Sciences, Maastricht University, The Netherlands
| | - Ann-Marie G de Lange
- Department of Psychology, University of Oslo, Oslo, Norway.,Norwegian Centre for Mental Disorders Research (NORMENT), Department of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,LREN, Centre for Research in Neurosciences - Department of Clinical Neurosciences, CHUV and University of Lausanne, Lausanne, Switzerland.,Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, UK
| | - Tobias Kaufmann
- Norwegian Centre for Mental Disorders Research (NORMENT), Department of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Alexey Shadrin
- Norwegian Centre for Mental Disorders Research (NORMENT), Department of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Oleksandr Frei
- Norwegian Centre for Mental Disorders Research (NORMENT), Department of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,Center for Bioinformatics, Department of Informatics, University of Oslo, Oslo, Norway
| | - Thomas Wolfers
- Department of Psychology, University of Oslo, Oslo, Norway.,Norwegian Centre for Mental Disorders Research (NORMENT), Department of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Lars T Westlye
- Department of Psychology, University of Oslo, Oslo, Norway.,Norwegian Centre for Mental Disorders Research (NORMENT), Department of Mental Health and Addiction, Oslo University Hospital and Institute of Clinical Medicine, University of Oslo, Oslo, Norway.,KG Jebsen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
| |
Collapse
|
43
|
Umezawa E, Ishihara D, Kato R. A Bayesian approach to diffusional kurtosis imaging. Magn Reson Med 2021; 86:1110-1124. [PMID: 33768579 DOI: 10.1002/mrm.28741] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2020] [Revised: 01/26/2021] [Accepted: 01/29/2021] [Indexed: 01/17/2023]
Abstract
PURPOSE Diffusional kurtosis metrics show high performance for detecting pathological changes and are therefore expected to be disease biomarkers. Kurtosis maps, however, tend to be noisy. The maps' visual quality is crucial for disease diagnosis, even when kurtosis is being used quantitatively. A Bayesian method was proposed to curtail the large statistical error inherent in kurtosis estimation while maintaining potential application to biomarkers. THEORY Gaussian priors are determined from first-step estimations implemented using the least-square method (LSM). The likelihood-function variance is determined from the residuals of the estimation. Although the proposed approach is similar to a regularized LSM, regularization parameters do not have to be artificially adjusted. An appropriate balance between denoising and preventing false shrinkages of metric dispersions is automatically achieved. METHODS Map qualities achieved using the conventional and proposed methods were compared. The receiver-operating characteristic analysis was performed for glioma-grade differentiation using simulated low- and high-grade glioma DWI datasets. Noninferiority of the proposed method was tested for areas under the curves (AUCs). RESULTS The noisier the conventional maps, the better the proposed Bayesian method improved them. Noninferiority of the proposed method was confirmed by AUC tests for all kurtosis-related metrics. Superiority of the proposed method was also established for several metrics. CONCLUSIONS The proposed approach improved noisy kurtosis maps while maintaining their performances as biomarkers without increasing data acquisition requirements or arbitrarily choosing LSM regularization parameters. This approach may enable the use of higher-order terms in diffusional kurtosis imaging (DKI) fitting functions by suppressing overfitting, thereby improving the DKI-estimation accuracy.
Collapse
Affiliation(s)
- Eizou Umezawa
- School of Medical Sciences, Fujita Health University, Toyoake, Japan
| | - Daichi Ishihara
- Department of Radiology, Nagoya City University Hospital, Nagoya, Japan
| | - Ryoichi Kato
- Department of Radiology, Fujita Health University Hospital, Toyoake, Japan
| |
Collapse
|
44
|
Associations between different white matter properties and reward-based performance modulation. Brain Struct Funct 2021; 226:1007-1021. [DOI: 10.1007/s00429-021-02222-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/29/2020] [Accepted: 01/14/2021] [Indexed: 12/13/2022]
|
45
|
Zhang F, Cho KIK, Tang Y, Zhang T, Kelly S, Biase MD, Xu L, Li H, Matcheri K, Whitfield-Gabrieli S, Niznikiewicz M, Stone WS, Wang J, Shenton ME, Pasternak O. MK-Curve improves sensitivity to identify white matter alterations in clinical high risk for psychosis. Neuroimage 2021; 226:117564. [PMID: 33285331 PMCID: PMC7873589 DOI: 10.1016/j.neuroimage.2020.117564] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2020] [Revised: 11/12/2020] [Accepted: 11/13/2020] [Indexed: 12/30/2022] Open
Abstract
Diffusion kurtosis imaging (DKI) is a diffusion MRI approach that enables the measurement of brain microstructural properties, reflecting molecular restrictions and tissue heterogeneity. DKI parameters such as mean kurtosis (MK) provide additional subtle information to that provided by popular diffusion tensor imaging (DTI) parameters, and thus have been considered useful to detect white matter abnormalities, especially in populations that are not expected to show severe brain pathologies. However, DKI parameters often yield artifactual output values that are outside of the biologically plausible range, which diminish sensitivity to identify true microstructural changes. Recently we have proposed the mean-kurtosis-curve (MK-Curve) method to correct voxels with implausible DKI parameters, and demonstrated its improved performance against other approaches that correct artifacts in DKI. In this work, we aimed to evaluate the utility of the MK-Curve method to improve the identification of white matter abnormalities in group comparisons. To do so, we compared group differences, with and without the MK-Curve correction, between 115 individuals at clinical high risk for psychosis (CHR) and 93 healthy controls (HCs). We also compared the correlation of the corrected and uncorrected DKI parameters with clinical characteristics. Following the MK-curve correction, the group differences had larger effect sizes and higher statistical significance (i.e., lower p-values), demonstrating increased sensitivity to detect group differences, in particular in MK. Furthermore, the MK-curve-corrected DKI parameters displayed stronger correlations with clinical variables in CHR individuals, demonstrating the clinical relevance of the corrected parameters. Overall, following the MK-curve correction our analyses found widespread lower MK in CHR that overlapped with lower fractional anisotropy (FA), and both measures were significantly correlated with a decline in functioning and with more severe symptoms. These observations further characterize white matter alterations in the CHR stage, demonstrating that MK and FA abnormalities are widespread, and mostly overlap. The improvement in group differences and stronger correlation with clinical variables suggest that applying MK-curve would be beneficial for the detection and characterization of subtle group differences in other experiments as well.
Collapse
Affiliation(s)
- Fan Zhang
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Kang Ik Kevin Cho
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Yingying Tang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Brain Science and Technology Research Center, Shanghai Jiao Tong University, Shanghai, China
| | - Tianhong Zhang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
| | - Sinead Kelly
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; The Massachusetts Mental Health Center, Public Psychiatry Division, Beth Israel Deaconess Medical Center, and Harvard Medical School, Boston, MA, USA
| | - Maria Di Biase
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, Victoria, Australia
| | - Lihua Xu
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Brain Science and Technology Research Center, Shanghai Jiao Tong University, Shanghai, China
| | - Huijun Li
- Department of Psychology, Florida A&M University, Tallahassee, FL,USA
| | - Keshevan Matcheri
- The Massachusetts Mental Health Center, Public Psychiatry Division, Beth Israel Deaconess Medical Center, and Harvard Medical School, Boston, MA, USA
| | - Susan Whitfield-Gabrieli
- Department of Psychology, Northeastern University, Boston, MA, USA; The McGovern Institute for Brain Research and the Poitras Center for Affective Disorders Research, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Margaret Niznikiewicz
- The Department of Psychiatry, Veterans Affairs Boston Healthcare System, Brockton Division, Brockton, MA, USA
| | - William S Stone
- The Massachusetts Mental Health Center, Public Psychiatry Division, Beth Israel Deaconess Medical Center, and Harvard Medical School, Boston, MA, USA
| | - Jijun Wang
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China; Brain Science and Technology Research Center, Shanghai Jiao Tong University, Shanghai, China.
| | - Martha E Shenton
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Carlton South, Victoria, Australia
| | - Ofer Pasternak
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
| |
Collapse
|
46
|
Mentzelopoulos A, Gkiatis K, Karanasiou I, Karavasilis E, Papathanasiou M, Efstathopoulos E, Kelekis N, Kouloulias V, Matsopoulos GK. Chemotherapy-Induced Brain Effects in Small-Cell Lung Cancer Patients: A Multimodal MRI Study. Brain Topogr 2021; 34:167-181. [PMID: 33403560 DOI: 10.1007/s10548-020-00811-3] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2020] [Accepted: 11/17/2020] [Indexed: 01/02/2023]
Abstract
The golden standard of treating Small Cell Lung Cancer (SCLC) entails application of platinum-based chemotherapy, is often accompanied by Prophylactic Cranial Irradiation (PCI), which have been linked to neurotoxic side-effects in cognitive functions. The related existing neuroimaging research mainly focuses on the effect of PCI treatment in life quality and expectancy, while little is known regarding the distinct adverse effects of chemotherapy. In this context, a multimodal MRI analysis based on structural and functional brain data is proposed in order to evaluate chemotherapy-specific effects on SCLC patients. Data from 20 patients (after chemotherapy and before PCI) and 14 healthy controls who underwent structural MRI, DTI and resting state fMRI were selected in this study. From a structural aspect, the proposed analysis included volumetry and thickness measurements on structural MRI data for assessing gray matter dissimilarities, as well as deterministic tractography and Tract-Based Spatial Statistics (TBSS) on DTI data, aiming to investigate potential white matter abnormalities. Functional data were also processed on the basis of connectivity analysis, evaluating brain network parameters to identify potential manifestation of functional inconsistencies. By comparing patients to healthy controls, the obtained results revealed statistically significant differences, with the patients' brains presenting reduced volumetry/thickness and fractional anisotropy values, accompanied by prominent differences in functional connectivity measurements. All above mentioned findings were observed in patients that underwent chemotherapy.
Collapse
Affiliation(s)
- Anastasios Mentzelopoulos
- School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece.
| | - Kostakis Gkiatis
- School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
| | | | | | - Matilda Papathanasiou
- Radiotherapy Unit, 2nd Department of Radiology, ATTIKON University Hospital, Athens, Greece
| | | | - Nikolaos Kelekis
- Radiotherapy Unit, 2nd Department of Radiology, ATTIKON University Hospital, Athens, Greece
| | - Vasileios Kouloulias
- Radiotherapy Unit, 2nd Department of Radiology, ATTIKON University Hospital, Athens, Greece
| | - George K Matsopoulos
- School of Electrical and Computer Engineering, National Technical University of Athens, Athens, Greece
| |
Collapse
|
47
|
Afzali M, Pieciak T, Newman S, Garyfallidis E, Özarslan E, Cheng H, Jones DK. The sensitivity of diffusion MRI to microstructural properties and experimental factors. J Neurosci Methods 2021; 347:108951. [PMID: 33017644 PMCID: PMC7762827 DOI: 10.1016/j.jneumeth.2020.108951] [Citation(s) in RCA: 55] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 08/27/2020] [Accepted: 09/15/2020] [Indexed: 12/13/2022]
Abstract
Diffusion MRI is a non-invasive technique to study brain microstructure. Differences in the microstructural properties of tissue, including size and anisotropy, can be represented in the signal if the appropriate method of acquisition is used. However, to depict the underlying properties, special care must be taken when designing the acquisition protocol as any changes in the procedure might impact on quantitative measurements. This work reviews state-of-the-art methods for studying brain microstructure using diffusion MRI and their sensitivity to microstructural differences and various experimental factors. Microstructural properties of the tissue at a micrometer scale can be linked to the diffusion signal at a millimeter-scale using modeling. In this paper, we first give an introduction to diffusion MRI and different encoding schemes. Then, signal representation-based methods and multi-compartment models are explained briefly. The sensitivity of the diffusion MRI signal to the microstructural components and the effects of curvedness of axonal trajectories on the diffusion signal are reviewed. Factors that impact on the quality (accuracy and precision) of derived metrics are then reviewed, including the impact of random noise, and variations in the acquisition parameters (i.e., number of sampled signals, b-value and number of acquisition shells). Finally, yet importantly, typical approaches to deal with experimental factors are depicted, including unbiased measures and harmonization. We conclude the review with some future directions and recommendations on this topic.
Collapse
Affiliation(s)
- Maryam Afzali
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom.
| | - Tomasz Pieciak
- AGH University of Science and Technology, Kraków, Poland; LPI, ETSI Telecomunicación, Universidad de Valladolid, Valladolid, Spain.
| | - Sharlene Newman
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA; Program of Neuroscience, Indiana University, Bloomington, IN 47405, USA.
| | - Eleftherios Garyfallidis
- Program of Neuroscience, Indiana University, Bloomington, IN 47405, USA; Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN 47408, USA.
| | - Evren Özarslan
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden; Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden.
| | - Hu Cheng
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA; Program of Neuroscience, Indiana University, Bloomington, IN 47405, USA.
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom.
| |
Collapse
|
48
|
Buimer EEL, Pas P, Brouwer RM, Froeling M, Hoogduin H, Leemans A, Luijten P, van Nierop BJ, Raemaekers M, Schnack HG, Teeuw J, Vink M, Visser F, Hulshoff Pol HE, Mandl RCW. The YOUth cohort study: MRI protocol and test-retest reliability in adults. Dev Cogn Neurosci 2020; 45:100816. [PMID: 33040972 PMCID: PMC7365929 DOI: 10.1016/j.dcn.2020.100816] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2019] [Revised: 06/09/2020] [Accepted: 07/02/2020] [Indexed: 11/30/2022] Open
Abstract
The YOUth cohort study is a unique longitudinal study on brain development in the general population. As part of the YOUth study, 2000 children will be included at 8, 9 or 10 years of age and planned to return every three years during adolescence. Magnetic resonance imaging (MRI) brain scans are collected, including structural T1-weighted imaging, diffusion-weighted imaging (DWI), resting-state functional MRI and task-based functional MRI. Here, we provide a comprehensive report of the MR acquisition in YOUth Child & Adolescent including the test-retest reliability of brain measures derived from each type of scan. To measure test-retest reliability, 17 adults were scanned twice with a week between sessions using the full YOUth MRI protocol. Intraclass correlation coefficients were calculated to quantify reliability. Global brain measures derived from structural T1-weighted and DWI scans were reliable. Resting-state functional connectivity was moderately reliable, as well as functional brain measures for both the inhibition task (stop versus go) and the emotion task (face versus house). Our results complement previous studies by presenting reliability results of regional brain measures collected with different MRI modalities. YOUth facilitates data sharing and aims for reliable and high-quality data. Here we show that using the state-of-the art YOUth MRI protocol brain measures can be estimated reliably.
Collapse
Affiliation(s)
- Elizabeth E L Buimer
- UMCU Brain Center, University Medical Center Utrecht, University Utrecht, Utrecht, the Netherlands
| | - Pascal Pas
- UMCU Brain Center, University Medical Center Utrecht, University Utrecht, Utrecht, the Netherlands
| | - Rachel M Brouwer
- UMCU Brain Center, University Medical Center Utrecht, University Utrecht, Utrecht, the Netherlands
| | - Martijn Froeling
- Image Sciences Institute, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands
| | - Hans Hoogduin
- Image Sciences Institute, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands
| | - Alexander Leemans
- Image Sciences Institute, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands
| | - Peter Luijten
- Department of Radiology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Bastiaan J van Nierop
- Image Sciences Institute, University Medical Center Utrecht and Utrecht University, Utrecht, the Netherlands; Department of Radiology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Mathijs Raemaekers
- UMCU Brain Center, University Medical Center Utrecht, University Utrecht, Utrecht, the Netherlands
| | - Hugo G Schnack
- UMCU Brain Center, University Medical Center Utrecht, University Utrecht, Utrecht, the Netherlands
| | - Jalmar Teeuw
- UMCU Brain Center, University Medical Center Utrecht, University Utrecht, Utrecht, the Netherlands
| | - Matthijs Vink
- UMCU Brain Center, University Medical Center Utrecht, University Utrecht, Utrecht, the Netherlands; Department of Psychology, Utrecht University, Utrecht, the Netherlands
| | | | - Hilleke E Hulshoff Pol
- UMCU Brain Center, University Medical Center Utrecht, University Utrecht, Utrecht, the Netherlands
| | - René C W Mandl
- UMCU Brain Center, University Medical Center Utrecht, University Utrecht, Utrecht, the Netherlands.
| |
Collapse
|
49
|
Elliott CA, Danyluk H, Aronyk KE, Au K, Wheatley BM, Gross DW, Sankar T, Beaulieu C. Intraoperative acquisition of DTI in cranial neurosurgery: readout-segmented DTI versus standard single-shot DTI. J Neurosurg 2020; 133:1210-1219. [PMID: 31419798 DOI: 10.3171/2019.5.jns19890] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2019] [Accepted: 05/21/2019] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Diffusion tensor imaging (DTI) tractography is commonly used in neurosurgical practice but is largely limited to the preoperative setting. This is due primarily to image degradation caused by susceptibility artifact when conventional single-shot (SS) echo-planar imaging (EPI) DTI (SS-DTI) is acquired for open cranial, surgical position intraoperative DTI (iDTI). Readout-segmented (RS) EPI DTI (RS-DTI) has been reported to reduce such artifact but has not yet been evaluated in the intraoperative MRI (iMRI) environment. The authors evaluated the performance of RS versus SS EPI for DTI of the human brain in the iMRI setting. METHODS Pre- and intraoperative 3-T 3D T1-weighted and 2D multislice RS-iDTI (called RESOLVE [readout segmentation of long variable echo-trains] on the Siemens platform) and SS-iDTI images were acquired in 22 adult patients undergoing intraaxial iMRI resections for suspected low-grade glioma (14; 64%), high-grade glioma (7; 32%), or focal cortical dysplasia. Regional susceptibility artifact, anatomical deviation relative to T1-weighted imaging, and tractographic output for surgically relevant tracts were compared between iDTI sequences as well as the intraoperative tract shifts from preoperative DTI. RESULTS RS-iDTI resulted in qualitatively less regional susceptibility artifact (resection cavity, orbitofrontal and anterior temporal cortices) and mean anatomical deviation in regions most prone to susceptibility artifact (RS-iDTI 2.7 ± 0.2 vs SS-iDTI 7.5 ± 0.4 mm) compared to SS-iDTI. Although tract reconstruction success did not significantly differ by DTI method, susceptibility artifact-related tractography failure (of at least 1 surgically relevant tract) occurred for SS-iDTI in 8/22 (36%) patients, and in 5 of these 8 patients RS-iDTI permitted successful reconstruction. Among cases with successful tractography for both sequences, maximal intersequence differences were substantial (mean 9.5 ± 5.7 mm, range -27.1 to 18.7 mm). CONCLUSIONS RS EPI enables higher quality and more accurate DTI for surgically relevant tractography of major white matter tracts in intraoperative, open cranium neurosurgical applications at 3 T.
Collapse
Affiliation(s)
| | | | | | | | | | | | | | - Christian Beaulieu
- 4Department of Biomedical Engineering, University of Alberta, Edmonton, Alberta, Canada
| |
Collapse
|
50
|
Andre QR, McMorris CA, Kar P, Ritter C, Gibbard WB, Tortorelli C, Lebel C. Different brain profiles in children with prenatal alcohol exposure with or without early adverse exposures. Hum Brain Mapp 2020; 41:4375-4385. [PMID: 32659051 PMCID: PMC7502833 DOI: 10.1002/hbm.25130] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2020] [Revised: 06/05/2020] [Accepted: 06/24/2020] [Indexed: 12/29/2022] Open
Abstract
Prenatal alcohol exposure (PAE) can alter brain development and impact mental health outcomes, and often occurs in conjunction with postnatal adversity (e.g., maltreatment). However, it is unclear how postnatal adverse exposures may moderate mental health and brain outcomes in children with PAE. T1‐weighted and diffusion magnetic resonance imaging were obtained from 66 participants aged 7–16 years. Twenty‐one participants had PAE and adverse postnatal exposures (PAE+), 12 had PAE without adverse postnatal exposures (PAE−), and 33 were age‐ and gender‐matched controls unexposed to either prenatal alcohol or postnatal adversity. Internalizing and externalizing mental health symptoms were assessed using the Behavioral Assessment System for Children II, Parent‐Rating Scale. ANCOVAs were used to compare mental health symptoms, limbic and prefrontal cortical volumes, and diffusion parameters of cortico‐limbic white matter tracts between groups, and to assess brain‐mental health relationships. Both PAE groups had worse externalizing behavior (higher scores) than controls. The PAE− group had lower fractional anisotropy (FA) in the bilateral cingulum and left uncinate fasciculus, and smaller volumes in the left anterior cingulate cortex than controls and the PAE+ group. The PAE− group also had higher mean diffusivity (MD) in the left uncinate than the PAE+ group, and smaller right anterior cingulate and superior frontal gyrus volumes than controls. These findings show different brain structure and mental health symptom profiles in children with PAE with and without postnatal adversity, highlighting the need to consider adverse postnatal exposures in individuals with PAE.
Collapse
Affiliation(s)
- Quinn R Andre
- Medical Science, University of Calgary, Calgary, Alberta, Canada.,Alberta Children's Hospital Research Institute, Calgary, Alberta, Canada.,Hotchkiss Brain Institute, Calgary, Alberta, Canada
| | - Carly A McMorris
- Alberta Children's Hospital Research Institute, Calgary, Alberta, Canada.,School & Applied Child Psychology, University of Calgary, Calgary, Alberta, Canada
| | - Preeti Kar
- Medical Science, University of Calgary, Calgary, Alberta, Canada.,Alberta Children's Hospital Research Institute, Calgary, Alberta, Canada.,Hotchkiss Brain Institute, Calgary, Alberta, Canada
| | - Chantel Ritter
- Alberta Children's Hospital Research Institute, Calgary, Alberta, Canada.,School & Applied Child Psychology, University of Calgary, Calgary, Alberta, Canada
| | - W Ben Gibbard
- Alberta Children's Hospital Research Institute, Calgary, Alberta, Canada.,Department of Pediatrics, University of Calgary, Calgary, Alberta, Canada
| | - Christina Tortorelli
- Department of Child Studies and Social Work, Mount Royal University, Calgary, Alberta, Canada
| | - Catherine Lebel
- Alberta Children's Hospital Research Institute, Calgary, Alberta, Canada.,Hotchkiss Brain Institute, Calgary, Alberta, Canada.,Department of Radiology, University of Calgary, Calgary, Alberta, Canada
| |
Collapse
|