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Verschuur AS, King R, Tax CMW, Boomsma MF, van Wezel-Meijler G, Leemans A, Leijser LM. Methodological considerations on diffusion MRI tractography in infants aged 0-2 years: a scoping review. Pediatr Res 2024:10.1038/s41390-024-03463-2. [PMID: 39143201 DOI: 10.1038/s41390-024-03463-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Revised: 07/20/2024] [Accepted: 07/24/2024] [Indexed: 08/16/2024]
Abstract
Diffusion MRI (dMRI) enables studying the complex architectural organization of the brain's white matter (WM) through virtual reconstruction of WM fiber tracts (tractography). Despite the anticipated clinical importance of applying tractography to study structural connectivity and tract development during the critical period of rapid infant brain maturation, detailed descriptions on how to approach tractography in young infants are limited. Over the past two decades, tractography from infant dMRI has mainly been applied in research settings and focused on diffusion tensor imaging (DTI). Only few studies used techniques superior to DTI in terms of disentangling information on the brain's organizational complexity, including crossing fibers. While more advanced techniques may enhance our understanding of the intricate processes of normal and abnormal brain development and extensive knowledge has been gained from application on adult scans, their applicability in infants has remained underexplored. This may partially be due to the higher technical requirements versus the need to limit scan time in young infants. We review various previously described methodological practices for tractography in the infant brain (0-2 years-of-age) and provide recommendations to optimize advanced tractography approaches to enable more accurate reconstructions of the brain WM's complexity. IMPACT: Diffusion tensor imaging is the technique most frequently used for fiber tracking in the developing infant brain but is limited in capability to disentangle the complex white matter organization. Advanced tractography techniques allow for reconstruction of crossing fiber bundles to better reflect the brain's complex organization. Yet, they pose practical and technical challenges in the fast developing young infant's brain. Methods on how to approach advanced tractography in the young infant's brain have hardly been described. Based on a literature review, recommendations are provided to optimize tractography for the developing infant brain, aiming to advance early diagnosis and neuroprotective strategies.
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Affiliation(s)
- Anouk S Verschuur
- Department of Radiology, Isala Hospital Zwolle, Zwolle, The Netherlands.
- Department of Pediatrics, Section of Newborn Critical Care, University of Calgary, Calgary, Canada.
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands.
| | - Regan King
- Department of Pediatrics, Section of Newborn Critical Care, University of Calgary, Calgary, Canada
| | - Chantal M W Tax
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
- CUBRIC, School of Physics and Astronomy, Cardiff University, Cardiff, United Kingdom
| | - Martijn F Boomsma
- Department of Radiology, Isala Hospital Zwolle, Zwolle, The Netherlands
- Division of Imaging and Oncology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Gerda van Wezel-Meijler
- Department of Neonatology, Isala Women and Children's Hospital Zwolle, Zwolle, The Netherlands
| | - Alexander Leemans
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Lara M Leijser
- Department of Pediatrics, Section of Newborn Critical Care, University of Calgary, Calgary, Canada
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Shirazinodeh A, Faraji H, Sharifzadeh Javidi S, Jafari AH, Nazemzadeh M, Saligheh Rad H. Main Paths of Brain Fibers in Diffusion Images Mixed with a Noise to Improve Performance of Tractography Algorithm-Evaluation in Phantom. J Biomed Phys Eng 2024; 14:357-364. [PMID: 39175552 PMCID: PMC11336046 DOI: 10.31661/jbpe.v0i0.2108-1387] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Accepted: 01/27/2022] [Indexed: 08/24/2024]
Abstract
Background Some voxels may alter the tractography results due to unintentional alteration of noises and other unwanted factors. Objective This study aimed to investigate the effect of local phase features on tractography results providing data are mixed by a Gaussian or random distribution noise. Material and Methods In this simulation study, a mask was firstly designed based on the local phase features to decrease false-negative and -positive tractography results. The local phase features are calculated according to the local structures of images, which can be zero-dimensional, meaning just one point (equivalent to noise in tractography algorithm), a line (equivalent to a simple fiber), or an edge (equivalent to structures more complex than a simple fiber). A digital phantom evaluated the feasibility current model with the maximum complexities of configurations in fibers, including crossing fibers. In this paper, the diffusion images were mixed separately by a Gaussian or random distribution noise in 2 forms a zero-mean noise and a noise with a mean of data. Results The local mask eliminates the pixels of unfitted values with the main structures of images, due to noise or other interferer factors. Conclusion The local phase features of diffusion images are an innovative solution to determine principal diffusion directions.
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Affiliation(s)
- Alireza Shirazinodeh
- Department of Medical Physics and Biomedical Engineering, Medicine School, Tehran University of Medical Sciences, Tehran, Iran
| | - Hadis Faraji
- Research Center for Molecular and Cellular Imaging Advanced Medical Technologies and Equipment, Tehran University of Medical Sciences, Tehran, Iran
| | - Sam Sharifzadeh Javidi
- Department of Medical Physics and Biomedical Engineering, Medicine School, Tehran University of Medical Sciences, Tehran, Iran
| | - Amir Homayoun Jafari
- Department of Medical Physics and Biomedical Engineering, Medicine School, Tehran University of Medical Sciences, Tehran, Iran
- Research Center for Biomedical Technologies and Robotics (RCBTR) Tehran University of Medical Sciences, Tehran, Iran
| | - Mohammadreza Nazemzadeh
- Department of Medical Physics and Biomedical Engineering, Medicine School, Tehran University of Medical Sciences, Tehran, Iran
- Research Center for Molecular and Cellular Imaging Advanced Medical Technologies and Equipment, Tehran University of Medical Sciences, Tehran, Iran
| | - Hamidreza Saligheh Rad
- Department of Medical Physics and Biomedical Engineering, Medicine School, Tehran University of Medical Sciences, Tehran, Iran
- Quantitative Medical Imaging Systems Group, Research Center for Molecular and Cellular Imaging, Tehran University of Medical Sciences, Tehran, Iran
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Li Z, Li Z, Bilgic B, Lee H, Ying K, Huang SY, Liao H, Tian Q. DIMOND: DIffusion Model OptimizatioN with Deep Learning. ADVANCED SCIENCE (WEINHEIM, BADEN-WURTTEMBERG, GERMANY) 2024; 11:e2307965. [PMID: 38634608 PMCID: PMC11200022 DOI: 10.1002/advs.202307965] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/21/2023] [Revised: 02/09/2024] [Indexed: 04/19/2024]
Abstract
Diffusion magnetic resonance imaging is an important tool for mapping tissue microstructure and structural connectivity non-invasively in the in vivo human brain. Numerous diffusion signal models are proposed to quantify microstructural properties. Nonetheless, accurate estimation of model parameters is computationally expensive and impeded by image noise. Supervised deep learning-based estimation approaches exhibit efficiency and superior performance but require additional training data and may be not generalizable. A new DIffusion Model OptimizatioN framework using physics-informed and self-supervised Deep learning entitled "DIMOND" is proposed to address this problem. DIMOND employs a neural network to map input image data to model parameters and optimizes the network by minimizing the difference between the input acquired data and synthetic data generated via the diffusion model parametrized by network outputs. DIMOND produces accurate diffusion tensor imaging results and is generalizable across subjects and datasets. Moreover, DIMOND outperforms conventional methods for fitting sophisticated microstructural models including the kurtosis and NODDI model. Importantly, DIMOND reduces NODDI model fitting time from hours to minutes, or seconds by leveraging transfer learning. In summary, the self-supervised manner, high efficacy, and efficiency of DIMOND increase the practical feasibility and adoption of microstructure and connectivity mapping in clinical and neuroscientific applications.
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Affiliation(s)
- Zihan Li
- School of Biomedical EngineeringTsinghua UniversityBeijing100084P. R. China
| | - Ziyu Li
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordOX3 9DUUK
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical ImagingMassachusetts General HospitalCharlestownMA02129USA
- Harvard Medical SchoolBostonMA02129USA
| | - Hong‐Hsi Lee
- Athinoula A. Martinos Center for Biomedical ImagingMassachusetts General HospitalCharlestownMA02129USA
- Harvard Medical SchoolBostonMA02129USA
| | - Kui Ying
- Department of Engineering PhysicsTsinghua UniversityBeijing100084P. R. China
| | - Susie Y. Huang
- Athinoula A. Martinos Center for Biomedical ImagingMassachusetts General HospitalCharlestownMA02129USA
- Harvard Medical SchoolBostonMA02129USA
| | - Hongen Liao
- School of Biomedical EngineeringTsinghua UniversityBeijing100084P. R. China
| | - Qiyuan Tian
- School of Biomedical EngineeringTsinghua UniversityBeijing100084P. R. China
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Patriat R, Palnitkar T, Chandrasekaran J, Sretavan K, Braun H, Yacoub E, McGovern RA, Aman J, Cooper SE, Vitek JL, Harel N. DiMANI: diffusion MRI for anatomical nuclei imaging-Application for the direct visualization of thalamic subnuclei. Front Hum Neurosci 2024; 18:1324710. [PMID: 38439939 PMCID: PMC10910100 DOI: 10.3389/fnhum.2024.1324710] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2023] [Accepted: 02/05/2024] [Indexed: 03/06/2024] Open
Abstract
The thalamus is a centrally located and heterogeneous brain structure that plays a critical role in various sensory, motor, and cognitive processes. However, visualizing the individual subnuclei of the thalamus using conventional MRI techniques is challenging. This difficulty has posed obstacles in targeting specific subnuclei for clinical interventions such as deep brain stimulation (DBS). In this paper, we present DiMANI, a novel method for directly visualizing the thalamic subnuclei using diffusion MRI (dMRI). The DiMANI contrast is computed by averaging, voxelwise, diffusion-weighted volumes enabling the direct distinction of thalamic subnuclei in individuals. We evaluated the reproducibility of DiMANI through multiple approaches. First, we utilized a unique dataset comprising 8 scans of a single participant collected over a 3-year period. Secondly, we quantitatively assessed manual segmentations of thalamic subnuclei for both intra-rater and inter-rater reliability. Thirdly, we qualitatively correlated DiMANI imaging data from several patients with Essential Tremor with the localization of implanted DBS electrodes and clinical observations. Lastly, we demonstrated that DiMANI can provide similar features at 3T and 7T MRI, using varying numbers of diffusion directions. Our results establish that DiMANI is a reproducible and clinically relevant method to directly visualize thalamic subnuclei. This has significant implications for the development of new DBS targets and the optimization of DBS therapy.
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Affiliation(s)
- Rémi Patriat
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, United States
| | - Tara Palnitkar
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, United States
| | - Jayashree Chandrasekaran
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, United States
| | - Karianne Sretavan
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, United States
- Graduate Program in Neuroscience, University of Minnesota, Minneapolis, MN, United States
| | - Henry Braun
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, United States
| | - Essa Yacoub
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, United States
| | - Robert A. McGovern
- Department of Neurosurgery, University of Minnesota, Minneapolis, MN, United States
| | - Joshua Aman
- Department of Neurology, University of Minnesota, Minneapolis, MN, United States
| | - Scott E. Cooper
- Department of Neurology, University of Minnesota, Minneapolis, MN, United States
| | - Jerrold L. Vitek
- Department of Neurology, University of Minnesota, Minneapolis, MN, United States
| | - Noam Harel
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, United States
- Department of Neurosurgery, University of Minnesota, Minneapolis, MN, United States
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Valcourt Caron A, Shmuel A, Hao Z, Descoteaux M. versaFlow: a versatile pipeline for resolution adapted diffusion MRI processing and its application to studying the variability of the PRIME-DE database. Front Neuroinform 2023; 17:1191200. [PMID: 37637471 PMCID: PMC10449583 DOI: 10.3389/fninf.2023.1191200] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 06/27/2023] [Indexed: 08/29/2023] Open
Abstract
The lack of "gold standards" in Diffusion Weighted Imaging (DWI) makes validation cumbersome. To tackle this task, studies use translational analysis where results in humans are benchmarked against findings in other species. Non-Human Primates (NHP) are particularly interesting for this, as their cytoarchitecture is closely related to humans. However, tools used for processing and analysis must be adapted and finely tuned to work well on NHP images. Here, we propose versaFlow, a modular pipeline implemented in Nextflow, designed for robustness and scalability. The pipeline is tailored to in vivo NHP DWI at any spatial resolution; it allows for maintainability and customization. Processes and workflows are implemented using cutting-edge and state-of-the-art Magnetic Resonance Imaging (MRI) processing technologies and diffusion modeling algorithms, namely Diffusion Tensor Imaging (DTI), Constrained Spherical Deconvolution (CSD), and DIstribution of Anisotropic MicrOstructural eNvironments in Diffusion-compartment imaging (DIAMOND). Using versaFlow, we provide an in-depth study of the variability of diffusion metrics computed on 32 subjects from 3 sites of the Primate Data Exchange (PRIME-DE), which contains anatomical T1-weighted (T1w) and T2-weighted (T2w) images, functional MRI (fMRI), and DWI of NHP brains. This dataset includes images acquired over a range of resolutions, using single and multi-shell gradient samplings, on multiple scanner vendors. We perform a reproducibility study of the processing of versaFlow using the Aix-Marseilles site's data, to ensure that our implementation has minimal impact on the variability observed in subsequent analyses. We report very high reproducibility for the majority of metrics; only gamma distribution parameters of DIAMOND display less reproducible behaviors, due to the absence of a mechanism to enforce a random number seed in the software we used. This should be taken into consideration when future applications are performed. We show that the PRIME-DE diffusion data exhibits a great level of variability, similar or greater than results obtained in human studies. Its usage should be done carefully to prevent instilling uncertainty in statistical analyses. This hints at a need for sufficient harmonization in acquisition protocols and for the development of robust algorithms capable of managing the variability induced in imaging due to differences in scanner models and/or vendors.
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Affiliation(s)
- Alex Valcourt Caron
- Sherbrooke Connectivity Imaging Laboratory, Computer Science Department, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Amir Shmuel
- Brain Imaging Signals Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Ziqi Hao
- Brain Imaging Signals Lab, McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Laboratory, Computer Science Department, Université de Sherbrooke, Sherbrooke, QC, Canada
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Schmid AB, Ridgway L, Hailey L, Tachrount M, Probert F, Martin KR, Scott W, Crombez G, Price C, Robinson C, Koushesh S, Ather S, Tampin B, Barbero M, Nanz D, Clare S, Fairbank J, Baskozos G. Factors predicting the transition from acute to persistent pain in people with 'sciatica': the FORECAST longitudinal prognostic factor cohort study protocol. BMJ Open 2023; 13:e072832. [PMID: 37019481 PMCID: PMC10111910 DOI: 10.1136/bmjopen-2023-072832] [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: 02/15/2023] [Accepted: 03/15/2023] [Indexed: 04/07/2023] Open
Abstract
INTRODUCTION Sciatica is a common condition and is associated with higher levels of pain, disability, poorer quality of life, and increased use of health resources compared with low back pain alone. Although many patients recover, a third develop persistent sciatica symptoms. It remains unclear, why some patients develop persistent sciatica as none of the traditionally considered clinical parameters (eg, symptom severity, routine MRI) are consistent prognostic factors.The FORECAST study (factors predicting the transition from acute to persistent pain in people with 'sciatica') will take a different approach by exploring mechanism-based subgroups in patients with sciatica and investigate whether a mechanism-based approach can identify factors that predict pain persistence in patients with sciatica. METHODS AND ANALYSIS We will perform a prospective longitudinal cohort study including 180 people with acute/subacute sciatica. N=168 healthy participants will provide normative data. A detailed set of variables will be assessed within 3 months after sciatica onset. This will include self-reported sensory and psychosocial profiles, quantitative sensory testing, blood inflammatory markers and advanced neuroimaging. We will determine outcome with the Sciatica Bothersomeness Index and a Numerical Pain Rating Scale for leg pain severity at 3 and 12 months.We will use principal component analysis followed by clustering methods to identify subgroups. Univariate associations and machine learning methods optimised for high dimensional small data sets will be used to identify the most powerful predictors and model selection/accuracy.The results will provide crucial information about the pathophysiological drivers of sciatica symptoms and may identify prognostic factors of pain persistence. ETHICS AND DISSEMINATION The FORECAST study has received ethical approval (South Central Oxford C, 18/SC/0263). The dissemination strategy will be guided by our patient and public engagement activities and will include peer-reviewed publications, conference presentations, social media and podcasts. TRIAL REGISTRATION NUMBER ISRCTN18170726; Pre-results.
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Affiliation(s)
- Annina B Schmid
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, Oxfordshire, UK
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, Oxfordshire, UK
| | - Lucy Ridgway
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, Oxfordshire, UK
| | - Louise Hailey
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, Oxfordshire, UK
- Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, Oxfordshire, UK
| | - Mohamed Tachrount
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, Oxfordshire, UK
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, Oxfordshire, UK
| | - Fay Probert
- Department of Chemistry, University of Oxford, Oxford, Oxfordshire, UK
| | - Kathryn R Martin
- Academic Primary Care, Institute of Applied Health Sciences, School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, UK
- Aberdeen Centre for Arhtritis and Musculoskeletal Health, School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, UK
| | - Whitney Scott
- Health Psychology Section, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
- INPUT Pain Management Unit, Guy's and St Thomas' NHS Foundation Trust, London, UK
| | - Geert Crombez
- Department of Experimental Clinical and Health Psychology, University of Ghent, Gent, Belgium
| | - Christine Price
- Patient partner FORECAST study, Oxford University, Oxford, UK
| | - Claire Robinson
- Patient partner FORECAST study, Oxford University, Oxford, UK
| | - Soraya Koushesh
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, Oxfordshire, UK
| | - Sarim Ather
- Department of Radiology, Oxford University Hospitals NHS Foundation Trust, Oxford, Oxfordshire, UK
| | - Brigitte Tampin
- Department of Physiotherapy, Sir Charles Gairdner Hospital, Perth, Western Australia, Australia
- Curtin School of Allied Health, Faculty of Health Sciences, Curtin University, Perth, Western Australia, Australia
- Faculty of Business and Social Sciences, University of Applied Sciences, Osnabrueck, Germany
| | - Marco Barbero
- Rehabilitation Research Laboratory 2rLab, Department of Business Economics Health and Social Care, University of Applied Sciences and Arts of Southern Switzerland, Manno, Ticino, Switzerland
| | - Daniel Nanz
- Swiss Center for Musculoskeletal Imaging, Balgrist Campus AG, Zurich, Zurich, Switzerland
- Medical Faculty, University of Zurich, Zurich, Zurich, Switzerland
| | - Stuart Clare
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, Oxfordshire, UK
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, Oxfordshire, UK
| | - Jeremy Fairbank
- Nuffield Department of Orthopaedics Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, Oxfordshire, UK
| | - Georgios Baskozos
- Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, Oxfordshire, UK
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Jha RR, Kumar BR, Pathak SK, Bhavsar A, Nigam A. TrGANet: Transforming 3T to 7T dMRI using Trapezoidal Rule and Graph based Attention Modules. Med Image Anal 2023; 87:102806. [PMID: 37030056 DOI: 10.1016/j.media.2023.102806] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2022] [Revised: 01/12/2023] [Accepted: 03/30/2023] [Indexed: 04/03/2023]
Abstract
Diffusion MRI (dMRI) is a non-invasive tool for assessing the white matter region of the brain by approximating the fiber streamlines, structural connectivity, and estimation of microstructure. This modality can yield useful information for diagnosing several mental diseases as well as for surgical planning. The higher angular resolution diffusion imaging (HARDI) technique is helpful in obtaining more robust fiber tracts by getting a good approximation of regions where fibers cross. Moreover, HARDI is more sensitive to tissue changes and can accurately represent anatomical details in the human brain at higher magnetic strengths. In other words, magnetic strengths affect the quality of the image, and hence high magnetic strength has good tissue contrast with better spatial resolution. However, a higher magnetic strength scanner (like 7T) is costly and unaffordable to most hospitals. Hence, in this work, we have proposed a novel CNN architecture for the transformation of 3T to 7T dMRI. Additionally, we have also reconstructed the multi-shell multi-tissue fiber orientation distribution function (MSMT fODF) at 7T from single-shell 3T. The proposed architecture consists of a CNN-based ODE solver utilizing the Trapezoidal rule and graph-based attention layer alongwith L1 and total variation loss. Finally, the model has been validated on the HCP data set quantitatively and qualitatively.
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8
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Diffusion Measures of Subcortical Structures Using High-Field MRI. Brain Sci 2023; 13:brainsci13030391. [PMID: 36979201 PMCID: PMC10046555 DOI: 10.3390/brainsci13030391] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2023] [Revised: 02/11/2023] [Accepted: 02/15/2023] [Indexed: 02/26/2023] Open
Abstract
The pathology of Parkinson’s disease (PD) involves the death of dopaminergic neurons in the substantia nigra (SN), which slowly influences downstream basal ganglia pathways as dopamine transport diminishes. Diffusion magnetic resonance imaging (MRI) has been used to diagnose PD by assessing white matter connectivity in some brain areas. For this study, we applied Lead-DBS to human connectome project data to automatically segment 11 subcortical structures of 49 human connectome project subjects, reducing the reliance on manual segmentation for more consistency. The Lead-connectome pipeline, which utilizes DSI Studio to generate structural connectomes from each 3T and 7T diffusion image, was applied to 3T and 7T data to investigate possible differences in diffusion measures due to different acquisition protocols. Significantly higher fractional anisotropy (FA) values were found in the 3T left SN; significantly higher MD values were found in the 3T left SN and the right amygdala, SN, and subthalamic nucleus (STN); significantly higher AD values were found in the right RN and STN; and significantly higher RD values were found in the left RN and right amygdala. We illustrate a methodology for obtaining diffusion measures of basal ganglia and basal ganglia connectivity using diffusion images, as well as show possible differences in diffusion measures that can arise due to the differences in MRI acquisitions.
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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: 5] [Impact Index Per Article: 2.5] [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.
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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.
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Seider NA, Adeyemo B, Miller R, Newbold DJ, Hampton JM, Scheidter KM, Rutlin J, Laumann TO, Roland JL, Montez DF, Van AN, Zheng A, Marek S, Kay BP, Bretthorst GL, Schlaggar BL, Greene DJ, Wang Y, Petersen SE, Barch DM, Gordon EM, Snyder AZ, Shimony JS, Dosenbach NUF. Accuracy and reliability of diffusion imaging models. Neuroimage 2022; 254:119138. [PMID: 35339687 PMCID: PMC9841915 DOI: 10.1016/j.neuroimage.2022.119138] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2021] [Revised: 03/01/2022] [Accepted: 03/22/2022] [Indexed: 01/19/2023] Open
Abstract
Diffusion imaging aims to non-invasively characterize the anatomy and integrity of the brain's white matter fibers. We evaluated the accuracy and reliability of commonly used diffusion imaging methods as a function of data quantity and analysis method, using both simulations and highly sampled individual-specific data (927-1442 diffusion weighted images [DWIs] per individual). Diffusion imaging methods that allow for crossing fibers (FSL's BedpostX [BPX], DSI Studio's Constant Solid Angle Q-Ball Imaging [CSA-QBI], MRtrix3's Constrained Spherical Deconvolution [CSD]) estimated excess fibers when insufficient data were present and/or when the data did not match the model priors. To reduce such overfitting, we developed a novel Bayesian Multi-tensor Model-selection (BaMM) method and applied it to the popular ball-and-stick model used in BedpostX within the FSL software package. BaMM was robust to overfitting and showed high reliability and the relatively best crossing-fiber accuracy with increasing amounts of diffusion data. Thus, sufficient data and an overfitting resistant analysis method enhance precision diffusion imaging. For potential clinical applications of diffusion imaging, such as neurosurgical planning and deep brain stimulation (DBS), the quantities of data required to achieve diffusion imaging reliability are lower than those needed for functional MRI.
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Affiliation(s)
- Nicole A Seider
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Babatunde Adeyemo
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Ryland Miller
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Dillan J Newbold
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Neurology, New York University Langone Medical Center, New York, NY 10016, United States of America
| | - Jacqueline M Hampton
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Kristen M Scheidter
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Jerrel Rutlin
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Timothy O Laumann
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Jarod L Roland
- Department of Neurological Surgery, Washington University School of Medicine, St Louis, MO 63110 United States of America
| | - David F Montez
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Andrew N Van
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Biomedical Engineering, Washington University in St Louis, St. Louis, MO 63110, United States of America
| | - Annie Zheng
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Scott Marek
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Benjamin P Kay
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - G Larry Bretthorst
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Chemistry, Washington University in St Louis, St. Louis, MO 63110, United States of America
| | - Bradley L Schlaggar
- Kennedy Krieger Institute, Baltimore, MD 21205, United States of America; Department of Neurology, Johns Hopkins University School of Medicine, Baltimore, MD 21287, United States of America; Department of Pediatrics, Johns Hopkins University School of Medicine, Baltimore, MD 21287, United States of America
| | - Deanna J Greene
- Department of Cognitive Science, University of California, San Diego, La Jolla, CA, United States of America
| | - Yong Wang
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Obstetrics and Gynecology, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Biomedical Engineering, Washington University in St Louis, St. Louis, MO 63110, United States of America
| | - Steven E Petersen
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Biomedical Engineering, Washington University in St Louis, St. Louis, MO 63110, United States of America; Department of Neuroscience, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Psychological and Brain Sciences, Washington University in St. Louis, MO 63110, United States of America
| | - Deanna M Barch
- Department of Psychiatry, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Psychological and Brain Sciences, Washington University in St. Louis, MO 63110, United States of America
| | - Evan M Gordon
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Abraham Z Snyder
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Joshua S Shimony
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Neuroscience, Washington University School of Medicine, St. Louis, MO 63110, United States of America
| | - Nico U F Dosenbach
- Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Biomedical Engineering, Washington University in St Louis, St. Louis, MO 63110, United States of America; Program in Occupational Therapy, Washington University School of Medicine, St. Louis, MO 63110, United States of America; Department of Pediatrics, Washington University School of Medicine, St. Louis, MO 63110, United States of America
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11
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Fan Q, Eichner C, Afzali M, Mueller L, Tax CMW, Davids M, Mahmutovic M, Keil B, Bilgic B, Setsompop K, Lee HH, Tian Q, Maffei C, Ramos-Llordén G, Nummenmaa A, Witzel T, Yendiki A, Song YQ, Huang CC, Lin CP, Weiskopf N, Anwander A, Jones DK, Rosen BR, Wald LL, Huang SY. Mapping the human connectome using diffusion MRI at 300 mT/m gradient strength: Methodological advances and scientific impact. Neuroimage 2022; 254:118958. [PMID: 35217204 PMCID: PMC9121330 DOI: 10.1016/j.neuroimage.2022.118958] [Citation(s) in RCA: 22] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2021] [Revised: 01/27/2022] [Accepted: 01/31/2022] [Indexed: 12/20/2022] Open
Abstract
Tremendous efforts have been made in the last decade to advance cutting-edge MRI technology in pursuit of mapping structural connectivity in the living human brain with unprecedented sensitivity and speed. The first Connectom 3T MRI scanner equipped with a 300 mT/m whole-body gradient system was installed at the Massachusetts General Hospital in 2011 and was specifically constructed as part of the Human Connectome Project. Since that time, numerous technological advances have been made to enable the broader use of the Connectom high gradient system for diffusion tractography and tissue microstructure studies and leverage its unique advantages and sensitivity to resolving macroscopic and microscopic structural information in neural tissue for clinical and neuroscientific studies. The goal of this review article is to summarize the technical developments that have emerged in the last decade to support and promote large-scale and scientific studies of the human brain using the Connectom scanner. We provide a brief historical perspective on the development of Connectom gradient technology and the efforts that led to the installation of three other Connectom 3T MRI scanners worldwide - one in the United Kingdom in Cardiff, Wales, another in continental Europe in Leipzig, Germany, and the latest in Asia in Shanghai, China. We summarize the key developments in gradient hardware and image acquisition technology that have formed the backbone of Connectom-related research efforts, including the rich array of high-sensitivity receiver coils, pulse sequences, image artifact correction strategies and data preprocessing methods needed to optimize the quality of high-gradient strength diffusion MRI data for subsequent analyses. Finally, we review the scientific impact of the Connectom MRI scanner, including advances in diffusion tractography, tissue microstructural imaging, ex vivo validation, and clinical investigations that have been enabled by Connectom technology. We conclude with brief insights into the unique value of strong gradients for diffusion MRI and where the field is headed in the coming years.
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Affiliation(s)
- Qiuyun Fan
- Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Cornelius Eichner
- Max Planck Institute for Human Cognitive and Brain Sciences, Department of Neuropsychology, Leipzig, Germany
| | - Maryam Afzali
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, Wales, UK; Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds LS2 9JT, UK
| | - Lars Mueller
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of Leeds, Leeds LS2 9JT, UK
| | - Chantal M W Tax
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, Wales, UK; Image Sciences Institute, University Medical Center (UMC) Utrecht, Utrecht, the Netherlands
| | - Mathias Davids
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA; Computer Assisted Clinical Medicine, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany
| | - Mirsad Mahmutovic
- Institute of Medical Physics and Radiation Protection (IMPS), TH-Mittelhessen University of Applied Sciences (THM), Giessen, Germany
| | - Boris Keil
- Institute of Medical Physics and Radiation Protection (IMPS), TH-Mittelhessen University of Applied Sciences (THM), Giessen, Germany
| | - Berkin Bilgic
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Kawin Setsompop
- Department of Radiology, Stanford University, Stanford, CA, USA; Department of Electrical Engineering, Stanford University, Stanford, CA, USA
| | - Hong-Hsi Lee
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Qiyuan Tian
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Chiara Maffei
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Gabriel Ramos-Llordén
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Aapo Nummenmaa
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA
| | | | - Anastasia Yendiki
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Yi-Qiao Song
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA USA
| | - Chu-Chung Huang
- Key Laboratory of Brain Functional Genomics (MOE & STCSM), Affiliated Mental Health Center (ECNU), School of Psychology and Cognitive Science, East China Normal University, Shanghai, China; Shanghai Changning Mental Health Center, Shanghai, China
| | - Ching-Po Lin
- Institute of Neuroscience, National Yang Ming Chiao Tung University, Taipei, Taiwan; Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
| | - Nikolaus Weiskopf
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth Sciences, Leipzig University, Leipzig, Germany
| | - Alfred Anwander
- Max Planck Institute for Human Cognitive and Brain Sciences, Department of Neuropsychology, Leipzig, Germany
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, Wales, UK
| | - Bruce R Rosen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Lawrence L Wald
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Susie Y Huang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA; Harvard Medical School, Boston, MA, USA; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA.
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12
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Hitti FL, Parker D, Yang AI, Brem S, Verma R. Laterality and Sex Differences of Human Lateral Habenula Afferent and Efferent Fiber Tracts. Front Neurosci 2022; 16:837624. [PMID: 35784832 PMCID: PMC9243380 DOI: 10.3389/fnins.2022.837624] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 05/27/2022] [Indexed: 11/26/2022] Open
Abstract
Introduction The lateral habenula (LHb) is an epithalamic nucleus associated with negative valence and affective disorders. It receives input via the stria medullaris (SM) and sends output via the fasciculus retroflexus (FR). Here, we use tractography to reconstruct and characterize this pathway. Methods Multi-shell human diffusion magnetic resonance imaging (dMRI) data was obtained from the human connectome project (HCP) (n = 20, 10 males) and from healthy controls (n = 10, 6 males) scanned at our institution. We generated LHb afferents and efferents using probabilistic tractography by selecting the pallidum as the seed region and the ventral tegmental area as the output target. Results We were able to reconstruct the intended streamlines in all individuals from the HCP dataset and our dataset. Our technique also aided in identification of the LHb. In right-handed individuals, the streamlines were significantly more numerous in the left hemisphere (mean ratio 1.59 ± 0.09, p = 0.04). In left-handed individuals, there was no hemispheric asymmetry on average (mean ratio 1.00 ± 0.09, p = 1.0). Additionally, these streamlines were significantly more numerous in females than in males (619.9 ± 159.7 vs. 225.9 ± 66.03, p = 0.04). Conclusion We developed a method to reconstruct the SM and FR without manual identification of the LHb. This technique enables targeting of these fiber tracts as well as the LHb. Furthermore, we have demonstrated that there are sex and hemispheric differences in streamline number. These findings may have therapeutic implications and warrant further investigation.
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Affiliation(s)
- Frederick L. Hitti
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, United States
- *Correspondence: Frederick L. Hitti,
| | - Drew Parker
- Diffusion and Connectomics in Precision Healthcare Research Lab, Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - Andrew I. Yang
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, United States
| | - Steven Brem
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, United States
| | - Ragini Verma
- Department of Neurosurgery, University of Pennsylvania, Philadelphia, PA, United States
- Diffusion and Connectomics in Precision Healthcare Research Lab, Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
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13
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Kornaropoulos EN, Winzeck S, Rumetshofer T, Wikstrom A, Knutsson L, Correia MM, Sundgren PC, Nilsson M. Sensitivity of Diffusion MRI to White Matter Pathology: Influence of Diffusion Protocol, Magnetic Field Strength, and Processing Pipeline in Systemic Lupus Erythematosus. Front Neurol 2022; 13:837385. [PMID: 35557624 PMCID: PMC9087851 DOI: 10.3389/fneur.2022.837385] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Accepted: 03/16/2022] [Indexed: 11/13/2022] Open
Abstract
There are many ways to acquire and process diffusion MRI (dMRI) data for group studies, but it is unknown which maximizes the sensitivity to white matter (WM) pathology. Inspired by this question, we analyzed data acquired for diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) at 3T (3T-DTI and 3T-DKI) and DTI at 7T in patients with systemic lupus erythematosus (SLE) and healthy controls (HC). Parameter estimates in 72 WM tracts were obtained using TractSeg. The impact on the sensitivity to WM pathology was evaluated for the diffusion protocol, the magnetic field strength, and the processing pipeline. Sensitivity was quantified in terms of Cohen's d for group comparison. Results showed that the choice of diffusion protocol had the largest impact on the effect size. The effect size in fractional anisotropy (FA) across all WM tracts was 0.26 higher when derived by DTI than by DKI and 0.20 higher in 3T compared with 7T. The difference due to the diffusion protocol was larger than the difference due to magnetic field strength for the majority of diffusion parameters. In contrast, the difference between including or excluding different processing steps was near negligible, except for the correction of distortions from eddy currents and motion which had a clearly positive impact. For example, effect sizes increased on average by 0.07 by including motion and eddy correction for FA derived from 3T-DTI. Effect sizes were slightly reduced by the incorporation of denoising and Gibbs-ringing removal (on average by 0.011 and 0.005, respectively). Smoothing prior to diffusion model fitting generally reduced effect sizes. In summary, 3T-DTI in combination with eddy current and motion correction yielded the highest sensitivity to WM pathology in patients with SLE. However, our results also indicated that the 3T-DKI and 7T-DTI protocols used here may be adjusted to increase effect sizes.
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Affiliation(s)
- Evgenios N. Kornaropoulos
- Clinical Sciences, Diagnostic Radiology, Lund University, Lund, Sweden
- Division of Anaesthesia, University of Cambridge, Cambridge, United Kingdom
| | - Stefan Winzeck
- Division of Anaesthesia, University of Cambridge, Cambridge, United Kingdom
- BioMedIA Group, Department of Computing, Imperial College London, London, United Kingdom
| | | | - Anna Wikstrom
- Clinical Sciences, Diagnostic Radiology, Lund University, Lund, Sweden
| | - Linda Knutsson
- Department of Medical Radiation Physics, Lund University, Lund, Sweden
- Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University, School of Medicine, Baltimore, MD, United States
- F.M. Kirby Research Center, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Marta M. Correia
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, United Kingdom
| | - Pia C. Sundgren
- Clinical Sciences, Diagnostic Radiology, Lund University, Lund, Sweden
- Lund University BioImaging Center, Lund University, Lund, Sweden
- Department of Medical Imaging and Physiology, Skåne University Hospital, Lund, Sweden
| | - Markus Nilsson
- Clinical Sciences, Diagnostic Radiology, Lund University, Lund, Sweden
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14
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Zhang F, Daducci A, He Y, Schiavi S, Seguin C, Smith RE, Yeh CH, Zhao T, O'Donnell LJ. Quantitative mapping of the brain's structural connectivity using diffusion MRI tractography: A review. Neuroimage 2022; 249:118870. [PMID: 34979249 PMCID: PMC9257891 DOI: 10.1016/j.neuroimage.2021.118870] [Citation(s) in RCA: 88] [Impact Index Per Article: 44.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 12/03/2021] [Accepted: 12/31/2021] [Indexed: 12/13/2022] Open
Abstract
Diffusion magnetic resonance imaging (dMRI) tractography is an advanced imaging technique that enables in vivo reconstruction of the brain's white matter connections at macro scale. It provides an important tool for quantitative mapping of the brain's structural connectivity using measures of connectivity or tissue microstructure. Over the last two decades, the study of brain connectivity using dMRI tractography has played a prominent role in the neuroimaging research landscape. In this paper, we provide a high-level overview of how tractography is used to enable quantitative analysis of the brain's structural connectivity in health and disease. We focus on two types of quantitative analyses of tractography, including: 1) tract-specific analysis that refers to research that is typically hypothesis-driven and studies particular anatomical fiber tracts, and 2) connectome-based analysis that refers to research that is more data-driven and generally studies the structural connectivity of the entire brain. We first provide a review of methodology involved in three main processing steps that are common across most approaches for quantitative analysis of tractography, including methods for tractography correction, segmentation and quantification. For each step, we aim to describe methodological choices, their popularity, and potential pros and cons. We then review studies that have used quantitative tractography approaches to study the brain's white matter, focusing on applications in neurodevelopment, aging, neurological disorders, mental disorders, and neurosurgery. We conclude that, while there have been considerable advancements in methodological technologies and breadth of applications, there nevertheless remains no consensus about the "best" methodology in quantitative analysis of tractography, and researchers should remain cautious when interpreting results in research and clinical applications.
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Affiliation(s)
- Fan Zhang
- Brigham and Women's Hospital, Harvard Medical School, Boston, USA.
| | | | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; Chinese Institute for Brain Research, Beijing, China
| | - Simona Schiavi
- Department of Computer Science, University of Verona, Verona, Italy
| | - Caio Seguin
- Melbourne Neuropsychiatry Centre, University of Melbourne and Melbourne Health, Melbourne, Australia; The University of Sydney, School of Biomedical Engineering, Sydney, Australia
| | - Robert E Smith
- The Florey Institute of Neuroscience and Mental Health, Melbourne, Australia; Florey Department of Neuroscience and Mental Health, The University of Melbourne, Melbourne, Australia
| | - Chun-Hung Yeh
- Institute for Radiological Research, Chang Gung University, Taoyuan, Taiwan; Department of Psychiatry, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
| | - Tengda Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
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15
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Fiber tracing and microstructural characterization among audiovisual integration brain regions in neonates compared with young adults. Neuroimage 2022; 254:119141. [PMID: 35342006 DOI: 10.1016/j.neuroimage.2022.119141] [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: 06/09/2021] [Revised: 02/23/2022] [Accepted: 03/21/2022] [Indexed: 11/23/2022] Open
Abstract
Audiovisual integration has been related with cognitive-processing and behavioral advantages, as well as with various socio-cognitive disorders. While some studies have identified brain regions instantiating this ability shortly after birth, little is known about the structural pathways connecting them. The goal of the present study was to reconstruct fiber tracts linking AVI regions in the newborn in-vivo brain and assess their adult-likeness by comparing them with analogous fiber tracts of young adults. We performed probabilistic tractography and compared connective probabilities between a sample of term-born neonates (N = 311; the Developing Human Connectome Project (dHCP, http://www.developingconnectome.org) and young adults (N = 311 The Human Connectome Project; https://www.humanconnectome.org/) by means of a classification algorithm. Furthermore, we computed Dice coefficients to assess between-group spatial similarity of the reconstructed fibers and used diffusion metrics to characterize neonates' AVI brain network in terms of microstructural properties, interhemispheric differences and the association with perinatal covariates and biological sex. Overall, our results indicate that the AVI fiber bundles were successfully reconstructed in a vast majority of neonates, similarly to adults. Connective probability distributional similarities and spatial overlaps of AVI fibers between the two groups differed across the reconstructed fibers. There was a rank-order correspondence of the fibers' connective strengths across the groups. Additionally, the study revealed patterns of diffusion metrics in line with early white matter developmental trajectories and a developmental advantage for females. Altogether, these findings deliver evidence of meaningful structural connections among AVI regions in the newborn in-vivo brain.
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Gard A, Al-Husseini A, Kornaropoulos EN, De Maio A, Tegner Y, Björkman-Burtscher I, Markenroth Bloch K, Nilsson M, Magnusson M, Marklund N. Post-Concussive Vestibular Dysfunction Is Related to Injury to the Inferior Vestibular Nerve. J Neurotrauma 2022; 39:829-840. [PMID: 35171721 PMCID: PMC9225415 DOI: 10.1089/neu.2021.0447] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/28/2022] Open
Abstract
Symptoms of vestibular dysfunction such as dizziness and vertigo are common after sports-related concussions (SRC) and associated with a worse outcome and a prolonged recovery. Vestibular dysfunction after SRC can be because of an impairment of the peripheral or central neural parts of the vestibular system. The aim of the present study was to establish the cause of vestibular impairment in athletes with SRC who have persisting post-concussive symptoms (PPCS). We recruited 42 participants-21 athletes with previous SRCs and PPCS ≥6 months and 21 healthy athletic age- and sex-matched controls-who underwent symptom rating, a detailed test battery of vestibular function and 7T magnetic resonance imaging with diffusion tensor imaging (DTI) and diffusion kurtosis imaging (DKI) of cerebellar white matter tracts, and T1-weighted imaging for cerebellar volumetrics. Vestibular dysfunction was observed in 13 SRC athletes and three controls (p = 0.001). Athletes with vestibular dysfunction reported more pronounced symptoms on the Dizziness Handicap Inventory (DHI; p < 0.001) and the Hospital Anxiety and Depression Scale (HADS; p < 0.001). No significant differences in DTI metrics were found, while in DKI two metrics were observed in the superior and/or inferior cerebellar tracts. Cerebellar gray and white matter volumes were similar in athletes with SRC and controls. Compared with controls, pathological video head impulse test results (vHIT; p < 0.001) and cervical vestibular evoked myogenic potentials (cVEMP; p = 0.002) were observed in athletes with SRC, indicating peripheral vestibular dysfunction and specifically suggesting injury to the inferior vestibular nerve. In athletes with persisting symptoms after SRC, vestibular dysfunction is associated with injury to the inferior vestibular nerve.
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Affiliation(s)
- Anna Gard
- Department of Clinical Sciences Lund, Lund University, Neurosurgery, Skåne University Hospital, Lund, Sweden
| | - Ali Al-Husseini
- Department of Clinical Sciences Lund, Lund University, Neurosurgery, Skåne University Hospital, Lund, Sweden
| | - Evgenios N. Kornaropoulos
- Department of Clinical Sciences Lund, Diagnostic Radiology, Lund University, Skåne University Hospital, Lund, Sweden
| | - Alessandro De Maio
- Department of Radiological, Oncological and Pathological Sciences. Policlinico Umberto I, Sapienza University of Rome, Rome, Italy
| | - Yelverton Tegner
- Department of Health Sciences, Luleå University of Technology, Luleå, Sweden
| | - Isabella Björkman-Burtscher
- Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | | | - Markus Nilsson
- Department of Clinical Sciences Lund, Diagnostic Radiology, Lund University, Skåne University Hospital, Lund, Sweden
| | - Måns Magnusson
- Department of Clinical Sciences Lund, Otorhinolaryngology, Lund University, Skåne University Hospital, Lund, Sweden
| | - Niklas Marklund
- Department of Clinical Sciences Lund, Lund University, Neurosurgery, Skåne University Hospital, Lund, Sweden
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17
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DeKraker J, Haast RAM, Yousif MD, Karat B, Lau JC, Köhler S, Khan AR. Automated hippocampal unfolding for morphometry and subfield segmentation with HippUnfold. eLife 2022; 11:77945. [PMID: 36519725 PMCID: PMC9831605 DOI: 10.7554/elife.77945] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2022] [Accepted: 12/13/2022] [Indexed: 12/16/2022] Open
Abstract
Like neocortical structures, the archicortical hippocampus differs in its folding patterns across individuals. Here, we present an automated and robust BIDS-App, HippUnfold, for defining and indexing individual-specific hippocampal folding in MRI, analogous to popular tools used in neocortical reconstruction. Such tailoring is critical for inter-individual alignment, with topology serving as the basis for homology. This topological framework enables qualitatively new analyses of morphological and laminar structure in the hippocampus or its subfields. It is critical for refining current neuroimaging analyses at a meso- as well as micro-scale. HippUnfold uses state-of-the-art deep learning combined with previously developed topological constraints to generate uniquely folded surfaces to fit a given subject's hippocampal conformation. It is designed to work with commonly employed sub-millimetric MRI acquisitions, with possible extension to microscopic resolution. In this paper, we describe the power of HippUnfold in feature extraction, and highlight its unique value compared to several extant hippocampal subfield analysis methods.
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Affiliation(s)
- Jordan DeKraker
- Robarts Research Institute, Schulich School of Medicine and Dentistry, The University of Western OntarioLondonCanada,Western Institute for Neuroscience, The University of Western OntarioLondonCanada
| | - Roy AM Haast
- Robarts Research Institute, Schulich School of Medicine and Dentistry, The University of Western OntarioLondonCanada
| | - Mohamed D Yousif
- Robarts Research Institute, Schulich School of Medicine and Dentistry, The University of Western OntarioLondonCanada
| | - Bradley Karat
- Robarts Research Institute, Schulich School of Medicine and Dentistry, The University of Western OntarioLondonCanada
| | - Jonathan C Lau
- Robarts Research Institute, Schulich School of Medicine and Dentistry, The University of Western OntarioLondonCanada,Department of Clinical Neurological Sciences, Division of Neurosurgery, Schulich School of Medicine & Dentistry, The University of Western OntarioLondonCanada,School of Biomedical Engineering, The University of Western OntarioLondonCanada
| | - Stefan Köhler
- Western Institute for Neuroscience, The University of Western OntarioLondonCanada,Department of Psychology, Faculty of Social Science, The University of Western OntarioLondonCanada
| | - Ali R Khan
- Robarts Research Institute, Schulich School of Medicine and Dentistry, The University of Western OntarioLondonCanada,Western Institute for Neuroscience, The University of Western OntarioLondonCanada,School of Biomedical Engineering, The University of Western OntarioLondonCanada,Department of Medical Biophysics, Schulich School of Medicine and Dentistry, The University of Western OntarioLondonCanada
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18
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Shim JH, Baek HM. White Matter Connectivity between Structures of the Basal Ganglia using 3T and 7T. Neuroscience 2021; 483:32-39. [PMID: 34974113 DOI: 10.1016/j.neuroscience.2021.12.034] [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: 06/17/2021] [Revised: 12/21/2021] [Accepted: 12/24/2021] [Indexed: 11/30/2022]
Abstract
Analysis of the basal ganglia has been important in investigating the effects of Parkinson's disease as well as treatments for Parkinson's disease. One method of analysis has been using MRI for non-invasively segmenting the basal ganglia, then investigating significant parameters that involve the basal ganglia, such as fiber orientations and positional markers for deep brain stimulation (DBS). Following enhancements to optimizations and improvements to 3T and 7T MRI acquisitions, we utilized Lead-DBS on human connectome project data to automatically segment the basal ganglia of 49 human connectome project subjects, reducing the reliance on manual segmentation for more consistency. We generated probabilistic tractography streamlines between each segmentation pair using 3T and 7T human connectome diffusion data to observe any major differences in tractography streamline patterns that can arise due to tradeoffs from different field strengths and acquisitions. Tractography streamlines generated between basal ganglia structures using 3T images showed less standard deviation in streamline count than using 7T images. Mean tractography streamline counts generated using 3T diffusion images were all higher in count than streamlines generated using 7T diffusion images. We illustrate a potential method for analyzing the structural connectivity between basal ganglia structures, as well as visualize possible differences in probabilistic tractography that can arise from different acquisition protocols.
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Affiliation(s)
- Jae-Hyuk Shim
- Department of Health Sciences and Technology, GAIHST, Gachon University, Incheon 21999, Republic of Korea
| | - Hyeon-Man Baek
- Department of Health Sciences and Technology, GAIHST, Gachon University, Incheon 21999, Republic of Korea.
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19
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Davis AD, Hassel S, Arnott SR, Hall GB, Harris JK, Zamyadi M, Downar J, Frey BN, Lam RW, Kennedy SH, Strother SC. Biophysical compartment models for single-shell diffusion MRI in the human brain: a model fitting comparison. Phys Med Biol 2021; 67. [PMID: 34965517 DOI: 10.1088/1361-6560/ac46de] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/05/2021] [Accepted: 12/29/2021] [Indexed: 11/11/2022]
Abstract
Clinically oriented studies commonly acquire diffusion MRI (dMRI) data with a single non-zero b-value (i.e. single-shell) and diffusion weighting ofb=1000 s/mm2. To produce microstructural parameter maps, the tensor model is usually used, despite known limitations. Although compartment models have demonstrated improved fits in multi-shell dMRI data, they are rarely used for single-shell parameter maps, where their effectiveness is unclear from the literature. Here, various compartment models combining isotropic balls and symmetric tensors were fitted to single-shell dMRI data to investigate model fitting optimization and extract the most information possible. Full testing was performed in 5 subjects, and 3 subjects with multi-shell data were included for comparison. The results were tested and confirmed in a further 50 subjects. The Markov chain Monte Carlo (MCMC) model fitting technique outperformed non-linear least squares. Using MCMC, the 2-fibre-orientation mono-exponential ball & stick model (BSME2) provided artifact-free, stable results, in little processing time. The analogous ball & zeppelin model (BZ2) also produced stable, low-noise parameter maps, though it required much greater computing resources (50 000 burn-in steps). In single-shell data, the gamma-distributed diffusivity ball & stick model (BSGD2) underperformed relative to other models, despite being an often-used software default. It produced artifacts in the diffusivity maps even with extremely long processing times. Neither increased diffusion weighting nor a greater number of gradient orientations improved BSGD2fits. In white matter (WM), the tensor produced the best fit as measured by Bayesian information criterion. This result contrasts with studies using multi-shell data. However, in crossing fibre regions the tensor confounded geometric effects with fractional anisotropy (FA): the planar/linear WM FA ratio was 49%, while BZ2and BSME2retained 76% and 83% of restricted fraction, respectively. As a result, the BZ2and BSME2models are strong candidates to optimize information extraction from single-shell dMRI studies.
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Affiliation(s)
- Andrew D Davis
- Rotman Research Institute, Baycrest Health Sciences, 3560 Bathurst St, Toronto, Ontario, M6A 2E1, CANADA
| | - Stefanie Hassel
- University of Calgary Cumming School of Medicine, 3330 Hospital Dr NW, Calgary, Alberta, T2N 4N1, CANADA
| | - Stephen R Arnott
- Rotman Research Institute, Baycrest Health Sciences, 3560 Bathurst St, Toronto, Ontario, M6A 2E1, CANADA
| | - Geoffrey B Hall
- Department of Psychology, Neuroscience & Behaviour, McMaster University, 1280 Main St W, Hamilton, Ontario, L8S 4K1, CANADA
| | - Jacqueline K Harris
- Department of Computing Science, University of Alberta, 8900 114 St NW, Edmonton, Alberta, T6G 2E8, CANADA
| | - Mojdeh Zamyadi
- Rotman Research Institute, Baycrest Health Sciences, 3560 Bathurst St, Toronto, Ontario, M6A 2E1, CANADA
| | - Jonathan Downar
- Institute of Medical Science, University of Toronto, 1 King's College Cir, Toronto, Ontario, M5S 1A8, CANADA
| | - Benicio N Frey
- McMaster University Department of Psychiatry and Behavioural Neurosciences, 100 West 5th Street, Hamilton, Ontario, L8N 3K7, CANADA
| | - Raymond W Lam
- Psychiatry, The University of British Columbia, 2255 Wesbrook Mall, Vancouver, British Columbia, V6T 2A1, CANADA
| | - Sidney H Kennedy
- University of Toronto Department of Psychiatry, 250 College Street, Toronto, Ontario, M5T 1R8, CANADA
| | - Stephen C Strother
- Rotman Research Institute, Baycrest Health Sciences, 3560 Bathurst St, Toronto, Ontario, M6A 2E1, CANADA
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20
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Syversen IF, Witter MP, Kobro-Flatmoen A, Goa PE, Navarro Schröder T, Doeller CF. Structural connectivity-based segmentation of the human entorhinal cortex. Neuroimage 2021; 245:118723. [PMID: 34780919 PMCID: PMC8756143 DOI: 10.1016/j.neuroimage.2021.118723] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 10/22/2021] [Accepted: 11/11/2021] [Indexed: 12/15/2022] Open
Abstract
The medial (MEC) and lateral entorhinal cortex (LEC), widely studied in rodents, are well defined and characterized. In humans, however, the exact locations of their homologues remain uncertain. Previous functional magnetic resonance imaging (fMRI) studies have subdivided the human EC into posteromedial (pmEC) and anterolateral (alEC) parts, but uncertainty remains about the choice of imaging modality and seed regions, in particular in light of a substantial revision of the classical model of EC connectivity based on novel insights from rodent anatomy. Here, we used structural, not functional imaging, namely diffusion tensor imaging (DTI) and probabilistic tractography to segment the human EC based on differential connectivity to other brain regions known to project selectively to MEC or LEC. We defined MEC as more strongly connected with presubiculum and retrosplenial cortex (RSC), and LEC as more strongly connected with distal CA1 and proximal subiculum (dCA1pSub) and lateral orbitofrontal cortex (OFC). Although our DTI segmentation had a larger medial-lateral component than in the previous fMRI studies, our results show that the human MEC and LEC homologues have a border oriented both towards the posterior-anterior and medial-lateral axes, supporting the differentiation between pmEC and alEC.
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Affiliation(s)
- Ingrid Framås Syversen
- Kavli Institute for Systems Neuroscience, NTNU - Norwegian University of Science and Technology, MH, NTNU, Postbox 8905, Trondheim 7491, Norway.
| | - Menno P Witter
- Kavli Institute for Systems Neuroscience, NTNU - Norwegian University of Science and Technology, MH, NTNU, Postbox 8905, Trondheim 7491, Norway
| | - Asgeir Kobro-Flatmoen
- Kavli Institute for Systems Neuroscience, NTNU - Norwegian University of Science and Technology, MH, NTNU, Postbox 8905, Trondheim 7491, Norway
| | - Pål Erik Goa
- Department of Physics, NTNU - Norwegian University of Science and Technology, Trondheim, Norway
| | - Tobias Navarro Schröder
- Kavli Institute for Systems Neuroscience, NTNU - Norwegian University of Science and Technology, MH, NTNU, Postbox 8905, Trondheim 7491, Norway
| | - Christian F Doeller
- Kavli Institute for Systems Neuroscience, NTNU - Norwegian University of Science and Technology, MH, NTNU, Postbox 8905, Trondheim 7491, Norway; Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Institute of Psychology, Leipzig University, Leipzig, Germany
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21
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Elam JS, Glasser MF, Harms MP, Sotiropoulos SN, Andersson JLR, Burgess GC, Curtiss SW, Oostenveld R, Larson-Prior LJ, Schoffelen JM, Hodge MR, Cler EA, Marcus DM, Barch DM, Yacoub E, Smith SM, Ugurbil K, Van Essen DC. The Human Connectome Project: A retrospective. Neuroimage 2021; 244:118543. [PMID: 34508893 PMCID: PMC9387634 DOI: 10.1016/j.neuroimage.2021.118543] [Citation(s) in RCA: 76] [Impact Index Per Article: 25.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 08/13/2021] [Accepted: 08/30/2021] [Indexed: 01/21/2023] Open
Abstract
The Human Connectome Project (HCP) was launched in 2010 as an ambitious effort to accelerate advances in human neuroimaging, particularly for measures of brain connectivity; apply these advances to study a large number of healthy young adults; and freely share the data and tools with the scientific community. NIH awarded grants to two consortia; this retrospective focuses on the "WU-Minn-Ox" HCP consortium centered at Washington University, the University of Minnesota, and University of Oxford. In just over 6 years, the WU-Minn-Ox consortium succeeded in its core objectives by: 1) improving MR scanner hardware, pulse sequence design, and image reconstruction methods, 2) acquiring and analyzing multimodal MRI and MEG data of unprecedented quality together with behavioral measures from more than 1100 HCP participants, and 3) freely sharing the data (via the ConnectomeDB database) and associated analysis and visualization tools. To date, more than 27 Petabytes of data have been shared, and 1538 papers acknowledging HCP data use have been published. The "HCP-style" neuroimaging paradigm has emerged as a set of best-practice strategies for optimizing data acquisition and analysis. This article reviews the history of the HCP, including comments on key events and decisions associated with major project components. We discuss several scientific advances using HCP data, including improved cortical parcellations, analyses of connectivity based on functional and diffusion MRI, and analyses of brain-behavior relationships. We also touch upon our efforts to develop and share a variety of associated data processing and analysis tools along with detailed documentation, tutorials, and an educational course to train the next generation of neuroimagers. We conclude with a look forward at opportunities and challenges facing the human neuroimaging field from the perspective of the HCP consortium.
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Affiliation(s)
| | | | - Michael P Harms
- Washington University School of Medicine, St. Louis, MO, USA
| | - Stamatios N Sotiropoulos
- Sir Peter Mansfield Imaging Centre & NIHR Nottingham Biomedical Research Centre, Queen's Medical Centre, School of Medicine, University of Nottingham, UK; Wellcome Centre for Integrative Neuroimaging, University of Oxford, UK
| | | | | | | | - Robert Oostenveld
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, the Netherlands
| | | | - Jan-Mathijs Schoffelen
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, the Netherlands
| | - Michael R Hodge
- Washington University School of Medicine, St. Louis, MO, USA
| | - Eileen A Cler
- Washington University School of Medicine, St. Louis, MO, USA
| | - Daniel M Marcus
- Washington University School of Medicine, St. Louis, MO, USA
| | - Deanna M Barch
- Washington University School of Medicine, St. Louis, MO, USA
| | - Essa Yacoub
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
| | - Stephen M Smith
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, UK
| | - Kamil Ugurbil
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
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22
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Improvement of magnetic resonance imaging using a wireless radiofrequency resonator array. Sci Rep 2021; 11:23034. [PMID: 34845314 PMCID: PMC8630230 DOI: 10.1038/s41598-021-02533-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Accepted: 11/17/2021] [Indexed: 11/21/2022] Open
Abstract
In recent years, new human magnetic resonance imaging systems operating at static magnetic fields strengths of 7 Tesla or higher have become available, providing better signal sensitivity compared with lower field strengths. However, imaging human-sized objects at such high field strength and associated precession frequencies is limited due to the technical challenges associated with the wavelength effect, which substantially disturb the transmit field uniformity over the human body when conventional coils are used. Here we report a novel passive inductively-coupled radiofrequency resonator array design with a simple structure that works in conjunction with conventional coils and requires only to be tuned to the scanner's operating frequency. We show that inductive-coupling between the resonator array and the coil improves the transmit efficiency and signal sensitivity in the targeted region. The simple structure, flexibility, and cost-efficiency make the proposed array design an attractive approach for altering the transmit field distribution specially at high field systems, where the wavelength is comparable with the tissue size.
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23
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Wu Y, Hong Y, Ahmad S, Yap PT. Active Cortex Tractography. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2021; 12907:467-476. [PMID: 35939282 PMCID: PMC9355463 DOI: 10.1007/978-3-030-87234-2_44] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
Most existing diffusion tractography algorithms are affected by gyral bias, causing the termination of streamlines at gyral crowns instead of sulcal banks. In this paper, we propose a tractography technique, called active cortex tractography (ACT), to overcome gyral bias by enabling fiber streamlines to curve naturally into the cortex. We show that the cortex can play an active role in cortical tractography by providing anatomical information to overcome orientation ambiguities as the streamlines enter the superficial white matter in gyral blades and approach the cortex. This is achieved by devising a direction scouting mechanism that takes into account the white matter surface normal vectors. The scouting mechanism allows probing of directions further in space to prepare the streamlines to turn at appropriate angles. The surface normal vectors guide the streamlines to turn into the cortex, perpendicular to the white-gray matter interface. Evaluation using synthetic, macaque and human data with different streamline seeding schemes demonstrates that ACT improves cortical tractography.
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Affiliation(s)
- Ye Wu
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, USA
| | - Yoonmi Hong
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, USA
| | - Sahar Ahmad
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, USA
| | - Pew-Thian Yap
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, USA
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24
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Baxter L, Moultrie F, Fitzgibbon S, Aspbury M, Mansfield R, Bastiani M, Rogers R, Jbabdi S, Duff E, Slater R. Functional and diffusion MRI reveal the neurophysiological basis of neonates' noxious-stimulus evoked brain activity. Nat Commun 2021; 12:2744. [PMID: 33980860 PMCID: PMC8115252 DOI: 10.1038/s41467-021-22960-0] [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: 04/13/2020] [Accepted: 04/05/2021] [Indexed: 11/20/2022] Open
Abstract
Understanding the neurophysiology underlying neonatal responses to noxious stimulation is central to improving early life pain management. In this neonatal multimodal MRI study, we use resting-state and diffusion MRI to investigate inter-individual variability in noxious-stimulus evoked brain activity. We observe that cerebral haemodynamic responses to experimental noxious stimulation can be predicted from separately acquired resting-state brain activity (n = 18). Applying this prediction model to independent Developing Human Connectome Project data (n = 215), we identify negative associations between predicted noxious-stimulus evoked responses and white matter mean diffusivity. These associations are subsequently confirmed in the original noxious stimulation paradigm dataset, validating the prediction model. Here, we observe that noxious-stimulus evoked brain activity in healthy neonates is coupled to resting-state activity and white matter microstructure, that neural features can be used to predict responses to noxious stimulation, and that the dHCP dataset could be utilised for future exploratory research of early life pain system neurophysiology.
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Affiliation(s)
- Luke Baxter
- Department of Paediatrics, University of Oxford, Oxford, UK
| | - Fiona Moultrie
- Department of Paediatrics, University of Oxford, Oxford, UK
| | - Sean Fitzgibbon
- FMRIB, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | | | | | - Matteo Bastiani
- FMRIB, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, UK
- NIHR Biomedical Research Centre, University of Nottingham, Nottingham, UK
| | - Richard Rogers
- Nuffield Department of Anaesthetics, John Radcliffe Hospital, Oxford, UK
| | - Saad Jbabdi
- FMRIB, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Eugene Duff
- Department of Paediatrics, University of Oxford, Oxford, UK
- FMRIB, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Rebeccah Slater
- Department of Paediatrics, University of Oxford, Oxford, UK.
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25
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Im S, Lee J, Kim S. Preliminary Comparison of Subcortical Structures in Elderly Subclinical Depression: Structural Analysis with 3T MRI. Exp Neurobiol 2021; 30:183-202. [PMID: 33972469 PMCID: PMC8118753 DOI: 10.5607/en20056] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2020] [Revised: 01/19/2021] [Accepted: 02/17/2021] [Indexed: 01/23/2023] Open
Abstract
Depression in the elderly population has shown increased likelihood of neurological disorders due to structural changes in the subcortical area. However, further investigation into depression related subcortical changes is needed due to mismatches in structural analysis results between studies as well as scarcities in research regarding subcortical connectivity patterns of subclinical depression populations. This study aims to investigate structural differences in subcortical regions of aged participants with subclinical depression using 3Tesla MRI. In structural analysis, volumes of each subcortical region were measured to observe the volumetric difference and asymmetry between groups, but no significant difference was found. In addition, fractional anisotropy (FA) and apparent diffusion coefficient (ADC) did not show any significant differences between groups. Structural analysis using probabilistic tractography indicated that the connection strength between left nucleus accumbens-right hippocampus, and right thalamus-right caudate was higher in the control group than the subclinical depression group. The differences in subcortical connection strength of subclinical depression groups, have shown to correlate with emotional and cognitive disorders, such as anxiety and memory impairment. We believe that the analysis of structural differences and cross-regional network measures in subcortical structures can help identify neurophysiological changes occurring in subclinical depression.
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Affiliation(s)
- SangJin Im
- Lee Gil Ya Cancer & Diabetes Institute, Gachon University, Incheon 21999, Korea
| | - Jeonghwan Lee
- Department of Psychiatry, Chungbuk National University College of Medicine, Cheongju 28644, Korea
| | - Siekyeong Kim
- Department of Psychiatry, Chungbuk National University College of Medicine, Cheongju 28644, Korea
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26
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Koike S, Tanaka SC, Okada T, Aso T, Yamashita A, Yamashita O, Asano M, Maikusa N, Morita K, Okada N, Fukunaga M, Uematsu A, Togo H, Miyazaki A, Murata K, Urushibata Y, Autio J, Ose T, Yoshimoto J, Araki T, Glasser MF, Van Essen DC, Maruyama M, Sadato N, Kawato M, Kasai K, Okamoto Y, Hanakawa T, Hayashi T. Brain/MINDS beyond human brain MRI project: A protocol for multi-level harmonization across brain disorders throughout the lifespan. Neuroimage Clin 2021; 30:102600. [PMID: 33741307 PMCID: PMC8209465 DOI: 10.1016/j.nicl.2021.102600] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2020] [Revised: 01/31/2021] [Accepted: 02/12/2021] [Indexed: 01/03/2023]
Abstract
Psychiatric and neurological disorders are afflictions of the brain that can affect individuals throughout their lifespan. Many brain magnetic resonance imaging (MRI) studies have been conducted; however, imaging-based biomarkers are not yet well established for diagnostic and therapeutic use. This article describes an outline of the planned study, the Brain/MINDS Beyond human brain MRI project (BMB-HBM, FY2018 ~ FY2023), which aims to establish clinically-relevant imaging biomarkers with multi-site harmonization by collecting data from healthy traveling subjects (TS) at 13 research sites. Collection of data in psychiatric and neurological disorders across the lifespan is also scheduled at 13 sites, whereas designing measurement procedures, developing and analyzing neuroimaging protocols, and databasing are done at three research sites. A high-quality scanning protocol, Harmonization Protocol (HARP), was established for five high-quality 3 T scanners to obtain multimodal brain images including T1 and T2-weighted, resting-state and task functional and diffusion-weighted MRI. Data are preprocessed and analyzed using approaches developed by the Human Connectome Project. Preliminary results in 30 TS demonstrated cortical thickness, myelin, functional connectivity measures are comparable across 5 scanners, suggesting sensitivity to subject-specific connectome. A total of 75 TS and more than two thousand patients with various psychiatric and neurological disorders are scheduled to participate in the project, allowing a mixed model statistical harmonization. The HARP protocols are publicly available online, and all the imaging, demographic and clinical information, harmonizing database will also be made available by 2024. To the best of our knowledge, this is the first project to implement a prospective, multi-level harmonization protocol with multi-site TS data. It explores intractable brain disorders across the lifespan and may help to identify the disease-specific pathophysiology and imaging biomarkers for clinical practice.
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Affiliation(s)
- Shinsuke Koike
- Center for Evolutionary Cognitive Sciences (ECS), Graduate School of Art and Sciences, The University of Tokyo, Meguro-ku, Tokyo 153-8902, Japan; University of Tokyo Institute for Diversity & Adaptation of Human Mind (UTIDAHM), Meguro-ku, Tokyo 153-8902, Japan; University of Tokyo Center for Integrative Science of Human Behavior (CiSHuB), 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan; The International Research Center for Neurointelligence (WPI-IRCN), Institutes for Advanced Study (UTIAS), University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8654, Japan
| | - Saori C Tanaka
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International (ATR), Kyoto 619-0288, Japan
| | - Tomohisa Okada
- Human Brain Research Center, Kyoto University, Kyoto 606-8507, Japan
| | - Toshihiko Aso
- Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, Hyogo 650-0047, Japan
| | - Ayumu Yamashita
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International (ATR), Kyoto 619-0288, Japan
| | - Okito Yamashita
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International (ATR), Kyoto 619-0288, Japan
| | - Michiko Asano
- Center for Evolutionary Cognitive Sciences (ECS), Graduate School of Art and Sciences, The University of Tokyo, Meguro-ku, Tokyo 153-8902, Japan
| | - Norihide Maikusa
- Center for Evolutionary Cognitive Sciences (ECS), Graduate School of Art and Sciences, The University of Tokyo, Meguro-ku, Tokyo 153-8902, Japan; Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Kodaira-shi, Tokyo 187-8551, Japan
| | - Kentaro Morita
- Department of Rehabilitation, Graduate School of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Naohiro Okada
- University of Tokyo Institute for Diversity & Adaptation of Human Mind (UTIDAHM), Meguro-ku, Tokyo 153-8902, Japan; The International Research Center for Neurointelligence (WPI-IRCN), Institutes for Advanced Study (UTIAS), University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8654, Japan; Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Masaki Fukunaga
- Division of Cerebral Integration, Department of System Neuroscience, National Institute for Physiological Sciences, Okazaki 444-8585, Japan
| | - Akiko Uematsu
- Center for Evolutionary Cognitive Sciences (ECS), Graduate School of Art and Sciences, The University of Tokyo, Meguro-ku, Tokyo 153-8902, Japan
| | - Hiroki Togo
- Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Kodaira-shi, Tokyo 187-8551, Japan
| | - Atsushi Miyazaki
- Tamagawa University Brain Science Institute, 6-1-1 Tamagawagakuen, Machida, Tokyo 194-8610, Japan
| | | | | | - Joonas Autio
- Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, Hyogo 650-0047, Japan
| | - Takayuki Ose
- Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, Hyogo 650-0047, Japan
| | - Junichiro Yoshimoto
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International (ATR), Kyoto 619-0288, Japan
| | - Toshiyuki Araki
- Department of Peripheral Nervous System Research, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Kodaira, Tokyo 187-8551, Japan
| | - Matthew F Glasser
- Department of Neuroscience, Washington University School of Medicine, St Louis, MO USA; Department of Radiology, Washington University School of Medicine, St Louis, MO, USA
| | - David C Van Essen
- Department of Neuroscience, Washington University School of Medicine, St Louis, MO USA
| | - Megumi Maruyama
- Research Enhancement Strategy Office, National Institute for Physiological Sciences, Okazaki 444-8585, Japan
| | - Norihiro Sadato
- Division of Cerebral Integration, Department of System Neuroscience, National Institute for Physiological Sciences, Okazaki 444-8585, Japan
| | - Mitsuo Kawato
- Brain Information Communication Research Laboratory Group, Advanced Telecommunications Research Institutes International (ATR), Kyoto 619-0288, Japan; Center for Advanced Intelligence Project, RIKEN, Tokyo 103-0027, Japan
| | - Kiyoto Kasai
- University of Tokyo Institute for Diversity & Adaptation of Human Mind (UTIDAHM), Meguro-ku, Tokyo 153-8902, Japan; University of Tokyo Center for Integrative Science of Human Behavior (CiSHuB), 3-8-1 Komaba, Meguro-ku, Tokyo 153-8902, Japan; The International Research Center for Neurointelligence (WPI-IRCN), Institutes for Advanced Study (UTIAS), University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-8654, Japan; Department of Neuropsychiatry, Graduate School of Medicine, The University of Tokyo, Bunkyo-ku, Tokyo 113-8655, Japan
| | - Yasumasa Okamoto
- Department of Psychiatry and Neurosciences, Hiroshima University, Hiroshima 734-8551, Japan
| | - Takashi Hanakawa
- Integrative Brain Imaging Center, National Center of Neurology and Psychiatry, Kodaira-shi, Tokyo 187-8551, Japan; Department of Integrated Neuroanatomy and Neuroimaging, Kyoto University Graduate School of Medicine, Kyoto 606-8303, Japan
| | - Takuya Hayashi
- Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, Hyogo 650-0047, Japan.
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Cottaar M, Bastiani M, Boddu N, Glasser MF, Haber S, van Essen DC, Sotiropoulos SN, Jbabdi S. Modelling white matter in gyral blades as a continuous vector field. Neuroimage 2020; 227:117693. [PMID: 33385545 PMCID: PMC7610793 DOI: 10.1016/j.neuroimage.2020.117693] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/28/2020] [Revised: 11/19/2020] [Accepted: 12/21/2020] [Indexed: 01/27/2023] Open
Abstract
Many brain imaging studies aim to measure structural connectivity with diffusion tractography. However, biases in tractography data, particularly near the boundary between white matter and cortical grey matter can limit the accuracy of such studies. When seeding from the white matter, streamlines tend to travel parallel to the convoluted cortical surface, largely avoiding sulcal fundi and terminating preferentially on gyral crowns. When seeding from the cortical grey matter, streamlines generally run near the cortical surface until reaching deep white matter. These so-called “gyral biases” limit the accuracy and effective resolution of cortical structural connectivity profiles estimated by tractography algorithms, and they do not reflect the expected distributions of axonal densities seen in invasive tracer studies or stains of myelinated fibres. We propose an algorithm that concurrently models fibre density and orientation using a divergence-free vector field within gyral blades to encourage an anatomically-justified streamline density distribution along the cortical white/grey-matter boundary while maintaining alignment with the diffusion MRI estimated fibre orientations. Using in vivo data from the Human Connectome Project, we show that this algorithm reduces tractography biases. We compare the structural connectomes to functional connectomes from resting-state fMRI, showing that our model improves cross-modal agreement. Finally, we find that after parcellation the changes in the structural connectome are very minor with slightly improved interhemispheric connections (i.e, more homotopic connectivity) and slightly worse intrahemi-spheric connections when compared to tracers.
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Affiliation(s)
- Michiel Cottaar
- Wellcome Centre for Integrative Neuroimaging (WIN), Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, UK.
| | - Matteo Bastiani
- Wellcome Centre for Integrative Neuroimaging (WIN), Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, UK; Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, UK
| | - Nikhil Boddu
- Wellcome Centre for Integrative Neuroimaging (WIN), Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, UK
| | - Matthew F Glasser
- Department of Neuroscience, Washington University Medical School, Saint Louis, MO 63110, USA; Department of Radiology, Washington University Medical School, Saint Louis, MO 63110, USA; St. Luke's Hospital, Saint Louis, MO 63017, USA
| | - Suzanne Haber
- McLean Hospital, Harvard Medical School, Belmont, USA; Department of Pharmacology and Physiology, University of Rochester School of Medicine & Dentistry, Rochester, USA
| | - David C van Essen
- Department of Neuroscience, Washington University Medical School, Saint Louis, MO 63110, USA
| | - Stamatios N Sotiropoulos
- Wellcome Centre for Integrative Neuroimaging (WIN), Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, UK; Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, UK
| | - Saad Jbabdi
- Wellcome Centre for Integrative Neuroimaging (WIN), Centre for Functional Magnetic Resonance Imaging of the Brain (FMRIB), University of Oxford, UK
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28
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Voineskos AN, Blumberger DM, Schifani C, Hawco C, Dickie EW, Rajji TK, Mulsant BH, Foussias G, Wang W, Daskalakis ZJ. Effects of Repetitive Transcranial Magnetic Stimulation on Working Memory Performance and Brain Structure in People With Schizophrenia Spectrum Disorders: A Double-Blind, Randomized, Sham-Controlled Trial. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2020; 6:449-458. [PMID: 33551284 DOI: 10.1016/j.bpsc.2020.11.011] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/13/2020] [Revised: 11/09/2020] [Accepted: 11/23/2020] [Indexed: 01/04/2023]
Abstract
BACKGROUND There are currently no approved treatments for working memory deficits in schizophrenia spectrum disorders (SSDs). The objective of the present study was to assess whether repetitive transcranial magnetic stimulation (rTMS) to the bilateral dorsolateral prefrontal cortex (DLPFC) in people with SSDs 1) improves working memory deficits and 2) changes brain structure. METHODS We conducted a double-blind, parallel, randomized, sham-controlled study at the Centre for Addiction and Mental Health in Toronto, Canada. We randomized 83 participants with SSDs to receive either active 20 Hz rTMS applied to the bilateral DLPFC or sham rTMS for 4 weeks. The participants also completed pre/posttreatment magnetic resonance imaging. Clinical and cognitive assessments were performed at baseline, treatment end, and 1 month later. The primary outcome was change in verbal n-back working memory performance accuracy (d-prime). The secondary outcome measures were change in DLPFC thickness and fractional anisotropy of white matter tracts connecting to the DLPFC. Prespecified exploratory outcome measures were changes in general cognition; positive, negative, and depressive symptoms. RESULTS Compared with sham treatment, active rTMS did not lead to significant change in working memory performance; it was associated with an increase in right DLPFC thickness but not fractional anisotropy. Prespecified exploratory analysis showed a significant decrease in depressive symptoms in the active group; the decrease in depressive symptoms was correlated with an increase in right DLPFC thickness. CONCLUSIONS Although rTMS applied to the bilateral DLPFC was not efficacious in treating working memory deficits in SSDs, it did increase right DLPFC thickness and decrease depressive symptoms. These findings deserve further study given the lack of efficacy of antidepressant medications in SSDs.
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Affiliation(s)
- Aristotle N Voineskos
- Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada.
| | - Daniel M Blumberger
- Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Christin Schifani
- Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Colin Hawco
- Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Erin W Dickie
- Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Tarek K Rajji
- Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Benoit H Mulsant
- Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - George Foussias
- Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Wei Wang
- Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada
| | - Zafiris J Daskalakis
- Centre for Addiction and Mental Health, Toronto, Ontario, Canada; Department of Psychiatry, University of Toronto, Toronto, Ontario, Canada; Department of Psychiatry, University of California San Diego School of Medicine, La Jolla, California
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29
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Pascual-Diaz S, Varriano F, Pineda J, Prats-Galino A. Structural characterization of the Extended Frontal Aslant Tract trajectory: A ML-validated laterality study in 3T and 7T. Neuroimage 2020; 222:117260. [PMID: 32798677 DOI: 10.1016/j.neuroimage.2020.117260] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2019] [Revised: 07/28/2020] [Accepted: 08/03/2020] [Indexed: 12/31/2022] Open
Abstract
The Extended Frontal Aslant Tract (exFAT) is a recently described tractography-based extension of the Frontal Aslant Tract connecting Broca's territory to both supplementary and pre-supplementary motor areas, and more anterior prefrontal regions. In this study, we aim to characterize the microstructural properties of the exFAT trajectories as a means to perform a laterality analysis to detect interhemispheric structural differences along the tracts using the Human Connectome Project (HCP) dataset. To that end, the bilateral exFAT was reconstructed for 3T and 7T HCP acquisitions in 120 randomly selected subjects. As a complementary exploration of the exFAT anatomy, we performed a white matter dissection of the exFAT trajectory of two ex-vivo left hemispheres that provide a qualitative assessment of the tract profiles. We assessed the lateralization structural differences in the exFAT by performing: (i) a laterality comparison between the mean microstructural diffusion-derived parameters for the exFAT trajectories, (ii) a laterality comparison between the tract profiles obtained by applying the Automated Fiber Quantification (AFQ) algorithm, and (iii) a cross-validated Machine Learning (ML) classifier analysis using single and combined tract profiles parameters for single-subject classification. The mean microstructural diffusion-derived parameter comparison showed statistically significant differences in mean FA values between left and right exFATs in the 3T sample. The diffusion parameters studied with the AFQ technique suggest that the inferiormost half of the exFAT trajectory has a hemispheric-dependent fingerprint of microstructural properties, with an increased measure of tissue hindrance in the orthogonal plane and a decreased measure of orientational dispersion along the main tract direction in the left exFAT compared to the right exFAT. The classification accuracy of the ML models showed a high agreement with the magnitude of those differences.
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Affiliation(s)
- Saül Pascual-Diaz
- Laboratory of Surgical Neuroanatomy, Universitat de Barcelona, Spain.
| | - Federico Varriano
- Laboratory of Surgical Neuroanatomy, Universitat de Barcelona, Spain
| | - Jose Pineda
- Laboratory of Surgical Neuroanatomy, Universitat de Barcelona, Spain
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30
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Non-negative data-driven mapping of structural connections with application to the neonatal brain. Neuroimage 2020; 222:117273. [PMID: 32818619 DOI: 10.1016/j.neuroimage.2020.117273] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2020] [Revised: 08/06/2020] [Accepted: 08/10/2020] [Indexed: 12/17/2022] Open
Abstract
Mapping connections in the neonatal brain can provide insight into the crucial early stages of neurodevelopment that shape brain organisation and lay the foundations for cognition and behaviour. Diffusion MRI and tractography provide unique opportunities for such explorations, through estimation of white matter bundles and brain connectivity. Atlas-based tractography protocols, i.e. a priori defined sets of masks and logical operations in a template space, have been commonly used in the adult brain to drive such explorations. However, rapid growth and maturation of the brain during early development make it challenging to ensure correspondence and validity of such atlas-based tractography approaches in the developing brain. An alternative can be provided by data-driven methods, which do not depend on predefined regions of interest. Here, we develop a novel data-driven framework to extract white matter bundles and their associated grey matter networks from neonatal tractography data, based on non-negative matrix factorisation that is inherently suited to the non-negative nature of structural connectivity data. We also develop a non-negative dual regression framework to map group-level components to individual subjects. Using in-silico simulations, we evaluate the accuracy of our approach in extracting connectivity components and compare with an alternative data-driven method, independent component analysis. We apply non-negative matrix factorisation to whole-brain connectivity obtained from publicly available datasets from the Developing Human Connectome Project, yielding grey matter components and their corresponding white matter bundles. We assess the validity and interpretability of these components against traditional tractography results and grey matter networks obtained from resting-state fMRI in the same subjects. We subsequently use them to generate a parcellation of the neonatal cortex using data from 323 new-born babies and we assess the robustness and reproducibility of this connectivity-driven parcellation.
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31
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Moeller S, Pisharady Kumar P, Andersson J, Akcakaya M, Harel N, Ma RE, Wu X, Yacoub E, Lenglet C, Ugurbil K. Diffusion Imaging in the Post HCP Era. J Magn Reson Imaging 2020; 54:36-57. [PMID: 32562456 DOI: 10.1002/jmri.27247] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/24/2020] [Revised: 05/11/2020] [Accepted: 05/13/2020] [Indexed: 02/06/2023] Open
Abstract
Diffusion imaging is a critical component in the pursuit of developing a better understanding of the human brain. Recent technical advances promise enabling the advancement in the quality of data that can be obtained. In this review the context for different approaches relative to the Human Connectome Project are compared. Significant new gains are anticipated from the use of high-performance head gradients. These gains can be particularly large when the high-performance gradients are employed together with ultrahigh magnetic fields. Transmit array designs are critical in realizing high accelerations in diffusion-weighted (d)MRI acquisitions, while maintaining large field of view (FOV) coverage, and several techniques for optimal signal-encoding are now available. Reconstruction and processing pipelines that precisely disentangle the acquired neuroanatomical information are established and provide the foundation for the application of deep learning in the advancement of dMRI for complex tissues. Level of Evidence: 3 Technical Efficacy Stage: Stage 3.
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Affiliation(s)
- Steen Moeller
- Center for Magnetic Resonance Research; Department of Radiology, University of Minnesota, Minneapolis, Minnesota, USA
| | - Pramod Pisharady Kumar
- Center for Magnetic Resonance Research; Department of Radiology, University of Minnesota, Minneapolis, Minnesota, USA
| | - Jesper Andersson
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Mehmet Akcakaya
- Center for Magnetic Resonance Research; Department of Radiology, University of Minnesota, Minneapolis, Minnesota, USA.,Electrical and Computer Engineering, University of Minnesota, Minneapolis, Minnesota, USA
| | - Noam Harel
- Center for Magnetic Resonance Research; Department of Radiology, University of Minnesota, Minneapolis, Minnesota, USA
| | - Ruoyun Emily Ma
- Center for Magnetic Resonance Research; Department of Radiology, University of Minnesota, Minneapolis, Minnesota, USA
| | - Xiaoping Wu
- Center for Magnetic Resonance Research; Department of Radiology, University of Minnesota, Minneapolis, Minnesota, USA
| | - Essa Yacoub
- Center for Magnetic Resonance Research; Department of Radiology, University of Minnesota, Minneapolis, Minnesota, USA
| | - Christophe Lenglet
- Center for Magnetic Resonance Research; Department of Radiology, University of Minnesota, Minneapolis, Minnesota, USA
| | - Kamil Ugurbil
- Center for Magnetic Resonance Research; Department of Radiology, University of Minnesota, Minneapolis, Minnesota, USA
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32
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Kim HK, Baek AR, Choi G, Lee JJ, Yang JU, Jung H, Lee T, Kim D, Kim M, Cho A, Lee GH, Chang Y. Highly brain-permeable apoferritin nanocage with high dysprosium loading capacity as a new T2 contrast agent for ultra-high field magnetic resonance imaging. Biomaterials 2020; 243:119939. [DOI: 10.1016/j.biomaterials.2020.119939] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2019] [Revised: 02/14/2020] [Accepted: 03/03/2020] [Indexed: 12/13/2022]
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33
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Tong Q, He H, Gong T, Li C, Liang P, Qian T, Sun Y, Ding Q, Li K, Zhong J. Multicenter dataset of multi-shell diffusion MRI in healthy traveling adults with identical settings. Sci Data 2020; 7:157. [PMID: 32461581 PMCID: PMC7253426 DOI: 10.1038/s41597-020-0493-8] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/02/2020] [Accepted: 04/16/2020] [Indexed: 01/09/2023] Open
Abstract
Multicenter diffusion magnetic resonance imaging (MRI) has drawn great attention recently due to the expanding need for large-scale brain imaging studies, whereas the variability in MRI scanners and data acquisition tends to confound reliable individual-based analysis of diffusion measures. In addition, a growing number of multi-shell diffusion models have been shown with the potential to generate various estimates of physio-pathological information, yet their reliability and reproducibility in multicenter studies remain to be assessed. In this article, we describe a multi-shell diffusion dataset collected from three traveling subjects with identical acquisition settings in ten imaging centers. Both the scanner type and imaging protocol for anatomical and diffusion imaging were well controlled. This dataset is expected to replenish individual reproducible studies via multicenter collaboration by providing an open resource for advanced and novel microstructural and tractography modelling and quantification.
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Grants
- National Natural Science Foundation of China (No. 81871428, 91632109), Shanghai Key Laboratory of Psychotic Disorders(No. 13dz2260500), Major Scientific Project of Zhejiang Lab (No. 2018DG0ZX01), Fundamental Research Funds for the Central Universities(No. 2019QNA5026, 2019XZZX001-01-08),and Zhejiang University Education Foundation Global Partnership Fund.
- Beijing Talents Foundation (No. 2016000021223TD07), Capacity Building for Sci-Tech Innovation - Fundamental Scientific Research Funds (No. 19530050157, 19530050184), and the Beijing Brain Initiative of Beijing Municipal Science & Technology Commission.
- Zhejiang Province Laboratory Work Research Project (No. YB201730).
- Beijing Municipal Science and Technology Project of Brain cognition and brain medicine (No. Z171100000117001), and Beijing Municipal Administration of Hospitals Clinical Medicine Development of Special Funding Support (No. ZYLX201609).
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Affiliation(s)
- Qiqi Tong
- Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Hongjian He
- Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, Zhejiang, China.
| | - Ting Gong
- Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Chen Li
- Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Peipeng Liang
- Beijing Key Laboratory of Learning and Cognition, School of Psychology, Capital Normal University, Beijing, China.
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, China.
| | - Tianyi Qian
- MR Collaboration NE Asia, Siemens Healthcare, Beijing, China
| | - Yi Sun
- MR Collaboration NE Asia, Siemens Healthcare, Shanghai, China
| | - Qiuping Ding
- Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Kuncheng Li
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, China
- Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China
| | - Jianhui Zhong
- Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, Zhejiang, China
- Department of Imaging Sciences, University of Rochester, Rochester, NY, USA
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34
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Forrester M, Crofts JJ, Sotiropoulos SN, Coombes S, O'Dea RD. The role of node dynamics in shaping emergent functional connectivity patterns in the brain. Netw Neurosci 2020; 4:467-483. [PMID: 32537537 PMCID: PMC7286301 DOI: 10.1162/netn_a_00130] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2019] [Accepted: 01/31/2020] [Indexed: 11/07/2022] Open
Abstract
The contribution of structural connectivity to functional brain states remains poorly understood. We present a mathematical and computational study suited to assess the structure–function issue, treating a system of Jansen–Rit neural mass nodes with heterogeneous structural connections estimated from diffusion MRI data provided by the Human Connectome Project. Via direct simulations we determine the similarity of functional (inferred from correlated activity between nodes) and structural connectivity matrices under variation of the parameters controlling single-node dynamics, highlighting a nontrivial structure–function relationship in regimes that support limit cycle oscillations. To determine their relationship, we firstly calculate network instabilities giving rise to oscillations, and the so-called ‘false bifurcations’ (for which a significant qualitative change in the orbit is observed, without a change of stability) occurring beyond this onset. We highlight that functional connectivity (FC) is inherited robustly from structure when node dynamics are poised near a Hopf bifurcation, whilst near false bifurcations, and structure only weakly influences FC. Secondly, we develop a weakly coupled oscillator description to analyse oscillatory phase-locked states and, furthermore, show how the modular structure of FC matrices can be predicted via linear stability analysis. This study thereby emphasises the substantial role that local dynamics can have in shaping large-scale functional brain states. Patterns of oscillation across the brain arise because of structural connections between brain regions. However, the type of oscillation at a site may also play a contributory role. We focus on an idealised model of a neural mass network, coupled using estimates of structural connections obtained via tractography on Human Connectome Project MRI data. Using a mixture of computational and mathematical techniques, we show that functional connectivity is inherited most strongly from structural connectivity when the network nodes are poised at a Hopf bifurcation. However, beyond the onset of this oscillatory instability a phase-locked network state can undergo a false bifurcation, and structural connectivity only weakly influences functional connectivity. This highlights the important effect that local dynamics can have on large-scale brain states.
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Affiliation(s)
- Michael Forrester
- Centre for Mathematical Medicine and Biology, School of Mathematical Sciences, University of Nottingham, Nottingham, UK
| | - Jonathan J Crofts
- Department of Physics and Mathematics, School of Science and Technology, Nottingham Trent University, Nottingham, UK
| | - Stamatios N Sotiropoulos
- Sir Peter Mansfield Imaging Centre, Queen's Medical Centre, University of Nottingham, Nottingham, UK
| | - Stephen Coombes
- Centre for Mathematical Medicine and Biology, School of Mathematical Sciences, University of Nottingham, Nottingham, UK
| | - Reuben D O'Dea
- Centre for Mathematical Medicine and Biology, School of Mathematical Sciences, University of Nottingham, Nottingham, UK
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35
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Denoise magnitude diffusion magnetic resonance images via variance-stabilizing transformation and optimal singular-value manipulation. Neuroimage 2020; 215:116852. [PMID: 32305566 PMCID: PMC7292796 DOI: 10.1016/j.neuroimage.2020.116852] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2019] [Revised: 04/07/2020] [Accepted: 04/10/2020] [Indexed: 12/12/2022] Open
Abstract
Although shown to have a great utility for a wide range of neuroscientific and clinical applications, diffusion-weighted magnetic resonance imaging (dMRI) faces a major challenge of low signal-to-noise ratio (SNR), especially when pushing the spatial resolution for improved delineation of brain's fine structure or increasing the diffusion weighting for increased angular contrast or both. Here, we introduce a comprehensive denoising framework for denoising magnitude dMRI. The framework synergistically combines the variance stabilizing transform (VST) with optimal singular value manipulation. The purpose of VST is to transform the Rician data to Gaussian-like data so that an asymptotically optimal singular value manipulation strategy tailored for Gaussian data can be used. The output of the framework is the estimated underlying diffusion signal for each voxel in the image domain. The usefulness of the proposed framework for denoising magnitude dMRI is demonstrated using both simulation and real-data experiments. Our results show that the proposed denoising framework can significantly improve SNR across the entire brain, leading to substantially enhanced performances for estimating diffusion tensor related indices and for resolving crossing fibers when compared to another competing method. More encouragingly, the proposed method when used to denoise a single average of 7 Tesla Human Connectome Project-style diffusion acquisition provided comparable performances relative to those achievable with ten averages for resolving multiple fiber populations across the brain. As such, the proposed denoising method is expected to have a great utility for high-quality, high-resolution whole-brain dMRI, desirable for many neuroscientific and clinical applications.
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36
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Ma R, Akçakaya M, Moeller S, Auerbach E, Uğurbil K, Van de Moortele PF. A field-monitoring-based approach for correcting eddy-current-induced artifacts of up to the 2 nd spatial order in human-connectome-project-style multiband diffusion MRI experiment at 7T: A pilot study. Neuroimage 2020; 216:116861. [PMID: 32305565 DOI: 10.1016/j.neuroimage.2020.116861] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/18/2019] [Revised: 04/09/2020] [Accepted: 04/14/2020] [Indexed: 01/30/2023] Open
Abstract
Over the recent years, significant advances in Spin-Echo (SE) Echo-Planar (EP) Diffusion MRI (dMRI) have enabled improved fiber tracking conspicuity in the human brain. At the same time, pushing the spatial resolution and using higher b-values inherently expose the acquired images to further eddy-current-induced distortion and blurring. Recently developed data-driven correction techniques, capable of significantly mitigating these defects, are included in the reconstruction pipelines developed for the Human Connectome Project (HCP) driven by the NIH BRAIN initiative. In this case, however, corrections are derived from the original diffusion-weighted (DW) magnitude images affected by distortion and blurring. Considering the complexity of k-space deviations in the presence of time varying high spatial order eddy currents, distortion and blurring may not be fully reversed when relying on magnitude DW images only. An alternative approach, consisting of iteratively reconstructing DW images based on the actual magnetic field spatiotemporal evolution measured with a magnetic field monitoring camera, has been successfully implemented at 3T in single band dMRI (Wilm et al., 2017, 2015). In this study, we aim to demonstrate the efficacy of this eddy current correction method in the challenging context of HCP-style multiband (MB = 2) dMRI protocol. The magnetic field evolution was measured during the EP-dMRI readout echo train with a field monitoring camera equipped with 16 19F NMR probes. The time variation of 0th, 1st and 2nd order spherical field harmonics were used to reconstruct DW images. Individual DW images reconstructed with and without field correction were compared. The impact of eddy current correction was evaluated by comparing the corresponding direction-averaged DW images and fractional anisotropy (FA) maps. 19F field monitoring data confirmed the existence of significant field deviations induced by the diffusion-encoding gradients, with variations depending on diffusion gradient amplitude and direction. In DW images reconstructed with the field correction, residual aliasing artifacts were reduced or eliminated, and when high b-values were applied, better gray/white matter delineation and sharper gyri contours were observed, indicating reduced signal blurring. The improvement in image quality further contributed to sharper contours and better gray/white matter delineation in mean DW images and FA maps. In conclusion, we demonstrate that up-to-2nd-order-eddy-current-induced field perturbation in multiband, in-plane accelerated HCP-style dMRI acquisition at 7T can be corrected by integrating the measured field evolution in image reconstruction.
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Affiliation(s)
- Ruoyun Ma
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
| | - Mehmet Akçakaya
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA; Electrical and Computer Engineering, University of Minnesota, Minneapolis, MN, USA
| | - Steen Moeller
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
| | - Edward Auerbach
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
| | - Kâmil Uğurbil
- Center for Magnetic Resonance Research, University of Minnesota, Minneapolis, MN, USA
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37
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Towards HCP-Style macaque connectomes: 24-Channel 3T multi-array coil, MRI sequences and preprocessing. Neuroimage 2020; 215:116800. [PMID: 32276072 DOI: 10.1016/j.neuroimage.2020.116800] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/25/2019] [Revised: 03/16/2020] [Accepted: 03/23/2020] [Indexed: 11/23/2022] Open
Abstract
Macaque monkeys are an important animal model where invasive investigations can lead to a better understanding of the cortical organization of primates including humans. However, the tools and methods for noninvasive image acquisition (e.g. MRI RF coils and pulse sequence protocols) and image data preprocessing have lagged behind those developed for humans. To resolve the structural and functional characteristics of the smaller macaque brain, high spatial, temporal, and angular resolutions combined with high signal-to-noise ratio are required to ensure good image quality. To address these challenges, we developed a macaque 24-channel receive coil for 3-T MRI with parallel imaging capabilities. This coil enables adaptation of the Human Connectome Project (HCP) image acquisition protocols to the in-vivo macaque brain. In addition, we adapted HCP preprocessing methods to the macaque brain, including spatial minimal preprocessing of structural, functional MRI (fMRI), and diffusion MRI (dMRI). The coil provides the necessary high signal-to-noise ratio and high efficiency in data acquisition, allowing four- and five-fold accelerations for dMRI and fMRI. Automated FreeSurfer segmentation of cortex, reconstruction of cortical surface, removal of artefacts and nuisance signals in fMRI, and distortion correction of dMRI all performed well, and the overall quality of basic neurobiological measures was comparable with those for the HCP. Analyses of functional connectivity in fMRI revealed high sensitivity as compared with those from publicly shared datasets. Tractography-based connectivity estimates correlated with tracer connectivity similarly to that achieved using ex-vivo dMRI. The resulting HCP-style in vivo macaque MRI data show considerable promise for analyzing cortical architecture and functional and structural connectivity using advanced methods that have previously only been available in studies of the human brain.
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38
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Polat HB, Kanat A, Celiker FB, Tufekci A, Beyazal M, Ardic G, Turan A. Rationalization of Using the MR Diffusion Imaging in B12 Deficiency. Ann Indian Acad Neurol 2020; 23:72-77. [PMID: 32055125 PMCID: PMC7001445 DOI: 10.4103/aian.aian_485_18] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/15/2022] Open
Abstract
Context: The structural imaging of brain does not demonstrate any changes in the vast majority of patients with vitamin B12 deficiency, even in the advanced stages. Aims: We investigated the microstructural changes in the brain with diffusion imaging among patients with biochemical evidence of B12 deficiency. Patients and Methods: We retrospectively analyzed all diffusion-weighted MRI images between the periods 2014–2016 who had biochemical evidence of B12. The age-sex matched controls were chosen from the group with normal B12 levels. Patients with pathological findings in conventional MRI images were excluded from the study. Results: About 37 patients were recruited (22 women, 15 men; mean age, 34.1 ± 9.9 years; age range). They were about thirty-four age-and sex-matched controls (with normal B12 levels), which were also included in the study. The mean apparent diffusion coefficient (ADC) value of amygdala (773.8 ± 49.9 vs. 742.2 ± 24.2, P = 0.01), hypothalamus (721.3 ± 39.2 vs. 700.2 ± 38.2, P = 0.02), striate cortex (737.6 ± 77.6 vs. 704.3 ± 58.2, P = 0.04), suprafrontal gyrus (740.7 ± 46.9 vs. 711.6 ± 40.7, P = 0.007) and medulla oblongata-olivary nucleus (787.3 ± 56.4 vs. 759.7 ± 46.2, P = 0.02) were significantly higher in B12 deficiency group compared to controls, whereas ADC values were similar at hippocampus, thalamus, insula, corpus striatum, cingulate gyrus, occipital gyrus, dentate nucleus, cerebral pedicle, tegmentum, pons, and posterior medulla oblongata. Conclusions: Our study indicates that a significant increase in ADC values occurs in multiple brain regions in patients with vitamin B12.
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Affiliation(s)
- Hatice B Polat
- Department of Internal Medicine, Medical Faculty, Recep Tayyip Erdogan University, Rize, Turkey
| | - Ayhan Kanat
- Department of Neurosurgery, Medical Faculty, Recep Tayyip Erdogan University, Rize, Turkey
| | - Fatma B Celiker
- Department of Radiology, Medical Faculty, Recep Tayyip Erdogan University, Rize, Turkey
| | - Ahmet Tufekci
- Department of Neurology, Medical Faculty, Recep Tayyip Erdogan University, Rize, Turkey
| | - Mehmet Beyazal
- Department of Radiology, Medical Faculty, Recep Tayyip Erdogan University, Rize, Turkey
| | - Gizem Ardic
- Department of Pharmacy, Rize Education and Research Hospital, Rize, Turkey
| | - Arzu Turan
- Department of Radiology, Medical Faculty, Recep Tayyip Erdogan University, Rize, Turkey
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39
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Wu Y, Hong Y, Feng Y, Shen D, Yap PT. Mitigating gyral bias in cortical tractography via asymmetric fiber orientation distributions. Med Image Anal 2020; 59:101543. [PMID: 31670139 PMCID: PMC6935166 DOI: 10.1016/j.media.2019.101543] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2019] [Revised: 06/14/2019] [Accepted: 08/08/2019] [Indexed: 11/19/2022]
Abstract
Diffusion tractography in brain connectomics often involves tracing axonal trajectories across gray-white matter boundaries in gyral blades of complex cortical convolutions. To date, gyral bias is observed in most tractography algorithms with streamlines predominantly terminating at gyral crowns instead of sulcal banks. This work demonstrates that asymmetric fiber orientation distribution functions (AFODFs), computed via a multi-tissue global estimation framework, can mitigate the effects of gyral bias, enabling fiber streamlines at gyral blades to make sharper turns into the cortical gray matter. We use ex-vivo data of an adult rhesus macaque and in-vivo data from the Human Connectome Project (HCP) to show that the fiber streamlines given by AFODFs bend more naturally into the cortex than the conventional symmetric FODFs in typical gyral blades. We demonstrate that AFODF tractography improves cortico-cortical connectivity and provides highly consistent outcomes between two different field strengths (3T and 7T).
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Affiliation(s)
- Ye Wu
- Department of Radiology and Biomedical Research Imaging Center (BRIC) University of North Carolina at Chapel Hill, NC, U.S.A.
| | - Yoonmi Hong
- Department of Radiology and Biomedical Research Imaging Center (BRIC) University of North Carolina at Chapel Hill, NC, U.S.A
| | - Yuanjing Feng
- Institute of Information Processing and Automation, Zhejiang University of Technology, Hangzhou, China.
| | - Dinggang Shen
- Department of Radiology and Biomedical Research Imaging Center (BRIC) University of North Carolina at Chapel Hill, NC, U.S.A; Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea.
| | - Pew-Thian Yap
- Department of Radiology and Biomedical Research Imaging Center (BRIC) University of North Carolina at Chapel Hill, NC, U.S.A.
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40
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Forrester M, Crofts JJ, Sotiropoulos SN, Coombes S, O'Dea RD. The role of node dynamics in shaping emergent functional connectivity patterns in the brain. Netw Neurosci 2020; 4:467-483. [PMID: 32537537 DOI: 10.1162/netn\_a_00130] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2019] [Accepted: 01/31/2020] [Indexed: 05/21/2023] Open
Abstract
The contribution of structural connectivity to functional brain states remains poorly understood. We present a mathematical and computational study suited to assess the structure-function issue, treating a system of Jansen-Rit neural mass nodes with heterogeneous structural connections estimated from diffusion MRI data provided by the Human Connectome Project. Via direct simulations we determine the similarity of functional (inferred from correlated activity between nodes) and structural connectivity matrices under variation of the parameters controlling single-node dynamics, highlighting a nontrivial structure-function relationship in regimes that support limit cycle oscillations. To determine their relationship, we firstly calculate network instabilities giving rise to oscillations, and the so-called 'false bifurcations' (for which a significant qualitative change in the orbit is observed, without a change of stability) occurring beyond this onset. We highlight that functional connectivity (FC) is inherited robustly from structure when node dynamics are poised near a Hopf bifurcation, whilst near false bifurcations, and structure only weakly influences FC. Secondly, we develop a weakly coupled oscillator description to analyse oscillatory phase-locked states and, furthermore, show how the modular structure of FC matrices can be predicted via linear stability analysis. This study thereby emphasises the substantial role that local dynamics can have in shaping large-scale functional brain states.
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Affiliation(s)
- Michael Forrester
- Centre for Mathematical Medicine and Biology, School of Mathematical Sciences, University of Nottingham, Nottingham, UK
| | - Jonathan J Crofts
- Department of Physics and Mathematics, School of Science and Technology, Nottingham Trent University, Nottingham, UK
| | - Stamatios N Sotiropoulos
- Sir Peter Mansfield Imaging Centre, Queen's Medical Centre, University of Nottingham, Nottingham, UK
| | - Stephen Coombes
- Centre for Mathematical Medicine and Biology, School of Mathematical Sciences, University of Nottingham, Nottingham, UK
| | - Reuben D O'Dea
- Centre for Mathematical Medicine and Biology, School of Mathematical Sciences, University of Nottingham, Nottingham, UK
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41
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Byrne Á, O'Dea RD, Forrester M, Ross J, Coombes S. Next-generation neural mass and field modeling. J Neurophysiol 2019; 123:726-742. [PMID: 31774370 DOI: 10.1152/jn.00406.2019] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/01/2023] Open
Abstract
The Wilson-Cowan population model of neural activity has greatly influenced our understanding of the mechanisms for the generation of brain rhythms and the emergence of structured brain activity. As well as the many insights that have been obtained from its mathematical analysis, it is now widely used in the computational neuroscience community for building large-scale in silico brain networks that can incorporate the increasing amount of knowledge from the Human Connectome Project. Here, we consider a neural population model in the spirit of that originally developed by Wilson and Cowan, albeit with the added advantage that it can account for the phenomena of event-related synchronization and desynchronization. This derived mean-field model provides a dynamic description for the evolution of synchrony, as measured by the Kuramoto order parameter, in a large population of quadratic integrate-and-fire model neurons. As in the original Wilson-Cowan framework, the population firing rate is at the heart of our new model; however, in a significant departure from the sigmoidal firing rate function approach, the population firing rate is now obtained as a real-valued function of the complex-valued population synchrony measure. To highlight the usefulness of this next-generation Wilson-Cowan style model, we deploy it in a number of neurobiological contexts, providing understanding of the changes in power spectra observed in electro- and magnetoencephalography neuroimaging studies of motor cortex during movement, insights into patterns of functional connectivity observed during rest and their disruption by transcranial magnetic stimulation, and to describe wave propagation across cortex.
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Affiliation(s)
- Áine Byrne
- Center for Neural Science, New York University, New York, New York.,School of Mathematics and Statistics, University College Dublin, Dublin, Ireland
| | - Reuben D O'Dea
- Centre for Mathematical Medicine and Biology, School of Mathematical Sciences, University of Nottingham, Nottingham, United Kingdom
| | - Michael Forrester
- Centre for Mathematical Medicine and Biology, School of Mathematical Sciences, University of Nottingham, Nottingham, United Kingdom
| | - James Ross
- Centre for Mathematical Medicine and Biology, School of Mathematical Sciences, University of Nottingham, Nottingham, United Kingdom
| | - Stephen Coombes
- Centre for Mathematical Medicine and Biology, School of Mathematical Sciences, University of Nottingham, Nottingham, United Kingdom
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42
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Default Mode Network structural alterations in Kocher-Monro trajectory white matter transection: A 3 and 7 tesla simulation modeling approach. PLoS One 2019; 14:e0224598. [PMID: 31697747 PMCID: PMC6837312 DOI: 10.1371/journal.pone.0224598] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/26/2018] [Accepted: 10/17/2019] [Indexed: 11/19/2022] Open
Abstract
The Kocher-Monro trajectory to the cerebral ventricular system represents one of the most common surgical procedures in the field of neurosurgery. Several studies have analyzed the specific white matter disruption produced during this intervention, which has no reported adverse neurological outcomes. In this study, a graph-theoretical approach was applied to quantify the structural alterations in whole-brain level connectivity. To this end, 132 subjects were randomly selected from the Human Connectome Project dataset and used to create 3 independent 44 subjects groups. Two of the groups underwent a simulated left/right Kocher-Monro trajectory and the third was kept as a control group. For the right Kocher-Monro approach, the nodal analysis revealed decreased strength in the anterior cingulate gyrus of the transected hemisphere. The network-based statistic analysis revealed a set of right lateralized subnetworks with decreased connectivity strength that is consistent with a subset of the Default Mode Network, Salience Network, and Cingulo-Opercular Network. These findings could allow for a better understanding of structural alterations caused by Kocher-Monro approaches that could reveal previously undetected clinical alterations and inform the process of designing safer and less invasive cerebral ventricular approaches.
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43
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Howard AF, Mollink J, Kleinnijenhuis M, Pallebage-Gamarallage M, Bastiani M, Cottaar M, Miller KL, Jbabdi S. Joint modelling of diffusion MRI and microscopy. Neuroimage 2019; 201:116014. [PMID: 31315062 PMCID: PMC6880780 DOI: 10.1016/j.neuroimage.2019.116014] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2019] [Revised: 06/06/2019] [Accepted: 07/11/2019] [Indexed: 11/20/2022] Open
Abstract
The combination of diffusion MRI (dMRI) with microscopy provides unique opportunities to study microstructural features of tissue, particularly when acquired in the same sample. Microscopy is frequently used to validate dMRI microstructure models, addressing the indirect nature of dMRI signals. Typically, these modalities are analysed separately, and microscopy is taken as a gold standard against which dMRI-derived parameters are validated. Here we propose an alternative approach in which we combine dMRI and microscopy data obtained from the same tissue sample to drive a single, joint model. This simultaneous analysis allows us to take advantage of the breadth of information provided by complementary data acquired from different modalities. By applying this framework to a spherical-deconvolution analysis, we are able to overcome a known degeneracy between fibre dispersion and radial diffusion. Spherical-deconvolution based approaches typically estimate a global fibre response function to determine the fibre orientation distribution in each voxel. However, the assumption of a 'brain-wide' fibre response function may be challenged if the diffusion characteristics of white matter vary across the brain. Using a generative joint dMRI-histology model, we demonstrate that the fibre response function is dependent on local anatomy, and that current spherical-deconvolution based models may be overestimating dispersion and underestimating the number of distinct fibre populations per voxel.
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Affiliation(s)
- Amy Fd Howard
- Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom.
| | - Jeroen Mollink
- Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom; Department of Anatomy, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Centre (Radboudumc), Nijmegen, Netherlands
| | - Michiel Kleinnijenhuis
- Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | | | - Matteo Bastiani
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, United Kingdom; Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Michiel Cottaar
- Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Karla L Miller
- Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Saad Jbabdi
- Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
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44
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Wu W, Koopmans PJ, Andersson JLR, Miller KL. Diffusion Acceleration with Gaussian process Estimated Reconstruction (DAGER). Magn Reson Med 2019; 82:107-125. [PMID: 30825243 PMCID: PMC6492188 DOI: 10.1002/mrm.27699] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2018] [Revised: 12/23/2018] [Accepted: 01/29/2019] [Indexed: 12/22/2022]
Abstract
PURPOSE Image acceleration provides multiple benefits to diffusion MRI, with in-plane acceleration reducing distortion and slice-wise acceleration increasing the number of directions that can be acquired in a given scan time. However, as acceleration factors increase, the reconstruction problem becomes ill-conditioned, particularly when using both in-plane acceleration and simultaneous multislice imaging. In this work, we develop a novel reconstruction method for in vivo MRI acquisition that provides acceleration beyond what conventional techniques can achieve. THEORY AND METHODS We propose to constrain the reconstruction in the spatial (k) domain by incorporating information from the angular (q) domain. This approach exploits smoothness of the signal in q-space using Gaussian processes, as has previously been exploited in post-reconstruction analysis. We demonstrate in-plane undersampling exceeding the theoretical parallel imaging limits, and simultaneous multislice combined with in-plane undersampling at a total factor of 12. This reconstruction is cast within a Bayesian framework that incorporates estimation of smoothness hyper-parameters, with no need for manual tuning. RESULTS Simulations and in vivo results demonstrate superior performance of the proposed method compared with conventional parallel imaging methods. These improvements are achieved without loss of spatial or angular resolution and require only a minor modification to standard pulse sequences. CONCLUSION The proposed method provides improvements over existing methods for diffusion acceleration, particularly for high simultaneous multislice acceleration with in-plane undersampling.
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Affiliation(s)
- Wenchuan Wu
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Peter J Koopmans
- Erwin L. Hahn Institute for Magnetic Resonance Imaging, University of Duisburg-Essen, Essen, Germany.,High Field and Hybrid MR Imaging, University Hospital Essen, University of Duisburg-Essen, Essen, Germany
| | - Jesper L R Andersson
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Karla L Miller
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
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45
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Karolak A, Markov DA, McCawley LJ, Rejniak KA. Towards personalized computational oncology: from spatial models of tumour spheroids, to organoids, to tissues. J R Soc Interface 2019; 15:rsif.2017.0703. [PMID: 29367239 DOI: 10.1098/rsif.2017.0703] [Citation(s) in RCA: 71] [Impact Index Per Article: 14.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2017] [Accepted: 01/02/2018] [Indexed: 02/06/2023] Open
Abstract
A main goal of mathematical and computational oncology is to develop quantitative tools to determine the most effective therapies for each individual patient. This involves predicting the right drug to be administered at the right time and at the right dose. Such an approach is known as precision medicine. Mathematical modelling can play an invaluable role in the development of such therapeutic strategies, since it allows for relatively fast, efficient and inexpensive simulations of a large number of treatment schedules in order to find the most effective. This review is a survey of mathematical models that explicitly take into account the spatial architecture of three-dimensional tumours and address tumour development, progression and response to treatments. In particular, we discuss models of epithelial acini, multicellular spheroids, normal and tumour spheroids and organoids, and multi-component tissues. Our intent is to showcase how these in silico models can be applied to patient-specific data to assess which therapeutic strategies will be the most efficient. We also present the concept of virtual clinical trials that integrate standard-of-care patient data, medical imaging, organ-on-chip experiments and computational models to determine personalized medical treatment strategies.
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Affiliation(s)
- Aleksandra Karolak
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA
| | - Dmitry A Markov
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA.,Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University, Nashville, TN, USA
| | - Lisa J McCawley
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, USA.,Vanderbilt Institute for Integrative Biosystems Research and Education, Vanderbilt University, Nashville, TN, USA
| | - Katarzyna A Rejniak
- Integrated Mathematical Oncology Department, H. Lee Moffitt Cancer Center & Research Institute, Tampa, FL, USA .,Department of Oncologic Sciences, Morsani College of Medicine, University of South Florida, Tampa, FL, USA
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46
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Popescu T, Cohen Kadosh R. Drawing the boundaries of expertise: Who is a mathematician? Cortex 2019; 117:421-424. [PMID: 31160035 DOI: 10.1016/j.cortex.2019.04.020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2019] [Revised: 04/18/2019] [Accepted: 04/25/2019] [Indexed: 11/16/2022]
Affiliation(s)
- Tudor Popescu
- Department of Experimental Psychology, University of Oxford, Oxford, UK; Wellcome Integrative Neuroscience Centre, University of Oxford, Oxford, UK.
| | - Roi Cohen Kadosh
- Department of Experimental Psychology, University of Oxford, Oxford, UK; Wellcome Integrative Neuroscience Centre, University of Oxford, Oxford, UK
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Sotiropoulos SN, Zalesky A. Building connectomes using diffusion MRI: why, how and but. NMR IN BIOMEDICINE 2019; 32:e3752. [PMID: 28654718 PMCID: PMC6491971 DOI: 10.1002/nbm.3752] [Citation(s) in RCA: 154] [Impact Index Per Article: 30.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/22/2016] [Revised: 04/05/2017] [Accepted: 05/03/2017] [Indexed: 05/14/2023]
Abstract
Why has diffusion MRI become a principal modality for mapping connectomes in vivo? How do different image acquisition parameters, fiber tracking algorithms and other methodological choices affect connectome estimation? What are the main factors that dictate the success and failure of connectome reconstruction? These are some of the key questions that we aim to address in this review. We provide an overview of the key methods that can be used to estimate the nodes and edges of macroscale connectomes, and we discuss open problems and inherent limitations. We argue that diffusion MRI-based connectome mapping methods are still in their infancy and caution against blind application of deep white matter tractography due to the challenges inherent to connectome reconstruction. We review a number of studies that provide evidence of useful microstructural and network properties that can be extracted in various independent and biologically relevant contexts. Finally, we highlight some of the key deficiencies of current macroscale connectome mapping methodologies and motivate future developments.
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Affiliation(s)
- Stamatios N. Sotiropoulos
- Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical NeurosciencesUniversity of OxfordOxfordUK
- Sir Peter Mansfield Imaging Centre, School of MedicineUniversity of NottinghamNottinghamUK
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre and Melbourne School of EngineeringUniversity of MelbourneVictoriaAustralia
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48
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Alexander DC, Dyrby TB, Nilsson M, Zhang H. Imaging brain microstructure with diffusion MRI: practicality and applications. NMR IN BIOMEDICINE 2019; 32:e3841. [PMID: 29193413 DOI: 10.1002/nbm.3841] [Citation(s) in RCA: 205] [Impact Index Per Article: 41.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/28/2016] [Revised: 07/09/2017] [Accepted: 09/11/2017] [Indexed: 05/22/2023]
Abstract
This article gives an overview of microstructure imaging of the brain with diffusion MRI and reviews the state of the art. The microstructure-imaging paradigm aims to estimate and map microscopic properties of tissue using a model that links these properties to the voxel scale MR signal. Imaging techniques of this type are just starting to make the transition from the technical research domain to wide application in biomedical studies. We focus here on the practicalities of both implementing such techniques and using them in applications. Specifically, the article summarizes the relevant aspects of brain microanatomy and the range of diffusion-weighted MR measurements that provide sensitivity to them. It then reviews the evolution of mathematical and computational models that relate the diffusion MR signal to brain tissue microstructure, as well as the expanding areas of application. Next we focus on practicalities of designing a working microstructure imaging technique: model selection, experiment design, parameter estimation, validation, and the pipeline of development of this class of technique. The article concludes with some future perspectives on opportunities in this topic and expectations on how the field will evolve in the short-to-medium term.
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Affiliation(s)
- Daniel C Alexander
- Centre for Medical Image Computing (CMIC), Department of Computer Science, UCL (University College London), Gower Street, London, UK
| | - Tim B Dyrby
- Danish Research Centre for Magnetic Resonance, Center for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Hvidovre, Hvidovre, Denmark
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Markus Nilsson
- Clinical Sciences Lund, Department of Radiology, Lund University, Lund, Sweden
| | - Hui Zhang
- Centre for Medical Image Computing (CMIC), Department of Computer Science, UCL (University College London), Gower Street, London, UK
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49
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Demirtaş M, Burt JB, Helmer M, Ji JL, Adkinson BD, Glasser MF, Van Essen DC, Sotiropoulos SN, Anticevic A, Murray JD. Hierarchical Heterogeneity across Human Cortex Shapes Large-Scale Neural Dynamics. Neuron 2019; 101:1181-1194.e13. [PMID: 30744986 PMCID: PMC6447428 DOI: 10.1016/j.neuron.2019.01.017] [Citation(s) in RCA: 195] [Impact Index Per Article: 39.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2018] [Revised: 12/04/2018] [Accepted: 01/10/2019] [Indexed: 01/20/2023]
Abstract
The large-scale organization of dynamical neural activity across cortex emerges through long-range interactions among local circuits. We hypothesized that large-scale dynamics are also shaped by heterogeneity of intrinsic local properties across cortical areas. One key axis along which microcircuit properties are specialized relates to hierarchical levels of cortical organization. We developed a large-scale dynamical circuit model of human cortex that incorporates heterogeneity of local synaptic strengths, following a hierarchical axis inferred from magnetic resonance imaging (MRI)-derived T1- to T2-weighted (T1w/T2w) mapping and fit the model using multimodal neuroimaging data. We found that incorporating hierarchical heterogeneity substantially improves the model fit to functional MRI (fMRI)-measured resting-state functional connectivity and captures sensory-association organization of multiple fMRI features. The model predicts hierarchically organized higher-frequency spectral power, which we tested with resting-state magnetoencephalography. These findings suggest circuit-level mechanisms linking spatiotemporal levels of analysis and highlight the importance of local properties and their hierarchical specialization on the large-scale organization of human cortical dynamics.
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Affiliation(s)
- Murat Demirtaş
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Joshua B. Burt
- Department of Physics, Yale University, New Haven, CT, USA,These authors contributed equally
| | - Markus Helmer
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA,These authors contributed equally
| | - Jie Lisa Ji
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Brendan D. Adkinson
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - Matthew F. Glasser
- Department of Neuroscience, Washington University School of Medicine, Saint Louis, MO, USA,St. Luke’s Hospital, Saint Louis, MO, USA
| | - David C. Van Essen
- Department of Neuroscience, Washington University School of Medicine, Saint Louis, MO, USA
| | - Stamatios N. Sotiropoulos
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, UK,Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain, Nuffield Department of Clinical Neuroscience, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Alan Anticevic
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA
| | - John D. Murray
- Department of Psychiatry, Yale University School of Medicine, New Haven, CT, USA,Department of Physics, Yale University, New Haven, CT, USA,Lead Contact,Correspondence:
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50
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Tan X, Zhou Z, Gao J, Meng F, Yu Y, Zhang J, He F, Wei R, Wang J, Peng G, Zhang X, Pan G, Luo B. Structural connectome alterations in patients with disorders of consciousness revealed by 7-tesla magnetic resonance imaging. NEUROIMAGE-CLINICAL 2019; 22:101702. [PMID: 30711681 PMCID: PMC6360803 DOI: 10.1016/j.nicl.2019.101702] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/03/2018] [Revised: 01/25/2019] [Accepted: 01/28/2019] [Indexed: 02/04/2023]
Abstract
Although the functional connectivity of patients with disorders of consciousness (DOC) has been widely examined, less is known about brain white matter connectivity. The aim of this study was to explore structural network alterations for the diagnosis and prognosis of patients with chronic DOC. Eleven DOC patients and 11 sex- and age-matched controls were included in the study. Participants underwent diffusion magnetic resonance imaging (MRI) and T1-weighted structural MRI at 7 tesla (7 T). Graph-theoretical analysis and network-based statistics were used to analyze the group differences. Two patients were scanned twice for a longitudinal study to examine the relationship between connectome metrics and the patients' prognoses. Compared with healthy controls, DOC patients showed significantly elevated transitivity (p < .001), local efficiency (p = .009), and clustering coefficient (p = .039). When comparing the connectome metrics within the three groups (healthy controls, minimally conscious state (MCS), and vegetative state/unresponsive wakefulness syndrome (VS/UWS)), significant group differences were observed in transitivity (p < .001) and local efficiency (p = .031). Significantly increased transitivity was observed in vegetative state/unresponsive wakefulness syndrome compared with minimally conscious state (p = .0217, Bonferroni corrected). Transitivity showed significant negative correlations with the Coma Recovery Scale-Revised score (r = -0.6902, p = .023), consistent with the longitudinal study results. A subnetwork with significantly decreased structural connections was identified using network-based statistical analysis comparing DOC patients with healthy controls, which was mainly located in the frontal cortex, limbic system, and occipital and parietal lobes. This preliminary study suggests that graph theoretical approaches for assessing white matter connectivity may enable various states of DOC to be distinguished. Of the metrics analyzed, transitivity had a critical role in distinguishing the diagnostic groups. Larger cohorts will be necessary to confirm the predictive value of 7 T MRI in the prognosis of DOC patients.
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Affiliation(s)
- Xufei Tan
- Department of Neurology, Brain Medical Centre, the First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Zhen Zhou
- State Key Lab of CAD&CG, Zhejiang University, Hangzhou, China; College of Computer Science and Technology, Zhejiang University, Hangzhou, China
| | - Jian Gao
- Department of Rehabilitation, Hangzhou Hospital of Zhejiang CAPR, Hangzhou, China
| | - Fanxia Meng
- Department of Neurology, Brain Medical Centre, the First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Yamei Yu
- Department of Neurology, Brain Medical Centre, the First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Jie Zhang
- Department of Rehabilitation Medicine, Zhejiang Provincial People's Hospital, People's Hospital of Hangzhou Medical College, Hangzhou, China
| | - Fangping He
- Department of Neurology, Brain Medical Centre, the First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Ruili Wei
- Department of Neurology, Brain Medical Centre, the First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Junyang Wang
- Department of Neurology, Brain Medical Centre, the First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Guoping Peng
- Department of Neurology, Brain Medical Centre, the First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China
| | - Xiaotong Zhang
- Interdisciplinary Institute of Neuroscience and Technology, Qiushi Academy for Advanced Studies, Zhejiang University, Hangzhou, China; Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, China
| | - Gang Pan
- State Key Lab of CAD&CG, Zhejiang University, Hangzhou, China; College of Computer Science and Technology, Zhejiang University, Hangzhou, China.
| | - Benyan Luo
- Department of Neurology, Brain Medical Centre, the First Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, China; School of Medicine, Zhejiang University, Collaborative Innovation Center for Brain Science, Hangzhou, China.
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