1
|
Graesser EA, Parsons MS, Dy CJ, Brogan DM. Advances in Imaging of Compressive Neuropathies. Hand Clin 2024; 40:325-336. [PMID: 38972677 DOI: 10.1016/j.hcl.2024.04.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 07/09/2024]
Abstract
Ultrasound and magnetic resonance neurography are useful modalities to aid in the assessment of compressive neuropathies, although they are still limited in their resolution of nerve microstructure and their capacity to monitor postoperative nerve recovery. Optical coherence tomography, a preclinical imaging modality, is promising in its ability to better identify structural and potential physiologic changes to peripheral nerves, but requires additional testing and research prior to widespread clinical implementation. Further advances in nerve imaging may elucidate the ability to visualize the zone of nerve injury intraoperatively, monitor the progression of nerve regeneration, and localize problems during nerve recovery.
Collapse
Affiliation(s)
- Elizabeth A Graesser
- Department of Orthopaedic Surgery, Washington University School of Medicine in St. Louis, 660 South Euclid Avenue, St Louis, MO 63110, USA
| | - Matthew S Parsons
- Division of Diagnostic Radiology, Section of Neuroradiology, Mallinckrodt Institute of Radiology, Washington University School of Medicine in St. Louis, 510 South Kingshighway Boulevard, St Louis, MO 63110, USA
| | - Christopher J Dy
- Division of Hand and Microsurgery, Department of Orthopaedic Surgery, Washington University School of Medicine in St. Louis, 660 South Euclid Avenue, St Louis, MO 63110, USA
| | - David M Brogan
- Division of Hand and Microsurgery, Department of Orthopaedic Surgery, Washington University School of Medicine in St. Louis, 660 South Euclid Avenue, St Louis, MO 63110, USA.
| |
Collapse
|
2
|
Paschoal AM, Woods JG, Pinto J, Bron EE, Petr J, Kennedy McConnell FA, Bell L, Dounavi ME, van Praag CG, Mutsaerts HJMM, Taylor AO, Zhao MY, Brumer I, Chan WSM, Toner J, Hu J, Zhang LX, Domingos C, Monteiro SP, Figueiredo P, Harms AGJ, Padrela BE, Tham C, Abdalle A, Croal PL, Anazodo U. Reproducibility of arterial spin labeling cerebral blood flow image processing: A report of the ISMRM open science initiative for perfusion imaging (OSIPI) and the ASL MRI challenge. Magn Reson Med 2024; 92:836-852. [PMID: 38502108 DOI: 10.1002/mrm.30081] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Revised: 02/20/2024] [Accepted: 02/21/2024] [Indexed: 03/20/2024]
Abstract
PURPOSE Arterial spin labeling (ASL) is a widely used contrast-free MRI method for assessing cerebral blood flow (CBF). Despite the generally adopted ASL acquisition guidelines, there is still wide variability in ASL analysis. We explored this variability through the ISMRM-OSIPI ASL-MRI Challenge, aiming to establish best practices for more reproducible ASL analysis. METHODS Eight teams analyzed the challenge data, which included a high-resolution T1-weighted anatomical image and 10 pseudo-continuous ASL datasets simulated using a digital reference object to generate ground-truth CBF values in normal and pathological states. We compared the accuracy of CBF quantification from each team's analysis to the ground truth across all voxels and within predefined brain regions. Reproducibility of CBF across analysis pipelines was assessed using the intra-class correlation coefficient (ICC), limits of agreement (LOA), and replicability of generating similar CBF estimates from different processing approaches. RESULTS Absolute errors in CBF estimates compared to ground-truth synthetic data ranged from 18.36 to 48.12 mL/100 g/min. Realistic motion incorporated into three datasets produced the largest absolute error and variability between teams, with the least agreement (ICC and LOA) with ground-truth results. Fifty percent of the submissions were replicated, and one produced three times larger CBF errors (46.59 mL/100 g/min) compared to submitted results. CONCLUSIONS Variability in CBF measurements, influenced by differences in image processing, especially to compensate for motion, highlights the significance of standardizing ASL analysis workflows. We provide a recommendation for ASL processing based on top-performing approaches as a step toward ASL standardization.
Collapse
Affiliation(s)
- Andre M Paschoal
- Institute of Physics, University of Campinas, Campinas, Brazil
- LIM44, Institute of Radiology, Department of Radiology and Oncology of Clinical Hospital, University of Sao Paulo, Sao Paulo, Brazil
| | - Joseph G Woods
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
- Department of Radiology, Center for Functional Magnetic Resonance Imaging, University of California, San Diego, La Jolla, California, USA
| | - Joana Pinto
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Esther E Bron
- Department of Radiology & Nuclear Medicine, Erasmus MC-University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Jan Petr
- Helmholtz-Zentrum Dresden-Rossendorf, Institute of Radiopharmaceutical Cancer Research, Dresden, Germany
| | - Flora A Kennedy McConnell
- Radiological Sciences, Division of Clinical Neuroscience, School of Medicine, University of Nottingham, Nottingham, UK
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, UK
- Nottingham Biomedical Research Centre, Queens Medical Centre, Nottingham, UK
| | - Laura Bell
- Clinical Imaging Group, Genentech, Inc., South San Francisco, California, USA
| | | | - Cassandra Gould van Praag
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
- Department of Psychiatry, University of Oxford, Oxford, UK
| | - Henk J M M Mutsaerts
- Department of Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, the Netherlands
| | | | - Moss Y Zhao
- Department of Radiology, Stanford University, Stanford, California, USA
| | - Irène Brumer
- Computer Assisted Clinical Medicine, Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - Wei Siang Marcus Chan
- Computer Assisted Clinical Medicine, Mannheim Institute for Intelligent Systems in Medicine, Medical Faculty Mannheim, Heidelberg University, Heidelberg, Germany
| | - Jack Toner
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, UK
- Mental Health & Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, UK
| | - Jian Hu
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, UK
- Mental Health & Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, UK
| | - Logan X Zhang
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | - Catarina Domingos
- Institute for Systems and Robotics-Lisboa and Department of Bioengineering, Instituto Superior Técnico-Universidade de Lisboa, Lisbon, Portugal
| | - Sara P Monteiro
- Institute for Systems and Robotics-Lisboa and Department of Bioengineering, Instituto Superior Técnico-Universidade de Lisboa, Lisbon, Portugal
| | - Patrícia Figueiredo
- Institute for Systems and Robotics-Lisboa and Department of Bioengineering, Instituto Superior Técnico-Universidade de Lisboa, Lisbon, Portugal
| | - Alexander G J Harms
- Department of Radiology & Nuclear Medicine, Erasmus MC-University Medical Center Rotterdam, Rotterdam, the Netherlands
| | - Beatriz E Padrela
- Department of Radiology and Nuclear Medicine, Vrije Universiteit Amsterdam, Amsterdam UMC Location VUmc, Amsterdam, the Netherlands
- Amsterdam Neuroscience, Brain Imaging, Amsterdam, the Netherlands
| | - Channelle Tham
- Department of Cognitive Neuroscience, Radboud University Medical Center, Nijmegen, the Netherlands
| | - Ahmed Abdalle
- Schulich School of Medicine and Dentistry, Western University, London, Ontario, Canada
| | - Paula L Croal
- Radiological Sciences, Division of Clinical Neuroscience, School of Medicine, University of Nottingham, Nottingham, UK
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, UK
| | - Udunna Anazodo
- Department of Neurology and Neurosurgery, Montreal Neurological Institute, McGill University, Montreal, Québec, Canada
| |
Collapse
|
3
|
Wade RG, Tam W, Perumal A, Pepple S, Griffiths TT, Flather R, Haroon HA, Shelley D, Plein S, Bourke G, Teh I. Comparison of distortion correction preprocessing pipelines for DTI in the upper limb. Magn Reson Med 2024; 91:773-783. [PMID: 37831659 PMCID: PMC10952179 DOI: 10.1002/mrm.29881] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/08/2023] [Revised: 09/12/2023] [Accepted: 09/12/2023] [Indexed: 10/15/2023]
Abstract
PURPOSE DTI characterizes tissue microstructure and provides proxy measures of nerve health. Echo-planar imaging is a popular method of acquiring DTI but is susceptible to various artifacts (e.g., susceptibility, motion, and eddy currents), which may be ameliorated via preprocessing. There are many pipelines available but limited data comparing their performance, which provides the rationale for this study. METHODS DTI was acquired from the upper limb of heathy volunteers at 3T in blip-up and blip-down directions. Data were independently corrected using (i) FSL's TOPUP & eddy, (ii) FSL's TOPUP, (iii) DSI Studio, and (iv) TORTOISE. DTI metrics were extracted from the median, radial, and ulnar nerves and compared (between pipelines) using mixed-effects linear regression. The geometric similarity of corrected b = 0 images and the slice matched T1-weighted (T1w) images were computed using the Sörenson-Dice coefficient. RESULTS Without preprocessing, the similarity coefficient of the blip-up and blip-down datasets to the T1w was 0·80 and 0·79, respectively. Preprocessing improved the geometric similarity by 1% with no difference between pipelines. Compared to TOPUP & eddy, DSI Studio and TORTOISE generated 2% and 6% lower estimates of fractional anisotropy, and 6% and 13% higher estimates of radial diffusivity, respectively. Estimates of anisotropy from TOPUP & eddy versus TOPUP were not different but TOPUP reduced radial diffusivity by 3%. The agreement of DTI metrics between pipelines was poor. CONCLUSIONS Preprocessing DTI from the upper limb improves geometric similarity but the choice of the pipeline introduces clinically important variability in diffusion parameter estimates from peripheral nerves.
Collapse
Affiliation(s)
- Ryckie G. Wade
- Leeds Institute for Medical Research, University of Leeds
LeedsUK
- Department of Plastic, Reconstructive and Hand SurgeryLeeds Teaching Hospitals TrustLeedsUK
| | - Winnie Tam
- Leeds Institute for Medical Research, University of Leeds
LeedsUK
| | - Antonia Perumal
- Leeds Institute for Medical Research, University of Leeds
LeedsUK
| | - Sophanit Pepple
- Leeds Institute for Medical Research, University of Leeds
LeedsUK
| | - Timothy T. Griffiths
- Leeds Institute for Medical Research, University of Leeds
LeedsUK
- Department of Plastic, Reconstructive and Hand SurgeryLeeds Teaching Hospitals TrustLeedsUK
| | - Robert Flather
- Leeds Institute for Medical Research, University of Leeds
LeedsUK
- Department of Plastic, Reconstructive and Hand SurgeryLeeds Teaching Hospitals TrustLeedsUK
| | - Hamied A. Haroon
- Division of Psychology, Communication & Human NeuroscienceThe University of ManchesterManchesterUK
| | | | - Sven Plein
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of LeedsLeedsUK
| | - Grainne Bourke
- Leeds Institute for Medical Research, University of Leeds
LeedsUK
- Department of Plastic, Reconstructive and Hand SurgeryLeeds Teaching Hospitals TrustLeedsUK
| | - Irvin Teh
- Leeds Institute of Cardiovascular and Metabolic Medicine, University of LeedsLeedsUK
| |
Collapse
|
4
|
Astolfi P, Verhagen R, Petit L, Olivetti E, Sarubbo S, Masci J, Boscaini D, Avesani P. Supervised tractogram filtering using Geometric Deep Learning. Med Image Anal 2023; 90:102893. [PMID: 37741032 DOI: 10.1016/j.media.2023.102893] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 04/18/2023] [Accepted: 07/07/2023] [Indexed: 09/25/2023]
Abstract
A tractogram is a virtual representation of the brain white matter. It is composed of millions of virtual fibers, encoded as 3D polylines, which approximate the white matter axonal pathways. To date, tractograms are the most accurate white matter representation and thus are used for tasks like presurgical planning and investigations of neuroplasticity, brain disorders, or brain networks. However, it is a well-known issue that a large portion of tractogram fibers is not anatomically plausible and can be considered artifacts of the tracking procedure. With Verifyber, we tackle the problem of filtering out such non-plausible fibers using a novel fully-supervised learning approach. Differently from other approaches based on signal reconstruction and/or brain topology regularization, we guide our method with the existing anatomical knowledge of the white matter. Using tractograms annotated according to anatomical principles, we train our model, Verifyber, to classify fibers as either anatomically plausible or non-plausible. The proposed Verifyber model is an original Geometric Deep Learning method that can deal with variable size fibers, while being invariant to fiber orientation. Our model considers each fiber as a graph of points, and by learning features of the edges between consecutive points via the proposed sequence Edge Convolution, it can capture the underlying anatomical properties. The output filtering results highly accurate and robust across an extensive set of experiments, and fast; with a 12GB GPU, filtering a tractogram of 1M fibers requires less than a minute.
Collapse
Affiliation(s)
- Pietro Astolfi
- NILab, TeV, Fondazione Bruno Kessler, Trento, Italy; PAVIS, Istituto Italiano di Tecnologia, Geonva, Italy; Center for Mind/Brain Sciences (CiMeC), University of Trento, Rovereto, Italy
| | | | - Laurent Petit
- GIN, IMN, CNRS, CEA, Université de Bordeaux, Bordeaux, France
| | - Emanuele Olivetti
- NILab, TeV, Fondazione Bruno Kessler, Trento, Italy; Center for Mind/Brain Sciences (CiMeC), University of Trento, Rovereto, Italy
| | - Silvio Sarubbo
- Center for Mind/Brain Sciences (CiMeC), University of Trento, Rovereto, Italy; Department of Neurosurgery, Azienda Provinciale per i Servizi Sanitari, "Santa Chiara" Hospital, Trento, Italy
| | | | | | - Paolo Avesani
- NILab, TeV, Fondazione Bruno Kessler, Trento, Italy; Center for Mind/Brain Sciences (CiMeC), University of Trento, Rovereto, Italy.
| |
Collapse
|
5
|
Wang D, Fan Q, Xiao X, He H, Yang Y, Li Y. Structural Fingerprinting of the Frontal Aslant Tract: Predicting Cognitive Control Capacity and Obsessive-Compulsive Symptoms. J Neurosci 2023; 43:7016-7027. [PMID: 37696666 PMCID: PMC10586535 DOI: 10.1523/jneurosci.0628-23.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2023] [Revised: 06/29/2023] [Accepted: 08/18/2023] [Indexed: 09/13/2023] Open
Abstract
White matter of the human brain is influenced by common genetic variations and shaped by neural activity-dependent experiences. Variations in microstructure of cerebral white matter across individuals and even across fiber tracts might underlie differences in cognitive capacity and vulnerabilities to mental disorders. The frontoparietal and cingulo-opercular networks of the brain constitute the central system supporting cognitive functions, and functional connectivity of these networks has been used to distinguish individuals known as "functional fingerprinting." The frontal aslant tract (FAT) that passes through the two networks has been implicated in executive functions. However, whether FAT can be used as a "structural fingerprint" to distinguish individuals and predict an individual's cognitive function and dysfunction is unknown. Here we investigated the fingerprinting property of FAT microstructural profiles using three independent diffusion MRI datasets with repeated scans on human participants including both females and males. We found that diffusion and geometric profiles of FAT can be used to distinguish individuals with a high accuracy. Next, we demonstrated that fractional anisotropy in different FAT segments predicted distinct cognitive functions, including working memory, inhibitory control, and relational reasoning. Finally, we assessed the contribution of altered FAT microstructural profiles to cognitive dysfunction in unmedicated patients with obsessive-compulsive disorders. We found that the altered microstructure in FAT was associated with the severity of obsessive-compulsive symptoms. Collectively, our findings suggest that the microstructural profiles of FAT can identify individuals with a high accuracy and may serve as an imaging marker for predicting an individual's cognitive capacity and disease severity.SIGNIFICANCE STATEMENT The frontoparietal network and cingulo-opercular network of the brain constitute a dual-network architecture for human cognitive functions, and functional connectivity of these two networks can be used as a "functional fingerprint" to distinguish individuals. However, the structural underpinnings of these networks subserving individual heterogeneities in their functional connectivity and cognitive ability remain unknown. We show here that the frontal aslant tract (FAT) that passes through the two networks distinguishes individuals with a high accuracy. Further, we demonstrate that the diffusion profiles of FAT predict distinct cognitive functions in healthy subjects and are associated with the clinical symptoms in patients with obsessive-compulsive disorders. Our findings suggest that the FAT may serve as a unique structural fingerprint underlying individual cognitive capability.
Collapse
Affiliation(s)
- Danni Wang
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, People's Republic of China
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, Maryland 21224
| | - Qing Fan
- Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, Shanghai 200030, People's Republic of China
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai 200030, People's Republic of China
| | - Xiang Xiao
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, Maryland 21224
| | - Hongjian He
- Center for Brain Imaging Science and Technology, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou 310027, People's Republic of China
- School of Physics, Zhejiang University, Hangzhou 310027, People's Republic of China
| | - Yihong Yang
- Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, Maryland 21224
| | - Yao Li
- School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai 200030, People's Republic of China
| |
Collapse
|
6
|
Girard G, Rafael-Patiño J, Truffet R, Aydogan DB, Adluru N, Nair VA, Prabhakaran V, Bendlin BB, Alexander AL, Bosticardo S, Gabusi I, Ocampo-Pineda M, Battocchio M, Piskorova Z, Bontempi P, Schiavi S, Daducci A, Stafiej A, Ciupek D, Bogusz F, Pieciak T, Frigo M, Sedlar S, Deslauriers-Gauthier S, Kojčić I, Zucchelli M, Laghrissi H, Ji Y, Deriche R, Schilling KG, Landman BA, Cacciola A, Basile GA, Bertino S, Newlin N, Kanakaraj P, Rheault F, Filipiak P, Shepherd TM, Lin YC, Placantonakis DG, Boada FE, Baete SH, Hernández-Gutiérrez E, Ramírez-Manzanares A, Coronado-Leija R, Stack-Sánchez P, Concha L, Descoteaux M, Mansour L S, Seguin C, Zalesky A, Marshall K, Canales-Rodríguez EJ, Wu Y, Ahmad S, Yap PT, Théberge A, Gagnon F, Massi F, Fischi-Gomez E, Gardier R, Haro JLV, Pizzolato M, Caruyer E, Thiran JP. Tractography passes the test: Results from the diffusion-simulated connectivity (disco) challenge. Neuroimage 2023; 277:120231. [PMID: 37330025 PMCID: PMC10771037 DOI: 10.1016/j.neuroimage.2023.120231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Revised: 05/12/2023] [Accepted: 06/14/2023] [Indexed: 06/19/2023] Open
Abstract
Estimating structural connectivity from diffusion-weighted magnetic resonance imaging is a challenging task, partly due to the presence of false-positive connections and the misestimation of connection weights. Building on previous efforts, the MICCAI-CDMRI Diffusion-Simulated Connectivity (DiSCo) challenge was carried out to evaluate state-of-the-art connectivity methods using novel large-scale numerical phantoms. The diffusion signal for the phantoms was obtained from Monte Carlo simulations. The results of the challenge suggest that methods selected by the 14 teams participating in the challenge can provide high correlations between estimated and ground-truth connectivity weights, in complex numerical environments. Additionally, the methods used by the participating teams were able to accurately identify the binary connectivity of the numerical dataset. However, specific false positive and false negative connections were consistently estimated across all methods. Although the challenge dataset doesn't capture the complexity of a real brain, it provided unique data with known macrostructure and microstructure ground-truth properties to facilitate the development of connectivity estimation methods.
Collapse
Affiliation(s)
- Gabriel Girard
- CIBM Center for Biomedical Imaging, Switzerland; Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland; Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
| | - Jonathan Rafael-Patiño
- Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland; Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland.
| | - Raphaël Truffet
- Univ Rennes, Inria, CNRS, Inserm, IRISA UMR 6074, Empenn ERL U-1228, Rennes, France
| | - Dogu Baran Aydogan
- A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, Kuopio, Finland; Department of Neuroscience and Biomedical Engineering, Aalto University, Espoo, Finland; Department of Psychiatry, Helsinki University Hospital, Helsinki, Finland
| | - Nagesh Adluru
- Waisman Center, University of Wisconsin-Madison, Madison, WI, United States; Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States
| | - Veena A Nair
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States
| | - Vivek Prabhakaran
- Department of Radiology, University of Wisconsin-Madison, Madison, WI, United States
| | - Barbara B Bendlin
- Department of Medicine, University of Wisconsin-Madison, Madison, WI, United States
| | - Andrew L Alexander
- Waisman Center, University of Wisconsin-Madison, Madison, WI, United States; Department of Medical Physics, University of Wisconsin-Madison, Madison, WI, United States; Department of Psychiatry, University of Wisconsin-Madison, Madison, WI, United States
| | - Sara Bosticardo
- Diffusion Imaging and Connectivity Estimation (DICE) Lab, Department of Computer Science, University of Verona, Verona, Italy; Translational Imaging in Neurology (ThINk), Department of Biomedical Engineering, University Hospital Basel and University of Basel, Basel, Switzerland
| | - Ilaria Gabusi
- Diffusion Imaging and Connectivity Estimation (DICE) Lab, Department of Computer Science, University of Verona, Verona, Italy; Department of Advanced Biomedical Sciences, University of Naples Federico II, Naples, Italy
| | - Mario Ocampo-Pineda
- Diffusion Imaging and Connectivity Estimation (DICE) Lab, Department of Computer Science, University of Verona, Verona, Italy
| | - Matteo Battocchio
- Diffusion Imaging and Connectivity Estimation (DICE) Lab, Department of Computer Science, University of Verona, Verona, Italy; Sherbrooke Connectivity Imaging Laboratory (SCIL), Department of Computer Science, University of Sherbrooke, Sherbrooke, QC, Canada
| | - Zuzana Piskorova
- Diffusion Imaging and Connectivity Estimation (DICE) Lab, Department of Computer Science, University of Verona, Verona, Italy; Brno Faculty of Electrical Engineering and Communication, Department of mathematics, University of Technology, Brno, Czech Republic
| | - Pietro Bontempi
- Diffusion Imaging and Connectivity Estimation (DICE) Lab, Department of Computer Science, University of Verona, Verona, Italy
| | - Simona Schiavi
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI), University of Genoa, Genoa, Italy
| | - Alessandro Daducci
- Diffusion Imaging and Connectivity Estimation (DICE) Lab, Department of Computer Science, University of Verona, Verona, Italy
| | | | - Dominika Ciupek
- Sano Centre for Computational Personalised Medicine, Kraków, Poland
| | - Fabian Bogusz
- AGH University of Science and Technology, Kraków, Poland
| | - Tomasz Pieciak
- AGH University of Science and Technology, Kraków, Poland; Laboratorio de Procesado de Imagen (LPI), ETSI Telecomunicación, Universidad de Valladolid, Valladolid, Spain
| | - Matteo Frigo
- Athena Project Team, Centre Inria d'Université Côte d'Azur, France
| | - Sara Sedlar
- Athena Project Team, Centre Inria d'Université Côte d'Azur, France
| | | | - Ivana Kojčić
- Athena Project Team, Centre Inria d'Université Côte d'Azur, France
| | - Mauro Zucchelli
- Athena Project Team, Centre Inria d'Université Côte d'Azur, France
| | - Hiba Laghrissi
- Athena Project Team, Centre Inria d'Université Côte d'Azur, France; Institut de Biologie de Valrose, Université Côte d'Azur, Nice, France
| | - Yang Ji
- Athena Project Team, Centre Inria d'Université Côte d'Azur, France
| | - Rachid Deriche
- Athena Project Team, Centre Inria d'Université Côte d'Azur, France
| | - Kurt G Schilling
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Bennett A Landman
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States; Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, United States
| | - Alberto Cacciola
- Brain Mapping Lab, Department of Biomedical, Dental Sciences and Morphological and Functional Images, University of Messina, Messina, Italy; Center for Complex Network Intelligence (CCNI), Tsinghua Laboratory of Brain and Intelligence (THBI), Tsinghua University, Beijing, China; Department of Biomedical Engineering, Tsinghua University, Beijing, China
| | - Gianpaolo Antonio Basile
- Brain Mapping Lab, Department of Biomedical, Dental Sciences and Morphological and Functional Images, University of Messina, Messina, Italy
| | - Salvatore Bertino
- Brain Mapping Lab, Department of Biomedical, Dental Sciences and Morphological and Functional Images, University of Messina, Messina, Italy
| | - Nancy Newlin
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, United States
| | - Praitayini Kanakaraj
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, United States
| | - Francois Rheault
- Department of Electrical and Computer Engineering, Vanderbilt University, Nashville, TN, United States
| | - Patryk Filipiak
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, NYU Langone Health, New York, NY, United States
| | - Timothy M Shepherd
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, NYU Langone Health, New York, NY, United States
| | - Ying-Chia Lin
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, NYU Langone Health, New York, NY, United States
| | - Dimitris G Placantonakis
- Department of Neurosurgery, Perlmutter Cancer Center, Neuroscience Institute, Kimmel Center for Stem Cell Biology, NYU Langone Health, New York, NY, United States
| | - Fernando E Boada
- Department of Radiology, Stanford University, Stanford, CA, United States
| | - Steven H Baete
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, NYU Langone Health, New York, NY, United States
| | - Erick Hernández-Gutiérrez
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Department of Computer Science, University of Sherbrooke, Sherbrooke, QC, Canada
| | | | - Ricardo Coronado-Leija
- Center for Advanced Imaging Innovation and Research (CAI2R), Department of Radiology, NYU Langone Health, New York, NY, United States
| | - Pablo Stack-Sánchez
- Computer Science Department, Centro de Investigación en Matemáticas A.C, Guanajuato, México
| | - Luis Concha
- Instituto de Neurobiología, Universidad Nacional Autónoma de México, Juriquilla, Querétaro, México
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Department of Computer Science, University of Sherbrooke, Sherbrooke, QC, Canada
| | - Sina Mansour L
- Department of Biomedical Engineering, The University of Melbourne, Parkville, Victoria, Australia; Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Parkville, Victoria, Australia
| | - Caio Seguin
- Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Parkville, Victoria, Australia; School of Biomedical Engineering, The University of Sydney, Sydney, Australia; Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, United States
| | - Andrew Zalesky
- Department of Biomedical Engineering, The University of Melbourne, Parkville, Victoria, Australia; Melbourne Neuropsychiatry Centre, Department of Psychiatry, The University of Melbourne and Melbourne Health, Parkville, Victoria, Australia
| | - Kenji Marshall
- Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; McGill University, Montréal, QC, Canada
| | - Erick J Canales-Rodríguez
- Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Ye Wu
- Department of Radiology and Biomedical Research Imaging Center (BRIC), The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States; School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China
| | - Sahar Ahmad
- Department of Radiology and Biomedical Research Imaging Center (BRIC), The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Pew-Thian Yap
- Department of Radiology and Biomedical Research Imaging Center (BRIC), The University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Antoine Théberge
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Department of Computer Science, University of Sherbrooke, Sherbrooke, QC, Canada
| | - Florence Gagnon
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Department of Computer Science, University of Sherbrooke, Sherbrooke, QC, Canada
| | - Frédéric Massi
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Department of Computer Science, University of Sherbrooke, Sherbrooke, QC, Canada
| | - Elda Fischi-Gomez
- CIBM Center for Biomedical Imaging, Switzerland; Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland; Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Rémy Gardier
- Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Juan Luis Villarreal Haro
- Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Marco Pizzolato
- Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland; Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark
| | - Emmanuel Caruyer
- Univ Rennes, Inria, CNRS, Inserm, IRISA UMR 6074, Empenn ERL U-1228, Rennes, France
| | - Jean-Philippe Thiran
- CIBM Center for Biomedical Imaging, Switzerland; Radiology Department, Centre Hospitalier Universitaire Vaudois and University of Lausanne, Lausanne, Switzerland; Signal Processing Laboratory (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| |
Collapse
|
7
|
Borrelli P, Savini G, Cavaliere C, Palesi F, Grazia Bruzzone M, Aquino D, Biagi L, Bosco P, Carne I, Ferraro S, Giulietti G, Napolitano A, Nigri A, Pavone L, Pirastru A, Redolfi A, Tagliavini F, Tosetti M, Salvatore M, Gandini Wheeler-Kingshott CAM, Aiello M. Normative values of the topological metrics of the structural connectome: A multi-site reproducibility study across the Italian Neuroscience network. Phys Med 2023; 112:102610. [PMID: 37331082 DOI: 10.1016/j.ejmp.2023.102610] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/30/2022] [Revised: 03/20/2023] [Accepted: 05/30/2023] [Indexed: 06/20/2023] Open
Abstract
PURPOSE The use of topological metrics to derive quantitative descriptors from structural connectomes is receiving increasing attention but deserves specific studies to investigate their reproducibility and variability in the clinical context. This work exploits the harmonization of diffusion-weighted acquisition for neuroimaging data performed by the Italian Neuroscience and Neurorehabilitation Network initiative to obtain normative values of topological metrics and to investigate their reproducibility and variability across centers. METHODS Different topological metrics, at global and local level, were calculated on multishell diffusion-weighted data acquired at high-field (e.g. 3 T) Magnetic Resonance Imaging scanners in 13 different centers, following the harmonization of the acquisition protocol, on young and healthy adults. A "traveling brains" dataset acquired on a subgroup of subjects at 3 different centers was also analyzed as reference data. All data were processed following a common processing pipeline that includes data pre-processing, tractography, generation of structural connectomes and calculation of graph-based metrics. The results were evaluated both with statistical analysis of variability and consistency among sites with the traveling brains range. In addition, inter-site reproducibility was assessed in terms of intra-class correlation variability. RESULTS The results show an inter-center and inter-subject variability of <10%, except for "clustering coefficient" (variability of 30%). Statistical analysis identifies significant differences among sites, as expected given the wide range of scanners' hardware. CONCLUSIONS The results show low variability of connectivity topological metrics across sites running a harmonised protocol.
Collapse
Affiliation(s)
| | | | | | - Fulvia Palesi
- Department of Brain and Behavioral Sciences, Università degli Studi di Pavia, Pavia, Italy
| | - Maria Grazia Bruzzone
- Neuroradiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Domenico Aquino
- Neuroradiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Laura Biagi
- Laboratory of Medical Physics and Magnetic Resonance, IRCCS Stella Maris Foundation, Pisa, Italy
| | - Paolo Bosco
- Laboratory of Medical Physics and Magnetic Resonance, IRCCS Stella Maris Foundation, Pisa, Italy
| | - Irene Carne
- Neuroradiology Unit, IRCCS Istituti Clinici Scientifici Maugeri, Pavia, Italy
| | - Stefania Ferraro
- Neuroradiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Giovanni Giulietti
- Neuroimaging Laboratory, IRCCS Santa Lucia Foundation, Rome, Italy; SAIMLAL Department, Sapienza University of Rome, Rome, Italy
| | - Antonio Napolitano
- Medical Physics, IRCCS Istituto Ospedale Pediatrico Bambino Gesù, Rome, Italy
| | - Anna Nigri
- Neuroradiology Unit, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | | | | | - Alberto Redolfi
- Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy
| | - Fabrizio Tagliavini
- Scientific Direction, Fondazione IRCCS Istituto Neurologico Carlo Besta, Milan, Italy
| | - Michela Tosetti
- Laboratory of Medical Physics and Magnetic Resonance, IRCCS Stella Maris Foundation, Pisa, Italy
| | | | - Claudia A M Gandini Wheeler-Kingshott
- Department of Brain and Behavioral Sciences, Università degli Studi di Pavia, Pavia, Italy; NMR Research Unit, Department of Neuroinflammation, Queen Square MS Centre, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
| | | |
Collapse
|
8
|
Wade RG, Lu F, Poruslrani Y, Karia C, Feltbower RG, Plein S, Bourke G, Teh I. Meta-analysis of the normal diffusion tensor imaging values of the peripheral nerves in the upper limb. Sci Rep 2023; 13:4852. [PMID: 36964186 PMCID: PMC10039047 DOI: 10.1038/s41598-023-31307-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Accepted: 03/09/2023] [Indexed: 03/26/2023] Open
Abstract
Peripheral neuropathy affects 1 in 10 adults over the age of 40 years. Given the absence of a reliable diagnostic test for peripheral neuropathy, there has been a surge of research into diffusion tensor imaging (DTI) because it characterises nerve microstructure and provides reproducible proxy measures of myelination, axon diameter, fibre density and organisation. Before researchers and clinicians can reliably use diffusion tensor imaging to assess the 'health' of the major nerves of the upper limb, we must understand the "normal" range of values and how they vary with experimental conditions. We searched PubMed, Embase, medRxiv and bioRxiv for studies which reported the findings of DTI of the upper limb in healthy adults. Four review authors independently triple extracted data. Using the meta suite of Stata 17, we estimated the normal fractional anisotropy (FA) and diffusivity (mean, MD; radial, RD; axial AD) values of the median, radial and ulnar nerve in the arm, elbow and forearm. Using meta-regression, we explored how DTI metrics varied with age and experimental conditions. We included 20 studies reporting data from 391 limbs, belonging to 346 adults (189 males and 154 females, ~ 1.2 M:1F) of mean age 34 years (median 31, range 20-80). In the arm, there was no difference in the FA (pooled mean 0.59 mm2/s [95% CI 0.57, 0.62]; I2 98%) or MD (pooled mean 1.13 × 10-3 mm2/s [95% CI 1.08, 1.18]; I2 99%) of the median, radial and ulnar nerves. Around the elbow, the ulnar nerve had a 12% lower FA than the median and radial nerves (95% CI - 0.25, 0.00) and significantly higher MD, RD and AD. In the forearm, the FA (pooled mean 0.55 [95% CI 0.59, 0.64]; I2 96%) and MD (pooled mean 1.03 × 10-3 mm2/s [95% CI 0.94, 1.12]; I2 99%) of the three nerves were similar. Multivariable meta regression showed that the b-value, TE, TR, spatial resolution and age of the subject were clinically important moderators of DTI parameters in peripheral nerves. We show that subject age, as well as the b-value, TE, TR and spatial resolution are important moderators of DTI metrics from healthy nerves in the adult upper limb. The normal ranges shown here may inform future clinical and research studies.
Collapse
Affiliation(s)
- Ryckie G Wade
- Leeds Institute for Medical Research, The Advanced Imaging Centre, Leeds General Infirmary, University of Leeds, Leeds, LS1 3EX, UK.
- Department of Plastic and Reconstructive Surgery, Leeds Teaching Hospitals Trust, Leeds, UK.
| | - Fangqing Lu
- Leeds Institute for Medical Research, The Advanced Imaging Centre, Leeds General Infirmary, University of Leeds, Leeds, LS1 3EX, UK
| | - Yohan Poruslrani
- Royal College of Surgeons in Ireland, University of Medicine and Health Sciences, Dublin, Ireland
| | - Chiraag Karia
- Department of Plastic and Reconstructive Surgery, Leeds Teaching Hospitals Trust, Leeds, UK
| | | | - Sven Plein
- Leeds Institute for Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK
- The Advanced Imaging Centre, Leeds Teaching Hospitals Trust, Leeds, UK
| | - Grainne Bourke
- Leeds Institute for Medical Research, The Advanced Imaging Centre, Leeds General Infirmary, University of Leeds, Leeds, LS1 3EX, UK
- Department of Plastic and Reconstructive Surgery, Leeds Teaching Hospitals Trust, Leeds, UK
| | - Irvin Teh
- Leeds Institute for Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK
- The Advanced Imaging Centre, Leeds Teaching Hospitals Trust, Leeds, UK
| |
Collapse
|
9
|
In-vivo along muscle fascicle strain heterogeneity is not affected by image registration parameters: Robustness testing of combined magnetic resonance-diffusion tensor imaging method. J Mech Behav Biomed Mater 2023; 139:105681. [PMID: 36708628 DOI: 10.1016/j.jmbbm.2023.105681] [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: 04/21/2022] [Revised: 12/14/2022] [Accepted: 01/13/2023] [Indexed: 01/19/2023]
Abstract
Coupled with diffusion tractography, non-rigid registration of high-resolution anatomical MR images allows the calculation of local strains along human skeletal muscle fascicles in-vivo. A reference study (passively imposed lengthening of gastrocnemius medial muscle) reported local shortening and lengthening occurring along the same muscle fascicles. However, the robustness of strain amplitudes and distribution patterns should be studied, as the heterogeneity of local length changes has major implications for muscle function. Using a previous image set of human medial gastrocnemius (GM) we aimed at testing: (1) the consistency of our MRI-DTI analysis workflow against changes made to the software environments, (2) the hypothesis that non-rigid demons algorithm tuning parameters (16 paired combinations were tested) are not a significant determinant of muscle fiber direction strain heterogeneity caused by passive knee extension. A profoundly altered analysis workflow did reproduce the original results well, showing a general pattern of proximally lengthened and distally shortened muscle fascicles (strain amplitude range: 21%-67%). Hierarchical shift function analyses and pairwise comparison of strain distributions between 10 equal parts of the tracked GM fascicles confirmed the hypothesis showing no significant effects of tuning parameters determining the in-vivo deformation field inhomogeneity. The findings show the robustness of the MRI-DTI method, and confirming the hypothesis, also the consistency of along muscle fascicle strain heterogeneity patterns against parameter selection. However, the strain amplitudes do vary with parameter choices. New studies are indicated to determine optimal tuning parameters to achieve accurate strain amplitudes compared to exact strains.
Collapse
|
10
|
Graesser EA, Dy CJ, Brogan DM. Future Considerations in the Diagnosis and Treatment of Compressive Neuropathies of the Upper Extremity. JOURNAL OF HAND SURGERY GLOBAL ONLINE 2022. [PMID: 37521547 PMCID: PMC10382897 DOI: 10.1016/j.jhsg.2022.10.009] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/05/2022] Open
Abstract
Compressive neuropathies of the upper extremity are among the most common conditions seen by hand surgeons. The diagnoses of carpal tunnel syndrome and cubital tunnel syndrome have traditionally been made by a combination of history, physical examination, and electrodiagnostic testing. However, findings can be nonspecific and electrodiagnostic testing is invasive for the patient. The diagnosis of compressive neuropathies continues to evolve as technology advances, and newer diagnostic modalities predominantly focus on preoperative diagnostic imaging with ultrasound and magnetic resonance imaging/neurography. With the advent of cheaper, faster, and less invasive imaging, the future may bring a paradigm shift away from electrophysiology as the gold standard for the preoperative diagnosis of compressive neuropathies. Intraoperative imaging of nerve health is an emerging concept that warrants further investigation, whereas postoperative imaging of nerve recovery with ultrasound and magnetic resonance imaging currently has a limited role because of nonspecific findings and potential for misinterpretation. Advances in surgical treatment of compressive neuropathies appear to center around the use of imaging for less invasive neurolysis techniques and other adjunctive treatments with nerve decompression. The management of failed peripheral nerve decompressions and recurrent compressive neuropathies remains challenging.
Collapse
|
11
|
Rheault F, Schilling KG, Obaid S, Begnoche JP, Cutting LE, Descoteaux M, Landman BA, Petit L. The influence of regions of interest on tractography virtual dissection protocols: general principles to learn and to follow. Brain Struct Funct 2022; 227:2191-2207. [PMID: 35672532 PMCID: PMC9884471 DOI: 10.1007/s00429-022-02518-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 05/22/2022] [Indexed: 01/31/2023]
Abstract
Efficient communication across fields of research is challenging, especially when they are at opposite ends of the physical and digital spectrum. Neuroanatomy and neuroimaging may seem close to each other. When neuroimaging studies try to isolate structures of interest, according to a specific anatomical definition, a variety of challenges emerge. It is a non-trivial task to convert the neuroanatomical knowledge to instructions and rules to be executed in neuroimaging software. In the process called "virtual dissection" used to isolate coherent white matter structure in tractography, each white matter pathway has its own set of landmarks (regions of interest) used as inclusion and exclusion criteria. The ability to segment and study these pathways is critical for scientific progress, yet, variability may depend on region placement, and be influenced by the person positioning the region (i.e., a rater). When raters' variability is taken into account, the impact made by each region of interest becomes even more difficult to interpret. A delicate balance between anatomical validity, impact on the virtual dissection and raters' reproducibility emerge. In this work, we investigate this balance by leveraging manual delineation data of a group of raters from a previous study to quantify which set of landmarks and criteria contribute most to variability in virtual dissection. To supplement our analysis, the variability of each pathway with a region-by-region exploration was performed. We present a detailed exploration and description of each region, the causes of variability and its impacts. Finally, we provide a brief overview of the lessons learned from our previous virtual dissection projects and propose recommendations for future virtual dissection protocols as well as perspectives to reach better community agreement when it comes to anatomical definitions of white matter pathways.
Collapse
Affiliation(s)
- Francois Rheault
- Electrical and Computer Engineering, Vanderbilt University, Nashville, USA
| | - Kurt G. Schilling
- Vanderbilt University Institute of Imaging, Nashville, USA,Department of Radiology and Radiological Science, Vanderbilt University Medical Center, Nashville, USA
| | - Sami Obaid
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Département d’Informatique, Université de Sherbrooke, Sherbrooke, Canada,Health Center Research Center, University of Montreal, Montreal, Canada
| | - John P. Begnoche
- The Center for Cognitive Medicine, Department of Psychiatry, Vanderbilt University Medical Center, Nashville, USA
| | - Laurie E. Cutting
- Vanderbilt Kennedy Center, University Medical Center, VanderbiltNashville, USA
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Département d’Informatique, Université de Sherbrooke, Sherbrooke, Canada
| | - Bennett A. Landman
- Electrical and Computer Engineering, Vanderbilt University, Nashville, USA,Vanderbilt University Institute of Imaging, Nashville, USA,Department of Radiology and Radiological Science, Vanderbilt University Medical Center, Nashville, USA,Computer Science, Vanderbilt University, Nashville, USA
| | - Laurent Petit
- Groupe d’Imagerie Neurofonctionnelle, Institut Des Maladies Neurodégénératives, CNRS, CEA University of Bordeaux, Bordeaux, France
| |
Collapse
|
12
|
Moon JY, Mukherjee P, Madduri RK, Markowitz AJ, Cai LT, Palacios EM, Manley GT, Bremer PT. The Case for Optimized Edge-Centric Tractography at Scale. Front Neuroinform 2022; 16:752471. [PMID: 35651721 PMCID: PMC9148990 DOI: 10.3389/fninf.2022.752471] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2021] [Accepted: 04/22/2022] [Indexed: 11/13/2022] Open
Abstract
The anatomic validity of structural connectomes remains a significant uncertainty in neuroimaging. Edge-centric tractography reconstructs streamlines in bundles between each pair of cortical or subcortical regions. Although edge bundles provides a stronger anatomic embedding than traditional connectomes, calculating them for each region-pair requires exponentially greater computation. We observe that major speedup can be achieved by reducing the number of streamlines used by probabilistic tractography algorithms. To ensure this does not degrade connectome quality, we calculate the identifiability of edge-centric connectomes between test and re-test sessions as a proxy for information content. We find that running PROBTRACKX2 with as few as 1 streamline per voxel per region-pair has no significant impact on identifiability. Variation in identifiability caused by streamline count is overshadowed by variation due to subject demographics. This finding even holds true in an entirely different tractography algorithm using MRTrix. Incidentally, we observe that Jaccard similarity is more effective than Pearson correlation in calculating identifiability for our subject population.
Collapse
Affiliation(s)
- Joseph Y. Moon
- Lawrence Livermore National Laboratory, Livermore, CA, United States
- *Correspondence: Joseph Y. Moon
| | - Pratik Mukherjee
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
- Pratik Mukherjee
| | | | - Amy J. Markowitz
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Lanya T. Cai
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Eva M. Palacios
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Geoffrey T. Manley
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Peer-Timo Bremer
- Lawrence Livermore National Laboratory, Livermore, CA, United States
| |
Collapse
|
13
|
Cai LY, Yang Q, Kanakaraj P, Nath V, Newton AT, Edmonson HA, Luci J, Conrad BN, Price GR, Hansen CB, Kerley CI, Ramadass K, Yeh FC, Kang H, Garyfallidis E, Descoteaux M, Rheault F, Schilling KG, Landman BA. MASiVar: Multisite, multiscanner, and multisubject acquisitions for studying variability in diffusion weighted MRI. Magn Reson Med 2021; 86:3304-3320. [PMID: 34270123 PMCID: PMC9087815 DOI: 10.1002/mrm.28926] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2021] [Revised: 06/21/2021] [Accepted: 06/23/2021] [Indexed: 12/18/2022]
Abstract
PURPOSE Diffusion-weighted imaging allows investigators to identify structural, microstructural, and connectivity-based differences between subjects, but variability due to session and scanner biases is a challenge. METHODS To investigate DWI variability, we present MASiVar, a multisite data set consisting of 319 diffusion scans acquired at 3 T from b = 1000 to 3000 s/mm2 across 14 healthy adults, 83 healthy children (5 to 8 years), three sites, and four scanners as a publicly available, preprocessed, and de-identified data set. With the adult data, we demonstrate the capacity of MASiVar to simultaneously quantify the intrasession, intersession, interscanner, and intersubject variability of four common DWI processing approaches: (1) a tensor signal representation, (2) a multi-compartment neurite orientation dispersion and density model, (3) white-matter bundle segmentation, and (4) structural connectomics. Respectively, we evaluate region-wise fractional anisotropy, mean diffusivity, and principal eigenvector; region-wise CSF volume fraction, intracellular volume fraction, and orientation dispersion index; bundle-wise shape, volume, fractional anisotropy, and length; and whole connectome correlation and maximized modularity, global efficiency, and characteristic path length. RESULTS We plot the variability in these measures at each level and find that it consistently increases with intrasession to intersession to interscanner to intersubject effects across all processing approaches and that sometimes interscanner variability can approach intersubject variability. CONCLUSIONS This study demonstrates the potential of MASiVar to more globally investigate DWI variability across multiple levels and processing approaches simultaneously and suggests harmonization between scanners for multisite analyses should be considered before inference of group differences on subjects.
Collapse
Affiliation(s)
- Leon Y. Cai
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
| | - Qi Yang
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA
| | - Praitayini Kanakaraj
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA
| | - Vishwesh Nath
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA
| | - Allen T. Newton
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee, USA
| | | | - Jeffrey Luci
- Department of Biomedical Engineering, The University of Texas at Austin, Austin, Texas, USA
- Department of Psychiatry, Robert Wood Johnson Medical School, Rutgers University, New Brunswick, New Jersey, USA
| | - Benjamin N. Conrad
- Neuroscience Graduate Program, Vanderbilt Brain Institute, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Department of Psychology and Human Development, Peabody College, Vanderbilt University, Nashville, Tennessee, USA
| | - Gavin R. Price
- Department of Psychology and Human Development, Peabody College, Vanderbilt University, Nashville, Tennessee, USA
| | - Colin B. Hansen
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA
| | - Cailey I. Kerley
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA
| | - Karthik Ramadass
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA
| | - Fang-Cheng Yeh
- Department of Neurological Surgery, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Hakmook Kang
- Department of Biostatistics, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | | | - Maxime Descoteaux
- Department of Computer Science, Université de Sherbrooke, Sherbrooke, Québec, Canada
| | - Francois Rheault
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA
- Department of Computer Science, Université de Sherbrooke, Sherbrooke, Québec, Canada
| | - Kurt G. Schilling
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee, USA
| | - Bennett A. Landman
- Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee, USA
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, Tennessee, USA
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, Tennessee, USA
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, Tennessee, USA
| |
Collapse
|
14
|
Rojoa D, Raheman F, Rassam J, Wade RG. Meta-analysis of the normal diffusion tensor imaging values of the median nerve and how they change in carpal tunnel syndrome. Sci Rep 2021; 11:20935. [PMID: 34686721 PMCID: PMC8536657 DOI: 10.1038/s41598-021-00353-z] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2021] [Accepted: 10/05/2021] [Indexed: 02/07/2023] Open
Abstract
Carpal tunnel syndrome (CTS) leads to distortion of axonal architecture, demyelination and fibrosis within the median nerve. Diffusion tensor imaging (DTI) characterises tissue microstructure and generates reproducible proxy measures of nerve 'health' which are sensitive to myelination, axon diameter, fiber density and organisation. This meta-analysis summarises the normal DTI values of the median nerve, and how they change in CTS. This systematic review included studies reporting DTI of the median nerve at the level of the wrist in adults. The primary outcome was to determine the normal fractional anisotropy (FA) and mean diffusivity (MD) of the median nerve. Secondarily, we show how the FA and MD differ between asymptomatic adults and patients with CTS, and how these differences are independent of the acquisition methods. We included 32 studies of 2643 wrists, belonging to 1575 asymptomatic adults and 1068 patients with CTS. The normal FA was 0.58 (95% CI 0.56, 0.59) and the normal MD was 1.138 × 10-3 mm2/s (95% CI 1.101, 1.174). Patients with CTS had a significantly lower FA than controls (mean difference 0.12 [95% CI 0.09, 0.16]). Similarly, the median nerve of patients with CTS had a significantly higher mean diffusivity (mean difference 0.16 × 10-3 mm2/s [95% CI 0.05, 0.27]). The differences were independent of experimental factors. We provide summary estimates of the normal FA and MD of the median nerve in asymptomatic adults. Furthermore, we show that diffusion throughout the length of the median nerve becomes more isotropic in patients with CTS.
Collapse
Affiliation(s)
- Djamila Rojoa
- grid.419248.20000 0004 0400 6485Department of Plastic and Reconstructive Surgery, Leicester Royal Infirmary, Leicester, UK
| | - Firas Raheman
- grid.419248.20000 0004 0400 6485Department of Plastic and Reconstructive Surgery, Leicester Royal Infirmary, Leicester, UK
| | - Joseph Rassam
- grid.419248.20000 0004 0400 6485Department of Plastic and Reconstructive Surgery, Leicester Royal Infirmary, Leicester, UK
| | - Ryckie G. Wade
- grid.415967.80000 0000 9965 1030Department of Plastic and Reconstructive Surgery, Leeds Teaching Hospitals Trust, Leeds, UK ,grid.9909.90000 0004 1936 8403Leeds Institute for Medical Research, Advanced Imaging Centre, University of Leeds, Leeds, LS1 3EX UK
| |
Collapse
|
15
|
Cotter DL, Azad A, Cabeen RP, Kim MS, Geffner ME, Sepehrband F, Herting MM. White Matter Microstructural Differences in Youth With Classical Congenital Adrenal Hyperplasia. J Clin Endocrinol Metab 2021; 106:3196-3212. [PMID: 34272858 PMCID: PMC8530716 DOI: 10.1210/clinem/dgab520] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Indexed: 11/19/2022]
Abstract
CONTEXT Gray matter morphology in the prefrontal cortex and subcortical regions, including the hippocampus and amygdala, are affected in youth with classical congenital adrenal hyperplasia (CAH). It remains unclear if white matter connecting these aforementioned brain regions is compromised in youth with CAH. OBJECTIVE To examine brain white matter microstructure in youth with CAH compared to controls. DESIGN A cross-sectional sample of 23 youths with CAH due to 21-hydroxylase deficiency (12.9 ± 3.5 year; 61% female) and 33 healthy controls (13.1 ± 2.8 year; 61% female) with 3T multishell diffusion-weighted magnetic resonance brain scans. MAIN OUTCOME MEASURES Complementary modeling approaches, including diffusion tensor imaging (DTI) and neurite orientation dispersion and density imaging (NODDI), to examine in vivo white matter microstructure in six white matter tracts that innervate the prefrontal and subcortical regions. RESULTS DTI showed CAH youth had lower fractional anisotropy in both the fornix and stria terminalis and higher mean diffusivity in the fornix compared to controls. NODDI modeling revealed that CAH youth have a significantly higher orientation dispersion index in the stria terminalis compared to controls. White matter microstructural integrity was associated with smaller hippocampal and amygdala volumes in CAH youth. CONCLUSIONS These patterns of microstructure reflect less restricted water diffusion likely due to less coherency in oriented microstructure. These results suggest that white matter microstructural integrity in the fornix and stria terminalis is compromised and may be an additional related brain phenotype alongside affected hippocampus and amygdala neurocircuitry in individuals with CAH.
Collapse
Affiliation(s)
- Devyn L Cotter
- Neuroscience Graduate Program, University of Southern California, Los Angeles, CA, USA
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Anisa Azad
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Ryan P Cabeen
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Mimi S Kim
- Center for Endocrinology, Diabetes, and Metabolism, and The Saban Research Institute at Children’s Hospital Los Angeles, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Mitchell E Geffner
- Center for Endocrinology, Diabetes, and Metabolism, and The Saban Research Institute at Children’s Hospital Los Angeles, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Farshid Sepehrband
- Laboratory of Neuro Imaging, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| | - Megan M Herting
- Department of Population and Public Health Sciences, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
- Center for Endocrinology, Diabetes, and Metabolism, and The Saban Research Institute at Children’s Hospital Los Angeles, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
| |
Collapse
|
16
|
Palesi F, Ferrante M, Gaviraghi M, Misiti A, Savini G, Lascialfari A, D'Angelo E, Gandini Wheeler‐Kingshott CAM. Motor and higher-order functions topography of the human dentate nuclei identified with tractography and clustering methods. Hum Brain Mapp 2021; 42:4348-4361. [PMID: 34087040 PMCID: PMC8356999 DOI: 10.1002/hbm.25551] [Citation(s) in RCA: 9] [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: 02/15/2021] [Revised: 05/11/2021] [Accepted: 05/17/2021] [Indexed: 01/29/2023] Open
Abstract
Deep gray matter nuclei are the synaptic relays, responsible to route signals between specific brain areas. Dentate nuclei (DNs) represent the main output channel of the cerebellum and yet are often unexplored especially in humans. We developed a multimodal MRI approach to identify DNs topography on the basis of their connectivity as well as their microstructural features. Based on results, we defined DN parcellations deputed to motor and to higher-order functions in humans in vivo. Whole-brain probabilistic tractography was performed on 25 healthy subjects from the Human Connectome Project to infer DN parcellations based on their connectivity with either the cerebral or the cerebellar cortex, in turn. A third DN atlas was created inputting microstructural diffusion-derived metrics in an unsupervised fuzzy c-means classification algorithm. All analyses were performed in native space, with probability atlas maps generated in standard space. Cerebellar lobule-specific connectivity identified one motor parcellation, accounting for about 30% of the DN volume, and two non-motor parcellations, one cognitive and one sensory, which occupied the remaining volume. The other two approaches provided overlapping results in terms of geometrical distribution with those identified with cerebellar lobule-specific connectivity, although with some differences in volumes. A gender effect was observed with respect to motor areas and higher-order function representations. This is the first study that indicates that more than half of the DN volumes is involved in non-motor functions and that connectivity-based and microstructure-based atlases provide complementary information. These results represent a step-ahead for the interpretation of pathological conditions involving cerebro-cerebellar circuits.
Collapse
Affiliation(s)
- Fulvia Palesi
- Department of Brain and Behavioral SciencesUniversity of PaviaPavia
| | | | - Marta Gaviraghi
- Department of Electrical, Computer, and Biomedical EngineeringUniversity of PaviaPaviaItaly
| | - Anastasia Misiti
- Department of Electrical, Computer, and Biomedical EngineeringUniversity of PaviaPaviaItaly
| | - Giovanni Savini
- Department of NeuroradiologyIRCCS Humanitas Research HospitalMilanItaly
| | | | - Egidio D'Angelo
- Department of Brain and Behavioral SciencesUniversity of PaviaPavia
- Brain Connectivity CenterIRCCS Mondino FoundationPavia
| | - Claudia A. M. Gandini Wheeler‐Kingshott
- Department of Brain and Behavioral SciencesUniversity of PaviaPavia
- Brain Connectivity CenterIRCCS Mondino FoundationPavia
- NMR Research Unit, Queen Square MS Centre, Department of NeuroinflammationUCL Queen Square Institute of NeurologyLondon
| |
Collapse
|
17
|
Griffiths TT, Flather R, Teh I, Haroon HA, Shelley D, Plein S, Bourke G, Wade RG. Diffusion tensor imaging in cubital tunnel syndrome. Sci Rep 2021; 11:14982. [PMID: 34294771 PMCID: PMC8298404 DOI: 10.1038/s41598-021-94211-7] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/18/2021] [Accepted: 06/25/2021] [Indexed: 11/09/2022] Open
Abstract
Cubital tunnel syndrome (CuTS) is the 2nd most common compressive neuropathy. To improve both diagnosis and the selection of patients for surgery, there is a pressing need to develop a reliable and objective test of ulnar nerve 'health'. Diffusion tensor imaging (DTI) characterises tissue microstructure and may identify differences in the normal ulnar from those affected by CuTS. The aim of this study was to compare the DTI metrics from the ulnar nerves of healthy (asymptomatic) adults and patients with CuTS awaiting surgery. DTI was acquired at 3.0 T using single-shot echo-planar imaging (55 axial slices, 3 mm thick, 1.5 mm2 in-plane) with 30 diffusion sensitising gradient directions, a b-value of 800 s/mm2 and 4 signal averages. The sequence was repeated with the phase-encoding direction reversed. Data were combined and corrected using the FMRIB Software Library (FSL) and reconstructed using generalized q-sampling imaging in DSI Studio. Throughout the length of the ulnar nerve, the fractional anisotropy (FA), quantitative anisotropy (QA), mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity (RD) were extracted, then compared using mixed-effects linear regression. Thirteen healthy controls (8 males, 5 females) and 8 patients with CuTS (6 males, 2 females) completed the study. Throughout the length of the ulnar nerve, diffusion was more isotropic in patients with CuTS. Overall, patients with CuTS had a 6% lower FA than controls, with the largest difference observed proximal to the cubital tunnel (mean difference 0.087 [95% CI 0.035, 0.141]). Patients with CuTS also had a higher RD than controls, with the largest disparity observed within the forearm (mean difference 0.252 × 10-4 mm2/s [95% CI 0.085 × 10-4, 0.419 × 10-4]). There were no significant differences between patients and controls in QA, MD or AD. Throughout the length of the ulnar nerve, the fractional anisotropy and radial diffusivity in patients with CuTS are different to healthy controls. These findings suggest that DTI may provide an objective assessment of the ulnar nerve and potentially, improve the management of CuTS.
Collapse
Affiliation(s)
- Timothy T Griffiths
- Leeds Institute for Medical Research, University of Leeds, Leeds, UK
- Department of Plastic, Reconstructive and Hand Surgery, Leeds Teaching Hospitals Trust, Leeds, UK
| | - Robert Flather
- Leeds Institute for Medical Research, University of Leeds, Leeds, UK
- Department of Plastic, Reconstructive and Hand Surgery, Leeds Teaching Hospitals Trust, Leeds, UK
| | - Irvin Teh
- Leeds Institute for Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK
| | - Hamied A Haroon
- Division of Neuroscience and Experimental Psychology, The University of Manchester, Manchester, UK
| | - David Shelley
- The Advanced Imaging Centre, Leeds Teaching Hospitals Trust, Leeds, UK
| | - Sven Plein
- Leeds Institute for Medical Research, University of Leeds, Leeds, UK
- Leeds Institute for Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK
| | - Grainne Bourke
- Leeds Institute for Medical Research, University of Leeds, Leeds, UK
- Department of Plastic, Reconstructive and Hand Surgery, Leeds Teaching Hospitals Trust, Leeds, UK
| | - Ryckie G Wade
- Leeds Institute for Medical Research, University of Leeds, Leeds, UK.
- Department of Plastic, Reconstructive and Hand Surgery, Leeds Teaching Hospitals Trust, Leeds, UK.
| |
Collapse
|
18
|
Tracking white-matter brain modifications in chronic non-bothersome acoustic trauma tinnitus. NEUROIMAGE-CLINICAL 2021; 31:102696. [PMID: 34029920 PMCID: PMC8163994 DOI: 10.1016/j.nicl.2021.102696] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 05/05/2021] [Accepted: 05/06/2021] [Indexed: 11/30/2022]
Abstract
Tractography was compared between two groups of tinnitus and control participants. Diffusion was modeled with ss3t-CSD allowing apparent fiber density (AFD) calculation. 27 bundles of interest were chosen for their link to the auditory and limbic systems. AFD was significantly increased in the tinnitus group in the right frontal isthmus. AFD in the acoustic radiations was not significantly different between the groups.
Subjective tinnitus is a symptom characterized by the perception of sound with no external acoustic source, most often accompanied by co-morbidities. To date, the specific role of white matter abnormalities related to tinnitus reaches no consensus in the literature. The goal of this study was to explore the structural connectivity related to tinnitus percept per se, thus focusing on a specific population presenting chronic non-bothersome tinnitus of similar etiology (noise induced) without co-morbidities. We acquired diffusion-weighted images with high angular resolution in a homogeneous group of mildly impacted tinnitus participants (n = 19) and their matched controls (n = 19). We focused the study on two subsets of fiber bundles of interest: on one hand, we extracted the acoustic radiation and further included any intersecting fiber bundles; on the other hand, we explored the tracts related to the limbic system. We modeled the diffusion signal using constrained spherical deconvolution. We conducted a deep-learning based tractography segmentation and mapped Apparent Fiber Density (AFD) on the bundles of interest. C, as well as Fractional Anisotropy (FA) and FOD peak amplitude for comparison. Between group statistical comparison was performed along the 27 tracts of interest controlling for confounding hearing loss, tinnitus severity, and duration since onset. We tested a potential correlation with hearing loss, tinnitus duration and tinnitus handicap score along these tracts. In the tinnitus group, we observed increased AFD related to chronic tinnitus percept after acoustic trauma in two main white matter regions. First, in the right hemisphere, in the isthmus between inferior temporal and inferior frontal cortices, in the uncinate fasciculus (UF), and in the inferior fronto-occipital bundle (IFO). Second, in the left hemisphere, underneath the superior parietal region in the thalamo parietal tract and parieto-occipital pontine tract. Between-group differences in the acoustic radiations were not significant with AFD but were with FA. Furthermore, significant correlations with hearing loss were found in the left hemisphere in the inferior longitudinal fasciculus and in the fronto-pontine tract. No additional correlation was found with tinnitus duration nor with tinnitus handicap, as reflected by THI scores. The regions that displayed tinnitus related increased AFD also displayed increased FA. The isthmus of the UF and IFO in the right hemisphere appear to be involved with a number of neuropsychiatric and traumatic disorders confirming the involvement of the limbic system even in chronic non-bothersome tinnitus subjects, potentially suggesting a common pathway between these pathologies. White matter changes underneath the superior parietal cortex found here in tinnitus participants supports the implication of an auditory-somatosensory pathway in tinnitus perception.
Collapse
|
19
|
Toescu SM, Hales PW, Kaden E, Lacerda LM, Aquilina K, Clark CA. Tractographic and Microstructural Analysis of the Dentato-Rubro-Thalamo-Cortical Tracts in Children Using Diffusion MRI. Cereb Cortex 2021; 31:2595-2609. [PMID: 33338201 DOI: 10.1093/cercor/bhaa377] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022] Open
Abstract
The dentato-rubro-thalamo-cortical tract (DRTC) is the main outflow pathway of the cerebellum, contributing to a finely balanced corticocerebellar loop involved in cognitive and sensorimotor functions. Damage to the DRTC has been implicated in cerebellar mutism syndrome seen in up to 25% of children after cerebellar tumor resection. Multi-shell diffusion MRI (dMRI) combined with quantitative constrained spherical deconvolution tractography and multi-compartment spherical mean technique modeling was used to explore the frontocerebellar connections and microstructural signature of the DRTC in 30 healthy children. The highest density of DRTC connections were to the precentral (M1) and superior frontal gyri (F1), and from cerebellar lobules I-IV and IX. The first evidence of a topographic organization of anterograde projections to the frontal cortex at the level of the superior cerebellar peduncle (SCP) is demonstrated, with streamlines terminating in F1 lying dorsomedially in the SCP compared to those terminating in M1. The orientation dispersion entropy of DRTC regions appears to exhibit greater contrast than that shown by fractional anisotropy. Analysis of a separate reproducibility cohort demonstrates good consistency in the dMRI metrics described. These novel anatomical insights into this well-studied pathway may prove to be of clinical relevance in the surgical resection of cerebellar tumors.
Collapse
Affiliation(s)
- Sebastian M Toescu
- Developmental Imaging and Biophysics Section, UCL-GOS Institute of Child Health, London WC1N 1EH, UK.,Department of Neurosurgery, Great Ormond Street Hospital, London WC1N 3JH, UK
| | - Patrick W Hales
- Developmental Imaging and Biophysics Section, UCL-GOS Institute of Child Health, London WC1N 1EH, UK
| | - Enrico Kaden
- Developmental Imaging and Biophysics Section, UCL-GOS Institute of Child Health, London WC1N 1EH, UK.,Centre for Medical Image Computing, University College London, London WC1V 6LJ, UK
| | - Luis M Lacerda
- Developmental Imaging and Biophysics Section, UCL-GOS Institute of Child Health, London WC1N 1EH, UK
| | - Kristian Aquilina
- Department of Neurosurgery, Great Ormond Street Hospital, London WC1N 3JH, UK
| | - Christopher A Clark
- Developmental Imaging and Biophysics Section, UCL-GOS Institute of Child Health, London WC1N 1EH, UK
| |
Collapse
|
20
|
Abstract
Tractography is an important technique that allows the in vivo reconstruction of structural connections in the brain using diffusion MRI. Although tracking algorithms have improved during the last two decades, results of validation studies and international challenges warn about the reliability of tractography and point out the need for improved algorithms. In propagation-based tracking, connections have traditionally been modeled as piece-wise linear segments. In this work, we propose a novel propagation-based tracker that is capable of generating geometrically smooth ( C1 ) curves using parallel transport frames. Notably, our approach does not increase the complexity of the propagation problem that remains two-dimensional. Moreover, our tracker has a novel mechanism to reduce noise related propagation errors by incorporating topographic regularity of connections, a neuroanatomic property of many brain pathways. We ran extensive experiments and compared our approach against deterministic and other probabilistic algorithms. Our experiments on FiberCup and ISMRM 2015 challenge datasets as well as on 56 subjects of the Human Connectome Project show highly promising results both visually and quantitatively. Open-source implementations of the algorithm are shared publicly.
Collapse
|
21
|
Wade RG, Teh I, Andersson G, Yeh FC, Wiberg M, Bourke G. Fractional anisotropy thresholding for deterministic tractography of the roots of the brachial plexus. Sci Rep 2021; 11:80. [PMID: 33420207 PMCID: PMC7794285 DOI: 10.1038/s41598-020-79840-8] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2020] [Accepted: 12/07/2020] [Indexed: 02/03/2023] Open
Abstract
Diffusion tensor imaging (DTI) metrics, such as the fractional anisotropy (FA) and estimates of diffusivity are sensitive to the microstructure of peripheral nerves and may be displayed as tractograms. However, the ideal conditions for tractography of the roots of the brachial plexus are unclear, which represents the rationale for this study. Ten healthy adults were scanned using a Siemens Prisma (3T) and single-shot echo-planar imaging (b-value 0/1000 s/mm2, 64 directions, 2.5 mm3 with 4 averages; repeated in opposing phase encoding directions). Susceptibility correction and tractography were performed in DSI Studio by two independent raters. The effect of FA thresholding at increments of 0.01 (from 0.04 to 0.10) were tested. The mean FA varied between subjects by 2% (95% CI 1%, 3%). FA thresholds of 0.04, 0.05 and 0.06 all propagated 96% of tracts representing the roots; thresholding at 0.07 yielded 4% fewer tracts (p = 0.2), 0.08 yielded 11% fewer tracts (p = 0.008), 0.09 yielded 15% fewer tracts (p = 0.001) and 0.1 yielded 20% fewer tracts (p < 0.001). There was < 0.1% inter-rater variability in the measured FA and 99% agreement for tractography (κ = 0.92, p < 0.001). The fractional anisotropy thresholds required to generate tractograms of the roots of the brachial plexus appears to be lower than those used in the brain. We provide estimates of the probability of generating true tracts for each spinal nerve root of the brachial plexus, at different fractional anisotropy thresholds.
Collapse
Affiliation(s)
- Ryckie G Wade
- Academic Plastic Surgery Office, Department of Plastic and Reconstructive Surgery, Leeds General Infirmary, Leeds Teaching Hospitals Trust, Leeds, LS1 3EX, UK. .,Faculty of Medicine and Health Sciences, University of Leeds, Leeds, UK.
| | - Irvin Teh
- Leeds Institute for Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, UK
| | - Gustav Andersson
- Department of Integrative Medical Biology, Faculty of Medicine, Umeå University, Umeå, Sweden.,Department of Surgical and Perioperative Science, Faculty of Medicine, Umeå University, Umeå, Sweden.,Wallenberg Centre for Molecular Medicine, Umeå University, Umeå, Sweden
| | - Fang-Cheng Yeh
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, USA
| | - Mikael Wiberg
- Department of Integrative Medical Biology, Faculty of Medicine, Umeå University, Umeå, Sweden.,Department of Surgical and Perioperative Science, Faculty of Medicine, Umeå University, Umeå, Sweden.,Wallenberg Centre for Molecular Medicine, Umeå University, Umeå, Sweden
| | - Grainne Bourke
- Academic Plastic Surgery Office, Department of Plastic and Reconstructive Surgery, Leeds General Infirmary, Leeds Teaching Hospitals Trust, Leeds, LS1 3EX, UK.,Faculty of Medicine and Health Sciences, University of Leeds, Leeds, UK.,Department of Integrative Medical Biology, Faculty of Medicine, Umeå University, Umeå, Sweden.,Department of Surgical and Perioperative Science, Faculty of Medicine, Umeå University, Umeå, Sweden
| |
Collapse
|
22
|
Diffusion tensor imaging of the roots of the brachial plexus: a systematic review and meta-analysis of normative values. Clin Transl Imaging 2020; 8:419-431. [PMID: 33282795 PMCID: PMC7708343 DOI: 10.1007/s40336-020-00393-x] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Accepted: 09/25/2020] [Indexed: 02/06/2023]
Abstract
Purpose Diffusion tensor magnetic resonance imaging (DTI) characterises tissue microstructure and provides proxy measures of myelination, axon diameter, fibre density and organisation. This may be valuable in the assessment of the roots of the brachial plexus in health and disease. Therefore, there is a need to define the normal DTI values. Methods The literature was systematically searched for studies of asymptomatic adults who underwent DTI of the brachial plexus. Participant characteristics, scanning protocols, and measurements of the fractional anisotropy (FA) and mean diffusivity (MD) of each spinal root were extracted by two independent review authors. Generalised linear modelling was used to estimate the effect of experimental conditions on the FA and MD. Meta-analysis of root-level estimates was performed using Cohen's method with random effects. Results Nine articles, describing 316 adults (1:1 male:female) of mean age 35 years (SD 6) were included. Increments of ten diffusion sensitising gradient directions reduced the mean FA by 0.01 (95% CI 0.01, 0.03). Each year of life reduced the mean MD by 0.03 × 10-3 mm2/s (95% CI 0.01, 0.04). At 3-T, the pooled mean FA of the roots was 0.36 (95% CI 0.34, 0.38; I 2 98%). The pooled mean MD of the roots was 1.51 × 10-3 mm2/s (95% CI 1.45, 1.56; I 2 99%). Conclusions The FA and MD of the roots of the brachial plexus vary according to experimental conditions and participant factors. We provide summary estimates of the normative values in different conditions which may be valuable to researchers and clinicians alike.
Collapse
|
23
|
Jelescu IO, Palombo M, Bagnato F, Schilling KG. Challenges for biophysical modeling of microstructure. J Neurosci Methods 2020; 344:108861. [PMID: 32692999 PMCID: PMC10163379 DOI: 10.1016/j.jneumeth.2020.108861] [Citation(s) in RCA: 65] [Impact Index Per Article: 16.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2020] [Revised: 07/10/2020] [Accepted: 07/14/2020] [Indexed: 02/07/2023]
Abstract
The biophysical modeling efforts in diffusion MRI have grown considerably over the past 25 years. In this review, we dwell on the various challenges along the journey of bringing a biophysical model from initial design to clinical implementation, identifying both hurdles that have been already overcome and outstanding issues. First, we describe the critical initial task of selecting which features of tissue microstructure can be estimated using a model and which acquisition protocol needs to be implemented to make the estimation possible. The model performance should necessarily be tested in realistic numerical simulations and in experimental data - adapting the fitting strategy accordingly, and parameter estimates should be validated against complementary techniques, when/if available. Secondly, the model performance and validity should be explored in pathological conditions, and, if appropriate, dedicated models for pathology should be developed. We build on examples from tumors, ischemia and demyelinating diseases. We then discuss the challenges associated with clinical translation and added value. Finally, we single out four major unresolved challenges that are related to: the availability of a microstructural ground truth, the validation of model parameters which cannot be accessed with complementary techniques, the development of a generalized standard model for any brain region and pathology, and the seamless communication between different parties involved in the development and application of biophysical models of diffusion.
Collapse
|
24
|
Palesi F, Lorenzi RM, Casellato C, Ritter P, Jirsa V, Gandini Wheeler-Kingshott CA, D’Angelo E. The Importance of Cerebellar Connectivity on Simulated Brain Dynamics. Front Cell Neurosci 2020; 14:240. [PMID: 32848628 PMCID: PMC7411185 DOI: 10.3389/fncel.2020.00240] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2020] [Accepted: 07/09/2020] [Indexed: 11/14/2022] Open
Abstract
The brain shows a complex multiscale organization that prevents a direct understanding of how structure, function and dynamics are correlated. To date, advances in neural modeling offer a unique opportunity for simulating global brain dynamics by embedding empirical data on different scales in a mathematical framework. The Virtual Brain (TVB) is an advanced data-driven model allowing to simulate brain dynamics starting from individual subjects' structural and functional connectivity obtained, for example, from magnetic resonance imaging (MRI). The use of TVB has been limited so far to cerebral connectivity but here, for the first time, we have introduced cerebellar nodes and interconnecting tracts to demonstrate the impact of cerebro-cerebellar loops on brain dynamics. Indeed, the matching between the empirical and simulated functional connectome was significantly improved when including the cerebro-cerebellar loops. This positive result should be considered as a first step, since issues remain open about the best strategy to reconstruct effective structural connectivity and the nature of the neural mass or mean-field models generating local activity in the nodes. For example, signal processing is known to differ remarkably between cortical and cerebellar microcircuits. Tackling these challenges is expected to further improve the predictive power of functional brain activity simulations, using TVB or other similar tools, in explaining not just global brain dynamics but also the role of cerebellum in determining brain states in physiological conditions and in the numerous pathologies affecting the cerebro-cerebellar loops.
Collapse
Affiliation(s)
- Fulvia Palesi
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy
| | | | - Claudia Casellato
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
| | - Petra Ritter
- Brain Simulation Section, Department of Neurology with Experimental Neurology, Charité – Universitätsmedizin Berlin and Berlin Institute of Health, Berlin, Germany
- Bernstein Center for Computational Neuroscience, Berlin, Germany
| | - Viktor Jirsa
- Institut de Neurosciences des Systèmes – Inserm UMR1106, Aix-Marseille Université, Marseille, France
| | - Claudia A.M. Gandini Wheeler-Kingshott
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Institute of Neurology, London, United Kingdom
| | - Egidio D’Angelo
- Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy
- Brain Connectivity Center, IRCCS Mondino Foundation, Pavia, Italy
| |
Collapse
|
25
|
Quartarone A, Cacciola A, Milardi D, Ghilardi MF, Calamuneri A, Chillemi G, Anastasi G, Rothwell J. New insights into cortico-basal-cerebellar connectome: clinical and physiological considerations. Brain 2020; 143:396-406. [PMID: 31628799 DOI: 10.1093/brain/awz310] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2019] [Revised: 08/06/2019] [Accepted: 08/12/2019] [Indexed: 12/15/2022] Open
Abstract
The current model of the basal ganglia system based on the 'direct', 'indirect' and 'hyperdirect' pathways provides striking predictions about basal ganglia function that have been used to develop deep brain stimulation approaches for Parkinson's disease and dystonia. The aim of this review is to challenge this scheme in light of new tract tracing information that has recently become available from the human brain using MRI-based tractography, thus providing a novel perspective on the basal ganglia system. We also explore the implications of additional direct pathways running from cortex to basal ganglia and between basal ganglia and cerebellum in the pathophysiology of movement disorders.
Collapse
Affiliation(s)
- Angelo Quartarone
- Department of Biomedical, Dental Sciences and Morphological and Functional Images, University of Messina, Messina, Italy
| | - Alberto Cacciola
- Department of Biomedical, Dental Sciences and Morphological and Functional Images, University of Messina, Messina, Italy
| | - Demetrio Milardi
- Department of Biomedical, Dental Sciences and Morphological and Functional Images, University of Messina, Messina, Italy.,IRCCS Centro Neurolesi 'Bonino Pulejo', Messina, Italy
| | | | | | | | - Giuseppe Anastasi
- Department of Biomedical, Dental Sciences and Morphological and Functional Images, University of Messina, Messina, Italy
| | - John Rothwell
- Sobell Department of Motor Neuroscience and Movement Disorders, Institute of Neurology, University College London, London, UK
| |
Collapse
|
26
|
Wade RG, Tanner SF, Teh I, Ridgway JP, Shelley D, Chaka B, Rankine JJ, Andersson G, Wiberg M, Bourke G. Diffusion Tensor Imaging for Diagnosing Root Avulsions in Traumatic Adult Brachial Plexus Injuries: A Proof-of-Concept Study. Front Surg 2020; 7:19. [PMID: 32373625 PMCID: PMC7177010 DOI: 10.3389/fsurg.2020.00019] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2019] [Accepted: 03/23/2020] [Indexed: 01/09/2023] Open
Abstract
Cross-sectional MRI has modest diagnostic accuracy for diagnosing traumatic brachial plexus root avulsions. Consequently, patients either undergo major exploratory surgery or months of surveillance to determine if and what nerve reconstruction is needed. This study aimed to develop a diffusion tensor imaging (DTI) protocol at 3 Tesla to visualize normal roots and identify traumatic root avulsions of the brachial plexus. Seven healthy adults and 12 adults with known (operatively explored) unilateral traumatic brachial plexus root avulsions were scanned. DTI was acquired using a single-shot echo-planar imaging sequence at 3 Tesla. The brachial plexus was visualized by deterministic tractography. Fractional anisotropy (FA) and mean diffusivity (MD) were calculated for injured and avulsed roots in the lateral recesses of the vertebral foramen. Compared to healthy nerves roots, the FA of avulsed nerve roots was lower (mean difference 0.1 [95% CI 0.07, 0.13]; p < 0.001) and the MD was greater (mean difference 0.32 × 10-3 mm2/s [95% CI 0.11, 0.53]; p < 0.001). Deterministic tractography reconstructed both normal roots and root avulsions of the brachial plexus; the negative-predictive value for at least one root avulsion was 100% (95% CI 78, 100). Therefore, DTI might help visualize both normal and injured roots of the brachial plexus aided by tractography. The precision of this technique and how it relates to neural microstructure will be further investigated in a prospective diagnostic accuracy study of patients with acute brachial plexus injuries.
Collapse
Affiliation(s)
- Ryckie G. Wade
- Department of Plastic and Reconstructive Surgery, Leeds Teaching Hospitals Trust, Leeds, United Kingdom
- Faculty of Medicine and Health Sciences, University of Leeds, Leeds, United Kingdom
| | - Steven F. Tanner
- National Institute for Health Research (NIHR), Leeds Biomedical Research Centre, Leeds, United Kingdom
- Department of Medical Physics and Engineering, Leeds Teaching Hospitals Trust, Leeds, United Kingdom
| | - Irvin Teh
- Leeds Institute for Cardiovascular and Metabolic Medicine, University of Leeds, Leeds, United Kingdom
| | - John P. Ridgway
- National Institute for Health Research (NIHR), Leeds Biomedical Research Centre, Leeds, United Kingdom
- Department of Medical Physics and Engineering, Leeds Teaching Hospitals Trust, Leeds, United Kingdom
| | - David Shelley
- The Advanced Imaging Centre, Leeds Teaching Hospitals Trust, Leeds, United Kingdom
| | - Brian Chaka
- National Institute for Health Research (NIHR), Leeds Biomedical Research Centre, Leeds, United Kingdom
| | - James J. Rankine
- Department of Radiology, Leeds Teaching Hospitals Trust, Leeds, United Kingdom
| | - Gustav Andersson
- Department of Integrative Medical Biology (Anatomy), Faculty of Medicine, Umeå University, Umeå, Sweden
- Department of Surgical and Perioperative Science (Hand and Plastic Surgery), Faculty of Medicine, Umeå University, Umeå, Sweden
- Wallenberg Centre for Molecular Medicine, Umeå University, Umeå, Sweden
| | - Mikael Wiberg
- Department of Integrative Medical Biology (Anatomy), Faculty of Medicine, Umeå University, Umeå, Sweden
- Department of Surgical and Perioperative Science (Hand and Plastic Surgery), Faculty of Medicine, Umeå University, Umeå, Sweden
| | - Grainne Bourke
- Department of Plastic and Reconstructive Surgery, Leeds Teaching Hospitals Trust, Leeds, United Kingdom
- Faculty of Medicine and Health Sciences, University of Leeds, Leeds, United Kingdom
- Department of Integrative Medical Biology (Anatomy), Faculty of Medicine, Umeå University, Umeå, Sweden
- Department of Surgical and Perioperative Science (Hand and Plastic Surgery), Faculty of Medicine, Umeå University, Umeå, Sweden
| |
Collapse
|
27
|
Fick RHJ, Wassermann D, Deriche R. The Dmipy Toolbox: Diffusion MRI Multi-Compartment Modeling and Microstructure Recovery Made Easy. Front Neuroinform 2019; 13:64. [PMID: 31680924 PMCID: PMC6803556 DOI: 10.3389/fninf.2019.00064] [Citation(s) in RCA: 39] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/21/2019] [Accepted: 09/04/2019] [Indexed: 12/22/2022] Open
Abstract
Non-invasive estimation of brain microstructure features using diffusion MRI (dMRI)—known as Microstructure Imaging—has become an increasingly diverse and complicated field over the last decades. Multi-compartment (MC)-models, representing the measured diffusion signal as a linear combination of signal models of distinct tissue types, have been developed in many forms to estimate these features. However, a generalized implementation of MC-modeling as a whole, providing deeper insights in its capabilities, remains missing. To address this fact, we present Diffusion Microstructure Imaging in Python (Dmipy), an open-source toolbox implementing PGSE-based MC-modeling in its most general form. Dmipy allows on-the-fly implementation, signal modeling, and optimization of any user-defined MC-model, for any PGSE acquisition scheme. Dmipy follows a “building block”-based philosophy to Microstructure Imaging, meaning MC-models are modularly constructed to include any number and type of tissue models, allowing simultaneous representation of a tissue's diffusivity, orientation, volume fractions, axon orientation dispersion, and axon diameter distribution. In particular, Dmipy is geared toward facilitating reproducible, reliable MC-modeling pipelines, often allowing the whole process from model construction to parameter map recovery in fewer than 10 lines of code. To demonstrate Dmipy's ease of use and potential, we implement a wide range of well-known MC-models, including IVIM, AxCaliber, NODDI(x), Bingham-NODDI, the spherical mean-based SMT and MC-MDI, and spherical convolution-based single- and multi-tissue CSD. By allowing parameter cascading between MC-models, Dmipy also facilitates implementation of advanced approaches like CSD with voxel-varying kernels and single-shell 3-tissue CSD. By providing a well-tested, user-friendly toolbox that simplifies the interaction with the otherwise complicated field of dMRI-based Microstructure Imaging, Dmipy contributes to more reproducible, high-quality research.
Collapse
Affiliation(s)
- Rutger H J Fick
- TheraPanacea, Paris, France.,Inria Sophia Antipolis-Méditerranée, Université Côte d'Azur, Sophia Antipolis, France
| | | | - Rachid Deriche
- Inria Sophia Antipolis-Méditerranée, Université Côte d'Azur, Sophia Antipolis, France
| |
Collapse
|
28
|
Calamante F. The Seven Deadly Sins of Measuring Brain Structural Connectivity Using Diffusion MRI Streamlines Fibre-Tracking. Diagnostics (Basel) 2019; 9:diagnostics9030115. [PMID: 31500098 PMCID: PMC6787694 DOI: 10.3390/diagnostics9030115] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2019] [Revised: 08/13/2019] [Accepted: 09/04/2019] [Indexed: 12/13/2022] Open
Abstract
There is great interest in the study of brain structural connectivity, as white matter abnormalities have been implicated in many disease states. Diffusion magnetic resonance imaging (MRI) provides a powerful means to characterise structural connectivity non-invasively, by using a fibre-tracking algorithm. The most widely used fibre-tracking strategy is based on the step-wise generation of streamlines. Despite their popularity and widespread use, there are a number of practical considerations that must be taken into account in order to increase the robustness of streamlines tracking results, particularly when these methods are used to study brain structural connectivity, and the connectome. This review article describes what we consider the ‘seven deadly sins’ of mapping structural connections using diffusion MRI streamlines fibre-tracking, with particular emphasis on ‘sins’ that can be practically avoided and they can have an important impact in the results. It is shown that there are important ‘deadly sins’ to be avoided at every step of the pipeline, such as during data acquisition, during data modelling to estimate local fibre architecture, during the fibre-tracking process itself, and during quantification of the tracking results. The recommendations here are intended to inform users on potential important shortcomings of their current tracking protocols, as well as to guide future users on some of the key issues and decisions that must be faced when designing their processing pipelines.
Collapse
Affiliation(s)
- Fernando Calamante
- Sydney Imaging, The University of Sydney, Sydney, New South Wales 2050, Australia.
- School of Aerospace, Mechanical and Mechatronic Engineering, The University of Sydney, Sydney, New South Wales 2006, Australia.
- Florey Department of Neuroscience and Mental Health, University of Melbourne, Melbourne, Victoria 3052, Australia.
- Brain and Mind Centre, The University of Sydney, 94 Mallett Street, Camperdown, NSW 2050, Australia.
| |
Collapse
|