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Herlin B, Uszynski I, Chauvel M, Dupont S, Poupon C. Sex-related variability of white matter tracts in the whole HCP cohort. Brain Struct Funct 2024; 229:1713-1735. [PMID: 39012482 PMCID: PMC11374878 DOI: 10.1007/s00429-024-02833-0] [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: 04/19/2024] [Accepted: 07/06/2024] [Indexed: 07/17/2024]
Abstract
Behavioral differences between men and women have been studied extensively, as have differences in brain anatomy. However, most studies have focused on differences in gray matter, while white matter has been much less studied. We conducted a comprehensive study of 77 deep white matter tracts to analyze their volumetric and microstructural variability between men and women in the full Human Connectome Project (HCP) cohort of 1065 healthy individuals aged 22-35 years. We found a significant difference in total brain volume between men and women (+ 12.6% in men), consistent with the literature. 16 tracts showed significant volumetric differences between men and women, one of which stood out due to a larger effect size: the corpus callosum genu, which was larger in women (+ 7.3% in women, p = 5.76 × 10-19). In addition, we found several differences in microstructural parameters between men and women, both using standard Diffusion Tensor Imaging (DTI) parameters and more complex microstructural parameters from the Neurite Orientation Dispersion and Density Imaging (NODDI) model, with the tracts showing the greatest differences belonging to motor (cortico-spinal tracts, cortico-cerebellar tracts) or limbic (cingulum, fornix, thalamo-temporal radiations) systems. These microstructural differences may be related to known behavioral differences between the sexes in timed motor performance, aggressiveness/impulsivity, and social cognition.
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Affiliation(s)
- B Herlin
- BAOBAB, NeuroSpin, Université Paris-Saclay, CNRS, CEA, Gif-Sur-Yvette, France.
- Rehabilitation Unit, AP-HP, Pitié-Salpêtrière Hospital, Paris, France.
- Université Paris Sorbonne, Paris, France.
| | - I Uszynski
- BAOBAB, NeuroSpin, Université Paris-Saclay, CNRS, CEA, Gif-Sur-Yvette, France
| | - M Chauvel
- BAOBAB, NeuroSpin, Université Paris-Saclay, CNRS, CEA, Gif-Sur-Yvette, France
| | - S Dupont
- Reference Center for Rare Epilepsies, Department of Neurology, Epileptology Unit, AP-HP, Pitié-Salpêtrière Hospital, Paris, France
- Rehabilitation Unit, AP-HP, Pitié-Salpêtrière Hospital, Paris, France
- Paris Brain Institute (ICM), Sorbonne-Université, Inserm U1127, CNRS 7225, Paris, France
- Université Paris Sorbonne, Paris, France
| | - C Poupon
- BAOBAB, NeuroSpin, Université Paris-Saclay, CNRS, CEA, Gif-Sur-Yvette, France
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2
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Asaridou SS, Cler GJ, Wiedemann A, Krishnan S, Smith HJ, Willis HE, Healy MP, Watkins KE. Microstructural Properties of the Cerebellar Peduncles in Children With Developmental Language Disorder. NEUROBIOLOGY OF LANGUAGE (CAMBRIDGE, MASS.) 2024; 5:774-794. [PMID: 39175782 PMCID: PMC11338306 DOI: 10.1162/nol_a_00142] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/06/2023] [Accepted: 03/13/2024] [Indexed: 08/24/2024]
Abstract
Children with developmental language disorder (DLD) struggle to learn their native language for no apparent reason. While research on the neurobiological underpinnings of the disorder has focused on the role of corticostriatal systems, little is known about the role of the cerebellum in DLD. Corticocerebellar circuits might be involved in the disorder as they contribute to complex sensorimotor skill learning, including the acquisition of spoken language. Here, we used diffusion-weighted imaging data from 77 typically developing and 54 children with DLD and performed probabilistic tractography to identify the cerebellum's white matter tracts: the inferior, middle, and superior cerebellar peduncles. Children with DLD showed lower fractional anisotropy (FA) in the inferior cerebellar peduncles (ICP), fiber tracts that carry motor and sensory input via the inferior olive to the cerebellum. Lower FA in DLD was driven by lower axial diffusivity. Probing this further with more sophisticated modeling of diffusion data, we found higher orientation dispersion but no difference in neurite density in the ICP of children with DLD. Reduced FA is therefore unlikely to be reflecting microstructural differences in myelination, rather the organization of axons in these pathways is disrupted. ICP microstructure was not associated with language or motor coordination performance in our sample. We also found no differences in the middle and superior peduncles, the main pathways connecting the cerebellum with the cortex. To conclude, it is not corticocerebellar but atypical olivocerebellar white matter connections that characterize DLD and suggest the involvement of the olivocerebellar system in speech and language acquisition and development.
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Affiliation(s)
- Salomi S. Asaridou
- Department of Experimental Psychology, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Gabriel J. Cler
- Department of Experimental Psychology, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
- Department of Speech & Hearing Sciences, University of Washington, Seattle, WA, USA
| | - Anna Wiedemann
- Department of Experimental Psychology, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Saloni Krishnan
- Department of Experimental Psychology, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
- Department of Psychology, Royal Holloway, University of London, Egham Hill, Surrey, UK
| | - Harriet J. Smith
- Department of Experimental Psychology, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Hanna E. Willis
- Department of Experimental Psychology, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
- Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, UK
| | - Máiréad P. Healy
- Department of Experimental Psychology, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
- Department of Psychology, University of Cambridge, Cambridge, UK
| | - Kate E. Watkins
- Department of Experimental Psychology, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
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3
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Verschuur AS, King R, Tax CMW, Boomsma MF, van Wezel-Meijler G, Leemans A, Leijser LM. Methodological considerations on diffusion MRI tractography in infants aged 0-2 years: a scoping review. Pediatr Res 2024:10.1038/s41390-024-03463-2. [PMID: 39143201 DOI: 10.1038/s41390-024-03463-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/12/2024] [Revised: 07/20/2024] [Accepted: 07/24/2024] [Indexed: 08/16/2024]
Abstract
Diffusion MRI (dMRI) enables studying the complex architectural organization of the brain's white matter (WM) through virtual reconstruction of WM fiber tracts (tractography). Despite the anticipated clinical importance of applying tractography to study structural connectivity and tract development during the critical period of rapid infant brain maturation, detailed descriptions on how to approach tractography in young infants are limited. Over the past two decades, tractography from infant dMRI has mainly been applied in research settings and focused on diffusion tensor imaging (DTI). Only few studies used techniques superior to DTI in terms of disentangling information on the brain's organizational complexity, including crossing fibers. While more advanced techniques may enhance our understanding of the intricate processes of normal and abnormal brain development and extensive knowledge has been gained from application on adult scans, their applicability in infants has remained underexplored. This may partially be due to the higher technical requirements versus the need to limit scan time in young infants. We review various previously described methodological practices for tractography in the infant brain (0-2 years-of-age) and provide recommendations to optimize advanced tractography approaches to enable more accurate reconstructions of the brain WM's complexity. IMPACT: Diffusion tensor imaging is the technique most frequently used for fiber tracking in the developing infant brain but is limited in capability to disentangle the complex white matter organization. Advanced tractography techniques allow for reconstruction of crossing fiber bundles to better reflect the brain's complex organization. Yet, they pose practical and technical challenges in the fast developing young infant's brain. Methods on how to approach advanced tractography in the young infant's brain have hardly been described. Based on a literature review, recommendations are provided to optimize tractography for the developing infant brain, aiming to advance early diagnosis and neuroprotective strategies.
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Affiliation(s)
- Anouk S Verschuur
- Department of Radiology, Isala Hospital Zwolle, Zwolle, The Netherlands.
- Department of Pediatrics, Section of Newborn Critical Care, University of Calgary, Calgary, Canada.
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands.
| | - Regan King
- Department of Pediatrics, Section of Newborn Critical Care, University of Calgary, Calgary, Canada
| | - Chantal M W Tax
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
- CUBRIC, School of Physics and Astronomy, Cardiff University, Cardiff, United Kingdom
| | - Martijn F Boomsma
- Department of Radiology, Isala Hospital Zwolle, Zwolle, The Netherlands
- Division of Imaging and Oncology, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Gerda van Wezel-Meijler
- Department of Neonatology, Isala Women and Children's Hospital Zwolle, Zwolle, The Netherlands
| | - Alexander Leemans
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Lara M Leijser
- Department of Pediatrics, Section of Newborn Critical Care, University of Calgary, Calgary, Canada
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4
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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.
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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
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5
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Wang H, Blanke N, Gong D, Ortug A, Alatorre Warren JL, Clickner C, Ammon W, Nolan J, Cotronis Z, van der Kouwe A, Takahashi E. Imaging of developing human brains with ex vivo PSOCT and dMRI. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.27.605383. [PMID: 39211119 PMCID: PMC11361116 DOI: 10.1101/2024.07.27.605383] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/04/2024]
Abstract
The human brain undergoes substantial developmental changes in the first five years of life. Particularly in the white matter, myelination of axons occurs near birth and continues at a rapid pace during the first 2 to 3 years. Diffusion MRI (dMRI) has revolutionized our understanding of developmental trajectories in white matter. However, the mm-resolution of in vivo techniques bears significant limitation in revealing the microstructure of the developing brain. Polarization sensitive optical coherence tomography (PSOCT) is a three-dimensional (3D) optical imaging technique that uses polarized light interferometry to target myelinated fiber tracts with micrometer resolution. Previous studies have shown that PSOCT contributes significantly to the elucidation of myelin content and quantification of fiber orientation in adult human brains. In this study, we utilized the PSOCT technique to study developing brains during the first 5 years of life in combination with ex vivo dMRI. The results showed that the optical properties of PSOCT quantitatively reveal the myelination process in young children. The imaging contrast of the optic axis orientation is a sensitive measure of fiber orientations in largely unmyelinated brains as young as 3-months-old. The micrometer resolution of PSOCT provides substantially enriched information about complex fiber networks and complements submillimeter dMRI. This new optical tool offers great potential to reveal the white matter structures in normal neurodevelopment and developmental disorders in unprecedented detail.
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6
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Calixto C, Soldatelli MD, Li B, Pierotich L, Gholipour A, Warfield SK, Karimi D. White matter tract crossing and bottleneck regions in the fetal brain. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.07.20.603804. [PMID: 39091823 PMCID: PMC11291018 DOI: 10.1101/2024.07.20.603804] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Indexed: 08/04/2024]
Abstract
There is a growing interest in using diffusion MRI to study the white matter tracts and structural connectivity of the fetal brain. Recent progress in data acquisition and processing suggests that this imaging modality has a unique role in elucidating the normal and abnormal patterns of neurodevelopment in utero. However, there have been no efforts to quantify the prevalence of crossing tracts and bottleneck regions, important issues that have been extensively researched for adult brains. In this work, we determined the brain regions with crossing tracts and bottlenecks between 23 and 36 gestational weeks. We performed probabilistic tractography on 59 fetal brain scans and extracted a set of 51 distinct white tracts, which we grouped into 10 major tract bundle groups. We analyzed the results to determine the patterns of tract crossings and bottlenecks. Our results showed that 20-25% of the white matter voxels included two or three crossing tracts. Bottlenecks were more prevalent. Between 75-80% of the voxels were characterized as bottlenecks, with more than 40% of the voxels involving four or more tracts. The results of this study highlight the challenge of fetal brain tractography and structural connectivity assessment and call for innovative image acquisition and analysis methods to mitigate these problems.
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Affiliation(s)
- Camilo Calixto
- Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Matheus D Soldatelli
- Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Bo Li
- Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Lana Pierotich
- Department of Pediatrics, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Ali Gholipour
- Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Simon K Warfield
- Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Davood Karimi
- Department of Radiology, Boston Children's Hospital and Harvard Medical School, Boston, Massachusetts, USA
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7
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Sanda P, Hlinka J, van den Berg M, Skoch A, Bazhenov M, Keliris GA, Krishnan GP. Cholinergic modulation supports dynamic switching of resting state networks through selective DMN suppression. PLoS Comput Biol 2024; 20:e1012099. [PMID: 38843298 PMCID: PMC11185486 DOI: 10.1371/journal.pcbi.1012099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/25/2023] [Revised: 06/18/2024] [Accepted: 04/23/2024] [Indexed: 06/19/2024] Open
Abstract
Brain activity during the resting state is widely used to examine brain organization, cognition and alterations in disease states. While it is known that neuromodulation and the state of alertness impact resting-state activity, neural mechanisms behind such modulation of resting-state activity are unknown. In this work, we used a computational model to demonstrate that change in excitability and recurrent connections, due to cholinergic modulation, impacts resting-state activity. The results of such modulation in the model match closely with experimental work on direct cholinergic modulation of Default Mode Network (DMN) in rodents. We further extended our study to the human connectome derived from diffusion-weighted MRI. In human resting-state simulations, an increase in cholinergic input resulted in a brain-wide reduction of functional connectivity. Furthermore, selective cholinergic modulation of DMN closely captured experimentally observed transitions between the baseline resting state and states with suppressed DMN fluctuations associated with attention to external tasks. Our study thus provides insight into potential neural mechanisms for the effects of cholinergic neuromodulation on resting-state activity and its dynamics.
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Affiliation(s)
- Pavel Sanda
- Institute of Computer Science of the Czech Academy of Sciences, Prague, Czech Republic
| | - Jaroslav Hlinka
- Institute of Computer Science of the Czech Academy of Sciences, Prague, Czech Republic
- National Institute of Mental Health, Klecany, Czech Republic
| | - Monica van den Berg
- Bio-Imaging Lab, University of Antwerp, Antwerp, Belgium
- μNEURO Research Centre of Excellence, University of Antwerp, Antwerp, Belgium
| | - Antonin Skoch
- National Institute of Mental Health, Klecany, Czech Republic
- MR Unit, Department of Diagnostic and Interventional Radiology, Institute for Clinical and Experimental Medicine, Prague, Czech Republic
| | - Maxim Bazhenov
- Department of Medicine, University of California San Diego, La Jolla, California, United States of America
| | - Georgios A. Keliris
- Bio-Imaging Lab, University of Antwerp, Antwerp, Belgium
- Institute of Computer Science, Foundation for Research and Technology - Hellas, Heraklion, Crete, Greece
| | - Giri P. Krishnan
- Department of Medicine, University of California San Diego, La Jolla, California, United States of America
- Georgia Institute of Technology, Atlanta, Georgia, United States of America
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8
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Gao C, Wu X, Cheng X, Madsen KH, Chu C, Yang Z, Fan L. Individualized brain mapping for navigated neuromodulation. Chin Med J (Engl) 2024; 137:508-523. [PMID: 38269482 PMCID: PMC10932519 DOI: 10.1097/cm9.0000000000002979] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Indexed: 01/26/2024] Open
Abstract
ABSTRACT The brain is a complex organ that requires precise mapping to understand its structure and function. Brain atlases provide a powerful tool for studying brain circuits, discovering biological markers for early diagnosis, and developing personalized treatments for neuropsychiatric disorders. Neuromodulation techniques, such as transcranial magnetic stimulation and deep brain stimulation, have revolutionized clinical therapies for neuropsychiatric disorders. However, the lack of fine-scale brain atlases limits the precision and effectiveness of these techniques. Advances in neuroimaging and machine learning techniques have led to the emergence of stereotactic-assisted neurosurgery and navigation systems. Still, the individual variability among patients and the diversity of brain diseases make it necessary to develop personalized solutions. The article provides an overview of recent advances in individualized brain mapping and navigated neuromodulation and discusses the methodological profiles, advantages, disadvantages, and future trends of these techniques. The article concludes by posing open questions about the future development of individualized brain mapping and navigated neuromodulation.
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Affiliation(s)
- Chaohong Gao
- Sino–Danish College, University of Chinese Academy of Sciences, Beijing 100190, China
| | - Xia Wu
- Brainnetome Center, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Xinle Cheng
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100190, China
| | - Kristoffer Hougaard Madsen
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby 2800, Denmark
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Hvidovre 2650, Denmark
| | - Congying Chu
- Brainnetome Center, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Zhengyi Yang
- Brainnetome Center, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Lingzhong Fan
- Sino–Danish College, University of Chinese Academy of Sciences, Beijing 100190, China
- Brainnetome Center, National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100190, China
- CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Health and Life Sciences, University of Health and Rehabilitation Sciences, Qingdao, Shandong 266000, China
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9
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Dai Z, Song L, Luo C, Liu D, Li M, Han Z. Hemispheric lateralization of language processing: insights from network-based symptom mapping and patient subgroups. Cereb Cortex 2024; 34:bhad437. [PMID: 38031356 DOI: 10.1093/cercor/bhad437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2023] [Revised: 10/27/2023] [Accepted: 10/27/2023] [Indexed: 12/01/2023] Open
Abstract
The hemispheric laterality of language processing has become a hot topic in modern neuroscience. Although most previous studies have reported left-lateralized language processing, other studies found it to be bilateral. A previous neurocomputational model has proposed a unified framework to explain that the above discrepancy might be from healthy and patient individuals. This model posits an initial symmetry but imbalanced capacity in language processing for healthy individuals, with this imbalance contributing to language recovery disparities following different hemispheric injuries. The present study investigated this model by analyzing the lateralization patterns of language subnetworks across multiple attributes with a group of 99 patients (compared to nonlanguage processing) and examining the lateralization patterns of language subnetworks in subgroups with damage to different hemispheres. Subnetworks were identified using a whole-brain network-based lesion-symptom mapping method, and the lateralization index was quantitatively measured. We found that all the subnetworks in language processing were left-lateralized, while subnetworks in nonlanguage processing had different lateralization patterns. Moreover, diverse hemisphere-injury subgroups exhibited distinct language recovery effects. These findings provide robust support for the proposed neurocomputational model of language processing.
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Affiliation(s)
- Zhiyun Dai
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Luping Song
- Shenzhen Sixth People's Hospital (Nanshan Hospital), Huazhong University of Science and Technology Union Shenzhen Hospital, Shenzhen 518052, China
| | - Chongjing Luo
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Di Liu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Mingyang Li
- Key Laboratory for Biomedical Engineering of Ministry of Education, Department of Biomedical Engineering, College of Biomedical Engineering & Instrument Science, Zhejiang University, Yuquan Campus, Hangzhou 310027, China
| | - Zaizhu Han
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
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10
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Nelson MC, Royer J, Lu WD, Leppert IR, Campbell JSW, Schiavi S, Jin H, Tavakol S, Vos de Wael R, Rodriguez-Cruces R, Pike GB, Bernhardt BC, Daducci A, Misic B, Tardif CL. The human brain connectome weighted by the myelin content and total intra-axonal cross-sectional area of white matter tracts. Netw Neurosci 2023; 7:1363-1388. [PMID: 38144691 PMCID: PMC10697181 DOI: 10.1162/netn_a_00330] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2023] [Accepted: 07/19/2023] [Indexed: 12/26/2023] Open
Abstract
A central goal in neuroscience is the development of a comprehensive mapping between structural and functional brain features, which facilitates mechanistic interpretation of brain function. However, the interpretability of structure-function brain models remains limited by a lack of biological detail. Here, we characterize human structural brain networks weighted by multiple white matter microstructural features including total intra-axonal cross-sectional area and myelin content. We report edge-weight-dependent spatial distributions, variance, small-worldness, rich club, hubs, as well as relationships with function, edge length, and myelin. Contrasting networks weighted by the total intra-axonal cross-sectional area and myelin content of white matter tracts, we find opposite relationships with functional connectivity, an edge-length-independent inverse relationship with each other, and the lack of a canonical rich club in myelin-weighted networks. When controlling for edge length, networks weighted by either fractional anisotropy, radial diffusivity, or neurite density show no relationship with whole-brain functional connectivity. We conclude that the co-utilization of structural networks weighted by total intra-axonal cross-sectional area and myelin content could improve our understanding of the mechanisms mediating the structure-function brain relationship.
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Affiliation(s)
- Mark C. Nelson
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
| | - Jessica Royer
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
| | - Wen Da Lu
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
- Department of Biomedical Engineering, McGill University, Montreal, QC, Canada
| | - Ilana R. Leppert
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
| | - Jennifer S. W. Campbell
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
| | - Simona Schiavi
- Department of Computer Science, University of Verona, Verona, Italy
| | - Hyerang Jin
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
| | - Shahin Tavakol
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
| | - Reinder Vos de Wael
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
| | - Raul Rodriguez-Cruces
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
| | - G. Bruce Pike
- Hotchkiss Brain Institute and Departments of Radiology and Clinical Neuroscience, University of Calgary, Calgary, Canada
| | - Boris C. Bernhardt
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
| | | | - Bratislav Misic
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
| | - Christine L. Tardif
- Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, Montreal, QC, Canada
- Department of Biomedical Engineering, McGill University, Montreal, QC, Canada
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11
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Deco G, Lynn CW, Sanz Perl Y, Kringelbach ML. Violations of the fluctuation-dissipation theorem reveal distinct nonequilibrium dynamics of brain states. Phys Rev E 2023; 108:064410. [PMID: 38243472 DOI: 10.1103/physreve.108.064410] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/14/2023] [Accepted: 10/16/2023] [Indexed: 01/21/2024]
Abstract
The brain is a nonequilibrium system whose dynamics change in different brain states, such as wakefulness and deep sleep. Thermodynamics provides the tools for revealing these nonequilibrium dynamics. We used violations of the fluctuation-dissipation theorem to describe the hierarchy of nonequilibrium dynamics associated with different brain states. Together with a whole-brain model fitted to empirical human neuroimaging data, and deriving the appropriate analytical expressions, we were able to capture the deviation from equilibrium in different brain states that arises from asymmetric interactions and hierarchical organization.
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Affiliation(s)
- Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona 08018, Spain
- Institució Catalana de la Recerca i Estudis Avançats (ICREA), Passeig Lluís Companys 23, Barcelona 08010, Spain
| | - Christopher W Lynn
- Initiative for the Theoretical Sciences, Graduate Center, City University of New York, New York, New York 10016, USA and Joseph Henry Laboratories of Physics, Princeton University, Princeton, New Jersey 08544, USA
| | - Yonatan Sanz Perl
- Center for Brain and Cognition, Computational Neuroscience Group, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona 08018, Spain
- Department of Physics, University of Buenos Aires, Buenos Aires 1428, Argentina and Paris Brain Institute (ICM), Paris 75013, France
| | - Morten L Kringelbach
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford OX3 9BX, United Kingdom; Department of Psychiatry, University of Oxford, Oxford OX3 7JX, United Kingdom; and Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Aarhus 8000, Denmark
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12
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Sarwar T, Ramamohanarao K, Daducci A, Schiavi S, Smith RE, Zalesky A. Evaluation of tractogram filtering methods using human-like connectome phantoms. Neuroimage 2023; 281:120376. [PMID: 37714389 DOI: 10.1016/j.neuroimage.2023.120376] [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: 07/19/2023] [Revised: 09/03/2023] [Accepted: 09/12/2023] [Indexed: 09/17/2023] Open
Abstract
Tractography algorithms are prone to reconstructing spurious connections. The set of streamlines generated with tractography can be post-processed to retain the streamlines that are most biologically plausible. Several microstructure-informed filtering algorithms are available for this purpose, however, the comparative performance of these methods has not been extensively evaluated. In this study, we aim to evaluate streamline filtering and post-processing algorithms using simulated connectome phantoms. We first establish a framework for generating connectome phantoms featuring brain-like white matter fiber architectures. We then use our phantoms to systematically evaluate the performance of a range of streamline filtering algorithms, including SIFT, COMMIT, and LiFE. We find that all filtering methods successfully improve connectome accuracy, although filter performance depends on the complexity of the underlying white matter fiber architecture. Filtering algorithms can markedly improve tractography accuracy for simple tubular fiber bundles (F-measure deterministic- unfiltered: 0.49 and best filter: 0.72; F-measure probabilistic- unfiltered: 0.37 and best filter: 0.81), but for more complex brain-like fiber architectures, the improvement is modest (F-measure deterministic- unfiltered: 0.53 and best filter: 0.54; F-measure probabilistic- unfiltered: 0.46 and best filter: 0.50). Overall, filtering algorithms have the potential to improve the accuracy of connectome mapping pipelines, particularly for weighted connectomes and pipelines using probabilistic tractography methods. Our results highlight the need for further advances tractography and streamline filtering to improve the accuracy of connectome mapping.
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Affiliation(s)
- Tabinda Sarwar
- School of Computing Technologies, RMIT University, Victoria, 3000, Australia.
| | | | | | - Simona Schiavi
- Department of Computer Science, University of Verona, 37129, Italy
| | - Robert E Smith
- Florey Institute of Neuroscience and Mental Health, Heidelberg, Victoria, 3084, Australia; Florey Department of Neuroscience and Mental Health, The University of Melbourne, Parkville, Victoria, 3052, Australia
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, The University of Melbourne, Victoria, 2010, Australia
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13
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Schilling KG, Li M, Rheault F, Gao Y, Cai L, Zhao Y, Xu L, Ding Z, Anderson AW, Landman BA, Gore JC. Whole-brain, gray, and white matter time-locked functional signal changes with simple tasks and model-free analysis. Proc Natl Acad Sci U S A 2023; 120:e2219666120. [PMID: 37824529 PMCID: PMC10589709 DOI: 10.1073/pnas.2219666120] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2022] [Accepted: 08/11/2023] [Indexed: 10/14/2023] Open
Abstract
Recent studies have revealed the production of time-locked blood oxygenation level-dependent (BOLD) functional MRI (fMRI) signals throughout the entire brain in response to tasks, challenging the existence of sparse and localized brain functions and highlighting the pervasiveness of potential false negative fMRI findings. "Whole-brain" actually refers to gray matter, the only tissue traditionally studied with fMRI. However, several reports have demonstrated reliable detection of BOLD signals in white matter, which have previously been largely ignored. Using simple tasks and analyses, we demonstrate BOLD signal changes across the whole brain, in both white and gray matters, in similar manner to previous reports of whole brain studies. We investigated whether white matter displays time-locked BOLD signals across multiple structural pathways in response to a stimulus in a similar manner to the cortex. We find that both white and gray matter show time-locked activations across the whole brain, with a majority of both tissue types showing statistically significant signal changes for all task stimuli investigated. We observed a wide range of signal responses to tasks, with different regions showing different BOLD signal changes to the same task. Moreover, we find that each region may display different BOLD responses to different stimuli. Overall, we present compelling evidence that, just like all gray matter, essentially all white matter in the brain shows time-locked BOLD signal changes in response to multiple stimuli, challenging the idea of sparse functional localization and the prevailing wisdom of treating white matter BOLD signals as artifacts to be removed.
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Affiliation(s)
- Kurt G. Schilling
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN37232
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN37232
| | - Muwei Li
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN37232
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN37232
| | - Francois Rheault
- Department of Electrical Engineering and Computer Engineering, Vanderbilt University, Nashville, TN37235
| | - Yurui Gao
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN37232
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN37235
| | - Leon Cai
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN37235
| | - Yu Zhao
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN37232
| | - Lyuan Xu
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN37232
| | - Zhaohua Ding
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN37232
| | - Adam W. Anderson
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN37232
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN37232
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN37235
| | - Bennett A. Landman
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN37232
- Department of Electrical Engineering and Computer Engineering, Vanderbilt University, Nashville, TN37235
| | - John C. Gore
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN37232
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN37232
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN37235
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14
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Al Busaidi A, Gangemi E, Wastling S, Berg ASVD, Mancini L, Yousry T. Functional MRI but not white matter fibre dissection identifies language dominance. Eur Radiol 2023; 33:6081-6093. [PMID: 37410110 DOI: 10.1007/s00330-023-09838-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 03/22/2023] [Accepted: 04/08/2023] [Indexed: 07/07/2023]
Abstract
OBJECTIVES Lateralisation of some language pathways has been reported in the literature using diffusion tractography, which is more feasible than functional magnetic resonance imaging (fMRI) in challenging patients. Our retrospective study investigates whether a correlation exists between threshold-independent fMRI language lateralisation and structural lateralisation using tractography in healthy controls and brain tumour patients. METHODS Fifteen healthy subjects and 61 patients underwent language fMRI and diffusion-weighted MRI. A regional fMRI laterality index (LI) was calculated. Tracts dissected were the arcuate fasciculus (long direct and short indirect tracts), uncinate fasciculus, inferior longitudinal fasciculus, inferior fronto-occipital fasciculus and frontal aslant tract. An asymmetry index (AI) for each tract was calculated using tract volume analysed with single tensor (ST) and spherical deconvolution (SD) models, as well as hindrance modulated orientational anisotropy (HMOA) for SD tracts. Linear regression assessed the correlation between LI and AI. RESULTS In all subjects, there was no significant correlation between LI and AI for any of the dissected tracts. Significant correlations were only found when handedness for controls and tumour volume for patients were included as covariates. In handedness subgroups, the average AI of some tracts showed the same laterality as LI, and some the opposite. Discordant results were observed for ST- and SD-based AIs. CONCLUSIONS Our results do not support using tractography in the assessment of language lateralisation. The discordant results between ST and SD indicate that either the structural lateralisation of dissected tracts is less robust than functional lateralisation, or tractography is not sensitive methodology. Other diffusion analysis approaches should be developed. CLINICAL RELEVANCE STATEMENT Although diffusion tractography may be more feasible than fMRI in challenging tumour patients and where sedation or anaesthesia is required, our results do not currently recommend replacing fMRI with tractography using volume or HMOA in the assessment of language lateralisation. KEY POINTS • No correlation found between fMRI and tractography in language lateralisation. • Discordance between asymmetry indices of different tractography models and metrics. • Tractography not currently recommended in language lateralisation assessment.
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Affiliation(s)
- Ayisha Al Busaidi
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, Queen Square, London, WC1N 3BG, UK.
- Department of Neuroradiology, King's College Hospital NHS Foundation Trust, London, Denmark Hill, SE5 9RS, UK.
| | - Emma Gangemi
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, Queen Square, London, WC1N 3BG, UK
- Radiology Department, Ospedale Dei Castelli, Via Nettunense, Km 11,5, 00040, Rome, Italy
| | - Stephen Wastling
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, Queen Square, London, WC1N 3BG, UK
- Neuroradiological Academic Unit, Department of Brain, Repair and Rehabilitation, University College London Institute of Neurology, Queen Square, WC1N 3BG, London, UK
| | - Aaike S van den Berg
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, Queen Square, London, WC1N 3BG, UK
- Department of Radiology & Nuclear Medicine, Erasmus MC - University Medical Center Rotterdam, Dr. Molewaterplein 40, 3015 GD, Rotterdam, the Netherlands
| | - Laura Mancini
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, Queen Square, London, WC1N 3BG, UK
- Neuroradiological Academic Unit, Department of Brain, Repair and Rehabilitation, University College London Institute of Neurology, Queen Square, WC1N 3BG, London, UK
| | - Tarek Yousry
- Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, University College London Hospitals NHS Foundation Trust, Queen Square, London, WC1N 3BG, UK
- Neuroradiological Academic Unit, Department of Brain, Repair and Rehabilitation, University College London Institute of Neurology, Queen Square, WC1N 3BG, London, UK
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15
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Thams F, Li SC, Flöel A, Antonenko D. Functional Connectivity and Microstructural Network Correlates of Interindividual Variability in Distinct Executive Functions of Healthy Older Adults. Neuroscience 2023; 526:61-73. [PMID: 37321368 DOI: 10.1016/j.neuroscience.2023.06.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2023] [Revised: 06/02/2023] [Accepted: 06/07/2023] [Indexed: 06/17/2023]
Abstract
Executive functions, essential for daily life, are known to be impaired in older age. Some executive functions, including working memory updating and value-based decision-making, are specifically sensitive to age-related deterioration. While their neural correlates in young adults are well-described, a comprehensive delineation of the underlying brain substrates in older populations, relevant to identify targets for modulation against cognitive decline, is missing. Here, we assessed letter updating and Markov decision-making task performance to operationalize these trainable functions in 48 older adults. Resting-state functional magnetic resonance imaging was acquired to quantify functional connectivity (FC) in task-relevant frontoparietal and default mode networks. Microstructure in white matter pathways mediating executive functions was assessed with diffusion tensor imaging and quantified by tract-based fractional anisotropy (FA). Superior letter updating performance correlated with higher FC between dorsolateral prefrontal cortex and left frontoparietal and hippocampal areas, while superior Markov decision-making performance correlated with decreased FC between basal ganglia and right angular gyrus. Furthermore, better working memory updating performance was related to higher FA in the cingulum bundle and the superior longitudinal fasciculus. Stepwise linear regression showed that cingulum bundle FA added significant incremental contribution to the variance explained by fronto-angular FC alone. Our findings provide a characterization of distinct functional and structural connectivity correlates associated with performance of specific executive functions. Thereby, this study contributes to the understanding of the neural correlates of updating and decision-making functions in older adults, paving the way for targeted modulation of specific networks by modulatory techniques such as behavioral interventions and non-invasive brain stimulation.
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Affiliation(s)
- Friederike Thams
- Department of Neurology, Universitätsmedizin Greifswald, Ferdinand-Sauerbruch-Straße, 17475 Greifswald, Germany.
| | - Shu-Chen Li
- Chair of Lifespan Developmental Neuroscience, Faculty of Psychology, TU Dresden, Zellescher Weg 17, 01062 Dresden, Germany; Centre for Tactile Internet with Human-in-the-Loop, TU Dresden, 01062 Dresden, Germany.
| | - Agnes Flöel
- Department of Neurology, Universitätsmedizin Greifswald, Ferdinand-Sauerbruch-Straße, 17475 Greifswald, Germany; German Centre for Neurodegenerative Diseases (DZNE) Standort Greifswald, 17475 Greifswald, Germany.
| | - Daria Antonenko
- Department of Neurology, Universitätsmedizin Greifswald, Ferdinand-Sauerbruch-Straße, 17475 Greifswald, Germany.
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16
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Asaridou SS, Cler GJ, Wiedemann A, Krishnan S, Smith HJ, Willis HE, Healy MP, Watkins KE. Microstructural Properties of the Cerebellar Peduncles in Children with Developmental Language Disorder. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.13.548858. [PMID: 37503009 PMCID: PMC10370025 DOI: 10.1101/2023.07.13.548858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Children with developmental language disorder (DLD) struggle to learn their native language for no apparent reason. While research on the neurobiological underpinnings of the disorder has focused on the role of cortico-striatal systems, little is known about the role of the cerebellum in DLD. Cortico-cerebellar circuits might be involved in the disorder as they contribute to complex sensorimotor skill learning, including the acquisition of spoken language. Here, we used diffusion-weighted imaging data from 77 typically developing and 54 children with DLD and performed probabilistic tractography to identify the cerebellum's white matter tracts: the inferior, middle, and superior cerebellar peduncles. Children with DLD showed lower fractional anisotropy (FA) in the inferior cerebellar peduncles (ICP), fiber tracts that carry motor and sensory input via the inferior olive to the cerebellum. Lower FA in DLD was driven by lower axial diffusivity. Probing this further with more sophisticated modeling of diffusion data, we found higher orientation dispersion but no difference in neurite density in the ICP of DLD. Reduced FA is therefore unlikely to be reflecting microstructural differences in myelination in this tract, rather the organization of axons in these pathways is disrupted. ICP microstructure was not associated with language or motor coordination performance in our sample. We also found no differences in the middle and superior peduncles, the main pathways connecting the cerebellum with the cortex. To conclude, it is not cortico-cerebellar but atypical olivocerebellar white matter connections that characterize DLD and suggest the involvement of the olivocerebellar system in speech acquisition and development.
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Affiliation(s)
- Salomi S. Asaridou
- Department of Experimental Psychology, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Gabriel J. Cler
- Department of Experimental Psychology, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
- Department of Speech & Hearing Sciences, University of Washington, Seattle, USA
| | - Anna Wiedemann
- Department of Experimental Psychology, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Saloni Krishnan
- Department of Experimental Psychology, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
- Department of Psychology, Royal Holloway, University of London, Egham Hill, Surrey, UK
| | - Harriet J. Smith
- Department of Experimental Psychology, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Hanna E. Willis
- Department of Experimental Psychology, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
- Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, UK
| | - Máiréad P. Healy
- Department of Experimental Psychology, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
- Department of Psychology, University of Cambridge, Cambridge, UK
| | - Kate E. Watkins
- Department of Experimental Psychology, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
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17
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Kebiri H, Gholipour A, Bach Cuadra M, Karimi D. Direct segmentation of brain white matter tracts in diffusion MRI. ARXIV 2023:arXiv:2307.02223v1. [PMID: 37461410 PMCID: PMC10350097] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 07/23/2023]
Abstract
The brain white matter consists of a set of tracts that connect distinct regions of the brain. Segmentation of these tracts is often needed for clinical and research studies. Diffusion-weighted MRI offers unique contrast to delineate these tracts. However, existing segmentation methods rely on intermediate computations such as tractography or estimation of fiber orientation density. These intermediate computations, in turn, entail complex computations that can result in unnecessary errors. Moreover, these intermediate computations often require dense multi-shell measurements that are unavailable in many clinical and research applications. As a result, current methods suffer from low accuracy and poor generalizability. Here, we propose a new deep learning method that segments these tracts directly from the diffusion MRI data, thereby sidestepping the intermediate computation errors. Our experiments show that this method can achieve segmentation accuracy that is on par with the state of the art methods (mean Dice Similarity Coefficient of 0.826). Compared with the state of the art, our method offers far superior generalizability to undersampled data that are typical of clinical studies and to data obtained with different acquisition protocols. Moreover, we propose a new method for detecting inaccurate segmentations and show that it is more accurate than standard methods that are based on estimation uncertainty quantification. The new methods can serve many critically important clinical and scientific applications that require accurate and reliable non-invasive segmentation of white matter tracts.
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Affiliation(s)
- Hamza Kebiri
- Computational Radiology Laboratory (CRL), Department of Radiology, Boston Children's Hospital, and Harvard Medical School, USA
- Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
| | - Ali Gholipour
- Computational Radiology Laboratory (CRL), Department of Radiology, Boston Children's Hospital, and Harvard Medical School, USA
| | - Meritxell Bach Cuadra
- Computational Radiology Laboratory (CRL), Department of Radiology, Boston Children's Hospital, and Harvard Medical School, USA
- Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
| | - Davood Karimi
- Computational Radiology Laboratory (CRL), Department of Radiology, Boston Children's Hospital, and Harvard Medical School, USA
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18
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Deco G, Perl YS, Ponce-Alvarez A, Tagliazucchi E, Whybrow P, Fuster J, Kringelbach ML. One ring to rule them all: The unifying role of prefrontal cortex in steering task-related brain dynamics. Prog Neurobiol 2023:102468. [PMID: 37301532 DOI: 10.1016/j.pneurobio.2023.102468] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 05/10/2023] [Accepted: 05/18/2023] [Indexed: 06/12/2023]
Abstract
Surviving and thriving in a complex world require intricate balancing of higher order brain functions with essential survival-related behaviours. Exactly how this is achieved is not fully understood but a large body of work has shown that different regions in the prefrontal cortex (PFC) play key roles for diverse cognitive and emotional tasks including emotion, control, response inhibition, mental set shifting and working memory. We hypothesised that the key regions are hierarchically organised and we developed a framework for discovering the driving brain regions at the top of the hierarchy, responsible for steering the brain dynamics of higher brain function. We fitted a time-dependent whole-brain model to the neuroimaging data from large-scale Human Connectome Project with over 1,000 participants and computed the entropy production for rest and seven tasks (covering the main domains of cognition). This thermodynamics framework allowed us to identify the main common, unifying drivers steering the orchestration of brain dynamics during difficult tasks; located in key regions of the PFC (inferior frontal gyrus, lateral orbitofrontal cortex, rostral and caudal frontal cortex and rostral anterior cingulate cortex). Selectively lesioning these regions in the whole-brain model demonstrated their causal mechanistic importance. Overall, this shows the existence of a 'ring' of specific PFC regions ruling over the orchestration of higher brain function.
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Affiliation(s)
- Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona, 08018, Spain; Institució Catalana de la Recerca i Estudis Avançats (ICREA), Passeig Lluís Companys 23, Barcelona, 08010, Spain
| | - Yonatan Sanz Perl
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona, 08018, Spain; Department of Physics, University of Buenos Aires, Buenos Aires, Argentina
| | - Adrián Ponce-Alvarez
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona, 08018, Spain
| | - Enzo Tagliazucchi
- Department of Physics, University of Buenos Aires, Buenos Aires, Argentina; Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibanez, Santiago, Chile
| | - Peter Whybrow
- University of California, Los Angeles, CA 90024, USA; Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, UK
| | - Joaquín Fuster
- University of California, Los Angeles, CA 90024, USA; Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, UK
| | - Morten L Kringelbach
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, UK; Department of Psychiatry, University of Oxford, Oxford, UK; Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Aarhus, DK
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19
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Zhou W, He J, Zhang C, Pan Y, Sang T, Qiu X. Fiber-specific white matter alterations in Parkinson's disease patients with freezing of gait. Brain Res 2023:148440. [PMID: 37271491 DOI: 10.1016/j.brainres.2023.148440] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 05/24/2023] [Accepted: 05/30/2023] [Indexed: 06/06/2023]
Abstract
Freezing of gait (FOG) is a gait disorder that usually occurs in advanced stages of Parkinson's disease (PD). Understanding the underlying mechanism of FOG is important for treatment and prevention. Previous studies have investigated white matter (WM) structure to explore the pathology of FOG. However, the pathology is still unclear, possibly due to the methodological limitation in identifying specific fiber tracts. This study aimed to investigate tract-specific WM structural changes in FOG patients and their relationships with clinical characteristics. We enrolled 19 PD patients with FOG (PD-FOG), 19 without FOG (PD-woFOG) and 21 controls. Fixel-based analysis is a novel framework to avoid the effect of crossing fibers, which provides the metrics to assess WM morphology. By combining a method for segmenting fibers, we identified abnormalities in the specific fiber tracts. Compared to PD-woFOG, PD-FOG showed significant increased fiber-bundle cross-section (FC) in the corpus callosum (CC), fornix (FX), inferior longitudinal fasciculus (ILF), striato-premotor (ST_PREM), superior thalamic radiation (STR), thalamo-premotor (T_PREM), increased fiber density and cross-section (FDC) in the STR, and decreased fiber density (FD) in the CC and ILF. Additionally, the ILF was correlated with motor, cognition and memory, the CC was correlated with anxiety, and the T_PREM was also correlated with cognition. In conclusion, in addition to impairments of WM found in PD-FOG, we found enhancements in WM, which may imply compensatory mechanisms. Furthermore, multiple fiber tracts were correlated with clinical characteristics, especially the ILF, validating the involvement of transmission circuits of multiple distinct information in mechanisms of FOG.
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Affiliation(s)
- Wenyang Zhou
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, People's Republic of China
| | - Jianzhong He
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, People's Republic of China
| | - Chengzhe Zhang
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, People's Republic of China
| | - Yiang Pan
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, People's Republic of China
| | - Tian Sang
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, People's Republic of China
| | - Xiang Qiu
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, People's Republic of China; Department of Automation, Zhejiang University of Technology, Hangzhou 310023, People's Republic of China.
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20
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Zhao C, Tapera TM, Bagautdinova J, Bourque J, Covitz S, Gur RE, Gur RC, Larsen B, Mehta K, Meisler SL, Murtha K, Muschelli J, Roalf DR, Sydnor VJ, Valcarcel AM, Shinohara RT, Cieslak M, Satterthwaite TD. ModelArray: An R package for statistical analysis of fixel-wise data. Neuroimage 2023; 271:120037. [PMID: 36931330 PMCID: PMC10119782 DOI: 10.1016/j.neuroimage.2023.120037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 03/08/2023] [Accepted: 03/14/2023] [Indexed: 03/17/2023] Open
Abstract
Diffusion MRI is the dominant non-invasive imaging method used to characterize white matter organization in health and disease. Increasingly, fiber-specific properties within a voxel are analyzed using fixels. While tools for conducting statistical analyses of fixel-wise data exist, currently available tools support only a limited number of statistical models. Here we introduce ModelArray, an R package for mass-univariate statistical analysis of fixel-wise data. At present, ModelArray supports linear models as well as generalized additive models (GAMs), which are particularly useful for studying nonlinear effects in lifespan data. In addition, ModelArray also aims for scalable analysis. With only several lines of code, even large fixel-wise datasets can be analyzed using a standard personal computer. Detailed memory profiling revealed that ModelArray required only limited memory even for large datasets. As an example, we applied ModelArray to fixel-wise data derived from diffusion images acquired as part of the Philadelphia Neurodevelopmental Cohort (n = 938). ModelArray revealed anticipated nonlinear developmental effects in white matter. Moving forward, ModelArray is supported by an open-source software development model that can incorporate additional statistical models and other imaging data types. Taken together, ModelArray provides a flexible and efficient platform for statistical analysis of fixel-wise data.
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Affiliation(s)
- Chenying Zhao
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA; Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA; Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Tinashe M Tapera
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA; Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Joëlle Bagautdinova
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA; Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Josiane Bourque
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA; Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Sydney Covitz
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA; Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Raquel E Gur
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Ruben C Gur
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Bart Larsen
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA; Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Kahini Mehta
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA; Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Steven L Meisler
- Program in Speech and Hearing Bioscience and Technology, Harvard University, Cambridge, MA 02139, USA
| | - Kristin Murtha
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA; Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - John Muschelli
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - David R Roalf
- Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Valerie J Sydnor
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA; Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Alessandra M Valcarcel
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Russell T Shinohara
- Penn Statistics in Imaging and Visualization Center, Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Matthew Cieslak
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA; Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Theodore D Satterthwaite
- Lifespan Informatics and Neuroimaging Center (PennLINC), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, 19104, USA; Penn/CHOP Lifespan Brain Institute, Perelman School of Medicine, Children's Hospital of Philadelphia Research Institute, Philadelphia, PA 19104, USA; Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA; Center for Biomedical Image Computation and Analytics, University of Pennsylvania, Philadelphia, PA 19104, USA.
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21
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Generative Sampling in Bundle Tractography using Autoencoders (GESTA). Med Image Anal 2023; 85:102761. [PMID: 36773366 DOI: 10.1016/j.media.2023.102761] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 01/11/2023] [Accepted: 01/24/2023] [Indexed: 02/04/2023]
Abstract
Current tractography methods use the local orientation information to propagate streamlines from seed locations. Many such seeds provide streamlines that stop prematurely or fail to map the true white matter pathways because some bundles are "harder-to-track" than others. This results in tractography reconstructions with poor white and gray matter spatial coverage. In this work, we propose a generative, autoencoder-based method, named GESTA (Generative Sampling in Bundle Tractography using Autoencoders), that produces streamlines achieving better spatial coverage. Compared to other deep learning methods, our autoencoder-based framework uses a single model to generate streamlines in a bundle-wise fashion, and does not require to propagate local orientations. GESTA produces new and complete streamlines for any given white matter bundle, including hard-to-track bundles. Applied on top of a given tractogram, GESTA is shown to be effective in improving the white matter volume coverage in poorly populated bundles, both on synthetic and human brain in vivo data. Our streamline evaluation framework ensures that the streamlines produced by GESTA are anatomically plausible and fit well to the local diffusion signal. The streamline evaluation criteria assess anatomy (white matter coverage), local orientation alignment (direction), and geometry features of streamlines, and optionally, gray matter connectivity. The GESTA framework offers considerable gains in bundle overlap using a reduced set of seeding streamlines with a 1.5x improvement for the "Fiber Cup", and 6x for the ISMRM 2015 Tractography Challenge datasets. Similarly, it provides a 4x white matter volume increase on the BIL&GIN callosal homotopic dataset, and successfully populates bundles on the multi-subject, multi-site, whole-brain in vivo TractoInferno dataset. GESTA is thus a novel deep generative bundle tractography method that can be used to improve the tractography reconstruction of the white matter.
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22
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Gruen J, Groeschel S, Schultz T. Spatially regularized low-rank tensor approximation for accurate and fast tractography. Neuroimage 2023; 271:120004. [PMID: 36898487 DOI: 10.1016/j.neuroimage.2023.120004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 03/03/2023] [Accepted: 03/06/2023] [Indexed: 03/11/2023] Open
Abstract
Tractography based on diffusion Magnetic Resonance Imaging (dMRI) is the prevalent approach to the in vivo delineation of white matter tracts in the human brain. Many tractography methods rely on models of multiple fiber compartments, but the local dMRI information is not always sufficient to reliably estimate the directions of secondary fibers. Therefore, we introduce two novel approaches that use spatial regularization to make multi-fiber tractography more stable. Both represent the fiber Orientation Distribution Function (fODF) as a symmetric fourth-order tensor, and recover multiple fiber orientations via low-rank approximation. Our first approach computes a joint approximation over suitably weighted local neighborhoods with an efficient alternating optimization. The second approach integrates the low-rank approximation into a current state-of-the-art tractography algorithm based on the unscented Kalman filter (UKF). These methods were applied in three different scenarios. First, we demonstrate that they improve tractography even in high-quality data from the Human Connectome Project, and that they maintain useful results with a small fraction of the measurements. Second, on the 2015 ISMRM tractography challenge, they increase overlap, while reducing overreach, compared to low-rank approximation without joint optimization or the traditional UKF, respectively. Finally, our methods permit a more comprehensive reconstruction of tracts surrounding a tumor in a clinical dataset. Overall, both approaches improve reconstruction quality. At the same time, our modified UKF significantly reduces the computational effort compared to its traditional counterpart, and to our joint approximation. However, when used with ROI-based seeding, joint approximation more fully recovers fiber spread.
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Affiliation(s)
- Johannes Gruen
- Institute for Computer Science, University of Bonn, Friedrich-Hirzebruch-Allee 8, Bonn, 53115, Germany; Bonn-Aachen International Center for Information Technology, University of Bonn, Friedrich-Hirzebruch-Allee 6, Bonn, 53115, Germany
| | - Samuel Groeschel
- Experimental Pediatric Neuroimaging and Department of Pediatric Neurology & Developmental Medicine, University Children's Hospital, Hoppe-Seyler-Straße 1, Tuebingen, 72076, Germany
| | - Thomas Schultz
- Bonn-Aachen International Center for Information Technology, University of Bonn, Friedrich-Hirzebruch-Allee 6, Bonn, 53115, Germany; Institute for Computer Science, University of Bonn, Friedrich-Hirzebruch-Allee 8, Bonn, 53115, Germany.
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23
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Lalgudi Srinivasan H, Pedro Lavrador J, Tambirajoo K, Pang G, Patel S, Gullan R, Vergani F, Bhangoo R, Shapey J, Vasan AK, Ashkan K. Tractography-Enhanced Biopsy of Central Core Motor Eloquent Tumours: A Simulation-Based Study. J Pers Med 2023; 13:jpm13030467. [PMID: 36983649 PMCID: PMC10051818 DOI: 10.3390/jpm13030467] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2022] [Revised: 02/25/2023] [Accepted: 02/27/2023] [Indexed: 03/08/2023] Open
Abstract
Safe Trajectory planning for navigation guided biopsy (nBx) of motor eloquent tumours (METs) is important to minimise neurological morbidity. Preliminary clinical data suggest that visualisation of the corticospinal tract (CST) and its relation to the tumour may aid in planning a safe trajectory. In this article we assess the impact of tractography in nBx planning in a simulation-based exercise. This single centre cross-sectional study was performed in March 2021 including 10 patients with METs divided into 2 groups: (1) tractography enhanced group (T-nBx; n = 5; CST merged with volumetric MRI); (2) anatomy-based group (A-nBx; n = 5; volumetric MRI only). A biopsy target was chosen on each tumour. Volunteer neurosurgical trainees had to plan a suitable biopsy trajectory on a Stealth S8® workstation for all patients in a single session. A trajectory safety index (TSI) was devised for each trajectory. Data collection and analysis included a comparison of trajectory planning time, trajectory/lobe changes and TSI. A total of 190 trajectories were analysed based on participation from 19 trainees. Mean trajectory planning time for the entire cohort was 225.1 ± 21.97 s. T-nBx required shorter time for planning (p = 0.01). Mean trajectory changes and lobe changes made per biopsy were 3.28 ± 0.29 and 0.45 ± 0.08, respectively. T-nBx required fewer trajectory/lobe changes (p = 0.01). TSI was better in the presence of tractography than A-nBx (p = 0.04). Neurosurgical experience of trainees had no significant impact on the measured parameters despite adjusted analysis. Irrespective of the level of neurosurgical training, surgical planning of navigation guided biopsy for METs may be achieved in less time with a safer trajectory if tractography imaging is available.
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Affiliation(s)
| | - Jose Pedro Lavrador
- Department of Neurosurgery, King’s College Hospital, London SE5 9RS, UK
- King’s NeuroLab, King’s College Hospital, London WC2R 2LS, UK
| | | | - Graeme Pang
- Department of Neurosurgery, King’s College Hospital, London SE5 9RS, UK
| | - Sabina Patel
- Department of Neurosurgery, King’s College Hospital, London SE5 9RS, UK
| | - Richard Gullan
- Department of Neurosurgery, King’s College Hospital, London SE5 9RS, UK
| | - Francesco Vergani
- Department of Neurosurgery, King’s College Hospital, London SE5 9RS, UK
| | - Ranjeev Bhangoo
- Department of Neurosurgery, King’s College Hospital, London SE5 9RS, UK
| | - Jonathan Shapey
- Department of Neurosurgery, King’s College Hospital, London SE5 9RS, UK
- King’s NeuroLab, King’s College Hospital, London WC2R 2LS, UK
- Department of Surgical Intervention and Engineering, School of Biomedical Engineering and Imaging Sciences, King’s College London, London WC2R 2LS, UK
| | - Ahilan Kailaya Vasan
- Department of Neurosurgery, King’s College Hospital, London SE5 9RS, UK
- King’s NeuroLab, King’s College Hospital, London WC2R 2LS, UK
| | - Keyoumars Ashkan
- Department of Neurosurgery, King’s College Hospital, London SE5 9RS, UK
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24
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Bero J, Li Y, Kumar A, Humphries C, Nag S, Lee H, Ahn WY, Hahn S, Constable RT, Kim H, Lee D. Coordinated anatomical and functional variability in the human brain during adolescence. Hum Brain Mapp 2023; 44:1767-1778. [PMID: 36479851 PMCID: PMC9921246 DOI: 10.1002/hbm.26173] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2022] [Revised: 10/26/2022] [Accepted: 11/25/2022] [Indexed: 12/13/2022] Open
Abstract
Adolescence represents a time of unparalleled brain development. In particular, developmental changes in morphometric and cytoarchitectural features are accompanied by maturation in the functional connectivity (FC). Here, we examined how three facets of the brain, including myelination, cortical thickness (CT), and resting-state FC, interact in children between the ages of 10 and 15. We investigated the pattern of coordination in these measures by computing correlation matrices for each measure as well as meta-correlations among them both at the regional and network levels. The results revealed consistently higher meta-correlations among myelin, CT, and FC in the sensory-motor cortical areas than in the association cortical areas. We also found that these meta-correlations were stable and little affected by age-related changes in each measure. In addition, regional variations in the meta-correlations were consistent with the previously identified gradient in the FC and therefore reflected the hierarchy of cortical information processing, and this relationship persists in the adult brain. These results demonstrate that heterogeneity in FC among multiple cortical areas are closely coordinated with the development of cortical myelination and thickness during adolescence.
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Affiliation(s)
- John Bero
- Neurogazer, Inc.BaltimoreMarylandUSA
| | - Yang Li
- Neurogazer, Inc.BaltimoreMarylandUSA
| | | | | | | | | | - Woo Young Ahn
- Department of PsychologySeoul National UniversitySeoulKorea
| | - Sowon Hahn
- Department of PsychologySeoul National UniversitySeoulKorea
| | - Robert Todd Constable
- Department of Diagnostic Radiology and NeurosurgeryYale School of MedicineNew HavenConnecticutUSA
| | - Hackjin Kim
- Department of PsychologyKorea UniversitySeoulKorea
| | - Daeyeol Lee
- Neurogazer, Inc.BaltimoreMarylandUSA
- The Zanvyl Krieger Mind/Brain Institute, Johns Hopkins UniversityBaltimoreMarylandUSA
- Department of NeuroscienceJohns Hopkins UniversityBaltimoreMarylandUSA
- Department of Psychological and Brain SciencesJohns Hopkins UniversityBaltimoreMarylandUSA
- Kavli Neuroscience Discovery Institute, Johns Hopkins UniversityBaltimoreMarylandUSA
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25
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Prediction of the Topography of the Corticospinal Tract on T1-Weighted MR Images Using Deep-Learning-Based Segmentation. Diagnostics (Basel) 2023; 13:diagnostics13050911. [PMID: 36900055 PMCID: PMC10000710 DOI: 10.3390/diagnostics13050911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Revised: 02/23/2023] [Accepted: 02/27/2023] [Indexed: 03/04/2023] Open
Abstract
INTRODUCTION Tractography is an invaluable tool in the planning of tumor surgery in the vicinity of functionally eloquent areas of the brain as well as in the research of normal development or of various diseases. The aim of our study was to compare the performance of a deep-learning-based image segmentation for the prediction of the topography of white matter tracts on T1-weighted MR images to the performance of a manual segmentation. METHODS T1-weighted MR images of 190 healthy subjects from 6 different datasets were utilized in this study. Using deterministic diffusion tensor imaging, we first reconstructed the corticospinal tract on both sides. After training a segmentation model on 90 subjects of the PIOP2 dataset using the nnU-Net in a cloud-based environment with graphical processing unit (Google Colab), we evaluated its performance using 100 subjects from 6 different datasets. RESULTS Our algorithm created a segmentation model that predicted the topography of the corticospinal pathway on T1-weighted images in healthy subjects. The average dice score was 0.5479 (0.3513-0.7184) on the validation dataset. CONCLUSIONS Deep-learning-based segmentation could be applicable in the future to predict the location of white matter pathways in T1-weighted scans.
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Schilling KG, Li M, Rheault F, Gao Y, Cai L, Zhao Y, Xu L, Ding Z, Anderson AW, Landman BA, Gore JC. Whole-brain, gray and white matter time-locked functional signal changes with simple tasks and model-free analysis. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.14.528557. [PMID: 36824784 PMCID: PMC9948951 DOI: 10.1101/2023.02.14.528557] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/17/2023]
Abstract
Recent studies have revealed the production of time-locked blood oxygenation-level dependent (BOLD) functional MRI (fMRI) signals throughout the entire brain in response to a task, challenging the idea of sparse and localized brain functions, and highlighting the pervasiveness of potential false negative fMRI findings. In these studies, 'whole-brain' refers to gray matter regions only, which is the only tissue traditionally studied with fMRI. However, recent reports have also demonstrated reliable detection and analyses of BOLD signals in white matter which have been largely ignored in previous reports. Here, using model-free analysis and simple tasks, we investigate BOLD signal changes in both white and gray matters. We aimed to evaluate whether white matter also displays time-locked BOLD signals across all structural pathways in response to a stimulus. We find that both white and gray matter show time-locked activations across the whole-brain, with a majority of both tissue types showing statistically significant signal changes for all task stimuli investigated. We observed a wide range of signal responses to tasks, with different regions showing very different BOLD signal changes to the same task. Moreover, we find that each region may display different BOLD responses to different stimuli. Overall, we present compelling evidence that the whole brain, including both white and gray matter, show time-locked activation to multiple stimuli, not only challenging the idea of sparse functional localization, but also the prevailing wisdom of treating white matter BOLD signals as artefacts to be removed.
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Affiliation(s)
- Kurt G Schilling
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Muwei Li
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Francois Rheault
- Department of Electrical Engineering and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - Yurui Gao
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States
| | - Leon Cai
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States
| | - Yu Zhao
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Lyuan Xu
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Zhaohua Ding
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Adam W Anderson
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States
| | - Bennett A Landman
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Electrical Engineering and Computer Engineering, Vanderbilt University, Nashville, TN, USA
| | - John C Gore
- Vanderbilt University Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States
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27
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Zhylka A, Leemans A, Pluim JPW, De Luca A. Anatomically informed multi-level fiber tractography for targeted virtual dissection. MAGMA (NEW YORK, N.Y.) 2023; 36:79-93. [PMID: 35904612 PMCID: PMC9992235 DOI: 10.1007/s10334-022-01033-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/06/2022] [Revised: 06/30/2022] [Accepted: 07/15/2022] [Indexed: 12/01/2022]
Abstract
OBJECTIVES Diffusion-weighted MRI can assist preoperative planning by reconstructing the trajectory of eloquent fiber pathways, such as the corticospinal tract (CST). However, accurate reconstruction of the full extent of the CST remains challenging with existing tractography methods. We suggest a novel tractography algorithm exploiting unused fiber orientations to produce more complete and reliable results. METHODS Our novel approach, referred to as multi-level fiber tractography (MLFT), reconstructs fiber pathways by progressively considering previously unused fiber orientations at multiple levels of tract propagation. Anatomical priors are used to minimize the number of false-positive pathways. The MLFT method was evaluated on synthetic data and in vivo data by reconstructing the CST while compared to conventional tractography approaches. RESULTS The radial extent of MLFT reconstructions is comparable to that of probabilistic reconstruction: [Formula: see text] for the left and [Formula: see text] for the right hemisphere according to Wilcoxon test, while achieving significantly higher topography preservation compared to probabilistic tractography: [Formula: see text]. DISCUSSION MLFT provides a novel way to reconstruct fiber pathways by adding the capability of including branching pathways in fiber tractography. Thanks to its robustness, feasible reconstruction extent and topography preservation, our approach may assist in clinical practice as well as in virtual dissection studies.
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Affiliation(s)
- Andrey Zhylka
- Biomedical Engineering, Eindhoven University of Technology, Rondom 70, 5612 AP, Eindhoven, The Netherlands.
| | - Alexander Leemans
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Josien P W Pluim
- Biomedical Engineering, Eindhoven University of Technology, Rondom 70, 5612 AP, Eindhoven, The Netherlands
| | - Alberto De Luca
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands.,Neurology Department, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, The Netherlands
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Schaechter JD, Kim M, Hightower BG, Ragas T, Loggia ML. Disruptions in Structural and Functional Connectivity Relate to Poststroke Fatigue. Brain Connect 2023; 13:15-27. [PMID: 35570655 PMCID: PMC9942175 DOI: 10.1089/brain.2022.0021] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Introduction: Poststroke fatigue (PSF) is a disabling condition with unclear etiology. The brain lesion is thought to be an important causal factor in PSF, although focal lesion characteristics such as size and location have not proven to be predictive. Given that the stroke lesion results not only in focal tissue death but also in widespread changes in brain networks that are structurally and functionally connected to damaged tissue, we hypothesized that PSF relates to disruptions in structural and functional connectivity. Materials and Methods: Twelve patients who incurred an ischemic stroke in the middle cerebral artery (MCA) territory 1-3 years prior, and currently experiencing a range of fatigue severity, were enrolled. The patients underwent structural and resting-state functional magnetic resonance imaging (MRI). The structural MRI data were used to measure structural disconnection of gray matter resulting from lesion to white matter pathways. The functional MRI data were used to measure network functional connectivity. Results: The patients showed structural disconnection in varying cortical and subcortical regions. Fatigue severity correlated significantly with structural disconnection of several frontal cortex regions in the ipsilesional (IL) and contralesional hemispheres. Fatigue-related structural disconnection was most severe in the IL rostral middle frontal cortex. Greater structural disconnection of a subset of fatigue-related frontal cortex regions, including the IL rostral middle frontal cortex, trended toward correlating significantly with greater loss in functional connectivity. Among identified fatigue-related frontal cortex regions, only the IL rostral middle frontal cortex showed loss in functional connectivity correlating significantly with fatigue severity. Conclusion: Our results provide evidence that loss in structural and functional connectivity of bihemispheric frontal cortex regions plays a role in PSF after MCA stroke, with connectivity disruptions of the IL rostral middle frontal cortex having a central role. Impact statement Poststroke fatigue (PSF) is a common disabling condition with unclear etiology. We hypothesized that PSF relates to disruptions in structural and functional connectivity secondary to the focal lesion. Using structural and resting-state functional connectivity magnetic resonance imaging (MRI) in patients with chronic middle cerebral artery (MCA) stroke, we found frontal cortex regions in the ipsilesional (IL) and contralesional hemispheres with greater structural disconnection correlating with greater fatigue. Among these fatigue-related cortices, the IL rostral middle frontal cortex showed loss in functional connectivity correlating with fatigue. These findings suggest that disruptions in structural and functional connectivity play a role in PSF after MCA stroke.
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Affiliation(s)
- Judith D. Schaechter
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
| | - Minhae Kim
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
| | - Baileigh G. Hightower
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
| | - Trevor Ragas
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
| | - Marco L. Loggia
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, Massachusetts, USA
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29
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Schilling KG, Archer D, Yeh FC, Rheault F, Cai LY, Shafer A, Resnick SM, Hohman T, Jefferson A, Anderson AW, Kang H, Landman BA. Short superficial white matter and aging: a longitudinal multi-site study of 1293 subjects and 2711 sessions. AGING BRAIN 2023; 3:100067. [PMID: 36817413 PMCID: PMC9937516 DOI: 10.1016/j.nbas.2023.100067] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
It is estimated that short association fibers running immediately beneath the cortex may make up as much as 60% of the total white matter volume. However, these have been understudied relative to the long-range association, projection, and commissural fibers of the brain. This is largely because of limitations of diffusion MRI fiber tractography, which is the primary methodology used to non-invasively study the white matter connections. Inspired by recent anatomical considerations and methodological improvements in superficial white matter (SWM) tractography, we aim to characterize changes in these fiber systems in cognitively normal aging, which provide insight into the biological foundation of age-related cognitive changes, and a better understanding of how age-related pathology differs from healthy aging. To do this, we used three large, longitudinal and cross-sectional datasets (N = 1293 subjects, 2711 sessions) to quantify microstructural features and length/volume features of several SWM systems. We find that axial, radial, and mean diffusivities show positive associations with age, while fractional anisotropy has negative associations with age in SWM throughout the entire brain. These associations were most pronounced in the frontal, temporal, and temporoparietal regions. Moreover, measures of SWM volume and length decrease with age in a heterogenous manner across the brain, with different rates of change in inter-gyri and intra-gyri SWM, and at slower rates than well-studied long-range white matter pathways. These features, and their variations with age, provide the background for characterizing normal aging, and, in combination with larger association pathways and gray matter microstructural features, may provide insight into fundamental mechanisms associated with aging and cognition.
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Affiliation(s)
- Kurt G Schilling
- Department of Radiology & Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN
| | - Derek Archer
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University Medical Center, Nashville, TN, USA,Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA,Vanderbilt Genetics Institute, Vanderbilt University School of Medicine, Nashville, TN
| | - Fang-Cheng Yeh
- Department of Neurological Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA, USA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Francois Rheault
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, United States
| | - Leon Y Cai
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, United States
| | - Andrea Shafer
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, United States of America
| | - Susan M. Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, United States of America
| | - Timothy Hohman
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University Medical Center, Nashville, TN, USA,Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA,Vanderbilt Genetics Institute, Vanderbilt University School of Medicine, Nashville, TN
| | - Angela Jefferson
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University Medical Center, Nashville, TN, USA,Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA,Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Adam W Anderson
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States
| | - Hakmook Kang
- Department of Biostatistics, Vanderbilt University, Nashville, TN, United States
| | - Bennett A Landman
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, United States
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30
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Karawun: a software package for assisting evaluation of advances in multimodal imaging for neurosurgical planning and intraoperative neuronavigation. Int J Comput Assist Radiol Surg 2023; 18:171-179. [PMID: 36070033 PMCID: PMC9883338 DOI: 10.1007/s11548-022-02736-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 08/09/2022] [Indexed: 02/01/2023]
Abstract
PURPOSE The neuroimaging research community-which includes a broad range of scientific, medical, statistical, and engineering disciplines-has developed many tools to advance our knowledge of brain structure, function, development, aging, and disease. Past research efforts have clearly shaped clinical practice. However, translation of new methodologies into clinical practice is challenging. Anything that can reduce these barriers has the potential to improve the rate at which research outcomes can contribute to clinical practice. In this article, we introduce Karawun, a file format conversion tool, that has become a key part of our work in translating advances in diffusion imaging acquisition and analysis into neurosurgical practice at our institution. METHODS Karawun links analysis workflows created using open-source neuroimaging software, to Brainlab (Brainlab AG, Munich, Germany), a commercially available surgical planning and navigation suite. Karawun achieves this using DICOM standards supporting representation of 3D structures, including tractography streamlines, and thus offers far more than traditional screenshot or color overlay approaches. RESULTS We show that neurosurgical planning data, created from multimodal imaging data using analysis methods implemented in open-source research software, can be imported into Brainlab. The datasets can be manipulated as if they were created by Brainlab, including 3D visualizations of white matter tracts and other objects. CONCLUSION Clinicians can explore and interact with the results of research neuroimaging pipelines using familiar tools within their standard clinical workflow, understand the impact of the new methods on their practice and provide feedback to methods developers. This capability has been important to the translation of advanced analysis techniques into practice at our institution.
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31
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Conrad BN, Pollack C, Yeo DJ, Price GR. Structural and functional connectivity of the inferior temporal numeral area. Cereb Cortex 2022; 33:6152-6170. [PMID: 36587366 PMCID: PMC10183753 DOI: 10.1093/cercor/bhac492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Revised: 11/14/2022] [Accepted: 11/17/2022] [Indexed: 01/02/2023] Open
Abstract
A growing body of evidence suggests that in adults, there is a spatially consistent "inferior temporal numeral area" (ITNA) in the occipitotemporal cortex that appears to preferentially process Arabic digits relative to non-numerical symbols and objects. However, very little is known about why the ITNA is spatially segregated from regions that process other orthographic stimuli such as letters, and why it is spatially consistent across individuals. In the present study, we used diffusion-weighted imaging and functional magnetic resonance imaging to contrast structural and functional connectivity between left and right hemisphere ITNAs and a left hemisphere letter-preferring region. We found that the left ITNA had stronger structural and functional connectivity than the letter region to inferior parietal regions involved in numerical magnitude representation and arithmetic. Between hemispheres, the left ITNA showed stronger structural connectivity with the left inferior frontal gyrus (Broca's area), while the right ITNA showed stronger structural connectivity to the ipsilateral inferior parietal cortex and stronger functional coupling with the bilateral IPS. Based on their relative connectivity, our results suggest that the left ITNA may be more readily involved in mapping digits to verbal number representations, while the right ITNA may support the mapping of digits to quantity representations.
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Affiliation(s)
- Benjamin N Conrad
- Department of Psychology & Human Development, Peabody College, Vanderbilt University, 230 Appleton Place, Nashville, TN, 37203, USA
| | - Courtney Pollack
- Department of Psychology & Human Development, Peabody College, Vanderbilt University, 230 Appleton Place, Nashville, TN, 37203, USA
| | - Darren J Yeo
- Department of Psychology & Human Development, Peabody College, Vanderbilt University, 230 Appleton Place, Nashville, TN, 37203, USA.,Division of Psychology, School of Social Sciences, Nanyang Technological University, 48 Nanyang Avenue, Singapore, 639818
| | - Gavin R Price
- Department of Psychology & Human Development, Peabody College, Vanderbilt University, 230 Appleton Place, Nashville, TN, 37203, USA.,Department of Psychology, University of Exeter, Washington Singer Building Perry Road, Exeter, EX4 4QG, United Kingdom
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32
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Poulin P, Theaud G, Rheault F, St-Onge E, Bore A, Renauld E, de Beaumont L, Guay S, Jodoin PM, Descoteaux M. TractoInferno - A large-scale, open-source, multi-site database for machine learning dMRI tractography. Sci Data 2022; 9:725. [PMID: 36433966 PMCID: PMC9700736 DOI: 10.1038/s41597-022-01833-1] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Accepted: 11/09/2022] [Indexed: 11/26/2022] Open
Abstract
TractoInferno is the world's largest open-source multi-site tractography database, including both research- and clinical-like human acquisitions, aimed specifically at machine learning tractography approaches and related ML algorithms. It provides 284 samples acquired from 3 T scanners across 6 different sites. Available data includes T1-weighted images, single-shell diffusion MRI (dMRI) acquisitions, spherical harmonics fitted to the dMRI signal, fiber ODFs, and reference streamlines for 30 delineated bundles generated using 4 tractography algorithms, as well as masks needed to run tractography algorithms. Manual quality control was additionally performed at multiple steps of the pipeline. We showcase TractoInferno by benchmarking the learn2track algorithm and 5 variations of the same recurrent neural network architecture. Creating the TractoInferno database required approximately 20,000 CPU-hours of processing power, 200 man-hours of manual QC, 3,000 GPU-hours of training baseline models, and 4 Tb of storage, to produce a final database of 350 Gb. By providing a standardized training dataset and evaluation protocol, TractoInferno is an excellent tool to address common issues in machine learning tractography.
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Affiliation(s)
- Philippe Poulin
- University of Sherbrooke, Computer Science Department, Sherbrooke, J1K 2R1, Canada.
| | - Guillaume Theaud
- University of Sherbrooke, Computer Science Department, Sherbrooke, J1K 2R1, Canada
| | - Francois Rheault
- University of Sherbrooke, Computer Science Department, Sherbrooke, J1K 2R1, Canada
| | - Etienne St-Onge
- University of Sherbrooke, Computer Science Department, Sherbrooke, J1K 2R1, Canada
| | - Arnaud Bore
- University of Sherbrooke, Computer Science Department, Sherbrooke, J1K 2R1, Canada
| | - Emmanuelle Renauld
- University of Sherbrooke, Computer Science Department, Sherbrooke, J1K 2R1, Canada
| | - Louis de Beaumont
- Montreal Sacred-Heart Hospital Research Centre, Montreal, H4J 1C5, Canada
- University of Montreal, Department of Surgery, Montreal, H3C 3J7, Canada
| | - Samuel Guay
- Montreal Sacred-Heart Hospital Research Centre, Montreal, H4J 1C5, Canada
- University of Montreal, Department of Surgery, Montreal, H3C 3J7, Canada
| | - Pierre-Marc Jodoin
- University of Sherbrooke, Computer Science Department, Sherbrooke, J1K 2R1, Canada
- Imeka, Sherbrooke, J1H 4A7, Canada
| | - Maxime Descoteaux
- University of Sherbrooke, Computer Science Department, Sherbrooke, J1K 2R1, Canada
- Imeka, Sherbrooke, J1H 4A7, Canada
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33
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Bae CR, Na Y, Cho M, Hwang YM, Tae WS, Pyun SB. Structural Changes in the Arcuate Fasciculus and Recovery of Post-stroke Aphasia: A 6-Month Follow-up Study using Diffusion Tensor Imaging. Neurorehabil Neural Repair 2022; 36:633-644. [PMID: 36036555 DOI: 10.1177/15459683221121752] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
BACKGROUND Temporal changes in the structural connectivity of major language tracts after stroke and their contribution to aphasia recovery are unclear. OBJECTIVE To investigate longitudinal arcuate fasciculus (AF) integrity changes and their relationship with post-stroke aphasia recovery using diffusion tensor imaging (DTI). METHODS Thirty-five patients with aphasia due to first-ever left hemispheric stroke underwent the Korean version of the Western Aphasia Battery and DTI at 1- and 6-month post stroke onset. Fractional anisotropy (FA), mean diffusivity (MD), radial diffusivity (RD), and axial diffusivity (AD) of both AF tracts were analyzed to evaluate the temporal changes in tract integrity and determine the correlation between changes (Δ; follow-up - initial) in DTI parameters and language scores. RESULTS At 6 months post-stroke, the mean FA decreased, and mean MD and RD increased in both hemispheres; however, compared with mean AD observed after 1 month, the mean observed at 6 months increased only in the left hemisphere (P < .05). ΔFA of the left AF and proportional change in the aphasia quotient showed a significant positive correlation (r = 0.365, P = .031). No correlation was found between changes in the right AF parameters and language score. The group with increased FA in the left AF showed more significant language improvement than the group with decreased FA. CONCLUSIONS During the subacute stage, the integrity of AF decreased in both hemispheres in patients with aphasia, and the change in structural connectivity of the left AF was associated with language improvement.
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Affiliation(s)
- Cho Rong Bae
- Department of Rehabilitation Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea
| | - Yoonhye Na
- Department of Biomedical Sciences, Korea University College of Medicine, Seoul, Republic of Korea.,Brain Convergence Research Center, Korea University College of Medicine, Seoul, Republic of Korea
| | - Minjae Cho
- Department of Biomedical Sciences, Korea University College of Medicine, Seoul, Republic of Korea.,Brain Convergence Research Center, Korea University College of Medicine, Seoul, Republic of Korea.,BK21 Graduate Program, Department of Biomedical Sciences, Korea University College of Medicine, Seoul, Republic of Korea
| | - Yu Mi Hwang
- Brain Convergence Research Center, Korea University College of Medicine, Seoul, Republic of Korea
| | - Woo-Suk Tae
- Brain Convergence Research Center, Korea University College of Medicine, Seoul, Republic of Korea
| | - Sung-Bom Pyun
- Department of Physical Medicine and Rehabilitation, Korea University College of Medicine, Seoul, Republic of Korea.,Department of Biomedical Sciences, Korea University College of Medicine, Seoul, Republic of Korea.,Brain Convergence Research Center, Korea University College of Medicine, Seoul, Republic of Korea
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34
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Alemán-Gómez Y, Griffa A, Houde JC, Najdenovska E, Magon S, Cuadra MB, Descoteaux M, Hagmann P. A multi-scale probabilistic atlas of the human connectome. Sci Data 2022; 9:516. [PMID: 35999243 PMCID: PMC9399115 DOI: 10.1038/s41597-022-01624-8] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Accepted: 08/03/2022] [Indexed: 11/10/2022] Open
Abstract
The human brain is a complex system that can be efficiently represented as a network of structural connectivity. Many imaging studies would benefit from such network information, which is not always available. In this work, we present a whole-brain multi-scale structural connectome atlas. This tool has been derived from a cohort of 66 healthy subjects imaged with optimal technology in the setting of the Human Connectome Project. From these data we created, using extensively validated diffusion-data processing, tractography and gray-matter parcellation tools, a multi-scale probabilistic atlas of the human connectome. In addition, we provide user-friendly and accessible code to match this atlas to individual brain imaging data to extract connection-specific quantitative information. This can be used to associate individual imaging findings, such as focal white-matter lesions or regional alterations, to specific connections and brain circuits. Accordingly, network-level consequences of regional changes can be analyzed even in absence of diffusion and tractography data. This method is expected to broaden the accessibility and lower the yield for connectome research.
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Affiliation(s)
- Yasser Alemán-Gómez
- Connectomics Lab, Department of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland.
- Center for Psychiatric Neuroscience, Department of Psychiatry, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Prilly, Switzerland.
| | - Alessandra Griffa
- Department of Radiology and Medical Informatics, University of Geneva, Geneva, Switzerland
- Medical Image Processing Laboratory, Neuro-X Institute, Ecole Polytechnique Fédérale de Lausanne (EPFL), Geneva, Switzerland
- Leenaards Memory Centre, Lausanne University Hospital and University of Lausanne, Lausanne, Switzerland
| | | | - Elena Najdenovska
- Department of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
- Medical Image Analysis Laboratory (MIAL), Centre d'Imagerie BioMédicale (CIBM), Lausanne, Switzerland
- Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Stefano Magon
- Pharma Research and Early Development, Roche Innovation Center Basel, F. Hoffmann-La Roche Ltd., Basel, Switzerland
| | - Meritxell Bach Cuadra
- Department of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
- Medical Image Analysis Laboratory (MIAL), Centre d'Imagerie BioMédicale (CIBM), Lausanne, Switzerland
- Signal Processing Laboratory (LTS5), Ecole Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Lab, Sherbrooke University, Sherbrooke, Canada
| | - Patric Hagmann
- Connectomics Lab, Department of Radiology, Centre Hospitalier Universitaire Vaudois (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland
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35
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Human brain structural connectivity matrices-ready for modelling. Sci Data 2022; 9:486. [PMID: 35945231 PMCID: PMC9363436 DOI: 10.1038/s41597-022-01596-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2021] [Accepted: 07/25/2022] [Indexed: 11/10/2022] Open
Abstract
The human brain represents a complex computational system, the function and structure of which may be measured using various neuroimaging techniques focusing on separate properties of the brain tissue and activity. We capture the organization of white matter fibers acquired by diffusion-weighted imaging using probabilistic diffusion tractography. By segmenting the results of tractography into larger anatomical units, it is possible to draw inferences about the structural relationships between these parts of the system. This pipeline results in a structural connectivity matrix, which contains an estimate of connection strength among all regions. However, raw data processing is complex, computationally intensive, and requires expert quality control, which may be discouraging for researchers with less experience in the field. We thus provide brain structural connectivity matrices in a form ready for modelling and analysis and thus usable by a wide community of scientists. The presented dataset contains brain structural connectivity matrices together with the underlying raw diffusion and structural data, as well as basic demographic data of 88 healthy subjects. Measurement(s) | Diffusion Weighted Imaging | Technology Type(s) | Magnetic Resonance Imaging | Sample Characteristic - Organism | Homo sapiens | Sample Characteristic - Location | Czech Republic |
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36
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Lucena O, Borges P, Cardoso J, Ashkan K, Sparks R, Ourselin S. Informative and Reliable Tract Segmentation for Preoperative Planning. FRONTIERS IN RADIOLOGY 2022; 2:866974. [PMID: 37492653 PMCID: PMC10365092 DOI: 10.3389/fradi.2022.866974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 03/31/2022] [Indexed: 07/27/2023]
Abstract
Identifying white matter (WM) tracts to locate eloquent areas for preoperative surgical planning is a challenging task. Manual WM tract annotations are often used but they are time-consuming, suffer from inter- and intra-rater variability, and noise intrinsic to diffusion MRI may make manual interpretation difficult. As a result, in clinical practice direct electrical stimulation is necessary to precisely locate WM tracts during surgery. A measure of WM tract segmentation unreliability could be important to guide surgical planning and operations. In this study, we use deep learning to perform reliable tract segmentation in combination with uncertainty quantification to measure segmentation unreliability. We use a 3D U-Net to segment white matter tracts. We then estimate model and data uncertainty using test time dropout and test time augmentation, respectively. We use a volume-based calibration approach to compute representative predicted probabilities from the estimated uncertainties. In our findings, we obtain a Dice of ≈0.82 which is comparable to the state-of-the-art for multi-label segmentation and Hausdorff distance <10mm. We demonstrate a high positive correlation between volume variance and segmentation errors, which indicates a good measure of reliability for tract segmentation ad uncertainty estimation. Finally, we show that calibrated predicted volumes are more likely to encompass the ground truth segmentation volume than uncalibrated predicted volumes. This study is a step toward more informed and reliable WM tract segmentation for clinical decision-making.
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Affiliation(s)
- Oeslle Lucena
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Pedro Borges
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Jorge Cardoso
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Keyoumars Ashkan
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- King's College Hospital Foundation Trust, London, United Kingdom
| | - Rachel Sparks
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Sebastien Ourselin
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
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37
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Silvestri E, Villani U, Moretto M, Colpo M, Salvalaggio A, Anglani M, Castellaro M, Facchini S, Monai E, D'Avella D, Puppa AD, Cecchin D, Corbetta M, Bertoldo A. Assessment of structural disconnections in gliomas: comparison of indirect and direct approaches. Brain Struct Funct 2022; 227:3109-3120. [PMID: 35503481 PMCID: PMC9653324 DOI: 10.1007/s00429-022-02494-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/27/2021] [Accepted: 04/04/2022] [Indexed: 12/24/2022]
Abstract
Gliomas are amongst the most common primary brain tumours in adults and are often associated with poor prognosis. Understanding the extent of white matter (WM) which is affected outside the tumoral lesion may be of paramount importance to explain cognitive deficits and the clinical progression of the disease. To this end, we explored both direct (i.e., tractography based) and indirect (i.e., atlas-based) approaches to quantifying WM structural disconnections in a cohort of 44 high- and low-grade glioma patients. While these methodologies have recently gained popularity in the context of stroke and other pathologies, to our knowledge, this is the first time they are applied in patients with brain tumours. More specifically, in this work, we present a quantitative comparison of the disconnection maps provided by the two methodologies by applying well-known metrics of spatial similarity, extension, and correlation. Given the important role the oedematous tissue plays in the physiopathology of tumours, we performed these analyses both by including and excluding it in the definition of the tumoral lesion. This was done to investigate possible differences determined by this choice. We found that direct and indirect approaches offer two distinct pictures of structural disconnections in patients affected by brain gliomas, presenting key differences in several regions of the brain. Following the outcomes of our analysis, we eventually discuss the strengths and pitfalls of these two approaches when applied in this critical field.
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Affiliation(s)
- Erica Silvestri
- Department of Information Engineering, University of Padova, 35131, Padua, Italy.,Padova Neuroscience Center, University of Padova, 35129, Padua, Italy
| | - Umberto Villani
- Department of Information Engineering, University of Padova, 35131, Padua, Italy.,Padova Neuroscience Center, University of Padova, 35129, Padua, Italy
| | - Manuela Moretto
- Department of Information Engineering, University of Padova, 35131, Padua, Italy.,Padova Neuroscience Center, University of Padova, 35129, Padua, Italy
| | - Maria Colpo
- Department of Information Engineering, University of Padova, 35131, Padua, Italy.,Padova Neuroscience Center, University of Padova, 35129, Padua, Italy
| | - Alessandro Salvalaggio
- Padova Neuroscience Center, University of Padova, 35129, Padua, Italy.,Department of Neuroscience, University of Padova, 35128, Padua, Italy
| | | | - Marco Castellaro
- Department of Information Engineering, University of Padova, 35131, Padua, Italy
| | - Silvia Facchini
- Padova Neuroscience Center, University of Padova, 35129, Padua, Italy.,Department of Neuroscience, University of Padova, 35128, Padua, Italy
| | - Elena Monai
- Padova Neuroscience Center, University of Padova, 35129, Padua, Italy.,Department of Neuroscience, University of Padova, 35128, Padua, Italy
| | - Domenico D'Avella
- Department of Neuroscience, University of Padova, 35128, Padua, Italy
| | - Alessandro Della Puppa
- Neurosurgery, Department of NEUROFARBA, University Hospital of Careggi, University of Florence, 50139, Florence, Italy
| | - Diego Cecchin
- Padova Neuroscience Center, University of Padova, 35129, Padua, Italy.,Department of Medicine, Unit of Nuclear Medicine, University of Padova, Padua, Italy
| | - Maurizio Corbetta
- Padova Neuroscience Center, University of Padova, 35129, Padua, Italy.,Department of Neuroscience, University of Padova, 35128, Padua, Italy.,Venetian Institute of Molecular Medicine, 35129, Padua, Italy
| | - Alessandra Bertoldo
- Department of Information Engineering, University of Padova, 35131, Padua, Italy. .,Padova Neuroscience Center, University of Padova, 35129, Padua, Italy.
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38
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Kannan L, Bhatt T, Zhang A, Ajilore O. Association of balance control mechanisms with brain structural integrity in older adults with mild cognitive impairment. Neurosci Lett 2022; 783:136699. [DOI: 10.1016/j.neulet.2022.136699] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Revised: 05/23/2022] [Accepted: 05/24/2022] [Indexed: 10/18/2022]
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39
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Kochanski RB, Slavin KV. The future perspectives of psychiatric neurosurgery. PROGRESS IN BRAIN RESEARCH 2022; 270:211-228. [PMID: 35396029 DOI: 10.1016/bs.pbr.2022.01.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 11/28/2022]
Abstract
The future of psychiatric neurosurgery can be viewed from two separate perspectives: the immediate future and the distant future. Both show promise, but the treatment strategy for mental diseases and the technology utilized during these separate periods will likely differ dramatically. It can be expected that the initial advancements will be built upon progress of neuroimaging and stereotactic targeting while surgical technology becomes adapted to patient-specific symptomatology and structural/functional imaging parameters. This individualized approach has already begun to show significant promise when applied to deep brain stimulation for treatment-resistant depression and obsessive-compulsive disorder. If effectiveness of these strategies is confirmed by well designed, double-blind, placebo-controlled clinical studies, further technological advances will continue into the distant future, and will likely involve precise neuromodulation at the cellular level, perhaps using wireless technology with or without closed-loop design. This approach, being theoretically less invasive and carrying less risk, may ultimately propel psychiatric neurosurgery to the forefront in the treatment algorithm of mental illness. Despite prominent development of non-invasive therapeutic options, such as stereotactic radiosurgery or transcranial magnetic resonance-guided focused ultrasound, chances are there will still be a need in surgical management of patients with most intractable psychiatric conditions.
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Affiliation(s)
- Ryan B Kochanski
- Neurosurgery, Methodist Healthcare System, San Antonio, TX, United States
| | - Konstantin V Slavin
- Department of Neurosurgery, University of Illinois at Chicago, Chicago, IL, United States; Neurology Service, Jesse Brown Veterans Administration Medical Center, Chicago, IL, United States.
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40
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Michele R, Ivana S, Maria DV, Luca B, Domenico L, Maria ZF, Alessandro DB, Silvio S, Khalid AO, Valeria M, Pietro A. Tracing in vivo the dorsal loop of the optic radiation: convergent perspectives from tractography and electrophysiology compared to a neuroanatomical ground truth. Brain Struct Funct 2022; 227:1357-1370. [PMID: 35320828 DOI: 10.1007/s00429-021-02430-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Accepted: 11/12/2021] [Indexed: 01/18/2023]
Abstract
The temporo-parietal junction (TPJ) is a cortical area contributing to a multiplicity of visual, language-related, and cognitive functions. In line with this functional richness, also the organization of the underlying white matter is highly complex and includes several bundles. The few studies tackling the outcome and neurological burdens of surgical operations addressing TPJ document the presence of language disturbances and visual field damages, with the latter hardly recovered in time. This observation advocates for identifying and functionally monitoring the optic radiation (OR) bundles that cross the white matter below the TPJ. In the present study, we adopted a multimodal approach to address the anatomo-functional correlates of the OR's dorsal loop. In particular, we combined cadavers' dissection with tractographic and electrophysiological data collected in drug-resistant epileptic patients explored by stereoelectroencephalography (SEEG). Cadaver dissection allowed us to appreciate the course and topography of the dorsal loop. More surprisingly, both tractographic and electrophysiological observations converged on a unitary picture highly coherent with the data obtained by neuroanatomical observation. The combination of diverse and multimodal observations allows overcoming the limitations intrinsic to single methodologies, defining a unitary picture which makes it possible to investigate the dorsal loop both presurgically and at the individual patient level, ultimately contributing to limit the postsurgical damages. Notwithstanding, such a combined approach could serve as a model of investigation for future neuroanatomical inquiries tackling white matter fibers anatomy and function through SEEG-derived neurophysiological data.
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Affiliation(s)
- Rizzi Michele
- "C.Munari" Epilepsy Surgery Centre, ASST GOM Niguarda, Piazza Dell'Ospedale Maggiore, 20162, Milan, Italy
| | - Sartori Ivana
- "C.Munari" Epilepsy Surgery Centre, ASST GOM Niguarda, Piazza Dell'Ospedale Maggiore, 20162, Milan, Italy.
| | - Del Vecchio Maria
- Institute of Neuroscience, National Research Council of Italy, Parma, Italy
| | - Berta Luca
- Department of Medical Physics, ASST GOM Niguarda, Milan, Italy
| | - Lizio Domenico
- Department of Medical Physics, ASST GOM Niguarda, Milan, Italy
| | - Zauli Flavia Maria
- "C.Munari" Epilepsy Surgery Centre, ASST GOM Niguarda, Piazza Dell'Ospedale Maggiore, 20162, Milan, Italy
- Department of Biomedical and Clinical Sciences "L. Sacco", University of Milan, Milan, Italy
| | - De Benedictis Alessandro
- Department of Neurosciences, Neurosurgery Unit, Bambino Gesù Children's Hospital, IRCCS, Rome, Italy
| | - Sarubbo Silvio
- Department of Neurosurgery, Ospedale Santa Chiara, Trento, Italy
| | - Al-Orabi Khalid
- "C.Munari" Epilepsy Surgery Centre, ASST GOM Niguarda, Piazza Dell'Ospedale Maggiore, 20162, Milan, Italy
| | - Mariani Valeria
- Neurology and Stroke Unit, ASST Sette Laghi-Ospedale di Circolo, Varese, Italy
| | - Avanzini Pietro
- Institute of Neuroscience, National Research Council of Italy, Parma, Italy
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41
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Schilling KG, Tax CMW, Rheault F, Landman BA, Anderson AW, Descoteaux M, Petit L. Prevalence of white matter pathways coming into a single white matter voxel orientation: The bottleneck issue in tractography. Hum Brain Mapp 2022; 43:1196-1213. [PMID: 34921473 PMCID: PMC8837578 DOI: 10.1002/hbm.25697] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2021] [Revised: 10/15/2021] [Accepted: 10/16/2021] [Indexed: 11/12/2022] Open
Abstract
Characterizing and understanding the limitations of diffusion MRI fiber tractography is a prerequisite for methodological advances and innovations which will allow these techniques to accurately map the connections of the human brain. The so-called "crossing fiber problem" has received tremendous attention and has continuously triggered the community to develop novel approaches for disentangling distinctly oriented fiber populations. Perhaps an even greater challenge occurs when multiple white matter bundles converge within a single voxel, or throughout a single brain region, and share the same parallel orientation, before diverging and continuing towards their final cortical or sub-cortical terminations. These so-called "bottleneck" regions contribute to the ill-posed nature of the tractography process, and lead to both false positive and false negative estimated connections. Yet, as opposed to the extent of crossing fibers, a thorough characterization of bottleneck regions has not been performed. The aim of this study is to quantify the prevalence of bottleneck regions. To do this, we use diffusion tractography to segment known white matter bundles of the brain, and assign each bundle to voxels they pass through and to specific orientations within those voxels (i.e. fixels). We demonstrate that bottlenecks occur in greater than 50-70% of fixels in the white matter of the human brain. We find that all projection, association, and commissural fibers contribute to, and are affected by, this phenomenon, and show that even regions traditionally considered "single fiber voxels" often contain multiple fiber populations. Together, this study shows that a majority of white matter presents bottlenecks for tractography which may lead to incorrect or erroneous estimates of brain connectivity or quantitative tractography (i.e., tractometry), and underscores the need for a paradigm shift in the process of tractography and bundle segmentation for studying the fiber pathways of the human brain.
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Affiliation(s)
- Kurt G. Schilling
- Department of Radiology & Radiological ScienceVanderbilt University Medical CenterNashvilleTennesseeUSA
- Vanderbilt University Institute of Imaging ScienceVanderbilt University Medical CenterNashvilleTennesseeUSA
| | - Chantal M. W. Tax
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United KingdomCardiffUK
- Image Sciences InstituteUniversity Medical Center UtrechtUtrechtThe Netherlands
| | - Francois Rheault
- Department of Electrical Engineering and Computer ScienceVanderbilt UniversityNashvilleTennesseeUSA
| | - Bennett A. Landman
- Department of Radiology & Radiological ScienceVanderbilt University Medical CenterNashvilleTennesseeUSA
- Vanderbilt University Institute of Imaging ScienceVanderbilt University Medical CenterNashvilleTennesseeUSA
- Department of Electrical Engineering and Computer ScienceVanderbilt UniversityNashvilleTennesseeUSA
- Department of Biomedical EngineeringVanderbilt UniversityNashvilleTennesseeUSA
| | - Adam W. Anderson
- Department of Radiology & Radiological ScienceVanderbilt University Medical CenterNashvilleTennesseeUSA
- Vanderbilt University Institute of Imaging ScienceVanderbilt University Medical CenterNashvilleTennesseeUSA
- Department of Biomedical EngineeringVanderbilt UniversityNashvilleTennesseeUSA
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science DepartmentUniversité de SherbrookeSherbrookeQuebecCanada
| | - Laurent Petit
- Groupe d'Imagerie NeurofonctionnelleInstitut Des Maladies Neurodégénératives, CNRS, CEA University of BordeauxBordeauxFrance
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42
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Doroshin A, Jillings S, Jeurissen B, Tomilovskaya E, Pechenkova E, Nosikova I, Rumshiskaya A, Litvinova L, Rukavishnikov I, De Laet C, Schoenmaekers C, Sijbers J, Laureys S, Petrovichev V, Van Ombergen A, Annen J, Sunaert S, Parizel PM, Sinitsyn V, zu Eulenburg P, Osipowicz K, Wuyts FL. Brain Connectometry Changes in Space Travelers After Long-Duration Spaceflight. Front Neural Circuits 2022; 16:815838. [PMID: 35250494 PMCID: PMC8894205 DOI: 10.3389/fncir.2022.815838] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 01/21/2022] [Indexed: 12/13/2022] Open
Abstract
Humans undergo extreme physiological changes when subjected to long periods of weightlessness, and as we continue to become a space-faring species, it is imperative that we fully understand the physiological changes that occur in the human body, including the brain. In this study, we present findings of brain structural changes associated with long-duration spaceflight based on diffusion magnetic resonance imaging (dMRI) data. Twelve cosmonauts who spent an average of six months aboard the International Space Station (ISS) were scanned in an MRI scanner pre-flight, ten days after flight, and at a follow-up time point seven months after flight. We performed differential tractography, a technique that confines white matter fiber tracking to voxels showing microstructural changes. We found significant microstructural changes in several large white matter tracts, such as the corpus callosum, arcuate fasciculus, corticospinal, corticostriatal, and cerebellar tracts. This is the first paper to use fiber tractography to investigate which specific tracts exhibit structural changes after long-duration spaceflight and may direct future research to investigate brain functional and behavioral changes associated with these white matter pathways.
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Affiliation(s)
- Andrei Doroshin
- Drexel University Neuroimaging Laboratory (DUN), Drexel University, Philadelphia, PA, United States
| | - Steven Jillings
- Lab for Equilibrium Investigations and Aerospace, University of Antwerp, Antwerp, Belgium
| | - Ben Jeurissen
- Imec-Vision Lab, University of Antwerp, Antwerp, Belgium
| | - Elena Tomilovskaya
- State Scientific Center of the Russian Federation – Institute for Biomedical Problem, Russian Academy of Sciences, Moscow, Russia
| | | | - Inna Nosikova
- State Scientific Center of the Russian Federation – Institute for Biomedical Problem, Russian Academy of Sciences, Moscow, Russia
| | - Alena Rumshiskaya
- Radiology Department, National Medical Research Treatment and Rehabilitation Centre of the Ministry of Health of Russia, Moscow, Russia
| | - Liudmila Litvinova
- Radiology Department, National Medical Research Treatment and Rehabilitation Centre of the Ministry of Health of Russia, Moscow, Russia
| | - Ilya Rukavishnikov
- State Scientific Center of the Russian Federation – Institute for Biomedical Problem, Russian Academy of Sciences, Moscow, Russia
| | - Chloë De Laet
- Lab for Equilibrium Investigations and Aerospace, University of Antwerp, Antwerp, Belgium
| | - Catho Schoenmaekers
- Lab for Equilibrium Investigations and Aerospace, University of Antwerp, Antwerp, Belgium
| | - Jan Sijbers
- Imec-Vision Lab, University of Antwerp, Antwerp, Belgium
| | - Steven Laureys
- Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium
| | - Victor Petrovichev
- Radiology Department, National Medical Research Treatment and Rehabilitation Centre of the Ministry of Health of Russia, Moscow, Russia
| | - Angelique Van Ombergen
- Lab for Equilibrium Investigations and Aerospace, University of Antwerp, Antwerp, Belgium
- Department of Translational Neurosciences-ENT, University of Antwerp, Antwerp, Belgium
| | - Jitka Annen
- Coma Science Group, GIGA Consciousness, University of Liège, Liège, Belgium
| | - Stefan Sunaert
- Department of Imaging & Pathology, Translational MRI, KU Leuven – University of Leuven, Leuven, Belgium
| | - Paul M. Parizel
- Department of Radiology, Royal Perth Hospital, University of Western Australia Medical School, Perth, WA, Australia
| | - Valentin Sinitsyn
- Faculty of Fundamental Medicine, Lomonosov Moscow State University, Moscow, Russia
| | - Peter zu Eulenburg
- Institute for Neuroradiology, Ludwig-Maximilians-University Munich, Munich, Germany
| | - Karol Osipowicz
- Drexel University Neuroimaging Laboratory (DUN), Drexel University, Philadelphia, PA, United States
| | - Floris L. Wuyts
- Lab for Equilibrium Investigations and Aerospace, University of Antwerp, Antwerp, Belgium
- *Correspondence: Floris L. Wuyts,
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43
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Kai J, Khan AR. Assessing the Reliability of Template-Based Clustering for Tractography in Healthy Human Adults. Front Neuroinform 2022; 16:777853. [PMID: 35250526 PMCID: PMC8891507 DOI: 10.3389/fninf.2022.777853] [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: 09/15/2021] [Accepted: 01/06/2022] [Indexed: 11/21/2022] Open
Abstract
Tractography is a non-invasive technique to investigate the brain’s structural pathways (also referred to as tracts) that connect different brain regions. A commonly used approach for identifying tracts is with template-based clustering, where unsupervised clustering is first performed on a template in order to label corresponding tracts in unseen data. However, the reliability of this approach has not been extensively studied. Here, an investigation into template-based clustering reliability was performed, assessing the output from two datasets: Human Connectome Project (HCP) and MyConnectome project. The effect of intersubject variability on template-based clustering reliability was investigated, as well as the reliability of both deep and superficial white matter tracts. Identified tracts were evaluated by assessing Euclidean distances from a dataset-specific tract average centroid, the volumetric overlap across corresponding tracts, and along-tract agreement of quantitative values. Further, two template-based techniques were employed to evaluate the reliability of different clustering approaches. Reliability assessment can increase the confidence of a tract identifying technique in future applications to study pathways of interest. The two different template-based approaches exhibited similar reliability for identifying both deep white matter tracts and the superficial white matter.
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Affiliation(s)
- Jason Kai
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, The University of Western Ontario, London, ON, Canada
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, The University of Western Ontario, London, ON, Canada
| | - Ali R. Khan
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, The University of Western Ontario, London, ON, Canada
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, The University of Western Ontario, London, ON, Canada
- *Correspondence: Ali R. Khan,
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44
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Sawicki J, Hartmann L, Bader R, Schöll E. Modelling the perception of music in brain network dynamics. FRONTIERS IN NETWORK PHYSIOLOGY 2022; 2:910920. [PMID: 36926090 PMCID: PMC10013054 DOI: 10.3389/fnetp.2022.910920] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/01/2022] [Accepted: 07/11/2022] [Indexed: 11/13/2022]
Abstract
We analyze the influence of music in a network of FitzHugh-Nagumo oscillators with empirical structural connectivity measured in healthy human subjects. We report an increase of coherence between the global dynamics in our network and the input signal induced by a specific music song. We show that the level of coherence depends crucially on the frequency band. We compare our results with experimental data, which also describe global neural synchronization between different brain regions in the gamma-band range in a time-dependent manner correlated with musical large-scale form, showing increased synchronization just before transitions between different parts in a musical piece (musical high-level events). The results also suggest a separation in musical form-related brain synchronization between high brain frequencies, associated with neocortical activity, and low frequencies in the range of dance movements, associated with interactivity between cortical and subcortical regions.
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Affiliation(s)
- Jakub Sawicki
- Potsdam Institute for Climate Impact Research, Potsdam, Germany.,Institut für Musikpädagogik, Universität der Künste Berlin, Berlin, Germany.,Fachhochschule Nordwestschweiz FHNW, Basel, Switzerland.,Institut für Theoretische Physik, Technische Universität Berlin, Berlin, Germany
| | - Lenz Hartmann
- Institute of Systematic Musicology, University of Hamburg, Hamburg, Germany
| | - Rolf Bader
- Institute of Systematic Musicology, University of Hamburg, Hamburg, Germany
| | - Eckehard Schöll
- Potsdam Institute for Climate Impact Research, Potsdam, Germany.,Institut für Theoretische Physik, Technische Universität Berlin, Berlin, Germany.,Bernstein Center for Computational Neuroscience Berlin, Humboldt-Universität, Berlin, Germany
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45
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Sarwar T, Ramamohanarao K, Zalesky A. A critical review of connectome validation studies. NMR IN BIOMEDICINE 2021; 34:e4605. [PMID: 34516016 DOI: 10.1002/nbm.4605] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 07/22/2021] [Accepted: 08/01/2021] [Indexed: 06/13/2023]
Abstract
Diffusion MRI tractography is the most widely used macroscale method for mapping connectomes in vivo. However, tractography is prone to various errors and biases, and thus tractography-derived connectomes require careful validation. Here, we critically review studies that have developed or utilized phantoms and tracer maps to validate tractography-derived connectomes, either quantitatively or qualitatively. We identify key factors impacting connectome reconstruction accuracy, including streamline seeding, propagation and filtering methods, and consider the strengths and limitations of state-of-the-art connectome phantoms and associated validation studies. These studies demonstrate the inherent limitations of current fiber orientation models and tractography algorithms and their impact on connectome reconstruction accuracy. Reconstructing connectomes with both high sensitivity and high specificity is challenging, given that some tractography methods can generate an abundance of spurious connections, while others can overlook genuine fiber bundles. We argue that streamline filtering can minimize spurious connections and potentially improve the biological plausibility of connectomes derived from tractography. We find that algorithmic choices such as the tractography seeding methodology, angular threshold, and streamline propagation method can substantially impact connectome reconstruction accuracy. Hence, careful application of tractography is necessary to reconstruct accurate connectomes. Improvements in diffusion MRI acquisition techniques will not necessarily overcome current tractography limitations without accompanying modeling and algorithmic advances.
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Affiliation(s)
- Tabinda Sarwar
- School of Computing Technologies, RMIT University, Melbourne, Victoria, Australia
| | - Kotagiri Ramamohanarao
- Department of Computing and Information Systems, The University of Melbourne, Melbourne, Victoria, Australia
| | - Andrew Zalesky
- Melbourne Neuropsychiatry Centre, The University of Melbourne, Melbourne, Victoria, Australia
- Department of Biomedical Engineering, The University of Melbourne, Melbourne, Victoria, Australia
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46
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Drobnjak I, Neher P, Poupon C, Sarwar T. Physical and digital phantoms for validating tractography and assessing artifacts. Neuroimage 2021; 245:118704. [PMID: 34748954 DOI: 10.1016/j.neuroimage.2021.118704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2021] [Revised: 10/01/2021] [Accepted: 11/01/2021] [Indexed: 11/17/2022] Open
Abstract
Fiber tractography is widely used to non-invasively map white-matter bundles in vivo using diffusion-weighted magnetic resonance imaging (dMRI). As it is the case for all scientific methods, proper validation is a key prerequisite for the successful application of fiber tractography, be it in the area of basic neuroscience or in a clinical setting. It is well-known that the indirect estimation of the fiber tracts from the local diffusion signal is highly ambiguous and extremely challenging. Furthermore, the validation of fiber tractography methods is hampered by the lack of a real ground truth, which is caused by the extremely complex brain microstructure that is not directly observable non-invasively and that is the basis of the huge network of long-range fiber connections in the brain that are the actual target of fiber tractography methods. As a substitute for in vivo data with a real ground truth that could be used for validation, a widely and successfully employed approach is the use of synthetic phantoms. In this work, we are providing an overview of the state-of-the-art in the area of physical and digital phantoms, answering the following guiding questions: "What are dMRI phantoms and what are they good for?", "What would the ideal phantom for validation fiber tractography look like?" and "What phantoms, phantom datasets and tools used for their creation are available to the research community?". We will further discuss the limitations and opportunities that come with the use of dMRI phantoms, and what future direction this field of research might take.
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Affiliation(s)
- Ivana Drobnjak
- Center for Medical Image Computing, Department of Computer Science, University College London, UK.
| | - Peter Neher
- Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Cyril Poupon
- BAOBAB, NeuroSpin, Commissariat à l'Energie Atomique, Institut des Sciences du Vivant Frédéric Joliot, Gif-sur-Yvette, France
| | - Tabinda Sarwar
- School of Computing Technologies, RMIT University, Australia
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47
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Schilling KG, Rheault F, Petit L, Hansen CB, Nath V, Yeh FC, Girard G, Barakovic M, Rafael-Patino J, Yu T, Fischi-Gomez E, Pizzolato M, Ocampo-Pineda M, Schiavi S, Canales-Rodríguez EJ, Daducci A, Granziera C, Innocenti G, Thiran JP, Mancini L, Wastling S, Cocozza S, Petracca M, Pontillo G, Mancini M, Vos SB, Vakharia VN, Duncan JS, Melero H, Manzanedo L, Sanz-Morales E, Peña-Melián Á, Calamante F, Attyé A, Cabeen RP, Korobova L, Toga AW, Vijayakumari AA, Parker D, Verma R, Radwan A, Sunaert S, Emsell L, De Luca A, Leemans A, Bajada CJ, Haroon H, Azadbakht H, Chamberland M, Genc S, Tax CMW, Yeh PH, Srikanchana R, Mcknight CD, Yang JYM, Chen J, Kelly CE, Yeh CH, Cochereau J, Maller JJ, Welton T, Almairac F, Seunarine KK, Clark CA, Zhang F, Makris N, Golby A, Rathi Y, O'Donnell LJ, Xia Y, Aydogan DB, Shi Y, Fernandes FG, Raemaekers M, Warrington S, Michielse S, Ramírez-Manzanares A, Concha L, Aranda R, Meraz MR, Lerma-Usabiaga G, Roitman L, Fekonja LS, Calarco N, Joseph M, Nakua H, Voineskos AN, Karan P, Grenier G, Legarreta JH, Adluru N, Nair VA, Prabhakaran V, Alexander AL, Kamagata K, Saito Y, Uchida W, Andica C, Abe M, Bayrak RG, Wheeler-Kingshott CAMG, D'Angelo E, Palesi F, Savini G, Rolandi N, Guevara P, Houenou J, López-López N, Mangin JF, Poupon C, Román C, Vázquez A, Maffei C, Arantes M, Andrade JP, Silva SM, Calhoun VD, Caverzasi E, Sacco S, Lauricella M, Pestilli F, Bullock D, Zhan Y, Brignoni-Perez E, Lebel C, Reynolds JE, Nestrasil I, Labounek R, Lenglet C, Paulson A, Aulicka S, Heilbronner SR, Heuer K, Chandio BQ, Guaje J, Tang W, Garyfallidis E, Raja R, Anderson AW, Landman BA, Descoteaux M. Tractography dissection variability: What happens when 42 groups dissect 14 white matter bundles on the same dataset? Neuroimage 2021; 243:118502. [PMID: 34433094 PMCID: PMC8855321 DOI: 10.1016/j.neuroimage.2021.118502] [Citation(s) in RCA: 65] [Impact Index Per Article: 21.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 08/10/2021] [Accepted: 08/21/2021] [Indexed: 10/20/2022] Open
Abstract
White matter bundle segmentation using diffusion MRI fiber tractography has become the method of choice to identify white matter fiber pathways in vivo in human brains. However, like other analyses of complex data, there is considerable variability in segmentation protocols and techniques. This can result in different reconstructions of the same intended white matter pathways, which directly affects tractography results, quantification, and interpretation. In this study, we aim to evaluate and quantify the variability that arises from different protocols for bundle segmentation. Through an open call to users of fiber tractography, including anatomists, clinicians, and algorithm developers, 42 independent teams were given processed sets of human whole-brain streamlines and asked to segment 14 white matter fascicles on six subjects. In total, we received 57 different bundle segmentation protocols, which enabled detailed volume-based and streamline-based analyses of agreement and disagreement among protocols for each fiber pathway. Results show that even when given the exact same sets of underlying streamlines, the variability across protocols for bundle segmentation is greater than all other sources of variability in the virtual dissection process, including variability within protocols and variability across subjects. In order to foster the use of tractography bundle dissection in routine clinical settings, and as a fundamental analytical tool, future endeavors must aim to resolve and reduce this heterogeneity. Although external validation is needed to verify the anatomical accuracy of bundle dissections, reducing heterogeneity is a step towards reproducible research and may be achieved through the use of standard nomenclature and definitions of white matter bundles and well-chosen constraints and decisions in the dissection process.
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Affiliation(s)
- Kurt G Schilling
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States.
| | | | - Laurent Petit
- Groupe dImagerie Neurofonctionnelle, Institut Des Maladies Neurodegeneratives, CNRS, CEA University of Bordeaux, Bordeaux, France
| | - Colin B Hansen
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, United States
| | - Vishwesh Nath
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, United States
| | - Fang-Cheng Yeh
- Department of Neurological Surgery, University of Pittsburgh, Pittsburgh, PA, United States
| | - Gabriel Girard
- CIBM Center for BioMedical Imaging, Lausanne, Switzerland
| | - Muhamed Barakovic
- Translational Imaging in Neurology (ThINK), Department of Medicine and Biomedical Engineering, University Hospital and University of Basel, Basel, Switzerland
| | - Jonathan Rafael-Patino
- Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Thomas Yu
- Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Elda Fischi-Gomez
- Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Marco Pizzolato
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark
| | | | - Simona Schiavi
- Department of Computer Science, University of Verona, Italy
| | | | | | - Cristina Granziera
- Translational Imaging in Neurology (ThINK), Department of Medicine and Biomedical Engineering, University Hospital and University of Basel, Basel, Switzerland
| | - Giorgio Innocenti
- Department of Neuroscience, Karolinska Institutet, Stockholm, Sweden
| | - Jean-Philippe Thiran
- Signal Processing Lab (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | - Laura Mancini
- Lysholm Department of Neuroradiology, National Hospital for Neurology & Neurosurgery, UCL Hospitals NHS Foundation Trust, London, United Kingdom
| | - Stephen Wastling
- Lysholm Department of Neuroradiology, National Hospital for Neurology & Neurosurgery, UCL Hospitals NHS Foundation Trust, London, United Kingdom
| | - Sirio Cocozza
- Department of Advanced Biomedical Sciences, University "Federico II", Naples, Italy
| | - Maria Petracca
- Department of Neurosciences and Reproductive and Odontostomatological Sciences, University "Federico II", Naples, Italy
| | - Giuseppe Pontillo
- Department of Advanced Biomedical Sciences, University "Federico II", Naples, Italy
| | - Matteo Mancini
- Department of Neuroscience, Brighton and Sussex Medical School, University of Sussex, Brighton, United Kingdom
| | - Sjoerd B Vos
- Centre for Medical Image Computing, University College London, London, United Kingdom
| | - Vejay N Vakharia
- Department of Clinical and Experimental Epilepsy, University College London, London, United Kingdom
| | - John S Duncan
- Epilepsy Society MRI Unit, Chalfont St Peter, United Kingdom
| | - Helena Melero
- Departamento de Psicobiología y Metodología en Ciencias del Comportamiento - Universidad Complutense de Madrid, Spain Laboratorio de Análisis de Imagen Médica y Biometría (LAIMBIO), Universidad Rey Juan Carlos, Madrid, Spain
| | - Lidia Manzanedo
- Facultad de Ciencias de la Salud, Universidad Rey Juan Carlos, Madrid, Spain
| | - Emilio Sanz-Morales
- Laboratorio de Análisis de Imagen Médica y Biometría (LAIMBIO), Universidad Rey Juan Carlos, Madrid, Spain
| | - Ángel Peña-Melián
- Departamento de Anatomía, Facultad de Medicina, Universidad Complutense de Madrid, Madrid, Spain
| | - Fernando Calamante
- Sydney Imaging and School of Biomedical Engineering, The University of Sydney, Sydney, Australia
| | - Arnaud Attyé
- School of Biomedical Engineering, The University of Sydney, Sydney, Australia
| | - Ryan P Cabeen
- Laboratory of Neuro Imaging, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States
| | - Laura Korobova
- Center for Integrative Connectomics, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States
| | - Arthur W Toga
- Laboratory of Neuro Imaging, Stevens Neuroimaging and Informatics Institute, Keck School of Medicine of USC, University of Southern California, Los Angeles, CA, United States
| | | | - Drew Parker
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - Ragini Verma
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, United States
| | - Ahmed Radwan
- KU Leuven, Department of Imaging and Pathology, Translational MRI, B-3000, Leuven, Belgium
| | - Stefan Sunaert
- KU Leuven, Department of Imaging and Pathology, Translational MRI, B-3000, Leuven, Belgium
| | - Louise Emsell
- KU Leuven, Department of Imaging and Pathology, Translational MRI, B-3000, Leuven, Belgium
| | | | | | - Claude J Bajada
- Department of Physiology and Biochemistry, Faculty of Medicine and Surgery, University of Malta, Malta
| | - Hamied Haroon
- Division of Neuroscience & Experimental Psychology, Faculty of Biology, Medicine and Health, The University of Manchester, Manchester Academic Health Science Centre, Manchester, United Kingdom
| | | | - Maxime Chamberland
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom
| | - Sila Genc
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom
| | - Chantal M W Tax
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, United Kingdom
| | - Ping-Hong Yeh
- National Intrepid Center of Excellence, Walter Reed National Military Medical Center, Bethesda, MD, USA
| | - Rujirutana Srikanchana
- National Intrepid Center of Excellence, Walter Reed National Military Medical Center, Bethesda, MD, USA
| | - Colin D Mcknight
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, United States
| | - Joseph Yuan-Mou Yang
- Department of Neurosurgery, Neuroscience Advanced Clinical Imaging Suite (NACIS), Royal Children's Hospital, Parkville, Melbourne, Australia
| | - Jian Chen
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, Australia
| | - Claire E Kelly
- Victorian Infant Brain Studies, Murdoch Children's Research Institute, Melbourne, Australia
| | - Chun-Hung Yeh
- Institute for Radiological Research, Chang Gung University & Chang Gung Memorial Hospital, Taoyuan, Taiwan
| | | | - Jerome J Maller
- MRI Clinical Science Specialist, General Electric Healthcare, Australia
| | | | - Fabien Almairac
- Neurosurgery department, Hôpital Pasteur, University Hospital of Nice, Côte d'Azur University, France
| | - Kiran K Seunarine
- Developmental Imaging and Biophysics Section, UCL GOS Institute of Child Health, London
| | - Chris A Clark
- Developmental Imaging and Biophysics Section, UCL GOS Institute of Child Health, London
| | - Fan Zhang
- Brigham & Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Nikos Makris
- Brigham & Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Alexandra Golby
- Brigham & Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Yogesh Rathi
- Brigham & Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Lauren J O'Donnell
- Brigham & Women's Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Yihao Xia
- University of Southern California, Keck School of Medicine, Neuroimaging and Informatics Institute, Los Angeles, California, United States
| | - Dogu Baran Aydogan
- Department of Neuroscience and Biomedical Engineering, Aalto University School of Science, Espoo, Finland
| | - Yonggang Shi
- University of Southern California, Keck School of Medicine, Neuroimaging and Informatics Institute, Los Angeles, California, United States
| | | | - Mathijs Raemaekers
- UMC Utrecht Brain Center, Department of Neurology&Neurosurgery, Utrecht, the Netherlands
| | - Shaun Warrington
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, UK
| | - Stijn Michielse
- Department of Neurosurgery, School for Mental Health and Neuroscience, Maastricht University
| | | | - Luis Concha
- Universidad Nacional Autonoma de Mexico, Institute of Neurobiology, Mexico City, Mexico
| | - Ramón Aranda
- Centro de Investigación Científica y de Educación Superior de Ensenada (CICESE-UT3), Cátedras-CONACyT, Ensenada, Mexico
| | | | | | - Lucas Roitman
- Department of Psychology, Stanford University, Stanford, California, USA
| | - Lucius S Fekonja
- Department of Neurosurgery, Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Navona Calarco
- Kimel Family Translational Imaging-Genetics Laboratory, Research Imaging Centre, Centre for Addiction and Mental Health, Toronto, Ontario
| | - Michael Joseph
- Kimel Family Translational Imaging-Genetics Laboratory, Research Imaging Centre, Centre for Addiction and Mental Health, Toronto, Ontario
| | - Hajer Nakua
- Kimel Family Translational Imaging-Genetics Laboratory, Research Imaging Centre, Centre for Addiction and Mental Health, Toronto, Ontario
| | - Aristotle N Voineskos
- Kimel Family Translational Imaging-Genetics Laboratory, Research Imaging Centre, Centre for Addiction and Mental Health, Toronto, Ontario
| | | | | | | | | | - Veena A Nair
- University of Wisconsin-Madison, Madison, WI, USA
| | | | | | - Koji Kamagata
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo Japan
| | - Yuya Saito
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo Japan
| | - Wataru Uchida
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo Japan
| | - Christina Andica
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo Japan
| | - Masahiro Abe
- Department of Radiology, Juntendo University Graduate School of Medicine, Tokyo Japan
| | - Roza G Bayrak
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, United States
| | - Claudia A M Gandini Wheeler-Kingshott
- NMR Research Unit, Queen Square MS Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, United Kingdom
| | - Egidio D'Angelo
- Department of Brain and Behavioral Sciences, University of Pavia, Italy
| | - Fulvia Palesi
- Department of Brain and Behavioral Sciences, University of Pavia, Italy
| | - Giovanni Savini
- Brain MRI 3T Research Center, IRCCS Mondino Foundation, Pavia, Italy
| | - Nicolò Rolandi
- Department of Brain and Behavioral Sciences, University of Pavia, Italy
| | - Pamela Guevara
- Universidad de Concepción, Faculty of Engineering, Concepción, Chile
| | - Josselin Houenou
- Université Paris-Saclay, CEA, CNRS, Neurospin, Gif-sur-Yvette, France
| | | | | | - Cyril Poupon
- Université Paris-Saclay, CEA, CNRS, Neurospin, Gif-sur-Yvette, France
| | - Claudio Román
- Universidad de Concepción, Faculty of Engineering, Concepción, Chile
| | - Andrea Vázquez
- Universidad de Concepción, Faculty of Engineering, Concepción, Chile
| | - Chiara Maffei
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts, USA
| | - Mavilde Arantes
- Department of Biomedicine, Unit of Anatomy, Faculty of Medicine of the University of Porto, Al. Professor Hernâni Monteiro, Porto, Portugal
| | - José Paulo Andrade
- Department of Biomedicine, Unit of Anatomy, Faculty of Medicine of the University of Porto, Al. Professor Hernâni Monteiro, Porto, Portugal
| | - Susana Maria Silva
- Department of Biomedicine, Unit of Anatomy, Faculty of Medicine of the University of Porto, Al. Professor Hernâni Monteiro, Porto, Portugal
| | - Vince D Calhoun
- Tri-institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, Emory University, Atlanta, GA 30303, United States
| | - Eduardo Caverzasi
- Neurology Department UCSF Weill Institute for Neurosciences, University of California, San Francisco
| | - Simone Sacco
- Neurology Department UCSF Weill Institute for Neurosciences, University of California, San Francisco
| | - Michael Lauricella
- Memory and Aging Center. UCSF Weill Institute for Neurosciences, University of California, San Francisco, USA
| | - Franco Pestilli
- Department of Psychology, The University of Texas at Austin, TX 78731, USA
| | - Daniel Bullock
- Department of Psychology, The University of Texas at Austin, TX 78731, USA
| | - Yang Zhan
- Brain Cognition and Brain Disease Institute, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China
| | - Edith Brignoni-Perez
- Developmental-Behavioral Pediatrics Division, Department of Pediatrics, Stanford School of Medicine, Stanford, CA, United States
| | - Catherine Lebel
- Department of Radiology, University of Calgary, 2500 University Drive NW, Calgary, AB, Canada, T2N 1N4
| | - Jess E Reynolds
- Department of Radiology, University of Calgary, 2500 University Drive NW, Calgary, AB, Canada, T2N 1N4
| | - Igor Nestrasil
- Division of Clinical Behavioral Neuroscience, Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - René Labounek
- Division of Clinical Behavioral Neuroscience, Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Christophe Lenglet
- Center for Magnetic Resonance Research, Department of Radiology, University of Minnesota, Minneapolis, MN, USA
| | - Amy Paulson
- Division of Clinical Behavioral Neuroscience, Department of Pediatrics, University of Minnesota, Minneapolis, MN, USA
| | - Stefania Aulicka
- Department of Paediatric Neurology, University Hospital and Medicine Faculty, Masaryk University, Brno, Czech Republic
| | | | - Katja Heuer
- Center for Research and Interdisciplinarity (CRI), INSERM U1284, Université de Paris, Paris, France
| | - Bramsh Qamar Chandio
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
| | - Javier Guaje
- Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN, USA
| | - Wei Tang
- Department of Computer Science, Indiana University, Bloomington, IN, USA
| | | | - Rajikha Raja
- University of Arkansas for Medical Sciences, Little Rock, AR, USA
| | - Adam W Anderson
- Department of Radiology and Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Bennett A Landman
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, United States
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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.
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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
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49
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De Luca A, Ianus A, Leemans A, Palombo M, Shemesh N, Zhang H, Alexander DC, Nilsson M, Froeling M, Biessels GJ, Zucchelli M, Frigo M, Albay E, Sedlar S, Alimi A, Deslauriers-Gauthier S, Deriche R, Fick R, Afzali M, Pieciak T, Bogusz F, Aja-Fernández S, Özarslan E, Jones DK, Chen H, Jin M, Zhang Z, Wang F, Nath V, Parvathaneni P, Morez J, Sijbers J, Jeurissen B, Fadnavis S, Endres S, Rokem A, Garyfallidis E, Sanchez I, Prchkovska V, Rodrigues P, Landman BA, Schilling KG. On the generalizability of diffusion MRI signal representations across acquisition parameters, sequences and tissue types: Chronicles of the MEMENTO challenge. Neuroimage 2021; 240:118367. [PMID: 34237442 PMCID: PMC7615259 DOI: 10.1016/j.neuroimage.2021.118367] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2021] [Revised: 06/09/2021] [Accepted: 07/04/2021] [Indexed: 12/29/2022] Open
Abstract
Diffusion MRI (dMRI) has become an invaluable tool to assess the microstructural organization of brain tissue. Depending on the specific acquisition settings, the dMRI signal encodes specific properties of the underlying diffusion process. In the last two decades, several signal representations have been proposed to fit the dMRI signal and decode such properties. Most methods, however, are tested and developed on a limited amount of data, and their applicability to other acquisition schemes remains unknown. With this work, we aimed to shed light on the generalizability of existing dMRI signal representations to different diffusion encoding parameters and brain tissue types. To this end, we organized a community challenge - named MEMENTO, making available the same datasets for fair comparisons across algorithms and techniques. We considered two state-of-the-art diffusion datasets, including single-diffusion-encoding (SDE) spin-echo data from a human brain with over 3820 unique diffusion weightings (the MASSIVE dataset), and double (oscillating) diffusion encoding data (DDE/DODE) of a mouse brain including over 2520 unique data points. A subset of the data sampled in 5 different voxels was openly distributed, and the challenge participants were asked to predict the remaining part of the data. After one year, eight participant teams submitted a total of 80 signal fits. For each submission, we evaluated the mean squared error, the variance of the prediction error and the Bayesian information criteria. The received submissions predicted either multi-shell SDE data (37%) or DODE data (22%), followed by cartesian SDE data (19%) and DDE (18%). Most submissions predicted the signals measured with SDE remarkably well, with the exception of low and very strong diffusion weightings. The prediction of DDE and DODE data seemed more challenging, likely because none of the submissions explicitly accounted for diffusion time and frequency. Next to the choice of the model, decisions on fit procedure and hyperparameters play a major role in the prediction performance, highlighting the importance of optimizing and reporting such choices. This work is a community effort to highlight strength and limitations of the field at representing dMRI acquired with trending encoding schemes, gaining insights into how different models generalize to different tissue types and fiber configurations over a large range of diffusion encodings.
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Affiliation(s)
- Alberto De Luca
- PROVIDI Lab, Image Sciences Institute, University Medical Center Utrecht, Utrecht, the Netherlands; Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands.
| | - Andrada Ianus
- Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Alexander Leemans
- PROVIDI Lab, Image Sciences Institute, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Marco Palombo
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
| | - Noam Shemesh
- Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Hui Zhang
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
| | - Daniel C Alexander
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
| | - Markus Nilsson
- Clinical Sciences Lund, Radiology, Lund University, Lund, Sweden
| | - Martijn Froeling
- Department of Radiology, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Geert-Jan Biessels
- Department of Neurology, UMC Utrecht Brain Center, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Mauro Zucchelli
- Inria Sophia Antipolis - Méditerranée, Université Côte d'Azur, Sophia Antipolis, France
| | - Matteo Frigo
- Inria Sophia Antipolis - Méditerranée, Université Côte d'Azur, Sophia Antipolis, France
| | - Enes Albay
- Inria Sophia Antipolis - Méditerranée, Université Côte d'Azur, Sophia Antipolis, France; Istanbul Technical University, Istanbul, Turkey
| | - Sara Sedlar
- Inria Sophia Antipolis - Méditerranée, Université Côte d'Azur, Sophia Antipolis, France
| | - Abib Alimi
- Inria Sophia Antipolis - Méditerranée, Université Côte d'Azur, Sophia Antipolis, France
| | | | - Rachid Deriche
- Inria Sophia Antipolis - Méditerranée, Université Côte d'Azur, Sophia Antipolis, France
| | | | - Maryam Afzali
- Cardiff University Brain Research, Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Tomasz Pieciak
- AGH University of Science and Technology, Kraków, Poland; LPI, ETSI Telecomunicación, Universidad de Valladolid, Valladolid, Spain
| | - Fabian Bogusz
- AGH University of Science and Technology, Kraków, Poland
| | | | - Evren Özarslan
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden; Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden
| | - Derek K Jones
- Cardiff University Brain Research, Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom
| | - Haoze Chen
- School of Instruments and Electronics, North University of China, Taiyuan, China
| | - Mingwu Jin
- Department of Physics, University of Texas at Arlington, Arlington, USA
| | - Zhijie Zhang
- School of Instruments and Electronics, North University of China, Taiyuan, China
| | - Fengxiang Wang
- School of Instruments and Electronics, North University of China, Taiyuan, China
| | | | | | - Jan Morez
- Imec-Vision lab, Department of Physics, University of Antwerp, Antwerp, Belgium
| | - Jan Sijbers
- Imec-Vision lab, Department of Physics, University of Antwerp, Antwerp, Belgium
| | - Ben Jeurissen
- Imec-Vision lab, Department of Physics, University of Antwerp, Antwerp, Belgium
| | - Shreyas Fadnavis
- Intelligent Systems Engineering, Indiana University Bloomington, Indiana, USA
| | - Stefan Endres
- Leibniz Institute for Materials Engineering - IWT, Faculty of Production Engineering, University of Bremen, Bremen, Germany
| | - Ariel Rokem
- Department of Psychology and the eScience Institute, University of Washington, Seattle, WA USA
| | | | | | | | | | - Bennet A Landman
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, USA
| | - Kurt G Schilling
- Vanderbilt University Institute of Imaging Science, Vanderbilt University, Nashville, USA; Department of Radiology and Radiological Science, Vanderbilt University Medical Center, Nashville, USA
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50
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Muckley MJ, Riemenschneider B, Radmanesh A, Kim S, Jeong G, Ko J, Jun Y, Shin H, Hwang D, Mostapha M, Arberet S, Nickel D, Ramzi Z, Ciuciu P, Starck JL, Teuwen J, Karkalousos D, Zhang C, Sriram A, Huang Z, Yakubova N, Lui YW, Knoll F. Results of the 2020 fastMRI Challenge for Machine Learning MR Image Reconstruction. IEEE TRANSACTIONS ON MEDICAL IMAGING 2021; 40:2306-2317. [PMID: 33929957 PMCID: PMC8428775 DOI: 10.1109/tmi.2021.3075856] [Citation(s) in RCA: 81] [Impact Index Per Article: 27.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Accelerating MRI scans is one of the principal outstanding problems in the MRI research community. Towards this goal, we hosted the second fastMRI competition targeted towards reconstructing MR images with subsampled k-space data. We provided participants with data from 7,299 clinical brain scans (de-identified via a HIPAA-compliant procedure by NYU Langone Health), holding back the fully-sampled data from 894 of these scans for challenge evaluation purposes. In contrast to the 2019 challenge, we focused our radiologist evaluations on pathological assessment in brain images. We also debuted a new Transfer track that required participants to submit models evaluated on MRI scanners from outside the training set. We received 19 submissions from eight different groups. Results showed one team scoring best in both SSIM scores and qualitative radiologist evaluations. We also performed analysis on alternative metrics to mitigate the effects of background noise and collected feedback from the participants to inform future challenges. Lastly, we identify common failure modes across the submissions, highlighting areas of need for future research in the MRI reconstruction community.
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