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Tremblay SA, Alasmar Z, Pirhadi A, Carbonell F, Iturria-Medina Y, Gauthier CJ, Steele CJ. MVComp toolbox: MultiVariate Comparisons of brain MRI features accounting for common information across metrics. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.02.27.582381. [PMID: 38463982 PMCID: PMC10925263 DOI: 10.1101/2024.02.27.582381] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Multivariate approaches have recently gained in popularity to address the physiological unspecificity of neuroimaging metrics and to better characterize the complexity of biological processes underlying behavior. However, commonly used approaches are biased by the intrinsic associations between variables, or they are computationally expensive and may be more complicated to implement than standard univariate approaches. Here, we propose using the Mahalanobis distance (D2), an individual-level measure of deviation relative to a reference distribution that accounts for covariance between metrics. To facilitate its use, we introduce an open-source python-based tool for computing D2 relative to a reference group or within a single individual: the MultiVariate Comparison (MVComp) toolbox. The toolbox allows different levels of analysis (i.e., group- or subject-level), resolutions (e.g., voxel-wise, ROI-wise) and dimensions considered (e.g., combining MRI metrics or WM tracts). Several example cases are presented to showcase the wide range of possible applications of MVComp and to demonstrate the functionality of the toolbox. The D2 framework was applied to the assessment of white matter (WM) microstructure at 1) the group-level, where D2 can be computed between a subject and a reference group to yield an individualized measure of deviation. We observed that clustering applied to D2 in the corpus callosum yields parcellations that highly resemble known topography based on neuroanatomy, suggesting that D2 provides an integrative index that meaningfully reflects the underlying microstructure. 2) At the subject level, D2 was computed between voxels to obtain a measure of (dis)similarity. The loadings of each MRI metric (i.e., its relative contribution to D2) were then extracted in voxels of interest to showcase a useful option of the MVComp toolbox. These relative contributions can provide important insights into the physiological underpinnings of differences observed. Integrative multivariate models are crucial to expand our understanding of the complex brain-behavior relationships and the multiple factors underlying disease development and progression. Our toolbox facilitates the implementation of a useful multivariate method, making it more widely accessible.
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Affiliation(s)
- Stefanie A Tremblay
- Department of Physics, Concordia University, Montreal, Canada
- School of Health, Concordia University, Montreal, Canada
- EPIC Centre, Montreal Heart Institute, Montreal, Canada
| | - Zaki Alasmar
- School of Health, Concordia University, Montreal, Canada
- Department of Psychology, Concordia University, Montreal, Canada
| | - Amir Pirhadi
- Department of Electrical Engineering, Concordia University, Montreal, Canada
- ViTAA medical solutions, Montreal, Canada
| | | | - Yasser Iturria-Medina
- Neurology and Neurosurgery Department, Montreal Neurological Institute, McGill University, Montreal, Canada
- McConnell Brain Imaging Center, Montreal Neurological Institute, Montreal, Canada
- Ludmer Center for NeuroInformatics and Mental Health, Montreal, Canada
| | - Claudine J Gauthier
- Department of Physics, Concordia University, Montreal, Canada
- School of Health, Concordia University, Montreal, Canada
- EPIC Centre, Montreal Heart Institute, Montreal, Canada
| | - Christopher J Steele
- School of Health, Concordia University, Montreal, Canada
- Department of Psychology, Concordia University, Montreal, Canada
- Department of Neurology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
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2
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Kyle K, Maller J, Barnett Y, Jonker B, Barnett M, D’Souza A, Calamante F, Maamary J, Peters J, Wang C, Tisch S. Tremor suppression following treatment with MRgFUS: skull density ratio consistency and degree of posterior dentatorubrothalamic tract lesioning predicts long-term clinical outcomes in essential tremor. Front Neurol 2023; 14:1129430. [PMID: 37181561 PMCID: PMC10166854 DOI: 10.3389/fneur.2023.1129430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Accepted: 03/27/2023] [Indexed: 05/16/2023] Open
Abstract
Objectives Magnetic resonance-guided focussed ultrasound (MRgFUS) is an incisionless ablative procedure, widely used for treatment of Parkinsonian and Essential Tremor (ET). Enhanced understanding of the patient- and treatment-specific factors that influence sustained long-term tremor suppression could help clinicians achieve superior outcomes via improved patient screening and treatment strategy. Methods We retrospectively analysed data from 31 subjects with ET, treated with MRgFUS at a single centre. Tremor severity was assessed with parts A, B and C of the Clinical Rating Scale for Tremor (CRST) as well as the combined CRST. Tremor in the dominant and non-dominant hand was assessed with Hand Tremor Scores (HTS), derived from the CRST. Pre- and post-treatment imaging data were analysed to determine ablation volume overlap with automated thalamic segmentations, and the dentatorubrothalamic tract (DRTT) and compared with percentage change in CRST and HTS following treatment. Results Tremor symptoms were significantly reduced following treatment. Combined pre-treatment CRST (mean: 60.7 ± 17.3) and HTS (mean: 19.2 ± 5.7) improved by an average of 45.5 and 62.6%, respectively. Percentage change in CRST was found to be significantly negatively associated with age (β = -0.375, p = 0.015), and SDR standard deviation (SDRSD; β = -0.324, p = 0.006), and positively associated with ablation overlap with the posterior DRTT (β = 0.535, p < 0.001). Percentage HTS improvement in the dominant hand decreased significantly with older age (β = -0.576, p < 0.01). Conclusion Our results suggest that increased lesioning of the posterior region of the DRTT could result in greater improvements in combined CRST and non-dominant hand HTS, and that subjects with lower SDR standard deviation tended to experience greater improvement in combined CRST.
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Affiliation(s)
- Kain Kyle
- Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia
- Sydney Neuroimaging Analysis Centre, Camperdown, NSW, Australia
| | | | - Yael Barnett
- Department of Medical Imaging, and Neurology, St Vincent’s Hospital, Darlinghurst, NSW, Australia
- Department of Neurology, St Vincent’s Hospital, Darlinghurst, NSW, Australia
| | - Benjamin Jonker
- Department of Neurosurgery, St Vincent’s Hospital, Darlinghurst, NSW, Australia
- Royal Prince Alfred Institute of Academic Surgery, University of Sydney, Camperdown, NSW, Australia
| | - Michael Barnett
- Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia
- Sydney Neuroimaging Analysis Centre, Camperdown, NSW, Australia
- Department of Neurology, Royal Prince Alfred Hospital, Camperdown, NSW, Australia
| | - Arkiev D’Souza
- Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia
| | - Fernando Calamante
- Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia
- School of Biomedical Engineering, The University of Sydney, Sydney, NSW, Australia
- Sydney Imaging, The University of Sydney, Sydney, NSW, Australia
| | - Joel Maamary
- Department of Neurology, St Vincent’s Hospital, Darlinghurst, NSW, Australia
- School of Medicine, University of New South Wales, Sydney, NSW, Australia
| | - James Peters
- Department of Neurology, St Vincent’s Hospital, Darlinghurst, NSW, Australia
| | - Chenyu Wang
- Brain and Mind Centre, The University of Sydney, Sydney, NSW, Australia
- Sydney Neuroimaging Analysis Centre, Camperdown, NSW, Australia
| | - Stephen Tisch
- Department of Neurology, St Vincent’s Hospital, Darlinghurst, NSW, Australia
- School of Medicine, University of New South Wales, Sydney, NSW, Australia
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3
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Giraldo DL, Smith RE, Struyfs H, Niemantsverdriet E, De Roeck E, Bjerke M, Engelborghs S, Romero E, Sijbers J, Jeurissen B. Investigating Tissue-Specific Abnormalities in Alzheimer's Disease with Multi-Shell Diffusion MRI. J Alzheimers Dis 2022; 90:1771-1791. [PMID: 36336929 PMCID: PMC9789487 DOI: 10.3233/jad-220551] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
BACKGROUND Most studies using diffusion-weighted MRI (DW-MRI) in Alzheimer's disease (AD) have focused their analyses on white matter (WM) microstructural changes using the diffusion (kurtosis) tensor model. Although recent works have addressed some limitations of the tensor model, such as the representation of crossing fibers and partial volume effects with cerebrospinal fluid (CSF), the focus remains in modeling and analyzing the WM. OBJECTIVE In this work, we present a brain analysis approach for DW-MRI that disentangles multiple tissue compartments as well as micro- and macroscopic effects to investigate differences between groups of subjects in the AD continuum and controls. METHODS By means of the multi-tissue constrained spherical deconvolution of multi-shell DW-MRI, underlying brain tissue is modeled with a WM fiber orientation distribution function along with the contributions of gray matter (GM) and CSF to the diffusion signal. From this multi-tissue model, a set of measures capturing tissue diffusivity properties and morphology are extracted. Group differences were interrogated following fixel-, voxel-, and tensor-based morphometry approaches while including strong FWE control across multiple comparisons. RESULTS Abnormalities related to AD stages were detected in WM tracts including the splenium, cingulum, longitudinal fasciculi, and corticospinal tract. Changes in tissue composition were identified, particularly in the medial temporal lobe and superior longitudinal fasciculus. CONCLUSION This analysis framework constitutes a comprehensive approach allowing simultaneous macro and microscopic assessment of WM, GM, and CSF, from a single DW-MRI dataset.
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Affiliation(s)
- Diana L. Giraldo
- Computer Imaging and Medical Applications Laboratory - Cim@Lab, Universidad Nacional de Colombia, Bogotá, Colombia,imec-Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium,μNEURO Research Center of Excellence, University of Antwerp, Antwerp, Belgium
| | - Robert E. Smith
- The Florey Institute of Neuroscience and Mental Health, Heidelberg, VIC, Australia,The Florey Department of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC, Australia
| | - Hanne Struyfs
- Reference Center for Biological Markers of Dementia (BIODEM), Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | - Ellis Niemantsverdriet
- Reference Center for Biological Markers of Dementia (BIODEM), Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium
| | - Ellen De Roeck
- Reference Center for Biological Markers of Dementia (BIODEM), Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium,Department of Neurology and Memory Clinic, Hospital Network Antwerp (ZNA) Middelheim and Hoge Beuken, Antwerp, Belgium
| | - Maria Bjerke
- Reference Center for Biological Markers of Dementia (BIODEM), Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium,Laboratory of Neurochemistry, Department of Clinical Chemistry, and Center for Neurosciences (C4N), Universitair Ziekenhuis Brussel, Vrije Universiteit Brussel (VUB), Brussels, Belgium
| | - Sebastiaan Engelborghs
- Reference Center for Biological Markers of Dementia (BIODEM), Department of Biomedical Sciences, University of Antwerp, Antwerp, Belgium,Department of Neurology, and Center for Neurosciences (C4N), Universitair Ziekenhuis Brussel, Vrije Universiteit Brussel (VUB), Brussels, Belgium
| | - Eduardo Romero
- Computer Imaging and Medical Applications Laboratory - Cim@Lab, Universidad Nacional de Colombia, Bogotá, Colombia
| | - Jan Sijbers
- imec-Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium,μNEURO Research Center of Excellence, University of Antwerp, Antwerp, Belgium
| | - Ben Jeurissen
- imec-Vision Lab, Department of Physics, University of Antwerp, Antwerp, Belgium,μNEURO Research Center of Excellence, University of Antwerp, Antwerp, Belgium,Lab for Equilibrium Investigations and Aerospace, Department of Physics, University of Antwerp, Antwerp, Belgium,Correspondence to: Ben Jeurissen, PhD, imec - Vision Lab, Department of Physics, University of Antwerp (CDE), Universiteitsplein 1, Building N, 2610 Antwerp, Belgium. Tel.: +32 3 265 24 77; E-mail:
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Ali TS, Lv J, Calamante F. Gradual changes in microarchitectural properties of cortex and juxtacortical white matter: Observed by anatomical and diffusion MRI. Magn Reson Med 2022; 88:2485-2503. [PMID: 36045582 DOI: 10.1002/mrm.29413] [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] [Received: 06/03/2022] [Revised: 07/18/2022] [Accepted: 07/22/2022] [Indexed: 01/07/2023]
Abstract
PURPOSE Characterization of cerebral cortex is challenged by the complexity and heterogeneity of its cyto- and myeloarchitecture. This study evaluates quantitative MRI metrics, measured across two cortical depths and in subcortical white matter (WM) adjacent to cortex (juxtacortical WM), indicative of myelin content, neurite density, and diffusion microenvironment, for a comprehensive characterization of cortical microarchitecture. METHODS High-quality structural and diffusion MRI data (N = 30) from the Human Connectome Project were processed to compute myelin index, neurite density index, fractional anisotropy, mean diffusivity, axial diffusivity, and radial diffusivity from superficial cortex, deep cortex, and juxtacortical WM. The distributional patterns of these metrics were analyzed individually, correlated to one another, and were compared to established parcellations. RESULTS Our results supported that myeloarchitectonic and the coexisting cytoarchitectonic structures influence the diffusion properties of water molecules residing in cortex. Full cortical thickness showed myelination patterns similar to those previously observed in humans. Higher myelin indices with similar distributional patterns were observed in deep cortex whereas lower myelin indices were observed in superficial cortex. Neurite density index and other diffusion MRI derived parameters provided complementary information to myelination. Reliable and reproducible correlations were identified among the cortical microarchitectural properties and fiber distributional patterns in proximal WM structures. CONCLUSION We demonstrated gradual changes across the cortical sheath by assessing depth-specific cortical micro-architecture using anatomical and diffusion MRI. Mutually independent but coexisting features of cortical layers and juxtacortical WM provided new insights towards structural organizational units and variabilities across cortical regions and through depth.
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Affiliation(s)
- Tonima S Ali
- School of Biomedical Engineering, The University of Sydney, Sydney, Australia.,Brain and Mind Centre, The University of Sydney, Sydney, Australia
| | - Jinglei Lv
- School of Biomedical Engineering, The University of Sydney, Sydney, Australia.,Brain and Mind Centre, The University of Sydney, Sydney, Australia.,Sydney Imaging, The University of Sydney, Sydney, Australia
| | - Fernando Calamante
- School of Biomedical Engineering, The University of Sydney, Sydney, Australia.,Brain and Mind Centre, The University of Sydney, Sydney, Australia.,Sydney Imaging, The University of Sydney, Sydney, Australia
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5
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Leveraging manifold learning techniques to explore white matter anomalies: An application of the TractLearn pipeline in epilepsy. Neuroimage Clin 2022; 36:103209. [PMID: 36162235 PMCID: PMC9668609 DOI: 10.1016/j.nicl.2022.103209] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Revised: 09/15/2022] [Accepted: 09/21/2022] [Indexed: 12/14/2022]
Abstract
An accurate description of brain white matter anatomy in vivo remains a challenge. However, technical progress allows us to analyze structural variations in an increasingly sophisticated way. Current methods of processing diffusion MRI data now make it possible to correct some limiting biases. In addition, the development of statistical learning algorithms offers the opportunity to analyze the data from a new perspective. We applied newly developed tractography models to extract quantitative white matter parameters in a group of patients with chronic temporal lobe epilepsy. Furthermore, we implemented a statistical learning workflow optimized for the MRI diffusion data - the TractLearn pipeline - to model inter-individual variability and predict structural changes in patients. Finally, we interpreted white matter abnormalities in the context of several other parameters reflecting clinical status, as well as neuronal and cognitive functioning for these patients. Overall, we show the relevance of such a diffusion data processing pipeline for the evaluation of clinical populations. The "global to fine scale" funnel statistical approach proposed in this study also contributes to the understanding of neuroplasticity mechanisms involved in refractory epilepsy, thus enriching previous findings.
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6
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Shepherd TM, Hoch MJ. MRI-Visible Anatomy of the Brainstem. Neuroimaging Clin N Am 2022; 32:553-564. [PMID: 35843662 DOI: 10.1016/j.nic.2022.04.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Human brainstem internal anatomy is intricate, complex, and essential to normal brain function. The brainstem is affected by stroke, multiple sclerosis, and most neurodegenerative diseases-a 1-mm focus of pathologic condition can have profound clinical consequences. Unfortunately, detailed internal brainstem anatomy is difficult to see with conventional MRI sequences. We review normal brainstem anatomy visualized on widely available clinical 3-T MRI scanners using fast gray matter acquisition T1 inversion recovery, probabilistic diffusion tractography, neuromelanin, and susceptibility-weighted imaging. Better anatomic localization using these recent innovations improves our ability to diagnose, localize, and treat brainstem diseases. We aim to provide an accessible review of the most clinically relevant brainstem neuroanatomy.
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Affiliation(s)
- Timothy M Shepherd
- Department of Radiology, New York University Langone School of Medicine, 660 First Avenue, Room 230D, New York, NY 10016, USA.
| | - Michael J Hoch
- Department of Radiology, University of Pennsylvania, 3400 Spruce Street, Suite 130, Philadelphia, PA 19104, USA. https://twitter.com/RVUhound
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7
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Hoch MJ, Shepherd TM. MRI-Visible Anatomy of the Basal Ganglia and Thalamus. Neuroimaging Clin N Am 2022; 32:529-541. [PMID: 35843660 DOI: 10.1016/j.nic.2022.05.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Conventional MR imaging does not discriminate basal ganglia and thalamic internal anatomy well. Radiology reports describe anatomic locations but not specific functional structures. Functional neurosurgery uses indirect targeting based on commissural coordinates or atlases that do not fully account for individual variability. We describe innovative MR imaging sequences that improve the visualization of normal anatomy in this complex brain region and may increase our understanding of basal ganglia and thalamic function. Better visualization also may improve treatments for movement disorders and other emerging functional neurosurgery targets. We aim to provide an accessible review of the most clinically-relevant neuroanatomy within the thalamus and basal ganglia.
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Affiliation(s)
- Michael J Hoch
- Department of Radiology, University of Pennsylvania, 3400 Spruce Street, Suite 130, Philadelphia, PA 19104, USA. https://twiter.com/@RVUhound
| | - Timothy M Shepherd
- Department of Radiology, New York University Langone School of Medicine, 660 First Avenue, Room 226, New York, NY 10016, USA.
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8
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Michels L, Moisa M, Stämpfli P, Hirsiger S, Baumgartner MR, Surbeck W, Seifritz E, Quednow BB. The impact of levamisole and alcohol on white matter microstructure in adult chronic cocaine users. Addict Biol 2022; 27:e13149. [PMID: 35394690 PMCID: PMC9287079 DOI: 10.1111/adb.13149] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 11/16/2021] [Accepted: 01/11/2022] [Indexed: 11/29/2022]
Abstract
Previous brain imaging studies with chronic cocaine users (CU) using diffusion tensor imaging (DTI) mostly focused on fractional anisotropy to investigate white matter (WM) integrity. However, a quantitative interpretation of fractional anisotropy (FA) alterations is often impeded by the inherent limitations of the underlying tensor model. A more fine-grained measure of WM alterations could be achieved by measuring fibre density (FD). This study investigates this novel DTI metric comparing 23 chronic CU and 32 healthy subjects. Quantitative hair analysis was used to determine intensity of cocaine and levamisole exposure-a cocaine adulterant with putative WM neurotoxicity. We first assessed the impact of cocaine use, levamisole exposure and alcohol use on group differences in WM integrity. Compared with healthy controls, all models revealed cortical reductions of FA and FD in CU. At the within-patient group level, we found that alcohol use and levamisole exposure exhibited regionally different FA and FD alterations than cocaine use. We found mostly negative correlations of tract-based WM associated with levamisole and weekly alcohol use. Specifically, levamisole exposure was linked with stronger WM reductions in the corpus callosum than alcohol use. Cocaine use duration correlated negatively with FA and FD in some regions. Yet, most of these correlations did not survive a correction for multiple testing. Our results suggest that chronic cocaine use, levamisole exposure and alcohol use were all linked to significant WM impairments in CU. We conclude that FD could be a sensitive marker to detect the impact of the use of multiple substances on WM integrity in cocaine but also other substance use disorders.
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Affiliation(s)
- Lars Michels
- Department of NeuroradiologyUniversity Hospital ZurichZurichSwitzerland
- Neuroscience Center ZurichUniversity of Zurich and Swiss Federal Institute of Technology ZurichZurichSwitzerland
| | - Marius Moisa
- Zurich Center for Neuroeconomics, Department of NeuroeconomicsUniversity of ZurichZurichSwitzerland
| | - Philipp Stämpfli
- Department of Psychiatry, Psychotherapy, and PsychosomaticsPsychiatric Hospital of the University of ZurichZurichSwitzerland
| | - Sarah Hirsiger
- Experimental and Clinical Pharmacopsychology, Department of Psychiatry, Psychotherapy, and PsychosomaticsPsychiatric Hospital of the University of ZurichZurichSwitzerland
| | - Markus R. Baumgartner
- Center of Forensic Hair Analytics, Institute of Forensic MedicineUniversity of ZurichZurichSwitzerland
| | - Werner Surbeck
- Experimental and Clinical Pharmacopsychology, Department of Psychiatry, Psychotherapy, and PsychosomaticsPsychiatric Hospital of the University of ZurichZurichSwitzerland
| | - Erich Seifritz
- Neuroscience Center ZurichUniversity of Zurich and Swiss Federal Institute of Technology ZurichZurichSwitzerland
- Department of Psychiatry, Psychotherapy, and PsychosomaticsPsychiatric Hospital of the University of ZurichZurichSwitzerland
| | - Boris B. Quednow
- Neuroscience Center ZurichUniversity of Zurich and Swiss Federal Institute of Technology ZurichZurichSwitzerland
- Experimental and Clinical Pharmacopsychology, Department of Psychiatry, Psychotherapy, and PsychosomaticsPsychiatric Hospital of the University of ZurichZurichSwitzerland
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9
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Roine T, Mohammadian M, Hirvonen J, Kurki T, Posti JP, Takala RS, Newcombe V, Tallus J, Katila AJ, Maanpää HR, Frantzen J, Menon D, Tenovuo O. Structural brain connectivity correlates with outcome in mild traumatic brain injury. J Neurotrauma 2022; 39:336-347. [PMID: 35018829 DOI: 10.1089/neu.2021.0093] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022] Open
Abstract
We investigated the topology of structural brain connectivity networks and its association to outcome following mild traumatic brain injury, a major cause of permanent disability. Eighty-five patients with mild traumatic brain injury underwent MRI twice, about three weeks and eight months after injury, and 30 age-matched orthopedic trauma control subjects were scanned. Outcome was assessed with Extended Glasgow Outcome Scale on average eight months after injury. We performed constrained spherical deconvolution based probabilistic streamlines tractography on diffusion MRI data and parcellated cortical and subcortical gray matter into 84 regions based on T1-weighted data to reconstruct structural brain connectivity networks weighted by the number of streamlines. Graph theoretical methods were employed to measure network properties in both patients and controls, and correlations between these properties and outcome were calculated. We found no global differences in the network properties between patients with mild traumatic brain injury and orthopedic control subjects at either stage. However, we found significantly increased betweenness centrality of the right pars opercularis in the chronic stage compared to control subjects. Furthermore, both global and local network properties correlated significantly with outcome. Higher normalized global efficiency, degree, and strength as well as lower small-worldness were associated with better outcome. Correlations between the outcome and the local network properties were the most prominent in the left putamen and the left postcentral gyrus. Our results indicate that both global and local network properties provide valuable information about the outcome already in the acute/subacute stage, and therefore, are promising biomarkers for prognostic purposes in mild traumatic brain injury.
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Affiliation(s)
- Timo Roine
- University of Turku, 8058, Turku Brain and Mind Center, Turku, Finland.,Aalto University School of Science, 313201, Department of Neuroscience and Biomedical Engineering, Espoo, Finland;
| | - Mehrbod Mohammadian
- University of Turku Faculty of Medicine, 60654, Department of Clinical Neurosciences, Turku, Finland.,Turku University Hospital, 60652, Turku Brain Injury Center, Neurocenter, Turku, Finland;
| | - Jussi Hirvonen
- TYKS Turku University Hospital, 60652, Department of Radiology, Turku, Varsinais-Suomi, Finland;
| | - Timo Kurki
- University of Turku Faculty of Medicine, 60654, Department of Clinical Neurosciences, Turku, Finland.,Turku University Hospital, 60652, Turku Brain Injury Center, Neurocenter, Turku, Finland.,TYKS Turku University Hospital, 60652, Department of Radiology, Turku, Varsinais-Suomi, Finland;
| | - Jussi P Posti
- University of Turku Faculty of Medicine, 60654, Department of Clinical Neurosciences, Turku, Finland.,Turku University Hospital, 60652, Turku Brain Injury Center, Neurocenter, Turku, Varsinais-Suomi, Finland.,TYKS Turku University Hospital, 60652, Department of Neurosurgery. Neurocenter, Turku, Varsinais-Suomi, Finland;
| | - Riikka Sk Takala
- Turku University Hospital, Perioperative Services, Intensive Care Medicine and Pain Management, Turku, Finland.,University of Turku, 8058, Anaesthesiology, Intensive Care, Emergency Care and Pain Medicine, Turku, Varsinais-Suomi, Finland;
| | - Virginia Newcombe
- University of Cambridge, Division of Anaesthesia, Addenbrooke's Hospital, Cambridge, United Kingdom of Great Britain and Northern Ireland;
| | - Jussi Tallus
- Turku University Hospital, 60652, Turku Brain Injury Center, Neurocenter, Turku, Varsinais-Suomi, Finland;
| | - Ari J Katila
- Turku University Hospital, Perioperative Services, Intensive Care Medicine and Pain Management, Turku, Varsinais-Suomi, Finland;
| | - Henna-Riikka Maanpää
- Turku University Hospital, 60652, Turku Brain Injury Center, Neurocenter, Turku, Varsinais-Suomi, Finland.,Turku University Hospital, Department of Neurosurgery, Neurocenter, Turku, Varsinais-Suomi, Finland;
| | - Janek Frantzen
- Turku University Hospital, Turku Brain Injury Center, Neurocenter, Turku, Finland.,Turku University Hospital, Department of Neurosurgery, Neurocenter, Turku, Varsinais-Suomi, Finland.,University of Turku Faculty of Medicine, 60654, Department of Clinical Neurosciences, Turku, Finland;
| | - David Menon
- University of Cambridge, Division of Anaesthesia, Addenbrooke's Hospital, Cambridge, United Kingdom of Great Britain and Northern Ireland;
| | - Olli Tenovuo
- University of Turku Faculty of Medicine, 60654, Department of Clinical Neurosciences, Turku, Finland.,Turku University Hospital, 60652, Turku Brain Injury Center, Neurocenter, Turku, Finland;
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10
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TractLearn: A geodesic learning framework for quantitative analysis of brain bundles. Neuroimage 2021; 233:117927. [PMID: 33689863 DOI: 10.1016/j.neuroimage.2021.117927] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2020] [Revised: 02/25/2021] [Accepted: 03/01/2021] [Indexed: 12/13/2022] Open
Abstract
Deep learning-based convolutional neural networks have recently proved their efficiency in providing fast segmentation of major brain fascicles structures, based on diffusion-weighted imaging. The quantitative analysis of brain fascicles then relies on metrics either coming from the tractography process itself or from each voxel along the bundle. Statistical detection of abnormal voxels in the context of disease usually relies on univariate and multivariate statistics models, such as the General Linear Model (GLM). Yet in the case of high-dimensional low sample size data, the GLM often implies high standard deviation range in controls due to anatomical variability, despite the commonly used smoothing process. This can lead to difficulties to detect subtle quantitative alterations from a brain bundle at the voxel scale. Here we introduce TractLearn, a unified framework for brain fascicles quantitative analyses by using geodesic learning as a data-driven learning task. TractLearn allows a mapping between the image high-dimensional domain and the reduced latent space of brain fascicles using a Riemannian approach. We illustrate the robustness of this method on a healthy population with test-retest acquisition of multi-shell diffusion MRI data, demonstrating that it is possible to separately study the global effect due to different MRI sessions from the effect of local bundle alterations. We have then tested the efficiency of our algorithm on a sample of 5 age-matched subjects referred with mild traumatic brain injury. Our contributions are to propose: 1/ A manifold approach to capture controls variability as standard reference instead of an atlas approach based on a Euclidean mean. 2/ A tool to detect global variation of voxels' quantitative values, which accounts for voxels' interactions in a structure rather than analyzing voxels independently. 3/ A ready-to-plug algorithm to highlight nonlinear variation of diffusion MRI metrics. With this regard, TractLearn is a ready-to-use algorithm for precision medicine.
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11
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Huynh V, Staempfli P, Luetolf R, Luechinger R, Curt A, Kollias S, Hubli M, Michels L. Investigation of Cerebral White Matter Changes After Spinal Cord Injury With a Measure of Fiber Density. Front Neurol 2021; 12:598336. [PMID: 33692736 PMCID: PMC7937730 DOI: 10.3389/fneur.2021.598336] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/26/2020] [Accepted: 01/18/2021] [Indexed: 02/06/2023] Open
Abstract
Remote neurodegenerative changes in supraspinal white matter (WM) can manifest after central lesions such as spinal cord injury (SCI). The majority of diffusion tensor imaging (DTI) studies use traditional metrics such as fractional anisotropy (FA) and mean diffusivity (MD) to investigate microstructural changes in cerebral WM after SCI. However, interpretation of FA readouts is often challenged by inherent limitations of the tensor model. Recent developments in novel diffusion markers, such as fiber density (FD), allows more accurate depictions of WM pathways and has shown more reliable quantification of WM alterations compared to FA in recent studies of neurological diseases. This study investigated if FD provides useful characterization of supraspinal WM integrity after SCI in addition to the traditional DTI readouts. FA, MD, and FD maps were derived from diffusion datasets of 20 patients with chronic SCI and compared with 19 healthy controls (HC). Group differences were investigated across whole brain WM using tract-based spatial statistics and averaged diffusion values of the corticospinal tract (CST) and thalamic radiation (TR) were extracted for comparisons between HC and SCI subgroups. We also related diffusion readouts of the CST and TR with clinical scores of sensorimotor function. To investigate which diffusion markers of the CST and TR delineate HC and patients with SCI a receiver operating characteristic (ROC) analysis was performed. Overall, patients with an SCI showed decreased FA of the TR and CST. ROC analysis differentiated HC and SCI based on diffusion markers of large WM tracts including FD of the TR. Furthermore, patients' motor function was positively correlated with greater microstructural integrity of the CST. While FD showed the strongest correlation, motor function was also associated with FA and MD of the CST. In summary, microstructural changes of supraspinal WM in patients with SCI can be detected using FD as a complementary marker to traditional DTI readouts and correlates with their clinical characteristics. Future DTI studies may benefit from utilizing this novel marker to investigate complex large WM tracts in patient cohorts with varying presentations of SCI or neurodegenerative diseases.
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Affiliation(s)
- Vincent Huynh
- Department of Neuroradiology, University Hospital of Zurich, Zurich, Switzerland.,Spinal Cord Injury Center, Balgrist University Hospital, University of Zurich, Zurich, Switzerland
| | - Philipp Staempfli
- MR-Center of the Psychiatric University Hospital and the Department of Child and Adolescent Psychiatry, University of Zurich, Zurich, Switzerland.,Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zurich, Zurich, Switzerland
| | - Robin Luetolf
- Spinal Cord Injury Center, Balgrist University Hospital, University of Zurich, Zurich, Switzerland
| | - Roger Luechinger
- Institute for Biomedical Engineering, University and ETH Zurich, Zurich, Switzerland
| | - Armin Curt
- Department of Neuroradiology, University Hospital of Zurich, Zurich, Switzerland
| | - Spyros Kollias
- Department of Neuroradiology, University Hospital of Zurich, Zurich, Switzerland
| | - Michèle Hubli
- Spinal Cord Injury Center, Balgrist University Hospital, University of Zurich, Zurich, Switzerland
| | - Lars Michels
- Department of Neuroradiology, University Hospital of Zurich, Zurich, Switzerland
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12
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Menegaux A, Hedderich DM, Bäuml JG, Manoliu A, Daamen M, Berg RC, Preibisch C, Zimmer C, Boecker H, Bartmann P, Wolke D, Sorg C, Stämpfli P. Reduced apparent fiber density in the white matter of premature-born adults. Sci Rep 2020; 10:17214. [PMID: 33057208 PMCID: PMC7560721 DOI: 10.1038/s41598-020-73717-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Accepted: 09/21/2020] [Indexed: 12/13/2022] Open
Abstract
Premature-born adults exhibit lasting white matter alterations as demonstrated by widespread reduction in fractional anisotropy (FA) based on diffusion-weighted imaging (DWI). FA reduction, however, is non-specific for microscopic underpinnings such as aberrant myelination or fiber density (FD). Using recent advances in DWI, we tested the hypothesis of reduced FD in premature-born adults and investigated its link with the degree of prematurity and cognition. 73 premature- and 89 mature-born adults aged 25-27 years underwent single-shell DWI, from which a FD measure was derived using convex optimization modeling for microstructure informed tractography (COMMIT). Premature-born adults exhibited lower FD in numerous tracts including the corpus callosum and corona radiata compared to mature-born adults. These FD alterations were associated with both the degree of prematurity, as assessed via gestational age and birth weight, as well as with reduced cognition as measured by full-scale IQ. Finally, lower FD overlapped with lower FA, suggesting lower FD underlie unspecific FA reductions. Results provide evidence that premature birth leads to lower FD in adulthood which links with lower full-scale IQ. Data suggest that lower FD partly underpins FA reductions of premature birth but that other processes such as hypomyelination might also take place.
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Affiliation(s)
- Aurore Menegaux
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany. .,TUM Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany.
| | - Dennis M Hedderich
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany.,TUM Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany
| | - Josef G Bäuml
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany.,TUM Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany
| | - Andrei Manoliu
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zurich, Zurich, Switzerland.,Wellcome Centre for Human Neuroimaging, University College London, London, UK.,Centre for Computational Psychiatry and Ageing Research, Max Planck University College London, London, UK
| | - Marcel Daamen
- Functional Neuroimaging Group, Department of Radiology, University Hospital Bonn, Bonn, Germany.,Department of Neonatology, University Hospital Bonn, Bonn, Germany
| | - Ronja C Berg
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany.,TUM Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany
| | - Christine Preibisch
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany.,TUM Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany
| | - Claus Zimmer
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany.,TUM Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany
| | - Henning Boecker
- Functional Neuroimaging Group, Department of Radiology, University Hospital Bonn, Bonn, Germany
| | - Peter Bartmann
- Department of Neonatology, University Hospital Bonn, Bonn, Germany
| | - Dieter Wolke
- Department of Psychology, University of Warwick, Coventry, UK.,Warwick Medical School, University of Warwick, Coventry, UK
| | - Christian Sorg
- Department of Diagnostic and Interventional Neuroradiology, School of Medicine, Technical University of Munich, Munich, Germany.,TUM Neuroimaging Center, School of Medicine, Technical University of Munich, Munich, Germany.,Department of Psychiatry, School of Medicine, Technical University of Munich, Munich, Germany
| | - Philipp Stämpfli
- Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zurich, Zurich, Switzerland.,MR-Center of the Department of Psychiatry, Psychotherapy, and Psychosomatics and the Department of Child and Adolescent Psychiatry, Psychiatric Hospital of the University of Zurich, University of Zurich, Zurich, Switzerland
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13
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Davidson B, Mithani K, Huang Y, Jones RM, Goubran M, Meng Y, Snell J, Hynynen K, Hamani C, Lipsman N. Technical and radiographic considerations for magnetic resonance imaging-guided focused ultrasound capsulotomy. J Neurosurg 2020; 135:291-299. [PMID: 32977311 DOI: 10.3171/2020.6.jns201302] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Accepted: 06/04/2020] [Indexed: 11/06/2022]
Abstract
OBJECTIVE Magnetic resonance imaging-guided focused ultrasound (MRgFUS) is an emerging treatment modality that enables incisionless ablative neurosurgical procedures. Bilateral MRgFUS capsulotomy has recently been demonstrated to be safe and effective in treating obsessive-compulsive disorder (OCD) and major depressive disorder (MDD). Preliminary evidence has suggested that bilateral MRgFUS capsulotomy can present increased difficulties in reaching lesional temperatures as compared to unilateral thalamotomy. The authors of this article aimed to study the parameters associated with successful MRgFUS capsulotomy lesioning and to present longitudinal radiographic findings following MRgFUS capsulotomy. METHODS Using data from 22 attempted MRgFUS capsulotomy treatments, the authors investigated the relationship between various sonication parameters and the maximal temperature achieved at the intracranial target. Lesion volume and morphology were analyzed longitudinally using structural and diffusion tensor imaging. A retreatment procedure was attempted in one patient, and their postoperative imaging is presented. RESULTS Skull density ratio (SDR), skull thickness, and angle of incidence were significantly correlated with the maximal temperature achieved. MRgFUS capsulotomy lesions appeared similar to those following MRgFUS thalamotomy, with three concentric zones observed on MRI. Lesion volumes regressed substantially over time following MRgFUS. Fractional anisotropy analysis revealed a disruption in white matter integrity, followed by a gradual return to near-baseline levels concurrent with lesion regression. In the patient who underwent retreatment, successful bilateral lesioning was achieved, and there were no adverse clinical or radiographic events. CONCLUSIONS With the current iteration of MRgFUS technology, skull-related parameters such as SDR, skull thickness, and angle of incidence should be considered when selecting patients suitable for MRgFUS capsulotomy. Lesions appear to follow morphological patterns similar to what is seen following MRgFUS thalamotomy. Retreatment appears to be safe, although additional cases will be necessary to further evaluate the associated safety profile.
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Affiliation(s)
- Benjamin Davidson
- 1Division of Neurosurgery, Sunnybrook Health Sciences Centre
- 2Harquail Centre for Neuromodulation and Hurvitz Brain Sciences Program
- 3Sunnybrook Research Institute
| | - Karim Mithani
- 1Division of Neurosurgery, Sunnybrook Health Sciences Centre
- 2Harquail Centre for Neuromodulation and Hurvitz Brain Sciences Program
- 3Sunnybrook Research Institute
| | - Yuexi Huang
- 3Sunnybrook Research Institute
- 4Physical Sciences Platform, Sunnybrook Research Institute, Toronto, Ontario, Canada
| | - Ryan M Jones
- 3Sunnybrook Research Institute
- 4Physical Sciences Platform, Sunnybrook Research Institute, Toronto, Ontario, Canada
| | - Maged Goubran
- 2Harquail Centre for Neuromodulation and Hurvitz Brain Sciences Program
- 3Sunnybrook Research Institute
- 4Physical Sciences Platform, Sunnybrook Research Institute, Toronto, Ontario, Canada
- 7Department of Medical Biophysics, University of Toronto; and
| | - Ying Meng
- 1Division of Neurosurgery, Sunnybrook Health Sciences Centre
- 2Harquail Centre for Neuromodulation and Hurvitz Brain Sciences Program
- 3Sunnybrook Research Institute
| | - John Snell
- 5The Focused Ultrasound Foundation, Charlottesville
- 6Department of Neurosurgery, University of Virginia, Charlottesville, Virginia
| | - Kullervo Hynynen
- 2Harquail Centre for Neuromodulation and Hurvitz Brain Sciences Program
- 3Sunnybrook Research Institute
- 4Physical Sciences Platform, Sunnybrook Research Institute, Toronto, Ontario, Canada
- 7Department of Medical Biophysics, University of Toronto; and
- 8Institute of Biomaterials and Biomedical Engineering, Toronto, Ontario, Canada
| | - Clement Hamani
- 1Division of Neurosurgery, Sunnybrook Health Sciences Centre
- 2Harquail Centre for Neuromodulation and Hurvitz Brain Sciences Program
- 3Sunnybrook Research Institute
| | - Nir Lipsman
- 1Division of Neurosurgery, Sunnybrook Health Sciences Centre
- 2Harquail Centre for Neuromodulation and Hurvitz Brain Sciences Program
- 3Sunnybrook Research Institute
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14
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Smith RE, Calamante F, Connelly A. Notes on "A cautionary note on the use of SIFT in pathological connectomes". Magn Reson Med 2020; 84:2303-2307. [PMID: 32716098 DOI: 10.1002/mrm.28266] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2020] [Revised: 03/02/2020] [Accepted: 03/03/2020] [Indexed: 11/09/2022]
Affiliation(s)
- Robert E Smith
- Imaging division, Florey Institute of Neuroscience and Mental Health, Heidelberg, Victoria, Australia.,Florey Department of Neuroscience and Mental Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Fernando Calamante
- Florey Department of Neuroscience and Mental Health, University of Melbourne, Melbourne, Victoria, Australia.,The University of Sydney, School of Aerospace, Mechanical and Mechatronic Engineering, University of Sydney, Camperdown, NSW, Australia.,The University of Sydney, Sydney Imaging, Camperdown, NSW, Australia
| | - Alan Connelly
- Imaging division, Florey Institute of Neuroscience and Mental Health, Heidelberg, Victoria, Australia.,Florey Department of Neuroscience and Mental Health, University of Melbourne, Melbourne, Victoria, Australia.,Department of Medicine, Austin Health and Northern Health, University of Melbourne, Melbourne, Victoria, Australia
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15
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Calamante F. The Seven Deadly Sins of Measuring Brain Structural Connectivity Using Diffusion MRI Streamlines Fibre-Tracking. Diagnostics (Basel) 2019; 9:diagnostics9030115. [PMID: 31500098 PMCID: PMC6787694 DOI: 10.3390/diagnostics9030115] [Citation(s) in RCA: 42] [Impact Index Per Article: 8.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2019] [Revised: 08/13/2019] [Accepted: 09/04/2019] [Indexed: 12/13/2022] Open
Abstract
There is great interest in the study of brain structural connectivity, as white matter abnormalities have been implicated in many disease states. Diffusion magnetic resonance imaging (MRI) provides a powerful means to characterise structural connectivity non-invasively, by using a fibre-tracking algorithm. The most widely used fibre-tracking strategy is based on the step-wise generation of streamlines. Despite their popularity and widespread use, there are a number of practical considerations that must be taken into account in order to increase the robustness of streamlines tracking results, particularly when these methods are used to study brain structural connectivity, and the connectome. This review article describes what we consider the ‘seven deadly sins’ of mapping structural connections using diffusion MRI streamlines fibre-tracking, with particular emphasis on ‘sins’ that can be practically avoided and they can have an important impact in the results. It is shown that there are important ‘deadly sins’ to be avoided at every step of the pipeline, such as during data acquisition, during data modelling to estimate local fibre architecture, during the fibre-tracking process itself, and during quantification of the tracking results. The recommendations here are intended to inform users on potential important shortcomings of their current tracking protocols, as well as to guide future users on some of the key issues and decisions that must be faced when designing their processing pipelines.
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Affiliation(s)
- Fernando Calamante
- Sydney Imaging, The University of Sydney, Sydney, New South Wales 2050, Australia.
- School of Aerospace, Mechanical and Mechatronic Engineering, The University of Sydney, Sydney, New South Wales 2006, Australia.
- Florey Department of Neuroscience and Mental Health, University of Melbourne, Melbourne, Victoria 3052, Australia.
- Brain and Mind Centre, The University of Sydney, 94 Mallett Street, Camperdown, NSW 2050, Australia.
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16
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Jeurissen B, Descoteaux M, Mori S, Leemans A. Diffusion MRI fiber tractography of the brain. NMR IN BIOMEDICINE 2019; 32:e3785. [PMID: 28945294 DOI: 10.1002/nbm.3785] [Citation(s) in RCA: 237] [Impact Index Per Article: 47.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/09/2017] [Revised: 07/10/2017] [Accepted: 07/11/2017] [Indexed: 06/07/2023]
Abstract
The ability of fiber tractography to delineate non-invasively the white matter fiber pathways of the brain raises possibilities for clinical applications and offers enormous potential for neuroscience. In the last decade, fiber tracking has become the method of choice to investigate quantitative MRI parameters in specific bundles of white matter. For neurosurgeons, it is quickly becoming an invaluable tool for the planning of surgery, allowing for visualization and localization of important white matter pathways before and even during surgery. Fiber tracking has also claimed a central role in the field of "connectomics," a technique that builds and studies comprehensive maps of the complex network of connections within the brain, and to which significant resources have been allocated worldwide. Despite its unique abilities and exciting applications, fiber tracking is not without controversy, in particular when it comes to its interpretation. As neuroscientists are eager to study the brain's connectivity, the quantification of tractography-derived "connection strengths" between distant brain regions is becoming increasingly popular. However, this practice is often frowned upon by fiber-tracking experts. In light of this controversy, this paper provides an overview of the key concepts of tractography, the technical considerations at play, and the different types of tractography algorithm, as well as the common misconceptions and mistakes that surround them. We also highlight the ongoing challenges related to fiber tracking. While recent methodological developments have vastly increased the biological accuracy of fiber tractograms, one should be aware that, even with state-of-the-art techniques, many issues that severely bias the resulting structural "connectomes" remain unresolved.
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Affiliation(s)
- Ben Jeurissen
- imec-Vision Lab, Dept. of Physics, University of Antwerp, Belgium
| | - Maxime Descoteaux
- Centre de Recherche CHUS, University of Sherbrooke, Sherbrooke, Canada
- Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Faculty of Science, University of Sherbrooke, Canada
| | - Susumu Mori
- Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD, USA
- F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, USA
| | - Alexander Leemans
- Image Sciences Institute, University Medical Center Utrecht, Utrecht, The Netherlands
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17
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Dell'Acqua F, Tournier J. Modelling white matter with spherical deconvolution: How and why? NMR IN BIOMEDICINE 2019; 32:e3945. [PMID: 30113753 PMCID: PMC6585735 DOI: 10.1002/nbm.3945] [Citation(s) in RCA: 68] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/16/2017] [Revised: 04/18/2018] [Accepted: 04/24/2018] [Indexed: 05/30/2023]
Abstract
Since the realization that diffusion MRI can probe the microstructural organization and orientation of biological tissue in vivo and non-invasively, a multitude of diffusion imaging methods have been developed and applied to study the living human brain. Diffusion tensor imaging was the first model to be widely adopted in clinical and neuroscience research, but it was also clear from the beginning that it suffered from limitations when mapping complex configurations, such as crossing fibres. In this review, we highlight the main steps that have led the field of diffusion imaging to move from the tensor model to the adoption of diffusion and fibre orientation density functions as a more effective way to describe the complexity of white matter organization within each brain voxel. Among several techniques, spherical deconvolution has emerged today as one of the main approaches to model multiple fibre orientations and for tractography applications. Here we illustrate the main concepts and the reasoning behind this technique, as well as the latest developments in the field. The final part of this review provides practical guidelines and recommendations on how to set up processing and acquisition protocols suitable for spherical deconvolution.
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Affiliation(s)
- Flavio Dell'Acqua
- Institute of Psychiatry Psychology and Neuroscience, King's College LondonDepartment of NeuroimagingUK
- Sackler Institute for Translational Neurodevelopment, Institute of Psychiatry Psychology and Neuroscience, King's College LondonDepartment of Forensic and Neurodevelopmental SciencesUK
| | - J.‐Donald Tournier
- King's College LondonDivision of Imaging Sciences and Biomedical EngineeringUK
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18
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Subtle white matter alterations in schizophrenia identified with a new measure of fiber density. Sci Rep 2019; 9:4636. [PMID: 30874571 PMCID: PMC6420505 DOI: 10.1038/s41598-019-40070-2] [Citation(s) in RCA: 21] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2018] [Accepted: 02/07/2019] [Indexed: 12/13/2022] Open
Abstract
Altered cerebral connectivity is one of the core pathophysiological mechanism underlying the development and progression of information-processing deficits in schizophrenia. To date, most diffusion tensor imaging (DTI) studies used fractional anisotropy (FA) to investigate disrupted white matter connections. However, a quantitative interpretation of FA changes is often impeded by the inherent limitations of the underlying tensor model. A more fine-grained measure of white matter alterations could be achieved by measuring fiber density (FD) - a novel non-tensor-derived diffusion marker. This study investigates, for the first time, FD alterations in schizophrenia patients. FD and FA maps were derived from diffusion data of 25 healthy controls (HC) and 21 patients with schizophrenia (SZ). Using tract-based spatial statistics (TBSS), group differences in FD and FA were investigated across the entire white matter. Furthermore, we performed a region of interest (ROI) analysis of frontal fasciculi to detect potential correlations between FD and positive symptoms. As a result, whole brain TBSS analysis revealed reduced FD in SZ patients compared to HC in several white matter tracts including the left and right thalamic radiation (TR), superior longitudinal fasciculus (SLF), corpus callosum (CC), and corticospinal tract (CST). In contrast, there were no significant FA differences between groups. Further, FD values in the TR were negatively correlated with the severity of positive symptoms and medication dose in SZ patients. In summary, a novel diffusion-weighted data analysis approach enabled us to identify widespread FD changes in SZ patients with most prominent white matter alterations in the frontal and subcortical regions. Our findings suggest that the new FD measure may be more sensitive to subtle changes in the white matter microstructure compared to FA, particularly in the given population. Therefore, investigating FD may be a promising approach to detect subtle changes in the white matter microstructure of altered connectivity in schizophrenia.
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19
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Tong Q, He H, Gong T, Li C, Liang P, Qian T, Sun Y, Ding Q, Li K, Zhong J. Reproducibility of multi-shell diffusion tractography on traveling subjects: A multicenter study prospective. Magn Reson Imaging 2019; 59:1-9. [PMID: 30797888 DOI: 10.1016/j.mri.2019.02.011] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2018] [Revised: 02/20/2019] [Accepted: 02/20/2019] [Indexed: 01/06/2023]
Abstract
Reproducibility of multicenter diffusion magnetic resonance imaging has drawn more attention recently due to rapidly increasing need for large-size brain imaging studies. Advanced multi-shell diffusion models are recommended for their potentials to provide variety of physio-pathological information. While previous studies have investigated the consistency of single-shell diffusion acquisition from various hardware and protocols, a well-controlled study with multi-shell acquisition would be necessary to understand the inherent factors of reproducibility from new complexity of such acquisition protocol. In this study, three traveling subjects were scanned at eight imaging centers equipped with the same type of scanners using the same multi-shell diffusion imaging protocol. Track density imaging and structure connectomes were investigated in local-scale distribution and in distal-scale connectivity, respectively. With evaluations of the coefficient of variation and the intra-class correlation coefficient, our results indicated: 1) similar to single-shell schemes, the intra-center reproducibility of multi-shell is higher than inter-center; 2) multi-shell schemes produce higher reproducibility and precision among centers compared to the single-shell schemes; and 3) in addition to the diffusion schemes, image quality and the presence of complex fiber structure could also associated with multicenter reproducibility.
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Affiliation(s)
- Qiqi Tong
- Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, Zhejiang, China.
| | - Hongjian He
- Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, Zhejiang, China.
| | - Ting Gong
- Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, Zhejiang, China.
| | - Chen Li
- Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, Zhejiang, China.
| | - Peipeng Liang
- School of Psychology, Capital Normal University, Beijing, China; Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, China.
| | - Tianyi Qian
- MR Collaboration NE Asia, Siemens Healthcare, Beijing, China.
| | - Yi Sun
- MR Collaboration NE Asia, Siemens Healthcare, Shanghai, China.
| | - Qiuping Ding
- Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, Zhejiang, China.
| | - Kuncheng Li
- Beijing Key Laboratory of Magnetic Resonance Imaging and Brain Informatics, Beijing, China; Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China.
| | - Jianhui Zhong
- Center for Brain Imaging Science and Technology, Key Laboratory for Biomedical Engineering of Ministry of Education, College of Biomedical Engineering and Instrumental Science, Zhejiang University, Hangzhou, Zhejiang, China; Department of Imaging Sciences, University of Rochester, Rochester, NY, USA.
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20
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Multivariate pattern classification of brain white matter connectivity predicts classic trigeminal neuralgia. Pain 2019; 159:2076-2087. [PMID: 29905649 DOI: 10.1097/j.pain.0000000000001312] [Citation(s) in RCA: 27] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2022]
Abstract
Trigeminal neuralgia (TN) is a severe form of chronic facial neuropathic pain. Increasing interest in the neuroimaging of pain has highlighted changes in the root entry zone in TN, but also group-level central nervous system gray and white matter (WM) abnormalities. Group differences in neuroimaging data are frequently evaluated with univariate statistics; however, this approach is limited because it is based on single, or clusters of, voxels. By contrast, multivariate pattern analyses consider all the model's neuroanatomical features to capture a specific distributed spatial pattern. This approach has potential use as a prediction tool at the individual level. We hypothesized that a multivariate pattern classification method can distinguish specific patterns of abnormal WM connectivity of classic TN from healthy controls (HCs). Diffusion-weighted scans in 23 right-sided TN and matched controls were processed to extract whole-brain interregional streamlines. We used a linear support vector machine algorithm to differentiate interregional normalized streamline count between TN and HC. This algorithm successfully differentiated between TN and HC with an accuracy of 88%. The structural pattern emphasized WM connectivity of regions that subserve sensory, affective, and cognitive dimensions of pain, including the insula, precuneus, inferior and superior parietal lobules, and inferior and medial orbital frontal gyri. Normalized streamline counts were associated with longer pain duration and WM metric abnormality between the connections. This study demonstrates that machine-learning algorithms can detect characteristic patterns of structural alterations in TN and highlights the role of structural brain imaging for identification of neuroanatomical features associated with neuropathic pain disorders.
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21
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Rheault F, St-Onge E, Sidhu J, Maier-Hein K, Tzourio-Mazoyer N, Petit L, Descoteaux M. Bundle-specific tractography with incorporated anatomical and orientational priors. Neuroimage 2018; 186:382-398. [PMID: 30453031 DOI: 10.1016/j.neuroimage.2018.11.018] [Citation(s) in RCA: 46] [Impact Index Per Article: 7.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2018] [Revised: 10/20/2018] [Accepted: 11/13/2018] [Indexed: 11/19/2022] Open
Abstract
Anatomical white matter bundles vary in shape, size, length, and complexity, making diffusion MRI tractography reconstruction of some bundles more difficult than others. As a result, bundles reconstruction often suffers from a poor spatial extent recovery. To fill-up the white matter volume as much and as best as possible, millions of streamlines can be generated and filtering techniques applied to address this issue. However, well-known problems and biases are introduced such as the creation of a large number of false positives and over-representation of easy-to-track parts of bundles and under-representation of hard-to-track. To address these challenges, we developed a Bundle-Specific Tractography (BST) algorithm. It incorporates anatomical and orientational prior knowledge during the process of streamline tracing to increase reproducibility, sensitivity, specificity and efficiency when reconstructing certain bundles of interest. BST outperforms classical deterministic, probabilistic, and global tractography methods. The increase in anatomically plausible streamlines, with larger spatial coverage, helps to accurately represent the full shape of bundles, which could greatly enhance and robustify tract-based and connectivity-based neuroimaging studies.
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Affiliation(s)
- Francois Rheault
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Université de Sherbrooke, Canada.
| | - Etienne St-Onge
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Université de Sherbrooke, Canada
| | - Jasmeen Sidhu
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Université de Sherbrooke, Canada
| | - Klaus Maier-Hein
- Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | | | - Laurent Petit
- Groupe d'Imagerie Neurofonctionnelle, IMN, UMR5293, CNRS, CEA, Université de Bordeaux, France
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Université de Sherbrooke, Canada
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22
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Li H, Chow HM, Chugani DC, Chugani HT. Linking spherical mean diffusion weighted signal with intra-axonal volume fraction. Magn Reson Imaging 2018; 57:75-82. [PMID: 30439515 DOI: 10.1016/j.mri.2018.11.006] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/16/2018] [Revised: 11/07/2018] [Accepted: 11/11/2018] [Indexed: 12/13/2022]
Abstract
Diffusion MRI has been widely used to assess brain tissue microstructure. However, the conventional diffusion tensor imaging (DTI) is inadequate for characterizing fiber direction or fiber density in voxels with crossing fibers in brain white matter. The constrained spherical deconvolution (CSD) technique has been proposed to measure the complex fiber orientation distribution (FOD) using a single high b-value (b ≥ 3000 s/mm2) to derive the intra-axonal volume fraction (Vin) from the calculated FOD. Recently, the spherical mean technique (SMT) was developed to fit Vin directly from a multi-compartment model with multi-shell b-values. Although different numbers of b-values are needed in the two techniques, both methods have been suggested to be related to the spherical mean diffusion weighted signal (S¯). The current study compared the two techniques on the same high-quality Human Connectome Project diffusion data and investigated the relation between S¯ and Vin systematically. At high b-values (b ≥ 3000 s/mm2), S¯ is linearly related to Vin, and S¯ provides similar contrast with Vin in white matter. At low b-values (b ~ 1000 s/mm2), the linear relation between S¯ and Vin is sensitive to the variations of intrinsic diffusivity. These results demonstrate that S¯ measured with the typical b-value of 1000 s/mm2 is not an indicator of Vin, and previous DTI studies acquired with b = 1000 s/mm2 cannot be re-analyzed to provide Vin-weighted contrast.
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Affiliation(s)
- Hua Li
- Katzin Diagnostic & Research PET/MR Center, Nemours - Alfred I. duPont Hospital for Children, Wilmington, DE 19803, USA.
| | - Ho Ming Chow
- Katzin Diagnostic & Research PET/MR Center, Nemours - Alfred I. duPont Hospital for Children, Wilmington, DE 19803, USA
| | - Diane C Chugani
- Katzin Diagnostic & Research PET/MR Center, Nemours - Alfred I. duPont Hospital for Children, Wilmington, DE 19803, USA; College of Health Sciences, University of Delaware, Newark, DE 19716, USA
| | - Harry T Chugani
- Katzin Diagnostic & Research PET/MR Center, Nemours - Alfred I. duPont Hospital for Children, Wilmington, DE 19803, USA; Department of Neurology, Thomas Jefferson University, Philadelphia, PA 19107, USA
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Li H, Chow HM, Chugani DC, Chugani HT. Minimal number of gradient directions for robust measurement of spherical mean diffusion weighted signal. Magn Reson Imaging 2018; 54:148-152. [PMID: 30171997 DOI: 10.1016/j.mri.2018.08.020] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2018] [Revised: 08/28/2018] [Accepted: 08/28/2018] [Indexed: 12/13/2022]
Abstract
PURPOSE Determination of the minimum number of gradient directions (Nmin) for robust measurement of spherical mean diffusion weighted signal (S¯). METHODS Computer simulations were employed to characterize the relative standard deviation (RSD) of the measured spherical mean signal as a function of the number of gradient directions (N). The effects of diffusion weighting b-value and signal-to-noise ratio (SNR) were investigated. Multi-shell high angular resolution Human Connectome Project diffusion data were analyzed to support the simulation results. RESULTS RSD decreases with increasing N, and the minimum number of N needed for RSD ≤ 5% is referred to as Nmin. At high SNRs, Nmin increases with increasing b-value to achieve sufficient sampling. Simulations showed that Nmin is linearly dependent on the b-value. At low SNRs, Nmin increases with increasing b-value to reduce the noise. RSD can be estimated as σS¯N, where σ = 1/SNR is the noise level. The experimental results were in good agreement with the simulation results. The spherical mean signal can be measured accurately with a subset of gradient directions. CONCLUSION As Nmin is affected by b-value and SNR, we recommend using 10 × b / b1 (b1 = 1 ms/μm2) uniformly distributed gradient directions for typical human diffusion studies with SNR ~ 20 for robust spherical mean signal measurement.
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Affiliation(s)
- Hua Li
- Katzin Diagnostic & Research PET/MR Center, Nemours - Alfred I. duPont Hospital for Children, Wilmington, DE 19803, USA.
| | - Ho Ming Chow
- Katzin Diagnostic & Research PET/MR Center, Nemours - Alfred I. duPont Hospital for Children, Wilmington, DE 19803, USA
| | - Diane C Chugani
- Katzin Diagnostic & Research PET/MR Center, Nemours - Alfred I. duPont Hospital for Children, Wilmington, DE 19803, USA; College of Health Sciences, University of Delaware, Newark, DE 19716, USA
| | - Harry T Chugani
- Katzin Diagnostic & Research PET/MR Center, Nemours - Alfred I. duPont Hospital for Children, Wilmington, DE 19803, USA; Department of Neurology, Thomas Jefferson University, Philadelphia, PA 19107, USA
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24
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Hanganu A, Houde JC, Fonov VS, Degroot C, Mejia-Constain B, Lafontaine AL, Soland V, Chouinard S, Collins LD, Descoteaux M, Monchi O. White matter degeneration profile in the cognitive cortico-subcortical tracts in Parkinson's disease. Mov Disord 2018; 33:1139-1150. [PMID: 29683523 DOI: 10.1002/mds.27364] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2017] [Revised: 01/22/2018] [Accepted: 01/24/2018] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND In Parkinson's disease cognitive impairment is an early nonmotor feature, but it is still unclear why some patients are able to maintain their cognitive performance at normal levels, as quantified by neuropsychological tests, whereas others cannot. The objectives of this study were to perform a cross-sectional study and analyze the white matter changes in the cognitive and motor bundles in patients with Parkinson's disease. METHODS Sixteen Parkinson's disease patients with normal cognitive performance, 19 with mild cognitive impairment (based on their performance of 1.5 standard deviations below the healthy population mean), and 16 healthy controls were compared with respect to their tractography patterns between the cortical cognitive / motor regions and subcortical structures, using high angular resolution diffusion imaging and constrained spherical deconvolution computation. RESULTS Motor bundles showed decreased apparent fiber density in both PD groups, associated with a significant increase in diffusivity metrics, number of reconstructed streamlines, and track volumes, compared with healthy controls. By contrast, in the cognitive bundles, decreased fiber density in both Parkinson's groups was compounded by the absence of changes in diffusivity in patients with normal cognition, whereas patients with cognitive impairment had increased diffusivity metrics, lower numbers of reconstructed streamlines, and lower track volumes. CONCLUSIONS Both PD groups showed similar patterns of white matter neurodegeneration in the motor bundles, whereas cognitive bundles showed a distinct pattern: Parkinson's patients with normal cognition had white matter diffusivity metrics similar to healthy controls, whereas in patients with cognitive impairment white matter showed a neurodegeneration pattern. © 2018 International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Alexandru Hanganu
- Department of Clinical Neurosciences and Department of Radiology, University of Calgary, Calgary, Alberta, Canada.,Cumming School of Medicine, Hotchkiss Brain Institute, Calgary, Alberta, Canada
| | - Jean-Christophe Houde
- Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Université de Sherbrooke, Sherbrooke, Quebec, Canada
| | - Vladimir S Fonov
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montréal, Quebec, Canada
| | - Clotilde Degroot
- Centre de Recherche, Institut Universitaire de Gériatrie de Montréal, Montréal, Quebec, Canada.,Department of Neurology and Neurosurgery, McGill University, Montréal, Quebec, Canada
| | - Beatriz Mejia-Constain
- Centre de Recherche, Institut Universitaire de Gériatrie de Montréal, Montréal, Quebec, Canada
| | - Anne-Louise Lafontaine
- Department of Neurology and Neurosurgery, McGill University, Montréal, Quebec, Canada.,Movement Disorders Unit, McGill University Health Center, Montréal, Quebec, Canada
| | - Valérie Soland
- Unité des Troubles du Mouvement André Barbeau, Centre Hospitalier de l'Université de Montréal, Montréal, Quebec, Canada
| | - Sylvain Chouinard
- Unité des Troubles du Mouvement André Barbeau, Centre Hospitalier de l'Université de Montréal, Montréal, Quebec, Canada
| | - Louis D Collins
- McConnell Brain Imaging Centre, Montreal Neurological Institute, McGill University, Montréal, Quebec, Canada
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Université de Sherbrooke, Sherbrooke, Quebec, Canada
| | - Oury Monchi
- Department of Clinical Neurosciences and Department of Radiology, University of Calgary, Calgary, Alberta, Canada.,Cumming School of Medicine, Hotchkiss Brain Institute, Calgary, Alberta, Canada.,Centre de Recherche, Institut Universitaire de Gériatrie de Montréal, Montréal, Quebec, Canada.,Department of Neurology and Neurosurgery, McGill University, Montréal, Quebec, Canada.,Department of Radiology, Faculty of Medicine, University of Montréal, Montréal, Quebec, Canada
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25
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Stämpfli P, Sommer S, Czell D, Kozerke S, Neuwirth C, Weber M, Sartoretti-Schefer S, Seifritz E, Gutzeit A, Reischauer C. Investigation of Neurodegenerative Processes in Amyotrophic Lateral Sclerosis Using White Matter Fiber Density. Clin Neuroradiol 2018; 29:493-503. [DOI: 10.1007/s00062-018-0670-8] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2017] [Accepted: 01/19/2018] [Indexed: 12/20/2022]
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26
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Sommer S, Kozerke S, Seifritz E, Staempfli P. Uniformity and Deviation of Intra-axonal Cross-sectional Area Coverage of the Gray-to-White Matter Interface. Front Neurosci 2017; 11:729. [PMID: 29311800 PMCID: PMC5743743 DOI: 10.3389/fnins.2017.00729] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2017] [Accepted: 12/14/2017] [Indexed: 12/13/2022] Open
Abstract
Diffusion magnetic resonance imaging (dMRI) is a compelling tool for investigating the structure and geometry of brain tissue based on indirect measurement of the diffusion anisotropy of water. Recent developments in global top-down tractogram optimizations enable the estimation of streamline weights, which characterize the connection between gray matter areas. In this work, the intra-axonal cross-sectional area coverage of the gray-to-white matter interface was examined by intersecting tractography streamlines with cortical regions of interest. The area coverage is the ratio of streamline weights divided by the surface area at the gray-to-white matter interface and assesses the estimated percentage which is covered by intra-axonal space. A high correlation (r = 0.935) between streamline weights and the cortical surface area was found across all regions of interest in all subjects. The variance across different cortical regions exhibits similarities to myelin maps. Additionally, we examined the effect of different diffusion gradient subsets at a lower, clinically feasible spatial resolution. Subsampling of the initial high-resolution diffusion dataset did not alter the tendency of the area coverage at the gray-to-white matter interface across cortical areas and subjects. However, single-shell acquisition schemes with lower b-values lead to a steady increase in area coverage in comparison to the full acquisition scheme at high resolution.
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Affiliation(s)
- Stefan Sommer
- Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland.,MR-Center of the Departments of Psychiatry, Psychotherapy and Psychosomatics and of Child and Youth Psychiatry and Psychotherapy, Psychiatric Hospital, University of Zurich, Zurich, Switzerland
| | - Sebastian Kozerke
- Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Erich Seifritz
- Department of Psychiatry, Psychotherapy and Psychosomatics, Hospital of Psychiatry, University of Zurich, Zurich, Switzerland
| | - Philipp Staempfli
- MR-Center of the Departments of Psychiatry, Psychotherapy and Psychosomatics and of Child and Youth Psychiatry and Psychotherapy, Psychiatric Hospital, University of Zurich, Zurich, Switzerland.,Department of Psychiatry, Psychotherapy and Psychosomatics, Hospital of Psychiatry, University of Zurich, Zurich, Switzerland
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27
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Super-Resolution Track-Density Imaging Reveals Fine Anatomical Features in Tree Shrew Primary Visual Cortex and Hippocampus. Neurosci Bull 2017; 34:438-448. [PMID: 29247318 DOI: 10.1007/s12264-017-0199-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2017] [Accepted: 10/07/2017] [Indexed: 12/21/2022] Open
Abstract
Diffusion-weighted magnetic resonance imaging (dMRI) is widely used to study white and gray matter (GM) micro-organization and structural connectivity in the brain. Super-resolution track-density imaging (TDI) is an image reconstruction method for dMRI data, which is capable of providing spatial resolution beyond the acquired data, as well as novel and meaningful anatomical contrast that cannot be obtained with conventional reconstruction methods. TDI has been used to reveal anatomical features in human and animal brains. In this study, we used short track TDI (stTDI), a variation of TDI with enhanced contrast for GM structures, to reconstruct direction-encoded color maps of fixed tree shrew brain. The results were compared with those obtained with the traditional diffusion tensor imaging (DTI) method. We demonstrated that fine microstructures in the tree shrew brain, such as Baillarger bands in the primary visual cortex and the longitudinal component of the mossy fibers within the hippocampal CA3 subfield, were observable with stTDI, but not with DTI reconstructions from the same dMRI data. The possible mechanisms underlying the enhanced GM contrast are discussed.
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28
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Calamante F, Jeurissen B, Smith RE, Tournier JD, Connelly A. The role of whole-brain diffusion MRI as a tool for studying human in vivo cortical segregation based on a measure of neurite density. Magn Reson Med 2017; 79:2738-2744. [PMID: 28921634 DOI: 10.1002/mrm.26917] [Citation(s) in RCA: 28] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2017] [Revised: 08/01/2017] [Accepted: 08/21/2017] [Indexed: 12/13/2022]
Abstract
PURPOSE To investigate whether diffusion MRI can be used to study cortical segregation based on a contrast related to neurite density, thus providing a complementary tool to myelin-based MRI techniques used for myeloarchitecture. METHODS Several myelin-sensitive MRI methods (e.g., based on T1 , T2 , and T2*) have been proposed to parcellate cortical areas based on their myeloarchitecture. Recent improvements in hardware, acquisition, and analysis methods have opened the possibility of achieving a more robust characterization of cortical microstructure using diffusion MRI. High-quality diffusion MRI data from the Human Connectome Project was combined with recent advances in fiber orientation modeling. The orientational average of the fiber orientation distribution was used as a summary parameter, which was displayed as inflated brain surface views. RESULTS Diffusion MRI identifies cortical patterns consistent with those previously seen by MRI methods used for studying myeloarchitecture, which have shown patterns of high myelination in the sensorimotor strip, visual cortex, and auditory areas and low myelination in frontal and anterior temporal areas. CONCLUSION In vivo human diffusion MRI provides a useful complementary noninvasive approach to myelin-based methods used to study whole-brain cortical parcellation, by exploiting a contrast based on tissue microstructure related to neurite density, rather than myelin itself. Magn Reson Med 79:2738-2744, 2018. © 2017 International Society for Magnetic Resonance in Medicine.
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Affiliation(s)
- Fernando Calamante
- Florey Institute of Neuroscience and Mental Health, Heidelberg, Victoria, Australia.,Florey Department of Neuroscience and Mental Health, University of Melbourne, Melbourne, Victoria, Australia.,Department of Medicine, Austin Health and Northern Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Ben Jeurissen
- iMec-Vision Lab, Department of Physics, University of Antwerp, Belgium
| | - Robert E Smith
- Florey Institute of Neuroscience and Mental Health, Heidelberg, Victoria, Australia
| | - Jacques-Donald Tournier
- Centre for the Developing Brain, King's College London, London, UK.,Department of Biomedical Engineering, King's College London, London, UK
| | - Alan Connelly
- Florey Institute of Neuroscience and Mental Health, Heidelberg, Victoria, Australia.,Florey Department of Neuroscience and Mental Health, University of Melbourne, Melbourne, Victoria, Australia.,Department of Medicine, Austin Health and Northern Health, University of Melbourne, Melbourne, Victoria, Australia
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29
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Cousineau M, Jodoin PM, Garyfallidis E, Côté MA, Morency FC, Rozanski V, Grand’Maison M, Bedell BJ, Descoteaux M. A test-retest study on Parkinson's PPMI dataset yields statistically significant white matter fascicles. Neuroimage Clin 2017; 16:222-233. [PMID: 28794981 PMCID: PMC5547250 DOI: 10.1016/j.nicl.2017.07.020] [Citation(s) in RCA: 83] [Impact Index Per Article: 11.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/12/2017] [Revised: 07/13/2017] [Accepted: 07/22/2017] [Indexed: 12/13/2022]
Abstract
In this work, we propose a diffusion MRI protocol for mining Parkinson's disease diffusion MRI datasets and recover robust disease-specific biomarkers. Using advanced high angular resolution diffusion imaging (HARDI) crossing fiber modeling and tractography robust to partial volume effects, we automatically dissected 50 white matter (WM) fascicles. These fascicles connect deep nuclei (thalamus, putamen, pallidum) to different cortical functional areas (associative, motor, sensorimotor, limbic), basal forebrain and substantia nigra. Then, among these 50 candidate WM fascicles, only the ones that passed a test-retest reproducibility procedure qualified for further tractometry analysis. Leveraging the unique 2-timepoints test-retest Parkinson's Progression Markers Initiative (PPMI) dataset of over 600 subjects, we found statistically significant differences in tract profiles along the subcortico-cortical pathways between Parkinson's disease patients and healthy controls. In particular, significant increases in FA, apparent fiber density, tract-density and generalized FA were detected in some locations of the nigro-subthalamo-putaminal-thalamo-cortical pathway. This connection is one of the major motor circuits balancing the coordination of motor output. Detailed and quantifiable knowledge on WM fascicles in these areas is thus essential to improve the quality and outcome of Deep Brain Stimulation, and to target new WM locations for investigation.
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Affiliation(s)
- Martin Cousineau
- Computer Science Department, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Pierre-Marc Jodoin
- Computer Science Department, Université de Sherbrooke, Sherbrooke, QC, Canada
- Imeka Solutions Inc., Sherbrooke, QC, Canada
| | - Eleftherios Garyfallidis
- Department of Intelligent Systems Engineering, School of Informatics and Computing, Indiana University, Bloomington, USA
| | - Marc-Alexandre Côté
- Computer Science Department, Université de Sherbrooke, Sherbrooke, QC, Canada
| | | | - Verena Rozanski
- Department of Neurology, Klinikum Grosshadern, University of Munich, Germany
| | | | - Barry J. Bedell
- Biospective Inc., Montréal, QC, Canada
- McGill University, Montréal, QC, Canada
| | - Maxime Descoteaux
- Computer Science Department, Université de Sherbrooke, Sherbrooke, QC, Canada
- Imeka Solutions Inc., Sherbrooke, QC, Canada
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30
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Rheault F, Houde JC, Descoteaux M. Visualization, Interaction and Tractometry: Dealing with Millions of Streamlines from Diffusion MRI Tractography. Front Neuroinform 2017; 11:42. [PMID: 28694776 PMCID: PMC5483435 DOI: 10.3389/fninf.2017.00042] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2016] [Accepted: 06/07/2017] [Indexed: 01/06/2023] Open
Abstract
Recently proposed tractography and connectomics approaches often require a very large number of streamlines, in the order of millions. Generating, storing and interacting with these datasets is currently quite difficult, since they require a lot of space in memory and processing time. Compression is a common approach to reduce data size. Recently such an approach has been proposed consisting in removing collinear points in the streamlines. Removing points from streamlines results in files that cannot be robustly post-processed and interacted with existing tools, which are for the most part point-based. The aim of this work is to improve visualization, interaction and tractometry algorithms to robustly handle compressed tractography datasets. Our proposed improvements are threefold: (i) An efficient loading procedure to improve visualization (reduce memory usage up to 95% for a 0.2 mm step size); (ii) interaction techniques robust to compressed tractograms; (iii) tractometry techniques robust to compressed tractograms to eliminate biased in tract-based statistics. The present work demonstrates the need of correctly handling compressed streamlines to avoid biases in future tractometry and connectomics studies.
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Affiliation(s)
- Francois Rheault
- Sherbrooke Connectivity Imaging Lab, Computer Science Department, University of SherbrookeSherbrooke, QC, Canada.,Sherbrooke Molecular Imaging Center, University of SherbrookeSherbrooke, QC, Canada.,Centre de Recherche, Centre Hospitalier Universitaire de Sherbrooke (CHUS), University of SherbrookeSherbrooke, QC, Canada
| | - Jean-Christophe Houde
- Sherbrooke Connectivity Imaging Lab, Computer Science Department, University of SherbrookeSherbrooke, QC, Canada.,Sherbrooke Molecular Imaging Center, University of SherbrookeSherbrooke, QC, Canada.,Centre de Recherche, Centre Hospitalier Universitaire de Sherbrooke (CHUS), University of SherbrookeSherbrooke, QC, Canada
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Lab, Computer Science Department, University of SherbrookeSherbrooke, QC, Canada.,Sherbrooke Molecular Imaging Center, University of SherbrookeSherbrooke, QC, Canada.,Centre de Recherche, Centre Hospitalier Universitaire de Sherbrooke (CHUS), University of SherbrookeSherbrooke, QC, Canada
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31
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Track-weighted imaging methods: extracting information from a streamlines tractogram. MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE 2017; 30:317-335. [DOI: 10.1007/s10334-017-0608-1] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2016] [Revised: 01/22/2017] [Accepted: 01/23/2017] [Indexed: 12/13/2022]
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32
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Toselli B, Tortora D, Severino M, Arnulfo G, Canessa A, Morana G, Rossi A, Fato MM. Improvement in White Matter Tract Reconstruction with Constrained Spherical Deconvolution and Track Density Mapping in Low Angular Resolution Data: A Pediatric Study and Literature Review. Front Pediatr 2017; 5:182. [PMID: 28913326 PMCID: PMC5582070 DOI: 10.3389/fped.2017.00182] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/08/2017] [Accepted: 08/10/2017] [Indexed: 12/14/2022] Open
Abstract
INTRODUCTION Diffusion-weighted magnetic resonance imaging (DW-MRI) allows noninvasive investigation of brain structure in vivo. Diffusion tensor imaging (DTI) is a frequently used application of DW-MRI that assumes a single main diffusion direction per voxel, and is therefore not well suited for reconstructing crossing fiber tracts. Among the solutions developed to overcome this problem, constrained spherical deconvolution with probabilistic tractography (CSD-PT) has provided superior quality results in clinical settings on adult subjects; however, it requires particular acquisition parameters and long sequences, which may limit clinical usage in the pediatric age group. The aim of this work was to compare the results of DTI with those of track density imaging (TDI) maps and CSD-PT on data from neonates and children, acquired with low angular resolution and low b-value diffusion sequences commonly used in pediatric clinical MRI examinations. MATERIALS AND METHODS We analyzed DW-MRI studies of 50 children (eight neonates aged 3-28 days, 20 infants aged 1-8 months, and 22 children aged 2-17 years) acquired on a 1.5 T Philips scanner using 34 gradient directions and a b-value of 1,000 s/mm2. Other sequence parameters included 60 axial slices; acquisition matrix, 128 × 128; average scan time, 5:34 min; voxel size, 1.75 mm × 1.75 mm × 2 mm; one b = 0 image. For each subject, we computed principal eigenvector (EV) maps and directionally encoded color TDI maps (DEC-TDI maps) from whole-brain tractograms obtained with CSD-PT; the cerebellar-thalamic, corticopontocerebellar, and corticospinal tracts were reconstructed using both CSD-PT and DTI. Results were compared by two neuroradiologists using a 5-point qualitative score. RESULTS The DEC-TDI maps obtained presented higher anatomical detail than EV maps, as assessed by visual inspection. In all subjects, white matter (WM) tracts were successfully reconstructed using both tractography methodologies. The mean qualitative scores of all tracts obtained with CSD-PT were significantly higher than those obtained with DTI (p-value < 0.05 for all comparisons). CONCLUSION CSD-PT can be successfully applied to DW-MRI studies acquired at 1.5 T with acquisition parameters adapted for pediatric subjects, thus providing TDI maps with greater anatomical detail. This methodology yields satisfactory results for clinical purposes in the pediatric age group.
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Affiliation(s)
- Benedetta Toselli
- Department of Informatics, Bioengineering, Robotics and System Engineering (DIBRIS), University of Genoa, Genoa, Italy
| | | | | | - Gabriele Arnulfo
- Department of Informatics, Bioengineering, Robotics and System Engineering (DIBRIS), University of Genoa, Genoa, Italy
| | - Andrea Canessa
- Department of Informatics, Bioengineering, Robotics and System Engineering (DIBRIS), University of Genoa, Genoa, Italy
| | - Giovanni Morana
- Neuroradiology Unit, Istituto Giannina Gaslini, Genoa, Italy
| | - Andrea Rossi
- Neuroradiology Unit, Istituto Giannina Gaslini, Genoa, Italy
| | - Marco Massimo Fato
- Department of Informatics, Bioengineering, Robotics and System Engineering (DIBRIS), University of Genoa, Genoa, Italy
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Farquharson S, Tournier JD, Calamante F, Mandelstam S, Burgess R, Schneider ME, Berkovic SF, Scheffer IE, Jackson GD, Connelly A. Periventricular Nodular Heterotopia: Detection of Abnormal Microanatomic Fiber Structures with Whole-Brain Diffusion MR Imaging Tractography. Radiology 2016; 281:896-906. [DOI: 10.1148/radiol.2016150852] [Citation(s) in RCA: 17] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
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34
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Renauld E, Descoteaux M, Bernier M, Garyfallidis E, Whittingstall K. Semi-Automatic Segmentation of Optic Radiations and LGN, and Their Relationship to EEG Alpha Waves. PLoS One 2016; 11:e0156436. [PMID: 27383146 PMCID: PMC4934857 DOI: 10.1371/journal.pone.0156436] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2016] [Accepted: 05/13/2016] [Indexed: 12/13/2022] Open
Abstract
At rest, healthy human brain activity is characterized by large electroencephalography (EEG) fluctuations in the 8-13 Hz range, commonly referred to as the alpha band. Although it is well known that EEG alpha activity varies across individuals, few studies have investigated how this may be related to underlying morphological variations in brain structure. Specifically, it is generally believed that the lateral geniculate nucleus (LGN) and its efferent fibres (optic radiation, OR) play a key role in alpha activity, yet it is unclear whether their shape or size variations contribute to its inter-subject variability. Given the widespread use of EEG alpha in basic and clinical research, addressing this is important, though difficult given the problems associated with reliably segmenting the LGN and OR. For this, we employed a multi-modal approach and combined diffusion magnetic resonance imaging (dMRI), functional magnetic resonance imaging (fMRI) and EEG in 20 healthy subjects to measure structure and function, respectively. For the former, we developed a new, semi-automated approach for segmenting the OR and LGN, from which we extracted several structural metrics such as volume, position and diffusivity. Although these measures corresponded well with known morphology based on previous post-mortem studies, we nonetheless found that their inter-subject variability was not significantly correlated to alpha power or peak frequency (p >0.05). Our results therefore suggest that alpha variability may be mediated by an alternative structural source and our proposed methodology may in general help in better understanding the influence of anatomy on function such as measured by EEG or fMRI.
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Affiliation(s)
- Emmanuelle Renauld
- Department of Nuclear Medecine and Radiobiology, Faculty of Medicine and Health Science, University of Sherbrooke, Sherbrooke, Qc, Canada
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Faculty of Science, University of Sherbrooke, Sherbrooke, Qc, Canada
- Centre Hospitalier Universitaire de Sherbrooke (CHUS), Sherbrooke, Qc, Canada
- Centre d’Imagerie Moléculaire de Sherbrooke (CIMS), Centre de Recherche du CHUS, Sherbrooke, Qc, Canada
| | - Michaël Bernier
- Department of Nuclear Medecine and Radiobiology, Faculty of Medicine and Health Science, University of Sherbrooke, Sherbrooke, Qc, Canada
| | - Eleftherios Garyfallidis
- Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Faculty of Science, University of Sherbrooke, Sherbrooke, Qc, Canada
| | - Kevin Whittingstall
- Department of Nuclear Medecine and Radiobiology, Faculty of Medicine and Health Science, University of Sherbrooke, Sherbrooke, Qc, Canada
- Department of Diagnostic Radiology, Faculty of Medicine and Health Science, University of Sherbrooke, Sherbrooke, Qc, Canada
- Centre Hospitalier Universitaire de Sherbrooke (CHUS), Sherbrooke, Qc, Canada
- Centre d’Imagerie Moléculaire de Sherbrooke (CIMS), Centre de Recherche du CHUS, Sherbrooke, Qc, Canada
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35
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Hoch MJ, Chung S, Ben-Eliezer N, Bruno MT, Fatterpekar GM, Shepherd TM. New Clinically Feasible 3T MRI Protocol to Discriminate Internal Brain Stem Anatomy. AJNR Am J Neuroradiol 2016; 37:1058-65. [PMID: 26869471 DOI: 10.3174/ajnr.a4685] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2015] [Accepted: 12/04/2015] [Indexed: 11/07/2022]
Abstract
Two new 3T MR imaging contrast methods, track density imaging and echo modulation curve T2 mapping, were combined with simultaneous multisection acquisition to reveal exquisite anatomic detail at 7 canonical levels of the brain stem. Compared with conventional MR imaging contrasts, many individual brain stem tracts and nuclear groups were directly visualized for the first time at 3T. This new approach is clinically practical and feasible (total scan time = 20 minutes), allowing better brain stem anatomic localization and characterization.
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Affiliation(s)
- M J Hoch
- From the Department of Radiology (M.J.H., S.C., N.B.-.E., M.T.B., G.M.F., T.M.S.) New York University Langone School of Medicine, New York, New York
| | - S Chung
- From the Department of Radiology (M.J.H., S.C., N.B.-.E., M.T.B., G.M.F., T.M.S.) New York University Langone School of Medicine, New York, New York Center for Advanced Imaging Innovation and Research (S.C., N.B.-.E., T.M.S.), New York, New York
| | - N Ben-Eliezer
- Center for Advanced Imaging Innovation and Research (S.C., N.B.-.E., T.M.S.), New York, New York
| | - M T Bruno
- From the Department of Radiology (M.J.H., S.C., N.B.-.E., M.T.B., G.M.F., T.M.S.) New York University Langone School of Medicine, New York, New York
| | - G M Fatterpekar
- From the Department of Radiology (M.J.H., S.C., N.B.-.E., M.T.B., G.M.F., T.M.S.) New York University Langone School of Medicine, New York, New York
| | - T M Shepherd
- From the Department of Radiology (M.J.H., S.C., N.B.-.E., M.T.B., G.M.F., T.M.S.) New York University Langone School of Medicine, New York, New York Center for Advanced Imaging Innovation and Research (S.C., N.B.-.E., T.M.S.), New York, New York.
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Wirsich J, Perry A, Ridley B, Proix T, Golos M, Bénar C, Ranjeva JP, Bartolomei F, Breakspear M, Jirsa V, Guye M. Whole-brain analytic measures of network communication reveal increased structure-function correlation in right temporal lobe epilepsy. NEUROIMAGE-CLINICAL 2016; 11:707-718. [PMID: 27330970 PMCID: PMC4909094 DOI: 10.1016/j.nicl.2016.05.010] [Citation(s) in RCA: 42] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 09/18/2015] [Revised: 03/15/2016] [Accepted: 05/18/2016] [Indexed: 12/13/2022]
Abstract
The in vivo structure-function relationship is key to understanding brain network reorganization due to pathologies. This relationship is likely to be particularly complex in brain network diseases such as temporal lobe epilepsy, in which disturbed large-scale systems are involved in both transient electrical events and long-lasting functional and structural impairments. Herein, we estimated this relationship by analyzing the correlation between structural connectivity and functional connectivity in terms of analytical network communication parameters. As such, we targeted the gradual topological structure-function reorganization caused by the pathology not only at the whole brain scale but also both in core and peripheral regions of the brain. We acquired diffusion (dMRI) and resting-state fMRI (rsfMRI) data in seven right-lateralized TLE (rTLE) patients and fourteen healthy controls and analyzed the structure-function relationship by using analytical network communication metrics derived from the structural connectome. In rTLE patients, we found a widespread hypercorrelated functional network. Network communication analysis revealed greater unspecific branching of the shortest path (search information) in the structural connectome and a higher global correlation between the structural and functional connectivity for the patient group. We also found evidence for a preserved structural rich-club in the patient group. In sum, global augmentation of structure-function correlation might be linked to a smaller functional repertoire in rTLE patients, while sparing the central core of the brain which may represent a pathway that facilitates the spread of seizures.
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Key Words
- CSD, constrained spherical deconvolution
- CSF, cerebrospinal fluid
- FA, fractional anisotropy
- FCA, analytic functional connectivity
- FCD, functional connectivity dynamics
- FOD, fiber orientation distribution
- Functional connectivity
- NBS, network based statistics
- Network based statistics
- Network communication
- Rich club
- Structural connectivity
- Temporal lobe epilepsy
- dMRI, diffusion magnetic resonance imaging
- rTLE, right temporal lobe epilepsy
- rsfMRI, resting state functional magnetic resonance imaging
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Affiliation(s)
- Jonathan Wirsich
- Aix-Marseille Université, CNRS, CRMBM UMR 7339, 13385 Marseille, France; APHM, Hôpitaux de la Timone, Pôle d'imagerie Médicale, CEMEREM, 13005 Marseille, France; Aix-Marseille Université, Institut de Neurosciences des Systèmes, 13385 Marseille, France; INSERM, UMR_S 1106, 13385 Marseille, France.
| | - Alistair Perry
- Centre for Healthy Brain Ageing (CHeBA), School of Psychiatry, University of New South Wales, Sydney, NSW, Australia; School of Psychiatry, University of New South Wales, Sydney, NSW 2052, Australia; Systems Neuroscience Group, QIMR Berghofer Medical Research Institute, 300 Herston Road, Herston, QLD 4006, Australia.
| | - Ben Ridley
- Aix-Marseille Université, CNRS, CRMBM UMR 7339, 13385 Marseille, France; APHM, Hôpitaux de la Timone, Pôle d'imagerie Médicale, CEMEREM, 13005 Marseille, France.
| | - Timothée Proix
- Aix-Marseille Université, Institut de Neurosciences des Systèmes, 13385 Marseille, France; INSERM, UMR_S 1106, 13385 Marseille, France.
| | - Mathieu Golos
- Aix-Marseille Université, Institut de Neurosciences des Systèmes, 13385 Marseille, France; INSERM, UMR_S 1106, 13385 Marseille, France.
| | - Christian Bénar
- Aix-Marseille Université, Institut de Neurosciences des Systèmes, 13385 Marseille, France; INSERM, UMR_S 1106, 13385 Marseille, France.
| | - Jean-Philippe Ranjeva
- Aix-Marseille Université, CNRS, CRMBM UMR 7339, 13385 Marseille, France; APHM, Hôpitaux de la Timone, Pôle d'imagerie Médicale, CEMEREM, 13005 Marseille, France.
| | - Fabrice Bartolomei
- Aix-Marseille Université, Institut de Neurosciences des Systèmes, 13385 Marseille, France; INSERM, UMR_S 1106, 13385 Marseille, France; APHM, Hôpitaux de la Timone, Pôle de Neurosciences Cliniques, Service de Neurophysiologie Clinique, 13005 Marseille, France.
| | - Michael Breakspear
- School of Psychiatry, University of New South Wales, Sydney, NSW 2052, Australia; Systems Neuroscience Group, QIMR Berghofer Medical Research Institute, 300 Herston Road, Herston, QLD 4006, Australia; Metro North Mental Health Services, Brisbane, QLD 4006, Australia.
| | - Viktor Jirsa
- Aix-Marseille Université, Institut de Neurosciences des Systèmes, 13385 Marseille, France; INSERM, UMR_S 1106, 13385 Marseille, France.
| | - Maxime Guye
- Aix-Marseille Université, CNRS, CRMBM UMR 7339, 13385 Marseille, France; APHM, Hôpitaux de la Timone, Pôle d'imagerie Médicale, CEMEREM, 13005 Marseille, France.
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Calamante F. Super-Resolution Track Density Imaging: Anatomic Detail versus Quantification. AJNR Am J Neuroradiol 2016; 37:1066-7. [PMID: 26915572 DOI: 10.3174/ajnr.a4721] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Affiliation(s)
- F Calamante
- Florey Institute of Neuroscience and Mental Health Heidelberg, Victoria, Australia Florey Department of Neuroscience and Mental Health University of Melbourne Melbourne, Victoria, Australia Department of Medicine Austin Health and Northern Health, University of Melbourne Melbourne, Victoria, Australia
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Vaessen MJ, Saj A, Lovblad KO, Gschwind M, Vuilleumier P. Structural white-matter connections mediating distinct behavioral components of spatial neglect in right brain-damaged patients. Cortex 2016; 77:54-68. [PMID: 26922504 DOI: 10.1016/j.cortex.2015.12.008] [Citation(s) in RCA: 49] [Impact Index Per Article: 6.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/19/2015] [Revised: 07/03/2015] [Accepted: 12/21/2015] [Indexed: 11/30/2022]
Abstract
Spatial neglect is a neuropsychological syndrome in which patients fail to perceive and orient to stimuli located in the space contralateral to the lesioned hemisphere. It is characterized by a wide heterogeneity in clinical symptoms which can be grouped into distinct behavioral components correlating with different lesion sites. Moreover, damage to white-matter (WM) fiber tracts has been suggested to disconnect brain networks that mediate different functions associated with spatial cognition and attention. However, it remains unclear what WM pathways are associated with functionally dissociable neglect components. In this study we examined nine patients with a focal right hemisphere stroke using a series of neuropsychological tests and diffusion tensor imaging (DTI) in order to disentangle the role of specific WM pathways in neglect symptoms. First, following previous work, the behavioral test scores of patients were factorized into three independent components reflecting perceptual, exploratory, and object-centered deficits in spatial awareness. We then examined the structural neural substrates of these components by correlating indices of WM integrity (fractional anisotropy) with the severity of deficits along each profile. Several locations in the right parietal and frontal WM correlated with neuropsychological scores. Fiber tracts projecting from these locations indicated that posterior parts of the superior longitudinal fasciculus (SLF), as well as nearby callosal fibers connecting ipsilateral and contralateral parietal areas, were associated with perceptual spatial deficits, whereas more anterior parts of SLF and inferior fronto-occipital fasciculus (IFOF) were predominantly associated with object-centered deficits. In addition, connections between frontal areas and superior colliculus were found to be associated with the exploratory deficits. Our results provide novel support to the view that neglect may result from disconnection lesions in distributed brain networks, but also extend these notions by highlighting the role of dissociable circuits in different functional components of the neglect syndrome. However these preliminary findings require replication with larger samples of patients.
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Affiliation(s)
- Maarten J Vaessen
- Laboratory for Neurology and Imaging of Cognition, Department of Neurosciences, University Medical Centre, Geneva, Switzerland; Department of Clinical Neurology, University Hospital of Geneva, Geneva, Switzerland.
| | - Arnaud Saj
- Laboratory for Neurology and Imaging of Cognition, Department of Neurosciences, University Medical Centre, Geneva, Switzerland; Department of Clinical Neurology, University Hospital of Geneva, Geneva, Switzerland
| | - Karl-Olof Lovblad
- Department of Radiology, University Hospital of Geneva, Geneva, Switzerland
| | - Markus Gschwind
- Laboratory for Neurology and Imaging of Cognition, Department of Neurosciences, University Medical Centre, Geneva, Switzerland; Department of Clinical Neurology, University Hospital of Geneva, Geneva, Switzerland
| | - Patrik Vuilleumier
- Laboratory for Neurology and Imaging of Cognition, Department of Neurosciences, University Medical Centre, Geneva, Switzerland; Department of Clinical Neurology, University Hospital of Geneva, Geneva, Switzerland
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