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Nabulsi L, Chandio BQ, McPhilemy G, Martyn FM, Roberts G, Hallahan B, Dannlowski U, Kircher T, Haarman B, Mitchell P, McDonald C, Cannon DM, Andreassen OA, Ching CRK, Thompson PM. Multi-Site Statistical Mapping of Along-Tract Microstructural Abnormalities in Bipolar Disorder with Diffusion MRI Tractometry. bioRxiv 2023:2023.08.17.553762. [PMID: 37662230 PMCID: PMC10473593 DOI: 10.1101/2023.08.17.553762] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Investigating alterations in brain circuitry associated with bipolar disorder (BD) may offer a valuable approach to discover brain biomarkers for genetic and interventional studies of the disorder and related mental illnesses. Some diffusion MRI studies report evidence of microstructural abnormalities in white matter regions of interest, but we lack a fine-scale spatial mapping of brain microstructural differences along tracts in BD. We also lack large-scale studies that integrate tractometry data from multiple sites, as larger datasets can greatly enhance power to detect subtle effects and assess whether effects replicate across larger international datasets. In this multisite diffusion MRI study, we used BUndle ANalytics (BUAN, Chandio 2020), a recently developed analytic approach for tractography, to extract, map, and visualize profiles of microstructural abnormalities on 3D models of fiber tracts in 148 participants with BD and 259 healthy controls from 6 independent scan sites. Modeling site differences as random effects, we investigated along-tract white matter (WM) microstructural differences between diagnostic groups. QQ plots showed that group differences were gradually enhanced as more sites were added. Using the BUAN pipeline, BD was associated with lower mean fractional anisotropy (FA) in fronto-limbic, interhemispheric, and posterior pathways; higher FA was also noted in posterior bundles, relative to controls. By integrating tractography and anatomical information, BUAN effectively captures unique effects along white matter (WM) tracts, providing valuable insights into anatomical variations that may assist in the classification of diseases.
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Affiliation(s)
- Leila Nabulsi
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, University of Southern California, Marina del Rey, CA 90292, USA
- Centre for Neuroimaging & Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, Galway Neuroscience Centre, College of Medicine Nursing and Health Sciences, University of Galway, H91 TK33 Galway, Ireland
| | - Bramsh Q Chandio
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, University of Southern California, Marina del Rey, CA 90292, USA
| | - Genevieve McPhilemy
- Centre for Neuroimaging & Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, Galway Neuroscience Centre, College of Medicine Nursing and Health Sciences, University of Galway, H91 TK33 Galway, Ireland
| | - Fiona M Martyn
- Centre for Neuroimaging & Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, Galway Neuroscience Centre, College of Medicine Nursing and Health Sciences, University of Galway, H91 TK33 Galway, Ireland
| | - Gloria Roberts
- Discipline of Psychiatry and Mental Health, University of New South Wales, Sydney, New South Wales, Australia
| | - Brian Hallahan
- Centre for Neuroimaging & Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, Galway Neuroscience Centre, College of Medicine Nursing and Health Sciences, University of Galway, H91 TK33 Galway, Ireland
| | - Udo Dannlowski
- Institute for Translational Psychiatry, University of Münster, Münster, Germany
| | - Tilo Kircher
- Department of Psychiatry and Psychotherapy, Philipps-University Marburg, Marburg, Germany
| | - Benno Haarman
- Department of Psychiatry, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Philip Mitchell
- Discipline of Psychiatry and Mental Health, University of New South Wales, Sydney, New South Wales, Australia
| | - Colm McDonald
- Centre for Neuroimaging & Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, Galway Neuroscience Centre, College of Medicine Nursing and Health Sciences, University of Galway, H91 TK33 Galway, Ireland
| | - Dara M Cannon
- Centre for Neuroimaging & Cognitive Genomics (NICOG), Clinical Neuroimaging Laboratory, Galway Neuroscience Centre, College of Medicine Nursing and Health Sciences, University of Galway, H91 TK33 Galway, Ireland
| | - Ole A Andreassen
- Norwegian Centre for Mental Disorders Research (NORMENT), Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Christopher R K Ching
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, University of Southern California, Marina del Rey, CA 90292, USA
| | - Paul M Thompson
- Imaging Genetics Center, Mark and Mary Stevens Neuroimaging & Informatics Institute, University of Southern California, Marina del Rey, CA 90292, USA
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Guerrero-Gonzalez JM, Yeske B, Kirk GR, Bell MJ, Ferrazzano PA, Alexander AL. Mahalanobis distance tractometry (MaD-Tract) - a framework for personalized white matter anomaly detection applied to TBI. Neuroimage 2022; 260:119475. [PMID: 35840117 PMCID: PMC9531540 DOI: 10.1016/j.neuroimage.2022.119475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Revised: 06/27/2022] [Accepted: 07/11/2022] [Indexed: 12/04/2022] Open
Abstract
Imaging-based quantitative measures from diffusion-weighted MRI (dMRI) offer the ability to non-invasively extract microscopic information from human brain tissues. Group-level comparisons of such measures represent an important approach to investigate abnormal brain conditions. These types of analyses are especially useful when the regions of abnormality spatially coincide across subjects. When this is not true, approaches for individualized analyses are necessary. Here we present a framework for single-subject multidimensional analysis based on the Mahalanobis distance. This is conducted along specific white matter pathways represented by tractography-derived streamline bundles. A definition for abnormality was constructed from Wilk’s criterion, which accounts for normative sample size, number of features used in the Mahalanobis distance, and multiple comparisons. One example of a condition exhibiting high heterogeneity across subjects is traumatic brain injury (TBI). Using the Mahalanobis distance computed from the three eigenvalues of the diffusion tensor along the cingulum, uncinate, and parcellated corpus callosum tractograms, 8 severe TBI patients were individually compared to a normative sample of 49 healthy controls. For all TBI patients, the analyses showed statistically significant deviations from the normative data at one or multiple locations along the analyzed bundles. The detected anomalies were widespread across the analyzed tracts, consistent with the expected heterogeneity that is hallmark of TBI. Each of the controls subjects was also compared to the remaining 48 subjects in the control group in a leave-one-out fashion. Only two segments were identified as abnormal out of the entire analysis in the control group, thus the method also demonstrated good specificity.
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Affiliation(s)
- Jose M Guerrero-Gonzalez
- Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; Waisman Center, University of Wisconsin-Madison, 1500 Highland Avenue, Room T129, Madison, WI 53705, USA.
| | - Benjamin Yeske
- Waisman Center, University of Wisconsin-Madison, 1500 Highland Avenue, Room T129, Madison, WI 53705, USA
| | - Gregory R Kirk
- Waisman Center, University of Wisconsin-Madison, 1500 Highland Avenue, Room T129, Madison, WI 53705, USA
| | - Michael J Bell
- Critical Care Medicine, Children's National Medical Center, Washington, DC, USA
| | - Peter A Ferrazzano
- Department of Pediatrics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; Waisman Center, University of Wisconsin-Madison, 1500 Highland Avenue, Room T129, Madison, WI 53705, USA
| | - Andrew L Alexander
- Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; Department of Psychiatry, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA; Waisman Center, University of Wisconsin-Madison, 1500 Highland Avenue, Room T129, Madison, WI 53705, USA
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Jandric D, Parker GJM, Haroon H, Tomassini V, Muhlert N, Lipp I. A tractometry principal component analysis of white matter tract network structure and relationships with cognitive function in relapsing-remitting multiple sclerosis. Neuroimage Clin 2022; 34:102995. [PMID: 35349892 PMCID: PMC8958271 DOI: 10.1016/j.nicl.2022.102995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Revised: 03/04/2022] [Accepted: 03/23/2022] [Indexed: 10/25/2022]
Abstract
Understanding the brain changes underlying cognitive dysfunction is a key priority in multiple sclerosis (MS) to improve monitoring and treatment of this debilitating symptom. Functional connectivity network changes are associated with cognitive dysfunction, but it is less well understood how changes in normal appearing white matter relate to cognitive symptoms. If white matter tracts have network structure it would be expected that tracts within a network share susceptibility to MS pathology. In the present study, we used a tractometry approach to explore patterns of variance in white matter metrics across white matter (WM) tracts, and assessed how such patterns relate to neuropsychological test performance across cognitive domains. A sample of 102 relapsing-remitting MS patients and 27 healthy controls underwent MRI and neuropsychological testing. Tractography was performed on diffusion MRI data to extract 40 WM tracts and microstructural measures were extracted from each tract. Principal component analysis (PCA) was used to decompose metrics from all tracts to assess the presence of any co-variance structure among the tracts. Similarly, PCA was applied to cognitive test scores to identify the main cognitive domains. Finally, we assessed the ability of tract co-variance patterns to predict test performance across cognitive domains. We found that a single co-variance pattern which captured microstructure across all tracts explained the most variance (65% variance explained) and that there was little evidence for separate, smaller network patterns of pathology. Variance in this pattern was explained by effects related to lesions, but one main co-variance pattern persisted after this effect was regressed out. This main WM tract co-variance pattern contributed to explaining a modest degree of variance in one of our four cognitive domains in MS. These findings highlight the need to investigate the relationship between the normal appearing white matter and cognitive impairment further and on a more granular level, to improve the understanding of the network structure of the brain in MS.
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Affiliation(s)
- Danka Jandric
- Division of Neuroscience & Experimental Psychology, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
| | - Geoff J M Parker
- Centre for Medical Image Computing, Department of Medical Physics & Biomedical Engineering and Department of Neuroinflammation, Queen Square Institute of Neurology, University College London, London, UK; Bioxydyn Limited, Manchester, UK
| | - Hamied Haroon
- Division of Neuroscience & Experimental Psychology, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
| | - Valentina Tomassini
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, UK; Institute for Advanced Biomedical Technologies (ITAB), Department of Neurosciences, Imaging and Clinical Sciences, University G. d'Annunzio of Chieti-Pescara, Chieti, Italy; Multiple Sclerosis Centre, Department of Neurology, SS. Annunziata University Hospital, Chieti, Italy
| | - Nils Muhlert
- Division of Neuroscience & Experimental Psychology, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
| | - Ilona Lipp
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, UK; Department of Neurophysics, Max Planck Institute for Human Cognitive & Brain Sciences, Leipzig, Germany.
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Winter M, Tallantyre EC, Brice TAW, Robertson NP, Jones DK, Chamberland M. Tract-specific MRI measures explain learning and recall differences in multiple sclerosis. Brain Commun 2021; 3:fcab065. [PMID: 33959710 PMCID: PMC8088789 DOI: 10.1093/braincomms/fcab065] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 02/07/2021] [Accepted: 03/01/2021] [Indexed: 12/19/2022] Open
Abstract
Cognitive difficulties are common and a key concern for people with multiple sclerosis. Advancing knowledge of the role of white matter pathology in multiple sclerosis-related cognitive impairment is essential as both occur early in the disease with implications for early intervention. Consequently, this cross-sectional study asked whether quantifying the relationships between lesions and specific white matter structures could better explain co-existing cognitive differences than whole brain imaging measures. Forty participants with relapse-onset multiple sclerosis underwent cognitive testing and MRI at 3 Tesla. They were classified as cognitively impaired (n = 24) or unimpaired (n = 16) and differed across verbal fluency, learning and recall tasks corrected for intelligence and education (corrected P-values = 0.007-0.04). The relationships between lesions and white matter were characterized across six measures: conventional voxel-based T2 lesion load, whole brain tractogram load (lesioned volume/whole tractogram volume), whole bundle volume, bundle load (lesioned volume/whole bundle volume), Tractometry (diffusion-tensor and high angular resolution diffusion measures sampled from all bundle streamlines) and lesionometry (diffusion measures sampled from streamlines traversing lesions only). The tract-specific measures were extracted from corpus callosum segments (genu and isthmus), striato-prefrontal and -parietal pathways, and the superior longitudinal fasciculi (sections I, II and III). White matter measure-task associations demonstrating at least moderate evidence against the null hypothesis (Bayes Factor threshold < 0.2) were examined using independent t-tests and covariate analyses (significance level P < 0.05). Tract-specific measures were significant predictors (all P-values < 0.05) of task-specific clinical scores and diminished the significant effect of group as a categorical predictor in Story Recall (isthmus bundle load), Figure Recall (right striato-parietal lesionometry) and Design Learning (left superior longitudinal fasciculus III volume). Lesion load explained the difference in List Learning, whereas Letter Fluency was not associated with any of the imaging measures. Overall, tract-specific measures outperformed the global lesion and tractogram load measures. Variation in regional lesion burden translated to group differences in tract-specific measures, which in turn, attenuated differences in individual cognitive tasks. The structural differences converged in temporo-parietal regions with particular influence on tasks requiring visuospatial-constructional processing. We highlight that measures quantifying the relationships between tract-specific structure and multiple sclerosis lesions uncovered associations with cognition masked by overall tract volumes and global lesion and tractogram loads. These tract-specific white matter quantifications show promise for elucidating the relationships between neuropathology and cognition in multiple sclerosis.
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Affiliation(s)
- Mia Winter
- Department of Clinical Neuropsychology, University Hospital of Wales, Cardiff, CF14 4XW, UK
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, CF24 4HQ, UK
| | - Emma C Tallantyre
- Division of Psychological Medicine and Clinical Neurosciences, Cardiff University School of Medicine, Cardiff, CF14 4XN, UK
- Helen Durham Centre for Neuroinflammation, University Hospital of Wales, Cardiff, CF14 4XW, UK
| | - Thomas A W Brice
- Division of Psychological Medicine and Clinical Neurosciences, Cardiff University School of Medicine, Cardiff, CF14 4XN, UK
| | - Neil P Robertson
- Division of Psychological Medicine and Clinical Neurosciences, Cardiff University School of Medicine, Cardiff, CF14 4XN, UK
- Helen Durham Centre for Neuroinflammation, University Hospital of Wales, Cardiff, CF14 4XW, UK
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, CF24 4HQ, UK
- Mary MacKillop Institute for Health Research, Australian Catholic University, Melbourne, Victoria 3000, Australia
| | - Maxime Chamberland
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, CF24 4HQ, UK
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Cocozza S, Schiavi S, Pontillo G, Battocchio M, Riccio E, Caccavallo S, Russo C, Di Risi T, Pisani A, Daducci A, Brunetti A. Microstructural damage of the cortico-striatal and thalamo-cortical fibers in Fabry disease: a diffusion MRI tractometry study. Neuroradiology 2020; 62:1459-1466. [PMID: 32700105 PMCID: PMC7568710 DOI: 10.1007/s00234-020-02497-7] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Accepted: 07/08/2020] [Indexed: 11/29/2022]
Abstract
Purpose Recent evidences have suggested the possible presence of an involvement of the extrapyramidal system in Fabry disease (FD), a rare X-linked lysosomal storage disorder. We aimed to investigate the microstructural integrity of the main tracts of the cortico-striatal-thalamo-cortical loop in FD patients. Methods Forty-seven FD patients (mean age = 42.3 ± 16.3 years, M/F = 28/21) and 49 healthy controls (mean age = 42.3 ± 13.1 years, M/F = 19/28) were enrolled in this study. Fractional anisotropy (FA), axial (AD), radial (RD), and mean diffusivity (MD) maps were computed for each subject, and connectomes were built using a standard atlas. Diffusion metrics and connectomes were then combined to carry on a diffusion MRI tractometry analysis. The main afferent and efferent pathways of the cortico-striatal-thalamo-cortical loop (namely, bundles connecting the precentral gyrus (PreCG) with the striatum and the thalamus) were evaluated. Results We found the presence of a microstructural involvement of cortico-striatal-thalamo-cortical loop in FD patients, predominantly affecting the left side. In particular, we found significant lower mean FA values of the left cortico-striatal fibers (p = 0.001), coupled to higher MD (p = 0.001) and RD (p < 0.001) values, as well as higher MD (p = 0.01) and RD (p = 0.01) values at the level of the thalamo-cortical fibers. Conclusion We confirmed the presence of an alteration of the extrapyramidal system in FD patients, in line with recent evidences suggesting the presence of brain changes as a possible reflection of the subtle motor symptoms present in this condition. Our results suggest that, along with functional changes, microstructural damage of this pathway is also present in FD patients.
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Affiliation(s)
- Sirio Cocozza
- Department of Advanced Biomedical Sciences, University "Federico II", Naples, Italy
| | - Simona Schiavi
- Department of Computer Science, University of Verona, Verona, Italy
| | - Giuseppe Pontillo
- Department of Advanced Biomedical Sciences, University "Federico II", Naples, Italy.
| | | | - Eleonora Riccio
- National Research Council of Italy (IRIB CNR), Institute for Biomedical Research and Innovation, Palermo, Italy
| | - Simona Caccavallo
- Department of Public Health, Nephrology Unit, University "Federico II", Naples, Italy
| | - Camilla Russo
- Department of Advanced Biomedical Sciences, University "Federico II", Naples, Italy
| | - Teodolinda Di Risi
- Department of Public Health, Nephrology Unit, University "Federico II", Naples, Italy.,CEINGE - Advanced Biotechnologies, Naples, Italy
| | - Antonio Pisani
- Department of Public Health, Nephrology Unit, University "Federico II", Naples, Italy
| | | | - Arturo Brunetti
- Department of Advanced Biomedical Sciences, University "Federico II", Naples, Italy
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Chamberland M, Raven EP, Genc S, Duffy K, Descoteaux M, Parker GD, Tax CMW, Jones DK. Dimensionality reduction of diffusion MRI measures for improved tractometry of the human brain. Neuroimage 2019; 200:89-100. [PMID: 31228638 DOI: 10.1016/j.neuroimage.2019.06.020] [Citation(s) in RCA: 65] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2019] [Revised: 06/05/2019] [Accepted: 06/07/2019] [Indexed: 12/13/2022] Open
Abstract
Various diffusion MRI (dMRI) measures have been proposed for characterising tissue microstructure over the last 15 years. Despite the growing number of experiments using different dMRI measures in assessments of white matter, there has been limited work on: 1) examining their covariance along specific pathways; and on 2) combining these different measures to study tissue microstructure. Indeed, it quickly becomes intractable for existing analysis pipelines to process multiple measurements at each voxel and at each vertex forming a streamline, highlighting the need for new ways to visualise or analyse such high-dimensional data. In a sample of 36 typically developing children aged 8–18 years, we profiled various commonly used dMRI measures across 22 brain pathways. Using a data-reduction approach, we identified two biologically-interpretable components that capture 80% of the variance in these dMRI measures. The first derived component captures properties related to hindrance and restriction in tissue microstructure, while the second component reflects characteristics related to tissue complexity and orientational dispersion. We then demonstrate that the components generated by this approach preserve the biological relevance of the original measurements by showing age-related effects across developmentally sensitive pathways. In summary, our findings demonstrate that dMRI analyses can benefit from dimensionality reduction techniques, to help disentangling the neurobiological underpinnings of white matter organisation.
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St-Jean S, Chamberland M, Viergever MA, Leemans A. Reducing variability in along-tract analysis with diffusion profile realignment. Neuroimage 2019; 199:663-679. [PMID: 31195073 DOI: 10.1016/j.neuroimage.2019.06.016] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2019] [Revised: 05/08/2019] [Accepted: 06/05/2019] [Indexed: 12/13/2022] Open
Abstract
Diffusion weighted magnetic resonance imaging (dMRI) provides a non invasive virtual reconstruction of the brain's white matter structures through tractography. Analyzing dMRI measures along the trajectory of white matter bundles can provide a more specific investigation than considering a region of interest or tract-averaged measurements. However, performing group analyses with this along-tract strategy requires correspondence between points of tract pathways across subjects. This is usually achieved by creating a new common space where the representative streamlines from every subject are resampled to the same number of points. If the underlying anatomy of some subjects was altered due to, e.g., disease or developmental changes, such information might be lost by resampling to a fixed number of points. In this work, we propose to address the issue of possible misalignment, which might be present even after resampling, by realigning the representative streamline of each subject in this 1D space with a new method, coined diffusion profile realignment (DPR). Experiments on synthetic datasets show that DPR reduces the coefficient of variation for the mean diffusivity, fractional anisotropy and apparent fiber density when compared to the unaligned case. Using 100 in vivo datasets from the human connectome project, we simulated changes in mean diffusivity, fractional anisotropy and apparent fiber density. Independent Student's t-tests between these altered subjects and the original subjects indicate that regional changes are identified after realignment with the DPR algorithm, while preserving differences previously detected in the unaligned case. This new correction strategy contributes to revealing effects of interest which might be hidden by misalignment and has the potential to improve the specificity in longitudinal population studies beyond the traditional region of interest based analysis and along-tract analysis workflows.
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Affiliation(s)
- Samuel St-Jean
- Image Sciences Institute, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, the Netherlands.
| | - Maxime Chamberland
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, United Kingdom.
| | - Max A Viergever
- Image Sciences Institute, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, the Netherlands.
| | - Alexander Leemans
- Image Sciences Institute, University Medical Center Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, the Netherlands.
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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] [What about the content of this article? (0)] [Affiliation(s)] [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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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: 81] [Impact Index Per Article: 11.6] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [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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10
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Campbell JSW, Pike GB. Potential and limitations of diffusion MRI tractography for the study of language. Brain Lang 2014; 131:65-73. [PMID: 23910928 DOI: 10.1016/j.bandl.2013.06.007] [Citation(s) in RCA: 48] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/20/2013] [Accepted: 06/29/2013] [Indexed: 06/02/2023]
Abstract
Diffusion magnetic resonance imaging (MRI) is a tremendously promising tool for imaging tissue microstructure, and for inferring large scale structural connectivity in vivo. However, the sensitivity of the technique is highly dependent on methodological details. Acquisition parameters, pre-processing steps, reconstruction models, and statistical analysis all affect the final sensitivity, specificity, and accuracy of a study. In the case of fiber pathway reconstruction in the central nervous system, termed tractography, false positive and false negative results abound, and interpretation of results must take into account the potential shortcomings of the techniques used. This article will review the strengths and limitations of different types of diffusion MRI tractography analysis, and highlight what one can realistically hope to learn from such imaging studies of the human brain.
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Affiliation(s)
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- Montreal Neurological Institute, McGill University, Montreal, Canada
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