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Jossinger S, Sares A, Zislis A, Sury D, Gracco V, Ben-Shachar M. White matter correlates of sensorimotor synchronization in persistent developmental stuttering. JOURNAL OF COMMUNICATION DISORDERS 2022; 95:106169. [PMID: 34856426 PMCID: PMC8821245 DOI: 10.1016/j.jcomdis.2021.106169] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/29/2021] [Revised: 10/25/2021] [Accepted: 11/11/2021] [Indexed: 06/13/2023]
Abstract
INTRODUCTION Individuals with persistent developmental stuttering display deficits in aligning motor actions to external cues (i.e., sensorimotor synchronization). Diffusion imaging studies point to stuttering-associated differences in dorsal, not ventral, white matter pathways, and in the cerebellar peduncles. Here, we studied microstructural white matter differences between adults who stutter (AWS) and fluent speakers using two complementary approaches to: (a) assess previously reported group differences in white matter diffusivity, and (b) evaluate the relationship between white matter diffusivity and sensorimotor synchronization in each group. METHODS Participants completed a sensorimotor synchronization task and a diffusion MRI scan. We identified the cerebellar peduncles and major dorsal- and ventral-stream language pathways in each individual and assessed correlations between sensorimotor synchronization and diffusion measures along the tracts. RESULTS The results demonstrated group differences in dorsal, not ventral, language tracts, in alignment with prior reports. Specifically, AWS had significantly lower fractional anisotropy (FA) in the left arcuate fasciculus, and significantly higher mean diffusivity (MD) in the bilateral frontal aslant tract compared to fluent speakers, while no significant group difference was detected in the inferior fronto-occipital fasciculus. We also found significant group differences in both FA and MD of the left middle cerebellar peduncle. Comparing patterns of association with sensorimotor synchronization revealed a novel double dissociation: MD within the left inferior cerebellar peduncle was significantly correlated with mean asynchrony in AWS but not in fluent speakers, while FA within the left arcuate fasciculus was significantly correlated with mean asynchrony in fluent speakers, but not in AWS. CONCLUSIONS Our results support the view that stuttering involves altered connectivity in dorsal tracts and that AWS may rely more heavily on cerebellar tracts to process timing information. Evaluating microstructural associations with sensitive behavioral measures provides a powerful tool for discovering additional functional differences in the underlying connectivity in AWS.
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Affiliation(s)
- Sivan Jossinger
- The Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat-Gan, Israel.
| | - Anastasia Sares
- Department of Psychology, Concordia University, Montréal, Canada; Centre for Research on Brain, Language and Music, McGill University, Montréal, Canada
| | - Avital Zislis
- The Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat-Gan, Israel
| | - Dana Sury
- The Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat-Gan, Israel
| | - Vincent Gracco
- Centre for Research on Brain, Language and Music, McGill University, Montréal, Canada; School of Communication Sciences and Disorders, McGill University, Montréal, Canada; Haskins Laboratories, New Haven, CT, United States
| | - Michal Ben-Shachar
- The Gonda Multidisciplinary Brain Research Center, Bar-Ilan University, Ramat-Gan, Israel; The Department of English Literature and Linguistics, Bar-Ilan University, Ramat-Gan, Israel
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Epstein SC, Bray TJP, Hall-Craggs MA, Zhang H. Task-driven assessment of experimental designs in diffusion MRI: A computational framework. PLoS One 2021; 16:e0258442. [PMID: 34624064 PMCID: PMC8500429 DOI: 10.1371/journal.pone.0258442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2021] [Accepted: 09/27/2021] [Indexed: 11/23/2022] Open
Abstract
This paper proposes a task-driven computational framework for assessing diffusion MRI experimental designs which, rather than relying on parameter-estimation metrics, directly measures quantitative task performance. Traditional computational experimental design (CED) methods may be ill-suited to experimental tasks, such as clinical classification, where outcome does not depend on parameter-estimation accuracy or precision alone. Current assessment metrics evaluate experiments’ ability to faithfully recover microstructural parameters rather than their task performance. The method we propose addresses this shortcoming. For a given MRI experimental design (protocol, parameter-estimation method, model, etc.), experiments are simulated start-to-finish and task performance is computed from receiver operating characteristic (ROC) curves and associated summary metrics (e.g. area under the curve (AUC)). Two experiments were performed: first, a validation of the pipeline’s task performance predictions against clinical results, comparing in-silico predictions to real-world ROC/AUC; and second, a demonstration of the pipeline’s advantages over traditional CED approaches, using two simulated clinical classification tasks. Comparison with clinical datasets validates our method’s predictions of (a) the qualitative form of ROC curves, (b) the relative task performance of different experimental designs, and (c) the absolute performance (AUC) of each experimental design. Furthermore, we show that our method outperforms traditional task-agnostic assessment methods, enabling improved, more useful experimental design. Our pipeline produces accurate, quantitative predictions of real-world task performance. Compared to current approaches, such task-driven assessment is more likely to identify experimental designs that perform well in practice. Our method is not limited to diffusion MRI; the pipeline generalises to any task-based quantitative MRI application, and provides the foundation for developing future task-driven end-to end CED frameworks.
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Affiliation(s)
- Sean C. Epstein
- Department of Computer Science & Centre for Medical Image Computing, University College London, London, United Kingdom
- * E-mail:
| | - Timothy J. P. Bray
- Centre for Medical Imaging, University College London, London, United Kingdom
| | | | - Hui Zhang
- Department of Computer Science & Centre for Medical Image Computing, University College London, London, United Kingdom
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Martinez-Heras E, Grussu F, Prados F, Solana E, Llufriu S. Diffusion-Weighted Imaging: Recent Advances and Applications. Semin Ultrasound CT MR 2021; 42:490-506. [PMID: 34537117 DOI: 10.1053/j.sult.2021.07.006] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
Abstract
Quantitative diffusion imaging techniques enable the characterization of tissue microstructural properties of the human brain "in vivo", and are widely used in neuroscientific and clinical contexts. In this review, we present the basic physical principles behind diffusion imaging and provide an overview of the current diffusion techniques, including standard and advanced techniques as well as their main clinical applications. Standard diffusion tensor imaging (DTI) offers sensitivity to changes in microstructure due to diseases and enables the characterization of single fiber distributions within a voxel as well as diffusion anisotropy. Nonetheless, its inability to represent complex intravoxel fiber topologies and the limited biological specificity of its metrics motivated the development of several advanced diffusion MRI techniques. For example, high-angular resolution diffusion imaging (HARDI) techniques enabled the characterization of fiber crossing areas and other complex fiber topologies in a single voxel and supported the development of higher-order signal representations aiming to decompose the diffusion MRI signal into distinct microstructure compartments. Biophysical models, often known by their acronym (e.g., CHARMED, WMTI, NODDI, DBSI, DIAMOND) contributed to capture the diffusion properties from each of such tissue compartments, enabling the computation of voxel-wise maps of axonal density and/or morphology that hold promise as clinically viable biomarkers in several neurological and neuroscientific applications; for example, to quantify tissue alterations due to disease or healthy processes. Current challenges and limitations of state-of-the-art models are discussed, including validation efforts. Finally, novel diffusion encoding approaches (e.g., b-tensor or double diffusion encoding) may increase the biological specificity of diffusion metrics towards intra-voxel diffusion heterogeneity in clinical settings, holding promise in neurological applications.
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Affiliation(s)
- Eloy Martinez-Heras
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona. Barcelona. Spain.
| | - Francesco Grussu
- Radiomics Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain; Queen Square MS Center, Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK
| | - Ferran Prados
- Queen Square MS Center, Queen Square Institute of Neurology, Faculty of Brain Sciences, University College London, London, UK; Center for Medical Image Computing (CMIC), Department of Medical Physics and Bioengineering, University College London, London, UK; E-health Center, Universitat Oberta de Catalunya. Barcelona. Spain
| | - Elisabeth Solana
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona. Barcelona. Spain
| | - Sara Llufriu
- Center of Neuroimmunology, Laboratory of Advanced Imaging in Neuroimmunological Diseases, Hospital Clinic Barcelona, Institut d'Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS) and Universitat de Barcelona. Barcelona. Spain
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4
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Oliviero S, Del Gratta C. Impact of the acquisition protocol on the sensitivity to demyelination and axonal loss of clinically feasible DWI techniques: a simulation study. MAGMA (NEW YORK, N.Y.) 2021; 34:523-543. [PMID: 33417079 DOI: 10.1007/s10334-020-00899-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Revised: 11/19/2020] [Accepted: 11/22/2020] [Indexed: 06/12/2023]
Abstract
OBJECTIVE To evaluate: (a) the specific effect that the demyelination and axonal loss have on the DW signal, and (b) the impact of the sequence parameters on the sensitivity to damage of two clinically feasible DWI techniques, i.e. DKI and NODDI. METHODS We performed a Monte Carlo simulation of water diffusion inside a novel synthetic model of white matter in the presence of axonal loss and demyelination, with three compartments with permeable boundaries between them. We compared DKI and NODDI in their ability to detect and assess the damage, using several acquisition protocols. We used the F test statistic as an index of the sensitivity for each DWI parameter to axonal loss and demyelination, respectively. RESULTS DKI parameters significantly changed with increasing axonal loss, but, in most cases, not with demyelination; all the NODDI parameters showed sensitivity to both the damage processes (at p < 0.01). However, the acquisition protocol strongly affected the sensitivity to damage of both the DKI and NODDI parameters and, especially for NODDI, the parameter absolute values also. DISCUSSION This work is expected to impact future choices for investigating white matter microstructure in focusing on specific stages of the disease, and for selecting the appropriate experimental framework to obtain optimal data quality given the purpose of the experiment.
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Affiliation(s)
- Stefania Oliviero
- Department Neurosciences, Imaging, and Clinical Sciences, Institute for Advanced Biomedical Technologies, ITAB, Gabriele D'Annunzio University, Chieti, Italy.
| | - Cosimo Del Gratta
- Department Neurosciences, Imaging, and Clinical Sciences, Institute for Advanced Biomedical Technologies, ITAB, Gabriele D'Annunzio University, Chieti, Italy
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Fritz FJ, Poser BA, Roebroeck A. MESMERISED: Super-accelerating T 1 relaxometry and diffusion MRI with STEAM at 7 T for quantitative multi-contrast and diffusion imaging. Neuroimage 2021; 239:118285. [PMID: 34147632 DOI: 10.1016/j.neuroimage.2021.118285] [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: 05/15/2020] [Revised: 06/14/2021] [Accepted: 06/16/2021] [Indexed: 12/17/2022] Open
Abstract
There is an increasing interest in quantitative imaging of T1, T2 and diffusion contrast in the brain due to greater robustness against bias fields and artifacts, as well as better biophysical interpretability in terms of microstructure. However, acquisition time constraints are a challenge, particularly when multiple quantitative contrasts are desired and when extensive sampling of diffusion directions, high b-values or long diffusion times are needed for multi-compartment microstructure modeling. Although ultra-high fields of 7 T and above have desirable properties for many MR modalities, the shortening T2 and the high specific absorption rate (SAR) of inversion and refocusing pulses bring great challenges to quantitative T1, T2 and diffusion imaging. Here, we present the MESMERISED sequence (Multiplexed Echo Shifted Multiband Excited and Recalled Imaging of STEAM Encoded Diffusion). MESMERISED removes the dead time in Stimulated Echo Acquisition Mode (STEAM) imaging by an echo-shifting mechanism. The echo-shift (ES) factor is independent of multiband (MB) acceleration and allows for very high multiplicative (ESxMB) acceleration factors, particularly under moderate and long mixing times. This results in super-acceleration and high time efficiency at 7 T for quantitative T1 and diffusion imaging, while also retaining the capacity to perform quantitative T2 and B1 mapping. We demonstrate the super-acceleration of MESMERISED for whole-brain T1 relaxometry with total acceleration factors up to 36 at 1.8 mm isotropic resolution, and up to 54 at 1.25 mm resolution qT1 imaging, corresponding to a 6x and 9x speedup, respectively, compared to MB-only accelerated acquisitions. We then demonstrate highly efficient diffusion MRI with high b-values and long diffusion times in two separate cases. First, we show that super-accelerated multi-shell diffusion acquisitions with 370 whole-brain diffusion volumes over 8 b-value shells up to b = 7000 s/mm2 can be generated at 2 mm isotropic in under 8 minutes, a data rate of almost a volume per second, or at 1.8 mm isotropic in under 11 minutes, achieving up to 3.4x speedup compared to MB-only. A comparison of b = 7000 s/mm2 MESMERISED against standard MB pulsed gradient spin echo (PGSE) diffusion imaging shows 70% higher SNR efficiency and greater effectiveness in supporting complex diffusion signal modeling. Second, we demonstrate time-efficient sampling of different diffusion times with 1.8 mm isotropic diffusion data acquired at four diffusion times up to 290 ms, which supports both Diffusion Tensor Imaging (DTI) and Diffusion Kurtosis Imaging (DKI) at each diffusion time. Finally, we demonstrate how adding quantitative T2 and B1+ mapping to super-accelerated qT1 and diffusion imaging enables efficient quantitative multi-contrast mapping with the same MESMERISED sequence and the same readout train. MESMERISED extends possibilities to efficiently probe T1, T2 and diffusion contrast for multi-component modeling of tissue microstructure.
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Affiliation(s)
- F J Fritz
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, the Netherlands; Institut für Systemische Neurowissenschaften, Zentrum für Experimentelle Medizin, Universitätklinikum Hamburg-Eppendorf (UKE), Hamburg, Deutschland
| | - B A Poser
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, the Netherlands
| | - A Roebroeck
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, the Netherlands.
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6
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Kelley S, Plass J, Bender AR, Polk TA. Age-Related Differences in White Matter: Understanding Tensor-Based Results Using Fixel-Based Analysis. Cereb Cortex 2021; 31:3881-3898. [PMID: 33791797 DOI: 10.1093/cercor/bhab056] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2020] [Revised: 01/19/2021] [Accepted: 02/16/2021] [Indexed: 12/13/2022] Open
Abstract
Aging is associated with widespread alterations in cerebral white matter (WM). Most prior studies of age differences in WM have used diffusion tensor imaging (DTI), but typical DTI metrics (e.g., fractional anisotropy; FA) can reflect multiple neurobiological features, making interpretation challenging. Here, we used fixel-based analysis (FBA) to investigate age-related WM differences observed using DTI in a sample of 45 older and 25 younger healthy adults. Age-related FA differences were widespread but were strongly associated with differences in multi-fiber complexity (CX), suggesting that they reflected differences in crossing fibers in addition to structural differences in individual fiber segments. FBA also revealed a frontolimbic locus of age-related effects and provided insights into distinct microstructural changes underlying them. Specifically, age differences in fiber density were prominent in fornix, bilateral anterior internal capsule, forceps minor, body of the corpus callosum, and corticospinal tract, while age differences in fiber cross section were largest in cingulum bundle and forceps minor. These results provide novel insights into specific structural differences underlying major WM differences associated with aging.
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Affiliation(s)
- Shannon Kelley
- Department of Psychology, University of Michigan, Ann Arbor, MI 48109, USA
| | - John Plass
- Department of Psychology, University of Michigan, Ann Arbor, MI 48109, USA
| | - Andrew R Bender
- Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, MI 48824, USA
| | - Thad A Polk
- Department of Psychology, University of Michigan, Ann Arbor, MI 48109, USA
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Yablonski M, Rastle K, Taylor J, Ben-Shachar M. Structural properties of the ventral reading pathways are associated with morphological processing in adult English readers. Cortex 2019; 116:268-285. [DOI: 10.1016/j.cortex.2018.06.011] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2017] [Revised: 06/25/2018] [Accepted: 06/25/2018] [Indexed: 12/27/2022]
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8
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Characterizing Microstructural Tissue Properties in Multiple Sclerosis with Diffusion MRI at 7 T and 3 T: The Impact of the Experimental Design. Neuroscience 2019; 403:17-26. [DOI: 10.1016/j.neuroscience.2018.03.048] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2017] [Revised: 03/23/2018] [Accepted: 03/28/2018] [Indexed: 11/21/2022]
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9
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Assaf Y, Johansen-Berg H, Thiebaut de Schotten M. The role of diffusion MRI in neuroscience. NMR IN BIOMEDICINE 2019; 32:e3762. [PMID: 28696013 DOI: 10.1002/nbm.3762] [Citation(s) in RCA: 69] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/30/2016] [Revised: 04/25/2017] [Accepted: 05/17/2017] [Indexed: 05/05/2023]
Abstract
Diffusion-weighted imaging has pushed the boundaries of neuroscience by allowing us to examine the white matter microstructure of the living human brain. By doing so, it has provided answers to fundamental neuroscientific questions, launching a new field of research that had been largely inaccessible. We briefly summarize key questions that have historically been raised in neuroscience concerning the brain's white matter. We then expand on the benefits of diffusion-weighted imaging and its contribution to the fields of brain anatomy, functional models and plasticity. In doing so, this review highlights the invaluable contribution of diffusion-weighted imaging in neuroscience, presents its limitations and proposes new challenges for future generations who may wish to exploit this powerful technology to gain novel insights.
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Affiliation(s)
- Yaniv Assaf
- Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel
- Department of Neurobiology, George S. Wise Faculty of Life Sciences, Tel Aviv University, Tel Aviv, Israel
| | - Heidi Johansen-Berg
- FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, John Radcliffe Hospital, Oxford, UK
| | - Michel Thiebaut de Schotten
- Brain Connectivity and Behaviour Group, Frontlab, Brain and Spine Institute, Paris, France
- Sorbonne Universités, UPMC Université Paris 06, Inserm, CNRS, Institut du cerveau et la moelle (ICM) - Hôpital Pitié-Salpêtrière, Boulevard de l'hôpital, Paris, France
- Centre de Neuroimagerie de Recherche CENIR, Groupe Hospitalier Pitié-Salpêtrière, Paris, France
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Tobisch A, Schultz T, Stirnberg R, Varela-Mattatall G, Knutsson H, Irarrázaval P, Stöcker T. Comparison of basis functions and q-space sampling schemes for robust compressed sensing reconstruction accelerating diffusion spectrum imaging. NMR IN BIOMEDICINE 2019; 32:e4055. [PMID: 30637831 DOI: 10.1002/nbm.4055] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/20/2018] [Revised: 11/06/2018] [Accepted: 11/13/2018] [Indexed: 06/09/2023]
Abstract
Time constraints placed on magnetic resonance imaging often restrict the application of advanced diffusion MRI (dMRI) protocols in clinical practice and in high throughput research studies. Therefore, acquisition strategies for accelerated dMRI have been investigated to allow for the collection of versatile and high quality imaging data, even if stringent scan time limits are imposed. Diffusion spectrum imaging (DSI), an advanced acquisition strategy that allows for a high resolution of intra-voxel microstructure, can be sufficiently accelerated by means of compressed sensing (CS) theory. CS theory describes a framework for the efficient collection of fewer samples of a data set than conventionally required followed by robust reconstruction to recover the full data set from sparse measurements. For an accurate recovery of DSI data, a suitable acquisition scheme for sparse q-space sampling and the sensing and sparsifying bases for CS reconstruction need to be selected. In this work we explore three different types of q-space undersampling schemes and two frameworks for CS reconstruction based on either Fourier or SHORE basis functions. After CS recovery, diffusion and microstructural parameters and orientational information are estimated from the reconstructed data by means of state-of-the-art processing techniques for dMRI analysis. By means of simulation, diffusion phantom and in vivo DSI data, an isotropic distribution of q-space samples was found to be optimal for sparse DSI. The CS reconstruction results indicate superior performance of Fourier-based CS-DSI compared to the SHORE-based approach. Based on these findings we outline an experimental design for accelerated DSI and robust CS reconstruction of the sparse measurements that is suitable for the application within time-limited studies.
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Affiliation(s)
- Alexandra Tobisch
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Computer Science, University of Bonn, Germany
| | - Thomas Schultz
- Department of Computer Science, University of Bonn, Germany
- Bonn-Aachen International Center for Information Technology, University of Bonn, Germany
| | | | - Gabriel Varela-Mattatall
- Biomedical Imaging Center, Pontificia Universidad Católica de Chile, Santiago, Chile
- Department of Electrical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
| | | | - Pablo Irarrázaval
- Biomedical Imaging Center, Pontificia Universidad Católica de Chile, Santiago, Chile
- Department of Electrical Engineering, Pontificia Universidad Católica de Chile, Santiago, Chile
| | - Tony Stöcker
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
- Department of Physics and Astronomy, University of Bonn, Germany
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De Santis S, Granberg T, Ouellette R, Treaba CA, Herranz E, Fan Q, Mainero C, Toschi N. Evidence of early microstructural white matter abnormalities in multiple sclerosis from multi-shell diffusion MRI. Neuroimage Clin 2019; 22:101699. [PMID: 30739842 PMCID: PMC6370560 DOI: 10.1016/j.nicl.2019.101699] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2018] [Revised: 12/07/2018] [Accepted: 01/28/2019] [Indexed: 12/29/2022]
Abstract
Irreversible white matter (WM) damage, including severe demyelination and axonal loss, is a main determinant of long-term disability in multiple sclerosis (MS). Non-invasive detection of changes in microstructural WM integrity in the disease is challenging since commonly used imaging metrics lack the necessary sensitivity, especially in the early phase of the disease. This study aims at assessing microstructural WM abnormalities in early-stage MS by using ultra-high gradient strength multi-shell diffusion MRI and the restricted signal fraction (FR) from the Composite Hindered and Restricted Model of Diffusion (CHARMED), a metric sensitive to the volume fraction of axons. In 22 early MS subjects (disease duration ≤5 years) and 15 age-matched healthy controls, restricted fraction estimates were obtained through the CHARMED model along with conventional Diffusion Tensor Imaging (DTI) metrics. All imaging parameters were compared cross-sectionally between the MS subjects and controls both in WM lesions and normal-appearing white matter (NAWM). We found a significant reduction in FR focally in WM lesions and widespread in the NAWM in MS patients relative to controls (corrected p < .05). Signal fraction changes in NAWM were not driven by perilesional tissue, nor were they influenced by proximity to the ventricles, challenging the hypothesis of an outside-in pathological process driven by CSF-mediated immune cytotoxic factors. No significant differences were found in conventional DTI parameters. In a cross-validated classification task, FR showed the largest effect size and outperformed all other diffusion imaging metrics in discerning lesions from contralateral NAWM. Taken together, our data provide evidence for the presence of widespread microstructural changes in the NAWM in early MS stages that are, at least in part, unrelated to focal demyelinating lesions. Interestingly, these pathological changes were not yet detectable by conventional diffusion imaging at this early disease stage, highlighting the sensitivity and value of multi-shell diffusion imaging for better characterizing axonal microstructure in MS.
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Affiliation(s)
- Silvia De Santis
- Instituto de Neurociencias de Alicante (CSIC-UMH), San Juan de Alicante, Spain; Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, UK
| | - Tobias Granberg
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden; Department of Radiology, Karolinska University Hospital, Stockholm, Sweden; Athinoula A. Martinos Center for Biomedical Imaging and Harvard Medical School, Boston, MA, USA
| | - Russell Ouellette
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden; Athinoula A. Martinos Center for Biomedical Imaging and Harvard Medical School, Boston, MA, USA
| | - Constantina A Treaba
- Athinoula A. Martinos Center for Biomedical Imaging and Harvard Medical School, Boston, MA, USA
| | - Elena Herranz
- Athinoula A. Martinos Center for Biomedical Imaging and Harvard Medical School, Boston, MA, USA
| | - Qiuyun Fan
- Athinoula A. Martinos Center for Biomedical Imaging and Harvard Medical School, Boston, MA, USA
| | - Caterina Mainero
- Athinoula A. Martinos Center for Biomedical Imaging and Harvard Medical School, Boston, MA, USA
| | - Nicola Toschi
- Athinoula A. Martinos Center for Biomedical Imaging and Harvard Medical School, Boston, MA, USA.; Department of Biomedicine and Prevention, University of Rome Tor Vergata, Rome, Italy.
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12
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Harms RL, Roebroeck A. Robust and Fast Markov Chain Monte Carlo Sampling of Diffusion MRI Microstructure Models. Front Neuroinform 2018; 12:97. [PMID: 30618702 PMCID: PMC6305549 DOI: 10.3389/fninf.2018.00097] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2018] [Accepted: 11/28/2018] [Indexed: 11/29/2022] Open
Abstract
In diffusion MRI analysis, advances in biophysical multi-compartment modeling have gained popularity over the conventional Diffusion Tensor Imaging (DTI), because they can obtain a greater specificity in relating the dMRI signal to underlying cellular microstructure. Biophysical multi-compartment models require a parameter estimation, typically performed using either the Maximum Likelihood Estimation (MLE) or the Markov Chain Monte Carlo (MCMC) sampling. Whereas, the MLE provides only a point estimate of the fitted model parameters, the MCMC recovers the entire posterior distribution of the model parameters given in the data, providing additional information such as parameter uncertainty and correlations. MCMC sampling is currently not routinely applied in dMRI microstructure modeling, as it requires adjustment and tuning, specific to each model, particularly in the choice of proposal distributions, burn-in length, thinning, and the number of samples to store. In addition, sampling often takes at least an order of magnitude, more time than non-linear optimization. Here we investigate the performance of the MCMC algorithm variations over multiple popular diffusion microstructure models, to examine whether a single, well performing variation could be applied efficiently and robustly to many models. Using an efficient GPU-based implementation, we showed that run times can be removed as a prohibitive constraint for the sampling of diffusion multi-compartment models. Using this implementation, we investigated the effectiveness of different adaptive MCMC algorithms, burn-in, initialization, and thinning. Finally we applied the theory of the Effective Sample Size, to the diffusion multi-compartment models, as a way of determining a relatively general target for the number of samples needed to characterize parameter distributions for different models and data sets. We conclude that adaptive Metropolis methods increase MCMC performance and select the Adaptive Metropolis-Within-Gibbs (AMWG) algorithm as the primary method. We furthermore advise to initialize the sampling with an MLE point estimate, in which case 100 to 200 samples are sufficient as a burn-in. Finally, we advise against thinning in most use-cases and as a relatively general target for the number of samples, we recommend a multivariate Effective Sample Size of 2,200.
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Affiliation(s)
- Robbert L. Harms
- Department of Cognitive Neuroscience, Faculty of Psychology & Neuroscience, Maastricht University, Maastricht, Netherlands
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13
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Filipiak P, Fick R, Petiet A, Santin M, Philippe AC, Lehericy S, Ciuciu P, Deriche R, Wassermann D. Reducing the number of samples in spatiotemporal dMRI acquisition design. Magn Reson Med 2018; 81:3218-3233. [DOI: 10.1002/mrm.27601] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2018] [Revised: 10/17/2018] [Accepted: 10/18/2018] [Indexed: 12/21/2022]
Affiliation(s)
- Patryk Filipiak
- Université Côte d'Azur-Inria Sophia Antipolis-Méditerranée; France
| | - Rutger Fick
- Université Côte d'Azur-Inria Sophia Antipolis-Méditerranée; France
| | - Alexandra Petiet
- CENIR-Center for NeuroImaging Research, ICM-Brain and Spine Institute; Paris France
| | - Mathieu Santin
- CENIR-Center for NeuroImaging Research, ICM-Brain and Spine Institute; Paris France
| | | | - Stephane Lehericy
- CENIR-Center for NeuroImaging Research, ICM-Brain and Spine Institute; Paris France
| | | | - Rachid Deriche
- Université Côte d'Azur-Inria Sophia Antipolis-Méditerranée; France
| | - Demian Wassermann
- Université Côte d'Azur-Inria Sophia Antipolis-Méditerranée; France
- Inria, CEA, Université Paris-Saclay; France
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14
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Abstract
The emergence of multiparametric diffusion models combining diffusion and relaxometry measurements provides powerful new ways to explore tissue microstructure, with the potential to provide new insights into tissue structure and function. However, their ability to provide rich analyses and the potential for clinical translation critically depends on the availability of efficient, integrated, multi-dimensional acquisitions. We propose a fully integrated sequence simultaneously sampling the acquisition parameter spaces required for T1 and T2* relaxometry and diffusion MRI. Slice-level interleaved diffusion encoding, multiple spin/gradient echoes and slice-shuffling are combined for higher efficiency, sampling flexibility and enhanced internal consistency. In-vivo data was successfully acquired on healthy adult brains. Obtained parametric maps as well as clustering results demonstrate the potential of the technique to provide eloquent data with an acceleration of roughly 20 compared to conventionally used approaches. The proposed integrated acquisition, which we call ZEBRA, offers significant acceleration and flexibility compared to existing diffusion-relaxometry studies, and thus facilitates wider use of these techniques both for research-driven and clinical applications.
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15
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Tobisch A, Stirnberg R, Harms RL, Schultz T, Roebroeck A, Breteler MMB, Stöcker T. Compressed Sensing Diffusion Spectrum Imaging for Accelerated Diffusion Microstructure MRI in Long-Term Population Imaging. Front Neurosci 2018; 12:650. [PMID: 30319336 PMCID: PMC6165908 DOI: 10.3389/fnins.2018.00650] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2018] [Accepted: 08/30/2018] [Indexed: 11/23/2022] Open
Abstract
Mapping non-invasively the complex microstructural architecture of the living human brain, diffusion magnetic resonance imaging (dMRI) is one of the core imaging modalities in current population studies. For the application in longitudinal population imaging, the dMRI protocol should deliver reliable data with maximum potential for future analysis. With the recent introduction of novel MRI hardware, advanced dMRI acquisition strategies can be applied within reasonable scan time. In this work we conducted a pilot study based on the requirements for high resolution dMRI in a long-term and high throughput population study. The key question was: can diffusion spectrum imaging accelerated by compressed sensing theory (CS-DSI) be used as an advanced imaging protocol for microstructure dMRI in a long-term population imaging study? As a minimum requirement we expected a high level of agreement of several diffusion metrics derived from both CS-DSI and a 3-shell high angular resolution diffusion imaging (HARDI) acquisition, an established imaging strategy used in other population studies. A wide spectrum of state-of-the-art diffusion processing and analysis techniques was applied to the pilot study data including quantitative diffusion and microstructural parameter mapping, fiber orientation estimation and white matter fiber tracking. When considering diffusion weighted images up to the same maximum diffusion weighting for both protocols, group analysis across 20 subjects indicates that CS-DSI performs comparable to 3-shell HARDI in the estimation of diffusion and microstructural parameters. Further, both protocols provide similar results in the estimation of fiber orientations and for local fiber tracking. CS-DSI provides high radial resolution while maintaining high angular resolution and it is well-suited for analysis strategies that require high b-value acquisitions, such as CHARMED modeling and biomarkers from the diffusion propagator.
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Affiliation(s)
- Alexandra Tobisch
- German Center for Neurodegenerative Diseases, Bonn, Germany.,Department of Computer Science, University of Bonn, Bonn, Germany
| | | | - Robbert L Harms
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Thomas Schultz
- Department of Computer Science, University of Bonn, Bonn, Germany.,Bonn-Aachen International Center for Information Technology, University of Bonn, Bonn, Germany
| | - Alard Roebroeck
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Monique M B Breteler
- German Center for Neurodegenerative Diseases, Bonn, Germany.,Faculty of Medicine, Institute for Medical Biometry, Informatics and Epidemiology, University of Bonn, Bonn, Germany
| | - Tony Stöcker
- German Center for Neurodegenerative Diseases, Bonn, Germany.,Department of Physics and Astronomy, University of Bonn, Bonn, Germany
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16
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Hutter J, Tournier JD, Price AN, Cordero‐Grande L, Hughes EJ, Malik S, Steinweg J, Bastiani M, Sotiropoulos SN, Jbabdi S, Andersson J, Edwards AD, Hajnal JV. Time-efficient and flexible design of optimized multishell HARDI diffusion. Magn Reson Med 2018; 79:1276-1292. [PMID: 28557055 PMCID: PMC5811841 DOI: 10.1002/mrm.26765] [Citation(s) in RCA: 52] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2016] [Revised: 05/02/2017] [Accepted: 05/03/2017] [Indexed: 02/01/2023]
Abstract
PURPOSE Advanced diffusion magnetic resonance imaging benefits from collecting as much data as is feasible but is highly sensitive to subject motion and the risk of data loss increases with longer acquisition times. Our purpose was to create a maximally time-efficient and flexible diffusion acquisition capability with built-in robustness to partially acquired or interrupted scans. Our framework has been developed for the developing Human Connectome Project, but different application domains are equally possible. METHODS Complete flexibility in the sampling of diffusion space combined with free choice of phase-encode-direction and the temporal ordering of the sampling scheme was developed taking into account motion robustness, internal consistency, and hardware limits. A split-diffusion-gradient preparation, multiband acceleration, and a restart capacity were added. RESULTS The framework was used to explore different parameters choices for the desired high angular resolution diffusion imaging diffusion sampling. For the developing Human Connectome Project, a high-angular resolution, maximally time-efficient (20 min) multishell protocol with 300 diffusion-weighted volumes was acquired in >400 neonates. An optimal design of a high-resolution (1.2 × 1.2 mm2 ) two-shell acquisition with 54 diffusion weighted volumes was obtained using a split-gradient design. CONCLUSION The presented framework provides flexibility to generate time-efficient and motion-robust diffusion magnetic resonance imaging acquisitions taking into account hardware constraints that might otherwise result in sub-optimal choices. Magn Reson Med 79:1276-1292, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
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Affiliation(s)
- Jana Hutter
- Centre for the Developing BrainKing's College LondonLondonUK
- Biomedical Engineering DepartmentKing's College LondonLondonUK
| | | | - Anthony N. Price
- Centre for the Developing BrainKing's College LondonLondonUK
- Biomedical Engineering DepartmentKing's College LondonLondonUK
| | - Lucilio Cordero‐Grande
- Centre for the Developing BrainKing's College LondonLondonUK
- Biomedical Engineering DepartmentKing's College LondonLondonUK
| | - Emer J. Hughes
- Centre for the Developing BrainKing's College LondonLondonUK
- Biomedical Engineering DepartmentKing's College LondonLondonUK
| | - Shaihan Malik
- Biomedical Engineering DepartmentKing's College LondonLondonUK
| | | | | | | | | | | | | | - Joseph V. Hajnal
- Centre for the Developing BrainKing's College LondonLondonUK
- Biomedical Engineering DepartmentKing's College LondonLondonUK
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17
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Cheng J, Shen D, Yap PT, Basser PJ. Single- and Multiple-Shell Uniform Sampling Schemes for Diffusion MRI Using Spherical Codes. IEEE TRANSACTIONS ON MEDICAL IMAGING 2018; 37:185-199. [PMID: 28952937 PMCID: PMC5867228 DOI: 10.1109/tmi.2017.2756072] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/07/2023]
Abstract
In diffusion MRI (dMRI), a good sampling scheme is important for efficient acquisition and robust reconstruction. Diffusion weighted signal is normally acquired on single or multiple shells in q-space. Signal samples are typically distributed uniformly on different shells to make them invariant to the orientation of structures within tissue, or the laboratory coordinate frame. The Electrostatic Energy Minimization (EEM) method, originally proposed for single shell sampling scheme in dMRI, was recently generalized to multi-shell schemes, called Generalized EEM (GEEM). GEEM has been successfully used in the Human Connectome Project (HCP). However, EEM does not directly address the goal of optimal sampling, i.e., achieving large angular separation between sampling points. In this paper, we propose a more natural formulation, called Spherical Code (SC), to directly maximize the minimal angle between different samples in single or multiple shells. We consider not only continuous problems to design single or multiple shell sampling schemes, but also discrete problems to uniformly extract sub-sampled schemes from an existing single or multiple shell scheme, and to order samples in an existing scheme. We propose five algorithms to solve the above problems, including an incremental SC (ISC), a sophisticated greedy algorithm called Iterative Maximum Overlap Construction (IMOC), an 1-Opt greedy method, a Mixed Integer Linear Programming (MILP) method, and a Constrained Non-Linear Optimization (CNLO) method. To our knowledge, this is the first work to use the SC formulation for single or multiple shell sampling schemes in dMRI. Experimental results indicate that SC methods obtain larger angular separation and better rotational invariance than the state-of-the-art EEM and GEEM. The related codes and a tutorial have been released in DMRITool.
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18
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Battiston M, Grussu F, Ianus A, Schneider T, Prados F, Fairney J, Ourselin S, Alexander DC, Cercignani M, Gandini Wheeler-Kingshott CAM, Samson RS. An optimized framework for quantitative magnetization transfer imaging of the cervical spinal cord in vivo. Magn Reson Med 2017; 79:2576-2588. [PMID: 28921614 PMCID: PMC5836910 DOI: 10.1002/mrm.26909] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/13/2017] [Revised: 08/15/2017] [Accepted: 08/16/2017] [Indexed: 11/06/2022]
Abstract
PURPOSE To develop a framework to fully characterize quantitative magnetization transfer indices in the human cervical cord in vivo within a clinically feasible time. METHODS A dedicated spinal cord imaging protocol for quantitative magnetization transfer was developed using a reduced field-of-view approach with echo planar imaging (EPI) readout. Sequence parameters were optimized based in the Cramer-Rao-lower bound. Quantitative model parameters (i.e., bound pool fraction, free and bound pool transverse relaxation times [ T2F, T2B], and forward exchange rate [kFB ]) were estimated implementing a numerical model capable of dealing with the novelties of the sequence adopted. The framework was tested on five healthy subjects. RESULTS Cramer-Rao-lower bound minimization produces optimal sampling schemes without requiring the establishment of a steady-state MT effect. The proposed framework allows quantitative voxel-wise estimation of model parameters at the resolution typically used for spinal cord imaging (i.e. 0.75 × 0.75 × 5 mm3 ), with a protocol duration of ∼35 min. Quantitative magnetization transfer parametric maps agree with literature values. Whole-cord mean values are: bound pool fraction = 0.11(±0.01), T2F = 46.5(±1.6) ms, T2B = 11.0(±0.2) µs, and kFB = 1.95(±0.06) Hz. Protocol optimization has a beneficial effect on reproducibility, especially for T2B and kFB . CONCLUSION The framework developed enables robust characterization of spinal cord microstructure in vivo using qMT. Magn Reson Med 79:2576-2588, 2018. © 2017 The Authors Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
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Affiliation(s)
- Marco Battiston
- Queen Square MS Centre, UCL Institute of Neurology, University College London, London, United Kingdom
| | - Francesco Grussu
- Queen Square MS Centre, UCL Institute of Neurology, University College London, London, United Kingdom
| | - Andrada Ianus
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom.,Champalimaud Neuroscience Programme, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | | | - Ferran Prados
- Queen Square MS Centre, UCL Institute of Neurology, University College London, London, United Kingdom.,Translational Imaging Group, Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - James Fairney
- Queen Square MS Centre, UCL Institute of Neurology, University College London, London, United Kingdom.,UCL Department of Medical Physics and Bioengineering, University College London, London, United Kingdom
| | - Sebastien Ourselin
- Translational Imaging Group, Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Daniel C Alexander
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, United Kingdom
| | - Mara Cercignani
- CISC, Department of Neuroscience, Brighton & Sussex Medical School, Brighton, Sussex, United Kingdom
| | - Claudia A M Gandini Wheeler-Kingshott
- Queen Square MS Centre, UCL Institute of Neurology, University College London, London, United Kingdom.,Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy.,Brain MRI 3T Mondino Research Center, C. Mondino National Neurological Institute, Pavia, Italy
| | - Rebecca S Samson
- Queen Square MS Centre, UCL Institute of Neurology, University College London, London, United Kingdom
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19
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Harms RL, Fritz FJ, Tobisch A, Goebel R, Roebroeck A. Robust and fast nonlinear optimization of diffusion MRI microstructure models. Neuroimage 2017; 155:82-96. [PMID: 28457975 PMCID: PMC5518773 DOI: 10.1016/j.neuroimage.2017.04.064] [Citation(s) in RCA: 71] [Impact Index Per Article: 10.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2016] [Revised: 04/07/2017] [Accepted: 04/09/2017] [Indexed: 02/07/2023] Open
Abstract
Advances in biophysical multi-compartment modeling for diffusion MRI (dMRI) have gained popularity because of greater specificity than DTI in relating the dMRI signal to underlying cellular microstructure. A large range of these diffusion microstructure models have been developed and each of the popular models comes with its own, often different, optimization algorithm, noise model and initialization strategy to estimate its parameter maps. Since data fit, accuracy and precision is hard to verify, this creates additional challenges to comparability and generalization of results from diffusion microstructure models. In addition, non-linear optimization is computationally expensive leading to very long run times, which can be prohibitive in large group or population studies. In this technical note we investigate the performance of several optimization algorithms and initialization strategies over a few of the most popular diffusion microstructure models, including NODDI and CHARMED. We evaluate whether a single well performing optimization approach exists that could be applied to many models and would equate both run time and fit aspects. All models, algorithms and strategies were implemented on the Graphics Processing Unit (GPU) to remove run time constraints, with which we achieve whole brain dataset fits in seconds to minutes. We then evaluated fit, accuracy, precision and run time for different models of differing complexity against three common optimization algorithms and three parameter initialization strategies. Variability of the achieved quality of fit in actual data was evaluated on ten subjects of each of two population studies with a different acquisition protocol. We find that optimization algorithms and multi-step optimization approaches have a considerable influence on performance and stability over subjects and over acquisition protocols. The gradient-free Powell conjugate-direction algorithm was found to outperform other common algorithms in terms of run time, fit, accuracy and precision. Parameter initialization approaches were found to be relevant especially for more complex models, such as those involving several fiber orientations per voxel. For these, a fitting cascade initializing or fixing parameter values in a later optimization step from simpler models in an earlier optimization step further improved run time, fit, accuracy and precision compared to a single step fit. This establishes and makes available standards by which robust fit and accuracy can be achieved in shorter run times. This is especially relevant for the use of diffusion microstructure modeling in large group or population studies and in combining microstructure parameter maps with tractography results. Evaluate robustness of fit, accuracy, precision for diffusion microstructure models Test three optimization algorithms and three parameter initialization strategies GPU implementation removes run time constraints; whole brain fit within minutes Powell conjugate-direction algorithm has superior fit, accuracy, precision Initialization approaches are important for crossing fiber microstructure models
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Affiliation(s)
- R L Harms
- Dept. of Cognitive Neuroscience, Faculty of Psychology & Neuroscience, Maastricht University, The Netherlands; Brain Innovation B.V., Maastricht, The Netherlands.
| | - F J Fritz
- Dept. of Cognitive Neuroscience, Faculty of Psychology & Neuroscience, Maastricht University, The Netherlands
| | - A Tobisch
- German Center for Neurodegenerative Diseases (DZNE), Bonn, Germany
| | - R Goebel
- Dept. of Cognitive Neuroscience, Faculty of Psychology & Neuroscience, Maastricht University, The Netherlands
| | - A Roebroeck
- Dept. of Cognitive Neuroscience, Faculty of Psychology & Neuroscience, Maastricht University, The Netherlands
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20
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Raffelt DA, Tournier JD, Smith RE, Vaughan DN, Jackson G, Ridgway GR, Connelly A. Investigating white matter fibre density and morphology using fixel-based analysis. Neuroimage 2016; 144:58-73. [PMID: 27639350 PMCID: PMC5182031 DOI: 10.1016/j.neuroimage.2016.09.029] [Citation(s) in RCA: 347] [Impact Index Per Article: 43.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2016] [Revised: 09/05/2016] [Accepted: 09/13/2016] [Indexed: 12/13/2022] Open
Abstract
Voxel-based analysis of diffusion MRI data is increasingly popular. However, most white matter voxels contain contributions from multiple fibre populations (often referred to as crossing fibres), and therefore voxel-averaged quantitative measures (e.g. fractional anisotropy) are not fibre-specific and have poor interpretability. Using higher-order diffusion models, parameters related to fibre density can be extracted for individual fibre populations within each voxel ('fixels'), and recent advances in statistics enable the multi-subject analysis of such data. However, investigating within-voxel microscopic fibre density alone does not account for macroscopic differences in the white matter morphology (e.g. the calibre of a fibre bundle). In this work, we introduce a novel method to investigate the latter, which we call fixel-based morphometry (FBM). To obtain a more complete measure related to the total number of white matter axons, information from both within-voxel microscopic fibre density and macroscopic morphology must be combined. We therefore present the FBM method as an integral piece within a comprehensive fixel-based analysis framework to investigate measures of fibre density, fibre-bundle morphology (cross-section), and a combined measure of fibre density and cross-section. We performed simulations to demonstrate the proposed measures using various transformations of a numerical fibre bundle phantom. Finally, we provide an example of such an analysis by comparing a clinical patient group to a healthy control group, which demonstrates that all three measures provide distinct and complementary information. By capturing information from both sources, the combined fibre density and cross-section measure is likely to be more sensitive to certain pathologies and more directly interpretable.
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Affiliation(s)
- David A Raffelt
- Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia.
| | - J-Donald Tournier
- Department of Biomedical Engineering, Division of Imaging Sciences & Biomedical Engineering, King's College London, London, UK; Centre for the Developing Brain, King's College London, London, UK
| | - Robert E Smith
- Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia
| | - David N Vaughan
- Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia; Florey Department of Neuroscience and Mental Health, University of Melbourne, Melbourne, Victoria, Australia; Department of Neurology, Austin Health and Northern Health, University of Melbourne, Melbourne, Victoria, Australia
| | - Graeme Jackson
- Florey Institute of Neuroscience and Mental Health, Melbourne, 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
| | - Gerard R Ridgway
- FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, UK; Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, London, UK
| | - Alan Connelly
- Florey Institute of Neuroscience and Mental Health, Melbourne, Victoria, Australia; Florey Department of Neuroscience and Mental Health, University of Melbourne, Melbourne, Victoria, Australia; Department of Neurology, Austin Health and Northern Health, University of Melbourne, Melbourne, Victoria, Australia
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21
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Dynamics of the Human Structural Connectome Underlying Working Memory Training. J Neurosci 2016; 36:4056-66. [PMID: 27053212 DOI: 10.1523/jneurosci.1973-15.2016] [Citation(s) in RCA: 58] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/22/2015] [Accepted: 02/04/2016] [Indexed: 01/09/2023] Open
Abstract
UNLABELLED Brain region-specific changes have been demonstrated with a variety of cognitive training interventions. The effect of cognitive training on brain subnetworks in humans, however, remains largely unknown, with studies limited to functional networks. Here, we used a well-established working memory training program and state-of-the art neuroimaging methods in 40 healthy adults (21 females, mean age 26.5 years). Near and far-transfer training effects were assessed using computerized working memory and executive function tasks. Adaptive working memory training led to improvement on (non)trained working memory tasks and generalization to tasks of reasoning and inhibition. Graph theoretical analysis of the structural (white matter) network connectivity ("connectome") revealed increased global integration within a frontoparietal attention network following adaptive working memory training compared with the nonadaptive group. Furthermore, the impact on the outcome of graph theoretical analyses of different white matter metrics to infer "connection strength" was evaluated. Increased efficiency of the frontoparietal network was best captured when using connection strengths derived from MR metrics that are thought to be more sensitive to differences in myelination (putatively indexed by the [quantitative] longitudinal relaxation rate, R1) than previously used diffusion MRI metrics (fractional anisotropy or fiber-tracking recovered streamlines). Our findings emphasize the critical role of specific microstructural markers in providing important hints toward the mechanisms underpinning training-induced plasticity that may drive working memory improvement in clinical populations. SIGNIFICANCE STATEMENT This is the first study to explore training-induced changes in the structural connectome using a well-controlled design to examine cognitive training with up-to-date neuroimaging methods. We found changes in global integration based on white matter connectivity within a frontoparietal attention network following adaptive working memory training compared with a nonadaptive comparison group. Furthermore, the impact of different diffusion MR metrics and more specific markers of white matter on the graph theoretical findings was evaluated. An increase in network global efficiency following working memory training was best captured when connection strengths were weighted by MR relaxation rates (influenced by myelination). These results are important for the optimization of cognitive training programs for healthy individuals and people with brain disease.
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22
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Froeling M, Tax CM, Vos SB, Luijten PR, Leemans A. “MASSIVE” brain dataset: Multiple acquisitions for standardization of structural imaging validation and evaluation. Magn Reson Med 2016; 77:1797-1809. [DOI: 10.1002/mrm.26259] [Citation(s) in RCA: 53] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2015] [Revised: 03/16/2016] [Accepted: 04/04/2016] [Indexed: 01/02/2023]
Affiliation(s)
- Martijn Froeling
- Department of RadiologyUniversity Medical Center UtrechtUtrecht Netherlands
| | - Chantal M.W. Tax
- Image Sciences InstituteUniversity Medical Center UtrechtUtrecht Netherlands
| | - Sjoerd B. Vos
- Image Sciences InstituteUniversity Medical Center UtrechtUtrecht Netherlands
- Translational Imaging Group, CMIC, University College LondonLondon United Kingdom
| | - Peter R. Luijten
- Department of RadiologyUniversity Medical Center UtrechtUtrecht Netherlands
| | - Alexander Leemans
- Image Sciences InstituteUniversity Medical Center UtrechtUtrecht Netherlands
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23
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Abstract
Progress in magnetic resonance imaging (MRI) now makes it possible to identify the major white matter tracts in the living human brain. These tracts are important because they carry many of the signals communicated between different brain regions. MRI methods coupled with biophysical modeling can measure the tissue properties and structural features of the tracts that impact our ability to think, feel, and perceive. This review describes the fundamental ideas of the MRI methods used to identify the major white matter tracts in the living human brain.
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Affiliation(s)
- Brian A Wandell
- Department of Psychology and Stanford Neurosciences Institute, Stanford University, Stanford, California 94305;
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24
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De Santis S, Jones DK, Roebroeck A. Including diffusion time dependence in the extra-axonal space improves in vivo estimates of axonal diameter and density in human white matter. Neuroimage 2016; 130:91-103. [PMID: 26826514 PMCID: PMC4819719 DOI: 10.1016/j.neuroimage.2016.01.047] [Citation(s) in RCA: 70] [Impact Index Per Article: 8.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2015] [Revised: 01/14/2016] [Accepted: 01/20/2016] [Indexed: 12/01/2022] Open
Abstract
Axonal density and diameter are two fundamental properties of brain white matter. Recently, advanced diffusion MRI techniques have made these two parameters accessible in vivo. However, the techniques available to estimate such parameters are still under development. For example, current methods to map axonal diameters capture relative trends over different structures, but consistently over-estimate absolute diameters. Axonal density estimates are more accessible experimentally, but different modeling approaches exist and the impact of the experimental parameters has not been thoroughly quantified, potentially leading to incompatibility of results obtained in different studies using different techniques. Here, we characterise the impact of diffusion time on axonal density and diameter estimates using Monte Carlo simulations and STEAM diffusion MRI at 7 T on 9 healthy volunteers. We show that axonal density and diameter estimates strongly depend on diffusion time, with diameters almost invariably overestimated and density both over and underestimated for some commonly used models. Crucially, we also demonstrate that these biases are reduced when the model accounts for diffusion time dependency in the extra-axonal space. For axonal density estimates, both upward and downward bias in different situations are removed by modeling extra-axonal time-dependence, showing increased accuracy in these estimates. For axonal diameter estimates, we report increased accuracy in ground truth simulations and axonal diameter estimates decreased away from high values given by earlier models and towards known values in the human corpus callosum when modeling extra-axonal time-dependence. Axonal diameter feasibility under both advanced and clinical settings is discussed in the light of the proposed advances.
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Affiliation(s)
- Silvia De Santis
- CUBRIC, School of Psychology, Cardiff University, Cardiff CF10 3AT, UK; Faculty of Psychology & Neuroscience, Maastricht University, Maastricht, The Netherlands.
| | - Derek K Jones
- CUBRIC, School of Psychology, Cardiff University, Cardiff CF10 3AT, UK; Neuroscience & Mental Health Research Institute, Cardiff University, CF10 3AT, UK
| | - Alard Roebroeck
- Faculty of Psychology & Neuroscience, Maastricht University, Maastricht, The Netherlands
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25
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K-Optimal Gradient Encoding Scheme for Fourth-Order Tensor-Based Diffusion Profile Imaging. BIOMED RESEARCH INTERNATIONAL 2015; 2015:760230. [PMID: 26451376 PMCID: PMC4584248 DOI: 10.1155/2015/760230] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/22/2015] [Revised: 08/26/2015] [Accepted: 08/27/2015] [Indexed: 11/18/2022]
Abstract
The design of an optimal gradient encoding scheme (GES) is a fundamental problem in diffusion MRI. It is well studied for the case of second-order tensor imaging (Gaussian diffusion). However, it has not been investigated for the wide range of non-Gaussian diffusion models. The optimal GES is the one that minimizes the variance of the estimated parameters. Such a GES can be realized by minimizing the condition number of the design matrix (K-optimal design). In this paper, we propose a new approach to solve the K-optimal GES design problem for fourth-order tensor-based diffusion profile imaging. The problem is a nonconvex experiment design problem. Using convex relaxation, we reformulate it as a tractable semidefinite programming problem. Solving this problem leads to several theoretical properties of K-optimal design: (i) the odd moments of the K-optimal design must be zero; (ii) the even moments of the K-optimal design are proportional to the total number of measurements; (iii) the K-optimal design is not unique, in general; and (iv) the proposed method can be used to compute the K-optimal design for an arbitrary number of measurements. Our Monte Carlo simulations support the theoretical results and show that, in comparison with existing designs, the K-optimal design leads to the minimum signal deviation.
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26
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Mueller BA, Lim KO, Hemmy L, Camchong J. Diffusion MRI and its Role in Neuropsychology. Neuropsychol Rev 2015; 25:250-71. [PMID: 26255305 PMCID: PMC4807614 DOI: 10.1007/s11065-015-9291-z] [Citation(s) in RCA: 26] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2015] [Accepted: 07/21/2015] [Indexed: 12/13/2022]
Abstract
Diffusion Magnetic Resonance Imaging (dMRI) is a popular method used by neuroscientists to uncover unique information about the structural connections within the brain. dMRI is a non-invasive imaging methodology in which image contrast is based on the diffusion of water molecules in tissue. While applicable to many tissues in the body, this review focuses exclusively on the use of dMRI to examine white matter in the brain. In this review, we begin with a definition of diffusion and how diffusion is measured with MRI. Next we introduce the diffusion tensor model, the predominant model used in dMRI. We then describe acquisition issues related to acquisition parameters and scanner hardware and software. Sources of artifacts are then discussed, followed by a brief review of analysis approaches. We provide an overview of the limitations of the traditional diffusion tensor model, and highlight several more sophisticated non-tensor models that better describe the complex architecture of the brain's white matter. We then touch on reliability and validity issues of diffusion measurements. Finally, we describe examples of ways in which dMRI has been applied to studies of brain disorders and how identified alterations relate to symptomatology and cognition.
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Winston GP. The potential role of novel diffusion imaging techniques in the understanding and treatment of epilepsy. Quant Imaging Med Surg 2015; 5:279-87. [PMID: 25853085 DOI: 10.3978/j.issn.2223-4292.2015.02.03] [Citation(s) in RCA: 19] [Impact Index Per Article: 2.1] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2015] [Accepted: 02/05/2015] [Indexed: 01/14/2023]
Abstract
Epilepsy is a common neurological disorder in which magnetic resonance imaging plays a key role. Diffusion imaging based on the molecular diffusion of water has been widely used clinically and in research for patients with epilepsy. Diffusion tensor imaging (DTI), the most common model, has been used for around two decades. Several parameters can be derived from DTI that are sensitive, but non-specific, to underlying structural changes. DTI assumes a single diffusion process following a Gaussian distribution within each voxel and is thus an overly simplistic representation of tissue microstructure. Several more advanced models of diffusion are now available that may have greater utility in the understanding of the effects of epilepsy on tissue microstructure. In this review, I summarise the principles, applications in epilepsy and future potential of three such techniques. Diffusion kurtosis imaging (DKI) characterises the degree to which diffusion deviates from Gaussian behaviour and gives an idea of the underlying tissue complexity. It has been used in both focal and generalised epilepsy and seems more sensitive than DTI. Multi-compartment models separate the signal from extra- and intra-axonal compartments in each voxel. The Composite Hindered and Restricted Model of Diffusion (CHARMED) can characterise axonal density but has not yet been applied in patients with epilepsy. The Neurite Orientation Dispersion and Density Imaging (NODDI) model can determine the intracellular volume fraction (ICVF) and degree of dispersion of neurite orientation. Preliminary data suggest it may more sensitive than conventional and diffusion imaging in localising focal epilepsy.
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Affiliation(s)
- Gavin P Winston
- 1 Epilepsy Society MRI Unit, Chesham Lane, Chalfont St Peter, Bucks SL9 0RJ, UK ; 2 Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, Queen Square, London, WC1N 3BG, UK
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De Santis S, Barazany D, Jones DK, Assaf Y. Resolving relaxometry and diffusion properties within the same voxel in the presence of crossing fibres by combining inversion recovery and diffusion-weighted acquisitions. Magn Reson Med 2015; 75:372-80. [PMID: 25735538 PMCID: PMC4737246 DOI: 10.1002/mrm.25644] [Citation(s) in RCA: 49] [Impact Index Per Article: 5.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/24/2014] [Revised: 11/18/2014] [Accepted: 12/10/2014] [Indexed: 12/13/2022]
Abstract
Purpose A comprehensive image‐based characterization of white matter should include the ability to quantify myelin and axonal attributes irrespective of the complexity of fibre organization within the voxel. While progress has been made with diffusion MRI‐based approaches to measure axonal morphology, to date available myelin metrics simply assign a single scalar value to the voxel, reflecting some form of average of its constituent fibres. Here, a new experimental framework that combines diffusion MRI and relaxometry is introduced. It provides, for the first time, the ability to assign to each unique fibre system within a voxel, a unique value of the longitudinal relaxation time, T1, which is largely influenced by the myelin content. Methods We demonstrate the method through simulations, in a crossing fibres phantom, in fixed brains and in vivo. Results The method is capable of recovering unique values of T1 for each fibre population. Conclusion The ability to extract fibre‐specific relaxometry properties will provide enhanced specificity and, therefore, sensitivity to differences in white matter architecture, which will be invaluable in many neuroimaging studies. Further the enhanced specificity should ultimately lead to earlier diagnosis and access to treatment in a range of white matter diseases where axons are affected. Magn Reson Med 75:372–380, 2016. © 2015 The Authors. Magnetic Resonance in Medicine Published by Wiley Periodicals, Inc. on behalf of International Society of Medicine in Resonance.
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Affiliation(s)
- Silvia De Santis
- CUBRIC, School of Psychology, Cardiff University, Cardiff, CF10 3AT, UK.,Department of Neurobiology, Faculty of Life Sciences, Tel Aviv University, Tel Aviv, 69978, Israel
| | - Daniel Barazany
- CUBRIC, School of Psychology, Cardiff University, Cardiff, CF10 3AT, UK.,Department of Neurobiology, Faculty of Life Sciences, Tel Aviv University, Tel Aviv, 69978, Israel
| | - Derek K Jones
- CUBRIC, School of Psychology, Cardiff University, Cardiff, CF10 3AT, UK.,Neuroscience & Mental Health Research Institute, Cardiff University, Cardiff, CF10 3AT, UK
| | - Yaniv Assaf
- Department of Neurobiology, Faculty of Life Sciences, Tel Aviv University, Tel Aviv, 69978, Israel
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Bracht T, Doidge AN, Keedwell PA, Jones DK. Hedonic tone is associated with left supero-lateral medial forebrain bundle microstructure. Psychol Med 2015; 45:865-874. [PMID: 25124530 PMCID: PMC4413785 DOI: 10.1017/s0033291714001949] [Citation(s) in RCA: 28] [Impact Index Per Article: 3.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/02/2013] [Revised: 07/17/2014] [Accepted: 07/17/2014] [Indexed: 12/28/2022]
Abstract
BACKGROUND The medial forebrain bundle (MFB) is an important pathway of the reward system. Two branches have been described using diffusion magnetic resonance imaging (MRI)-based tractography: the infero-medial MFB (imMFB) and the supero-lateral MFB (slMFB). Previous studies point to white-matter microstructural alterations of the slMFB in major depressive disorder (MDD) during acute episodes. To extend this finding, this study investigates whether white-matter microstructure is also altered in MDD patients that are in remission. Further, we explore associations between diffusion MRI-based metrics of white-matter microstructure of imMFB, slMFB and hedonic tone, the ability to derive pleasure. METHOD Eighteen remitted depressed (RD) and 22 never depressed (ND) participants underwent high angular resolution diffusion-weighted imaging (HARDI) scans. To reconstruct the two pathways of the MFB (imMFB and slMFB) we used the damped Richardson-Lucy (dRL) algorithm. Mean fractional anisotropy (FA) was sampled along the tracts. RESULTS Mean FA of imMFB, slMFB and a comparison tract (the middle cerebellar peduncle) did not differ between ND and RD participants. Hedonic capacity correlated negatively with mean FA of the left slMFB, explaining 21% of the variance. CONCLUSIONS Diffusion MRI-based metrics of white-matter microstructure of the MFB in RD do not differ from ND. Hedonic capacity is associated with altered white-matter microstructure of the slMFB.
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Affiliation(s)
- T. Bracht
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
- Neuroscience, Mental Health Research Institute, Cardiff University, Cardiff, UK
| | - A. N. Doidge
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
- Neuroscience, Mental Health Research Institute, Cardiff University, Cardiff, UK
| | - P. A. Keedwell
- Neuroscience, Mental Health Research Institute, Cardiff University, Cardiff, UK
| | - D. K. Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, UK
- Neuroscience, Mental Health Research Institute, Cardiff University, Cardiff, UK
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De Santis S, Drakesmith M, Bells S, Assaf Y, Jones DK. Why diffusion tensor MRI does well only some of the time: variance and covariance of white matter tissue microstructure attributes in the living human brain. Neuroimage 2013; 89:35-44. [PMID: 24342225 PMCID: PMC3988851 DOI: 10.1016/j.neuroimage.2013.12.003] [Citation(s) in RCA: 159] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/07/2013] [Revised: 12/01/2013] [Accepted: 12/03/2013] [Indexed: 01/07/2023] Open
Abstract
Fundamental to increasing our understanding of the role of white matter microstructure in normal/abnormal function in the living human is the development of MR-based metrics that provide increased specificity to distinct attributes of the white matter (e.g., local fibre architecture, axon morphology, and myelin content). In recent years, different approaches have been developed to enhance this specificity, and the Tractometry framework was introduced to combine the resulting multi-parametric data for a comprehensive assessment of white matter properties. The present work exploits that framework to characterise the statistical properties, specifically the variance and covariance, of these advanced microstructural indices across the major white matter pathways, with the aim of giving clear indications on the preferred metric(s) given the specific research question. A cohort of healthy subjects was scanned with a protocol that combined multi-component relaxometry with conventional and advanced diffusion MRI acquisitions to build the first comprehensive MRI atlas of white matter microstructure. The mean and standard deviation of the different metrics were analysed in order to understand how they vary across different brain regions/individuals and the correlation between them. Characterising the fibre architectural complexity (in terms of number of fibre populations in a voxel) provides clear insights into correlation/lack of correlation between the different metrics and explains why DT-MRI is a good model for white matter only some of the time. The study also identifies the metrics that account for the largest inter-subject variability and reports the minimal sample size required to detect differences in means, showing that, on the other hand, conventional DT-MRI indices might still be the safest choice in many contexts. We report an atlas of key white matter pathways in standard space. CHARMED provide more specific measures of axonal properties than DT-MRI metrics. Crossing fibres explain the correlation between myelin and diffusion indices. DT-MRI metrics need the smallest sample size to detect differences between groups.
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Affiliation(s)
- Silvia De Santis
- CUBRIC, School of Psychology, Cardiff University, Cardiff CF10 3AT, UK; Neuroscience & Mental Health Research Institute, Cardiff University, CF10 3AT, UK.
| | - Mark Drakesmith
- CUBRIC, School of Psychology, Cardiff University, Cardiff CF10 3AT, UK; Neuroscience & Mental Health Research Institute, Cardiff University, CF10 3AT, UK
| | - Sonya Bells
- CUBRIC, School of Psychology, Cardiff University, Cardiff CF10 3AT, UK; Neuroscience & Mental Health Research Institute, Cardiff University, CF10 3AT, UK
| | - Yaniv Assaf
- Department of Neurobiology, Faculty of Life Sciences, Tel Aviv University, Tel Aviv 69978 Israel
| | - Derek K Jones
- CUBRIC, School of Psychology, Cardiff University, Cardiff CF10 3AT, UK; Neuroscience & Mental Health Research Institute, Cardiff University, CF10 3AT, UK
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Li S, Wang B, Xu P, Lin Q, Gong G, Peng X, Fan Y, He Y, Huang R. Increased global and local efficiency of human brain anatomical networks detected with FLAIR-DTI compared to non-FLAIR-DTI. PLoS One 2013; 8:e71229. [PMID: 23967170 PMCID: PMC3742791 DOI: 10.1371/journal.pone.0071229] [Citation(s) in RCA: 7] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2012] [Accepted: 06/30/2013] [Indexed: 11/30/2022] Open
Abstract
Diffusion-weighted MRI (DW-MRI), the only non-invasive technique for probing human brain white matter structures in vivo, has been widely used in both fundamental studies and clinical applications. Many studies have utilized diffusion tensor imaging (DTI) and tractography approaches to explore the topological properties of human brain anatomical networks by using the single tensor model, the basic model to quantify DTI indices and tractography. However, the conventional DTI technique does not take into account contamination by the cerebrospinal fluid (CSF), which has been known to affect the estimated DTI measures and tractography in the single tensor model. Previous studies have shown that the Fluid-Attenuated Inversion Recovery (FLAIR) technique can suppress the contribution of the CSF to the DW-MRI signal. We acquired DTI datasets from twenty-two subjects using both FLAIR-DTI and conventional DTI (non-FLAIR-DTI) techniques, constructed brain anatomical networks using deterministic tractography, and compared the topological properties of the anatomical networks derived from the two types of DTI techniques. Although the brain anatomical networks derived from both types of DTI datasets showed small-world properties, we found that the brain anatomical networks derived from the FLAIR-DTI showed significantly increased global and local network efficiency compared with those derived from the conventional DTI. The increases in the network regional topological properties derived from the FLAIR-DTI technique were observed in CSF-filled regions, including the postcentral gyrus, periventricular regions, inferior frontal and temporal gyri, and regions in the visual cortex. Because brain anatomical networks derived from conventional DTI datasets with tractography have been widely used in many studies, our findings may have important implications for studying human brain anatomical networks derived from DW-MRI data and tractography.
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Affiliation(s)
- Shumei Li
- Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, South China Normal University, Guangzhou, P. R. China
| | - Bin Wang
- Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, South China Normal University, Guangzhou, P. R. China
| | - Pengfei Xu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, P. R. China
| | - Qixiang Lin
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, P. R. China
| | - Gaolang Gong
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, P. R. China
| | - Xiaoling Peng
- Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, South China Normal University, Guangzhou, P. R. China
| | - Yuanyuan Fan
- Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, South China Normal University, Guangzhou, P. R. China
| | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, P. R. China
| | - Ruiwang Huang
- Center for the Study of Applied Psychology, Key Laboratory of Mental Health and Cognitive Science of Guangdong Province, School of Psychology, South China Normal University, Guangzhou, P. R. China
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Koay CG, Özarslan E. Conceptual Foundations of Diffusion in Magnetic Resonance. CONCEPTS IN MAGNETIC RESONANCE. PART A, BRIDGING EDUCATION AND RESEARCH 2013; 42:116-129. [PMID: 26997923 PMCID: PMC4793283 DOI: 10.1002/cmr.a.21269] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/22/2023]
Abstract
A thorough review of the q-space technique is presented starting from a discussion of Fick's laws. The work presented here is primarily conceptual, theoretical and hopefully pedagogical. We offered the notion of molecular concentration to unify Fick's laws and diffusion MRI within a coherent conceptual framework. The fundamental relationship between diffusion MRI and the Fick's laws are carefully established. The conceptual and theoretical basis of the q-space technique is investigated from first principles.
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Affiliation(s)
- Cheng Guan Koay
- Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, WI 53705
| | - Evren Özarslan
- Department of Radiology, Brigham & Women's Hospital, Harvard Medical School, Boston, MA 02215
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Caruyer E, Lenglet C, Sapiro G, Deriche R. Design of multishell sampling schemes with uniform coverage in diffusion MRI. Magn Reson Med 2013; 69:1534-40. [PMID: 23625329 PMCID: PMC5381389 DOI: 10.1002/mrm.24736] [Citation(s) in RCA: 173] [Impact Index Per Article: 15.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/14/2012] [Revised: 02/20/2013] [Accepted: 02/20/2013] [Indexed: 11/11/2022]
Abstract
PURPOSE In diffusion MRI, a technique known as diffusion spectrum imaging reconstructs the propagator with a discrete Fourier transform, from a Cartesian sampling of the diffusion signal. Alternatively, it is possible to directly reconstruct the orientation distribution function in q-ball imaging, providing so-called high angular resolution diffusion imaging. In between these two techniques, acquisitions on several spheres in q-space offer an interesting trade-off between the angular resolution and the radial information gathered in diffusion MRI. A careful design is central in the success of multishell acquisition and reconstruction techniques. METHODS The design of acquisition in multishell is still an open and active field of research, however. In this work, we provide a general method to design multishell acquisition with uniform angular coverage. This method is based on a generalization of electrostatic repulsion to multishell. RESULTS We evaluate the impact of our method using simulations, on the angular resolution in one and two bundles of fiber configurations. Compared to more commonly used radial sampling, we show that our method improves the angular resolution, as well as fiber crossing discrimination. DISCUSSION We propose a novel method to design sampling schemes with optimal angular coverage and show the positive impact on angular resolution in diffusion MRI.
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Affiliation(s)
- Emmanuel Caruyer
- Athena Project-Team, Inria Sophia Antipolis-Méditerranée, Sophia Antipolis, France.
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