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Baxi M, Cetin-Karayumak S, Papadimitriou G, Makris N, van der Kouwe A, Jenkins B, Moore TL, Rosene DL, Kubicki M, Rathi Y. Investigating the contribution of cytoarchitecture to diffusion MRI measures in gray matter using histology. FRONTIERS IN NEUROIMAGING 2022; 1:947526. [PMID: 37555179 PMCID: PMC10406256 DOI: 10.3389/fnimg.2022.947526] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 05/18/2022] [Accepted: 07/19/2022] [Indexed: 08/10/2023]
Abstract
Postmortem studies are currently considered a gold standard for investigating brain structure at the cellular level. To investigate cellular changes in the context of human development, aging, or disease treatment, non-invasive in-vivo imaging methods such as diffusion MRI (dMRI) are needed. However, dMRI measures are only indirect measures and require validation in gray matter (GM) in the context of their sensitivity to the underlying cytoarchitecture, which has been lacking. Therefore, in this study we conducted direct comparisons between in-vivo dMRI measures and histology acquired from the same four rhesus monkeys. Average and heterogeneity of fractional anisotropy and trace from diffusion tensor imaging and mean squared displacement (MSD) and return-to-origin-probability from biexponential model were calculated in nine cytoarchitectonically different GM regions using dMRI data. DMRI measures were compared with corresponding histology measures of regional average and heterogeneity in cell area density. Results show that both average and heterogeneity in trace and MSD measures are sensitive to the underlying cytoarchitecture (cell area density) and capture different aspects of cell composition and organization. Trace and MSD thus would prove valuable as non-invasive imaging biomarkers in future studies investigating GM cytoarchitectural changes related to development and aging as well as abnormal cellular pathologies in clinical studies.
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Affiliation(s)
- Madhura Baxi
- Graduate Program for Neuroscience, Boston University, Boston, MA, United States
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Suheyla Cetin-Karayumak
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - George Papadimitriou
- Center for Morphometric Analysis, Massachusetts General Hospital, Charlestown, MA, United States
| | - Nikos Makris
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
| | - Andre van der Kouwe
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
| | - Bruce Jenkins
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
| | - Tara L. Moore
- Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA, United States
- Center for Systems Neuroscience, Boston, MA, United States
| | - Douglas L. Rosene
- Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA, United States
- Center for Systems Neuroscience, Boston, MA, United States
| | - Marek Kubicki
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
| | - Yogesh Rathi
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, United States
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States
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2
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Detecting microstructural white matter abnormalities of frontal pathways in children with ADHD using advanced diffusion models. Brain Imaging Behav 2021; 14:981-997. [PMID: 31041662 DOI: 10.1007/s11682-019-00108-5] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/26/2022]
Abstract
Studies using diffusion tensor imaging (DTI) have documented alterations in the attention and executive system in children and adolescents with attention-deficit/hyperactivity disorder (ADHD). While abnormalities in the frontal lobe have also been reported, the associated white matter fiber bundles have not been investigated comprehensively due to the complexity in tracing them through fiber crossings. Furthermore, most studies have used a non-specific DTI model to understand white matter abnormalities. We present results from a first study that uses a multi-shell diffusion MRI (dMRI) data set coupled with an advanced multi-fiber tractography algorithm to probe microstructural measures related to axonal/cellular density and volume of fronto-striato-thalamic pathways in children with ADHD (N = 30) and healthy controls (N = 28). Head motion was firstly examined as a priority in order to assure that no group difference existed. We investigated 45 different white matter fiber bundles in the brain. After correcting for multiple comparisons, we found lower axonal/cellular packing density and volume in ADHD children in 8 of the 45 fiber bundles, primarily in the right hemisphere as follows: 1) Superior longitudinal fasciculus-II (SLF-II) (right), 2) Thalamus to precentral gyrus (right), 3) Thalamus to superior-frontal gyrus (right), 4) Caudate to medial orbitofrontal gyrus (right), 5) Caudate to precentral gyrus (right), 6) Thalamus to paracentral gyrus (left), 7) Caudate to caudal middlefrontal gyrus (left), and 8) Cingulum (bilateral). Our results demonstrate reduced axonal/cellular density and volume in certain frontal lobe white matter fiber tracts, which sub-serve the attention function and executive control systems. Further, our work shows specific microstructural abnormalities in the striato-thalamo-cortical connections, which have not been previously reported in children with ADHD.
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Afzali M, Pieciak T, Newman S, Garyfallidis E, Özarslan E, Cheng H, Jones DK. The sensitivity of diffusion MRI to microstructural properties and experimental factors. J Neurosci Methods 2021; 347:108951. [PMID: 33017644 PMCID: PMC7762827 DOI: 10.1016/j.jneumeth.2020.108951] [Citation(s) in RCA: 38] [Impact Index Per Article: 12.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 08/27/2020] [Accepted: 09/15/2020] [Indexed: 12/13/2022]
Abstract
Diffusion MRI is a non-invasive technique to study brain microstructure. Differences in the microstructural properties of tissue, including size and anisotropy, can be represented in the signal if the appropriate method of acquisition is used. However, to depict the underlying properties, special care must be taken when designing the acquisition protocol as any changes in the procedure might impact on quantitative measurements. This work reviews state-of-the-art methods for studying brain microstructure using diffusion MRI and their sensitivity to microstructural differences and various experimental factors. Microstructural properties of the tissue at a micrometer scale can be linked to the diffusion signal at a millimeter-scale using modeling. In this paper, we first give an introduction to diffusion MRI and different encoding schemes. Then, signal representation-based methods and multi-compartment models are explained briefly. The sensitivity of the diffusion MRI signal to the microstructural components and the effects of curvedness of axonal trajectories on the diffusion signal are reviewed. Factors that impact on the quality (accuracy and precision) of derived metrics are then reviewed, including the impact of random noise, and variations in the acquisition parameters (i.e., number of sampled signals, b-value and number of acquisition shells). Finally, yet importantly, typical approaches to deal with experimental factors are depicted, including unbiased measures and harmonization. We conclude the review with some future directions and recommendations on this topic.
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Affiliation(s)
- Maryam Afzali
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom.
| | - Tomasz Pieciak
- AGH University of Science and Technology, Kraków, Poland; LPI, ETSI Telecomunicación, Universidad de Valladolid, Valladolid, Spain.
| | - Sharlene Newman
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA; Program of Neuroscience, Indiana University, Bloomington, IN 47405, USA.
| | - Eleftherios Garyfallidis
- Program of Neuroscience, Indiana University, Bloomington, IN 47405, USA; Department of Intelligent Systems Engineering, Indiana University, Bloomington, IN 47408, USA.
| | - Evren Özarslan
- Department of Biomedical Engineering, Linköping University, Linköping, Sweden; Center for Medical Image Science and Visualization, Linköping University, Linköping, Sweden.
| | - Hu Cheng
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA; Program of Neuroscience, Indiana University, Bloomington, IN 47405, USA.
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Cardiff, United Kingdom.
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4
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Baxi M, Di Biase MA, Lyall AE, Cetin-Karayumak S, Seitz J, Ning L, Makris N, Rosene D, Kubicki M, Rathi Y. Quantifying Genetic and Environmental Influence on Gray Matter Microstructure Using Diffusion MRI. Cereb Cortex 2020; 30:6191-6205. [PMID: 32676671 DOI: 10.1093/cercor/bhaa174] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Revised: 05/28/2020] [Accepted: 05/28/2020] [Indexed: 01/10/2023] Open
Abstract
Early neuroimaging work in twin studies focused on studying genetic and environmental influence on gray matter macrostructure. However, it is also important to understand how gray matter microstructure is influenced by genes and environment to facilitate future investigations of their influence in mental disorders. Advanced diffusion MRI (dMRI) measures allow more accurate assessment of gray matter microstructure compared with conventional diffusion tensor measures. To understand genetic and environmental influence on gray matter, we used diffusion and structural MRI data from a large twin and sibling study (N = 840) and computed advanced dMRI measures including return to origin probability (RTOP), which is heavily weighted toward intracellular and intra-axonal restricted spaces, and mean squared displacement (MSD), more heavily weighted to diffusion in extracellular space and large cell bodies in gray matter. We show that while macrostructural features like brain volume are mainly genetically influenced, RTOP and MSD can together tap into both genetic and environmental influence on microstructure.
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Affiliation(s)
- Madhura Baxi
- Graduate Program of Neuroscience, Boston University, Boston, MA 02118, USA.,Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02215, USA
| | - Maria A Di Biase
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02215, USA
| | - Amanda E Lyall
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02215, USA.,Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02114, USA
| | - Suheyla Cetin-Karayumak
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02215, USA
| | - Johanna Seitz
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02215, USA
| | - Lipeng Ning
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02215, USA
| | - Nikos Makris
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02215, USA.,Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02114, USA
| | - Douglas Rosene
- Department of Anatomy and Neurobiology, Boston University School of Medicine, Boston, MA 02118, USA
| | - Marek Kubicki
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02215, USA.,Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02114, USA
| | - Yogesh Rathi
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02215, USA.,Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02114, USA.,Laboratory of Mathematics in Imaging, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02215, USA
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Haehn D, Franke L, Zhang F, Cetin-Karayumak S, Pieper S, O'Donnell LJ, Rathi Y. TRAKO: Efficient Transmission of Tractography Data for Visualization. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2020; 12267:322-332. [PMID: 33135015 DOI: 10.1007/978-3-030-59728-3_32] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Fiber tracking produces large tractography datasets that are tens of gigabytes in size consisting of millions of streamlines. Such vast amounts of data require formats that allow for efficient storage, transfer, and visualization. We present TRAKO, a new data format based on the Graphics Layer Transmission Format (glTF) that enables immediate graphical and hardware-accelerated processing. We integrate a state-of-the-art compression technique for vertices, streamlines, and attached scalar and property data. We then compare TRAKO to existing tractography storage methods and provide a detailed evaluation on eight datasets. TRAKO can achieve data reductions of over 28x without loss of statistical significance when used to replicate analysis from previously published studies.
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Ning L, Gagoski B, Szczepankiewicz F, Westin CF, Rathi Y. Joint RElaxation-Diffusion Imaging Moments to Probe Neurite Microstructure. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:668-677. [PMID: 31398113 PMCID: PMC7164590 DOI: 10.1109/tmi.2019.2933982] [Citation(s) in RCA: 22] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/23/2023]
Abstract
Joint relaxation-diffusion measurements can provide new insight about the tissue microstructural properties. Most recent methods have focused on inverting the Laplace transform to recover the joint distribution of relaxation-diffusion. However, as is well-known, this problem is notoriously ill-posed and numerically unstable. In this work, we address this issue by directly computing the joint moments of transverse relaxation rate and diffusivity, which can be robustly estimated. To zoom into different parts of the joint distribution, we further enhance our method by applying multiplicative filters to the joint probability density function of relaxation and diffusion and compute the corresponding moments. We propose an approach to use these moments to compute several novel scalar indices to characterize specific properties of the underlying tissue microstructure. Furthermore, for the first time, we propose an algorithm to estimate diffusion signals that are independent of echo time based on the moments of the marginal probability density function of diffusion. We demonstrate its utility in extracting tissue information not contaminated with multiple intra-voxel relaxation rates. We compare the performance of four types of filters that zoom into tissue components with different relaxation and diffusion properties and demonstrate it on an in-vivo human dataset. Experimental results show that these filters are able to characterize heterogeneous tissue microstructure. Moreover, the filtered diffusion signals are also able to distinguish fiber bundles with similar orientations but different relaxation rates. The proposed method thus allows to characterize the neural microstructure information in a robust and unique manner not possible using existing techniques.
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7
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Zhang C, Arefin TM, Nakarmi U, Lee CH, Li H, Liang D, Zhang J, Ying L. Acceleration of three-dimensional diffusion magnetic resonance imaging using a kernel low-rank compressed sensing method. Neuroimage 2020; 210:116584. [PMID: 32004717 DOI: 10.1016/j.neuroimage.2020.116584] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2019] [Revised: 12/23/2019] [Accepted: 01/23/2020] [Indexed: 12/13/2022] Open
Abstract
Diffusion Magnetic Resonance Imaging (dMRI) has shown great potential in probing tissue microstructure and structural connectivity in the brain but is often limited by the lengthy scan time needed to sample the diffusion profile by acquiring multiple diffusion weighted images (DWIs). Although parallel imaging technique has improved the speed of dMRI acquisition, attaining high resolution three dimensional (3D) dMRI on preclinical MRI systems remained still time consuming. In this paper, kernel principal component analysis, a machine learning approach, was employed to estimate the correlation among DWIs. We demonstrated the feasibility of such correlation estimation from low-resolution training DWIs and used the correlation as a constraint to reconstruct high-resolution DWIs from highly under-sampled k-space data, which significantly reduced the scan time. Using full k-space 3D dMRI data of post-mortem mouse brains, we retrospectively compared the performance of the so-called kernel low rank (KLR) method with a conventional compressed sensing (CS) method in terms of image quality and ability to resolve complex fiber orientations and connectivity. The results demonstrated that the KLR-CS method outperformed the conventional CS method for acceleration factors up to 8 and was likely to enhance our ability to investigate brain microstructure and connectivity using high-resolution 3D dMRI.
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Affiliation(s)
- Chaoyi Zhang
- Electrical Engineering, University at Buffalo, State University of New York, Buffalo, NY, United States
| | - Tanzil Mahmud Arefin
- Radiology, New York University School of Medicine, New York City, NY, United States
| | - Ukash Nakarmi
- Radiology, Stanford University, Stanford, CA, United States
| | - Choong Heon Lee
- Radiology, New York University School of Medicine, New York City, NY, United States
| | - Hongyu Li
- Electrical Engineering, University at Buffalo, State University of New York, Buffalo, NY, United States
| | - Dong Liang
- Paul C. Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Shenzhen, People's Republic of China
| | - Jiangyang Zhang
- Radiology, New York University School of Medicine, New York City, NY, United States
| | - Leslie Ying
- Electrical Engineering, University at Buffalo, State University of New York, Buffalo, NY, United States; Biomedical Engineering, University at Buffalo, State University at New York, Buffalo, NY, United States.
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8
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Cetin Karayumak S, Bouix S, Ning L, James A, Crow T, Shenton M, Kubicki M, Rathi Y. Retrospective harmonization of multi-site diffusion MRI data acquired with different acquisition parameters. Neuroimage 2019; 184:180-200. [PMID: 30205206 PMCID: PMC6230479 DOI: 10.1016/j.neuroimage.2018.08.073] [Citation(s) in RCA: 89] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2018] [Revised: 08/17/2018] [Accepted: 08/29/2018] [Indexed: 01/17/2023] Open
Abstract
A joint and integrated analysis of multi-site diffusion MRI (dMRI) datasets can dramatically increase the statistical power of neuroimaging studies and enable comparative studies pertaining to several brain disorders. However, dMRI data sets acquired on multiple scanners cannot be naively pooled for joint analysis due to scanner specific nonlinear effects as well as differences in acquisition parameters. Consequently, for joint analysis, the dMRI data has to be harmonized, which involves removing scanner-specific differences from the raw dMRI signal. In this work, we propose a dMRI harmonization method that is capable of removing scanner-specific effects, while accounting for minor differences in acquisition parameters such as b-value, spatial resolution and number of gradient directions. We validate our algorithm on dMRI data acquired from two sites: Philadelphia Neurodevelopmental Cohort (PNC) with 800 healthy adolescents (ages 8-22 years) and Brigham and Women's Hospital (BWH) with 70 healthy subjects (ages 14-54 years). In particular, we show that gender and age-related maturation differences in different age groups are preserved after harmonization, as measured using effect sizes (small, medium and large), irrespective of the test sample size. Since we use matched control subjects from different scanners to estimate scanner-specific effects, our goal in this work is also to determine the minimum number of well-matched subjects needed from each site to achieve best harmonization results. Our results indicate that at-least 16 to 18 well-matched healthy controls from each site are needed to reliably capture scanner related differences. The proposed method can thus be used for retrospective harmonization of raw dMRI data across sites despite differences in acquisition parameters, while preserving inter-subject anatomical variability.
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Affiliation(s)
- Suheyla Cetin Karayumak
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital and Harvard Medical School, USA.
| | - Sylvain Bouix
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital and Harvard Medical School, USA
| | - Lipeng Ning
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital and Harvard Medical School, USA
| | - Anthony James
- Highfield Family and Adolescent Unit, Warneford Hospital, Oxford, UK
| | - Tim Crow
- Sane Powic, University Department of Psychiatry, Warneford Hospital, Oxford, UK
| | - Martha Shenton
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital and Harvard Medical School, USA; VA Boston Healthcare System, Brockton Division, Brockton, USA
| | - Marek Kubicki
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital and Harvard Medical School, USA
| | - Yogesh Rathi
- Psychiatry Neuroimaging Laboratory, Brigham and Women's Hospital and Harvard Medical School, USA
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Zhang F, Wu W, Ning L, McAnulty G, Waber D, Gagoski B, Sarill K, Hamoda HM, Song Y, Cai W, Rathi Y, O'Donnell LJ. Suprathreshold fiber cluster statistics: Leveraging white matter geometry to enhance tractography statistical analysis. Neuroimage 2018; 171:341-354. [PMID: 29337279 DOI: 10.1016/j.neuroimage.2018.01.006] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2017] [Revised: 01/04/2018] [Accepted: 01/05/2018] [Indexed: 12/13/2022] Open
Abstract
This work presents a suprathreshold fiber cluster (STFC) method that leverages the whole brain fiber geometry to enhance statistical group difference analyses. The proposed method consists of 1) a well-established study-specific data-driven tractography parcellation to obtain white matter tract parcels and 2) a newly proposed nonparametric, permutation-test-based STFC method to identify significant differences between study populations. The basic idea of our method is that a white matter parcel's neighborhood (nearby parcels with similar white matter anatomy) can support the parcel's statistical significance when correcting for multiple comparisons. We propose an adaptive parcel neighborhood strategy to allow suprathreshold fiber cluster formation that is robust to anatomically varying inter-parcel distances. The method is demonstrated by application to a multi-shell diffusion MRI dataset from 59 individuals, including 30 attention deficit hyperactivity disorder patients and 29 healthy controls. Evaluations are conducted using both synthetic and in-vivo data. The results indicate that the STFC method gives greater sensitivity in finding group differences in white matter tract parcels compared to several traditional multiple comparison correction methods.
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Affiliation(s)
- Fan Zhang
- Brigham and Women's Hospital, Harvard Medical School, Boston, USA.
| | - Weining Wu
- College of Computer Science and Technology, Harbin Engineering University, Harbin, China; Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Lipeng Ning
- Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Gloria McAnulty
- Boston Children's Hospital, Harvard Medical School, Boston, USA
| | - Deborah Waber
- Boston Children's Hospital, Harvard Medical School, Boston, USA
| | - Borjan Gagoski
- Boston Children's Hospital, Harvard Medical School, Boston, USA
| | - Kiera Sarill
- Boston Children's Hospital, Harvard Medical School, Boston, USA
| | - Hesham M Hamoda
- Department of Psychiatry, Boston Children's Hospital, Harvard Medical School, Boston, USA
| | - Yang Song
- School of Information Technologies, The University of Sydney, Sydney, Australia
| | - Weidong Cai
- School of Information Technologies, The University of Sydney, Sydney, Australia
| | - Yogesh Rathi
- Brigham and Women's Hospital, Harvard Medical School, Boston, USA
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Variability and anatomical specificity of the orbitofrontothalamic fibers of passage in the ventral capsule/ventral striatum (VC/VS): precision care for patient-specific tractography-guided targeting of deep brain stimulation (DBS) in obsessive compulsive disorder (OCD). Brain Imaging Behav 2017; 10:1054-1067. [PMID: 26518214 DOI: 10.1007/s11682-015-9462-9] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
Deep Brain Stimulation (DBS) is a neurosurgical procedure that can reduce symptoms in medically intractable obsessive-compulsive disorder (OCD). Conceptually, DBS of the ventral capsule/ventral striatum (VC/VS) region targets reciprocal excitatory connections between the orbitofrontal cortex (OFC) and thalamus, decreasing abnormal reverberant activity within the OFC-caudate-pallidal-thalamic circuit. In this study, we investigated these connections using diffusion magnetic resonance imaging (dMRI) on human connectome datasets of twenty-nine healthy young-adult volunteers with two-tensor unscented Kalman filter based tractography. We studied the morphology of the lateral and medial orbitofrontothalamic connections and estimated their topographic variability within the VC/VS region. Our results showed that the morphology of the individual orbitofrontothalamic fibers of passage in the VC/VS region is complex and inter-individual variability in their topography is high. We applied this method to an example OCD patient case who underwent DBS surgery, formulating an initial proof of concept for a tractography-guided patient-specific approach in DBS for medically intractable OCD. This may improve on current surgical practice, which involves implanting all patients at identical stereotactic coordinates within the VC/VS region.
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11
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Liao R, Ning L, Chen Z, Rigolo L, Gong S, Pasternak O, Golby AJ, Rathi Y, O'Donnell LJ. Performance of unscented Kalman filter tractography in edema: Analysis of the two-tensor model. NEUROIMAGE-CLINICAL 2017; 15:819-831. [PMID: 28725549 PMCID: PMC5506885 DOI: 10.1016/j.nicl.2017.06.027] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/10/2016] [Revised: 06/01/2017] [Accepted: 06/19/2017] [Indexed: 11/30/2022]
Abstract
Diffusion MRI tractography is increasingly used in pre-operative neurosurgical planning to visualize critical fiber tracts. However, a major challenge for conventional tractography, especially in patients with brain tumors, is tracing fiber tracts that are affected by vasogenic edema, which increases water content in the tissue and lowers diffusion anisotropy. One strategy for improving fiber tracking is to use a tractography method that is more sensitive than the traditional single-tensor streamline tractography. We performed experiments to assess the performance of two-tensor unscented Kalman filter (UKF) tractography in edema. UKF tractography fits a diffusion model to the data during fiber tracking, taking advantage of prior information from the previous step along the fiber. We studied UKF performance in a synthetic diffusion MRI digital phantom with simulated edema and in retrospective data from two neurosurgical patients with edema affecting the arcuate fasciculus and corticospinal tracts. We compared the performance of several tractography methods including traditional streamline, UKF single-tensor, and UKF two-tensor. To provide practical guidance on how the UKF method could be employed, we evaluated the impact of using various seed regions both inside and outside the edematous regions, as well as the impact of parameter settings on the tractography sensitivity. We quantified the sensitivity of different methods by measuring the percentage of the patient-specific fMRI activation that was reached by the tractography. We expected that diffusion anisotropy threshold parameters, as well as the inclusion of a free water model, would significantly influence the reconstruction of edematous WM fiber tracts, because edema increases water content in the tissue and lowers anisotropy. Contrary to our initial expectations, varying the fractional anisotropy threshold and including a free water model did not affect the UKF two-tensor tractography output appreciably in these two patient datasets. The most effective parameter for increasing tracking sensitivity was the generalized anisotropy (GA) threshold, which increased the length of tracked fibers when reduced to 0.075. In addition, the most effective seeding strategy was seeding in the whole brain or in a large region outside of the edema. Overall, the main contribution of this study is to provide insight into how UKF tractography can work, using a two-tensor model, to begin to address the challenge of fiber tract reconstruction in edematous regions near brain tumors. Reconstruction of edematous white matter from diffusion MRI is investigated. The performance of two–tensor unscented Kalman filter (UKF) tractography is assessed. The two–tensor model in UKF is analyzed in phantom and patient data experiments. Practical guidance on employing the UKF method in neurosurgical patients is provided
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Affiliation(s)
- Ruizhi Liao
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Lipeng Ning
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Zhenrui Chen
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Laura Rigolo
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Shun Gong
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA; Shanghai Changzheng Hospital, Shanghai, China
| | - Ofer Pasternak
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Alexandra J Golby
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Yogesh Rathi
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
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12
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Ning L, Setsompop K, Michailovich O, Makris N, Shenton ME, Westin CF, Rathi Y. A joint compressed-sensing and super-resolution approach for very high-resolution diffusion imaging. Neuroimage 2016; 125:386-400. [PMID: 26505296 PMCID: PMC4691422 DOI: 10.1016/j.neuroimage.2015.10.061] [Citation(s) in RCA: 45] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2015] [Revised: 09/11/2015] [Accepted: 10/20/2015] [Indexed: 11/24/2022] Open
Abstract
Diffusion MRI (dMRI) can provide invaluable information about the structure of different tissue types in the brain. Standard dMRI acquisitions facilitate a proper analysis (e.g. tracing) of medium-to-large white matter bundles. However, smaller fiber bundles connecting very small cortical or sub-cortical regions cannot be traced accurately in images with large voxel sizes. Yet, the ability to trace such fiber bundles is critical for several applications such as deep brain stimulation and neurosurgery. In this work, we propose a novel acquisition and reconstruction scheme for obtaining high spatial resolution dMRI images using multiple low resolution (LR) images, which is effective in reducing acquisition time while improving the signal-to-noise ratio (SNR). The proposed method called compressed-sensing super resolution reconstruction (CS-SRR), uses multiple overlapping thick-slice dMRI volumes that are under-sampled in q-space to reconstruct diffusion signal with complex orientations. The proposed method combines the twin concepts of compressed sensing and super-resolution to model the diffusion signal (at a given b-value) in a basis of spherical ridgelets with total-variation (TV) regularization to account for signal correlation in neighboring voxels. A computationally efficient algorithm based on the alternating direction method of multipliers (ADMM) is introduced for solving the CS-SRR problem. The performance of the proposed method is quantitatively evaluated on several in-vivo human data sets including a true SRR scenario. Our experimental results demonstrate that the proposed method can be used for reconstructing sub-millimeter super resolution dMRI data with very good data fidelity in clinically feasible acquisition time.
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Affiliation(s)
- Lipeng Ning
- Brigham and Women's Hospital, Harvard Medical School, Boston, USA.
| | - Kawin Setsompop
- Massachusetts General Hospital, Harvard Medical School, Boston, USA
| | | | - Nikos Makris
- Massachusetts General Hospital, Harvard Medical School, Boston, USA
| | - Martha E Shenton
- Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | | | - Yogesh Rathi
- Brigham and Women's Hospital, Harvard Medical School, Boston, USA
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Koerte IK, Lin AP, Willems A, Muehlmann M, Hufschmidt J, Coleman MJ, Green I, Liao H, Tate DF, Wilde EA, Pasternak O, Bouix S, Rathi Y, Bigler ED, Stern RA, Shenton ME. A review of neuroimaging findings in repetitive brain trauma. Brain Pathol 2015; 25:318-49. [PMID: 25904047 PMCID: PMC5699448 DOI: 10.1111/bpa.12249] [Citation(s) in RCA: 82] [Impact Index Per Article: 9.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2015] [Accepted: 02/05/2015] [Indexed: 12/14/2022] Open
Abstract
Chronic traumatic encephalopathy (CTE) is a neurodegenerative disease confirmed at postmortem. Those at highest risk are professional athletes who participate in contact sports and military personnel who are exposed to repetitive blast events. All neuropathologically confirmed CTE cases, to date, have had a history of repetitive head impacts. This suggests that repetitive head impacts may be necessary for the initiation of the pathogenetic cascade that, in some cases, leads to CTE. Importantly, while all CTE appears to result from repetitive brain trauma, not all repetitive brain trauma results in CTE. Magnetic resonance imaging has great potential for understanding better the underlying mechanisms of repetitive brain trauma. In this review, we provide an overview of advanced imaging techniques currently used to investigate brain anomalies. We also provide an overview of neuroimaging findings in those exposed to repetitive head impacts in the acute/subacute and chronic phase of injury and in more neurodegenerative phases of injury, as well as in military personnel exposed to repetitive head impacts. Finally, we discuss future directions for research that will likely lead to a better understanding of the underlying mechanisms separating those who recover from repetitive brain trauma vs. those who go on to develop CTE.
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Affiliation(s)
- Inga K. Koerte
- Psychiatry Neuroimaging LaboratoryDepartments of Psychiatry and RadiologyBrigham and Women's Hospital and Harvard Medical SchoolBostonMA
- Department of Child and Adolescent PsychiatryPsychosomatic and PsychotherapyDr. von Hauner Children's HospitalLudwig‐Maximilian UniversityMunichGermany
| | - Alexander P. Lin
- Psychiatry Neuroimaging LaboratoryDepartments of Psychiatry and RadiologyBrigham and Women's Hospital and Harvard Medical SchoolBostonMA
- Center for Clinical SpectroscopyDepartment of RadiologyBrigham and Women's Hospital and Harvard Medical SchoolBostonMA
| | - Anna Willems
- Psychiatry Neuroimaging LaboratoryDepartments of Psychiatry and RadiologyBrigham and Women's Hospital and Harvard Medical SchoolBostonMA
- Department of Child and Adolescent PsychiatryPsychosomatic and PsychotherapyDr. von Hauner Children's HospitalLudwig‐Maximilian UniversityMunichGermany
| | - Marc Muehlmann
- Psychiatry Neuroimaging LaboratoryDepartments of Psychiatry and RadiologyBrigham and Women's Hospital and Harvard Medical SchoolBostonMA
- Department of Child and Adolescent PsychiatryPsychosomatic and PsychotherapyDr. von Hauner Children's HospitalLudwig‐Maximilian UniversityMunichGermany
| | - Jakob Hufschmidt
- Psychiatry Neuroimaging LaboratoryDepartments of Psychiatry and RadiologyBrigham and Women's Hospital and Harvard Medical SchoolBostonMA
- Department of Pediatric NeurologyDr. von Hauner Children's HospitalLudwig‐Maximilian UniversityMunichGermany
| | - Michael J. Coleman
- Psychiatry Neuroimaging LaboratoryDepartments of Psychiatry and RadiologyBrigham and Women's Hospital and Harvard Medical SchoolBostonMA
| | - Isobel Green
- Psychiatry Neuroimaging LaboratoryDepartments of Psychiatry and RadiologyBrigham and Women's Hospital and Harvard Medical SchoolBostonMA
| | - Huijun Liao
- Center for Clinical SpectroscopyDepartment of RadiologyBrigham and Women's Hospital and Harvard Medical SchoolBostonMA
| | - David F. Tate
- General Dynamic Information Technologies ContractorDefense and Veterans Brain Injury CentersSan Antonio Military Medical CenterSan AntonioTX
| | - Elisabeth A. Wilde
- Departments of Physical Medicine and RehabilitationNeurology and RadiologyBaylor College of MedicineSan AntonioTX
- Michael E. DeBakey VA Medical CenterSan AntonioTX
| | - Ofer Pasternak
- Psychiatry Neuroimaging LaboratoryDepartments of Psychiatry and RadiologyBrigham and Women's Hospital and Harvard Medical SchoolBostonMA
| | - Sylvain Bouix
- Psychiatry Neuroimaging LaboratoryDepartments of Psychiatry and RadiologyBrigham and Women's Hospital and Harvard Medical SchoolBostonMA
| | - Yogesh Rathi
- Psychiatry Neuroimaging LaboratoryDepartments of Psychiatry and RadiologyBrigham and Women's Hospital and Harvard Medical SchoolBostonMA
| | - Erin D. Bigler
- Neuroscience Center and Department of PsychologyBrigham Young UniversityProvoUT
| | - Robert A. Stern
- Departments of Neurology, Neurosurgery, and Anatomy and Neurobiology, Boston University Alzheimer's Disease CenterBoston University School of MedicineBostonMA
| | - Martha E. Shenton
- Psychiatry Neuroimaging LaboratoryDepartments of Psychiatry and RadiologyBrigham and Women's Hospital and Harvard Medical SchoolBostonMA
- VA Boston Healthcare SystemBostonMA
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Ning L, Setsompop K, Michailovich O, Makris N, Westin CF, Rathi Y. A Compressed-Sensing Approach for Super-Resolution Reconstruction of Diffusion MRI. INFORMATION PROCESSING IN MEDICAL IMAGING : PROCEEDINGS OF THE ... CONFERENCE 2015; 24. [PMID: 26221667 PMCID: PMC4578654 DOI: 10.1007/978-3-319-19992-4_5] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
We present an innovative framework for reconstructing high-spatial-resolution diffusion magnetic resonance imaging (dMRI) from multiple low-resolution (LR) images. Our approach combines the twin concepts of compressed sensing (CS) and classical super-resolution to reduce acquisition time while increasing spatial resolution. We use subpixel-shifted LR images with down-sampled and non-overlapping diffusion directions to reduce acquisition time. The diffusion signal in the high resolution (HR) image is represented in a sparsifying basis of spherical ridgelets to model complex fiber orientations with reduced number of measurements. The HR image is obtained as the solution of a convex optimization problem which can be solved using the proposed algorithm based on the alternating direction method of multipliers (ADMM). We qualitatively and quantitatively evaluate the performance of our method on two sets of in-vivo human brain data and show its effectiveness in accurately recovering very high resolution diffusion images.
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