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Calixto C, Soldatelli MD, Jaimes C, Warfield SK, Gholipour A, Karimi D. A detailed spatio-temporal atlas of the white matter tracts for the fetal brain. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.26.590815. [PMID: 38712296 PMCID: PMC11071632 DOI: 10.1101/2024.04.26.590815] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/08/2024]
Abstract
This study presents the construction of a comprehensive spatiotemporal atlas detailing the development of white matter tracts in the fetal brain using diffusion magnetic resonance imaging (dMRI). Our research leverages data collected from fetal MRI scans conducted between 22 and 37 weeks of gestation, capturing the dynamic changes in the brain's microstructure during this critical period. The atlas includes 60 distinct white matter tracts, including commissural, projection, and association fibers. We employed advanced fetal dMRI processing techniques and tractography to map and characterize the developmental trajectories of these tracts. Our findings reveal that the development of these tracts is characterized by complex patterns of fractional anisotropy (FA) and mean diffusivity (MD), reflecting key neurodevelopmental processes such as axonal growth, involution of the radial-glial scaffolding, and synaptic pruning. This atlas can serve as a useful resource for neuroscience research and clinical practice, improving our understanding of the fetal brain and potentially aiding in the early diagnosis of neurodevelopmental disorders. By detailing the normal progression of white matter tract development, the atlas can be used as a benchmark for identifying deviations that may indicate neurological anomalies or predispositions to disorders.
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He J, Zhang F, Pan Y, Feng Y, Rushmore J, Torio E, Rathi Y, Makris N, Kikinis R, Golby AJ, O'Donnell LJ. Reconstructing the somatotopic organization of the corticospinal tract remains a challenge for modern tractography methods. Hum Brain Mapp 2023; 44:6055-6073. [PMID: 37792280 PMCID: PMC10619402 DOI: 10.1002/hbm.26497] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/07/2023] [Revised: 09/09/2023] [Accepted: 09/13/2023] [Indexed: 10/05/2023] Open
Abstract
The corticospinal tract (CST) is a critically important white matter fiber tract in the human brain that enables control of voluntary movements of the body. The CST exhibits a somatotopic organization, which means that the motor neurons that control specific body parts are arranged in order within the CST. Diffusion magnetic resonance imaging (MRI) tractography is increasingly used to study the anatomy of the CST. However, despite many advances in tractography algorithms over the past decade, modern, state-of-the-art methods still face challenges. In this study, we compare the performance of six widely used tractography methods for reconstructing the CST and its somatotopic organization. These methods include constrained spherical deconvolution (CSD) based probabilistic (iFOD1) and deterministic (SD-Stream) methods, unscented Kalman filter (UKF) tractography methods including multi-fiber (UKF2T) and single-fiber (UKF1T) models, the generalized q-sampling imaging (GQI) based deterministic tractography method, and the TractSeg method. We investigate CST somatotopy by dividing the CST into four subdivisions per hemisphere that originate in the leg, trunk, hand, and face areas of the primary motor cortex. A quantitative and visual comparison is performed using diffusion MRI data (N = 100 subjects) from the Human Connectome Project. Quantitative evaluations include the reconstruction rate of the eight anatomical subdivisions, the percentage of streamlines in each subdivision, and the coverage of the white matter-gray matter (WM-GM) interface. CST somatotopy is further evaluated by comparing the percentage of streamlines in each subdivision to the cortical volumes for the leg, trunk, hand, and face areas. Overall, UKF2T has the highest reconstruction rate and cortical coverage. It is the only method with a significant positive correlation between the percentage of streamlines in each subdivision and the volume of the corresponding motor cortex. However, our experimental results show that all compared tractography methods are biased toward generating many trunk streamlines (ranging from 35.10% to 71.66% of total streamlines across methods). Furthermore, the coverage of the WM-GM interface in the largest motor area (face) is generally low (under 40%) for all compared tractography methods. Different tractography methods give conflicting results regarding the percentage of streamlines in each subdivision and the volume of the corresponding motor cortex, indicating that there is generally no clear relationship, and that reconstruction of CST somatotopy is still a large challenge. Overall, we conclude that while current tractography methods have made progress toward the well-known challenge of improving the reconstruction of the lateral projections of the CST, the overall problem of performing a comprehensive CST reconstruction, including clinically important projections in the lateral (hand and face areas) and medial portions (leg area), remains an important challenge for diffusion MRI tractography.
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Affiliation(s)
- Jianzhong He
- Institution of Information Processing and AutomationZhejiang University of TechnologyHangzhouChina
| | - Fan Zhang
- Department of Radiology, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
- University of Electronic Science and Technology of ChinaChengduSichuanChina
| | - Yiang Pan
- Institution of Information Processing and AutomationZhejiang University of TechnologyHangzhouChina
| | - Yuanjing Feng
- Institution of Information Processing and AutomationZhejiang University of TechnologyHangzhouChina
| | - Jarrett Rushmore
- Departments of Psychiatry, Neurology and RadiologyMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Department of Anatomy and NeurobiologyBoston University School of MedicineBostonMassachusettsUSA
| | - Erickson Torio
- Department of NeurosurgeryBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Yogesh Rathi
- Department of Radiology, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
- Department of PsychiatryBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Nikos Makris
- Departments of Psychiatry, Neurology and RadiologyMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Department of PsychiatryBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Ron Kikinis
- Department of Radiology, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Alexandra J. Golby
- Department of Radiology, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
- Department of NeurosurgeryBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Lauren J. O'Donnell
- Department of Radiology, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
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Chen Y, Zhang C, Xue T, Song Y, Makris N, Rathi Y, Cai W, Zhang F, O'Donnell LJ. Deep fiber clustering: Anatomically informed fiber clustering with self-supervised deep learning for fast and effective tractography parcellation. Neuroimage 2023; 273:120086. [PMID: 37019346 PMCID: PMC10958986 DOI: 10.1016/j.neuroimage.2023.120086] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 04/02/2023] [Indexed: 04/05/2023] Open
Abstract
White matter fiber clustering is an important strategy for white matter parcellation, which enables quantitative analysis of brain connections in health and disease. In combination with expert neuroanatomical labeling, data-driven white matter fiber clustering is a powerful tool for creating atlases that can model white matter anatomy across individuals. While widely used fiber clustering approaches have shown good performance using classical unsupervised machine learning techniques, recent advances in deep learning reveal a promising direction toward fast and effective fiber clustering. In this work, we propose a novel deep learning framework for white matter fiber clustering, Deep Fiber Clustering (DFC), which solves the unsupervised clustering problem as a self-supervised learning task with a domain-specific pretext task to predict pairwise fiber distances. This process learns a high-dimensional embedding feature representation for each fiber, regardless of the order of fiber points reconstructed during tractography. We design a novel network architecture that represents input fibers as point clouds and allows the incorporation of additional sources of input information from gray matter parcellation. Thus, DFC makes use of combined information about white matter fiber geometry and gray matter anatomy to improve the anatomical coherence of fiber clusters. In addition, DFC conducts outlier removal naturally by rejecting fibers with low cluster assignment probability. We evaluate DFC on three independently acquired cohorts, including data from 220 individuals across genders, ages (young and elderly adults), and different health conditions (healthy control and multiple neuropsychiatric disorders). We compare DFC to several state-of-the-art white matter fiber clustering algorithms. Experimental results demonstrate superior performance of DFC in terms of cluster compactness, generalization ability, anatomical coherence, and computational efficiency.
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Affiliation(s)
- Yuqian Chen
- Harvard Medical School, MA, USA; The University of Sydney, NSW, Australia
| | | | - Tengfei Xue
- Harvard Medical School, MA, USA; The University of Sydney, NSW, Australia
| | - Yang Song
- The University of New South Wales, NSW, Australia
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Ahtam B, Solti M, Doo JM, Feldman HA, Vyas R, Zhang F, O'Donnell LJ, Rathi Y, Smith ER, Orbach D, See AP, Grant PE, Lehman LL. Diffusion-Weighted Magnetic Resonance Imaging Demonstrates White Matter Alterations in Watershed Regions in Children With Moyamoya Without Stroke or Silent Infarct. Pediatr Neurol 2023; 143:89-94. [PMID: 37054515 DOI: 10.1016/j.pediatrneurol.2023.03.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Revised: 02/25/2023] [Accepted: 03/12/2023] [Indexed: 04/15/2023]
Abstract
BACKGROUND Moyamoya is a disease with progressive cerebral arterial stenosis leading to stroke and silent infarct. Diffusion-weighted magnetic resonance imaging (dMRI) studies show that adults with moyamoya have significantly lower fractional anisotropy (FA) and higher mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD) compared with controls, which raises concern for unrecognized white matter injury. Children with moyamoya have significantly lower FA and higher MD in their white matter compared with controls. However, it is unknown which white matter tracts are affected in children with moyamoya. METHODS We present a cohort of 15 children with moyamoya with 24 affected hemispheres without stroke or silent infarct compared with 25 controls. We analyzed dMRI data using unscented Kalman filter tractography and extracted major white matter pathways with a fiber clustering method. We compared the FA, MD, AD, and RD in each segmented white matter tract and combined white matter tracts found within the watershed region using analysis of variance. RESULTS Age and sex were not significantly different between children with moyamoya and controls. Specific white matter tracts affected included inferior fronto-occipital fasciculus, inferior longitudinal fasciculus, superior longitudinal fasciculus, thalamofrontal, uncinate fasciculus, and arcuate fasciculus. Combined watershed region white matter tracts in children with moyamoya had significantly lower FA (-7.7% ± 3.2%, P = 0.02) and higher MD (4.8% ± 1.9%, P = 0.01) and RD (8.7% ± 2.8%, P = 0.002). CONCLUSIONS Lower FA with higher MD and RD is concerning for unrecognized white matter injury. Affected tracts were located in watershed regions suggesting that the findings may be due to chronic hypoperfusion. These findings support the concern that children with moyamoya without overt stroke or silent infarction are sustaining ongoing injury to their white matter microstructure and provide practitioners with a noninvasive method of more accurately assessing disease burden in children with moyamoya.
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Affiliation(s)
- Banu Ahtam
- Division of Newborn Medicine, Department of Pediatrics, Boston Children's Hospital, Boston, Massachusetts; Harvard Medical School, Boston, Massachusetts
| | - Marina Solti
- Division of Newborn Medicine, Department of Pediatrics, Boston Children's Hospital, Boston, Massachusetts
| | - Justin M Doo
- Division of Newborn Medicine, Department of Pediatrics, Boston Children's Hospital, Boston, Massachusetts
| | - Henry A Feldman
- Division of Newborn Medicine, Department of Pediatrics, Boston Children's Hospital, Boston, Massachusetts; Harvard Medical School, Boston, Massachusetts
| | - Rutvi Vyas
- Division of Newborn Medicine, Department of Pediatrics, Boston Children's Hospital, Boston, Massachusetts
| | - Fan Zhang
- Harvard Medical School, Boston, Massachusetts; Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts
| | - Lauren J O'Donnell
- Harvard Medical School, Boston, Massachusetts; Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts
| | - Yogesh Rathi
- Harvard Medical School, Boston, Massachusetts; Department of Radiology, Brigham and Women's Hospital, Boston, Massachusetts
| | - Edward R Smith
- Harvard Medical School, Boston, Massachusetts; Department of Neurosurgery, Boston Children's Hospital, Boston, Massachusetts
| | - Darren Orbach
- Harvard Medical School, Boston, Massachusetts; Department of Radiology, Boston Children's Hospital, Boston, Massachusetts
| | - Alfred P See
- Harvard Medical School, Boston, Massachusetts; Department of Neurosurgery, Boston Children's Hospital, Boston, Massachusetts; Department of Radiology, Boston Children's Hospital, Boston, Massachusetts
| | - P Ellen Grant
- Division of Newborn Medicine, Department of Pediatrics, Boston Children's Hospital, Boston, Massachusetts; Harvard Medical School, Boston, Massachusetts
| | - Laura L Lehman
- Harvard Medical School, Boston, Massachusetts; Department of Neurology, Boston Children's Hospital, Boston, Massachusetts.
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Rojczyk P, Seitz-Holland J, Kaufmann E, Sydnor VJ, Kim CL, Umminger LF, Wiegand TLT, Guenette JP, Zhang F, Rathi Y, Bouix S, Pasternak O, Fortier CB, Salat D, Hinds SR, Heinen F, O’Donnell LJ, Milberg WP, McGlinchey RE, Shenton ME, Koerte IK. Sleep Quality Disturbances Are Associated with White Matter Alterations in Veterans with Post-Traumatic Stress Disorder and Mild Traumatic Brain Injury. J Clin Med 2023; 12:2079. [PMID: 36902865 PMCID: PMC10004675 DOI: 10.3390/jcm12052079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2023] [Revised: 03/02/2023] [Accepted: 03/03/2023] [Indexed: 03/09/2023] Open
Abstract
Sleep disturbances are strongly associated with mild traumatic brain injury (mTBI) and post-traumatic stress disorder (PTSD). PTSD and mTBI have been linked to alterations in white matter (WM) microstructure, but whether poor sleep quality has a compounding effect on WM remains largely unknown. We evaluated sleep and diffusion magnetic resonance imaging (dMRI) data from 180 male post-9/11 veterans diagnosed with (1) PTSD (n = 38), (2) mTBI (n = 25), (3) comorbid PTSD+mTBI (n = 94), and (4) a control group with neither PTSD nor mTBI (n = 23). We compared sleep quality (Pittsburgh Sleep Quality Index, PSQI) between groups using ANCOVAs and calculated regression and mediation models to assess associations between PTSD, mTBI, sleep quality, and WM. Veterans with PTSD and comorbid PTSD+mTBI reported poorer sleep quality than those with mTBI or no history of PTSD or mTBI (p = 0.012 to <0.001). Poor sleep quality was associated with abnormal WM microstructure in veterans with comorbid PTSD+mTBI (p < 0.001). Most importantly, poor sleep quality fully mediated the association between greater PTSD symptom severity and impaired WM microstructure (p < 0.001). Our findings highlight the significant impact of sleep disturbances on brain health in veterans with PTSD+mTBI, calling for sleep-targeted interventions.
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Affiliation(s)
- Philine Rojczyk
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02145, USA
- cBRAIN, Department of Child and Adolescent Psychiatry, Psychosomatics, and Psychotherapy, Ludwig-Maximilians-University, 80336 Munich, Germany
| | - Johanna Seitz-Holland
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02145, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
| | - Elisabeth Kaufmann
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02145, USA
- cBRAIN, Department of Child and Adolescent Psychiatry, Psychosomatics, and Psychotherapy, Ludwig-Maximilians-University, 80336 Munich, Germany
- Department of Neurology, Ludwig-Maximilians-University, 81377 Munich, Germany
| | - Valerie J. Sydnor
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02145, USA
| | - Cara L. Kim
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02145, USA
- cBRAIN, Department of Child and Adolescent Psychiatry, Psychosomatics, and Psychotherapy, Ludwig-Maximilians-University, 80336 Munich, Germany
| | - Lisa F. Umminger
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02145, USA
- cBRAIN, Department of Child and Adolescent Psychiatry, Psychosomatics, and Psychotherapy, Ludwig-Maximilians-University, 80336 Munich, Germany
| | - Tim L. T. Wiegand
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02145, USA
- cBRAIN, Department of Child and Adolescent Psychiatry, Psychosomatics, and Psychotherapy, Ludwig-Maximilians-University, 80336 Munich, Germany
| | - Jeffrey P. Guenette
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02145, USA
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Fan Zhang
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Yogesh Rathi
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02145, USA
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Sylvain Bouix
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02145, USA
- Department of Software Engineering and IT, École de Technologie Supérieure, Montreal, QC H3C 1K3, Canada
| | - Ofer Pasternak
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02145, USA
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Catherine B. Fortier
- Translational Research Center for TBI and Stress Disorders (TRACTS) and Geriatric Research, Education and Clinical Center (GRECC), VA Boston Healthcare System, Boston, MA 02130, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA 02215, USA
| | - David Salat
- Translational Research Center for TBI and Stress Disorders (TRACTS) and Geriatric Research, Education and Clinical Center (GRECC), VA Boston Healthcare System, Boston, MA 02130, USA
- Neuroimaging Research for Veterans (NeRVe) Center, VA Boston Healthcare System, Boston, 02115 MA, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Department of Radiology, Boston, MA 02129, USA
| | - Sidney R. Hinds
- Department of Neurology, Uniformed Services University, Bethesda, MD 20814, USA
| | - Florian Heinen
- Department of Pediatric Neurology and Developmental Medicine and LMU Center for Children with Medical Complexity, Dr. von Hauner Children’s Hospital, Ludwig-Maximilians-University, 80337 Munich, Germany
| | - Lauren J. O’Donnell
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - William P. Milberg
- Translational Research Center for TBI and Stress Disorders (TRACTS) and Geriatric Research, Education and Clinical Center (GRECC), VA Boston Healthcare System, Boston, MA 02130, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA 02215, USA
- Neuroimaging Research for Veterans (NeRVe) Center, VA Boston Healthcare System, Boston, 02115 MA, USA
| | - Regina E. McGlinchey
- Translational Research Center for TBI and Stress Disorders (TRACTS) and Geriatric Research, Education and Clinical Center (GRECC), VA Boston Healthcare System, Boston, MA 02130, USA
- Department of Psychiatry, Harvard Medical School, Boston, MA 02215, USA
- Neuroimaging Research for Veterans (NeRVe) Center, VA Boston Healthcare System, Boston, 02115 MA, USA
| | - Martha E. Shenton
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02145, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
- Department of Radiology, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA
| | - Inga K. Koerte
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02145, USA
- cBRAIN, Department of Child and Adolescent Psychiatry, Psychosomatics, and Psychotherapy, Ludwig-Maximilians-University, 80336 Munich, Germany
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA
- Graduate School of Systemic Neurosciences, Ludwig-Maximilians-University, 82152 Munich, Germany
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Prediction of the Topography of the Corticospinal Tract on T1-Weighted MR Images Using Deep-Learning-Based Segmentation. Diagnostics (Basel) 2023; 13:diagnostics13050911. [PMID: 36900055 PMCID: PMC10000710 DOI: 10.3390/diagnostics13050911] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2023] [Revised: 02/23/2023] [Accepted: 02/27/2023] [Indexed: 03/04/2023] Open
Abstract
INTRODUCTION Tractography is an invaluable tool in the planning of tumor surgery in the vicinity of functionally eloquent areas of the brain as well as in the research of normal development or of various diseases. The aim of our study was to compare the performance of a deep-learning-based image segmentation for the prediction of the topography of white matter tracts on T1-weighted MR images to the performance of a manual segmentation. METHODS T1-weighted MR images of 190 healthy subjects from 6 different datasets were utilized in this study. Using deterministic diffusion tensor imaging, we first reconstructed the corticospinal tract on both sides. After training a segmentation model on 90 subjects of the PIOP2 dataset using the nnU-Net in a cloud-based environment with graphical processing unit (Google Colab), we evaluated its performance using 100 subjects from 6 different datasets. RESULTS Our algorithm created a segmentation model that predicted the topography of the corticospinal pathway on T1-weighted images in healthy subjects. The average dice score was 0.5479 (0.3513-0.7184) on the validation dataset. CONCLUSIONS Deep-learning-based segmentation could be applicable in the future to predict the location of white matter pathways in T1-weighted scans.
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Marcolini S, Rojczyk P, Seitz-Holland J, Koerte IK, Alosco ML, Bouix S. Posttraumatic Stress and Traumatic Brain Injury: Cognition, Behavior, and Neuroimaging Markers in Vietnam Veterans. J Alzheimers Dis 2023; 95:1427-1448. [PMID: 37694363 PMCID: PMC10578246 DOI: 10.3233/jad-221304] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/24/2023] [Indexed: 09/12/2023]
Abstract
BACKGROUND Posttraumatic stress disorder (PTSD) and traumatic brain injury (TBI) are common in Veterans and linked to behavioral disturbances, increased risk of cognitive decline, and Alzheimer's disease. OBJECTIVE We studied the synergistic effects of PTSD and TBI on behavioral, cognitive, and neuroimaging measures in Vietnam war Veterans. METHODS Data were acquired at baseline and after about one-year from male Veterans categorized into: PTSD, TBI, PTSD+TBI, and Veteran controls without PTSD or TBI. We applied manual tractography to examine white matter microstructure of three fiber tracts: uncinate fasciculus (N = 91), cingulum (N = 87), and inferior longitudinal fasciculus (N = 95). ANCOVAs were used to compare Veterans' baseline behavioral and cognitive functioning (N = 285), white matter microstructure, amyloid-β (N = 230), and tau PET (N = 120). Additional ANCOVAs examined scores' differences from baseline to follow-up. RESULTS Veterans with PTSD and PTSD+TBI, but not Veterans with TBI only, exhibited poorer behavioral and cognitive functioning at baseline than controls. The groups did not differ in baseline white matter, amyloid-β, or tau, nor in behavioral and cognitive functioning, and tau accumulation change. Progression of white matter abnormalities of the uncinate fasciculus in Veterans with PTSD compared to controls was observed; analyses in TBI and PTSD+TBI were not run due to insufficient sample size. CONCLUSIONS PTSD and PTSD+TBI negatively affect behavioral and cognitive functioning, while TBI does not contribute independently. Whether progressive decline in uncinate fasciculus microstructure in Veterans with PTSD might account for cognitive decline should be further studied. Findings did not support an association between PTSD, TBI, and Alzheimer's disease pathology based on amyloid and tau PET.
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Affiliation(s)
- Sofia Marcolini
- Department of Neurology and Alzheimer Center, University Medical Center Groningen, Groningen, The Netherlands
| | - Philine Rojczyk
- Department of Psychiatry, Psychiatry Neuroimaging Laboratory, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- cBRAIN, Department of Child and Adolescent Psychiatry, Psychosomatics, and Psychotherapy, University Hospital, Ludwig Maximilian University Munich, Germany
| | - Johanna Seitz-Holland
- Department of Psychiatry, Psychiatry Neuroimaging Laboratory, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Inga K. Koerte
- Department of Psychiatry, Psychiatry Neuroimaging Laboratory, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- cBRAIN, Department of Child and Adolescent Psychiatry, Psychosomatics, and Psychotherapy, University Hospital, Ludwig Maximilian University Munich, Germany
| | - Michael L. Alosco
- Department of Neurology, Boston University Alzheimer’s Disease Research Center, Boston University CTE Center, Boston University Chobanian & Avedisian School of Medicine, Boston, MA, USA
| | - Sylvain Bouix
- Department of Psychiatry, Psychiatry Neuroimaging Laboratory, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
- Department of Software Engineering and Information Technology, École de Technologie Supe´rieure, Montre´al, Canada
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Wu D, Zhang M, Geng J, Chen X. Noninvasive Prediction of Language Lateralization Through Arcuate Fasciculus Tractography in Patients With Low-Grade Gliomas: Correlation With The Wada Test. Front Oncol 2022; 12:936228. [PMID: 35936675 PMCID: PMC9354698 DOI: 10.3389/fonc.2022.936228] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2022] [Accepted: 06/15/2022] [Indexed: 11/13/2022] Open
Abstract
Language lateralization is unique to humans, so clarifying dominant side is helpful for removing gliomas involving language areas. This study investigated the arcuate fasciculus (AF) reconstructed by diffusion tensor imaging–based tractography (DTT) in predicting language lateralization in patients with low-grade gliomas. Wada test was performed to determine the language Dominant Hemisphere (DH) and the Contralateral Hemisphere. DTI data [1.5-T magnetic resonance imaging (MRI)] was used to reconstruct AF by two independent operators using a DTT method. Fiber number, volume, and fractional anisotropy (FA) of bilateral reconstructed AF were measured. Lateralization indexes (LIs), including Number Index (NI), Volume Index (VI), and FA Index (FI), were accordingly calculated by mean values. A total of 21 patients with WHO Grade II gliomas in the left hemisphere were included. Every patient received a successful Wada test and reconstruction of bilateral AF. DTT metrics of reconstructed AF, such as fiber number, volume, and FA, showed significantly asymmetric between hemispheres. All the LI (NI, VI, and FI) values were statistically higher in the DH determined by the Wada test. No discrepancy was found between the prediction using the cutoff values of DTT metrics and the results of WADA test. The Kappa values were 0.829, 0.696, and 0.611, indicating NI and VI as more reliable predictor than FI although FI itself may also be feasible. Compared with the Wada test, we consider that DTT of AF is a non-invasive, simple, relatively accurate, and feasible method in predicting language lateralization in patients with low-grade gliomas.
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Affiliation(s)
- Dongdong Wu
- Department of Neurosurgery, Chinese People's Liberation Army (PLA) General Hospital, Beijing, China
| | - Meng Zhang
- Department of Neurosurgery, The Second Hospital of Southern Theater of Chinese Navy, Sanya, China
| | - Jiefeng Geng
- Department of Neurosurgery, First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Xiaolei Chen
- Department of Neurosurgery, Chinese People's Liberation Army (PLA) General Hospital, Beijing, China
- *Correspondence: Xiaolei Chen,
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Sydnor VJ, Cieslak M, Duprat R, Deluisi J, Flounders MW, Long H, Scully M, Balderston NL, Sheline YI, Bassett DS, Satterthwaite TD, Oathes DJ. Cortical-subcortical structural connections support transcranial magnetic stimulation engagement of the amygdala. SCIENCE ADVANCES 2022; 8:eabn5803. [PMID: 35731882 PMCID: PMC9217085 DOI: 10.1126/sciadv.abn5803] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Accepted: 05/04/2022] [Indexed: 05/31/2023]
Abstract
The amygdala processes valenced stimuli, influences emotion, and exhibits aberrant activity across anxiety disorders, depression, and PTSD. Interventions modulating amygdala activity hold promise as transdiagnostic psychiatric treatments. In 45 healthy participants, we investigated whether transcranial magnetic stimulation (TMS) elicits indirect changes in amygdala activity when applied to ventrolateral prefrontal cortex (vlPFC), a region important for emotion regulation. Harnessing in-scanner interleaved TMS/functional MRI (fMRI), we reveal that vlPFC neurostimulation evoked acute and focal modulations of amygdala fMRI BOLD signal. Larger TMS-evoked changes in the amygdala were associated with higher fiber density in a vlPFC-amygdala white matter pathway when stimulating vlPFC but not an anatomical control, suggesting this pathway facilitated stimulation-induced communication between cortex and subcortex. This work provides evidence of amygdala engagement by TMS, highlighting stimulation of vlPFC-amygdala circuits as a candidate treatment for transdiagnostic psychopathology. More broadly, it indicates that targeting cortical-subcortical structural connections may enhance the impact of TMS on subcortical neural activity and, by extension, subcortex-subserved behaviors.
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Affiliation(s)
- Valerie J. Sydnor
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Matthew Cieslak
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Romain Duprat
- Center for Neuromodulation in Depression and Stress (CNDS), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Joseph Deluisi
- Center for Neuromodulation in Depression and Stress (CNDS), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Matthew W. Flounders
- Center for Neuromodulation in Depression and Stress (CNDS), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Hannah Long
- Center for Neuromodulation in Depression and Stress (CNDS), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Morgan Scully
- Center for Neuromodulation in Depression and Stress (CNDS), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Nicholas L. Balderston
- Center for Neuromodulation in Depression and Stress (CNDS), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Yvette I. Sheline
- Center for Neuromodulation in Depression and Stress (CNDS), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Dani S. Bassett
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Bioengineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Electrical and Systems Engineering, School of Engineering and Applied Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Physics and Astronomy, College of Arts and Sciences, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Santa Fe Institute, Santa Fe, NM 87501, USA
| | - Theodore D. Satterthwaite
- Penn Lifespan Informatics and Neuroimaging Center (PennLINC), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Center for Biomedical Image Computing and Analytics (CBICA), University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Desmond J. Oathes
- Center for Neuromodulation in Depression and Stress (CNDS), Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
- Penn Brain Science, Translation, Innovation, and Modulation Center (brainSTIM), Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA 19104, USA
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10
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Lucena O, Borges P, Cardoso J, Ashkan K, Sparks R, Ourselin S. Informative and Reliable Tract Segmentation for Preoperative Planning. FRONTIERS IN RADIOLOGY 2022; 2:866974. [PMID: 37492653 PMCID: PMC10365092 DOI: 10.3389/fradi.2022.866974] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2022] [Accepted: 03/31/2022] [Indexed: 07/27/2023]
Abstract
Identifying white matter (WM) tracts to locate eloquent areas for preoperative surgical planning is a challenging task. Manual WM tract annotations are often used but they are time-consuming, suffer from inter- and intra-rater variability, and noise intrinsic to diffusion MRI may make manual interpretation difficult. As a result, in clinical practice direct electrical stimulation is necessary to precisely locate WM tracts during surgery. A measure of WM tract segmentation unreliability could be important to guide surgical planning and operations. In this study, we use deep learning to perform reliable tract segmentation in combination with uncertainty quantification to measure segmentation unreliability. We use a 3D U-Net to segment white matter tracts. We then estimate model and data uncertainty using test time dropout and test time augmentation, respectively. We use a volume-based calibration approach to compute representative predicted probabilities from the estimated uncertainties. In our findings, we obtain a Dice of ≈0.82 which is comparable to the state-of-the-art for multi-label segmentation and Hausdorff distance <10mm. We demonstrate a high positive correlation between volume variance and segmentation errors, which indicates a good measure of reliability for tract segmentation ad uncertainty estimation. Finally, we show that calibrated predicted volumes are more likely to encompass the ground truth segmentation volume than uncalibrated predicted volumes. This study is a step toward more informed and reliable WM tract segmentation for clinical decision-making.
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Affiliation(s)
- Oeslle Lucena
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Pedro Borges
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Jorge Cardoso
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Keyoumars Ashkan
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
- King's College Hospital Foundation Trust, London, United Kingdom
| | - Rachel Sparks
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
| | - Sebastien Ourselin
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom
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11
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Zhang F, Daducci A, He Y, Schiavi S, Seguin C, Smith RE, Yeh CH, Zhao T, O'Donnell LJ. Quantitative mapping of the brain's structural connectivity using diffusion MRI tractography: A review. Neuroimage 2022; 249:118870. [PMID: 34979249 PMCID: PMC9257891 DOI: 10.1016/j.neuroimage.2021.118870] [Citation(s) in RCA: 75] [Impact Index Per Article: 37.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 12/03/2021] [Accepted: 12/31/2021] [Indexed: 12/13/2022] Open
Abstract
Diffusion magnetic resonance imaging (dMRI) tractography is an advanced imaging technique that enables in vivo reconstruction of the brain's white matter connections at macro scale. It provides an important tool for quantitative mapping of the brain's structural connectivity using measures of connectivity or tissue microstructure. Over the last two decades, the study of brain connectivity using dMRI tractography has played a prominent role in the neuroimaging research landscape. In this paper, we provide a high-level overview of how tractography is used to enable quantitative analysis of the brain's structural connectivity in health and disease. We focus on two types of quantitative analyses of tractography, including: 1) tract-specific analysis that refers to research that is typically hypothesis-driven and studies particular anatomical fiber tracts, and 2) connectome-based analysis that refers to research that is more data-driven and generally studies the structural connectivity of the entire brain. We first provide a review of methodology involved in three main processing steps that are common across most approaches for quantitative analysis of tractography, including methods for tractography correction, segmentation and quantification. For each step, we aim to describe methodological choices, their popularity, and potential pros and cons. We then review studies that have used quantitative tractography approaches to study the brain's white matter, focusing on applications in neurodevelopment, aging, neurological disorders, mental disorders, and neurosurgery. We conclude that, while there have been considerable advancements in methodological technologies and breadth of applications, there nevertheless remains no consensus about the "best" methodology in quantitative analysis of tractography, and researchers should remain cautious when interpreting results in research and clinical applications.
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Affiliation(s)
- Fan Zhang
- Brigham and Women's Hospital, Harvard Medical School, Boston, USA.
| | | | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; Chinese Institute for Brain Research, Beijing, China
| | - Simona Schiavi
- Department of Computer Science, University of Verona, Verona, Italy
| | - Caio Seguin
- Melbourne Neuropsychiatry Centre, University of Melbourne and Melbourne Health, Melbourne, Australia; The University of Sydney, School of Biomedical Engineering, Sydney, Australia
| | - Robert E Smith
- The Florey Institute of Neuroscience and Mental Health, Melbourne, Australia; Florey Department of Neuroscience and Mental Health, The University of Melbourne, Melbourne, Australia
| | - Chun-Hung Yeh
- Institute for Radiological Research, Chang Gung University, Taoyuan, Taiwan; Department of Psychiatry, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
| | - Tengda Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
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12
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Zhang Y, Furst AJ. Brainstem Diffusion Tensor Tractography and Clinical Applications in Pain. FRONTIERS IN PAIN RESEARCH (LAUSANNE, SWITZERLAND) 2022; 3:840328. [PMID: 35399154 PMCID: PMC8989264 DOI: 10.3389/fpain.2022.840328] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 12/21/2021] [Accepted: 02/15/2022] [Indexed: 11/13/2022]
Abstract
The brainstem is one of the most vulnerable brain structures in many neurological conditions, such as pain, sleep problems, autonomic dysfunctions, and neurodegenerative disorders. Diffusion tensor imaging and tractography provide structural details and quantitative measures of brainstem fiber pathways. Until recently, diffusion tensor tractographic studies have mainly focused on whole-brain MRI acquisition. Due to the brainstem's spatial localization, size, and tissue characteristics, and limits of imaging techniques, brainstem diffusion MRI poses particular challenges in tractography. We provide a brief overview on recent advances in diffusion tensor tractography in revealing human pathways connecting the brainstem to the subcortical regions (e.g., basal ganglia, mesolimbic, basal forebrain), and cortical regions. Each of these pathways contains different distributions of fiber tracts from known neurotransmitter-specific nuclei in the brainstem. We compare the brainstem tractographic approaches in literature and our in-lab developed automated brainstem tractography in terms of atlas building, technical advantages, and neuroanatomical implications on neurotransmitter systems. Lastly, we summarize recent investigations of using brainstem tractography as a promising tool in association with pain.
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Affiliation(s)
- Yu Zhang
- War Related Illness and Injury Study Center (WRIISC), VA Palo Alto Health Care System, Palo Alto, CA, United States,*Correspondence: Yu Zhang ;
| | - Ansgar J. Furst
- War Related Illness and Injury Study Center (WRIISC), VA Palo Alto Health Care System, Palo Alto, CA, United States,Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Palo Alto, CA, United States,Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Palo Alto, CA, United States,Polytrauma System of Care (PSC), VA Palo Alto Health Care System, Palo Alto, CA, United States
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13
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Spencer APC, Byrne H, Lee-Kelland R, Jary S, Thoresen M, Cowan FM, Chakkarapani E, Brooks JCW. An Age-Specific Atlas for Delineation of White Matter Pathways in Children Aged 6-8 Years. Brain Connect 2021; 12:402-416. [PMID: 34210166 PMCID: PMC7612846 DOI: 10.1089/brain.2021.0058] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Introduction Diffusion MRI allows non-invasive assessment of white matter connectivity in typical development and of changes due to brain injury or pathology. Probabilistic white matter atlases allow diffusion metrics to be measured in specific white matter pathways, and are a critical component in spatial normalisation for group analysis. However, given the known developmental changes in white matter it may be sub-optimal to use an adult template when assessing data acquired from children. Methods By averaging subject-specific fibre bundles from 28 children aged from 6 to 8 years, we created an age-specific probabilistic white matter atlas for 12 major white matter tracts. Using both the newly developed and Johns Hopkins adult atlases, we compared the atlas to subject-specific fibre bundles in two independent validation cohorts, assessing accuracy in terms of volumetric overlap and measured diffusion metrics. Results Our age-specific atlas gave better overall performance than the adult atlas, achieving higher volumetric overlap with subject-specific fibre tracking and higher correlation of FA measurements with those measured from subject-specific fibre bundles. Specifically, estimates of FA values for cortico-spinal tract, uncinate fasciculus, forceps minor, cingulate gyrus part of the cingulum and anterior thalamic radiation were all significantly more accurate when estimated with an age-specific atlas. Discussion The age-specific atlas allows delineation of white matter tracts in children aged 6-8 years, without the need for tractography, more accurately than when normalising to an adult atlas. To our knowledge, this is the first publicly available probabilistic atlas of white matter tracts for this age group.
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Affiliation(s)
- Arthur P C Spencer
- Clinical Research and Imaging Centre, University of Bristol, Bristol, United Kingdom.,Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Hollie Byrne
- Clinical Research and Imaging Centre, University of Bristol, Bristol, United Kingdom
| | - Richard Lee-Kelland
- Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Sally Jary
- Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Marianne Thoresen
- Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom.,Faculty of Medicine, Institute of Basic Medical Sciences, University of Oslo, Oslo, Norway
| | - Frances M Cowan
- Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom.,Department of Paediatrics, Imperial College London, London, United Kingdom
| | - Ela Chakkarapani
- Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Jonathan C W Brooks
- Clinical Research and Imaging Centre, University of Bristol, Bristol, United Kingdom.,School of Psychology, University of East Anglia, Norwich, United Kingdom
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14
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Legarreta JH, Petit L, Rheault F, Theaud G, Lemaire C, Descoteaux M, Jodoin PM. Filtering in tractography using autoencoders (FINTA). Med Image Anal 2021; 72:102126. [PMID: 34161915 DOI: 10.1016/j.media.2021.102126] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2020] [Revised: 04/20/2021] [Accepted: 05/26/2021] [Indexed: 10/21/2022]
Abstract
Current brain white matter fiber tracking techniques show a number of problems, including: generating large proportions of streamlines that do not accurately describe the underlying anatomy; extracting streamlines that are not supported by the underlying diffusion signal; and under-representing some fiber populations, among others. In this paper, we describe a novel autoencoder-based learning method to filter streamlines from diffusion MRI tractography, and hence, to obtain more reliable tractograms. Our method, dubbed FINTA (Filtering in Tractography using Autoencoders) uses raw, unlabeled tractograms to train the autoencoder, and to learn a robust representation of brain streamlines. Such an embedding is then used to filter undesired streamline samples using a nearest neighbor algorithm. Our experiments on both synthetic and in vivo human brain diffusion MRI tractography data obtain accuracy scores exceeding the 90% threshold on the test set. Results reveal that FINTA has a superior filtering performance compared to conventional, anatomy-based methods, and the RecoBundles state-of-the-art method. Additionally, we demonstrate that FINTA can be applied to partial tractograms without requiring changes to the framework. We also show that the proposed method generalizes well across different tracking methods and datasets, and shortens significantly the computation time for large (>1 M streamlines) tractograms. Together, this work brings forward a new deep learning framework in tractography based on autoencoders, which offers a flexible and powerful method for white matter filtering and bundling that could enhance tractometry and connectivity analyses.
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Affiliation(s)
- Jon Haitz Legarreta
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Department of Computer Science, Université de Sherbrooke, 2500, boul. de l'Université, Sherbrooke, Québec J1K 2R1, Canada; Videos & Images Theory and Analytics Laboratory (VITAL), Department of Computer Science, Université de Sherbrooke, 2500, boul. de l'Université, Sherbrooke, Québec J1K 2R1, Canada.
| | - Laurent Petit
- Groupe d'Imagerie Neurofonctionnelle (GIN), Univ. Bordeaux, CNRS, CEA, IMN, UMR 5293, Bordeaux F-33000, France
| | - François Rheault
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Department of Computer Science, Université de Sherbrooke, 2500, boul. de l'Université, Sherbrooke, Québec J1K 2R1, Canada
| | - Guillaume Theaud
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Department of Computer Science, Université de Sherbrooke, 2500, boul. de l'Université, Sherbrooke, Québec J1K 2R1, Canada
| | - Carl Lemaire
- Centre de Calcul Scientifique, Université de Sherbrooke, 2500, boul. de l'Université, Sherbrooke, Québec J1K 2R1, Canada
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Laboratory (SCIL), Department of Computer Science, Université de Sherbrooke, 2500, boul. de l'Université, Sherbrooke, Québec J1K 2R1, Canada
| | - Pierre-Marc Jodoin
- Videos & Images Theory and Analytics Laboratory (VITAL), Department of Computer Science, Université de Sherbrooke, 2500, boul. de l'Université, Sherbrooke, Québec J1K 2R1, Canada
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15
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Feng Y, Song J, Yan W, Wang J, Zhao C, Zeng Q. Investigation of Local White Matter Properties in Professional Chess Player: A Diffusion Magnetic Resonance Imaging Study Based on Automatic Annotation Fiber Clustering. IEEE Trans Cogn Dev Syst 2021. [DOI: 10.1109/tcds.2020.2968116] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
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16
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Bopp MHA, Emde J, Carl B, Nimsky C, Saß B. Diffusion Kurtosis Imaging Fiber Tractography of Major White Matter Tracts in Neurosurgery. Brain Sci 2021; 11:brainsci11030381. [PMID: 33802710 PMCID: PMC8002557 DOI: 10.3390/brainsci11030381] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2021] [Revised: 03/08/2021] [Accepted: 03/14/2021] [Indexed: 01/31/2023] Open
Abstract
Diffusion tensor imaging (DTI)-based fiber tractography is routinely used in clinical applications to visualize major white matter tracts, such as the corticospinal tract (CST), optic radiation (OR), and arcuate fascicle (AF). Nevertheless, DTI is limited due to its capability of resolving intra-voxel multi-fiber populations. Sophisticated models often require long acquisition times not applicable in clinical practice. Diffusion kurtosis imaging (DKI), as an extension of DTI, combines sophisticated modeling of the diffusion process with short acquisition times but has rarely been investigated in fiber tractography. In this study, DTI- and DKI-based fiber tractography of the CST, OR, and AF was investigated in healthy volunteers and glioma patients. For the CST, significantly larger tract volumes were seen in DKI-based fiber tractography. Similar results were obtained for the OR, except for the right OR in patients. In the case of the AF, results of both models were comparable with DTI-based fiber tractography showing even significantly larger tract volumes in patients. In the case of the CST and OR, DKI-based fiber tractography contributes to advanced visualization under clinical time constraints, whereas for the AF, other models should be considered.
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Affiliation(s)
- Miriam H. A. Bopp
- Department of Neurosurgery, University of Marburg, Baldingerstrasse, 35043 Marburg, Germany; (J.E.); (B.C.); (C.N.); (B.S.)
- Center for Mind, Brain and Behavior (CMBB), 35043 Marburg, Germany
- Correspondence:
| | - Julia Emde
- Department of Neurosurgery, University of Marburg, Baldingerstrasse, 35043 Marburg, Germany; (J.E.); (B.C.); (C.N.); (B.S.)
| | - Barbara Carl
- Department of Neurosurgery, University of Marburg, Baldingerstrasse, 35043 Marburg, Germany; (J.E.); (B.C.); (C.N.); (B.S.)
- Department of Neurosurgery, Helios Dr. Horst Schmidt Kliniken, Ludwig-Erhard-Strasse 100, 65199 Wiesbaden, Germany
| | - Christopher Nimsky
- Department of Neurosurgery, University of Marburg, Baldingerstrasse, 35043 Marburg, Germany; (J.E.); (B.C.); (C.N.); (B.S.)
- Center for Mind, Brain and Behavior (CMBB), 35043 Marburg, Germany
| | - Benjamin Saß
- Department of Neurosurgery, University of Marburg, Baldingerstrasse, 35043 Marburg, Germany; (J.E.); (B.C.); (C.N.); (B.S.)
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17
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Steinmann S, Lyall AE, Langhein M, Nägele FL, Rauh J, Cetin-Karayumak S, Zhang F, Mussmann M, Billah T, Makris N, Pasternak O, O'Donnell LJ, Rathi Y, Kubicki M, Leicht G, Shenton ME, Mulert C. Sex-Related Differences in White Matter Asymmetry and Its Implications for Verbal Working Memory in Psychosis High-Risk State. Front Psychiatry 2021; 12:686967. [PMID: 34194350 PMCID: PMC8236502 DOI: 10.3389/fpsyt.2021.686967] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/28/2021] [Accepted: 05/17/2021] [Indexed: 12/19/2022] Open
Abstract
Objective: Sexual dimorphism has been investigated in schizophrenia, although sex-specific differences among individuals who are at clinical high-risk (CHR) for developing psychosis have been inconclusive. This study aims to characterize sexual dimorphism of language areas in the brain by investigating the asymmetry of four white matter tracts relevant to verbal working memory in CHR patients compared to healthy controls (HC). HC typically show a leftward asymmetry of these tracts. Moreover, structural abnormalities in asymmetry and verbal working memory dysfunctions have been associated with neurodevelopmental abnormalities and are considered core features of schizophrenia. Methods: Twenty-nine subjects with CHR (17 female/12 male) for developing psychosis and twenty-one HC (11 female/10 male) matched for age, sex, and education were included in the study. Two-tensor unscented Kalman filter tractography, followed by an automated, atlas-guided fiber clustering approach, were used to identify four fiber tracts related to verbal working memory: the superior longitudinal fasciculi (SLF) I, II and III, and the superior occipitofrontal fasciculus (SOFF). Using fractional anisotropy (FA) of tissue as the primary measure, we calculated the laterality index for each tract. Results: There was a significantly greater right>left asymmetry of the SLF-III in CHR females compared to HC females, but no hemispheric difference between CHR vs. HC males. Moreover, the laterality index of SLF-III for CHR females correlated negatively with Backward Digit Span performance, suggesting a greater rightward asymmetry was associated with poorer working memory functioning. Conclusion: This study suggests increased rightward asymmetry of the SLF-III in CHR females. This finding of sexual dimorphism in white matter asymmetry in a language-related area of the brain in CHR highlights the need for a deeper understanding of the role of sex in the high-risk state. Future work investigating early sex-specific pathophysiological mechanisms, may lead to the development of novel personalized treatment strategies aimed at preventing transition to a more chronic and difficult-to-treat disorder.
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Affiliation(s)
- Saskia Steinmann
- Psychiatry Neuroimaging Branch, Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.,Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Boston, MA, United States
| | - Amanda E Lyall
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Boston, MA, United States.,Department of Psychiatry, Massachusetts General Hospital Harvard Medical School, Boston, MA, United States
| | - Mina Langhein
- Psychiatry Neuroimaging Branch, Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.,Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Boston, MA, United States
| | - Felix L Nägele
- Psychiatry Neuroimaging Branch, Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Jonas Rauh
- Psychiatry Neuroimaging Branch, Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Suheyla Cetin-Karayumak
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Boston, MA, United States
| | - Fan Zhang
- Department of Radiology, Brigham and Women's Hospital Harvard Medical School, Boston, MA, United States
| | - Marius Mussmann
- Psychiatry Neuroimaging Branch, Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Tashrif Billah
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Boston, MA, United States
| | - Nikos Makris
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Boston, MA, United States.,Department of Psychiatry, Massachusetts General Hospital Harvard Medical School, Boston, MA, United States
| | - Ofer Pasternak
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Boston, MA, United States.,Department of Radiology, Brigham and Women's Hospital Harvard Medical School, Boston, MA, United States
| | - Lauren J O'Donnell
- Department of Radiology, Brigham and Women's Hospital Harvard Medical School, Boston, MA, United States
| | - Yogesh Rathi
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Boston, MA, United States.,Department of Radiology, Brigham and Women's Hospital Harvard Medical School, Boston, MA, United States
| | - Marek Kubicki
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Boston, MA, United States.,Department of Psychiatry, Massachusetts General Hospital Harvard Medical School, Boston, MA, United States
| | - Gregor Leicht
- Psychiatry Neuroimaging Branch, Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany
| | - Martha E Shenton
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Boston, MA, United States.,Department of Psychiatry, Massachusetts General Hospital Harvard Medical School, Boston, MA, United States.,Department of Radiology, Brigham and Women's Hospital Harvard Medical School, Boston, MA, United States
| | - Christoph Mulert
- Psychiatry Neuroimaging Branch, Department of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.,Center for Psychiatry and Psychotherapy, Justus-Liebig-University Giessen, Giessen, Germany
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18
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Li FY, Liu HY, Zhang J, Sun ZH, Zhang JS, Sun GC, Yu XG, Chen XL, Xu BN. Identification of risk factors for poor language outcome in surgical resection of glioma involving the arcuate fasciculus: an observational study. Neural Regen Res 2021; 16:333-337. [PMID: 32859793 PMCID: PMC7896210 DOI: 10.4103/1673-5374.290901] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/12/2023] Open
Abstract
The arcuate fasciculus is a critical component of the neural substrate of human language function. Surgical resection of glioma adjacent to the arcuate fasciculus likely damages this region. In this study, we evaluated the outcome of surgical resection of glioma adjacent to the arcuate fasciculus under the guidance of magnetic resonance imaging and diffusion tensor imaging, and we aimed to identify the risk factors for postoperative linguistic deficit. In total, 54 patients with primary glioma adjacent to the arcuate fasciculus were included in this observational study. These patients comprised 38 men and 16 women (aged 43 ± 11 years). All patients underwent surgical resenction of glioma under the guidance of magnetic resonance imaging and diffusion tensor imaging. Intraoperative images were updated when necessary for further resection. The gross total resection rate of the 54 patients increased from 38.9% to 70.4% by intraoperative magnetic resonance imaging. Preoperative language function and glioma-to-arcuate fasciculus distance were associated with poor language outcome. Multivariable logistic regression analyses showed that glioma-to-arcuate fasciculus distance was the major independent risk factor for poor outcome. The cutoff point of glioma-to-arcuate fasciculus distance for poor outcome was 3.2 mm. These findings suggest that intraoperative magnetic resonance imaging combined with diffusion tensor imaging of the arcuate fasciculus can help optimize tumor resection and result in the least damage to the arcuate fasciculus. Notably, glioma-to-arcuate fasciculus distance is a key independent risk factor for poor postoperative language outcome. This study was approved by the Ethics Committee of the Chinese PLA General Hospital, China (approval No. S2014-096-01) on October 11, 2014.
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Affiliation(s)
- Fang-Ye Li
- Department of Neurosurgery, First Medical Center of Chinese PLA General Hospital, Beijing; Department of Neurosurgery, Hainan Hospital of Chinese PLA General Hospital, Sanya, Hainan Province, China
| | - Hong-Yu Liu
- Department of Neurosurgery, Hainan Hospital of Chinese PLA General Hospital, Sanya, Hainan Province, China
| | - Jun Zhang
- Department of Neurosurgery, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Zheng-Hui Sun
- Department of Neurosurgery, First Medical Center of Chinese PLA General Hospital, Beijing; Department of Neurosurgery, Hainan Hospital of Chinese PLA General Hospital, Sanya, Hainan Province, China
| | - Jia-Shu Zhang
- Department of Neurosurgery, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Guo-Chen Sun
- Department of Neurosurgery, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Xin-Guang Yu
- Department of Neurosurgery, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Xiao-Lei Chen
- Department of Neurosurgery, First Medical Center of Chinese PLA General Hospital, Beijing, China
| | - Bai-Nan Xu
- Department of Neurosurgery, First Medical Center of Chinese PLA General Hospital, Beijing, China
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19
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Somuncu ÖS, Berns HM, Sanchez JG. New Pioneers of Optogenetics in Neuroscience. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2020; 1288:47-60. [PMID: 31983055 DOI: 10.1007/5584_2019_473] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/12/2023]
Abstract
Optogenetics have recently increased in popularity as tools to study behavior in response to the brain and how these trends relate back to a neuronal circuit. Additionally, the high demand for human cerebral tissue in research has led to the generation of a new model to investigate human brain development and disease. Human Pluripotent Stem Cells (hPSCs) have been previously used to recapitulate the development of several tissues such as intestine, stomach and liver and to model disease in a human context, recently new improvements have been made in the field of hPSC-derived brain organoids to better understand overall brain development but more specifically, to mimic inter-neuronal communication. This review aims to highlight the recent advances in these two separate approaches of brain research and to emphasize the need for overlap. These two novel approaches would combine the study of behavior along with the specific circuits required to produce the signals causing such behavior. This review is focused on the current state of the field, as well as the development of novel optogenetic technologies and their potential for current scientific study and potential therapeutic use.
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Affiliation(s)
- Ö Sezin Somuncu
- Department of Medical Biology, Bahçeşehir University Faculty of Medicine, İstanbul, Turkey.
| | - H Matthew Berns
- Heritage College of Osteopathic Medicine, Ohio University, Athens, OH, USA
| | - J Guillermo Sanchez
- Division of Developmental Biology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
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20
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Dimond D, Heo S, Ip A, Rohr CS, Tansey R, Graff K, Dhollander T, Smith RE, Lebel C, Dewey D, Connelly A, Bray S. Maturation and interhemispheric asymmetry in neurite density and orientation dispersion in early childhood. Neuroimage 2020; 221:117168. [DOI: 10.1016/j.neuroimage.2020.117168] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2019] [Revised: 06/15/2020] [Accepted: 07/12/2020] [Indexed: 12/13/2022] Open
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21
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Denier N, Walther S, Schneider C, Federspiel A, Wiest R, Bracht T. Reduced tract length of the medial forebrain bundle and the anterior thalamic radiation in bipolar disorder with melancholic depression. J Affect Disord 2020; 274:8-14. [PMID: 32469836 DOI: 10.1016/j.jad.2020.05.008] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Revised: 04/29/2020] [Accepted: 05/06/2020] [Indexed: 12/11/2022]
Abstract
BACKGROUND The supero-lateral medial forebrain bundle (slMFB) and the anterior thalamic radiation (ATR) play a core role in reward anticipation and motivational processes. In this study, the slMFB and the ATR were investigated in a group of depressed bipolar disorder (BD) and in healthy controls (HC) using tract length as a measure of fibre geometry and fractional anisotropy (FA) as a measure of white matter microstructure. We hypothesized reduced tract length and FA of the slMFB and the ATR in BD. We expect alterations to be driven by the melancholic subtype. METHODS Nineteen depressed patients with BD and 19 HC matched for age and gender underwent diffusion-weighted magnetic resonance imaging (MRI) scans. Diffusion tensor imaging (DTI) based tractography was used to reconstruct bilateral slMFB and ATR. Mean tract length and FA were computed for the slMFB and the ATR. Mixed-model ANCOVAs and post-hoc ANCOVAs, controlling for age and intracranial volume, were used to compare tract length and FA of bilateral slMFB and ATR between HC and BD and between HC and subgroups with melancholic and non-melancholic symptoms. RESULTS In BD we found a significantly shortened tract length of the right slMFB and ATR in BD compared to HC. Subgroup analyses showed that these findings were driven by the melancholic subgroup. Mean-FA did not differ between HC and BD. LIMITATIONS Sample size CONCLUSIONS: Tract length of the right slMFB and the right ATR is reduced in BD. Those changes of fibre geometry are driven by the melancholic subtype.
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Affiliation(s)
- Niklaus Denier
- University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland; Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Sebastian Walther
- University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland; Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Christoph Schneider
- University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland; Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Andrea Federspiel
- Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland
| | - Roland Wiest
- Institute of Diagnostic and Interventional Neuroradiology, University of Bern, Bern, Switzerland
| | - Tobias Bracht
- University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland; Translational Research Center, University Hospital of Psychiatry and Psychotherapy, University of Bern, Bern, Switzerland.
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22
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Neuro4Neuro: A neural network approach for neural tract segmentation using large-scale population-based diffusion imaging. Neuroimage 2020; 218:116993. [DOI: 10.1016/j.neuroimage.2020.116993] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2019] [Revised: 03/06/2020] [Accepted: 05/21/2020] [Indexed: 12/21/2022] Open
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23
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Avital I, Nelkenbaum I, Tsarfaty G, Konen E, Kiryati N, Mayer A. Neural Segmentation of Seeding ROIs (sROIs) for Pre-Surgical Brain Tractography. IEEE TRANSACTIONS ON MEDICAL IMAGING 2020; 39:1655-1667. [PMID: 31751233 DOI: 10.1109/tmi.2019.2954477] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/10/2023]
Abstract
White matter tractography mapping is an important tool for neuro-surgical planning and navigation. It relies on the accurate manual delineation of anatomical seeding ROIs (sROIs) by neuroanatomy experts. Stringent pre-operative time-constraints and limited availability of experts suggest that automation tools are strongly needed for the task. In this article, we propose and compare several multi-modal fully convolutional network architectures for segmentation of sROIs. Inspired by their manual segmentation practice, anatomical information from T1w maps is fused by the network with directionally encoded color (DEC) maps to compute the segmentation. Qualitative and quantitative validation was performed on image data from 75 real tumor resection candidates for the sROIs of the motor tract, the arcuate fasciculus, and optic radiation. Favorable comparison was also obtained with state-of-the-art methods for the tumor dataset as well as the ISMRM 2017 traCED challenge dataset. The proposed networks showed promising results, indicating they may significantly improve the efficiency of pre-surgical tractography mapping, without compromising its quality.
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24
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Zhang F, Wu Y, Norton I, Rathi Y, Golby AJ, O'Donnell LJ. Test-retest reproducibility of white matter parcellation using diffusion MRI tractography fiber clustering. Hum Brain Mapp 2019; 40:3041-3057. [PMID: 30875144 PMCID: PMC6548665 DOI: 10.1002/hbm.24579] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/07/2019] [Revised: 02/28/2019] [Accepted: 03/07/2019] [Indexed: 01/22/2023] Open
Abstract
There are two popular approaches for automated white matter parcellation using diffusion MRI tractography, including fiber clustering strategies that group white matter fibers according to their geometric trajectories and cortical-parcellation-based strategies that focus on the structural connectivity among different brain regions of interest. While multiple studies have assessed test-retest reproducibility of automated white matter parcellations using cortical-parcellation-based strategies, there are no existing studies of test-retest reproducibility of fiber clustering parcellation. In this work, we perform what we believe is the first study of fiber clustering white matter parcellation test-retest reproducibility. The assessment is performed on three test-retest diffusion MRI datasets including a total of 255 subjects across genders, a broad age range (5-82 years), health conditions (autism, Parkinson's disease and healthy subjects), and imaging acquisition protocols (three different sites). A comprehensive evaluation is conducted for a fiber clustering method that leverages an anatomically curated fiber clustering white matter atlas, with comparison to a popular cortical-parcellation-based method. The two methods are compared for the two main white matter parcellation applications of dividing the entire white matter into parcels (i.e., whole brain white matter parcellation) and identifying particular anatomical fiber tracts (i.e., anatomical fiber tract parcellation). Test-retest reproducibility is measured using both geometric and diffusion features, including volumetric overlap (wDice) and relative difference of fractional anisotropy. Our experimental results in general indicate that the fiber clustering method produced more reproducible white matter parcellations than the cortical-parcellation-based method.
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Affiliation(s)
- Fan Zhang
- Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusetts
| | - Ye Wu
- Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusetts
| | - Isaiah Norton
- Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusetts
| | - Yogesh Rathi
- Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusetts
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25
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Mancini M, Vos SB, Vakharia VN, O'Keeffe AG, Trimmel K, Barkhof F, Dorfer C, Soman S, Winston GP, Wu C, Duncan JS, Sparks R, Ourselin S. Automated fiber tract reconstruction for surgery planning: Extensive validation in language-related white matter tracts. Neuroimage Clin 2019; 23:101883. [PMID: 31163386 PMCID: PMC6545442 DOI: 10.1016/j.nicl.2019.101883] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2019] [Revised: 04/18/2019] [Accepted: 05/25/2019] [Indexed: 12/30/2022]
Abstract
Diffusion MRI and tractography hold great potential for surgery planning, especially to preserve eloquent white matter during resections. However, fiber tract reconstruction requires an expert with detailed understanding of neuroanatomy. Several automated approaches have been proposed, using different strategies to reconstruct the white matter tracts in a supervised fashion. However, validation is often limited to comparison with manual delineation by overlap-based measures, which is limited in characterizing morphological and topological differences. In this work, we set up a fully automated pipeline based on anatomical criteria that does not require manual intervention, taking advantage of atlas-based criteria and advanced acquisition protocols available on clinical-grade MRI scanners. Then, we extensively validated it on epilepsy patients with specific focus on language-related bundles. The validation procedure encompasses different approaches, including simple overlap with manual segmentations from two experts, feasibility ratings from external multiple clinical raters and relation with task-based functional MRI. Overall, our results demonstrate good quantitative agreement between automated and manual segmentation, in most cases better performances of the proposed method in qualitative terms, and meaningful relationships with task-based fMRI. In addition, we observed significant differences between experts in terms of both manual segmentation and external ratings. These results offer important insights on how different levels of validation complement each other, supporting the idea that overlap-based measures, although quantitative, do not offer a full perspective on the similarities and differences between automated and manual methods.
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Affiliation(s)
- Matteo Mancini
- Centre for Medical Image Computing, University College London, London, United Kingdom.
| | - Sjoerd B Vos
- Centre for Medical Image Computing, University College London, London, United Kingdom; Epilepsy Society MRI Unit, Chalfont St Peter, United Kingdom
| | - Vejay N Vakharia
- Department of Clinical and Experimental Epilepsy, University College London, London, United Kingdom; National Hospital for Neurology and Neurosurgery, Queen Square, London, UK
| | - Aidan G O'Keeffe
- Department of Statistical Science, University College London, London, UK
| | - Karin Trimmel
- Epilepsy Society MRI Unit, Chalfont St Peter, United Kingdom; Department of Clinical and Experimental Epilepsy, University College London, London, United Kingdom; National Hospital for Neurology and Neurosurgery, Queen Square, London, UK; Department of Neurology, Medical University of Vienna, Vienna, Austria
| | - Frederik Barkhof
- Centre for Medical Image Computing, University College London, London, United Kingdom; Brain Repair and Rehabilitation, University College London, London, UK; Radiology & Nuclear Medicine, VU University Medical Centre, Amsterdam, Netherlands
| | - Christian Dorfer
- Department of Neurosurgery, Vienna General Hospital, Medical University of Vienna, Vienna, Austria
| | - Salil Soman
- Harvard Medical School, Beth Israel Deaconess Medical Center, Department of Radiology, Boston, MA 00215, United States
| | - Gavin P Winston
- Epilepsy Society MRI Unit, Chalfont St Peter, United Kingdom; Department of Clinical and Experimental Epilepsy, University College London, London, United Kingdom; Department of Medicine, Division of Neurology, Queen's University, Kingston, Ontario, Canada
| | - Chengyuan Wu
- Department of Neurosurgery, Thomas Jefferson University, Philadelphia, PA, USA
| | - John S Duncan
- Epilepsy Society MRI Unit, Chalfont St Peter, United Kingdom; Department of Clinical and Experimental Epilepsy, University College London, London, United Kingdom; National Hospital for Neurology and Neurosurgery, Queen Square, London, UK
| | - Rachel Sparks
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
| | - Sebastien Ourselin
- School of Biomedical Engineering and Imaging Sciences, King's College London, London, UK
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26
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Bain JS, Yeatman JD, Schurr R, Rokem A, Mezer AA. Evaluating arcuate fasciculus laterality measurements across dataset and tractography pipelines. Hum Brain Mapp 2019; 40:3695-3711. [PMID: 31106944 DOI: 10.1002/hbm.24626] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2019] [Revised: 04/18/2019] [Accepted: 05/01/2019] [Indexed: 11/11/2022] Open
Abstract
The arcuate fasciculi are white-matter pathways that connect frontal and temporal lobes in each hemisphere. The arcuate plays a key role in the language network and is believed to be left-lateralized, in line with left hemisphere dominance for language. Measuring the arcuate in vivo requires diffusion magnetic resonance imaging-based tractography, but asymmetry of the in vivo arcuate is not always reliably detected in previous studies. It is unknown how the choice of tractography algorithm, with each method's freedoms, constraints, and vulnerabilities to false-positive and -negative errors, impacts findings of arcuate asymmetry. Here, we identify the arcuate in two independent datasets using a number of tractography strategies and methodological constraints, and assess their impact on estimates of arcuate laterality. We test three tractography methods: a deterministic, a probabilistic, and a tractography-evaluation (LiFE) algorithm. We extract the arcuate from the whole-brain tractogram, and compare it to an arcuate bundle constrained even further by selecting only those streamlines that connect to anatomically relevant cortical regions. We test arcuate macrostructure laterality, and also evaluate microstructure profiles for properties such as fractional anisotropy and quantitative R1. We find that both tractography choice and implementing the cortical constraints substantially impact estimates of all indices of arcuate laterality. Together, these results emphasize the effect of the tractography pipeline on estimates of arcuate laterality in both macrostructure and microstructure.
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Affiliation(s)
- Jonathan S Bain
- The Edmond & Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Jason D Yeatman
- Institute for Learning & Brain Sciences and Department of Speech and Hearing Science, The University of Washington, Seattle, Washington, USA
| | - Roey Schurr
- The Edmond & Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Ariel Rokem
- The University of Washington eScience Institute, The University of Washington, Seattle, Washington, USA
| | - Aviv A Mezer
- The Edmond & Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
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27
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Wu Y, Zhang F, Makris N, Ning Y, Norton I, She S, Peng H, Rathi Y, Feng Y, Wu H, O'Donnell LJ. Investigation into local white matter abnormality in emotional processing and sensorimotor areas using an automatically annotated fiber clustering in major depressive disorder. Neuroimage 2018; 181:16-29. [PMID: 29890329 PMCID: PMC6415925 DOI: 10.1016/j.neuroimage.2018.06.019] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2018] [Revised: 06/02/2018] [Accepted: 06/05/2018] [Indexed: 01/17/2023] Open
Abstract
This work presents an automatically annotated fiber cluster (AAFC) method to enable identification of anatomically meaningful white matter structures from the whole brain tractography. The proposed method consists of 1) a study-specific whole brain white matter parcellation using a well-established data-driven groupwise fiber clustering pipeline to segment tractography into multiple fiber clusters, and 2) a novel cluster annotation method to automatically assign an anatomical tract annotation to each fiber cluster by employing cortical parcellation information across multiple subjects. The novelty of the AAFC method is that it leverages group-wise information about the fiber clusters, including their fiber geometry and cortical terminations, to compute a tract anatomical label for each cluster in an automated fashion. We demonstrate the proposed AAFC method in an application of investigating white matter abnormality in emotional processing and sensorimotor areas in major depressive disorder (MDD). Seven tracts of interest related to emotional processing and sensorimotor functions are automatically identified using the proposed AAFC method as well as a comparable method that uses a cortical parcellation alone. Experimental results indicate that our proposed method is more consistent in identifying the tracts across subjects and across hemispheres in terms of the number of fibers. In addition, we perform a between-group statistical analysis in 31 MDD patients and 62 healthy subjects on the identified tracts using our AAFC method. We find statistical differences in diffusion measures in local regions within a fiber tract (e.g. 4 fiber clusters within the identified left hemisphere cingulum bundle (consisting of 14 clusters) are significantly different between the two groups), suggesting the ability of our method in identifying potential abnormality specific to subdivisions of a white matter structure.
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Affiliation(s)
- Ye Wu
- Institution of Information Processing and Automation, Zhejiang University of Technology, Hangzhou, China; Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Fan Zhang
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Nikos Makris
- Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Yuping Ning
- Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Hui'ai Hospital), Guangzhou, China
| | - Isaiah Norton
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Shenglin She
- Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Hui'ai Hospital), Guangzhou, China
| | - Hongjun Peng
- Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Hui'ai Hospital), Guangzhou, China
| | - Yogesh Rathi
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Yuanjing Feng
- Institution of Information Processing and Automation, Zhejiang University of Technology, Hangzhou, China
| | - Huawang Wu
- Affiliated Brain Hospital of Guangzhou Medical University (Guangzhou Hui'ai Hospital), Guangzhou, China.
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28
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Zhang F, Wu Y, Norton I, Rigolo L, Rathi Y, Makris N, O'Donnell LJ. An anatomically curated fiber clustering white matter atlas for consistent white matter tract parcellation across the lifespan. Neuroimage 2018; 179:429-447. [PMID: 29920375 PMCID: PMC6080311 DOI: 10.1016/j.neuroimage.2018.06.027] [Citation(s) in RCA: 106] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2018] [Revised: 05/01/2018] [Accepted: 06/08/2018] [Indexed: 12/15/2022] Open
Abstract
This work presents an anatomically curated white matter atlas to enable consistent white matter tract parcellation across different populations. Leveraging a well-established computational pipeline for fiber clustering, we create a tract-based white matter atlas including information from 100 subjects. A novel anatomical annotation method is proposed that leverages population-based brain anatomical information and expert neuroanatomical knowledge to annotate and categorize the fiber clusters. A total of 256 white matter structures are annotated in the proposed atlas, which provides one of the most comprehensive tract-based white matter atlases covering the entire brain to date. These structures are composed of 58 deep white matter tracts including major long range association and projection tracts, commissural tracts, and tracts related to the brainstem and cerebellar connections, plus 198 short and medium range superficial fiber clusters organized into 16 categories according to the brain lobes they connect. Potential false positive connections are annotated in the atlas to enable their exclusion from analysis or visualization. In addition, the proposed atlas allows for a whole brain white matter parcellation into 800 fiber clusters to enable whole brain connectivity analyses. The atlas and related computational tools are open-source and publicly available. We evaluate the proposed atlas using a testing dataset of 584 diffusion MRI scans from multiple independently acquired populations, across genders, the lifespan (1 day-82 years), and different health conditions (healthy control, neuropsychiatric disorders, and brain tumor patients). Experimental results show successful white matter parcellation across subjects from different populations acquired on multiple scanners, irrespective of age, gender or disease indications. Over 99% of the fiber tracts annotated in the atlas were detected in all subjects on average. One advantage in terms of robustness is that the tract-based pipeline does not require any cortical or subcortical segmentations, which can have limited success in young children and patients with brain tumors or other structural lesions. We believe this is the first demonstration of consistent automated white matter tract parcellation across the full lifespan from birth to advanced age.
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Affiliation(s)
- Fan Zhang
- Harvard Medical School, Boston, USA.
| | - Ye Wu
- Harvard Medical School, Boston, USA
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