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Zeng Q, Yu J, Hu Q, Yin K, Li Q, Huang J, Xie L, Wang J, Zhang C, Wang J, Zhang J, Feng Y. Investigation into white matter microstructure differences in visual training by using an automated fiber tract subclassification segmentation quantification method. Neurosci Lett 2024; 821:137574. [PMID: 38036084 DOI: 10.1016/j.neulet.2023.137574] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2023] [Revised: 11/25/2023] [Accepted: 11/27/2023] [Indexed: 12/02/2023]
Abstract
Visual training has emerged as a useful framework for investigating training-related brain plasticity, a highly complex task involving the interaction of visual orientation, attention, reasoning, and cognitive functions. However, the effects of long-term visual training on microstructural changes within white matter (WM) is poorly understood. Therefore, a set of visual training programs was designed, and automated fiber tract subclassification segmentation quantification based on diffusion magnetic resonance imaging was performed to obtain the anatomical changes in the brains of visual trainees. First, 40 healthy matched participants were randomly assigned to the training group or the control group. The training group underwent 10 consecutive weeks of visual training. Then, the fiber tracts of the subjects were automatically identified and further classified into fiber clusters to determine the differences between the two groups on a detailed scale. Next, each fiber cluster was divided into segments that can analyze specific areas of a fiber cluster. Lastly, the diffusion metrics of the two groups were comparatively analyzed to delineate the effects of visual training on WM microstructure. Our results showed that there were significant differences in the fiber clusters of the cingulate bundle, thalamus frontal, uncinate fasciculus, and corpus callosum between the training group compared and the control group. In addition, the training group exhibited lower mean fractional anisotropy, higher mean diffusivity and radial diffusivity than the control group. Therefore, the long-term cognitive activities, such as visual training, may systematically influence the WM properties of cognition, attention, memory, and processing speed.
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Affiliation(s)
- Qingrun Zeng
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China; Zhejiang Provincial United Key Laboratory of Embedded Systems, Hangzhou 310023, China
| | - Jiangli Yu
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China; Zhejiang Provincial United Key Laboratory of Embedded Systems, Hangzhou 310023, China
| | - Qiming Hu
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China; Zhejiang Provincial United Key Laboratory of Embedded Systems, Hangzhou 310023, China
| | - Kuiying Yin
- Nanjing Research Institute of Electronic Technology, Nanjing 210012, China
| | - Qixue Li
- Nanjing Research Institute of Electronic Technology, Nanjing 210012, China
| | - Jiahao Huang
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China; Zhejiang Provincial United Key Laboratory of Embedded Systems, Hangzhou 310023, China
| | - Lei Xie
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China; Zhejiang Provincial United Key Laboratory of Embedded Systems, Hangzhou 310023, China
| | - Jingqiang Wang
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China; Zhejiang Provincial United Key Laboratory of Embedded Systems, Hangzhou 310023, China
| | - Chengzhe Zhang
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China; Zhejiang Provincial United Key Laboratory of Embedded Systems, Hangzhou 310023, China
| | - Jiafeng Wang
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China; Zhejiang Provincial United Key Laboratory of Embedded Systems, Hangzhou 310023, China
| | - Jiawei Zhang
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China; Zhejiang Provincial United Key Laboratory of Embedded Systems, Hangzhou 310023, China
| | - Yuanjing Feng
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China; Zhejiang Provincial United Key Laboratory of Embedded Systems, Hangzhou 310023, China.
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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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Dong QY, Lin JH, Wu Y, Cao YB, Zhou MX, Chen HJ. White matter microstructural disruption in minimal hepatic encephalopathy: a neurite orientation dispersion and density imaging (NODDI) study. Neuroradiology 2023; 65:1589-1604. [PMID: 37486421 DOI: 10.1007/s00234-023-03201-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 07/11/2023] [Indexed: 07/25/2023]
Abstract
PURPOSE To evaluate the ability of neurite orientation dispersion and density imaging (NODDI) for detecting white matter (WM) microstructural abnormalities in minimal hepatic encephalopathy (MHE). METHODS Diffusion-weighted images, enabling the estimation of NODDI and diffusion tensor imaging (DTI) parameters, were acquired from 20 healthy controls (HC), 22 cirrhotic patients without MHE (NHE), and 15 cirrhotic patients with MHE. Tract-based spatial statistics were used to determine differences in DTI (including fractional anisotropy [FA] and mean/axial/radial diffusivity [MD/AD/RD]) and NODDI parameters (including neurite density index [NDI], orientation dispersion index [ODI], and isotropic volume fraction [ISO]). Voxel-wise analyses of correlations between diffusion parameters and neurocognitive performance determined by Psychometric Hepatic Encephalopathy Score (PHES) were completed. RESULTS MHE patients had extensive NDI reduction and rare ODI reduction, primarily involving the genu and body of corpus callosum and the bilateral frontal lobe, corona radiata, external capsule, anterior limb of internal capsule, temporal lobe, posterior thalamic radiation, and brainstem. The extent of NDI and ODI reduction expanded from NHE to MHE. In both MHE and NHE groups, the extent of NDI change was quite larger than that of FA change. No significant intergroup difference in ISO/MD/AD/RD was observed. Tissue specificity afforded by NODDI revealed the underpinning of FA reduction in MHE. The NDI in left frontal lobe was significantly correlated with PHES. CONCLUSION MHE is characterized by diffuse WM microstructural impairment (especially neurite density reduction). NODDI can improve the detection of WM microstructural impairments in MHE and provides more precise information about MHE-related pathology than DTI.
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Affiliation(s)
- Qiu-Yi Dong
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - Jia-Hui Lin
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - Ye Wu
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, 210094, China
| | - Yun-Bin Cao
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, 350001, China
| | - Min-Xiong Zhou
- College of Medical Imaging, Shang Hai University of Medicine & Health Sciences, Shanghai, 201318, China
| | - Hua-Jun Chen
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, 350001, China.
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Zhao J, Jing B, Liu J, Chen F, Wu Y, Li H. Probing bundle-wise abnormalities in patients infected with human immunodeficiency virus using fixel-based analysis: new insights into neurocognitive impairments. Chin Med J (Engl) 2023; 136:2178-2186. [PMID: 37605986 PMCID: PMC10508508 DOI: 10.1097/cm9.0000000000002829] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2022] [Indexed: 08/23/2023] Open
Abstract
BACKGROUND Changes in white matter (WM) underlie the neurocognitive damages induced by a human immunodeficiency virus (HIV) infection. This study aimed to examine using a bundle-associated fixel-based analysis (FBA) pipeline for investigating the microstructural and macrostructural alterations in the WM of the brain of HIV patients. METHODS This study collected 93 HIV infected patients and 45 age/education/handedness matched healthy controls (HCs) at the Beijing Youan Hospital between January 1, 2016 and December 30, 2016.All HIV patients underwent neurocognitive evaluation and laboratory testing followed by magnetic resonance imaging (MRI) scanning. In order to detect the bundle-wise WM abnormalities accurately, a specific WM bundle template with 56 tracts of interest was firstly generated by an automated fiber clustering method using a subset of subjects. Fixel-based analysis was used to investigate bundle-wise differences between HIV patients and HCs in three perspectives: fiber density (FD), fiber cross-section (FC), and fiber density and cross-section (FDC). The between-group differences were detected by a two-sample t -test with the false discovery rate (FDR) correction ( P <0.05). Furthermore, the covarying relationship in FD, FC and FDC between any pair of bundles was also accessed by the constructed covariance networks, which was subsequently compared between HIV and HCs via permutation t -tests. The correlations between abnormal WM metrics and the cognitive functions of HIV patients were explored via partial correlation analysis after controlling age and gender. RESULTS Among FD, FC and FDC, FD was the only metric that showed significant bundle-wise alterations in HIV patients compared to HCs. Increased FD values were observed in the bilateral fronto pontine tract, corona radiata frontal, left arcuate fasciculus, left corona radiata parietal, left superior longitudinal fasciculus III, and right superficial frontal parietal (SFP) (all FDR P <0.05). In bundle-wise covariance network, HIV patients displayed decreased FD and increased FC covarying patterns in comparison to HC ( P <0.05) , especially between associated pathways. Finally, the FCs of several tracts exhibited a significant correlation with language and attention-related functions. CONCLUSIONS Our study demonstrated the utility of FBA on detecting the WM alterations related to HIV infection. The bundle-wise FBA method provides a new perspective for investigating HIV-induced microstructural and macrostructural WM-related changes, which may help to understand cognitive dysfunction in HIV patients thoroughly.
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Affiliation(s)
- Jing Zhao
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100069, China
- Department of Radiology, Beijing Youan Hospital, Capital Medical University, Beijing 100069, China
| | - Bin Jing
- School of Biomedical Engineering, Capital Medical University, Beijing 100069, China
- Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application,School of Biomedical Engineering, Capital Medical University, Beijing 100069, China
| | - Jiaojiao Liu
- Department of Radiology, Beijing Youan Hospital, Capital Medical University, Beijing 100069, China
| | - Feng Chen
- Department of Radiology, Beijing Youan Hospital, Capital Medical University, Beijing 100069, China
| | - Ye Wu
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, Jiangsu 210094, China
| | - Hongjun Li
- School of Biological Science and Medical Engineering, Beihang University, Beijing 100069, China
- Department of Radiology, Beijing Youan Hospital, Capital Medical University, Beijing 100069, China
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Li J, Li J, Shen L, Wang H, Zheng T, Hui Y, Li X. Investigating the causal association of postpartum depression with cerebrovascular diseases and cognitive impairment: a Mendelian randomization study. Front Psychiatry 2023; 14:1196055. [PMID: 37426101 PMCID: PMC10324563 DOI: 10.3389/fpsyt.2023.1196055] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/29/2023] [Accepted: 05/18/2023] [Indexed: 07/11/2023] Open
Abstract
Background Postpartum depression (PPD) is considered the most widespread puerperium complication. The associations of major depressive disorder with certain types of cerebrovascular diseases and cognitive function have been proposed, but the potential causal effects of PPD on these phenotypes are still unknown. Methods A Mendelian randomization (MR) research design with various methods (e.g., inverse-variance weighted method and MR pleiotropy residual sum and outlier test) was adopted to establish a causal relationship between PPD with cerebrovascular diseases and cognitive impairment. Results No causal relationship between PPD with carotid intima media thickness and cerebrovascular diseases (i.e., stroke, ischemic stroke, and cerebral aneurysm) was found. However, MR analyses indicated a causal association between PPD and decreased cognitive function (P = 3.55 × 10-3), which remained significant even after multiple comparison corrections using the Bonferroni method. Sensitivity analyses using weighted median and MR-Egger methods indicated a consistent direction of the association. Conclusion The causal association between PPD and cognitive impairment indicates that cognitive impairment is a critical aspect of PPD and thus cannot be regarded as an epiphenomenon. Addressing cognitive impairment and lessening the symptoms associated with PPD independently play significant roles in the treatment of PPD.
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Affiliation(s)
- Jia Li
- Department of Nursing, Zhuhai Campus of Zunyi Medical University, Guangdong, China
| | - Jinqiu Li
- Department of Nursing, Zhuhai Campus of Zunyi Medical University, Guangdong, China
| | - Lan Shen
- Department of Nursing, Zhuhai Campus of Zunyi Medical University, Guangdong, China
| | - Huan Wang
- Department of Pediatrics, The Fifth Affiliated Hospital of Zunyi Medical University, Guangdong, China
| | - Tian Zheng
- Department of Nursing, Zhuhai Campus of Zunyi Medical University, Guangdong, China
| | - Ying Hui
- Department of Nursing, Zhuhai Campus of Zunyi Medical University, Guangdong, China
| | - Xiaoxuan Li
- Department of Obstetrics, Zhuhai Maternity and Child Health Care Hospital, Guangdong, China
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Levitt JJ, Zhang F, Vangel M, Nestor PG, Rathi Y, Cetin-Karayumak S, Kubicki M, Coleman MJ, Lewandowski KE, Holt DJ, Keshavan M, Bouix S, Öngür D, Breier A, Shenton ME, O'Donnell LJ. The organization of frontostriatal brain wiring in non-affective early psychosis compared with healthy subjects using a novel diffusion imaging fiber cluster analysis. Mol Psychiatry 2023; 28:2301-2311. [PMID: 37173451 DOI: 10.1038/s41380-023-02031-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Revised: 02/13/2023] [Accepted: 03/08/2023] [Indexed: 05/15/2023]
Abstract
BACKGROUND Alterations in brain connectivity may underlie neuropsychiatric conditions such as schizophrenia. We here assessed the degree of convergence of frontostriatal fiber projections in 56 young adult healthy controls (HCs) and 108 matched Early Psychosis-Non-Affective patients (EP-NAs) using our novel fiber cluster analysis of whole brain diffusion magnetic resonance imaging tractography. METHODS Using whole brain tractography and our fiber clustering methodology on harmonized diffusion magnetic resonance imaging data from the Human Connectome Project for Early Psychosis we identified 17 white matter fiber clusters that connect frontal cortex (FCtx) and caudate (Cd) per hemisphere in each group. To quantify the degree of convergence and, hence, topographical relationship of these fiber clusters, we measured the inter-cluster mean distances between the endpoints of the fiber clusters at the level of the FCtx and of the Cd, respectively. RESULTS We found (1) in both groups, bilaterally, a non-linear relationship, yielding convex curves, between FCtx and Cd distances for FCtx-Cd connecting fiber clusters, driven by a cluster projecting from inferior frontal gyrus; however, in the right hemisphere, the convex curve was more flattened in EP-NAs; (2) that cluster pairs in the right (p = 0.03), but not left (p = 0.13), hemisphere were significantly more convergent in HCs vs EP-NAs; (3) in both groups, bilaterally, similar clusters projected significantly convergently to the Cd; and, (4) a significant group by fiber cluster pair interaction for 2 right hemisphere fiber clusters (numbers 5, 11; p = .00023; p = .00023) originating in selective PFC subregions. CONCLUSIONS In both groups, we found the FCtx-Cd wiring pattern deviated from a strictly topographic relationship and that similar clusters projected significantly more convergently to the Cd. Interestingly, we also found a significantly more convergent pattern of connectivity in HCs in the right hemisphere and that 2 clusters from PFC subregions in the right hemisphere significantly differed in their pattern of connectivity between groups.
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Affiliation(s)
- J J Levitt
- Department of Psychiatry, VA Boston Healthcare System, Brockton Division, Brockton, MA, 02301, USA.
- Harvard Medical School, Boston, MA, 02115, USA.
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA.
| | - F Zhang
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - M Vangel
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
| | - P G Nestor
- Department of Psychiatry, VA Boston Healthcare System, Brockton Division, Brockton, MA, 02301, USA
- Harvard Medical School, Boston, MA, 02115, USA
- Department of Psychology, University of Massachusetts, Boston, MA, 02125, USA
| | - Y Rathi
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
| | - S Cetin-Karayumak
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - M Kubicki
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
| | - M J Coleman
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
| | - K E Lewandowski
- McLean Hospital, Harvard Medical School, Belmont, MA, 02478, USA
| | - D J Holt
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
| | - M Keshavan
- Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, 02215, USA
| | - S Bouix
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Department of Software Engineering and Information Technology, École de technologie supérieure, Université du Québec, Montréal, QC, H3C 1K3, Canada
| | - D Öngür
- McLean Hospital, Harvard Medical School, Belmont, MA, 02478, USA
| | - A Breier
- Indiana University School of Medicine, Indianapolis, IN, 46202, USA
| | - M E Shenton
- Psychiatry Neuroimaging Laboratory, Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
- Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, 02114, USA
| | - L J O'Donnell
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA
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Xu EP, Nguyen L, Leibenluft E, Stange JP, Linke JO. A meta-analysis on the uncinate fasciculus in depression. Psychol Med 2023; 53:2721-2731. [PMID: 37051913 PMCID: PMC10235669 DOI: 10.1017/s0033291723000107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/22/2022] [Revised: 12/29/2022] [Accepted: 01/09/2023] [Indexed: 04/14/2023]
Abstract
Aberrant microstructure of the uncinate fasciculus (UNC), a white matter (WM) tract implicated in emotion regulation, has been hypothesized as a neurobiological mechanism of depression. However, studies testing this hypothesis have yielded inconsistent results. The present meta-analysis consolidates evidence from 44 studies comparing fractional anisotropy (FA) and radial diffusivity (RD), two metrics characterizing WM microstructure, of the UNC in individuals with depression (n = 5016) to healthy individuals (n = 18 425). We conduct meta-regressions to identify demographic and clinical characteristics that contribute to cross-study heterogeneity in UNC findings. UNC FA was reduced in individuals with depression compared to healthy individuals. UNC RD was comparable between individuals with depression and healthy individuals. Comorbid anxiety explained inter-study heterogeneity in UNC findings. Depression is associated with perturbations in UNC microstructure, specifically with respect to UNC FA and not UNC RD. The association between depression and UNC microstructure appears to be moderated by anxiety. Future work should unravel the cellular mechanisms contributing to aberrant UNC microstructure in depression; clarify the relationship between UNC microstructure, depression, and anxiety; and link UNC microstructure to psychological processes, such as emotion regulation.
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Affiliation(s)
- Ellie P. Xu
- Department of Psychology, University of Southern California, Los Angeles, CA, USA
| | - Lynn Nguyen
- National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Ellen Leibenluft
- National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
| | - Jonathan P. Stange
- Department of Psychology, University of Southern California, Los Angeles, CA, USA
- Department of Psychiatry and Behavioral Sciences, University of Southern California, Los Angeles, CA, USA
| | - Julia O. Linke
- National Institute of Mental Health, National Institutes of Health, Bethesda, MD, USA
- Louis A. Faillace, MD, Department of Psychiatry and Behavioral Sciences, University of Texas Health Science Center at Houston, Houston, TX, USA
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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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9
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Zhang C, Yuan Y, Sang T, Yu L, Yu Y, Liu X, Zhou W, Zeng Q, Wang J, Peng G, Feng Y. Local white matter abnormalities in Parkinson's disease with mild cognitive impairment: Assessed with neurite orientation dispersion and density imaging. J Neurosci Res 2023; 101:1154-1169. [PMID: 36854050 DOI: 10.1002/jnr.25179] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Revised: 12/06/2022] [Accepted: 01/31/2023] [Indexed: 03/02/2023]
Abstract
Mild cognitive impairment is a nonmotor complication in Parkinson's disease (PD) that have a high risk of developing dementia. White matter is associated with cognitive function in PD and the alterations may occur before the symptoms of the disease. Previous diffusion tensor imaging (DTI) studies lacked specificity to characterize the concrete contributions of distinct white matter tissue properties. This may lead to inconsistent conclusions about the alteration of white matter microstructure. Here, we used neurite orientation dispersion and density imaging (NODDI) and white matter fiber clustering method to uncover local white matter microstructures in PD with mild cognitive impairment (PD-MCI). This study included 23 PD-MCI and 20 PD with normal cognition (PD-NC) and 21 healthy controls (HC). To probe specific and fine-grained differences, metrics of NODDI and DTI in white matter fiber clusters were evaluated using along-tract analysis. Our results showed that PD-MCI patients had significantly lower neurite density index (NDI) and orientation dispersion index (ODI) in white matter fiber clusters in the prefrontal region. Correlation analysis and receiver operating characteristic (ROC) analysis revealed that the diagnostic performance of NODDI-derived metrics in cingulum bundle (2 clusters) and thalamo-frontal (2 clusters) were superior to DTI metrics. Our study provides a more specific insight to uncover local white matter abnormalities in PD-MCI, which benefit understanding the underlying mechanism of cognitive decline in PD and predicting the disease in advance.
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Affiliation(s)
- Chengzhe Zhang
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Yuan Yuan
- Department of Neurology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Tian Sang
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Lihua Yu
- Department of Neurology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yan Yu
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Xiaoming Liu
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Wenyang Zhou
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Qingrun Zeng
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Jingqiang Wang
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Guoping Peng
- Department of Neurology, The First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Yuanjing Feng
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
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10
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Lin JH, Chen XH, Wu Y, Cao YB, Chen HJ, Huang NX. Altered isotropic volume fraction in gray matter after sleep deprivation and its association with visuospatial memory: A neurite orientation dispersion and density imaging study. Front Neurosci 2023; 17:1144802. [PMID: 37034160 PMCID: PMC10076534 DOI: 10.3389/fnins.2023.1144802] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Accepted: 03/06/2023] [Indexed: 04/11/2023] Open
Abstract
Background and aims Diffusion magnetic resonance imaging (dMRI) studies have revealed microstructural abnormalities in white matter resulting from sleep deprivation (SD). This study aimed to adopt neurite orientation dispersion and density imaging (NODDI) to investigate the effect of SD on gray matter (GM) microstructural properties and its association to visuospatial memory (VSM). Methods Twenty-four healthy women underwent two sessions of dMRI scanning and visuospatial ability assessment by Complex Figure Test (CFT), once during rested wakefulness (RW) and once after 24 h of SD. We calculated NODDI metrics, including intracellular volume fraction (ICVF), orientation dispersion index (ODI), and isotropic volume fraction (ISO). Differences in NODDI-related metrics between RW and SD were determined using a voxel-wise paired t-test. We identified an association between NODDI metrics and CFT results using Spearman's correlation coefficient. Results Sleep deprivation worsened subjects' performance in the delayed-CFT trial. We observed no significant difference in ICVF and ODI between RW and SD. After SD, subjects showed decreases in ISO, primarily in the prefrontal cortex and temporal lobe, while exhibiting ISO increases in the anterior and posterior cerebellar lobe and cerebellar vermis. Furthermore, ISO change in the left superior, middle and inferior frontal gyrus was significantly correlated with completion time change in delayed-CFT trial performance. Conclusion Our results suggested that SD hardly affected the density and spatial organization of neurites in GM, but the extra-neurite water molecule diffusion process was affected (perhaps resulting from neuroinflammation), which contributed to VSM dysfunction.
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Affiliation(s)
- Jia-Hui Lin
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, China
| | - Xu-Hui Chen
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, China
| | - Ye Wu
- School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing, China
| | - Yun-Bin Cao
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, China
| | - Hua-Jun Chen
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, China
- *Correspondence: Hua-Jun Chen,
| | - Nao-Xin Huang
- Department of Radiology, Fujian Medical University Union Hospital, Fuzhou, China
- Nao-Xin Huang,
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11
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Barch DM, Hua X, Kandala S, Harms MP, Sanders A, Brady R, Tillman R, Luby JL. White matter alterations associated with lifetime and current depression in adolescents: Evidence for cingulum disruptions. Depress Anxiety 2022; 39:881-890. [PMID: 36321433 PMCID: PMC10848013 DOI: 10.1002/da.23294] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/29/2022] [Revised: 10/15/2022] [Accepted: 10/22/2022] [Indexed: 12/12/2022] Open
Abstract
INTRODUCTION Compared to research on adults with depression, relatively little work has examined white matter microstructure differences in depression arising earlier in life. Here we tested hypotheses about disruptions to white matter structure in adolescents with current and past depression, with an a priori focus on the cingulum bundles, uncinate fasciculi, corpus collosum, and superior longitudinal fasciculus. METHODS One hundred thirty-one children from the Preschool Depression Study were assessed using a Human Connectome Project style diffusion imaging sequence which was processed with HCP pipelines and TRACULA to generate estimates of fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD) and radial diffusivity (RD). RESULTS We found that reduced FA, reduced AD, and increased RD in the dorsal cingulum bundle were associated with a lifetime diagnosis of major depression and greater cumulative and current depression severity. Reduced FA, reduced AD, and increased RD in the ventral cingulum were associated with greater cumulative depression severity. CONCLUSION These findings support the emergence of white matter differences detected in adolescence associated with earlier life and concurrent depression. They also highlight the importance of connections of the cingulate to other brain regions in association with depression, potentially relevant to understanding emotion dysregulation and functional connectivity differences in depression.
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Affiliation(s)
- Deanna M. Barch
- Departments of Psychological & Brain Sciences, Psychiatry, and Radiology, Washington University in St. Louis, St. Louis, Missouri, USA
- Department of Psychiatry, Washington University in St. Louis, St. Louis, Missouri, USA
- Department of Radiology, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Xiao Hua
- Department of Psychological & Brain Sciences, Imaging Sciences Program, Washington University in St. Louis, Missouri, St. Louis, USA
| | - Sridhar Kandala
- Department of Psychiatry, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Michael P. Harms
- Department of Psychiatry, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Ashley Sanders
- Department of Psychiatry, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Rebecca Brady
- Department of Psychiatry, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Rebecca Tillman
- Department of Psychiatry, Washington University in St. Louis, St. Louis, Missouri, USA
| | - Joan L. Luby
- Department of Psychiatry, Washington University in St. Louis, St. Louis, Missouri, USA
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12
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Wang J, Liu X, Wang X, Hu Y, Zeng Q, Lin Z, Xiong N, Feng Y. Alterations of white matter tracts and topological properties of structural networks in hemifacial spasm. NMR IN BIOMEDICINE 2022; 35:e4756. [PMID: 35488376 DOI: 10.1002/nbm.4756] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/03/2021] [Revised: 03/31/2022] [Accepted: 04/28/2022] [Indexed: 06/14/2023]
Abstract
Hemifacial spasm (HFS) is characterized by involuntary and paroxysmal muscle contractions on the hemiface. It is generally believed that HFS is caused by neurovascular compression at the root exit zone of the facial nerve. In recent years, the structural alterations of brains with HFS have aroused growing concern. However, little attention has been directed towards the possible involvement of specific white matter (WM) tracts and the topological properties of structural networks in HFS. In the present study, diffusion magnetic resonance imaging tractography was utilized to construct structural networks and perform tractometric analysis. The diffusion tensor imaging scalar parameters along with the WM tracts, and the topological parameters of global networks and subnetworks, were assessed in 62 HFS patients and 57 demographically matched healthy controls (HCs). Moreover, we investigated the correlation of these parameters with disease-clinical-level (DCL) and disease-duration-time (DDT) of HFS patients. Compared with HCs, HFS patients had additional hub regions including the amygdala, ventromedial putamen, lateral occipital cortex, and rostral cuneus gyrus. Furthermore, HFS patients showed significant alternations with specific topological properties in some structural subnetworks, including the limbic, default mode, dorsal attention, somato-motor, and control networks, as well as diffusion properties in some WM tracts, including the superior longitudinal fasciculus, cingulum bundle, thalamo-frontal, and corpus callosum. These subnetworks and tracts were associated with the regulation of emotion, motor function, vision, and attention. Notably, we also found that the parameters with subnetworks and tracts exhibited correlations with DCL and DDT. In addition to corroborating previous findings in HFS, this study demonstrates the changed microstructures in specific locations along with the fiber tracts and changed topological properties in structural subnetworks.
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Affiliation(s)
- Jingqiang Wang
- Institution of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Xiaoming Liu
- Department of Radiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
- Hubei Province Key Laboratory of Molecular Imaging, Wuhan, Hubei, China
| | - Xinyi Wang
- Department of Neurology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yuhuan Hu
- Institution of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Qingrun Zeng
- Institution of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Zhicheng Lin
- Mclean Hospital, Harvard Medical School, Belmont, Massachusetts, USA
| | - Nian Xiong
- Department of Neurology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, China
| | - Yuanjing Feng
- Institution of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
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13
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Wang J, Wen C, Li J, Chen J, Feng Y. Automated quantification of brain connectivity in Alzheimer's disease using ClusterMetric. Neurosci Lett 2022; 785:136724. [PMID: 35697157 DOI: 10.1016/j.neulet.2022.136724] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2022] [Revised: 04/26/2022] [Accepted: 06/08/2022] [Indexed: 11/18/2022]
Abstract
Diffusion magnetic resonance imaging tractography allows investigating brain structural connections in a noninvasive way and has been widely used for understanding neurological disease. Quantification of brain connectivity along with its length by dividing a fiber bundle into multiple segments (node) is a powerful approach to assess biological properties, which is termed as tractometry. However, current tractometry methods face challenges in node identification along with the length of complex bundles whose morphology is difficult to summarize. In addition, the anatomic measure reflecting the macroscopic fiber cross-section has not been followed in previous tractometry. In this paper, we propose an automated fiber bundle quantification, which we refer to as ClusterMetric. The ClusterMetric uses a data-driven approach to identify fiber clusters corresponding to subdivisions of the white matter anatomy and identify consistent space nodes along the length of clusters across individuals. The proposed method is demonstrated by applicating to our collected dataset including 23 Alzheimer's disease (AD) patients and 22 healthy controls (HCs) and a public dataset of ADNI including 53 AD patients and 85 HCs. The altered white matter tracts in AD group are observed using both datasets, which involve several major fiber tracts including the corpus callosum, corona-radiata-frontal, arcuate fasciculus, inferior occipito-frontal fasciculus, uncinate fasciculus, thalamo-frontal, superior longitudinal fasciculus, inferior cerebellar peduncle, cingulum bundle, and extreme capsule. These fiber clusters represent the white matter connections that could be most affected in AD, suggesting the ability of our method in identifying potential abnormalities specific to local regions within a fiber cluster.
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Affiliation(s)
- Jingqiang Wang
- Institution of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Caiyun Wen
- The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Jinwen Li
- Institution of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
| | | | - Yuanjing Feng
- Institution of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou, China.
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14
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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: 72] [Impact Index Per Article: 36.0] [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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15
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Kai J, Khan AR. Assessing the Reliability of Template-Based Clustering for Tractography in Healthy Human Adults. Front Neuroinform 2022; 16:777853. [PMID: 35250526 PMCID: PMC8891507 DOI: 10.3389/fninf.2022.777853] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/15/2021] [Accepted: 01/06/2022] [Indexed: 11/21/2022] Open
Abstract
Tractography is a non-invasive technique to investigate the brain’s structural pathways (also referred to as tracts) that connect different brain regions. A commonly used approach for identifying tracts is with template-based clustering, where unsupervised clustering is first performed on a template in order to label corresponding tracts in unseen data. However, the reliability of this approach has not been extensively studied. Here, an investigation into template-based clustering reliability was performed, assessing the output from two datasets: Human Connectome Project (HCP) and MyConnectome project. The effect of intersubject variability on template-based clustering reliability was investigated, as well as the reliability of both deep and superficial white matter tracts. Identified tracts were evaluated by assessing Euclidean distances from a dataset-specific tract average centroid, the volumetric overlap across corresponding tracts, and along-tract agreement of quantitative values. Further, two template-based techniques were employed to evaluate the reliability of different clustering approaches. Reliability assessment can increase the confidence of a tract identifying technique in future applications to study pathways of interest. The two different template-based approaches exhibited similar reliability for identifying both deep white matter tracts and the superficial white matter.
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Affiliation(s)
- Jason Kai
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, The University of Western Ontario, London, ON, Canada
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, The University of Western Ontario, London, ON, Canada
| | - Ali R. Khan
- Department of Medical Biophysics, Schulich School of Medicine and Dentistry, The University of Western Ontario, London, ON, Canada
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, The University of Western Ontario, London, ON, Canada
- *Correspondence: Ali R. Khan,
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16
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Huang J, Li M, Zeng Q, Xie L, He J, Chen G, Liang J, Li M, Feng Y. Automatic oculomotor nerve identification based on
data‐driven
fiber clustering. Hum Brain Mapp 2022; 43:2164-2180. [PMID: 35092135 PMCID: PMC8996358 DOI: 10.1002/hbm.25779] [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: 09/07/2021] [Revised: 12/09/2021] [Accepted: 12/26/2021] [Indexed: 11/10/2022] Open
Abstract
The oculomotor nerve (OCN) is the main motor nerve innervating eye muscles and can be involved in multiple flammatory, compressive, or pathologies. The diffusion magnetic resonance imaging (dMRI) tractography is now widely used to describe the trajectory of the OCN. However, the complex cranial structure leads to difficulties in fiber orientation distribution (FOD) modeling, fiber tracking, and region of interest (ROI) selection. Currently, the identification of OCN relies on expert manual operation, resulting in challenges, such as the carries high clinical, time‐consuming, and labor costs. Thus, we propose a method that can automatically identify OCN from dMRI tractography. First, we choose the multi‐shell multi‐tissue constraint spherical deconvolution (MSMT‐CSD) FOD estimation model and deterministic tractography to describe the 3D trajectory of the OCN. Then, we rely on the well‐established computational pipeline and anatomical expertise to create a data‐driven OCN tractography atlas from 40 HCP data. We identify six clusters belonging to the OCN from the atlas, including the structures of three kinds of positional relationships (pass between, pass through, and go around) with the red nuclei and two kinds of positional relationships with medial longitudinal fasciculus. Finally, we apply the proposed OCN atlas to identify the OCN automatically from 40 new HCP subjects and two patients with brainstem cavernous malformation. In terms of spatial overlap and visualization, experiment results show that the automatically and manually identified OCN fibers are consistent. Our proposed OCN atlas provides an effective tool for identifying OCN by avoiding the traditional selection strategy of ROIs.
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Affiliation(s)
- Jiahao Huang
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology Hangzhou China
- Zhejiang Provincial United Key Laboratory of Embedded Systems Hangzhou China
| | - Mengjun Li
- Department of Radiology, Second Xiangya Hospital Central South University Hunan China
- Department of Neurosurgery Capital Medical University Xuanwu Hospital Beijing China
| | - Qingrun Zeng
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology Hangzhou China
- Zhejiang Provincial United Key Laboratory of Embedded Systems Hangzhou China
| | - Lei Xie
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology Hangzhou China
- Zhejiang Provincial United Key Laboratory of Embedded Systems Hangzhou China
| | - Jianzhong He
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology Hangzhou China
- Zhejiang Provincial United Key Laboratory of Embedded Systems Hangzhou China
| | - Ge Chen
- Department of Neurosurgery Capital Medical University Xuanwu Hospital Beijing China
| | - Jiantao Liang
- Department of Neurosurgery Capital Medical University Xuanwu Hospital Beijing China
| | - Mingchu Li
- Department of Neurosurgery Capital Medical University Xuanwu Hospital Beijing China
| | - Yuanjing Feng
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology Hangzhou China
- Zhejiang Provincial United Key Laboratory of Embedded Systems Hangzhou China
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17
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Li M, Yeh FC, Zeng Q, Wu X, Wang X, Zhu Z, Liu X, Liang J, Chen G, Zhang H, Feng Y, Li M. The trajectory of the medial longitudinal fasciculus in the human brain: A diffusion imaging-based tractography study. Hum Brain Mapp 2021; 42:6070-6086. [PMID: 34597450 PMCID: PMC8596984 DOI: 10.1002/hbm.25670] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2021] [Revised: 09/04/2021] [Accepted: 09/10/2021] [Indexed: 01/23/2023] Open
Abstract
The aim of this study is to investigate the trajectory of medial longitudinal fasciculus (MLF) and explore its anatomical relationship with the oculomotor nerve using tractography technique. The MLF and oculomotor nerve were reconstructed at the same time with preset three region of interests (ROIs): one set at the area of rostral midbrain, one placed on the MLF area at the upper pons, and one placed at the cisternal part of the oculomotor nerve. This mapping protocol was tested in an HCP‐1065 template, 35 health subjects from Massachusetts General Hospital (MGH), 20 healthy adults and 6 brainstem cavernous malformation (BCM) patients with generalized q‐sampling imaging (GQI)‐based tractography. Finally, the 200 μm brainstem template from Center for In Vivo Microscopy, Duke University (Duke CIVM), was used to validate the trajectory of reconstructed MLF. The MLF and oculomotor nerve were reconstructed in the HCP‐1065 template, 35 MGH health subjects, 20 healthy adults and 6 BCM patients. The MLF was in conjunction with the ipsilateral mesencephalic part of the oculomotor nerve. The displacement of MLF was identified in all BCM patients. Decreased QA, RDI and FA were found in the MLF of lesion side, indicating axonal loss and/or edema of displaced MLF. The reconstructed MLF in Duke CIVM brainstem 200 μm template corresponded well with histological anatomy. The MLF and oculomotor nerve were visualized accurately with our protocol using GQI‐based fiber tracking. This GQI‐based tractography is an important tool in the reconstruction and evaluation of MLF.
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Affiliation(s)
- Mengjun Li
- Department of Neurosurgery, Capital Medical University Xuanwu Hospital, Beijing, China
| | - Fang-Cheng Yeh
- Department of Neurological Surgery, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA.,Department of Bioengineering, University of Pittsburgh School of Medicine, Pittsburgh, Pennsylvania, USA
| | - Qingrun Zeng
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou, China.,Zhejiang Provincial United Key Laboratory of Embedded Systems, Hangzhou, China
| | - Xiaolong Wu
- Department of Neurosurgery, Capital Medical University Xuanwu Hospital, Beijing, China
| | - Xu Wang
- Department of Neurosurgery, Capital Medical University Xuanwu Hospital, Beijing, China
| | - Zixin Zhu
- Department of Neurosurgery, Capital Medical University Xuanwu Hospital, Beijing, China
| | - Xiaohai Liu
- Department of Neurosurgery, Capital Medical University Xuanwu Hospital, Beijing, China
| | - Jiantao Liang
- Department of Neurosurgery, Capital Medical University Xuanwu Hospital, Beijing, China
| | - Ge Chen
- Department of Neurosurgery, Capital Medical University Xuanwu Hospital, Beijing, China
| | - Hongqi Zhang
- Department of Neurosurgery, Capital Medical University Xuanwu Hospital, Beijing, China
| | - Yuanjing Feng
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou, China.,Zhejiang Provincial United Key Laboratory of Embedded Systems, Hangzhou, China
| | - Mingchu Li
- Department of Neurosurgery, Capital Medical University Xuanwu Hospital, Beijing, China
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18
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Zeng Q, Li M, Yuan S, He J, Wang J, Chen Z, Zhao C, Chen G, Liang J, Li M, Feng Y. Automated facial-vestibulocochlear nerve complex identification based on data-driven tractography clustering. NMR IN BIOMEDICINE 2021; 34:e4607. [PMID: 34486766 DOI: 10.1002/nbm.4607] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/11/2021] [Revised: 08/04/2021] [Accepted: 08/07/2021] [Indexed: 06/13/2023]
Abstract
Small size and intricate anatomical environment are the main difficulties facing tractography of the facial-vestibulocochlear nerve complex (FVN), and lead to challenges in fiber orientation distribution (FOD) modeling, fiber tracking, region-of-interest selection, and fiber filtering. Experts need rich experience in anatomy and tractography, as well as substantial labor costs, to identify the FVN. Thus, we present a pipeline to identify the FVN automatically, in what we believe is the first study of the automated identification of the FVN. First, we created an FVN template. Forty high-resolution multishell data were used to perform data-driven fiber clustering based on the multishell multitissue constraint spherical deconvolution FOD model and deterministic tractography. We selected the brainstem and cerebellum (BS-CB) region as the seed region and removed the fibers that reach other brain regions. We then performed spectral fiber clustering twice. The first clustering was to create a BS-CB atlas and separate the fibers that pass through the cerebellopontine angle, and the other one was to extract the FVN. Second, we registered the subject-specific fibers in the space of the FVN template and assigned each fiber to the closest cluster to identify the FVN automatically by spectral embedding. We applied the proposed method to different acquirement sites, including two different healthy datasets and two tumor patient datasets. Experimental results showed that our automatic identification results have ideal colocalization with expert manual identification in terms of spatial overlap and visualization. Importantly, we successfully applied our method to tumor patient data. The FVNs identified by the proposed method were in agreement with intraoperative findings.
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Affiliation(s)
- Qingrun Zeng
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
- Zhejiang Provincial United Key Laboratory of Embedded Systems, Hangzhou, China
| | - Mengjun Li
- Department of Radiology, Second Xiangya Hospital, Central South University, Changsha, China
- Department of Neurosurgery, Capital Medical University Xuanwu Hospital, Beijing, China
| | - Shaonan Yuan
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
- Zhejiang Provincial United Key Laboratory of Embedded Systems, Hangzhou, China
| | - Jianzhong He
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
- Zhejiang Provincial United Key Laboratory of Embedded Systems, Hangzhou, China
| | - Jingqiang Wang
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
- Zhejiang Provincial United Key Laboratory of Embedded Systems, Hangzhou, China
| | - Zan Chen
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
- Zhejiang Provincial United Key Laboratory of Embedded Systems, Hangzhou, China
| | - Changchen Zhao
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
- Zhejiang Provincial United Key Laboratory of Embedded Systems, Hangzhou, China
| | - Ge Chen
- Department of Neurosurgery, Capital Medical University Xuanwu Hospital, Beijing, China
| | - Jiantao Liang
- Department of Neurosurgery, Capital Medical University Xuanwu Hospital, Beijing, China
| | - Mingchu Li
- Department of Neurosurgery, Capital Medical University Xuanwu Hospital, Beijing, China
| | - Yuanjing Feng
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
- Zhejiang Provincial United Key Laboratory of Embedded Systems, Hangzhou, China
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Li M, Ribas EC, Zhang Z, Wu X, Wang X, Liu X, Liang J, Chen G, Li M. Tractography of the ansa lenticularis in the human brain. Clin Anat 2021; 35:269-279. [PMID: 34535922 DOI: 10.1002/ca.23788] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Revised: 09/02/2021] [Accepted: 09/08/2021] [Indexed: 12/31/2022]
Abstract
The aim of this study was to make a thorough investigation of the trajectory of the ansa lenticularis (AL) and its subcomponents using high-resolution fiber-tracking tractography. The subcomponents of the AL were reconstructed from one region of interest (ROI) in the area of the globus pallidus combined with another ROI in the red nucleus, substantia nigra, subthalamic nucleus, or thalamus. This fiber-tracking protocol was tested in an HCP-1065 template, 35 healthy subjects from Massachusetts General Hospital (MGH), and 20 healthy subjects from the human connectome project (HCP) using generalized q-sampling imaging (GQI)-based tractography. Quantitative anisotropy and fractional anisotropy were also computed for the AL subcomponents. The subcomponents of the AL could be reconstructed in the HCP-1065 template, 35 MGH healthy subjects, and 20 HCP healthy subjects. The AL descends from the globus pallidus and joins the ansa peduncularis for a short distance, subdividing later into fibers that continue separately to the red nucleus, substantia nigra, subthalamic nucleus, and thalamus. The study demonstrated the trajectory of the ansa lenticularis and its subcomponents using GQI-based tractography, improving our understanding of the anatomical connectivity between the globus pallidus and the thalamo-subthalamic region in the human brain. One Sentence Summary The investigation of the ansa lenticularis and its subcomponents using high-resolution diffusion images based tractography.
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Affiliation(s)
- Mengjun Li
- Department of Neurosurgery, Samii Clinical Neuroanatomy Research & Education Center, Capital Medical University Xuanwu Hospital, China International Neuroscience Institute (China-INI), Beijing, China
| | - Eduardo Carvalhal Ribas
- Division of Neurosurgery, Hospital das Clínicas, University of São Paulo Medical School, São Paulo, Brazil
| | - Zhiping Zhang
- Department of Neurosurgery, Samii Clinical Neuroanatomy Research & Education Center, Capital Medical University Xuanwu Hospital, China International Neuroscience Institute (China-INI), Beijing, China
| | - Xiaolong Wu
- Department of Neurosurgery, Samii Clinical Neuroanatomy Research & Education Center, Capital Medical University Xuanwu Hospital, China International Neuroscience Institute (China-INI), Beijing, China
| | - Xu Wang
- Department of Neurosurgery, Samii Clinical Neuroanatomy Research & Education Center, Capital Medical University Xuanwu Hospital, China International Neuroscience Institute (China-INI), Beijing, China
| | - Xiaohai Liu
- Department of Neurosurgery, Samii Clinical Neuroanatomy Research & Education Center, Capital Medical University Xuanwu Hospital, China International Neuroscience Institute (China-INI), Beijing, China
| | - Jiantao Liang
- Department of Neurosurgery, Samii Clinical Neuroanatomy Research & Education Center, Capital Medical University Xuanwu Hospital, China International Neuroscience Institute (China-INI), Beijing, China
| | - Ge Chen
- Department of Neurosurgery, Samii Clinical Neuroanatomy Research & Education Center, Capital Medical University Xuanwu Hospital, China International Neuroscience Institute (China-INI), Beijing, China
| | - Mingchu Li
- Department of Neurosurgery, Samii Clinical Neuroanatomy Research & Education Center, Capital Medical University Xuanwu Hospital, China International Neuroscience Institute (China-INI), Beijing, China
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20
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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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21
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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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22
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Xu C, Sun G, Liang R, Xu X. Vector Field Streamline Clustering Framework for Brain Fiber Tract Segmentation. IEEE Trans Cogn Dev Syst 2021. [DOI: 10.1109/tcds.2021.3094555] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
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23
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Zhang F, Xie G, Leung L, Mooney MA, Epprecht L, Norton I, Rathi Y, Kikinis R, Al-Mefty O, Makris N, Golby AJ, O'Donnell LJ. Creation of a novel trigeminal tractography atlas for automated trigeminal nerve identification. Neuroimage 2020; 220:117063. [PMID: 32574805 PMCID: PMC7572753 DOI: 10.1016/j.neuroimage.2020.117063] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2020] [Revised: 06/07/2020] [Accepted: 06/14/2020] [Indexed: 12/29/2022] Open
Abstract
Diffusion MRI (dMRI) tractography has been successfully used to study the trigeminal nerves (TGNs) in many clinical and research applications. Currently, identification of the TGN in tractography data requires expert nerve selection using manually drawn regions of interest (ROIs), which is prone to inter-observer variability, time-consuming and carries high clinical and labor costs. To overcome these issues, we propose to create a novel anatomically curated TGN tractography atlas that enables automated identification of the TGN from dMRI tractography. In this paper, we first illustrate the creation of a trigeminal tractography atlas. Leveraging a well-established computational pipeline and expert neuroanatomical knowledge, we generate a data-driven TGN fiber clustering atlas using tractography data from 50 subjects from the Human Connectome Project. Then, we demonstrate the application of the proposed atlas for automated TGN identification in new subjects, without relying on expert ROI placement. Quantitative and visual experiments are performed with comparison to expert TGN identification using dMRI data from two different acquisition sites. We show highly comparable results between the automatically and manually identified TGNs in terms of spatial overlap and visualization, while our proposed method has several advantages. First, our method performs automated TGN identification, and thus it provides an efficient tool to reduce expert labor costs and inter-operator bias relative to expert manual selection. Second, our method is robust to potential imaging artifacts and/or noise that can prevent successful manual ROI placement for TGN selection and hence yields a higher successful TGN identification rate.
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Affiliation(s)
- Fan Zhang
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA.
| | - Guoqiang Xie
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; Department of Neurosurgery, Nuclear Industry 215 Hospital of Shaanxi Province, Xianyang, China
| | - Laura Leung
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; Faculty of Medicine, The Chinese University of Hong Kong, Hong Kong, China
| | - Michael A Mooney
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Lorenz Epprecht
- Department of Otolaryngology, Head and Neck Surgery, University Hospital Zurich, Zurich, Switzerland
| | - Isaiah Norton
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Yogesh Rathi
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Ron Kikinis
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Ossama Al-Mefty
- Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Nikos Makris
- Department of Psychiatry, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; Departments of Psychiatry, Neurology and Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, USA
| | - Alexandra J Golby
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA; Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Lauren J O'Donnell
- Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
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24
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Zhang F, Cetin Karayumak S, Hoffmann N, Rathi Y, Golby AJ, O'Donnell LJ. Deep white matter analysis (DeepWMA): Fast and consistent tractography segmentation. Med Image Anal 2020; 65:101761. [PMID: 32622304 PMCID: PMC7483951 DOI: 10.1016/j.media.2020.101761] [Citation(s) in RCA: 37] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/23/2019] [Revised: 06/16/2020] [Accepted: 06/18/2020] [Indexed: 02/07/2023]
Abstract
White matter tract segmentation, i.e. identifying tractography fibers (streamline trajectories) belonging to anatomically meaningful fiber tracts, is an essential step to enable tract quantification and visualization. In this study, we present a deep learning tractography segmentation method (DeepWMA) that allows fast and consistent identification of 54 major deep white matter fiber tracts from the whole brain. We create a large-scale training tractography dataset of 1 million labeled fiber samples, and we propose a novel 2D multi-channel feature descriptor (FiberMap) that encodes spatial coordinates of points along each fiber. We learn a convolutional neural network (CNN) fiber classification model based on FiberMap and obtain a high fiber classification accuracy of 90.99% on the training tractography data with ground truth fiber labels. Then, the method is evaluated on a test dataset of 597 diffusion MRI scans from six independently acquired populations across genders, the lifespan (1 day - 82 years), and different health conditions (healthy control, neuropsychiatric disorders, and brain tumor patients). We perform comparisons with two state-of-the-art tract segmentation methods. Experimental results show that our method obtains a highly consistent tract segmentation result, where on average over 99% of the fiber tracts are successfully identified across all subjects under study, most importantly, including neonates and patients with space-occupying brain tumors. We also demonstrate good generalization of the method to tractography data from multiple different fiber tracking methods. The proposed method leverages deep learning techniques and provides a fast and efficient tool for brain white matter segmentation in large diffusion MRI tractography datasets.
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Affiliation(s)
- Fan Zhang
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
| | | | - Nico Hoffmann
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Yogesh Rathi
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Alexandra J Golby
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
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25
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Wang J, Zhang F, Zhao C, Zeng Q, He J, O'Donnell LJ, Feng Y. Investigation of local white matter abnormality in Parkinson's disease by using an automatic fiber tract parcellation. Behav Brain Res 2020; 394:112805. [PMID: 32673707 DOI: 10.1016/j.bbr.2020.112805] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/21/2019] [Revised: 07/01/2020] [Accepted: 07/08/2020] [Indexed: 11/18/2022]
Abstract
The deficits of white matter (WM) microstructure are involved during Parkinson's disease (PD) progression. Most current methods identify key WM tracts relying on cortical regions of interest (ROIs). However, such ROI methods can be challenged due to low diffusion anisotropy near the gray matter (GM), which could result in a low sensitivity of tract identification. This work proposes an automatic WM parcellation method to improve the accuracy of WM tract identification and locate abnormal tracts by using sensitive features. The proposed method consists of 1) whole brain WM parcellation using an established fiber clustering method, without using any ROIs, 2) features of fasciculus were calculated to quantify diffusion measures at each equal cross-section along the whole cluster. Then, we use the proposed features to investigate the WM difference in PD compared with healthy controls (HC). We also use these features to investigate the relationship of clinical symptoms and specific fiber tracts. The novelty of the proposed method is that it automatically identifies the abnormal WM fibers in cluster degree. Experiment results indicated that the proposed method had advantage in detecting the local WM abnormality by performing between-group statistical analysis in 30 patients with PD and 28 HC. We found 13 hemisphere clusters and 8 commissural clusters had significant group difference (p < 0.05, corrected by FDR method) in local regions, which belonged to multiple fiber tracts including cingulum bundle (CB), inferior occipito-frontal fasciculus (IoFF), corpus callosum (CC), external capsule (EC), uncinate fasciculus (UF), superior longitudinal fasciculus (SLF) and thalamo front (TF). We also found clusters that had relevance with clinical indices of cognitive function (2 clusters), athletic function (6 clusters), and depressive state (2 clusters) in these significant clusters. From the experiment results, it confirmed the ability of the proposed method to identify potential WM microstructure abnormality.
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Affiliation(s)
- Jingqiang Wang
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Fan Zhang
- Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Changchen Zhao
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Qingrun Zeng
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
| | - Jianzhong He
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
| | | | - Yuanjing Feng
- College of Information Engineering, Zhejiang University of Technology, Hangzhou, China
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26
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Vázquez A, López-López N, Sánchez A, Houenou J, Poupon C, Mangin JF, Hernández C, Guevara P. FFClust: Fast fiber clustering for large tractography datasets for a detailed study of brain connectivity. Neuroimage 2020; 220:117070. [PMID: 32599269 DOI: 10.1016/j.neuroimage.2020.117070] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2019] [Revised: 03/19/2020] [Accepted: 06/16/2020] [Indexed: 01/31/2023] Open
Abstract
Automated methods that can identify white matter bundles from large tractography datasets have several applications in neuroscience research. In these applications, clustering algorithms have shown to play an important role in the analysis and visualization of white matter structure, generating useful data which can be the basis for further studies. This work proposes FFClust, an efficient fiber clustering method for large tractography datasets containing millions of fibers. Resulting clusters describe the whole set of main white matter fascicles present on an individual brain. The method aims to identify compact and homogeneous clusters, which enables several applications. In individuals, the clusters can be used to study the local connectivity in pathological brains, while at population level, the processing and analysis of reproducible bundles, and other post-processing algorithms can be carried out to study the brain connectivity and create new white matter bundle atlases. The proposed method was evaluated in terms of quality and execution time performance versus the state-of-the-art clustering techniques used in the area. Results show that FFClust is effective in the creation of compact clusters, with a low intra-cluster distance, while keeping a good quality Davies-Bouldin index, which is a metric that quantifies the quality of clustering approaches. Furthermore, it is about 8.6 times faster than the most efficient state-of-the-art method for one million fibers dataset. In addition, we show that FFClust is able to correctly identify atlas bundles connecting different brain regions, as an example of application and the utility of compact clusters.
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Affiliation(s)
- Andrea Vázquez
- Universidad de Concepción, Department of Computer Science, Concepción, Chile
| | - Narciso López-López
- Universidad de Concepción, Department of Computer Science, Concepción, Chile; Universidade da Coruña, Centro de investigación CITIC, A Coruña, Spain
| | - Alexis Sánchez
- Universidad de Concepción, Department of Computer Science, Concepción, Chile
| | - Josselin Houenou
- Université Paris-Saclay, CEA, CNRS, Baobab, Neurospin, Gif-sur-Yvette, France; INSERM U955 Unit, Mondor Institute for Biomedical Research, Team 15 "Translational Psychiatry", Créteil, France; Fondation Fondamental, Créteil, France; AP-HP, Department of Psychiatry and Addictology, Mondor University Hospitals, School of Medicine, DHU PePsy, Créteil, France
| | - Cyril Poupon
- Université Paris-Saclay, CEA, CNRS, Baobab, Neurospin, Gif-sur-Yvette, France
| | | | - Cecilia Hernández
- Universidad de Concepción, Department of Computer Science, Concepción, Chile; Center for Biotechnology and Bioengineering (CeBiB), Santiago, Chile
| | - Pamela Guevara
- Universidad de Concepción, Department of Electrical Engineering, Concepción, Chile.
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27
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Feng Y, Yan W, Wang J, Song J, Zeng Q, Zhao C. Local White Matter Fiber Clustering Differentiates Parkinson's Disease Diagnoses. Neuroscience 2020; 435:146-160. [PMID: 32272152 DOI: 10.1016/j.neuroscience.2020.03.049] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2019] [Revised: 03/30/2020] [Accepted: 03/31/2020] [Indexed: 10/24/2022]
Abstract
Scans without evidence of dopaminergic deficit (SWEDD) patients are often misdiagnosed with Parkinson's disease (PD) but have normal dopamine transporter scans. We hypothesised that white matter tracts associated with motor and cognition functions may be affected differently by SWEDD and PD. Automatically annotated fibre clustering (AAFC) is a novel clustering method based on diffusion magnetic resonance imaging (dMRI) tractography that enables highly robust reconstruction of white matter tracts that are composed of corresponding clusters. This study aimed to investigate the white matter properties in the subdivisions of white matter tracts among SWEDD and PD groups. We applied AAFC to identify white matter tracts related to motion and cognition functions in the dataset consisting of SWEDD (n = 22), PD (n = 30) and normal control (NC) (n = 30). Then, we resampled 200 nodes along fibres of cluster, and the diffusion metric values corresponding to each node were calculated and used for comparison. Compared with NC, PD showed significant difference (p < 0.05) in two clusters in thalamo-frontal (TF), one cluster in thalamo-parietal (TP) and one cluster in thalamo-occipital (TO), whereas SWEDD presented no significant difference. Three clusters in cingulum bundle (CB) commonly exhibited significant differences in PD versus SWEDD and NC versus SWEDD. The support vector machine classifier achieved high accuracies in PD-NC, PD-SWEDD and NC-SWEDD classifications. This outcome validated these local white matter differences were useful to separate the three groups. These results suggest that PD exerts more significant effects on thalamo tracts than SWEDD, and unique microstructural changes occur in CB tract in SWEDD.
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Affiliation(s)
- Yuanjing Feng
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China; Zhejiang Provincial United Key Laboratory of Embedded Systems, Hangzhou 310023, China.
| | - Wenxuan Yan
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Jingqiang Wang
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Jiahao Song
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Qingrun Zeng
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
| | - Changchen Zhao
- Institute of Information Processing and Automation, College of Information Engineering, Zhejiang University of Technology, Hangzhou 310023, China
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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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Jin L, Zeng Q, He J, Feng Y, Zhou S, Wu Y. A ReliefF-SVM-based method for marking dopamine-based disease characteristics: A study on SWEDD and Parkinson’s disease. Behav Brain Res 2019; 356:400-407. [DOI: 10.1016/j.bbr.2018.09.003] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2018] [Revised: 09/05/2018] [Accepted: 09/07/2018] [Indexed: 12/17/2022]
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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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