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Bai J, He M, Gao E, Yang G, Zhang C, Yang H, Dong J, Ma X, Gao Y, Zhang H, Yan X, Zhang Y, Cheng J, Zhao G. High-performance presurgical differentiation of glioblastoma and metastasis by means of multiparametric neurite orientation dispersion and density imaging (NODDI) radiomics. Eur Radiol 2024:10.1007/s00330-024-10686-8. [PMID: 38485749 DOI: 10.1007/s00330-024-10686-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/19/2023] [Revised: 02/06/2024] [Accepted: 02/10/2024] [Indexed: 04/19/2024]
Abstract
OBJECTIVES To evaluate the performance of multiparametric neurite orientation dispersion and density imaging (NODDI) radiomics in distinguishing between glioblastoma (Gb) and solitary brain metastasis (SBM). MATERIALS AND METHODS In this retrospective study, NODDI images were curated from 109 patients with Gb (n = 57) or SBM (n = 52). Automatically segmented multiple volumes of interest (VOIs) encompassed the main tumor regions, including necrosis, solid tumor, and peritumoral edema. Radiomics features were extracted for each main tumor region, using three NODDI parameter maps. Radiomics models were developed based on these three NODDI parameter maps and their amalgamation to differentiate between Gb and SBM. Additionally, radiomics models were constructed based on morphological magnetic resonance imaging (MRI) and diffusion imaging (diffusion-weighted imaging [DWI]; diffusion tensor imaging [DTI]) for performance comparison. RESULTS The validation dataset results revealed that the performance of a single NODDI parameter map model was inferior to that of the combined NODDI model. In the necrotic regions, the combined NODDI radiomics model exhibited less than ideal discriminative capabilities (area under the receiver operating characteristic curve [AUC] = 0.701). For peritumoral edema regions, the combined NODDI radiomics model achieved a moderate level of discrimination (AUC = 0.820). Within the solid tumor regions, the combined NODDI radiomics model demonstrated superior performance (AUC = 0.904), surpassing the models of other VOIs. The comparison results demonstrated that the NODDI model was better than the DWI and DTI models, while those of the morphological MRI and NODDI models were similar. CONCLUSION The NODDI radiomics model showed promising performance for preoperative discrimination between Gb and SBM. CLINICAL RELEVANCE STATEMENT The NODDI radiomics model showed promising performance for preoperative discrimination between Gb and SBM, and radiomics features can be incorporated into the multidimensional phenotypic features that describe tumor heterogeneity. KEY POINTS • The neurite orientation dispersion and density imaging (NODDI) radiomics model showed promising performance for preoperative discrimination between glioblastoma and solitary brain metastasis. • Compared with other tumor volumes of interest, the NODDI radiomics model based on solid tumor regions performed best in distinguishing the two types of tumors. • The performance of the single-parameter NODDI model was inferior to that of the combined-parameter NODDI model.
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Affiliation(s)
- Jie Bai
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
- Henan Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment, Zhengzhou, 450052, China
| | - Mengyang He
- School of Cyber Science and Engineering, Zhengzhou University, Zhengzhou, 450001, China
| | - Eryuan Gao
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
- Henan Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment, Zhengzhou, 450052, China
| | - Guang Yang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, 200062, China
| | - Chengxiu Zhang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, 200062, China
| | - Hongxi Yang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, 200062, China
| | - Jie Dong
- School of Information Engineering, North China University of Water Resources and Electric Power, Zhengzhou, 450046, China
| | - Xiaoyue Ma
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
- Henan Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment, Zhengzhou, 450052, China
| | - Yufei Gao
- School of Cyber Science and Engineering, Zhengzhou University, Zhengzhou, 450001, China
| | - Huiting Zhang
- MR Research Collaboration, Siemens Healthineers, Wuhan, 201318, China
| | - Xu Yan
- MR Research Collaboration, Siemens Healthineers, Wuhan, 201318, China
| | - Yong Zhang
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
- Henan Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment, Zhengzhou, 450052, China
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China
- Henan Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment, Zhengzhou, 450052, China
| | - Guohua Zhao
- Department of Magnetic Resonance Imaging, the First Affiliated Hospital of Zhengzhou University, Zhengzhou, 450052, China.
- Henan Engineering Research Center of Medical Imaging Intelligent Diagnosis and Treatment, Zhengzhou, 450052, China.
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Gao J, Pan P, Li J, Tang M, Yan X, Zhang X, Wang M, Ai K, Lei X, Zhang X, Zhang D. Analysis of white matter tract integrity using diffusion kurtosis imaging reveals the correlation of white matter microstructural abnormalities with cognitive impairment in type 2 diabetes mellitus. Front Endocrinol (Lausanne) 2024; 15:1327339. [PMID: 38487342 PMCID: PMC10937453 DOI: 10.3389/fendo.2024.1327339] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/24/2023] [Accepted: 02/13/2024] [Indexed: 03/17/2024] Open
Abstract
Background This study aimed to identify disruptions in white matter integrity in type 2 diabetes mellitus (T2DM) patients by utilizing the white matter tract integrity (WMTI) model, which describes compartment-specific diffusivities in the intra- and extra-axonal spaces, and to investigate the relationship between WMTI metrics and clinical and cognitive measurements. Methods A total of 73 patients with T2DM and 57 healthy controls (HCs) matched for age, sex, and education level were enrolled and underwent diffusional kurtosis imaging and cognitive assessments. Tract-based spatial statistics (TBSS) and atlas-based region of interest (ROI) analysis were performed to compare group differences in diffusional metrics, including fractional anisotropy (FA), mean diffusivity (MD), axonal water fraction (AWF), intra-axonal diffusivity (Daxon), axial extra-axonal space diffusivity (De,//), and radial extra-axonal space diffusivity (De,⊥) in multiple white matter (WM) regions. Relationships between diffusional metrics and clinical and cognitive functions were characterized. Results In the TBSS analysis, the T2DM group exhibited decreased FA and AWF and increased MD, De,∥, and De,⊥ in widespread WM regions in comparison with the HC group, which involved 56.28%, 32.07%, 73.77%, 50.47%, and 75.96% of the mean WM skeleton, respectively (P < 0.05, TFCE-corrected). De,⊥ detected most of the WM changes, which were mainly located in the corpus callosum, internal capsule, external capsule, corona radiata, posterior thalamic radiations, sagittal stratum, cingulum (cingulate gyrus), fornix (stria terminalis), superior longitudinal fasciculus, and uniform fasciculus. Additionally, De,⊥ in the genu of the corpus callosum was significantly correlated with worse performance in TMT-A (β = 0.433, P < 0.001) and a longer disease duration (β = 0.438, P < 0.001). Conclusions WMTI is more sensitive than diffusion tensor imaging in detecting T2DM-related WM microstructure abnormalities and can provide novel insights into the possible pathological changes underlying WM degeneration in T2DM. De,⊥ could be a potential imaging marker in monitoring disease progression in the brain and early intervention treatment for the cognitive impairment in T2DM.
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Affiliation(s)
- Jie Gao
- Department of MRI, Shaanxi Provincial People’s Hospital, Xi’an, China
| | - Peichun Pan
- Department of Graduate, Shaanxi University of Chinese Medicine, Xianyang, China
| | - Jing Li
- Department of Graduate, Xi’an Medical University, Xi’an, China
| | - Min Tang
- Department of MRI, Shaanxi Provincial People’s Hospital, Xi’an, China
| | - Xuejiao Yan
- Department of MRI, Shaanxi Provincial People’s Hospital, Xi’an, China
| | - Xin Zhang
- Department of MRI, Shaanxi Provincial People’s Hospital, Xi’an, China
| | - Man Wang
- Department of MRI, Shaanxi Provincial People’s Hospital, Xi’an, China
| | - Kai Ai
- Department of Clinical Science, Philips Healthcare, Xi’an, China
| | - Xiaoyan Lei
- Department of MRI, Shaanxi Provincial People’s Hospital, Xi’an, China
| | - Xiaoling Zhang
- Department of MRI, Shaanxi Provincial People’s Hospital, Xi’an, China
| | - Dongsheng Zhang
- Department of MRI, Shaanxi Provincial People’s Hospital, Xi’an, China
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Chan SY, Low XZ, Ngoh ZM, Ong ZY, Kee MZL, Huang P, Kumar S, Rifkin-Graboi A, Chong YS, Chen H, Tan KH, Chan JKY, Fortier MV, Gluckman PD, Zhou JH, Meaney MJ, Tan AP. Neonatal Nucleus Accumbens Microstructure Modulates Individual Susceptibility to Preconception Maternal Stress in Relation to Externalizing Behaviors. J Am Acad Child Adolesc Psychiatry 2024:S0890-8567(24)00071-6. [PMID: 38423282 DOI: 10.1016/j.jaac.2023.12.011] [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: 03/16/2023] [Revised: 11/20/2023] [Accepted: 02/20/2024] [Indexed: 03/02/2024]
Abstract
OBJECTIVE Maternal stress influences in utero brain development and is a modifiable risk factor for offspring psychopathologies. Reward circuitry dysfunction underlies various internalizing and externalizing psychopathologies. This study examined (1) the association between maternal stress and microstructural characteristics of the neonatal nucleus accumbens (NAcc), a major node of the reward circuitry, and (2) whether neonatal NAcc microstructure modulates individual susceptibility to maternal stress in relation to childhood behavioral problems. METHOD K-means longitudinal cluster analysis was performed to determine trajectories of maternal stress measures (Perceived Stress Scale [PSS], hair cortisol) from preconception to the third trimester. Neonatal NAcc microstructural measures (orientation density index [ODI] and intracellular volume fraction [ICVF]) were compared across trajectories. We then examined the interaction between maternal stress and neonatal NAcc microstructure on child internalizing and externalizing behaviors, assessed between ages 3 and 4 years. RESULTS Two trajectories of maternal stress magnitude ("low"/"high") were identified for both PSS (n = 287) and hair cortisol (n = 336). Right neonatal NAcc ODI (rNAcc-ODI) was significantly lower in "low" relative to "high" PSS trajectories (n = 77, p = .04). PSS at preconception had the strongest association with rNAcc-ODI (r = 0.293, p = .029). No differences in NAcc microstructure were found between hair cortisol trajectories. A significant interaction between preconception PSS and rNAcc-ODI on externalizing behavior was observed (n = 47, p = .047). CONCLUSION Our study showed that the preconception period contributes to in utero NAcc development, and that NAcc microstructure modulates individual susceptibility to preconception maternal stress in relation to externalizing problems.
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Affiliation(s)
- Shi Yu Chan
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A∗STAR), Singapore, Singapore
| | - Xi Zhen Low
- National University Health System, Singapore, Singapore
| | - Zhen Ming Ngoh
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A∗STAR), Singapore, Singapore
| | - Zi Yan Ong
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A∗STAR), Singapore, Singapore
| | - Michelle Z L Kee
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A∗STAR), Singapore, Singapore
| | - Pei Huang
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A∗STAR), Singapore, Singapore
| | | | - Anne Rifkin-Graboi
- National Institute of Education, Nanyang Technological University, Singapore, Singapore
| | - Yap-Seng Chong
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A∗STAR), Singapore, Singapore; National University Health System, Singapore, Singapore; National University of Singapore, Singapore, Singapore
| | - Helen Chen
- KK Women's and Children's Hospital, Duke-National University of Singapore, Singapore, Singapore
| | - Kok Hian Tan
- KK Women's and Children's Hospital, Duke-National University of Singapore, Singapore, Singapore
| | - Jerry K Y Chan
- KK Women's and Children's Hospital, Duke-National University of Singapore, Singapore, Singapore
| | - Marielle V Fortier
- KK Women's and Children's Hospital, Duke-National University of Singapore, Singapore, Singapore
| | - Peter D Gluckman
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A∗STAR), Singapore, Singapore
| | | | - Michael J Meaney
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A∗STAR), Singapore, Singapore; National University of Singapore, Singapore, Singapore; Douglas Mental Health University Institute, McGill University, Montreal, Canada
| | - Ai Peng Tan
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A∗STAR), Singapore, Singapore; National University Health System, Singapore, Singapore; National University of Singapore, Singapore, Singapore.
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Coelho S, Liao Y, Szczepankiewicz F, Veraart J, Chung S, Lui YW, Novikov DS, Fieremans E. Assessment of Precision and Accuracy of Brain White Matter Microstructure using Combined Diffusion MRI and Relaxometry. ARXIV 2024:arXiv:2402.17175v1. [PMID: 38463511 PMCID: PMC10925389] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Subscribe] [Scholar Register] [Indexed: 03/12/2024]
Abstract
Joint modeling of diffusion and relaxation has seen growing interest due to its potential to provide complementary information about tissue microstructure. For brain white matter, we designed an optimal diffusion-relaxometry MRI protocol that samples multiple b-values, B-tensor shapes, and echo times (TE). This variable-TE protocol (27 min) has as subsets a fixed-TE protocol (15 min) and a 2-shell dMRI protocol (7 min), both characterizing diffusion only. We assessed the sensitivity, specificity and reproducibility of these protocols with synthetic experiments and in six healthy volunteers. Compared with the fixed-TE protocol, the variable-TE protocol enables estimation of free water fractions while also capturing compartmental T 2 relaxation times. Jointly measuring diffusion and relaxation offers increased sensitivity and specificity to microstructure parameters in brain white matter with voxelwise coefficients of variation below 10%.
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Affiliation(s)
- Santiago Coelho
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
| | - Ying Liao
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
| | | | - Jelle Veraart
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
| | - Sohae Chung
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
| | - Yvonne W Lui
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
| | - Dmitry S Novikov
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
| | - Els Fieremans
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
- Center for Advanced Imaging Innovation and Research (CAIR), Department of Radiology, New York University Grossman School of Medicine, New York, NY, USA
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Zhao K, Gao A, Gao E, Qi J, Chen T, Zhao G, Zhao G, Wang P, Wang W, Bai J, Zhang Y, Zhang H, Yang G, Ma X, Cheng J. Multiple diffusion metrics in differentiating solid glioma from brain inflammation. Front Neurosci 2024; 17:1320296. [PMID: 38352939 PMCID: PMC10861663 DOI: 10.3389/fnins.2023.1320296] [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: 10/12/2023] [Accepted: 12/19/2023] [Indexed: 02/16/2024] Open
Abstract
Background and purpose The differential diagnosis between solid glioma and brain inflammation is necessary but sometimes difficult. We assessed the effectiveness of multiple diffusion metrics of diffusion-weighted imaging (DWI) in differentiating solid glioma from brain inflammation and compared the diagnostic performance of different DWI models. Materials and methods Participants diagnosed with either glioma or brain inflammation with a solid lesion on MRI were enrolled in this prospective study from May 2016 to April 2023. Diffusion-weighted imaging was performed using a spin-echo echo-planar imaging sequence with five b values (500, 1,000, 1,500, 2000, and 2,500 s/mm2) in 30 directions for each b value, and one b value of 0 was included. The mean values of multiple diffusion metrics based on diffusion tensor imaging (DTI), diffusion kurtosis imaging (DKI), mean apparent propagator (MAP), and neurite orientation dispersion and density imaging (NODDI) in the abnormal signal area were calculated. Comparisons between glioma and inflammation were performed. The area under the curve (AUC) of the receiver operating characteristic curve (ROC) of diffusion metrics were calculated. Results 57 patients (39 patients with glioma and 18 patients with inflammation) were finally included. MAP model, with its metric non-Gaussianity (NG), shows the greatest diagnostic performance (AUC = 0.879) for differentiation of inflammation and glioma with atypical MRI manifestation. The AUC of DKI model, with its metric mean kurtosis (MK) are comparable to NG (AUC = 0.855), followed by NODDI model with intracellular volume fraction (ICVF) (AUC = 0.825). The lowest value was obtained in DTI with mean diffusivity (MD) (AUC = 0.758). Conclusion Multiple diffusion metrics can be used in differentiation of inflammation and solid glioma. Non-Gaussianity (NG) from mean apparent propagator (MAP) model shows the greatest diagnostic performance for differentiation of inflammation and glioma.
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Affiliation(s)
- Kai Zhao
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Ankang Gao
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Eryuan Gao
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jinbo Qi
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Ting Chen
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Guohua Zhao
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Gaoyang Zhao
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Peipei Wang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Weijian Wang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jie Bai
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Yong Zhang
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Huiting Zhang
- MR Research Collaboration, Siemens Healthineers Ltd., Wuhan, China
| | - Guang Yang
- Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai, China
| | - Xiaoyue Ma
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
| | - Jingliang Cheng
- Department of Magnetic Resonance Imaging, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, China
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Xue X, Lu R, Li H, Chen Z, Sheng D, Jin Z, Liu S, Liu A, Yan X, Tang W, Chen S, Hua Y. In Vivo Characterization of Cerebellar Peduncles in Chronic Ankle Instability: A Single and Multishell Diffusion-Weighted Imaging Study. Sports Health 2024; 16:38-46. [PMID: 38112261 PMCID: PMC10732106 DOI: 10.1177/19417381231156544] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/21/2023] Open
Abstract
BACKGROUND Ankle sprain causes proprioceptor injuries and prolonged joint deafferentation, which might lead to maladaptive neuroplasticity in patients with chronic ankle instability (CAI), especially in the cerebellum. Previous studies have indicated the impairment of superior cerebellar peduncle (SCP), but the inferior cerebellar peduncle (ICP) and middle cerebellar peduncle (MCP) have not been fully analyzed. HYPOTHESIS The cerebellar peduncles of participants with CAI would have altered fractional anisotropy (FA) and orientation dispersion index (ODI) in comparison with healthy controls without ankle injury history. In addition, FA and ODI would be correlated with the duration or severity of the sensorimotor deficits in CAI. STUDY DESIGN Cross-sectional study. LEVEL OF EVIDENCE Level 3. METHODS A group of 27 participants with CAI and 26 healthy controls underwent diffusion-weighted imaging scanning, with the cerebellar peduncles as the regions of interest. The measures obtained by single-shell diffusion tensor imaging and the multishell neurite orientation dispersion and density imaging were used. Correlation analyses were performed to examine the potential relationship between the FA/ODI and both the normalized Y-balance scores and the durations of ankle instability. RESULTS The ipsilateral ICP of the injured ankle in participants with CAI showed significantly lower FA (Cohen d 95% CI, -1.33 to -0.21; P = 0.04) and marginally significant higher ODI (Cohen d 95% CI, 0.10 to 1.20, P = 0.08) when compared with the same measures in the control group, with the ODI being positively correlated with the duration of ankle instability (r = 0.42, P = 0.03). CONCLUSION The ICP in participants with CAI exhibited impaired integrity and a trend of abnormally organized neurites in comparison with a healthy control group. CLINICAL RELEVANCE The impairments of ICP might be an ongoing part of the pathological process of CAI, having the potential to become a target for the diagnostic evaluation of this clinical entity.
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Affiliation(s)
- Xiao’ao Xue
- Department of Sports Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Rong Lu
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Hong Li
- Department of Sports Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Ziyi Chen
- Department of Sports Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Dandan Sheng
- Department of Sports Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Zhengbiao Jin
- Department of Sports Medicine, Huashan Hospital, Fudan University, Shanghai, China
| | - Shiyin Liu
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Ailin Liu
- Department of Medical Ultrasound, Huashan Hospital, Fudan University, Shanghai, China
| | - Xu Yan
- Scientific Marketing, Siemens Healthcare Ltd., Shanghai, China
| | - Weijun Tang
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Shuang Chen
- Department of Radiology, Huashan Hospital, Fudan University, Shanghai, China
| | - Yinghui Hua
- Department of Sports Medicine, Huashan Hospital, Fudan University, Shanghai, China
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7
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Lampinen B, Szczepankiewicz F, Lätt J, Knutsson L, Mårtensson J, Björkman-Burtscher IM, van Westen D, Sundgren PC, Ståhlberg F, Nilsson M. Probing brain tissue microstructure with MRI: principles, challenges, and the role of multidimensional diffusion-relaxation encoding. Neuroimage 2023; 282:120338. [PMID: 37598814 DOI: 10.1016/j.neuroimage.2023.120338] [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: 02/02/2023] [Revised: 06/30/2023] [Accepted: 08/17/2023] [Indexed: 08/22/2023] Open
Abstract
Diffusion MRI uses the random displacement of water molecules to sensitize the signal to brain microstructure and to properties such as the density and shape of cells. Microstructure modeling techniques aim to estimate these properties from acquired data by separating the signal between virtual tissue 'compartments' such as the intra-neurite and the extra-cellular space. A key challenge is that the diffusion MRI signal is relatively featureless compared with the complexity of brain tissue. Another challenge is that the tissue microstructure is wildly different within the gray and white matter of the brain. In this review, we use results from multidimensional diffusion encoding techniques to discuss these challenges and their tentative solutions. Multidimensional encoding increases the information content of the data by varying not only the b-value and the encoding direction but also additional experimental parameters such as the shape of the b-tensor and the echo time. Three main insights have emerged from such encoding. First, multidimensional data contradict common model assumptions on diffusion and T2 relaxation, and illustrates how the use of these assumptions cause erroneous interpretations in both healthy brain and pathology. Second, many model assumptions can be dispensed with if data are acquired with multidimensional encoding. The necessary data can be easily acquired in vivo using protocols optimized to minimize Cramér-Rao lower bounds. Third, microscopic diffusion anisotropy reflects the presence of axons but not dendrites. This insight stands in contrast to current 'neurite models' of brain tissue, which assume that axons in white matter and dendrites in gray matter feature highly similar diffusion. Nevertheless, as an axon-based contrast, microscopic anisotropy can differentiate gray and white matter when myelin alterations confound conventional MRI contrasts.
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Affiliation(s)
- Björn Lampinen
- Clinical Sciences Lund, Diagnostic Radiology, Lund University, Lund, Sweden.
| | | | - Jimmy Lätt
- Department of Medical Imaging and Physiology, Skåne University Hospital Lund, Lund, Sweden
| | - Linda Knutsson
- Clinical Sciences Lund, Medical Radiation Physics, Lund University, Lund, Sweden; Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States; F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, MD, United States
| | - Johan Mårtensson
- Clinical Sciences Lund, Logopedics, Phoniatrics and Audiology, Lund University, Lund, Sweden
| | - Isabella M Björkman-Burtscher
- Department of Radiology, Institute of Clinical Sciences, Sahlgrenska Academy, University of Gothenburg and Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Danielle van Westen
- Clinical Sciences Lund, Diagnostic Radiology, Lund University, Lund, Sweden; Department of Medical Imaging and Physiology, Skåne University Hospital Lund, Lund, Sweden
| | - Pia C Sundgren
- Clinical Sciences Lund, Diagnostic Radiology, Lund University, Lund, Sweden; Department of Medical Imaging and Physiology, Skåne University Hospital Lund, Lund, Sweden; Lund University BioImaging Centre (LBIC), Lund University, Lund, Sweden
| | - Freddy Ståhlberg
- Clinical Sciences Lund, Diagnostic Radiology, Lund University, Lund, Sweden; Clinical Sciences Lund, Medical Radiation Physics, Lund University, Lund, Sweden
| | - Markus Nilsson
- Clinical Sciences Lund, Diagnostic Radiology, Lund University, Lund, Sweden
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Karat BG, DeKraker J, Hussain U, Köhler S, Khan AR. Mapping the macrostructure and microstructure of the in vivo human hippocampus using diffusion MRI. Hum Brain Mapp 2023; 44:5485-5503. [PMID: 37615057 PMCID: PMC10543110 DOI: 10.1002/hbm.26461] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Revised: 06/12/2023] [Accepted: 08/06/2023] [Indexed: 08/25/2023] Open
Abstract
The hippocampus is classically divided into mesoscopic subfields which contain varying microstructure that contribute to their unique functional roles. It has been challenging to characterize this microstructure with current magnetic resonance based neuroimaging techniques. In this work, we used diffusion magnetic resonance imaging (dMRI) and a novel surface-based approach in the hippocampus which revealed distinct microstructural distributions of neurite density and dispersion, T1w/T2w ratio as a proxy for myelin content, fractional anisotropy, and mean diffusivity. We used the neurite orientation dispersion and density imaging (NODDI) model optimized for grey matter diffusivity to characterize neurite density and dispersion. We found that neurite dispersion was highest in the cornu ammonis (CA) 1 and subiculum subfields which likely captures the large heterogeneity of tangential and radial fibres, such as the Schaffer collaterals, perforant path, and pyramidal neurons. Neurite density and T1w/T2w were highest in the subiculum and CA3 and lowest in CA1, which may reflect known myeloarchitectonic differences between these subfields. Using a simple logistic regression model, we showed that neurite density, dispersion, and T1w/T2w measures were separable across the subfields, suggesting that they may be sensitive to the known variability in subfield cyto- and myeloarchitecture. We report macrostructural measures of gyrification, thickness, and curvature that were in line with ex vivo descriptions of hippocampal anatomy. We employed a multivariate orthogonal projective non-negative matrix factorization (OPNNMF) approach to capture co-varying regions of macro- and microstructure across the hippocampus. The clusters were highly variable along the medial-lateral (proximal-distal) direction, likely reflecting known differences in morphology, cytoarchitectonic profiles, and connectivity. Finally, we show that by examining the main direction of diffusion relative to canonical hippocampal axes, we could identify regions with stereotyped microstructural orientations that may map onto specific fibre pathways, such as the Schaffer collaterals, perforant path, fimbria, and alveus. These results highlight the value of combining in vivo dMRI with computational approaches for capturing hippocampal microstructure, which may provide useful features for understanding cognition and for diagnosis of disease states.
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Affiliation(s)
- Bradley G. Karat
- Robarts Research Institute, Schulich School of Medicine and DentistryUniversity of Western OntarioLondonOntarioCanada
- Neuroscience Graduate ProgramUniversity of Western OntarioLondonOntarioCanada
| | - Jordan DeKraker
- Robarts Research Institute, Schulich School of Medicine and DentistryUniversity of Western OntarioLondonOntarioCanada
- Montreal Neurological InstituteMcGill UniversityMontrealQuebecCanada
| | | | - Stefan Köhler
- Department of PsychologyUniversity of Western OntarioLondonOntarioCanada
| | - Ali R. Khan
- Robarts Research Institute, Schulich School of Medicine and DentistryUniversity of Western OntarioLondonOntarioCanada
- Western Institute for NeuroscienceUniversity of Western OntarioLondonOntarioCanada
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9
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Schiavi S, Palombo M, Zacà D, Tazza F, Lapucci C, Castellan L, Costagli M, Inglese M. Mapping tissue microstructure across the human brain on a clinical scanner with soma and neurite density image metrics. Hum Brain Mapp 2023; 44:4792-4811. [PMID: 37461286 PMCID: PMC10400787 DOI: 10.1002/hbm.26416] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/01/2022] [Revised: 05/02/2023] [Accepted: 06/23/2023] [Indexed: 08/05/2023] Open
Abstract
Soma and neurite density image (SANDI) is an advanced diffusion magnetic resonance imaging biophysical signal model devised to probe in vivo microstructural information in the gray matter (GM). This model requires acquisitions that include b values that are at least six times higher than those used in clinical practice. Such high b values are required to disentangle the signal contribution of water diffusing in soma from that diffusing in neurites and extracellular space, while keeping the diffusion time as short as possible to minimize potential bias due to water exchange. These requirements have limited the use of SANDI only to preclinical or cutting-edge human scanners. Here, we investigate the potential impact of neglecting water exchange in the SANDI model and present a 10-min acquisition protocol that enables to characterize both GM and white matter (WM) on 3 T scanners. We implemented analytical simulations to (i) evaluate the stability of the fitting of SANDI parameters when diminishing the number of shells; (ii) estimate the bias due to potential exchange between neurites and extracellular space in such reduced acquisition scheme, comparing it with the bias due to experimental noise. Then, we demonstrated the feasibility and assessed the repeatability and reproducibility of our approach by computing microstructural metrics of SANDI with AMICO toolbox and other state-of-the-art models on five healthy subjects. Finally, we applied our protocol to five multiple sclerosis patients. Results suggest that SANDI is a practical method to characterize WM and GM tissues in vivo on performant clinical scanners.
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Affiliation(s)
- Simona Schiavi
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI)University of GenoaGenoaItaly
| | - Marco Palombo
- CUBRIC, School of PsychologyCardiff UniversityCardiffUK
- School of Computer Science and InformaticsCardiff UniversityCardiffUK
| | | | - Francesco Tazza
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI)University of GenoaGenoaItaly
| | - Caterina Lapucci
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI)University of GenoaGenoaItaly
- HNSR, IRRCS Ospedale Policlinico San MartinoGenoaItaly
| | - Lucio Castellan
- Department of NeuroradiologyIRCCS Ospedale Policlinico San MartinoGenoaItaly
| | - Mauro Costagli
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI)University of GenoaGenoaItaly
- Laboratory of Medical Physics and Magnetic ResonanceIRCCS Stella MarisPisaItaly
| | - Matilde Inglese
- Department of Neuroscience, Rehabilitation, Ophthalmology, Genetics, Maternal and Child Health (DINOGMI)University of GenoaGenoaItaly
- IRCCS Ospedale Policlinico San MartinoGenoaItaly
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10
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Maximov II, Westlye LT. Comparison of different neurite density metrics with brain asymmetry evaluation. Z Med Phys 2023:S0939-3889(23)00085-5. [PMID: 37562999 DOI: 10.1016/j.zemedi.2023.07.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 07/05/2023] [Accepted: 07/13/2023] [Indexed: 08/12/2023]
Abstract
The standard diffusion MRI model with intra- and extra-axonal water pools offers a set of microstructural parameters describing brain white matter architecture. However, non-linearities in the standard model and diffusion data contamination by noise and imaging artefacts make estimation of diffusion metrics challenging. In order to develop reliable diffusion approaches and to avoid computational model degeneracy, additional theoretical assumptions allowing stable numerical implementations are required. Advanced diffusion approaches allow for estimation of intra-axonal water fraction (AWF), describing a key structural characteristic of brain tissue. AWF can be interpreted as an indirect measure or proxy of neurite density and has a potential as useful clinical biomarker. Established diffusion approaches such as white matter tract integrity, neurite orientation dispersion and density imaging (NODDI), and spherical mean technique provide estimates of AWF within their respective theoretical frameworks. In the present study, we estimated AWF metrics using different diffusion approaches and compared measures of brain asymmetry between the different metrics in a sub-sample of 182 subjects from the UK Biobank. Multivariate decomposition by mean of linked independent component analysis revealed that the various AWF proxies derived from the different diffusion approaches reflect partly non-overlapping variance of independent components, with distinct anatomical distributions and sensitivity to age. Further, voxel-wise analysis revealed age-related differences in AWF-based brain asymmetry, indicating less apparent left-right hemisphere difference with higher age. Finally, we demonstrated that NODDI metrics suffer from a quite strong dependence on used numerical algorithms and post-processing pipeline. The analysis based on AWF metrics strongly depends on the used diffusion approach and leads to poorly reproducible results.
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Affiliation(s)
- Ivan I Maximov
- Department of Psychology, University of Oslo, Oslo, Norway; Norwegian Centre for Mental Disorders Research (NORMENT), Department of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; Department of Health and Functioning, Western Norway University of Applied Sciences, Bergen, Norway.
| | - Lars T Westlye
- Department of Psychology, University of Oslo, Oslo, Norway; Norwegian Centre for Mental Disorders Research (NORMENT), Department of Mental Health and Addiction, Oslo University Hospital, Oslo, Norway; KG Jensen Centre for Neurodevelopmental Disorders, University of Oslo, Oslo, Norway
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11
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Asaridou SS, Cler GJ, Wiedemann A, Krishnan S, Smith HJ, Willis HE, Healy MP, Watkins KE. Microstructural Properties of the Cerebellar Peduncles in Children with Developmental Language Disorder. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.07.13.548858. [PMID: 37503009 PMCID: PMC10370025 DOI: 10.1101/2023.07.13.548858] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/29/2023]
Abstract
Children with developmental language disorder (DLD) struggle to learn their native language for no apparent reason. While research on the neurobiological underpinnings of the disorder has focused on the role of cortico-striatal systems, little is known about the role of the cerebellum in DLD. Cortico-cerebellar circuits might be involved in the disorder as they contribute to complex sensorimotor skill learning, including the acquisition of spoken language. Here, we used diffusion-weighted imaging data from 77 typically developing and 54 children with DLD and performed probabilistic tractography to identify the cerebellum's white matter tracts: the inferior, middle, and superior cerebellar peduncles. Children with DLD showed lower fractional anisotropy (FA) in the inferior cerebellar peduncles (ICP), fiber tracts that carry motor and sensory input via the inferior olive to the cerebellum. Lower FA in DLD was driven by lower axial diffusivity. Probing this further with more sophisticated modeling of diffusion data, we found higher orientation dispersion but no difference in neurite density in the ICP of DLD. Reduced FA is therefore unlikely to be reflecting microstructural differences in myelination in this tract, rather the organization of axons in these pathways is disrupted. ICP microstructure was not associated with language or motor coordination performance in our sample. We also found no differences in the middle and superior peduncles, the main pathways connecting the cerebellum with the cortex. To conclude, it is not cortico-cerebellar but atypical olivocerebellar white matter connections that characterize DLD and suggest the involvement of the olivocerebellar system in speech acquisition and development.
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Affiliation(s)
- Salomi S. Asaridou
- Department of Experimental Psychology, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Gabriel J. Cler
- Department of Experimental Psychology, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
- Department of Speech & Hearing Sciences, University of Washington, Seattle, USA
| | - Anna Wiedemann
- Department of Experimental Psychology, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
- Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Saloni Krishnan
- Department of Experimental Psychology, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
- Department of Psychology, Royal Holloway, University of London, Egham Hill, Surrey, UK
| | - Harriet J. Smith
- Department of Experimental Psychology, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - Hanna E. Willis
- Department of Experimental Psychology, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
- Nuffield Department of Clinical Neuroscience, University of Oxford, Oxford, UK
| | - Máiréad P. Healy
- Department of Experimental Psychology, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
- Department of Psychology, University of Cambridge, Cambridge, UK
| | - Kate E. Watkins
- Department of Experimental Psychology, Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
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12
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Villaseñor PJ, Cortés-Servín D, Pérez-Moriel A, Aquiles A, Luna-Munguía H, Ramirez-Manzanares A, Coronado-Leija R, Larriva-Sahd J, Concha L. Multi-tensor diffusion abnormalities of gray matter in an animal model of cortical dysplasia. Front Neurol 2023; 14:1124282. [PMID: 37342776 PMCID: PMC10278582 DOI: 10.3389/fneur.2023.1124282] [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: 12/15/2022] [Accepted: 04/18/2023] [Indexed: 06/23/2023] Open
Abstract
Focal cortical dysplasias are a type of malformations of cortical development that are a common cause of drug-resistant focal epilepsy. Surgical treatment is a viable option for some of these patients, with their outcome being highly related to complete surgical resection of lesions visible in magnetic resonance imaging (MRI). However, subtle lesions often go undetected on conventional imaging. Several methods to analyze MRI have been proposed, with the common goal of rendering subtle cortical lesions visible. However, most image-processing methods are targeted to detect the macroscopic characteristics of cortical dysplasias, which do not always correspond to the microstructural disarrangement of these cortical malformations. Quantitative analysis of diffusion-weighted MRI (dMRI) enables the inference of tissue characteristics, and novel methods provide valuable microstructural features of complex tissue, including gray matter. We investigated the ability of advanced dMRI descriptors to detect diffusion abnormalities in an animal model of cortical dysplasia. For this purpose, we induced cortical dysplasia in 18 animals that were scanned at 30 postnatal days (along with 19 control animals). We obtained multi-shell dMRI, to which we fitted single and multi-tensor representations. Quantitative dMRI parameters derived from these methods were queried using a curvilinear coordinate system to sample the cortical mantle, providing inter-subject anatomical correspondence. We found region- and layer-specific diffusion abnormalities in experimental animals. Moreover, we were able to distinguish diffusion abnormalities related to altered intra-cortical tangential fibers from those associated with radial cortical fibers. Histological examinations revealed myelo-architectural abnormalities that explain the alterations observed through dMRI. The methods for dMRI acquisition and analysis used here are available in clinical settings and our work shows their clinical relevance to detect subtle cortical dysplasias through analysis of their microstructural properties.
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Affiliation(s)
- Paulina J. Villaseñor
- Instituto de Neurobiología, Universidad Nacional Autónoma de México Campus Juriquilla, Querétaro, Mexico
| | - David Cortés-Servín
- Instituto de Neurobiología, Universidad Nacional Autónoma de México Campus Juriquilla, Querétaro, Mexico
| | | | - Ana Aquiles
- Instituto de Neurobiología, Universidad Nacional Autónoma de México Campus Juriquilla, Querétaro, Mexico
| | - Hiram Luna-Munguía
- Instituto de Neurobiología, Universidad Nacional Autónoma de México Campus Juriquilla, Querétaro, Mexico
| | | | - Ricardo Coronado-Leija
- Bernard and Irene Schwartz Center for Biomedical Imaging, Department of Radiology, New York University School of Medicine, New York, NY, United States
| | - Jorge Larriva-Sahd
- Instituto de Neurobiología, Universidad Nacional Autónoma de México Campus Juriquilla, Querétaro, Mexico
| | - Luis Concha
- Instituto de Neurobiología, Universidad Nacional Autónoma de México Campus Juriquilla, Querétaro, Mexico
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13
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Drobyshevsky A, Synowiec S, Goussakov I, Lu J, Gascoigne D, Aksenov DP, Yarnykh V. Temporal trajectories of normal myelination and axonal development assessed by quantitative macromolecular and diffusion MRI: Ultrastructural and immunochemical validation in a rabbit model. Neuroimage 2023; 270:119974. [PMID: 36848973 PMCID: PMC10103444 DOI: 10.1016/j.neuroimage.2023.119974] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2022] [Revised: 02/15/2023] [Accepted: 02/22/2023] [Indexed: 02/27/2023] Open
Abstract
INTRODUCTION Quantitative and non-invasive measures of brain myelination and maturation during development are of great importance to both clinical and translational research communities. While the metrics derived from diffusion tensor imaging, are sensitive to developmental changes and some pathologies, they remain difficult to relate to the actual microstructure of the brain tissue. The advent of advanced model-based microstructural metrics requires histological validation. The purpose of the study was to validate novel, model-based MRI techniques, such as macromolecular proton fraction mapping (MPF) and neurite orientation and dispersion indexing (NODDI), against histologically derived indexes of myelination and microstructural maturation at various stages of development. METHODS New Zealand White rabbit kits underwent serial in-vivo MRI examination at postnatal days 1, 5, 11, 18, and 25, and as adults. Multi-shell, diffusion-weighted experiments were processed to fit NODDI model to obtain estimates, intracellular volume fraction (ICVF) and orientation dispersion index (ODI). Macromolecular proton fraction (MPF) maps were obtained from three source (MT-, PD-, and T1-weighted) images. After MRI sessions, a subset of animals was euthanized and regional samples of gray and white matter were taken for western blot analysis, to determine myelin basic protein (MBP), and electron microscopy, to estimate axonal, myelin fractions and g-ratio. RESULTS MPF of white matter regions showed a period of fast growth between P5 and P11 in the internal capsule, with a later onset in the corpus callosum. This MPF trajectory was in agreement with levels of myelination in the corresponding brain region, as assessed by western blot and electron microscopy. In the cortex, the greatest increase of MPF occurred between P18 and P26. In contrast, myelin, according to MBP western blot, saw the largest hike between P5 and P11 in the sensorimotor cortex and between P11 and P18 in the frontal cortex, which then seemingly plateaued after P11 and P18 respectively. G-ratio by MRI markers decreased with age in the white matter. However, electron microscopy suggest a relatively stable g-ratio throughout development. CONCLUSION Developmental trajectories of MPF accurately reflected regional differences of myelination rate in different cortical regions and white matter tracts. MRI-derived estimation of g-ratio was inaccurate during early development, likely due to the overestimation of axonal volume fraction by NODDI due to the presence of a large proportion of unmyelinated axons.
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Affiliation(s)
- Alexander Drobyshevsky
- Department of Pediatrics, NorthShore University HealthSystem Research Institute, Evanston, IL, USA.
| | - Sylvia Synowiec
- Department of Pediatrics, NorthShore University HealthSystem Research Institute, Evanston, IL, USA
| | - Ivan Goussakov
- Department of Pediatrics, NorthShore University HealthSystem Research Institute, Evanston, IL, USA
| | - Jing Lu
- Department of Pediatrics, University of Chicago, Chicago, IL, USA
| | - David Gascoigne
- Center for Basic MR Research, NorthShore University HealthSystem Research Institute, Evanston, IL, USA
| | - Daniil P Aksenov
- Center for Basic MR Research, NorthShore University HealthSystem Research Institute, Evanston, IL, USA
| | - Vasily Yarnykh
- Department of Radiology, University of Washington, Seattle, WA, USA
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14
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Pizzolato M, Canales-Rodríguez EJ, Andersson M, Dyrby TB. Axial and radial axonal diffusivities and radii from single encoding strongly diffusion-weighted MRI. Med Image Anal 2023; 86:102767. [PMID: 36867913 DOI: 10.1016/j.media.2023.102767] [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: 07/26/2022] [Revised: 12/13/2022] [Accepted: 02/08/2023] [Indexed: 02/18/2023]
Abstract
We enable the estimation of the per-axon axial diffusivity from single encoding, strongly diffusion-weighted, pulsed gradient spin echo data. Additionally, we improve the estimation of the per-axon radial diffusivity compared to estimates based on spherical averaging. The use of strong diffusion weightings in magnetic resonance imaging (MRI) allows to approximate the signal in white matter as the sum of the contributions from only axons. At the same time, spherical averaging leads to a major simplification of the modeling by removing the need to explicitly account for the unknown distribution of axonal orientations. However, the spherically averaged signal acquired at strong diffusion weightings is not sensitive to the axial diffusivity, which cannot therefore be estimated although needed for modeling axons - especially in the context of multi-compartmental modeling. We introduce a new general method for the estimation of both the axial and radial axonal diffusivities at strong diffusion weightings based on kernel zonal modeling. The method could lead to estimates that are free from partial volume bias with gray matter or other isotropic compartments. The method is tested on publicly available data from the MGH Adult Diffusion Human Connectome project. We report reference values of axonal diffusivities based on 34 subjects, and derive estimates of axonal radii from only two shells. The estimation problem is also addressed from the angle of the required data preprocessing, the presence of biases related to modeling assumptions, current limitations, and future possibilities.
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Affiliation(s)
- Marco Pizzolato
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark; Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Copenhagen, Denmark.
| | | | - Mariam Andersson
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Copenhagen, Denmark
| | - Tim B Dyrby
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kgs. Lyngby, Denmark; Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital Amager and Hvidovre, Copenhagen, Denmark
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15
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Öztekin I, Garic D, Bayat M, Hernandez ML, Finlayson MA, Graziano PA, Dick AS. Structural and diffusion-weighted brain imaging predictors of attention-deficit/hyperactivity disorder and its symptomology in very young (4- to 7-year-old) children. Eur J Neurosci 2022; 56:6239-6257. [PMID: 36215144 PMCID: PMC10165616 DOI: 10.1111/ejn.15842] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2021] [Revised: 09/15/2022] [Accepted: 09/21/2022] [Indexed: 12/29/2022]
Abstract
The current study aimed to identify the key neurobiology of attention-deficit/hyperactivity disorder (ADHD), as it relates to ADHD diagnostic category and symptoms of hyperactive/impulsive behaviour and inattention. To do so, we adapted a predictive modelling approach to identify the key structural and diffusion-weighted brain imaging measures and their relative standing with respect to teacher ratings of executive function (EF) (measured by the Metacognition Index of the Behavior Rating Inventory of Executive Function [BRIEF]) and negativity and emotion regulation (ER) (measured by the Emotion Regulation Checklist [ERC]), in a critical young age range (ages 4 to 7, mean age 5.52 years, 82.2% Hispanic/Latino), where initial contact with educators and clinicians typically take place. Teacher ratings of EF and ER were predictive of both ADHD diagnostic category and symptoms of hyperactive/impulsive behaviour and inattention. Among the neural measures evaluated, the current study identified the critical importance of the largely understudied diffusion-weighted imaging measures for the underlying neurobiology of ADHD and its associated symptomology. Specifically, our analyses implicated the inferior frontal gyrus as a critical predictor of ADHD diagnostic category and its associated symptomology, above and beyond teacher ratings of EF and ER. Collectively, the current set of findings have implications for theories of ADHD, the relative utility of neurobiological measures with respect to teacher ratings of EF and ER, and the developmental trajectory of its underlying neurobiology.
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Affiliation(s)
- Ilke Öztekin
- Center for Children and Families and Department of Psychology, Florida International University, Miami, Florida, USA.,Exponent, Inc., Philadelphia, Pennsylvania, USA
| | - Dea Garic
- Carolina Institute for Developmental Disabilities, The University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Mohammadreza Bayat
- Center for Children and Families and Department of Psychology, Florida International University, Miami, Florida, USA
| | - Melissa L Hernandez
- Center for Children and Families and Department of Psychology, Florida International University, Miami, Florida, USA
| | - Mark A Finlayson
- School of Computing and Information Sciences, Florida International University, Miami, Florida, USA
| | - Paulo A Graziano
- Center for Children and Families and Department of Psychology, Florida International University, Miami, Florida, USA
| | - Anthony Steven Dick
- Center for Children and Families and Department of Psychology, Florida International University, Miami, Florida, USA
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16
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Kurz FT, Schlemmer HP. Imaging in translational cancer research. Cancer Biol Med 2022; 19:j.issn.2095-3941.2022.0677. [PMID: 36476372 PMCID: PMC9724222 DOI: 10.20892/j.issn.2095-3941.2022.0677] [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] [Indexed: 12/12/2022] Open
Abstract
This review is aimed at presenting some of the recent developments in translational cancer imaging research, with a focus on novel, recently established, or soon to be established cross-sectional imaging techniques for computed tomography (CT), magnetic resonance imaging (MRI), and positron-emission tomography (PET) imaging, including computational investigations based on machine-learning techniques.
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Affiliation(s)
- Felix T. Kurz
- Department of Radiology, German Cancer Research Center, Heidelberg 69120, Germany,Correspondence to: Felix T. Kurz and Heinz-Peter Schlemmer, E-mail: and
| | - Heinz-Peter Schlemmer
- Department of Radiology, German Cancer Research Center, Heidelberg 69120, Germany,Correspondence to: Felix T. Kurz and Heinz-Peter Schlemmer, E-mail: and
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17
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Reveley C, Ye FQ, Mars RB, Matrov D, Chudasama Y, Leopold DA. Diffusion MRI anisotropy in the cerebral cortex is determined by unmyelinated tissue features. Nat Commun 2022; 13:6702. [PMID: 36335105 PMCID: PMC9637141 DOI: 10.1038/s41467-022-34328-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Accepted: 10/19/2022] [Indexed: 11/07/2022] Open
Abstract
Diffusion magnetic resonance imaging (dMRI) is commonly used to assess the tissue and cellular substructure of the human brain. In the white matter, myelinated axons are the principal neural elements that shape dMRI through the restriction of water diffusion; however, in the gray matter the relative contributions of myelinated axons and other tissue features to dMRI are poorly understood. Here we investigate the determinants of diffusion in the cerebral cortex. Specifically, we ask whether myelinated axons significantly shape dMRI fractional anisotropy (dMRI-FA), a measure commonly used to characterize tissue properties in humans. We compared ultra-high resolution ex vivo dMRI data from the brain of a marmoset monkey with both myelin- and Nissl-stained histological sections obtained from the same brain after scanning. We found that the dMRI-FA did not match the spatial distribution of myelin in the gray matter. Instead dMRI-FA was more closely related to the anisotropy of stained tissue features, most prominently those revealed by Nissl staining and to a lesser extent those revealed by myelin staining. Our results suggest that unmyelinated neurites such as large caliber apical dendrites are the primary features shaping dMRI measures in the cerebral cortex.
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Affiliation(s)
- Colin Reveley
- grid.4991.50000 0004 1936 8948Wellcome Centre for Integrative Neuroimaging, Centre for fMRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Headington, Oxford, OX9 3DU UK ,grid.12082.390000 0004 1936 7590Department of Informatics, University of Sussex, Falmer, Brighton, BN1 9QJ UK
| | - Frank Q. Ye
- grid.94365.3d0000 0001 2297 5165Neurophysiology Imaging Facility, National Institute of Mental Health, National Institute of Neurological Disorders and Stroke, National Eye Institute, National Institutes of Health, Bethesda, MD USA
| | - Rogier B. Mars
- grid.4991.50000 0004 1936 8948Wellcome Centre for Integrative Neuroimaging, Centre for fMRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Headington, Oxford, OX9 3DU UK ,grid.5590.90000000122931605Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands
| | - Denis Matrov
- grid.94365.3d0000 0001 2297 5165Section on Behavioral Neuroscience, National Institute of Mental Health, National Institutes of Health, Bethesda, MD USA
| | - Yogita Chudasama
- grid.94365.3d0000 0001 2297 5165Section on Behavioral Neuroscience, National Institute of Mental Health, National Institutes of Health, Bethesda, MD USA
| | - David A. Leopold
- grid.94365.3d0000 0001 2297 5165Neurophysiology Imaging Facility, National Institute of Mental Health, National Institute of Neurological Disorders and Stroke, National Eye Institute, National Institutes of Health, Bethesda, MD USA ,grid.94365.3d0000 0001 2297 5165Section on Cognitive Neurophysiology and Imaging, Laboratory of Neuropsychology, National Institute of Mental Health, National Institutes of Health, Bethesda, MD USA
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18
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Zachariou V, Bauer CE, Pappas C, Gold BT. High cortical iron is associated with the disruption of white matter tracts supporting cognitive function in healthy older adults. Cereb Cortex 2022; 33:4815-4828. [PMID: 36182267 PMCID: PMC10110441 DOI: 10.1093/cercor/bhac382] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2022] [Revised: 08/29/2022] [Accepted: 08/30/2022] [Indexed: 01/25/2023] Open
Abstract
Aging is associated with brain iron accumulation, which has been linked to cognitive decline. However, how brain iron affects the structure and function of cognitive brain networks remains unclear. Here, we explored the possibility that iron load in gray matter is associated with disruption of white matter (WM) microstructure within a network supporting cognitive function, in a cohort of 95 cognitively normal older adults (age range: 60-86). Functional magnetic resonance imaging was used to localize a set of brain regions involved in working memory and diffusion tensor imaging based probabilistic tractography was used to identify a network of WM tracts connecting the functionally defined regions. Brain iron concentration within these regions was evaluated using quantitative susceptibility mapping and microstructural properties were assessed within the identified tracts using neurite orientation dispersion and density imaging. Results indicated that high brain iron concentration was associated with low neurite density (ND) within the task-relevant WM network. Further, regional associations were observed such that brain iron in cortical regions was linked with lower ND in neighboring but not distant WM tracts. Our results provide novel evidence suggesting that age-related increases in brain iron concentration are associated with the disruption of WM tracts supporting cognitive function in normal aging.
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Affiliation(s)
- Valentinos Zachariou
- Department of Neuroscience, University of Kentucky, Lexington, KY 40536-0298, United States.,College of Medicine, University of Kentucky, Lexington, KY 40536-0298, United States
| | - Christopher E Bauer
- Department of Neuroscience, University of Kentucky, Lexington, KY 40536-0298, United States.,College of Medicine, University of Kentucky, Lexington, KY 40536-0298, United States
| | - Colleen Pappas
- Department of Neuroscience, University of Kentucky, Lexington, KY 40536-0298, United States.,College of Medicine, University of Kentucky, Lexington, KY 40536-0298, United States
| | - Brian T Gold
- Department of Neuroscience, University of Kentucky, Lexington, KY 40536-0298, United States.,College of Medicine, University of Kentucky, Lexington, KY 40536-0298, United States.,Sanders-Brown Center on Aging, University of Kentucky, Lexington, KY 40536-0298, United States.,Magnetic Resonance Imaging and Spectroscopy Center, University of Kentucky, Lexington, KY 40536-0298, United States
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19
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Jensen JH. Impact of intra-axonal kurtosis on fiber orientation density functions estimated with fiber ball imaging. Magn Reson Med 2022; 88:1347-1354. [PMID: 35436362 PMCID: PMC9246967 DOI: 10.1002/mrm.29270] [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] [Subscribe] [Scholar Register] [Received: 12/13/2021] [Revised: 02/27/2022] [Accepted: 03/27/2022] [Indexed: 11/09/2022]
Abstract
PURPOSE To determine the impact of an intra-axonal kurtosis on estimates of the fiber orientation density function (fODF) obtained with fiber ball imaging (FBI). THEORY AND METHODS Standard FBI assumes Gaussian diffusion within individual axons and estimates the fODF by applying an inverse generalized Funk transform to diffusion MRI data for b-values of 4000 s/mm2 or higher. However, recent work based on numeric simulations shows that diffusion inside axons is non-Gaussian with an intra-axonal kurtosis of ∼ 0.4. Here, the theory underlying FBI is extended to incorporate an intra-axonal kurtosis. This is done to first order in the intra-axonal kurtosis without making assumptions about the details of the diffusion dynamics and to all orders for a specific model based on a gamma distribution of diffusivities. The first order approximation is used to assess the effect of an intra-axonal kurtosis on FBI estimates for the fODF and axonal water fraction. The gamma distribution model is used to test the validity of the approximation. RESULTS The first order approximation indicates the estimated fODF is altered by a few percent for an intra-axonal kurtosis of 0.4 in comparison to predictions of standard FBI. If one neglects the intra-axonal kurtosis, the angular resolution of the point spread function for the fODF is changed by <1°, whereas the axonal water fraction is overestimated by ∼ 5%. The gamma distribution model shows that the first order approximation is accurate to within a few percent. CONCLUSION The intra-axonal kurtosis has a small impact on fODFs estimated with FBI.
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Affiliation(s)
- Jens H. Jensen
- Center for Biomedical Imaging, Medical University of South Carolina, Charleston, South Carolina
- Department of Neuroscience, Medical University of South Carolina, Charleston, South Carolina
- Department of Radiology and Radiological Science, Medical University of South Carolina, Charleston, South Carolina
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20
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Reproducibility of the Standard Model of diffusion in white matter on clinical MRI systems. Neuroimage 2022; 257:119290. [PMID: 35545197 PMCID: PMC9248353 DOI: 10.1016/j.neuroimage.2022.119290] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2022] [Revised: 04/06/2022] [Accepted: 05/06/2022] [Indexed: 12/13/2022] Open
Abstract
Estimating intra- and extra-axonal microstructure parameters, such as volume fractions and diffusivities, has been one of the major efforts in brain microstructure imaging with MRI. The Standard Model (SM) of diffusion in white matter has unified various modeling approaches based on impermeable narrow cylinders embedded in locally anisotropic extra-axonal space. However, estimating the SM parameters from a set of conventional diffusion MRI (dMRI) measurements is ill-conditioned. Multidimensional dMRI helps resolve the estimation degeneracies, but there remains a need for clinically feasible acquisitions that yield robust parameter maps. Here we find optimal multidimensional protocols by minimizing the mean-squared error of machine learning-based SM parameter estimates for two 3T scanners with corresponding gradient strengths of 40and80mT/m. We assess intra-scanner and inter-scanner repeatability for 15-minute optimal protocols by scanning 20 healthy volunteers twice on both scanners. The coefficients of variation all SM parameters except free water fraction are ≲10% voxelwise and 1-4% for their region-averaged values. As the achieved SM reproducibility outcomes are similar to those of conventional diffusion tensor imaging, our results enable robust in vivo mapping of white matter microstructure in neuroscience research and in the clinic.
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21
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Jelescu IO, de Skowronski A, Geffroy F, Palombo M, Novikov DS. Neurite Exchange Imaging (NEXI): A minimal model of diffusion in gray matter with inter-compartment water exchange. Neuroimage 2022; 256:119277. [PMID: 35523369 PMCID: PMC10363376 DOI: 10.1016/j.neuroimage.2022.119277] [Citation(s) in RCA: 29] [Impact Index Per Article: 14.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2022] [Revised: 04/26/2022] [Accepted: 05/01/2022] [Indexed: 01/18/2023] Open
Abstract
Biophysical models of diffusion in white matter have been center-stage over the past two decades and are essentially based on what is now commonly referred to as the "Standard Model" (SM) of non-exchanging anisotropic compartments with Gaussian diffusion. In this work, we focus on diffusion MRI in gray matter, which requires rethinking basic microstructure modeling blocks. In particular, at least three contributions beyond the SM need to be considered for gray matter: water exchange across the cell membrane - between neurites and the extracellular space; non-Gaussian diffusion along neuronal and glial processes - resulting from structural disorder; and signal contribution from soma. For the first contribution, we propose Neurite Exchange Imaging (NEXI) as an extension of the SM of diffusion, which builds on the anisotropic Kärger model of two exchanging compartments. Using datasets acquired at multiple diffusion weightings (b) and diffusion times (t) in the rat brain in vivo, we investigate the suitability of NEXI to describe the diffusion signal in the gray matter, compared to the other two possible contributions. Our results for the diffusion time window 20-45 ms show minimal diffusivity time-dependence and more pronounced kurtosis decay with time, which is well fit by the exchange model. Moreover, we observe lower signal for longer diffusion times at high b. In light of these observations, we identify exchange as the mechanism that best explains these signal signatures in both low-b and high-b regime, and thereby propose NEXI as the minimal model for gray matter microstructure mapping. We finally highlight multi-b multi-t acquisition protocols as being best suited to estimate NEXI model parameters reliably. Using this approach, we estimate the inter-compartment water exchange time to be 15 - 60 ms in the rat cortex and hippocampus in vivo, which is of the same order or shorter than the diffusion time in typical diffusion MRI acquisitions. This suggests water exchange as an essential component for interpreting diffusion MRI measurements in gray matter.
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Affiliation(s)
- Ileana O Jelescu
- CIBM Center for Biomedical Imaging, Animal Imaging and Technology, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland; Department of Radiology, Lausanne University Hospital (CHUV), Lausanne, Switzerland; School of Biology and Medicine, University of Lausanne (UNIL), Lausanne, Switzerland.
| | - Alexandre de Skowronski
- CIBM Center for Biomedical Imaging, Animal Imaging and Technology, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland
| | | | - Marco Palombo
- School of Psychology, Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Cardiff, UK; School of Computer Science and Informatics, Cardiff University, Cardiff, UK; Department of Computer Science, Centre for Medical Image Computing, University College London, London, UK
| | - Dmitry S Novikov
- Department of Radiology, Center for Biomedical Imaging, New York University School of Medicine, New York, NY, USA
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22
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Yendiki A, Aggarwal M, Axer M, Howard AF, van Cappellen van Walsum AM, Haber SN. Post mortem mapping of connectional anatomy for the validation of diffusion MRI. Neuroimage 2022; 256:119146. [PMID: 35346838 PMCID: PMC9832921 DOI: 10.1016/j.neuroimage.2022.119146] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2021] [Revised: 03/02/2022] [Accepted: 03/23/2022] [Indexed: 01/13/2023] Open
Abstract
Diffusion MRI (dMRI) is a unique tool for the study of brain circuitry, as it allows us to image both the macroscopic trajectories and the microstructural properties of axon bundles in vivo. The Human Connectome Project ushered in an era of impressive advances in dMRI acquisition and analysis. As a result of these efforts, the quality of dMRI data that could be acquired in vivo improved substantially, and large collections of such data became widely available. Despite this progress, the main limitation of dMRI remains: it does not image axons directly, but only provides indirect measurements based on the diffusion of water molecules. Thus, it must be validated by methods that allow direct visualization of axons but that can only be performed in post mortem brain tissue. In this review, we discuss methods for validating the various features of connectional anatomy that are extracted from dMRI, both at the macro-scale (trajectories of axon bundles), and at micro-scale (axonal orientations and other microstructural properties). We present a range of validation tools, including anatomic tracer studies, Klingler's dissection, myelin stains, label-free optical imaging techniques, and others. We provide an overview of the basic principles of each technique, its limitations, and what it has taught us so far about the accuracy of different dMRI acquisition and analysis approaches.
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Affiliation(s)
- Anastasia Yendiki
- Department of Radiology, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, United States,Corresponding author (A. Yendiki)
| | - Manisha Aggarwal
- Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, MD, United States
| | - Markus Axer
- Forschungszentrum Jülich, Institute of Neuroscience and Medicine, Jülich, Germany,Department of Physics, University of Wuppertal Germany
| | - Amy F.D. Howard
- Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Anne-Marie van Cappellen van Walsum
- Department of Medical Imaging, Anatomy, Radboud University Medical Center, Nijmegen, the Netherland,Cognition and Behaviour, Donders Institute for Brain, Nijmegen, the Netherland
| | - Suzanne N. Haber
- Department of Pharmacology and Physiology, University of Rochester, Rochester, NY, United States,McLean Hospital, Belmont, MA, United States
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23
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San Martín Molina I, Salo RA, Gröhn O, Tohka J, Sierra A. Histopathological modeling of status epilepticus-induced brain damage based on in vivo diffusion tensor imaging in rats. Front Neurosci 2022; 16:944432. [PMID: 35968364 PMCID: PMC9372371 DOI: 10.3389/fnins.2022.944432] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2022] [Accepted: 07/07/2022] [Indexed: 11/13/2022] Open
Abstract
Non-invasive magnetic resonance imaging (MRI) methods have proved useful in the diagnosis and prognosis of neurodegenerative diseases. However, the interpretation of imaging outcomes in terms of tissue pathology is still challenging. This study goes beyond the current interpretation of in vivo diffusion tensor imaging (DTI) by constructing multivariate models of quantitative tissue microstructure in status epilepticus (SE)-induced brain damage. We performed in vivo DTI and histology in rats at 79 days after SE and control animals. The analyses focused on the corpus callosum, hippocampal subfield CA3b, and layers V and VI of the parietal cortex. Comparison between control and SE rats indicated that a combination of microstructural tissue changes occurring after SE, such as cellularity, organization of myelinated axons, and/or morphology of astrocytes, affect DTI parameters. Subsequently, we constructed a multivariate regression model for explaining and predicting histological parameters based on DTI. The model revealed that DTI predicted well the organization of myelinated axons (cross-validated R = 0.876) and astrocyte processes (cross-validated R = 0.909) and possessed a predictive value for cell density (CD) (cross-validated R = 0.489). However, the morphology of astrocytes (cross-validated R > 0.05) was not well predicted. The inclusion of parameters from CA3b was necessary for modeling histopathology. Moreover, the multivariate DTI model explained better histological parameters than any univariate model. In conclusion, we demonstrate that combining several analytical and statistical tools can help interpret imaging outcomes to microstructural tissue changes, opening new avenues to improve the non-invasive diagnosis and prognosis of brain tissue damage.
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24
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Wei L, Ding M, Zhang Y, Wang H. Decoding transcriptional signatures of the association between free water and macroscale organizations in healthy adolescents. Neuroimage 2022; 261:119514. [PMID: 35901916 DOI: 10.1016/j.neuroimage.2022.119514] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 07/11/2022] [Accepted: 07/22/2022] [Indexed: 11/16/2022] Open
Abstract
We leveraged a novel index of diffusion MRI to investigate the relationships among cortical free water, macro-organizations and gene expression in healthy adults. Few research has been conducted to investigate the role of free water in the healthy adults due to it can easily be affected also by aging diseases. High quality data of 350 subjects from Human Connectome Project were used in our study. Cortical free water was estimated by using a bi-tensor model. The free water was high in the limbic, insular and somatosensory cortex, while being lower in motor and association cortex. The negative correlation between the free water and cortical thickness has been consistently identified in almost all the cortical regions. Negative correlation between the cortical free water and structural covariance (rho=-0.38, pspin=0.005) revealed the free water was sensitive to cortical heterogeneity. Using human gene expression dataset, we found the gene expression pattern of the relationship between the free water and cortical thickness spatially coupled with primary gradient of structural covariance network (rho=0.40, pspin=0.004). Our findings indicated the free water was sensitive to the cortical cellular status. The relationship between free water and macroscale organization also reflected hierarchal structures of cerebral cortex.
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Affiliation(s)
- Lei Wei
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, PR China.
| | - Ming Ding
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, PR China
| | - Yuwen Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, PR China
| | - He Wang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, PR China; Human Phenome Institute, Fudan University, Shanghai, PR China; Department of Neurology, Zhongshan Hospital, Fudan University, Shanghai, PR China.
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25
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Ianuş A, Carvalho J, Fernandes FF, Cruz R, Chavarrias C, Palombo M, Shemesh N. Soma and Neurite Density MRI (SANDI) of the in-vivo mouse brain and comparison with the Allen Brain Atlas. Neuroimage 2022; 254:119135. [PMID: 35339686 DOI: 10.1016/j.neuroimage.2022.119135] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2021] [Revised: 02/15/2022] [Accepted: 03/22/2022] [Indexed: 10/18/2022] Open
Abstract
Diffusion MRI (dMRI) provides unique insights into the neural tissue milieu by probing interactions between diffusing molecules and tissue microstructure. Most dMRI techniques focus on white matter (WM) tissues, nevertheless, interest in gray matter characterizations is growing. The Soma and Neurite Density MRI (SANDI) methodology harnesses a model incorporating water diffusion in spherical objects (assumed to be associated with cell bodies) and in impermeable "sticks" (assumed to represent neurites), which potentially enables the characterization of cellular and neurite densities. Recognising the importance of rodents in animal models of development, aging, plasticity, and disease, we here employ SANDI for in-vivo preclinical imaging and provide a first validation of the methodology by comparing SANDI metrics with cellular density reflected by the Allen mouse brain atlas. SANDI was implemented on a 9.4T scanner equipped with a cryogenic coil, and in-vivo experiments were carried out on N = 6 mice. Pixelwise, ROI-based, and atlas comparisons were performed, magnitude vs. real-valued analyses were compared, and shorter acquisitions with reduced the number of b-value shells were investigated. Our findings reveal good reproducibility of the SANDI parameters, including the sphere and stick fractions, as well as sphere size (CoV < 7%, 12% and 3%, respectively). Additionally, we find a very good rank correlation between SANDI-driven sphere fraction and Allen mouse brain atlas contrast that represents cellular density. We conclude that SANDI is a viable preclinical MRI technique that can greatly contribute to research on brain tissue microstructure.
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Affiliation(s)
- Andrada Ianuş
- Champalimaud Research, Champalimaud Foundation, Av. Brasilia, Lisbon 1400-038, Portugal.
| | - Joana Carvalho
- Champalimaud Research, Champalimaud Foundation, Av. Brasilia, Lisbon 1400-038, Portugal
| | - Francisca F Fernandes
- Champalimaud Research, Champalimaud Foundation, Av. Brasilia, Lisbon 1400-038, Portugal
| | - Renata Cruz
- Champalimaud Research, Champalimaud Foundation, Av. Brasilia, Lisbon 1400-038, Portugal
| | - Cristina Chavarrias
- Champalimaud Research, Champalimaud Foundation, Av. Brasilia, Lisbon 1400-038, Portugal
| | - Marco Palombo
- Center for Medical Image Computing, Department of Computer Science, University College London, UK; Cardiff University Brain Research Imaging Centre, School of Psychology, Cardiff University, UK; School of Computer Science and Informatics, Cardiff University, UK
| | - Noam Shemesh
- Champalimaud Research, Champalimaud Foundation, Av. Brasilia, Lisbon 1400-038, Portugal.
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26
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Ling M, Zhou J, Pang XQ, Liang J, Qin YF, Huang S, Liang GY, Li YF, Zeng ZS. White Matter Microstructural Abnormalities of the Visual Pathway in Type 2 Diabetes Mellitus: A Generalized Q-sampling Imaging Study. Acad Radiol 2022; 29 Suppl 3:S166-S174. [PMID: 34930656 DOI: 10.1016/j.acra.2021.10.021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2021] [Revised: 10/16/2021] [Accepted: 10/21/2021] [Indexed: 11/01/2022]
Abstract
RATIONALE AND OBJECTIVES Neurodegeneration is an early event in the pathogenesis of diabetic retinopathy (DR). We assessed the white matter microstructural integrity of the visual pathway in diabetes patients vs. healthy subjects, and investigated the advantages of generalized Q-sampling imaging (GQI) in the assessment of the visual pathway. MATERIALS AND METHODS T1-weighted, T2-weighted fluid-attenuated inversion recovery, and simultaneous multislice- diffusion sequences were acquired from 21 DR patients, 29 diabetes patients without DR (NDR group), and 28 age- and gender-matched healthy controls. Diffusion source images were reconstructed to GQI. Region of interest (ROI)-based analysis was utilized to evaluate microstructural alterations in the visual pathway. Multivariate linear regression analysis (forward stepwise method) was performed to investigate associations between clinical data and mean GQI parameters. RESULTS ROI-based analyses indicated that the GQI parameters generalized fractional anisotropy, quantitative anisotropy (QA), and normalized QA (NQA) were significantly lower in the NDR group than in the healthy controls, and even lower in the DR group than in the NDR group. Disease duration was significantly and negatively correlated with mean generalized fractional anisotropy and mean NQA. CONCLUSION GQI could sensitively and non-invasively evaluate the visual pathway in diabetes patients. The nerve fibers of the visual pathway were damaged before the onset of retinopathy, and this damage was aggravated after retinopathy onset, as a consequence of long exposure to hyperglycemia.
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27
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Faiyaz A, Doyley M, Schifitto G, Zhong J, Uddin MN. Single-shell NODDI using dictionary-learner-estimated isotropic volume fraction. NMR IN BIOMEDICINE 2022; 35:e4628. [PMID: 34642974 DOI: 10.1002/nbm.4628] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/05/2021] [Revised: 09/13/2021] [Accepted: 09/14/2021] [Indexed: 06/13/2023]
Abstract
Neurite orientation dispersion and density imaging (NODDI) enables the assessment of intracellular, extracellular, and free water signals from multi-shell diffusion MRI data. It is an insightful approach to characterize brain tissue microstructure. Single-shell reconstruction for NODDI parameters has been discouraged in previous studies caused by failure when fitting, especially for the neurite density index (NDI). Here, we investigated the possibility of creating robust NODDI parameter maps with single-shell data, using the isotropic volume fraction (fISO ) as a prior. Prior estimation was made independent of the NODDI model constraint using a dictionary learning approach. First, we used a stochastic sparse dictionary-based network (DictNet), which is trained with data obtained from in vivo and simulated diffusion MRI data, to predict fISO . In single-shell cases, the mean diffusivity and raw T2 signal with no diffusion weighting (S0 ) was incorporated in the dictionary for the fISO estimation. Then, the NODDI framework was used with the known fISO to estimate the NDI and orientation dispersion index (ODI). The fISO estimated using our model was compared with other fISO estimators in the simulation. Further, using both synthetic data simulation and human data collected on a 3 T scanner (both high-quality HCP and clinical dataset), we compared the performance of our dictionary-based learning prior NODDI (DLpN) with the original NODDI for both single-shell and multi-shell data. Our results suggest that DLpN-derived NDI and ODI parameters for single-shell protocols are comparable with original multi-shell NODDI, and the protocol with b = 2000 s/mm2 performs the best (error ~ 5% in white and gray matter). This may allow NODDI evaluation of studies on single-shell data by multi-shell scanning of two subjects for DictNet fISO training.
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Affiliation(s)
- Abrar Faiyaz
- Department of Electrical and Computer Engineering, University of Rochester, Rochester, New York
| | - Marvin Doyley
- Department of Electrical and Computer Engineering, University of Rochester, Rochester, New York
- Department of Imaging Sciences, University of Rochester, Rochester, New York
- Department of Biomedical Engineering, University of Rochester, Rochester, New York
| | - Giovanni Schifitto
- Department of Electrical and Computer Engineering, University of Rochester, Rochester, New York
- Department of Imaging Sciences, University of Rochester, Rochester, New York
- Department of Neurology, University of Rochester, Rochester, New York
| | - Jianhui Zhong
- Department of Imaging Sciences, University of Rochester, Rochester, New York
- Department of Biomedical Engineering, University of Rochester, Rochester, New York
- Department of Physics and Astronomy, University of Rochester, Rochester, New York
| | - Md Nasir Uddin
- Department of Neurology, University of Rochester, Rochester, New York
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28
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Olesen JL, Østergaard L, Shemesh N, Jespersen SN. Diffusion time dependence, power-law scaling, and exchange in gray matter. Neuroimage 2022; 251:118976. [PMID: 35168088 PMCID: PMC8961002 DOI: 10.1016/j.neuroimage.2022.118976] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2021] [Revised: 12/24/2021] [Accepted: 02/04/2022] [Indexed: 12/27/2022] Open
Abstract
Characterizing neural tissue microstructure is a critical goal for future neuroimaging. Diffusion MRI (dMRI) provides contrasts that reflect diffusing spins’ interactions with myriad microstructural features of biological systems. However, the specificity of dMRI remains limited due to the ambiguity of its signals vis-à-vis the underlying microstructure. To improve specificity, biophysical models of white matter (WM) typically express dMRI signals according to the Standard Model (SM) and have more recently in gray matter (GM) taken spherical compartments into account (the SANDI model) in attempts to represent cell soma. The validity of the assumptions underlying these models, however, remains largely undetermined, especially in GM. To validate these assumptions experimentally, observing their unique, functional properties, such as the b−1/2 power-law associated with one-dimensional diffusion, has emerged as a fruitful strategy. The absence of this signature in GM, in turn, has been explained by neurite water exchange, non-linear morphology, and/or by obscuring soma signal contributions. Here, we present diffusion simulations in realistic neurons demonstrating that curvature and branching does not destroy the stick power-law behavior in impermeable neurites, but also that their signal is drowned by the soma signal under typical experimental conditions. Nevertheless, by studying the GM dMRI signal's behavior as a function of diffusion weighting as well as time, we identify an attainable experimental regime in which the neurite signal dominates. Furthermore, we find that exchange-driven time dependence produces a signal behavior opposite to that which would be expected from restricted diffusion, thereby providing a functional signature that disambiguates the two effects. We present data from dMRI experiments in ex vivo rat brain at ultrahigh field of 16.4T and observe a time dependence that is consistent with substantial exchange but also with a GM stick power-law. The first finding suggests significant water exchange between neurites and the extracellular space while the second suggests a small sub-population of impermeable neurites. To quantify these observations, we harness the Kärger exchange model and incorporate the corresponding signal time dependence in the SM and SANDI models.
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29
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Schlüter C, Fraenz C, Friedrich P, Güntürkün O, Genç E. Neurite density imaging in amygdala nuclei reveals interindividual differences in neuroticism. Hum Brain Mapp 2022; 43:2051-2063. [PMID: 35049113 PMCID: PMC8933246 DOI: 10.1002/hbm.25775] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2021] [Revised: 12/23/2021] [Accepted: 12/30/2021] [Indexed: 11/23/2022] Open
Abstract
Neuroticism is known to have significant health implications. While previous research revealed that interindividual differences in the amygdala function are associated with interindividual differences in neuroticism, the impact of the amygdala’s structure and especially microstructure on variations in neuroticism remains unclear. Here, we present the first study using NODDI to examine the association between the in vivo microstructural architecture of the amygdala and neuroticism at the level of neurites. We, therefore, acquired brain images from 221 healthy participants using advanced multi‐shell diffusion‐weighted imaging. Because the amygdala comprises several nuclei, we, moreover, used a high‐resolution T1 image to automatically segment the amygdala into eight different nuclei. Neuroticism and its facets have been assessed using the NEO‐PI‐R. Finally, we associated neuroticism and its facets with the volume and microstructure of the amygdala nuclei. Statistical analysis revealed that lower neurite density in the lateral amygdala nucleus (La) was significantly associated with higher scores in depression, one of the six neuroticism facets. The La is the sensory relay of the amygdala, filtering incoming information based on previous experiences. Reduced neurite density and related changes in the dendritic structure of the La could impair its filtering function. This again might cause harmless sensory information to be misevaluated as threatening and lead to the altered amygdala responsivity as reported in previous studies investigating the functional correlates of neuroticism and neuroticism‐related disorders like depression.
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Affiliation(s)
- Caroline Schlüter
- Department of Biopsychology, Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Bochum, Germany
| | - Christoph Fraenz
- Department of Biopsychology, Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Bochum, Germany.,Department of Psychology and Neurosciences, Leibniz Research Centre for Working Environment and Human Factors (IfADo), Dortmund, Germany
| | - Patrick Friedrich
- Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Centre Jülich, Jülich, Germany
| | - Onur Güntürkün
- Department of Biopsychology, Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Bochum, Germany
| | - Erhan Genç
- Department of Biopsychology, Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr University Bochum, Bochum, Germany.,Department of Psychology and Neurosciences, Leibniz Research Centre for Working Environment and Human Factors (IfADo), Dortmund, Germany
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Vogt NM, Hunt JFV, Adluru N, Ma Y, Van Hulle CA, Dean DC, Kecskemeti SR, Chin NA, Carlsson CM, Asthana S, Johnson SC, Kollmorgen G, Batrla R, Wild N, Buck K, Zetterberg H, Alexander AL, Blennow K, Bendlin BB. Interaction of amyloid and tau on cortical microstructure in cognitively unimpaired adults. Alzheimers Dement 2022; 18:65-76. [PMID: 33984184 PMCID: PMC8589921 DOI: 10.1002/alz.12364] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2020] [Revised: 03/31/2021] [Accepted: 04/12/2021] [Indexed: 01/03/2023]
Abstract
INTRODUCTION Neurite orientation dispersion and density imaging (NODDI), a multi-compartment diffusion-weighted imaging (DWI) model, may be useful for detecting early cortical microstructural alterations in Alzheimer's disease prior to cognitive impairment. METHODS Using neuroimaging (NODDI and T1-weighted magnetic resonance imaging [MRI]) and cerebrospinal fluid (CSF) biomarker data (measured using Elecsys® CSF immunoassays) from 219 cognitively unimpaired participants, we tested the main and interactive effects of CSF amyloid beta (Aβ)42 /Aβ40 and phosphorylated tau (p-tau) on cortical NODDI metrics and cortical thickness, controlling for age, sex, and apolipoprotein E ε4. RESULTS We observed a significant CSF Aβ42 /Aβ40 × p-tau interaction on cortical neurite density index (NDI), but not orientation dispersion index or cortical thickness. The directionality of these interactive effects indicated: (1) among individuals with lower CSF p-tau, greater amyloid burden was associated with higher cortical NDI; and (2) individuals with greater amyloid and p-tau burden had lower cortical NDI, consistent with cortical neurodegenerative changes. DISCUSSION NDI is a particularly sensitive marker for early cortical changes that occur prior to gross atrophy or development of cognitive impairment.
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Affiliation(s)
- Nicholas M. Vogt
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Jack F. V. Hunt
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Nagesh Adluru
- Waisman Laboratory for Brain Imaging and Behavior, Waisman Center, University of Wisconsin-Madison, Madison, WI, USA
| | - Yue Ma
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Carol A. Van Hulle
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Douglas C. Dean
- Waisman Laboratory for Brain Imaging and Behavior, Waisman Center, University of Wisconsin-Madison, Madison, WI, USA
- Department of Pediatrics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
- Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Steven R. Kecskemeti
- Waisman Laboratory for Brain Imaging and Behavior, Waisman Center, University of Wisconsin-Madison, Madison, WI, USA
| | - Nathaniel A. Chin
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Cynthia M. Carlsson
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
- Geriatric Research Education and Clinical Center, William S. Middleton Memorial Veterans Hospital, Madison, WI, USA
| | - Sanjay Asthana
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
- Geriatric Research Education and Clinical Center, William S. Middleton Memorial Veterans Hospital, Madison, WI, USA
| | - Sterling C. Johnson
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
- Geriatric Research Education and Clinical Center, William S. Middleton Memorial Veterans Hospital, Madison, WI, USA
| | | | - Richard Batrla
- Roche Diagnostics International AG, Rotkreuz, Switzerland
| | | | | | - Henrik Zetterberg
- Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry, The Sahlgrenska Academy at the University of Gothenburg, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
- Department of Neurodegenerative Disease, UCL Institute of Neurology, London, UK
- UK Dementia Research Institute at University College London, London, UK
| | - Andrew L. Alexander
- Waisman Laboratory for Brain Imaging and Behavior, Waisman Center, University of Wisconsin-Madison, Madison, WI, USA
- Department of Medical Physics, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
| | - Kaj Blennow
- Institute of Neuroscience and Physiology, Department of Psychiatry and Neurochemistry, The Sahlgrenska Academy at the University of Gothenburg, Sweden
- Clinical Neurochemistry Laboratory, Sahlgrenska University Hospital, Mölndal, Sweden
| | - Barbara B. Bendlin
- Wisconsin Alzheimer’s Disease Research Center, University of Wisconsin School of Medicine and Public Health, Madison, WI, USA
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31
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Veale T, Malone IB, Poole T, Parker TD, Slattery CF, Paterson RW, Foulkes AJM, Thomas DL, Schott JM, Zhang H, Fox NC, Cash DM. Loss and dispersion of superficial white matter in Alzheimer's disease: a diffusion MRI study. Brain Commun 2021; 3:fcab272. [PMID: 34859218 PMCID: PMC8633427 DOI: 10.1093/braincomms/fcab272] [Citation(s) in RCA: 16] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 09/24/2021] [Accepted: 10/18/2021] [Indexed: 11/22/2022] Open
Abstract
Pathological cerebral white matter changes in Alzheimer's disease have been shown using diffusion tensor imaging. Superficial white matter changes are relatively understudied despite their importance in cortico-cortical connections. Measuring superficial white matter degeneration using diffusion tensor imaging is challenging due to its complex organizational structure and proximity to the cortex. To overcome this, we investigated diffusion MRI changes in young-onset Alzheimer's disease using standard diffusion tensor imaging and Neurite Orientation Dispersion and Density Imaging to distinguish between disease-related changes that are degenerative (e.g. loss of myelinated fibres) and organizational (e.g. increased fibre dispersion). Twenty-nine young-onset Alzheimer's disease patients and 22 healthy controls had both single-shell and multi-shell diffusion MRI. We calculated fractional anisotropy, mean diffusivity, neurite density index, orientation dispersion index and tissue fraction (1-free water fraction). Diffusion metrics were sampled in 15 a priori regions of interest at four points along the cortical profile: cortical grey matter, grey/white boundary, superficial white matter (1 mm below grey/white boundary) and superficial/deeper white matter (2 mm below grey/white boundary). To estimate cross-sectional group differences, we used average marginal effects from linear mixed effect models of participants' diffusion metrics along the cortical profile. The superficial white matter of young-onset Alzheimer's disease individuals had lower neurite density index compared to controls in five regions (superior and inferior parietal, precuneus, entorhinal and parahippocampus) (all P < 0.05), and higher orientation dispersion index in three regions (fusiform, entorhinal and parahippocampus) (all P < 0.05). Young-onset Alzheimer's disease individuals had lower fractional anisotropy in the entorhinal and parahippocampus regions (both P < 0.05) and higher fractional anisotropy within the postcentral region (P < 0.05). Mean diffusivity was higher in the young-onset Alzheimer's disease group in the parahippocampal region (P < 0.05) and lower in the postcentral, precentral and superior temporal regions (all P < 0.05). In the overlying grey matter, disease-related changes were largely consistent with superficial white matter findings when using neurite density index and fractional anisotropy, but appeared at odds with orientation dispersion and mean diffusivity. Tissue fraction was significantly lower across all grey matter regions in young-onset Alzheimer's disease individuals (all P < 0.001) but group differences reduced in magnitude and coverage when moving towards the superficial white matter. These results show that microstructural changes occur within superficial white matter and along the cortical profile in individuals with young-onset Alzheimer's disease. Lower neurite density and higher orientation dispersion suggests underlying fibres undergo neurodegeneration and organizational changes, two effects previously indiscernible using standard diffusion tensor metrics in superficial white matter.
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Affiliation(s)
- Thomas Veale
- The Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
- UK Dementia Research Institute at UCL, University College London, London, UK
| | - Ian B Malone
- The Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
| | - Teresa Poole
- Department of Medical Statistics, London School of Hygiene & Tropical Medicine, London, UK
| | - Thomas D Parker
- The Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
- Department of Brain Sciences, Imperial College London, London, UK
| | - Catherine F Slattery
- The Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
| | - Ross W Paterson
- The Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
- UK Dementia Research Institute at UCL, University College London, London, UK
| | - Alexander J M Foulkes
- The Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
| | - David L Thomas
- The Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, UK
- Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, London, UK
| | - Jonathan M Schott
- The Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
| | - Hui Zhang
- Department of Computer Science and Centre for Medical Image Computing, UCL, London, UK
| | - Nick C Fox
- The Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
- UK Dementia Research Institute at UCL, University College London, London, UK
| | - David M Cash
- The Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
- UK Dementia Research Institute at UCL, University College London, London, UK
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Experimental Imaging Study of Encephalomalacia Fluid-Attenuated Inversion Recovery (FLAIR) Hyperintense Lesions in Posttraumatic Epilepsy. Neural Plast 2021; 2021:2678379. [PMID: 34754305 PMCID: PMC8572636 DOI: 10.1155/2021/2678379] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Revised: 09/24/2021] [Accepted: 10/04/2021] [Indexed: 01/20/2023] Open
Abstract
This study introduced new MRI techniques such as neurite orientation dispersion and density imaging (NODDI); NODDI applies a three-compartment tissue model to multishell DWI data that allows the examination of both the intra- and extracellular properties of white matter tissue. This, in turn, enables us to distinguish the two key aspects of axonal pathology-the packing density of axons in the white matter and the spatial organization of axons (orientation dispersion (OD)). NODDI is used to detect possible abnormalities of posttraumatic encephalomalacia fluid-attenuated inversion recovery (FLAIR) hyperintense lesions in neurite density and dispersion. Methods. 26 epilepsy patients associated with FLAIR hyperintensity around the trauma encephalomalacia region were in the epilepsy group. 18 posttraumatic patients with a FLAIR hyperintense encephalomalacia region were in the nonepilepsy group. Neurite density and dispersion affection in FLAIR hyperintense lesions around encephalomalacia were measured by NODDI using intracellular volume fraction (ICVF), and we compare these findings with conventional diffusion MRI parameters, namely, fractional anisotropy (FA) and apparent diffusion coefficient (ADC). Differences were compared between the epilepsy and nonepilepsy groups, as well as in the FLAIR hyperintense part and in the FLAIR hypointense part to try to find neurite density and dispersion differences in these parts. Results. ICVF of FLAIR hyperintense lesions in the epilepsy group was significantly higher than that in the nonepilepsy group (P < 0.001). ICVF reveals more information of FLAIR(+) and FLAIR(-) parts of encephalomalacia than OD and FA and ADC. Conclusion. The FLAIR hyperintense part around encephalomalacia in the epilepsy group showed higher ICVF, indicating that this part may have more neurite density and dispersion and may be contributing to epilepsy. NODDI indicated high neurite density with the intensity of myelin in the FLAIR hyperintense lesion. Therefore, NODDI likely shows that neurite density may be a more sensitive marker of pathology than FA.
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Lunkova E, Guberman GI, Ptito A, Saluja RS. Noninvasive magnetic resonance imaging techniques in mild traumatic brain injury research and diagnosis. Hum Brain Mapp 2021; 42:5477-5494. [PMID: 34427960 PMCID: PMC8519871 DOI: 10.1002/hbm.25630] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/05/2021] [Revised: 08/06/2021] [Accepted: 08/07/2021] [Indexed: 12/13/2022] Open
Abstract
Mild traumatic brain injury (mTBI), frequently referred to as concussion, is one of the most common neurological disorders. The underlying neural mechanisms of functional disturbances in the brains of concussed individuals remain elusive. Novel forms of brain imaging have been developed to assess patients postconcussion, including functional magnetic resonance imaging (fMRI), susceptibility-weighted imaging (SWI), diffusion MRI (dMRI), and perfusion MRI [arterial spin labeling (ASL)], but results have been mixed with a more common utilization in the research environment and a slower integration into the clinical setting. In this review, the benefits and drawbacks of the methods are described: fMRI is an effective method in the diagnosis of concussion but it is expensive and time-consuming making it difficult for regular use in everyday practice; SWI allows detection of microhemorrhages in acute and chronic phases of concussion; dMRI is primarily used for the detection of white matter abnormalities, especially axonal injury, specific for mTBI; and ASL is an alternative to the BOLD method with its ability to track cerebral blood flow alterations. Thus, the absence of a universal diagnostic neuroimaging method suggests a need for the adoption of a multimodal approach to the neuroimaging of mTBI. Taken together, these methods, with their underlying functional and structural features, can contribute from different angles to a deeper understanding of mTBI mechanisms such that a comprehensive diagnosis of mTBI becomes feasible for the clinician.
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Affiliation(s)
- Ekaterina Lunkova
- Department of Neurology & NeurosurgeryMcGill UniversityMontrealQuebecCanada
| | - Guido I. Guberman
- Department of Neurology & NeurosurgeryMcGill UniversityMontrealQuebecCanada
| | - Alain Ptito
- Department of Neurology & NeurosurgeryMcGill UniversityMontrealQuebecCanada
- Montreal Neurological InstituteMontrealQuebecCanada
- Department of PsychologyMcGill University Health CentreMontrealQuebecCanada
| | - Rajeet Singh Saluja
- Department of Neurology & NeurosurgeryMcGill UniversityMontrealQuebecCanada
- McGill University Health Centre Research InstituteMontrealQuebecCanada
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Sato J, Vandewouw MM, Bando N, Branson HM, O'Connor DL, Unger SL, Taylor MJ. White matter alterations and cognitive outcomes in children born very low birth weight. Neuroimage Clin 2021; 32:102843. [PMID: 34601309 PMCID: PMC8496319 DOI: 10.1016/j.nicl.2021.102843] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/29/2021] [Revised: 09/23/2021] [Accepted: 09/24/2021] [Indexed: 10/25/2022]
Abstract
BACKGROUND Very low birth weight (VLBW) infants are at risk for disrupted white matter maturation, yet little is known about the contributing factors, particularly at preschool-age when cognitive difficulties begin to emerge. We examined white matter microstructure in five-year-old VLBW and full-term (FT) children, and its association with cognitive outcomes and birth weight. METHODS Multi-shell diffusion and MR images were obtained for 41 VLBW (mean birth weight: 1028.6 ± 256.8 g) and 26 FT (3295.4 ± 493.9 g) children. Fractional anisotropy (FA), radial diffusivity (RD), neurite orientation dispersion index (ODI) and density index (NDI) were estimated using diffusion tensor and neurite orientation dispersion and density imaging models. Between-group analyses used a general linear model with group and sex as explanatory variables. Within-group associations between white matter microstructure, cognitive outcomes and birth weight were also investigated. RESULTS VLBW compared to FT children showed lower FA and NDI across widespread white matter regions. Smaller clusters of atypical ODI were also found in VLBW children. Within-group analyses in FT children revealed that lower RD and higher NDI were associated with vocabulary acquisition and working memory. In VLBW children, higher FA and NDI, and lower RD and ODI, were associated with improved processing speed. In both groups, FA was positively associated with birth weight. CONCLUSIONS Our findings demonstrate white matter alterations in young VLBW children, including widespread reductions in axon density that may reflect sustained myelination disruptions. The associations with cognitive outcomes may also highlight which of the VLBW children are at higher risk for later cognitive difficulties.
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Affiliation(s)
- Julie Sato
- Diagnostic Imaging, Hospital for Sick Children, Toronto, Ontario, Canada; Psychology, University of Toronto, Toronto, Ontario, Canada; Neurosciences & Mental Health, Hospital for Sick Children, Toronto, Ontario, Canada.
| | - Marlee M Vandewouw
- Diagnostic Imaging, Hospital for Sick Children, Toronto, Ontario, Canada; Neurosciences & Mental Health, Hospital for Sick Children, Toronto, Ontario, Canada; Autism Research Centre, Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON, Canada; Institute of Biomedical Engineering, University of Toronto, Toronto, Ontario, Canada
| | - Nicole Bando
- Translational Medicine, SickKids Research Institute, Toronto, Ontario, Canada
| | - Helen M Branson
- Diagnostic Imaging, Hospital for Sick Children, Toronto, Ontario, Canada; Medical Imaging, University of Toronto, Ontario, Canada
| | - Deborah L O'Connor
- Translational Medicine, SickKids Research Institute, Toronto, Ontario, Canada; Nutritional Sciences, University of Toronto, Toronto, Ontario, Canada; Paediatrics, Mount Sinai Health, Toronto, Ontario, Canada
| | - Sharon L Unger
- Nutritional Sciences, University of Toronto, Toronto, Ontario, Canada; Paediatrics, University of Toronto, Toronto, Ontario, Canada; Paediatrics, Mount Sinai Health, Toronto, Ontario, Canada; Division of Neonatology, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Margot J Taylor
- Diagnostic Imaging, Hospital for Sick Children, Toronto, Ontario, Canada; Psychology, University of Toronto, Toronto, Ontario, Canada; Neurosciences & Mental Health, Hospital for Sick Children, Toronto, Ontario, Canada; Medical Imaging, University of Toronto, Ontario, Canada; Paediatrics, University of Toronto, Toronto, Ontario, Canada
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Barletta V, Herranz E, Treaba CA, Mehndiratta A, Ouellette R, Mangeat G, Granberg T, Sloane JA, Klawiter EC, Cohen-Adad J, Mainero C. Quantitative 7-Tesla Imaging of Cortical Myelin Changes in Early Multiple Sclerosis. Front Neurol 2021; 12:714820. [PMID: 34539559 PMCID: PMC8446537 DOI: 10.3389/fneur.2021.714820] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/25/2021] [Accepted: 07/16/2021] [Indexed: 11/21/2022] Open
Abstract
Cortical demyelination occurs early in multiple sclerosis (MS) and relates to disease outcome. The brain cortex has endogenous propensity for remyelination as proven from histopathology study. In this study, we aimed at characterizing cortical microstructural abnormalities related to myelin content by applying a novel quantitative MRI technique in early MS. A combined myelin estimation (CME) cortical map was obtained from quantitative 7-Tesla (7T) T2* and T1 acquisitions in 25 patients with early MS and 19 healthy volunteers. Cortical lesions in MS patients were classified based on their myelin content by comparison with CME values in healthy controls as demyelinated, partially demyelinated, or non-demyelinated. At follow-up, we registered changes in cortical lesions as increased, decreased, or stable CME. Vertex-wise analysis compared cortical CME in the normal-appearing cortex in 25 MS patients vs. 19 healthy controls at baseline and investigated longitudinal changes at 1 year in 10 MS patients. Measurements from the neurite orientation dispersion and density imaging (NODDI) diffusion model were obtained to account for cortical neurite/dendrite loss at baseline and follow-up. Finally, CME maps were correlated with clinical metrics. CME was overall low in cortical lesions (p = 0.03) and several normal-appearing cortical areas (p < 0.05) in the absence of NODDI abnormalities. Individual cortical lesion analysis revealed, however, heterogeneous CME patterns from extensive to partial or absent demyelination. At follow-up, CME overall decreased in cortical lesions and non-lesioned cortex, with few areas showing an increase (p < 0.05). Cortical CME maps correlated with processing speed in several areas across the cortex. In conclusion, CME allows detection of cortical microstructural changes related to coexisting demyelination and remyelination since the early phases of MS, and shows to be more sensitive than NODDI and relates to cognitive performance.
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Affiliation(s)
- Valeria Barletta
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, United States.,Harvard Medical School, Harvard University, Boston, MA, United States
| | - Elena Herranz
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, United States.,Harvard Medical School, Harvard University, Boston, MA, United States
| | - Constantina A Treaba
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, United States.,Harvard Medical School, Harvard University, Boston, MA, United States
| | - Ambica Mehndiratta
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, United States
| | - Russell Ouellette
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, United States.,Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.,Department of Neuroradiology, Karolinska University Hospital, Stockholm, Sweden
| | - Gabriel Mangeat
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
| | - Tobias Granberg
- Department of Clinical Neuroscience, Karolinska Institutet, Stockholm, Sweden.,Department of Neuroradiology, Karolinska University Hospital, Stockholm, Sweden
| | - Jacob A Sloane
- Harvard Medical School, Harvard University, Boston, MA, United States.,Department of Neurology, Beth Israel Deaconess Medical Center, Boston, MA, United States
| | - Eric C Klawiter
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, United States.,Harvard Medical School, Harvard University, Boston, MA, United States
| | - Julien Cohen-Adad
- NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
| | - Caterina Mainero
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, MA, United States.,Harvard Medical School, Harvard University, Boston, MA, United States
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Borghesani V, Wang C, Watson C, Bouhali F, Caverzasi E, Battistella G, Bogley R, Yabut NA, Deleon J, Miller ZA, Hoeft F, Mandelli ML, Gorno-Tempini ML. Functional and morphological correlates of developmental dyslexia: A multimodal investigation of the ventral occipitotemporal cortex. J Neuroimaging 2021; 31:962-972. [PMID: 34115429 PMCID: PMC10832296 DOI: 10.1111/jon.12892] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2021] [Revised: 05/03/2021] [Accepted: 05/21/2021] [Indexed: 12/26/2022] Open
Abstract
BACKGROUND AND PURPOSE The ventral occipitotemporal cortex (vOT) is a region crucial for reading acquisition through selective tuning to printed words. Developmental dyslexia is a disorder of reading with underlying neurobiological bases often associated with atypical neural responses to printed words. Previous studies have discovered anomalous structural development and function of the vOT in individuals with dyslexia. However, it remains unclear if or how structural abnormalities relate to functional alterations. METHODS In this study, we acquired structural, functional (words and faces processing), and diffusion MRI data from 26 children with dyslexia (average age = 10.4 ± 2.0 years) and 14 age-matched typically developing readers (average age = 10.4 ± 1.6 years). Morphological indices of local gyrification, neurite density (i.e., dendritic arborization structure), and orientation dispersion (i.e., dendritic arborization orientation) were analyzed within the vOT region that showed preferential activation in typically developing readers for words (as compared to face stimuli). RESULTS The two cohorts diverged significantly in both functional and structural measures. Compared to typically developing controls, children with dyslexia did not show selectivity for words in the left vOT (contrast: words > false fonts). This lack of tuning to printed words was associated with greater neurite dispersion heterogeneity in the dyslexia cohort, but similar neurite density. These group differences were not present in the homologous contralateral area, the right vOT. CONCLUSIONS Our findings provide new insight into the neurobiology of the lack of vOT word tuning in dyslexia by linking behavior, alterations in functional activation, and neurite organization.
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Affiliation(s)
- Valentina Borghesani
- Department of Neurology, University of California, San Francisco, San Francisco, California, USA
| | - Cheng Wang
- Department of Neurology, University of California, San Francisco, San Francisco, California, USA
- Dyslexia Center, University of California, San Francisco, San Francisco, California, USA
| | - Christa Watson
- Department of Neurology, University of California, San Francisco, San Francisco, California, USA
- Dyslexia Center, University of California, San Francisco, San Francisco, California, USA
| | - Florence Bouhali
- Department of Psychiatry and Behavioral Science, University of California, San Francisco, San Francisco, California, USA
- Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California, USA
| | - Eduardo Caverzasi
- Department of Neurology, University of California, San Francisco, San Francisco, California, USA
- Dyslexia Center, University of California, San Francisco, San Francisco, California, USA
| | - Giovanni Battistella
- Department of Neurology, University of California, San Francisco, San Francisco, California, USA
- Dyslexia Center, University of California, San Francisco, San Francisco, California, USA
| | - Rian Bogley
- Department of Neurology, University of California, San Francisco, San Francisco, California, USA
- Dyslexia Center, University of California, San Francisco, San Francisco, California, USA
| | - Nicole A Yabut
- Department of Neurology, University of California, San Francisco, San Francisco, California, USA
- Dyslexia Center, University of California, San Francisco, San Francisco, California, USA
| | - Jessica Deleon
- Department of Neurology, University of California, San Francisco, San Francisco, California, USA
- Dyslexia Center, University of California, San Francisco, San Francisco, California, USA
| | - Zachary A Miller
- Department of Neurology, University of California, San Francisco, San Francisco, California, USA
- Dyslexia Center, University of California, San Francisco, San Francisco, California, USA
| | - Fumiko Hoeft
- Department of Psychiatry and Behavioral Science, University of California, San Francisco, San Francisco, California, USA
- Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California, USA
- Department of Psychological Sciences, University of Connecticut, Mansfield, Connecticut, USA
- Brain Imaging Research Center, University of Connecticut, Mansfield, Connecticut, USA
| | - Maria Luisa Mandelli
- Department of Neurology, University of California, San Francisco, San Francisco, California, USA
- Dyslexia Center, University of California, San Francisco, San Francisco, California, USA
| | - Maria Luisa Gorno-Tempini
- Department of Neurology, University of California, San Francisco, San Francisco, California, USA
- Dyslexia Center, University of California, San Francisco, San Francisco, California, USA
- Department of Psychiatry and Behavioral Science, University of California, San Francisco, San Francisco, California, USA
- Weill Institute for Neurosciences, University of California, San Francisco, San Francisco, California, USA
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Oehr LE, Yang JYM, Chen J, Maller JJ, Seal ML, Anderson JFI. Investigating White Matter Tract Microstructural Changes at Six-Twelve Weeks following Mild Traumatic Brain Injury: A Combined Diffusion Tensor Imaging and Neurite Orientation Dispersion and Density Imaging Study. J Neurotrauma 2021; 38:2255-2263. [PMID: 33307950 DOI: 10.1089/neu.2020.7310] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/12/2022] Open
Abstract
Using diffusion-weighted imaging (DWI), research has demonstrated changes suggestive of damage to white matter tracts (WMT) following mild traumatic brain injury (mTBI). Yet due to the predominant use of the diffusion tensor imaging (DTI) model, which has numerous well-established limitations, it has not yet been possible to clearly examine the nature of changes to WMT microstructure following mTBI. This study used a second DWI-based technique, neurite orientation dispersion and density imaging (NODDI), in combination with DTI to measure microstructural changes within the corpus callosum, three long association and one projection WMTs at 6-12 weeks following mTBI, compared with matched trauma controls (TC). Between-groups differences were identified across all WMT for the DTI metric fractional anisotropy (FA), and the NODDI metrics orientation dispersion index (ODI) and isotropic volume fraction (ISO). No statistically significant between-groups differences were found for other DTI and NODDI metrics. Our study revealed that reduced FA was accompanied by increased ODI, suggesting that mTBI results in reduced coherence of axonal fiber bundles within the studied WMTs. These between-groups differences in WMT microstructure were found at 6-12 weeks post-injury, which suggests that structural recovery is not yet complete towards end of the typical 3-month recovery period.
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Affiliation(s)
- Lucy E Oehr
- Melbourne School of Psychological Sciences, University of Melbourne, Victoria, Australia
| | - Joseph Yuan-Mou Yang
- Department of Neuroscience Research, Murdoch Childrens Research Institute, Melbourne, Victoria, Australia
- Department of Developmental Imaging, Murdoch Childrens Research Institute, Melbourne, Victoria, Australia
- Department of Neurosurgery, Royal Children's Hospital, Melbourne, Victoria, Australia
- Department of Pediatrics, University of Melbourne, Victoria, Australia
| | - Jian Chen
- Department of Developmental Imaging, Murdoch Childrens Research Institute, Melbourne, Victoria, Australia
| | - Jerome J Maller
- General Electric Healthcare, Melbourne, Victoria, Australia
- Monash Alfred Psychiatry Research Center, Melbourne, Victoria, Australia
| | - Marc L Seal
- Department of Developmental Imaging, Murdoch Childrens Research Institute, Melbourne, Victoria, Australia
| | - Jacqueline F I Anderson
- Melbourne School of Psychological Sciences, University of Melbourne, Victoria, Australia
- Department of Psychology, Alfred Hospital, Melbourne, Victoria, Australia
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38
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Huber E, Mezer A, Yeatman JD. Neurobiological underpinnings of rapid white matter plasticity during intensive reading instruction. Neuroimage 2021; 243:118453. [PMID: 34358657 DOI: 10.1016/j.neuroimage.2021.118453] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2019] [Revised: 07/24/2021] [Accepted: 08/03/2021] [Indexed: 01/18/2023] Open
Abstract
Diffusion MRI is a powerful tool for imaging brain structure, but it is challenging to discern the biological underpinnings of plasticity inferred from these and other non-invasive MR measurements. Biophysical modeling of the diffusion signal aims to render a more biologically rich image of tissue microstructure, but the application of these models comes with important caveats. A separate approach for gaining biological specificity has been to seek converging evidence from multi-modal datasets. Here we use metrics derived from diffusion kurtosis imaging (DKI) and the white matter tract integrity (WMTI) model along with quantitative MRI measurements of T1 relaxation to characterize changes throughout the white matter during an 8-week, intensive reading intervention (160 total hours of instruction). Behavioral measures, multi-shell diffusion MRI data, and quantitative T1 data were collected at regular intervals during the intervention in a group of 33 children with reading difficulties (7-12 years old), and over the same period in an age-matched non-intervention control group. Throughout the white matter, mean 'extra-axonal' diffusivity was inversely related to intervention time. In contrast, model estimated axonal water fraction (AWF), overall diffusion kurtosis, and T1 relaxation time showed no significant change over the intervention period. Both diffusion and quantitative T1 based metrics were correlated with pre-intervention reading performance, albeit with distinct anatomical distributions. These results are consistent with the view that rapid changes in diffusion properties reflect phenomena other than widespread changes in myelin density. We discuss this result in light of recent work highlighting non-axonal factors in experience-dependent plasticity and learning.
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Affiliation(s)
- Elizabeth Huber
- Institute for Learning and Brain Sciences and Department of Speech and Hearing Sciences, University of Washington, Seattle, WA 98195, USA.
| | - Aviv Mezer
- Edmond and Lily Safra Center for Brain Sciences, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Jason D Yeatman
- Graduate School of Education, Stanford University, Stanford, CA 94305, USA; Division of Developmental-Behavioral Pediatrics, Stanford University School of Medicine, Stanford, CA 95305, USA
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39
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Gozdas E, Fingerhut H, Dacorro L, Bruno JL, Hosseini SMH. Neurite Imaging Reveals Widespread Alterations in Gray and White Matter Neurite Morphology in Healthy Aging and Amnestic Mild Cognitive Impairment. Cereb Cortex 2021; 31:5570-5578. [PMID: 34313731 DOI: 10.1093/cercor/bhab180] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Revised: 04/09/2021] [Accepted: 05/19/2021] [Indexed: 12/26/2022] Open
Abstract
Aging is the major risk factor for neurodegenerative diseases and affects neurite distributions throughout the brain, yet underlying neurobiological mechanisms remain unclear. Multi-shell diffusion-weighted imaging and neurite orientation dispersion and density imaging (NODDI) now provide in vivo biophysical measurements that explain these biological processes in the cortex and white matter. In this study, neurite distributions were evaluated in the cortex and white matter in healthy older adults and patients with amnestic mild cognitive impairment (aMCI) that provides fundamental contributions regarding healthy aging and neurodegeneration. Older age was associated with reduced neurite density and neurite orientation dispersion (ODI) in widespread cortical regions. In contrast, increased ODI was only observed in the right thalamus and hippocampus with age. For the first time, we also reported a widespread age-associated decrease in neurite density along major white matter tracts correlated with decreased cortical neurite density in the tract endpoints in healthy older adults. We further examined alterations in cortical and white matter neurite microstructures in aMCI patients and found significant neurite morphology deficits in memory networks correlated with memory performance. Our findings indicate that neurite parameters provide valuable information regarding cortical and white matter microstructure and complement myeloarchitectural information in healthy aging and aMCI.
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Affiliation(s)
- Elveda Gozdas
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94304, USA
| | - Hannah Fingerhut
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94304, USA
| | - Lauren Dacorro
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94304, USA
| | - Jennifer L Bruno
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94304, USA
| | - S M Hadi Hosseini
- Department of Psychiatry and Behavioral Sciences, Stanford University School of Medicine, Stanford, CA 94304, USA
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40
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Ianus A, Alexander DC, Zhang H, Palombo M. Mapping complex cell morphology in the grey matter with double diffusion encoding MR: A simulation study. Neuroimage 2021; 241:118424. [PMID: 34311067 PMCID: PMC8961003 DOI: 10.1016/j.neuroimage.2021.118424] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/08/2021] [Revised: 07/13/2021] [Accepted: 07/21/2021] [Indexed: 01/18/2023] Open
Abstract
This paper investigates the impact of cell body (namely soma) size and branching of cellular projections on diffusion MR imaging (dMRI) and spectroscopy (dMRS) signals for both standard single diffusion encoding (SDE) and more advanced double diffusion encoding (DDE) measurements using numerical simulations. The aim is to investigate the ability of dMRI/dMRS to characterize the complex morphology of brain cells focusing on these two distinctive features of brain grey matter. To this end, we employ a recently developed computational framework to create three dimensional meshes of neuron-like structures for Monte Carlo simulations, using diffusion coefficients typical of water and brain metabolites. Modelling the cellular structure as realistically connected spherical soma and cylindrical cellular projections, we cover a wide range of combinations of sphere radii and branching order of cellular projections, characteristic of various grey matter cells. We assess the impact of spherical soma size and branching order on the b-value dependence of the SDE signal as well as the time dependence of the mean diffusivity (MD) and mean kurtosis (MK). Moreover, we also assess the impact of spherical soma size and branching order on the angular modulation of DDE signal at different mixing times, together with the mixing time dependence of the apparent microscopic anisotropy (μA), a promising contrast derived from DDE measurements. The SDE results show that spherical soma size has a measurable impact on both the b-value dependence of the SDE signal and the MD and MK diffusion time dependence for both water and metabolites. On the other hand, we show that branching order has little impact on either, especially for water. In contrast, the DDE results show that spherical soma size has a measurable impact on the DDE signal's angular modulation at short mixing times and the branching order of cellular projections significantly impacts the mixing time dependence of the DDE signal's angular modulation as well as of the derived μA, for both water and metabolites. Our results confirm that SDE based techniques may be sensitive to spherical soma size, and most importantly, show for the first time that DDE measurements may be more sensitive to the dendritic tree complexity (as parametrized by the branching order of cellular projections), paving the way for new ways of characterizing grey matter morphology, non-invasively using dMRS and potentially dMRI.
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Affiliation(s)
- A Ianus
- Centre for Medical Image Computing and Department of Computer Science, University College London, London, United Kingdom; Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - D C Alexander
- Centre for Medical Image Computing and Department of Computer Science, University College London, London, United Kingdom
| | - H Zhang
- Centre for Medical Image Computing and Department of Computer Science, University College London, London, United Kingdom
| | - M Palombo
- Centre for Medical Image Computing and Department of Computer Science, University College London, London, United Kingdom.
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41
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Koirala N, Perdue MV, Su X, Grigorenko EL, Landi N. Neurite density and arborization is associated with reading skill and phonological processing in children. Neuroimage 2021; 241:118426. [PMID: 34303796 PMCID: PMC8539928 DOI: 10.1016/j.neuroimage.2021.118426] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 06/25/2021] [Accepted: 07/21/2021] [Indexed: 12/03/2022] Open
Abstract
Background: Studies exploring neuroanatomic correlates of reading have associated white matter tissue properties with reading disability and related componential skills (e.g., phonological and single-word reading skills). Mean diffusivity (MD) and fractional anisotropy (FA) are widely used surrogate measures of tissue microstructure with high sensitivity; however, they lack specificity for individual microstructural features. Here we investigated neurite features with higher specificity in order to explore the underlying microstructural architecture. Methods: Diffusion weighted images (DWI) and a battery of behavioral and neuropsychological assessments were obtained from 412 children (6 – 16 years of age). Neurite indices influenced by orientation and density were attained from 23 major white matter tracts. Partial correlations were calculated between neurite indices and indicators of phonological processing and single-word reading skills using age, sex, and image quality metrics as covariates. In addition, mediation analysis was performed using structural equation modeling (SEM) to evaluate the indirect effect of phonological processing on reading skills. Results: We observed that orientation dispersion index (ODI) and neurite density index (NDI) were negatively correlated with single-word reading and phonological processing skills in several tracts previously shown to have structural correlates with reading efficiency. We also observed a significant and substantial effect in which phonological processing mediated the relationship between neurite indices and reading skills in most tracts. Conclusions: In sum, we established that better reading and phonological processing skills are associated with greater tract coherence (lower ODI) and lower neurite density (lower NDI). We interpret these findings as evidence that reading is associated with neural architecture and its efficiency.
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Affiliation(s)
- Nabin Koirala
- Haskins Laboratories, New Haven, Connecticut, United States.
| | - Meaghan V Perdue
- Haskins Laboratories, New Haven, Connecticut, United States; Department of Psychological Sciences, University of Connecticut, Connecticut, United States
| | - Xing Su
- Haskins Laboratories, New Haven, Connecticut, United States
| | - Elena L Grigorenko
- Haskins Laboratories, New Haven, Connecticut, United States; Department of Psychology, University of Houston, Texas, United States
| | - Nicole Landi
- Haskins Laboratories, New Haven, Connecticut, United States; Department of Psychological Sciences, University of Connecticut, Connecticut, United States
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42
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Surgent O, Dean DC, Alexander AL, Dadalko OI, Guerrero-Gonzalez J, Taylor D, Skaletski E, Travers BG. Neurobiological and behavioural outcomes of biofeedback-based training in autism: a randomized controlled trial. Brain Commun 2021; 3:fcab112. [PMID: 34250479 PMCID: PMC8254423 DOI: 10.1093/braincomms/fcab112] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2021] [Revised: 03/26/2021] [Accepted: 04/28/2021] [Indexed: 11/13/2022] Open
Abstract
The human brain has demonstrated the power to structurally change as a result of movement-based interventions. However, it is unclear whether these structural brain changes differ in autistic individuals compared to non-autistic individuals. The purpose of the present study was to pilot a randomized controlled trial to investigate brain, balance, autism symptom severity and daily living skill changes that result from a biofeedback-based balance intervention in autistic adolescents (13-17 years old). Thirty-four autistic participants and 28 age-matched non-autistic participants underwent diagnostic testing and pre-training assessment (neuroimaging, cognitive, autism symptom severity and motor assessments) and were then randomly assigned to 6 weeks of a balance-training intervention or a sedentary-control condition. After the 6 weeks, neuroimaging, symptom severity and motor assessments were repeated. Results found that both the autistic and non-autistic participants demonstrated similar and significant increases in balance times with training. Furthermore, individuals in the balance-training condition showed significantly greater improvements in postural sway and reductions in autism symptom severity compared to individuals in the control condition. Daily living scores did not change with training, nor did we observe hypothesized changes to the microstructural properties of the corticospinal tract. However, follow-up voxel-based analyses found a wide range of balance-related structures that showed changes across the brain. Many of these brain changes were specific to the autistic participants compared to the non-autistic participants, suggesting distinct structural neuroplasticity in response to balance training in autistic participants. Altogether, these findings suggest that biofeedback-based balance training may target postural stability challenges, reduce core autism symptoms and influence neurobiological change. Future research is encouraged to examine the superior cerebellar peduncle in response to balance training and symptom severity changes in autistic individuals, as the current study produced overlapping findings in this brain region.
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Affiliation(s)
- Olivia Surgent
- Waisman Center, University of Wisconsin-Madison, Madison, WI 53705, USA
- Neuroscience Training Program, University of Wisconsin-Madison, Madison, WI 53705, USA
| | - Douglas C Dean
- Waisman Center, University of Wisconsin-Madison, Madison, WI 53705, USA
- Pediatrics, University of Wisconsin-Madison, Madison, WI 53792, USA
- Medical Physics, University of Wisconsin-Madison, Madison, WI 53705, USA
| | - Andrew L Alexander
- Waisman Center, University of Wisconsin-Madison, Madison, WI 53705, USA
- Medical Physics, University of Wisconsin-Madison, Madison, WI 53705, USA
- Psychiatry, University of Wisconsin-Madison, Madison, WI 53719, USA
| | - Olga I Dadalko
- Waisman Center, University of Wisconsin-Madison, Madison, WI 53705, USA
| | - Jose Guerrero-Gonzalez
- Waisman Center, University of Wisconsin-Madison, Madison, WI 53705, USA
- Medical Physics, University of Wisconsin-Madison, Madison, WI 53705, USA
| | - Desiree Taylor
- Waisman Center, University of Wisconsin-Madison, Madison, WI 53705, USA
- Occupational Therapy Program in Kinesiology, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Emily Skaletski
- Waisman Center, University of Wisconsin-Madison, Madison, WI 53705, USA
- Occupational Therapy Program in Kinesiology, University of Wisconsin-Madison, Madison, WI 53706, USA
| | - Brittany G Travers
- Waisman Center, University of Wisconsin-Madison, Madison, WI 53705, USA
- Occupational Therapy Program in Kinesiology, University of Wisconsin-Madison, Madison, WI 53706, USA
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43
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Johnson D, Ricciardi A, Brownlee W, Kanber B, Prados F, Collorone S, Kaden E, Toosy A, Alexander DC, Gandini Wheeler-Kingshott CAM, Ciccarelli O, Grussu F. Comparison of Neurite Orientation Dispersion and Density Imaging and Two-Compartment Spherical Mean Technique Parameter Maps in Multiple Sclerosis. Front Neurol 2021; 12:662855. [PMID: 34194382 PMCID: PMC8236830 DOI: 10.3389/fneur.2021.662855] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/01/2021] [Accepted: 05/17/2021] [Indexed: 01/03/2023] Open
Abstract
Background: Neurite orientation dispersion and density imaging (NODDI) and the spherical mean technique (SMT) are diffusion MRI methods providing metrics with sensitivity to similar characteristics of white matter microstructure. There has been limited comparison of changes in NODDI and SMT parameters due to multiple sclerosis (MS) pathology in clinical settings. Purpose: To compare group-wise differences between healthy controls and MS patients in NODDI and SMT metrics, investigating associations with disability and correlations with diffusion tensor imaging (DTI) metrics. Methods: Sixty three relapsing-remitting MS patients were compared to 28 healthy controls. NODDI and SMT metrics corresponding to intracellular volume fraction (vin), orientation dispersion (ODI and ODE), diffusivity (D) (SMT only) and isotropic volume fraction (viso) (NODDI only) were calculated from diffusion MRI data, alongside DTI metrics (fractional anisotropy, FA; axial/mean/radial diffusivity, AD/MD/RD). Correlations between all pairs of MRI metrics were calculated in normal-appearing white matter (NAWM). Associations with expanded disability status scale (EDSS), controlling for age and gender, were evaluated. Patient-control differences were assessed voxel-by-voxel in MNI space controlling for age and gender at the 5% significance level, correcting for multiple comparisons. Spatial overlap of areas showing significant differences were compared using Dice coefficients. Results: NODDI and SMT show significant associations with EDSS (standardised beta coefficient −0.34 in NAWM and −0.37 in lesions for NODDI vin; 0.38 and −0.31 for SMT ODE and vin in lesions; p < 0.05). Significant correlations in NAWM are observed between DTI and NODDI/SMT metrics. NODDI vin and SMT vin strongly correlated (r = 0.72, p < 0.05), likewise NODDI ODI and SMT ODE (r = −0.80, p < 0.05). All DTI, NODDI and SMT metrics detect widespread differences between patients and controls in NAWM (12.57% and 11.90% of MNI brain mask for SMT and NODDI vin, Dice overlap of 0.42). Data Conclusion: SMT and NODDI detect significant differences in white matter microstructure between MS patients and controls, concurring on the direction of these changes, providing consistent descriptors of tissue microstructure that correlate with disability and show alterations beyond focal damage. Our study suggests that NODDI and SMT may play a role in monitoring MS in clinical trials and practice.
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Affiliation(s)
- Daniel Johnson
- Department of Neuroinflammation, Faculty of Brain Sciences, Queen Square Multiple Sclerosis (MS) Centre, University College London (UCL) Queen Square Institute of Neurology, University College London, London, United Kingdom.,Addenbrooke's Hospital, Cambridge, United Kingdom
| | - Antonio Ricciardi
- Department of Neuroinflammation, Faculty of Brain Sciences, Queen Square Multiple Sclerosis (MS) Centre, University College London (UCL) Queen Square Institute of Neurology, University College London, London, United Kingdom.,Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing, University College London, London, United Kingdom
| | - Wallace Brownlee
- Department of Neuroinflammation, Faculty of Brain Sciences, Queen Square Multiple Sclerosis (MS) Centre, University College London (UCL) Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Baris Kanber
- Department of Neuroinflammation, Faculty of Brain Sciences, Queen Square Multiple Sclerosis (MS) Centre, University College London (UCL) Queen Square Institute of Neurology, University College London, London, United Kingdom.,Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing, University College London, London, United Kingdom
| | - Ferran Prados
- Department of Neuroinflammation, Faculty of Brain Sciences, Queen Square Multiple Sclerosis (MS) Centre, University College London (UCL) Queen Square Institute of Neurology, University College London, London, United Kingdom.,Department of Medical Physics and Biomedical Engineering, Centre for Medical Image Computing, University College London, London, United Kingdom.,e-Health Centre, Universitat Oberta de Catalunya, Barcelona, Spain
| | - Sara Collorone
- Department of Neuroinflammation, Faculty of Brain Sciences, Queen Square Multiple Sclerosis (MS) Centre, University College London (UCL) Queen Square Institute of Neurology, University College London, London, United Kingdom
| | - Enrico Kaden
- Department of Computer Science, Centre for Medical Image Computing, University College London, London, United Kingdom.,Great Ormond Street Institute of Child Health, University College London, London, United Kingdom
| | - Ahmed Toosy
- Department of Neuroinflammation, Faculty of Brain Sciences, Queen Square Multiple Sclerosis (MS) Centre, University College London (UCL) Queen Square Institute of Neurology, University College London, London, United Kingdom.,National Institute for Health Research (NIHR) University College London Hospitals Biomedical Research Centre, London, United Kingdom
| | - Daniel C Alexander
- Department of Computer Science, Centre for Medical Image Computing, University College London, London, United Kingdom
| | - Claudia A M Gandini Wheeler-Kingshott
- Department of Neuroinflammation, Faculty of Brain Sciences, Queen Square Multiple Sclerosis (MS) Centre, University College London (UCL) Queen Square Institute of Neurology, University College London, London, United Kingdom.,Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy.,Brain Magnetic Resonance Imaging (MRI) 3T Research Centre, Istituto di Ricovero e Cura a Carattere Scientifico (IRCCS) Mondino Foundation, Pavia, Italy
| | - Olga Ciccarelli
- Department of Neuroinflammation, Faculty of Brain Sciences, Queen Square Multiple Sclerosis (MS) Centre, University College London (UCL) Queen Square Institute of Neurology, University College London, London, United Kingdom.,National Institute for Health Research (NIHR) University College London Hospitals Biomedical Research Centre, London, United Kingdom
| | - Francesco Grussu
- Department of Neuroinflammation, Faculty of Brain Sciences, Queen Square Multiple Sclerosis (MS) Centre, University College London (UCL) Queen Square Institute of Neurology, University College London, London, United Kingdom.,Radiomics Group, Vall d'Hebron Institute of Oncology, Vall d'Hebron Barcelona Hospital Campus, Barcelona, Spain
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44
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Differentiating Glioblastomas from Solitary Brain Metastases: An Update on the Current Literature of Advanced Imaging Modalities. Cancers (Basel) 2021; 13:cancers13122960. [PMID: 34199151 PMCID: PMC8231515 DOI: 10.3390/cancers13122960] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2021] [Revised: 06/09/2021] [Accepted: 06/09/2021] [Indexed: 12/12/2022] Open
Abstract
Differentiating between glioblastomas and solitary brain metastases proves to be a challenging diagnosis for neuroradiologists, as both present with imaging patterns consisting of peritumoral hyperintensities with similar intratumoral texture on traditional magnetic resonance imaging sequences. Early diagnosis is paramount, as each pathology has completely different methods of clinical assessment. In the past decade, recent developments in advanced imaging modalities enabled providers to acquire a more accurate diagnosis earlier in the patient's clinical assessment, thus optimizing clinical outcome. Dynamic susceptibility contrast has been optimized for detecting relative cerebral blood flow and relative cerebral blood volume. Diffusion tensor imaging can be used to detect changes in mean diffusivity. Neurite orientation dispersion and density imaging is an innovative modality detecting changes in intracellular volume fraction, isotropic volume fraction, and extracellular volume fraction. Magnetic resonance spectroscopy is able to assist by providing a metabolic descriptor while detecting variable ratios of choline/N-acetylaspartate, choline/creatine, and N-acetylaspartate/creatine. Finally, radiomics and machine learning algorithms have been devised to assist in improving diagnostic accuracy while often utilizing more than one advanced imaging protocol per patient. In this review, we provide an update on all the current evidence regarding the identification and differentiation of glioblastomas from solitary brain metastases.
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45
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Lazari A, Lipp I. Can MRI measure myelin? Systematic review, qualitative assessment, and meta-analysis of studies validating microstructural imaging with myelin histology. Neuroimage 2021; 230:117744. [PMID: 33524576 PMCID: PMC8063174 DOI: 10.1016/j.neuroimage.2021.117744] [Citation(s) in RCA: 74] [Impact Index Per Article: 24.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2020] [Revised: 01/05/2021] [Accepted: 01/09/2021] [Indexed: 12/16/2022] Open
Abstract
Recent years have seen an increased understanding of the importance of myelination in healthy brain function and neuropsychiatric diseases. Non-invasive microstructural magnetic resonance imaging (MRI) holds the potential to expand and translate these insights to basic and clinical human research, but the sensitivity and specificity of different MR markers to myelination is a subject of debate. To consolidate current knowledge on the topic, we perform a systematic review and meta-analysis of studies that validate microstructural imaging by combining it with myelin histology. We find meta-analytic evidence for correlations between various myelin histology metrics and markers from different MRI modalities, including fractional anisotropy, radial diffusivity, macromolecular pool, magnetization transfer ratio, susceptibility and longitudinal relaxation rate, but not mean diffusivity. Meta-analytic correlation effect sizes range widely, between R2 = 0.26 and R2 = 0.82. However, formal comparisons between MRI-based myelin markers are limited by methodological variability, inconsistent reporting and potential for publication bias, thus preventing the establishment of a single most sensitive strategy to measure myelin with MRI. To facilitate further progress, we provide a detailed characterisation of the evaluated studies as an online resource. We also share a set of 12 recommendations for future studies validating putative MR-based myelin markers and deploying them in vivo in humans.
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Affiliation(s)
- Alberto Lazari
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, UK
| | - Ilona Lipp
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany.
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46
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Zhao X, Shi J, Dai F, Wei L, Zhang B, Yu X, Wang C, Zhu W, Wang H. Brain Development From Newborn to Adolescence: Evaluation by Neurite Orientation Dispersion and Density Imaging. Front Hum Neurosci 2021; 15:616132. [PMID: 33790750 PMCID: PMC8005551 DOI: 10.3389/fnhum.2021.616132] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/11/2020] [Accepted: 02/22/2021] [Indexed: 11/15/2022] Open
Abstract
Neurite orientation dispersion and density imaging (NODDI) is a diffusion model specifically designed for brain magnetic resonance imaging. Despite recent studies suggesting that NODDI modeling might be more sensitive to brain development than diffusion tensor imaging (DTI), these studies were limited to a relatively small age range and mainly based on the manually operated region of interest analysis. Therefore, this study applied NODDI to investigate brain development in a large sample size of 214 subjects ranging in ages from 0 to 14. The whole brain was automatically segmented into 122 regions. The maturation trajectory of each region was characterized by the time course of diffusion metrics and further quantified using nonlinear regression. The NODDI-derived metrics, neurite density index (NDI) and orientation dispersion index (ODI), increased with age. And these two metrics were superior to the DTI-derived metrics in SVM regression models of age. The NDI in white matter exhibited a more rapid growth than that in gray matter (including the cortex and deep nucleus). These diffusion indicators experienced conspicuous increases during early childhood and the growth speed slowed down in adolescence. Region-specific maturation patterns were described throughout the brain, including white matter, cortical and deep gray matter. These development patterns were evaluated and discussed on the basis of NODDI’s model assumptions. To summarize, this study verified the high sensitivity of NODDI to age over a crucial developmental period from newborn to adolescence. Moreover, the existing knowledge of brain development has been complemented, suggesting that NODDI has a potential capability in the investigation of brain development.
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Affiliation(s)
- Xueying Zhao
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
| | - Jingjing Shi
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Fei Dai
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
| | - Lei Wei
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
| | - Boyu Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
| | - Xuchen Yu
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
| | - Chengyan Wang
- Human Phenome Institute, Fudan University, Shanghai, China
| | - Wenzhen Zhu
- Department of Radiology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - He Wang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.,Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China.,Human Phenome Institute, Fudan University, Shanghai, China
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Mark VW. Retention of Physical Gains in the Community Following Physical Training for Multiple Sclerosis: A Systematic Review and Implications. Semin Neurol 2021; 41:177-188. [PMID: 33690875 DOI: 10.1055/s-0041-1725139] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Abstract
Multiple sclerosis (MS) is a progressive neurological illness whose typically young adult onset results in a nearly entire lifetime of worsening disability. But despite being an unrelenting neurodegenerative disease, numerous clinical trials over the past 40 years for MS have vigorously attempted to improve or at least stabilize declining physical function. Although the vast majority of the studies assessed training effects only within controlled laboratory or clinic settings, in recent years a growing interest has emerged to test whether newer therapies can instead benefit real-life activities in the community. Nonetheless, comparatively little attention has been paid to whether the training gains can be retained for meaningful periods. This review discusses the comparative success of various physical training methods to benefit within-community activities in MS, and whether the gains can be retained long afterward. This review will suggest future research directions toward establishing efficacious treatments that can allow persons with MS to reclaim their physical abilities and maximize functionality for meaningful periods.
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Affiliation(s)
- Victor W Mark
- Departments of Physical Medicine and Rehabilitation, Neurology, and Psychology, University of Alabama at Birmingham, Birmingham, Alabama
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48
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Czéh B, Simon M. Benefits of animal models to understand the pathophysiology of depressive disorders. Prog Neuropsychopharmacol Biol Psychiatry 2021; 106:110049. [PMID: 32735913 DOI: 10.1016/j.pnpbp.2020.110049] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Revised: 07/13/2020] [Accepted: 07/21/2020] [Indexed: 12/14/2022]
Abstract
Major depressive disorder (MDD) is a potentially life-threatening mental disorder imposing severe social and economic burden worldwide. Despite the existence of effective antidepressant treatment strategies the exact pathophysiology of the disease is still unknown. Large number of animal models of MDD have been developed over the years, but all of them suffer from significant shortcomings. Despite their limitations these models have been extensively used in academic research and drug development. The aim of this review is to highlight the benefits of animal models of MDD. We focus here on recent experimental data where animal models were used to examine current theories of this complex disease. We argue, that despite their evident imperfections, these models provide invaluable help to understand cellular and molecular mechanisms contributing to the development of MDD. Furthermore, animal models are utilized in research to find clinically useful biomarkers. We discuss recent neuroimaging and microRNA studies since these investigations yielded promising candidates for biomarkers. Finally, we briefly summarize recent progresses in drug development, i.e. the FDA approval of two novel antidepressant drugs: S-ketamine and brexanolone (allopregnanolone). Deeper understanding of the exact molecular and cellular mechanisms of action responsible for the antidepressant efficacy of these rapid acting drugs could aid us to design further compounds with similar effectiveness, but less side effects. Animal studies are likely to provide valuable help in this endeavor.
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Affiliation(s)
- Boldizsár Czéh
- Neurobiology of Stress Research Group, Szentágothai Research Centre, University of Pécs, Pécs, Hungary; Department of Laboratory Medicine, Medical School, University of Pécs, Pécs, Hungary.
| | - Maria Simon
- Neurobiology of Stress Research Group, Szentágothai Research Centre, University of Pécs, Pécs, Hungary; Department of Psychiatry and Psychotherapy, Medical School, University of Pécs, Hungary
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49
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MORC1 methylation and BDI are associated with microstructural features of the hippocampus and medial prefrontal cortex. J Affect Disord 2021; 282:91-97. [PMID: 33401128 DOI: 10.1016/j.jad.2020.12.056] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/18/2020] [Revised: 12/14/2020] [Accepted: 12/17/2020] [Indexed: 01/16/2023]
Abstract
BACKGROUND Alterations in the hippocampus and prefrontal cortex (PFC) have frequently been reported in depressed patients. These parameters might prove to be a consistent finding in depression. In addition, peripheral DNA methylation of the MORC1 gene promoter showed stable associations with depression across independent samples. However, the question arises whether MORC1, supposedly acting as transcription factor, might also be involved in neurobiological alterations accompanying depression. This study further analyses the role of MORC1 in depression by investigating a potential correlation between peripheral MORC1 DNA methylation and neuronal structural properties previously associated with depression in humans. METHODS Beck Depression Inventory (BDI) was assessed in 52 healthy participants. DNA was extracted from buccal cells and MORC1 methylation correlated with micro- and macrostructural properties derived from magnetic resonance imaging (MRI) and neurite orientation dispersion and density imaging (NODDI) in the hippocampus and medial prefrontal cortex (mPFC). RESULTS MORC1 methylation was associated with volume reduction and neurite orientation dispersion and density markers in the hippocampus and mPFC. BDI was positively associated with neurite orientation dispersion and density markers in the hippocampus. LIMITATIONS The study was conducted in a small sample of healthy participants with subclinical depressive symptoms. Peripheral tissue was analyzed. CONCLUSION We found significant negative associations between peripheral MORC1 methylation and macro- and microstructural markers in the hippocampus and mPFC. Thus, MORC1 might be involved in neurobiological properties. Studies investigating neuronal methylation patterns of MORC1 are needed to support this hypothesis.
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50
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Olesen JL, Østergaard L, Shemesh N, Jespersen SN. Beyond the diffusion standard model in fixed rat spinal cord with combined linear and planar encoding. Neuroimage 2021; 231:117849. [PMID: 33582270 DOI: 10.1016/j.neuroimage.2021.117849] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Revised: 01/20/2021] [Accepted: 02/04/2021] [Indexed: 10/22/2022] Open
Abstract
Information about tissue on the microscopic and mesoscopic scales can be accessed by modelling diffusion MRI signals, with the aim of extracting microstructure-specific biomarkers. The standard model (SM) of diffusion, currently the most broadly adopted microstructural model, describes diffusion in white matter (WM) tissues by two Gaussian components, one of which has zero radial diffusivity, to represent diffusion in intra- and extra-axonal water, respectively. Here, we reappraise these SM assumptions by collecting comprehensive double diffusion encoded (DDE) MRI data with both linear and planar encodings, which was recently shown to substantially enhance the ability to estimate SM parameters. We find however, that the SM is unable to account for data recorded in fixed rat spinal cord at an ultrahigh field of 16.4 T, suggesting that its underlying assumptions are violated in our experimental data. We offer three model extensions to mitigate this problem: first, we generalize the SM to accommodate finite radii (axons) by releasing the constraint of zero radial diffusivity in the intra-axonal compartment. Second, we include intracompartmental kurtosis to account for non-Gaussian behaviour. Third, we introduce an additional (third) compartment. The ability of these models to account for our experimental data are compared based on parameter feasibility and Bayesian information criterion. Our analysis identifies the three-compartment description as the optimal model. The third compartment exhibits slow diffusion with a minor but non-negligible signal fraction (∼12%). We demonstrate how failure to take the presence of such a compartment into account severely misguides inferences about WM microstructure. Our findings bear significance for microstructural modelling at large and can impact the interpretation of biomarkers extracted from the standard model of diffusion.
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Affiliation(s)
- Jonas L Olesen
- Center of Functionally Integrative Neuroscience (CFIN) and MINDLab, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; Department of Physics and Astronomy, Aarhus University, Aarhus, Denmark
| | - Leif Østergaard
- Center of Functionally Integrative Neuroscience (CFIN) and MINDLab, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Noam Shemesh
- Champalimaud Research, Champalimaud Centre for the Unknown, Lisbon, Portugal
| | - Sune N Jespersen
- Center of Functionally Integrative Neuroscience (CFIN) and MINDLab, Department of Clinical Medicine, Aarhus University, Aarhus, Denmark; Department of Physics and Astronomy, Aarhus University, Aarhus, Denmark.
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