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Zhong T, Wang Y, Xu X, Wu X, Liang S, Ning Z, Wang L, Niu Y, Li G, Zhang Y. A brain subcortical segmentation tool based on anatomy attentional fusion network for developing macaques. Comput Med Imaging Graph 2024; 116:102404. [PMID: 38870599 DOI: 10.1016/j.compmedimag.2024.102404] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2024] [Revised: 05/21/2024] [Accepted: 05/22/2024] [Indexed: 06/15/2024]
Abstract
Magnetic Resonance Imaging (MRI) plays a pivotal role in the accurate measurement of brain subcortical structures in macaques, which is crucial for unraveling the complexities of brain structure and function, thereby enhancing our understanding of neurodegenerative diseases and brain development. However, due to significant differences in brain size, structure, and imaging characteristics between humans and macaques, computational tools developed for human neuroimaging studies often encounter obstacles when applied to macaques. In this context, we propose an Anatomy Attentional Fusion Network (AAF-Net), which integrates multimodal MRI data with anatomical constraints in a multi-scale framework to address the challenges posed by the dynamic development, regional heterogeneity, and age-related size variations of the juvenile macaque brain, thus achieving precise subcortical segmentation. Specifically, we generate a Signed Distance Map (SDM) based on the initial rough segmentation of the subcortical region by a network as an anatomical constraint, providing comprehensive information on positions, structures, and morphology. Then we construct AAF-Net to fully fuse the SDM anatomical constraints and multimodal images for refined segmentation. To thoroughly evaluate the performance of our proposed tool, over 700 macaque MRIs from 19 datasets were used in this study. Specifically, we employed two manually labeled longitudinal macaque datasets to develop the tool and complete four-fold cross-validations. Furthermore, we incorporated various external datasets to demonstrate the proposed tool's generalization capabilities and promise in brain development research. We have made this tool available as an open-source resource at https://github.com/TaoZhong11/Macaque_subcortical_segmentation for direct application.
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Affiliation(s)
- Tao Zhong
- School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing and Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, China
| | - Ya Wang
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, USA
| | - Xiaotong Xu
- School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing and Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, China
| | - Xueyang Wu
- School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing and Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, China
| | - Shujun Liang
- School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing and Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, China
| | - Zhenyuan Ning
- School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing and Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, China
| | - Li Wang
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, USA
| | - Yuyu Niu
- Yunnan Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, China
| | - Gang Li
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, USA.
| | - Yu Zhang
- School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing and Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, China.
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Zhong T, Wu X, Liang S, Ning Z, Wang L, Niu Y, Yang S, Kang Z, Feng Q, Li G, Zhang Y. nBEST: Deep-learning-based non-human primates Brain Extraction and Segmentation Toolbox across ages, sites and species. Neuroimage 2024; 295:120652. [PMID: 38797384 DOI: 10.1016/j.neuroimage.2024.120652] [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: 02/07/2024] [Revised: 05/21/2024] [Accepted: 05/22/2024] [Indexed: 05/29/2024] Open
Abstract
Accurate processing and analysis of non-human primate (NHP) brain magnetic resonance imaging (MRI) serves an indispensable role in understanding brain evolution, development, aging, and diseases. Despite the accumulation of diverse NHP brain MRI datasets at various developmental stages and from various imaging sites/scanners, existing computational tools designed for human MRI typically perform poor on NHP data, due to huge differences in brain sizes, morphologies, and imaging appearances across species, sites, and ages, highlighting the imperative for NHP-specialized MRI processing tools. To address this issue, in this paper, we present a robust, generic, and fully automated computational pipeline, called non-human primates Brain Extraction and Segmentation Toolbox (nBEST), whose main functionality includes brain extraction, non-cerebrum removal, and tissue segmentation. Building on cutting-edge deep learning techniques by employing lifelong learning to flexibly integrate data from diverse NHP populations and innovatively constructing 3D U-NeXt architecture, nBEST can well handle structural NHP brain MR images from multi-species, multi-site, and multi-developmental-stage (from neonates to the elderly). We extensively validated nBEST based on, to our knowledge, the largest assemblage dataset in NHP brain studies, encompassing 1,469 scans with 11 species (e.g., rhesus macaques, cynomolgus macaques, chimpanzees, marmosets, squirrel monkeys, etc.) from 23 independent datasets. Compared to alternative tools, nBEST outperforms in precision, applicability, robustness, comprehensiveness, and generalizability, greatly benefiting downstream longitudinal, cross-sectional, and cross-species quantitative analyses. We have made nBEST an open-source toolbox (https://github.com/TaoZhong11/nBEST) and we are committed to its continual refinement through lifelong learning with incoming data to greatly contribute to the research field.
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Affiliation(s)
- Tao Zhong
- School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing and Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
| | - Xueyang Wu
- School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing and Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
| | - Shujun Liang
- School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing and Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
| | - Zhenyuan Ning
- School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing and Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
| | - Li Wang
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, USA
| | - Yuyu Niu
- Yunnan Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming, China
| | - Shihua Yang
- College of Veterinary Medicine, South China Agricultural University, Guangzhou, China
| | - Zhuang Kang
- Department of Radiology, The Third Affiliated Hospital, Sun Yat-sen University, Guangzhou, China
| | - Qianjin Feng
- School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing and Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China
| | - Gang Li
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, USA.
| | - Yu Zhang
- School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing and Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology, Southern Medical University, Guangzhou, China.
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Kim J, Barcus R, Lipford ME, Yuan H, Ririe DG, Jung Y, Vlasova RM, Styner M, Nader MA, Whitlow CT. Effects of multiple anesthetic exposures on rhesus macaque brain development: a longitudinal structural MRI analysis. Cereb Cortex 2024; 34:bhad463. [PMID: 38142289 PMCID: PMC10793576 DOI: 10.1093/cercor/bhad463] [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: 03/06/2023] [Revised: 11/10/2023] [Accepted: 11/13/2023] [Indexed: 12/25/2023] Open
Abstract
Concerns about the potential neurotoxic effects of anesthetics on developing brain exist. When making clinical decisions, the timing and dosage of anesthetic exposure are critical factors to consider due to their associated risks. In our study, we investigated the impact of repeated anesthetic exposures on the brain development trajectory of a cohort of rhesus monkeys (n = 26) over their first 2 yr of life, utilizing longitudinal magnetic resonance imaging data. We hypothesized that early or high-dose anesthesia exposure could negatively influence structural brain development. By employing the generalized additive mixed model, we traced the longitudinal trajectories of brain volume, cortical thickness, and white matter integrity. The interaction analysis revealed that age and cumulative anesthetic dose were variably linked to white matter integrity but not to morphometric measures. Early high-dose exposure was associated with increased mean, axial, and radial diffusivities across all white matter regions, compared to late-low-dose exposure. Our findings indicate that early or high-dose anesthesia exposure during infancy disrupts structural brain development in rhesus monkeys. Consequently, the timing of elective surgeries and procedures that require anesthesia for children and pregnant women should be strategically planned to account for the cumulative dose of volatile anesthetics, aiming to minimize the potential risks to brain development.
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Affiliation(s)
- Jeongchul Kim
- Radiology Informatics and Image Processing Laboratory (RIIPL), Wake Forest School of Medicine, Winston-Salem, NC, United States
- Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC, United States
- School of Biomedical Engineering and Sciences, Virginia Tech-Wake Forest University, Winston-Salem, NC, United States
| | - Richard Barcus
- Radiology Informatics and Image Processing Laboratory (RIIPL), Wake Forest School of Medicine, Winston-Salem, NC, United States
- Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC, United States
| | - Megan E Lipford
- Radiology Informatics and Image Processing Laboratory (RIIPL), Wake Forest School of Medicine, Winston-Salem, NC, United States
- Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC, United States
- School of Biomedical Engineering and Sciences, Virginia Tech-Wake Forest University, Winston-Salem, NC, United States
| | - Hongyu Yuan
- Radiology Informatics and Image Processing Laboratory (RIIPL), Wake Forest School of Medicine, Winston-Salem, NC, United States
- Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC, United States
| | - Douglas G Ririe
- Pain Mechanisms Lab, Department of Anesthesiology, Wake Forest School of Medicine, Winston-Salem, NC, United States
| | - Youngkyoo Jung
- Department of Biomedical Engineering, University of California Davis, Davis, CA, United States
| | - Roza M Vlasova
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Martin Styner
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Michael A Nader
- Department of Physiology and Pharmacology, Wake Forest School of Medicine, Winston-Salem, NC, United States
- Center for Research on Substance Use and Addiction, Wake Forest School of Medicine, Winston-Salem, NC, United States
- Clinical and Translational Science Institute, Wake Forest School of Medicine, Winston-Salem, NC, United States
| | - Christopher T Whitlow
- Radiology Informatics and Image Processing Laboratory (RIIPL), Wake Forest School of Medicine, Winston-Salem, NC, United States
- Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC, United States
- School of Biomedical Engineering and Sciences, Virginia Tech-Wake Forest University, Winston-Salem, NC, United States
- Center for Research on Substance Use and Addiction, Wake Forest School of Medicine, Winston-Salem, NC, United States
- Clinical and Translational Science Institute, Wake Forest School of Medicine, Winston-Salem, NC, United States
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC, United States
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吴 雪, 张 煜, 张 华, 钟 涛. [Whole-brain parcellation for macaque brain magnetic resonance images based on attention mechanism and multi-modality feature fusion]. NAN FANG YI KE DA XUE XUE BAO = JOURNAL OF SOUTHERN MEDICAL UNIVERSITY 2023; 43:2118-2125. [PMID: 38189399 PMCID: PMC10774098 DOI: 10.12122/j.issn.1673-4254.2023.12.17] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Subscribe] [Scholar Register] [Received: 10/12/2023] [Indexed: 01/09/2024]
Abstract
OBJECTIVE We propose a novel deep learning algorithm based on attention mechanism and multimodality feature fusion (DDAM) to achieve whole-brain parcellation of macaque brain magnetic resonance (MR) images. METHODS We collected multimodality brain MR images of 68 macaques (aged 13 to 36 months) with corresponding ground truth labels. To address the complexity and complementary nature of the multimodality data, we employed a multi- encoder structure to adapt to different modalities and performed feature extraction. In the decoder, an attention mechanism was introduced to construct the Attention-based Multimodality Feature Fusion module (AMFF). Leveraging the rich and complementary information between modalities, AMFF effectively integrated multimodality features of varying scales and complexities to enhance the performance of parcellation. We conducted ablation experiments and statistically analyzed the results. RESULTS The incorporation of the multi- encoder structure and attention mechanism significantly improved the performance of the network for integrating the multimodality features, and achieved an average DSC of 0.904 and an ASD as low as 0.131 for macaque whole-brain parcellation (P < 0.05). The ablation experiments validated the effectiveness of each component of the DDAM method. CONCLUSION The proposed DDAM method, with its enhanced ability to extract and integrate multimodality features, can significantly improve the accuracy of whole-brain MR image segmentation.
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Affiliation(s)
- 雪扬 吴
- />南方医科大学生物医学工程学院//广东省医学图像处理重点实验室,广东 广州 510515School of Biomedical Engineering//Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou 510515, China
| | - 煜 张
- />南方医科大学生物医学工程学院//广东省医学图像处理重点实验室,广东 广州 510515School of Biomedical Engineering//Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou 510515, China
| | - 华 张
- />南方医科大学生物医学工程学院//广东省医学图像处理重点实验室,广东 广州 510515School of Biomedical Engineering//Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou 510515, China
| | - 涛 钟
- />南方医科大学生物医学工程学院//广东省医学图像处理重点实验室,广东 广州 510515School of Biomedical Engineering//Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou 510515, China
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Jeffery N, Manson A. Postnatal growth and spatial conformity of the cranium, brain, eyeballs and masseter muscles in the macaque (Macaca mulatta). J Anat 2023; 243:590-604. [PMID: 37300248 PMCID: PMC10485578 DOI: 10.1111/joa.13911] [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: 10/04/2022] [Revised: 05/15/2023] [Accepted: 05/26/2023] [Indexed: 06/12/2023] Open
Abstract
Spatial growth constraints in the head region can lead to coordinated patterns of morphological variation that pleiotropically modify genetically defined phenotypes as the tissues compete for space. Here we test for such architectural modifications during rhesus macaque (Macaca mulatta) postnatal ontogeny. We captured cranium and brain shape from 153 MRI datasets spanning 13 to 1090 postnatal days and tested for patterns of covariation with measurements of relative brain, eyeball, and masseter muscle size as well as callosal tract length. We find that the shape of the infant (<365 days) macaque cranium was most closely aligned to masseter muscle and brain size measured relative to face size. Infant brain and juvenile (365-1090 days) cranium shape were more closely linked with brain size relative to basicranium and face size. Meanwhile, the juvenile macaque brain shape was dominated by the size of the brain relative to that of the basicranium. Associations with relative eyeball size and commissural tract lengths were weaker. Our results are consistent with a spatial-packing regime operating during postnatal macaque ontogeny, in which relative growth of the masseter, face and basicranium have a greater influence than brain growth on the overall shape of the cranium and brain.
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Affiliation(s)
- Nathan Jeffery
- Department of Musculoskeletal & Ageing Science, Institute of Life Course & Medical Sciences (ILCaMS) and Human Anatomy Resource Centre (HARC), Education Directorate, University of Liverpool, Liverpool, UK
| | - Amy Manson
- Department of Musculoskeletal & Ageing Science, Institute of Life Course & Medical Sciences (ILCaMS) and Human Anatomy Resource Centre (HARC), Education Directorate, University of Liverpool, Liverpool, UK
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Xia J, Wang F, Wang Y, Wang L, Li G. Longitudinal mapping of the development of cortical thickness and surface area in rhesus macaques during the first three years. Proc Natl Acad Sci U S A 2023; 120:e2303313120. [PMID: 37523547 PMCID: PMC10410744 DOI: 10.1073/pnas.2303313120] [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: 02/26/2023] [Accepted: 07/03/2023] [Indexed: 08/02/2023] Open
Abstract
Studying dynamic spatiotemporal patterns of early brain development in macaque monkeys is critical for understanding the cortical organization and evolution in humans, given the phylogenetic closeness between humans and macaques. However, due to huge challenges in the analysis of early brain Magnetic Resonance Imaging (MRI) data typically with extremely low contrast and dynamic imaging appearances, our knowledge of the early macaque cortical development remains scarce. To fill this critical gap, this paper characterizes the early developmental patterns of cortical thickness and surface area in rhesus macaques by leveraging advanced computing tools tailored for early developing brains based on a densely sampled longitudinal dataset with 140 rhesus macaque MRI scans seamlessly covering from birth to 36 mo of age. The average cortical thickness exhibits an inverted U-shaped trajectory with peak thickness at around 4.3 mo of age, which is remarkably in line with the age of peak thickness at 14 mo in humans, considering the around 3:1 age ratio of human to macaque. The total cortical surface area in macaques increases monotonically but with relatively lower expansions than in humans. The spatial distributions of thicker and thinner regions are quite consistent during development, with gyri having a thicker cortex than sulci. By 4 mo of age, over 81% of cortical vertices have reached their peaks in thickness, except for the insula and medial temporal cortices, while most cortical vertices keep expanding in surface area, except for the occipital cortex. These findings provide important insights into early brain development and evolution in primates.
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Affiliation(s)
- Jing Xia
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC27599
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC27599
| | - Fan Wang
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC27599
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC27599
| | - Ya Wang
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC27599
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC27599
| | - Li Wang
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC27599
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC27599
| | - Gang Li
- Department of Radiology, University of North Carolina at Chapel Hill, Chapel Hill, NC27599
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC27599
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Tomlinson C, Vlasova R, Al-Ali K, Young JT, Shi Y, Lubach GR, Alexander AL, Coe CL, Styner M, Fine J. Effects of anesthesia exposure on postnatal maturation of white matter in rhesus monkeys. Dev Psychobiol 2023; 65:e22396. [PMID: 37338252 PMCID: PMC11000522 DOI: 10.1002/dev.22396] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 09/21/2022] [Accepted: 03/24/2023] [Indexed: 06/21/2023]
Abstract
There is increasing concern about the potential effects of anesthesia exposure on the developing brain. The effects of relatively brief anesthesia exposures used repeatedly to acquire serial magnetic resonance imaging scans could be examined prospectively in rhesus macaques. We analyzed magnetic resonance diffusion tensor imaging (DTI) of 32 rhesus macaques (14 females, 18 males) aged 2 weeks to 36 months to assess postnatal white matter (WM) maturation. We investigated the longitudinal relationships between each DTI property and anesthesia exposure, taking age, sex, and weight of the monkeys into consideration. Quantification of anesthesia exposure was normalized to account for variation in exposures. Segmented linear regression with two knots provided the best model for quantifying WM DTI properties across brain development as well as the summative effect of anesthesia exposure. The resulting model revealed statistically significant age and anesthesia effects in most WM tracts. Our analysis indicated there were major effects on WM associated with low levels of anesthesia even when repeated as few as three times. Fractional anisotropy values were reduced across several WM tracts in the brain, indicating that anesthesia exposure may delay WM maturation, and highlight the potential clinical concerns with even a few exposures in young children.
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Affiliation(s)
- Chalmer Tomlinson
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Roza Vlasova
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Khalid Al-Ali
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Jeffrey T Young
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Yundi Shi
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Gabriele R Lubach
- Harlow Center for Biological Psychology, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Andrew L Alexander
- Waisman Laboratory for Brain Imaging and Behavior, University of Wisconsin-Madison, Madison, Wisconsin, USA
- Department of Medical Physics, University of Wisconsin-Madison, Madison, Wisconsin, USA
- Department of Psychiatry, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Christopher L Coe
- Harlow Center for Biological Psychology, University of Wisconsin-Madison, Madison, Wisconsin, USA
| | - Martin Styner
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Jason Fine
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
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Zhang S, Chavoshnejad P, Li X, Guo L, Jiang X, Han J, Wang L, Li G, Wang X, Liu T, Razavi MJ, Zhang S, Zhang T. Gyral peaks: Novel gyral landmarks in developing macaque brains. Hum Brain Mapp 2022; 43:4540-4555. [PMID: 35713202 PMCID: PMC9491295 DOI: 10.1002/hbm.25971] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2021] [Revised: 04/22/2022] [Accepted: 05/23/2022] [Indexed: 11/09/2022] Open
Abstract
Cerebral cortex development undergoes a variety of processes, which provide valuable information for the study of the developmental mechanism of cortical folding as well as its relationship to brain structural architectures and brain functions. Despite the variability in the anatomy-function relationship on the higher-order cortex, recent studies have succeeded in identifying typical cortical landmarks, such as sulcal pits, that bestow specific functional and cognitive patterns and remain invariant across subjects and ages with their invariance being related to a gene-mediated proto-map. Inspired by the success of these studies, we aim in this study at defining and identifying novel cortical landmarks, termed gyral peaks, which are the local highest foci on gyri. By analyzing data from 156 MRI scans of 32 macaque monkeys with the age spanned from 0 to 36 months, we identified 39 and 37 gyral peaks on the left and right hemispheres, respectively. Our investigation suggests that these gyral peaks are spatially consistent across individuals and relatively stable within the age range of this dataset. Moreover, compared with other gyri, gyral peaks have a thicker cortex, higher mean curvature, more pronounced hub-like features in structural connective networks, and are closer to the borders of structural connectivity-based cortical parcellations. The spatial distribution of gyral peaks was shown to correlate with that of other cortical landmarks, including sulcal pits. These results provide insights into the spatial arrangement and temporal development of gyral peaks as well as their relation to brain structure and function.
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Affiliation(s)
- Songyao Zhang
- School of AutomationNorthwestern Polytechnical UniversityXi'anChina
| | - Poorya Chavoshnejad
- Department of Mechanical EngineeringState University of New York at BinghamtonNew YorkUSA
| | - Xiao Li
- School of Information TechnologyNorthwest UniversityXi'anChina
| | - Lei Guo
- School of AutomationNorthwestern Polytechnical UniversityXi'anChina
| | - Xi Jiang
- School of Life Science and TechnologyUniversity of Electronic Science and Technology of ChinaChengduChina
| | - Junwei Han
- School of AutomationNorthwestern Polytechnical UniversityXi'anChina
| | - Li Wang
- Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillNorth CarolinaUSA
| | - Gang Li
- Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillNorth CarolinaUSA
| | - Xianqiao Wang
- College of EngineeringThe University of GeorgiaAthensGeorgiaUSA
| | - Tianming Liu
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research CenterThe University of GeorgiaAthensGeorgiaUSA
| | - Mir Jalil Razavi
- Department of Mechanical EngineeringState University of New York at BinghamtonNew YorkUSA
| | - Shu Zhang
- Center for Brain and Brain‐Inspired Computing Research, Department of Computer ScienceNorthwestern Polytechnical UniversityXi'anChina
| | - Tuo Zhang
- School of AutomationNorthwestern Polytechnical UniversityXi'anChina
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Rossano S, Toyonaga T, Berg E, Lorence I, Fowles K, Nabulsi N, Ropchan J, Li S, Ye Y, Felchner Z, Kukis D, Huang Y, Benveniste H, Tarantal AF, Groman S, Carson RE. Imaging the fetal nonhuman primate brain with SV2A positron emission tomography (PET). Eur J Nucl Med Mol Imaging 2022; 49:3679-3691. [PMID: 35633376 PMCID: PMC9826644 DOI: 10.1007/s00259-022-05825-6] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 04/26/2022] [Indexed: 01/11/2023]
Abstract
PURPOSE Exploring synaptic density changes during brain growth is crucial to understanding brain development. Previous studies in nonhuman primates report a rapid increase in synapse number between the late gestational period and the early neonatal period, such that synaptic density approaches adult levels by birth. Prenatal synaptic development may have an enduring impact on postnatal brain development, but precisely how synaptic density changes in utero are unknown because current methods to quantify synaptic density are invasive and require post-mortem brain tissue. METHODS We used synaptic vesicle glycoprotein 2A (SV2A) positron emission tomography (PET) radioligands [11C]UCB-J and [18F]Syn-VesT-1 to conduct the first assessment of synaptic density in the developing fetal brain in gravid rhesus monkeys. Eight pregnant monkeys were scanned twice during the third trimester at two imaging sites. Fetal post-mortem samples were collected near term in a subset of subjects to quantify SV2A density by Western blot. RESULTS Image-derived fetal brain SV2A measures increased during the third trimester. SV2A concentrations were greater in subcortical regions than in cortical regions at both gestational ages. Near term, SV2A density was higher in primary motor and visual areas than respective associative regions. Post-mortem quantification of SV2A density was significantly correlated with regional SV2A PET measures. CONCLUSION While further study is needed to determine the exact relationship of SV2A and synaptic density, the imaging paradigm developed in the current study allows for the effective in vivo study of SV2A development in the fetal brain.
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Affiliation(s)
- Samantha Rossano
- Department of Radiology and Biomedical Imaging, Yale PET Center, Yale School of Medicine, New Haven, CT, USA.
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA.
| | - Takuya Toyonaga
- Department of Radiology and Biomedical Imaging, Yale PET Center, Yale School of Medicine, New Haven, CT, USA
| | - Eric Berg
- Department of Biomedical Engineering, University of California, Davis, CA, USA
| | - Isabella Lorence
- Department of Radiology and Biomedical Imaging, Yale PET Center, Yale School of Medicine, New Haven, CT, USA
| | - Krista Fowles
- Department of Radiology and Biomedical Imaging, Yale PET Center, Yale School of Medicine, New Haven, CT, USA
| | - Nabeel Nabulsi
- Department of Radiology and Biomedical Imaging, Yale PET Center, Yale School of Medicine, New Haven, CT, USA
| | - Jim Ropchan
- Department of Radiology and Biomedical Imaging, Yale PET Center, Yale School of Medicine, New Haven, CT, USA
| | - Songye Li
- Department of Radiology and Biomedical Imaging, Yale PET Center, Yale School of Medicine, New Haven, CT, USA
| | - Yunpeng Ye
- Department of Radiology and Biomedical Imaging, Yale PET Center, Yale School of Medicine, New Haven, CT, USA
| | - Zachary Felchner
- Department of Radiology and Biomedical Imaging, Yale PET Center, Yale School of Medicine, New Haven, CT, USA
| | - David Kukis
- Center for Molecular and Genomic Imaging, University of California, Davis, CA, USA
| | - Yiyun Huang
- Department of Radiology and Biomedical Imaging, Yale PET Center, Yale School of Medicine, New Haven, CT, USA
| | - Helene Benveniste
- Department of Anesthesiology, Yale School of Medicine, Yale University, New Haven, CT, USA
| | - Alice F Tarantal
- Departments of Pediatrics and Cell Biology and Human Anatomy, School of Medicine, and California National Primate Research Center, University of California, Davis, CA, USA
| | - Stephanie Groman
- Department of Psychiatry, Yale University, New Haven, CT, USA
- Department of Neuroscience, University of Minnesota, Minneapolis, MN, USA
| | - Richard E Carson
- Department of Radiology and Biomedical Imaging, Yale PET Center, Yale School of Medicine, New Haven, CT, USA
- Department of Biomedical Engineering, Yale University, New Haven, CT, USA
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10
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Charvet CJ, Ofori K, Baucum C, Sun J, Modrell MS, Hekmatyar K, Edlow BL, van der Kouwe AJ. Tracing Modification to Cortical Circuits in Human and Nonhuman Primates from High-Resolution Tractography, Transcription, and Temporal Dimensions. J Neurosci 2022; 42:3749-3767. [PMID: 35332086 PMCID: PMC9087811 DOI: 10.1523/jneurosci.1506-21.2022] [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: 07/22/2021] [Revised: 03/11/2022] [Accepted: 03/15/2022] [Indexed: 11/21/2022] Open
Abstract
The neural circuits that support human cognition are a topic of enduring interest. Yet, there are limited tools available to map brain circuits in the human and nonhuman primate brain. We harnessed high-resolution diffusion MR tractography, anatomic, and transcriptomic data from individuals of either sex to investigate the evolution and development of frontal cortex circuitry. We applied machine learning to RNA sequencing data to find corresponding ages between humans and macaques and to compare the development of circuits across species. We transcriptionally defined neural circuits by testing for associations between gene expression and white matter maturation. We then considered transcriptional and structural growth to test whether frontal cortex circuit maturation is unusually extended in humans relative to other species. We also considered gene expression and high-resolution diffusion MR tractography of adult brains to test for cross-species variation in frontal cortex circuits. We found that frontal cortex circuitry development is extended in primates, and concomitant with an expansion in corticocortical pathways compared with mice in adulthood. Importantly, we found that these parameters varied relatively little across humans and studied primates. These data identify a surprising collection of conserved features in frontal cortex circuits across humans and Old World monkeys. Our work demonstrates that integrating transcriptional and structural data across temporal dimensions is a robust approach to trace the evolution of brain pathways in primates.SIGNIFICANCE STATEMENT Diffusion MR tractography is an exciting method to explore pathways, but there are uncertainties in the accuracy of reconstructed tracts. We broaden the repertoire of toolkits to enhance our ability to trace human brain pathways from diffusion MR tractography. Our integrative approach finds corresponding ages across species and transcriptionally defines neural circuits. We used this information to test for variation in circuit maturation across species and found a surprising constellation of similar features in frontal cortex neural circuits across humans and primates. Integrating across scales of biological organization expands the repertoire of tools available to study pathways in primates, which opens new avenues to study pathways in health and diseases of the human brain.
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Affiliation(s)
- Christine J Charvet
- Department of Anatomy, Physiology and Pharmacology, College of Veterinary Medicine, Auburn University, Auburn, Alabama 36849-5518
- Delaware Center for Neuroscience, Delaware State University, Dover, Delaware 19901
| | - Kwadwo Ofori
- PhD Program in Neuroscience, Department in Biology, Delaware State University, Dover, Delaware 19901
| | - Christine Baucum
- Department of Biology, Bath Spa University, Bath BA2 9BN, United Kingdom
| | - Jianli Sun
- Delaware Center for Neuroscience, Delaware State University, Dover, Delaware 19901
| | - Melinda S Modrell
- Delaware Center for Neuroscience, Delaware State University, Dover, Delaware 19901
| | - Khan Hekmatyar
- Center for Biomedical and Brain Imaging Center, University of Delaware, Wilmington, Delaware 19716
| | - Brian L Edlow
- Center for Neurotechnology and Neurorecovery, Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts 02114
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts 02129
| | - Andre J van der Kouwe
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts 02129
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11
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Li X, Zhang S, Jiang X, Zhang S, Han J, Guo L, Zhang T. Cortical development coupling between surface area and sulcal depth on macaque brains. Brain Struct Funct 2022; 227:1013-1029. [PMID: 34989870 DOI: 10.1007/s00429-021-02444-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Accepted: 12/15/2021] [Indexed: 02/06/2023]
Abstract
Postnatal development of cerebral cortex is associated with a variety of neuronal processes and is thus critical to development of brain function and cognition. Longitudinal changes of cortical morphology and topology, such as postnatal cortical thinning and flattening have been widely studied. However, thorough and systematic investigation of such cortical change, including how to quantify it from multiple spatial directions and how to relate it to surface topology, is rarely found. In this work, based on a longitudinal macaque neuroimaging dataset, we quantified local changes in gyral white matter's surface area and sulcal depth during early development. We also investigated how these two metrics are coupled and how this coupling is linked to cortical surface topology, underlying white matter, and positions of functional areas. Semi-parametric generalized additive models were adopted to quantify the longitudinal changes of surface area (A) and sulcal depth (D), and the coupling patterns between them. This resulted in four classes of regions, according to how they change compared with global change throughout early development: slower surface area change and slower sulcal depth change (slowA_slowD), slower surface area change and faster sulcal depth change (slowA_fastD), faster surface area change and slower sulcal depth change (fastA_slowD), and faster surface area change and faster sulcal depth change (fastA_fastD). We found that cortex-related metrics, including folding pattern and cortical thickness, vary along slowA_fastD-fastA_slowD axis, and structural connection-related metrics vary along fastA_fastD-slowA_slowD axis, with which brain functional sites align better. It is also found that cortical landmarks, including sulcal pits and gyral hinges, spatially reside on the borders of the four patterns. These findings shed new lights on the relationship between cortex development, surface topology, axonal wiring pattern and brain functions.
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Affiliation(s)
- Xiao Li
- School of Automation, Northwestern Polytechnical University, Xi'an, China
| | - Songyao Zhang
- School of Automation, Northwestern Polytechnical University, Xi'an, China
| | - Xi Jiang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Shu Zhang
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Junwei Han
- School of Automation, Northwestern Polytechnical University, Xi'an, China
| | - Lei Guo
- School of Automation, Northwestern Polytechnical University, Xi'an, China
| | - Tuo Zhang
- School of Automation, Northwestern Polytechnical University, Xi'an, China.
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12
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Scan Once, Analyse Many: Using Large Open-Access Neuroimaging Datasets to Understand the Brain. Neuroinformatics 2022; 20:109-137. [PMID: 33974213 PMCID: PMC8111663 DOI: 10.1007/s12021-021-09519-6] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/07/2021] [Indexed: 02/06/2023]
Abstract
We are now in a time of readily available brain imaging data. Not only are researchers now sharing data more than ever before, but additionally large-scale data collecting initiatives are underway with the vision that many future researchers will use the data for secondary analyses. Here I provide an overview of available datasets and some example use cases. Example use cases include examining individual differences, more robust findings, reproducibility-both in public input data and availability as a replication sample, and methods development. I further discuss a variety of considerations associated with using existing data and the opportunities associated with large datasets. Suggestions for further readings on general neuroimaging and topic-specific discussions are also provided.
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13
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Yokoyama C, Autio JA, Ikeda T, Sallet J, Mars RB, Van Essen DC, Glasser MF, Sadato N, Hayashi T. Comparative connectomics of the primate social brain. Neuroimage 2021; 245:118693. [PMID: 34732327 PMCID: PMC9159291 DOI: 10.1016/j.neuroimage.2021.118693] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/13/2021] [Revised: 09/27/2021] [Accepted: 10/29/2021] [Indexed: 01/13/2023] Open
Abstract
Social interaction is thought to provide a selection pressure for human intelligence, yet little is known about its neurobiological basis and evolution throughout the primate lineage. Recent advances in neuroimaging have enabled whole brain investigation of brain structure, function, and connectivity in humans and non-human primates (NHPs), leading to a nascent field of comparative connectomics. However, linking social behavior to brain organization across the primates remains challenging. Here, we review the current understanding of the macroscale neural mechanisms of social behaviors from the viewpoint of system neuroscience. We first demonstrate an association between the number of cortical neurons and the size of social groups across primates, suggesting a link between neural information-processing capacity and social capabilities. Moreover, by capitalizing on recent advances in species-harmonized functional MRI, we demonstrate that portions of the mirror neuron system and default-mode networks, which are thought to be important for representation of the other's actions and sense of self, respectively, exhibit similarities in functional organization in macaque monkeys and humans, suggesting possible homologies. With respect to these two networks, we describe recent developments in the neurobiology of social perception, joint attention, personality and social complexity. Together, the Human Connectome Project (HCP)-style comparative neuroimaging, hyperscanning, behavioral, and other multi-modal investigations are expected to yield important insights into the evolutionary foundations of human social behavior.
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Affiliation(s)
- Chihiro Yokoyama
- Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, 6-7-3 Minatojima-minamimachi, Chuo-ku, Kobe 650-0047, Japan.
| | - Joonas A Autio
- Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, 6-7-3 Minatojima-minamimachi, Chuo-ku, Kobe 650-0047, Japan
| | - Takuro Ikeda
- Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, 6-7-3 Minatojima-minamimachi, Chuo-ku, Kobe 650-0047, Japan
| | - Jérôme Sallet
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, Oxford University, Oxford, United Kingdom; University of Lyon, Université Lyon 1, Inserm, Stem Cell and Brain Research Institute U1208, Bron, France
| | - Rogier B Mars
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom; Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, the Netherlands
| | - David C Van Essen
- Departments of Neuroscience, Washington University Medical School, St Louis, MO, United States of America
| | - Matthew F Glasser
- Departments of Neuroscience, Washington University Medical School, St Louis, MO, United States of America; Department of Radiology, Washington University Medical School, St Louis, MO, United States of America
| | - Norihiro Sadato
- National Institute for Physiological Sciences, Okazaki, Japan; The Graduate University for Advanced Studies (SOKENDAI), Kanagawa, Japan
| | - Takuya Hayashi
- Laboratory for Brain Connectomics Imaging, RIKEN Center for Biosystems Dynamics Research, 6-7-3 Minatojima-minamimachi, Chuo-ku, Kobe 650-0047, Japan; School of Medicine, Kyoto University, Kyoto, Japan.
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14
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Darayi M, Hoffman ME, Sayut J, Wang S, Demirci N, Consolini J, Holland MA. Computational models of cortical folding: A review of common approaches. J Biomech 2021; 139:110851. [PMID: 34802706 DOI: 10.1016/j.jbiomech.2021.110851] [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: 04/02/2021] [Revised: 08/09/2021] [Accepted: 10/26/2021] [Indexed: 11/29/2022]
Abstract
The process of gyrification, by which the brain develops the intricate pattern of gyral hills and sulcal valleys, is the result of interactions between biological and mechanical processes during brain development. Researchers have developed a vast array of computational models in order to investigate cortical folding. This review aims to summarize these studies, focusing on five essential elements of the brain that affect development and gyrification and how they are represented in computational models: (i) the constraints of skull, meninges, and cerebrospinal fluid; (ii) heterogeneity of cortical layers and regions; (iii) anisotropic behavior of subcortical fiber tracts; (iv) material properties of brain tissue; and (v) the complex geometry of the brain. Finally, we highlight areas of need for future simulations of brain development.
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Affiliation(s)
- Mohsen Darayi
- Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN 46556, USA
| | - Mia E Hoffman
- Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN 46556, USA
| | - John Sayut
- Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN 46556, USA
| | - Shuolun Wang
- Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN 46556, USA
| | - Nagehan Demirci
- Bioengineering Graduate Program, University of Notre Dame, Notre Dame, IN 46556, USA
| | - Jack Consolini
- Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN 46556, USA
| | - Maria A Holland
- Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN 46556, USA; Bioengineering Graduate Program, University of Notre Dame, Notre Dame, IN 46556, USA.
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15
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Song X, García-Saldivar P, Kindred N, Wang Y, Merchant H, Meguerditchian A, Yang Y, Stein EA, Bradberry CW, Ben Hamed S, Jedema HP, Poirier C. Strengths and challenges of longitudinal non-human primate neuroimaging. Neuroimage 2021; 236:118009. [PMID: 33794361 PMCID: PMC8270888 DOI: 10.1016/j.neuroimage.2021.118009] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/21/2020] [Revised: 03/16/2021] [Accepted: 03/23/2021] [Indexed: 01/20/2023] Open
Abstract
Longitudinal non-human primate neuroimaging has the potential to greatly enhance our understanding of primate brain structure and function. Here we describe its specific strengths, compared to both cross-sectional non-human primate neuroimaging and longitudinal human neuroimaging, but also its associated challenges. We elaborate on factors guiding the use of different analytical tools, subject-specific versus age-specific templates for analyses, and issues related to statistical power.
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Affiliation(s)
- Xiaowei Song
- Preclinical Pharmacology Section, Intramural Research Program, NIDA, NIH, Baltimore, MD 21224, USA
| | - Pamela García-Saldivar
- Instituto de Neurobiología, UNAM, Campus Juriquilla. Boulevard Juriquilla No. 3001 Querétaro, Qro. 76230, México
| | - Nathan Kindred
- Biosciences Institute & Centre for Behaviour and Evolution, Faculty of Medical Sciences, Newcastle University, United Kingdom
| | - Yujiang Wang
- CNNP Lab (www.cnnp-lab.com), Interdisciplinary Complex Systems Group, School of Computing, Newcastle University, United Kingdom
| | - Hugo Merchant
- Instituto de Neurobiología, UNAM, Campus Juriquilla. Boulevard Juriquilla No. 3001 Querétaro, Qro. 76230, México
| | - Adrien Meguerditchian
- Laboratoire de Psychologie Cognitive, UMR7290, Université Aix-Marseille/CNRS, Institut Language, Communication and the Brain 13331 Marseille, France
| | - Yihong Yang
- Neuroimaging Research Branch, Intramural Research Program, NIDA, NIH, Baltimore, MD 21224, USA
| | - Elliot A Stein
- Neuroimaging Research Branch, Intramural Research Program, NIDA, NIH, Baltimore, MD 21224, USA
| | - Charles W Bradberry
- Preclinical Pharmacology Section, Intramural Research Program, NIDA, NIH, Baltimore, MD 21224, USA
| | - Suliann Ben Hamed
- Institut des Sciences Cognitives Marc Jeannerod, UMR 5229, Université de Lyon - CNRS, France
| | - Hank P Jedema
- Preclinical Pharmacology Section, Intramural Research Program, NIDA, NIH, Baltimore, MD 21224, USA.
| | - Colline Poirier
- Biosciences Institute & Centre for Behaviour and Evolution, Faculty of Medical Sciences, Newcastle University, United Kingdom.
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16
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Lepage C, Wagstyl K, Jung B, Seidlitz J, Sponheim C, Ungerleider L, Wang X, Evans AC, Messinger A. CIVET-Macaque: An automated pipeline for MRI-based cortical surface generation and cortical thickness in macaques. Neuroimage 2021; 227:117622. [PMID: 33301944 PMCID: PMC7615896 DOI: 10.1016/j.neuroimage.2020.117622] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/10/2020] [Revised: 10/27/2020] [Accepted: 11/28/2020] [Indexed: 12/21/2022] Open
Abstract
The MNI CIVET pipeline for automated extraction of cortical surfaces and evaluation of cortical thickness from in-vivo human MRI has been extended for processing macaque brains. Processing is performed based on the NIMH Macaque Template (NMT), as the reference template, with the anatomical parcellation of the surface following the D99 and CHARM atlases. The modifications needed to adapt CIVET to the macaque brain are detailed. Results have been obtained using CIVET-macaque to process the anatomical scans of the 31 macaques used to generate the NMT and another 95 macaques from the PRIME-DE initiative. It is anticipated that the open usage of CIVET-macaque will promote collaborative efforts in data collection and processing, sharing, and automated analyses from which the non-human primate brain imaging field will advance.
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Affiliation(s)
- Claude Lepage
- Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
| | - Konrad Wagstyl
- Montreal Neurological Institute (MNI), McGill University, Montreal, Canada; Wellcome Centre for Human Neuroimaging, University College London, London, UK
| | - Benjamin Jung
- Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD, USA; Department of Neuroscience, Brown University, Providence, RI, USA
| | - Jakob Seidlitz
- Department of Child and Adolescent Psychiatry and Behavioral Science, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Caleb Sponheim
- Department of Organismal Biology and Anatomy, University of Chicago, Chicago, IL, USA
| | - Leslie Ungerleider
- Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD, USA
| | - Xindi Wang
- Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
| | - Alan C Evans
- Montreal Neurological Institute (MNI), McGill University, Montreal, Canada
| | - Adam Messinger
- Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD, USA.
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17
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Young JT, Vlasova RM, Howell BR, Knickmeyer RC, Morin E, Kuitchoua KI, Lubach GR, Noel J, Hu X, Shi Y, Caudill G, Alexander AL, Niethammer M, Paule MG, Coe CL, Sanchez M, Styner M. General anaesthesia during infancy reduces white matter micro-organisation in developing rhesus monkeys. Br J Anaesth 2021; 126:845-853. [PMID: 33549320 DOI: 10.1016/j.bja.2020.12.029] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Revised: 12/04/2020] [Accepted: 12/24/2020] [Indexed: 11/17/2022] Open
Abstract
BACKGROUND Non-human primates are commonly used in neuroimaging research for which general anaesthesia or sedation is typically required for data acquisition. In this analysis, the cumulative effects of exposure to ketamine, Telazol® (tiletamine and zolazepam), and the inhaled anaesthetic isoflurane on early brain development were evaluated in two independent cohorts of typically developing rhesus macaques. METHODS Diffusion MRI scans were analysed from 43 rhesus macaques (20 females and 23 males) at either 12 or 18 months of age from two separate primate colonies. RESULTS Significant, widespread reductions in fractional anisotropy with corresponding increased axial, mean, and radial diffusivity were observed across the brain as a result of repeated anaesthesia exposures. These effects were dose dependent and remained after accounting for age and sex at time of exposure in a generalised linear model. Decreases of up to 40% in fractional anisotropy were detected in some brain regions. CONCLUSIONS Multiple exposures to commonly used anaesthetics were associated with marked changes in white matter microstructure. This study is amongst the first to examine clinically relevant anaesthesia exposures on the developing primate brain. It will be important to examine if, or to what degree, the maturing brain can recover from these white matter changes.
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Affiliation(s)
- Jeffrey T Young
- Department of Psychiatry University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Roza M Vlasova
- Department of Psychiatry University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
| | - Brittany R Howell
- Yerkes National Primate Research Center, Emory University, Atlanta, GA, USA
| | - Rebecca C Knickmeyer
- Department of Psychiatry University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Elyse Morin
- Yerkes National Primate Research Center, Emory University, Atlanta, GA, USA
| | - Kaela I Kuitchoua
- Yerkes National Primate Research Center, Emory University, Atlanta, GA, USA
| | - Gabriele R Lubach
- Harlow Center for Biological Psychology, University of Wisconsin-Madison, Madison, WI, USA
| | - Jean Noel
- Department of Psychiatry University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Xiaoping Hu
- Department of Bioengineering, University of California, Riverside, CA, USA
| | - Yundi Shi
- Department of Psychiatry University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Gibson Caudill
- Department of Psychiatry University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Andrew L Alexander
- Waisman Laboratory for Brain Imaging and Behavior, University of Wisconsin-Madison, Madison, WI, USA
| | - Marc Niethammer
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Merle G Paule
- Division of Neurotoxicology, National Center for Toxicological Research, US Food & Drug Administration, Jefferson, AR, USA
| | - Christopher L Coe
- Harlow Center for Biological Psychology, University of Wisconsin-Madison, Madison, WI, USA
| | - Mar Sanchez
- Yerkes National Primate Research Center, Emory University, Atlanta, GA, USA; Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Emory University, Atlanta, GA, USA
| | - Martin Styner
- Department of Psychiatry University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
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18
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Knickmeyer RC, Nguyen CT, Young JT, Haunton A, Kosorok MR, Gilmore JH, Styner M, Rothmond DA, Noble PL, Lenroot R, Weickert CS. Impact of gonadectomy on maturational changes in brain volume in adolescent macaques. Psychoneuroendocrinology 2021; 124:105068. [PMID: 33260081 PMCID: PMC8121100 DOI: 10.1016/j.psyneuen.2020.105068] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/17/2019] [Revised: 09/29/2020] [Accepted: 11/12/2020] [Indexed: 10/23/2022]
Abstract
Adolescence is a transitional period between childhood and adulthood characterized by significant changes in global and regional brain tissue volumes. It is also a period of increasing vulnerability to psychiatric illness. The relationship between these patterns and increased levels of circulating sex steroids during adolescence remains unclear. The objective of the current study was to determine whether gonadectomy, prior to puberty, alters adolescent brain development in male rhesus macaques. Ninety-six structural MRI scans were acquired from 12 male rhesus macaques (8 time points per animal over a two-year period). Six animals underwent gonadectomy and 6 animals underwent a sham operation at 29 months of age. Mixed-effects models were used to determine whether gonadectomy altered developmental trajectories of global and regional brain tissue volumes. We observed a significant effect of gonadectomy on the developmental trajectory of prefrontal gray matter (GM), with intact males showing peak volumes around 3.5 years of age with a subsequent decline. In contrast, prefrontal GM volumes continued to increase in gonadectomized males until the end of the study. We did not observe a significant effect of gonadectomy on prefrontal white matter or on any other global or regional brain tissue volumes, though we cannot rule out that effects might be detected in a larger sample. Results suggest that the prefrontal cortex is more vulnerable to gonadectomy than other brain regions.
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Affiliation(s)
- Rebecca C. Knickmeyer
- Michigan State University, Institute for Quantitative Health Science and Engineering, Room 2114, Bio Engineering Facility, 775 Woodlot Dr., East Lansing, MI, 48824 USA,University of North Carolina at Chapel Hill, Department of Psychiatry, Campus Box #7160, Chapel Hill, NC 27599-7160, USA
| | - Crystal T. Nguyen
- University of North Carolina at Chapel Hill, Department of Biostatistics, Campus Box #7420, Chapel Hill, NC 27599-7420, USA
| | - Jeffrey T. Young
- University of North Carolina at Chapel Hill, Department of Psychiatry, Campus Box #7160, Chapel Hill, NC 27599-7160, USA
| | - Anne Haunton
- North Carolina School of Science and Mathematics, 1219 Broad St, Durham, NC 27705, USA.
| | - Michael R. Kosorok
- University of North Carolina at Chapel Hill, Department of Biostatistics, Campus Box #7420, Chapel Hill, NC 27599-7420, USA
| | - John H. Gilmore
- University of North Carolina at Chapel Hill, Department of Psychiatry, Campus Box #7160, Chapel Hill, NC 27599-7160, USA
| | - Martin Styner
- University of North Carolina at Chapel Hill, Department of Psychiatry, Campus Box #7160, Chapel Hill, NC 27599-7160, USA; University of North Carolina at Chapel Hill, Department of Computer Science, Campus Box #3175, Chapel Hill, NC 27599-3175, USA.
| | - Debora A. Rothmond
- Schizophrenia Research Laboratory, Neuroscience Research Australia, Randwick NSW 2031, Australia
| | - Pamela L. Noble
- Laboratory of Neuropsychology, National Institute for Mental Health, National Institutes of Health, Bethesda, MD 20892-9663
| | - Rhoshel Lenroot
- University of New Mexico, Department of Psychiatry and Behavioral Sciences, Albuquerque, NM 87131, USA.
| | - Cynthia Shannon Weickert
- School of Psychiatry, Faculty of Medicine, University of New South Wales, Sydney, NSW 2052, Australia; Department of Neuroscience & Physiology, Upstate Medical University, Syracuse, NY 13210, USA.
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19
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Lv Q, Yan M, Shen X, Wu J, Yu W, Yan S, Yang F, Zeljic K, Shi Y, Zhou Z, Lv L, Hu X, Menon R, Wang Z. Normative Analysis of Individual Brain Differences Based on a Population MRI-Based Atlas of Cynomolgus Macaques. Cereb Cortex 2021; 31:341-355. [PMID: 32844170 PMCID: PMC7727342 DOI: 10.1093/cercor/bhaa229] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2020] [Revised: 07/05/2020] [Accepted: 07/27/2020] [Indexed: 01/09/2023] Open
Abstract
The developmental trajectory of the primate brain varies substantially with aging across subjects. However, this ubiquitous variability between individuals in brain structure is difficult to quantify and has thus essentially been ignored. Based on a large-scale structural magnetic resonance imaging dataset acquired from 162 cynomolgus macaques, we create a species-specific 3D template atlas of the macaque brain, and deploy normative modeling to characterize individual variations of cortical thickness (CT) and regional gray matter volume (GMV). We observed an overall decrease in total GMV and mean CT, and an increase in white matter volume from juvenile to early adult. Specifically, CT and regional GMV were greater in prefrontal and temporal cortices relative to early unimodal areas. Age-dependent trajectories of thickness and volume for each cortical region revealed an increase in the medial temporal lobe, and decreases in all other regions. A low percentage of highly individualized deviations of CT and GMV were identified (0.0021%, 0.0043%, respectively, P < 0.05, false discovery rate [FDR]-corrected). Our approach provides a natural framework to parse individual neuroanatomical differences for use as a reference standard in macaque brain research, potentially enabling inferences regarding the degree to which behavioral or symptomatic variables map onto brain structure in future disease studies.
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Affiliation(s)
- Qiming Lv
- National Resource Center for Non-human Primates, Kunming Primate Research Center, and National Research Facility for Phenotypic & Genetic Analysis of Model Animals (Primate Facility), Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
- University of Chinese Academy of Sciences, Beijing, China
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, State Key Laboratory of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Chinese Academy of Sciences, Shanghai, China
| | - Mingchao Yan
- University of Chinese Academy of Sciences, Beijing, China
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, State Key Laboratory of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Chinese Academy of Sciences, Shanghai, China
| | - Xiangyu Shen
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, State Key Laboratory of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Chinese Academy of Sciences, Shanghai, China
| | - Jing Wu
- National Resource Center for Non-human Primates, Kunming Primate Research Center, and National Research Facility for Phenotypic & Genetic Analysis of Model Animals (Primate Facility), Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
| | - Wenwen Yu
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, State Key Laboratory of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Chinese Academy of Sciences, Shanghai, China
| | - Shengyao Yan
- University of Chinese Academy of Sciences, Beijing, China
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, State Key Laboratory of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Chinese Academy of Sciences, Shanghai, China
| | - Feng Yang
- University of Chinese Academy of Sciences, Beijing, China
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, State Key Laboratory of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Chinese Academy of Sciences, Shanghai, China
| | - Kristina Zeljic
- University of Chinese Academy of Sciences, Beijing, China
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, State Key Laboratory of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Chinese Academy of Sciences, Shanghai, China
| | - Yuequan Shi
- Department of Radiology, Fujian Provincial Maternity and Children’s Hospital of Fujian Medical University, Fuzhou, Fujian, China
| | - Zuofu Zhou
- Department of Radiology, Fujian Provincial Maternity and Children’s Hospital of Fujian Medical University, Fuzhou, Fujian, China
| | - Longbao Lv
- National Resource Center for Non-human Primates, Kunming Primate Research Center, and National Research Facility for Phenotypic & Genetic Analysis of Model Animals (Primate Facility), Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
| | - Xintian Hu
- National Resource Center for Non-human Primates, Kunming Primate Research Center, and National Research Facility for Phenotypic & Genetic Analysis of Model Animals (Primate Facility), Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
| | - Ravi Menon
- Centre for Functional and Metabolic Mapping, Robarts Research Institute, Western University, London, Ontario, Canada
- Department of Medical Biophysics, Western University, London, Ontario, Canada
| | - Zheng Wang
- National Resource Center for Non-human Primates, Kunming Primate Research Center, and National Research Facility for Phenotypic & Genetic Analysis of Model Animals (Primate Facility), Kunming Institute of Zoology, Chinese Academy of Sciences, Kunming, Yunnan, China
- University of Chinese Academy of Sciences, Beijing, China
- Institute of Neuroscience, CAS Center for Excellence in Brain Science and Intelligence Technology, State Key Laboratory of Neuroscience, CAS Key Laboratory of Primate Neurobiology, Chinese Academy of Sciences, Shanghai, China
- Shanghai Center for Brain Science and Brain-inspired Intelligence Technology, Shanghai, China
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20
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Garcia-Saldivar P, Garimella A, Garza-Villarreal EA, Mendez FA, Concha L, Merchant H. PREEMACS: Pipeline for preprocessing and extraction of the macaque brain surface. Neuroimage 2020; 227:117671. [PMID: 33359348 DOI: 10.1016/j.neuroimage.2020.117671] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 12/04/2020] [Accepted: 12/16/2020] [Indexed: 01/18/2023] Open
Abstract
Accurate extraction of the cortical brain surface is critical for cortical thickness estimation and a key element to perform multimodal imaging analysis, where different metrics are integrated and compared in a common space. While brain surface extraction has become widespread practice in human studies, several challenges unique to neuroimaging of non-human primates (NHP) have hindered its adoption for the study of macaques. Although, some of these difficulties can be addressed at the acquisition stage, several common artifacts can be minimized through image preprocessing. Likewise, there are several image analysis pipelines for human MRIs, but very few automated methods for extraction of cortical surfaces have been reported for NHPs and none have been tested on data from diverse sources. We present PREEMACS, a pipeline that standardizes the preprocessing of structural MRI images (T1- and T2-weighted) and carries out an automatic surface extraction of the macaque brain. Building upon and extending pre-existing tools, the first module performs volume orientation, image cropping, intensity non-uniformity correction, and volume averaging, before skull-stripping through a convolutional neural network. The second module performs quality control using an adaptation of MRIqc method to extract objective quality metrics that are then used to determine the likelihood of accurate brain surface estimation. The third and final module estimates the white matter (wm) and pial surfaces from the T1-weighted volume (T1w) using an NHP customized version of FreeSurfer aided by the T2-weighted volumes (T2w). To evaluate the generalizability of PREEMACS, we tested the pipeline using 57 T1w/T2w NHP volumes acquired at 11 different sites from the PRIME-DE public dataset. Results showed an accurate and robust automatic brain surface extraction from images that passed the quality control segment of our pipeline. This work offers a robust, efficient and generalizable pipeline for the automatic standardization of MRI surface analysis on NHP.
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Affiliation(s)
- Pamela Garcia-Saldivar
- Institute of Neurobiology, Universidad Nacional Autónoma de México, Campus Juriquilla. Blvd. Juriquilla, 3001 Querétaro, Querétaro, México
| | - Arun Garimella
- Institute of Neurobiology, Universidad Nacional Autónoma de México, Campus Juriquilla. Blvd. Juriquilla, 3001 Querétaro, Querétaro, México; International Institute of Information Technology, Hyderabad, India
| | - Eduardo A Garza-Villarreal
- Institute of Neurobiology, Universidad Nacional Autónoma de México, Campus Juriquilla. Blvd. Juriquilla, 3001 Querétaro, Querétaro, México
| | - Felipe A Mendez
- Institute of Neurobiology, Universidad Nacional Autónoma de México, Campus Juriquilla. Blvd. Juriquilla, 3001 Querétaro, Querétaro, México
| | - Luis Concha
- Institute of Neurobiology, Universidad Nacional Autónoma de México, Campus Juriquilla. Blvd. Juriquilla, 3001 Querétaro, Querétaro, México.
| | - Hugo Merchant
- Institute of Neurobiology, Universidad Nacional Autónoma de México, Campus Juriquilla. Blvd. Juriquilla, 3001 Querétaro, Querétaro, México.
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21
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DIKA-Nets: Domain-invariant knowledge-guided attention networks for brain skull stripping of early developing macaques. Neuroimage 2020; 227:117649. [PMID: 33338616 DOI: 10.1016/j.neuroimage.2020.117649] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2020] [Revised: 12/02/2020] [Accepted: 12/03/2020] [Indexed: 01/18/2023] Open
Abstract
As non-human primates, macaques have a close phylogenetic relationship to human beings and have been proven to be a valuable and widely used animal model in human neuroscience research. Accurate skull stripping (aka. brain extraction) of brain magnetic resonance imaging (MRI) is a crucial prerequisite in neuroimaging analysis of macaques. Most of the current skull stripping methods can achieve satisfactory results for human brains, but when applied to macaque brains, especially during early brain development, the results are often unsatisfactory. In fact, the early dynamic, regionally-heterogeneous development of macaque brains, accompanied by poor and age-related contrast between different anatomical structures, poses significant challenges for accurate skull stripping. To overcome these challenges, we propose a fully-automated framework to effectively fuse the age-specific intensity information and domain-invariant prior knowledge as important guiding information for robust skull stripping of developing macaques from 0 to 36 months of age. Specifically, we generate Signed Distance Map (SDM) and Center of Gravity Distance Map (CGDM) based on the intermediate segmentation results as guidance. Instead of using local convolution, we fuse all information using the Dual Self-Attention Module (DSAM), which can capture global spatial and channel-dependent information of feature maps. To extensively evaluate the performance, we adopt two relatively-large challenging MRI datasets from rhesus macaques and cynomolgus macaques, respectively, with a total of 361 scans from two different scanners with different imaging protocols. We perform cross-validation by using one dataset for training and the other one for testing. Our method outperforms five popular brain extraction tools and three deep-learning-based methods on cross-source MRI datasets without any transfer learning.
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22
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Weiss AR, Liu Z, Wang X, Liguore WA, Kroenke CD, McBride JL. The macaque brain ONPRC18 template with combined gray and white matter labelmap for multimodal neuroimaging studies of Nonhuman Primates. Neuroimage 2020; 225:117517. [PMID: 33137475 PMCID: PMC7833476 DOI: 10.1016/j.neuroimage.2020.117517] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/20/2020] [Revised: 09/25/2020] [Accepted: 10/24/2020] [Indexed: 11/30/2022] Open
Abstract
Macaques are the most common nonhuman primate (NHP) species used in neuroscience research. With the advancement of many neuroimaging techniques, new studies are beginning to apply multiple types of in vivo mag netic resonance imaging (MRI), such as structural imaging (sMRI) with T1 and T2 weighted contrasts alongside diffusion weighed (DW) imaging. In studies involving rhesus macaques, this approach can be used to better under stand micro-structural changes that occur during development, in various disease states or with normative aging. However, many of the available rhesus brain atlases have been designed for only one imaging modality, making it difficult to consistently define the same brain regions across multiple imaging modalities in the same subject. To address this, we created a brain atlas from 18 adult rhesus macaques that includes co-registered templates constructed from images frequently used to characterize macroscopic brain structure (T2/SPACE and T1/MP-RAGE), and a diffusion tensor imaging (DTI) template. The DTI template was up-sampled from 1 mm isotropic resolution to resolution match to the T1 and T2-weighted images (0.5 mm isotropic), and the parameter maps were derived for FA, AD, RD and MD. The labelmap volumes delineate 57 gray matter regions of interest (ROIs; 36 cortical regions and 21 subcortical structures), as well as 74 white matter tracts. Importantly, the labelmap overlays both the structural and diffusion templates, enabling the same regions to be consistently identified across imaging modalities. A specialized condensed version of the labelmap ROIs are also included to further extend the usefulness of this tool for imaging data with lower spatial resolution, such as functional MRI (fMRI) or positron emission tomography (PET).
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Affiliation(s)
- Alison R Weiss
- Division of Neuroscience, Oregon National Primate Research Center, Beaverton, OR, USA, 97006
| | - Zheng Liu
- Division of Neuroscience, Oregon National Primate Research Center, Beaverton, OR, USA, 97006; Advanced Imaging Research Center, Oregon Health and Science University, Portland, OR, USA, 97239
| | - Xiaojie Wang
- Division of Neuroscience, Oregon National Primate Research Center, Beaverton, OR, USA, 97006; Advanced Imaging Research Center, Oregon Health and Science University, Portland, OR, USA, 97239
| | - William A Liguore
- Division of Neuroscience, Oregon National Primate Research Center, Beaverton, OR, USA, 97006
| | - Christopher D Kroenke
- Division of Neuroscience, Oregon National Primate Research Center, Beaverton, OR, USA, 97006; Advanced Imaging Research Center, Oregon Health and Science University, Portland, OR, USA, 97239; Department of Behavioral Neuroscience, Oregon Health and Science University, Portland OR, USA, 97239
| | - Jodi L McBride
- Division of Neuroscience, Oregon National Primate Research Center, Beaverton, OR, USA, 97006; Department of Behavioral Neuroscience, Oregon Health and Science University, Portland OR, USA, 97239; Department of Behavioral Neurology, Oregon Health and Science University, Portland OR, USA, 97239.
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23
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Müller HP, Roselli F, Rasche V, Kassubek J. Diffusion Tensor Imaging-Based Studies at the Group-Level Applied to Animal Models of Neurodegenerative Diseases. Front Neurosci 2020; 14:734. [PMID: 32982659 PMCID: PMC7487414 DOI: 10.3389/fnins.2020.00734] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2020] [Accepted: 06/22/2020] [Indexed: 12/11/2022] Open
Abstract
The understanding of human and non-human microstructural brain alterations in the course of neurodegenerative diseases has substantially improved by the non-invasive magnetic resonance imaging (MRI) technique of diffusion tensor imaging (DTI). Animal models (including disease or knockout models) allow for a variety of experimental manipulations, which are not applicable to humans. Thus, the DTI approach provides a promising tool for cross-species cross-sectional and longitudinal investigations of the neurobiological targets and mechanisms of neurodegeneration. This overview with a systematic review focuses on the principles of DTI analysis as used in studies at the group level in living preclinical models of neurodegeneration. The translational aspect from in-vivo animal models toward (clinical) applications in humans is covered as well as the DTI-based research of the non-human brains' microstructure, the methodological aspects in data processing and analysis, and data interpretation at different abstraction levels. The aim of integrating DTI in multiparametric or multimodal imaging protocols will allow the interrogation of DTI data in terms of directional flow of information and may identify the microstructural underpinnings of neurodegeneration-related patterns.
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Affiliation(s)
| | - Francesco Roselli
- Department of Neurology, University of Ulm, Ulm, Germany.,German Center for Neurodegenerative Diseases (DZNE), Ulm, Germany
| | - Volker Rasche
- Core Facility Small Animal MRI, University of Ulm, Ulm, Germany
| | - Jan Kassubek
- Department of Neurology, University of Ulm, Ulm, Germany
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24
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Kim J, Jung Y, Barcus R, Bachevalier JH, Sanchez MM, Nader MA, Whitlow CT. Rhesus Macaque Brain Developmental Trajectory: A Longitudinal Analysis Using Tensor-Based Structural Morphometry and Diffusion Tensor Imaging. Cereb Cortex 2020; 30:4325-4335. [PMID: 32239147 PMCID: PMC7325797 DOI: 10.1093/cercor/bhaa015] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2019] [Revised: 01/09/2020] [Accepted: 01/17/2020] [Indexed: 12/28/2022] Open
Abstract
The typical developmental trajectory of brain structure among nonhuman primates (NHPs) remains poorly understood. In this study, we characterized the normative trajectory of developmental change among a cohort of rhesus monkeys (n = 28), ranging in age from 2 to 22 months, using structural MRI datasets that were longitudinally acquired every 3-4 months. We hypothesized that NHP-specific transient intracranial volume decreases reported during late infancy would be part of the typical developmental process, which is driven by volumetric contraction of gray matter in primary functional areas. To this end, we performed multiscale analyses from the whole brain to voxel level, characterizing regional heterogeneity, hemispheric asymmetry, and sexual dimorphism in developmental patterns. The longitudinal trajectory of brain development was explained by three different regional volumetric growth patterns (exponentially decreasing, undulating, and linearly increasing), which resulted in developmental brain volume curves with transient brain volumetric decreases. White matter (WM) fractional anisotropy increased with age, corresponding to WM volume increases, while mean diffusivity (MD) showed biphasic patterns. The longitudinal trajectory of brain development in young rhesus monkeys follows typical maturation patterns seen in humans, but regional volumetric and MD changes are more dynamic in rhesus monkeys compared with humans, with marked decreases followed by "rebound-like" increases.
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Affiliation(s)
- Jeongchul Kim
- Radiology Informatics and Image Processing Laboratory (RIIPL), Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
- Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
| | - Youngkyoo Jung
- Radiology Informatics and Image Processing Laboratory (RIIPL), Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
- Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
- Department of Biomedical Engineering, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
| | - Richard Barcus
- Radiology Informatics and Image Processing Laboratory (RIIPL), Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
- Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
| | - Jocelyne H Bachevalier
- Department of Psychology, Emory University, Atlanta, GA 30322, USA
- Division of Developmental and Cognitive Neuroscience, Yerkes National Primate Research Center, Emory University, Atlanta, GA 30329, USA
| | - Mar M Sanchez
- Division of Developmental and Cognitive Neuroscience, Yerkes National Primate Research Center, Emory University, Atlanta, GA 30329, USA
- Department of Psychiatry and Behavioral Sciences, Emory University School of Medicine, Atlanta, GA 30322, USA
| | - Michael A Nader
- Department of Physiology and Pharmacology, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
- Center for Research on Substance Use and Addiction, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
- Clinical and Translational Science Institute, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
| | - Christopher T Whitlow
- Radiology Informatics and Image Processing Laboratory (RIIPL), Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
- Department of Radiology, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
- Department of Biomedical Engineering, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
- Center for Research on Substance Use and Addiction, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
- Clinical and Translational Science Institute, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
- Department of Biostatistics and Data Science, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
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25
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Charvet CJ, Das A, Song JW, Tindal-Burgess DJ, Kabaria P, Dai G, Kane T, Takahashi E. High Angular Resolution Diffusion MRI Reveals Conserved and Deviant Programs in the Paths that Guide Human Cortical Circuitry. Cereb Cortex 2020; 30:1447-1464. [PMID: 31667494 PMCID: PMC7132938 DOI: 10.1093/cercor/bhz178] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/15/2019] [Revised: 06/13/2019] [Accepted: 07/10/2019] [Indexed: 02/07/2023] Open
Abstract
Diffusion magnetic resonance (MR) tractography represents a novel opportunity to investigate conserved and deviant developmental programs between humans and other species such as mice. To that end, we acquired high angular resolution diffusion MR scans of mice [embryonic day (E) 10.5 to postnatal week 4] and human brains [gestational week (GW) 17-30] at successive stages of fetal development to investigate potential evolutionary changes in radial organization and emerging pathways between humans and mice. We compare radial glial development as well as commissural development (e.g., corpus callosum), primarily because our findings can be integrated with previous work. We also compare corpus callosal growth trajectories across primates (i.e., humans and rhesus macaques) and rodents (i.e., mice). One major finding is that the developing cortex of humans is predominated by pathways likely associated with a radial glial organization at GW 17-20, which is not as evident in age-matched mice (E 16.5, 17.5). Another finding is that, early in development, the corpus callosum follows a similar developmental timetable in primates (i.e., macaques and humans) as in mice. However, the corpus callosum grows for an extended period of time in primates compared with rodents. Taken together, these findings highlight deviant developmental programs underlying the emergence of cortical pathways in the human brain.
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Affiliation(s)
| | - Avilash Das
- Medical Sciences in the College of Arts and Sciences, Boston University, Boston, MA 02215, USA
- Division of Newborn Medicine, Department of Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02215, USA
- Fetal-Neonatal Brain Imaging and Developmental Science Center, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02215, USA
| | - Jae W Song
- Division of Neuroradiology, Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA
| | | | - Priya Kabaria
- Department of Behavioral Neuroscience, Northeastern University, Boston, MA 02115, USA
| | - Guangping Dai
- Science Center, Wellesley College, Wellesley, MA 02481, USA
| | - Tara Kane
- Department of Behavioral Neuroscience, Northeastern University, Boston, MA 02115, USA
| | - Emi Takahashi
- Division of Newborn Medicine, Department of Medicine, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02215, USA
- Fetal-Neonatal Brain Imaging and Developmental Science Center, Boston Children’s Hospital, Harvard Medical School, Boston, MA 02215, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Charlestown, MA 02129, USA
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26
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Xia J, Wang F, Wu Z, Wang L, Zhang C, Shen D, Li G. Mapping hemispheric asymmetries of the macaque cerebral cortex during early brain development. Hum Brain Mapp 2019; 41:95-106. [PMID: 31532054 PMCID: PMC7267900 DOI: 10.1002/hbm.24789] [Citation(s) in RCA: 20] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/25/2019] [Revised: 05/29/2019] [Accepted: 06/05/2019] [Indexed: 11/10/2022] Open
Abstract
Studying cortical hemispheric asymmetries during the dynamic early postnatal stages in macaque monkeys (with close phylogenetic relationship to humans) would increase our limited understanding on the possible origins, developmental trajectories, and evolutional mechanisms of brain asymmetries in nonhuman primates, but remains a blind spot to the community. Via cortical surface-based morphometry, we comprehensively analyze hemispheric structural asymmetries in 134 longitudinal MRI scans from birth to 20 months of age from 32 healthy macaque monkeys. We reveal that most clusters of hemispheric asymmetries of cortical properties, such as surface area, cortical thickness, sulcal depth, and vertex positions, expand in the first 4 months of life, and evolve only moderately thereafter. Prominent hemispheric asymmetries are found at the inferior frontal gyrus, precentral gyrus, posterior temporal cortex, superior temporal gyrus (STG), superior temporal sulcus (STS), and cingulate cortex. Specifically, the left planum temporale and left STG consistently have larger area and thicker cortices than those on the right hemisphere, while the right STS, right cingulate cortex, and right anterior insula are consistently deeper than the left ones, partially consistent with the findings in human infants and adults. Our results thus provide a valuable reference in studying early brain development and evolution.
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Affiliation(s)
- Jing Xia
- Department of Computer Science and Technology, Shandong University, Jinan, Shandong, China.,Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Fan Wang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Zhengwang Wu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Li Wang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Caiming Zhang
- Department of Computer Science and Technology, Shandong University, Jinan, Shandong, China
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.,Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea
| | - Gang Li
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
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27
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Heuer K, Gulban OF, Bazin PL, Osoianu A, Valabregue R, Santin M, Herbin M, Toro R. Evolution of neocortical folding: A phylogenetic comparative analysis of MRI from 34 primate species. Cortex 2019; 118:275-291. [DOI: 10.1016/j.cortex.2019.04.011] [Citation(s) in RCA: 34] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/04/2018] [Revised: 04/08/2019] [Accepted: 04/09/2019] [Indexed: 01/14/2023]
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28
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Thiebaut de Schotten M, Croxson PL, Mars RB. Large-scale comparative neuroimaging: Where are we and what do we need? Cortex 2019; 118:188-202. [PMID: 30661736 PMCID: PMC6699599 DOI: 10.1016/j.cortex.2018.11.028] [Citation(s) in RCA: 30] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2018] [Revised: 11/26/2018] [Accepted: 11/29/2018] [Indexed: 01/26/2023]
Abstract
Neuroimaging has a lot to offer comparative neuroscience. Although invasive "gold standard" techniques have a better spatial resolution, neuroimaging allows fast, whole-brain, repeatable, and multi-modal measurements of structure and function in living animals and post-mortem tissue. In the past years, comparative neuroimaging has increased in popularity. However, we argue that its most significant potential lies in its ability to collect large-scale datasets of many species to investigate principles of variability in brain organisation across whole orders of species-an ambition that is presently unfulfilled but achievable. We briefly review the current state of the field and explore what the current obstacles to such an approach are. We propose some calls to action.
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Affiliation(s)
- Michel Thiebaut de Schotten
- Brain Connectivity and Behaviour Group, Sorbonne Universities, Paris France; Frontlab, Institut du Cerveau et de la Moelle épinière (ICM), UPMC UMRS 1127, Inserm U 1127, CNRS UMR, Paris, France; Groupe d'Imagerie Neurofonctionnelle, Institut des Maladies Neurodégénératives-UMR 5293, CNRS, CEA University of Bordeaux, Bordeaux, France.
| | - Paula L Croxson
- Friedman Brain Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA; Mortimer B. Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY, USA.
| | - Rogier B Mars
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom; Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands.
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29
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atlasBREX: Automated template-derived brain extraction in animal MRI. Sci Rep 2019; 9:12219. [PMID: 31434923 PMCID: PMC6704255 DOI: 10.1038/s41598-019-48489-3] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2019] [Accepted: 06/03/2019] [Indexed: 11/25/2022] Open
Abstract
We proposed a generic template-derived approach for (semi-) automated brain extraction in animal MRI studies and evaluated our implementation with different animal models (macaque, marmoset, rodent) and MRI protocols (T1, T2). While conventional MR-neuroimaging studies perform brain extraction as an initial step priming subsequent image-registration from subject to template, our proposed approach propagates an anatomical template to (whole-head) individual subjects in reverse order, which is challenging due to the surrounding extracranial tissue, greater differences in contrast pattern and larger areas with field inhomogeneity. As a novel approach, the herein introduced brain extraction algorithm derives whole-brain segmentation using rigid and non-rigid deformation based on unbiased anatomical atlas building with a priori estimates from study-cohort and an initial approximate brain extraction. We evaluated our proposed method in comparison to several other technical approaches including “Marker based watershed scalper”, “Brain-Extraction-Tool”, “3dSkullStrip”, “Primatologist-Toolbox”, “Rapid Automatic Tissue Segmentation” and “Robust automatic rodent brain extraction using 3D pulse-coupled neural networks” with manual skull-stripping as reference standard. ABX demonstrated best performance with accurate (≥92%) and consistent results throughout datasets and across species, age and MRI protocols. ABX was made available to the public with documentation, templates and sample material (https://www.github.com/jlohmeier/atlasBREX).
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Xia J, Wang F, Wu Z, Wang L, Wang Y, Zhang C, Lin W, Shen D, Li G. CHARTING DEVELOPMENT-BASED JOINT PARCELLATION MAPS OF HUMAN AND MACAQUE BRAINS DURING INFANCY. PROCEEDINGS. IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING 2019; 2019:422-425. [PMID: 31354917 DOI: 10.1109/isbi.2019.8759379] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Comparative characterization of early brain development between human and macaque using neuroimaging data is crucial to understand the mechanisms of brain development and evolution. To this end, joint cortical parcellation maps of human and macaque infant brains with corresponding regions are highly desirable, since they provide basic cortical parcels for both region-based and network-based studies of two closely-related species. To address this issue, we propose to leverage developmental patterns of cortical properties of both human and macaque infants for creating joint parcellation maps with inter-species comparability. The motivation is that the developmental patterns of cortical properties indicate underlying rapid changes of microstructures, which determine the molecular and functional principles of the cortex. Thus, developmental patterns are well suitable for defining distinct cortical regions in both structures and functions. To comprehensively capture the similarities of developmental patterns of vertices on cortical surfaces, for each species, we first construct two complementary similarity matrices: a low-order matrix and a high-order matrix. Then, we non-linearly fuse these four matrices together as a single matrix in a hierarchical manner, thus capturing the common and complementary information of both human and macaque infants. Finally, based on the fused similarity matrix, we apply the spectral clustering to derive the joint parcellation maps. By applying our method to 210 longitudinal human infant MRI scans and 140 longitudinal macaque infant MRI scans, we generate the first biologically-meaningful joint parcellation maps of human and macaque infants.
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Affiliation(s)
- Jing Xia
- Department of Computer Science and Technology, Shandong University, Shandong, China.,Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA
| | - Fan Wang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA
| | - Zhengwang Wu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA
| | - Li Wang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA
| | - Yaping Wang
- School of Information Engineering, Zhengzhou University, Henan, China
| | - Caiming Zhang
- Department of Computer Science and Technology, Shandong University, Shandong, China
| | - Weili Lin
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA
| | - Gang Li
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA
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31
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Individual variation in longitudinal postnatal development of the primate brain. Brain Struct Funct 2019; 224:1185-1201. [PMID: 30637493 DOI: 10.1007/s00429-019-01829-5] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2018] [Accepted: 01/07/2019] [Indexed: 12/18/2022]
Abstract
Quantifying individual variation in postnatal brain development can provide insight into cognitive diversity within a population and the aetiology of common neuropsychiatric and neurodevelopmental disorders. Non-invasive studies of the non-human primate can aid understanding of human brain development, facilitating longitudinal analysis during early postnatal development when comparative human populations are difficult to sample. In this study, we perform analysis of a longitudinal MRI dataset of 32 macaques, each with up to five magnetic resonance imaging (MRI) scans acquired between 3 and 36 months of age. Using nonlinear mixed effects model we derive growth trajectories for whole brain, cortical and subcortical grey matter, cerebral white matter and cerebellar volume. We then test the association between individual variation in postnatal tissue volumes and birth weight. We report nonlinear growth models for all tissue compartments, as well as significant variation in total intracranial volume between individuals. We also demonstrate that regional subcortical grey matter varies both in total volume and rate of change between individuals and is associated with differences in birth weight. This supports evidence that birth weight may act as a marker of subsequent brain development and highlights the importance of longitudinal MRI analysis in developmental studies.
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32
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Zhang T, Kong J, Jing K, Chen H, Jiang X, Li L, Guo L, Lu J, Hu X, Liu T. Optimization of macaque brain DMRI connectome by neuron tracing and myelin stain data. Comput Med Imaging Graph 2018; 69:9-20. [PMID: 30170273 PMCID: PMC6176488 DOI: 10.1016/j.compmedimag.2018.06.001] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2017] [Revised: 04/26/2018] [Accepted: 06/18/2018] [Indexed: 12/11/2022]
Abstract
Accurate assessment of connectional anatomy of primate brains can be an important avenue to better understand the structural and functional organization of brains. To this end, numerous connectome projects have been initiated to create a comprehensive map of the connectional anatomy over a large spatial expanse. Tractography based on diffusion MRI (dMRI) data has been used as a tool by many connectome projects in that it is widely used to visualize axonal pathways and reveal microstructural features on living brains. However, the measures obtained from dMRI are indirect inference of microstructures. This intrinsic limitation reduces the reliability of dMRI in constructing connectomes for brains. In this work, we proposed a framework to increase the accuracy of constructing a dMRI-based connectome on macaque brains by integrating meso-scale connective information from tract-tracing data and micro-scale axonal orientation information from myelin stain data. Our results suggest that this integrative framework could advance the mapping accuracy of dMRI based connections and axonal pathways, and demonstrate the prospect of the proposed framework in constructing a large-scale connectome on living primate brains.
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Affiliation(s)
- Tuo Zhang
- School of Automation and Brain Decoding Research Center, Northwestern Polytechnical University, Xi'an, Shaanxi, China
| | - Jun Kong
- Emory University, Atlanta, GA, United States
| | - Ke Jing
- Nanjing University of Science and Technology, Nanjing, Jiangsu, China
| | - Hanbo Chen
- Cortical Architecture Imaging and Discovery Lab, The University of Georgia, Athens, GA, United States
| | - Xi Jiang
- Cortical Architecture Imaging and Discovery Lab, The University of Georgia, Athens, GA, United States
| | - Longchuan Li
- Marcus Autism Center, Children's Healthcare of Atlanta, Emory University School of Medicine, Atlanta, GA, United States
| | - Lei Guo
- School of Automation and Brain Decoding Research Center, Northwestern Polytechnical University, Xi'an, Shaanxi, China
| | - Jianfeng Lu
- Nanjing University of Science and Technology, Nanjing, Jiangsu, China
| | - Xiaoping Hu
- University of California, Riverside, CA, United States
| | - Tianming Liu
- Cortical Architecture Imaging and Discovery Lab, The University of Georgia, Athens, GA, United States.
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33
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Milham MP, Ai L, Koo B, Xu T, Amiez C, Balezeau F, Baxter MG, Blezer ELA, Brochier T, Chen A, Croxson PL, Damatac CG, Dehaene S, Everling S, Fair DA, Fleysher L, Freiwald W, Froudist-Walsh S, Griffiths TD, Guedj C, Hadj-Bouziane F, Ben Hamed S, Harel N, Hiba B, Jarraya B, Jung B, Kastner S, Klink PC, Kwok SC, Laland KN, Leopold DA, Lindenfors P, Mars RB, Menon RS, Messinger A, Meunier M, Mok K, Morrison JH, Nacef J, Nagy J, Rios MO, Petkov CI, Pinsk M, Poirier C, Procyk E, Rajimehr R, Reader SM, Roelfsema PR, Rudko DA, Rushworth MFS, Russ BE, Sallet J, Schmid MC, Schwiedrzik CM, Seidlitz J, Sein J, Shmuel A, Sullivan EL, Ungerleider L, Thiele A, Todorov OS, Tsao D, Wang Z, Wilson CRE, Yacoub E, Ye FQ, Zarco W, Zhou YD, Margulies DS, Schroeder CE. An Open Resource for Non-human Primate Imaging. Neuron 2018; 100:61-74.e2. [PMID: 30269990 PMCID: PMC6231397 DOI: 10.1016/j.neuron.2018.08.039] [Citation(s) in RCA: 139] [Impact Index Per Article: 23.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/16/2018] [Revised: 03/02/2018] [Accepted: 08/30/2018] [Indexed: 01/11/2023]
Abstract
Non-human primate neuroimaging is a rapidly growing area of research that promises to transform and scale translational and cross-species comparative neuroscience. Unfortunately, the technological and methodological advances of the past two decades have outpaced the accrual of data, which is particularly challenging given the relatively few centers that have the necessary facilities and capabilities. The PRIMatE Data Exchange (PRIME-DE) addresses this challenge by aggregating independently acquired non-human primate magnetic resonance imaging (MRI) datasets and openly sharing them via the International Neuroimaging Data-sharing Initiative (INDI). Here, we present the rationale, design, and procedures for the PRIME-DE consortium, as well as the initial release, consisting of 25 independent data collections aggregated across 22 sites (total = 217 non-human primates). We also outline the unique pitfalls and challenges that should be considered in the analysis of non-human primate MRI datasets, including providing automated quality assessment of the contributed datasets.
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Affiliation(s)
- Michael P Milham
- Center for the Developing Brain, Child Mind Institute, New York, NY 10022, USA; Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY 10962, USA.
| | - Lei Ai
- Center for the Developing Brain, Child Mind Institute, New York, NY 10022, USA
| | - Bonhwang Koo
- Center for the Developing Brain, Child Mind Institute, New York, NY 10022, USA
| | - Ting Xu
- Center for the Developing Brain, Child Mind Institute, New York, NY 10022, USA
| | - Céline Amiez
- University of Lyon, Université Claude Bernard Lyon 1, INSERM, Stem Cell and Brain Research Institute U1208, Lyon, France
| | - Fabien Balezeau
- Institute of Neuroscience, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
| | - Mark G Baxter
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Erwin L A Blezer
- Biomedical MR Imaging and Spectroscopy Group, Center for Image Sciences, University Medical Center Utrecht, Utrecht, The Netherlands
| | - Thomas Brochier
- Institut de Neurosciences de la Timone, CNRS & Aix-Marseille Université, UMR 7289, Marseille, France
| | - Aihua Chen
- Key Laboratory of Brain Functional Genomics (Ministry of Education & Science and Technology Commission of Shanghai Municipality), School of Life Sciences, East China Normal University, Shanghai 200062, China
| | - Paula L Croxson
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Christienne G Damatac
- Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen, 6525 EN Nijmegen, Netherlands
| | - Stanislas Dehaene
- NeuroSpin, CEA, INSERM U992, Université Paris-Saclay, 91191 Gif-sur-Yvette, France
| | - Stefan Everling
- Centre for Functional and Metabolic Mapping, The University of Western Ontario, London, ON N6A 3K7, Canada
| | - Damian A Fair
- Department of Behavior Neuroscience, Department of Psychiatry, Advanced Imaging Research Center, Oregon Health and Science University, Portland, OR, USA
| | - Lazar Fleysher
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Winrich Freiwald
- Laboratory of Neural Systems, The Rockefeller University, New York, NY, USA
| | | | - Timothy D Griffiths
- Institute of Neuroscience, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
| | - Carole Guedj
- INSERM, U1028, CNRS UMR5292, Lyon Neuroscience Research Center, Lyon, France
| | | | - Suliann Ben Hamed
- Institut des Sciences Cognitives - Marc Jeannerod, UMR5229, CNRS-Université de Lyon, Lyon, France
| | - Noam Harel
- Center for Magnetic Resonance Research, University of Minnesota Medical School, Minneapolis, MN 55455, USA
| | - Bassem Hiba
- Institut des Sciences Cognitives - Marc Jeannerod, UMR5229, CNRS-Université de Lyon, Lyon, France
| | - Bechir Jarraya
- NeuroSpin, CEA, INSERM U992, Université Paris-Saclay, 91191 Gif-sur-Yvette, France
| | - Benjamin Jung
- Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD 20892, USA
| | - Sabine Kastner
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08540, USA
| | - P Christiaan Klink
- Netherlands Institute for Neuroscience, Royal Netherlands Academy of Arts and Sciences, 1105 BA Amsterdam, the Netherlands; Department of Psychiatry, Amsterdam UMC, University of Amsterdam, 1105 AZ Amsterdam, the Netherlands
| | - Sze Chai Kwok
- Shanghai Key Laboratory of Brain Functional Genomics, School of Psychology and Cognitive Science, Key Laboratory of Brain Functional Genomics (Ministry of Education), East China Normal University, Shanghai 200062, China; Shanghai Key Laboratory of Magnetic Resonance, East China Normal University, Shanghai 200062, China; NYU-ECNU Institute of Brain and Cognitive Science at NYU Shanghai, Shanghai 200062, China
| | - Kevin N Laland
- Centre for Social Learning and Cognitive Evolution, School of Biology, University of St. Andrews, St. Andrews, UK
| | - David A Leopold
- Section on Cognitive Neurophysiology and Imaging, National Institute of Mental Health, Bethesda, MD 20892, USA; Neurophysiology Imaging Facility, National Institute of Mental Health, National Institute of Neurological Disorders and Stroke, National Eye Institute, Bethesda, MD 20892, USA
| | - Patrik Lindenfors
- Institute for Future Studies, Stockholm, Sweden; Centre for Cultural Evolution & Department of Zoology, Stockholm University, Stockholm, Sweden
| | - Rogier B Mars
- Donders Institute for Brain, Cognition and Behavior, Radboud University Nijmegen, 6525 EN Nijmegen, Netherlands; Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DU, UK
| | - Ravi S Menon
- Centre for Functional and Metabolic Mapping, The University of Western Ontario, London, ON N6A 3K7, Canada
| | - Adam Messinger
- Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD 20892, USA
| | - Martine Meunier
- INSERM, U1028, CNRS UMR5292, Lyon Neuroscience Research Center, Lyon, France
| | - Kelvin Mok
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Departments of Neurology, Neurosurgery, and Biomedical Engineering, McGill University, Montreal, QC H3A 0G4, Canada
| | - John H Morrison
- California National Primate Research Center, Davis, CA 95616, USA; Department of Neurology, School of Medicine, University of California, Davis, CA 95616, USA
| | - Jennifer Nacef
- Institute of Neuroscience, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
| | - Jamie Nagy
- Department of Neuroscience, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Michael Ortiz Rios
- Institute of Neuroscience, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
| | - Christopher I Petkov
- Institute of Neuroscience, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
| | - Mark Pinsk
- Princeton Neuroscience Institute, Princeton University, Princeton, NJ 08540, USA
| | - Colline Poirier
- Institute of Neuroscience, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
| | - Emmanuel Procyk
- University of Lyon, Université Claude Bernard Lyon 1, INSERM, Stem Cell and Brain Research Institute U1208, Lyon, France
| | - Reza Rajimehr
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
| | - Simon M Reader
- Department of Biology and Helmholtz Institute, Utrecht University, 35 84 CH Utrecht, The Netherlands; Department of Biology, McGill University, Montreal, QC H3A 1BA, Canada
| | - Pieter R Roelfsema
- Netherlands Institute for Neuroscience, Royal Netherlands Academy of Arts and Sciences, 1105 BA Amsterdam, the Netherlands; Department of Psychiatry, Amsterdam UMC, University of Amsterdam, 1105 AZ Amsterdam, the Netherlands; Department of Integrative Neurophysiology, Center for Neurogenomics and Cognitive Research, Vrije Universiteit, 1081 HV Amsterdam, the Netherlands
| | - David A Rudko
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Departments of Neurology, Neurosurgery, and Biomedical Engineering, McGill University, Montreal, QC H3A 0G4, Canada
| | - Matthew F S Rushworth
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford OX3 9DU, UK; Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford OX1 3AQ, UK
| | - Brian E Russ
- Section on Cognitive Neurophysiology and Imaging, National Institute of Mental Health, Bethesda, MD 20892, USA
| | - Jerome Sallet
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford OX1 3AQ, UK
| | | | | | - Jakob Seidlitz
- Developmental Neurogenomics Unit, National Institute of Mental Health, Bethesda, MD 20892, USA; Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK
| | - Julien Sein
- Institut de Neurosciences de la Timone, CNRS & Aix-Marseille Université, UMR 7289, Marseille, France
| | - Amir Shmuel
- McConnell Brain Imaging Centre, Montreal Neurological Institute, Departments of Neurology, Neurosurgery, and Biomedical Engineering, McGill University, Montreal, QC H3A 0G4, Canada
| | - Elinor L Sullivan
- Divisions of Neuroscience and Cardiometabolic Health, Oregon National Primate Research Center, Beaverton, OR, USA; Department of Human Physiology, University of Oregon, Eugene, OR, USA
| | - Leslie Ungerleider
- Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD 20892, USA
| | - Alexander Thiele
- Institute of Neuroscience, Newcastle University, Newcastle upon Tyne NE1 7RU, UK
| | - Orlin S Todorov
- Department of Biology and Helmholtz Institute, Utrecht University, 35 84 CH Utrecht, The Netherlands
| | - Doris Tsao
- Department of Computation and Neural Systems, California Institute of Technology, Pasadena, CA 91125, USA
| | - Zheng Wang
- Institute of Neuroscience, Key Laboratory of Primate Neurobiology, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai, China
| | - Charles R E Wilson
- University of Lyon, Université Claude Bernard Lyon 1, INSERM, Stem Cell and Brain Research Institute U1208, Lyon, France
| | - Essa Yacoub
- Center for Magnetic Resonance Research, University of Minnesota Medical School, Minneapolis, MN 55455, USA
| | - Frank Q Ye
- Neurophysiology Imaging Facility, National Institute of Mental Health, National Institute of Neurological Disorders and Stroke, National Eye Institute, Bethesda, MD 20892, USA
| | - Wilbert Zarco
- Laboratory of Neural Systems, The Rockefeller University, New York, NY, USA
| | - Yong-di Zhou
- Krieger Mind/Brain Institute, Department of Neurosurgery, Johns Hopkins University, Baltimore, MD 21287, USA
| | - Daniel S Margulies
- Max Planck Research Group for Neuroanatomy and Connectivity, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany; Centre national de la recherche scientifique, CNRS UMR 7225, Institut du Cerveau et de la Moelle épinière, 75013 Paris, France
| | - Charles E Schroeder
- Center for Biomedical Imaging and Neuromodulation, Nathan S. Kline Institute for Psychiatric Research, Orangeburg, NY 10962, USA; Department of Neurological Surgery, Columbia University College of Physicians and Surgeons, New York, NY 10032, USA; Department of Psychiatry, Columbia University College of Physicians and Surgeons, New York, NY 10032, USA
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Wang F, Lian C, Xia J, Wu Z, Duan D, Wang L, Shen D, Li G. CONSTRUCTION OF SPATIOTEMPORAL INFANT CORTICAL SURFACE ATLAS OF RHESUS MACAQUE. PROCEEDINGS. IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING 2018; 2018:704-707. [PMID: 30416671 DOI: 10.1109/isbi.2018.8363671] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2023]
Abstract
As a widely used animal model in MR imaging studies, rhesus macaque helps to better understand both normal and abnormal neural development in the human brain. However, the available adult macaque brain atlases are not well suitable for study of brain development at the early postnatal stage, since this stage undergoes dramatic changes in brain appearances and structures. Building age matched atlases for this critical period is thus highly desirable yet still lacking. In this paper, we construct the first spatiotemporal (4D) cortical surface atlases for rhesus macaques from 2 weeks to 24 months, using 138 longitudinal MRI scans from 32 healthy rhesus monkeys. Specifically, we first perform intra-subject cortical surface registration to obtain within-subject mean cortical surfaces. Then, we perform inter-subject registration of within-subject mean surfaces to obtain unbiased and longitudinally-consistent 4D cortical surface atlases. Based on our 4D rhesus monkey atlases, we further chart the first developmental-trajectories-based parcellation maps using the local surface area and spectral clustering algorithm. Our 4D macaque surface atlases and parcellation maps will greatly facilitate early brain development studies of macaques.
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Affiliation(s)
- Fan Wang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA
| | - Chunfeng Lian
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA
| | - Jing Xia
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA
| | - Zhengwang Wu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA
| | - Dingna Duan
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA
| | - Li Wang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA
| | - Gang Li
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA
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35
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Seidlitz J, Váša F, Shinn M, Romero-Garcia R, Whitaker KJ, Vértes PE, Wagstyl K, Kirkpatrick Reardon P, Clasen L, Liu S, Messinger A, Leopold DA, Fonagy P, Dolan RJ, Jones PB, Goodyer IM, Raznahan A, Bullmore ET. Morphometric Similarity Networks Detect Microscale Cortical Organization and Predict Inter-Individual Cognitive Variation. Neuron 2017; 97:231-247.e7. [PMID: 29276055 DOI: 10.1016/j.neuron.2017.11.039] [Citation(s) in RCA: 239] [Impact Index Per Article: 34.1] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2017] [Revised: 10/05/2017] [Accepted: 11/22/2017] [Indexed: 12/22/2022]
Abstract
Macroscopic cortical networks are important for cognitive function, but it remains challenging to construct anatomically plausible individual structural connectomes from human neuroimaging. We introduce a new technique for cortical network mapping based on inter-regional similarity of multiple morphometric parameters measured using multimodal MRI. In three cohorts (two human, one macaque), we find that the resulting morphometric similarity networks (MSNs) have a complex topological organization comprising modules and high-degree hubs. Human MSN modules recapitulate known cortical cytoarchitectonic divisions, and greater inter-regional morphometric similarity was associated with stronger inter-regional co-expression of genes enriched for neuronal terms. Comparing macaque MSNs with tract-tracing data confirmed that morphometric similarity was related to axonal connectivity. Finally, variation in the degree of human MSN nodes accounted for about 40% of between-subject variability in IQ. Morphometric similarity mapping provides a novel, robust, and biologically plausible approach to understanding how human cortical networks underpin individual differences in psychological functions.
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Affiliation(s)
- Jakob Seidlitz
- University of Cambridge, Department of Psychiatry, Cambridge CB2 0SZ, UK; Developmental Neurogenomics Unit, National Institute of Mental Health, Bethesda, MD 20892, USA.
| | - František Váša
- University of Cambridge, Department of Psychiatry, Cambridge CB2 0SZ, UK
| | - Maxwell Shinn
- University of Cambridge, Department of Psychiatry, Cambridge CB2 0SZ, UK
| | | | - Kirstie J Whitaker
- University of Cambridge, Department of Psychiatry, Cambridge CB2 0SZ, UK
| | - Petra E Vértes
- University of Cambridge, Department of Psychiatry, Cambridge CB2 0SZ, UK
| | - Konrad Wagstyl
- University of Cambridge, Department of Psychiatry, Cambridge CB2 0SZ, UK
| | | | - Liv Clasen
- Developmental Neurogenomics Unit, National Institute of Mental Health, Bethesda, MD 20892, USA
| | - Siyuan Liu
- Developmental Neurogenomics Unit, National Institute of Mental Health, Bethesda, MD 20892, USA
| | - Adam Messinger
- Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD 20892, USA
| | - David A Leopold
- Laboratory of Neuropsychology, National Institute of Mental Health, Bethesda, MD 20892, USA; Neurophysiology Imaging Facility, National Institute of Mental Health, National Institute of Neurological Disorders and Stroke, National Eye Institute, Bethesda, MD 20892, USA
| | - Peter Fonagy
- Research Department of Clinical, Educational and Health Psychology, University College London, London WC1E 6BT, UK
| | - Raymond J Dolan
- Wellcome Trust Centre for Neuroimaging, UCL Institute of Neurology, University College London, London WC1N 3BG, UK; Max Planck UCL Centre for Computational Psychiatry and Ageing Research, London WC1B 5EH, UK
| | - Peter B Jones
- University of Cambridge, Department of Psychiatry, Cambridge CB2 0SZ, UK; Cambridgeshire and Peterborough NHS Foundation Trust, Huntingdon PE29 3RJ, UK
| | - Ian M Goodyer
- University of Cambridge, Department of Psychiatry, Cambridge CB2 0SZ, UK; Cambridgeshire and Peterborough NHS Foundation Trust, Huntingdon PE29 3RJ, UK
| | | | - Armin Raznahan
- Developmental Neurogenomics Unit, National Institute of Mental Health, Bethesda, MD 20892, USA
| | - Edward T Bullmore
- University of Cambridge, Department of Psychiatry, Cambridge CB2 0SZ, UK; Cambridgeshire and Peterborough NHS Foundation Trust, Huntingdon PE29 3RJ, UK; ImmunoPsychiatry, Immuno-Inflammation Therapeutic Area Unit, GlaxoSmithKline R&D, Stevenage SG1 2NY, UK
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36
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Balbastre Y, Rivière D, Souedet N, Fischer C, Hérard AS, Williams S, Vandenberghe ME, Flament J, Aron-Badin R, Hantraye P, Mangin JF, Delzescaux T. Primatologist: A modular segmentation pipeline for macaque brain morphometry. Neuroimage 2017; 162:306-321. [PMID: 28899745 DOI: 10.1016/j.neuroimage.2017.09.007] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/28/2017] [Revised: 08/10/2017] [Accepted: 09/04/2017] [Indexed: 02/08/2023] Open
Abstract
Because they bridge the genetic gap between rodents and humans, non-human primates (NHPs) play a major role in therapy development and evaluation for neurological disorders. However, translational research success from NHPs to patients requires an accurate phenotyping of the models. In patients, magnetic resonance imaging (MRI) combined with automated segmentation methods has offered the unique opportunity to assess in vivo brain morphological changes. Meanwhile, specific challenges caused by brain size and high field contrasts make existing algorithms hard to use routinely in NHPs. To tackle this issue, we propose a complete pipeline, Primatologist, for multi-region segmentation. Tissue segmentation is based on a modular statistical model that includes random field regularization, bias correction and denoising and is optimized by expectation-maximization. To deal with the broad variety of structures with different relaxing times at 7 T, images are segmented into 17 anatomical classes, including subcortical regions. Pre-processing steps insure a good initialization of the parameters and thus the robustness of the pipeline. It is validated on 10 T2-weighted MRIs of healthy macaque brains. Classification scores are compared with those of a non-linear atlas registration, and the impact of each module on classification scores is thoroughly evaluated.
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Affiliation(s)
- Yaël Balbastre
- UMR9199, CNRS, CEA, Paris-Sud Univ., Univ. Paris-Saclay, Fontenay-aux-Roses, France; MIRCen, Institut de biologie François Jacob, DRF, CEA, Fontenay-aux-Roses, France; UNATI, NeuroSpin, Institut des sciences du vivant Frédéric Joliot, DRF, CEA, Univ. Paris-Saclay, Gif-sur-Yvette, France
| | - Denis Rivière
- UNATI, NeuroSpin, Institut des sciences du vivant Frédéric Joliot, DRF, CEA, Univ. Paris-Saclay, Gif-sur-Yvette, France; CATI Multicenter Neuroimaging Platform, France
| | - Nicolas Souedet
- UMR9199, CNRS, CEA, Paris-Sud Univ., Univ. Paris-Saclay, Fontenay-aux-Roses, France; MIRCen, Institut de biologie François Jacob, DRF, CEA, Fontenay-aux-Roses, France
| | - Clara Fischer
- UNATI, NeuroSpin, Institut des sciences du vivant Frédéric Joliot, DRF, CEA, Univ. Paris-Saclay, Gif-sur-Yvette, France; CATI Multicenter Neuroimaging Platform, France
| | - Anne-Sophie Hérard
- UMR9199, CNRS, CEA, Paris-Sud Univ., Univ. Paris-Saclay, Fontenay-aux-Roses, France; MIRCen, Institut de biologie François Jacob, DRF, CEA, Fontenay-aux-Roses, France
| | - Susannah Williams
- UMR9199, CNRS, CEA, Paris-Sud Univ., Univ. Paris-Saclay, Fontenay-aux-Roses, France; MIRCen, Institut de biologie François Jacob, DRF, CEA, Fontenay-aux-Roses, France
| | - Michel E Vandenberghe
- UMR9199, CNRS, CEA, Paris-Sud Univ., Univ. Paris-Saclay, Fontenay-aux-Roses, France; MIRCen, Institut de biologie François Jacob, DRF, CEA, Fontenay-aux-Roses, France
| | - Julien Flament
- MIRCen, Institut de biologie François Jacob, DRF, CEA, Fontenay-aux-Roses, France; US27, INSERM, Fontenay-aux-Roses, France
| | - Romina Aron-Badin
- UMR9199, CNRS, CEA, Paris-Sud Univ., Univ. Paris-Saclay, Fontenay-aux-Roses, France; MIRCen, Institut de biologie François Jacob, DRF, CEA, Fontenay-aux-Roses, France
| | - Philippe Hantraye
- UMR9199, CNRS, CEA, Paris-Sud Univ., Univ. Paris-Saclay, Fontenay-aux-Roses, France; MIRCen, Institut de biologie François Jacob, DRF, CEA, Fontenay-aux-Roses, France; US27, INSERM, Fontenay-aux-Roses, France
| | - Jean-François Mangin
- UNATI, NeuroSpin, Institut des sciences du vivant Frédéric Joliot, DRF, CEA, Univ. Paris-Saclay, Gif-sur-Yvette, France; CATI Multicenter Neuroimaging Platform, France
| | - Thierry Delzescaux
- UMR9199, CNRS, CEA, Paris-Sud Univ., Univ. Paris-Saclay, Fontenay-aux-Roses, France; MIRCen, Institut de biologie François Jacob, DRF, CEA, Fontenay-aux-Roses, France; Sorbonne Universités, Université Pierre and Marie Curie, Paris, France.
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Bauman MD, Schumann CM. Advances in nonhuman primate models of autism: Integrating neuroscience and behavior. Exp Neurol 2017; 299:252-265. [PMID: 28774750 DOI: 10.1016/j.expneurol.2017.07.021] [Citation(s) in RCA: 44] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2017] [Revised: 07/27/2017] [Accepted: 07/30/2017] [Indexed: 12/28/2022]
Abstract
Given the prevalence and societal impact of autism spectrum disorders (ASD), there is an urgent need to develop innovative preventative strategies and treatments to reduce the alarming number of cases and improve core symptoms for afflicted individuals. Translational efforts between clinical and preclinical research are needed to (i) identify and evaluate putative causes of ASD, (ii) determine the underlying neurobiological mechanisms, (iii) develop and test novel therapeutic approaches and (iv) ultimately translate basic research into safe and effective clinical practices. However, modeling a uniquely human brain disorder, such as ASD, will require sophisticated animal models that capitalize on unique advantages of diverse species including drosophila, zebra fish, mice, rats, and ultimately, species more closely related to humans, such as the nonhuman primate. Here we discuss the unique contributions of the rhesus monkey (Macaca mulatta) model to ongoing efforts to understand the neurobiology of the disorder, focusing on the convergence of brain and behavior outcome measures that parallel features of human ASD.
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Affiliation(s)
- M D Bauman
- The UC Davis MIND Institute, University of California, Davis, USA; Department of Psychiatry and Behavioral Sciences, University of California, Davis, USA; California National Primate Research Center, University of California, Davis, USA.
| | - C M Schumann
- The UC Davis MIND Institute, University of California, Davis, USA; Department of Psychiatry and Behavioral Sciences, University of California, Davis, USA
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38
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Seidlitz J, Sponheim C, Glen D, Ye FQ, Saleem KS, Leopold DA, Ungerleider L, Messinger A. A population MRI brain template and analysis tools for the macaque. Neuroimage 2017; 170:121-131. [PMID: 28461058 DOI: 10.1016/j.neuroimage.2017.04.063] [Citation(s) in RCA: 122] [Impact Index Per Article: 17.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/23/2017] [Revised: 03/24/2017] [Accepted: 04/26/2017] [Indexed: 12/28/2022] Open
Abstract
The use of standard anatomical templates is common in human neuroimaging, as it facilitates data analysis and comparison across subjects and studies. For non-human primates, previous in vivo templates have lacked sufficient contrast to reliably validate known anatomical brain regions and have not provided tools for automated single-subject processing. Here we present the "National Institute of Mental Health Macaque Template", or NMT for short. The NMT is a high-resolution in vivo MRI template of the average macaque brain generated from 31 subjects, as well as a neuroimaging tool for improved data analysis and visualization. From the NMT volume, we generated maps of tissue segmentation and cortical thickness. Surface reconstructions and transformations to previously published digital brain atlases are also provided. We further provide an analysis pipeline using the NMT that automates and standardizes the time-consuming processes of brain extraction, tissue segmentation, and morphometric feature estimation for anatomical scans of individual subjects. The NMT and associated tools thus provide a common platform for precise single-subject data analysis and for characterizations of neuroimaging results across subjects and studies.
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Affiliation(s)
- Jakob Seidlitz
- Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD 20892, USA; Developmental Neurogenomics Unit, National Institute of Mental Health, Bethesda, MD 20892, USA; Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge CB2 1TN, UK.
| | - Caleb Sponheim
- Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD 20892, USA
| | - Daniel Glen
- Scientific and Statistical Computing Core, National Institute of Mental Health, Bethesda, MD 20892, USA
| | - Frank Q Ye
- Neurophysiology Imaging Facility, National Institute of Mental Health, National Institute of Neurological Disorders and Stroke, National Eye Institute, Bethesda, MD 20892, USA
| | - Kadharbatcha S Saleem
- Laboratory of Neuropsychology, National Institute of Mental Health, Bethesda, MD 20892, USA
| | - David A Leopold
- Laboratory of Neuropsychology, National Institute of Mental Health, Bethesda, MD 20892, USA; Neurophysiology Imaging Facility, National Institute of Mental Health, National Institute of Neurological Disorders and Stroke, National Eye Institute, Bethesda, MD 20892, USA
| | - Leslie Ungerleider
- Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD 20892, USA
| | - Adam Messinger
- Laboratory of Brain and Cognition, National Institute of Mental Health, Bethesda, MD 20892, USA
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