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Lu H, Li J. MRI-informed machine learning-driven brain age models for classifying mild cognitive impairment converters. J Cent Nerv Syst Dis 2024; 16:11795735241266556. [PMID: 39049837 PMCID: PMC11268046 DOI: 10.1177/11795735241266556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Accepted: 06/02/2024] [Indexed: 07/27/2024] Open
Abstract
BACKGROUND Brain age model, including estimated brain age and brain-predicted age difference (brain-PAD), has shown great potentials for serving as imaging markers for monitoring normal ageing, as well as for identifying the individuals in the pre-diagnostic phase of neurodegenerative diseases. PURPOSE This study aimed to investigate the brain age models in normal ageing and mild cognitive impairments (MCI) converters and their values in classifying MCI conversion. METHODS Pre-trained brain age model was constructed using the structural magnetic resonance imaging (MRI) data from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) project (N = 609). The tested brain age model was built using the baseline, 1-year and 3-year follow-up MRI data from normal ageing (NA) adults (n = 32) and MCI converters (n = 22) drew from the Open Access Series of Imaging Studies (OASIS-2). The quantitative measures of morphometry included total intracranial volume (TIV), gray matter volume (GMV) and cortical thickness. Brain age models were calculated based on the individual's morphometric features using the support vector machine (SVM) algorithm. RESULTS With comparable chronological age, MCI converters showed significant increased TIV-based (Baseline: P = 0.021; 1-year follow-up: P = 0.037; 3-year follow-up: P = 0.001) and left GMV-based brain age than NA adults at all time points. Higher brain-PAD scores were associated with worse global cognition. Acceptable classification performance of TIV-based (AUC = 0.698) and left GMV-based brain age (AUC = 0.703) was found, which could differentiate the MCI converters from NA adults at the baseline. CONCLUSIONS This is the first demonstration that MRI-informed brain age models exhibit feature-specific patterns. The greater GMV-based brain age observed in MCI converters may provide new evidence for identifying the individuals at the early stage of neurodegeneration. Our findings added value to existing quantitative imaging markers and might help to improve disease monitoring and accelerate personalized treatments in clinical practice.
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Affiliation(s)
- Hanna Lu
- Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong, China
- Department of Neurology, The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Jing Li
- Department of Psychiatry, The Chinese University of Hong Kong, Hong Kong, China
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Huang Y, Wu Z, Li T, Wang X, Wang Y, Xing L, Zhu H, Lin W, Wang L, Guo L, Gilmore JH, Li G. Mapping Genetic Topography of Cortical Thickness and Surface Area in Neonatal Brains. J Neurosci 2023; 43:6010-6020. [PMID: 37369585 PMCID: PMC10451118 DOI: 10.1523/jneurosci.1841-22.2023] [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: 09/26/2022] [Revised: 06/05/2023] [Accepted: 06/10/2023] [Indexed: 06/29/2023] Open
Abstract
Adult twin neuroimaging studies have revealed that cortical thickness (CT) and surface area (SA) are differentially influenced by genetic information, leading to their spatially distinct genetic patterning and topography. However, the postnatal origins of the genetic topography of CT and SA remain unclear, given the dramatic cortical development from neonates to adults. To fill this critical gap, this study unprecedentedly explored how genetic information differentially regulates the spatial topography of CT and SA in the neonatal brain by leveraging brain magnetic resonance (MR) images from 202 twin neonates with minimal influence by the complicated postnatal environmental factors. We capitalized on infant-dedicated computational tools and a data-driven spectral clustering method to parcellate the cerebral cortex into a set of distinct regions purely according to the genetic correlation of cortical vertices in terms of CT and SA, respectively, and accordingly created the first genetically informed cortical parcellation maps of neonatal brains. Both genetic parcellation maps exhibit bilaterally symmetric and hierarchical patterns, but distinct spatial layouts. For CT, regions with closer genetic relationships demonstrate an anterior-posterior (A-P) division, while for SA, regions with greater genetic proximity are typically within the same lobe. Certain genetically informed regions exhibit strong similarities between neonates and adults, with the most striking similarities in the medial surface in terms of SA, despite their overall substantial differences in genetic parcellation maps. These results greatly advance our understanding of the development of genetic influences on the spatial patterning of cortical morphology.SIGNIFICANCE STATEMENT Genetic influences on cortical thickness (CT) and surface area (SA) are complex and could evolve throughout the lifespan. However, studies revealing distinct genetic topography of CT and SA have been limited to adults. Using brain structural magnetic resonance (MR) images of twins, we unprecedentedly discovered the distinct genetically-informed parcellation maps of CT and SA in neonatal brains, respectively. Each genetic parcellation map comprises a distinct spatial layout of cortical regions, where vertices within the same region share high genetic correlation. These genetic parcellation maps of CT and SA of neonates largely differ from those of adults, despite their highly remarkable similarities in the medial cortex of SA. These discoveries provide important insights into the genetic organization of the early cerebral cortex development.
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Affiliation(s)
- Ying Huang
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27514
| | - Zhengwang Wu
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27514
| | - Tengfei Li
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27514
| | - Xifeng Wang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27516
| | - Ya Wang
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27514
| | - Lei Xing
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27516
| | - Weili Lin
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27514
| | - Li Wang
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27514
| | - Lei Guo
- School of Automation, Northwestern Polytechnical University, Xi'an, Shaanxi 710129, China
| | - John H Gilmore
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27514
| | - Gang Li
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27514
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Wang F, Zhang H, Wu Z, Hu D, Zhou Z, Girault JB, Wang L, Lin W, Li G. Fine-grained functional parcellation maps of the infant cerebral cortex. eLife 2023; 12:e75401. [PMID: 37526293 PMCID: PMC10393291 DOI: 10.7554/elife.75401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2021] [Accepted: 07/17/2023] [Indexed: 08/02/2023] Open
Abstract
Resting-state functional MRI (rs-fMRI) is widely used to examine the dynamic brain functional development of infants, but these studies typically require precise cortical parcellation maps, which cannot be directly borrowed from adult-based functional parcellation maps due to the substantial differences in functional brain organization between infants and adults. Creating infant-specific cortical parcellation maps is thus highly desired but remains challenging due to difficulties in acquiring and processing infant brain MRIs. In this study, we leveraged 1064 high-resolution longitudinal rs-fMRIs from 197 typically developing infants and toddlers from birth to 24 months who participated in the Baby Connectome Project to develop the first set of infant-specific, fine-grained, surface-based cortical functional parcellation maps. To establish meaningful cortical functional correspondence across individuals, we performed cortical co-registration using both the cortical folding geometric features and the local gradient of functional connectivity (FC). Then we generated both age-related and age-independent cortical parcellation maps with over 800 fine-grained parcels during infancy based on aligned and averaged local gradient maps of FC across individuals. These parcellation maps reveal complex functional developmental patterns, such as changes in local gradient, network size, and local efficiency, especially during the first 9 postnatal months. Our generated fine-grained infant cortical functional parcellation maps are publicly available at https://www.nitrc.org/projects/infantsurfatlas/ for advancing the pediatric neuroimaging field.
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Affiliation(s)
- Fan Wang
- Key Laboratory of Biomedical Information Engineering of Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong UniversityXi'anChina
- Department of Radiology and Biomedical Research Imaging Center, the University of North Carolina at Chapel HillChapel HillUnited States
| | - Han Zhang
- Department of Radiology and Biomedical Research Imaging Center, the University of North Carolina at Chapel HillChapel HillUnited States
| | - Zhengwang Wu
- Department of Radiology and Biomedical Research Imaging Center, the University of North Carolina at Chapel HillChapel HillUnited States
| | - Dan Hu
- Department of Radiology and Biomedical Research Imaging Center, the University of North Carolina at Chapel HillChapel HillUnited States
| | - Zhen Zhou
- Department of Radiology and Biomedical Research Imaging Center, the University of North Carolina at Chapel HillChapel HillUnited States
| | - Jessica B Girault
- Department of Psychiatry, the University of North Carolina at Chapel HillChapel HillUnited States
| | - Li Wang
- Department of Radiology and Biomedical Research Imaging Center, the University of North Carolina at Chapel HillChapel HillUnited States
| | - Weili Lin
- Department of Radiology and Biomedical Research Imaging Center, the University of North Carolina at Chapel HillChapel HillUnited States
| | - Gang Li
- Department of Radiology and Biomedical Research Imaging Center, the University of North Carolina at Chapel HillChapel HillUnited States
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Lu H, Li J, Chan SSM, Yue WWY, Lam LCW. Decoding the radiomic features of dorsolateral prefrontal cortex in individuals with accelerated cortical changes: implications for personalized transcranial magnetic stimulation. J Med Imaging (Bellingham) 2023; 10:015001. [PMID: 36619873 PMCID: PMC9811135 DOI: 10.1117/1.jmi.10.1.015001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2022] [Accepted: 12/20/2022] [Indexed: 01/05/2023] Open
Abstract
Purpose Image-guided transcranial magnetic stimulation (TMS) is an emerging research field in neuroscience and rehabilitation medicine. Cortical morphometry, as a radiomic phenotype of aging, plays a vital role in developing personalized TMS model, yet few studies are afoot to examine the aging effects on region-specific morphometry and use it in the estimation of TMS-induced electric fields. Our study was aimed to investigate the radiomic features of bilateral dorsolateral prefrontal cortex (DLPFC) and quantify the TMS-induced electric fields during aging. Approach Baseline, 1-year and 3-year structural magnetic resonance imaging (MRI) scans from normal aging (NA) adults ( n = 32 ) and mild cognitive impairment (MCI) converters ( n = 22 ) were drawn from the Open Access Series of Imaging Studies. The quantitative measures of radiomics included cortical thickness, folding, and scalp-to-cortex distance. Realistic head models were developed to simulate the impacts of radiomic features on TMS-induced E-fields using the finite-element method. Results A pronounced aging-related decrease was found in the gyrification of left DLPFC in MCI converters ( t = 2.21 , p = 0.035 ), which could predict the decline of global cognition at 3-year follow up. Along with the decreased gyrification in left DLPFC, the magnitude of TMS-induced E-fields was rapidly decreased in MCI converters ( t = 2.56 , p = 0.018 ). Conclusions MRI-informed radiomic features of the treatment targets have significant effects on the intensity and distribution of the stimulation-induced electric fields in prodromal dementia patients. Our findings highlight the importance of region-specific radiomics when conducting the transcranial brain stimulation in individuals with accelerated cortical changes, such as Alzheimer's disease.
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Affiliation(s)
- Hanna Lu
- The Chinese University of Hong Kong, Department of Psychiatry, Hong Kong, China
- The Affiliated Brain Hospital of Guangzhou Medical University, Centre for Neuromodulation and Rehabilitation, Guangzhou, China
- The Affiliated Brain Hospital of Guangzhou Medical University, Guangzhou, China
| | - Jing Li
- The Chinese University of Hong Kong, Department of Psychiatry, Hong Kong, China
| | - Sandra Sau Man Chan
- The Chinese University of Hong Kong, Department of Psychiatry, Hong Kong, China
| | | | - Linda Chiu Wa Lam
- The Chinese University of Hong Kong, Department of Psychiatry, Hong Kong, China
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Alex AM, Ruvio T, Xia K, Jha SC, Girault JB, Wang L, Li G, Shen D, Cornea E, Styner MA, Gilmore JH, Knickmeyer RC. Influence of gonadal steroids on cortical surface area in infancy. Cereb Cortex 2022; 32:3206-3223. [PMID: 34952542 PMCID: PMC9340392 DOI: 10.1093/cercor/bhab410] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2021] [Revised: 09/13/2021] [Accepted: 09/14/2021] [Indexed: 12/27/2022] Open
Abstract
Sex differences in the human brain emerge as early as mid-gestation and have been linked to sex hormones, particularly testosterone. Here, we analyzed the influence of markers of early sex hormone exposure (polygenic risk score (PRS) for testosterone, salivary testosterone, number of CAG repeats, digit ratios, and PRS for estradiol) on the growth pattern of cortical surface area in a longitudinal cohort of 722 infants. We found PRS for testosterone and right-hand digit ratio to be significantly associated with surface area, but only in females. PRS for testosterone at the most stringent P value threshold was positively associated with surface area development over time. Higher right-hand digit ratio, which is indicative of low prenatal testosterone levels, was negatively related to surface area in females. The current work suggests that variation in testosterone levels during both the prenatal and postnatal period may contribute to cortical surface area development in female infants.
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Affiliation(s)
- Ann Mary Alex
- Neuroengineering Division, Institute for Quantitative Health Sciences and Engineering, Michigan State University, East Lansing, MI 48824, USA
| | - Tom Ruvio
- Neuroengineering Division, Institute for Quantitative Health Sciences and Engineering, Michigan State University, East Lansing, MI 48824, USA
| | - Kai Xia
- Department of Psychiatry, University of North Carolina Chapel Hill, Chapel Hill, NC 27599, USA
| | - Shaili C Jha
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
| | - Jessica B Girault
- Department of Psychiatry, University of North Carolina Chapel Hill, Chapel Hill, NC 27599, USA
- Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Li Wang
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Gang Li
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Dinggang Shen
- School of Biomedical Engineering, ShanghaiTech University, Shanghai 201210, China
- Department of Artificial Intelligence, Korea University, Seoul 02841, Republic of Korea
| | - Emil Cornea
- Department of Psychiatry, University of North Carolina Chapel Hill, Chapel Hill, NC 27599, USA
| | - Martin A Styner
- Department of Psychiatry, University of North Carolina Chapel Hill, Chapel Hill, NC 27599, USA
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - John H Gilmore
- Department of Psychiatry, University of North Carolina Chapel Hill, Chapel Hill, NC 27599, USA
| | - Rebecca C Knickmeyer
- Address correspondence to Rebecca C. Knickmeyer, Institute for Quantitative Health Science and Engineering, 775 Woodlot Dr, East Lansing, MI 48824, USA.
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6
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Cheng J, Zhang X, Ni H, Li C, Xu X, Wu Z, Wang L, Lin W, Li G. Path Signature Neural Network of Cortical Features for Prediction of Infant Cognitive Scores. IEEE TRANSACTIONS ON MEDICAL IMAGING 2022; 41:1665-1676. [PMID: 35089858 PMCID: PMC9246848 DOI: 10.1109/tmi.2022.3147690] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Studies have shown that there is a tight connection between cognition skills and brain morphology during infancy. Nonetheless, it is still a great challenge to predict individual cognitive scores using their brain morphological features, considering issues like the excessive feature dimension, small sample size and missing data. Due to the limited data, a compact but expressive feature set is desirable as it can reduce the dimension and avoid the potential overfitting issue. Therefore, we pioneer the path signature method to further explore the essential hidden dynamic patterns of longitudinal cortical features. To form a hierarchical and more informative temporal representation, in this work, a novel cortical feature based path signature neural network (CF-PSNet) is proposed with stacked differentiable temporal path signature layers for prediction of individual cognitive scores. By introducing the existence embedding in path generation, we can improve the robustness against the missing data. Benefiting from the global temporal receptive field of CF-PSNet, characteristics consisted in the existing data can be fully leveraged. Further, as there is no need for the whole brain to work for a certain cognitive ability, a top K selection module is used to select the most influential brain regions, decreasing the model size and the risk of overfitting. Extensive experiments are conducted on an in-house longitudinal infant dataset within 9 time points. By comparing with several recent algorithms, we illustrate the state-of-the-art performance of our CF-PSNet (i.e., root mean square error of 0.027 with the time latency of 518 milliseconds for each sample).
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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] [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 Automation, Northwestern Polytechnical University, Xi'an, China
| | - Poorya Chavoshnejad
- Department of Mechanical Engineering, State University of New York at Binghamton, New York, USA
| | - Xiao Li
- School of Information Technology, Northwest University, Xi'an, China
| | - Lei Guo
- 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
| | - Junwei Han
- School of Automation, Northwestern Polytechnical University, Xi'an, China
| | - Li Wang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Gang Li
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, USA
| | - Xianqiao Wang
- College of Engineering, The University of Georgia, Athens, Georgia, USA
| | - Tianming Liu
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, Georgia, USA
| | - Mir Jalil Razavi
- Department of Mechanical Engineering, State University of New York at Binghamton, New York, USA
| | - Shu Zhang
- Center for Brain and Brain-Inspired Computing Research, Department of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Tuo Zhang
- School of Automation, Northwestern Polytechnical University, Xi'an, China
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Wei J, Wu Z, Wang L, Bui TD, Qu L, Yap PT, Xia Y, Li G, Shen D. A cascaded nested network for 3T brain MR image segmentation guided by 7T labeling. PATTERN RECOGNITION 2022; 124:108420. [PMID: 38469076 PMCID: PMC10927017 DOI: 10.1016/j.patcog.2021.108420] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/13/2024]
Abstract
Accurate segmentation of the brain into gray matter, white matter, and cerebrospinal fluid using magnetic resonance (MR) imaging is critical for visualization and quantification of brain anatomy. Compared to 3T MR images, 7T MR images exhibit higher tissue contrast that is contributive to accurate tissue delineation for training segmentation models. In this paper, we propose a cascaded nested network (CaNes-Net) for segmentation of 3T brain MR images, trained by tissue labels delineated from the corresponding 7T images. We first train a nested network (Nes-Net) for a rough segmentation. The second Nes-Net uses tissue-specific geodesic distance maps as contextual information to refine the segmentation. This process is iterated to build CaNes-Net with a cascade of Nes-Net modules to gradually refine the segmentation. To alleviate the misalignment between 3T and corresponding 7T MR images, we incorporate a correlation coefficient map to allow well-aligned voxels to play a more important role in supervising the training process. We compared CaNes-Net with SPM and FSL tools, as well as four deep learning models on 18 adult subjects and the ADNI dataset. Our results indicate that CaNes-Net reduces segmentation errors caused by the misalignment and improves segmentation accuracy substantially over the competing methods.
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Affiliation(s)
- Jie Wei
- National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an 710072, China
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Zhengwang Wu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Li Wang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Toan Duc Bui
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Liangqiong Qu
- Department of Biomedical Data Science at Stanford University, Stanford, CA 94305, USA
| | - Pew-Thian Yap
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Yong Xia
- National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology, School of Computer Science and Engineering, Northwestern Polytechnical University, Xi’an 710072, China
| | - Gang Li
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Dinggang Shen
- School of Biomedical Engineering, ShanghaiTech University, Shanghai 201210, China
- Shanghai United Imaging Intelligence Co., Ltd., Shanghai 200232, China
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Wang Y, Hu D, Wu Z, Wang L, Huang W, Li G. Developmental abnormalities of structural covariance networks of cortical thickness and surface area in autistic infants within the first 2 years. Cereb Cortex 2022; 32:3786-3798. [PMID: 35034115 PMCID: PMC9433424 DOI: 10.1093/cercor/bhab448] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2021] [Revised: 10/18/2021] [Accepted: 11/01/2021] [Indexed: 01/19/2023] Open
Abstract
Converging evidence supports that a collection of brain regions is functionally or anatomically abnormal in autistic subjects. Structural covariance networks (SCNs) representing patterns of coordinated regional maturation are widely used to study abnormalities associated with neurodisorders. However, the possible developmental changes of SCNs in autistic individuals during the first 2 postnatal years, which features dynamic development and can potentially serve as biomarkers, remain unexplored. To fill this gap, for the first time, SCNs of cortical thickness and surface area were constructed and investigated in infants at high familial risk for autism and typically developing infants in this study. Group differences of SCNs emerge at 12 months of age in surface area. By 24 months of age, the autism group shows significantly increased integration, decreased segregation, and decreased small-worldness, compared with controls. The SCNs of surface area are deteriorated and shifted toward randomness in autistic infants. The abnormal brain regions changed during development, and the group differences of the left lateral occipital cortex become more prominent with age. These results indicate that autism has more significant influences on coordinated development of surface area than that of cortical thickness and the occipital cortex maybe an important biomarker of autism during infancy.
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Affiliation(s)
- Ya Wang
- National Key Discipline of Human Anatomy, School of Basic Medical Sciences, Southern Medical University, Guangzhou 510515, China,Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Dan Hu
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Zhengwang Wu
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Li Wang
- Department of Radiology and Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Wenhua Huang
- Address correspondence to Wenhua Huang, National Key Discipline of Human Anatomy, School of Basic Medical Sciences, 11th floor, Southern Medical University, Guangzhou 510515, China. ; Gang Li, The University of North Carolina at Chapel Hill, Bioinformatics Building #3104, Chapel Hill, NC 27599.
| | - Gang Li
- Address correspondence to Wenhua Huang, National Key Discipline of Human Anatomy, School of Basic Medical Sciences, 11th floor, Southern Medical University, Guangzhou 510515, China. ; Gang Li, The University of North Carolina at Chapel Hill, Bioinformatics Building #3104, Chapel Hill, NC 27599.
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Xia K, Schmitt JE, Jha SC, Girault JB, Cornea E, Li G, Shen D, Styner M, Gilmore JH. Genetic Influences on Longitudinal Trajectories of Cortical Thickness and Surface Area during the First 2 Years of Life. Cereb Cortex 2022; 32:367-379. [PMID: 34231837 PMCID: PMC8897991 DOI: 10.1093/cercor/bhab213] [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: 01/15/2021] [Revised: 06/09/2021] [Accepted: 06/09/2021] [Indexed: 11/14/2022] Open
Abstract
Genetic influences on cortical thickness (CT) and surface area (SA) are known to vary across the life span. Little is known about the extent to which genetic factors influence CT and SA in infancy and toddlerhood. We performed the first longitudinal assessment of genetic influences on variation in CT and SA in 501 twins who were aged 0-2 years. We observed substantial additive genetic influences on both average CT (0.48 in neonates, 0.37 in 1-year-olds, and 0.44 in 2-year-olds) and total SA (0.59 in neonates, 0.74 in 1-year-olds, and 0.73 in 2-year-olds). In addition, we found strong heritability of the change in average CT (0.49) from neonates to 1-year-olds, but not from 1- to 2-year-olds. Moreover, we found strong genetic correlations for average CT (rG = 0.92) between 1- and 2-year-olds and strong genetic correlations for total SA across all timepoints (rG = 0.96 between neonates and 1-year-olds, rG = 1 between 1- and 2-year-olds). In addition, we found CT and SA are strongly genetic correlated at birth, but weaken over time. Overall, results suggest a dynamic genetic relationship between CT and SA during first 2 years of life and provide novel insights into how genetic influences shape the cortical structure during early brain development.
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Affiliation(s)
- Kai Xia
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC 27599-7160, USA
| | - J Eric Schmitt
- Brain Behavior Laboratory, Department of Psychiatry, Neuropsychiatry Section, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Shaili C Jha
- Department of Epidemiology, Harvard T. H. Chan School of Public Health, Boston, MA 02115, USA
| | - Jessica B Girault
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC 27599-7160, USA
| | - Emil Cornea
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC 27599-7160, USA
| | - Gang Li
- Department of Radiology, University of North Carolina, Chapel Hill, NC 27599-7320, USA
| | - Dinggang Shen
- Department of Radiology, University of North Carolina, Chapel Hill, NC 27599-7320, USA
| | - Martin Styner
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC 27599-7160, USA
| | - John H Gilmore
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC 27599-7160, USA
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11
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Peng B, Gong Z, Zhang Y, Shen B, Pang C, Zhang L, Dai Y. Self-paced learning and privileged information based KRR classification algorithm for diagnosis of Parkinson's disease. Neurosci Lett 2022; 766:136312. [PMID: 34757107 DOI: 10.1016/j.neulet.2021.136312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Revised: 10/14/2021] [Accepted: 10/24/2021] [Indexed: 10/20/2022]
Abstract
Computer aided diagnosis (CAD) methods for Parkinson's disease (PD) can assist clinicians in diagnosis and treatment. Magnetic resonance imaging (MRI) based CAD methods can help reveal structural changes in brain. Classifier is a key component in CAD system, which directly affects the classification performance. Privileged information (PI) can assist to train the classifier by providing additional information, which makes test samples have less error and improves the classification accuracy. In this paper, we proposed a PI based kernel ridge regression plus (KRR+) in diagnosis of PD. Specifically, morphometric features and brain network features are extracted from MRI. Then, empirical kernel mapping feature expression method is used to make the data separable in high-dimensional space. Besides, we introduce self-paced learning that can adaptively select the sample in training of the model, which can further improve the classification performance. The experimental results show that the proposed method is effective for PD diagnosis, its performance is superior to existing classification model. This method is helpful to assist clinicians to find out possible neuroimaging biomarkers in the diagnosis of PD.
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Affiliation(s)
- Bo Peng
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Science, Suzhou 215163, China; Jinan Guoke Medical Engineering Technology Development co., LTD, Jinan 250000, China
| | - Zhenjia Gong
- School of Life Science and Technology, Changchun University of Science and Technology, Changchun 130000, China
| | - Yu Zhang
- School of Life Science and Technology, Changchun University of Science and Technology, Changchun 130000, China
| | - Bo Shen
- Nanjing Medical University and Nanjing Brain Hospital, Nanjing 210029, China
| | - Chunying Pang
- School of Life Science and Technology, Changchun University of Science and Technology, Changchun 130000, China
| | - Li Zhang
- Nanjing Medical University and Nanjing Brain Hospital, Nanjing 210029, China.
| | - Yakang Dai
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Science, Suzhou 215163, China; Jinan Guoke Medical Engineering Technology Development co., LTD, Jinan 250000, China.
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12
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Hong Y, Ahmad S, Wu Y, Liu S, Yap PT. Vox2Surf: Implicit Surface Reconstruction from Volumetric Data. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2021; 12966:644-653. [PMID: 36222819 PMCID: PMC9542254 DOI: 10.1007/978-3-030-87589-3_66] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/13/2023]
Abstract
Surface reconstruction from volumetric T1-weighted and T2-weighted images is a time-consuming multi-step process that often involves careful parameter fine-tuning, hindering a more wide-spread utilization of surface-based analysis particularly in large-scale studies. In this work, we propose a fast surface reconstruction method that is based on directly learning a continuous-valued signed distance function (SDF) as implicit surface representation. This continuous representation implicitly encodes the boundary of the surface as the zero isosurface. Given the predicted SDF, the target 3D surface is reconstructed by applying the marching cubes algorithm. Our implicit reconstruction method concurrently predicts the surfaces of the brain parenchyma, the white matter and pial surfaces, the subcortical structures, and the ventricles. Evaluation based on data from the Human Connectome Project indicates that surface reconstruction of a total of 22 cortical and subcortical structures can be completed in less than 20 min.
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Affiliation(s)
- Yoonmi Hong
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, NC, USA
| | - Sahar Ahmad
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, NC, USA
| | - Ye Wu
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, NC, USA
| | - Siyuan Liu
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, NC, USA
| | - Pew-Thian Yap
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, NC, USA
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13
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Peng B, Yu X, Ma X, Xue Z, Wang J, Cai Z, Pang C, Zhu J, Dai Y. Improving MRI-based analysis of brain structural changes in patients with hypertension via a privileged information learning algorithm. Methods 2021; 202:103-109. [PMID: 34252532 DOI: 10.1016/j.ymeth.2021.07.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Revised: 07/03/2021] [Accepted: 07/07/2021] [Indexed: 11/16/2022] Open
Abstract
Hypertension can lead to changes in the brain structure and function, and different blood pressure levels (2017ACC/AHA) have different effects on brain structure. It is important to analyze these changes by machine learning methods, and various characteristics can provide rich information for the analysis of these changes. However, multiple feature extraction involves complex data processing. How to make a single feature achieve the same diagnosis effect as multiple features do is worth of study. Kernel ridge regression (KRR) is a kind of machine learning method, which shows faster learning speed and generalization ability in classification tasks. In order to knowledge transfer, we use privileged information (PI) to transfer information of multiple types of feature to single feature. This allows only one feature type to be used during the test stage. In the process of feature fusion, we need to consider all the samples' attribution making the classifier better. In this work, we propose a multi-kernel KRR+ framework based on self-paced learning to analyze the changes of the brain structure in patients with different blood pressure levels. Specifically, one kind of a feature is taken as main feature, and other features are input into the multi-kernel KRR as PI. These two inputs are fed into the final KRR classifier together. In addition, a self-paced learning method is introduced into sample selecting to avoid training the classifier using samples with a large loss value firstly, which improves the generalization performance of the classifier. Experimental results show that the proposed method can make full use of the information of various features and achieve better classification performance. This shows self-paced learning based KRR can help analyze brain structure of patients with different blood pressure levels. The discriminative features may help clinicians to make judgments of hypertension degrees on brain MRI images.
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Affiliation(s)
- Bo Peng
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China; Suzhou Key Laboratory of Medical and Health Information Technology, Suzhou, China; Jinan Guoke Medical Engineering Technology Development Co., LTD, Jinan, China
| | - Xinying Yu
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| | - Xinwei Ma
- The Affiliated Suzhou Science & Technology Town Hospital of Nanjing Medical University, Suzhou, Jiangsu, China; Suzhou Science & Technology Town Hospital, Suzhou, Jiangsu, China
| | - Zeyu Xue
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China
| | - Jingyu Wang
- World Leading School Association Academy, Shanghai, China
| | - Zenglin Cai
- The Affiliated Suzhou Science & Technology Town Hospital of Nanjing Medical University, Suzhou, Jiangsu, China; Suzhou Science & Technology Town Hospital, Suzhou, Jiangsu, China
| | - Chunying Pang
- School of Life Science and Technology, Changchun University of Science and Technology, Changchun, China
| | - Jianbing Zhu
- The Affiliated Suzhou Science & Technology Town Hospital of Nanjing Medical University, Suzhou, Jiangsu, China; Suzhou Science & Technology Town Hospital, Suzhou, Jiangsu, China.
| | - Yakang Dai
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, China; Suzhou Key Laboratory of Medical and Health Information Technology, Suzhou, China; Jinan Guoke Medical Engineering Technology Development Co., LTD, Jinan, China.
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14
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Girault JB, Cornea E, Goldman BD, Jha SC, Murphy VA, Li G, Wang L, Shen D, Knickmeyer RC, Styner M, Gilmore JH. Cortical Structure and Cognition in Infants and Toddlers. Cereb Cortex 2021; 30:786-800. [PMID: 31365070 DOI: 10.1093/cercor/bhz126] [Citation(s) in RCA: 25] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2018] [Revised: 05/21/2019] [Accepted: 05/22/2019] [Indexed: 12/21/2022] Open
Abstract
Cortical structure has been consistently related to cognitive abilities in children and adults, yet we know little about how the cortex develops to support emergent cognition in infancy and toddlerhood when cortical thickness (CT) and surface area (SA) are maturing rapidly. In this report, we assessed how regional and global measures of CT and SA in a sample (N = 487) of healthy neonates, 1-year-olds, and 2-year-olds related to motor, language, visual reception, and general cognitive ability. We report novel findings that thicker cortices at ages 1 and 2 and larger SA at birth, age 1, and age 2 confer a cognitive advantage in infancy and toddlerhood. While several expected brain-cognition relationships were observed, overlapping cortical regions were also implicated across cognitive domains, suggesting that infancy marks a period of plasticity and refinement in cortical structure to support burgeoning motor, language, and cognitive abilities. CT may be a particularly important morphological indicator of ability, but its impact on cognition is relatively weak when compared with gestational age and maternal education. Findings suggest that prenatal and early postnatal cortical developments are important for cognition in infants and toddlers but should be considered in relation to other child and demographic factors.
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Affiliation(s)
- Jessica B Girault
- Carolina Institute for Developmental Disabilities, University of North Carolina, Chapel Hill, NC, USA
| | - Emil Cornea
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA
| | - Barbara D Goldman
- Department of Psychology & Neuroscience and FPG Child Development Institute, University of North Carolina, Chapel Hill, NC, USA
| | - Shaili C Jha
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Veronica A Murphy
- Neuroscience Curriculum, University of North Carolina, Chapel Hill, NC, USA
| | - Gang Li
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Li Wang
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Dinggang Shen
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Rebecca C Knickmeyer
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA.,Department of Pediatrics and Human Development, Institute for Quantitative Health Sciences and Engineering, Michigan State University, East Lansing, Michigan, USA
| | - Martin Styner
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA.,Department of Computer Science, University of North Carolina, Chapel Hill, NC, USA
| | - John H Gilmore
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA
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15
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Woodburn M, Bricken CL, Wu Z, Li G, Wang L, Lin W, Sheridan MA, Cohen JR. The maturation and cognitive relevance of structural brain network organization from early infancy to childhood. Neuroimage 2021; 238:118232. [PMID: 34091033 PMCID: PMC8372198 DOI: 10.1016/j.neuroimage.2021.118232] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2020] [Revised: 04/30/2021] [Accepted: 06/01/2021] [Indexed: 01/14/2023] Open
Abstract
The interactions of brain regions with other regions at the network level likely provide the infrastructure necessary for cognitive processes to develop. Specifically, it has been theorized that in infancy brain networks become more modular, or segregated, to support early cognitive specialization, before integration across networks increases to support the emergence of higher-order cognition. The present study examined the maturation of structural covariance networks (SCNs) derived from longitudinal cortical thickness data collected between infancy and childhood (0–6 years). We assessed modularity as a measure of network segregation and global efficiency as a measure of network integration. At the group level, we observed trajectories of increasing modularity and decreasing global efficiency between early infancy and six years. We further examined subject-based maturational coupling networks (sbMCNs) in a subset of this cohort with cognitive outcome data at 8–10 years, which allowed us to relate the network organization of longitudinal cortical thickness maturation to cognitive outcomes in middle childhood. We found that lower global efficiency of sbMCNs throughout early development (across the first year) related to greater motor learning at 8–10 years. Together, these results provide novel evidence characterizing the maturation of brain network segregation and integration across the first six years of life, and suggest that specific trajectories of brain network maturation contribute to later cognitive outcomes.
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Affiliation(s)
- Mackenzie Woodburn
- Department of Psychology & Neuroscience, University of North Carolina, Chapel Hill, United States.
| | - Cheyenne L Bricken
- Department of Psychology & Neuroscience, University of North Carolina, Chapel Hill, United States
| | - Zhengwang Wu
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, United States; Department of Radiology, University of North Carolina, Chapel Hill, United States
| | - Gang Li
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, United States; Department of Radiology, University of North Carolina, Chapel Hill, United States
| | - Li Wang
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, United States; Department of Radiology, University of North Carolina, Chapel Hill, United States
| | - Weili Lin
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, United States; Department of Radiology, University of North Carolina, Chapel Hill, United States
| | - Margaret A Sheridan
- Department of Psychology & Neuroscience, University of North Carolina, Chapel Hill, United States; Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, United States; Carolina Institute of Developmental Disabilities, University of North Carolina, Chapel Hill, United States
| | - Jessica R Cohen
- Department of Psychology & Neuroscience, University of North Carolina, Chapel Hill, United States; Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, United States; Carolina Institute of Developmental Disabilities, University of North Carolina, Chapel Hill, United States
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16
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Liu M, Lepage C, Kim SY, Jeon S, Kim SH, Simon JP, Tanaka N, Yuan S, Islam T, Peng B, Arutyunyan K, Surento W, Kim J, Jahanshad N, Styner MA, Toga AW, Barkovich AJ, Xu D, Evans AC, Kim H. Robust Cortical Thickness Morphometry of Neonatal Brain and Systematic Evaluation Using Multi-Site MRI Datasets. Front Neurosci 2021; 15:650082. [PMID: 33815050 PMCID: PMC8010150 DOI: 10.3389/fnins.2021.650082] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/06/2021] [Accepted: 02/17/2021] [Indexed: 11/13/2022] Open
Abstract
The human brain grows the most dramatically during the perinatal and early post-natal periods, during which pre-term birth or perinatal injury that may alter brain structure and lead to developmental anomalies. Thus, characterizing cortical thickness of developing brains remains an important goal. However, this task is often complicated by inaccurate cortical surface extraction due to small-size brains. Here, we propose a novel complex framework for the reconstruction of neonatal WM and pial surfaces, accounting for large partial volumes due to small-size brains. The proposed approach relies only on T1-weighted images unlike previous T2-weighted image-based approaches while only T1-weighted images are sometimes available under the different clinical/research setting. Deep neural networks are first introduced to the neonatal magnetic resonance imaging (MRI) pipeline to address the mis-segmentation of brain tissues. Furthermore, this pipeline enhances cortical boundary delineation using combined models of the cerebrospinal fluid (CSF)/GM boundary detection with edge gradient information and a new skeletonization of sulcal folding where no CSF voxels are seen due to the limited resolution. We also proposed a systematic evaluation using three independent datasets comprising 736 pre-term and 97 term neonates. Qualitative assessment for reconstructed cortical surfaces shows that 86.9% are rated as accurate across the three site datasets. In addition, our landmark-based evaluation shows that the mean displacement of the cortical surfaces from the true boundaries was less than a voxel size (0.532 ± 0.035 mm). Evaluating the proposed pipeline (namely NEOCIVET 2.0) shows the robustness and reproducibility across different sites and different age-groups. The mean cortical thickness measured positively correlated with post-menstrual age (PMA) at scan (p < 0.0001); Cingulate cortical areas grew the most rapidly whereas the inferior temporal cortex grew the least rapidly. The range of the cortical thickness measured was biologically congruent (1.3 mm at 28 weeks of PMA to 1.8 mm at term equivalent). Cortical thickness measured on T1 MRI using NEOCIVET 2.0 was compared with that on T2 using the established dHCP pipeline. It was difficult to conclude that either T1 or T2 imaging is more ideal to construct cortical surfaces. NEOCIVET 2.0 has been open to the public through CBRAIN (https://mcin-cnim.ca/technology/cbrain/), a web-based platform for processing brain imaging data.
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Affiliation(s)
- Mengting Liu
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Claude Lepage
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Sharon Y Kim
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Seun Jeon
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Sun Hyung Kim
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Julia Pia Simon
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Nina Tanaka
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Shiyu Yuan
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Tasfiya Islam
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Bailin Peng
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Knarik Arutyunyan
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Wesley Surento
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Justin Kim
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Neda Jahanshad
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Martin A Styner
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States.,Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Arthur W Toga
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
| | - Anthony James Barkovich
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Duan Xu
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States
| | - Alan C Evans
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Hosung Kim
- Department of Neurology, USC Stevens Neuroimaging and Informatics Institute, Keck School of Medicine, University of Southern California, Los Angeles, CA, United States
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17
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Tustison NJ, Holbrook AJ, Avants BB, Roberts JM, Cook PA, Reagh ZM, Duda JT, Stone JR, Gillen DL, Yassa MA. Longitudinal Mapping of Cortical Thickness Measurements: An Alzheimer's Disease Neuroimaging Initiative-Based Evaluation Study. J Alzheimers Dis 2020; 71:165-183. [PMID: 31356207 DOI: 10.3233/jad-190283] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
Longitudinal studies of development and disease in the human brain have motivated the acquisition of large neuroimaging data sets and the concomitant development of robust methodological and statistical tools for quantifying neurostructural changes. Longitudinal-specific strategies for acquisition and processing have potentially significant benefits including more consistent estimates of intra-subject measurements while retaining predictive power. Using the first phase of the Alzheimer's Disease Neuroimaging Initiative (ADNI-1) data, comprising over 600 subjects with multiple time points from baseline to 36 months, we evaluate the utility of longitudinal FreeSurfer and Advanced Normalization Tools (ANTs) surrogate thickness values in the context of a linear mixed-effects (LME) modeling strategy. Specifically, we estimate the residual variability and between-subject variability associated with each processing stream as it is known from the statistical literature that minimizing the former while simultaneously maximizing the latter leads to greater scientific interpretability in terms of tighter confidence intervals in calculated mean trends, smaller prediction intervals, and narrower confidence intervals for determining cross-sectional effects. This strategy is evaluated over the entire cortex, as defined by the Desikan-Killiany-Tourville labeling protocol, where comparisons are made with the cross-sectional and longitudinal FreeSurfer processing streams. Subsequent linear mixed effects modeling for identifying diagnostic groupings within the ADNI cohort is provided as supporting evidence for the utility of the proposed ANTs longitudinal framework which provides unbiased structural neuroimage processing and competitive to superior power for longitudinal structural change detection.
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Affiliation(s)
- Nicholas J Tustison
- Department of Radiology & Medical Imaging, University of Virginia, Charlottesville, VA, USA.,Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA, USA
| | | | - Brian B Avants
- Department of Radiology & Medical Imaging, University of Virginia, Charlottesville, VA, USA
| | - Jared M Roberts
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA, USA
| | - Philip A Cook
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - Zachariah M Reagh
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA, USA
| | - Jeffrey T Duda
- Department of Radiology, University of Pennsylvania, Philadelphia, PA, USA
| | - James R Stone
- Department of Radiology & Medical Imaging, University of Virginia, Charlottesville, VA, USA
| | - Daniel L Gillen
- Department of Statistics, University of California, Irvine, CA, USA
| | - Michael A Yassa
- Department of Neurobiology and Behavior, University of California, Irvine, Irvine, CA, USA
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18
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Zöllei L, Iglesias JE, Ou Y, Grant PE, Fischl B. Infant FreeSurfer: An automated segmentation and surface extraction pipeline for T1-weighted neuroimaging data of infants 0-2 years. Neuroimage 2020; 218:116946. [PMID: 32442637 PMCID: PMC7415702 DOI: 10.1016/j.neuroimage.2020.116946] [Citation(s) in RCA: 73] [Impact Index Per Article: 18.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2019] [Revised: 03/03/2020] [Accepted: 05/12/2020] [Indexed: 01/23/2023] Open
Abstract
The development of automated tools for brain morphometric analysis in infants has lagged significantly behind analogous tools for adults. This gap reflects the greater challenges in this domain due to: 1) a smaller-scaled region of interest, 2) increased motion corruption, 3) regional changes in geometry due to heterochronous growth, and 4) regional variations in contrast properties corresponding to ongoing myelination and other maturation processes. Nevertheless, there is a great need for automated image-processing tools to quantify differences between infant groups and other individuals, because aberrant cortical morphologic measurements (including volume, thickness, surface area, and curvature) have been associated with neuropsychiatric, neurologic, and developmental disorders in children. In this paper we present an automated segmentation and surface extraction pipeline designed to accommodate clinical MRI studies of infant brains in a population 0-2 year-olds. The algorithm relies on a single channel of T1-weighted MR images to achieve automated segmentation of cortical and subcortical brain areas, producing volumes of subcortical structures and surface models of the cerebral cortex. We evaluated the algorithm both qualitatively and quantitatively using manually labeled datasets, relevant comparator software solutions cited in the literature, and expert evaluations. The computational tools and atlases described in this paper will be distributed to the research community as part of the FreeSurfer image analysis package.
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Affiliation(s)
- Lilla Zöllei
- Laboratory for Computational Neuroimaging, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, USA.
| | - Juan Eugenio Iglesias
- Laboratory for Computational Neuroimaging, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, USA; Center for Medical Image Computing, University College London, United Kingdom; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, USA
| | - Yangming Ou
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, USA
| | - P Ellen Grant
- Fetal-Neonatal Neuroimaging and Developmental Science Center, Boston Children's Hospital, USA
| | - Bruce Fischl
- Laboratory for Computational Neuroimaging, Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, USA
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19
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Makarov SN, Wartman WA, Daneshzand M, Fujimoto K, Raij T, Nummenmaa A. A software toolkit for TMS electric-field modeling with boundary element fast multipole method: an efficient MATLAB implementation. J Neural Eng 2020; 17:046023. [PMID: 32235065 DOI: 10.1088/1741-2552/ab85b3] [Citation(s) in RCA: 26] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2022]
Abstract
Objective To present and disseminate our transcranial magnetic stimulation (TMS) modeling software toolkit, including several new algorithmic developments, and to apply this software to realistic TMS modeling scenarios given a high-resolution model of the human head including cortical geometry and an accurate coil model. Approach The recently developed charge-based boundary element fast multipole method (BEM-FMM) is employed as an alternative to the 1st order finite element method (FEM) most commonly used today. The BEM-FMM approach provides high accuracy and unconstrained numerical field resolution close to and across cortical interfaces. Here, the previously proposed BEM-FMM algorithm has been improved in several novel ways. Main results The improvements resulted in a threefold increase in computational speed while maintaining the same solution accuracy. The computational code based on the MATLAB® platform is made available to all interested researchers, along with a coil model repository and examples to create custom coils, head model repository, and supporting documentation. The presented software toolkit may be useful for post-hoc analyses of navigated TMS data using high-resolution subject-specific head models as well as accurate and fast modeling for the purposes of TMS coil/hardware development. Significance TMS is currently the only non-invasive neurostimulation modality that enables painless and safe supra-threshold stimulation by employing electromagnetic induction to efficiently penetrate the skull. Accurate, fast, and high resolution modeling of the electric fields may significantly improve individualized targeting and dosing of TMS and therefore enhance the efficiency of existing clinical protocols as well as help establish new application domains.
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Affiliation(s)
- Sergey N Makarov
- Electrical & Computer Engineering Department, Worcester Polytechnic Institute, Worcester, MA 01609 United States of America. Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02115 United States of America. Author to whom any correspondence should be addressed
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20
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Jha SC, Xia K, Ahn M, Girault JB, Li G, Wang L, Shen D, Zou F, Zhu H, Styner M, Gilmore JH, Knickmeyer RC. Environmental Influences on Infant Cortical Thickness and Surface Area. Cereb Cortex 2020; 29:1139-1149. [PMID: 29420697 DOI: 10.1093/cercor/bhy020] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2017] [Indexed: 01/07/2023] Open
Abstract
Cortical thickness (CT) and surface area (SA) vary widely between individuals and are associated with intellectual ability and risk for various psychiatric and neurodevelopmental conditions. Factors influencing this variability remain poorly understood, but the radial unit hypothesis, as well as the more recent supragranular cortex expansion hypothesis, suggests that prenatal and perinatal influences may be particularly important. In this report, we examine the impact of 17 major demographic and obstetric history variables on interindividual variation in CT and SA in a unique sample of 805 neonates who received MRI scans of the brain around 2 weeks of age. Birth weight, postnatal age at MRI, gestational age at birth, and sex emerged as important predictors of SA. Postnatal age at MRI, paternal education, and maternal ethnicity emerged as important predictors of CT. These findings suggest that individual variation in infant CT and SA is explained by different sets of environmental factors with neonatal SA more strongly influenced by sex and obstetric history and CT more strongly influenced by socioeconomic and ethnic disparities. Findings raise the possibility that interventions aimed at reducing disparities and improving obstetric outcomes may alter prenatal/perinatal cortical development.
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Affiliation(s)
- Shaili C Jha
- Curriculum in Neuroscience, University of North Carolina, Chapel Hill, NC, USA
| | - Kai Xia
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA
| | - Mihye Ahn
- Department of Mathematics and Statistics, University of Nevada, Reno, NV, USA
| | - Jessica B Girault
- Curriculum in Neuroscience, University of North Carolina, Chapel Hill, NC, USA
| | - Gang Li
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Li Wang
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Dinggang Shen
- Biomedical Research Imaging Center, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.,Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea
| | - Fei Zou
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina, Chapel Hill, NC, USA.,Department of Biostatistics, University of Texas, MD Andersen Cancer Center, Houston, TX, USA
| | - Martin Styner
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA
| | - John H Gilmore
- Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA
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21
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Murphy VA, Shen MD, Kim SH, Cornea E, Styner M, Gilmore JH. Extra-axial Cerebrospinal Fluid Relationships to Infant Brain Structure, Cognitive Development, and Risk for Schizophrenia. BIOLOGICAL PSYCHIATRY: COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2020; 5:651-659. [PMID: 32457022 DOI: 10.1016/j.bpsc.2020.03.008] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/11/2019] [Revised: 03/13/2020] [Accepted: 03/16/2020] [Indexed: 10/24/2022]
Abstract
BACKGROUND Increased volume of extra-axial cerebrospinal fluid (EA-CSF) is associated with autism spectrum disorder diagnosis in young children. However, little is known about EA-CSF development in typically developing (TD) children or in children at risk for schizophrenia (SCZHR). METHODS 3T magnetic resonance imaging scans were obtained in TD children (n = 105) and in SCZHR children (n = 38) at 1 and 2 years of age. EA-CSF volume and several measures of brain structure were generated, including global tissue volumes, cortical thickness, and surface area. Cognitive and motor abilities at 1 and 2 years of age were assessed using the Mullen Scales of Early Learning. RESULTS In the TD children, EA-CSF volume was positively associated with total brain volume, gray and white matter volumes, and total surface area at 1 and 2 years of age. In contrast, EA-CSF volume was negatively associated with average cortical thickness. Lower motor ability was associated with increased EA-CSF volume at 1 year of age. EA-CSF was not significantly increased in SCZHR children compared with TD children. CONCLUSIONS EA-CSF volume is positively associated with overall brain size and cortical surface area but negatively associated with cortical thickness. Increased EA-CSF is associated with delayed motor development at 1 year of age, similar to studies of children at risk for autism, suggesting that increased EA-CSF may be an early biomarker of abnormal brain development in infancy. Infants in the SCZHR group did not exhibit significantly increased EA-CSF, suggesting that increased EA-CSF could be specific to neurodevelopmental disorders with an earlier onset, such as autism.
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Affiliation(s)
- Veronica A Murphy
- Curriculum in Neuroscience, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Mark D Shen
- Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, North Carolina; Department of Psychiatry, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, North Carolina
| | - Sun Hyung Kim
- Department of Psychiatry, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, North Carolina
| | - Emil Cornea
- Department of Psychiatry, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, North Carolina
| | - Martin Styner
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina; Department of Psychiatry, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, North Carolina
| | - John H Gilmore
- Department of Psychiatry, University of North Carolina at Chapel Hill School of Medicine, Chapel Hill, North Carolina.
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22
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Duan D, Xia S, Rekik I, Wu Z, Wang L, Lin W, Gilmore JH, Shen D, Li G. Individual identification and individual variability analysis based on cortical folding features in developing infant singletons and twins. Hum Brain Mapp 2020; 41:1985-2003. [PMID: 31930620 PMCID: PMC7198353 DOI: 10.1002/hbm.24924] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2019] [Revised: 12/31/2019] [Accepted: 01/02/2020] [Indexed: 01/07/2023] Open
Abstract
Studying the early dynamic development of cortical folding with remarkable individual variability is critical for understanding normal early brain development and related neurodevelopmental disorders. This study focuses on the fingerprinting capability and the individual variability of cortical folding during early brain development. Specifically, we aim to explore (a) whether the developing neonatal cortical folding is unique enough to be considered as a "fingerprint" that can reliably identify an individual within a cohort of infants; (b) which cortical regions manifest more individual variability and thus contribute more for infant identification; (c) whether the infant twins can be distinguished by cortical folding. Hence, for the first time, we conduct infant individual identification and individual variability analysis involving twins based on the developing cortical folding features (mean curvature, average convexity, and sulcal depth) in 472 neonates with 1,141 longitudinal MRI scans. Experimental results show that the infant individual identification achieves 100% accuracy when using the neonatal cortical folding features to predict the identities of 1- and 2-year-olds. Besides, we observe high identification capability in the high-order association cortices (i.e., prefrontal, lateral temporal, and inferior parietal regions) and two unimodal cortices (i.e., precentral gyrus and lateral occipital cortex), which largely overlap with the regions encoding remarkable individual variability in cortical folding during the first 2 years. For twins study, we show that even for monozygotic twins with identical genes and similar developmental environments, their cortical folding features are unique enough for accurate individual identification; and in some high-order association cortices, the differences between monozygotic twin pairs are significantly lower than those between dizygotic twins. This study thus provides important insights into individual identification and individual variability based on cortical folding during infancy.
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Affiliation(s)
- Dingna Duan
- Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, Zhejiang, China.,Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Shunren Xia
- Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, Hangzhou, Zhejiang, China
| | - Islem Rekik
- BASIRA Lab, Faculty of Computer and Informatics, Istanbul Technical University, Istanbul, Turkey.,Computing, School of Science and Engineering, University of Dundee, Dundee, UK
| | - 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
| | - Weili Lin
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - John H Gilmore
- Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - 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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23
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Yu X, Peng B, Xue Z, Rad HS, Cai Z, Shi J, Zhu J, Dai Y. Analyzing brain structural differences associated with categories of blood pressure in adults using empirical kernel mapping-based kernel ELM. Biomed Eng Online 2019; 18:124. [PMID: 31881897 PMCID: PMC6935092 DOI: 10.1186/s12938-019-0740-4] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2019] [Accepted: 12/06/2019] [Indexed: 01/23/2023] Open
Abstract
Background Hypertension increases the risk of angiocardiopathy and cognitive disorder. Blood pressure has four categories: normal, elevated, hypertension stage 1 and hypertension stage 2. The quantitative analysis of hypertension helps determine disease status, prognosis assessment, guidance and management, but is not well studied in the framework of machine learning. Methods We proposed empirical kernel mapping-based kernel extreme learning machine plus (EKM–KELM+) classifier to discriminate different blood pressure grades in adults from structural brain MR images. ELM+ is the extended version of ELM, which integrates the additional privileged information about training samples in ELM to help train a more effective classifier. In this work, we extracted gray matter volume (GMV), white matter volume, cerebrospinal fluid volume, cortical surface area, cortical thickness from structural brain MR images, and constructed brain network features based on thickness. After feature selection and EKM, the enhanced features are obtained. Then, we select one feature type as the main feature to feed into KELM+, and the rest of the feature types are PI to assist the main feature to train 5 KELM+ classifiers. Finally, the 5 KELM+ classifiers are ensemble to predict classification result in the test stage, while PI is not used during testing. Results We evaluated the performance of the proposed EKM–KELM+ method using four grades of hypertension data (73 samples for each grade). The experimental results show that the GMV performs observably better than any other feature types with a comparatively higher classification accuracy of 77.37% (Grade 1 vs. Grade 2), 93.19% (Grade 1 vs. Grade 3), and 95.15% (Grade 1 vs. Grade 4). The most discriminative brain regions found using our method are olfactory, orbitofrontal cortex (inferior), supplementary motor area, etc. Conclusions Using region of interest features and brain network features, EKM–KELM+ is proposed to study the most discriminative regions that have obvious structural changes in different blood pressure grades. The discriminative features that are selected using our method are consistent with the existing neuroimaging studies. Moreover, our study provides a potential approach to take effective interventions in the early period, when the blood pressure makes minor impacts on the brain structure and function.
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Affiliation(s)
- Xinying Yu
- Shanghai Institute for Advanced Communication and Data Science, School of Communication and Information Engineering, Shanghai University, Shanghai, China.,Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Science, Suzhou, Jiangsu, China
| | - Bo Peng
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Science, Suzhou, Jiangsu, China
| | - Zeyu Xue
- Shanghai Institute for Advanced Communication and Data Science, School of Communication and Information Engineering, Shanghai University, Shanghai, China.,Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Science, Suzhou, Jiangsu, China
| | - Hamidreza Saligheh Rad
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Science, Suzhou, Jiangsu, China.,Quantitative Medical Imaging Systems Group, Research Center for Molecular and Cellular Imaging, Institute for Advanced Medical Technologies and Devices, Tehran University of Medical Sciences, Tehran, Iran
| | - Zhenlin Cai
- The Affiliated Suzhou Science & Technology Town Hospital of Nanjing Medical University, Suzhou, Jiangsu, China.,Suzhou Science & Technology Town Hospital, Suzhou, 215153, Jiangsu, China
| | - Jun Shi
- Shanghai Institute for Advanced Communication and Data Science, School of Communication and Information Engineering, Shanghai University, Shanghai, China
| | - Jianbing Zhu
- The Affiliated Suzhou Science & Technology Town Hospital of Nanjing Medical University, Suzhou, Jiangsu, China. .,Suzhou Science & Technology Town Hospital, Suzhou, 215153, Jiangsu, China.
| | - Yakang Dai
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Science, Suzhou, Jiangsu, China. .,Suzhou Key Laboratory of Medical and Health Information Technology, Suzhou, China. .,Nanjing Guoke Medical Engineering Technology Development Co., Ltd, Nanjing, China. .,Jinan Guoke Medical Engineering Technology Development Co., Ltd, Jinan, China.
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24
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Ahmad S, Wu Z, Li G, Wang L, Lin W, Yap PT, Shen D. Surface-constrained volumetric registration for the early developing brain. Med Image Anal 2019; 58:101540. [PMID: 31398617 PMCID: PMC6815721 DOI: 10.1016/j.media.2019.101540] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2018] [Revised: 07/26/2019] [Accepted: 07/29/2019] [Indexed: 12/24/2022]
Abstract
The T1-weighted and T2-weighted MRI contrasts of the infant brain evolve drastically during the first year of life. This poses significant challenges to inter- and intra-subject registration, which is key to subsequent statistical analyses. Existing registration methods that do not consider temporal contrast changes are ineffective for infant brain MRI data. To address this problem, we present in this paper a method for deformable registration of infant brain MRI. The key advantage of our method is threefold: (i) To deal with appearance changes, registration is performed based on segmented tissue maps instead of image intensity. Segmentation is performed by using an infant-centric algorithm previously developed by our group. (ii) Registration is carried out with respect to both cortical surfaces and volumetric tissue maps, thus allowing precise alignment of both cortical and subcortical structures. (iii) A dynamic elasticity model is utilized to allow large non-linear deformation. Experimental results in comparison with well-established registration methods indicate that our method yields superior accuracy in both cortical and subcortical alignment.
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Affiliation(s)
- Sahar Ahmad
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, United States
| | - Zhengwang Wu
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, United States
| | - Gang Li
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, United States
| | - Li Wang
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, United States
| | - Weili Lin
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, United States
| | - Pew-Thian Yap
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, United States.
| | - Dinggang Shen
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina, Chapel Hill, United States; Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea.
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25
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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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26
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Wu Z, Wang L, Lin W, Gilmore JH, Li G, Shen D. Construction of 4D infant cortical surface atlases with sharp folding patterns via spherical patch-based group-wise sparse representation. Hum Brain Mapp 2019; 40:3860-3880. [PMID: 31115143 DOI: 10.1002/hbm.24636] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/21/2018] [Revised: 04/14/2019] [Accepted: 05/09/2019] [Indexed: 11/08/2022] Open
Abstract
4D (spatial + temporal) infant cortical surface atlases covering dense time points are highly needed for understanding dynamic early brain development. In this article, we construct a set of 4D infant cortical surface atlases with longitudinally consistent and sharp cortical attribute patterns at 11 time points in the first six postnatal years, that is, at 1, 3, 6, 9, 12, 18, 24, 36, 48, 60, and 72 months of age, which is targeted for better normalization of the dynamic changing early brain cortical surfaces. To ensure longitudinal consistency and unbiasedness, we adopt a two-stage group-wise surface registration. To preserve sharp cortical attribute patterns on the atlas, instead of simply averaging over the coregistered cortical surfaces, we leverage a spherical patch-based sparse representation using the augmented dictionary to overcome the potential registration errors. Our atlases provide not only geometric attributes of the cortical folding, but also cortical thickness and myelin content. Therefore, to address the consistency across different cortical attributes on the atlas, instead of sparsely representing each attribute independently, we jointly represent all cortical attributes with a group-wise sparsity constraint. In addition, to further facilitate region-based analysis using our atlases, we have also provided two widely used parcellations, that is, FreeSurfer parcellation and multimodal parcellation, on our 4D infant cortical surface atlases. Compared to cortical surface atlases constructed with other methods, our cortical surface atlases preserve sharper cortical folding attribute patterns, thus leading to better accuracy in registration of individual infant cortical surfaces to the atlas.
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Affiliation(s)
- 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
| | - Weili Lin
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - John H Gilmore
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Gang Li
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - 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
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27
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Xia J, Wang F, Benkarim OM, Sanroma G, Piella G, González Ballester MA, Hahner N, Eixarch E, Zhang C, Shen D, Li G. Fetal cortical surface atlas parcellation based on growth patterns. Hum Brain Mapp 2019; 40:3881-3899. [PMID: 31106942 DOI: 10.1002/hbm.24637] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2019] [Revised: 04/22/2019] [Accepted: 04/27/2019] [Indexed: 12/13/2022] Open
Abstract
Defining anatomically and functionally meaningful parcellation maps on cortical surface atlases is of great importance in surface-based neuroimaging analysis. The conventional cortical parcellation maps are typically defined based on anatomical cortical folding landmarks in adult surface atlases. However, they are not suitable for fetal brain studies, due to dramatic differences in brain size, shape, and properties between adults and fetuses. To address this issue, we propose a novel data-driven method for parcellation of fetal cortical surface atlases into distinct regions based on the dynamic "growth patterns" of cortical properties (e.g., surface area) from a population of fetuses. Our motivation is that the growth patterns of cortical properties indicate the underlying rapid changes of microstructures, which determine the molecular and functional principles of the cortex. Thus, growth patterns are well suitable for defining distinct cortical regions in development, structure, and function. To comprehensively capture the similarities of cortical growth patterns among vertices, we construct two complementary similarity matrices. One is directly based on the growth trajectories of vertices, and the other is based on the correlation profiles of vertices' growth trajectories in relation to a set of reference points. Then, we nonlinearly fuse these two similarity matrices into a single one, which can better capture both their common and complementary information than by simply averaging them. Finally, based on this fused similarity matrix, we perform spectral clustering to divide the fetal cortical surface atlases into distinct regions. By applying our method on 25 normal fetuses from 26 to 29 gestational weeks, we construct age-specific fetal cortical surface atlases equipped with biologically meaningful parcellation maps based on cortical growth patterns. Importantly, our generated parcellation maps reveal spatially contiguous, hierarchical and bilaterally relatively symmetric patterns of fetal cortical surface development.
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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, Chapel Hills, North Carolina
| | - Fan Wang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hills, North Carolina
| | | | - Gerard Sanroma
- BCN Medtech, Universitat Pompeu Fabra, Barcelona, Spain.,German Center for Neurodegenerative Diseases, Bonn, Germany
| | - Gemma Piella
- BCN Medtech, Universitat Pompeu Fabra, Barcelona, Spain
| | | | - Nadine Hahner
- Fetal i+D Fetal Medicine Research Center, BCNatal, Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Déu), Institut Clínic de Ginecologia, Obstetricia i Neonatologia, Institut d'Investigacions Biomèdiques August Pi i Sunyer, Universitat de Barcelona, Barcelona, Spain
| | - Elisenda Eixarch
- Fetal i+D Fetal Medicine Research Center, BCNatal, Barcelona Center for Maternal-Fetal and Neonatal Medicine (Hospital Clínic and Hospital Sant Joan de Déu), Institut Clínic de Ginecologia, Obstetricia i Neonatologia, Institut d'Investigacions Biomèdiques August Pi i Sunyer, Universitat de Barcelona, Barcelona, Spain.,Centre for Biomedical Research on Rare Diseases (CIBER-ER), Barcelona, Spain
| | - Caiming Zhang
- Digital Media Technology Key Lab of Shandong Province, Jinan, China.,Department of Software, Shandong University, Jinan, China
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hills, 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 Hills, North Carolina
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28
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Xu X, He P, Yap PT, Zhang H, Nie J, Shen D. Meta-Network Analysis of Structural Correlation Networks Provides Insights Into Brain Network Development. Front Hum Neurosci 2019; 13:93. [PMID: 30971907 PMCID: PMC6444117 DOI: 10.3389/fnhum.2019.00093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2018] [Accepted: 02/27/2019] [Indexed: 11/25/2022] Open
Abstract
Analysis of developmental brain networks is fundamentally important for basic developmental neuroscience. In this paper, we focus on the temporally-covarying connection patterns, called meta-networks, and develop a new mathematical model for meta-network decomposition. With the proposed model, we decompose the developmental structural correlation networks of cortical thickness into five meta-networks. Each meta-network exhibits a distinctive spatial connection pattern, and its covarying trajectory highlights the temporal contribution of the meta-network along development. Systematic analysis of the meta-networks and covarying trajectories provides insights into three important aspects of brain network development.
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Affiliation(s)
- Xiaohua Xu
- Department of Computer Science, Yangzhou University, Yangzhou, China
| | - Ping He
- Department of Computer Science, Yangzhou University, Yangzhou, China
| | - Pew-Thian Yap
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Han Zhang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Jingxin Nie
- School of Psychology, South China Normal University, Guangzhou, China
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
- Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea
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29
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Girault JB, Munsell BC, Puechmaille D, Goldman BD, Prieto JC, Styner M, Gilmore JH. White matter connectomes at birth accurately predict cognitive abilities at age 2. Neuroimage 2019; 192:145-155. [PMID: 30825656 DOI: 10.1016/j.neuroimage.2019.02.060] [Citation(s) in RCA: 37] [Impact Index Per Article: 7.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2018] [Revised: 02/18/2019] [Accepted: 02/22/2019] [Indexed: 12/14/2022] Open
Abstract
Cognitive ability is an important predictor of mental health outcomes that is influenced by neurodevelopment. Evidence suggests that the foundational wiring of the human brain is in place by birth, and that the white matter (WM) connectome supports developing brain function. It is unknown, however, how the WM connectome at birth supports emergent cognition. In this study, a deep learning model was trained using cross-validation to classify full-term infants (n = 75) as scoring above or below the median at age 2 using WM connectomes generated from diffusion weighted magnetic resonance images at birth. Results from this model were used to predict individual cognitive scores. We additionally identified WM connections important for classification. The model was also evaluated in a separate set of preterm infants (n = 37) scanned at term-age equivalent. Findings revealed that WM connectomes at birth predicted 2-year cognitive score group with high accuracy in both full-term (89.5%) and preterm (83.8%) infants. Scores predicted by the model were strongly correlated with actual scores (r = 0.98 for full-term and r = 0.96 for preterm). Connections within the frontal lobe, and between the frontal lobe and other brain areas were found to be important for classification. This work suggests that WM connectomes at birth can accurately predict a child's 2-year cognitive group and individual score in full-term and preterm infants. The WM connectome at birth appears to be a useful neuroimaging biomarker of subsequent cognitive development that deserves further study.
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Affiliation(s)
- Jessica B Girault
- Department of Psychiatry, UNC Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Brent C Munsell
- Department of Computer Science, College of Charleston, Charleston, SC, 29424, USA
| | | | - Barbara D Goldman
- Department of Psychology & Neuroscience, UNC Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Juan C Prieto
- Department of Psychiatry, UNC Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Martin Styner
- Department of Psychiatry, UNC Chapel Hill, Chapel Hill, NC, 27599, USA
| | - John H Gilmore
- Department of Psychiatry, UNC Chapel Hill, Chapel Hill, NC, 27599, USA.
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30
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Zhang H, Shen D, Lin W. Resting-state functional MRI studies on infant brains: A decade of gap-filling efforts. Neuroimage 2019; 185:664-684. [PMID: 29990581 PMCID: PMC6289773 DOI: 10.1016/j.neuroimage.2018.07.004] [Citation(s) in RCA: 68] [Impact Index Per Article: 13.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2017] [Revised: 05/19/2018] [Accepted: 07/02/2018] [Indexed: 12/16/2022] Open
Abstract
Resting-state functional MRI (rs-fMRI) is one of the most prevalent brain functional imaging modalities. Previous rs-fMRI studies have mainly focused on adults and elderly subjects. Recently, infant rs-fMRI studies have become an area of active research. After a decade of gap filling studies, many facets of the brain functional development from early infancy to toddler has been uncovered. However, infant rs-fMRI is still in its infancy. The image analysis tools for neonates and young infants can be quite different from those for adults. From data analysis to result interpretation, more questions and issues have been raised, and new hypotheses have been formed. With the anticipated availability of unprecedented high-resolution rs-fMRI and dedicated analysis pipelines from the Baby Connectome Project (BCP), it is important now to revisit previous findings and hypotheses, discuss and comment existing issues and problems, and make a "to-do-list" for the future studies. This review article aims to comprehensively review a decade of the findings, unveiling hidden jewels of the fields of developmental neuroscience and neuroimage computing. Emphases will be given to early infancy, particularly the first few years of life. In this review, an end-to-end summary, from infant rs-fMRI experimental design to data processing, and from the development of individual functional systems to large-scale brain functional networks, is provided. A comprehensive summary of the rs-fMRI findings in developmental patterns is highlighted. Furthermore, an extensive summary of the neurodevelopmental disorders and the effects of other hazardous factors is provided. Finally, future research trends focusing on emerging dynamic functional connectivity and state-of-the-art functional connectome analysis are summarized. In next decade, early infant rs-fMRI and developmental connectome study could be one of the shining research topics.
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Affiliation(s)
- Han Zhang
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, NC, 27599, USA
| | - Dinggang Shen
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, NC, 27599, USA; Department of Brain and Cognitive Engineering, Korea University, Seoul, 02841, Republic of Korea.
| | - Weili Lin
- Department of Radiology and Biomedical Research Imaging Center (BRIC), University of North Carolina at Chapel Hill, NC, 27599, USA.
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Duan D, Xia S, Rekik I, Meng Y, Wu Z, Wang L, Lin W, Gilmore JH, Shen D, Li G. Exploring folding patterns of infant cerebral cortex based on multi-view curvature features: Methods and applications. Neuroimage 2019; 185:575-592. [PMID: 30130646 PMCID: PMC6289765 DOI: 10.1016/j.neuroimage.2018.08.041] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/29/2017] [Revised: 08/15/2018] [Accepted: 08/17/2018] [Indexed: 12/30/2022] Open
Abstract
The highly convoluted cortical folding of the human brain is intriguingly complex and variable across individuals. Exploring the underlying representative patterns of cortical folding is of great importance for many neuroimaging studies. At term birth, all major cortical folds are established and are minimally affected by the complicated postnatal environments; hence, neonates are the ideal candidates for exploring early postnatal cortical folding patterns, which yet remain largely unexplored. In this paper, we propose a novel method for exploring the representative regional folding patterns of infant brains. Specifically, first, multi-view curvature features are constructed to comprehensively characterize the complex characteristics of cortical folding. Second, for each view of curvature features, a similarity matrix is computed to measure the similarity of cortical folding in a specific region between any pair of subjects. Next, a similarity network fusion method is adopted to nonlinearly and adaptively fuse all the similarity matrices into a single one for retaining both shared and complementary similarity information of the multiple characteristics of cortical folding. Finally, based on the fused similarity matrix and a hierarchical affinity propagation clustering approach, all subjects are automatically grouped into several clusters to obtain the representative folding patterns. To show the applications, we have applied the proposed method to a large-scale dataset with 595 normal neonates and discovered representative folding patterns in several cortical regions, i.e., the superior temporal gyrus (STG), inferior frontal gyrus (IFG), precuneus, and cingulate cortex. Meanwhile, we have revealed sex difference in STG, IFG, and cingulate cortex, as well as hemispheric asymmetries in STG and cingulate cortex in terms of cortical folding patterns. Moreover, we have also validated the proposed method on a public adult dataset, i.e., the Human Connectome Project (HCP), and revealed that certain major cortical folding patterns of adults are largely established at term birth.
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Affiliation(s)
- Dingna Duan
- Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, China; Department of Radiology and BRIC, University of North Carolina at Chapel Hill, USA
| | - Shunren Xia
- Key Laboratory of Biomedical Engineering of Ministry of Education, Zhejiang University, China
| | - Islem Rekik
- BASIRA Lab, CVIP, Computing, School of Science and Engineering, University of Dundee, UK
| | - Yu Meng
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, USA
| | - Zhengwang Wu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, USA
| | - Li Wang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, USA
| | - Weili Lin
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, USA
| | - John H Gilmore
- Department of Psychiatry, University of North Carolina at Chapel Hill, USA
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, USA; Department of Brain and Cognitive Engineering, Korea University, Seoul, 02841, Republic of Korea.
| | - Gang Li
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, USA.
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32
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Wen X, Zhang H, Li G, Liu M, Yin W, Lin W, Zhang J, Shen D. First-year development of modules and hubs in infant brain functional networks. Neuroimage 2019; 185:222-235. [PMID: 30315911 PMCID: PMC6289727 DOI: 10.1016/j.neuroimage.2018.10.019] [Citation(s) in RCA: 50] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2018] [Revised: 09/09/2018] [Accepted: 10/04/2018] [Indexed: 11/19/2022] Open
Abstract
The human brain develops rapidly in the first postnatal year, in which rewired functional brain networks could shape later behavioral and cognitive performance. Resting-state functional magnetic resonances imaging (rs-fMRI) and complex network analysis have been widely used for characterizing the developmental brain functional connectome. Yet, such studies focusing on the first year of postnatal life are still very limited. Leveraging normally developing longitudinal infant rs-fMRI scans from neonate to one year of age, we investigated how brain functional networks develop at a fine temporal scale (every 3 months). Considering challenges in the infant fMRI-based network analysis, we developed a novel algorithm to construct the robust, temporally consistent and modular structure augmented group-level network based on which functional modules were detected at each age. Our study reveals that the brain functional network is gradually subdivided into an increasing number of functional modules accompanied by the strengthened intra- and inter-modular connectivities. Based on the developing modules, we found connector hubs (the high-centrality regions connecting different modules) emerging and increasing, while provincial hubs (the high-centrality regions connecting regions in the same module) diminishing. Further region-wise longitudinal analysis validates that different hubs have distinct developmental trajectories of the intra- and inter-modular connections suggesting different types of role transitions in network, such as non-hubs to hubs or provincial hubs to connector hubs et al. All findings indicate that functional segregation and integration are both increased in the first year of postnatal life. The module reorganization and hub transition lead to more efficient brain networks, featuring increasingly segregated modular structure and more connector hubs. This study provides the first comprehensive report of the development of functional brain networks at a 3-month interval throughout the first postnatal year of life, which provides essential information to the future neurodevelopmental and developmental disorder studies.
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Affiliation(s)
- Xuyun Wen
- School of Data and Computer Science, Sun-Yat Sen University, China; Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Han Zhang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.
| | - Gang Li
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Mingxia Liu
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Weiyan Yin
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Weili Lin
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jun Zhang
- School of Computer Science and Engineering, South China University of Technology, Guangzhou, China.
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Brain and Cognitive Engineering, Korea University, Seoul, 02841, Republic of Korea.
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Xia J, Wang F, Meng Y, Wu Z, Wang L, Lin W, Zhang C, Shen D, Li G. A computational method for longitudinal mapping of orientation-specific expansion of cortical surface in infants. Med Image Anal 2018; 49:46-59. [PMID: 30092545 PMCID: PMC6276374 DOI: 10.1016/j.media.2018.07.006] [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: 01/09/2018] [Revised: 05/24/2018] [Accepted: 07/17/2018] [Indexed: 12/29/2022]
Abstract
The cortical surface of the human brain expands dynamically and regionally heterogeneously during the first postnatal year. As all primary and secondary cortical folds as well as many tertiary cortical folds are well established at term birth, the cortical surface area expansion during this stage is largely driven by the increase of surface area in two orthogonal orientations in the tangent plane: 1) the expansion parallel to the folding orientation (i.e., increasing the lengths of folds) and 2) the expansion perpendicular to the folding orientation (i.e., increasing the depths of folds). This information would help us better understand the mechanisms of cortical development and provide important insights into neurodevelopmental disorders, but still remains largely unknown due to lack of dedicated computational methods. To address this issue, we propose a novel method for longitudinal mapping of orientation-specific expansion of cortical surface area in these two orthogonal orientations during early infancy. First, to derive the two orientation fields perpendicular and parallel to cortical folds, we propose to adaptively and smoothly fuse the gradient field of sulcal depth and also the maximum principal direction field, by leveraging their region-specific reliability. Specifically, we formulate this task as a discrete labeling problem, in which each vertex is assigned to an orientation label, and solve it by graph cuts. Then, based on the computed longitudinal deformation of the cortical surface, we estimate the Jacobian matrix at each vertex by solving a least-squares problem and derive its corresponding stretch tensor. Finally, to obtain the orientation-specific cortical surface expansion, we project the stretch tensor into the two orthogonal orientations separately. We have applied the proposed method to 30 healthy infants, and for the first time we revealed the orientation-specific longitudinal cortical surface expansion maps during the first postnatal year.
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Affiliation(s)
- Jing Xia
- Department of Computer Science and Technology, Shandong University, Jinan 250100, 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
| | - Yu Meng
- 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
| | - Weili Lin
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA
| | - Caiming Zhang
- Digital Media Technology Key Lab of Shandong Province, Jinan 250061, China; Department of Software, Shandong University, Jinan 250100, China
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA; Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea.
| | - Gang Li
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC 27599, USA.
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Wu Z, Li G, Li W, Shi F, Lin W, Gilmore JH, Shen D. Registration-Free Infant Cortical Surface Parcellation using Deep Convolutional Neural Networks. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2018; 11072:672-680. [PMID: 31263805 DOI: 10.1007/978-3-030-00931-1_77] [Citation(s) in RCA: 15] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
Automatic parcellation of infant cortical surfaces into anatomical regions of interest (ROIs) is of great importance in brain structural and functional analysis. Conventional cortical surface parcellation methods suffer from two main issues: 1) Cortical surface registration is needed for establishing the atlas-to-individual correspondences; 2) The mapping from cortical shape to the parcellation labels requires designing of specific hand-crafted features. To address these issues, in this paper, we propose a novel cortical surface parcellation method, which is free of surface registration and designing of hand-crafted features, based on deep convolutional neural network (DCNN). Our main idea is to formulate surface parcellation as a patch-wise classification problem. Briefly, we use DCNN to train a classifier, whose inputs are the local cortical surface patches with multi-channel cortical shape descriptors such as mean curvature, sulcal depth, and average convexity; while the outputs are the parcellation label probabilities of cortical vertices. To enable effective convolutional operation on the surface data, we project each spherical surface patch onto its intrinsic tangent plane by a geodesic-distance-preserving mapping. Then, after classification, we further adopt the graph cuts method to improve spatial consistency of the parcellation. We have validated our method based on 90 neonatal cortical surfaces with manual parcellations, showing superior accuracy and efficiency of our proposed method.
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Affiliation(s)
- Zhengwang Wu
- Depart. of Radiology, University of North Carolina at Chapel Hill, NC, USA
| | - Gang Li
- Depart. of Radiology, University of North Carolina at Chapel Hill, NC, USA
| | - Wang Li
- Depart. of Radiology, University of North Carolina at Chapel Hill, NC, USA
| | - Feng Shi
- Shanghai United Imaging Intelligence Co., Ltd., Shanghai, China
| | - Weili Lin
- Depart. of Radiology, University of North Carolina at Chapel Hill, NC, USA
| | - John H Gilmore
- Depart. of Radiology, University of North Carolina at Chapel Hill, NC, USA
| | - Dinggang Shen
- Depart. of Radiology, University of North Carolina at Chapel Hill, NC, USA
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35
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Jha SC, Xia K, Schmitt JE, Ahn M, Girault JB, Murphy VA, Li G, Wang L, Shen D, Zou F, Zhu H, Styner M, Knickmeyer RC, Gilmore JH. Genetic influences on neonatal cortical thickness and surface area. Hum Brain Mapp 2018; 39:4998-5013. [PMID: 30144223 DOI: 10.1002/hbm.24340] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/16/2018] [Revised: 07/18/2018] [Accepted: 07/20/2018] [Indexed: 01/07/2023] Open
Abstract
Genetic and environmental influences on cortical thickness (CT) and surface area (SA) are thought to vary in a complex and dynamic way across the lifespan. It has been established that CT and SA are genetically distinct in older children, adolescents, and adults, and that heritability varies across cortical regions. Very little, however, is known about how genetic and environmental factors influence infant CT and SA. Using structural MRI, we performed the first assessment of genetic and environmental influences on normal variation of SA and CT in 360 twin neonates. We observed strong and significant additive genetic influences on total SA (a2 = 0.78) and small and nonsignificant genetic influences on average CT (a2 = 0.29). Moreover, we found significant genetic overlap (genetic correlation = 0.65) between these global cortical measures. Regionally, there were minimal genetic influences across the cortex for both CT and SA measures and no distinct patterns of genetic regionalization. Overall, outcomes from this study suggest a dynamic relationship between CT and SA during the neonatal period and provide novel insights into how genetic influences shape cortical structure during early development.
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Affiliation(s)
- Shaili C Jha
- Department of Psychiatry, University of North Carolina, Chapel Hill, North Carolina
| | - Kai Xia
- Department of Psychiatry, University of North Carolina, Chapel Hill, North Carolina
| | - James Eric Schmitt
- Brain Behavior Laboratory, Departments of Radiology and Psychiatry, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Mihye Ahn
- Department of Mathematics and Statistics, University of Nevada, Reno, Nevada
| | - Jessica B Girault
- Department of Psychiatry, University of North Carolina, Chapel Hill, North Carolina
| | - Veronica A Murphy
- Department of Psychiatry, University of North Carolina, Chapel Hill, North Carolina.,Curriculum in Neuroscience, University of North Carolina, Chapel Hill, North Carolina
| | - Gang Li
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, North Carolina
| | - Li Wang
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, North Carolina
| | - Dinggang Shen
- Biomedical Research Imaging Center, University of North Carolina, Chapel Hill, North Carolina
| | - Fei Zou
- Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina, Chapel Hill, North Carolina.,Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Martin Styner
- Department of Psychiatry, University of North Carolina, Chapel Hill, North Carolina.,Department of Computer Science, University of North Carolina, Chapel Hill, North Carolina
| | - Rebecca C Knickmeyer
- Department of Psychiatry, University of North Carolina, Chapel Hill, North Carolina
| | - John H Gilmore
- Department of Psychiatry, University of North Carolina, Chapel Hill, North Carolina
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36
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He P, Xu X, Zhang H, Li G, Nie J, Yap PT, Shen D. Spatiotemporal Analysis of Developing Brain Networks. Front Neuroinform 2018; 12:48. [PMID: 30108496 PMCID: PMC6080575 DOI: 10.3389/fninf.2018.00048] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2018] [Accepted: 07/10/2018] [Indexed: 11/13/2022] Open
Abstract
Recent advances in MRI have made it easier to collect data for studying human structural and functional connectivity networks. Computational methods can reveal complex spatiotemporal dynamics of the human developing brain. In this paper, we propose a Developmental Meta-network Decomposition (DMD) method to decompose a series of developmental networks into a set of Developmental Meta-networks (DMs), which reveal the underlying changes in connectivity over development. DMD circumvents the limitations of traditional static network decomposition methods by providing a novel exploratory approach to capture the spatiotemporal dynamics of developmental networks. We apply this method to structural correlation networks of cortical thickness across subjects at 3-20 years of age, and identify four DMs that smoothly evolve over three stages, i.e., 3-6, 7-12, and 13-20 years of age. We analyze and highlight the characteristic connections of each DM in relation to brain development.
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Affiliation(s)
- Ping He
- Department of Computer Science, Yangzhou University, Yangzhou, China
| | - Xiaohua Xu
- Department of Computer Science, Yangzhou University, Yangzhou, China
| | - Han Zhang
- Department of Radiology and BRIC, University of North Carolina, Chapel Hill, NC, United States
| | - Gang Li
- Department of Radiology and BRIC, University of North Carolina, Chapel Hill, NC, United States
| | - Jingxin Nie
- School of Psychology, South China Normal University, Guangzhou, China
| | - Pew-Thian Yap
- Department of Radiology and BRIC, University of North Carolina, Chapel Hill, NC, United States
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina, Chapel Hill, NC, United States
- Department of Brain and Cognitive Engineering, Korea University, Seoul, South Korea
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37
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Wu Z, Li G, Wang L, Lin W, Gilmore JH, Shen D. CONSTRUCTION OF SPATIOTEMPORAL NEONATAL CORTICAL SURFACE ATLASES USING A LARGE-SCALE DATASET. PROCEEDINGS. IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING 2018; 2018:1056-1059. [PMID: 30416672 PMCID: PMC6223307 DOI: 10.1109/isbi.2018.8363753] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/09/2023]
Abstract
The cortical surface atlases constructed from a large representative population of neonates are highly needed in the neonatal neuroimaging studies. However, existing neonatal cortical surface atlases are typically constructed from small datasets, e.g., tens of subjects, which are inherently biased and thus are not representative to the neonatal population. In this paper, we construct neonatal cortical surface atlases based on a large-scale dataset with 764 subjects. To better characterize the dynamic cortical development during the first postnatal weeks, instead of constructing just a single atlas, we construct a set of spatiotemporal atlases at each week from 39 to 44 gestational weeks. The central idea is that, for all cortical surfaces, we first group-wisely register them into the common space to ensure the unbiasedness. Then, rather than simply averaging over the co-registered cortical surfaces, which generally leads to over-smoothed cortical folding patterns, we adopt a spherical patch-based sparse representation using an augmented dictionary to overcome the noises and potential registration errors. Through the group-wise sparsity constraint, we obtain consistent geometric cortical folding attributes on the atlases. Our atlases preserve the sharp cortical folding patterns, thus leading to better registration accuracy when aligning new subjects onto the atlases.
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Affiliation(s)
- Zhengwang Wu
- 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
| | - Li Wang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, 27599, USA
| | - Weili Lin
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, 27599, USA
| | - John H Gilmore
- Department of Psychiatry, 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
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38
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Computational neuroanatomy of baby brains: A review. Neuroimage 2018; 185:906-925. [PMID: 29574033 DOI: 10.1016/j.neuroimage.2018.03.042] [Citation(s) in RCA: 95] [Impact Index Per Article: 15.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2017] [Revised: 02/23/2018] [Accepted: 03/19/2018] [Indexed: 12/12/2022] Open
Abstract
The first postnatal years are an exceptionally dynamic and critical period of structural, functional and connectivity development of the human brain. The increasing availability of non-invasive infant brain MR images provides unprecedented opportunities for accurate and reliable charting of dynamic early brain developmental trajectories in understanding normative and aberrant growth. However, infant brain MR images typically exhibit reduced tissue contrast (especially around 6 months of age), large within-tissue intensity variations, and regionally-heterogeneous, dynamic changes, in comparison with adult brain MR images. Consequently, the existing computational tools developed typically for adult brains are not suitable for infant brain MR image processing. To address these challenges, many infant-tailored computational methods have been proposed for computational neuroanatomy of infant brains. In this review paper, we provide a comprehensive review of the state-of-the-art computational methods for infant brain MRI processing and analysis, which have advanced our understanding of early postnatal brain development. We also summarize publically available infant-dedicated resources, including MRI datasets, computational tools, grand challenges, and brain atlases. Finally, we discuss the limitations in current research and suggest potential future research directions.
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39
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Makropoulos A, Robinson EC, Schuh A, Wright R, Fitzgibbon S, Bozek J, Counsell SJ, Steinweg J, Vecchiato K, Passerat-Palmbach J, Lenz G, Mortari F, Tenev T, Duff EP, Bastiani M, Cordero-Grande L, Hughes E, Tusor N, Tournier JD, Hutter J, Price AN, Teixeira RPAG, Murgasova M, Victor S, Kelly C, Rutherford MA, Smith SM, Edwards AD, Hajnal JV, Jenkinson M, Rueckert D. The developing human connectome project: A minimal processing pipeline for neonatal cortical surface reconstruction. Neuroimage 2018. [PMID: 29409960 DOI: 10.1101/125526] [Citation(s) in RCA: 20] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/06/2023] Open
Abstract
The Developing Human Connectome Project (dHCP) seeks to create the first 4-dimensional connectome of early life. Understanding this connectome in detail may provide insights into normal as well as abnormal patterns of brain development. Following established best practices adopted by the WU-MINN Human Connectome Project (HCP), and pioneered by FreeSurfer, the project utilises cortical surface-based processing pipelines. In this paper, we propose a fully automated processing pipeline for the structural Magnetic Resonance Imaging (MRI) of the developing neonatal brain. This proposed pipeline consists of a refined framework for cortical and sub-cortical volume segmentation, cortical surface extraction, and cortical surface inflation, which has been specifically designed to address considerable differences between adult and neonatal brains, as imaged using MRI. Using the proposed pipeline our results demonstrate that images collected from 465 subjects ranging from 28 to 45 weeks post-menstrual age (PMA) can be processed fully automatically; generating cortical surface models that are topologically correct, and correspond well with manual evaluations of tissue boundaries in 85% of cases. Results improve on state-of-the-art neonatal tissue segmentation models and significant errors were found in only 2% of cases, where these corresponded to subjects with high motion. Downstream, these surfaces will enhance comparisons of functional and diffusion MRI datasets, supporting the modelling of emerging patterns of brain connectivity.
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Affiliation(s)
- Antonios Makropoulos
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Emma C Robinson
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom; Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom.
| | - Andreas Schuh
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Robert Wright
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Sean Fitzgibbon
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Jelena Bozek
- Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia
| | - Serena J Counsell
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Johannes Steinweg
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Katy Vecchiato
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Jonathan Passerat-Palmbach
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Gregor Lenz
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Filippo Mortari
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Tencho Tenev
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Eugene P Duff
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Matteo Bastiani
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Emer Hughes
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Nora Tusor
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Jacques-Donald Tournier
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Jana Hutter
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Anthony N Price
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Rui Pedro A G Teixeira
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Maria Murgasova
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Suresh Victor
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Christopher Kelly
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Mary A Rutherford
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Stephen M Smith
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - A David Edwards
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Joseph V Hajnal
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Mark Jenkinson
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
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40
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Makropoulos A, Robinson EC, Schuh A, Wright R, Fitzgibbon S, Bozek J, Counsell SJ, Steinweg J, Vecchiato K, Passerat-Palmbach J, Lenz G, Mortari F, Tenev T, Duff EP, Bastiani M, Cordero-Grande L, Hughes E, Tusor N, Tournier JD, Hutter J, Price AN, Teixeira RPAG, Murgasova M, Victor S, Kelly C, Rutherford MA, Smith SM, Edwards AD, Hajnal JV, Jenkinson M, Rueckert D. The developing human connectome project: A minimal processing pipeline for neonatal cortical surface reconstruction. Neuroimage 2018; 173:88-112. [PMID: 29409960 DOI: 10.1016/j.neuroimage.2018.01.054] [Citation(s) in RCA: 219] [Impact Index Per Article: 36.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/07/2017] [Revised: 01/19/2018] [Accepted: 01/21/2018] [Indexed: 12/11/2022] Open
Abstract
The Developing Human Connectome Project (dHCP) seeks to create the first 4-dimensional connectome of early life. Understanding this connectome in detail may provide insights into normal as well as abnormal patterns of brain development. Following established best practices adopted by the WU-MINN Human Connectome Project (HCP), and pioneered by FreeSurfer, the project utilises cortical surface-based processing pipelines. In this paper, we propose a fully automated processing pipeline for the structural Magnetic Resonance Imaging (MRI) of the developing neonatal brain. This proposed pipeline consists of a refined framework for cortical and sub-cortical volume segmentation, cortical surface extraction, and cortical surface inflation, which has been specifically designed to address considerable differences between adult and neonatal brains, as imaged using MRI. Using the proposed pipeline our results demonstrate that images collected from 465 subjects ranging from 28 to 45 weeks post-menstrual age (PMA) can be processed fully automatically; generating cortical surface models that are topologically correct, and correspond well with manual evaluations of tissue boundaries in 85% of cases. Results improve on state-of-the-art neonatal tissue segmentation models and significant errors were found in only 2% of cases, where these corresponded to subjects with high motion. Downstream, these surfaces will enhance comparisons of functional and diffusion MRI datasets, supporting the modelling of emerging patterns of brain connectivity.
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Affiliation(s)
- Antonios Makropoulos
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Emma C Robinson
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom; Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom.
| | - Andreas Schuh
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Robert Wright
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Sean Fitzgibbon
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Jelena Bozek
- Faculty of Electrical Engineering and Computing, University of Zagreb, Zagreb, Croatia
| | - Serena J Counsell
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Johannes Steinweg
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Katy Vecchiato
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Jonathan Passerat-Palmbach
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Gregor Lenz
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Filippo Mortari
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Tencho Tenev
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
| | - Eugene P Duff
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Matteo Bastiani
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Lucilio Cordero-Grande
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Emer Hughes
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Nora Tusor
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Jacques-Donald Tournier
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Jana Hutter
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Anthony N Price
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Rui Pedro A G Teixeira
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Maria Murgasova
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Suresh Victor
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Christopher Kelly
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Mary A Rutherford
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Stephen M Smith
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - A David Edwards
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Joseph V Hajnal
- Centre for the Developing Brain, School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom
| | - Mark Jenkinson
- Wellcome Centre for Integrative Neuroimaging, FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
| | - Daniel Rueckert
- Biomedical Image Analysis Group, Department of Computing, Imperial College London, London, United Kingdom
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41
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Meng Y, Li G, Gao Y, Lin W, Shen D. Learning-based subject-specific estimation of dynamic maps of cortical morphology at missing time points in longitudinal infant studies. Hum Brain Mapp 2018; 37:4129-4147. [PMID: 27380969 DOI: 10.1002/hbm.23301] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/30/2015] [Revised: 05/20/2016] [Accepted: 06/20/2016] [Indexed: 12/13/2022] Open
Abstract
Longitudinal neuroimaging analysis of the dynamic brain development in infants has received increasing attention recently. Many studies expect a complete longitudinal dataset in order to accurately chart the brain developmental trajectories. However, in practice, a large portion of subjects in longitudinal studies often have missing data at certain time points, due to various reasons such as the absence of scan or poor image quality. To make better use of these incomplete longitudinal data, in this paper, we propose a novel machine learning-based method to estimate the subject-specific, vertex-wise cortical morphological attributes at the missing time points in longitudinal infant studies. Specifically, we develop a customized regression forest, named dynamically assembled regression forest (DARF), as the core regression tool. DARF ensures the spatial smoothness of the estimated maps for vertex-wise cortical morphological attributes and also greatly reduces the computational cost. By employing a pairwise estimation followed by a joint refinement, our method is able to fully exploit the available information from both subjects with complete scans and subjects with missing scans for estimation of the missing cortical attribute maps. The proposed method has been applied to estimating the dynamic cortical thickness maps at missing time points in an incomplete longitudinal infant dataset, which includes 31 healthy infant subjects, each having up to five time points in the first postnatal year. The experimental results indicate that our proposed framework can accurately estimate the subject-specific vertex-wise cortical thickness maps at missing time points, with the average error less than 0.23 mm. Hum Brain Mapp 37:4129-4147, 2016. © 2016 Wiley Periodicals, Inc.
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Affiliation(s)
- Yu Meng
- Department of Computer Science, University of North Carolina at Chapel Hill, North Carolina.,Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina
| | - Gang Li
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina.
| | - Yaozong Gao
- Department of Computer Science, University of North Carolina at Chapel Hill, North Carolina.,Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina
| | - Weili Lin
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, North Carolina. .,Department of Brain and Cognitive Engineering, Korea University, Seoul, Republic of Korea.
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42
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Meng Y, Li G, Rekik I, Zhang H, Gao Y, Lin W, Shen D. Can we predict subject-specific dynamic cortical thickness maps during infancy from birth? Hum Brain Mapp 2017; 38:2865-2874. [PMID: 28295833 PMCID: PMC5426957 DOI: 10.1002/hbm.23555] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/18/2016] [Revised: 01/28/2017] [Accepted: 02/21/2017] [Indexed: 11/08/2022] Open
Abstract
Understanding the early dynamic development of the human cerebral cortex remains a challenging problem. Cortical thickness, as one of the most important morphological attributes of the cerebral cortex, is a sensitive indicator for both normal neurodevelopment and neuropsychiatric disorders, but its early postnatal development remains largely unexplored. In this study, we investigate a key question in neurodevelopmental science: can we predict the future dynamic development of cortical thickness map in an individual infant based on its available MRI data at birth? If this is possible, we might be able to better model and understand the early brain development and also early detect abnormal brain development during infancy. To this end, we develop a novel learning-based method, called Dynamically-Assembled Regression Forest (DARF), to predict the development of the cortical thickness map during the first postnatal year, based on neonatal MRI features. We applied our method to 15 healthy infants and predicted their cortical thickness maps at 3, 6, 9, and 12 months of age, with respectively mean absolute errors of 0.209 mm, 0.332 mm, 0.340 mm, and 0.321 mm. Moreover, we found that the prediction precision is region-specific, with high precision in the unimodal cortex and relatively low precision in the high-order association cortex, which may be associated with their differential developmental patterns. Additional experiments also suggest that using more early time points for prediction can further significantly improve the prediction accuracy. Hum Brain Mapp 38:2865-2874, 2017. © 2017 Wiley Periodicals, Inc.
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Affiliation(s)
- Yu Meng
- Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillNorth Carolina
- Department of Computer ScienceUniversity of North Carolina at Chapel HillChapel HillNorth Carolina
| | - Gang Li
- Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillNorth Carolina
| | - Islem Rekik
- Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillNorth Carolina
| | - Han Zhang
- Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillNorth Carolina
| | - Yaozong Gao
- Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillNorth Carolina
- Department of Computer ScienceUniversity of North Carolina at Chapel HillChapel HillNorth Carolina
| | - Weili Lin
- Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillNorth Carolina
| | - Dinggang Shen
- Department of Radiology and BRICUniversity of North Carolina at Chapel HillChapel HillNorth Carolina
- Department of Brain and Cognitive EngineeringKorea UniversitySeoulRepublic of Korea
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43
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Peng B, Lu J, Saxena A, Zhou Z, Zhang T, Wang S, Dai Y. Examining Brain Morphometry Associated with Self-Esteem in Young Adults Using Multilevel-ROI-Features-Based Classification Method. Front Comput Neurosci 2017; 11:37. [PMID: 28588470 PMCID: PMC5438414 DOI: 10.3389/fncom.2017.00037] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2016] [Accepted: 05/04/2017] [Indexed: 11/29/2022] Open
Abstract
Purpose: This study is to exam self-esteem related brain morphometry on brain magnetic resonance (MR) images using multilevel-features-based classification method. Method: The multilevel region of interest (ROI) features consist of two types of features: (i) ROI features, which include gray matter volume, white matter volume, cerebrospinal fluid volume, cortical thickness, and cortical surface area, and (ii) similarity features, which are based on similarity calculation of cortical thickness between ROIs. For each feature type, a hybrid feature selection method, comprising of filter-based and wrapper-based algorithms, is used to select the most discriminating features. ROI features and similarity features are integrated by using multi-kernel support vector machines (SVMs) with appropriate weighting factor. Results: The classification performance is improved by using multilevel ROI features with an accuracy of 96.66%, a specificity of 96.62%, and a sensitivity of 95.67%. The most discriminating ROI features that are related to self-esteem spread over occipital lobe, frontal lobe, parietal lobe, limbic lobe, temporal lobe, and central region, mainly involving white matter and cortical thickness. The most discriminating similarity features are distributed in both the right and left hemisphere, including frontal lobe, occipital lobe, limbic lobe, parietal lobe, and central region, which conveys information of structural connections between different brain regions. Conclusion: By using ROI features and similarity features to exam self-esteem related brain morphometry, this paper provides a pilot evidence that self-esteem is linked to specific ROIs and structural connections between different brain regions.
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Affiliation(s)
- Bo Peng
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of SciencesSuzhou, China.,University of Chinese Academy of SciencesBeijing, China.,Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of SciencesChangchun, China
| | - Jieru Lu
- School of Information Science and Engineering, Changzhou UniversityChangzhou, China
| | - Aditya Saxena
- Trauma Center, Khandwa District HospitalKhandwa, India
| | - Zhiyong Zhou
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of SciencesSuzhou, China
| | - Tao Zhang
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of SciencesChangchun, China
| | - Suhong Wang
- Department of Neuroscience, The Third Affiliated Hospital of Soochow UniversityChangzhou, China
| | - Yakang Dai
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of SciencesSuzhou, China
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Rekik I, Li G, Yap PT, Chen G, Lin W, Shen D. Joint prediction of longitudinal development of cortical surfaces and white matter fibers from neonatal MRI. Neuroimage 2017; 152:411-424. [PMID: 28284800 PMCID: PMC5432411 DOI: 10.1016/j.neuroimage.2017.03.012] [Citation(s) in RCA: 6] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/06/2016] [Revised: 03/05/2017] [Accepted: 03/06/2017] [Indexed: 10/20/2022] Open
Abstract
The human brain can be modeled as multiple interrelated shapes (or a multishape), each for characterizing one aspect of the brain, such as the cortex and white matter pathways. Predicting the developing multishape is a very challenging task due to the contrasting nature of the developmental trajectories of the constituent shapes: smooth for the cortical surface and non-smooth for white matter tracts due to changes such as bifurcation. We recently addressed this problem and proposed an approach for predicting the multishape developmental spatiotemporal trajectories of infant brains based only on neonatal MRI data using a set of geometric, dynamic, and fiber-to-surface connectivity features. In this paper, we propose two key innovations to further improve the prediction of multishape evolution. First, for a more accurate cortical surface prediction, instead of simply relying on one neonatal atlas to guide the prediction of the multishape, we propose to use multiple neonatal atlases to build a spatially heterogeneous atlas using the multidirectional varifold representation. This individualizes the atlas by locally maximizing its similarity to the testing baseline cortical shape for each cortical region, thereby better representing the baseline testing cortical surface, which founds the multishape prediction process. Second, for temporally consistent fiber prediction, we propose to reliably estimate spatiotemporal connectivity features using low-rank tensor completion, thereby capturing the variability and richness of the temporal development of fibers. Experimental results confirm that the proposed variants significantly improve the prediction performance of our original multishape prediction framework for both cortical surfaces and fiber tracts shape at 3, 6, and 9 months of age. Our pioneering model will pave the way for learning how to predict the evolution of anatomical shapes with abnormal changes. Ultimately, devising accurate shape evolution prediction models that can help quantify and predict the severity of a brain disorder as it progresses will be of great aid in individualized treatment planning.
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Affiliation(s)
- Islem Rekik
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA; CVIP, Computing, School of Science and Engineering, University of Dundee, UK
| | - Gang Li
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA
| | - Pew-Thian Yap
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA
| | - Geng Chen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA
| | - Weili Lin
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA; Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea.
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45
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Peng B, Wang S, Zhou Z, Liu Y, Tong B, Zhang T, Dai Y. A multilevel-ROI-features-based machine learning method for detection of morphometric biomarkers in Parkinson's disease. Neurosci Lett 2017; 651:88-94. [PMID: 28435046 DOI: 10.1016/j.neulet.2017.04.034] [Citation(s) in RCA: 42] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2017] [Revised: 04/07/2017] [Accepted: 04/18/2017] [Indexed: 11/16/2022]
Abstract
Machine learning methods have been widely used in recent years for detection of neuroimaging biomarkers in regions of interest (ROIs) and assisting diagnosis of neurodegenerative diseases. The innovation of this study is to use multilevel-ROI-features-based machine learning method to detect sensitive morphometric biomarkers in Parkinson's disease (PD). Specifically, the low-level ROI features (gray matter volume, cortical thickness, etc.) and high-level correlative features (connectivity between ROIs) are integrated to construct the multilevel ROI features. Filter- and wrapper- based feature selection method and multi-kernel support vector machine (SVM) are used in the classification algorithm. T1-weighted brain magnetic resonance (MR) images of 69 PD patients and 103 normal controls from the Parkinson's Progression Markers Initiative (PPMI) dataset are included in the study. The machine learning method performs well in classification between PD patients and normal controls with an accuracy of 85.78%, a specificity of 87.79%, and a sensitivity of 87.64%. The most sensitive biomarkers between PD patients and normal controls are mainly distributed in frontal lobe, parental lobe, limbic lobe, temporal lobe, and central region. The classification performance of our method with multilevel ROI features is significantly improved comparing with other classification methods using single-level features. The proposed method shows promising identification ability for detecting morphometric biomarkers in PD, thus confirming the potentiality of our method in assisting diagnosis of the disease.
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Affiliation(s)
- Bo Peng
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu 215163, China; University of Chinese Academy of Sciences, Beijing 100049, China; Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, Jilin 130033, China
| | - Suhong Wang
- Department of Neuroscience, The Third Affiliated Hospital of Soochow University, Changzhou, Jiangsu 213003, China
| | - Zhiyong Zhou
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu 215163, China
| | - Yan Liu
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu 215163, China
| | - Baotong Tong
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu 215163, China
| | - Tao Zhang
- Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, Jilin 130033, China
| | - Yakang Dai
- Suzhou Institute of Biomedical Engineering and Technology, Chinese Academy of Sciences, Suzhou, Jiangsu 215163, China.
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46
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Examining population differences in cerebral morphometry between Chinese and Indian undergraduate students. Neurosci Lett 2017; 636:290-297. [PMID: 27838451 DOI: 10.1016/j.neulet.2016.11.021] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2016] [Revised: 10/25/2016] [Accepted: 11/08/2016] [Indexed: 11/24/2022]
Abstract
The aim of this study is to examine potential population differences in brain morphometry using magnetic resonance imaging (MRI). Thirty-six Chinese and thirty-two Indian undergraduate students are included in this study. All images are processed using BrainLab toolbox to obtain the morphometric values of gray matter volume, cortical thickness, and cortical surface area in each region of interest (ROI). We use ROI-based analysis to investigate ethnic differences using the three types of measurements. Cerebral variations of the brain between Chinese and Indian groups are mostly distributed in the frontal lobe, temporal lobe, and occipital lobe. Subgroup analysis reveals sex differences between the two groups. Our study demonstrates population-related differences in brain morphometry (gray matter volume, cortical thickness, and cortical surface area) between Chinese and Indian undergraduates.
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47
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Rekik I, Li G, Lin W, Shen D. Multidirectional and Topography-based Dynamic-scale Varifold Representations with Application to Matching Developing Cortical Surfaces. Neuroimage 2016; 135:152-62. [PMID: 27138207 DOI: 10.1016/j.neuroimage.2016.04.037] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2015] [Revised: 04/13/2016] [Accepted: 04/15/2016] [Indexed: 01/22/2023] Open
Abstract
The human cerebral cortex is marked by great complexity as well as substantial dynamic changes during early postnatal development. To obtain a fairly comprehensive picture of its age-induced and/or disorder-related cortical changes, one needs to match cortical surfaces to one another, while maximizing their anatomical alignment. Methods that geodesically shoot surfaces into one another as currents (a distribution of oriented normals) and varifolds (a distribution of non-oriented normals) provide an elegant Riemannian framework for generic surface matching and reliable statistical analysis. However, both conventional current and varifold matching methods have two key limitations. First, they only use the normals of the surface to measure its geometry and guide the warping process, which overlooks the importance of the orientations of the inherently convoluted cortical sulcal and gyral folds. Second, the 'conversion' of a surface into a current or a varifold operates at a fixed scale under which geometric surface details will be neglected, which ignores the dynamic scales of cortical foldings. To overcome these limitations and improve varifold-based cortical surface registration, we propose two different strategies. The first strategy decomposes each cortical surface into its normal and tangent varifold representations, by integrating principal curvature direction field into the varifold matching framework, thus providing rich information of the orientation of cortical folding and better characterization of the complex cortical geometry. The second strategy explores the informative cortical geometric features to perform a dynamic-scale measurement of the cortical surface that depends on the local surface topography (e.g., principal curvature), thereby we introduce the concept of a topography-based dynamic-scale varifold. We tested the proposed varifold variants for registering 12 pairs of dynamically developing cortical surfaces from 0 to 6 months of age. Both variants improved the matching accuracy in terms of closeness to the target surface and the goodness of alignment with regional anatomical boundaries, when compared with three state-of-the-art methods: (1) diffeomorphic spectral matching, (2) conventional current-based surface matching, and (3) conventional varifold-based surface matching.
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Affiliation(s)
- Islem Rekik
- 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
| | - 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; Department of Brain and Cognitive Engineering, Korea University, Seoul 02841, Republic of Korea.
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48
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Rekik I, Li G, Lin W, Shen D. Predicting infant cortical surface development using a 4D varifold-based learning framework and local topography-based shape morphing. Med Image Anal 2016; 28:1-12. [PMID: 26619188 PMCID: PMC4914136 DOI: 10.1016/j.media.2015.10.007] [Citation(s) in RCA: 15] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/28/2015] [Revised: 08/20/2015] [Accepted: 10/23/2015] [Indexed: 12/27/2022]
Abstract
Longitudinal neuroimaging analysis methods have remarkably advanced our understanding of early postnatal brain development. However, learning predictive models to trace forth the evolution trajectories of both normal and abnormal cortical shapes remains broadly absent. To fill this critical gap, we pioneered the first prediction model for longitudinal developing cortical surfaces in infants using a spatiotemporal current-based learning framework solely from the baseline cortical surface. In this paper, we detail this prediction model and even further improve its performance by introducing two key variants. First, we use the varifold metric to overcome the limitations of the current metric for surface registration that was used in our preliminary study. We also extend the conventional varifold-based surface registration model for pairwise registration to a spatiotemporal surface regression model. Second, we propose a morphing process of the baseline surface using its topographic attributes such as normal direction and principal curvature sign. Specifically, our method learns from longitudinal data both the geometric (vertices positions) and dynamic (temporal evolution trajectories) features of the infant cortical surface, comprising a training stage and a prediction stage. In the training stage, we use the proposed varifold-based shape regression model to estimate geodesic cortical shape evolution trajectories for each training subject. We then build an empirical mean spatiotemporal surface atlas. In the prediction stage, given an infant, we select the best learnt features from training subjects to simultaneously predict the cortical surface shapes at all later timepoints, based on similarity metrics between this baseline surface and the learnt baseline population average surface atlas. We used a leave-one-out cross validation method to predict the inner cortical surface shape at 3, 6, 9 and 12 months of age from the baseline cortical surface shape at birth. Our method attained a higher prediction accuracy and better captured the spatiotemporal dynamic change of the highly folded cortical surface than the previous proposed prediction method.
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Affiliation(s)
- Islem Rekik
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA.
| | - Gang Li
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA
| | - Weili Lin
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA.
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49
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Li G, Liu T, Ni D, Lin W, Gilmore JH, Shen D. Spatiotemporal patterns of cortical fiber density in developing infants, and their relationship with cortical thickness. Hum Brain Mapp 2015; 36:5183-95. [PMID: 26417847 PMCID: PMC4715737 DOI: 10.1002/hbm.23003] [Citation(s) in RCA: 27] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2014] [Revised: 09/14/2015] [Accepted: 09/15/2015] [Indexed: 12/20/2022] Open
Abstract
The intrinsic relationship between the convoluted cortical folding and the underlying complex whiter matter fiber connections has received increasing attention in current neuroscience studies. Recently, the axonal pushing hypothesis of cortical folding has been proposed to explain the finding that the axonal fibers (derived from diffusion tensor images) connecting to gyri are significantly denser than those connecting to sulci in both adult human and non-human primate brains. However, it is still unclear about the spatiotemporal patterns of the fiber density on the cortical surface of the developing infant brains from birth to 2 years of age, which is the most dynamic phase of postnatal brain development. In this paper, for the first time, we systemically characterized the spatial distributions and longitudinal developmental trajectories of the cortical fiber density in the first 2 postnatal years, via joint analysis of longitudinal structural and diffusion tensor imaging from 33 healthy infants. We found that the cortical fiber density increases dramatically in the first year and then keeps relatively stable in the second year. Moreover, we revealed that the cortical fiber density on gyral regions was significantly higher at 0, 1, and 2 years of age than that on sulcal regions in the frontal, temporal, and parietal lobes. Meanwhile, the cortical fiber density was strongly positively correlated with cortical thickness at several three-hinge junction regions of gyri. These results significantly advanced our understanding of the intrinsic relationship between the cortical folding, cortical thickness and axonal wiring during early postnatal stages.
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Affiliation(s)
- Gang Li
- Department of Radiology and BRICUniversity of North Carolina at Chapel HillNorth Carolina
| | - Tianming Liu
- Department of Computer Science and Bioimaging Research CenterThe University of GeorgiaAthensGeorgia
| | - Dong Ni
- Department of Biomedical Engineering, The Guangdong Key Laboratory for Biomedical Measurements and Ultrasound ImagingShenzhen UniversityShenzhenChina
| | - Weili Lin
- Department of Radiology and BRICUniversity of North Carolina at Chapel HillNorth Carolina
| | - John H. Gilmore
- Department of PsychiatryUniversity of North Carolina at Chapel HillNorth Carolina
| | - Dinggang Shen
- Department of Radiology and BRICUniversity of North Carolina at Chapel HillNorth Carolina
- Department of Brain and Cognitive EngineeringKorea UniversitySeoulRepublic of Korea
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Li G, Wang L, Gilmore JH, Lin W, Shen D. Parcellation of Infant Surface Atlas Using Developmental Trajectories of Multidimensional Cortical Attributes. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2015; 9351:543-550. [PMID: 27169139 PMCID: PMC4860270 DOI: 10.1007/978-3-319-24574-4_65] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/13/2023]
Abstract
Cortical surface atlases, equipped with anatomically and functionally defined parcellations, are of fundamental importance in neuroimaging studies. Typically, parcellations of surface atlases are derived based on the sulcal-gyral landmarks, which are extremely variable across individuals and poorly matched with microstructural and functional boundaries. Cortical developmental trajectories in infants reflect underlying changes of microstructures, which essentially determines the molecular organization and functional principles of the cortex, thus allowing better definition of developmentally, microstructurally, and functionally distinct regions, compared to conventional sulcal-gyral landmarks. Accordingly, a parcellation of infant cortical surface atlas was proposed, based on the developmental trajectories of cortical thickness in infants, revealing regional patterning of cortical growth. However, cortical anatomy is jointly characterized by biologically-distinct, multidimensional cortical attributes, i.e., cortical thickness, surface area, and local gyrification, each with its distinct genetic underpinning, cellular mechanism, and developmental trajectories. To date, the parcellations based on the development of surface area and local gyrification is still missing. To bridge this critical gap, for the first time, we parcellate an infant cortical surface atlas into distinct regions based solely on developmental trajectories of surface area and local gyrification, respectively. For each cortical attribute, we first nonlinearly fuse the subject-specific similarity matrices of vertices' developmental trajectories of all subjects into a single matrix, which helps better capture common and complementary information of the population than the conventional method of simple averaging of all subjects' matrices. Then, we perform spectral clustering based on this fused matrix. We have applied our method to parcellate an infant surface atlas using the developmental trajectories of surface area and local gyrification from 35 healthy infants, each with up to 7 time points in the first two postnatal years, revealing biologically more meaningful growth patterning than the conventional method.
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Affiliation(s)
- Gang Li
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA
| | - Li Wang
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA
| | - John H. Gilmore
- Department of Psychiatry, University of North Carolina at Chapel Hill, NC 27599, USA
| | - Weili Lin
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA
| | - Dinggang Shen
- Department of Radiology and BRIC, University of North Carolina at Chapel Hill, NC, USA
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