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Lin Q, Jin S, Yin G, Li J, Asgher U, Qiu S, Wang J. Cortical Morphological Networks Differ Between Gyri and Sulci. Neurosci Bull 2024:10.1007/s12264-024-01262-7. [PMID: 39044060 DOI: 10.1007/s12264-024-01262-7] [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: 12/07/2023] [Accepted: 03/28/2024] [Indexed: 07/25/2024] Open
Abstract
This study explored how the human cortical folding pattern composed of convex gyri and concave sulci affected single-subject morphological brain networks, which are becoming an important method for studying the human brain connectome. We found that gyri-gyri networks exhibited higher morphological similarity, lower small-world parameters, and lower long-term test-retest reliability than sulci-sulci networks for cortical thickness- and gyrification index-based networks, while opposite patterns were observed for fractal dimension-based networks. Further behavioral association analysis revealed that gyri-gyri networks and connections between gyral and sulcal regions significantly explained inter-individual variance in Cognition and Motor domains for fractal dimension- and sulcal depth-based networks. Finally, the clinical application showed that only sulci-sulci networks exhibited morphological similarity reductions in major depressive disorder for cortical thickness-, fractal dimension-, and gyrification index-based networks. Taken together, these findings provide novel insights into the constraint of the cortical folding pattern to the network organization of the human brain.
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Affiliation(s)
- Qingchun Lin
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, 510631, China
| | - Suhui Jin
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, 510631, China
| | - Guole Yin
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, 510631, China
| | - Junle Li
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, 510631, China
| | - Umer Asgher
- Department of Air Transport, Faculty of Transportation Sciences, Czech Technical University in Prague (CTU), Prague, 128 00, Czech Republic
- School of Interdisciplinary Engineering and Sciences (SINES), National University of Science and Technology (NUST), Islamabad, 44000, Pakistan
| | - Shijun Qiu
- Department of Radiology, The First Affiliated Hospital of Guangzhou University of Chinese Medicine, Guangzhou, 510405, China
| | - Jinhui Wang
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, 510631, China.
- Key Laboratory of Brain, Cognition and Education Sciences, Ministry of Education, Guangzhou, 510631, China.
- Center for Studies of Psychological Application, South China Normal University, Guangzhou, 510631, China.
- Guangdong Key Laboratory of Mental Health and Cognitive Science, South China Normal University, Guangzhou, 510631, China.
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Cao C, Li Y, Hu F, Gao X. Modeling refined differences of cortical folding patterns via spatial, morphological, and temporal fusion representations. Cereb Cortex 2024; 34:bhae146. [PMID: 38602743 DOI: 10.1093/cercor/bhae146] [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: 12/19/2023] [Revised: 03/18/2024] [Accepted: 03/20/2024] [Indexed: 04/12/2024] Open
Abstract
The gyrus, a pivotal cortical folding pattern, is essential for integrating brain structure-function. This study focuses on 2-Hinge and 3-Hinge folds, characterized by the gyral convergence from various directions. Existing voxel-level studies may not adequately capture the precise spatial relationships within cortical folding patterns, especially when relying solely on local cortical characteristics due to their variable shapes and homogeneous frequency-specific features. To overcome these challenges, we introduced a novel model that combines spatial distribution, morphological structure, and functional magnetic resonance imaging data. We utilized spatio-morphological residual representations to enhance and extract subtle variations in cortical spatial distribution and morphological structure during blood oxygenation, integrating these with functional magnetic resonance imaging embeddings using self-attention for spatio-morphological-temporal representations. Testing these representations for identifying cortical folding patterns, including sulci, gyri, 2-Hinge, and 2-Hinge folds, and evaluating the impact of phenotypic data (e.g. stimulus) on recognition, our experimental results demonstrate the model's superior performance, revealing significant differences in cortical folding patterns under various stimulus. These differences are also evident in the characteristics of sulci and gyri folds between genders, with 3-Hinge showing more variations. Our findings indicate that our representations of cortical folding patterns could serve as biomarkers for understanding brain structure-function correlations.
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Affiliation(s)
- Chunhong Cao
- The MOE Key Laboratory of Intelligent Computing and Information Processing, Xiangtan University, 411005 Xiangtan, China
| | - Yongquan Li
- The MOE Key Laboratory of Intelligent Computing and Information Processing, Xiangtan University, 411005 Xiangtan, China
| | - Fang Hu
- The Key Laboratory of Medical Imaging and Artificial Intelligence of Hunan Province, Xiangnan University, 423043 Chenzhou, China
| | - Xieping Gao
- The Hunan Provincial Key Laboratory of Intelligent Computing and Language Information Processing, Hunan Normal University, 410081 Changsha, China
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3
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Zhao L, Dai H, Wu Z, Jiang X, Zhu D, Zhang T, Liu T. Hierarchical functional differences between gyri and sulci at different scales. Cereb Cortex 2024; 34:bhae057. [PMID: 38483143 DOI: 10.1093/cercor/bhae057] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2023] [Revised: 01/16/2024] [Accepted: 01/18/2024] [Indexed: 03/26/2024] Open
Abstract
Gyri and sulci are 2 fundamental cortical folding patterns of the human brain. Recent studies have suggested that gyri and sulci may play different functional roles given their structural and functional heterogeneity. However, our understanding of the functional differences between gyri and sulci remains limited due to several factors. Firstly, previous studies have typically focused on either the spatial or temporal domain, neglecting the inherently spatiotemporal nature of brain functions. Secondly, analyses have often been restricted to either local or global scales, leaving the question of hierarchical functional differences unresolved. Lastly, there has been a lack of appropriate analytical tools for interpreting the hierarchical spatiotemporal features that could provide insights into these differences. To overcome these limitations, in this paper, we proposed a novel hierarchical interpretable autoencoder (HIAE) to explore the hierarchical functional difference between gyri and sulci. Central to our approach is its capability to extract hierarchical features via a deep convolutional autoencoder and then to map these features into an embedding vector using a carefully designed feature interpreter. This process transforms the features into interpretable spatiotemporal patterns, which are pivotal in investigating the functional disparities between gyri and sulci. We evaluate the proposed framework on Human Connectome Project task functional magnetic resonance imaging dataset. The experiments demonstrate that the HIAE model can effectively extract and interpret hierarchical spatiotemporal features that are neuroscientifically meaningful. The analyses based on the interpreted features suggest that gyri are more globally activated, whereas sulci are more locally activated, demonstrating a distinct transition in activation patterns as the scale shifts from local to global. Overall, our study provides novel insights into the brain's anatomy-function relationship.
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Affiliation(s)
- Lin Zhao
- School of Computing, University of Georgia, Athens, GA 30602, USA
| | - Haixing Dai
- School of Computing, University of Georgia, Athens, GA 30602, USA
| | - Zihao Wu
- School of Computing, University of Georgia, Athens, GA 30602, USA
| | - Xi Jiang
- MOE Key Laboratory for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 611731, China
| | - Dajiang Zhu
- Department of Computer Science and Engineering, University of Texas at Arlington, Arlington, TX 76013, USA
| | - Tuo Zhang
- School of Automation, Northwestern Polytechnical University, Xi'an 710129, China
| | - Tianming Liu
- School of Computing, University of Georgia, Athens, GA 30602, USA
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Wang L, Yang Y, Hu X, Zhao S, Jiang X, Guo L, Han J, Liu T. Frequency-specific functional difference between gyri and sulci in naturalistic paradigm fMRI. Brain Struct Funct 2024; 229:431-442. [PMID: 38193918 DOI: 10.1007/s00429-023-02746-4] [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: 08/23/2023] [Accepted: 12/04/2023] [Indexed: 01/10/2024]
Abstract
Disentangling functional difference between cortical folding patterns of gyri and sulci provides novel insights into the relationship between brain structure and function. Previous studies using resting-state functional magnetic resonance imaging (rsfMRI) have revealed that sulcal signals exhibit stronger high-frequency but weaker low-frequency components compared to gyral ones, suggesting that gyri may serve as functional integration centers while sulci are segregated local processing units. In this study, we utilize naturalistic paradigm fMRI (nfMRI) to explore the functional difference between gyri and sulci as it has proven to record stronger functional integrations compared to rsfMRI. We adopt a convolutional neural network (CNN) to classify gyral and sulcal fMRI signals in the whole brain (the global model) and within functional brain networks (the local models). The frequency-specific difference between gyri and sulci is then inferred from the power spectral density (PSD) profiles of the learned filters in the CNN model. Our experimental results show that nfMRI shows higher gyral-sulcal PSD contrast effect sizes in the global model compared to rsfMRI. In the local models, the effect sizes are either increased or decreased depending on frequency bands and functional complexity of the FBNs. This study highlights the advantages of nfMRI in depicting the functional difference between gyri and sulci, and provides novel insights into unraveling the relationship between brain structure and function.
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Affiliation(s)
- Liting Wang
- School of Automation, Northwestern Polytechnical University, Xi'an, Shaanxi, China
| | - Yang Yang
- School of Automation, Northwestern Polytechnical University, Xi'an, Shaanxi, China
| | - Xintao Hu
- School of Automation, Northwestern Polytechnical University, Xi'an, Shaanxi, China.
| | - Shijie Zhao
- School of Automation, Northwestern Polytechnical University, Xi'an, Shaanxi, China
| | - Xi Jiang
- School of Life Science and Technology, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Lei Guo
- School of Automation, Northwestern Polytechnical University, Xi'an, Shaanxi, China
| | - Junwei Han
- School of Automation, Northwestern Polytechnical University, Xi'an, Shaanxi, China
| | - Tianming Liu
- School of Computing, University of Georgia, Athens, USA
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Barresi M, Hickmott RA, Bosakhar A, Quezada S, Quigley A, Kawasaki H, Walker D, Tolcos M. Toward a better understanding of how a gyrified brain develops. Cereb Cortex 2024; 34:bhae055. [PMID: 38425213 DOI: 10.1093/cercor/bhae055] [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: 04/24/2023] [Revised: 01/26/2024] [Accepted: 01/28/2024] [Indexed: 03/02/2024] Open
Abstract
The size and shape of the cerebral cortex have changed dramatically across evolution. For some species, the cortex remains smooth (lissencephalic) throughout their lifetime, while for other species, including humans and other primates, the cortex increases substantially in size and becomes folded (gyrencephalic). A folded cortex boasts substantially increased surface area, cortical thickness, and neuronal density, and it is therefore associated with higher-order cognitive abilities. The mechanisms that drive gyrification in some species, while others remain lissencephalic despite many shared neurodevelopmental features, have been a topic of investigation for many decades, giving rise to multiple perspectives of how the gyrified cerebral cortex acquires its unique shape. Recently, a structurally unique germinal layer, known as the outer subventricular zone, and the specialized cell type that populates it, called basal radial glial cells, were identified, and these have been shown to be indispensable for cortical expansion and folding. Transcriptional analyses and gene manipulation models have provided an invaluable insight into many of the key cellular and genetic drivers of gyrification. However, the degree to which certain biomechanical, genetic, and cellular processes drive gyrification remains under investigation. This review considers the key aspects of cerebral expansion and folding that have been identified to date and how theories of gyrification have evolved to incorporate this new knowledge.
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Affiliation(s)
- Mikaela Barresi
- School of Health and Biomedical Sciences, RMIT University, Plenty Road, Bundoora, VIC 3083, Australia
- ACMD, St Vincent's Hospital Melbourne, Regent Street, Fitzroy, VIC 3065, Australia
| | - Ryan Alexander Hickmott
- School of Health and Biomedical Sciences, RMIT University, Plenty Road, Bundoora, VIC 3083, Australia
- ACMD, St Vincent's Hospital Melbourne, Regent Street, Fitzroy, VIC 3065, Australia
| | - Abdulhameed Bosakhar
- School of Health and Biomedical Sciences, RMIT University, Plenty Road, Bundoora, VIC 3083, Australia
| | - Sebastian Quezada
- School of Health and Biomedical Sciences, RMIT University, Plenty Road, Bundoora, VIC 3083, Australia
| | - Anita Quigley
- School of Health and Biomedical Sciences, RMIT University, Plenty Road, Bundoora, VIC 3083, Australia
- ACMD, St Vincent's Hospital Melbourne, Regent Street, Fitzroy, VIC 3065, Australia
- School of Engineering, RMIT University, La Trobe Street, Melbourne, VIC 3000, Australia
- Department of Medicine, University of Melbourne, St Vincent's Hospital, Regent Street, Fitzroy, VIC 3065, Australia
| | - Hiroshi Kawasaki
- Department of Medical Neuroscience, Graduate School of Medical Sciences, Kanazawa University, Takara-machi 13-1, Kanazawa, Ishikawa 920-8640, Japan
| | - David Walker
- School of Health and Biomedical Sciences, RMIT University, Plenty Road, Bundoora, VIC 3083, Australia
| | - Mary Tolcos
- School of Health and Biomedical Sciences, RMIT University, Plenty Road, Bundoora, VIC 3083, Australia
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Wang Q, Zhao S, He Z, Zhang S, Jiang X, Zhang T, Liu T, Liu C, Han J. Modeling functional difference between gyri and sulci within intrinsic connectivity networks. Cereb Cortex 2023; 33:933-947. [PMID: 35332916 DOI: 10.1093/cercor/bhac111] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Revised: 02/17/2022] [Accepted: 02/18/2022] [Indexed: 11/12/2022] Open
Abstract
Recently, the functional roles of the human cortical folding patterns have attracted increasing interest in the neuroimaging community. However, most existing studies have focused on the gyro-sulcal functional relationship on a whole-brain scale but possibly overlooked the localized and subtle functional differences of brain networks. Actually, accumulating evidences suggest that functional brain networks are the basic unit to realize the brain function; thus, the functional relationships between gyri and sulci still need to be further explored within different functional brain networks. Inspired by these evidences, we proposed a novel intrinsic connectivity network (ICN)-guided pooling-trimmed convolutional neural network (I-ptFCN) to revisit the functional difference between gyri and sulci. By testing the proposed model on the task functional magnetic resonance imaging (fMRI) datasets of the Human Connectome Project, we found that the classification accuracy of gyral and sulcal fMRI signals varied significantly for different ICNs, indicating functional heterogeneity of cortical folding patterns in different brain networks. The heterogeneity may be contributed by sulci, as only sulcal signals show heterogeneous frequency features across different ICNs, whereas the frequency features of gyri are homogeneous. These results offer novel insights into the functional difference between gyri and sulci and enlighten the functional roles of cortical folding patterns.
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Affiliation(s)
- Qiyu Wang
- School of Automation, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, China
| | - Shijie Zhao
- School of Automation, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, China
| | - Zhibin He
- School of Automation, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, China
| | - Shu Zhang
- School of Computer Science, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, China
| | - Xi Jiang
- School of Life Science and Technology, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, Sichuan 611731, China
| | - Tuo Zhang
- School of Automation, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, China
| | - Tianming Liu
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA 30605, United States
| | - Cirong Liu
- CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Neuroscience, Chinese Academy of Sciences, Shanghai 200031, China
| | - Junwei Han
- School of Automation, Northwestern Polytechnical University, Xi'an, Shaanxi 710072, China
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NAS-optimized topology-preserving transfer learning for differentiating cortical folding patterns. Med Image Anal 2021; 77:102316. [PMID: 34979433 DOI: 10.1016/j.media.2021.102316] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Revised: 11/21/2021] [Accepted: 11/23/2021] [Indexed: 11/21/2022]
Abstract
Increasing evidence suggests that cortical folding patterns of human cerebral cortex manifest overt structural and functional differences. However, for interpretability, few studies leverage advanced techniques (e.g., deep learning) to investigate the difference among cortical folds, resulting in more differences yet to be extensively explored. To this end, we proposed an effective topology-preserving transfer learning framework to differentiate cortical fMRI time series extracted from cortical folds. Our framework consists of three main parts: (1) Neural architecture search (NAS), which is used to devise a well-performing network structure based on an initialized hand-designed super-graph in an image dataset; (2) Topology-preserving transfer, which takes the model searched by NAS as the source network, keeping the topological connectivity in the network unchanged, while transforming all 2D operations including convolution and pooling into 1D, therefore resulting in a topology-preserving network, named TPNAS-Net; (3) Classification and correlation analysis, which involves using the TPNAS-Net to classify 1D cortical fMRI time series for each individual brain, and performing a group difference analysis between autism spectrum disorder (ASD) and healthy control (HC) and correlation analysis with clinical information (i.e., age). Extensive experiments on two ASD datasets obtain consistent results, demonstrating that the TPNAS-Net not only discriminates cortical folding patterns at high classification accuracy, but also captures subtle differences between ASD and HC (p-value = 0.042). In addition, we discover that there is a positive correlation between the classification accuracy and age in ASD (r = 0.39, p-value = 0.04). These findings together suggest that structural and functional differences in cortical folding patterns between ASD and HC may provide a potentially useful biomarker for the diagnosis of ASD.
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He Z, Du L, Huang Y, Jiang X, Lv J, Guo L, Zhang S, Zhang T. Gyral Hinges Account for the Highest Cost and the Highest Communication Capacity in a Corticocortical Network. Cereb Cortex 2021; 32:3359-3376. [PMID: 34875041 DOI: 10.1093/cercor/bhab420] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 10/22/2021] [Accepted: 10/23/2021] [Indexed: 12/11/2022] Open
Abstract
Prior studies reported the global structure of brain networks exhibits the "small-world" and "rich-world" attributes. However, the underlying structural and functional architecture highlighted by these graph theory findings hasn't been explicitly related to the morphology of the cortex. This could be attributed to the lower resolution of used folding patterns, such as gyro-sulcal patterns. By defining a novel gyral folding pattern, termed gyral hinge (GH), which is the conjunction of ordinary gyri from multiple directions, we found GHs possess the highest length and cost in the white matter fiber connective network, and the shortest paths in the network tend to travel through GHs in their middle part. Based on these findings, we would hypothesize GHs could reside in the centers of a network core, thereby accounting for the highest cost and the highest communication capacity in a corticocortical network. The following results further support our hypothesis: 1) GHs possess stronger functional network integration capacity. 2) Higher cost is found on the connection with GHs to hinges and GHs to GHs. 3) Moving GHs introduces higher extra network cost. Our findings and hypotheses could reveal a profound relationship among the cortical folding patterns, axonal wiring architectures, and brain functions.
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Affiliation(s)
- Zhibin He
- School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
| | - Lei Du
- School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
| | - Ying Huang
- School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
| | - Xi Jiang
- School of Life Science and Technology, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Jinglei Lv
- School of Biomedical Engineering, Sydney Imaging, Brain and Mind Centre, The University of Sydney, Camperdown, NSW 2050, Australia
| | - Lei Guo
- School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
| | - Shu Zhang
- School of Computer Science, Northwestern Polytechnical University, Xi'an 710072, China
| | - Tuo Zhang
- School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
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Zhao L, Zhang T, Guo L, Liu T, Jiang X. Gyral-sulcal contrast in intrinsic functional brain networks across task performances. Brain Imaging Behav 2021; 15:1483-1498. [PMID: 32700255 DOI: 10.1007/s11682-020-00347-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Abstract
Functional mechanism of the brain and its relationship with the brain structural substrate have been an interest for multiple disciplines for centuries. Recently, gyri and sulci, two basic cortical folding patterns, have been demonstrated to act different functional roles. Specifically, a variety of functional MRI (fMRI) studies have consistently suggested that gyri represent a global functional center while sulci serve as a local functional unit under either resting state or task stimulus, which are further supported by brain structural analysis reporting that gyri have thicker cortex and denser long-distance axonal fibers. However, the consistency of such gyral-sulcal functional difference across different task stimuli, as well as its association with task conditions, remains to be explored. To this end, we used intrinsic networks as the testbed for cross-task comparison, and adopted a computational framework of dictionary learning and sparse representation of whole-brain fMRI signals to systematically examine the potential gyral-sulcal difference in signal representation residual (SRR) which reflected the degree of global functional communication. Using all seven task-based fMRI datasets in Human Connectome Project Q1 release, we found that within the intrinsic functional networks, the fMRI SRR was significantly smaller on gyral regions than on sulcal regions across different task stimuli, indicating that gyral regions were more involved in global functions of the brain and interregional communications. Moreover, the magnitudes of such gyral-sulcal difference varied across task conditions and intrinsic networks. Our work adds novel explanation and insight to the existing knowledge of functional differences between gyri and sulci.
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Affiliation(s)
- Lin Zhao
- School of Automation, Northwestern Polytechnical University, Xi'an, China.,Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA
| | - Tuo Zhang
- School of Automation, Northwestern Polytechnical University, Xi'an, China.
| | - Lei Guo
- School of Automation, Northwestern Polytechnical University, Xi'an, China
| | - Tianming Liu
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA
| | - Xi Jiang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China.
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Li Y, Wang N, Wang H, Lv Y, Zou Q, Wang J. Surface-based single-subject morphological brain networks: Effects of morphological index, brain parcellation and similarity measure, sample size-varying stability and test-retest reliability. Neuroimage 2021; 235:118018. [PMID: 33794358 DOI: 10.1016/j.neuroimage.2021.118018] [Citation(s) in RCA: 43] [Impact Index Per Article: 14.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/24/2020] [Revised: 12/04/2020] [Accepted: 03/22/2021] [Indexed: 12/14/2022] Open
Abstract
Morphological brain networks, in particular those at the individual level, have become an important approach for studying the human brain connectome; however, relevant methodology is far from being well-established in their formation, description and reproducibility. Here, we extended our previous study by constructing and characterizing single-subject morphological similarity networks from brain volume to surface space and systematically evaluated their reproducibility with respect to effects of different choices of morphological index, brain parcellation atlas and similarity measure, sample size-varying stability and test-retest reliability. Using the Human Connectome Project dataset, we found that surface-based single-subject morphological similarity networks shared common small-world organization, high parallel efficiency, modular architecture and bilaterally distributed hubs regardless of different analytical strategies. Nevertheless, quantitative values of all interregional similarities, global network measures and nodal centralities were significantly affected by choices of morphological index, brain parcellation atlas and similarity measure. Moreover, the morphological similarity networks varied along with the number of participants and approached stability until the sample size exceeded ~70. Using an independent test-retest dataset, we found fair to good, even excellent, reliability for most interregional similarities and network measures, which were also modulated by different analytical strategies, in particular choices of morphological index. Specifically, fractal dimension and sulcal depth outperformed gyrification index and cortical thickness, higher-resolution atlases outperformed lower-resolution atlases, and Jensen-Shannon divergence-based similarity outperformed Kullback-Leibler divergence-based similarity. Altogether, our findings propose surface-based single-subject morphological similarity networks as a reliable method to characterize the human brain connectome and provide methodological recommendations and guidance for future research.
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Affiliation(s)
- Yinzhi Li
- Institute for Brain Research and Rehabilitation, Guangdong Key Laboratory of Mental Health and Cognitive Science, Center for Studies of Psychological Application, South China Normal University, Guangzhou, China
| | - Ningkai Wang
- Institute for Brain Research and Rehabilitation, Guangdong Key Laboratory of Mental Health and Cognitive Science, Center for Studies of Psychological Application, South China Normal University, Guangzhou, China
| | - Hao Wang
- Institute of Fundamental and Frontier Sciences, University of Electronic Science and Technology of China, Chengdu, China
| | - Yating Lv
- Institute of Psychological Sciences, Hangzhou Normal University, Hangzhou, Zhejiang, China
| | - Qihong Zou
- Center for MRI Research, Academy for Advanced Interdisciplinary Studies, Peking University, Beijing, China
| | - Jinhui Wang
- Institute for Brain Research and Rehabilitation, Guangdong Key Laboratory of Mental Health and Cognitive Science, Center for Studies of Psychological Application, South China Normal University, Guangzhou, China; Key Laboratory of Brain, Cognition and Education Sciences (South China Normal University), Ministry of Education.
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11
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Jiang X, Zhang T, Zhang S, Kendrick KM, Liu T. Fundamental functional differences between gyri and sulci: implications for brain function, cognition, and behavior. PSYCHORADIOLOGY 2021; 1:23-41. [PMID: 38665307 PMCID: PMC10939337 DOI: 10.1093/psyrad/kkab002] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/30/2020] [Revised: 01/24/2021] [Accepted: 02/02/2021] [Indexed: 04/28/2024]
Abstract
Folding of the cerebral cortex is a prominent characteristic of mammalian brains. Alterations or deficits in cortical folding are strongly correlated with abnormal brain function, cognition, and behavior. Therefore, a precise mapping between the anatomy and function of the brain is critical to our understanding of the mechanisms of brain structural architecture in both health and diseases. Gyri and sulci, the standard nomenclature for cortical anatomy, serve as building blocks to make up complex folding patterns, providing a window to decipher cortical anatomy and its relation with brain functions. Huge efforts have been devoted to this research topic from a variety of disciplines including genetics, cell biology, anatomy, neuroimaging, and neurology, as well as involving computational approaches based on machine learning and artificial intelligence algorithms. However, despite increasing progress, our understanding of the functional anatomy of gyro-sulcal patterns is still in its infancy. In this review, we present the current state of this field and provide our perspectives of the methodologies and conclusions concerning functional differentiation between gyri and sulci, as well as the supporting information from genetic, cell biology, and brain structure research. In particular, we will further present a proposed framework for attempting to interpret the dynamic mechanisms of the functional interplay between gyri and sulci. Hopefully, this review will provide a comprehensive summary of anatomo-functional relationships in the cortical gyro-sulcal system together with a consideration of how these contribute to brain function, cognition, and behavior, as well as to mental disorders.
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Affiliation(s)
- Xi Jiang
- School of Life Science and Technology, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Tuo Zhang
- School of Automation, Northwestern Polytechnical University, Xi'an 710129, China
| | - Shu Zhang
- Center for Brain and Brain-Inspired Computing Research, School of Computer Science, Northwestern Polytechnical University, Xi'an 710129, China
| | - Keith M Kendrick
- School of Life Science and Technology, MOE Key Laboratory for Neuroinformation, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Tianming Liu
- Cortical Architecture Imaging and Discovery Laboratory, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA 30605, USA
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Lin HY, Huang CC, Chou KH, Yang AC, Lo CYZ, Tsai SJ, Lin CP. Differential Patterns of Gyral and Sulcal Morphological Changes During Normal Aging Process. Front Aging Neurosci 2021; 13:625931. [PMID: 33613271 PMCID: PMC7886979 DOI: 10.3389/fnagi.2021.625931] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/04/2020] [Accepted: 01/08/2021] [Indexed: 11/24/2022] Open
Abstract
The cerebral cortex is a highly convoluted structure with distinct morphologic features, namely the gyri and sulci, which are associated with the functional segregation or integration in the human brain. During the lifespan, the brain atrophy that is accompanied by cognitive decline is a well-accepted aging phenotype. However, the detailed patterns of cortical folding change during aging, especially the changing age-dependencies of gyri and sulci, which is essential to brain functioning, remain unclear. In this study, we investigated the morphology of the gyral and sulcal regions from pial and white matter surfaces using MR imaging data of 417 healthy participants across adulthood to old age (21–92 years). To elucidate the age-related changes in the cortical pattern, we fitted cortical thickness and intrinsic curvature of gyri and sulci using the quadratic model to evaluate their age-dependencies during normal aging. Our findings show that comparing to gyri, the sulcal thinning is the most prominent pattern during the aging process, and the gyrification of pial and white matter surfaces were also affected differently, which implies the vulnerability of functional segregation during aging. Taken together, we propose a morphological model of aging that may provide a framework for understanding the mechanisms underlying gray matter degeneration.
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Affiliation(s)
- Hsin-Yu Lin
- Centre for Research and Development in Learning, Nanyang Technological University, Singapore, Singapore.,Institute of Neuroscience, National Yang-Ming University, Taipei, Taiwan
| | - Chu-Chung Huang
- Institute of Neuroscience, National Yang-Ming University, Taipei, Taiwan.,School of Psychology and Cognitive Science, East China Normal University, Institute of Cognitive Neuroscience, Shanghai, China
| | - Kun-Hsien Chou
- Institute of Neuroscience, National Yang-Ming University, Taipei, Taiwan.,Brain Research Center, National Yang-Ming University, Taipei, Taiwan
| | - Albert C Yang
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan.,Division of Interdisciplinary Medicine and Biotechnology, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, United States
| | - Chun-Yi Zac Lo
- Institute of Science and Technology for Brain Inspired Intelligence, Fudan University, Shanghai, China
| | - Shih-Jen Tsai
- Department of Psychiatry, Taipei Veterans General Hospital, Taipei, Taiwan.,School of Medicine, National Yang-Ming University, Taipei, Taiwan
| | - Ching-Po Lin
- Institute of Neuroscience, National Yang-Ming University, Taipei, Taiwan.,Brain Research Center, National Yang-Ming University, Taipei, Taiwan
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13
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Liu H, Zhang S, Jiang X, Zhang T, Huang H, Ge F, Zhao L, Li X, Hu X, Han J, Guo L, Liu T. The Cerebral Cortex is Bisectionally Segregated into Two Fundamentally Different Functional Units of Gyri and Sulci. Cereb Cortex 2019; 29:4238-4252. [PMID: 30541110 PMCID: PMC6735260 DOI: 10.1093/cercor/bhy305] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2018] [Revised: 11/08/2018] [Accepted: 11/12/2018] [Indexed: 01/05/2023] Open
Abstract
The human cerebral cortex is highly folded into diverse gyri and sulci. Accumulating evidences suggest that gyri and sulci exhibit anatomical, morphological, and connectional differences. Inspired by these evidences, we performed a series of experiments to explore the frequency-specific differences between gyral and sulcal neural activities from resting-state and task-based functional magnetic resonance imaging (fMRI) data. Specifically, we designed a convolutional neural network (CNN) based classifier, which can differentiate gyral and sulcal fMRI signals with reasonable accuracies. Further investigations of learned CNN models imply that sulcal fMRI signals are more diverse and more high frequency than gyral signals, suggesting that gyri and sulci truly play different functional roles. These differences are significantly associated with axonal fiber wiring and cortical thickness patterns, suggesting that these differences might be deeply rooted in their structural and cellular underpinnings. Further wavelet entropy analyses demonstrated the validity of CNN-based findings. In general, our collective observations support a new concept that the cerebral cortex is bisectionally segregated into 2 functionally different units of gyri and sulci.
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Affiliation(s)
- Huan Liu
- School of Automation, Northwestern Polytechnical University, Xi’an, China
| | - Shu Zhang
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA
| | - Xi Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Tuo Zhang
- School of Automation, Brain Decoding Research Center, Northwestern Polytechnical University, Xi’an, China
| | - Heng Huang
- School of Automation, Northwestern Polytechnical University, Xi’an, China
| | - Fangfei Ge
- School of Automation, Northwestern Polytechnical University, Xi’an, China
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA
| | - Lin Zhao
- School of Automation, Northwestern Polytechnical University, Xi’an, China
| | - Xiao Li
- School of Automation, Northwestern Polytechnical University, Xi’an, China
| | - Xintao Hu
- School of Automation, Northwestern Polytechnical University, Xi’an, China
| | - Junwei Han
- School of Automation, Northwestern Polytechnical University, Xi’an, China
| | - Lei Guo
- School of Automation, Northwestern Polytechnical University, Xi’an, China
| | - Tianming Liu
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA
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14
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Yang S, Zhao Z, Cui H, Zhang T, Zhao L, He Z, Liu H, Guo L, Liu T, Becker B, Kendrick KM, Jiang X. Temporal Variability of Cortical Gyral-Sulcal Resting State Functional Activity Correlates With Fluid Intelligence. Front Neural Circuits 2019; 13:36. [PMID: 31156400 PMCID: PMC6529596 DOI: 10.3389/fncir.2019.00036] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2018] [Accepted: 05/02/2019] [Indexed: 12/29/2022] Open
Abstract
The human cerebral cortex is highly convoluted as convex gyri and concave sulci. In the past decades, extensive studies have consistently revealed substantial differences between gyri and sulci in terms of genetics, anatomy, morphology, axonal fiber connections, and function. Although interesting findings have been reported to date to elucidate the functional difference between gyri and sulci, the temporal variability of functional activity, which could explain individual differences in learning and higher-order cognitive functions, and as well as differences in gyri and sulci, remains to be explored. The present study explored the temporal variability of cortical gyral-sulcal resting state functional activity and its association with fluid intelligence measures on the Human Connectome Project dataset. We found that the temporal variance of resting state fMRI BOLD signal was significantly larger in gyri than in sulci. We also found that the temporal variability of certain regions including middle frontal cortex, inferior parietal lobe and visual cortex was positively associated with fluid intelligence. Moreover, those regions were predominately located in gyri rather than in sulci. This study reports initial evidence for temporal variability difference of functional activity between gyri and sulci, and its association with fluid intelligence measures, and thus provides novel insights to understand the mechanism and functional relevance of gyri and sulci.
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Affiliation(s)
- Shimin Yang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Zhongbo Zhao
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Han Cui
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Tuo Zhang
- School of Automation, Northwestern Polytechnical University, Xi’an, China
| | - Lin Zhao
- School of Automation, Northwestern Polytechnical University, Xi’an, China
| | - Zhibin He
- School of Automation, Northwestern Polytechnical University, Xi’an, China
| | - Huan Liu
- School of Automation, Northwestern Polytechnical University, Xi’an, China
| | - Lei Guo
- School of Automation, Northwestern Polytechnical University, Xi’an, China
| | - Tianming Liu
- Department of Computer Science, Bioimaging Research Center, The University of Georgia, Athens, GA, United States
| | - Benjamin Becker
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Keith M. Kendrick
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Xi Jiang
- The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
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15
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Zhang S, Liu H, Huang H, Zhao Y, Jiang X, Bowers B, Guo L, Hu X, Sanchez M, Liu T. Deep Learning Models Unveiled Functional Difference Between Cortical Gyri and Sulci. IEEE Trans Biomed Eng 2018; 66:1297-1308. [PMID: 30281424 DOI: 10.1109/tbme.2018.2872726] [Citation(s) in RCA: 11] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/27/2022]
Abstract
It is largely unknown whether there is functional role difference between cortical gyral and sulcal regions. Recent advancements in neuroimaging studies demonstrate clear difference of structural connection profiles in gyral and sulcal areas, suggesting possible functional role difference in these convex and concave cortical regions. To explore and confirm such possible functional difference, we design and apply a powerful deep learning model of convolutional neural networks (CNN) that has been proven to be superior in learning discriminative and meaningful patterns on fMRI. By using the CNN model, gyral and sulcal fMRI signals are learned and predicted, and the prediction performance is adopted to demonstrate the functional difference between gyri and sulci. By using the Human Connectome Project (HCP) fMRI data and macaque brain fMRI data, an average of 83% and 90% classification accuracy has been achieved to separate gyral/sulcal HCP task fMRI signals at the population and individual subject level, respectively; 81% and 86% classification accuracy for resting state fMRI signals at the group and individual subject level, respectively. In addition, 78% classification accuracy has been achieved to separate gyral/sulcal resting state fMRI signals in macaque brains. Importantly, further analysis reveals that the discriminative features that are learned by CNNs to differentiate gyral/sulcal fMRI signals can be meaningfully interpreted, thus unveiling the fundamental functional difference between cortical gyri and sulci. That is, gyri are more global functional integration centers with simpler lower frequency signal components, while sulci are more local processing units with more complex higher frequency signal components.
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16
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Zhang T, Chen H, Razavi MJ, Li Y, Ge F, Guo L, Wang X, Liu T. Exploring 3-hinge gyral folding patterns among HCP Q3 868 human subjects. Hum Brain Mapp 2018; 39:4134-4149. [PMID: 29947164 DOI: 10.1002/hbm.24237] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2018] [Revised: 04/22/2018] [Accepted: 05/21/2018] [Indexed: 12/21/2022] Open
Abstract
Comparison and integration of neuroimaging data from different brains and populations are fundamental in neuroscience. Over the past decades, the neuroimaging field has largely depended on image registration to compare and integrate neuroimaging data from individuals in a common reference space, with a basic assumption that the brains are similar. However, the intrinsic neuroanatomical complexity and huge interindividual cortical folding variation remain underexplored. Here we focus on a specific cortical convolution pattern, termed 3-hinge gyral folding, which is the conjunction of gyri from multiple orientations and has unique and consistent anatomically, structurally, and functionally connective patterns across subjects. By developing a novel shape descriptor and a two-stage clustering pipeline, we devise an automatic method to identify 3-hinges in the Human Connectome Project Q3 868 human brains, and further parameterize the complexity of such a pattern and quantify its regularity and variation in terms of 3-hinge number, position, and morphology. Our results not only exhibit the huge interindividual variations, but also reveal regular relationship between gyral hinges and other factors, such as their locations and cortical morphologies. It is found that "line-shape" cortices have relatively more consistent 3-hinge shape pattern distributions, and certain types of 3-hinge patterns favor particular cortical morphologies. In addition, more 3-hinges are found on "line-shape" cortices while their numbers vary more across subjects than those on "non-line-shape" cortices. This study adds new insights into a better understanding of the regularity and variability of human brain anatomy, and their functional aspects.
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Affiliation(s)
- Tuo Zhang
- Brain Decoding Research Center and School of Automation, Northwestern Polytechnical University Xi'an, Shaanxi, China
| | - Hanbo Chen
- Cortical Architecture Imaging and Discovery Lab Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, Georgia
| | - Mir Jalil Razavi
- College of Engineering, The University of Georgia, Athens, Georgia
| | - Yujie Li
- Cortical Architecture Imaging and Discovery Lab Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, Georgia
| | - Fangfei Ge
- Cortical Architecture Imaging and Discovery Lab Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, Georgia
| | - Lei Guo
- Brain Decoding Research Center and School of Automation, Northwestern Polytechnical University Xi'an, Shaanxi, China
| | - Xianqiao Wang
- College of Engineering, The University of Georgia, Athens, Georgia
| | - Tianming Liu
- Cortical Architecture Imaging and Discovery Lab Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, Georgia
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