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Zhang F, Chen Y, Ning L, Rushmore J, Liu Q, Du M, Hassanzadeh‐Behbahani S, Legarreta J, Yeterian E, Makris N, Rathi Y, O'Donnell L. Assessment of the Depiction of Superficial White Matter Using Ultra-High-Resolution Diffusion MRI. Hum Brain Mapp 2024; 45:e70041. [PMID: 39392220 PMCID: PMC11467805 DOI: 10.1002/hbm.70041] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2024] [Accepted: 09/22/2024] [Indexed: 10/12/2024] Open
Abstract
The superficial white matter (SWM) consists of numerous short-range association fibers connecting adjacent and nearby gyri and plays an important role in brain function, development, aging, and various neurological disorders. Diffusion MRI (dMRI) tractography is an advanced imaging technique that enables in vivo mapping of the SWM. However, detailed imaging of the small, highly-curved fibers of the SWM is a challenge for current clinical and research dMRI acquisitions. This work investigates the efficacy of mapping the SWM using in vivo ultra-high-resolution dMRI data. We compare the SWM mapping performance from two dMRI acquisitions: a high-resolution 0.76-mm isotropic acquisition using the generalized slice-dithered enhanced resolution (gSlider) protocol and a lower resolution 1.25-mm isotropic acquisition obtained from the Human Connectome Project Young Adult (HCP-YA) database. Our results demonstrate significant differences in the cortico-cortical anatomical connectivity that is depicted by these two acquisitions. We perform a detailed assessment of the anatomical plausibility of these results with respect to the nonhuman primate (macaque) tract-tracing literature. We find that the high-resolution gSlider dataset is more successful at depicting a large number of true positive anatomical connections in the SWM. An additional cortical coverage analysis demonstrates significantly higher cortical coverage in the gSlider dataset for SWM streamlines under 40 mm in length. Overall, we conclude that the spatial resolution of the dMRI data is one important factor that can significantly affect the mapping of SWM. Considering the relatively long acquisition time, the application of dMRI tractography for SWM mapping in future work should consider the balance of data acquisition efforts and the efficacy of SWM depiction.
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Affiliation(s)
- Fan Zhang
- School of Information and Communication Engineering, University of Electronic Science and Technology of ChinaChengduChina
- Department of RadiologyBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Yuqian Chen
- Department of RadiologyBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Lipeng Ning
- Department of PsychiatryBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Jarrett Rushmore
- Department of PsychiatryBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Department of Anatomy and NeurobiologyBoston University School of MedicineBostonMassachusettsUSA
| | - Qiang Liu
- Department of PsychiatryBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Mubai Du
- School of Information and Communication Engineering, University of Electronic Science and Technology of ChinaChengduChina
| | | | - Jon Haitz Legarreta
- Department of RadiologyBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Edward Yeterian
- Department of Anatomy and NeurobiologyBoston University School of MedicineBostonMassachusettsUSA
- Department of PsychologyColby CollegeWatervilleMaineUSA
| | - Nikos Makris
- Department of PsychiatryBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Yogesh Rathi
- Department of PsychiatryBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
| | - Lauren J. O'Donnell
- School of Information and Communication Engineering, University of Electronic Science and Technology of ChinaChengduChina
- Department of RadiologyBrigham and Women's Hospital, Harvard Medical SchoolBostonMassachusettsUSA
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2
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Zhang D, Zong F, Zhang Q, Yue Y, Zhang F, Zhao K, Wang D, Wang P, Zhang X, Liu Y. Anat-SFSeg: Anatomically-guided superficial fiber segmentation with point-cloud deep learning. Med Image Anal 2024; 95:103165. [PMID: 38608510 DOI: 10.1016/j.media.2024.103165] [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: 09/29/2023] [Revised: 03/28/2024] [Accepted: 04/05/2024] [Indexed: 04/14/2024]
Abstract
Diffusion magnetic resonance imaging (dMRI) tractography is a critical technique to map the brain's structural connectivity. Accurate segmentation of white matter, particularly the superficial white matter (SWM), is essential for neuroscience and clinical research. However, it is challenging to segment SWM due to the short adjacent gyri connection in a U-shaped pattern. In this work, we propose an Anatomically-guided Superficial Fiber Segmentation (Anat-SFSeg) framework to improve the performance on SWM segmentation. The framework consists of a unique fiber anatomical descriptor (named FiberAnatMap) and a deep learning network based on point-cloud data. The spatial coordinates of fibers represented as point clouds, as well as the anatomical features at both the individual and group levels, are fed into a neural network. The network is trained on Human Connectome Project (HCP) datasets and tested on the subjects with a range of cognitive impairment levels. One new metric named fiber anatomical region proportion (FARP), quantifies the ratio of fibers in the defined brain regions and enables the comparison with other methods. Another metric named anatomical region fiber count (ARFC), represents the average fiber number in each cluster for the assessment of inter-subject differences. The experimental results demonstrate that Anat-SFSeg achieves the highest accuracy on HCP datasets and exhibits great generalization on clinical datasets. Diffusion tensor metrics and ARFC show disorder severity associated alterations in patients with Alzheimer's disease (AD) and mild cognitive impairments (MCI). Correlations with cognitive grades show that these metrics are potential neuroimaging biomarkers for AD. Furthermore, Anat-SFSeg could be utilized to explore other neurodegenerative, neurodevelopmental or psychiatric disorders.
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Affiliation(s)
- Di Zhang
- School of Airtificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | - Fangrong Zong
- School of Airtificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China.
| | - Qichen Zhang
- School of Airtificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | - Yunhui Yue
- School of Airtificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | - Fan Zhang
- School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, China
| | - Kun Zhao
- School of Airtificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China
| | - Dawei Wang
- Department of Radiology, Qilu Hospital of Shandong University, Jinan, China; Department of Epidemiology and Health Statistics, School of Public Health, Shandong University, Jinan, China; Institute of Brain and Brain-Inspired Science, Shandong University, Jinan, China
| | - Pan Wang
- Department of Neurology, Tianjin Huanhu Hospital, Tianjin, China
| | - Xi Zhang
- Department of Neurology, the Second Medical Centre, National Clinical Research Centre for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Yong Liu
- School of Airtificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, 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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4
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Yoshino M, Shiraishi Y, Saito K, Kameya N, Hamabe-Horiike T, Shinmyo Y, Nakada M, Ozaki N, Kawasaki H. Distinct subdivisions of subcortical U-fiber regions in the gyrencephalic ferret brain. Neurosci Res 2024; 200:1-7. [PMID: 37866527 DOI: 10.1016/j.neures.2023.10.004] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Revised: 09/29/2023] [Accepted: 10/17/2023] [Indexed: 10/24/2023]
Abstract
The human cerebrum contains a large amount of cortico-cortical association fibers. Among them, U-fibers are short-range association fibers located in white matter immediately deep to gray matter. Although U-fibers are thought to be crucial for higher cognitive functions, the organization within U-fiber regions are still unclear. Here we investigated the properties of U-fiber regions in the ferret cerebrum using neurochemical, neuronal tracing, immunohistochemical and electron microscopic techniques. We found that U-fiber regions can be subdivided into two regions, which we named outer and inner U-fiber regions. We further uncovered that outer U-fiber regions have smaller-diameter axons with thinner myelin compared with inner U-fiber regions. These findings may indicate functional complexity within U-fiber regions in the cerebrum.
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Affiliation(s)
- Mayuko Yoshino
- Department of Medical Neuroscience, Graduate School of Medical Sciences, Kanazawa University, Ishikawa 920-8640, Japan
| | - Yoshitake Shiraishi
- Department of Functional Anatomy, Graduate School of Medical Sciences, Kanazawa University, Ishikawa 920-8640, Japan; Engineering and Technology Department, Kanazawa University, Ishikawa 920-8640, Japan
| | - Kengo Saito
- Department of Medical Neuroscience, Graduate School of Medical Sciences, Kanazawa University, Ishikawa 920-8640, Japan
| | - Narufumi Kameya
- Department of Medical Neuroscience, Graduate School of Medical Sciences, Kanazawa University, Ishikawa 920-8640, Japan
| | - Toshihide Hamabe-Horiike
- Department of Medical Neuroscience, Graduate School of Medical Sciences, Kanazawa University, Ishikawa 920-8640, Japan
| | - Yohei Shinmyo
- Department of Medical Neuroscience, Graduate School of Medical Sciences, Kanazawa University, Ishikawa 920-8640, Japan
| | - Mitsutoshi Nakada
- Department of Neurosurgery, Graduate School of Medical Sciences, Kanazawa University, Ishikawa 920-8641, Japan
| | - Noriyuki Ozaki
- Department of Functional Anatomy, Graduate School of Medical Sciences, Kanazawa University, Ishikawa 920-8640, Japan
| | - Hiroshi Kawasaki
- Department of Medical Neuroscience, Graduate School of Medical Sciences, Kanazawa University, Ishikawa 920-8640, Japan.
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5
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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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Chauvel M, Uszynski I, Herlin B, Popov A, Leprince Y, Mangin JF, Hopkins WD, Poupon C. In vivo mapping of the deep and superficial white matter connectivity in the chimpanzee brain. Neuroimage 2023; 282:120362. [PMID: 37722605 DOI: 10.1016/j.neuroimage.2023.120362] [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: 04/05/2023] [Revised: 06/27/2023] [Accepted: 09/03/2023] [Indexed: 09/20/2023] Open
Abstract
Mapping the chimpanzee brain connectome and comparing it to that of humans is key to our understanding of similarities and differences in primate evolution that occurred after the split from their common ancestor around 6 million years ago. In contrast to studies on macaque species' brains, fewer studies have specifically addressed the structural connectivity of the chimpanzee brain and its comparison with the human brain. Most comparative studies in the literature focus on the anatomy of the cortex and deep nuclei to evaluate how their morphology and asymmetry differ from that of the human brain, and some studies have emerged concerning the study of brain connectivity among humans, monkeys, and apes. In this work, we established a new white matter atlas of the deep and superficial white matter structural connectivity in chimpanzees. In vivo anatomical and diffusion-weighted magnetic resonance imaging (MRI) data were collected on a 3-Tesla MRI system from 39 chimpanzees. These datasets were subsequently processed using a novel fiber clustering pipeline adapted to the chimpanzee brain, enabling us to create two novel deep and superficial white matter connectivity atlases representative of the chimpanzee brain. These atlases provide the scientific community with an important and novel set of reference data for understanding the commonalities and differences in structural connectivity between the human and chimpanzee brains. We believe this study to be innovative both in its novel approach and in mapping the superficial white matter bundles in the chimpanzee brain, which will contribute to a better understanding of hominin brain evolution.
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Affiliation(s)
- Maëlig Chauvel
- BAOBAB, UMR 9027, NeuroSpin, Université Paris-Saclay, CNRS, CEA, Gif-sur-Yvette, France.
| | - Ivy Uszynski
- BAOBAB, UMR 9027, NeuroSpin, Université Paris-Saclay, CNRS, CEA, Gif-sur-Yvette, France
| | - Bastien Herlin
- BAOBAB, UMR 9027, NeuroSpin, Université Paris-Saclay, CNRS, CEA, Gif-sur-Yvette, France; Hôpital de la Pitié-Salpêtrière, 47-83 Boulevard de l'Hôpital, 75013 Paris, France
| | - Alexandros Popov
- BAOBAB, UMR 9027, NeuroSpin, Université Paris-Saclay, CNRS, CEA, Gif-sur-Yvette, France
| | - Yann Leprince
- UNIACT, NeuroSpin, Université Paris-Saclay, CEA, Gif-sur-Yvette, France
| | - Jean-François Mangin
- BAOBAB, UMR 9027, NeuroSpin, Université Paris-Saclay, CNRS, CEA, Gif-sur-Yvette, France
| | - William D Hopkins
- Department of Comparative Medicine, Michale E Keeling Center for Comparative Medicine and Research, The University of Texas MD Anderson Cancer Center, Bastrop, TX, United States of America
| | - Cyril Poupon
- BAOBAB, UMR 9027, NeuroSpin, Université Paris-Saclay, CNRS, CEA, Gif-sur-Yvette, France.
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7
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Huang Y, Zhang T, Zhang S, Zhang W, Yang L, Zhu D, Liu T, Jiang X, Han J, Guo L. Genetic Influence on Gyral Peaks. Neuroimage 2023; 280:120344. [PMID: 37619794 DOI: 10.1016/j.neuroimage.2023.120344] [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: 05/10/2023] [Accepted: 08/21/2023] [Indexed: 08/26/2023] Open
Abstract
Genetic mechanisms have been hypothesized to be a major determinant in the formation of cortical folding. Although there is an increasing number of studies examining the heritability of cortical folding, most of them focus on sulcal pits rather than gyral peaks. Gyral peaks, which reflect the highest local foci on gyri and are consistent across individuals, remain unstudied in terms of heritability. To address this knowledge gap, we used high-resolution data from the Human Connectome Project (HCP) to perform classical twin analysis and estimate the heritability of gyral peaks across various brain regions. Our results showed that the heritability of gyral peaks was heterogeneous across different cortical regions, but relatively symmetric between hemispheres. We also found that pits and peaks are different in a variety of anatomic and functional measures. Further, we explored the relationship between the levels of heritability and the formation of cortical folding by utilizing the evolutionary timeline of gyrification. Our findings indicate that the heritability estimates of both gyral peaks and sulcal pits decrease linearly with the evolution timeline of gyrification. This suggests that the cortical folds which formed earlier during gyrification are subject to stronger genetic influences than the later ones. Moreover, the pits and peaks coupled by their time of appearance are also positively correlated in respect of their heritability estimates. These results fill the knowledge gap regarding genetic influences on gyral peaks and significantly advance our understanding of how genetic factors shape the formation of cortical folding. The comparison between peaks and pits suggests that peaks are not a simple morphological mirror of pits but could help complete the understanding of folding patterns.
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Affiliation(s)
- Ying Huang
- School of Automation, Northwestern Polytechnical University, Xi'an 710129, China; School of Information and Technology, Northwest University, Xi'an 710127, China
| | - Tuo Zhang
- School of Automation, Northwestern Polytechnical University, Xi'an 710129, China.
| | - Songyao Zhang
- School of Automation, Northwestern Polytechnical University, Xi'an 710129, China
| | - Weihan Zhang
- School of Automation, Northwestern Polytechnical University, Xi'an 710129, China
| | - Li Yang
- School of Automation, Northwestern Polytechnical University, Xi'an 710129, China
| | - Dajiang Zhu
- Computer Science & Engineering, University of Texas at Arlington, TX 76010, USA
| | - Tianming Liu
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA 30602, USA
| | - Xi Jiang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610056, China
| | - Junwei Han
- School of Automation, Northwestern Polytechnical University, Xi'an 710129, China
| | - Lei Guo
- School of Automation, Northwestern Polytechnical University, Xi'an 710129, China
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8
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Wang Q, Zhao S, Liu T, Han J, Liu C. Temporal fingerprints of cortical gyrification in marmosets and humans. Cereb Cortex 2023; 33:9802-9814. [PMID: 37434368 PMCID: PMC10656951 DOI: 10.1093/cercor/bhad245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2023] [Revised: 06/01/2023] [Accepted: 06/02/2023] [Indexed: 07/13/2023] Open
Abstract
Recent neuroimaging studies in humans have reported distinct temporal dynamics of gyri and sulci, which may be associated with putative functions of cortical gyrification. However, the complex folding patterns of the human cortex make it difficult to explain temporal patterns of gyrification. In this study, we used the common marmoset as a simplified model to examine the temporal characteristics and compare them with the complex gyrification of humans. Using a brain-inspired deep neural network, we obtained reliable temporal-frequency fingerprints of gyri and sulci from the awake rs-fMRI data of marmosets and humans. Notably, the temporal fingerprints of one region successfully classified the gyrus/sulcus of another region in both marmosets and humans. Additionally, the temporal-frequency fingerprints were remarkably similar in both species. We then analyzed the resulting fingerprints in several domains and adopted the Wavelet Transform Coherence approach to characterize the gyro-sulcal coupling patterns. In both humans and marmosets, sulci exhibited higher frequency bands than gyri, and the two were temporally coupled within the same range of phase angles. This study supports the notion that gyri and sulci possess unique and evolutionarily conserved features that are consistent across functional areas, and advances our understanding of the functional role of cortical gyrification.
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Affiliation(s)
- Qiyu Wang
- School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
- CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Neuroscience, Chinese Academy of Sciences, Shanghai 200031, China
| | - Shijie Zhao
- School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
- Research & Development Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen 518063, China
| | - Tianming Liu
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA 30602, United States
| | - Junwei Han
- School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
| | - Cirong Liu
- CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Neuroscience, Chinese Academy of Sciences, Shanghai 200031, China
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Zhang S, Zhang T, He Z, Li X, Zhang L, Zhu D, Jiang X, Liu T, Han J, Guo L. Gyral peaks and patterns in human brains. Cereb Cortex 2023; 33:6708-6722. [PMID: 36646465 PMCID: PMC10422926 DOI: 10.1093/cercor/bhac537] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/08/2022] [Revised: 12/19/2022] [Accepted: 12/20/2022] [Indexed: 01/18/2023] Open
Abstract
Cortical folding patterns are related to brain function, cognition, and behavior. Since the relationship has not been fully explained on a coarse scale, many efforts have been devoted to the identification of finer grained cortical landmarks, such as sulcal pits and gyral peaks, which were found to remain invariant across subjects and ages and the invariance may be related to gene mediated proto-map. However, gyral peaks were only investigated on macaque monkey brains, but not on human brains where the investigation is challenged due to high inter-individual variabilities. To this end, in this work, we successfully identified 96 gyral peaks both on the left and right hemispheres of human brains, respectively. These peaks are spatially consistent across individuals. Higher or sharper peaks are more consistent across subjects. Both structural and functional graph metrics of peaks are significantly different from other cortical regions, and more importantly, these nodal graph metrics are anti-correlated with the spatial consistency metrics within peaks. In addition, the distribution of peaks and various cortical anatomical, structural/functional connective features show hemispheric symmetry. These findings provide new clues to understanding the cortical landmarks, as well as their relationship with brain functions, cognition, behavior in both healthy and aberrant brains.
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Affiliation(s)
- Songyao Zhang
- School of Automation, School of Information Technology, and School of Life Science and Technology, Northwestern Polytechnical University, Xi’an 710000, China
| | - Tuo Zhang
- School of Automation, School of Information Technology, and School of Life Science and Technology, Northwestern Polytechnical University, Xi’an 710000, China
| | - Zhibin He
- School of Automation, School of Information Technology, and School of Life Science and Technology, Northwestern Polytechnical University, Xi’an 710000, China
| | - Xiao Li
- School of Automation, School of Information Technology, and School of Life Science and Technology, Northwest University, Xi’an, China
| | - Lu Zhang
- Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX, United States
| | - Dajiang Zhu
- Department of Computer Science and Engineering, The University of Texas at Arlington, Arlington, TX, United States
| | - Xi Jiang
- School of Automation, School of Information Technology, and School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, 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
| | - Junwei Han
- School of Automation, School of Information Technology, and School of Life Science and Technology, Northwestern Polytechnical University, Xi’an 710000, China
| | - Lei Guo
- School of Automation, School of Information Technology, and School of Life Science and Technology, Northwestern Polytechnical University, Xi’an 710000, China
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10
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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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11
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Schilling KG, Archer D, Yeh FC, Rheault F, Cai LY, Shafer A, Resnick SM, Hohman T, Jefferson A, Anderson AW, Kang H, Landman BA. Short superficial white matter and aging: a longitudinal multi-site study of 1293 subjects and 2711 sessions. AGING BRAIN 2023; 3:100067. [PMID: 36817413 PMCID: PMC9937516 DOI: 10.1016/j.nbas.2023.100067] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2023] Open
Abstract
It is estimated that short association fibers running immediately beneath the cortex may make up as much as 60% of the total white matter volume. However, these have been understudied relative to the long-range association, projection, and commissural fibers of the brain. This is largely because of limitations of diffusion MRI fiber tractography, which is the primary methodology used to non-invasively study the white matter connections. Inspired by recent anatomical considerations and methodological improvements in superficial white matter (SWM) tractography, we aim to characterize changes in these fiber systems in cognitively normal aging, which provide insight into the biological foundation of age-related cognitive changes, and a better understanding of how age-related pathology differs from healthy aging. To do this, we used three large, longitudinal and cross-sectional datasets (N = 1293 subjects, 2711 sessions) to quantify microstructural features and length/volume features of several SWM systems. We find that axial, radial, and mean diffusivities show positive associations with age, while fractional anisotropy has negative associations with age in SWM throughout the entire brain. These associations were most pronounced in the frontal, temporal, and temporoparietal regions. Moreover, measures of SWM volume and length decrease with age in a heterogenous manner across the brain, with different rates of change in inter-gyri and intra-gyri SWM, and at slower rates than well-studied long-range white matter pathways. These features, and their variations with age, provide the background for characterizing normal aging, and, in combination with larger association pathways and gray matter microstructural features, may provide insight into fundamental mechanisms associated with aging and cognition.
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Affiliation(s)
- Kurt G Schilling
- Department of Radiology & Radiological Sciences, Vanderbilt University Medical Center, Nashville, TN
| | - Derek Archer
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University Medical Center, Nashville, TN, USA,Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA,Vanderbilt Genetics Institute, Vanderbilt University School of Medicine, Nashville, TN
| | - Fang-Cheng Yeh
- Department of Neurological Surgery, University of Pittsburgh Medical Center, Pittsburgh, PA, USA; Department of Bioengineering, University of Pittsburgh, Pittsburgh, PA, USA
| | - Francois Rheault
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, United States
| | - Leon Y Cai
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, United States
| | - Andrea Shafer
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, United States of America
| | - Susan M. Resnick
- Laboratory of Behavioral Neuroscience, National Institute on Aging, National Institutes of Health, Baltimore, MD, United States of America
| | - Timothy Hohman
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University Medical Center, Nashville, TN, USA,Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA,Vanderbilt Genetics Institute, Vanderbilt University School of Medicine, Nashville, TN
| | - Angela Jefferson
- Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University Medical Center, Nashville, TN, USA,Department of Neurology, Vanderbilt University Medical Center, Nashville, TN, USA,Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Adam W Anderson
- Department of Biomedical Engineering, Vanderbilt University, Nashville, TN, United States
| | - Hakmook Kang
- Department of Biostatistics, Vanderbilt University, Nashville, TN, United States
| | - Bennett A Landman
- Department of Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, United States
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12
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Ma J, Liu F, Wang Y, Ma L, Niu Y, Wang J, Ye Z, Zhang J. Frequency-dependent white-matter functional network changes associated with cognitive deficits in subcortical vascular cognitive impairment. Neuroimage Clin 2022; 36:103245. [PMID: 36451351 PMCID: PMC9668649 DOI: 10.1016/j.nicl.2022.103245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/14/2022] [Revised: 10/07/2022] [Accepted: 10/21/2022] [Indexed: 11/11/2022]
Abstract
Vascular cognitive impairment (VCI) refers to all forms of cognitive decline associated with cerebrovascular diseases, in which white matter (WM) is highly vulnerable. Although previous studies have shown that blood oxygen level-dependent (BOLD) signals inside WM can effectively reflect neural activities, whether WM BOLD signal alterations are present and their roles underlying cognitive impairment in VCI remain largely unknown. In this study, 36 subcortical VCI (SVCI) patients and 36 healthy controls were enrolled to evaluate WM dysfunction. Specifically, fourteen distinct WM networks were identified from resting-state functional MRI using K-means clustering analysis. Subsequently, between-network functional connectivity (FC) and within-network BOLD signal amplitude of WM networks were calculated in three frequency bands (band A: 0.01-0.15 Hz, band B: 0.08-0.15 Hz, and band C: 0.01-0.08 Hz). Patients with SVCI manifested decreased FC mainly in bilateral parietal WM regions, forceps major, superior and inferior longitudinal fasciculi. These connections extensively linked with distinct WM networks and with gray-matter networks such as frontoparietal control, dorsal and ventral attention networks, which exhibited frequency-specific alterations in SVCI. Additionally, extensive amplitude reductions were found in SVCI, showing frequency-dependent properties in parietal, anterior corona radiate, pre/post central, superior and inferior longitudinal fasciculus networks. Furthermore, these decreased FC and amplitudes showed significant positive correlations with cognitive performances in SVCI, and high diagnostic performances for SVCI especially combining all bands. Our study indicated that VCI-related cognitive deficits were characterized by frequency-dependent WM functional abnormalities, which offered novel applicable neuromarkers for VCI.
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Affiliation(s)
- Juanwei Ma
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China; National Clinical Research Center for Cancer, Tianjin, China; Key Laboratory of Cancer Prevention and Therapy, Tianjin, China; Tianjin's Clinical Research Center for Cancer, Tianjin, China; Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Feng Liu
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Yang Wang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Lin Ma
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China
| | - Yali Niu
- Department of Rehabilitation, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Jing Wang
- Department of Rehabilitation, Tianjin Medical University General Hospital, Tianjin 300052, China
| | - Zhaoxiang Ye
- Department of Radiology, Tianjin Medical University Cancer Institute and Hospital, Tianjin, China; National Clinical Research Center for Cancer, Tianjin, China; Key Laboratory of Cancer Prevention and Therapy, Tianjin, China; Tianjin's Clinical Research Center for Cancer, Tianjin, China.
| | - Jing Zhang
- Department of Radiology and Tianjin Key Laboratory of Functional Imaging, Tianjin Medical University General Hospital, Tianjin, China.
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13
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Shastin D, Genc S, Parker GD, Koller K, Tax CMW, Evans J, Hamandi K, Gray WP, Jones DK, Chamberland M. Surface-based tracking for short association fibre tractography. Neuroimage 2022; 260:119423. [PMID: 35809886 PMCID: PMC10009610 DOI: 10.1016/j.neuroimage.2022.119423] [Citation(s) in RCA: 18] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2021] [Revised: 05/30/2022] [Accepted: 06/29/2022] [Indexed: 11/16/2022] Open
Abstract
It is estimated that in the human brain, short association fibres (SAF) represent more than half of the total white matter volume and their involvement has been implicated in a range of neurological and psychiatric conditions. This population of fibres, however, remains relatively understudied in the neuroimaging literature. Some of the challenges pertinent to the mapping of SAF include their variable anatomical course and proximity to the cortical mantle, leading to partial volume effects and potentially affecting streamline trajectory estimation. This work considers the impact of seeding and filtering strategies and choice of scanner, acquisition, data resampling to propose a whole-brain, surface-based short (≤30-40 mm) SAF tractography approach. The framework is shown to produce longer streamlines with a predilection for connecting gyri as well as high cortical coverage. We further demonstrate that certain areas of subcortical white matter become disproportionally underrepresented in diffusion-weighted MRI data with lower angular and spatial resolution and weaker diffusion weighting; however, collecting data with stronger gradients than are usually available clinically has minimal impact, making our framework translatable to data collected on commonly available hardware. Finally, the tractograms are examined using voxel- and surface-based measures of consistency, demonstrating moderate reliability, low repeatability and high between-subject variability, urging caution when streamline count-based analyses of SAF are performed.
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Affiliation(s)
- Dmitri Shastin
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Maindy Rd, Cardiff CF24 4HQ, United Kingdom; Department of Neurosurgery, University Hospital of Wales, Cardiff, United Kingdom; BRAIN Biomedical Research Unit, Health & Care Research Wales, Cardiff, United Kingdom.
| | - Sila Genc
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Maindy Rd, Cardiff CF24 4HQ, United Kingdom
| | - Greg D Parker
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Maindy Rd, Cardiff CF24 4HQ, United Kingdom
| | - Kristin Koller
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Maindy Rd, Cardiff CF24 4HQ, United Kingdom
| | - Chantal M W Tax
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Maindy Rd, Cardiff CF24 4HQ, United Kingdom; Image Sciences Institute, University Medical Center Utrecht, Utrecht, Netherlands
| | - John Evans
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Maindy Rd, Cardiff CF24 4HQ, United Kingdom
| | - Khalid Hamandi
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Maindy Rd, Cardiff CF24 4HQ, United Kingdom; BRAIN Biomedical Research Unit, Health & Care Research Wales, Cardiff, United Kingdom; Department of Neurology, University Hospital of Wales, Cardiff, United Kingdom
| | - William P Gray
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Maindy Rd, Cardiff CF24 4HQ, United Kingdom; Department of Neurosurgery, University Hospital of Wales, Cardiff, United Kingdom; BRAIN Biomedical Research Unit, Health & Care Research Wales, Cardiff, United Kingdom
| | - Derek K Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Maindy Rd, Cardiff CF24 4HQ, United Kingdom; BRAIN Biomedical Research Unit, Health & Care Research Wales, Cardiff, United Kingdom
| | - Maxime Chamberland
- Cardiff University Brain Research Imaging Centre (CUBRIC), Cardiff University, Maindy Rd, Cardiff CF24 4HQ, United Kingdom; Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, the Netherlands
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14
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Kruggel F, Solodkin A. Gyral and sulcal connectivity in the human cerebral cortex. Cereb Cortex 2022; 33:4216-4229. [PMID: 36104856 DOI: 10.1093/cercor/bhac338] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2022] [Revised: 07/28/2022] [Accepted: 08/02/2022] [Indexed: 11/13/2022] Open
Abstract
Abstract
The rapid evolution of image acquisition and data analytic methods has established in vivo whole-brain tractography as a routine technology over the last 20 years. Imaging-based methods provide an additional approach to classic neuroanatomical studies focusing on biomechanical principles of anatomical organization and can in turn overcome the complexity of inter-individual variability associated with histological and tractography studies. In this work we propose a novel, reliable framework for determining brain tracts resolving the anatomical variance of brain regions. We distinguished 4 region types based on anatomical considerations: (i) gyral regions at borders between cortical communities; (ii) gyral regions within communities; (iii) sulcal regions at invariant locations across subjects; and (iv) other sulcal regions. Region types showed strikingly different anatomical and connection properties. Results allowed complementing the current understanding of the brain’s communication structure with a model of its anatomical underpinnings.
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Affiliation(s)
- Frithjof Kruggel
- Department of Biomedical Engineering, University of California , Irvine, CA92697-2755 , United States
| | - Ana Solodkin
- School of Behavioral and Brain Sciences, University of Texas , Richardson, TX75080-3021 , United States
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15
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Sperber C, Griffis J, Kasties V. Indirect structural disconnection-symptom mapping. Brain Struct Funct 2022; 227:3129-3144. [PMID: 36048282 DOI: 10.1007/s00429-022-02559-x] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2022] [Accepted: 08/24/2022] [Indexed: 01/01/2023]
Abstract
In vivo tracking of white matter fibres catalysed a modern perspective on the pivotal role of brain connectome disruption in neuropsychological deficits. However, the examination of white matter integrity in neurological patients by diffusion-weighted magnetic resonance imaging bears conceptual limitations and is not widely applicable, as it requires imaging-compatible patients and resources beyond the capabilities of many researchers. The indirect estimation of structural disconnection offers an elegant and economical alternative. For this approach, a patient's structural lesion information and normative connectome data are combined to estimate different measures of lesion-induced structural disconnection. Using one of several toolboxes, this method is relatively easy to implement and is even available to scientists without expertise in fibre tracking analyses. Nevertheless, the anatomo-behavioural statistical mapping of structural brain disconnection requires analysis steps that are not covered by these toolboxes. In this paper, we first review the current state of indirect lesion disconnection estimation, the different existing measures, and the available software. Second, we aim to fill the remaining methodological gap in statistical disconnection-symptom mapping by providing an overview and guide to disconnection data and the statistical mapping of their relationship to behavioural measurements using either univariate or multivariate statistical modelling. To assist in the practical implementation of statistical analyses, we have included software tutorials and analysis scripts.
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Affiliation(s)
- Christoph Sperber
- University of Tubingen: Eberhard Karls Universitat Tubingen, Tubingen, Germany.
| | - Joseph Griffis
- University of Tubingen: Eberhard Karls Universitat Tubingen, Tubingen, Germany
| | - Vanessa Kasties
- Centre of Neurology, Hertie-Institute for Clinical Brain Research, University of Tubingen, Tubingen, Germany
- Child Development Center, University Childrens Hospital Zurich, University of Zurich, Zurich, Switzerland
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16
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Rosenzopf H, Wiesen D, Basilakos A, Yourganov G, Bonilha L, Rorden C, Fridriksson J, Karnath HO, Sperber C. Mapping the human praxis network: an investigation of white matter disconnection in limb apraxia of gesture production. Brain Commun 2022; 4:fcac004. [PMID: 35169709 PMCID: PMC8833454 DOI: 10.1093/braincomms/fcac004] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 11/19/2021] [Accepted: 01/07/2022] [Indexed: 11/14/2022] Open
Abstract
Left hemispheric cerebral stroke can cause apraxia, a motor cognitive disorder characterized by deficits of higher-order motor skills such as the failure to accurately produce meaningful gestures. This disorder provides unique insights into the anatomical and cognitive architecture of the human praxis system. The present study aimed to map the structural brain network that is damaged in apraxia. We assessed the ability to perform meaningful gestures with the hand in 101 patients with chronic left hemisphere stroke. Structural white matter fibre damage was directly assessed by diffusion tensor imaging and fractional anisotropy mapping. We used multivariate topographical inference on tract-based fractional anisotropy topographies to identify white matter disconnection associated with apraxia. We found relevant pathological white matter alterations in a densely connected fronto-temporo-parietal network of short and long association fibres. Hence, the findings suggest that heterogeneous topographical results in previous lesion mapping studies might not only result from differences in study design, but also from the general methodological limitations of univariate topographical mapping in uncovering the structural praxis network. A striking role of middle and superior temporal lobe disconnection, including temporo-temporal short association fibres, was found, suggesting strong involvement of the temporal lobe in the praxis network. Further, the results stressed the importance of subcortical disconnections for the emergence of apractic symptoms. Our study provides a fine-grain view into the structural connectivity of the human praxis network and suggests a potential value of disconnection measures in the clinical prediction of behavioural post-stroke outcome.
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Affiliation(s)
- Hannah Rosenzopf
- Centre of Neurology, Division of Neuropsychology, Hertie-Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Daniel Wiesen
- Centre of Neurology, Division of Neuropsychology, Hertie-Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
| | - Alexandra Basilakos
- Department of Communication Sciences and Disorders, University of South Carolina, Columbia, SC, USA
| | - Grigori Yourganov
- Department of Psychology, University of South Carolina, Columbia, SC, USA
| | - Leonardo Bonilha
- Department of Neurology, Medical University of South Carolina, Charleston, SC, USA
| | - Christopher Rorden
- Department of Psychology, University of South Carolina, Columbia, SC, USA
| | - Julius Fridriksson
- Department of Communication Sciences and Disorders, University of South Carolina, Columbia, SC, USA
| | - Hans-Otto Karnath
- Centre of Neurology, Division of Neuropsychology, Hertie-Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
- Department of Psychology, University of South Carolina, Columbia, SC, USA
| | - Christoph Sperber
- Centre of Neurology, Division of Neuropsychology, Hertie-Institute for Clinical Brain Research, University of Tübingen, Tübingen, Germany
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17
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Li X, Zhang S, Jiang X, Zhang S, Han J, Guo L, Zhang T. Cortical development coupling between surface area and sulcal depth on macaque brains. Brain Struct Funct 2022; 227:1013-1029. [PMID: 34989870 DOI: 10.1007/s00429-021-02444-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/23/2021] [Accepted: 12/15/2021] [Indexed: 02/06/2023]
Abstract
Postnatal development of cerebral cortex is associated with a variety of neuronal processes and is thus critical to development of brain function and cognition. Longitudinal changes of cortical morphology and topology, such as postnatal cortical thinning and flattening have been widely studied. However, thorough and systematic investigation of such cortical change, including how to quantify it from multiple spatial directions and how to relate it to surface topology, is rarely found. In this work, based on a longitudinal macaque neuroimaging dataset, we quantified local changes in gyral white matter's surface area and sulcal depth during early development. We also investigated how these two metrics are coupled and how this coupling is linked to cortical surface topology, underlying white matter, and positions of functional areas. Semi-parametric generalized additive models were adopted to quantify the longitudinal changes of surface area (A) and sulcal depth (D), and the coupling patterns between them. This resulted in four classes of regions, according to how they change compared with global change throughout early development: slower surface area change and slower sulcal depth change (slowA_slowD), slower surface area change and faster sulcal depth change (slowA_fastD), faster surface area change and slower sulcal depth change (fastA_slowD), and faster surface area change and faster sulcal depth change (fastA_fastD). We found that cortex-related metrics, including folding pattern and cortical thickness, vary along slowA_fastD-fastA_slowD axis, and structural connection-related metrics vary along fastA_fastD-slowA_slowD axis, with which brain functional sites align better. It is also found that cortical landmarks, including sulcal pits and gyral hinges, spatially reside on the borders of the four patterns. These findings shed new lights on the relationship between cortex development, surface topology, axonal wiring pattern and brain functions.
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Affiliation(s)
- Xiao Li
- School of Automation, Northwestern Polytechnical University, Xi'an, China
| | - Songyao Zhang
- School of Automation, Northwestern Polytechnical University, Xi'an, China
| | - Xi Jiang
- School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu, China
| | - Shu Zhang
- School of Computer Science, Northwestern Polytechnical University, Xi'an, China
| | - Junwei Han
- School of Automation, Northwestern Polytechnical University, Xi'an, China
| | - Lei Guo
- School of Automation, Northwestern Polytechnical University, Xi'an, China
| | - Tuo Zhang
- School of Automation, Northwestern Polytechnical University, Xi'an, China.
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18
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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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19
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Veale T, Malone IB, Poole T, Parker TD, Slattery CF, Paterson RW, Foulkes AJM, Thomas DL, Schott JM, Zhang H, Fox NC, Cash DM. Loss and dispersion of superficial white matter in Alzheimer's disease: a diffusion MRI study. Brain Commun 2021; 3:fcab272. [PMID: 34859218 PMCID: PMC8633427 DOI: 10.1093/braincomms/fcab272] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2021] [Revised: 09/24/2021] [Accepted: 10/18/2021] [Indexed: 11/22/2022] Open
Abstract
Pathological cerebral white matter changes in Alzheimer's disease have been shown using diffusion tensor imaging. Superficial white matter changes are relatively understudied despite their importance in cortico-cortical connections. Measuring superficial white matter degeneration using diffusion tensor imaging is challenging due to its complex organizational structure and proximity to the cortex. To overcome this, we investigated diffusion MRI changes in young-onset Alzheimer's disease using standard diffusion tensor imaging and Neurite Orientation Dispersion and Density Imaging to distinguish between disease-related changes that are degenerative (e.g. loss of myelinated fibres) and organizational (e.g. increased fibre dispersion). Twenty-nine young-onset Alzheimer's disease patients and 22 healthy controls had both single-shell and multi-shell diffusion MRI. We calculated fractional anisotropy, mean diffusivity, neurite density index, orientation dispersion index and tissue fraction (1-free water fraction). Diffusion metrics were sampled in 15 a priori regions of interest at four points along the cortical profile: cortical grey matter, grey/white boundary, superficial white matter (1 mm below grey/white boundary) and superficial/deeper white matter (2 mm below grey/white boundary). To estimate cross-sectional group differences, we used average marginal effects from linear mixed effect models of participants' diffusion metrics along the cortical profile. The superficial white matter of young-onset Alzheimer's disease individuals had lower neurite density index compared to controls in five regions (superior and inferior parietal, precuneus, entorhinal and parahippocampus) (all P < 0.05), and higher orientation dispersion index in three regions (fusiform, entorhinal and parahippocampus) (all P < 0.05). Young-onset Alzheimer's disease individuals had lower fractional anisotropy in the entorhinal and parahippocampus regions (both P < 0.05) and higher fractional anisotropy within the postcentral region (P < 0.05). Mean diffusivity was higher in the young-onset Alzheimer's disease group in the parahippocampal region (P < 0.05) and lower in the postcentral, precentral and superior temporal regions (all P < 0.05). In the overlying grey matter, disease-related changes were largely consistent with superficial white matter findings when using neurite density index and fractional anisotropy, but appeared at odds with orientation dispersion and mean diffusivity. Tissue fraction was significantly lower across all grey matter regions in young-onset Alzheimer's disease individuals (all P < 0.001) but group differences reduced in magnitude and coverage when moving towards the superficial white matter. These results show that microstructural changes occur within superficial white matter and along the cortical profile in individuals with young-onset Alzheimer's disease. Lower neurite density and higher orientation dispersion suggests underlying fibres undergo neurodegeneration and organizational changes, two effects previously indiscernible using standard diffusion tensor metrics in superficial white matter.
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Affiliation(s)
- Thomas Veale
- The Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
- UK Dementia Research Institute at UCL, University College London, London, UK
| | - Ian B Malone
- The Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
| | - Teresa Poole
- Department of Medical Statistics, London School of Hygiene & Tropical Medicine, London, UK
| | - Thomas D Parker
- The Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
- Department of Brain Sciences, Imperial College London, London, UK
| | - Catherine F Slattery
- The Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
| | - Ross W Paterson
- The Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
- UK Dementia Research Institute at UCL, University College London, London, UK
| | - Alexander J M Foulkes
- The Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
| | - David L Thomas
- The Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
- Wellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, London, UK
- Department of Brain Repair and Rehabilitation, UCL Queen Square Institute of Neurology, London, UK
| | - Jonathan M Schott
- The Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
| | - Hui Zhang
- Department of Computer Science and Centre for Medical Image Computing, UCL, London, UK
| | - Nick C Fox
- The Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
- UK Dementia Research Institute at UCL, University College London, London, UK
| | - David M Cash
- The Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, London, UK
- UK Dementia Research Institute at UCL, University College London, London, UK
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20
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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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21
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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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22
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Chavoshnejad P, Li X, Zhang S, Dai W, Vasung L, Liu T, Zhang T, Wang X, Razavi MJ. Role of axonal fibers in the cortical folding patterns: A tale of variability and regularity. BRAIN MULTIPHYSICS 2021. [DOI: 10.1016/j.brain.2021.100029] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022] Open
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23
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Zhang T, Li X, Jiang X, Ge F, Zhang S, Zhao L, Liu H, Huang Y, Wang X, Yang J, Guo L, Hu X, Liu T. Cortical 3-hinges could serve as hubs in cortico-cortical connective network. Brain Imaging Behav 2020; 14:2512-2529. [PMID: 31950404 PMCID: PMC7647986 DOI: 10.1007/s11682-019-00204-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
Mapping the relation between cortical convolution and structural/functional brain architectures could provide deep insights into the mechanisms of brain development, evolution and diseases. In our previous studies, we found a unique gyral folding pattern, termed a 3-hinge, which was defined as the conjunction of three gyral crests. The uniqueness of the 3-hinge was evidenced by its thicker cortex and stronger fiber connections than other gyral regions. However, the role that 3-hinges play in cortico-cortical connective architecture remains unclear. To this end, we conducted MRI studies by constructing structural cortico-cortical connective networks based on a fine-granular cortical parcellation, the parcels of which were automatically labeled as 3-hinge, 2-hinge (ordinary gyrus) or sulcus. On human brains, 3-hinges possess significantly higher degrees, strengths and betweennesses than 2-hinges, suggesting that 3-hinges could serve more like hubs in the cortico-cortical connective network. This hypothesis gains supports from human functional network analyses, in which 3-hinges are involved in more global functional networks than ordinary gyri. In addition, 3-hinges could serve as 'connector' hubs rather than 'provincial' hubs and they account for a dominant proportion of nodes in the high-level 'backbone' of the network. These structural results are reproduced on chimpanzee and macaque brains, while the roles of 3-hinges as hubs become more pronounced in higher order primates. Our new findings could provide a new window to the relation between cortical convolution, anatomical connection and brain function.
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Affiliation(s)
- Tuo Zhang
- School of Automation, Northwestern Polytechnical University, #127, West Youyi Road, Xi'an, 710072, Shaanxi, China.
| | - Xiao Li
- School of Automation, Northwestern Polytechnical University, #127, West Youyi Road, Xi'an, 710072, Shaanxi, 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
| | - Fangfei Ge
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA
| | - Shu Zhang
- 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, #127, West Youyi Road, Xi'an, 710072, Shaanxi, China
| | - Huan Liu
- School of Automation, Northwestern Polytechnical University, #127, West Youyi Road, Xi'an, 710072, Shaanxi, China
| | - Ying Huang
- School of Automation, Northwestern Polytechnical University, #127, West Youyi Road, Xi'an, 710072, Shaanxi, China
| | - Xianqiao Wang
- College of Engineering, The University of Georgia, Athens, GA, USA
| | - Jian Yang
- Radiology Department of the First Affiliated Hospital, Xi'an Jiaotong University, Xi'an, China
- The Key Laboratory of Biomedical Information Engineering, Ministry of Education, Department of Biomedical Engineering, School of Life Science and Technology, Xi'an Jiaotong University, Xi'an, China
| | - Lei Guo
- School of Automation, Northwestern Polytechnical University, #127, West Youyi Road, Xi'an, 710072, Shaanxi, China
| | - Xiaoping Hu
- Department of Bioengineering, University of California Riverside, Riverside, CA, USA
| | - 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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24
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Li X, Liu T, Li Y, Li Q, Wang X, Hu X, Guo L, Zhang T, Liu T. Marmoset Brain ISH Data Revealed Molecular Difference Between Cortical Folding Patterns. Cereb Cortex 2020; 31:1660-1674. [PMID: 33152757 DOI: 10.1093/cercor/bhaa317] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Revised: 08/23/2020] [Accepted: 09/29/2020] [Indexed: 01/14/2023] Open
Abstract
Literature studies have demonstrated the structural, connectional, and functional differences between cortical folding patterns in mammalian brains, such as convex and concave patterns. However, the molecular underpinning of such convex/concave differences remains largely unknown. Thanks to public access to a recently released set of marmoset whole-brain in situ hybridization data by RIKEN, Japan; this data's accessibility empowers us to improve our understanding of the organization, regulation, and function of genes and their relation to macroscale metrics of brains. In this work, magnetic resonance imaging and diffusion tensor imaging macroscale neuroimaging data in this dataset were used to delineate convex/concave patterns in marmoset and to examine their structural features. Machine learning and visualization tools were employed to investigate the possible transcriptome difference between cortical convex and concave patterns. Experimental results demonstrated that a collection of genes is differentially expressed in convex and concave patterns, and their expression profiles can robustly characterize and differentiate the two folding patterns. More importantly, neuroscientific interpretations of these differentially expressed genes, as well as axonal guidance pathway analysis and gene enrichment analysis, offer novel understanding of structural and functional differences between cortical folding patterns in different regions from a molecular perspective.
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Affiliation(s)
- Xiao Li
- Key Laboratory of Information Fusion Technology, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
| | - Tao Liu
- Center for Genomics and Computational Biology, College of Science, North China University of Science and Technology, 063210, China.,Center of Computational Biology, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 611731, China
| | - Yujie Li
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA 30602, USA
| | - Qing Li
- The Information Processing Laboratory, School of Artificial Intelligence, Beijing Normal University, Beijing 100875, China
| | - Xianqiao Wang
- Computational Nano/Bio-Mechanics Lab, College of Engineering, The University of Georgia, Athens, GA 30602, USA
| | - Xintao Hu
- Key Laboratory of Information Fusion Technology, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
| | - Lei Guo
- Key Laboratory of Information Fusion Technology, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
| | - Tuo Zhang
- Key Laboratory of Information Fusion Technology, School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
| | - Tianming Liu
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA 30602, USA
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25
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Bodin C, Pron A, Le Mao M, Régis J, Belin P, Coulon O. Plis de passage in the superior temporal sulcus: Morphology and local connectivity. Neuroimage 2020; 225:117513. [PMID: 33130271 DOI: 10.1016/j.neuroimage.2020.117513] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Revised: 10/21/2020] [Accepted: 10/23/2020] [Indexed: 12/31/2022] Open
Abstract
While there is a profusion of functional investigations involving the superior temporal sulcus (STS), our knowledge of the anatomy of this sulcus is still limited by a large individual variability. In particular, an accurate characterization of the "plis de passage" (PPs), annectant gyri inside the fold, is lacking to explain this variability. Performed on 90 subjects of the HCP database, our study revealed that PPs constitute landmarks that can be identified from the geometry of the STS walls. They were found associated with a specific U-shape white-matter connectivity between the two banks of the sulcus, the amount of connectivity being related to the depth of the PPs. These findings raise new hypotheses regarding the spatial organization of PPs, the relation between cortical anatomy and structural connectivity, as well as the possible role of PPs in the regional functional organization.
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Affiliation(s)
- C Bodin
- CNRS, UMR 7289, Institut de Neurosciences de la Timone, Aix-Marseille Université, Marseille, France; Institute for Language, Communication, and the Brain, Aix-Marseille University, Marseille, France.
| | - A Pron
- CNRS, UMR 7289, Institut de Neurosciences de la Timone, Aix-Marseille Université, Marseille, France
| | - M Le Mao
- CNRS, UMR 7289, Institut de Neurosciences de la Timone, Aix-Marseille Université, Marseille, France
| | - J Régis
- INSERM U1106, Institut de Neurosciences des Systèmes, Aix-Marseille Université, Marseille, France
| | - P Belin
- CNRS, UMR 7289, Institut de Neurosciences de la Timone, Aix-Marseille Université, Marseille, France; Département de Psychologie, Université de Montréal, Montréal, Canada; Institute for Language, Communication, and the Brain, Aix-Marseille University, Marseille, France
| | - O Coulon
- CNRS, UMR 7289, Institut de Neurosciences de la Timone, Aix-Marseille Université, Marseille, France; Institute for Language, Communication, and the Brain, Aix-Marseille University, Marseille, France
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26
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López-López N, Vázquez A, Houenou J, Poupon C, Mangin JF, Ladra S, Guevara P. From Coarse to Fine-Grained Parcellation of the Cortical Surface Using a Fiber-Bundle Atlas. Front Neuroinform 2020; 14:32. [PMID: 33071768 PMCID: PMC7533645 DOI: 10.3389/fninf.2020.00032] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/09/2020] [Accepted: 06/19/2020] [Indexed: 12/12/2022] Open
Abstract
In this article, we present a hybrid method to create fine-grained parcellations of the cortical surface, from a coarse-grained parcellation according to an anatomical atlas, based on cortico-cortical connectivity. The connectivity information is obtained from segmented superficial and deep white matter bundles, according to bundle atlases, instead of the whole tractography. Thus, a direct matching between the fiber bundles and the cortical regions is obtained, avoiding the problem of finding the correspondence of the cortical parcels among subjects. Generating parcels from segmented fiber bundles can provide a good representation of the human brain connectome since they are based on bundle atlases that contain the most reproducible short and long connections found on a population of subjects. The method first processes the tractography of each subject and extracts the bundles of the atlas, based on a segmentation algorithm. Next, the intersection between the fiber bundles and the cortical mesh is calculated, to define the initial and final intersection points of each fiber. A fiber filtering is then applied to eliminate misclassified fibers, based on the anatomical definition of each bundle and the labels of Desikan-Killiany anatomical parcellation. A parcellation algorithm is then performed to create a subdivision of the anatomical regions of the cortex, which is reproducible across subjects. This step resolves the overlapping of the fiber bundle extremities over the cortical mesh within each anatomical region. For the analysis, the density of the connections and the degree of overlapping, is considered and represented with a graph. One of our parcellations, an atlas composed of 160 parcels, achieves a reproducibility across subjects of ≈0.74, based on the average Dice's coefficient between subject's connectivity matrices, rather than ≈0.73 obtained for a macro anatomical parcellation of 150 parcels. Moreover, we compared two of our parcellations with state-of-the-art atlases, finding a degree of similarity with dMRI, functional, anatomical, and multi-modal atlases. The higher similarity was found for our parcellation composed of 185 sub-parcels with another parcellation based on dMRI data from the same database, but created with a different approach, leading to 130 parcels in common based on a Dice's coefficient ≥0.5.
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Affiliation(s)
- Narciso López-López
- Faculty of Engineering, Universidad de Concepción, Concepción, Chile.,Universidade da Coruña, CITIC, Department of Computer Science and Information Technologies, A Coruña, Spain
| | - Andrea Vázquez
- Faculty of Engineering, Universidad de Concepción, Concepción, Chile
| | - Josselin Houenou
- Université Paris-Saclay, CEA, CNRS, Baobab, Neurospin, Gif-sur-Yvette, France.,INSERM U955 Unit, Mondor Institute for Biomedical Research, Team 15 "Translational Psychiatry", Paris, France.,Fondation Fondamental, Paris, France.,AP-HP, Department of Psychiatry and Addictology, School of Medicine, Mondor University Hospitals, DHU PePsy, Paris, France
| | - Cyril Poupon
- Université Paris-Saclay, CEA, CNRS, Baobab, Neurospin, Gif-sur-Yvette, France
| | | | - Susana Ladra
- Universidade da Coruña, CITIC, Department of Computer Science and Information Technologies, A Coruña, Spain
| | - Pamela Guevara
- Faculty of Engineering, Universidad de Concepción, Concepción, Chile
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27
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Guevara M, Guevara P, Román C, Mangin JF. Superficial white matter: A review on the dMRI analysis methods and applications. Neuroimage 2020; 212:116673. [DOI: 10.1016/j.neuroimage.2020.116673] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2019] [Revised: 02/17/2020] [Accepted: 02/19/2020] [Indexed: 12/12/2022] Open
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28
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Zhang T, Huang Y, Zhao L, He Z, Jiang X, Guo L, Hu X, Liu T. Identifying Cross-individual Correspondences of 3-hinge Gyri. Med Image Anal 2020; 63:101700. [PMID: 32361590 DOI: 10.1016/j.media.2020.101700] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2019] [Revised: 04/04/2020] [Accepted: 04/07/2020] [Indexed: 01/16/2023]
Abstract
Human brain alignment based on imaging data has long been an intriguing research topic. One of the challenges is the huge inter-individual variabilities, which are pronounced not only in cortical folding patterns, but also in the underlying structural and functional patterns. Also, it is still not fully understood how to link the cross-subject similarity of cortical folding patterns to the correspondences of structural brain wiring diagrams and brain functions. Recently, a specific cortical gyral folding pattern was identified, which is the conjunction of gyri from multiple directions and termed a "gyral hinge". These gyral hinges are characterized by the thickest cortices, the densest long-range fibers, and the most complex functional profiles in contrast to other gyri. In addition to their structural and functional importance, a small portion of 3-hinges found correspondences across subjects and even species by manual labeling. However, it is unclear if such cross-subject correspondences can be found for all 3-hinges, or if the correspondences are interpretable from structural and functional aspects. Given the huge variability of cortical folding patterns, we proposed a novel algorithm which jointly uses structural MRI-derived cortical folding patterns and diffusion-MRI-derived fiber shape features to estimate the correspondences. This algorithm was executed in a group-wise manner, whereby 3-hinges of all subjects were simultaneously aligned. The effectiveness of the algorithm was demonstrated by higher cross-subject 3-hinges' consistency with respect to structural and functional metrics, when compared with other methods. Our findings provide a novel approach to brain alignment and an insight to the linkage between cortical folding patterns and the underlying structural connective diagrams and brain functions.
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Affiliation(s)
- Tuo Zhang
- School of Automation, Northwestern Polytechnical University, Xi'an, China.
| | - Ying Huang
- School of Automation, Northwestern Polytechnical University, Xi'an, China
| | - 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
| | - Zhibin He
- School of Automation, Northwestern Polytechnical University, Xi'an, China
| | - Xi Jiang
- School of Life Science and Technology, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China
| | - Lei Guo
- School of Automation, Northwestern Polytechnical University, Xi'an, China
| | - Xiaoping Hu
- Department of Bioengineering, University of California Riverside, Riverside, CA, USA
| | - 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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29
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Movahedian Attar F, Kirilina E, Haenelt D, Pine KJ, Trampel R, Edwards LJ, Weiskopf N. Mapping Short Association Fibers in the Early Cortical Visual Processing Stream Using In Vivo Diffusion Tractography. Cereb Cortex 2020; 30:4496-4514. [PMID: 32297628 PMCID: PMC7325803 DOI: 10.1093/cercor/bhaa049] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022] Open
Abstract
Short association fibers (U-fibers) connect proximal cortical areas and constitute the majority of white matter connections in the human brain. U-fibers play an important role in brain development, function, and pathology but are underrepresented in current descriptions of the human brain connectome, primarily due to methodological challenges in diffusion magnetic resonance imaging (dMRI) of these fibers. High spatial resolution and dedicated fiber and tractography models are required to reliably map the U-fibers. Moreover, limited quantitative knowledge of their geometry and distribution makes validation of U-fiber tractography challenging. Submillimeter resolution diffusion MRI—facilitated by a cutting-edge MRI scanner with 300 mT/m maximum gradient amplitude—was used to map U-fiber connectivity between primary and secondary visual cortical areas (V1 and V2, respectively) in vivo. V1 and V2 retinotopic maps were obtained using functional MRI at 7T. The mapped V1–V2 connectivity was retinotopically organized, demonstrating higher connectivity for retinotopically corresponding areas in V1 and V2 as expected. The results were highly reproducible, as demonstrated by repeated measurements in the same participants and by an independent replication group study. This study demonstrates a robust U-fiber connectivity mapping in vivo and is an important step toward construction of a more complete human brain connectome.
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Affiliation(s)
- Fakhereh Movahedian Attar
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany
| | - Evgeniya Kirilina
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany.,Department of Education and Psychology, Center for Cognitive Neuroscience Berlin, Free University Berlin, 14195 Berlin, Germany
| | - Daniel Haenelt
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany
| | - Kerrin J Pine
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany
| | - Robert Trampel
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany
| | - Luke J Edwards
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany
| | - Nikolaus Weiskopf
- Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, 04103 Leipzig, Germany.,Felix Bloch Institute for Solid State Physics, Faculty of Physics and Earth Sciences, Leipzig University, 04109 Leipzig, Germany
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30
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The origin and development of subcortical U-fibers in gyrencephalic ferrets. Mol Brain 2020; 13:37. [PMID: 32156301 PMCID: PMC7063767 DOI: 10.1186/s13041-020-00575-8] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2019] [Accepted: 02/27/2020] [Indexed: 11/17/2022] Open
Abstract
In the white matter of the human cerebrum, the majority of cortico-cortical fibers are of short range, connecting neighboring cortical areas. U-fibers represent connections between neighboring areas and are located in the white matter immediately deep to the cerebral cortex. Using gyrencephalic carnivore ferrets, here we investigated the neurochemical, anatomical and developmental features of U-fibers. We demonstrate that U-fibers were derived from neighboring cortical areas in ferrets. U-fiber regions in ferrets were intensely stained with Gallyas myelin staining and Turnbull blue iron staining. We further found that U-fibers were derived from neurons in both upper and lower layers in neighboring areas of the cerebral cortex and that U-fibers were formed later than axons in the deep white matter during development. Our findings shed light on the fundamental features of U-fibers in the gyrencephalic cerebral cortex. Because genetic manipulation techniques for ferrets are now available, ferrets should be an important option for investigating the development, functions and pathophysiological changes of U-fibers.
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31
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Registration-free analysis of diffusion MRI tractography data across subjects through the human lifespan. Neuroimage 2020; 214:116703. [PMID: 32151759 PMCID: PMC8482444 DOI: 10.1016/j.neuroimage.2020.116703] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2019] [Revised: 01/21/2020] [Accepted: 03/02/2020] [Indexed: 02/05/2023] Open
Abstract
Diffusion MRI tractography produces massive sets of streamlines that need to be clustered into anatomically meaningful white-matter bundles. Conventional clustering techniques group streamlines based on their proximity in Euclidean space. We have developed AnatomiCuts, an unsupervised method for clustering tractography streamlines based on their neighboring anatomical structures, rather than their coordinates in Euclidean space. In this work, we show that the anatomical similarity metric used in AnatomiCuts can be extended to find corresponding clusters across subjects and across hemispheres, without inter-subject or inter-hemispheric registration. Our proposed approach enables group-wise tract cluster analysis, as well as studies of hemispheric asymmetry. We evaluate our approach on data from the pilot MGH-Harvard-USC Lifespan Human Connectome project, showing improved correspondence in tract clusters across 184 subjects aged 8-90. Our method shows up to 38% improvement in the overlap of corresponding clusters when comparing subjects with large age differences. The techniques presented here do not require registration to a template and can thus be applied to populations with large inter-subject variability, e.g., due to brain development, aging, or neurological disorders.
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32
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Lizarazu M, Gil-Robles S, Pomposo I, Nara S, Amoruso L, Quiñones I, Carreiras M. Spatiotemporal dynamics of postoperative functional plasticity in patients with brain tumors in language areas. BRAIN AND LANGUAGE 2020; 202:104741. [PMID: 31931399 DOI: 10.1016/j.bandl.2019.104741] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/28/2019] [Revised: 12/17/2019] [Accepted: 12/27/2019] [Indexed: 06/10/2023]
Abstract
Postoperative functional neuroimaging provides a unique opportunity to investigate the neural mechanisms that facilitate language network reorganization. Previous studies in patients with low grade gliomas (LGGs) in language areas suggest that postoperative recovery is likely due to functional neuroplasticity in peritumoral and contra-tumoral healthy regions, but have attributed varying degrees of importance to specific regions. In this study, we used Magnetoencephalography (MEG) to investigate functional connectivity changes in peritumoral and contra-tumoral regions after brain tumor resection. MEG recordings of cortical activity during resting-state were obtained from 12 patients with LGGs in left-hemisphere language brain areas. MEG data were recorded before (Pre session), and 3 (Post_1 session) and 6 (Post_2 session) months after awake craniotomy. For each MEG session, we measured the functional connectivity of the peritumoral and contra-tumoral regions to the rest of the brain across the 1-100 Hz frequency band. We found that functional connectivity in the Post_1 and Post_2 sessions was higher than in the Pre session only in peritumoral regions and within the alpha frequency band. Functional connectivity in peritumoral regions did not differ between the Post_1 and Post_2 sessions. Alpha connectivity enhancement in peritumoral regions was observed in all patients regardless of the LGG location. Together, these results suggest that postoperative language functional reorganization occurs in peritumoral regions regardless of the location of the tumor and mostly develops within 3 months after surgery.
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Affiliation(s)
- Mikel Lizarazu
- BCBL, Basque Center on Cognition, Brain and Language, Donostia/San Sebastián, Spain; Laboratoire de Sciences Cognitives et Psycholinguistique (ENS, EHESS, CNRS), Ecole Normale Supérieure, PSL Research University, Paris, France.
| | - Santiago Gil-Robles
- Department of Neurosurgery, Hospital Quirón, Madrid, Spain; BioCruces Research Institute, Bilbao, Spain
| | | | - Sanjeev Nara
- BCBL, Basque Center on Cognition, Brain and Language, Donostia/San Sebastián, Spain
| | - Lucía Amoruso
- BCBL, Basque Center on Cognition, Brain and Language, Donostia/San Sebastián, Spain
| | - Ileana Quiñones
- BCBL, Basque Center on Cognition, Brain and Language, Donostia/San Sebastián, Spain
| | - Manuel Carreiras
- BCBL, Basque Center on Cognition, Brain and Language, Donostia/San Sebastián, Spain; Ikerbasque, Basque Foundation for Science, Bilbao, Spain; University of the Basque Country, UPV/EHU, Bilbao, Spain
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Bigham B, Zamanpour SA, Zemorshidi F, Boroumand F, Zare H. Identification of Superficial White Matter Abnormalities in Alzheimer's Disease and Mild Cognitive Impairment Using Diffusion Tensor Imaging. J Alzheimers Dis Rep 2020; 4:49-59. [PMID: 32206757 PMCID: PMC7081087 DOI: 10.3233/adr-190149] [Citation(s) in RCA: 17] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 02/03/2020] [Indexed: 01/15/2023] Open
Abstract
BACKGROUND Diffusion tensor imaging (DTI) estimates the microstructural alterations of the brain, as a magnetic resonance imaging (MRI)-based neuroimaging technique. Prior DTI studies reported decreased structural integrity of the superficial white matter (SWM) in the brain diseases. OBJECTIVE This study aimed to determine the diffusion characteristics of SWM in Alzheimer's disease (AD) and mild cognitive impairment (MCI) using tractography and region of interest (ROI) approaches. METHODS The diffusion MRI data were downloaded from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database on 24 patients with AD, 24 with MCI, and 24 normal control (NC) subjects. DTI processing was performed using DSI Studio software. First, for ROI-based analysis, The superficial white matter was divided into right and left frontal, parietal, temporal, insula, limbic and occipital regions by the Talairach Atlas, Then, for tractography-based analysis, the tractography of each of these regions was performed with 100000 seeds. Finally, the average diffusion values were extracted from voxels within the ROIs and tracts. RESULTS Both tractography and ROI analyses showed a significant difference in radial, axial and mean diffusivity values between the three groups (p < 0.05) across most of the SWM. Furthermore, The Mini-Mental State Examination was significantly correlated with radial, axial, and mean diffusivity values in parietal and temporal lobes SWM in the AD group (p < 0.05). CONCLUSION DTI provided information indicating microstructural changes in the SWM of patients with AD and MCI. Therefore, assessment of the SWM using DTI may be helpful for the clinical diagnosis of patients with AD and MCI.
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Affiliation(s)
- Bahare Bigham
- Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Seyed Amir Zamanpour
- Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Fariba Zemorshidi
- Department of Neurology, Ghaem Hospital, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Farzaneh Boroumand
- Student Research Committee, Social Determinants of Health Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
| | - Hoda Zare
- Department of Medical Physics, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
- Medical Physics Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
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Gahm JK, Shi Y. Surface-based Tracking of U-fibers in the Superficial White Matter. MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION : MICCAI ... INTERNATIONAL CONFERENCE ON MEDICAL IMAGE COMPUTING AND COMPUTER-ASSISTED INTERVENTION 2019; 11766:538-546. [PMID: 33860288 PMCID: PMC8046261] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
The superficial white matter (SWM) lies directly underneath the cortical ribbon and contains the short association fibers, or U-fibers, that connect neighboring gyri. Connectivity of these U-fibers is important for various neuroscientific research from the development to the aging of the brain. Nonetheless, conventional tractography methods can only provide a partial representation of these connections. Moreover, previous studies on U-fibers mainly extract tracts based on their shape characteristics without imposing the biologically critical condition that they should tightly follow the cortical surface. In this work we leverage the high resolution diffusion imaging data from the Human Connectome Project (HCP), and develop a novel surface-based framework for reconstructing the U-fibers. Guided by the projected fiber orientation distributions (FODs) on cortical surfaces, our method tracks the U-fibers from sulcal seed regions to neighboring gyrus on the triangular mesh representation of the cortex. Compared to volume-based tractography, the main advantage of our method is that it is intrinsic to the cortical geometry. More specifically, we define a novel approach for measuring the change of angles on the tangent space of the surface and use them to determine the U-fiber passing through a sulcal seed point. In experimental results, we compare our surface-based method with state-of-the-art FOD-based tractography from MRtrix on a large-scale dataset of 484 HCP subjects, and demonstrate that our method clearly achieves superior performance on the reconstruction of U-fibers between the precentral and postcentral gyrus.
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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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Choi SH, Kim YB, Cho ZH. Fiber-distance-based unsupervised clustering of MR tractography data. J Neurosci Methods 2019; 325:108361. [PMID: 31336061 DOI: 10.1016/j.jneumeth.2019.108361] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/03/2019] [Revised: 06/25/2019] [Accepted: 07/11/2019] [Indexed: 11/28/2022]
Abstract
BACKGROUND MR tractography from diffusion tensor imaging provides a non-invasive way to explore white matter pathways in the human brain. However, a challenge to extracting reliable anatomical information from these data is the use of reliable and effective clustering methodologies. In this paper, we implemented a new version of a robust unsupervised clustering method from MR tractography data using the density-based spatial clustering of applications with noise (DBSCAN) algorithm. NEW METHOD Conventional DBSCAN clustering methods for MR tractography data use each fiber's start and end point as well as the distance between start and end points. Instead, in this study, we extracted and used a fiber-distance matrix generated for all fiber combinations from the tractography dataset in DBSCAN clustering. The two DBSCAN parameters-minimum point number and maximum radius of the neighborhood-were selected according to the value generated with the cluster stability index (CSI). RESULTS Performing the proposed CSI-optimized DBSCAN-based clustering method on MR tractography data of the superior longitudinal fasciculus generated 6 robust, non-overlapping, clusters that are neuroanatomically related. COMPARISON WITH EXISTING METHODS Conventional DBSCAN-based clustering methods have intrinsic error potential in the clustering results due to deviations in fiber shape and fiber location. The proposed method did not exhibit clustering error caused by deviation in fiber trajectory or fiber location. CONCLUSIONS We implemented a new, robust DBSCAN-based fiber clustering method for MR tractography data. The CSI-optimized DBSCAN-based unsupervised clustering is applicable to investigation of the neuroconnectome and the fiber structure of the brain.
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Affiliation(s)
- Sang-Han Choi
- Neuroscience Research Institute, Suwon University, 17, Wauangil, Bongdam-eup, Hwaseong-si, Gyeonggi-do, South Korea.
| | - Young-Bo Kim
- Depart of Neurosurgery, School of Medicine Gachon University, Incheon, South Korea.
| | - Zang-Hee Cho
- Neuroscience Research Institute, Suwon University, 17, Wauangil, Bongdam-eup, Hwaseong-si, Gyeonggi-do, South Korea; AICT, Seoul National University, Seoul, South Korea.
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Ostrowski LM, Song DY, Thorn EL, Ross EE, Stoyell SM, Chinappen DM, Eden UT, Kramer MA, Emerton BC, Morgan AK, Stufflebeam SM, Chu CJ. Dysmature superficial white matter microstructure in developmental focal epilepsy. Brain Commun 2019; 1:fcz002. [PMID: 31608323 PMCID: PMC6777514 DOI: 10.1093/braincomms/fcz002] [Citation(s) in RCA: 16] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2019] [Revised: 05/07/2019] [Accepted: 05/08/2019] [Indexed: 01/09/2023] Open
Abstract
Benign epilepsy with centrotemporal spikes is a common childhood epilepsy syndrome that predominantly affects boys, characterized by self-limited focal seizures arising from the perirolandic cortex and fine motor abnormalities. Concurrent with the age-specific presentation of this syndrome, the brain undergoes a developmentally choreographed sequence of white matter microstructural changes, including maturation of association u-fibres abutting the cortex. These short fibres mediate local cortico-cortical communication and provide an age-sensitive structural substrate that could support a focal disease process. To test this hypothesis, we evaluated the microstructural properties of superficial white matter in regions corresponding to u-fibres underlying the perirolandic seizure onset zone in children with this epilepsy syndrome compared with healthy controls. To verify the spatial specificity of these features, we characterized global superficial and deep white matter properties. We further evaluated the characteristics of the perirolandic white matter in relation to performance on a fine motor task, gender and abnormalities observed on EEG. Children with benign epilepsy with centrotemporal spikes (n = 20) and healthy controls (n = 14) underwent multimodal testing with high-resolution MRI including diffusion tensor imaging sequences, sleep EEG recordings and fine motor assessment. We compared white matter microstructural characteristics (axial, radial and mean diffusivity, and fractional anisotropy) between groups in each region. We found distinct abnormalities corresponding to the perirolandic u-fibre region, with increased axial, radial and mean diffusivity and fractional anisotropy values in children with epilepsy (P = 0.039, P = 0.035, P = 0.042 and P = 0.017, respectively). Increased fractional anisotropy in this region, consistent with decreased integrity of crossing sensorimotor u-fibres, correlated with inferior fine motor performance (P = 0.029). There were gender-specific differences in white matter microstructure in the perirolandic region; males and females with epilepsy and healthy males had higher diffusion and fractional anisotropy values than healthy females (P ≤ 0.035 for all measures), suggesting that typical patterns of white matter development disproportionately predispose boys to this developmental epilepsy syndrome. Perirolandic white matter microstructure showed no relationship to epilepsy duration, duration seizure free, or epileptiform burden. There were no group differences in diffusivity or fractional anisotropy in superficial white matter outside of the perirolandic region. Children with epilepsy had increased radial diffusivity (P = 0.022) and decreased fractional anisotropy (P = 0.027) in deep white matter, consistent with a global delay in white matter maturation. These data provide evidence that atypical maturation of white matter microstructure is a basic feature in benign epilepsy with centrotemporal spikes and may contribute to the epilepsy, male predisposition and clinical comorbidities observed in this disorder.
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Affiliation(s)
- Lauren M Ostrowski
- Department of Neuroscience, Brown University, Providence, RI 02912, USA
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Daniel Y Song
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Emily L Thorn
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Erin E Ross
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Sally M Stoyell
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA
| | | | - Uri T Eden
- Department of Mathematics and Statistics, Boston University, Boston, MA 02215, USA
| | - Mark A Kramer
- Department of Mathematics and Statistics, Boston University, Boston, MA 02215, USA
| | - Britt C Emerton
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Amy K Morgan
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Steven M Stufflebeam
- Department of Radiology, Massachusetts General Hospital, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
| | - Catherine J Chu
- Department of Neurology, Massachusetts General Hospital, Boston, MA 02114, USA
- Harvard Medical School, Boston, MA 02115, USA
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Fan X, Duan Y, Cheng S, Zhang Y, Cheng H. Fast density-peaks clustering for registration-free pediatric white matter tract analysis. Artif Intell Med 2019; 96:1-11. [PMID: 31164202 DOI: 10.1016/j.artmed.2019.03.002] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2018] [Revised: 02/27/2019] [Accepted: 03/01/2019] [Indexed: 11/25/2022]
Abstract
Clustering white matter (WM) tracts from diffusion tensor imaging (DTI) is primarily important for quantitative analysis on pediatric brain development. A recently developed algorithm, density peaks (DP) clustering, demonstrates great robustness to the complex structural variations of WM tracts without any prior templates. Nevertheless, the calculation of densities, the core step of DP, is time consuming especially when the number of WM fibers is huge. In this paper, we propose a fast algorithm that accelerates the density computation about 50 times over the original one. We convert the global calculation for the density as well as critical parameter in the process into local computations, and develop a binary tree structure to orderly store the neighbors for these local computations. Hence, the density computation turns out to be a direct access of the structure, rendering significantly computational saving. Performing experiments on synthetic point data and the JHU-DTI data set and comparing results of our fast DP algorithm and existing clustering methods, we can validate the efficiency and effectiveness of our fast DP algorithm. Finally, we demonstrate the application of the proposed algorithm on the analysis of pediatric WM tract development.
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Affiliation(s)
- Xin Fan
- DUT-RU International School of Information Science and Technology, Dalian University of Technology, Dalian, China; Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, Dalian, China
| | - Yuzhuo Duan
- DUT-RU International School of Information Science and Technology, Dalian University of Technology, Dalian, China; Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, Dalian, China
| | - Shichao Cheng
- Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, Dalian, China; School of Mathematical Science, Dalian University of Technology, Dalian, China
| | - Yuxi Zhang
- DUT-RU International School of Information Science and Technology, Dalian University of Technology, Dalian, China; Key Laboratory for Ubiquitous Network and Service Software of Liaoning Province, Dalian, China
| | - Hua Cheng
- Department of Radiology, Beijing Children's Hospital, Captital Medical University, National Center for Children's Health, China.
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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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Ge F, Li X, Razavi MJ, Chen H, Zhang T, Zhang S, Guo L, Hu X, Wang X, Liu T. Denser Growing Fiber Connections Induce 3-hinge Gyral Folding. Cereb Cortex 2019; 28:1064-1075. [PMID: 28968837 DOI: 10.1093/cercor/bhx227] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/23/2017] [Accepted: 08/11/2017] [Indexed: 01/13/2023] Open
Abstract
Recent studies have shown that quantitative description of gyral shape patterns offers a novel window to examine the relationship between brain structure and function. Along this research line, this paper examines a unique and interesting type of cortical gyral region where 3 different gyral crests meet, termed 3-hinge gyral region. We extracted 3-hinge gyral regions in macaque/chimpanzee/human brains, quantified and compared the relevant DTI-derived fiber densities in 3-hinge and 2-hinge gyral regions. Our observations consistently showed that DTI-derived fiber densities in 3-hinge regions are much higher than those in 2-hinge regions. Therefore, we hypothesize that besides the cortical expansion, denser fiber connections can induce the formation of 3-hinge gyri. To examine the biomechanical basis of this hypothesis, we constructed a series of 3-dimensional finite element soft tissue models based on continuum growth theory to investigate fundamental biomechanical mechanisms of consistent 3-hinge gyri formation. Our computational simulation results consistently showed that during gyrification gyral regions with higher concentrations of growing axonal fibers tend to form 3-hinge gyri. Our integrative approach combining neuroimaging data analysis and computational modeling appears effective in probing a plausible theory of 3-hinge gyri formation and providing new insights into structural and functional cortical architectures and their relationship.
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Affiliation(s)
- 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
| | - Xiao Li
- School of Automation, Northwestern Polytechnical University, Xi'an, China
| | - Mir Jalil Razavi
- College of Engineering, The University of Georgia, Athens, GA, USA
| | - Hanbo Chen
- 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.,Brain Decoding Research Center, 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
| | - Lei Guo
- School of Automation, Northwestern Polytechnical University, Xi'an, China
| | - Xiaoping Hu
- Department of Bioengineering, UC Riverside, Riverside, CA, USA
| | - Xianqiao Wang
- College of Engineering, The University of Georgia, Athens, GA, USA
| | - 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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Jiang X, Zhao L, Liu H, Guo L, Kendrick KM, Liu T. A Cortical Folding Pattern-Guided Model of Intrinsic Functional Brain Networks in Emotion Processing. Front Neurosci 2018; 12:575. [PMID: 30186102 PMCID: PMC6110906 DOI: 10.3389/fnins.2018.00575] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/05/2018] [Accepted: 07/30/2018] [Indexed: 01/21/2023] Open
Abstract
There have been increasing studies demonstrating that emotion processing in humans is realized by the interaction within or among the large-scale intrinsic functional brain networks. Identifying those meaningful intrinsic functional networks based on task-based functional magnetic resonance imaging (task fMRI) with specific emotional stimuli and responses, and exploring the underlying functional working mechanisms of interregional neural communication within the intrinsic functional networks are thus of great importance to understand the neural basis of emotion processing. In this paper, we propose a novel cortical folding pattern-guided model of intrinsic networks in emotion processing: gyri serve as global functional connection centers that perform interregional neural communication among distinct regions via long distance dense axonal fibers, and sulci serve as local functional units that directly communicate with neighboring gyri via short distance fibers and indirectly communicate with other distinct regions via the neighboring gyri. We test the proposed model by adopting a computational framework of dictionary learning and sparse representation of emotion task fMRI data of 68 subjects in the publicly released Human Connectome Project. The proposed model provides novel insights of functional mechanisms in emotion processing.
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Affiliation(s)
- 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
| | - Lin Zhao
- 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
| | - 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
| | - Tianming Liu
- Department of Computer Science and Bioimaging Research Center, University of Georgia, Athens, GA, United States
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Le Guen Y, Leroy F, Auzias G, Riviere D, Grigis A, Mangin JF, Coulon O, Dehaene-Lambertz G, Frouin V. The chaotic morphology of the left superior temporal sulcus is genetically constrained. Neuroimage 2018; 174:297-307. [DOI: 10.1016/j.neuroimage.2018.03.046] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2017] [Revised: 03/01/2018] [Accepted: 03/19/2018] [Indexed: 12/31/2022] Open
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Wang Y, Metoki A, Alm KH, Olson IR. White matter pathways and social cognition. Neurosci Biobehav Rev 2018; 90:350-370. [PMID: 29684403 PMCID: PMC5993647 DOI: 10.1016/j.neubiorev.2018.04.015] [Citation(s) in RCA: 65] [Impact Index Per Article: 10.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/09/2017] [Revised: 03/02/2018] [Accepted: 04/15/2018] [Indexed: 12/13/2022]
Abstract
There is a growing consensus that social cognition and behavior emerge from interactions across distributed regions of the "social brain". Researchers have traditionally focused their attention on functional response properties of these gray matter networks and neglected the vital role of white matter connections in establishing such networks and their functions. In this article, we conduct a comprehensive review of prior research on structural connectivity in social neuroscience and highlight the importance of this literature in clarifying brain mechanisms of social cognition. We pay particular attention to three key social processes: face processing, embodied cognition, and theory of mind, and their respective underlying neural networks. To fully identify and characterize the anatomical architecture of these networks, we further implement probabilistic tractography on a large sample of diffusion-weighted imaging data. The combination of an in-depth literature review and the empirical investigation gives us an unprecedented, well-defined landscape of white matter pathways underlying major social brain networks. Finally, we discuss current problems in the field, outline suggestions for best practice in diffusion-imaging data collection and analysis, and offer new directions for future research.
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Affiliation(s)
- Yin Wang
- Department of Psychology, Temple University, Philadelphia, 19107, USA.
| | - Athanasia Metoki
- Department of Psychology, Temple University, Philadelphia, 19107, USA
| | - Kylie H Alm
- Department of Psychology, Temple University, Philadelphia, 19107, USA
| | - Ingrid R Olson
- Department of Psychology, Temple University, Philadelphia, 19107, USA.
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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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45
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Zhang Z, Descoteaux M, Zhang J, Girard G, Chamberland M, Dunson D, Srivastava A, Zhu H. Mapping population-based structural connectomes. Neuroimage 2018; 172:130-145. [PMID: 29355769 PMCID: PMC5910206 DOI: 10.1016/j.neuroimage.2017.12.064] [Citation(s) in RCA: 51] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2017] [Revised: 11/09/2017] [Accepted: 12/20/2017] [Indexed: 11/23/2022] Open
Abstract
Advances in understanding the structural connectomes of human brain require improved approaches for the construction, comparison and integration of high-dimensional whole-brain tractography data from a large number of individuals. This article develops a population-based structural connectome (PSC) mapping framework to address these challenges. PSC simultaneously characterizes a large number of white matter bundles within and across different subjects by registering different subjects' brains based on coarse cortical parcellations, compressing the bundles of each connection, and extracting novel connection weights. A robust tractography algorithm and streamline post-processing techniques, including dilation of gray matter regions, streamline cutting, and outlier streamline removal are applied to improve the robustness of the extracted structural connectomes. The developed PSC framework can be used to reproducibly extract binary networks, weighted networks and streamline-based brain connectomes. We apply the PSC to Human Connectome Project data to illustrate its application in characterizing normal variations and heritability of structural connectomes in healthy subjects.
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Affiliation(s)
- Zhengwu Zhang
- Department of Biostatistics and Computational Biology, Rochester, NY, USA; Statistical and Applied Mathematical Sciences Institute, Durham, NC, USA
| | - Maxime Descoteaux
- Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Jingwen Zhang
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Gabriel Girard
- Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Maxime Chamberland
- Sherbrooke Connectivity Imaging Lab (SCIL), Computer Science Department, Université de Sherbrooke, Sherbrooke, QC, Canada
| | - David Dunson
- Department of Statistical Science, Duke University, Durham, NC, USA
| | - Anuj Srivastava
- Department of Statistics, Florida State University, Tallahassee, FL, USA
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA; Department of Biostatistics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.
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Siless V, Chang K, Fischl B, Yendiki A. AnatomiCuts: Hierarchical clustering of tractography streamlines based on anatomical similarity. Neuroimage 2018; 166:32-45. [PMID: 29100937 PMCID: PMC6152885 DOI: 10.1016/j.neuroimage.2017.10.058] [Citation(s) in RCA: 40] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2017] [Revised: 10/18/2017] [Accepted: 10/26/2017] [Indexed: 01/25/2023] Open
Abstract
Diffusion MRI tractography produces massive sets of streamlines that contain a wealth of information on brain connections. The size of these datasets creates a need for automated clustering methods to group the streamlines into meaningful bundles. Conventional clustering techniques group streamlines based on their spatial coordinates. Neuroanatomists, however, define white-matter bundles based on the anatomical structures that they go through or next to, rather than their spatial coordinates. Thus we propose a similarity measure for clustering streamlines based on their position relative to cortical and subcortical brain regions. We incorporate this measure into a hierarchical clustering algorithm and compare it to a measure that relies on Euclidean distance, using data from the Human Connectome Project. We show that the anatomical similarity measure leads to a 20% improvement in the overlap of clusters with manually labeled tracts. Importantly, this is achieved without introducing any prior information from a tract atlas into the clustering algorithm, therefore without imposing the existence of any named tracts.
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Affiliation(s)
- Viviana Siless
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA.
| | - Ken Chang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Bruce Fischl
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Anastasia Yendiki
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
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Chen H, Li Y, Ge F, Li G, Shen D, Liu T. Gyral net: A new representation of cortical folding organization. Med Image Anal 2017; 42:14-25. [PMID: 28732269 PMCID: PMC5654690 DOI: 10.1016/j.media.2017.07.001] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.9] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2017] [Revised: 06/09/2017] [Accepted: 07/14/2017] [Indexed: 12/30/2022]
Abstract
One distinct feature of the cerebral cortex is its convex (gyri) and concave (sulci) folding patterns. Due to the remarkable complexity and variability of gyral/sulcal shapes, it has been challenging to quantitatively model their organization patterns. Inspired by the observation that the lines of gyral crests can form a connected graph on each brain hemisphere, we propose a new representation of cortical gyri/sulci organization pattern - gyral net, which models cortical architecture from a graph perspective, starting with nodes and edges obtained from the reconstructed cortical surfaces. A novel computational framework is developed to efficiently and automatically construct gyral nets from surface meshes, and four measurements are devised to quantify the folding patterns. Using an MRI dataset for autism study as a test bed, we identified reduced local connectivity cost and increased curviness of gyral net bilaterally on the parietal lobe, occipital lobe, and temporal lobe in autistic patients. Additionally, we found that the cortical thickness and the gyral straightness of gyral joints are higher than the rest of gyral crest regions. The proposed representation offers a new tool for a comprehensive and reliable characterization of the cortical folding organization. This novel computational framework will enable large-scale analyses of cortical folding patterns in the future.
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Affiliation(s)
- Hanbo Chen
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA
| | - Yujie Li
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA
| | - Fangfei Ge
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA, USA
| | - Gang Li
- 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
| | - 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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48
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Razavi MJ, Zhang T, Chen H, Li Y, Platt S, Zhao Y, Guo L, Hu X, Wang X, Liu T. Radial Structure Scaffolds Convolution Patterns of Developing Cerebral Cortex. Front Comput Neurosci 2017; 11:76. [PMID: 28860983 PMCID: PMC5559440 DOI: 10.3389/fncom.2017.00076] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/06/2015] [Accepted: 07/28/2017] [Indexed: 01/08/2023] Open
Abstract
Commonly-preserved radial convolution is a prominent characteristic of the mammalian cerebral cortex. Endeavors from multiple disciplines have been devoted for decades to explore the causes for this enigmatic structure. However, the underlying mechanisms that lead to consistent cortical convolution patterns still remain poorly understood. In this work, inspired by prior studies, we propose and evaluate a plausible theory that radial convolution during the early development of the brain is sculptured by radial structures consisting of radial glial cells (RGCs) and maturing axons. Specifically, the regionally heterogeneous development and distribution of RGCs controlled by Trnp1 regulate the convex and concave convolution patterns (gyri and sulci) in the radial direction, while the interplay of RGCs' effects on convolution and axons regulates the convex (gyral) convolution patterns. This theory is assessed by observations and measurements in literature from multiple disciplines such as neurobiology, genetics, biomechanics, etc., at multiple scales to date. Particularly, this theory is further validated by multimodal imaging data analysis and computational simulations in this study. We offer a versatile and descriptive study model that can provide reasonable explanations of observations, experiments, and simulations of the characteristic mammalian cortical folding.
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Affiliation(s)
- Mir Jalil Razavi
- School of Environmental, Civil, Agricultural and Mechanical Engineering, College of Engineering, University of GeorgiaAthens, GA, United States
| | - Tuo Zhang
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, University of GeorgiaAthens, GA, United States.,School of Automation, Northwestern Polytechnic UniversityXi'an, China
| | - Hanbo Chen
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, University of GeorgiaAthens, GA, United States
| | - Yujie Li
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, University of GeorgiaAthens, GA, United States
| | - Simon Platt
- Department of Small Animal Medicine & Surgery, College of Veterinary Medicine, University of GeorgiaAthens, GA, United States
| | - Yu Zhao
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, University of GeorgiaAthens, GA, United States
| | - Lei Guo
- School of Automation, Northwestern Polytechnic UniversityXi'an, China
| | - Xiaoping Hu
- Biomedical Imaging Technology Center, Emory UniversityAtlanta, GA, United States
| | - Xianqiao Wang
- School of Environmental, Civil, Agricultural and Mechanical Engineering, College of Engineering, University of GeorgiaAthens, GA, United States
| | - Tianming Liu
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, University of GeorgiaAthens, GA, United States
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49
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Liu H, Jiang X, Zhang T, Ren Y, Hu X, Guo L, Han J, Liu T. Elucidating functional differences between cortical gyri and sulci via sparse representation HCP grayordinate fMRI data. Brain Res 2017; 1672:81-90. [PMID: 28760438 DOI: 10.1016/j.brainres.2017.07.018] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2017] [Revised: 07/20/2017] [Accepted: 07/21/2017] [Indexed: 12/31/2022]
Abstract
The highly convoluted cerebral cortex is characterized by two different topographic structures: convex gyri and concave sulci. Increasing studies have demonstrated that cortical gyri and sulci exhibit different structural connectivity patterns. Inspired by the intrinsic structural differences between gyri and sulci, in this paper, we present a data-driven framework based on sparse representation of fMRI data for functional network inferences, then examine the interactions within and across gyral and sulcal functional networks and finally elucidate possible functional differences using graph theory based properties. We apply the proposed framework to the high-resolution Human Connectome Project (HCP) grayordinate fMRI data. Extensive experimental results on both resting state fMRI data and task-based fMRI data consistently suggested that gyri are more functionally integrated, while sulci are more functionally segregated in the organizational architecture of cerebral cortex, offering novel understanding of the byzantine cerebral cortex.
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Affiliation(s)
- Huan Liu
- School of Automation, Northwestern Polytechnical University, Xi'an, China
| | - Xi Jiang
- 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
| | - Yudan Ren
- School of Automation, Northwestern Polytechnical University, Xi'an, China
| | - Xintao Hu
- School of Automation, Northwestern Polytechnical University, Xi'an, China
| | - Lei Guo
- School of Automation, Northwestern Polytechnical University, Xi'an, China
| | - Junwei Han
- 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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50
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Reproducibility of superficial white matter tracts using diffusion-weighted imaging tractography. Neuroimage 2016; 147:703-725. [PMID: 28034765 DOI: 10.1016/j.neuroimage.2016.11.066] [Citation(s) in RCA: 69] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2016] [Revised: 11/23/2016] [Accepted: 11/26/2016] [Indexed: 11/21/2022] Open
Abstract
Human brain connection map is far from being complete. In particular the study of the superficial white matter (SWM) is an unachieved task. Its description is essential for the understanding of human brain function and the study of pathogenesis triggered by abnormal connectivity. In this work we automatically created a multi-subject atlas of SWM diffusion-based bundles of the whole brain. For each subject, the complete cortico-cortical tractogram is first split into sub-tractograms connecting pairs of gyri. Then intra-subject shape-based fiber clustering performs compression of each sub-tractogram into a set of bundles. Proceeding further with shape-based clustering provides a match of the bundles across subjects. Bundles found in most of the subjects are instantiated in the atlas. To increase robustness, this procedure was performed with two independent groups of subjects, in order to discard bundles without match across the two independent atlases. Finally, the resulting intersection atlas was projected on a third independent group of subjects in order to filter out bundles without reproducible and reliable projection. The final multi-subject diffusion-based U-fiber atlas is composed of 100 bundles in total, 50 per hemisphere, from which 35 are common to both hemispheres.
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