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Petersen SE, Seitzman BA, Nelson SM, Wig GS, Gordon EM. Principles of cortical areas and their implications for neuroimaging. Neuron 2024; 112:2837-2853. [PMID: 38834069 DOI: 10.1016/j.neuron.2024.05.008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2023] [Revised: 04/11/2024] [Accepted: 05/08/2024] [Indexed: 06/06/2024]
Abstract
Cortical organization should constrain the study of how the brain performs behavior and cognition. A fundamental concept in cortical organization is that of arealization: that the cortex is parceled into discrete areas. In part one of this report, we review how non-human animal studies have illuminated principles of cortical arealization by revealing: (1) what defines a cortical area, (2) how cortical areas are formed, (3) how cortical areas interact with one another, and (4) what "computations" or "functions" areas perform. In part two, we discuss how these principles apply to neuroimaging research. In doing so, we highlight several examples where the commonly accepted interpretation of neuroimaging observations requires assumptions that violate the principles of arealization, including nonstationary areas that move on short time scales, large-scale gradients as organizing features, and cortical areas with singular functionality that perfectly map psychological constructs. Our belief is that principles of neurobiology should strongly guide the nature of computational explanations.
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Affiliation(s)
- Steven E Petersen
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA; Department of Neurology, Washington University School of Medicine, St. Louis, MO 63110, USA; Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA; Department of Psychological and Brain Sciences, Washington University in St. Louis, St. Louis, MO 63130, USA; Department of Pediatrics, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Benjamin A Seitzman
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Steven M Nelson
- Department of Pediatrics, University of Minnesota Medical School, Minneapolis, MN 55455, USA; Masonic Institute for the Developing Brain, University of Minnesota, Minneapolis, MN 55455, USA
| | - Gagan S Wig
- Center for Vital Longevity, School of Behavioral and Brain Sciences, University of Texas at Dallas, Dallas, TX 75235, USA; Department of Psychiatry, University of Texas Southwestern Medical Center, Dallas, TX 75390, USA
| | - Evan M Gordon
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA.
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2
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Tchetchenian A, Zekelman L, Chen Y, Rushmore J, Zhang F, Yeterian EH, Makris N, Rathi Y, Meijering E, Song Y, O'Donnell LJ. Deep multimodal saliency parcellation of cerebellar pathways: Linking microstructure and individual function through explainable multitask learning. Hum Brain Mapp 2024; 45:e70008. [PMID: 39185598 PMCID: PMC11345609 DOI: 10.1002/hbm.70008] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2024] [Revised: 07/18/2024] [Accepted: 08/10/2024] [Indexed: 08/27/2024] Open
Abstract
Parcellation of human cerebellar pathways is essential for advancing our understanding of the human brain. Existing diffusion magnetic resonance imaging tractography parcellation methods have been successful in defining major cerebellar fibre tracts, while relying solely on fibre tract structure. However, each fibre tract may relay information related to multiple cognitive and motor functions of the cerebellum. Hence, it may be beneficial for parcellation to consider the potential importance of the fibre tracts for individual motor and cognitive functional performance measures. In this work, we propose a multimodal data-driven method for cerebellar pathway parcellation, which incorporates both measures of microstructure and connectivity, and measures of individual functional performance. Our method involves first training a multitask deep network to predict various cognitive and motor measures from a set of fibre tract structural features. The importance of each structural feature for predicting each functional measure is then computed, resulting in a set of structure-function saliency values that are clustered to parcellate cerebellar pathways. We refer to our method as Deep Multimodal Saliency Parcellation (DeepMSP), as it computes the saliency of structural measures for predicting cognitive and motor functional performance, with these saliencies being applied to the task of parcellation. Applying DeepMSP to a large-scale dataset from the Human Connectome Project Young Adult study (n = 1065), we found that it was feasible to identify multiple cerebellar pathway parcels with unique structure-function saliency patterns that were stable across training folds. We thoroughly experimented with all stages of the DeepMSP pipeline, including network selection, structure-function saliency representation, clustering algorithm, and cluster count. We found that a 1D convolutional neural network architecture and a transformer network architecture both performed comparably for the multitask prediction of endurance, strength, reading decoding, and vocabulary comprehension, with both architectures outperforming a fully connected network architecture. Quantitative experiments demonstrated that a proposed low-dimensional saliency representation with an explicit measure of motor versus cognitive category bias achieved the best parcellation results, while a parcel count of four was most successful according to standard cluster quality metrics. Our results suggested that motor and cognitive saliencies are distributed across the cerebellar white matter pathways. Inspection of the final k = 4 parcellation revealed that the highest-saliency parcel was most salient for the prediction of both motor and cognitive performance scores and included parts of the middle and superior cerebellar peduncles. Our proposed saliency-based parcellation framework, DeepMSP, enables multimodal, data-driven tractography parcellation. Through utilising both structural features and functional performance measures, this parcellation strategy may have the potential to enhance the study of structure-function relationships of the cerebellar pathways.
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Affiliation(s)
- Ari Tchetchenian
- Biomedical Image Computing Group, School of Computer Science and EngineeringUniversity of New South Wales (UNSW)SydneyNew South WalesAustralia
| | - Leo Zekelman
- Department of Radiology, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
- Harvard UniversityCambridgeMassachusettsUSA
| | - Yuqian Chen
- Department of Radiology, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Jarrett Rushmore
- Department of PsychiatryMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Department of NeurologyMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Department of RadiologyMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Department of Anatomy and NeurobiologyBoston University School of MedicineBostonMassachusettsUSA
| | - Fan Zhang
- School of Information and Communication EngineeringUniversity of Electronic Science and Technology of ChinaChengduChina
| | | | - Nikos Makris
- Department of PsychiatryMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Department of NeurologyMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Department of RadiologyMassachusetts General Hospital, Harvard Medical SchoolBostonMassachusettsUSA
- Department of Psychiatry, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Yogesh Rathi
- Department of Radiology, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
- Department of Psychiatry, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
| | - Erik Meijering
- Biomedical Image Computing Group, School of Computer Science and EngineeringUniversity of New South Wales (UNSW)SydneyNew South WalesAustralia
| | - Yang Song
- Biomedical Image Computing Group, School of Computer Science and EngineeringUniversity of New South Wales (UNSW)SydneyNew South WalesAustralia
| | - Lauren J. O'Donnell
- Department of Radiology, Brigham and Women's HospitalHarvard Medical SchoolBostonMassachusettsUSA
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Zahnert F, Kleinholdermann U, Belke M, Keil B, Menzler K, Pedrosa DJ, Timmermann L, Kircher T, Nenadić I, Knake S. The connectivity-based architecture of the human piriform cortex. Neuroimage 2024; 297:120747. [PMID: 39033790 DOI: 10.1016/j.neuroimage.2024.120747] [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: 12/28/2023] [Revised: 07/16/2024] [Accepted: 07/18/2024] [Indexed: 07/23/2024] Open
Abstract
The anatomy of the human piriform cortex (PC) is poorly understood. We used a bimodal connectivity-based-parcellation approach to investigate subregions of the PC and its connectional differentiation from the amygdala. One hundred (55 % female) genetically unrelated subjects from the Human Connectome Project were included. A region of interest (ROI) was delineated bilaterally covering PC and amygdala, and functional and structural connectivity of this ROI with the whole gray matter was computed. Spectral clustering was performed to obtain bilateral parcellations at granularities of k = 2-10 clusters and combined bimodal parcellations were computed. Validity of parcellations was assessed via their mean individual-to-group similarity per adjusted rand index (ARI). Individual-to-group similarity was higher than chance in both modalities and in all clustering solutions. The amygdala was clearly distinguished from PC in structural parcellations, and olfactory amygdala was connectionally more similar to amygdala than to PC. At higher granularities, an anterior and ventrotemporal and a posterior frontal cluster emerged within PC, as well as an additional temporal cluster at their boundary. Functional parcellations also showed a frontal piriform cluster, and similar temporal clusters were observed with less consistency. Results from bimodal parcellations were similar to the structural parcellations. Consistent results were obtained in a validation cohort. Distinction of the human PC from the amygdala, including its olfactory subregions, is possible based on its structural connectivity alone. The canonical fronto-temporal boundary within PC was reproduced in both modalities and with consistency. All obtained parcellations are freely available.
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Affiliation(s)
- F Zahnert
- Epilepsy Center Hesse, Department of Neurology, University Hospital Marburg, Philipps-University Marburg, Germany.
| | - U Kleinholdermann
- Department of Neurology, University Hospital Marburg, Philipps University Marburg, Germany; Department of Psychiatry and Psychotherapy, University Hospital Marburg, Philipps University Marburg, Germany
| | - M Belke
- Epilepsy Center Hesse, Department of Neurology, University Hospital Marburg, Philipps-University Marburg, Germany; Center for Personalized Translational Epilepsy Research, Goethe University Frankfurt, Germany
| | - B Keil
- Institute of Medical Physics and Radiation Protection, Mittelhessen University of Applied Sciences, Giessen, Germany; Department of Diagnostic and Interventional Radiology, University Hospital Marburg, Philipps University of Marburg, Marburg, Germany; Center for Mind, Brain and Behavior, Philipps University Marburg, Germany
| | - K Menzler
- Epilepsy Center Hesse, Department of Neurology, University Hospital Marburg, Philipps-University Marburg, Germany; Center for Mind, Brain and Behavior, Philipps University Marburg, Germany
| | - D J Pedrosa
- Department of Neurology, University Hospital Marburg, Philipps University Marburg, Germany
| | - L Timmermann
- Department of Neurology, University Hospital Marburg, Philipps University Marburg, Germany; Center for Mind, Brain and Behavior, Philipps University Marburg, Germany
| | - T Kircher
- Department of Psychiatry and Psychotherapy, University Hospital Marburg, Philipps University Marburg, Germany; Center for Mind, Brain and Behavior, Philipps University Marburg, Germany
| | - I Nenadić
- Department of Psychiatry and Psychotherapy, University Hospital Marburg, Philipps University Marburg, Germany; Center for Mind, Brain and Behavior, Philipps University Marburg, Germany
| | - S Knake
- Epilepsy Center Hesse, Department of Neurology, University Hospital Marburg, Philipps-University Marburg, Germany; Center for Personalized Translational Epilepsy Research, Goethe University Frankfurt, Germany; Center for Mind, Brain and Behavior, Philipps University Marburg, Germany; Core Facility Brain Imaging, Philipps University Marburg, Germany
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Ling Q, Liu A, Li Y, McKeown MJ, Chen X. fMRI-based spatio-temporal parcellations of the human brain. Curr Opin Neurol 2024; 37:369-380. [PMID: 38804205 DOI: 10.1097/wco.0000000000001280] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/29/2024]
Abstract
PURPOSE OF REVIEW Human brain parcellation based on functional magnetic resonance imaging (fMRI) plays an essential role in neuroscience research. By segmenting vast and intricate fMRI data into functionally similar units, researchers can better decipher the brain's structure in both healthy and diseased states. This article reviews current methodologies and ideas in this field, while also outlining the obstacles and directions for future research. RECENT FINDINGS Traditional brain parcellation techniques, which often rely on cytoarchitectonic criteria, overlook the functional and temporal information accessible through fMRI. The adoption of machine learning techniques, notably deep learning, offers the potential to harness both spatial and temporal information for more nuanced brain segmentation. However, the search for a one-size-fits-all solution to brain segmentation is impractical, with the choice between group-level or individual-level models and the intended downstream analysis influencing the optimal parcellation strategy. Additionally, evaluating these models is complicated by our incomplete understanding of brain function and the absence of a definitive "ground truth". SUMMARY While recent methodological advancements have significantly enhanced our grasp of the brain's spatial and temporal dynamics, challenges persist in advancing fMRI-based spatio-temporal representations. Future efforts will likely focus on refining model evaluation and selection as well as developing methods that offer clear interpretability for clinical usage, thereby facilitating further breakthroughs in our comprehension of the brain.
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Affiliation(s)
- Qinrui Ling
- Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, 230027, China
| | - Aiping Liu
- Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, 230027, China
| | - Yu Li
- Institute of Dataspace, Hefei Comprehensive National Science Center, Hefei 230088, China
| | - Martin J McKeown
- Department of Medicine, University of British Columbia, Vancouver, Vancouver V6T2B5, Canada
| | - Xun Chen
- Department of Electronic Engineering and Information Science, University of Science and Technology of China, Hefei, 230027, China
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Wang Y, Cheng L, Li D, Lu Y, Wang C, Wang Y, Gao C, Wang H, Vanduffel W, Hopkins WD, Sherwood CC, Jiang T, Chu C, Fan L. Comparative Analysis of Human-Chimpanzee Divergence in Brain Connectivity and its Genetic Correlates. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.03.597252. [PMID: 38895242 PMCID: PMC11185649 DOI: 10.1101/2024.06.03.597252] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/21/2024]
Abstract
Chimpanzees (Pan troglodytes) are humans' closest living relatives, making them the most directly relevant comparison point for understanding human brain evolution. Zeroing in on the differences in brain connectivity between humans and chimpanzees can provide key insights into the specific evolutionary changes that might have occured along the human lineage. However, conducting comparisons of brain connectivity between humans and chimpanzees remains challenging, as cross-species brain atlases established within the same framework are currently lacking. Without the availability of cross-species brain atlases, the region-wise connectivity patterns between humans and chimpanzees cannot be directly compared. To address this gap, we built the first Chimpanzee Brainnetome Atlas (ChimpBNA) by following a well-established connectivity-based parcellation framework. Leveraging this new resource, we found substantial divergence in connectivity patterns across most association cortices, notably in the lateral temporal and dorsolateral prefrontal cortex between the two species. Intriguingly, these patterns significantly deviate from the patterns of cortical expansion observed in humans compared to chimpanzees. Additionally, we identified regions displaying connectional asymmetries that differed between species, likely resulting from evolutionary divergence. Genes associated with these divergent connectivities were found to be enriched in cell types crucial for cortical projection circuits and synapse formation. These genes exhibited more pronounced differences in expression patterns in regions with higher connectivity divergence, suggesting a potential foundation for brain connectivity evolution. Therefore, our study not only provides a fine-scale brain atlas of chimpanzees but also highlights the connectivity divergence between humans and chimpanzees in a more rigorous and comparative manner and suggests potential genetic correlates for the observed divergence in brain connectivity patterns between the two species. This can help us better understand the origins and development of uniquely human cognitive capabilities.
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Affiliation(s)
- Yufan Wang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100190, China
| | - Luqi Cheng
- School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, China
- Research Center for Augmented Intelligence, Zhejiang Lab, Hangzhou 311100, China
| | - Deying Li
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100190, China
| | - Yuheng Lu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100190, China
| | - Changshuo Wang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- Sino-Danish College, University of Chinese Academy of Sciences, Beijing 100190, China
| | - Yaping Wang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- Sino-Danish College, University of Chinese Academy of Sciences, Beijing 100190, China
| | - Chaohong Gao
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- Sino-Danish College, University of Chinese Academy of Sciences, Beijing 100190, China
| | - Haiyan Wang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- Department of Neurosciences, Laboratory of Neuro- and Psychophysiology, KU Leuven Medical School, 3000 Leuven, Belgium
| | - Wim Vanduffel
- Department of Neurosciences, Laboratory of Neuro- and Psychophysiology, KU Leuven Medical School, 3000 Leuven, Belgium
- Leuven Brain Institute, KU Leuven, 3000 Leuven, Belgium
- Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA 02129, USA
- Department of Radiology, Harvard Medical School, Boston, MA 02144, USA
| | - William D. Hopkins
- Department of Comparative Medicine, University of Texas MD Anderson Cancer Center, Bastrop, TX 78602, USA
| | - Chet C. Sherwood
- Department of Anthropology and Center for the Advanced Study of Human Paleobiology, The George Washington University, Washington, DC 20052, USA
| | - Tianzi Jiang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100190, China
- Research Center for Augmented Intelligence, Zhejiang Lab, Hangzhou 311100, China
| | - Congying Chu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100190, China
| | - Lingzhong Fan
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
- School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100190, China
- Sino-Danish College, University of Chinese Academy of Sciences, Beijing 100190, China
- School of Health and Life Sciences, University of Health and Rehabilitation Sciences, Qingdao 266000, China
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Wen X, Yang M, Qi S, Wu X, Zhang D. Automated individual cortical parcellation via consensus graph representation learning. Neuroimage 2024; 293:120616. [PMID: 38697587 DOI: 10.1016/j.neuroimage.2024.120616] [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: 12/28/2023] [Revised: 03/28/2024] [Accepted: 04/15/2024] [Indexed: 05/05/2024] Open
Abstract
Cortical parcellation plays a pivotal role in elucidating the brain organization. Despite the growing efforts to develop parcellation algorithms using functional magnetic resonance imaging, achieving a balance between intra-individual specificity and inter-individual consistency proves challenging, making the generation of high-quality, subject-consistent cortical parcellations particularly elusive. To solve this problem, our paper proposes a fully automated individual cortical parcellation method based on consensus graph representation learning. The method integrates spectral embedding with low-rank tensor learning into a unified optimization model, which uses group-common connectivity patterns captured by low-rank tensor learning to optimize subjects' functional networks. This not only ensures consistency in brain representations across different subjects but also enhances the quality of each subject's representation matrix by eliminating spurious connections. More importantly, it achieves an adaptive balance between intra-individual specificity and inter-individual consistency during this process. Experiments conducted on a test-retest dataset from the Human Connectome Project (HCP) demonstrate that our method outperforms existing methods in terms of reproducibility, functional homogeneity, and alignment with task activation. Extensive network-based comparisons on the HCP S900 dataset reveal that the functional network derived from our cortical parcellation method exhibits greater capabilities in gender identification and behavior prediction than other approaches.
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Affiliation(s)
- Xuyun Wen
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China; Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing, Jiangsu, China.
| | - Mengting Yang
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China
| | - Shile Qi
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China; Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing, Jiangsu, China
| | - Xia Wu
- School of Computer Science and Technology, Beijing Institute of Technology, Beijing, China.
| | - Daoqiang Zhang
- College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, Jiangsu, China; Key Laboratory of Brain-Machine Intelligence Technology, Ministry of Education, Nanjing, Jiangsu, China.
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Huang C, Li A, Pang Y, Yang J, Zhang J, Wu X, Mei L. How the intrinsic functional connectivity patterns of the semantic network support semantic processing. Brain Imaging Behav 2024; 18:539-554. [PMID: 38261218 DOI: 10.1007/s11682-024-00849-y] [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] [Accepted: 01/05/2024] [Indexed: 01/24/2024]
Abstract
Semantic processing, a core of language comprehension, involves the activation of brain regions dispersed extensively across the frontal, temporal, and parietal cortices that compose the semantic network. To comprehend the functional structure of this semantic network and how it prepares for semantic processing, we investigated its intrinsic functional connectivity (FC) and the relation between this pattern and semantic processing ability in a large sample from the Human Connectome Project (HCP) dataset. We first defined a well-studied brain network for semantic processing, and then we characterized the within-network connectivity (WNC) and the between-network connectivity (BNC) within this network using a voxel-based global brain connectivity (GBC) method based on resting-state functional magnetic resonance imaging (fMRI). The results showed that 97.73% of the voxels in the semantic network displayed considerably greater WNC than BNC, demonstrating that the semantic network is a fairly encapsulated network. Moreover, multiple connector hubs in the semantic network were identified after applying the criterion of WNC > 1 SD above the mean WNC of the semantic network. More importantly, three of these connector hubs (i.e., the left anterior temporal lobe, angular gyrus, and orbital part of the inferior frontal gyrus) were reliably associated with semantic processing ability. Our findings suggest that the three identified regions use WNC as the central mechanism for supporting semantic processing and that task-independent spontaneous connectivity in the semantic network is essential for semantic processing.
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Affiliation(s)
- Chengmei Huang
- Philosophy and Social Science Laboratory of Reading and Development in Children and Adolescents (South China Normal University), Ministry of Education, Guangzhou, 510631, China
- School of Psychology, South China Normal University, Guangzhou, 510631, China
- Center for Studies of Psychological Application, South China Normal University, Guangzhou, 510631, China
| | - Aqian Li
- Philosophy and Social Science Laboratory of Reading and Development in Children and Adolescents (South China Normal University), Ministry of Education, Guangzhou, 510631, China
- School of Psychology, South China Normal University, Guangzhou, 510631, China
- Center for Studies of Psychological Application, South China Normal University, Guangzhou, 510631, China
| | - Yingdan Pang
- Philosophy and Social Science Laboratory of Reading and Development in Children and Adolescents (South China Normal University), Ministry of Education, Guangzhou, 510631, China
- School of Psychology, South China Normal University, Guangzhou, 510631, China
- Center for Studies of Psychological Application, South China Normal University, Guangzhou, 510631, China
| | - Jiayi Yang
- Philosophy and Social Science Laboratory of Reading and Development in Children and Adolescents (South China Normal University), Ministry of Education, Guangzhou, 510631, China
- School of Psychology, South China Normal University, Guangzhou, 510631, China
- Center for Studies of Psychological Application, South China Normal University, Guangzhou, 510631, China
| | - Jingxian Zhang
- Philosophy and Social Science Laboratory of Reading and Development in Children and Adolescents (South China Normal University), Ministry of Education, Guangzhou, 510631, China
- School of Psychology, South China Normal University, Guangzhou, 510631, China
- Center for Studies of Psychological Application, South China Normal University, Guangzhou, 510631, China
| | - Xiaoyan Wu
- Philosophy and Social Science Laboratory of Reading and Development in Children and Adolescents (South China Normal University), Ministry of Education, Guangzhou, 510631, China
- School of Psychology, South China Normal University, Guangzhou, 510631, China
- Center for Studies of Psychological Application, South China Normal University, Guangzhou, 510631, China
| | - Leilei Mei
- Philosophy and Social Science Laboratory of Reading and Development in Children and Adolescents (South China Normal University), Ministry of Education, Guangzhou, 510631, China.
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Hakonen M, Dahmani L, Lankinen K, Ren J, Barbaro J, Blazejewska A, Cui W, Kotlarz P, Li M, Polimeni JR, Turpin T, Uluç I, Wang D, Liu H, Ahveninen J. Individual connectivity-based parcellations reflect functional properties of human auditory cortex. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.20.576475. [PMID: 38293021 PMCID: PMC10827228 DOI: 10.1101/2024.01.20.576475] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
Neuroimaging studies of the functional organization of human auditory cortex have focused on group-level analyses to identify tendencies that represent the typical brain. Here, we mapped auditory areas of the human superior temporal cortex (STC) in 30 participants by combining functional network analysis and 1-mm isotropic resolution 7T functional magnetic resonance imaging (fMRI). Two resting-state fMRI sessions, and one or two auditory and audiovisual speech localizer sessions, were collected on 3-4 separate days. We generated a set of functional network-based parcellations from these data. Solutions with 4, 6, and 11 networks were selected for closer examination based on local maxima of Dice and Silhouette values. The resulting parcellation of auditory cortices showed high intraindividual reproducibility both between resting state sessions (Dice coefficient: 69-78%) and between resting state and task sessions (Dice coefficient: 62-73%). This demonstrates that auditory areas in STC can be reliably segmented into functional subareas. The interindividual variability was significantly larger than intraindividual variability (Dice coefficient: 57%-68%, p<0.001), indicating that the parcellations also captured meaningful interindividual variability. The individual-specific parcellations yielded the highest alignment with task response topographies, suggesting that individual variability in parcellations reflects individual variability in auditory function. Connectional homogeneity within networks was also highest for the individual-specific parcellations. Furthermore, the similarity in the functional parcellations was not explainable by the similarity of macroanatomical properties of auditory cortex. Our findings suggest that individual-level parcellations capture meaningful idiosyncrasies in auditory cortex organization.
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Affiliation(s)
- M Hakonen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - L Dahmani
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - K Lankinen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - J Ren
- Division of Brain Sciences, Changping Laboratory, Beijing, China
| | - J Barbaro
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital Charlestown, MA, USA
| | - A Blazejewska
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - W Cui
- Division of Brain Sciences, Changping Laboratory, Beijing, China
| | - P Kotlarz
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital Charlestown, MA, USA
| | - M Li
- Division of Brain Sciences, Changping Laboratory, Beijing, China
| | - J R Polimeni
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
- Harvard-MIT Program in Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - T Turpin
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital Charlestown, MA, USA
| | - I Uluç
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - D Wang
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
| | - H Liu
- Division of Brain Sciences, Changping Laboratory, Beijing, China
- Biomedical Pioneering Innovation Center (BIOPIC), Peking University, Beijing, China
| | - J Ahveninen
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital Charlestown, MA, USA
- Department of Radiology, Harvard Medical School, Boston, MA, USA
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9
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Oka S, Ogawa A, Osada T, Tanaka M, Nakajima K, Kamagata K, Aoki S, Oshima Y, Tanaka S, Kirino E, Nakamura TJ, Konishi S. Diurnal Variation of Brain Activity in the Human Suprachiasmatic Nucleus. J Neurosci 2024; 44:e1730232024. [PMID: 38238074 PMCID: PMC10883613 DOI: 10.1523/jneurosci.1730-23.2024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2023] [Revised: 11/29/2023] [Accepted: 01/09/2024] [Indexed: 02/23/2024] Open
Abstract
The suprachiasmatic nucleus (SCN) is the central clock for circadian rhythms. Animal studies have revealed daily rhythms in the neuronal activity in the SCN. However, the circadian activity of the human SCN has remained elusive. In this study, to reveal the diurnal variation of the SCN activity in humans, we localized the SCN by employing an areal boundary mapping technique to resting-state functional images and investigated the SCN activity using perfusion imaging. In the first experiment (n = 27, including both sexes), we scanned each participant four times a day, every 6 h. Higher activity was observed at noon, while lower activity was recorded in the early morning. In the second experiment (n = 20, including both sexes), the SCN activity was measured every 30 min for 6 h from midnight to dawn. The results showed that the SCN activity gradually decreased and was not associated with the electroencephalography. Furthermore, the SCN activity was compatible with the rodent SCN activity after switching off the lights. These results suggest that the diurnal variation of the human SCN follows the zeitgeber cycles of nocturnal and diurnal mammals and is modulated by physical lights rather than the local time.
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Affiliation(s)
- Satoshi Oka
- Department of Neurophysiology, Juntendo University School of Medicine, Tokyo 113-8421, Japan
| | - Akitoshi Ogawa
- Department of Neurophysiology, Juntendo University School of Medicine, Tokyo 113-8421, Japan
| | - Takahiro Osada
- Department of Neurophysiology, Juntendo University School of Medicine, Tokyo 113-8421, Japan
| | - Masaki Tanaka
- Department of Neurophysiology, Juntendo University School of Medicine, Tokyo 113-8421, Japan
| | - Koji Nakajima
- Department of Neurophysiology, Juntendo University School of Medicine, Tokyo 113-8421, Japan
- Department of Orthopaedic Surgery, The University of Tokyo School of Medicine, Tokyo 113-0033, Japan
| | - Koji Kamagata
- Department of Radiology, Juntendo University School of Medicine, Tokyo 113-8421, Japan
| | - Shigeki Aoki
- Department of Radiology, Juntendo University School of Medicine, Tokyo 113-8421, Japan
| | - Yasushi Oshima
- Department of Orthopaedic Surgery, The University of Tokyo School of Medicine, Tokyo 113-0033, Japan
| | - Sakae Tanaka
- Department of Orthopaedic Surgery, The University of Tokyo School of Medicine, Tokyo 113-0033, Japan
| | - Eiji Kirino
- Department of Psychiatry, Juntendo Shizuoka Hospital, Shizuoka 410-2211, Japan
| | - Takahiro J Nakamura
- Laboratory of Animal Physiology, School of Agriculture, Meiji University, Kanagawa 214-8571, Japan
| | - Seiki Konishi
- Department of Neurophysiology, Juntendo University School of Medicine, Tokyo 113-8421, Japan
- Sportology Center, Juntendo University School of Medicine, Tokyo 113-8421, Japan
- Research Institute for Diseases of Old Age, Juntendo University School of Medicine, Tokyo 113-8421, Japan
- Advanced Research Institute for Health Science, Juntendo University School of Medicine, Tokyo 113-8421, Japan
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10
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Sypré L, Sharma S, Mantini D, Nelissen K. Intrinsic functional clustering of the macaque insular cortex. Front Integr Neurosci 2024; 17:1272529. [PMID: 38250745 PMCID: PMC10797002 DOI: 10.3389/fnint.2023.1272529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2023] [Accepted: 12/18/2023] [Indexed: 01/23/2024] Open
Abstract
The functional organization of the primate insula has been studied using a variety of techniques focussing on regional differences in either architecture, connectivity, or function. These complementary methods offered insights into the complex organization of the insula and proposed distinct parcellation schemes at varying levels of detail and complexity. The advent of imaging techniques that allow non-invasive assessment of structural and functional connectivity, has popularized data-driven connectivity-based parcellation methods to investigate the organization of the human insula. Yet, it remains unclear if the subdivisions derived from these data-driven clustering methods reflect meaningful descriptions of the functional specialization of the insula. In this study, we employed hierarchical clustering to examine the cluster parcellations of the macaque insula. As our aim was exploratory, we examined parcellations consisting of two up to ten clusters. Three different cluster validation methods (fingerprinting, silhouette, elbow) converged on a four-cluster solution as the most optimal representation of our data. Examining functional response properties of these clusters, in addition to their brain-wide functional connectivity suggested a functional specialization related to processing gustatory, somato-motor, vestibular and social visual cues. However, a more detailed functional differentiation aligning with previous functional investigations of insula subfields became evident at higher cluster numbers beyond the proposed optimal four clusters. Overall, our findings demonstrate that resting-state-based hierarchical clustering can provide a meaningful description of the insula's functional organization at some level of detail. Nonetheless, cluster parcellations derived from this method are best combined with data obtained through other modalities, to provide a more comprehensive and detailed account of the insula's complex functional organization.
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Affiliation(s)
- Lotte Sypré
- Laboratory for Neuro- & Psychophysiology, Department of Neurosciences, KU Leuven, Leuven, Belgium
- Leuven Brain Institute, KU Leuven, Leuven, Belgium
| | | | - Dante Mantini
- Leuven Brain Institute, KU Leuven, Leuven, Belgium
- Movement Control & Neuroplasticity Research Group, KU Leuven, Leuven, Belgium
| | - Koen Nelissen
- Laboratory for Neuro- & Psychophysiology, Department of Neurosciences, KU Leuven, Leuven, Belgium
- Leuven Brain Institute, KU Leuven, Leuven, Belgium
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11
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Gajwani M, Oldham S, Pang JC, Arnatkevičiūtė A, Tiego J, Bellgrove MA, Fornito A. Can hubs of the human connectome be identified consistently with diffusion MRI? Netw Neurosci 2023; 7:1326-1350. [PMID: 38144690 PMCID: PMC10631793 DOI: 10.1162/netn_a_00324] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/27/2022] [Accepted: 05/17/2023] [Indexed: 12/26/2023] Open
Abstract
Recent years have seen a surge in the use of diffusion MRI to map connectomes in humans, paralleled by a similar increase in processing and analysis choices. Yet these different steps and their effects are rarely compared systematically. Here, in a healthy young adult population (n = 294), we characterized the impact of a range of analysis pipelines on one widely studied property of the human connectome: its degree distribution. We evaluated the effects of 40 pipelines (comparing common choices of parcellation, streamline seeding, tractography algorithm, and streamline propagation constraint) and 44 group-representative connectome reconstruction schemes on highly connected hub regions. We found that hub location is highly variable between pipelines. The choice of parcellation has a major influence on hub architecture, and hub connectivity is highly correlated with regional surface area in most of the assessed pipelines (ρ > 0.70 in 69% of the pipelines), particularly when using weighted networks. Overall, our results demonstrate the need for prudent decision-making when processing diffusion MRI data, and for carefully considering how different processing choices can influence connectome organization.
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Affiliation(s)
- Mehul Gajwani
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, Australia
| | - Stuart Oldham
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, Australia
- Developmental Imaging, Murdoch Children’s Research Institute, The Royal Children’s Hospital, Melbourne, Victoria, Australia
| | - James C. Pang
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, Australia
| | - Aurina Arnatkevičiūtė
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, Australia
| | - Jeggan Tiego
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, Australia
| | - Mark A. Bellgrove
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, Australia
| | - Alex Fornito
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, Australia
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12
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Diveica V, Riedel MC, Salo T, Laird AR, Jackson RL, Binney RJ. Graded functional organization in the left inferior frontal gyrus: evidence from task-free and task-based functional connectivity. Cereb Cortex 2023; 33:11384-11399. [PMID: 37833772 PMCID: PMC10690868 DOI: 10.1093/cercor/bhad373] [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: 02/10/2023] [Revised: 08/17/2023] [Accepted: 09/18/2023] [Indexed: 10/15/2023] Open
Abstract
The left inferior frontal gyrus has been ascribed key roles in numerous cognitive domains, such as language and executive function. However, its functional organization is unclear. Possibilities include a singular domain-general function, or multiple functions that can be mapped onto distinct subregions. Furthermore, spatial transition in function may be either abrupt or graded. The present study explored the topographical organization of the left inferior frontal gyrus using a bimodal data-driven approach. We extracted functional connectivity gradients from (i) resting-state fMRI time-series and (ii) coactivation patterns derived meta-analytically from heterogenous sets of task data. We then sought to characterize the functional connectivity differences underpinning these gradients with seed-based resting-state functional connectivity, meta-analytic coactivation modeling and functional decoding analyses. Both analytic approaches converged on graded functional connectivity changes along 2 main organizational axes. An anterior-posterior gradient shifted from being preferentially associated with high-level control networks (anterior functional connectivity) to being more tightly coupled with perceptually driven networks (posterior). A second dorsal-ventral axis was characterized by higher connectivity with domain-general control networks on one hand (dorsal functional connectivity), and with the semantic network, on the other (ventral). These results provide novel insights into an overarching graded functional organization of the functional connectivity that explains its role in multiple cognitive domains.
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Affiliation(s)
- Veronica Diveica
- Department of Psychology & Cognitive Neuroscience Institute, Bangor University, Bangor, Wales LL57 2AS, United Kingdom
- Department of Neurology and Neurosurgery & Montreal Neurological Institute, McGill University, Montreal, QC H3A 2B4, Canada
| | - Michael C Riedel
- Department of Physics, Florida International University, Miami, FL 33199, United States
| | - Taylor Salo
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, 19104, United States
| | - Angela R Laird
- Department of Physics, Florida International University, Miami, FL 33199, United States
| | - Rebecca L Jackson
- Department of Psychology & York Biomedical Research Institute, University of York, York, YO10 5DD, United Kingdom
| | - Richard J Binney
- Department of Psychology & Cognitive Neuroscience Institute, Bangor University, Bangor, Wales LL57 2AS, United Kingdom
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13
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Achard S, Coeurjolly JF, de Micheaux PL, Lbath H, Richiardi J. Inter-regional correlation estimators for functional magnetic resonance imaging. Neuroimage 2023; 282:120388. [PMID: 37805021 DOI: 10.1016/j.neuroimage.2023.120388] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Revised: 09/05/2023] [Accepted: 09/22/2023] [Indexed: 10/09/2023] Open
Abstract
Functional magnetic resonance imaging (fMRI) functional connectivity between brain regions is often computed using parcellations defined by functional or structural atlases. Typically, some kind of voxel averaging is performed to obtain a single temporal correlation estimate per region pair. However, several estimators can be defined for this task, with various assumptions and degrees of robustness to local noise, global noise, and region size. In this paper, we systematically present and study the properties of 9 different functional connectivity estimators taking into account the spatial structure of fMRI data, based on a simple fMRI data spatial model. These include 3 existing estimators and 6 novel estimators. We demonstrate the empirical properties of the estimators using synthetic, animal, and human data, in terms of graph structure, repeatability and reproducibility, discriminability, dependence on region size, as well as local and global noise robustness. We prove analytically the link between regional intra-correlation and inter-region correlation, and show that the choice of estimator has a strong influence on inter-correlation values. Some estimators, including the commonly used correlation of averages (ca), are positively biased, and have more dependence to region size and intra-correlation than robust alternatives, resulting in spatially-dependent bias. We define the new local correlation of averages estimator with better theoretical guarantees, lower bias, significantly lower dependence on region size (Spearman correlation 0.40 vs 0.55, paired t-test T=27.2, p=1.1e-47), at negligible cost to discriminative power, compared to the ca estimator. The difference in connectivity pattern between the estimators is not distributed uniformly throughout the brain, but rather shows a clear ventral-dorsal gradient, suggesting that region size and intra-correlation plays an important role in shaping functional networks defined using the ca estimator, and leading to non-trivial differences in their connectivity structure. We provide an open source R package and equivalent Python implementation to facilitate the use of the new estimators, together with preprocessed rat time-series.
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Affiliation(s)
- Sophie Achard
- Univ. Grenoble Alpes, CNRS, Inria, Grenoble-INP, LJK, 38000 Grenoble, France.
| | | | - Pierre Lafaye de Micheaux
- AMIS, Université Paul Valéry Montpellier 3, France; PreMeDICaL - Precision Medicine by Data Integration and Causal Learning, Inria Sophia Antipolis, France; Desbrest Institute of Epidemiology and Public Health, Univ Montpellier, INSERM, Montpellier, France
| | - Hanâ Lbath
- Univ. Grenoble Alpes, CNRS, Inria, Grenoble-INP, LJK, 38000 Grenoble, France
| | - Jonas Richiardi
- Department of Radiology, Lausanne University Hospital and University of Lausanne, Switzerland
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14
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Guyonnet-Hencke T, Reimann MW. A parcellation scheme of mouse isocortex based on reversals in connectivity gradients. Netw Neurosci 2023; 7:999-1021. [PMID: 37781146 PMCID: PMC10473268 DOI: 10.1162/netn_a_00312] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Accepted: 03/02/2023] [Indexed: 10/03/2023] Open
Abstract
The brain is composed of several anatomically clearly separated structures. This parcellation is often extended into the isocortex, based on anatomical, physiological, or functional differences. Here, we derive a parcellation scheme based purely on the spatial structure of long-range synaptic connections within the cortex. To that end, we analyzed a publicly available dataset of average mouse brain connectivity, and split the isocortex into disjunct regions. Instead of clustering connectivity based on modularity, our scheme is inspired by methods that split sensory cortices into subregions where gradients of neuronal response properties, such as the location of the receptive field, reverse. We calculated comparable gradients from voxelized brain connectivity data and automatically detected reversals in them. This approach better respects the known presence of functional gradients within brain regions than clustering-based approaches. Placing borders at the reversals resulted in a parcellation into 41 subregions that differs significantly from an established scheme in nonrandom ways, but is comparable in terms of the modularity of connectivity between regions. It reveals unexpected trends of connectivity, such as a tripartite split of somatomotor regions along an anterior to posterior gradient. The method can be readily adapted to other organisms and data sources, such as human functional connectivity.
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Affiliation(s)
- Timothé Guyonnet-Hencke
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
| | - Michael W. Reimann
- Blue Brain Project, École polytechnique fédérale de Lausanne (EPFL), Campus Biotech, Geneva, Switzerland
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15
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Liu R, Li M, Dunson DB. PPA: Principal parcellation analysis for brain connectomes and multiple traits. Neuroimage 2023; 276:120214. [PMID: 37286151 DOI: 10.1016/j.neuroimage.2023.120214] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Accepted: 05/31/2023] [Indexed: 06/09/2023] Open
Abstract
Our understanding of the structure of the brain and its relationships with human traits is largely determined by how we represent the structural connectome. Standard practice divides the brain into regions of interest (ROIs) and represents the connectome as an adjacency matrix having cells measuring connectivity between pairs of ROIs. Statistical analyses are then heavily driven by the (largely arbitrary) choice of ROIs. In this article, we propose a human trait prediction framework utilizing a tractography-based representation of the brain connectome, which clusters fiber endpoints to define a data-driven white matter parcellation targeted to explain variation among individuals and predict human traits. This leads to Principal Parcellation Analysis (PPA), representing individual brain connectomes by compositional vectors building on a basis system of fiber bundles that captures the connectivity at the population level. PPA eliminates the need to choose atlases and ROIs a priori, and provides a simpler, vector-valued representation that facilitates easier statistical analysis compared to the complex graph structures encountered in classical connectome analyses. We illustrate the proposed approach through applications to data from the Human Connectome Project (HCP) and show that PPA connectomes improve power in predicting human traits over state-of-the-art methods based on classical connectomes, while dramatically improving parsimony and maintaining interpretability. Our PPA package is publicly available on GitHub, and can be implemented routinely for diffusion image data.
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Affiliation(s)
- Rongjie Liu
- Department of Statistics, Florida State University, Tallahassee, FL, USA
| | - Meng Li
- Department of Statistics, Rice University, Houston, TX, USA.
| | - David B Dunson
- Department of Statistical Science, Duke University, Durham, NC, USA
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16
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Tregidgo HFJ, Soskic S, Althonayan J, Maffei C, Van Leemput K, Golland P, Insausti R, Lerma-Usabiaga G, Caballero-Gaudes C, Paz-Alonso PM, Yendiki A, Alexander DC, Bocchetta M, Rohrer JD, Iglesias JE. Accurate Bayesian segmentation of thalamic nuclei using diffusion MRI and an improved histological atlas. Neuroimage 2023; 274:120129. [PMID: 37088323 PMCID: PMC10636587 DOI: 10.1016/j.neuroimage.2023.120129] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/20/2022] [Revised: 03/30/2023] [Accepted: 04/20/2023] [Indexed: 04/25/2023] Open
Abstract
The human thalamus is a highly connected brain structure, which is key for the control of numerous functions and is involved in several neurological disorders. Recently, neuroimaging studies have increasingly focused on the volume and connectivity of the specific nuclei comprising this structure, rather than looking at the thalamus as a whole. However, accurate identification of cytoarchitectonically designed histological nuclei on standard in vivo structural MRI is hampered by the lack of image contrast that can be used to distinguish nuclei from each other and from surrounding white matter tracts. While diffusion MRI may offer such contrast, it has lower resolution and lacks some boundaries visible in structural imaging. In this work, we present a Bayesian segmentation algorithm for the thalamus. This algorithm combines prior information from a probabilistic atlas with likelihood models for both structural and diffusion MRI, allowing segmentation of 25 thalamic labels per hemisphere informed by both modalities. We present an improved probabilistic atlas, incorporating thalamic nuclei identified from histology and 45 white matter tracts surrounding the thalamus identified in ultra-high gradient strength diffusion imaging. We present a family of likelihood models for diffusion tensor imaging, ensuring compatibility with the vast majority of neuroimaging datasets that include diffusion MRI data. The use of these diffusion likelihood models greatly improves identification of nuclear groups versus segmentation based solely on structural MRI. Dice comparison of 5 manually identifiable groups of nuclei to ground truth segmentations show improvements of up to 10 percentage points. Additionally, our chosen model shows a high degree of reliability, with median test-retest Dice scores above 0.85 for four out of five nuclei groups, whilst also offering improved detection of differential thalamic involvement in Alzheimer's disease (AUROC 81.98%). The probabilistic atlas and segmentation tool will be made publicly available as part of the neuroimaging package FreeSurfer (https://freesurfer.net/fswiki/ThalamicNucleiDTI).
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Affiliation(s)
- Henry F J Tregidgo
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, UK.
| | - Sonja Soskic
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, UK
| | - Juri Althonayan
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, UK
| | - Chiara Maffei
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, USA
| | - Koen Van Leemput
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, USA; Department of Health Technology, Technical University of Denmark, Denmark
| | - Polina Golland
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, USA
| | - Ricardo Insausti
- Human Neuroanatomy Laboratory, University of Castilla-La Mancha, Spain
| | - Garikoitz Lerma-Usabiaga
- BCBL. Basque Center on Cognition, Brain and Language, Spain; Ikerbasque, Basque Foundation for Science, Bilbao, Spain
| | | | - Pedro M Paz-Alonso
- BCBL. Basque Center on Cognition, Brain and Language, Spain; Ikerbasque, Basque Foundation for Science, Bilbao, Spain
| | - Anastasia Yendiki
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, USA
| | - Daniel C Alexander
- Centre for Medical Image Computing, Department of Computer Science, University College London, UK
| | - Martina Bocchetta
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, UK; Centre for Cognitive and Clinical Neuroscience, Department of Life Sciences, College of Health, Medicine and Life Sciences, Brunel University London, UK
| | - Jonathan D Rohrer
- Dementia Research Centre, Department of Neurodegenerative Disease, UCL Queen Square Institute of Neurology, University College London, UK
| | - Juan Eugenio Iglesias
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, UK; Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, USA
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17
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Plachti A, Latzman RD, Balajoo SM, Hoffstaedter F, Madsen KS, Baare W, Siebner HR, Eickhoff SB, Genon S. Hippocampal anterior- posterior shift in childhood and adolescence. Prog Neurobiol 2023; 225:102447. [PMID: 36967075 PMCID: PMC10185869 DOI: 10.1016/j.pneurobio.2023.102447] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2022] [Revised: 03/14/2023] [Accepted: 03/23/2023] [Indexed: 04/07/2023]
Abstract
Hippocampal-cortical networks play an important role in neurocognitive development. Applying the method of Connectivity-Based Parcellation (CBP) on hippocampal-cortical structural covariance (SC) networks computed from T1-weighted magnetic resonance images, we examined how the hippocampus differentiates into subregions during childhood and adolescence (N = 1105, 6-18 years). In late childhood, the hippocampus mainly differentiated along the anterior-posterior axis similar to previous reported functional differentiation patterns of the hippocampus. In contrast, in adolescence a differentiation along the medial-lateral axis was evident, reminiscent of the cytoarchitectonic division into cornu ammonis and subiculum. Further meta-analytical characterization of hippocampal subregions in terms of related structural co-maturation networks, behavioural and gene profiling suggested that the hippocampal head is related to higher order functions (e.g. language, theory of mind, autobiographical memory) in late childhood morphologically co-varying with almost the whole brain. In early adolescence but not in childhood, posterior subicular SC networks were associated with action-oriented and reward systems. The findings point to late childhood as an important developmental period for hippocampal head morphology and to early adolescence as a crucial period for hippocampal integration into action- and reward-oriented cognition. The latter may constitute a developmental feature that conveys increased propensity for addictive disorders.
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Affiliation(s)
- Anna Plachti
- Institute of Neuroscience and Medicine (INM-7), Research Centre Jülich, Jülich, Germany; Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital -Amager and Hvidovre, Copenhagen, Denmark
| | - Robert D Latzman
- Data Sciences Institute, Takeda Pharmaceutical, Cambridge, MA, USA
| | | | - Felix Hoffstaedter
- Institute of Neuroscience and Medicine (INM-7), Research Centre Jülich, Jülich, Germany
| | - Kathrine Skak Madsen
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital -Amager and Hvidovre, Copenhagen, Denmark; Radiography, Department of Technology, University College Copenhagen, Copenhagen, Denmark
| | - William Baare
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital -Amager and Hvidovre, Copenhagen, Denmark
| | - Hartwig R Siebner
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital -Amager and Hvidovre, Copenhagen, Denmark; Department of Neurology, Copenhagen University Hospital Bispebjerg and Frederiksberg, Copenhagen, Denmark; Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Sarah Genon
- Institute of Neuroscience and Medicine (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, Düsseldorf, Germany; GIGA-CRC In vivo Imaging, University of Liege, Liege, Belgium.
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18
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Yan X, Kong R, Xue A, Yang Q, Orban C, An L, Holmes AJ, Qian X, Chen J, Zuo XN, Zhou JH, Fortier MV, Tan AP, Gluckman P, Chong YS, Meaney MJ, Bzdok D, Eickhoff SB, Yeo BTT. Homotopic local-global parcellation of the human cerebral cortex from resting-state functional connectivity. Neuroimage 2023; 273:120010. [PMID: 36918136 PMCID: PMC10212507 DOI: 10.1016/j.neuroimage.2023.120010] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 02/25/2023] [Accepted: 03/08/2023] [Indexed: 03/13/2023] Open
Abstract
Resting-state fMRI is commonly used to derive brain parcellations, which are widely used for dimensionality reduction and interpreting human neuroscience studies. We previously developed a model that integrates local and global approaches for estimating areal-level cortical parcellations. The resulting local-global parcellations are often referred to as the Schaefer parcellations. However, the lack of homotopic correspondence between left and right Schaefer parcels has limited their use for brain lateralization studies. Here, we extend our previous model to derive homotopic areal-level parcellations. Using resting-fMRI and task-fMRI across diverse scanners, acquisition protocols, preprocessing and demographics, we show that the resulting homotopic parcellations are as homogeneous as the Schaefer parcellations, while being more homogeneous than five publicly available parcellations. Furthermore, weaker correlations between homotopic parcels are associated with greater lateralization in resting network organization, as well as lateralization in language and motor task activation. Finally, the homotopic parcellations agree with the boundaries of a number of cortical areas estimated from histology and visuotopic fMRI, while capturing sub-areal (e.g., somatotopic and visuotopic) features. Overall, these results suggest that the homotopic local-global parcellations represent neurobiologically meaningful subdivisions of the human cerebral cortex and will be a useful resource for future studies. Multi-resolution parcellations estimated from 1479 participants are publicly available (https://github.com/ThomasYeoLab/CBIG/tree/master/stable_projects/brain_parcellation/Yan2023_homotopic).
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Affiliation(s)
- Xiaoxuan Yan
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health and Institute for Digital Medicine (WisDM), National University of Singapore, Singapore; Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore
| | - Ru Kong
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health and Institute for Digital Medicine (WisDM), National University of Singapore, Singapore
| | - Aihuiping Xue
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health and Institute for Digital Medicine (WisDM), National University of Singapore, Singapore
| | - Qing Yang
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health and Institute for Digital Medicine (WisDM), National University of Singapore, Singapore
| | - Csaba Orban
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health and Institute for Digital Medicine (WisDM), National University of Singapore, Singapore
| | - Lijun An
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health and Institute for Digital Medicine (WisDM), National University of Singapore, Singapore
| | - Avram J Holmes
- Yale University, Departments of Psychology and Psychiatry, New Haven, CT, Unites States of America
| | - Xing Qian
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Jianzhong Chen
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health and Institute for Digital Medicine (WisDM), National University of Singapore, Singapore
| | - Xi-Nian Zuo
- State Key Laboratory of Cognitive Neuroscience and Learning/IDG McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; National Basic Public Science Data Center, China
| | - Juan Helen Zhou
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore
| | - Marielle V Fortier
- Department of Diagnostic and Interventional Imaging, KK Women's and Children's Hospital, Singapore; Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), Singapore
| | - Ai Peng Tan
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), Singapore; Department of Diagnostic Radiology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore
| | - Peter Gluckman
- UK Centre for Human Evolution, Adaptation and Disease, Liggins Institute, University of Auckland, Auckland, New Zealand
| | - Yap Seng Chong
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), Singapore; Department of Obstetrics and Gynaecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Michael J Meaney
- Singapore Institute for Clinical Sciences (SICS), Agency for Science, Technology and Research (A*STAR), Singapore; Department of Neurology and Neurosurgery, McGill University, Montreal, QC, Canada
| | - Danilo Bzdok
- Department of Biomedical Engineering, Montreal Neurological Institute, McGill University, Montreal, QC, Canada; Mila - Quebec AI Institute, Montreal, QC, Canada
| | - Simon B Eickhoff
- Institute for Systems Neuroscience, Medical Faculty, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany; Institute of Neuroscience and Medicine, Brain & Behaviour (INM-7), Research Center Jülich, Jülich, Germany
| | - B T Thomas Yeo
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Department of Electrical and Computer Engineering, National University of Singapore, Singapore; N.1 Institute for Health and Institute for Digital Medicine (WisDM), National University of Singapore, Singapore; Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore; Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, Unites States of America.
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Diveica V, Riedel MC, Salo T, Laird AR, Jackson RL, Binney RJ. Graded functional organisation in the left inferior frontal gyrus: evidence from task-free and task-based functional connectivity. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.02.02.526818. [PMID: 36778322 PMCID: PMC9915604 DOI: 10.1101/2023.02.02.526818] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/05/2023]
Abstract
The left inferior frontal gyrus (LIFG) has been ascribed key roles in numerous cognitive domains, including language, executive function and social cognition. However, its functional organisation, and how the specific areas implicated in these cognitive domains relate to each other, is unclear. Possibilities include that the LIFG underpins a domain-general function or, alternatively, that it is characterized by functional differentiation, which might occur in either a discrete or a graded pattern. The aim of the present study was to explore the topographical organisation of the LIFG using a bimodal data-driven approach. To this end, we extracted functional connectivity (FC) gradients from 1) the resting-state fMRI time-series of 150 participants (77 female), and 2) patterns of co-activation derived meta-analytically from task data across a diverse set of cognitive domains. We then sought to characterize the FC differences driving these gradients with seed-based resting-state FC and meta-analytic co-activation modelling analyses. Both analytic approaches converged on an FC profile that shifted in a graded fashion along two main organisational axes. An anterior-posterior gradient shifted from being preferentially associated with high-level control networks (anterior LIFG) to being more tightly coupled with perceptually-driven networks (posterior). A second dorsal-ventral axis was characterized by higher connectivity with domain-general control networks on one hand (dorsal LIFG), and with the semantic network, on the other (ventral). These results provide novel insights into a graded functional organisation of the LIFG underpinning both task-free and task-constrained mental states, and suggest that the LIFG is an interface between distinct large-scale functional networks.
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Affiliation(s)
- Veronica Diveica
- Cognitive Neuroscience Institute, Department of Psychology, School of Human and Behavioural Sciences, Bangor University, Wales, UK
| | - Michael C. Riedel
- Department of Physics, Florida International University, Miami, FL, USA
| | - Taylor Salo
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA, USA
| | - Angela R. Laird
- Department of Physics, Florida International University, Miami, FL, USA
| | - Rebecca L. Jackson
- Department of Psychology & York Biomedical Research Institute, University of York, UK
| | - Richard J. Binney
- Cognitive Neuroscience Institute, Department of Psychology, School of Human and Behavioural Sciences, Bangor University, Wales, UK
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20
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Ogawa A, Osada T, Tanaka M, Suda A, Nakajima K, Oka S, Kamagata K, Aoki S, Oshima Y, Tanaka S, Hattori N, Konishi S. Hypothalamic interaction with reward-related regions during subjective evaluation of foods. Neuroimage 2022; 264:119744. [PMID: 36368500 DOI: 10.1016/j.neuroimage.2022.119744] [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: 06/14/2022] [Revised: 10/14/2022] [Accepted: 11/08/2022] [Indexed: 11/11/2022] Open
Abstract
The reward system implemented in the midbrain, ventral striatum, orbitofrontal cortex, and ventromedial prefrontal cortex evaluates and compares various types of rewards given to the organisms. It has been suggested that autonomic factors influence reward-related processing via the hypothalamus, but how the hypothalamus modulates the reward system remains elusive. In this functional magnetic resonance imaging study, the hypothalamus was parcellated into individual hypothalamic nuclei performing different autonomic functions using boundary mapping parcellation analyses. The effective interaction during subjective evaluation of foods in a reward task was then investigated between the human hypothalamic nuclei and the reward-related regions. We found significant brain activity decrease in the paraventricular nucleus (PVH) and lateral nucleus in the hypothalamus in food evaluation compared with monetary evaluation. A psychophysiological interaction analysis revealed dual interactions between the PVH and (1) midbrain region and (2) ventromedial prefrontal cortex, with the former correlated with the stronger tendency of participants toward food-seeking. A dynamic causal modeling analysis further revealed unidirectional interactions from the PVH to the midbrain and ventromedial prefrontal cortex. These results suggest that the PVH in the human hypothalamus interacts with the reward-related regions in the cerebral cortex via multiple pathways (i.e., the midbrain pathway and ventromedial prefrontal pathway) to evaluate rewards for subsequent decision-making.
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Affiliation(s)
- Akitoshi Ogawa
- Department of Neurophysiology, Juntendo University School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo 113-8421, Japan
| | - Takahiro Osada
- Department of Neurophysiology, Juntendo University School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo 113-8421, Japan
| | - Masaki Tanaka
- Department of Neurophysiology, Juntendo University School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo 113-8421, Japan
| | - Akimitsu Suda
- Department of Neurophysiology, Juntendo University School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo 113-8421, Japan; Department of Neurology, Juntendo University School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo 113-8421, Japan
| | - Koji Nakajima
- Department of Neurophysiology, Juntendo University School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo 113-8421, Japan; Department of Orthopaedic Surgery, The University of Tokyo School of Medicine, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Satoshi Oka
- Department of Neurophysiology, Juntendo University School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo 113-8421, Japan
| | - Koji Kamagata
- Department of Radiology, Juntendo University School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo 113-8421, Japan
| | - Shigeki Aoki
- Department of Radiology, Juntendo University School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo 113-8421, Japan
| | - Yasushi Oshima
- Department of Orthopaedic Surgery, The University of Tokyo School of Medicine, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Sakae Tanaka
- Department of Orthopaedic Surgery, The University of Tokyo School of Medicine, 7-3-1 Hongo, Bunkyo-ku, Tokyo 113-0033, Japan
| | - Nobutaka Hattori
- Department of Neurology, Juntendo University School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo 113-8421, Japan
| | - Seiki Konishi
- Department of Neurophysiology, Juntendo University School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo 113-8421, Japan; Research Institute for Diseases of Old Age, Juntendo University School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo 113-8421, Japan; Sportology Center, Juntendo University School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo 113-8421, Japan; Advanced Research Institute for Health Science, Juntendo University School of Medicine, 2-1-1 Hongo, Bunkyo-ku, Tokyo 113-8421, Japan.
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21
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Rogojin A, Gorbet DJ, Hawkins KM, Sergio LE. Differences in resting state functional connectivity underlie visuomotor performance declines in older adults with a genetic risk (APOE ε4) for Alzheimer’s disease. Front Aging Neurosci 2022; 14:1054523. [DOI: 10.3389/fnagi.2022.1054523] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/26/2022] [Accepted: 11/15/2022] [Indexed: 12/04/2022] Open
Abstract
IntroductionNon-standard visuomotor integration requires the interaction of large networks in the brain. Previous findings have shown that non-standard visuomotor performance is impaired in individuals with specific dementia risk factors (family history of dementia and presence of the APOE ε4 allele) in advance of any cognitive impairments. These findings suggest that visuomotor impairments are associated with early dementia-related brain changes. The current study examined the underlying resting state functional connectivity (RSFC) associated with impaired non-standard visuomotor performance, as well as the impacts of dementia family history, sex, and APOE status.MethodsCognitively healthy older adults (n = 48) were tested on four visuomotor tasks where reach and gaze were increasingly spatially dissociated. Participants who had a family history of dementia or the APOE ε4 allele were considered to be at an increased risk for AD. To quantify RSFC within networks of interest, an EPI sequence sensitive to BOLD contrast was collected. The networks of interest were the default mode network (DMN), somatomotor network (SMN), dorsal attention network (DAN), ventral attention network (VAN), and frontoparietal control network (FPN).ResultsIndividuals with the ε4 allele showed abnormalities in RSFC between posterior DMN nodes that predicted poorer non-standard visuomotor performance. Specifically, multiple linear regression analyses revealed lower RSFC between the precuneus/posterior cingulate cortex and the left inferior parietal lobule as well as the left parahippocampal cortex. Presence of the APOE ε4 allele also modified the relationship between mean DAN RSFC and visuomotor control, where lower mean RSFC in the DAN predicted worse non-standard visuomotor performance only in APOE ε4 carriers. There were otherwise no effects of family history, APOE ε4 status, or sex on the relationship between RSFC and visuomotor performance for any of the other resting networks.ConclusionThe preliminary findings provide insight into the impact of APOE ε4-related genetic risk on neural networks underlying complex visuomotor transformations, and demonstrate that the non-standard visuomotor task paradigm discussed in this study may be used as a non-invasive, easily accessible assessment tool for dementia risk.
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22
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McGrath H, Zaveri HP, Collins E, Jafar T, Chishti O, Obaid S, Ksendzovsky A, Wu K, Papademetris X, Spencer DD. High-resolution cortical parcellation based on conserved brain landmarks for localization of multimodal data to the nearest centimeter. Sci Rep 2022; 12:18778. [PMID: 36335146 PMCID: PMC9637135 DOI: 10.1038/s41598-022-21543-3] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Accepted: 09/28/2022] [Indexed: 11/06/2022] Open
Abstract
Precise cortical brain localization presents an important challenge in the literature. Brain atlases provide data-guided parcellation based on functional and structural brain metrics, and each atlas has its own unique benefits for localization. We offer a parcellation guided by intracranial electroencephalography, a technique which has historically provided pioneering advances in our understanding of brain structure-function relationships. We used a consensus boundary mapping approach combining anatomical designations in Duvernoy's Atlas of the Human Brain, a widely recognized textbook of human brain anatomy, with the anatomy of the MNI152 template and the magnetic resonance imaging scans of an epilepsy surgery cohort. The Yale Brain Atlas consists of 690 one-square centimeter parcels based around conserved anatomical features and each with a unique identifier to communicate anatomically unambiguous localization. We report on the methodology we used to create the Atlas along with the findings of a neuroimaging study assessing the accuracy and clinical usefulness of cortical localization using the Atlas. We also share our vision for the Atlas as a tool in the clinical and research neurosciences, where it may facilitate precise localization of data on the cortex, accurate description of anatomical locations, and modern data science approaches using standardized brain regions.
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Affiliation(s)
- Hari McGrath
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA.
- GKT School of Medical Education, King's College London, London, UK.
| | - Hitten P Zaveri
- Department of Neurology, Yale School of Medicine, New Haven, CT, USA
| | - Evan Collins
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
- Yale School of Engineering and Applied Science, New Haven, CT, USA
- Department of Biological Engineering, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Tamara Jafar
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
| | - Omar Chishti
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
- Yale School of Engineering and Applied Science, New Haven, CT, USA
| | - Sami Obaid
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
| | - Alexander Ksendzovsky
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
- Department of Neurosurgery, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Kun Wu
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
| | - Xenophon Papademetris
- Department of Radiology and Biomedical Engineering, Yale School of Medicine, New Haven, CT, USA
- Department of Biomedical Engineering, Yale School of Medicine, New Haven, CT, USA
| | - Dennis D Spencer
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
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23
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Cao L, Li H, Liu J, Jiang J, Li B, Li X, Zhang S, Gao Y, Liang K, Hu X, Bao W, Qiu H, Lu L, Zhang L, Hu X, Gong Q, Huang X. Disorganized functional architecture of amygdala subregional networks in obsessive-compulsive disorder. Commun Biol 2022; 5:1184. [PMCID: PMC9636402 DOI: 10.1038/s42003-022-04115-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2022] [Accepted: 10/14/2022] [Indexed: 11/06/2022] Open
Abstract
AbstractA precise understanding of amygdala-centered subtle networks may help refine neurocircuitry models of obsessive-compulsive disorder (OCD). We applied connectivity-based parcellation methodology to segment the amygdala based on resting-state fMRI data of 92 medication-free OCD patients without comorbidity and 90 matched healthy controls (HC). The amygdala was parcellated into two subregions corresponding to basolateral amygdala (BLA) and centromedial amygdala (CMA). Amygdala subregional functional connectivity (FC) maps were generated and group differences were evaluated with diagnosis-by-subregion flexible factorial ANOVA. We found significant diagnosis × subregion FC interactions in insula, supplementary motor area (SMA), midcingulate cortex (MCC), superior temporal gyrus (STG) and postcentral gyrus (PCG). In HC, the BLA demonstrated stronger connectivity with above regions compared to CMA, whereas in OCD, the connectivity pattern reversed to stronger CMA connectivity comparing to BLA. Relative to HC, OCD patients exhibited hypoconnectivity between left BLA and left insula, and hyperconnectivity between right CMA and SMA, MCC, insula, STG, and PCG. Moreover, OCD patients showed reduced volume of left BLA and right CMA compared to HC. Our findings characterized disorganized functional architecture of amygdala subregional networks in accordance with structural defects, providing direct evidence regarding the specific role of amygdala subregions in the neurocircuitry models of OCD.
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24
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Foster JC, Cohodes EM, Brieant AE, McCauley S, Odriozola P, Zacharek SJ, Pierre JC, Hodges H, Kribakaran S, Haberman JT, Holt-Gosselin B, Gee DG. Associations between early-life stress exposure and internalizing symptomatology during the COVID-19 pandemic: Assessing the role of neurobehavioral mediators. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2022; 3:S2667-1743(22)00090-8. [PMID: 35959474 PMCID: PMC9356644 DOI: 10.1016/j.bpsgos.2022.07.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2021] [Revised: 07/12/2022] [Accepted: 07/16/2022] [Indexed: 11/23/2022] Open
Abstract
Background The ongoing COVID-19 pandemic is a major stressor that has been associated with increased risk for psychiatric illness in the general population. Recent work has highlighted that experiences of early-life stress (ELS) may impact individuals' psychological functioning and vulnerability for developing internalizing psychopathology in response to pandemic-related stress. However, little is known about the neurobehavioral factors that may mediate the association between ELS exposure and COVID-related internalizing symptomatology. The current study sought to examine the mediating roles of pre-pandemic resting-state frontoamygdala connectivity and concurrent emotion regulation (ER) in the association between ELS and pandemic-related internalizing symptomatology. Methods Retrospective life-stress histories, concurrent self-reported ER strategies (i.e., reappraisal and suppression), concurrent self-reported internalizing symptomatology (i.e., depression- and anxiety-related symptomatology), and resting-state functional connectivity data from a sample of adults (N = 64, M age = 22.12, female = 68.75%) were utilized. Results There were no significant direct associations between ELS and COVID-related internalizing symptomatology. Neither frontoamygdala functional connectivity nor ER strategy use mediated an association between ELS and COVID-related internalizing symptomatology (ps > 0.05). Exploratory analyses identified a significant moderating effect of reappraisal use on the association between ELS and internalizing symptomatology (β = -0.818, p = 0.047), such that increased reappraisal use buffered the impact of ELS on psychopathology. Conclusions While frontoamygdala connectivity and ER do not appear to mediate the association between ELS and COVID-related internalizing symptomatology, our findings suggest that the use of reappraisal may buffer against the effect of ELS on mental health during the pandemic.
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Affiliation(s)
- Jordan C. Foster
- Department of Psychology, Yale University, New Haven, Connecticut
| | - Emily M. Cohodes
- Department of Psychology, Yale University, New Haven, Connecticut
| | | | - Sarah McCauley
- Department of Psychology, Yale University, New Haven, Connecticut
| | - Paola Odriozola
- Department of Psychology, Yale University, New Haven, Connecticut
| | - Sadie J. Zacharek
- Department of Psychology, Yale University, New Haven, Connecticut
- Department of Brain and Cognitive Sciences, Massachusetts Institute of Technology, Cambridge, Massachusetts
| | | | - H.R. Hodges
- Department of Psychology, Yale University, New Haven, Connecticut
| | | | | | | | - Dylan G. Gee
- Department of Psychology, Yale University, New Haven, Connecticut
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25
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Raghu ALB, Martin SC, Parker T, Aziz TZ, Green AL. Connectivity-based thalamus parcellation and surgical targeting of somatosensory subnuclei. J Neurosurg 2022; 137:209-216. [PMID: 34798607 DOI: 10.3171/2021.7.jns211140] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/06/2021] [Accepted: 07/12/2021] [Indexed: 11/06/2022]
Abstract
OBJECTIVE The anatomy of the posterolateral thalamus varies substantially between individuals, presenting a challenge for surgical targeting. Patient-specific, connectivity-based parcellation of the thalamus may effectively approximate the ventrocaudal nucleus (Vc). This remains to be robustly validated or assessed as a method to guide surgical targeting. The authors assessed the validity of connectivity-based parcellation for targeting the Vc and its potential for improving clinical outcomes of pain surgery. METHODS A cohort of 19 patients with regional, chronic neuropathic pain underwent preoperative structural and diffusion MRI, then progressed to deep brain stimulation targeting the Vc based on traditional atlas coordinates. Surgical thalami were retrospectively segmented and then parcellated based on tractography estimates of thalamocortical connectivity. The location of each patient's electrode array was analyzed with respect to their primary somatosensory cortex (S1) parcel and compared across patients with reference to the thalamic homunculus. RESULTS Ten patients achieved long-term pain relief. Sixty-one percent of an average array (interquartile range 42%-74%) was located in the S1 parcel. In patients who achieved long-term benefit from surgery, array location in the individually generated S1 parcels was medial for face pain, centromedial for arm pain, and centrolateral for leg pain. Patients who did not benefit from surgery did not follow this pattern. Standard stereotactic coordinates of electrode locations diverged from this pattern. CONCLUSIONS Connectivity-based parcellation of the thalamus appears to be a reliable method for segmenting the Vc. Identifying the Vc in this way, and targeting mediolaterally as appropriate for the region of pain, merits exploration in an effort to increase the yield of successful surgical procedures.
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Affiliation(s)
- Ashley L B Raghu
- 1Oxford Functional Neurosurgery, Nuffield Department of Surgical Sciences, University of Oxford, Oxford, United Kingdom; and
| | - Sean C Martin
- 1Oxford Functional Neurosurgery, Nuffield Department of Surgical Sciences, University of Oxford, Oxford, United Kingdom; and
- 2Department of Neurosurgery, John Radcliffe Hospital, Oxford University NHS Foundation Trust, Oxford, United Kingdom
| | - Tariq Parker
- 1Oxford Functional Neurosurgery, Nuffield Department of Surgical Sciences, University of Oxford, Oxford, United Kingdom; and
| | - Tipu Z Aziz
- 1Oxford Functional Neurosurgery, Nuffield Department of Surgical Sciences, University of Oxford, Oxford, United Kingdom; and
- 2Department of Neurosurgery, John Radcliffe Hospital, Oxford University NHS Foundation Trust, Oxford, United Kingdom
| | - Alexander L Green
- 1Oxford Functional Neurosurgery, Nuffield Department of Surgical Sciences, University of Oxford, Oxford, United Kingdom; and
- 2Department of Neurosurgery, John Radcliffe Hospital, Oxford University NHS Foundation Trust, Oxford, United Kingdom
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26
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Mancuso L, Cavuoti-Cabanillas S, Liloia D, Manuello J, Buzi G, Cauda F, Costa T. Tasks activating the default mode network map multiple functional systems. Brain Struct Funct 2022; 227:1711-1734. [PMID: 35179638 PMCID: PMC9098625 DOI: 10.1007/s00429-022-02467-0] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/05/2021] [Accepted: 01/31/2022] [Indexed: 12/30/2022]
Abstract
Recent developments in network neuroscience suggest reconsidering what we thought we knew about the default mode network (DMN). Although this network has always been seen as unitary and associated with the resting state, a new deconstructive line of research is pointing out that the DMN could be divided into multiple subsystems supporting different functions. By now, it is well known that the DMN is not only deactivated by tasks, but also involved in affective, mnestic, and social paradigms, among others. Nonetheless, it is starting to become clear that the array of activities in which it is involved, might also be extended to more extrinsic functions. The present meta-analytic study is meant to push this boundary a bit further. The BrainMap database was searched for all experimental paradigms activating the DMN, and their activation likelihood estimation maps were then computed. An additional map of task-induced deactivations was also created. A multidimensional scaling indicated that such maps could be arranged along an anatomo-psychological gradient, which goes from midline core activations, associated with the most internal functions, to that of lateral cortices, involved in more external tasks. Further multivariate investigations suggested that such extrinsic mode is especially related to reward, semantic, and emotional functions. However, an important finding was that the various activation maps were often different from the canonical representation of the resting-state DMN, sometimes overlapping with it only in some peripheral nodes, and including external regions such as the insula. Altogether, our findings suggest that the intrinsic-extrinsic opposition may be better understood in the form of a continuous scale, rather than a dichotomy.
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Affiliation(s)
- Lorenzo Mancuso
- FOCUS Lab Department of Psychology, University of Turin, Via Giuseppe Verdi 10, 10124, Turin, Italy
| | | | - Donato Liloia
- FOCUS Lab Department of Psychology, University of Turin, Via Giuseppe Verdi 10, 10124, Turin, Italy
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy
| | - Jordi Manuello
- FOCUS Lab Department of Psychology, University of Turin, Via Giuseppe Verdi 10, 10124, Turin, Italy
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy
| | - Giulia Buzi
- FOCUS Lab Department of Psychology, University of Turin, Via Giuseppe Verdi 10, 10124, Turin, Italy
| | - Franco Cauda
- FOCUS Lab Department of Psychology, University of Turin, Via Giuseppe Verdi 10, 10124, Turin, Italy
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy
| | - Tommaso Costa
- FOCUS Lab Department of Psychology, University of Turin, Via Giuseppe Verdi 10, 10124, Turin, Italy.
- GCS-fMRI, Koelliker Hospital and Department of Psychology, University of Turin, Turin, Italy.
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27
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Cohesive parcellation of the human brain using resting-state fMRI. J Neurosci Methods 2022; 377:109629. [PMID: 35618164 DOI: 10.1016/j.jneumeth.2022.109629] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/17/2021] [Revised: 04/14/2022] [Accepted: 05/19/2022] [Indexed: 11/23/2022]
Abstract
BACKGROUND The data burden for resting-state fMRI analysis rises with increasing resolutions available at ultrahigh fields. Therefore, a fundamental preprocessing step in brain network analysis is to reduce the data, usually by performing some kind of data parcellation. Most functional parcellations based on rsfMRI connectivity are synthesized from the dense connectome. In contrast, most network analyses begin by reducing each parcel to a single exemplar time series. This disconnect between parcel formation and usage assumes that parcel exemplars adequately represent their member voxels, which is not always the case for commonly used parcellations.
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28
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Krendl AC, Betzel RF. Social cognitive network neuroscience. Soc Cogn Affect Neurosci 2022; 17:510-529. [PMID: 35352125 PMCID: PMC9071476 DOI: 10.1093/scan/nsac020] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/03/2021] [Revised: 01/27/2022] [Accepted: 03/10/2022] [Indexed: 12/31/2022] Open
Abstract
Over the past three decades, research from the field of social neuroscience has identified a constellation of brain regions that relate to social cognition. Although these studies have provided important insights into the specific neural regions underlying social behavior, they may overlook the broader neural context in which those regions and the interactions between them are embedded. Network neuroscience is an emerging discipline that focuses on modeling and analyzing brain networks-collections of interacting neural elements. Because human cognition requires integrating information across multiple brain regions and systems, we argue that a novel social cognitive network neuroscience approach-which leverages methods from the field of network neuroscience and graph theory-can advance our understanding of how brain systems give rise to social behavior. This review provides an overview of the field of network neuroscience, discusses studies that have leveraged this approach to advance social neuroscience research, highlights the potential contributions of social cognitive network neuroscience to understanding social behavior and provides suggested tools and resources for conducting network neuroscience research.
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Affiliation(s)
- Anne C Krendl
- Department of Psychological & Brain Sciences, Indiana University, Bloomington, IN 47405, USA
| | - Richard F Betzel
- Department of Psychological & Brain Sciences, Indiana University, Bloomington, IN 47405, USA
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29
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Chen T, Mandal A, Zhu H, Liu R. Imaging Genetic Based Mediation Analysis for Human Cognition. Front Neurosci 2022; 16:824069. [PMID: 35573299 PMCID: PMC9097855 DOI: 10.3389/fnins.2022.824069] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/28/2021] [Accepted: 03/23/2022] [Indexed: 11/30/2022] Open
Abstract
The brain connectome maps the structural and functional connectivity that forms an important neurobiological basis for the analysis of human cognitive traits while the genetic predisposition and our cognition ability are frequently found in close association. The issue of how genetic architecture and brain connectome causally affect human behaviors remains unknown. To seek for the potential causal relationship, in this paper, we carried out the causal pathway analysis from single nucleotide polymorphism (SNP) data to four common human cognitive traits, mediated by the brain connectome. Specifically, we selected 942 SNPs that are significantly associated with the brain connectome, and then estimated the direct and indirect effect on the human traits for each SNP. We found out that a majority of the selected SNPs have significant direct effects on human traits and discussed the trait-related brain regions and their implications.
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Affiliation(s)
- Tingan Chen
- Department of Statistics, Florida State University, Tallahassee, FL, United States
| | - Abhishek Mandal
- Department of Statistics, Florida State University, Tallahassee, FL, United States
| | - Hongtu Zhu
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States
| | - Rongjie Liu
- Department of Statistics, Florida State University, Tallahassee, FL, United States
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30
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Zhi D, King M, Hernandez-Castillo CR, Diedrichsen J. Evaluating brain parcellations using the distance-controlled boundary coefficient. Hum Brain Mapp 2022; 43:3706-3720. [PMID: 35451538 PMCID: PMC9294308 DOI: 10.1002/hbm.25878] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2021] [Revised: 02/13/2022] [Accepted: 03/21/2022] [Indexed: 11/18/2022] Open
Abstract
One important approach to human brain mapping is to define a set of distinct regions that can be linked to unique functions. Numerous brain parcellations have been proposed, using cytoarchitectonic, structural, or functional magnetic resonance imaging (fMRI) data. The intrinsic smoothness of brain data, however, poses a problem for current methods seeking to compare different parcellations. For example, criteria that simply compare within‐parcel to between‐parcel similarity provide even random parcellations with a high value. Furthermore, the evaluation is biased by the spatial scale of the parcellation. To address this problem, we propose the distance‐controlled boundary coefficient (DCBC), an unbiased criterion to evaluate discrete parcellations. We employ this new criterion to evaluate existing parcellations of the human neocortex in their power to predict functional boundaries for an fMRI data set with many different tasks, as well as for resting‐state data. We find that common anatomical parcellations do not perform better than chance, suggesting that task‐based functional boundaries do not align well with sulcal landmarks. Parcellations based on resting‐state fMRI data perform well; in some cases, as well as a parcellation defined on the evaluation data itself. Finally, multi‐modal parcellations that combine functional and anatomical criteria perform substantially worse than those based on functional data alone, indicating that functionally homogeneous regions often span major anatomical landmarks. Overall, the DCBC advances the field of functional brain mapping by providing an unbiased metric that compares the predictive ability of different brain parcellations to define brain regions that are functionally maximally distinct.
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Affiliation(s)
- Da Zhi
- Brain and Mind Institute, Western University, London, Ontario, Canada.,Department of Computer Science, Western University, London, Ontario, Canada
| | - Maedbh King
- Department of Psychology, University of California, Berkeley, California, USA
| | | | - Jörn Diedrichsen
- Brain and Mind Institute, Western University, London, Ontario, Canada.,Department of Computer Science, Western University, London, Ontario, Canada.,Department of Statistical and Actuarial Sciences, Western University, London, Ontario, Canada
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31
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Yang Z, Caldwell JZK, Cummings JL, Ritter A, Kinney JW, Cordes D. Sex Modulates the Pathological Aging Effect on Caudate Functional Connectivity in Mild Cognitive Impairment. Front Psychiatry 2022; 13:804168. [PMID: 35479489 PMCID: PMC9037326 DOI: 10.3389/fpsyt.2022.804168] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/04/2021] [Accepted: 03/14/2022] [Indexed: 11/13/2022] Open
Abstract
Purpose To assess the pathological aging effect on caudate functional connectivity among mild cognitive impairment (MCI) participants and examine whether and how sex and amyloid contribute to this process. Materials and Methods Two hundred and seventy-seven functional magnetic resonance imaging (fMRI) sessions from 163 cognitive normal (CN) older adults and 309 sessions from 139 participants with MCI were included as the main sample in our analysis. Pearson's correlation was used to characterize the functional connectivity (FC) between caudate nuclei and each brain region, then caudate nodal strength was computed to quantify the overall caudate FC strength. Association analysis between caudate nodal strength and age was carried out in MCI and CN separately using linear mixed effect (LME) model with covariates (education, handedness, sex, Apolipoprotein E4, and intra-subject effect). Analysis of covariance was conducted to investigate sex, amyloid status, and their interaction effects on aging with the fMRI data subset having amyloid status available. LME model was applied to women and men separately within MCI group to evaluate aging effects on caudate nodal strength and each region's connectivity with caudate nuclei. We then evaluated the roles of sex and amyloid status in the associations of neuropsychological scores with age or caudate nodal strength. An independent cohort was used to validate the sex-dependent aging effects in MCI. Results The MCI group had significantly stronger age-related increase of caudate nodal strength compared to the CN group. Analyzing women and men separately revealed that the aging effect on caudate nodal strength among MCI participants was significant only for women (left: P = 6.23 × 10-7, right: P = 3.37 × 10-8), but not for men (P > 0.3 for bilateral caudate nuclei). The aging effects on caudate nodal strength were not significantly mediated by brain amyloid burden. Caudate connectivity with ventral prefrontal cortex substantially contributed to the aging effect on caudate nodal strength in women with MCI. Higher caudate nodal strength is significantly related to worse cognitive performance in women but not in men with MCI. Conclusion Sex modulates the pathological aging effects on caudate nodal strength in MCI regardless of amyloid status. Caudate nodal strength may be a sensitive biomarker of pathological aging in women with MCI.
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Affiliation(s)
- Zhengshi Yang
- Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, United States
- Department of Brain Health, University of Nevada Las Vegas, Las Vegas, NV, United States
| | | | - Jeffrey L. Cummings
- Chambers-Grundy Center for Transformative Neuroscience, Department of Brain Health, School of Integrated Health Sciences, University of Nevada Las Vegas, Las Vegas, NV, United States
| | - Aaron Ritter
- Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, United States
| | - Jefferson W. Kinney
- Chambers-Grundy Center for Transformative Neuroscience, Department of Brain Health, School of Integrated Health Sciences, University of Nevada Las Vegas, Las Vegas, NV, United States
| | - Dietmar Cordes
- Cleveland Clinic Lou Ruvo Center for Brain Health, Las Vegas, NV, United States
- Department of Brain Health, University of Nevada Las Vegas, Las Vegas, NV, United States
- Department of Psychology and Neuroscience, University of Colorado, Boulder, CO, United States
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32
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Dugré JR, Eickhoff SB, Potvin S. Meta-analytical transdiagnostic neural correlates in common pediatric psychiatric disorders. Sci Rep 2022; 12:4909. [PMID: 35318371 PMCID: PMC8941086 DOI: 10.1038/s41598-022-08909-3] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2021] [Accepted: 03/09/2022] [Indexed: 01/04/2023] Open
Abstract
In the last decades, neuroimaging studies have attempted to unveil the neurobiological markers underlying pediatric psychiatric disorders. Yet, the vast majority of neuroimaging studies still focus on a single nosological category, which limit our understanding of the shared/specific neural correlates between these disorders. Therefore, we aimed to investigate the transdiagnostic neural correlates through a novel and data-driven meta-analytical method. A data-driven meta-analysis was carried out which grouped similar experiments’ topographic map together, irrespectively of nosological categories and task-characteristics. Then, activation likelihood estimation meta-analysis was performed on each group of experiments to extract spatially convergent brain regions. One hundred forty-seven experiments were retrieved (3124 cases compared to 3100 controls): 79 attention-deficit/hyperactivity disorder, 32 conduct/oppositional defiant disorder, 14 anxiety disorders, 22 major depressive disorders. Four significant groups of experiments were observed. Functional characterization suggested that these groups of aberrant brain regions may be implicated internally/externally directed processes, attentional control of affect, somato-motor and visual processes. Furthermore, despite that some differences in rates of studies involving major depressive disorders were noticed, nosological categories were evenly distributed between these four sets of regions. Our results may reflect transdiagnostic neural correlates of pediatric psychiatric disorders, but also underscore the importance of studying pediatric psychiatric disorders simultaneously rather than independently to examine differences between disorders.
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Affiliation(s)
- Jules R Dugré
- Research Center of the Institut Universitaire en Santé Mentale de Montréal, 7331 Hochelaga, Montreal, QC, H1N 3V2, Canada. .,Department of Psychiatry and Addictology, Faculty of Medicine, University of Montreal, Montreal, Canada.
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine (INM-7), Jülich, Germany.,Institute for Systems Neuroscience, Heinrich Heine University, Düsseldorf, Germany
| | - Stéphane Potvin
- Research Center of the Institut Universitaire en Santé Mentale de Montréal, 7331 Hochelaga, Montreal, QC, H1N 3V2, Canada. .,Department of Psychiatry and Addictology, Faculty of Medicine, University of Montreal, Montreal, Canada.
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33
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Coelho A, Magalhães R, Moreira PS, Amorim L, Portugal-Nunes C, Castanho T, Santos NC, Sousa N, Fernandes HM. A novel method for estimating connectivity-based parcellation of the human brain from diffusion MRI: Application to an aging cohort. Hum Brain Mapp 2022; 43:2419-2443. [PMID: 35274787 PMCID: PMC9057102 DOI: 10.1002/hbm.25773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Revised: 12/20/2021] [Accepted: 12/27/2021] [Indexed: 11/18/2022] Open
Abstract
Connectivity‐based parcellation (CBP) methods are used to define homogenous and biologically meaningful parcels or nodes—the foundations of brain network fingerprinting—by grouping voxels with similar patterns of brain connectivity. However, we still lack a gold standard method and the use of CBPs to study the aging brain remains scarce. Our study proposes a novel CBP method from diffusion MRI data and shows its potential to produce a more accurate characterization of the longitudinal alterations in brain network topology occurring in aging. For this, we constructed whole‐brain connectivity maps from diffusion MRI data of two datasets: an aging cohort evaluated at two timepoints (mean interval time: 52.8 ± 7.24 months) and a normative adult cohort—MGH‐HCP. State‐of‐the‐art clustering techniques were used to identify the best performing technique. Furthermore, we developed a new metric (connectivity homogeneity fingerprint [CHF]) to evaluate the success of the final CBP in improving regional/global structural connectivity homogeneity. Our results show that our method successfully generates highly homogeneous parcels, as described by the significantly larger CHF score of the resulting parcellation, when compared to the original. Additionally, we demonstrated that the developed parcellation provides a robust anatomical framework to assess longitudinal changes in the aging brain. Our results reveal that aging is characterized by a reorganization of the brain's structural network involving the decrease of intra‐hemispheric, increase of inter‐hemispheric connectivity, and topological rearrangement. Overall, this study proposes a new methodology to perform accurate and robust evaluations of CBP of the human brain.
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Affiliation(s)
- Ana Coelho
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal.,ICVS/3B's-PT Government Associate Laboratory, Braga/Guimarães, Portugal.,Clinical Academic Center - Braga, Braga, Portugal
| | - Ricardo Magalhães
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal.,ICVS/3B's-PT Government Associate Laboratory, Braga/Guimarães, Portugal.,Clinical Academic Center - Braga, Braga, Portugal
| | - Pedro S Moreira
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal.,ICVS/3B's-PT Government Associate Laboratory, Braga/Guimarães, Portugal.,Clinical Academic Center - Braga, Braga, Portugal
| | - Liliana Amorim
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal.,ICVS/3B's-PT Government Associate Laboratory, Braga/Guimarães, Portugal.,Clinical Academic Center - Braga, Braga, Portugal
| | - Carlos Portugal-Nunes
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal.,ICVS/3B's-PT Government Associate Laboratory, Braga/Guimarães, Portugal.,Clinical Academic Center - Braga, Braga, Portugal
| | - Teresa Castanho
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal.,ICVS/3B's-PT Government Associate Laboratory, Braga/Guimarães, Portugal.,Clinical Academic Center - Braga, Braga, Portugal
| | - Nadine Correia Santos
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal.,ICVS/3B's-PT Government Associate Laboratory, Braga/Guimarães, Portugal.,Clinical Academic Center - Braga, Braga, Portugal
| | - Nuno Sousa
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal.,ICVS/3B's-PT Government Associate Laboratory, Braga/Guimarães, Portugal.,Clinical Academic Center - Braga, Braga, Portugal
| | - Henrique M Fernandes
- Center for Music in the Brain (MIB), Aarhus University, Aarhus, Denmark.,Department of Psychiatry, University of Oxford, Oxford, UK
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34
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Albers KJ, Liptrot MG, Ambrosen KS, Røge R, Herlau T, Andersen KW, Siebner HR, Hansen LK, Dyrby TB, Madsen KH, Schmidt MN, Mørup M. Uncovering Cortical Units of Processing From Multi-Layered Connectomes. Front Neurosci 2022; 16:836259. [PMID: 35360166 PMCID: PMC8960198 DOI: 10.3389/fnins.2022.836259] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/15/2021] [Accepted: 02/09/2022] [Indexed: 11/13/2022] Open
Abstract
Modern diffusion and functional magnetic resonance imaging (dMRI/fMRI) provide non-invasive high-resolution images from which multi-layered networks of whole-brain structural and functional connectivity can be derived. Unfortunately, the lack of observed correspondence between the connectivity profiles of the two modalities challenges the understanding of the relationship between the functional and structural connectome. Rather than focusing on correspondence at the level of connections we presently investigate correspondence in terms of modular organization according to shared canonical processing units. We use a stochastic block-model (SBM) as a data-driven approach for clustering high-resolution multi-layer whole-brain connectivity networks and use prediction to quantify the extent to which a given clustering accounts for the connectome within a modality. The employed SBM assumes a single underlying parcellation exists across modalities whilst permitting each modality to possess an independent connectivity structure between parcels thereby imposing concurrent functional and structural units but different structural and functional connectivity profiles. We contrast the joint processing units to their modality specific counterparts and find that even though data-driven structural and functional parcellations exhibit substantial differences, attributed to modality specific biases, the joint model is able to achieve a consensus representation that well accounts for both the functional and structural connectome providing improved representations of functional connectivity compared to using functional data alone. This implies that a representation persists in the consensus model that is shared by the individual modalities. We find additional support for this viewpoint when the anatomical correspondence between modalities is removed from the joint modeling. The resultant drop in predictive performance is in general substantial, confirming that the anatomical correspondence of processing units is indeed present between the two modalities. Our findings illustrate how multi-modal integration admits consensus representations well-characterizing each individual modality despite their biases and points to the importance of multi-layered connectomes as providing supplementary information regarding the brain's canonical processing units.
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Affiliation(s)
- Kristoffer Jon Albers
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
| | - Matthew G. Liptrot
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
| | - Karen Sandø Ambrosen
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
| | - Rasmus Røge
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
| | - Tue Herlau
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
| | - Kasper Winther Andersen
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital - Amager and Hvidovre, Copenhagen, Denmark
| | - Hartwig R. Siebner
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital - Amager and Hvidovre, Copenhagen, Denmark
- Department of Neurology, Copenhagen University Hospital Bispebjerg and Frederiksberg, Copenhagen, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Lars Kai Hansen
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
| | - Tim B. Dyrby
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital - Amager and Hvidovre, Copenhagen, Denmark
| | - Kristoffer H. Madsen
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
- Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital - Amager and Hvidovre, Copenhagen, Denmark
| | - Mikkel N. Schmidt
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
| | - Morten Mørup
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, Lyngby, Denmark
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35
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Qiu W, Ma L, Jiang T, Zhang Y. Unrevealing Reliable Cortical Parcellation of Individual Brains Using Resting-State Functional Magnetic Resonance Imaging and Masked Graph Convolutions. Front Neurosci 2022; 16:838347. [PMID: 35356058 PMCID: PMC8959420 DOI: 10.3389/fnins.2022.838347] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/17/2021] [Accepted: 01/24/2022] [Indexed: 11/20/2022] Open
Abstract
Brain parcellation helps to understand the structural and functional organization of the cerebral cortex. Resting-state functional magnetic resonance imaging (fMRI) and connectivity analysis provide useful information to delineate individual brain parcels in vivo. We proposed an individualized cortical parcellation based on graph neural networks (GNN) to learn the reliable functional characteristics of each brain parcel on a large fMRI dataset and to infer the areal probability of each vertex on unseen subjects. A subject-specific confidence mask was implemented in the GNN model to account for the tradeoff between the topographic alignment across subjects and functional homogeneity of brain parcels on individual brains. The individualized brain parcellation achieved better functional homogeneity at rest and during cognitive tasks compared with the group-registered atlas (p-values < 0.05). In addition, highly reliable and replicable parcellation maps were generated on multiple sessions of the same subject (intrasubject similarity = 0.89), while notable variations in the topographic organization were captured across subjects (intersubject similarity = 0.81). Moreover, the intersubject variability of brain parcellation indicated large variations in the association cortices while keeping a stable parcellation on the primary cortex. Such topographic variability was strongly associated with the functional connectivity variability, significantly predicted cognitive behaviors, and generally followed the myelination, cytoarchitecture, and functional organization of the human brain. This study provides new avenues to the precise individualized mapping of the cortical areas through deep learning and shows high potentials in the personalized localization diagnosis and treatment of neurological disorders.
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Affiliation(s)
- Wenyuan Qiu
- Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, China
| | - Liang Ma
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Tianzi Jiang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing, China
- National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing, China
| | - Yu Zhang
- Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, China
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36
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Rivière D, Leprince Y, Labra N, Vindas N, Foubet O, Cagna B, Loh KK, Hopkins W, Balzeau A, Mancip M, Lebenberg J, Cointepas Y, Coulon O, Mangin JF. Browsing Multiple Subjects When the Atlas Adaptation Cannot Be Achieved via a Warping Strategy. Front Neuroinform 2022; 16:803934. [PMID: 35311005 PMCID: PMC8928460 DOI: 10.3389/fninf.2022.803934] [Citation(s) in RCA: 4] [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: 10/28/2021] [Accepted: 01/17/2022] [Indexed: 11/14/2022] Open
Abstract
Brain mapping studies often need to identify brain structures or functional circuits into a set of individual brains. To this end, multiple atlases have been published to represent such structures based on different modalities, subject sets, and techniques. The mainstream approach to exploit these atlases consists in spatially deforming each individual data onto a given atlas using dense deformation fields, which supposes the existence of a continuous mapping between atlases and individuals. However, this continuity is not always verified, and this "iconic" approach has limits. We present in this study an alternative, complementary, "structural" approach, which consists in extracting structures from the individual data, and comparing them without deformation. A "structural atlas" is thus a collection of annotated individual data with a common structure nomenclature. It may be used to characterize structure shape variability across individuals or species, or to train machine learning systems. This study exhibits Anatomist, a powerful structural 3D visualization software dedicated to building, exploring, and editing structural atlases involving a large number of subjects. It has been developed primarily to decipher the cortical folding variability; cortical sulci vary enormously in both size and shape, and some may be missing or have various topologies, which makes iconic approaches inefficient to study them. We, therefore, had to build structural atlases for cortical sulci, and use them to train sulci identification algorithms. Anatomist can display multiple subject data in multiple views, supports all kinds of neuroimaging data, including compound structural object graphs, handles arbitrary coordinate transformation chains between data, and has multiple display features. It is designed as a programming library in both C++ and Python languages, and may be extended or used to build dedicated custom applications. Its generic design makes all the display and structural aspects used to explore the variability of the cortical folding pattern work in other applications, for instance, to browse axonal fiber bundles, deep nuclei, functional activations, or other kinds of cortical parcellations. Multimodal, multi-individual, or inter-species display is supported, and adaptations to large scale screen walls have been developed. These very original features make it a unique viewer for structural atlas browsing.
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Affiliation(s)
- Denis Rivière
- Université Paris-Saclay, CEA, CNRS UMR 9027, Baobab, NeuroSpin, Gif-sur-Yvette, France
| | - Yann Leprince
- Université Paris-Saclay, CEA, CNRS UMR 9027, Baobab, NeuroSpin, Gif-sur-Yvette, France
| | - Nicole Labra
- Université Paris-Saclay, CEA, CNRS UMR 9027, Baobab, NeuroSpin, Gif-sur-Yvette, France
- PaleoFED Team, UMR 7194, CNRS, Département Homme et Environnement, Muséum National d’Histoire Naturelle, Musée de l’Homme, Paris, France
| | - Nabil Vindas
- Université Paris-Saclay, CEA, CNRS UMR 9027, Baobab, NeuroSpin, Gif-sur-Yvette, France
| | - Ophélie Foubet
- Université Paris-Saclay, CEA, CNRS UMR 9027, Baobab, NeuroSpin, Gif-sur-Yvette, France
| | - Bastien Cagna
- Université Paris-Saclay, CEA, CNRS UMR 9027, Baobab, NeuroSpin, Gif-sur-Yvette, France
| | - Kep Kee Loh
- INT - Institut de Neurosciences de la Timone, Aix-Marseille Univ, CNRS UMR 7289, Marseille, France
| | - William Hopkins
- Department of Comparative Medicine, University of Texas MD Anderson Cancer Center, Bastrop, TX, United States
| | - Antoine Balzeau
- PaleoFED Team, UMR 7194, CNRS, Département Homme et Environnement, Muséum National d’Histoire Naturelle, Musée de l’Homme, Paris, France
- Department of African Zoology, Royal Museum for Central Africa, Tervuren, Belgium
| | - Martial Mancip
- Maison de la Simulation, CNRS, CEA Saclay, Gif-sur-Yvette, France
| | - Jessica Lebenberg
- Université Paris-Saclay, CEA, CNRS UMR 9027, Baobab, NeuroSpin, Gif-sur-Yvette, France
- Université de Paris, INSERM UMR 1141, NeuroDiderot, Paris, France
| | - Yann Cointepas
- Université Paris-Saclay, CEA, CNRS UMR 9027, Baobab, NeuroSpin, Gif-sur-Yvette, France
| | - Olivier Coulon
- INT - Institut de Neurosciences de la Timone, Aix-Marseille Univ, CNRS UMR 7289, Marseille, France
| | - Jean-François Mangin
- Université Paris-Saclay, CEA, CNRS UMR 9027, Baobab, NeuroSpin, Gif-sur-Yvette, France
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37
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Kazemivash B, Calhoun VD. A novel 5D brain parcellation approach based on spatio-temporal encoding of resting fMRI data from deep residual learning. J Neurosci Methods 2022; 369:109478. [PMID: 35031344 PMCID: PMC9394484 DOI: 10.1016/j.jneumeth.2022.109478] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/10/2021] [Revised: 12/15/2021] [Accepted: 01/06/2022] [Indexed: 10/19/2022]
Abstract
OBJECTIVE Brain parcellation is an essential aspect of computational neuroimaging research and deals with segmenting the brain into (possibly overlapping) sub-regions employed to study brain anatomy or function. In the context of functional parcellation, brain organization which is often measured via temporal metrics such as coherence, is highly dynamic. This dynamic aspect is ignored in most research, which typically applies anatomically based, fixed regions for each individual, and can produce misleading results. METHODS In this work, we propose a novel spatio-temporal-network (5D) brain parcellation scheme utilizing a deep residual network to predict the probability of each voxel belonging to a brain network at each point in time. RESULTS We trained 53 4D brain networks and evaluate the ability of these networks to capture spatial and temporal dynamics as well as to show sensitivity to individual or group-level variation (in our case with age). CONCLUSION The proposed system generates informative spatio-temporal networks that vary not only across individuals but also over time and space. SIGNIFICANCE The dynamic 5D nature of the developed approach provides a powerful framework that expands on existing work and has potential to identify novel and typically ignored findings when studying the healthy and disordered brain.
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Affiliation(s)
- Behnam Kazemivash
- Department of Computer Science, Georgia State University, Atlanta, GA 30332, USA.
| | - Vince D. Calhoun
- Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS), Georgia State University, Georgia Institute of Technology, and Emory University, Atlanta GA 30303
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Bottino F, Lucignani M, Pasquini L, Mastrogiovanni M, Gazzellini S, Ritrovato M, Longo D, Figà-Talamanca L, Rossi Espagnet MC, Napolitano A. Spatial Stability of Functional Networks: A Measure to Assess the Robustness of Graph-Theoretical Metrics to Spatial Errors Related to Brain Parcellation. Front Neurosci 2022; 15:736524. [PMID: 35250432 PMCID: PMC8894326 DOI: 10.3389/fnins.2021.736524] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/05/2021] [Accepted: 12/28/2021] [Indexed: 12/12/2022] Open
Abstract
There is growing interest in studying human brain connectivity and in modelling the brain functional structure as a network. Brain network creation requires parcellation of the cerebral cortex to define nodes. Parcellation might be affected by possible errors due to inter- and intra-subject variability as a consequence of brain structural and physiological characteristics and shape variations related to ageing and diseases, acquisition noise, and misregistration. These errors could induce a knock-on effect on network measure variability. The aim of this study was to investigate spatial stability, a measure of functional connectivity variations induced by parcellation errors. We simulated parcellation variability with random small spatial changes and evaluated its effects on twenty-seven graph-theoretical measures. The study included subjects from three public online datasets. Two brain parcellations were performed using FreeSurfer with geometric atlases. Starting from these, 100 new parcellations were created by increasing the area of 30% of parcels, reducing the area of neighbour parcels, with a rearrangement of vertices. fMRI data were filtered with linear regression, CompCor, and motion correction. Adjacency matrices were constructed with 0.1, 0.2, 0.3, and 0.4 thresholds. Differences in spatial stability between datasets, atlases, and threshold were evaluated. The higher spatial stability resulted for Characteristic-path-length, Density, Transitivity, and Closeness-centrality, and the lower spatial stability resulted for Bonacich and Katz. Multivariate analysis showed a significant effect of atlas, datasets, and thresholds. Katz and Bonacich centrality, which was subject to larger variations, can be considered an unconventional graph measure, poorly implemented in the clinical field and not yet investigated for reliability assessment. Spatial stability (SS) is affected by threshold, and it decreases with increasing threshold for several measures. Moreover, SS seems to depend on atlas choice and scanning parameters. Our study highlights the importance of paying close attention to possible parcellation-related spatial errors, which may affect the reliability of functional connectivity measures.
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Affiliation(s)
- Francesca Bottino
- Medical Physics Department, Bambino Gesù Children’s Hospital IRCCS, Rome, Italy
| | - Martina Lucignani
- Medical Physics Department, Bambino Gesù Children’s Hospital IRCCS, Rome, Italy
| | - Luca Pasquini
- Neuroradiology Unit, NESMOS Department, Sant’Andrea Hospital, La Sapienza University, Rome, Italy
- Neuroradiology Service, Department of Radiology, Memorial Sloan Kettering Cancer Center, New York, NY, United States
| | | | - Simone Gazzellini
- Neuroscience and Neurorehabilitation Department, Bambino Gesù Children’s Hospital – IRCCS, Rome, Italy
| | - Matteo Ritrovato
- Health Technology and Safety Research Unit, Bambino Gesù Children’s Hospital – IRCCS, Rome, Italy
| | - Daniela Longo
- Neuroradiology Unit, Imaging Department, Bambino Gesù Children’s Hospital, IRCCS, Rome, Italy
| | - Lorenzo Figà-Talamanca
- Neuroradiology Unit, Imaging Department, Bambino Gesù Children’s Hospital, IRCCS, Rome, Italy
| | - Maria Camilla Rossi Espagnet
- Neuroradiology Unit, Imaging Department, Bambino Gesù Children’s Hospital, IRCCS, Rome, Italy
- NESMOS, Neuroradiology Department, S. Andrea Hospital Sapienza Rome University, Rome, Italy
| | - Antonio Napolitano
- Medical Physics Department, Bambino Gesù Children’s Hospital IRCCS, Rome, Italy
- *Correspondence: Antonio Napolitano,
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van Albada SJ, Morales-Gregorio A, Dickscheid T, Goulas A, Bakker R, Bludau S, Palm G, Hilgetag CC, Diesmann M. Bringing Anatomical Information into Neuronal Network Models. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1359:201-234. [DOI: 10.1007/978-3-030-89439-9_9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
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40
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Blazquez Freches G, Haak KV, Beckmann CF, Mars RB. Connectivity gradients on tractography data: Pipeline and example applications. Hum Brain Mapp 2021; 42:5827-5845. [PMID: 34559432 PMCID: PMC8596970 DOI: 10.1002/hbm.25623] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2020] [Revised: 07/03/2021] [Accepted: 07/30/2021] [Indexed: 11/08/2022] Open
Abstract
Gray matter connectivity can be described in terms of its topographical organization, but the differential role of white matter connections underlying that organization is often unknown. In this study, we propose a method for unveiling principles of organization of both gray and white matter based on white matter connectivity as assessed using diffusion magnetic ressonance imaging (MRI) tractography with spectral embedding gradient mapping. A key feature of the proposed approach is its capacity to project the individual connectivity gradients it reveals back onto its input data in the form of projection images, allowing one to assess the contributions of specific white matter tracts to the observed gradients. We demonstrate the ability of our proposed pipeline to identify connectivity gradients in prefrontal and occipital gray matter. Finally, leveraging the use of tractography, we demonstrate that it is possible to observe gradients within the white matter bundles themselves. Together, the proposed framework presents a generalized way to assess both the topographical organization of structural brain connectivity and the anatomical features driving it.
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Affiliation(s)
- Guilherme Blazquez Freches
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical CenterNijmegenThe Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud UniversityNijmegenThe Netherlands
| | - Koen V. Haak
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical CenterNijmegenThe Netherlands
| | - Christian F. Beckmann
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical CenterNijmegenThe Netherlands
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nufeld Department of Clinical NeurosciencesJohn Radclife Hospital, University of OxfordOxfordUK
| | - Rogier B. Mars
- Donders Institute for Brain, Cognition and Behaviour, Radboud UniversityNijmegenThe Netherlands
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41
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Genon S, Bernhardt BC, La Joie R, Amunts K, Eickhoff SB. The many dimensions of human hippocampal organization and (dys)function. Trends Neurosci 2021; 44:977-989. [PMID: 34756460 DOI: 10.1016/j.tins.2021.10.003] [Citation(s) in RCA: 45] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2021] [Revised: 09/06/2021] [Accepted: 10/05/2021] [Indexed: 11/19/2022]
Abstract
The internal organization of hippocampal formation has been studied for more than a century. Although early accounts emphasized its subfields along the medial-lateral axis, findings in recent decades have highlighted also the anterior-to-posterior (i.e., longitudinal) axis as a key contributor to this brain region's functional organization. Hence, understanding of hippocampal function likely demands characterizing both medial-to-lateral and anterior-to-posterior axes, an approach that has been concretized by recent advances in in vivo parcellation and gradient mapping techniques. Following a short historical overview, we review the evidence provided by these approaches in brain-mapping studies, as well as the perspectives they open for addressing the behavioral relevance of the interacting organizational axes in healthy and clinical populations.
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Affiliation(s)
- Sarah Genon
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany.
| | | | - Renaud La Joie
- Memory and Aging Center, Weill Institute for Neuroscience, University of California, San Francisco, San Francisco, CA, USA
| | - Katrin Amunts
- Institute of Neuroscience and Medicine, Structural and Functional Organisation of the Brain (INM-1), Research Centre Jülich, Jülich, Germany; C. & O. Vogt Institute for Brain Research, Medical Faculty, University Hospital Düsseldorf, Heinrich Heine University, Düsseldorf, Germany
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine, Brain and Behaviour (INM-7), Research Centre Jülich, Jülich, Germany; Institute of Systems Neuroscience, Medical Faculty, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
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42
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Vergara C, Silva F, Huerta I, Lopez-Lopez N, Vazquez A, Houenou J, Poupon C, Mangin JF, Hernandez C, Guevara P. Group-Wise Cortical Surface Parcellation Based on Inter-Subject Fiber Clustering . ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2021; 2021:2655-2659. [PMID: 34891798 DOI: 10.1109/embc46164.2021.9631099] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
We present an automatic algorithm for the group-wise parcellation of the cortical surface. The method is based on the structural connectivity obtained from representative brain fiber clusters, calculated via an inter-subject clustering scheme. Preliminary regions were defined from cluster-cortical mesh intersection points. The final parcellation was obtained using parcel probability maps to model and integrate the connectivity information of all subjects, and graphs to represent the overlap between parcels. Two inter-subject clustering schemes were tested, generating a total of 171 and 109 parcels, respectively. The resulting parcels were quantitatively compared with three state-of-the-art atlases. The best parcellation returned 69 parcels with a Dice similarity coefficient greater than 0.5. To the best of our knowledge, this is the first diffusion-based cortex parcellation method based on whole-brain inter-subject fiber clustering.
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43
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YAN JUNYI, YANG JINZHU, ZHAO DAZHE. MULTIMODAL HUMAN BRAIN LONGITUDINAL PARCELLATION ACROSS LIFESPAN. J MECH MED BIOL 2021. [DOI: 10.1142/s0219519421400595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/18/2022]
Abstract
Subdividing the human brain into several functionally distinct and spatially contiguous areas is important to understand the amazingly complex human cerebral cortex. However, adult aging is related to differences in the structure, function, and connectivity of brain areas, so that the single population subdivision does not apply to multiple age groups. Moreover, different modalities could provide affirmative and complementary information for the human brain subdivision. To obtain a more reasonable subdivision of the cerebral cortex, we make use of multimodal information to subdivide the human cerebral cortex across lifespan. Specifically, we first construct a population average functional connectivity matrix for each modality of each age group. Second, we separately calculate the population average similarity matrix for the cortical thickness and myelin modality of each age group. Finally, we fuse these population average matrixes to obtain the multimodal similarity matrix and feed it into the spectral clustering algorithm to generate the brain parcellation for each age group.
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Affiliation(s)
- JUNYI YAN
- School of Computer Science and Engineering, Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education, Northeastern University, Shenyang, P. R. China
| | - JINZHU YANG
- School of Computer Science and Engineering, Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education, Northeastern University, Shenyang, P. R. China
| | - DAZHE ZHAO
- School of Computer Science and Engineering, Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education, Northeastern University, Shenyang, P. R. China
- Neusoft Corporation, Shenyang, P. R. China
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44
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Benkarim O, Paquola C, Park BY, Hong SJ, Royer J, Vos de Wael R, Lariviere S, Valk S, Bzdok D, Mottron L, C Bernhardt B. Connectivity alterations in autism reflect functional idiosyncrasy. Commun Biol 2021; 4:1078. [PMID: 34526654 PMCID: PMC8443598 DOI: 10.1038/s42003-021-02572-6] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/22/2021] [Accepted: 08/17/2021] [Indexed: 02/08/2023] Open
Abstract
Autism spectrum disorder (ASD) is commonly understood as an alteration of brain networks, yet case-control analyses against typically-developing controls (TD) have yielded inconsistent results. Here, we devised a novel approach to profile the inter-individual variability in functional network organization and tested whether such idiosyncrasy contributes to connectivity alterations in ASD. Studying a multi-centric dataset with 157 ASD and 172 TD, we obtained robust evidence for increased idiosyncrasy in ASD relative to TD in default mode, somatomotor and attention networks, but also reduced idiosyncrasy in lateral temporal cortices. Idiosyncrasy increased with age and significantly correlated with symptom severity in ASD. Furthermore, while patterns of functional idiosyncrasy were not correlated with ASD-related cortical thickness alterations, they co-localized with the expression patterns of ASD risk genes. Notably, we could demonstrate that patterns of atypical idiosyncrasy in ASD closely overlapped with connectivity alterations that are measurable with conventional case-control designs and may, thus, be a principal driver of inconsistency in the autism connectomics literature. These findings support important interactions between inter-individual heterogeneity in autism and functional signatures. Our findings provide novel biomarkers to study atypical brain development and may consolidate prior research findings on the variable nature of connectome level anomalies in autism.
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Affiliation(s)
- Oualid Benkarim
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada.
| | - Casey Paquola
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Bo-Yong Park
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Seok-Jun Hong
- Center for the Developing Brain, Child Mind Institute, New York, NY, USA
- Center for Neuroscience Imaging Research, Institute for Basic Science, Sungkyunkwan University, Suwon, South Korea
- Department of Biomedical Engineering, Sungkyunkwan University, Suwon, South Korea
| | - Jessica Royer
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Reinder Vos de Wael
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Sara Lariviere
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Sofie Valk
- Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
- INM-7, FZ Jülich, Jülich, Germany
| | - Danilo Bzdok
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
- Department of Biomedical Engineering, Faculty of Medicine, McGill University, Montreal, QC, Canada
- Mila - Quebec Artificial Intelligence Institute, Montreal, QC, Canada
| | - Laurent Mottron
- Centre de recherche du CIUSSSNIM et Département de Psychiatrie, Université de Montréal, Montreal, QC, Canada
| | - Boris C Bernhardt
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada.
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45
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Pegg EJ, McKavanagh A, Bracewell RM, Chen Y, Das K, Denby C, Kreilkamp BAK, Laiou P, Marson A, Mohanraj R, Taylor JR, Keller SS. Functional network topology in drug resistant and well-controlled idiopathic generalized epilepsy: a resting state functional MRI study. Brain Commun 2021; 3:fcab196. [PMID: 34514400 PMCID: PMC8417840 DOI: 10.1093/braincomms/fcab196] [Citation(s) in RCA: 11] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 05/18/2021] [Indexed: 11/23/2022] Open
Abstract
Despite an increasing number of drug treatment options for people with idiopathic generalized epilepsy (IGE), drug resistance remains a significant issue and the mechanisms underlying it remain poorly understood. Previous studies have largely focused on potential cellular or genetic explanations for drug resistance. However, epilepsy is understood to be a network disorder and there is a growing body of literature suggesting altered topology of large-scale resting networks in people with epilepsy compared with controls. We hypothesize that network alterations may also play a role in seizure control. The aim of this study was to compare resting state functional network structure between well-controlled IGE (WC-IGE), drug resistant IGE (DR-IGE) and healthy controls. Thirty-three participants with IGE (10 with WC-IGE and 23 with DR-IGE) and 34 controls were included. Resting state functional MRI networks were constructed using the Functional Connectivity Toolbox (CONN). Global graph theoretic network measures of average node strength (an equivalent measure to mean degree in a network that is fully connected), node strength distribution variance, characteristic path length, average clustering coefficient, small-world index and average betweenness centrality were computed. Graphs were constructed separately for positively weighted connections and for absolute values. Individual nodal values of strength and betweenness centrality were also measured and ‘hub nodes’ were compared between groups. Outcome measures were assessed across the three groups and between both groups with IGE and controls. The IGE group as a whole had a higher average node strength, characteristic path length and average betweenness centrality. There were no clear differences between groups according to seizure control. Outcome metrics were sensitive to whether negatively correlated connections were included in network construction. There were no clear differences in the location of ‘hub nodes’ between groups. The results suggest that, irrespective of seizure control, IGE interictal network topology is more regular and has a higher global connectivity compared to controls, with no alteration in hub node locations. These alterations may produce a resting state network that is more vulnerable to transitioning to the seizure state. It is possible that the lack of apparent influence of seizure control on network topology is limited by challenges in classifying drug response. It is also demonstrated that network topological features are influenced by the sign of connectivity weights and therefore future methodological work is warranted to account for anticorrelations in graph theoretic studies.
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Affiliation(s)
- Emily J Pegg
- Department of Neurology, Manchester Centre for Clinical Neurosciences, Salford Royal NHS Foundation Trust, Salford, UK.,Division of Neuroscience and Experimental Psychology, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Andrea McKavanagh
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
| | | | - Yachin Chen
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
| | - Kumar Das
- The Walton Centre NHS Foundation Trust, Liverpool, UK
| | | | - Barbara A K Kreilkamp
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK.,Department of Neurology, University Medical Center Göttingen, Göttingen, Germany
| | - Petroula Laiou
- Department of Biostatistics and Health Informatics, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
| | - Anthony Marson
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
| | - Rajiv Mohanraj
- Department of Neurology, Manchester Centre for Clinical Neurosciences, Salford Royal NHS Foundation Trust, Salford, UK.,Division of Neuroscience and Experimental Psychology, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK
| | - Jason R Taylor
- Division of Neuroscience and Experimental Psychology, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester, UK.,Manchester Academic Health Sciences Centre, University of Manchester, Manchester, UK
| | - Simon S Keller
- Department of Pharmacology and Therapeutics, Institute of Systems, Molecular and Integrative Biology, University of Liverpool, Liverpool, UK
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46
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Hierarchical correction of p-values via an ultrametric tree running Ornstein-Uhlenbeck process. Comput Stat 2021. [DOI: 10.1007/s00180-021-01148-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/20/2022]
Abstract
AbstractStatistical testing is classically used as an exploratory tool to search for association between a phenotype and many possible explanatory variables. This approach often leads to multiple testing under dependence. We assume a hierarchical structure between tests via an Ornstein-Uhlenbeck process on a tree. The process correlation structure is used for smoothing the p-values. We design a penalized estimation of the mean of the Ornstein-Uhlenbeck process for p-value computation. The performances of the algorithm are assessed via simulations. Its ability to discover new associations is demonstrated on a metagenomic dataset. The corresponding R package is available from https://github.com/abichat/zazou.
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47
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Bertino S, Basile GA, Bramanti A, Ciurleo R, Tisano A, Anastasi GP, Milardi D, Cacciola A. Ventral intermediate nucleus structural connectivity-derived segmentation: anatomical reliability and variability. Neuroimage 2021; 243:118519. [PMID: 34461233 DOI: 10.1016/j.neuroimage.2021.118519] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2021] [Revised: 07/24/2021] [Accepted: 08/25/2021] [Indexed: 12/30/2022] Open
Abstract
The Ventral intermediate nucleus (Vim) of thalamus is the most targeted structure for the treatment of drug-refractory tremors. Since methodological differences across existing studies are remarkable and no gold-standard pipeline is available, in this study, we tested different parcellation pipelines for tractography-derived putative Vim identification. Thalamic parcellation was performed on a high quality, multi-shell dataset and a downsampled, clinical-like dataset using two different diffusion signal modeling techniques and two different voxel classification criteria, thus implementing a total of four parcellation pipelines. The most reliable pipeline in terms of inter-subject variability has been picked and parcels putatively corresponding to motor thalamic nuclei have been selected by calculating similarity with a histology-based mask of Vim. Then, spatial relations with optimal stimulation points for the treatment of essential tremor have been quantified. Finally, effect of data quality and parcellation pipelines on a volumetric index of connectivity clusters has been assessed. We found that the pipeline characterized by higher-order signal modeling and threshold-based voxel classification criteria was the most reliable in terms of inter-subject variability regardless data quality. The maps putatively corresponding to Vim were those derived by precentral and dentate nucleus-thalamic connectivity. However, tractography-derived functional targets showed remarkable differences in shape and sizes when compared to a ground truth model based on histochemical staining on seriate sections of human brain. Thalamic voxels connected to contralateral dentate nucleus resulted to be the closest to literature-derived stimulation points for essential tremor but at the same time showing the most remarkable inter-subject variability. Finally, the volume of connectivity parcels resulted to be significantly influenced by data quality and parcellation pipelines. Hence, caution is warranted when performing thalamic connectivity-based segmentation for stereotactic targeting.
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Affiliation(s)
- Salvatore Bertino
- Brain Mapping Lab, Department of Biomedical, Dental Sciences and Morphological and Functional Images, University of Messina, Messina, Italy
| | - Gianpaolo Antonio Basile
- Brain Mapping Lab, Department of Biomedical, Dental Sciences and Morphological and Functional Images, University of Messina, Messina, Italy
| | | | | | - Adriana Tisano
- Department of Clinical and Experimental Medicine, University of Messina, Messina, Italy
| | - Giuseppe Pio Anastasi
- Brain Mapping Lab, Department of Biomedical, Dental Sciences and Morphological and Functional Images, University of Messina, Messina, Italy
| | - Demetrio Milardi
- Brain Mapping Lab, Department of Biomedical, Dental Sciences and Morphological and Functional Images, University of Messina, Messina, Italy
| | - Alberto Cacciola
- Brain Mapping Lab, Department of Biomedical, Dental Sciences and Morphological and Functional Images, University of Messina, Messina, Italy.
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48
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Korhonen O, Zanin M, Papo D. Principles and open questions in functional brain network reconstruction. Hum Brain Mapp 2021; 42:3680-3711. [PMID: 34013636 PMCID: PMC8249902 DOI: 10.1002/hbm.25462] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 03/11/2021] [Accepted: 04/10/2021] [Indexed: 12/12/2022] Open
Abstract
Graph theory is now becoming a standard tool in system-level neuroscience. However, endowing observed brain anatomy and dynamics with a complex network representation involves often covert theoretical assumptions and methodological choices which affect the way networks are reconstructed from experimental data, and ultimately the resulting network properties and their interpretation. Here, we review some fundamental conceptual underpinnings and technical issues associated with brain network reconstruction, and discuss how their mutual influence concurs in clarifying the organization of brain function.
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Affiliation(s)
- Onerva Korhonen
- Department of Computer ScienceAalto University, School of ScienceHelsinki
- Centre for Biomedical TechnologyUniversidad Politécnica de MadridPozuelo de Alarcón
| | - Massimiliano Zanin
- Instituto de Física Interdisciplinar y Sistemas Complejos IFISC (CSIC‐UIB), Campus UIBPalma de MallorcaSpain
| | - David Papo
- Fondazione Istituto Italiano di TecnologiaFerrara
- Department of Neuroscience and Rehabilitation, Section of PhysiologyUniversity of FerraraFerrara
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49
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Basile GA, Bertino S, Bramanti A, Ciurleo R, Anastasi GP, Milardi D, Cacciola A. In Vivo Super-Resolution Track-Density Imaging for Thalamic Nuclei Identification. Cereb Cortex 2021; 31:5613-5636. [PMID: 34296740 DOI: 10.1093/cercor/bhab184] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Revised: 05/23/2021] [Accepted: 05/25/2021] [Indexed: 11/12/2022] Open
Abstract
The development of novel techniques for the in vivo, non-invasive visualization and identification of thalamic nuclei has represented a major challenge for human neuroimaging research in the last decades. Thalamic nuclei have important implications in various key aspects of brain physiology and many of them show selective alterations in various neurologic and psychiatric disorders. In addition, both surgical stimulation and ablation of specific thalamic nuclei have been proven to be useful for the treatment of different neuropsychiatric diseases. The present work aimed at describing a novel protocol for histologically guided delineation of thalamic nuclei based on short-tracks track-density imaging (stTDI), which is an advanced imaging technique exploiting high angular resolution diffusion tractography to obtain super-resolved white matter maps. We demonstrated that this approach can identify up to 13 distinct thalamic nuclei bilaterally with very high inter-subject (ICC: 0.996, 95% CI: 0.993-0.998) and inter-rater (ICC:0.981; 95% CI:0.963-0.989) reliability, and that both subject-based and group-level thalamic parcellation show a fair share of similarity to a recent standard-space histological thalamic atlas. Finally, we showed that stTDI-derived thalamic maps can be successfully employed to study structural and functional connectivity of the thalamus and may have potential implications both for basic and translational research, as well as for presurgical planning purposes.
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Affiliation(s)
- Gianpaolo Antonio Basile
- Brain Mapping Lab, Department of Biomedical, Dental Sciences and Morphological and Functional Images, University of Messina, 98124 Messina, Italy
| | - Salvatore Bertino
- Brain Mapping Lab, Department of Biomedical, Dental Sciences and Morphological and Functional Images, University of Messina, 98124 Messina, Italy
| | - Alessia Bramanti
- Department of Medicine, Surgery and Dentistry "Medical School of Salerno", University of Salerno, 84084 Baronissi, Italy
| | - Rosella Ciurleo
- IRCCS Centro Neurolesi "Bonino Pulejo", 98124 Messina, Italy
| | - Giuseppe Pio Anastasi
- Brain Mapping Lab, Department of Biomedical, Dental Sciences and Morphological and Functional Images, University of Messina, 98124 Messina, Italy
| | - Demetrio Milardi
- Brain Mapping Lab, Department of Biomedical, Dental Sciences and Morphological and Functional Images, University of Messina, 98124 Messina, Italy
| | - Alberto Cacciola
- Brain Mapping Lab, Department of Biomedical, Dental Sciences and Morphological and Functional Images, University of Messina, 98124 Messina, Italy
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Liu X, Eickhoff SB, Caspers S, Wu J, Genon S, Hoffstaedter F, Mars RB, Sommer IE, Eickhoff CR, Chen J, Jardri R, Reetz K, Dogan I, Aleman A, Kogler L, Gruber O, Caspers J, Mathys C, Patil KR. Functional parcellation of human and macaque striatum reveals human-specific connectivity in the dorsal caudate. Neuroimage 2021; 235:118006. [PMID: 33819611 PMCID: PMC8214073 DOI: 10.1016/j.neuroimage.2021.118006] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Revised: 02/10/2021] [Accepted: 03/17/2021] [Indexed: 12/12/2022] Open
Abstract
A wide homology between human and macaque striatum is often assumed as in both the striatum is involved in cognition, emotion and executive functions. However, differences in functional and structural organization between human and macaque striatum may reveal evolutionary divergence and shed light on human vulnerability to neuropsychiatric diseases. For instance, dopaminergic dysfunction of the human striatum is considered to be a pathophysiological underpinning of different disorders, such as Parkinson's disease (PD) and schizophrenia (SCZ). Previous investigations have found a wide similarity in structural connectivity of the striatum between human and macaque, leaving the cross-species comparison of its functional organization unknown. In this study, resting-state functional connectivity (RSFC) derived striatal parcels were compared based on their homologous cortico-striatal connectivity. The goal here was to identify striatal parcels whose connectivity is human-specific compared to macaque parcels. Functional parcellation revealed that the human striatum was split into dorsal, dorsomedial, and rostral caudate and ventral, central, and caudal putamen, while the macaque striatum was divided into dorsal, and rostral caudate and rostral, and caudal putamen. Cross-species comparison indicated dissimilar cortico-striatal RSFC of the topographically similar dorsal caudate. We probed clinical relevance of the striatal clusters by examining differences in their cortico-striatal RSFC and gray matter (GM) volume between patients (with PD and SCZ) and healthy controls. We found abnormal RSFC not only between dorsal caudate, but also between rostral caudate, ventral, central and caudal putamen and widespread cortical regions for both PD and SCZ patients. Also, we observed significant structural atrophy in rostral caudate, ventral and central putamen for both PD and SCZ while atrophy in the dorsal caudate was specific to PD. Taken together, our cross-species comparative results revealed shared and human-specific RSFC of different striatal clusters reinforcing the complex organization and function of the striatum. In addition, we provided a testable hypothesis that abnormalities in a region with human-specific connectivity, i.e., dorsal caudate, might be associated with neuropsychiatric disorders.
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Affiliation(s)
- Xiaojin Liu
- Institute of Neuroscience and Medicine (INM-7), Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Systems Neuroscience, Research Centre Jülich, Jülich, Germany
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine (INM-7), Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Systems Neuroscience, Research Centre Jülich, Jülich, Germany
| | - Svenja Caspers
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany; Institute for Anatomy I, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Jianxiao Wu
- Institute of Neuroscience and Medicine (INM-7), Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Systems Neuroscience, Research Centre Jülich, Jülich, Germany
| | - Sarah Genon
- Institute of Neuroscience and Medicine (INM-7), Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Systems Neuroscience, Research Centre Jülich, Jülich, Germany
| | - Felix Hoffstaedter
- Institute of Neuroscience and Medicine (INM-7), Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Systems Neuroscience, Research Centre Jülich, Jülich, Germany
| | - Rogier B Mars
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom; Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands
| | - Iris E Sommer
- Department of Biomedical Sciences of Cells & Systems, University Medical Center Groningen, Groningen, Netherlands
| | - Claudia R Eickhoff
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany; Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, University of Düsseldorf, Düsseldorf, Germany
| | - Ji Chen
- Institute of Neuroscience and Medicine (INM-7), Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Systems Neuroscience, Research Centre Jülich, Jülich, Germany
| | - Renaud Jardri
- Division of Psychiatry, University of Lille, CNRS UMR9193, SCALab & CHU Lille, Fontan Hospital, CURE platform, Lille, France
| | - Kathrin Reetz
- JARA-BRAIN Institute Molecular Neuroscience and Neuroimaging, Forschungszentrum Jülich, RWTH Aachen University, Aachen, Germany; Department of Neurology, RWTH Aachen University, Aachen, Germany
| | - Imis Dogan
- JARA-BRAIN Institute Molecular Neuroscience and Neuroimaging, Forschungszentrum Jülich, RWTH Aachen University, Aachen, Germany; Department of Neurology, RWTH Aachen University, Aachen, Germany
| | - André Aleman
- Department of Neuroscience, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Lydia Kogler
- Department of Psychiatry and Psychotherapy, Medical School, University of Tübingen, Germany
| | - Oliver Gruber
- Section for Experimental Psychopathology and Neuroimaging, Department of General Psychiatry, Heidelberg University, Germany
| | - Julian Caspers
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany; Department of Diagnostic and Interventional Radiology, Medical Faculty, University of Düsseldorf, Düsseldorf, Germany
| | - Christian Mathys
- Department of Diagnostic and Interventional Radiology, Medical Faculty, University of Düsseldorf, Düsseldorf, Germany; Research Center Neurosensory Science, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany; Institute of Radiology and Neuroradiology, Evangelisches Krankenhaus, University of Oldenburg, Oldenburg, Germany
| | - Kaustubh R Patil
- Institute of Neuroscience and Medicine (INM-7), Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Systems Neuroscience, Research Centre Jülich, Jülich, Germany.
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