1
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Qu J, Zhu R, Wu Y, Xu G, Wang D. Abnormal structural‒functional coupling patterning in progressive supranuclear palsy is associated with diverse gradients and histological features. Commun Biol 2024; 7:1195. [PMID: 39341965 PMCID: PMC11439051 DOI: 10.1038/s42003-024-06877-0] [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: 05/16/2024] [Accepted: 09/10/2024] [Indexed: 10/01/2024] Open
Abstract
The anatomy of the brain supports inherent processes, fostering mental abilities and eventually facilitating adaptive behavior. Recent studies have shown that progressive supranuclear palsy (PSP) is accompanied by alterations in functional and structural networks. However, how the structure and function of PSP coordinates change is not clear, and the relationships between structural‒functional coupling (SFC) and the gradient of hierarchical structure and cellular histology remain largely unknown. Here, we use neuroimaging data from two independent cohorts and a public histological dataset to investigate the relationships among the cellular histology, hierarchical structure, and SFC of PSP patients. We find that the SFC of the entire cortex in PSP is severely disrupted, with higher coupling in the visual network (VN). Moreover, coupling differences in PSP follow a macroscopic organizational principle from unimodal to transmodal gradients. Finally, we elucidate greater laminar differentiation in VN regions sensitive to SFC changes in PSP, which is related mainly to the higher cellular density and smaller size of the internal-granular layer. In conclusion, our findings provide an interpretable framework for understanding SFC changes in PSP and provide new insights into the consistency of structural and functional changes in PSP regarding hierarchical structure and cellular histology.
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Affiliation(s)
- Junyu Qu
- Department of Radiology, Qilu Hospital of Shandong University; Qilu Medical Imaging Institute of Shandong University, Jinan, China
| | - Rui Zhu
- Department of Radiology, Qilu Hospital of Shandong University; Qilu Medical Imaging Institute of Shandong University, Jinan, China
| | - Yongsheng Wu
- Department of Radiology, Qilu Hospital of Shandong University; Qilu Medical Imaging Institute of Shandong University, Jinan, China
| | - Guihua Xu
- Department of Radiology, Qilu Hospital of Shandong University; Qilu Medical Imaging Institute of Shandong University, Jinan, China
| | - Dawei Wang
- Department of Radiology, Qilu Hospital of Shandong University; Qilu Medical Imaging Institute of Shandong University, Jinan, China.
- Research Institute of Shandong University: Magnetic Field-free Medicine & Functional Imaging, Jinan, China.
- Shandong Key Laboratory: Magnetic Field-free Medicine & Functional Imaging (MF), Jinan, China.
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2
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Soylu F. A new ontology for numerical cognition: Integrating evolutionary, embodied, and data informatics approaches. Acta Psychol (Amst) 2024; 249:104416. [PMID: 39121614 DOI: 10.1016/j.actpsy.2024.104416] [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: 03/07/2022] [Revised: 04/07/2024] [Accepted: 07/16/2024] [Indexed: 08/12/2024] Open
Abstract
Numerical cognition is a field that investigates the sociocultural, developmental, cognitive, and biological aspects of mathematical abilities. Recent findings in cognitive neuroscience suggest that cognitive skills are facilitated by distributed, transient, and dynamic networks in the brain, rather than isolated functional modules. Further, research on the bodily and evolutionary bases of cognition reveals that our cognitive skills harness capacities originally evolved for action and that cognition is best understood in conjunction with perceptuomotor capacities. Despite these insights, neural models of numerical cognition struggle to capture the relation between mathematical skills and perceptuomotor systems. One front to addressing this issue is to identify building block sensorimotor processes (BBPs) in the brain that support numerical skills and develop a new ontology connecting the sensorimotor system with mathematical cognition. BBPs here are identified as sensorimotor functions, associated with distributed networks in the brain, and are consistently identified as supporting different cognitive abilities. BBPs can be identified with new approaches to neuroimaging; by examining an array of sensorimotor and cognitive tasks in experimental designs, employing data-driven informatics approaches to identify sensorimotor networks supporting cognitive processes, and interpreting the results considering the evolutionary and bodily foundations of mathematical abilities. New empirical insights on the BBPs can eventually lead to a revamped embodied cognitive ontology in numerical cognition. Among other mathematical skills, numerical magnitude processing and its sensorimotor origins are discussed to substantiate the arguments presented. Additionally, an fMRI study design is provided to illustrate the application of the arguments presented in empirical research.
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Affiliation(s)
- Firat Soylu
- Educational Psychology Program, The University of Alabama, Tuscaloosa, AL, United States.
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3
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Ducret M, Giacometti C, Dirheimer M, Dureux A, Autran-Clavagnier D, Hadj-Bouziane F, Verstraete C, Lamberton F, Wilson CRE, Amiez C, Procyk E. Medial to lateral frontal functional connectivity mapping reveals the organization of cingulate cortex. Cereb Cortex 2024; 34:bhae322. [PMID: 39129533 DOI: 10.1093/cercor/bhae322] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/23/2024] [Revised: 07/12/2024] [Accepted: 07/25/2024] [Indexed: 08/13/2024] Open
Abstract
The functional organization of the frontal lobe is a source of debate, focusing on broad functional subdivisions, large-scale networks, or local refined specificities. Multiple neurocognitive models have tried to explain how functional interactions between cingulate and lateral frontal regions contribute to decision making and cognitive control, but their neuroanatomical bases remain unclear. We provide a detailed description of the functional connectivity between cingulate and lateral frontal regions using resting-state functional MRI in rhesus macaques. The analysis focuses on the functional connectivity of the rostral part of the cingulate sulcus with the lateral frontal cortex. Data-driven and seed-based analysis revealed three clusters within the cingulate sulcus organized along the rostro-caudal axis: the anterior, mid, and posterior clusters display increased functional connectivity with, respectively, the anterior lateral prefrontal regions, face-eye lateral frontal motor cortical areas, and hand lateral frontal motor cortex. The location of these clusters can be predicted in individual subjects based on morphological landmarks. These results suggest that the anterior cluster corresponds to the anterior cingulate cortex, whereas the posterior clusters correspond to the face-eye and hand cingulate motor areas within the anterior midcingulate cortex. These data provide a comprehensive framework to identify cingulate subregions based on functional connectivity and local organization.
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Affiliation(s)
- Marion Ducret
- Université Lyon 1, Inserm, Stem Cell and Brain Research Institute, INSERM U1208, 18 avenue du Doyen Jean Lépine, 69500 Bron, France
| | - Camille Giacometti
- Université Lyon 1, Inserm, Stem Cell and Brain Research Institute, INSERM U1208, 18 avenue du Doyen Jean Lépine, 69500 Bron, France
| | - Manon Dirheimer
- Integrative Multisensory Perception Action and Cognition Team (ImpAct), INSERM U1028, CNRS UMR5292, Lyon Neuroscience Research Center (CRNL), 16 avenue du doyen Lépine, 69500 Bron, France
- University of Lyon 1, Lyon, France
| | - Audrey Dureux
- Integrative Multisensory Perception Action and Cognition Team (ImpAct), INSERM U1028, CNRS UMR5292, Lyon Neuroscience Research Center (CRNL), 16 avenue du doyen Lépine, 69500 Bron, France
- University of Lyon 1, Lyon, France
| | | | - Fadila Hadj-Bouziane
- Integrative Multisensory Perception Action and Cognition Team (ImpAct), INSERM U1028, CNRS UMR5292, Lyon Neuroscience Research Center (CRNL), 16 avenue du doyen Lépine, 69500 Bron, France
- University of Lyon 1, Lyon, France
| | - Charles Verstraete
- Université Lyon 1, Inserm, Stem Cell and Brain Research Institute, INSERM U1208, 18 avenue du Doyen Jean Lépine, 69500 Bron, France
- Institut de neuromodulation, GHU Paris psychiatrie et neurosciences, Centre Hospitalier Sainte-Anne, pôle hospitalo-universitaire 15, Université Paris Cité, Paris, France
| | - Franck Lamberton
- CERMEP, Imagerie du Vivant, 95 Boulevard Pinel, F-69677 Bron, Auvergne-Rhône-Alpes, France
- SFR Lyon-Est, Université Lyon 1, CNRS UAR3453, INSERM US7, U69500, Lyon, France
| | - Charles R E Wilson
- Université Lyon 1, Inserm, Stem Cell and Brain Research Institute, INSERM U1208, 18 avenue du Doyen Jean Lépine, 69500 Bron, France
| | - Céline Amiez
- Université Lyon 1, Inserm, Stem Cell and Brain Research Institute, INSERM U1208, 18 avenue du Doyen Jean Lépine, 69500 Bron, France
| | - Emmanuel Procyk
- Université Lyon 1, Inserm, Stem Cell and Brain Research Institute, INSERM U1208, 18 avenue du Doyen Jean Lépine, 69500 Bron, France
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4
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Lu Y, Cui Y, Cao L, Dong Z, Cheng L, Wu W, Wang C, Liu X, Liu Y, Zhang B, Li D, Zhao B, Wang H, Li K, Ma L, Shi W, Li W, Ma Y, Du Z, Zhang J, Xiong H, Luo N, Liu Y, Hou X, Han J, Sun H, Cai T, Peng Q, Feng L, Wang J, Paxinos G, Yang Z, Fan L, Jiang T. Macaque Brainnetome Atlas: A multifaceted brain map with parcellation, connection, and histology. Sci Bull (Beijing) 2024; 69:2241-2259. [PMID: 38580551 DOI: 10.1016/j.scib.2024.03.031] [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: 10/12/2023] [Revised: 01/18/2024] [Accepted: 03/11/2024] [Indexed: 04/07/2024]
Abstract
The rhesus macaque (Macaca mulatta) is a crucial experimental animal that shares many genetic, brain organizational, and behavioral characteristics with humans. A macaque brain atlas is fundamental to biomedical and evolutionary research. However, even though connectivity is vital for understanding brain functions, a connectivity-based whole-brain atlas of the macaque has not previously been made. In this study, we created a new whole-brain map, the Macaque Brainnetome Atlas (MacBNA), based on the anatomical connectivity profiles provided by high angular and spatial resolution ex vivo diffusion MRI data. The new atlas consists of 248 cortical and 56 subcortical regions as well as their structural and functional connections. The parcellation and the diffusion-based tractography were evaluated with invasive neuronal-tracing and Nissl-stained images. As a demonstrative application, the structural connectivity divergence between macaque and human brains was mapped using the Brainnetome atlases of those two species to uncover the genetic underpinnings of the evolutionary changes in brain structure. The resulting resource includes: (1) the thoroughly delineated Macaque Brainnetome Atlas (MacBNA), (2) regional connectivity profiles, (3) the postmortem high-resolution macaque diffusion and T2-weighted MRI dataset (Brainnetome-8), and (4) multi-contrast MRI, neuronal-tracing, and histological images collected from a single macaque. MacBNA can serve as a common reference frame for mapping multifaceted features across modalities and spatial scales and for integrative investigation and characterization of brain organization and function. Therefore, it will enrich the collaborative resource platform for nonhuman primates and facilitate translational and comparative neuroscience research.
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Affiliation(s)
- 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 100049, China
| | - Yue Cui
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Long Cao
- Henan Key Laboratory of Imaging and Intelligent Processing, PLA Strategic Support Force Information Engineering University, Zhengzhou 450001, China; Key Laboratory for NeuroInformation of the Ministry of Education, School of Life Science and Technology, University of Electronic Science and Technology of China, Chengdu 610054, China
| | - Zhenwei Dong
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Luqi Cheng
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Life and Environmental Sciences, Guilin University of Electronic Technology, Guilin 541004, China; Research Center for Augmented Intelligence, Zhejiang Lab, Hangzhou 311100, China
| | - Wen Wu
- Research Center for Augmented Intelligence, Zhejiang Lab, Hangzhou 311100, China
| | - Changshuo Wang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China; Sino-Danish College, University of Chinese Academy of Science, Beijing 100049, China
| | - Xinyi Liu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Youtong Liu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Baogui Zhang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, 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 100049, China
| | - Bokai Zhao
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Haiyan Wang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Kaixin Li
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Mechanical and Power Engineering, Harbin University of Science and Technology, Harbin 150080, China
| | - Liang Ma
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Weiyang Shi
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Wen Li
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China
| | - Yawei Ma
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China; Sino-Danish College, University of Chinese Academy of Science, Beijing 100049, China
| | - Zongchang Du
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Jiaqi Zhang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Hui Xiong
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Na Luo
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Yanyan Liu
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Xiaoxiao Hou
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Jinglu Han
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; University of Chinese Academy of Sciences, Beijing 100049, China; Sino-Danish College, University of Chinese Academy of Science, Beijing 100049, China
| | - Hongji Sun
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
| | - Tao Cai
- Research Center for Augmented Intelligence, Zhejiang Lab, Hangzhou 311100, China
| | - Qiang Peng
- Research Center for Augmented Intelligence, Zhejiang Lab, Hangzhou 311100, China
| | - Linqing Feng
- Research Center for Augmented Intelligence, Zhejiang Lab, Hangzhou 311100, China
| | - Jiaojian Wang
- State Key Laboratory of Primate Biomedical Research, Institute of Primate Translational Medicine, Kunming University of Science and Technology, Kunming 650500, China
| | - George Paxinos
- Neuroscience Research Australia and The University of New South Wales, Sydney NSW 2031, Australia
| | - Zhengyi Yang
- Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China; Xiaoxiang Institute for Brain Health and Yongzhou Central Hospital, Yongzhou 425000, 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 100049, China.
| | - 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 100049, China; Research Center for Augmented Intelligence, Zhejiang Lab, Hangzhou 311100, China; Xiaoxiang Institute for Brain Health and Yongzhou Central Hospital, Yongzhou 425000, China.
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5
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Rudroff T. Artificial Intelligence as a Replacement for Animal Experiments in Neurology: Potential, Progress, and Challenges. Neurol Int 2024; 16:805-820. [PMID: 39195562 DOI: 10.3390/neurolint16040060] [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: 06/05/2024] [Revised: 07/19/2024] [Accepted: 07/26/2024] [Indexed: 08/29/2024] Open
Abstract
Animal experimentation has long been a cornerstone of neurology research, but it faces growing scientific, ethical, and economic challenges. Advances in artificial intelligence (AI) are providing new opportunities to replace animal testing with more human-relevant and efficient methods. This article explores the potential of AI technologies such as brain organoids, computational models, and machine learning to revolutionize neurology research and reduce reliance on animal models. These approaches can better recapitulate human brain physiology, predict drug responses, and uncover novel insights into neurological disorders. They also offer faster, cheaper, and more ethical alternatives to animal experiments. Case studies demonstrate AI's ability to accelerate drug discovery for Alzheimer's, predict neurotoxicity, personalize treatments for Parkinson's, and restore movement in paralysis. While challenges remain in validating and integrating these technologies, the scientific, economic, practical, and moral advantages are driving a paradigm shift towards AI-based, animal-free research in neurology. With continued investment and collaboration across sectors, AI promises to accelerate the development of safer and more effective therapies for neurological conditions while significantly reducing animal use. The path forward requires the ongoing development and validation of these technologies, but a future in which they largely replace animal experiments in neurology appears increasingly likely. This transition heralds a new era of more humane, human-relevant, and innovative brain research.
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Affiliation(s)
- Thorsten Rudroff
- Department of Health and Human Physiology, University of Iowa, Iowa City, IA 52242, USA
- Department of Neurology, University of Iowa Hospitals and Clinics, Iowa City, IA 52242, USA
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6
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Wang F, Lu X, Chen X, Wang Q, Li Q, Li H. Connectivity Reveals the Relationships between Human Brain Areas Associated with High-Level Linguistic Processing and Macaque Brain Areas. Tomography 2024; 10:1089-1098. [PMID: 39058054 PMCID: PMC11280774 DOI: 10.3390/tomography10070082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/09/2024] [Revised: 06/14/2024] [Accepted: 07/04/2024] [Indexed: 07/28/2024] Open
Abstract
Cross-species research has advanced human understanding of brain regions, with cross-species comparisons using magnetic resonance imaging technology becoming increasingly common. Currently, cross-species research on human language regions has primarily focused on traditional brain areas such as the Broca region. While some studies have indicated that human language function also involves other language regions, the corresponding relationships between these brain regions in humans and macaques remain unclear. This study calculated the strength of the connections between the high-level language processing regions in human and macaque brains, identified homologous target areas based on the structural connections of white-matter fiber bundles, and compared the connectivity profiles of both species. The results of the experiment demonstrated that macaques possess brain regions which exhibit connectivity patterns resembling those found in human high-level language processing regions. This discovery suggests that while the function of a human brain region is specialized, it still maintains a structural connectivity similar to that seen in macaques.
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Affiliation(s)
- Fangyuan Wang
- School of Computer Information Engineering, Shanxi Technology and Business University, Taiyuan 030024, China; (F.W.); (X.L.)
| | - Xiaohua Lu
- School of Computer Information Engineering, Shanxi Technology and Business University, Taiyuan 030024, China; (F.W.); (X.L.)
| | - Xiaofeng Chen
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (X.C.); (Q.W.); (Q.L.)
| | - Qianshan Wang
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (X.C.); (Q.W.); (Q.L.)
| | - Qi Li
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (X.C.); (Q.W.); (Q.L.)
| | - Haifang Li
- School of Computer Information Engineering, Shanxi Technology and Business University, Taiyuan 030024, China; (F.W.); (X.L.)
- College of Computer Science and Technology, Taiyuan University of Technology, Taiyuan 030024, China; (X.C.); (Q.W.); (Q.L.)
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7
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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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8
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Ma L, Zhang Y, Zhang H, Cheng L, Yang Z, Lu Y, Shi W, Li W, Zhuo J, Wang J, Fan L, Jiang T. BAI-Net: Individualized Anatomical Cerebral Cartography Using Graph Neural Network. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2024; 35:7446-7457. [PMID: 36315537 DOI: 10.1109/tnnls.2022.3213581] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/16/2023]
Abstract
Brain atlas is an important tool in the diagnosis and treatment of neurological disorders. However, due to large variations in the organizational principles of individual brains, many challenges remain in clinical applications. Brain atlas individualization network (BAI-Net) is an algorithm that subdivides individual cerebral cortex into segregated areas using brain morphology and connectomes. The presented method integrates group priors derived from a population atlas, adjusts areal probabilities using the context of connectivity fingerprints derived from the fiber-tract embedding of tractography, and provides reliable and explainable individualized brain areas across multiple sessions and scanners. We demonstrate that BAI-Net outperforms the conventional iterative clustering approach by capturing significantly heritable topographic variations in individualized cartographies. The topographic variability of BAI-Net cartographies has shown strong associations with individual variability in brain morphology, connectivity as well as higher relationship on individual cognitive behaviors and genetics. This study provides an explainable framework for individualized brain cartography that may be useful in the precise localization of neuromodulation and treatments on individual brains.
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9
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Lazari A, Tachrount M, Valverde JM, Papp D, Beauchamp A, McCarthy P, Ellegood J, Grandjean J, Johansen-Berg H, Zerbi V, Lerch JP, Mars RB. The mouse motor system contains multiple premotor areas and partially follows human organizational principles. Cell Rep 2024; 43:114191. [PMID: 38717901 DOI: 10.1016/j.celrep.2024.114191] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2023] [Revised: 12/10/2023] [Accepted: 04/17/2024] [Indexed: 06/01/2024] Open
Abstract
While humans are known to have several premotor cortical areas, secondary motor cortex (M2) is often considered to be the only higher-order motor area of the mouse brain and is thought to combine properties of various human premotor cortices. Here, we show that axonal tracer, functional connectivity, myelin mapping, gene expression, and optogenetics data contradict this notion. Our analyses reveal three premotor areas in the mouse, anterior-lateral motor cortex (ALM), anterior-lateral M2 (aM2), and posterior-medial M2 (pM2), with distinct structural, functional, and behavioral properties. By using the same techniques across mice and humans, we show that ALM has strikingly similar functional and microstructural properties to human anterior ventral premotor areas and that aM2 and pM2 amalgamate properties of human pre-SMA and cingulate cortex. These results provide evidence for the existence of multiple premotor areas in the mouse and chart a comparative map between the motor systems of humans and mice.
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Affiliation(s)
- Alberto Lazari
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
| | - Mohamed Tachrount
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Juan Miguel Valverde
- DTU Compute, Technical University of Denmark, Kongens Lyngby, Denmark; A.I. Virtanen Institute for Molecular Sciences, University of Eastern Finland, 70150 Kuopio, Finland
| | - Daniel Papp
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; NeuroPoly Lab, Institute of Biomedical Engineering, Polytechnique Montreal, Montreal, QC, Canada
| | - Antoine Beauchamp
- Mouse Imaging Centre, The Hospital for Sick Children, Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Paul McCarthy
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Jacob Ellegood
- Mouse Imaging Centre, The Hospital for Sick Children, Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada; Bloorview Research Institute, Holland Bloorview Kids Rehabilitation Hospital, Toronto, ON, Canada
| | - Joanes Grandjean
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands
| | - Heidi Johansen-Berg
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Valerio Zerbi
- Neuro-X Institute, School of Engineering (STI), EPFL, 1015 Lausanne, Switzerland; CIBM Center for Biomedical Imaging, 1015 Lausanne, Switzerland
| | - Jason P Lerch
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Mouse Imaging Centre, The Hospital for Sick Children, Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
| | - Rogier B Mars
- Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK; Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands
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10
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van Hout ATB, van Heukelum S, Rushworth MFS, Grandjean J, Mars RB. Comparing mouse and human cingulate cortex organization using functional connectivity. Brain Struct Funct 2024:10.1007/s00429-024-02773-9. [PMID: 38739155 DOI: 10.1007/s00429-024-02773-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/06/2023] [Accepted: 01/30/2024] [Indexed: 05/14/2024]
Abstract
The subdivisions of the extended cingulate cortex of the human brain are implicated in a number of high-level behaviors and affected by a range of neuropsychiatric disorders. Its anatomy, function, and response to therapeutics are often studied using non-human animals, including the mouse. However, the similarity of human and mouse frontal cortex, including cingulate areas, is still not fully understood. Some accounts emphasize resemblances between mouse cingulate cortex and human cingulate cortex while others emphasize similarities with human granular prefrontal cortex. We use comparative neuroimaging to study the connectivity of the cingulate cortex in the mouse and human, allowing comparisons between mouse 'gold standard' tracer and imaging data, and, in addition, comparison between the mouse and the human using comparable imaging data. We find overall similarities in organization of the cingulate between species, including anterior and midcingulate areas and a retrosplenial area. However, human cingulate contains subareas with a more fine-grained organization than is apparent in the mouse and it has connections to prefrontal areas not present in the mouse. Results such as these help formally address between-species brain organization and aim to improve the translation from preclinical to human results.
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Affiliation(s)
- Aran T B van Hout
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands
| | - Sabrina van Heukelum
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands
| | - Matthew F S Rushworth
- Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
- Department of Experimental Psychology, University of Oxford, Oxford, UK
| | - Joanes Grandjean
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands
- Department for Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands
| | - Rogier B Mars
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands.
- Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, UK.
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11
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Li J, Wang X, Liu M, Yin Y, Wu Y, Xu G, Ma X. Sex-specific grey matter abnormalities in individuals with chronic insomnia. Neurol Sci 2024; 45:2301-2310. [PMID: 38063921 DOI: 10.1007/s10072-023-07224-7] [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: 09/20/2023] [Accepted: 11/23/2023] [Indexed: 04/17/2024]
Abstract
Previous studies have reported sex differences in altered brain function in patients with chronic insomnia (CI). However, sex-related alterations in brain morphology have rarely been investigated. This study aimed to investigate sex-specific grey matter (GM) alterations in patients with CI and to examine the relationship between GM alterations and neuropsychological assessments. Ninety-three (65 females and 28 males) patients and 78 healthy (50 females and 28 males) controls were recruited. Structural magnetic resonance imaging data were analysed using voxel-based morphometry to test for interactions between sex and diagnosis. Spearman's correlation was used to assess the associations among structure, disease duration, and sleep-, mood-, and cognition-related assessments. Males with CI showed reduced GM volume in the left inferior parietal lobe, left middle cingulate cortex, and right supramarginal gyrus. Females with CI showed increased GM volume in the right Rolandic operculum. Moreover, mood-related assessments were negatively correlated with GM volumes in the right supramarginal gyrus and left inferior parietal lobe in the male patients, and cognitive-related assessments were positively correlated with GM volumes in the Rolandic operculum in the female patients. Our findings indicate sex-specific alterations in brain morphology in CI, thereby broadening our understanding of sex differences in CI and potentially providing complementary evidence for the development of more effective therapies and individual treatments.
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Affiliation(s)
- Jingwen Li
- Department of Nuclear medicine, Guangdong Second Provincial General Hospital, No.466 Road XinGang, Guangzhou, 510317, P. R. China
- The Second School of Clinical Medicine, Southern Medial University, No. 253 Industrial Avenue Central, Guangzhou, 510260, P. R. China
| | - Xinzhi Wang
- Department of Nuclear medicine, Guangdong Second Provincial General Hospital, No.466 Road XinGang, Guangzhou, 510317, P. R. China
| | - Mengchen Liu
- Department of Nuclear medicine, Guangdong Second Provincial General Hospital, No.466 Road XinGang, Guangzhou, 510317, P. R. China
| | - Yi Yin
- Department of Nuclear medicine, Guangdong Second Provincial General Hospital, No.466 Road XinGang, Guangzhou, 510317, P. R. China
| | - Yunfan Wu
- Department of Nuclear medicine, Guangdong Second Provincial General Hospital, No.466 Road XinGang, Guangzhou, 510317, P. R. China
| | - Guang Xu
- Department of Neurology, Guangdong Second Provincial General Hospital, No.466 Road XinGang, Guangzhou, 510317, P. R. China
| | - Xiaofen Ma
- Department of Nuclear medicine, Guangdong Second Provincial General Hospital, No.466 Road XinGang, Guangzhou, 510317, P. R. China.
- The Second School of Clinical Medicine, Southern Medial University, No. 253 Industrial Avenue Central, Guangzhou, 510260, P. R. China.
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12
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Zárate-Rochín AM. Contemporary neurocognitive models of memory: A descriptive comparative analysis. Neuropsychologia 2024; 196:108846. [PMID: 38430963 DOI: 10.1016/j.neuropsychologia.2024.108846] [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: 11/03/2023] [Revised: 02/27/2024] [Accepted: 02/27/2024] [Indexed: 03/05/2024]
Abstract
The great complexity involved in the study of memory has given rise to numerous hypotheses and models associated with various phenomena at different levels of analysis. This has allowed us to delve deeper in our knowledge about memory but has also made it difficult to synthesize and integrate data from different lines of research. In this context, this work presents a descriptive comparative analysis of contemporary models that address the structure and function of multiple memory systems. The main goal is to outline a panoramic view of the key elements that constitute these models in order to visualize both the current state of research and possible future directions. The elements that stand out from different levels of analysis are distributed neural networks, hierarchical organization, predictive coding, homeostasis, and evolutionary perspective.
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Affiliation(s)
- Alba Marcela Zárate-Rochín
- Instituto de Investigaciones Cerebrales, Universidad Veracruzana, Dr. Castelazo Ayala s/n, Industrial Animas, 91190, Xalapa-Enríquez, Veracruz, Mexico.
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13
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Deshpande G, Zhao S, Waggoner P, Beyers R, Morrison E, Huynh N, Vodyanoy V, Denney TS, Katz JS. Two Separate Brain Networks for Predicting Trainability and Tracking Training-Related Plasticity in Working Dogs. Animals (Basel) 2024; 14:1082. [PMID: 38612321 PMCID: PMC11010877 DOI: 10.3390/ani14071082] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2024] [Revised: 03/28/2024] [Accepted: 03/29/2024] [Indexed: 04/14/2024] Open
Abstract
Functional brain connectivity based on resting-state functional magnetic resonance imaging (fMRI) has been shown to be correlated with human personality and behavior. In this study, we sought to know whether capabilities and traits in dogs can be predicted from their resting-state connectivity, as in humans. We trained awake dogs to keep their head still inside a 3T MRI scanner while resting-state fMRI data was acquired. Canine behavior was characterized by an integrated behavioral score capturing their hunting, retrieving, and environmental soundness. Functional scans and behavioral measures were acquired at three different time points across detector dog training. The first time point (TP1) was prior to the dogs entering formal working detector dog training. The second time point (TP2) was soon after formal detector dog training. The third time point (TP3) was three months' post detector dog training while the dogs were engaged in a program of maintenance training for detection work. We hypothesized that the correlation between resting-state FC in the dog brain and behavior measures would significantly change during their detection training process (from TP1 to TP2) and would maintain for the subsequent several months of detection work (from TP2 to TP3). To further study the resting-state FC features that can predict the success of training, dogs at TP1 were divided into a successful group and a non-successful group. We observed a core brain network which showed relatively stable (with respect to time) patterns of interaction that were significantly stronger in successful detector dogs compared to failures and whose connectivity strength at the first time point predicted whether a given dog was eventually successful in becoming a detector dog. A second ontologically based flexible peripheral network was observed whose changes in connectivity strength with detection training tracked corresponding changes in behavior over the training program. Comparing dog and human brains, the functional connectivity between the brain stem and the frontal cortex in dogs corresponded to that between the locus coeruleus and left middle frontal gyrus in humans, suggestive of a shared mechanism for learning and retrieval of odors. Overall, the findings point toward the influence of phylogeny and ontogeny in dogs producing two dissociable functional neural networks.
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Affiliation(s)
- Gopikrishna Deshpande
- Auburn University Neuroimaging Center, Department of Electrical & Computer Engineering, Auburn University, Auburn, AL 36849, USA; (S.Z.); (R.B.); (N.H.); (T.S.D.J.)
- Department of Psychological Sciences, Auburn University, Auburn, AL 36849, USA
- Alabama Advanced Imaging Consortium, Birmingham, AL 36849, USA
- Center for Neuroscience, Auburn University, Auburn, AL 36849, USA
- Department of Psychiatry, National Institute of Mental Health and Neurosciences, Bangalore 560029, India
- Department of Heritage Science and Technology, Indian Institute of Technology, Hyderabad 502285, India
| | - Sinan Zhao
- Auburn University Neuroimaging Center, Department of Electrical & Computer Engineering, Auburn University, Auburn, AL 36849, USA; (S.Z.); (R.B.); (N.H.); (T.S.D.J.)
| | - Paul Waggoner
- Canine Performance Sciences Program, College of Veterinary Medicine, Auburn University, Auburn, AL 36849, USA;
| | - Ronald Beyers
- Auburn University Neuroimaging Center, Department of Electrical & Computer Engineering, Auburn University, Auburn, AL 36849, USA; (S.Z.); (R.B.); (N.H.); (T.S.D.J.)
| | - Edward Morrison
- Department of Anatomy, Physiology & Pharmacology, Auburn University, Auburn, AL 36849, USA; (E.M.); (V.V.)
| | - Nguyen Huynh
- Auburn University Neuroimaging Center, Department of Electrical & Computer Engineering, Auburn University, Auburn, AL 36849, USA; (S.Z.); (R.B.); (N.H.); (T.S.D.J.)
| | - Vitaly Vodyanoy
- Department of Anatomy, Physiology & Pharmacology, Auburn University, Auburn, AL 36849, USA; (E.M.); (V.V.)
| | - Thomas S. Denney
- Auburn University Neuroimaging Center, Department of Electrical & Computer Engineering, Auburn University, Auburn, AL 36849, USA; (S.Z.); (R.B.); (N.H.); (T.S.D.J.)
- Department of Psychological Sciences, Auburn University, Auburn, AL 36849, USA
- Alabama Advanced Imaging Consortium, Birmingham, AL 36849, USA
- Center for Neuroscience, Auburn University, Auburn, AL 36849, USA
| | - Jeffrey S. Katz
- Auburn University Neuroimaging Center, Department of Electrical & Computer Engineering, Auburn University, Auburn, AL 36849, USA; (S.Z.); (R.B.); (N.H.); (T.S.D.J.)
- Department of Psychological Sciences, Auburn University, Auburn, AL 36849, USA
- Alabama Advanced Imaging Consortium, Birmingham, AL 36849, USA
- Center for Neuroscience, Auburn University, Auburn, AL 36849, USA
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14
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Molnár F, Horvát S, Ribeiro Gomes AR, Martinez Armas J, Molnár B, Ercsey-Ravasz M, Knoblauch K, Kennedy H, Toroczkai Z. Predictability of cortico-cortical connections in the mammalian brain. Netw Neurosci 2024; 8:138-157. [PMID: 38562298 PMCID: PMC10861169 DOI: 10.1162/netn_a_00345] [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: 08/02/2022] [Accepted: 10/23/2023] [Indexed: 04/04/2024] Open
Abstract
Despite a five order of magnitude range in size, the brains of mammals share many anatomical and functional characteristics that translate into cortical network commonalities. Here we develop a machine learning framework to quantify the degree of predictability of the weighted interareal cortical matrix. Partial network connectivity data were obtained with retrograde tract-tracing experiments generated with a consistent methodology, supplemented by projection length measurements in a nonhuman primate (macaque) and a rodent (mouse). We show that there is a significant level of predictability embedded in the interareal cortical networks of both species. At the binary level, links are predictable with an area under the ROC curve of at least 0.8 for the macaque. Weighted medium and strong links are predictable with an 85%-90% accuracy (mouse) and 70%-80% (macaque), whereas weak links are not predictable in either species. These observations reinforce earlier observations that the formation and evolution of the cortical network at the mesoscale is, to a large extent, rule based. Using the methodology presented here, we performed imputations on all area pairs, generating samples for the complete interareal network in both species. These are necessary for comparative studies of the connectome with minimal bias, both within and across species.
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Affiliation(s)
- Ferenc Molnár
- Department of Physics, University of Notre Dame, Notre Dame, IN, USA
| | - Szabolcs Horvát
- Center for Systems Biology Dresden, Dresden, Germany
- Max Planck Institute for Cell Biology and Genetics, Dresden, Germany
- Max Planck Institute for the Physics of Complex Systems, Dresden, Germany
- Department of Computer Science, Reykjavik University, Reykjavík, Iceland
| | - Ana R. Ribeiro Gomes
- Univ Lyon, Université Claude Bernard Lyon 1, INSERM, Stem Cell and Brain Research Institute, Bron, France
| | | | - Botond Molnár
- Faculty of Mathematics and Computer Science, Babeş-Bolyai University, Cluj-Napoca, Romania
- Faculty of Physics, Babeş-Bolyai University, Cluj-Napoca, Romania
- Transylvanian Institute of Neuroscience, Cluj-Napoca, Romania
| | - Mária Ercsey-Ravasz
- Faculty of Physics, Babeş-Bolyai University, Cluj-Napoca, Romania
- Transylvanian Institute of Neuroscience, Cluj-Napoca, Romania
| | - Kenneth Knoblauch
- National Centre for Optics, Vision and Eye Care, Faculty of Health and Social Sciences, University of South-Eastern Norway, Kongsberg, Norway
| | - Henry Kennedy
- Institute of Neuroscience, Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
- Shanghai Center for Brain Science and Brain-Inspired Intelligence Technology, Shanghai, China
| | - Zoltan Toroczkai
- Department of Physics, University of Notre Dame, Notre Dame, IN, USA
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15
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Yi H, Xiao M, Chen X, Yan Q, Yang Y, Liu Y, Song S, Gao X, Chen H. Resting-state functional network connectivity underlying conscientiousness in school-aged children. Child Neuropsychol 2024; 30:486-502. [PMID: 37278282 DOI: 10.1080/09297049.2023.2221757] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2022] [Accepted: 05/31/2023] [Indexed: 06/07/2023]
Abstract
Conscientiousness is a personality trait that matures from early childhood to late adolescence, yet little is known about its underlying brain mechanisms during this period. To investigate this, our study examined the resting-state functional network connectivity (rsFNC) of 69 school-aged children (mean age = 10.12 years, range = 9-12) using a whole-brain region-of-interest (ROI) based analysis, based on functional magnetic resonance imaging (fMRI). The results indicated a positive association between conscientiousness and the rsFNC between the fronto-parietal network (FPN) and two brain networks: the somatosensory motor-hand network (SMHN) and the auditory network (AN). However, conscientiousness was negatively associated with the rsFNC between FPN and two other networks: the salience network (SN); the default mode network (DMN). Moreover, our results suggest that the FPN may play a hub role in the neural performance of children's conscientiousness. Intrinsic brain networks, particularly those involved in higher-order cognitive functions, impact children's conscientiousness. Therefore, FPN plays an important role in the development of children's personality, providing insight into the neural mechanisms underlying children's personality.
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Affiliation(s)
- Haijing Yi
- Key Laboratory of Cognition and Personality (Ministry of Education), Southwest University, Chongqing, China
- Department of Psychology, Southwest University, Chongqing, China
| | - Mingyue Xiao
- Key Laboratory of Cognition and Personality (Ministry of Education), Southwest University, Chongqing, China
- Department of Psychology, Southwest University, Chongqing, China
| | - Ximei Chen
- Key Laboratory of Cognition and Personality (Ministry of Education), Southwest University, Chongqing, China
- Department of Psychology, Southwest University, Chongqing, China
| | - Qiaoling Yan
- Key Laboratory of Cognition and Personality (Ministry of Education), Southwest University, Chongqing, China
- Department of Psychology, Southwest University, Chongqing, China
| | - Yue Yang
- Key Laboratory of Cognition and Personality (Ministry of Education), Southwest University, Chongqing, China
- Department of Psychology, Southwest University, Chongqing, China
| | - Yong Liu
- Key Laboratory of Cognition and Personality (Ministry of Education), Southwest University, Chongqing, China
- Department of Psychology, Southwest University, Chongqing, China
| | - Shiqing Song
- Key Laboratory of Cognition and Personality (Ministry of Education), Southwest University, Chongqing, China
- Department of Psychology, Southwest University, Chongqing, China
| | - Xiao Gao
- Key Laboratory of Cognition and Personality (Ministry of Education), Southwest University, Chongqing, China
- Department of Psychology, Southwest University, Chongqing, China
| | - Hong Chen
- Key Laboratory of Cognition and Personality (Ministry of Education), Southwest University, Chongqing, China
- Department of Psychology, Southwest University, Chongqing, China
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16
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García-Rosales F, Schaworonkow N, Hechavarria JC. Oscillatory Waveform Shape and Temporal Spike Correlations Differ across Bat Frontal and Auditory Cortex. J Neurosci 2024; 44:e1236232023. [PMID: 38262724 PMCID: PMC10919256 DOI: 10.1523/jneurosci.1236-23.2023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/03/2023] [Revised: 11/01/2023] [Accepted: 11/29/2023] [Indexed: 01/25/2024] Open
Abstract
Neural oscillations are associated with diverse computations in the mammalian brain. The waveform shape of oscillatory activity measured in the cortex relates to local physiology and can be informative about aberrant or dynamically changing states. However, how waveform shape differs across distant yet functionally and anatomically related cortical regions is largely unknown. In this study, we capitalize on simultaneous recordings of local field potentials (LFPs) in the auditory and frontal cortices of awake, male Carollia perspicillata bats to examine, on a cycle-by-cycle basis, waveform shape differences across cortical regions. We find that waveform shape differs markedly in the fronto-auditory circuit even for temporally correlated rhythmic activity in comparable frequency ranges (i.e., in the delta and gamma bands) during spontaneous activity. In addition, we report consistent differences between areas in the variability of waveform shape across individual cycles. A conceptual model predicts higher spike-spike and spike-LFP correlations in regions with more asymmetric shapes, a phenomenon that was observed in the data: spike-spike and spike-LFP correlations were higher in the frontal cortex. The model suggests a relationship between waveform shape differences and differences in spike correlations across cortical areas. Altogether, these results indicate that oscillatory activity in the frontal and auditory cortex possesses distinct dynamics related to the anatomical and functional diversity of the fronto-auditory circuit.
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Affiliation(s)
- Francisco García-Rosales
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Frankfurt am Main 60528, Germany
| | - Natalie Schaworonkow
- Ernst Strüngmann Institute (ESI) for Neuroscience in Cooperation with Max Planck Society, Frankfurt am Main 60528, Germany
| | - Julio C Hechavarria
- Institut für Zellbiologie und Neurowissenschaft, Goethe-Universität, Frankfurt am Main 60438, Germany
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17
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Zang Z, Chi X, Luan M, Hu S, Zhou K, Liu J. Inter-individual, hemispheric and sex variability of brain activations during numerosity processing. Brain Struct Funct 2024; 229:459-475. [PMID: 38197958 PMCID: PMC10917853 DOI: 10.1007/s00429-023-02747-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/12/2023] [Accepted: 12/04/2023] [Indexed: 01/11/2024]
Abstract
Numerosity perception is a fundamental and innate cognitive function shared by both humans and many animal species. Previous research has primarily focused on exploring the spatial and functional consistency of neural activations that were associated with the processing of numerosity information. However, the inter-individual variability of brain activations of numerosity perception remains unclear. In the present study, with a large-sample functional magnetic resonance imaging (fMRI) dataset (n = 460), we aimed to localize the functional regions related to numerosity perceptions and explore the inter-individual, hemispheric, and sex differences within these brain regions. Fifteen subject-specific activated regions, including the anterior intraparietal sulcus (aIPS), posterior intraparietal sulcus (pIPS), insula, inferior frontal gyrus (IFG), inferior temporal gyrus (ITG), premotor area (PM), middle occipital gyrus (MOG) and anterior cingulate cortex (ACC), were delineated in each individual and then used to create a functional probabilistic atlas to quantify individual variability in brain activations of numerosity processing. Though the activation percentages of most regions were higher than 60%, the intersections of most regions across individuals were considerably lower, falling below 50%, indicating substantial variations in brain activations related to numerosity processing among individuals. Furthermore, significant hemispheric and sex differences in activation location, extent, and magnitude were also found in these regions. Most activated regions in the right hemisphere had larger activation volumes and activation magnitudes, and were located more lateral and anterior than their counterparts in the left hemisphere. In addition, in most of these regions, males displayed stronger activations than females. Our findings demonstrate large inter-individual, hemispheric, and sex differences in brain activations related to numerosity processing, and our probabilistic atlas can serve as a robust functional and spatial reference for mapping the numerosity-related neural networks.
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Affiliation(s)
- Zhongyao Zang
- Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education (Beijing Normal University), Faculty of Psychology, Beijing Normal University, Beijing, 100875, China
| | - Xiaoyue Chi
- Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education (Beijing Normal University), Faculty of Psychology, Beijing Normal University, Beijing, 100875, China
| | - Mengkai Luan
- Department of Psychology, Shanghai University of Sport, 650 Qing Yuan Huan Road, Shanghai, 200438, People's Republic of China
| | - Siyuan Hu
- Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education (Beijing Normal University), Faculty of Psychology, Beijing Normal University, Beijing, 100875, China.
| | - Ke Zhou
- Beijing Key Laboratory of Applied Experimental Psychology, National Demonstration Center for Experimental Psychology Education (Beijing Normal University), Faculty of Psychology, Beijing Normal University, Beijing, 100875, China.
| | - Jia Liu
- Tsinghua Laboratory of Brain and Intelligence, Department of Psychology, Tsinghua University, Beijing, 100084, China
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18
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Levy R. The prefrontal cortex: from monkey to man. Brain 2024; 147:794-815. [PMID: 37972282 PMCID: PMC10907097 DOI: 10.1093/brain/awad389] [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: 06/05/2023] [Revised: 10/01/2023] [Accepted: 11/02/2023] [Indexed: 11/19/2023] Open
Abstract
The prefrontal cortex is so important to human beings that, if deprived of it, our behaviour is reduced to action-reactions and automatisms, with no ability to make deliberate decisions. Why does the prefrontal cortex hold such importance in humans? In answer, this review draws on the proximity between humans and other primates, which enables us, through comparative anatomical-functional analysis, to understand the cognitive functions we have in common and specify those that distinguish humans from their closest cousins. First, a focus on the lateral region of the prefrontal cortex illustrates the existence of a continuum between rhesus monkeys (the most studied primates in neuroscience) and humans for most of the major cognitive functions in which this region of the brain plays a central role. This continuum involves the presence of elementary mental operations in the rhesus monkey (e.g. working memory or response inhibition) that are constitutive of 'macro-functions' such as planning, problem-solving and even language production. Second, the human prefrontal cortex has developed dramatically compared to that of other primates. This increase seems to concern the most anterior part (the frontopolar cortex). In humans, the development of the most anterior prefrontal cortex is associated with three major and interrelated cognitive changes: (i) a greater working memory capacity, allowing for greater integration of past experiences and prospective futures; (ii) a greater capacity to link discontinuous or distant data, whether temporal or semantic; and (iii) a greater capacity for abstraction, allowing humans to classify knowledge in different ways, to engage in analogical reasoning or to acquire abstract values that give rise to our beliefs and morals. Together, these new skills enable us, among other things, to develop highly sophisticated social interactions based on language, enabling us to conceive beliefs and moral judgements and to conceptualize, create and extend our vision of our environment beyond what we can physically grasp. Finally, a model of the transition of prefrontal functions between humans and non-human primates concludes this review.
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Affiliation(s)
- Richard Levy
- AP–HP, Groupe Hospitalier Pitié-Salpêtrière, Department of Neurology, Sorbonne Université, Institute of Memory and Alzheimer’s Disease, 75013 Paris, France
- Sorbonne Université, INSERM U1127, CNRS 7225, Paris Brain Institute- ICM, 75013 Paris, France
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19
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Assimopoulos S, Warrington S, Bryant KL, Pszczolkowski S, Jbabdi S, Mars RB, Sotiropoulos SN. Generalising XTRACT tractography protocols across common macaque brain templates. Brain Struct Funct 2024:10.1007/s00429-024-02760-0. [PMID: 38388696 DOI: 10.1007/s00429-024-02760-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/03/2023] [Accepted: 01/09/2024] [Indexed: 02/24/2024]
Abstract
Non-human primates are extensively used in neuroscience research as models of the human brain, with the rhesus macaque being a prominent example. We have previously introduced a set of tractography protocols (XTRACT) for reconstructing 42 corresponding white matter (WM) bundles in the human and the macaque brain and have shown cross-species comparisons using such bundles as WM landmarks. Our original XTRACT protocols were developed using the F99 macaque brain template. However, additional macaque template brains are becoming increasingly common. Here, we generalise the XTRACT tractography protocol definitions across five macaque brain templates, including the F99, D99, INIA, Yerkes and NMT. We demonstrate equivalence of such protocols in two ways: (a) Firstly by comparing the bodies of the tracts derived using protocols defined across the different templates considered, (b) Secondly by comparing the projection patterns of the reconstructed tracts across the different templates in two cross-species (human-macaque) comparison tasks. The results confirm similarity of all predictions regardless of the macaque brain template used, providing direct evidence for the generalisability of these tractography protocols across the five considered templates.
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Affiliation(s)
- Stephania Assimopoulos
- Sir Peter Mansfield Imaging Centre, Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, UK
| | - Shaun Warrington
- Sir Peter Mansfield Imaging Centre, Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, UK
| | - Katherine L Bryant
- Laboratoire de Psychologie Cognitive, Aix-Marseille Université, Marseille, France
- Wellcome Centre for Integrative Neuroimaging (WIN-FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Stefan Pszczolkowski
- Sir Peter Mansfield Imaging Centre, Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, UK
- NIHR Nottingham Biomedical Research Centre, University of Nottingham, Nottingham, UK
| | - Saad Jbabdi
- Wellcome Centre for Integrative Neuroimaging (WIN-FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Rogier B Mars
- Wellcome Centre for Integrative Neuroimaging (WIN-FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Stamatios N Sotiropoulos
- Sir Peter Mansfield Imaging Centre, Mental Health and Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, UK.
- Wellcome Centre for Integrative Neuroimaging (WIN-FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
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20
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Huang W, Dong X, Zhao T, Kucikova L, Fu A, Shu N. DCP: A pipeline toolbox for diffusion connectome. Hum Brain Mapp 2024; 45:e26626. [PMID: 38375916 PMCID: PMC10877999 DOI: 10.1002/hbm.26626] [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: 06/08/2023] [Revised: 12/29/2023] [Accepted: 02/02/2024] [Indexed: 02/21/2024] Open
Abstract
The brain structural network derived from diffusion magnetic resonance imaging (dMRI) reflects the white matter connections between brain regions, which can quantitatively describe the anatomical connection pattern of the entire brain. The development of structural brain connectome leads to the emergence of a large number of dMRI processing packages and network analysis toolboxes. However, the fully automated network analysis based on dMRI data remains challenging. In this study, we developed a cross-platform MATLAB toolbox named "Diffusion Connectome Pipeline" (DCP) for automatically constructing brain structural networks and calculating topological attributes of the networks. The toolbox integrates a few developed packages, including FSL, Diffusion Toolkit, SPM, Camino, MRtrix3, and MRIcron. It can process raw dMRI data collected from any number of participants, and it is also compatible with preprocessed files from public datasets such as HCP and UK Biobank. Moreover, a friendly graphical user interface allows users to configure their processing pipeline without any programming. To prove the capacity and validity of the DCP, two tests were conducted with using DCP. The results showed that DCP can reproduce the findings in our previous studies. However, there are some limitations of DCP, such as relying on MATLAB and being unable to fixel-based metrics weighted network. Despite these limitations, overall, the DCP software provides a standardized, fully automated computational workflow for white matter network construction and analysis, which is beneficial for advancing future human brain connectomics application research.
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Affiliation(s)
- Weijie Huang
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingPR China
- School of Systems Science, Beijing Normal UniversityBeijingPR China
- Department of NeuroscienceSheffield Institute for Translational Neuroscience, Medical School and Insigneo Institute for in Silico Medicine, University of SheffieldSheffieldUK
| | - Xinyi Dong
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingPR China
| | - Tengda Zhao
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingPR China
| | - Ludmila Kucikova
- Department of NeuroscienceSheffield Institute for Translational Neuroscience, Medical School and Insigneo Institute for in Silico Medicine, University of SheffieldSheffieldUK
| | - Anguo Fu
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingPR China
| | - Ni Shu
- State Key Laboratory of Cognitive Neuroscience and LearningBeijing Normal UniversityBeijingPR China
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21
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Luo J, Qin P, Bi Q, Wu K, Gong G. Individual variability in functional connectivity of human auditory cortex. Cereb Cortex 2024; 34:bhae007. [PMID: 38282455 DOI: 10.1093/cercor/bhae007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/19/2023] [Revised: 01/04/2024] [Accepted: 01/05/2024] [Indexed: 01/30/2024] Open
Abstract
Individual variability in functional connectivity underlies individual differences in cognition and behaviors, yet its association with functional specialization in the auditory cortex remains elusive. Using resting-state functional magnetic resonance imaging data from the Human Connectome Project, this study was designed to investigate the spatial distribution of auditory cortex individual variability in its whole-brain functional network architecture. An inherent hierarchical axis of the variability was discerned, which radiates from the medial to lateral orientation, with the left auditory cortex demonstrating more pronounced variations than the right. This variability exhibited a significant correlation with the variations in structural and functional metrics in the auditory cortex. Four auditory cortex subregions, which were identified from a clustering analysis based on this variability, exhibited unique connectional fingerprints and cognitive maps, with certain subregions showing specificity to speech perception functional activation. Moreover, the lateralization of the connectional fingerprint exhibited a U-shaped trajectory across the subregions. These findings emphasize the role of individual variability in functional connectivity in understanding cortical functional organization, as well as in revealing its association with functional specialization from the activation, connectome, and cognition perspectives.
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Affiliation(s)
- Junhao Luo
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Peipei Qin
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Qiuhui Bi
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
- School of Artificial Intelligence, Beijing Normal University, Beijing 100875, China
| | - Ke Wu
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Gaolang Gong
- State Key Laboratory of Cognitive Neuroscience and Learning & IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China
- Chinese Institute for Brain Research, Beijing 102206, China
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22
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Wang X, Leprince Y, Lebenberg J, Langlet C, Mohlberg H, Rivière D, Auzias G, Dickscheid T, Amunts K, Mangin JF. A framework to improve the alignment of individual cytoarchitectonic maps of the Julich-Brain atlas using cortical folding landmarks. Cereb Cortex 2024; 34:bhad538. [PMID: 38236742 DOI: 10.1093/cercor/bhad538] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/25/2023] [Revised: 12/21/2023] [Accepted: 12/22/2023] [Indexed: 02/06/2024] Open
Abstract
The segregation of the cortical mantle into cytoarchitectonic areas provides a structural basis for the specialization of different brain regions. In vivo neuroimaging experiments can be linked to this postmortem cytoarchitectonic parcellation via Julich-Brain. This atlas embeds probabilistic maps that account for inter-individual variability in the localization of cytoarchitectonic areas in the reference spaces targeted by spatial normalization. We built a framework to improve the alignment of architectural areas across brains using cortical folding landmarks. This framework, initially designed for in vivo imaging, was adapted to postmortem histological data. We applied this to the first 14 brains used to establish the Julich-Brain atlas to infer a refined atlas with more focal probabilistic maps. The improvement achieved is significant in the primary regions and some of the associative areas. This framework also provides a tool for exploring the relationship between cortical folding patterns and cytoarchitectonic areas in different cortical regions to establish new landmarks in the remainder of the cortex.
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Affiliation(s)
- Xiaoyu Wang
- Université Paris-Saclay, CEA, CNRS, Neurospin, Baobab, Saclay, France
| | - Yann Leprince
- Université Paris-Saclay, CEA, CNRS, Neurospin, Baobab, Saclay, France
- UNIACT, NeuroSpin, CEA, Université Paris-Saclay, F-91191 Gif-sur-Yvette, France
| | - Jessica Lebenberg
- Université Paris-Saclay, CEA, CNRS, Neurospin, Baobab, Saclay, France
- Lariboisière University Hospital, APHP, Translational Neurovascular Centre and Department of Neurology, FHU NeuroVasc, Paris, France
| | - Clement Langlet
- Université Paris-Saclay, CEA, CNRS, Neurospin, Baobab, Saclay, France
| | - Hartmut Mohlberg
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, D-52425 Jülich, Germany
| | - Denis Rivière
- Université Paris-Saclay, CEA, CNRS, Neurospin, Baobab, Saclay, France
| | - Guillaume Auzias
- Institut de Neurosciences de la Timone, UMR 7289, Aix Marseille Université, CNRS, Marseille, France
| | - Timo Dickscheid
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, D-52425 Jülich, Germany
- Institute of Computer Science, Heinrich-Heine University Düsseldorf, D-40225 Düsseldorf, Germany
| | - Katrin Amunts
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, D-52425 Jülich, Germany
- Cecile und Oskar Vogt Institut für Hirnforschung, University Hospital Düsseldorf, Heinrich-Heine Universität Düsseldorf, D-40225 Düsseldorf, Germany
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23
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Okuno T, Ichinohe N, Woodward A. A reappraisal of the default mode and frontoparietal networks in the common marmoset brain. FRONTIERS IN NEUROIMAGING 2024; 2:1345643. [PMID: 38264540 PMCID: PMC10803424 DOI: 10.3389/fnimg.2023.1345643] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/28/2023] [Accepted: 12/20/2023] [Indexed: 01/25/2024]
Abstract
In recent years the common marmoset homolog of the human default mode network (DMN) has been a hot topic of discussion in the marmoset research field. Previously, the posterior cingulate cortex regions (PGM, A19M) and posterior parietal cortex regions (LIP, MIP) were defined as the DMN, but some studies claim that these form the frontoparietal network (FPN). We restarted from a neuroanatomical point of view and identified two DMN candidates: Comp-A (which has been called both the DMN and FPN) and Comp-B. We performed GLM analysis on auditory task-fMRI and found Comp-B to be more appropriate as the DMN, and Comp-A as the FPN. Additionally, through fingerprint analysis, a DMN and FPN in the tasking human was closer to the resting common marmoset. The human DMN appears to have an advanced function that may be underdeveloped in the common marmoset brain.
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Affiliation(s)
- Takuto Okuno
- Connectome Analysis Unit, RIKEN Center for Brain Science, Wako, Saitama, Japan
| | - Noritaka Ichinohe
- Laboratory for Ultrastructure Research, National Institute of Neuroscience, National Center of Neurology and Psychiatry, Kodaira, Japan
| | - Alexander Woodward
- Connectome Analysis Unit, RIKEN Center for Brain Science, Wako, Saitama, Japan
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24
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Yee Y, Ellegood J, French L, Lerch JP. Organization of thalamocortical structural covariance and a corresponding 3D atlas of the mouse thalamus. Neuroimage 2024; 285:120453. [PMID: 37979895 DOI: 10.1016/j.neuroimage.2023.120453] [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: 03/14/2022] [Revised: 10/29/2023] [Accepted: 11/08/2023] [Indexed: 11/20/2023] Open
Abstract
For information from sensory organs to be processed by the brain, it is usually passed to appropriate areas of the cerebral cortex. Almost all of this information passes through the thalamus, a relay structure that reciprocally connects to the vast majority of the cortex. The thalamus facilitates this information transfer through a set of thalamocortical connections that vary in cellular structure, molecular profiles, innervation patterns, and firing rates. Additionally, corticothalamic connections allow for intracortical information transfer through the thalamus. These efferent and afferent connections between the thalamus and cortex have been the focus of many studies, and the importance of cortical connectivity in defining thalamus anatomy is demonstrated by multiple studies that parcellate the thalamus based on cortical connectivity profiles. Here, we examine correlated morphological variation between the thalamus and cortex, or thalamocortical structural covariance. For each voxel in the thalamus as a seed, we construct a cortical structural covariance map that represents correlated cortical volume variation, and examine whether high structural covariance is observed in cortical areas that are functionally relevant to the seed. Then, using these cortical structural covariance maps as features, we subdivide the thalamus into six non-overlapping regions (clusters of voxels), and assess whether cortical structural covariance is associated with cortical connectivity that specifically originates from these regions. We show that cortical structural covariance is high in areas of the cortex that are functionally related to the seed voxel, cortical structural covariance varies along cortical depth, and sharp transitions in cortical structural covariance profiles are observed when varying seed locations in the thalamus. Subdividing the thalamus based on structural covariance, we additionally demonstrate that the six thalamic clusters of voxels stratify cortical structural covariance along the dorsal-ventral, medial-lateral, and anterior-posterior axes. These cluster-associated structural covariance patterns are prominently detected in cortical regions innervated by fibers projecting out of their related thalamic subdivisions. Together, these results advance our understanding of how the thalamus and the cortex couple in their volumes. Our results indicate that these volume correlations reflect functional organization and structural connectivity, and further provides a novel segmentation of the mouse thalamus that can be used to examine thalamic structural variation and thalamocortical structural covariation in disease models.
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Affiliation(s)
- Yohan Yee
- Mouse Imaging Centre, The Hospital for Sick Children, Toronto, Canada; Department of Medical Biophysics, University of Toronto, Toronto, Canada.
| | - Jacob Ellegood
- Mouse Imaging Centre, The Hospital for Sick Children, Toronto, Canada
| | - Leon French
- Department of Psychiatry, University of Toronto, Toronto, Canada
| | - Jason P Lerch
- Department of Medical Biophysics, University of Toronto, Toronto, Canada; Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, United Kingdom
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25
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Bazinet V, Hansen JY, Misic B. Towards a biologically annotated brain connectome. Nat Rev Neurosci 2023; 24:747-760. [PMID: 37848663 DOI: 10.1038/s41583-023-00752-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 09/20/2023] [Indexed: 10/19/2023]
Abstract
The brain is a network of interleaved neural circuits. In modern connectomics, brain connectivity is typically encoded as a network of nodes and edges, abstracting away the rich biological detail of local neuronal populations. Yet biological annotations for network nodes - such as gene expression, cytoarchitecture, neurotransmitter receptors or intrinsic dynamics - can be readily measured and overlaid on network models. Here we review how connectomes can be represented and analysed as annotated networks. Annotated connectomes allow us to reconceptualize architectural features of networks and to relate the connection patterns of brain regions to their underlying biology. Emerging work demonstrates that annotated connectomes help to make more veridical models of brain network formation, neural dynamics and disease propagation. Finally, annotations can be used to infer entirely new inter-regional relationships and to construct new types of network that complement existing connectome representations. In summary, biologically annotated connectomes offer a compelling way to study neural wiring in concert with local biological features.
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Affiliation(s)
- Vincent Bazinet
- Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada
| | - Justine Y Hansen
- Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada
| | - Bratislav Misic
- Montréal Neurological Institute, McGill University, Montréal, Quebec, Canada.
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26
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Hong Y, Cornea E, Girault JB, Bagonis M, Foster M, Kim SH, Prieto JC, Chen H, Gao W, Styner MA, Gilmore JH. Structural and functional connectome relationships in early childhood. Dev Cogn Neurosci 2023; 64:101314. [PMID: 37898019 PMCID: PMC10630618 DOI: 10.1016/j.dcn.2023.101314] [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: 06/13/2023] [Revised: 09/27/2023] [Accepted: 10/12/2023] [Indexed: 10/30/2023] Open
Abstract
There is strong evidence that the functional connectome is highly related to the white matter connectome in older children and adults, though little is known about structure-function relationships in early childhood. We investigated the development of cortical structure-function coupling in children longitudinally scanned at 1, 2, 4, and 6 years of age (N = 360) and in a comparison sample of adults (N = 89). We also applied a novel graph convolutional neural network-based deep learning model with a new loss function to better capture inter-subject heterogeneity and predict an individual's functional connectivity from the corresponding structural connectivity. We found regional patterns of structure-function coupling in early childhood that were consistent with adult patterns. In addition, our deep learning model improved the prediction of individual functional connectivity from its structural counterpart compared to existing models.
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Affiliation(s)
- Yoonmi Hong
- Department of Psychiatry, University of North Carolina at Chapel Hill, United States of America.
| | - Emil Cornea
- Department of Psychiatry, University of North Carolina at Chapel Hill, United States of America
| | - Jessica B Girault
- Department of Psychiatry, University of North Carolina at Chapel Hill, United States of America; Carolina Institute for Developmental Disabilities, University of North Carolina at Chapel Hill, United States of America
| | - Maria Bagonis
- Department of Psychiatry, University of North Carolina at Chapel Hill, United States of America
| | - Mark Foster
- Department of Psychiatry, University of North Carolina at Chapel Hill, United States of America
| | - Sun Hyung Kim
- Department of Psychiatry, University of North Carolina at Chapel Hill, United States of America
| | - Juan Carlos Prieto
- Department of Psychiatry, University of North Carolina at Chapel Hill, United States of America
| | - Haitao Chen
- Biomedical Imaging Research Institute (BIRI), Department of Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, United States of America
| | - Wei Gao
- Biomedical Imaging Research Institute (BIRI), Department of Biomedical Sciences and Imaging, Cedars-Sinai Medical Center, United States of America
| | - Martin A Styner
- Department of Psychiatry, University of North Carolina at Chapel Hill, United States of America; Department of Computer Science, University of North Carolina at Chapel Hill, United States of America
| | - John H Gilmore
- Department of Psychiatry, University of North Carolina at Chapel Hill, United States of America
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27
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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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28
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Abstract
Neurological insults, such as congenital blindness, deafness, amputation, and stroke, often result in surprising and impressive behavioural changes. Cortical reorganisation, which refers to preserved brain tissue taking on a new functional role, is often invoked to account for these behavioural changes. Here, we revisit many of the classical animal and patient cortical remapping studies that spawned this notion of reorganisation. We highlight empirical, methodological, and conceptual problems that call this notion into doubt. We argue that appeal to the idea of reorganisation is attributable in part to the way that cortical maps are empirically derived. Specifically, cortical maps are often defined based on oversimplified assumptions of 'winner-takes-all', which in turn leads to an erroneous interpretation of what it means when these maps appear to change. Conceptually, remapping is interpreted as a circuit receiving novel input and processing it in a way unrelated to its original function. This implies that neurons are either pluripotent enough to change what they are tuned to or that a circuit can change what it computes. Instead of reorganisation, we argue that remapping is more likely to occur due to potentiation of pre-existing architecture that already has the requisite representational and computational capacity pre-injury. This architecture can be facilitated via Hebbian and homeostatic plasticity mechanisms. Crucially, our revised framework proposes that opportunities for functional change are constrained throughout the lifespan by the underlying structural 'blueprint'. At no period, including early in development, does the cortex offer structural opportunities for functional pluripotency. We conclude that reorganisation as a distinct form of cortical plasticity, ubiquitously evoked with words such as 'take-over'' and 'rewiring', does not exist.
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Affiliation(s)
- Tamar R Makin
- MRC Cognition and Brain Sciences Unit, University of CambridgeCambridgeUnited Kingdom
| | - John W Krakauer
- Department of Neuroscience, Johns Hopkins University School of MedicineBaltimoreUnited States
- Department of Neurology, Johns Hopkins University School of MedicineBaltimoreUnited States
- The Santa Fe InstituteSanta FeUnited States
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29
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Karadachka K, Assem M, Mitchell DJ, Duncan J, Medendorp WP, Mars RB. Structural connectivity of the multiple demand network in humans and comparison to the macaque brain. Cereb Cortex 2023; 33:10959-10971. [PMID: 37798142 PMCID: PMC10646692 DOI: 10.1093/cercor/bhad314] [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: 05/01/2023] [Revised: 08/10/2023] [Accepted: 08/11/2023] [Indexed: 10/07/2023] Open
Abstract
Fluid intelligence encompasses a wide range of abilities such as working memory, problem-solving, and relational reasoning. In the human brain, these abilities are associated with the Multiple Demand Network, traditionally thought to involve combined activity of specific regions predominantly in the prefrontal and parietal cortices. However, the structural basis of the interactions between areas in the Multiple Demand Network, as well as their evolutionary basis among primates, remains largely unexplored. Here, we exploit diffusion MRI to elucidate the major white matter pathways connecting areas of the human core and extended Multiple Demand Network. We then investigate whether similar pathways can be identified in the putative homologous areas of the Multiple Demand Network in the macaque monkey. Finally, we contrast human and monkey networks using a recently proposed approach to compare different species' brains within a common organizational space. Our results indicate that the core Multiple Demand Network relies mostly on dorsal longitudinal connections and, although present in the macaque, these connections are more pronounced in the human brain. The extended Multiple Demand Network relies on distinct pathways and communicates with the core Multiple Demand Network through connections that also appear enhanced in the human compared with the macaque.
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Affiliation(s)
- Katrin Karadachka
- Donders Institute for Brain, Cognition and Behaviour, Faculty of Social Sciences, Radboud University Nijmegen, 6525HR Nijmegen, The Netherlands
| | - Moataz Assem
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, United Kingdom
| | - Daniel J Mitchell
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, United Kingdom
| | - John Duncan
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge CB2 7EF, United Kingdom
| | - W Pieter Medendorp
- Donders Institute for Brain, Cognition and Behaviour, Faculty of Social Sciences, Radboud University Nijmegen, 6525HR Nijmegen, The Netherlands
| | - Rogier B Mars
- Donders Institute for Brain, Cognition and Behaviour, Faculty of Social Sciences, Radboud University Nijmegen, 6525HR Nijmegen, The Netherlands
- Wellcome Centre for Integrative Neuroimaging Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Headington, Oxford OX3 9DU, United Kingdom
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30
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He Y, Li Q, Fu Z, Zeng D, Han Y, Li S. Functional gradients reveal altered functional segregation in patients with amnestic mild cognitive impairment and Alzheimer's disease. Cereb Cortex 2023; 33:10836-10847. [PMID: 37718155 DOI: 10.1093/cercor/bhad328] [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: 03/15/2023] [Revised: 07/26/2023] [Accepted: 08/23/2023] [Indexed: 09/19/2023] Open
Abstract
Alzheimer's disease and amnestic mild cognitive impairment are associated with disrupted functional organization in brain networks, involved with alteration of functional segregation. Connectome gradients are a new tool representing brain functional topological organization to smoothly capture the human macroscale hierarchy. Here, we examined altered topological organization in amnestic mild cognitive impairment and Alzheimer's disease by connectome gradient mapping. We further quantified functional segregation by gradient dispersion. Then, we systematically compared the alterations observed in amnestic mild cognitive impairment and Alzheimer's disease patients with those in normal controls in a two-dimensional functional gradient space from both the whole-brain level and module level. Compared with normal controls, the first gradient, which described the neocortical hierarchy from unimodal to transmodal regions, showed a more distributed and significant suppression in Alzheimer's disease than amnestic mild cognitive impairment patients. Furthermore, gradient dispersion showed significant decreases in Alzheimer's disease at both the global level and module level, whereas this alteration was limited only to limbic areas in amnestic mild cognitive impairment. Notably, we demonstrated that suppressed gradient dispersion in amnestic mild cognitive impairment and Alzheimer's disease was associated with cognitive scores. These findings provide new evidence for altered brain hierarchy in amnestic mild cognitive impairment and Alzheimer's disease, which strengthens our understanding of the progressive mechanism of cognitive decline.
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Affiliation(s)
- Yirong He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
| | - Qiongling Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
- Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing 100875, China
- IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing 100875, China
| | - Zhenrong Fu
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science & Medical Engineering, Beihang University, Beijing 100083, China
| | - Debin Zeng
- Beijing Advanced Innovation Center for Biomedical Engineering, School of Biological Science & Medical Engineering, Beihang University, Beijing 100083, China
| | - Ying Han
- Department of Neurology, Xuanwu Hospital of Capital Medical University, Beijing 100053, China
- Biomedical Engineering Institute, Hainan University, Haikou 570228, China
- Center of Alzheimer's Disease, Beijing Institute for Brain Disorders, Beijing 100050, China
- National Clinical Research Center for Geriatric Disorders, Beijing 100053, China
| | - Shuyu Li
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing 100875, China
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31
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Dehdar K, Raoufy MR. Brain structural and functional alterations related to anxiety in allergic asthma. Brain Res Bull 2023; 202:110727. [PMID: 37562517 DOI: 10.1016/j.brainresbull.2023.110727] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/01/2023] [Revised: 07/29/2023] [Accepted: 08/03/2023] [Indexed: 08/12/2023]
Abstract
Psychiatric disorders are common in patients with allergic asthma, and they can have a significant impact on their quality of life and disease control. Recent studies have suggested that there may be potential immune-brain communication mechanisms in asthma, which can activate inflammatory responses in different brain areas, leading to structural and functional alterations and behavioral changes. However, the precise mechanisms underlying these alterations remain unclear. In this paper, we comprehensively review the relevant research on asthma-induced brain structural and functional alterations that lead to the initiation and promotion of anxiety. We summarize the possible pathways for peripheral inflammation to affect the brain's structure and function. Our review highlights the importance of addressing neuropsychiatric disorders in the clinical guidelines of asthma, to improve the quality of life of these patients. We suggest that a better understanding of the mechanisms underlying psychiatric comorbidities in asthma could lead to the development of more effective treatments for these patients.
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Affiliation(s)
- Kolsoum Dehdar
- Department of Physiology, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran
| | - Mohammad Reza Raoufy
- Department of Physiology, Faculty of Medical Sciences, Tarbiat Modares University, Tehran, Iran.
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32
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Milisav F, Bazinet V, Iturria-Medina Y, Misic B. Resolving inter-regional communication capacity in the human connectome. Netw Neurosci 2023; 7:1051-1079. [PMID: 37781139 PMCID: PMC10473316 DOI: 10.1162/netn_a_00318] [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: 10/11/2022] [Accepted: 04/03/2023] [Indexed: 10/03/2023] Open
Abstract
Applications of graph theory to the connectome have inspired several models of how neural signaling unfolds atop its structure. Analytic measures derived from these communication models have mainly been used to extract global characteristics of brain networks, obscuring potentially informative inter-regional relationships. Here we develop a simple standardization method to investigate polysynaptic communication pathways between pairs of cortical regions. This procedure allows us to determine which pairs of nodes are topologically closer and which are further than expected on the basis of their degree. We find that communication pathways delineate canonical functional systems. Relating nodal communication capacity to meta-analytic probabilistic patterns of functional specialization, we also show that areas that are most closely integrated within the network are associated with higher order cognitive functions. We find that these regions' proclivity towards functional integration could naturally arise from the brain's anatomical configuration through evenly distributed connections among multiple specialized communities. Throughout, we consider two increasingly constrained null models to disentangle the effects of the network's topology from those passively endowed by spatial embedding. Altogether, the present findings uncover relationships between polysynaptic communication pathways and the brain's functional organization across multiple topological levels of analysis and demonstrate that network integration facilitates cognitive integration.
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Affiliation(s)
- Filip Milisav
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Vincent Bazinet
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Yasser Iturria-Medina
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Bratislav Misic
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
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33
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Omont-Lescieux S, Menu I, Salvia E, Poirel N, Oppenheim C, Houdé O, Cachia A, Borst G. Lateralization of the cerebral network of inhibition in children before and after cognitive training. Dev Cogn Neurosci 2023; 63:101293. [PMID: 37683326 PMCID: PMC10498008 DOI: 10.1016/j.dcn.2023.101293] [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: 03/07/2023] [Revised: 08/31/2023] [Accepted: 09/01/2023] [Indexed: 09/10/2023] Open
Abstract
Inhibitory control (IC) plays a critical role in cognitive and socio-emotional development. IC relies on a lateralized cortico-subcortical brain network including the inferior frontal cortex, anterior parts of insula, anterior cingulate cortex, caudate nucleus and putamen. Brain asymmetries play a critical role for IC efficiency. In parallel to age-related changes, IC can be improved following training. The aim of this study was to (1) assess the lateralization of IC network in children (N = 60, 9-10 y.o.) and (2) examine possible changes in neural asymmetry of this network from anatomical (structural MRI) and functional (resting-state fMRI) levels after 5-week computerized IC vs. active control (AC) training. We observed that IC training, but not AC training, led to a leftward lateralization of the putamen anatomy, similarly to what is observed in adults, supporting that training could accelerate the maturation of this structure.
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Affiliation(s)
- Sixtine Omont-Lescieux
- Université Paris Cité, LaPsyDÉ, CNRS, F-75005, Paris, France; Université Paris Cité, Institute of Psychiatry and Neuroscience of Paris (IPNP), INSERM U1266, Imaging biomarkers for brain development and disorders, 75014 Paris, France; GHU-Paris Psychiatrie et Neurosciences, Hôpital Sainte Anne, F-75014 Paris, France
| | - Iris Menu
- Université Paris Cité, LaPsyDÉ, CNRS, F-75005, Paris, France; Université Paris Cité, Institute of Psychiatry and Neuroscience of Paris (IPNP), INSERM U1266, Imaging biomarkers for brain development and disorders, 75014 Paris, France; GHU-Paris Psychiatrie et Neurosciences, Hôpital Sainte Anne, F-75014 Paris, France
| | - Emilie Salvia
- Université Paris Cité, LaPsyDÉ, CNRS, F-75005, Paris, France; GHU-Paris Psychiatrie et Neurosciences, Hôpital Sainte Anne, F-75014 Paris, France
| | - Nicolas Poirel
- Université Paris Cité, LaPsyDÉ, CNRS, F-75005, Paris, France; GIP Cyceron, Caen, France
| | - Catherine Oppenheim
- Université Paris Cité, Institute of Psychiatry and Neuroscience of Paris (IPNP), INSERM U1266, Imaging biomarkers for brain development and disorders, 75014 Paris, France; GHU-Paris Psychiatrie et Neurosciences, Hôpital Sainte Anne, F-75014 Paris, France
| | - Olivier Houdé
- Université Paris Cité, LaPsyDÉ, CNRS, F-75005, Paris, France; GHU-Paris Psychiatrie et Neurosciences, Hôpital Sainte Anne, F-75014 Paris, France; Institut Universitaire de France, Paris, France
| | - Arnaud Cachia
- Université Paris Cité, LaPsyDÉ, CNRS, F-75005, Paris, France; Université Paris Cité, Institute of Psychiatry and Neuroscience of Paris (IPNP), INSERM U1266, Imaging biomarkers for brain development and disorders, 75014 Paris, France; GHU-Paris Psychiatrie et Neurosciences, Hôpital Sainte Anne, F-75014 Paris, France
| | - Grégoire Borst
- Université Paris Cité, LaPsyDÉ, CNRS, F-75005, Paris, France; GHU-Paris Psychiatrie et Neurosciences, Hôpital Sainte Anne, F-75014 Paris, France; Institut Universitaire de France, Paris, France.
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34
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Ford A, Ammar Z, Li L, Shultz S. Lateralization of major white matter tracts during infancy is time-varying and tract-specific. Cereb Cortex 2023; 33:10221-10233. [PMID: 37595203 PMCID: PMC10545441 DOI: 10.1093/cercor/bhad277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/24/2023] [Revised: 07/08/2023] [Accepted: 07/10/2023] [Indexed: 08/20/2023] Open
Abstract
Lateralization patterns are a major structural feature of brain white matter and have been investigated as a neural architecture that indicates and supports the specialization of cognitive processing and observed behaviors, e.g. language skills. Many neurodevelopmental disorders have been associated with atypical lateralization, reinforcing the need for careful measurement and study of this structural characteristic. Unfortunately, there is little consensus on the direction and magnitude of lateralization in major white matter tracts during the first months and years of life-the period of most rapid postnatal brain growth and cognitive maturation. In addition, no studies have examined white matter lateralization in a longitudinal pediatric sample-preventing confirmation of if and how white matter lateralization changes over time. Using a densely sampled longitudinal data set from neurotypical infants aged 0-6 months, we aim to (i) chart trajectories of white matter lateralization in 9 major tracts and (ii) link variable findings from cross-sectional studies of white matter lateralization in early infancy. We show that patterns of lateralization are time-varying and tract-specific and that differences in lateralization results during this period may reflect the dynamic nature of lateralization through development, which can be missed in cross-sectional studies.
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Affiliation(s)
- Aiden Ford
- Neuroscience Program, Emory University, Atlanta, GA 30322, United States
- Marcus Autism Center, Children’s Healthcare of Atlanta, Atlanta, GA 30329, United States
| | - Zeena Ammar
- Neuroscience Program, Emory University, Atlanta, GA 30322, United States
- Marcus Autism Center, Children’s Healthcare of Atlanta, Atlanta, GA 30329, United States
| | - Longchuan Li
- Marcus Autism Center, Children’s Healthcare of Atlanta, Atlanta, GA 30329, United States
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA 30322, United States
| | - Sarah Shultz
- Neuroscience Program, Emory University, Atlanta, GA 30322, United States
- Marcus Autism Center, Children’s Healthcare of Atlanta, Atlanta, GA 30329, United States
- Department of Pediatrics, Emory University School of Medicine, Atlanta, GA 30322, United States
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35
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Wang Q, Zhao S, Liu T, Han J, Liu C. Temporal fingerprints of cortical gyrification in marmosets and humans. Cereb Cortex 2023; 33:9802-9814. [PMID: 37434368 PMCID: PMC10656951 DOI: 10.1093/cercor/bhad245] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/18/2023] [Revised: 06/01/2023] [Accepted: 06/02/2023] [Indexed: 07/13/2023] Open
Abstract
Recent neuroimaging studies in humans have reported distinct temporal dynamics of gyri and sulci, which may be associated with putative functions of cortical gyrification. However, the complex folding patterns of the human cortex make it difficult to explain temporal patterns of gyrification. In this study, we used the common marmoset as a simplified model to examine the temporal characteristics and compare them with the complex gyrification of humans. Using a brain-inspired deep neural network, we obtained reliable temporal-frequency fingerprints of gyri and sulci from the awake rs-fMRI data of marmosets and humans. Notably, the temporal fingerprints of one region successfully classified the gyrus/sulcus of another region in both marmosets and humans. Additionally, the temporal-frequency fingerprints were remarkably similar in both species. We then analyzed the resulting fingerprints in several domains and adopted the Wavelet Transform Coherence approach to characterize the gyro-sulcal coupling patterns. In both humans and marmosets, sulci exhibited higher frequency bands than gyri, and the two were temporally coupled within the same range of phase angles. This study supports the notion that gyri and sulci possess unique and evolutionarily conserved features that are consistent across functional areas, and advances our understanding of the functional role of cortical gyrification.
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Affiliation(s)
- Qiyu Wang
- School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
- CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Neuroscience, Chinese Academy of Sciences, Shanghai 200031, China
| | - Shijie Zhao
- School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
- Research & Development Institute of Northwestern Polytechnical University in Shenzhen, Shenzhen 518063, China
| | - Tianming Liu
- Cortical Architecture Imaging and Discovery Lab, Department of Computer Science and Bioimaging Research Center, The University of Georgia, Athens, GA 30602, United States
| | - Junwei Han
- School of Automation, Northwestern Polytechnical University, Xi'an 710072, China
| | - Cirong Liu
- CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Neuroscience, Chinese Academy of Sciences, Shanghai 200031, China
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36
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Nebe S, Reutter M, Baker DH, Bölte J, Domes G, Gamer M, Gärtner A, Gießing C, Gurr C, Hilger K, Jawinski P, Kulke L, Lischke A, Markett S, Meier M, Merz CJ, Popov T, Puhlmann LMC, Quintana DS, Schäfer T, Schubert AL, Sperl MFJ, Vehlen A, Lonsdorf TB, Feld GB. Enhancing precision in human neuroscience. eLife 2023; 12:e85980. [PMID: 37555830 PMCID: PMC10411974 DOI: 10.7554/elife.85980] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/20/2023] [Accepted: 07/23/2023] [Indexed: 08/10/2023] Open
Abstract
Human neuroscience has always been pushing the boundary of what is measurable. During the last decade, concerns about statistical power and replicability - in science in general, but also specifically in human neuroscience - have fueled an extensive debate. One important insight from this discourse is the need for larger samples, which naturally increases statistical power. An alternative is to increase the precision of measurements, which is the focus of this review. This option is often overlooked, even though statistical power benefits from increasing precision as much as from increasing sample size. Nonetheless, precision has always been at the heart of good scientific practice in human neuroscience, with researchers relying on lab traditions or rules of thumb to ensure sufficient precision for their studies. In this review, we encourage a more systematic approach to precision. We start by introducing measurement precision and its importance for well-powered studies in human neuroscience. Then, determinants for precision in a range of neuroscientific methods (MRI, M/EEG, EDA, Eye-Tracking, and Endocrinology) are elaborated. We end by discussing how a more systematic evaluation of precision and the application of respective insights can lead to an increase in reproducibility in human neuroscience.
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Affiliation(s)
- Stephan Nebe
- Zurich Center for Neuroeconomics, Department of Economics, University of ZurichZurichSwitzerland
| | - Mario Reutter
- Department of Psychology, Julius-Maximilians-UniversityWürzburgGermany
| | - Daniel H Baker
- Department of Psychology and York Biomedical Research Institute, University of YorkYorkUnited Kingdom
| | - Jens Bölte
- Institute for Psychology, University of Münster, Otto-Creuzfeldt Center for Cognitive and Behavioral NeuroscienceMünsterGermany
| | - Gregor Domes
- Department of Biological and Clinical Psychology, University of TrierTrierGermany
- Institute for Cognitive and Affective NeuroscienceTrierGermany
| | - Matthias Gamer
- Department of Psychology, Julius-Maximilians-UniversityWürzburgGermany
| | - Anne Gärtner
- Faculty of Psychology, Technische Universität DresdenDresdenGermany
| | - Carsten Gießing
- Biological Psychology, Department of Psychology, School of Medicine and Health Sciences, Carl von Ossietzky University of OldenburgOldenburgGermany
| | - Caroline Gurr
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital, Goethe UniversityFrankfurtGermany
- Brain Imaging Center, Goethe UniversityFrankfurtGermany
| | - Kirsten Hilger
- Department of Psychology, Julius-Maximilians-UniversityWürzburgGermany
- Department of Psychology, Psychological Diagnostics and Intervention, Catholic University of Eichstätt-IngolstadtEichstättGermany
| | - Philippe Jawinski
- Department of Psychology, Humboldt-Universität zu BerlinBerlinGermany
| | - Louisa Kulke
- Department of Developmental with Educational Psychology, University of BremenBremenGermany
| | - Alexander Lischke
- Department of Psychology, Medical School HamburgHamburgGermany
- Institute of Clinical Psychology and Psychotherapy, Medical School HamburgHamburgGermany
| | - Sebastian Markett
- Department of Psychology, Humboldt-Universität zu BerlinBerlinGermany
| | - Maria Meier
- Department of Psychology, University of KonstanzKonstanzGermany
- University Psychiatric Hospitals, Child and Adolescent Psychiatric Research Department (UPKKJ), University of BaselBaselSwitzerland
| | - Christian J Merz
- Department of Cognitive Psychology, Institute of Cognitive Neuroscience, Faculty of Psychology, Ruhr University BochumBochumGermany
| | - Tzvetan Popov
- Department of Psychology, Methods of Plasticity Research, University of ZurichZurichSwitzerland
| | - Lara MC Puhlmann
- Leibniz Institute for Resilience ResearchMainzGermany
- Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
| | - Daniel S Quintana
- Max Planck Institute for Human Cognitive and Brain SciencesLeipzigGermany
- NevSom, Department of Rare Disorders & Disabilities, Oslo University HospitalOsloNorway
- KG Jebsen Centre for Neurodevelopmental Disorders, University of OsloOsloNorway
- Norwegian Centre for Mental Disorders Research (NORMENT), University of OsloOsloNorway
| | - Tim Schäfer
- Department of Child and Adolescent Psychiatry, Psychosomatics and Psychotherapy, University Hospital, Goethe UniversityFrankfurtGermany
- Brain Imaging Center, Goethe UniversityFrankfurtGermany
| | | | - Matthias FJ Sperl
- Department of Clinical Psychology and Psychotherapy, University of GiessenGiessenGermany
- Center for Mind, Brain and Behavior, Universities of Marburg and GiessenGiessenGermany
| | - Antonia Vehlen
- Department of Biological and Clinical Psychology, University of TrierTrierGermany
| | - Tina B Lonsdorf
- Department of Systems Neuroscience, University Medical Center Hamburg-EppendorfHamburgGermany
- Department of Psychology, Biological Psychology and Cognitive Neuroscience, University of BielefeldBielefeldGermany
| | - Gordon B Feld
- Department of Clinical Psychology, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg UniversityMannheimGermany
- Department of Psychology, Heidelberg UniversityHeidelbergGermany
- Department of Addiction Behavior and Addiction Medicine, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg UniversityMannheimGermany
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, Heidelberg UniversityMannheimGermany
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37
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Shi Y, Cui H, Li X, Chen L, Zhang C, Zhao X, Li X, Shao Q, Sun Q, Yan K, Wang G. Laminar and dorsoventral organization of layer 1 interneuronal microcircuitry in superficial layers of the medial entorhinal cortex. Cell Rep 2023; 42:112782. [PMID: 37436894 DOI: 10.1016/j.celrep.2023.112782] [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: 11/01/2022] [Revised: 04/03/2023] [Accepted: 06/24/2023] [Indexed: 07/14/2023] Open
Abstract
Layer 1 (L1) interneurons (INs) participate in various brain functions by gating information flow in the neocortex, but their role in the medial entorhinal cortex (MEC) is still unknown, largely due to scant knowledge of MEC L1 microcircuitry. Using simultaneous triple-octuple whole-cell recordings and morphological reconstructions, we comprehensively depict L1IN networks in the MEC. We identify three morphologically distinct types of L1INs with characteristic electrophysiological properties. We dissect intra- and inter-laminar cell-type-specific microcircuits of L1INs, showing connectivity patterns different from those in the neocortex. Remarkably, motif analysis reveals transitive and clustered features of L1 networks, as well as over-represented trans-laminar motifs. Finally, we demonstrate the dorsoventral gradient of L1IN microcircuits, with dorsal L1 neurogliaform cells receiving fewer intra-laminar inputs but exerting more inhibition on L2 principal neurons. These results thus present a more comprehensive picture of L1IN microcircuitry, which is indispensable for deciphering the function of L1INs in the MEC.
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Affiliation(s)
- Yuying Shi
- HIT Center for Life Sciences, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Hui Cui
- HIT Center for Life Sciences, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Xiaoyue Li
- HIT Center for Life Sciences, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Ligu Chen
- HIT Center for Life Sciences, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Chen Zhang
- HIT Center for Life Sciences, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Xinran Zhao
- HIT Center for Life Sciences, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Xiaowan Li
- HIT Center for Life Sciences, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Qiming Shao
- HIT Center for Life Sciences, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Qiang Sun
- HIT Center for Life Sciences, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Kaiyue Yan
- HIT Center for Life Sciences, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China
| | - Guangfu Wang
- HIT Center for Life Sciences, School of Life Science and Technology, Harbin Institute of Technology, Harbin 150001, China.
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38
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Buzsáki G, Tingley D. Cognition from the Body-Brain Partnership: Exaptation of Memory. Annu Rev Neurosci 2023; 46:191-210. [PMID: 36917822 PMCID: PMC10793243 DOI: 10.1146/annurev-neuro-101222-110632] [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] [Indexed: 03/16/2023]
Abstract
Examination of cognition has historically been approached from language and introspection. However, human language-dependent definitions ignore the evolutionary roots of brain mechanisms and constrain their study in experimental animals. We promote an alternative view, namely that cognition, including memory, can be explained by exaptation and expansion of the circuits and algorithms serving bodily functions. Regulation and protection of metabolic and energetic processes require time-evolving brain computations enabling the organism to prepare for altered future states. Exaptation of such circuits was likely exploited for exploration of the organism's niche. We illustrate that exploration gives rise to a cognitive map, and in turn, environment-disengaged computation allows for mental travel into the past (memory) and the future (planning). Such brain-body interactions not only occur during waking but also persist during sleep. These exaptation steps are illustrated by the dual, endocrine-homeostatic and memory, contributions of the hippocampal system, particularly during hippocampal sharp-wave ripples.
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Affiliation(s)
- György Buzsáki
- Neuroscience Institute and Department of Neurology, NYU Grossman School of Medicine, New York University, New York, NY, USA;
- Center for Neural Science, New York University, New York, NY, USA
| | - David Tingley
- Neuroscience Institute and Department of Neurology, NYU Grossman School of Medicine, New York University, New York, NY, USA;
- Division of Endocrinology, Diabetes and Metabolism, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, Massachusetts, USA
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39
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Deco G, Perl YS, Ponce-Alvarez A, Tagliazucchi E, Whybrow P, Fuster J, Kringelbach ML. One ring to rule them all: The unifying role of prefrontal cortex in steering task-related brain dynamics. Prog Neurobiol 2023:102468. [PMID: 37301532 DOI: 10.1016/j.pneurobio.2023.102468] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 05/10/2023] [Accepted: 05/18/2023] [Indexed: 06/12/2023]
Abstract
Surviving and thriving in a complex world require intricate balancing of higher order brain functions with essential survival-related behaviours. Exactly how this is achieved is not fully understood but a large body of work has shown that different regions in the prefrontal cortex (PFC) play key roles for diverse cognitive and emotional tasks including emotion, control, response inhibition, mental set shifting and working memory. We hypothesised that the key regions are hierarchically organised and we developed a framework for discovering the driving brain regions at the top of the hierarchy, responsible for steering the brain dynamics of higher brain function. We fitted a time-dependent whole-brain model to the neuroimaging data from large-scale Human Connectome Project with over 1,000 participants and computed the entropy production for rest and seven tasks (covering the main domains of cognition). This thermodynamics framework allowed us to identify the main common, unifying drivers steering the orchestration of brain dynamics during difficult tasks; located in key regions of the PFC (inferior frontal gyrus, lateral orbitofrontal cortex, rostral and caudal frontal cortex and rostral anterior cingulate cortex). Selectively lesioning these regions in the whole-brain model demonstrated their causal mechanistic importance. Overall, this shows the existence of a 'ring' of specific PFC regions ruling over the orchestration of higher brain function.
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Affiliation(s)
- Gustavo Deco
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona, 08018, Spain; Institució Catalana de la Recerca i Estudis Avançats (ICREA), Passeig Lluís Companys 23, Barcelona, 08010, Spain
| | - Yonatan Sanz Perl
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona, 08018, Spain; Department of Physics, University of Buenos Aires, Buenos Aires, Argentina
| | - Adrián Ponce-Alvarez
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Roc Boronat 138, Barcelona, 08018, Spain
| | - Enzo Tagliazucchi
- Department of Physics, University of Buenos Aires, Buenos Aires, Argentina; Latin American Brain Health Institute (BrainLat), Universidad Adolfo Ibanez, Santiago, Chile
| | - Peter Whybrow
- University of California, Los Angeles, CA 90024, USA; Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, UK
| | - Joaquín Fuster
- University of California, Los Angeles, CA 90024, USA; Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, UK
| | - Morten L Kringelbach
- Centre for Eudaimonia and Human Flourishing, Linacre College, University of Oxford, Oxford, UK; Department of Psychiatry, University of Oxford, Oxford, UK; Center for Music in the Brain, Department of Clinical Medicine, Aarhus University, Aarhus, DK
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40
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Frank LE, Zeithamova D. Evaluating methods for measuring background connectivity in slow event-related functional magnetic resonance imaging designs. Brain Behav 2023; 13:e3015. [PMID: 37062880 PMCID: PMC10275534 DOI: 10.1002/brb3.3015] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/02/2022] [Revised: 03/10/2023] [Accepted: 03/20/2023] [Indexed: 04/18/2023] Open
Abstract
INTRODUCTION Resting-state functional magnetic resonance imaging (fMRI) is widely used for measuring functional interactions between brain regions, significantly contributing to our understanding of large-scale brain networks and brain-behavior relationships. Furthermore, idiosyncratic patterns of resting-state connections can be leveraged to identify individuals and predict individual differences in clinical symptoms, cognitive abilities, and other individual factors. Idiosyncratic connectivity patterns are thought to persist across task states, suggesting task-based fMRI can be similarly leveraged for individual differences analyses. METHOD Here, we tested the degree to which functional interactions occurring in the background of a task during slow event-related fMRI parallel or differ from those captured during resting-state fMRI. We compared two approaches for removing task-evoked activity from task-based fMRI: (1) applying a low-pass filter to remove task-related frequencies in the signal, or (2) extracting residuals from a general linear model (GLM) that accounts for task-evoked responses. RESULT We found that the organization of large-scale cortical networks and individual's idiosyncratic connectivity patterns are preserved during task-based fMRI. In contrast, individual differences in connection strength can vary more substantially between rest and task. Compared to low-pass filtering, background connectivity obtained from GLM residuals produced idiosyncratic connectivity patterns and individual differences in connection strength that more resembled rest. However, all background connectivity measures were highly similar when derived from the low-pass-filtered signal or GLM residuals, indicating that both methods are suitable for measuring background connectivity. CONCLUSION Together, our results highlight new avenues for the analysis of task-based fMRI datasets and the utility of each background connectivity method.
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Affiliation(s)
- Lea E. Frank
- Department of PsychologyUniversity of OregonEugeneOregonUSA
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41
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Ware AL, Onicas AI, Abdeen N, Beauchamp MH, Beaulieu C, Bjornson BH, Craig W, Dehaes M, Deschenes S, Doan Q, Freedman SB, Goodyear BG, Gravel J, Ledoux AA, Zemek R, Yeates KO, Lebel C. Altered longitudinal structural connectome in paediatric mild traumatic brain injury: an Advancing Concussion Assessment in Paediatrics study. Brain Commun 2023; 5:fcad173. [PMID: 37324241 PMCID: PMC10265725 DOI: 10.1093/braincomms/fcad173] [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: 10/28/2022] [Revised: 04/18/2023] [Accepted: 05/30/2023] [Indexed: 06/17/2023] Open
Abstract
Advanced diffusion-weighted imaging techniques have increased understanding of the neuropathology of paediatric mild traumatic brain injury (i.e. concussion). Most studies have examined discrete white-matter pathways, which may not capture the characteristically subtle, diffuse and heterogenous effects of paediatric concussion on brain microstructure. This study compared the structural connectome of children with concussion to those with mild orthopaedic injury to determine whether network metrics and their trajectories across time post-injury differentiate paediatric concussion from mild traumatic injury more generally. Data were drawn from of a large study of outcomes in paediatric concussion. Children aged 8-16.99 years were recruited from five paediatric emergency departments within 48 h of sustaining a concussion (n = 360; 56% male) or mild orthopaedic injury (n = 196; 62% male). A reliable change score was used to classify children with concussion into two groups: concussion with or without persistent symptoms. Children completed 3 T MRI at post-acute (2-33 days) and/or chronic (3 or 6 months, via random assignment) post-injury follow-ups. Diffusion-weighted images were used to calculate the diffusion tensor, conduct deterministic whole-brain fibre tractography and compute connectivity matrices in native (diffusion) space for 90 supratentorial regions. Weighted adjacency matrices were constructed using average fractional anisotropy and used to calculate global and local (regional) graph theory metrics. Linear mixed effects modelling was performed to compare groups, correcting for multiple comparisons. Groups did not differ in global network metrics. However, the clustering coefficient, betweenness centrality and efficiency of the insula, cingulate, parietal, occipital and subcortical regions differed among groups, with differences moderated by time (days) post-injury, biological sex and age at time of injury. Post-acute differences were minimal, whereas more robust alterations emerged at 3 and especially 6 months in children with concussion with persistent symptoms, albeit differently by sex and age. In the largest neuroimaging study to date, post-acute regional network metrics distinguished concussion from mild orthopaedic injury and predicted symptom recovery 1-month post-injury. Regional network parameters alterations were more robust and widespread at chronic timepoints than post-acutely after concussion. Results suggest that increased regional and local subnetwork segregation (modularity) and inefficiency occurs across time after concussion, emerging after post-concussive symptom resolve in most children. These differences persist up to 6 months after concussion, especially in children who showed persistent symptoms. While prognostic, the small to modest effect size of group differences and the moderating effects of sex likely would preclude effective clinical application in individual patients.
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Affiliation(s)
- Ashley L Ware
- Correspondence to: Ashley L. Ware, PhD Department of Psychology, Georgia State University 140 Decatur Street SE, Atlanta, GA 30303, USA E-mail:
| | - Adrian I Onicas
- Department of Psychology, University of Calgary, Calgary, AB T2N 0V2, Canada
- Computer Vision Group, Sano Centre for Computational Medicine, Kraków 30-054, Poland
| | - Nishard Abdeen
- Department of Radiology, Children’s Hospital of Eastern Ontario Research Institute, University of Ottawa,Ottawa, ON, Canada K1H 8L1
| | - Miriam H Beauchamp
- Department of Psychology, University of Montreal and CHU Sainte-Justine Hospital Research Center, Montréal, QC, Canada H3C 3J7
| | - Christian Beaulieu
- Department of Biomedical Engineering, 1098 Research Transition Facility, University of Alberta, Edmonton, AB, Canada T6G 2V2
| | - Bruce H Bjornson
- Division of Neurology, Department of Pediatrics, University of British Columbia, Vancouver, BC, Canada V6H 3V4
- BC Children’s Hospital Research Institute, Vancouver, BC, Canada V6H 3V4
| | - William Craig
- University of Alberta and Stollery Children’s Hospital, Edmonton, AB, Canada T6G 1C9
| | - Mathieu Dehaes
- Department of Radiology, Radio-oncology and Nuclear Medicine, Institute of Biomedical Engineering, University of Montreal, Montréal, QC, Canada H3T1J4
- CHU Sainte-Justine Research Center, Montréal, QC, Canada H3T1C5
| | - Sylvain Deschenes
- CHU Sainte-Justine Research Center, Montréal, QC, Canada H3T1C5
- Department of Radiology, Radio-oncology and Nuclear Medicine, University of Montreal, Montréal, QC, CHU Sainte-Justine Research Center, Montréal, QC, Canada H3T1C5
| | - Quynh Doan
- Department of Pediatrics University of British Columbia, BC Children’s Hospital Research Institute, Vancouver, BC, Canada V5Z 4H4
| | - Stephen B Freedman
- Departments of Pediatric and Emergency Medicine, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada T3B 6A8
| | - Bradley G Goodyear
- Alberta Children's Hospital Research Institute and Hotchkiss Brain Institute, University of Calgary, AB T2N 0V2, Canada
- Department of Radiology, University of Calgary, Calgary, AB T2N 0V2, Canada
| | - Jocelyn Gravel
- Pediatric Emergency Department, CHU Sainte-Justine, Montréal, QC H3T1C5, Canada
- Department of Pediatric, Université de Montréal, Montréal, QC H3T 1C5, Canada
| | - Andrée-Anne Ledoux
- Department of Cellular Molecular Medicine, University of Ottawa, Children’s Hospital of Eastern Ontario Research Institute, Ottawa, ON, Canada K1H8L1
| | - Roger Zemek
- Department of Pediatrics and Emergency Medicine, University of Ottawa, Children’s Hospital of Eastern Ontario Research Institute, Ottawa, ON, Canada K1H8L1
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Bazinet V, Hansen JY, Vos de Wael R, Bernhardt BC, van den Heuvel MP, Misic B. Assortative mixing in micro-architecturally annotated brain connectomes. Nat Commun 2023; 14:2850. [PMID: 37202416 DOI: 10.1038/s41467-023-38585-4] [Citation(s) in RCA: 9] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/03/2022] [Accepted: 05/08/2023] [Indexed: 05/20/2023] Open
Abstract
The wiring of the brain connects micro-architecturally diverse neuronal populations, but the conventional graph model, which encodes macroscale brain connectivity as a network of nodes and edges, abstracts away the rich biological detail of each regional node. Here, we annotate connectomes with multiple biological attributes and formally study assortative mixing in annotated connectomes. Namely, we quantify the tendency for regions to be connected based on the similarity of their micro-architectural attributes. We perform all experiments using four cortico-cortical connectome datasets from three different species, and consider a range of molecular, cellular, and laminar annotations. We show that mixing between micro-architecturally diverse neuronal populations is supported by long-distance connections and find that the arrangement of connections with respect to biological annotations is associated to patterns of regional functional specialization. By bridging scales of cortical organization, from microscale attributes to macroscale connectivity, this work lays the foundation for next-generation annotated connectomics.
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Affiliation(s)
- Vincent Bazinet
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Justine Y Hansen
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Reinder Vos de Wael
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Boris C Bernhardt
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada
| | - Martijn P van den Heuvel
- Center for Neurogenomics and Cognitive Research, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Bratislav Misic
- McConnell Brain Imaging Centre, Montréal Neurological Institute, McGill University, Montréal, Canada.
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43
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Bedini M, Olivetti E, Avesani P, Baldauf D. Accurate localization and coactivation profiles of the frontal eye field and inferior frontal junction: an ALE and MACM fMRI meta-analysis. Brain Struct Funct 2023; 228:997-1017. [PMID: 37093304 PMCID: PMC10147761 DOI: 10.1007/s00429-023-02641-y] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2022] [Accepted: 04/08/2023] [Indexed: 04/25/2023]
Abstract
The frontal eye field (FEF) and the inferior frontal junction (IFJ) are prefrontal structures involved in mediating multiple aspects of goal-driven behavior. Despite being recognized as prominent nodes of the networks underlying spatial attention and oculomotor control, and working memory and cognitive control, respectively, the limited quantitative evidence on their precise localization has considerably impeded the detailed understanding of their structure and connectivity. In this study, we performed an activation likelihood estimation (ALE) fMRI meta-analysis by selecting studies that employed standard paradigms to accurately infer the localization of these regions in stereotaxic space. For the FEF, we found the highest spatial convergence of activations for prosaccade and antisaccade paradigms at the junction of the precentral sulcus and superior frontal sulcus. For the IFJ, we found consistent activations across oddball/attention, working memory, task-switching and Stroop paradigms at the junction of the inferior precentral sulcus and inferior frontal sulcus. We related these clusters to previous meta-analyses, sulcal/gyral neuroanatomy, and a comprehensive brain parcellation, highlighting important differences compared to their results and taxonomy. Finally, we leveraged the ALE peak coordinates as seeds to perform a meta-analytic connectivity modeling (MACM) analysis, which revealed systematic coactivation patterns spanning the frontal, parietal, and temporal cortices. We decoded the behavioral domains associated with these coactivations, suggesting that these may allow FEF and IFJ to support their specialized roles in flexible behavior. Our study provides the meta-analytic groundwork for investigating the relationship between functional specialization and connectivity of two crucial control structures of the prefrontal cortex.
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Affiliation(s)
- Marco Bedini
- Center for Mind/Brain Sciences (CIMeC), University of Trento, Via delle Regole 101, 38123, Trento, Italy.
- Department of Psychology, University of California, San Diego, McGill Hall 9500 Gilman Dr, La Jolla, CA, 92093-0109, USA.
| | - Emanuele Olivetti
- Center for Mind/Brain Sciences (CIMeC), University of Trento, Via delle Regole 101, 38123, Trento, Italy
- NILab, Bruno Kessler Foundation (FBK), Via delle Regole 101, 38123, Trento, Italy
| | - Paolo Avesani
- Center for Mind/Brain Sciences (CIMeC), University of Trento, Via delle Regole 101, 38123, Trento, Italy
- NILab, Bruno Kessler Foundation (FBK), Via delle Regole 101, 38123, Trento, Italy
| | - Daniel Baldauf
- Center for Mind/Brain Sciences (CIMeC), University of Trento, Via delle Regole 101, 38123, Trento, Italy
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44
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Rahimi S, Jackson R, Farahibozorg SR, Hauk O. Time-Lagged Multidimensional Pattern Connectivity (TL-MDPC): An EEG/MEG pattern transformation based functional connectivity metric. Neuroimage 2023; 270:119958. [PMID: 36813063 PMCID: PMC10030313 DOI: 10.1016/j.neuroimage.2023.119958] [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: 09/15/2022] [Revised: 01/16/2023] [Accepted: 02/19/2023] [Indexed: 02/23/2023] Open
Abstract
Functional and effective connectivity methods are essential to study the complex information flow in brain networks underlying human cognition. Only recently have connectivity methods begun to emerge that make use of the full multidimensional information contained in patterns of brain activation, rather than unidimensional summary measures of these patterns. To date, these methods have mostly been applied to fMRI data, and no method allows vertex-to-vertex transformations with the temporal specificity of EEG/MEG data. Here, we introduce time-lagged multidimensional pattern connectivity (TL-MDPC) as a novel bivariate functional connectivity metric for EEG/MEG research. TL-MDPC estimates the vertex-to-vertex transformations among multiple brain regions and across different latency ranges. It determines how well patterns in ROI X at time point tx can linearly predict patterns of ROI Y at time point ty. In the present study, we use simulations to demonstrate TL-MDPC's increased sensitivity to multidimensional effects compared to a unidimensional approach across realistic choices of number of trials and signal-to-noise ratios. We applied TL-MDPC, as well as its unidimensional counterpart, to an existing dataset varying the depth of semantic processing of visually presented words by contrasting a semantic decision and a lexical decision task. TL-MDPC detected significant effects beginning very early on, and showed stronger task modulations than the unidimensional approach, suggesting that it is capable of capturing more information. With TL-MDPC only, we observed rich connectivity between core semantic representation (left and right anterior temporal lobes) and semantic control (inferior frontal gyrus and posterior temporal cortex) areas with greater semantic demands. TL-MDPC is a promising approach to identify multidimensional connectivity patterns, typically missed by unidimensional approaches.
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Affiliation(s)
- Setareh Rahimi
- MRC Cognition and Brain Sciences Unit, University of Cambridge, 15 Chaucer Road, Cambridge CB2 7EF United Kingdom.
| | - Rebecca Jackson
- Department of Psychology & York Biomedical Research Institute, University of York, United Kingdom; MRC Cognition and Brain Sciences Unit, University of Cambridge, 15 Chaucer Road, Cambridge CB2 7EF United Kingdom
| | - Seyedeh-Rezvan Farahibozorg
- Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, United Kingdom
| | - Olaf Hauk
- MRC Cognition and Brain Sciences Unit, University of Cambridge, 15 Chaucer Road, Cambridge CB2 7EF United Kingdom
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45
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Yang Z, Chen Y, Hou X, Xu Y, Bai F. Topologically convergent and divergent large scale complex networks among Alzheimer's disease spectrum patients: A systematic review. Heliyon 2023; 9:e15389. [PMID: 37101638 PMCID: PMC10123263 DOI: 10.1016/j.heliyon.2023.e15389] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2022] [Revised: 03/16/2023] [Accepted: 04/05/2023] [Indexed: 04/28/2023] Open
Abstract
Alzheimer's disease (AD) is associated with disruption at the level of a large-scale complex network. To explore the underlying mechanisms in the progression of AD, graph theory was used to quantitatively analyze the topological properties of structural and functional connections. Although an increasing number of studies have shown altered global and nodal network properties, little is known about the topologically convergent and divergent patterns between structural and functional networks among AD-spectrum patients. In this review, we summarized the topological patterns of the large-scale complex networks using multimodal neuroimaging graph theory analysis in AD spectrum patients. Convergent deficits in the connectivity characteristics were primarily in the default mode network (DMN) itself both in the structural and functional networks, while a divergent changes in the neighboring regions of the DMN were also observed between the patient groups. Together, the application of graph theory to large-scale complex brain networks provides quantitative insights into topological principles of brain network organization, which may lead to increasing attention in identifying the underlying neuroimaging pathological changes and predicting the progression of AD.
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Affiliation(s)
- Zhiyuan Yang
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China
| | - Ya Chen
- Department of Neurology, Nanjing Drum Tower Hospital Clinical College of Nanjing University of Chinese Medicine, Nanjing 210008, China
| | - Xinle Hou
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China
| | - Yun Xu
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China
- Department of Neurology, Nanjing Drum Tower Hospital, State Key Laboratory of Pharmaceutical Biotechnology, Nanjing University, Nanjing 210008, China
| | - Feng Bai
- Department of Neurology, Nanjing Drum Tower Hospital, Affiliated Hospital of Medical School, Nanjing University, Nanjing 210008, China
- Geriatric Medicine Center, Affiliated Taikang Xianlin Drum Tower Hospital, Medical School of Nanjing University, Nanjing 210008, China
- Correspondence to: 321 Zhongshan Road, Nanjing, 210008, China.
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46
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Maliske LZ, Schurz M, Kanske P. Interactions within the social brain: Co-activation and connectivity among networks enabling empathy and Theory of Mind. Neurosci Biobehav Rev 2023; 147:105080. [PMID: 36764638 DOI: 10.1016/j.neubiorev.2023.105080] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/28/2021] [Revised: 09/08/2022] [Accepted: 02/05/2023] [Indexed: 02/11/2023]
Abstract
Empathy and Theory of Mind (ToM) have classically been studied as separate social functions, however, recent advances demonstrate the need to investigate the two in interaction: naturalistic settings often blur the distinction of affect and cognition and demand the simultaneous processing of such different stimulus dimensions. Here, we investigate how empathy and ToM related brain networks interact in contexts wherein multiple cognitive and affective demands must be processed simultaneously. Building on the findings of a recent meta-analysis and hierarchical clustering analysis, we perform meta-analytic connectivity modeling to determine patterns of task-context specific network changes. We analyze 140 studies including classical empathy and ToM tasks, as well as complex social tasks. For studies at the intersection of empathy and ToM, neural co-activation patterns included areas typically associated with both empathy and ToM. Network integration is discussed as a means of combining mechanisms across unique behavioral domains. Such integration may enable adaptive behavior in complex, naturalistic social settings that require simultaneous processing of a multitude of different affective and cognitive information.
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Affiliation(s)
- Lara Z Maliske
- Clinical Psychology and Behavioral Neuroscience, Faculty of Psychology, Technische Universität Dresden, Chemnitzer Straße 46, 01187 Dresden, Germany.
| | - Matthias Schurz
- Institute of Psychology and Digital Science Center, University of Innsbruck, Innrain 52, 6020 Innsbruck, Austria; Donders Institute for Brain, Cognition, & Behaviour, Radboud University, Heyendaalseweg 135, 6525 Nijmegen, Netherlands; Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, 13 Mansifield Road, Oxford OX1 3SR, United Kingdom
| | - Philipp Kanske
- Clinical Psychology and Behavioral Neuroscience, Faculty of Psychology, Technische Universität Dresden, Chemnitzer Straße 46, 01187 Dresden, Germany; Max Planck Institute for Human Cognitive and Brain Sciences, Stephanstraße 1a, 04103 Leipzig, Germany
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Chiarion G, Sparacino L, Antonacci Y, Faes L, Mesin L. Connectivity Analysis in EEG Data: A Tutorial Review of the State of the Art and Emerging Trends. Bioengineering (Basel) 2023; 10:bioengineering10030372. [PMID: 36978763 PMCID: PMC10044923 DOI: 10.3390/bioengineering10030372] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 03/10/2023] [Accepted: 03/14/2023] [Indexed: 03/30/2023] Open
Abstract
Understanding how different areas of the human brain communicate with each other is a crucial issue in neuroscience. The concepts of structural, functional and effective connectivity have been widely exploited to describe the human connectome, consisting of brain networks, their structural connections and functional interactions. Despite high-spatial-resolution imaging techniques such as functional magnetic resonance imaging (fMRI) being widely used to map this complex network of multiple interactions, electroencephalographic (EEG) recordings claim high temporal resolution and are thus perfectly suitable to describe either spatially distributed and temporally dynamic patterns of neural activation and connectivity. In this work, we provide a technical account and a categorization of the most-used data-driven approaches to assess brain-functional connectivity, intended as the study of the statistical dependencies between the recorded EEG signals. Different pairwise and multivariate, as well as directed and non-directed connectivity metrics are discussed with a pros-cons approach, in the time, frequency, and information-theoretic domains. The establishment of conceptual and mathematical relationships between metrics from these three frameworks, and the discussion of novel methodological approaches, will allow the reader to go deep into the problem of inferring functional connectivity in complex networks. Furthermore, emerging trends for the description of extended forms of connectivity (e.g., high-order interactions) are also discussed, along with graph-theory tools exploring the topological properties of the network of connections provided by the proposed metrics. Applications to EEG data are reviewed. In addition, the importance of source localization, and the impacts of signal acquisition and pre-processing techniques (e.g., filtering, source localization, and artifact rejection) on the connectivity estimates are recognized and discussed. By going through this review, the reader could delve deeply into the entire process of EEG pre-processing and analysis for the study of brain functional connectivity and learning, thereby exploiting novel methodologies and approaches to the problem of inferring connectivity within complex networks.
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Affiliation(s)
- Giovanni Chiarion
- Mathematical Biology and Physiology, Department Electronics and Telecommunications, Politecnico di Torino, 10129 Turin, Italy
| | - Laura Sparacino
- Department of Engineering, University of Palermo, 90128 Palermo, Italy
| | - Yuri Antonacci
- Department of Engineering, University of Palermo, 90128 Palermo, Italy
| | - Luca Faes
- Department of Engineering, University of Palermo, 90128 Palermo, Italy
| | - Luca Mesin
- Mathematical Biology and Physiology, Department Electronics and Telecommunications, Politecnico di Torino, 10129 Turin, Italy
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Sullivan KE, Kraus L, Kapustina M, Wang L, Stach TR, Lemire AL, Clements J, Cembrowski MS. Sharp cell-type-identity changes differentiate the retrosplenial cortex from the neocortex. Cell Rep 2023; 42:112206. [PMID: 36881508 DOI: 10.1016/j.celrep.2023.112206] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2022] [Revised: 12/22/2022] [Accepted: 02/15/2023] [Indexed: 03/08/2023] Open
Abstract
The laminae of the neocortex are fundamental processing layers of the mammalian brain. Notably, such laminae are believed to be relatively stereotyped across short spatial scales such that shared laminae between nearby brain regions exhibit similar constituent cells. Here, we consider a potential exception to this rule by studying the retrosplenial cortex (RSC), a brain region known for sharp cytoarchitectonic differences across its granular-dysgranular border. Using a variety of transcriptomics techniques, we identify, spatially map, and interpret the excitatory cell-type landscape of the mouse RSC. In doing so, we uncover that RSC gene expression and cell types change sharply at the granular-dysgranular border. Additionally, supposedly homologous laminae between the RSC and the neocortex are effectively wholly distinct in their cell-type composition. In collection, the RSC exhibits a variety of intrinsic cell-type specializations and embodies an organizational principle wherein cell-type identities can vary sharply within and between brain regions.
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Affiliation(s)
- Kaitlin E Sullivan
- Department of Cellular and Physiological Sciences, Life Sciences Institute, University of British Columbia, 2350 Health Sciences Boulevard, Vancouver, BC, Canada
| | - Larissa Kraus
- Department of Cellular and Physiological Sciences, Life Sciences Institute, University of British Columbia, 2350 Health Sciences Boulevard, Vancouver, BC, Canada
| | - Margarita Kapustina
- Department of Cellular and Physiological Sciences, Life Sciences Institute, University of British Columbia, 2350 Health Sciences Boulevard, Vancouver, BC, Canada
| | - Lihua Wang
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, 2215 Wesbrook Mall, Vancouver, BC, Canada
| | - Tara R Stach
- School of Biomedical Engineering, Biomedical Research Centre, University of British Columbia, 2222 Health Sciences Mall, Vancouver, BC, Canada
| | - Andrew L Lemire
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, 2215 Wesbrook Mall, Vancouver, BC, Canada
| | - Jody Clements
- Djavad Mowafaghian Centre for Brain Health, University of British Columbia, 2215 Wesbrook Mall, Vancouver, BC, Canada
| | - Mark S Cembrowski
- Department of Cellular and Physiological Sciences, Life Sciences Institute, University of British Columbia, 2350 Health Sciences Boulevard, Vancouver, BC, Canada; Djavad Mowafaghian Centre for Brain Health, University of British Columbia, 2215 Wesbrook Mall, Vancouver, BC, Canada; Janelia Research Campus, HHMI, 19700 Helix Dr, Ashburn, VA, USA.
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49
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Pessoa L. Disentangling Some Conceptual Knots. J Cogn Neurosci 2023; 35:391-395. [PMID: 36626350 PMCID: PMC11019943 DOI: 10.1162/jocn_a_01961] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
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50
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Sha Z, Schijven D, Fisher SE, Francks C. Genetic architecture of the white matter connectome of the human brain. SCIENCE ADVANCES 2023; 9:eadd2870. [PMID: 36800424 PMCID: PMC9937579 DOI: 10.1126/sciadv.add2870] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Accepted: 01/13/2023] [Indexed: 06/18/2023]
Abstract
White matter tracts form the structural basis of large-scale brain networks. We applied brain-wide tractography to diffusion images from 30,810 adults (U.K. Biobank) and found significant heritability for 90 node-level and 851 edge-level network connectivity measures. Multivariate genome-wide association analyses identified 325 genetic loci, of which 80% had not been previously associated with brain metrics. Enrichment analyses implicated neurodevelopmental processes including neurogenesis, neural differentiation, neural migration, neural projection guidance, and axon development, as well as prenatal brain expression especially in stem cells, astrocytes, microglia, and neurons. The multivariate association profiles implicated 31 loci in connectivity between core regions of the left-hemisphere language network. Polygenic scores for psychiatric, neurological, and behavioral traits also showed significant multivariate associations with structural connectivity, each implicating distinct sets of brain regions with trait-relevant functional profiles. This large-scale mapping study revealed common genetic contributions to variation in the structural connectome of the human brain.
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Affiliation(s)
- Zhiqiang Sha
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, Netherlands
| | - Dick Schijven
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, Netherlands
| | - Simon E. Fisher
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
| | - Clyde Francks
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, Netherlands
- Department of Human Genetics, Radboud University Medical Center, Nijmegen, Netherlands
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