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Meta-connectomic analysis maps consistent, reproducible, and transcriptionally relevant functional connectome hubs in the human brain. Commun Biol 2022; 5:1056. [PMID: 36195744 PMCID: PMC9532385 DOI: 10.1038/s42003-022-04028-x] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2022] [Accepted: 09/23/2022] [Indexed: 11/10/2022] Open
Abstract
Human brain connectomes include sets of densely connected hub regions. However, the consistency and reproducibility of functional connectome hubs have not been established to date and the genetic signatures underlying robust hubs remain unknown. Here, we conduct a worldwide harmonized meta-connectomic analysis by pooling resting-state functional MRI data of 5212 healthy young adults across 61 independent cohorts. We identify highly consistent and reproducible connectome hubs in heteromodal and unimodal regions both across cohorts and across individuals, with the greatest effects observed in lateral parietal cortex. These hubs show heterogeneous connectivity profiles and are critical for both intra- and inter-network communications. Using post-mortem transcriptome datasets, we show that as compared to non-hubs, connectome hubs have a spatiotemporally distinctive transcriptomic pattern dominated by genes involved in the neuropeptide signaling pathway, neurodevelopmental processes, and metabolic processes. These results highlight the robustness of macroscopic connectome hubs and their potential cellular and molecular underpinnings, which markedly furthers our understanding of how connectome hubs emerge in development, support complex cognition in health, and are involved in disease. A worldwide harmonized meta-connectomic analysis of 5212 healthy young adults across 61 independent cohorts provides consistent, reproducible, and transcriptionally relevant functional connectome hubs in the resting human brain.
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52
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Cao YH, Ding J, Tang QH, Zhang J, Huang ZY, Tang XM, Liu JB, Ma YS, Fu D. Deciphering cell-cell interactions and communication in the tumor microenvironment and unraveling intratumoral genetic heterogeneity via single-cell genomic sequencing. Bioengineered 2022; 13:14974-14986. [PMID: 37105769 DOI: 10.1080/21655979.2023.2185434] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/29/2023] Open
Abstract
A tumor's heterogeneity has important implications in terms of its clonal origin, progression, stemness, and drug resistance. Therefore, because of its significance in treatment, it is important to understand the gene expression pattern of a single cell, track gene expression or mutation in heterogeneous cells, evaluate the clonal origin of cancer cells, and determine the selective evolution of different subpopulations of cancer cells. Researchers are able to trace a cell's mutation and identify different types of tumor cells by measuring the whole transcriptome with single-cell sequencing (scRNA-seq). This technology provides a better understanding of the molecular mechanisms driving tumor growth than that offered by traditional RNA sequencing methods. In addition, it has revealed changes in the mutations and functions of somatic cells as a tumor evolves; it has also clarified immune cell infiltration and activation. Research on scRNA-seq technology has recently advanced significantly, suggesting new strategies for the treatment of cancer. In short, cancer researchers have become increasingly dependent on scRNA-seq. This paper reviews the development, detection principles, and processes of scRNA-seq technology and their application in tumor research. It also considers potential clinical applications.
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Affiliation(s)
- Ya-Hong Cao
- Department of Respiratory, Nantong Traditional Chinese Medicine Hospital, Affiliated Nantong Traditional Chinese Medicine Hospital of Nantong University, Nantong, Jiangsu, China
| | - Jie Ding
- Department of Clinical Laboratory, Jingjiang Traditional Chinese Medicine Hospital, Jingjiang, Jiangsu, China
| | - Qing-Hai Tang
- Hunan Key Laboratory for Conservation and Utilization of Biological Resources in the Nanyue Mountainous Region and College of Life Sciences and Environment, Hengyang Normal University, Hengyang, Hunan, China
| | - Jie Zhang
- Department of Immunology, School of Medicine, Nantong University, Nantong, Jiangsu, China
| | - Zhong-Yan Huang
- Institute of Pancreatic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, Huangpu, China
| | - Xiao-Mei Tang
- Institute of Pancreatic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, Huangpu, China
| | - Ji-Bin Liu
- Institute of Oncology, Affiliated Tumor Hospital of Nantong University, Nantong, Jiangsu, China
| | - Yu-Shui Ma
- Cancer Institute, Longhua Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, Xuhui, China
| | - Da Fu
- Institute of Pancreatic Diseases, Ruijin Hospital, Shanghai Jiaotong University School of Medicine, Shanghai, Huangpu, China
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53
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Giacomel A, Martins D, Frigo M, Turkheimer F, Williams SC, Dipasquale O, Veronese M. Integrating neuroimaging and gene expression data using the imaging transcriptomics toolbox. STAR Protoc 2022; 3:101315. [PMID: 35479111 PMCID: PMC9036395 DOI: 10.1016/j.xpro.2022.101315] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/29/2022] Open
Abstract
The integration of neuroimaging and transcriptomics data, Imaging Transcriptomics, is becoming increasingly popular but standardized workflows for its implementation are still lacking. We describe the Imaging Transcriptomics toolbox, a new package that implements a full imaging transcriptomics pipeline using a user-friendly, command line interface. This toolbox allows the user to identify patterns of gene expression which correlates with a specific neuroimaging phenotype and perform gene set enrichment analyses to inform the biological interpretation of the findings using up-to-date methods. For complete details on the use and execution of this protocol, please refer to Martins et al. (2021). Protocol for using the Imaging Transcriptomics toolbox Identifies transcriptomic correlates of neuroimaging data Performs gene enrichment analysis to contextualize findings Standardized and user-friendly pipeline using robust statistics
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Affiliation(s)
- Alessio Giacomel
- Department of Neuroimaging, IoPPN, King’s College London, London, UK
- Corresponding author
| | - Daniel Martins
- Department of Neuroimaging, IoPPN, King’s College London, London, UK
- Corresponding author
| | - Matteo Frigo
- Corsmed AB, Stockholm, Sweden
- ATHENA Project Team, Inria Sophia Antipolis - Mediterranée, Université Côte d’Azur, Nice, France
| | | | | | | | - Mattia Veronese
- Department of Neuroimaging, IoPPN, King’s College London, London, UK
- Department of Information Engineering, University of Padova, Padova, Italy
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54
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Abstract
Recent advances in imaging and tracing technology provide increasingly detailed reconstructions of brain connectomes. Concomitant analytic advances enable rigorous identification and quantification of functionally important features of brain network architecture. Null models are a flexible tool to statistically benchmark the presence or magnitude of features of interest, by selectively preserving specific architectural properties of brain networks while systematically randomizing others. Here we describe the logic, implementation and interpretation of null models of connectomes. We introduce randomization and generative approaches to constructing null networks, and outline a taxonomy of network methods for statistical inference. We highlight the spectrum of null models - from liberal models that control few network properties, to conservative models that recapitulate multiple properties of empirical networks - that allow us to operationalize and test detailed hypotheses about the structure and function of brain networks. We review emerging scenarios for the application of null models in network neuroscience, including for spatially embedded networks, annotated networks and correlation-derived networks. Finally, we consider the limits of null models, as well as outstanding questions for the field.
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55
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Wang G, Wu W, Xu Y, Yang Z, Xiao B, Long L. Imaging Genetics in Epilepsy: Current Knowledge and New Perspectives. Front Mol Neurosci 2022; 15:891621. [PMID: 35706428 PMCID: PMC9189397 DOI: 10.3389/fnmol.2022.891621] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 05/06/2022] [Indexed: 12/11/2022] Open
Abstract
Epilepsy is a neurological network disease with genetics playing a much greater role than was previously appreciated. Unfortunately, the relationship between genetic basis and imaging phenotype is by no means simple. Imaging genetics integrates multidimensional datasets within a unified framework, providing a unique opportunity to pursue a global vision for epilepsy. This review delineates the current knowledge of underlying genetic mechanisms for brain networks in different epilepsy syndromes, particularly from a neural developmental perspective. Further, endophenotypes and their potential value are discussed. Finally, we highlight current challenges and provide perspectives for the future development of imaging genetics in epilepsy.
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Affiliation(s)
- Ge Wang
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
- Clinical Research Center for Epileptic Disease of Hunan Province, Central South University, Changsha, China
| | - Wenyue Wu
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
- Department of Neurology, The Second Affiliated Hospital of Nanchang University, Jiangxi, China
| | - Yuchen Xu
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
- Department of Neurology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, China
| | - Zhuanyi Yang
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
- Clinical Research Center for Epileptic Disease of Hunan Province, Central South University, Changsha, China
- Department of Neurosurgery, Xiangya Hospital, Central South University, Changsha, China
| | - Bo Xiao
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
- Clinical Research Center for Epileptic Disease of Hunan Province, Central South University, Changsha, China
| | - Lili Long
- Department of Neurology, Xiangya Hospital, Central South University, Changsha, China
- National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, China
- Clinical Research Center for Epileptic Disease of Hunan Province, Central South University, Changsha, China
- *Correspondence: Lili Long
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56
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Dorfschmidt L, Bethlehem RA, Seidlitz J, Váša F, White SR, Romero-García R, Kitzbichler MG, Aruldass AR, Morgan SE, Goodyer IM, Fonagy P, Jones PB, Dolan RJ, Harrison NA, Vértes PE, Bullmore ET. Sexually divergent development of depression-related brain networks during healthy human adolescence. SCIENCE ADVANCES 2022; 8:eabm7825. [PMID: 35622918 PMCID: PMC9140984 DOI: 10.1126/sciadv.abm7825] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/12/2021] [Accepted: 04/12/2022] [Indexed: 05/20/2023]
Abstract
Sexual differences in human brain development could be relevant to sex differences in the incidence of depression during adolescence. We tested for sex differences in parameters of normative brain network development using fMRI data on N = 298 healthy adolescents, aged 14 to 26 years, each scanned one to three times. Sexually divergent development of functional connectivity was located in the default mode network, limbic cortex, and subcortical nuclei. Females had a more "disruptive" pattern of development, where weak functional connectivity at age 14 became stronger during adolescence. This fMRI-derived map of sexually divergent brain network development was robustly colocated with i prior loci of reward-related brain activation ii a map of functional dysconnectivity in major depressive disorder (MDD), and iii an adult brain gene transcriptional pattern enriched for genes on the X chromosome, neurodevelopmental genes, and risk genes for MDD. We found normative sexual divergence in adolescent development of a cortico-subcortical brain functional network that is relevant to depression.
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Affiliation(s)
- Lena Dorfschmidt
- Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK
| | | | - Jakob Seidlitz
- Department of Psychiatry, University of Pennsylvania, Philadelphia, PA 19104, USA
- Department of Child and Adolescent Psychiatry and Behavioral Science, University of Pennsylvania, Philadelphia, PA 19104, USA
- Lifespan Brain Institute, Children’s Hospital of Philadelphia and Penn Medicine, Philadelphia, PA 19104, USA
| | - František Váša
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King’s College London, London SE5 8AF, UK
| | - Simon R. White
- Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK
| | | | | | - Athina R. Aruldass
- Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK
| | - Sarah E. Morgan
- Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK
- The Alan Turing Institute, London NW1 2DB, UK
- Department of Computer Science and Technology, University of Cambridge, Cambridge CB2 0SZ, UK
| | - Ian M. Goodyer
- Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK
| | - Peter Fonagy
- Research Department of Clinical, Educational and Health Psychology, University College London, London WC1E 6BT, UK
| | - Peter B. Jones
- Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK
- Cambridgeshire and Peterborough NHS Foundation Trust, Huntingdon PE29 3RJ, UK
| | - Ray J. Dolan
- Wellcome Trust Centre for Neuroimaging, University College London Queen Square Institute of Neurology
- Max Planck University College London Centre for Computational Psychiatry and Ageing Research, University College London, London WC1B 5EH, UK
| | | | - Neil A. Harrison
- Department of Neuroscience, Brighton and Sussex Medical School, University of Sussex Campus, Brighton BN1 9RY, UK
- Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff CF24 4HQ, UK
| | - Petra E. Vértes
- Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK
| | - Edward T. Bullmore
- Department of Psychiatry, University of Cambridge, Cambridge CB2 0SZ, UK
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57
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Luppi AI, Mediano PAM, Rosas FE, Holland N, Fryer TD, O'Brien JT, Rowe JB, Menon DK, Bor D, Stamatakis EA. A synergistic core for human brain evolution and cognition. Nat Neurosci 2022; 25:771-782. [PMID: 35618951 DOI: 10.1038/s41593-022-01070-0] [Citation(s) in RCA: 61] [Impact Index Per Article: 30.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/20/2020] [Accepted: 03/30/2022] [Indexed: 12/11/2022]
Abstract
How does the organization of neural information processing enable humans' sophisticated cognition? Here we decompose functional interactions between brain regions into synergistic and redundant components, revealing their distinct information-processing roles. Combining functional and structural neuroimaging with meta-analytic results, we demonstrate that redundant interactions are predominantly associated with structurally coupled, modular sensorimotor processing. Synergistic interactions instead support integrative processes and complex cognition across higher-order brain networks. The human brain leverages synergistic information to a greater extent than nonhuman primates, with high-synergy association cortices exhibiting the highest degree of evolutionary cortical expansion. Synaptic density mapping from positron emission tomography and convergent molecular and metabolic evidence demonstrate that synergistic interactions are supported by receptor diversity and human-accelerated genes underpinning synaptic function. This information-resolved approach provides analytic tools to disentangle information integration from coupling, enabling richer, more accurate interpretations of functional connectivity, and illuminating how the human neurocognitive architecture navigates the trade-off between robustness and integration.
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Affiliation(s)
- Andrea I Luppi
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Cambridge, UK. .,Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK. .,Leverhulme Centre for the Future of Intelligence, University of Cambridge, Cambridge, UK. .,The Alan Turing Institute, London, UK.
| | - Pedro A M Mediano
- Department of Psychology, University of Cambridge, Cambridge, UK.,Department of Psychology, Queen Mary University of London, London, UK
| | - Fernando E Rosas
- Center for Psychedelic Research, Department of Brain Science, Imperial College London, London, UK.,Data Science Institute, Imperial College London, London, UK.,Center for Complexity Science, Imperial College London, London, UK
| | - Negin Holland
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
| | - Tim D Fryer
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.,Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, UK
| | - John T O'Brien
- Department of Psychiatry, University of Cambridge, Cambridge, UK.,Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK
| | - James B Rowe
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK.,Cambridge University Hospitals NHS Foundation Trust, Cambridge, UK.,MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
| | - David K Menon
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Cambridge, UK.,Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, UK
| | - Daniel Bor
- Department of Psychology, University of Cambridge, Cambridge, UK.,Department of Psychology, Queen Mary University of London, London, UK
| | - Emmanuel A Stamatakis
- Division of Anaesthesia, School of Clinical Medicine, University of Cambridge, Cambridge, UK.,Department of Clinical Neurosciences, University of Cambridge, Cambridge, UK
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58
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Zheng B, Fang L. Spatially resolved transcriptomics provide a new method for cancer research. JOURNAL OF EXPERIMENTAL & CLINICAL CANCER RESEARCH : CR 2022; 41:179. [PMID: 35590346 PMCID: PMC9118771 DOI: 10.1186/s13046-022-02385-3] [Citation(s) in RCA: 17] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/26/2022] [Accepted: 05/06/2022] [Indexed: 12/22/2022]
Abstract
A major feature of cancer is the heterogeneity, both intratumoral and intertumoral. Traditional single-cell techniques have given us a comprehensive understanding of the biological characteristics of individual tumor cells, but the lack of spatial context of the transcriptome has limited the study of cell-to-cell interaction patterns and hindered further exploration of tumor heterogeneity. In recent years, the advent of spatially resolved transcriptomics (SRT) technology has made possible the multidimensional analysis of the tumor microenvironment in the context of intact tissues. Different SRT methods are applicable to different working ranges due to different working principles. In this paper, we review the advantages and disadvantages of various current SRT methods and the overall idea of applying these techniques to oncology studies, hoping to help researchers find breakthroughs. Finally, we discussed the future direction of SRT technology, and deeper investigation into the complex mechanisms of tumor development from different perspectives through multi-omics fusion, paving the way for precisely targeted tumor therapy.
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Affiliation(s)
- Bowen Zheng
- Department of Breast and Thyroid Surgery, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, 200072, People's Republic of China
| | - Lin Fang
- Department of Breast and Thyroid Surgery, Shanghai Tenth People's Hospital, School of Medicine, Tongji University, Shanghai, 200072, People's Republic of China.
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59
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Wang Y, Chai L, Chu C, Li D, Gao C, Wu X, Yang Z, Zhang Y, Xu J, Nyengaard JR, Eickhoff SB, Liu B, Madsen KH, Jiang T, Fan L. Uncovering the genetic profiles underlying the intrinsic organization of the human cerebellum. Mol Psychiatry 2022; 27:2619-2634. [PMID: 35264730 DOI: 10.1038/s41380-022-01489-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 02/01/2022] [Accepted: 02/14/2022] [Indexed: 11/09/2022]
Abstract
The functional diversity of the human cerebellum is largely believed to be derived more from its extensive connections rather than being limited to its mostly invariant architecture. However, whether and how the determination of cerebellar connections in its intrinsic organization interact with microscale gene expression is still unknown. Here we decode the genetic profiles of the cerebellar functional organization by investigating the genetic substrates simultaneously linking cerebellar functional heterogeneity and its drivers, i.e., the connections. We not only identified 443 network-specific genes but also discovered that their co-expression pattern correlated strongly with intra-cerebellar functional connectivity (FC). Ninety of these genes were also linked to the FC of cortico-cerebellar cognitive-limbic networks. To further discover the biological functions of these genes, we performed a "virtual gene knock-out" by observing the change in the coupling between gene co-expression and FC and divided the genes into two subsets, i.e., a positive gene contribution indicator (GCI+) involved in cerebellar neurodevelopment and a negative gene set (GCI-) related to neurotransmission. A more interesting finding is that GCI- is significantly linked with the cerebellar connectivity-behavior association and many recognized brain diseases that are closely linked with the cerebellar functional abnormalities. Our results could collectively help to rethink the genetic substrates underlying the cerebellar functional organization and offer possible micro-macro interacted mechanistic interpretations of the cerebellum-involved high order functions and dysfunctions in neuropsychiatric disorders.
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Affiliation(s)
- Yaping Wang
- Sino-Danish Center, University of Chinese Academy of Sciences, 100190, Beijing, China.,University of Chinese Academy of Sciences, 100190, Beijing, China.,Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China
| | - Lin Chai
- University of Chinese Academy of Sciences, 100190, Beijing, China.,Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China
| | - Congying Chu
- University of Chinese Academy of Sciences, 100190, Beijing, China. .,Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China. .,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China.
| | - Deying Li
- University of Chinese Academy of Sciences, 100190, Beijing, China.,Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China
| | - Chaohong Gao
- Sino-Danish Center, University of Chinese Academy of Sciences, 100190, Beijing, China.,University of Chinese Academy of Sciences, 100190, Beijing, China.,Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China
| | - Xia Wu
- University of Chinese Academy of Sciences, 100190, Beijing, China.,Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China
| | - Zhengyi Yang
- University of Chinese Academy of Sciences, 100190, Beijing, China.,Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China
| | - Yu Zhang
- Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, 311100, China
| | - Junhai Xu
- School of Computer Science and Technology, Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin University, Tianjin, 300350, China
| | - Jens Randel Nyengaard
- Sino-Danish Center, University of Chinese Academy of Sciences, 100190, Beijing, China.,Core Centre for Molecular Morphology, Section for Stereology and Microscopy, Department of Clinical Medicine, Aarhus University, 8000, Aarhus, Denmark.,Department of Pathology, Aarhus University Hospital, 8200, Aarhus, Denmark
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, 52425, Jülich, Germany.,Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
| | - Bing Liu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, 100875, Beijing, China
| | - Kristoffer Hougaard Madsen
- Sino-Danish Center, University of Chinese Academy of Sciences, 100190, Beijing, China.,Department of Informatics and Mathematical Modelling, Technical University of Denmark, 2800, Kongens Lyngby, Denmark.,Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital-Amager and Hvidovre, 2650, Hvidovre, Denmark
| | - Tianzi Jiang
- Sino-Danish Center, University of Chinese Academy of Sciences, 100190, Beijing, China.,University of Chinese Academy of Sciences, 100190, Beijing, China.,Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China.,CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China
| | - Lingzhong Fan
- Sino-Danish Center, University of Chinese Academy of Sciences, 100190, Beijing, China. .,University of Chinese Academy of Sciences, 100190, Beijing, China. .,Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China. .,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China. .,CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China.
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60
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Transcriptomic and cellular decoding of functional brain connectivity changes reveal regional brain vulnerability to pro- and anti-inflammatory therapies. Brain Behav Immun 2022; 102:312-323. [PMID: 35259429 DOI: 10.1016/j.bbi.2022.03.004] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/01/2021] [Revised: 02/16/2022] [Accepted: 03/03/2022] [Indexed: 12/18/2022] Open
Abstract
BACKGROUND Systemic inflammation induces acute changes in mood, motivation and cognition that closely resemble those observed in depressed individuals. However, the mechanistic pathways linking peripheral inflammation to depression-like psychopathology via intermediate effects on brain function remain incompletely understood. METHODS We combined data from 30 patients initiating interferon-α treatment for Hepatitis-C and 20 anti-tumour necrosis factor (TNF) therapy for inflammatory arthritis and used resting-state functional magnetic resonance imaging to investigate acute effects of each treatment on regional global brain connectivity (GBC). We leveraged transcriptomic data from the Allen Human Brain Atlas to uncover potential biological and cellular pathways underpinning regional vulnerability to GBC changes induced by each treatment. RESULTS Interferon-α and anti-TNF therapies both produced differential small-to-medium sized decreases in regional GBC. However, these were observed within distinct brain regions and the regional patterns of GBC changes induced by each treatment did not correlate suggesting independent underlying processes. Further, the spatial distribution of these differential GBC decreases could be captured by multivariate patterns of constitutive regional expression of genes respectively related to: i) neuroinflammation and glial cells; and ii) glutamatergic neurotransmission and neurons. The extent to which each participant expressed patterns of GBC changes aligning with these patterns of transcriptomic vulnerability also correlated with both acute treatment-induced changes in interleukin-6 (IL-6) and, for Interferon-α, longer-term treatment-associated changes in depressive symptoms. CONCLUSIONS Together, we present two transcriptomic models separately linking regional vulnerability to the acute effects of interferon-α and anti-TNF treatments on brain function to glial neuroinflammation and glutamatergic neurotransmission. These findings generate hypotheses about two potential brain mechanisms through which bidirectional changes in peripheral inflammation may contribute to the development/resolution of psychopathology.
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61
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Petrican R, Fornito A, Jones N. Psychological Resilience and Neurodegenerative Risk: A Connectomics-Transcriptomics Investigation in Healthy Adolescent and Middle-Aged Females. Neuroimage 2022; 255:119209. [PMID: 35429627 DOI: 10.1016/j.neuroimage.2022.119209] [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: 09/14/2021] [Revised: 04/05/2022] [Accepted: 04/11/2022] [Indexed: 11/25/2022] Open
Abstract
Adverse life events can inflict substantial long-term damage, which, paradoxically, has been posited to stem from initially adaptative responses to the challenges encountered in one's environment. Thus, identification of the mechanisms linking resilience against recent stressors to longer-term psychological vulnerability is key to understanding optimal functioning across multiple timescales. To address this issue, our study tested the relevance of neuro-reproductive maturation and senescence, respectively, to both resilience and longer-term risk for pathologies characterised by accelerated brain aging, specifically, Alzheimer's Disease (AD). Graph theoretical and partial least squares analyses were conducted on multimodal imaging, reported biological aging and recent adverse experience data from the Lifespan Human Connectome Project (HCP). Availability of reproductive maturation/senescence measures restricted our investigation to adolescent (N =178) and middle-aged (N=146) females. Psychological resilience was linked to age-specific brain senescence patterns suggestive of precocious functional development of somatomotor and control-relevant networks (adolescence) and earlier aging of default mode and salience/ventral attention systems (middle adulthood). Biological aging showed complementary associations with the neural patterns relevant to resilience in adolescence (positive relationship) versus middle-age (negative relationship). Transcriptomic and expression quantitative trait locus data analyses linked the neural aging patterns correlated with psychological resilience in middle adulthood to gene expression patterns suggestive of increased AD risk. Our results imply a partially antagonistic relationship between resilience against proximal stressors and longer-term psychological adjustment in later life. They thus underscore the importance of fine-tuning extant views on successful coping by considering the multiple timescales across which age-specific processes may unfold.
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Affiliation(s)
- Raluca Petrican
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, United Kingdom.
| | - Alex Fornito
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia
| | - Natalie Jones
- Cardiff University Brain Research Imaging Centre (CUBRIC), School of Psychology, Cardiff University, Maindy Road, Cardiff, CF24 4HQ, United Kingdom
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62
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Basaia S, Agosta F, Diez I, Bueichekú E, d'Oleire Uquillas F, Delgado-Alvarado M, Caballero-Gaudes C, Rodriguez-Oroz M, Stojkovic T, Kostic VS, Filippi M, Sepulcre J. Neurogenetic traits outline vulnerability to cortical disruption in Parkinson's disease. Neuroimage Clin 2022; 33:102941. [PMID: 35091253 PMCID: PMC8800137 DOI: 10.1016/j.nicl.2022.102941] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2021] [Revised: 12/03/2021] [Accepted: 01/10/2022] [Indexed: 01/18/2023]
Abstract
The genetic traits that underlie vulnerability to neuronal damage across specific brain circuits in Parkinson's disease (PD) remain to be elucidated. In this study, we characterized the brain topological intersection between propagating connectivity networks in controls and PD participants and gene expression patterns across the human cortex - such as the SNCA gene. We observed that brain connectivity originated from PD-related pathology epicenters in the brainstem recapitulated the anatomical distribution of alpha-synuclein histopathology in postmortem data. We also discovered that the gene set most related to cortical propagation patterns of PD-related pathology was primarily involved in microtubule cellular components. Thus, this study sheds light on new avenues for enhancing detection of PD neuronal vulnerability via an evaluation of in vivo connectivity trajectories across the human brain and successful integration of neuroimaging-genetic strategies.
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Affiliation(s)
- Silvia Basaia
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy; Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Federica Agosta
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy; Neurology Unit, IRCCS San Raffaele Scientific Institute and Vita-Salute San Raffaele University, Milan, Italy
| | - Ibai Diez
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Elisenda Bueichekú
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
| | - Federico d'Oleire Uquillas
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Princeton Neuroscience Institute, Princeton University, Princeton, NJ, USA
| | - Manuel Delgado-Alvarado
- Neurology Department, Sierrallana Hospital, Torrelavega, Spain; IDIVAL, Valdecilla Biomedical Research Institute, Santander, Spain; Biomedical Research Networking Center for Mental Health (CIBERSAM), Madrid, Spain
| | | | - MariCruz Rodriguez-Oroz
- Neurology Department, Clínica Universidad de Navarra, Neuroscience Unit, CIMA Universidad de Navarra, Spain
| | - Tanja Stojkovic
- Clinic of Neurology, Faculty of Medicine, University of Belgrade, Belgrade, Serbia
| | - Vladimir S Kostic
- Clinic of Neurology, Faculty of Medicine, University of Belgrade, Belgrade, Serbia
| | - Massimo Filippi
- Neuroimaging Research Unit, Division of Neuroscience, IRCCS San Raffaele Scientific Institute, Milan, Italy; Neurology Unit, IRCCS San Raffaele Scientific Institute and Vita-Salute San Raffaele University, Milan, Italy
| | - Jorge Sepulcre
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA; Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Charlestown, MA, USA.
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63
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Tsvetanov KA, Spindler LRB, Stamatakis EA, Newcombe VFJ, Lupson VC, Chatfield DA, Manktelow AE, Outtrim JG, Elmer A, Kingston N, Bradley JR, Bullmore ET, Rowe JB, Menon DK. Hospitalisation for COVID-19 predicts long lasting cerebrovascular impairment: A prospective observational cohort study. Neuroimage Clin 2022; 36:103253. [PMID: 36451358 PMCID: PMC9639388 DOI: 10.1016/j.nicl.2022.103253] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/19/2022] [Revised: 10/06/2022] [Accepted: 10/31/2022] [Indexed: 11/09/2022]
Abstract
Human coronavirus disease 2019 (COVID-19) due to severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) has multiple neurological consequences, but its long-term effect on brain health is still uncertain. The cerebrovascular consequences of COVID-19 may also affect brain health. We studied the chronic effect of COVID-19 on cerebrovascular health, in relation to acute severity, adverse clinical outcomes and in contrast to control group data. Here we assess cerebrovascular health in 45 patients six months after hospitalisation for acute COVID-19 using the resting state fluctuation amplitudes (RSFA) from functional magnetic resonance imaging, in relation to disease severity and in contrast with 42 controls. Acute COVID-19 severity was indexed by COVID-19 WHO Progression Scale, inflammatory and coagulatory biomarkers. Chronic widespread changes in frontoparietal RSFA were related to the severity of the acute COVID-19 episode. This relationship was not explained by chronic cardiorespiratory dysfunction, age, or sex. The level of cerebrovascular dysfunction was associated with cognitive, mental, and physical health at follow-up. The principal findings were consistent across univariate and multivariate approaches. The results indicate chronic cerebrovascular impairment following severe acute COVID-19, with the potential for long-term consequences on cognitive function and mental wellbeing.
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Affiliation(s)
- Kamen A Tsvetanov
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom; Department of Psychology, University of Cambridge, Cambridge, United Kingdom.
| | - Lennart R B Spindler
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom; Division of Anaesthesia, Department of Medicine, University Cambridge, Cambridge, United Kingdom
| | - Emmanuel A Stamatakis
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom; Division of Anaesthesia, Department of Medicine, University Cambridge, Cambridge, United Kingdom
| | - Virginia F J Newcombe
- Division of Anaesthesia, Department of Medicine, University Cambridge, Cambridge, United Kingdom; Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, United Kingdom
| | - Victoria C Lupson
- Division of Anaesthesia, Department of Medicine, University Cambridge, Cambridge, United Kingdom; Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, United Kingdom
| | - Doris A Chatfield
- Division of Anaesthesia, Department of Medicine, University Cambridge, Cambridge, United Kingdom
| | - Anne E Manktelow
- Division of Anaesthesia, Department of Medicine, University Cambridge, Cambridge, United Kingdom
| | - Joanne G Outtrim
- Division of Anaesthesia, Department of Medicine, University Cambridge, Cambridge, United Kingdom
| | - Anne Elmer
- Cambridge Clinical Research Centre, NIHR Clinical Research Facility, Cambridge University Hospitals NHS Foundation Trust, Addenbrooke's Hospital, Cambridge, United Kingdom
| | - Nathalie Kingston
- NIHR BioResource, Cambridge University Hospitals NHS Foundation, Cambridge Biomedical Campus, Cambridge, United Kingdom; Department of Haematology, School of Clinical Medicine, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
| | - John R Bradley
- NIHR BioResource, Cambridge University Hospitals NHS Foundation, Cambridge Biomedical Campus, Cambridge, United Kingdom; Department of Medicine, University of Cambridge, Addenbrooke's Hospital, Cambridge, United Kingdom
| | - Edward T Bullmore
- Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, United Kingdom; Department of Psychiatry, University of Cambridge, Cambridge Biomedical Campus, Cambridge, United Kingdom
| | - James B Rowe
- Department of Clinical Neurosciences, University of Cambridge, Cambridge, United Kingdom; Medical Research Council Cognition and Brain Sciences Unit, Department of Psychiatry, Cambridge, United Kingdom
| | - David K Menon
- Division of Anaesthesia, Department of Medicine, University Cambridge, Cambridge, United Kingdom; Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, United Kingdom; Cambridge Clinical Research Centre, NIHR Clinical Research Facility, Cambridge University Hospitals NHS Foundation Trust, Addenbrooke's Hospital, Cambridge, United Kingdom; Department of Medicine, University of Cambridge, Addenbrooke's Hospital, Cambridge, United Kingdom
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64
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Shine JM, Arnatkeviciute A, Fornito A, Fulcher BD. Navigating a Complex Landscape: Using Transcriptomics to Parcellate the Human Cortex. BIOLOGICAL PSYCHIATRY. COGNITIVE NEUROSCIENCE AND NEUROIMAGING 2022; 7:3-4. [PMID: 34998482 DOI: 10.1016/j.bpsc.2021.10.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 10/02/2021] [Indexed: 06/14/2023]
Affiliation(s)
- James M Shine
- School of Medical Sciences, The University of Sydney, Sydney, Australia.
| | - Aurina Arnatkeviciute
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, Australia
| | - Alex Fornito
- Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Victoria, Australia
| | - Ben D Fulcher
- School of Physics, The University of Sydney, Sydney, Australia
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65
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Martins D, Giacomel A, Williams SCR, Turkheimer F, Dipasquale O, Veronese M. Imaging transcriptomics: Convergent cellular, transcriptomic, and molecular neuroimaging signatures in the healthy adult human brain. Cell Rep 2021; 37:110173. [PMID: 34965413 DOI: 10.1016/j.celrep.2021.110173] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2021] [Revised: 09/30/2021] [Accepted: 12/03/2021] [Indexed: 12/12/2022] Open
Abstract
The integration of transcriptomic and neuroimaging data, "imaging transcriptomics," has recently emerged to generate hypotheses about potential biological pathways underlying regional variability in neuroimaging features. However, the validity of this approach is yet to be examined in depth. Here, we sought to bridge this gap by performing transcriptomic decoding of the regional distribution of well-known molecular markers spanning different elements of the biology of the healthy human brain. Imaging transcriptomics identifies biological and cell pathways that are consistent with the known biology of a wide range of molecular neuroimaging markers. The extent to which it can capture patterns of gene expression that align well with elements of the biology of the neuroinflammatory axis, at least in healthy controls without a proinflammatory challenge, is inconclusive. Imaging transcriptomics might constitute an interesting approach to improve our understanding of the biological pathways underlying regional variability in a wide range of neuroimaging phenotypes.
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Affiliation(s)
- Daniel Martins
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London SE5 8AF, UK.
| | - Alessio Giacomel
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London SE5 8AF, UK
| | - Steven C R Williams
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London SE5 8AF, UK
| | - Federico Turkheimer
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London SE5 8AF, UK
| | - Ottavia Dipasquale
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London SE5 8AF, UK
| | - Mattia Veronese
- Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, De Crespigny Park, London SE5 8AF, UK; Department of Information Engineering, University of Padua, Via Gradenigo, 6/b, 35131 Padova, Italy.
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66
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Li L, Wei Y, Zhang J, Ma J, Yi Y, Gu Y, Li LMW, Lin Y, Dai Z. Gene expression associated with individual variability in intrinsic functional connectivity. Neuroimage 2021; 245:118743. [PMID: 34800667 DOI: 10.1016/j.neuroimage.2021.118743] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2021] [Revised: 10/28/2021] [Accepted: 11/16/2021] [Indexed: 10/19/2022] Open
Abstract
It has been revealed that intersubject variability (ISV) in intrinsic functional connectivity (FC) is associated with a wide variety of cognitive and behavioral performances. However, the underlying organizational principle of ISV in FC and its related gene transcriptional profiles remain unclear. Using resting-state fMRI data from the Human Connectome Project (299 adult participants) and microarray gene expression data from the Allen Human Brain Atlas, we conducted a transcription-neuroimaging association study to investigate the spatial configurations of ISV in intrinsic FC and their associations with spatial gene transcriptional profiles. We found that the multimodal association cortices showed the greatest ISV in FC, while the unimodal cortices and subcortical areas showed the least ISV. Importantly, partial least squares regression analysis revealed that the transcriptional profiles of genes associated with human accelerated regions (HARs) could explain 31.29% of the variation in the spatial distribution of ISV in FC. The top-related genes in the transcriptional profiles were enriched for the development of the central nervous system, neurogenesis and the cellular components of synapse. Moreover, we observed that the effect of gene expression profile on the heterogeneous distribution of ISV in FC was significantly mediated by the cerebral blood flow configuration. These findings highlighted the spatial arrangement of ISV in FC and their coupling with variations in transcriptional profiles and cerebral blood flow supply.
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Affiliation(s)
- Liangfang Li
- Department of Psychology, Sun Yat-sen University, Guangzhou 510006, China
| | - Yongbin Wei
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, Netherlands
| | - Jinbo Zhang
- Department of Psychology, Sun Yat-sen University, Guangzhou 510006, China
| | - Junji Ma
- Department of Psychology, Sun Yat-sen University, Guangzhou 510006, China
| | - Yangyang Yi
- Department of Psychology, Sun Yat-sen University, Guangzhou 510006, China
| | - Yue Gu
- Department of Psychology, Sun Yat-sen University, Guangzhou 510006, China
| | - Liman Man Wai Li
- Department of Psychology and Centre for Psychosocial Health, The Education University of Hong Kong, Hong Kong SAR, China
| | - Ying Lin
- Department of Psychology, Sun Yat-sen University, Guangzhou 510006, China
| | - Zhengjia Dai
- Department of Psychology, Sun Yat-sen University, Guangzhou 510006, China.
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67
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Wei Y, de Lange SC, Pijnenburg R, Scholtens LH, Ardesch DJ, Watanabe K, Posthuma D, van den Heuvel MP. Statistical testing in transcriptomic-neuroimaging studies: A how-to and evaluation of methods assessing spatial and gene specificity. Hum Brain Mapp 2021; 43:885-901. [PMID: 34862695 PMCID: PMC8764473 DOI: 10.1002/hbm.25711] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/03/2021] [Revised: 10/22/2021] [Accepted: 10/23/2021] [Indexed: 11/14/2022] Open
Abstract
Multiscale integration of gene transcriptomic and neuroimaging data is becoming a widely used approach for exploring the molecular underpinnings of large‐scale brain organization in health and disease. Proper statistical evaluation of determined associations between imaging‐based phenotypic and transcriptomic data is key in these explorations, in particular to establish whether observed associations exceed “chance level” of random, nonspecific effects. Recent approaches have shown the importance of statistical models that can correct for spatial autocorrelation effects in the data to avoid inflation of reported statistics. Here, we discuss the need for examination of a second category of statistical models in transcriptomic‐neuroimaging analyses, namely those that can provide “gene specificity.” By means of a couple of simple examples of commonly performed transcriptomic‐neuroimaging analyses, we illustrate some of the potentials and challenges of transcriptomic‐imaging analyses, showing that providing gene specificity on observed transcriptomic‐neuroimaging effects is of high importance to avoid reports of nonspecific effects. Through means of simulations we show that the rate of reported nonspecific effects (i.e., effects that cannot be specifically linked to a specific gene or gene‐set) can run as high as 60%, with only less than 5% of transcriptomic‐neuroimaging associations observed through ordinary linear regression analyses showing both spatial and gene specificity. We provide a discussion, a tutorial, and an easy‐to‐use toolbox for the different options of null models in transcriptomic‐neuroimaging analyses.
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Affiliation(s)
- Yongbin Wei
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Siemon C de Lange
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.,Department of Sleep and Cognition, Netherlands Institute for Neuroscience (NIN), Royal Netherlands Academy of Arts and Sciences, Amsterdam, The Netherlands
| | - Rory Pijnenburg
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Lianne H Scholtens
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Dirk Jan Ardesch
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Kyoko Watanabe
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Danielle Posthuma
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.,Department of Child and Adolescent Psychiatry and Psychology, Section Complex Trait Genetics, Amsterdam Neuroscience, Vrije Universiteit Medical Center, Amsterdam UMC, Amsterdam, The Netherlands
| | - Martijn P van den Heuvel
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.,Department of Child and Adolescent Psychiatry and Psychology, Section Complex Trait Genetics, Amsterdam Neuroscience, Vrije Universiteit Medical Center, Amsterdam UMC, Amsterdam, The Netherlands
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68
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Krupa O, Fragola G, Hadden-Ford E, Mory JT, Liu T, Humphrey Z, Rees BW, Krishnamurthy A, Snider WD, Zylka MJ, Wu G, Xing L, Stein JL. NuMorph: Tools for cortical cellular phenotyping in tissue-cleared whole-brain images. Cell Rep 2021; 37:109802. [PMID: 34644582 PMCID: PMC8530274 DOI: 10.1016/j.celrep.2021.109802] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/28/2020] [Revised: 07/07/2021] [Accepted: 09/15/2021] [Indexed: 01/18/2023] Open
Abstract
Tissue-clearing methods allow every cell in the mouse brain to be imaged without physical sectioning. However, the computational tools currently available for cell quantification in cleared tissue images have been limited to counting sparse cell populations in stereotypical mice. Here, we introduce NuMorph, a group of analysis tools to quantify all nuclei and nuclear markers within the mouse cortex after clearing and imaging by light-sheet microscopy. We apply NuMorph to investigate two distinct mouse models: a Topoisomerase 1 (Top1) model with severe neurodegenerative deficits and a Neurofibromin 1 (Nf1) model with a more subtle brain overgrowth phenotype. In each case, we identify differential effects of gene deletion on individual cell-type counts and distribution across cortical regions that manifest as alterations of gross brain morphology. These results underline the value of whole-brain imaging approaches, and the tools are widely applicable for studying brain structure phenotypes at cellular resolution.
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Affiliation(s)
- Oleh Krupa
- Joint Department of Biomedical Engineering, University of North Carolina at Chapel Hill and North Carolina State University, Chapel Hill, NC 27514, USA; UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Giulia Fragola
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Department of Cell Biology and Physiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA; Department of Neurology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Ellie Hadden-Ford
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Jessica T Mory
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Tianyi Liu
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Zachary Humphrey
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Benjamin W Rees
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Ashok Krishnamurthy
- Renaissance Computing Institute, Chapel Hill, NC 27517, USA; Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - William D Snider
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA
| | - Mark J Zylka
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Department of Cell Biology and Physiology, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, USA
| | - Guorong Wu
- Department of Computer Science, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Department of Psychiatry, University of North Carolina at Chapel Hill, Chapel Hill, NC 27514, USA
| | - Lei Xing
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
| | - Jason L Stein
- UNC Neuroscience Center, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA; Department of Genetics, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA.
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69
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Bueichekú E, Gonzalez-de-Echavarri JM, Ortiz-Teran L, Montal V, d'Oleire Uquillas F, De Marcos L, Orwig W, Kim CM, Ortiz-Teran E, Basaia S, Diez I, Sepulcre J. Divergent connectomic organization delineates genetic evolutionary traits in the human brain. Sci Rep 2021; 11:19692. [PMID: 34608211 PMCID: PMC8490416 DOI: 10.1038/s41598-021-99082-6] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Accepted: 09/07/2021] [Indexed: 02/08/2023] Open
Abstract
The relationship between human brain connectomics and genetic evolutionary traits remains elusive due to the inherent challenges in combining complex associations within cerebral tissue. In this study, insights are provided about the relationship between connectomics, gene expression and divergent evolutionary pathways from non-human primates to humans. Using in vivo human brain resting-state data, we detected two co-existing idiosyncratic functional systems: the segregation network, in charge of module specialization, and the integration network, responsible for information flow. Their topology was approximated to whole-brain genetic expression (Allen Human Brain Atlas) and the co-localization patterns yielded that neuron communication functionalities-linked to Neuron Projection-were overrepresented cell traits. Homologue-orthologue comparisons using dN/dS-ratios bridged the gap between neurogenetic outcomes and biological data, summarizing the known evolutionary divergent pathways within the Homo Sapiens lineage. Evidence suggests that a crosstalk between functional specialization and information flow reflects putative biological qualities of brain architecture, such as neurite cellular functions like axonal or dendrite processes, hypothesized to have been selectively conserved in the species through positive selection. These findings expand our understanding of human brain function and unveil aspects of our cognitive trajectory in relation to our simian ancestors previously left unexplored.
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Affiliation(s)
- Elisenda Bueichekú
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, USA
| | - Jose M Gonzalez-de-Echavarri
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, USA
- Barcelona βeta Brain Research Center, Barcelona, Spain
| | - Laura Ortiz-Teran
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, USA
- Department of Radiology, Division of Nuclear Medicine and Molecular Imaging, Brigham and Women's Hospital, Harvard Medical School, Boston, USA
| | - Victor Montal
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, USA
- Memory Unit, Department of Neurology, Hospital de la Santa Creu i Sant Pau, Biomedical Research Institute Sant Pau, Universitat Autonoma de Barcelona, Barcelona, Spain
- Centro de Investigacón Biomédica en Red de Enfermedades Neurodegenerativas (CIBERNED), Madrid, Spain
| | - Federico d'Oleire Uquillas
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, USA
- Princeton Neuroscience Institute, Princeton University, Princeton, USA
| | - Lola De Marcos
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, USA
- University of Navarra School of Medicine, University of Navarra, Pamplona, Navarra, Spain
| | - William Orwig
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, USA
| | - Chan-Mi Kim
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, USA
| | - Elena Ortiz-Teran
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, USA
- Facultad de Ciencias Jurídicas y Sociales, Universidad Rey Juan Carlos, Madrid, Spain
| | - Silvia Basaia
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, USA
- Neuroimaging Research Unit, San Raffaele Scientific Institute, Vita-Salute San Raffaele University, Milan, Italy
| | - Ibai Diez
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, USA
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, USA
| | - Jorge Sepulcre
- Gordon Center for Medical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, USA.
- Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Charlestown, USA.
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70
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Arnatkeviciute A, Fulcher BD, Bellgrove MA, Fornito A. Imaging Transcriptomics of Brain Disorders. BIOLOGICAL PSYCHIATRY GLOBAL OPEN SCIENCE 2021; 2:319-331. [PMID: 36324650 PMCID: PMC9616271 DOI: 10.1016/j.bpsgos.2021.10.002] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/11/2021] [Revised: 10/06/2021] [Accepted: 10/11/2021] [Indexed: 01/05/2023] Open
Abstract
Noninvasive neuroimaging is a powerful tool for quantifying diverse aspects of brain structure and function in vivo, and it has been used extensively to map the neural changes associated with various brain disorders. However, most neuroimaging techniques offer only indirect measures of underlying pathological mechanisms. The recent development of anatomically comprehensive gene expression atlases has opened new opportunities for studying the transcriptional correlates of noninvasively measured neural phenotypes, offering a rich framework for evaluating pathophysiological hypotheses and putative mechanisms. Here, we provide an overview of some fundamental methods in imaging transcriptomics and outline their application to understanding brain disorders of neurodevelopment, adulthood, and neurodegeneration. Converging evidence indicates that spatial variations in gene expression are linked to normative changes in brain structure during age-related maturation and neurodegeneration that are in part associated with cell-specific gene expression markers of gene expression. Transcriptional correlates of disorder-related neuroimaging phenotypes are also linked to transcriptionally dysregulated genes identified in ex vivo analyses of patient brains. Modeling studies demonstrate that spatial patterns of gene expression are involved in regional vulnerability to neurodegeneration and the spread of disease across the brain. This growing body of work supports the utility of transcriptional atlases in testing hypotheses about the molecular mechanism driving disease-related changes in macroscopic neuroimaging phenotypes.
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Affiliation(s)
- Aurina Arnatkeviciute
- Turner Institute for Brain and Mental Health, School of Psychological Science, Monash University, Melbourne, Victoria, Australia
- Address correspondence to Aurina Arnatkeviciute, Ph.D
| | - Ben D. Fulcher
- School of Physics, The University of Sydney, Camperdown, New South Wales, Australia
| | - Mark A. Bellgrove
- Turner Institute for Brain and Mental Health, School of Psychological Science, Monash University, Melbourne, Victoria, Australia
| | - Alex Fornito
- Turner Institute for Brain and Mental Health, School of Psychological Science, Monash University, Melbourne, Victoria, Australia
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Arnatkeviciute A, Fulcher BD, Oldham S, Tiego J, Paquola C, Gerring Z, Aquino K, Hawi Z, Johnson B, Ball G, Klein M, Deco G, Franke B, Bellgrove MA, Fornito A. Genetic influences on hub connectivity of the human connectome. Nat Commun 2021; 12:4237. [PMID: 34244483 PMCID: PMC8271018 DOI: 10.1038/s41467-021-24306-2] [Citation(s) in RCA: 68] [Impact Index Per Article: 22.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2021] [Accepted: 06/03/2021] [Indexed: 02/06/2023] Open
Abstract
Brain network hubs are both highly connected and highly inter-connected, forming a critical communication backbone for coherent neural dynamics. The mechanisms driving this organization are poorly understood. Using diffusion-weighted magnetic resonance imaging in twins, we identify a major role for genes, showing that they preferentially influence connectivity strength between network hubs of the human connectome. Using transcriptomic atlas data, we show that connected hubs demonstrate tight coupling of transcriptional activity related to metabolic and cytoarchitectonic similarity. Finally, comparing over thirteen generative models of network growth, we show that purely stochastic processes cannot explain the precise wiring patterns of hubs, and that model performance can be improved by incorporating genetic constraints. Our findings indicate that genes play a strong and preferential role in shaping the functionally valuable, metabolically costly connections between connectome hubs.
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Affiliation(s)
- Aurina Arnatkeviciute
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia.
| | - Ben D Fulcher
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia
- School of Physics, The University of Sydney, Camperdown, NSW, Australia
| | - Stuart Oldham
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia
| | - Jeggan Tiego
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia
| | - Casey Paquola
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
- Institute of Neuroscience and Medicine (INM-1), Forschungszentrum Jülich, Jülich, Germany
| | - Zachary Gerring
- Translational Neurogenomics Laboratory, QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
| | - Kevin Aquino
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia
- School of Physics, The University of Sydney, Camperdown, NSW, Australia
| | - Ziarih Hawi
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia
| | - Beth Johnson
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia
| | - Gareth Ball
- Developmental Imaging, Murdoch Children's Research Institute, Melbourne, VIC, Australia
- Department of Paediatrics, University of Melbourne, Melbourne, VIC, Australia
| | - Marieke Klein
- Department of Human Genetics, Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
- Department of Psychiatry, University of California San Diego, La Jolla, CA, USA
| | - Gustavo Deco
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia
- Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Barcelona, Spain
- Universitat Pompeu Fabra, Barcelona, Spain
- Institució Catalana de la Recerca i Estudis Avançats (ICREA), Barcelona, Spain
- Department of Neuropsychology, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany
| | - Barbara Franke
- Department of Human Genetics, Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
- Department of Psychiatry, Radboud University Medical Center, Donders Institute for Brain, Cognition and Behaviour, Nijmegen, The Netherlands
| | - Mark A Bellgrove
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia
| | - Alex Fornito
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, and Monash Biomedical Imaging, Monash University, Melbourne, VIC, Australia
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72
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Akarca D, Vértes PE, Bullmore ET, Astle DE. A generative network model of neurodevelopmental diversity in structural brain organization. Nat Commun 2021; 12:4216. [PMID: 34244490 PMCID: PMC8270998 DOI: 10.1038/s41467-021-24430-z] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Accepted: 05/27/2021] [Indexed: 02/07/2023] Open
Abstract
The formation of large-scale brain networks, and their continual refinement, represent crucial developmental processes that can drive individual differences in cognition and which are associated with multiple neurodevelopmental conditions. But how does this organization arise, and what mechanisms drive diversity in organization? We use generative network modeling to provide a computational framework for understanding neurodevelopmental diversity. Within this framework macroscopic brain organization, complete with spatial embedding of its organization, is an emergent property of a generative wiring equation that optimizes its connectivity by renegotiating its biological costs and topological values continuously over time. The rules that govern these iterative wiring properties are controlled by a set of tightly framed parameters, with subtle differences in these parameters steering network growth towards different neurodiverse outcomes. Regional expression of genes associated with the simulations converge on biological processes and cellular components predominantly involved in synaptic signaling, neuronal projection, catabolic intracellular processes and protein transport. Together, this provides a unifying computational framework for conceptualizing the mechanisms and diversity in neurodevelopment, capable of integrating different levels of analysis-from genes to cognition.
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Affiliation(s)
- Danyal Akarca
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK.
| | - Petra E Vértes
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- The Alan Turing Institute, London, UK
| | - Edward T Bullmore
- Department of Psychiatry, University of Cambridge, Cambridge, UK
- Department of Clinical Neurosciences, Wolfson Brain Imaging Centre, University of Cambridge, Cambridge, UK
| | - Duncan E Astle
- MRC Cognition and Brain Sciences Unit, University of Cambridge, Cambridge, UK
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