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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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Mitteroecker P, Merola GP. The cliff edge model of the evolution of schizophrenia: Mathematical, epidemiological, and genetic evidence. Neurosci Biobehav Rev 2024; 160:105636. [PMID: 38522813 DOI: 10.1016/j.neubiorev.2024.105636] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/19/2023] [Revised: 02/27/2024] [Accepted: 03/16/2024] [Indexed: 03/26/2024]
Abstract
How has schizophrenia, a condition that significantly reduces an individual's evolutionary fitness, remained common across generations and cultures? Numerous theories about the evolution of schizophrenia have been proposed, most of which are not consistent with modern epidemiological and genetic evidence. Here, we briefly review this evidence and explore the cliff edge model of schizophrenia. It suggests that schizophrenia is the extreme manifestation of a polygenic trait or a combination of traits that, within a normal range of variation, confer cognitive, linguistic, and/or social advantages. Only beyond a certain threshold, these traits precipitate the onset of schizophrenia and reduce fitness. We provide the first mathematical model of this qualitative concept and show that it requires only very weak positive selection of the underlying trait(s) to explain today's schizophrenia prevalence. This prediction, along with expectations about the effect size of schizophrenia risk alleles, are surprisingly well matched by empirical evidence. The cliff edge model predicts a dynamic change of selection of risk alleles, which explains the contradictory findings of evolutionary genetic studies.
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Affiliation(s)
- Philipp Mitteroecker
- Unit for Theoretical Biology, Department of Evolutionary Biology, University of Vienna, Djerassiplatz 1, Vienna, Austria; Konrad Lorenz Institute for Evolution and Cognition Research, Martinstrasse 12, Klosterneuburg, Vienna, Austria.
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3
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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:S2095-9273(24)00187-7. [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] [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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4
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Wainberg M, Forde NJ, Mansour S, Kerrebijn I, Medland SE, Hawco C, Tripathy SJ. Genetic architecture of the structural connectome. Nat Commun 2024; 15:1962. [PMID: 38438384 PMCID: PMC10912129 DOI: 10.1038/s41467-024-46023-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/13/2022] [Accepted: 02/12/2024] [Indexed: 03/06/2024] Open
Abstract
Myelinated axons form long-range connections that enable rapid communication between distant brain regions, but how genetics governs the strength and organization of these connections remains unclear. We perform genome-wide association studies of 206 structural connectivity measures derived from diffusion magnetic resonance imaging tractography of 26,333 UK Biobank participants, each representing the density of myelinated connections within or between a pair of cortical networks, subcortical structures or cortical hemispheres. We identify 30 independent genome-wide significant variants after Bonferroni correction for the number of measures studied (126 variants at nominal genome-wide significance) implicating genes involved in myelination (SEMA3A), neurite elongation and guidance (NUAK1, STRN, DPYSL2, EPHA3, SEMA3A, HGF, SHTN1), neural cell proliferation and differentiation (GMNC, CELF4, HGF), neuronal migration (CCDC88C), cytoskeletal organization (CTTNBP2, MAPT, DAAM1, MYO16, PLEC), and brain metal transport (SLC39A8). These variants have four broad patterns of spatial association with structural connectivity: some have disproportionately strong associations with corticothalamic connectivity, interhemispheric connectivity, or both, while others are more spatially diffuse. Structural connectivity measures are highly polygenic, with a median of 9.1 percent of common variants estimated to have non-zero effects on each measure, and exhibited signatures of negative selection. Structural connectivity measures have significant genetic correlations with a variety of neuropsychiatric and cognitive traits, indicating that connectivity-altering variants tend to influence brain health and cognitive function. Heritability is enriched in regions with increased chromatin accessibility in adult oligodendrocytes (as well as microglia, inhibitory neurons and astrocytes) and multiple fetal cell types, suggesting that genetic control of structural connectivity is partially mediated by effects on myelination and early brain development. Our results indicate pervasive, pleiotropic, and spatially structured genetic control of white-matter structural connectivity via diverse neurodevelopmental pathways, and support the relevance of this genetic control to healthy brain function.
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Affiliation(s)
- Michael Wainberg
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada.
- Prosserman Centre for Population Health Research, Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON, Canada.
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada.
- Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada.
| | - Natalie J Forde
- Department of Cognitive Neuroscience, Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen Medical Centre, Nijmegen, The Netherlands
| | - Salim Mansour
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Isabel Kerrebijn
- Prosserman Centre for Population Health Research, Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, ON, Canada
- Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada
| | - Sarah E Medland
- QIMR Berghofer Medical Research Institute, Brisbane, QLD, Australia
- School of Psychology, University of Queensland, Brisbane, QLD, Australia
- Faculty of Medicine, University of Queensland, Brisbane, QLD, Australia
| | - Colin Hawco
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada.
- Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada.
- Campbell Family Mental Health Research Institute, Centre for Addiction and Mental Health, Toronto, ON, Canada.
| | - Shreejoy J Tripathy
- Krembil Centre for Neuroinformatics, Centre for Addiction and Mental Health, Toronto, ON, Canada.
- Department of Psychiatry, University of Toronto, Toronto, ON, Canada.
- Institute of Medical Sciences, University of Toronto, Toronto, ON, Canada.
- Department of Physiology, University of Toronto, Toronto, ON, Canada.
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5
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Guardiola-Ripoll M, Almodóvar-Payá C, Arias-Magnasco A, Latorre-Guardia M, Papiol S, Canales-Rodríguez EJ, García-León MÁ, Fuentes-Claramonte P, Salavert J, Tristany J, Torres L, Rodríguez-Cano E, Salvador R, Pomarol-Clotet E, Fatjó-Vilas M. Human-specific evolutionary markers linked to foetal neurodevelopment modulate brain surface area in schizophrenia. Commun Biol 2023; 6:1040. [PMID: 37833414 PMCID: PMC10576001 DOI: 10.1038/s42003-023-05356-2] [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: 03/29/2023] [Accepted: 09/13/2023] [Indexed: 10/15/2023] Open
Abstract
Schizophrenia may represent a trade-off in the evolution of human-specific ontogenetic mechanisms that guide neurodevelopment. Human Accelerated Regions (HARs) are evolutionary markers functioning as neurodevelopmental transcription enhancers that have been associated with brain configuration, neural information processing, and schizophrenia risk. Here, we have investigated the influence of HARs' polygenic load on neuroanatomical measures through a case-control approach (128 patients with schizophrenia and 115 controls). To this end, we have calculated the global schizophrenia Polygenic Risk Score (Global PRSSZ) and that specific to HARs (HARs PRSSZ). We have also estimated the polygenic burden restricted to the HARs linked to transcriptional regulatory elements active in the foetal brain (FB-HARs PRSSZ) and the adult brain (AB-HARs PRSSZ). We have explored the main effects of the PRSs and the PRSs x diagnosis interactions on brain regional cortical thickness (CT) and surface area (SA). The results indicate that a higher FB-HARs PRSSZ is associated with patients' lower SA in the lateral orbitofrontal cortex, the superior temporal cortex, the pars triangularis and the paracentral lobule. While noHARs-derived PRSs show an effect on the risk, our neuroanatomical findings suggest that the human-specific transcriptional regulation during the prenatal period underlies SA variability, highlighting the role of these evolutionary markers in the schizophrenia genomic architecture.
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Affiliation(s)
- Maria Guardiola-Ripoll
- FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Spain.
- CIBERSAM (Biomedical Research Network in Mental Health; Instituto de Salud Carlos III), Madrid, Spain.
| | - Carmen Almodóvar-Payá
- FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Spain
- CIBERSAM (Biomedical Research Network in Mental Health; Instituto de Salud Carlos III), Madrid, Spain
| | | | | | - Sergi Papiol
- CIBERSAM (Biomedical Research Network in Mental Health; Instituto de Salud Carlos III), Madrid, Spain
- Institute of Psychiatric Phenomics and Genomics (IPPG), University Hospital, LMU Munich, Munich, Germany
- Max Planck Institute of Psychiatry, Munich, Germany
| | - Erick J Canales-Rodríguez
- CIBERSAM (Biomedical Research Network in Mental Health; Instituto de Salud Carlos III), Madrid, Spain
- Signal Processing Laboratory 5 (LTS5), École Polytechnique Fédérale de Lausanne (EPFL), Lausanne, Switzerland
| | - María Ángeles García-León
- FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Spain
- CIBERSAM (Biomedical Research Network in Mental Health; Instituto de Salud Carlos III), Madrid, Spain
| | - Paola Fuentes-Claramonte
- FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Spain
- CIBERSAM (Biomedical Research Network in Mental Health; Instituto de Salud Carlos III), Madrid, Spain
| | - Josep Salavert
- Hospital Sant Rafael, Germanes Hospitalàries, Barcelona, Spain
| | - Josep Tristany
- Hospital Sagrat Cor, Germanes Hospitalàries, Martorell, Spain
| | - Llanos Torres
- Hospital Mare de Déu de la Mercè, Germanes Hospitalàries, Barcelona, Spain
| | - Elena Rodríguez-Cano
- FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Spain
- CIBERSAM (Biomedical Research Network in Mental Health; Instituto de Salud Carlos III), Madrid, Spain
- Hospital Benito Menni, Germanes Hospitalàries, Sant Boi de Llobregat, Spain
| | - Raymond Salvador
- FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Spain
- CIBERSAM (Biomedical Research Network in Mental Health; Instituto de Salud Carlos III), Madrid, Spain
| | - Edith Pomarol-Clotet
- FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Spain
- CIBERSAM (Biomedical Research Network in Mental Health; Instituto de Salud Carlos III), Madrid, Spain
| | - Mar Fatjó-Vilas
- FIDMAG Germanes Hospitalàries Research Foundation, Barcelona, Spain.
- CIBERSAM (Biomedical Research Network in Mental Health; Instituto de Salud Carlos III), Madrid, Spain.
- Departament de Biologia Evolutiva, Ecologia i Ciències Ambientals, Universitat de Barcelona, Barcelona, Spain.
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Mars RB, Palomero-Gallagher N. Towards multi-modal, multi-species brain atlases: part one. Brain Struct Funct 2023; 228:1041-1044. [PMID: 37227517 PMCID: PMC10250418 DOI: 10.1007/s00429-023-02656-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 05/26/2023]
Affiliation(s)
- Rogier B Mars
- Wellcome Centre for Integrative Neuroimaging, Centre for fMRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, OX3 9DU, UK.
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, AJ 6525, Nijmegen, The Netherlands.
| | - Nicola Palomero-Gallagher
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, 52425, Jülich, Germany.
- C. & O. Vogt Institute for Brain Research, Heinrich-Heine-University, 40225, Dusseldorf, Germany.
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7
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Gao Z, Xiao Y, Zhu F, Tao B, Yu W, Lui S. The whole-brain connectome landscape in patients with schizophrenia: a systematic review and meta-analysis of graph theoretical characteristics. Neurosci Biobehav Rev 2023; 148:105144. [PMID: 36990373 DOI: 10.1016/j.neubiorev.2023.105144] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 03/14/2023] [Accepted: 03/24/2023] [Indexed: 03/29/2023]
Abstract
The alterations of connectome in schizophrenia have been reported, but the results remain inconsistent. We conducted a systematic review and random-effects meta-analysis on structural or functional connectome MRI studies comparing global graph theoretical characteristics between schizophrenia and healthy controls. Meta-regression and subgroup analyses were performed to examine confounding effects. Based on the included 48 studies, Structural connectome in schizophrenia showed a significant decrease in segregation (lower clustering coefficient and local efficiency, Hedge's g= -0.352 and -0.864, respectively) and integration (higher characteristic path length and lower global efficiency, Hedge's g= 0.532 and -0.577 respectively). The functional connectome showed no difference between groups except γ. Moderator analysis indicated that clinical and methodological factors exerted a potential effect on the graph theoretical characteristics. Our analysis revealed a weaker small-worldization trend in structural connectome of schizophrenia. For the relatively unchanged functional connectome, more homogenous and high-quality studies are warranted to elucidate whether the change was blurred by heterogeneity or the presentation of pathophysiological reconfiguration.
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Guma E, Beauchamp A, Liu S, Levitis E, Clasen LS, Torres E, Blumenthal J, Lalonde F, Qiu LR, Hrncir H, MacKenzie-Graham A, Yang X, Arnold AP, Lerch JP, Raznahan A. A Cross-Species Neuroimaging Study of Sex Chromosome Dosage Effects on Human and Mouse Brain Anatomy. J Neurosci 2023; 43:1321-1333. [PMID: 36631267 PMCID: PMC9987571 DOI: 10.1523/jneurosci.1761-22.2022] [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: 09/12/2022] [Revised: 12/12/2022] [Accepted: 12/22/2022] [Indexed: 01/13/2023] Open
Abstract
All eutherian mammals show chromosomal sex determination with contrasting sex chromosome dosages (SCDs) between males (XY) and females (XX). Studies in transgenic mice and humans with sex chromosome trisomy (SCT) have revealed direct SCD effects on regional mammalian brain anatomy, but we lack a formal test for cross-species conservation of these effects. Here, we develop a harmonized framework for comparative structural neuroimaging and apply this to systematically profile SCD effects on regional brain anatomy in both humans and mice by contrasting groups with SCT (XXY and XYY) versus XY controls. Total brain size was substantially altered by SCT in humans (significantly decreased by XXY and increased by XYY), but not in mice. Robust and spatially convergent effects of XXY and XYY on regional brain volume were observed in humans, but not mice, when controlling for global volume differences. However, mice do show subtle effects of XXY and XYY on regional volume, although there is not a general spatial convergence in these effects within mice or between species. Notwithstanding this general lack of conservation in SCT effects, we detect several brain regions that show overlapping effects of XXY and XYY both within and between species (cerebellar, parietal, and orbitofrontal cortex), thereby nominating high priority targets for future translational dissection of SCD effects on the mammalian brain. Our study introduces a generalizable framework for comparative neuroimaging in humans and mice and applies this to achieve a cross-species comparison of SCD effects on the mammalian brain through the lens of SCT.SIGNIFICANCE STATEMENT Sex chromosome dosage (SCD) affects neuroanatomy and risk for psychopathology in humans. Performing mechanistic studies in the human brain is challenging but possible in mouse models. Here, we develop a framework for cross-species neuroimaging analysis and use this to show that an added X- or Y-chromosome significantly alters human brain anatomy but has muted effects in the mouse brain. However, we do find evidence for conserved cross-species impact of an added chromosome in the fronto-parietal cortices and cerebellum, which point to regions for future mechanistic dissection of sex chromosome dosage effects on brain development.
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Affiliation(s)
- Elisa Guma
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental Health, Bethesda, 20892, Maryland
| | - Antoine Beauchamp
- Mouse Imaging Centre, Toronto, Ontario M5T 3H7, Canada
- The Hospital for Sick Children, Toronto, Ontario M5G 1X8, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario M5G 1L7, Canada
| | - Siyuan Liu
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental Health, Bethesda, 20892, Maryland
| | - Elizabeth Levitis
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental Health, Bethesda, 20892, Maryland
| | - Liv S. Clasen
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental Health, Bethesda, 20892, Maryland
| | - Erin Torres
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental Health, Bethesda, 20892, Maryland
| | - Jonathan Blumenthal
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental Health, Bethesda, 20892, Maryland
| | - Francois Lalonde
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental Health, Bethesda, 20892, Maryland
| | - Lily R. Qiu
- Mouse Imaging Centre, Toronto, Ontario M5T 3H7, Canada
- The Hospital for Sick Children, Toronto, Ontario M5G 1X8, Canada
| | - Haley Hrncir
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, California 90095
| | - Allan MacKenzie-Graham
- Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, California 90095
| | - Xia Yang
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, California 90095
| | - Arthur P. Arnold
- Department of Integrative Biology and Physiology, University of California, Los Angeles, Los Angeles, California 90095
| | - Jason P. Lerch
- Mouse Imaging Centre, Toronto, Ontario M5T 3H7, Canada
- The Hospital for Sick Children, Toronto, Ontario M5G 1X8, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario M5G 1L7, Canada
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, OX3 9DU, United Kingdom
| | - Armin Raznahan
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental Health, Bethesda, 20892, Maryland
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9
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A Systematic Review of the Human Accelerated Regions in Schizophrenia and Related Disorders: Where the Evolutionary and Neurodevelopmental Hypotheses Converge. Int J Mol Sci 2023; 24:ijms24043597. [PMID: 36835010 PMCID: PMC9962562 DOI: 10.3390/ijms24043597] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2022] [Revised: 02/03/2023] [Accepted: 02/07/2023] [Indexed: 02/15/2023] Open
Abstract
Schizophrenia is a psychiatric disorder that results from genetic and environmental factors interacting and disrupting neurodevelopmental trajectories. Human Accelerated Regions (HARs) are evolutionarily conserved genomic regions that have accumulated human-specific sequence changes. Thus, studies on the impact of HARs in the context of neurodevelopment, as well as with respect to adult brain phenotypes, have increased considerably in the last few years. Through a systematic approach, we aim to offer a comprehensive review of HARs' role in terms of human brain development, configuration, and cognitive abilities, as well as whether HARs modulate the susceptibility to neurodevelopmental psychiatric disorders such as schizophrenia. First, the evidence in this review highlights HARs' molecular functions in the context of the neurodevelopmental regulatory genetic machinery. Second, brain phenotypic analyses indicate that HAR genes' expression spatially correlates with the regions that suffered human-specific cortical expansion, as well as with the regional interactions for synergistic information processing. Lastly, studies based on candidate HAR genes and the global "HARome" variability describe the involvement of these regions in the genetic background of schizophrenia, but also in other neurodevelopmental psychiatric disorders. Overall, the data considered in this review emphasise the crucial role of HARs in human-specific neurodevelopment processes and encourage future research on this evolutionary marker for a better understanding of the genetic basis of schizophrenia and other neurodevelopmental-related psychiatric disorders. Accordingly, HARs emerge as interesting genomic regions that require further study in order to bridge the neurodevelopmental and evolutionary hypotheses in schizophrenia and other related disorders and phenotypes.
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Lanooij SD, Eisel ULM, Drinkenburg WHIM, van der Zee EA, Kas MJH. Influencing cognitive performance via social interactions: a novel therapeutic approach for brain disorders based on neuroanatomical mapping? Mol Psychiatry 2023; 28:28-33. [PMID: 35858991 PMCID: PMC9812764 DOI: 10.1038/s41380-022-01698-1] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/22/2021] [Revised: 06/28/2022] [Accepted: 07/01/2022] [Indexed: 01/09/2023]
Abstract
Many psychiatric and neurological disorders present deficits in both the social and cognitive domain. In this perspectives article, we provide an overview and the potential of the existence of an extensive neurobiological substrate underlying the close relationship between these two domains. By mapping the rodent brain regions involved in the social and/or cognitive domain, we show that the vast majority of brain regions involved in the cognitive domain are also involved in the social domain. The identified neuroanatomical overlap has an evolutionary basis, as complex social behavior requires cognitive skills, and aligns with the reported functional interactions of processes underlying cognitive and social performance. Based on the neuroanatomical mapping, recent (pre-)clinical findings, and the evolutionary perspective, we emphasize that the social domain requires more focus as an important treatment target and/or biomarker, especially considering the presently limited treatment strategies for these disorders.
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Affiliation(s)
- Suzanne D. Lanooij
- grid.4830.f0000 0004 0407 1981Groningen Institute for Evolutionary Life Sciences (GELIFES), Neurobiology, University of Groningen, Nijenborgh 7, 9747 AG Groningen, The Netherlands
| | - Ulrich L. M. Eisel
- grid.4830.f0000 0004 0407 1981Groningen Institute for Evolutionary Life Sciences (GELIFES), Neurobiology, University of Groningen, Nijenborgh 7, 9747 AG Groningen, The Netherlands
| | - Wilhelmus H. I. M. Drinkenburg
- grid.4830.f0000 0004 0407 1981Groningen Institute for Evolutionary Life Sciences (GELIFES), Neurobiology, University of Groningen, Nijenborgh 7, 9747 AG Groningen, The Netherlands ,grid.419619.20000 0004 0623 0341Department of Neuroscience, Janssen Research & Development, a Division of Janssen Pharmaceutica NV, Turnhoutseweg 30, B-2340 Beerse, Belgium
| | - Eddy A. van der Zee
- grid.4830.f0000 0004 0407 1981Groningen Institute for Evolutionary Life Sciences (GELIFES), Neurobiology, University of Groningen, Nijenborgh 7, 9747 AG Groningen, The Netherlands
| | - Martien J. H. Kas
- grid.4830.f0000 0004 0407 1981Groningen Institute for Evolutionary Life Sciences (GELIFES), Neurobiology, University of Groningen, Nijenborgh 7, 9747 AG Groningen, The Netherlands
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11
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A low-dimensional cognitive-network space in Alzheimer's disease and frontotemporal dementia. Alzheimers Res Ther 2022; 14:199. [PMID: 36581943 PMCID: PMC9798659 DOI: 10.1186/s13195-022-01145-x] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 12/14/2022] [Indexed: 12/30/2022]
Abstract
BACKGROUND Alzheimer's disease (AD) and frontotemporal dementia (FTD) show network dysfunctions linked with cognitive deficits. Within this framework, network abnormalities between AD and FTD show both convergent and divergent patterns. However, these functional patterns are far from being established and their relevance to cognitive processes remains to be elucidated. METHODS We investigated the relationship between cognition and functional connectivity of major cognitive networks in these diseases. Twenty-three bvFTD (age: 71±10), 22 AD (age: 72±6), and 20 controls (age: 72±6) underwent cognitive evaluation and resting-state functional MRI. Principal component analysis was used to describe cognitive variance across participants. Brain network connectivity was estimated with connectome analysis. Connectivity matrices were created assessing correlations between parcels within each functional network. The following cognitive networks were considered: default mode (DMN), dorsal attention (DAN), ventral attention (VAN), and frontoparietal (FPN) networks. The relationship between cognition and connectivity was assessed using a bootstrapping correlation and interaction analyses. RESULTS Three principal cognitive components explained more than 80% of the cognitive variance: the first component (cogPC1) loaded on memory, the second component (cogPC2) loaded on emotion and language, and the third component (cogPC3) loaded on the visuo-spatial and attentional domains. Compared to HC, AD and bvFTD showed impairment in all cogPCs (p<0.002), and bvFTD scored worse than AD in cogPC2 (p=0.031). At the network level, the DMN showed a significant association in the whole group with cogPC1 and cogPC2 and the VAN with cogPC2. By contrast, DAN and FPN showed a divergent pattern between diagnosis and connectivity for cogPC2. We confirmed these results by means of a multivariate analysis (canonical correlation). CONCLUSIONS A low-dimensional representation can account for a large variance in cognitive scores in the continuum from normal to pathological aging. Moreover, cognitive components showed both convergent and divergent patterns with connectivity across AD and bvFTD. The convergent pattern was observed across the networks primarily involved in these diseases (i.e., the DMN and VAN), while a divergent FC-cognitive pattern was mainly observed between attention/executive networks and the language/emotion cognitive component, suggesting the co-existence of compensatory and detrimental mechanisms underlying these components.
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12
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Chang X, Zhao W, Kang J, Xiang S, Xie C, Corona-Hernández H, Palaniyappan L, Feng J. Language abnormalities in schizophrenia: binding core symptoms through contemporary empirical evidence. SCHIZOPHRENIA (HEIDELBERG, GERMANY) 2022; 8:95. [PMID: 36371445 PMCID: PMC9653408 DOI: 10.1038/s41537-022-00308-x] [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: 06/07/2022] [Accepted: 10/26/2022] [Indexed: 06/16/2023]
Abstract
Both the ability to speak and to infer complex linguistic messages from sounds have been claimed as uniquely human phenomena. In schizophrenia, formal thought disorder (FTD) and auditory verbal hallucinations (AVHs) are manifestations respectively relating to concrete disruptions of those abilities. From an evolutionary perspective, Crow (1997) proposed that "schizophrenia is the price that Homo sapiens pays for the faculty of language". Epidemiological and experimental evidence points to an overlap between FTD and AVHs, yet a thorough investigation examining their shared neural mechanism in schizophrenia is lacking. In this review, we synthesize observations from three key domains. First, neuroanatomical evidence indicates substantial shared abnormalities in language-processing regions between FTD and AVHs, even in the early phases of schizophrenia. Second, neurochemical studies point to a glutamate-related dysfunction in these language-processing brain regions, contributing to verbal production deficits. Third, genetic findings further show how genes that overlap between schizophrenia and language disorders influence neurodevelopment and neurotransmission. We argue that these observations converge into the possibility that a glutamatergic dysfunction in language-processing brain regions might be a shared neural basis of both FTD and AVHs. Investigations of language pathology in schizophrenia could facilitate the development of diagnostic tools and treatments, so we call for multilevel confirmatory analyses focused on modulations of the language network as a therapeutic goal in schizophrenia.
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Affiliation(s)
- Xiao Chang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China
- Zhangjiang Fudan International Innovation Center, Shanghai, China
| | - Wei Zhao
- MOE-LCSM, School of Mathematics and Statistics, Hunan Normal University, Changsha, PR China
| | - Jujiao Kang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
- Shanghai Center for Mathematical Sciences, Shanghai, China
| | - Shitong Xiang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
| | - Chao Xie
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China
| | - Hugo Corona-Hernández
- Department of Biomedical Sciences of Cells & Systems, University Medical Center Groningen, University of Groningen, Groningen, The Netherlands
| | - Lena Palaniyappan
- Douglas Mental Health University Institute, Department of Psychiatry, McGill University, Montreal, Quebec, Canada.
- Robarts Research Institute, University of Western Ontario, London, Ontario, Canada.
- Lawson Health Research Institute, London, Ontario, Canada.
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
- Key Laboratory of Computational Neuroscience and Brain-Inspired Intelligence, Fudan University, Ministry of Education, Shanghai, China.
- MOE Frontiers Center for Brain Science, Fudan University, Shanghai, China.
- Zhangjiang Fudan International Innovation Center, Shanghai, China.
- Shanghai Center for Mathematical Sciences, Shanghai, China.
- Department of Computer Science, University of Warwick, Coventry, UK.
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13
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Warrington S, Thompson E, Bastiani M, Dubois J, Baxter L, Slater R, Jbabdi S, Mars RB, Sotiropoulos SN. Concurrent mapping of brain ontogeny and phylogeny within a common space: Standardized tractography and applications. SCIENCE ADVANCES 2022; 8:eabq2022. [PMID: 36260675 PMCID: PMC9581484 DOI: 10.1126/sciadv.abq2022] [Citation(s) in RCA: 9] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 03/24/2022] [Accepted: 08/31/2022] [Indexed: 06/16/2023]
Abstract
Developmental and evolutionary effects on brain organization are complex, yet linked, as evidenced by the correspondence in cortical area expansion across these vastly different time scales. However, it is still not possible to study concurrently the ontogeny and phylogeny of cortical areal connections, which is arguably more relevant to brain function than allometric measurements. Here, we propose a novel framework that allows the integration of structural connectivity maps from humans (adults and neonates) and nonhuman primates (macaques) onto a common space. We use white matter bundles to anchor the common space and use the uniqueness of cortical connection patterns to these bundles to probe area specialization. This enabled us to quantitatively study divergences and similarities in connectivity over evolutionary and developmental scales, to reveal brain maturation trajectories, including the effect of premature birth, and to translate cortical atlases between diverse brains. Our findings open new avenues for an integrative approach to imaging neuroanatomy.
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Affiliation(s)
- Shaun Warrington
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, UK
| | - Elinor Thompson
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, UK
- Centre for Medical Image Computing, Department of Computer Science, University College London, London, UK
| | - Matteo Bastiani
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, UK
| | - Jessica Dubois
- Université Paris Cité, Inserm, NeuroDiderot Unit, Paris, France
- University Paris-Saclay, CEA, NeuroSpin, Gif-sur-Yvette, France
| | - Luke Baxter
- Department of Paediatrics, University of Oxford, Oxford, UK
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Rebeccah Slater
- Department of Paediatrics, University of Oxford, Oxford, UK
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Saad Jbabdi
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
| | - Rogier B. Mars
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
- Donders Institute for Brain, Cognition, and Behaviour, Radboud University, Nijmegen, Netherlands
| | - Stamatios N. Sotiropoulos
- Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, UK
- Wellcome Centre for Integrative Neuroimaging, University of Oxford, Oxford, UK
- National Institute for Health Research (NIHR) Nottingham Biomedical Research Centre, Queens Medical Centre, Nottingham, UK
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14
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Safron A, Çatal O, Verbelen T. Generalized Simultaneous Localization and Mapping (G-SLAM) as unification framework for natural and artificial intelligences: towards reverse engineering the hippocampal/entorhinal system and principles of high-level cognition. Front Syst Neurosci 2022; 16:787659. [PMID: 36246500 PMCID: PMC9563348 DOI: 10.3389/fnsys.2022.787659] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2021] [Accepted: 09/02/2022] [Indexed: 11/24/2022] Open
Abstract
Simultaneous localization and mapping (SLAM) represents a fundamental problem for autonomous embodied systems, for which the hippocampal/entorhinal system (H/E-S) has been optimized over the course of evolution. We have developed a biologically-inspired SLAM architecture based on latent variable generative modeling within the Free Energy Principle and Active Inference (FEP-AI) framework, which affords flexible navigation and planning in mobile robots. We have primarily focused on attempting to reverse engineer H/E-S "design" properties, but here we consider ways in which SLAM principles from robotics may help us better understand nervous systems and emergent minds. After reviewing LatentSLAM and notable features of this control architecture, we consider how the H/E-S may realize these functional properties not only for physical navigation, but also with respect to high-level cognition understood as generalized simultaneous localization and mapping (G-SLAM). We focus on loop-closure, graph-relaxation, and node duplication as particularly impactful architectural features, suggesting these computational phenomena may contribute to understanding cognitive insight (as proto-causal-inference), accommodation (as integration into existing schemas), and assimilation (as category formation). All these operations can similarly be describable in terms of structure/category learning on multiple levels of abstraction. However, here we adopt an ecological rationality perspective, framing H/E-S functions as orchestrating SLAM processes within both concrete and abstract hypothesis spaces. In this navigation/search process, adaptive cognitive equilibration between assimilation and accommodation involves balancing tradeoffs between exploration and exploitation; this dynamic equilibrium may be near optimally realized in FEP-AI, wherein control systems governed by expected free energy objective functions naturally balance model simplicity and accuracy. With respect to structure learning, such a balance would involve constructing models and categories that are neither too inclusive nor exclusive. We propose these (generalized) SLAM phenomena may represent some of the most impactful sources of variation in cognition both within and between individuals, suggesting that modulators of H/E-S functioning may potentially illuminate their adaptive significances as fundamental cybernetic control parameters. Finally, we discuss how understanding H/E-S contributions to G-SLAM may provide a unifying framework for high-level cognition and its potential realization in artificial intelligences.
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Affiliation(s)
- Adam Safron
- Center for Psychedelic and Consciousness Research, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Cognitive Science Program, Indiana University, Bloomington, IN, United States
- Institute for Advanced Consciousness Studies, Santa Monica, CA, United States
| | - Ozan Çatal
- IDLab, Department of Information Technology, Ghent University—imec, Ghent, Belgium
| | - Tim Verbelen
- IDLab, Department of Information Technology, Ghent University—imec, Ghent, Belgium
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15
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Genetic variations in evolutionary accelerated regions disrupt cognition in schizophrenia. Psychiatry Res 2022; 314:114586. [PMID: 35623238 PMCID: PMC10150587 DOI: 10.1016/j.psychres.2022.114586] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/19/2021] [Revised: 04/03/2022] [Accepted: 04/30/2022] [Indexed: 02/03/2023]
Abstract
Cognition is believed to be a product of human evolution, while schizophrenia is ascribed as the by-product with cognitive impairment as it's genetically mediated endophenotype. Genomic loci associated with these traits are enriched with recent evolutionary markers such as Human accelerated regions (HARs). HARs are markedly different in humans since their divergence with chimpanzees and mostly regulate gene expression by binding to transcription factors and/or modulating chromatin interactions. We hypothesize that variants within HARs may alter such functions and thus contribute to disease pathogenesis. 49 systematically prioritized variants from 2737 genome-wide HARs were genotyped in a north-Indian schizophrenia cohort (331 cases, 235 controls). Six variants were significantly associated with cognitive impairment in schizophrenia, thirteen with general cognition in healthy individuals. These variants were mapped to 122 genes; predicted to alter 79 transcription factors binding sites and overlapped with promoters, enhancers and/or repressors. These genes and TFs are implicated in neurocognitive phenotypes, autism, schizophrenia and bipolar disorders; a few are targets of common or repurposable antipsychotics suggesting their draggability; and enriched for immune response and brain developmental pathways. Immune response has been more strongly targeted by natural selection during human evolution and has a prominent role in neurodevelopment. Thus, its disruption may have deleterious consequences for neuronal and cognitive functions. Importantly, among the 15 associated SNPs, 12 showed association in several independent GWASs of different neurocognitive functions. Further analysis of HARs may be valuable to understand their role in cognition biology and identify improved therapeutics for schizophrenia.
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16
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Scholtens LH, Pijnenburg R, de Lange SC, Huitinga I, van den Heuvel MP. Common Microscale and Macroscale Principles of Connectivity in the Human Brain. J Neurosci 2022; 42:4147-4163. [PMID: 35422441 PMCID: PMC9121834 DOI: 10.1523/jneurosci.1572-21.2022] [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: 07/30/2021] [Revised: 01/27/2022] [Accepted: 03/04/2022] [Indexed: 11/21/2022] Open
Abstract
The brain requires efficient information transfer between neurons and large-scale brain regions. Brain connectivity follows predictable organizational principles. At the cellular level, larger supragranular pyramidal neurons have larger, more branched dendritic trees, more synapses, and perform more complex computations; at the macroscale, region-to-region connections display a diverse architecture with highly connected hub areas facilitating complex information integration and computation. Here, we explore the hypothesis that the branching structure of large-scale region-to-region connectivity follows similar organizational principles as the neuronal scale. We examine microscale connectivity of basal dendritic trees of supragranular pyramidal neurons (300+) across 10 cortical areas in five human donor brains (1 male, 4 female). Dendritic complexity was quantified as the number of branch points, tree length, spine count, spine density, and overall branching complexity. High-resolution diffusion-weighted MRI was used to construct white matter trees of corticocortical wiring. Examining complexity of the resulting white matter trees using the same measures as for dendritic trees shows heteromodal association areas to have larger, more complex white matter trees than primary areas (p < 0.0001) and macroscale complexity to run in parallel with microscale measures, in terms of number of inputs (r = 0.677, p = 0.032), branch points (r = 0.797, p = 0.006), tree length (r = 0.664, p = 0.036), and branching complexity (r = 0.724, p = 0.018). Our findings support the integrative theory that brain connectivity follows similar principles of connectivity at neuronal and macroscale levels and provide a framework to study connectivity changes in brain conditions at multiple levels of organization.SIGNIFICANCE STATEMENT Within the human brain, cortical areas are involved in a wide range of processes, requiring different levels of information integration and local computation. At the cellular level, these regional differences reflect a predictable organizational principle with larger, more complexly branched supragranular pyramidal neurons in higher order regions. We hypothesized that the 3D branching structure of macroscale corticocortical connections follows the same organizational principles as the cellular scale. Comparing branching complexity of dendritic trees of supragranular pyramidal neurons and of MRI-based regional white matter trees of macroscale connectivity, we show that macroscale branching complexity is larger in higher order areas and that microscale and macroscale complexity go hand in hand. Our findings contribute to a multiscale integrative theory of brain connectivity.
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Affiliation(s)
- Lianne H Scholtens
- Complex Traits Genetics Department, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
| | - Rory Pijnenburg
- Complex Traits Genetics Department, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
| | - Siemon C de Lange
- Complex Traits Genetics Department, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
| | - Inge Huitinga
- Neuroimmunology Research Group, Netherlands Institute for Neuroscience, 1105 BA Amsterdam, The Netherlands
- Brain Plasticity Group, Swammerdam Institute for Life Sciences, University of Amsterdam, 1098 XH Amsterdam, The Netherlands
| | - Martijn P van den Heuvel
- Complex Traits Genetics Department, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands
- Department of Child Psychiatry, Amsterdam Neuroscience, Amsterdam University Medical Center, 1081 HV Amsterdam, The Netherlands
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17
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Gupta C, Chandrashekar P, Jin T, He C, Khullar S, Chang Q, Wang D. Bringing machine learning to research on intellectual and developmental disabilities: taking inspiration from neurological diseases. J Neurodev Disord 2022; 14:28. [PMID: 35501679 PMCID: PMC9059371 DOI: 10.1186/s11689-022-09438-w] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/19/2021] [Accepted: 04/07/2022] [Indexed: 12/31/2022] Open
Abstract
Intellectual and Developmental Disabilities (IDDs), such as Down syndrome, Fragile X syndrome, Rett syndrome, and autism spectrum disorder, usually manifest at birth or early childhood. IDDs are characterized by significant impairment in intellectual and adaptive functioning, and both genetic and environmental factors underpin IDD biology. Molecular and genetic stratification of IDDs remain challenging mainly due to overlapping factors and comorbidity. Advances in high throughput sequencing, imaging, and tools to record behavioral data at scale have greatly enhanced our understanding of the molecular, cellular, structural, and environmental basis of some IDDs. Fueled by the "big data" revolution, artificial intelligence (AI) and machine learning (ML) technologies have brought a whole new paradigm shift in computational biology. Evidently, the ML-driven approach to clinical diagnoses has the potential to augment classical methods that use symptoms and external observations, hoping to push the personalized treatment plan forward. Therefore, integrative analyses and applications of ML technology have a direct bearing on discoveries in IDDs. The application of ML to IDDs can potentially improve screening and early diagnosis, advance our understanding of the complexity of comorbidity, and accelerate the identification of biomarkers for clinical research and drug development. For more than five decades, the IDDRC network has supported a nexus of investigators at centers across the USA, all striving to understand the interplay between various factors underlying IDDs. In this review, we introduced fast-increasing multi-modal data types, highlighted example studies that employed ML technologies to illuminate factors and biological mechanisms underlying IDDs, as well as recent advances in ML technologies and their applications to IDDs and other neurological diseases. We discussed various molecular, clinical, and environmental data collection modes, including genetic, imaging, phenotypical, and behavioral data types, along with multiple repositories that store and share such data. Furthermore, we outlined some fundamental concepts of machine learning algorithms and presented our opinion on specific gaps that will need to be filled to accomplish, for example, reliable implementation of ML-based diagnosis technology in IDD clinics. We anticipate that this review will guide researchers to formulate AI and ML-based approaches to investigate IDDs and related conditions.
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Affiliation(s)
- Chirag Gupta
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA.,Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Pramod Chandrashekar
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA.,Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Ting Jin
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA.,Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Chenfeng He
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA.,Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Saniya Khullar
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA.,Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53706, USA
| | - Qiang Chang
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA.,Department of Medical Genetics, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, 53705, USA.,Department of Neurology, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, 53705, USA
| | - Daifeng Wang
- Waisman Center, University of Wisconsin-Madison, Madison, WI, 53705, USA. .,Department of Biostatistics and Medical Informatics, University of Wisconsin-Madison, Madison, WI, 53706, USA. .,Department of Computer Sciences, University of Wisconsin-Madison, Madison, WI, 53706, USA.
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Zhang F, Daducci A, He Y, Schiavi S, Seguin C, Smith RE, Yeh CH, Zhao T, O'Donnell LJ. Quantitative mapping of the brain's structural connectivity using diffusion MRI tractography: A review. Neuroimage 2022; 249:118870. [PMID: 34979249 PMCID: PMC9257891 DOI: 10.1016/j.neuroimage.2021.118870] [Citation(s) in RCA: 78] [Impact Index Per Article: 39.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/15/2021] [Revised: 12/03/2021] [Accepted: 12/31/2021] [Indexed: 12/13/2022] Open
Abstract
Diffusion magnetic resonance imaging (dMRI) tractography is an advanced imaging technique that enables in vivo reconstruction of the brain's white matter connections at macro scale. It provides an important tool for quantitative mapping of the brain's structural connectivity using measures of connectivity or tissue microstructure. Over the last two decades, the study of brain connectivity using dMRI tractography has played a prominent role in the neuroimaging research landscape. In this paper, we provide a high-level overview of how tractography is used to enable quantitative analysis of the brain's structural connectivity in health and disease. We focus on two types of quantitative analyses of tractography, including: 1) tract-specific analysis that refers to research that is typically hypothesis-driven and studies particular anatomical fiber tracts, and 2) connectome-based analysis that refers to research that is more data-driven and generally studies the structural connectivity of the entire brain. We first provide a review of methodology involved in three main processing steps that are common across most approaches for quantitative analysis of tractography, including methods for tractography correction, segmentation and quantification. For each step, we aim to describe methodological choices, their popularity, and potential pros and cons. We then review studies that have used quantitative tractography approaches to study the brain's white matter, focusing on applications in neurodevelopment, aging, neurological disorders, mental disorders, and neurosurgery. We conclude that, while there have been considerable advancements in methodological technologies and breadth of applications, there nevertheless remains no consensus about the "best" methodology in quantitative analysis of tractography, and researchers should remain cautious when interpreting results in research and clinical applications.
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Affiliation(s)
- Fan Zhang
- Brigham and Women's Hospital, Harvard Medical School, Boston, USA.
| | | | - Yong He
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China; Chinese Institute for Brain Research, Beijing, China
| | - Simona Schiavi
- Department of Computer Science, University of Verona, Verona, Italy
| | - Caio Seguin
- Melbourne Neuropsychiatry Centre, University of Melbourne and Melbourne Health, Melbourne, Australia; The University of Sydney, School of Biomedical Engineering, Sydney, Australia
| | - Robert E Smith
- The Florey Institute of Neuroscience and Mental Health, Melbourne, Australia; Florey Department of Neuroscience and Mental Health, The University of Melbourne, Melbourne, Australia
| | - Chun-Hung Yeh
- Institute for Radiological Research, Chang Gung University, Taoyuan, Taiwan; Department of Psychiatry, Chang Gung Memorial Hospital at Linkou, Taoyuan, Taiwan
| | - Tengda Zhao
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, Beijing, China; Beijing Key Laboratory of Brain Imaging and Connectomics, Beijing Normal University, Beijing, China; IDG/McGovern Institute for Brain Research, Beijing Normal University, Beijing, China
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19
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Cong S, Yao X, Xie L, Yan J, Shen L. Genetic Influence Underlying Brain Connectivity Phenotype: A Study on Two Age-Specific Cohorts. Front Genet 2022; 12:782953. [PMID: 35237294 PMCID: PMC8884108 DOI: 10.3389/fgene.2021.782953] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/25/2021] [Accepted: 11/16/2021] [Indexed: 11/29/2022] Open
Abstract
Background: Human brain structural connectivity is an important imaging quantitative trait for brain development and aging. Mapping the network connectivity to the phenotypic variation provides fundamental insights in understanding the relationship between detailed brain topological architecture, function, and dysfunction. However, the underlying neurobiological mechanism from gene to brain connectome, and to phenotypic outcomes, and whether this mechanism changes over time, remain unclear. Methods: This study analyzes diffusion-weighted imaging data from two age-specific neuroimaging cohorts, extracts structural connectome topological network measures, performs genome-wide association studies of the measures, and examines the causality of genetic influences on phenotypic outcomes mediated via connectivity measures. Results: Our empirical study has yielded several significant findings: 1) It identified genetic makeup underlying structural connectivity changes in the human brain connectome for both age groups. Specifically, it revealed a novel association between the minor allele (G) of rs7937515 and the decreased network segregation measures of the left middle temporal gyrus across young and elderly adults, indicating a consistent genetic effect on brain connectivity across the lifespan. 2) It revealed rs7937515 as a genetic marker for body mass index in young adults but not in elderly adults. 3) It discovered brain network segregation alterations as a potential neuroimaging biomarker for obesity. 4) It demonstrated the hemispheric asymmetry of structural network organization in genetic association analyses and outcome-relevant studies. Discussion: These imaging genetic findings underlying brain connectome warrant further investigation for exploring their potential influences on brain-related complex diseases, given the significant involvement of altered connectivity in neurological, psychiatric and physical disorders.
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Affiliation(s)
- Shan Cong
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Xiaohui Yao
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
| | - Linhui Xie
- Department of Electrical and Computer Engineering, School of Engineering, Indiana University Purdue University Indianapolis, Indianapolis, IN, United States
| | - Jingwen Yan
- Department of BioHealth Informatics, School of Informatics and Computing, Indiana University Purdue University Indianapolis, Indianapolis, IN, United States
| | - Li Shen
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, United States
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20
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Safron A, Klimaj V, Hipólito I. On the Importance of Being Flexible: Dynamic Brain Networks and Their Potential Functional Significances. Front Syst Neurosci 2022; 15:688424. [PMID: 35126062 PMCID: PMC8814434 DOI: 10.3389/fnsys.2021.688424] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Accepted: 11/10/2021] [Indexed: 12/14/2022] Open
Abstract
In this theoretical review, we begin by discussing brains and minds from a dynamical systems perspective, and then go on to describe methods for characterizing the flexibility of dynamic networks. We discuss how varying degrees and kinds of flexibility may be adaptive (or maladaptive) in different contexts, specifically focusing on measures related to either more disjoint or cohesive dynamics. While disjointed flexibility may be useful for assessing neural entropy, cohesive flexibility may potentially serve as a proxy for self-organized criticality as a fundamental property enabling adaptive behavior in complex systems. Particular attention is given to recent studies in which flexibility methods have been used to investigate neurological and cognitive maturation, as well as the breakdown of conscious processing under varying levels of anesthesia. We further discuss how these findings and methods might be contextualized within the Free Energy Principle with respect to the fundamentals of brain organization and biological functioning more generally, and describe potential methodological advances from this paradigm. Finally, with relevance to computational psychiatry, we propose a research program for obtaining a better understanding of ways that dynamic networks may relate to different forms of psychological flexibility, which may be the single most important factor for ensuring human flourishing.
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Affiliation(s)
- Adam Safron
- Center for Psychedelic and Consciousness Research, Department of Psychiatry and Behavioral Sciences, Johns Hopkins University School of Medicine, Baltimore, MD, United States
- Kinsey Institute, Indiana University, Bloomington, IN, United States
- Cognitive Science Program, Indiana University, Bloomington, IN, United States
| | - Victoria Klimaj
- Cognitive Science Program, Indiana University, Bloomington, IN, United States
- Complex Networks and Systems, Informatics Department, Indiana University, Bloomington, IN, United States
| | - Inês Hipólito
- Department of Philosophy, Berlin School of Mind and Brain, Humboldt-Universität zu Berlin, Berlin, Germany
- Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom
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21
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Palaniyappan L, Du J, Zhang J, Feng J. Reply to: "Historical pursuits of the language pathway hypothesis of schizophrenia". NPJ SCHIZOPHRENIA 2021; 7:54. [PMID: 34753936 PMCID: PMC8578441 DOI: 10.1038/s41537-021-00183-y] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Key Words] [Track Full Text] [Download PDF] [Subscribe] [Scholar Register] [Received: 06/27/2021] [Accepted: 09/13/2021] [Indexed: 12/12/2022]
Affiliation(s)
- Lena Palaniyappan
- Department of Psychiatry and Robarts Research Institute, University of Western Ontario, London, Ontario, Canada
- Lawson Health Research Institute, London, Ontario, Canada
| | - Jingnan Du
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China
- Shanghai Key Laboratory of Psychotic Disorders, Shanghai Mental Health Center, Shanghai Jiao Tong University School of Medicine, 200030, Shanghai, China
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China
| | - Jie Zhang
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China.
| | - Jianfeng Feng
- Institute of Science and Technology for Brain-Inspired Intelligence, Fudan University, Shanghai, China.
- Key Laboratory of Computational Neuroscience and Brain Inspired Intelligence (Fudan University), Ministry of Education, Shanghai, China.
- Department of Computer Science, University of Warwick, Coventry, CV4 7AL, UK.
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22
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Bryant KL, Ardesch DJ, Roumazeilles L, Scholtens LH, Khrapitchev AA, Tendler BC, Wu W, Miller KL, Sallet J, van den Heuvel MP, Mars RB. Diffusion MRI data, sulcal anatomy, and tractography for eight species from the Primate Brain Bank. Brain Struct Funct 2021; 226:2497-2509. [PMID: 34264391 PMCID: PMC8608778 DOI: 10.1007/s00429-021-02268-x] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/27/2021] [Accepted: 03/26/2021] [Indexed: 12/16/2022]
Abstract
Large-scale comparative neuroscience requires data from many species and, ideally, at multiple levels of description. Here, we contribute to this endeavor by presenting diffusion and structural MRI data from eight primate species that have not or rarely been described in the literature. The selected samples from the Primate Brain Bank cover a prosimian, New and Old World monkeys, and a great ape. We present preliminary labelling of the cortical sulci and tractography of the optic radiation, dorsal part of the cingulum bundle, and dorsal parietal-frontal and ventral temporal-frontal longitudinal white matter tracts. Both dorsal and ventral association fiber systems could be observed in all samples, with the dorsal tracts occupying much less relative volume in the prosimian than in other species. We discuss the results in the context of known primate specializations and present hypotheses for further research. All data and results presented here are available online as a resource for the scientific community.
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Affiliation(s)
- Katherine L Bryant
- Wellcome Centre for Integrative Neuroimaging, Centre for fMRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Headington, Oxford, OX9 3DU, UK
| | - Dirk Jan Ardesch
- Department of Complex Trait Genetics, Centre for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Lea Roumazeilles
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford, UK
| | - Lianne H Scholtens
- Department of Complex Trait Genetics, Centre for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Alexandre A Khrapitchev
- Department of Oncology, University of Oxford, Cancer Research UK and Medical Research Council Oxford Institute for Radiation Oncology, Oxford, UK
| | - Benjamin C Tendler
- Wellcome Centre for Integrative Neuroimaging, Centre for fMRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Headington, Oxford, OX9 3DU, UK
| | - Wenchuan Wu
- Wellcome Centre for Integrative Neuroimaging, Centre for fMRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Headington, Oxford, OX9 3DU, UK
| | - Karla L Miller
- Wellcome Centre for Integrative Neuroimaging, Centre for fMRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Headington, Oxford, OX9 3DU, UK
| | - Jerome Sallet
- Wellcome Centre for Integrative Neuroimaging, Department of Experimental Psychology, University of Oxford, Oxford, UK
- Univ Lyon, Université Lyon 1, Inserm, Stem Cell and Brain Research Institute U1208, 69500, Bron, France
| | - Martijn P van den Heuvel
- Department of Complex Trait Genetics, Centre for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Department of Clinical Genetics, Amsterdam Neuroscience, Amsterdam UMC, Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - Rogier B Mars
- Wellcome Centre for Integrative Neuroimaging, Centre for fMRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Headington, Oxford, OX9 3DU, UK.
- Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, The Netherlands.
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23
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Si X, Xiang S, Zhang L, Li S, Zhang K, Ming D. Acupuncture With deqi Modulates the Hemodynamic Response and Functional Connectivity of the Prefrontal-Motor Cortical Network. Front Neurosci 2021; 15:693623. [PMID: 34483822 PMCID: PMC8415569 DOI: 10.3389/fnins.2021.693623] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/11/2021] [Accepted: 06/07/2021] [Indexed: 11/25/2022] Open
Abstract
As a world intangible cultural heritage, acupuncture is considered an essential modality of complementary and alternative therapy to Western medicine. Despite acupuncture’s long history and public acceptance, how the cortical network is modulated by acupuncture remains largely unclear. Moreover, as the basic acupuncture unit for regulating the central nervous system, how the cortical network is modulated during acupuncture at the Hegu acupoint is mostly unclear. Here, multi-channel functional near-infrared spectroscopy (fNIRS) data were recorded from twenty healthy subjects for acupuncture manipulation, pre- and post-manipulation tactile controls, and pre- and post-acupuncture rest controls. Results showed that: (1) acupuncture manipulation caused significantly increased acupuncture behavioral deqi performance compared with tactile controls. (2) The bilateral prefrontal cortex (PFC) and motor cortex were significantly inhibited during acupuncture manipulation than controls, which was evidenced by the decreased power of oxygenated hemoglobin (HbO) concentration. (3) The bilateral PFC’s hemodynamic responses showed a positive correlation trend with acupuncture behavioral performance. (4) The network connections with bilateral PFC as nodes showed significantly increased functional connectivity during acupuncture manipulation compared with controls. (5) Meanwhile, the network’s efficiency was improved by acupuncture manipulation, evidenced by the increased global efficiency and decreased shortest path length. Taken together, these results reveal that a cooperative PFC-Motor functional network could be modulated by acupuncture manipulation at the Hegu acupoint. This study provides neuroimaging evidence that explains acupuncture’s neuromodulation effects on the cortical network.
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Affiliation(s)
- Xiaopeng Si
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.,Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin, China.,Tianjin International Engineering Institute, Tianjin University, Tianjin, China.,Institute of Applied Psychology, Tianjin University, Tianjin, China
| | - Shaoxin Xiang
- Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin, China.,Tianjin International Engineering Institute, Tianjin University, Tianjin, China
| | - Ludan Zhang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.,Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin, China
| | - Sicheng Li
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.,Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin, China
| | - Kuo Zhang
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.,Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin, China
| | - Dong Ming
- Academy of Medical Engineering and Translational Medicine, Tianjin University, Tianjin, China.,Tianjin Key Laboratory of Brain Science and Neural Engineering, Tianjin University, Tianjin, China
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24
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Moreau CA, Raznahan A, Bellec P, Chakravarty M, Thompson PM, Jacquemont S. Dissecting autism and schizophrenia through neuroimaging genomics. Brain 2021; 144:1943-1957. [PMID: 33704401 PMCID: PMC8370419 DOI: 10.1093/brain/awab096] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Revised: 12/24/2020] [Accepted: 01/08/2021] [Indexed: 12/23/2022] Open
Abstract
Neuroimaging genomic studies of autism spectrum disorder and schizophrenia have mainly adopted a 'top-down' approach, beginning with the behavioural diagnosis, and moving down to intermediate brain phenotypes and underlying genetic factors. Advances in imaging and genomics have been successfully applied to increasingly large case-control studies. As opposed to diagnostic-first approaches, the bottom-up strategy begins at the level of molecular factors enabling the study of mechanisms related to biological risk, irrespective of diagnoses or clinical manifestations. The latter strategy has emerged from questions raised by top-down studies: why are mutations and brain phenotypes over-represented in individuals with a psychiatric diagnosis? Are they related to core symptoms of the disease or to comorbidities? Why are mutations and brain phenotypes associated with several psychiatric diagnoses? Do they impact a single dimension contributing to all diagnoses? In this review, we aimed at summarizing imaging genomic findings in autism and schizophrenia as well as neuropsychiatric variants associated with these conditions. Top-down studies of autism and schizophrenia identified patterns of neuroimaging alterations with small effect-sizes and an extreme polygenic architecture. Genomic variants and neuroimaging patterns are shared across diagnostic categories suggesting pleiotropic mechanisms at the molecular and brain network levels. Although the field is gaining traction; characterizing increasingly reproducible results, it is unlikely that top-down approaches alone will be able to disentangle mechanisms involved in autism or schizophrenia. In stark contrast with top-down approaches, bottom-up studies showed that the effect-sizes of high-risk neuropsychiatric mutations are equally large for neuroimaging and behavioural traits. Low specificity has been perplexing with studies showing that broad classes of genomic variants affect a similar range of behavioural and cognitive dimensions, which may be consistent with the highly polygenic architecture of psychiatric conditions. The surprisingly discordant effect sizes observed between genetic and diagnostic first approaches underscore the necessity to decompose the heterogeneity hindering case-control studies in idiopathic conditions. We propose a systematic investigation across a broad spectrum of neuropsychiatric variants to identify putative latent dimensions underlying idiopathic conditions. Gene expression data on temporal, spatial and cell type organization in the brain have also considerable potential for parsing the mechanisms contributing to these dimensions' phenotypes. While large neuroimaging genomic datasets are now available in unselected populations, there is an urgent need for data on individuals with a range of psychiatric symptoms and high-risk genomic variants. Such efforts together with more standardized methods will improve mechanistically informed predictive modelling for diagnosis and clinical outcomes.
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Affiliation(s)
- Clara A Moreau
- Sainte Justine Research Center, University of Montréal, Montréal, Québec H3T 1C5, Canada
- Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, Montreal, Québec H3W 1W5, Canada
- Human Genetics and Cognitive Functions, CNRS UMR 3571, Université de Paris, Institut Pasteur, Paris, France
| | - Armin Raznahan
- Section on Developmental Neurogenomics, Human Genetics Branch, National Institute of Mental Health Intramural Research Program, Bethesda, MD 20892, USA
| | - Pierre Bellec
- Centre de Recherche de l’Institut Universitaire de Gériatrie de Montréal, Montreal, Québec H3W 1W5, Canada
| | - Mallar Chakravarty
- Cerebral Imaging Centre, Douglas Hospital Mental Health University Institute, Verdun, Québec H4H 1R3, Canada
| | - Paul M Thompson
- Imaging Genetics Center, Stevens Institute for Neuroimaging and Informatics, USC Keck School of Medicine, Marina del Rey, CA 90033, USA
| | - Sebastien Jacquemont
- Sainte Justine Research Center, University of Montréal, Montréal, Québec H3T 1C5, Canada
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25
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Abstract
Although rodent research provides important insights into neural correlates of human psychology, new cortical areas, connections, and cognitive abilities emerged during primate evolution, including human evolution. Comparison of human brains with those of nonhuman primates reveals two aspects of human brain evolution particularly relevant to emotional disorders: expansion of homotypical association areas and expansion of the hippocampus. Two uniquely human cognitive capacities link these phylogenetic developments with emotion: a subjective sense of participating in and reexperiencing remembered events and a limitless capacity to imagine details of future events. These abilities provided evolving humans with selective advantages, but they also created proclivities for emotional problems. The first capacity evokes the "reliving" of past events in the "here-and-now," accompanied by emotional responses that occurred during memory encoding. It contributes to risk for stress-related syndromes, such as posttraumatic stress disorder. The second capacity, an ability to imagine future events without temporal limitations, facilitates flexible, goal-related behavior by drawing on and creating a uniquely rich array of mental representations. It promotes goal achievement and reduces errors, but the mental construction of future events also contributes to developmental aspects of anxiety and mood disorders. With maturation of homotypical association areas, the concrete concerns of childhood expand to encompass the abstract apprehensions of adolescence and adulthood. These cognitive capacities and their dysfunction are amenable to a research agenda that melds experimental therapeutic interventions, cognitive neuropsychology, and developmental psychology in both humans and nonhuman primates.
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Affiliation(s)
- Daniel S. Pine
- Section on Development and Affective Neuroscience, Emotion and Development Branch, National Institute of Mental Health, Bethesda, MD 20892
| | - Steven P. Wise
- Olschefskie Institute for the Neurobiology of Knowledge, Bethesda, MD 20814
| | - Elisabeth A. Murray
- Section on the Neurobiology of Learning and Memory, Laboratory of Neuropsychology, National Institute of Mental Health, Bethesda, MD 20892
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26
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Liu X, Eickhoff SB, Caspers S, Wu J, Genon S, Hoffstaedter F, Mars RB, Sommer IE, Eickhoff CR, Chen J, Jardri R, Reetz K, Dogan I, Aleman A, Kogler L, Gruber O, Caspers J, Mathys C, Patil KR. Functional parcellation of human and macaque striatum reveals human-specific connectivity in the dorsal caudate. Neuroimage 2021; 235:118006. [PMID: 33819611 PMCID: PMC8214073 DOI: 10.1016/j.neuroimage.2021.118006] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/15/2020] [Revised: 02/10/2021] [Accepted: 03/17/2021] [Indexed: 12/12/2022] Open
Abstract
A wide homology between human and macaque striatum is often assumed as in both the striatum is involved in cognition, emotion and executive functions. However, differences in functional and structural organization between human and macaque striatum may reveal evolutionary divergence and shed light on human vulnerability to neuropsychiatric diseases. For instance, dopaminergic dysfunction of the human striatum is considered to be a pathophysiological underpinning of different disorders, such as Parkinson's disease (PD) and schizophrenia (SCZ). Previous investigations have found a wide similarity in structural connectivity of the striatum between human and macaque, leaving the cross-species comparison of its functional organization unknown. In this study, resting-state functional connectivity (RSFC) derived striatal parcels were compared based on their homologous cortico-striatal connectivity. The goal here was to identify striatal parcels whose connectivity is human-specific compared to macaque parcels. Functional parcellation revealed that the human striatum was split into dorsal, dorsomedial, and rostral caudate and ventral, central, and caudal putamen, while the macaque striatum was divided into dorsal, and rostral caudate and rostral, and caudal putamen. Cross-species comparison indicated dissimilar cortico-striatal RSFC of the topographically similar dorsal caudate. We probed clinical relevance of the striatal clusters by examining differences in their cortico-striatal RSFC and gray matter (GM) volume between patients (with PD and SCZ) and healthy controls. We found abnormal RSFC not only between dorsal caudate, but also between rostral caudate, ventral, central and caudal putamen and widespread cortical regions for both PD and SCZ patients. Also, we observed significant structural atrophy in rostral caudate, ventral and central putamen for both PD and SCZ while atrophy in the dorsal caudate was specific to PD. Taken together, our cross-species comparative results revealed shared and human-specific RSFC of different striatal clusters reinforcing the complex organization and function of the striatum. In addition, we provided a testable hypothesis that abnormalities in a region with human-specific connectivity, i.e., dorsal caudate, might be associated with neuropsychiatric disorders.
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Affiliation(s)
- Xiaojin Liu
- Institute of Neuroscience and Medicine (INM-7), Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Systems Neuroscience, Research Centre Jülich, Jülich, Germany
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine (INM-7), Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Systems Neuroscience, Research Centre Jülich, Jülich, Germany
| | - Svenja Caspers
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany; Institute for Anatomy I, Medical Faculty, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Jianxiao Wu
- Institute of Neuroscience and Medicine (INM-7), Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Systems Neuroscience, Research Centre Jülich, Jülich, Germany
| | - Sarah Genon
- Institute of Neuroscience and Medicine (INM-7), Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Systems Neuroscience, Research Centre Jülich, Jülich, Germany
| | - Felix Hoffstaedter
- Institute of Neuroscience and Medicine (INM-7), Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Systems Neuroscience, Research Centre Jülich, Jülich, Germany
| | - Rogier B Mars
- Wellcome Centre for Integrative Neuroimaging, Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, John Radcliffe Hospital, University of Oxford, Oxford, United Kingdom; Donders Institute for Brain, Cognition and Behaviour, Radboud University Nijmegen, Nijmegen, Netherlands
| | - Iris E Sommer
- Department of Biomedical Sciences of Cells & Systems, University Medical Center Groningen, Groningen, Netherlands
| | - Claudia R Eickhoff
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany; Institute of Clinical Neuroscience and Medical Psychology, Medical Faculty, University of Düsseldorf, Düsseldorf, Germany
| | - Ji Chen
- Institute of Neuroscience and Medicine (INM-7), Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Systems Neuroscience, Research Centre Jülich, Jülich, Germany
| | - Renaud Jardri
- Division of Psychiatry, University of Lille, CNRS UMR9193, SCALab & CHU Lille, Fontan Hospital, CURE platform, Lille, France
| | - Kathrin Reetz
- JARA-BRAIN Institute Molecular Neuroscience and Neuroimaging, Forschungszentrum Jülich, RWTH Aachen University, Aachen, Germany; Department of Neurology, RWTH Aachen University, Aachen, Germany
| | - Imis Dogan
- JARA-BRAIN Institute Molecular Neuroscience and Neuroimaging, Forschungszentrum Jülich, RWTH Aachen University, Aachen, Germany; Department of Neurology, RWTH Aachen University, Aachen, Germany
| | - André Aleman
- Department of Neuroscience, University Medical Center Groningen, University of Groningen, Groningen, Netherlands
| | - Lydia Kogler
- Department of Psychiatry and Psychotherapy, Medical School, University of Tübingen, Germany
| | - Oliver Gruber
- Section for Experimental Psychopathology and Neuroimaging, Department of General Psychiatry, Heidelberg University, Germany
| | - Julian Caspers
- Institute of Neuroscience and Medicine (INM-1), Research Centre Jülich, Jülich, Germany; Department of Diagnostic and Interventional Radiology, Medical Faculty, University of Düsseldorf, Düsseldorf, Germany
| | - Christian Mathys
- Department of Diagnostic and Interventional Radiology, Medical Faculty, University of Düsseldorf, Düsseldorf, Germany; Research Center Neurosensory Science, Carl von Ossietzky Universität Oldenburg, Oldenburg, Germany; Institute of Radiology and Neuroradiology, Evangelisches Krankenhaus, University of Oldenburg, Oldenburg, Germany
| | - Kaustubh R Patil
- Institute of Neuroscience and Medicine (INM-7), Heinrich Heine University Düsseldorf, Düsseldorf, Germany; Institute of Systems Neuroscience, Research Centre Jülich, Jülich, Germany.
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27
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Inter-individual body mass variations relate to fractionated functional brain hierarchies. Commun Biol 2021; 4:735. [PMID: 34127795 PMCID: PMC8203627 DOI: 10.1038/s42003-021-02268-x] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/10/2020] [Accepted: 05/06/2021] [Indexed: 02/05/2023] Open
Abstract
Variations in body mass index (BMI) have been suggested to relate to atypical brain organization, yet connectome-level substrates of BMI and their neurobiological underpinnings remain unclear. Studying 325 healthy young adults, we examined associations between functional connectivity and inter-individual BMI variations. We utilized non-linear connectome manifold learning techniques to represent macroscale functional organization along continuous hierarchical axes that dissociate low level and higher order brain systems. We observed an increased differentiation between unimodal and heteromodal association networks in individuals with higher BMI, indicative of a disrupted modular architecture and hierarchy of the brain. Transcriptomic decoding and gene enrichment analyses identified genes previously implicated in genome-wide associations to BMI and specific cortical, striatal, and cerebellar cell types. These findings illustrate functional connectome substrates of BMI variations in healthy young adults and point to potential molecular associations.
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Bhattacharyya U, Deshpande SN, Bhatia T, Thelma BK. Revisiting Schizophrenia from an Evolutionary Perspective: An Association Study of Recent Evolutionary Markers and Schizophrenia. Schizophr Bull 2021; 47:827-836. [PMID: 33350444 PMCID: PMC8759809 DOI: 10.1093/schbul/sbaa179] [Citation(s) in RCA: 9] [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] [Indexed: 02/03/2023]
Abstract
The persistence of schizophrenia in human populations at a high prevalence and with a large heritability estimate despite reduced fertility and increased mortality rate is a Darwinian paradox. This may be likely if the genomic components that predispose to schizophrenia are also advantageous for the acquisition of important human traits, such as language and cognition. Accordingly, an emerging group of genomic markers of recent evolution in humans, namely human accelerated regions (HARs), since our divergence from chimpanzees, are gaining importance for neurodevelopmental disorders, such as schizophrenia. We hypothesize that variants within HARs may affect the expression of genes under their control, thus contributing to disease etiology. A total of 49 HAR single nucleotide polymorphisms (SNPs) were prioritized from the complete repertoire of HARs (n = 2737) based on their functional relevance and prevalence in the South Asian population. Test of association using 2 independent schizophrenia case-control cohorts of north Indian ethnicity (discovery: n = 930; replication: n = 1104) revealed 3 SNPs (rs3800926, rs3801844, and rs764453) from chromosome 7 and rs77047799 from chromosome 3 to be significantly associated (combined analysis: Bonferroni corrected P < .002-.000004). Of note, these SNPs were found to alter the expression of neurodevelopmental genes such as SLC25A13, MAD1L1, and ULK4; a few from the HOX gene family; and a few genes that are implicated in mitochondrial function. These SNPs may most likely alter binding sites of transcription factors, including TFCP2, MAFK, SREBF2, E2F1, and/or methylation signatures around these genes. These findings reiterate a neurodevelopmental basis of schizophrenia and also open up a promising avenue to investigate HAR-mediated mitochondrial dysfunction in schizophrenia etiology.
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Affiliation(s)
| | | | | | - B K Thelma
- Department of Genetics, University of Delhi South Campus, New Delhi, India
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29
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Ovsepian SV, O'Leary VB, Hoschl C, Zaborszky L. Integrated phylogeny of the human brain and pathobiology of Alzheimer's disease: A unifying hypothesis. Neurosci Lett 2021; 755:135895. [PMID: 33862141 DOI: 10.1016/j.neulet.2021.135895] [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: 02/21/2021] [Revised: 04/05/2021] [Accepted: 04/09/2021] [Indexed: 02/08/2023]
Abstract
The disproportionate evolutionary expansion of the human cerebral cortex with reinforcement of cholinergic innervations warranted a major rise in the functional and metabolic load of the conserved basal forebrain (BF) cholinergic system. Given that acetylcholine (ACh) regulates properties of the microtubule-associated protein (MAP) tau and promotes non-amyloidogenic processing of amyloid precursor protein (APP), growing neocortex predicts higher demands for ACh, while the emerging role of BF cholinergic projections in Aβ clearance infers greater exposure of source neurons and their innervation fields to amyloid pathology. The higher exposure of evolutionary most recent cortical areas to the amyloid pathology of Alzheimer's disease (AD) with synaptic impairments and atrophy, therefore, might involve attenuated homeostatic effects of BF cholinergic projections, in addition to fall-outs of inherent processes of expanding association areas. This unifying model, thus, views amyloid pathology and loss of cholinergic cells as a quid pro quo of the allometric evolution of the human brain, which in combination with increase in life expectancy overwhelm the fine homeostatic balance and trigger the disease process.
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Affiliation(s)
- Saak V Ovsepian
- Department of Experimental Neurobiology, National Institute of Mental Health, Topolová 748, 250 67, Klecany, Czech Republic.
| | - Valerie B O'Leary
- Department of Medical Genetics, Third Faculty of Medicine, Charles University, Ruská 87, 100 00, Praha 10, Czech Republic
| | - Cyril Hoschl
- Department of Experimental Neurobiology, National Institute of Mental Health, Topolová 748, 250 67, Klecany, Czech Republic; Department of Psychiatry and Medical Psychology, Third Faculty of Medicine, Charles University, Ruská 87, 100 00, Prague 10, Czech Republic
| | - Laszlo Zaborszky
- Center for Molecular and Behavioral Neuroscience, Rutgers, the State University of New Jersey, Newark, NJ, USA
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Park BY, Hong SJ, Valk SL, Paquola C, Benkarim O, Bethlehem RAI, Di Martino A, Milham MP, Gozzi A, Yeo BTT, Smallwood J, Bernhardt BC. Differences in subcortico-cortical interactions identified from connectome and microcircuit models in autism. Nat Commun 2021; 12:2225. [PMID: 33850128 PMCID: PMC8044226 DOI: 10.1038/s41467-021-21732-0] [Citation(s) in RCA: 42] [Impact Index Per Article: 14.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2020] [Accepted: 02/05/2021] [Indexed: 01/14/2023] Open
Abstract
The pathophysiology of autism has been suggested to involve a combination of both macroscale connectome miswiring and microcircuit anomalies. Here, we combine connectome-wide manifold learning with biophysical simulation models to understand associations between global network perturbations and microcircuit dysfunctions in autism. We studied neuroimaging and phenotypic data in 47 individuals with autism and 37 typically developing controls obtained from the Autism Brain Imaging Data Exchange initiative. Our analysis establishes significant differences in structural connectome organization in individuals with autism relative to controls, with strong between-group effects in low-level somatosensory regions and moderate effects in high-level association cortices. Computational models reveal that the degree of macroscale anomalies is related to atypical increases of recurrent excitation/inhibition, as well as subcortical inputs into cortical microcircuits, especially in sensory and motor areas. Transcriptomic association analysis based on postmortem datasets identifies genes expressed in cortical and thalamic areas from childhood to young adulthood. Finally, supervised machine learning finds that the macroscale perturbations are associated with symptom severity scores on the Autism Diagnostic Observation Schedule. Together, our analyses suggest that atypical subcortico-cortical interactions are associated with both microcircuit and macroscale connectome differences in autism.
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Affiliation(s)
- Bo-Yong Park
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada.
- Department of Data Science, Inha University, Incheon, South Korea.
| | - Seok-Jun Hong
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
- Center for the Developing Brain, Child Mind Institute, New York City, NY, USA
- Center for Neuroscience Imaging Research, Institute for Basic Science, Sungkyunkwan University, Suwon, South Korea
- Department of Biomedical Engineering, Sungkyunkwan University, Suwon, South Korea
| | - Sofie L Valk
- Forschungszentrum, Julich, Germany
- Max Planck Institute for Cognitive and Brain Sciences, Leipzig, Germany
| | - Casey Paquola
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Oualid Benkarim
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada
| | - Richard A I Bethlehem
- Autism Research Centre, Department of Psychiatry, University of Cambridge, Cambridge, UK
- Brain Mapping Unit, Department of Psychiatry, University of Cambridge, Cambridge, UK
| | - Adriana Di Martino
- Center for the Developing Brain, Child Mind Institute, New York City, NY, USA
| | - Michael P Milham
- Center for the Developing Brain, Child Mind Institute, New York City, NY, USA
| | - Alessandro Gozzi
- Istituto Italiano di Tecnologia, Centre for Neuroscience and Cognitive Systems @ UNITN, Rovereto, Italy
| | - B T Thomas Yeo
- Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore
- Centre for Sleep and Cognition (CSC) & Centre for Translational Magnetic Resonance Research (TMR), National University of Singapore, Singapore, Singapore
- N.1 Institute for Health & Institute for Digital Medicine (WisDM), National University of Singapore, Singapore, Singapore
- Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, USA
- Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore, Singapore
| | - Jonathan Smallwood
- Department of Psychology, York Neuroimaging Centre, University of York, York, UK
- Department of Psychology, Queen's University, Kingston, ON, Canada
| | - Boris C Bernhardt
- McConnell Brain Imaging Centre, Montreal Neurological Institute and Hospital, McGill University, Montreal, QC, Canada.
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31
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Ijichi S, Kawaike Y, Ijichi N, Ijichi Y, Hirakata M, Yamaguchi Y, Kamachi A, Imamura C, Fushuku S, Nagata J, Tanuma R, Sameshima H, Morioka H. Hypothetical novel simulations to explain the evolutionary survival of the hypo-reproductive extreme tail in the complex human diversity. Biosystems 2021; 204:104393. [PMID: 33640397 DOI: 10.1016/j.biosystems.2021.104393] [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: 01/11/2021] [Accepted: 02/20/2021] [Indexed: 10/22/2022]
Abstract
Hierarchical structures which lie hidden between human complex conditions and reproductivity cannot be simple, and trends of each population component does not necessarily pertain to evolutionary theories. As an illustration, the fitness of individuals with heritable extreme conditions can be low across continuing generations in observational data. Autism and schizophrenia are characterized by such evolutionary paradox of survival and hypo-reproductivity in the complex human diversity. Theoretical mechanisms for the observational fact were evaluated using a simple formula which was established to simulate stochastic epistasis-mediated phenotypic diversity. The survival of the hypo-reproductive extreme tail could be imitated just by the predominant presence of stochastic epistasis mechanism, suggesting that stochastic epistasis might be a genetic prerequisite for the evolutionary paradox. As supplemental cofactors of stochastic epistasis, a random link of the extreme tail to both un- and hyper-reproductivity and group assortative mating were shown to be effective for the paradox. Especially, the mixed localization of un- and hyper-reproductivity in the tail of a generational population evidently induced the continuous survival of outliers and extremes. These hypothetical considerations and mathematical simulations may suggest the significance of stochastic epistasis as the essential genetic background of complex human diversity.
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Affiliation(s)
- Shinji Ijichi
- Health Service Center, Kagoshima University, Kagoshima, Japan; Institute for Externalization of Gifts and Talents, Kagoshima, Japan.
| | - Yoichi Kawaike
- Health Service Center, Kagoshima University, Kagoshima, Japan
| | - Naomi Ijichi
- Institute for Externalization of Gifts and Talents, Kagoshima, Japan
| | - Yukina Ijichi
- Institute for Externalization of Gifts and Talents, Kagoshima, Japan
| | - Mai Hirakata
- Health Service Center, Kagoshima University, Kagoshima, Japan
| | - Yuka Yamaguchi
- Health Service Center, Kagoshima University, Kagoshima, Japan
| | - Akiyo Kamachi
- Health Service Center, Kagoshima University, Kagoshima, Japan
| | - Chikako Imamura
- Support Center for Students with Disabilities, Kagoshima University, Kagoshima, Japan
| | - Sayuri Fushuku
- Health Service Center, Kagoshima University, Kagoshima, Japan
| | - Junko Nagata
- Health Service Center, Kagoshima University, Kagoshima, Japan
| | - Rie Tanuma
- Health Service Center, Kagoshima University, Kagoshima, Japan
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32
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Reynolds T, Johnson EC, Huggett SB, Bubier JA, Palmer RHC, Agrawal A, Baker EJ, Chesler EJ. Interpretation of psychiatric genome-wide association studies with multispecies heterogeneous functional genomic data integration. Neuropsychopharmacology 2021; 46:86-97. [PMID: 32791514 PMCID: PMC7688940 DOI: 10.1038/s41386-020-00795-5] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/01/2020] [Revised: 07/27/2020] [Accepted: 07/29/2020] [Indexed: 02/08/2023]
Abstract
Genome-wide association studies and other discovery genetics methods provide a means to identify previously unknown biological mechanisms underlying behavioral disorders that may point to new therapeutic avenues, augment diagnostic tools, and yield a deeper understanding of the biology of psychiatric conditions. Recent advances in psychiatric genetics have been made possible through large-scale collaborative efforts. These studies have begun to unearth many novel genetic variants associated with psychiatric disorders and behavioral traits in human populations. Significant challenges remain in characterizing the resulting disease-associated genetic variants and prioritizing functional follow-up to make them useful for mechanistic understanding and development of therapeutics. Model organism research has generated extensive genomic data that can provide insight into the neurobiological mechanisms of variant action, but a cohesive effort must be made to establish which aspects of the biological modulation of behavioral traits are evolutionarily conserved across species. Scalable computing, new data integration strategies, and advanced analysis methods outlined in this review provide a framework to efficiently harness model organism data in support of clinically relevant psychiatric phenotypes.
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Affiliation(s)
- Timothy Reynolds
- The Jackson Laboratory, Bar Harbor, ME, USA
- Computer Science Department, Baylor University, Waco, TX, USA
| | - Emma C Johnson
- Department of Psychiatry, Washington University in St Louis, St Louis, MO, USA
| | | | | | | | - Arpana Agrawal
- Department of Psychiatry, Washington University in St Louis, St Louis, MO, USA
| | - Erich J Baker
- Computer Science Department, Baylor University, Waco, TX, USA
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33
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Úbeda Y, Fatjó J, Rostán C, Crailsheim D, Gomara A, Almunia J, Llorente M. A preliminary investigation on the evaluation of psychopathologies in a group of ex-pet and ex-performer chimpanzees (Pan troglodytes): A rating approach based on the Diagnostic and Statistical Manual of Mental Disorders (DSM). J Vet Behav 2021. [DOI: 10.1016/j.jveb.2020.08.006] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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34
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Zhang X, Braun U, Harneit A, Zang Z, Geiger LS, Betzel RF, Chen J, Schweiger JI, Schwarz K, Reinwald JR, Fritze S, Witt S, Rietschel M, Nöthen MM, Degenhardt F, Schwarz E, Hirjak D, Meyer-Lindenberg A, Bassett DS, Tost H. Generative network models of altered structural brain connectivity in schizophrenia. Neuroimage 2020; 225:117510. [PMID: 33160087 DOI: 10.1016/j.neuroimage.2020.117510] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2020] [Revised: 10/20/2020] [Accepted: 10/22/2020] [Indexed: 12/30/2022] Open
Abstract
Alterations in the structural connectome of schizophrenia patients have been widely characterized, but the mechanisms remain largely unknown. Generative network models have recently been introduced as a tool to test the biological underpinnings of altered brain network formation. We evaluated different generative network models in healthy controls (n=152), schizophrenia patients (n=66), and their unaffected first-degree relatives (n=32), and we identified spatial and topological factors contributing to network formation. We further investigated how these factors relate to cognition and to polygenic risk for schizophrenia. Our data show that among the four tested classes of generative network models, structural brain networks were optimally accounted for by a two-factor model combining spatial constraints and topological neighborhood structure. The same wiring model explained brain network formation across study groups. However, relatives and schizophrenia patients exhibited significantly lower spatial constraints and lower topological facilitation compared to healthy controls. Further exploratory analyses point to potential associations of the model parameter reflecting spatial constraints with the polygenic risk for schizophrenia and cognitive performance. Our results identify spatial constraints and local topological structure as two interrelated mechanisms contributing to regular brain network formation as well as altered connectomes in schizophrenia and healthy individuals at familial risk for schizophrenia. On an exploratory level, our data further point to the potential relevance of spatial constraints for the genetic risk for schizophrenia and general cognitive functioning, thereby encouraging future studies in following up on these observations to gain further insights into the biological basis and behavioral relevance of model parameters.
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Affiliation(s)
- Xiaolong Zhang
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, J5 68159 Mannheim, Germany
| | - Urs Braun
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, J5 68159 Mannheim, Germany; Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA.
| | - Anais Harneit
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, J5 68159 Mannheim, Germany
| | - Zhenxiang Zang
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, J5 68159 Mannheim, Germany
| | - Lena S Geiger
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, J5 68159 Mannheim, Germany
| | - Richard F Betzel
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Junfang Chen
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, J5 68159 Mannheim, Germany
| | - Janina I Schweiger
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, J5 68159 Mannheim, Germany
| | - Kristina Schwarz
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, J5 68159 Mannheim, Germany
| | - Jonathan Rochus Reinwald
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, J5 68159 Mannheim, Germany
| | - Stefan Fritze
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, J5 68159 Mannheim, Germany
| | - Stephanie Witt
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Marcella Rietschel
- Department of Genetic Epidemiology in Psychiatry, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany
| | - Markus M Nöthen
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany
| | - Franziska Degenhardt
- Institute of Human Genetics, University of Bonn, School of Medicine & University Hospital Bonn, Bonn, Germany
| | - Emanuel Schwarz
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, J5 68159 Mannheim, Germany
| | - Dusan Hirjak
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, J5 68159 Mannheim, Germany
| | - Andreas Meyer-Lindenberg
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, J5 68159 Mannheim, Germany
| | - Danielle S Bassett
- Department of Bioengineering, University of Pennsylvania, Philadelphia, PA, USA; Department of Psychiatry, Department of Electrical & Systems Engineering, Department of Neurology, and Department of Physics & Astronomy, University of Pennsylvania, Philadelphia, PA, USA; Santa Fe Institute, Santa Fe, NM USA
| | - Heike Tost
- Department of Psychiatry and Psychotherapy, Central Institute of Mental Health, Medical Faculty Mannheim, University of Heidelberg, J5 68159 Mannheim, Germany
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Takahashi Y, Terada T, Muto Y. Systems Level Analysis and Identification of Pathways and Key Genes Associated with Delirium. Genes (Basel) 2020; 11:genes11101225. [PMID: 33086708 PMCID: PMC7590056 DOI: 10.3390/genes11101225] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2020] [Revised: 10/15/2020] [Accepted: 10/16/2020] [Indexed: 11/16/2022] Open
Abstract
Delirium is a complex pathophysiological process, and multiple contributing mechanisms have been identified. However, it is largely unclear how the genes associated with delirium contribute and which of them play key roles. In this study, the genes associated with delirium were retrieved from the Comparative Toxicogenomics Database (CTD) and integrated through a protein-protein interaction (PPI) network. Delirium-associated genes formed a highly interconnected PPI subnetwork, indicating a high tendency to interact and agglomerate. Using the Molecular Complex Detection (MCODE) algorithm, we identified the top two delirium-relevant network modules, M1 and M5, that have the most significant enrichments for the delirium-related gene sets. Functional enrichment analysis showed that genes related to neurotransmitter receptor activity were enriched in both modules. Moreover, analyses with genes located in human accelerated regions (HARs) provided evidence that HAR-Brain genes were overrepresented in the delirium-relevant network modules. We found that four of the HAR-Brain genes, namely APP, PLCB1, NPY, and HTR2A, in the M1 module were highly connected and appeared to exhibit hub properties, which might play vital roles in delirium development. Further understanding of the function of the identified modules and member genes could help to identify therapeutic intervention targets and diagnostic biomarkers for delirium.
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Affiliation(s)
- Yukiko Takahashi
- United Graduate School of Drug Discovery and Medical Information Sciences, Gifu University, 1-1, Yanagido, Gifu 501-1194, Japan; (Y.T.); (T.T.)
- Department of Adult Nursing (Acute phase), Gifu University School of Medicine, 1-1, Yanagido, Gifu 501-1193, Japan
| | - Tomoyoshi Terada
- United Graduate School of Drug Discovery and Medical Information Sciences, Gifu University, 1-1, Yanagido, Gifu 501-1194, Japan; (Y.T.); (T.T.)
- Department of Functional Bioscience, Gifu University School of Medicine, 1-1, Yanagido, Gifu 501-1193, Japan
| | - Yoshinori Muto
- United Graduate School of Drug Discovery and Medical Information Sciences, Gifu University, 1-1, Yanagido, Gifu 501-1194, Japan; (Y.T.); (T.T.)
- Department of Functional Bioscience, Gifu University School of Medicine, 1-1, Yanagido, Gifu 501-1193, Japan
- Correspondence: ; Tel.: +81-58-293-3241
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Di Plinio S, Ebisch SJH. Combining local and global evolutionary trajectories of brain-behaviour relationships through game theory. Eur J Neurosci 2020; 52:4198-4213. [PMID: 32594640 DOI: 10.1111/ejn.14883] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2020] [Revised: 06/15/2020] [Accepted: 06/20/2020] [Indexed: 01/05/2023]
Abstract
The study of the evolution of brain-behaviour relationships concerns understanding the causes and repercussions of cross- and within-species variability. Understanding such variability is a main objective of evolutionary and cognitive neuroscience, and it may help explaining the appearance of psychopathological phenotypes. Although brain evolution is related to the progressive action of selection and adaptation through multiple paths (e.g. mosaic vs. concerted evolution, metabolic vs. structural and functional constraints), a coherent, integrative framework is needed to combine evolutionary paths and neuroscientific evidence. Here, we review the literature on evolutionary pressures focusing on structural-functional changes and developmental constraints. Taking advantage of recent progress in neuroimaging and cognitive neuroscience, we propose a twofold hypothetical model of brain evolution. Within this model, global and local trajectories imply rearrangements of neural subunits and subsystems and of behavioural repertoires of a species, respectively. We incorporate these two processes in a game in which the global trajectory shapes the structural-functional neural substrates (i.e. players), while the local trajectory shapes the behavioural repertoires (i.e. stochastic payoffs).
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Affiliation(s)
- Simone Di Plinio
- Department of Neuroscience, Imaging, and Clinical Sciences, G D'Annunzio University of Chieti-Pescara, Chieti, Italy
| | - Sjoerd J H Ebisch
- Department of Neuroscience, Imaging, and Clinical Sciences, G D'Annunzio University of Chieti-Pescara, Chieti, Italy.,Institute for Advanced Biomedical Technologies (ITAB), G D'Annunzio University of Chieti Pescara, Chieti, Italy
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37
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Diederich NJ, Uchihara T, Grillner S, Goetz CG. The Evolution-Driven Signature of Parkinson's Disease. Trends Neurosci 2020; 43:475-492. [PMID: 32499047 DOI: 10.1016/j.tins.2020.05.001] [Citation(s) in RCA: 16] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/30/2020] [Revised: 04/14/2020] [Accepted: 05/04/2020] [Indexed: 12/19/2022]
Abstract
In this review, we approach Parkinson's disease (PD) in the context of an evolutionary mismatch of central nervous system functions. The neurons at risk have hyperbranched axons, extensive transmitter release sites, display spontaneous spiking, and elevated mitochondrial stress. They function in networks largely unchanged throughout vertebrate evolution, but now connecting to the expanded human cortex. Their breakdown is favoured by longevity. At the cellular level, mitochondrial dysfunction starts at the synapses, then involves axons and cell bodies. At the behavioural level, network dysfunctions provoke the core motor syndrome of parkinsonism including freezing and failed gait automatization, and non-motor deficits including inactive blindsight and autonomic dysregulation. The proposed evolutionary re-interpretation of PD-prone cellular phenotypes and of prototypical clinical symptoms allows a new conceptual framework for future research.
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Affiliation(s)
- Nico J Diederich
- Department of Neurosciences, Centre Hospitalier de Luxembourg, L-1210 Luxembourg City, Luxembourg.
| | - Toshiki Uchihara
- Neurology Clinic with Neuromorphomics Laboratory, Nitobe-Memorial Nakano General Hospital, Tokyo 164-8607, Japan; Department of Neurology and Neurological Science, Tokyo Medical and Dental University, Tokyo 113-8519, Japan
| | - Sten Grillner
- Department of Neuroscience, Karolinska Institute, SE-17177 Stockholm, Sweden
| | - Christopher G Goetz
- Department of Neurological Sciences, Rush University, Chicago, IL 60612, USA
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38
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Affiliation(s)
- René S Kahn
- Department of Psychiatry and Behavioral Health System, Icahn School of Medicine at Mount Sinai, N.Y.; and VISN 2 Mental Illness Research, Education, and Clinical Center, James J. Peters VA Medical Center, Bronx, N.Y
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Abstract
This scientific commentary refers to ‘Evolutionary modifications in human brain connectivity associated with schizophrenia’ by van den Heuvel et al. (doi:10.1093/brain/awz330).
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Affiliation(s)
- Petra E Vértes
- University of Cambridge, Department of Psychiatry, Cambridge CB2 0SZ, UK.,The Alan Turing Institute, London NW1 2DB, UK
| | - Jakob Seidlitz
- Developmental Neurogenomics Unit, National Institute of Mental Health, Bethesda, MD 20892, USA
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40
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Wei Y, de Lange SC, Scholtens LH, Watanabe K, Ardesch DJ, Jansen PR, Savage JE, Li L, Preuss TM, Rilling JK, Posthuma D, van den Heuvel MP. Genetic mapping and evolutionary analysis of human-expanded cognitive networks. Nat Commun 2019; 10:4839. [PMID: 31649260 PMCID: PMC6813316 DOI: 10.1038/s41467-019-12764-8] [Citation(s) in RCA: 69] [Impact Index Per Article: 13.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2019] [Accepted: 09/24/2019] [Indexed: 01/04/2023] Open
Abstract
Cognitive brain networks such as the default-mode network (DMN), frontoparietal network, and salience network, are key functional networks of the human brain. Here we show that the rapid evolutionary cortical expansion of cognitive networks in the human brain, and most pronounced the DMN, runs parallel with high expression of human-accelerated genes (HAR genes). Using comparative transcriptomics analysis, we present that HAR genes are differentially more expressed in higher-order cognitive networks in humans compared to chimpanzees and macaques and that genes with high expression in the DMN are involved in synapse and dendrite formation. Moreover, HAR and DMN genes show significant associations with individual variations in DMN functional activity, intelligence, sociability, and mental conditions such as schizophrenia and autism. Our results suggest that the expansion of higher-order functional networks subserving increasing cognitive properties has been an important locus of genetic changes in recent human brain evolution.
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Affiliation(s)
- Yongbin Wei
- Connectome Lab, Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, 1081 HV, Amsterdam, The Netherlands
| | - Siemon C de Lange
- Connectome Lab, Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, 1081 HV, Amsterdam, The Netherlands
| | - Lianne H Scholtens
- Connectome Lab, Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, 1081 HV, Amsterdam, The Netherlands
| | - Kyoko Watanabe
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, 1081 HV, Amsterdam, The Netherlands
| | - Dirk Jan Ardesch
- Connectome Lab, Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, 1081 HV, Amsterdam, The Netherlands
| | - Philip R Jansen
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, 1081 HV, Amsterdam, The Netherlands
- Department of Child and Adolescent Psychiatry, Erasmus Medical Center, 3015 GD, Rotterdam, The Netherlands
| | - Jeanne E Savage
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, 1081 HV, Amsterdam, The Netherlands
| | - Longchuan Li
- Marcus Autism Center, Children's Healthcare of Atlanta, Emory University School of Medicine, Atlanta, GA, 30322, USA
| | - Todd M Preuss
- Division of Neuropharmacology and Neurologic Diseases, Emory University, Atlanta, GA, 30322, USA
- Center for Translational Social Neuroscience, Emory University, Atlanta, GA, 30322, USA
- Yerkes National Primate Research Center, Emory University, Atlanta, GA, 30322, USA
| | - James K Rilling
- Center for Translational Social Neuroscience, Emory University, Atlanta, GA, 30322, USA
- Yerkes National Primate Research Center, Emory University, Atlanta, GA, 30322, USA
- Department of Anthropology, Emory University, Atlanta, GA, 30322, USA
- Center for Behavioral Neuroscience, Emory University, Atlanta, GA, 30322, USA
- Department of Psychiatry and Behavioral Sciences, Emory University, Atlanta, GA, 30322, USA
| | - Danielle Posthuma
- Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, 1081 HV, Amsterdam, The Netherlands
- Department of Clinical Genetics, Amsterdam Neuroscience, Amsterdam UMC, 1081 HV, Amsterdam, The Netherlands
| | - Martijn P van den Heuvel
- Connectome Lab, Department of Complex Trait Genetics, Center for Neurogenomics and Cognitive Research, Amsterdam Neuroscience, Vrije Universiteit Amsterdam, 1081 HV, Amsterdam, The Netherlands.
- Department of Clinical Genetics, Amsterdam Neuroscience, Amsterdam UMC, 1081 HV, Amsterdam, The Netherlands.
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