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Nayak S, Coleman PL, Ladányi E, Nitin R, Gustavson DE, Fisher SE, Magne CL, Gordon RL. The Musical Abilities, Pleiotropy, Language, and Environment (MAPLE) Framework for Understanding Musicality-Language Links Across the Lifespan. NEUROBIOLOGY OF LANGUAGE (CAMBRIDGE, MASS.) 2022; 3:615-664. [PMID: 36742012 PMCID: PMC9893227 DOI: 10.1162/nol_a_00079] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/29/2021] [Accepted: 08/08/2022] [Indexed: 04/18/2023]
Abstract
Using individual differences approaches, a growing body of literature finds positive associations between musicality and language-related abilities, complementing prior findings of links between musical training and language skills. Despite these associations, musicality has been often overlooked in mainstream models of individual differences in language acquisition and development. To better understand the biological basis of these individual differences, we propose the Musical Abilities, Pleiotropy, Language, and Environment (MAPLE) framework. This novel integrative framework posits that musical and language-related abilities likely share some common genetic architecture (i.e., genetic pleiotropy) in addition to some degree of overlapping neural endophenotypes, and genetic influences on musically and linguistically enriched environments. Drawing upon recent advances in genomic methodologies for unraveling pleiotropy, we outline testable predictions for future research on language development and how its underlying neurobiological substrates may be supported by genetic pleiotropy with musicality. In support of the MAPLE framework, we review and discuss findings from over seventy behavioral and neural studies, highlighting that musicality is robustly associated with individual differences in a range of speech-language skills required for communication and development. These include speech perception-in-noise, prosodic perception, morphosyntactic skills, phonological skills, reading skills, and aspects of second/foreign language learning. Overall, the current work provides a clear agenda and framework for studying musicality-language links using individual differences approaches, with an emphasis on leveraging advances in the genomics of complex musicality and language traits.
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Affiliation(s)
- Srishti Nayak
- Department of Otolaryngology – Head & Neck Surgery, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Psychology, Middle Tennessee State University, Murfreesboro, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt University School of Medicine, Vanderbilt University, TN, USA
| | - Peyton L. Coleman
- Department of Otolaryngology – Head & Neck Surgery, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Enikő Ladányi
- Department of Otolaryngology – Head & Neck Surgery, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Linguistics, Potsdam University, Potsdam, Germany
| | - Rachana Nitin
- Department of Otolaryngology – Head & Neck Surgery, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN, USA
| | - Daniel E. Gustavson
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
- Institute for Behavioral Genetics, University of Colorado Boulder, Boulder, CO, USA
| | - Simon E. Fisher
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen, The Netherlands
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Nijmegen, The Netherlands
| | - Cyrille L. Magne
- Department of Psychology, Middle Tennessee State University, Murfreesboro, TN, USA
- PhD Program in Literacy Studies, Middle Tennessee State University, Murfreesboro, TN, USA
| | - Reyna L. Gordon
- Department of Otolaryngology – Head & Neck Surgery, Vanderbilt University Medical Center, Nashville, TN, USA
- Vanderbilt Brain Institute, Vanderbilt University, Nashville, TN, USA
- Vanderbilt Genetics Institute, Vanderbilt University Medical Center, Nashville, TN, USA
- Curb Center for Art, Enterprise, and Public Policy, Vanderbilt University, Nashville, TN, USA
- Vanderbilt Kennedy Center, Vanderbilt University Medical Center, TN, USA
- Vanderbilt University School of Medicine, Vanderbilt University, TN, USA
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4
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Wang Y, Chai L, Chu C, Li D, Gao C, Wu X, Yang Z, Zhang Y, Xu J, Nyengaard JR, Eickhoff SB, Liu B, Madsen KH, Jiang T, Fan L. Uncovering the genetic profiles underlying the intrinsic organization of the human cerebellum. Mol Psychiatry 2022; 27:2619-2634. [PMID: 35264730 DOI: 10.1038/s41380-022-01489-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/18/2021] [Revised: 02/01/2022] [Accepted: 02/14/2022] [Indexed: 11/09/2022]
Abstract
The functional diversity of the human cerebellum is largely believed to be derived more from its extensive connections rather than being limited to its mostly invariant architecture. However, whether and how the determination of cerebellar connections in its intrinsic organization interact with microscale gene expression is still unknown. Here we decode the genetic profiles of the cerebellar functional organization by investigating the genetic substrates simultaneously linking cerebellar functional heterogeneity and its drivers, i.e., the connections. We not only identified 443 network-specific genes but also discovered that their co-expression pattern correlated strongly with intra-cerebellar functional connectivity (FC). Ninety of these genes were also linked to the FC of cortico-cerebellar cognitive-limbic networks. To further discover the biological functions of these genes, we performed a "virtual gene knock-out" by observing the change in the coupling between gene co-expression and FC and divided the genes into two subsets, i.e., a positive gene contribution indicator (GCI+) involved in cerebellar neurodevelopment and a negative gene set (GCI-) related to neurotransmission. A more interesting finding is that GCI- is significantly linked with the cerebellar connectivity-behavior association and many recognized brain diseases that are closely linked with the cerebellar functional abnormalities. Our results could collectively help to rethink the genetic substrates underlying the cerebellar functional organization and offer possible micro-macro interacted mechanistic interpretations of the cerebellum-involved high order functions and dysfunctions in neuropsychiatric disorders.
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Affiliation(s)
- Yaping Wang
- Sino-Danish Center, University of Chinese Academy of Sciences, 100190, Beijing, China.,University of Chinese Academy of Sciences, 100190, Beijing, China.,Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China
| | - Lin Chai
- University of Chinese Academy of Sciences, 100190, Beijing, China.,Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China
| | - Congying Chu
- University of Chinese Academy of Sciences, 100190, Beijing, China. .,Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China. .,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China.
| | - Deying Li
- University of Chinese Academy of Sciences, 100190, Beijing, China.,Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China
| | - Chaohong Gao
- Sino-Danish Center, University of Chinese Academy of Sciences, 100190, Beijing, China.,University of Chinese Academy of Sciences, 100190, Beijing, China.,Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China
| | - Xia Wu
- University of Chinese Academy of Sciences, 100190, Beijing, China.,Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China
| | - Zhengyi Yang
- University of Chinese Academy of Sciences, 100190, Beijing, China.,Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China
| | - Yu Zhang
- Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, 311100, China
| | - Junhai Xu
- School of Computer Science and Technology, Tianjin Key Laboratory of Cognitive Computing and Application, Tianjin University, Tianjin, 300350, China
| | - Jens Randel Nyengaard
- Sino-Danish Center, University of Chinese Academy of Sciences, 100190, Beijing, China.,Core Centre for Molecular Morphology, Section for Stereology and Microscopy, Department of Clinical Medicine, Aarhus University, 8000, Aarhus, Denmark.,Department of Pathology, Aarhus University Hospital, 8200, Aarhus, Denmark
| | - Simon B Eickhoff
- Institute of Neuroscience and Medicine (INM-7: Brain and Behaviour), Research Centre Jülich, 52425, Jülich, Germany.,Institute of Systems Neuroscience, Heinrich Heine University Düsseldorf, 40225, Düsseldorf, Germany
| | - Bing Liu
- State Key Laboratory of Cognitive Neuroscience and Learning, Beijing Normal University, 100875, Beijing, China
| | - Kristoffer Hougaard Madsen
- Sino-Danish Center, University of Chinese Academy of Sciences, 100190, Beijing, China.,Department of Informatics and Mathematical Modelling, Technical University of Denmark, 2800, Kongens Lyngby, Denmark.,Danish Research Centre for Magnetic Resonance, Centre for Functional and Diagnostic Imaging and Research, Copenhagen University Hospital-Amager and Hvidovre, 2650, Hvidovre, Denmark
| | - Tianzi Jiang
- Sino-Danish Center, University of Chinese Academy of Sciences, 100190, Beijing, China.,University of Chinese Academy of Sciences, 100190, Beijing, China.,Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China.,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China.,CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China
| | - Lingzhong Fan
- Sino-Danish Center, University of Chinese Academy of Sciences, 100190, Beijing, China. .,University of Chinese Academy of Sciences, 100190, Beijing, China. .,Brainnetome Center, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China. .,National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China. .,CAS Center for Excellence in Brain Science and Intelligence Technology, Institute of Automation, Chinese Academy of Sciences, 100190, Beijing, China.
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6
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Kong XZ, Postema M, Schijven D, Castillo AC, Pepe A, Crivello F, Joliot M, Mazoyer B, Fisher SE, Francks C. Large-Scale Phenomic and Genomic Analysis of Brain Asymmetrical Skew. Cereb Cortex 2021; 31:4151-4168. [PMID: 33836062 PMCID: PMC8328207 DOI: 10.1093/cercor/bhab075] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/04/2020] [Revised: 02/15/2021] [Accepted: 03/10/2021] [Indexed: 12/29/2022] Open
Abstract
The human cerebral hemispheres show a left-right asymmetrical torque pattern, which has been claimed to be absent in chimpanzees. The functional significance and developmental mechanisms are unknown. Here, we carried out the largest-ever analysis of global brain shape asymmetry in magnetic resonance imaging data. Three population datasets were used, UK Biobank (N = 39 678), Human Connectome Project (N = 1113), and BIL&GIN (N = 453). At the population level, there was an anterior and dorsal skew of the right hemisphere, relative to the left. Both skews were associated independently with handedness, and various regional gray and white matter metrics oppositely in the two hemispheres, as well as other variables related to cognitive functions, sociodemographic factors, and physical and mental health. The two skews showed single nucleotide polymorphisms-based heritabilities of 4-13%, but also substantial polygenicity in causal mixture model analysis, and no individually significant loci were found in genome-wide association studies for either skew. There was evidence for a significant genetic correlation between horizontal brain skew and autism, which requires future replication. These results provide the first large-scale description of population-average brain skews and their inter-individual variations, their replicable associations with handedness, and insights into biological and other factors which associate with human brain asymmetry.
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Affiliation(s)
- Xiang-Zhen Kong
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen 6525 XD, The Netherlands
- Department of Psychology and Behavioral Sciences, Zhejiang University, Hangzhou 310028, China
| | - Merel Postema
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen 6525 XD, The Netherlands
| | - Dick Schijven
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen 6525 XD, The Netherlands
| | - Amaia Carrión Castillo
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen 6525 XD, The Netherlands
| | - Antonietta Pepe
- Institut des Maladies Neurodégénératives, UMR5293, Groupe d’Imagerie Neurofonctionnelle, Commissariat à l'énergie atomique et aux énergies alternatives, CNRS, Université de Bordeaux, Bordeaux cedex 33076, France
| | - Fabrice Crivello
- Institut des Maladies Neurodégénératives, UMR5293, Groupe d’Imagerie Neurofonctionnelle, Commissariat à l'énergie atomique et aux énergies alternatives, CNRS, Université de Bordeaux, Bordeaux cedex 33076, France
| | - Marc Joliot
- Institut des Maladies Neurodégénératives, UMR5293, Groupe d’Imagerie Neurofonctionnelle, Commissariat à l'énergie atomique et aux énergies alternatives, CNRS, Université de Bordeaux, Bordeaux cedex 33076, France
| | - Bernard Mazoyer
- Institut des Maladies Neurodégénératives, UMR5293, Groupe d’Imagerie Neurofonctionnelle, Commissariat à l'énergie atomique et aux énergies alternatives, CNRS, Université de Bordeaux, Bordeaux cedex 33076, France
| | - Simon E Fisher
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen 6525 XD, The Netherlands
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen 6525 EN, The Netherlands
| | - Clyde Francks
- Language and Genetics Department, Max Planck Institute for Psycholinguistics, Nijmegen 6525 XD, The Netherlands
- Donders Institute for Brain, Cognition and Behavior, Radboud University, Nijmegen 6525 EN, The Netherlands
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