Neurocomputational Consequences of Evolutionary Connectivity Changes in Perisylvian Language Cortex
The human brain sets itself apart from that of its primate relatives by specific neuroanatomical features, especially the strong linkage of left perisylvian language areas (frontal and temporal cortex) by way of the arcuate fasciculus (AF). AF connectivity has been shown to correlate with verbal wor...
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Veröffentlicht in: | The Journal of neuroscience 2017-03, Vol.37 (11), p.3045-3055 |
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description | The human brain sets itself apart from that of its primate relatives by specific neuroanatomical features, especially the strong linkage of left perisylvian language areas (frontal and temporal cortex) by way of the arcuate fasciculus (AF). AF connectivity has been shown to correlate with verbal working memory-a specifically human trait providing the foundation for language abilities-but a mechanistic explanation of any related causal link between anatomical structure and cognitive function is still missing. Here, we provide a possible explanation and link, by using neurocomputational simulations in neuroanatomically structured models of the perisylvian language cortex. We compare networks mimicking key features of cortical connectivity in monkeys and humans, specifically the presence of relatively stronger higher-order "jumping links" between nonadjacent perisylvian cortical areas in the latter, and demonstrate that the emergence of working memory for syllables and word forms is a functional consequence of this structural evolutionary change. We also show that a mere increase of learning time is not sufficient, but that this specific structural feature, which entails higher connectivity degree of relevant areas and shorter sensorimotor path length, is crucial. These results offer a better understanding of specifically human anatomical features underlying the language faculty and their evolutionary selection advantage.
Why do humans have superior language abilities compared to primates? Recently, a uniquely human neuroanatomical feature has been demonstrated in the strength of the arcuate fasciculus (AF), a fiber pathway interlinking the left-hemispheric language areas. Although AF anatomy has been related to linguistic skills, an explanation of how this fiber bundle may support language abilities is still missing. We use neuroanatomically structured computational models to investigate the consequences of evolutionary changes in language area connectivity and demonstrate that the human-specific higher connectivity degree and comparatively shorter sensorimotor path length implicated by the AF entail emergence of verbal working memory, a prerequisite for language learning. These results offer a better understanding of specifically human anatomical features for language and their evolutionary selection advantage. |
doi_str_mv | 10.1523/JNEUROSCI.2693-16.2017 |
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Why do humans have superior language abilities compared to primates? Recently, a uniquely human neuroanatomical feature has been demonstrated in the strength of the arcuate fasciculus (AF), a fiber pathway interlinking the left-hemispheric language areas. Although AF anatomy has been related to linguistic skills, an explanation of how this fiber bundle may support language abilities is still missing. We use neuroanatomically structured computational models to investigate the consequences of evolutionary changes in language area connectivity and demonstrate that the human-specific higher connectivity degree and comparatively shorter sensorimotor path length implicated by the AF entail emergence of verbal working memory, a prerequisite for language learning. These results offer a better understanding of specifically human anatomical features for language and their evolutionary selection advantage.</description><identifier>ISSN: 0270-6474</identifier><identifier>EISSN: 1529-2401</identifier><identifier>DOI: 10.1523/JNEUROSCI.2693-16.2017</identifier><identifier>PMID: 28193685</identifier><language>eng</language><publisher>United States: Society for Neuroscience</publisher><subject>Animals ; Biological Evolution ; Cerebral Aqueduct - physiology ; Cerebral Cortex - physiology ; Computer Simulation ; Connectome - methods ; Haplorhini ; Humans ; Language ; Macaca ; Models, Genetic ; Models, Neurological ; Neuronal Plasticity - genetics ; Pan troglodytes ; Species Specificity</subject><ispartof>The Journal of neuroscience, 2017-03, Vol.37 (11), p.3045-3055</ispartof><rights>Copyright © 2017 Schomers et al.</rights><rights>Copyright © 2017 Schomers et al. 2017</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c500t-82e67337b14aa7f73caab530730634beb7ace9d2817c78f4301ac4ced9a583a13</citedby><cites>FETCH-LOGICAL-c500t-82e67337b14aa7f73caab530730634beb7ace9d2817c78f4301ac4ced9a583a13</cites><orcidid>0000-0003-2104-1074 ; 0000-0002-4506-1697</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5354338/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5354338/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,727,780,784,885,27922,27923,53789,53791</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/28193685$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Schomers, Malte R</creatorcontrib><creatorcontrib>Garagnani, Max</creatorcontrib><creatorcontrib>Pulvermüller, Friedemann</creatorcontrib><title>Neurocomputational Consequences of Evolutionary Connectivity Changes in Perisylvian Language Cortex</title><title>The Journal of neuroscience</title><addtitle>J Neurosci</addtitle><description>The human brain sets itself apart from that of its primate relatives by specific neuroanatomical features, especially the strong linkage of left perisylvian language areas (frontal and temporal cortex) by way of the arcuate fasciculus (AF). AF connectivity has been shown to correlate with verbal working memory-a specifically human trait providing the foundation for language abilities-but a mechanistic explanation of any related causal link between anatomical structure and cognitive function is still missing. Here, we provide a possible explanation and link, by using neurocomputational simulations in neuroanatomically structured models of the perisylvian language cortex. We compare networks mimicking key features of cortical connectivity in monkeys and humans, specifically the presence of relatively stronger higher-order "jumping links" between nonadjacent perisylvian cortical areas in the latter, and demonstrate that the emergence of working memory for syllables and word forms is a functional consequence of this structural evolutionary change. We also show that a mere increase of learning time is not sufficient, but that this specific structural feature, which entails higher connectivity degree of relevant areas and shorter sensorimotor path length, is crucial. These results offer a better understanding of specifically human anatomical features underlying the language faculty and their evolutionary selection advantage.
Why do humans have superior language abilities compared to primates? Recently, a uniquely human neuroanatomical feature has been demonstrated in the strength of the arcuate fasciculus (AF), a fiber pathway interlinking the left-hemispheric language areas. Although AF anatomy has been related to linguistic skills, an explanation of how this fiber bundle may support language abilities is still missing. We use neuroanatomically structured computational models to investigate the consequences of evolutionary changes in language area connectivity and demonstrate that the human-specific higher connectivity degree and comparatively shorter sensorimotor path length implicated by the AF entail emergence of verbal working memory, a prerequisite for language learning. These results offer a better understanding of specifically human anatomical features for language and their evolutionary selection advantage.</description><subject>Animals</subject><subject>Biological Evolution</subject><subject>Cerebral Aqueduct - physiology</subject><subject>Cerebral Cortex - physiology</subject><subject>Computer Simulation</subject><subject>Connectome - methods</subject><subject>Haplorhini</subject><subject>Humans</subject><subject>Language</subject><subject>Macaca</subject><subject>Models, Genetic</subject><subject>Models, Neurological</subject><subject>Neuronal Plasticity - genetics</subject><subject>Pan troglodytes</subject><subject>Species Specificity</subject><issn>0270-6474</issn><issn>1529-2401</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqNkUFvEzEQhS0EoqHwF6o9ctlg7-za3gsSikIpitqq0LM168ymRhs72LsR_fd1aBuVGyfLft_MvPFj7EzwuWgq-PT9cnl7c_VjcTGvZAulkPOKC_WKzbLallXNxWs245XipaxVfcLepfSLc64y9JadVFq0IHUzY_aSphhs2O6mEUcXPA7FIvhEvyfyllIR-mK5D8P0V4v3B9GTHd3ejflyh36TIeeLa4ou3Q97h75Y5dcJN5ThONKf9-xNj0OiD0_nKbv9uvy5-Faurs4vFl9WpW04H0tdkVQAqhM1ouoVWMSuAa6AS6g76hRaatfZu7JK9zVwgba2tG6x0YACTtnnx767qdvS2pIfIw5mF902OzcBnflX8e7ObMLeNNDUADo3-PjUIIa8fxrN1iVLw4CewpSM0Hl2k_-2-g9Uamh1tp9R-YjaGFKK1B8dCW4OYZpjmOYQphHSHMLMhWcv9zmWPacHD3nnnxQ</recordid><startdate>20170315</startdate><enddate>20170315</enddate><creator>Schomers, Malte R</creator><creator>Garagnani, Max</creator><creator>Pulvermüller, Friedemann</creator><general>Society for Neuroscience</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7X8</scope><scope>7TK</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0003-2104-1074</orcidid><orcidid>https://orcid.org/0000-0002-4506-1697</orcidid></search><sort><creationdate>20170315</creationdate><title>Neurocomputational Consequences of Evolutionary Connectivity Changes in Perisylvian Language Cortex</title><author>Schomers, Malte R ; Garagnani, Max ; Pulvermüller, Friedemann</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c500t-82e67337b14aa7f73caab530730634beb7ace9d2817c78f4301ac4ced9a583a13</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Animals</topic><topic>Biological Evolution</topic><topic>Cerebral Aqueduct - physiology</topic><topic>Cerebral Cortex - physiology</topic><topic>Computer Simulation</topic><topic>Connectome - methods</topic><topic>Haplorhini</topic><topic>Humans</topic><topic>Language</topic><topic>Macaca</topic><topic>Models, Genetic</topic><topic>Models, Neurological</topic><topic>Neuronal Plasticity - genetics</topic><topic>Pan troglodytes</topic><topic>Species Specificity</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Schomers, Malte R</creatorcontrib><creatorcontrib>Garagnani, Max</creatorcontrib><creatorcontrib>Pulvermüller, Friedemann</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>MEDLINE - Academic</collection><collection>Neurosciences Abstracts</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>The Journal of neuroscience</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Schomers, Malte R</au><au>Garagnani, Max</au><au>Pulvermüller, Friedemann</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Neurocomputational Consequences of Evolutionary Connectivity Changes in Perisylvian Language Cortex</atitle><jtitle>The Journal of neuroscience</jtitle><addtitle>J Neurosci</addtitle><date>2017-03-15</date><risdate>2017</risdate><volume>37</volume><issue>11</issue><spage>3045</spage><epage>3055</epage><pages>3045-3055</pages><issn>0270-6474</issn><eissn>1529-2401</eissn><abstract>The human brain sets itself apart from that of its primate relatives by specific neuroanatomical features, especially the strong linkage of left perisylvian language areas (frontal and temporal cortex) by way of the arcuate fasciculus (AF). AF connectivity has been shown to correlate with verbal working memory-a specifically human trait providing the foundation for language abilities-but a mechanistic explanation of any related causal link between anatomical structure and cognitive function is still missing. Here, we provide a possible explanation and link, by using neurocomputational simulations in neuroanatomically structured models of the perisylvian language cortex. We compare networks mimicking key features of cortical connectivity in monkeys and humans, specifically the presence of relatively stronger higher-order "jumping links" between nonadjacent perisylvian cortical areas in the latter, and demonstrate that the emergence of working memory for syllables and word forms is a functional consequence of this structural evolutionary change. We also show that a mere increase of learning time is not sufficient, but that this specific structural feature, which entails higher connectivity degree of relevant areas and shorter sensorimotor path length, is crucial. These results offer a better understanding of specifically human anatomical features underlying the language faculty and their evolutionary selection advantage.
Why do humans have superior language abilities compared to primates? Recently, a uniquely human neuroanatomical feature has been demonstrated in the strength of the arcuate fasciculus (AF), a fiber pathway interlinking the left-hemispheric language areas. Although AF anatomy has been related to linguistic skills, an explanation of how this fiber bundle may support language abilities is still missing. We use neuroanatomically structured computational models to investigate the consequences of evolutionary changes in language area connectivity and demonstrate that the human-specific higher connectivity degree and comparatively shorter sensorimotor path length implicated by the AF entail emergence of verbal working memory, a prerequisite for language learning. These results offer a better understanding of specifically human anatomical features for language and their evolutionary selection advantage.</abstract><cop>United States</cop><pub>Society for Neuroscience</pub><pmid>28193685</pmid><doi>10.1523/JNEUROSCI.2693-16.2017</doi><tpages>11</tpages><orcidid>https://orcid.org/0000-0003-2104-1074</orcidid><orcidid>https://orcid.org/0000-0002-4506-1697</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Animals Biological Evolution Cerebral Aqueduct - physiology Cerebral Cortex - physiology Computer Simulation Connectome - methods Haplorhini Humans Language Macaca Models, Genetic Models, Neurological Neuronal Plasticity - genetics Pan troglodytes Species Specificity |
title | Neurocomputational Consequences of Evolutionary Connectivity Changes in Perisylvian Language Cortex |
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