Dynamic shifts in brain network activation during supracapacity working memory task performance
Despite significant advances in understanding how brain networks support working memory (WM) and cognitive control, relatively little is known about how these networks respond when cognitive capabilities are overtaxed. We used a fine‐grained manipulation of memory load within a single trial to excee...
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description | Despite significant advances in understanding how brain networks support working memory (WM) and cognitive control, relatively little is known about how these networks respond when cognitive capabilities are overtaxed. We used a fine‐grained manipulation of memory load within a single trial to exceed WM capacity during functional magnetic resonance imaging to investigate how these networks respond to support task performance when WM capacity is exceeded. Analyzing correct trials only, we observed a nonmonotonic (inverted‐U) response to WM load throughout the classic WM network (including bilateral dorsolateral prefrontal cortex, posterior parietal cortex, and presupplementary motor areas) that peaked later in individuals with greater WM capacity. We also observed a relative increase in activity in medial anterior prefrontal cortex, posterior cingulate/precuneus, and lateral temporal and parietal regions at the highest WM loads, and a set of predominantly subcortical and prefrontal regions whose activation was greatest at the lowest WM loads. At the individual subject level, the inverted‐U pattern was associated with poorer performance while expression of the early and late activating patterns was predictive of better performance. In addition, greater activation in bilateral fusiform gyrus and right occipital lobe at the highest WM loads predicted better performance. These results demonstrate dynamic and behaviorally relevant changes in the level of activation of multiple brain networks in response to increasing WM load that are not well accounted for by present models of how the brain subserves the cognitive ability to hold and manipulate information on‐line. Hum Brain Mapp 36:1245–1264, 2015. © 2014 Wiley Periodicals, Inc. |
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We used a fine‐grained manipulation of memory load within a single trial to exceed WM capacity during functional magnetic resonance imaging to investigate how these networks respond to support task performance when WM capacity is exceeded. Analyzing correct trials only, we observed a nonmonotonic (inverted‐U) response to WM load throughout the classic WM network (including bilateral dorsolateral prefrontal cortex, posterior parietal cortex, and presupplementary motor areas) that peaked later in individuals with greater WM capacity. We also observed a relative increase in activity in medial anterior prefrontal cortex, posterior cingulate/precuneus, and lateral temporal and parietal regions at the highest WM loads, and a set of predominantly subcortical and prefrontal regions whose activation was greatest at the lowest WM loads. At the individual subject level, the inverted‐U pattern was associated with poorer performance while expression of the early and late activating patterns was predictive of better performance. In addition, greater activation in bilateral fusiform gyrus and right occipital lobe at the highest WM loads predicted better performance. These results demonstrate dynamic and behaviorally relevant changes in the level of activation of multiple brain networks in response to increasing WM load that are not well accounted for by present models of how the brain subserves the cognitive ability to hold and manipulate information on‐line. Hum Brain Mapp 36:1245–1264, 2015. © 2014 Wiley Periodicals, Inc.</description><identifier>ISSN: 1065-9471</identifier><identifier>EISSN: 1097-0193</identifier><identifier>DOI: 10.1002/hbm.22699</identifier><identifier>PMID: 25422039</identifier><language>eng</language><publisher>United States: Blackwell Publishing Ltd</publisher><subject>Adult ; Brain - physiology ; Brain Mapping - methods ; cognition ; Female ; Humans ; magnetic resonance imaging ; Magnetic Resonance Imaging - methods ; Male ; memory ; Memory, Short-Term - physiology ; memory, short‐term ; Middle Aged ; Models, Statistical ; Neural Pathways - physiology ; Neuropsychological Tests ; prefrontal cortex ; short-term ; Signal Processing, Computer-Assisted ; task performance and analysis ; Young Adult</subject><ispartof>Human brain mapping, 2015-04, Vol.36 (4), p.1245-1264</ispartof><rights>2014 Wiley Periodicals, Inc.</rights><rights>2015 Wiley Periodicals, Inc.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c5149-7569f438083b607700654b30e36133729b5b924e4df9032c1afdc05de35b0eb63</citedby><cites>FETCH-LOGICAL-c5149-7569f438083b607700654b30e36133729b5b924e4df9032c1afdc05de35b0eb63</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4722876/pdf/$$EPDF$$P50$$Gpubmedcentral$$H</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC4722876/$$EHTML$$P50$$Gpubmedcentral$$H</linktohtml><link.rule.ids>230,314,723,776,780,881,1411,27901,27902,45550,45551,53766,53768</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/25422039$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Van Snellenberg, Jared X.</creatorcontrib><creatorcontrib>Slifstein, Mark</creatorcontrib><creatorcontrib>Read, Christina</creatorcontrib><creatorcontrib>Weber, Jochen</creatorcontrib><creatorcontrib>Thompson, Judy L.</creatorcontrib><creatorcontrib>Wager, Tor D.</creatorcontrib><creatorcontrib>Shohamy, Daphna</creatorcontrib><creatorcontrib>Abi-Dargham, Anissa</creatorcontrib><creatorcontrib>Smith, Edward E.</creatorcontrib><title>Dynamic shifts in brain network activation during supracapacity working memory task performance</title><title>Human brain mapping</title><addtitle>Hum. Brain Mapp</addtitle><description>Despite significant advances in understanding how brain networks support working memory (WM) and cognitive control, relatively little is known about how these networks respond when cognitive capabilities are overtaxed. We used a fine‐grained manipulation of memory load within a single trial to exceed WM capacity during functional magnetic resonance imaging to investigate how these networks respond to support task performance when WM capacity is exceeded. Analyzing correct trials only, we observed a nonmonotonic (inverted‐U) response to WM load throughout the classic WM network (including bilateral dorsolateral prefrontal cortex, posterior parietal cortex, and presupplementary motor areas) that peaked later in individuals with greater WM capacity. We also observed a relative increase in activity in medial anterior prefrontal cortex, posterior cingulate/precuneus, and lateral temporal and parietal regions at the highest WM loads, and a set of predominantly subcortical and prefrontal regions whose activation was greatest at the lowest WM loads. At the individual subject level, the inverted‐U pattern was associated with poorer performance while expression of the early and late activating patterns was predictive of better performance. In addition, greater activation in bilateral fusiform gyrus and right occipital lobe at the highest WM loads predicted better performance. These results demonstrate dynamic and behaviorally relevant changes in the level of activation of multiple brain networks in response to increasing WM load that are not well accounted for by present models of how the brain subserves the cognitive ability to hold and manipulate information on‐line. Hum Brain Mapp 36:1245–1264, 2015. © 2014 Wiley Periodicals, Inc.</description><subject>Adult</subject><subject>Brain - physiology</subject><subject>Brain Mapping - methods</subject><subject>cognition</subject><subject>Female</subject><subject>Humans</subject><subject>magnetic resonance imaging</subject><subject>Magnetic Resonance Imaging - methods</subject><subject>Male</subject><subject>memory</subject><subject>Memory, Short-Term - physiology</subject><subject>memory, short‐term</subject><subject>Middle Aged</subject><subject>Models, Statistical</subject><subject>Neural Pathways - physiology</subject><subject>Neuropsychological Tests</subject><subject>prefrontal cortex</subject><subject>short-term</subject><subject>Signal Processing, Computer-Assisted</subject><subject>task performance and analysis</subject><subject>Young Adult</subject><issn>1065-9471</issn><issn>1097-0193</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2015</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNqNkd1v1SAYh4nRuA-98B8wJN5sF934KHC4MXGb20y2GRPNEm8IpXSHnRYqtJv976We7USXmHgDBJ734YUfAG8wOsAIkcNl1R0QwqV8BrYxkqJAWNLn85qzQpYCb4GdlG4Rwpgh_BJsEVYSgqjcBupk8rpzBqala4YEnYdV1Hn0drgPcQW1GdydHlzwsB6j8zcwjX3URvfauGGCMzTvdrYLcYKDTivY29iE2Glv7CvwotFtsq8f5l3w7fTj1-Pz4uLz2afjDxeFYbiUhWBcNiVdoAWtOBIC5c7LiiJLOaZUEFmxSpLSlnUjESUG66Y2iNWWsgrZitNd8H7t7ceqs7Wxfoi6VX10nY6TCtqpv0-8W6qbcKdKQchCzIK9B0EMP0abBtW5ZGzbam_DmBTmfEEYZ0z8D8oFoRSTjL57gt6GMfr8EzOV75aslJnaX1MmhpSibTZ9Y6TmhFVOWP1OOLNv_3zohnyMNAOHa-DetXb6t0mdH10-Kot1hUuD_bmp0HGluKCCqeurM3VKr79c0e9HCtNfLKq_fg</recordid><startdate>201504</startdate><enddate>201504</enddate><creator>Van Snellenberg, Jared X.</creator><creator>Slifstein, Mark</creator><creator>Read, Christina</creator><creator>Weber, Jochen</creator><creator>Thompson, Judy L.</creator><creator>Wager, Tor D.</creator><creator>Shohamy, Daphna</creator><creator>Abi-Dargham, Anissa</creator><creator>Smith, Edward E.</creator><general>Blackwell Publishing Ltd</general><general>John Wiley & Sons, Inc</general><general>John Wiley and Sons Inc</general><scope>BSCLL</scope><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>7QR</scope><scope>7TK</scope><scope>7U7</scope><scope>8FD</scope><scope>C1K</scope><scope>FR3</scope><scope>K9.</scope><scope>P64</scope><scope>7X8</scope><scope>5PM</scope></search><sort><creationdate>201504</creationdate><title>Dynamic shifts in brain network activation during supracapacity working memory task performance</title><author>Van Snellenberg, Jared X. ; Slifstein, Mark ; Read, Christina ; Weber, Jochen ; Thompson, Judy L. ; Wager, Tor D. ; Shohamy, Daphna ; Abi-Dargham, Anissa ; Smith, Edward E.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c5149-7569f438083b607700654b30e36133729b5b924e4df9032c1afdc05de35b0eb63</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2015</creationdate><topic>Adult</topic><topic>Brain - physiology</topic><topic>Brain Mapping - methods</topic><topic>cognition</topic><topic>Female</topic><topic>Humans</topic><topic>magnetic resonance imaging</topic><topic>Magnetic Resonance Imaging - methods</topic><topic>Male</topic><topic>memory</topic><topic>Memory, Short-Term - physiology</topic><topic>memory, short‐term</topic><topic>Middle Aged</topic><topic>Models, Statistical</topic><topic>Neural Pathways - physiology</topic><topic>Neuropsychological Tests</topic><topic>prefrontal cortex</topic><topic>short-term</topic><topic>Signal Processing, Computer-Assisted</topic><topic>task performance and analysis</topic><topic>Young Adult</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Van Snellenberg, Jared X.</creatorcontrib><creatorcontrib>Slifstein, Mark</creatorcontrib><creatorcontrib>Read, Christina</creatorcontrib><creatorcontrib>Weber, Jochen</creatorcontrib><creatorcontrib>Thompson, Judy L.</creatorcontrib><creatorcontrib>Wager, Tor D.</creatorcontrib><creatorcontrib>Shohamy, Daphna</creatorcontrib><creatorcontrib>Abi-Dargham, Anissa</creatorcontrib><creatorcontrib>Smith, Edward E.</creatorcontrib><collection>Istex</collection><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Chemoreception Abstracts</collection><collection>Neurosciences Abstracts</collection><collection>Toxicology Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Engineering Research Database</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Human brain mapping</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Van Snellenberg, Jared X.</au><au>Slifstein, Mark</au><au>Read, Christina</au><au>Weber, Jochen</au><au>Thompson, Judy L.</au><au>Wager, Tor D.</au><au>Shohamy, Daphna</au><au>Abi-Dargham, Anissa</au><au>Smith, Edward E.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Dynamic shifts in brain network activation during supracapacity working memory task performance</atitle><jtitle>Human brain mapping</jtitle><addtitle>Hum. Brain Mapp</addtitle><date>2015-04</date><risdate>2015</risdate><volume>36</volume><issue>4</issue><spage>1245</spage><epage>1264</epage><pages>1245-1264</pages><issn>1065-9471</issn><eissn>1097-0193</eissn><abstract>Despite significant advances in understanding how brain networks support working memory (WM) and cognitive control, relatively little is known about how these networks respond when cognitive capabilities are overtaxed. We used a fine‐grained manipulation of memory load within a single trial to exceed WM capacity during functional magnetic resonance imaging to investigate how these networks respond to support task performance when WM capacity is exceeded. Analyzing correct trials only, we observed a nonmonotonic (inverted‐U) response to WM load throughout the classic WM network (including bilateral dorsolateral prefrontal cortex, posterior parietal cortex, and presupplementary motor areas) that peaked later in individuals with greater WM capacity. We also observed a relative increase in activity in medial anterior prefrontal cortex, posterior cingulate/precuneus, and lateral temporal and parietal regions at the highest WM loads, and a set of predominantly subcortical and prefrontal regions whose activation was greatest at the lowest WM loads. At the individual subject level, the inverted‐U pattern was associated with poorer performance while expression of the early and late activating patterns was predictive of better performance. In addition, greater activation in bilateral fusiform gyrus and right occipital lobe at the highest WM loads predicted better performance. These results demonstrate dynamic and behaviorally relevant changes in the level of activation of multiple brain networks in response to increasing WM load that are not well accounted for by present models of how the brain subserves the cognitive ability to hold and manipulate information on‐line. Hum Brain Mapp 36:1245–1264, 2015. © 2014 Wiley Periodicals, Inc.</abstract><cop>United States</cop><pub>Blackwell Publishing Ltd</pub><pmid>25422039</pmid><doi>10.1002/hbm.22699</doi><tpages>20</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Adult Brain - physiology Brain Mapping - methods cognition Female Humans magnetic resonance imaging Magnetic Resonance Imaging - methods Male memory Memory, Short-Term - physiology memory, short‐term Middle Aged Models, Statistical Neural Pathways - physiology Neuropsychological Tests prefrontal cortex short-term Signal Processing, Computer-Assisted task performance and analysis Young Adult |
title | Dynamic shifts in brain network activation during supracapacity working memory task performance |
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