000851776 001__ 851776
000851776 005__ 20210129235018.0
000851776 0247_ $$2doi$$a10.1016/j.neuroimage.2018.09.017
000851776 0247_ $$2ISSN$$a1053-8119
000851776 0247_ $$2ISSN$$a1095-9572
000851776 0247_ $$2pmid$$apmid:30205209
000851776 0247_ $$2WOS$$aWOS:000449385000013
000851776 0247_ $$2altmetric$$aaltmetric:48154954
000851776 0247_ $$2Handle$$a2128/23414
000851776 037__ $$aFZJ-2018-05291
000851776 082__ $$a610
000851776 1001_ $$0P:(DE-Juel1)166200$$aMengotti, P.$$b0$$eCorresponding author
000851776 245__ $$aNeural correlates of the energetic value of food during visual processing and response inhibition
000851776 260__ $$aOrlando, Fla.$$bAcademic Press$$c2019
000851776 3367_ $$2DRIVER$$aarticle
000851776 3367_ $$2DataCite$$aOutput Types/Journal article
000851776 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1539246706_26612
000851776 3367_ $$2BibTeX$$aARTICLE
000851776 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000851776 3367_ $$00$$2EndNote$$aJournal Article
000851776 520__ $$aPrevious research showed that human brain regions involved in reward and cognitive control are responsive to visually presented food stimuli, in particular high-energy foods. However, it is still to be determined whether the preference towards high-energy foods depends on their higher energy density (kcal/gram), or is based on the difference in energy content of the food items (total amount of kcal). Here we report the results of an fMRI study in which normal-weight healthy participants processed food images during a one-back task or were required to inhibit their response towards food stimuli during a Go/No-Go task. High-energy density (HD) and low-energy density (LD) foods were matched for energy content displayed. Food-related kitchen objects (OBJ) were used as control stimuli. The lateral occipital complex and the orbitofrontal cortex showed consistent higher activity in response to HD than LD foods, both during visual processing and response inhibition. This result suggests that images of HD foods, even when the amount of food shown is not associated with a higher energy content, elicit preferential visual processing - possibly involving attentional processes - and trigger a response from the reward system. We conclude that the human brain is able to distinguish food energy densities of food items during both active visual processing and response inhibition.
000851776 536__ $$0G:(DE-HGF)POF3-572$$a572 - (Dys-)function and Plasticity (POF3-572)$$cPOF3-572$$fPOF III$$x0
000851776 588__ $$aDataset connected to CrossRef
000851776 7001_ $$0P:(DE-HGF)0$$aForoni, F.$$b1
000851776 7001_ $$0P:(DE-HGF)0$$aRumiati, R. I.$$b2
000851776 773__ $$0PERI:(DE-600)1471418-8$$a10.1016/j.neuroimage.2018.09.017$$gp. S1053811918308024$$p130-139$$tNeuroImage$$v184$$x1053-8119$$y2019
000851776 8564_ $$uhttps://juser.fz-juelich.de/record/851776/files/1-s2.0-S1053811918308024-main.pdf$$yRestricted
000851776 8564_ $$uhttps://juser.fz-juelich.de/record/851776/files/1-s2.0-S1053811918308024-main.pdf?subformat=pdfa$$xpdfa$$yRestricted
000851776 8564_ $$uhttps://juser.fz-juelich.de/record/851776/files/Mengotti_NeuroImage_2019_Post%20Print_Neural%20correlates%20of%20the%20energetic%20value%20of%20food%20during%20visual%20processing%20and%20response%20inhibition.pdf$$yOpenAccess
000851776 909CO $$ooai:juser.fz-juelich.de:851776$$pdnbdelivery$$pdriver$$pVDB$$popen_access$$popenaire
000851776 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)166200$$aForschungszentrum Jülich$$b0$$kFZJ
000851776 9131_ $$0G:(DE-HGF)POF3-572$$1G:(DE-HGF)POF3-570$$2G:(DE-HGF)POF3-500$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bKey Technologies$$lDecoding the Human Brain$$v(Dys-)function and Plasticity$$x0
000851776 9141_ $$y2019
000851776 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS
000851776 915__ $$0StatID:(DE-HGF)1030$$2StatID$$aDBCoverage$$bCurrent Contents - Life Sciences
000851776 915__ $$0StatID:(DE-HGF)0600$$2StatID$$aDBCoverage$$bEbsco Academic Search
000851776 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bNEUROIMAGE : 2015
000851776 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection
000851776 915__ $$0StatID:(DE-HGF)0110$$2StatID$$aWoS$$bScience Citation Index
000851776 915__ $$0StatID:(DE-HGF)0111$$2StatID$$aWoS$$bScience Citation Index Expanded
000851776 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
000851776 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bASC
000851776 915__ $$0StatID:(DE-HGF)9905$$2StatID$$aIF >= 5$$bNEUROIMAGE : 2015
000851776 915__ $$0StatID:(DE-HGF)0310$$2StatID$$aDBCoverage$$bNCBI Molecular Biology Database
000851776 915__ $$0StatID:(DE-HGF)1050$$2StatID$$aDBCoverage$$bBIOSIS Previews
000851776 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline
000851776 915__ $$0StatID:(DE-HGF)0420$$2StatID$$aNationallizenz
000851776 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bThomson Reuters Master Journal List
000851776 920__ $$lyes
000851776 9201_ $$0I:(DE-Juel1)INM-3-20090406$$kINM-3$$lKognitive Neurowissenschaften$$x0
000851776 980__ $$ajournal
000851776 980__ $$aVDB
000851776 980__ $$aUNRESTRICTED
000851776 980__ $$aI:(DE-Juel1)INM-3-20090406
000851776 9801_ $$aFullTexts