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Intensity patterns at the peaks of brain activity in fMRI and PET are highly correlated with neural models of spatial integration.

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Title: Intensity patterns at the peaks of brain activity in fMRI and PET are highly correlated with neural models of spatial integration.
Authors: Sadoun, Amirouche (AUTHOR), Chauhan, Tushar (AUTHOR), Zhang, Yi Fan (AUTHOR), Gallois, Yohan (AUTHOR), Marx, Mathieu (AUTHOR), Deguine, Olivier (AUTHOR), Barone, Pascal (AUTHOR), Strelnikov, Kuzma (AUTHOR)
Source: European Journal of Neuroscience. Nov2021, Vol. 54 Issue 9, p7141-7151. 11p. 1 Color Photograph, 2 Diagrams, 1 Chart, 2 Graphs.
Subjects: Positron emission tomography, Functional magnetic resonance imaging, Brain mapping
Abstract: Spatial integration during the brain's cognitive activity prompts changes in energy used by different neuroglial populations. Nevertheless, the organisation of such integration in 3D ‐brain activity remains undescribed from a quantitative standpoint. In response, we applied a cross‐correlation between brain activity and integrative models, which yielded a deeper understanding of information integration in functional brain mapping. We analysed four datasets obtained via fundamentally different neuroimaging techniques (functional magnetic resonance imaging [fMRI] and positron emission tomography [PET]) and found that models of spatial integration with an increasing input to each step of integration were significantly more correlated with brain activity than models with a constant input to each step of integration. In addition, marking the voxels with the maximal correlation, we found exceptionally high intersubject consistency with the initial brain activity at the peaks. Our method demonstrated for the first time that the network of peaks of brain activity is organised strictly according to the models of spatial integration independent of neuroimaging techniques. The highest correlation with models integrating an increasing at each step input suggests that brain activity reflects a network of integrative processes where the results of integration in some neuroglial populations serve as an input to other neuroglial populations. [ABSTRACT FROM AUTHOR]
Copyright of European Journal of Neuroscience is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Psychology and Behavioral Sciences Collection
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  Data: Intensity patterns at the peaks of brain activity in fMRI and PET are highly correlated with neural models of spatial integration.
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  Data: <searchLink fieldCode="AR" term="%22Sadoun%2C+Amirouche%22">Sadoun, Amirouche</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chauhan%2C+Tushar%22">Chauhan, Tushar</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zhang%2C+Yi+Fan%22">Zhang, Yi Fan</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Gallois%2C+Yohan%22">Gallois, Yohan</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Marx%2C+Mathieu%22">Marx, Mathieu</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Deguine%2C+Olivier%22">Deguine, Olivier</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Barone%2C+Pascal%22">Barone, Pascal</searchLink> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Strelnikov%2C+Kuzma%22">Strelnikov, Kuzma</searchLink> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22European+Journal+of+Neuroscience%22">European Journal of Neuroscience</searchLink>. Nov2021, Vol. 54 Issue 9, p7141-7151. 11p. 1 Color Photograph, 2 Diagrams, 1 Chart, 2 Graphs.
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  Data: <searchLink fieldCode="DE" term="%22Positron+emission+tomography%22">Positron emission tomography</searchLink><br /><searchLink fieldCode="DE" term="%22Functional+magnetic+resonance+imaging%22">Functional magnetic resonance imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Brain+mapping%22">Brain mapping</searchLink>
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  Data: Spatial integration during the brain's cognitive activity prompts changes in energy used by different neuroglial populations. Nevertheless, the organisation of such integration in 3D ‐brain activity remains undescribed from a quantitative standpoint. In response, we applied a cross‐correlation between brain activity and integrative models, which yielded a deeper understanding of information integration in functional brain mapping. We analysed four datasets obtained via fundamentally different neuroimaging techniques (functional magnetic resonance imaging [fMRI] and positron emission tomography [PET]) and found that models of spatial integration with an increasing input to each step of integration were significantly more correlated with brain activity than models with a constant input to each step of integration. In addition, marking the voxels with the maximal correlation, we found exceptionally high intersubject consistency with the initial brain activity at the peaks. Our method demonstrated for the first time that the network of peaks of brain activity is organised strictly according to the models of spatial integration independent of neuroimaging techniques. The highest correlation with models integrating an increasing at each step input suggests that brain activity reflects a network of integrative processes where the results of integration in some neuroglial populations serve as an input to other neuroglial populations. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of European Journal of Neuroscience is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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              Text: Nov2021
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