Brain working memory network indices as landmarks of intelligence
Identifying the neural correlates of intelligence has long been a goal in neuroscience. Recently, the field of network neuroscience has attracted researchers' attention as a means for answering this question. In network neuroscience, the brain is considered as an integrated system whose systema...
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Elsevier
2023-06-01
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| Series: | NeuroImage: Reports |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666956023000107 |
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| author | Mohammadreza Khodaei Paul J. Laurienti Dale Dagenbach Sean L. Simpson |
| author_facet | Mohammadreza Khodaei Paul J. Laurienti Dale Dagenbach Sean L. Simpson |
| author_sort | Mohammadreza Khodaei |
| collection | DOAJ |
| description | Identifying the neural correlates of intelligence has long been a goal in neuroscience. Recently, the field of network neuroscience has attracted researchers' attention as a means for answering this question. In network neuroscience, the brain is considered as an integrated system whose systematic properties provide profound insights into health and behavioral outcomes. However, most network studies of intelligence have used univariate methods to investigate topological network measures, with their focus limited to a few measures. Furthermore, most studies have focused on resting state networks despite the fact that brain activation during working memory tasks has been linked to intelligence. Finally, the literature is still missing an investigation of the association between network assortativity and intelligence. To address these issues, here we employ a recently developed mixed-modeling framework for analyzing multi-task brain networks to elucidate the most critical working memory task network topological properties corresponding to individuals' intelligence differences. We used a data set of 379 subjects (22–35 y/o) from the Human Connectome Project (HCP). Each subject's data included composite intelligence scores, and fMRI during resting state and a 2-back working memory task. Following comprehensive quality control and preprocessing of the minimally preprocessed fMRI data, we extracted a set of the main topological network features, including global efficiency, degree, leverage centrality, modularity, and clustering coefficient. The estimated network features and subject's confounders were then incorporated into the multi-task mixed-modeling framework to investigate how brain network changes between working memory and resting state relate to intelligence score. Our results indicate that the general intelligence score (cognitive composite score) is associated with a change in the relationship between connection strength and multiple network topological properties, including global efficiency, leverage centrality, and degree difference during working memory as it is compared to resting state. More specifically, we observed a higher increase in the positive association between global efficiency and connection strength for the high intelligence group when they switch from resting state to working memory. The strong connections might form superhighways for a more efficient global flow of information through the brain network. Furthermore, we found an increase in the negative association between degree difference and leverage centrality with connection strength during working memory tasks for the high intelligence group. These indicate higher network resilience and assortativity along with higher circuit-specific information flow during working memory for those with a higher intelligence score. Although the exact neurobiological implications of our results are speculative at this point, our results provide evidence for the significant association of intelligence with hallmark properties of brain networks during working memory. |
| format | Article |
| id | doaj-art-2a31daa8bba54bfaa43f6ef3994dbcd4 |
| institution | OA Journals |
| issn | 2666-9560 |
| language | English |
| publishDate | 2023-06-01 |
| publisher | Elsevier |
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| series | NeuroImage: Reports |
| spelling | doaj-art-2a31daa8bba54bfaa43f6ef3994dbcd42025-08-20T02:04:00ZengElsevierNeuroImage: Reports2666-95602023-06-013210016510.1016/j.ynirp.2023.100165Brain working memory network indices as landmarks of intelligenceMohammadreza Khodaei0Paul J. Laurienti1Dale Dagenbach2Sean L. Simpson3Virginia Tech-Wake Forest University School of Biomedical Engineering and Sciences, Wake Forest University School of Medicine, Winston-Salem, NC, USAVirginia Tech-Wake Forest University School of Biomedical Engineering and Sciences, Wake Forest University School of Medicine, Winston-Salem, NC, USA; Department of Radiology, Wake Forest University School of Medicine, Winston-Salem, NC, USADepartment of Psychology, Wake Forest University, Winston-Salem, NC, USAVirginia Tech-Wake Forest University School of Biomedical Engineering and Sciences, Wake Forest University School of Medicine, Winston-Salem, NC, USA; Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, NC, USA; Corresponding author. Department of Biostatistics and Data Science, Wake Forest University School of Medicine, Winston-Salem, NC, USA.Identifying the neural correlates of intelligence has long been a goal in neuroscience. Recently, the field of network neuroscience has attracted researchers' attention as a means for answering this question. In network neuroscience, the brain is considered as an integrated system whose systematic properties provide profound insights into health and behavioral outcomes. However, most network studies of intelligence have used univariate methods to investigate topological network measures, with their focus limited to a few measures. Furthermore, most studies have focused on resting state networks despite the fact that brain activation during working memory tasks has been linked to intelligence. Finally, the literature is still missing an investigation of the association between network assortativity and intelligence. To address these issues, here we employ a recently developed mixed-modeling framework for analyzing multi-task brain networks to elucidate the most critical working memory task network topological properties corresponding to individuals' intelligence differences. We used a data set of 379 subjects (22–35 y/o) from the Human Connectome Project (HCP). Each subject's data included composite intelligence scores, and fMRI during resting state and a 2-back working memory task. Following comprehensive quality control and preprocessing of the minimally preprocessed fMRI data, we extracted a set of the main topological network features, including global efficiency, degree, leverage centrality, modularity, and clustering coefficient. The estimated network features and subject's confounders were then incorporated into the multi-task mixed-modeling framework to investigate how brain network changes between working memory and resting state relate to intelligence score. Our results indicate that the general intelligence score (cognitive composite score) is associated with a change in the relationship between connection strength and multiple network topological properties, including global efficiency, leverage centrality, and degree difference during working memory as it is compared to resting state. More specifically, we observed a higher increase in the positive association between global efficiency and connection strength for the high intelligence group when they switch from resting state to working memory. The strong connections might form superhighways for a more efficient global flow of information through the brain network. Furthermore, we found an increase in the negative association between degree difference and leverage centrality with connection strength during working memory tasks for the high intelligence group. These indicate higher network resilience and assortativity along with higher circuit-specific information flow during working memory for those with a higher intelligence score. Although the exact neurobiological implications of our results are speculative at this point, our results provide evidence for the significant association of intelligence with hallmark properties of brain networks during working memory.http://www.sciencedirect.com/science/article/pii/S2666956023000107IntelligenceBrain networkWorking memoryResting statefMRIFluid and crystalized intelligence |
| spellingShingle | Mohammadreza Khodaei Paul J. Laurienti Dale Dagenbach Sean L. Simpson Brain working memory network indices as landmarks of intelligence NeuroImage: Reports Intelligence Brain network Working memory Resting state fMRI Fluid and crystalized intelligence |
| title | Brain working memory network indices as landmarks of intelligence |
| title_full | Brain working memory network indices as landmarks of intelligence |
| title_fullStr | Brain working memory network indices as landmarks of intelligence |
| title_full_unstemmed | Brain working memory network indices as landmarks of intelligence |
| title_short | Brain working memory network indices as landmarks of intelligence |
| title_sort | brain working memory network indices as landmarks of intelligence |
| topic | Intelligence Brain network Working memory Resting state fMRI Fluid and crystalized intelligence |
| url | http://www.sciencedirect.com/science/article/pii/S2666956023000107 |
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