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Top-down attention mechanisms require the selection of specific objects or locations; however, the brain mechanism involved when attention is allocated across different modalities is not well understood. The aim of this study was to use functional magnetic resonance imaging to define the neural mechanisms underlying divided and selective spatial attention. A concurrent audiovisual stimulus was used, and subjects were prompted to focus on a visual, auditory and audiovisual stimulus in a Posner paradigm. Our behavioral results confirmed the better performance of selective attention compared to devided attention. We found differences in the activation level of the frontoparietal network, visual/auditory cortex, the putamen and the salience network under different attention conditions. We further used Granger causality (GC) to explore effective connectivity differences between tasks. Differences in GC connectivity between visual and auditory selective tasks reflected the visual dominance effect under spatial attention. In addition, our results supported the role of the putamen in redistributing attention and the functional separation of the salience network. In summary, we explored the audiovisual top-down allocation of attention and observed the differences in neural mechanisms under endogenous attention modes, which revealed the differences in cross-modal expression in visual and auditory attention under attentional modulation.
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1 School of Biomedical Engineering, Capital Medical University, Beijing 100069, China; Beijing Advanced Innovation Center for Big Data-based Precision Medicine, Capital Medical University, Beijing 100069, China; Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing 100069, China; These authors contributed equally to this work
2 Peking University First Hospital, Beijing 100034, China; These authors contributed equally to this work
3 Biomedical Engineering Laboratory, Graduate School of Natural Science and Technology, Okayama University, 3-1-1 Tsushima-naka, Okayama, Japan
4 Biomedical Engineering Laboratory, Graduate School of Natural Science and Technology, Okayama University, 3-1-1 Tsushima-naka, Okayama, Japan; Key Laboratory of Biomimetic Robots and Systems, Ministry of Education, Beijing Institute of Technology, Beijing 100081, China
5 Computational Image Analysis, Banner Alzheimer’s Institute and Banner Good Samaritan Medical Centre, PET Centre, Phoenix, AZ 85006, USA
6 Radiology, Peking University Third Hospital, Beijing 100191, China
7 Neuroscience Research Institute/ Peking University Third Hospital, Beijing 100191, China
8 School of Biomedical Engineering, Capital Medical University, Beijing 100069, China; Beijing Advanced Innovation Center for Big Data-based Precision Medicine, Capital Medical University, Beijing 100069, China; Beijing Key Laboratory of Fundamental Research on Biomechanics in Clinical Application, Capital Medical University, Beijing 100069, China