Attention is the cornerstone of effective functioning in a complex and information-rich world.While the neural activity of attention has been extensively studied in the cortex,the brain-wide neural activity patterns a...Attention is the cornerstone of effective functioning in a complex and information-rich world.While the neural activity of attention has been extensively studied in the cortex,the brain-wide neural activity patterns are largely unknown.In this study,we conducted a comprehensive analysis of neural activity across the mouse brain during attentional processing using EEG and c-Fos staining,utilizing hierarchical clustering and c-Fos-based functional network analysis to evaluate the c-Fos activation patterns.Our findings reveal that a wide range of brain regions are activated,notably in the high-order cortex,thalamus,and brain stem regions involved in advanced cognition and arousal regulation,with the central lateral nucleus of the thalamus as a strong hub,suggesting the crucial role of the thalamus in attention control.These results provide valuable insights into the neural network mechanisms underlying attention,offering a foundation for formulating functional hypotheses and conducting circuit-level testing.展开更多
文摘Attention is the cornerstone of effective functioning in a complex and information-rich world.While the neural activity of attention has been extensively studied in the cortex,the brain-wide neural activity patterns are largely unknown.In this study,we conducted a comprehensive analysis of neural activity across the mouse brain during attentional processing using EEG and c-Fos staining,utilizing hierarchical clustering and c-Fos-based functional network analysis to evaluate the c-Fos activation patterns.Our findings reveal that a wide range of brain regions are activated,notably in the high-order cortex,thalamus,and brain stem regions involved in advanced cognition and arousal regulation,with the central lateral nucleus of the thalamus as a strong hub,suggesting the crucial role of the thalamus in attention control.These results provide valuable insights into the neural network mechanisms underlying attention,offering a foundation for formulating functional hypotheses and conducting circuit-level testing.
