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.
Animals ; Attention/physiology* ; Mice ; Brain/physiology* ; Male ; Electroencephalography ; Reaction Time/physiology* ; Brain Mapping ; Mice, Inbred C57BL ; Choice Behavior/physiology* ; Proto-Oncogene Proteins c-fos/metabolism*

Objective To study binocular rivalry (BR)objectively and the correlation between fusiform face area (FFA)and visual cortex.Methods Six subjects participated in this study,with one eye presented a normal face expres-sion picture flickered at 8.57 Hz,while the other presented a fearful face flickered at 12 Hz or 15 Hz,respectively.Electro-encephalogram(EEG)was recorded during this process.Steady state visual evoked potential(SSVEP)evoked by two flick-ering rates was analyzed by time-frequency analysis of short time fourier transformation(STFT).The time index of BR was estimated and the correlation coefficient between FFA and visual cortex compared.Results The total average time was (411.6 ±73.8)ms for the left eye and (547.6 ±126.7)ms for the right eye.The switch rate of the two groups was not different,but the left FFA was more sensitive than the right FFA in process of the fearful face.Neither side of FFA had any frequency preference to the flickered fearful face.Conclusion SSVEP can be used as a frequency tag of BR or as a tool to evaluate visual sensation under BR objectively.SSVEP combined with BR can be used in research of neural mechanisms of visual awareness.

A recently developed machine learning algorithm referred to as Extreme Learning Machine (ELM) was used to classify machine control commands out of time series of spike trains of ensembles of CA1 hippocampus neurons (n=34) of a rat, which was performing a target-to-goal task on a two-dimensional space through a brain-machine interface system. Performance of ELM was analyzed in terms of training time and classification accuracy. The results showed that some processes such as class code prefix, redundancy code suffix and smoothing effect of the classifiers' outputs could improve the accuracy of classification of robot control commands for a brain-machine interface system.
Aniline Compounds ; Animals ; Brain-Computer Interfaces ; Hippocampus ; Learning ; Neural Prostheses ; Neurons ; Rats ; Machine Learning