Modulation Of Brain Signal Variability By Face Recognition

Neural oscillation is the intrinsic characteristic of dynamic brain activity.The traditional electrophysiological studies have revealed a lot of some characteristics of high frequency neural oscillation.However,due to the limitation of imaging techniques,the mechanisms of low frequency oscillation are still poorly understood.In recent years,with the help of functional magnetic resonance imaging(fMRI),low frequency oscillations have been studied more often.The current studies have found that low frequency oscillations of blood oxygen level dependent(BOLD)signal can reflect many important physiological information,such as synchronization characteristics of neural activity,inherent brain structure and functional network,cognitive aging process and level,behavior performance and pathogenesis of mental illness.And these physiological activities and cognitive abilities also show a strong frequency-dependent characteristic.The current work focused on low frequency oscillation of BOLD signal and dedicated to investigate the characteristics of the brain signal variability under face recognition task.Specifically,we explored the relationship of low-frequency steady-state brain response(lfSSBR)and variability,and the changes of cognitive task to temporal and spatial funcational organizations.On the one hand,through the analysis of power and variability at multiple frequency bands,we found power increase at the fundamental frequency and two harmonic frequencies and power decrease within the infra-slow frequency band,suggesting a multifrequency energy reallocation.The consistency of power and variability was demonstrated by the high correlation of their spatial distribution and brain–behavior relationship at all frequency bands.Additionally,the reallocation of finite energy was observed across various brain regions and frequency bands,forming a particular spatiotemporal pattern.Results from this study strongly suggest that frequency-specific power and variability may measure the same underlying brain activity and that these results may shed light on different mechanisms between lfSSBR and brain activation,and spatiotemporal characteristics of energy reallocation induced by cognitive tasks.On the other hand,to explore the temporal and spatial reorganization,we introduced the temporal and spatial multiscale entropy(MSE)to neuroimaging data at fundamental frequency in a face recognition task with low frequency steady-state brain response(lfSSBR)paradigm.Results showed that the spatial MSE was reduced by the task and correlated with lfSSBR and behavioral stability in a phase-dependent way,in accord with the phase synchronization mechanism of lfSSBR.Furthermore,reduced temporal MSE was also found in the core and extended face areas,suggesting task-related reorganization of neural dynamics.Overall,the sMSE provides complementary information to temporal MSE by directly measuring the spatial configuration of brain activity and its time course in elucidating the flexibility/stability of brain function during cognition.In conclusion,this current dissertation used two different methods of brain signal variability under lfSSBR paradigm and found multiscale reorganization of brain function and temporal and spatial reorganization of brain activity at fundamental frequency in face recognition.It provides important research direction for the mechanism and application of brain signal variability and lfSSBR,and makes us understand the cognitive processing mechanism of our brain more thoroughly.