Research On Spontaneous Low Frequency Oscillation Characteristics In Cerebral Cortex

In this paper,the mouse somatosensory cortex was used as the research object.Based on the intrinsic optical imaging equipment,the effects of anesthetic concentration and nitric oxide synthase inhibitor on the spontaneous low frequency oscillation(LFO)were studied,and then under the two-photon microscope.Cortical artery-venous separation was achieved.The research work and contributions of this paper are summarized as follows:The effect of anesthetic concentration on LFO.The optical signals of KM mice are recorded herein.The effects of changes in anesthetic concentration on the deoxygenated hemoglobin content and vascular blood volume LFO signal were explored under red and green light,respectively.The artery,venous and cortex area was selected and the signal of different anesthetic concentrations under red and green light was recorded.In this paper,the time-space characteristics of LFO were explored by changing the concentration of anesthetic.It was found that the increase of anesthetic concentration significantly increased the frequency of LFO signal under green light,but it was not affected under red light.It is speculated that the source of the LFO signal is not just neurons.The effect of nitric oxide synthase inhibitors on LFO.Mouse optical data before and after injection of the nitric oxide inhibitor were collected using intrinsic optical imaging equipment.By comparing the corresponding results,it was found that on the red light the amplitude of the LFO signal on the arterial,venous and cortex was enhanced after administration.Under the green light,the frequency of the LFO signal on the cortical area is significantly reduced.This may be because after inhibition of nitric oxide,vasoconstriction performance is not regulated by neurons,and hemoglobin concentration changes are not stable.The LFO signal amplitude is therefore enhanced.Two-photon artery-vein separation.This paper collects images under TPLSM based on the research described above.Firstly,the image is corrected by the crosscorrelation method,and then the connected set method is used to extract the vascular network.Next,the multi-window spectrum estimation method is used to extract the spectral value characteristics of the blood vessel pixels,and the average value of the low frequency band and the high frequency band amplitude is used as the characteristic value.The spectral clustering algorithm achieves artery-vein separation,and the classification accuracy rate is up to 98.7%.