Resting-state Beta-burst Patterns Of Depression:a Meg Study

Recent researches have proposed new viewpoints that neural oscillations contain not only smooth oscillations but also transient and isolated β-burst events,which might drive network coordination.Analysis of depression based on β-burst would provide fresh insights into the underlying neuropathological changes and develop our knowledge of brain beta oscillations.In this study,resting-state MEG recordings were collected from 30 depressed patients and a matched 40 healthy controls.MEG β-burst was characterized in healthy and depression group from the sensor level and the source level.The relationship between β-burst characteristics,β-burst connectome and severity of depression were analyzed in order to excavate the evaluation indicators of depression severity.The main contents are as follows:1.Based on the sensor level MEG data,the threshold method and Hidden Markov Model(HMM)are compared in detection of β-burst state to explore the impaired oscillatory activity of depression.HMM avoids the selection of threshold and band-pass filtering through regularization,provides rich spectrum details,and has higher analysis granularity in depression MEG data.Compared with the healthy group,the depressed group showed increased β-burst amplitude in the bilateral central area and left frontal area,reflecting the increase of cortical excitation and metabolic activity in this region.However,sensor level signals are not enough to accurately reflect the changes of cerebral cortical activity,and the regional indicators derived from channel level studies are not enough to assess the severity of depression.2.Based on Hidden Markov model,β-burst was characterized at the source level across cortex to further explore the biological markers related to the severity of depression.Compared with the healthy group,the depressed group showed abnormal β-burst characteristics in the core brain region.The increase of β-burst amplitude of orbitalfrontal cortex was related to the severity of sleep disturbance,and β-burst rate was correlated with the severity of anxiety.The changes of orbitofrontal cortex β-burst characteristics were sensitive biological index of patients with depression,which reveals the neural oscillation of symptom related changes in the potential neural mechanism of depression.3.Abnormal functional connectivity pairs under β-burst functional connections closely associated with severity of depression can be regarded as neuroimaging biomarkers.Temporal coordination between coincident bursts and phase stability of burst state were used to measure the stability of functional connection inner regions.Compared with the healthy group,the depressed group showed three significant orbitofrontal connections.It was found that the functional connection between orbitofrontal and dorsolateral superior frontal gyrus was related to sleep disturbance,and the connection strength between orbitofrontal and medial superior frontal gyrus was significantly related to anxiety symptoms,indicating that the structural framework of neural synchronization in depression is damaged,thus damaging the network function.In summary,this study has explored the distribution of β-burst characteristics across the cortex and inter-regional functional connectivity.The depression results in abnormal β-burst characteristics and a loss in connectivity,which impairs the dynamic coordination of neural network.abnormal β-burst oscillation is a potential neural mechanism of sleep disorders and mood disorders in patients with depression.This study shows that abnormal β-burst oscillation is a potential neural mechanism of sleep disturbance and anxiety in depressed patients.