Study On Characteristic And Clinical Application Of EEG Microstate In Patients With First-Episode Depressive Disorder

Objective:Major depressive disorder(MDD)exhibits a high prevalence and low cure rate,and the solution to this problem lies in understanding its pathogenesis and treatment mechanisms.Numerous studies have attempted to unravel the multifactorial pathogenesis of depressive disorder,and although some progress has been made,there is still no definitive conclusion.Numerous studies have suggested that depressive disorder is a disease with abnormal neural activity and connectivity in the brain.EEG in neurophysiological examination has been widely used in brain function research and clinical applications of neuropsychiatric disorders and other diseases by its low cost,non-invasive,immediate responsiveness,and high temporal resolution in milliseconds.Depressive disorders EEG has been extensively studied,but previous EEG research methods ignored the temporal dynamics of whole-brain neural networks.EEG microstates are typical voltage topographies reflecting transient activation of brain networks,allowing continuous temporal analysis of EEG at the topological level,which can complement the study of dynamic changes in sustained mental activity in disease.Only a few previous studies have focused on resting EEG microstates in patients with depressive disorder,and these studies suggest that EEG microstates in depressive disorder are abnormal and correlate with the severity of depressive symptoms,and can be altered with treatment.However,most of the previous studies have focused on the differences between depressed and normal subjects,while few studies have focused on patients with first-episode depressive disorder and EEG microstates associated with treatment with selective serotonin reuptake inhibitor(SSRI).In this study,we explored the neurophysiological characteristics of patients with first-episode major depressive disorder and assessed the changes in brain network dynamics before and after antidepressant treatment with SSRIs by using resting-state EEG microstate analysis,to find biological indicators of depressive disorder and predictors of efficacy.Methods:1.Research subjects,intervention follow-up,and data collection2.A cross-sectional case-control and longitudinal follow-up study design were used for the primary screening group,with 101 patients with first-episode untreated major depressive disorder(MDD baseline/pre-treatment group)and 45 healthy subjects(HC)collecting general information,clinical scales,cognitive tests,and 64-lead resting-state EEG from January 2020 to July 2021.After enrollment,all MDD patients were treated with SSRIs and followed up.A total of 76 MDD patients(MDD 8-week/post-treatment group)completed the 8-week follow-up and the above information was collected again,of which only 51 patients completed a 64-lead resting-state EEG again.3.The first untreated MDD patients were divided into 26 patients with clinical cure prediction(RP)and 26 patients without clinical cure prediction(non-remission prediction)based on the 8-week HAMD scale score and the rate of reduction of the SSRIs in the primary screening group.The first untreated MDD patients were divided into 26 patients with clinical cure prediction(RP)and 50 patients without clinical cure prediction(non-remission prediction,NRP).The validation group was designed as a case-control study with 30 patients with first untreated major depressive disorder(v MDD)and 30 healthy subjects(v HC)collected from August 2021 to November 2021,and the above information was collected again.4.Analysis methods(1)EEG microstate analysis: resting-state EEG data were preprocessed first,and then EEG microstate analysis was performed on all preprocessed EEG data of each group separately: calculating global field power,finding peak brain topographies,clustering,fitting,and finally deriving EEG microstate indexes of each group.(2)Depressive disorder EEG microstate characterization and validation: the primary screening group compared the differences of EEG microstate parameters between MDD baseline group and HC group;the validation group compared the differences of EEG microstate indexes between v MDD baseline group and v HC group.(3)Association of EEG microstates with clinical and cognitive symptoms in depressive disorder: correlation analysis of EEG microstates indexes with HAMD and RBANS factor scores in all MDD patients in the primary screening and validation groups.(4)Changes in EEG microstates during treatment with SSRIs: The changes in EEG microstates indicators between the MDD baseline group and the post-treatment group were compared in the primary screening group;the differences in EEG microstates between the MDD post-treatment group and the HC group were compared.(5)EEG microstates for predicting the efficacy of SSRIs: compare the differences of EEG microstates indicators between the clinical cure prediction group(RP)and the non-clinical cure prediction group(NRP);correlation analysis of EEG microstates indicators at baseline with HAMD scores at 0 and 8 weeks and the rate of subtraction.(6)The value of EEG microstate indicators for auxiliary diagnosis and efficacy prediction:ROC curve analysis of the value of EEG microstate indicators and combined indicators after logistic regression for auxiliary diagnosis of depressive disorder and efficacy prediction of SSRIs.Results:1.EEG microstate topographies.The clustering in each group of subjects after EEG microstate analysis yielded 4EEG microstates(A,B,C,D)consistent with previous studies and the proportion of explained variance for these 4 EEG microstates ranged from 65% to 84%.2.Analysis and validation of EEG microstate characteristics in depressive disorder.(1)Primary screening group: compared with the normal population,the mean duration of EEG microstates D was reduced in patients with first untreated depressive disorder(t=1.984,p=0.049),while the differences in the mean duration of EEG microstates A,B,and C were not significant;the frequency of EEG microstates A(t=-2.109,p=0.037)and EEG microstates B(t=-2.287,p= 0.024)increased in frequency,while there was no significant difference in the frequency of EEG microstates C and D.The proportion of time spent in the four EEG microstates and the probability of interconversion of the four EEG microstates were not statistically different between the two groups.(2)Validation group: compared with normal controls,the mean duration of EEG microstate D in the validation group v MDD group at week 0 was lower than that in the v HC group(t=3.335,p=0.001),while the differences in the mean duration of EEG microstates A,B and C were not significant;and the frequency of EEG microstate A(t=-2.685,p=0.009)was significantly increased,while in the other EEG microstates B,C,and D frequencies were not significantly different;the time ratio of the four EEG microstates and the probability of interconversion of the four EEG microstates in the v MDD and v HC groups were not significantly different.3.Correlation analysis of EEG microstates of depressive disorder with clinical and cognitive symptoms.(1)Correlation analysis of EEG microstate indicators of depressive disorder with HAMD factor scores.The anxiety/somatization factor was positively correlated with the probability of EEG microstates D to C conversion(r=0.234,p=0.019).The weight factor was positively correlated with mean duration of EEG microstate C(r=0.224,p=0.025),mean duration of EEG microstate D(r=0.238,p=0.017);and with frequency of EEG microstate B(r=-0.319,p=0.001),proportion of EEG microstate B time(r=-0.204,p=0.041),EEG microstate D to B transition probability(r=-0.265,p=0.007)were negatively correlated.The cognitive impairment factor was positively correlated with the mean duration of EEG microstate D(r=0.231,p=0.021);and negatively correlated with the frequency of EEG microstate B(r=-0.240,p=0.016).The blocking factor was positively correlated with the mean duration of EEG microstate D(r=0.198,p=0.048).The sleep disturbance factor was positively correlated with EEG microstate C frequency(r=0.236,p=0.018),EEG microstate D frequency(r=0.241,p=0.016),and EEG microstate C to D transition probability(r=0.198,p=0.048).(2)Correlation analysis of EEG microstate indicators of depressive disorder with each factor of RBANS.Immediate memory was positively correlated with mean duration of EEG microstate A(r=0.260,p=0.009)and mean duration of EEG microstate B(r=0.200,p=0.047);and negatively correlated with frequency of EEG microstate A(r=-0.210,p=0.036)and frequency of EEG microstate D(r=-0.233,p=0.019).Visual breadth was positively correlated with the mean duration of EEG microstate B(r=0.206,p=0.040);and negatively correlated with EEG microstate D frequency(r=-0.263,p=0.008),and EEG microstate C to D transition probability(r=-0.223,p=0.026).Speech function was positively correlated with the mean duration of EEG microstate B(r=0.276,p=0.005)and EEG microstate C(r=0.296,p=0.003);and positively correlated with EEG microstate A frequency(r=-0.217,p=0.030),EEG microstate D frequency(r=-0.361,p=0.0002),EEG microstate D time ratio(r=-0.306,p=0.002)were negatively correlated.Delayed memory was positively correlated with the mean duration of EEG microstate A(r=0.273,p=0.006),EEG microstate B(r=0.220,p=0.028),and EEG microstate C(r=0.206,p=0.040);and positively correlated with the mean duration of EEG microstate A(r=-0.234,p=0.019)and EEG microstate D(r=-0.289,p=0.004)in frequency,and EEG microstate C to D transition probability(r=-0.199,p=0.048)were negatively correlated.Attention function was not significantly correlated with EEG microstate parameters.4.Analysis of EEG microstates and SSRI efficacy in depressive disorder.(1)Analysis of differences in EEG microstates before and after SSRI treatment for depressive disorder.Compared with the MDD group before treatment for 0 weeks,the mean duration(t=-2.544,p=0.014)and time proportion(t=-2.299,p=0.026)of EEG microstates D and the frequency of EEG microstates A decreased significantly(t=2.268,p=0.028)in MDD patients after 8 weeks of SSRIs;the EEG microstates D to A was significantly decreased(t=2.676,p=0.010)and the probability of transition from EEG microstates B to D(t=-2.007,p=0.050)and D to B(t=-2.441,p=0.018)were significantly increased.(2)Characteristics of EEG microstates in patients with effective treatment with SSRIs for depressive disorderRelative to the HC group,there were no statistical differences in each of the four EEG microstates(mean duration,frequency,temporal ratio,and mutual transfer probability)in the MDD group after 8 weeks of treatment.5.depressive disorder EEG microstates on SSRI drug efficacy prediction study.(1)Differences in EEG microstates between the clinical cure prediction group and the group that did not reach clinical cure prediction The mean duration of EEG microstates D in the RP group was shorter than that in the NRP group(t=2.001,p=0.049),while the mean duration of other EEG microstates was not statistically different;the frequency of EEG microstates C in the RP group was higher than that in the NRP group(t=-2.381,p=0.020),while the frequency of other EEG microstates was not statistically different.the time ratio of the four EEG microstates was not There was no statistical difference in the time ratio of the four EEG microstates;the probability of transition from EEG microstate D to B was significantly lower in the RP group than in the NRP group(t=2.111,p=0.038);there was no statistical difference in the probability of transition between the other EEG microstates.(2)Correlation between EEG microstates of depressive disorder and the total score/subtraction rate of HAMDThe frequency of EEG microstate C was negatively correlated with the 8-week total score of HAMD(r=-0.253,p=0.028)and positively correlated with the subtraction rate(r=0.250,p=0.029).The time proportion of EEG microstates C was negatively correlated with the total 8-week HAMD score(r=-0.245,p=0.033)and positively correlated with the rate of subtraction(r=0.256,p=0.025).The probability of EEG microstate A to C transition was negatively correlated with HAMD 8-week total score(r=-0.226,p=0.049)and positively correlated with the rate of score reduction(r=0.261,p=0.023).The probability of EEG microstate D to B conversion was positively correlated with the8-week total score of HAMD(r=0.285,p=0.012).6.Diagnostic applications of EEG microstates in depressive disorder.(1)ROC curve analysis of EEG microstate indexes in depressive disorder on the auxiliary diagnosis The AUC of the mean duration of EEG microstate D was 0.662,with a sensitivity of50.4% and specificity: 77.3%;the AUC of EEG microstate A frequency was 0.632,with a sensitivity of 80.0% and specificity: 49.6%;the AUC of EEG microstate B frequency was0.629,with a sensitivity of 88.0% and specificity: 35.1%.After logistic regression of all EEG microstate indicators,ROC curve analysis was done,and the AUC of the joint diagnosis after logistic regression was 0.787.(2)ROC curve analysis of EEG microstate indexes of depressive disorder for prediction of efficacy The AUC of the mean duration of EEG microstate D was 0.641,with a sensitivity of42.3% and specificity: 90.0%;the AUC of the frequency of EEG microstate C was 0.668,with a sensitivity of 69.2% and specificity: 60.0%;the AUC of the transition probability from EEG microstate D to B was 0.645,with a sensitivity of 66.0% and specificity:68.6%,respectively.The AUC for the combined prediction of efficacy after logistic regression was 0.835.Conclusions:1.There are abnormalities in the dynamic brain network of EEG microstates in patients with depressive disorder,and the mean duration of EEG microstate D and microstate A frequency may be biometric indicators of the temporal dynamics of brain activity in depressive disorder.2.The mean duration of EEG microstates D and microstate A frequency may be state indicators reflecting the development of depressive disorder.3.The mean duration of EEG microstate D and microstate C frequency may be the neurophysiological indicators for predicting the efficacy of SSRIs in patients with depressive disorder.4.The combined EEG microstate logistic regression indexes are valuable for aiding depressive disorder diagnosis and predicting the efficacy of SSRIs.