Value of resting state electroencephalogram in the diagnosis of Alzheimer's disease
10.11886/scjsws20230327006
- VernacularTitle:静息态脑电在阿尔茨海默病诊断中的价值
- Author:
Yaxin ZHOU
1
;
Yuan SHAO
2
;
Yuanlong WANG
1
;
Ya'nan LIN
1
;
Liangying ZHANG
2
;
Yongjun WANG
1
Author Information
1. School of Mental Health and Psychological Sciences， Anhui Medical University， Hefei 230032， China
2. Shenzhen Kangning Hospital， Shenzhen 518020， China
- Publication Type:Journal Article
- Keywords:
Alzheimer's disease;
Electroencephalography;
Cognitive function;
Correlation analysis
- From:
Sichuan Mental Health
2023;36(4):313-319
- CountryChina
- Language:Chinese
-
Abstract:
BackgroundThe diagnosis of Alzheimer's disease （AD） still faces great challenges， and the advantage of electroencephalogram （EEG） diagnosis lies in its portable and non-invasive nature， so the EEG diagnosis of AD has occupied an important place in clinical research. ObjectiveTo evaluate the value of resting state EEG for AD diagnosis， and to provide references for early recognition of AD in clinical practice. MethodsClinical data of AD patients （n=59） in an Inpatient Geriatric Psychiatry Unit of Shenzhen Kangning Hospital from May 2019 to May 2022 were retrospectively analyzed， and healthy elderly individuals attending outpatient clinics at the hospital during the same period were enrolled as control group （n=54）. Eight-channel resting state EEG data were acquired， and the absolute power values in the α， β， θ and δ frequency bands and the α/θ ratio were obtained and calculated using Fast Fourier Transform （FFT）. Cognitive function assessments of patients were done by Mini-Mental State Examination （MMSE） and Montreal Cognitive Assessment （MoCA）. Spearman correlation analysis was used to examine the correlation between EEG findings and MMSE and MoCA scores of AD patienrs. Logistic regression prediction model for AD was built using currently available EEG and clinical variables， and the model performance was assessed using the receiver operating characteristic （ROC） curve and the area under curve （AUC）. ResultsThe θ-band absolute powers in the right mid-frontal （F4） and mid-lateral （F7， F8） regions were higher in AD patients than those in healthy controls， with statistically significant difference （t=-2.844， -2.825， -3.014， P<0.05 or 0.01）. The absolute powers of α/θ ratio in prefrontal （Fp1， Fp2）， mid-frontal （F3， F4） and mid-lateral （F7， F8） regions showed a notable reduction in AD patients compared with healthy controls， with statistical difference （t=2.081， 2.327， 3.423， 2.358， 3.272， 2.445， P<0.05 or 0.01）. Spearman correlation analysis denoted that MMSE score was positively correlated with the absolute powers of α-band， β-band and α/θ ratio （r=0.206， 0.288， 0.372， P<0.05 or 0.01）. MoCA score was positively correlated with β absolute powers and α/θ ratio （r=0.201， 0.315， P<0.05 or 0.01）， and negatively correlated with θ absolute power （r=-0.218， P<0.05）. ROC curve revealed an AUC of 0.882 （95% CI： 0.820~0.943）， a sensitivity of 0.966 and a specificity of 0.673 for the AD prediction model based on EEG variables， while the prediction model for AD using comprehensive variables achieved better predictive efficacy， reaching an AUC， sensitivity and specificity of 0.946 （95% CI： 0.905~0.986）， 0.948 and 0.873， respectively. ConclusionResting state EEG of AD patients is correlated with cognitive function， and are of great value in the diagnosis of AD， with θ absolute power and α/θ ratio in EEG being the most strongly correlated with AD.
