Abstract Background Patients with mild cognitive impairment(MCI) are a clinically important group because of the increased risk for the progression to Alzheimer’s dementia(AD). Amyloid positron emission tomography(PET) was useful as a predictor for identifying AD pathology in the pre‐dementia stage. We determined the rates of conversion to AD in MCI patients based on amyloid positivity by visual and quantitative analysis over 2 years. We also investigated the clinical and neuroimaging predictors for conversion to AD. Method Individuals aged 50 and above with MCI according to Petersen’s criteria were eligible. Subjects underwent laboratory tests including APOE genotyping, neuropsychological tests, brain magnetic resonance imaging(MRI), and amyloid PET assessed by 18 F‐FC119S. Amyloid positivity was determined in each lobe of the brain and quantitatively analyzed. The follow‐up period was at least 2 years. Cox regression analysis was performed to identify prognostic factors which are independently related to time to AD conversion. Result We recruited 50 patients and 39 subjects were enrolled. 5 patients dropped out at 2 years, so 34 subjects were finally included. 13 of 34(38.2%) by visual analysis and 12 of 34(35.3%) by quantitative analysis of 18 F‐FC119S PET showed amyloid positive. The basic demographics were not significantly different between the groups except for Korea‐Instrumental Activities of Daily Living(K‐IADL) at baseline(p = 0.004) and 1 year(p = 0.001), Mini‐mental State Examination(MMSE) at 2 years(p = 0.047) and Global Deterioration Scale(GDS) at 2 years(p = 0.029). 4 of 13(30.8%) by visual analysis, 4 of 12(66.7%) by quantitative analysis amyloid‐positive MCI patients converted to AD. Amyloid positive in posterior cingulate was marginally significant(p = 0.066). AD conversion group had a higher Clinical Dementia Rating‐Sum of box(CDR‐SOB) at baseline(p = 0.002) compared with remained MCI group. In multivariate cox regression, CDR‐SOB at baseline, MMSE at 1 year and K‐IADL at 2 years were included. Conclusion Amyloid‐positive MCI group was more likely to progress to AD. But long‐term follow‐up was required. By quantitative analysis of 18 F‐FC119S PET, amyloid deposition in posterior cingulate was highly associated with the AD conversion group. CDR‐SOB at baseline, MMSE at 1 year and K‐IADL at 2 years were considered as clinically valuable prognostic predictors of disease progression. Especially, CDR‐SOB is important as a predictive biomarker in the pre‐dementia stage.

Abstract Background Early screening of dementia at its pre‐clinical stage is crucial, seeing as how deteriorated cognitive functions can be recovered through appropriate medicinal practices. The pre‐clinical stage, which we refer to as mild cognitive impairment (MCI) is known to affect the alpha rhythm of the brain activity. Hence, our study aims to establish quantitative EEG (qEEG)‐based alpha asymmetry biomarker that aids the diagnoses of MCI. Method The qEEG dataset were composed of age‐ and sex‐ matched subjects (N = 634, 317 healthy control (HC), 317 MCI), acquired in accordance with the 10‐20 system and under resting state with their eyes being closed. Subjects were recruited by Chung‐Ang University (153 HC, 111 MCI), iMediSync(164 HC, 76 MCI), Dong‐a Medical Center(84 MCI) and Pusan Medical Center(46 MCI). The features were classified into four types: the absolute and relative region alpha power (AAP/RAP) of six regions (Prefrontal, Frontal, Temporal, Central, Parietal, Occipital); alpha power asymmetry (APA) between regions that align in anterior‐posterior and lateral planes; occipital alpha peak frequency (OPF) that analyzes peak frequencies. In order to highlight the alpha variability, EEG signals were bandpass filtered (6.5‐12Hz). Filtered signal at each channel was categorized into slow (6.5‐8Hz), middle (8‐10Hz) and fast alpha (10‐12Hz). The mean and standard deviation of their FFT were computed through moving time window. Henceforth, the feature set were used to train machine learning algorithms. Data were split into 8(training N = 507) to 2(test N = 127) ratio. 101 subjects were randomly selected and used as validation set. The algorithm that exhibited the best classification performance were determined through 5‐fold cross validation (CV) results. Result Support vector machine (SVM) showed the best 5‐fold CV results – 84.61% average accuracy, 74.02% sensitivity, 95.25% specificity, 94.15% precision and F1‐Score of 0.8273. The RAP decreased for all regions and OPF were slower in MCI group when compared to HC. APA patterns of the right intra‐hemisphere exhibited most significant differences. Conclusion This study illustrates the potential of APA as a biomarker for MCI. Current applications of APA are confined to emotional‐related studies, such as depressive disorder. The reported cases of MCI‐associated depression also uphold the validity of APA as a biomarker for MCI.