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Background: Currently positron emission tomography (PET) is used as the initial or sole biomarker of β‐amyloid (Aβ) brain pathology, which may inhibit Alzheimer's disease (AD) drug development and clinical use due to cost, access, and tolerability. Machine learning (ML)‐based EEG biomarkers may address these challenges. Previous studies have confirmed their ability to accurately discriminate between normal, mild cognitive impairment (MCI) and Alzheimer's dementia. However, few quantitative EEG (QEEG) biomarkers have been validated with Aβ PET. We developed a QEEG‐ML algorithm to predict brain Aβ pathology among subjective cognitive decline (SCD) and MCI patients, and validated it using Aβ PET. Method: EEG (19‐channel, eye‐closed, resting‐state) and Aβ PET data were collected from 160 human subjects with MCI (77 Aβ+, 83 Aβ‐). We excluded 45 data (23 Aβ+, 22 Aβ‐) for test verification. QEEG absolute power, relative power, power ratio, and connectivity between channels (iCoherence) comprised the input features, from which the most relevant predictive features were identified using four methods (Random Forest Importance (GBM, XGB), ElasticNet, Whitney‐Mann). We then trained six ML algorithms (SVM, Logistic, KNN, Naive Bayes, Random Forest(GBM/XGB)) using each relevant feature set, yielding 24 models (4 sets * 6 algorithms). The 45 test data sets were input into each model to compare their performance. Then, 111 MCI data (56 Aβ+, 55 Aβ‐) and 165 SCD data (31 Aβ+, 134 Aβ‐) were classified by the model for 3rd validation. Result: In test validation, the best‐performing model showed 77.8% accuracy, 81.8% sensitivity, 73.9% specificity in discriminating Aβ+ from Aβ‐. In 3rd data validation, the best‐performing model showed 72.1% accuracy, 71.4% sensitivity, 72.7% specificity in MCI. In SCD, it showed, 69.1% accuracy, 64.5% sensitivity, 70.2% specificity. Conclusion: These findings suggest that our novel ML‐based QEEG biomarker can accurately predict the presence of brain Aβ plaque. Additional benefits of such a biomarker include reduced expense, wide availability, and high‐throughput screening and response monitoring. Future studies will assess utility as primary AD screens, adjunctive use with PET, and as a support in clinical rationales for Aβ PET and treatment choice in AD. [ABSTRACT FROM AUTHOR]

Background: Our everyday actions and decisions involve a complex coordination of various cognitive functions. Severe deterioration of such functions, commonly referred to as dementia, adversely affects quality of our lives. However, through early screening of the disease at its pre‐clinical stage and appropriate treatment, they can be reverted. Current diagnostic criteria for dementia utilize questionnaire‐based cognitive function assessments, such as the mini‐mental state examination. However, it is statistically less robust, where further tests such as positron emission tomography are required for accurate diagnosis. This is laborious, expensive and expose patients to harmful radiation. Therefore, we propose a quantitative electroencephalography (QEEG) based approach that distinguishes Alzheimer's disease dementia (ADD) from a non‐ADD group that include mild cognitive impairment and subjective cognitive decline data. Method: 19 channel EEG data employed in the present study were clinically acquired from Chung‐Ang university hospital, in eyes‐closed resting state. Artefacts were eliminated through independent component analysis and bad epoch rejection. Through spectral analysis, the powers of a frequency spectrum (1‐45Hz) were computed with 0.25Hz resolution. Henceforth, the Z‐score of each value were calculated through iMediSync's age‐ and sex‐differentiated normative database and were mapped into a rectangular arrangement in accordance with channel locations on the scalp. The rearranged Z‐score matrices were visualized, clearly exhibiting both spectral and spatial information. Gamma spectrum (30‐45Hz) was excluded due to its vulnerability to contaminations induced by external noises and muscle movements. The final dataset (N = 732; 137 ADD, 595 non‐ADD) was established, where the 75% of non‐ADD were first excluded as test data due to data imbalance. The remaining data were split into 2 to 8 ratio for testing (N = 503; 27 ADD; 476 non‐ADD) and training (N = 229; 110 ADD; 119 non‐ADD). Result: The final 18‐layer Residual network model showed test accuracy at 88.5% with ADD sensitivity at 88.9% and specificity at 88.4%. Explainable artificial intelligence algorithm was utilized for the inference and verification of classification criteria. Conclusion: The outstanding classification results of the established model upholds QEEG utility in the distinguishment of dementia from its pre‐clinical stages. Continuous refinement will bolster its potential in the diagnosis several other neurological diseases. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: Alzheimer's disease dementia (ADD) is a predominant neurodegenerative disorder that results severe abnormalities in cognitive functions. However, most diagnoses rely on assessments made by individual clinical experts. Hence, we aim to establish a novel biomarker which can provide both accuracy and objectivity. Electroencephalogram (EEG) data were utilized for the training of artificial intelligence (AI) models, making use of its cost‐advantage and simple utility. Method: 19‐channel EEG data were recorded at Chung‐Ang University hospital in accordance with international 10‐20 system at resting state with eyes being closed. We first converted 19 channels into 68 regions of interest (ROIs) using standardized low resolution brain electromagnetic tomography (sLORETA). Henceforth, imaginary part of coherence (iCoh) between each ROI were calculated, reflecting functional connectivity between the ROIs. The iCoh values were then utilized in the calculation of the brain's functional brain network. The subjects were divided into non‐ADD and ADD group. The resulting training dataset consisted of 118 non‐ADD and 110 ADD subjects, where the number of non‐ADD training data were adjusted in order to account for data imbalance. The test dataset included 476 non‐ADD and 27 ADD subjects. The classification model was trained using 68 ROI source powers and functional brain network features. Only statistically significant features were selected for feature reduction purposes. Through primary classification via light gradient boosting method (LGBM), top 30 features with high importance values were selected and were employed in the training of our final LGBM classification model. Result: Through appropriate hyperparameter tuning, the LGBM classification model resulted in 87.67% accuracy, 88.89% sensitivity and 87.61% specificity. From the receiver operating characteristic (ROC) curve, 0.9506 area under ROC curve (AUC) was achieved. Conclusion: The LGBM model resulted in promising classification performance, solely through EEG features. In addition, functional brain network features had higher importance in comparison with source power features, indicating high potential of EEG brain network features in distinguishment of neurodegenerative diseases. [ABSTRACT FROM AUTHOR]

Background: The pre‐clinical stage of dementia, mild cognitive impairment (MCI) carries various pathological pathways and respective prognoses. The two foremost causes of MCI and dementia that have been identified through autopsy diagnoses are Amyloid plaques and Lewy body, commonly referred to as Alzheimer's disease (AD) and Lewy body disease (LBD) respectively. Although correct identification of pathological pathways plays a crucial role in effective designing of treatment methods, MCI subtypes are underexploited in the current diagnostic paradigm due to multiple reasons, mainly cost‐effectiveness. Hence, we utilized quantitative electroencephalography (QEEG) that are cheap and convenient to record in the distinguishment of MCI subtypes. Method: A total of 151 MCI patients' EEG data were aggregated into 3 groups, pure AD (n = 29), pure LBD (n = 88), and mixed (n = 34). The mixed type was further subdivided into main AD (n = 31) and main LBD (n = 3) groups, in accordance with AD/LBD tendency exhibited by the patients. The clinical labelling was brought by the experienced experts of Yonsei Severance hospital, South Korea. 1D SE‐ResNet‐based classification model was established for quantitative investigation of AD and LBD propensity. The power spectrum density (PSD) in dB/Hz scale were computed from the EEG data and were used to train the model. Due to the small number of data, augmentation was applied to the training data. The final dataset was split into 8 to 1 to 1 ratio (Train n = 7092: 2274 pure AD + main AD; 3694 pure LBD + main LBD, Validation n = 14: 6 pure AD + main AD; 8 Pure LBD + main LBD, Test n = 15: 6 pure AD + main AD; 9 pure LBD + main LBD). Result: The classification model showed validation results of 85.7% accuracy, 83.3% AD sensitivity and 87.5% LBD sensitivity. Test results were at 92.8% accuracy, 83.3% AD sensitivity and 88.9% LBD sensitivity. Conclusion: QEEG‐based deep learning classifier developed in this study successfully distinguished the two main causes of cognitive degeneration in MCI patients. Our model can help identify subtype‐specific spectral trends, which could also make contributions in the establishment of effective treatment methods for MCI. [ABSTRACT FROM AUTHOR]

Background: Early screening and prevention of dementia are widely discussed and studied in the current healthcare paradigm. However, about 70 percent of dementia diagnoses are categorized into Alzheimer's disease (AD), neglecting the presence and influence of Lewy body. The present study aims to distinguish Alzheimer's disease dementia (pure AD) and Lewy body dementia (pure LBD) through quantitative electroencephalogram (QEEG)‐based classification algorithm. The mixed dementia that exhibits both AD and LBD related propensities were further tested for the verification of the established classification model. Method: 19 channel EEG data were recorded in accordance with the 10‐20 system in eyes‐closed resting state. The acquired data were bandpass filtered into the range of interest (1‐45Hz) and re‐referenced into common average reference. Artifacts were removed by bad epoch rejection and independent component analysis (ICA) through an automated, cloud‐based QEEG analysis platform iSyncBrain®. Henceforth, sensor‐level features were extracted and computed for the training of TabNet structure. Due to the data imbalance (N = 146, Pure AD = 48, Pure LBD = 102), time window‐based augmentation has been applied to establish the final dataset (N = 7292, Pure AD = 3376, Pure LBD = 3916), which were then split into 8 to 2 ratio for the training and testing of the classification model. In addition, mixed dementia (N = 136) data were used to further validate the model's performance. Result: The best classification model yielded test accuracy of 80% with pure AD sensitivity at 80% and pure LBD sensitivity 80%. Moreover, the predicted probability distribution for the mixed dementia data were prominently concentrated around 0.5, which reflect that both pure AD and LBD properties exist. Conclusion: Along with the promising classification performance of the established classification model, the centered probability distribution of the mixed dementia data reflect that the model can well distinguish the characteristics that are specific to pure AD and LBD. [ABSTRACT FROM AUTHOR]

Background: Currently positron emission tomography (PET) is used as the initial or sole biomarker of β‐amyloid (Aβ) brain pathology, which may inhibit Alzheimer's disease (AD) drug development and clinical use due to cost, access, and tolerability. Machine learning (ML)‐based EEG biomarkers may address these challenges. Previous studies have confirmed their ability to accurately discriminate between normal, mild cognitive impairment (MCI) and Alzheimer's dementia. However, few quantitative EEG (QEEG) biomarkers have been validated with Aβ PET. We developed a QEEG‐ML algorithm to predict brain Aβ pathology among subjective cognitive decline (SCD) and MCI patients, and validated it using Aβ PET. Method: EEG (19‐channel, eye‐closed, resting‐state) and Aβ PET data were collected from 311 subjects with SCD (Aβ+: N = 36, Aβ‐: N = 160) or MCI (Aβ+: N = 54, Aβ‐: N = 61). We randomly excluded data for 76 subjects (SCD, Aβ+: N = 7, Aβ‐: N = 28; MCI, Aβ+: N = 6, Aβ‐: N = 7) for use in subsequent verification. QEEG absolute power, relative power, power ratio, and connectivity between channels (iCoherence) comprised the input features, from which the most relevant predictive features were identified using several methods (Random Forest Importance (GBM, XGB), ElasticNet, Whitney‐Mann). We then trained four ML algorithms (SVM, Logistic, KNN, Naive Bayes, Random Forest(GBM/XGB)) using each relevant feature set, yielding 24 models (4 sets * 6 algorithms). The 76 validation data sets were input into each model to compare their performance. Result: The best‐performing model (random forest importance * SVM) showed 82.9% accuracy, 90.9% sensitivity, 76.7% specificity, and 75% positive predictive value in discriminating Aβ+ from Aβ‐, regardless of MCI/SCD. In MCI alone, the model showed 82.1% accuracy, 90.0% sensitivity, 78.9% specificity, and 81.8% positive predictive value. In SCD alone, it showed, 81.1% accuracy, 92.3% sensitivity, 75% specificity, and 66.7% positive predictive value. Conclusion: These findings suggest that our novel ML‐based QEEG biomarker can accurately predict the presence of brain Aβ plaque. Additional benefits of such a biomarker include reduced expense, wide availability, and high‐throughput screening and response monitoring. Future studies will assess utility as primary AD screens, adjunctive use with PET, and as a support in clinical rationales for Aβ PET and treatment choice in AD. [ABSTRACT FROM AUTHOR]

Background: Alzheimer's disease (AD) is the most common cause of dementia. However, accumulation of beta amyloid plague in the brain which is main pathology of AD could show the various spectrum of cognitive functional stage from preclinical level to overt dementia. It is important to understand functional differences between dementia and non‐dementia of AD to predict progression forward to dementia at prodromal or preclinical stage of AD. Method: Nineteen channels of resting state EEGs were measured from 45 Alzheimer's disease subjects who were all confirmed by brain amyloid‐beta PET. Neurocognitive test, activity of daily living (ADL) and MRI were conducted for clinical diagnosis. EEG power spectrum analysis and band‐power ratio, cortical source current density estimated by standardized low resolution brain electromagnetic tomography(sLORETA), functional connectivity between 68 region of interest (ROI) based on imaginary coherence(iCoh) and complex brain networks were analyzed. Result: 20 AD subjects (age: 76.8±7.54) were assessed as dementia, and the other 25 (age: 72.1±6.89) as mild cognitive impairment (MCI) or subjective cognitive decline (SCD) depending on delayed recall memory score. Average CDR was 0.23 in non‐dementia and 1.15 in dementia group. There was no significant difference at delta, but theta power of AD dementia group was significantly increased at all 19 scalp channels. Mid frontal channel (Fz) showed the most distinctive enhancement of theta power. In other hands, beta1 (12∼15Hz) was significantly increased at both temporoparietal area in non‐dementia AD group. These patterns about theta increase and beta 1 decrease in AD dementia group were observed very similarly in cortical source current density. iCoh among ROIs of default mode network (DMN) was significantly decreased at alpha2 band (10∼12Hz). At bilateral frontal pole, rostral middle frontal, anterior cingulate, parahippocampus and precuneus, the degree centrality and characteristic path length significant increased at theta band. In contrast, theta of cluster coefficient and closeness centrality significantly decreased. Conclusion: AD dementia showed increase of theta and decrease of bet1 both at scalp and cortical level comparing to non‐dementia AD subjects. Both global and local network efficiencies via theta wave were significantly deteriorated in AD dementia. QEEG could well discriminate the differences between dementia and non‐dementia in ADs. [ABSTRACT FROM AUTHOR]

Background: As existence of alpha synucleniopathy could affect progression of cognitive impairment in Alzheimer's disease (AD), it's very important to differentiate comorbidity of alpha synucleniopathy in AD to predict progression. To develop affordable EEG‐based discriminating machine‐learning(ML) algorithm for existence of alpha synucleniopathy, we explore differences between AD with vs without it. Method: Based on pattern of cognitive impairment and 3 types of PET scan [18F‐Florbetaben brain amyloid‐beta, FDG, DAT‐PET] , dementia due to Alzheimer's disease [pure ADD] or Lewy Body (pure LBD) or mixed type (AD with LB disease) were clinically classified. We measured 19ch resting state EEG based on international 10‐20. Quantitative analysis of EEG was done by iSyncBrain®. Result: Pure ADD showed the characteristics that general EEG slowing with low total power, relative delta enhancement and desynchronized alpha with low amplitude. Pure LBD showed the pattern of slow alpha peak frequency with intact synchronization, relatively higher EEG total power and frontotemporal theta enhancement. Compared to pure ADD, ADD with LB showed mixed pattern of ADD and LDB. Total power was higher, alpha wave showed intact synchronization but theta power was rather enhanced at frontal(Fz; p‐value < 0.05) and bilateral temporal(T3,5, T4,6; p‐value < 0.05) area that was reported as characteristic pattern of progressive type of AD by previous studies. Conclusion: These findings imply that ADD could have different EEG oscillation characteristics according to the Lewybody disease. Next step, we will develop the machine‐learning algorithm to discriminate the Lewybody disease based on specific EEG features and validate it. [ABSTRACT FROM AUTHOR]

Purpose: Recent study suggests that psychological issues and eating habits are closely related. In this study, we aimed to find the association between eating habits and intakes of artificial sweeteners with emotional states of schoolchildren using quantitatively analyzing objective biosignals.Methods: The study was conducted at the National Standard Reference Data Center for Korean EEG as a cross-sectional study. Three hundred eighteen healthy children who have not been diagnosed with neurologic or psychiatric disorders were evaluated (168 girls and 150 boys; mean age of 11.8 ± 3.6 years). Analysis indicators were a dietary intake checklist for children's nutrition-related behavior score (NBS), consisting of 19 items; food frequency questionnaires (FFQs), consisting of 76 items; the Child Depression Inventory (CDI); State-Trait Anxiety Inventory-State (STAI-S); State-Trait Anxiety Inventory-Trait (STAI-T); electroencephalograph (EEG); and heart rate variability (HRV).Results: Higher scores on the CDI, STAI-S, and STAI-T indicate negative emotions, and these scores were significantly decreased from the first to the fourth quartiles. The HRV results showed that the standard deviation of all normal-to-normal (SDNN) intervals was significantly higher in the first quartile than in the fourth quartile (p < 0.05). The intakes of artificial sweeteners and processed foods such as hamburgers correlate with higher theta/beta ratios, and intakes of natural foods such as legumes and fruits correlate with lower theta/beta ratios (p < 0.05).Conclusions and Implications: From this result we confirmed a link between overall nutritional behavior, food additive intakes, and emotion in apparently healthy children and adolescents. [ABSTRACT FROM AUTHOR]

Background: Alzheimer's disease is the most common cause of dementia, which destroys nerve cells in the brain through the formation and accumulation of amyloid plaques. However, various other pathologies of dementia also exist, such as Lewy body pathology where the destruction is caused by the accumulation of Lewy body peptides. Differing dementia pathologies carry distinguishing symptoms and treatment methods; hence we cannot disregard the importance of correct identification of the pathology. Currently, the correct pathology can be identified through positron emission tomography (PET), which is expensive and results in exposure to harmful ionizing radiation. Therefore, the present study aims to overcome the disadvantages of current screening methods and distinguish the quantitative electroencephalography (QEEG) of two dementia pathologies through machine learning. Method: EEG data employed in the present study were recorded at the 19 electrode locations defined by the international 10‐20 system, in eyes‐closed resting‐state condition. The acquired data were then re‐referenced to a common average reference. Hereafter, bandpass filtering at 0.1‐45.5Hz, bad epoch rejection and independent component analysis (ICA) was performed to remove noise components from the data. Standardized low resolution brain electromagnetic tomography (sLORETA) was further used for mathematical estimation of source cortical activity, yielding data for 68 cortical regions based on the Desikan‐Killiany atlas. Gamma wave (30‐45Hz) features were excluded from the data due to is vulnerability to electromyography. Further feature reduction was also performed, through the designated criteria on p‐value threshold (0.05) and feature importance threshold determined by shapely values. The final dataset (N = 104; 30 ADD, 74 DLB) was established, where 20% of the data (N =21; 6 ADD, 15 DLB) were randomly selected and excluded as test data for model verification. Result: The final machine learning model trained and tested through the dataset yielded test accuracy at 85.7% with ADD sensitivity at 83.3%, DLB sensitivity at 86.7%. The 5 fold cross validation accuracy was at 82.1%. Conclusion: The model developed in the present study yielded a promising classification performance, through EEG data which is cheap and harmless to record. Such QEEG‐based classification models may sufficiently replace the PET in future, resolving current disadvantages. [ABSTRACT FROM AUTHOR]

Background: Subjective cognitive decline(SCD) has been considered as at risk state to progress to mild cognitive impairment(MCI) or Alzheimer's disease(AD) dementia. The purpose of this study is to set up cohort and to identify risk factors for the progression to MCI or dementia from amnestic SCD Method: We enrolled subjects aged 60 years or older complaining of persistent cognitive decline. To include higher risk SCD patients with rapidly progression, subjects needs to have 7% to 50% of the verbal memory and over 7% of the rest subtests of comprehensive neuropsychological battery. We named this state of SCD as an amnestic SCD. Brain magnetic resonance imaging (MRI) volumetry, visual and standardized uptake value ratio (SUVR) analysis of 18F‐Florbetaben brain amyloid‐beta Positron Emission Tomography (PET) were. Blood amyloid oligomerization was measured by the Multimer Detection System‐Oligomeric Aβ (MDS‐OAβ) method. Quantitative EEG were also analyzed. Sleep time and physical activity were collected with wearable device (Fitbit alta). Gait speed, body mass index, and muscle strength were measured. Result: We analyzed 120 SCD subjects (male 53, female 67). Mean age and education were 70.9 and 11.2 years. 25 (20.8%) showed amyloid positive PET and 26(21.6%) had APOE ε4 allele. Positive amyloid PET SCD had higher frequency of APOE ε4 (48%) compared to amyloid negative SCD (14%). Positive amyloid PET SCD also had lower delayed verbal memory score and lower frontal inferior, anterior, medial and lateral temporal and lateral parietal regional brain volume ratio. Power spectral analysis of EEG showed increased relative theta /alpha ratio in the bilateral frontal regions. sLORETA showed increased theta spectrum in the left superior temporal, transverse temporal and fusiform area in amyloid positive SCD. MDS‐OAβ was positive in 55.1% which was not associated with SUVR value of amyloid PET. Conclusion: Amnestic SCD with amyloid PET positive showed higher APOE ε4 allele, lower regional brain volume in AD related areas, lower verbal memory score and higher relative theta/alpha ratio in the frontal area. Longitudinal follow‐up for the next 3 years will identify risk factors for the progression to MCI or dementia and clinical significance of blood MDS‐OAβ value. [ABSTRACT FROM AUTHOR]

Background: Early screening biomarker has been required to discriminate higher risk of progressive subgroup in subjective cognitive decline(SCD) at preclinical stage. Study purpose was to identify quantitative EEG patterns among different condition of amnestic SCD Method: From 6 dementia clinics in Korea, we enrolled participants aged over 60 years with complaining of persistent cognitive decline, performance range of 7% to 50% of the verbal memory tests and over 7% of the rest subtests of comprehensive neuropsychological battery. Both 18F‐Florbetaben brain amyloid‐beta PET and genetic test for APOE ε4 allele were conducted. EEG was measured at 19 channels of international 10‐20 system under resting state, and spectrum power and power ratio were calculated. Source cortical activity was mathematically estimated by standardized low resolution brain electromagnetic tomography(sLORETA) Result: 120 SCD subjects (male 53, female 67) were included. Amyloid PET(+) was 25 (20.8%) and APOE ε4 allele (+) was 26(21.6%) independently. Amyloid PET (+) SCD had higher frequency of APOE e4 (48%) compared to amyloid PET (‐) SCD (14%). We selected 67 amyloid PET (‐) SCD older than 65 years to escape age differences between 2 groups. Amyloid PET (+) SCD showed significant increase of relative theta power at left frontotemporal(Fp1, F7, T5), both occipital(O1,2) and right parietal(P4) channels. Similarly, significant enhancement of theta was observed at left superior and transverse temporal, fusiform and cuneus at sLORETA in amyloid PET (+) SCD. Among amyloid PET (+) SCD, APOE ε4 (+) subgroup showed significant increase of theta/beta ratio at both superior frontal and anterior cingulate, right parietal and occipital area Conclusion: Amnestic SCD with amyloid (+) showed relative enhancement of slow wave in resting EEG, which have been similarly reported about amnestic MCI or AD dementia. QEEG could be tentative biomarker to screen AD or to predict progressiveness of SCD at preclinical stage. Longitudinal study and development of single quantitative index of QEEG to evaluate individual subject will be needed to confirm its clinical significance. [ABSTRACT FROM AUTHOR]

Background: Nowadays virtual reality (VR) and mixed reality (MR) is used as a psychological and occupational therapeutic option to the patients of depression, phobia, post traumatic stress disorder and stroke patients. VR and MR have a number of advantages because it is entertaining, thus motivating the patient. Therefore VR and MR can be used for cognitive therapy to the elderly cognitive declined people because it can give attention, motivation and stimulation to them. Our previous pilot study showed that cognitive training program using VR and MR improved cognitive function, depressive mood and electrophysiologic brain activity on Mild cognitive impairment (MCI) patients. This is the extensive study to show the usefulness of VR and MR using cognitive training on MCI patients, which added more session, program and enrolled more patient in comparison with previous pilot study. Method: This study was an 8‐week, single‐blind study of 20 patients with MCI. The cognitive training program included art therapy, aerobic exercises, memory training, including face‐name association, object recall training, and spaced retrieval; visuo‐motor organization using software; similarity and ruled based categorization; and behavior modification and sequencing (e.g., making change, paying bills) using VR and MR and computerized program. Neuropsychological function was assessed using K‐MMSE (Korean Mini‐Mental State Examination), Clinical dementia rating(CDR), Alzheimer's Disease Assessment Scale‐Cognition(ADAS‐cog), Korean version of short form Geriatric Depression Scale(SGDS‐K) and Neuropsychiatric Inventory(NPI). Quantitative electroencephalogram (EEG) was assessed on cognitive training days before treatment and after each treatment phase. Then EEG was analyzed by using "iSyncbrain" which is cloud‐based, AI‐driven EEG analysis platform. Result: After two months (8 sessions) of treatment, cognitive function, neuropsychiatric symptoms and depression, which were assessed by using MMSE, NPI and SGDS respectively, showed significant improvements (p<0.05). Also there were significant increase of the EEG alpha/theta ratio on both dorsolateral prefrontal cortex and right parietal cortex, which may imply improvement of frontal and visuospatial function. However, there were no statistically significant differences except Source ROI Power and connectivity. Conclusion: Cognitive training program using VR and MR showed improvement of cognitive function, depressive mood and electrophysiologic brain activity. [ABSTRACT FROM AUTHOR]

Abstract: Objective: This systematic review aimed to critically appraise published clinical trials designed to assess the effect of Tai Chi on psychosocial well-being. Data Sources: Databases searched included MEDLINE, CINAHL, EMBASE, HEALT, PsycINFO, CISCOM, the Cochrane Central Register of Controlled Trials of the Cochrane Library, and dissertations and conference proceedings from inception to August 2008. Review Methods: Methodological quality was assessed using a modified Jadad scale. A total of 15 studies met the inclusion criteria (i.e. English publications of randomized controlled trials with Tai Chi as an intervention and psychological well-being as an outcome measure), of which eight were high quality trials. The psychosocial outcomes measured included anxiety (eight studies), depression (eight studies), mood (four studies), stress (two studies), general mental health three studies), anger, positive and negative effect, self-esteem, life satisfaction, social interaction and self-rated health (one study each). Results: Tai Chi intervention was found to have a significant effect in 13 studies, especially in the management of depression and anxiety. Although the results seemed to suggest Tai Chi is effective, they should be interpreted cautiously as the quality of the trials varied substantially. Furthermore, significant findings were shown in only six high quality studies. Moreover, significant between group differences after Tai Chi intervention was demonstrated in only one high quality study (the other three significant results were observed in non-high quality studies). Two high quality studies in fact found no significant Tai Chi effects. Conclusion: It is still premature to make any conclusive remarks on the effect of Tai Chi on psychosocial well-being. [Copyright &y& Elsevier]

Background: Subjective cognitive decline (SCD) has been considered as at risk state to progress to mild cognitive impairment(MCI) or Alzheimer's disease(AD). Apolipoprotein E e4 allele is a well‐known genetic risk for rapid deterioration of cognitive and dementia development. We investigated the effect of APOE ε4 on the brain amyloid pathology and other clinical features in the elderly with SCD. Method: One hundred and nineteen elderly subjects, 60 or older, complaining of persistent cognitive decline, with 7% to 50% of the memory test score and over 7% of the other tests score were included through neuropsychological tests(Seoul neuropsychological battery, SNSB). Brain magnetic resonance imaging (MRI) volumetry, visual and standardized uptake value ratio (SUVR) analysis of 18F‐Florbetaben brain amyloid‐beta Positron Emission Tomography (PET) were performed. Demographics, physical data, social and physiological functions were also obtained. Result: Of the 119 SCD subjects, 26 (21.8%) had APOE ε4 and 26 (21.8%) had amyloid PET positive. Age was not different between two groups (70.9 V.S. 70.9, p=0.880). Test Z‐score was only marginally lower in Digit span test‐forward (p=0.053) in SCD with APOE ε4. Amyloid PET SUVR value was higher in SCD with APOE ε4 (1.2±0.2 V.S. 1.5±0.3, p=0.001). Regional brain volume including hippocampus and entorhinal cortex were not different in SPM based volumetric ananlysis. Korean_Everyday Cognition subscores, depression scores measured with Patient Health Questionnaire 9, sleep quality measured with Pittsburgh Sleep Quality Index total score, Body Mass Index, and gait speed were not different between two groups. Conclusion: With the baseline data, APOE ε4 was associated with decreased attention and higher brain amyloid accumulation in amnestic SCD. Longitudinal data will be needed to clarify whether APOE ε4 is associated with progression of cognitive dysfunction in SCD. This study was supported by a grant from the Ministry of Health and Welfare, HI18C0530. [ABSTRACT FROM AUTHOR]

Background: Amnestic mild cognitive impairment (aMCI) has been considered as prodromal stage of Alzheimer's disease. Memory impairment can be classified into encoding failure (EF) and retrieval failure (RF). Objects: The aim of this study is to classify patients with single‐dominant aMCI according to the type of memory impairment (EF and RF) and to examine the quantitative EEG and MRI brain volume with significant differences between the groups. Method: This study used the EEG data of patients who visited Chung‐Ang University Hospital Department of Neurology from January 2012 to May 2019 and were diagnosed with single‐dominant aMCI. A total of 165 collected EEG data were classified into 87 EF (73.76 ±8.26 years) and 78 RF (73.42 ± 7.88 years) based on the results of the Seoul Verbal Learning Test. EEG was done using the standard 10–20 system (21 electrodes) using a digital electroencephalograph (Comet AS40 amplifier EEG GRASS; Telefactor, USA). The stored EEG data were subjected to quantitative EEG analysis through noise preprocessing using iSyncBrain™ (iMediSync, Inc., Korea), and group analysis was performed according to two types. Subjects were also performed 3D T1 MRI and voxel based morphometry using Computational Anatomy Toolbox for SPM 12. Result: The beta2 band power(frequency range : 15∼20Hz) in the relative power spectrum showed statistically significant difference between the two groups in the Cz and parietal electrodes(P3, Pz, P4) (p<0.05). Also, broad area around occipito‐parietal region showed significant increase of TB2R(Theta/Beta2 power ratio) both sensor and source level analysis. Increasing pattern of TB2R was also observed in both parahippocampal area. There was no significant difference of regional brain volumes between aMCI with EF and RF. Conclusion: EF and RF of aMCI showed no structural differences, but functional differences, suggesting the possibility of different stages or pathologies of cognitive impairments. [ABSTRACT FROM AUTHOR]

Background: aMCI is the most progressive prodromal stage of dementia. For early screening of dementia, it is very important to discriminate aMCI from age‐related physiologic cognitive decline. In addition, community‐based screening tool requires appropriate, less expensive technology. We developed QEEG biomarker algorithm using mixed machine‐learning methods, and validated it Method: Nineteen channels of EEGs were measured from 382 community‐based normal subjects and 182 aMCI who showed standardized delayed verbal memory score under 16 percentiles. Various linear and non‐linear quantitative features were generated from single EEG such as frequency band power, power ratio, peak alpha frequency, cortical source current density, functional connectivity based on imaginary coherence and non‐linear complexity. Independent t‐test and least absolute shrinkage and selection operator (LASSO) were used to select the best combination of features. Probability index score for aMCI was developed by combination with logistic regression and penalized linear regression methods with 73 aMCI and 75 community‐based normal subjects. Probability index score could be ranged from 0 to 100, and higher score was referred to as more severe memory impairment due to underlying pathologic progression. Five fold validation was done to fit the best model for largest area under curve (AUC). Result: The probability index score for aMCI was validated with blinded independent EEG dataset from 3 hospitals in Korea. The test set consisted of 307 community‐based normal and 109 aMCI subjects. When index score of 60 was applied at cut‐off value to discriminated aMCI from age‐related normal memory declined, the sensitivity was 93% and specificity was 90%. AUC was 0.93 (95% CI: 0.89–0.96) Conclusion: QEEG findings of aMCI were different from those of age‐related normal memory declined. Above aMCI probability index score showed promising result. Individual EEGs are much various, and just single specific EEG feature or single machine‐learning method couldn't be enough to show the significant discriminating power. Careful selection of EEG features and curation of different machine‐learning and statistical models is recommended. This EEG biomarker should be tested for larger aMCI and normal subject populations, and continuously revised and upgraded. [ABSTRACT FROM AUTHOR]

Background: This is a preliminary study to research the efficacy of a multicomponent and structured high intensity interval physical exercise intervention (PEI) on cognitive function in the not‐demented Korean elderly. Method: We enrolled 6 not‐demented participants over the age of 70 from a community health center (77.0±4.7 years, 33.3% female). The PEI was conducted at the community health center for 4 weeks, 3 times a week, and 1 hour a day. It was a multicomponent structured physical exercise program consisting of muscle strength, flexibility, balance and aerobic exercises using elastic bands and ladder‐shaped floor plate, which was also a high intensity interval exercise program that allowed participants to maintain a 75 percent increase in their heart rate during exercise rather than the heart rate measured at rest. The outcome of this study was the effect of our PEI and was intended to be verified by conducting the physical fitness, cognitive function evaluation and electroencephalography, which were analyzed by paired t‐tests. Result: After the PEI, some items of participants' physical fitness and cognitive function evaluation were improved: 30‐seconds sit‐stand test (16.2±7.0 vs 24.8±5.5, p<0.05), 2‐minutes stationary march (98.3±27.2 vs 143.7±36.9, p<0.05), literacy independent cognitive assessment (LICA) memory domain score (89.6±21.6 vs 111.0±19.1, p<0.05), LICA executive domain score (33.3±5.3 vs 37.0±5.1, p<0.05) and LICA total score (214.6±30.6 vs 241.6±22.8, p<0.05). In addition, the relative theta and delta power on temporal brain region were decreased and the relative beta power on frontal brain region was increased (p<0.05) after the PEI. Conclusion: This study suggests that our high intensity interval PEI program could be helpful for the elderly with cognitive brain function as well as physical fitness, lower extremity muscular and cardiopulmonary endurance. [ABSTRACT FROM AUTHOR]