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Background: Early diagnosis of mild cognitive impairment (MCI) is essential for timely treatment planning. With recent advances in the wearable technology, interest has increasingly shifted toward computer-aided self-diagnosis of MCI using wearable electroencephalography (EEG) devices in daily life. However, no study so far has investigated the optimal electrode configurations for the efficient diagnosis of MCI while considering the design factors of wearable EEG devices. In this study, we aimed to determine the optimal channel configurations of wearable EEG devices for the computer-aided diagnosis of MCI. Method: We employed an EEG dataset collected from 21 patients with MCI and 21 healthy control subjects. After evaluating the classification accuracies for all possible electrode configurations for the two-, four-, six-, and eight-electrode conditions using a support vector machine, the optimal electrode configurations that provide the highest diagnostic accuracy were suggested for each electrode condition. Results: The highest classification accuracies of 74.04% ± 4.82, 82.43% ± 6.14, 86.28% ± 2.81, and 86.85% ± 4.97 were achieved for the optimal two-, four-, six-, and eight-electrode configurations, respectively, which demonstrated the possibility of precise machine-learning-based diagnosis of MCI with a limited number of EEG electrodes. Additionally, further simulations with the EEG dataset revealed that the optimal electrode configurations had significantly higher classification accuracies than commercial EEG devices with the same number of electrodes, which suggested the importance of electrode configuration optimization for wearable EEG devices based on clinical EEG datasets. Conclusions: This study highlighted that the optimization of the electrode configuration, assuming the wearable EEG devices can potentially be utilized for daily life monitoring of MCI, is necessary to enhance the performance and portability. [ABSTRACT FROM AUTHOR]

Background: Recently, several studies suggested potential involvements of α-synuclein in Alzheimer's disease (AD) pathophysiology. Higher concentrations of α-synuclein were reported in cerebrospinal fluid (CSF) of AD patients with a positive correlation towards CSF tau, indicating its possible role in AD. We analyzed the CSF biomarkers to verify whether α-synuclein could be an additional supported biomarker in AD diagnosis. Methods: In this cross-sectional study, CSF samples of 71 early-onset AD, 34 late-onset AD, 11 mild cognitive impairment, 17 subjective cognitive decline, 45 Parkinson's disease, and 32 healthy control (HC) were collected. CSF amyloid-β1-42 (A), total tau (N), and phosphorylated tau181 (T) were measured by commercial ELISA kits, and in-house ELISA kit was developed to quantify α-synuclein. The cognitive assessments and amyloid-PET imaging were also performed. Results: CSF α-synuclein manifested a tendency to increase in AD and to decreased in Parkinson's disease compared to HC. The equilibrium states of total tau and α-synuclein concentrations were changed significantly in AD, and the ratio of total tau/α-synuclein (N/αS) was dramatically increased in AD than HC. Remarkably, N/αS revealed a strong positive correlation with tau phosphorylation rate. Also, the combination of N/αS with amyloid-β1-42/phosphorylated tau181 ratio had the best diagnosis performance (AUC = 0.956, sensitivity = 96%, specificity = 87%). In concordance analysis, N/αS showed the higher diagnostic agreement with amyloid-β1-42 and amyloid-PET. Analysis of biomarker profiling with N/αS had distinctive characteristics and clustering of each group. Especially, among the group of suspected non-Alzheimer's disease pathophysiology, all A−T+N+ patients with N/αS+ were reintegrated into AD. Conclusions: The high correlation of α-synuclein with tau and the elevated N/αS in AD supported the involvement of α-synuclein in AD pathophysiology. Importantly, N/αS improved the diagnostic performance, confirming the needs of incorporating α-synuclein as a biomarker for neurodegenerative disorders. The incorporation of a biomarker group [N/αS] could contribute to provide better understanding and diagnosis of neurodegenerative disorders. [ABSTRACT FROM AUTHOR]

Background: Neuropsychological tests (NPTs) are important tools for informing diagnoses of cognitive impairment (CI). However, interpreting NPTs requires specialists and is thus time-consuming. To streamline the application of NPTs in clinical settings, we developed and evaluated the accuracy of a machine learning algorithm using multi-center NPT data.Methods: Multi-center data were obtained from 14,926 formal neuropsychological assessments (Seoul Neuropsychological Screening Battery), which were classified into normal cognition (NC), mild cognitive impairment (MCI) and Alzheimer's disease dementia (ADD). We trained a machine learning model with artificial neural network algorithm using TensorFlow (https://www.tensorflow.org) to distinguish cognitive state with the 46-variable data and measured prediction accuracies from 10 randomly selected datasets. The features of the NPT were listed in order of their contribution to the outcome using Recursive Feature Elimination.Results: The ten times mean accuracies of identifying CI (MCI and ADD) achieved by 96.66 ± 0.52% of the balanced dataset and 97.23 ± 0.32% of the clinic-based dataset, and the accuracies for predicting cognitive states (NC, MCI or ADD) were 95.49 ± 0.53 and 96.34 ± 1.03%. The sensitivity to the detection CI and MCI in the balanced dataset were 96.0 and 96.0%, and the specificity were 96.8 and 97.4%, respectively. The 'time orientation' and '3-word recall' score of MMSE were highly ranked features in predicting CI and cognitive state. The twelve features reduced from 46 variable of NPTs with age and education had contributed to more than 90% accuracy in predicting cognitive impairment.Conclusions: The machine learning algorithm for NPTs has suggested potential use as a reference in differentiating cognitive impairment in the clinical setting. [ABSTRACT FROM AUTHOR]

Background: Imaging agents capable of quantifying the brain's tau aggregates will allow a more precise staging of Alzheimer's disease (AD). The aim of the present study was to examine the in vitro properties as well as the in vivo kinetics, using gold standard methods, of the novel positron emission tomography (PET) tau imaging agent [18F]MK-6240. Methods: In vitro properties of [18F]MK-6240 were estimated with autoradiography in postmortem brain tissues of 14 subjects (seven AD patients and seven age-matched controls). In vivo quantification of [18F]MK-6240 binding was performed in 16 subjects (four AD patients, three mild cognitive impairment patients, six healthy elderly individuals, and three healthy young individuals) who underwent 180-min dynamic scans; six subjects had arterial sampling for metabolite correction. Simplified approaches for [18F]MK-6240 quantification were validated using full kinetic modeling with metabolite-corrected arterial input function. All participants also underwent amyloid-PET and structural magnetic resonance imaging. Results: In vitro [18F]MK-6240 uptake was higher in AD patients than in age-matched controls in brain regions expected to contain tangles such as the hippocampus, whereas no difference was found in the cerebellar gray matter. In vivo, [18F]MK-6240 displayed favorable kinetics with rapid brain delivery and washout. The cerebellar gray matter had low binding across individuals, showing potential for use as a reference region. A reversible two-tissue compartment model well described the time–activity curves across individuals and brain regions. Distribution volume ratios using the plasma input and standardized uptake value ratios (SUVRs) calculated after the binding approached equilibrium (90 min) were correlated and higher in mild cognitive impairment or AD dementia patients than in controls. Reliability analysis revealed robust SUVRs calculated from 90 to 110 min, while earlier time points provided inaccurate estimates. Conclusions: This evaluation shows an [18F]MK-6240 distribution in concordance with postmortem studies and that simplified quantitative approaches such as the SUVR offer valid estimates of neurofibrillary tangle load 90 min post injection. [18F]MK-6240 is a promising tau tracer with the potential to be applied in the disease diagnosis and assessment of therapeutic interventions. [ABSTRACT FROM AUTHOR]

Introduction: Oligomeric amyloid-ß is a major toxic species associated with Alzheimer's disease pathogenesis. Methods used to measure oligomeric amyloid-β in the blood have increased in number in recent years. The Multimer Detection System-Oligomeric Amyloid-β (MDS-OAβ) is a specific method to measure oligomerization tendencies in the blood. The objective of this study was to determine the association between amyloid-ß oligomerization in the plasma and structural changes of the brain. Methods: We studied 162 subjects composed of 92 community-based normal healthy subjects, 17 with subjective cognitive decline, 14 with mild cognitive impairment and 39 with Alzheimer's disease dementia. All subjects underwent MDS-OAβ and three-dimensional T1 magnetic resonance imaging. To determine the structural changes of the brain that are statistically correlated with MDS-OAβ level, we used voxel-based morphometry with corrections for age and total intracranial volume covariates. Results: We found brain volume reduction in the bilateral temporal, amygdala, parahippocampal and lower parietal lobe and left cingulate and precuneus regions (family-wise error, p < 0.05). Reduction was also found in white matter in proximity to the left temporal and bilateral lower parietal lobes and posterior corpus callosum (family-wise error, p < 0.05). Brain volume increment was not observed in any regions within grey or white matter. Discussion: Findings suggest that substantial correlation exists between amyloid ß oligomerization in the blood and brain volume reduction in the form of Alzheimer's disease despite of uncertainty in the casual relationship. [ABSTRACT FROM AUTHOR]

Background: Visinin-like protein-1 (VILIP-1) and chitinase-3-like protein 1 (CHI3L1 or YKL-40) in cerebrospinal fluid (CSF) are newly discovered markers indicating neuronal damage and microglial activation, respectively. Phosphorylated tau (p-tau) reflects the neuropathology of Alzheimer's disease (AD) and is useful as diagnostic markers for AD. However, it is unknown whether these biomarkers have similar or complementary information in AD. Methods: We stratified 121 participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database into cognitively normal (CN), stable mild cognitive impairment (sMCI), progressive MCI (pMCI), and dementia due to AD. Analysis of covariance (ANOVA) and chi-square analyses, Spearman correlation, and logistic regression models were performed to test the demographic, associations between biomarkers, and diagnostic accuracies, respectively. Linear mixed-effects models were used to evaluate the effects of CSF amyloid-β (Aβ) on above biomarkers within diagnostic groups, the combination of diagnostic group and Aβ status as predictor, and CSF biomarkers as predictors of AD features, including cognition measured by Mini–Mental State Examination (MMSE) and brain structure and white matter hyperintensity (WMH) measured by magnetic resonance imaging (MRI). Results: P-tau, VILIP-1, and YKL-40 were all predictors of AD diagnosis, but combinations of biomarkers did not improve the diagnostic accuracy (AUC 0.924 for p-tau, VILIP-1, and YKL-40) compared to p-tau (AUC 0.922). P-tau and VILIP-1 were highly correlated (r = 0.639, p < 0.001) and strongly associated with Aβ pathology across clinical stages of AD, while YKL-40 was correlated with Aβ pathology in CN and AD groups. VILIP-1 was associated with acceleration of cognitive decline, hippocampal atrophy, and expansion of ventricles in longitudinal analyses. YKL-40 was associated with hippocampal atrophy at baseline and follow-up, while p-tau was only associated with worsening WMH at baseline. Conclusions: CSF levels of p-tau, VILIP-1, and YKL-40 may have utility for discriminating between cognitively normal subjects and patients with AD. Increased levels of both VILIP-1 and YKL-40 may be associated with disease degeneration. These CSF biomarkers should be considered for future assessment in the characterization of the natural history of AD. [ABSTRACT FROM AUTHOR]