Tong Chen

Background: Mild cognitive impairment due to Alzheimer’s disease (MCI due to AD) is a crucial stage for the early identification of Alzheimer’s disease (AD), and timely detection at this stage may provide opportunities for earlier intervention and potentially delay disease progression. Methods: This study proposed a digital early-screening method based on a touchscreen maze hand-interaction kinetic paradigm, which integrates digital biomarkers from the visuospatial/executive and episodic memory domains to support the screening of MCI due to AD. A customized maze task was administered to 40 patients with clinically diagnosed MCI due to AD and 40 healthy controls (HC). Behavioral data were collected, and two categories of digital biomarkers were extracted: (1) visuospatial/executive digital biomarkers, such as task completion time ( VSETT ) and average movement speed ( VSES ); and (2) episodic memory digital biomarkers, such as episodic memory total time ( EMTT ) and number of correct choices ( EMCC ). Significant digital biomarkers were identified through between-group comparisons, and their combined classification performance was evaluated using binary logistic regression and receiver operating characteristic (ROC) analysis. Results: The integrated digital biomarker model showed promising apparent discriminative performance in the full cohort, with an AUC of 0.899 (95% CI: 0.831–0.967). To reduce potential optimism associated with biomarker selection, model development, and model evaluation within the same dataset, internal validation was performed using full-pipeline repeated stratified five-fold cross-validation with all 16 candidate digital biomarkers entered into the validation procedure and biomarker selection repeated within each training fold. The internally validated model retained good discriminative performance, with a mean cross-validated AUC of 0.842, an empirical 95% interval of 0.779–0.878, an accuracy of 0.783, a sensitivity of 0.772, and a specificity of 0.795. Conclusion: These findings suggest that the proposed touchscreen maze-based digital assessment method may provide a promising and objective approach to supporting the early screening of MCI due to AD.

Background: In response to the shortcomings of the current Alzheimer’s disease (AD) early populations assessment, which is based on neuropsychological scales with high subjectivity, low accuracy of repeated measurements, tedious process and dependence on physicians, it was found that digital biomarkers based on the writing process can effectively characterize the cognitive deficits of patients with mild cognitive impairment (MCI) due to AD.Methods: This study designed a digital writing assessment paradigm, extracted dynamic handwriting and image data during the paradigm assessment process, and analyzed digital biomarkers of the writing process to assess subjects’ cognitive functions. A total of 72 subjects, including 34 health controls (HC) and 38 MCI due to AD, were enrolled in this study.Results: Their combined screening efficacy of digital biomarkers based on the MCI writing process due to AD populations having an area under curve (AUC) of 0.918, and a confidence interval (CI) of 0.854–0.982, was higher than the Montreal Cognitive Assessment Scale (AUC = 0.859, CI = 0.772–0.947) and the Mini-mental State Examination Scale (AUC = 0.783, CI = 0.678–0.888).Conclusion: Therefore, digital biomarkers based on the writing process can characterize and quantify the cognitive function of MCI due to AD populations at a fine-grained level, which is expected to be a new method for intelligent screening and early warning of early AD populations in a community-based physician-free setting.

Background: Mild cognitive impairment (MCI) is common in Alzheimer’s disease (AD) and Parkinson’s disease (PD), but there are differences in pathogenesis and cognitive performance between Mild cognitive impairment due to Alzheimer’s disease (AD-MCI) and Parkinson’s disease with Mild cognitive impairment (PD-MCI) populations. Studies have shown that assessments based on the digital clock drawing test (dCDT) can effectively reflect cognitive deficits. Based on this, we proposed the following research hypothesis: there is a difference in cognitive functioning between AD-MCI and PD-MCI populations in the CDT, and the two populations can be effectively distinguished based on this feature.Methods: To test this hypothesis, we designed the dCDT to extract digital biomarkers that can characterize and quantify cognitive function differences between AD-MCI and PD-MCI populations. We enrolled a total of 40 AD-MCI patients, 40 PD-MCI patients, 41 PD with normal cognition (PD-NC) patients and 40 normal cognition (NC) controls.Results: Through a cross-sectional study, we revealed a difference in cognitive function between AD-MCI and PD-MCI populations in the dCDT, which distinguished AD-MCI from PD-MCI patients, the area under the roc curve (AUC) = 0.923, 95% confidence interval (CI) = 0.866–0.983. The AUC for effective differentiation between AD-MCI and PD-MCI patients with high education (≥12 years of education) was 0.968, CI = 0.927–1.000. By correlation analysis, we found that the overall plotting of task performance score (VFDB₁) correlated with the [visuospatial/executive] subtest score on the Montreal Cognitive Assessment (MoCA) scale (Spearman rank correlation coefficient [R] = 0.472, p < 0.001).Conclusion: The dCDT is a tool that can rapidly and accurately characterize and quantify differences in cognitive functioning in AD-MCI and PD-MCI populations.

Background: Depressive disorders are one of the most common mental disorders among young people. However, there is still a lack of objective means to identify and evaluate young people with depressive disorders quickly. Cognitive impairment is one of the core characteristics of depressive disorders, which is of great value in the identification and evaluation of young people with depressive disorders.Methods: This study proposes a new method for identifying and evaluating depressive disorders in young people based on cognitive neurocomputing. The method evaluates cognitive impairments such as reduced attention, executive dysfunction, and slowed information processing speed that may exist in the youth depressive disorder population through an independently designed digital evaluation paradigm. It also mines digital biomarkers that can effectively identify these cognitive impairments. A total of 50 young patients with depressive disorders and 47 healthy controls were included in this study to validate the method’s identification and evaluation capability.Results: The differences analysis results showed that the digital biomarkers of cognitive function on attention, executive function, and information processing speed extracted in this study were significantly different between young depressive disorder patients and healthy controls. Through stepwise regression analysis, four digital biomarkers of cognitive function were finally screened. The area under the curve for them to jointly distinguish patients with depressive disorders from healthy controls was 0.927.Conclusion: This new method rapidly characterizes and quantifies cognitive impairment in young people with depressive disorders. It provides a new way for organizations, such as schools, to quickly identify and evaluate the population of young people with depressive disorders based on human-computer interaction.

Mild cognitive impairment in Parkinson’s disease (PD-MCI) as an independent risk factor for dementia in Parkinson’s disease has prognostic value in predicting dementia in PD patients. It was found that the calculation of cognitive function decision-making could better evaluate the cognitive function of PD-MCI. Therefore, this study explored deficits in decision-making cognitive function in PD-MCI population, and mined novel digital biomarkers for recognizing early cognitive decline in PD-MCI through an independently designed maze decision-making digital assessment paradigm. This study included 30 healthy controls 37 PD with normal cognition (PD-NC) and 40 PD-MCI patients. Through difference comparison and stepwise regression analysis, two digital decision-making biomarkers, total decision time and performance average acceleration, were screened, and their joint area under curve for the ability to discriminate between PD-MCI and PD-NC was 0.909, and for the ability to discriminate between PD-MCI and NC was 0.942. In addition, it was found that maze digital decision-making biomarkers had greater early warning efficacy in men than in women. Unlike traditional methods, this study used digital dynamic assessment to reveal possible decision-making cognitive deficits in the PD-MCI populations, which provides new ideas for effective screening for PD-MCI.