With the growing prevalence of cognitive impairment, early detection has become increasingly critical. Prior studies have examined the association between neuropsychiatric symptoms (NPS) and cognitive impairment, identifying potential predictive relationships. However, they hardly evaluated the heterogeneous relationships between serial patterns of NPS and evolving cognition status of the patients. To address this limitation, we investigate the statistical causal relationship between NPS and cognitive impairment, as well as the dynamic changes in their predictive effects over time, with a specific focus on sex differences. Our approach accounts for the fluctuating nature of NPS and varying follow-up durations across participants by implementing a bootstrap strategy that repeatedly samples a fixed number of visits per participant in a temporal order. Then, we apply causal discovery techniques and counterfactual framework-based causal inference methods to estimate the independent effects of NPS over time. Our findings highlight apathy as a key predictive symptom of cognitive impairment. Moreover, its predictive effect peaks earlier in females than in males, indicating that early-stage tracking is particularly informative in female participants. This suggests sex-specific monitoring strategies may improve early detection and intervention of cognitive impairment.

Mild Cognitive Impairment (MCI) is a transitional stage between normal cognitive aging and dementia. Some individuals with MCI revert to normal, while others progress to dementia. There are limited studies using explainable artificial intelligence on longitudinal data, particularly including genotypes, biomarkers and chronic diseases, to explore these differences. This study introduces a novel approach to understanding MCI progression using explainable graph neural networks. Utilizing longitudinal temporal data, we constructed a comprehensive graph representation of each individual in the study cohort. Our temporal graph convolutional network achieved 72.4% accuracy in predicting MCI transitions, while our causal explanation method outperformed existing explanation techniques in stability, accuracy, and faithfulness. We identified a causal subgraph with informative variables including hypertension, arrhythmia, congestive heart failure, coronary artery disease, stroke, lipid-related issues, and sex.

Dementia is among the leading causes of cognitive and functional loss and disability in older adults. Past studies suggested sex differences in health conditions and progression of cognitive decline. Existing studies on the temporal trajectory of health conditions for patient characterization after dementia diagnosis are scarce and ambiguous. Thus, there's limited and unclear research on how health conditions change over time after a dementia diagnosis. To this end, we aim to analyze the shift in medical conditions and examine sex-specific changes in patterns of chronic health conditions after dementia diagnosis. We centered our analysis on a 15-year window around the point of dementia diagnosis, encompassing the 5 years leading up to the diagnosis and the 10 years following it. We introduce (i) MedMet, a network metric to quantify the contribution of each medical condition, and (ii) growth and decay function for temporal trajectory analysis of medical conditions. Our experiments demonstrate that certain health conditions are more prevalent among females than males. Thus, our findings underscore the pressing need to examine differences between men and women, which could be important for healthcare utilization after a dementia diagnosis.

This paper presents a machine learning-based prediction for dementia, leveraging transfer learning to reuse the knowledge learned from prediction of mild cognitive impairment, a precursor of dementia. We also examine the impacts of temporal aspects of longitudinal data and sex differences. The methodology encompasses key components such as setting the duration window, comparing different modeling strategies, conducting comprehensive evaluations, and examining the sex-specific impacts of simulated scenarios. The findings reveal that cognitive deficits in females, once detected at the mild cognitive impairment stage, tend to deteriorate over time, while males exhibit more diverse decline across various characteristics without highlighting specific ones. However, the underlying reasons for these sex differences remain unknown and warrant further investigation.

more than 44 million people have been diagnosed with dementia worldwide, and this number is estimated to triple by next three decades. Given this increasing trend of older adults with cognitive impairment (CI; dementia and mild cognitive impairment) and its significant underdiagnosis, early identification of CI and understanding its progression is a critical step towards a better quality of life for the aging population. Early alert of individual health changes could facilitate better ways for clinicians to diagnose CI in its early stages and come up with more effective treatment plans. However, there is a lack of approaches to characterize patient health conditions accounting for temporal information in an unsupervised manner. Limited CI cases and its costly ascertainment in clinical settings also make unsupervised learning more promising in CI research. In this paper, a streaming clustering model was used to determine distinct patterns of older adults' health changes from their clinical visits in Mayo Clinic Study of Aging. The streaming clustering was also examined to study its ability to generate early alerts for potential incidents of CI. Our analysis demonstrated that temporal characteristics incorporated in a streaming clustering model has a promising potential to increase power in predicting CI.

About 44.4 million people have been diagnosed with dementia worldwide, and it is estimated that this number will be almost tripled by 2050. Predicting mild cognitive impairment (MCI), an intermediate state between normal cognition and dementia and an important risk factor for the development of dementia is crucial in aging populations. MCI is formally determined by health professionals through a comprehensive cognitive evaluation, together with a clinical examination, medical history and often the input of an informant (an individual that know the patient very well). However, this is not routinely performed in primary care visits, and could result in a significant delay in diagnosis. In this study, we used deep learning and machine learning techniques to predict the progression from cognitively unimpaired to MCI and also to analyze the potential for patient clustering using routinely-collected electronic health records (EHRs). Our analysis of EHRs indicates that temporal characteristics of patient data incorporated in a deep learning model provides increased power in predicting MCI.

This paper presents a machine-learning-based joint model of brain age and cognitive performance, and demonstrates its superior performance relative to isolated models. Previous studies have chosen to study those two measures of brain health separately for two reasons: 1) although cognition can be measured regardless of an individual's health, brain-age ground-truth can be defined only for healthy individuals; and 2) while brain-age models are developed using neuroimaging data alone, modeling of cognitive performance additionally requires measures of cognitive reserve and biomarkers of cognitive disorders. However, those two measures are biologically related to each other, because they both depend on brain structure. Hence, we developed a joint model by 1) explicitly defining the commonalities and differences between them in a graph, and 2) converting that graph into a multitask-learning model to facilitate learning from population-level data. Our model took as inputs structural neuroimaging data and information related to cognitive reserve and disorders, and predicted brain age and cognitive performance in terms of a Mini-Mental State Examination (MMSE) score. We implemented linear and nonlinear joint models and compared them against isolated models. Our results indicate that joint modeling substantially improves the accuracy of the modeling of individual measures, relative to isolated models.