Short-Term Memory Binding Distinguishing Amnestic Mild Cognitive Impairment from Healthy Aging: A Machine Learning Study

Applications of Artificial Intelligence in the Neuropsychological Assessment of Dementia: A Systematic Review

In the context of advancing healthcare, the diagnosis and treatment of cognitive disorders, particularly Mild Cognitive Impairment (MCI) and Alzheimer's Disease (AD), pose significant challenges. This review explores Artificial Intelligence (AI) and Machine Learning (ML) in neuropsychological assessment for the early detection and personalized treatment of MCI and AD. The review includes 37 articles that demonstrate that AI could be an useful instrument for optimizing diagnostic procedures, predicting cognitive decline, and outperforming traditional tests. Three main categories of applications are identified: (1) combining neuropsychological assessment with clinical data, (2) optimizing existing test batteries using ML techniques, and (3) employing virtual reality and games to overcome the limitations of traditional tests. Despite advancements, the review highlights a gap in developing tools that simplify the clinician's workflow and underscores the need for explainable AI in healthcare decision making. Future studies should bridge the gap between technical performance measures and practical clinical utility to yield accurate results and facilitate clinicians' roles. The successful integration of AI/ML in predicting dementia onset could reduce global healthcare costs and benefit aging societies.

XGBoost-SHAP-based interpretable diagnostic framework for alzheimer's disease

BACKGROUND

Due to the class imbalance issue faced when Alzheimer's disease (AD) develops from normal cognition (NC) to mild cognitive impairment (MCI), present clinical practice is met with challenges regarding the auxiliary diagnosis of AD using machine learning (ML). This leads to low diagnosis performance. We aimed to construct an interpretable framework, extreme gradient boosting-Shapley additive explanations (XGBoost-SHAP), to handle the imbalance among different AD progression statuses at the algorithmic level. We also sought to achieve multiclassification of NC, MCI, and AD.

METHODS

We obtained patient data from the Alzheimer's Disease Neuroimaging Initiative (ADNI) database, including clinical information, neuropsychological test results, neuroimaging-derived biomarkers, and APOE-ε4 gene statuses. First, three feature selection algorithms were applied, and they were then included in the XGBoost algorithm. Due to the imbalance among the three classes, we changed the sample weight distribution to achieve multiclassification of NC, MCI, and AD. Then, the SHAP method was linked to XGBoost to form an interpretable framework. This framework utilized attribution ideas that quantified the impacts of model predictions into numerical values and analysed them based on their directions and sizes. Subsequently, the top 10 features (optimal subset) were used to simplify the clinical decision-making process, and their performance was compared with that of a random forest (RF), Bagging, AdaBoost, and a naive Bayes (NB) classifier. Finally, the National Alzheimer's Coordinating Center (NACC) dataset was employed to assess the impact path consistency of the features within the optimal subset.

RESULTS

Compared to the RF, Bagging, AdaBoost, NB and XGBoost (unweighted), the interpretable framework had higher classification performance with accuracy improvements of 0.74%, 0.74%, 1.46%, 13.18%, and 0.83%, respectively. The framework achieved high sensitivity (81.21%/74.85%), specificity (92.18%/89.86%), accuracy (87.57%/80.52%), area under the receiver operating characteristic curve (AUC) (0.91/0.88), positive clinical utility index (0.71/0.56), and negative clinical utility index (0.75/0.68) on the ADNI and NACC datasets, respectively. In the ADNI dataset, the top 10 features were found to have varying associations with the risk of AD onset based on their SHAP values. Specifically, the higher SHAP values of CDRSB, ADAS13, ADAS11, ventricle volume, ADASQ4, and FAQ were associated with higher risks of AD onset. Conversely, the higher SHAP values of LDELTOTAL, mPACCdigit, RAVLT_immediate, and MMSE were associated with lower risks of AD onset. Similar results were found for the NACC dataset.

CONCLUSIONS

The proposed interpretable framework contributes to achieving excellent performance in imbalanced AD multiclassification tasks and provides scientific guidance (optimal subset) for clinical decision-making, thereby facilitating disease management and offering new research ideas for optimizing AD prevention and treatment programs.

Sex Differences in Memory: Do Female Reproductive Factors Explain the Differences?

Background

The sex differences in memory impairment were inconclusive, and the effect of female reproductive factors (age at menarche, age at menopause, and reproductive period) on the differences was not clear. We aimed to examine the sex differences in objective and subjective memory impairment in postmenopausal women and age- and education-matched men and explore whether the differences were differed by female reproductive factors.

Methods

Data were obtained from the China Health and Retirement Longitudinal Study. Using the case-control matching method, 3,218 paired postmenopausal women and men matched for age and education were selected. Memory was assessed using the three-word recall task and a self-rated question. Poisson regression models with a robust error variance were used.

Results

The relative risk was 1.22 (95% confidence interval 1.08-1.38) for objective memory impairment in women compared with men (23.87% vs. 27.36%), and 1.51 (1.36-1.67) for subjective memory impairment (39.34% vs. 28.25%) after adjusting the confounders. The higher risk of objective memory impairment in women was different among groups of age at menarche in a linear pattern, with younger age at menarche associated with higher risks of objective memory impairment (p < 0.001 for trend). It was also different among groups of menopausal age and reproductive period in an approximate U-shaped pattern, with a similar risk of objective memory with men in women menopause at 52-53 years and having a reproductive period of 31-33 years and higher risks in women with earlier or later menopause (RRs raging form 1.17 to1.41) and a shorter or longer period of reproduction (RR, 1.23-1.29). The higher risks of subjective memory impairment in women were not different among different groups of reproductive factors.

Conclusions

Postmenopausal women were at an increased risk of objective and subjective memory impairment than men. The higher risks in objective memory, but not subjective memory, were varied by age at menarche, age at menopause, and reproductive periods, which may help understand the underlying mechanisms of sex differences in cognitive ageing and guide precise intervention to preventing dementia among older women and men.