Neuroimaging and analytical methods for studying the pathways from mild cognitive impairment to Alzheimer's disease: protocol for a rapid systematic review

Prediction of Alzheimer's disease progression within 6 years using speech: A novel approach leveraging language models

INTRODUCTION

Identification of individuals with mild cognitive impairment (MCI) who are at risk of developing Alzheimer's disease (AD) is crucial for early intervention and selection of clinical trials.

METHODS

We applied natural language processing techniques along with machine learning methods to develop a method for automated prediction of progression to AD within 6 years using speech. The study design was evaluated on the neuropsychological test interviews of n = 166 participants from the Framingham Heart Study, comprising 90 progressive MCI and 76 stable MCI cases.

RESULTS

Our best models, which used features generated from speech data, as well as age, sex, and education level, achieved an accuracy of 78.5% and a sensitivity of 81.1% to predict MCI-to-AD progression within 6 years.

DISCUSSION

The proposed method offers a fully automated procedure, providing an opportunity to develop an inexpensive, broadly accessible, and easy-to-administer screening tool for MCI-to-AD progression prediction, facilitating development of remote assessment.

HIGHLIGHTS

Voice recordings from neuropsychological exams coupled with basic demographics can lead to strong predictive models of progression to dementia from mild cognitive impairment. The study leveraged AI methods for speech recognition and processed the resulting text using language models. The developed AI-powered pipeline can lead to fully automated assessment that could enable remote and cost-effective screening and prognosis for Alzehimer's disease.

Evaluating frontoparietal network topography for diagnostic markers of Alzheimer's disease

Numerous prospective biomarkers are being studied for their ability to diagnose various stages of Alzheimer's disease (AD). High-density electroencephalogram (EEG) methods show promise as an accurate, economical, non-invasive approach to measuring the electrical potentials of brains associated with AD. Event-related potentials (ERPs) may serve as clinically useful biomarkers of AD. Through analysis of secondary data, the present study examined the performance and distribution of N4/P6 ERPs across the frontoparietal network (FPN) using EEG topographic mapping. ERP measures and memory as a function of reaction time (RT) were compared between a group of (n = 63) mild untreated AD patients and a control group of (n = 73) healthy age-matched adults. Based on the literature presented, it was expected that healthy controls would outperform patients in peak amplitude and mean component latency across three parameters of memory when measured at optimal N4 (frontal) and P6 (parietal) locations. It was also predicted that the control group would exhibit neural cohesion through FPN integration during cross-modal tasks, thus demonstrating healthy cognitive functioning consistent with older healthy adults. By targeting select frontal and parietal EEG reference channels based on N4/P6 component time windows and positivity, our findings demonstrated statistically significant group variations between controls and patients in N4/P6 peak amplitudes and latencies during cross-modal testing. Our results also support that the N4 ERP might be stronger than its P6 counterpart as a possible candidate biomarker. We conclude through topographic mapping that FPN integration occurs in healthy controls but is absent in AD patients during cross-modal memory tasks.

Online COVID-19 diagnosis prediction using complete blood count: an innovative tool for public health

BACKGROUND

COVID-19, caused by SARS-CoV-2, presents distinct diagnostic challenges due to its wide range of clinical manifestations and the overlapping symptoms with other common respiratory diseases. This study focuses on addressing these difficulties by employing machine learning (ML) methodologies, particularly the XGBoost algorithm, to utilize Complete Blood Count (CBC) parameters for predictive analysis.

METHODS

We performed a retrospective study involving 2114 COVID-19 patients treated between December 2022 and January 2023 at our healthcare facility. These patients were classified into fever (1057 patients) and pneumonia groups (1057 patients), based on their clinical symptoms. The CBC data were utilized to create predictive models, with model performance evaluated through metrics like Area Under the Receiver Operating Characteristics Curve (AUC), accuracy, sensitivity, specificity, and precision. We selected the top 10 predictive variables based on their significance in disease prediction. The data were then split into a training set (70% of patients) and a validation set (30% of patients) for model validation.

RESULTS

We identified 31 indicators with significant disparities. The XGBoost model outperformed others, with an AUC of 0.920 and high precision, sensitivity, specificity, and accuracy. The top 10 features (Age, Monocyte%, Mean Platelet Volume, Lymphocyte%, SIRI, Eosinophil count, Platelet count, Hemoglobin, Platelet Distribution Width, and Neutrophil count.) were crucial in constructing a more precise predictive model. The model demonstrated strong performance on both training (AUC = 0.977) and validation (AUC = 0.912) datasets, validated by decision curve analysis and calibration curve.

CONCLUSION

ML models that incorporate CBC parameters offer an innovative and effective tool for data analysis in COVID-19. They potentially enhance diagnostic accuracy and the efficacy of therapeutic interventions, ultimately contributing to a reduction in the mortality rate of this infectious disease.

Neuroimaging and machine learning for studying the pathways from mild cognitive impairment to alzheimer's disease: a systematic review

BACKGROUND

This systematic review synthesizes the most recent neuroimaging procedures and machine learning approaches for the prediction of conversion from mild cognitive impairment to Alzheimer's disease dementia.

METHODS

We systematically searched PubMed, SCOPUS, and Web of Science databases following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) systematic review guidelines.

RESULTS

Our search returned 2572 articles, 56 of which met the criteria for inclusion in the final selection. The multimodality framework and deep learning techniques showed potential for predicting the conversion of MCI to AD dementia.

CONCLUSION

Findings of this systematic review identified that the possibility of using neuroimaging data processed by advanced learning algorithms is promising for the prediction of AD progression. We also provided a detailed description of the challenges that researchers are faced along with future research directions. The protocol has been registered in the International Prospective Register of Systematic Reviews- CRD42019133402 and published in the Systematic Reviews journal.

Association of Perceived Neighborhood Environments With Cognitive Function in Older Adults

The current study examined the associations between perceptions of the social and physical neighborhood environments and cognitive function in older adults. This cross-sectional study analyzed 821 adults aged ≥65 years from the Adult Changes in Thought study. Perceived neighborhood attributes were measured by the Physical Activity Neighborhood Environment Scale. Cognitive function was assessed using the Cognitive Ability Screening Instrument. The associations were tested using multivariate linear regression. One point greater perceived access to public transit was associated with 0.56 points greater cognitive function score (95% confidence interval [CI] [0.25, 0.88]), and an additional one point of perceived sidewalk coverage was related to 0.22 points higher cognitive function score (95% CI [0.00, 0.45]) after controlling for sociodemographic factors. The perception of neighborhood attributes alongside physical infrastructure may play an important role in supporting older adults' cognitive function. [Journal of Gerontological Nursing, 49(8), 35-41.].

A novelty detection approach to effectively predict conversion from mild cognitive impairment to Alzheimer’s disease

Accurately recognising patients with progressive mild cognitive impairment (pMCI) who will develop Alzheimer’s disease (AD) in subsequent years is very important, as early identification of those patients will enable interventions to potentially reduce the number of those transitioning from MCI to AD. Most studies in this area have concentrated on high-dimensional neuroimaging data with supervised binary/multi-class classification algorithms. However, neuroimaging data is more costly to obtain than non-imaging, and healthcare datasets are normally imbalanced which may reduce classification performance and reliability. To address these challenges, we proposed a new strategy that employs unsupervised novelty detection (ND) techniques to predict pMCI from the AD neuroimaging initiative non-imaging data. ND algorithms, including the k-nearest neighbours (kNN), k-means, Gaussian mixture model (GMM), isolation forest (IF) and extreme learning machine (ELM), were employed and compared with supervised binary support vector machine (SVM) and random forest (RF). We introduced optimisation with nested cross-validation and focused on maximising the adjusted F measure to ensure maximum generalisation of the proposed system by minimising false negative rates. Our extensive experimental results show that ND algorithms (0.727 ± 0.029 kNN, 0.7179 ± 0.0523 GMM, 0.7276 ± 0.0281 ELM) obtained comparable performance to supervised binary SVM (0.7359 ± 0.0451) with 20% stable MCI misclassification tolerance and were significantly better than RF (0.4771 ± 0.0167). Moreover, we found that the non-invasive, readily obtainable, and cost-effective cognitive and functional assessment was the most efficient predictor for predicting the pMCI within 2 years with ND techniques. Importantly, we presented an accessible and cost-effective approach to pMCI prediction, which does not require labelled data.

Analysis of Risk Factors in Dementia Through Machine Learning

Background: Sociodemographic data indicate the progressive increase in life expectancy and the prevalence of Alzheimer’s disease (AD). AD is raised as one of the greatest public health problems. Its etiology is twofold: on the one hand, non-modifiable factors and on the other, modifiable. Objective: This study aims to develop a processing framework based on machine learning (ML) and optimization algorithms to study sociodemographic, clinical, and analytical variables, selecting the best combination among them for an accurate discrimination between controls and subjects with major neurocognitive disorder (MNCD). Methods: This research is based on an observational-analytical design. Two research groups were established: MNCD group (n = 46) and control group (n = 38). ML and optimization algorithms were employed to automatically diagnose MNCD. Results: Twelve out of 37 variables were identified in the validation set as the most relevant for MNCD diagnosis. Sensitivity of 100%and specificity of 71%were achieved using a Random Forest classifier. Conclusion: ML is a potential tool for automatic prediction of MNCD which can be applied to relatively small preclinical and clinical data sets. These results can be interpreted to support the influence of the environment on the development of AD.