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

Multivariate longitudinal clustering reveals neuropsychological factors as dementia predictors in an Alzheimer's disease progression study

Dementia due to Alzheimer's disease (AD) is a multifaceted neurodegenerative disorder characterized by various cognitive and behavioral decline factors. In this work, we propose an extension of the traditional k-means clustering for multivariate time series data to cluster joint trajectories of different features describing progression over time. The algorithm we propose here enables the joint analysis of various longitudinal features to explore co-occurring trajectory factors among markers indicative of cognitive decline in individuals participating in an AD progression study. By examining how multiple variables co-vary and evolve together, we identify distinct subgroups within the cohort based on their longitudinal trajectories. Our clustering method enhances the understanding of individual development across multiple dimensions and provides deeper medical insights into the trajectories of cognitive decline. In addition, the proposed algorithm is also able to make a selection of the most significant features in separating clusters by considering trajectories over time. This process, together with a preliminary pre-processing on the OASIS-3 dataset, reveals an important role of some neuropsychological factors. In particular, the proposed method has identified a significant profile compatible with a syndrome known as Mild Behavioral Impairment (MBI), displaying behavioral manifestations of individuals that may precede the cognitive symptoms typically observed in AD patients. The findings underscore the importance of considering multiple longitudinal features in clinical modeling, ultimately supporting more effective and individualized patient management strategies.

Effectiveness of Serious Games in Evaluating Cognitive Status of the Elderly: A Systematic Review and Meta-Analysis

Early diagnosis of mild cognitive impairment (MCI) and Alzheimer's disease (AD) is very important in better management of these diseases, and serious games play an effective role in helping to diagnose these diseases more accurately owing to their innovative features. With respect to the diversity of available games, the purpose of this study was to investigate the effectiveness of using serious games to assess the cognitive status of the elderly at risk of MCI/AD. A systematic review was conducted and the correlation of serious game results with cognitive test scores were extracted from eligible studies for meta-analysis. We analyzed the correlation between the results of serious games with the scores of mini-mental state examination (MMSE), Addenbrooke's Cognitive Examination-revised edition (ACE-R), and Montreal Cognitive Assessment (MoCA) tests to evaluate cognitive status of the elderly at risk of MCI/AD, as well as the cognitive aspects examined by these tests. The random-effects model was used to obtain the overall correlation coefficient to assess the relationship between the results of serious games and the above mentioned paper-and-pencil tests. The correlation of game results with the MMSE, ACE-R, and MoCA was 0.604, 0.682, with 0.682, respectively. The correlation between the results of the games with the score of each cognitive aspect was also calculated. Overall, there is a positive correlation between serious game scores in terms of accurate patients' reactions with the scores of MMSE, ACE-R, and MoCA tests. Among the cognitive aspects, the highest correlation was obtained for fluency (0.591). For abstraction, however, the correlation was the lowest (0.036). In all three tests, the correlation was >0.6 and in cognitive aspects was <0.6. Thus, more studies should be conducted to develop serious games that are more in line with cognitive tests.

The Potential of Automated Assessment of Cognitive Function Using Non-Neuroimaging Data: A Systematic Review

Background/Objectives: The growing incidence of cognitive impairment among older adults has a significant impact on individuals, family members, caregivers, and society. Current conventional cognitive assessment tools are faced with some limitations. Recent evidence suggests that automating cognitive assessment holds promise, potentially resulting in earlier diagnosis, timely intervention, improved patient outcomes, and higher chances of response to treatment. Despite the advantages of automated assessment and technological advancements, automated cognitive assessment has yet to gain widespread use, especially in low and lower middle-income countries. This review highlights the potential of automated cognitive assessment tools and presents an overview of existing tools. Methods: This review includes 87 studies carried out with non-neuroimaging data alongside their performance metrics. Results: The identified articles automated the cognitive assessment process and were grouped into five categories either based on the tools' design or the data analysis approach. These categories include game-based, digital versions of conventional tools, original computerized tests and batteries, virtual reality/wearable sensors/smart home technologies, and artificial intelligence-based (AI-based) tools. These categories are further explained, and evaluation of their strengths and limitations is discussed to strengthen their adoption in clinical practice. Conclusions: The comparative metrics of both conventional and automated approaches of assessment suggest that the automated approach is a strong alternative to the conventional approach. Additionally, the results of the review show that the use of automated assessment tools is more prominent in countries ranked as high-income and upper middle-income countries. This trend merits further social and economic studies to understand the impact of this global reality.

AI-driven innovations in Alzheimer's disease: Integrating early diagnosis, personalized treatment, and prognostic modelling

Alzheimer's disease (AD) presents a significant challenge in neurodegenerative research and clinical practice due to its complex etiology and progressive nature. The integration of artificial intelligence (AI) into the diagnosis, treatment, and prognostic modelling of AD holds promising potential to transform the landscape of dementia care. This review explores recent advancements in AI applications across various stages of AD management. In early diagnosis, AI-enhanced neuroimaging techniques, including MRI, PET, and CT scans, enable precise detection of AD biomarkers. Machine learning models analyze these images to identify patterns indicative of early cognitive decline. Additionally, AI algorithms are employed to detect genetic and proteomic biomarkers, facilitating early intervention. Cognitive and behavioral assessments have also benefited from AI, with tools that enhance the accuracy of neuropsychological tests and analyze speech and language patterns for early signs of dementia. Personalized treatment strategies have been revolutionized by AI-driven approaches. In drug discovery, virtual screening and drug repurposing, guided by predictive modelling, accelerate the identification of effective treatments. AI also aids in tailoring therapeutic interventions by predicting individual responses to treatments and monitoring patient progress, allowing for dynamic adjustment of care plans. Prognostic modelling, another critical area, utilizes AI to predict disease progression through longitudinal data analysis and risk prediction models. The integration of multi-modal data, combining clinical, genetic, and imaging information, enhances the accuracy of these predictions. Deep learning techniques are particularly effective in fusing diverse data types to uncover new insights into disease mechanisms and progression. Despite these advancements, challenges remain, including ethical considerations, data privacy, and the need for seamless integration of AI tools into clinical workflows. This review underscores the transformative potential of AI in AD management while highlighting areas for future research and development. By leveraging AI, the healthcare community can improve early diagnosis, personalize treatments, and predict disease outcomes more accurately, ultimately enhancing the quality of life for individuals with AD.

Postoperative cognitive disorders and delirium in gynecologic surgery: Which surgery and anesthetic techniques to use to reduce the risk?

Despite their general good health, an increasing proportion of elderly individuals require surgery due to an increase in average lifespan. However, because of their increased vulnerability, these patients need to be handled carefully to make sure that surgery does not cause more harm than good. Age-related postoperative cognitive disorders (POCD) and postoperative delirium (POD), two serious consequences that are marked by adverse neuropsychologic alterations after surgery, are particularly dangerous for the elderly. In the context of gynecologic procedures, POCD and POD are examined in this narrative review. The main question is how to limit the rates of POCD and POD in older women undergoing gynecologic procedures by maximizing the risk-benefit balance. Three crucial endpoints are considered: (1) surgical procedures to lower the rates of POCD and POD, (2) anesthetic techniques to lessen the occurrence and (3) the identification of individuals at high risk for post-surgery cognitive impairments. Risks associated with laparoscopic gynecologic procedures include the Trendelenburg posture and CO2 exposure during pneumoperitoneum, despite statistical similarities in POD and POCD frequency between laparoscopic and laparotomy techniques. Numerous risk factors are associated with surgical interventions, such as blood loss, length of operation, and position holding, all of which reduce the chance of complications when they are minimized. In order to emphasize the essential role that anesthesia and surgery play in patient care, anesthesiologists are vital in making sure that anesthesia is given as sparingly and quickly as feasible. In addition, people who are genetically predisposed to POCD may be more susceptible to the disorder. The significance of a thorough strategy combining surgical and anesthetic concerns is highlighted in this article, in order to maximize results for senior patients having gynecologic surgery.

Integrating Machine Learning with Multi-Omics Technologies in Geroscience: Towards Personalized Medicine

Aging is a fundamental biological process characterized by a progressive decline in physiological functions and an increased susceptibility to diseases. Understanding aging at the molecular level is crucial for developing interventions that could delay or reverse its effects. This review explores the integration of machine learning (ML) with multi-omics technologies-including genomics, transcriptomics, epigenomics, proteomics, and metabolomics-in studying the molecular hallmarks of aging to develop personalized medicine interventions. These hallmarks include genomic instability, telomere attrition, epigenetic alterations, loss of proteostasis, disabled macroautophagy, deregulated nutrient sensing, mitochondrial dysfunction, cellular senescence, stem cell exhaustion, altered intercellular communication, chronic inflammation, and dysbiosis. Using ML to analyze big and complex datasets helps uncover detailed molecular interactions and pathways that play a role in aging. The advances of ML can facilitate the discovery of biomarkers and therapeutic targets, offering insights into personalized anti-aging strategies. With these developments, the future points toward a better understanding of the aging process, aiming ultimately to promote healthy aging and extend life expectancy.

Neuropsychological Assessment for Early Detection and Diagnosis of Dementia: Current Knowledge and New Insights

Dementia remains an underdiagnosed syndrome, and there is a need to improve the early detection of cognitive decline. This narrative review examines the role of neuropsychological assessment in the characterization of cognitive changes associated with dementia syndrome at different states. The first section describes the early indicators of cognitive decline and the major barriers to their identification. Further, the optimal cognitive screening conditions and the most widely accepted tests are described. The second section analyzes the main differences in cognitive performance between Alzheimer's disease and other subtypes of dementia. Finally, the current challenges of neuropsychological assessment in aging/dementia and future approaches are discussed. Essentially, we find that current research is beginning to uncover early cognitive changes that precede dementia, while continuing to improve and refine the differential diagnosis of neurodegenerative disorders that cause dementia. However, neuropsychology faces several barriers, including the cultural diversity of the populations, a limited implementation in public health systems, and the adaptation to technological advances. Nowadays, neuropsychological assessment plays a fundamental role in characterizing cognitive decline in the different stages of dementia, but more efforts are needed to develop harmonized procedures that facilitate its use in different clinical contexts and research protocols.

A machine learning approach to evaluate the impact of virtual balance/cognitive training on fall risk in older women

Introduction

Novel technologies based on virtual reality (VR) are creating attractive virtual environments with high ecological value, used both in basic/clinical neuroscience and modern medical practice. The study aimed to evaluate the effects of VR-based training in an elderly population.

Materials and methods

The study included 36 women over the age of 60, who were randomly divided into two groups subjected to balance-strength and balance-cognitive training. The research applied both conventional clinical tests, such as (a) the Timed Up and Go test, (b) the five-times sit-to-stand test, and (c) the posturographic exam with the Romberg test with eyes open and closed. Training in both groups was conducted for 10 sessions and embraced exercises on a bicycle ergometer and exercises using non-immersive VR created by the ActivLife platform. Machine learning methods with a k-nearest neighbors classifier, which are very effective and popular, were proposed to statistically evaluate the differences in training effects in the two groups.

Results and conclusion

The study showed that training using VR brought beneficial improvement in clinical tests and changes in the pattern of posturographic trajectories were observed. An important finding of the research was a statistically significant reduction in the risk of falls in the study population. The use of virtual environments in exercise/training has great potential in promoting healthy aging and preventing balance loss and falls among seniors.

Is Virtual Reality Orientation Therapy Useful to Optimize Cognitive and Behavioral Functioning Following Severe Acquired Brain Injury? An Exploratory Study

INTRODUCTION

Severe acquired brain injury (SABI) is a leading cause of death and disability, and it is defined as a brain injury that occurs after birth due to traumatic or non-traumatic causes. Reality orientation therapy (ROT) uses repeated time-place-person orientation and meaningful stimuli to develop a better understanding of the environment and has great potential as an effective strategy to improve cognitive and behavioral functioning.

OBJECTIVE

This study aims to investigate the feasibility and potential effects of virtual reality orientation therapy (VR-rot) on optimizing cognitive and behavioral functioning and depressive symptoms post-SABI.

METHOD

Forty patients with SABI were enrolled from October 2022 to December 2023 and divided into two groups: the experimental group (EG, n = 20) received VR_rot, while the control group (CG, n = 20) received standard ROT (S_rot). All patients were evaluated with a psychometric battery, including the Mini-Mental State Examination (MMSE) and the Hamilton Rating Scale for Depression (HRS-D), administered before (T0) and after the end (T1) of rehabilitation.

RESULTS

Within-group comparisons indicated a statistically significant change in MMSE scores from T0 to T1 in the EG and CG, with the EG showing a greater improvement than the CG. Regarding HRS-D scores, the EG showed a statistically significant change. VR-ROT could be a valuable tool for improving cognitive-behavioral functioning in SABI patients.

CONCLUSIONS

The VRRS can help reduce depressive symptoms and improve the reality orientation deficit caused by traumatic brain injury and stroke on brain tissue. This study highlights the benefits of virtual reality.

Towards a Deeper Understanding: Utilizing Machine Learning to Investigate the Association between Obesity and Cognitive Decline-A Systematic Review

Background/Objectives: Several studies have shown a relation between obesity and cognitive decline, highlighting a significant global health challenge. In recent years, artificial intelligence (AI) and machine learning (ML) have been integrated into clinical practice for analyzing datasets to identify new risk factors, build predictive models, and develop personalized interventions, thereby providing useful information to healthcare professionals. This systematic review aims to evaluate the potential of AI and ML techniques in addressing the relationship between obesity, its associated health consequences, and cognitive decline. Methods: Systematic searches were performed in PubMed, Cochrane, Web of Science, Scopus, Embase, and PsycInfo databases, which yielded eight studies. After reading the full text of the selected studies and applying predefined inclusion criteria, eight studies were included based on pertinence and relevance to the topic. Results: The findings underscore the utility of AI and ML in assessing risk and predicting cognitive decline in obese patients. Furthermore, these new technology models identified key risk factors and predictive biomarkers, paving the way for tailored prevention strategies and treatment plans. Conclusions: The early detection, prevention, and personalized interventions facilitated by these technologies can significantly reduce costs and time. Future research should assess ethical considerations, data privacy, and equitable access for all.

Pioneering Methods in Brain Optimization and Mental Health Treatment

This Patent Highlight explores ground-breaking advancements in neurostimulation, psychedelic therapy, and brain function optimization from recent innovations. It examines methods for altering brain states through AI-driven non-invasive neurostimulation, potentially contributing to personalized brain therapy. The publication also delves into the therapeutic potential of N-isopropyl tryptamines and tryptamine derivatives. Furthermore, it discusses the therapeutic applications of psilocybin and psilocin crystalline forms in mental health and central nervous system disorders. By comparing and contrasting these diverse approaches, this work highlights their mechanistic insights, therapeutic implications, and contributions to the evolving fields of neuroscience and mental health.