Low-Cost Robotic Assessment of Visuo-Motor Deficits in Alzheimer's Disease

Investigating the associations between upper limb motor function and cognitive impairment: a scoping review

Upper limb motor function is a potential new biomarker of cognitive impairment and may aid discrimination from healthy ageing. However, it remains unclear which assessments to use. This study aimed to explore what methods have been used and to describe associations between upper limb function and cognitive impairment. A scoping review was conducted using PubMed, CINAHL and Web of Science. A systematic search was undertaken, including synonyms for key concepts 'upper limb', 'motor function' and 'cognitive impairment'. Selection criteria included tests of upper limb motor function and impaired cognition in adults. Analysis was by narrative synthesis. Sixty papers published between 1998 and 2022, comprising 41,800 participants, were included. The most common assessment tasks were finger tapping, Purdue Pegboard Test and functional tasks such as writing. Protocols were diverse in terms of equipment used and recording duration. Most participants were recruited from clinical settings. Alzheimer's Disease was the most common cause of cognitive impairment. Results were mixed but, generally, slower speed, more errors, and greater variability in upper limb movement variables was associated with cognitive impairment. This review maps the upper limb motor function assessments used and summarises the available evidence on how these associate with cognitive impairment. It identifies research gaps and may help guide protocols for future research. There is potential for upper limb motor function to be used in assessments of cognitive impairment.

Revolutionizing the Early Detection of Alzheimer's Disease through Non-Invasive Biomarkers: The Role of Artificial Intelligence and Deep Learning

Alzheimer's disease (AD) is now classified as a silent pandemic due to concerning current statistics and future predictions. Despite this, no effective treatment or accurate diagnosis currently exists. The negative impacts of invasive techniques and the failure of clinical trials have prompted a shift in research towards non-invasive treatments. In light of this, there is a growing need for early detection of AD through non-invasive approaches. The abundance of data generated by non-invasive techniques such as blood component monitoring, imaging, wearable sensors, and bio-sensors not only offers a platform for more accurate and reliable bio-marker developments but also significantly reduces patient pain, psychological impact, risk of complications, and cost. Nevertheless, there are challenges concerning the computational analysis of the large quantities of data generated, which can provide crucial information for the early diagnosis of AD. Hence, the integration of artificial intelligence and deep learning is critical to addressing these challenges. This work attempts to examine some of the facts and the current situation of these approaches to AD diagnosis by leveraging the potential of these tools and utilizing the vast amount of non-invasive data in order to revolutionize the early detection of AD according to the principles of a new non-invasive medicine era.

Reach-to-grasp kinematics and kinetics with and without visual feedback in early-stage Alzheimer’s disease

This study aimed to investigate the effects of early-stage Alzheimer’s disease (AD) on the reach-to-grasp kinematics and kinetics with and without visual supervision of the grasping arm and hand. Seventeen patients who had been diagnosed with early-stage AD and 17 age- and gender-matched, cognitive normal (CN) adults participated in the experiment. A mirror operating system was designed to block the visual feedback of their grasping hand and forearms but to virtually show grasped targets. The target for reach-to-grasp kinematics was a reflective marker installed on a base; and the target for reach-to-grasp kinetics was a custom-made apparatus installed with two six-component force/torque transducers. Kinematics and kinetic parameters were used to quantify the reach-to-grasp performances. Results showed that the early-stage AD remarkably decreased the reaching speed, reduced the grasping accuracy and increased the transportation variability for reach-to-grasp kinematics. For kinetic analysis, early-stage AD extended the preload duration, disturbed the grip and lift forces coordination, and increased the feedforward proportion in the grasping force control. The AD-related changes in the reach-to-grasp kinematic and kinetic parameters depended on visual feedback and were associated with nervous system function according to correlation analyses with the neuropsychological testing. These results suggest that the abnormal kinematic and kinetic characteristics may correlate with the neuropsychological status of early-stage AD, and that the reach-to-grasp kinematic and kinetic maneuver could potentially be used as a novel tool for non-invasive screening or evaluation of early-stage AD.

Exploratory Research on Key Technology of Human-Computer Interactive 2.5-Minute Fast Digital Early Warning for Mild Cognitive Impairment

Objective. As the preclinical stage of Alzheimer’s disease (AD), Mild Cognitive Impairment (MCI) is characterized by hidden onset, which is difficult to detect early. Traditional neuropsychological scales are main tools used for assessing MCI. However, due to its strong subjectivity and the influence of many factors such as subjects’ educational background, language and hearing ability, and time cost, its accuracy as the standard of early screening is low. Therefore, the purpose of this paper is to propose a new key technology of fast digital early warning for MCI based on eye movement objective data analysis. Methodology. Firstly, four exploratory indexes (test durations, correlation degree, lengths of gaze trajectory, and drift rate) of MCI early warning are determined based on the relevant literature research and semistructured expert interview; secondly, the eye movement state is captured based on the eye tracker to realize the data extraction of four exploratory indexes. On this basis, the human-computer interactive 2.5-minute fast digital early warning paradigm for MCI is designed; thirdly, the rationality of the four early warning indexes proposed in this paper and their early warning effectiveness on MCI are verified. Results. Through the small sample test of human-computer interactive 2.5 fast digital early warning paradigm for MCI conducted by 32 elderly people aged 70–90 in a medical institution in Hangzhou, the two indexes of “correlation degree” and “drift rate” with statistical differences are selected. The experiment results show that AUC of this MCI early warning paradigm is 0.824. Conclusion. The key technology of human-computer interactive 2.5 fast digital early warning for MCI proposed in this paper overcomes the limitations of the existing MCI early warning tools, such as low objectification level, high dependence on professional doctors, long test time, requiring high educational level, and so on. The experiment results show that the early warning technology, as a new generation of objective and effective digital early warning tool, can realize 2.5-minute fast and high-precision preliminary screening and early warning for MCI in the elderly.

Technological Solutions for Diagnosis, Management and Treatment of Alzheimer's Disease-Related Symptoms: A Structured Review of the Recent Scientific Literature

In people with Alzheimer's disease (PwAD), there is a need for specific tools for the timely diagnosis, management, and treatment of symptoms. New technological solutions, including digital devices, application programs (apps), sensors and virtual reality, represent promising possibilities for objective and reliable assessment, monitoring and intervention strategies in this field. Our structured review presents an up-to-date summary of the technological solutions for the (i) diagnosis, (ii) management and (iii) treatment of AD-related symptoms. To this end, we searched electronic databases (i.e., PubMed, Web of Science, and Cochrane Library) for studies published over the last 10 years. Two authors of the review extracted data of interest. A total of eight manuscripts were included. In the last decade, a series of technological solutions across AD stages have been proposed. These include: (i) innovative strategies for the early detection of deficits in finger dexterity, visuo-spatial abilities (including spatial navigation), divided attention and instrumental autonomy; (ii) tools to activate the patient's responsiveness in terms of alertness and mood improvement; and (iii) useful interventions for retrieving memories, increasing body movements and improving spatial cognition. Methodological limitations, mainly pertaining to the paucity of randomized controlled trials and comprehensive assessments, were observed. Advances in technology currently provide the potential for designing innovative methods for evaluating, controlling and handling AD-related symptoms. The co-creation of technological solutions with all stakeholders represents the best way to design effective strategies for PwAD.

Visuomotor integration in early Alzheimer's disease: A TMS study

BACKGROUND AND PURPOSE

Cortical visuomotor integration is altered in Alzheimer's disease (AD), even at an early stage of the disease. The aim of this study was to assess the connections between the primary visual (V1) and motor (M1) areas in patients with early AD using a paired-pulse, twin-coil transcranial magnetic stimulation (TMS) technique.

METHODS

Visuomotor connections (VMCs) were assessed in 13 subjects with probable AD and 16 healthy control subjects. A conditioning stimulus over the V1 phosphene hotspot was followed at interstimulus intervals (ISIs) of 18 and 40 ms by a test stimulus over M1, to elicit motor evoked potentials (MEPs) in the contralateral first dorsal interosseous muscle.

RESULTS

Significant effects due to VMCs, consisting of enhanced MEP suppression at ISI of 18 and 40 ms, were observed in the AD patients. Patients with AD showed an excessive inhibitory response of the right M1 to inputs travelling from V1 at given ISIs.

CONCLUSIONS

This study provides neurophysiological evidence of altered functional connectivity between visual and motor areas in AD.

Intelligent Sensing Technologies for the Diagnosis, Monitoring and Therapy of Alzheimer's Disease: A Systematic Review

Alzheimer's disease is a lifelong progressive neurological disorder. It is associated with high disease management and caregiver costs. Intelligent sensing systems have the capability to provide context-aware adaptive feedback. These can assist Alzheimer's patients with, continuous monitoring, functional support and timely therapeutic interventions for whom these are of paramount importance. This review aims to present a summary of such systems reported in the extant literature for the management of Alzheimer's disease. Four databases were searched, and 253 English language articles were identified published between the years 2015 to 2020. Through a series of filtering mechanisms, 20 articles were found suitable to be included in this review. This study gives an overview of the depth and breadth of the efficacy as well as the limitations of these intelligent systems proposed for Alzheimer's. Results indicate two broad categories of intelligent technologies, distributed systems and self-contained devices. Distributed systems base their outcomes mostly on long-term monitoring activity patterns of individuals whereas handheld devices give quick assessments through touch, vision and voice. The review concludes by discussing the potential of these intelligent technologies for clinical practice while highlighting future considerations for improvements in the design of these solutions for Alzheimer's disease.

Visuomotor integration deficits are common to familial and sporadic preclinical Alzheimer's disease

We investigated whether subtle visuomotor deficits were detectable in familial and sporadic preclinical Alzheimer's disease. A circle-tracing task-with direct and indirect visual feedback, and dual-task subtraction-was completed by 31 individuals at 50% risk of familial Alzheimer's disease (19 presymptomatic mutation carriers; 12 non-carriers) and 390 cognitively normal older adults (members of the British 1946 Birth Cohort, all born during the same week; age range at assessment = 69-71 years), who also underwent β-amyloid-PET/MRI to derive amyloid status (positive/negative), whole-brain volume and white matter hyperintensity volume. We compared preclinical Alzheimer's groups against controls cross-sectionally (mutation carriers versus non-carriers; amyloid-positive versus amyloid-negative) on speed and accuracy of circle-tracing and subtraction. Mutation carriers (mean 7 years before expected onset) and amyloid-positive older adults traced disproportionately less accurately than controls when visual feedback was indirect, and were slower at dual-task subtraction. In the older adults, the same pattern of associations was found when considering amyloid burden as a continuous variable (Standardized Uptake Value Ratio). The effect of amyloid was independent of white matter hyperintensity and brain volumes, which themselves were associated with different aspects of performance: greater white matter hyperintensity volume was also associated with disproportionately poorer tracing accuracy when visual feedback was indirect, whereas larger brain volume was associated with faster tracing and faster subtraction. Mutation carriers also showed evidence of poorer tracing accuracy when visual feedback was direct. This study provides the first evidence of visuomotor integration deficits common to familial and sporadic preclinical Alzheimer's disease, which may precede the onset of clinical symptoms by several years.