AI and Non AI Assessments for Dementia

Current progress in the artificial intelligence domain has led to the development of various types of AI-powered dementia assessments, which can be employed to identify patients at the early stage of dementia. It can revolutionize the dementia care settings. It is essential that the medical community be aware of various AI assessments and choose them considering their degrees of validity, efficiency, practicality, reliability, and accuracy concerning the early identification of patients with dementia (PwD). On the other hand, AI developers should be informed about various non-AI assessments as well as recently developed AI assessments. Thus, this paper, which can be readable by both clinicians and AI engineers, fills the gap in the literature in explaining the existing solutions for the recognition of dementia to clinicians, as well as the techniques used and the most widespread dementia datasets to AI engineers. It follows a review of papers on AI and non-AI assessments for dementia to provide valuable information about various dementia assessments for both the AI and medical communities. The discussion and conclusion highlight the most prominent research directions and the maturity of existing solutions.Comment: 49 page

Is brain connectome research the future frontier for subjective cognitive decline? A systematic review

Objective We performed a systematic literature review on Subjective Cognitive Decline (SCD) in order to examine whether the resemblance of brain connectome and functional connectivity (FC) alterations in SCD with respect to MCI, AD and HC can help us draw conclusions on the progression of SCD to more advanced stages of dementia. Methods We searched for studies that used any neuroimaging tool to investigate potential differences/similarities of brain connectome in SCD with respect to HC, MCI, and AD. Results Sixteen studies were finally included in the review. Apparent FC connections and disruptions were observed in the white matter, default mode and gray matter networks in SCD with regards to HC, MCI, and AD. Interestingly, more apparent connections in SCD were located over the posterior regions, while an increase of FC over anterior regions was observed as the disease progressed. Conclusions Elders with SCD display a significant disruption of the brain network, which in most of the cases is worse than HC across multiple network parameters. Significance The present review provides comprehensive and balanced coverage of a timely target research activity around SCD with the intention to identify similarities/differences across patient groups on the basis of brain connectome properties

Wearable devices for assessing function in Alzheimer’s disease: a European public involvement activity about the features and preferences of patients and caregivers

Background: Alzheimer's Disease (AD) impairs the ability to carry out daily activities, reduces independence and quality of life and increases caregiver burden. Our understanding of functional decline has traditionally relied on reports by family and caregivers, which are subjective and vulnerable to recall bias. The Internet of Things (IoT) and wearable sensor technologies promise to provide objective, affordable, and reliable means for monitoring and understanding function. However, human factors for its acceptance are relatively unexplored. Objective: The Public Involvement (PI) activity presented in this paper aims to capture the preferences, priorities and concerns of people with AD and their caregivers for using monitoring wearables. Their feedback will drive device selection for clinical research, starting with the study of the RADAR-AD project. Method: The PI activity involved the Patient Advisory Board (PAB) of the RADAR-AD project, comprised of people with dementia across Europe and their caregivers (11 and 10, respectively). A set of four devices that optimally represent various combinations of aspects and features from the variety of currently available wearables (e.g., weight, size, comfort, battery life, screen types, water-resistance, and metrics) was presented and experienced hands-on. Afterwards, sets of cards were used to rate and rank devices and features and freely discuss preferences. Results: Overall, the PAB was willing to accept and incorporate devices into their daily lives. For the presented devices, the aspects most important to them included comfort, convenience and affordability. For devices in general, the features they prioritized were appearance/style, battery life and water resistance, followed by price, having an emergency button and a screen with metrics. The metrics valuable to them included activity levels and heart rate, followed by respiration rate, sleep quality and distance. Some concerns were the potential complexity, forgetting to charge the device, the potential stigma and data privacy. Conclusions: The PI activity explored the preferences, priorities and concerns of the PAB, a group of people with dementia and caregivers across Europe, regarding devices for monitoring function and decline, after a hands-on experience and explanation. They highlighted some expected aspects, metrics and features (e.g., comfort and convenience), but also some less expected (e.g., screen with metrics)

Assessing the cognitive decline of people in the spectrum of AD by monitoring their activities of daily living in an IoT-enabled smart home environment: a cross-sectional pilot study

IntroductionAssessing functional decline related to activities of daily living (ADLs) is deemed significant for the early diagnosis of dementia. As current assessment methods for ADLs often lack the ability to capture subtle changes, technology-based approaches are perceived as advantageous. Specifically, digital biomarkers are emerging, offering a promising avenue for research, as they allow unobtrusive and objective monitoring.MethodsA study was conducted with the involvement of 36 participants assigned to three known groups (Healthy Controls, participants with Subjective Cognitive Decline and participants with Mild Cognitive Impairment). Participants visited the CERTH-IT Smart Home, an environment that simulates a fully functional residence, and were asked to follow a protocol describing different ADL Tasks (namely Task 1 – Meal, Task 2 – Beverage and Task 3 – Snack Preparation). By utilizing data from fixed in-home sensors installed in the Smart Home, the identification of the performed Tasks and their derived features was explored through the developed CARL platform. Furthermore, differences between groups were investigated. Finally, overall feasibility and study satisfaction were evaluated.ResultsThe composition of the ADLs was attainable, and differentiation among the HC group compared to the SCD and the MCI groups considering the feature “Activity Duration” in Task 1 – Meal Preparation was possible, while no difference could be noted between the SCD and the MCI groups.DiscussionThis ecologically valid study was determined as feasible, with participants expressing positive feedback. The findings additionally reinforce the interest and need to include people in preclinical stages of dementia in research to further evolve and develop clinically relevant digital biomarkers

EEG-Based Brain–Computer Interfaces for Communication and Rehabilitation of People with Motor Impairment: A Novel Approach of the 21st Century

People with severe neurological impairments face many challenges in sensorimotor functions and communication with the environment; therefore they have increased demand for advanced, adaptive and personalized rehabilitation. During the last several decades, numerous studies have developed brain–computer interfaces (BCIs) with the goals ranging from providing means of communication to functional rehabilitation. Here we review the research on non-invasive, electroencephalography (EEG)-based BCI systems for communication and rehabilitation. We focus on the approaches intended to help severely paralyzed and locked-in patients regain communication using three different BCI modalities: slow cortical potentials, sensorimotor rhythms and P300 potentials, as operational mechanisms. We also review BCI systems for restoration of motor function in patients with spinal cord injury and chronic stroke. We discuss the advantages and limitations of these approaches and the challenges that need to be addressed in the future

Tools, indicators and innovative approaches for the diagnosis of subjective cognitive decline in elderly-dwelling population: a prospective case-control study

Background: Despite a proliferation of publications on various aspects of subjective cognitive decline (SCD), the critical issue of how to optimize classification, particularly in community‐based studies, has received almost no attention. However, assessment using EEG, exploration of patients’ requirements and investigation of multiple parameters is very crucial for SCD research. Methods: Initially we extensively investigated the literature for detecting the most suitable and valuable screening tools which are currently used globally, we searched also for the metrics that are being used and all the available in market sensors for remote monitoring. We conducted the first ever reported validation of two neuropsychometric tests (Memory Alternation Test - M@T and Subjective Cognitive Decline – Questionnaire - SCD-Q) in elderly-dwelling population with SCD and MCI. By taking into account the suggested by NHS and EU User-Centered Design of conducting a research, we developed two questionnaires for investigating patients’ requirements, preferences and needs of people with cognitive impairment with regards to technologies and wearable sensors. Moreover, we conducted a focus groups through a Patient and Public Involvement including patients with cognitive impairment in collaboration with Alzheimer Europe. After creating the platform for collecting and interpreting the data from wearable sensors, we deployed the FitBit charge 3 sensors in patients with SCD, MCI and HC for long-term use in order to record daily functionality such as sleep, heart rate and physical activity. Moreover, we investigated EEG recordings of high-density EEG during resting state and visual attention memory test for brain connectome analysis in parietal-occipital electrodes with EGI GES 300 with 256 channels.Results: The extensive literature search showed that brain connectome of SCD people presents similar changes and alterations with more advanced stages of AD continuum, while preserves in general global network properties, local connections seem to become apparent. However, no brain connectome study has ever applied EEG analysis in SCD to detect changes in brain connectome. Moreover, the exploration of users and patients’ requirements through the PPI studies we conducted improved the methodology as well as the quality of the study and the protocol. Additionally, the validation of the questionnaires showed that the total score of M@T was statistical significant different between healthy controls, SCD and MCI. Regarding the EEG analysis, SCD participants showed statistical significant difference of clustering coefficient and strength locally in parietal area during memory coding and memory retrieval of visual attention test. In particular, SCD participants demonstrated extensive interruptions in local network with intermediate values between HC and MCI. Finally, the present doctoral constitutes the first ever conducted research using wearable sensors to detect changes in SCD participants and showed very important findings with regards to changes in sleep parameters and sleep stages. Therefore, our results highlight that there is a strong connection between brain connectome interruptions and SCD. Conclusions: The present findings show an interrupted pattern of brain connectome, physiological problems and neuropsychological tests in people with AD that begins in people with subjective memory complaints. The results show that these complains are of outmost importance for AD progression and future studies should focus on that stageΘέμα: Η υποκειμενική νοητική διαταραχή (ΥΝΔ) είναι μια άγνωστη κατάσταση που αποτελεί ένα προ-κλινικό στάδιο της νόσου του Alzheimer (ΝΑ) πριν από την ήπια νοητική διαταραχή (ΗΝΔ). Ωστόσο, η μελέτη αυτής με τη χρήση υψηλής ευκρίνειας ηλεκτροεγκεφαλογραφήματος (ΗΕΓ) και αισθητήρων κίνησης σε όλα τα προ-κλινικά στάδια έχει μελετηθεί ελάχιστα ενώ σημαντικό πρόβλημα αποτελεί η έλλειψη σταθμισμένων νευροψυχολογικών εργαλείων στη πρωτοβάθμια φροντίδα υγείας για τον έγκαιρο εντοπισμό της ΥΝΔ. Μέθοδος: Αρχικά διερευνήθηκε εκτενώς η βιβλιογραφία για εύρεση των κατάλληλων εργαλείων αυτό-αναφοράς που χρησιμοποιούνται σε Παγκόσμιο επίπεδο, των δεικτών που θα αξιολογηθούν από το ΗΕΓ και των διαθέσιμων αισθητήρων στην αγορά για την εξ’ αποστάσεως παρακολούθηση των ασθενών. Πραγματοποιήσαμε την πρώτη στάθμιση σε Ελληνικό πληθυσμό με ΥΝΔ και ΗΝΔ δύο ευρέως χρησιμοποιούμενων ερωτηματολογίων αυτό-αναφοράς (Memory Alternation Test - M@T και Subjective Cognitive Decline – Questionnaire - SCD-Q). Ενώ ακολουθώντας το προτεινόμενο από την Ευρωπαϊκή Ένωση και το NHS User-Centered Design κατασκευάσαμε δυο ερωτηματολόγια για να καταγράψουμε τις τεχνολογικές απαιτήσεις για τη χρήση αισθητήρων κίνησης και εφαρμογών παράλληλα υλοποιήσαμε ένα focus group σε συνεργασία με την Alzheimer Europe για να συλλέξουμε τις προτιμήσεις και τις επιθυμίες ατόμων με νοητική διαταραχή μέσω Patient and Public Involvement μελέτης. Στη συνέχεια αφού δημιουργήσαμε τη πλατφόρμα και το σύστημα συλλογής και αναπαράστασης δεδομένων από τους αισθητήρες, εγκαταστήσαμε τους αισθητήρες fitbit charge 3 σε ασθενείς με ΥΝΔ, ΗΝΔ και υγιείς για μεγάλο χρονικό διάστημα με στόχο την καθημερινή καταγραφή φυσιολογικών δραστηριοτήτων (ύπνος, καρδιακοί παλμοί, φυσική δραστηριότητα κτλ). Επιπροσθέτως μελετήθηκαν εκτενώς καταγραφές με ΗΕΓ υψηλής ευκρίνειας HD-EEG (EGI GES 300) σε κατάσταση ηρεμίας και κατόπιν χρήσης οπτικού ερεθίσματος με σκοπό την αξιολόγηση της οπτικής προσοχής και κωδικοποίησης και άμεσης ανάκλησης πληροφορίας σχετιζόμενης με το ερέθισμα με στόχο τον υπολογισμό μετρικών δικτύου του εγκεφάλου σε συγκεκριμένες περιοχές βρεγματο-ινιακά. Αποτελέσματα: Η εκτενής βιβλιογραφική ανασκόπηση που διενεργήθηκε έδειξε ότι το δίκτυο εγκεφάλου ατόμων με ΥΝΔ παρουσιάζει διαταραχές συγκριτικά με αυτό της ΗΝΔ ωστόσο σε μικρότερο βαθμό διατηρώντας τις σφαιρικές ιδιότητες δικτύου ενώ σε τοπικό επίπεδο εντοπίζονται αλλοιώσεις. Η συλλογή απαιτήσεων και οι PPI μελέτες που πραγματοποιήθηκαν διασφάλισαν την ποιότητα της μεθοδολογίας της μελέτης μας προσφέροντας με αυτό τον τρόπο ενίσχυση της συμμετοχής των ιδίων των ασθενών στη διαμόρφωση του πρωτοκόλλου. Επίσης, η στάθμιση των δύο ερωτηματολογίων έδειξε ότι η συνολική βαθμολογία M@T ήταν στατιστικά σημαντικά διαφορετική μεταξύ υγιών και ΥΝΔ και μεταξύ υγιών και ΗΝΔ και μεταξύ ΥΝΔ και ΗΝΔ. Παράλληλα, τα δεδομένα του ΗΕΓ απέδειξαν ότι οι ασθενείς με ΥΝΔ παρουσίασαν στατιστικά σημαντικά μειωμένο συντελεστή συσταδιοποίησης και ισχύος τοπικά στη περιοχή του βρεγματικού λοβού κατά τη διάρκεια κωδικοποίησης και ανάκλησης της πληροφορίας. Πιο συγκεκριμένα, οι ασθενείς με ΥΝΔ παρουσιάζουν σημαντική διαταραχή των δικτύων του εγκεφάλου σε τοπικό επίπεδο, παρουσιάζοντας ενδιάμεσες τιμές μεταξύ της ομάδας των υγιών και των ασθενών με ΗΝΔ. Τέλος, η παρούσα εργασία αποτέλεσε την πρώτη μελέτη με αισθητήρες και ΥΝΔ που διενεργήθηκε και μας έδειξε πολύ σημαντικές παρατηρήσεις όπως ότι ο ύπνος και τα στάδια αυτού επηρεάζονται σημαντικά στην ΥΝΔ και μπορεί να προσφέρει πολύ σημαντικές κλινικές πληροφορίες. Συμπεράσματα: Τα αποτελέσματα αυτά αποδεικνύουν ότι οι υποκειμενικές ανησυχίες αποτελούν βασικό παράγοντα στην πρόοδο της ΝΑ που μπορεί να αντικατοπτρίζει δυναμικά την εξέλιξη της ΝΑ και έτσι να αντιπροσωπεύει έναν πιθανό δείκτη για την έγκαιρη διάγνωση της ΝΑ με στόχο την πρόληψη, πρόγνωση και εφαρμογή εξατομικευμένων παρεμβάσεων

Detection of Health-Related Events and Behaviours from Wearable Sensor Lifestyle Data Using Symbolic Intelligence: A Proof-of-Concept Application in the Care of Multiple Sclerosis

In this paper, we demonstrate the potential of a knowledge-driven framework to improve the efficiency and effectiveness of care through remote and intelligent assessment. More specifically, we present a rule-based approach to detect health related problems from wearable lifestyle sensor data that add clinical value to take informed decisions on follow-up and intervention. We use OWL 2 ontologies as the underlying knowledge representation formalism for modelling contextual information and high-level concepts and relations among them. The conceptual model of our framework is defined on top of existing modelling standards, such as SOSA and WADM, promoting the creation of interoperable knowledge graphs. On top of the symbolic knowledge graphs, we define a rule-based framework for infusing expert knowledge in the form of SHACL constraints and rules to recognise patterns, anomalies and situations of interest based on the predefined and stored rules and conditions. A dashboard visualizes both sensor data and detected events to facilitate clinical supervision and decision making. Preliminary results on the performance and scalability are presented, while a focus group of clinicians involved in an exploratory research study revealed their preferences and perspectives to shape future clinical research using the framework

A Novel Connectome-based Electrophysiological Study of Subjective Cognitive Decline Related to Alzheimer’s Disease by Using Resting-state High-density EEG EGI GES 300

Aim: To investigate for the first time the brain network in the Alzheimer’s disease (AD) spectrum by implementing a high-density electroencephalography (HD-EEG - EGI GES 300) study with 256 channels in order to seek if the brain connectome can be effectively used to distinguish cognitive impairment in preclinical stages. Methods: Twenty participants with AD, 30 with mild cognitive impairment (MCI), 20 with subjective cognitive decline (SCD) and 22 healthy controls (HC) were examined with a detailed neuropsychological battery and 10 min resting state HD-EEG. We extracted correlation matrices by using Pearson correlation coefficients for each subject and constructed weighted undirected networks for calculating clustering coefficient (CC), strength (S) and betweenness centrality (BC) at global (256 electrodes) and local levels (29 parietal electrodes). Results: One-way ANOVA presented a statistically significant difference among the four groups at local level in CC [F (3, 88) = 4.76, p = 0.004] and S [F (3, 88) = 4.69, p = 0.004]. However, no statistically significant difference was found at a global level. According to the independent sample t-test, local CC was higher for HC [M (SD) = 0.79 (0.07)] compared with SCD [M (SD) = 0.72 (0.09)]; t (40) = 2.39, p = 0.02, MCI [M (SD) = 0.71 (0.09)]; t (50) = 0.41, p = 0.004 and AD [M (SD) = 0.68 (0.11)]; t (40) = 3.62, p = 0.001 as well, while BC showed an increase at a local level but a decrease at a global level as the disease progresses. These findings provide evidence that disruptions in brain networks in parietal organization may potentially represent a key factor in the ability to distinguish people at early stages of the AD continuum. Conclusions: The above findings reveal a dynamically disrupted network organization of preclinical stages, showing that SCD exhibits network disorganization with intermediate values between MCI and HC. Additionally, these pieces of evidence provide information on the usefulness of the 256 HD-EEG in network construction

A Semantic Framework to Detect Problems in Activities of Daily Living Monitored through Smart Home Sensors

Activities of daily living (ADLs) are fundamental routine tasks that the majority of physically and mentally healthy people can independently execute. In this paper, we present a semantic framework for detecting problems in ADLs execution, monitored through smart home sensors. In the context of this work, we conducted a pilot study, gathering raw data from various sensors and devices installed in a smart home environment. The proposed framework combines multiple Semantic Web technologies (i.e., ontology, RDF, triplestore) to handle and transform these raw data into meaningful representations, forming a knowledge graph. Subsequently, SPARQL queries are used to define and construct explicit rules to detect problematic behaviors in ADL execution, a procedure that leads to generating new implicit knowledge. Finally, all available results are visualized in a clinician dashboard. The proposed framework can monitor the deterioration of ADLs performance for people across the dementia spectrum by offering a comprehensive way for clinicians to describe problematic behaviors in the everyday life of an individual

The Effects of playing the COSMA Cognitive Games in Dementia

Brain Training games are increasingly gaining attention as a non-pharmacological intervention to promote well-being and quality of life in people living with dementia. Herein we present the COSMA software and a pilot study to evaluate its impact on the emotions of people in the spectrum of dementia. The software was created in accordance to the UK National Institute for Health and Care Excellence (NICE) guidelines as a ‘brain-stimulating’ software for use by people with cognitive impairment i.e., mild cognitive impairment (MCI) and early dementia. The pilot study aims to investigate whether the current COSMA game designs have an impact on emotions in people with MCI and early dementia. The emotional evaluation before and after playing COSMA games was carried out using the Positive and Negative Affect Schedule (PANAS). Our findings demonstrated a small, but significant increase in positive emotions (MCI: p= 0.041; early dementia: p= 0.042) and decrease in negative emotions (MCI: p= 0.001; early dementia: p< 0.001). These preliminary results showed that people with MCI and early dementia experienced positive emotions while playing the COSMA games, suggesting that people with cognitive impairment may benefit from using the COSMA software regularly