The association between mental status, personality traits, and discrepancy in social isolation and perceived loneliness among community dwellers.

Background: Social isolation and loneliness can co-occur; however, they are distinct concepts. There is discrepancy as some people feel lonely in social isolation, while others do not. This study sought to enhance our understanding of this discrepancy between social isolation and loneliness by investigating its related factors, with a specific focus on mental status and personality traits.

Web-Based Group Conversational Intervention on Cognitive Function and Comprehensive Functional Status Among Japanese Older Adults: Protocol for a 6-Month Randomized Controlled Trial.

Background: Social communication is a key factor in maintaining cognitive function and contributes to well-being in later life.

Objective: This study will examine the effects of "Photo-Integrated Conversation Moderated by Application version 2" (PICMOA-2), which is a web-based conversational intervention, on cognitive performance, frailty, and social and psychological indicators among community-dwelling older adults.

Methods: This study is a randomized controlled trial with an open-label, 2-parallel group trial and 1:1 allocation design.

Home-Based Cognitive Intervention for Healthy Older Adults Through Asking Robots Questions: Randomized Controlled Trial.

Background: Asking questions is common in conversations, and while asking questions, we need to listen carefully to what others say and consider the perspective our questions adopt. However, difficulties persist in verifying the effect of asking questions on older adults' cognitive function due to the lack of a standardized system for conducting experiments at participants' homes.

Objective: This study examined the intervention effect of cognitive training moderated by robots on healthy older adults.

Mild cognitive impairment prediction and cognitive score regression in the elderly using EEG topological data analysis and machine learning with awareness assessed in affective reminiscent paradigm.

Introduction: The main objective of this study is to evaluate working memory and determine EEG biomarkers that can assist in the field of health neuroscience. Our ultimate goal is to utilize this approach to predict the early signs of mild cognitive impairment (MCI) in healthy elderly individuals, which could potentially lead to dementia. The advancements in health neuroscience research have revealed that affective reminiscence stimulation is an effective method for developing EEG-based neuro-biomarkers that can detect the signs of MCI.

Mild Cognitive Impairment Detection with Machine Learning and Topological Data Analysis Applied to EEG Time-series in Facial Emotion Oddball Paradigm.

We report a novel approach to dementia neurobiomarker development from EEG time series using topological data analysis (TDA) methodology and machine learning (ML) tools in the 'AI for social good' application domain, with possible following application to home-based point of care diagnostics and cognitive intervention monitoring. We propose a new approach to a digital dementia neurobiomarker for early-onset mild cognitive impairment (MCI) prognosis. We report the best median accuracies in a range of upper 85% linear discriminant analysis (LDA), as well above 90% for linear SVM and deep fully connected neural network classifier models in leave-one-out-subject cross-validation, which presents very encouraging results in a binary healthy cognitive aging versus MCI stages using TDA features applied to brainwave time series patterns captured from a four-channel EEG wearable.

Eye-tracking paradigms for the assessment of mild cognitive impairment: a systematic review.

Mild cognitive impairment (MCI), representing the 'transitional zone' between normal cognition and dementia, has become a novel topic in clinical research. Although early detection is crucial, it remains logistically challenging at the same time. While traditional pen-and-paper tests require in-depth training to ensure standardized administration and accurate interpretation of findings, significant technological advancements are leading to the development of procedures for the early detection of Alzheimer's disease (AD) and facilitating the diagnostic process.

Machine learning approach for early onset dementia neurobiomarker using EEG network topology features.

Introduction: Modern neurotechnology research employing state-of-the-art machine learning algorithms within the so-called "AI for social good" domain contributes to improving the well-being of individuals with a disability. Using digital health technologies, home-based self-diagnostics, or cognitive decline managing approaches with neuro-biomarker feedback may be helpful for older adults to remain independent and improve their wellbeing. We report research results on early-onset dementia neuro-biomarkers to scrutinize cognitive-behavioral intervention management and digital non-pharmacological therapies.

Effect of home-based group conversation intervention using smartphone application on cognitive health and psychological well-being of older adults with subjective cognitive concerns in Japan: a randomized controlled trial protocol.

Background: Social activity is a key component in the prevention of cognitive decline. However, face-to-face social intervention has limited accessibility. To address this issue, we developed the "Photo-Integrated Conversation Moderated by Application" (PICMOA), a home-based group conversation intervention using smartphones.

Word-producing brain: Contribution of the left anterior middle temporal gyrus to word production patterns in spoken language.

Vocabulary is based on semantic knowledge. The anterior temporal lobe (ATL) has been considered an essential region for processing semantic knowledge; nonetheless, the association between word production patterns and the structural and functional characteristics of the ATL remains unclear. To examine this, we analyzed over one million words from group conversations among community-dwelling older adults and their multimodal magnetic resonance imaging data.

Association between social comparison orientation and hippocampal properties in older adults: A multimodal MRI study.

Social comparison orientation (SCO) refers to the tendency to compare oneself with others and has two distinct dimensions: one about opinions and the other about abilities. Although dissociable neural mechanisms underlying the two dimensions of social comparison can be assumed, little is known about how each dimension of SCO is associated with cognitive and brain health among older adults. To investigate this, we analyzed the SCO scale questionnaire data, neuropsychological assessment data, and multimodal MRI data collected from 90 community-dwelling older adults.

Voice Over Body? Older Adults' Reactions to Robot and Voice Assistant Facilitators of Group Conversation.

Unlabelled: Intelligent agents have great potential as facilitators of group conversation among older adults. However, little is known about how to design agents for this purpose and user group, especially in terms of agent embodiment. To this end, we conducted a mixed methods study of older adults' reactions to voice and body in a group conversation facilitation agent.

Scenario-based dialogue system based on pause detection toward daily health monitoring.

Introduction: We have conducted research on building a robot dialogue system to support the independent living of older adults. In order to provide appropriate support for them, it is necessary to obtain as much information, particularly related to their health condition, as possible. As the first step, we have examined a method to allow dialogue to continue for longer periods.

Dementia Digital Neuro-biomarker Study from Theta-band EEG Fluctuation Analysis in Facial and Emotional Identification Short-term Memory Oddball Paradigm.

An efficient machine learning (ML) implementation in the so-called 'AI for social good' domain shall contribute to dementia digital neuro-biomarker development for early-onset prognosis of a possible cognitive decline. We report encouraging initial developments of wearable EEG-derived theta-band fluctuations examination and a successive classification embracing a time-series complexity examination with a multifractal detrended fluctuation analysis (MFDFA) in the face or emotion video-clip identification short-term oddball memory tasks. We also report findings from a thirty-five elderly volunteer pilot study that EEG responses to instructed to ignore (inhibited) oddball paradigm stimulation results in more informative MFDFA features, leading to better machine learning classification results.

Tract-Based Spatial Statistics Analysis of Diffusion Tensor Imaging in Older Adults After the PICMOR Intervention Program: A Pilot Study.

Diffusion tensor imaging (DTI) enables the investigation of white matter properties by applying a tensor model to the diffusion of water molecules in the brain. Using DTI metrics including fractional anisotropy (FA), mean diffusivity (MD), axial diffusivity (AD), and radial diffusivity (RD), an attempt has been made to detect age-related alterations in the white matter microstructure in aging research. However, the use of comprehensive DTI measures to examine the effects of cognitive intervention/training on white matter fiber health in older adults remains limited.

Patterns of social relationships among community-dwelling older adults in Japan: latent class analysis.

Background: Social relationships may be the key to successful aging among older adults. However, little is known about the variability of social relationships among community-dwelling older people. This study aimed to describe the patterns of social relationships and examine the differences in sociodemographic characteristics and mental and physical health status among these patterns.

A pilot voxel-based morphometry study of older adults after the PICMOR intervention program.

Background: Age-related decline in cognitive function, such as executive function, is associated with structural changes in the neural substrates, such as volume reductions in the lateral prefrontal cortex. To prevent or delay age-related changes in cognitive function, cognitive intervention methods that employ social activity, including conversations, have been proposed in some intervention studies. Interestingly, previous studies have consistently reported that verbal fluency ability can be trained by conversation-based interventions in healthy older adults.

Neurotechnology and AI Approach for Early Dementia Onset Biomarker from EEG in Emotional Stimulus Evaluation Task.

We present an efficient utilization of a machine learning (ML) method concentrating on the 'AI for social good' application. We develop a digital dementia biomarker for early-onset dementia forecast. The paper demonstrates encouraging preliminary results of EEG-wearable-based signal analysis and a subsequent classification adopting a signal complexity test of a multifractal detrended fluctuation analysis (MFDFA) in emotional faces working memory training and evaluation tasks.

Older Adult Mild Cognitive Impairment Prediction from Multiscale Entropy EEG Patterns in Reminiscent Interior Image Working Memory Paradigm.

We discuss the practical employment of a machine learning (ML) technique within AI for a social good application. We present an application for elderly adult dementia onset prognostication. First, the paper explains our encouraging preliminary study results of EEG responses analysis using a signal complexity measure of multiscale entropy (MSE) in reminiscent interior working memory evaluation tasks.

A Dialogue-Based System with Photo and Storytelling for Older Adults: Toward Daily Cognitive Training.

As the elderly population grows worldwide, living a healthy and full life as an older adult is becoming a topic of great interest. One key factor and severe challenge to maintaining quality of life in older adults is cognitive decline. Assistive robots for helping older adults have been proposed to solve issues such as social isolation and dependent living.

Types of social networks and starting leisure activities in later life: A longitudinal Japan Gerontological Evaluation Study (JAGES).

Considering beneficial effects of leisure activities in later life on well-being and health, we investigated which type of social network among older adults is associated with starting their participation in leisure activities. We used data from a longitudinal Japan Gerontological Evaluation Study (JAGES) conducted in Japan every three years from 2010 to 2016. We extracted types of social networks of older adults who did not participate in leisure activities in 2013 and responded to items related to social networks (n = 3436) relying on latent class analysis to examine changes in leisure activity participation over a three-year period within each latent class while controlling for participants' activity in 2010.

Cognitive Intervention Through Photo-Integrated Conversation Moderated by Robots (PICMOR) Program: A Randomized Controlled Trial.

Social interaction might prevent or delay dementia, but little is known about the specific effects of various social activity interventions on cognition. This study conducted a single-site randomized controlled trial (RCT) of Photo-Integrated Conversation Moderated by Robots (PICMOR), a group conversation intervention program for resilience against cognitive decline and dementia. In the RCT, PICMOR was compared to an unstructured group conversation condition.

Scaling laws in natural conversations among elderly people.

Language is a result of brain function; thus, impairment in cognitive function can result in language disorders. Understanding the aging of brain functions in terms of language processing is crucial for modern aging societies. Previous studies have shown that language characteristics, such as verbal fluency, are associated with cognitive functions.

Characteristics of resting-state functional connectivity in older adults after the PICMOR intervention program: a preliminary report.

Background: The present study aimed to provide a basis for future research examining the neural mechanisms that underlie the beneficial effect of an intervention program, Photo-Integrated Conversation Moderated by Robots (PICMOR), on verbal fluency in older adults as identified in our previous randomized controlled trial. In this preliminary report, we conducted an additional experiment using resting-state functional magnetic resonance imaging (rsfMRI) after the intervention period. Specifically, we investigated the resting-state functional connectivity (rsFC) characteristics of the intervention group (INT) compared to the control group (CONT).

Classifying Mild Cognitive Impairment from Behavioral Responses in Emotional Arousal and Valence Evaluation Task - AI Approach for Early Dementia Biomarker in Aging Societies.

The presented paper discusses a practical application of machine learning (ML) in the so-called 'AI for social good' domain and in particular concerning the problem of a potential elderly adult dementia onset prediction. An increase in dementia cases is producing a significant medical and economic weight in many countries. Approximately 47 million older adults live with a dementia spectrum of neurocognitive disorders, according to an up-to-date statement of the World Health Organization (WHO), and this amount will triple within the next thirty years.

AI for social good: unlocking the opportunity for positive impact.

Advances in machine learning (ML) and artificial intelligence (AI) present an opportunity to build better tools and solutions to help address some of the world's most pressing challenges, and deliver positive social impact in accordance with the priorities outlined in the United Nations' 17 Sustainable Development Goals (SDGs). The AI for Social Good (AI4SG) movement aims to establish interdisciplinary partnerships centred around AI applications towards SDGs. We provide a set of guidelines for establishing successful long-term collaborations between AI researchers and application-domain experts, relate them to existing AI4SG projects and identify key opportunities for future AI applications targeted towards social good.