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

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. Community dwellers aged 65 years and older were enrolled in the trial and divided into the intervention and control groups. The intervention group receives the PICMOA-2 program, a web-based group conversation, once every 2 weeks for 6 months. The primary outcome is verbal fluency, including phonemic and semantic fluency. The secondary outcomes are other neuropsychiatric batteries, including the Mini-Mental State Examination, Logical Memory (immediate and delay), verbal paired associates, and comprehensive functional status evaluated by questionnaires, including frailty, social status, and well-being. The effect of the intervention will be examined using a mixed linear model. As a secondary aim, we will test whether the intervention effects vary with the covariates at baseline to examine the effective target attributes.

RESULTS

Recruitment was completed in July 2023. A total of 66 participants were randomly allocated to intervention or control groups. As of January 1, 2024, the intervention is ongoing. Participants are expected to complete the intervention at the end of February 2024, and the postintervention evaluation will be conducted in March 2024.

CONCLUSIONS

This protocol outlines the randomized controlled trial study design evaluating the effect of a 6-month intervention with PICMOA-2. This study will provide evidence on the effectiveness of social interventions on cognitive function and identify effective target images for remote social intervention.

TRIAL REGISTRATION

UMIN Clinical Trials UMIN000050877; https://tinyurl.com/5eahsy66.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/56608.

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. A focus on the feasibility of the intervention at participants' homes was also maintained. Feasibility was evaluated by considering both the dropout rate during the intervention and the number of questions posed to each participant during the experiment.

Methods

We conducted a randomized controlled trial with 81 adults older than 65 years. Participants were recruited through postal invitations and then randomized into 2 groups. The intervention group (n=40) received sessions where participants listened to photo-integrated stories and posed questions to the robots. The control group (n=41) received sessions where participants listened to photo-integrated stories and only thanked the robots for confirming participation. The participants participated in 12 dialogue sessions for 2-3 weeks. Scores of global cognitive functioning tests, recall tests, and verbal fluency tasks measured before and after the intervention were compared between the 2 groups.

Results

There was no significant intervention effect on the Telephone Interview for Cognitive Status-Japanese scores, recall tests, and verbal fluency tasks. Additionally, our study successfully concluded with no participant dropouts at follow-up, confirming the feasibility of our approach.

Conclusions

There was no statistically significant evidence indicating intervention benefits for cognitive functioning. Although the feasibility of home-based interventions was demonstrated, we identified areas for improvement in the future, such as setting up more efficient session themes. Further research is required to identify the effectiveness of an improved cognitive intervention involving the act of asking questions.

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. This paper introduces the PICMOA intervention and the protocol of the ongoing randomized controlled trial (RCT), which aims to evaluate the effects of PICMOA on the cognitive functioning and psychological well-being of Japanese community dwelling older adults at the risk of cognitive function decline.

Methods

This study uses an RCT design in two parallel group trials with 1:1 allocation. The participants are community dwelling older adults aged 65 years and above, living in an urban city in Japan, with subjective cognitive concerns. In total, 81 participants were allocated to the intervention or control groups. The intervention group receives 30 min of weekly PICMOA sessions at their home for 12 weeks. The PICMOA intervention consists of (1) a photo preparation period before the session and (2) a structured group conversation session talking about the photos that participants took according to a specific theme. The control group receives 30 min of weekly health education videos on a tablet device. The primary outcome is cognitive functioning at pre- and post-phases of the 12-week intervention measured using the Telephone Interview for Cognitive Status in Japanese, semantic and phonemic fluency tests, and the Digit Span Forward and Backward tests. The secondary outcomes are psychological and social aspects including mental status, well-being, loneliness, and social support.

Discussion

Interest is growing in internet-based activities for preventing social isolation. However, the effect of remote conversation interventions on cognitive functioning remains unclear. This study addresses this issue and provides a new avenue of social participation for older adults.

Clinical trial registration

https://www.umin.ac.jp/ctr/, identifier: UMIN000047247.

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 in vivo 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. Recently, we developed a cognitive intervention program called Photo-Integrated Conversation Moderated by Robots (PICMOR), which utilizes one of the most intellectual activities of daily life, conversations. To examine the effects of PICMOR on cognitive function in older adults, we conducted a randomized controlled trial and found that verbal fluency task scores were improved by this intervention. Based on these behavioral findings, we collected in this pilot study diffusion-weighted images from the participants to identify candidate structures for white matter microstructural changes induced by this intervention. The results from tract-based spatial statistics analyses showed that the intervention group, who participated in PICMOR-based conversations, had significantly higher FA values or lower MD, AD, or RD values across various fiber tracts, including the left anterior corona radiata, external capsule, and anterior limb of the internal capsule, compared to the control group, who participated in unstructured free conversations. Furthermore, a larger improvement in verbal fluency task scores throughout the intervention was associated with smaller AD values in clusters, including the left side of these frontal regions. The present findings suggest that left frontal white matter structures are candidates for the neural underpinnings responsible for the enhancement of verbal fluency. Although our findings are limited by the lack of comparable data at baseline, we successfully confirmed the hypothesized pattern of group differences in DTI indices after the intervention, which fits well with the results of other cognitive intervention studies. To confirm whether this pattern reflects intervention-induced white matter alterations, longitudinal data acquisition is needed in future research.

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. However, little is known about the neural substrates that underlie the beneficial effect of conversation-based interventions on cognitive function. In this pilot study, we aimed to provide candidate brain regions that are responsible for the enhancement of cognitive function, by analyzing structural magnetic resonance imaging (MRI) data that were additionally obtained from participants in our previous intervention study.

Methods

A voxel-based morphometric analysis was applied to the structural MRI data. In the analysis, the regional brain volume was compared between the intervention group, who participated in a group conversation-based intervention program named Photo-Integrated Conversation Moderated by Robots (PICMOR), and the control group, who joined in a control program based on unstructured free conversations. Furthermore, regions whose volume was positively correlated with an increase in verbal fluency task scores throughout the intervention period were explored.

Results

Results showed that the volume of several regions, including the superior frontal gyrus, parahippocampal gyrus/hippocampus, posterior middle temporal gyrus, and postcentral gyrus, was greater in the intervention group than in the control group. In contrast, no regions showed greater volume in the control group than in the intervention group. The region whose volume showed a positive correlation with the increased task scores was identified in the inferior parietal lobule.

Conclusions

Although definitive conclusions cannot be drawn from this study due to a lack of MRI data from the pre-intervention period, it achieved the exploratory purpose by successfully identifying candidate brain regions that reflect the beneficial effect of conversation-based interventions on cognitive function, including the lateral prefrontal cortex, which plays an important role in executive functions.

Trial registration

The trial was retrospectively registered on 7 May 2019 (UMIN Clinical Trials Registry number: UMIN000036667).

Supplementary Information

The online version contains supplementary material available at 10.1186/s12877-021-02669-x.

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. For the digital biomarker of dementia onset detection, we examine shallow- and deep-learning machine learning models. We report the best median accuracies in a range of 90% for random forest and fully connected neural network classifier models in both emotional faces learning and evaluation experimental tasks. In addition, the classifiers are trained in a ten-fold cross-validation regime to discriminate normal versus mild cognitive impairment (MCI) cognition stages using MFDFA patterns from four-channel EEG recordings. Thirty-five volunteer elderly subjects participate in the current study concentrating on simple wearable EEG-based objective dementia biomarker progression. The reported outcomes showcase an essential social benefit of artificial intelligence (AI) employment for early dementia prediction. Furthermore, we improve ML employment for the succeeding application in an uncomplicated and applied EEG-wearable examination.

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. Then, we compare shallow and deep learning machine learning models for a digital biomarker of dementia onset detection. The evaluated machine-learning models succeed in the most reliable median accuracies above 80% using random forest and fully connected neural network classifiers in automatic discrimination of normal cognition versus a mild cognitive impairment (MCI) task. The classifier input features consist of MSE patterns only derived from four dry EEG electrodes. Fifteen elderly subjects voluntarily participate in the reported study focusing on EEG-based objective dementia biomarker advancement. The results showcase the essential social advantages of artificial intelligence (AI) application for the dementia prognosis and advance ML for the subsequent use for simple objective EEG-based examination.Clinical relevance- This manuscript introduces an objective biomarker from EEG recorded by a wearable for a plausible replacement of a mild cognitive impairment (MCI) evaluation using usual biased paper and pencil examinations.