Participants
In total, 65 healthy community-dwelling older adults, who participated in our previous RCT study (32 participants in INT and 33 participants in CONT) [4], were recruited in the present study. A total of 61 participants, including 31 participants in INT and 30 participants in CONT, took part in MRI scanning. MRI data from four RCT participants were not collected in this study because one participant in INT and one participant in CONT had claustrophobia, one participant in CONT was equipped with a heart pacemaker, and one participant in CONT declined to participate in the MRI scanning. All participants were right-handed and native Japanese-speaking individuals. We confirmed that there were no significant differences in age [t(df = 59) = 1.01, p = 0.32], gender [χ2(df = 1, n = 61) = 0.15, p = 0.70], and educational level [χ2(df = 1, n = 61) = 0.13, p = 0.71] between the two groups (educational level was binarized with a border of 13 years to categorize participants depending on whether they went to university/college or not) (see Table 1).
Table 1
Participants’ characteristics
| INT (N = 31) | CONT (N = 30) |
Age (mean ± SD) | 72.84 ± 3.45 | 72.03 ± 2.72 |
Gender (males : females) | 16 : 15 | 13 : 17 |
Educational level (≥ 13 years : < 13 years) | 20 : 11 | 17 : 13 |
Amount of talking time in group conversations during the intervention period** (sec) (mean ± SD) | 5000.52 ± 800.63 | 7627.19 ± 3377.06 |
PVFT score at the pre-intervention (mean ± SD) | 12.03 ± 3.61 | 11.13 ± 4.07 |
PVFT score at the post-intervention** (mean ± SD) | 13.71 ± 3.55 | 11.07 ± 2.95 |
Notes: We lack conversational data of one out of 12 group conversations from two CONT groups due to technical errors. Asterisks represent the variables showing significant difference between INT and CONT (p < 0.01). |
Abbreviations: INT, the intervention group; CONT, the control group; PVFT, phonemic verbal fluency task; SD, standard deviation. |
Intervention program
The procedures of the PICMOR intervention and control programs are described in detail below. Both the intervention and control programs were based on a group conversation. A total of 65 people, who were recruited from the Silver Human Resources Center, were divided into 16 groups (8 for INT and 8 for CONT), each with four members (only one group in CONT has five members). The participants were required to participate in the group conversation once a week for 12 weeks. One of the major differences in the two programs is whether the programs were designed to train executive functions or not.
In the group conversation offered by PICMOR, a robot acted as a chair to lead the conversations and prompted one of the four members to speak about an event they experienced in their daily life for 1 minute. The topic was a predetermined subject that changed every week. During the 1-minute talk, the other three members had to listen carefully to be able to ask questions during the subsequent discussion period. The 1-minute talk period was repeated without a break, in which they talked about another event related to the topic for 1 minute (i.e., each participant was assigned a total of 2 minutes to make a speech). Following the 1-minute talks, there were 2-minute discussion periods for each event during which the speaker was required to answer questions raised by the other three members. During the discussion, the robot automatically encouraged and stopped the participants’ utterances to balance the amount of talking time for each participant. For example, when the robot detected that one participant had spent less time talking in the conversation compared to others, it directly prompted the participant to provide questions or comments. After the 2-minute discussion periods, another member was assigned as a speaker and the 1-minute talks for the participant were followed. A series of this procedure (i.e., the 1-minute talks and the 2-minute discussions) was repeated for all members in group conversations. There were two major reasons to employ a robot but not a human as a chair in the group conversation. First, we could force the participants to make a speech during the predetermined time period and finish their talk when the time was over, excluding uncontrollable personal factors originated from the human chair, such as hesitation. Time management by the robot made it possible to give each member an equally predetermined time (i.e., 1 minute) to talk about an event. Second, it would be difficult for a human chairperson to play the moderator role to prompt and stop conversations in real-time based on the talking time for each person.
In contrast, in the group conversations offered by the control program, four members were required to talk freely without any robotic facilitation or predetermined theme, similar to how they take part in conversations in their daily life. As shown in Table 1, the variance of the amount of talking time in group conversations during the intervention period was smaller in INT than in CONT. In an F-test to compare the variances between the two groups, a null hypothesis, i.e., true ratio of variances is 1, was rejected (ratio of variances = 17.79, p < 0.01). This suggests that our experimental manipulation by robotic moderation to balance the amount of talking time for each participant in INT was successful. We hypothesized that repeated trainings in group conversations in PICMOR compared to the control program would exercise executive functions, such as flexibility, planning, working memory, and response inhibition, given that the participants have to make a speech within a limited time span (i.e., 1 minute), flexibly ask and answer questions, intentionally store and manipulate the information to ask questions, and suppress the interruption of other members in a group conversation. As noted earlier, executive control abilities can be measured via the verbal fluency task [6]. To successfully perform this task, participants must flexibly retrieve words along with the task rules, keep previously produced words in their working memory to avoid repetition, and suppress inappropriate words or task-irrelevant thoughts [6, 8, 9]. Given that the characteristics of the training demands in PICMOR involve exercising executive functions, it is reasonable to predict that the ability to produce words within a limited time could be enhanced in INT compared with CONT between the pre- and post-intervention periods. Consistent with this idea, a significantly larger improvement in the PVFT score was observed in INT than in CONT in our previous RCT study [4]. The beneficial intervention effect on verbal fluency could not be explained by the amount of talking time in group conversations during the intervention period, given that the talking time was significantly shorter in INT than in CONT [t(df = 59) = 4.21, p < 0.01, Cohen’s d = 1.08] (see Table 1).
Data acquisition
All MRI data were acquired with a Philips Achieva 3.0 MRI scanner, located in the Advanced Imaging Center Yaesu Clinic, Tokyo. The data were collected only after the intervention. During the MRI scanning, participants were equipped with a set of earplugs and headphones to reduce the effects of scanner noise and a belt with foam pads around their head to minimize head motion. There was no significant difference in the time period from the last day of the intervention period to the day of MRI data acquisition between INT (mean ± SD = 9.67 ± 0.76 weeks) and CONT (mean ± SD = 9.68 ± 0.56 weeks) [t(df = 59) = 0.05, p = 0.96].
Firstly, three directional T1-weighted anatomical planes were scanned to localize the subsequent anatomical and functional images. Subsequently, anatomical structures were scanned by a high-resolution T1-weighted image [repetition time (TR) = 6.41 ms, echo time (TE) = 3.00 ms, field of view (FOV) = 24.0 cm × 24.0 cm, matrix size = 256 × 256, slice thickness/gap = 1.2/0 mm, 170 sagittal slices]. Finally, resting-state functional images were scanned by a pulse sequence of gradient-echo echo-planar imaging, which is sensitive to blood oxygenation level-dependent (BOLD) contrasts (TR = 3000 ms, TE = 30 ms, flip angle = 80 degrees, FOV = 24.0 cm × 24.0 cm, matrix size = 80 × 80, slice thickness/gap = 4.0/0 mm, 35 horizontal slices). All participants were instructed to remain awake with their eyes open and think of nothing during the entire rsfMRI scanning for 10 minutes. The rsfMRI run began with dummy scans that were discarded from further analyses.
Data analysis
All MRI data were analyzed by the CONN functional connectivity toolbox v.17.f (www.nitrc.org/projects/conn) [18] for Statistical Parametric Mapping 12 (SPM 12) (www.fil.ion.ucl.ac.uk/spm/), implemented in MATLAB→. Resting-state functional images were preprocessed along the default pipeline in CONN. The images were realigned and corrected for slice timing. After the outlier detection, the functional and structural images were segmented and normalized to the Montreal Neurological Institute (MNI) space with a resolution of 2 × 2 × 2 mm3 voxels. Then, these normalized functional images were spatially smoothed by a Gaussian kernel of 8 mm full-width at half-maximum. After that, a temporal correction was performed using the component-based noise correction method [19]. In this step, five principle components were extracted from the white matter and cerebrospinal fluid regions. Along with six bulk motion parameters, the first-order derivative for each of the motion parameters, and scrubbing parameter, the five principal components were regressed out from the signal of interests. The scrubbing parameter was provided by the Artifact Detection Tools in CONN that could detect outliers based on the variance of whole movements. A band-pass filter with a frequency window of 0.008–0.09 Hz and detrending were then applied to the data.
In the present study, we employed seed-to-voxel analyses for the rsfMRI data. Seed regions were defined as spheres with a 5 mm radius, around (− 50, 12, 24), (− 48, 28, 14), (− 52, 12, 0), (− 42, 8, 36), (− 54, 2, 46), and (− 44, 18, 6) in MNI coordinates, located in the left inferior and middle frontal gyri, based on a previous meta-analytic neuroimaging study demonstrating that these regions consistently show significant activation during PVFTs [12]. The anatomical mask of these seed regions was created using MarsBaR (www.marsbar.sourceforge.net). In the individual level analysis, the mean BOLD time course was extracted from each seed region and correlation coefficients were calculated with the BOLD time course of each voxel, throughout the whole brain. The coefficients were converted to normally distributed scores using Fisher’s transformation. This procedure yielded individual rsFC maps for each seed region. In the group level analysis, the rsFC maps identified in the first level analysis of INT and CONT were compared by two-sample t-tests. The models included participants’ age, gender, and educational level as covariates. In this second level analysis, the threshold at the cluster level was corrected for whole-brain multiple comparisons [false discovery rate (FDR); p < 0.05]. Given that we selected six seed regions for analysis, we also employed a stringent statistical significance in the two-sample t-tests. In this case, the height threshold was divided by the number of seed regions (FDR, p < 0.05/6).
For complementary analysis, we performed regression analyses for rsFCs using the raw scores of PVFT, which were collected from all participants before and after the intervention. In this analysis, regions showing significant correlations between the rsFCs with seed regions and the difference in the individual PVFT score between the pre- and post-intervention periods (i.e., post- minus pre-intervention) as a dependent variable were explored at the whole-brain level. This analysis enabled us to find regions modulating the increase in the score by interacting with the left inferior and middle frontal gyri as seed regions. Participants’ age, gender, and educational level were also included as covariates in the analysis. The threshold at the cluster level was corrected for whole-brain multiple comparisons (FDR, p < 0.05).