Edited by:
Reviewed by:
*Correspondence:
†
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Neuroimaging studies have documented that aging can disrupt certain higher cognitive systems such as the default mode network (DMN), the salience network and the central executive network (CEN). The effect of cognitive training on higher cognitive systems remains unclear. This study used a 1-year longitudinal design to explore the cognitive training effect on three higher cognitive networks in healthy older adults. The community-living healthy older adults were divided into two groups: the multi-domain cognitive training group (24 sessions of cognitive training over a 3-months period) and the wait-list control group. All subjects underwent cognitive measurements and resting-state functional magnetic resonance imaging scanning at baseline and at 1 year after the training ended. We examined training-related changes in functional connectivity (FC) within and between three networks. Compared with the baseline, we observed maintained or increased FC within all three networks after training. The scans after training also showed maintained anti-correlation of FC between the DMN and CEN compared to the baseline. These findings demonstrated that cognitive training maintained or improved the functional integration within networks and the coupling between the DMN and CEN in older adults. Our findings suggested that multi-domain cognitive training can mitigate the aging-related dysfunction of higher cognitive networks.
As people age, they experience cognitive decline, which involves working memory, executive function and attention, among other functions, and this occurs with particular frequency in late life, resulting in an increasingly poor quality of life (
Recent studies suggest that functional brain networks, a higher level of organization of brain regions, are needed to better understand improved cognitive function after intervention (
Previous studies have shown that brain networks are affected by interventions, such as the DMN and CEN, associated with improved behavioral outcomes and cognitive performances (
The purpose of this study is to explore the effects of cognitive training on resting-state FCs within and between the three networks in old adults. We hypothesized that multi-domain cognitive training would result in changes of resting-state FC within and among the three networks in healthy older adults, which might be beneficial for counteracting decreased integration within network and impaired coupling between networks with aging. In our work, we investigated the resting-state FC of three networks using seed-based FC analysis at baseline and at 1 year after training ended. The relationship between cognitive improvements and the changes of FC was also analyzed.
The current work included 40 healthy older adults from two randomized groups [the multi-domain training group (
Cognitive training employed a randomized, controlled design. The multi-domain training group was divided into a small group, and the training procedure took place in a classroom in Tongji Hospital. All participants received 24 sessions of cognitive training over a 3-months period at a frequency of twice a week. The multi-domain cognitive training targeted memory, reasoning, and problem-solving strategies using approaches such as visual-spatial map reading, handcraft making, healthy living, and physical exercise. Each session lasted 60 min. A lecture about a common disease in aging people was presented during the first 15 min of each session. Then, the trainer taught the participants about a special cognitive strategy or technique via slide lecture during the second 30 min. The newly practiced skills were consolidated by dealing with some real-life problems during the last 15 min. The wait-list control group served as a match for the social contact associated with cognitive training. The multi-domain training group and the control group attended a lecture about healthy living every 2 months. More details about the training are provided in our previous study (
To evaluate the effects of intervention on cognitive function, composite outcome measurements were created, including the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS, Form A), which shows good validity and reliability in a Chinese community-living elderly sample (
To examine the effect of cognitive training on cognition, we compared the pre- and post-training changes between the multi-domain cognitive group and the control group using one-way analyses of covariance (ANCOVAs) with the differences between the 1-year post-test and baseline measurements as dependent variables and scores at baseline as covariates to exclude the possibility that any pre-existing difference in the measures between the groups affected the results of each measure (
All functional and structural imaging data were acquired using a Siemens 3T MRI scanner (Erlangen, Germany) at East China Normal University, Shanghai, China. All participants underwent scanning twice, at baseline and at 1 year after training. To minimize head motion, we fixed the subjects’ heads using foam pads. Resting-state functional images were collected using a single-shot, gradient-recalled echo planar imaging sequence [repetition time (TR) = 2000 ms, echo time (TE) = 25 ms and flip angle (FA) = 90°, field of view (FOV) = 240 mm × 240 mm, in-plane matrix = 64 × 64, slice thickness = 5 mm, voxel size = 3.75 mm × 3.75 mm × 5 mm], generating 32 slices. The functional scanning lasted for 310 s, yielding a total of 155 volumes. To ensure magnetic field stabilization, the first five volumes were discarded. During the resting-state scanning, the subjects were asked to lie with their eyes closed, not to fall asleep, and not to think of anything in particular. Axial T1-weighted anatomical images were acquired using a magnetisation-prepared rapid gradient-echo sequence, generating 160 slices (TR = 1900 ms, TE = 3.43 ms, FA = 90°, matrix size = 256 × 256, FOV = 240 mm × 240 mm, slice thickness = 1 mm, voxel size = 0.9375 mm × 0.9375 mm × 1 mm).
Imaging data preprocessing was performed using the SPM8 software package
where
The FD0 was set to zero and rotational displacements were converted from degrees to millimeters (
Previous studies provided the evidence of the loss of gray matter volume (GMV) with aging and of the potential effects on the functional results (
To evaluate our hypothesis, a seed-based FC analysis was performed to examine the training effect on FC within and between the DMN, SN, and CEN. Three seeds were selected based on previous studies: the PCC (0, -52, 20), the right AI (38, 26, -10) and the right dorsolateral prefrontal cortex [right DLPFC (44, 36, 20)] to constitute the DMN, SN, and CEN, respectively (
To evaluate the training effect on FC within and between the three networks, a whole-brain voxel-wise 2 (between-subject factor: training and control groups) × 2 (within-subject factor: baseline and 1-year after training ending) repeated analysis of variance (ANOVA) was performed using the explicit mask from the union set of the one-sample
To explore the relationship between FC and cognitive measurements, z-transformed connectivity values were extracted from regions that showed significant training-related effect, and the difference between the baseline and post-test was computed. Partial correlations were performed between the changes of resting-state FC and the pre- and post-training differences in the scores on the cognitive measures, while controlling for age, gender, and education years.
At baseline, 23 subjects (one left-handed) from the multi-domain training group and 17 subjects from the control group underwent cognitive measurements and the fMRI scanning. Eighteen subjects in the multi-domain training group and 14 in the control group finished both the cognitive measurements and the fMRI scanning at 1 year after the intervention, and they were involved in the final analysis. A total of eight participants withdrew (one death, two cancers, one operation, and four rejecting scanning). No significant differences in age, gender, education years, FD and MMSE score were found between the multi-domain training group and the control group (
Demographic information about the subjects at different timepoints.
Multi-domain training group | Control group | |||
---|---|---|---|---|
Age (year) | Baseline | 70.61 ± 3.29 | 68.59 ± 3.24 | 0.838 |
(mean ± SD) | One-year post-test | 72.39 ± 3.43 | 70.85 ± 4.05 | 0.782 |
Gender (male) | Baseline | 23 (16) | 17 (9) | 0.283 |
One-year post-test | 18 (13) | 14 (9) | 0.631 | |
Education (year) | Baseline | 10.91 ± 3.65 | 10.64 ± 3.06 | 0.452 |
(mean ± SD) | One-year post-test | 11.11 ± 4.25 | 10.09 ± 3.34 | 0.383 |
MMSE | Baseline | 27.57 ± 2.57 | 28.17 ± 1.94 | 0.505 |
(mean ± SD) | One-year post-test | 27.72 ± 2.16 | 27.85 ± 2.31 | 0.900 |
FD | Baseline | 0.12 ± 0.04 | 0.14 ± 0.05 | 0.448 |
(mean ± SD) | One-year post-test | 0.12 ± 0.04 | 0.16 ± 0.07 | 0.068 |
No significant differences in all cognitive measurements were found at baseline between the two groups (
Baseline and 1-year post-test scores for psychological measures (mean ± SD).
Multi-domain training |
Contorl |
|||||
---|---|---|---|---|---|---|
Baseline | One-year Post-test | Baseline | One-year Post-test | |||
RBANS total score | 93.17 ± 16.10 | 106.94 ± 12.90 | 93.07 ± 15.00 | 99.57 ± 13.75 | 0.058 | 0.971 |
Immediate memory | 86.22 ± 16.45 | 103.17 ± 23.35 | 84.00 ± 14.34 | 100.5 ± 14.33 | 0.695 | 0.694 |
Visuospatial | 106.78 ± 12.86 | 103.9 ± 11.56 | 106.86 ± 19.05 | 103.00 ± 16.81 | 0.981 | 0.922 |
Language | 92.44 ± 12.92 | 101.00 ± 8.39 | 93.29 ± 7.12 | 95.86 ± 5.86 | 0.861 | |
Attention | 90.67 ± 19.39 | 94.94 ± 15.17 | 91.5 ± 15.25 | 88.93 ± 15.24 | 0.22 | 0.776 |
Delayed memory | 98.28 ± 18.86 | 118.50 ± 12.41 | 99.00 ± 13.13 | 110.21 ± 14.80 | 0.999 | |
The CWST | ||||||
Color interfere | 20.16 ± 13.95 | 27.33 ± 17.01 | 14.57 ± 7.62 | 23.85 ± 14.33 | 0.884 | 0.125 |
Word interfere | 38.61 ± 15.56 | 39.89 ± 17.35 | 39.71 ± 14.10 | 42.00 ± 19.00 | 0.669 | 0.642 |
The Visual Reasoning Test | 5.78 ± 1.35 | 6.56 ± 1.89 | 5.86 ± 2.74 | 5.93 ± 2.46 | 0.169 | 0.894 |
The TMT | ||||||
Trail A complete time | 79.50 ± 23.60 | 73.72 ± 33.93 | 87.21 ± 37.37 | 77.07 ± 29.22 | 0.739 | 0.381 |
Trail B complete time | 157.89 ± 55.41 | 138.5 ± 48.27 | 153.5 ± 68.11 | 142.57 ± 49.6 | 0.634 | 0.830 |
Three networks were constituted by seed-based FC analysis, seeding at the PCC for the DMN, at the right AI for the SN and at the right DLPFC for the CEN. The DMN mainly involved the PCC/precuneus, VMPFC, DMPFC, bilateral IPL, bilateral temporal lobe, cerebellar lobule IX and crus II (Supplementary Figure
The 2 × 2 repeated measure ANOVA revealed significant interactions (
The ANOVA revealed the significant interactions for the FC within the DMN [PCC – left superior frontal gyrus (SFG) and PCC – cerebellar lobule IX]; within the SN (right AI – right middle/posterior insula, and right AI – left frontoinsula) and within the CEN (right DLPFC – bilateral DLPFC and right DLPFC – right SFG).
Brain areas showing significant interactions for resting-state functional connectivity using a 2 (between-subject factor: training and control groups) × 2 (within-subject factor: baseline and 1-year after training ended) repeated ANOVA.
Seed | Brain region | BA | MNI coordinate |
Cluster size (mm3) | |||
---|---|---|---|---|---|---|---|
PCC | Left dorsolateral prefrontal lobe | 20 | -33 | 15 | 24 | 18.64 | 2187 |
Left cerebellar lobule IX | -6 | -51 | -54 | 16.2 | 2241 | ||
Right putamen | 9 | 18 | 15 | -9 | 13.17 | 1377 | |
Right dorsolateral prefrontal lobe | 51 | 0 | 30 | 12.86 | 1782 | ||
Left superior frontal gyrus | 20 | -24 | 36 | 39 | 11.66 | 1323 | |
Right AI | Right insula | 45 | 30 | -9 | -18 | 15.82 | 4644 |
Left frontoinsula | 45 | -51 | 33 | -12 | 11.67 | 2538 | |
Right DLPFC | Right superior frontal cortex | 11 | 15 | 27 | 54 | 13.30 | 3186 |
Left dorsolateral prefrontal cortex | 9 | -39 | 30 | 42 | 12.78 | 1647 | |
Left inferior parietal lobe | -30 | -63 | 30 | 13 | 1431 | ||
Right superior frontal gyrus | 42 | 27 | 42 | 12.12 | 2970 |
Our results also revealed significant interactions in the FCs between networks, including between DMN and CEN and between DMN and SN.
We found a significant relationship between the changes in FC (between the two fMRI sessions) and the improvements in cognitive performances (
In the present study, we used resting-state fMRI analysis to examine the effects of multi-domain cognitive training on FC within and among three networks (DMN, SN, and CEN). Consistent with our
This study found training-related enhanced integration of FC within the DMN, SN, and CEN. In particular, we observed that FCs between PCC and SFG and between PCC and cerebellar lobule IX were maintained in the multi-domain training group, but decreased in the control group. Previous studies have provided consistent evidence that healthy aging is accompanied by decreased FC within the DMN, particularly an anterior–posterior disruption of FC (
Recent studies demonstrated that the decreased FC within the SN is an important feature of normal aging and is associated with the cognitive impairments (
The triple network model of the DMN, SN, and CEN has been used to understand cognitive dysfunction in neurological and psychiatric disorders (
Interpreting the present findings requires the consideration of a few key limitations. First, our demographic characteristics, such as sample size and educational level, might affect the generalisability of the results. A larger sample size and a more consistent educational level among the subjects are necessary to verify the effect of cognitive training on the resting-state FC of three networks. Second, because the imaging data were not acquired at intermediate stages and or immediately after the completion of training completion, the effects of training on FCs within and between networks at additional intermediate timepoints and immediately after the completion of training are not studied. Third, we considered the effects of brain atrophy on FC in this work. However, the other focal damage (i.e., vascular lesions) is prevalent in the population and may have contributed to our findings. Fourth, the effects of cognitive training on resting-state FCs of three networks were observed in our study. This result suggested that the training was valid in the current population; however, further work should be performed to verify these conclusions in other populations. Finally, another control group of younger subjects should be considered in future work to determine whether the effects of cognitive training are specific to older adults.
We found changes in resting-state FC within networks and between the three networks at 1 year post-training, perhaps reflecting that cognitive training enhanced integration within network and maintenance of segregation between DMN and CEN. Our findings provide evidence that multi-domain cognitive training could mitigate the age-related functional alterations involving DMN, SN and CEN, thereby helping older adults maintain brain health.
Conceived and designed the work: WC, Cheng Luo, Chunbo Li, DY. Acquired the data: YC, XC, TL, LJ. Analyzed the data: WC, CH, XC, YC. Wrote the paper: WC, Cheng Luo. All authors revised the work for important intellectual content. All of the authors have read and approved the manuscript.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
This project was funded by the National Nature Science Foundation of China (81330032, 81371505, 91232725, 81271547, and 30770769), the Science and Technology Commission of Shanghai Municipality (134119a2501, 13dz2260500) and SHSMU-ION Research Center for Brain Disorders. This study was also sponsored by the ‘111’ project of China (No. B12027).
The Supplementary Material for this article can be found online at: