Resting State Networks Related to the Maintenance of Good Cognitive Performance During Healthy Aging
Purpose: Maintenance of cognitive performance is important for healthy aging. This study aims to elucidate the relationship between brain networks and cognitive function in subjects maintaining relatively good cognitive performance.Methods: A total of 120 subjects, with equal number of participants...
Guardado en:
| Autores principales: | , , , , , , , , , , , , , , , |
|---|---|
| Formato: | article |
| Lenguaje: | EN |
| Publicado: |
Frontiers Media S.A.
2021
|
| Materias: | |
| Acceso en línea: | https://doaj.org/article/9ee705e984c7400ca2594656fd18a053 |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| id |
oai:doaj.org-article:9ee705e984c7400ca2594656fd18a053 |
|---|---|
| record_format |
dspace |
| spelling |
oai:doaj.org-article:9ee705e984c7400ca2594656fd18a0532021-11-05T14:18:43ZResting State Networks Related to the Maintenance of Good Cognitive Performance During Healthy Aging1662-516110.3389/fnhum.2021.753836https://doaj.org/article/9ee705e984c7400ca2594656fd18a0532021-11-01T00:00:00Zhttps://www.frontiersin.org/articles/10.3389/fnhum.2021.753836/fullhttps://doaj.org/toc/1662-5161Purpose: Maintenance of cognitive performance is important for healthy aging. This study aims to elucidate the relationship between brain networks and cognitive function in subjects maintaining relatively good cognitive performance.Methods: A total of 120 subjects, with equal number of participants from each age group between 20 and 70 years, were included in this study. Only participants with Addenbrooke’s Cognitive Examination – Revised (ACE-R) total score greater than 83 were included. Anatomical T1-weighted MR images and resting-state functional MR images (rsfMRIs) were taken from all participants using a 3-tesla MRI scanner. After preprocessing, several factors associated with age including the ACE-R total score, scores of five domains, sub-scores of ACE-R, and brain volumes were tested. Morphometric changes associated with age were analyzed using voxel based morphometry (VBM) and changes in resting state networks (RSNs) were examined using dual regression analysis.Results: Significant negative correlations with age were seen in the total gray matter volume (GMV, r = −0.58), and in the memory, attention, and visuospatial domains. Among the different sub-scores, the score of the delayed recall (DR) showed the highest negative correlation with age (r = −0.55, p < 0.001). In VBM analysis, widespread regions demonstrated negative correlation with age, but none with any of the cognitive scores. Quadratic approximations of cognitive scores as functions of age showed relatively delayed decline compared to total GMV loss. In dual regression analysis, some cognitive networks, including the dorsal default mode network, the lateral dorsal attention network, the right / left executive control network, the posterior salience network, and the language network, did not demonstrate negative correlation with age. Some regions in the sensorimotor networks showed positive correlation with the DR, memory, and fluency scores.Conclusion: Some domains of the cognitive test did not correlate with age, and even the highly correlated sub-scores such as the DR score, showed delayed decline compared to the loss of total GMV. Some RSNs, especially involving cognitive control regions, were relatively maintained with age. Furthermore, the scores of memory, fluency, and the DR were correlated with the within-network functional connectivity values of the sensorimotor network, which supported the importance of exercise for maintenance of cognition.Satoshi MaesawaSatoshi MaesawaSatomi MizunoEpifanio BagarinaoHirohisa WatanabeHirohisa WatanabeKazuya KawabataKazuya KawabataKazuhiro HaraReiko OhdakeAya OguraDaisuke MoriDaisuke MoriDaisuke NakatsuboHaruo IsodaMinoru HoshiyamaMasahisa KatsunoRyuta SaitoNorio OzakiNorio OzakiGen SobueGen SobueFrontiers Media S.A.articleresting state networkaginghealthy cohortcognitiondelayed recallNeurosciences. Biological psychiatry. NeuropsychiatryRC321-571ENFrontiers in Human Neuroscience, Vol 15 (2021) |
| institution |
DOAJ |
| collection |
DOAJ |
| language |
EN |
| topic |
resting state network aging healthy cohort cognition delayed recall Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 |
| spellingShingle |
resting state network aging healthy cohort cognition delayed recall Neurosciences. Biological psychiatry. Neuropsychiatry RC321-571 Satoshi Maesawa Satoshi Maesawa Satomi Mizuno Epifanio Bagarinao Hirohisa Watanabe Hirohisa Watanabe Kazuya Kawabata Kazuya Kawabata Kazuhiro Hara Reiko Ohdake Aya Ogura Daisuke Mori Daisuke Mori Daisuke Nakatsubo Haruo Isoda Minoru Hoshiyama Masahisa Katsuno Ryuta Saito Norio Ozaki Norio Ozaki Gen Sobue Gen Sobue Resting State Networks Related to the Maintenance of Good Cognitive Performance During Healthy Aging |
| description |
Purpose: Maintenance of cognitive performance is important for healthy aging. This study aims to elucidate the relationship between brain networks and cognitive function in subjects maintaining relatively good cognitive performance.Methods: A total of 120 subjects, with equal number of participants from each age group between 20 and 70 years, were included in this study. Only participants with Addenbrooke’s Cognitive Examination – Revised (ACE-R) total score greater than 83 were included. Anatomical T1-weighted MR images and resting-state functional MR images (rsfMRIs) were taken from all participants using a 3-tesla MRI scanner. After preprocessing, several factors associated with age including the ACE-R total score, scores of five domains, sub-scores of ACE-R, and brain volumes were tested. Morphometric changes associated with age were analyzed using voxel based morphometry (VBM) and changes in resting state networks (RSNs) were examined using dual regression analysis.Results: Significant negative correlations with age were seen in the total gray matter volume (GMV, r = −0.58), and in the memory, attention, and visuospatial domains. Among the different sub-scores, the score of the delayed recall (DR) showed the highest negative correlation with age (r = −0.55, p < 0.001). In VBM analysis, widespread regions demonstrated negative correlation with age, but none with any of the cognitive scores. Quadratic approximations of cognitive scores as functions of age showed relatively delayed decline compared to total GMV loss. In dual regression analysis, some cognitive networks, including the dorsal default mode network, the lateral dorsal attention network, the right / left executive control network, the posterior salience network, and the language network, did not demonstrate negative correlation with age. Some regions in the sensorimotor networks showed positive correlation with the DR, memory, and fluency scores.Conclusion: Some domains of the cognitive test did not correlate with age, and even the highly correlated sub-scores such as the DR score, showed delayed decline compared to the loss of total GMV. Some RSNs, especially involving cognitive control regions, were relatively maintained with age. Furthermore, the scores of memory, fluency, and the DR were correlated with the within-network functional connectivity values of the sensorimotor network, which supported the importance of exercise for maintenance of cognition. |
| format |
article |
| author |
Satoshi Maesawa Satoshi Maesawa Satomi Mizuno Epifanio Bagarinao Hirohisa Watanabe Hirohisa Watanabe Kazuya Kawabata Kazuya Kawabata Kazuhiro Hara Reiko Ohdake Aya Ogura Daisuke Mori Daisuke Mori Daisuke Nakatsubo Haruo Isoda Minoru Hoshiyama Masahisa Katsuno Ryuta Saito Norio Ozaki Norio Ozaki Gen Sobue Gen Sobue |
| author_facet |
Satoshi Maesawa Satoshi Maesawa Satomi Mizuno Epifanio Bagarinao Hirohisa Watanabe Hirohisa Watanabe Kazuya Kawabata Kazuya Kawabata Kazuhiro Hara Reiko Ohdake Aya Ogura Daisuke Mori Daisuke Mori Daisuke Nakatsubo Haruo Isoda Minoru Hoshiyama Masahisa Katsuno Ryuta Saito Norio Ozaki Norio Ozaki Gen Sobue Gen Sobue |
| author_sort |
Satoshi Maesawa |
| title |
Resting State Networks Related to the Maintenance of Good Cognitive Performance During Healthy Aging |
| title_short |
Resting State Networks Related to the Maintenance of Good Cognitive Performance During Healthy Aging |
| title_full |
Resting State Networks Related to the Maintenance of Good Cognitive Performance During Healthy Aging |
| title_fullStr |
Resting State Networks Related to the Maintenance of Good Cognitive Performance During Healthy Aging |
| title_full_unstemmed |
Resting State Networks Related to the Maintenance of Good Cognitive Performance During Healthy Aging |
| title_sort |
resting state networks related to the maintenance of good cognitive performance during healthy aging |
| publisher |
Frontiers Media S.A. |
| publishDate |
2021 |
| url |
https://doaj.org/article/9ee705e984c7400ca2594656fd18a053 |
| work_keys_str_mv |
AT satoshimaesawa restingstatenetworksrelatedtothemaintenanceofgoodcognitiveperformanceduringhealthyaging AT satoshimaesawa restingstatenetworksrelatedtothemaintenanceofgoodcognitiveperformanceduringhealthyaging AT satomimizuno restingstatenetworksrelatedtothemaintenanceofgoodcognitiveperformanceduringhealthyaging AT epifaniobagarinao restingstatenetworksrelatedtothemaintenanceofgoodcognitiveperformanceduringhealthyaging AT hirohisawatanabe restingstatenetworksrelatedtothemaintenanceofgoodcognitiveperformanceduringhealthyaging AT hirohisawatanabe restingstatenetworksrelatedtothemaintenanceofgoodcognitiveperformanceduringhealthyaging AT kazuyakawabata restingstatenetworksrelatedtothemaintenanceofgoodcognitiveperformanceduringhealthyaging AT kazuyakawabata restingstatenetworksrelatedtothemaintenanceofgoodcognitiveperformanceduringhealthyaging AT kazuhirohara restingstatenetworksrelatedtothemaintenanceofgoodcognitiveperformanceduringhealthyaging AT reikoohdake restingstatenetworksrelatedtothemaintenanceofgoodcognitiveperformanceduringhealthyaging AT ayaogura restingstatenetworksrelatedtothemaintenanceofgoodcognitiveperformanceduringhealthyaging AT daisukemori restingstatenetworksrelatedtothemaintenanceofgoodcognitiveperformanceduringhealthyaging AT daisukemori restingstatenetworksrelatedtothemaintenanceofgoodcognitiveperformanceduringhealthyaging AT daisukenakatsubo restingstatenetworksrelatedtothemaintenanceofgoodcognitiveperformanceduringhealthyaging AT haruoisoda restingstatenetworksrelatedtothemaintenanceofgoodcognitiveperformanceduringhealthyaging AT minoruhoshiyama restingstatenetworksrelatedtothemaintenanceofgoodcognitiveperformanceduringhealthyaging AT masahisakatsuno restingstatenetworksrelatedtothemaintenanceofgoodcognitiveperformanceduringhealthyaging AT ryutasaito restingstatenetworksrelatedtothemaintenanceofgoodcognitiveperformanceduringhealthyaging AT norioozaki restingstatenetworksrelatedtothemaintenanceofgoodcognitiveperformanceduringhealthyaging AT norioozaki restingstatenetworksrelatedtothemaintenanceofgoodcognitiveperformanceduringhealthyaging AT gensobue restingstatenetworksrelatedtothemaintenanceofgoodcognitiveperformanceduringhealthyaging AT gensobue restingstatenetworksrelatedtothemaintenanceofgoodcognitiveperformanceduringhealthyaging |
| _version_ |
1718444245776334848 |