Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Quantifying Brain and Cognitive Maintenance as Key Indicators for Sustainable Cognitive Aging: Insights from the UK Biobank
Age-related cognitive decline is a global phenomenon that affects individuals worldwide. The course and extent of this decline are influenced by numerous factors, such as genetics, lifestyle, education, and cognitive engagement. The theory of brain and cognitive reserve/maintenance posits that these factors have a significant impact on the degree of cognitive decline and overall brain health. However, the absence of standardized definitions and measurements for these terms creates ambiguity in research. To address this issue, we utilized a robust and systematic experimental paradigm, employing a considerably large subject pool comprising 17,030 participants from the UK Biobank. Utilizing advanced machine learning methodologies, we were able to accurately quantify both brain maintenance (BM) and cognitive maintenance (CM), making use of six distinct MRI modalities and nine distinct cognitive capabilities. Our study successfully identified several significant features that were meaningfully associated with both BM and CM outcomes. The results of our study demonstrate that lifestyle factors play a significant role in influencing both BM and CM through unique and independent mechanisms. Specifically, our study found that health status is a critical determinant of BM, while diabetes was found to be moderately associated with CM. Furthermore, our study revealed a positive correlation between BM/CM and cognitive reserve. By carefully considering the unique and independent mechanisms that govern both BM and CM, as well as their correlation with cognitive reserve, our study has provided valuable insight into the various strategies that may be leveraged to promote sustainable interventions to enhance cognitive and brain health across the lifespan.
Quantifying Brain and Cognitive Maintenance as Key Indicators for Sustainable Cognitive Aging: Insights from the UK Biobank
Age-related cognitive decline is a global phenomenon that affects individuals worldwide. The course and extent of this decline are influenced by numerous factors, such as genetics, lifestyle, education, and cognitive engagement. The theory of brain and cognitive reserve/maintenance posits that these factors have a significant impact on the degree of cognitive decline and overall brain health. However, the absence of standardized definitions and measurements for these terms creates ambiguity in research. To address this issue, we utilized a robust and systematic experimental paradigm, employing a considerably large subject pool comprising 17,030 participants from the UK Biobank. Utilizing advanced machine learning methodologies, we were able to accurately quantify both brain maintenance (BM) and cognitive maintenance (CM), making use of six distinct MRI modalities and nine distinct cognitive capabilities. Our study successfully identified several significant features that were meaningfully associated with both BM and CM outcomes. The results of our study demonstrate that lifestyle factors play a significant role in influencing both BM and CM through unique and independent mechanisms. Specifically, our study found that health status is a critical determinant of BM, while diabetes was found to be moderately associated with CM. Furthermore, our study revealed a positive correlation between BM/CM and cognitive reserve. By carefully considering the unique and independent mechanisms that govern both BM and CM, as well as their correlation with cognitive reserve, our study has provided valuable insight into the various strategies that may be leveraged to promote sustainable interventions to enhance cognitive and brain health across the lifespan.
Quantifying Brain and Cognitive Maintenance as Key Indicators for Sustainable Cognitive Aging: Insights from the UK Biobank
Lan Lin (Autor:in) / Min Xiong (Autor:in) / Yue Jin (Autor:in) / Wenjie Kang (Autor:in) / Shuicai Wu (Autor:in) / Shen Sun (Autor:in) / Zhenrong Fu (Autor:in)
2023
Aufsatz (Zeitschrift)
Elektronische Ressource
Unbekannt
Metadata by DOAJ is licensed under CC BY-SA 1.0
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