Longitudinal Brain Age Predicts Future Executive Function in Asian Children and Older Adults

Grunnleggende konsepter

Longitudinal changes in brain age are associated with future executive function performance in both Asian children and older adults, with distinct brain features contributing to age prediction in the two populations.

Sammendrag

The study investigated the generalizability of a pretrained deep learning brain age model to Singaporean elderly participants and children. The pretrained model performed well in the elderly participants, but required finetuning for the children.
In the elderly participants from the Epidemiology of Dementia in Singapore (EDIS) study, higher baseline brain age gap (BAG) was associated with poorer baseline cognitive performance, particularly in executive function. In the longitudinal Singapore Longitudinal Aging Brain Study (SLABS) dataset, the early rate of change in BAG was negatively associated with future decline in executive function, independent of baseline BAG.
In the children from the Growing Up in Singapore Towards healthy Outcomes (GUSTO) study, the early rate of change in BAG from 4.5 to 7.5 years old was positively associated with better future inhibitory control at 8.5 years old, independent of baseline BAG.
The model interpretability analysis revealed that the finetuned brain age models focused on distinct brain features in the elderly versus children. In the elderly, regions near the lateral ventricles, frontal/association areas, and subcortical regions were most salient. In children, the prominence of white matter regions, especially in posterior areas, increased compared to the elderly.
These findings suggest that longitudinal changes in brain age can capture ongoing processes of healthy brain aging and development, with distinct neural mechanisms underlying the associations with future executive function in the two populations.

Statistikk

The brain age gap (BAG) is calculated by subtracting chronological age from predicted brain age.
The early rate of change in BAG was calculated from a linear regression of BAG over time for each participant.
The long-term rate of change in cognitive performance was calculated from a linear regression of cognitive scores over time for each participant.

Sitater

"Longitudinal changes in brain age are associated with future executive function performance in both Asian children and older adults, with distinct brain features contributing to age prediction in the two populations."
"In the elderly participants, higher baseline brain age gap (BAG) was associated with poorer baseline cognitive performance, particularly in executive function."
"In the children, the early rate of change in BAG from 4.5 to 7.5 years old was positively associated with better future inhibitory control at 8.5 years old."

Viktige innsikter hentet fra

Rate of brain aging associates with future executive function in Asian children and older adults

by Cheng,S. F.,... klokken www.biorxiv.org 11-28-2023

https://www.biorxiv.org/content/10.1101/2023.11.27.568184v4

Dypere Spørsmål

How do the associations between longitudinal brain age changes and cognition differ across other cognitive domains beyond executive function?

In the study, the associations between longitudinal brain age changes and cognition were explored in various cognitive domains beyond executive function. The findings indicated that the relationship between brain age changes and cognition varied across different cognitive domains. While the study showed a significant negative association between brain age changes and executive function in both elderly participants and children, the associations with other cognitive domains differed.
For example, in elderly participants, the associations with attention, language, visuomotor speed, visuoconstruction, verbal memory, and visual memory were also examined. The results revealed significant negative associations between baseline brain age gap (BAG) and baseline cognitive performance in these domains. However, when looking at the longitudinal changes in brain age gap and future cognitive decline, the associations were not consistently significant across all cognitive domains. This suggests that the impact of brain age changes on cognitive function may vary depending on the specific cognitive domain being considered.
In children, the study focused on inhibition and switching as key cognitive domains. While the association between brain age changes and inhibition was found to be positively significant, the association with switching was not significant after correcting for multiple comparisons. This indicates that the relationship between brain age changes and cognitive function may manifest differently in children compared to elderly participants, highlighting the importance of considering age-specific cognitive domains in understanding the impact of brain aging on cognitive abilities.

How can the insights from brain age model interpretability be leveraged to develop more targeted interventions for promoting healthy brain aging and development?

The insights gained from brain age model interpretability, particularly in understanding the salient features that contribute to brain age predictions in different age groups, can be instrumental in developing targeted interventions for promoting healthy brain aging and development. By identifying the specific brain regions and networks that play a crucial role in predicting brain age, researchers and healthcare professionals can tailor interventions to focus on enhancing the health and function of these areas.

Personalized Interventions: Understanding the regions that contribute most to brain age predictions can help in personalizing interventions based on an individual's unique brain aging profile. For example, if certain white matter tracts or subcortical structures are identified as key contributors to brain age predictions, interventions targeting the health and maintenance of these specific areas can be prioritized for individuals at risk of accelerated brain aging.

Early Intervention Strategies: By recognizing the features that are most influential in predicting brain age changes in children, interventions can be designed to support healthy brain development from an early age. For instance, if specific white matter regions are crucial for predicting brain age in children, interventions focused on enhancing the development and integrity of these areas can be implemented to promote optimal cognitive function and brain health.

Monitoring and Feedback: Utilizing the insights from brain age model interpretability, monitoring tools can be developed to track changes in the salient brain regions over time. This feedback mechanism can provide individuals with real-time information about their brain health and aging process, enabling them to make informed lifestyle choices and engage in targeted interventions to maintain cognitive function and overall brain health.

Educational Programs: Educational programs can be designed based on the identified salient features to raise awareness about the importance of specific brain regions in healthy brain aging. By educating individuals about the role of these regions in cognitive function and brain health, they can be empowered to adopt lifestyle practices and behaviors that support the well-being of these critical brain areas.

In conclusion, leveraging the insights from brain age model interpretability can pave the way for the development of personalized, targeted interventions aimed at promoting healthy brain aging and development across different age groups. By focusing on the specific brain regions and networks identified through interpretability analyses, interventions can be tailored to address individual needs and optimize cognitive function and brain health.