| Application: | 23773, Modeling the determinants of age-related declines in cognitive abilities |
| Title: | The neural determinants of age-related changes in fluid intelligence: a pre-registered, longitudinal analysis in UK Biobank |
| Size: | 2.0 MB |
| Archived: | 4 Feb 2020 |
| Stability: | Complete |
| Personal: | No individual-level data |
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Notes
In this project, we wanted to understand the neural mechanisms that underlie age related differences between people in their ability to figure out verbal and abstract problems. We found a weak but significant effect of cross-sectional age on the mean fluid intelligence score, such that older individuals scored slightly lower. Some of these differences could be explained by differences in brain structure, specifically in terms of grey matter (the cell bodies) and white matter integrity (the connections between brain regions). Generally, people with more grey matter, and more intact connections between regions, had slightly higher scores on the abstract puzzle tests. The strongest effects were such that higher grey matter volume in the frontal pole and greater white matter microstructure in the posterior thalamic radiations were associated with higher fluid intelligence scores.Application 23773
Modeling the determinants of age-related declines in cognitive abilities
One of the biggest challenges in aging research is to understand how, and why, cognitive abilities tend to worsen as we age. Recently, theories have proposed that various cognitive abilities, such as memory, reasoning, speed and language, have a mutually beneficial effect on each other. In other words, if, for example, your memory keeps working well, that will benefit other skills such as language (remembering words, phrases, names) or reasoning (remembering a problem you?re trying to solve). Additionally, we will examine whether brain health and lifestyle factors help combat age-related decline in mental abilities. This project offers new avenues for diagnosis (groups of individuals defined by both current scores and developmental trajectories), prevention (what lifestyle factors determine the shape of age-related decline) and the promotion of health (identifying which cognitive abilities are most promising targets to slow down decline). We will focus on answering three key questions in healthy cognitive aging: -Is there any evidence that cognitive abilities interact positively, such that they together help slow decline? -Does brain health play an important role in these interactions? -Are there lifestyle factors that set apart individuals who age more slowly, or not at all? We will use statistical methods called growth curve models and latent change models to study how people change across their lifespan. To study this topic, we need a dataset that is large (many people), broad (many cognitive domains) and longitudinal (people measured multiple times). Specifically, we will quantify decline and improvement over time on multiple cognitive abilities to see if changes across domains influence each other, and how these changes are related to brain health. The richness and size of the Biobank sample offers a unique avenue to investigate these questions. Full cohort
| Lead investigator: | Dr Rogier Kievit |
| Lead institution: | University of Cambridge |
1 Publication
| Pub ID | Title | Author(s) | Year | Journal |
|---|---|---|---|---|
| 1968 | The neural determinants of age-related changes in fluid intelligence: a pre-registered, longitudinal analysis in UK Biobank | Kievit RA et al. | 2018 | Wellcome Open Res |
Enabling scientific discoveries that improve human health