Mental Health in the UK Biobank: An Updated Roadmap for Brain-behavior Associations
Presented During:
Friday, June 27, 2025: 11:30 AM - 12:45 PM
Brisbane Convention & Exhibition Centre
Room:
M3 (Mezzanine Level)
Poster No:
1818
Submission Type:
Abstract Submission
Authors:
Xuqian Li1, Sarah Khalife1, Lena Oestreich1
Institutions:
1The University of Queensland, Brisbane, Queensland
First Author:
Co-Author(s):
Introduction:
The UK Biobank (UKB) is an essential resource for research in neuroimaging and population mental health (Dutt et al., 2021). However, the dataset has become increasingly complex to navigate due to the ongoing collection and release of follow-up data. In this study, we aimed to establish an up-to-date roadmap for neuroimaging research in mental health using the UKB dataset, with a focus on updating the univariate effect sizes for associations between imaging-derived phenotypes (IDPs) and all available mental health phenotypes (MHPs). To set the stage for future longitudinal studies, we also calculated effect sizes for repeated measures correlations between IDPs and MHPs assessed at two timepoints (Bakdash & Marusich, 2017).
Methods:
We included 4,939 participants who completed the initial imaging visit (2014+) and the first repeat imaging visit (2019+), as well as the Mental Health questionnaire (2016+) the Mental Well-being questionnaire (2022+) administered online. During the imaging visits, participants underwent MRI scans and completed touchscreen questionnaires that included questions on mental health. A total of 4,554 IDPs from all MRI modalities, except for fMRI, and 356 self-reported MHPs were included after variable preprocessing and de-confounding. The dataset was randomly divided into a discovery (N = 2,469) and a validation subset (N = 2,470). Pearson's correlation was performed for each IDP and MHP pair in each subset. 2,227 IDPs measured across two imaging visits and 95 MHPs from the touchscreen and online questionnaires were identified as repeated measures variables. Repeated measures correlations were conducted between these IDPs and MHPs within each subset.
Results:
Across all Pearson's correlations, the median effect size (|r|) was .02, with the top 1% of correlations exceeding a value of .21. Out of the 1,621,224 correlations, 670 were significant after FDR correction, and 393 of these were replicated and remained significant after correction. For the initial and repeat imaging visits, median |r| for correlations between IDPs and touchscreen MHPs acquired on the same day were both .02 (Figure 1a). The top 1% of correlations exceeded |r| values of .11 and .12 for the initial and repeat visits, respectively. Similar distributions were observed for correlations between IDPs from the initial imaging visit and touchscreen MHPs from the repeat visit, and vice versa (Figure 1b). For correlations with MHPs from the Mental Health questionnaire, the median |r| were .02 for IDPs from both initial and repeat imaging visits, with the top 1% reaching an |r| above .11 and .14, respectively (Figure 1c). For correlations with MHPs from the Mental Well-being questionnaire, median |r| were .02 and .03 for IDPs from the initial and repeat visits, with the top correlations reaching values greater than .24 and .33 (Figure 1d). Across all repeated measures correlations, the median effect size (|rrm|) was .04, with the top 1% of correlations exceeding an |rrm| value of .41. Out of the 211,565 repeated measures correlations, 45,306 were significant after FDR correction, and 37,111 of these were replicated and remained significant after correction. For correlations between IDPs and MHPs from touchscreen questionnaires, median |rrm| was .02, with the top 1% reaching values above .15 (Figure 2a). For correlations between IDPs and MHPs from online questionnaires, median |rrm| was .05, with the top 1% reaching values above .44 (Figure 2b).
Conclusions:
Pearson's correlations between IDPs and MHPs from the Mental Well-being questionnaire exhibited the largest effect sizes. Repeated measures correlations revealed stronger associations between IDPs and MHPs. These effect sizes were replicated in the validation subset, indicating that our study offers a realistic and robust estimation of the effect sizes for neuroimaging research in mental health.
Neuroinformatics and Data Sharing:
Databasing and Data Sharing 1
Informatics Other 2
Keywords:
Other - UK Biobank; Mental health; Brain-behavior relationships; Effect size
1|2Indicates the priority used for review
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Provide references using APA citation style.
Dutt, R. K. et al. (2022). Mental health in the UK Biobank: A roadmap to self‐report measures and neuroimaging correlates. Human Brain Mapping, 43(2), 816-832.