Predicting Mental Health&Mortality: Insights from Functional Connectivity&Cross-Dataset Validation
Presented During:
Friday, June 27, 2025: 11:30 AM - 12:45 PM
Brisbane Convention & Exhibition Centre
Room:
Great Hall
Poster No:
443
Submission Type:
Abstract Submission
Authors:
Thuan Tinh Nguyen1,2, Kwun Kei Ng1,2, Voon Hao Liew1,2, Janice Koi3, Juan Helen Zhou1,2,4
Institutions:
1Centre for Sleep and Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore, 2Healthy Longevity & Human Potential Translational Research Program and Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore, 3Nanyang Technological University, Singapore, Singapore, 4Department of Electrical and Computer Engineering & Integrative Sciences and Engineering Programme (ISEP), NUS Graduate School, National University of Singapore, Singapore, Singapore
First Author:
Thuan Tinh Nguyen
Centre for Sleep and Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore|Healthy Longevity & Human Potential Translational Research Program and Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore
Singapore, Singapore|Singapore, Singapore
Centre for Sleep and Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore|Healthy Longevity & Human Potential Translational Research Program and Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore
Singapore, Singapore|Singapore, Singapore
Co-Author(s):
Eric Kwun Kei Ng
Centre for Sleep and Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore|Healthy Longevity & Human Potential Translational Research Program and Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore
Singapore, Singapore|Singapore, Singapore
Centre for Sleep and Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore|Healthy Longevity & Human Potential Translational Research Program and Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore
Singapore, Singapore|Singapore, Singapore
Voon Hao Liew
Centre for Sleep and Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore|Healthy Longevity & Human Potential Translational Research Program and Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore
Singapore, Singapore|Singapore, Singapore
Centre for Sleep and Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore|Healthy Longevity & Human Potential Translational Research Program and Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore
Singapore, Singapore|Singapore, Singapore
Juan Helen Zhou, Ph.D.
Centre for Sleep and Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore|Healthy Longevity & Human Potential Translational Research Program and Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore|Department of Electrical and Computer Engineering & Integrative Sciences and Engineering Programme (ISEP), NUS Graduate School, National University of Singapore
Singapore, Singapore|Singapore, Singapore|Singapore, Singapore
Centre for Sleep and Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore|Healthy Longevity & Human Potential Translational Research Program and Department of Medicine, Yong Loo Lin School of Medicine, National University of Singapore|Department of Electrical and Computer Engineering & Integrative Sciences and Engineering Programme (ISEP), NUS Graduate School, National University of Singapore
Singapore, Singapore|Singapore, Singapore|Singapore, Singapore
Introduction:
A recent symptom-based model of psychopathology suggests the existence of hierarchical organization across disorders[1]. Studies in youth utilizing functional connectivity (FC) have uncovered both shared and distinct neural patterns associated with disorder dimensions[2, 3]. However, limited research has explored the relationship between psychopathology dimensions and imaging biomarkers in adults, particularly in middle-aged populations. In this study, we attempt to address this gap by investigating the brain functional phenotypes relating to mental health in the middle-aged and older adults using the UK Biobank cohort, while assessing its predictive utility within and across datasets.
Methods:
We utilized participants from UK Biobank (N=6529, 62.9±7.5 years, 2914 males) who completed the online mental health questionnaire and had brain imaging data. Using the questionnaire, we derived 36 measures characterizing alcohol use, depression, anxiety, PTSD and psychosis[4]. fMRI data were subject to standard preprocessing[5] with additional steps following previous work[6]. FC matrices were computed among 420 cortical and subcortical regions[7].
We then investigated how brain FC relate to 36 outcome items after regressing out a set of simple confounders[8] using partial least squares (PLS) correlation[9]. From there we can obtain brain scores to reflect participants' expression of specific connectivity profile.
For predicting future mental health, participants were categorized based on changes in alcohol addiction, anxiety, or depression status, and group differences in baseline brain scores were analyzed using ANOVA and Tukey tests.
Mortality risk was assessed by correlating brain scores with mortality data through Cox proportional hazards (CPH) regression, controlling for age, sex, years of education and social economic status measured by Townsend Deprivation Index[10].
Findings were validated externally using the HCP-Aging dataset (N=376, 47.85±7.02 years, 160 males), correlating brain scores with total mental health problems measured by the sum of Achenbach Adults Self-Report questionnaire items.
We then investigated how brain FC relate to 36 outcome items after regressing out a set of simple confounders[8] using partial least squares (PLS) correlation[9]. From there we can obtain brain scores to reflect participants' expression of specific connectivity profile.
For predicting future mental health, participants were categorized based on changes in alcohol addiction, anxiety, or depression status, and group differences in baseline brain scores were analyzed using ANOVA and Tukey tests.
Mortality risk was assessed by correlating brain scores with mortality data through Cox proportional hazards (CPH) regression, controlling for age, sex, years of education and social economic status measured by Townsend Deprivation Index[10].
Findings were validated externally using the HCP-Aging dataset (N=376, 47.85±7.02 years, 160 males), correlating brain scores with total mental health problems measured by the sum of Achenbach Adults Self-Report questionnaire items.
Results:
The top two significant LVs from the PLS explained most of the covariance, with the first reflecting general psychopathology while the second outlining a divergence between alcohol use and depression/PTSD symptoms.
Looking at the PLS-derived brain scores across different diagnostic groups across time points, we noted that there were significant differences in LV1 brain scores across groups for all three problems, namely anxiety (FDR adjusted p=3*10-7, Fig.1A), depression (FDR adjusted p=4*10-10, Fig.1B) and hazardous drinking (FDR adjusted p=0.0004, Fig.1C). Meanwhile for LV2, the group differences were only seen in hazardous drinking (FDR adjusted p=1*10-10, Fig.1D).
CPH model revealed that the LV1 brain score reflecting overall psychopathology burden but not the LV2 brain score (Fig.2) was a significant predictor of mortality (hazard ratio:1.84, 95%CI:1.13-2.99, p=0.014), even after accounting for demographics and socioeconomic status.
Analysis with HCP-Aging data revealed significant positive correlation between the projected LV1 brain score and total mental health problems (r=0.11, p=0.035) and not the LV2 brain score (r=-0.07, p=0.17).
All in all, the findings highlight the specificity and reliability of the brain phenotypes, demonstrating their alignment with the behavioral phenotypes represented by each LV.
Looking at the PLS-derived brain scores across different diagnostic groups across time points, we noted that there were significant differences in LV1 brain scores across groups for all three problems, namely anxiety (FDR adjusted p=3*10-7, Fig.1A), depression (FDR adjusted p=4*10-10, Fig.1B) and hazardous drinking (FDR adjusted p=0.0004, Fig.1C). Meanwhile for LV2, the group differences were only seen in hazardous drinking (FDR adjusted p=1*10-10, Fig.1D).
CPH model revealed that the LV1 brain score reflecting overall psychopathology burden but not the LV2 brain score (Fig.2) was a significant predictor of mortality (hazard ratio:1.84, 95%CI:1.13-2.99, p=0.014), even after accounting for demographics and socioeconomic status.
Analysis with HCP-Aging data revealed significant positive correlation between the projected LV1 brain score and total mental health problems (r=0.11, p=0.035) and not the LV2 brain score (r=-0.07, p=0.17).
All in all, the findings highlight the specificity and reliability of the brain phenotypes, demonstrating their alignment with the behavioral phenotypes represented by each LV.
Conclusions:
Across middle-aged and older adults, we identified a general disease factor and another reflecting the divergence between alcohol use and depression/PTSD related symptoms, consistent with the hierarchical model of psychopathology. We also demonstrated the potential prognostic utility of baseline brain scores in predicting future mental health and mortality risk. These findings highlighted the importance of comprehending brain network phenotypes related to mental health disorders within a dimensional framework.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 1
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 2
Multivariate Approaches
Keywords:
Anxiety
FUNCTIONAL MRI
Other - alcohol addiction; anxiety; depression; PTSD; psychosis; multivariate
1|2Indicates the priority used for review
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2. Xia, C.H., et al. (2018), Linked dimensions of psychopathology and connectivity in functional brain networks. Nature Communications. 9(1): p. 3003.
3. Lees, B., et al. (2020), Altered Neurocognitive Functional Connectivity and Activation Patterns Underlie Psychopathology in Preadolescence. Biological Psychiatry: Cognitive Neuroscience and Neuroimaging.
4. Davis, K.A.S., et al. (2020), Mental health in UK Biobank - development, implementation and results from an online questionnaire completed by 157 366 participants: a reanalysis. BJPsych open. 6(2): p. e18-e18.
5. Alfaro-Almagro, F., et al. (2018), Image processing and Quality Control for the first 10,000 brain imaging datasets from UK Biobank. Neuroimage. 166: p. 400-424.
6. Chong, J.S.X., et al. (2017), Influence of cerebrovascular disease on brain networks in prodromal and clinical Alzheimer’s disease. Brain. 140(11): p. 3012-3022.
7. Schaefer, A., et al. (2018), Local-Global Parcellation of the Human Cerebral Cortex from Intrinsic Functional Connectivity MRI. Cereb Cortex. 28(9): p. 3095-3114.
8. Alfaro-Almagro, F., et al. (2021), Confound modelling in UK Biobank brain imaging. NeuroImage. 224: p. 117002.
9. McIntosh, A.R., W.K. Chau, and A.B. Protzner (2004), Spatiotemporal analysis of event-related fMRI data using partial least squares. NeuroImage. 23(2): p. 764-775.
10. Rask-Andersen, M., et al. (2021), Modification of Heritability for Educational Attainment and Fluid Intelligence by Socioeconomic Deprivation in the UK Biobank. American Journal of Psychiatry. 178(7): p. 625-634.