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Functional connectome phenotype of multiple cerebrovascular disease markers and its interaction with plasma p-tau181 on downstream outcomes
Joanna Su Xian Chong1, Fang Ji1, Saima Hilal2,3,4, Joyce Ruifen Chong3,4, Jia Ming Lau1, Boon Yeow Tan5, Narayanaswamy Venketasubramanian3,6, Mitchell Kim Peng Lai3,4, Christopher Li-Hsian Chen3,4, and Juan Helen Zhou1,7,8
1Centre for Sleep and Cognition & Centre for Translational Magnetic Resonance Research, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore, 2Saw Swee Hock School of Public Health, National University of Singapore and National University Health System, Singapore, Singapore, 3Memory Ageing & Cognition Centre, National University Health System, Singapore, Singapore, 4Department of Pharmacology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore, 5St Luke’s Hospital, Singapore, Singapore, 6Raffles Neuroscience Centre, Raffles Hospital, Singapore, Singapore, 7Integrative Sciences and Engineering Programme (ISEP), National University of Singapore, Singapore, Singapore, 8Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore

Synopsis

Keywords: Functional Connectivity, Alzheimer's Disease, Cerebrovascular disease

Motivation: Cerebrovascular disease (CeVD) is assessed by several MRI markers, but their impact on brain functional connectivity (FC) remains unclear.

Goal(s): To examine how multiple CeVD markers influence FC, and how CeVD-related FC changes interact with Alzheimer’s disease pathology to influence downstream outcomes.

Approach: We studied multivariate associations between four CeVD markers and whole-brain FC in 529 participants, and how this CeVD-related FC phenotype interacted with plasma p-tau181 to influence longitudinal brain atrophy and cognitive decline.

Results: We identified a FC phenotype linked to high CeVD burden across all markers. This phenotype and p-tau181 contributed additively, but not synergistically, to atrophy and cognitive decline.

Impact: Using a multivariate approach, our study demonstrated that CeVD exerted widespread, non-MRI marker-specific effects on the whole-brain functional connectome. Further, we showed that AD and CeVD have additive but not synergistic effects on neurodegeneration and cognitive changes over time.

Introduction

Cerebrovascular disease (CeVD) is a major cause of cognitive impairment in older adults1, and frequently co-occurs with Alzheimer’s disease (AD)2. Despite the focal nature of its lesions, CeVD is associated with a wide range of clinical symptoms and thus has global effects3. In this regard, fMRI-derived functional connectivity (FC) measures have been instrumental in understanding how CeVD leads to diverse symptoms via its effects on the functional connectome3,4. Disrupted FC across large-scale distributed networks has been reported in individuals with CeVD3,4, and further linked to poorer cognition5,6. To date, studies have typically quantified CeVD burden using a single MRI marker. However, CeVD is assessed by several MRI markers that are heterogeneous in aetiology and disease severity3. As such, CeVD burden may not be fully reflected by a single marker. Our study thus sought to examine the effects of multiple CeVD MRI markers on whole-brain FC using a multivariate approach. Given the global effects of CeVD3, we hypothesize that CeVD would be linked to widespread FC alterations in a non-marker-specific manner. Further, we investigated how such CeVD-related FC changes interacted with plasma p-tau181, an AD marker, to influence longitudinal changes in neurodegeneration and cognition/behaviour. Converging evidence suggests additive, rather than synergistic effects of AD and CeVD on cognitive decline7-11. Accordingly, we hypothesized that expression of the CeVD-related FC phenotype and p-tau181 would have additive, but not synergistic effects on cognition/behaviour and grey matter volumes.

Methods

Cross-sectional task-free fMRI, CeVD (four MRI markers: age-related white matter changes score (measuring white matter hyperintensity), number of lacunes, cerebral microbleeds and cortical microinfarcts) and plasma p-tau181 data, and longitudinal T1-weighted and cognitive data from 529 participants across the dementia spectrum12 were analyzed. Individual FC matrices were first computed for 114 cortical13 and 30 subcortical14,15 regions. FC and CeVD residuals were then obtained by regressing out age, sex, ethnicity, education, handedness, and total intracranial volume (TIV). To derive the CeVD-related FC phenotype, multivariate associations between FC and CeVD residuals were examined using partial least squares correlation (PLSC). Brain scores, indicating the extent to which each individual expressed the CeVD-related phenotype, were then derived from the PLSC analyses. Finally, main and interactive effects of brain scores and log-transformed plasma p-tau181 on baseline and longitudinal changes in grey matter volumes and cognition/behaviour were examined using linear mixed-effects models, with time as random effect, and age, sex, ethnicity, education, handedness, diagnosis, TIV, p-tau181 and brain score as fixed effects.

Results

PLSC analyses revealed a significant latent variable that explained 76.6% of the covariance between FC and CeVD markers (Figure 1). This phenotype was linked to high burden across all CeVD markers, and characterized by lower within-network and higher between-network cortical FC, as well as lower subcortical FC to associative networks but higher subcortical FC to sensorimotor networks. Further, we demonstrated additive, but not synergistic effects, of the CeVD-related FC phenotype and p-tau181 on grey matter volumes and cognition/behaviour. Brain scores had widespread effects on baseline grey matter volumes and cognition, but only influenced longitudinal changes in global cognition and neuropsychiatric symptoms. By comparison, p-tau181 showed circumscribed effects on baseline cognitive performance of AD-related domains (e.g., memory) and grey matter volumes of AD-related regions, but widespread effects on atrophy and cognition over time (Figures 2-3).

Discussion

We identified a phenotype that was linked to high CeVD burden across all markers and widespread FC changes, supporting studies showing widespread, global effects of CeVD on FC3,4. Importantly, we demonstrated that expression of the CeVD-related FC phenotype and p-tau181 showed additive, but not synergistic effects on both baseline and longitudinal changes in grey matter volumes and cognitive/behavioral performance, corroborating previous studies showing differential effects of AD and CeVD on cognitive function7,11, cortical thickness7, and FC9,10. Specifically, the effects of CeVD-related phenotype on neurodegeneration and cognition were widespread at baseline but limited over time, which is consistent with the global, but heterogeneous effects of CeVD on clinical symptoms3. Conversely, the effects of p-tau181 were limited to AD-related cognitive domains and brain regions at baseline, but widespread across multiple brain networks and cognitive domains over time, which mirrors AD symptom progression from early memory impairments to more widespread neurodegeneration and cognitive deficits at later stages16,17.

Conclusion

Using a multivariate approach, we found widespread, non-MRI marker-specific effects of CeVD on FC. Further, we demonstrated that expression of this phenotype and plasma p-tau181 had additive, but not synergistic effects on baseline and longitudinal changes in neurodegeneration and cognition/behaviour. Collectively, our findings provide insight on the effects of CeVD on functional networks in older adults, which may be a therapeutic target.

Acknowledgements

This study was supported by the National Medical Research Council Singapore Translational Research Investigator Award (MOH-000707-00) (C. L.-H. Chen), National Medical Research Council NMRC/CIRG/1485/2018 (C. L.-H. Chen), National Medical Research Council Centre Grant - NMRC/CG/M009/2017_NUH/NUHS (C. L.-H. Chen) and National Medical Research Council Senior Clinician Scientist Award 2017-2022 (NMRC/CSA-SI/0007/2016) (C. L.-H. Chen). This work was also supported by the Singapore National Medical Research Council (NMRC/OFLCG19May-0035, NMRC/CIRG/1485/2018, NMRC/MOH-00707-01, NMRC/OFLCG19May-0035) (J. H. Zhou), RIE2020 AME Programmatic Fund from A*STAR, Singapore (No. A20G8b0102) (J. H. Zhou) and Yong Loo Lin School of Medicine Research Core Funding, National University of Singapore, Singapore (J. H. Zhou).

References

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Figures

Figure 1. PLSC analyses reveal a brain phenotype linked to widespread FC changes and high burden across multiple CeVD markers. We identified a significant latent variable that explained 76.6% of covariance between FC and CeVD markers. Matrix (left) displays significant bootstrap ratios of functional connections corresponding to the latent variable. Bar chart (right) displays mean correlations between brain score and each CeVD marker (error bars denote 95% bootstrapped confidence intervals).

Figure 2: CeVD-related FC phenotype and plasma p-tau181 contribute additively to baseline and longitudinal changes in grey matter volumes. Bar plots indicate T-statistic values for the effects of (A) brain score, (B) p-tau181, (C) brain score × time and (D) p-tau × time on grey matter volumes of 10 networks. * indicates significant effects (p < 0.05). Corresponding brain maps display T-statistic values of regions whose grey matter volumes showed significant effects.

Figure 3: CeVD-related FC phenotype and plasma p-tau181 contribute additively to baseline and longitudinal changes in cognition/behaviour. Bar plots indicate T-statistic values for the effects of (A) brain score, (B) p-tau181, (C) brain score × time and (D) p-tau × time on cognitive and behavioural scores. * indicates significant effects (p < 0.05).

Proc. Intl. Soc. Mag. Reson. Med. 32 (2024)
4964
DOI: https://doi.org/10.58530/2024/4964