Locus coeruleus integrity predicts changes in segregation of functional networks
Poster No:
916
Submission Type:
Abstract Submission
Authors:
Alfie Wearn1, Colleen Hughes2, Giulia Baracchini3, Roni Setton4, Christine Tardif5, Ilana Leppert6, Sylvia Villeneuve5, Judes Poirier5, John Breitner5, Gary Turner7, Nathan Spreng5
Institutions:
1Montreal Neurological Institute, Montreal, Quebec, 2Indiana University, Bloomington, IN, 3The University of Sydney, Sydney, New South Wales, 4Harvard University, Boston, MA, 5McGill University, Montreal, Quebec, 6McGill, Montreal, QC, 7York University, Toronto, Ontario
First Author:
Co-Author(s):
Introduction:
Resting-state functional brain networks become less segregated (i.e. specialised into distinct processing modules) in older adulthood (Chan et al., 2014; Setton et al., 2022). Older adults with less segregated networks at rest show poor modulation in response to task demands and worse episodic memory performance (Cassady et al., 2023), but little is known about why reduced resting-state segregation emerges.
The noradrenergic system may directly promote task-relevant network integration, inducing a state of attentiveness and arousal (Shine, 2019). Moreover, the locus coeruleus (LC; the primary source of noradrenaline to the cortex) degenerates with age (Liu et al., 2019). We thus tested the model of noradrenergic control of network integration while teasing apart complex interactions with age. We hypothesised that greater LC integrity, measured in vivo using neuromelanin-sensitive MRI, would be associated with greater network integration (lower segregation), above and beyond an association with age.
The noradrenergic system may directly promote task-relevant network integration, inducing a state of attentiveness and arousal (Shine, 2019). Moreover, the locus coeruleus (LC; the primary source of noradrenaline to the cortex) degenerates with age (Liu et al., 2019). We thus tested the model of noradrenergic control of network integration while teasing apart complex interactions with age. We hypothesised that greater LC integrity, measured in vivo using neuromelanin-sensitive MRI, would be associated with greater network integration (lower segregation), above and beyond an association with age.
Methods:
160 healthy older adults were included from the PREVENT-AD cohort (Tremblay-Mercier et al., 2021) (mean age 68.3 ± 5.28y, 74% female). 129 subjects were also examined 32.1± 5.12 months later.
3T MRI sequences:
o T1w MPRAGE: 1mm3, TR/TE/TI=2300/2.96/900ms, FA=9°, TA=5:30
o Neuromelanin-sensitive MRI: 0.7x0.7x1.8mm (brainstem slab), TR/TE=600/10 ms, FA=120°, TA=8:27
o Multi-echo resting-state fMRI: 3mm3, TR=1000 ms, TEs=12/30.11/48.22 ms, FA=50°, TA=10:24
Image Processing:
LC integrity: LC was automatically delineated on individual neuromelanin-sensitive brainstem scans by identifying 10 brightest connected voxels within an approximate region of interest and calculating their contrast to noise relative to a pontine control region (LCCNR). Described further by (Nobileau et al., 2023)
Functional processing: Rest fMRI scans were pre-processed using fMRIPrep (Esteban et al., 2019) and denoised with tedana (DuPre et al., 2021). Cortical parcellation was performed using the Group Prior Individual Parcellation method (Chong et al., 2017). Functional connectivity (Fisher's r-to-z correlations) was estimated between each pair of parcels without thresholding. Network segregation (Chan et al., 2014) for each subject/time was calculated as the proportion of within-network strength greater than between-network strength. The assignment of regions to networks followed the well-characterized Yeo networks at less (7) and more (17) fine-grained levels of analysis.
Statistical Analysis:
We tested the relationship between LCCNR and network segregation using a robust linear mixed effects model with a random intercept of subject. Change over time was tested as an interaction term:
Segregation ~ LCCNR * Time + age at baseline + sex + education + (1 | subject)
Segregation measures were negative reciprocal transformed due to heavy right skew. Extreme outlier values (3*IQR) were excluded.
3T MRI sequences:
o T1w MPRAGE: 1mm3, TR/TE/TI=2300/2.96/900ms, FA=9°, TA=5:30
o Neuromelanin-sensitive MRI: 0.7x0.7x1.8mm (brainstem slab), TR/TE=600/10 ms, FA=120°, TA=8:27
o Multi-echo resting-state fMRI: 3mm3, TR=1000 ms, TEs=12/30.11/48.22 ms, FA=50°, TA=10:24
Image Processing:
LC integrity: LC was automatically delineated on individual neuromelanin-sensitive brainstem scans by identifying 10 brightest connected voxels within an approximate region of interest and calculating their contrast to noise relative to a pontine control region (LCCNR). Described further by (Nobileau et al., 2023)
Functional processing: Rest fMRI scans were pre-processed using fMRIPrep (Esteban et al., 2019) and denoised with tedana (DuPre et al., 2021). Cortical parcellation was performed using the Group Prior Individual Parcellation method (Chong et al., 2017). Functional connectivity (Fisher's r-to-z correlations) was estimated between each pair of parcels without thresholding. Network segregation (Chan et al., 2014) for each subject/time was calculated as the proportion of within-network strength greater than between-network strength. The assignment of regions to networks followed the well-characterized Yeo networks at less (7) and more (17) fine-grained levels of analysis.
Statistical Analysis:
We tested the relationship between LCCNR and network segregation using a robust linear mixed effects model with a random intercept of subject. Change over time was tested as an interaction term:
Segregation ~ LCCNR * Time + age at baseline + sex + education + (1 | subject)
Segregation measures were negative reciprocal transformed due to heavy right skew. Extreme outlier values (3*IQR) were excluded.
Results:
No association between LCCNR and network segregation was observed at baseline, but greater LCCNR was associated with a decline in network segregation over time (17net (Figure 1): t(152) = -2.42, p = 0.017; 7net: t(158) = -2.58, p = 0.011).
Older age was associated with lower values of network segregation (7net: t(173) = -2.45, p = 0.015, 17net: t(169) = -2.68, p = 0.008)
Older age was associated with lower values of network segregation (7net: t(173) = -2.45, p = 0.015, 17net: t(169) = -2.68, p = 0.008)
·Figure 1
Conclusions:
We show that those with the greatest LC integrity experience a greater shift to more integrated network organisation than those with lower LC integrity. This effect is above and beyond the expected trend that networks become more integrated with older age. Our finding is line with the model that noradrenergic systems support network integration (Shine, 2019), and hints at complex mechanisms of large scale functional organisation of brain networks. Future work will explore network specificity of these effects, and associations with markers of Alzheimer's disease, which is known to cause selective LC degeneration in preclinical stages.
Lifespan Development:
Aging 1
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
fMRI Connectivity and Network Modeling 2
Task-Independent and Resting-State Analysis
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Subcortical Structures
Keywords:
Aging
FUNCTIONAL MRI
MRI
Noradrenaline
Norpinephrine
STRUCTURAL MRI
Sub-Cortical
Other - Neuromelanin; Network modelling; Locus Coeruleus
1|2Indicates the priority used for review
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Provide references using APA citation style.
Chan, M.Y. et al. (2014) ‘Decreased segregation of brain systems across the healthy adult lifespan’, Proceedings of the National Academy of Sciences, 111(46), pp. E4997–E5006. Available at: https://doi.org/10.1073/pnas.1415122111.
Chong, M. et al. (2017) ‘Individual Parcellation of Resting fMRI with a Group Functional Connectivity Prior’, NeuroImage, 156, pp. 87–100. Available at: https://doi.org/10.1016/j.neuroimage.2017.04.054.
DuPre, E. et al. (2021) ‘TE-dependent analysis of multi-echo fMRI with *tedana*’, Journal of Open Source Software, 6(66), p. 3669. Available at: https://doi.org/10.21105/joss.03669.
Esteban, O. et al. (2019) ‘fMRIPrep: a robust preprocessing pipeline for functional MRI’, Nature Methods, 16(1), pp. 111–116. Available at: https://doi.org/10.1038/s41592-018-0235-4.
Liu, K.Y. et al. (2019) ‘In vivo visualization of age-related differences in the locus coeruleus’, Neurobiology of Aging, 74, pp. 101–111. Available at: https://doi.org/10.1016/j.neurobiolaging.2018.10.014.
Nobileau, A. et al. (2023) ‘Neuromelanin-Sensitive Magnetic Resonance Imaging Changes in the Locus Coeruleus/Subcoeruleus Complex in Patients with Typical and Atypical Parkinsonism’, Movement Disorders, 38(3), pp. 479–484. Available at: https://doi.org/10.1002/mds.29309.
Setton, R. et al. (2022) ‘Age differences in the functional architecture of the human brain’, Cerebral Cortex (New York, NY), 33(1), pp. 114–134. Available at: https://doi.org/10.1093/cercor/bhac056.
Shine, J.M. (2019) ‘Neuromodulatory Influences on Integration and Segregation in the Brain’, Trends in Cognitive Sciences, 23(7), pp. 572–583. Available at: https://doi.org/10.1016/j.tics.2019.04.002.
Tremblay-Mercier, J. et al. (2021) ‘Open science datasets from PREVENT-AD, a longitudinal cohort of pre-symptomatic Alzheimer’s disease’, NeuroImage: Clinical, 31, p. 102733. Available at: https://doi.org/10.1016/j.nicl.2021.102733.