Cortical similarity networks are sensitive to age effects in functional domains of the macaque brain
Presented During:
Thursday, June 26, 2025: 11:30 AM - 12:45 PM
Brisbane Convention & Exhibition Centre
Room:
Great Hall
Poster No:
1246
Submission Type:
Abstract Submission
Authors:
Melina Tsotras1, Joey Charbonneau2, Jelle Veraart3, Claude Lepage4, Jeff Bennett5, Eliza Bliss-Moreau6, Erika Raven7
Institutions:
1Center for Data Science, New York University, New York, NY, 2Center for Neural Science, New York University, New York, NY, 3Department of Radiology, NYU Grossman School of Medicine, New York, NY, 4Brain Imaging Center, Montreal Neurological Institute, McGill University, Montreal, Quebec, 5California National Primate Research Center, Davis, CA, 6University of California, Davis, Davis, CA, 7NYU School of Medicine, New York, NY
First Author:
Co-Author(s):
Claude Lepage
Brain Imaging Center, Montreal Neurological Institute, McGill University
Montreal, Quebec
Brain Imaging Center, Montreal Neurological Institute, McGill University
Montreal, Quebec
Introduction:
Biological aging, in both health and disease, is linked to changes in brain architecture. Neuroimaging has been used extensively to study the impact of aging on brain structure, revealing reductions in cortical volume, surface area, thickness, and gyrification. New evidence suggests that these structural changes reflect dysconnectivity within brain networks that are particularly vulnerable to aging [1].
Structural connectivity is often studied using tractography derived from diffusion-weighted MRI data [2]. Yet, this approach is sensitive to false-positive connections and does not leverage information sensitive to cytoarchitectural changes (i.e. T1w, T2w, T2*). Morphometric Inverse Divergence (MIND) has been proposed as an alternative strategy, providing a measure of within-subject architectonic similarity between cortical areas based on multivariate distributions of vertex-wise MRI data for macro and microstructural features between region pair [3].
In this study, we will evaluate the sensitivity of MIND in detecting lifespan changes in global brain architecture in a cohort of N=63 macaque monkeys.
Structural connectivity is often studied using tractography derived from diffusion-weighted MRI data [2]. Yet, this approach is sensitive to false-positive connections and does not leverage information sensitive to cytoarchitectural changes (i.e. T1w, T2w, T2*). Morphometric Inverse Divergence (MIND) has been proposed as an alternative strategy, providing a measure of within-subject architectonic similarity between cortical areas based on multivariate distributions of vertex-wise MRI data for macro and microstructural features between region pair [3].
In this study, we will evaluate the sensitivity of MIND in detecting lifespan changes in global brain architecture in a cohort of N=63 macaque monkeys.
Methods:
The cortical surfaces of 63 rhesus macaque monkeys with ages from 1.98 to 26.4 (age: mean±SD=11.53±6.64, male: N=35) were reconstructed from T1 and T2 weighted MRIs [4]. Using a surface-based approach, we derived MRI feature data for 82k vertices, covering the cortex for T1w/T2w ratio, thickness, surface area, gray matter volume, sulcal depth, mean curvature and gaussian curvature. The D99 macaque atlas [5] was used to group vertices into 280 (140 per hemisphere) cortical nodes (Fig. 2B).
For each subject, nodal pair similarity was computed using symmetrized KL divergence, estimated using a k-nearest neighbors approach, from vertex-wise distributions of MRI features, transformed to range from 0 to 1 (low to high similarity) [3]. We observe a 280x280 MIND matrix for each subject.
MIND-based network properties were computed using the Brain Connectivity Toolbox for Python [6][7], including clustering coefficient and mean strength per node, and global efficiency for each whole-brain graph.
To investigate age-related patterns of network characteristics across the seven functional networks [8], we applied linear mixed-effects modeling using lme4 [9], with hemisphere and subject ID as random effects when applicable, on a global, network-wise (Fig.1) and regional scale (Fig. 2A). FDR correction was applied to network and regional analyses.
For each subject, nodal pair similarity was computed using symmetrized KL divergence, estimated using a k-nearest neighbors approach, from vertex-wise distributions of MRI features, transformed to range from 0 to 1 (low to high similarity) [3]. We observe a 280x280 MIND matrix for each subject.
MIND-based network properties were computed using the Brain Connectivity Toolbox for Python [6][7], including clustering coefficient and mean strength per node, and global efficiency for each whole-brain graph.
To investigate age-related patterns of network characteristics across the seven functional networks [8], we applied linear mixed-effects modeling using lme4 [9], with hemisphere and subject ID as random effects when applicable, on a global, network-wise (Fig.1) and regional scale (Fig. 2A). FDR correction was applied to network and regional analyses.
Results:
Linear models of the whole brain showed negative relationships between age and clustering coefficient (t-val=-3.69, pFDR<0.001), global efficiency (t-val=-3.69, pFDR=0.0013), and mean strength (t-val=-3.73, pFDR<0.001) (Fig. 1).
A significant negative relationship between age and clustering coefficient was found in all networks. Significant negative relationships were observed between age and global efficiency in the Default Mode (t-val=-6.56,pFDR<10-7), Frontoparietal (t-val=-4.90, pFDR<0.0001), and Ventral Attention (t-val=-3.25, pFDR=0.0013) networks. Age and mean strength showed significant negative relationships in all networks except Default Mode (Fig. 1).
Global and network trends were consistent at the regional level, demonstrating robustness of the analysis (Fig. 2D).
A significant negative relationship between age and clustering coefficient was found in all networks. Significant negative relationships were observed between age and global efficiency in the Default Mode (t-val=-6.56,pFDR<10-7), Frontoparietal (t-val=-4.90, pFDR<0.0001), and Ventral Attention (t-val=-3.25, pFDR=0.0013) networks. Age and mean strength showed significant negative relationships in all networks except Default Mode (Fig. 1).
Global and network trends were consistent at the regional level, demonstrating robustness of the analysis (Fig. 2D).
Conclusions:
This is the first study to investigate age-related lifespan trajectories of cortical similarity networks in the macaque brain. In most comparisons, we observe linear decreases in the strength and organization of MIND networks at the global, network and regional level. This suggests functional networks might become less specialized or segregated within the brain, similar to human based fMRI studies [10].
In addition, Default Mode, Frontoparietal, and Ventral Attention were the only networks to show reduced global efficiency, highlighting potential vulnerabilities of higher order information processing with age.
In addition, Default Mode, Frontoparietal, and Ventral Attention were the only networks to show reduced global efficiency, highlighting potential vulnerabilities of higher order information processing with age.
Lifespan Development:
Lifespan Development Other 2
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 1
Multivariate Approaches
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Cortical Anatomy and Brain Mapping
Novel Imaging Acquisition Methods:
Multi-Modal Imaging
Keywords:
Aging
ANIMAL STUDIES
Cortex
Multivariate
STRUCTURAL MRI
Other - Connectivity
1|2Indicates the priority used for review
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