Assortative mixing in micro-architecturally annotated brain connectomes
Presented During:
Tuesday, June 25, 2024: 12:00 PM - 1:15 PM
COEX
Room:
Grand Ballroom 104-105
Poster No:
1537
Submission Type:
Abstract Submission
Authors:
Vincent Bazinet1, Justine Hansen1, Reinder Vos de Wael1, Boris Bernhardt1, Martijn van den Heuvel2, Bratislav Misic1
Institutions:
1McConnell Brain Imaging Centre, Montréal Neurological Institute, Montreal, Quebec, 2Vrije Universiteit, Amsterdam, N/A
First Author:
Co-Author(s):
Reinder Vos de Wael
McConnell Brain Imaging Centre, Montréal Neurological Institute
Montreal, Quebec
McConnell Brain Imaging Centre, Montréal Neurological Institute
Montreal, Quebec
Introduction:
The wiring of the brain connects micro-architecturally diverse neuronal populations. These neuronal populations have distinct anatomical and cellular makeups and thanks to modern technological advances, this heterogeneity in the brain's micro-architecture can be imaged with unprecedented detail and depth. The conventional graph model, however, encodes brain connectivity as a network of nodes and edges, and abstracts away the rich biological detail of each node [1].
Methods:
In this work, we investigated the systematic arrangement of brain network connections with respect to a range of molecular, cellular, and genetic attributes. More specifically, we used the assortativity coefficient [2] to ask whether brain regions with similar annotations (i.e. biological attributes) are more likely to be connected with each other (Fig. 1a). To disentangle the relationships between the brain's connectivity, regional heterogeneity, and spatial embedding, we implemented novel null models that control for the spatial autocorrelation of nodal attributes (Fig. 1b) [3]. We performed all experiments using four brain network datasets from three different species (human, macaque, and mouse; Fig. 1c). This allowed us to uncover universal principles of organization across network reconstruction techniques, species, spatial scales, and attributes.
Results:
While we find that all annotations are positively assortative (i.e., connected brain regions tend to have similar annotations), we find that only a few are significantly assortative when compared to spatial autocorrelation-preserving null annotations (Fig. 2a). These are gene PC1, T1w/T2w ratio and cortical thickness for the functional connectome, and neuron density and T1w/T2w ratio for the macaque connectome.
To explore how the mixing properties of our annotations vary as we consider connections of different lengths, we computed the assortativity of our annotations in thresholded connectomes where a given percentile of the shortest connections are removed. As short-distance connections are removed, leaving behind the longest connections, the standardized assortativity of all annotations in all four connectomes decreases (Fig. 2b). This result suggests that long-distance connections increase the diversity of a region's inputs and outputs and support the integration of information between micro-architecturally dissimilar regions [4].
We next asked if the heterogeneous distribution of pairs of attributes from multi-member classes of annotation – neurotransmitter receptor profiles and laminar differentiation – is reflected in the connectivity of the brain. We find that laminar thicknesses of layers III, V and VI are assortative with respect to each other, but disassortative with respect to layer IV. For neurotransmitter receptors, we find evidence of disassortative mixing for pairs of receptors and transporters predominantly expressed in brain regions on opposite ends of the functional hierarchy as well as a significant assortative relationship between VaChT and NAT (Fig. 2c). This supports the idea that the cholinergic and norepinephrine systems interact with each other and with the brain's topology to influence large-scale dynamical processes [5].
Finally, using meta-analytic decoding [6], we find that the arrangement of connectivity patterns with respect to biological attributes shape patterns of regional functional specialization. Specifically, regions that connect to biologically similar regions are associated with executive function while regions that connect with biologically dissimilar regions are associated with memory function (Fig. 2d).
To explore how the mixing properties of our annotations vary as we consider connections of different lengths, we computed the assortativity of our annotations in thresholded connectomes where a given percentile of the shortest connections are removed. As short-distance connections are removed, leaving behind the longest connections, the standardized assortativity of all annotations in all four connectomes decreases (Fig. 2b). This result suggests that long-distance connections increase the diversity of a region's inputs and outputs and support the integration of information between micro-architecturally dissimilar regions [4].
We next asked if the heterogeneous distribution of pairs of attributes from multi-member classes of annotation – neurotransmitter receptor profiles and laminar differentiation – is reflected in the connectivity of the brain. We find that laminar thicknesses of layers III, V and VI are assortative with respect to each other, but disassortative with respect to layer IV. For neurotransmitter receptors, we find evidence of disassortative mixing for pairs of receptors and transporters predominantly expressed in brain regions on opposite ends of the functional hierarchy as well as a significant assortative relationship between VaChT and NAT (Fig. 2c). This supports the idea that the cholinergic and norepinephrine systems interact with each other and with the brain's topology to influence large-scale dynamical processes [5].
Finally, using meta-analytic decoding [6], we find that the arrangement of connectivity patterns with respect to biological attributes shape patterns of regional functional specialization. Specifically, regions that connect to biologically similar regions are associated with executive function while regions that connect with biologically dissimilar regions are associated with memory function (Fig. 2d).
Conclusions:
In summary, the present work bridges microscale attributes and macroscale connectivity. While carefully controlling for the background effect of the brain's spatial embedding, we systematically assessed how connectivity is interdigitated with a broad range of micro-architectural attributes and empirically tested multiple theories about the wiring of cortical brain networks.
Genetics:
Transcriptomics
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 1
fMRI Connectivity and Network Modeling 2
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Cortical Cyto- and Myeloarchitecture
Transmitter Receptors
Keywords:
Computational Neuroscience
Other - Connectomics
1|2Indicates the priority used for review
Provide references using author date format
[2] Newman, M. E. (2003). Mixing patterns in networks. Physical review E, 67(2), 026126.
[3] Markello, R. D., & Misic, B. (2021). Comparing spatial null models for brain maps. NeuroImage, 236, 118052.
[4] Betzel, R. F. (2018). Specificity and robustness of long-distance connections in weighted, interareal connectomes. Proceedings of the National Academy of Sciences, 115(21), E4880-E4889.
[5] Shine, J. M. (2019). Neuromodulatory influences on integration and segregation in the brain. Trends in cognitive sciences, 23(7), 572-583.
[6] Yarkoni, T. (2011). Large-scale automated synthesis of human functional neuroimaging data. Nature methods, 8(8), 665-670.