Neurotransmission Connectomes
Poster No:
1187
Submission Type:
Abstract Submission
Authors:
Filip Milisav1, Justine Hansen1, Bratislav Misic1
Institutions:
1Montréal Neurological Institute, McGill University, Montréal, Canada
First Author:
Co-Author(s):
Introduction:
The development of biologically resolved wiring diagrams is crucial to understanding brain function and dysfunction. While the brain's structural network can mediate disease propagation (Fornito, 2015), the vulnerability of brain regions is also influenced by their physiochemical makeup (Saxena, 2011). However, structural networks abstract away the rich biological detail of individual brain regions. Here, we develop a framework for annotating structural network edges using chemoarchitectural features of glutamate and GABA neurotransmitter systems to build a signaling-informed connectome.
Methods:
Neurotransmitters, together with neurotransmitter receptors, modulate neuronal excitability and mediate the propagation of action potentials, ultimately shaping patterns of brain-wide communication. To account for the cortical patterning of neurotransmitter synthesis and neurotransmitter receptors, and relate it to interregional anatomical connections, we use a receptor density atlas acquired from PET tracer images (Hansen, 2022) for 2 glutamate receptors (mGluR5 and NMDA) and 1 GABA receptor (GABAa) and gene expression data from the Allen Human Brain Atlas (Hawrylycz, 2012) for the rate-limiting enzymes associated with both neurotransmitters (GLS and GAD1 for glutamate and GABA, respectively). Specifically, we weight each edge of the structural brain network by the product of gene expression and receptor density in the respective sender and target regions (Fig. 1a). The resulting neurotransmitter signaling networks provide a directed transmitter-receptor resolved perspective on inter-regional synaptic signaling. To relate structure to function in neurotransmission connectomes, we use group-representative structural and functional brain networks reconstructed from diffusion-weighted and resting-state functional MRI data, respectively (n = 327; Human Connectome Project; Van Essen, 2013). To bridge sparse structural and neurotransmission networks with fully connected functional networks, we apply the mean first-passage time measure of diffusive communication (Noh, 2004). Functional specialization is analyzed using meta-analytic probabilistic functional activation maps from the Neurosynth platform (Yarkoni, 2011). To measure the accessibility of interregional topological shortest paths, we use search information (Goñi, 2013). Finally, we assess the role of system-specific neurotransmission in disease spreading using node-neighbour atrophy analysis (Shafiei, 2020) in a dataset of abnormal cortical thickness patterns from 10 different neurological, psychiatric, and neurodevelopmental diseases and disorders from the Enhancing Neuroimaging Genetics through Meta-Analysis (ENIGMA) consortium (Larivière, 2021).
Results:
We find that neurotransmission connectomes significantly improve predictions of regional functional connectivity compared to using structural connectivity alone (Fig. 1b, top; orange: pspin < 0.05). Specifically, neurotransmission connectomes outperform the structural connectome in regions subserving higher-order cognitive functions (Fig. 1b, bottom). Additionally, they require significantly less search information than the structural connectome (two-tailed Wilcoxon-Mann-Whitney–WMW–tests , p < 10−17), which indicates that their optimal communication pathways are more accessible (Fig. 1c). We observe that communication pathways where accessibility is most improved originate or terminate in limbic and visual regions. Finally, across 10 diseases, we find that cortical thickness abnormalities correlate strongly with abnormalities in neighbouring regions weighted by neurotransmitter-informed communication, with the best correspondence observed for temporal lobe epilepsy using glutamate-NMDA-mediated communication (Fig. 1d).
Conclusions:
In summary, annotating brain structure with neurotransmitter chemoarchitecture enhances neural signaling representations and offers potential applications in disease modeling.
Disorders of the Nervous System:
Neurodevelopmental/ Early Life (eg. ADHD, autism)
Psychiatric (eg. Depression, Anxiety, Schizophrenia)
Genetics:
Transcriptomics
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural) 1
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Transmitter Systems 2
Keywords:
DISORDERS
FUNCTIONAL MRI
GABA
Glutamate
Modeling
Positron Emission Tomography (PET)
RECEPTORS
Other - connectome
1|2Indicates the priority used for review
Abstract Information
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Provide references using APA citation style.
Goñi, J. (2014). Resting-brain functional connectivity predicted by analytic measures of network communication. Proceedings of the National Academy of Sciences, 111(2), 833-838.
Hansen, J. Y. (2022). Mapping neurotransmitter systems to the structural and functional organization of the human neocortex. Nature Neuroscience, 25(11), 1569-1581.
Hawrylycz, M. J. (2012). An anatomically comprehensive atlas of the adult human brain transcriptome. Nature, 489(7416), 391-399.
Larivière, S. (2021). The ENIGMA Toolbox: multiscale neural contextualization of multisite neuroimaging datasets. Nature Methods, 18(7), 698-700.
Noh, J. D. (2004). Random walks on complex networks. Physical review letters, 92(11), 118701.
Saxena, S. (2011). Selective neuronal vulnerability in neurodegenerative diseases: from stressor thresholds to degeneration. Neuron, 71(1), 35-48.
Shafiei, G. (2020). Spatial patterning of tissue volume loss in schizophrenia reflects brain network architecture. Biological Psychiatry, 87(8), 727-735.
Van Essen, D. C. (2013). The WU-Minn human connectome project: an overview. Neuroimage, 80, 62-79.
Yarkoni, T. (2011). Large-scale automated synthesis of human functional neuroimaging data. Nature Methods, 8(8), 665-670.