Mapping Glutamate-Receptor Spatial Covariance Using GluCEST MRI and Neuromaps
Poster No:
2117
Submission Type:
Abstract Submission
Authors:
Maggie Pecsok1, Golia Shafiei1, Ally Atkins1, Jacquelyn Stifelman1, Melanie Matyi1, Heather Robinson2, Monica Calkins1, Raquel Gur1, Ruben Gur1, Ravi Nanga1, Ravinder Reddy1, Kosha Ruparel1, Kristin Linn1, Corey McMillan1, Russell Shinohara1, Daniel Wolf1, Aaron Alexander-Bloch1, Theodore Satterthwaite1, David Roalf1
Institutions:
1University of Pennsylvania, Philadelphia, PA, 2University of Connecticut, Arjona, CT
First Author:
Co-Author(s):
Introduction:
Glutamate-weighted Chemical Exchange Saturation Transfer (GluCEST) MRI provides non-invasive, high-resolution mapping of total glutamate (Glu) and has highlighted glutamatergic abnormalities in psychosis spectrum disorder (PSY) and other disorders (Cember et al., 2023). However, its association with regional excitatory and inhibitory neurotransmitter receptor density remains unexplored. Investigating this spatial covariance could elucidate biological correlates of GluCEST signal and highlight patterns of receptor functional heterogeneity or glutamatergic dysfunction. Here, we used Neuromaps (Markello et al., 2022), a repository of normative PET-derived receptor data, to examine the spatial covariance of GluCEST with NMDA, mGluR5, and GABAA receptor expression across the cortex in a transdiagnostic cohort of PSY patients and healthy controls (HC). We hypothesized that GluCEST signal would be spatially correlated with glutamatergic receptor density.
Methods:
91 adults (36 HC, 55 PSY) underwent 7T GluCEST MRI. GluCEST data were processed in-house (Roalf, 2022), while normative PET data were curated using Neuromaps (Hansen et al., 2022). All data were parcellated using the Cammoun500 atlas (Cammoun et al., 2012). Associations between parcel-wise GluCEST level and PET receptor density were assessed in the HC group via linear regression, with age and sex as covariates. Spatial null (spin) tests were used to assess statistical significance (Alexander-Bloch et al., 2018). Post-hoc interaction models evaluated GluCEST-receptor associations across cytoarchitecturally defined von Economo (VE) regions (Scholtens et al., 2018). Finally, GluCEST-receptor associations were compared in HC versus PSY in each VE region using an interaction model. Analyses were completed in Python and R. FDR correction was employed.
Results:
Across all parcels in the HC group, there was a positive association between GluCEST and both NMDA (r=0.47, p(spin)=0.0001) and GABAA (r=0.67, p(spin)=0.0001), but not mGluR5 (r=0.25, p(spin)=0.3) (Figure 1C-E). Models predicting GluCEST revealed interactions between VE region and receptor expression for NMDA (F(87)=7.24, p<0.001), GABA (F(87)=32, p<0.0001), and mGluR5 (F(87)=32, p<0.0001) (Figure 1F-H). There was also an interaction between receptor density and diagnosis group in the Primary/Secondary Sensory region for mGluR5 (F=5.7, p=0.02) and GABAA (F=7.4, p<0.01), with weaker GluCEST-receptor associations in PSY (Figure 2).
Conclusions:
Our study shows that GluCEST is spatially correlated with normative NMDA and GABAA density in HC. The robust GluCEST-NMDA association likely reflects elevated extracellular glutamate in regions with a high density of NMDA receptors, a key ionotropic Glu receptor. GluCEST-GABAA coupling may indicate stringent excitation-inhibition regulation. Across all parcels, there was no association with mGluR5, a metabotropic receptor that does not directly mediate Glu synaptic transmission (Scheefhals et al., 2023).
The receptor*VE region interaction for all three receptors suggests regional variation in receptor-mediated Glu transmission or metabolism. For example, in the Primary/Secondary Sensory region, mGluR5 is negatively correlated with GluCEST, potentially reflecting its region-specific role in long-term potentiation in the visual cortex (Li et al., 2017). In the same region, weaker GluCEST associations with mGluR5 and GABAA in PSY complement prior reports of Glu-GABA disruptions in the visual cortex (Thakkar et al., 2017), indicating local receptor dysfunction.
Overall, we present a novel combination of in vivo Glu imaging modalities to show that GluCEST covaries with normative maps of Glu-related receptor expression. With unprecedented spatial resolution, we show differential Glu-receptor coupling across regions and disease states. These results warrant further investigation and highlight the need to collect multimodal imaging data within-subjects and in clinical populations.
The receptor*VE region interaction for all three receptors suggests regional variation in receptor-mediated Glu transmission or metabolism. For example, in the Primary/Secondary Sensory region, mGluR5 is negatively correlated with GluCEST, potentially reflecting its region-specific role in long-term potentiation in the visual cortex (Li et al., 2017). In the same region, weaker GluCEST associations with mGluR5 and GABAA in PSY complement prior reports of Glu-GABA disruptions in the visual cortex (Thakkar et al., 2017), indicating local receptor dysfunction.
Overall, we present a novel combination of in vivo Glu imaging modalities to show that GluCEST covaries with normative maps of Glu-related receptor expression. With unprecedented spatial resolution, we show differential Glu-receptor coupling across regions and disease states. These results warrant further investigation and highlight the need to collect multimodal imaging data within-subjects and in clinical populations.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia) 2
Neuroanatomy, Physiology, Metabolism and Neurotransmission:
Transmitter Receptors
Transmitter Systems
Physiology, Metabolism and Neurotransmission:
Neurophysiology of Imaging Signals 1
Keywords:
GABA
Glutamate
MRI
Neurotransmitter
Positron Emission Tomography (PET)
RECEPTORS
Schizophrenia
1|2Indicates the priority used for review
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Provide references using APA citation style.
Cammoun, L. (2012). Mapping the human connectome at multiple scales with diffusion spectrum MRI. Journal of neuroscience methods, 203(2), 386-397.
Cember, A. T. (2023). Glutamate‐weighted CEST (gluCEST) imaging for mapping neurometabolism: An update on the state of the art and emerging findings from in vivo applications. NMR in Biomedicine, 36(6), e4780.
Li, S. (2017). Brief novel visual experience fundamentally changes synaptic plasticity in the mouse visual cortex. Journal of Neuroscience, 37(39), 9353-9360.
Markello, R. D. (2022). Neuromaps: structural and functional interpretation of brain maps. Nature Methods, 19(11), 1472-1479.
Hansen, J. Y. (2022). Mapping neurotransmitter systems to the structural and functional organization of the human neocortex. Nature neuroscience, 25(11), 1569-1581.
Roalf, D.R., 2022. An automated and robust analysis pipeline for 7T GluCEST data.
Scheefhals, N. (2023). mGluR5 is transiently confined in perisynaptic nanodomains to shape synaptic function. Nature communications, 14(1), 244.
Scholtens, L. H. (2018). An mri von economo–koskinas atlas. NeuroImage, 170, 249-256.
Thakkar, K. N. (2017). 7T proton magnetic resonance spectroscopy of gamma-aminobutyric acid, glutamate, and glutamine reveals altered concentrations in patients with schizophrenia and healthy siblings. Biological psychiatry, 81(6), 525-535.