Glutamatergic Underpinnings of Within-Network Functional Connectivity in a Transdiagnostic Cohort
Poster No:
2381
Submission Type:
Abstract Submission
Authors:
Margaret Pecsok1, Ally Atkins2, Alfredo Lucas1, Monica Calkins2, Adam Czernuszenko3, Ruben Gur4, Ravi Prakash Nanga Reddy5, Ravinder Reddy5, Heather Robinson1, Kosha Ruparel2, Nick Wellman6, Daniel Wolf1, Theodore Satterthwaite4, David Roalf7
Institutions:
1University of Pennsylvania, Philadelphia, PA, 2University of Pennsylvania, Department of Psychiatry, Philadelphia, PA, 3University of Pennsylvania, Department of Psychiatry, Philadephia, PA, 4UPenn, Philadelphia, PA, 5University of Pennsylvania, Department of Radiology, Philadelphia, PA, 6University of Pennsylvania, Department of Psychiatry, Philadelphia , PA, 7Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA
First Author:
Co-Author(s):
David Roalf
Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania
Philadelphia, PA
Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania
Philadelphia, PA
Introduction:
Alterations in glutamate (Glu) concentration and resting state functional connectivity (FC) in cortical networks have independently been linked to psychosis spectrum (PS) symptoms (e.g., Sydnor & Roalf, 2020; O'Neill et al., 2019). However, the relationship between regional Glu and FC patterns in PS cohorts has been sparsely explored. The present study addresses this gap by leveraging 3T resting-state functional magnetic resonance imaging (rsfMRI) in conjunction with ultra-high field (7T) Glutamate Chemical Exchange Saturation Transfer (GluCEST), which allows for non-invasive, in vivo mapping of brain Glu with unprecedented sensitivity and spatial resolution. We seek to gain insights into the functional implications of cortical glutamatergic deficits across the sensorimotor-association (SA) axis in PS individuals and healthy controls (HC).
Methods:
A transdiagnostic sample of 51 adults (23 HC, 28 PS) underwent 7T GluCEST and 3T rsfMRI within a 6-month period. The 2D GluCEST imaging parameters were: slice thickness=5 mm, FOV: 220×200, in-plane resolution=1.15×1.15 mm2, GRE read out TR=6.2 ms, TE=3 ms, number of averages=1, shot TR=10 500 ms, shots per slice=2, with a CEST saturation pulse at a B1rms of 3.06 μT with 800 ms duration. GluCEST data from cortical gray matter were processed using an in-house Python-based pipeline. In addition to GluCEST, participants also underwent 10 minutes of resting state fMRI (T2*-weighted gradient EPI, TR=3,000 ms, voxel size=2.0×2.0×2.0 mm3) obtained using a Siemens 3T scanner (Magnetom TrioTim). fMRI data were processed using fMRIPrep and XCP-D (Esteban et al., 2019; Mehta et al., 2023). All post-processing analyses were performed in Python. First, Glu levels were compared within and across 7 functional networks as defined by the Schaefer100 atlas (Schaefer et al., 2018). Finally, linear regression was used to compare within-network functional connectivity and network-wide GluCEST across all participants and within clinical groups.
Results:
Across all participants, GluCEST was lower in the Limbic network (Limb) and higher in the visual network (Vis) compared to all other networks (p<1*10-4 and p<1*10-8, respectively, for all comparisons). Compared to HCs, PS participants had significantly lower GluCEST in the Salience/Ventral Attention network (SalVentAttn; p=0.029) and somatomotor network (SomMot; p=0.038). Linear regression revealed a positive association between SomMot GluCEST and FC (R2=0.14, p=0.01). A negative association between dorsal attention network (DorsAttn) GluCEST and FC (R2=0.22, p=0.03) was identified exclusively in the PS group.
Conclusions:
The present analysis provides valuable preliminary insights into Glu-FC relationships. First, we replicated previous findings (Pecsok et al., 2022) of higher GluCEST in Vis and lower GluCEST in Limb; GluCEST may capture stable cortical features of these networks, potentially related to cortical architecture, synaptic plasticity, or baseline metabolic activity. Previous reports of lower Glu in PS groups (Pecsok et al., 2022, Roalf et al., 2017, Sydnor & Roalf, 2020) were also replicated, with lower GluCEST in PS within the SomMot and SalVentAttn networks. The positive association between SomMot GluCEST and FC suggests that the robustness of local excitatory glutamatergic signaling may underlie macroscale connectivity dynamics in this network. Interestingly, despite comparable GluCEST levels in DorsAttn, only the PS group showed a negative correlation between GluCEST and FC. This may stem from heightened vulnerability to Glu-mediated excitotoxicity in this region. Further investigation of Glu-FC coupling across cortical and subcortical networks is indicated.
Disorders of the Nervous System:
Psychiatric (eg. Depression, Anxiety, Schizophrenia)
Modeling and Analysis Methods:
Connectivity (eg. functional, effective, structural)
fMRI Connectivity and Network Modeling 2
Novel Imaging Acquisition Methods:
Imaging Methods Other 1
Keywords:
FUNCTIONAL MRI
Glutamate
Psychiatric Disorders
1|2Indicates the priority used for review
Provide references using author date format
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O’Neill, A., Mechelli, A., & Bhattacharyya, S. (2019). Dysconnectivity of large-scale functional networks in early psychosis: a meta-analysis. Schizophrenia bulletin, 45(3), 579-590.
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