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Abstract

Local measures of neurotransmitters such as glutamate and GABA provide insights into possible neurobiological changes underlying altered functional connectivity (FC) in mental disorders. However, as the signal-to-noise ratio of conventional magnetic resonance spectroscopy (MRS) is low, a single MRS voxel may cover regions with distinct cyto- and receptorarchitecture, and, therefore, distinct FC profiles. Here, we propose a novel, multi-modal approach offering a more nuanced prediction of glutamate and GABA in an MRS voxel. We used 7 Tesla imaging data from 88 healthy male and female participants and employed resting-state functional connectivity parcellation (FCBP) of a pregenual anterior cingulate (pgACC) MRS voxel and a cytoarchitecture-based parcellation (CABP) of the same region (Palomero-Gallagher et al., 2018, Cerebral Cortex). FCBP recovered two functionally distinct subregions, corresponding to the cytoarchitectonic division of the pgACC into areas p24 and p32. Using two complementary, data-driven methods (elastic net and partial least squares regression) we predicted Glu and GABA from cluster-wise FC. Cluster p32 predicted pgACC glutamate better than chance using elastic net and explained more variance compared to cluster p24 FC using both methods. In contrast, we found limited evidence supporting the robust prediction of GABA using cluster-wise FC. Crucially, predictions using cytoarchitectonic ROIs showed a similar pattern for glutamate and GABA suggesting that the results are robust regarding the partitioning method employed. Collectively, our results show that multimodal imaging may help to overcome the fundamental limitations of a single method, as fMRI can improve the spatial specificity of local neurometabolites assessed with conventional MRS.