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Localized prediction of glutamate from whole-brain functional connectivity of the pregenual anterior cingulate cortex

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Martens,  L
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Walter,  M
Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society;
Max Planck Institute for Biological Cybernetics, Max Planck Society;

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Citation

Martens, L., Kroemer, N., Teckentrup, V., Colic, L., Palomero-Gallagher, N., Li, M., et al. (2020). Localized prediction of glutamate from whole-brain functional connectivity of the pregenual anterior cingulate cortex. The Journal of Neuroscience, 40(47), 9028-9042. doi:10.1523/JNEUROSCI.0897-20.2020.


Cite as: http://hdl.handle.net/21.11116/0000-0005-C42D-C
Abstract
Local measures of neurotransmitters provide crucial insights into neurobiological changes underlying altered functional connectivity in psychiatric disorders. However, non-invasive neuroimaging techniques such as magnetic resonance spectroscopy (MRS) may cover anatomically and functionally distinct areas, such as p32 and p24 of the pregenual anterior cingulate cortex (pgACC). Here, we aimed to overcome this low spatial specificity of MRS by predicting local glutamate and GABA based on functional characteristics and neuroanatomy in a sample of 88 human participants (35 females), using complementary machine learning approaches. Functional connectivity profiles of pgACC area p32 predicted pgACC glutamate better than chance (R2 = .324) and explained more variance compared to area p24 using both elastic net and partial least squares regression. In contrast, GABA could not be robustly predicted. To summarize, machine learning helps exploit the high resolution of fMRI to improve the interpretation of local neurometabolism. Our augmented multimodal imaging analysis can deliver novel insights into neurobiology by using complementary information. Magnetic resonance spectroscopy (MRS) measures local glutamate and GABA non-invasively. However, conventional MRS requires large voxels compared to fMRI, due to its inherently low signal-to-noise ratio. Consequently, a single MRS voxel may cover areas with distinct cytoarchitecture. In the largest multimodal 7 Tesla machine learning study to date, we overcome this limitation by capitalizing on the spatial resolution of fMRI to predict local neurotransmitters in the PFC. Critically, we found that prefrontal glutamate could be robustly and exclusively predicted from the functional connectivity fingerprint of one out of two anatomically and functionally defined areas that form the pregenual anterior cingulate cortex. Our approach provides greater spatial specificity on neurotransmitter levels, potentially improving understanding of altered functional connectivity in mental disorders.