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

Martens, L., Kroemer, N., Teckentrup, V., Colic, L., Palomero-Gallagher, N., Li, M., et al. (submitted). Localized prediction of glutamate from whole-brain functional connectivity of the pregenual anterior cingulate cortex.

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Item Permalink: http://hdl.handle.net/21.11116/0000-0005-C42D-C Version Permalink: http://hdl.handle.net/21.11116/0000-0006-9F0F-8
Genre: Paper

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 Creators:
Martens, L1, 2, Author              
Kroemer, NB, Author
Teckentrup, V, Author
Colic, L, Author
Palomero-Gallagher, N, Author
Li, M, Author              
Walter, M1, 2, Author              
Affiliations:
1Department High-Field Magnetic Resonance, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497796              
2Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497794              

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 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, 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.

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 Dates: 2020-02
 Publication Status: Submitted
 Pages: -
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 Rev. Method: -
 Identifiers: DOI: 10.1101/2020.02.26.966259
 Degree: -

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