Journal
JOURNAL OF NEUROSCIENCE
Volume 40, Issue 47, Pages 9028-9042Publisher
SOC NEUROSCIENCE
DOI: 10.1523/JNEUROSCI.0897-20.2020
Keywords
anterior cingulate cortex; functional connectivity; glutamate; machine learning; MRS
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Funding
- German Research Foundation (DFG) [SFB779/A06, Wa2673/4-1]
- Center for Behavioral Brain Sciences [CBBS NN05]
- University of Tubingen, Faculty of Medicine [2453-0-0]
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Local measures of neurotransmitters provide crucial insights into neurobiological changes underlying altered functional con-nectivity in psychiatric disorders. However, noninvasive 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 (R-2 = 0.324) and explained more variance compared with 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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