TY  - JOUR
AU  - Martens, L.
AU  - Kroemer, N. B.
AU  - Teckentrup, V.
AU  - Colic, L.
AU  - Palomero-Gallagher, Nicola
AU  - Li, M.
TI  - Localized prediction of glutamate from whole-brain functional connectivity of the pregenual anterior cingulate cortex
JO  - The journal of neuroscience
VL  - 40
IS  - 47
SN  - 0270-6474
CY  - Washington, DC
PB  - Soc.
M1  - FZJ-2020-04195
SP  - 9028-9042
PY  - 2020
AB  - Local measures of neurotransmitters provide crucial insights into neurobiological changes underlying altered functional connectivity 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 (R2 = 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.
LB  - PUB:(DE-HGF)16
C6  - pmid:33046545
UR  - <Go to ISI:>//WOS:000591200900004
DO  - DOI:10.1523/JNEUROSCI.0897-20.2020
UR  - https://juser.fz-juelich.de/record/885964
ER  -