This title appears in the Scientific Report :
2020
Please use the identifier:
http://dx.doi.org/10.1523/JNEUROSCI.0897-20.2020 in citations.
Please use the identifier: http://hdl.handle.net/2128/26382 in citations.
Localized prediction of glutamate from whole-brain functional connectivity of the pregenual anterior cingulate cortex
Localized prediction of glutamate from whole-brain functional connectivity of the pregenual anterior cingulate cortex
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 a...
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Personal Name(s): | Martens, L. (Corresponding author) |
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Kroemer, N. B. / Teckentrup, V. / Colic, L. / Palomero-Gallagher, Nicola / Li, M. | |
Contributing Institute: |
Strukturelle und funktionelle Organisation des Gehirns; INM-1 |
Published in: | The journal of neuroscience, 40 (2020) 47, S. 9028-9042 |
Imprint: |
Washington, DC
Soc.
2020
|
DOI: |
10.1523/JNEUROSCI.0897-20.2020 |
PubMed ID: |
33046545 |
Document Type: |
Journal Article |
Research Program: |
Connectivity and Activity |
Link: |
Published on 2020-11-18. Available in OpenAccess from 2021-05-18. Published on 2020-11-18. Available in OpenAccess from 2021-05-18. Published on 2020-11-18. Available in OpenAccess from 2021-05-18. |
Publikationsportal JuSER |
Please use the identifier: http://hdl.handle.net/2128/26382 in citations.
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. |