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@ARTICLE{Martens:885964,
      author       = {Martens, L. and Kroemer, N. B. and Teckentrup, V. and
                      Colic, L. and Palomero-Gallagher, Nicola and Li, M.},
      title        = {{L}ocalized prediction of glutamate from whole-brain
                      functional connectivity of the pregenual anterior cingulate
                      cortex},
      journal      = {The journal of neuroscience},
      volume       = {40},
      number       = {47},
      issn         = {0270-6474},
      address      = {Washington, DC},
      publisher    = {Soc.},
      reportid     = {FZJ-2020-04195},
      pages        = {9028-9042},
      year         = {2020},
      abstract     = {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.},
      cin          = {INM-1},
      ddc          = {610},
      cid          = {I:(DE-Juel1)INM-1-20090406},
      pnm          = {571 - Connectivity and Activity (POF3-571)},
      pid          = {G:(DE-HGF)POF3-571},
      typ          = {PUB:(DE-HGF)16},
      pubmed       = {pmid:33046545},
      UT           = {WOS:000591200900004},
      doi          = {10.1523/JNEUROSCI.0897-20.2020},
      url          = {https://juser.fz-juelich.de/record/885964},
}