% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@INPROCEEDINGS{MalekiBalajoo:905315,
      author       = {Maleki Balajoo, Somayeh and Eickhoff, Simon and Kharabian,
                      Shahrzad and Plachti, Anna and Waite, Laura and
                      Hoffstaedter, Felix and Palomero-Gallagher, Nicola and
                      GENON, Sarah},
      title        = {{H}ippocampal {M}etabolic {S}ubregions in {H}ealthy {O}lder
                      and{T}heir {P}rofiles in {N}eurodegeneration},
      reportid     = {FZJ-2022-00586},
      year         = {2021},
      abstract     = {Hippocampus dysfunction is the hallmark of Alzheimer’s
                      pathology and is frequently investigated with FDG-PET
                      metabolism measurements. However, while metabolic changes
                      are a key aspect of Alzheimer’s disease (AD), different
                      hippocampus’ subregions with their specific metabolic
                      covariance (MC) networks haven’t been identified in
                      healthy populations. It is also unclear to what extent these
                      are affected by AD pathophysiology. As the hippocampus
                      portrays cytoarchitectural, connectional and functional
                      heterogeneity, heterogenous patterns of MC could be
                      expected, leading to hippocampal subregions being
                      differentially affected by AD pathology. We investigated MC
                      as correlations in metabolism between hippocampus and brain
                      voxels in a large cohort of healthy older participants
                      (n=362). To identify how the pattern of brain MC changes
                      spatially within the hippocampus, we used a data-driven
                      approach to cluster hippocampal voxels based on their whole
                      brain co-metabolism profile (Eickhoff, Yeo, $\&$ Genon,
                      2018). The stability of different parcellation levels was
                      measured using split-half cross-validation. We then examined
                      the whole brain co-metabolism profile of each subregion
                      using the general linear model. To examine whether the local
                      metabolism between the metabolically-identified subregions
                      in healthy older is influenced by AD pathology, we also
                      performed a two-way ANOVA in the healthy older and in a
                      cohort of ADNI patients (n=581) with the mean glucose uptake
                      value as a dependent variable and both the subregions and
                      diagnostic groups as factors. The ANOVA was followed by
                      post-hoc analyses to identify which particular group
                      differences are statistically significant while correcting
                      for multiple comparisons. The results were compared with
                      results of the same analysis using the structurally-defined
                      and widely used FreeSurfer’s subfields.A stable 5-clusters
                      parcellation could be identified which included an
                      Anterior-subiculum(Red), an Anterior-CA(Yellow), an
                      Intermediate-subregion(Pink), a Posterior-subiculum(Blue)
                      and a Posterior-CA(Green) subregions (Fig. 1-A). As
                      illustrated in Fig. 1-C, the Anterior-subiculum(Red)
                      subregion mainly relates to orbito-frontal and temporal
                      regions while the Intermediate-subregion (Pink) is a
                      transitional subregion towards the Posterior-subiculum(Blue)
                      subregion which has a wide pattern of cortical MC. The
                      Anterior-CA(Yellow) subregion mainly relates to the amygdala
                      while the Posterior-CA(Green) subregion mainly relates to
                      other subcortical structures (Fig. 1-C). For both
                      hippocampal parcellations, the two-way ANOVA revealed both
                      significant main and interaction effects. Nevertheless,
                      overall, the differentiation between CA subregions as
                      provided by FreeSurfer did not exhibit specific group
                      differences while the anterior-vs-posterior distinction
                      offered by our parcellation revealed specific group
                      differences, in particular in the early stages (Table 1).
                      Overall, our results suggest a MC based differentiation
                      within the hippocampus that follows the CAs vs Subiculum
                      differentiation known from local microstructure mapping
                      (Fig. 1B) and anatomical connectivity (Fig. 1D). The MC
                      patterns of the identified subregions suggested three main
                      networks relating the anterior subregions to orbitofrontal
                      and anterior temporal regions, the Posterior-CA(Green)
                      subregion to subcortical structures around the ventricles,
                      and the Posterior-subiculum(Blue) subregion to an extended
                      cortical pattern (Fig. 1C). These MC patterns converge with
                      the patterns of structural covariance previously shown in
                      healthy aging (Plachti et al., 2020)(Fig. 1E), as well as
                      the different factors of brain atrophy reported in AD (Zhang
                      et al., 2016)(Fig. 1F) reinforcing the relevance of the
                      derived differentiation in pathological aging. Finally, our
                      parcellation allows the identification of specific
                      diagnostic group differences in regional hippocampus
                      metabolism.},
      month         = {Jun},
      date          = {2021-06-21},
      organization  = {27th annual meeting of the
                       Organization for Human Brain Mapping,
                       Virtual (Germany), 21 Jun 2021 - 25 Jun
                       2021},
      cin          = {INM-7},
      cid          = {I:(DE-Juel1)INM-7-20090406},
      pnm          = {5252 - Brain Dysfunction and Plasticity (POF4-525) / 5251 -
                      Multilevel Brain Organization and Variability (POF4-525) /
                      5253 - Neuroimaging (POF4-525)},
      pid          = {G:(DE-HGF)POF4-5252 / G:(DE-HGF)POF4-5251 /
                      G:(DE-HGF)POF4-5253},
      typ          = {PUB:(DE-HGF)1},
      url          = {https://juser.fz-juelich.de/record/905315},
}