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Maleki Balajoo, Somayeh [VerfasserIn]; Eickhoff, Simon [VerfasserIn]; Kharabian, Shahrzad [VerfasserIn]; Plachti, Anna [VerfasserIn]; Waite, Laura [VerfasserIn]; Hoffstaedter, Felix [VerfasserIn]; Palomero-Gallagher, Nicola [VerfasserIn]; GENON, Sarah [VerfasserIn]

Hippocampal Metabolic Subregions in Healthy Older andTheir Profiles in Neurodegeneration

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  • Medientyp: Elektronischer Konferenzbericht
  • Titel: Hippocampal Metabolic Subregions in Healthy Older andTheir Profiles in Neurodegeneration
  • Beteiligte: Maleki Balajoo, Somayeh [VerfasserIn]; Eickhoff, Simon [VerfasserIn]; Kharabian, Shahrzad [VerfasserIn]; Plachti, Anna [VerfasserIn]; Waite, Laura [VerfasserIn]; Hoffstaedter, Felix [VerfasserIn]; Palomero-Gallagher, Nicola [VerfasserIn]; GENON, Sarah [VerfasserIn]
  • Erschienen: Forschungszentrum Jülich: JuSER (Juelich Shared Electronic Resources), 2021
  • Erschienen in: 27th annual meeting of the Organization for Human Brain Mapping, Virtual, Germany, 2021-06-21 - 2021-06-25
  • Sprache: Englisch
  • Entstehung:
  • Anmerkungen: Diese Datenquelle enthält auch Bestandsnachweise, die nicht zu einem Volltext führen.
  • Beschreibung: 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 ...
  • Zugangsstatus: Freier Zugang