| Literature DB >> 34080613 |
Boris-Stephan Rauchmann1,2, Ersin Ersoezlue2, Sophia Stoecklein1, Daniel Keeser1,2, Frederic Brosseron3,4, Katharina Buerger5,6, Peter Dechent7, Laura Dobisch8, Birgit Ertl-Wagner1,9, Klaus Fliessbach3,4, John Dylan Haynes10, Michael T Heneka3,4, Enise I Incesoy11,12, Daniel Janowitz6, Ingo Kilimann13,14, Christoph Laske15,16, Coraline D Metzger8,17,18, Matthias H Munk15,16, Oliver Peters11,12, Josef Priller11,19, Alfredo Ramirez3,4,20, Sandra Roeske3, Nina Roy3, Klaus Scheffler21, Anja Schneider3,4, Annika Spottke3,22, Eike Jakob Spruth11,19, Stefan Teipel13,14, Maike Tscheuschler23, Ruth Vukovich24, Michael Wagner3,4, Jens Wiltfang24,25,26, Renat Yakupov8, Emrah Duezel8,17, Frank Jessen3,23,27, Robert Perneczky2,5,28,29.
Abstract
Several Alzheimer's disease (AD) atrophy subtypes were identified, but their brain network properties are unclear. We analyzed data from two independent datasets, including 166 participants (103 AD/63 controls) from the DZNE-longitudinal cognitive impairment and dementia study and 151 participants (121 AD/30 controls) from the AD neuroimaging initiative cohorts, aiming to identify differences between AD atrophy subtypes in resting-state functional magnetic resonance imaging intra-network connectivity (INC) and global and nodal network properties. Using a data-driven clustering approach, we identified four AD atrophy subtypes with differences in functional connectivity, accompanied by clinical and biomarker alterations, including a medio-temporal-predominant (S-MT), a limbic-predominant (S-L), a diffuse (S-D), and a mild-atrophy (S-MA) subtype. S-MT and S-D showed INC reduction in the default mode, dorsal attention, visual and limbic network, and a pronounced reduction of "global efficiency" and decrease of the "clustering coefficient" in parietal and temporal lobes. Despite severe atrophy in limbic areas, the S-L exhibited only marginal global network but substantial nodal network failure. S-MA, in contrast, showed limited impairment in clinical and cognitive scores but pronounced global network failure. Our results contribute toward a better understanding of heterogeneity in AD with the detection of distinct differences in functional connectivity networks accompanied by CSF biomarker and cognitive differences in AD subtypes.Entities:
Keywords: Alzheimer’s disease; brain structure; graph theory; independent component analysis; resting-state connectivity
Mesh:
Year: 2021 PMID: 34080613 PMCID: PMC8491689 DOI: 10.1093/cercor/bhab130
Source DB: PubMed Journal: Cereb Cortex ISSN: 1047-3211 Impact factor: 4.861