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Topology-based Clustering of Functional Brain Networks in an Alzheimer's Disease Cohort
Ist Teil von
AMIA Summits on Translational Science proceedings, 2024, Vol.2024, p.449-458
Ort / Verlag
United States
Erscheinungsjahr
2024
Quelle
EZB Electronic Journals Library
Beschreibungen/Notizen
Alzheimer's disease is a progressive neurodegenerative disease with many identifying biomarkers for diagnosis. However, whole-brain phenomena, particularly in functional MRI modalities, are not fully understood nor characterized. Here we employ the novel application of topological data analysis (TDA)-based methods of persistent homology to functional brain networks from ADNI-3 cohort to perform a subtyping experiment using unsupervised clustering techniques. We then investigate variations in QT-PAD challenge features across the identified clusters. Using a Wasserstein distance kernel with a variety of clustering algorithms, we found that the 0
-homology Wasserstein distance kernel and spectral clustering yielded clusters with significant differences in whole brain and medial temporal lobe (MTL) volume, thus demonstrating an intrinsic link between whole brain functional topology and brain morphometric structure. These findings demonstrate the importance of MTL in functional connectivity and the efficacy of using TDA-based machine learning methods in network neuroscience and neurodegenerative disease subtyping.
Sprache
Englisch
Identifikatoren
eISSN: 2153-4063
Titel-ID: cdi_proquest_miscellaneous_3064139777
Format
–
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