Hierarchical spectral clustering reveals brain size and shape changes in asymptomatic carriers of C9orf72.

ABSTRACT: Traditional methods for detecting asymptomatic brain changes in neurodegenerative diseases such as Alzheimer's disease or frontotemporal degeneration typically evaluate changes in volume at a predefined level of granularity, e.g. voxel-wise or in a priori defined cortical volumes of interest. Here, we apply a method based on hierarchical spectral clustering, a graph-based partitioning technique. Our method uses multiple levels of segmentation for detecting changes in a data-driven, unbiased, comprehensive manner within a standard statistical framework. Furthermore, spectral clustering allows for detection of changes in shape along with changes in size. We performed tensor-based morphometry to detect changes in the Genetic Frontotemporal dementia Initiative asymptomatic and symptomatic fron

SUBMITTER: Bruffaerts R 

PROVIDER: S-EPMC9311825 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature