Applying Topological Methods to Cortical Thickness Data
Topological methods have been utilized to analyze differences in cortical thickness, particularly in individuals with autism. This involves using Python code to obtain necessary data files, calculating simplicial complexes, and visualizing persistence plots. The next steps involve computing these plots for different age groups and potentially comparing them with Ghudi tools.
Uploaded on Feb 26, 2025 | 0 Views
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Presentation Transcript
Can we apply topological methods to cortical thickness data?
Topological methods and cortical thickness Chung et al., 2009 discovered differences in cortical thickness comparing individuals with autism to controls using topology
Work flow HCP: Use Python code to get .mat file: 1) 3xN array for triangles (mesh) 2) 1xM array for vertex values ( thickness) subject+'.R.corrThickn ess.32k_fs_LR.shape.gi i subject+'.R.pial.32k_fs _LR.surf.gii Matlab or Octave: Calculate simplicial complex Save_weighted_triangle_su rface.m Command line: Sublevel-filtration Persistance diagram Dipha <input> <output> Matlab or Octave: plot_persistence_di agram.m
Visualization of persistence plot Data: rosanna_trianles_788746.filtration 1 5 4 3 Dimension Death 2 1 Essential Ordinary 0 0 0 1 2 3 4 5 Birth
Next steps Compute persistence plots for younger and older participants Use Ghudi tools to compare group?
