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Uncovering the Topology of Time-Varying fMRI Data using Cubical Persistence

NeurIPS 2020 · Probabilistic and scientific modeling › Dynamical systems · approach: Functional connectivity modeling · 34 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use the infinity-norm persistence summary instead of the 1-norm summary (ablation)Cubical persistence age prediction · BMCC: 0.46 -> 0.61Helped
Use the infinity-norm persistence summary instead of the 1-norm summary (ablation)Cubical persistence age prediction · OMCC: 0.67 -> 0.77Helped
Use the infinity-norm persistence summary instead of the 1-norm summary (ablation)Cubical persistence age prediction · XMCC: 0.48 -> 0.73Helped
Use parcellated voxel values in the cubical volume (ablation)||D||1 age prediction · BMCC: 0.46 -> 0.32Hurt
Use parcellated voxel values in the cubical volume (ablation)||D||1 age prediction · OMCC: 0.67 -> 0.50Hurt
Use parcellated voxel values in the cubical volume (ablation)||D||1 age prediction · XMCC: 0.48 -> 0.34Hurt
Use parcellated voxel values in the cubical volume (ablation)||D||infinity age prediction · BMCC: 0.61 -> 0.67Helped
Use parcellated voxel values in the cubical volume (ablation)||D||infinity age prediction · OMCC: 0.77 -> 0.50Hurt
Use parcellated voxel values in the cubical volume (ablation)||D||infinity age prediction · XMCC: 0.73 -> 0.33Hurt
Use topological summary statistics rather than baseline-ppAge prediction · BMCC: 0.41 -> 0.61Helped
Use topological summary statistics rather than baseline-ppAge prediction · OMCC: 0.40 -> 0.77Helped
Use topological summary statistics rather than baseline-ppAge prediction · XMCC: 0.40 -> 0.73Helped

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