Abstract
Functional brain connectivity changes dynamically over time, making its representation challenging for learning on non-Euclidean data. We present a framework that encodes dynamic functional connectivity as an image representation of evolving network topology. Persistent graph homology summarizes global organization across scales, yielding Wasserstein distance-preserving embeddings stable under resolution changes. Stacking these embeddings forms a topological image that captures temporal reconfiguration of brain networks. This design enables convolutional architectures and transfer learning from pretrained foundational models to operate effectively under limited and imbalanced data. Applied to early-stage cognitive impairment assessment on OASIS-3, the approach achieves strong CDR–SB prediction performance, supporting dynamic topological imaging as a promising framework for capturing functional alterations relevant to Alzheimer’s disease.
Links to Paper and Supplementary Materials
Main Paper (Open Access Version): https://papers.miccai.org/miccai-2026-sat/paper/GRAIL_010.pdf
SharedIt Link: Not yet available
SpringerLink (DOI): Not yet available
Supplementary Material: Not Submitted
Link to Open Review
Open Review Page: https://openreview.net/forum?id=nMufOopZr8
BibTex
@InProceedings{HePei_Imaging_MICCAISAT2026,
author = { He, Peilin AND Songdechakraiwut, Tananun},
title = { { Imaging the Topology of Dynamic Brain Connectivity } },
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026 Workshops and Challenges},
year = {2026},
publisher = {Springer Nature Switzerland},
volume = {LNCS 17278},
month = {pending},
page = {pending}
}