Accelerating Benchmarking of Functional Connectivity Modeling via Structure-aware Core-set Selection

• URL: http://arxiv.org/abs/2602.05667v1
• Date: Thu, 05 Feb 2026 13:50:39 GMT
• Title: Accelerating Benchmarking of Functional Connectivity Modeling via Structure-aware Core-set Selection
• Authors: Ling Zhan, Zhen Li, Junjie Huang, Tao Jia,
• Abstract summary: We formalize Contrastive Learning for Core-set Selection (SCLCS) as a ranking- subset selection problem.<n>SCLCS identifies stable samples via a top-k ranking, Structural Perturbation Score, and density-balanced sampling strategy.<n>On the large-scale REST-meta-MDD dataset, SCLCS preserves the ground-truth model ranking with just 10% of the data, outperforming state-of-the-art (SOTA) core-set selection methods by up to 23.2% in ranking consistency (nDCG@k)
• Score: 8.347306013377041
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Benchmarking the hundreds of functional connectivity (FC) modeling methods on large-scale fMRI datasets is critical for reproducible neuroscience. However, the combinatorial explosion of model-data pairings makes exhaustive evaluation computationally prohibitive, preventing such assessments from becoming a routine pre-analysis step. To break this bottleneck, we reframe the challenge of FC benchmarking by selecting a small, representative core-set whose sole purpose is to preserve the relative performance ranking of FC operators. We formalize this as a ranking-preserving subset selection problem and propose Structure-aware Contrastive Learning for Core-set Selection (SCLCS), a self-supervised framework to select these core-sets. SCLCS first uses an adaptive Transformer to learn each sample's unique FC structure. It then introduces a novel Structural Perturbation Score (SPS) to quantify the stability of these learned structures during training, identifying samples that represent foundational connectivity archetypes. Finally, while SCLCS identifies stable samples via a top-k ranking, we further introduce a density-balanced sampling strategy as a necessary correction to promote diversity, ensuring the final core-set is both structurally robust and distributionally representative. On the large-scale REST-meta-MDD dataset, SCLCS preserves the ground-truth model ranking with just 10% of the data, outperforming state-of-the-art (SOTA) core-set selection methods by up to 23.2% in ranking consistency (nDCG@k). To our knowledge, this is the first work to formalize core-set selection for FC operator benchmarking, thereby making large-scale operators comparisons a feasible and integral part of computational neuroscience. Code is publicly available on https://github.com/lzhan94swu/SCLCS

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