arXiv cs.AI· Sheethal Bhat, Mahfuzur Rahman Chowdhury, Imam Ahmed Shaik, Rose Dawn Bharath, Siming Bayer, Andreas Maier·· 4 小时前AI 评分30
ADNI 静息态 fMRI 认知预测中个体化功能网络归因的可靠性分析
How Reliable Is Individualized Functional-Network Attribution in Resting-State fMRI Cognitive Prediction in ADNI?
AI 导读
一项研究评估了基于 ADNI 的 875 名受试者训练的 Schaefer-200 FC 预测模型（预测 MMSE/ADAS-Cog13）的逐受试者网络消融归因可靠性。
正文 · 原文
Abstract:Individualized attribution estimates which functional networks drive a model prediction for a given subject and is often proposed as an interpretable resting-state fMRI (rs-fMRI) biomarker, but its test-retest reliability is rarely quantified. We evaluate per-subject network-ablation attribution for a frozen resting-state functional connectivity (FC) predictor (Schaefer-200 FC predicting MMSE/ADAS-Cog13) trained on N=875 subjects from the Alzheimer's Disease Neuroimaging Initiative (ADNI), using, and (iii) a subject-level permutation null. Split-half reliability is near-ceiling (Spearman p=0.948/0.950) but drops on scan-rescan (p=0.609/0.616). Under the mismatched-subject null, correlations remain substantial (p~0.46-0.47), indicating strong population-shared structure, yet matched pairs exceed this null (z~6.6-6.9, p<=10-4). Thus, in ADNI, individualized network attribution contains a modest but non-zero person-specific component atop a strong population-shared pattern. We recommend reporting scan-rescan reliability together with a permutation-null control, rather than within-session proxies alone, to avoid overstating individual specificity.
| Subjects: | Computer Vision and Pattern Recognition (cs.CV); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.37472 [cs.CV] |
| (or arXiv:2610.37472v1 [cs.CV] for this version) | |
| https://doi.org/10.48550/arXiv.2610.37472 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Sheethal Bhat [view email]
[v1]
Tue, 27 Oct 2026 16:04:54 UTC (442 KB)
来源：arXiv cs.AI · arxiv.org