不用固定脑图谱，直接从个体fMRI数据构建脑网络，提升分析精度与泛化能力。
Atlas-free Brain Network Transformer
- 基于个体静息态fMRI数据生成个性化脑区划分，摆脱固定图谱依赖。
- 在性别分类和脑连接组年龄预测任务中优于多种主流方法。
- 适合需要高精度脑网络分析的神经科学与精准医疗研究者。
当前基于图谱的脑网络分析严重依赖标准化解剖或功能驱动的脑图谱。然而，这些固定图谱常带来空间错位、区域功能异质性及图谱选择偏差等问题，影响脑网络的可靠性与可解释性。为此，我们提出一种新型无图谱脑网络变压器（atlas-free BNT），直接利用受试者特异性静息态fMRI数据生成个性化脑区划分。该方法在标准化体素特征空间中计算脑区-体素连接特征，并通过BNT架构生成可比较的个体级嵌入表示。在性别分类与脑连接组年龄预测任务上的实验表明，所提方法持续优于包括弹性网、BrainGNN、Graphormer及原始BNT在内的多种先进图谱基方法。该无图谱策略显著提升了脑网络分析的精度、鲁棒性与泛化能力，为神经影像生物标志物与临床诊断工具的发展提供新可能。代码已开源：https://github.com/shuai-huang/atlas_free_bnt。
原文摘要 · Abstract (English)
Current atlas-based approaches to brain network analysis rely heavily on standardized anatomical or connectivity-driven brain atlases. However, these fixed atlases often introduce significant limitations, such as spatial misalignment across individuals, functional heterogeneity within predefined regions, and atlas-selection biases, collectively undermining the reliability and interpretability of the derived brain networks. To address these challenges, we propose a novel atlas-free brain network transformer (atlas-free BNT) that leverages individualized brain parcellations derived directly from subject-specific resting-state fMRI data. Our approach computes ROI-to-voxel connectivity features in a standardized voxel-based feature space, which are subsequently processed using the BNT architecture to produce comparable subject-level embeddings. Experimental evaluations on sex classification and brain-connectome age prediction tasks demonstrate that our atlas-free BNT consistently outperforms state-of-the-art atlas-based methods, including elastic net, BrainGNN, Graphormer and the original BNT. Our atlas-free approach significantly improves the precision, robustness, and generalizability of brain network analyses. This advancement holds great potential to enhance neuroimaging biomarkers and clinical diagnostic tools for personalized precision medicine. Reproducible code is available at https://github.com/shuai-huang/atlas_free_bnt
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