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

翻译：当前基于图谱的脑网络分析方法严重依赖于标准化的解剖学或连接驱动脑图谱。然而，这些固定图谱通常带来显著局限性，例如个体间的空间错位、预定义区域内的功能异质性以及图谱选择偏差，共同削弱了所推导脑网络的可靠性与可解释性。为应对这些挑战，我们提出一种新颖的无图谱脑网络Transformer（atlas-free BNT），该方法利用直接从受试者特异性静息态fMRI数据导出的个体化脑区划分。我们的方法在基于标准体素的特征空间中计算ROI到体素的连接特征，随后通过BNT架构进行处理以生成可比较的受试者水平嵌入。在性别分类和脑连接组年龄预测任务上的实验评估表明，我们的无图谱BNT始终优于最先进的基于图谱的方法，包括弹性网络、BrainGNN、Graphormer及原始BNT。我们的无图谱方法显著提升了脑网络分析的精确性、鲁棒性和泛化能力。这一进展对于增强神经影像生物标志物及面向个性化精准医学的临床诊断工具具有重要潜力。可复现代码详见https://github.com/shuai-huang/atlas_free_bnt