Resting-state fMRI (rs-fMRI) is an effective tool for quantifying functional connectivity (FC), which plays a crucial role in exploring various brain diseases. Due to the high dimensionality of fMRI data, FC is typically computed based on the region of interest (ROI), whose parcellation relies on a pre-defined atlas. However, utilizing the brain atlas poses several challenges including 1) subjective selection bias in choosing from various brain atlases, 2) parcellation of each subject’s brain with the same atlas yet disregarding individual specificity; 3) lack of interaction between brain region parcellation and downstream ROI-based FC analysis. To address these limitations, we propose a novel randomizing strategy for generating brain function representation to facilitate neural disease diagnosis. Specifically, we randomly sample brain patches, thus avoiding ROI parcellations of the brain atlas. Then, we introduce a new brain function representation framework for the sampled patches. Each patch has its function description by referring to anchor patches, as well as the position description. Furthermore, we design an adaptive-selection-assisted Transformer network to optimize and integrate the function representations of all sampled patches within each brain for neural disease diagnosis. To validate our framework, we conduct extensive evaluations on three datasets, and the experimental results establish the effectiveness and generality of our proposed method, offering a promising avenue for advancing neural disease diagnosis beyond the confines of traditional atlas-based methods. Our code is available at https://github.com/mjliu2020/RandomFR.

中文翻译：

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随机化人脑功能表征以进行脑疾病诊断


静息态功能磁共振成像（rs-fMRI）是量化功能连接（FC）的有效工具，在探索各种脑部疾病中发挥着至关重要的作用。由于 fMRI 数据的高维性，FC 通常是根据感兴趣区域 (ROI) 计算的，其分割依赖于预定义的图集。然而，利用大脑图谱带来了一些挑战，包括1）从不同的大脑图谱中进行选择时的主观选择偏差，2）使用相同的图谱对每个受试者的大脑进行分区，但忽略个体特异性； 3）大脑区域分割和下游基于 ROI 的 FC 分析之间缺乏相互作用。为了解决这些限制，我们提出了一种新的随机化策略来生成大脑功能表示以促进神经疾病的诊断。具体来说，我们随机对大脑斑块进行采样，从而避免大脑图谱的 ROI 分割。然后，我们为采样的补丁引入了一个新的大脑功能表示框架。每个补丁都有其参考锚点补丁的功能描述，以及位置描述。此外，我们设计了一个自适应选择辅助的 Transformer 网络来优化和集成每个大脑内所有采样斑块的功能表示，以进行神经疾病诊断。为了验证我们的框架，我们对三个数据集进行了广泛的评估，实验结果证实了我们提出的方法的有效性和通用性，为超越传统基于图集的方法的范围推进神经疾病诊断提供了一条有前途的途径。我们的代码可在 https://github.com/mjliu2020/RandomFR 获取。