Accurate mapping of brain functional subregions at an individual level is crucial. Task-based functional MRI (tfMRI) captures subject-specific activation patterns during various functions and behaviors, facilitating the individual localization of functionally distinct subregions. However, acquiring high-quality tfMRI is time-consuming and resource-intensive in both scientific and clinical settings. The present study proposes a two-stage network model, TS-AI, to individualize an atlas on cortical surfaces through the prediction of tfMRI data. TS-AI first synthesizes a battery of task contrast maps for each individual by leveraging tract-wise anatomical connectivity and resting-state networks. These synthesized maps, along with feature maps of tract-wise anatomical connectivity and resting-state networks, are then fed into an end-to-end deep neural network to individualize an atlas. TS-AI enables the synthesized task contrast maps to be used in individual parcellation without the acquisition of actual task fMRI scans. In addition, a novel feature consistency loss is designed to assign vertices with similar features to the same parcel, which increases individual specificity and mitigates overfitting risks caused by the absence of individual parcellation ground truth. The individualized parcellations were validated by assessing test-retest reliability, homogeneity, and cognitive behavior prediction using diverse reference atlases and datasets, demonstrating the superior performance and generalizability of TS-AI. Sensitivity analysis yielded insights into region-specific features influencing individual variation in functional regionalization. Additionally, TS-AI identified accelerated shrinkage in the medial temporal and cingulate parcels during the progression of Alzheimer's disease, suggesting its potential in clinical research and applications.

中文翻译：

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TS-AI：用于具有任务对比合成的多模式特定主题分割的深度学习管道


在个体水平上准确绘制大脑功能分区至关重要。基于任务的功能性 MRI (tfMRI) 可捕获各种功能和行为期间受试者特定的激活模式，从而促进功能不同子区域的个体定位。然而，在科学和临床环境中，获取高质量的 tfMRI 既耗时又耗费资源。本研究提出了一种两阶段网络模型 TS-AI，通过预测 tfMRI 数据来个性化皮质表面的图谱。 TS-AI 首先利用逐道解剖连接和静息态网络为每个人合成一组任务对比图。然后，这些合成图以及束方向解剖连接性和静息状态网络的特征图被输入端到端深度神经网络以个性化图谱。 TS-AI 使合成的任务对比图能够用于单独的分区，而无需采集实际的任务 fMRI 扫描。此外，设计了一种新颖的特征一致性损失，将具有相似特征的顶点分配给同一个地块，这增加了个体特异性并减轻了由于缺乏个体地块地面实况而导致的过度拟合风险。通过使用不同的参考图集和数据集评估重测可靠性、同质性和认知行为预测来验证个性化分区，证明了 TS-AI 的卓越性能和通用性。敏感性分析深入了解了影响功能区域化个体差异的区域特定特征。 此外，TS-AI 还发现，在阿尔茨海默氏病的进展过程中，内侧颞叶和扣带回包裹加速收缩，这表明其在临床研究和应用中的潜力。