Brain imaging has been used to predict language skills during development and neuropathology but its accuracy in predicting language performance in healthy adults has been poorly investigated. To address this shortcoming, we studied the ability to predict reading accuracy and single‐word comprehension scores from rest‐ and task‐based functional magnetic resonance imaging (fMRI) datasets of 424 healthy adults. Using connectome‐based predictive modeling, we identified functional brain networks with >400 edges that predicted language scores and were reproducible in independent data sets. To simplify these complex models we identified the overlapping edges derived from the three task‐fMRI sessions (language, working memory, and motor tasks), and found 12 edges for reading recognition and 11 edges for vocabulary comprehension that accounted for 20% of the variance of these scores, both in the training sample and in the independent sample. The overlapping edges predominantly emanated from language areas within the frontoparietal and default‐mode networks, with a strong precuneus prominence. These findings identify a small subset of edges that accounted for a significant fraction of the variance in language performance that might serve as neuromarkers for neuromodulation interventions to improve language performance or for presurgical planning to minimize language impairments.

中文翻译：

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网络连接可预测健康成人的语言处理。

脑成像已被用于预测发育和神经病理学过程中的语言技能，但其在预测健康成人语言表现方面的准确性尚未得到很好的研究。为了解决这个缺点，我们研究了从 424 名健康成年人的基于休息和基于任务的功能磁共振成像 (fMRI) 数据集预测阅读准确性和单字理解分数的能力。使用基于连接组的预测模型，我们确定了具有超过 400 个边缘的功能性大脑网络，可以预测语言分数并且可以在独立数据集中重现。为了简化这些复杂的模型，我们确定了来自三个任务-fMRI 会话（语言、工作记忆和运动任务）的重叠边缘，并在训练样本和独立样本中发现了 12 个阅读识别边缘和 11 个词汇理解边缘占这些分数方差的 20%。重叠边缘主要来自额顶和默认模式网络内的语言区域，具有强烈的楔前叶突出。这些发现确定了一小部分边缘，这些边缘占语言表现差异的很大一部分，这些边缘可能作为神经调节干预的神经标志物，以提高语言表现或用于术前计划以最大限度地减少语言障碍。具有强烈的楔前叶突出。这些发现确定了一小部分边缘，这些边缘占语言表现差异的很大一部分，这些边缘可能作为神经调节干预的神经标志物，以提高语言表现或用于术前计划以最大限度地减少语言障碍。具有强烈的楔前叶突出。这些发现确定了一小部分边缘，这些边缘占语言表现差异的很大一部分，这些边缘可能作为神经调节干预的神经标志物，以提高语言表现或用于术前计划以最大限度地减少语言障碍。