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A functional connectivity-based neuromarker of sustained attention generalizes to predict recall in a reading task

机译:基于功能的连接性的神经标志物持续注意,推广以预测阅读任务中的召回

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Abstract Sustaining attention to the task at hand is a crucial part of everyday life, from following a lecture at school to maintaining focus while driving. Lapses in sustained attention are frequent and often problematic, with conditions such as attention deficit hyperactivity disorder affecting millions of people worldwide. Recent work has had some success in finding signatures of sustained attention in whole-brain functional connectivity (FC) measures during basic tasks, but since FC can be dynamic and task-dependent, it remains unclear how fully these signatures would generalize to a more complex and naturalistic scenario. To this end, we used a previously defined whole-brain FC network – a marker of attention that was derived from a sustained attention task – to predict the ability of participants to recall material during a free-viewing reading task. Though the predictive network was trained on a different task and set of participants, the strength of FC in the sustained attention network predicted reading recall significantly better than permutation tests where behavior was scrambled to simulate chance performance. To test the generalization of the method used to derive the sustained attention network, we applied the same method to our reading task data to find a new FC network whose strength specifically predicts reading recall. Even though the sustained attention network provided significant prediction of recall, the reading network was more predictive of recall accuracy. The new reading network's spatial distribution indicates that reading recall is highest when temporal pole regions have higher FC with left occipital regions and lower FC with bilateral supramarginal gyrus. Right cerebellar to right frontal connectivity is also indicative of poor reading recall. We examine these and other differences between the two predictive FC networks, providing new insight into the task-dependent nature of FC-based performance metrics. Highlights ? Whole-brain FC marker of attention predicts reading recall in new subjects. ? A new FC network derived from reading task data provides stronger prediction. ? A limited set of 73 FC connections performs as well as a larger set. ? The networks' spatial distributions are linked to specifics of their tasks.
机译:摘要在手中的任务的持续注意是日常生活的关键部分,从学校进行讲课,以在驾驶时保持重点。持续关注的失误经常和经常有问题,有些条件,诸如受关注缺陷多动障碍,影响全球数百万人。最近的工作在基本任务期间发现全脑功能连通性(FC)措施的持续关注的签名,但由于FC可以是动态和任务相关的,因此仍然不清楚这些签名将概括为更复杂的方式和自然主义情景。为此,我们使用先前定义的全脑FC网络 - 一种引起的关注标记,这些标记来自持续的注意任务 - 预测参与者在自由观看阅读任务期间召回材料的能力。虽然预测网络在不同的任务和参与者集中培训,但是FC在持续关注网络中的强度预测读数召回比排列测试显着更好,其中行为被争夺以模拟机会性能。为了测试用于导出持续注意网络的方法的概括,我们将相同的方法应用于我们的阅读任务数据,找到一个新的FC网络,其强度专门预测阅读召回。尽管持续关注网络提供了重新推出的显着预测,但读取网络更加预测了召回的准确性。新的阅读网络的空间分布表明,当颞杆区具有较高的FC具有左枕区和具有双侧上敷基的FC的FC具有更高的FC,阅读召回是最高的。右前方连接的右头脑也表示糟糕的阅读召回。我们在两个预测性FC网络之间检查这些和其他差异,从而新的洞察基于FC的性能度量的任务相关性。强调 ?全脑FC关注标记预测新科目的阅读召回。还是从读取任务数据派生的新FC网络提供了更强烈的预测。还是一组有限的73个FC连接执行以及更大的集合。还是网络的空间分布与其任务的细节链接。

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