Decoding Subject-Driven Cognitive States with Whole-Brain Connectivity Patterns
Decoding Subject-Driven Cognitive States with Whole-Brain Connectivity Patterns
复制标题
DOI：
10.1093/cercor/bhr099
复制
发表时间：
2012-01-01
期刊：
影响因子：
3.7
通讯作者：
Greicius, M. D.
中科院分区：
文献类型：
--
作者：
Shirer, W. R.;Ryali, S.;Greicius, M. D.
从大脑活动中解码特定的认知状态是神经科学的主要目标。以前对大脑状态分类的研究主要集中在解码简短的离散事件上，并要求知道这些事件的时间。到目前为止，解码更连续和纯粹的主体驱动的认知状态的方法还没有。在这里，我们证明了自由流主体驱动的认知状态可以使用一种新的全脑功能连接分析解码。在14个大规模静息态脑网络中定义了90个感兴趣的功能区（ROI），以生成反映全脑连接的3960个细胞矩阵。我们训练了一个分类器，以识别受试者安静休息时全脑连接的特定模式，记住他们一天中的事件，减去数字，或（默默地）唱歌词。在留一法交叉验证中，分类器以84%的准确率识别了这4种认知状态。更重要的是，当在第二个独立的受试者队列中识别这些状态时，分类器达到了85%的准确率。成像运行时间短至30-60 s时，分类准确度仍然很高。在所有的时间间隔评估，90个功能定义的ROI优于一组112个常用的结构ROI分类认知状态。这种方法应该能够从简短的成像数据样本中解码无数受试者驱动的认知状态。
Decoding specific cognitive states from brain activity constitutes a major goal of neuroscience. Previous studies of brain-state classification have focused largely on decoding brief, discrete events and have required the timing of these events to be known. To date, methods for decoding more continuous and purely subject-driven cognitive states have not been available. Here, we demonstrate that free-streaming subject-driven cognitive states can be decoded using a novel whole-brain functional connectivity analysis. Ninety functional regions of interest (ROIs) were defined across 14 large-scale resting-state brain networks to generate a 3960 cell matrix reflecting whole-brain connectivity. We trained a classifier to identify specific patterns of whole-brain connectivity as subjects rested quietly, remembered the events of their day, subtracted numbers, or (silently) sang lyrics. In a leave-one-out cross-validation, the classifier identified these 4 cognitive states with 84% accuracy. More critically, the classifier achieved 85% accuracy when identifying these states in a second, independent cohort of subjects. Classification accuracy remained high with imaging runs as short as 30-60 s. At all temporal intervals assessed, the 90 functionally defined ROIs outperformed a set of 112 commonly used structural ROIs in classifying cognitive states. This approach should enable decoding a myriad of subject-driven cognitive states from brief imaging data samples.