Functional Imaging in Neuropsychiatric Disorders (FIND) Lab, Department of Neurology and Neurological Sciences, Stanford School of Medicine, Stanford, CA 94305, USA.
Cereb Cortex. 2012 Jan;22(1):158-65. doi: 10.1093/cercor/bhr099. Epub 2011 May 26.
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.
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之一。先前的大脑状态分类研究主要集中在解码短暂、离散的事件上，并且需要知道这些事件的时间。迄今为止，还没有用于解码更连续和纯粹由主体驱动的认知状态的方法。在这里，我们展示了使用一种新的全脑功能连接分析可以解码自由流动的主体驱动的认知状态。在 14 个大规模静息态大脑网络中定义了 90 个功能感兴趣区 (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来识别特定的全脑连接模式，这些模式是在受试者安静休息、回忆当天的事件、做减法或（无声地）唱歌时产生的。在一次留一法交叉验证中，该分类器以 84%的准确率识别了这 4 种认知状态。更重要的是，当在第二组独立的受试者中识别这些状态时，分类器的准确率达到了 85%。即使在成像运行时间短至 30-60 秒的情况下，分类准确率仍然很高。在评估的所有时间间隔内，这 90 个功能定义的 ROI 在分类认知状态方面的表现优于一组 112 个常用的结构 ROI。这种方法应该能够从短暂的成像数据样本中解码出无数的主体驱动的认知状态。
