Trinity College Institute of Neuroscience, School of Psychology, Trinity College Dublin Dublin, Ireland ; Neural Control of Movement Lab, Department of Health Sciences and Technology, ETH Zurich Zurich, Switzerland.
Front Hum Neurosci. 2013 May 16;7:207. doi: 10.3389/fnhum.2013.00207. eCollection 2013.
Electrophysiology studies routinely investigate the relationship between neural oscillations and task performance. However, the sluggish nature of the BOLD response means that few researchers have investigated the spectral properties of the BOLD signal in a similar manner. For the first time we have applied group ICA to fMRI data collected during a standard working memory task (delayed match-to-sample) and using a multivariate analysis, we investigate the relationship between working memory performance (accuracy and reaction time) and BOLD spectral power within functional networks. Our results indicate that BOLD spectral power within specific networks (visual, temporal-parietal, posterior default-mode network, salience network, basal ganglia) correlated with task accuracy. Multivariate analyses show that the relationship between task accuracy and BOLD spectral power is stronger than the relationship between BOLD spectral power and other variables (age, gender, head movement, and neuropsychological measures). A traditional General Linear Model (GLM) analysis found no significant group differences, or regions that covaried in signal intensity with task accuracy, suggesting that BOLD spectral power holds unique information that is lost in a standard GLM approach. We suggest that the combination of ICA and BOLD spectral power is a useful novel index of cognitive performance that may be more sensitive to brain-behavior relationships than traditional approaches.
电生理学研究通常研究神经振荡与任务表现之间的关系。然而，BOLD 反应的迟缓性质意味着很少有研究人员以类似的方式研究 BOLD 信号的光谱特性。我们首次将组 ICA 应用于在标准工作记忆任务（延迟匹配样本）期间采集的 fMRI 数据，并使用多元分析，我们研究了工作记忆性能（准确性和反应时间）与功能网络内 BOLD 光谱功率之间的关系。我们的结果表明，特定网络（视觉、颞顶网络、后默认模式网络、突显网络、基底神经节）内的 BOLD 光谱功率与任务准确性相关。多元分析表明，任务准确性和 BOLD 光谱功率之间的关系强于 BOLD 光谱功率与其他变量（年龄、性别、头部运动和神经心理学测量）之间的关系。传统的一般线性模型（GLM）分析未发现显著的组间差异，或与任务准确性信号强度共变的区域，表明 BOLD 光谱功率具有独特的信息，而在标准 GLM 方法中会丢失这些信息。我们建议，ICA 和 BOLD 光谱功率的组合是认知表现的有用新指标，与传统方法相比，它可能对大脑-行为关系更敏感。
