Jarrahi Behnaz, Mantini Dante
Annu Int Conf IEEE Eng Med Biol Soc. 2016 Aug;2016:5567-5570. doi: 10.1109/EMBC.2016.7591988.
Advanced multivariate analyses of functional magnetic resonance imaging (fMRI) data based on blood oxygen level-dependent (BOLD) contras have revealed that the human brain organizes its activities into multiple intrinsic connectivity networks (ICNs). Several fMRI studies have evaluated the modulations of these networks during different cognitive or emotional tasks using blind source separation techniques particularly the independent component analysis (ICA). In this exploratory study, we applied ICA methodology to examine ICN modulations under different interoceptive conditions. Fifteen right-handed healthy subjects (age range 21-48 years) underwent a series of eyes-open resting-state and interoceptive task fMRI scans. Using a high-order ICA model, the functional imaging data were decomposed into 75 independent components and 36 were identified as non-artifactual ICNs. ICN spatial modulations were evaluated in terms of the network volume and maximum activations. ICN temporal modulations were assessed based on the power density frequency spectra. Following a false discovery rate multiple comparison correction threshold of ρ <; 0.05, we found significant changes in spatial feature of the attention/cognitive, default-mode, visual and salience networks. More liberal statistical criteria (uncorrected ρ <; 0.05) also indicated differences in network volumes between different states especially involving the sensorimotor, subcortical, cerebellar and brainstem networks. Significant power spectra changes were also found in several attention/cognitive and visual networks as well as the sensorimotor, salience, and subcortical networks especially when resting-states where compared with the interoceptive task fMRI. Further investigations of how interoceptive sensations alter the spatial and temporal features of the human brain networks can elucidate the foundational underpinnings of brain-body relation.
基于血氧水平依赖(BOLD)对比的功能磁共振成像(fMRI)数据的高级多变量分析表明,人类大脑将其活动组织成多个内在连接网络(ICN)。几项fMRI研究使用盲源分离技术,特别是独立成分分析(ICA),评估了这些网络在不同认知或情感任务期间的调制情况。在这项探索性研究中,我们应用ICA方法来检查不同内感受条件下的ICN调制。15名右利手健康受试者(年龄范围21 - 48岁)接受了一系列睁眼静息态和内感受任务fMRI扫描。使用高阶ICA模型,将功能成像数据分解为75个独立成分,其中36个被确定为非伪影ICN。根据网络体积和最大激活来评估ICN的空间调制。基于功率密度频谱评估ICN的时间调制。在错误发现率多重比较校正阈值ρ < 0.05之后,我们发现注意力/认知、默认模式、视觉和突显网络的空间特征有显著变化。更宽松的统计标准(未校正ρ < 0.05)也表明不同状态之间网络体积存在差异,特别是涉及感觉运动、皮层下、小脑和脑干网络。在几个注意力/认知和视觉网络以及感觉运动、突显和皮层下网络中也发现了显著的功率谱变化,特别是在将静息态与内感受任务fMRI进行比较时。对内感受感觉如何改变人类大脑网络的空间和时间特征的进一步研究可以阐明脑 - 身关系的基础支撑。