Hierarchical subdivision and effect of ICA model dimensionality on the interoceptive task-derived brain networks
Hierarchical subdivision and effect of ICA model dimensionality on the interoceptive task-derived brain networks
复制标题
ICA模型维度的层次细分和对内感受任务衍生脑网络的影响
DOI：
10.1109/bhi.2016.7455834
复制
发表时间：
2016
期刊：
影响因子：
--
通讯作者：
D. Mantini
中科院分区：
文献类型：
--
作者：
B. Jarrahi;D. Mantini
网络分析已成为研究功能性磁共振成像（fMRI）数据的首选工具。在这项研究中，我们使用独立成分分析（伊卡）来表征人脑网络相关的内感受。将组ICA应用于从15名健康受试者收集的血氧水平依赖性（BOLD）fMRI数据，这些受试者在四个不同的伊卡维度（即，K = 10、20、30和40个分量），以评估分层分解（细分）和伊卡模型维度对源自内脏内感受成像的内在脑网络特征的影响。默认模式网络、视觉网络、额下回/颞下回网络、边缘系统关联网络、脑干和小脑网络即使在最低维度10时也被识别。中央执行网络、显著性网络、自我参照网络、背侧注意网络和感觉运动网络仅在伊卡模型维度至少为20时出现。腹侧注意力网络，丘脑网络，和额外的默认模式，显着性，边缘系统的关联，和脑干网络确定与40个组件的分解，这表明一些网络的检测需要增加模型的维数。此外，缺乏特定的网络细分可能与进行伊卡的BOLD fMRI数据的性质有关。
Network analysis has become a tool of choice for studying functional Magnetic Resonance Imaging (fMRI) data. In this study, we used Independent Component Analysis (ICA) to characterize human brain networks related to interoception. Group-ICA was applied to blood oxygenation level-dependent (BOLD) fMRI data collected from 15 healthy subjects, who underwent intravesical stimulation, at four different ICA dimensionalities (i.e., K = 10, 20, 30, and 40 components) to assess hierarchical breakdown (subdivision) and the impact of ICA model dimensionality on the characteristics of intrinsic brain networks derived from imaging visceral interoception. The default mode network, the visual network, the network of inferior frontal/inferior temporal gyri, the limbic association network, the brainstem, and cerebellar networks were identified even at the lowest dimensionality of 10. The central executive network, the salience network, the self-referential network, the dorsal attention network, and the sensorimotor network appeared only with the ICA model dimensionality of at least 20. The ventral attention network, the thalamic network, and additional default mode, salience, limbic association, and brainstem networks identified with decomposition of 40 components suggesting that detection of some networks requires increasing model dimensionality. Furthermore, the lack of specific network subdivision might be related to the nature of the BOLD fMRI data upon which ICA was performed.