Department of Mathematical Information Technology, University of Jyväskylä, Finland.
Department of Mathematical Information Technology, University of Jyväskylä, Finland.
J Neurosci Methods. 2014 Feb 15;223:74-84. doi: 10.1016/j.jneumeth.2013.11.025. Epub 2013 Dec 11.
Independent component analysis (ICA) has been often used to decompose fMRI data mostly for the resting-state, block and event-related designs due to its outstanding advantage. For fMRI data during free-listening experiences, only a few exploratory studies applied ICA.
For processing the fMRI data elicited by 512-s modern tango, a FFT based band-pass filter was used to further pre-process the fMRI data to remove sources of no interest and noise. Then, a fast model order selection method was applied to estimate the number of sources. Next, both individual ICA and group ICA were performed. Subsequently, ICA components whose temporal courses were significantly correlated with musical features were selected. Finally, for individual ICA, common components across majority of participants were found by diffusion map and spectral clustering.
The extracted spatial maps (by the new ICA approach) common across most participants evidenced slightly right-lateralized activity within and surrounding the auditory cortices. Meanwhile, they were found associated with the musical features.
COMPARISON WITH EXISTING METHOD(S): Compared with the conventional ICA approach, more participants were found to have the common spatial maps extracted by the new ICA approach. Conventional model order selection methods underestimated the true number of sources in the conventionally pre-processed fMRI data for the individual ICA.
Pre-processing the fMRI data by using a reasonable band-pass digital filter can greatly benefit the following model order selection and ICA with fMRI data by naturalistic paradigms. Diffusion map and spectral clustering are straightforward tools to find common ICA spatial maps.
独立成分分析（ICA）由于其突出的优势，常被用于分解 fMRI 数据，主要用于静息态、块和事件相关设计。对于自由聆听体验期间的 fMRI 数据，只有少数探索性研究应用了 ICA。
为了处理由 512 秒现代探戈引起的 fMRI 数据，使用基于 FFT 的带通滤波器进一步预处理 fMRI 数据，以去除不感兴趣的来源和噪声。然后，应用快速模型阶数选择方法估计源的数量。接下来，进行个体 ICA 和组 ICA。接下来，选择与音乐特征具有显著相关性的 ICA 成分。最后，对于个体 ICA，通过扩散图和谱聚类找到大多数参与者共有的共同成分。
大多数参与者共同提取的空间图谱（通过新的 ICA 方法）显示，听觉皮层内及其周围存在轻微的右侧活动。同时，它们与音乐特征有关。
与传统的 ICA 方法相比，通过新的 ICA 方法提取的共同空间图谱在更多的参与者中被发现。在个体 ICA 中，传统预处理 fMRI 数据的传统模型阶数选择方法低估了真实源的数量。
使用合理的带通数字滤波器预处理 fMRI 数据可以极大地促进自然范式下 fMRI 数据的模型阶数选择和 ICA。扩散图和谱聚类是找到共同 ICA 空间图谱的直接工具。
