[论文解读] Mining the Mind: Linear Discriminant Analysis of MEG source reconstruction time series supports dynamic changes in deep brain regions during meditation sessions

Daniela Calvetti, Brian P. Johnson+4 more|arXiv (Cornell University)|Jan 29, 2021

Functional Brain Connectivity Studies参考文献 107被引用 9

一句话总结

本研究利用脑磁图（MEG）源重建时间序列的线性判别分析（LDA），识别冥想过程中动态脑活动的变化。结果表明，深层脑区——特别是伏隔核、尾状核、壳核、丘脑、杏仁核、岛叶和扣带皮层——在不同频率带（θ、α、β、γ）中表现出可与静息态区分的状态特异性谱变化，支持其在冥想状态中的作用。

ABSTRACT

Meditation practices have been claimed to have a positive effect on the regulation of mood and emotion for quite some time by practitioners, and in recent times there has been a sustained effort to provide a more precise description of the changes induced by meditation on human brain. Longitudinal studies have reported morphological changes in cortical thickness and volume in selected brain regions due to meditation practice, which is interpreted as evidence for effectiveness of it beyond the subjective self reporting. Evidence based on real time monitoring of meditating brain by functional imaging modalities such as MEG or EEG remains a challenge. In this article we consider MEG data collected during meditation sessions of experienced Buddhist monks practicing focused attention (Samatha) and open monitoring (Vipassana) meditation, contrasted by resting state with eyes closed. The MEG data is first mapped to time series of brain activity averaged over brain regions corresponding to a standard Destrieux brain atlas, and further by bootstrapping and spectral analysis to data matrices representing a random sample of power spectral densities over bandwidths corresponding to $\alpha$, $\beta$, $\gamma$, and $ heta$ bands in the spectral range. We demonstrate using linear discriminant analysis (LDA) that the samples corresponding to different meditative or resting states contain enough fingerprints of the brain state to allow a separation between different states, and we identify the brain regions that appear to contribute to the separation. Our findings suggest that cingulate cortex, insular cortex and some of the internal structures, most notably accumbens, caudate and putamen nuclei, thalamus and amygdalae stand out as separating regions, which seems to correlate well with earlier findings based on longitudinal studies.

研究动机与目标

• 利用MEG数据量化冥想过程中的动态脑活动变化。
• 识别在专注观想（Focused Attention）和开放觉察（Open Monitoring）冥想状态与静息态之间活动存在显著差异的脑区。
• 验证基于数据驱动分析，深层脑区的谱功率变化是否能可靠地区分冥想状态。
• 将研究发现与既往关于冥想诱导脑区结构与功能改变的纵向神经影像研究相联系。

提出的方法

• 对经验丰富的佛教僧侣在静息态、专注观想（Samatha）和开放觉察（Vipassana）冥想状态下采集的MEG数据，采用独立成分分析（ICA）进行预处理以去除伪影。
• 通过分层贝叶斯方法求解MEG逆问题，估算基于Destrieux图谱定义的脑区的时间分辨源活动。
• 应用谱分析，利用自 resampling 的周期图法计算θ、α、β和γ频段的功率谱密度。
• 应用线性判别分析（LDA）对基于谱功率矩阵的脑状态进行分类，识别最具判别力的脑区。
• 该方法为每位受试者和每种状态生成带注释的数据集，支持跨受试者和跨会话的一致性分析。
• 通过识别判别载荷最高的脑区解释结果，表明其对状态分离的贡献。

实验结果

研究问题

• RQ1 基于MEG的脑区谱功率模式是否能可靠地区分冥想状态与静息态？
• RQ2 哪些深层脑区在冥想过程中表现出最显著的谱活动动态变化？
• RQ3 基于LDA的冥想状态分类在个体之间和不同冥想会话之间是否具有一致性？
• RQ4 所识别的脑区在多大程度上与情绪调节、注意力和奖赏系统的已知神经解剖结构一致？

主要发现

• 伏隔核、尾状核、壳核、丘脑和杏仁核在所有频率带（θ、α、β、γ）中均持续作为状态分离的主要贡献者。
• 岛叶皮层和扣带皮层被识别为关键皮层区域，在冥想期间与静息态相比表现出显著的谱活动差异。
• LDA能够可靠地区分冥想状态与静息态，且同一受试者在多次冥想会话中结果保持一致。
• 研究结果与纵向研究结果一致，后者显示长期冥想练习可导致这些区域的结构与功能改变。
• 尾状核和壳核的参与支持其在目标导向行为和习惯学习中的作用，与Samatha和Vipassana冥想的认知需求一致。
• 左杏仁核在所有频率带中的一致性激活与焦虑和压力水平降低相关，支持其在冥想过程中情绪调节中的作用。