Weng Helen Y, Lewis-Peacock Jarrod A, Hecht Frederick M, Uncapher Melina R, Ziegler David A, Farb Norman A S, Goldman Veronica, Skinner Sasha, Duncan Larissa G, Chao Maria T, Gazzaley Adam
Osher Center for Integrative Medicine, University of California, San Francisco, San Francisco, CA, United States.
Neuroscape Center, University of California, San Francisco, San Francisco, CA, United States.
Front Hum Neurosci. 2020 Aug 28;14:336. doi: 10.3389/fnhum.2020.00336. eCollection 2020.
Meditation practices are often used to cultivate interoception or internally-oriented attention to bodily sensations, which may improve health cognitive and emotional regulation of bodily signals. However, it remains unclear how meditation impacts internal attention (IA) states due to lack of measurement tools that can objectively assess mental states during meditation practice itself, and produce time estimates of internal focus at individual or group levels. To address these measurement gaps, we tested the feasibility of applying multi-voxel pattern analysis (MVPA) to single-subject fMRI data to: (1) learn and recognize internal attentional states relevant for meditation during a directed IA task; and (2) decode or estimate the presence of those IA states during an independent meditation session. Within a mixed sample of experienced meditators and novice controls ( = 16), we first used MVPA to develop single-subject brain classifiers for five modes of attention during an IA task in which subjects were specifically instructed to engage in one of five states [i.e., meditation-related states: breath attention, mind wandering (MW), and self-referential processing, and control states: attention to feet and sounds]. Using standard cross-validation procedures, MVPA classifiers were trained in five of six IA blocks for each subject, and predictive accuracy was tested on the independent sixth block (iterated until all volumes were tested, = 2,160). Across participants, all five IA states were significantly recognized well above chance (>41% vs. 20% chance). At the individual level, IA states were recognized in most participants (87.5%), suggesting that recognition of IA neural patterns may be generalizable for most participants, particularly experienced meditators. Next, for those who showed accurate IA neural patterns, the originally trained classifiers were applied to a separate meditation run (10-min) to make an inference about the percentage time engaged in each IA state (breath attention, MW, or self-referential processing). Preliminary group-level analyses demonstrated that during meditation practice, participants spent more time attending to breath compared to MW or self-referential processing. This paradigm established the feasibility of using MVPA classifiers to objectively assess mental states during meditation at the participant level, which holds promise for improved measurement of internal attention states cultivated by meditation.
冥想练习通常用于培养内感受或对身体感觉的内在导向性关注,这可能会改善对身体信号的健康认知和情绪调节。然而,由于缺乏能够在冥想练习本身期间客观评估心理状态并在个体或群体层面产生内部专注时间估计的测量工具,冥想如何影响内部注意力(IA)状态仍不清楚。为了填补这些测量空白,我们测试了将多体素模式分析(MVPA)应用于单受试者功能磁共振成像(fMRI)数据的可行性,以:(1)在定向IA任务期间学习和识别与冥想相关的内部注意力状态;以及(2)在独立的冥想 session 期间解码或估计那些IA状态的存在。在经验丰富的冥想者和新手对照组(n = 16)的混合样本中,我们首先使用MVPA在IA任务期间为五种注意力模式开发单受试者脑分类器,在该任务中,受试者被特别指示进入五种状态之一[即与冥想相关的状态:呼吸注意力、走神(MW)和自我参照加工,以及对照状态:对脚部和声音的注意力]。使用标准的交叉验证程序,为每个受试者在六个IA块中的五个中训练MVPA分类器,并在独立的第六个块上测试预测准确性(迭代直到所有体积都经过测试,n = 2,1)。在所有参与者中,所有五种IA状态的识别准确率均显著高于随机水平(>41% 对 20% 的随机概率)。在个体层面,大多数参与者(87.5%)识别出了IA状态,这表明IA神经模式的识别可能对大多数参与者,特别是经验丰富的冥想者具有普遍性。接下来,对于那些显示出准确IA神经模式的人,将最初训练的分类器应用于单独的冥想运行(1分钟),以推断参与每种IA状态(呼吸注意力、MW或自我参照加工)的时间百分比。初步的群体层面分析表明,在冥想练习期间,与MW或自我参照加工相比,参与者花更多时间关注呼吸。这种范式确立了使用MVPA分类器在参与者层面客观评估冥想期间心理状态的可行性,这有望改进对冥想培养的内部注意力状态的测量。