Meditation practices are often used to cultivate interoception or internally-oriented attention to bodily sensations, which may improve health via 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 (N = 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, N = 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.

中文翻译：

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专注于呼吸：大脑解码揭示冥想期间注意力的内部状态


冥想练习通常用于培养内感受或对身体感觉的内在关注，这可以通过身体信号的认知和情绪调节来改善健康。然而，由于缺乏能够客观评估冥想练习本身的心理状态并在个人或团体层面上产生内部注意力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。为了解决这些测量差距，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； (2) 在独立冥想过程中解码或估计这些 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 开发单受试者大脑分类器，用于 IA 任务期间的五种注意力模式，其中受试者被专门指示参与五种状态之一。即，与冥想相关的状态：呼吸注意力、走神（MW）和自我参照处理，以及控制状态：对脚和声音的注意力]。使用标准交叉验证程序，MVPA 分类器在每个受试者的六个 IA 块中的五个中进行训练，并在独立的第六个块上测试预测准确性（迭代直到测试所有卷，N = 2,160）。在参与者中，所有五个 IA 状态的识别率均远高于概率（>41% 与 20% 的概率）。在个人层面上，大多数参与者（87.5%）都认识到 IA 状态，这表明对 IA 神经模式的认识可能适用于大多数参与者，特别是经验丰富的冥想者。 接下来，对于那些表现出准确 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（10 分钟），以推断参与每个 IA 状态（呼吸注意力、MW 或自我参照）的时间百分比加工）。初步的小组分析表明，在冥想练习期间，与 MW 或自我参照处理相比，参与者花更多的时间关注呼吸。该范式确立了使用 MVPA 分类器客观评估参与者冥想期间心理状态的可行性，这有望改善对冥想培养的内部注意力状态的测量。