Jhana advanced concentration absorption meditation (ACAM-J) is related to profound changes in consciousness and cognitive processing, making the study of their neural correlates vital for insights into consciousness and well-being. This study evaluates whether functional MRI-derived regional homogeneity (ReHo) can be used to classify ACAM-J using machine-learning approaches. We collected group-level fMRI data from 20 advanced meditators to train the classifiers, and intensive single-case data from an advanced practitioner performing ACAM-J and control tasks to evaluate generalization. ReHo maps were computed, and features were extracted from predefined brain regions of interest. We trained multiple machine learning classifiers using stratified cross-validation to evaluate whether ReHo patterns distinguish ACAM-J from non-meditative states. Ensemble models achieved 66.82% (p < 0.05) accuracy in distinguishing ACAM-J from control conditions. Feature-importance analysis indicated that prefrontal and anterior cingulate areas contributed most to model decisions, aligning with established involvement of these regions in attentional regulation and metacognitive processes. Moreover, moderate agreement reflected in Cohen's kappa supports the feasibility of using machine learning to distinguish ACAM-J from non-meditative states. These findings advocate machine-learning's feasibility in classifying advanced meditation states, future research on neuromodulation and mechanistic models of advanced meditation.

翻译：禅那高级专注吸收冥想（ACAM-J）与意识和认知加工的深刻变化相关，因此研究其神经关联对于理解意识与幸福感至关重要。本研究评估了基于功能磁共振成像的区域同质性（ReHo）是否可用于通过机器学习方法对ACAM-J进行分类。我们收集了20名高级冥想者的组水平fMRI数据用于训练分类器，并收集了一名高级练习者执行ACAM-J及控制任务的密集单案例数据以评估泛化能力。计算了ReHo图谱，并从预定义的感兴趣脑区提取特征。我们使用分层交叉验证训练了多种机器学习分类器，以评估ReHo模式是否能区分ACAM-J与非冥想状态。集成模型在区分ACAM-J与控制条件时达到了66.82%（p < 0.05）的准确率。特征重要性分析表明，前额叶和前扣带回区域对模型决策贡献最大，这与这些区域在注意调节和元认知过程中已知的参与作用一致。此外，Cohen's kappa所反映的中等一致性支持了使用机器学习区分ACAM-J与非冥想状态的可行性。这些发现论证了机器学习在分类高级冥想状态方面的可行性，并为未来关于高级冥想的神经调控及机制模型研究提供了方向。