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[2602.13008] Machine Learning-Based Classification of Jhana Advanced Concentrative Absorption Meditation (ACAM-J) using 7T fMRI
Summary
This study explores the use of machine learning to classify Jhana advanced concentration absorption meditation (ACAM-J) through 7T fMRI, achieving notable accuracy in distinguishing meditative states.
Why It Matters
Understanding the neural correlates of advanced meditation practices like ACAM-J can enhance insights into consciousness and mental well-being. This research demonstrates the potential of machine learning in neuroscience, paving the way for future studies on meditation's effects on the brain.
Key Takeaways
- Machine learning classifiers can distinguish ACAM-J from non-meditative states with 66.82% accuracy.
- Prefrontal and anterior cingulate regions are crucial in differentiating meditative states.
- The study supports the feasibility of using fMRI data for advanced meditation research.
- Feature-importance analysis provides insights into brain areas involved in attentional regulation.
- Findings advocate for further exploration of neuromodulation in meditation practices.
Computer Science > Machine Learning arXiv:2602.13008 (cs) [Submitted on 13 Feb 2026] Title:Machine Learning-Based Classification of Jhana Advanced Concentrative Absorption Meditation (ACAM-J) using 7T fMRI Authors:Puneet Kumar, Winson F. Z. Yang, Alakhsimar Singh, Xiaobai Li, Matthew D. Sacchet View a PDF of the paper titled Machine Learning-Based Classification of Jhana Advanced Concentrative Absorption Meditation (ACAM-J) using 7T fMRI, by Puneet Kumar and 4 other authors View PDF Abstract: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 pre...