Machine Learning-Based Classification of Jhana Advanced Concentrative Absorption Meditation (ACAM-J) using 7T fMRI
Puneet Kumar, W. F. Yang, Alakhsimar Singh, Xiaobai Li, Matthew D. Sacchet
arXiv.org February 13, 2026 preprint DOI: 10.48550/arxiv.2602.13008 (opens in new tab)
Study at a glance
AI-extracted from the abstract| Characteristics | Observational cohort with machine learning classification |
|---|---|
| Sample size | 20 |
| Population | Advanced meditators |
| Intervention | Jhana advanced concentration absorption meditation (ACAM-J) |
| Topics | Meditation |
| Key findings | Machine learning classifiers using fMRI-derived regional homogeneity achieved 66.82% accuracy in distinguishing Jhana advanced concentration absorption meditation from non-meditative states, with prefrontal and anterior cingulate regions contributing most to classification. |
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 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.
Comparable studies
Other observational and cohort studies on meditation, most cited first.
| Study | Year | Design | Participants |
|---|---|---|---|
| Using Self-Report Assessment Methods to Explore Facets of Mindfulness Undergraduate students | 2006 | Observational cohort | |
| Effectiveness of a meditation-based stress reduction program in the treatment of anxiety disorders Patients with generalized anxiety disorder or panic disorder with or without agoraphobia | 1992 | Observational cohort | n = 22 |
| Meditation experience is associated with increased cortical thickness Participants with extensive Insight meditation experience and matched controls | 2005 | Observational cohort | n = 20 |
| Meditation experience is associated with differences in default mode network activity and connectivity Experienced meditators and matched meditation-naive controls | 2011 | Observational cohort | |
| Long-term meditators self-induce high-amplitude gamma synchrony during mental practice Long-term Buddhist practitioners and controls | 2004 | Observational cohort |