Key result
The ensemble learning algorithms Random Forest and Gradient Boosted Tree differentiated long-term and short-term Rajayoga practitioners with an accuracy of 62% using functional connectivity metrics.
Why the study?
Meditation provides health benefits and complementary treatment for various ailments, prompting the use of fMRI to assess its long-term effects on functional connectivity in the brain.
Can machine learning algorithms using fMRI functional connectivity metrics classify subjects based on the duration of their Rajayoga meditation practice?
Cross-Sectional (n=22)
No
Can machine learning algorithms using fMRI functional connectivity metrics classify subjects based on the duration of their Rajayoga meditation practice?
Effect estimate: 62% accuracy
Functional connectivity metrics derived from fMRI can be used by machine learning algorithms to differentiate between long-term and short-term Rajayoga meditators.
Should not inform clinical meditation guidance; leaves open whether fMRI-ML classifiers predict outcomes or require prospective validation.
Meditation has several health benefits and is also used as a complementary treatment for various ailments. Neuroimaging studies have shed light on the effects of meditation, especially on the brain. Functional Magnetic Resonance Imaging, a powerful non-invasive imaging technique is used in this study to determine the functional connectivity in meditator’s brain. In this study, long-term effects of Rajayoga Meditation practice were considered where the difference in functional connectivity between two groups of subjects one with long duration and the other with short duration of Rajayogameditation practice was found. Two groups of subjects with long-term and short-term practice of Rajayoga meditation were recruited. Task-based fMRI was acquired as the subject performed a Neurocognitive task. Functional connectivity among the regions of Resting-State Networks was performed and four functional connectivity metrics were derived. Machine learning algorithms were used to classify these two groups based on functional connectivity metrics used as features. It was found that the…… classifier could differentiate the two groups with …. Accuracy.
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Savanth et al. (2021) conducted a cross-sectional in Healthy subjects practicing Rajayoga meditation (n=22). Long-term Rajayoga meditation practice vs. Short-term Rajayoga meditation practice was evaluated on Classification accuracy using all four Functional Connectivity metrics (62% accuracy). The ensemble learning algorithms Random Forest and Gradient Boosted Tree differentiated long-term and short-term Rajayoga practitioners with an accuracy of 62% using functional connectivity metrics.
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