Revealing Changes in Brain Functional Networks Caused by Focused-Attention Meditation Using Tucker3 Clustering

A clustering method called Tucker3 simultaneously selects brain regions and feature metrics that distinguish resting from meditative states. Breath-counting meditation, a focused-attention practice, was studied using functional magnetic resonance imaging in novice meditators. Clustering coefficients increased in eight brain regions—including parts of the frontal, occipital, and cingulate cortex, the cerebellum, and the parahippocampal gyrus—during meditation compared to rest. The work offers a data-driven framework for analyzing functional brain states and demonstrates its utility in studying the neural basis of focused-attention meditation.

Study at a glance

Abstract

This study examines the effects of focused-attention meditation on functional brain states in novice meditators. There are a number of feature metrics for functional brain states, such as functional connectivity, graph theoretical metrics, and amplitude of low frequency fluctuation (ALFF). It is necessary to choose appropriate metrics and also to specify the region of interests (ROIs) from a number of brain regions. Here, we use a Tucker3 clustering method, which simultaneously selects the feature vectors (graph theoretical metrics and fractional ALFF) and the ROIs that can discriminate between resting and meditative states based on the characteristics of the given data. In this study, breath-counting meditation, one of the most popular forms of focused-attention meditation, was used and brain activities during resting and meditation states were measured by functional magnetic resonance imaging. The results indicated that the clustering coefficients of the eight brain regions, Frontal Inf Oper L, Occipital Inf R, ParaHippocampal R, Cerebellum 10 R, Cingulum Mid R, Cerebellum Crus1 L, Occipital Inf L, and Paracentral Lobule R increased through the meditation. Our study also provided the framework of data-driven brain functional analysis and confirmed its effectiveness on analyzing neural basis of focused-attention meditation.