Title:

Functional Connectivity during Mindful Meditation: Activation Likelihood Estimate & Meta-Analytic Connectivity Modelling

The overall incidence of mental health disorders (MHD) is high, with an estimated 1 in 5 adults in the United States having a diagnosed MHD (National Institute of Mental Health (NIMH), 2023). Such disorders are often associated with changes and dysregulation or dysfunction of brain structures (NIH, 2007). Due to the high prevalence of MHD, interventions and models have been proposed to target specific neural networks associated with dysregulation (e.g., Triple Network Model). Some of the most highly efficacious treatments involve mindful meditation as a component, one such treatment being Acceptance & Commitment Therapy. Mindful meditation is a practice that has been utilized for decades, focusing on awareness of one’s moment-to-moment state (Van Dam et al., 2017). Research on mindful meditation has involved a variety of paradigms and tasks making comparisons across them difficult, hence this study proposes a meta-analytic connectivity approach where all studies independent of the paradigm or task can be included. Functional connectivity is the statistical relationship between specified brain regions, and whether these regions consistently correspond with each other (Eickhoff & Muller, 2015). Obtaining an understanding of functional connectivity associated with mindfulness will help inform intervention approaches and push their development to target salient networks. Therefore, this study is designed as a meta-analytic connectivity modelling approach to mindfulness through the use of BrainMap software. Data modelled in this meta-analytic approach were taken from 20 function Magnetic Resonance Imaging (fMRI) or Positron Emission Topography (PET) studies investigating mindfulness and meditation. These data were used to first conduct an Activation Likelihood Estimation (ALE) which reveals which regions of the brain are active. Next, a Meta-Analytic Connectivity Model (MACM) was developed based on the regions found active within ALE to explore the network connectivity. In addition, BrainMap allows for the analysis of Paradigm Class (i.e., the experimental task within a study) and Behavioral Domain (i.e., action contrasts within an experiment) analysis. ALE unveiled eight peak activation clusters (right superior temporal gyrus [R STG], left anterior cingulate cortex [L ACC], left middle frontal gyrus [L MFG], left superior frontal gyrus [L SFG], right medial frontal gyrus [R MFG], left inferior parietal lobe [L IPL], left lentiform nucleus [L LN], and a second cluster within the left superior frontal gyrus [L SFG]). Analysis revealed bidirectional connectivity between L LN and L ACC, as well as unidirectional connectivity within the R STG, L MFG, L SFG, and R MFG. Ultimately, these findings establish functional connectivity within meditation-involved brain regions and overlap within hypothesized neural networks (i.e., TNM). Additionally, the practicality of translational neuroimaging and contributions to databases, such as BrainMap, is discussed, in addition to clinical application.

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