Attentional and cognitive monitoring brain networks in long-term meditators depend on meditation states and expertise

Abstract

Meditation practice is suggested to engage training of cognitive control systems in the brain. To evaluate the functional involvement of attentional and cognitive monitoring processes during meditation, the present study analysed the electroencephalographic synchronization of fronto-parietal (FP) and medial-frontal (MF) brain networks in highly experienced meditators during different meditation states (focused attention, open monitoring and loving kindness meditation). The aim was to assess whether and how the connectivity patterns of FP and MF networks are modulated by meditation style and expertise. Compared to novice meditators, (1) highly experienced meditators exhibited a strong theta synchronization of both FP and MF networks in left parietal regions in all mediation styles, and (2) only the connectivity of lateralized beta MF networks differentiated meditation styles. The connectivity of intra-hemispheric theta FP networks depended non-linearly on meditation expertise, with opposite expertise-dependent patterns found in the left and the right hemisphere. In contrast, inter-hemispheric FP connectivity in faster frequency bands (fast alpha and beta) increased linearly as a function of expertise. The results confirm that executive control systems play a major role in maintaining states of meditation. The distinctive lateralized involvement of FP and MF networks appears to represent a major functional mechanism that supports both generic and style-specific meditation states. The observed expertise-dependent effects suggest that functional plasticity within executive control networks may underpin the emergence of unique meditation states in expert meditators.

An updated classification of meditation methods using principles of taxonomy and systematics

“…That paper cited research from the broader fields of cognitive and affective neuroscience, as well as from the field of CoNS, which supported the notion that these enhanced cognitive, affective, and null states demonstrated distinctly different and measurable neurophysiological correlates (e.g., Lehmann et al, 2001;Dalgleish, 2004;Carter et al, 2005;Cahn and Polich, 2006;Hankey, 2006;Holzel et al, 2007Holzel et al, , 2008Lutz et al, 2007Lutz et al, , 2008Davidson, 2010;Travis and Shear, 2010;Josipovic et al, 2011;Leung et al, 2013). 8 Since then, our original thesis has been supported by more recent research as well (e.g., Dahl et al, 2015;Brandmeyer et al, 2019;Josipovic, 2019;Raffone et al, 2019;Afonso et al, 2020;Yordanova et al, 2020Yordanova et al, , 2021. Lee et al (2012, p.7) concluded that "different forms of meditation have meditation-specific effects on neural activity, rather than a common neural mechanism"; "different forms of meditation practice create domain-specific plastic changes in neural activity"; and "each form of meditation is associated with a dissociable pattern of neural activity."…”

“…Since then, our original thesis has been supported by more recent research as well (e.g., Dahl et al, 2015 ; Brandmeyer et al, 2019 ; Josipovic, 2019 ; Raffone et al, 2019 ; Afonso et al, 2020 ; Yordanova et al, 2020 , 2021 ). Lee et al (2012 , p.7) concluded that “different forms of meditation have meditation-specific effects on neural activity, rather than a common neural mechanism”; “different forms of meditation practice create domain-specific plastic changes in neural activity”; and “each form of meditation is associated with a dissociable pattern of neural activity.” In a review of meditation research findings to date, Travis concurs: “the assumption that a common brain marker would emerge by combining different meditation practices together in one analysis is flawed.…”

“…Transcendental meditation has been associated with increases in the frequency of peak EEG power (Travis and Pearson, 2000). Importantly, Yordanova and colleagues showed that there were similarities and differences between three different practices-focused attention, open monitoring, and loving kindness meditation (Yordanova et al, 2020(Yordanova et al, , 2021. All meditation practices were associated with a common connectivity pattern characterized by increased connectivity of broadly distributed delta networks, lefthemispheric theta networks, and right-hemispheric alpha networks.…”

Effect of Heartfulness Meditation Among Long-Term, Short-Term and Non-meditators on Prefrontal Cortex Activity of Brain Using Machine Learning Classification: A Cross-Sectional Study

“…Where Cov(i,j) is the cross-spectral density between two signals, and Var(i)and Var(j) are the auto spectral densities for signals i and j, respectively. The correlation value lies between +1 to -1, where +1 indicates that signals are perfect positive correlation, -1 indicates a perfect negative correlation, and 0 indicates that the two signals are perfectly uncorrelated [18,26] .…”

“…Functional connectivity can be used for analyzing cognitive activity [11], disorders like schizophrenia [12], depression [13], chronic pain [14], yoga, and meditation [15][16][17]. Therefore, we have used functional connectivity parameters in the present study such as Pearson correlation (r) [18], phase locking value (PLV) [19], clustering coefficient (CC), and shortest path (SP) [20]. Previous meditation reported an increase in functional connectivity of the brain during meditation as compared to the resting state [21].…”