RESUMO
Background: The investigation of mindfulness meditation practice, classically divided into focused attention meditation (FAM), and open monitoring meditation (OMM) styles, has seen a long tradition of theoretical, affective, neurophysiological and clinical studies. In particular, the high temporal resolution of magnetoencephalography (MEG) or electroencephalography (EEG) has been exploited to fill the gap between the personal experience of meditation practice and its neural correlates. Mounting evidence, in fact, shows that human brain activity is highly dynamic, transiting between different brain states (microstates). In this study, we aimed at exploring MEG microstates at source-level during FAM, OMM and in the resting state, as well as the complexity and criticality of dynamic transitions between microstates. Methods: Ten right-handed Theravada Buddhist monks with a meditative expertise of minimum 2,265 h participated in the experiment. MEG data were acquired during a randomized block design task (6 min FAM, 6 min OMM, with each meditative block preceded and followed by 3 min resting state). Source reconstruction was performed using eLORETA on individual cortical space, and then parcellated according to the Human Connect Project atlas. Microstate analysis was then applied to parcel level signals in order to derive microstate topographies and indices. In addition, from microstate sequences, the Hurst exponent and the Lempel-Ziv complexity (LZC) were computed. Results: Our results show that the coverage and occurrence of specific microstates are modulated either by being in a meditative state or by performing a specific meditation style. Hurst exponent values in both meditation conditions are reduced with respect to the value observed during rest, LZC shows significant differences between OMM, FAM, and REST, with a progressive increase from REST to FAM to OMM. Discussion: Importantly, we report changes in brain criticality indices during meditation and between meditation styles, in line with a state-like effect of meditation on cognitive performance. In line with previous reports, we suggest that the change in cognitive state experienced in meditation is paralleled by a shift with respect to critical points in brain dynamics.
RESUMO
Background Photon-counting detector (PCD) CT provides comprehensive spectral data with every acquisition, but studies evaluating myocardial extracellular volume (ECV) quantification with use of PCD CT compared with an MRI reference remain lacking. Purpose To compare ECV quantification for myocardial tissue characterization between a first-generation PCD CT system and cardiac MRI. Materials and Methods In this single-center prospective study, adults without contraindication to iodine-based contrast media underwent same-day cardiac PCD CT and MRI with native and postcontrast T1 mapping and late gadolinium enhancement for various clinical indications for cardiac MRI (the reference standard) between July 2021 and January 2022. Global and midventricular ECV were assessed with use of three methods: single-energy PCD CT, dual-energy PCD CT, and MRI T1 mapping. Quantitative comparisons among all techniques were performed. Correlation and reliability between different methods of ECV quantification were assessed with use of the Pearson correlation coefficient (r) and the intraclass correlation coefficient. Results The final sample included 29 study participants (mean age ± SD, 54 years ± 17; 15 men). There was a strong correlation of ECV between dual- and single-energy PCD CT (r = 0.91, P < .001). Radiation dose was 40% lower with dual-energy versus single-energy PCD CT (volume CT dose index, 10.1 mGy vs 16.8 mGy, respectively; P < .001). In comparison with MRI, dual-energy PCD CT showed strong correlation (r = 0.82 and 0.91, both P < .001) and good to excellent reliability (intraclass correlation coefficients, 0.81 and 0.90) for midventricular and global ECV quantification, but it overestimated ECV by approximately 2%. Single-energy PCD CT showed similar relationship with MRI but underestimated ECV by 3%. Conclusion Myocardial tissue characterization with photon-counting detector CT-based quantitative extracellular volume analysis showed a strong correlation to MRI. © RSNA, 2023 Supplemental material is available for this article.