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Independent component analysis (ICA) is one of the leading approaches for studying brain functional networks. There is increasing interest in neuroscience studies to investigate individual differences in brain networks and their association with demographic characteristics and clinical outcomes. In this work, we develop a sparse Bayesian group hierarchical ICA model that offers significant improvements over existing ICA techniques for identifying covariate effects on the brain network. Specifically, we model the population‐level ICA source signals for brain networks using a Dirichlet process mixture. To reliably capture individual differences on brain networks, we propose sparse estimation of the covariate effects in the hierarchical ICA model via a horseshoe prior. Through extensive simulation studies, we show that our approach performs considerably better in detecting covariate effects in comparison with the leading group ICA methods. We then perform an ICA decomposition of a between‐subject meditation study. Our method is able to identify significant effects related to meditative practice in brain regions that are consistent with previous research into the default mode network, whereas other group ICA approaches find few to no effects. [ABSTRACT FROM AUTHOR]

Investigating the similarity and changes in brain networks under different mental conditions has become increasingly important in neuroscience research. A standard separate estimation strategy fails to pool information across networks and hence has reduced estimation accuracy and power to detect between-network differences. Motivated by an fMRI Stroop task experiment that involves multiple related tasks, we develop an integrative Bayesian approach for jointly modeling multiple brain networks that provides a systematic inferential framework for network comparisons. The proposed approach explicitly models shared and differential patterns via flexible Dirichlet process-based priors on edge probabilities. Conditional on edges, the connection strengths are modeled via Bayesian spike-and-slab prior on the precision matrix off-diagonals. Numerical simulations illustrate that the proposed approach has increased power to detect true differential edges while providing adequate control on false positives and achieves greater network estimation accuracy compared to existing methods. The Stroop task data analysis reveals greater connectivity differences between task and fixation that are concentrated in brain regions previously identified as differentially activated in Stroop task, and more nuanced connectivity differences between exertion and relaxed task. In contrast, penalized modeling approaches involving computationally burdensome permutation tests reveal negligible network differences between conditions that seem biologically implausible. for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement. [ABSTRACT FROM AUTHOR]

The theory of predictive processing in the comprehensive articulation proposed by Karl Friston is a framework that boasts an impressively wide explanatory power in neurobiology, where processes apparently as diverse as perception, action, attention, and learning unfold, and are coherently orchestrated, according to the single general mandate of free-energy minimization. In the present opinion piece, I argue that the adoption of this theoretical perspective can provide a much needed unitary framework for contemplative research as well, whose explosive growth in terms of the number of published studies and amount of collected data has not been matched yet by a similarly extensive effort to theoretically organize the findings, so that a deeper understanding of meditation-related processes can be attained. After an introduction to the basic notions of predictive processing, a tentative application of the latter to the meditative exercise is discussed, taking as a paradigmatic example the Japanese Zen meditation practice of shikantaza. Finally, I provide a short list of experimental paradigms that seem particularly useful to test the hypotheses born out of the predictive processing approach to contemplative research., (© 2019 Elsevier B.V. All rights reserved.)