Tri-process model of interpersonal mindfulness: theoretical framework and study protocol.

According to the Center for Disease Control and Prevention, over 14% of the US population practice mindfulness meditation. The effects of mindfulness training on physical and mental health have been consistently documented, but its effects on interpersonal relationships are not yet fully understood or investigated. Interpersonal relationships play a crucial role in the wellbeing of individuals and society, and therefore, warrants further study. The aim of this paper is to present a tri-process theoretical model of interpersonal mindfulness and a study protocol to validate the proposed model. Specifically, according to the proposed model, mindfulness meditation training increases the self-awareness, self-regulation, and prosociality of those receiving the training, which ameliorates the quality of interpersonal interactions and the socioemotional support provided to other individuals. Finally, better socioemotional support increases the support receiver's ability to regulate their emotions. Using a multiphasic longitudinal design involving 640 participants randomized into 480 dyads, the proposed protocol aims to validate the tri-process model and to investigate its mechanisms of actions. The proposed study has important theoretical and social implications and will allow devising new and more effective interpersonal mindfulness programs with applications in multiple fields.

Popular and Scientific Discourse on Autism: Representational Cross-Cultural Analysis of Epistemic Communities to Inform Policy and Practice.

BACKGROUND: Social media provide a window onto the circulation of ideas in everyday folk psychiatry, revealing the themes and issues discussed both by the public and by various scientific communities. OBJECTIVE: This study explores the trends in health information about autism spectrum disorder within popular and scientific communities through the systematic semantic exploration of big data gathered from Twitter and PubMed. METHODS: First, we performed a natural language processing by text-mining analysis and with unsupervised (machine learning) topic modeling on a sample of the last 10,000 tweets in English posted with the term #autism (January 2021). We built a network of words to visualize the main dimensions representing these data. Second, we performed precisely the same analysis with all the articles using the term "autism" in PubMed without time restriction. Lastly, we compared the results of the 2 databases. RESULTS: We retrieved 121,556 terms related to autism in 10,000 tweets and 5.7x109 terms in 57,121 biomedical scientific articles. The 4 main dimensions extracted from Twitter were as follows: integration and social support, understanding and mental health, child welfare, and daily challenges and difficulties. The 4 main dimensions extracted from PubMed were as follows: diagnostic and skills, research challenges, clinical and therapeutical challenges, and neuropsychology and behavior. CONCLUSIONS: This study provides the first systematic and rigorous comparison between 2 corpora of interests, in terms of lay representations and scientific research, regarding the significant increase in information available on autism spectrum disorder and of the difficulty to connect fragments of knowledge from the general population. The results suggest a clear distinction between the focus of topics used in the social media and that of scientific communities. This distinction highlights the importance of knowledge mobilization and exchange to better align research priorities with personal concerns and to address dimensions of well-being, adaptation, and resilience. Health care professionals and researchers can use these dimensions as a framework in their consultations to engage in discussions on issues that matter to beneficiaries and develop clinical approaches and research policies in line with these interests. Finally, our study can inform policy makers on the health and social needs and concerns of individuals with autism and their caregivers, especially to define health indicators based on important issues for beneficiaries.

Binaural Beats through the Auditory Pathway: From Brainstem to Connectivity Patterns.

Binaural beating is a perceptual auditory illusion occurring when presenting two neighboring frequencies to each ear separately. Several controversial claims have been attributed to binaural beats regarding their ability to entrain human brain activity and mood, in both the scientific literature and the marketing realm. Here, we sought to address those questions in a robust fashion using a single-blind, active-controlled protocol. To do so, we compared the effects of binaural beats with a control beat stimulation (monaural beats, known to entrain brain activity but not mood) across four distinct levels in the human auditory pathway: subcortical and cortical entrainment, scalp-level functional connectivity and self-reports. Both stimuli elicited standard subcortical responses at the pure tone frequencies of the stimulus [i.e., frequency following response (FFR)], and entrained the cortex at the beat frequency [i.e., auditory steady state response (ASSR)]. Furthermore, functional connectivity patterns were modulated differentially by both kinds of stimuli, with binaural beats being the only one eliciting cross-frequency activity. Despite this, we did not find any mood modulation related to our experimental manipulation. Our results provide evidence that binaural beats elicit cross frequency connectivity patterns, but weakly entrain the cortex when compared with monaural beat stimuli. Whether binaural beats have an impact on cognitive performance or other mood measurements remains to be seen and can be further investigated within the proposed methodological framework.