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This document describes the steps and methods of the modeling and statistical analyses to implement in order to answer the research question of the study expressed by the primary and secondary objectives of the protocol LONGIMED (ClinicalTrials.gov Identifier: NCT04449913, sponsor Hospices Civils de Lyon, France). This basic science, randomized, longitudinal study was conducted on healthy participants in 2020 and 2021 in France. This document was preregistrated on OSF prior to these analyses and before the closure of the study.

The COVID-19 pandemic has sparked an intense debate about the hidden factors underlying the dynamics of the outbreak. Several computational models have been proposed to inform effective social and healthcare strategies. Crucially, the predictive validity of these models often depends upon incorporating behavioral and social responses to infection. Among these tools, the analytic framework known as "dynamic causal modeling" (DCM) has been applied to the COVID-19 pandemic, shedding new light on the factors underlying the dynamics of the outbreak. We have applied DCM to data from northern Italian regions, the first areas in Europe to contend with the outbreak, and analyzed the predictive validity of the model and also its suitability in highlighting the hidden factors governing the pandemic diffusion. By taking into account data from the beginning of the pandemic, the model could faithfully predict the dynamics of outbreak diffusion varying from region to region. The DCM appears to be a reliable tool to investigate the mechanisms governing the spread of the SARS-CoV-2 to identify the containment and control strategies that could efficiently be used to counteract further waves of infection.

According to constructivist theories of emotion, members of different populations experience emotion differently as a result of assembling different neural resources to produce it. To test this prediction, we sampled individuals from three populations (experienced meditators, cancer survivors, matched controls) and used functional magnetic resonance imaging (fMRI) to assess their neural responses to four kinds of affective phrases (positive cancer, negative cancer, positive non-cancer, negative non- cancer). As predicted, the three populations responded differently to these affective phrases. When assessing neural activity for positive vs. negative phrases, controls showed the standard negativity bias reported in the emotion literature, cancer survivors showed a positivity bias (perhaps related to personal growth following cancer), and experienced meditators showed no bias (perhaps reflecting the mental quality of equanimity established through regular meditation practice). When assessing neural activity for positively-valenced cancer vs. non-cancer phrases, controls exhibited difficulty engaging with the cancer phrases, engaging much more with the non-cancer phrases instead. In contrast, experienced meditators and cancer survivors engaged more with the cancer phrases, while exhibiting different affective styles. Together these results indicate that experienced meditators, cancer survivors, and matched controls assemble different neural resources to process emotion.