Empirical descriptions of the phenomenology of meditation states rely on practitioners' ability to provide accurate information on their experience. We present a meditation training protocol that was designed to equip naive participants with a theoretical background and experiential knowledge that would enable them to share their experience. Subsequently, novices carried on with daily practice during several weeks before participating in experiments. Using a neurophenomenological experiment designed to explore two different meditation states (focused attention and open monitoring), we found that self-reported phenomenological ratings (i) were sensitive to meditation states, (ii) reflected meditation dose and fatigue effects, and (iii) correlated with behavioral measures (variability of response time). Each of these effects was better predicted by features of participants' daily practice than by desirable responding. Our results provide evidence that novice practitioners can reliably report their experience along phenomenological dimensions and warrant the future investigation of this training protocol with a longitudinal design.

A major characteristic of neural networks is the complexity of their organization at various spatial scales, from microscopic local circuits to macroscopic brain-scale areas. Understanding how neural information is processed thus entails the ability to study them at multiple scales simultaneously. This is made possible using microelectrodes array (MEA) technology. Indeed, high-density MEAs provide large-scale covering (several mm²) of whole neural structures combined with microscopic resolution (about 50µm) of unit activity. Yet, current options for spatiotemporal representation of MEA-collected data remain limited. Here we present NeuroMap, a new interactive Matlab-based software for spatiotemporal mapping of MEA data. NeuroMap uses thin plate spline interpolation, which provides several assets with respect to conventional mapping methods used currently. First, any MEA design can be considered, including 2D or 3D, regular or irregular, arrangements of electrodes. Second, spline interpolation allows the estimation of activity across the tissue with local extrema not necessarily at recording sites. Finally, this interpolation approach provides a straightforward analytical estimation of the spatial Laplacian for better current sources localization. In this software, coregistration of 2D MEA data on the anatomy of the neural tissue is made possible by fine matching of anatomical data with electrode positions using rigid deformation based correction of anatomical pictures. Overall, NeuroMap provides substantial material for detailed spatiotemporal analysis of MEA data. The package is distributed under GNU General Public License (GPL) and available at http://sites.google.com/site/neuromapsoftware.

Despite attempts to unify the different theoretical accounts of the mismatch negativity (MMN), there is still an ongoing debate on the neurophysiological mechanisms underlying this complex brain response. On one hand, neuronal adaptation to recurrent stimuli is able to explain many of the observed properties of the MMN, such as its sensitivity to controlled experimental parameters. On the other hand, several modeling studies reported evidence in favor of Bayesian learning models for explaining the trial-to-trial dynamics of the human MMN. However, direct comparisons of these two main hypotheses are scarce, and previous modeling studies suffered from methodological limitations. Based on reports indicating spatial and temporal dissociation of physiological mechanisms within the timecourse of mismatch responses in animals, we hypothesized that different computational models would best fit different temporal phases of the human MMN. Using electroencephalographic data from two independent studies of a simple auditory oddball task (n = 82), we compared adaptation and Bayesian learning models’ ability to explain the sequential dynamics of auditory deviance detection in a time-resolved fashion. We first ran simulations to evaluate the capacity of our design to dissociate the tested models and found that they were sufficiently distinguishable above a certain level of signal-to-noise ratio (SNR). In subjects with a sufficient SNR, our time-resolved approach revealed a temporal dissociation between the two model families, with high evidence for adaptation during the early MMN window (from 90 to 150-190 ms post-stimulus depending on the dataset) and for Bayesian learning later in time (170-180 ms or 200-220ms). In addition, Bayesian model averaging of fixed-parameter models within the adaptation family revealed a gradient of adaptation rates, resembling the anatomical gradient in the auditory cortical hierarchy reported in animal studies.

A significant share of meditation research relies on trait psychometric measures of mindfulness, which neglect the complex and dynamic unfolding of experiential processes entailed by meditation over time. However, there have been a few attempts to capture ongoing changes in experience during meditation. In this study, we integrated and expanded on 3 of these previous approaches to create a novel instrument, which we called the Lyon Assessment of Meditation Phenomenology (LAMP). This questionnaire encompasses contextual, conative, affective, somatic, attentional, cognitive, and metacognitive domains. Fifty-three experienced meditators completed the LAMP after each meditation session during an intensive 10-day retreat. We modeled the time courses of the individual answers to each question using generalized additive modeling and automatically clustered participants using group-based trajectory modeling. Over 60% of the dimensions assessed by LAMP exhibited significant change during the retreat across the group, following distinct temporal trajectories. These trajectories reflected differences between meditation types (chiefly, focused attention and open monitoring) and individual expertise, supporting a previously proposed multidimensional phenomenological model of mindfulness-related practices. We also identified 3 clusters of individual temporal trajectories associated with prior meditative experience and difficulties experienced during the retreat. Finally, we replicated and extended core findings from mindfulness research on pain regulation. The proposed multidimensional experience-sampling approach provides a rich characterization of the dynamic aspects of meditative experience and may be used to deepen our knowledge of the phenomenology and mechanisms of meditation and meditation-based interventions.