The heartbeat evoked potential does not support strong interoceptive sensibility in trait mindfulness.

The enhancement of body awareness is proposed as one of the cognitive mechanisms that characterize mindfulness. To date, this hypothesis is supported by self-report and behavioral measures but still lacks physiological evidence. The current study investigated relation between trait mindfulness (i.e., individual differences in the ability to be mindful in daily life) and body awareness in combining a self-report measure (Multidimensional Assessment of Interoceptive Awareness [MAIA] questionnaire) with analysis of the heartbeat evoked potential (HEP), which is an event-related potential reflecting the cortical processing of the heartbeat. The HEP data were collected from 17 healthy participants under five minutes of resting-state condition. In addition, each participant completed the Freiburg Mindfulness Inventory and the MAIA questionnaire. Taking account of the important variability of HEP effects, analyses were replicated with the same participants three times (in three distinct sessions). First, group-level analyses showed that HEP amplitude and trait mindfulness do not correlate. Secondly, we observed that HEP amplitude could positively correlate with self-reported body awareness; however, this association was unreliable over time. Interestingly, we found that HEP measure shows very poor reliability over time at the individual level, potentially explaining the lack of reliable association between HEP and psychological traits. Lastly, a reliable positive correlation was found between self-reported trait mindfulness and body awareness. Taken together, these findings provide preliminary evidence that the HEP might not support the increased subjective body awareness in trait mindfulness, thus suggesting that perhaps objective and subjective measures of body awareness could be independent.

Towards a Pragmatic Approach to a Psychophysiological Unit of Analysis for Mental and Brain Disorders: An EEG-Copeia for Neurofeedback.

This article proposes what we call an "EEG-Copeia" for neurofeedback, like the "Pharmacopeia" for psychopharmacology. This paper proposes to define an "EEG-Copeia" as an organized list of scientifically validated EEG markers, characterized by a specific association with an identified cognitive process, that define a psychophysiological unit of analysis useful for mental or brain disorder evaluation and treatment. A characteristic of EEG neurofeedback for mental and brain disorders is that it targets a EEG markers related to a supposed cognitive process, whereas conventional treatments target clinical manifestations. This could explain why EEG neurofeedback studies encounter difficulty in achieving reproducibility and validation. The present paper suggests that a first step to optimize EEG neurofeedback protocols and future research is to target a valid EEG marker. The specificity of the cognitive skills trained and learned during real time feedback of the EEG marker could be enhanced and both the reliability of neurofeedback training and the therapeutic impact optimized. However, several of the most well-known EEG markers have seldom been applied for neurofeedback. Moreover, we lack a reliable and valid EEG targets library for further RCT to evaluate the efficacy of neurofeedback in mental and brain disorders. With the present manuscript, our aim is to foster dialogues between cognitive neuroscience and EEG neurofeedback according to a psychophysiological perspective. The primary objective of this review was to identify the most robust EEG target. EEG markers linked with one or several clearly identified cognitive-related processes will be identified. The secondary objective was to organize these EEG markers and related cognitive process in a psychophysiological unit of analysis matrix inspired by the Research Domain Criteria (RDoC) project.

Robust contour tracking in ultrasound tongue image sequences.

A new contour-tracking algorithm is presented for ultrasound tongue image sequences, which can follow the motion of tongue contours over long durations with good robustness. To cope with missing segments caused by noise, or by the tongue midsagittal surface being parallel to the direction of ultrasound wave propagation, active contours with a contour-similarity constraint are introduced, which can be used to provide 'prior' shape information. Also, in order to address accumulation of tracking errors over long sequences, we present an automatic re-initialization technique, based on the complex wavelet image similarity index. Experiments on synthetic data and on real 60 frame per second (fps) data from different subjects demonstrate that the proposed method gives good contour tracking for ultrasound image sequences even over durations of minutes, which can be useful in applications such as speech recognition where very long sequences must be analyzed in their entirety.