Progressive modulation of resting-state brain activity during neurofeedback of positive-social emotion regulation networks

Neurofeedback allows for the self-regulation of brain circuits implicated in specific maladaptive behaviors, leading to persistent changes in brain activity and connectivity. Positive-social emotion regulation neurofeedback enhances emotion regulation capabilities, which is critical for reducing the severity of various psychiatric disorders. Training dorsomedial prefrontal cortex (dmPFC) to exert a top-down influence on bilateral amygdala during positive-social emotion regulation progressively (linearly) modulates connectivity within the trained network and induces positive mood. However, the processes during rest that interleave the neurofeedback training remain poorly understood. We hypothesized that short resting periods at the end of training sessions of positive-social emotion regulation neurofeedback would show alterations within emotion regulation and neurofeedback learning networks. We used complementary model-based and data-driven approaches to assess how resting-state connectivity relates to neurofeedback changes at the end of training sessions. In the experimental group, we found lower progressive dmPFC self-inhibition and an increase of connectivity in networks engaged in emotion regulation, neurofeedback learning, visuospatial processing, and memory. Our findings highlight a large-scale synergy between neurofeedback and resting-state brain activity and connectivity changes within the target network and beyond. This work contributes to our understanding of concomitant learning mechanisms post training and facilitates development of efficient neurofeedback training

Supplementary Material for: Audio Feature Analysis for Acoustic Pain Detection in Term Newborns

Introduction: Crying newborns signal a need or discomfort as part of the innate communication system. Exposure to pain is related to infants’ unfavorable neurodevelopmental outcomes. There is a tremendous need for more objective methods to assess neonatal pain. An audio analysis of acoustic utterances could provide specific information on the patient’s pain level. Methods: We analyzed 67 videos of 33 term-born newborns recorded during a planned capillary blood sample, including the stimuli, non-noxious thermal stimulus, short noxious stimulus, and prolonged unpleasant stimulus, between December 2020 and March 2021. Two expert raters evaluated the infants’ pain responses using the Neonatal Facial Coding System (NFCS). The mean values of 123 timbre features of the recorded audio data were analyzed by using specific toolboxes and libraries from the following programming environments: MIRtoolbox (MATLAB), MiningSuite (MATLAB), Essentia (Python), AudioCommons timbral models (Python), and Librosa (Python). Results: The NFCS values were significantly higher during the short noxious stimulus (p p p = 0.79). Brightness, roughness, percussive energy, and attack times were identified as the features having the highest impact on the NFCS. Conclusion: This hypothesis-generating study identified several salient acoustic features highly associated with pain responses in term newborns. Our analysis is an encouraging starting point for the targeted analysis of pain-specific acoustic features of neonatal cries and vocalizations from the perspective of real-time acoustic processing

A Hybrid approach for biomedical relation extraction using finite state automata and random forest-weighted fusion

Comunicació presentada a: The 18th International Conference on Computational Linguistics and Intelligent Text Processing (CICLing 2017), celebrada a Budapest, Hungria, del 17 al 23 d'abril de 2017.The automatic extraction of relations between medical entities found in related texts is considered to be a very important task, due to the multitude of applications that it can support, from question answering systems to the devel-opment of medical ontologies. Many different methodologies have been pre-sented and applied to this task over the years. Of particular interest are hybrid approaches, in which different techniques are combined in order to improve the individual performance of either one of them. In this study, we extend a previ-ously established hybrid framework for medical relation extraction, which we modify by enhancing the pattern-based part of the framework and by applying a more sophisticated weighting method. Most notably, we replace the use of regu-lar expressions with finite state automata for the pattern-building part, while the fusion part is replaced by a weighting strategy that is based on the operational capabilities of the Random Forests algorithm. The experimental results indicate the superiority of the proposed approach against the aforementioned well-established hybrid methodology and other state-of-the-art approaches.This work was supported by the project KRISTINA (H2020-645012), funded by the European Commission. Deidentified clinical records used in this research were provided by the i2b2 National Center for Biomedical Computing funded by U54LM008748 and were originally prepared for the Shared Tasks for Challenges in NLP for Clinical Data organized by Dr. Ozlem Uzuner, i2b2 and SUNY