The Story behind the Mask: A Narrative Review on Hypomimia in Parkinson's Disease.

Facial movements are crucial for social and emotional interaction and well-being. Reduced facial expressions (i.e., hypomimia) is a common feature in patients with Parkinson's disease (PD) and previous studies linked this manifestation to both motor symptoms of the disease and altered emotion recognition and processing. Nevertheless, research on facial motor impairment in PD has been rather scarce and only a limited number of clinical evaluation tools are available, often suffering from poor validation processes and high inter- and intra-rater variability. In recent years, the availability of technology-enhanced quantification methods of facial movements, such as automated video analysis and machine learning application, led to increasing interest in studying hypomimia in PD. In this narrative review, we summarize the current knowledge on pathophysiological hypotheses at the basis of hypomimia in PD, with particular focus on the association between reduced facial expressions and emotional processing and analyze the current evaluation tools and management strategies for this symptom, as well as future research perspectives.

An Adaptive Model Predictive Controller to Address the Biovariability in Blood Clotting Response During Therapy With Warfarin.

OBJECTIVE: Effective dosing of anticoagulants aims to prevent blood clot formation while avoiding hemorrhages. This complex task is challenged by several disturbing factors and drug-effect uncertainties, requesting frequent monitoring and adjustment. Biovariability in drug absorption and action further complicates titration and calls for individualized strategies. In this paper, we propose an adaptive closed-loop control algorithm to assist in warfarin therapy management. METHODS: The controller was designed and tested in silico using an established pharmacometrics model of warfarin, which accounts for inter-subject variability. The control algorithm is an adaptive Model Predictive Control (a-MPC) that leverages a simplified patient model, whose parameters are updated with a Bayesian strategy. Performance was quantitatively evaluated in simulations performed on a population of virtual subjects against an algorithm reproducing medical guidelines (MG) and an MPC controller available in the literature (l-MPC). RESULTS: The proposed a-MPC significantly (p 0.05) lowers rising time (2.8 vs. 4.4 and 11.2 days) and time out of range (3.3 vs. 7.2 and 12.9 days) with respect to both MG and l-MPC, respectively. Adaptivity grants a significantly (p 0.05) lower number of subjects reaching unsafe INR values compared to when this feature is not present (8.9% vs.15% of subjects presenting an overshoot outside the target range and 0.08% vs. 0.28% of subjects reaching dangerous INR values). CONCLUSION: The a-MPC algorithm improve warfarin therapy compared to the benchmark therapies. SIGNIFICANCE: This in-silico validation proves effectiveness of the a-MPC algorithm for anticoagulant administration, paving the way for clinical testing.

Quantitative Evaluation of Hypomimia in Parkinson's Disease: A Face Tracking Approach.

Parkinson's disease (PD) is a neurological disorder that mainly affects the motor system. Among other symptoms, hypomimia is considered one of the clinical hallmarks of the disease. Despite its great impact on patients' quality of life, it remains still under-investigated. The aim of this work is to provide a quantitative index for hypomimia that can distinguish pathological and healthy subjects and that can be used in the classification of emotions. A face tracking algorithm was implemented based on the Facial Action Coding System. A new easy-to-interpret metric (face mobility index, FMI) was defined considering distances between pairs of geometric features and a classification based on this metric was proposed. Comparison was also provided between healthy controls and PD patients. Results of the study suggest that this index can quantify the degree of impairment in PD and can be used in the classification of emotions. Statistically significant differences were observed for all emotions when distances were taken into account, and for happiness and anger when FMI was considered. The best classification results were obtained with Random Forest and kNN according to the AUC metric.

Using high density EEG to assess TMS treatment in patients with schizophrenia.

We present preliminary results from the ongoing study entitled "Icelandic AVH-TMS" which aim is to study the effectiveness of repetitive transcranial magnetic stimulation (rTMS) treatment for patients with schizophrenia and with persistent auditory verbal hallucinations (AVH) using symptoms and psychometric scales and high-density EEG system (256 channels). The aim of the present work was to describe cortical topography of the auditory evoked responses like P50 and N100-P300 complex in healthy participants and patients with schizophrenia and to define a robust methodology of signal quantification using dense-array EEG. Preliminary data is shown for three healthy participants and three patients in baseline conditions and for two patients we show the results recorded before and after 10 days rTMS treatment. Our results show differences in sensory gating (P50 suppresion) and a stronger N100-P300 response to rare audio stimulus after the treatment. Moreover we show the value of assessing brain electrical activity from high-density EEG (256 channels) analyzing the results in different regions of interest. However, it is premature and hazardous to assume that rTMS treatment effectiveness in patients with AVH can be assessed using P50 suppression ratio.