Voluntary and spontaneous facial mimicry toward other's emotional expression in patients with Parkinson's disease

Psychosocial functioning in patients with altered facial expression: a scoping review in five neurological diseases

PURPOSE

To perform a scoping review to investigate the psychosocial impact of having an altered facial expression in five neurological diseases.

METHODS

A systematic literature search was performed. Studies were on Bell's palsy, facioscapulohumeral muscular dystrophy (FSHD), Moebius syndrome, myotonic dystrophy type 1, or Parkinson's disease patients; had a focus on altered facial expression; and had any form of psychosocial outcome measure. Data extraction focused on psychosocial outcomes.

RESULTS

Bell's palsy, myotonic dystrophy type 1, and Parkinson's disease patients more often experienced some degree of psychosocial distress than healthy controls. In FSHD, facial weakness negatively influenced communication and was experienced as a burden. The psychosocial distress applied especially to women (Bell's palsy and Parkinson's disease), and patients with more severely altered facial expression (Bell's palsy), but not for Moebius syndrome patients. Furthermore, Parkinson's disease patients with more pronounced hypomimia were perceived more negatively by observers. Various strategies were reported to compensate for altered facial expression.

CONCLUSIONS

This review showed that patients with altered facial expression in four of five included neurological diseases had reduced psychosocial functioning. Future research recommendations include studies on observers' judgements of patients during social interactions and on the effectiveness of compensation strategies in enhancing psychosocial functioning.

Tabular data augmentation for video-based detection of hypomimia in Parkinson's disease

BACKGROUND AND OBJECTIVE

This paper presents a method for the computerized detection of hypomimia in people with Parkinson's disease (PD). It overcomes the difficulty of the small and unbalanced size of available datasets.

METHODS

A public dataset consisting of features of the video recordings of people with PD with four facial expressions was used. Synthetic data was generated using a Conditional Generative Adversarial Network (CGAN) for training augmentation. After training the model, Test-Time Augmentation was performed. The classification was conducted using the original test set to prevent bias in the results.

RESULTS

The employment of CGAN followed by Test-Time Augmentation led to an accuracy of classification of the videos of 83%, specificity of 82%, and sensitivity of 85% in the test set that the prevalence of PD was around 7% and where real data was used for testing. This is a significant improvement compared with other similar studies. The results show that while the technique was able to detect people with PD, there were a number of false positives. Hence this is suitable for applications such as population screening or assisting clinicians, but at this stage is not suitable for diagnosis.

CONCLUSIONS

This work has the potential for assisting neurologists to perform online diagnose and monitoring their patients. However, it is essential to test this for different ethnicity and to test its repeatability.

Exploring facial expressions and action unit domains for Parkinson detection

BACKGROUND AND OBJECTIVE

Patients suffering from Parkinson's disease (PD) present a reduction in facial movements called hypomimia. In this work, we propose to use machine learning facial expression analysis from face images based on action unit domains to improve PD detection. We propose different domain adaptation techniques to exploit the latest advances in automatic face analysis and face action unit detection.

METHODS

Three different approaches are explored to model facial expressions of PD patients: (i) face analysis using single frame images and also using sequences of images, (ii) transfer learning from face analysis to action units recognition, and (iii) triplet-loss functions to improve the automatic classification between patients and healthy subjects.

RESULTS

Real face images from PD patients show that it is possible to properly model elicited facial expressions using image sequences (neutral, onset-transition, apex, offset-transition, and neutral) with accuracy improvements of up to 5.5% (from 72.9% to 78.4%) with respect to single-image PD detection. We also show that our proposed action unit domain adaptation provides improvements of up to 8.9% (from 78.4% to 87.3%) with respect to face analysis. Finally, we also show that triplet-loss functions provide improvements of up to 3.6% (from 78.8% to 82.4%) with respect to action unit domain adaptation applied upon models created from scratch. The code of the experiments is available at https://github.com/luisf-gomez/Explorer-FE-AU-in-PD.

CONCLUSIONS

Domain adaptation via transfer learning methods seem to be a promising strategy to model hypomimia in PD patients. Considering the good results and also the fact that only up to five images per participant are considered in each sequence, we believe that this work is a step forward in the development of inexpensive computational systems suitable to model and quantify problems of PD patients in their facial expressions.

The association of negative mood with automatic and effortful facial expression mimicry

The natural process of mimicking the facial expressions of others is well established, as are the deficits in this reflexive behavior for individuals with clinical disorders such as depression. This study examines the extent of this deficit in non-clinical individuals with high transient negative mood, and whether it extends to both automatic and effortful emotion expression behavior. One hundred and thirty-six participants were shown happy, sad, and neutral faces, while electromyography (EMG) recorded facial muscle responses. Automatic (reflexive) mimicry was assessed while participants simply viewed facially expressive photographs, while effortful mimicry was monitored when individuals were told to intentionally copy the expressions in the photographs. Results indicated that high levels of negative mood were primarily associated with deficits in effortful mimicry of happy expressions, although some similar evidence was found in automatic mimicry of happy faces. Surprisingly, there were also ties between negative moods and inaccuracies in effortful mimicry of sad expressions (but not automatic mimicry). Inaccurate automatic and effortful mimicry were also tied with lower self-reported social support and greater loneliness. These results indicate that even in healthy individuals, transient and minor changes in negative mood are tied to deficiencies in facial mimicry at both the automatic and effortful level.

Measurement of the facial nerve thickness and its correlation with freezing phenomenon and hypomimia in Parkinson's disease

OBJECTIVE

Hypomimia is one of the diagnostic features in Parkinson's disease, and freezing blocks may also occur with the degenerative process. We investigated that the thickness of the cranial facial nerve that innervates facial expressions, and the relationship between bradymimia and freezing phenomena in these patients.

METHODS

We included 70 patients and healthy participants in this cross-sectional study. Clinical characteristics, modified Hoehn and Yahr (mHY) stages, Unified Parkinson's Disease Rating Scale (UPDRS) scores and Freezing of Gait Questionnaire (FOG-Q) scores of Parkinson's patients were recorded. FOG-Q was scored between 1 and 4 points. The thickness of the facial nerve and its neighborhoods of numerically equal groups were measured radiologically in the internal acoustic channel (IAC) with magnetic resonance imaging of temporal lobe. Right and left facial nerve thicknesses were compared.

RESULTS

The right facial nerve thicknesses of the patient and control groups were measured as 0.97 ± 0.12 mm and 1.20 ± 0.10 mm at the proximal level and 0.71 (0.69-0.81) mm and 1.21 (1.13-1.24) mm at the distal level, respectively (P < 0.001). Notably, the facial nerve was more atrophied on the right than on the left (P < 0.001). Facial nerve to IAC ratio (%) was significantly lower and cerebrospinal fluid thickness distance (%) measurement was significantly higher (P < 0.001). Also, the FOG-Q and facial nerve to IAC ratio were negatively correlated (P = 0.049, rho = -0.335).

CONCLUSIONS

Our study provides new information about the facial nerve and its neighborhoods and clinical relationships in individuals with PD. In studies investigating hypomimia and FOG in Parkinson's, neuroimaging of the facial nerve can also be used. These results need to be proven with more comprehensive studies.

Social Symptoms of Parkinson's Disease

Parkinson's disease (PD) is typically well recognized by its characteristic motor symptoms (e.g., bradykinesia, rigidity, and tremor). The cognitive symptoms of PD are increasingly being acknowledged by clinicians and researchers alike. However, PD also involves a host of emotional and communicative changes which can cause major disruptions to social functioning. These incude problems producing emotional facial expressions (i.e., facial masking) and emotional speech (i.e., dysarthria), as well as difficulties recognizing the verbal and nonverbal emotional cues of others. These social symptoms of PD can result in severe negative social consequences, including stigma, dehumanization, and loneliness, which might affect quality of life to an even greater extent than more well-recognized motor or cognitive symptoms. It is, therefore, imperative that researchers and clinicans become aware of these potential social symptoms and their negative effects, in order to properly investigate and manage the socioemotional aspects of PD. This narrative review provides an examination of the current research surrounding some of the most common social symptoms of PD and their related social consequences and argues that proactively and adequately addressing these issues might improve disease outcomes.