Facial expressions recognition and discrimination in Parkinson’s disease

Abstract

Emotion processing impairment is a common non‐motor symptom in Parkinson’s Disease (PD). Previous literature reported conflicting results concerning, in particular, the performance for different emotions, the relation with cognitive and neuropsychiatric symptoms and the affected stage of processing. This study aims at assessing emotion recognition and discrimination in PD. Recognition of six facial expressions was studied in order to clarify its relationship with motor, cognitive and neuropsychiatric symptoms. Sensitivity in discriminating happy and fearful faces was investigated to address controversial findings on impairment in early stages of emotion processing. To do so, seventy PD patients were tested with the Ekman 60 Faces test and compared with 46 neurologically unimpaired participants. Patients’ performances were correlated with clinical scales and neuropsychological tests. A subsample of 25 PD patients and 25 control participants were also tested with a backward masking paradigm for sensitivity in happiness and fear discrimination. Results showed that PD patients were impaired in facial emotion recognition, especially for fearful expressions. The performance correlated with perceptual, executive and general cognitive abilities, but facial expression recognition deficits were present even in cognitively unimpaired patients. In contrast, patients’ sensitivity in backward masking tasks was not reduced as compared to controls. Taken together our data demonstrate that facial emotion recognition, and fear expression in particular, is critically affected by neurodegeneration in PD and related to cognitive abilities; however, it appears before other cognitive impairments. Preserved performances in discriminating shortly presented facial expressions, suggest unimpaired early stages of emotion processing.

The Effects of Facial Expressions on Face Biometric System’s Reliability

“…In terms of the FE recognition applications, the deficits of FE in Huntington's diseases have been studied by Yitzhak et al in [29] to improve the FE recognition using the predicting the severity of their motor symptoms. Mattavellt [30] studied it in Parkinson's diseases. Flynn et al [31] assessed the effectiveness of automated emotion recognition in adults and children for the benefits of different applications, such as identification of children's emotions before clinical investigations.…”

“…For instance, in [15,16] investigated the utilization of the selected features and landmarks for face recognition purposes only. Although the accuracy was the highest when both slopes and distances were used in [12], this study will use distances only as it analyzes which muscles and facial features are affected by FE, not for recognition purposes [21][22][23][24][25][26][27][28][29][30][31][32][33], evaluated the performance of FE classifications. While utilizing the periocular as a biometric trait in [33] has its failures when the face presents posture changes, occlusions, closed eyes, and other changes, in the FB, the recognition process can use other features than the one that exposes failure.…”

Tunable Q wavelet transform based emotion classification in Parkinson’s disease using Electroencephalography

“…Several methods have been proposed to detect emotions in PD such as facial expressions, speech, gestures, and biosignals. Facial expressions based emotions detection proved to be promising but its performance can be deliberately altered by intensional changes in facial expressions [8][9][10]. To overcome these limitations of emotion recognition based on facial expressions, electroencephalogram (EEG) signals can be utilized.…”

“…The multiple features extracted from EEG signals have been classified by the decision tree classification method in [17]. The analysis of delta (< 4 Hz), theta (4)(5)(6)(7)(8), alpha (8)(9)(10)(11)(12), beta (13)(14)(15)(16)(17)(18)(19)(20)(21)(22)(23)(24)(25)(26)(27)(28)(29)(30), and gamma (> 30 Hz) rhythms have been studied widely to detect the emotions in PD. In [18], the delta, theta, alpha, and beta power, and [19,20], the rhythmic study of power spectral density has been analyzed with analysis of variance (ANOVA) test.…”

Editorial: Explicit and Implicit Emotion Processing: Neural Basis, Perceptual and Cognitive Mechanisms

“…Indeed, deficits in the recognition of distinct emotions result from focal lesions in different brain areas, like the amygdala, anterior insula, basal ganglia and frontal cortex (Calder et al, 2001 ; Celeghin et al, 2017 ; Mattavelli et al, 2019 ). Likewise, patients with neurodegenerative diseases involving the insula and basal ganglia (such as Huntington's and Parkinson's diseases) showed poorer performance in emotion recognition, with possible dissociations related to the stages of processing (Novak et al, 2012 ; Mattavelli et al, 2020 ). In their review, Wagenbreth et al focus on patients with Parkinson and deep brain stimulation (DBS) in the subthalamic nucleus.…”