Exploring facial emotion perception in schizophrenia using transcranial magnetic stimulation and spatial filtering

Within the framework of interpersonal and cognitive theories of depression, we investigated whether the perception of facial emotions was associated with subsequent relapse into depression. The 23 inpatients with major depression who remitted (65 admitted patients) were studied at admission (T0), at discharge (T1), and 6 months thereafter to assess relapse. They judged schematic faces with respect to the expression of positive and negative emotions. Six patients (26.1%) relapsed. High levels of perception of negative emotions in faces, either assessed at T0 or at T1, were associated with relapse. Moreover, subjects saw more negative emotions in depressed than in remitted state. Significant results were confined to ambiguous faces, i.e., faces expressing equal amounts of positive and negative emotions. Our data support the hypothesis that a bias toward the perception of others' facial emotions as negative is an enduring vulnerability factor to depression relapse and depressed mood amplifies this negative bias in perception.

Deficits in facial emotion perception have been linked to poorer functional outcome in schizophrenia. However, the relationship between abnormal emotion perception and functional outcome remains poorly understood. To better understand the nature of facial emotion perception deficits in schizophrenia, we used the Bubbles Facial Emotion Perception Task to identify differences in usage of visual facial information in schizophrenia patients (n = 20) and controls (n = 20), when differentiating between angry and neutral facial expressions. As hypothesized, schizophrenia patients required more facial information than controls to accurately differentiate between angry and neutral facial expressions, and they relied on different facial features and spatial frequencies to differentiate these facial expressions. Specifically, schizophrenia patients underutilized the eye regions, overutilized the nose and mouth regions, and virtually ignored information presented at the lowest levels of spatial frequency. In addition, a post hoc one-tailed t test revealed a positive relationship of moderate strength between the degree of divergence from "normal" visual facial information usage in the eye region and lower overall social functioning. These findings provide direct support for aberrant patterns of visual facial information usage in schizophrenia in differentiating between socially salient emotional states.

Objects we see in daily life are often occluded and rarely appear in full form. The present study investigates how people recognize emotions from partially occluded faces. Previous studies have shown that prolonged exposure to a complete emotional face biases the perception of emotion of subsequent faces, the facial expression aftereffect (FEA). However, less is known about the influences of an obscured emotional face to the perception of subsequent facial emotions. People with autism spectrum disorder (ASD) have been found to suffer from compromised ability to process facial emotions. Autistic traits typically exhibited in people with ASD are also demonstrated in neurotypical individuals (NT) to milder degrees, however, the association between autistic traits and the perception of facial emotion in NT is understudied. The present thesis aims to bridge the gap by

Magnocellular (M) deficit theory indicates that individuals with developmental dyslexia (DD) have low sensitivity to stimuli with high temporal frequencies (HTF) and low spatial frequencies (LSF). However, some studies found that temporal processing and spatial processing were correlated with different reading-related skills. Chinese is a logographic language, and visual skills are particularly important for reading in Chinese. It is necessary to investigate the temporal and spatial processing abilities in the M pathway of Chinese children with DD. Using electrophysiological recordings, the present study examined the mean amplitude and latency of P1 during a grating direction judgment task in 13 children with DD and 13 age-matched normal children. Dyslexic children showed a low amplitude and long latency of P1 in the HTF condition and LSF condition compared with age-matched children. In the HTF condition, the amplitude of P1 correlated with phonological awareness, and the latency of P1 correlated with reading fluency and rapid naming of digits. The amplitude of P1 in the LSF condition correlated with reading accuracy. This result suggested that Chinese children with DD had difficulties in both temporal and spatial processing in the M pathway. However, temporal processing and spatial processing played different roles in Chinese reading.

It is generally agreed that schizophrenia patients show a markedly reduced ability to perceive and express facial emotions. Previous studies have shown, however, that such deficits are emotion-specific in schizophrenia and not generalized. Three kinds of studies were examined: decoding studies dealing with schizophrenia patients' ability to perceive universally recognized facial expressions of emotions, encoding studies dealing with schizophrenia patients' ability to express certain facial emotions, and studies of subjective reactions of patients' sensitivity toward universally recognized facial expressions of emotions. A review of these studies shows that schizophrenia patients, despite a general impairment of perception or expression of facial emotions, are highly sensitive to certain negative emotions of fear and anger. These observations are discussed in the light of hemispheric theory, which accounts for a generalized performance deficit, and social-cognitive theory, which accounts for an emotion-specific deficit in schizophrenia.

Reduced white matter integrity and facial emotion perception in never-medicated patients with first-episode schizophrenia: A diffusion tensor imaging study

FC19-06 - Visual backward masking - an excellent endophenotype for Schizophrenia ?

Facial emotion recognition deficits in abstinent cannabis dependent patients

Deficits in facial emotion perception in adults with recent traumatic brain injury

Adolescent onset depression places a high burden on those who suffer from it, and is difficult to treat. An improved understanding of mechanisms underlying susceptibility to adolescent depression may be useful in early detection and as target in treatment. Facial emotion identification bias has been suggested as trait marker for depression, but results have been inconclusive. To explore whether facial emotion identification biases may be trait markers for depression, we tested whether the speed with which young adolescents identified happy, sad, angry and fearful facial emotions predicted the onset of depression during an eight-year follow-up period. We hypothesized that facial emotion identification speed predicts depression in a symptom-congruent way and differentially predicts symptoms of anhedonia and sadness. Data were collected as part of the TRacking Adolescents’ Individual Lives Survey (TRAILS), and involved 1840 adolescents who participated in a facial emotion identification test at age 11 and were subjected to the World Health Organization Composite International Diagnostic Interview (CIDI) at age 19. In a multi-emotion model, slow identification of happy facial emotions tentatively predicted onset of depressive disorder within the follow-up period. Slow identification of happy emotions and fast identification of sad emotions predicted symptoms of anhedonia, but not symptoms of sadness. Our results suggest that the relative speed of identification of happiness in relation to the identification of sadness is a better predictor of depression than the identification of either facial emotion alone. A possible mechanism underlying the predictive role of facial emotion identification may be a less reactive reward system.Electronic supplementary materialThe online version of this article (doi:10.1007/s00787-016-0846-1) contains supplementary material, which is available to authorized users.

ObjectiveDespite the fact that facial emotion recognition (FER) tasks using Western faces should be applied with caution to non-Western participants or patients, there are few psychometrically sound and validated FER tasks featuring Easterners' facial expressions for emotions. Thus, we aimed to develop and establish the psychometric properties of the Korean Facial Emotion Identification Task (K-FEIT) and the Korean Facial Emotion Discrimination Task (K-FEDT) for individuals with schizophrenia.MethodsThe K-FEIT and K-FEDT were administered to 42 Korean individuals with schizophrenia to evaluate their psychometric properties. To test the convergent and divergent validities, the Social Behavior Sequencing Task (SBST) and hinting task were administered as social-cognitive measures, and the Trail Making Test (TMT)-A and -B were administered as neurocognitive measures.ResultsAverage accuracy on the K-FEIT and K-FEDT were 63% and 74%, respectively, and internal consistencies of the K-FEIT and K-FEDT were 0.82 and 0.95, respectively. The K-FEIT and K-FEDT were significantly correlated with SBST and Hinting Task, but not with TMT-A and B.ConclusionFollowing replication studies in a larger sample, the K-FEIT and K-FEDT are expected to facilitate future studies targeting facial emotion recognition in schizophrenia in Korea. Limitations and directions for future research are discussed.

Recent research on forward masking and backward masking has led us to the hypothesis that combined forward and backward masking may involve at least two different types of interaction patterns. The previously documented interaction results in masking are greater than predicted by the simple summation of masking effects obtained with each masker alone (Lynn & Small, 1977; Patterson, 1971; Wilson & Carhart, 1971). Another possible type of interaction is based upon the finding that backward masking, but not forward masking, can be reduced by cues providing timing information (Puleo & Pastore, 1980). A forward masker appears to involve minimum temporal uncertainty (Pastore & Freda, 1980) and, therefore, should be able to act as such a source of timing information to reduce the contribution of a later occurring backward masker. Both types of interaction patterns were found, with the specific forward and backward masker parameters determining which pattern is observed.

Visual image contains broadband information and is processed by different neural channels that are tuned to different spatial frequencies. Here we investigated whether or not our ability in gender identification on emotional faces was influenced by this early visual processing. Four types of emotional (happy, anger, sad, and fear) faces were used, and all of the stimuli were processed by spatial frequency analysis. Spatial frequency content in the original faces was filtered by using a high-pass filter (cut-off frequency was 24 cycles/image) for the HSF stimuli, and a low-pass filter (cut-off frequency was 6 cycles/image) for the LSF stimuli. Participants needed to identify the gender of the faces. The results showed that the participants responded faster and had higher accuracy to LSF faces than to HSF ones. They also responded faster and had higher accuracy to male faces than to female faces. Further analysis revealed that the identification of an angry man and a happy woman had advantage among combinations of genders and emotions in LSF conditions. However, this advantage was not manifested in HSF conditions. We concluded that the identification of gender with different emotions may rely on the processing of low spatial frequency channels.

Deficits in emotion processing are prominent in schizophrenia, and flat affect is resistant to treatment and portends poor outcome. Investigation of the underlying neural circuitry can elucidate affective dysfunction. To examine the brain circuitry for facial emotion processing, dissecting response to task demands from effects of the appearance of facial expressions. A facial emotion identification task was presented during high-field (4-T) magnetic resonance imaging. Blood oxygenation level-dependent changes were contrasted for task compared with a scrambled face baseline (blocked analysis) and for the appearance of each of the following 4 target expressions compared with neutral faces (event related): happy, sad, anger, and fear. Participants from the Schizophrenia Research Center underwent a functional magnetic resonance imaging study at the University of Pennsylvania Medical Center. Patients with DSM-IV-defined schizophrenia (n = 16) and healthy controls (n = 17) were recruited from the community. The percentage of signal change for each contrast and performance and clinical symptom severity ratings. Patients showed reduced limbic activation compared with controls for the emotion identification task. However, event-related analysis revealed that whereas in controls greater amygdala activation was associated with correct identifications of threat-related (anger and fear) expressions, patients showed the opposite effect of greater limbic activation, portending misidentifications. Furthermore, greater amygdala activation to the presentation of fearful faces was highly correlated with greater severity of flat affect. Abnormal amygdala activation in schizophrenia in response to presentation of fearful faces is paradoxically associated with failure to recognize the emotion and with more severe flat affect. This finding suggests that flat affect in schizophrenia relates to overstimulation of the limbic system.

In this article, local monotonic pattern (LMP) paired with grey level co‐occurrence matrix (GLCM) methods are suggested to identify facial emotions with a high identification rate even when the face pictures are rotated. The proposed method extracts image features using the properties of the LMP algorithm and features extracted from the GLCM, which are then fed into the Support Vector Machine (SVM) process that reduces the dimensionality of the features vector and classifies the output into different facial expressions or emotions. The SVM performance rate is then compared to the K‐nearest neighbour approach (KNN) to see which method produces the best facial emotion identification and categorization. The study identified facial emotions in the images using advanced algorithms of GLCM and LMP models to be compared. As a result, the accuracy of SVM and KNN was utilized to determine the method's usefulness in classification using the application of MATLAB. A result of more than 93% was achieved using the SVM method compared with 89.4% using the KNN for the recognition process. The study also demonstrated that this approach would lead to more classification outcomes if the LMP and GLCM are combined with an edge‐based technique yielding a new method that is more efficient and more effective.