The Associations between Visual Attention and Facial Expression Identification in Patients with Schizophrenia

Association between fractional amplitude of low-frequency fluctuation (fALFF) and facial emotion recognition ability in first-episode schizophrenia patients: a fMRI study

It was still unclear that the correlation between the resting-state intrinsic activity in brain regions and facial emotion recognition (FER) ability in patients with first-episode schizophrenia (FSZ). Our aim was to analyse the correlation between the fractional amplitude of low-frequency fluctuation (fALFF) and FER ability in FSZ patients. A total of 28 patients with FSZ and 33 healthy controls (HCs) completed visual search tasks for FER ability. Regions of interest (ROIs) related to facial emotion were obtained from a previous meta-analysis. Pearson correlation analysis was performed to understand the correlation between fALFF and FER ability. Our results indicated that the patients performed worse than the HCs in the accuracy performances of happy FER and fearful FER. The previous meta-analysis results showed that the brain regions related to FER included the bilateral amygdala (AMY)/hippocampus (HIP), right fusiform gyrus (FFG), and right supplementary motor area (SMA). Partial correlation analysis showed that the fALFF of the right FFG was associated with high-load fearful FER accuracy (r = - 0.60, p = 0.004). Our study indicated that FER ability is correlated with resting-state intrinsic activity in brain regions related to facial emotion, which may provide a reference for the study of FER deficiency in schizophrenia.

Prediction of Facial Emotion Recognition Ability in Patients With First-Episode Schizophrenia Using Amplitude of Low-Frequency Fluctuation-Based Support Vector Regression Model

OBJECTIVE

There were few studies that had attempted to predict facial emotion recognition (FER) ability at the individual level in schizophrenia patients. In this study, we developed a model for the prediction of FER ability in Chinese Han patients with the first-episode schizophrenia (FSZ).

MATERIALS AND METHODS

A total of 28 patients with FSZ and 33 healthy controls (HCs) were recruited. All subjects underwent resting-state fMRI (rs-fMRI). The amplitude of low-frequency fluctuation (ALFF) method was selected to analyze voxel-level spontaneous neuronal activity. The visual search experiments were selected to evaluate the FER, while the support vector regression (SVR) model was selected to develop a model based on individual rs-fMRI brain scan.

RESULTS

Group difference in FER ability showed statistical significance (P < 0.05). In FSZ patients, increased mALFF value were observed in the limbic lobe and frontal lobe, while decreased mALFF value were observed in the frontal lobe, parietal lobe, and occipital lobe (P < 0.05, AlphaSim correction). SVR analysis showed that abnormal spontaneous activity in multiple brain regions, especially in the right posterior cingulate, right precuneus, and left calcarine could effectively predict fearful FER accuracy (r = 0.64, P = 0.011) in patients.

CONCLUSION

Our study provides an evidence that abnormal spontaneous activity in specific brain regions may serve as a predictive biomarker for fearful FER ability in schizophrenia.

In (deficit) schizophrenia, a general cognitive decline partly mediates the effects of neuro-immune and neuro-oxidative toxicity on the symptomatome and quality of life

BACKGROUND

Schizophrenia and deficit schizophrenia are accompanied by neurocognitive impairments. The aim of this study was to examine whether a general factor underpins impairments in key Cambridge Neuropsychological Test Automated Battery (CANTAB) probes, verbal fluency test (VFT), world list memory (WLM), True Recall, and mini mental state examination (MMSE).

METHODS

We recruited 80 patients with schizophrenia and 40 healthy controls. All patients were assessed using CANTAB tests, namely paired-association learning, rapid visual information processing, spatial working memory, one touch stockings of Cambridge, intra/extradimensional set-shifting (IED), and emotional recognition test.

RESULTS

We found that a general factor, which is essentially unidimensional, underlies those CANTAB, VFT, WLM, True Recall, and MMSE scores. This common factor shows excellent psychometric properties and fits a reflective model and, therefore, reflects a general cognitive decline (G-CoDe) comprising deficits in semantic and episodic memory, recall, executive functions, strategy use, rule acquisition, visual sustained attention, attentional set-shifting, and emotional recognition. Partial least squares analysis showed that 40.5% of the variance in G-CoDe is explained by C-C motif ligand 11, IgA to tryptophan catabolites, and increased oxidative toxicity, and that G-CoDe explains 44.8% of the variance in a general factor extracted from psychosis, hostility, excitation, mannerism, negative symptoms, formal thought disorders, and psychomotor retardation, and 40.9% in quality-of-life scores. The G-CoDe is significantly greater in deficit than in nondeficit schizophrenia.

CONCLUSIONS

A common core shared by a multitude of neurocognitive impairments (G-CoDe) mediates the effects of neurotoxic pathways on the phenome of (deficit) schizophrenia.

RETRACTED: The effects of perceptual load in processing emotional facial expression in psychotic disorders

This article has been retracted: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been retracted at the request of the authors and Editor-in-Chief. The Authors have recently discovered an error in the database that affects the results and, consequently, the discussion, conclusions and abstract of this article on the effects of perceptual load and task-irrelevant facial expression during a target-letter discrimination task in psychotic disorders. The error was discovered by the Authors during the analysis of new data using the same experimental task, but collected from a distinct clinical sample, and involves a systematic error in the database: pictures of one actor were coded as depicting the same valence (angry, happy, neutral) during the programming of the experimental task, regardless of the real facial expression portraited by the picture in each trial. As a result, the information provided by the ‘Emotion’ variable in the database, created by the E-Prime software for each participant during the performance of the task, was not correct. Because the Authors used the ‘Emotion’ variable to perform the statistical analysis, the results of lower accuracy for individuals diagnosed with psychotic disorders in trials where the task-irrelevant stimuli are happy facial expressions - as revealed by a significant interaction between group and facial expression (p = .016) and between group, perceptual load and facial expression (p = .027) - are not correct, thus partially affecting the discussion, conclusions and abstract of the manuscript. Besides the ‘Emotion’ variable, the raw database contains the filename of the picture presented in each trial. Therefore, the Authors were able to correct the error and to re-analyse the data, and will proceed with the submission for publication of the corrected version of the manuscript. The error had no influence on the design and procedure of the experimental task, as the order of the pictures was fully randomized per participant. The Authors apologize for the inconvenience caused by these mistakes and agree with the retraction. The Authors advise all researchers to pay special attention when scripting/coding the stimuli to be used in the experimental tasks, and to confirm all scripting/coding with independent researchers, whenever possible, to prevent mistakes (Strand, 2021). The Journal apologizes that these errors were not detected during peer review.

Emotion Recognition Ability Test Using JACFEE Photos: A Validity/Reliability Study of a War Veterans' Sample and Their Offspring

Emotion recognition is very important for social interaction. Several mental disorders influence facial emotion recognition. War veterans and their offspring are subject to an increased risk of developing psychopathology. Emotion recognition is an important aspect that needs to be addressed in this population. To our knowledge, no test exists that is validated for use with war veterans and their offspring. The current study aimed to validate the JACFEE photo set to study facial emotion recognition in war veterans and their offspring. The JACFEE photo set was presented to 135 participants, comprised of 62 male war veterans and 73 war veterans’ offspring. The participants identified the facial emotion presented from amongst the possible seven emotions that were tested for: anger, contempt, disgust, fear, happiness, sadness, and surprise. A loglinear model was used to evaluate whether the agreement between the intended and the chosen emotions was higher than the expected. Overall agreement between chosen and intended emotions was 76.3% (Cohen kappa = 0.72). The agreement ranged from 63% (sadness expressions) to 91% (happiness expressions). The reliability by emotion ranged from 0.617 to 0.843 and the overall JACFEE photo set Cronbach alpha was 0.911. The offspring showed higher agreement when compared with the veterans (RR: 41.52 vs 12.12, p < 0.001), which confirms the construct validity of the test. The JACFEE set of photos showed good validity and reliability indices, which makes it an adequate instrument for researching emotion recognition ability in the study sample of war veterans and their respective offspring.

Schizophrenia and Depression Co-Morbidity: What We have Learned from Animal Models

Patients with schizophrenia are at an increased risk for the development of depression. Overlap in the symptoms and genetic risk factors between the two disorders suggests a common etiological mechanism may underlie the presentation of comorbid depression in schizophrenia. Understanding these shared mechanisms will be important in informing the development of new treatments. Rodent models are powerful tools for understanding gene function as it relates to behavior. Examining rodent models relevant to both schizophrenia and depression reveals a number of common mechanisms. Current models which demonstrate endophenotypes of both schizophrenia and depression are reviewed here, including models of CUB and SUSHI multiple domains 1, PDZ and LIM domain 5, glutamate Delta 1 receptor, diabetic db/db mice, neuropeptide Y, disrupted in schizophrenia 1, and its interacting partners, reelin, maternal immune activation, and social isolation. Neurotransmission, brain connectivity, the immune system, the environment, and metabolism emerge as potential common mechanisms linking these models and potentially explaining comorbid depression in schizophrenia.

Disrupted-In-Schizophrenia-1 (DISC1) interactome and mental disorders: impact of mouse models

Disrupted-In-Schizophrenia-1 (DISC1) has captured much attention because it predisposes individuals to a wide range of mental illnesses. Notably, a number of genes encoding proteins interacting with DISC1 are also considered to be relevant risk factors of mental disorders. We reasoned that the understanding of DISC1-associated mental disorders in the context of network principles will help to address fundamental properties of DISC1 as a disease gene. Systematic integration of behavioural phenotypes of genetic mouse lines carrying perturbation in DISC1 interacting proteins would contribute to a better resolution of neurobiological mechanisms of mental disorders associated with the impaired DISC1 interactome and lead to a development of network medicine. This review also makes specific recommendations of how to assess DISC1 associated mental disorders in mouse models and discuss future directions.