Facial emotion processing in schizophrenia : a non-specific neuropsychological deficit?

Original article can be found at : http://journals.cambridge.org/ Copyright Cambridge University PressBackground: Identification of facial emotions has been found to be impaired in schizophrenia but there are uncertainties about the neuropsychological specificity of the finding. Method: Twenty-two patients with schizophrenia and 20 healthy controls were given tests requiring identification of facial emotion, judgement of the intensity of emotional expressions without identification, familiar face recognition and the Benton Facial Recognition Test (BFRT). The schizophrenia patients were selected to be relatively intellectually preserved. Results: The patients with schizophrenia showed no deficit in identifying facial emotion, although they were slower than the controls. They were, however, impaired on judging the intensity of emotional expression without identification. They showed impairment in recognizing familiar faces but not on the BFRT. Conclusions: When steps are taken to reduce the effects of general intellectual impairment, there is no deficit in identifying facial emotions in schizophrenia. There may, however, be a deficit in judging emotional intensity. The impairment found in naming familiar faces is consistent with other evidence of semantic memory impairment in the disorder.Peer reviewe

Brain functional abnormality in schizo-affective disorder: an fMRI study.

Background.Schizo-affective disorder has not been studied to any significant extent using functional imaging. The aim of this study was to examine patterns of brain activation and deactivation in patients meeting strict diagnostic criteria for the disorder. METHOD: Thirty-two patients meeting research diagnostic criteria (RDC) for schizo-affective disorder (16 schizomanic and 16 schizodepressive) and 32 matched healthy controls underwent functional magnetic resonance imaging (fMRI) during performance of the n-back task. Linear models were used to obtain maps of activations and deactivations in the groups. RESULTS: Controls showed activation in a network of frontal and other areas and also deactivation in the medial frontal cortex, the precuneus and the parietal cortex. Schizo-affective patients activated significantly less in prefrontal, parietal and temporal regions than the controls, and also showed failure of deactivation in the medial frontal cortex. When task performance was controlled for, the reduced activation in the dorsolateral prefrontal cortex (DLPFC) and the failure of deactivation of the medial frontal cortex remained significant. CONCLUSIONS: Schizo-affective disorder shows a similar pattern of reduced frontal activation to schizophrenia. The disorder is also characterized by failure of deactivation suggestive of default mode network dysfunction

A comparison of various MRI feature types for characterizing whole brain anatomical differences using linear pattern recognition methods

There is a widespread interest in applying pattern recognition methods to anatomical neuroimaging data, but so far, there has been relatively little investigation into how best to derive image features in order to make the most accurate predictions. In this work, a Gaussian Process machine learning approach was used for predicting age, gender and body mass index (BMI) of subjects in the IXI dataset, as well as age, gender and diagnostic status using the ABIDE and COBRE datasets. MRI data were segmented and aligned using SPM12, and a variety of feature representations were derived from this preprocessing. We compared classification and regression accuracy using the different sorts of features, and with various degrees of spatial smoothing. Results suggested that feature sets that did not ignore the implicit background tissue class, tended to result in better overall performance, whereas some of the most commonly used feature sets performed relatively poorly

Multimodal Brain Changes in First-Episode Mania: A Voxel-Based Morphometry, Functional Magnetic Resonance Imaging, and Connectivity Study

Background: Brain structural and functional changes in bipolar disorder (BD) are well-established findings, but it is uncertain whether these changes are already present in first episode mania (FEM). Methods: We compared 31 FEM subjects, with 31 healthy individuals matched for age, sex, and premorbid IQ. Whole-brain voxel-wise morphometry, functional magnetic resonance imaging during the n-back task, and a functional connectivity analysis were performed. Results: There were no volumetric differences between the 2 groups. During the 2-back task, FEM patients did not perform differently from controls and activated similar regions, but they showed less deactivation in the ventromedial prefrontal cortex (vmPFC), the anterior hub of the default mode network (DMN). They showed preserved functional connectivity between the vmPFC and other regions of the DMN, but increased connectivity with the superior frontal gyrus. Conclusions: The absence of volumetric changes in FEM patients suggests that these changes could be related to progression of the illness. On the other hand, the failure of deactivation of the anterior hub of the DMN is present from the onset of the illness and may represent a core pathophysiological feature of BD

Removing the effects of the site in brain imaging machine-learning Measurement and extendable benchmark

Multisite machine-learning neuroimaging studies, such as those conducted by the ENIGMA Consortium, need to remove the differences between sites to avoid effects of the site (EoS) that may prevent or fraudulently help the creation of prediction models, leading to impoverished or inflated prediction accuracy. Unfortunately, we have shown earlier that current Methods Aiming to Remove the EoS (MAREoS, e.g., ComBat) cannot remove complex EoS (e.g., including interactions between regions). And complex EoS may bias the accuracy. To overcome this hurdle, groups worldwide are developing novel MAREoS. However, we cannot assess their effectiveness because EoS may either inflate or shrink the accuracy, and MAREoS may both remove the EoS and degrade the data. In this work, we propose a strategy to measure the effectiveness of a MAREoS in removing different types of EoS. FOR MAREOS DEVELOPERS, we provide two multisite MRI datasets with only simple true effects (i.e., detectable by most machine-learning algorithms) and two with only simple EoS (i.e., removable by most MAREoS). First, they should use these datasets to fit machine-learning algorithms after applying the MAREoS. Second, they should use the formulas we provide to calculate the relative accuracy change associated with the MAREoS in each dataset and derive an EoS-removal effectiveness statistic. We also offer similar datasets and formulas for complex true effects and EoS that include first-order interactions. FOR MACHINE-LEARNING RESEARCHERS, we provide an extendable benchmark website to show: a) the types of EoS they should remove for each given machine-learning algorithm and b) the effectiveness of each MAREoS for removing each type of EoS. Relevantly, a MAREoS only able to remove the simple EoS may suffice for simple machine-learning algorithms, whereas more complex algorithms need a MAREoS that can remove more complex EoS. For instance, ComBat removes all simple EoS as needed for predictions based on simple lasso algorithms, but it leaves residual complex EoS that may bias the predictions based on standard support vector machine algorithms

Executive dysfunction and memory impairment in schizoaffective disorder: a comparison with bipolar disorder, schizophrenia and healthy controls.

BACKGROUND: Deficits in memory and executive performance are well-established features of bipolar disorder and schizophrenia. By contrast, data on cognitive impairment in schizoaffective disorder are scarce and the findings are conflicting. METHOD: We used the Wechsler Memory Scale (WMS-III) and the Behavioural Assessment of the Dysexecutive Syndrome (BADS) to test memory and executive function in 45 schizophrenic patients, 26 schizomanic patients and 51 manic bipolar patients in comparison to 65 healthy controls. The patients were tested when acutely ill. RESULTS: All three patient groups performed significantly more poorly than the controls on global measures of memory and executive functioning, but there were no differences among the patient groups. There were few differences in memory and executive function subtest scores within the patient groups. There were no differences in any test scores between manic patients with and without psychotic symptoms. CONCLUSIONS: Schizophrenic, schizomanic and manic patients show a broadly similar degree of executive and memory deficits in the acute phase of illness. Our results do not support a categorical differentiation across different psychotic categories with regard to neuropsychological deficits

Neutrophil count is associated with reduced gray matter and enlarged ventricles in first-episode psychosis

Although there is recent evidence that cells from the peripheral immune system can gain access to the central nervous system in certain conditions such as multiple sclerosis, their role has not been assessed in psychosis. Here, we aimed to explore whether blood cell count was associated with brain volume and/or clinical symptomatology. A total of 218 participants (137 first-episode psychosis patients [FEP] and 81 healthy controls [HC]) were included in the study. For each participant, a T1 structural image was acquired, from which brain tissue volumes were calculated. We found that, in FEP, neutrophil count was associated with reduced gray matter (GM) volume (ß = -0.117, P < .001) and increased cerebrospinal fluid volume (ß = 0.191, P = .007). No associations were observed in HC. GM reduction was generalized but more prominent in certain regions, notably the thalamus, the anterior insula, and the left Heschl''s gyrus, among many others. Neutrophil count was also associated with the total PANSS score (ß = 0.173, P = .038), including those items assessing hallucinations (ß = 0.182, P = .028) and avolition (ß = 0.197, P = .018). Several confounders, such as antipsychotic medication, body mass index, and smoking, were controlled for. Overall, the present study may represent the first indirect evidence of brain tissue loss associated with neutrophils in psychosis, and lends support to the hypothesis of a dysregulated immune system. Higher neutrophil count was also associated with more severe clinical symptomatology, which renders it a promising indicator of schizophrenia severity and could even give rise to new therapies

Brain structural changes in schizoaffective disorder compared to schizophrenia and bipolar disorder

Brain structural changes in schizoaffective disorder, and how far they resemble those seen in schizophrenia and bipolar disorder, have only been studied to a limited extent. Forty-five patients meeting - and criteria for schizoaffective disorder, groups of patients with 45 matched schizophrenia and bipolar disorder, and 45 matched healthy controls were examined using voxel-based morphometry (). Analyses comparing each patient group with the healthy control subjects found that the patients with schizoaffective disorder and the patients with schizophrenia showed widespread and overlapping areas of significant volume reduction, but the patients with bipolar disorder did not. A subsequent analysis compared the combined group of patients with the controls followed by extraction of clusters. In regions where the patients differed significantly from the controls, no significant differences in mean volume between patients with schizoaffective disorder and patients with schizophrenia in any of five regions of volume reduction were found, but mean volumes in the patients with bipolar disorder were significantly smaller in three of five. The findings provide evidence that, in terms of structural gray matter brain abnormality, schizoaffective disorder resembles schizophrenia more than bipolar disorder

A longitudinal study of gene expression in first-episode schizophrenia; exploring relapse mechanisms by co-expression analysis in peripheral blood

Little is known about the pathophysiological mechanisms of relapse in first-episode schizophrenia, which limits the study of potential biomarkers. To explore relapse mechanisms and identify potential biomarkers for relapse prediction, we analyzed gene expression in peripheral blood in a cohort of first-episode schizophrenia patients with less than 5 years of evolution who had been evaluated over a 3-year follow-up period. A total of 91 participants of the 2EPs project formed the sample for baseline gene expression analysis. Of these, 67 provided biological samples at follow-up (36 after 3 years and 31 at relapse). Gene expression was assessed using the Clariom S Human Array. Weighted gene co-expression network analysis was applied to identify modules of co-expressed genes and to analyze their preservation after 3 years of follow-up or at relapse. Among the 25 modules identified, one module was semi-conserved at relapse (DarkTurquoise) and was enriched with risk genes for schizophrenia, showing a dysregulation of the TCF4 gene network in the module. Two modules were semi-conserved both at relapse and after 3 years of follow-up (DarkRed and DarkGrey) and were found to be biologically associated with protein modification and protein location processes. Higher expression of DarkRed genes was associated with higher risk of suffering a relapse and early appearance of relapse (p = 0.045). Our findings suggest that a dysregulation of the TCF4 network could be an important step in the biological process that leads to relapse and suggest that genes related to the ubiquitin proteosome system could be potential biomarkers of relapse. © 2021, The Author(s)

Reply to: New Meta- and Mega-analyses of Magnetic Resonance Imaging Findings in Schizophrenia: Do They Really Increase Our Knowledge About the Nature of the Disease Process?

This work was supported by National Institute of Biomedical Imaging and Bioengineering Grant No. U54EB020403 (to the ENIGMA consortium)