Cognition sociale dans la schizophrénie et les troubles du spectre de l’autisme : points de convergences et différences fonctionnelles

Identification of genetic features that are associated with amplitude of low-frequency fluctuation changes in schizophrenia using omics analysis

Genetic risk for schizophrenia is thought to trigger variation in clinical features of schizophrenia, but biological processes associated with neuronal activity in brain regions remain elusive. In this study, gene expression features were mapped to various sub-regions of the brain by integrating low-frequency amplitude features and gene expression data from the schizophrenia brain and using gene co-expression network analysis of the Allen Transcriptome Atlas of the human brain from six donors to identify genetic features of brain regions and important associations with neuronal features. The results indicate that changes in the dynamic amplitude of low-frequency fluctuation (dALFF) are mainly associated with transcriptome signature factors such as cortical layer synthesis, immune response, and expanded membrane transport. Further modular disease enrichment analysis revealed that the same set of signature genes associated with dALFF levels was enriched for multiple neurological biological processes. Finally, genetic profiling of individual modules identified multiple core genes closely related to schizophrenia, also potentially associated with neuronal activity. Thus, this paper explores genetic features of brain regions in the schizophrenia closely related to low-frequency amplitude ratio levels based on imaging genetics, which suggests structural endophenotypes associated with schizophrenia.

Eye-movement analysis on facial expression for identifying children and adults with neurodevelopmental disorders

Experienced psychiatrists identify people with autism spectrum disorder (ASD) and schizophrenia (Sz) through interviews based on diagnostic criteria, their responses, and various neuropsychological tests. To improve the clinical diagnosis of neurodevelopmental disorders such as ASD and Sz, the discovery of disorder-specific biomarkers and behavioral indicators with sufficient sensitivity is important. In recent years, studies have been conducted using machine learning to make more accurate predictions. Among various indicators, eye movement, which can be easily obtained, has attracted much attention and various studies have been conducted for ASD and Sz. Eye movement specificity during facial expression recognition has been studied extensively in the past, but modeling taking into account differences in specificity among facial expressions has not been conducted. In this paper, we propose a method to detect ASD or Sz from eye movement during the Facial Emotion Identification Test (FEIT) while considering differences in eye movement due to the facial expressions presented. We also confirm that weighting using the differences improves classification accuracy. Our data set sample consisted of 15 adults with ASD and Sz, 16 controls, and 15 children with ASD and 17 controls. Random forest was used to weight each test and classify the participants as control, ASD, or Sz. The most successful approach used heat maps and convolutional neural networks (CNN) for eye retention. This method classified Sz in adults with 64.5% accuracy, ASD in adults with up to 71.0% accuracy, and ASD in children with 66.7% accuracy. Classifying of ASD result was significantly different (p<.05) by the binomial test with chance rate. The results show a 10% and 16.7% improvement in accuracy, respectively, compared to a model that does not take facial expressions into account. In ASD, this indicates that modeling is effective, which weights the output of each image.

Fine structure analysis of perineuronal nets in the ketamine model of schizophrenia

Perineuronal nets (PNNs) represent a highly condensed specialized form of brain extracellular matrix (ECM) enwrapping mostly parvalbumin-positive interneurons in the brain in a mesh-like fashion. PNNs not only regulate the onset and completion of the critical period during postnatal brain development, control cell excitability, and synaptic transmission but are also implicated in several brain disorders including schizophrenia. Holes in the perineuronal nets, harboring the synaptic contacts, along with hole-surrounding ECM barrier can be viewed as PNN compartmentalization units that might determine the properties of synapses and heterosynaptic communication. In this study, we developed a novel open-source script for Fiji (ImageJ) to semi-automatically quantify structural alterations of PNNs such as the number of PNN units, area, mean intensity of PNN marker expression in 2D and 3D, shape parameters of PNN units in the ketamine-treated Sprague-Dawley rat model of schizophrenia using high-resolution confocal microscopic images. We discovered that the mean intensity of ECM within PNN units is inversely correlated with the area and the perimeter of the PNN holes. The intensity, size, and shape of PNN units proved to be three major principal factors to describe their variability. Ketamine-treated rats had more numerous but smaller and less circular PNN units than control rats. These parameters allowed to correctly classify individual PNNs as derived from control or ketamine-treated groups with ≈85% reliability. Thus, the proposed multidimensional analysis of PNN units provided a robust and comprehensive morphometric fingerprinting of fine ECM structure abnormalities in the experimental model of schizophrenia.