Neuronal Signatures of Negative and Positive Schemas towards the Self and Others in Patients with Early Stage Schizophrenia.

OBJECTIVE: The present study investigated the functional neuroanatomy underlying negative and positive schemas towards the self and others in patients with early stage schizophrenia spectrum disorders (SSDs) using a task-based fMRI procedure. METHODS: This study included 50 patients with SSDs and 52 controls. The schema-evoking task consisted of four active conditions and neutral condition. Differences in brain activation were compared between the two groups. Correlation analysis was performed between task-related activation and psychopathology. RESULTS: The SSD patients exhibited higher activity of the left middle and inferior frontal gyri under the negative-others minus neutral contrast as well as greater activation of the left superior and middle frontal gyri and right medial superior frontal gyrus under the positive- self minus neutral and positive-others minus neutral contrasts. Under the positive-others minus neutral contrast, negative correlation was observed between activity of the right inferior parietal gyrus and right angular and total score of the Positive and Negative Syndrome Scale (PANSS), whereas positive correlation between activity of the left middle cingulate gyrus and left/right precuneus and positive-others score of the Brief Core Schema Scales (BCSS). CONCLUSION: The present findings suggest that the frontal brain regions of SSD patients are more sensitive to negative and positive schemas towards the self and/or others compared to those of controls.

Altered amygdala-based functional connectivity in individuals with attenuated psychosis syndrome and first-episode schizophrenia.

Altered resting-state functional connectivity (FC) of the amygdala (AMY) has been demonstrated to be implicated in schizophrenia (SZ) and attenuated psychosis syndrome (APS). Specifically, no prior work has investigated FC in individuals with APS using subregions of the AMY as seed regions of interest. The present study examined AMY subregion-based FC in individuals with APS and first-episode schizophrenia (FES) and healthy controls (HCs). The resting state FC maps of the three AMY subregions were computed and compared across the three groups. Correlation analysis was also performed to examine the relationship between the Z-values of regions showing significant group differences and symptom rating scores. Individuals with APS showed hyperconnectivity between the right centromedial AMY (CMA) and left frontal pole cortex (FPC) and between the laterobasal AMY and brain stem and right inferior lateral occipital cortex compared to HCs. Patients with FES showed hyperconnectivity between the right superficial AMY and left occipital pole cortex and between the left CMA and left thalamus compared to the APS and HCs respectively. A negative relationship was observed between the connectivity strength of the CMA with the FPC and negative-others score of the Brief Core Schema Scales in the APS group. We observed different altered FC with subregions of the AMY in individuals with APS and FES compared to HCs. These results shed light on the pathogenetic mechanisms underpinning the development of APS and SZ.

Classification of schizophrenia and normal controls using 3D convolutional neural network and outcome visualization.

BACKGROUND: The recent deep learning-based studies on the classification of schizophrenia (SCZ) using MRI data rely on manual extraction of feature vector, which destroys the 3D structure of MRI data. In order to both identify SCZ and find relevant biomarkers, preserving the 3D structure in classification pipeline is critical. OBJECTIVES: The present study investigated whether the proposed 3D convolutional neural network (CNN) model produces higher accuracy compared to the support vector machine (SVM) and other 3D-CNN models in distinguishing individuals with SCZ spectrum disorders (SSDs) from healthy controls. We sought to construct saliency map using class saliency visualization (CSV) method. METHODS: Task-based fMRI data were obtained from 103 patients with SSDs and 41 normal controls. To preserve spatial locality, we used 3D activation map as input for the 3D convolutional autoencoder (3D-CAE)-based CNN model. Data on 62 patients with SSDs were used for unsupervised pretraining with 3D-CAE. Data on the remaining 41 patients and 41 normal controls were processed for training and testing with CNN. The performance of our model was analyzed and compared with SVM and other 3D-CNN models. The learned CNN model was visualized using CSV method. RESULTS: Using task-based fMRI data, our model achieved 84.15%∼84.43% classification accuracies, outperforming SVM and other 3D-CNN models. The inferior and middle temporal lobes were identified as key regions for classification. CONCLUSIONS: Our findings suggest that the proposed 3D-CAE-based CNN can classify patients with SSDs and controls with higher accuracy compared to other models. Visualization of salient regions provides important clinical information.