The representation of mental state information in schizophrenia and first-degree relatives: a multivariate pattern analysis of fMRI data

Abstract

Individuals with a schizophrenia-spectrum disorder (SSD) and those at familial high-risk (FHR) for SSDs experience social difficulties that are related to neural abnormalities in the network of brain regions recruited during theory of mind (ToM). Prior work with these groups has focused almost exclusively on characterizing involvement of these regions in ToM. Here, we examine the representational content of these regions using multivariate pattern analysis. We analyzed two previously collected datasets of SSD, FHR, and control participants who, while undergoing fMRI, completed the false-belief task in which they read stories describing beliefs or physical representations (e.g., photographs). Univariate and multivariate analyses were performed in regions-of-interest to evaluate group differences in task-based activation and representational content, respectively. Compared to non-SSD, SSD showed reduced decoding accuracy for the category of mental states in right temporo-parietal junction (RTPJ)—which was related to false-belief accuracy—and dorsal medial prefrontal cortex (DMPFC), and reduced involvement of DMPFC for mental state understanding. FHR showed no differences in decoding accuracy or involvement compared to non-FHR. Given prior studies of disrupted neural involvement in FHR, and the lack of decoding differences observed here, the onset of illness may involve processes that corrupt how mental state information is represented.

[Retracted] Multidimensional State Data Reduction and Evaluation of College Students’ Mental Health Based on SVM

“…In a study on student suicide risk prediction, Kumari et al used keywords extracted from text data published by microblog users for data analysis and used MLP models to achieve a predictive assessment of suicide risk based on microblog text data [8]. However, one of the obvious drawbacks of these studies is that they only focus on users who have social network accounts, such as Sina Weibo, and not every student uses Sina Weibo; in fact, according to the survey, the average number of active users of Sina Weibo is only 4 to 5 [9]. ere are no studies that show a psychological difference between Weibo users and non-Twitter users.…”

Resting-State Networks Associated with Behavioral and Self-Reported Measures of Persecutory Ideation in Psychosis

“…The significant network found in our study extended from the OFC into the dmPFC, a region associated with social processes [35]. Dodell-Feder et al [36] found that psychotic patients showed reduced recruitment of the dmPFC during mental state interpretation, while Vargas et al [37] found that dmPFC efficiency in the mentalizing network was compromised in the clinically at-risk sample for psychosis. Persecutory ideation may be the result of the breakdown of the cognitive control system on social processing and outcome prediction.…”

Intelligent Analysis of Exercise Health Big Data Based on Deep Convolutional Neural Network

“…e serial operation merges all local features to form a new global representation [17]. e core idea of the pairwise additive operation, on the other hand, is to give more information to the feature map without adding additional parameters to the network.…”

“…Since the network does not really learn the spatial distribution of human joints but only fits the training set with a huge number of parameters, the final model cannot be applied to the actual scene. The serial operation merges all local features to form a new global representation [ 17 ]. The core idea of the pairwise additive operation, on the other hand, is to give more information to the feature map without adding additional parameters to the network.…”