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

Training volitional control of the theory of mind network with real-time fMRI neurofeedback

Is there a way improve our ability to understand the minds of others? Towards addressing this question, here, we conducted a single-arm, proof-of-concept study to evaluate whether real-time fMRI neurofeedback (rtfMRI-NF) from the temporo-parietal junction (TPJ) leads to volitional control of the neural network subserving theory of mind (ToM; the process by which we attribute and reason about the mental states of others). As additional aims, we evaluated the strategies used to self-regulate the network and whether volitional control of the ToM network was moderated by participant characteristics and associated with improved performance on behavioral measures. Sixteen participants underwent fMRI while completing a task designed to individually-localize the TPJ, and then three separate rtfMRI-NF scans during which they completed multiple runs of a training task while receiving intermittent, activation-based feedback from the TPJ, and one run of a transfer task in which no neurofeedback was provided. Region-of-interest analyses demonstrated volitional control in most regions during the training tasks and during the transfer task, although the effects were smaller in magnitude and not observed in one of the neurofeedback targets for the transfer task. Text analysis demonstrated that volitional control was most strongly associated with thinking about prior social experiences when up-regulating the neural signal. Analysis of behavioral performance and brain-behavior associations largely did not reveal behavior changes except for a positive association between volitional control in RTPJ and changes in performance on one ToM task. Exploratory analysis suggested neurofeedback-related learning occurred, although some degree of volitional control appeared to be conferred with the initial self-regulation strategy provided to participants (i.e., without the neurofeedback signal). Critical study limitations include the lack of a control group and pre-rtfMRI transfer scan, which prevents a more direct assessment of neurofeedback-induced volitional control, and a small sample size, which may have led to an overestimate and/or unreliable estimate of study effects. Nonetheless, together, this study demonstrates the feasibility of training volitional control of a social cognitive brain network, which may have important clinical applications. Given the study's limitations, findings from this study should be replicated with more robust experimental designs.

Resting-state functional connectivity correlates of antipsychotic treatment in unmedicated schizophrenia

BACKGROUND

Antipsychotics may modulate the resting state functional connectivity(rsFC) to improve clinical symptoms in schizophrenia(Sz). Existing literature has potential confounders like past medication effects and evaluating preselected regions/networks. We aimed to evaluate connectivity pattern changes with antipsychotics in unmedicated Sz using Multivariate pattern analysis(MVPA), a data-driven technique for whole-brain connectome analysis.

METHODS

Forty-seven unmedicated patients with Sz(DSM-IV-TR) underwent clinical evaluation and neuroimaging at baseline and after 3-months of antipsychotic treatment. Resting-state functional MRI was analysed using group-MVPA to derive 5-components. The brain region with significant connectivity pattern changes with antipsychotics was identified, and post-hoc seed-to-voxel analysis was performed to identify connectivity changes and their association with symptom changes.

RESULTS

Connectome-MVPA analysis revealed the connectivity pattern of a cluster localised to left anterior cingulate and paracingulate gyri (ACC/PCG) (peak coordinates:x = -04,y = +30,z = +26;k = 12;cluster-p_FWE=0.002) to differ significantly after antipsychotics. Specifically, its connections with clusters of precuneus/posterior cingulate cortex(PCC) and left inferior temporal gyrus(ITG) correlated with improvement in positive and negative symptoms scores, respectively.

CONCLUSION

ACC/PCG, a hub of the default mode network, seems to mediate the antipsychotic effects in unmedicated Sz. Evaluating causality models with data from randomised controlled design using the MVPA approach would further enhance our understanding of therapeutic connectomics in Sz.

Social cognition in youth with a first-degree relative with schizophrenia: A systematic scoping review

Social-cognitive deficits are present in individuals at familial high-risk (FHR) for schizophrenia and may play a role in the onset of the illness. No literature review has examined the social-cognitive profiles of youth at FHR who are within the peak window of risk for developing schizophrenia, which could provide insight on the endophenotypic role of social cognition. This systematic scoping review (1) summarizes the evidence on social-cognitive deficits in youth at FHR, (2) explores brain correlates, and (3) describes social-cognitive deficits and prodromal symptom associations. We searched PsycInfo and PubMed for studies investigating social cognition in FHR youth aged 35 or younger and included 19 studies (FHR=639; controls=689). Studies report that youth at FHR have difficulty recognizing negative emotions, particularly fear. Youth at FHR also have difficulty performing complex theory of mind tasks. Abnormality in corticolimbic and temporoparietal regions are observed in youth at FHR during social-cognitive tasks, but results are inconsistent. Finally, there is evidence for negative associations between prodromal symptoms and performance on emotion regulation and theory of mind tasks, but the research is scarce. This review highlights the need for studies on youth at FHR using longitudinal designs and extensive social-cognitive, brain imaging and clinical measures.

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

In this paper, the algorithm of the deep convolutional neural network is used to conduct in-depth research and analysis of sports health big data, and an intelligent analysis system is designed for the practical process. A convolutional neural network is one of the most popular methods of deep learning today. The convolutional neural network has the feature of local perception, which allows a complete image to be divided into several small parts, by learning the characteristic features of each local part and then merging the local information at the high level to get the full representation information. In this paper, we first apply a convolutional neural network for four classifications of brainwave data and analyze the accuracy and recall of the model. The model is then further optimized to improve its accuracy and is compared with other models to confirm its effectiveness. A demonstration platform of emotional fatigue detection with multimodal data feature fusion was established to realize data acquisition, emotional fatigue detection, and emotion feedback functions. The emotional fatigue detection platform was tested to verify that the proposed model can be used for time-series data feature learning. According to the platform requirement analysis and detailed functional design, the development of each functional module of the platform was completed and system testing was conducted. The big data platform constructed in this study can meet the basic needs of health monitoring for data analysis, which is conducive to the formation of a good situation of orderly and effective interaction among multiple subjects, thus improving the information service level of health monitoring and promoting comprehensive health development.

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

Persecutory ideations are self-referential delusions of being the target of malevolence despite a lack of evidence. Wisner et al. (2021) found that reduced connectivity between the left frontoparietal (lFP) network and parts of the orbitofrontal cortex (OFC) correlated with increased persecutory behaviors among psychotic patients performing in an economic social decision-making task that can measure the anticipation of a partner’s spiteful behavior. If this pattern could be observed in the resting state, it would suggest a functional-structural prior predisposing individuals to persecutory ideation. Forty-four patients in the early course of a psychotic disorder provided data for resting-state functional connectivity magnetic resonance imaging across nine brain networks that included the FP network and a similar OFC region. As predicted, we found a significant and negative correlation between the lFP–OFC at rest and the level of suspicious mistrust on the decision-making task using a within-group correlational design. Additionally, self-reported persecutory ideation correlated significantly with the connectivity between the right frontoparietal (rFP) network and the OFC. We extended the previous finding of reduced connectivity between the lFP network and the OFC in psychosis patients to the resting state, and observed a possible hemispheric difference, such that greater rFP–OFC connectivity predicted elevated self-reported persecutory ideation, suggesting potential differences between the lFP and rFP roles in persecutory social interactions.