Neuronal dysfunction in individuals at early stage of schizophrenia, A resting-state fMRI study

Social intelligence mediates the protective role of resting-state brain activity in the social cognition network against social anxiety

Background

Social intelligence refers to an important psychosocial skill set encompassing an array of abilities, including effective self-expression, understanding of social contexts, and acting wisely in social interactions. While there is ample evidence of its importance in various mental health outcomes, particularly social anxiety, little is known on the brain correlates underlying social intelligence and how it can mitigate social anxiety.

Objective

This research aims to investigate the functional neural markers of social intelligence and their relations to social anxiety.

Methods

Data of resting-state functional magnetic resonance imaging and behavioral measures were collected from 231 normal students aged 16 to 20 years (48% male). Whole-brain voxel-wise correlation analysis was conducted to detect the functional brain clusters related to social intelligence. Correlation and mediation analyses explored the potential role of social intelligence in the linkage of resting-state brain activities to social anxiety.

Results

Social intelligence was correlated with neural activities (assessed as the fractional amplitude of low-frequency fluctuations, fALFF) among two key brain clusters in the social cognition networks: negatively correlated in left superior frontal gyrus (SFG) and positively correlated in right middle temporal gyrus. Further, the left SFG fALFF was positively correlated with social anxiety; brain-personality-symptom analysis revealed that this relationship was mediated by social intelligence.

Conclusion

These results indicate that resting-state activities in the social cognition networks might influence a person's social anxiety via social intelligence: lower left SFG activity → higher social intelligence → lower social anxiety. These may have implication for developing neurobehavioral interventions to mitigate social anxiety.

Functional magnetic resonance imaging in schizophrenia: current evidence, methodological advances, limitations and future directions

Functional neuroimaging emerged with great promise and has provided fundamental insights into the neurobiology of schizophrenia. However, it has faced challenges and criticisms, most notably a lack of clinical translation. This paper provides a comprehensive review and critical summary of the literature on functional neuroimaging, in particular functional magnetic resonance imaging (fMRI), in schizophrenia. We begin by reviewing research on fMRI biomarkers in schizophrenia and the clinical high risk phase through a historical lens, moving from case-control regional brain activation to global connectivity and advanced analytical approaches, and more recent machine learning algorithms to identify predictive neuroimaging features. Findings from fMRI studies of negative symptoms as well as of neurocognitive and social cognitive deficits are then reviewed. Functional neural markers of these symptoms and deficits may represent promising treatment targets in schizophrenia. Next, we summarize fMRI research related to antipsychotic medication, psychotherapy and psychosocial interventions, and neurostimulation, including treatment response and resistance, therapeutic mechanisms, and treatment targeting. We also review the utility of fMRI and data-driven approaches to dissect the heterogeneity of schizophrenia, moving beyond case-control comparisons, as well as methodological considerations and advances, including consortia and precision fMRI. Lastly, limitations and future directions of research in the field are discussed. Our comprehensive review suggests that, in order for fMRI to be clinically useful in the care of patients with schizophrenia, research should address potentially actionable clinical decisions that are routine in schizophrenia treatment, such as which antipsychotic should be prescribed or whether a given patient is likely to have persistent functional impairment. The potential clinical utility of fMRI is influenced by and must be weighed against cost and accessibility factors. Future evaluations of the utility of fMRI in prognostic and treatment response studies may consider including a health economics analysis.

A neuromarker for deficit syndrome in schizophrenia from a combination of structural and functional magnetic resonance imaging

AIM

Deficit schizophrenia (DS), defined by primary and enduring negative symptoms, has been proposed as a promising homogeneous subtype of schizophrenia. It has been demonstrated that unimodal neuroimaging characteristics of DS were different from non-deficit schizophrenia (NDS), however, whether multimodal-based neuroimaging features could identify deficit syndrome remains to be determined.

METHODS

Functional and structural multimodal magnetic resonance imaging of DS, NDS and healthy controls were scanned. Voxel-based features of gray matter volume, fractional amplitude of low-frequency fluctuations, and regional homogeneity were extracted. The support vector machine classification models were constructed using these features separately and jointly. The most discriminative features were defined as the first 10% of features with the greatest weights. Moreover, relevance vector regression was applied to explore the predictive values of these top-weighted features in predicting negative symptoms.

RESULTS

The multimodal classifier achieved a higher accuracy (75.48%) compared with the single modal model in distinguishing DS from NDS. The most predictive brain regions were mainly located in the default mode and visual networks, exhibiting differences between functional and structural features. Further, the identified discriminative features significantly predicted scores of diminished expressivity factor in DS but not NDS.

CONCLUSIONS

The present study demonstrated that local properties of brain regions extracted from multimodal imaging data could distinguish DS from NDS with a machine learning-based approach and confirmed the relationship between distinctive features and the negative symptoms subdomain. These findings may improve the identification of potential neuroimaging signatures and improve the clinical assessment of the deficit syndrome.

Revisiting Functional Dysconnectivity: a Review of Three Model Frameworks in Schizophrenia

PURPOSE OF REVIEW

Over the last decade, evidence suggests that a combination of behavioral and neuroimaging findings can help illuminate changes in functional dysconnectivity in schizophrenia. We review the recent connectivity literature considering several vital models, considering connectivity findings, and relationships with clinical symptoms. We reviewed resting state fMRI studies from 2017 to 2023. We summarized the role of two sets of brain networks (cerebello-thalamo-cortical (CTCC) and the triple network set) across three hypothesized models of schizophrenia etiology (neurodevelopmental, vulnerability-stress, and neurotransmitter hypotheses).

RECENT FINDINGS

The neurotransmitter and neurodevelopmental models best explained CTCC-subcortical dysfunction, which was consistently connected to symptom severity and motor symptoms. Triple network dysconnectivity was linked to deficits in executive functioning, and the salience network (SN)-default mode network dysconnectivity was tied to disordered thought and attentional deficits. This paper links behavioral symptoms of schizophrenia (symptom severity, motor, executive functioning, and attentional deficits) to various hypothesized mechanisms.