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Journal of Psychiatry & Neuroscience : JPN logoLink to Journal of Psychiatry & Neuroscience : JPN
. 2023 Nov 7;48(6):E421–E430. doi: 10.1503/jpn.230049

Neural mechanism underlying the beneficial effect of Theory of Mind psychotherapy on early-onset schizophrenia: a randomized controlled trial

Siyu Liu 1,*, Hui Zhong 1,*, Yinfeng Qian 1, Huanhuan Cai 1, Yan-Bin Jia 1,✉, Jiajia Zhu 1,✉
PMCID: PMC10635708  PMID: 37935475

Abstract

Background:

Psychosocial interventions have emerged as an important component of a comprehensive therapeutic approach in early-onset schizophrenia, typically representing a more severe form of the disorder. Despite the feasibility and efficacy of Theory of Mind (ToM) psychotherapy for schizophrenia, relatively little is known regarding the neural mechanism underlying its effect on early-onset schizophrenia.

Methods:

We performed a randomized, active controlled trial in patients with early-onset schizophrenia, who were randomly allocated into either an intervention (ToM psychotherapy) or an active control (health education) group. Diffusion tensor imaging data were collected to construct brain structural networks, with both global and regional topological properties measured using graph theory.

Results:

We enrolled 28 patients with early-onset schizophrenia in our study. After 5 weeks of treatment, both the intervention and active control groups showed significant improvement in psychotic symptoms, yet the improvement was greater in the intervention group. Importantly, in contrast with no brain structural network change after treatment in the active control group, the intervention group showed increased nodal centrality of the left insula that was associated with psychotic symptom improvement.

Limitations:

We did not collect important information concerning the participants’ cognitive abilities, particularly ToM performance.

Conclusion:

These findings suggest a potential neural mechanism by which ToM psychotherapy exerts a beneficial effect on early-onset schizophrenia via strengthening the coordination capacity of the insula in brain structural networks, which may provide a clinically translatable biomarker for monitoring or predicting responses to ToM psychotherapy.

Clinical trial registration: NCT05577338; ClinicalTrials.gov

Introduction

Schizophrenia is a chronic mental disorder expressed as a complex behavioural syndrome with a heterogeneous combination of psychotic symptoms (e.g., delusions, hallucinations and disorganization) and cognitive dysfunctions, which may be attributable to genetic and/or environmental disruption of brain development.1,2 Early-onset schizophrenia, defined as onset of schizophrenia before the age of 18 years, occurs in a substantial proportion of patients with schizophrenia. 3–5 Despite evidence for a similar profile of clinical manifestations and neurobiological abnormalities between early- and adult-onset schizophrenia, extensive research has shown that those with early onset have more severe premorbid deficits, higher levels of psychotic symptoms, worse cognitive abilities, poorer outcomes and greater genetic predisposition than those with adult onset,3,5–18 jointly supporting a plausible hypothesis that early-onset schizophrenia represents a more severe form of the disorder. As such, studying early-onset schizophrenia might provide important insights into the neurodevelopmental origins of the disorder and the complexity by which genetic and environmental factors interact to modulate disease phenotype.

Cognitive impairments are a prominent and debilitating feature of early-onset schizophrenia that predicts illness chronicity and contributes to poor functional outcomes.13,19 Given that pharmacological treatment alone has yielded limited clinical benefits in improving cognitive dysfunctions in patients with early-onset schizophrenia,20 there is an urgent need for effective therapeutic interventions for cognitive deficits in this population.20,21 Under such circumstances, psychosocial interventions (e.g., cognitive remediation) have emerged as an important component of a comprehensive therapeutic approach in early-onset schizophrenia.22,23 It is now increasingly recognized that cognitive remediation therapy for schizophrenia is a behavioural training–based intervention that aims to improve cognition and functional outcome with the goal of durability and generalization. 24 An expanding body of evidence indicates that cognitive remediation therapy benefits patients with early-onset schizophrenia.25 Theory of Mind (ToM) refers to the ability to consider various viewpoints in the process of attributing mental states to others and plays a central role in regulating social interactions.26 It is well documented that patients with schizophrenia show impairments in ToM,27 which influence real-world social functioning and are strongly associated with community outcomes.28,29 Targeted ToM interventions are specifically designed to improve patients’ ability to attribute mental states to others. Prior data have established the feasibility and efficacy of cognitive remediation targeting ToM functions for schizophrenia.26,30–34 However, relatively little is known regarding the neural mechanism underlying the effect of ToM psychotherapy on early-onset schizophrenia.

The human brain is a complex, integrated system of highly interconnected regions via white matter fibres, typically referred to as brain structural networks. Modern neuroimaging techniques, and diffusion MRI in particular, have made it feasible to reconstruct comprehensive maps of white matter structural connections in vivo.35,36 Leveraging diffusion MRI, a large number of studies have found structural connectivity damage in early- and adult-onset schizophrenia, characterized by alterations in multiple diffusion measures (e.g., fractional anisotropy [FA]) reflecting white matter microstructural integrity.37–46 In parallel, advances in network science and graph theory have improved our ability to study the topological organization of brain structural networks that exists above and beyond lower-order structural connectivity. 47–49 In this framework, there is strong empirical evidence for structural network topological disruptions in schizophrenia, manifested as changes in global and regional network properties.50–57 Prior work focusing exclusively on early-onset schizophrenia has also reported abnormal structural network topology in this condition.58 Collectively, these findings suggest that brain structural network measures derived from a combination of diffusion MRI and graph theory may serve as useful imaging biomarkers for early-onset schizophrenia and its treatment.

In this study, we performed a randomized, active controlled trial to investigate the neural mechanism underlying the effect of ToM psychotherapy on patients with early-onset schizophrenia, by integrating diffusion tensor imaging (DTI) data with graph theoretical analysis. In light of the aforementioned evidence from the literature, we hypothesized that ToM psychotherapy would lead to alterations in brain structural networks through which ToM psychotherapy exerts its beneficial effect on patients with early-onset schizophrenia.

Methods

Participants

This randomized controlled trial was registered with ClinicalTrials. gov (NCT05577338). We recruited right-handed adolescent patients with early-onset schizophrenia from Anhui Mental Health Center. Written informed consent was obtained from all participants, and the study was approved by the ethics committee of The First Affiliated Hospital of Anhui Medical University (PJ2022–10–37). Diagnosis of schizophrenia was determined by the consensus of 2 psychiatrists using the MINI-International Neuro-psychiatric Interview in accordance with the International Classification of Diseases (ICD-10) criteria. Right handedness was determined using the Edinburgh handedness inventory. 59 The other inclusion criteria were age 13–18 years; a capacity for understanding, judging and expressing; and an ability to complete the experiment independently. The exclusion criteria were present or any history of neurologic or major physical diseases, history of head injury with loss of consciousness, history of substance abuse, history of electroconvulsive therapy, mental retardation and MRI contraindications. For all patients, the Positive and Negative Syndrome Scale (PANSS)60 was used to assess the severity of psychotic symptoms.

Study design

Following successful completion of screening, half the patients were randomly assigned to the intervention group and half to the active control group, controlling for equal distribution of female and male participants between the groups. Patients in the intervention group underwent semi-structured, multidomain, tailored group psychotherapy for 5 weeks (a 2-hour session twice a week) using an appropriately adapted ToM intervention strategy.30 In brief, the ToM intervention was conducted by a well-trained psychiatrist on groups of 10 members using comic strips and cartoons depicting human social interactions. Patients were instructed to note the relevant details, to decipher meaningful information (i.e., place, time, emotions, characters’ actions and physical features), to read the verbal part of the comic strips and to identify their literal meaning. They were then asked to interpret hidden meaning using all the information collected and to hypothesize interpretations of scenes on the basis of expressed emotions, relationships between characters, implicit motivations and mental states. The active control group received group health education about the disease (e.g., epidemiology, diagnosis, clinical features, treatment), the duration of which was identical to that of psychotherapy. Both groups received their regular antipsychotic medication as prescribed by the attending psychiatrists during this study. All patients completed 2 study visits: baseline (before psychotherapy or health education) and follow-up (after 5 weeks of psychotherapy or health education). Clinical assessment and MRI examination occurred at both baseline and follow-up.

Image acquisition

The MRI data were collected on a 3 T MR system (Discovery MR750w, General Electric) with a 24-channel head coil. During scanning, tight but comfortable foam and earplugs were used to minimize head movement and scanner noise. All participants were instructed to relax, keep their eyes closed but not fall asleep, think of nothing in particular and move as little as possible. Structural images were obtained using a high-resolution 3-dimensional T1-weighted brain volume (BRAVO) sequence with the following parameters: repetition time (TR) 8.5 ms; echo time (TE) 3.2 ms; inversion time (TI) 450 ms; flip angle 12°; field of view (FOV) 256 mm × 256 mm; matrix size 256 × 256; slice thickness 1 mm, no gap; voxel size 1 mm × 1 mm × 1 mm; 188 sagittal slices; and acquisition time 296 s. The DTI data were acquired using a spin-echo single-shot echo planar imaging (SE-SS-EPI) sequence with the following parameters: TR 10 000 ms; TE 74 ms; flip angle 90°; FOV 256 mm × 256 mm; matrix 128 × 128; slice thickness 3 mm, no gap; 50 axial slices; 64 diffusion gradient directions (b = 1000 s/mm2 plus 5 b = 0 reference images); and acquisition time 700 s. Routine T2-weighted images were also obtained to exclude any organic brain abnormality. All images were visually inspected to ensure that only images without visible artifacts were included in subsequent analyses.

DTI data processing

The software packages FMRIB Software Library (FSL; http://www.fmrib.ox.ac.uk/fsl),61 Diffusion Toolkit (DTK; http://trackvis.org/dtk) and Pipeline for Analyzing braiN Diffusion imAges (PANDA; http://www.nitrc.org/projects/panda)62 were used for DTI data processing. Eddy currents in the gradient coils induce stretches and shears in the diffusion-weighted images. These distortions are different for different gradient directions. Eddy current correction implemented in FSL was adopted to correct for these distortions and simple head motions by registering the diffusion-weighted images to a reference volume (i.e., the first b0 image) using affine transformations. Correspondingly, the diffusion gradient direction of each diffusion-weighted image was rotated according to the resultant affine transformation information.63 Then, brain tissues were extracted using the FSL Brain Extraction Tool (http://www.fmrib.ox.ac.uk/fsl/bet2). Next, the 3-dimensional maps of the diffusion tensor and FA were calculated using the DTIFIT toolbox. Finally, a deterministic streamline tracking algorithm, Fiber Assignment by Continuous Tracking (FACT), was performed to obtain the whole-brain fibre tractography.64–66 The fibre tracking procedure started from the deep white matter regions and ended at a voxel with a turning angle greater than 45° or with an FA less than 0.2.

Brain structural network construction

Because brain structural networks were constructed in native diffusion space, the automated anatomic labelling (AAL) template67 in the Montreal Neurological Institute (MNI) space was initially transformed to individual native diffusion space. Briefly, individual structural images were first coregistered to their b0 images using a linear transformation. Then, the coregistered structural images were normalized to the MNI space using a nonlinear transformation. The derived deformation parameters were inverted and used to transform the AAL template from the MNI space to individual native diffusion space. Nodes and edges are 2 basic elements of a brain network. Here, 90 cortical and subcortical regions within the AAL template were defined as nodes. To define edges, we calculated the number of fibres (with end points located in both nodes during the fibre tracking) between any pairs of nodes, yielding a 90 × 90 fibre number (FN) matrix for each participant. To balance the sensitivity and specificity, a threshold of 3 fibres was applied to all FN matrices; that is, 2 nodes were considered connected if the FN between them was greater than or equal to 3.68,69 Finally, each FN matrix was thresholded and converted into a binary matrix.

Structural network analysis

Graph theoretical analysis was carried out on the resulting structural networks using GRETNA software (http://www.nitrc.org/projects/gretna).70 Both global and regional network properties were measured. For global property, we calculated 2 network efficiency measures: global efficiency (reflecting the capability of parallel information transfer over the network) and local efficiency (representing the fault tolerance of the network that indicates how well the information is communicated within the neighbours of a given node when this node is eliminated).71–73 For regional property, we focused primarily on betweenness centrality, a commonly used measure to describe the importance of individual nodes within a network.74 Betweenness centrality is defined as the fraction of all shortest paths in the network that pass through a given node. Brain regions with higher betweenness centrality are considered hubs that are assumed to play a pivotal role in global brain communication because of their central embedding in the overall network. The calculation procedure of these global and regional network measures is detailed in the previous literature.47

Statistical analysis

The statistical analyses of demographic and clinical data were conducted using the SPSS 23.0 software package (SPSS Inc.). For cross-sectional analyses of baseline data, we compared age, education, illness duration and psychotic symptoms (PANSS scores) between the intervention and active control groups using 2-sample t tests. The Pearson χ2 test was adopted to test group difference in gender. For longitudinal analyses of PANSS scores, we used 2-way mixed analyses of variance (ANOVA), with group as a between-subjects factor (intervention v. active control) and time as a within-subjects factor (baseline v. follow-up). We focused our analysis principally on group × time interactions, followed by post hoc paired t tests for comparing time points within each group separately. The significance level was set at p < 0.05.

In terms of neuroimaging data, we made use of the above-mentioned 2-way mixed ANOVA to examine group × time interactions on structural network measures, followed by post hoc paired t tests. For global and local efficiency, the significance threshold was set at p < 0.05. For betweenness centrality of 90 nodes, correction for multiple comparisons was performed using a false-positive correction (i.e., p < [1/90] = 0.011), which is not as conservative as a Bonferroni or false discovery rate correction and thus does not yield strong type I error control for exploratory analysis at a regional level of network organization.75 In addition, we calculated longitudinal changes (follow-up – baseline) in network measures and PANSS scores, followed by Pearson correlation analyses to evaluate their associations within each group.

Results

Demographic and clinical characteristics

We recruited 30 participants; 15 were assigned to the intervention group and 15 to the active control group. Two male participants in the active control group were lost to follow-up and were not included in the analysis, leaving a final sample of 28 patients (Figure 1). Demographic and clinical data of the patients at baseline are presented in Table 1. There were no significant differences in gender, age, education, illness duration or PANSS scores between the intervention and active control groups. Two-way mixed ANOVA showed significant group × time interaction effects on PANSS total (F = 7.93, p = 0.010), positive (F = 4.45, p = 0.047) and general (F = 7.17, p = 0.014) scores. Post hoc analyses revealed decreased PANSS total, positive and general scores from baseline to follow-up in both groups, but a greater decrease in the intervention group (Figure 2 and Table 2). For PANSS negative score, we found a significant decrease from baseline to follow-up in both groups, but a marginally significant group × time interaction indicating a trend toward a greater decrease in the intervention group (F = 3.46, p = 0.076; Figure 2 and Table 2).

Figure 1.

Figure 1

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Flow of participants through the study. MRI = magnetic resonance imaging.

Table 1.

Demographic and clinical characteristics of the patients with early-onset schizophrenia at baseline

Characteristic Group, mean ± SD* Statistical test p value
Intervention, n = 15 Active control, n = 13
Gender, F/M 9/6 9/4 χ2 = 0.258 0.705
Age, yr 16.20 ± 1.32 16.54 ± 1.39 t = −0.657 0.517
Education, yr 10.00 ± 1.41 10.23 ± 1.59 t = −0.407 0.688
Illness duration, mo 20.13 ± 15.91 20.85 ± 18.28 t = −0.110 0.913
PANSS score
 Total 58.07 ± 13.88 53.69 ± 12.57 t = 0.868 0.393
 Positive 13.47 ± 4.81 12.69 ± 3.64 t = 0.474 0.639
 Negative 12.53 ± 3.04 12.54 ± 4.79 t = −0.003 0.997
 General 28.27 ± 7.12 25.15 ± 5.73 t = 1.261 0.218
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F = female; M = male; PANSS = Positive and Negative Syndrome Scale; SD = standard deviation.

*

Unless indicated otherwise.

Figure 2.

Figure 2

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Longitudinal changes in psychotic symptoms. PANSS = Positive and Negative Syndrome Scale.

Table 2.

Longitudinal changes in psychotic symptoms of patients with early-onset schizophrenia

PANSS score Intervention group Active control group
Change,* mean ± SD paired t test p value Change,* mean ± SD paired t test p value
Total −15.20 ± 8.86 −6.646 1.10 × 10−5 −6.69 ± 7.62 −3.167 0.008
Positive −4.33 ± 3.44 −4.884 2.42 × 10−4 −2.23 ± 2.20 −3.649 0.003
Negative −2.93 ± 1.16 −9.769 1.25 × 10−7 −1.77 ± 2.86 −2.229 0.046
General −7.13 ± 4.85 −5.693 5.60 × 10−5 −2.85 ± 3.29 −3.122 0.009
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PANSS = Positive and Negative Syndrome Scale.

*

Change = follow-up – baseline.

Brain structural network changes

With respect to global network property, 2-way mixed ANOVA showed no significant group × time interactions for global (F = 1.98, p = 0.171) or local (F = 0.97, p = 0.333) efficiency. As to regional network property, we found a significant group × time interaction effect on betweenness centrality of the left insula (F = 15.22, p = 6.04 × 10−4) that survived a false-positive correction. Post hoc analyses showed increased betweenness centrality of the left insula from baseline to follow-up in the intervention group (t = 4.58, p = 4.28 × 10−4), but no change in the active control group (t = −0.63, p = 0.540) (Figure 3). The group × time interaction results for other brain regions are shown in Appendix 1, Table S1, available at https://www.jpn.ca/lookup/doi/10.1503/jpn.230049/tab-related-content. For completeness, we conducted the same analyses using other regional network measures (degree centrality and nodal efficiency), but found no significant results (Appendix 1, Tables S2 and S3). Moreover, we observed a significant PANSS total score change × group interaction effect on betweenness centrality change of the left insula (F = 19.57, p = 0.002), which was driven by a significant negative correlation between PANSS total score change and betweenness centrality change of the left insula in the intervention group (r = −0.529, p = 0.043) (Figure 4) and a nonsignificant correlation in the active control group (r = 0.41, p = 0.163).

Figure 3.

Figure 3

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Longitudinal change in betweenness centrality of the left insula. Betweenness centrality is defined as the fraction of all shortest paths in the network that pass through a given node. The red path passing through the left insula (red node) represents the shortest path between the 2 green nodes.

Figure 4.

Figure 4

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Scatter plot of the negative correlation between PANSS total score change and betweenness centrality change of the left insula in the intervention group. Change = follow-up – baseline. PANSS = Positive and Negative Syndrome Scale.

Discussion

By applying graph theoretical analysis to DTI data, we performed, to our knowledge, the first randomized, active controlled trial to examine the neural mechanism underlying the effect of ToM psychotherapy on patients with early-onset schizophrenia. After 5 weeks of treatment, both the intervention (ToM psychotherapy + antipsychotics) and active control (health education + antipsychotics) groups showed significant improvement in psychotic symptoms; yet, the improvement was greater in the intervention group. In contrast with the active control group, which showed no change in brain structural network after treatment, the intervention group showed increased nodal centrality of the left insula that was associated with improvement of psychotic symptoms. These findings suggest that ToM psychotherapy may contribute to alterations in brain structural networks, exerting a beneficial effect on patients with early-onset schizophrenia.

It is well established that psychotherapy holds promise as an effective treatment in adults with schizophrenia.76,77 There is also recent evidence in favour of its potential usefulness in adolescents with early-onset schizophrenia.22,23 For example, a pioneering study suggested that cognitive behavioural therapy adapted to the needs of adolescents with early-onset schizophrenia was a safe and tolerable treatment approach for improving negative symptoms, global functioning and quality of life.78 Puig and colleagues reported that cognitive remediation therapy induced significant, reliable, medium-to-large cognitive improvements and significant functional gains in adolescents with early-onset schizophrenia.25 Complementing the pilot work, our data showed that ToM psychotherapy could help alleviate psychotic symptoms, especially positive symptoms and general psychopathology, in patients with early-onset schizophrenia. Our findings, taken with those from the earlier reports, indicate that a combination of psychotherapy and antipsychotics may be a better intervention strategy for early-onset schizophrenia.

Extending the behavioural evidence, our neuroimaging analysis further showed that ToM psychotherapy led to increased nodal centrality of the left insula that was associated with improvement of psychotic symptoms in patients with early-onset schizophrenia. It is generally accepted that the insula acts as a key cortical hub engaged in a wide variety of functions ranging from lower-order sensorimotor processes to higher-order cognition and emotion,79–84 deficits of which have been frequently reported in schizophrenia.85–87 Moreover, numerous clinical neuroimaging studies have documented structural and functional abnormalities of the insula in schizophrenia including early-onset schizophrenia,87–98 highlighting its prominent role in the disease neuropathology. 87 From a large-scale brain organization perspective, the insula-anchored salience network has been suggested to play a central role in the psychopathology and cognitive dysfunction in schizophrenia,99 which is often referred to as a salience dysregulation disorder.100 In parallel, the involvement of the insula in ToM functions has been evident,101,102 implying its potential contribution to the neural effect of ToM psychotherapy. That said, increased nodal centrality of the insula may reflect its strengthened role in coordinating whole-brain structural networks, presumably in response to ToM intervention. Combined, these data invite us to speculate that ToM psychotherapy might induce intervention-dependent neuroplasticity in the insula, expressed as its elevated importance in global information integration, which may in turn give rise to improvement of psychotic symptoms in patients affected by early-onset schizophrenia. Nevertheless, given the lack of a healthy control group, it is not clear whether the nodal centrality increase represents normalization or compensation. Note that the lateralization of the effect observed in the left insula is consistent with previous findings showing a pattern of grey matter abnormalities in the left hemisphere in individuals with early-onset schizophrenia.103,104 Furthermore, a recent coordinate-based meta-analysis showed left-lateralized insular grey matter reduction in individuals with recently diagnosed schizophrenia, which appeared to be bilateral in those with chronic schizophrenia.105 From the perspective of core psychotic symptoms, the left-lateralized grey matter abnormality of the insula has been thought to contribute significantly to auditory hallucinations in individuals with schizophrenia.106–108

Limitations

Our study has a few limitations. First, while our small sample size is commonplace for within-subject neuroimaging study designs, it limits the statistical power and the generalizability of the findings. This is a well-known challenge in early-onset schizophrenia research, as larger samples are difficult to recruit because of the uncommon early onset of schizophrenia and the moderate compliance of patients. Second, the patients with early-onset schizophrenia were receiving their regular antipsychotic medication during the study. The confounding effect of antipsychotics on our results merits further investigation but was beyond the scope of this research. Third, the intervention duration of 5 weeks was relatively short and may have resulted in transient and subtle neuroimaging changes. Extending the duration of the intervention would help detect and estimate the stability and reliability of the treatment effects. Fourth, we did not collect important information concerning the participants’ cognitive abilities, particularly ToM, which is a key outcome to assess when investigating the impact of a ToM intervention. Further analysis of this information may help in the interpretation of our findings. Fifth, considering our small sample size, a lenient false-positive correction was used to adjust for multiple comparisons. Application of a more rigorous Bonferroni or false discovery rate correction to a large data set is warranted to validate our preliminary results. Sixth, our sample included more females than males, in contrast with a higher prevalence of schizophrenia among males. This may have led to gender bias, which should be addressed in future studies. Finally, neither the participants nor the individuals conducting the analysis were blinded to the study conditions, which could have introduced biases.

Conclusion

Our randomized, active controlled trial in patients with early-onset schizophrenia suggests a potential neural mechanism by which ToM psychotherapy exerts its antipsychotic efficacy via strengthening the coordination capacity of the insula in brain structural networks. More generally, our findings may provide a clinically translatable biomarker for monitoring or predicting responses to ToM psychotherapy and expose the insula as a promising anatomic target for novel interventions (e.g., transcranial magnetic stimulation) in patients with early-onset schizophrenia.

Supplementary Material

Appendix 1
JPN-230049-at-1.pdf (249.1KB, pdf)

Acknowledgement

The authors are grateful to study participants for volunteering their time.

Footnotes

Competing interests: None declared.

Contributors: Y.-B. Jia and J. Zhu designed the study. H. Zhong and H. Cai acquired the data, which S. Liu and Y. Qian analyzed. S. Liu wrote the article. All of the authors critically revised it for important intellectual content and gave final approval of the version to be published.

Funding: The study was supported by the National Natural Science Foundation of China (grant no. 82071905), the Outstanding Youth Support Project of Anhui Province Universities (grant no. gxyqZD2022026), the Scientific Research Key Project of Anhui Province Universities (grant no. 2022AH051135), the Scientific Research Foundation of Anhui Medical University (grant no. 2022xkj143) and the Anhui University Collaborative Innovation Project (grant no. GXXT-2021-065).

Data sharing: Data that support the findings of this study are publicly available in the study’s Open Science Framework repository (https://osf.io/j9fc2/).

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Appendix 1
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