By the end of 2022, an estimated 108.4 million individuals worldwide experienced forced displacement. Identifying modifiable factors associated with the mental illness of refugees is crucial for promoting successful integration and developing effective health policies. This study aims to examine the associations between the changes in the diversity of social participation and psychological distress among refugees throughout the resettlement process, specifically focusing on gender differences.

Utilizing data from three waves of a longitudinal, nationally representative cohort study conducted in Australia, this study involved 2399 refugees interviewed during Wave 1, 1894 individuals interviewed during Wave 3 and 1881 respondents during Wave 5. At each wave, we assessed psychological distress and 10 types of social participation across 3 distinct dimensions, including social activities, employment and education. The primary analysis employed mixed linear models and time-varying Cox models. Gender-stratified analyses and sensitivity analyses were performed.

Refugees engaging in one type or two or more types of social participation, compared with those not engaging in any, consistently had lower psychological distress scores (β = −0.62 [95% confidence interval (CI), −1.07 to −0.17] for one type of social participation; β = −0.57 [95% CI, −1.04 to −0.10] for two or more types of social participation) and a reduced risk of experiencing psychological distress (hazard ratio [HR] = 0.81 [95% CI, 0.65–0.99] for one type of social participation; HR = 0.77 [95% CI, 0.61–0.97] for two or more types of social participation) during the resettlement period. When stratifying the results by gender, these associations in the adjusted models only remained significant in male refugees. Moreover, three specific types of social participation, namely sporting activities, leisure activities and current employment status, were most prominently associated with a reduced risk of psychological distress.

The findings of this cohort study suggest that social participation was consistently associated with reduced risks of psychological distress among male refugees during resettlement. These findings highlight the significance of promoting meaningful social participation and interaction may be an effective strategy to improve the mental health of refugees and facilitate their successful integration into society, especially among male refugees.

Microstates of an electroencephalogram (EEG) are canonical voltage topographies that remain quasi-stable for 90 ms, serving as the foundational elements of brain dynamics. Different changes in EEG microstates can be observed in psychiatric disorders like schizophrenia (SCZ), major depressive disorder (MDD), and bipolar disorder (BD). However, the similarities and disparatenesses in whole-brain dynamics on a subsecond timescale among individuals diagnosed with SCZ, BD, and MDD are unclear.

This study included 1112 participants (380 individuals diagnosed with SCZ, 330 with BD, 212 with MDD, and 190 demographically matched healthy controls [HCs]). We assembled resting-state EEG data and completed a microstate analysis of all participants using a cross-sectional design.

Our research indicates that SCZ, BD, and MDD exhibit distinct patterns of transition among the four EEG microstate states (A, B, C, and D). The analysis of transition probabilities showed a higher frequency of switching from microstates A to B and from B to A in each patient group compared to the HC group, and less frequent transitions from microstates A to C and from C to A in the SCZ and MDD groups compared to the HC group. And the probability of the microstate switching from C to D and D to C in the SCZ group significantly increased compared to those in the patient and HC groups.

Our findings provide crucial insights into the abnormalities involved in distributing neural assets and enabling proper transitions between different microstates in patients with major psychiatric disorders.

Although dopaminergic disturbances are well-known in schizophrenia, the understanding of dopamine-related brain dynamics remains limited. This study investigates the dynamic coactivation patterns (CAPs) associated with the substantia nigra (SN), a key dopaminergic nucleus, in first-episode treatment-naïve patients with schizophrenia (FES).

Resting-state fMRI data were collected from 84 FES and 94 healthy controls (HCs). Frame-wise clustering was implemented to generate CAPs related to SN activation or deactivation. Connectome features of each CAP were derived using an edge-centric method. The occurrence for each CAP and the balance ratio for antagonistic CAPs were calculated and compared between two groups, and correlations between temporal dynamic metrics and symptom burdens were explored.

Functional reconfigurations in CAPs exhibited significant differences between the activation and deactivation states of SN. During SN activation, FES more frequently recruited a CAP characterized by activated default network, language network, control network, and the caudate, compared to HCs (F = 8.54, FDR-p = 0.030). Moreover, FES displayed a tilted balance towards a CAP featuring SN-coactivation with the control network, caudate, and thalamus, as opposed to its antagonistic CAP (F = 7.48, FDR-p = 0.030). During SN deactivation, FES exhibited increased recruitment of a CAP with activated visual and dorsal attention networks but decreased recruitment of its opposing CAP (F = 6.58, FDR-p = 0.034).

Our results suggest that neuroregulatory dysfunction in dopaminergic pathways involving SN potentially mediates aberrant time-varying functional reorganizations in schizophrenia. This finding enriches the dopamine hypothesis of schizophrenia from the perspective of brain dynamics.

The preschool children born very low birth weight(VLBW) still have executive functions(EFs) deficits even with normal early development(Ni, Huang, & Guo, 2011). Consequently, early intervention might be more important than expected. This study aims to investigate the follow-up outcome of the therapeutic effects of integrative neuropsychological training model(INTM) focused on EFs for school-age VLBW children with EFs deficits.

The VLBW children, recruited from the Regional Cohort Network for premature infants who were admitted to neonatal intensive care units, had normal scores in Bayley and Wechsler Intelligence systems before 6 years old. They also received follow-up neuropsychological assessment for EFs at 6 or 8-year-old. The deficits of EFs were defined from the result of Digit Span Subtest of WISC-IV, Knox's Cube Test(KCT), Tower of London(ToL), Wisconsin Card Sorting Test(WCST), and Comprehensive Nonverbal Attention Test Battery(CNAT). A total of 8 VLBW children with EFs deficits were recruited and received EFs training at 6 or 8-year-old. The INTM combined with Comprehensive Memory Training System(CMTS), Executive FUNction Training(EFT), and multi-ecological materials focused on enhancing the four aspects of EFs, including working memory, planning, cognitive flexibility, and inhibition ability. Then, they received follow-up neuropsychological assessment for EFs at 8 or 10-year-old.

The results showed that all children got benefits from 20 hours of INTM and most of the EF aspects remained improved at follow-up. A total of 8 VLBW children with an average of 3.4 aspects EFs deficits had an average of 1.4 aspects of EFs deficits left at the follow-up. More precisely, 5 of them had 1 aspects of EFs deficits and 3 of them had 2 aspects of EFs deficits.

This study revealed that such a short-term INTM had long-term effects in enhancing the EFs of those VLBW children who had normal early development but later grew into EFs deficits at school-age. Besides, their EFs are still improving even after two years of intervention. Further study on more subjects with longer follow-up might help VLBW children to achieve better neuropsychological function.

Previous studies had shown that very low birth weight(VLBW) preterm children with normal early development had poorer cognitive executive functions(CEFs) and emotional executive functions(EEFs) at preschool-aged. There were still about 73% of children with deficits of CEFs and 74% of them with deficits of EEFs at school-aged. (Ni et al., 2011; Chiang et al., 2019; Lee et al., 2022). Besides, former studies less discuss the core neuropsychological ability related to the EFs development. In this study, meta-attention was chosen as the core ability. This study applied longitudinal design aimed to discuss the predictive power of meta-attention at preschool-aged on the CEFs and EEFs at school-aged for VLBW preterm and typically developing children.

The VLBW group was referred by Premature Baby Foundation of Taiwan. These children have been followed up with Bayley Scales of Infant Development(BSID) II or III administered at the age of 12 months and 24 months and Wechsler Preschool and Primary Scale of Intelligence-Revised(WPPSI-R) at the age of 5-year-old. Children with visual impairment, auditory impairment, cerebral palsy developmental indices of BSID-II or III less than 70, or full-scale IQ of WPPSI-R less than 70 were excluded.

The typical group was recruited from the community and included 30 children whose development was typical. Both preterm and typically developing children completed the CEFs and EEFs test at 6 and 8. Four types of CEFs including 33 indicators were assessed: Meta-attention including 18 indicators through Comprehensive Nonverbal Attention Test Battery(CNAT), working memory including 2 indicators through Digit Span Subtest of Wechsler Intelligence Scale for Children-IV(WISC-IV) and Knox’s Cube Test(KCT), planning ability including 6 indicators through Tower of London(ToL), and cognitive flexibility including 7 indicators through Wisconsin Card Sorting Test(WCST).

Two types of EEFs including 5 indicators were assessed. We designed Emotional EF Ecological Assessment Battery for Children in Taiwan to assess EEFs, including 3 indicators of theory of mind and 2 indicators of emotion regulation. Data were analyzed with correlation analysis and independent sample t-test.

Meta-attention at 6 among the VLBW group significantly correlated with 73.3% of CEFs and 80% of EEFs indicators at 8 and had significant predictive power on working memory, planning ability, cognitive flexibility, emotion regulation, and theory of mind. (p<.05) When the significance level was 0.01, meta-attention still correlated with 33% of CEFs and 80% of EEFs indicators and can predict those CEFs and EEFs among the VLBW group. Meta-attention at 6 among the typical group significantly correlated with 26.7% of CEFs and 80% of EEFs indicators at 8 and had significant predictive power on working memory, planning ability, emotion regulation, and theory of mind. (p<.05)

When the significance level was 0.01, meta-attention only correlated with 80% of EEFs indicators and can’t predict any CEFs and EEFs among the typical group.

Meta-attention at 6 can predict CEFs and EEFs among VLBW preterm children at 8, while the effect didn’t be found among typically developing children. Thus, meta-attention can be served as a clinical cut-point for VLBW preterm children to find the deficits and intervene early.

Convergent evidence has suggested atypical relationships between brain structure and function in major psychiatric disorders, yet how the abnormal patterns coincide and/or differ across different disorders remains largely unknown. Here, we aim to investigate the common and/or unique dynamic structure–function coupling patterns across major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SZ).

We quantified the dynamic structure–function coupling in 452 patients with psychiatric disorders (MDD/BD/SZ = 166/168/118) and 205 unaffected controls at three distinct brain network levels, such as global, meso-, and local levels. We also correlated dynamic structure–function coupling with the topological features of functional networks to examine how the structure–function relationship facilitates brain information communication over time.

The dynamic structure–function coupling is preserved for the three disorders at the global network level. Similar abnormalities in the rich-club organization are found in two distinct functional configuration states at the meso-level and are associated with the disease severity of MDD, BD, and SZ. At the local level, shared and unique alterations are observed in the brain regions involving the visual, cognitive control, and default mode networks. In addition, the relationships between structure–function coupling and the topological features of functional networks are altered in a manner indicative of state specificity.

These findings suggest both transdiagnostic and illness-specific alterations in the dynamic structure–function relationship of large-scale brain networks across MDD, BD, and SZ, providing new insights and potential biomarkers into the neurodevelopmental basis underlying the behavioral and cognitive deficits observed in these disorders.

Inflammation plays a crucial role in the pathogenesis of major depressive disorder (MDD) and bipolar disorder (BD). This study aimed to examine whether the dysregulation of complement components contributes to brain structural defects in patients with mood disorders.

A total of 52 BD patients, 35 MDD patients, and 53 controls were recruited. The human complement immunology assay was used to measure the levels of complement factors. Whole brain-based analysis was performed to investigate differences in gray matter volume (GMV) and cortical thickness (CT) among the BD, MDD, and control groups, and relationships were explored between neuroanatomical differences and levels of complement components.

GMV in the medial orbital frontal cortex (mOFC) and middle cingulum was lower in both patient groups than in controls, while the CT of the left precentral gyrus and left superior frontal gyrus were affected differently in the two disorders. Concentrations of C1q, C4, factor B, factor H, and properdin were higher in both patient groups than in controls, while concentrations of C3, C4 and factor H were significantly higher in BD than in MDD. Concentrations of C1q, factor H, and properdin showed a significant negative correlation with GMV in the mOFC at the voxel-wise level.

BD and MDD are associated with shared and different alterations in levels of complement factors and structural impairment in the brain. Structural defects in mOFC may be associated with elevated levels of certain complement factors, providing insight into the shared neuro-inflammatory pathogenesis of mood disorders.

Treatment non-response and recurrence are the main sources of disease burden in major depressive disorder (MDD). However, little is known about its neurobiological mechanism concerning the brain network changes accompanying pharmacotherapy. The present study investigated the changes in the intrinsic brain networks during 6-month antidepressant treatment phase associated with the treatment response and recurrence in MDD.

Resting-state functional magnetic resonance imaging was acquired from untreated patients with MDD and healthy controls at baseline. The patients' depressive symptoms were monitored by using the Hamilton Rating Scale for Depression (HAMD). After 6 months of antidepressant treatment, patients were re-scanned and followed up every 6 months over 2 years. Traditional statistical analysis as well as machine learning approaches were conducted to investigate the longitudinal changes in macro-scale resting-state functional network connectivity (rsFNC) strength and micro-scale resting-state functional connectivity (rsFC) associated with long-term treatment outcome in MDD.

Repeated measures of the general linear model demonstrated a significant difference in the default mode network (DMN) rsFNC change before and after the 6-month antidepressant treatment between remitters and non-remitters. The difference in the rsFNC change over the 6-month antidepressant treatment between recurring and stable MDD was also specific to DMN. Machine learning analysis results revealed that only the DMN rsFC change successfully distinguished non-remitters from the remitters at 6 months and recurring from stable MDD during the 2-year follow-up.

Our findings demonstrated that the intrinsic DMN connectivity could be a unique and important target for treatment and recurrence prevention in MDD.

Previous analyses of grey and white matter volumes have reported that schizophrenia is associated with structural changes. Deep learning is a data-driven approach that can capture highly compact hierarchical non-linear relationships among high-dimensional features, and therefore can facilitate the development of clinical tools for making a more accurate and earlier diagnosis of schizophrenia.

To identify consistent grey matter abnormalities in patients with schizophrenia, 662 people with schizophrenia and 613 healthy controls were recruited from eight centres across China, and the data from these independent sites were used to validate deep-learning classifiers.

We used a prospective image-based meta-analysis of whole-brain voxel-based morphometry. We also automatically differentiated patients with schizophrenia from healthy controls using combined grey matter, white matter and cerebrospinal fluid volumetric features, incorporated a deep neural network approach on an individual basis, and tested the generalisability of the classification models using independent validation sites.

We found that statistically reliable schizophrenia-related grey matter abnormalities primarily occurred in regions that included the superior temporal gyrus extending to the temporal pole, insular cortex, orbital and middle frontal cortices, middle cingulum and thalamus. Evaluated using leave-one-site-out cross-validation, the performance of the classification of schizophrenia achieved by our findings from eight independent research sites were: accuracy, 77.19–85.74%; sensitivity, 75.31–89.29% and area under the receiver operating characteristic curve, 0.797–0.909.

These results suggest that, by using deep-learning techniques, multidimensional neuroanatomical changes in schizophrenia are capable of robustly discriminating patients with schizophrenia from healthy controls, findings which could facilitate clinical diagnosis and treatment in schizophrenia.