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.

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.

To identify risk genes whose expression are regulated by the reported risk variants and to explore the potential regulatory mechanism in schizophrenia (SCZ).

We systematically integrated three independent brain expression quantitative traits (eQTLs) (CommonMind, GTEx, and BrainSeq Phase 2, a total of 1039 individuals) and GWAS data (56 418 cases and 78 818 controls), with the use of transcriptome-wide association study (TWAS). Diffusion magnetic resonance imaging was utilized to quantify the integrity of white matter bundles and determine whether polygenic risk of novel genes linked to brain structure was present in patients with first-episode antipsychotic SCZ.

TWAS showed that eight risk genes (CORO7, DDAH2, DDHD2, ELAC2, GLT8D1, PCDHA8, THOC7, and TYW5) reached transcriptome-wide significance (TWS) level. These findings were confirmed by an independent integrative approach (i.e. Sherlock). We further conducted conditional analyses and identified the potential risk genes that driven the TWAS association signal in each locus. Gene expression analysis showed that several TWS genes (including CORO7, DDAH2, DDHD2, ELAC2, GLT8D1, THOC7 and TYW5) were dysregulated in the dorsolateral prefrontal cortex of SCZ cases compared with controls. TWS genes were mainly expressed on the surface of glutamatergic neurons, GABAergic neurons, and microglia. Finally, SCZ cases had a substantially greater TWS genes-based polygenic risk (PRS) compared to controls, and we showed that fractional anisotropy of the cingulum-hippocampus mediates the influence of TWS genes PRS on SCZ.

Our findings identified novel SCZ risk genes and highlighted the importance of the TWS genes in frontal-limbic dysfunctions in SCZ, indicating possible therapeutic targets.

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.

Whether borderline personality disorder (BPD) and bipolar disorder are the same or different disorders lacks consistency.

To detect whether grey matter volume (GMV) and grey matter density (GMD) alterations show any similarities or differences between BPD and bipolar disorder.

Web-based publication databases were searched to conduct a meta-analysis of all voxel-based studies that compared BPD or bipolar disorder with healthy controls. We included 13 BPD studies (395 patients with BPD and 415 healthy controls) and 47 bipolar disorder studies (2111 patients with bipolar disorder and 3261 healthy controls). Peak coordinates from clusters with significant group differences were extracted. Effect-size signed differential mapping meta-analysis was performed to analyse peak coordinates of clusters and thresholds (P < 0.005, uncorrected). Conjunction analyses identified regions in which disorders showed common patterns of volumetric alteration. Correlation analyses were also performed.

Patients with BPD showed decreased GMV and GMD in the bilateral medial prefrontal cortex network (mPFC), bilateral amygdala and right parahippocampal gyrus; patients with bipolar disorder showed decreased GMV and GMD in the bilateral medial orbital frontal cortex (mOFC), right insula and right thalamus, and increased GMV and GMD in the right putamen. Multi-modal analysis indicated smaller volumes in both disorders in clusters in the right medial orbital frontal cortex. Decreased bilateral mPFC in BPD was partly mediated by patient age. Increased GMV and GMD of the right putamen was positively correlated with Young Mania Rating Scale scores in bipolar disorder.

Our results show different patterns of GMV and GMD alteration and do not support the hypothesis that bipolar disorder and BPD are on the same affective spectrum.

None.

Previous studies have inferred a strong genetic component in schizophrenia. However, the genetic variants involved in the susceptibility to schizophrenia remain unclear.

To detect potential gene pathways and networks associated with schizophrenia, and to explore the relationship between common and rare variants in these pathways and abnormal white matter integrity in schizophrenia.

The analysis included 100 first-episode treatment-naïve patients with schizophrenia and 140 healthy controls. A network-based analysis was carried out on the data collected from the Psychiatric Genomics Consortium Phase I (PGC-I). Based on our genome-wide association study and whole-exome sequencing data-sets, we performed a gene-set analysis to detect associations between the combining effects of common and rare genetic variants and abnormal white matter integrity in schizophrenia.

Patients had significantly reduced functional anisotropy in the left and right anterior cingulate cortex, left and right precuneus and extra-nuclear (t = 4.61–5.10, PFDR < 0.01), compared with controls. Generated from co-expression network analysis of the PGC-1 summary statistics of schizophrenia, a subnetwork of 207 genes associated with schizophrenia was identified (P < 0.01), and 176 genes were co-expressed in four gene modules. Functional enrichment analysis for genes in each module revealed that the yellow module was enriched with highly co-expressed, innate immune response genes. Furthermore, rare variants of enriched genes in the yellow module were associated with reduced functional anisotropy in the left anterior cingulate cortex (P = 0.006; Padjusted = 0.024) in patients only.

The pathogenesis of schizophrenia may be substantially influenced by genes involved in the immune system, via both pathway and network.

None.