There’s large heterogeneity present in major depressive disorder (MDD) and controversial evidence on alterations of brain functional connectivity (FC), making it hard to elucidate the neurobiological basis of MDD. Subtyping is one promising solution to characterize this heterogeneity.

To identify neurophysiological subtypes of MDD based on FC derived from resting-state functional magnetic resonance imaging using large multisite data and investigate the differences in genetic mechanisms and neurotransmitter basis of FC alterations, and the differences of FC-related cognition between each subtype.

Consensus clustering of FC patterns was applied to a population of 829 MDD patients from REST-Meta-MDD database after data cleaning and image quality control. Gene transcriptomic data derived from Allen Human Brain Atlas and neurotransmitter receptor/transporter density data acquired by using neuromap toolbox were used to characterize the molecular mechanism underlying each FC-based subtype by identifying the gene set and neurotransmitters/transporters showing high spatial similarity with the profiles of FC alterations between each subtype and 770 healthy controls. The FC-related cognition in each subtype was also selected by lasso regression.

Two stable neurophysiological MDD subtypes were found and labeled as hypoconnectivity (n=527) and hyperconnectivity (n=299) characterized by the FC differences in each subtype relative to controls, respectively. The two subtypes did not differ in age, sex, and scores of Hamilton Depression/Anxiety Scale.

The genes related to FC alterations were enriched in ion transmembrane transport, synaptic transmission/organization, axon development, and regulation of neurotransmitter level for both subtypes, but specifically enriched in glial cell differentiation for hypoconnectivity subtype, while enriched in regulation of presynaptic membrane and regulation of neuron differentiation for hyperconnectivity subtype.

FC alterations were associated with the density of 5-HT2a receptor in both subtypes. For hyperconnectivity subtype, FC alterations were also correlated with the density of norepinephrine transporter, glutamate receptor, GABA receptor, 5-HT1b receptor, and cannabinoid receptor.

Both subtypes showed correlations between FC and categorization, motor inhibition, and localization. The FC in hypoconnectivity subtype correlated with response inhibition, selective attention, face recognition, sleep, empathy, expertise, uncertainty, and anticipation, while that was related to inference, speech perception, and reward anticipation in hyperconnectivity subtype.

Our findings suggested the presence of two neuroimaging subtypes of MDD characterized by hypo or hyper-connectivity. The two subtypes had both shared and distinct genetic mechanisms, neurotransmitter receptor/transporter profiles, and cognition types.

None Declared

Major depressive disorder (MDD) is characterized by both clinical symptoms and cognitive deficits. Prior studies have typically examined either symptoms or cognition correlated with brain measures, thus causing a notable paucity of stable brain markers that capture the full characteristics of MDD. Brain controllability derived from newly proposed brain model integrating both metabolism (energy cost) and dynamics from a control perspective has been considered as a sensitive biomarker for characterizing brain function. Thus, identifying such a biomarker of controllability related to both symptoms and cognition may provide a promising state monitor of MDD.

To assess the associations between two multi-dimensional clinical (symptoms and cognition) and brain controllability data of MDD in an integrative model.

Sparse canonical correlation analysis (sCCA) was used to investigate the association between brain controllability at a network level and both clinical symptoms and cognition in 99 first-episode medication-naïve patients with MDD. The potential mediation effect of cognition on relationship between controllability and symptoms was also tested.

Average controllability was significantly correlated with both symptoms and cognition (rmean=0.54, PBonferroni=0.03). Average controllability of dorsal attention network (DAN) (r=0.46) and visual network (r=0.29) had the highest correlation with both symptoms and cognition. Among clinical variables, depressed mood (r=-0.23) , suicide(r=-0.25), work and activities(r=-0.27), gastrointestinal symptoms (r=-0.25) were significantly negatively associated with average controllability, while cognitive flexibility (r=0.29) was most strongly positively correlated with average controllability. Additionally, cognitive flexibility fully mediated the association between average controllability of DAN and depressed mood (indirect effect=-0.11, 95% CI [-0.18, -0.04], P=0.001) in MDD.

Brain average controllability was correlated with both clinical symptoms and cognition in first-episode medication-naïve patients with MDD. The results suggest that average controllability of DAN and visual network reached high associations with clinical variates in MDD, thus these brain features may serve as stable biomarkers to control the brain functional states transitions to be relevant to cognitions deficits and clinical symptoms of MDD. Additionally, altered average controllability of DAN in patients could induce impairment of cognitive flexibility, and thus cause severe depressed mood, indicating that controllability of DAN may be a potential intervention target for alleviating depressed mood through improving cognitive flexibility in MDD.

None Declared

Apart from the psychiatric symptoms, cognitive deficits are also the core symptoms of schizophrenia. Brain network control theory provided information on the role of a specific brain region in the cognitive control process, helping understand the neural mechanism of cognitive impairment in schizophrenia.

To characterize the control properties of functional brain network in first-episode untreated patients with schizophrenia and the relationships between controllability and psychiatric symptoms, as well as exploring the predictive value of controllability in differentiating patients from healthy controls (HCs).

Average and modal controllability of brain networks were calculated and compared between 133 first-episode untreated patients with schizophrenia and 135 HCs. The associations between controllability and clinical symptoms were evaluated using sparse canonical correlation analysis. Support vector machine (SVM) and SVM-recursive feature elimination combined with the controllability were performed to establish the individual prediction model.

Compared to HCs, the patients with schizophrenia showed increased average controllability and decreased modal controllability in dorsal anterior cingulate cortex (dACC). Brain controllability predominantly in somatomotor, default mode, and visual networks was associated with the positive symptomatology of schizophrenia. The established model could identify patients with an accuracy of 0.68. Furthermore, the most discriminative features were located in dACC, medial prefrontal lobe, precuneus and superior temporal gyrus.

Altered controllability in dACC may play a critical role in the neuropathological mechanisms of cognitive deficit in schizophrenia, which could drive the brain function to different states to cope with varied cognitive tasks. As symptom-related biomarkers, controllability could be also beneficial to individual prediction in schizophrenia.

No significant relationships.

There is a growing consensus on brain networks that it is not immutable but rather a dynamic complex system for adapting environment. The neuroimaging research studying how brain regions work collaboratively with dynamic methods had demonstrated its effectiveness in revealing the neural mechanisms of schizophrenia.

To investigate altered dynamic brain functional topology in first-episode untreated schizophrenia patients (SZs) and establish classification models to find objective brain imaging biomarkers.

Resting-state-functional magnetic resonance data for SZs and matched healthy controls were obtained(Table1).

Power-264-template was used to extract nodes and sliding-window approach was carried out to establish functional connectivity matrices. Functional topology was assessed by eigenvector centrality(EC) and node efficiency and its time-fluctuating was evaluated with coefficient of variation(CV). Group differences of dynamic topology and correlation analysis between Positive and Negative Syndrome Scale(PANSS) scores and topology indices showing group differences, which also were used in establishing classification models, was examed.

The CV of node efficiency in angular and paracingulate gyrus was larger in SZs. There are 13 nodes assigned into several brain networks displaying altered CV of EC between groups(Figure1.A). Fluctuation of EC of the node in DMN, which was lower in SZs, showed negative correlation with PANSS total scores(Figure1.B). Dynamic functional topology of above nodes was used to train classification models and demonstrated 80% and 71% accuracy for support vector classification(SVC) and random forest(RF), respectively(Figure2).

Dynamic functional topology illustrated a capability in identifying SZs. Aberrated dynamics of DMN relevant to severity of patient’s symptoms could reveal the reason why it contributed to classification.

No significant relationships.

The extant findings have been of great heterogeneity due to partial volume effects in the investigation of cortical gray matter volume (GMV), high comorbidity with other psychiatric disorders, and concomitant therapy in the neuroimaging studies of social anxiety disorder (SAD).

To identity gray matter deficits in cortical and subcortical structures in non-comorbid never-treated patients, so as to explore the “pure” SAD-specific pathophysiology and neurobiology.

Thirty-two non-comorbid free-of-treatment patients with SAD and 32 demography-matched healthy controls were recruited to undergo high-resolution 3.0-Tesla T1-weighted MRI. Cortical thickness (CT) and subcortical GMV were estimated using FreeSurfer; then the whole-brain vertex-wise analysis was performed to compare group differences in CT. Besides, differences in subcortical GMV of priori selected regions-of-interest: amygdala, hippocampus, putamen, and pallidum were compared by an analysis of covariance with age, gender, and total subcortical GMV as covariates.

The SAD patients demonstrated significantly decreased CT near-symmetrically in the bilateral prefrontal cortex (Monte Carlo simulations of P < 0.05). Besides, smaller GMV in the left hippocampus and pallidum were also observed in the SAD cohort (two-sample t-test of P < 0.05).

For the first time, the current study investigated the structural alterations of CT and subcortical GMV in non-comorbid never-treated patients with SAD. Our findings provide preliminary evidences that structural deficits in cortical-striatal-limbic circuit may contribute to the psychopathological basis of SAD, and offer more detailed structural substrates for the involvement of such aberrant circuit in the imbalance between defective bottom-up response and top-down control to external stimuli in SAD.

No significant relationships.

One of the most perplexing and characteristic symptoms of the schizophrenia (SZ) patients is hallucination. The occurrence of hallucinations to be associated with altered activity in the auditory and visual cortex but is not well understood from the brain functional network dynamics in SZ.

To explore the brain abnormal basis of hallucinations in SZ with the dynamic functional connectivity (dFC).

Using magnetic resonance imaging for 83 SZ patients and 83 matched healthy controls and independent component analysis, 52 independent components (ICs) were identified as nodes and assigned into eight intrinsic connectivity networks (Figure 1A). Subsequently, we established dFC matrices and clustered them into four discrete states (Figure 1B) and three state transition metrics were obtained. To further explore the changes in the centrality of each component, eigenvector centrality (EC) was calculated and its time-varying was evaluated.

Compared to controls with FDR correction, we found that patients had more mean dwell times and fractional time in state 1 (P=0.0081 and P=0.0018), mainly with hypoconnectivity between auditory and visual network and other networks and hyperconnectivity between language and default-mode network (DMN). While, patients had less dwell times and fractional time in state 3 (P=0.0018 and P=0.0009), and decreased FC between visual network and executive control network (ECN) and increased FC between ECN and DMN than controls (Figure 2).

EC statistics showed that SZs displayed increased temporal dynamics in visual-related regions (Figure 3).

SZ was mainly manifested as altered dFC and temporal variability of nodal centrality in auditory and visual networks.

No significant relationships.

Though schizophrenia (SZ) and obsessive-compulsive disorder (OCD) are conceptualized as distinct clinical entities, they do have notable symptom overlap and a tight association. Graph-theoretical analysis of the brain connectome provides more indicators to describe the functional organization of the brain, which may help us understand the shared and disorder-specific neural basis of the two disorders.

To explore the static and dynamic topological organization of OCD and SZ as well as the relationship between topological metrics and clinical variables.

Resting state functional magnetic resonance imaging data of 31 OCD patients, 49 SZ patients, and 45 healthy controls (HC) were involved in this study (Table 1). Using independent component analysis to obtain independent components (ICs) (Figure 1), which were defined as nodes for static and dynamic topological analysis.

Static analysis showed the global efficiency of SZ was higher than HC. For nodal degree centrality, OCD exhibited decreased degree centrality in IC59 (located in visiual network) (P = 0.03) and increased degree centrality in IC38 (located in salience network) (P = 0.002) compared with HC. Dynamic analysis showed OCD exhibited decreased dynamics of degree centrality in IC38 (P = 0.003) compared with HC, which showed a negative correlation with clinical scores in OCD. While SZ showed decreased dynamics of degree centrality in IC76 (located in sensory motor network) compared with OCD (P=0.009), which showed a positive correlation with clinical scores in SZ (Figure 2).

These changes are suggestive of disorder-specific alternation of static and dynamic brain topological organization in OCD and SZ.

Obsessive-compulsive disorder (OCD) and schizophrenia (SZ) are both severe psychiatric disorders. Though these two disorders have distinct typical symptoms, there are partial polygenic overlap and comorbidity between the two disorders. However, few studies have explored the shared and disorder-specific brain function underlying the neural pathophysiology of the two disorders, especially in the aspect of dynamics.

To explore the abnormal characteristics of the dynamic functional connectivity (dFC) in OCD and SZ as well as the association between dFC metrics and symptom severity.

The resting state functional magnetic resonance imaging data of 31 patients with OCD, 49 patients with SZ, and 45 healthy controls were analyzed using independent component analysis to obtain independent components (ICs) and assigned them into eight brain networks (Figure 1), then used the sliding-window approach to generate dFC matrices. Using k-means clustering, we obtained three reoccurring dFC states (Figure 2), and state transition metrics were obtained

In a sparsely connected state (state 1), SZ showed both increased fractional time and mean dwell time than controls (P=0.047 and P=0.033) and OCD (P=0.001 and P=0.003). In a state characterized by negative FC between networks (state 2), OCD showed both increased fractional time and mean dwell time than controls (P=0.032 and P=0.013) and SZ (P=0.005 and P=0.003). Moreover, the fractional time of state 2 was positively correlated with anxiety scores in OCD (r=0.535, P=0.021, FDR corrected) (Figure 3).

OCD and SZ patients showed distinct alternations of brain functional dynamics.

No significant relationships.

The coronavirus disease 2019 (COVID-19) pandemic represents an unprecedented threat to mental health. Herein, we assessed the impact of COVID-19 on subthreshold depressive symptoms and identified potential mitigating factors.

Participants were from Depression Cohort in China (ChiCTR registry number 1900022145). Adults (n = 1722) with subthreshold depressive symptoms were enrolled between March and October 2019 in a 6-month, community-based interventional study that aimed to prevent clinical depression using psychoeducation. A total of 1506 participants completed the study in Shenzhen, China: 726 participants, who completed the study between March 2019 and January 2020 (i.e. before COVID-19), comprised the ‘wave 1’ group; 780 participants, who were enrolled before COVID-19 and completed the 6-month endpoint assessment during COVID-19, comprised ‘wave 2’. Symptoms of depression, anxiety and insomnia were assessed at baseline and endpoint (i.e. 6-month follow-up) using the Patient Health Questionnaire-9 (PHQ-9), Generalised Anxiety Disorder-7 (GAD-7) and Insomnia Severity Index (ISI), respectively. Measures of resilience and regular exercise were assessed at baseline. We compared the mental health outcomes between wave 1 and wave 2 groups. We additionally investigated how mental health outcomes changed across disparate stages of the COVID-19 pandemic in China, i.e. peak (7–13 February), post-peak (14–27 February), remission plateau (28 February−present).

COVID-19 increased the risk for three mental outcomes: (1) depression (odds ratio [OR] = 1.30, 95% confidence interval [CI]: 1.04–1.62); (2) anxiety (OR = 1.47, 95% CI: 1.16–1.88) and (3) insomnia (OR = 1.37, 95% CI: 1.07–1.77). The highest proportion of probable depression and anxiety was observed post-peak, with 52.9% and 41.4%, respectively. Greater baseline resilience scores had a protective effect on the three main outcomes (depression: OR = 0.26, 95% CI: 0.19–0.37; anxiety: OR = 1.22, 95% CI: 0.14–0.33 and insomnia: OR = 0.18, 95% CI: 0.11–0.28). Furthermore, regular physical activity mitigated the risk for depression (OR = 0.79, 95% CI: 0.79–0.99).

The COVID-19 pandemic exerted a highly significant and negative impact on symptoms of depression, anxiety and insomnia. Mental health outcomes fluctuated as a function of the duration of the pandemic and were alleviated to some extent with the observed decline in community-based transmission. Augmenting resiliency and regular exercise provide an opportunity to mitigate the risk for mental health symptoms during this severe public health crisis.

White matter abnormalities have been repeatedly reported in both schizophrenia and bipolar disorder (BD) diseases from diffusion tensor imaging (DTI) studies respectively, while the empirical evidences about the diagnostic specificity of white matter abnormalities in these disorders are still limited.

25 patients with paranoid schizophrenia and 18 patients with bipolar mania were recruited from the in-patient unit of the Mental Health Centre, West China Hospital, China.

Patients were diagnosed according to the criteria of Diagnostic and Statistical Manual of Mental Disorders-Version IV (DSM- IV). 30 healthy controls were recruited from the community by means of leaflets distributed throughout Chengdu city.

This study sought to investigate the alterations in fractional anisotropy (FA) in white matter throughout the entire brain of patients from Chengdu, China with paranoid schizophrenia and bipolar mania.

Diffusion tensor imaging (DTI) was used to assess white matter integrity in patients with paranoid schizophrenia and bipolar mania, as well as in normal controls. The differences in FA were measured by use of voxel-based analysis.

Reduced FA was found in the left posterior corona radiate (PCR) in patients with bipolar mania and paranoid schizophrenia compared to the controls. Patients with bipolar mania also showed a significant reduction in FA in right posterior corona radiate and in right anterior thalamic radiation (ATR).

Common abnormalities in the left PCR might imply an overlap in white matter pathology of both diseases and might be related to the shared risk factors for both disorders.

Although the deviations of brain volume deficits in sporadic and familial first-episode schizophrenia patients (FEP) had been presented, the difference of brain asymmetries remained unidentified.

To assess the potential differences of volumetric asymmetries of gray matter (GM) and white matter (WM) between groups.

To find out the different injury alteration of sporadic FEP and familial FEP.

42 sporadic and 30 familiar drug-naïve FEP with and 72 matched normal controls (NC) were recruited. Participants were assessed with neuropsychological tests and scanned by a 3.0T MRI to obtain T1-weighted and DTI images. Lateralization distribution maps of GM and WM volume were generated by employing optimized voxel-based morphometry. The asymmetries were analyzed by comparing calculating Laterality Index (LI) voxel by voxel.

All three groups showed similar overall brain torque. Familiar FEP have more regional extensive GM asymmetry brain lesions compared to sporadic FEP. There was no shared regional lesion between two groups. LIGM and LIWM in right superior temporal were negatively correlated. Significant negative correlations were also found between LIGM of left superior parietal lobule and LIWM of right superior parietal lobule, and between LIGM of right inferior parietal lobule and LIWM of left inferior parietal lobule. The asymmetry in distinct brain regions were related to cognitive deficits especially in the domains of language and memory.

The two patient groups had different alteration in injuries of brain asymmetry. Familiar FEP has more GM extensive asymmetry brain region, which may correlate with their high genetic burdens.

Despite strong evidence that the pathophysiology of tic disorders (TD) involves structural and functional disturbances of the basal ganglia, inconsistent findings from several TD imaging studies have supported contradictory conclusions.

To find brain structural differences between children with of TD and the health children and verify the pathogenesis hypothesis of that basal ganglia play an important role in this disorder.

The right handedness, first-episode TD children were chosen. Yale global tic severity scale (YGTSS) was used to assess the tic severity. MRI scan was performed on TD children and the controls. The volumes of caudate nucleus, putamen, globus pallidus and total intracranial volume were measured on high resolution MR images. We compared the volumes, relative volumes and asymmetry index, AI between groups.

Totally 11 patients finished this study with two excluded for the unclear image caused by tic and 18 subjects (9 TD patients and 9 controls) were finally analyzed. The right globus pallidus is significantly larger in TD patients. The volumes of left caudate increased significantly in both TD patients and controls. There was no significant difference in asymmetry index between two groups, relative volumes did not correlate significantly with the severity of tic and the course of disease.

The right globus pallidus may be the primary pathological change of TD. Asymmetry indexes between the two groups are not significantly different. The relative volume of any structure of basal ganglia has no significant correlation with the severity of tic and the course of disease.

The catechol-O-methyltransferase (COMT) gene is related to dopamine degradation and has been suggested to be involved in the pathogenesis of major depressive disorder (MDD). However, how this gene affects brain function properties in MDD is still unclear.

Fifty patients with MDD and 35 cognitively normal participants underwent a resting-state functional magnetic resonance imaging scan. A voxelwise and data-drive global functional connectivity density (gFCD) analysis was used to investigate the main effects and the interactions of disease states and COMT rs4680 gene polymorphism on brain function.

We found significant group differences of the gFCD in bilateral fusiform area (FFA), post-central and pre-central cortex, left superior temporal gyrus (STG), rectal and superior temporal gyrus and right ventrolateral prefrontal cortex (vlPFC); abnormal gFCDs in left STG were positively correlated with severity of depression in MDD group. Significant disease × COMT interaction effects were found in the bilateral calcarine gyrus, right vlPFC, hippocampus and thalamus, and left SFG and FFA. Further post-hoc tests showed a nonlinear modulation effect of COMT on gFCD in the development of MDD. Interestingly, an inverted U-shaped modulation was found in the prefrontal cortex (control system) but U-shaped modulations were found in the hippocampus, thalamus and occipital cortex (processing system).

Our study demonstrated nonlinear modulation of the interaction between COMT and depression on brain function. These findings expand our understanding of the COMT effect underlying the pathophysiology of MDD.

To investigate the morphology and dimensions of the vestibular aqueduct on axial, single-oblique and double-oblique computed tomography images.

The computed tomography temporal bone scans of 112 patients were retrospectively evaluated. Midpoint and opercular measurements were performed using axial, single-oblique and double-oblique images. Morphometric analyses were also conducted. The vestibular aqueduct sizes on axial, single-oblique and double-oblique images were compared.

At the midpoint, the mean (± standard deviation) vestibular aqueduct measured 0.61 ± 0.23 mm, 0.74 ± 0.27 mm and 0.82 ± 0.38 mm on axial, single-oblique and double-oblique images, respectively; at the operculum, the vestibular aqueduct measured 0.91 ± 0.30 mm, 1.11 ± 0.45 mm and 1.66 ± 1.07 mm on the respective images. The co-efficients of variation of the vestibular aqueduct measured at the midpoint were 37.4 per cent, 36.5 per cent and 47.5 per cent on axial, single-oblique and double-oblique images, respectively; at the operculum, the measurements were 33.0 per cent, 40.5 per cent and 64.5 per cent. Regarding morphology, the vestibular aqueduct was fissured (33.5 per cent), tubular (64.3 per cent) or invisible (2.2 per cent).

The morphology and dimensions of the vestibular aqueduct were highly variable among axial, single-oblique and double-oblique images.