Disrupted brain functional asymmetry at rest in patients with major depressive disorder associated with sleep disturbances.

Sleep disturbances (SD) are common in major depressive disorder (MDD) patients. Brain functional asymmetry is crucial for understanding MDD pathophysiology. Previous studies using the parameter of asymmetry (PAS) approach have found brain functional asymmetry disruption in MDD. However, this has not been explored in MDD patients with SD. This study examined 26 MDD patients with SD, 34 MDD patients without SD, and 34 healthy controls using resting-state functional magnetic resonance imaging scans. SD symptoms were quantified using the 17-item Hamilton Rating Scale for Depression. PAS approach was used to evaluate functional asymmetry. MDD patients with SD displayed increased PAS in the left middle frontal gyrus (MFG)/inferior frontal gyrus (IFG) and decreased PAS in the left parahippocampal gyrus (PHG) compared to MDD patients without SD. Increased PAS in the left MFG/IFG was positively correlated with SD severity, and a negative correlation was found between decreased PAS in the left PHG and SD scores in all MDD patients. Receiver operating characteristic analysis indicated that increased PAS in the left MFG/IFG and decreased PAS in the left PHG may serve as potential neuroimaging markers to differentiate MDD patients with SD from those without SD with Area Under Curve values of 0.8157 and 0.8068, respectively. These results highlighted that increased PAS in the left MFG/IFG and decreased PAS in the left PHG may be considered a prominent feature associated with SD symptoms of MDD patients, potentially serving as imaging markers to discriminate between MDD patients with and without SD.

Influence of panic disorder and paroxetine on brain functional hubs in drug-free patients.

BACKGROUND: The effects of panic disorder (PD) and pharmacotherapy on brain functional hubs in drug-free patients, and the utility of their degree centrality (DC) in diagnosing and predicting treatment response (TR) for PD, remained unclear. AIMS: This study aimed to assess the effects of PD and paroxetine on brain functional hubs in drug-free patients and to identify neuroimaging biomarkers for diagnosing and predicting TR in patients with PD. METHODS: Imaging data from 54 medication-free PD patients and 54 matched healthy controls (HCs) underwent DC and functional connectivity (FC) analyses before and after a 4-week paroxetine treatment. Diagnosis and prediction of TR models for PD were constructed using support vector machine (SVM) and support vector regression (SVR), with DC as features. RESULTS: Patients with PD showed aberrant DC and FC in the anterior cingulum, temporal, and occipital areas compared with HCs at baseline. After treatment, DC of the patients increased in the calcarine cortex, lingual gyrus, and cerebellum IV/V, along with improved clinical symptoms. Utilizing voxel-wise DC values at baseline, the SVM effectively distinguished patients with PD from HCs with an accuracy of 83.33%. In SVR, the predicted TR significantly correlated with the observed TR (correlation coefficient (r) = 0.893, Mean Squared Error = 0.009). CONCLUSION: Patients with PD exhibited abnormal DC and FC, notably in the limbic network, temporal, and occipital regions. Paroxetine ameliorated patients' symptoms while altering their brain FC. SVM and SVR models, utilizing baseline DC, effectively distinguished the patients from HCs and accurately predicted TR.

DNA Hyper-methylation Associated With Schizophrenia May Lead to Increased Levels of Autoantibodies.

Background and Hypothesis: Environmental stressors may influence immune surveillance in B lymphocytes and stimulate autoimmune responses via epigenetic DNA methylation modifications in schizophrenia (SCZ). Study Design: A total of 2722, Chinese Han origin subjects were recruited in this study (2005-2011), which included a discovery follow-up cohort with 40 remitters of SCZ (RSCZ), 40 nonremitters of SCZ (NRSCZ), and 40 controls (CTL), and a replication follow-up cohort (64 RSCZ, 16 NRSCZ, and 84 CTL), as well as a case-control validation cohort (1230 SCZ and 1208 CTL). Genomic DNA methylation, target gene mRNA transcripts, and plasma autoantibody levels were measured across cohorts. Study Results: We found extensive differences in global DNA methylation profiles between RSCZ and NRSCZ groups, wherein differential methylation sites (DMS) were enriched with immune cell maturation and activation in the RSCZ group. Out of 2722 participants, the foremost DMS cg14341177 was hyper-methylated in the SCZ group and it inhibited the alternative splicing of its target gene BICD2 and may have increased its autoantigen exposure, leading to an increase in plasma anti-BICD2 IgG antibody levels. The levels of cg14341177 methylation and anti-BICD2 IgG decreased significantly in RSCZ endpoint samples but not in NRSCZ endpoint samples. There are strong positive correlations between cg14341177 methylation, anti-BICD2 IgG, and positive and negative syndrome scale (PANSS) scores in the RSCZ groups, but not in the NRSCZ groups. Conclusions: These data suggest that abnormal DNA methylation could affect autoreactive responses in SCZ, and that cg14341177 methylation and anti-BICD2 IgG levels may potentially serve as useful biomarkers.

Neural substrates of predicting anhedonia symptoms in major depressive disorder via connectome-based modeling.

MAIN PROBLEM: Anhedonia is a critical diagnostic symptom of major depressive disorder (MDD), being associated with poor prognosis. Understanding the neural mechanisms underlying anhedonia is of great significance for individuals with MDD, and it encourages the search for objective indicators that can reliably identify anhedonia. METHODS: A predictive model used connectome-based predictive modeling (CPM) for anhedonia symptoms was developed by utilizing pre-treatment functional connectivity (FC) data from 59 patients with MDD. Node-based FC analysis was employed to compare differences in FC patterns between melancholic and non-melancholic MDD patients. The support vector machines (SVM) method was then applied for classifying these two subtypes of MDD patients. RESULTS: CPM could successfully predict anhedonia symptoms in MDD patients (positive network: r = 0.4719, p < 0.0020, mean squared error = 23.5125, 5000 iterations). Compared to non-melancholic MDD patients, melancholic MDD patients showed decreased FC between the left cingulate gyrus and the right parahippocampus gyrus (p_bonferroni = 0.0303). This distinct FC pattern effectively discriminated between melancholic and non-melancholic MDD patients, achieving a sensitivity of 93.54%, specificity of 67.86%, and an overall accuracy of 81.36% using the SVM method. CONCLUSIONS: This study successfully established a network model for predicting anhedonia symptoms in MDD based on FC, as well as a classification model to differentiate between melancholic and non-melancholic MDD patients. These findings provide guidance for clinical treatment.

Exploring the latent cognitive structure in schizophrenia: implications for antipsychotic treatment responses.

BACKGROUND: Individuals diagnosed with schizophrenia present diverse degrees and types of cognitive impairment, leading to variations in responses to antipsychotic treatments. Understanding the underlying cognitive structures is crucial for assessing this heterogeneity. Utilizing latent profile analysis (LPA) enables the delineation of latent categories of cognitive function. Integrating this approach with a dimensional perspective allows for the exploration of the relationship between cognitive function and treatment response. METHODS: This study examined 647 patients from two distinct cohorts. Utilizing LPA within the discovery cohort (n = 333) and the replication cohort (n = 314), latent subtypes were identified categorically. The stability of cognitive structures was evaluated employing Latent Transition Analysis (LTA). The relationship between cognitive function and treatment response were investigated by comparing Positive and Negative Syndrome Scale (PANSS) reduction rates across diverse cognitive subtypes. Furthermore, dimensional insights were gained through correlation analyses between cognitive tests and PANSS reduction rates. RESULTS: In terms of categorical, individuals diagnosed with schizophrenia can be categorized into three distinct subtypes: those 'without cognitive deficit', those 'with mild-moderate cognitive 'eficit', and those 'with moderate-severe cognitive deficit'. There are significant differences in PANSS reduction rates among patients belonging to these subtypes following antipsychotic treatment (p < 0.05). Furthermore, from a dimensional perspective, processing speed at baseline is positively correlated with PANSS score reduction rates at week 8/week 10 (p < 0.01). CONCLUSIONS: Our findings have unveiled the latent subtypes of cognitive function in schizophrenia, illuminating the association between cognitive function and responses to antipsychotic treatment from both categorical and dimensional perspectives.

Similar imaging changes and their relations to genetic profiles in bipolar disorder across different clinical stages.

Bipolar disorder (BD) across different clinical stages may present shared and distinct changes in brain activity. We aimed to reveal the neuroimaging homogeneity and heterogeneity of BD and its relationship with clinical variables and genetic variations. In present study, we conducted fractional amplitude of low-frequency fluctuations (fALFF), functional connectivity (FC) and genetic neuroimaging association analyses with 32 depressed, 26 manic, 35 euthymic BD patients and 87 healthy controls (HCs). Significant differences were found in the bilateral pre/subgenual anterior cingulate cortex (ACC) across the four groups, and all bipolar patients exhibited decreased fALFF values in the ACC when compared to HCs. Furthermore, positive associations were significantly observed between fALFF values in the pre/subgenual ACC and participants' cognitive functioning. No significant changes were found in ACC-based FC. We identified fALFF-alteration-related genes in BD, with enrichment in biological progress including synaptic and ion transmission. Taken together, abnormal activity in ACC is a characteristic change associated with BD, regardless of specific mood stages, serving as a potential neuroimaging feature in BD patients. Our genetic neuroimaging association analysis highlights possible heterogeneity in biological processes that could be responsible for different clinical stages in BD.

Association between abnormal default mode network homogeneity and sleep disturbances in major depressive disorder.

Background: Sleep disturbance is a common comorbidity of major depressive disorder (MDD). However, network homogeneity (NH) changes of the default mode network (DMN) in MDD with sleep disturbances are unclear. Aims: The purpose of this study was to probe the abnormal NH in the DMN in MDD with sleep disturbances and to reveal the differences between MDD with or without sleep disturbances. Methods: Twenty-four patients with MDD and sleep disturbances (Pa_s), 33 patients with MDD without sleep disturbances (Pa_ns) and 32 healthy controls (HCs) were recruited in this study. Resting-state functional imaging data were analysed using NH. Results: Compared with Pa_ns and HCs, Pa_s showed decreased NH in the left superior medial prefrontal cortex and increased NH in the right precuneus. There was a negative correlation between NH in the left superior medial prefrontal cortex and sleep disturbances (r=-0.42, p=0.001) as well as a positive correlation between NH in the right precuneus and sleep disturbances (r=0.41, p=0.002) in patients with MDD. Conclusions: MDD with sleep disturbances is associated with abnormal NH in the DMN, which could differentiate pa_s from pa_ns. The DMN may play a crucial role in the neurobiological mechanisms of MDD with sleep disturbances.

Development and validation of a risk score model for predicting autism based on pre- and perinatal factors.

Background: The use of pre- and perinatal risk factors as predictive factors may lower the age limit for reliable autism prediction. The objective of this study was to develop a clinical model based on these risk factors to predict autism. Methods: A stepwise logistic regression analysis was conducted to explore the relationships between 28 candidate risk factors and autism risk among 615 Han Chinese children with autism and 615 unrelated typically developing children. The significant factors were subsequently used to create a clinical risk score model. A chi-square automatic interaction detector (CHAID) decision tree was used to validate the selected predictors included in the model. The predictive performance of the model was evaluated by an independent cohort. Results: Five factors (pregnancy influenza-like illness, pregnancy stressors, maternal allergic/autoimmune disease, cesarean section, and hypoxia) were found to be significantly associated with autism risk. A receiver operating characteristic (ROC) curve indicated that the risk score model had good discrimination ability for autism, with an area under the curve (AUC) of 0.711 (95% CI=0.679-0.744); in the external validation cohort, the model showed slightly worse but overall similar predictive performance. Further subgroup analysis indicated that a higher risk score was associated with more behavioral problems. The risk score also exhibited robustness in a subgroup analysis of patients with mild autism. Conclusion: This risk score model could lower the age limit for autism prediction with good discrimination performance, and it has unique advantages in clinical application.

Trajectories of social function in patients with first-episode schizophrenia: Analysis of data from a 10-year follow-up study.

BACKGROUND: Patients with schizophrenia may have diverse functional outcomes. However, the long-term functional trajectories of patients with first-episode schizophrenia (FES) are unclear. METHODS: We extracted data from the Chinese First-Episode Schizophrenia Trial, a 10-year prospective study of antipsychotic-naïve patients with FES. We applied K means cluster modelling to longitudinal data on the social function of patients with FES and examined associations of the empirically derived trajectories with baseline clinical characteristics of the 10-year follow-up. OUTCOMES: Three distinct functional trajectories emerged: improving-favorable (39·3%), improving-poor (17·8%) and improving-stable (42·9%). All three trajectories demonstrated Personal and Social Performance (PSP) score improvement in the first six months. The improving-poor trajectory demonstrated PSP score decline during the second six months and thereafter, while PSP scores in the other two trajectories were mainly stable during the same period. Patients in the improving-favorable trajectory had higher baseline PSP scores than those in the improving-poor trajectory (OR=0·904 [0·852, 0·961], p < 0·05) and the improving-stable trajectory (OR=0·870 [0·825, 0·918], p < 0·001) and were more likely to be female than those in the improving-stable trajectory (OR=2·699 [1·030, 7·074], p < 0·05). CONCLUSIONS: Patients with FES demonstrated varied long-term functional recovery profiles. The first year, especially the second half of the first year, is a key period for social function interventions that improve long-term functional outcomes. Male patients and patients with poor baseline function may particularly benefit from such interventions.

Abnormal regional activity in the prefrontal-limbic circuit at rest: Potential imaging markers and treatment predictors in drug-naive anxiety disorders.

BACKGROUND: Previous research has identified functional impairments within the prefrontal-limbic circuit in individuals with anxiety disorders. However, the link between these deficiencies, clinical symptoms, and responses to antipsychotic treatment is still not fully understood. This study aimed to investigate abnormal regional activity within the prefrontal-limbic circuit among drug-naive individuals diagnosed with generalized anxiety disorder (GAD) and panic disorder (PD) and to analyze changes following treatment. METHODS: Resting-state magnetic resonance imaging was performed on a cohort of 118 anxiety disorder patients (64 GAD, 54 PD) and 61 healthy controls (HCs) at baseline. Among them, 52 patients with GAD and 44 patients with PD underwent a 4-week treatment regimen of paroxetine. Fractional amplitude of low-frequency fluctuation (fALFF) measurements and pattern classification techniques were employed to analyze the data in accordance with the human Brainnetome atlas. RESULTS: Both patients with GAD and PD demonstrated decreased fALFF in the right cHipp subregion of the hippocampus and increased fALFF in specified subregions of the cingulate and orbitofrontal lobe. Notably, patients with PD exhibited significantly higher fALFF in the left A24cd subregion compared to patients with GAD, while other ROI subregions showed no significant variations between the two patient groups. Whole-brain analysis revealed abnormal fALFF in both patient groups, primarily in specific areas of the cingulate and parasingulate gyrus, as well as the inferior and medial orbitofrontal gyrus (OFG). Following a 4-week treatment period, specific subregions in the GAD and PD groups showed a significant decrease in fALFF. Further analysis using support vector regression indicated that fALFF measurements in the right A13 and right A24cd subregions may be predictive of treatment response among anxiety disorder patients. CONCLUSIONS: Aberrant functional activity in certain subregions of the prefrontal-limbic circuit appears to be linked to the manifestation of anxiety disorders. These findings suggest potential imaging indicators for individual responses to antipsychotic treatment.

Shared and distinctive neural substrates of generalized anxiety disorder with or without depressive symptoms and their roles in prognostic prediction.

BACKGROUND: The neurophysiological mechanisms underlying generalized anxiety disorder (GAD) with or without depressive symptoms are obscure. This study aimed to uncover them and assess their predictive value for treatment response. METHODS: We enrolled 98 GAD patients [58 (age: 33.22 ± 10.23 years old, males/females: 25/33) with and 40 (age: 33.65 ± 10.49 years old, males/females: 14/26) without depressive symptoms] and 54 healthy controls (HCs, age: 32.28 ± 10.56 years old, males/females: 21/33). Patients underwent clinical assessments and resting-state functional MRI (rs-fMRI) at baseline and after 4-week treatment with paroxetine, while HCs underwent rs-fMRI at baseline only. Regional homogeneity (ReHo) was employed to measure intrinsic brain activity. We compared ReHo in patients to HCs and examined changes in ReHo within the patient groups after treatment. Support vector regression (SVR) analyses were conducted separately for each patient group to predict the patients' treatment response. RESULTS: Both patient groups exhibited higher ReHo in the middle/superior frontal gyrus decreased ReHo in different brain regions compared to HCs. Furthermore, differences in ReHo were detected between the two patient groups. After treatment, the patient groups displayed distinct ReHo change patterns. By utilizing SVR based on baseline abnormal ReHo, we effectively predicted treatment response of patients (p-value for correlation < 0.05). LIMITATIONS: The dropout rate was relatively high. CONCLUSIONS: This study identified shared and unique neural substrates in GAD patients with or without depressive symptoms, potentially serving as biomarkers for treatment response prediction. Comorbid depressive symptoms were associated with differences in disease manifestation and treatment response compared to pure GAD cases.

Shared and distinctive dysconnectivity patterns underlying pure generalized anxiety disorder (GAD) and comorbid GAD and depressive symptoms.

The resting-state connectivity features underlying pure generalized anxiety disorder (GAD, G1) and comorbid GAD and depressive symptoms (G2) have not been directly compared. Furthermore, it is unclear whether these features might serve as potential prognostic biomarkers and change with treatment. Degree centrality (DC) in G1 (40 subjects), G2 (58 subjects), and healthy controls (HCs, 54 subjects) was compared before treatment, and the DC of G1 or G2 at baseline was compared with that after 4 weeks of paroxetine treatment. Using support vector regression (SVR), voxel-wise DC across the entire brain and abnormal DC at baseline were employed to predict treatment response. At baseline, G1 and G2 exhibited lower DC in the left mid-cingulate cortex and vermis IV/V compared to HCs. Additionally, compared to HCs, G1 had lower DC in the left middle temporal gyrus, while G2 showed higher DC in the right inferior temporal/fusiform gyrus. However, there was no significant difference in DC between G1 and G2. The SVR based on abnormal DC at baseline could successfully predict treatment response in responders in G2 or in G1 and G2. Notably, the predictive performance based on abnormal DC at baseline surpassed that based on DC across the entire brain. After treatment, G2 responders showed lower DC in the right medial orbital frontal gyrus, while no change in DC was identified in G1 responders. The G1 and G2 showed common and distinct dysconnectivity patterns and they could potentially serve as prognostic biomarkers. Furthermore, DC in patients with GAD could change with treatment.

Disrupted functional connectivity associated with cognitive impairment in generalized anxiety disorder (GAD) and comorbid GAD and depression: a follow-up fMRI study.

BACKGROUND: Impaired functional connectivity between the bilateral hemispheres may serve as the neural substrate for anxiety and depressive disorders, yet its role in comorbid generalized anxiety disorder (GAD) and depression, as well as the effect of treatment on this connectivity, remains unclear. We sought to examine functional connectivity between homotopic regions of the 2 hemispheres (voxel-mirrored homotopic connectivity [VMHC]) among people with GAD with and without comorbid depression at baseline and after a 4-week paroxetine treatment. METHODS: Drug-naïve patients with GAD, with or without comorbid depression and healthy controls underwent functional magnetic resonance imaging and clinical assessments at baseline and after treatment. We compared VMHC and seed-based functional connectivity across the 3 groups. We performed correlation analysis and support vector regression (SVR) to examine the intrinsic relationships between VMHC and symptoms. RESULTS: Both patient groups (n = 40 with GAD only, n = 58 with GAD and depression) showed decreased VMHC in the precuneus, posterior cingulate cortex and lingual gyrus compared with healthy controls (n = 54). Moreover, they showed decreased VMHC in different brain regions compared with healthy controls. However, we did not observe any significant differences between the 2 patient groups. Seeds from abnormal VMHC clusters in patient groups had decreased functional connectivity. Voxel-mirrored homotopic connectivity in the precuneus, posterior cingulate cortex and lingual gyrus was negatively correlated with cognitive impairment among patients with GAD only and among all patients. The SVR analysis based on abnormal VMHC showed significant positive correlations (p < 0.0001) between predicted and actual treatment responses. However, we did not observe significant differences in VMHC or functional connectivity after treatment. LIMITATIONS: A notable dropout rate and intergroup somatic symptom variations may have biased the results. CONCLUSION: Patients with GAD with or without comorbid depression exhibited shared and distinct abnormal VMHC patterns, which might be linked to their cognitive deficits. These patterns have the potential to serve as prognostic biomarkers for GAD.Clinical trial registration: ClinicalTrials.gov NCT03894085.

Identifying major depressive disorder with associated sleep disturbances through fMRI regional homogeneity at rest.

BACKGROUND: Anomalies in regional homogeneity (ReHo) have been documented in patients with major depressive disorder (MDD) and sleep disturbances (SDs). This investigation aimed to scrutinize changes in ReHo in MDD patients with comorbid SD, and to devise potential diagnostic biomarkers for detecting sleep-related conditions in patients with MDD. METHODS: Patients with MDD and healthy controls underwent resting-state functional magnetic resonance imaging scans. SD severity was quantified using the 17-item Hamilton Rating Scale for Depression. Subsequent to the acquisition of imaging data, ReHo analysis was performed, and a support vector machine (SVM) method was employed to assess the utility of ReHo in discriminating MDD patients with SD. RESULTS: Compared with MDD patients without SD, MDD patients with SD exhibited increased ReHo values in the right posterior cingulate cortex (PCC)/precuneus, right median cingulate cortex, left postcentral gyrus (postCG), and right inferior temporal gyrus (ITG). Furthermore, the ReHo values in the right PCC/precuneus and ITG displayed a positive correlation with clinical symptoms across all patients. SVM classification results showed that a combination of abnormal ReHo in the left postCG and right ITG achieved an overall accuracy of 84.21%, a sensitivity of 81.82%, and a specificity of 87.50% in identifying MDD patients with SD from those without SD. CONCLUSION: We identified disrupted ReHo patterns in MDD patients with SD, and presented a prospective neuroimaging-based diagnostic biomarker for these patients.

Metformin improves cognitive impairment in patients with schizophrenia: associated with enhanced functional connectivity of dorsolateral prefrontal cortex.

Cognitive impairment is a core feature of schizophrenia, which is aggravated by antipsychotics-induced metabolic disturbance and lacks effective pharmacologic treatments in clinical practice. Our previous study demonstrated the efficiency of metformin in alleviating metabolic disturbance following antipsychotic administration. Here we report that metformin could ameliorate cognitive impairment and improve functional connectivity (FC) in prefrontal regions. This is an open-labeled, evaluator-blinded study. Clinically stable patients with schizophrenia were randomly assigned to receive antipsychotics plus metformin (N = 48) or antipsychotics alone (N = 24) for 24 weeks. The improvement in cognition was assessed by the MATRICS Consensus Cognitive Battery (MCCB). Its association with metabolic measurements, and voxel-wise whole-brain FC with dorsolateral prefrontal cortex (DLPFC) subregions as seeds were evaluated. When compared to the antipsychotics alone group, the addition of metformin resulted in significantly greater improvements in the MCCB composite score, speed of processing, working memory, verbal learning, and visual learning. A significant time × group interaction effect of increased FC between DLPFC and the anterior cingulate cortex (ACC)/middle cingulate cortex (MCC), and between DLPFC subregions were observed after metformin treatment, which was positively correlated with MCCB cognitive performance. Furthermore, the FC between left DLPFC A9/46d to right ACC/MCC significantly mediated metformin-induced speed of processing improvement; the FC between left A46 to right ACC significantly mediated metformin-induced verbal learning improvement. Collectively, these findings demonstrate that metformin can improve cognitive impairments in schizophrenia patients and is partly related to the FC changes in the DLPFC. Trial Registration: The trial was registered with ClinicalTrials.gov (NCT03271866). The full trial protocol is provided in Supplementary Material.

Breaking the Fear Barrier: Aberrant Activity of Fear Networks as a Prognostic Biomarker in Patients with Panic Disorder Normalized by Pharmacotherapy.

Panic disorder (PD) is a prevalent type of anxiety disorder. Previous studies have reported abnormal brain activity in the fear network of patients with PD. Nonetheless, it remains uncertain whether pharmacotherapy can effectively normalize these abnormalities. This longitudinal resting-state functional magnetic resonance imaging study aimed to investigate the spontaneous neural activity in patients with PD and its changes after pharmacotherapy, with a focus on determining whether it could predict treatment response. The study included 54 drug-naive patients with PD and 54 healthy controls (HCs). Spontaneous neural activity was measured using regional homogeneity (ReHo). Additionally, support vector regression (SVR) was employed to predict treatment response from ReHo. At baseline, PD patients had aberrant ReHo in the fear network compared to HCs. After 4 weeks of paroxetine treatment (20 mg/day), a significant increase in ReHo was observed in the left fusiform gyrus, which had shown reduced ReHo before treatment. The SVR analysis showed significantly positive correlations (p < 0.0001) between the predicted and actual reduction rates of the severity of anxiety and depressive symptoms. Here, we show patients with PD had abnormal spontaneous neural activities in the fear networks. Furthermore, these abnormal spontaneous neural activities can be partially normalized by pharmacotherapy and serve as candidate predictors of treatment response. Gaining insight into the trajectories of brain activity normalization following treatment holds the potential to provide vital insights for managing PD.

Minocycline and antipsychotics inhibit inflammatory responses in BV-2 microglia activated by LPS via regulating the MAPKs/ JAK-STAT signaling pathway.

BACKGROUND: Abnormal activation of microglia is involved in the pathogenesis of schizophrenia. Minocycline and antipsychotics have been reported to be effective in inhibiting the activation of microglia and thus alleviating the negative symptoms of patients with schizophrenia. However, the specific molecular mechanism by which minocycline and antipsychotics inhibit microglial activation is not clear. In this study, we aimed to explore the molecular mechanism of treatment effect of minocycline and antipsychotics on schizophrenia. METHODS: Microglia cells were activated by lipopolysaccharide (LPS) and further treated with minocycline, haloperidol, and risperidone. Then cell morphology, specific marker, cytokines, and nitric oxide production process, and the proteins in related molecular signaling pathways in LPS-activated microglia were compared among groups. RESULTS: The study found that minocycline, risperidone, and haloperidol significantly inhibited morphological changes and reduced the expression of OX-42 protein induced by LPS. Minocycline significantly decreased the production of interleukin-6 (IL-6), tumor necrosis factor-alpha (TNF-α), and interleukin-1beta (IL-1ß). Risperidone also showed significant decrease in the production of IL-6 and TNF-α, while haloperidol only showed significant decrease in the production of IL-6. Minocycline, risperidone, and haloperidol were found to significantly inhibit nitric oxide (NO) expression, but had no effect on inducible nitric oxide synthase (iNOS) expression. Both minocycline and risperidone were effective in decreasing the activity of c­Jun N­terminal kinase (JNK) and extracellular signal-regulated kinase (ERK) in the mitogen-activated protein kinases (MAPKs) signal pathway. Additionally, minocycline and risperidone were found to increase the activity of phosphorylated-p38. In contrast, haloperidol only suppressed the activity of ERK. Minocycline also suppressed the activation of janus kinase 2 (JAK2) and signal transducer and activator of transcription 3 (STAT3), while risperidone and haloperidol only suppressed the activation of STAT3. CONCLUSIONS: The results demonstrated that minocycline and risperidone exert stronger anti-inflammatory and neuroprotective effects stronger than haloperidol, through MAPKs and Janus kinase-signal transducer and activator of transcription (JAK-STAT) signaling pathways in BV2 cells stimulated with LPS, revealing the underlying mechanisms of minocycline and atypical antipsychotics in the treatment of negative schizophrenia symptoms.

Early non-response as a predictor of later non-response to antipsychotics in schizophrenia: a randomized trial.

BACKGROUND: It remains a challenge to predict the long-term response to antipsychotics in patients with schizophrenia who do not respond at an early stage. This study aimed to investigate the optimal predictive cut-off value for early non-response that would better predict later non-response to antipsychotics in patients with schizophrenia. METHODS: This multicenter, 8-week, open-label, randomized trial was conducted at 19 psychiatric centers throughout China. All enrolled participants were assigned to olanzapine, risperidone, amisulpride, or aripiprazole monotherapy for 8 weeks. The positive and negative syndrome scale (PANSS) was evaluated at baseline, week 2, week 4, and week 8. The main outcome was the prediction of nonresponse. Nonresponse is defined as a < 20% reduction in the total scores of PANSS from baseline to endpoint. Severity ratings of mild, moderate, and severe illness corresponded to baseline PANSS total scores of 58, 75, and 95, respectively. RESULTS: At week 2, a reduction of < 5% in the PANSS total score showed the highest total accuracy in the severe and mild schizophrenia patients (total accuracy, 75.0% and 80.8%, respectively), and patients who were treated with the risperidone and amisulpride groups (total accuracy, 82.4%, and 78.2%, respectively). A 10% decrease exhibited the best overall accuracy in the moderate schizophrenia patients (total accuracy, 84.0%), olanzapine (total accuracy, 79.2%), and aripiprazole group (total accuracy, 77.4%). At week 4, the best predictive cut-off value was < 20%, regardless of the antipsychotic or severity of illness (total accuracy ranging from 89.8 to 92.1%). CONCLUSIONS: Symptom reduction at week 2 has acceptable discrimination in predicting later non-response to antipsychotics in schizophrenia, and a more accurate predictive cut-off value should be determined according to the medication regimen and baseline illness severity. The response to treatment during the next 2 weeks after week 2 could be further assessed to determine whether there is a need to change antipsychotic medication during the first four weeks. TRIAL REGISTRATION: This study was registered on Clinicaltrials.gov (NCT03451734).

Abnormal causal connectivity of prefrontal-limbic circuit by structural deficits in drug-naive anxiety disorders.

BACKGROUND: Structural and functional deficits in the prefrontal-limbic circuit have been revealed in patients with anxiety disorders. However, the effect of structural abnormalities on causal connectivity within this circuit remains unclear. This study aimed to investigate causal connectivity in the prefrontal-limbic circuit associated with structural deficits in drug-naive patients with generalized anxiety disorder (GAD) and panic disorder (PD) and the changes after treatment. METHODS: A total of 64 GAD patients, 54 PD patients and 61 healthy controls (HCs) completed the resting-state magnetic resonance imaging scans at baseline. Among them, 96 patients with anxiety disorders (52 in GAD group and 44 in PD group) completed a 4-week paroxetine treatment. Voxel-based morphometry and Granger causality analysis (GCA) were applied to analyze the data based on the human brainnetome atlas. RESULTS: Patients with GAD and PD showed decreased gray matter volume (GMV) in the bilateral A24cd subregions of cingulate gyrus. Whole-brain analysis revealed decreased GMV in the left cingulate gyrus in patients with PD. Hence, the left A24cd subregion was selected as a seed. Compared with HCs, unidirectional causal connectivity between the limbic-superior temporal gyrus (STG) temporal pole and the limbic-precentral/middle frontal gyrus was enhanced in patients with GAD and PD (from the left A24cd subregion of cingulate gyrus to the right STG temporal pole, and from the left A24cd subregion of cingulate gyrus to the right precentral/middle frontal gyrus). Compared with patients with PD, the limbic-precuneus unidirectional causal connectivity was enhanced in patients with GAD, and the cerebellum crus1 - limbic connectivity has a positive feedback effect. CONCLUSIONS: The anatomical defects in the left A24cd subregion of cingulate gyrus may partially affect the prefrontal-limbic circuit, and the unidirectional causal effect from the left A24cd subregion to the right STG temporal pole may be an imaging change shared by anxiety disorders. The causal effect of the left A24cd subregion of cingulate gyrus to precuneus might be related to the neurobiology of GAD.

The clinical application of Chinese herbal medication to depression: A narrative review.

Depression severely impairs psychosocial functioning and quality of life, which places a huge burden on patients and their families. However, the physiological mechanism of depression remains unknown. Treatment with existing antidepressant medications is effective in around 50% of patients according to various studies, but is associated with severe side effects including nausea and headaches. Chinese herbal medicine (CHM) has been approved and widely used for depression as an alternative medicine in Chinese culture for decades. It has certain advantages and potential in the prevention and treatment of depression. In this review, we summarize the currently available evidence for the efficacy of CHM for the treatment of depression and physiological diseases comorbid with depression. We further discuss the possible mechanisms of action of CHM and the relationships to our current understanding of depression. The majority of current evidence has suggested that the combined treatment with CHM and mainstream antidepressants improves the response rate and reduces the side effects, while CHM alone could be more effective than placebo. However, the results should be carefully interpreted due to the shortcomings of existing clinical trials and a high risk of bias in meta-analyses. Our review provides a summary of the current applications and understanding of widely used CHMs for depression.