Yingying Yue

Divergent topological architecture of the default mode network as a pretreatment predictor of early antidepressant response in major depressive disorder

Identifying a robust pretreatment neuroimaging marker would be helpful for the selection of an optimal therapy for major depressive disorder (MDD). We recruited 82 MDD patients [n = 42 treatment-responsive depression (RD) and n = 40 non-responding depression (NRD)] and 50 healthy controls (HC) for this study. Based on the thresholded partial correlation matrices of 58 specific brain regions, a graph theory approach was applied to analyse the topological properties. When compared to HC, both RD and NRD patients exhibited a lower nodal degree (Dnodal) in the left anterior cingulate gyrus; as for RD, the Dnodal of the left superior medial orbitofrontal gyrus was significantly reduced, but the right inferior orbitofrontal gyrus was increased (all P < 0.017, FDR corrected). Moreover, the nodal degree in the right dorsolateral superior frontal cortex (SFGdor) was significantly lower in RD than in NRD. Receiver operating characteristic curve analysis demonstrated that the λ and nodal degree in the right SFGdor exhibited a good ability to distinguish nonresponding patients from responsive patients, which could serve as a specific maker to predict an early response to antidepressants. The disrupted topological configurations in the present study extend the understanding of pretreatment neuroimaging predictors for antidepressant medication.

Reliability and validity of a new post-stroke depression scale in Chinese population

BACKGROUND Nowadays there is still a lack of effective method to evaluate post-stroke depression. To distinguish patients with and without depression after stroke reliably, this study proposes a new Post-Stroke Depression Scale (PSDS). METHODS PSDS was developed based on various depression scales and clinician experiences. 158 stroke patients who were able to finish PSDS and Hamilton Depression Rating Scale (HDRS) were recruited. Cronbach α, Spearman rank coefficient and Kruskal-Wallis test were respectively used to examine reliability, internal consistency and discriminate validity. Then the Receiver Operating Characteristic (ROC) curve was used to determine the ability of scale and categorized scales to the range of depression. Finally, the factors of the PSDS were classified by average clustering analysis. RESULTS The Cronbach α of PSDS was 0.797 (95% CI) indicted a good reliability. The Spearman correlation coefficient between PSDS and HDRS was 0.822 (P<0.001) showed an excellent congruent validity. The discriminate validity displayed significant difference between patients with and without depression (P<0.001). 6/24 was set to be the cut-off value by ROC analysis. Moreover, the different severity was distinguished by the value 6/24, 15/24 and 17/24. LIMITATIONS The small sample size maybe the main limitation, the larger sample used in different fields according sex, age and side-lesion was needed to verity the results. The cut off value calculated by ROC curve maybe react the severity of the disease to some extent, but it is not absolute. CONCLUSIONS PSDS is a valid, reliable and specific tool for evaluating post-stroke depression patients and can be conveniently utilized.

Plasminogen Activator Inhibitor-1 in depression: Results from Animal and Clinical Studies

Evidence suggests that plasminogen activator inhibitor-1 (PAI-1) is a stress-related factor, and serum PAI-1 levels are increased in patients with major depressive disorders (MDD). Herein, we analysed PAI-1 protein levels in the brain, cerebrospinal fluid (CSF) and serum of rodents exposed to chronic unpredictable mild stress or treated with escitalopram. In addition, we examined PAI-1 concentrations in serum obtained from 17 drug-free depressed patients before and after escitalopram treatment. We found that PAI-1 expression was increased in area 1 of the cingulate cortex and prelimbic cortex of the medial prefrontal cortex as well as in the hippocampal cornu ammonis 3 and dentate gyrus in stressed rats. A downregulation of PAI-1 following chronic escitalopram treatment was also found. PAI-1 levels were higher in the CSF and serum in stressed rats than in controls, although the difference did not reach statistical significance in the serum. Escitalopram treatment significantly decreased PAI-1 levels in the serum, but not in the CSF. MDD patients had significantly greater serum PAI-1 concentrations than controls. Our results suggest that PAI-1 is implicated in the pathophysiology of depression.

Abnormal brain functional connectivity leads to impaired mood and cognition in hyperthyroidism: a resting-state functional MRI study

Patients with hyperthyroidism frequently have neuropsychiatric complaints such as lack of concentration, poor memory, depression, anxiety, nervousness, and irritability, suggesting brain dysfunction. However, the underlying process of these symptoms remains unclear. Using resting-state functional magnetic resonance imaging (rs-fMRI), we depicted the altered graph theoretical metric degree centrality (DC) and seed-based resting-state functional connectivity (FC) in 33 hyperthyroid patients relative to 33 healthy controls. The peak points of significantly altered DC between the two groups were defined as the seed regions to calculate FC to the whole brain. Then, partial correlation analyses were performed between abnormal DC, FC and neuropsychological performances, as well as some clinical indexes. The decreased intrinsic functional connectivity in the posterior lobe of cerebellum (PLC) and medial frontal gyrus (MeFG), as well as the abnormal seed-based FC anchored in default mode network (DMN), attention network, visual network and cognitive network in this study, possibly constitutes the latent mechanism for emotional and cognitive changes in hyperthyroidism, including anxiety and impaired processing speed.

Structural and Functional Connectivity of Default Mode Network underlying the Cognitive Impairment in Late-onset Depression

To identify the association between the functional and structural changes of default mode network (DMN) underlying the cognitive impairment in Late-onset depression (LOD), 32 LOD patients and 39 normal controls were recruited and underwent resting-state fMRI, DTI scans, and cognitive assessments. Seed-based correlation analysis was conducted to explore the functional connectivity (FC) of the DMN. Deterministic tractography between FC-impaired regions was performed to examine the structural connectivity (SC). Partial correlation analyses were employed to evaluate the cognitive association of those altered FC and SC. Compared with controls, LOD patients showed decreased FC between DMN and the cingulo-opercular network (CON), as well as the thalamus. Decreased FA and increased RD of these fiber tracts connecting DMN with CON were found in LOD patient. The DMN-CON FC and the FA, RD of the fiber tracts were both significantly correlated with the cognitive performance. Therefore, the cognitive impairment in LOD might be associated with the decreased FC between the DMN and the CON, which probably resulted from the demyelination of the white matter.

Towards a multi protein and mRNA expression of biological predictive and distinguish model for post stroke depression

Previous studies suggest that neurotrophic factors participate in the development of stroke and depression. So we investigated the utility of these biomarkers as predictive and distinguish model for post stroke depression (PSD). 159 individuals including PSD, stroke without depression (Non-PSD), major depressive disorder (MDD) and normal control groups were recruited and examined the protein and mRNA expression levels of vascular endothelial growth factor (VEGF), vascular endothelial growth factor receptors (VEGFR2), placental growth factor (PIGF), insulin-like growth factor (IGF-1) and insulin-like growth factor receptors (IGF-1R). The chi-square test was used to evaluate categorical variable, while nonparametric test and one-way analysis of variance were applied to continuous variables of general characteristics, clinical and biological changes. In order to explore the predictive and distinguish role of these factors in PSD, discriminant analysis and receiver operating characteristic curve were calculated. The four groups had statistical differences in these neurotrophic factors (all P < 0.05) except VEGF concentration and IGF-1R mRNA (P = 0.776, P = 0.102 respectively). We identified these mRNA expression and protein analytes with general predictive performance for PSD and Non-PSD groups [area under the curve (AUC): 0.805, 95% CI, 0.704-0.907, P < 0.001]. Importantly, there is an excellent predictive performance (AUC: 0.984, 95% CI, 0.964-1.000, P < 0.001) to differentiate PSD patients from MDD patients. This was the first study to explore the changes of neurotrophic factors family in PSD patients, the results intriguingly demonstrated that the combination of protein and mRNA expression of biological factors could use as a predictive and discriminant model for PSD.

The protein and mRNA expression levels of glial cell line-derived neurotrophic factor in post stroke depression and major depressive disorder

Previous studies have indicated that the level of glial cell line-derived neurotrophic factor (GDNF) may be correlated with stroke and depression. Here, we investigated whether GDNF can be a discriminant indicator for post stroke depression (PSD). 159 participants were divided into four groups: PSD, stroke without depression (Non-PSD), major depressive disorder (MDD) and normal control (NC) group, and the protein and mRNA expression levels of GDNF in serum were measured. The results showed that only MDD group had statistical difference in protein and mRNA levels compared with the other three groups (Bonferroni test, P < 0.05). The results of receiver operating curve (ROC) analysis supported GDNF as general distinguishing models in PSD and MDD groups with the area under the curve (AUC) at 0.797 (P < 0.001) and 0.831 (P < 0.001) respectively. In addition, the Spearman analysis demonstrated that the GDNF protein level negatively correlated with the value of Hamilton depression rating scale (HAMD) in PSD patients (correlation coefficient = −0.328, P = 0.047). Together, these findings suggest the protein and mRNA expression levels of GDNF decreased in patients with depression. GDNF may serve as a potential biomarker for differential diagnosis of PSD from MDD patients.

A risk prediction model for post-stroke depression in Chinese stroke survivors based on clinical and socio-psychological features

Background Post-stroke depression (PSD) is a frequent complication that worsens rehabilitation outcomes and patient quality of life. This study developed a risk prediction model for PSD based on patient clinical and socio-psychology features for the early detection of high risk PSD patients. Results Risk predictors included a history of brain cerebral infarction (odds ratio [OR], 3.84; 95% confidence interval [CI], 2.22-6.70; P < 0.0001) and four socio-psychological factors including Eysenck Personality Questionnaire with Neuroticism/Stability (OR, 1.18; 95% CI, 1.12-1.20; P < 0.0001), life event scale (OR, 0.99; 95% CI, 0.98-0.99; P = 0.0007), 20 items Toronto Alexithymia Scale (OR, 1.06; 95% CI, 1.02-1.10; P = 0.002) and Social Support Rating Scale (OR, 0.91; 95% CI, 0.87-0.90; P < 0.001) in the logistic model. In addition, 11 rules were generated in the tree model. The areas under the curve of the ROC and the accuracy for the tree model were 0.85 and 0.86, respectively. Methods This study recruited 562 stroke patients in China who were assessed for demographic data, medical history, vascular risk factors, functional status post-stroke, and socio-psychological factors. Multivariate backward logistic regression was used to extract risk factors for depression in 1-month after stroke. We converted the logistic model to a visible tree model using the decision tree method. Receiver operating characteristic (ROC) was used to evaluate the performance of the model. Conclusion This study provided an effective risk model for PSD and indicated that the socio-psychological factors were important risk factors of PSD.BACKGROUND Post-stroke depression (PSD) is a frequent complication that worsens rehabilitation outcomes and patient quality of life. This study developed a risk prediction model for PSD based on patient clinical and socio-psychology features for the early detection of high risk PSD patients. RESULTS Risk predictors included a history of brain cerebral infarction (odds ratio [OR], 3.84; 95% confidence interval [CI], 2.22-6.70; P < 0.0001) and four socio-psychological factors including Eysenck Personality Questionnaire with Neuroticism/Stability (OR, 1.18; 95% CI, 1.12-1.20; P < 0.0001), life event scale (OR, 0.99; 95% CI, 0.98-0.99; P = 0.0007), 20 items Toronto Alexithymia Scale (OR, 1.06; 95% CI, 1.02-1.10; P = 0.002) and Social Support Rating Scale (OR, 0.91; 95% CI, 0.87-0.90; P < 0.001) in the logistic model. In addition, 11 rules were generated in the tree model. The areas under the curve of the ROC and the accuracy for the tree model were 0.85 and 0.86, respectively. METHODS This study recruited 562 stroke patients in China who were assessed for demographic data, medical history, vascular risk factors, functional status post-stroke, and socio-psychological factors. Multivariate backward logistic regression was used to extract risk factors for depression in 1-month after stroke. We converted the logistic model to a visible tree model using the decision tree method. Receiver operating characteristic (ROC) was used to evaluate the performance of the model. CONCLUSION This study provided an effective risk model for PSD and indicated that the socio-psychological factors were important risk factors of PSD.

New opinion on the subtypes of poststroke depression in Chinese stroke survivors

Aim Poststroke depression (PSD) is the most common complication of stroke. However, some stroke survivors with depression cannot meet the diagnostic criteria of PSD. The aim of this study was to propose the new conception of stroke patients with depression and then make them to receive reasonable diagnosis and treatment. Methods We first put forward the opinion that the general PSD should consist of PSD disorder (PSDD) and PSD symptoms (PSDS) according to the Diagnostic and Statistical Manual of Mental Disorder – Fifth Edition (DSM-5) and ZhongDa diagnostic criteria – first edition (ZD-1), respectively. The ZD-1 was established based on the suggestions of 65 Chinese chief doctors considering that the symptoms of PSDS might be different from those of PSDD and the duration of DSM-5 was too strict. Then, 166 stroke inpatients were recruited, and the study was conducted using the diagnosis and classification of PSD to verify the new concept. Results A total of 24 (14.46%) and 80 (48.19%) stroke patients were diagnosed with PSDD and PSDS, respectively, according to individual diagnosis criteria. Moreover, patients meeting the diagnostic criteria of PSDD should satisfy the criteria of PSDS first. The distribution frequencies of depressive symptoms were different, which suggested that there might be discrepant depressive symptoms between PSDS and PSDD. Conclusion The present study proposes new opinion about the classification and diagnosis of depression in stroke survivors. The definition and criteria of PSDS are beneficial to explore phenomenological consistency and provide useful information for early recognition and appropriate interventions.

Risk factors associated with cognitions for late-onset depression based on anterior and posterior default mode sub-networks

BACKGROUND Abnormal functional connectivity (FC) in the default mode network (DMN) plays an important role in late-onset depression (LOD) patients. In this study, the risk predictors of LOD based on anterior and posterior DMN are explored. METHODS A total of 27 LOD patients and 40 healthy controls (HC) underwent resting-state functional magnetic resonance imaging and cognitive assessments. Firstly, FCs within DMN sub-networks were determined by placing seeds in the ventral medial prefrontal cortex (vmPFC) and posterior cingulate cortex (PCC). Secondly, multivariable logistic regression was used to identify risk factors for LOD patients. Finally, correlation analysis was performed to investigate the relationship between risk factors and the cognitive value. RESULTS Multivariable logistic regression showed that the FCs between the vmPFC and right middle temporal gyrus (MTG) (vmPFC-MTG_R), FCs between the vmPFC and left precuneus (PCu), and FCs between the PCC and left PCu (PCC-PCu_L) were the risk factors for LOD. Furthermore, FCs of the vmPFC-MTG_R and PCC-PCu_L correlated with processing speed (R = 0.35, P = 0.002; R = 0.32, P = 0.009), and FCs of the vmPFC-MTG_R correlated with semantic memory (R = 0.41, P = 0.001). LIMITATIONS The study was a cross-sectional study. The results may be potentially biased because of a small sample. CONCLUSIONS In this study, we confirmed that LOD patients mainly present cognitive deficits in processing speed and semantic memory. Moreover, our findings further suggested that FCs within DMN sub-networks associated with cognitions were risk factors, which may be used for the prediction of LOD.