Aberrant interhemispheric functional connectivity in major depressive disorder with and without anhedonia.

OBJECTIVE: Anhedonia is a core feature of major depressive disorder (MDD), and as a subtype of depression, MDD with anhedonia may have exceptional neurobiological mechanisms. However, the neuropathology of anhedonia in MDD remains unclear. Thus, this study aimed to investigate the brain functional differences between MDD with and without anhedonia. METHODS: A total of 62 individuals including 22 MDD patients with anhedonia, 20 MDD patients without anhedonia, and 20 healthy controls (HCs) were recruited for this study. All participants underwent 3.0-T functional magnetic resonance imaging scan. Voxel-mirrored homotopic connectivity (VMHC) was employed to quantitatively describe bilateral functional connectivity. Analyses of variance (ANOVA) were performed to obtain brain regions with significant differences among three groups and then post hoc tests were calculated for inter-group comparisons. RESULTS: The ANOVA revealed significant VMHC differences among three groups in the bilateral middle temporal gyrus (MTG), superior frontal gyrus (SFG), and inferior parietal lobule (IPL) (F = 10.47 ~ 15.09, p < 0.05, AlphaSim corrected). Relative to HCs, MDD with anhedonia showed significantly decreased VMHC in the bilateral MTG (t = -5.368, p < 0.05, AlphaSim corrected), as well as increased VMHC in the bilateral SFG (t = -4.696, p < 0.05, AlphaSim corrected). Compared to MDD without anhedonia, MDD with anhedonia showed significantly decreased VMHC in the bilateral MTG and IPL (t = -5.629 ~ -4.330, p < 0.05, AlphaSim corrected), while increased VMHC in the bilateral SFG (t = 3.926, p < 0.05, AlphaSim corrected). However, no significant difference was found between MDD without anhedonia and HCs. CONCLUSION: The present findings suggest that MDD with and without anhedonia exhibit different patterns of interhemispheric connectivity. Anhedonia in MDD is related to aberrant interhemispheric connectivity within brain regions involved in the frontal-temporal-parietal circuit.

Optimizing early neurological deterioration prediction in acute ischemic stroke patients following intravenous thrombolysis: a LASSO regression model approach.

Background: Acute ischemic stroke (AIS) remains a leading cause of disability and mortality globally among adults. Despite Intravenous Thrombolysis (IVT) with recombinant tissue plasminogen activator (rt-PA) emerging as the standard treatment for AIS, approximately 6-40% of patients undergoing IVT experience Early Neurological Deterioration (END), significantly impacting treatment efficacy and patient prognosis. Objective: This study aimed to develop and validate a predictive model for END in AIS patients post rt-PA administration using the Least Absolute Shrinkage and Selection Operator (LASSO) regression approach. Methods: In this retrospective cohort study, data from 531 AIS patients treated with intravenous alteplase across two hospitals were analyzed. LASSO regression was employed to identify significant predictors of END, leading to the construction of a multivariate predictive model. Results: Six key predictors significantly associated with END were identified through LASSO regression analysis: previous stroke history, Body Mass Index (BMI), age, Onset to Treatment Time (OTT), lymphocyte count, and glucose levels. A predictive nomogram incorporating these factors was developed, effectively estimating the probability of END post-IVT. The model demonstrated robust predictive performance, with an Area Under the Curve (AUC) of 0.867 in the training set and 0.880 in the validation set. Conclusion: The LASSO regression-based predictive model accurately identifies critical risk factors leading to END in AIS patients following IVT. This model facilitates timely identification of high-risk patients by clinicians, enabling more personalized treatment strategies and optimizing patient management and outcomes.

Unraveling the Predictors of Enlarged Perivascular Spaces: A Comprehensive Logistic Regression Approach in Cerebral Small Vessel Disease.

Background: This study addresses the predictive modeling of Enlarged Perivascular Spaces (EPVS) in neuroradiology and neurology, focusing on their impact on Cerebral Small Vessel Disease (CSVD) and neurodegenerative disorders. Methods: A retrospective analysis was conducted on 587 neurology inpatients, utilizing LASSO regression for variable selection and logistic regression for model development. The study included comprehensive demographic, medical history, and laboratory data analyses. Results: The model identified key predictors of EPVS, including Age, Hypertension, Stroke, Lipoprotein a, Platelet Large Cell Ratio, Uric Acid, and Albumin to Globulin Ratio. The predictive nomogram demonstrated strong efficacy in EPVS risk assessment, validated through ROC curve analysis, calibration plots, and Decision Curve Analysis. Conclusion: The study presents a novel, robust EPVS predictive model, providing deeper insights into EPVS mechanisms and risk factors. It underscores the potential for early diagnosis and improved management strategies in neuro-radiology and neurology, highlighting the need for future research in diverse populations and longitudinal settings.

Task-state skin potential abnormalities can distinguish major depressive disorder and bipolar depression from healthy controls.

Early detection of bipolar depression (BPD) and major depressive disorder (MDD) has been challenging due to the lack of reliable and easily measurable biological markers. This study aimed to investigate the accuracy of discriminating patients with mood disorders from healthy controls based on task state skin potential characteristics and their correlation with individual indicators of oxidative stress. A total of 77 patients with BPD, 53 patients with MDD, and 79 healthy controls were recruited. A custom-made device, previously shown to be sufficiently accurate, was used to collect skin potential data during six emotion-inducing tasks involving video, pictorial, or textual stimuli. Blood indicators reflecting individual levels of oxidative stress were collected. A discriminant model based on the support vector machine (SVM) algorithm was constructed for discriminant analysis. MDD and BPD patients were found to have abnormal skin potential characteristics on most tasks. The accuracy of the SVM model built with SP features to discriminate MDD patients from healthy controls was 78% (sensitivity 78%, specificity 82%). The SVM model gave an accuracy of 59% (sensitivity 59%, specificity 79%) in classifying BPD patients, MDD patients, and healthy controls into three groups. Significant correlations were also found between oxidative stress indicators in the blood of patients and certain SP features. Patients with depression and bipolar depression have abnormalities in task-state skin potential that partially reflect the pathological mechanism of the illness, and the abnormalities are potential biological markers of affective disorders.

Effects of neuronavigation-guided rTMS on serum BDNF, TrkB and VGF levels in depressive patients with suicidal ideation.

BACKGROUND: Neuronavigation-guided high-dose repetitive transcranial magnetic stimulation (rTMS) could rapidly treat depressive patients with suicidal ideation. But the mechanism of rTMS still needs to be elucidated. This study aims to investigate if rTMS improves suicidal ideation and depressive symptoms by influencing brain-derived neurotrophic factor (BDNF), tropomysin receptor kinase B (TrkB) and VGF levels. METHODS: In the present 1-week study, 59 treatment-naive depressive patients with suicidal ideation were randomly assigned to the active (n = 31) or sham (n = 28) rTMS group. The severity of suicidal ideation and depression were measured by the Beck Scale for Suicide Ideation, the Hamilton Depression Rating Scale and Montgomery-Asberg Depression Rating Scale. Fasting venous blood samples were collected at baseline and after treatment. Serum protein concentrations of BDNF, TrkB and VGF were measured by enzyme linked immunosorbent assay. RESULTS: We found after treatment the levels of BDNF in the active rTMS group were higher than the sham group (p = 0.011), TrkB levels were decreased in the active group (p < 0.001), VGF levels were increased in the active group (p = 0.005). Post-treatment VGF levels in the active group were higher than the sham group (p = 0.008). However, there were no significant correlation between changes in BDNF, TrkB and VGF levels and the changes in clinical variables. LIMITATIONS: Participants taking medication may affect the results. CONCLUSIONS: Our results suggest that the BDNF-TrkB pathway and VGF may be implicated in the mechanisms underlying neuronavigation-guided rTMS for treating depressive patients with suicidal ideation.

The Corrosion Behavior of AZ91D Magnesium Alloy in Simulated Haze Aqueous Solution.

The corrosion process of AZ91D magnesium alloy in simulated haze aqueous solution has been studied by electrochemical measurements, immersion tests and morphology characterization. Results show that AZ91D was corroded heavily in simulated haze aqueous solution due to the loose and breakable product film on the surface providing little corrosion barrier. The effect of different ions was investigated. It was found that both N O 3 − and N H 4 + played an important role in the corrosion process. N O 3 − helped to form passive film to protect the matrix, yet N H 4 + consumed OH−, resulting in the absence of Mg(OH)2 and serious corrosion. Meanwhile, S O 4 2 − and Cl− had influence on pitting corrosion. Magnesium aluminum oxide and MgAl2(SO4)4·22H2O instead of Mg(OH)2 were the dominate products, which is different from the former study. Corrosion rate changed with time, especially in the first 3 h. A two-stage corrosion mechanism is proposed after considering both the corrosion process and the influence of ions.