Identify Potential Causal Relationships Between Cortical Thickness, Mismatch Negativity, Neurocognition, and Psychosocial Functioning in Drug-Naïve First-Episode Psychosis Patients.

BACKGROUND: Cortical thickness (CT) alterations, mismatch negativity (MMN) reductions, and cognitive deficits are robust findings in first-episode psychosis (FEP). However, most studies focused on medicated patients, leaving gaps in our understanding of the interrelationships between CT, MMN, neurocognition, and psychosocial functioning in unmedicated FEP. This study aimed to employ multiple mediation analysis to investigate potential pathways among these variables in unmedicated drug-naïve FEP. METHODS: We enrolled 28 drug-naïve FEP and 34 age and sex-matched healthy controls. Clinical symptoms, neurocognition, psychosocial functioning, auditory duration MMN, and T1 structural magnetic resonance imaging data were collected. We measured CT in the superior temporal gyrus (STG), a primary MMN-generating region. RESULTS: We found a significant negative correlation between MMN amplitude and bilateral CT of STG (CT_STG) in FEP (left: r = -.709, P < .001; right: r = -.612, P = .008). Multiple mediation models revealed that a thinner left STG cortex affected functioning through both direct (24.66%) and indirect effects (75.34%). In contrast, the effects of the right CT_STG on functioning were mainly mediated through MMN and neurocognitive pathways. CONCLUSIONS: Bilateral CT_STG showed significant association with MMN, and MMN plays a mediating role between CT and cognition. Both MMN alone and its interaction with cognition mediated the effects of structural alterations on psychosocial function. The decline in overall function in FEP may stem from decreased CT_STG, leading to subsequent MMN deficits and neurocognitive dysfunction. These findings underline the crucial role of MMN in elucidating how subtle structural alterations can impact neurocognition and psychosocial function in FEP.

Aromatic and magnetic properties in a series of heavy rare earth-doped Ge6 cluster anions.

A series of pentagonal bipyramidal anionic germanium clusters doped with heavy rare earth elements, REGe 6 - (RE = Gd, Tb, Dy, Ho, Er, Tm, Yb, and Lu), have been identified at the PBE0/def2-TZVP level using density functional theory (DFT). Our findings reveal that the centrally doped pentagonal ring structure demonstrates enhanced stability and heightened aromaticity due to its uniform bonding characteristics and a larger charge transfer region. Through natural population analysis and spin density diagrams, we observed a monotonic decrease in the magnetic moment from Gd to Yb. This is attributed to the decreasing number of unpaired electrons in the 4f orbitals of the heavy rare earth atoms. Interestingly, the system doped with Er atoms showed lower stability and anti-aromaticity, likely due to the involvement of the 4f orbitals in bonding. Conversely, the systems doped with Gd and Tb atoms stood out for their high magnetism and stability, making them potential building blocks for rare earth-doped semiconductor materials.

Exploring the stability and aromaticity of rare earth doped tin cluster MSn16- (M = Sc, Y, La).

Rare earth elements have high chemical reactivity, and doping them into semiconductor clusters can induce novel physicochemical properties. The study of the physicochemical mechanisms of interactions between rare earth and tin atoms will enhance our understanding of rare earth functional materials from a microscopic perspective. Hence, the structure, electronic characteristics, stability, and aromaticity of endohedral cages MSn16- (M = Sc, Y, La) have been investigated using a combination of the hybrid PBE0 functional, stochastic kicking, and artificial bee colony global search technology. By comparing the simulated results with experimental photoelectron spectra, it is determined that the most stable structure of these clusters is the Frank-Kasper polyhedron. The doping of atoms has a minimal influence on density of states of the pure tin system, except for causing a widening of the energy gap. Various methods such as ab initio molecular dynamics simulations, the spherical jellium model, adaptive natural density partitioning, localized orbital locator, and electron density difference are employed to analyze the stability of these clusters. The aromaticity of the clusters is examined using iso-chemical shielding surfaces and the gauge-including magnetically induced currents. This study demonstrates that the stability and aromaticity of a tin cage can be systematically adjusted through doping.

Making Sense of the Growth Behavior of Ultra-High Magnetic Gd2-Doped Silicon Clusters.

The growth behavior, stability, electronic and magnetic properties of the Gd2Sin- (n = 3-12) clusters are reported, which are investigated using density functional theory calculations combined with the Saunders 'Kick' and the Artificial Bee Colony algorithm. The lowest-lying structures of Gd2Sin- (n = 3-12) are all exohedral structures with two Gd atoms face-capping the Sin frameworks. Results show that the pentagonal bipyramid (PB) shape is the basic framework for the nascent growth process of the present clusters, and forming the PB structure begins with n = 5. The Gd2Si5- is the potential magic cluster due to significantly higher average binding energies and second order difference energies, which can also be further verified by localized orbital locator and adaptive natural density partitioning methods. Moreover, the localized f-electron can be observed by natural atomic orbital analysis, implying that these electrons are not affected by the pure silicon atoms and scarcely participate in bonding. Hence, the implantation of these elements into a silicon substrate could present a potential alternative strategy for designing and synthesizing rare earth magnetic silicon-based materials.

Optimizing multi-domain hematologic biomarkers and clinical features for the differential diagnosis of unipolar depression and bipolar depression.

There is a lack of objective features for the differential diagnosis of unipolar and bipolar depression, especially those that are readily available in practical settings. We investigated whether clinical features of disease course, biomarkers from complete blood count, and blood biochemical markers could accurately classify unipolar and bipolar depression using machine learning methods. This retrospective study included 1160 eligible patients (918 with unipolar depression and 242 with bipolar depression). Patient data were randomly split into training (85%) and open test (15%) sets 1000 times, and the average performance was reported. XGBoost achieved the optimal open-test performance using selected biomarkers and clinical features-AUC 0.889, sensitivity 0.831, specificity 0.839, and accuracy 0.863. The importance of features for differential diagnosis was measured using SHapley Additive exPlanations (SHAP) values. The most informative features include (1) clinical features of disease duration and age of onset, (2) biochemical markers of albumin, low density lipoprotein (LDL), and potassium, and (3) complete blood count-derived biomarkers of white blood cell count (WBC), platelet-to-lymphocyte ratio (PLR), and monocytes (MONO). Overall, onset features and hematologic biomarkers appear to be reliable information that can be readily obtained in clinical settings to facilitate the differential diagnosis of unipolar and bipolar depression.

A bidirectional association between internet addiction and depression: A large-sample longitudinal study among Chinese university students.

BACKGROUND: Internet addiction (IA) is associated with adverse consequences, especially for younger people. Evidence indicates that IA is associated with depression, but no studies have yet investigated potential common vulnerability between them. METHODS: IA (measured by the Young's 20-item Internet Addiction Test Scale) and depressive symptoms (measured by the Patient Health Questionnaire-9 Scale) among 12 043 undergraduates were surveyed at baseline and at a respective 12 month follow-up for each participant. Application of a cross-lagged panel model approach (CLPM) revealed an association between IA and depression after adjusting for demographic variables. RESULTS: Rates of baseline IA and depression were 5.47% (95% CI: 5.07%, 5.88%) and 3.85% (95% CI: 3.51%, 4.20%), respectively; increasing to 9.47% (95% CI: 8.94%, 9.99%) and 5.58% (95% CI: 5.17%,5.99%), respectively, at follow-up. Rates of new-incidences of IA and depression over 12 months were 7.43% (95% CI: 6.95%, 7.91%) and 4.47% (95% CI: 4.09%, 4.84%), respectively. Models in the present analysis revealed that baseline depression had a significant net-predictive effect on follow-up IA, and baseline IA had a significant net-predictive effect on follow-up depression. LIMITATIONS: The follow-up survey response rate was moderate (54.69%) in this analysis of university students. Moreover, the IAT-20 scale did not allow differentiate between specific forms of Internet activity. CONCLUSIONS: Common vulnerability and bidirectional cross-causal effects may both contribute to the association between IA and depression, with common vulnerability likely playing a more significant role than cross-causal effects.

Testing extra-linearity across a psychosis continuum.

BACKGROUND: It is unclear whether psychotic experiences (PEs) gradually merge into states of clinical psychosis along a continuum which correspond to a dimensional classification or whether latent classes appear above a certain severity threshold which correspond better to diagnostic categories of psychosis. METHODS: Annual cross-sectional surveys, 2014-19, among Chinese undergraduates (N = 47,004) measured PEs, depression and etiological risk factors using standardized self-report instruments. We created a psychosis continuum with five levels and tested linear and extra-linear contrasts in associated etiological risk factors, before and after adjustment for depression. We carried out latent class analysis. RESULTS: Categorical expression of psychosis, including hallucinations and delusions, nuclear symptoms, and nuclear symptoms and depression were found at severe level 5. Etiological risk factors which impacted linearly across the continuum were more common for depression. Child maltreatment impacted extra-linearly on both psychosis and depression. Family history of psychosis impacted linearly on psychosis; male sex and urban birth impacted extra-linearly and were specific for psychosis. Four latent classes were found, but only at level 5. These corresponded to nuclear schizophrenia symptoms, nuclear schizophrenia and depressive symptoms, severe depression, and an unclassified category with moderate prevalence of PEs. CONCLUSION: Quantitative and qualitative changes in the underlying structure of psychosis were observed at the most severe level along a psychosis continuum, where four latent classes emerged. These corresponded to existing categorical classifications but require confirmation with clinical interview. PEs are non-specific and our findings suggest some are on a continuum with depression, whilst others are on a continuum with non-affective psychosis. Differing patterns of impact from etiological risk factors across the spectrum of psychopathology determine outcome at the most severe level of these continua.

[Resting-state electroencephalogram classification of patients with schizophrenia or depression].

The clinical manifestations of patients with schizophrenia and patients with depression not only have a certain similarity, but also change with the patient's mood, and thus lead to misdiagnosis in clinical diagnosis. Electroencephalogram (EEG) analysis provides an important reference and objective basis for accurate differentiation and diagnosis between patients with schizophrenia and patients with depression. In order to solve the problem of misdiagnosis between patients with schizophrenia and patients with depression, and to improve the accuracy of the classification and diagnosis of these two diseases, in this study we extracted the resting-state EEG features from 100 patients with depression and 100 patients with schizophrenia, including information entropy, sample entropy and approximate entropy, statistical properties feature and relative power spectral density (rPSD) of each EEG rhythm (δ, θ, α, ß). Then feature vectors were formed to classify these two types of patients using the support vector machine (SVM) and the naive Bayes (NB) classifier. Experimental results indicate that: ① The rPSD feature vector P performs the best in classification, achieving an average accuracy of 84.2% and a highest accuracy of 86.3%; ② The accuracy of SVM is obviously better than that of NB; ③ For the rPSD of each rhythm, the ß rhythm performs the best with the highest accuracy of 76%; ④ Electrodes with large feature weight are mainly concentrated in the frontal lobe and parietal lobe. The results of this study indicate that the rPSD feature vector P in conjunction with SVM can effectively distinguish depression and schizophrenia, and can also play an auxiliary role in the relevant clinical diagnosis.

Fronto-limbic disconnection in depressed patients with suicidal ideation: A resting-state functional connectivity study.

BACKGROUND: Suicidal ideation (SI) is highly prevalent and a known symptom of Major Depressive Disorder (MDD), but its underlying biological mechanisms are relatively unknown. Several studies linked suicidal ideation to dysfunctional brain circuits, specifically fronto-limbic connections. The purpose of this work was to investigate fronto-limbic disconnection in MDD patients with or without SI. METHODS: MDD patients with SI (SI, n=28) or without SI (NSI, n=20), identified by the Scale for Suicide Ideation and healthy controls (HCs, n=30) underwent resting-state functional MRI scanning. The functional properties of correlations in neural activity (intrinsic functional connectivity, iFC) of the rostral anterior cingulate cortex (rACC) were analyzed among the three groups. Furthermore, correlation analyses between iFC, SI severity and depression severity were performed. RESULTS: We found that the SI group exhibited decreased iFC between the rACC, the orbitomedial prefrontal cortex and the right middle temporal pole compared to HCs and NSI groups. The NSI group showed decreased iFC between the rACC and the orbitomedial prefrontal cortex compared to HCs. In the SI group, iFC strength between the right rACC and the middle temporal pole positively correlated with SI severity. CONCLUSION: Transdiagnostic and diagnosis-specific alterations of fronto-limbic iFC were found in MDD patients with or without SI. Disrupted fronto-limbic circuits may impact decision-making and emotional processing in SI. These results provide useful information about the pathophysiological mechanisms of MDD patients with SI.

Antidepressant Effects of Electroconvulsive Therapy Correlate With Subgenual Anterior Cingulate Activity and Connectivity in Depression.

The mechanisms underlying the effects of electroconvulsive therapy (ECT) in major depressive disorder (MDD) are not fully understood. Resting-state functional magnetic resonance imaging (rs-fMRI) is a new tool to study the effects of brain stimulation interventions, particularly ECT. The authors aim to investigate the mechanisms of ECT in MDD by rs-fMRI.They used rs-fMRI to measure functional changes in the brain of first-episode, treatment-naive MDD patients (n = 23) immediately before and then following 8 ECT sessions (brief-pulse square-wave apparatus, bitemporal). They also computed voxel-wise amplitude of low-frequency fluctuation (ALFF) as a measure of regional brain activity and selected the left subgenual anterior cingulate cortex (sgACC) to evaluate functional connectivity between the sgACC and other brain regions.Increased regional brain activity measured by ALFF mainly in the left sgACC following ECT. Functional connectivity of the left sgACC increased in the ipsilateral parahippocampal gyrus, pregenual ACC, contralateral middle temporal pole, and orbitofrontal cortex. Importantly, reduction in depressive symptoms were negatively correlated with increased ALFF in the left sgACC and left hippocampus, and with distant functional connectivity between the left sgACC and contralateral middle temporal pole. That is, across subjects, as depression improved, regional brain activity in sgACC and its functional connectivity increased in the brain.Eight ECT sessions in MDD patients modulated activity in the sgACC and its networks. The antidepressant effects of ECT were negatively correlated with sgACC brain activity and connectivity. These findings suggest that sgACC-associated prefrontal-limbic structures are associated with the therapeutic effects of ECT in MDD.

Reorganization of Anatomical Connectome following Electroconvulsive Therapy in Major Depressive Disorder.

Objective. Electroconvulsive therapy (ECT) is considered one of the most effective and fast-acting treatment options for depressive episodes. Little is known, however, about ECT's enabling brain (neuro)plasticity effects, particular for plasticity of white matter pathway. Materials and Methods. We collected longitudinal diffusion tensor imaging in the first-episode, drug-naïve major depressive disorder (MDD) patients (n = 24) before and after a predefined time window ECT treatment. We constructed large-scale anatomical networks derived from white matter fiber tractography and evaluated the topological reorganization using graph theoretical analysis. We also assessed the relationship between topological reorganization with improvements in depressive symptoms. Results. Our investigation revealed three main findings: (1) the small-worldness was persistent after ECT series; (2) anatomical connections changes were found in limbic structure, temporal and frontal lobes, in which the connection changes between amygdala and parahippocampus correlate with depressive symptom reduction; (3) significant nodal strength changes were found in right paralimbic network. Conclusions. ECT elicits neuroplastic processes associated with improvements in depressive symptoms that act to specific local ventral frontolimbic circuits, but not small-world property. Overall, ECT induced topological reorganization in large-scale brain structural network, opening up new avenues to better understand the mode of ECT action in MDD.