Unraveling a Concerted Proton-Coupled Electron Transfer Pathway in Atomically Precise Gold Nanoclusters.

Metal nanoclusters (MNCs) represent a promising class of materials for catalytic carbon dioxide and proton reduction as well as dihydrogen oxidation. In such reactions, multiple proton-coupled electron transfer (PCET) processes are typically involved, and the current understanding of PCET mechanisms in MNCs has primarily focused on the sequential transfer mode. However, a concerted transfer pathway, i.e., concerted electron-proton transfer (CEPT), despite its potential for a higher catalytic rate and lower reaction barrier, still lacks comprehensive elucidation. Herein, we introduce an experimental paradigm to test the feasibility of the CEPT process in MNCs, by employing Au18(SR)14 (SR denotes thiolate ligand), Au22(SR)18, and Au25(SR)18- as model clusters. Detailed investigations indicate that the photoinduced PCET reactions in the designed system proceed via an CEPT pathway. Furthermore, the rate constants of gold nanoclusters (AuNCs) have been found to be correlated with both the size of the cluster and the flexibility of the Au-S framework. This newly identified PCET behavior in AuNCs is prominently different from that observed in semiconductor quantum dots and plasmonic metal nanoparticles. Our findings are of crucial importance for unveiling the catalytic mechanisms of quantum-confined metal nanomaterials and for the future rational design of more efficient catalysts.

Altered dynamic amplitude of low-frequency fluctuation in individuals at high risk for Alzheimer's disease.

The brain's dynamic spontaneous neural activity is significant in supporting cognition; however, how brain dynamics go awry in subjective cognitive decline (SCD) and mild cognitive impairment (MCI) remains unclear. Thus, the current study aimed to investigate the dynamic amplitude of low-frequency fluctuation (dALFF) alterations in patients at high risk for Alzheimer's disease and to explore its correlation with clinical cognitive assessment scales, to identify an early imaging sign for these special populations. A total of 152 participants, including 72 SCD patients, 44 MCI patients and 36 healthy controls (HCs), underwent a resting-state functional magnetic resonance imaging and were assessed with various neuropsychological tests. The dALFF was measured using sliding-window analysis. We employed canonical correlation analysis (CCA) to examine the bi-multivariate correlations between neuropsychological scales and altered dALFF among multiple regions in SCD and MCI patients. Compared to those in the HC group, both the MCI and SCD groups showed higher dALFF values in the right opercular inferior frontal gyrus (voxel P < .001, cluster P < .05, correction). Moreover, the CCA models revealed that behavioural tests relevant to inattention correlated with the dALFF of the right middle frontal gyrus and right opercular inferior frontal gyrus, which are involved in frontoparietal networks (R = .43, P = .024). In conclusion, the brain dynamics of neural activity in frontal areas provide insights into the shared neural basis underlying SCD and MCI.

Investigating the spontaneous brain activities of patients with subjective cognitive decline and mild cognitive impairment: an amplitude of low-frequency fluctuation functional magnetic resonance imaging study.

Background: Subjective cognitive decline (SCD) and mild cognitive impairment (MCI) are neurodegenerative processing stages of Alzheimer's disease (AD). Cognitive decline is thought to manifest in intrinsic brain activity changes, but research results yielded conflicting and few studies have explored the roles of brain regions in cognitive decline, and sensitivity of the cognitive field to changes in the altered intrinsic brain activity. Methods: In this cross-sectional study, 158 elderly participants were recruited from the memory clinic of the First Affiliated Hospital of Nanjing Medical University from July 2019 to May 2021, and grouped into SCD (n=73), MCI (n=46), and normal controls (NC) (n=39). The amplitude of low-frequency fluctuation (ALFF) was calculated and evaluated among the groups. Then canonical correlation analysis (CCA) was conducted to investigate the associations between imaging outcomes and cognitive behaviors. Results: Neuropsychological tests in different cognitive dimensions and ALFF values of the prefrontal, parietal, and temporal gyrus, were significantly different (P<0.05) among the three groups, with no appreciable decline in daily activity. The changes in intrinsic activities were closely related to the decline in cognitive function (R=0.73, P=0.002). ALFF values in the left middle occipital gyrus, right middle frontal gyrus, left superior frontal gyrus, left angular gyrus, and superior temporal gyrus played significant roles in the analysis, while the Montreal Cognitive Assessment (MoCA) and Auditory-Verbal Learning Test scores were found to be more sensitive to changes in ALFF values. Conclusions: Spontaneous brain activity is a stable imaging biomarker of cognitive impairment. ALFF changes of the prefrontal, occipital, left angular, and temporal gyrus were sensitive to identifying cognitive decline, and the scores of the Auditory-Verbal Learning Test and MoCA could predict the abnormal intrinsic activities.

Classification of bipolar disorders using the multilayer modularity in dynamic minimum spanning tree from resting state fMRI.

The diagnosis of bipolar disorders (BD) mainly depends on the clinical history and behavior observation, while only using clinical tools often limits the diagnosis accuracy. The study aimed to create a novel BD diagnosis framework using multilayer modularity in the dynamic minimum spanning tree (MST). We collected 45 un-medicated BD patients and 47 healthy controls (HC). The sliding window approach was utilized to construct dynamic MST via resting-state functional magnetic resonance imaging (fMRI) data. Firstly, we used three null models to explore the effectiveness of multilayer modularity in dynamic MST. Furthermore, the module allegiance exacted from dynamic MST was applied to train a classifier to discriminate BD patients. Finally, we explored the influence of the FC estimator and MST scale on the performance of the model. The findings indicated that multilayer modularity in the dynamic MST was not a random process in the human brain. And the model achieved an accuracy of 83.70% for identifying BD patients. In addition, we found the default mode network, subcortical network (SubC), and attention network played a key role in the classification. These findings suggested that the multilayer modularity in dynamic MST could highlight the difference between HC and BD patients, which opened up a new diagnostic tool for BD patients. Supplementary Information: The online version contains supplementary material available at 10.1007/s11571-022-09907-x.

Synthesis of lignin nanoparticle­manganese dioxide complex and its adsorption of methyl orange.

Lignin nanoparticles (LNPs) were synthesized using an anti-solvent method and subsequently loaded with manganese dioxide (MnO2) via potassium permanganate treatment, resulting in the formation of MnO2@LNPs. An extensive investigation was conducted to elucidate the influence of MnO2@LNPs on the decolorization of methyl orange solution. The LNPs were successfully obtained by adjusting the preparation parameters, yielding particles exhibited average sizes ranging from 300 to 600 nm, and the synthesis process exhibited a high yield of up to 87.3% and excellent dispersion characteristics. Notably, LNPs size was reduced by decreasing initial concentration, increasing stirring rate, and adding water. In the acetone-water two-phase system, LNPs self-assembled into spherical particles driven by π-π interactions and hydrogen bond forces. Oxidation modification using potassium permanganate led to the formation of nanoscale MnO2, which effectively combined with LNPs. Remarkably, the resulting MnO2@LNPs demonstrated a two-fold increase in methyl orange adsorption capacity (227 mg/g) compared to unmodified LNPs. The process followed the Langmuir isotherm model and was exothermic.

Local and large-scale resting-state oscillatory dysfunctions for early antidepressant response prediction in major depressive disorder.

BACKGROUND: Magnetoencephalography (MEG) could explore and resolve brain signals with realistic temporal resolution to investigate the underlying electrophysiology of major depressive disorder (MDD) and the treatment efficacy. Here, we explore whether neuro-electrophysiological features of MDD at baseline can be used as a neural marker to predict their early antidepressant response. METHODS: Sixty-six medication-free patients with MDD and 48 healthy controls were enrolled and underwent resting-state MEG scans. Hamilton depression rating scale (HAMD-17) was assessed at both baseline and after two-week pharmacotherapy. We measured local and large-scale resting-state oscillatory dysfunctions with a data-driven model, the Fitting Oscillations & One-Over F algorithm. Then, we quantified band-limited regional power and functional connectivity between brain regions. RESULTS: After two-week follow-up, 52 patients completed the re-interviews. Thirty-one patients showed early response (ER) to pharmacotherapy and 21 patients did not. Treatment response was defined as at least 50 % reduction of severity reflected by HAMD-17. We observed decreased regional periodic power in patients with MDD comparing to controls. However, patients with ER exhibited that functional couplings across brain regions in both alpha and beta band were increased and significantly correlated with severity of depressive symptoms after treatment. Receiver operating characteristic curves (ROC) further confirmed the predictive ability of baseline large-scale functional connectivity for early antidepressant efficacy (AUC = 0.9969). LIMITATIONS: Relatively small sample size and not a double-blind design. CONCLUSIONS: The current study demonstrated the electrophysiological dysfunctions of local neural oscillatory related with depression and highlighted the identification ability of large-scale couplings biomarkers in early antidepressant response prediction.

Altered beta band spatial-temporal interactions during negative emotional processing in major depressive disorder: An MEG study.

BACKGROUND: The mood-concordance bias is a key feature of major depressive disorder (MDD), but the spatiotemporal neural activity associated with emotional processing in MDD remains unclear. Understanding the dysregulated connectivity patterns during emotional processing and their relationship with clinical symptoms could provide insights into MDD neuropathology. METHODS: We enrolled 108 MDD patients and 64 healthy controls (HCs) who performed an emotion recognition task during magnetoencephalography recording. Network-based statistics (NBS) was used to analyze whole-brain functional connectivity (FC) across different frequency ranges during distinct temporal periods. The relationship between the aberrant FC and affective symptoms was explored. RESULTS: MDD patients exhibited decreased FC strength in the beta band (13-30 Hz) compared to HCs. During the early stage of emotional processing (0-100 ms), reduced FC was observed between the left parahippocampal gyrus and the left cuneus. In the late stage (250-400 ms), aberrant FC was primarily found in the cortex-limbic-striatum systems. Moreover, the FC strength between the right fusiform gyrus and left thalamus, and between the left calcarine fissure and left inferior temporal gyrus were negatively associated with Hamilton Depression Rating Scale (HAMD) scores. LIMITATIONS: Medication information was not involved. CONCLUSION: MDD patients exhibited abnormal temporal-spatial neural interactions in the beta band, ranging from early sensory to later cognitive processing stages. These aberrant interactions involve the cortex-limbic-striatum circuit. Notably, aberrant FC in may serve as a potential biomarker for assessing depression severity.

Efficacy and safety of Ginkgo biloba dropping pills in the treatment of coronary heart disease with stable angina pectoris and depression: study protocol for a randomised, placebo-controlled, parallel-group, double-blind and multicentre clinical trial.

BACKGROUND: Coronary heart disease(CHD) with stable angina pectoris is a common cardiovascular disease. It has been reported that 10%-81.4% of these patients suffer from psychological conditions,such as depression, which has been associated with more frequent angina, lower treatment satisfaction and lower perceived quality of life. Ginkgo biloba extract (GBE), the raw material of Ginkgo biloba dropping pills (GBDPs), is widely used to treat various conditions, including cardiovascular disease, ischaemic cerebrovascular disease, and depression. This clinical trial aimed to examine the efficacy and safety of GBDPs in improving the frequency of angina pectoris and the life quality of patients with stable angina pectoris and depression symptoms. METHODS: This randomised, double-blind, placebo-controlled, parallel-group and multicentre clinical trial will be conducted in four medical centres in China. We aim to recruit approximately 72 participants aged 18-75 years with depression and coronary heart disease with stable angina pectoris. Based on conventional drug treatment, participants will be randomly assignedto the treatment group (GBDPs group; n=36) or the control group (placebo group; n=36) at a 1:1 allocation ratio. After randomisation,follow-up will be done at 4 weeks, 8 weeks and 12 weeks (±3 days). Additionally, 30 healthy individuals will be enrolled to investigate the underlying pharmacological mechanisms of the effects of GBE. The primary outcomes will be the Seattle Angina Questionnaire score and the frequency of angina pectoris-related symptoms each week. The secondary outcomes will include the 36-item Short Form Health Survey quality-of-life scale, Hamilton Depression Scale and composite endpoint incidence of major adverse cardiovascular events. ETHICS AND DISSEMINATION: This trial has been approved by the Research Ethics Committee of the First Affiliated Hospital of Guangzhou University of Chinese Medicine, China (approval number: ZYYECK [2020]030). Written informed consent will be obtained from all participants. The results of this trial will be publicly shared through academic conferences and peer-reviewed journals. TRIAL REGISTRATION NUMBER: NCT04529148 and ChiCTR2200066908.

Paeonol improves renal and vascular angiotensin II type 1 receptor function via inhibiting oxidative stress in spontaneously hypertensive rats.

BACKGROUND: Oxidative stress has been shown to play a critical role in the pathogenesis of hypertension. Paeonol, a major phenolic component extracted from Moutan Cortex, exerts a beneficial effect in preventing cardiovascular disease via reducing oxidative stress. The present study investigated the protective mechanism of paeonol against high blood pressure in spontaneous hypertension rats (SHRs). METHODS: Wistar-Kyoto (WKY) rats and SHRs received vehicle or peaonol in the drinking water for 5 weeks. Blood pressure was measured by tail-cuff plethysmography and oxidative stress in kidney and vascular tissue was examined by enzyme-linked immunosorbed assay. The functions of angiotensin II type 1 receptors (AT1R) in the kidney and mesenteric artery were measured by natriuresis and vasoconstrictor response, respectively. RESULTS: Compared with vehicle-treated WKY rats, vehicle-treated SHRs exhibited higher blood pressure, increased oxidative stress, accompanied by exaggerated diuretic and natriuretic responses to candesartan (AT1 receptor antagonist) and vasoconstrictor responses to angiotensin II (Ang II). Moreover, SHRs had higher ACE and AT1R in the kidney and mesenteric artery, and higher Ang II and lower renin levels. Interestingly, paeonol treatment reduced the candesartan-induced increase in diuresis and natriuresis and vasoconstrictor responses to Ang II, and lowered blood pressure in SHRs, accompanied by reducing AT1R protein expression in the kidney and mesenteric artery of SHR, and Ang II levels in plasma and increasing renin levels in renal cortex. In addition, these changes were associated with reducing oxidative stress. CONCLUSIONS: The present study suggests that paeonol improves renal and vascular AT1R functions by inhibition of oxidative stress, thus lowering blood pressure in SHRs.

Prediction of suicidality in bipolar disorder using variability of intrinsic brain activity and machine learning.

Bipolar disorder (BD) is associated with marked suicidal susceptibility, particularly during a major depressive episode. However, the evaluation of suicidal risk remains challenging since it relies mainly on self-reported information from patients. Hence, it is necessary to complement neuroimaging features with advanced machine learning techniques in order to predict suicidal behavior in BD patients. In this study, a total of 288 participants, including 75 BD suicide attempters, 101 BD nonattempters and 112 healthy controls, underwent a resting-state functional magnetic resonance imaging (rs-fMRI). Intrinsic brain activity was measured by amplitude of low-frequency fluctuation (ALFF). We trained and tested a two-level k-nearest neighbors (k-NN) model based on resting-state variability of ALFF with fivefold cross-validation. BD suicide attempters had increased dynamic ALFF values in the right anterior cingulate cortex, left thalamus and right precuneus. Compared to other machine learning methods, our proposed framework had a promising performance with 83.52% accuracy, 78.75% sensitivity and 87.50% specificity. The trained models could also replicate and validate the results in an independent cohort with 72.72% accuracy. These findings based on a relatively large data set, provide a promising way of combining fMRI data with machine learning technique to reliably predict suicide attempt at an individual level in bipolar depression. Overall, this work might enhance our understanding of the neurobiology of suicidal behavior by detecting clinically defined disruptions in the dynamics of instinct brain activity.

Radiomics analysis of CT imaging improves preoperative prediction of cervical lymph node metastasis in laryngeal squamous cell carcinoma.

OBJECTIVES: To investigate the role of CT radiomics for preoperative prediction of lymph node metastasis (LNM) in laryngeal squamous cell carcinoma (LSCC). METHODS: LSCC patients who received open surgery and lymphadenectomy were enrolled and randomized into primary and validation cohorts at a ratio of 7:3 (325 vs. 139). In the primary cohort, we extracted radiomics features from whole intratumoral regions on venous-phase CT images and constructed a radiomics signature by least absolute shrinkage and selection operator (LASSO) regression. A radiomics model incorporating the radiomic signature and independent clinical factors was established via multivariable logistic regression and presented as a nomogram. Nomogram performance was compared with a clinical model and traditional CT report with respect to its discrimination and clinical usefulness. The radiomics nomogram was internally tested in an independent validation cohort. RESULTS: The radiomics signature, composed of 9 stable features, was associated with LNM in both the primary and validation cohorts (both p < .001). A radiomics model incorporating independent predictors of LNM (the radiomics signature, tumor subsite, and CT report) showed significantly better discrimination of nodal status than either the clinical model or the CT report in the primary cohort (AUC 0.91 vs. 0.84 vs. 0.68) and validation cohort (AUC 0.89 vs. 0.83 vs. 0.70). Decision curve analysis confirmed that the radiomics nomogram was superior to the clinical model and traditional CT report. CONCLUSIONS: The CT-based radiomics nomogram may improve preoperative identification of nodal status and help in clinical decision-making in LSCC. KEY POINTS: • The radiomics model showed favorable performance for predicting LN metastasis in LSCC patients. • The radiomics model may help in clinical decision-making and define patient subsets benefiting most from neck treatment.

Convergent and divergent cognitive impairment of unipolar and bipolar depression: A magnetoencephalography resting-state study.

BACKGROUND: Unipolar depression (UD) and bipolar depression (BD) showed convergent and divergent cognitive impairments. Neural oscillations are linked to the foundational cognitive processes. We aimed to investigate the underpinning spectral neuronal power patterns by magnetoencephalography (MEG), which combinates high spatial and temporal resolution. We hypothesized that patients with UD and BD exhibit common and distinct patterns, which may contribute to their cognitive impairments. METHODS: Group cognitive tests were performed. Eyes closed resting-state MEG data were collected from 61 UD, 55 BD, and 52 healthy controls (HC). Nonparametric cluster-based permutation tests were performed to deal with the multiple comparison problem on channel-frequency MEG data. Correlation analysis of cognitive dysfunction scores and MEG oscillation were conducted by Spearman or partial correlation analysis. RESULTS: Wisconsin Card Sorting Test showed similar cognitive impairment in patients with UD and BD. Moreover, patients with BD exhibited extensive cognitive deficits in verbal executive functions and visuospatial processing. Compare to HC, both patients with UD and BD showed increased frontal-central beta power while high gamma power was decreased in UD groups during the resting-state. The significant correlations between cognitive function and average beta power were observed. CONCLUSIONS: Patients with BD had more cognitive impairments on different dimensions than those with UD, involving disrupted beta power modulations. Our investigation provides a better understanding of the neuroelectrophysiological process underlying cognitive impairments in patients with UD and BD.

Association Between Antidepressant Efficacy and Interactions of Three Core Depression-Related Brain Networks in Major Depressive Disorder.

Background: The central executive network (CEN), salience network (SN), and default mode network (DMN) are the three most studied depression-related brain networks. Many studies have shown that they are related to depression symptoms and treatment effects. However, few studies have related these three networks and their activity frequency bands to depressive symptoms and treatment efficacy. Methods: Sixty-six medication-free patients with major depressive disorder (MDD) were enrolled. Magnetoencephalography (MEG) was administered at baseline to calculate imaging indicators such as the power and functional connectivity (FC) of each brain network. The Hamilton Rating Score for Depression (HRSD-17) was assessed at baseline and weekly for 4 weeks. Pearson correlation and receiver operating characteristic curves (ROC) analyses were used to explore the relationship between brain imaging indicators and antidepressant efficacy. Results: The difference between therapeutically effective and ineffective groups was mainly manifested in the beta power of the SN. The FC of beta waves between the three networks was related to antidepressant efficacy, with ROC analysis results of AUC = 0.794, P = 0.004, sensitivity = 76.7%, and specificity = 81.8%. Limitations: The sample size was small and a healthy control group was not available. Conclusions: The interaction between the three networks is related to antidepressant efficacy and the relief of depressive symptoms.

Dynamic connectivity alterations in anterior cingulate cortex associated with suicide attempts in bipolar disorders with a current major depressive episode.

OBJECTIVE: Considering that the physiological mechanism of the anterior cingulate cortex (ACC) in suicide brain remains elusive for bipolar disorder (BD) patients. The study aims to investigate the intrinsic relevance between ACC and suicide attempts (SA) through transient functional connectivity (FC). METHODS: We enrolled 50 un-medicated BD patients with at least one SA, 67 none-suicide attempt patients (NSA) and 75 healthy controls (HCs). The sliding window approach was utilized to study the dynamic FC of ACC via resting-state functional MRI data. Subsequently, we probed into the temporal properties of dynamic FC and then estimated the relationship between dynamic characteristics and clinical variables using the Pearson correlation. RESULTS: We found six distinct FC states in all populations, with one of them being more associated with SA. Compared with NSA and HCs, the suicide-related functional state showed significantly reduced dwell time in SA patients, accompanied by a significantly increased FC strength between the right ACC and the regions within the subcortical (SubC) network. In addition, the number of transitions was significantly increased in SA patients relative to other groups. All these altered indicators were significantly correlated with the suicide risk. CONCLUSIONS: The results suggested that the dysfunction of ACC was relevant to SA from a dynamic FC perspective in BD patients. It highlights the temporal properties in dynamic FC of ACC that could be used as a putative target of suicide risk assessment for BD patients.

Flow-through strategy to fractionate lignin from eucalyptus with formic acid/hydrochloric solution under mild conditions.

Formic acid is an attractive solvent for the fractionation of lignocellulose for the production of biomaterials and chemicals, while the operation conducted in a batch manner is not conducive to mass transfer in separation process. In this research, eucalyptus was fractionated with formic acid/hydrochloric solution in a flow-through reactor at 95 °C, and the structural characteristics and the composition of fractionated lignin in different stages were investigated. Results showed that the fractionation efficiency was notably improved with a flow-through reactor, as evidenced by the low solid residue yield of 49.5% and the lignin removal rate of 79.4% as compared to the batch manner. During the fractionation process, the dissolution rate of lignin decreased gradually, and the obtained lignin samples showed low molecular weight (<3000), good uniformity (<2), and high thermal stability. The structure analysis showed that ß-O-4, ß-ß, and ß-5 linkages in lignin were degraded to varying degrees with increased time, and the degradation of G units was more severe than S ones.

Spontaneous transient states of fronto-temporal and default-mode networks altered by suicide attempt in major depressive disorder.

Major depressive disorder (MDD) is associated with increased suicidality, and it's still challenging to identify suicide in clinical practice. Although suicide attempt (SA) is the most relevant precursor with multiple functional abnormalities reported from neuroimaging studies, little is known about how the spontaneous transient activated patterns organize and coordinate brain networks underlying SA. Thus, we obtained resting-state magnetoencephalography data for two MDD subgroups of 44 non-suicide patients and 34 suicide-attempted patients, together with 49 matched health-controls. For the source-space signals, Hidden Markov Model (HMM) helped to capture the sub-second dynamic activity via a hidden sequence of finite number of states. Temporal parameters and spectral activation were acquired for each state and then compared between groups. Here, HMM states characterized the spatiotemporal signatures of eight networks. The activity of suicide attempters switches more frequently into the fronto-temporal network, as the time spent occupancy of fronto-temporal state is increased and interval time is decreased compared with the non-suicide patients. Moreover, these changes are significantly correlated with Nurses' Global Assessment of Suicide Risk scores. Suicide attempters also exhibit increased state-wise activations in the theta band (4-8 Hz) in the posterior default mode network centered on posterior cingulate cortex, which can't be detected in the static spectral analysis. These alternations may disturb the time allocations of cognitive control regulations and cause inflexible decision making to SA. As the better sensitivity of dynamic study in reflecting SA diathesis than the static is validated, dynamic stability could serve as a potential neuronal marker for SA.

An Investigation into the Association Between Dopamine Receptor D1 Multilocus Genetic Variation, Multiparametric Magnetic Resonance Imaging, and Antidepressant Treatment.

BACKGROUND: Combining genetic variants with neuroimaging phenotypes may facilitate understanding of the biological mechanisms for the etiology and pharmacology of antidepressant treatment of major depressive disorder (MDD). PURPOSE: To explore the latent pathway of dopamine gene-hierarchical brain network-antidepressant treatment. STUDY TYPE: Retrospective. POPULATION: One hundred and sixty-eight MDD inpatients divided into responders (N = 98) or nonresponders (N = 70) based on the treatment outcome of antidepressant. FIELD STRENGTH/SEQUENCE: Diffusion tensors imaging and resting-state functional magnetic resonance imaging at 3.0T using echo-planar sequence. ASSESSMENT: Four genetic variations of the dopamine receptor D1 (DRD1) were genotyped. Strengths of rich-club, feeder, and local connections were calculated based on the rich-club organizations of structural and functional brain networks at baseline and following 4 weeks of selective serotonin reuptake inhibitor (SSRI) therapy. STATISTICAL TESTS: Logistic and linear regressions were used to analyze the impact of DRD1 multilocus genetic profile score on the treatment response of SSRI, and their associations with strengths of rich-club, feeder, and local connections. Mediation models were developed to explore the mediation role of rich-club organizations on the relationship between DRD1 and SSRI therapy response. A P value <0.05 was considered to be statistically significant. RESULTS: Multiple genetic variations of DRD1 were significantly related to the strengths of feeder connections both in structural and functional networks, and to the treatment response of SSRI. Furthermore, the strength of the structural feeder connection significantly modulated the effect of DRD1 variants on SSRI treatment outcome. DATA CONCLUSION: DRD1 displayed close connections both with SSRI treatment outcome and rich-club organizations of structural and functional data. Moreover, structural feeder connection played a mediating role in the relationship between DRD1 and antidepressant therapy. LEVEL OF EVIDENCE: 3 TECHNICAL EFFICACY STAGE: 4.

Diurnal mood variation symptoms in major depressive disorder associated with evening chronotype: Evidence from a neuroimaging study.

BACKGROUND: Major depressive disorder (MDD) is often accompanied with classic diurnal mood variation (DMV) symptoms. Patients with DMV symptoms feel a mood improvement and prefer activities at dusk or in the evening, which is consistent with the evening chronotype. Their neural alterations are unclear. In this study, we aimed to explore the neuropathological mechanisms underlying the circadian rhythm of mood and the association with chronotype in MDD. METHODS: A total of 126 depressed patients, including 48 with DMV, 78 without, and 67 age/gender-matched healthy controls (HC) were recruited and underwent a resting-state functional magnetic resonance imaging. Spontaneous neural activity was investigated using amplitude of low-frequency fluctuation (ALFF) and region of interest (ROI)-based functional connectivity (FC) analyses were conducted. The Morningness-Eveningness Questionnaire (MEQ) was utilized to evaluate participant chronotypes and Pearson correlations were calculated between altered ALFF/FC values and MEQ scores in patients with MDD. RESULTS: Compared with NMV, DMV group exhibited lower MEQ scores, and increased ALFF values in the right orbital superior frontal gyrus (oSFG). We observed that increased FC between the left suprachiasmatic nucleus (SCN) and supramarginal gyrus (SMG). ALFF in the oSFG and FC of rSCN-SMG were negatively correlated with MEQ scores. LIMITATION: Some people's chronotypes information is missing. CONCLUSION: Patients with DMV tended to be evening type and exhibited abnormal brain functions in frontal lobes. The synergistic changes between frontotemporal lobe, SCN-SMG maybe the characteristic of patients with DMV symptoms.

Predicting Treatment Selections for Individuals with Major Depressive Disorder According to Functional Connectivity Subgroups.

Background: Major depressive disorder (MDD) is a highly prevalent and disabling disease. Currently, patients' treatment choices depend on their clinical symptoms observed by clinicians, which are subjective. Rich evidence suggests that different functional networks' dysfunctions correspond to different intervention preferences. In this study, we aimed to develop a prediction model based on data-driven subgroups to provide treatment recommendations. Methods: All 630 participants enrolled from four sites underwent functional magnetic resonances imaging at baseline. In the discovery data set (n = 228), we first identified MDD subgroups by the hierarchical clustering method using the canonical variates of resting-state functional connectivity (FC) through canonical correlation analyses. The demographic symptom improvement and FC were compared among subgroups. The preference intervention for each subgroup was also determined. Next, we predicted the individual treatment strategy. Specifically, a patient was assigned into predefined subgroups based on FC similarities and then his/her treatment strategy was determined by the subgroups' preferred interventions. Results: Three subgroups with specific treatment recommendations were emerged, including (1) a selective serotonin reuptake inhibitors-oriented subgroup with early improvements in working and activities, (2) a stimulation-oriented subgroup with more alleviation in suicide, and (3) a selective serotonin noradrenaline reuptake inhibitors-oriented subgroup with more alleviation in hypochondriasis. Through cross-dataset testing, respectively, conducted on three testing data sets, results showed an overall accuracy of 72.83%. Conclusions: Our works revealed the correspondences between subgroups and their treatment preferences and predicted individual treatment strategy based on such correspondences. Our model has the potential to support psychiatrists in early clinical decision making for better treatment outcomes. Impact statement This study proposes a novel framework to provide treatment recommendations by integrating resting-state functional connectivity and advanced machine learning technique in a large data set. Our data-driven approach is able to objectively and automatically cluster patients into different subgroups and recommends the optimal treatment strategies based on specific brain circuits and clinical symptoms. Our results have the potential to support psychiatrists in early clinical decision making for better treatment outcomes.

Attenuated alpha-gamma coupling in emotional dual pathways with right-Amygdala predicting ineffective antidepressant response.

AIMS: The diversity of treatment outcomes for major depressive disorder (MDD) remains uncertain in neuropathology. The current study aimed at exploring electrophysiological biomarkers associated with treatment response. METHODS: The present study recruited 130 subjects including 100 MDD patients and 30 healthy controls. All subjects participated in a sad expression recognition task while their magnetoencephalography data were recorded. Patients who had a reduction of at least 50% in disorder severity at endpoint (>2 weeks) were considered as responders. Within-frequency power and phase-amplitude coupling were measured for the brain regions involved in the emotional visual information processing pathways. RESULTS: The significant alpha-gamma decoupling from the right thalamus to the right amygdala in unconscious processing and from right orbital frontal cortices to the right amygdala in conscious processing was found in non-responders relative to responders and healthy controls. These kinds of dysregulation could also predict the potential treatment response. CONCLUSION: The attenuated alpha-gamma coupling in dual pathways indicated increased sensitivity to the negative emotional information and reduced moderated effect of the amygdala, which might cause insensitivity to antidepressant treatment and could be regarded as potential neural mechanisms for treatment response prediction.