Major Depressive Disorder Patients Research Articles

A hierarchical random effects state-space model for modeling brain activities from electroencephalogram data.

Mental disorders present challenges in diagnosis and treatment due to their complex and heterogeneous nature. Electroencephalogram (EEG) has shown promise as a source of potential biomarkers for these disorders. However, existing methods for analyzing EEG signals have limitations in addressing heterogeneity and capturing complex brain activity patterns between regions. This paper proposes a novel random effects state-space model (RESSM) for analyzing large-scale multi-channel resting-state EEG signals, accounting for the heterogeneity of brain connectivities between groups and individual subjects. We incorporate multi-level random effects for temporal dynamical and spatial mapping matrices and address non-stationarity so that the brain connectivity patterns can vary over time. The model is fitted under a Bayesian hierarchical model framework coupled with a Gibbs sampler. Compared to previous mixed-effects state-space models, we directly model high-dimensional random effects matrices of interest without structural constraints and tackle the challenge of identifiability. Through extensive simulation studies, we demonstrate that our approach yields valid estimation and inference. We apply RESSM to a multi-site clinical trial of major depressive disorder (MDD). Our analysis uncovers significant differences in resting-state brain temporal dynamics among MDD patients compared to healthy individuals. In addition, we show the subject-level EEG features derived from RESSM exhibit a superior predictive value for the heterogeneous treatment effect compared to the EEG frequency band power, suggesting the potential of EEG as a valuable biomarker for MDD.

Effect of childhood trauma on cognitive function in individuals with major depressive disorder and healthy controls

BackgroundIndividuals with major depressive disorder (MDD) exhibit cognitive impairment, while childhood trauma (CT) is associated with an elevated risk of both MDD and cognitive dysfunction. The effect of CT on cognitive function in MDD patients and healthy controls (HCs) is unclear. MethodsMDD patients and HCs were enrolled between December 2013 and December 2016. The Childhood Trauma Questionnaire (CTQ) was used to assess CT. Depressive symptoms and cognitive function were assessed at baseline and after 8-week acute-phase treatment with selective serotonin reuptake inhibitors (SSRIs) in MDD patients. ResultsA total of 909 people were included in the analysis. The interaction between MDD and CT had a main effect on Digit Symbol-Coding Test (DSCT), Stroop Color Test (SCT), and Stroop Color-Word Test (SCWT) scores. The effect of CT on cognitive function disappeared after adjusting for MDD diagnosis and years of education. Neglect could predict poor performance on SCT and SCWT in the HC group. After acute-phase treatment with SSRIs, CT did not significantly predict changes in cognitive function or depressive symptoms. LimitationsThe CTQ assessment might cause recall bias, and the cross-sectional design could not establish the causal link between CT and cognitive function. ConclusionThe effect of CT on cognitive function was modulated by MDD diagnosis and years of education. CT did not predict changes in depressive symptoms or cognitive function after acute-phase treatment with SSRIs. The direct influence of CT on cognitive function in MDD patients may be over-estimated. Trial registrationClinicalTrials.gov: NCT02023567; registration date: December 2013.

An exploratory analysis on the association between suicidal ideation and the microbiome in patients with or without major depressive disorder

BackgroundScarce research has investigated associations between suicidal ideation and the gut microbiota. We aimed to explore variations in the gut microbiome associated with suicidal ideation and major depressive disorder (MDD). MethodA case-control study compared abundances of fecal microbiota and biomarkers of gut permeability among patients with MDD, with or without suicidal ideation, and healthy volunteers without depression. Information on demographic variables and assessments of suicidal ideation (Beck Suicidal Ideation Scale), depression (Hamilton Depression Scale, Patient Health Questionnaire, Hospital Anxiety and Depression Scale- Depression), as well as anxiety (Hospital Anxiety and Depression Scale- Anxiety), were obtained. Univariate and multivariate regression model was performed to explore the possible predictors of suicidal ideation. ResultsAmong the 140 participants, significant differences in Beta diversity were found between MDD patients with (n = 43) or without suicidal ideation (n = 34), and healthy volunteers (n = 42) (all p < 0.001). The strain of g-Phascolarctobacterium was found to have significant positive associations with scores of BSSI and BSSI Part 1 (suicidal ideation), particularly in MDD patients with suicidal ideation, after controlling for demographic and mood covariates. Mediation analyses revealed that g-Phascolarctobacterium may be a partial mediator between depression and suicidal ideation; however, it is also possible that the association between g-Phascolarctobacterium and suicidal ideation was partially mediated by the level of depression. ConclusionWe found different compositions, diversities, and possible mediating of the gut microbiome associated with suicidal ideations. Potential mechanisms need further investigation to establish whether this reflects a biological process that might be the focus for intervention development. SynopsisOur objective was to investigate whether the diversities and abundances of the gut microbiome varied in people with or without suicidal ideation and with or without MDD after considering possible demographic and mood confounders.

Comparative analysis of gut microbiota in major depressive disorder and schizophrenia during hospitalisation - the case-control, post hoc study

The aim of this study was to investigate the relationship between gut microbiota and major depressive disorder (MDD) and schizophrenia (SCZ) by comparing 36 inpatients with these conditions to 29 healthy controls (HC) matched for age, sex, and body mass index (BMI). Individuals with SCZ exhibited greater microbiota richness compared to HC (FDR P(Q)=0.028). Taxonomically, while no significant differences were observed between the microbiota of MDD and SCZ patients in a head-to-head comparison, both patient groups differed significantly when compared to HC. Interestingly, besides common patterns (such as a higher abundance of Erysipelotrichaceae UCG-003 and Streptococcus, and a lower abundance of Lachnospiraceae ND3007 group), unique patterns were exhibited only in MDD (with a higher abundance of Anaerostipes, Q=0.004) or SCZ (with a higher abundance of Sutterella, Q=0.001, and a lower abundance of Clostridium sensu stricto 1, Q=0.002). The Random Forest algorithm identified Ruminococcus torques group, Lachnospiraceae UCG-001, and Erysipelotrichaceae UCG-003 as highly discriminative features for both SCZ and MDD, while Suturella and Holdemania were unique features for SZC, and Lachnospiraceae genus CAG-56 and Anaerostipes for MDD. Additionally, between 50 % and 60 % of the differentially abundant taxa were found among the top 10 influential features in the RF models. In conclusion, while no significant differences were found between the microbiota of MDD and SCZ patients, distinct microbial patterns were found in each group when compared to HC. The study did not confirm universal microbial biomarkers reported in other studies but showed that the observed differences concern the bacteria associated with inflammation, the production of short chain fatty acids (SCFA), and the synthesis of metabolites linked to mental health (lactic acid, gamma-aminobutyric acid - GABA). The application of machine learning holds promise for further understanding the complex relationship between microbiota and these psychiatric disorders. The observed results should be treated with caution due to the limitations of this study (mainly sample size), therefore further researches under standardized environmental conditions with consistent analytical and bioinformatics approaches are warranted.

Modulation effects of repetitive transcranial magnetic stimulation on target and indirect target nodes in patients with major depressive disorder

Clinical studies intensively highlight two critical brain regions, i,e, dorsal lateral prefrontal cortex (DLPFC) (target node) and subgenual anterior cingulate cortex (sgACC) (indirect target node) for the treatment of neuroimaging-guided repetitive transcranial magnetic stimulation (rTMS) in major depressive disorder (MDD). However, it remains unclear whether the clinical rTMS treatment could modulate the activity of the target and indirect target nodes in MDD patients. We aim to identify the rTMS-induced alteration of brain local and functional connectivity (FC) activities in the target and indirect target nodes. 38 patients with MDD were recruited for taking part in the 2-week rTMS treatment. We identified left DLPFC and right sgACC as the target and indirect target nodes for each participant, using the neuroimaging guided method, and further explored the rTMS-induced modulation on the brain functional activity of the two nodes. Ultimately, 28 patients were included in the analysis. We found that subjects had significant improvement in depressive symptoms, and their brain functional activities were reorganized. rTMS reduced the FC activity between the target and indirect target nodes, while the brain local activity in these nodes did not show rTMS-induced changes. The FC reduction was not associated with improvement in depressive symptoms. These results confirmed the clinical significance of the target node (DLPFC) and indirect target node (sgACC) in the rTMS treatment of MDD, and further shed light on the brain functional reorganization underpinning clinical practice of rTMS.

The differences in testosterone and stress hormones between unipolar and bipolar depression in adolescents and adults

BackgroundWhile patients with major depressive disorder (MDD) and bipolar disorder (BD) often exhibit aberrant hormones, it is still unknown whether the hormones differ between MDD and BD across the age spectrum. We aimed to investigate the differences in testosterone and stress hormones between depressed patients with MDD and BD in adolescents and adults, and the impact of suicidal ideation on these. MethodsA total of 432 depressed patients (270 MDD and 162 BD) were recruited, including 177 adolescents and 255 adults. Plasma levels of adrenocorticotropic hormone (ACTH), cortisol (CORT), testosterone (T), and prolactin (PRL) were measured in all patients. Suicidal ideation was assessed by item 3 of the Hamilton Depression Rating Scale. ResultsIn adolescents, plasma T levels were higher in MDD than in BD (p=0.018), MDD patients with suicidal ideation exhibited higher T levels than BD patients with suicidal ideation (p=0.036), and plasma T levels were associated with diagnosis (ORadjus=0.777, p=0.023). In adults, plasma ACTH levels were elevated in MDD versus BD (p=0.012) and were also diagnosis-related (ORadjus=0.972, p=0.019). Plasma levels of other hormones were not significantly different between MDD and BD in adolescents or adults (all p>0.05). ConclusionsThere was an age-specific difference in the T and ACTH between depressed patients with MDD and BD. Suicidal ideation was linked to T in adolescents.

Transdiagnostic white matter controllability deficits across patients with affective and anxiety spectrum disorders

BackgroundAffective and anxiety disorders including major depression disorder (MDD), post-traumatic stress disorder (PTSD), and social anxiety disorder (SAD) are characterized by network dysconnectivity. Network controllability quantifies the capability of specific brain regions to impact functional dynamics based on the underlying structural connectome. This study aimed to investigate transdiagnostic and illness-specific network controllability alterations across these three disorders. Materials and methodsThe study enrolled 233 currently untreated and non-comorbid subjects, including 68 MDD patients, 51 PTSD patients, 46 SAD patients, and 68 healthy controls (HCs). White matter network controllability was compared among the four groups, and its associations with symptom severity and duration of untreated illness were evaluated. ResultsCompared with HCs, patients with PTSD, MDD and SAD exhibited reduced average controllability in the somatomotor, subcortical, and default mode network, notably in brain regions such as the superior frontal gyrus, postcentral gyrus, paracentral gyrus, pallidum, posterior cingulate, and putamen. MDD and SAD patients exhibited reduced average controllability in the left lateral occipital gyrus and bilateral accumbens. SAD patients showed reduced average controllability in the dorsal attention network. These controllability changes did not correlate with illness duration or symptom severity. LimitationsThe cross-sectional design limits causal inference, and adjusting for age and sex differences may not completely eliminate their influence on the results. ConclusionThe present study revealed shared and specific alterations of network controllability in MDD, PTSD, and SAD, suggesting reduced ability of specific brain regions/networks in driving the brain system into different functional states across these disorders.

Contribution of resting-state functional connectivity of the subgenual anterior cingulate to prediction of antidepressant efficacy in patients with major depressive disorder

This study investigated how resting-state functional connectivity (rsFC) of the subgenual anterior cingulate cortex (sgACC) predicts antidepressant response in patients with major depressive disorder (MDD). Eighty-seven medication-free MDD patients underwent baseline resting-state functional MRI scans. After 12 weeks of escitalopram treatment, patients were classified into remission depression (RD, n = 42) and nonremission depression (NRD, n = 45) groups. We conducted two analyses: a voxel-wise rsFC analysis using sgACC as a seed to identify group differences, and a prediction model based on the sgACC rsFC map to predict treatment efficacy. Haufe transformation was used to interpret the predictive rsFC features. The RD group showed significantly higher rsFC between the sgACC and regions in the fronto-parietal network (FPN), including the bilateral dorsolateral prefrontal cortex (DLPFC) and bilateral inferior parietal lobule (IPL), compared to the NRD group. These sgACC rsFC measures correlated positively with symptom improvement. Baseline sgACC rsFC also significantly predicted treatment response after 12 weeks, with a mean accuracy of 72.64% (p < 0.001), mean area under the curve of 0.74 (p < 0.001), mean specificity of 0.82, and mean sensitivity of 0.70 in 10-fold cross-validation. The predictive voxels were mainly within the FPN. The rsFC between the sgACC and FPN is a valuable predictor of antidepressant response in MDD patients. These findings enhance our understanding of the neurobiological mechanisms underlying treatment response and could help inform personalized treatment strategies for MDD.

Why Do Some Depressive Patients Have Suicidal Ideation but Others Not? Suicidal Ideation From the Perspective of Affective Neuroscience Personality Traits.

Although major depression is the disorder most frequently associated with suicidal behavior, it is unclear that major depressive disorder patients may develop suicidal ideation. Basic affective system theory may provide a novel and beneficial viewpoint in this field. The goal of this study was to investigate the basic affective system in relation to suicidal ideation in individuals with depression. The study population comprised 160 participants who had been formally diagnosed with major depressive disorder. Participants were divided into two groups according to whether they had suicide ideation (depression with suicide [DS]) (N=93) or not (depression with no suicidal ideation [DNS]) (N=67). The Beck Depression Inventory (BDI), the Suicide Probability Scale (SPS), the Beck Hopelessness Scale (BHS), and the Affective Neuroscience Personality Scale (ANPS) were applied. Statistical Product and Service Solutions (SPSS) 24 and the SPSS macroprocess, which were specifically developed for assessing complex models including serial mediators, were used to analyze the data. The mean age of all participants was 31.1±11 years, and most of them were female (65%). The DS group had a higher family history of psychiatric medication use and suicidal attempts. In addition, BDI, BHS, and SPS total scores were higher, as well as ANGER subscale scores were higher in the DS group. In mediation analysis, the ANGER subscale significantly predicted the presence of suicide ideation. We observed a direct effect of the ANGER subscale score on suicide ideation as well as an indirect effect of the ANGER subscale score on suicidal ideation via depression severity. Higher scores on ANGER are associated with suicidal ideation. Neurobiological correlates, including the ANGER system, may be promising in understanding suicidal behaviors.

Alterations in orbitofrontal cortex communication relate to suicidal attempts in patients with major depressive disorder

BackgroundInvestigating how the interaction between the orbitofrontal cortex (OFC) and various brain regions/functional networks in major depressive disorder (MDD) patients with a history of suicide attempt (SA) holds importance for understanding the neurobiology of this population. MethodsWe employed resting-state functional magnetic resonance imaging (rs-fMRI) to analyze the OFC's functional segregation in 586 healthy individuals. A network analysis framework was then applied to rs-fMRI data from 86 MDD-SA patients and 85 MDD-Control patients, utilizing seed mappings of OFC subregions and a multi-connectivity-indicator strategy involving cross-correlation, total interdependencies, Granger causality, and machine learning. ResultsFour functional subregions of left and right OFC, were designated as seed regions of interest. Relative to the MDD-Control group, the MDD-SA group exhibited enhanced functional connectivity (FC) and attenuated interaction between the OFC and the sensorimotor network, imbalanced communication between the OFC and the default mode network, enhanced FC and interaction between the OFC and the ventral attention network, enhanced interaction between the OFC and the salience network, and attenuated FC between the OFC and the frontoparietal network. LimitationsThe medication and treatment condition of patients with MDD was not controlled, so the medication effect on the alteration model cannot be affirmed. ConclusionThe findings suggest an imbalanced interaction pattern between the OFC subregions and a set of cognition- and emotion-related functional networks/regions in the MDD-SA group.

Effect of slow breathing exercise on heart rate variability: A non linear analysis in female major depressive disorder patients

Background: Major depressive disorder (MDD) is a mental health problem, associated with cardiac autonomic dysfunction. Cardiac autonomic function (CANF) can be assessed by non linear analysis of heart rate variability (HRV), a simple non-invasive software based analytical method. Yoga based slow breathing exercise (SBE) has role to improve autonomic dysfunction in some disease condition. Objective: To observe the effect of SBE on non linear measures of HRV in newly diagnosed female MDD patients. Methods: The quasi-experimental study was conducted in 2023 on sixty (60) newly diagnosed female MDD patients of 20-40 years aged, collected from the Department of Psychiatry, Bangabandhu Sheikh Mujib Medical University (BSMMU). Among them 30 were assigned with SBE and remaining 30 patients without SBE were followed up after 90 days. Both patient groups received conventional treatment during this period. Thirty apparently healthy female subjects with similar age &amp; BMI were included as control for comparison and they did not perform breathing exercise. Data were taken at baseline and after 90 days in all the subjects. CANF was assessed by analysing non linear (poincare plot) of HRV by using ECG based data acquisition device, Power lab 8/35. Data were expressed as mean±SD. For statistical analysis, ANOVA followed by post hoc Bonferroni test and paired sample “t” test were used. Results: At baseline pulse rate and BP were significantly lower in both group of MDD patients compared to control. The pre-intervention values of standard deviation of short term RR interval variability (SD1), standard deviation of long term RR interval variability (SD2) and ratio of short term and long term RR interval variability (SD1/SD2) were found significantly (p&lt; 0.001) lower in MDD patients compared to healthy control. Following 90 days of SBE, significant (p&lt; 0.001, p&lt; 0.05) rise of these poincare plot parameters was noted in MDD patients whereas no significant changes of these parameters were observed in patients without SBE after 90 days of follow-up. Moreover, the pulse BP almost reached control level though the non linear parameters were still below control level after 3 months of SBE.. Conclusion: Autonomic dysfunction occured in MDD patients SBE can improve cardiac autonomic dysfunction and sympathovagal balance in female MDD patients. J Bangladesh Soc Physiol 2023;18(2): 45-52

Insomnia Catastrophizing and Affective Temperaments in Major Depressive Disorder

Background and Objective We aim to assess the role of affective temperaments, insomnia severity, sleep quality, and depressive anxiety symptoms in predicting catastrophizing insomnia in patients with major depressive disorder (MDD).Methods Our study employed a case-control design, involving 90 drug-free MDD patients and 90 healthy controls (HCs) with identical sociodemographic characteristics. Participants completed a comprehensive set of assessments, including the Beck Depression Inventory (BDI), Beck Anxiety Inventory (BAI), Insomnia Severity Index (ISI), Pittsburgh Sleep Quality Index (PSQI), Insomnia Catastrophizing Scale (ICS), and Temperament Evaluation of Memphis, Pisa, Paris, San Diego Autoquestionnaire (TEMPS-A).Results Among the 90 MDD patients, 80% were female (n = 72), with a mean age of 32.2 ± 13.6 years. The MDD group exhibited significantly higher scores on the BDI, BAI, ISI, PSQI, ICS night, ICS daytime, and ICS total scores, along with depressive, cyclothymic, irritable, and anxious temperament scores, compared to the HCs (p &lt; 0.001). Notably, the hyperthymic temperament showed no correlation with insomnia catastrophizing in either group. Our findings revealed that the ISI score was a predictive factor for the ICS night; age and ISI were predictive factors for the ICS daytime; and age, anxious temperament, and ISI were predictive factors for the ICS total in the MDD group.Conclusions The severity of insomnia, young age, and an anxious temperament were identified as positive predictors of insomnia catastrophizing.

Brain electrical activity in female Major Depressive Disorder patients

Background: Quantitative EEG provides insight to the brain activity associated with depressive illness. Frontal asymmetry and changes of slow wave frequencies brain activity have been found as the characteristic features of Major Depressive Disorder (MDD). Objective: To assess the brain activity by power spectral analysis of EEG in female patients with MDD. Methods: This observational study was carried on 20 female MDD patients (age 20-40 years) enrolled from the Department of Psychiatry, Bangabandhu Sheikh Mujib Medical University, Shahbag, Dhaka. Twenty (20) healthy volunteers of the same age were considered the control group. For the study, EEG was recorded for 3 minutes in eye closed position by EEG data acquisition device, EEG Traveler BrainTech 32+ CMEEG-01, India. Power spectral analysis of EEG data was done by BT40 analysis software. For statistical analysis, the Mann-Whitney U test was done. Results: Significantly higher Absolute power of delta and theta brain wave, and lower alpha and beta brain wave activity was observed in female MDD patients compared to control in all most all electrodes in all cortical regions. Conclusion: MDD patients showed low excitable brain state and characteristic feature of high slow frequency wave and low fast frequency wave in MDD which can be diagnosed by quantitative EEG analysis. J Bangladesh Soc Physiol 2023;18(2): 53-62

Evaluating the Effects of Ketamine on Serum Glutamate Dehydrogenase, Glutamine Synthetase and Asparagine Synthetase Levels in Patients with Major Depressive Disorder Before and After Ketamine Treatment

Introduction: It has been revealed that major depressive disorder (MDD)is a common and debilitating psychiatric disorder. Dysfunctional enzymes involved in neurotransmission glutamate dehydrogenase (GDH), Glutamine synthetase (GS) and Asparagine synthetase (ASNS) may underlie the pathophysiology of MDD. The present project aimed to explore the effects of Ketamine on GDH, GS and ASNS in MDD patients. This study provides evidence of interactions between ketamine and GDH, ASNS and GS levels in patients with MDD. Materials and Methods: Patients with MDD are referred to the psychiatric ward of Shaheed Yahyanejad Hospital for regular follow-up. Patients diagnosed with MDD were based on the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), structured clinical interviews, as well as the severity of depression based on clinical criteria determined by a specialist physician. Serum samples from MDD patients before and after ketamine administration were taken to examine the changes in GDH, ASNS and GS levels. In this project, 29 patients with MDD therapy were evaluated with ketamine (0.75 mg/kg). Results: Our study showed that after administrating ketamine, the level of ASNS and Glutamate dehydrogenase GDH in patients with MDD was reduced compared to pre-treatment. However, the level of GS was increased compared to before treatment. The results show that ketamine occurs at the metabolism level in MDD patients. Also, our results demonstrated that GDH, ASNS, and GS levels can be measured to evaluate the effect of Ketamine on MDD patients. Conclusions: Patient success has improved depression after two months of administrating ketamine. Marginally, more than 80% of patients diagnosed with MDD treated with ketamine showed complete remission. Current results may be helpful in understanding the mechanisms responsible for ketamine’s clinical efficacy.

The relationship between plasma interleukin-6 and cognition based on curve correlation in drug-naïve patients with major depressive disorder

BackgroundThe effect of interleukin-6 (IL-6) on cognition in patients with major depressive disorder (MDD) remains unclear. The aim of the present study was to investigate for the first time the non-linear relationship between plasma IL-6 and cognition and its sex-specific associations in patients with drug-naïve MDD. MethodsA total of 326 participants, including 237 drug-naïve MDD patients and 89 healthy controls (HCs), were included in this study. All participants completed the Repeatable Battery for the Assessment of Neuropsychological Status (RBANS) and fasting venous blood was collected for IL-6 measurement. Patients with MDD completed the Hamilton Depression Scale-17 (HAMD-17) and the Hamilton Anxiety Scale-14 (HAMA-14) assessments. Two-way analysis of variance and curve estimation analyses were used to explore the effects of IL-6 on cognition and its sex differences. ResultsWe found that IL-6 and cognition were associated in different patterns in HCs and MDD patients. The best model for curve estimation between IL-6 and attention (F = 2.736, p = 0.045) and HAMA (F = 6.416, p < 0.001) in females with MDD was the cubic model. In male MDD patients, the best model for curve estimation between IL-6 and immediate memory was the cubic model (F = 3.077, p = 0.034). However, in HCs, the best model for curve estimation analysis between IL-6 and language was the quadratic model (F = 3.803, p = 0.026). LimitationsThe main limitations were cross-sectional design. ConclusionThere was a non-linear and sex-specific relationship between IL-6 levels and cognition in patients with MDD.

Structural-functional connectomics in major depressive disorder following aiTBS treatment

Major depressive disorder (MDD) has been associated with changes in the structural (SC) and functional connectivity (FC) of the brain. This study investigated the effects of accelerated intermittent theta burst stimulation (aiTBS) on SC-FC coupling and graph theory measures, focusing on the association between baseline SC-FC coupling of the dorsolateral prefrontal cortex (dlPFC) and clinical improvement. In a randomized, sham-controlled, quadruple-blind, crossover study, aiTBS was delivered to the left dlPFC of depressed patients with MDD, and diffusion tensor imaging (DTI) and resting-state functional magnetic resonance imaging (rsfMRI) data were acquired. In 77 MDD patients, significantly increased whole-brain SC-FC coupling was observed, primarily driven by default mode network (DMN) SC-FC coupling, along with increased somatomotor network FC, and decreased FC between the DMN hubs and limbic regions after active aiTBS. Furthermore, significant increases were observed in structural global and local efficiency measures that were not specific to the stimulation condition (active/sham aiTBS). However, these changes did not significantly correlate with clinical improvement. Notably, baseline SC-FC coupling of the left dlPFC was a significant predictor of clinical improvement. Our findings highlight the potential of left dlPFC SC-FC coupling as a predictor of aiTBS treatment outcomes, as well as the effect of aiTBS in enhancing SC-FC coupling.

Distinct connectivity patterns in bipolar and unipolar depression: a functional connectivity multivariate pattern analysis study

BackgroundPatients with bipolar disorder (BD) and major depressive disorder (MDD) exhibit depressive episodes with similar symptoms despite having different and poorly understood underlying neurobiology, often leading to misdiagnosis and improper treatment. This exploratory study examined whole-brain functional connectivity (FC) using FC multivariate pattern analysis (fc-MVPA) to identify the FC patterns with the greatest ability to distinguish between currently depressed patients with BD type I (BD I) and those with MDD.MethodologyIn a cross-sectional design, 41 BD I, 40 MDD patients and 63 control participants completed resting state functional magnetic resonance imaging scans. Data-driven fc-MVPA, as implemented in the CONN toolbox, was used to identify clusters with differential FC patterns between BD patients and MDD patients. The identified cluster was used as a seed in a post hoc seed-based analysis (SBA) to reveal associated connectivity patterns, followed by a secondary ROI-to-ROI analysis to characterize differences in connectivity between these patterns among BD I patients, MDD patients and controls.ResultsFC-MVPA identified one cluster located in the right frontal pole (RFP). The subsequent SBA revealed greater FC between the RFP and posterior cingulate cortex (PCC) and between the RFP and the left inferior/middle temporal gyrus (LI/MTG) and lower FC between the RFP and the left precentral gyrus (LPCG), left lingual gyrus/occipital cortex (LLG/OCC) and right occipital cortex (ROCC) in MDD patients than in BD patients. Compared with the controls, ROI-to-ROI analysis revealed lower FC between the RFP and the PCC and greater FC between the RFP and the LPCG, LLG/OCC and ROCC in BD patients; in MDD patients, the analysis revealed lower FC between the RFP and the LLG/OCC and ROCC and greater FC between the RFP and the LI/MTG.ConclusionsDifferences in the RFP FC patterns between currently depressed patients with BD and those with MDD suggest potential neuroimaging markers that should be further examined. Specifically, BD patients exhibit increased FC between the RFP and the motor and visual networks, which is associated with psychomotor symptoms and heightened compensatory frontoparietal FC to counter distractibility. In contrast, MDD patients exhibit increased FC between the RFP and the default mode network, corresponding to sustained self-focus and rumination.

Exploring miRNA Biomarkers in Major Depressive Disorder: A Molecular Medicine Perspective.

Major depressive disorder (MDD) is a complex mental health condition with a multifaceted and incompletely elucidated pathophysiology. MicroRNAs (miRNAs) have emerged as potential biomarkers due to their role in gene regulation and the observed dysregulation in MDD. The aim of this study is to detect the presence of specific molecular diagnostic biomarkers in major depressive disorder. This cross-sectional study analyzed plasma miRNA expression in ten MDD patients and eight healthy controls using real-time PCR. Differentially expressed miRNAs were identified using independent t-tests, and their diagnostic potential was assessed with ROC curve analysis. Fifteen miRNAs exhibited significant dysregulation in MDD patients. Notably, hsa-miR-29c-3p, hsa-miR-376a-3p, hsa-miR-532-5p, and hsa-miR-339-5p showed excellent discriminatory power (AUC > 0.8). This study identifies differentially expressed plasma miRNAs in MDD, suggesting their potential for improved diagnosis and personalized treatment. However, further validation in larger cohorts and investigation into their functional roles are warranted.

Structural connectivity of dopaminergic pathways in major depressive disorder: An ultra-high resolution 7-Tesla diffusion MRI study

Accumulating evidence points to imbalanced dopamine (DA) signaling and circulating levels in the pathophysiology of major depressive disorder (MDD). However, the use of conventional MRI scanners and acquisition techniques has prevented a thorough examination of DA neural pathways in MDD. We uniquely employed ultra-high field diffusion MRI at 7.0 Tesla to map the white matter architecture and integrity of several DA pathways in MDD patients. Fifty-three MDD patients and 12 healthy controls (HCs) were enrolled in the final analysis. Images were acquired using a 7.0 Tesla MRI scanner. FreeSurfer was used to segment components of DA pathways, and MRtrix was used to perform preprocessing and tractography of mesolimbic, mesocortical, nigrostriatal, and unconventional DA pathways. Bayesian analyses assessed the impact of MDD and clinical features on DA tracts. MDD was associated with perturbed white matter microstructural properties of the nigrostriatal pathway, while several MDD features (severity of depression/age of onset/insomnia) related to connectivity changes within mesocortical, nigrostriatal, and unconventional pathways. MDD is associated with microstructural differences in the nigrostriatal pathway. The findings provide insight into the structural architecture and integrity of several DA pathways in MDD, and implicate their involvement in the clinical manifestation of MDD.

Optimizing the individual dosing of paroxetine in major depressive disorder with therapeutic drug monitoring.

Previous studies have examined the correlation between paroxetine concentrations and therapeutic efficacy in patients diagnosed with major depressive disorder (MDD), but findings have been contradictory. This study aimed to investigate the relationships among plasma concentrations, severity of symptoms, and adverse drug reactions (ADRs) to optimize individual dosing. Eighty-seven MDD patients, after completing treatment with paroxetine, were divided into low-concentration (LC, n = 38), medium-concentration (MC, n = 27), and high-concentration (HC, n = 22) groups, based on cutoff value concentrations with the 50% response rate and the laboratory alert level from the 2017 consensus guidelines for therapeutic drug monitoring in neuropsychopharmacology. The severity of depression and anxiety was evaluated using a 17-item Hamilton Depression Scale (HAMD-17) and Hamilton Anxiety Scale (HAMA), respectively. Dosage, plasma concentrations, scale scores, and ADRs were recorded across the three groups at different treatment stages to define the therapeutic reference range. The 4-week plasma concentration of paroxetine (65.00 ng/mL) could predict the clinical response in MDD patients at 8 weeks. Symptom relief in patients with 4-week paroxetine concentrations ranging from 65.00 to 120.00 ng/mL at 8 weeks was greater than in those with concentrations below 65.00 ng/mL, with no significant difference observed above this range. In addition, more cases of liver injury and weight gain were observed in patients with high paroxetine concentrations. Our results support that early paroxetine concentration may predict clinical efficacy and the incidence of ADRs, thus improving individual dosing regimens for MDD patients.