Bipolar spectrum in major depressive disorders

Distinct global brain connectivity alterations in depressed adolescents with subthreshold mania and the relationship with processing speed: Evidence from sBEAD Cohort

BACKGROUND

Bipolar disorder (BD) is a progressive condition. Investigating the neuroimaging mechanisms in depressed adolescents with subthreshold mania (SubMD) facilitates the early identification of BD. However, the global brain connectivity (GBC) patterns in SubMD patients, as well as the relationship with processing speed before the onset of full-blown BD, remain unclear.

METHODS

The study involved 72 SubMD, 77 depressed adolescents without subthreshold mania (nSubMD), and 69 gender- and age-matched healthy adolescents (HCs). All patients underwent a clinical follow-up ranging from six to twelve months. We calculated the voxel-based graph theory analysis of the GBC map and conducted the TMT-A test to measure the processing speed.

RESULTS

Compared to HCs and nSubMD, SubMD patients displayed distinctive GBC index patterns: GBC index decreased in the right Medial Superior Frontal Gyrus (SFGmed.R)/Superior Frontal Gyrus (SFG) while increased in the right Precuneus and left Postcentral Gyrus. Both patient groups showed increased GBC index in the right Inferior Temporal Gyrus. An increased GBC value in the right Supplementary Motor Area was exclusively observed in the nSubMD-group. There were opposite changes in the GBC index in SFGmed.R/SFG between two patient groups, with an AUC of 0.727. Additionally, GBC values in SFGmed.R/SFG exhibited a positive correlation with TMT-A scores in SubMD-group.

LIMITATIONS

Relatively shorter follow-up duration, medications confounding, and modest sample size.

CONCLUSION

These findings suggest that adolescents with subthreshold BD have specific impairments patterns at the whole brain connectivity level associated with processing speed impairments, providing insights into early identification and intervention strategies for BD.

Comparative Analysis of Personality Traits in Major Depressive Disorder and Bipolar Disorder: Impact, Differences, and Associations with Symptoms

Purpose

This cross-sectional study aimed to compare the personality traits of patients with major depressive disorder (MDD) and bipolar disorder (BD) with those of healthy individuals. The goal was to gain insight into the potential impact of personality traits on the development and manifestation of mood disorders.

Methods

One hundred seventy-eight patients with mood disorders were analyzed as either MDD or BD, with each group containing euthymic and depressive members: e-MDD, d-MDD, e-BD, and d-BD. Mood status was assessed using the Young Mania Rating Scale (YMRS), and the 17-item Hamilton Depression Rating Scale (HAMD-17). Ninety-five healthy individuals served as controls. Personality traits were assessed with the Eysenck Personality Questionnaire.

Results

The scores for neuroticism in the patient groups were comparable, but each group had higher scores compared to the control group (P < 0.001). Each patient group exhibited significantly lower scores for extraversion compared to the control group, with e-MDD, d-MDD, and d-BD showing particularly notable differences (P < 0.001); these groups scored significantly lower than the e-BD (P = 0.041, 0.009, 0.038). In patients with BD, there was an inverted association between extraversion score and HAMD total score (P = 0.010, r = -0.27), and a positive association with the YMRS total score (P = 0.022, r = 0.24). In the MDD group, there was a positive association between the neuroticism score and HAMD total score (P = 0.021, r = 0.25).

Conclusion

Patients with mood disorders are characterized by lower extraversion and higher neuroticism. Level of neuroticism associated with depression severity in MDD. Patients with BD may be more extraverted, but their extraversion can be affected by depressive episodes. Extraversion may be a feature of BD, and may differentiate BD from MDD. Personality traits are related to disease diathesis and state, and shaped by symptom manifestations.

Classifying and clustering mood disorder patients using smartphone data from a feasibility study

Differentiating between bipolar disorder and major depressive disorder can be challenging for clinicians. The diagnostic process might benefit from new ways of monitoring the phenotypes of these disorders. Smartphone data might offer insight in this regard. Today, smartphones collect dense, multimodal data from which behavioral metrics can be derived. Distinct patterns in these metrics have the potential to differentiate the two conditions. To examine the feasibility of smartphone-based phenotyping, two study sites (Mayo Clinic, Johns Hopkins University) recruited patients with bipolar I disorder (BPI), bipolar II disorder (BPII), major depressive disorder (MDD), and undiagnosed controls for a 12-week observational study. On their smartphones, study participants used a digital phenotyping app (mindLAMP) for data collection. While in use, mindLAMP gathered real-time geolocation, accelerometer, and screen-state (on/off) data. mindLAMP was also used for EMA delivery. MindLAMP data was then used as input variables in binary classification, three-group k-nearest neighbors (KNN) classification, and k-means clustering. The best-performing binary classification model was able to classify patients as control or non-control with an AUC of 0.91 (random forest). The model that performed best at classifying patients as having MDD or bipolar I/II had an AUC of 0.62 (logistic regression). The k-means clustering model had a silhouette score of 0.46 and an ARI of 0.27. Results support the potential for digital phenotyping methods to cluster depression, bipolar disorder, and healthy controls. However, due to inconsistencies in accuracy, more data streams are required before these methods can be applied to clinical practice.

Privacy-Preserving Federated Model Predicting Bipolar Transition in Patients With Depression: Prediction Model Development Study

BACKGROUND

Mood disorder has emerged as a serious concern for public health; in particular, bipolar disorder has a less favorable prognosis than depression. Although prompt recognition of depression conversion to bipolar disorder is needed, early prediction is challenging due to overlapping symptoms. Recently, there have been attempts to develop a prediction model by using federated learning. Federated learning in medical fields is a method for training multi-institutional machine learning models without patient-level data sharing.

OBJECTIVE

This study aims to develop and validate a federated, differentially private multi-institutional bipolar transition prediction model.

METHODS

This retrospective study enrolled patients diagnosed with the first depressive episode at 5 tertiary hospitals in South Korea. We developed models for predicting bipolar transition by using data from 17,631 patients in 4 institutions. Further, we used data from 4541 patients for external validation from 1 institution. We created standardized pipelines to extract large-scale clinical features from the 4 institutions without any code modification. Moreover, we performed feature selection in a federated environment for computational efficiency and applied differential privacy to gradient updates. Finally, we compared the federated and the 4 local models developed with each hospital's data on internal and external validation data sets.

RESULTS

In the internal data set, 279 out of 17,631 patients showed bipolar disorder transition. In the external data set, 39 out of 4541 patients showed bipolar disorder transition. The average performance of the federated model in the internal test (area under the curve [AUC] 0.726) and external validation (AUC 0.719) data sets was higher than that of the other locally developed models (AUC 0.642-0.707 and AUC 0.642-0.699, respectively). In the federated model, classifications were driven by several predictors such as the Charlson index (low scores were associated with bipolar transition, which may be due to younger age), severe depression, anxiolytics, young age, and visiting months (the bipolar transition was associated with seasonality, especially during the spring and summer months).

CONCLUSIONS

We developed and validated a differentially private federated model by using distributed multi-institutional psychiatric data with standardized pipelines in a real-world environment. The federated model performed better than models using local data only.

Digital Phenotyping for Differential Diagnosis of Major Depressive Episode: Narrative Review

BACKGROUND

Major depressive episode (MDE) is a common clinical syndrome. It can be found in different pathologies such as major depressive disorder (MDD), bipolar disorder (BD), posttraumatic stress disorder (PTSD), or even occur in the context of psychological trauma. However, only 1 syndrome is described in international classifications (Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition [DSM-5]/International Classification of Diseases 11th Revision [ICD-11]), which do not take into account the underlying pathology at the origin of the MDE. Clinical interviews are currently the best source of information to obtain the etiological diagnosis of MDE. Nevertheless, it does not allow an early diagnosis and there are no objective measures of extracted clinical information. To remedy this, the use of digital tools and their correlation with clinical symptomatology could be useful.

OBJECTIVE

We aimed to review the current application of digital tools for MDE diagnosis while highlighting shortcomings for further research. In addition, our work was focused on digital devices easy to use during clinical interview and mental health issues where depression is common.

METHODS

We conducted a narrative review of the use of digital tools during clinical interviews for MDE by searching papers published in PubMed/MEDLINE, Web of Science, and Google Scholar databases since February 2010. The search was conducted from June to September 2021. Potentially relevant papers were then compared against a checklist for relevance and reviewed independently for inclusion, with focus on 4 allocated topics of (1) automated voice analysis, behavior analysis by (2) video and physiological measures, (3) heart rate variability (HRV), and (4) electrodermal activity (EDA). For this purpose, we were interested in 4 frequently found clinical conditions in which MDE can occur: (1) MDD, (2) BD, (3) PTSD, and (4) psychological trauma.

RESULTS

A total of 74 relevant papers on the subject were qualitatively analyzed and the information was synthesized. Thus, a digital phenotype of MDE seems to emerge consisting of modifications in speech features (namely, temporal, prosodic, spectral, source, and formants) and in speech content, modifications in nonverbal behavior (head, hand, body and eyes movement, facial expressivity, and gaze), and a decrease in physiological measurements (HRV and EDA). We not only found similarities but also differences when MDE occurs in MDD, BD, PTSD, or psychological trauma. However, comparative studies were rare in BD or PTSD conditions, which does not allow us to identify clear and distinct digital phenotypes.

CONCLUSIONS

Our search identified markers from several modalities that hold promise for helping with a more objective diagnosis of MDE. To validate their potential, further longitudinal and prospective studies are needed.

Clinical research diagnostic criteria for bipolar illness (CRDC-BP): rationale and validity

BACKGROUND

In the 1970 s, scientific research on psychiatric nosology was summarized in Research Diagnostic Criteria (RDC), based solely on empirical data, an important source for the third revision of the official nomenclature of the American Psychiatric Association in 1980, the Diagnostic and Statistical Manual, Third Edition (DSM-III). The intervening years, especially with the fourth edition in 1994, saw a shift to a more overtly "pragmatic" approach to diagnostic definitions, which were constructed for many purposes, with research evidence being only one consideration. The latest editions have been criticized as failing to be useful for research. Biological and clinical research rests on the validity of diagnostic definitions that are supported by firm empirical foundations, but critics note that DSM criteria have failed to prioritize research data in favor of "pragmatic" considerations.

RESULTS

Based on prior work of the International Society for Bipolar Diagnostic Guidelines Task Force, we propose here Clinical Research Diagnostic Criteria for Bipolar Illness (CRDC-BP) for use in research studies, with the hope that these criteria may lead to further refinement of diagnostic definitions for other major mental illnesses in the future. New proposals are provided for mixed states, mood temperaments, and duration of episodes.

CONCLUSIONS

A new CRDC could provide guidance toward an empirically-based, scientific psychiatric nosology, and provide an alternative clinical diagnostic approach to the DSM system.

Predictors of diagnostic transition from major depressive disorder to bipolar disorder: a retrospective observational network study

Many patients with bipolar disorder (BD) are initially misdiagnosed with major depressive disorder (MDD) and are treated with antidepressants, whose potential iatrogenic effects are widely discussed. It is unknown whether MDD is a comorbidity of BD or its earlier stage, and no consensus exists on individual conversion predictors, delaying BD's timely recognition and treatment. We aimed to build a predictive model of MDD to BD conversion and to validate it across a multi-national network of patient databases using the standardization afforded by the Observational Medical Outcomes Partnership (OMOP) common data model. Five "training" US databases were retrospectively analyzed: IBM MarketScan CCAE, MDCR, MDCD, Optum EHR, and Optum Claims. Cyclops regularized logistic regression models were developed on one-year MDD-BD conversion with all standard covariates from the HADES PatientLevelPrediction package. Time-to-conversion Kaplan-Meier analysis was performed up to a decade after MDD, stratified by model-estimated risk. External validation of the final prediction model was performed across 9 patient record databases within the Observational Health Data Sciences and Informatics (OHDSI) network internationally. The model's area under the curve (AUC) varied 0.633-0.745 (µ = 0.689) across the five US training databases. Nine variables predicted one-year MDD-BD transition. Factors that increased risk were: younger age, severe depression, psychosis, anxiety, substance misuse, self-harm thoughts/actions, and prior mental disorder. AUCs of the validation datasets ranged 0.570-0.785 (µ = 0.664). An assessment algorithm was built for MDD to BD conversion that allows distinguishing as much as 100-fold risk differences among patients and validates well across multiple international data sources.

Factors influencing lithium versus valproate prescription preference in the maintenance treatment of bipolar patients: a report from the Italian Early Career Psychiatrists (SOPSI-GG)

BACKGROUND

Objective of the present manuscript is to investigate, among Italian early career psychiatrists (ECPs), prescriber and patient-related factors associated with lithium or valproate preference to treat patients affected by Bipolar Disorder (BD).

METHODS

An on-line survey was carried out among 252 ECPs, investigating their prescription patterns in relation to lithium and the differences with prescription of valproate. Collected data were compared according to lithium or valproate prescription preference in the long-term treatment of BD by χ² tests for qualitative variables.

RESULTS

Over two thirds of ECPs preferred lithium over valproate for the maintenance treatment of BD. Less than half of the sample used lithium as first-line agent for mania or major depression, and less than one third for mixed episodes. Factors associated with lithium preference as first-line maintenance treatment include perception of having a good knowledge of lithium (p < 0.001) and complete satisfaction with education on lithium (p < 0.001). One of the main factors to prefer valproate was the concern about long-term side effects of lithium (p < 0.001).

CONCLUSIONS

Type of education, source of information, clinical experience and safety concerns influence the choice of lithium versus valproate in the long-term treatment of BD. Present findings may guide educational training of ECPs.KEY POINTSLithium has been less prescribed in the last years for long-term treatment of Bipolar Disorder.Educational and clinical factors seem to influence the attitude to prescribe lithium.Only half of the Italian early career psychiatrists declare to have at least an adequate knowledge of lithium.Residency program in psychiatry should consider the implementation of education on lithium.

The Co-Morbidity between Bipolar and Panic Disorder in Fibromyalgia Syndrome

About half of the patients with fibromyalgia (FM) had a lifetime major depression episode and one third had a panic disorder (PD). Because the co-morbidity between bipolar disorder (BD) and PD marks a specific subtype of BD we aimed to investigate if co-morbid BD/PD (comBD/PD) occurs more frequently than the single disorder in FM patients and evaluate the clinical significance and timing of this co-morbidity. Further, we explored the role of co-morbid subthreshold BD and PD. In 118 patients with FM, lifetime threshold and sub-threshold mood disorders and PD were diagnosed with Diagnostic and Statistical Manual of Mental Disorders-IV-Text Revision (DSM-IV-TR) Clinical Interview. Demographic and clinical variables were compared in co-morbid BD/PD (comBD/PD) and not co-morbid BD/PD (nocomBD/PD) subgroups. The co-morbidity BD/PD was seen in 46.6% of FM patients and in 68.6% when patients with minor bipolar (MinBD) and sub-threshold panic were included. These rates are higher than those of the general population and BD outpatients. There were no statistically significant differences between threshold and sub-threshold comBD/PD and nocom-BD/PD subgroups in demographic and clinical parameters. In the majority of patients (78.2%), the onset of comBD/PD preceded or was contemporary with FM. These findings support the hypothesis that comBD/PD is related to the development of FM in a subgroup of patients.

Pharmacotherapeutic interventions for bipolar disorder type II: addressing multiple symptoms and approaches with a particular emphasis on strategies in lower and middle-income countries

Introduction: Appropriately managing mental disorders is a growing priority across countries in view of the impact on morbidity and mortality. This includes patients with bipolar disorders (BD). Management of BD is a concern as this is a complex disease with often misdiagnosis, which is a major issue in lower and middle-income countries (LMICs) with typically a limited number of trained personnel and resources. This needs to be addressed.Areas covered: Medicines are the cornerstone of managing patients with Bipolar II across countries including LMICs. The choice of medicines, especially antipsychotics, is important in LMICs with high rates of diabetes and HIV. However, care is currently compromised in LMICs by issues such as the stigma, cultural beliefs, a limited number of trained professionals and high patient co-payments.Expert opinion: Encouragingly, some LMICs have introduced guidelines for patients with BD; however, this is very variable. Strategies for the future include addressing the lack of national guidelines for patients with BD, improving resources for mental disorders including personnel, improving medicine availability and patients' rights, and monitoring prescribing against agreed guidelines. A number of strategies have been identified to improve the treatment of patients with Bipolar II in LMICs, and will be followed up.

"Do depressive and manic symptoms differentially impact on functioning in acute depression? Results from a large, cross-sectional study"

BACKGROUND

Diagnostic criteria for a major depressive episode capture heterogeneous presentations across unipolar (UD) and bipolar (BD) and first-onset (FDE) depression. We evaluated the contribution of each depressive and (hypo)manic symptom to worse functioning in UD/BD/FDE subgroups.

METHODS

A post-hoc analysis of the BRIDGE-II-Mix study. Acutely depressed patients were stratified into UD, BD and FDE. Each (hypo)manic or depressive symptom was included in a diagnosis-specific logistic regression model with functioning as dependent variable. Better/worse functioning was set with median diagnosis-specific GAF scores cutoffs. All p values were two-tailed. Statistical significance was set at p < 0.05.

RESULTS

A total of 2768/2811 depressed individuals were enrolled. In BD (N = 716), "recurrent thoughts of death" (OR 2.48, p < 0.0001) and "feelings of worthlessness" (OR 2.28, p < 0.0001) among depressive symptoms, "aggressiveness" (OR 1.67, p = 0.022) as the unique (hypo)manic symptom, significantly contributed to worse functioning. In UD (N = 1357), "depressed mood" (OR 5.6, p = 0.031) and "diminished interest or pleasure" (OR 4.77, p < 0.0001) among depressive, "grandiosity" (OR 3.5, p = 0.014) among (hypo)manic symptoms, most significantly contributed to worse functioning. In FDE (N = 677) "recurrent thoughts of death" (OR 1.99, p < 0.0001) and "insomnia/hypersomnia" (OR 1.88, p = 0.039) among depressive, "grandiosity" (OR 5.98, p = 0.038) as (hypo)manic symptoms significantly contributed to worse functioning.

LIMITATIONS

The post-hoc and cross-sectional design do not allow for prognostic or causal inferences.

CONCLUSIONS

Key depressive and (hypo)manic symptoms distinctively associate with worse functional outcome in acute depression, with differential diagnostic-specific magnitude of effect. Core depressive symptoms are associated with worse functioning in unipolar depression, but not in bipolar or first-episode depression.

Connectivity of the Frontal Cortical Oscillatory Dynamics Underlying Inhibitory Control During a Go/No-Go Task as a Predictive Biomarker in Major Depression

BACKGROUND

Major depressive disorder (MDD) is characterized by core functional deficits in cognitive inhibition, which is crucial for emotion regulation. To assess the response to ruminative and negative mood states, it was hypothesized that MDD patients have prolonged disparities in the oscillatory dynamics of the frontal cortical regions across the life course of the disease.

METHOD

A "go/no-go" response inhibition paradigm was tested in 31 MDD patients and 19 age-matched healthy controls after magnetoencephalography (MEG) scanning. The use of minimum norm estimates (MNE) examined the changes of inhibitory control network which included the right inferior frontal gyrus (rIFG), pre-supplementary motor area (preSMA), and left primary motor cortex (lM1). The power spectrum (PS) within each node and the functional connectivity (FC) between nodes were compared between two groups. Furthermore, Pearson correlation was calculated to estimate the relationship between altered FC and clinical features.

RESULT

PS was significantly reduced in left motor and preSMA of MDD patients in both beta (13-30 Hz) and low gamma (30-50 Hz) bands. Compared to the HC group, the MDD group demonstrated higher connectivity between lM1 and preSMA in the beta band (t = 3.214, p = 0.002, FDR corrected) and showed reduced connectivity between preSMA and rIFG in the low gamma band (t = -2.612, p = 0.012, FDR corrected). The FC between lM1 and preSMA in the beta band was positively correlated with illness duration (r = 0.475, p = 0.005, FDR corrected), while the FC between preSMA and rIFG in the low gamma band was negatively correlated with illness duration (r = -0.509, p = 0.002, FDR corrected) and retardation factor scores (r = -0.288, p = 0.022, uncorrected).

CONCLUSION

In this study, a clinical neurophysiological signature of cognitive inhibition leading to sustained negative affect as well as functional non-recovery in MDD patients is highlighted. Duration of illness (DI) plays a key role in negative emotional processing, heighten rumination, impulsivity, and disinhibition.

Lamotrigine augmentation in treatment-resistant unipolar depression: A comprehensive meta-analysis of efficacy and safety

OBJECTIVE

Augmentation strategies are commonly applied when an individual is unresponsive to antidepressant monotherapy. Lamotrigine is currently considered at best only as second line augmentation for treatment-resistant unipolar depression while its clinical efficacy and safety profiles remain inconclusive. We intended to assess the therapeutic effects and safety profiles of lamotrigine augmentation in patients with treatment-resistant unipolar depression by conducting a meta-analysis.

METHODS

MEDLINE, Embase, EBSCO, Cochrane, Web of Science, Scopus, Wanfang and Ariniti databases were searched. Coprimary outcomes, including changes in severity of depression and response rate, were measured in this study. Secondary outcomes were defined as the safety profile of the intervention, including reported discontinuation rate and adverse events.

RESULTS

Eight double-blinded randomized controlled trials with 677 patients overall were included. Significant improvements in Hamilton Rating Scale for Depression scores and response rates were shown in lamotrigine augmentation groups compared with control groups, of which the pooled result of six Chinese studies showed positive effects of Hamilton Rating Scale for Depression improvement while the pooled result of two non-Chinese studies was statistically non-significant. Patients with more severe illness and longer duration of illness were more effectively treated with lamotrigine augmentation. The magnitude of depression improvement after lamotrigine augmentation was higher in patients treated with selective serotonin reuptake inhibitors than those treated with serotonin-norepinephrine reuptake inhibitors. Lamotrigine augmentation is well-tolerated in terms of all-cause discontinuation rate and adverse events.

CONCLUSIONS

Lamotrigine augmentation may serve as a possible choice for patients with treatment-resistant unipolar depression and further trials are warranted to clarify the optimal dosage of lamotrigine augmentation together with the treatment duration and safety over time.

Early symptom non-improvement and aggravation are associated with the treatment response to SSRIs in MDD: a real-world study

PURPOSE

Early improvement in major depressive disorder is defined as a reduction of ≥20% in the 17-item Hamilton Depression Rating Scale (HAM-D-17) score at the second week after initiation of treatment, predicting long-term treatment response. However, there remains no effective strategy for switching medications when a patient fails to reach early improvement at the second week. This study focused on the predictive value of early symptom changes in each item of the HAM-D-17 scale for treatment response to selective serotonin reuptake inhibitor (SSRI) monotherapy and to provide a reference for switching antidepressants to enhance early treatment efficacy.

PATIENTS AND METHODS

Our study was an observational, real-world study that enrolled 90 treatment-naïve patients experiencing their first episode of major depressive disorder in the outpatient department of Huashan Hospital. Patients who did not achieve the threshold of early improvement in the second week after starting treatment were switched to alternative SSRI monotherapy. Patient follow-up occurred at 2, 4, 8, and 12 weeks after the initiation of treatment. We analyzed the relationship between the change in each symptom on the HAM-D-17 scale and treatment efficacy.

RESULTS

Early improvement predicted the treatment response at 12 weeks (χ ²=19.249, P<0.001), whereas early non-improvement in insomnia and anxiety was associated with a poor response (OR =9.487, 95% CI: 1.312-68.588 and OR =12.947, 95% CI: 1.99-82.246, respectively). At week 2, general somatic symptom aggravation was associated with a poorer response (OR =73.337, 95% CI: 2.232->999.999); treatment-emergent headache and tremor were associated with treatment efficacy (t=-9.521, P<0.001 and t=3.660, P=0.001, respectively). In addition, the increase in suicidal thoughts, once treatment began, had no relationship with the treatment response (OR =0.821, P=0.872).

CONCLUSION

This study suggested that patients with early non-improvement in insomnia and anxiety were not suitable for switches in SSRI monotherapy. Patients with treatment-emergent symptoms, especially headaches and tremors, were not suitable for switching from monotherapy to another SSRI.

EEG Resting-State Large-Scale Brain Network Dynamics Are Related to Depressive Symptoms

Background: The few previous studies on resting-state electroencephalography (EEG) microstates in depressive patients suggest altered temporal characteristics of microstates compared to those of healthy subjects. We tested whether resting-state microstate temporal characteristics could capture large-scale brain network dynamic activity relevant to depressive symptomatology. Methods: To evaluate a possible relationship between the resting-state large-scale brain network dynamics and depressive symptoms, we performed EEG microstate analysis in 19 patients with moderate to severe depression in bipolar affective disorder, depressive episode, and recurrent depressive disorder and in 19 healthy controls. Results: Microstate analysis revealed six classes of microstates (A-F) in global clustering across all subjects. There were no between-group differences in the temporal characteristics of microstates. In the patient group, higher depressive symptomatology on the Montgomery-Åsberg Depression Rating Scale correlated with higher occurrence of microstate A (Spearman's rank correlation, r = 0.70, p < 0.01). Conclusion: Our results suggest that the observed interindividual differences in resting-state EEG microstate parameters could reflect altered large-scale brain network dynamics relevant to depressive symptomatology during depressive episodes. Replication in larger cohort is needed to assess the utility of the microstate analysis approach in an objective depression assessment at the individual level.