Recurrent Depressive Episodes Research Articles

Defining "High Recurrence" of Depressive Episodes for Predicting Diagnostic Conversion from Major Depressive Disorder to Bipolar Disorder: A 5-year Retrospective Study.

This study determined the threshold for recurrent depressive episodes that predicted conversion from major depressive disorder (MDD) to bipolar disorder (BD). We retrospectively reviewed the medical records of 296 patients diagnosed with MDD for a minimum of 5 years in two university hospitals. We examined their the Diagnostic and Statistical Manual of Mental Disorders, 5th edition diagnoses and detailed clinical information at the initial admission and yearly assessments after discharge to establish the threshold for recurrent depressive episodes indicating a risk of diagnostic conversion from MDD to BD. Optimal cut-offs were derived using receiver operating characteristic (ROC) curves. ROC curve analysis revealed that more than four recurrent depressive episodes was indicative of potential diagnostic conversion from MDD to BD (area under the curve, 0.604; sensitivity, 0.353; specificity, 0.855; positive predictive value, 0.421; negative predictive value, 0.816). These findings suggest that the best predictor of conversion from MDD to BD is more than four recurrent depressive episodes. Our findings have the potential to enhance diagnostic accuracy and treatment efficiency. To validate our results, longitudinal prospective studies are necessary.

Concurrent inflammation-related brain reorganization in multiple sclerosis and depression

BackgroundNeuroinflammation affects brain tissue integrity in multiple sclerosis (MS) and may have a role in major depressive disorder (MDD). Whether advanced magnetic resonance imaging characteristics of the gray-to-white matter border serve as proxy of neuroinflammatory activity in MDD and MS remain unknown. MethodsWe included 684 participants (132 MDD patients with recurrent depressive episodes (RDE), 70 MDD patients with a single depressive episode (SDE), 222 MS patients without depressive symptoms (nMS), 58 MS patients with depressive symptoms (dMS), and 202 healthy controls (HC)). 3 T-T1w MRI-derived gray-to-white matter contrast (GWc) was used to reconstruct and characterize connectivity alterations of GWc-covariance networks by means of modularity, clustering coefficient, and degree. A cross-validated support vector machine was used to test the ability of GWc to stratify groups according to their depression symptoms, measured with BDI, at the single-subject level in MS and MDD independently. FindingsMS and MDD patients showed increased modularity (ANOVA partial-η2 = 0.3) and clustering (partial-η2 = 0.1) compared to HC. In the subgroups, a linear trend analysis attested a gradient of modularity increases in the form: HC, dMS, nMS, SDE, and RDE (ANOVA partial-η2 = 0.28, p < 0.001) while this trend was less evident for clustering coefficient. Reduced morphological integrity (GWc) was seen in patients with increased depressive symptoms (partial-η2 = 0.42, P < 0.001) and was associated with depression scores across patient groups (r = -0.2, P < 0.001). Depressive symptoms in MS were robustly classified (88 %). ConclusionsSimilar structural network alterations in MDD and MS exist, suggesting possible common inflammatory events like demyelination, neuroinflammation that are caught by GWc analyses. These alterations may vary depending on the severity of symptoms and in the case of MS may elucidate the occurrence of comorbid depression.

Neural foundation of the diathesis-stress model: longitudinal gray matter volume changes in response to stressful life events in major depressive disorder and healthy controls.

Recurrences of depressive episodes in major depressive disorder (MDD) can be explained by the diathesis-stress model, suggesting that stressful life events (SLEs) can trigger MDD episodes in individuals with pre-existing vulnerabilities. However, the longitudinal neurobiological impact of SLEs on gray matter volume (GMV) in MDD and its interaction with early-life adversity remains unresolved. In 754 participants aged 18-65 years (362 MDD patients; 392 healthy controls; HCs), we assessed longitudinal associations between SLEs (Life Events Questionnaire) and whole-brain GMV changes (3 Tesla MRI) during a 2-year interval, using voxel-based morphometry in SPM12/CAT12. We also explored the potential moderating role of childhood maltreatment (Childhood Trauma Questionnaire) on these associations. Over the 2-year interval, HCs demonstrated significant GMV reductions in the middle frontal, precentral, and postcentral gyri in response to higher levels of SLEs, while MDD patients showed no such GMV changes. Childhood maltreatment did not moderate these associations in either group. However, MDD patients who had at least one depressive episode during the 2-year interval, compared to those who did not, or HCs, showed GMV increases in the middle frontal, precentral, and postcentral gyri associated with an increase in SLEs and childhood maltreatment. Our findings indicate distinct GMV changes in response to SLEs between MDD patients and HCs. GMV decreases in HCs may represent adaptive responses to stress, whereas GMV increases in MDD patients with both childhood maltreatment and a depressive episode during the 2-year interval may indicate maladaptive changes, suggesting a neural foundation for the diathesis-stress model in MDD recurrences.

Computerized cognitive control training to reduce rumination in major depression: A randomized controlled trial

ObjectiveRumination is a major risk factor for the onset and recurrence of depressive episodes and has been associated with deficits in updating working memory content. This randomized controlled trial examines whether training updating-specific cognitive control processes reduces daily ruminative thoughts in clinically depressed individuals. MethodsSixty-five individuals with a current major depressive episode were randomized to 10 sessions of either cognitive control training (N = 31) or placebo training (N = 34). The frequency and negativity of individuals’ daily ruminative thoughts were assessed for seven days before training, after training, and at a 3-month follow-up using experience sampling methodology. Secondary outcomes were depressive symptoms, depressed mood, and level of disability. ResultsCognitive control training led to stronger improvements in the trained task than placebo training. However, cognitive control training did not lead to greater reductions in the frequency or negativity of daily ruminative thoughts than placebo training. There were no training-specific effects on participants' depressive symptoms or level of disability. ConclusionsThe robustness of the present null-findings, combined with the methodological strengths of the study, suggest that training currently depressed individuals to update emotional content in working memory does not affect the frequency or negativity of their daily ruminative thoughts.

Monitoring emotional intensity and variability to forecast depression recurrence in real time in remitted adults.

Recurrent depressive episodes are preceded by changing mean levels of repeatedly assessed emotions (e.g., feeling restless), which can be detected in real time using statistical process control (SPC). This study investigated whether monitoring changes in the standard deviation (SD) of emotions and negative thinking improves the early detection of recurrent depression. Formerly depressed adults (N = 41) monitored their emotions five times a day for 4 consecutive months. During the study, 22 individuals experienced recurrent depression. We used SPC to detect warning signs (i.e., changing means and SDs) of four emotions (positive and negative affect with high or low arousal) and negative thinking. SD-based warning signs only preceded 23%-36% of recurrences, but almost never reflected a false alarm (0%-16%). Correspondingly, SD-based warnings had a high specificity (at the cost of sensitivity), while mean-based warnings had a higher sensitivity (but lower specificity). There was little overlap in mean- and SD-based warning signs. For the majority of emotions, monitoring for high SDs alongside monitoring changes in mean levels improved the detection of depression (p < .015) compared to when only monitoring for changing mean levels. Warning signs for depression manifest not only in changing mean levels of emotions and cognitions but also in increasing SDs. These warnings could eventually be used to detect not just who is at increased risk for depression but also when risk is rising. Further research is needed to evaluate the clinical utility of depression SPC. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Multi modality fusion transformer with spatio-temporal feature aggregation module for psychiatric disorder diagnosis

Bipolar disorder (BD) is characterized by recurrent episodes of depression and mild mania. In this paper, to address the common issue of insufficient accuracy in existing methods and meet the requirements of clinical diagnosis, we propose a framework called Spatio-temporal Feature Fusion Transformer (STF2Former). It improves on our previous work — MFFormer by introducing a Spatio-temporal Feature Aggregation Module (STFAM) to learn the temporal and spatial features of rs-fMRI data. It promotes intra-modality attention and information fusion across different modalities. Specifically, this method decouples the temporal and spatial dimensions and designs two feature extraction modules for extracting temporal and spatial information separately. Extensive experiments demonstrate the effectiveness of our proposed STFAM in extracting features from rs-fMRI, and prove that our STF2Former can significantly outperform MFFormer and achieve much better results among other state-of-the-art methods.

The current state of the problem of diagnosis and therapy of depressive disorders (a literature review)

The article presents the results of an analytical review of current literature data on the problem of diagnosis and treatment of depressive disorders. The issues of a high incidence and polymorphism of the clinical presentation of these disorders are considered. The tendency towards formalization and objectification in the process of diagnosing depressive disorders in modern psychiatric discourse is covered, in particular, through the use of neuroimaging, neurophysiological and biochemical methods with the involvement of machine learning and artificial intelligence technologies. The classic method of a structured or semi-structured interview, as a tool for diagnosing clinical depression, currently remains one of the most common, but it has certain limitations and disadvantages, the most significant of which are a considerable degree of the method subjectivity and its dependence on the perception by specialists conducting the diagnosis, that causes low sensitivity and specificity of this method. The significant role of a general practitioner as a key link in the identification and treatment of the described phenomena, which automatically presents to these specialists a number of professional, qualification and personal requirements, such as knowing pathogenesis of depressive disorders, the features of their pharmacotherapy, skills to build compliant relationships with patients, is highlighted apart. The article also emphasizes the lack of information in available sources about measures to assess the risk of depressive episode recurrence, as well as insufficient attention to the methods of neurophysiological examination and their narrow arsenal. Thus, based on the results of the review, it has been found that a promising direction to improve diagnosis and assessment of therapeutic measure effectiveness, as a part of follow-up among patients with depression, in addition to classical psychodiagnostic tools, is the development and implementation of ethological and neurophysiological methods of examination for objectifying evaluation of patients’ condition and elimination of subjective factors associated with specialists’ perception of patients, as well as symptoms of anosognosia or aggravation.

Effectiveness of vortioxetine for winter depression in bipolar disorder: A case report

AbstractBackgroundWe present a case report on the efficacy of the short‐term application of vortioxetine in managing winter depression in patients with seasonal bipolar disorder (BP). Standard treatment strategies for BP may not adequately address seasonal depressive symptoms during winter in patients with seasonal BP patterns. Depressive symptoms during winter may be linked to seasonal changes in serotonin transporter binding, such as a decrease in synaptic serotonin levels, necessitating alternative approaches. Although antidepressants, including vortioxetine, are effective in treating seasonal monopolar depression, their efficacy and safety in treating depression in patients with seasonal BP patterns remain unclear.Case PresentationThis case report focuses on a 44‐year‐old male patient diagnosed with seasonal BP who had recurrent depressive episodes, specifically during winter. Notably, the patient had a significant decrease in recurrent episodes after short‐term seasonal vortioxetine use without inducing mania or rapid cycling.ConclusionOur study highlights the potential effectiveness of a seasonal, short‐term treatment strategy with antidepressants, including vortioxetine, for winter depression in individuals with BP.

Psychology of bipolar depression: revisiting past and present researches, prospects ahead, and moving toward future directions

Bipolar disorder (BD) is a debilitating psychiatric disorder characterized by recurrent depression, mania, and hypomania episodes. The interaction of psychological, neuropsychological, and neurobiological factors (cognitive, behavioral, and emotional) is implicated in the development and persistence of BD. Accordingly, almost all investigators confirm that BD is the outcome of psychological and genetic interactions. Therefore, researchers should consider various factors in the psychopathology and psychotherapy of BD. This selective review first reviews research on these factors, then points to a variety of therapeutic methods for BD [interpersonal and social rhythm therapy (IPSRT), cognitive behavioral therapy (CBT), dialectical behavior therapy (DBT), mindfulness-based cognitive therapy (MBCT), and family-focused therapy (FFT)], and finally suggested a new comprehensive integrated model for the assessment and therapy of BD.

Evaluating the impact of adjunct bright light therapy on subjective sleep quality in major depressive disorder

BackgroundSleep disturbances are a fundamental feature of depression, with their persistence after remission serving as a key risk factor for recurrence of depressive episodes, suicide, and hypnotics abuse. Though Adjunct Bright Light Therapy (BLT) has shown efficacy in treating depression by improving sleep duration and timing, its impact on subjective sleep quality remains underexplored. ObjectiveThis study investigates the effect of adjunct BLT on the subjective experience of sleep quality of Major Depressive Disorder (MDD) inpatients. MethodsA randomized controlled trial was undertaken with 100 MDD consecutively admitted inpatients on consistent antidepressant regimens. Participants were divided into two groups; Group A, received pharmacotherapy augmented with BLT, Group B, received pharmacotherapy alone. The Hamilton Depression Rating Scale assessed depressive symptoms, while the Pittsburgh Sleep Quality Index (PSQI) evaluated subjective sleep quality. ResultsWhile both groups displayed enhanced depressive symptomatology, only Group A manifested significant improvement in perceived sleep quality (PSQI scores: A T0 8.05 ± 5.07 vs. T1 5.64 ± 3.64, p < 0.001; B T0 7.11 ± 3.17 vs. T1 6.50 ± 3.04, p = 0.072). LimitationsStudy limitations include its single-site design, lack of objective sleep measurement, and exclusive SSRI use, suggesting caution in generalizing findings. Further, the absence of placebo control and unmeasured expectancy effects may influence treatment outcomes. ConclusionsThese findings underscore the criticality of subjective sleep quality in clinical evaluations and highlight the potential of adjunct BLT as an augmentation therapeutic strategy to ameliorate sleep perception in MDD patients, emphasizing its potential role in enhancing therapeutic outcomes.

Protein folding and mental health: The impact of protein misfolding on neurological disorders

Major depressive disorder (MDD), bipolar disorder (BD), and schizophrenia (SCZ) are psychiatric disorders that significantly impact an individual's ability to function effectively within society. MDD is characterized by persistent low mood, leading to symptoms such as anhedonia, feelings of worthlessness, and cognitive impairments. BD, another mood disorder, is typified by recurrent episodes of depression and mania or hypomania. The disorder is associated with increased risk of suicide, comorbid psychiatric conditions, and impaired functioning across various domains. SCZ, a severe psychiatric disorder primarily characterized by hallucinations, delusions, and disorganized thinking, affects approximately 1% of the global population. The cognitive, social, and occupational impairments associated with SCZ impose a substantial burden on affected individuals and their families. Even though it sounds like three completely different mental disorders, because of the high degree of matching of symptoms, we firmly believe that there is a relationship between these mental disorders. Protein folding is crucial for proper protein function, and misfolding can lead to neurodegenerative disorders. Through recent research findings, we found that the changes in A plaques and tau tangles appeared in the typical psychiatric diseases mentioned above. This paper explored the impact of protein misfolding, changes in A plaques, and tau tangles on neurological disorders, with a focus on depression, bipolar disorder, and schizophrenia, also examines the role of molecular chaperones in preventing misfolding and the impact of protein misfolding.

OP63 Incorporating Machine Learning Methods In Health Economic Evaluations: A Case Study On Depression Prevention

IntroductionNew methodologies such as machine learning are becoming widely available and are increasingly used. However, more guidance on their role in the context of economic evaluations would be beneficial.MethodsWe developed a machine learning model to predict recurrent depressive episodes and incorporated the model outcomes in a health economic model to assess the cost effectiveness of offering targeted prevention of recurrent depression. We considered the impact on cost effectiveness (defined as cost per quality-adjusted life-year) for machine learning models with different thresholds for classifying a patient as being at risk, resulting in different precision-recall pairs.ResultsTargeted prevention of recurrent depression could enhance the cost effectiveness of depression treatment by preventing a small number of recurrent depressive episodes in patients where the estimated risk of recurrence is relatively high. More depressive episodes could be prevented with the trade-off of less cost effectiveness for the healthcare system.ConclusionsHealth economic modeling approaches can be augmented with machine learning methods, which broadens the areas in which evidence can be generated for policy makers to base their budget allocation. The precision of such predictive machine learning models must be high enough to be able to improve a care-as-usual healthcare system. Machine learning models generally let you set the level of precision acquired, at the cost of a possibly low recall, thereby limiting the impact on the healthcare system as a whole. More and better data for training these machine learning models will allow developed models to better distinguish patients who will and won’t develop a recurrent depressive episode, and for higher recall given a desired precision threshold. This will translate into a more substantial improvement in the treatment of depressive disorders in the healthcare system.

PSYCHOSOCIAL INTERVENTION FOR REDUCING RELAPSE AND IMPROVING TREATMENT ADHERENCE IN BIPOLAR AFFECTIVE DISORDER WITH POOR SOCIO-OCCUPATIONAL FUNCTIONING AND HIGH EXPRESSED EMOTION IN THE FAMILY: A CASE REPORT

Introduction: Bipolar affective disorder (BPAD) is a chronic psychiatric disorder with an admixture of frequent and recurrent episodes of mania, hypomania, depression, or mixed episodes. More than 1% of the world's population and at least 0.3% of the Indian population are affected by BPAD. We have chosen a case of BPAD with multiple episodes to study the impact of psychosocial intervention in the management of the illness of the affected person and family. The index client, a male, Presentation of the case: 32 years of age, visited the Institute of Psychiatry (IOP) with caregivers and was referred to the psychiatric social work department of the IOP on April 24, 2019, with symptoms of mania and psychosis. He had 7-years history of psychiatric illness, including multiple episodes of mania and depression, as well as frequent irregular medication; burden and conict, insufcient social support, poor communication and interaction patterns, poor coping and problem-solving skills in the family. Therapists imparted Interpersonal and Social Rhythm Therapy (IPSRT) and family-focused therapy to the client and family members. It was found that there Discussion: was a signicant difference between pre- and postpsychosocial interventions in the life and wellbeing of a person with BPAD and his family. Post intervention, client's level of social and occupational functioning was increased, high expressed emotion had been notably decreased, adaptive patterns had been improved, and cohesion in the family had been strengthened. The client and his family members learned to prevent development of future episodes. Conclusion: Psychiatric social work interventions have been a key area in the management of BPAD involving the family, which helps in maintaining a prolonged euthymic period and better treatment compliance.

Effects of Emotional Schema Therapy and Dialectical Behavior Therapy on Psychological Distress and Cognitive-Behavioral Avoidance in Patients with Bipolar II Disorder

Background: Bipolar II disorder (BD-II) is a chronic mental illness with recurrent episodes of depression that causes emotional disorders in patients. Objectives: The present study aimed to investigate the effects of Emotional Schema Therapy (EST) and Dialectical Behavior Therapy (DBT) on psychological distress and cognitive-behavioral avoidance in patients with BD-II. Methods: This study was carried out using a quasi-experimental pretest-posttest design with a control group. The study population consisted of all patients with BD-II visiting the counseling centers in Dezful, Iran, in 2021, and the research sample included 45 eligible individuals selected using purposive sampling. The patients were randomly assigned to two intervention groups and one control group (n = 15 per group). The participants in the first and second intervention groups attended eleven 90-minute sessions of EST and DBT, respectively. However, those in the control group were placed on the waiting list. The research instruments included the Kessler Psychological Distress Scale (K10) and the Cognitive-Behavioral Avoidance Scale. The data were analyzed using analysis of covariance (ANCOVA) and Bonferroni post hoc test in SPSS software (version 26). Results: According to the results, there was a significant reduction in levels of psychological distress and cognitive-behavioral avoidance among the participants of the EST and DBT groups, compared to those in the control group (P &lt; 0.001). Conclusions: Emotional Schema Therapy and Dialectical Behavior Therapy reduced psychological distress and cognitive-behavioral avoidance in patients with BD-II. Therefore, therapists and health professionals can use EST and DBT interventions, along with other effective therapeutic approaches, to reduce psychological distress and cognitive-behavioral avoidance in patients with BD-II.

24 Mindfulness-based cognitive therapy enhances executive control in recurrent depression in a randomized wait-list controlled trial

Objective:Adults with recurrent depression have been shown to have cognitive deficits also while in remission. Thus, it has been suggested that with a chronic course of depression, poorer executive control can be a vulnerability factor for depressive relapse. This has led to research on how cognitive remediation training can protect against recurrent depressive episodes. Findings indicate that such training has short term effects on cognitive functioning, and small effect on depression symptoms. Less focus has been on how “standard” psychotherapy can have positive effects on executive and attentional control. Mindfulness-based cognitive therapy (MBCT) has been shown to be as effective as antidepressant medication in preventing relapses of depressive episodes. Mindfulness training in healthy samples seems to improve executive and attentional control. However, the few studies of MBCT in recurrent depression show mixed effects on executive and attentional control. As far as we know, no prior study has investigated the effect of MBCT in recurrent depression with the revised version of the attention network test (ANT-R). In a randomized controlled trial, we expected that the MBCT group would show enhanced executive control and lower levels of attentional fluctuations than the wait-list controls (WLC) from pre (T0) to post (T1) treatment. We further investigated if positive effects of MBCT on executive and attentional control were associated with reduction in depression symptoms.Participants and Methods:Adults with recurrent depression in partial or full remission (N = 64) were randomized to MBCT or WLC. In the MBCT and WLC groups, 25 and 29, respectively, performed the ANT-R at T0 and T1. The attention network reaction time scores of executive control, alerting, and orienting were calculated in addition to attention fluctuations scores of intra-individual reaction time variability (IIVRT) and exgaussian-mean of longer reaction times (TAU). Self-reported depression symptoms were measured with BDI-II. The two groups were compared at baseline on full-scale IQ (WASI), executive control (D-KEFS Stroop), and processing speed (D-KEFS TMT).Results:The MBCT and WCL groups did not differ significantly in age or gender distribution, education, full-scale IQ or in baseline executive and attentional control as measured with the ANT-R, Stroop and TMT. The MBCT group showed a higher efficiency in conflict detection as measured with the executive control score from T0 to T1 compared to the WLC. This positive effect of MBCT on executive control was independent from the greater reductions in depression symptoms in the MBCT group compared to in the WLCs. However, reduction in depression symptoms at T1 was associated with enhanced efficiency in responding to alerting cues in conflict detection. No effects of MBCT compared to WLC were found at T1 on the attention fluctuation measures (IIVRT and TAU).Conclusions:The findings from the current study indicates that MBCT enhances executive control in adults with recurrent depression. As such, MBCT may target an important cognitive vulnerability factor in the chronic course of recurrent depression that may contribute to its efficacy in preventing depressive relapses. It was also observed that reductions in depression symptoms led to higher alertness in conflict detection.

Comparative Study of Heart Rate Variability in Patients with Major Depressive Disorder and Normal Subjects

Introduction: Major Depressive Disorder is a mood disorder diagnosed on the basis of DSM-5 criteria. Cardiovascular disorders are among the most common causes of deaths worldwide, accounting for about one-third of the mortality in both genders. Cardiac illnesses seem to have a bidirectional relationship with depression. Altered cardiac autonomic tone remains one of the most plausible explanations, which also appears to be a central biological substrate linking depression to a number of concurrent physical (e.g., cardiovascular) dysfunctions. Heart Rate Variability has become the conventionally accepted term to describe variations of both instantaneous heart rate and RR intervals (measured in ms) obtained from ECG or plethysmogram. Material and Method: This study was a prospective observational study involved 150 individuals divided into two groups. Group A – Patients having first episode major depression or new recurrent depressive episode recruited from psychiatric OPD (n=75), Group B– Age, Sex, Height and Weight matched healthy controls (n=75). Results: In our study, among the time domain indices, Mean heart rate was increased in MDD patients in comparison to healthy controls and the difference was statistically significant. Root mean square of successive differences was reduced in MDD patients in comparison to healthy controls and the difference was statistically significant. Standard deviation of NN intervals and percentage of NN intervals differing by &gt;50 ms also showed reduction in depressed patients than controls, but the difference was not statistically significant. The frequency domain indices Total power, very low frequency, High frequency were reduced in MDD patients in comparison to healthy controls and the difference was statistically significant. Low Frequency and Low Frequency / High Frequency ratio were increased in patients of MDD in comparison to healthy controls and the difference was statistically significant. Conclusion: From our study we can conclude that HRV is reduced in MDD patients. We suggest that HRV parameters can be used as diagnostic or predictive biomarkers of depression. Keywords: Major Depressive Disorder (MDD), cardiovascular disease (CVD), Heart Rate Variability (HRV), Time Domain Indices, Frequency Domain Indices.

Depression recurrence is accompanied by longer periods in default mode and more frequent attentional and reward processing dynamic brain-states during resting-state activity.

Recurrence in major depressivedisorder (MDD) is common, but neurobiological models capturing vulnerability for recurrences are scarce. Disturbances in multiple resting-state networks have been linked to MDD, but most approaches focus on stable (vs. dynamic) network characteristics. We investigated how the brain's dynamical repertoire changes after patients transition from remission to recurrence of a new depressive episode. Sixty two drug-free, MDD-patients with ≥2 episodes underwent a baseline resting-state fMRI scan when in remission. Over 30-months follow-up, 11 patients with a recurrence and 17 matched-remitted MDD-patients without a recurrence underwent a second fMRI scan. Recurrent patterns of functional connectivity were characterized by applying Leading Eigenvector Dynamics Analysis (LEiDA). Differences between baseline and follow-up were identified for the 11 non-remitted patients, while data from the 17 matched-remitted patients was used as a validation dataset. After the transition into a depressive state, basal ganglia-anterior cingulate cortex (ACC) and visuo-attentional networks were detected significantly more often, whereas default mode network activity was found to have a longer duration. Additionally, the fMRI signal in the basal ganglia-ACC areas underlying the reward network, were significantly less synchronized with the rest of the brain after recurrence (compared to a state of remission). No significant changes were observed in the matched-remitted patients who were scanned twice while in remission. These findings characterize changes that may be associated with the transition from remission to recurrence and provide initial evidence of altered dynamical exploration of the brain's repertoire of functional networks when a recurrent depressive episode occurs.

Bipolar disorder: Diagnosis, treatment and future directions.

Bipolar disorder is a relatively common mental illness, characterised by recurrent episodes of mania (or hypomania) and major depression, and associated with a significant burden of morbidity and premature mortality. Physicians across all specialities are likely to encounter individuals with the condition within their clinical practice. This short review provides an up-to-date overview of the clinical features, epidemiology, pathophysiology, evidence-based management, prognosis and future directions for treatment and research in bipolar disorder. Aspects of cross-specialty relevance are highlighted, including the physical health burden associated with the condition, and the side effects and safety considerations of medication regimes used in bipolar disorder.

The immunomodulatory effect of lithium as a mechanism of action in bipolar disorder.

Bipolar disorder (BD) is a chronic mental disorder characterized by recurrent episodes of mania and depression alternating with periods of euthymia. Although environmental and genetic factors have been described, their pathogenesis is not fully understood. Much evidence suggests a role for inflammatory mediators and immune dysregulation in the development of BD. The first-line treatment in BD are mood-stabilizing agents, one of which is lithium (Li) salts. The Li mechanism of action is not fully understood, but it has been proposed that its robust immunomodulatory properties might be one of the mechanisms responsible for its effectiveness. In this article, the authors present the current knowledge about immune system changes accompanying BD, as well as the immunomodulatory effect of lithium. The results of studies describing connections between immune system changes and lithium effectiveness are often incoherent. Further research is needed to understand the connection between immune system modulation and the therapeutic action of lithium in BD.

Psychiatric comorbidity in patients with migraine

ABSTRACT Background: Comorbidity of various psychiatric conditions with migraine is common and important. A thorough analysis of mental conditions in migraine is essential to encourage the holistic care of such patients. Aim: To assess psychiatric comorbidity in patients with migraine using a well-standardized clinical diagnostic tool. Methods: This cross-sectional study was conducted in the outpatient department of a tertiary care hospital in North India. Persons with migraine attending the facility were enrolled as per the inclusion and exclusion criteria set for this study. The ICD-10 mental disorder symptom checklist was the principal tool to assess the psychiatric comorbidity in the patients. Results: Total Patients recruited were 146 out of which 70.5% had a psychiatric comorbidity. Depressive disorder (17.1%) was the most common while delusional disorder (4.1%) was the least common psychiatric disorder comorbid with migraine. Other comorbid psychiatric diagnoses were recurrent depressive episodes (6.8%), dysthymia (10.3%), bipolar affective disorder (7.5%), panic disorder (5.5%), generalized anxiety disorder (8.2%), mixed and other anxiety disorders (6.2%), obsessive-compulsive disorder (6.2%), post-traumatic stress disorder (4.8%), adjustment disorders (5.5%), dissociative (conversion) disorders (6.8%), and somatoform disorders (6.2%). Lower socioeconomic status and the presence of aura with migraine are two important patient-related factors linked with the occurrence of any psychiatric comorbidity in migraine. Conclusion: The prevalence of comorbid psychiatric disorders in migraine patients is very high. The relationship between demographic and medical information of people with migraine with individual psychiatric comorbidities is complex.