Investigating the association between metabolic syndrome conditions and perinatal mental illness: a national administrative claims study.

BACKGROUND: Although the association between mental disorder and metabolic syndrome as a bidirectional relationship has been demonstrated, there is little knowledge of the cumulative and individual effect of these conditions on peripartum mental health. This study aims to investigate the association between metabolic syndrome conditions (MetS-C) and maternal mental illness in the perinatal period, while exploring time to incident mental disorder diagnosis in postpartum women. METHODS: This observational study identified perinatal women continuously enrolled 1 year prior to and 1 year post-delivery using Optum's de-identified Clinformatics® Data Mart Database (CDM) from 2014 to 2019 with MetS-C i.e. obesity, diabetes, high blood pressure, high triglycerides, or low HDL (1-year prior to delivery); perinatal comorbidities (9-months prior to and 4-month postpartum); and mental disorder (1-year prior to and 1-year post-delivery). Additionally, demographics and the number of days until mental disorder diagnosis were evaluated in this cohort. The analysis included descriptive statistics and multivariable logistic regression. MetS-C, perinatal comorbidities, and mental disorder were assessed using the International Classification of Diseases, Ninth, and Tenth Revision diagnosis codes. RESULTS: 372,895 deliveries met inclusion/exclusion criteria. The prevalence of MetS-C was 13.43%. Multivariable logistic regression revealed prenatal prevalence (1.64, CI = 1.59-1.70) and postpartum incident (1.30, CI = 1.25-1.34) diagnosis of mental health disorder were significantly higher in those with at least one MetS-C. Further, the adjusted odds of having postpartum incident mental illness were 1.51 times higher (CI = 1.39-1.66) in those with 2 MetS-C and 2.12 times higher (CI = 1.21-4.01) in those with 3 or more MetS-C. Young women (under the age of 18 years) were more likely to have an incident mental health diagnosis as opposed to other age groups. Lastly, time from hospital discharge to incident mental disorder diagnosis revealed an average of 157 days (SD = 103 days). CONCLUSION: The risk of mental disorder (both prenatal and incident) has a significant association with MetS-C. An incremental relationship between incident mental illness diagnosis and the number of MetS-C, a significant association with younger mothers along with a relatively long period of diagnosis mental illness highlights the need for more screening and treatment during pregnancy and postpartum.

Deep brain stimulation of the "medial forebrain bundle": sustained efficacy of antidepressant effect over years.

Deep brain stimulation (DBS) to the superolateral branch of the medial forebrain bundle (MFB) has emerged as a quite efficacious therapy for treatment resistant depression (TRD), leading to rapid antidepressant effects. In this study, we complete our assessment of our first 10 enrolled patients throughout one year post-implantation, showing sustained antidepressant effect up to 5 years. The primary outcome measure was a 50% reduction in Montgomery-Åsberg Depression Rating Scale (MADRS) score, which was interpreted as a response. Deterministic fiber tracking was used to individually map the target area. An insertional effect was seen during the 4-week sham stimulation phase (29% mean MADRS reduction, p = 0.02). However, after 2 weeks of initiating stimulation, five patients met response criteria (47% mean MADRS reduction, p < 0.001). One patient withdrew from study participation at 6 weeks. Twelve weeks after initiating stimulation, six of nine remaining patients had a >50% decrease in MADRS scores relative to baseline (52% mean MADRS reduction, p = 0.001); these same six patients continued to meet response criteria at 52 weeks (63% overall mean MADRS reduction, p < 0.001). Four of five patients who achieved the 5-year time point analysis continued to be responders (81% mean MADRS reduction, p < 0.001). Evaluation of modulated fiber tracts reveals significant common prefrontal/orbitofrontal connectivity to the target region in all responders. Key points learned from this study that we can incorporate in future protocols to better elucidate the effect of this therapy are a longer blinded sham stimulation phase and use of scheduled discontinuation concomitant with functional imaging.

Alterations in brain synaptic proteins and mRNAs in mood disorders: a systematic review and meta-analysis of postmortem brain studies.

The pathophysiological mechanisms underlying bipolar (BD) and major depressive disorders (MDD) are multifactorial but likely involve synaptic dysfunction and dysregulation. There are multiple synaptic proteins but three synaptic proteins, namely SNAP-25, PSD-95, and synaptophysin, have been widely studied for their role in synaptic function in human brain postmortem studies in BD and MDD. These studies have yielded contradictory results, possibly due to the small sample size and sourcing material from different cortical regions of the brain. We performed a systematic review and meta-analysis to understand the role of these three synaptic proteins and other synaptic proteins, messenger RNA (mRNA) and their regional localizations in BD and MDD. A systematic literature search was conducted and the review is reported in accordance with the MOOSE Guidelines. Meta-analysis was performed to compare synaptic marker levels between BD/MDD groups and controls separately. 1811 papers were identified in the literature search and screened against the preset inclusion and exclusion criteria. A total of 72 studies were screened in the full text, of which 47 were identified as eligible to be included in the systematic review. 24 of these 47 papers were included in the meta-analysis. The meta-analysis indicated that SNAP-25 protein levels were significantly lower in BD. On average, PSD-95 mRNA levels were lower in BD, and protein levels of SNAP-25, PSD-95, and syntaxin were lower in MDD. Localization analysis showed decreased levels of PSD-95 protein in the frontal cortex. We found specific alterations in synaptic proteins and RNAs in both BD and MDD. The review was prospectively registered online in PROSPERO international prospective register of systematic reviews, registration no. CRD42020196932.

Blood-based biomarkers of antidepressant response to ketamine and esketamine: A systematic review and meta-analysis.

(R,S)-ketamine (ketamine) and its enantiomer (S)-ketamine (esketamine) can produce rapid and substantial antidepressant effects. However, individual response to ketamine/esketamine is variable, and there are no well-accepted methods to differentiate persons who are more likely to benefit. Numerous potential peripheral biomarkers have been reported, but their current utility is unclear. We conducted a systematic review/meta-analysis examining the association between baseline levels and longitudinal changes in blood-based biomarkers, and response to ketamine/esketamine. Of the 5611 citations identified, 56 manuscripts were included (N = 2801 participants), and 26 were compatible with meta-analytical calculations. Random-effect models were used, and effect sizes were reported as standardized mean differences (SMD). Our assessments revealed that more than 460 individual biomarkers were examined. Frequently studied groups included neurotrophic factors (n = 15), levels of ketamine and ketamine metabolites (n = 13), and inflammatory markers (n = 12). There were no consistent associations between baseline levels of blood-based biomarkers, and response to ketamine. However, in a longitudinal analysis, ketamine responders had statistically significant increases in brain-derived neurotrophic factor (BDNF) when compared to pre-treatment levels (SMD [95% CI] = 0.26 [0.03, 0.48], p = 0.02), whereas non-responders showed no significant changes in BDNF levels (SMD [95% CI] = 0.05 [-0.19, 0.28], p = 0.70). There was no consistent evidence to support any additional longitudinal biomarkers. Findings were inconclusive for esketamine due to the small number of studies (n = 2). Despite a diverse and substantial literature, there is limited evidence that blood-based biomarkers are associated with response to ketamine, and no current evidence of clinical utility.

Digital health technologies for peripartum depression management among low-socioeconomic populations: perspectives from patients, providers, and social media channels.

BACKGROUND: Peripartum Depression (PPD) affects approximately 10-15% of perinatal women in the U.S., with those of low socioeconomic status (low-SES) more likely to develop symptoms. Multilevel treatment barriers including social stigma and not having appropriate access to mental health resources have played a major role in PPD-related disparities. Emerging advances in digital technologies and analytics provide opportunities to identify and address access barriers, knowledge gaps, and engagement issues. However, most market solutions for PPD prevention and management are produced generically without considering the specialized needs of low-SES populations. In this study, we examine and portray the information and technology needs of low-SES women by considering their unique perspectives and providers' current experiences. We supplement our understanding of women's needs by harvesting online social discourse in PPD-related forums, which we identify as valuable information resources among these populations. METHODS: We conducted (a) 2 focus groups (n = 9), (b) semi-structured interviews with care providers (n = 9) and low SES women (n = 10), and (c) secondary analysis of online messages (n = 1,424). Qualitative data were inductively analyzed using a grounded theory approach. RESULTS: A total of 134 open concepts resulted from patient interviews, 185 from provider interviews, and 106 from focus groups. These revealed six core themes for PPD management, including "Use of Technology/Features", "Access to Care", and "Pregnancy Education". Our social media analysis revealed six PPD topics of importance in online messages, including "Physical and Mental Health" (n = 725 messages), and "Social Support" (n = 674). CONCLUSION: Our data triangulation allowed us to analyze PPD information and technology needs at different levels of granularity. Differences between patients and providers included a focus from providers on needing better support from administrative staff, as well as better PPD clinical decision support. Our results can inform future research and development efforts to address PPD health disparities.

Within subject cross-tissue analyzes of epigenetic clocks in substance use disorder postmortem brain and blood.

There is a possible accelerated biological aging in patients with substance use disorders (SUD). The evaluation of epigenetic clocks, which are accurate estimators of biological aging based on DNA methylation changes, has been limited to blood tissue in patients with SUD. Consequently, the impact of biological aging in the brain of individuals with SUD remains unknown. In this study, we evaluated multiple epigenetic clocks (DNAmAge, DNAmAgeHannum, DNAmAgeSkinBlood, DNAmPhenoAge, DNAmGrimAge, and DNAmTL) in individuals with SUD (n = 42), including alcohol (n = 10), opioid (n = 19), and stimulant use disorder (n = 13), and controls (n = 10) in postmortem brain (prefrontal cortex) and blood tissue obtained from the same individuals. We found a higher DNAmPhenoAge (ß = 0.191, p-value = 0.0104) and a nominally lower DNAmTL (ß = -0.149, p-value = 0.0603) in blood from individuals with SUD compared to controls. SUD subgroup analysis showed a nominally lower brain DNAmTL in subjects with alcohol use disorder, compared to stimulant use disorder and controls (ß = 0.0150, p-value = 0.087). Cross-tissue analyzes indicated a lower blood DNAmTL and a higher blood DNAmAge compared to their respective brain values in the SUD group. This study highlights the relevance of tissue specificity in biological aging studies and suggests that peripheral measures of epigenetic clocks in SUD may depend on the specific type of drug used.

Personalizing repetitive transcranial magnetic stimulation for precision depression treatment based on functional brain network controllability and optimal control analysis.

Brain neuromodulation effectively treats neurological diseases and psychiatric disorders such as Depression. However, due to patient heterogeneity, neuromodulation treatment outcomes are often highly variable, requiring patient-specific stimulation protocols throughout the recovery stages to optimize treatment outcomes. Therefore, it is critical to personalize neuromodulation protocol to optimize the patient-specific stimulation targets and parameters by accommodating inherent interpatient variability and intersession alteration during treatments. The study aims to develop a personalized repetitive transcranial magnetic stimulation (rTMS) protocol and evaluate its feasibility in optimizing the treatment efficiency using an existing dataset from an antidepressant experimental imaging study in depression. The personalization of the rTMS treatment protocol was achieved by personalizing both stimulation targets and parameters via a novel approach integrating the functional brain network controllability analysis and optimal control analysis. First, the functional brain network controllability analysis was performed to identify the optimal rTMS stimulation target from the effective connectivity network constructed from patient-specific resting-state functional magnetic resonance imaging data. The optimal control algorithm was then applied to optimize the rTMS stimulation parameters based on the optimized target. The performance of the proposed personalized rTMS technique was evaluated using datasets collected from a longitudinal antidepressant experimental imaging study in depression (n = 20). Simulation models demonstrated that the proposed personalized rTMS protocol outperformed the standard rTMS treatment by efficiently steering a depressive resting brain state to a healthy resting brain state, indicated by the significantly less control energy needed and higher model fitting accuracy achieved. The node with the maximum average controllability of each patient was designated as the optimal target region for the personalized rTMS protocol. Our results also demonstrated the theoretical feasibility of achieving comparable neuromodulation efficacy by stimulating a single node compared to stimulating multiple driver nodes. The findings support the feasibility of developing personalized neuromodulation protocols to more efficiently treat depression and other neurological diseases.

Angiogenic gene networks are dysregulated in opioid use disorder: evidence from multi-omics and imaging of postmortem human brain.

Opioid use disorder (OUD) is a public health crisis in the U.S. that causes over 50 thousand deaths annually due to overdose. Using next-generation RNA sequencing and proteomics techniques, we identified 394 differentially expressed (DE) coding and long noncoding (lnc) RNAs as well as 213 DE proteins in Brodmann Area 9 of OUD subjects. The RNA and protein changes converged on pro-angiogenic gene networks and cytokine signaling pathways. Four genes (LGALS3, SLC2A1, PCLD1, and VAMP1) were dysregulated in both RNA and protein. Dissecting these DE genes and networks, we found cell type-specific effects with enrichment in astrocyte, endothelial, and microglia correlated genes. Weighted-genome correlation network analysis (WGCNA) revealed cell-type correlated networks including an astrocytic/endothelial/microglia network involved in angiogenic cytokine signaling as well as a neuronal network involved in synaptic vesicle formation. In addition, using ex vivo magnetic resonance imaging, we identified increased vascularization in postmortem brains from a subset of subjects with OUD. This is the first study integrating dysregulation of angiogenic gene networks in OUD with qualitative imaging evidence of hypervascularization in postmortem brain. Understanding the neurovascular effects of OUD is critical in this time of widespread opioid use.

Correlations between peripheral levels of inflammatory mediators and frontolimbic structures in bipolar disorder: an exploratory analysis.

BACKGROUND: Altered peripheral immune/inflammatory system and brain volumetric changes have been implicated in the pathophysiology of bipolar disorder (BD). This study aimed to evaluate how peripheral levels of cytokines are related to volumetric brain changes in euthymic patients with BD. METHODS: Euthymic patients with BD (n = 21) and healthy controls (n = 22) were enrolled in this exploratory study. Blood samples were collected on the same day of clinical assessment and neuroimaging. Cytokines were measured through cytometric bead array method. Neuroimaging data were acquired using a sagittal three-dimensional magnetic resonance imaging T1-weighted fast field echo sequence and was processed using FreeSurfer. RESULTS: Compared to controls, BD patients had significantly lower volumes in the cingulate, medial-orbitofrontal (MOF) and parahippocampal regions. We found a negative correlation between right MOF volume and interferon-gamma levels (ß = -0.431, P = .049) and a positive correlation between interleukin-10 levels and left posterior cingulate volume (ß = 0.457, P = .048). CONCLUSION: Our results support the involvement of inflammatory pathways in structural brain changes in BD.

Neuroinflammation trajectories precede cognitive impairment after experimental meningitis-evidence from an in vivo PET study.

BACKGROUND: Bacterial meningitis is a devastating central nervous system (CNS) infection with acute and long-term neurological consequences, including cognitive impairment. The aim of this study was to understand the association between activated microglia-induced neuroinflammation and post-meningitis cognitive impairment. METHOD: Meningitis was induced in male Wistar rats by injecting Streptococcus pneumoniae into the brain through the cisterna magna, and rats were then treated with ceftriaxone. Twenty-four hours and 10 days after meningitis induction, rats were imaged with positron emission tomography (PET) using [11C]PBR28, a specific translocator protein (TSPO) radiotracer, to determine in vivo microglial activation. Following imaging, the expression of TSPO, cardiolipin, and cytochrome c, inflammatory mediators, oxidative stress markers, and glial activation markers were evaluated in the prefrontal cortex and hippocampus. Ten days after meningitis induction, animals were subjected to behavioral tests, such as the open-field, step-down inhibitory avoidance, and novel object recognition tests. RESULTS: Both 24-h (acute) and 10-day (long-term) groups of rats demonstrated increased [11C]PBR28 uptake and microglial activation in the whole brain compared to levels in the control group. Although free from infection, 10-day group rats exhibited increased expression levels of cytokines and markers of oxidative stress, microglial activation (IBA-1), and astrocyte activation (GFAP) similar to those seen in the 24-h group. Acute meningitis induction also elevated TSPO, cytochrome c, and caspase-3 levels with no change in caspase-9 levels. Furthermore, upregulated levels of TSPO, cytochrome c, and caspase-3 and caspase-9 were observed in the rat hippocampus 10 days after meningitis induction with a simultaneous reduction in cardiolipin levels. Animals showed a cognitive decline in all tasks compared with the control group, and this impairment may be at least partially mediated by activating a glia-mediated immune response and upregulating TSPO. CONCLUSIONS: TSPO-PET could potentially be used as an imaging biomarker for microglial activation and long-term cognitive impairment post-meningitis. Additionally, this study opens a new avenue for the potential use of TSPO ligands after infection-induced neurological sequelae.

Computational meta-analysis of statistical parametric maps in major depression.

OBJECTIVE: Several neuroimaging meta-analyses have summarized structural brain changes in major depression using coordinate-based methods. These methods might be biased toward brain regions where significant differences were found in the original studies. In this study, a novel voxel-based technique is implemented that estimates and meta-analyses between-group differences in grey matter from individual MRI studies, which are then applied to the study of major depression. METHODS: A systematic review and meta-analysis of voxel-based morphometry studies were conducted comparing participants with major depression and healthy controls by using statistical parametric maps. Summary effect sizes were computed correcting for multiple comparisons at the voxel level. Publication bias and heterogeneity were also estimated and the excess of heterogeneity was investigated with metaregression analyses. RESULTS: Patients with major depression were characterized by diffuse bilateral grey matter loss in ventrolateral and ventromedial frontal systems extending into temporal gyri compared to healthy controls. Grey matter reduction was also detected in the right parahippocampal and fusiform gyri, hippocampus, and bilateral thalamus. Other areas included parietal lobes and cerebellum. There was no evidence of statistically significant publication bias or heterogeneity. CONCLUSIONS: The novel computational meta-analytic approach used in this study identified extensive grey matter loss in key brain regions implicated in emotion generation and regulation. Results are not biased toward the findings of the original studies because they include all available imaging data, irrespective of statistically significant regions, resulting in enhanced detection of additional areas of grey matter loss.

Development and validation of a brain maturation index using longitudinal neuroanatomical scans.

BACKGROUND: Major psychiatric disorders are increasingly being conceptualized as 'neurodevelopmental', because they are associated with aberrant brain maturation. Several studies have hypothesized that a brain maturation index integrating patterns of neuroanatomical measurements may reliably identify individual subjects deviating from a normative neurodevelopmental trajectory. However, while recent studies have shown great promise in developing accurate brain maturation indices using neuroimaging data and multivariate machine learning techniques, this approach has not been validated using a large sample of longitudinal data from children and adolescents. METHODS: T1-weighted scans from 303 healthy subjects aged 4.88 to 18.35years were acquired from the National Institute of Health (NIH) pediatric repository (http://www.pediatricmri.nih.gov). Out of the 303 subjects, 115 subjects were re-scanned after 2years. The least absolute shrinkage and selection operator algorithm (LASSO) was 'trained' to integrate neuroanatomical changes across chronological age and predict each individual's brain maturity. The resulting brain maturation index was developed using first-visit scans only, and was validated using second-visit scans. RESULTS: We report a high correlation between the first-visit chronological age and brain maturation index (r=0.82, mean absolute error or MAE=1.69years), and a high correlation between the second-visit chronological age and brain maturation index (r=0.83, MAE=1.71years). The brain maturation index captured neuroanatomical volume changes between the first and second visits with an MAE of 0.27years. CONCLUSIONS: The brain maturation index developed in this study accurately predicted individual subjects' brain maturation longitudinally. Due to its strong clinical potentials in identifying individuals with an abnormal brain maturation trajectory, the brain maturation index may allow timely clinical interventions for individuals at risk for psychiatric disorders.

Risk of suicidal behavior in patients with major depression and bipolar disorder - A systematic review and meta-analysis of registry-based studies.

Suicide is a health priority and one of the most common causes of death in mood disorders. One of the limitations of this type of research is that studies often establish rates of suicide behaviors in mood disorders by using diverse comparison groups or simply monitoring cohort of patients over a time period. In this registry-based systematic review, national registers were identified through searches in six academic databases, and information about the occurrence of suicide behaviors in mood disorders was systematically extracted. Odds ratios were subsequently calculated comparing rates of death by suicide in mood disorders in comparison with age and period matched rates of death by suicide in the general population obtained from country-wide national registers. The aim was to provide the most recent summary of epidemiological and clinical factors associated to suicide in mood disorders whilst calculating the likelihood of death by suicide in mood disorders in comparison with non-affected individuals according to national databases. The study follows the Preferred Reporting Guidelines for Systematic Reviews and Meta-analyses and was prespecify registered on Prospero (CRD42020186857). Results suggest that patients with mood disorders are at substantially increased risk of attempting and dying by suicide. Several epidemiological, clinical and social factors are reported to be associated with clinical populations at risk of suicide. Meta-analyses of completed deaths by suicide suggest that the likelihood for dying by suicide in mood disorders is 8.62 times higher in major depression and 8.66 times higher in bipolar disorder with higher number of untoward events in women compared to men in both conditions. The likelihood of dying by suicide in major depressive disorders is higher in the first year following discharge. Clinical guidelines might consider longer periods of monitoring following discharge from hospital. Overall, due to the higher risk of suicide in mood disorders, efforts should be made to increase detection and prevention whilst focusing on reducing risk in the most severe forms of illness with appropriate treatment to promote response and remission at the earliest convenience.

Predicting Antidepressant Treatment Response Using Functional Brain Controllability Analysis.

Introduction: For decades, predicting response to the antidepressant medication has been a critical unmet need in depression treatment in clinic, and a technical challenge in depression research. Methods: In this study, a recently developed functional brain network controllability (fBNC) analysis approach was employed to identify the antidepressant treatment responders and nonresponders from depression patients at the pretreatment period. The fBNC, which captures the ability of brain regions to guide the brain's behavior from an initial state to a desired state with suitable choice of inputs, may provide valuable features for antidepressant response prediction. The performance of prediction was evaluated using resting-state functional magnetic resonance imaging data collected from a 6-week longitudinal clinical trial with escitalopram in treating unmedicated depression patients (n = 20). Treatment outcomes were assessed using the Hamilton Depression Rating Scale (HAMD) scores. Patients were considered as the treatment responders if their post-treatment HAMD scores were decreased by 50% or more at 6 weeks post-treatment. Results: Results showed significantly larger global average controllability and lower global modal controllability, greater regional average controllability, and smaller regional modal controllability of default mode network in treatment responders compared with the treatment nonresponders at the pretreatment period. By performing optimal control analysis, our results showed no significant difference of the neuromodulation effects between the treatment responders and nonresponders. Discussion: Our results suggest that the fBNC measures may be utilized as novel biomarkers to predict antidepressant response on depression and provide theoretical support to employ neuromodulation for treating antidepressant nonresponders. Impact statement In this study, by employing the novel functional brain controllability analysis on top of the brain connectivity network, we identified a set of biomarkers to identify the groups of depressive patients who responded to the antidepressant treatments from those who did not. We further provided the theoretical support to utilize neuromodulation for treating antidepressant nonresponders. These findings have clinical implications as accurate identification of antidepressant treatment response before starting the treatment may reduce patients' suffering and costs and increase the treatment outcomes by adjusting and personalizing the treatment protocol.

Noninvasive Brain Stimulation Techniques for Treatment-Resistant Depression: Transcranial Magnetic Stimulation and Transcranial Direct Current Stimulation.

Transcranial magnetic stimulation is a safe, effective, and well-tolerated intervention for depression; it is currently approved for treatment-resistant depression. This article summarizes the mechanism of action, evidence of clinical efficacy, and the clinical aspects of this intervention, including patient evaluation, stimulation parameters selection, and safety considerations. Transcranial direct current stimulation is another neuromodulation treatment for depression; although promising, the technique is not currently approved for clinical use in the United States. The final section outlines the open challenges and future directions of the field.

Temporal multi-step predictive modeling of remission in major depressive disorder using early stage treatment data; STAR*D based machine learning approach.

BACKGROUND: Artificial intelligence is currently being used to facilitate early disease detection, better understand disease progression, optimize medication/treatment dosages, and uncover promising novel treatments and potential outcomes. METHODS: Utilizing the Sequenced Treatment Alternatives to Relieve Depression (STAR*D) dataset, we built a machine learning model to predict depression remission rates using same clinical data as features for each of the first three antidepressant treatment steps in STAR*D. We only used early treatment data (baseline and first follow up) in each STAR*D step to temporally analyze predictive features of remission at the end of the step. RESULTS: Our model showed significant prediction performance across the three treatment steps, At step 1, Model accuracy was 66 %; sensitivity-65 %, specificity-67 %, positive predictive value (PPV)-65.5 %, and negative predictive value (NPV)-66.6 %. At step 2, model accuracy was 71.3 %, sensitivity-74.3 %, specificity-69 %, PPV-64.5 %, and NPV-77.9 %. At step 3, accuracy reached 84.6 %; sensitivity-69 %, specificity-88.8 %, PPV-67 %, and NPV-91.1 %. Across all three steps, the early Quick Inventory of Depressive Symptomatology-Self-Report (QIDS-SR) scores were key elements in predicting the final treatment outcome. The model also identified key sociodemographic factors that predicted treatment remission at different steps. LIMITATIONS: The retrospective design, lack of replication in an independent dataset, and the use of "a complete case analysis" model in our analysis. CONCLUSIONS: This proof-of-concept study showed that using early treatment data, multi-step temporal prediction of depressive symptom remission results in clinically useful accuracy rates. Whether these predictive models are generalizable deserves further study.

Effects of an experimentally induced inflammatory stimulus on motivational behavior in remitted depressed patients.

BACKGROUND: Acute inflammation is associated with sickness behavior characterized by reduced motivation for pleasurable activities in humans. The current study investigated the effect of an experimentally induced inflammatory stimulus on motivational reward in people who remitted from depression. METHODS: This randomized, double-blind crossover study involved 12 participants, 5 with remitted major depressive disorder (rMDD) and 7 healthy controls (HC), who received an injection of typhoid vaccine and placebo (or vice-versa) intramuscularly at least one week apart. At baseline and between 4 and 6 h post-injection on both days, participant mood was measured using the profile of mood states (POMS), and injection blood samples were collected for cytokines measurement. All participants completed the Effort Expenditure for Rewards Task (EEfRT), a behavioral paradigm measuring effort-based decision-making before and 4 h post-both injections. Generalized linear mixed modeling was used to evaluate group differences in choosing the hard over easy task to obtain a monetary reward. RESULTS: Typhoid vaccine increased IL-6 in all participants. On the EEfRT, a significant interaction between treatment condition (typhoid vs. placebo) and participant group (HC vs. rMDD) was found (p = .004). Analyses of simple effects within treatment conditions found that after placebo, HCs were more likely to choose the harder task than rMDD (OR = 3.21; p = .013). However, after the typhoid vaccine, no differences were found between rMDD and HC (p = .397). Analyses within participant groups found that the probability of choosing a hard task was higher after placebo for HC (OR = 1.37; p = .045), but not different within rMDD (p = .241). For HC at baseline, mood was significantly lower following injection with typhoid vaccine, relative to placebo (b = -1.03, p < .001); however, this effect should be considered coincidental, given that mood rating was taken prior to injection. For rMDD patients 4-6 h post-injection, mood was significantly lower following typhoid vaccine, relative to placebo (b = -0.981, p < .001 b = -0.77, p < .001). Finally, for HC receiving placebo, mood was significantly lower 4-6 h post-injection, relative to baseline (b = -1.76, p < .001). CONCLUSIONS: Our preliminary findings suggest persistent deficits in motivational reward processing function despite clinical improvement in remitted depressed patients.

Processing speed - A potential candidate cognitive endophenotype for bipolar disorder.

Background: Bipolar disorder (BD) is a chronic multifactorial disorder that presents with cognitive impairment as one of its main features, in patients as well as in their first-degree relatives. However, the profile of cognitive dysfunction in BD patients and their relatives is not yet well defined. Various neurocognitive deficits have been proposed as endophenotypes for BD. In the present study, we explored the susceptibility to neurocognitive deficits in BD patients and their siblings compared to healthy controls. Method: A sample consisting of patients diagnosed with BD (N=37), their unaffected siblings (N=30) and a healthy control group (N=39) was assessed using the Brief Assessment of Cognition for Affective Disorders (BAC-A) battery of tests in various cognitive domains: memory, processing speed, working memory, reasoning and problem solving, and affective processing. Results: Compared to healthy controls, BD patients and their unaffected siblings showed deficits in attention and motor speed, or processing speed as measured by the Symbol coding task (p = 0.008), as well as a similar degree of impairment (p = 1.000). Limitations: The lack of statistically significant findings in the other cognitive domains could be related to differences in task difficulty. Most patients were taking psychotropic medication with varying effects on cognition and being treated as outpatients, implying a currently higher level of functioning, which may limit extrapolation of the sample to the general population of BD patients. Conclusions: These results support the view of considering processing speed as an endophenotype for bipolar disorder.

Feasibility of a meditation intervention for stroke survivors and informal caregivers: a randomized controlled trial.

BACKGROUND: Depressive symptoms are a significant psychological complication of stroke, impacting both survivors and informal caregivers of survivors. Randomized controlled trials are needed to determine optimal non-pharmacological strategies to prevent or ameliorate depressive symptoms in stroke survivors and their informal caregivers. METHODS: A prospective, randomized, parallel-group, single-center, feasibility study. Participants were assigned to a 4-week meditation intervention or expressive writing control group. The intervention comprised four facilitator-led group meditation sessions, one session per week and building upon prior session(s). Descriptive statistics were used to examine the proportion of eligible individuals who enrolled, retention and adherence rates, and the proportion of questionnaires completed. Data were collected at baseline, immediately after the 4-week intervention period, and 4 and 8 weeks after the intervention period. Secondary analysis tested for changes in symptoms of depression (Center for Epidemiologic Studies-Depression [CES-D]), anxiety [State-Trait Anxiety Inventory for Adults (STAI)], and pain (Brief Pain Inventory-Short Form) in the intervention group via paired t tests. Linear mixed models were used to compare longitudinal changes in the measures between the groups. Intervention and trial design acceptability were preliminary explored. RESULTS: Seventy-one (77%) individuals enrolled and 26 (37%) completed the study (baseline and 8-week post-intervention visits completed). Forty-two (66%) participants completed baseline and immediate post-intervention visits. Mean questionnaire completion rate was 95%. The median meditation group session attendance rate for the intervention group was 75.0%, and the mean attendance rate was 55%. Non-significant reductions in CES-D scores were found. Paired t tests for stroke survivors indicated a significant reduction from baseline through week 8 in BPI-sf severity scores (p = 0.0270). Repeated measures analysis with linear mixed models for informal caregivers indicated a significant reduction in in STAI-Trait scores (F [3,16.2] = 3.28, p = 0.0479) and paired t test showed a significant reduction from baseline to week 4 in STAI-Trait scores (mean = - 9.1250, 95% CI [- 16.8060 to 1.4440], p = 0.0262). No between-group differences were found. CONCLUSIONS: Future trials will require strategies to optimize retention and adherence before definitive efficacy testing of the meditation intervention. TRIAL REGISTRATION: ClinicalTrials.gov Identifier: NCT03239132. Registration date: 03/08/2017.