Suicidal ideation arises from a complex interplay of multiple interacting risk factors over time. Recently, ecological momentary assessment (EMA) has increased our understanding of factors associated with real-time suicidal ideation, as well as those predicting ideation at the level of hours and days. Here we used statistical network methods to investigate which cognitive-affective risk and protective factors are associated with the temporal dynamics of suicidal ideation.

The SAFE study is a longitudinal cohort study of 82 participants with current suicidal ideation who completed 4×/day EMA over 21 days. We modeled contemporaneous (t) and temporal (t + 1) associations of three suicidal ideation components (passive ideation, active ideation, and acquired capability) and their predictors (positive and negative affect, anxiety, hopelessness, loneliness, burdensomeness, and optimism) using multilevel vector auto-regression models.

Contemporaneously, passive suicidal ideation was positively associated with sadness, hopelessness, loneliness, and burdensomeness, and negatively with happiness, calmness, and optimism; active suicidal ideation was positively associated with passive suicidal ideation, sadness, and shame; and acquired capability only with passive and active suicidal ideation. Acquired capability and hopelessness positively predicted passive ideation at t + 1, which in turn predicted active ideation; acquired capability was positively predicted at t + 1 by shame, and negatively by burdensomeness.

Our findings show that systematic real-time associations exist between suicidal ideation and its predictors, and that different factors may uniquely influence distinct components of ideation. These factors may represent important targets for safety planning and risk detection.

Depression is a highly recurrent disorder, with more than 50% of those affected experiencing a subsequent episode. Although there is relatively little stability in symptoms across episodes, some evidence indicates that suicidal ideation may be an exception. However, these findings warrant replication, especially over longer periods and across multiple episodes.

To assess the relative stability of suicidal ideation in comparison with other non-core depressive symptoms across episodes.

We examined 490 individuals with current major depressive disorder (MDD) at baseline and at least one subsequent episode during 9-year follow-up within the Netherlands Study of Depression and Anxiety (NESDA). The Inventory of Depressive Symptomatology (IDS) was used to assess DSM-5 non-core MDD symptoms (fatigue, appetite/weight change, sleep disturbance, psychomotor disturbance, concentration difficulties, worthlessness/guilt, suicidal ideation) at baseline and 2-, 4-, 6- and 9-year follow-up. We examined consistency in symptom presentation (i.e. whether the symptom met the diagnostic threshold, based on a binary categorisation of the IDS) using kappa (κ) and percentage agreement, and stability in symptom severity using Spearman correlation, based on the continuous IDS scores.

Out of all non-core depressive symptoms, insomnia appeared the most stable across episodes (r = 0.55–0.69, κ = 0.31–0.47) and weight decrease the least stable (r = 0.03–0.33, κ = 0.06–0.19). For suicidal ideation, correlations across episodes ranged from r = 0.36 to r = 0.55 and consistency ranged from κ = 0.28 to κ = 0.49.

Suicidal ideation is moderately stable in recurrent depression over 9 years. Contrary to prior reports, however, it does not exhibit substantially more stability than most other non-core symptoms of depression.

Recently, artificial intelligence-powered devices have been put forward as potentially powerful tools for the improvement of mental healthcare. An important question is how these devices impact the physician-patient interaction.

Aifred is an artificial intelligence-powered clinical decision support system (CDSS) for the treatment of major depression. Here, we explore the use of a simulation centre environment in evaluating the usability of Aifred, particularly its impact on the physician–patient interaction.

Twenty psychiatry and family medicine attending staff and residents were recruited to complete a 2.5-h study at a clinical interaction simulation centre with standardised patients. Each physician had the option of using the CDSS to inform their treatment choice in three 10-min clinical scenarios with standardised patients portraying mild, moderate and severe episodes of major depression. Feasibility and acceptability data were collected through self-report questionnaires, scenario observations, interviews and standardised patient feedback.

All 20 participants completed the study. Initial results indicate that the tool was acceptable to clinicians and feasible for use during clinical encounters. Clinicians indicated a willingness to use the tool in real clinical practice, a significant degree of trust in the system's predictions to assist with treatment selection, and reported that the tool helped increase patient understanding of and trust in treatment. The simulation environment allowed for the evaluation of the tool's impact on the physician–patient interaction.

The simulation centre allowed for direct observations of clinician use and impact of the tool on the clinician–patient interaction before clinical studies. It may therefore offer a useful and important environment in the early testing of new technological tools. The present results will inform further tool development and clinician training materials.

While taxonomy segregates anxiety symptoms into diagnoses, patients typically present with multiple diagnoses; this poses major challenges, particularly for youth, where mixed presentation is particularly common. Anxiety comorbidity could reflect multivariate, cross-domain interactions insufficiently emphasized in current taxonomy. We utilize network analytic approaches that model these interactions by characterizing pediatric anxiety as involving distinct, inter-connected, symptom domains. Quantifying this network structure could inform views of pediatric anxiety that shape clinical practice and research.

Participants were 4964 youths (ages 5–17 years) from seven international sites. Participants completed standard symptom inventory assessing severity along distinct domains that follow pediatric anxiety diagnostic categories. We first applied network analytic tools to quantify the anxiety domain network structure. We then examined whether variation in the network structure related to age (3-year longitudinal assessments) and sex, key moderators of pediatric anxiety expression.

The anxiety network featured a highly inter-connected structure; all domains correlated positively but to varying degrees. Anxiety patients and healthy youth differed in severity but demonstrated a comparable network structure. We noted specific sex differences in the network structure; longitudinal data indicated additional structural changes during childhood. Generalized-anxiety and panic symptoms consistently emerged as central domains.

Pediatric anxiety manifests along multiple, inter-connected symptom domains. By quantifying cross-domain associations and related moderation effects, the current study might shape views on the diagnosis, treatment, and study of pediatric anxiety.

We compared cohorts of raters from different countries who received training on the PANSS. We attempted to determine if there was any consistent by-country impact on specific items, factors, or subscales. We also queried raters about their perceptions of the instrument they were asked to use vis-à-vis their local patient population.

The data set comes from standardized rater training events involving raters from four countries: India (n = 83), Russia (n = 59), the US (n = 63), and Romania (n = 76). Raters scored interviews of schizophrenic patients using the PANSS. Scores were compared and intra-class correlation coefficients (ICCs) and rater agreement with “gold standard” scores were evaluated. The results were viewed against raters’ responses to questions about how well the PANSS items correlated to the presentation of symptoms.

Raters from the US and Russia demonstrated a higher level of inter-rater consistency with ICCs of 0.883 and 0.835, respectively. For eight PANSS items, all raters demonstrated at least 80% agreement with the gold standard scores. For ten PANSS items, there was at least one country whose raters scored below 60% agreement. The PANSS items with the lower inter-rater reliability were the same items raters indicated as problematic in local settings.

The differences in rater performance indicate that standardized rater training is broadly effective but that there are some important differences in the way in which different groups conceptualize symptomatology and corresponding PANSS items. This suggests a need to tailor training to ensure reliability and validity in the use of this instrument.

Though rater drift in clinical trials has long been understood to negatively impact trial results, few studies have systematically quantified this. We examined training data for the HAM-D (Hamilton Depression Scale, 17-item version) at two time points to measure the impact.

Raters participating in a standardized training scored the HAM-D based on two videotaped interviews of depressed patients. To assess drift, data from an initial, post-online training session was compared to data obtained 12 months later. Intra-class correlation coefficients (Shrout & Fliess, 1979) and concordance with expert ratings were compared.

Intra-class correlation coefficients (ICC) for raters (n = 167) following initial training were good to excellent for individual raters (.695–.976, p < .0001) and good for the overall cohort (.752, p < .0001). Concordance with expert ratings was excellent at 99.3%. The overall ICC fell to .730 at the second assessment and although the upper bound of individual performance remained in the good to excellent range, the frequency of scores in the poor to fair range (< .65) increased. Concordance also fell slightly to 87%.

Rater drift occurred over 12 months, as gauged by the metrics of reliability and concordance. Drift was apparent in a limited portion of the cohort but resulted in a lower overall ICC at the second time point. Because studies are generally powered assuming that the ICC remains stable, there are implications for both this power calculation and the required sample size.

Recent advances in biomarker technology have allowed for the development of highly predictive tests for Alzheimer's disease (AD) when combined with standard psychometric tests. Current research in AD utilizes the ADAS-Cog and/or the MMSE as standard measures; they do not exclusively address the specific deficits expected in an amnesic syndrome of the hippocampal type as express with AD.

Because episodic memory degradation is most strongly predictive of conversion from mild cognitive impairment (MCI) to AD, a clinical measure targeting this deficit is warranted.

To utilize current knowledge of neural correlates of different stages of episodic memory function and their modulation by AD to develop a psychometrically sound instrument.

The authors developed a brief scale that captures registration, storage and retrieval of information along four identified domains of episodic memory in AD. A second stage was to confirm BEMA in institutionalized subjects, and assess reliability and validity.

Preliminary results indicate good test-retest reliability and adequate sensitivity and specificity. the BEMA was positively and significantly correlated with other measures of episodic memory. the [insert scale name or abbreviation] yields a total score, scores for 3 lifetime periods and the duration of episodic memory impairment.

Findings suggest that a richer understanding of the memory deficits in AD can lead to the development of an instrument which taps different aspects of episodic memory function. This scale can aid in the screening, assessment and treatment of early AD and complement the newly developed one-plus-one strategy.

Prognostic models discriminate between groups of individuals likely to experience better or worse outcomes and to predict response to treatment.

The premise of the analysis was the assumption that baseline PANSS measurements could be a prognostic factor to inform decisions on the expected response (completion or early-termination) to treatment during participation in a clinical trial.

To examine early patterns/profiles based on PANSS and response to treatment (Study-Completer (SC), Early-Termination (ET)).

Receiver Operating Curves (ROC) was conducted on 809 subjects with SC versus ET. Factor structure assessed whether psychopathology constructs are comparable across SC and ET.

Positive-Symptoms: P5.Grandiosity, P7.Hostility and P4.Excitement are not as good as others in predicting ET. 91.1% ET would have scores of 5, 6 or 7 on P1.Delusions.

Negative-Symptoms: N5. Difficulty in Abstract Thinking and N6.Lack of Spontaneity and Flow of Conversation are not as good in predicting ET. 67.9% ET may have scores of 5, 6 or 7 on N1.Blunted Affect. General-Psychopathology: G3.Guilt Feelings, G6.Depression, G7.Motor Retardation, and G10.Disorientation are not as good in predicting ET. 73.2% ET have scores of 5, 6 or 7 on G9.Unusual Thought Content. Positive Factor accounted for the most variance 15.885%, then Negative factor=14.592%, then Hostile-Excitement=11.973% for SC. For ET, Negative Factor=13.713% variance, cognitive factor=12.451%, Excitement Factor=10.396%.

These findings represent patterns of early detection of response in clinical trials, and have led to the development of sophisticated algorithms that may allow investigators to identify ET and SC, which is important in trial success.

Evidence has suggested that immune imbalance is involved with bipolar disorder (BD); however, its precise mechanism is poorly understood.

This study investigated whether biochemical changes in the serum from BD patients could modulate the phenotype of macrophages.

Eighteen subjects with BD and healthy individuals (n = 5) were included in this study. The human monocyte cell line U-937 was activated with PMA (phorbol 12-myristate 13-acetate) and polarization was induced with RPMI-1640 media supplemented with 10% serum from each patient for 24 h. Gene expression of selected M1 and M2 markers was assessed by qPCR.

Macrophages exposed to serum of manic and depressive BD patients displayed an increase of IL-1β (6.40 ± 3.47 and 9.04 ± 5.84 versus 0.23 ± 0.11; P < 0.05) and TNF-α (2.23 ± 0.91 and 2.03 ± 0.45 versus 0.62 ± 0.24; P = 0.002 and P = 0.004, respectively) compared to remitted group. In parallel, U-937 macrophages treated with serum of patients in acute episode displayed a down-regulation of CXCL9 (0.29 ± 0.20 versus 1.86 ± 1.61; P = 0.006) and CXCL10 expression (0.36 ± 0.15 and 0.86 ± 0.24 versus 1.83 ± 0.88; P < 0.000 and P = 0.04) compared to remitters.

Our results are consistent with previous studies showing that changes in peripheral blood markers could modulate M1/M2 polarization in BD. The evidence of macrophages as source of inflammatory cytokines might be helpful to unravel how the mononuclear phagocyte system can be involved in the etiology of BD.

The authors have not supplied their declaration of competing interest.

Studies investigating the link between depressive symptoms and inflammation have yielded inconsistent results, which may be due to two factors. First, studies differed regarding the specific inflammatory markers studied and covariates accounted for. Second, specific depressive symptoms may be differentially related to inflammation. We address both challenges using network psychometrics.

We estimated seven regularized Mixed Graphical Models in the Netherlands Study of Depression and Anxiety (NESDA) data (N = 2321) to explore shared variances among (1) depression severity, modeled via depression sum-score, nine DSM-5 symptoms, or 28 individual depressive symptoms; (2) inflammatory markers C-reactive protein (CRP), interleukin 6 (IL-6), and tumor necrosis factor α (TNF-α); (3) before and after adjusting for sex, age, body mass index (BMI), exercise, smoking, alcohol, and chronic diseases.

The depression sum-score was related to both IL-6 and CRP before, and only to IL-6 after covariate adjustment. When modeling the DSM-5 symptoms and CRP in a conceptual replication of Jokela et al., CRP was associated with ‘sleep problems’, ‘energy level’, and ‘weight/appetite changes’; only the first two links survived covariate adjustment. In a conservative model with all 38 variables, symptoms and markers were unrelated. Following recent psychometric work, we re-estimated the full model without regularization: the depressive symptoms ‘insomnia’, ‘hypersomnia’, and ‘aches and pain’ showed unique positive relations to all inflammatory markers.

We found evidence for differential relations between markers, depressive symptoms, and covariates. Associations between symptoms and markers were attenuated after covariate adjustment; BMI and sex consistently showed strong relations with inflammatory markers.

Network analyses on psychopathological data focus on the network structure and its derivatives such as node centrality. One conclusion one can draw from centrality measures is that the node with the highest centrality is likely to be the node that is determined most by its neighboring nodes. However, centrality is a relative measure: knowing that a node is highly central gives no information about the extent to which it is determined by its neighbors. Here we provide an absolute measure of determination (or controllability) of a node – its predictability. We introduce predictability, estimate the predictability of all nodes in 18 prior empirical network papers on psychopathology, and statistically relate it to centrality.

We carried out a literature review and collected 25 datasets from 18 published papers in the field (several mood and anxiety disorders, substance abuse, psychosis, autism, and transdiagnostic data). We fit state-of-the-art network models to all datasets, and computed the predictability of all nodes.

Predictability was unrelated to sample size, moderately high in most symptom networks, and differed considerable both within and between datasets. Predictability was higher in community than clinical samples, highest for mood and anxiety disorders, and lowest for psychosis.

Predictability is an important additional characterization of symptom networks because it gives an absolute measure of the controllability of each node. It allows conclusions about how self-determined a symptom network is, and may help to inform intervention strategies. Limitations of predictability along with future directions are discussed.

Researchers have studied psychological disorders extensively from a common cause perspective, in which symptoms are treated as independent indicators of an underlying disease. In contrast, the causal systems perspective seeks to understand the importance of individual symptoms and symptom-to-symptom relationships. In the current study, we used network analysis to examine the relationships between and among depression and anxiety symptoms from the causal systems perspective.

We utilized data from a large psychiatric sample at admission and discharge from a partial hospital program (N = 1029, mean treatment duration = 8 days). We investigated features of the depression/anxiety network including topology, network centrality, stability of the network at admission and discharge, as well as change in the network over the course of treatment.

Individual symptoms of depression and anxiety were more related to other symptoms within each disorder than to symptoms between disorders. Sad mood and worry were among the most central symptoms in the network. The network structure was stable both at admission and between admission and discharge, although the overall strength of symptom relationships increased as symptom severity decreased over the course of treatment.

Examining depression and anxiety symptoms as dynamic systems may provide novel insights into the maintenance of these mental health problems.