Previous research showed that behavioural activation is as effective as cognitive–behavioural therapy for general depression. However, it remains unclear if it leads to greater improvement in depressive symptoms when compared with standard treatment for post-stroke depression.

To compare the effectiveness of behavioural activation against control conditions in reducing depression symptoms in individuals with post-stroke depression.

This review searched five databases from inception until 13 July 2021 (updated 15 September 2023) for randomised controlled trials comparing behavioural activation and any control conditions for post-stroke depression. Risk of bias was assessed with the Cochrane Collaboration's Risk-of-Bias 2 tool. The primary outcome was improvement in depressive symptoms in individuals with post-stroke depression. We calculated a random-effects, inverse variance weighting meta-analysis.

Of 922 initial studies, five randomised controlled trials with 425 participants met the inclusion criteria. Meta-analysis showed that behavioural activation was associated with reduced depressive symptoms in individuals with post-stroke depression at 6-month follow-up (Hedges’ g −0.39; 95% CI −0.64 to −0.14). The risk of bias was low for two (40%) of five trials, and the remaining three (60%) trials were rated as having a high risk of bias. Heterogeneity was low, with no indication of inconsistency.

Evidence from this review was too little to confirm the effectiveness of behavioural activation as a useful treatment for post-stroke depression when compared with control conditions. Further high-quality studies are needed to conclusively establish the efficacy of behavioural activation as a treatment option for post-stroke depression.

Post-stroke depression (PSD) is the most common psychiatric complication facing stroke survivors and has been associated with increased distress, physical disability, poor rehabilitation, and suicidal ideation. However, the pathophysiological mechanisms underlying PSD remain unknown, and no objective laboratory-based test is available to aid PSD diagnosis or monitor progression.

Here, an isobaric tags for relative and absolute quantitation (iTRAQ)-based quantitative proteomic approach was performed to identify differentially expressed proteins in plasma samples obtained from PSD, stroke, and healthy control subjects.

The significantly differentiated proteins were primarily involved in lipid metabolism and immunoregulation. Six proteins associated with these processes – apolipoprotein A-IV (ApoA-IV), apolipoprotein C-II (ApoC-II), C-reactive protein (CRP), gelsolin, haptoglobin, and leucine-rich alpha-2-glycoprotein (LRG) – were selected for Western blotting validation. ApoA-IV expression was significantly upregulated in PSD as compared to stroke subjects. ApoC-II, LRG, and CRP expression were significantly downregulated in both PSD and HC subjects relative to stroke subjects. Gelsolin and haptoglobin expression were significantly dysregulated across all three groups with the following expression profiles: gelsolin, healthy control > PSD > stroke subjects; haptoglobin, stroke > PSD > healthy control.

Early perturbation of lipid metabolism and immunoregulation may be involved in the pathophysiology of PSD. The combination of increased gelsolin levels accompanied by decreased haptoglobin levels shows promise as a plasma-based diagnostic biomarker panel for detecting increased PSD risk in post-stroke patients.

Finding the prediction factors for the risks of post-stroke depression (PSD) is important to stroke survivors. However, most existing studies focused only on general clinical data, which limited the predictive ability. To improve the predictive ability, this study proposed a comprehensive PSD risk prediction model with social psychological factors, neurological, cognitive functional factors and general clinical factors.

The study recruited 188 stroke patients. Patients were diagnosed by DSM-IV criteria. Predictors were collected within a week after stroke. Boosted regression trees (BRT) was used to classify these predictors, and then a predictive model was constructed based on the selected predictors. The receiver operating characteristic (ROC) curve was used to determine the performance of the predictive model .

The risk prediction model was constructed with 6 factors: Body Mass Index (BMI), cerebral infraction history (CI), Social Support Rating Scale (SSRS), Eysenck Personality Questionnaire-Neuroticism (EPQ-N), factor 1 of the 20 items Toronto Alexithymia Scale (TAS-F1) and Snaith-Hamilton-Pleasure Scale (SHARPS). In the contribution of risk prediction factors, social psychological factors was more than 0.60. ROC curve of prediction model was 0.826 (p<0.001; 95% CI) and the accuracy of prediction was 0.81 (p<0.001). Transforming the prediction model to a tree diagram, it was convenient to clinic operation.

A PSD risk prediction model with good prediction performance was constructed to achieve diagnose concisely and clearly. The social psychological factors play an important role for diagnosing PSD in the early period.

Poststroke depression (PSD) is a public health issue, affecting one-third of stroke survivors, and is associated with multiple negative consequences. Reviews tried to identify PSD risk factors with discrepant results, highlighting the lack of comparability of the analyzed studies. We carried out a meta-analysis in order to identify clinical risk factors that can predict PSD.

PubMed and Web of Science were searched for papers. Only papers with a strictly defined Diagnostic and Statistical Manual of Mental Disorders depression assessment, at least 2 weeks after stroke, were selected. Two authors independently evaluated potentially eligible studies that were identified by our search and independently extracted data using standardized spreadsheets. Analyses were performed using MetaWin®, the role of each variable being given as a risk ratio (RR).

Eighteen studies were included in the meta-analysis. Identified risk factors for PSD with RR significantly above 1 were previous history of depression (RR 2.19, confidence interval (CI) 1.52–3.15), disability (RR 2.00, CI 1.58–2.52), previous history of stroke (RR 1.68, CI 1.06–2.66), aphasia (RR 1.47, CI 1.13–1.91), and female gender (RR 1.35, CI 1.14–1.61). Fixed effects model leads to identification of two more risk factors: early depressive symptoms with an RR of 2.32 (CI 1.43–3.79) and tobacco consumption (RR 1.40, CI 1.09–1.81). Time bias was found for alcohol consumption. Sample size was significantly involved to explain the role of “alcohol consumption” and “cognitive impairment.”

Five items were significantly predictive of PSD. It might be of clinical interest that depressive-related risk factors (such as past depressive episodes) were having the largest impact.

Depression is a common post-stroke complication. Pre-stroke depression may be an important contributor, however the epidemiology of pre-stroke depression is poorly understood. Using systematic review and meta-analysis, we described the prevalence of pre-stroke depression and its association with post-stroke depression.

We searched multiple cross-disciplinary databases from inception to July 2017 and extracted data on the prevalence of pre-stroke depression and its association with post-stroke depression. We assessed the risk of bias (RoB) using validated tools. We described summary estimates of prevalence and summary odds ratio (OR) for association with post-stroke depression, using random-effects models. We performed subgroup analysis describing the effect of depression assessment method. We used a funnel plot to describe potential publication bias. The strength of evidence presented in this review was summarised via ‘GRADE’.

Of 11 884 studies identified, 29 were included (total participants n = 164 993). Pre-stroke depression pooled prevalence was 11.6% [95% confidence interval (CI) 9.2–14.7]; range: 0.4–24% (I2 95.8). Prevalence of pre-stroke depression varied by assessment method (p = 0.02) with clinical interview suggesting greater pre-stroke depression prevalence (~17%) than case-note review (9%) or self-report (11%). Pre-stroke depression was associated with increased odds of post-stroke depression; summary OR 3.0 (95% CI 2.3–4.0). All studies were judged to be at RoB: 59% of included studies had an uncertain RoB in stroke assessment; 83% had high or uncertain RoB for pre-stroke depression assessment. Funnel plot indicated no risk of publication bias. The strength of evidence based on GRADE was ‘very low’.

One in six stroke patients have had pre-stroke depression. Reported rates may be routinely underestimated due to limitations around assessment. Pre-stroke depression significantly increases odds of post-stroke depression.

PROSPERO identifier: CRD42017065544

Epidemiological research on post-stroke affective disorders has been mainly focusing on post-stroke depression (PSD). In contrast, research on post-stroke anxiety (PSA) is in its early stages. The present study proposes a broad picture on post-stroke affective disorders, including PSD and PSA in German stroke in-patients during rehabilitation. In addition, we investigated whether lifetime affective disorders predict the emergence of PSD and PSA.

289 stroke patients were assessed in the early weeks following stroke for a range of mood and anxiety disorders by means of the Structured Clinical Interview relying on the Diagnostic and Statistical Manual of Mental Disorders IV. This assessment was conducted for two periods: for post-stroke and retroactively for the period preceding stroke (lifetime). The covariation between PSD and PSA was investigated using Spearman-ρ correlation. Predictors of PSD and PSA prevalence based on the respective lifetime prevalence were investigated using logistic regression analyses.

PSD prevalence was 31.1%, PSA prevalence was 20.4%. We also found significant correlations between depression and anxiety at post-stroke and for the lifetime period. Interestingly, lifetime depression could not predict the emergence of PSD. In contrast, lifetime anxiety was a good predictor of PSA.

We were able to highlight the complexity of post-stroke affective disorders by strengthening the comorbidity of depression and anxiety. In addition, we contrasted the predictability of PSA based on its lifetime history compared to PSD which was not predictable based on lifetime depression.

Lacunar stroke is a small (<2 cm) infarction that accounts for approximately 20% of all strokes. While a third of all stroke patients experience depressive symptoms, the prevalence of depression in the lacunar stroke patient population is unclear. This meta-analysis aimed to synthesize the evidence on the effect of lacunar stroke and deep white matter disease on depressive symptoms.

A systematic search of electronic databases was conducted, resulting in the inclusion of 12 studies. Analyses were performed on the effects of lacunar stroke, volume and location of lacunes on depression prevalence, and the effect on depression severity. The effects estimates were calculated in random-effects models.

None of the analyses produced statistically significant results. Lacunar stroke patients had a non-significantly higher prevalence of depression compared to patients with non-lacunar cerebrovascular diseases (OR = 1.46, 95% CI: 0.88–2.43, p = 0.15). Neither thalamic (OR = 1.37 (0.85–2.20), p = 0.19), deep white matter (RR = 1.16 (0.85–1.57), p = 0.35), multiple lacunes (OR = 1.34 (0.81–2.22), p = 0.25), or the volume of lacunes (MD = −4.71 (−351.59–342.18), p = 0.98) had an effect on depression prevalence. Lastly, lacunar stroke did not influence depressive symptom severity (MD = 0.96 (−1.57–3.48), p = 0.46).

The pooled group of patients with lacunar stroke and deep white matter disease appear to have a similar prevalence of depression compared to those with other types of cerebrovascular diseases. However, the small number of studies, heterogeneous comparison groups, and high statistical heterogeneity between studies posed an obstacle to the meta-analysis. To determine appropriate screening and treatment approaches, future research will need to separate lacunar stroke and deep white matter disease patients, and include larger sample sizes and healthy control groups to determine their distinct contributions to depression.