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Objective To explore whether poor sleep is associated with post‐stroke anxiety (PSA) in Chinese patients with acute ischemic stroke (AIS) and to verify whether poor sleep is a predictor of PSA. Methods A total of 327 patients with AIS were enrolled and followed up for 1 month. Sleep quality within 1 month before stroke was evaluated using the Pittsburgh Sleep Quality Index (PSQI) at admission. The patients were divided into the poor sleep group (PSQI > 7, n = 76) and good sleep group (PSQI ≤ 7, n = 251). One month after stroke, patients with obvious anxiety symptoms and a Hamilton Anxiety Scale score >7 were diagnosed with PSA. Results Eighty‐seven patients (26.6%) were diagnosed with PSA. Compared to the good sleep quality group, the incidence of PSA in patients with poor sleep quality was higher (42.1% vs. 21.9%, p = .001). Poor sleep quality is more common in patients with PSA (35.6% vs. 18.8%, p = .001). A logistic regression analysis indicated that poor sleep quality was significantly associated with PSA (OR: 2.265, 95% CI: 1.262–4.067, p = .003). After adjusting for conventional and identified risk factors, poor sleep quality was found to be independently associated with PSA (OR: 2.676, 95% CI: 1.451–4.936, p = .001). Conclusions Poor sleep quality before stroke was associated with PSA and may be an independent risk factor of PSA 1 month after AIS onset. We found that the incidence rate of poor sleep quality is significantly higher in PSA patients than in non‐PSA patients. Poor sleep quality before stroke is independently associated with the development of PSA, even after adjustment for several conventional confounders. These findings suggested poor sleep quality before stroke could provide important predictive information for anxiety after acute ischemic stroke.

Aim To determine the potential profile classes of anxiety reported by ischaemic stroke survivors in rural China, and to explore the characteristics of patients having different types of post‐stroke anxiety. Design A cross‐sectional survey. Methods A cross‐sectional survey was conducted by using convenience sampling to collect data from 661 ischaemic stroke survivors in rural Anyang city, Henan Province, China, from July 2021 to September 2021. The parameters included in the study were the socio‐demographic characteristics, self‐rating anxiety scale (SAS), self‐rating depression scale (SDS) and the Barthel index of daily activity ability. Potential profile analysis was done to recognize subgroups of post‐stroke anxiety. The Chi‐square test was performed to explore the characteristics of individuals with different types of post‐stroke anxiety. Results The model fitting indices of stroke survivors supported three classes of anxiety models which were as follows: (a) Class 1, low‐level, stable group (65.3%, N = 431); (b) Class 2, moderate‐level, unstable group (17.9%, N = 118) and (c) Class 3, high‐level, stable group (16.9%, N = 112). The risk factors associated with post‐stroke anxiety were female patients, lower levels of education, living alone, lower monthly household income, other chronic diseases, impaired daily activity ability and depression. Conclusions This study identified three different subgroups of post‐ischaemic stroke anxiety and their characteristics in patients in rural China. Impact This study has significance in providing evidence for the development of targeted intervention measures to reduce negative emotions in different subgroups of post‐stroke anxiety patients. Patient or Public Contribution In this study, the researchers arranged the time of questionnaire collection with the village committee in advance, gathered the patients to the village committee for face‐to‐face questionnaire survey and collected the household data of the patients with mobility difficulties.

ABSTRACTBackgroundEpidemiological 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. Methods289 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. ResultsPSD 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. ConclusionsWe 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.

Background Malnutrition, post-stroke depression (PSD), post-stroke anxiety (PSA), and post-stroke fatigue (PSF) in stroke survivors have complex relationships and are associated with adverse stroke outcomes. Aims This research aims to explore the temporal and directional relationships between malnutrition, PSD, PSA, and PSF after stroke in older adults. Methods Patients aged 65 years and older with their first ischemic stroke from two centers were selected and assessed at baseline, 3 months and 12 months. Malnutrition was evaluated using the Controlling Nutritional Status (CONUT) score, the Geriatric Nutritional Risk Index (GNRI), and the Prognostic Nutritional Index (PNI). PSD, PSA and PSF were measured with 24-item Hamilton Depression Scale (HAMD-24), 14-item Hamilton Anxiety Scale (HAMA-14) and Fatigue Severity Scale (FSS), respectively. The cross-lagged panel model (CLPM) was employed to investigate the temporal and directional relationships among these variables. Results Among the 381 older patients included, 54.33%, 43.57%, and 7.87% were found to have malnutrition according to the CONUT, GNRI, and PNI scores, respectively. Significant bidirectional relationships were found between malnutrition and PSD, as well as between PSD, PSA, and PSF, but no significant bidirectional relationships between malnutrition, PSA and PSF were observed, irrespective of the malnutrition index used (CONUT, GNRI, or PNI). Conclusions Nutritional status and post-stroke neuropsychiatric disorders in older stroke survivors are worthy of attention. Specifically, early malnutrition after stroke can predict later PSD and vice versa. PSD, PSA, and PSF are mutually predictable. Further studies are required to investigate the mechanisms of these findings.

To examine prefrontal hemodynamic changes in patients with post-stroke anxiety (PSA), both at rest and during cognitive task engagement, with the aim of elucidating the underlying neural mechanisms of PSA and identifying potential neural correlates for clinical application. Fifty patients with PSA and 45 post-stroke patients without anxiety symptoms were recruited. PSA was diagnosed using the Hamilton Anxiety Rating Scale (HAMA ≥ 7), and comorbid depression was screened using the 17-item Hamilton Depression Rating Scale (HAMD-17 ≥ 8). Patients with significant cognitive impairment were excluded. Functional near-infrared spectroscopy (fNIRS) was used to measure resting-state functional connectivity in the frontopolar cortex (FPC) and dorsolateral prefrontal cortex (DLPFC), as well as task-evoked activation during the verbal fluency task (VFT). Demographic and clinical characteristics showed no significant differences between groups except for stroke type. Between-group comparisons were conducted to identify PSA-related differences in prefrontal network characteristics. Subgroup analyses were performed to explore the influence of comorbid depression on neural alterations. There were no significant differences between the PSA and non-PSA groups in demographic or clinical characteristics, including age, sex, and disease duration ( > 0.05). Compared to the non-PSA group, patients with PSA exhibited significantly reduced activation in the bilateral FPC during the VFT ( < 0.05). Within the PSA group, those with comorbid depression showed further reductions in activation in the bilateral FPC and the left DLPFC ( < 0.05). No significant differences in resting-state functional connectivity were observed between groups ( > 0.05). Reduced activation in the bilateral FPC may represent a key neural substrate associated with post-stroke anxiety. In addition, altered activation patterns in the bilateral FPC and left DLPFC may reflect neural correlates related to depressive symptoms in patients with PSA, providing candidate targets for future mechanistic and clinical studies.

Post-stroke anxiety (PSA) manifests as anxiety symptoms after stroke, with unclear mechanisms and limited treatment strategies. Endocannabinoids, reported to mitigate fear, anxiety, and stress, undergo dynamic alterations after stroke linked to prognosis intricately. However, endocannabinoid metabolism in ischemic microenvironment and their associations with post-stroke anxiety-like behavior remain largely uncovered. Our findings indicated that endocannabinoid metabolism was dysregulated after stroke, characterized by elevated N-palmitoylethanolamide ( PEA) hydrolase N-acylethanolamine-acid amidase (NAAA) in activated microglia from ischemic area, accompanied by rapid PEA exhaustion. Microglial PEA metabolite exhaustion is directly associated with more severe pathological damage, anxiety symptoms and pain sensitivity. Naaa knockout or pharmacological supplementation to boost PEA pool content can effectively promote stroke recovery and alleviate anxiety-like behaviors. In addition, maintaining PEA pool content in ischemic area reduces overactivated microglia by confronting against mitochondria dysfunction and inflammasome cascade triggered IL-18 release and diffusion to contralateral hemisphere. Meanwhile, maintenance of microglial PEA pool content in ischemic-damaged lesion can preserve contralateral vCA1 synaptic integrity, enhancing anxiolytic pBLA-vCA1 Calb1+ circuit activity by alleviating microglial phagocytosis-mediated synaptic loss. Thus, we conclude that microglial NAAA-regulated lipid signaling in the ischemic focus remodels contralateral anxiolytic circuit to participate in post-stroke anxiety progression. Blocking PEA signaling breakdown promotes stroke recovery and mitigates anxiety-like symptoms. Graphical Abstract Microglial NAAA-regulated lipid signaling involves with post-stroke anxiety. Highlights 1. PEA metabolite is exhausted in microglia after ischemic stroke. 2. NAAA signaling activation exacerbates ischemic brain injury and abnormal behavior. 3. PEA metabolism represses IL-18 inflammatory cascade mediated contralateral vCA1 circuit remodeling. 4. Boosting PEA metabolite mitigates anxiety-like behavior and improves function recovery after stroke.

Post-stroke anxiety (PSA) has caused wide public concern in recent years, and the study on risk factors analysis and prediction is still an open issue. With the deepening of the research, machine learning has been widely applied to various scenarios and make great achievements increasingly, which brings new approaches to this field. In this paper, 395 patients with acute ischemic stroke are collected and evaluated by anxiety scales (i.e., HADS-A, HAMA, and SAS), hence the patients are divided into anxiety group and non-anxiety group. Afterward, the results of demographic data and general laboratory examination between the two groups are compared to identify the risk factors with statistical differences accordingly. Then the factors with statistical differences are incorporated into a multivariate logistic regression to obtain risk factors and protective factors of PSA. Statistical analysis shows great differences in gender, age, serious stroke, hypertension, diabetes mellitus, drinking, and HDL-C level between PSA group and non-anxiety group with HADS-A and HAMA evaluation. Meanwhile, as evaluated by SAS scale, gender, serious stroke, hypertension, diabetes mellitus, drinking, and HDL-C level differ in the PSA group and the non-anxiety group. Multivariate logistic regression analysis of HADS-A, HAMA, and SAS scales suggest that hypertension, diabetes mellitus, drinking, high NIHSS score, and low serum HDL-C level are related to PSA. In other words, gender, age, disability, hypertension, diabetes mellitus, HDL-C, and drinking are closely related to anxiety during the acute stage of ischemic stroke. Hypertension, diabetes mellitus, drinking, and disability increased the risk of PSA, and higher serum HDL-C level decreased the risk of PSA. Several machine learning methods are employed to predict PSA according to HADS-A, HAMA, and SAS scores, respectively. The experimental results indicate that random forest outperforms the competitive methods in PSA prediction, which contributes to early intervention for clinical treatment.

Depression and anxiety are common comorbidities of stroke. Research has shown that about 30% of stroke survivors develop depression and about 20% develop anxiety. Stroke survivors with such adverse mental outcomes are often attributed to poorer health outcomes, such as higher mortality rates. The objective of this study is to use deep learning (DL) methods to predict the risk of a stroke survivor experiencing post-stroke depression and/or post-stroke anxiety, which is collectively known as post-stroke adverse mental outcomes (PSAMO). This study studied 179 patients with stroke, who were further classified into PSAMO versus no PSAMO group based on the results of validated depression and anxiety questionnaires, which are the industry’s gold standard. This study collected demographic and sociological data, quality of life scores, stroke-related information, medical and medication history, and comorbidities. In addition, sequential data such as daily lab results taken seven consecutive days after admission are also collected. The combination of using DL algorithms, such as multi-layer perceptron (MLP) and long short-term memory (LSTM), which can process complex patterns in the data, and the inclusion of new data types, such as sequential data, helped to improve model performance. Accurate prediction of PSAMO helps clinicians make early intervention care plans and potentially reduce the incidence of PSAMO.

Background: Post-stroke anxiety (PSA) remains a challenging medical problem. Integrated rehabilitation involves a combination of traditional Chinese medicine (TCM) and Western conventional rehabilitation techniques. Theoretically, integrated rehabilitation is likely to have significant advantages in treating PSA. Nevertheless, the therapeutic effect of integrated rehabilitation needs to be verified based on large-scale trials with sound methodology. Thus, the aim of this trial is to assess the efficacy and safety of integrated rehabilitation on PSA. Methods: The study is a prospective, multicenter, randomized, controlled trial involving 188 PSA patients from four clinical centers in China. Eligible participants will be randomly divided into the integrated rehabilitation group or the standard care group. Participants in the integrated rehabilitation group will receive a combination of TCM and Western conventional rehabilitation methods, including acupuncture, repeated transcranial magnetic stimulation, traditional Chinese herbal medicine, and standard care. The primary outcome will be the Hamilton Anxiety Rating Scale (HAM-A). The secondary outcomes will include the Self-Rating Anxiety Scale (SAS), the Activities of Daily Living (ADL) scale, the Montreal Cognitive Assessment (MoCA) scale, the simplified Fugl--Meyer Assessment of motor function (FMA) scale, and the Pittsburgh Sleep Quality Index (PSQI). Outcome measurements will be performed at baseline, at the end of the 4-week treatment and the 8-week follow-up. Conclusion: Results of this trial will ascertain the efficacy and safety of integrated rehabilitation on PSA, thereby providing evidence regarding integrated rehabilitation strategies for treating PSA. It will also promote up-to-date evidence for patients, clinicians, and policy-makers. Trial Registration: ClinicalTrials.gov NCT05147077. Keywords: post-stroke anxiety, traditional Chinese medicine, Western medicine, repeated transcranial magnetic stimulation, rehabilitation, randomized controlled trial

Stroke survivors often experience social isolation, which can lead to post-stroke depression (PSD) and post-stroke anxiety (PSA) that can compromise neurogenesis and impede functional recovery following the stroke. The present study aimed to investigate the effects and mechanisms of post-stroke social isolation-mediated PSD and PSA on hippocampal neurogenesis and cognitive function. The effects of the natural antidepressant hyperforin on post-stroke social isolation-mediated PSD and PSA were also investigated. In the present study, a model of PSD and PSA using C57BL/6J male mice was successfully established using middle cerebral artery occlusion combined with post-stroke isolated housing conditions. It was observed that PSD and PSA were more prominent in the isolated mice compared with the pair-housed mice at 14 days post-ischemia (dpi). Mice isolated 3 dpi exhibited decreased transforming growth factor-β (TGF-β) levels and impairment of hippocampal neurogenesis and memory function at 14 dpi. Intracerebroventricular administration of recombinant TGF-β for 7 consecutive days, starting at 7 dpi, restored the reduced hippocampal neurogenesis and memory function induced by social isolation. Furthermore, intranasal administration of hyperforin for 7 consecutive days starting at 7 dpi improved PSD and PSA and promoted hippocampal neurogenesis and memory function in the isolated mice at 14 dpi. The inhibition of TGF-β with a neutralizing antibody prevented the effects of hyperforin. In conclusion, the results revealed a previously uncharacterized role of hyperforin in improving post-stroke social isolation-induced exaggeration of PSD and PSA and, in turn, promoting hippocampal neurogenesis and cognitive function via TGF-β.