INTRODUCTION
Intracerebral hemorrhage, as a highly fatal and disabling type of stroke, accounts for approximately half of the incidence and mortality of strokes. The early mortality rate can be as high as 40% to 50%, which is about twice that of ischemic stroke. Among them, more than one-third of the survivors developed post-stroke depression (PSD)[1-3]. Post-cerebral hemorrhage depression shows significant differences from post-ischemic stroke depression in terms of lesion nature, hematoma clearance mechanism and intensity of inflammatory response, which has a great negative impact on the subsequent survival function, rehabilitation and quality of life of patients, hinders the recovery of physical and cognitive functions, reduces the quality of life and increases the mortality rate. It is a key factor hindering the overall prognosis improvement of patients with depression after cerebral hemorrhage[4,5].
In recent years, with the increasing emphasis on mental health management after stroke, post-cerebral hemorrhage depression has gradually become a research hotspot in the interdisciplinary field of neurology and psychiatry. At present, the exploration of the influencing factors of depression after cerebral hemorrhage mainly focuses on neurological deficits, psychosocial factors, age, nutritional status, etc. However, the interaction mechanisms among these factors are not yet clear, and there is a lack of risk prediction models that have both high discriminatory efficacy and clinical operability[6,7]. Studies have shown that different brain region functions, the size of the hematoma, and whether brain regions related to emotion regulation are involved can also increase the risk of depression after cerebral hemorrhage[8-10]. At present, there are no unified standards for post-cerebral hemorrhage depression in terms of pathogenesis, early screening, risk stratification, intervention timing and management measures, which is worthy of further exploration. This article, as a review of viewpoints, systematically sorts out the cutting-edge research progress in the field of post-cerebral hemorrhage depression, analyzes the core controversies and limitations of current research, and explains the multi-dimensional risk network of post-cerebral hemorrhage depression based on the three-dimensional interactive framework of "neural structure damage - inflammatory metabolic disorder - psychosocial stress". It aims to provide a reference for promoting the precise identification, risk stratification and individualized intervention of depression after cerebral hemorrhage.
MULTI-DIMENSIONAL RISK FACTORS FOR DEPRESSION AFTER CEREBRAL HEMORRHAGE
Neuroanatomical factors
Deep hematoma, midline displacement, hematoma volume, and involved brain regions are the core risk factors for depression after cerebral hemorrhage[11]. There are extensive neural fiber connections between the deep brain regions and the limbic system as well as the prefrontal cortex. Disrupting the emotional regulation circuits and shifting the brain lines can aggravate limbic system damage through secondary brain compression, further reducing the patient's emotional regulation ability[12]. In addition, patients with cerebral hemorrhage secondary to intraventricular hemorrhage and subarachnoid hemorrhage have a significantly increased risk of depression, which may be related to chronic brain tissue edema caused by cerebrospinal fluid circulation disorders[13].
The results of the meta-analysis conducted by Bora et al[14] indicated that there was a structural change of volume reduction in the prefrontal cortex region of patients with depression. The research by Peng et al[15] and Qiu et al[16] further found that the thickness of the temporal pole, right orbitofrontal gyrus and paracentral area of the frontal cortex in patients with newly diagnosed depression significantly increased, and at the same time, pathological changes occurred in the surface area of the frontal cortex. Visible abnormal brain structure imaging indicators provide an important theoretical basis for revealing the neuropathological process of early depression. Therefore, when the lesion of cerebral hemorrhage directly causes organic damage to the above-mentioned brain regions, there is a potential to induce depression. However, at present, both at home and abroad, there is still a lack of systematic and in-depth research on the rigorous correlation between brain region damage caused by cerebral hemorrhage and the onset of depression.
Inflammation and metabolic factors
Low albumin (ALB) has been identified as one of the independent risk factors for depression after cerebral hemorrhage. As an important anti-inflammatory substance and nutrient carrier in the body, it can activate neuroinflammation through nutrient depletion and elevated acute-phase response proteins, thereby damaging synaptic plasticity[17-19]. Studies have shown that elevated levels of inflammatory factors such as C-reactive protein (CRP), tumor necrosis factor-α (TNF-α), interleukin-6 (IL-6), and granulocyte-lymphoid ratio (NLR) are significantly associated with the occurrence of depression after cerebral hemorrhage, confirming the core role of the neuro-immune axis in the pathogenesis[20,21]. Villa et al[22] found that after cerebral hemorrhage, pro-inflammatory cytokines stimulate the hypothalamic-pituitary-adrenal (HPA) axis, causing its regulation to be disordered, which leads to the imbalance of hormones (such as glucocorticoids) and neurotransmitters (serotonin), mediating the occurrence of depression.
Neurological function and physical factors
Elevated modified Rankin Scale (mRS) score reflecting moderate to severe neurological deficits is one of the important predictive factors for depression after cerebral hemorrhage[23,24]. Loss of the ability to perform daily activities caused by physical movement disorders, language function deficits, etc., not only increases the physical burden on patients but also triggers psychological stress by disrupting their social roles. In addition, physical complications such as sleep disorders, pain, and cognitive impairment can also significantly increase the risk of depression after cerebral hemorrhage[25].
Psychosocial and demographic factors
Increasing age, gender, and insufficient social support are classic demographic and psychosocial risk factors for depression after cerebral hemorrhage[25]. Elderly patients have a decline in brain tissue repair ability, reduced hippocampal neural plasticity, multiple chronic diseases, and relatively weak psychological tolerance[26,27]. Due to differences in neuroendocrine characteristics, women are more sensitive to emotional stress after brain injury[28,29]. Relevant studies have shown that female patients have a 1.3 times to 1.5 times higher risk of developing PSD than male patients[30]. This gender difference gradually disappeared six months after the onset of the disease. The underlying neuroendocrine mechanism and clinical significance still need further exploration. Research shows that the social withdrawal rate of patients with depression after cerebral hemorrhage is as high as 76%. Insufficient social support will lead to a lack of emotional and practical support for patients during the rehabilitation process, further aggravating their sense of helplessness and stigma[31].
THE PATHOGENESIS AND MULTI-DIMENSIONAL INTERACTION OF RISK FACTORS FOR DEPRESSION AFTER CEREBRAL HEMORRHAGE
The pathophysiological mechanism of depression after cerebral hemorrhage is not mediated by a single pathway, but rather the result of complex interactions among multiple pathways such as neural structure damage, inflammatory oxidative stress, and neurotransmitter disorders. Various risk factors form a vicious cycle through a cascade reaction, jointly constituting the pathogenesis network of depression after cerebral hemorrhage. Studies have shown that secondary brain injury caused by cerebral hemorrhage can activate multiple signaling pathways such as inflammation, oxidative stress, autophagy and apoptosis, which is the core pathological basis for inducing depressive states in patients with cerebral hemorrhage[32-34]. At present, there are still obvious limitations in the construction of animal models and the effect evaluation system for depression after cerebral hemorrhage. Most of the existing modeling methods adopt the middle cerebral artery occlusion model combined with chronic mild unpredictable stress or chronic restraint stress. Specific animal models for cerebral hemorrhage are relatively lacking. Meanwhile, the determination of successful modeling mostly relies on indirect behavioral indicators such as sugar water preference experiments and forced swimming experiments, lacking direct and quantified evaluation criteria for depressive states. This leads to insufficient stability and repeatability of experimental results, restricting in-depth exploration of the pathogenesis of depression after cerebral hemorrhage and translational research on its therapeutic targets[35].
In terms of risk factors, existing studies have identified the related factors of depression after cerebral hemorrhage from multiple dimensions such as neuroanatomy, inflammatory metabolism, neurological function, psychosocial and demography. There are complex interactions among these factors, revealing the disease’s pathogenesis from different levels. Increasing age, as an important risk factor, may be related to the decline in brain tissue repair ability and the reduction of hippocampal neural plasticity in elderly patients, leading to an imbalance of neurotransmitters related to emotion regulation. Meanwhile, elderly patients often have multiple chronic diseases, relatively weak social support, and weakened psychological tolerance, which further increases their susceptibility to depression[36,37]. When advanced age coexists with low ALB, the risk of depression after cerebral hemorrhage shows a significant superimposed effect compared with the presence of a single factor. Studies have shown that low ALB exhibits the strongest predictive weight (with a significant odds ratio value) in the multi-factor prediction model, suggesting that systemic inflammation and nutritional metabolic disorders may play a more crucial role in the mechanism of depression through the neuroimmune axis than ever before[38]. The increase of inflammatory indicators such as CRP, TNF-α, IL-6, and NLR, in synergy with low ALB, further aggravates neuroinflammation and synaptic plasticity damage, promoting the research perspective to shift from simple “brain injury” to the brand-new pathological framework of “brain-body interaction”[39,40].
It can be seen from this that the above-mentioned risk factors do not exist in isolation. Their multi-dimensional interactions form a vicious cycle of “neural structure damage-functional disorder-metabolic disorder-psychological stress”. In addition, the degree of functional dependence reflected by the mRS score also interacts with age factors. Functional dependence in elderly patients significantly weakens their psychological resilience and amplifies the pathogenic effects of other risk factors. At the same time, the social isolation and insufficient social support caused by functional dependence will further aggravate the patient's sense of helplessness and stigma, forming a two-way vicious cycle with functional impairment. The superimposed risk effects of various factors further verified the diversity and interactivity of the pathogenesis of depression after cerebral hemorrhage, as shown in Figure 1.
Figure 1 Variety and interaction of depression pathogenesis after intracerebral hemorrhage.
mRS: Modified Rankin Scale.
THE DEVELOPMENT AND APPLICATION OF PREDICTIVE MODELS
With the continuous deepening of the understanding of the pathological mechanism of post-cerebral hemorrhage depression, researchers have gradually realized that post-cerebral hemorrhage depression is the result of the complex interaction of multiple pathways such as neuroanatomical damage, inflammatory metabolic disorders, neurological dysfunction, and psychosocial stress. Therefore, the shift from a single predictor to a multi-dimensional integrated model has become an inevitable trend. Constructing efficient and practical risk prediction models has become an important strategy for early identification and intervention[41]. In terms of modeling methods, the traditional logistic regression model has achieved multi-factor integration. The study incorporated neuroanatomical features (hematoma location, midbrain line displacement), inflammatory metabolic indicators (ALB, CRP), neurological functional status (mRS score), and demographic factors (age, gender) into the regression model, confirming that the area under the curve (AUC) value predicted by the multi-dimensional combination was significantly better than that of the single-factor model[42,43]. The nomogram model visualizes the predictive factors of each dimension into intuitive risk scores, facilitating clinicians to complete individualized risk assessment at the bedside[44].
In recent years, machine learning algorithms have demonstrated unique advantages in multi-dimensional integrated models. Algorithms such as random forest, extreme gradient boosting, and support vector machine can effectively handle the nonlinear interactions among various risk factors, such as the superimposed effect of age and functional dependence, the synergistic pathogenic effect of low ALB and inflammatory factors, and the interactive influence of neuroanatomical injury and psychosocial stress, etc.[45,46]. Research has confirmed that the predictive performance of multi-dimensional machine learning models is significantly superior to that of traditional regression models, with an AUC value ranging from 0.82 to 0.92[47,48]. With the in-depth research on predictive models, model construction and external validation based on multi-center, large sample, and prospective cohorts have gradually become mainstream. However, current research mostly focuses on the ischemic stroke population. Specific multi-center predictive models for patients with cerebral hemorrhage are still relatively scarce, and most studies have not yet undergone strict external validation. Therefore, the construction of depression risk prediction models for cerebral hemorrhage has gradually shifted from single-dimensional statistical analysis to multi-modal and multi-factor comprehensive prediction. In the future, while strengthening model validation, attention should be paid to its clinical operability and dynamic update capability to provide more precise tools for the mental health management of stroke patients.
CLINICAL MANAGEMENT STRATEGIES FOR DEPRESSION AFTER CEREBRAL HEMORRHAGE
Within 24 hours to 72 hours after the onset of cerebral hemorrhage in patients, multi-dimensional data such as demographic characteristics (gender, age), neuroimaging features (hematoma location, brain line displacement, involved brain regions, etc.), laboratory indicators (serum ALB level), functional prognosis indicators (mRS score), and levels of inflammatory metabolic factors can be systematically collected. Based on the above indicators, a risk prediction model for depression occurrence is constructed, and the depression risk score of each patient is calculated, thereby achieving early identification and risk stratification of high-risk groups. For ultra-high-risk and high-risk patients, it is recommended to establish special follow-up files, implement individualized depressive symptom monitoring programs, and regularly use standardized assessment tools such as the Patient Health Questionnaire-9 scale and Self-Rating Depression Scale for symptom assessment, so as to detect the early signs of depression after cerebral hemorrhage in a timely manner and take corresponding intervention measures[49,50].
In terms of intervention strategies, post-cerebral hemorrhage depression mainly includes drug treatment, traditional Chinese medicine (TCM) treatment, psychological treatment and rehabilitation treatment. Currently, there is no unified clinical guideline recommendation plan. Selective serotonin reuptake inhibitors (SSRIs) are the first-line drugs for drug treatment, but their use is controversial: Some studies suggest that they may increase the risk of cerebral hemorrhage recurrence and mortality, while others suggest that the risk is lower and they are safe and effective. The latest view regards SSRIs as first-line drugs and tricyclic antidepressants as second-line drugs, emphasizing that when using SSRIs, the potential risks of improving depressive symptoms and the recurrence of cerebral hemorrhage need to be weighed[51,52]. However, the long-term use of antidepressants has led to a series of adverse reactions. Studies have shown that although drug treatment remains the first-line option at present, about 40% to 60% of patients have poor therapeutic effects, and 30% may develop into treatment-resistant depression[53]. Therefore, since 2021, TCM has gradually begun to exert a certain influence in the treatment of depression caused by cerebral hemorrhage. According to TCM, depression after cerebral hemorrhage is often caused by deficiency of both liver and kidney, qi stagnation and blood stasis. The pathogenesis is the imbalance of Yin, Yang, blood and qi in the Zang-fu organs. The treatment principles are to regulate qi and relieve depression, smooth qi movement and promote blood circulation and unblock meridians. Studies have shown[54-56] that acupuncture combined with psychological counseling can significantly alleviate depressive moods; the therapeutic effect of Xiaoyaosan combined with Dailixin is superior to that of using Western medicine alone. Zhu Yu Kai Yu Decoction, St. John’s Wort extract and others can also help improve depressive symptoms and neurological function. The combination of TCM, acupuncture, Western medicine and psychotherapy can significantly improve symptoms. However, the numerous syndrome differentiation types and treatment methods in TCM theory make it difficult to diagnose depression caused by cerebral hemorrhage with objective and direct standards. This results in researchers lacking a unified TCM diagnostic standard for case classification during the process of collecting cases. Psychotherapy mainly consists of cognitive behavioral therapy (CBT) and interpersonal therapy, which effectively improves patients' negative cognition and enhances their psychological resilience[57]. Relevant studies have found that CBT can indirectly improve the levels of brain-derived neurotrophic factor (BDNF) and 5-hydroxytryptamine by reducing the overresponse of the HPA axis and decreasing the release of stress cortisol[58]. Rehabilitation therapy indirectly alleviates depressive symptoms by improving patients' neurological deficits. When combined with medication and psychotherapy, it can enhance the overall intervention effect. Early relevant rehabilitation treatment training can effectively reduce the neurological deficits of stroke survivors, improve the degree of limb rehabilitation and Activity of Daily Living Scale score, thereby significantly enhancing their quality of life, improving their psychological condition, and reducing the risk of depression caused by cerebral hemorrhage. Studies have shown that the diversity of rehabilitation methods, such as speech rehabilitation, fine motor skills, and swallowing function rehabilitation, has a positive impact on the prognosis of patients, thereby improving their quality of life[59]. In recent years, mechanism-oriented targeted intervention has gradually become a research hotspot[60]. Nutritional support and anti-inflammatory treatment for hypoalbuminemia, the application of anti-inflammatory factors for neuroinflammation, and the supplementation treatment of BDNF for impaired neural plasticity have all shown potential efficacy in animal experiments, but there is still a lack of verification in large-sample clinical studies[61,62].
CONCLUSION
Post-cerebral hemorrhage depression is a core complication in the rehabilitation process of patients with cerebral hemorrhage. Its pathogenesis involves complex interactions in multiple dimensions such as neural structure, inflammatory metabolism, and psychosocial aspects. Existing research has identified multiple risk factors and constructed multi-dimensional prediction models, but there are still disputes and limitations in aspects such as mechanism interpretation, risk stratification, and intervention strategies. This article clarifies the cascade reactions, interaction mechanisms and vicious cycles of various dimensional pathways, providing a brand-new theoretical perspective on the pathogenesis of depression after cerebral hemorrhage.
Based on this framework, the clinical management of depression after cerebral hemorrhage should follow the principles of “early screening, risk stratification, targeted intervention, and multidisciplinary integration”, incorporate multi-dimensional prediction models into routine clinical evaluations, carry out individualized intervention targeting pathways, and construct a multidisciplinary joint management model. Future research should focus on multi-center model validation, exploration of risk network mechanisms, mechanism-oriented targeted intervention clinical trials, and the construction of long-term management systems. At the same time, it is necessary to promote the application of artificial intelligence and big data technologies to achieve precise and standardized management of depression after cerebral hemorrhage.
With the continuous deepening of research, it is believed that in the future, the multi-dimensional risk network interaction mechanism of post-cerebral hemorrhage depression will be gradually clarified, more accurate prediction tools and more effective targeted intervention plans will be developed, and ultimately early prevention, precise identification and individualized treatment of post-cerebral hemorrhage depression will be achieved, significantly improving the emotional health and overall prognosis of patients with cerebral hemorrhage promote the development of post-stroke mental health management.
Peer review: Externally peer reviewed.
Peer-review model: Single blind
Corresponding Author's Membership in Professional Societies: American Society for Peripheral Neurosurgery, No. 5300190.
Specialty type: Psychiatry
Country of origin: China
Peer-review report’s classification
Scientific quality: Grade B, Grade B
Novelty: Grade B, Grade B
Creativity or innovation: Grade B, Grade B
Scientific significance: Grade B, Grade B
P-Reviewer: He MY, Assistant Professor, China; Hou WM, MD, China S-Editor: Qu XL L-Editor: A P-Editor: Zhao YQ
