Microstructural changes in the reward system are associated with post-stroke depression

Feasibility of Observing Cerebrovascular Disease Phenotypes with Smartphone Monitoring: Study Design Considerations for Real-World Studies

Accelerated by the adoption of remote monitoring during the COVID-19 pandemic, interest in using digitally captured behavioral data to predict patient outcomes has grown; however, it is unclear how feasible digital phenotyping studies may be in patients with recent ischemic stroke or transient ischemic attack. In this perspective, we present participant feedback and relevant smartphone data metrics suggesting that digital phenotyping of post-stroke depression is feasible. Additionally, we proffer thoughtful considerations for designing feasible real-world study protocols tracking cerebrovascular dysfunction with smartphone sensors.

Post-Stroke Depression in Older Adults: An Overview

Detailed data on post-stroke depression (PSD) in older adults are limited in spite of the high vulnerability of this population to stroke. In fact, PSD prevalence in older adults ranges from 16.0 to 43.9%; however, timing and instruments of evaluation often differ significantly across all available studies. The etiology, genetic and inflammatory factors, as well as structural brain alterations, are claimed as part of a multifaceted mechanism of action in PSD onset. Thus, the aim of this narrative review was to further elaborate on the prevalence, etiology, diagnosis, consequences and treatment of PSD in older adults. The consequences of PSD in older adults may be devastating, including a poor functional outcome after rehabilitation and lower medication adherence. In addition, lower quality of life and reduced social participation, higher risk of new stroke, rehospitalization, and mortality have been reported. In this scenario, treating PSD represents a crucial step to prevent these complications. Both pharmacological and non-pharmacological therapies are currently available. The pharmacological treatment utilizes antidepressant drugs, such as selective serotonin reuptake inhibitors (SSRIs), serotonin-norepinephrine reuptake inhibitors (SNRIs), monoamine oxidase inhibitors (MAOIs), tricyclic antidepressants (TAs) and new multimodal antidepressants (NMAs). Non-pharmacological therapies include psychological interventions and non-invasive brain stimulation techniques, while excluding drug administration. In the general population experiencing PSD, SSRIs (sertraline in particular) are the most prescribed, whereas the combination of antidepressants and psychotherapy is underused. Furthermore, about one-third of patients do not receive treatment for PSD. In regard to older adults with PSD, the possibility of more adverse effects or contraindications to antidepressant prescription due to comorbidities may limit the therapeutic window. Although drugs such as citalopram, escitalopram, sertraline, venlafaxine, and vortioxetine are usually well tolerated by older patients with PSD, the few randomized controlled trials (RCTs) specifically considering older adults with PSD have been conducted with fluoxetine, fluvoxamine, reboxetine, citalopram and nortriptyline, often with very small patient samples. Furthermore, data regarding the results of non-pharmacological therapies are scarce. High-quality RCTs recruiting large samples of older adults are needed in order to better manage PSD in this population. In addition, adequate screening and diagnosis instruments, with reliable timing of evaluation, should be applied.

Network analysis of neuropsychiatric, cognitive, and functional complications of stroke: implications for novel treatment targets

AIM

Recovery from stroke is adversely affected by neuropsychiatric complications, cognitive impairment, and functional disability. Better knowledge of their mutual relationships is required to inform effective interventions. Network theory enables the conceptualization of symptoms and impairments as dynamic and mutually interacting systems. We aimed to identify interactions of poststroke complications using network analysis in diverse stroke samples.

METHODS

Data from 2185 patients were sourced from member studies of STROKOG (Stroke and Cognition Consortium), an international collaboration of stroke studies. Networks were generated for each cohort, whereby nodes represented neuropsychiatric symptoms, cognitive deficits, and disabilities on activities of daily living. Edges characterized associations between them. Centrality measures were used to identify hub items.

RESULTS

Across cohorts, a single network of interrelated poststroke complications emerged. Networks exhibited dissociable depression, apathy, fatigue, cognitive impairment, and functional disability modules. Worry was the most central symptom across cohorts, irrespective of the depression scale used. Items relating to activities of daily living were also highly central nodes. Follow-up analysis in two studies revealed that individuals who worried had more densely connected networks than those free of worry (CASPER [Cognition and Affect after Stroke: Prospective Evaluation of Risks] study: S = 9.72, P = 0.038; SSS [Sydney Stroke Study]: S = 13.56, P = 0.069).

CONCLUSION

Neuropsychiatric symptoms are highly interconnected with cognitive deficits and functional disabilities resulting from stroke. Given their central position and high level of connectedness, worry and activities of daily living have the potential to drive multimorbidity and mutual reinforcement between domains of poststroke complications. Targeting these factors early after stroke may have benefits that extend to other complications, leading to better stroke outcomes.

Establishment and verification of a nomogram model for predicting the risk of post-stroke depression

Objective

The purpose of this study was to establish a nomogram predictive model of clinical risk factors for post-stroke depression (PSD).

Patients and Methods

We used the data of 202 stroke patients collected from Xuanwu Hospital from October 2018 to September 2020 as training data to develop a predictive model. Nineteen clinical factors were selected to evaluate their risk. Minimum absolute contraction and selection operator (LASSO, least absolute shrinkage and selection operator) regression were used to select the best patient attributes, and seven predictive factors with predictive ability were selected, and then multi-factor logistic regression analysis was carried out to determine six predictive factors and establish a nomogram prediction model. The C-index, calibration chart, and decision curve analyses were used to evaluate the predictive ability, accuracy, and clinical practicability of the prediction model. We then used the data of 156 stroke patients collected by Xiangya Hospital from June 2019 to September 2020 for external verification.

Results

The selected predictors including work style, number of children, time from onset to hospitalization, history of hyperlipidemia, stroke area, and the National Institutes of Health Stroke Scale (NIHSS) score. The model showed good prediction ability and a C index of 0.773 (95% confidence interval: [0.696-0.850]). It reached a high C-index value of 0.71 in bootstrap verification, and its C index was observed to be as high as 0.702 (95% confidence interval: [0.616-0.788]) in external verification. Decision curve analyses further showed that the nomogram of post-stroke depression has high clinical usefulness when the threshold probability was 6%.

Conclusion

This novel nomogram, which combines patients' work style, number of children, time from onset to hospitalization, history of hyperlipidemia, stroke area, and NIHSS score, can help clinicians to assess the risk of depression in patients with acute stroke much earlier in the timeline of the disease, and to implement early intervention treatment so as to reduce the incidence of PSD.

Hyperconnectivity and altered interactions of a nucleus accumbens network in post-stroke depression

Post-stroke depression is a common complication of stroke. To date, no consistent locus of injury is associated with this complication. Here, we probed network dynamics and structural alterations in post-stroke depression in four functional circuits linked to major depressive disorder and a visual network, which served as a control network. Forty-four participants with recent stroke (mean age = 69.03, standard deviation age = 8.59, age range = 51–86 and gender: female = 10) and 16 healthy volunteers (mean age = 71.53, standard deviation age = 10.62, age range = 51–84 and gender: female = 11) were imaged with 3-Tesla structural, diffusion and resting-state functional MRI. The Geriatric Depression Scale was administered to measure depression severity. Associations between depression severity and functional connectivity were investigated within networks seeded from nucleus accumbens, amygdala, dorsolateral prefrontal cortex and primary visual cortex. In addition, the default mode network was identified by connectivity with medial prefrontal cortex and posterior cingulate cortex. Circuits that exhibited altered activity associated with depression severity were further investigated by extracting within-network volumetric and microstructural measures from structural images. In the stroke group, functional connectivity within the nucleus accumbens-seeded network (left hemisphere: P = 0.001; and right hemisphere: P = 0.004) and default mode network (cluster one: P < 0.001; and cluster two: P < 0.001) correlated positively with depressive symptoms. Normal anticorrelations between these two networks were absent in patients with post-stroke depression. Grey matter volume of the right posterior cingulate cortex (Pearson correlation coefficient = −0.286, P = 0.03), as well as microstructural measures in the posterior cingulate cortex (right: Pearson correlation coefficient = 0.4, P = 0.024; and left: Pearson correlation coefficient = 0.3, P = 0.048), right medial prefrontal cortex (Pearson correlation coefficient = 0.312, P = 0.039) and the medial forebrain bundle (Pearson correlation coefficient = 0.450, P = 0.003), a major projection pathway interconnecting the nucleus accumbens-seeded network and linking to medial prefrontal cortex, were associated with depression severity. Depression after stroke is marked by reduced mutual inhibition between functional circuits involving nucleus accumbens and default mode network as well as volumetric and microstructural changes within these networks. Aberrant network dynamics present in patients with post-stroke depression are therefore likely to be influenced by secondary, pervasive alterations in grey and white matter, remote from the site of injury.

Transdiagnostic In Vivo Magnetic Resonance Imaging Markers of Neuroinflammation

Accumulating evidence suggests that inflammation is not limited to archetypal inflammatory diseases such as multiple sclerosis, but instead represents an intrinsic feature of many psychiatric and neurological disorders not typically classified as neuroinflammatory. A growing body of research suggests that neuroinflammation can be observed in early and prodromal stages of these disorders and, under certain circumstances, may lead to tissue damage. Traditional methods to assess neuroinflammation include serum or cerebrospinal fluid markers and positron emission tomography. These methods require invasive procedures or radiation exposure and lack the exquisite spatial resolution of magnetic resonance imaging (MRI). There is, therefore, an increasing interest in noninvasive neuroimaging tools to evaluate neuroinflammation reliably and with high specificity. While MRI does not provide information at a cellular level, it facilitates the characterization of several biophysical tissue properties that are closely linked to neuroinflammatory processes. The purpose of this review is to evaluate the potential of MRI as a noninvasive, accessible, and cost-effective technology to image neuroinflammation across neurological and psychiatric disorders. We provide an overview of current and developing MRI methods used to study different aspects of neuroinflammation and weigh their strengths and shortcomings. Novel MRI contrast agents are increasingly able to target inflammatory processes directly, therefore offering a high degree of specificity, particularly if used in conjunction with multitissue, biophysical diffusion MRI compartment models. The capability of these methods to characterize several aspects of the neuroinflammatory milieu will likely push MRI to the forefront of neuroimaging modalities used to characterize neuroinflammation transdiagnostically.

Symptomatic plaque enhancement is associated with early-onset post-stroke depression

BACKGROUND

The association between imaging features closely associated with symptomatic intracranial atherosclerotic plaques and early-onset post-stroke depression (PSD) is currently unclear.

MATERIALS AND METHODS

76 ischemic stroke patients who underwent high-resolution vessel wall magnetic resonance imaging (HR-VWI) were divided into PSD and non-PSD groups according to their DSM-V diagnoses and HAMD-17 scores at 14 days after onset. Clinical data and the imaging features associated with symptomatic plaques (including the enhancement index (EI), remodeling index, and plaque surface irregularity) were compared between groups. Multifactorial logistic regression analysis was used to find independent predictors of early-onset PSD. Spearman rank correlation analysis explores the association between clinical data, symptomatic plaque imaging features, and HAMD-17 in patients.

RESULTS

The sample comprised 36 patients with early-onset PSD. The symptomatic plaque EI and infarct volume were significantly higher in depressed patients than in patients without depression (P < 0.05). Multivariate logistic regression showed that symptomatic plaque EI could be used as an independent predictor of early-onset PSD after correcting for the confounding factor of infarct volume (OR = 1.034, 95% CI:1.014-1.055, P = 0.001). In the total sample, symptomatic plaque EI, infarct volume, and HAMD-17 had a significant positive correlation with each other (P < 0.05).

LIMITATIONS

This study focused only on the patients' symptomatic plaques and did not monitor patients' systemic inflammation levels at the time of HR-VWI.

CONCLUSIONS

The degree of symptomatic plaque enhancement is an independent predictive imaging marker of early-onset PSD and can be used the early diagnosis of early-onset PSD.

Improving Depressive Symptoms of Post-stroke Depression Using the Shugan Jieyu Capsule: A Resting-State Functional Magnetic Resonance Imaging Study

Regional homogeneity (ReHo) and fractional amplitude of low-frequency fluctuation (fALFF) were used to detect the neuroimaging mechanism of Shugan Jieyu Capsule (SG) in ameliorating depression of post-stroke depression (PSD) patients. Fifteen PSD patients took SG for 8 weeks, completed the 24-item Hamilton Depression Scale (HAMD) assessment at the baseline and 8 weeks later, and underwent functional magnetic resonance imaging (fMRI) scanning. Twenty-one healthy controls (HCs) underwent these assessments at the baseline. We found that SG improved depression of PSD patients, in which ReHo values decreased in the left calcarine sulcus (CAL.L) and increased in the left superior frontal gyrus (SFG.L) of PSD patients at the baseline. The fALFF values of the left inferior parietal cortex (IPL.L) decreased in PSD patients at the baseline. Abnormal functional activities in the brain regions were reversed to normal levels after the administration of SG for 8 weeks. Receiver operating characteristic (ROC) analysis found that the changes in three altered brain regions could be used to differentiate PSD patients at the baseline and HCs. Average signal values of altered regions were related to depression in all subjects at the baseline. Our results suggest that SG may ameliorate depression of PSD patients by affecting brain region activity and local synchronization.

Neural Substrates of Poststroke Depression: Current Opinions and Methodology Trends

Poststroke depression (PSD), affecting about one-third of stroke survivors, exerts significant impact on patients’ functional outcome and mortality. Great efforts have been made since the 1970s to unravel the neuroanatomical substrate and the brain-behavior mechanism of PSD. Thanks to advances in neuroimaging and computational neuroscience in the past two decades, new techniques for uncovering the neural basis of symptoms or behavioral deficits caused by focal brain damage have been emerging. From the time of lesion analysis to the era of brain networks, our knowledge and understanding of the neural substrates for PSD are increasing. Pooled evidence from traditional lesion analysis, univariate or multivariate lesion-symptom mapping, regional structural and functional analyses, direct or indirect connectome analysis, and neuromodulation clinical trials for PSD, to some extent, echoes the frontal-limbic theory of depression. The neural substrates of PSD may be used for risk stratification and personalized therapeutic target identification in the future. In this review, we provide an update on the recent advances about the neural basis of PSD with the clinical implications and trends of methodology as the main features of interest.

Reward System Dysfunction and the Motoric-Cognitive Risk Syndrome in Older Persons

During aging, many physiological systems spontaneously change independent of the presence of chronic diseases. The reward system is not an exception and its dysfunction generally includes a reduction in dopamine and glutamate activities and the loss of neurons of the ventral tegmental area (VTA). These impairments are even more pronounced in older persons who have neurodegenerative diseases and/or are affected by cognitive and motoric frailty. All these changes may result in the occurrence of cognitive and motoric frailty and accelerated progression of neurodegenerative diseases, such as Alzheimer’s and Parkinson’s diseases. In particular, the loss of neurons in VTA may determine an acceleration of depressive symptoms and cognitive and motor frailty trajectory, producing an increased risk of disability and mortality. Thus, we hypothesize the existence of a loop between reward system dysfunction, depression, and neurodegenerative diseases in older persons. Longitudinal studies are needed to evaluate the determinant role of the reward system in the onset of motoric-cognitive risk syndrome.

The Structural and Functional Neuroanatomy of Post-Stroke Depression and Executive Dysfunction: A Review of Neuroimaging Findings and Implications for Treatment

Post-stroke depression and executive dysfunction co-occur and are highly debilitating. Few treatments alleviate both depression and executive dysfunction after stroke. Understanding the brain network changes underlying post-stroke depression with executive dysfunction can inform the development of targeted and efficacious treatment. In this review, we synthesize neuroimaging findings in post-stroke depression and post-stroke executive dysfunction and highlight the network commonalities that may underlie this comorbidity. Structural and functional alterations in the cognitive control network, salience network, and default mode network are associated with depression and executive dysfunction after stroke. Specifically, post-stroke depression and executive dysfunction are both linked to changes in intrinsic functional connectivity within resting state networks, functional over-connectivity between the default mode and salience/cognitive control networks, and reduced cross-hemispheric frontoparietal functional connectivity. Cognitive training and noninvasive brain stimulation targeted at these brain network abnormalities and specific clinical phenotypes may help advance treatment for post-stroke depression with executive dysfunction.

Pathomechanisms of Vascular Depression in Older Adults

Depression in older individuals is a common complex mood disorder with high comorbidity of both psychiatric and physical diseases, associated with high disability, cognitive decline, and increased mortality The factors predicting the risk of late-life depression (LLD) are incompletely understood. The reciprocal relationship of depressive disorder and age- and disease-related processes has generated pathogenic hypotheses and provided various treatment options. The heterogeneity of depression complicates research into the underlying pathogenic cascade, and factors involved in LLD considerably differ from those involved in early life depression. Evidence suggests that a variety of vascular mechanisms, in particular cerebral small vessel disease, generalized microvascular, and endothelial dysfunction, as well as metabolic risk factors, including diabetes, and inflammation that may induce subcortical white and gray matter lesions by compromising fronto-limbic and other important neuronal networks, may contribute to the development of LLD. The "vascular depression" hypothesis postulates that cerebrovascular disease or vascular risk factors can predispose, precipitate, and perpetuate geriatric depression syndromes, based on their comorbidity with cerebrovascular lesions and the frequent development of depression after stroke. Vascular burden is associated with cognitive deficits and a specific form of LLD, vascular depression, which is marked by decreased white matter integrity, executive dysfunction, functional disability, and poorer response to antidepressive therapy than major depressive disorder without vascular risk factors. Other pathogenic factors of LLD, such as neurodegeneration or neuroimmune regulatory dysmechanisms, are briefly discussed. Treatment planning should consider a modest response of LLD to antidepressants, while vascular and metabolic factors may provide promising targets for its successful prevention and treatment. However, their effectiveness needs further investigation, and intervention studies are needed to assess which interventions are appropriate and effective in clinical practice.