Next Article in Journal
G Protein-Coupled Receptors and the Rise of Type 2 Diabetes in Children
Previous Article in Journal
Influence of Muscle Mass and Strength on Bone Mineralisation with Consideration of Sclerostin Concentration
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Biomedicines
Volume 11
Issue 6
10.3390/biomedicines11061575
biomedicines-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Causal Associations between Functional/Structural Connectivity and Stroke: A Bidirectional Mendelian Randomization Study

by
Yisong Wang
1,†,
Longtao Yang
1,† and
Jun Liu
1,2,3,*
1
Department of Radiology, The Second Xiangya Hospital of Central South University, Changsha 410011, China
2
Clinical Research Center for Medical Imaging in Hunan Province, Changsha 410011, China
3
Department of Radiology Quality Control Center in Hunan Province, Changsha 410011, China
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Biomedicines 2023, 11(6), 1575; https://doi.org/10.3390/biomedicines11061575
Submission received: 27 April 2023 / Revised: 21 May 2023 / Accepted: 26 May 2023 / Published: 29 May 2023
(This article belongs to the Special Issue Reperfusion in Ischemic Stroke)
Versions Notes

Abstract

:
Disruption of brain resting-state networks (RSNs) is known to be related to stroke exposure, but determining causality can be difficult in epidemiological studies. We used data on genetic variants associated with the levels of functional (FC) and structural connectivity (SC) within 7 RSNs identified from a genome-wide association study (GWAS) meta-analysis among 24,336 European ancestries. The data for stroke and its subtypes were obtained from the MEGASTROKE consortium, including up to 520,000 participants. We conducted a two-sample bidirectional Mendelian randomization (MR) study to investigate the causality relationship between FC and SC within 7 RSNs and stroke and its subtypes. The results showed that lower global mean FC and limbic network FC were associated with a higher risk of any ischemic stroke and small vessel stroke separately. Moreover, ventral attention network FC and default mode network SC have a positive causal relationship with the risk of small vessel stroke and large artery stroke, respectively. In the inverse MR analysis, any stroke and large artery stroke were causally related to dorsal attention network FC and somatomotor FC, respectively. The present study provides genetic support that levels of FC or SC within different RSNs have contrasting causal effects on stroke and its subtypes. Moreover, there is a combination of injury and compensatory physiological processes in brain RSNs following a stroke. Further studies are necessary to validate our results and explain the physiological mechanisms.

1. Introduction

Based on the 2019 Global Burden of Disease (GBD) study, stroke has been the second leading cause of death and a predominant contributor to increasing total disability-adjusted life years (DALYs) worldwide [1], which poses a significant burden at both individual and societal levels, especially in low- and middle-income countries. As the global burden of stroke increases, it is vital to identify potential risks and protective factors for stroke prevention. According to neuropathology, stroke can be divided into two main categories: ischemic stroke (IS) and hemorrhagic stroke, of which the former is the most frequent [2]. Common etiologic subtypes of IS consist in large artery stroke (LAS), cardioembolic stroke (CES), and small vessel stroke (SVS). Pathophysiological mechanisms are variable between these IS subtypes [3]. As a result, studies of IS as a whole may not be sufficient to assess the impact of risk factors under the influence of subtype distribution. Although the risk factors for stroke are numerous, the fact that genetic predisposition to functional and structural connectivity (FC/SC) within seven resting-state networks (RSNs), including default mode network, ventral attention network, dorsal attention network, visual network, limbic network, somatomotor network, and frontoparietal network, was associated with the risk of stroke subtypes has scarcely been studied. Studying the relationships between risk factors and specific stroke subtypes might provide information for obtaining more targeted neuro-imaging biomarkers.
It is estimated that approximately one-third of stroke survivors will undergo cognitive disorders manifesting in various cognitive domains within a few months (i.e., post-stroke cognitive impairment (PSCI)), which leads to a tremendous burden on victims, their families, and the community as a whole [3,4]. Illuminating the association between stroke and cognitive dysfunction is critical for a targeted intervention development of treatment strategies. FC and SC within RSNs are vital for cognitive performance. Genetic variants in RSN FC affect biological processes relevant to brain disorders. For example, lower default mode network FC was associated with a higher genetic risk of Alzheimer’s disease [5]. PSCI highlights the causal relationship between stroke and cognitive impairment. Brain network connections have shown dynamic changes throughout stroke [6]. Many studies have found that alterations of FC and SC within and between brain RSNs correlate with cognitive decline and recovery after a stroke exposure [7,8,9]. However, these observational studies focusing on different affected cognition behaviors following stroke are unclear about whether there are causal relationships between specific stroke subtypes with changes of FC and SC within 7 RSNs due to the existing cross-sectional design. Stroke lesions in brain regions have various effects on cognitive functions, and these impacts should be interpreted from an RSN perspective [10].
Most recently, Mendelian randomization (MR) has become an appealing method for inferring causal associations between exposures and outcomes using genetic variants as instrumental variables (IVs). MR analysis is based on three key hypotheses: (1) instrumental variables were closely correlated with exposure; (2) there were no observed associations between instrumental variables and confounders; and (3) the effects of instrumental variables on outcome occurred exclusively through exposure.
Therefore, we first performed bidirectional MR analysis to investigate whether FC and SC within 7 RSNs could predict the risk of stroke and its subtypes and validated whether stroke is causally related to FC and SC.

2. Materials and Methods

2.1. Data Sources

We used data on genetic variants associated with FC and SC from a recent GWAS meta-analysis, which included 24,336 participants. GWAS summary statistics for stroke and its subtypes were retrieved from the MEGASTROKE consortium, involving 40,585 stroke cases and 406,111 controls of European ancestry. IS cases were further subdivided into large artery stroke (LAS, n = 4373), cardioembolic stroke (CES, n = 7193), and small vessel stroke (SVS, n = 5386) based on the Trial of Org 10172 in Acute Stroke Treatment criteria. In order to perform bidirectional MR analysis, GWAS of the FC and SC was used as the outcome dataset.

2.2. Selection of Genetic Variants

We selected single nucleotide polymorphisms (SNPs) associated with FC and SC, stroke and its subtypes at the genome-wide significance threshold (p < 5 × 10−6) as candidate IVs for the following MR analysis. Then, F-statistics were calculated to test the strength of these selected SNPs. IVs are generally considered a robust instrument when F-statistics are above 10. We chose independent SNPs (r2 < 0.001), calculated the F-statistic, and manually excluded SNPs correlated with confounders.

2.3. Statistical Analysis

The inverse-variance-weighted (IVW) method was selected as a top-choice approach for determining the causal effects of exposures on outcomes. In sensitive analysis, we used other MR approaches to ensure the robustness of our results, as follows: (1) We performed MR Egger, weighted median, simple mode, and weighted mode as complementary methods to detect the stability and reliability of our results; (2) Statistical heterogeneity among SNPs was measured using Cochran’s Q test in the IVW technique (p < 0.05); (3) MR-Egger regression’s intercept was calculated to evaluate whether genetic variants have pleiotropy; (4) In addition, MR-pleiotropy residual sum and outlier (MR-PRESSO) global test was also employed to identify outlier SNPs and potential horizontal pleiotropy (p was set at 0.05). After observing heterogeneity or horizontal pleiotropy, we recalculated MR estimates after deleting the outlier SNPs detected by MR-PRESSO.

3. Results

3.1. The Causal Effects of FC and SC Levels on Stroke

Significant SNPs (p < 5 × 10−6) associated with FC and SC in RSNs were obtained (Table S1). The harmonized SNPs are displayed in Table S2. The complete results of MR analysis are shown in Table S3. In the MR analysis, two exposures were significantly associated with SVS: limbic network FC (β: −33.048, 95% CI: [−56.856, −9.240], p = 0.007) and ventral attention network FC (β: 122.976, 95% CI: [57.513, 188.438], p = 0.00023). Moreover, the IVW method indicated that genetically increased global mean FC was associated with a lower risk of AIS (β: −20.549, 95% CI: [−40.059, −1.039], p = 0.039), whereas increased default mode network SC indicated a higher risk of LAS (β: 37.737, 95% CI: [5.786, 69.687], p = 0.021). The results evaluated by other supplementary MR methods were directionally consistent with the IVW method, as shown in Table 1. Heterogeneity tests indicated no heterogeneities of the genetic variants; pleiotropy tests indicated no pleiotropy of the genetic variants except the MR estimate between global mean FC and CES, as shown in Table 2 and Table S4.

3.2. The Causal Effects of Stroke on FC and SC Levels

To further explore the causal effects of stroke and its subtypes on FC and SC levels, we performed a bidirectional MR analysis. Significant SNPs associated with stroke types were obtained (Table S5). A total of 47, 45, 34, 35, and 31 SNPs significantly associated with AS, AIS, LAS, CES, and SVS were harmonized (Table S6). We found that genetic predisposition to AS (β: 2.938 × 10−4, 95% CI: [1.420 × 10−5, 5.732 × 10−4], p = 0.047) and genetic predisposition to LAS (β: −5.079 × 10−4, 95% CI: [−1.010 × 10−3, −5.617 × 10−6], p = 0.039) were highly correlated with dorsal attention network FC and somatomotor network FC, respectively (Table 1 and Table S7). No heterogeneity and pleiotropy were identified in the analysis of AS (all p > 0.1) (Table 2 and Table S8).

4. Discussion

To our knowledge, this is the first study to investigate whether the strength levels of FC and SC within 7 RSNs are causally associated with the risk of stroke subtypes and to further evaluate whether there are reverse causal effects of stroke subtypes on FC and SC by using the bidirectional MR method. The results, supported by multiple statistical methods, showed that lower global mean FC and limbic network FC were associated with a higher risk of AIS and SVS, separately. Moreover, this study provides evidence for positively causal relationships of ventral attention network FC and default mode network SC with the risk of SVS and LAS, respectively. Inverse MR analysis indicated that AS and LAS were causally related to dorsal attention network FC and somatomotor FC, respectively.
In previous research, risk factors of stroke have been intensively studied [11,12], while little was known about whether there were causal effects of FC and SC levels within RSNs on stroke and its subtypes. The associations between FC and SC within RSNs and stroke were complicated. Until now, 10 modifiable factors have been correlated with an increased risk of stroke, such as high blood pressure, diabetes mellitus, hyperlipidemia, and smoking, which account for nearly 90% of the risk of stroke [13]. Growing evidence recently demonstrated an inverse causal association between education and IS [14,15,16]. Several MR studies also proved that a higher educational attainment was a protective factor for hypertension and type 2 diabetes, further supporting that high education could reduce IS risk [17,18]. Furthermore, the education level might influence stroke survivors’ cognitive status [19,20]. Higher education levels were less likely to affect their cognitive functioning because they had a larger brain reserve capacity [21]. Indeed, cognition and education are mutually related and inseparable, with solid genetic evidence supporting a bidirectional relationship between educational attainment and cognitive function from previous GWAS studies [18,22,23]. Higher educational attainment can improve cognitive performance. Maintaining cognitive performance depends on FC and SC within 7 RSNs. Higher global mean FC indicates a better cognitive function. The limbic network is commonly responsible for emotion regulation, episodic memory, and action-outcome learning, where the hippocampus is associated with learning, cognitive functions, and memory, in particular converting short-term memory into long-term memory. Given that education and cognition are strongly genetically correlated (rg = 0.662) [24], we speculate that educational attainment is somewhat reflected in the strength of global mean FC and limbic network FC. Thus, genetically predicted global and limbic network FCs were inversely associated with IS risk. Public health, educational policy and governmental decision-making should reflect the importance of education for stroke prevention.
The potential mechanism according to which higher ventral attention network FC and default mode network FC are positively associated with the risk of LAS and SVS separately might be attributed to the neuro-vascular coupling hypothesis. Blood perfusion is fine-tuned to deliver blood with a higher need [25]. Therefore, brain RSNs with high FC and SC have a higher spontaneous neuronal activity and, thus, a higher metabolic demand, resulting in more cerebral perfusion. Traditional vascular risk factors promote the progression of atherosclerotic stenosis and thrombus in atherosclerotic arteries. On this basis, elevated levels of circulating blood flow due to an increased activity of RSNs may be a potential mechanism leading to an increased risk of IS. This MR study did not identify any causal role with CES. The possible reason for this may be that most CES cases are caused by atrial fibrillation, which has different risk factors than other stroke subtypes [12].
Reverse MR analysis suggested that the occurrence of LAS was a risk factor for reduced FC strength in the somatomotor network. Previous epidemiological studies consistently reported disruptions and recovery of functional brain networks over time after a stroke exposure. Most studies ignored that there might be causal associations between IS subtypes and the strength of FC and SC within specific RSNs [8,9,26]. Extensive functional network connectivity changes occur immediately after stroke and have important prognostic implications [27]. Post-stroke changes in brain networks may be a combination of neurological damage and compensation. Actually, the impaired cognitive domains after stroke might vary depending on the stroke characteristics, such as stroke subtypes, volume, number, location, and severity [28,29]. Attention, working memory, and executive function are most commonly affected [20,30]. Prior RSN FC research demonstrated that the functional connectivity between the ipsilesional and the contralesional primary sensorimotor cortex was significantly impaired at the early stage of stroke [31,32]. Functional connections cannot be separated from integrated structural connections. Following a stroke, patients’ brains will suffer structural damage due to ischemia or hemorrhage [33,34]. The underlying mechanisms of neurological deficits following stroke are complex. For instance, ischemic injury will result in a sequence of cascade reactions involving hypoperfusion, calcium overload, oxidative stress, blood-brain barrier dysfunction, and neuroinflammation responses. From a pathological perspective, stroke damages structural and functional synaptic plasticity, impairing synaptic connections’ strength and the efficiency of synaptic transmission [35,36]. These findings shed light on neuro-mechanisms from a network-level prospective, which may contribute to explaining why patients after stroke exposure show deficits of behavior and cognition.
This study also found that a higher genetic risk of AIS was associated with higher dorsal attention network FC. Increased functional connectivity in the dorsal attention network might represent a compensatory effect, which contributed to maintaining normal cognitive performance in the early stages of stroke. In this study, the findings did not indicate any causal role between stroke subtypes and other RSNs. One possible reason was that primary motor- and vision-related functions depend on specific RSNs. In contrast, advanced cognitive functions, such as attention and memory, rely more on the communication between several RSNs than on the function of any individual RSN [37].
This MR study has several strengths. First, this study effectively avoided the disadvantages of traditional observational research methods, such as uncertain confounding factors and reverse causality. Second, our summary-level data were derived from the latest GWAS of populations with European ancestry, which increased our statistical power and provided compelling evidence of associations. Moreover, we used some alternative approaches to estimate possible pleiotropic bias and attained consistent effect estimates, which confirmed the robustness of our findings. Finally, we analyzed the relationship between FC and SC within 7 RSNs and stroke and its subtypes for the first time, which, to some extent, had yet to be investigated in previous studies.
Despite these advantages, one should be aware of some limitations in this study. First, this analysis was primarily based on populations of European ancestry, restricting the generalizability to other people. Second, in this study, the β value might not represent the severity of influence, merely indicating that there was a causal relationship. Third, there was significant pleiotropy in the MR estimate between global mean FC and CES, even though the MR result was significant. Finally, our study focused exclusively on abnormalities of FC and SC within a single RSN associated with stroke and its subtypes. Brain functions, such as language, motor, and primary sensory functions, are relatively confined to specific brain networks, whereas high-level cortical functions, such as memory and executive functions, appear to depend on communication between several brain RSNs. In addition to causing damage at the location of the injury, stroke also affects long-distance anatomical connections and disrupts the functional link between different brain RSNs. Moreover, though our reverse MR analysis results were not significant enough, they could be suggestive. Further investigations with representative samples will be required to investigate causal relationships between cerebral connectivity and stroke as well as its subtypes.

5. Conclusions

This study provides genetic evidence that the levels of FC and SC within different RSNs have opposite causal effects on stroke and its subtypes. Moreover, there is a combination of injury and compensatory physiological processes in brain RSNs following a stroke. Further clinical trials are warranted to investigate the direct causal association of FC and SC within brain RSNs and stroke and its subtypes.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/biomedicines11061575/s1, Table S1: GWAS summary data of functional/structural connectivity at p-value < 5.0 × 10−6; Table S2: Detailed information of instrumental variables used in MR analyses; Table S3: Full result of MR estimates for the association between functional/structural connectivity and stroke types; Table S4: Sensitivity analysis of instrumental variables used in MR analyses; Table S5: GWAS summary data of stroke types at p-value < 5.0 × 10−6; Table S6: Detailed information of instrumental variables used in inverse MR analyses; Table S7: Full result of inverse MR estimates for the association between functional/structural connectivity and stroke types; Table S8: Sensitivity analysis of instrumental variables used in inverse MR analyses.

Author Contributions

Y.W. wrote the paper and was in charge of overall direction and planning. L.Y. performed experiments and analyzed data. J.L. conceived the study, discussed the results and commented on the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by the National Natural Science Foundation of China 61971451, Innovative Province special construction foundation of Hunan Province 2019SK2131, the science and technology innovation program of Hunan province 2021RC4016 and Clinical Research Center For Medical Imaging In Hunan Province in China 2020SK4001.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

Functional/structural connectivity within RSNs data from https://www.fmrib.ox.ac.uk/ukbiobank/gwas_resources/index.html and stroke data from https://megastroke.org are both open access.

Acknowledgments

The consortium’s release of stroke types from GWAS summary statistics was appreciated by the authors. The authors also wish to thank the authors of the GWAS summary statistics for FC/SC properties from UK Biobank Resource.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. GBD. 2019 Diseases and Injuries Collaborators Global Burden of 369 Diseases and Injuries in 204 Countries and Territories, 1990–2019: A Systematic Analysis for the Global Burden of Disease Study 2019. Lancet 2020, 396, 1204–1222. [Google Scholar] [CrossRef] [PubMed]
  2. Li, Y.; Lu, J.; Wang, J.; Deng, P.; Meng, C.; Tang, H. Inflammatory Cytokines and Risk of Ischemic Stroke: A Mendelian Randomization Study. Front. Pharmacol. 2021, 12, 779899. [Google Scholar] [CrossRef] [PubMed]
  3. Huang, Y.-Y.; Chen, S.-D.; Leng, X.-Y.; Kuo, K.; Wang, Z.-T.; Cui, M.; Tan, L.; Wang, K.; Dong, Q.; Yu, J.-T. Post-Stroke Cognitive Impairment: Epidemiology, Risk Factors, and Management. J. Alzheimer’s Dis. 2022, 86, 983–999. [Google Scholar] [CrossRef]
  4. Delavaran, H.; Jönsson, A.-C.; Lövkvist, H.; Iwarsson, S.; Elmståhl, S.; Norrving, B.; Lindgren, A. Cognitive Function in Stroke Survivors: A 10-Year Follow-up Study. Acta Neurol. Scand. 2017, 136, 187–194. [Google Scholar] [CrossRef]
  5. Tissink, E.; Werme, J.; de Lange, S.C.; Savage, J.E.; Wei, Y.; de Leeuw, C.A.; Nagel, M.; Posthuma, D.; van den Heuvel, M.P. The Genetic Architectures of Functional and Structural Connectivity Properties within Cerebral Resting-State Networks. Eneuro 2023, 10, ENEURO.0242-22.2023. [Google Scholar] [CrossRef] [PubMed]
  6. Xu, X.; Ren, C.; Fang, H.; Li, Z.; Chen, P.; Yang, J.; Wang, T. Exploring the Functional Connectivity Characteristics of Brain Networks in Post-Stroke Patients with Global Aphasia: A Healthy Control Based Resting-State FMRI Study. Ann. Palliat. Med. 2021, 10, 12113–12128. [Google Scholar] [CrossRef]
  7. Baldassarre, A.; Ramsey, L.E.; Siegel, J.S.; Shulman, G.L.; Corbetta, M. Brain Connectivity and Neurological Disorders after Stroke. Curr. Opin. Neurol. 2016, 29, 706–713. [Google Scholar] [CrossRef]
  8. Yue, X.; Li, Z.; Li, Y.; Gao, J.; Han, H.; Zhang, G.; Li, X.; Shen, Y.; Wei, W.; Bai, Y.; et al. Altered Static and Dynamic Functional Network Connectivity in Post-Stroke Cognitive Impairment. Neurosci. Lett. 2023, 799, 137097. [Google Scholar] [CrossRef]
  9. Geng, W.; Zhang, J.; Shang, S.; Chen, H.; Shi, M.; Jiang, L.; Yin, X.; Chen, Y.-C. Reduced Functional Network Connectivity Is Associated with Upper Limb Dysfunction in Acute Ischemic Brainstem Stroke. Brain Imaging Behav. 2022, 16, 802–810. [Google Scholar] [CrossRef]
  10. Lim, J.-S.; Lee, J.-J.; Woo, C.-W. Post-Stroke Cognitive Impairment: Pathophysiological Insights into Brain Disconnectome from Advanced Neuroimaging Analysis Techniques. J. Stroke 2021, 23, 297–311. [Google Scholar] [CrossRef]
  11. Hopewell, J.C.; Clarke, R. Emerging Risk Factors for Stroke: What Have We Learned From Mendelian Randomization Studies? Stroke 2016, 47, 1673–1678. [Google Scholar] [CrossRef] [PubMed]
  12. Harshfield, E.L.; Georgakis, M.K.; Malik, R.; Dichgans, M.; Markus, H.S. Modifiable Lifestyle Factors and Risk of Stroke: A Mendelian Randomization Analysis. Stroke 2021, 52, 931–936. [Google Scholar] [CrossRef] [PubMed]
  13. Kalkonde, Y.V.; Alladi, S.; Kaul, S.; Hachinski, V. Stroke Prevention Strategies in the Developing World. Stroke 2018, 49, 3092–3097. [Google Scholar] [CrossRef]
  14. Xiuyun, W.; Qian, W.; Minjun, X.; Weidong, L.; Lizhen, L. Education and Stroke: Evidence from Epidemiology and Mendelian Randomization Study. Sci. Rep. 2020, 10, 21208. [Google Scholar] [CrossRef] [PubMed]
  15. Gill, D.; Efstathiadou, A.; Cawood, K.; Tzoulaki, I.; Dehghan, A. Education Protects against Coronary Heart Disease and Stroke Independently of Cognitive Function: Evidence from Mendelian Randomization. Int. J. Epidemiol. 2019, 48, 1468–1477. [Google Scholar] [CrossRef] [PubMed]
  16. Gao, L.; Wang, K.; Ni, Q.-B.; Fan, H.; Zhao, L.; Huang, L.; Yang, M.; Li, H. Educational Attainment and Ischemic Stroke: A Mendelian Randomization Study. Front. Genet. 2021, 12, 794820. [Google Scholar] [CrossRef]
  17. Liang, J.; Cai, H.; Liang, G.; Liu, Z.; Fang, L.; Zhu, B.; Liu, B.; Zhang, H. Educational Attainment Protects against Type 2 Diabetes Independently of Cognitive Performance: A Mendelian Randomization Study. Acta Diabetol. 2021, 58, 567–574. [Google Scholar] [CrossRef] [PubMed]
  18. Wang, Y.; Ye, C.; Kong, L.; Zheng, J.; Xu, M.; Xu, Y.; Li, M.; Zhao, Z.; Lu, J.; Chen, Y.; et al. Independent Associations of Education, Intelligence, and Cognition with Hypertension and the Mediating Effects of Cardiometabolic Risk Factors: A Mendelian Randomization Study. Hypertension 2023, 80, 192–203. [Google Scholar] [CrossRef]
  19. Koren, D.; Slavkovska, M.; Vitkova, M.; Gdovinova, Z. Importance of Retesting for the Final Diagnosis of Post-Stroke Cognitive Impairment. Medicina 2023, 59, 637. [Google Scholar] [CrossRef]
  20. Li, J.; You, S.-J.; Xu, Y.-N.; Yuan, W.; Shen, Y.; Huang, J.-Y.; Xiong, K.-P.; Liu, C.-F. Cognitive Impairment and Sleep Disturbances after Minor Ischemic Stroke. Sleep Breath. 2019, 23, 455–462. [Google Scholar] [CrossRef]
  21. Mohd Zulkifly, M.F.; Ghazali, S.E.; Che Din, N.; Singh, D.K.A.; Subramaniam, P. A Review of Risk Factors for Cognitive Impairment in Stroke Survivors. Sci. World J. 2016, 2016, 3456943. [Google Scholar] [CrossRef] [PubMed]
  22. Savage, J.E.; Jansen, P.R.; Stringer, S.; Watanabe, K.; Bryois, J.; de Leeuw, C.A.; Nagel, M.; Awasthi, S.; Barr, P.B.; Coleman, J.R.I.; et al. Genome-Wide Association Meta-Analysis in 269,867 Individuals Identifies New Genetic and Functional Links to Intelligence. Nat. Genet. 2018, 50, 912–919. [Google Scholar] [CrossRef] [PubMed]
  23. Anderson, E.L.; Howe, L.D.; Wade, K.H.; Ben-Shlomo, Y.; Hill, W.D.; Deary, I.J.; Sanderson, E.C.; Zheng, J.; Korologou-Linden, R.; Stergiakouli, E.; et al. Education, Intelligence and Alzheimer’s Disease: Evidence from a Multivariable Two-Sample Mendelian Randomization Study. Int. J. Epidemiol. 2020, 49, 1163–1172. [Google Scholar] [CrossRef]
  24. Lee, J.J.; Wedow, R.; Okbay, A.; Kong, E.; Maghzian, O.; Zacher, M.; Nguyen-Viet, T.A.; Bowers, P.; Sidorenko, J.; Karlsson Linnér, R.; et al. Gene Discovery and Polygenic Prediction from a Genome-Wide Association Study of Educational Attainment in 1.1 Million Individuals. Nat. Genet. 2018, 50, 1112–1121. [Google Scholar] [CrossRef] [PubMed]
  25. Iadecola, C. The Neurovascular Unit Coming of Age: A Journey through Neurovascular Coupling in Health and Disease. Neuron 2017, 96, 17–42. [Google Scholar] [CrossRef]
  26. Li, Y.; Yu, Z.; Wu, P.; Chen, J. Ability of an Altered Functional Coupling between Resting-State Networks to Predict Behavioral Outcomes in Subcortical Ischemic Stroke: A Longitudinal Study. Front. Aging Neurosci. 2022, 14, 933567. [Google Scholar] [CrossRef]
  27. Fan, Y.-T.; Wu, C.-Y.; Liu, H.-L.; Lin, K.-C.; Wai, Y.-Y.; Chen, Y.-L. Neuroplastic Changes in Resting-State Functional Connectivity after Stroke Rehabilitation. Front. Hum. Neurosci. 2015, 9, 546. [Google Scholar] [CrossRef]
  28. Pantoni, L.; Salvadori, E. Location of Infarcts and Post-Stroke Cognitive Impairment. Lancet Neurol. 2021, 20, 413–414. [Google Scholar] [CrossRef]
  29. Kalaria, R.N.; Akinyemi, R.; Ihara, M. Stroke Injury, Cognitive Impairment and Vascular Dementia. Biochim. Biophys. Acta 2016, 1862, 915–925. [Google Scholar] [CrossRef]
  30. Kramer, J.H.; Reed, B.R.; Mungas, D.; Weiner, M.W.; Chui, H.C. Executive Dysfunction in Subcortical Ischaemic Vascular Disease. J. Neurol. Neurosurg. Psychiatry 2002, 72, 217–220. [Google Scholar] [CrossRef]
  31. Park, C.; Chang, W.H.; Ohn, S.H.; Kim, S.T.; Bang, O.Y.; Pascual-Leone, A.; Kim, Y.-H. Longitudinal Changes of Resting-State Functional Connectivity during Motor Recovery after Stroke. Stroke 2011, 42, 1357–1362. [Google Scholar] [CrossRef] [PubMed]
  32. Golestani, A.-M.; Tymchuk, S.; Demchuk, A.; Goodyear, B.G.; VISION-2 Study Group. Longitudinal Evaluation of Resting-State FMRI after Acute Stroke with Hemiparesis. Neurorehabil. Neural Repair. 2013, 27, 153–163. [Google Scholar] [CrossRef] [PubMed]
  33. Zhang, P.; Wang, C.; Wu, J.; Zhang, S. A Systematic Review of the Predictive Value of Plasma D-Dimer Levels for Predicting Stroke Outcome. Front. Neurol. 2021, 12, 693524. [Google Scholar] [CrossRef]
  34. Woodruff, T.M.; Thundyil, J.; Tang, S.-C.; Sobey, C.G.; Taylor, S.M.; Arumugam, T.V. Pathophysiology, Treatment, and Animal and Cellular Models of Human Ischemic Stroke. Mol. Neurodegener. 2011, 6, 11. [Google Scholar] [CrossRef] [PubMed]
  35. Chi, X.; Wang, L.; Liu, H.; Zhang, Y.; Shen, W. Post-Stroke Cognitive Impairment and Synaptic Plasticity: A Review about the Mechanisms and Chinese Herbal Drugs Strategies. Front. Neurosci. 2023, 17, 1123817. [Google Scholar] [CrossRef] [PubMed]
  36. Joy, M.T.; Carmichael, S.T. Encouraging an Excitable Brain State: Mechanisms of Brain Repair in Stroke. Nat. Rev. Neurosci. 2021, 22, 38–53. [Google Scholar] [CrossRef] [PubMed]
  37. Siegel, J.S.; Ramsey, L.E.; Snyder, A.Z.; Metcalf, N.V.; Chacko, R.V.; Weinberger, K.; Baldassarre, A.; Hacker, C.D.; Shulman, G.L.; Corbetta, M. Disruptions of Network Connectivity Predict Impairment in Multiple Behavioral Domains after Stroke. Proc. Natl. Acad. Sci. USA 2016, 113, E4367–E4376. [Google Scholar] [CrossRef]
Table
Table 1. Full positive results of MR estimate for the association between functional/structural connectivity and strokes.
Table 1. Full positive results of MR estimate for the association between functional/structural connectivity and strokes.
Functional/Structural Connectivity (Exposure)Stroke Type (Outcome)MR MethodNo. SNPFβ (95% CI)p-Value
Global mean FCAISIVW(MRE)825.52−20.549 (−40.059, −1.039)0.03899
MR Egger8 −59.964 (−109.570, −10.357)
Weighted median8 −24.254 (−55.788, 7.279)
Simple mode8 −28.694 (−76.351, 18.964)
Weighted mode8 −28.132 (77.790, 21.525)
Limbic network FCSVSIVW (MRE)1022.63−33.048 (−56.856, −9.240)0.00652
MR Egger10 −22.480 (−164.960, 120.000)
Weighted median10 −43.583 (−89.394, 2.229)
Simple mode10 −53.689 (−126.947, 19.568)
Weighted mode10 −50.592 (−114.007, 12.823)
Ventral attention network FCSVSIVW (MRE)421.63122.976 (57.513, 188.438)0.00023
MR Egger4 129.368 (−145.057, 403.792)
Weighted median4 103.820 (−33.168, 240.808)
Simple mode4 66.841 (−107.596, 241.278)
Weighted mode4 171.079 (−6.787, 348.945)
Default mode network SCLASIVW (MRE)1421.0637.737 (5.786, 69.687)0.02062
MR Egger14 85.152 (−2.216, 172.521)
Weighted median14 53.524 (5.341, 101.706)
Simple mode14 67.920 (−15.131, 150.971)
Weighted mode14 66.506 (−6.566, 139.577)
/
Stroke Type (Exposure)Functional/Structural Connectivity (Outcome)MR MethodNo. SNPFβ (95% CI)p-Value
ASDorsal attention network FCIVW (MRE)4748.412.938 × 10−4 (1.420 × 10−5, 5.732 × 10−4)0.03946
MR Egger47 6.562 × 10−4 (−2.428 × 10−4, 1.555 × 10−3)
Weighted median47 2.888 × 10−4 (1.086 × 10−4, 6.862 × 10−4)
Simple mode47 3.669 × 10−4 (−5.547 × 10−4, 1.288 × 10−3)
Weighted mode47 3.669 × 10−4 (5.267 × 10−4, 1.261 × 10−3)
LASSomatomotor network FCIVW (MRE)3429.97−5.079 × 10−4 (−1.010 × 10−3, −5.617 × 10−6)0.04749
MR Egger34 −2.450 × 10−6 (−1.420 × 10−3, 1.414 × 10−3)
Weighted median34 −5.920 × 10−4 (−1.538 × 10−3, 3.539 × 10−4)
Simple mode34 −5.828 × 10−4 (−2.644 × 10−3, 1.478 × 10−3)
Weighted mode34 −6.024 × 10−4 (−2.333 × 10−3, 1.129 × 10−3)
IVW: inverse variance weighted; MRE: multiplicative random effects model; MR Egger: Mendelian randomization-Egger; OR: odds ratio; CI: confidence interval; No. SNP: number of single nucleotide polymorphisms; AS: any stroke; AIS: any ischemic stroke; LAS: large artery stroke; CES: cardioembolic stroke; SVS: small vessel stroke; FC: functional connectivity; SC: structural connectivity.
Table
Table 2. Sensitivity analyses for association between functional/structural connectivity and strokes.
Table 2. Sensitivity analyses for association between functional/structural connectivity and strokes.
Functional/Structural Connectivity (Exposure)Stroke Type (Outcome)No. SNPPleiotropyHeterogeneity
MR-PRESSO Global p-ValueMR-Egger p-ValueIVW Test p-ValueMR-Egger p-Value
Global mean FCAIS80.72660.12440.97000.7185
Limbic network FCSVS100.90650.88450.90100.8451
Ventral attention network FCSVS40.77680.96480.78210.5837
Default mode net-work SCLAS 140.73420.26490.68940.7293
/
Stroke Type (Exposure)Functional/Structural Connectivity (Outcome)No. SNPPleiotropyHeterogeneity
MR-PRESSO Global p-ValueMR-Egger p-ValueIVW Test p-ValueMR-Egger p-Value
ASDorsal attention network FC470.44790.40990.44840.4357
LASSomatomotor network FC340.99200.42790.98940.9890
MR-PRESSO: Mendelian Randomization Pleiotropy RESidual Sum and Outlier; MR-egger: Mendelian randomization-Egger; IVW: inverse variance weighted; No. SNP: number of single nucleotide polymorphisms; AIS: any ischemic stroke; SVS: small vessel stroke; LAS: large artery stroke; AS: any stroke; FC: functional connectivity; SC: structural connectivity.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Wang, Y.; Yang, L.; Liu, J. Causal Associations between Functional/Structural Connectivity and Stroke: A Bidirectional Mendelian Randomization Study. Biomedicines 2023, 11, 1575. https://doi.org/10.3390/biomedicines11061575

AMA Style

Wang Y, Yang L, Liu J. Causal Associations between Functional/Structural Connectivity and Stroke: A Bidirectional Mendelian Randomization Study. Biomedicines. 2023; 11(6):1575. https://doi.org/10.3390/biomedicines11061575

Chicago/Turabian Style

Wang, Yisong, Longtao Yang, and Jun Liu. 2023. "Causal Associations between Functional/Structural Connectivity and Stroke: A Bidirectional Mendelian Randomization Study" Biomedicines 11, no. 6: 1575. https://doi.org/10.3390/biomedicines11061575

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop