Abstract
Background
Previous studies have found that patients with epilepsy are more likely to suffer impulsivity. However, the causal relationship between impulsivity and epilepsy is unknown. In this study, we conduct a bidirectional Mendelian randomization (MR) study to explore the causal relationship between impulsivity and epilepsy with recurrent seizure.
Methods
Data of the genome-wide association studies (GWAS) on 14 impulsivity traits and epilepsy were obtained from the GWAS catalog and UK Biobank. Inverse-variance weighted (IVW) and weighted median (WM) methods were utilized for MR estimates. IVW, MR-Egger regression, and MR-pleiotropy residual sum and outlier (MR-PRESSO) methods were used to assess heterogeneity and pleiotropy.
Results
Single-nucleotide polymorphisms (SNPs) related to the lack of perseverance were associated with a decreased risk of epilepsy with recurrent seizures according to the results of IVW (odd ratio [OR] = 0.93, 95% confident interval [CI] = 0.90–0.97, P = 0.001) and WM (OR = 0.93, 95%CI = 0.87–0.98, P = 0.007). Meanwhile, heterogeneity was not observed with a Cochran Q-derived P value of 0.819 for MR egger and a P value of 0.808 for IVW. Pleiotropy was not found according to the MR-PRESSO (P = 0.273). The other 13 impulsivity traits had no causal effect on epilepsy with recurrent seizures. Meanwhile, SNPs related with epilepsy with recurrent seizures had no causal effect on the 14 impulsivity traits.
Conclusions
This MR study suggests that lack of perseverance may be a protective factor against epilepsy with recurrent seizures. However, epilepsy with recurrent seizures does not affect impulsivity.
Keywords: Mendelian randomization analysis, Epilepsy, Impulsivity, Causal effect
Background
Impulsivity refers to the predisposition toward rapid, unplanned reactions to external or internal stimuli without consideration of the negative consequences of these responses to the impulsive individuals or to others [1]. A previous study of impulsivity measures found three distinct domains, including impulsive choice, impulsive action, and impulsive personality traits measured through self-reported questionnaires [2]. Epilepsy is a chronic neurological disease characterized by sudden abnormal discharges of neurons in the brain, resulting in transient brain dysfunction [3]. Epilepsy with recurrent seizures could result in hippocampal sclerosis [4, 5], and the hippocampus mediates behavior [6]. Furthermore, hippocampal-prefrontal interactions are involved in emotional behaviors [7]. Previous studies found that epilepsy is associated with impulsivity, and impulsivity affects the quality of life of epileptic patients [8–15]. Furthermore, a functional brain magnetic resonance imaging (MRI) study found that the abnormality of anatomical connections, including prefrontal-limbic network, temporo-occipital network, and frontostriatal circuit, might underlie the association between impulsivity and epilepsy [15]. But the causal effect between impulsivity and epilepsy remains unclear.
Fortunately, a recent genome-wide association study (GWAS) revealed the genetic basis of different impulsivity phenotypes [16]. Mendelian randomization (MR) is a method that establishes the causal relationship between different phenotypes by using genetic variants linked to modifiable exposures as proxies for the phenotype [17–19]. In this study, we used bidirectional MR to explore the causal effect between impulsivity and epilepsy with recurrent seizures.
Methods
A two-sample MR study was performed to explore the causal relationship between impulsivity and epilepsy with recurrent seizures. First, we employed the genetic variants associated with 14 impulsivity traits as instrumental variables (IVs) to determine their causal effect on epilepsy with recurrent seizures. Then, we employed the genetic variants associated with epilepsy with recurrent seizures as IVs to determine its causal effect on impulsivity. All of data analyzed in this study were obtained from public databases.
Data source for impulsivity
The most recent impulsivity-associated GWAS study was utilized for the selection of genetic instruments (Table 1) [16]. That study included 1534 participants with European ancestry. The impulsivity included three distinct domains, including impulsive choice, impulsive action, and impulsive personality, which were measured by the Monetary Choice Questionnaire, Go/No-Go task, and the S-UPPS-P scale, respectively [16]. The impulsivity traits included 14 different variables, which are impulsive action factor (IAF), impulsive choice factor (ICF), impulsive personality factor (IPF), impulsive action with No-Go trial accuracy (IA-NGTA), impulsive action with Go trial accuracy (IA-GTA), delayed reward discounting (DRD), delayed reward discounting with small magnitude rewards (DRD-SMR), delayed reward discounting with medium magnitude rewards (DRD-MMR), delayed reward discounting with large magnitude rewards (DRD-LMR), negative urgency, lack of perseverance, lack of premeditation, positive urgency, and sensation seeking [16].
Table 1.
Sources of GWAS data used in this study
| Trait | Consortium | Sample size | Number of cases |
|---|---|---|---|
| Epilepsy, recurrent seizures, convulsions | UK Biobank | 400,296 | 5087 |
| IAF | Deng et al.a | 1534 | NA |
| ICF | Deng et al.a | 1534 | NA |
| IPF | Deng et al.a | 1534 | NA |
| IA-NGTA | Deng et al.a | 1534 | NA |
| IA-GTA | Deng et al.a | 1534 | NA |
| DRD | Deng et al.a | 1534 | NA |
| DRD-SMR | Deng et al.a | 1534 | NA |
| DRD-MMR | Deng et al.a | 1534 | NA |
| DRD-LMR | Deng et al.a | 1534 | NA |
| Negative urgency | Deng et al.a | 1534 | NA |
| Lack of perseverance | Deng et al.a | 1534 | NA |
| Lack of premeditation | Deng et al.a | 1534 | NA |
| Positive urgency | Deng et al.a | 1534 | NA |
| Sensation seeking | Deng et al.a | 1534 | NA |
GWAS Genome-wide association study, NA Not applicable, IAF Impulsive action factor, ICF Impulsive choice factor, IPF Impulsive personality factor, IA-NGTA Impulsive action with No-Go trial accuracy, IA-GTA Impulsive action with Go trial accuracy, DRD Delayed reward discounting, DRD-SMR Delayed reward discounting with small magnitude rewards, DRD-MMR Delayed reward discounting with medium magnitude rewards, DRD-LMR Delayed reward discounting with large magnitude rewards
ameans the reference of Deng et al. [16]
Data source for epilepsy with recurrent seizures
The GWAS data for epilepsy with recurrent seizures were obtained from the UK Biobank (Table 1), which were analyzed by the SAIGE (Scalable and Accurate Implementation of Generalized) mixed model, a method that diminishes the type I error caused by unbalanced case-control phenotypes [20]. The cohort included 5087 epileptic patients with recurrent seizures and 395,209 controls [20].
Selection of genetic instruments
Single-nucleotide polymorphisms (SNPs) serve as a genetic instrument to indicate the condition of impulsivity or epilepsy with recurrent seizure. The correlation of genetic instruments with the phenotype should meet the following criteria: (1) a GWAS-correlated P-value less than 5 × 10−8, or a P-value less than 5 × 10−5 with a F statistic > 10 when no SNPs met the tight criteria; and (2) a linkage disequilibrium coefficient r2 less than 0.001 [17, 21].
Statistical analyses
We used two methods to determine MR estimates, the inverse-variance weighted (IVW) method and the weighted median (WM) method [17, 18]. Multiple approaches were employed because their underlying assumptions for horizontal pleiotropy varied [22]. The IVW method assumes that all genetic variants are valid IVs, i.e., the genetic instruments can only affect the outcome through exposure and not through any other pathway [22]. The WM method was performed to supplement IVW estimates [22]. Bonferroni-corrected P values less than 0.004 were considered as statistical significant [23].
Sensitivity analysis, including bias analysis for pleiotropy and the MR estimates, was performed. Heterogeneity was evaluated by the IVW method and MR-Egger regression, with a Cochran Q-derived P < 0.05 indicating significant heterogeneity. Pleiotropy assessment was performed with the MR-pleiotropy residual sum and outlier methods (MR-PRESSO) [19]. Analyses were performed using the packages TwoSampleMR (version 0.5.6) and MendelR (version 2.1.2) in R software (version 4.0).
Results
All SNPs selected as IVs had a GWAS-correlated P value of no more than 5 × 10−5 and a F statistics more than 10. No SNPs fulfilled the criterion of P < 5 × 10−8 (Table 2).
Table 2.
Number of instrumental variables and associated phenotypic variance
| Exposure | Outcome | Number of SNPs | R 2 | Mean F (Min, Max) |
|---|---|---|---|---|
| IAF | EWRS | 48 | 0.50 | 16.25 (13.86, 22.99) |
| ICF | EWRS | 36 | 0.41 | 17.60 (15.17, 21.26) |
| IPF | EWRS | 35 | 0.32 | 14.33 (12.12, 18.22) |
| IA-NGTA | EWRS | 45 | 0.55 | 18.56 (15.88, 25.65) |
| IA-GTA | EWRS | 57 | 0.67 | 17.64 (15.27, 22.79) |
| DRD | EWRS | 40 | 0.48 | 18.52 (16.33, 22.06) |
| DRD-SMR | EWRS | 34 | 0.39 | 17.77 (15.51, 22.74) |
| DRD-MMR | EWRS | 40 | 0.45 | 17.35 (15.17, 22.54) |
| DRD-LMR | EWRS | 41 | 0.48 | 17.96 (15.50, 23.76) |
| Negative urgency | EWRS | 34 | 0.42 | 19.20 (16.78, 24.46) |
| Lack of perseverance | EWRS | 41 | 0.49 | 18.52 (16.20, 23.75) |
| Lack of premeditation | EWRS | 40 | 0.49 | 19.17 (15.77, 26.44) |
| Positive urgency | EWRS | 40 | 0.49 | 18.90 (15.98, 24.81) |
| Sensation seeking | EWRS | 35 | 0.44 | 19.33 (16.81, 25.23) |
| EWRS | IAF | 8 | 0.03 | 1443.27 (1356.41, 1569.39) |
| EWRS | ICF | 13 | 0.05 | 1462.62 (1369.72, 1669.24) |
| EWRS | IPF | 13 | 0.05 | 1444.14 (1363.59, 1634.59) |
| EWRS | IA-NGTA | 7 | 0.03 | 1483.92 (1363.59, 1613.57) |
| EWRS | IA-GTA | 9 | 0.03 | 1529.20 (1383.33, 2010.72) |
| EWRS | DRD | 10 | 0.04 | 1470.89 (1392.86, 1669.24) |
| EWRS | DRD-SMR | 11 | 0.04 | 1537.79 (1369.72, 2052.16) |
| EWRS | DRD-MMR | 10 | 0.04 | 1537.36 (1369.72, 2052.16) |
| EWRS | DRD-LMR | 11 | 0.04 | 1519.80 (1392.86, 2052.16) |
| EWRS | Negative urgency | 12 | 0.04 | 1490.17 (1363.59, 1930.68) |
| EWRS | Lack of perseverance | 11 | 0.04 | 1442.50 (1363.59, 1582.68) |
| EWRS | Lack of premeditation | 4 | 0.01 | 1390.28 (1363.59, 1434.94) |
| EWRS | Positive urgency | 12 | 0.04 | 1494.70 (1363.59, 1634.59) |
| EWRS | Sensation seeking | 11 | 0.04 | 1500.58 (1363.59, 1779.39) |
IAF Impulsive action factor, ICF Impulsive choice factor, IPF Impulsive personality factor, IA-NGTA Impulsive action with No-Go trial accuracy, IA-GTA Impulsive action with Go trial accuracy, DRD Delayed reward discounting, DRD-SMR Delayed reward discounting with small magnitude rewards, DRD-MMR Delayed reward discounting with medium magnitude rewards, DRD-LMR Delayed reward discounting with large magnitude rewards, EWRS Epilepsy with recurrent seizure
Causal effect of impulsivity on epilepsy with recurrent seizures
There were 48, 36, 35, 45, 57, 40, 34, 40, 41, 34, 41, 40, 40 and 35 independent SNPs associated with IAF, ICF, IPF, IA-NGTA, IA-GTA, DRD, DRD-SMR, DRD-MMR, DRD-LMR, negative urgency, lack of perseverance, lack of premeditation, positive urgency, and sensation seeking, respectively. The R2, which stands for phenotypic variance explained by the SNPs, and F statistics of every IV, are shown in Table 2.
The SNPs related with lack of perseverance were associated with a decreased risk of epilepsy with recurrent seizures according to the IVW (odd ratio [OR] = 0.93, 95% confident interval [CI] = 0.90–0.97, P = 0.001) and WM (OR = 0.93, 95%CI = 0.87–0.98, P = 0.007) methods (Fig. 1). Meanwhile, heterogeneity was not observed with a Cochran Q-derived P value of 0.819 for MR egger and a P value of 0.808 for IVW. Pleiotropy was not found according to the MR-PRESSO (P = 0.273) (Table 3). We also found that there was no significant causal effect of other SNPs related with impulsivity traits on the risk of epilepsy with recurrent seizures (Fig. 1).
Fig. 1.
The forest plot showing the causal effect of 14 impulsivity traits on epilepsy with recurrent seizures, estimated with the IVW and WM methods. Lack of perseverance was shown to reduce the risk of epilepsy with recurrent seizures
IVW: inverse variance weighted; WM: weight medium; OR: odd ratio; CI: confidence interval; IAF: impulsive action factor; ICF: impulsive choice factor; IPF: impulsive personality factor; IA-NGTA: impulsive action with No-Go trial accuracy; IA-GTA: impulsive action with Go trial accuracy; DRD: delayed reward discounting; DRD-SMR: delayed reward discounting with small magnitude rewards; DRD-MMR: delayed reward discounting with medium magnitude rewards; DRD-LMR: delayed reward discounting with large magnitude rewards
Table 3.
Heterogeneity and pleiotropy
| Heterogeneity | Pleiotropy | ||
|---|---|---|---|
| MR Egger | IVW | MR-PRESSO | |
| Exposure | |||
| IAF | 0.443 | 0.477 | 0.669 |
| ICF | 0.555 | 0.577 | 0.471 |
| IPF | 0.108 | 0.116 | 0.459 |
| IA-NGTA | 0.252 | 0.273 | 0.551 |
| IA-GTA | 0.220 | 0.169 | 0.097 |
| DRD | 0.603 | 0.576 | 0.221 |
| DRD-SMR | 0.991 | 0.993 | 0.703 |
| DRD-MMR | 0.392 | 0.432 | 0.742 |
| DRD-LMR | 0.993 | 0.982 | 0.090 |
| Negative urgency | 0.297 | 0.262 | 0.190 |
| Lack of perseverance | 0.819 | 0.808 | 0.273 |
| Lack of premeditation | 0.380 | 0.176 | 0.014 |
| Positive urgency | 0.469 | 0.430 | 0.176 |
| Sensation seeking | 0.616 | 0.647 | 0.572 |
| Outcome | |||
| IAF | 1.000 | 1.000 | 0.905 |
| ICF | 1.000 | 1.000 | 0.948 |
| IPF | 1.000 | 1.000 | 0.896 |
| IA-NGTA | 1.000 | 1.000 | 0.964 |
| IA-GTA | 1.000 | 1.000 | 0.902 |
| DRD | 1.000 | 1.000 | 0.931 |
| DRD-SMR | 1.000 | 1.000 | 0.841 |
| DRD-MMR | 1.000 | 1.000 | 0.946 |
| DRD-LMR | 1.000 | 1.000 | 0.914 |
| Negative urgency | 1.000 | 1.000 | 0.878 |
| Lack of perseverance | 1.000 | 1.000 | 0.949 |
| Lack of premeditation | 0.990 | 0.999 | 0.986 |
| Positive urgency | 1.000 | 1.000 | 0.798 |
| Sensation seeking | 1.000 | 1.000 | 0.945 |
IVW Inverse variance weighted, MR-PRESSO MR-Pleiotropy Residual Sum and Outlier methods, IAF Impulsive action factor, ICF Impulsive choice factor, IPF Impulsive personality factor, IA-NGTA Impulsive action with No-Go trial accuracy, IA-GTA Impulsive action with Go trial accuracy, DRD Delayed reward discounting, DRD-SMR Delayed reward discounting with small magnitude rewards, DRD-MMR Delayed reward discounting with medium magnitude rewards, DRD-LMR Delayed reward discounting with large magnitude rewards
Causal effect of epilepsy with recurrent seizures on impulsivity traits
The number of SNPs associated with epilepsy with recurrent seizures, selected as IVs, were different after harmonizing with different impulsivity traits (Fig. 2). The R2 and F statistic of every IV are shown in Table 2. There were no causal effects of SNPs related with epilepsy with recurrent seizures on 14 different impulsivity traits (Fig. 2). In addition, no heterogeneity or pleiotropy was observed (Table 3).
Fig. 2.
Forest plot showing the causal effect of epilepsy with recurrent seizures on 14 impulsivity traits estimated with the IVW and WM methods. Results showed that there were no causal effects of epilepsy with recurrent seizures on the 14 impulsivity traits. IVW: inverse variance weighted; WM: weight medium; OR: odd ratio; CI: confidence interval; IAF: impulsive action factor; ICF: impulsive choice factor; IPF: impulsive personality factor; IA-NGTA: impulsive action with No-Go trial accuracy; IA-GTA: impulsive action with Go trial accuracy; DRD: delayed reward discounting; DRD-SMR: delayed reward discounting with small magnitude rewards; DRD-MMR: delayed reward discounting with medium magnitude rewards; DRD-LMR: delayed reward discounting with large magnitude rewards
Discussion
Our study was the first MR study exploring the bidirectional causal effects between impulsivity and epilepsy with recurrent seizures. The results showed that lack of perseverance is a protective factor against epilepsy with recurrent seizures. Meanwhile, epilepsy with recurrent seizures did not affect the 14 impulsivity traits.
Previous clinical studies have found that impulsivity is associated with epilepsy. Patients with juvenile myoclonic epilepsy show increased impulsivity [9, 10, 12, 14, 24, 25]. Patients with idiopathic generalized epilepsy or frontal lobe epilepsy are more likely to have high impulsivity [15]. Furthermore, impulsivity is associated with the severity of epileptic seizures [26]. However, in a previous study, patients with temporal lobe epilepsy showed no differences in impulsivity, as compared with healthy controls [15]. Our MR analysis was the first to analyze the relationship between three distinct domains of impulsivity and epilepsy with recurrent seizures. We found that epilepsy could not induce impulsivity. However, we found that lack of perseverance is a protective factor against epilepsy with recurrent seizures. The mechanism of this causal relationship is unknown, which may involve the frontal lobe networks [15, 27].
Perseverance, also known as grit, is defined as the passion for long-term goals. Perseverance is an important noncognitive trait [28]. In our study, lack of perseverance was utilized for measuring the impulsive personality traits [16]. A study found that the volume of nucleus accumbens is associated with interindividual differences in perseverance [29]. In another study with 217 healthy adolescent students, Wang et al. found that perseverance is negatively associated with the fractional amplitude of low-frequency fluctuations in the right dorsomedial prefrontal cortex [30]. These results demonstrated that the prefrontal cortex and the basal ganglia are the neural basis of perseverance. Furthermore, Myers et al. found that perseverance was associated with the connectivity between dorsal and ventral striatum and the dorsal anterior cingulate cortex [31]. These neural basis and neural network of perseverance might affect epileptic seizures through hippocampal-prefrontal interactions, which need to be verified in the future [7].
This study had some limitations. First, the data used in this study were all from European ancestors, so the conclusions should be applied with caution to other ethnic populations. Second, the mechanisms of the causal effect between lack of perseverance and epilepsy are unclear. Brain functional imaging can help clarify the mechanisms. Third, the results might be affected by age, gender, and ethnicity. Future studies with participant stratification according to these confounders are needed. Finally, our study did not classify the types of epilepsy, which might cause false-negative results.
Conclusions
In conclusion, the MR study provides evidence that the lack of perseverance may be a protective factor against epilepsy with recurrent seizures. However, epilepsy with recurrent seizures do not affect impulsivity.
Acknowledgements
We would like to thank the participants and investigators of all the GWAS studies, including the UK Biobank.
Abbreviations
- CI
Confidence interval
- DRD
Delayed reward discounting
- DRD-LMR
Delayed reward discounting with large magnitude rewards
- DRD-MMR
Delayed reward discounting with medium magnitude rewards
- DRD-SMR
Delayed reward discounting with small magnitude rewards
- GWAS
Genome-wide association studies
- IAF
Impulsive action factor
- IA-GTA
Impulsive action with Go trial accuracy
- IA-NGTA
Impulsive action with No-Go trial accuracy
- ICF
Impulsive choice factor
- IPF
Impulsive personality factor
- IVs
Instrumental variables
- IVW
Inverse variance weighted
- MR
Mendelian randomization
- MRI
Magnetic resonance imaging
- MR-PRESSO
MR-pleiotropy residual sum and outlier
- OR
Odd ratio
- SNPs
Single-nucleotide polymorphisms
- WM
Weight medium
Authors’ contributions
HP concepted the study; CT and LY acquired the data. CT and LY analyzed the data. HP wrote the first draft; CT and LY revised the draft. All authors have approved the final version.
Funding
The study was not funded by any project.
Availability of data and materials
The data analyzed in this study are available on the UK Biobank (https://pheweb.org/UKB-SAIGE/) and GWAS catalog (https://www.ebi.ac.uk/gwas/).
Declarations
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors reported no competing interests.
Footnotes
Tao Chen and Yuqi Liao contributed equally to this work.
References
- 1.Moeller FG, Barratt ES, Dougherty DM, Schmitz JM, Swann AC. Psychiatric aspects of impulsivity. Am J Psychiatry. 2001;158(11):1783–93. [DOI] [PubMed] [Google Scholar]
- 2.MacKillop J, Weafer J, Oshri JCG, Palmer A, de Wit A. The latent structure of impulsivity: impulsive choice, impulsive action, and impulsive personality traits. Psychopharmacology. 2016;233(18):3361–70. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Asadi-Pooya AA, Brigo F, Lattanzi S, Blumcke I. Adult epilepsy. Lancet. 2023;402(10399):412–24. [DOI] [PubMed] [Google Scholar]
- 4.Aronica E, Mühlebner A. Neuropathology of epilepsy. Handb Clin Neurol. 2017;145:193–216. [DOI] [PubMed] [Google Scholar]
- 5.Thom M. Review: hippocampal sclerosis in epilepsy: a neuropathology review. Neuropathol Appl Neurobiol. 2014;40(5):520–43. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Strange BA, Witter MP, Lein ES, Moser EI. Functional organization of the hippocampal longitudinal axis. Nat Rev Neurosci. 2014;15(10):655–69. [DOI] [PubMed] [Google Scholar]
- 7.Sigurdsson T, Duvarci S. Hippocampal-prefrontal interactions in Cognition, Behavior and Psychiatric Disease. Front Syst Neurosci. 2015;9:190. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Moschetta S, Fiore LA, Fuentes D, Gois J, Valente KD. Personality traits in patients with juvenile myoclonic epilepsy. Epilepsy Behav: E&B. 2011;21(4):473–7. [DOI] [PubMed] [Google Scholar]
- 9.Moschetta S, Valente KD. Impulsivity and seizure frequency, but not cognitive deficits, impact social adjustment in patients with juvenile myoclonic epilepsy. Epilepsia. 2013;54(5):866–70. [DOI] [PubMed] [Google Scholar]
- 10.Rzezak P, Moschetta SP, Lima E, Castro CX, Vincentiis S, Coan AC, et al. Distinct domains of impulsivity are impaired in juvenile myoclonic epilepsy but not in temporal lobe epilepsy. Epilepsy Behav: E&B. 2015;45:44–8. [DOI] [PubMed]
- 11.Syvertsen M, Selmer K, Enger U, Nakken KO, Pal DK, Smith A, et al. Psychosocial complications in juvenile myoclonic epilepsy. Epilepsy Behav: E&B. 2019;90:122–8. [DOI] [PubMed] [Google Scholar]
- 12.Syvertsen M, Koht J, Selmer K, Enger U, Pal DK, Smith A. Trait impulsivity correlates with active myoclonic seizures in genetic generalized epilepsy. Epilepsy Behav: E&B. 2020;112:107260. [DOI] [PubMed] [Google Scholar]
- 13.Luca A, Giuliano L, Manna R, D’Agate C, Maira G, Sofia V, et al. Impulsivity traits in eyelid myoclonia with absences. Seizure. 2021;91:393–6. [DOI] [PubMed] [Google Scholar]
- 14.Shakeshaft A, Panjwani N, McDowall R, Crudgington H, Peña Ceballos J, Andrade DM, et al. Trait impulsivity in Juvenile Myoclonic Epilepsy. Ann Clin Transl Neurol. 2021;8(1):138–52. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 15.Lee SA, Yang HR, Im K, Choi EJ, Jeon JY, Han SH, et al. Comparisons of impulsivity among patients with different subtypes of epilepsy. Epilepsy Res. 2022;186:106997. [DOI] [PubMed] [Google Scholar]
- 16.Deng WQ, Belisario K, Gray JC, Levitt EE, Mohammadi-Shemirani P, Singh D, et al. Leveraging related health phenotypes for polygenic prediction of impulsive choice, impulsive action, and impulsive personality traits in 1534 European ancestry community adults. Genes Brain Behav. 2023;22(3):e12848. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Bowden J, Davey Smith G, Burgess S. Mendelian randomization with invalid instruments: effect estimation and bias detection through Egger regression. Int J Epidemiol. 2015;44(2):512–25. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Burgess S, Thompson SG. Interpreting findings from mendelian randomization using the MR-Egger method. Eur J Epidemiol. 2017;32(5):377–89. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Ong JS, MacGregor S. Implementing MR-PRESSO and GCTA-GSMR for pleiotropy assessment in mendelian randomization studies from a practitioner’s perspective. Genet Epidemiol. 2019;43(6):609–16. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Zhou W, Nielsen JB, Fritsche LG, Dey R, Gabrielsen ME, Wolford BN, et al. Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies. Nat Genet. 2018;50(9):1335–41. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Sanderson E, Glymour MM, Holmes MV, Kang H, Morrison J, Munafò MR, et al. Mendelian randomization. Nat Rev Methods Primers. 2022;2:6. [DOI] [PMC free article] [PubMed]
- 22.Chen X, Kong J, Diao X, Cai J, Zheng J, Xie W, et al. Depression and prostate cancer risk: a mendelian randomization study. Cancer Med. 2020;9(23):9160–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 23.Shi S, Chen Y, Chu X, Shi P, Wang B, Cai Q, He D, Zhang N, Qin X, Wei W, et al. Evaluating the associations between intelligence quotient and multi-tissue proteome from the brain, CSF and plasma. Brain Commun. 2024;6(4):fcae207. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 24.Schmitz B, Yacubian EM, Feucht M, Hermann B, Trimble M. Neuropsychology and behavior in juvenile myoclonic epilepsy. Epilepsy Behav: E&B. 2013;28(Suppl 1):S72–73. [DOI] [PubMed] [Google Scholar]
- 25.Gama AP, Taura M, Alonso NB, Sousa AM, Noffs M, Yacubian EM, et al. Impulsiveness, personality traits and executive functioning in patients with juvenile myoclonic epilepsy. Seizure. 2020;82:125–32. [DOI] [PubMed] [Google Scholar]
- 26.Lee SA, Choi EJ, Jeon JY, Han SH, Kim HW, Lee GH, et al. Impulsivity in persons with epilepsy: Association with seizure severity and suicide risk. Epilepsy Res. 2022;179:106825. [DOI] [PubMed] [Google Scholar]
- 27.Yang H, Ren J, Wang Q. Abnormal Brain Network in Epilepsy and Associated Comorbidites. Neuropsychiatry. 2018;8(3).
- 28.Duckworth AL, Peterson C, Matthews MD, Kelly DR. Grit: perseverance and passion for long-term goals. J Personal Soc Psychol. 2007;92(6):1087–101. [DOI] [PubMed] [Google Scholar]
- 29.Nemmi F, Nymberg C, Helander E, Klingberg T. Grit is Associated with structure of Nucleus Accumbens and gains in cognitive training. J Cogn Neurosci. 2016;28(11):1688–99. [DOI] [PubMed] [Google Scholar]
- 30.Wang S, Zhou M, Chen T, Yang X, Chen G, Wang M, et al. Grit and the brain: spontaneous activity of the dorsomedial prefrontal cortex mediates the relationship between the trait grit and academic performance. Soc Cognit Affect Neurosci. 2017;12(3):452–60. [DOI] [PMC free article] [PubMed]
- 31.Myers CA, Wang C, Black JM, Bugescu N, Hoeft F. The matter of motivation: striatal resting-state connectivity is dissociable between grit and growth mindset. Soc Cognit Affect Neurosci. 2016;11(10):1521–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data analyzed in this study are available on the UK Biobank (https://pheweb.org/UKB-SAIGE/) and GWAS catalog (https://www.ebi.ac.uk/gwas/).