Skip to main content
Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

The impact of maternal prenatal psychological distress on the development of epilepsy in offspring: The Japan Environment and Children’s Study

Abstract

The relationship between maternal prenatal psychological distress and epilepsy development in offspring has not yet been clarified. Herein, we used a dataset obtained from the Japan Environment and Children’s Study, a nationwide birth cohort study, to evaluate the association between six-item Kessler Psychological Distress Scale (K6) scores and epilepsy among 1–3 years old. The data of 97,484 children were retrospectively analyzed. The K6 was administered to women twice: during the first half (M-T1) and second half (M-T2) of pregnancy. M-T1 ranged from 12.3–18.9 (median 15.1) weeks, and M-T2 ranged from 25.3–30.1 (median 27.4) weeks. Participants were divided into six groups based on K6 scores of two ranges (≤4 and ≥5) at M-T1 and M-T2. The numbers of children diagnosed with epilepsy at the ages of 1, 2, and 3 years were 89 (0.1%), 129 (0.2%), and 149 (0.2%), respectively. A maternal K6 score of ≥5 at both M-T1 and M-T2 was associated with epilepsy diagnosis ratios among 1-, 2-, and 3-year-old children in the univariate analysis. Moreover, multivariate analysis revealed that a maternal K6 score of ≥5 at both M-T1 and M-T2 was associated with epilepsy diagnosis ratios among 1-, 2-, and 3-year-olds. Continuous moderate-level maternal psychological distress from the first to the second half of pregnancy is associated with epilepsy among 1-, 2-, and 3-year-old children. Hence, environmental adjustments to promote relaxation such as mindfulness in pregnant women might be necessary.

Introduction

Epilepsy, affecting 65 million individuals globally, is the most prevalent, persistent, and severe neurological disorder worldwide [1]. Individuals living with epilepsy often encounter discrimination, misunderstanding, and social stigma [2], and they endure the stress of living with a chronic, unpredictable disease that may lead to a loss of autonomy in activities of daily living. Epilepsy is one of the most common neurological disorders in childhood [3], while epilepsy onset before the age of three is particularly associated with high rates of drug resistance and developmental delays [4]. As such, it is crucial to develop preventive measures to reduce the incidence of epilepsy in children under the age of three. Previous studies examining big data on the perinatal environment have identified abruptio placenta, eclampsia, infection in pregnancy, low birth weight, and artificial milk feeding as risk factors for the onset of early childhood epilepsy [5].

The fetal programming theory proposes that the environment status during fetal development significantly impacts health and disease risk throughout an individual’s lifetime [610]. This theory underscores the critical importance of the fetal environment, providing a novel perspective in preventive medicine by highlighting its significant impact on the future health of children. Studies using animal models have shown that maternal distress can influence the development of the fetal nervous system and the function of the hypothalamic-pituitary-adrenal (HPA) axis, leading to long-term negative effects on the offspring’s learning, motor development, and behavior [1114]. Previous research has demonstrated that maternal psychological stress can lead to neuropsychological disorders in children, such as anxiety, depression, and attention deficit hyperactivity disorder [15,16]. On the other hand, epilepsy is a neurological complication whose association with prenatal stress has not yet been fully established. However, some reports have suggested that prenatal distress may alter the glutamatergic [17,18], gamma-aminobutyric acidergic [19,20], and adrenergic systems [21,22], which are closely related to seizures in epilepsy patients, through the HPA axis, indicating a potential link to the onset of epilepsy. Moreover, while the pathophysiology of epilepsy results from abnormalities in brain networks [23], the synthesis of glucocorticoids and release of cytokines induced by prenatal maternal stress influence the programming of the functional structure and connectivity of the offspring’s brain [2428]. Therefore, maternal stress during pregnancy may be directly or indirectly associated with the development of epilepsy in children.

In Japan, identified concerns include shortened maternal sleep duration during pregnancy [29] and prolonged working hours [30]. From the perspective of the fetal programming theory, these environmental factors are believed to exacerbate maternal stress and psychological burden, thus potentially intensifying the neurological impacts on the fetus. If prenatal stress is found to be associated with the onset of childhood epilepsy, then addressing and improving such environmental factors would become a necessity.

The Japan Environment and Children’s Study (JECS) is a nationwide government-funded birth cohort study conducted by the Japanese Ministry of the Environment. This extensive national study encompasses 100,000 sets of parents and children and is designed to explore the relationships between environmental factors and child development [31,32]. Within this vast dataset, various perinatal environmental factors, including maternal psychological stress, and outcome factors, including epilepsy, were recorded. We hypothesized that prenatal maternal stress influences the onset of epilepsy in children and aimed to investigate the hypothesis using the JECS data.

Material and methods

Design and participants

This study utilized the JECS dataset to retrospectively investigate the relationship between maternal prenatal psychological distress and epilepsy in children aged 1, 2, and 3 years.

The JECS methodology has been detailed in past publications [31,32]. The JECS recruited pregnant women from across the nation from January 2011 to March 2014. The study is ongoing, with plans to continue monitoring participants until the children reach the age of 13 years. As inclusion criteria, we utilized all 104,062 mother-child pairs from the jeсs-ta-20190930 dataset, which was released in October 2019 and revised in 2022. Cases involving abortion or stillbirth, as well as those with missing data on the six-item Kessler Psychological Distress Scale (K6), were excluded.

Maternal psychological distress

The JECS protocol was structured to facilitate the administration of K6 on two occasions during pregnancy: in the first (M-T1) and second (M-T2) halves of gestation [32]. The K6 is a self-administered psychological distress scale commonly utilized in population-based research and primary healthcare settings [3335]. The six items of the K6 (feeling nervous, hopeless, restless or fidgety, worthless, sad, and that everything is an effort) were grouped into depressive and anxiety symptoms. This was based on the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, reflecting the preceding four weeks on a scale of 0 to 4 [36]. Previous reports have demonstrated the efficacy of the K6 as a screening scale to detect depressive and anxiety disorders [33]. The total score is the sum of the six items and ranges from 0 to 24, with a cutoff ≥5 to identify cases of moderate-level psychological distress [37]. We used the Japanese version of the K6 with a cutoff ≥5, which has been used in previous studies involving the general population and specific communities in Japan [3840].

The progression of fetal development differs between the early and mid/late stages of pregnancy [41]. In the early stages, major organs and structures such as the brain, heart, and limbs develop. Subsequently, from the mid to late stages, myelination and the formation of brain grooves occur, establishing neural networks within the brain. Given that the impact of stress on the fetus may vary depending on the timing and the specific organs affected, we classified the participants into six groups based on their maternal K6 scores at M-T1 and M-T2: (1) K6 scores of ≥5 at M-T1; (2) K6 scores of ≥5 at M-T2; (3) K6 scores of ≤4 at M-T1 and M-T2; (4) K6 scores of ≥5 at M-T1 and ≤4 at M-T2; (5) K6 scores of ≤4 at M-T1 and ≥5 at M-T2; and (6) K6 scores of ≥5 at M-T1 and M-T2.

Outcome: Epilepsy among 1-, 2-, and 3-year-old children

Based on data obtained from the C-1-, -2-, and -3-year questionnaires (performed when the child was 1, 2, and 3 years of age, respectively), we estimated the incidence of epilepsy each year. In the self-reported questionnaire, mothers were asked, “Has your child been diagnosed with epilepsy by a physician?”. Children whose mothers answered “Yes” were defined as having a diagnosis of epilepsy.

Data collection of potential prognostic factors

We collected data about potential prognostic factors associated with neurodevelopment from the questionnaires, including maternal academic and social history [42,43], history of maternal neuropsychiatric disorders [44,45], psychoactive drug use during pregnancy [46], maternal epilepsy [47], maternal alcohol consumption during pregnancy [48], hypertensive disorders complicating pregnancy and labor abruption, premature birth of children, low birth weight, chromosome abnormalities, and nutrition at 1 month of age [5].

Statistical analyses and covariables

Data analysis was performed to determine the association between K6 scores of ≥5 and epilepsy among 1-, 2-, and 3-year-old children. First, we performed univariate logistic regression analyses to evaluate potential prognostic factors for K6 score variables individually. Subsequently, we performed multivariate logistic regression analyses to adjust potential confounding biases involving factors detected in the univariate analyses (p<0.05). Multicollinearity occurs when variables are highly correlated, making it difficult to precisely identify the extent to which each variable influences the dependent variable. Therefore, we further assessed multicollinearity through multivariate analysis. The variance inflation factor (VIF) was used to assess for multicollinearity in multivariate regression models. VIF>10 was defined as serious multicollinearity. Data analysis was performed using IBM SPSS Statistics version 25.0 (IBM Japan, Tokyo, Japan).

Ethical approval

This study was conducted according to the guidelines of the Declaration of Helsinki. The JECS protocol was reviewed and approved by the Ministry of the Environment’s Institutional Review Board on Epidemiological Studies (no. 100910001) and by the Ethics Committees of all participating institutions. Written informed consent was obtained from all participating mothers and fathers. Children for whom parental written consent could not be obtained were excluded from the study.

Results

Participants

Of the 104,062 entries in the dataset, 3,759 in which pregnancies with miscarriages or stillbirths occurred and 2,819 with missing K6 scale data for either the M-T1 or M-T2 were excluded. As a result, data from 97,484 children were analyzed (Fig 1). The demographic information of the participants is presented in Table 1. At M-T1, the maternal prenatal K6 score was determined at a median of 15.1 (interquartile range 12.3–18.9) weeks of gestation. At M-T2, the maternal prenatal K6 score was assessed at a median of 27.4 (interquartile range 25.3–30.1) weeks of gestation.

thumbnail
Fig 1. Flow chart showing the selection process of study participants.

https://doi.org/10.1371/journal.pone.0311666.g001

The means of K6 scores during M-T1 and the M-T2 were 3.6 and 3.5, respectively. The numbers (ratios) of the datasets in the six groups were as follows: (1) K6 scores ≥5 at M-T1: 31,075 mothers (31.9%); (2) K6 scores ≥5 at M-T2: 28,124 (28.8%); (3) K6 scores ≤4 at M-T1 and M-T2: 56,512 mothers (58.0%); (4) K6 scores ≥5 at M-T1 and ≤4 at M-T2: 12,848 (13.2%); (5) K6 scores ≤4 at M-T1 and ≥5 at M-T2: 9,897 (10.2%); and (6) K6 scores ≥5 at M-T1 and M-T2: 18,227 (18.7%). Children diagnosed with epilepsy at the ages of 1, 2, and 3 years were 89 (0.1%), 129 (0.2%), and 149 (0.2%), respectively.

Statistical results

Univariate analysis.

Table 2 presents the results of univariate logistic regression analyses for maternal K6 score and epilepsy of 1-, 2-, and 3-year-old children. The analyses revealed that a maternal K6 scores of ≥5 at M-T1 was not associated with epilepsy among 1- (odds ratio [OR]: 1.4, 95% confidence interval [CI], 0.92–2.2, p = 0.11), 2- (OR: 1.2, 95% CI, 0.85–1.8, p = 0.29) and 3-year-old children (OR: 1.1, 95% CI, 0.8–1.6, p = 0.47). A maternal K6 scores of ≥5 at M-T2 was not associated with epilepsy among 1- (OR: 1.5, 95% CI, 0.98–2.3, p = 0.063), 2- (OR: 1.3, 95% CI, 0.86–1.8, p = 0.24) and 3-year-old children (OR: 1.4, 95% CI, 0.95–1.9, p = 0.092). K6 scores ≤4 at M-T1 and M-T2 was not associated with epilepsy among 1- (OR: 0.73, 95% CI, 0.48–1.1, p = 0.13), 2- (OR: 0.97, 95% CI, 0.68–1.4, p = 0.86) and 3-year-old children (OR: 0.95, 95% CI, 0.68–1.3, p = 0.78). K6 scores ≥5 at M-T1 and ≤4 at M-T2 was not associated with epilepsy among 1- (OR: 0.92, 95% CI, 0.49–1.7, p = 0.79), 2- (OR: 0.68, 95% CI, 0.38–1.2, p = 0.21) and 3-year-old children (OR: 0.57, 95% CI, 0.31–1.1, p = 0.74). K6 scores ≤4 at M-T1 and ≥5 at M-T2 was not associated with epilepsy among 1- (OR: 1.01, 95% CI, 0.51–2.0, p = 0.97), 2- (OR: 0.61, 95% CI, 0.30–1.3, p = 0.18) and 3-year-old children (OR: 0.81, 95% CI, 0.44–1.5, p = 0.51). However, a maternal K6 score of ≥5 at both M-T1 and M-T2 was associated with epilepsy among 1- (OR: 1.7, 95% CI, 1.04–2.7, p = 0.033), 2- (OR: 1.6, 95% CI, 1.1–2.4, p = 0.017) and 3-year-old children (OR: 1.6, 95% CI, 1.1–2.4, p = 0.014).

thumbnail
Table 2. Association between the development of epilepsy in children at 1-, 2-, and 3-years of age and maternal K6 score based on univariate logistic regression analyses.

https://doi.org/10.1371/journal.pone.0311666.t002

Multivariate analyses.

Table 3 presents the results of the multivariate logistic regression analysis for a maternal K6 score of ≥5 at M-T1 and M-T2 and epilepsy of 1-, 2-, and 3-year-old children. As the covariates for the analysis, potential prognostic factors and a maternal K6 score of ≥5 at both M-T1 and M-T2, which were associated with epilepsy in the univariate logistic regression analysis (Table 2), were included. The analysis demonstrated that a maternal K6 score of ≥5 at both M-T1 and M-T2 was associated with epilepsy among 1- (OR: 1.7, 95% CI, 1.1–2.9, p = 0.030), 2- (OR: 1.7, 95% CI, 1.1–2.6, p = 0.012) and 3-year-old children (OR: 1.7, 95% CI, 1.1–2.6, p = 0.012). Moreover, birth weight <2500 g was associated with epilepsy in 1-year-olds (OR: 2.8, 95% CI, 1.4–4.7, p = 0.005), and nutrition with artificial milk at 1 month of age was associated with epilepsy in 2-year-olds (OR: 3.03, 95% CI, 1.2–7.6, p = 0.022). Additionally, chromosome abnormalities were associated with epilepsy in 1- (OR: 22.0, 95% CI, 7.3–79.2, p<0.001), 2- (OR: 16.7, 95% CI, 5.1–54.6, p<0.001), and 3-year-olds (OR: 11.0, 95% CI, 2.6–44.6, p = 0.001). Among epilepsy in 1-, 2- and 3-year-old children, the VIF of maternal academic history: junior high school, maternal alcohol consumption during pregnancy, household income during pregnancy: <4000 yen/year, maternal epilepsy, maternal neuropsychiatric disorder, psychoactive drug use during pregnancy, hypertensive disorders of pregnancy complication, labor abruption, gestation week: <37, birth weight of children: <2500 g, chromosome abnormalities, nutrition at 1 month of age: milk, and M-T1; K6 ≥ 5 and M-T2; were <10 respectively, indicating no multicollinearity among these variables.

thumbnail
Table 3. Association between the development of epilepsy in children at 1-, 2-, and 3-years of age and maternal K6 score based on multivariate logistic regression analyses.

https://doi.org/10.1371/journal.pone.0311666.t003

Discussion

Fetal programming theory posits that environmental factors, such as stress during pregnancy, can have long-term effects on fetal development [610]. Recent reports have increasingly indicated that prenatal stress can impact the central nervous system of the offspring in human study [27,40,49,50]. Consequently, we investigated the correlation between prenatal stress and the onset of epilepsy in offspring. The JECS was based on self-reported data from participants; however, the incidence of epilepsy up to age three was similar to data from the United Kingdom [4]. This suggests that our data is generally comprehensive for developed countries. As a result, this study demonstrated that continuous moderate-level maternal psychological distress from the first to the second half of pregnancy was associated with an increased risk of epilepsy among 1-, 2-, and 3-year-old children. Furthermore, nutrition with artificial milk, low birth weight, and chromosomal abnormalities were identified as risk factors.

Prenatal stress is associated with the onset of epilepsy in children. Our findings support previous reports indicating that prenatal stress increases the risk of neurological disorders in offspring and affirm the fetal programming theory [610,40,51]. Significant stress during pregnancy may potentially affect epileptogenesis through mechanisms such as changes in the DNA and histone methylation [52]. Moreover, both animal and human studies have revealed that prenatal exposure to maternal anxiety or depression is associated with changes in fetal brain structure and function, especially in the prefrontal cortex, hippocampus, and amygdala [2428,53,54]. Epilepsy is caused by the generation of abnormal networks, and certain types of epilepsy exhibit network abnormalities in these areas [23,5557]. Brain networks develop over several years after birth [58]. As such, these factors are associated with the development of epilepsy in children under 3 years of age.

Artificial milk nutrition has been associated with epilepsy in 2-year-olds. Previous reports indicate that breastfeeding may prevent the onset of epilepsy in children [5,59], and our findings are consistent with these reports. Breast milk contains various bioactive agents required for optimal infant brain development, such as arachidonic acid (AA) and docosahexaenoic acid (DHA) [60]. During the last trimester and neonatal period, brain tissue is rapidly synthesized, and cell differentiation and development of active synapses in the brain have specific requirements for DHA and AA [60]. The literature recommends the addition of AA and DHA to regular artificial milk as their levels do not meet those found in breast milk [61]. As such, the deficiency of essential components necessary for normal neurodevelopment in artificial milk may have resulted in an increased risk of developing epilepsy.

Low birth weight has been found to be associated with epilepsy in 1-year-olds, as previously reported [62]. The birth weight of children with epilepsy is lower than that of healthy controls [63]. Low birth weight is associated with brain structure abnormalities, including ventricular dilation, smaller brain volume, reduced cortical surface area, regional cortical thinning, and white matter abnormalities [64]. Low birth weight is also associated with network abnormalities in the hippocampus and amygdala [65]. Abnormal brain structure and function resulting from low birth weight have been considered to be associated with the onset of epilepsy.

Chromosome abnormalities were also found to be associated with epilepsy in 1-, 2-, and 3-year-olds. Approximately 400 chromosomal imbalances associated with seizures or EEG abnormalities have been reported [66]. Some chromosomal disorders are strongly associated with the onset of epilepsy throughout childhood [66,67]. However, due to the lack of detailed chromosomal information, we were unable to determine which specific chromosomal abnormalities contributed to the onset of childhood epilepsy in this study.

Preventing the onset of epilepsy is necessary as this condition is strongly associated with psychological and social problems in everyday life [2]. Concerning artificial milk nutrition and low birth weight, nutrition guidance during pregnancy, avoidance of smoking, and recommendation of breastfeeding may be effective [60,68]. Moreover, relaxation therapies, including yoga, music, Benson therapy, progressive muscle relaxation, deep breathing relaxation, guided imagery, mindfulness, and hypnosis have been shown to reduce maternal stress and anxiety and alleviate depressive symptoms [6971]. As such, relaxation interventions for pregnant women may also be effective in preventing the onset of epilepsy in offspring. Furthermore, in Japan, interventions to dissolve the problems in sleep quality [72] and workplace environment [73] during pregnancy may be particularly important to alleviate stress in pregnant women.

This study has some limitations. First, we could not conduct a detailed evaluation of the types of epilepsy in the affected offspring. Moreover, the diagnosis of epilepsy was based on responses from parents via questionnaires, and cases where there was a lack of understanding of the diagnosis may not be included in the aggregate data. The detailed classification of epilepsy by medical doctors rather than non-medical personnel will be necessary in future research. Second, the presence of confounding factors could not be ruled out completely. We have confirmed that there was no confounding among the potential factors in this study based on the VIF analysis. However, other potential confounding factors, such as genetic or chromosomal abnormalities associated with both epilepsy and stress [66,74], as well as paternal mental status during pregnancy [75], require further investigation in future studies. Thirdly, it is crucial to acknowledge the limitations of the K6. The K6 assessment is based on the past 30 days and might not reflect the overall stress experienced during the first half and second half of pregnancy [33,37,76]. Additionally, it does not provide specific information on the types of stress or psychological disorders that the mother may have encountered [77]. Moreover, data collection on non-psychological stress factors during early pregnancy has been limited, preventing a comprehensive evaluation.

In conclusion, the present study found that continuous moderate-level maternal psychological distress from the first to the second half of pregnancy was associated with epilepsy among 1-, 2-, and 3-year-old children. As previously reported, additional risk factors include artificial milk nutrition, low birth weight, and chromosomal abnormalities. Therefore, environmental adjustments to promote relaxation in pregnant women are needed to prevent the development of epilepsy in their offspring.

Acknowledgments

The authors are grateful to all study participants and all participants of the JECS. We also thank the following members of the JECS Group as of 2023: Michihiro Kamijima (principal investigator, Nagoya City University, Nagoya, Japan), Shin Yamazaki (National Institute for Environmental Studies, Tsukuba, Japan), Yukihiro Ohya (National Center for Child Health and Development, Tokyo, Japan), Reiko Kishi (Hokkaido University, Sapporo, Japan), Nobuo Yaegashi (Tohoku University, Sendai, Japan), Koichi Hashimoto (Fukushima Medical University, Fukushima, Japan), Chisato Mori (Chiba University, Chiba, Japan), Shuichi Ito (Yokohama City University, Yokohama, Japan), Zentaro Yamagata (University of Yamanashi, Chuo, Japan), Hidekuni Inadera (University of Toyama, Toyama, Japan), Takeo Nakayama (Kyoto University, Kyoto, Japan), Tomotaka Sobue (Osaka University, Suita, Japan), Masayuki Shima (Hyogo Medical University, Nishinomiya, Japan), Seiji Kageyama (Tottori University, Yonago, Japan), Narufumi Suganuma (Kochi University, Nankoku, Japan), Shoichi Ohga (Kyushu University, Fukuoka, Japan), and Takahiko Katoh (Kumamoto University, Kumamoto, Japan). We thank Editage for the English editing of the manuscript.

References

  1. 1. Thurman DJ, Beghi E, Begley CE, Berg AT, Buchhalter JR, Ding D, et al. Standards for epidemiologic studies and surveillance of epilepsy. Epilepsia. 2011;52(Suppl 7):2–26. pmid:21899536
  2. 2. Quintas R, Raggi A, Giovannetti AM, Pagani M, Sabariego C, Cieza A, et al. Psychosocial difficulties in people with epilepsy: a systematic review of literature from 2005 until 2010. Epilepsy Behav. 2012;25: 60–67. pmid:22749606
  3. 3. Wallace S. Epilepsy in children. London: Chapman & Hall Medical, 1996.
  4. 4. Symonds JD, Elliott KS, Shetty J, Armstrong M, Brunklaus A, Cutcutache I, et al. Early childhood epilepsies: epidemiology, classification, aetiology, and socio-economic determinants. Brain. 2021;144:2879–2891. pmid:34687210
  5. 5. Whitehead E, Dodds L, Joseph KS, Gordon KE, Wood E, Allen AC, et al. Relation of pregnancy and neonatal factors to subsequent development of childhood epilepsy: a population-based cohort study. Pediatrics. 2006;117: 1298–1306. pmid:16585327
  6. 6. Gluckman PD, Hanson MA, Cooper C, Thornburg KL. Effect of in utero and early-life conditions on adult health and disease. N Engl J Med. 2008;359: 61–73. pmid:18596274
  7. 7. Hanson M, Gluckman P. Developmental origins of noncommunicable disease: population and public health implications. Am J Clin Nutr. 2011;94(6 Suppl): 1754S–8S. pmid:21525196
  8. 8. Seckl JR, Holmes MC. Mechanisms of disease: glucocorticoids, their placental metabolism and fetal ‘programming’ of adult pathophysiology. Nat Clin Pract Endocrinol Metab. 2007;3: 479–488. pmid:17515892
  9. 9. Van den Bergh BRH. Developmental programming of early brain and behaviour development and mental health: a conceptual framework. Dev Med Child Neurol. 2011;53(Suppl 4): 19–23. pmid:21950389
  10. 10. Dunkel Schetter C, Tanner L. Anxiety, depression and stress in pregnancy: implications for mothers, children, research, and practice. Curr Opin Psychiatry. 2012;25:141–8. pmid:22262028
  11. 11. Matthews SG. Antenatal glucocorticoids and programming of the developing CNS. Pediatr Res. 2000;47: 291–300. pmid:10709726
  12. 12. Matthews SG. Early programming of the hypothalamo-pituitary-adrenal axis. Trends Endocrinol Metab. 2002;13: 373–380. pmid:12367818
  13. 13. Schwab M, Antonow-Schlorke I, Kühn B, Müller T, Schubert H, Walter B, et al. Effect of antenatal betamethasone treatment on microtubule-associated proteins MAP1B and MAP2 in fetal sheep. J Physiol. 2001;530: 497–506. pmid:11158279
  14. 14. Wyrwoll CS, Holmes MC. Prenatal excess glucocorticoid exposure and adult affective disorders: a role for serotonergic and catecholamine pathways. Neuroendocrinology. 2012;95: 47–55. pmid:22042385
  15. 15. Van den Bergh BR, Marcoen A. High antenatal maternal anxiety is related to ADHD symptoms, externalizing problems, and anxiety in 8- and 9-year-olds. Child Dev. 2004;75:1085–97. pmid:15260866
  16. 16. O’Connor TG, Heron J, Golding J, Glover V; ALSPAC Study Team. Maternal antenatal anxiety and behavioural/emotional problems in children: a test of a programming hypothesis. J Child Psychol Psychiatry. 2003;44:1025–36. pmid:14531585
  17. 17. Berger MA, Barros VG, Sarchi MI, Tarazi FI, Antonelli MC. Long-term effects of prenatal stress on dopamine and glutamate receptors in adult rat brain. Neurochem Res 2002;27:1525–33.
  18. 18. Son GH, Geum D, Chung S, Kim EJ, Jo JH, Kim CM, et al. Maternal stress produces learning deficits associated with impairment of NMDA receptor-mediated synaptic plasticity. J Neurosci 2006;26:3309–18. pmid:16554481
  19. 19. Nejatbakhsh M, Saboory E, Bagheri M. Effect of prenatal stress on ɑ5 GABAA receptor subunit gene expression in hippocampus and pilocarpine induced seizure in rats. Int J Dev Neurosci 2018;68:66–71. pmid:29758348
  20. 20. Stell BM, Brickley SG, Tang C, Farrant M, Mody I. Neuroactive steroids reduce neuronal excitability by selectively enhancing tonic inhibition mediated by δ subunitcontaining GABAA receptors. Proc Natl Acad Sci 2003;100:14439–44.
  21. 21. Fitzgerald PJ. Is elevated norepinephrine an etiological factor in some cases of epilepsy? Seizure 2010;19:311–8. pmid:20493725
  22. 22. Piquer B, Fonseca JL, Lara HE. Gestational stress, placental norepinephrine transporter and offspring fertility. Reproduction 2017;153:147–55. pmid:27815561
  23. 23. Gleichgerrcht E, Kocher M, Bonilha L. Connectomics and graph theory analyses: novel insights into network abnormalities in epilepsy. Epilepsia. 2015;56: 1660–1668. pmid:26391203
  24. 24. Entringer S. Impact of stress and stress physiology during pregnancy on child metabolic function and obesity risk. Curr Opin Clin Nutr Metab Care. 2013;16: 320–327. pmid:23400210
  25. 25. Soe NN, Wen DJ, Poh JS, Li Y, Broekman BF, Chen H, et al. Pre- and post-natal maternal depressive symptoms in relation with infant frontal function, connectivity, and behaviors. PLoS One. 2016;11: e0152991. pmid:27073881
  26. 26. Qiu A, Anh TT, Li Y, Chen H, Rifkin-Graboi A, Broekman BF, et al. Prenatal maternal depression alters amygdala functional connectivity in 6-month-old infants. Transl Psychiatry. 2015;53: e508. pmid:25689569
  27. 27. Scheinost D, Kwon SH, Lacadie C, Sze G, Sinha R, Constable RT, et al. Prenatal stress alters amygdala functional connectivity in preterm neonates. Neuroimage Clin. 2016;12: 381–388. pmid:27622134
  28. 28. Posner J, Cha J, Roy AK, Peterson BS, Bansal R, Gustafsson HC, et al. Alterations in amygdala–prefrontal circuits in infants exposed to prenatal maternal depression. Transl Psychiatry. 2016;6: e935. pmid:27801896
  29. 29. Kawakami SI, Ikegami A, Komada Y. Sleep habits and problems across gestational progress in Japanese women. J Obstet Gynaecol Res 2023;49:1137–1143. pmid:36746646
  30. 30. Suzumori N, Ebara T, Matsuki T, Yamada Y, Kato S, Omori T, et al. Effects of long working hours and shift work during pregnancy on obstetric and perinatal outcomes: A large prospective cohort study-Japan Environment and Children’s Study. Birth 2020;47:67–79. pmid:31667913
  31. 31. Kawamoto T, Nitta H, Murata K, Toda E, Tsukamoto N, Hasegawa M, et al. Rationale and study design of the Japan environment and children’s study (JECS). BMC Public Health. 2014;14: 25. pmid:24410977
  32. 32. Michikawa T, Nitta H, Nakayama SF, Yamazaki S, Isobe T, Tamura K, et al. Baseline profile of participants in the Japan Environment and Children’s Study (JECS). J Epidemiol. 2018;28: 99–104. pmid:29093304
  33. 33. Kessler RC, Barker PR, Colpe LJ, Epstein JF, Gfroerer JC, Hiripi E, et al. Screening for serious mental illness in the general population. Arch Gen Psychiatry. 2003;60:184–189. pmid:12578436
  34. 34. Patel V, Araya R, Chowdhary N, King M, Kirkwood B, Nayak S, et al. Detecting common mental disorders in primary care in India: a comparison of five screening questionnaires. Psychol Med. 2008;38: 221–228. pmid:18047768
  35. 35. Chan SM, Fung TCT. Reliability and validity of K10 and K6 in screening depressive symptoms in Hong Kong adolescents. Vulnerable Child Youth Stud. 2014;9: 75–85.
  36. 36. Drapeau A, Marchand A, Beaulieu-Prévost D. Epidemiology of psychological distress. In: Mental illnesses: Understanding, prediction and control. InTech, 2012;105–134. Available from:
  37. 37. Prochaska JJ, Sung HY, Max W, Shi Y, Ong M. Validity study of the K6 scale as a measure of moderate mental distress based on mental health treatment need and utilization. Int J Methods Psychiatr Res. 2012;21: 88–97. pmid:22351472
  38. 38. Sakurai K, Nishi A, Kondo K, Yanagida K, Kawakami N. Screening performance of K6/K10 and other screening instruments for mood and anxiety disorders in Japan. Psychiatry Clin Neurosci. 2011;65: 434–441. pmid:21851452
  39. 39. Kuroda Y, Goto A, Koyama Y, Hosoya M, Fujimori K, Yasumura S, et al. Antenatal and postnatal association of maternal bonding and mental health in Fukushima after the Great East Japan Earthquake of 2011: the Japan Environment and Children’s Study (JECS). J Affect Disord. 2021;278: 244–251. pmid:32971317
  40. 40. Nishigori T, Hashimoto K, Mori M, Suzuki T, Watanabe M, Imaizumi K, et al. Association between maternal prenatal psychological distress and autism spectrum disorder among 3-year-old children: the Japan Environment and Children’s Study. J Dev Orig Health Dis. 2023;14: 70–76. pmid:35801288
  41. 41. O’Rahilly R, Müller F. Developmental stages in human embryos: revised and new measurements. Cells Tissues Organs. 2010;192:73–84. pmid:20185898
  42. 42. Saurel-Cubizolles MJ, Marchand-Martin L, Pierrat V, Arnaud C, Burguet A, Fresson J, et al. Maternal employment and socio-economic status of families raising children born very preterm with motor or cognitive impairments: the EPIPAGE cohort study. Dev Med Child Neurol. 2020;62: 1182–1190. pmid:32557556
  43. 43. Joseph RM , O’Shea TM, Allred EN, Heeren T, Kuban KK. Maternal educational status at birth, maternal educational advancement, and neurocognitive outcomes at age 10 years among children born extremely preterm. Pediatr Res. 2018;83: 767–777. pmid:29072866
  44. 44. Vigod SN, Fung K, Amartey A, Bartsch E, Felemban R, Saunders N, et al. Maternal schizophrenia and adverse birth outcomes: what mediates the risk? Soc Psychiatry Psychiatr Epidemiol. 2020;55: 561–570. pmid:31811316
  45. 45. Hizkiyahu R, Levy A, Sheiner E. Pregnancy outcome of patients with schizophrenia. Am J Perinatol. 2010;27: 19–23. pmid:19565434
  46. 46. Habibi M, Hart F, Bainbridge J. The impact of psychoactive drugs on seizures and antiepileptic drugs. Curr Neurol Neurosci Rep. 2016;16: 71. pmid:27315249
  47. 47. Ottman R, Annegers JF, Hauser WA, Kurland LT. Higher risk of seizures in offspring of mothers than of fathers with epilepsy. Am J Hum Genet. 1988;43:257–64. pmid:3414683
  48. 48. Nicita F, Verrotti A, Pruna D, Striano P, Capovilla G, Savasta S, et al. Seizures in fetal alcohol spectrum disorders: evaluation of clinical, electroencephalographic, and neuroradiologic features in a pediatric case series. Epilepsia. 2014;55:e60–6. pmid:24815902
  49. 49. Wu Y, Lu YC, Jacobs M, Pradhan S, Kapse K, Zhao L, et al. Association of prenatal maternal psychological distress with fetal brain growth, metabolism, and cortical maturation. JAMA Netw Open. 2020;3: e1919940. pmid:31995213
  50. 50. Glover V. Maternal depression, anxiety and stress during pregnancy and child outcome; what needs to be done. Best Pract Res Clin Obstet Gynaecol. 2014;28: 25–35. pmid:24090740
  51. 51. Graignic-Philippe R, Dayan J, Chokron S, Jacquet AY, Tordjman S. Effects of prenatal stress on fetal and child development: a critical literature review. Neurosci Biobehav Rev. 2014;43:137–62. pmid:24747487
  52. 52. Saboory E, Mohammadi S, Dindarian S, Mohammadi H. Prenatal stress and elevated seizure susceptibility: Molecular inheritable changes. Epilepsy Behav. 2019;96:122–131. pmid:31132613
  53. 53. Wu Y, Lu YC, Jacobs M, Pradhan S, Kapse K, Zhao L, et al. Association of prenatal maternal psychological distress with fetal brain growth, metabolism, and cortical maturation. JAMA Netw Open. 2020;3: e1919940. pmid:31995213
  54. 54. Weinstock M. Sex-dependent changes induced by prenatal stress in cortical and hippocampal morphology and behaviour in rats: an update. Stress. 2011;14: 604–613. pmid:21790452
  55. 55. Vlooswijk MC, Jansen JF, Jeukens CR, Majoie HJ, Hofman PA, de Krom MC, et al. Memory processes and prefrontal network dysfunction in cryptogenic epilepsy. Epilepsia. 2011;52: 1467–1475. pmid:21635235
  56. 56. Milton CK O’Neal CM, Conner AK. Functional connectivity of hippocampus in temporal lobe epilepsy depends on hippocampal dominance: a systematic review of the literature. J Neurol. 2022;269: 221–232. pmid:33564915
  57. 57. Doucet GE, Skidmore C, Sharan AD, Sperling MR, Tracy JI. Functional connectivity abnormalities vary by amygdala subdivision and are associated with psychiatric symptoms in unilateral temporal epilepsy. Brain Cogn. 2013;83: 171–182. pmid:24036129
  58. 58. Gao W, Alcauter S, Smith JK, Gilmore JH, Lin W. Development of human brain cortical network architecture during infancy. Brain Struct Funct. 2015;220: 1173–1186. pmid:24469153
  59. 59. Sun Y, Vestergaard M, Christensen J, Olsen J. Breastfeeding and risk of epilepsy in childhood: a birth cohort study. J Pediatr. 2011;158:924–9. pmid:21232762
  60. 60. Martin CR, Ling PR, Blackburn GL. Review of infant feeding: key features of breast milk and infant formula. Nutrients. 2016;8: 279. pmid:27187450
  61. 61. Lien EL, Richard C, Hoffman DR. DHA and ARA addition to infant formula: Current status and future research directions. Prostaglandins Leukot Essent Fatty Acids. 2018;128: 26–40. pmid:29413359
  62. 62. Fukao T, Sano F, Nemoto A, Naito A, Yanagisawa T, Imai K, et al. Factors associated with the development of epilepsy in very low birth weight infants. Pediatr Neonatol. 2023;28: S1875-9572(23)00072-4. pmid:37117074
  63. 63. Jackson DC, Lin JJ, Chambers KL, Kessler-Jones A, Jones JE, Hsu DA, et al. Birth weight and cognition in children with epilepsy. Epilepsia. 2014;55: 901–908. pmid:24735169
  64. 64. Farajdokht F, Sadigh-Eteghad S, Dehghani R, Mohaddes G, Abedi L, Bughchechi R, et al. Very low birth weight is associated with brain structure abnormalities and cognitive function impairments: a systematic review. Brain Cogn. 2017;118: 80–89. pmid:28802183
  65. 65. Hayward DA, Pomares F, Casey KF, Ismaylova E, Levesque M, Greenlaw K, et al. Birth weight is associated with adolescent brain development: a multimodal imaging study in monozygotic twins. Hum Brain Mapp. 2020;41: 5228–5239. pmid:32881198
  66. 66. Singh R, Gardner RJ, Crossland KM, Scheffer IE, Berkovic SF. Chromosomal abnormalities and epilepsy: a review for clinicians and gene hunters. Epilepsia. 2002;43: 127–140. pmid:11903458
  67. 67. Battaglia A, Guerrini R. Chromosomal disorders associated with epilepsy. Epileptic Disord. 2005;7:181–92. pmid:16162426
  68. 68. Sema A, Tesfaye F, Belay Y, Amsalu B, Bekele D, Desalew A. Associated factors with low birth weight in Dire Dawa city, Eastern Ethiopia: a cross-sectional study. Biomed Res Int. 2019;2019: 2965094. pmid:31886197
  69. 69. Glover V. Maternal depression, anxiety and stress during pregnancy and child outcome; what needs to be done. Best Pract Res Clin Obstet Gynaecol. 2014;28: 25–35. pmid:24090740
  70. 70. Corbijn van Willenswaard K, Lynn F, McNeill J, McQueen K, Dennis CL, Lobel M, et al. Music interventions to reduce stress and anxiety in pregnancy: a systematic review and meta-analysis. BMC Psychiatry. 2017;17: 271. pmid:28750631
  71. 71. Abera M, Hanlon C, Daniel B, Tesfaye M, Workicho A, Girma T, et al. Effects of relaxation interventions during pregnancy on maternal mental health, and pregnancy and newborn outcomes: A systematic review and meta-analysis. PLoS One 2024;19:e0278432. pmid:38271440
  72. 72. Bacaro V, Benz F, Pappaccogli A, De Bartolo P, Johann AF, Palagini L, et al. Interventions for sleep problems during pregnancy: A systematic review. Sleep Med Rev. 2020;50:101234. pmid:31801099
  73. 73. Andersen DR, Momsen AH, Pedersen P, Maimburg RD. Reflections on workplace adjustments for pregnant employees: a qualitative study of the experiences of pregnant employees and their managers. BMC Pregnancy Childbirth. 2022;22:456. pmid:35650542
  74. 74. Jhaveri DJ, McGonigal A, Becker C, Benoliel JJ, Nandam LS, Soncin L, et al. Stress and Epilepsy: Towards Understanding of Neurobiological Mechanisms for Better Management. eNeuro. 2023;10:ENEURO.0200-23.2023. pmid:37923391
  75. 75. Ashraf S, Shah K, Vadukapuram R, Shah B, Jaiswal S, Mansuri Z, et al.Impact of Paternal Depression on Child Neurodevelopmental Outcomes and Disorders. Prim Care Companion CNS Disord. 2023;25:22r03303. pmid:36763820
  76. 76. Tokuda N, Kobayashi Y, Tanaka H, Sawai H, Shibahara H, Takeshima Y, et al. Feelings about pregnancy and mother-infant bonding as predictors of persistent psychological distress in the perinatal period: The Japan Environment and Children’s Study. J Psychiatr Res. 2021;140:132–140. pmid:34116439
  77. 77. Dunkel Schetter C, Tanner L. Anxiety, depression and stress in pregnancy: implications for mothers, children, research, and practice. Curr Opin Psychiatry. 2012;25:141–8. pmid:22262028