Abstract
Postpartum Depression (PPD) and Childbirth Post-Traumatic Stress Disorder (CB-PTSD) are psychiatric conditions that cause significant distress. Yet despite their high prevalence and decades of research, knowledge about causal cognitive mechanisms that may assist in predicting or preventing these conditions is still missing. One characteristic of PPD and CB-PTSD that may contribute to their early prevention is the existence of cognitive biases concerning future parenting. Cognitive biases have been shown to play an important role in the etiology of various other psychiatric disorders, including depression and PTSD, suggesting they might have a similar role in PPD and CB-PTSD. From a theoretical perspective, understanding the associations between cognitive biases, PPD, and CB-PTSD may lead to novel theoretical models and research avenues. Additionally, understanding the cognitive mechanisms underlying PPD and CB-PTSD has several important clinical implications, such as early detection, preventative care, and developing individually tailored cognitive therapies focusing on these specific biases.
Introduction
Approximately 10–20% of women experience Postpartum Depression (PPD)* or Childbirth-Related Post-Traumatic Stress Disorder (CB-PTSD) after giving birth* (1, 2). Both conditions are associated with significant psychological, health, and familial challenges (3, 4) including child neglect and, in extreme cases, maternal suicide or infanticide (4–6). Women with high-risk pregnancies are more vulnerable (7, 8). Moreover, 8% to 25% of fathers or partners also experience PPD symptoms (9). Beyond the individual suffering of mothers123+; and partners, PPD and CB-PTSD are associated with problems in attachment between mothers and their newborns (10, 11). Children of mothers with PPD are also more likely to exhibit psychological and cognitive-emotional difficulties during adolescence (12).
This paper proposes that cognitive biases may serve as early markers, or warning signs, for the potential development of PPD and CB-PTSD. Identifying such markers could contribute to the prevention of these disorders. To support this proposal, the paper provides a brief review of relevant literature, explores the role of cognitive biases as early indicators, and outlines directions for future research.
Postpartum depression and CB-PTSD: comorbidity, prevalence, and symptoms
PPD is categorized as a mood disorder and a subtype of major depressive disorder (MDD), with symptoms typically emerging within the first four weeks after childbirth (1, 13). Common symptoms include depressed mood, fatigue, anhedonia, low self-esteem, difficulty concentrating, loneliness, irritability, anxiety, feelings of guilt, hopelessness, and reduced self-confidence (14). The prevalence of PPD ranges from 10% to 15% in the general postpartum population (6, 15) and rises to 27%–44% among women with high-risk pregnancies (8). A history of depression is one of the most robust predictors of PPD (16). Other risk factors include low social support (17), socioeconomic disadvantage, unplanned pregnancy (1), poor sleep quality (18), perceived low quality of life (19), older maternal age, low relationship satisfaction (20), excessive worry about motherhood during pregnancy (21), acute pain following childbirth (22), self-reported pain during pregnancy (23) and “maternity blues”, a temporary condition experienced during the first days postpartum, may increase the likelihood of developing PPD (24, 25).
Several biological mechanisms have been implicated in the development of PPD. Lower levels of salivary cortisol have been observed in mothers, fathers, and infants within families affected by PPD (26). Mothers with PPD are more likely to exhibit lower levels of the GG genotype versus the A-carrying variant of the oxytocin receptor gene, a hormone associated with affective touch and lower empathic engagement with their babies (27). Gonadal steroid fluctuations also appear to play a significant role in the onset of postpartum mood disturbances (28).
Another condition during the postpartum period is CB-PTSD, classified within the category of trauma- and stress-related disorders, and follows the same diagnostic criteria as PTSD, when the traumatic event must be the childbirth experience (1, 13). According to DSM-V (13), PTSD symptoms include intrusive re-experiencing of the event, avoidance of reminders or places associated with the trauma, and heightened physiological arousal (29). In the context of CB-PTSD, affected individuals may experience distressing and intrusive memories or nightmares about childbirth, irritability, hypervigilance, sleep disturbances, difficulty concentrating, emotional withdrawal, and feelings of guilt. They may also avoid discussing or thinking about the labor or birth experience (30–32).
The prevalence of CB-PTSD in community samples ranges from 1% to 6% (33), with significantly higher rates among women who experienced high-risk pregnancies, 9.9% for those with clinically significant symptoms, and up to 11.9% for those with subclinical symptoms (34). Several psychological and social risk factors have been identified, including perceiving the birth experience as traumatic or negative, intense fears for one’s own health or the baby’s health during labor (2), and a history of childhood sexual abuse or domestic violence (1).
Additional physiological and obstetric variables have been linked to the development of CB-PTSD. These include prolonged labor (lasting more than six hours), postpartum hemorrhage (35), and severe perineal tearing (36). Women who experienced hyperemesis gravidarum reported significantly higher CB-PTSD symptom levels compared to those with either no or milder forms of pregnancy-related nausea, with effects lasting up to two years postpartum (37). Despite this evidence, results are mixed, and other studies found no significant associations between CB-PTSD and obstetric factors such as type or duration of labor, severity of hemorrhage, low APGAR scores, or breastfeeding challenges (38).
Research consistently demonstrates a strong relationship between PPD and CB-PTSD, including shared risk factors and high comorbidity (1, 39–41). Evidence further shows that PPD and CB-PTSD are associated with postpartum anxiety (15, 42, 43) and with maternal bonding and attachment (44).
Preventive treatment of postpartum depression and CB-PTSD
Similar to other psychiatric disorders, there is growing medical and scientific interest in shifting toward preventive care and early detection of PPD and CB-PTSD, particularly by identifying behavioral and physiological markers (45, 46). Currently, both conditions are often diagnosed only after symptoms have become pronounced. Although maternal depression screening is recommended during routine pediatric care, many women underreport symptoms during pediatric visits (47). This underreporting can result in missed referrals, especially when relying on current screening thresholds (47). The delay in diagnosis is often influenced by sociocultural pressures, such as the desire to appear as a “good” mother, internalized stigma around mental health (47), guilt or shame about not feeling joy after childbirth, and societal expectations of motherhood (40).
Compounding this issue, research has identified multiple sociodemographic, physiological, psychological, environmental, and pregnancy-related risk factors for PPD and CB-PTSD (48, 49), such as older age, lack of sleep, psychiatric history, high anxiety, and lack of social support (1, 18, 20, 39, 40). While each self-report survey may be a quick and cost-effective screening tool, the implementation of these questionnaires lacks standardized protocols. Comprehensive screening requires multiple questionnaires, adequate clinical training, and structured referral pathways, elements frequently missing in routine care (47, 48, 50, 51). Moreover, CB-PTSD is often overlooked during postpartum screening, due to its symptom overlap with PPD and its lower public and clinical visibility (39). To address these gaps, researchers recommend the development and validation of comprehensive prenatal screening tools that capture a broader range of risk factors, while also improving objectivity and medical relevance (47, 48, 51).
These obstacles underscore the urgency of identifying early behavioral markers that can support timely and targeted interventions. In this context, cognitive biases hold promise as early indicators, not only to enhance early detection, but also to guide preventive cognitive interventions in pregnant individuals at increased risk for PPD and CB-PTSD.
Cognitive biases associated with postpartum depression and CB-PTSD
Cognitive biases refer to systematic distortions in the processing of emotional information. These can include biased attention toward threatening stimuli, distorted interpretation of ambiguous situations, altered memory for negative events, or negative expectations about the future (52, 53). Such biases are known to contribute to the onset and maintenance of various psychiatric conditions (54), including depression (55, 56), anxiety disorders (56, 57), phobias (58, 59), PTSD (60, 61), and eating disorders (62).
Accumulating research demonstrates that specific cognitive biases characterize PPD and CB-PTSD (Table 1). For example, mothers with PPD show greater attention bias toward images of infants compared to adults (63–65). Depressed pregnant women also disengage more quickly from distressed infant faces than from non-distressed ones, a pattern not observed in non-depressed women (64, 66). Notably, this tendency to stronger attentional bias towards distressed infant faces was associated with more positive mother–infant bonding reported after birth (66).
Table 1
| Author, year, and country. | Tasks | Population | Time of assessment |
Main finding |
|---|---|---|---|---|
| Gil et al. (2011), France (63). | Emotion recognition of infants' and adults' faces: -The emotional facial expression task |
Postpartum: Sub-clinical PPD and healthy women | Postpartum: 3 days after delivery. |
|
| Flanagan et al. (2011), USA. (65) |
Emotion recognition in adults. 1. Ekman 60 Faces Test: recognizing prototypical facial expressions. 2. Emotion Hexagon Task |
Postpartum: PPD MDD Healthy non-mothers. |
Up to one month after childbirth |
|
| Pearson et al. (2010), UK (64). | Attention and inhibition bias measures of Infants' and adults' emotional pictures: -Go/No-Go task. |
Prepartum: with and without depressive symptoms. | Up to 12 weeks of pregnancy |
|
| Pearson et al. (2011), UK (66). | Attention and inhibition bias measures of infants’ emotional pictures: -Go/No-Go task, prepartum depression, and bonding with the newborn after delivery. |
Prepartum: with and without depressive symptoms. | Late pregnancy (range 34–39 weeks). |
|
| Webb & Ayers (2015), UK (67). | Systematic review for cognitive biases and peripartum, related to PPD and CB-PTSD. | Not apply | Not apply |
|
| Arteche et al. (2011), USA (69). | Emotion recognition in infants through the identification of babies' facial emotions. | Postpartum: PPD, GAD, Healthy non-mothers. | Postpartum: between 10 and 18 months after delivery |
|
| Bloch (2022), Israel (28) | Emotional cognitive measures through: 1. Self−referential encoding and incidental recall task (SRET). 2. Dot−probe task (DPT). 3. Emotional Stroop. |
Postpartum: with and without history of PPD. | Postpartum: at least one year after delivery. Two assessments: once during the follicular phase (days 10-12) and once during the luteal phase (days 24-26). |
|
| Bjertrup et al. (2021), Denmark (68). | Emotion recognition and emotion intensity ratings of infant faces. 1. The facial expression recognition task (FERT) 2. Ratings of Emotional Infant Stimuli 3. The Screen for Cognitive Impairment in Psychiatry (SCIP) |
Prepartum: MDD, Bipolar Depression, Healthy Pregnant, Healthy non-pregnant women. Postpartum: Sub-clinical PPD symptoms | Prepartum: 27-40 weeks of pregnancy Postpartum 1: 2–25 days after birth. Postpartum 2: 10–36 months after birth. |
|
| Bjertrup et al. (2023), Denmark (72) | Emotion recognition of infants' sounds.
|
Pregnant: Healthy Postpartum: PPD and Healthy |
Prepartum: second and third trimesters of pregnancy. |
|
| Edvinsson et al. (2017), Sweden (71). | Emotional attention bias in pregnancy: -The emotional Stroop task, presenting emotionally charged delivery-related words |
Prepartum: MDD (with and without comorbid anxiety) Healthy Postpartum: PPD (with and without comorbid anxiety) Healthy | Prepartum: pregnancy weeks 35–39 |
|
| Dale-Hewitt et al. (2012), UK (73). |
Emotional attention bias in postpartum women with CB-PTSD symptoms: -The emotional Stroop task, presenting emotionally charged delivery-related words |
Postpartum: women with CB-PTSD symptoms (PTSD-Q and IES). | Postpartum: Between 1.5 and 6 months after delivery. |
|
| Hampson et al. (2015), Canada (86). | Working memory (WM) tasks related to hormones: Neuropsychological tests: -Spatial working memory -Self-Ordered Pointing -Paragraph Recall -Corsi Blocks -Mooney–Harshman Closure Six hormones: (progesterone, estriol, estradiol, testosterone, cortisol, dehydroepiandrosterone) |
Prepartum: Pregnant (third trimester) and non-pregnant women. Postpartum: two groups of pregnant women and non-pregnant women, within the 4 months after delivery. With and without MDD or PPD. |
Pregnant women: the first visit between 34 and 38 weeks of gestation and the second postpartum, 3 to 4 months later. Postpartum control group: the first visit 4 to 12 weeks after delivery, and the second visit 3 to 4 months later. |
|
| Pio de Almeida et al. (2012), Brazil (17). |
Recall and working memory tasks in postpartum women and men. Neuropsychological test: word span test, to evaluate: -Working (immediate) -Short-term (recalled) memories. |
Postpartum: Women and men (parents). | Does not specify: live births, born from March to December 2008. |
|
| Messinis et al. (2010), Greece (87). | Multiple cognitive tasks to evaluate: -Cognitive attention and psychomotor speed. -Working memory (visual and auditory). -Recall, interference, and delay recall auditory memory. -Controlled Search Speed. -Executive functioning. Neuropsychological test: -Verbal fluency test composed of phonological and semantic cues. -The Rey Auditory Verbal Learning Test (RAVLT). -Trail Making Test, parts A and B (TMT). -Ruff 2 and 7 Selective Attention Test. |
Postpartum: women after delivery. With and without PPD, and a non-pregnant healthy women group. | Postpartum: between one and two months after delivery. |
|
Summary of papers described in this article on cognitive biases and processes.
GAD, general anxiety disorder; PPD, postpartum depression; CB-PTSD, child-birth-related posttraumatic stress disorder; PTSD, posttraumatic stress disorder; WM, working memory; MADRS, Montgomery-Åsberg Depression Rating Scale; MDD, major depressive disorder; EPDS, Edinburgh Postnatal Depression Scale; IES, impact of events scale; PTSD-Q, Posttraumatic Stress Disorder Questionnaire.
The table includes only studies that focus on computerized tasks designed to measure cognitive biases.
In terms of interpretation bias, the tendency is not yet clear. Mothers with PPD tend to rate neutral infant faces as sad (63) and are more sensitive to negative facial expressions in infants compared to happy ones (63, 67, 68). While Gil et al. [2011 (63)] found no significant impact of depressive symptoms on the recognition of positive emotions (e.g., smiling infants) among mothers with PPD three days after delivery, Arteche et al. [2011 (69)] reported that women with PPD more than 10 months postpartum did not differ from nonpregnant women in the recognition of sad infant faces, and were less accurate in identifying expressions of happiness in infant faces. The same tendencies were reported for postpartum women with anxiety disorder (see (67) for more information, or Table 1 in this manuscript).
Research suggests that women with a history of PPD may continue to exhibit cognitive vulnerabilities beyond the postpartum period, particularly during the luteal phase of the menstrual cycle (28). During this phase, they tend to show more negative self-evaluations and display memory and attentional biases toward negative information, even when not currently experiencing depressive or mood symptoms (28). As noted above, a history of MDD (16, 70) or bipolar disorder (68) increases the risk of developing PPD. PPD can also emerge in women without a prior psychiatric history (40); however, different cognitive bias profiles have been observed in women with and without a prior history of MDD or bipolar disorder (68, 71).
Finally, reduced accuracy in recognizing happy infant faces may signal impaired attunement to a newborn’s needs. This reduced responsiveness may negatively affect early mother-infant bonding, contributing to relational difficulties and potentially hindering the child’s emotional development (66, 67, 69).
Can cognitive biases predict future development of postpartum depression and CB-PTSD?
Despite the importance of identifying cognitive biases in perinatal mental health, only a few studies have investigated biases during pregnancy and related them to the development of postpartum disorders. One study found that stronger negative reactions to a crying infant during pregnancy significantly predicted later development of PPD, even after controlling for subclinical depressive symptoms (68). Another study used an emotional Stroop task and showed that depressed pregnant women undergoing treatment exhibited greater emotional interference from negatively valenced obstetric words (e.g., “pain,” “vacuum extraction,” “brain injury”) than either untreated depressed women or healthy pregnant controls (71). Notably, participants who later developed PPD showed faster reaction times to both positive and negative obstetric words, suggesting a generalized attentional bias to emotional obstetric information.
Recent findings further support the predictive value of interpretation biases. When asked to assess the emotional states of infants in short videos and audio clips, pregnant women who interpreted infant distress more negatively were more likely to develop depressive symptoms within the first two months postpartum (72). Moreover, harsher evaluations of the most distressing infant cries were particularly predictive of higher depressive symptom levels, but the emotional reactivity to the video had higher predictive validity. These results suggest that even adjusting for prenatal depressive symptoms, prior depression history, income, financial stress, and childhood trauma, the association of pregnant women with PPD is still stronger in this study, and negative interpretation biases may be at elevated risk for developing PPD (68, 72).
Research investigating the predictive utility of cognitive biases for CB-PTSD remains limited compared to that for PPD. Much of the available literature focuses on women with prior PTSD or those who experienced trauma during pregnancy, rather than on trauma stemming specifically from the childbirth experience. Nevertheless, a few studies provide important insights. One study used an implicit, computerized Stroop task to assess attention bias in women with subclinical symptoms of CB-PTSD. Attention bias away from labor-related words was associated with higher post-traumatic stress and more negative childbirth experiences (73). Additional research explored more explicit, reflective measures related to memory and narrative processing. For example, studies have examined how writing about the childbirth experience, memory distortions of labor pain, and cultural perceptions of unplanned or traumatic births may influence the risk of developing CB-PTSD (74–78).
These studies provide preliminary evidence that cognitive biases may serve as early indicators of vulnerability to PPD and CB-PTSD. By examining both correlational and causal links between specific biases and postpartum psychopathology, research can begin to define cognitive profiles for individuals at elevated risk. Recent developments in computational psychiatry further support this approach. Studies using machine learning (ML) and nonlinear analytic methods have successfully predicted symptoms of anxiety and depression based on cognitive bias patterns (52, 79, 80), as an addition to the traditional analysis. Recent studies demonstrate the potential of ML to improve predictive accuracy. For example, a natural language processing (NLP) model analyzing women’s childbirth narratives achieved 85% sensitivity and 75% specificity in identifying CB-PTSD symptoms (81). Similarly, a population-based study applying Random Forest models to predict PPD reported an AUC of 0.884, indicating high diagnostic accuracy (82), with implied sensitivity and specificity often ranging between 70–90% and 60–85%, depending on the dataset and thresholds used.
These advances highlight the potential to identify and differentiate several variables specifically related to PPD and CB-PTSD, revealing a complex relationship in the data without assuming the relationships (predictor or predicted) (48), and enabling more accurate predictions for the population through actual or retrospective data (83).
Cognitive control difficulties and their relationship with postpartum depression and CB-PTSD
Cognitive biases are commonly associated with reduced cognitive control over emotional processing and reactions (54). Cognitive control is essential for flexible, goal-directed behavior, particularly in uncertain situations. It enables individuals to override automatic responses, inhibit irrelevant information, shift attention, and update working memory, skills that help allocate mental resources and prioritize task-relevant information (84, 85).
In the context of PPD and CB-PTSD, evidence suggests impairments in executive functions. For instance, Hampson et al. (86) demonstrated working memory decline in pregnant women with depressive symptoms; Pio de Almeida et al. (14) reported deficits in working and short-term memory in depressed parents; and Messinis et al. (87) found that women with postnatal depression performed worse on tasks involving learning and attention-switching compared to controls. Despite these findings, research on cognitive control in postpartum psychopathology remains limited and warrants further exploration.
Some studies have investigated interventions targeting these cognitive processes. Di Blasio et al. (41) showed that writing about the childbirth experience significantly reduced PPD symptoms 96 hours after delivery, with further reductions in both PPD and CB-PTSD, observed following a similar intervention three months postpartum.
DeJong et al. (85) proposed a cognitive control model linking PPD, rumination, parenting, and child development. They suggest that women with PPD tend to focus on negative information, which then shapes ruminative thinking. Supporting this, Denis and Luminet (88) found that rumination predicted PPD symptoms and maternal self-esteem at one month postpartum, while neuroticism predicted outcomes at one year.
Ford et al. (89) adapted a general PTSD cognitive model to CB-PTSD, including social support as a potential predictor. Their model explained 23% of the variance in CB-PTSD symptoms at three weeks postpartum, with no improved prediction when social support was added to the model. However, at three months postpartum, social support, partially mediated by post-traumatic cognitions, improved the model’s predictive power (16% variance explained), suggesting that social support becomes more critical over time. These findings affirm that CB-PTSD over time is predicted by the influence of early social support.
Peñacoba et al. (90) examined cognitive variables linked to PPD were positively associated with neuroticism and negatively associated with extroversion. Worries and an external locus of control influenced both anxiety and PPD, whereas expectations about childbirth affected only anxiety. Additionally, anxiety during pregnancy emerged as a strong independent predictor of PPD.
Optimism was associated with fewer depressive symptoms during pregnancy and at two weeks postpartum, while self-esteem consistently predicted lower depression levels across the six months. Self-esteem may serve as a more stable protective factor against early postpartum depression (91, 92). Similarly, women with poor or moderate coping strategies were more likely to report symptoms of CB-PTSD compared to those with effective coping mechanisms (93). Cultural context also shapes coping behaviors such as accepted social support, emotion-focused coping, or religious coping. Importantly, higher self-esteem among postpartum women was linked to increased use of positive coping strategies (51, 92). Moreover, women who demonstrated higher levels of positive coping during the third trimester were less likely to develop postpartum depression (51).
Collectively, these models underscore the potential role of executive control with the effects of the personality traits in PPD and CB-PTSD. However, more research is required to reach firm conclusions and establish gold-standard theoretical models.
Summary and future directions
Accumulating evidence proposes that cognitive biases may serve as behavioral markers for early diagnosis and prevention of PPD and CB-PTSD. Few studies have explored attentional biases across genders (9, 94). None have examined differences between biological and non-biological caregiver parents.
Systematic studies comparing cognitive biases in large cohorts of pregnant women are scarce. Additionally, existing research often focuses on isolated cognitive biases or risk factors, neglecting how different factors might interact. The combined cognitive bias hypothesis (95), originally proposed in the context of depression, suggests that biases interact. Applying this integrated perspective to PPD and CB-PTSD may clarify their underlying mechanisms. Supporting this, negative interpretation bias predicted higher levels of fear of childbirth, whereas attention and memory biases showed no significant associations (96).
Cognitive bias measures may offer an implicit and objective alternative to traditional self-report tools, which are prone to subjective biases (97, 98). These tasks are low-cost, easy to administer, and require no special equipment, making them accessible and practical for early screening and preventive care globally. When used alongside self-reports, they may improve diagnostic accuracy. Emerging evidence also suggests that cognitive interventions, such as attention bias modification and cognitive bias modification (99, 100), could alleviate symptoms of PPD and CB-PTSD by targeting maladaptive thought patterns (biased attention-orienting or negative interpretations). These interventions leverage neuroplasticity to improve emotional and cognitive outcomes and provide a direct method for testing the causal role of cognitive mechanisms in symptom change (101).
Although mixed findings exist [for discussion, see (102)]. Cognitive training has shown effectiveness in reducing symptoms of depression (56, 103), anxiety (100), social anxiety (104), PTSD (105), rumination (106), and schizophrenia (107). However, its use in PPD and CB-PTSD remains underexplored. Hirsch et al. (108) conducted the first training with pregnant women (>16 weeks gestation) reporting high worry. Their cognitive bias modification for interpretation training promoted positive interpretations of ambiguous scenarios, reduced negative thought intrusions, and induced a more positive interpretation bias, supporting a causal link between interpretation bias and worry during pregnancy.
Conclusions
Cognitive biases may play a key role in the development and maintenance of PPD and CB-PTSD. They may function as early warning signs for their development, offering potential avenues for early identification and prevention. Growing evidence indicates that distinct cognitive biases are characteristic of both disorders. Future research should expand on this evidence, focusing on diverse populations, integrated bias models, and longitudinal designs to refine theoretical understanding and enhance clinical practice.
Statements
Data availability statement
The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.
Author contributions
VC: Project administration, Methodology, Investigation, Conceptualization, Writing – review & editing, Writing – original draft. IS: Funding acquisition, Resources, Writing – review & editing. NG-B: Writing – review & editing, Conceptualization, Methodology. EF: Writing – review & editing, Conceptualization, Funding acquisition, Resources. HO-S: Project administration, Conceptualization, Methodology, Writing – review & editing, Supervision.
Funding
The authors declare that financial support was received for the research and/or publication of this article. We gratefully acknowledge the support of the Data Science Research Center, Dr. Mike Divon, and the Haber Wolf Trust for their funding assistance.
Conflict of interest
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
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Footnotes
1.^In this paper, we refer to the term women because currently, the research relevant to the topic under discussion has only been conducted on women.
2.^*This paper uses the terms postpartum depression (‘PPD’) and postnatal depression (‘PND’) interchangeably. Both terms refer to depression that is delivery-related, in line with recent literature. The same is true for the use of postpartum post-traumatic stress disorder (‘PP-PTSD’) and childbirth PTSD (‘CB-PTSD’).
3.^+;In this paper, the term 'mother' refers to the partner or caregiver who predominantly fulfills the traditional maternal role.
References
1
Liu Y Zhang L Guo N Jiang H . Postpartum depression and postpartum post-traumatic stress disorder: prevalence and associated factors. BMC Psychiatry. (2021) 21:1–11. doi: 10.1186/s12888-021-03432-7
2
Dekel S Stuebe C Dishy G . Childbirth induced posttraumatic stress syndrome: A systematic review of prevalence and risk factors. Front Psychol. (2017) 8:560. doi: 10.3389/fpsyg.2017.00560
3
Brummelte S Galea LAM . Postpartum depression: Etiology, treatment and consequences for maternal care. Horm Behav. (2016) 77:153–66. doi: 10.1016/j.yhbeh.2015.08.008
4
Jacques N . Prenatal and postnatal maternal depression and infant hospitalization and mortality in the first year of life: A systematic review and meta-analysis. J Affect Disord. (2019) 243:201–8. doi: 10.1016/j.jad.2018.09.055
5
Shi P Ren H Li H Dai Q . Maternal depression and suicide at immediate prenatal and early postpartum periods and psychosocial risk factors. Psychiatry Res. (2018) 261:298–306. doi: 10.1016/j.psychres.2017.12.085
6
Dodson KD . “Mental illness, infanticide, and neonaticide.,” The encyclopedia of women and crime. Hoboken, New Jersey: John Wiley & Sons, Inc (2019) p. 1–3. doi: 10.1002/9781118929803.ewac0349
7
Zadeh MA Khajehei M Sharif F Hadzic M . High-risk pregnancy: Effects on postpartum depression and anxiety. Br J Midwifery. (2012) 20:104–13. doi: 10.12968/bjom.2012.20.2.104
8
Sade S Sheiner E Wainstock T Hermon N Salem SY Kosef T et al . Risk for depressive symptoms among hospitalized women in high-risk pregnancy units during the covid-19 pandemic. J Clin Med. (2020) 9:1–11. doi: 10.3390/JCM9082449
9
Koch S De Pascalis L Vivian F Meurer Renner A Murray L Arteche A . Effects of male postpartum depression on father–infant interaction: The mediating role of face processing. Infant Ment Health J. (2019) 40:263–76. doi: 10.1002/imhj.21769
10
Śliwerski A Kossakowska K Jarecka K Świtalska J Bielawska-Batorowicz E . The effect of maternal depression on infant attachment: A systematic review. Int J Environ Res Public Health. (2020) 17:2675. doi: 10.3390/ijerph17082675
11
Dekel S Thiel F Dishy G Ashenfarb AL . Is childbirth-induced PTSD associated with low maternal attachment? Arch Womens Ment Health. (2019) 22:119–22. doi: 10.1007/s00737-018-0853-y
12
Gollan J Rosebrock L Hoxha D Wisner K . Changes in attentional processing and affective reactivity in pregnancy and postpartum. Neurosci Neuroecon. (2014) 3:99–109. doi: 10.2147/nan.s35912
13
American Psychiatric Association DS, Association AP . Diagnostic and statistical manual of mental disorders: DSM-5. 5th ed. Washington, D.C: American Psychiatric Association (2013).
14
Pio De Almeida LS Jansen K Köhler CA Pinheiro RT Da Silva RA Bonini JS . Working and short-term memories are impaired in postpartum depression. J Affect Disord. (2012) 136:1238–42. doi: 10.1016/j.jad.2011.09.031
15
Dikmen-Yildiz P Ayers S Phillips L . Depression, anxiety, PTSD and comorbidity in perinatal women in Turkey: A longitudinal population-based study. Midwifery. (2017) 55:29–37. doi: 10.1016/j.midw.2017.09.001
16
Silverman ME Reichenberg A Savitz DA Cnattingius S Lichtenstein P Hultman CM et al . The risk factors for postpartum depression: A population-based study. Depress Anxiety. (2017) 34:178–87. doi: 10.1002/da.22597
17
Cho H Lee K Choi E Cho HN Park B Suh M et al . Association between social support and postpartum depression. Sci Rep. (2022) 12:3128. doi: 10.1038/s41598-022-07248-7
18
Tikotzky L . Postpartum maternal sleep, maternal depressive symptoms and self-perceived mother–infant emotional relationship. Behav Sleep Med. (2016) 14:5–22. doi: 10.1080/15402002.2014.940111
19
Abbaszadeh F Kafaei M Masoudi Alavi N Bagheri A Sadat Z Karimian Z . Relationship between quality of life and depression in pregnant women. Nurs Midwifery Stud. (2013) 1:193–7. doi: 10.5812/nms.8518
20
Meifen W Xiaoyi L Bin F Hao W Chunbo Q Zhang W . Poor sleep quality of third-trimester pregnancy is a risk factor for postpartum depression. Med Sci Monitor. (2014) 20:2740–5. doi: 10.12659/MSM.891222
21
Osborne LM Voegtline K Standeven LR Sundel B Pangtey M Hantsoo L et al . High worry in pregnancy predicts postpartum depression. J Affect Disord. (2021) 294:701–6. doi: 10.1016/j.jad.2021.07.009
22
Eisenach JC Pan PH Smiley R Lavand’homme P Landau R Houle TT . Severity of acute pain after childbirth, but not type of delivery, predicts persistent pain and postpartum depression. Pain. (2008) 140:87–94. doi: 10.1016/J.PAIN.2008.07.011
23
Huang Y Alvernaz S Kim SJ Maki P Dai Y Peñalver Bernabé B . Predicting prenatal depression and assessing model bias using machine learning models. Biol Psychiatry Global Open Sci. (2024) 4:1–11. doi: 10.1016/J.BPSGOS.2024.100376
24
Reck C Stehle E Reinig K Mundt C . Maternity blues as a predictor of DSM-IV depression and anxiety disorders in the first three months postpartum. J Affect Disord. (2009) 113:77–87. doi: 10.1016/j.jad.2008.05.003
25
Rezaie-Keikhaie K Arbabshastan ME Rafiemanesh H Amirshahi M Ostadkelayeh SM Arbabisarjou A . Systematic review and meta-analysis of the prevalence of the maternity blues in the postpartum period. JOGNN - J Obstetric Gynecologic Neonatal Nurs. (2020) 49:127–36. doi: 10.1016/j.jogn.2020.01.001
26
Apter-Levy Y Feldman M Vakart A Ebstein RP Feldman R . Impact of maternal depression across the first 6 years of life on the child’s mental health, social engagement, and empathy: the moderating role of oxytocin. Am J Psychiatry. (2013) 170:1161–8. doi: 10.1176/appi.ajp.2013.12121597
27
Oyetunji A Chandra P . Postpartum stress and infant outcome: A review of current literature. Psychiatry Res. (2020) 284:112769. doi: 10.1016/j.psychres.2020.112769
28
Bloch M Helpman L Gilboa-Schechtman E Fried-Zaig I . Cognitive processing of emotional information during menstrual phases in women with and without postpartum depression: differential sensitivity to changes in gonadal steroids. Arch Womens Ment Health. (2022) 25:753–62. doi: 10.1007/s00737-022-01235-7
29
Flory JD Yehuda R . Comorbidity between post-traumatic stress disorder and major depressive disorder: alternative explanations and treatment considerations. Dialogues Clin Neurosci. (2015) 17:141–50. doi: 10.31887/DCNS.2015.17.2/jflory
30
Yehuda R Hoge CW McFarlane AC Vermetten E Lanius RA Nievergelt CM et al . Post-traumatic stress disorder. Nat Rev Dis Primers. (2015) 1:15057. doi: 10.1038/nrdp.2015.57
31
Bayri Bingol F Demirgoz Bal M . The risk factors for postpartum posttraumatic stress disorder and depression. Perspect Psychiatr Care. (2020) 56:851–7. doi: 10.1111/ppc.12501
32
Thiel F Ein-Dor T Dishy G King A Dekel S . Examining symptom clusters of childbirth-related posttraumatic stress disorder. Prim Care Companion CNS Disord. (2018) 20:1–8. doi: 10.4088/PCC.18m02322
33
Garthus-Niegel S Horsch A Ayers S Junge-Hoffmeister J Weidner K Eberhard-Gran M . The influence of postpartum PTSD on breastfeeding: A longitudinal population-based study. Birth. (2018) 45:193–201. doi: 10.1111/birt.12328
34
Shlomi Polachek I Dulitzky M Margolis-Dorfman L Simchen MJ . A simple model for prediction postpartum PTSD in high-risk pregnancies. Arch Womens Ment Health. (2016) 19:483–90. doi: 10.1007/s00737-015-0582-4
35
Froeliger A Deneux-Tharaux C Seco A Sentilhes L . Posttraumatic stress disorder symptoms 2 months after vaginal delivery. Obstetrics gynecology. (2022) 139:63–72. doi: 10.1097/AOG.0000000000004611
36
Baumann S Staudt A Horesh D Eberhard-Gran M Garthus-Niegel S Horsch A . Perineal tear and childbirth-related posttraumatic stress: A prospective cohort study. Acta Psychiatr Scand. (2023). doi: 10.1111/acps.13595
37
Kjeldgaard HK Vikanes Å Benth JŠ Junge C Garthus-Niegel S Eberhard-Gran M . The association between the degree of nausea in pregnancy and subsequent posttraumatic stress. Arch Womens Ment Health. (2019) 22:493–501. doi: 10.1007/s00737-018-0909-z
38
Gankanda WI Gunathilake IAGMP Kahawala NL Ranaweera AKP . Prevalence and associated factors of post-traumatic stress disorder (PTSD) among a cohort of Srilankan post-partum mothers: a cross-sectional study. BMC Pregnancy Childbirth. (2021) 21:626–33. doi: 10.1186/s12884-021-04058-z
39
Dekel S Ein-Dor T Dishy GA Mayopoulos PA . Beyond postpartum depression: posttraumatic stress-depressive response following childbirth. Arch Womens Ment Health. (2020) 23:557–64. doi: 10.1007/s00737-019-01006-x
40
Pampaka D Papatheodorou SI AlSeaidan M Al Wotayan R Wright RJ Buring JE et al . Postnatal depressive symptoms in women with and without antenatal depressive symptoms: results from a prospective cohort study. Arch Womens Ment Health. (2019) 22:93–103. doi: 10.1007/s00737-018-0880-8
41
Di Blasio P Miragoli S Camisasca E Di Vita AM Pizzo R Pipitone L . Emotional distress following childbirth: an intervention to buffer depressive and PTSD symptoms. Eur J Psychol. (2015) 11:214–32. doi: 10.5964/ejop.v11i2.779
42
Haagen JFG Moerbeek M Olde E van der Hart O Kleber RJ . PTSD after childbirth: A predictive ethological model for symptom development. J Affect Disord. (2015) 185:135–43. doi: 10.1016/j.jad.2015.06.049
43
Zhou Y Shi H Liu Z Peng S Wang R Qi L et al . The prevalence of psychiatric symptoms of pregnant and non-pregnant women during the COVID-19 epidemic. Transl Psychiatry. (2020) 10:319–26. doi: 10.1038/s41398-020-01006-x
44
Handelzalts JE Levy S Molmen-Lichter M Ayers S Krissi H Wiznitzer A et al . The association of attachment style, postpartum PTSD and depression with bonding- A longitudinal path analysis model, from childbirth to six months. J Affect Disord. (2021) 280:17–25. doi: 10.1016/j.jad.2020.10.068
45
Arango C Díaz-Caneja CM McGorry PD Rapoport J Sommer IE Vorstman JA et al . Preventive strategies for mental health. Lancet Psychiatry. (2018) 5:591–604. doi: 10.1016/S2215-0366(18)30057-9
46
Neumann PJ Sanders GD Russell LB Siegel JE Ganiats TG . Cost-effectiveness in health and medicine. 2nd Ed Oxford Univ Press. (2016). doi: 10.1093/acprof:oso/9780190492939.001.0001
47
White LK Perlstein S Gorgone A Himes MM Kornfield SL Shanmugan S et al . Evidence for missed cases of postpartum depression based on paediatric clinical care screenings. Br J Psychiatry. (2025) 226:421–3. doi: 10.1192/bjp.2025.81
48
Hoorelbeke K Fried EI Koster EHW . A comprehensive network analysis of biopsychosocial factors associated with postpartum depression. J Affect Disord. (2025) 390:119808. doi: 10.1016/j.jad.2025.119808
49
Chan SJ Ein-Dor T Mayopoulos PA Mesa MM Sunda RM McCarthy BF et al . Risk factors for developing posttraumatic stress disorder following childbirth. Psychiatry Res. (2020) 290:113090. doi: 10.1016/j.psychres.2020.113090
50
Kantrowitz-Gordon I . Internet confessions of postpartum depression. Issues Ment Health Nurs. (2013) 34:874–82. doi: 10.3109/01612840.2013.806618
51
Yu M Gong W Taylor B Cai Y Xu D . (Roman). Coping styles in pregnancy, their demographic and psychological influences, and their association with postpartum depression: A longitudinal study of women in China. Int J Environ Res Public Health. (2020) 17:3654. doi: 10.3390/ijerph17103654
52
Richter T Fishbain B Richter-Levin G Okon-Singer H . Machine learning-based behavioral diagnostic tools for depression: advances, challenges, and future directions. J Pers Med. (2021) 11:957. doi: 10.3390/jpm11100957
53
Haselton MG Nettle D Murray DR . “The evolution of cognitive bias.,” The handbook of evolutionary psychology. Wiley (2015) p. 968–87. doi: 10.1002/9781119125563.evpsych241
54
Aue T Okon-Singer H Aue . Cognitive biases in health and psychiatric disorders: Neurophysiological foundations. Okon-SingerH, editors. Elsevier Academic Press (2020). doi: 10.1016/C2018-0-00401-6
55
Badura-Brack AS Naim R Ryan TJ Levy O Abend R Khanna MM et al . Effect of attention training on attention bias variability and PTSD symptoms: Randomized controlled trials in Israeli and U.S. Combat Veterans. Am J Psychiatry. (2015) 172:1233–41. doi: 10.1176/appi.ajp.2015.14121578
56
Cristea IA Kok RN Cuijpers P . Efficacy of cognitive bias modification interventions in anxiety and depression: Meta-analysis. Br J Psychiatry. (2015) 206:7–16. doi: 10.1192/bjp.bp.114.146761
57
Dalgleish T Taghavi R Neshat-Doost H Moradi A Canterbury R Yule W . Patterns of processing bias for emotional information across clinical disorders: A comparison of attention, memory, and prospective cognition in children and adolescents with depression, generalized anxiety, and posttraumatic stress disorder. J Clin Child Adolesc Psychol. (2003) 32:10–21. doi: 10.1207/S15374424JCCP3201_02
58
Abado E Aue T Pourtois G Okon-Singer H . Expectancy and attention bias to spiders: Dissecting anticipation and allocation processes using ERPs. Psychophysiology. (2024) 61:e14546. doi: 10.1111/psyp.14546
59
Givon-Benjio N Oren-Yagoda R Aderka IM Okon-Singer H . Biased distance estimation in social anxiety disorder: A new avenue for understanding avoidance behavior. Depress Anxiety. (2020) 37:1243–52. doi: 10.1002/da.23086
60
Fani N Tone EB Phifer J Norrholm SD Bradley B Ressler KJ et al . Attention bias toward threat is associated with exaggerated fear expression and impaired extinction in PTSD. Psychol Med. (2012) 42:533–43. doi: 10.1017/S0033291711001565
61
Wald I Shechner T Bitton S Holoshitz Y Charney DS Muller D et al . Attention bias away from threat during life threatening danger predicts PTSD symptoms at one-year follow-up. Depress Anxiety. (2011) 28:406–11. doi: 10.1002/da.20808
62
Rowlands K Grafton B Cerea S Simic M Hirsch C Cruwys T et al . A multifaceted study of interpersonal functioning and cognitive biases towards social stimuli in adolescents with eating disorders and healthy controls. J Affect Disord. (2021) 295:397–404. doi: 10.1016/j.jad.2021.07.013
63
Gil S Teissèdre F Chambres P Droit-Volet S . The evaluation of emotional facial expressions in early postpartum depression mood: A difference between adult and baby faces? Psychiatry Res. (2011) 186:281–6. doi: 10.1016/j.psychres.2010.06.015
64
Pearson RM Cooper RM Penton-Voak IS Lightman SL Evans J . Depressive symptoms in early pregnancy disrupt attentional processing of infant emotion. Psychol Med. (2010) 40:621–31. doi: 10.1017/S0033291709990961
65
Flanagan TJ White H Carter BG . Differential impairments in emotion face recognition in postpartum and nonpostpartum depressed women. J Affect Disord. (2011) 128:314–8. doi: 10.1016/J.JAD.2010.07.021
66
Pearson RM Lightman SL Evans J . Attentional processing of infant emotion during late pregnancy and mother-infant relations after birth. Arch Womens Ment Health. (2011) 14:23–31. doi: 10.1007/s00737-010-0180-4
67
Webb R Ayers S . Cognitive biases in processing infant emotion by women with depression, anxiety and post-traumatic stress disorder in pregnancy or after birth: A systematic review. Cognit Emot. (2015) 29:1278–94. doi: 10.1080/02699931.2014.977849
68
Bjertrup AJ Jensen MB Schjødt MS Parsons CE Kjærbye-Thygesen A Mikkelsen RL et al . Cognitive processing of infant stimuli in pregnant women with and without affective disorders and the association to postpartum depression. Eur Neuropsychopharmacol. (2021) 42:97–109. doi: 10.1016/j.euroneuro.2020.10.006
69
Arteche A Joormann J Harvey A Craske M Gotlib IH Lehtonen A et al . The effects of postnatal maternal depression and anxiety on the processing of infant faces. J Affect Disord. (2011) 133:197–203. doi: 10.1016/j.jad.2011.04.015
70
Silverman ME Reichenberg A Lichtenstein P Sandin S . Is depression more likely following childbirth? A population-based study. Arch Womens Ment Health. (2019) 22:253–8. doi: 10.1007/s00737-018-0891-5
71
Edvinsson Å Skalkidou A Hellgren C Gingnell M Ekselius L Willebrand M et al . Different patterns of attentional bias in antenatal and postpartum depression. Brain Behav. (2017) 7:1–11. doi: 10.1002/brb3.844
72
Bjertrup AJ Væver MS Miskowiak KW . Prediction of postpartum depression with an online neurocognitive risk screening tool for pregnant women. Eur Neuropsychopharmacol. (2023) 73:36–47. doi: 10.1016/j.euroneuro.2023.04.014
73
Dale-Hewitt V Slade P Wright I Cree M Tully C . Patterns of attention and experiences of post-traumatic stress symptoms following childbirth: an experimental study. Arch Womens Ment Health. (2012) 15:289–96. doi: 10.1007/s00737-012-0290-2
74
Simonelli MC Gennaro S O’Connor C Doyle LT . Women construct their birth narratives and process unplanned cesarean births through storytelling. J Obstetric Gynecologic Neonatal Nurs. (2021) 50:30–9. doi: 10.1016/j.jogn.2020.09.157
75
Callister LC . Making meaning: women’s birth narratives. J Obstetric Gynecologic Neonatal Nurs. (2004) 33:508–18. doi: 10.1177/0884217504266898
76
Thiel F Berman Z Dishy GA Chan SJ Seth H Tokala M et al . Traumatic memories of childbirth relate to maternal postpartum posttraumatic stress disorder. J Anxiety Disord. (2021) 77:102342. doi: 10.1016/j.janxdis.2020.102342
77
Bartal A Jagodnik KM Chan SJ Babu MS Dekel S . Identifying women with postdelivery posttraumatic stress disorder using natural language processing of personal childbirth narratives. Am J Obstet Gynecol MFM. (2023) 5:100834. doi: 10.1016/j.ajogmf.2022.100834
78
Santoro E Stagni-Brenca E Olivari MG Confalonieri E Di Blasio P . Childbirth narratives of women with posttraumatic stress symptoms in the postpartum period. J Obstetric Gynecologic Neonatal Nurs. (2018) 47:333–41. doi: 10.1016/j.jogn.2018.02.009
79
Richter T Fishbain B Fruchter E Richter-Levin G Okon-Singer H . Machine learning-based diagnosis support system for differentiating between clinical anxiety and depression disorders. J Psychiatr Res. (2021) 141:199–205. doi: 10.1016/j.jpsychires.2021.06.044
80
Richter T Fishbain B Markus A Richter-Levin G Okon-Singer H . Using machine learning-based analysis for behavioral differentiation between anxiety and depression. Sci Rep. (2020) 10:16381. doi: 10.1038/s41598-020-72289-9
81
Bartal A Jagodnik KM Chan SJ Dekel S . AI and narrative embeddings detect PTSD following childbirth via birth stories. Sci Rep. (2024) 14:8336. doi: 10.1038/s41598-024-54242-2
82
Shin D Lee KJ Adeluwa T Hur J . Machine learning-based predictive modeling of postpartum depression. J Clin Med. (2020) 9:2899. doi: 10.3390/jcm9092899
83
Kessler RC van Loo HM Wardenaar KJ Bossarte RM Brenner LA Cai T et al . Testing a machine-learning algorithm to predict the persistence and severity of major depressive disorder from baseline self-reports. Mol Psychiatry. (2016) 21:1366–71. doi: 10.1038/mp.2015.198
84
Mackie MA Van Dam NT Fan J . Cognitive control and attentional functions. Brain Cognit. (2013) 82:301. doi: 10.1016/J.BANDC.2013.05.004
85
DeJong H Fox E Stein A . Rumination and postnatal depression: A systematic review and a cognitive model. Behav Res Ther. (2016) 82:38–49. doi: 10.1016/j.brat.2016.05.003
86
Hampson E Phillips S-D Duff-Canning SJ Evans KL Merrill M Pinsonneault JK et al . Working memory in pregnant women: Relation to estrogen and antepartum depression. Horm Behav. (2015) 74:218–27. doi: 10.1016/j.yhbeh.2015.07.006
87
Messinis L Vlahou CH Tsapanos V Tsapanos A Spilioti D Papathanasopoulos P . Neuropsychological functioning in postpartum depressed versus nondepressed females and nonpostpartum controls. J Clin Exp Neuropsychol. (2010) 32:661–6. doi: 10.1080/13803390903468863
88
Denis A Luminet O . Cognitive factors and post-partum depression: What is the influence of general personality traits, rumination, maternal self-esteem, and alexithymia? Clin Psychol Psychother. (2018) 25:359–67. doi: 10.1002/cpp.2168
89
Ford E Ayers S Bradley R . Exploration of a cognitive model to predict post-traumatic stress symptoms following childbirth. J Anxiety Disord. (2010) 24:353–9. doi: 10.1016/j.janxdis.2010.01.008
90
Peñacoba-Puente C Marín-Morales D Carmona-Monge FJ Velasco Furlong L . Post-partum depression, personality, and cognitive-emotional factors: A longitudinal study on spanish pregnant women. Health Care Women Int. (2016) 37:97–117. doi: 10.1080/07399332.2015.1066788
91
Fontaine KR Jones LC . Self-esteem, optimism, and postpartum depression. J Clin Psychol. (1997) 53:59–63. doi: 10.1002/(SICI)1097-4679(199701)53:1<59::AID-JCLP8>3.0.CO;2-Q
92
Motofelea N Motofelea AC Tamasan IF Hoinoiu T Ioana JTM Vilibić M et al . Predictive validity of screening tools and role of self-esteem and coping in postpartum depression risk. Diagnostics. (2025) 15:1152. doi: 10.3390/diagnostics15091152
93
van Heumen MA Hollander MH van Pampus MG van Dillen J Stramrood CAI . Psychosocial predictors of postpartum posttraumatic stress disorder in women with a traumatic childbirth experience. Front Psychiatry. (2018) 9:348. doi: 10.3389/fpsyt.2018.00348
94
Bohne A Nordahl D Lindahl ÅAW Ulvenes P Wang CEA Pfuhl G . Emotional infant face processing in women with major depression and expecting parents with depressive symptoms. Front Psychol. (2021) 12:experience Postpartum Depression657269. doi: 10.3389/fpsyg.2021.657269
95
Everaert J Tierens M Uzieblo K Koster EHW . The indirect effect of attention bias on memory via interpretation bias: Evidence for the combined cognitive bias hypothesis in subclinical depression. Cognit Emot. (2013) 27:1450–9. doi: 10.1080/02699931.2013.787972
96
Beal EM Slade P Krahé C . Cognitive processing biases associated with fear of childbirth. J Anxiety Disord. (2023) 99:102761. doi: 10.1016/j.janxdis.2023.102761
97
Zhang XC Kuchinke L Woud ML Velten J Margraf J . Survey method matters: Online/offline questionnaires and face-to-face or telephone interviews differ. Comput Hum Behav. (2017) 71:172–80. doi: 10.1016/j.chb.2017.02.006
98
Johnson S Seaton SE Manktelow BN Smith LK Field D Draper ES et al . Telephone interviews and online questionnaires can be used to improve neurodevelopmental follow-up rates. BMC Res Notes. (2014) 7:1–8. doi: 10.1186/1756-0500-7-219
99
Shani R Tal S Derakshan N Cohen N Enock PM McNally RJ et al . Personalized cognitive training: Protocol for individual-level meta-analysis implementing machine learning methods. J Psychiatr Res. (2021) 138:342–8. doi: 10.1016/j.jpsychires.2021.03.043
100
Hallion LS Ruscio AM . A meta-analysis of the effect of cognitive bias modification on anxiety and depression. Psychol Bull. (2011) 137:940–58. doi: 10.1037/a0024355
101
Derakhshan N . Attentional control and cognitive biases as determinants of vulnerability and resilience in anxiety and depression. In: Cognitive biases in health and psychiatric disorders: neurophysiological foundations (Elsevier: Academic Press) (2020). p. 261–74. doi: 10.1016/B978-0-12-816660-4.00012-X
102
Richter T Shani R Tal S Derakshan N Cohen N Enock PM et al . Machine learning meta-analysis identifies individual characteristics moderating cognitive intervention efficacy for anxiety and depression symptoms. NPJ Digit Med. (2025) 8:65–0. doi: 10.1038/s41746-025-01449-w
103
Joormann J Waugh CE Gotlib IH . Cognitive bias modification for interpretation in major depression. Clin psychol Sci. (2015) 3:126–39. doi: 10.1177/2167702614560748
104
Enock PM Hofmann SG McNally RJ . Attention bias modification training via smartphone to reduce social anxiety: A randomized, controlled multi-session experiment. Cognit Ther Res. (2014) 38:200–16. doi: 10.1007/s10608-014-9606-z
105
Wald I Bitton S Levi O Zusmanovich S Fruchter E Ginat K et al . Acute delivery of attention bias modification training (ABMT) moderates the association between combat exposure and posttraumatic symptoms: A feasibility study. Biol Psychol. (2017) 122:93–7. doi: 10.1016/j.biopsycho.2016.01.005
106
Cohen N Mor N Henik A . Linking executive control and emotional response. Clin psychol Sci. (2015) 3:15–25. doi: 10.1177/2167702614530114
107
Perez VB Tarasenko M Miyakoshi M Pianka ST Makeig SD Braff DL et al . Mismatch negativity is a sensitive and predictive biomarker of perceptual learning during auditory cognitive training in schizophrenia. Neuropsychopharmacology. (2017) 42:2206–13. doi: 10.1038/npp.2017.25
108
Hirsch CR Meeten F Newby JM O’Halloran S Gordon C Krzyzanowski H et al . Looking on the bright side reduces worry in pregnancy: Training interpretations in pregnant women. Clin Psychol Europe. (2021) 3:1–17. doi: 10.32872/cpe.3781
Summary
Keywords
postpartum depression, postnatal depression, childbirth post-traumatic stress disorder, cognitive biases, preventative care, cognitive training, attention bias, interpretation bias
Citation
Cywiak V, Solt I, Givon-Benjio N, Fruchter E and Okon-Singer H (2025) Cognitive biases: potential behavioral marker for future development of postpartum depression and childbirth-related post-traumatic stress disorder. Front. Psychiatry 16:1650453. doi: 10.3389/fpsyt.2025.1650453
Received
19 June 2025
Accepted
30 July 2025
Published
20 August 2025
Volume
16 - 2025
Edited by
Laura Orsolini, Marche Polytechnic University, Italy
Reviewed by
Nadica Motofelea, Victor Babes University of Medicine and Pharmacy, Romania
Updates
Copyright
© 2025 Cywiak, Solt, Givon-Benjio, Fruchter and Okon-Singer.
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Vanessa Cywiak, vanecywiak@gmail.com
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