Neural correlates of peripartum depression: a systematic review, meta-analysis and comparison to major depressive disorder

Neural correlates of peripartum depression: a systematic review, meta-analysis and comparison to major depressive disorder | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Systematic Review Neural correlates of peripartum depression: a systematic review, meta-analysis and comparison to major depressive disorder Mónica Sobral, Raquel Guiomar, Manya Rezaeian, Maria Vasileiadi, and 10 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7008488/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 07 Sep, 2025 Read the published version in Molecular Psychiatry → Version 1 posted You are reading this latest preprint version Abstract Peripartum depression (PPD) is a form of major depressive disorder (MDD) that begins during the peripartum period and poses a significant mental health challenge affecting 10–29% of women. This systematic review and multimodal activation likelihood estimation (ALE) meta-analysis explored the distinct structural, functional, and metabolic features of the PPD brain as compared to female non-peripartum MDD. For this purpose, we conducted a comprehensive literature search in PubMed, Embase and PsycINFO databases to identify peer-reviewed original studies investigating the neural correlates associated with PPD or fMDD. Forty-five studies in PPD and 55 in fMDD were included in the qualitative synthesis. From these, 27 PPD and 32 fMDD studies were included in the meta-analysis. Both shared and distinct neural underpinnings of PPD and fMDD were observed. Specifically, we found alterations in the cognitive control, salience and default mode networks for both PPD and fMDD, although with reversed structural and functional activity patterns in the insula, amygdala, precentral gyrus and precuneus. These findings support the consistent pattern of dysregulation associated with emotional regulation, cognition and maternal caregiving in women with PPD, as well as possible differential sensitivity to hormonal influences, highlighting the need for targeted interventions. Neurobiology of Disease Psychology Psychiatry Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Peripartum depression (PPD) is a major depressive disorder (MDD) with onset during pregnancy or after childbirth ( 1 , 2 ). It is characterised by sadness, restlessness/agitation, impaired concentration, and sleep/appetite disturbances ( 3 ). Multiple systematic reviews and meta-analyses yielded an estimated prevalence of PPD ranging between 10–29% ( 2 , 4 – 7 ), constituting a serious mental health issue with well-established detrimental effects on the mother's well-being and infant’s emotional, behavioural and cognitive development ( 8 ). Emerging literature has demonstrated that PPD renders altered brain structure and functional connectivity in peripartum women ( 9 , 10 ). However, the brain response patterns appear to differ from those reported in similar symptom profiles outside the peripartum period, such as MDD ( 9 , 11 ). The peripartum period encompasses profound environmental/social, psychological, and hormonal changes that impact brain plasticity, influencing maternal behavior and caregiving toward infants ( 12 ). Neurobiological adaptations are observed in brain regions associated with emotion processing (e.g., prefrontal cortex [PFC]), salience/threat detection (e.g., dorsal anterior cingulate cortex [ACC], anterior insula), reward/motivation (e.g., striatum, medial PFC, thalamus) and social cognition (e.g., posterior cingulate [PCC], temporoparietal regions; 11, 13). These adaptations can enhance maternal responsiveness and bonding by facilitating the acquisition of experience-dependent skills and knowledge related to motherhood tasks (e.g., threat vigilance, inferring what the infants’ feelings and needs are; 11–12, 14). Maternal brain plasticity, alongside hormonal fluctuations and external stressors, may also increase vulnerability to peripartum mental disorders, including PPD ( 11 ). Structural, functional, and molecular studies in PPD have consistently revealed changes in brain areas associated with both depression and maternal caregiving, such as the hypothalamus, amygdala (AMY), ACC, orbitofrontal (OFC) and dorsolateral prefrontal cortices (DLPFC), insula and striatum ( 9 , 11 , 15 ). The abnormal correlates in these regions may be indicative of the neural mechanisms of PPD and consequent impaired caregiving abilities ( 16 ). However, the literature is hindered by several limitations, including small sample sizes and underpowered studies (generally involving only 4 to 30 PPD women). This poses a significant challenge when attempting to interpret and synthesise the existing PPD imaging literature. Diagnostically, PPD is often considered a subtype of MDD (with specifiers for peripartum onset in the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition, Text Revision [DSM-5-TR] and the International Classification of Disorders, 11th Edition [ICD-11]), but other evidence suggests that PPD has distinct clinical characteristics compared to non-peripartum MDD. For instance, PPD is associated with more common and/or severe symptoms of anxiety, irritability, psychomotor restlessness and agitation, obsessive thoughts, fatigue, loss of energy and impaired concentration and decision-making, as well as specific guilt related to motherhood but less sad mood and suicidal ideation ( 17 , 18 ). However, evidence for differentiating PPD from MDD is inconsistent, partly due to differences in the definition of the postpartum period. Studies focusing on depression in the early postpartum period suggest that PPD may be characterized by unique features related to symptom severity, heritability, and epigenetic factors and may stem from biological factors (e.g., 19). In contrast, depression occurring in the later postpartum period may resemble MDD observed outside the peripartum period and be more influenced by psychosocial factors ( 20 ). Reviews comparing brain response patterns in PPD and non-peripartum MDD have also revealed notable differences ( 3 , 9 , 11 ), with reversed activation patterns in the AMY and PFC ( 16 ). For example, women diagnosed with PPD typically show a blunted AMY response to non-infant-related negative stimuli (e.g., 21), whereas MDD patients have a heightened AMY response ( 22 ). Understanding these distinct neurobiological profiles is essential for developing appropriate treatment approaches, as depression related to the female reproductive cycle (such as PPD) may represent a distinct biotype ( 11 , 19 ). Several reviews have explored the neurobiological underpinnings of PPD (e.g., 15, 23, 24), but a comprehensive meta-analysis or direct comparison with female-only non-peripartum unipolar depression (fMDD) remains lacking in the literature. To address this gap, our study aims to extend previous reviews of the neural correlates of PPD relative to the healthy postpartum brain and to conduct a formal comparison with fMDD in relation to the healthy female brain. Considering symptom presentations observed in PPD (e.g., obsessive thoughts, increased anxiety and impaired concentration and decision-making; 17), our focus is on the cognitive control network (CCN), particularly the ACC-DLPFC axis, due to its involvement in emotional and social regulation, its interaction with attention and default mode networks (DMN) and impact on treatment outcomes ( 25 – 27 ). 2. Methods 2.1. Registration and protocol The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines (28; checklist in supplementary Table S1). The study protocol was preregistered on the International Prospective Register of Systematic Reviews (PROSPERO; CRD42021281870). 2.2. Eligibility criteria We included English peer-reviewed original studies in which: ( 1 ) participants presented PPD (search 1) or MDD (search 2) and no other clinical diagnoses, except for anxiety symptoms/diagnosis; ( 2 ) all participants were aged between 18–60 years old; ( 3 ) for PPD, participants were assessed from pregnancy up to 1 year postpartum (current pregnancy/postpartum); ( 4 ) structural, functional or metabolic differences were assessed using structural or functional magnetic resonance imaging (MRI), diffusion tensor/kurtosis imaging (DTI/DKI), computed tomography (CT), positron emission tomography (PET), near-infrared spectroscopy (NIRS) or magnetic resonance spectroscopy (MRS); ( 5 ) a within- or between-group comparison or correlation with symptom severity was reported. For the quantitative analysis, peak coordinates of significant contrast of interest findings were reported in Montreal Neurological Institute (MNI) or Talairach standard spaces. Studies with imaging modalities lacking coordinate information, such as NIRS and MRS, were included in the qualitative synthesis of results. Based on the authors expertise an additional search was performed for Portuguese, Spanish, German, French, Dutch, and Greek articles in the PubMed database. This search did not result in any additional hits. Additional exclusion criteria were: ( 1 ) nonempirical studies (e.g., review, meta-analysis); ( 2 ) not published in English language or any language native to the authors; ( 3 ) depressive disorder diagnosed prior to pregnancy (past history or euthymic patients excluded); ( 4 ) patients were subject to an intervention, unless the study reported a baseline comparison with healthy controls (HC); ( 5 ) electroencephalogram (EEG) or magnetoencephalogram (MEG) studies. 2.3. Information sources and search strategy A systematic literature search was performed in the PubMed, PsycINFO (through Ovid), and Embase electronic databases, from inception until 24 February 2021. Updated searches were conducted on 27 July 2024. Search terms related to different imaging modalities including “magnetic resonance imaging”, “magnetic resonance spectroscopy”, “diffusion tensor imaging”, “computed tomography”, "near infrared spectrometry" or “positron emission tomography” and terms denoting “pregnancy” and “birth” and “depression” were included. For the search strategy on fMDD-related correlates, terms denoting “pregnancy” and “birth” were removed. The complete search strategy for all databases is reported in supplementary Table S2. Manual searches of reference lists from relevant reviews and included studies were also conducted to identify any additional studies that met the eligibility criteria. 2.4. Study selection and data extraction In search 1, the title and abstract screening, as well as the full-text review and data extraction of retrieved reports, were conducted independently by two researchers (MR [search 1])/MS [updated search] and RG). For Search 2, due to the high number of results, seven reviewers (MS, RG, MR, MV, FP, RP-C, JP-N) were involved in screening the abstracts and full-texts and data extraction of the reports, with one designated reviewer (MS) cross-verifying all the decisions made by the team to ensure consistency and accuracy. The screening was performed using the Rayyan software ( 29 ). Any conflicts or discrepancies during this process were resolved through discussions until a consensus was reached. The extracted data consisted of demographic and clinical data (number of participants, age, age range, diagnostic criteria and/or assessment instrument and cut-off scores, comorbidity, symptom severity, treatment status, parity [only in search 1], peripartum timepoint [only in search 1] and pregnancy history [only in search 2]), methodological details (study design, imaging technique, task/measure) and main findings (direction of effect, qualitative and peak coordinates of significant correlates). 2.5. Risk of bias assessment We assessed the risk of bias in the included studies in PPD and fMDD using the Newcastle-Ottawa Scale (NOS; 30) for cohort studies and the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Analytical Cross-Sectional Studies ( 31 ) for cross-sectional studies. The assessment was conducted independently by two reviewers (MS, RG). Disagreements were resolved by consensus. Following the literature, we derived an overall summary risk of bias judgement in NOS (low quality [0–2 items], moderate quality [3–5 items] and good/high quality [6–7 items]) and JBI (low quality [0–3 items]; moderate quality [4–5 items]; high quality [6–8 items]). While the overall appraisal results serve as an additional source of information regarding the quality of the studies, they were not used to exclude any reports. 2.6. Synthesis methods and statistical analysis Reports were grouped based on the imaging modality (structural, task-based and resting-state functional and molecular imaging ) and synthesised using a tabular and narrative format. For the comparison analysis, we selected a subgroup of MDD studies ( n = 55) focused exclusively on female participants to minimize sex effects. We also contacted the authors of these studies to request additional information about participants’ previous and current history of pregnancy (a possible confounding factor). A coordinate-based ALE meta-analysis was conducted to combine peak coordinates from included studies, using GingerALE 3.0.2 ( https://brainmap.org/ ; 32,33). Prior to analysis, all activation foci coordinates reported in Talairach space (n = 10) were transformed into MNI space using the Lancaster transform, implemented in the GingerALE toolbox (icbm2tal; 32). To avoid repeated inclusion of the same sample and spurious findings ( 34 ), we carefully examined the included studies for overlap (team members, location, recruitment interval, sample size and age) and aggregated data when it coincided (n = 3; 35–37). We used GingerALE's less conservative gray matter mask and subject-based Full-Width Half-Max (FWHM) values ( 33 ). To address our research question, imaging method-specific differences (PPD / fMDD vs HC as measured by structural MRI/DTI, resting-state and task-based fMRI). For the PPD / fMDD multimodal analyses, all coordinates from multiple experiments (i.e., different imaging modalities or tasks and contrasts) of the same study were merged, guaranteeing that each sample was only represented by one experiment. We then performed conjunction and contrast analyses between PPD vs fMDD. Exploratory analyses were conducted to contrast PDD and fMDD with HC considering: direction of effect (PPD / fMDD brain structure / function greater than HC and PPD / fMDD less than HC). To assess the impact of age, we conducted an exploratory analysis by considering only a subgroup of studies with age-matched fMDD participants. According to best practices ( 38 ), for the imaging-specific and multimodal, results were thresholded for significance using cluster-level inference of p < 0.05 with a cluster-forming threshold of p < 0.001, with 10000 thresholding permutations. For contrast analyses (PPD vs. fMDD) significance level was set to a p-value of below 0.001 shuffling through 10000 permutations. For the exploratory direction of effect, conjunction and age-matched analyses, we adopted a less conservative statistical threshold of uncorrected p < 0.05. All resulting coordinate clusters are reported in MNI space and overlaid on an MNI-normalised template using MRIcroGL ( http://www.mccauslandcenter.sc.edu/mricrogl/ ) or Surf Ice ( https://www.nitrc.org/projects/surfice/ ). Additionally, we submitted the MNI coordinates of peak values to Neurosynth ( http://www.neurosynth.org ) to explore functional networks in PPD and fMDD, through seed-based connectivity analysis. Moreover, in order to evaluate the overlap between common depression symptom networks and PPD and fMDD, we calculated dice indices between the dysphoric and anxiosomatic networks according to Sidiqqi et al. ( 39 ). In order to account for specificity of our PPD ALE results we created a dummy dataset (n = 25) consisting of comparable numbers of participants and MNI-coordinates to the PPD dataset. The dummy data can be found in the Supplementary Materials. 3. Results 3.1. Study selection Our database search yielded 1048 records for search 1 (PPD) and 13338 for search 2 (MDD). After removing duplicates, we carefully screened 704 and 9399 records and conducted a thorough review of 60 and 916 full-text documents, respectively. We included 45 articles that met our inclusion criteria for PPD ( 21 , 35 – 37 , 40 – 80 ) and 55 for fMDD (55; 81–134; a flow chart is available in supplementary Figure S1). For a multimodal meta-analysis of both female and male MDD participants (literature review until 2021), please refer to supplementary Table S3 and Figure S2. 3.2. Study characteristics and risk of bias Demographic, methodological and outcome characteristics of the included studies in PPD and fMDD are summarised in Tables 1 and 2 (a comparison of PPD and fMDD studies main characteristics is available in supplementary Table S4 and Figures S3-S5). PPD reports include data on DTI/DKI ( n = 3), structural MRI ( n = 7), resting-state ( n = 21), and task-based fMRI ( n = 8), fNIRS ( n = 2), MRS (n = 5) and PET ( n = 2). PPD was diagnosed according to standardized diagnostic criteria (e.g., DSM; major depressive episode with peripartum onset) in most studies, except for four studies where cut-off scores from validated self-report questionnaires were used ( 21 , 68 , 74 , 76 ). The majority of reports (96%) focused exclusively on the postpartum period, ranging from early ( 21 , 35 – 37 , 41 – 45 , 49 – 50 , 52 , 57 – 59 , 61 , 64 – 67 , 69 – 72 , 77 , 80 ) to late postpartum ( 62 , 68 , 73 , 75 – 76 , 78 ) and unspecified (up to 1 year postpartum; 40, 46–48, 51, 55–56, 60, 63, 79), with only two studies collecting data antenatally (2nd or 3rd trimester; 53–54). Twelve studies indicated concomitant anxiety disorders/symptoms ( 42 , 45 , 50 , 57 – 58 , 67 , 69 – 70 , 72 – 73 , 75 , 78 ), while the remaining studies did not report any clinical comorbidity. Additionally, several studies reported first episode PPD (history of previous mental disorder excluded, including depression; n = 23; 21, 35–37, 40–41, 43–44, 47–49, 51–56, 59–61, 64–65, 71), although others included participants with a previous history of non-peripartum MDD (n = 8; 42, 57–58, 67, 70, 76–78) and/or previous history of PPD (n = 5; 42, 57–58, 70, 76). Regarding treatment status, participants across studies were either treatment-naive, not undergoing treatment at the time of the experiment or medication-free, while in four studies antidepressants or psychotherapy were accepted ( 66 , 72 , 73 , 78 ). For fMDD, studies used DTI/DKI/DWI ( n = 2), structural MRI ( n = 12), resting-state ( n = 15) and task-based fMRI ( n = 19), NIRS ( n = 2), MRS ( n = 9) and PET ( n = 1). The majority of studies did not provide information on the previous history or current pregnancy status of participants, with 24 studies explicitly excluding pregnant and/or breastfeeding participants (81, 85–86, 90, 92, 96, 98, 99–103, 105, 108, 113, 120, 122, 124, 126, 129–131, 133–134. Eighteen studies included participants in the reproductive stage (18–49 years old; 55, 88, 95–96, 98, 100, 101, 104, 105, 113, 115, 117, 122, 126, 129–131, 133), while in the remaining studies the age range surpassed 50 years or was unspecified. All studies used standardized diagnostic criteria to assess MDD, except for one study where the method used is unclear ( 94 ). Of the studies reviewed, most reported no comorbid conditions, while in four anxiety disorders or symptoms were present ( 91 , 93 , 95 , 108 ). Nine studies had either unavailable or unclear data regarding clinical comorbidity ( 83 , 90 , 109 , 112 , 113 , 115 , 118 , 124 , 130 , 131 ). Finally, 29 studies were conducted with participants who were antidepressant/medication free or naive ( 55 , 82 , 86 , 88 , 91 – 93 , 95 – 97 , 99 – 101 , 104 , 105 , 109 , 110 , 114 , 115 , 122 – 124 , 128 – 134 ), while in 22 studies participants were using antidepressants, undergoing neuromodulation, or receiving psychotherapy ( 81 , 83 , 84 , 87 , 89 , 90 , 98 , 103 , 106 – 108 , 111 , 112 , 116 – 121 , 125 – 127 ). Four studies had unclear or unavailable data regarding current treatment status ( 85 , 94 , 102 , 113 ). [Tables 1 and 2 about here.] Overall, studies ranged from moderate to high quality (supplementary tables S5-S7). Notably, bias in cross-sectional designs primarily stemmed from a lack of detailed descriptions regarding study participants and design (e.g., failing to specify the postpartum timepoint) and an inadequate identification and control for confounding factors. Concerning cohort studies, a prevalent source of bias centred around the adequacy of follow-up, with instances of follow-up rates falling below 80% or lacking sufficient information. 3.2.1. Peripartum Depression vs Healthy Controls 3.2.1.1. Structural correlates In white matter, PPD has been associated with increased mean diffusivity (MD) in temporo-parietal areas, superior longitudinal fasciculus, corticospinal tract, cingulum, body and splenium of the corpus callosum, external capsule, internal capsule, inferior longitudinal fasciculus, and putamen ( 41 ). Additionally, decreased fractional anisotropy (FA) has been found in the superior longitudinal fasciculus, corticospinal tract, thalamus ( 41 ), and in the left anterior limb of the internal capsule ( 42 ), along with reduced radial diffusivity (RD) in the cingulum tract ( 40 ). In contrast, increased FA was observed in the right anterior thalamic radiation and cingulum tracts ( 40 ). In grey matter, PPD participants had increased volume (GMV) in the left DLPFC ( 43 , 44 ), right anterior insula ( 44 ) and OFC ( 43 ) and reduced GMV in AMY ( 46 ). Significant volumetric differences were also noted in the right ACC and left middle-PCC ( 45 ), as well as increased cortical thickness (CT) in the left superior frontal gyrus, cuneus and fusiform gyrus ( 49 ), but decreased CT in the right inferior parietal lobule ( 47 ). Additionally, increased surface area was observed in the left superior frontal gyrus, caudal middle frontal gyrus (MFG), middle temporal gyrus (MTG) and insula, along with increased mean curvature in the parietal lobules ( 47 ). Four studies ( 42 – 44 , 47 ) on 136 participants were included in the structural meta-analysis and no significant clusters were identified. 3.2.1.2. Functional correlates In the PFC, increased resting-state connectivity was observed between the left DLPFC and right ACC ( 43 ), while connectivity was decreased between the dorsomedial PFC and left ventral striatum ( 35 ), precuneus and PCC ( 58 ) and between the DLPFC, ACC and AMY ( 57 ). Decreased sample entropy was found in the left medial PFC ( 37 ), as well as reduced regional homogeneity (ReHo) in left DLPFC ( 53 , 59 ) and decreased voxel-mirrored homotopic connectivity (VMHC) in bilateral dorsomedial PFC ( 65 ). Increased values in amplitude of low-frequency fluctuations (ALFF) were found in left medial PFC and DLPFC ( 54 ). For the OFC, increased connectivity was observed with the right MFG and left inferior occipital gyrus ( 43 ), as well as decreased ALFF ( 54 ) and VMHC ( 65 ). Regarding the ACC, its subgenual part (sgACC) showed increased connectivity with the ventral anterior insula ( 35 , 36 ) and decreased connectivity with the superior and MTG ( 36 ). Within the right hippocampus, degree centrality and ReHo were increased ( 59 , 64 ), as well as connectivity with the left precuneus and left superior frontal gyrus ( 59 ), while connectivity was reduced with the right MFG and left median cingulate and paracingulate gyri ( 64 ). The PCC showed reduced connectivity with the right AMY ( 50 ) and with the right paracentral lobule ( 52 ) and increased ReHo ( 61 ). Finally, ReHo and VHMC reductions were found in the right insula ( 53 , 54 , 59 , 63 ) and AMY ( 53 , 63 ). Eighteen studies were included in the resting-state meta-analysis (16 experiments; 35–37, 43, 50, 51, 53–59, 61–65; 367 participants), which highlighted abnormalities in the left MFG (Fig. 1 ; Table S8). [Figure 1 about here] Studies using infant stimuli in fMRI report differential emotional processing in PPD. Specifically, Dudin et al. ( 66 ) found an increased right AMY response to unfamiliar smiling infants, while Wonch et al. ( 72 ) observed a general increase in BOLD response in the right AMY. The latter also reported decreased bilateral AMY-right insular cortex connectivity when participants viewed faces of their own versus other infants. Lenzi et al. ( 68 ) found increased deactivation in the orbital and medial PFC and an increase in right AMY reactivity. Finnegan et al. ( 67 ) observed a differential response to infant versus non-infant stimuli in brain regions such as the right dorsolateral superior, middle, and inferior frontal gyri, the left inferior and middle temporal lobe, and bilateral angular gyri. Interestingly, the authors found that a history of depressive episodes did not independently impact these neural responses. For non-infant stimuli, Moses-Kolko et al. ( 69 ) observed increased nonlinear attenuation of left ventral striatal activity after reward in PPD. In response to negative stimuli, decreased activation was observed in bilateral OFC, cingulate, putamen, precuneus, DLPFC, ACC ( 21 ), right AMY ( 21 , 71 ), left AMY and left dorsomedial PFC ( 70 ), alongside increased activity in the bilateral insula ( 21 ). For positive stimuli, decreased activity was found in striatum, cingulate gyrus, DLPFC and precentral gyrus ( 21 ). Using fNIRS, increased depression severity was found to be associated with decreased connectivity between the temporoparietal junction (TPJ) with lateral PFC and increased connectivity between TPJ with anterior medial PFC ( 73 ). In contrast, Song et al. ( 74 ) found no differences in integral or centroid values. Six studies were included in the task-based meta-analysis (21, 68–72; 67 participants) and no significant clusters were found. 3.2.1.3. Metabolic correlates An increase in monoamine oxidase A in the PFC and ACC was found in PPD ( 79 ) but no differences in D2/3 receptor binding potential ( 80 ). MRS studies identified a decrease in glutamate-glutamine (Glx) and N-acetylaspartate (NAA) levels in the left DLPFC ( 78 ). Additionally, McEwen et al. ( 77 ) found increased glutamate (Glu) levels in the medial PFC in PPD, though other metabolite levels (NAA, creatine [Cr] and choline [Cho]) did not show significant differences. There was also a trend towards decreased cortical gamma-aminobutyric acid (GABA) levels in PPD ( 76 ), although Deligiannidis et al. ( 58 ) found no significant differences in GABA/Cr concentrations in the pregenual ACC or occipital cortex. 3.2.1.4. Multimodal meta-analysis of PPD correlates In the pooled meta-analysis of all included studies (25 experiments, 542 participants), women diagnosed with PPD exhibited structural and functional changes in right putamen, right amygdala and left MFG (Fig. 2 and Table S8). Additional exploratory direction of effect analyses results are provided in supplementary Table S9 and Figure S6. [Figure 2 about here.] 3.2.1.5. Network effects In the seed-based connectivity analysis using the left DLPFC (MFG) as the seed region, significant positive connectivity was observed across several cortical areas (Fig. 3 ). Regions with the strongest connectivity (yellow) include areas of the bilateral DLPFC and angular gyrus. Additional activation is seen in adjacent prefrontal regions, as well as posterior parietal areas. Areas with lower but still significant connectivity (red) extend into occipital and temporal cortices. [Figure 3 about here.] 3.2.2. Female Non-Peripartum Major Depression vs Healthy Controls 3.2.2.1. Structural correlates In white matter, decreased fibre-density was observed in the left and right frontal projections of the corpus callosum, right anterior limb of the internal capsule, tapetum, and right inferior longitudinal fasciculus ( 82 ). Additionally, reductions in FA were widespread in the genu of the corpus callosum, bilateral cerebral peduncles, forceps minor and major, bilateral inferior fronto-occipital fasciculus ( 82 ), left bilateral uncinate fasciculi ( 81 , 82 ), inferior and superior longitudinal fasciculi ( 81 ). For gray matter, reduced GMV was found in bilateral ventral ACC ( 91 ), right AMY ( 91 , 93 ), bilateral caudate extending into the anterior nucleus of the thalamus ( 87 ), medial PFC ( 84 ), left lingual gyrus extending to the parahippocampal gyrus, cerebellum ( 92 – 93 ), bilateral insula, bilateral putamen, and caudal middle-frontal region ( 93 ). However, another study did not find GMV differences within the AMY, hippocampus, sgACC or putamen ( 87 ). Reductions in AMY volume were observed across studies ( 85 , 90 ). Nine studies were included in the structural meta-analysis (82, 84, 86–89, 91–93; 251 participants). Women diagnosed with MDD manifested structural alterations in left putamen gray matter and bilateral sub-lobar extra-nuclear white matter (Table S10; Fig. 1 ). 3.2.2.2. Functional correlates Decreased connectivity was observed between: the right AMY and the ventrolateral PFC, bilateral insula, and bilateral putamen ( 93 ); the left hippocampus and temporo-occipital regions, including the bilateral lingual gyrus and fusiform ( 103 ); and the left middle occipital gyrus and the left OFC ( 104 ). Increased connectivity was found between the left middle occipital gyrus and the left medial prefrontal gyrus and the left hippocampus ( 104 ) and between the left MFG and bilateral putamen ( 96 ). ALFF reductions were reported in the right putamen, right MTG ( 106 ), left middle occipital gyrus ( 104 ), right postcentral gyrus ( 96 ) and right superior occipital gyrus ( 105 ), while increases were observed in the left medial PFC ( 106 ), left MFG ( 102 , 106 ), left precentral gyrus ( 102 ) and left temporal pole ( 55 ). There were no observed differences in fractional ALFF in the ACC and insula ( 98 ). ReHo was elevated in the left sgACC and left thalamus ( 55 ). Conversely, BOLD signal variability was reduced in bilateral cerebellum ( 100 ) and the DLPFC ( 101 ). Nine studies were included in the resting-state meta-analysis (55, 84, 96, 100, 102–106, 192 participants). No significant clusters were found. The ACC showed increased activation during the presentation of positive stimuli ( 107 , 119 ), emotional approach and withdrawal conditions ( 109 ), incongruent conditions ( 123 ) and rejection ( 125 ), while reduced activity was observed in response to negative stimuli ( 116 ). Connectivity of the ACC was reduced with the AMY during negative stimuli and with the DLPFC during high-attention stimuli ( 120 ). Different patterns emerged in response to positive and fearful stimuli, with increased inverse connectivity between the left-sided sgACC and AMY to happy faces, and increased positive connectivity between the same regions to fearful faces ( 108 ). The DLPFC also showed increased activation during expectation of negative stimuli ( 107 ), incongruent versus congruent contrasts ( 123 ) and painful stimuli ( 110 ). In contrast, decreased activation was found during low-risk cheating choices ( 121 ) and decreased connectivity with the right AMY during high-attention stimuli ( 120 ). Reduced activity was also found in: the dorsal putamen and anterior insula during low-risk cheating choices ( 121 ) and in frontoparietal network and salience networks, irrespective of stress ( 115 ); in the right caudate during the recall of positive specific memories. On the other hand, increased activation was noted in the PCC, insula, and thalamus during the recall of negative specific memories ( 124 ). Using fNIRS, a significant correlation between depression scores and changes in oxy-Hb in the right frontal brain region was observed ( 126 ), as well as reduced oxy-Hb activation in the DLPFC ( 127 ). Fourteen studies were included in the task-based meta-analysis (107, 110–112, 116–125; 225 participants). Women diagnosed with MDD manifested alterations in the left ACC (Fig. 1 ; Table S10). 3.2.2.3. Metabolic correlates In the medial PFC, there were no significant differences in Glu, GABA, or Glx levels ( 106 , 130 ). Similarly, Glu levels in the left DLPFC were not significantly different, although there was a reduction in GABA + levels and in the GABA + to Glu ratio in this region ( 131 ). In the ACC, there was a reduction in GABA levels ( 115 ) and in NAA to phosphocreatine plus creatine (NAA/PCr + Cr) ratio ( 133 ). In the ventral prefrontal white matter, reduced Cho/Cr ratios were observed bilaterally, while NAA/Cr levels showed no significant differences ( 132 ). In the hippocampus, there were increased total Cho levels ( 103 ) and decreased taurine concentration ( 129 ). The only PET imaging study revealed a global reduction in regionally normalized cerebral blood flow in the DLPFC ( 134 ). 3.2.2.4. Multimodal meta-analysis of fMDD neural correlates In the pooled meta-analysis of all included studies (32 experiments, 652 participants, 107, 110–112, 116–125), women diagnosed with MDD exhibited changes in right putamen and amygdala (Fig. 2 ). Direction of effect analyses are provided in supplementary Table S11 and Figure S7. In order to investigate if there was a potential effect of age in the fMDD sample, we calculated an exploratory subgroup analysis for fMDD studies that was age-matched to PPD samples. The general pattern of ALE results was maintained in this age-matched exploratory analysis (supplementary Figure S8). 3.2.2.5. Network effects Following the seed-based functional connectivity analysis using the right amygdala as the seed region (Fig. 3 ), regions with the strongest connectivity (yellow) include somatosensory and motor cortices and anterior temporal lobe. Lower connectivity (in red) extends into the posterior temporal cortex and insula. 3.2.3. Peripartum Depression vs Non-Peripartum Female Major Depression 3.2.3.1. Structural correlates Both PPD and fMDD show widespread reductions in FA in several tracts, including the superior longitudinal fasciculus ( 41 , 81 ). PPD is associated with increased GMV in the right insula and right precentral gyrus ( 43 , 44 ), while MDD exhibits reduced GMV in the same areas ( 89 , 93 ). Both conditions show reduced AMY volumes ( 46 , 91 , 93 ). 3.2.3.2. Functional correlates PDD and fMDD present both shared and distinct patterns of altered brain function. Shared findings include increased ALFF in left medial PFC ( 54 , 106 ), increased ReHo in thalamus ( 55 ) and decreased activity in the ACC and cingulate in response to negative stimuli ( 21 , 116 ). Additionally, there is reduced activity in the cingulate gyrus in response to positive stimuli (21 116), and reduced AMY-insula connectivity ( 72 , 93 ). However, PPD is characterized by decreased activity in AMY and precuneus, along with increased activity in the insula, in response to negative stimuli ( 21 , 71 ). Conversely, fMDD has increased activity in the AMY and precuneus and decreased activity in the insula ( 107 , 116 ). One study directly compared PPD, fMDD and HC to identity shared and different resting-state neural circuits ( 55 ). Both PPD and fMDD groups showed higher fALFF in the left temporal pole (vs HC). The fMDD group showed a specifically increased FC in the right cerebellum, whereas PPD had specifically decreased fALFF in the left supplementary motor area and the posterior MTG, and reduced posterior MTG-precuneus and left-right sgACC connectivity. Additionally, there were significant ReHo differences in the left thalamus and left sgACC (PPD > fMDD > HC). 3.2.3.3. Metabolic correlates In the medial PFC, PPD shows increased Glu levels ( 77 ). In fMDD, however, there were no significant differences in Glu, GABA, or Glx concentrations in this region ( 106 , 130 ). 3.2.3.4. Conjunction and contrast analyses The conjunction analysis (57 experiments, 1194 participants) identified regions in the right insula, left ventral lateral nucleus (thalamus), left caudate, right amygdala, left cingulate gyrus and bilateral putamina demonstrating convergent brain changes in both PPD and fMDD (Table S12; Fig. 4 ). [Figure 4 about here.] 3.2.3.5 Network effects Dice coefficients for PPD and fMDD were low for both symptom networks: 0.0858 (PPD/anxiosomatic), 0.0924 (PPD/dysphoric), 0.0845 (fMDD/anxiosomatic) and 0.0995 (fMDD/dysphoric). 3.2.3.6 Specificity of results The dummy dataset of randomly assigned coordinates did not result in any significant cluster accounting for strict thresholding. This suggests that our meta-analytical results on PPD are not merely the consequence of compiling heterogeneous study results for analysis. 4. Discussion This systematic review and meta-analysis aimed at exploring the neural correlates of PPD and compares them with non-peripartum fMDD. We discuss our findings within the theoretical framework of network biotypes proposed by Leanne Williams’ team ( 135 – 137 ), which include the DMN, salience (SN), frontoparietal attention (AN), negative affect (NA), positive affect (PA) and CCN. These biotypes have been shown to be psychometrically reliable and were recently validated in patients with depression and anxiety ( 138 ). 4.1. Neural alterations in PPD Our comprehensive analysis identified structural, functional, and metabolic alterations within the SN, NA, DMN, and CCN networks in women experiencing PPD, compared to HC. The meta-analysis pinpointed changes in the left MFG/DLPFC and right putamen and AMY, while the qualitative synthesis additionally highlighted alterations in the ACC, insula, medial PFC, OFC, putamen, thalamus, hippocampus, PCC, precuneus, MTG, superior frontal, fusiform, cingulate, precentral and angular gyri. These alterations in networks governing emotional and cognitive processes may contribute to the diverse manifestation of symptoms and behaviors in PPD. The SN (encompassing the ACC, the anterior insula and the temporal pole) plays a crucial role in detecting salient internal sensations and external changes, guiding cognitive processing/control and coordinating behavioral responses essential for threat detection ( 139 ), particularly relevant in motherhood ( 11 ). Disruptions in this circuit may contribute to anxious arousal and avoidance behaviors (i.e., difficulties in discerning relevant cues and avoidance of overwhelming situations; 135, 136) and biased affective processing. In PPD, reduced ACC activity and increased insula activity in response to negative stimuli was found, as well as sgACC-insula hyperconnectivity and ACC-AMY hypoconnectivity. These disruptions can manifest as symptoms commonly observed in PPD, including anxiety, somatic complaints (e.g., fatigability, sleep disturbances; 140) and psychomotor symptoms (agitation/restlessness; 17, 141). The CCN (involving the DLPFC, the dorsal ACC and the dorsal parietal cortex) is crucial for higher executive functions, namely working memory, selective attention, and cognitive flexibility ( 135 , 136 ). Due to its heightened connectivity with networks involved in emotion and reward, the CCN may have down-stream effects on affective circuits ( 142 , 143 ). Reduced activity in response to both negative and positive stimuli in DLPFC was found in PPD patients, alongside decreased Glx and NAA levels and increased GMV. Additionally, the left DLPFC had a strong connectivity with areas of the frontoparietal network and occipital and temporal cortices. Dysfunction in the CCN has been associated with heightened anxiety anticipation ( 15 ) and inattention/cognitive dyscontrol (poor concentration, difficulty paying attention, indecisiveness; 144), as well as maladaptive emotion regulation (e.g., difficulties in suppressing negative emotions). In depressed postpartum women, impaired concentration/decision-making are prominent symptoms ( 17 ). Also, studies have shown that emotion regulation difficulties are associated with depressive and anxiety symptoms during pregnancy and across the postpartum period ( 145 , 146 ). Finally, impairments in cognitive control, including repetitive negative thinking and worry, are particularly relevant for the postpartum period due to the executive function demands of parenting, which include planning, attention and working memory abilities ( 147 ). Recent findings of cognitive biotypes of MDD (with impairments in executive function and response inhibition, insomnia, and poor psychosocial function; 148) and cognitive mechanisms of postpartum depression ( 149 ) may indicate that cognitive control dysfunction underlies depression in general ( 147 ) and constitutes a transdiagnostic factor, as many psychiatric disorders are associated with deficits in cognitive control ( 150 ). However, negative repetitive thinking in peripartum women with depressive symptoms tends to focus on peripartum-specific concerns within self, motherhood, and interpersonal domains, such as unmet high expectations, thoughts about harming the infant and parenting efficacy, which highlights the existence of factors unique to the peripartum period ( 17 , 151 ). Dysfunction in the SN and CCN can also contribute to negative interpersonal and attentional biases ( 15 ). Women with PPD exhibit a negative bias perception of ambiguous and distressful infant stimuli, which has been associated with an increased vulnerability for PPD ( 152 ) and may impair mothers’ evaluations of their parenting and ability to detect and respond to their infants' needs, thereby affecting maternal sensitivity ( 153 ). The DMN (medial PFC, PCC, angular gyrus, TPJ) is a resting-state network involved in self-referential processing, emotion regulation, mentalizing and metacognitive processing of psychological states, which has been associated with self-criticism and rumination ( 154 ). In PPD, a consistent pattern of attenuated resting-state connectivity within the DMN has been found, particularly between the PCC-AMY, TPJ-anterior medial PFC and the dorsomedial PFC, precuneus, PCC and the angular gyrus. Reduced medial PFC activity in response to negative stimuli and increased Glu levels has also been observed. This DMN dysfunction might contribute to negative self-perceptions, feeling overwhelmed, excessive worry about parenting abilities, and difficulty disengaging from negative thoughts, leading to increased feelings of guilt in PPD ( 17 ). Additionally, altered DMN function may impact how attentive the mother is to her newborn, potentially contributing to the bonding deficits commonly observed in PPD ( 50 ). 4.2. Comparative analysis with non-peripartum female MDD Our comparison between women diagnosed with PPD and non-peripartum women diagnosed with MDD revealed shared alterations in regions of the DMN (e.g., increased ALFF in medial PFC), SN (e.g., decreased activity in ACC to negative stimuli, higher fALFF in left temporal pole) and NA (e.g., AMY-insula hypoconnectivity). These altered correlates suggest a shared dysfunction in emotion processing, threat sensitivity and mood regulation in female depression subtypes. However, distinct structural and functional patterns emerged across networks (SN, NA and AN; qualitative comparison). In PPD, there was increased volume and activity in the right insula in response to negative stimuli, as well as decreased activity in AMY and precuneus. In contrast, fMDD was associated with reduced GMV and decreased activity in the insula, alongside increased activity in AMY and precuneus in response to negative stimuli. Additionally, a study directly comparing PPD and fMDD ( 55 ) discovered that fMDD had increased DC in the right cerebellum, whereas PPD showed decreased fALFF in the left supplementary motor area and the posterior MTG, and reduced MTG-precuneus and left-right sgACC connectivity. These differences extend to the metabolic level, where, in the medial PFC, women with PPD show increased Glu levels, while no significant differences were present in fMDD. Finally, exploratory analyses suggested low correspondence between PPD and fMDD ALE maps and common symptom networks ( 39 ). This could be due to distinctive underlying mechanisms, symptom interactions, or network connectivity for PPD and fMDD, in the anxiosomatic and the dysphoric domains. Further systematic research is needed to unravel the neurosymptomatic interactions in PPD and fMDD. We further observed reduced AMY activation in response to negative stimuli in PPD and an increased response to positive infant stimuli. This is in contrast with findings in non-peripartum MDD literature, where patients commonly exhibit amygdala hypoactivity in response to positive stimuli and heightened amygdala activation when exposed to negative stimuli. Notably, O’Brien and colleagues ( 157 ) compared women with a history of postpartum depression with women with a history of non-postpartum MDD during the late luteal phase of the menstrual cycle. Their findings revealed hypoactivity in response to positive emotional faces in the right amygdala in women with previous PPD. These inconsistent neural profiles of hyper and hypo-amygdalar activity support the hypothesis of different biotypes of neural circuit dysfunction ( 136 ) and that women with and at risk for PPD may constitute a unique subgroup with divergent sensitivity to hormonal influences ( 155 ). Furthermore, research has shown that PPD is also a heterogeneous disorder comprised of different clinical subtypes (e.g., 156, 157), based on timing of onset, duration and severity, which can be distinguished considering biological, psychological and social factors ( 17 ). For example, Fox and colleagues ( 156 ) found six different symptom clusters of postpartum depression, namely worry (e.g., anxiety and guilt), anger, emotional/circadian/energetic dysregulation (e.g., agitation, fatigue, sadness), appetite, somatic/cognitive (e.g., inability to focus) and distress display (e.g., crying, sad affect display), which may reflect the different neural networks involved (e.g., SN, NA, and CCN). In summary, while there are shared neural mechanisms underlying depressive disorders, the peripartum period may introduce distinct neurobiological changes that contribute to a specific manifestation of depression. The influence of hormonal fluctuations, reproductive-related neuroadaptations, and the socio-environmental context during the peripartum period may contribute to the observed differences ( 20 ). However, several confounding factors may influence the neural correlates observed in PPD and its comparison with fMDD. Firstly, PPD generally affects women within a narrow reproductive age range (18–49 years), whereas fMDD spans a broader age range (from young adulthood to later life) and contexts (not exclusively related to the peripartum period). These variations in age can introduce significant differences in brain structure, hormones, and life circumstances, which may hinder the interpretation of neural and clinical findings. Research comparing PPD and fMDD in women aged 21–42 identified both overlapping and distinct resting-state neural circuits ( 55 ). Our ALE results were maintained when selecting a subgroup of age-matched fMDD studies. Secondly, previous history of depressive episodes (a strong psychological risk factor for PPD; 5, 17) may lead to long-term changes in neural circuits. In our review, only 51% of studies focused on first-episode PPD, leaving potential prior episodes unaccounted for. 4.3. Clinical relevance Recognizing differences in symptom presentation and neural correlates between PPD and non-peripartum MDD can help improve the identification of PPD cases ( 109 ). Considering the unique psychosocial and physiological changes, alongside altered brain function across the peripartum period, our findings are in line with the need for the extension of the onset specifier to one year postpartum ( 17 ). Treatment for PPD usually follows standard MDD guidelines and is based on pharmacotherapy or psychotherapy (e.g., 158) to reduce symptoms, improve quality of life and general functioning ( 20 ). Although serotonin reuptake inhibitors (SSRIs) are among the first-line treatments for PPD ( 159 ), pregnant or breastfeeding women often present concerns regarding side effects and potential effects on fetal and infant development ( 160 ). As a result, treatment may be refused, or doses may be reduced below what is clinically advised ( 161 ). To achieve widespread access to high-quality peripartum mental health care, new solutions are therefore required. Brain circuit organization and function have emerged as both an explanatory model and a foundation for designing interventions, aiming to address major circuits and neurotransmitter pathways disrupted in psychiatric disorders ( 162 ). The evolving approach of targeting specific brain circuits associated with distinct symptom clusters offers a promising avenue for more personalized treatment strategies ( 26 , 162 ). Tailoring therapeutic approaches based on neural signatures may enhance the effectiveness of treatments for both MDD and PPD and, with increasing evidence, may guide intervention choice. For example, non-invasive brain stimulation techniques (NIBS) have been proposed as alternatives to traditional therapies for PPD, with meta-analyses hinting towards the effectiveness, safety and acceptability of repetitive transcranial magnetic stimulation (rTMS) treatment for PPD ( 163 – 165 ). Understanding the disrupted neural networks involved in PPD will allow for personalized interventions (e.g., increased precision in defining the target areas for stimulation). Additionally, while SSRIs and other treatment approaches may impact biological transdiagnostic factors, the unique physiological changes of the peripartum period calls for more tailored treatments, such as addressing GABAergic dysfunction ( 20 ). Evidence also suggests that distinct activation patterns in AMY-PFC can predict treatment responses, especially to antidepressants ( 137 ). Thus, neuroimaging measures hold promise for guiding the selection of the most effective treatment for different psychiatric biotypes. 4.4. Future directions and limitations In conclusion, this systematic review and meta-analysis provides a comprehensive overview of the neural correlates of PPD, offering insights into both shared and distinct features compared to non-peripartum fMDD. The identified alterations in brain regions associated with emotion processing and cognitive functions emphasize the need for targeted interventions in the management of PPD. Future research, guided by larger, well-controlled longitudinal studies, is crucial for advancing our understanding of the neurobiological underpinnings of PPD and informing innovative treatment approaches. Despite the contributions of this study, some limitations must be acknowledged. The majority of studies focused exclusively on the postpartum period, leaving a gap in our understanding of the antenatal neural changes associated with PPD. Emerging evidence highlights differences between pre- and postpartum symptom networks, suggesting that antenatal and postpartum maternal mood and anxiety may have different presentations. Additionally, this study does not quantitatively compare PPD and fMDD, but instead performs indirect comparisons through their respective differences with HC or correlation with depression severity. In relation to MDD, although we attempted to minimize the effects of sex by considering female only participants, studies did not report on previous history or current pregnancy, which may impact the comparison results. Moreover, bias in studies, including insufficient participant descriptions and confounding factor control, poses challenges to the generalizability of findings. Analyses split for different modalities suggest that cortical effects in ALE are largely driven by resting-state results, which might be explained by the higher proportion of these studies. For fMDD some results were stronger driven by task-based fMRI or structural imaging. Although this might be a limitation to interpretability, we would like to stress that some clusters in the multimodal analysis only emerged via merging cross-modal data. The exclusion of other non-English language studies (e.g., Chinese) may impact the comprehensiveness of the review. Although we conducted an additional exploratory search for studies in Portuguese, Spanish, German, French, Dutch, and Greek in the PubMed database, this search did not yield any additional articles that met our inclusion criteria. We highlight the need for future studies in PPD to adequately characterize participants (e.g., postpartum timepoint, parity, previous history of depression) and data acquisition parameters. It would be further interesting to compare different pre-processing protocols (quality control, pre-processing steps, correction for multiple comparisons) and assess the validity and replicability of implemented paradigms. Additionally, future synthesis may consider further specificity testing (i.e., how the neural correlates of PPD differ from those of other brain disorders associated with pregnancy and childbirth) and individual profiles of participants, through individual participant data meta-analysis (IPD; 166). Finally, exploring antenatal neural changes, investigating the influence of hormonal fluctuations, and considering socio-environmental factors will further enrich our understanding of PPD, as well as studies directly comparing PPD and fMDD while considering confounding variables such as reproductive age, psychosocial stress factors and peripartum timepoints. Declarations Funding Mónica Sobral is supported by a doctoral grant from the Portuguese Foundation for Science and Technology (FCT; 2021.07006.BD). The Center for Research in Neuropsychology and Cognitive and Behavioral Intervention (CINEICC) of the Faculty of Psychology and Educational Sciences of the University of Coimbra is supported by national funds through FCT – Fundação para a Ciência e Tecnologia, by project reference UIDP/00730/2020. Acknowledgements This paper is part of the COST Action Riseup-PPD CA18138 and was supported by COST under COST Action Riseup-PPD CA18138. Sara Cruz is supported by the Psychology for Development Research Center, Lusíada University, Portugal, supported by FCT – Fundação para a Ciência e Tecnologia, I.P., by project reference UIDB/04375/2020 and DOI identifier . Ana Ganho-Ávila is supported by the Portuguese Foundation for Science and Technology (FCT) Grants 2020.02059.CEECIND (https://doi.org/10.54499/2020.02059.CEECIND/CP1609/CT0015). We thank Professors Allison Nugent, Ian Gotlib, Robert Wolf, Gabriel Robert, Chaejoon Cheong and Karl-Jürgen Bär for the additional information provided upon request. Conflict of interest The authors state no conflict of interest. Contributors M.S.: conceptualization, methodology, formal analysis, investigation, writing-– original draft preparation, writing - review and editing, visualization; R.G.: conceptualization, methodology, formal analysis, investigation, writing - review and editing; M.R.: methodology (selection and data extraction process), writing - review and editing; M.V.: methodology (selection and data extraction process), writing - review and editing; S.C.: writing - review and editing; F.P.: methodology (selection process), writing - review and editing; V.M.: conceptualization, methodology (selection process), writing - review and editing; R.P-C: methodology (selection process), writing - review and editing; J.P-N.: methodology (selection process), writing - review and editing; A.W.: conceptualization, writing - review and editing; H.M.: conceptualization, writing - review and editing; M.T.: conceptualization, writing - review and editing, validation; A.G.-A: conceptualization, validation, writing - review and editing, supervision; A-L.S.: conceptualization, methodology, formal analysis, investigation, validation, writing - review and editing, supervision. 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J Nurse Pract. 2023;19(2):104488. Riley RD, Lambert PC, Abo-Zaid G. Meta-analysis of individual participant data: rationale, conduct, and reporting. BMJ. 2010;340:c221. Tables Tables 1 and 2 are not available with this version. Additional Declarations The authors declare no competing interests. Supplementary Files SupplementaryMaterialPPD.docx Supplementary Materials Cite Share Download PDF Status: Published Journal Publication published 07 Sep, 2025 Read the published version in Molecular Psychiatry → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7008488","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":478307795,"identity":"a3deb9ca-b53b-4a26-825c-da44eff91bdf","order_by":0,"name":"Mónica Sobral","email":"","orcid":"https://orcid.org/0000-0001-6003-1931","institution":"University of Coimbra, Center for Research in Neuropsychology and Cognitive-Behavior Interventions, Faculty of Psychology and Educational Sciences, Coimbra, Portugal; Human Developmental Sciences Graduate Program and Mackenzie Center for Research in Childhood and Adolescence, Center for Biological and Health Sciences, Mackenzie Presbyterian University, São Paulo, Brazil","correspondingAuthor":false,"prefix":"","firstName":"Mónica","middleName":"","lastName":"Sobral","suffix":""},{"id":478307796,"identity":"2da49e90-0902-4096-bb0e-39b99c90f36a","order_by":1,"name":"Raquel Guiomar","email":"","orcid":"","institution":"University of Coimbra, Center for Research in Neuropsychology and Cognitive-Behavior Interventions, Faculty of Psychology and Educational Sciences, Coimbra, Portugal","correspondingAuthor":false,"prefix":"","firstName":"Raquel","middleName":"","lastName":"Guiomar","suffix":""},{"id":478307797,"identity":"cdba7f68-9201-4b26-a4b4-7669e2bbe578","order_by":2,"name":"Manya Rezaeian","email":"","orcid":"","institution":"Counseling Center of Tehran University, Tehran, Iran","correspondingAuthor":false,"prefix":"","firstName":"Manya","middleName":"","lastName":"Rezaeian","suffix":""},{"id":478307798,"identity":"61eb0597-6ff2-4ac9-b73d-8be27fab2436","order_by":3,"name":"Maria Vasileiadi","email":"","orcid":"","institution":"Medical University of Vienna, Vienna, Austria","correspondingAuthor":false,"prefix":"","firstName":"Maria","middleName":"","lastName":"Vasileiadi","suffix":""},{"id":478307799,"identity":"2ea67a62-fcf3-4dc2-9244-3bbe21e15b06","order_by":4,"name":"Sara Cruz","email":"","orcid":"","institution":"Department of Psychology, School of Philosophy, Psychology \u0026 Language Sciences, University of Edinburgh, UK","correspondingAuthor":false,"prefix":"","firstName":"Sara","middleName":"","lastName":"Cruz","suffix":""},{"id":478307800,"identity":"ccd1a2e5-f122-4610-8ba5-c78f13cb44c0","order_by":5,"name":"Francisca Pacheco","email":"","orcid":"","institution":"University of Coimbra, Center for Research in Neuropsychology and Cognitive-Behavior Interventions, Faculty of Psychology and Educational Sciences, Coimbra, Portugal","correspondingAuthor":false,"prefix":"","firstName":"Francisca","middleName":"","lastName":"Pacheco","suffix":""},{"id":478307801,"identity":"122ee9ae-591f-4f99-9d4c-b19b670310f4","order_by":6,"name":"Vera Mateus","email":"","orcid":"","institution":"University of Coimbra, Center for Research in Neuropsychology and Cognitive-Behavior Interventions, Faculty of Psychology and Educational Sciences, Coimbra, Portugal","correspondingAuthor":false,"prefix":"","firstName":"Vera","middleName":"","lastName":"Mateus","suffix":""},{"id":478307802,"identity":"f948e8f8-979e-4b38-a0a8-f253170c67fb","order_by":7,"name":"Roser Palau-Costafreda","email":"","orcid":"","institution":"ESIMar (Mar Nursing School), Parc de Salut Mar, Universitat Pompeu Fabra-affiliated, Barcelona, Spain. SDHEd (Social Determinants and Health Education Research Group), IMIM (Hospital del Mar Medical Research Institute), Barcelona, Spain","correspondingAuthor":false,"prefix":"","firstName":"Roser","middleName":"","lastName":"Palau-Costafreda","suffix":""},{"id":478307803,"identity":"67bea58b-cc3f-4877-b3d0-1ef2a3297d51","order_by":8,"name":"Johanna Pozo-Neira","email":"","orcid":"","institution":"Institute of Neuroscience, Universidad Católica de Cuenca, Cuenca, Ecuador","correspondingAuthor":false,"prefix":"","firstName":"Johanna","middleName":"","lastName":"Pozo-Neira","suffix":""},{"id":478307804,"identity":"87132335-8512-4343-9fad-29b862ce9887","order_by":9,"name":"Ana Weidenauer","email":"","orcid":"","institution":"Medical University of Vienna, Vienna, Austria","correspondingAuthor":false,"prefix":"","firstName":"Ana","middleName":"","lastName":"Weidenauer","suffix":""},{"id":478307805,"identity":"bac0a99a-1259-4c9f-bbf9-737271d38660","order_by":10,"name":"Helena Moreira","email":"","orcid":"","institution":"University of Coimbra, Center for Research in Neuropsychology and Cognitive-Behavior Interventions, Faculty of Psychology and Educational Sciences, Coimbra, Portugal","correspondingAuthor":false,"prefix":"","firstName":"Helena","middleName":"","lastName":"Moreira","suffix":""},{"id":478307806,"identity":"f3bf3f56-a4e0-4d2a-bbbf-3fe1c986ceda","order_by":11,"name":"Martin Tik","email":"","orcid":"","institution":"Medical University of Vienna, Vienna, Austria; Stanford University Department of Psychiatry and Behavioral Sciences, Palo Alto, CA, USA","correspondingAuthor":false,"prefix":"","firstName":"Martin","middleName":"","lastName":"Tik","suffix":""},{"id":478307807,"identity":"be294eb6-f489-49f9-9561-39484b4993f0","order_by":12,"name":"Ana Ganho-Ávila","email":"","orcid":"","institution":"University of Coimbra, Center for Research in Neuropsychology and Cognitive-Behavior Interventions, Faculty of Psychology and Educational Sciences, Coimbra, Portugal","correspondingAuthor":false,"prefix":"","firstName":"Ana","middleName":"","lastName":"Ganho-Ávila","suffix":""},{"id":478307808,"identity":"88bc5be4-5363-448b-8347-86bfc602044a","order_by":13,"name":"Anna-Lisa Schuler","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIie2PMQrCQBBFJ9oGbdfGXGElIB5n0pjGtbGxiLJVUtoGvISl5UIgacY+gmjEC3gCMYsIVm7SCe4rh3n8/wEslp/EBagAhsDeB9VEQQC/vRLIxoqXHHKGy3O42SZV57hfQa+Q3xVO8ylDWoj0RLwrqIABGWI4c8csiFHs2Ay6Is6Bl2golmrlgSFnYfVSLpVhTKkVicgZ1sXiqE4xGJxm/gRzHOktmSDlDshULKFReY/Q62+T603s18NeoQwxnyhHZm6Lf40j1y0Ni8Vi+Qeen6JG+kMNxCsAAAAASUVORK5CYII=","orcid":"","institution":"Research Group Cognition and Plasticity, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany","correspondingAuthor":true,"prefix":"","firstName":"Anna-Lisa","middleName":"","lastName":"Schuler","suffix":""}],"badges":[],"createdAt":"2025-06-30 09:01:56","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7008488/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7008488/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41380-025-03227-2","type":"published","date":"2025-09-08T00:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":85982996,"identity":"d9414eb0-9b47-4a3e-b6b3-19cee02fd178","added_by":"auto","created_at":"2025-07-04 02:19:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":2587294,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eResults of the imaging (structural, resting-state and task-based fMRI) ALE meta-analyses showing clusters with significant ALE maxima in PPD and fMDD patients. \u003c/strong\u003eWhile results for MFG in PPD seem to have been driven by resting state data (upper panel), for fMDD subcortical results in amygdala and putamina were driven by structural and VLMT by functional investigations (lower panel).\u003c/p\u003e","description":"","filename":"Figure11.png","url":"https://assets-eu.researchsquare.com/files/rs-7008488/v1/d838636c25efb7f8e56657a7.png"},{"id":85982995,"identity":"ceb81038-850c-4a17-8b48-1a477b63c7ea","added_by":"auto","created_at":"2025-07-04 02:19:15","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":2059737,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eResults of the multimodal ALE meta-analyses showing clusters with significant ALE maxima for PPD and fMDD.\u003c/strong\u003e While multimodal analysis for PPD revealed significant clusters in the right putamen, amygdala and left MFG, in fMDD there were only subcortical clusters in the right putamen and amygdala.\u003c/p\u003e","description":"","filename":"Figure21.png","url":"https://assets-eu.researchsquare.com/files/rs-7008488/v1/1056963c3ec80c3dbb1b6559.png"},{"id":85983587,"identity":"8c7c36b2-d866-418d-b1e9-f04c8ec6996c","added_by":"auto","created_at":"2025-07-04 02:27:15","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":958034,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eResults of seed-based connectivity analysis with the left DLPFC (MFG) as a seed in PPD and right amygdala as a seed in fMDD.\u003c/strong\u003e While the PPD derived network was marked by involvement of bilateral DLPFC and angular gyrus (upper panel), fMDD derived network was marked by involvement of somatosensory, motor and anterior temporal cortices.\u003c/p\u003e","description":"","filename":"Figure31.png","url":"https://assets-eu.researchsquare.com/files/rs-7008488/v1/b78805aaf2d86027aca83ee7.png"},{"id":85983585,"identity":"7b95c34a-2c27-40e5-9ded-90604ac8fb9c","added_by":"auto","created_at":"2025-07-04 02:27:15","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3392465,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eResults of conjunction ALE meta-analysis in PPD and fMDD patients.\u003c/strong\u003e \u003cu\u003ePanel A.\u003c/u\u003e In PPD there were more prominent alterations in big parts of the temporal lobes and somatosensory cortices as compared to fMDD (warm colors), while in fMDD there was stronger involvement of DLPFC and ACC (cool colors). \u003cu\u003ePanel B.\u003c/u\u003e There was a significant overlap between PPD and fMDD in the right insula, left ventral lateral nucleus (thalamus), left caudate, right amygdala, left cingulate gyrus and bilateral putamina.\u003c/p\u003e","description":"","filename":"Figure41.png","url":"https://assets-eu.researchsquare.com/files/rs-7008488/v1/6cbed23c8ee5211ccdcb32aa.png"},{"id":90912750,"identity":"1f0ca9de-e45f-437c-822a-930dd534d200","added_by":"auto","created_at":"2025-09-09 13:52:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":11569553,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7008488/v1/4913fe6b-273f-4341-ac39-b85046c2031b.pdf"},{"id":85982999,"identity":"318bcdec-2c6a-4ebb-8693-623ee000a2c1","added_by":"auto","created_at":"2025-07-04 02:19:15","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":4947176,"visible":true,"origin":"","legend":"\u003cp\u003eSupplementary Materials\u003c/p\u003e","description":"","filename":"SupplementaryMaterialPPD.docx","url":"https://assets-eu.researchsquare.com/files/rs-7008488/v1/b369e8c17ee52d819a5b6851.docx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eNeural correlates of peripartum depression: a systematic review, meta-analysis and comparison to major depressive disorder\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003ePeripartum depression (PPD) is a major depressive disorder (MDD) with onset during pregnancy or after childbirth (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). It is characterised by sadness, restlessness/agitation, impaired concentration, and sleep/appetite disturbances (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e). Multiple systematic reviews and meta-analyses yielded an estimated prevalence of PPD ranging between 10\u0026ndash;29% (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan additionalcitationids=\"CR5 CR6\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e), constituting a serious mental health issue with well-established detrimental effects on the mother's well-being and infant\u0026rsquo;s emotional, behavioural and cognitive development (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Emerging literature has demonstrated that PPD renders altered brain structure and functional connectivity in peripartum women (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). However, the brain response patterns appear to differ from those reported in similar symptom profiles outside the peripartum period, such as MDD (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe peripartum period encompasses profound environmental/social, psychological, and hormonal changes that impact brain plasticity, influencing maternal behavior and caregiving toward infants (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Neurobiological adaptations are observed in brain regions associated with emotion processing (e.g., prefrontal cortex [PFC]), salience/threat detection (e.g., dorsal anterior cingulate cortex [ACC], anterior insula), reward/motivation (e.g., striatum, medial PFC, thalamus) and social cognition (e.g., posterior cingulate [PCC], temporoparietal regions; 11, 13). These adaptations can enhance maternal responsiveness and bonding by facilitating the acquisition of experience-dependent skills and knowledge related to motherhood tasks (e.g., threat vigilance, inferring what the infants\u0026rsquo; feelings and needs are; 11\u0026ndash;12, 14).\u003c/p\u003e \u003cp\u003eMaternal brain plasticity, alongside hormonal fluctuations and external stressors, may also increase vulnerability to peripartum mental disorders, including PPD (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Structural, functional, and molecular studies in PPD have consistently revealed changes in brain areas associated with both depression and maternal caregiving, such as the hypothalamus, amygdala (AMY), ACC, orbitofrontal (OFC) and dorsolateral prefrontal cortices (DLPFC), insula and striatum (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). The abnormal correlates in these regions may be indicative of the neural mechanisms of PPD and consequent impaired caregiving abilities (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). However, the literature is hindered by several limitations, including small sample sizes and underpowered studies (generally involving only 4 to 30 PPD women). This poses a significant challenge when attempting to interpret and synthesise the existing PPD imaging literature.\u003c/p\u003e \u003cp\u003eDiagnostically, PPD is often considered a subtype of MDD (with specifiers for peripartum onset in the Diagnostic and Statistical Manual of Mental Disorders, 5th Edition, Text Revision [DSM-5-TR] and the International Classification of Disorders, 11th Edition [ICD-11]), but other evidence suggests that PPD has distinct clinical characteristics compared to non-peripartum MDD. For instance, PPD is associated with more common and/or severe symptoms of anxiety, irritability, psychomotor restlessness and agitation, obsessive thoughts, fatigue, loss of energy and impaired concentration and decision-making, as well as specific guilt related to motherhood but less sad mood and suicidal ideation (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). However, evidence for differentiating PPD from MDD is inconsistent, partly due to differences in the definition of the postpartum period. Studies focusing on depression in the early postpartum period suggest that PPD may be characterized by unique features related to symptom severity, heritability, and epigenetic factors and may stem from biological factors (e.g., 19). In contrast, depression occurring in the later postpartum period may resemble MDD observed outside the peripartum period and be more influenced by psychosocial factors (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eReviews comparing brain response patterns in PPD and non-peripartum MDD have also revealed notable differences (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e), with reversed activation patterns in the AMY and PFC (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). For example, women diagnosed with PPD typically show a blunted AMY response to non-infant-related negative stimuli (e.g., 21), whereas MDD patients have a heightened AMY response (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Understanding these distinct neurobiological profiles is essential for developing appropriate treatment approaches, as depression related to the female reproductive cycle (such as PPD) may represent a distinct biotype (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSeveral reviews have explored the neurobiological underpinnings of PPD (e.g., 15, 23, 24), but a comprehensive meta-analysis or direct comparison with female-only non-peripartum unipolar depression (fMDD) remains lacking in the literature. To address this gap, our study aims to extend previous reviews of the neural correlates of PPD relative to the healthy postpartum brain and to conduct a formal comparison with fMDD in relation to the healthy female brain. Considering symptom presentations observed in PPD (e.g., obsessive thoughts, increased anxiety and impaired concentration and decision-making; 17), our focus is on the cognitive control network (CCN), particularly the ACC-DLPFC axis, due to its involvement in emotional and social regulation, its interaction with attention and default mode networks (DMN) and impact on treatment outcomes (\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e).\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Registration and protocol\u003c/h2\u003e \u003cp\u003eThe review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines (28; checklist in supplementary Table S1). The study protocol was preregistered on the International Prospective Register of Systematic Reviews (PROSPERO; CRD42021281870).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Eligibility criteria\u003c/h2\u003e \u003cp\u003eWe included English peer-reviewed original studies in which: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) participants presented PPD (search 1) or MDD (search 2) and no other clinical diagnoses, except for anxiety symptoms/diagnosis; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) all participants were aged between 18\u0026ndash;60 years old; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) for PPD, participants were assessed from pregnancy up to 1 year postpartum (current pregnancy/postpartum); (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) structural, functional or metabolic differences were assessed using structural or functional magnetic resonance imaging (MRI), diffusion tensor/kurtosis imaging (DTI/DKI), computed tomography (CT), positron emission tomography (PET), near-infrared spectroscopy (NIRS) or magnetic resonance spectroscopy (MRS); (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) a within- or between-group comparison or correlation with symptom severity was reported. For the quantitative analysis, peak coordinates of significant contrast of interest findings were reported in Montreal Neurological Institute (MNI) or Talairach standard spaces. Studies with imaging modalities lacking coordinate information, such as NIRS and MRS, were included in the qualitative synthesis of results. Based on the authors expertise an additional search was performed for Portuguese, Spanish, German, French, Dutch, and Greek articles in the PubMed database. This search did not result in any additional hits.\u003c/p\u003e \u003cp\u003eAdditional exclusion criteria were: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) nonempirical studies (e.g., review, meta-analysis); (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) not published in English language or any language native to the authors; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) depressive disorder diagnosed prior to pregnancy (past history or euthymic patients excluded); (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) patients were subject to an intervention, unless the study reported a baseline comparison with healthy controls (HC); (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) electroencephalogram (EEG) or magnetoencephalogram (MEG) studies.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Information sources and search strategy\u003c/h2\u003e \u003cp\u003eA systematic literature search was performed in the PubMed, PsycINFO (through Ovid), and Embase electronic databases, from inception until 24 February 2021. Updated searches were conducted on 27 July 2024. Search terms related to different imaging modalities including \u0026ldquo;magnetic resonance imaging\u0026rdquo;, \u0026ldquo;magnetic resonance spectroscopy\u0026rdquo;, \u0026ldquo;diffusion tensor imaging\u0026rdquo;, \u0026ldquo;computed tomography\u0026rdquo;, \"near infrared spectrometry\" or \u0026ldquo;positron emission tomography\u0026rdquo; and terms denoting \u0026ldquo;pregnancy\u0026rdquo; and \u0026ldquo;birth\u0026rdquo; and \u0026ldquo;depression\u0026rdquo; were included. For the search strategy on fMDD-related correlates, terms denoting \u0026ldquo;pregnancy\u0026rdquo; and \u0026ldquo;birth\u0026rdquo; were removed. The complete search strategy for all databases is reported in supplementary Table S2. Manual searches of reference lists from relevant reviews and included studies were also conducted to identify any additional studies that met the eligibility criteria.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Study selection and data extraction\u003c/h2\u003e \u003cp\u003eIn search 1, the title and abstract screening, as well as the full-text review and data extraction of retrieved reports, were conducted independently by two researchers (MR [search 1])/MS [updated search] and RG). For Search 2, due to the high number of results, seven reviewers (MS, RG, MR, MV, FP, RP-C, JP-N) were involved in screening the abstracts and full-texts and data extraction of the reports, with one designated reviewer (MS) cross-verifying all the decisions made by the team to ensure consistency and accuracy. The screening was performed using the Rayyan software (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Any conflicts or discrepancies during this process were resolved through discussions until a consensus was reached.\u003c/p\u003e \u003cp\u003eThe extracted data consisted of demographic and clinical data (number of participants, age, age range, diagnostic criteria and/or assessment instrument and cut-off scores, comorbidity, symptom severity, treatment status, parity [only in search 1], peripartum timepoint [only in search 1] and pregnancy history [only in search 2]), methodological details (study design, imaging technique, task/measure) and main findings (direction of effect, qualitative and peak coordinates of significant correlates).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Risk of bias assessment\u003c/h2\u003e \u003cp\u003eWe assessed the risk of bias in the included studies in PPD and fMDD using the Newcastle-Ottawa Scale (NOS; 30) for cohort studies and the Joanna Briggs Institute (JBI) Critical Appraisal Checklist for Analytical Cross-Sectional Studies (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e) for cross-sectional studies. The assessment was conducted independently by two reviewers (MS, RG). Disagreements were resolved by consensus. Following the literature, we derived an overall summary risk of bias judgement in NOS (low quality [0\u0026ndash;2 items], moderate quality [3\u0026ndash;5 items] and good/high quality [6\u0026ndash;7 items]) and JBI (low quality [0\u0026ndash;3 items]; moderate quality [4\u0026ndash;5 items]; high quality [6\u0026ndash;8 items]). While the overall appraisal results serve as an additional source of information regarding the quality of the studies, they were not used to exclude any reports.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Synthesis methods and statistical analysis\u003c/h2\u003e \u003cp\u003eReports were grouped based on the imaging modality (structural, task-based and resting-state functional and molecular imaging ) and synthesised using a tabular and narrative format. For the comparison analysis, we selected a subgroup of MDD studies (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;55) focused exclusively on female participants to minimize sex effects. We also contacted the authors of these studies to request additional information about participants\u0026rsquo; previous and current history of pregnancy (a possible confounding factor).\u003c/p\u003e \u003cp\u003eA coordinate-based ALE meta-analysis was conducted to combine peak coordinates from included studies, using GingerALE 3.0.2 (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://brainmap.org/\u003c/span\u003e\u003cspan address=\"https://brainmap.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e; 32,33). Prior to analysis, all activation foci coordinates reported in Talairach space (n\u0026thinsp;=\u0026thinsp;10) were transformed into MNI space using the Lancaster transform, implemented in the GingerALE toolbox (icbm2tal; 32). To avoid repeated inclusion of the same sample and spurious findings (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e), we carefully examined the included studies for overlap (team members, location, recruitment interval, sample size and age) and aggregated data when it coincided (n\u0026thinsp;=\u0026thinsp;3; 35\u0026ndash;37). We used GingerALE's less conservative gray matter mask and subject-based Full-Width Half-Max (FWHM) values (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). To address our research question, imaging method-specific differences (PPD / fMDD vs HC as measured by structural MRI/DTI, resting-state and task-based fMRI). For the PPD / fMDD multimodal analyses, all coordinates from multiple experiments (i.e., different imaging modalities or tasks and contrasts) of the same study were merged, guaranteeing that each sample was only represented by one experiment. We then performed conjunction and contrast analyses between PPD vs fMDD. Exploratory analyses were conducted to contrast PDD and fMDD with HC considering: direction of effect (PPD / fMDD brain structure / function greater than HC and PPD / fMDD less than HC). To assess the impact of age, we conducted an exploratory analysis by considering only a subgroup of studies with age-matched fMDD participants.\u003c/p\u003e \u003cp\u003eAccording to best practices (\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e), for the imaging-specific and multimodal, results were thresholded for significance using cluster-level inference of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 with a cluster-forming threshold of p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, with 10000 thresholding permutations. For contrast analyses (PPD vs. fMDD) significance level was set to a p-value of below 0.001 shuffling through 10000 permutations. For the exploratory direction of effect, conjunction and age-matched analyses, we adopted a less conservative statistical threshold of uncorrected p\u0026thinsp;\u0026lt;\u0026thinsp;0.05. All resulting coordinate clusters are reported in MNI space and overlaid on an MNI-normalised template using MRIcroGL (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.mccauslandcenter.sc.edu/mricrogl/\u003c/span\u003e\u003cspan address=\"http://www.mccauslandcenter.sc.edu/mricrogl/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) or Surf Ice (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.nitrc.org/projects/surfice/\u003c/span\u003e\u003cspan address=\"https://www.nitrc.org/projects/surfice/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAdditionally, we submitted the MNI coordinates of peak values to Neurosynth (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.neurosynth.org\u003c/span\u003e\u003cspan address=\"http://www.neurosynth.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) to explore functional networks in PPD and fMDD, through seed-based connectivity analysis. Moreover, in order to evaluate the overlap between common depression symptom networks and PPD and fMDD, we calculated dice indices between the dysphoric and anxiosomatic networks according to Sidiqqi et al. (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn order to account for specificity of our PPD ALE results we created a dummy dataset (n\u0026thinsp;=\u0026thinsp;25) consisting of comparable numbers of participants and MNI-coordinates to the PPD dataset. The dummy data can be found in the Supplementary Materials.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Study selection\u003c/h2\u003e \u003cp\u003eOur database search yielded 1048 records for search 1 (PPD) and 13338 for search 2 (MDD). After removing duplicates, we carefully screened 704 and 9399 records and conducted a thorough review of 60 and 916 full-text documents, respectively. We included 45 articles that met our inclusion criteria for PPD (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan additionalcitationids=\"CR41 CR42 CR43 CR44 CR45 CR46 CR47 CR48 CR49 CR50 CR51 CR52 CR53 CR54 CR55 CR56 CR57 CR58 CR59 CR60 CR61 CR62 CR63 CR64 CR65 CR66 CR67 CR68 CR69 CR70 CR71 CR72 CR73 CR74 CR75 CR76 CR77 CR78 CR79\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e) and 55 for fMDD (55; 81\u0026ndash;134; a flow chart is available in supplementary Figure S1). For a multimodal meta-analysis of both female and male MDD participants (literature review until 2021), please refer to supplementary Table S3 and Figure S2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Study characteristics and risk of bias\u003c/h2\u003e \u003cp\u003eDemographic, methodological and outcome characteristics of the included studies in PPD and fMDD are summarised in Tables\u0026nbsp;1 and 2 (a comparison of PPD and fMDD studies main characteristics is available in supplementary Table S4 and Figures S3-S5). PPD reports include data on DTI/DKI (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;3), structural MRI (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7), resting-state (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;21), and task-based fMRI (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;8), fNIRS (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2), MRS (n\u0026thinsp;=\u0026thinsp;5) and PET (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2). PPD was diagnosed according to standardized diagnostic criteria (e.g., DSM; major depressive episode with peripartum onset) in most studies, except for four studies where cut-off scores from validated self-report questionnaires were used (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e). The majority of reports (96%) focused exclusively on the postpartum period, ranging from early (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan additionalcitationids=\"CR42 CR43 CR44\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan additionalcitationids=\"CR58\" citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan additionalcitationids=\"CR65 CR66\" citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e, \u003cspan additionalcitationids=\"CR70 CR71\" citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e, \u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e) to late postpartum (\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e) and unspecified (up to 1 year postpartum; 40, 46\u0026ndash;48, 51, 55\u0026ndash;56, 60, 63, 79), with only two studies collecting data antenatally (2nd or 3rd trimester; 53\u0026ndash;54).\u003c/p\u003e \u003cp\u003eTwelve studies indicated concomitant anxiety disorders/symptoms (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e, \u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e, \u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e), while the remaining studies did not report any clinical comorbidity. Additionally, several studies reported first episode PPD (history of previous mental disorder excluded, including depression; n\u0026thinsp;=\u0026thinsp;23; 21, 35\u0026ndash;37, 40\u0026ndash;41, 43\u0026ndash;44, 47\u0026ndash;49, 51\u0026ndash;56, 59\u0026ndash;61, 64\u0026ndash;65, 71), although others included participants with a previous history of non-peripartum MDD (n\u0026thinsp;=\u0026thinsp;8; 42, 57\u0026ndash;58, 67, 70, 76\u0026ndash;78) and/or previous history of PPD (n\u0026thinsp;=\u0026thinsp;5; 42, 57\u0026ndash;58, 70, 76). Regarding treatment status, participants across studies were either treatment-naive, not undergoing treatment at the time of the experiment or medication-free, while in four studies antidepressants or psychotherapy were accepted (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e, \u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e, \u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor fMDD, studies used DTI/DKI/DWI (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2), structural MRI (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;12), resting-state (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;15) and task-based fMRI (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;19), NIRS (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2), MRS (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9) and PET (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1). The majority of studies did not provide information on the previous history or current pregnancy status of participants, with 24 studies explicitly excluding pregnant and/or breastfeeding participants (81, 85\u0026ndash;86, 90, 92, 96, 98, 99\u0026ndash;103, 105, 108, 113, 120, 122, 124, 126, 129\u0026ndash;131, 133\u0026ndash;134. Eighteen studies included participants in the reproductive stage (18\u0026ndash;49 years old; 55, 88, 95\u0026ndash;96, 98, 100, 101, 104, 105, 113, 115, 117, 122, 126, 129\u0026ndash;131, 133), while in the remaining studies the age range surpassed 50 years or was unspecified. All studies used standardized diagnostic criteria to assess MDD, except for one study where the method used is unclear (\u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOf the studies reviewed, most reported no comorbid conditions, while in four anxiety disorders or symptoms were present (\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e, \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e, \u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e108\u003c/span\u003e). Nine studies had either unavailable or unclear data regarding clinical comorbidity (\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e, \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e109\u003c/span\u003e, \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e112\u003c/span\u003e, \u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e113\u003c/span\u003e, \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e115\u003c/span\u003e, \u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e118\u003c/span\u003e, \u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e124\u003c/span\u003e, \u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e130\u003c/span\u003e, \u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e131\u003c/span\u003e). Finally, 29 studies were conducted with participants who were antidepressant/medication free or naive (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e, \u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e, \u003cspan citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e, \u003cspan additionalcitationids=\"CR92\" citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e, \u003cspan additionalcitationids=\"CR96\" citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e, \u003cspan additionalcitationids=\"CR100\" citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e, \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e, \u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e, \u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e109\u003c/span\u003e, \u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e110\u003c/span\u003e, \u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e114\u003c/span\u003e, \u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e115\u003c/span\u003e, \u003cspan additionalcitationids=\"CR123\" citationid=\"CR122\" class=\"CitationRef\"\u003e122\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e124\u003c/span\u003e, \u003cspan additionalcitationids=\"CR129 CR130 CR131 CR132 CR133\" citationid=\"CR128\" class=\"CitationRef\"\u003e128\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR134\" class=\"CitationRef\"\u003e134\u003c/span\u003e), while in 22 studies participants were using antidepressants, undergoing neuromodulation, or receiving psychotherapy (\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e, \u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e, \u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e, \u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e, \u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e, \u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e, \u003cspan additionalcitationids=\"CR107\" citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e108\u003c/span\u003e, \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e111\u003c/span\u003e, \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e112\u003c/span\u003e, \u003cspan additionalcitationids=\"CR117 CR118 CR119 CR120\" citationid=\"CR116\" class=\"CitationRef\"\u003e116\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e121\u003c/span\u003e, \u003cspan additionalcitationids=\"CR126\" citationid=\"CR125\" class=\"CitationRef\"\u003e125\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR127\" class=\"CitationRef\"\u003e127\u003c/span\u003e). Four studies had unclear or unavailable data regarding current treatment status (\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e, \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e, \u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e, \u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e113\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e[Tables\u0026nbsp;1 and 2 about here.]\u003c/p\u003e \u003cp\u003eOverall, studies ranged from moderate to high quality (supplementary tables S5-S7). Notably, bias in cross-sectional designs primarily stemmed from a lack of detailed descriptions regarding study participants and design (e.g., failing to specify the postpartum timepoint) and an inadequate identification and control for confounding factors. Concerning cohort studies, a prevalent source of bias centred around the adequacy of follow-up, with instances of follow-up rates falling below 80% or lacking sufficient information.\u003c/p\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1. Peripartum Depression vs Healthy Controls\u003c/h2\u003e \u003cdiv id=\"Sec13\" class=\"Section4\"\u003e \u003ch2\u003e3.2.1.1. Structural correlates\u003c/h2\u003e \u003cp\u003eIn white matter, PPD has been associated with increased mean diffusivity (MD) in temporo-parietal areas, superior longitudinal fasciculus, corticospinal tract, cingulum, body and splenium of the corpus callosum, external capsule, internal capsule, inferior longitudinal fasciculus, and putamen (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e). Additionally, decreased fractional anisotropy (FA) has been found in the superior longitudinal fasciculus, corticospinal tract, thalamus (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e), and in the left anterior limb of the internal capsule (\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e), along with reduced radial diffusivity (RD) in the cingulum tract (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e). In contrast, increased FA was observed in the right anterior thalamic radiation and cingulum tracts (\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn grey matter, PPD participants had increased volume (GMV) in the left DLPFC (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e), right anterior insula (\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e) and OFC (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e) and reduced GMV in AMY (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e). Significant volumetric differences were also noted in the right ACC and left middle-PCC (\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e), as well as increased cortical thickness (CT) in the left superior frontal gyrus, cuneus and fusiform gyrus (\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e), but decreased CT in the right inferior parietal lobule (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e). Additionally, increased surface area was observed in the left superior frontal gyrus, caudal middle frontal gyrus (MFG), middle temporal gyrus (MTG) and insula, along with increased mean curvature in the parietal lobules (\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFour studies (\u003cspan additionalcitationids=\"CR43\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e) on 136 participants were included in the structural meta-analysis and no significant clusters were identified.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section4\"\u003e \u003ch2\u003e3.2.1.2. Functional correlates\u003c/h2\u003e \u003cp\u003eIn the PFC, increased resting-state connectivity was observed between the left DLPFC and right ACC (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e), while connectivity was decreased between the dorsomedial PFC and left ventral striatum (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e), precuneus and PCC (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e) and between the DLPFC, ACC and AMY (\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e). Decreased sample entropy was found in the left medial PFC (\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e), as well as reduced regional homogeneity (ReHo) in left DLPFC (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e) and decreased voxel-mirrored homotopic connectivity (VMHC) in bilateral dorsomedial PFC (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e). Increased values in amplitude of low-frequency fluctuations (ALFF) were found in left medial PFC and DLPFC (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e). For the OFC, increased connectivity was observed with the right MFG and left inferior occipital gyrus (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e), as well as decreased ALFF (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e) and VMHC (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRegarding the ACC, its subgenual part (sgACC) showed increased connectivity with the ventral anterior insula (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e) and decreased connectivity with the superior and MTG (\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e). Within the right hippocampus, degree centrality and ReHo were increased (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e), as well as connectivity with the left precuneus and left superior frontal gyrus (\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e), while connectivity was reduced with the right MFG and left median cingulate and paracingulate gyri (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e). The PCC showed reduced connectivity with the right AMY (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e) and with the right paracentral lobule (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e) and increased ReHo (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e). Finally, ReHo and VHMC reductions were found in the right insula (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e) and AMY (\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEighteen studies were included in the resting-state meta-analysis (16 experiments; 35\u0026ndash;37, 43, 50, 51, 53\u0026ndash;59, 61\u0026ndash;65; 367 participants), which highlighted abnormalities in the left MFG (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Table S8).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e[Figure \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e about here]\u003c/p\u003e \u003cp\u003eStudies using infant stimuli in fMRI report differential emotional processing in PPD. Specifically, Dudin et al. (\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e) found an increased right AMY response to unfamiliar smiling infants, while Wonch et al. (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e) observed a general increase in BOLD response in the right AMY. The latter also reported decreased bilateral AMY-right insular cortex connectivity when participants viewed faces of their own versus other infants. Lenzi et al. (\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e) found increased deactivation in the orbital and medial PFC and an increase in right AMY reactivity. Finnegan et al. (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e) observed a differential response to infant versus non-infant stimuli in brain regions such as the right dorsolateral superior, middle, and inferior frontal gyri, the left inferior and middle temporal lobe, and bilateral angular gyri. Interestingly, the authors found that a history of depressive episodes did not independently impact these neural responses.\u003c/p\u003e \u003cp\u003eFor non-infant stimuli, Moses-Kolko et al. (\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e) observed increased nonlinear attenuation of left ventral striatal activity after reward in PPD. In response to negative stimuli, decreased activation was observed in bilateral OFC, cingulate, putamen, precuneus, DLPFC, ACC (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e), right AMY (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e), left AMY and left dorsomedial PFC (\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e), alongside increased activity in the bilateral insula (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). For positive stimuli, decreased activity was found in striatum, cingulate gyrus, DLPFC and precentral gyrus (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Using fNIRS, increased depression severity was found to be associated with decreased connectivity between the temporoparietal junction (TPJ) with lateral PFC and increased connectivity between TPJ with anterior medial PFC (\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e). In contrast, Song et al. (\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e) found no differences in integral or centroid values.\u003c/p\u003e \u003cp\u003eSix studies were included in the task-based meta-analysis (21, 68\u0026ndash;72; 67 participants) and no significant clusters were found.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section4\"\u003e \u003ch2\u003e3.2.1.3. Metabolic correlates\u003c/h2\u003e \u003cp\u003eAn increase in monoamine oxidase A in the PFC and ACC was found in PPD (\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e) but no differences in D2/3 receptor binding potential (\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e). MRS studies identified a decrease in glutamate-glutamine (Glx) and N-acetylaspartate (NAA) levels in the left DLPFC (\u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e). Additionally, McEwen et al. (\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e) found increased glutamate (Glu) levels in the medial PFC in PPD, though other metabolite levels (NAA, creatine [Cr] and choline [Cho]) did not show significant differences. There was also a trend towards decreased cortical gamma-aminobutyric acid (GABA) levels in PPD (\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e), although Deligiannidis et al. (\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e) found no significant differences in GABA/Cr concentrations in the pregenual ACC or occipital cortex.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section4\"\u003e \u003ch2\u003e3.2.1.4. Multimodal meta-analysis of PPD correlates\u003c/h2\u003e \u003cp\u003eIn the pooled meta-analysis of all included studies (25 experiments, 542 participants), women diagnosed with PPD exhibited structural and functional changes in right putamen, right amygdala and left MFG (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table S8). Additional exploratory direction of effect analyses results are provided in supplementary Table S9 and Figure S6.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e[Figure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e about here.]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section4\"\u003e \u003ch2\u003e3.2.1.5. Network effects\u003c/h2\u003e \u003cp\u003eIn the seed-based connectivity analysis using the left DLPFC (MFG) as the seed region, significant positive connectivity was observed across several cortical areas (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Regions with the strongest connectivity (yellow) include areas of the bilateral DLPFC and angular gyrus. Additional activation is seen in adjacent prefrontal regions, as well as posterior parietal areas. Areas with lower but still significant connectivity (red) extend into occipital and temporal cortices.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e[Figure \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e about here.]\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2. Female Non-Peripartum Major Depression vs Healthy Controls\u003c/h2\u003e \u003cdiv id=\"Sec19\" class=\"Section4\"\u003e \u003ch2\u003e3.2.2.1. Structural correlates\u003c/h2\u003e \u003cp\u003eIn white matter, decreased fibre-density was observed in the left and right frontal projections of the corpus callosum, right anterior limb of the internal capsule, tapetum, and right inferior longitudinal fasciculus (\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e). Additionally, reductions in FA were widespread in the genu of the corpus callosum, bilateral cerebral peduncles, forceps minor and major, bilateral inferior fronto-occipital fasciculus (\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e), left bilateral uncinate fasciculi (\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e), inferior and superior longitudinal fasciculi (\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFor gray matter, reduced GMV was found in bilateral ventral ACC (\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e), right AMY (\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e), bilateral caudate extending into the anterior nucleus of the thalamus (\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e), medial PFC (\u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e), left lingual gyrus extending to the parahippocampal gyrus, cerebellum (\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e), bilateral insula, bilateral putamen, and caudal middle-frontal region (\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e). However, another study did not find GMV differences within the AMY, hippocampus, sgACC or putamen (\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e). Reductions in AMY volume were observed across studies (\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e, \u003cspan citationid=\"CR90\" class=\"CitationRef\"\u003e90\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eNine studies were included in the structural meta-analysis (82, 84, 86\u0026ndash;89, 91\u0026ndash;93; 251 participants). Women diagnosed with MDD manifested structural alterations in left putamen gray matter and bilateral sub-lobar extra-nuclear white matter (Table S10; Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section4\"\u003e \u003ch2\u003e3.2.2.2. Functional correlates\u003c/h2\u003e \u003cp\u003eDecreased connectivity was observed between: the right AMY and the ventrolateral PFC, bilateral insula, and bilateral putamen (\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e); the left hippocampus and temporo-occipital regions, including the bilateral lingual gyrus and fusiform (\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e); and the left middle occipital gyrus and the left OFC (\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e). Increased connectivity was found between the left middle occipital gyrus and the left medial prefrontal gyrus and the left hippocampus (\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e) and between the left MFG and bilateral putamen (\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e). ALFF reductions were reported in the right putamen, right MTG (\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e), left middle occipital gyrus (\u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e), right postcentral gyrus (\u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e) and right superior occipital gyrus (\u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e), while increases were observed in the left medial PFC (\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e), left MFG (\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e, \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e), left precentral gyrus (\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e) and left temporal pole (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e). There were no observed differences in fractional ALFF in the ACC and insula (\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e). ReHo was elevated in the left sgACC and left thalamus (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e). Conversely, BOLD signal variability was reduced in bilateral cerebellum (\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e) and the DLPFC (\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e). Nine studies were included in the resting-state meta-analysis (55, 84, 96, 100, 102\u0026ndash;106, 192 participants). No significant clusters were found.\u003c/p\u003e \u003cp\u003eThe ACC showed increased activation during the presentation of positive stimuli (\u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e, \u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e119\u003c/span\u003e), emotional approach and withdrawal conditions (\u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e109\u003c/span\u003e), incongruent conditions (\u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e123\u003c/span\u003e) and rejection (\u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e125\u003c/span\u003e), while reduced activity was observed in response to negative stimuli (\u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e116\u003c/span\u003e). Connectivity of the ACC was reduced with the AMY during negative stimuli and with the DLPFC during high-attention stimuli (\u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e120\u003c/span\u003e). Different patterns emerged in response to positive and fearful stimuli, with increased inverse connectivity between the left-sided sgACC and AMY to happy faces, and increased positive connectivity between the same regions to fearful faces (\u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e108\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe DLPFC also showed increased activation during expectation of negative stimuli (\u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e), incongruent versus congruent contrasts (\u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e123\u003c/span\u003e) and painful stimuli (\u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e110\u003c/span\u003e). In contrast, decreased activation was found during low-risk cheating choices (\u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e121\u003c/span\u003e) and decreased connectivity with the right AMY during high-attention stimuli (\u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e120\u003c/span\u003e). Reduced activity was also found in: the dorsal putamen and anterior insula during low-risk cheating choices (\u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e121\u003c/span\u003e) and in frontoparietal network and salience networks, irrespective of stress (\u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e115\u003c/span\u003e); in the right caudate during the recall of positive specific memories. On the other hand, increased activation was noted in the PCC, insula, and thalamus during the recall of negative specific memories (\u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e124\u003c/span\u003e). Using fNIRS, a significant correlation between depression scores and changes in oxy-Hb in the right frontal brain region was observed (\u003cspan citationid=\"CR126\" class=\"CitationRef\"\u003e126\u003c/span\u003e), as well as reduced oxy-Hb activation in the DLPFC (\u003cspan citationid=\"CR127\" class=\"CitationRef\"\u003e127\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFourteen studies were included in the task-based meta-analysis (107, 110\u0026ndash;112, 116\u0026ndash;125; 225 participants). Women diagnosed with MDD manifested alterations in the left ACC (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e; Table S10).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section4\"\u003e \u003ch2\u003e3.2.2.3. Metabolic correlates\u003c/h2\u003e \u003cp\u003eIn the medial PFC, there were no significant differences in Glu, GABA, or Glx levels (\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e, \u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e130\u003c/span\u003e). Similarly, Glu levels in the left DLPFC were not significantly different, although there was a reduction in GABA\u0026thinsp;+\u0026thinsp;levels and in the GABA\u0026thinsp;+\u0026thinsp;to Glu ratio in this region (\u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e131\u003c/span\u003e). In the ACC, there was a reduction in GABA levels (\u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e115\u003c/span\u003e) and in NAA to phosphocreatine plus creatine (NAA/PCr\u0026thinsp;+\u0026thinsp;Cr) ratio (\u003cspan citationid=\"CR133\" class=\"CitationRef\"\u003e133\u003c/span\u003e). In the ventral prefrontal white matter, reduced Cho/Cr ratios were observed bilaterally, while NAA/Cr levels showed no significant differences (\u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e132\u003c/span\u003e). In the hippocampus, there were increased total Cho levels (\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e) and decreased taurine concentration (\u003cspan citationid=\"CR129\" class=\"CitationRef\"\u003e129\u003c/span\u003e). The only PET imaging study revealed a global reduction in regionally normalized cerebral blood flow in the DLPFC (\u003cspan citationid=\"CR134\" class=\"CitationRef\"\u003e134\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section4\"\u003e \u003ch2\u003e3.2.2.4. Multimodal meta-analysis of fMDD neural correlates\u003c/h2\u003e \u003cp\u003eIn the pooled meta-analysis of all included studies (32 experiments, 652 participants, 107, 110\u0026ndash;112, 116\u0026ndash;125), women diagnosed with MDD exhibited changes in right putamen and amygdala (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Direction of effect analyses are provided in supplementary Table S11 and Figure S7. In order to investigate if there was a potential effect of age in the fMDD sample, we calculated an exploratory subgroup analysis for fMDD studies that was age-matched to PPD samples. The general pattern of ALE results was maintained in this age-matched exploratory analysis (supplementary Figure S8).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section4\"\u003e \u003ch2\u003e3.2.2.5. Network effects\u003c/h2\u003e \u003cp\u003eFollowing the seed-based functional connectivity analysis using the right amygdala as the seed region (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e), regions with the strongest connectivity (yellow) include somatosensory and motor cortices and anterior temporal lobe. Lower connectivity (in red) extends into the posterior temporal cortex and insula.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3. Peripartum Depression vs Non-Peripartum Female Major Depression\u003c/h2\u003e \u003cdiv id=\"Sec25\" class=\"Section4\"\u003e \u003ch2\u003e3.2.3.1. Structural correlates\u003c/h2\u003e \u003cp\u003eBoth PPD and fMDD show widespread reductions in FA in several tracts, including the superior longitudinal fasciculus (\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e). PPD is associated with increased GMV in the right insula and right precentral gyrus (\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e), while MDD exhibits reduced GMV in the same areas (\u003cspan citationid=\"CR89\" class=\"CitationRef\"\u003e89\u003c/span\u003e, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e). Both conditions show reduced AMY volumes (\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section4\"\u003e \u003ch2\u003e3.2.3.2. Functional correlates\u003c/h2\u003e \u003cp\u003ePDD and fMDD present both shared and distinct patterns of altered brain function. Shared findings include increased ALFF in left medial PFC (\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e, \u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e), increased ReHo in thalamus (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e) and decreased activity in the ACC and cingulate in response to negative stimuli (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e116\u003c/span\u003e). Additionally, there is reduced activity in the cingulate gyrus in response to positive stimuli (21 116), and reduced AMY-insula connectivity (\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e, \u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e). However, PPD is characterized by decreased activity in AMY and precuneus, along with increased activity in the insula, in response to negative stimuli (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e). Conversely, fMDD has increased activity in the AMY and precuneus and decreased activity in the insula (\u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e, \u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e116\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOne study directly compared PPD, fMDD and HC to identity shared and different resting-state neural circuits (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e). Both PPD and fMDD groups showed higher fALFF in the left temporal pole (vs HC). The fMDD group showed a specifically increased FC in the right cerebellum, whereas PPD had specifically decreased fALFF in the left supplementary motor area and the posterior MTG, and reduced posterior MTG-precuneus and left-right sgACC connectivity. Additionally, there were significant ReHo differences in the left thalamus and left sgACC (PPD\u0026thinsp;\u0026gt;\u0026thinsp;fMDD\u0026thinsp;\u0026gt;\u0026thinsp;HC).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section4\"\u003e \u003ch2\u003e3.2.3.3. Metabolic correlates\u003c/h2\u003e \u003cp\u003eIn the medial PFC, PPD shows increased Glu levels (\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e). In fMDD, however, there were no significant differences in Glu, GABA, or Glx concentrations in this region (\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e, \u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e130\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section4\"\u003e \u003ch2\u003e3.2.3.4. Conjunction and contrast analyses\u003c/h2\u003e \u003cp\u003eThe conjunction analysis (57 experiments, 1194 participants) identified regions in the right insula, left ventral lateral nucleus (thalamus), left caudate, right amygdala, left cingulate gyrus and bilateral putamina demonstrating convergent brain changes in both PPD and fMDD (Table S12; Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e[Figure \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e about here.]\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section4\"\u003e \u003ch2\u003e3.2.3.5 Network effects\u003c/h2\u003e \u003cp\u003eDice coefficients for PPD and fMDD were low for both symptom networks: 0.0858 (PPD/anxiosomatic), 0.0924 (PPD/dysphoric), 0.0845 (fMDD/anxiosomatic) and 0.0995 (fMDD/dysphoric).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section4\"\u003e \u003ch2\u003e3.2.3.6 Specificity of results\u003c/h2\u003e \u003cp\u003eThe dummy dataset of randomly assigned coordinates did not result in any significant cluster accounting for strict thresholding. This suggests that our meta-analytical results on PPD are not merely the consequence of compiling heterogeneous study results for analysis.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThis systematic review and meta-analysis aimed at exploring the neural correlates of PPD and compares them with non-peripartum fMDD. We discuss our findings within the theoretical framework of network biotypes proposed by Leanne Williams\u0026rsquo; team (\u003cspan additionalcitationids=\"CR136\" citationid=\"CR135\" class=\"CitationRef\"\u003e135\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR137\" class=\"CitationRef\"\u003e137\u003c/span\u003e), which include the DMN, salience (SN), frontoparietal attention (AN), negative affect (NA), positive affect (PA) and CCN. These biotypes have been shown to be psychometrically reliable and were recently validated in patients with depression and anxiety (\u003cspan citationid=\"CR138\" class=\"CitationRef\"\u003e138\u003c/span\u003e).\u003c/p\u003e \u003cdiv id=\"Sec32\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Neural alterations in PPD\u003c/h2\u003e \u003cp\u003eOur comprehensive analysis identified structural, functional, and metabolic alterations within the SN, NA, DMN, and CCN networks in women experiencing PPD, compared to HC. The meta-analysis pinpointed changes in the left MFG/DLPFC and right putamen and AMY, while the qualitative synthesis additionally highlighted alterations in the ACC, insula, medial PFC, OFC, putamen, thalamus, hippocampus, PCC, precuneus, MTG, superior frontal, fusiform, cingulate, precentral and angular gyri. These alterations in networks governing emotional and cognitive processes may contribute to the diverse manifestation of symptoms and behaviors in PPD.\u003c/p\u003e \u003cp\u003eThe SN (encompassing the ACC, the anterior insula and the temporal pole) plays a crucial role in detecting salient internal sensations and external changes, guiding cognitive processing/control and coordinating behavioral responses essential for threat detection (\u003cspan citationid=\"CR139\" class=\"CitationRef\"\u003e139\u003c/span\u003e), particularly relevant in motherhood (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Disruptions in this circuit may contribute to anxious arousal and avoidance behaviors (i.e., difficulties in discerning relevant cues and avoidance of overwhelming situations; 135, 136) and biased affective processing. In PPD, reduced ACC activity and increased insula activity in response to negative stimuli was found, as well as sgACC-insula hyperconnectivity and ACC-AMY hypoconnectivity. These disruptions can manifest as symptoms commonly observed in PPD, including anxiety, somatic complaints (e.g., fatigability, sleep disturbances; 140) and psychomotor symptoms (agitation/restlessness; 17, 141).\u003c/p\u003e \u003cp\u003eThe CCN (involving the DLPFC, the dorsal ACC and the dorsal parietal cortex) is crucial for higher executive functions, namely working memory, selective attention, and cognitive flexibility (\u003cspan citationid=\"CR135\" class=\"CitationRef\"\u003e135\u003c/span\u003e, \u003cspan citationid=\"CR136\" class=\"CitationRef\"\u003e136\u003c/span\u003e). Due to its heightened connectivity with networks involved in emotion and reward, the CCN may have down-stream effects on affective circuits (\u003cspan citationid=\"CR142\" class=\"CitationRef\"\u003e142\u003c/span\u003e, \u003cspan citationid=\"CR143\" class=\"CitationRef\"\u003e143\u003c/span\u003e). Reduced activity in response to both negative and positive stimuli in DLPFC was found in PPD patients, alongside decreased Glx and NAA levels and increased GMV. Additionally, the left DLPFC had a strong connectivity with areas of the frontoparietal network and occipital and temporal cortices. Dysfunction in the CCN has been associated with heightened anxiety anticipation (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e) and inattention/cognitive dyscontrol (poor concentration, difficulty paying attention, indecisiveness; 144), as well as maladaptive emotion regulation (e.g., difficulties in suppressing negative emotions). In depressed postpartum women, impaired concentration/decision-making are prominent symptoms (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Also, studies have shown that emotion regulation difficulties are associated with depressive and anxiety symptoms during pregnancy and across the postpartum period (\u003cspan citationid=\"CR145\" class=\"CitationRef\"\u003e145\u003c/span\u003e, \u003cspan citationid=\"CR146\" class=\"CitationRef\"\u003e146\u003c/span\u003e). Finally, impairments in cognitive control, including repetitive negative thinking and worry, are particularly relevant for the postpartum period due to the executive function demands of parenting, which include planning, attention and working memory abilities (\u003cspan citationid=\"CR147\" class=\"CitationRef\"\u003e147\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eRecent findings of cognitive biotypes of MDD (with impairments in executive function and response inhibition, insomnia, and poor psychosocial function; 148) and cognitive mechanisms of postpartum depression (\u003cspan citationid=\"CR149\" class=\"CitationRef\"\u003e149\u003c/span\u003e) may indicate that cognitive control dysfunction underlies depression in general (\u003cspan citationid=\"CR147\" class=\"CitationRef\"\u003e147\u003c/span\u003e) and constitutes a transdiagnostic factor, as many psychiatric disorders are associated with deficits in cognitive control (\u003cspan citationid=\"CR150\" class=\"CitationRef\"\u003e150\u003c/span\u003e). However, negative repetitive thinking in peripartum women with depressive symptoms tends to focus on peripartum-specific concerns within self, motherhood, and interpersonal domains, such as unmet high expectations, thoughts about harming the infant and parenting efficacy, which highlights the existence of factors unique to the peripartum period (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR151\" class=\"CitationRef\"\u003e151\u003c/span\u003e). Dysfunction in the SN and CCN can also contribute to negative interpersonal and attentional biases (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Women with PPD exhibit a negative bias perception of ambiguous and distressful infant stimuli, which has been associated with an increased vulnerability for PPD (\u003cspan citationid=\"CR152\" class=\"CitationRef\"\u003e152\u003c/span\u003e) and may impair mothers\u0026rsquo; evaluations of their parenting and ability to detect and respond to their infants' needs, thereby affecting maternal sensitivity (\u003cspan citationid=\"CR153\" class=\"CitationRef\"\u003e153\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe DMN (medial PFC, PCC, angular gyrus, TPJ) is a resting-state network involved in self-referential processing, emotion regulation, mentalizing and metacognitive processing of psychological states, which has been associated with self-criticism and rumination (\u003cspan citationid=\"CR154\" class=\"CitationRef\"\u003e154\u003c/span\u003e). In PPD, a consistent pattern of attenuated resting-state connectivity within the DMN has been found, particularly between the PCC-AMY, TPJ-anterior medial PFC and the dorsomedial PFC, precuneus, PCC and the angular gyrus. Reduced medial PFC activity in response to negative stimuli and increased Glu levels has also been observed. This DMN dysfunction might contribute to negative self-perceptions, feeling overwhelmed, excessive worry about parenting abilities, and difficulty disengaging from negative thoughts, leading to increased feelings of guilt in PPD (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). Additionally, altered DMN function may impact how attentive the mother is to her newborn, potentially contributing to the bonding deficits commonly observed in PPD (\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec33\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Comparative analysis with non-peripartum female MDD\u003c/h2\u003e \u003cp\u003eOur comparison between women diagnosed with PPD and non-peripartum women diagnosed with MDD revealed shared alterations in regions of the DMN (e.g., increased ALFF in medial PFC), SN (e.g., decreased activity in ACC to negative stimuli, higher fALFF in left temporal pole) and NA (e.g., AMY-insula hypoconnectivity). These altered correlates suggest a shared dysfunction in emotion processing, threat sensitivity and mood regulation in female depression subtypes.\u003c/p\u003e \u003cp\u003eHowever, distinct structural and functional patterns emerged across networks (SN, NA and AN; qualitative comparison). In PPD, there was increased volume and activity in the right insula in response to negative stimuli, as well as decreased activity in AMY and precuneus. In contrast, fMDD was associated with reduced GMV and decreased activity in the insula, alongside increased activity in AMY and precuneus in response to negative stimuli. Additionally, a study directly comparing PPD and fMDD (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e) discovered that fMDD had increased DC in the right cerebellum, whereas PPD showed decreased fALFF in the left supplementary motor area and the posterior MTG, and reduced MTG-precuneus and left-right sgACC connectivity. These differences extend to the metabolic level, where, in the medial PFC, women with PPD show increased Glu levels, while no significant differences were present in fMDD. Finally, exploratory analyses suggested low correspondence between PPD and fMDD ALE maps and common symptom networks (\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e). This could be due to distinctive underlying mechanisms, symptom interactions, or network connectivity for PPD and fMDD, in the anxiosomatic and the dysphoric domains. Further systematic research is needed to unravel the neurosymptomatic interactions in PPD and fMDD.\u003c/p\u003e \u003cp\u003eWe further observed reduced AMY activation in response to negative stimuli in PPD and an increased response to positive infant stimuli. This is in contrast with findings in non-peripartum MDD literature, where patients commonly exhibit amygdala hypoactivity in response to positive stimuli and heightened amygdala activation when exposed to negative stimuli. Notably, O\u0026rsquo;Brien and colleagues (\u003cspan citationid=\"CR157\" class=\"CitationRef\"\u003e157\u003c/span\u003e) compared women with a history of postpartum depression with women with a history of non-postpartum MDD during the late luteal phase of the menstrual cycle. Their findings revealed hypoactivity in response to positive emotional faces in the right amygdala in women with previous PPD. These inconsistent neural profiles of hyper and hypo-amygdalar activity support the hypothesis of different biotypes of neural circuit dysfunction (\u003cspan citationid=\"CR136\" class=\"CitationRef\"\u003e136\u003c/span\u003e) and that women with and at risk for PPD may constitute a unique subgroup with divergent sensitivity to hormonal influences (\u003cspan citationid=\"CR155\" class=\"CitationRef\"\u003e155\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurthermore, research has shown that PPD is also a heterogeneous disorder comprised of different clinical subtypes (e.g., 156, 157), based on timing of onset, duration and severity, which can be distinguished considering biological, psychological and social factors (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). For example, Fox and colleagues (\u003cspan citationid=\"CR156\" class=\"CitationRef\"\u003e156\u003c/span\u003e) found six different symptom clusters of postpartum depression, namely worry (e.g., anxiety and guilt), anger, emotional/circadian/energetic dysregulation (e.g., agitation, fatigue, sadness), appetite, somatic/cognitive (e.g., inability to focus) and distress display (e.g., crying, sad affect display), which may reflect the different neural networks involved (e.g., SN, NA, and CCN).\u003c/p\u003e \u003cp\u003eIn summary, while there are shared neural mechanisms underlying depressive disorders, the peripartum period may introduce distinct neurobiological changes that contribute to a specific manifestation of depression. The influence of hormonal fluctuations, reproductive-related neuroadaptations, and the socio-environmental context during the peripartum period may contribute to the observed differences (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). However, several confounding factors may influence the neural correlates observed in PPD and its comparison with fMDD. Firstly, PPD generally affects women within a narrow reproductive age range (18\u0026ndash;49 years), whereas fMDD spans a broader age range (from young adulthood to later life) and contexts (not exclusively related to the peripartum period). These variations in age can introduce significant differences in brain structure, hormones, and life circumstances, which may hinder the interpretation of neural and clinical findings. Research comparing PPD and fMDD in women aged 21\u0026ndash;42 identified both overlapping and distinct resting-state neural circuits (\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e). Our ALE results were maintained when selecting a subgroup of age-matched fMDD studies. Secondly, previous history of depressive episodes (a strong psychological risk factor for PPD; 5, 17) may lead to long-term changes in neural circuits. In our review, only 51% of studies focused on first-episode PPD, leaving potential prior episodes unaccounted for.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec34\" class=\"Section2\"\u003e \u003ch2\u003e4.3. Clinical relevance\u003c/h2\u003e \u003cp\u003eRecognizing differences in symptom presentation and neural correlates between PPD and non-peripartum MDD can help improve the identification of PPD cases (\u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e109\u003c/span\u003e). Considering the unique psychosocial and physiological changes, alongside altered brain function across the peripartum period, our findings are in line with the need for the extension of the onset specifier to one year postpartum (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eTreatment for PPD usually follows standard MDD guidelines and is based on pharmacotherapy or psychotherapy (e.g., 158) to reduce symptoms, improve quality of life and general functioning (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Although serotonin reuptake inhibitors (SSRIs) are among the first-line treatments for PPD (\u003cspan citationid=\"CR159\" class=\"CitationRef\"\u003e159\u003c/span\u003e), pregnant or breastfeeding women often present concerns regarding side effects and potential effects on fetal and infant development (\u003cspan citationid=\"CR160\" class=\"CitationRef\"\u003e160\u003c/span\u003e). As a result, treatment may be refused, or doses may be reduced below what is clinically advised (\u003cspan citationid=\"CR161\" class=\"CitationRef\"\u003e161\u003c/span\u003e). To achieve widespread access to high-quality peripartum mental health care, new solutions are therefore required.\u003c/p\u003e \u003cp\u003eBrain circuit organization and function have emerged as both an explanatory model and a foundation for designing interventions, aiming to address major circuits and neurotransmitter pathways disrupted in psychiatric disorders (\u003cspan citationid=\"CR162\" class=\"CitationRef\"\u003e162\u003c/span\u003e). The evolving approach of targeting specific brain circuits associated with distinct symptom clusters offers a promising avenue for more personalized treatment strategies (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR162\" class=\"CitationRef\"\u003e162\u003c/span\u003e). Tailoring therapeutic approaches based on neural signatures may enhance the effectiveness of treatments for both MDD and PPD and, with increasing evidence, may guide intervention choice. For example, non-invasive brain stimulation techniques (NIBS) have been proposed as alternatives to traditional therapies for PPD, with meta-analyses hinting towards the effectiveness, safety and acceptability of repetitive transcranial magnetic stimulation (rTMS) treatment for PPD (\u003cspan additionalcitationids=\"CR164\" citationid=\"CR163\" class=\"CitationRef\"\u003e163\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR165\" class=\"CitationRef\"\u003e165\u003c/span\u003e). Understanding the disrupted neural networks involved in PPD will allow for personalized interventions (e.g., increased precision in defining the target areas for stimulation).\u003c/p\u003e \u003cp\u003eAdditionally, while SSRIs and other treatment approaches may impact biological transdiagnostic factors, the unique physiological changes of the peripartum period calls for more tailored treatments, such as addressing GABAergic dysfunction (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Evidence also suggests that distinct activation patterns in AMY-PFC can predict treatment responses, especially to antidepressants (\u003cspan citationid=\"CR137\" class=\"CitationRef\"\u003e137\u003c/span\u003e). Thus, neuroimaging measures hold promise for guiding the selection of the most effective treatment for different psychiatric biotypes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec35\" class=\"Section2\"\u003e \u003ch2\u003e4.4. Future directions and limitations\u003c/h2\u003e \u003cp\u003eIn conclusion, this systematic review and meta-analysis provides a comprehensive overview of the neural correlates of PPD, offering insights into both shared and distinct features compared to non-peripartum fMDD. The identified alterations in brain regions associated with emotion processing and cognitive functions emphasize the need for targeted interventions in the management of PPD. Future research, guided by larger, well-controlled longitudinal studies, is crucial for advancing our understanding of the neurobiological underpinnings of PPD and informing innovative treatment approaches.\u003c/p\u003e \u003cp\u003eDespite the contributions of this study, some limitations must be acknowledged. The majority of studies focused exclusively on the postpartum period, leaving a gap in our understanding of the antenatal neural changes associated with PPD. Emerging evidence highlights differences between pre- and postpartum symptom networks, suggesting that antenatal and postpartum maternal mood and anxiety may have different presentations. Additionally, this study does not quantitatively compare PPD and fMDD, but instead performs indirect comparisons through their respective differences with HC or correlation with depression severity. In relation to MDD, although we attempted to minimize the effects of sex by considering female only participants, studies did not report on previous history or current pregnancy, which may impact the comparison results. Moreover, bias in studies, including insufficient participant descriptions and confounding factor control, poses challenges to the generalizability of findings. Analyses split for different modalities suggest that cortical effects in ALE are largely driven by resting-state results, which might be explained by the higher proportion of these studies. For fMDD some results were stronger driven by task-based fMRI or structural imaging. Although this might be a limitation to interpretability, we would like to stress that some clusters in the multimodal analysis only emerged via merging cross-modal data. The exclusion of other non-English language studies (e.g., Chinese) may impact the comprehensiveness of the review. Although we conducted an additional exploratory search for studies in Portuguese, Spanish, German, French, Dutch, and Greek in the PubMed database, this search did not yield any additional articles that met our inclusion criteria.\u003c/p\u003e \u003cp\u003eWe highlight the need for future studies in PPD to adequately characterize participants (e.g., postpartum timepoint, parity, previous history of depression) and data acquisition parameters. It would be further interesting to compare different pre-processing protocols (quality control, pre-processing steps, correction for multiple comparisons) and assess the validity and replicability of implemented paradigms. Additionally, future synthesis may consider further specificity testing (i.e., how the neural correlates of PPD differ from those of other brain disorders associated with pregnancy and childbirth) and individual profiles of participants, through individual participant data meta-analysis (IPD; 166). Finally, exploring antenatal neural changes, investigating the influence of hormonal fluctuations, and considering socio-environmental factors will further enrich our understanding of PPD, as well as studies directly comparing PPD and fMDD while considering confounding variables such as reproductive age, psychosocial stress factors and peripartum timepoints.\u003c/p\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eM\u0026oacute;nica Sobral is supported by a doctoral grant from the Portuguese Foundation for Science and Technology (FCT; 2021.07006.BD). The Center for Research in Neuropsychology and Cognitive and Behavioral Intervention (CINEICC) of the Faculty of Psychology and Educational Sciences of the University of Coimbra is supported by national funds through FCT \u0026ndash; Funda\u0026ccedil;\u0026atilde;o para a Ci\u0026ecirc;ncia e Tecnologia, by project reference UIDP/00730/2020.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis paper is part of the COST Action Riseup-PPD CA18138 and was supported by COST under COST Action Riseup-PPD CA18138. Sara Cruz is supported by the Psychology for Development Research Center, Lus\u0026iacute;ada University, Portugal, supported by FCT \u0026ndash; Funda\u0026ccedil;\u0026atilde;o para a Ci\u0026ecirc;ncia e Tecnologia, I.P., by project reference UIDB/04375/2020 and DOI identifier \u0026lt;10.54499/UIDB/04375/2020 (https://doi.org/10.54499/UIBD/04375/2020)\u0026gt;. Ana Ganho-\u0026Aacute;vila is supported by the Portuguese Foundation for Science and Technology (FCT) Grants 2020.02059.CEECIND (https://doi.org/10.54499/2020.02059.CEECIND/CP1609/CT0015).\u003c/p\u003e\n\u003cp\u003eWe thank Professors Allison Nugent, Ian Gotlib, Robert Wolf, Gabriel Robert,\u0026nbsp;Chaejoon Cheong and Karl-J\u0026uuml;rgen B\u0026auml;r for the additional information provided upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors state no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributors\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eM.S.: conceptualization, methodology, formal analysis, investigation, writing-\u0026ndash; original draft preparation, writing - review and editing, visualization; R.G.: conceptualization, methodology, formal analysis, investigation, writing - review and editing; M.R.: methodology (selection and data extraction process), writing - review and editing; M.V.: methodology (selection and data extraction process), writing - review and editing; S.C.: writing - review and editing; F.P.: methodology (selection process), writing - review and editing; V.M.: conceptualization, methodology (selection process), writing - review and editing; R.P-C: methodology (selection process), writing - review and editing; J.P-N.: methodology (selection process), writing - review and editing; A.W.: conceptualization, writing - review and editing; H.M.: conceptualization, writing - review and editing; M.T.: conceptualization, writing - review and editing, validation; \u0026nbsp; A.G.-A: conceptualization, validation, writing - review and editing, supervision; A-L.S.: conceptualization, methodology, formal analysis, investigation, validation, writing - review and editing, supervision. All authors contributed to and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003eSupplementary information is available at MP\u0026rsquo;s website.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAmerican Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition. American Psychiatric Publishing, Inc; 2013. \u003c/li\u003e\n\u003cli\u003eShorey S, Chee CYI, Ng ED, Chan YH, Tam WWS, Chong YS. Prevalence and incidence of postpartum depression among healthy mothers: A systematic review and meta-analysis. J Psychiatr Res. 2018;104:235\u0026ndash;48. \u003c/li\u003e\n\u003cli\u003ePawluski JL, Lonstein JS, Fleming AS. The neurobiology of postpartum anxiety and depression. Trends Neurosci. 2017;40(2):106\u0026ndash;20.\u003c/li\u003e\n\u003cli\u003eAl-abri K, Edge D, Armitage CJ. Prevalence and correlates of perinatal depression. 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BMJ. 2010;340:c221.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1 and 2 are not available with this version.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[{"identity":"82e2bf3d-153c-4049-a992-494417638413","identifier":"10.13039/501100001871","name":"Fundação para a Ciência e a Tecnologia","awardNumber":"2021.07006.BD","order_by":0}],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"Center for Research in Neuropsychology and Cognitive and Behavioral Intervention (CINEICC) of the Faculty of Psychology and Educational Sciences of the University of Coimbra ","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7008488/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7008488/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ePeripartum depression (PPD) is a form of major depressive disorder (MDD) that begins during the peripartum period and poses a significant mental health challenge affecting 10\u0026ndash;29% of women. This systematic review and multimodal activation likelihood estimation (ALE) meta-analysis explored the distinct structural, functional, and metabolic features of the PPD brain as compared to female non-peripartum MDD. For this purpose, we conducted a comprehensive literature search in PubMed, Embase and PsycINFO databases to identify peer-reviewed original studies investigating the neural correlates associated with PPD or fMDD. Forty-five studies in PPD and 55 in fMDD were included in the qualitative synthesis. From these, 27 PPD and 32 fMDD studies were included in the meta-analysis. Both shared and distinct neural underpinnings of PPD and fMDD were observed. Specifically, we found alterations in the cognitive control, salience and default mode networks for both PPD and fMDD, although with reversed structural and functional activity patterns in the insula, amygdala, precentral gyrus and precuneus. These findings support the consistent pattern of dysregulation associated with emotional regulation, cognition and maternal caregiving in women with PPD, as well as possible differential sensitivity to hormonal influences, highlighting the need for targeted interventions.\u003c/p\u003e","manuscriptTitle":"Neural correlates of peripartum depression: a systematic review, meta-analysis and comparison to major depressive disorder","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-04 02:19:10","doi":"10.21203/rs.3.rs-7008488/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"bb08657c-7e1c-4af6-9953-fa94fb3d0f96","owner":[],"postedDate":"July 4th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":50779197,"name":"Neurobiology of Disease"},{"id":50779198,"name":"Psychology"},{"id":50779199,"name":"Psychiatry"}],"tags":[],"updatedAt":"2025-09-09T13:52:39+00:00","versionOfRecord":{"articleIdentity":"rs-7008488","link":"https://doi.org/10.1038/s41380-025-03227-2","journal":{"identity":"molecular-psychiatry","isVorOnly":false,"title":"Molecular Psychiatry"},"publishedOn":"2025-09-08 00:00:00","publishedOnDateReadable":"September 8th, 2025"},"versionCreatedAt":"2025-07-04 02:19:10","video":"","vorDoi":"10.1038/s41380-025-03227-2","vorDoiUrl":"https://doi.org/10.1038/s41380-025-03227-2","workflowStages":[]},"version":"v1","identity":"rs-7008488","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7008488","identity":"rs-7008488","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}