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Fetal frontolimbic connectivity prospectively associates with aggression in toddlers

Open AccessPublished:September 21, 2022DOI:https://doi.org/10.1016/j.bpsgos.2022.09.003

      Abstract

      Background

      Aggression is a major public health concern that emerges early in development and lacks optimized treatment, highlighting need for improved mechanistic understanding of aggression etiology. The present study leverages fetal resting-state functional MRI (rsfMRI) to identify candidate neurocircuitry for the onset of aggressive behaviors, prior to symptom emergence.

      Methods

      Pregnant mothers were recruited during the third trimester of pregnancy to complete a fetal rsfMRI scan. Mothers subsequently completed the Child Behavior Checklist to assess child aggression at 3 years postpartum (N=79). Independent component analysis was used to define frontal and limbic regions of interest.

      Results

      Child aggression was not related to within network connectivity of subcortical limbic regions or within medial prefrontal network connectivity in fetuses. However, weaker functional coupling between the subcortical limbic network and medial prefrontal network in fetuses was prospectively associated with greater maternal-rated child aggression at 3 years of age even after controlling for maternal emotion dysregulation and toddler language ability. We observed similar, but weaker, associations between fetal frontolimbic FC and toddler internalizing symptoms.

      Conclusion

      Neural correlates of aggressive behavior may be detectable in utero, well before the onset of aggression symptomatology. These preliminary results highlight frontolimbic connections as potential candidate neurocircuitry that should be further investigated in relation to the unfolding of child behavior and psychiatric risk.

      Keywords

      Introduction

      Aggression, defined as verbal or physical behaviors intended to cause harm, emerges early in human life. Aggressive acts peak during the preschool period, with over 90% of preschoolers engaging in at least occasional aggressive acts (
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      ). Although the mean frequency of aggressive behavior steadily decreases after 4 years of age, stability of aggression is typically high such that young children with the highest levels of aggression are most likely to be aggressive adults as well (
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      Teymoori A, Côté SM, Jones BL, Nagin DS, Boivin M, Vitaro F, et al. (2018): Risk Factors Associated With Boys’ and Girls’ Developmental Trajectories of Physical Aggression From Early Childhood Through Early Adolescence. JAMA Netw Open 1: e186364–e186364.

      ). When aggression does extend into adolescence and adulthood, it is a strong predictor of negative life outcomes, including psychosocial problems, psychopathology, physical violence, delinquency, and school dropout (
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      ). Indeed, current multimodal treatments for aggression and delinquency that are implemented during adolescence, such as multisystemic therapy, have inconsistent success rates that are often not superior to community treatment as usual (
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      ). Improved mechanistic understanding of aggression etiology, and earlier intervention, is a critical public health goal.
      Basic neuroscience studies that illuminate candidate neurocircuitry prior to the entrenchment of pathological and maladaptive levels of aggression are lacking. However, translational preclinical research converges with fMRI experiments conducted in human adults to isolate dysregulated co-activation of limbic regions, including the amygdala, and the medial prefrontal cortex (mPFC) in the expression of aggressive behaviors. Among individuals who display more aggressive behaviors, dysregulated frontolimbic circuitry manifests both as increased excitatory signals from limbic regions (
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      ) and decreased mPFC-initiated inhibition of limbic activity (
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      ,
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      ). Yet frontolimbic circuitry undergoes significant changes across development that may alter the nature of these brain-behavior associations. For instance, the amygdala and mPFC demonstrate positive functional coupling during infancy and childhood (
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      Hendrix CL, Dilks DD, McKenna BG, Dunlop AL, Corwin EJ, Brennan PA (2021): Maternal Childhood Adversity Associates With Frontoamygdala Connectivity in Neonates. Biol Psychiatry Cogn Neurosci Neuroimaging. https://doi.org/10.1016/j.bpsc.2020.11.003

      ,
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      ) but become negatively coupled during adolescence and adulthood (
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      ). It is imperative to examine brain-behavior associations across the spectrum of development to identify consistent and diverging patterns. Several studies have examined the relevance of infant frontolimbic circuitry for the development of internalizing symptoms (
      • Salzwedel A.P.
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      • et al.
      Maternal Interleukin-6 concentration during pregnancy is associated with variation in frontolimbic white matter and cognitive development in early life.
      ), but none have examined whether early life frontolimbic connectivity is associated with externalizing symptomatology. Determining the extent to which individual differences in neural circuitry are related to aggression early in human development represents a foundational gap in our knowledge.
      Although it is often assumed that the brain causes behavior, work conducted in adolescents reveals that externalizing behaviors can precede certain neural alterations (

      Luking KR, Jirsaraie RJ, Tillman R, Luby JL, Barch DM, Sotiras A (2021): Timing and Type of Early Psychopathology Symptoms Predict Longitudinal Change in Cortical Thickness From Middle Childhood Into Early Adolescence. Biol Psychiatry Cogn Neurosci Neuroimaging. https://doi.org/10.1016/J.BPSC.2021.06.013

      ,
      • Saxbe D.
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      • Kaplan J.T.
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      ), suggesting bidirectional associations between disease states, behavioral phenotypes, and neural development. A particularly rigorous method of parsing the temporal unfolding of biobehavioral risk is to measure brain development prior to birth, and prior to both the onset of aggression and postnatal exposures that enhance risk for, or buffer against, this behavioral phenotype. Establishing this temporal unfolding is an essential, although not sufficient, step towards establishing a causal association between brain and behavior.
      Frontolimbic brain structures are an especially promising candidate for such work, as they develop early in human life, rendering them feasible targets for examination in utero. Histological work reveals the amygdala is differentiated by 17-18 weeks of gestation (
      • Vasung L.
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      • Jovanov-Milošević N.
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      • Mori S.
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      ) and has reached a high degree of structural maturity by 36 weeks gestational age (
      • Ulfig N.
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      ). Although the prefrontal cortex is later to develop (
      • Kolk S.M.
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      Development of prefrontal cortex.
      ), early anatomical connections from the amygdala to the forebrain form between the 24th and 26th week of gestation (
      • Vasung L.
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      • Pletikos M.
      • Mori S.
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      ). Available literature thus suggests that although it continues to be refined by pre- and postnatal experiences, the core scaffolding for frontolimbic circuitry is in place during the third trimester of pregnancy. Indeed, one study confirms that fetal resting-state fMRI (rsfMRI) demonstrates capacity to non-invasively quantify individual differences in this circuit prior to birth (

      Thomason ME, Hect JL, Waller R, Curtin P (2021): Interactive relations between maternal prenatal stress, fetal brain connectivity, and gestational age at delivery. Neuropsychopharmacol 2021 4610 46: 1839–1847.

      ). Fetal rsfMRI presents the unique opportunity to examine prospective associations between individual differences in the development of frontal and limbic neurocircuitry in utero and subsequent aggressive behavior.
      The current study sought to determine whether and how frontolimbic circuitry in utero was associated with childhood aggression using a longitudinal dataset including fetal rsfMRI scans. We specifically tested the novel hypothesis that inter-individual variation in frontolimbic functional circuitry prior to the child’s birth will be associated with aggressive behavior at age 3 years. We also explored whether select comorbid risk factors modified these brain-behavior associations. In the present study, we examined 2 risk factors that have been tied to heightened offspring aggression: child language development and maternal emotion dysregulation (
      • Roberts M.Y.
      • Curtis P.
      • Estabrook R.
      • Norton E.S.
      • Davis M.
      • Burns J.
      • et al.
      Talking tots and the terrible twos: Early language and disruptive behavior in toddlers.
      ,
      • Girard L.C.
      • Pingault J.B.
      • Falissard B.
      • Boivin M.
      • Dionne G.
      • Tremblay R.E.
      Physical aggression and language ability from 17 to 72 months: Cross-lagged effects in a population sample.
      ,
      • Gilliam M.
      • Forbes E.E.
      • Gianaros P.J.
      • Erickson K.I.
      • Brennan L.M.
      • Shaw D.S.
      Maternal depression in childhood and aggression in young adulthood: evidence for mediation by offspring amygdala–hippocampal volume ratio.
      ). By providing needed exploration of a core neurodevelopmental pathway that may contribute to heightened aggression early in life, these findings have potential to deepen our understanding of aggression etiology and illuminate sensitive periods for intervention timing.

      Materials & Methods

      Participants & Procedures. Healthy mothers with singleton pregnancies were recruited from Hutzel Women’s Hospital in Detroit, Michigan during routine obstetrical appointments. Exclusion criteria included 1) non-native English speaker, 2) less than 18 years of age, or 3) presence of anatomical fetal brain abnormalities identified during ultrasound and/or MRI examination. Fetal MRI scans occurred at Wayne State University when fetuses were between 22- and 39-weeks gestational age (GA). Manually segmented and quality assured functional MRI data was available for 165 fetuses at the time of this analysis. From this quality assured data, fetuses were excluded if they were scanned prior to 25 weeks GA (n=9) or had low birthweight or were born very preterm (<1800g or <33 weeks GA; n=14). We also excluded fetuses with high average motion (>1.5 mm max excursion, >0.5 mm mean, rotational: >2°) or had fewer than 100 functional volumes after scrubbing (n=22). When children were 3 years old (M=36.43 months, SD=1.83), 79 of these mothers completed follow up questionnaires about their child’s behavioral and socioemotional development (Figure 1A). The final sample consisted of 79 mother-fetus dyads who were predominantly from low to middle income households. Consistent with our exclusion criteria, included dyads had an older gestational age at the fetal MRI scan and at birth and less motion during the fetal MRI scan (Table 1). The Wayne State University Institutional Review Board approved all study procedures and informed written consent was provided by participating mothers. Data from this sample has previously been used to examine the impact of prenatal stress (

      Thomason ME, Hect JL, Waller R, Curtin P (2021): Interactive relations between maternal prenatal stress, fetal brain connectivity, and gestational age at delivery. Neuropsychopharmacol 2021 4610 46: 1839–1847.

      ,
      • Hendrix C.L.
      • Srinivasan H.
      • Feliciano I.
      • Carré J.M.
      • Thomason M.E.
      Fetal Hippocampal Connectivity Shows Dissociable Associations with Maternal Cortisol and Self-Reported Distress during Pregnancy.
      ,
      • van den Heuvel M.I.
      • Hect J.L.
      • Smarr B.L.
      • Qawasmeh T.
      • Kriegsfeld L.J.
      • Barcelona J.
      • et al.
      Maternal stress during pregnancy alters fetal cortico-cerebellar connectivity in utero and increases child sleep problems after birth.
      ) and cannabis use (
      • Thomason M.E.
      • Palopoli A.C.
      • Jariwala N.N.
      • Werchan D.M.
      • Chen A.
      • Adhikari S.
      • et al.
      Miswiring the brain: Human prenatal Δ9-tetrahydrocannabinol use associated with altered fetal hippocampal brain network connectivity.
      ) on the developing brain, examine associations between preterm birth and functional network development (

      Thomason ME, Scheinost D, Manning JH, Grove LE, Hect J, Marshall N, et al. (2016): Weak functional connectivity in the human fetal brain prior to preterm birth. https://doi.org/10.1038/srep39286

      ), and to optimize fetal fMRI preprocessing (

      Ji L, Hendrix CL, Thomason ME (2022): Empirical optimization of human fetal fMRI preprocessing steps. Networks Neurosci.

      ,
      • Rutherford S.
      • Sturmfels P.
      • Angstadt M.
      • Hect J.
      • Wiens J.
      • van den Heuvel M.I.
      • et al.
      Automated Brain Masking of Fetal Functional MRI with Open Data.
      ).
      Figure thumbnail gr1
      Figure 1Participant ages, attrition, and endorsement of aggressive toddler behavior. (A) 120 mothers completed a resting-state functional MRI (rsfMRI) scan between 26- and 39-weeks gestational age. A subset of these mothers (n=79) additionally completed a follow-up visit when their child was 3 years of age. (B) Mothers rated children’s aggressive behaviors using the preschool version of the Child Behavior Checklist (CBCL). The number of toddlers displaying each aggressive behavior is plotted. (C) Distribution of total aggression scores in the sample. In the present sample, aggression scores ranged from 0-27 (mean=8.76, median=8.00, SD=6.43). 91% of mothers endorsed at least one aggressive behavior in their child.
      Table 1Participant sociodemographic variables
      Final sample (n=79)Excluded sample (n=86)Differences between included and excluded dyads
      M (SD) or N (%)M (SD) or N (%)Stats.
      Sociodemographic variables
      Maternal age at fetal MRI25.54 (4.66) years25.71 (5.49)t=0.21, p=0.83
      GA at fetal MRI32.96 (3.71) weeks31.02 (4.50)t=-3.00, p=0.003
      Maternal raceX2=3.01, p=0.56
      Black/African American64 (86%)70 (84%)
      White/Caucasian7 (9%)7 (8%)
      Bi-racial3 (4%)4 (5%)
      Asian American1 (1%)0 (0%)
      Other0 (0%)2 (2%)
      Maternal education44 (59%) HS diploma/GED or less43 (52%)X2=0.75, p=0.39
      Maternal income23 (34%) <$20,00024 (32%)X2=0.12, p=0.73
      Maternal marital status46 (62%) single45 (52%)X2=0.70, p=0.40
      GA at birth39.01 (1.60) weeks37.85 (3.22)t=-2.97, p=0.002
      Preterm (<36 weeks)4 (5%)14 (16%)
      Fetal sex34 (43%) female38 (44%)X2=0.02, p=0.88
      rsfMRI Characteristics
      Number low-motion volumes169.28 (54.15)158.31 (53.50)t=-1.07, p=0.29
      Mean XYZ translation0.23 (0.10) mm0.25 (0.11)t=1.17, p=0.24
      Mean PYR rotation0.38 (0.17) mm0.47 (0.20)t=3.15, p=0.002
      3-year child follow up measures
      CBCL-P Aggression8.76 (6.43)8.13 (5.76)t=-0.47, p=0.64
      CBCL-P Hyperactivity2.47 (2.12)2.53 (2.00)t=0.14, p=0.89
      CBCL-P Internalizing7.48 (7.81)7.36 (6.75)t=-0.08, p=0.94
      Bayley Cognitive Composite91.03 (7.13)88.40 (10.74)t=-1.49, p=0.14
      Bayley Motor Composite99.27 (10.40)93.87 (14.64)t=-2.38, p=0.02
      Bayley Language Composite93.04 (8.24)89.91 (14.01)t=-1.39, p=0.17
      Maternal emotion regulation measures
      Prenatal internalizing symptoms composite-0.10 (0.88)-0.14 (0.95)t=-0.26, p=0.80
      3-year internalizing symptoms composite0.02 (0.85)-0.26 (0.56)t=-2.11, p=0.04
      Quick to anger17 (24%)25 (34%)X2=1.71, p=0.19
      Note. Dyads included in our final analyses had an older gestational age at the scan and at birth, less motion during the fetal fMRI scan, higher scores on the Bayley Motor Composite, and fewer maternal anxiety/depression symptoms at the 3-year follow up visit compared to excluded dyads. There were no other sociodemographic differences or differences in rsfMRI data characteristics between dyads who or were not included in our final analyses. GA = gestational age. 41 dyads were excluded because they were missing 3-year follow-up data, so excluded sample estimates for 3-year outcomes are based on the subgroup who completed the visit but were excluded from analyses for other reasons as described in the methods section (n=39).

      Measures.

      Fetal fMRI

      Fetal functional MRI images were collected using a 3T Siemens Verio 70 cm open-bore system with a 550g abdominal 4-channel Siemens Flex coil. For each participant, 360 axial frames (12 minutes) of EPI-BOLD data were collected with the following scan sequence parameters: TR=2000 ms; TE=30ms; flip-angle: 80 degrees, slice-gap: none; voxel-size: 3.4 mm x 3.4 mm x 4 mm; matrix-size: 96 x 96 x 25. This sequence was repeated when time permitted. On average, 169 low-motion resting-state fMRI frames were included in analyses (range across fetuses: 100-344).

      Maternal emotion dysregulation

      Mothers completed the Center for Epidemiological Studies Depression Scale (CES-D; Radloff, 1977) and the Spielberg State Trait Anxiety Inventory-Trait subscale (STAI-T; Spielberger et al., 1970) at the fetal scan and again at a 3-year follow up visit to assess current depression and anxiety symptoms, respectively. Both measures demonstrated acceptable internal consistency in the present sample (CES-D α=0.91; STAI-T α=0.71). Due to correlation between these measures (fetal scan: r=0.75, p<0.001; 3-year visit: r=0.46, p<0.001), we standardized and averaged the CES-D and STAI-T total scores to create a single composite variable representing maternal internalizing symptoms during pregnancy, and a second composite representing maternal internalizing symptoms at the 3-year visit. The 3-year internalizing composite was included as a covariate in all analyses examining maternal-reported toddler aggression. Additionally, we leveraged a single yes/no question from the WIDUS scale (
      • Ondersma S.J.
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      • Connors-Burge V.
      Development and preliminary validation of an indirect screener for drug use in the perinatal period.
      ) as a measure of maternal quickness to anger: “I get mad easily and feel a need to off some steam”. Mothers completed the WIDUS at the fetal scan. In our sample, 17 (24%) mothers reported being quick to anger. All 3 maternal dysregulation variables were explored as potential modifiers of brain-behavior associations in children.

      Toddler aggression

      Mothers completed the preschool version of the Child Behavior Checklist (CBCL-P; Achenbach and Rescorla, 2000) when their child was 3 years old. The CBCL-P aggressive behavior subscale was the primary outcome in the present study, which showed good internal consistency (cronbach’s α=0.92). Aggressive problems reported by parents on the CBCL-P have demonstrated construct validity with aggressive behaviors assessed in the laboratory (

      Calkins SD, Gill K, Williford A (2010): Externalizing Problems in Two-Year-Olds: Implications for Patterns of Social Behavior and Peers’ Responses to Aggression. https://doi.org/101207/s15566935eed1003_3 10: 267–288.

      ), and this subscale demonstrates acceptable measurement equivalence in minoritized samples in the United States (
      • Gross D.
      • Fogg L.
      • Young M.
      • Ridge A.
      • Cowell J.M.
      • Richardson R.
      • Sivan A.
      The equivalence of the Child Behavior Checklist/11/2-5 across parent race/ethnicity, income level, and language.
      ,
      • Jastrowski Mano K.E.
      • Davies Hobart W.
      • Klein-Tasman B.P.
      • Adesso V.J.
      Measurement equivalence of the child behavior checklist among parents of African American adolescents.
      ,
      • Tyson E.H.
      • Teasley M.
      • Ryan S.
      Using the child behavior checklist with African American and Caucasian American Adopted Youth.
      ). Additional analyses used the attention subscale (α=0.79) and internalizing subscale (α=0.84) to determine specificity of findings (see Figure 1C and Table 1 for descriptive information). Other CBCL-P syndrome scales showed questionable or poor internal consistency in the present sample (α’s<0.68) so were not examined in subsequent analyses.

      Toddler neurodevelopment

      Children were administered the Bayley Scales of Infant and Toddler Development, 2nd Edition (

      Bayley N (2006): Bayley Scales of Infant and Toddler Development, Third. Bloomington, MN: NCS Pearson, Inc.

      ) during the 3-year visit by a trained examiner. Bayley composite scores were used to identify children with developmental delays in the present sample for descriptive purposes (see Table S1). Given that language delay can co-occur with aggressive acts in children (
      • Roberts M.Y.
      • Curtis P.
      • Estabrook R.
      • Norton E.S.
      • Davis M.
      • Burns J.
      • et al.
      Talking tots and the terrible twos: Early language and disruptive behavior in toddlers.
      ,
      • Girard L.C.
      • Pingault J.B.
      • Falissard B.
      • Boivin M.
      • Dionne G.
      • Tremblay R.E.
      Physical aggression and language ability from 17 to 72 months: Cross-lagged effects in a population sample.
      ), the Bayley language composite was also included as a covariate in sensitivity analyses.

      fMRI preprocessing.

      Preprocessing was performed using a combination of FSL (https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/) and Statistical Parametric Mapping (SPM12) software (http://icatb.source- forge.net). First, low-motion segments were manually selected using FSL’s image viewer. Next, Brainsuite was used to manually draw three-dimensional masks around single reference images, and these masks were applied to all other volumes within the corresponding low-motion segment (
      • Shattuck D.W.
      • Leahy R.M.
      BrainSuite: An automated cortical surface identification tool.
      ). All manually drawn masks were quality assured by a second reviewer. A subset of images was independently masked by separate reviewers, and we calculated the percent overlap between resulting images (i.e., the dice coefficient). These dice coefficients revealed exceptional overlap in fetal brain masks created by separate reviewers (mean=0.993, range=0.986-0.997). Subsequent preprocessing included brain extraction, reorientation, within segment motion correction, normalization to a 32-week fetal brain template (
      • Serag A.
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      A Multi-channel 4D Probabilistic Atlas of the Developing Brain: Application to Fetuses and Neonates.
      ), concatenation of volumes across low-motion segments, realignment to correct for potential normalization misalignments between segments, reapplication of the fetal brain mask, ICA-based denoising with “FSL Melodic” toolbox, and spatial smoothing with a 4-mm FWHM Gaussian kernel. Stringent ICA denoising has been shown to successfully remove motion artifacts from fMRI data without needing to censor high motion spikes and provides superior removal of motion-related signal contamination compared to traditional spike censoring and regression of motion parameters (
      • Pruim R.H.R.
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      • Llera A.
      • Buitelaar J.K.
      • Beckmann C.F.
      ICA-AROMA: A robust ICA-based strategy for removing motion artifacts from fMRI data.
      ). For more information about the ICA-denoising used in our fetal data, please see (

      Ji L, Hendrix CL, Thomason ME (2022): Empirical optimization of human fetal fMRI preprocessing steps. Networks Neurosci.

      ).

      Data Analysis.

      Identification of resting-state networks

      A data-driven approach was used to define fetal frontal and limbic networks. Specifically, independent component analysis (ICA) decomposed the whole brain data for all fetuses into 35 spatially independent components, each of which exhibited a unique time course profile. ICA was performed using the GIFT Functional MRI Toolbox (v3.0b, https://trendscenter.org/software /gift/), with the Infomax algorithm as the optimization principle. Thirty-five components were derived based on image quality, using the minimum description length approach (
      • Rissanen J.
      Modeling by shortest data description.
      ). Reliability and stability of the algorithm was ensured by repeating the component estimation 20 times using ICASSO (
      • Himberg J.
      • Hyvärinen A.
      • Esposito F.
      Validating the independent components of neuroimaging time series via clustering and visualization.
      ). Subject-specific spatial maps and time courses were obtained using the back-reconstruction approach (GICA, Calhoun et al., 2004) and converted to z-scores. Three components were discarded because they represented signal from cerebrospinal fluid or other sources of noise, and the remaining group-level components were manually organized to be spatially consistent with established resting-state networks in neonates (
      • Smyser C.D.
      • Inder T.E.
      • Shimony J.S.
      • Hill J.E.
      • Degnan A.J.
      • Snyder A.Z.
      • Neil J.J.
      Longitudinal analysis of neural network development in preterm infants.
      ) and fetuses (
      • Thomason M.E.
      • Grove L.E.
      • Lozon T.A.
      • Vila A.M.
      • Ye Y.
      • Nye M.J.
      • et al.
      Age-related increases in long-range connectivity in fetal functional neural connectivity networks in utero.
      ) as shown in Supplemental Figure S3. The present analyses focus on a medial prefrontal network (consisting of 2 ICA-derived components) and a subcortical limbic network (consisting of 1 ICA-derived component). All ICA-derived networks are publicly available for download (https://www.brainnexus.com/projects-2/fetal-resting-state-ica-templates).

      Calculation of within and between-network functional connectivity

      We extracted the mean BOLD timeseries from a 4mm sphere surrounding the peak voxel within each component of interest (Figure 2, Table 2) for every fetus (n=79). Peaks were selected on the aggregate component map and applied to each normalized fetal brain to account for variability and signal spread in individual component maps. There were four signal intensity peaks across the mPFC and limbic networks. Specifically, in the subcortical limbic network, 2 peaks were isolated in each of the left and right hemispheres, and the peak was drawn from each of the left and right mPFC components. Pearson correlations were calculated between all 4 extracted average timeseries to create an unthresholded resting-state functional connectivity (FC) matrix for each fetus, inclusive of both negative and positive associations. Within-network FC for each fetus was defined as the correlation coefficient between the two homologous regions of interest (ROIs) within a given network. Between-network FC was calculated by averaging the correlations between each ROI within the medial prefrontal network with the two ROIs in the subcortical limbic network. ROI-to-ROI analyses were Bonferroni corrected (p<0.017).
      Figure thumbnail gr2
      Figure 2Fetal frontal and limbic networks based on group-level ICA. The group-level ICA decomposed all rsfMRI data for all fetuses into a set of spatially independent components, each of which exhibited a unique time course. Two networks consisting of ICA-derived components were explored in the subsequent analyses: (A) a subcortical limbic network consisting of the bilateral amygdala and (B) a medial prefrontal network comprised of the left and right medial prefrontal cortex (mPFC). The peak of each component is marked with black edges.
      Table 2Location of ICA-derived network peaks.
      XYZ
      Limbic Network FC
      R limbic39-17
      L limbic-69-17
      mPFC Network FC
      R mPFC1536-2
      L mPFC-1536-2
      Note. A 4mm sphere was drawn around the above coordinates to define our ICA-derived limbic and mPFC seeds. Coordinates reflect the central voxel of each ROI, normalized to the Serag et al. (2012) fetal atlas (
      • Serag A.
      • Kyriakopoulou V.
      • Rutherford M.A.
      • Edwards A.D.
      • Hajnal J.V.
      • Aljabar P.
      • et al.
      A Multi-channel 4D Probabilistic Atlas of the Developing Brain: Application to Fetuses and Neonates.
      ).

      Child behavior analyses

      Descriptives and measures of variability were examined for all variables. The CBCL aggression subscale showed acceptable distribution normality (aggression skew=0.53, see Figures 1B-C). Hierarchical linear regression was used to examine associations between fetal functional connectivity and behavioral outcomes at 3 years of age, with relevant covariates entered into the first step, and fetal within/between network connectivity entered in the second step. Because of their theoretical relevance to early brain development, we included the following covariates in the first step of all analyses: number of rsfMRI frames included in the analysis, fetal motion parameters, fetal GA at scan, fetal sex, GA at birth, maternal education, and partner status at the fetal MRI scan. All analyses also controlled for a composite maternal anxiety and depression variable at the 3-year visit. Linear regression assumptions were assessed in several ways. Unstandardized residuals were visually examined using histograms to determine normality and residuals were plotted against predicted values to ensure homoscedasticity. Finally, Cook’s D was used to identify potential outliers (i.e., values >1) since it considers both leverage and discrepancy. Moderation analyses were implemented with the PROCESS Macro and controlled for the same covariates included in our primary analyses (

      Hayes A (2018): Introduction to Mediation, Moderation, and Conditional Process Analysis: A Regression Based Approach, Second Edi. New York, NY: The Guildford Press.

      ). We also conducted exploratory seed connectivity analyses to examine whether variation in child aggression was related to altered frontal and limbic connections to other neural regions. These analyses are described in Supplemental Materials.

      Results

      Toddler Aggression

      91% of mothers (n=72) reported that their toddler engaged in at least 1 aggression-related behavior. The most frequent problem behaviors reported were not being able to wait, being stubborn, and wanting attention (Figure 1B). Extreme acts of physical violence, such as hurting animals or attacking people, were rarely endorsed. Overall aggression scores in the sample are displayed in Figure 1C.

      Fetal Frontolimbic Networks

      The current study centered on two networks: a subcortical limbic network and a medial prefrontal network (see Figure 2). The average strength of within and between network functional connectivity (FC) across fetuses is visually displayed in Figure 3 and fetal FC descriptive statistics are in Supplemental Table S2.
      Figure thumbnail gr3
      Figure 3Network connectivity patterns vary across fetuses. On average, fetuses showed positive functional correlations (FC) between frontal and limbic regions. mPFC=medial prefrontal cortex.

      Fetal Frontolimbic Connectivity & Toddler Aggression

      Greater scores on the aggression scale at 3 years of age was associated with lower limbic-medial prefrontal FC prior to birth (ß=-0.30, R2=0.43, ΔR2=0.09, p=0.003, 95%CI b[-11.91, -2.49], see Figure 4). This association was specific to limbic-medial prefrontal FC; neither within-network medial prefrontal FC (ß=-0.16, R2=0.36, ΔR2=0.02, p=0.14, 95%CI b[-5.63, 0.84]) nor within-network limbic FC (ß=0.01, R2=0.34, ΔR2<0.01, p=0.90, 95%CI b[-4.33, 4.91]) in fetuses was associated with child aggression at 3 years of age.
      Figure thumbnail gr4
      Figure 4Fetal frontolimbic connectivity is associated with child aggression at age 3 years. We conducted hierarchical linear regressions examining the association between fetal frontolimbic functional connectivity with child aggression at 3 years of age. All analyses controlled for number of rsfMRI frames included in the analysis, fetal motion parameters, fetal GA at scan, fetal sex, birth weight, GA at birth, maternal education, maternal age, partner status, and maternal anxiety and depressive symptoms at 3 years postpartum. (A) Functional connectivity between the subcortical limbic network and medial prefrontal network prior to birth prospectively explained 9% of variance in maternal-reported child aggression at 3 years of age above and beyond covariates. (B) Neither within-network connectivity of the mPFC or of the (C) subcortical limbic network among fetuses were associated with child aggression after covariate control. mPFC=medial prefrontal cortex.
      Next, we conducted follow-up analyses to determine whether effects were lateralized. Lower FC between the left subcortical limbic ROI and left mPFC was not associated with greater child aggression at 3 years of age (ß=-0.18, R2=0.37, ΔR2=0.03, p=0.10, 95%CI b[-6.50, 0.57]). Lower right ipsilateral FC between the subcortical limbic region and mPFC continued to be significantly related to greater child aggression (ß=-0.23, R2=0.39, ΔR2=0.05, p=0.03, 95%CI b[-7.21, -0.33]). Lower contralateral limbic-mPFC FC was also associated with greater child aggression (left mPFC to right amygdala: ß=-0.24, R2=0.39, ΔR2=0.05, p=0.03, 95%CI b[-7.51, -0.47]; right mPFC to left amygdala: ß=-0.22, R2=0.38, ΔR2=0.05, p=0.04, 95%CI b[-6.49, -0.15]). No lateralized associations survive Bonferroni correction for 4 comparisons (p<0.01).

      Specificity of Aggression Findings

      We next examined whether fetal bilateral limbic-mPFC FC was uniquely associated with subsequent aggressive symptoms or was instead related to a range of child symptomatology. Toddler language abilities did not associate with fetal limbic-mPFC FC (ß=0.01, R2=0.11, ΔR2<0.01, p=0.94, 95%CI b[-7.02, 7.58]), and frontolimbic FC continued to relate to child aggression even after adding toddler language ability to the model (ß=-0.28, R2=0.43, ΔR2=0.07, p=0.009, 95%CI b[-11.51, -1.74]). Toddler cognitive and motor development also did not explain our brain-aggression findings (see Supplemental Materials). Fetal limbic-mPFC was not associated with toddler inattention/hyperactivity symptoms after controlling for the same covariates included in our primary analyses (ß=-0.11, R2=0.29, ΔR2=0.01, p=0.31, 95%CI b[-2.59, 0.84]). However, lower fetal limbic-mPFC FC was associated with greater toddler internalizing symptoms after covariate control (ß=-0.27, R2=0.29, ΔR2=0.07, p=0.02, 95%CI b[-0.77, -0.08]).

      Associations between Maternal Emotion Dysregulation and Child Aggression

      We also explored whether maternal emotion dysregulation correlated with child aggression and whether it interacted with fetal neural phenotype to predict child aggression. Maternal internalizing symptoms during pregnancy were not associated with toddler aggression at age 3 years (ß=-0.18, 95%CI b[-3.05, 0.47], p=0.15) and did not moderate the association between fetal limbic-mPFC FC and toddler aggression (b=-4.36, 95%CI [-9.57, 0.84], p=0.10). Mothers with greater internalizing symptoms at the 3-year follow up reported that their toddler engaged in more aggressive behaviors (ß=0.45, 95%CI b[1.81, 5.36], p<0.001), but maternal symptoms at 3 years did not moderate the association between fetal limbic-mPFC FC and child aggression (b=-0.24, 95%CI[-6.16, 5.68], p=0.93). Finally, there were no differences in aggression between children whose mothers endorsed being quick to anger versus mothers who denied quickness to anger (ß=0.05, 95%CI b[-3.00, 4.38], p=0.71). Maternal quickness to anger did not moderate the association between fetal limbic-mPFC FC and toddler aggression (b=-3.24, 95%CI[-17.24, 10.75], p=0.64). These moderation analyses should be considered preliminary given our smaller sample size. None of our maternal emotion dysregulation measures associated with fetal frontolimbic FC (p’s>0.69).

      Discussion

      In a prospective study of 79 mother-child dyads, we found that lower intrinsic functional coupling between medial prefrontal and limbic regions prior to birth was associated with greater maternal report of aggressive behavior when children reached 3 years of age. This association was specific to between network coactivation, as neither within-network connectivity of the mPFC nor within-network connectivity of the limbic network was associated with subsequent child aggression. Our results are consistent with extant fMRI studies showing links between aggressive behavior and altered frontolimbic circuitry in childhood (

      Sukhodolsky D, Kalvin C, Jordan R (2021): Increased Amygdala and Decreased Frontolimbic Resting State Functional Connectivity in Children with Aggressive Behavior. Soc Cogn Affect Neurosci. https://doi.org/10.1093/scan/nsab128

      ), adolescence (

      Ibrahim K, Kalvin C, Morand-Beaulieu S, He G, Pelphrey KA, McCarthy G, Sukhodolsky DG (2022): Amygdala–Prefrontal Connectivity in Children with Maladaptive Aggression is Modulated by Social Impairment. Cereb Cortex. https://doi.org/10.1093/CERCOR/BHAB489

      ,

      Lickley RA, Sebastian CL (2018): The neural basis of reactive aggression and its development in adolescence. https://doi.org/101080/1068316X20171420187 24: 313–333.

      ), and adulthood (
      • Siep N.
      • Tonnaer F.
      • van de Ven V.
      • Arntz A.
      • Raine A.
      • Cima M.
      Anger provocation increases limbic and decreases medial prefrontal cortex connectivity with the left amygdala in reactive aggressive violent offenders.
      ), and extend these findings to demonstrate prospective associations with frontolimbic connections measured prior to the onset of symptomatology and prior to birth.
      A critical finding from the present study was that individual differences in frontolimbic circuitry prior to birth, specifically weaker coupling of the amygdala and mPFC, precede and relate to greater subsequent aggression in toddlerhood. Rather than directly causing more mature emotion and behavior regulation abilities, which is typically reflected in negative functional coupling of limbic and frontal regions at older ages (
      • Wu M.
      • Kujawa A.
      • Lu L.H.
      • Fitzgerald D.A.
      • Klumpp H.
      • Fitzgerald K.D.
      • et al.
      Age-related changes in amygdala–frontal connectivity during emotional face processing from childhood into young adulthood.
      ), enhanced excitatory inputs are believed to drive the positive coactivation of limbic and mPFC regions beginning in gestation, which is postulated to entrain this circuitry and stimulate development of inhibitory connections from the PFC to limbic areas postnatally (
      • Werchan D.M.
      • Amso D.
      A novel ecological account of prefrontal cortex functional development.
      ,
      • Berkes P.
      • Orbán G.
      • Lengyel M.
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      Spontaneous cortical activity reveals hallmarks of an optimal internal model of the environment.
      ,

      Gabard-Durnam LJ, Gee DG, Goff B, Flannery J, Telzer E, Humphreys KL, et al. (2016): Stimulus-Elicited Connectivity Influences Resting-State Connectivity Years Later in Human Development: A Prospective Study. https://doi.org/10.1523/JNEUROSCI.0598-16.2016

      ,
      • Mackey A.P.
      • Singley A.T.M.
      • Bunge S.A.
      Intensive Reasoning Training Alters Patterns of Brain Connectivity at Rest.
      ). Longitudinal research has found that frontolimbic activation in response to negative stimuli at 7 to 12 years of age prospectively predicts stronger intrinsic connectivity within this same circuitry on average 2 years later; this association was strongest when stimulus-elicited activity was measured earlier in development, a time when neural plasticity is more pronounced (

      Gabard-Durnam LJ, Gee DG, Goff B, Flannery J, Telzer E, Humphreys KL, et al. (2016): Stimulus-Elicited Connectivity Influences Resting-State Connectivity Years Later in Human Development: A Prospective Study. https://doi.org/10.1523/JNEUROSCI.0598-16.2016

      ).
      An alternative mechanistic explanation of our results may relate to the postnatal caregiving environment. It is possible that fetuses who show stronger positive frontolimbic connectivity in utero exhibit higher levels of behavioral and emotional arousal after birth, which elicits caregiving behaviors that externally regulate the infant (

      Fox NA, Calkins SD (2003, March): The development of self-control of emotion: Intrinsic and extrinsic influences. Motivation and Emotion, vol. 27. Springer, pp 7–26.

      ). The repeated external regulation provided by caregivers early in life may scaffold the postnatal development of inhibitory connections from the mPFC to subcortical limbic regions, ultimately resulting in more effective behavioral control, less intense emotional reactions in early childhood, and fewer aggressive behaviors (
      • Gaffrey M.S.
      • Barch D.M.
      • Luby J.L.
      • Petersen S.E.
      Amygdala Functional Connectivity Is Associated With Emotion Regulation and Amygdala Reactivity in 4- to 6-Year-Olds.
      ,
      • Tottenham N.
      Early Adversity and the Neotenous Human Brain.
      ). This explanation is consistent with our finding that fetal frontolimbic circuitry also correlated with internalizing symptomatology. It is possible that entrainment of frontolimbic circuitry in utero holds broader implications for child emotional reactivity, and that heightened reactivity simply manifests most strongly as aggression during this early stage of development.
      Heterogeneity in the development of frontal and limbic connections in utero is likely explained by a combination of genetic and environmental factors. MRIs conducted with twin neonates suggests that while global measures of brain volume and structural variability in motor and visual regions are highly heritable, variation in the development of frontal regions tends to be more strongly influenced by environmental rather than genetic factors (
      • Maggioni E.
      • Squarcina L.
      • Dusi N.
      • Diwadkar V.A.
      • Brambilla P.
      Twin MRI studies on genetic and environmental determinants of brain morphology and function in the early lifespan.
      ). Behavioral genetics studies in 7 to 9 year old twins also estimates that unique and shared environmental factors are more strongly associated with interindividual variance in amygdala-prefrontal connectivity than shared genetics (
      • Achterberg M.
      • Bakermans-Kranenburg M.J.
      • van Ijzendoorn M.H.
      • van der Meulen M.
      • Tottenham N.
      • Crone E.A.
      Distinctive heritability patterns of subcortical-prefrontal cortex resting state connectivity in childhood: A twin study.
      ). Although examining neural development in utero accounts for the influence of postnatal environmental inputs, preconception and prenatal exposures likely shape developing frontolimbic circuitry. For example, maternal childhood maltreatment (

      Hendrix CL, Dilks DD, McKenna BG, Dunlop AL, Corwin EJ, Brennan PA (2021): Maternal Childhood Adversity Associates With Frontoamygdala Connectivity in Neonates. Biol Psychiatry Cogn Neurosci Neuroimaging. https://doi.org/10.1016/j.bpsc.2020.11.003

      ), negative affect and stress during pregnancy (

      Thomason ME, Hect JL, Waller R, Curtin P (2021): Interactive relations between maternal prenatal stress, fetal brain connectivity, and gestational age at delivery. Neuropsychopharmacol 2021 4610 46: 1839–1847.

      ), and maternal inflammation during pregnancy (
      • Rasmussen J.M.
      • Graham A.M.
      • Entringer S.
      • Gilmore J.H.
      • Styner M.
      • Fair D.A.
      • et al.
      Maternal Interleukin-6 concentration during pregnancy is associated with variation in frontolimbic white matter and cognitive development in early life.
      ) are all correlated with the intrinsic functional coupling of limbic and prefrontal regions in infants. Altered frontolimbic connections in utero may therefore reflect even earlier environmental exposures that have potent programming effects.
      Our analyses focused solely on a single frontal network and a subcortical limbic network in the fetal brain, but these are not the only neural regions implicated in the unfolding of aggressive behaviors. Leading models propose that aggression emerges from a combination of circuitry alterations that subserve salience detection (e.g., amygdala, cingulate, and insula), reward processing (e.g., ventral striatum), and cognitive control (e.g., medial and lateral PFC and inferior parietal lobe; 12,14). Indeed, exploratory seed-based connectivity analyses in the present study (see Supplemental Materials) revealed that altered limbic and mPFC connections to the cingulate, inferior parietal, and cerebellum also correlated with increased aggression in toddlers. Frontolimbic connections are therefore part of a more complex network that may support individual differences in aggressive behaviors.
      Of note, we examined normative variation in aggressive behaviors within a typically developing sample of children and we lacked information about whether any children exhibited clinically significant levels of aggression that require intervention. Engagement of a typically developing rather than clinical sample is of high interest given that aggressive behaviors in preschool-aged children are a common response to frustration and an important aspect of development (
      • Dirks M.A.
      • Recchia H.E.
      • Estabrook R.
      • Howe N.
      • Petitclerc A.
      • Burns J.L.
      • et al.
      Differentiating typical from atypical perpetration of sibling-directed aggression during the preschool years.
      ). Nonetheless, there is value in future research examining neural precursors of clinically significant levels of aggression. In addition, the families in this study were predominantly Black mother-child dyads from low to middle income households. We view this as a strength of the current study, given that racial and ethnic minority groups are markedly underrepresented in developmental (
      • Nketia J.
      • Amso D.
      • Brito N.H.
      Towards a more inclusive and equitable developmental cognitive neuroscience.
      ) and neuroimaging research (
      • Falk E.B.
      • Hyde L.W.
      • Mitchell C.
      • Faul J.
      • Gonzalez R.
      • Heitzeg M.M.
      • et al.
      What is a representative brain? Neuroscience meets population science.
      ). Nevertheless, these analyses should be replicated in a population-representative sample to ascertain the generalizability of these results across diverse cultural, racial, and sociodemographic groups.
      In vivo fetal fMRI is a powerful, but relatively young tool for parsing neural mechanisms that contribute to individual differences in behavior and clinical risk. The present results must therefore be interpreted with caution and in the context of analytic limitations. First, fetal fMRI data are contaminated by large fetal movements, resulting in significant data loss; it is common to discard 35-56% of rsfMRI frames due to fetal motion (
      • van den Heuvel M.I.
      • Hect J.L.
      • Smarr B.L.
      • Qawasmeh T.
      • Kriegsfeld L.J.
      • Barcelona J.
      • et al.
      Maternal stress during pregnancy alters fetal cortico-cerebellar connectivity in utero and increases child sleep problems after birth.
      ,
      • Pecco N.
      • Canini M.
      • Mosser K.H.H.
      • Caglioni M.
      • Scifo P.
      • Castellano A.
      • et al.
      RS-FetMRI: a MATLAB-SPM Based Tool for Pre-processing Fetal Resting-State fMRI Data.
      ). In the present study, some fetuses had as few as 100 frames of rsfMRI data. Due to concerns that FC estimates are less reliable when examining low amounts of data (
      • Birn R.M.
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      ), we repeated our analyses only in fetuses who had relatively greater amounts of data. Our results held (see Supplemental Materials). Nonetheless, our findings are based on a relatively low amount of rsfMRI data per fetus so it will be imperative to assess whether these findings replicate in studies that are able to collect longer fetal rsfMRI scans. We also used a more liberal motion threshold than what is typically applied in adult imaging in order to maximize data retention. For this reason, we used a stringent ICA denoising strategy that was effective at minimizing motion-related artifacts in the present data (

      Ji L, Hendrix CL, Thomason ME (2022): Empirical optimization of human fetal fMRI preprocessing steps. Networks Neurosci.

      ). Emerging advanced motion correction approaches, such as slice-to-volume registration, may further improve our ability to reduce noise and enhance signal in fetal fMRI (
      • Sobotka D.
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      • Schwartz E.
      • Nenning K.H.
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      • Vercauteren T.
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      ). Outside of motion-related challenges, fetal fMRI preprocessing presently requires a number of manual steps (e.g., selection of low-motion segments based on visual inspection, manual brain masking) that hold potential to introduce unmeasured bias. Several research groups are making directed efforts to automate and quantify the impact of different preprocessing steps (

      Ji L, Hendrix CL, Thomason ME (2022): Empirical optimization of human fetal fMRI preprocessing steps. Networks Neurosci.

      ,
      • Rutherford S.
      • Sturmfels P.
      • Angstadt M.
      • Hect J.
      • Wiens J.
      • van den Heuvel M.I.
      • et al.
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      ,
      • Pecco N.
      • Canini M.
      • Mosser K.H.H.
      • Caglioni M.
      • Scifo P.
      • Castellano A.
      • et al.
      RS-FetMRI: a MATLAB-SPM Based Tool for Pre-processing Fetal Resting-State fMRI Data.
      ), which will continue to enhance the rigor of fetal fMRI.
      Fetuses were scanned during a fairly wide age range (i.e., between 26 and 39 weeks gestation). Although histological, DTI, and fMRI studies in the fetal brain suggest that limbic to forebrain connections are in place by 26 weeks (
      • Vasung L.
      • Huang H.
      • Jovanov-Milošević N.
      • Pletikos M.
      • Mori S.
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      Development of axonal pathways in the human fetal fronto-limbic brain: Histochemical characterization and diffusion tensor imaging.
      ), the brain undergoes significant growth and refinement of connections across this stage of development (
      • Jakab A.
      • Schwartz E.
      • Kasprian G.
      • Gruber G.M.
      • Prayer D.
      • Schöpf V.
      • Langs G.
      Fetal functional imaging portrays heterogeneous development of emerging human brain networks.
      ). To facilitate group comparisons, we elected to normalize all fetal brains to a single template representing the average age of our sample. However, we cannot be certain that all spatially aligned areas are functionally identical across fetuses in this wide age range. ICA was used to define our regions of interest based on underlying fMRI signal, which helps to ameliorate, but does not entirely address, this concern. Related to our use of ICA, our measure of within-network connectivity only measures cross-hemisphere connectivity of homologous regions and does not measure within-hemisphere connections within a given network. Additional research is needed to determine whether within-network associations with child aggression emerge when alternative methods are used to define fetal networks (
      • Wheelock M.D.
      • Hect J.L.
      • Hernandez-Andrade E.
      • Hassan S.S.
      • Romero R.
      • Eggebrecht A.T.
      • Thomason M.E.
      Sex differences in functional connectivity during fetal brain development.
      ).
      Aggression is a complex behavioral phenotype that is shaped by both environmental and biological factors. In the present study, we found that individual differences in fetal frontolimbic circuitry is one such factor that prospectively correlates with the emergence of aggressive behaviors. We postulate that stronger coactivation of frontolimbic regions during gestation may entrain this circuitry and stimulate the postnatal development of inhibitory connections from the PFC to limbic structures, which subsequently leads to greater emotional regulation in toddlerhood. Aggression was examined in the present study given its early developmental onset, ability to be measured reliably at young ages and in diverse samples, and public health significance. Yet we saw similar associations between fetal neurocircuitry and toddler internalizing symptomatology. Stronger positive frontolimbic coupling prior to birth may thus be a neural phenotype that is associated with emotion dysregulation more broadly, or even with a general psychopathology factor (
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      ). In utero neural phenotypes are predisposing rather than prescriptive and are only the beginning of complex trajectories that subserve child health and wellness. Nonetheless, isolating neural beginnings that relate to the manifestation of human behavior is a necessary step in our efforts to understand the biological etiology of psychiatric illness.

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      Acknowledgements

      First and foremost, we are grateful to all of our participant families for their time, energy, and trust. We also thank Ava Palopoli and Alexis Taylor for their careful organization and care of the data.

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