Large-scale functional brain networks of maladaptive childhood aggression identified by connectome-based predictive modeling

Mapping Lesion-Related Human Aggression to a Common Brain Network

BACKGROUND

Aggression exacts a significant toll on human societies and is highly prevalent among neuropsychiatric patients. The neural mechanisms of aggression are unclear and treatment options are limited.

METHODS

Using a recently validated lesion network mapping technique, we derived an aggression-associated network by analyzing data from 182 patients who had experienced penetrating head injuries during their service in the Vietnam War. To test whether damage to this lesion-derived network would increase the risk of aggression-related neuropsychiatric symptoms, we used the Harvard Lesion Repository (N = 852). To explore potential therapeutic relevance of this network, we used an independent deep brain stimulation dataset of 25 patients with epilepsy, in which irritability and aggression are known potential side effects.

RESULTS

We found that lesions associated with aggression occurred in many different brain locations but were characterized by a specific brain network defined by functional connectivity to a hub region in the right prefrontal cortex. This network involves positive connectivity to the ventromedial prefrontal cortex, dorsolateral prefrontal cortex, frontal pole, posterior cingulate cortex, anterior cingulate cortex, temporal-parietal junction, and lateral temporal lobe and negative connectivity to the amygdala, hippocampus, insula, and visual cortex. Among all 24 neuropsychiatric symptoms included in the Harvard Lesion Repository, criminality demonstrated the most alignment with our aggression-associated network. Deep brain stimulation site connectivity to this same network was associated with increased irritability.

CONCLUSIONS

We conclude that brain lesions associated with aggression map to a specific human brain circuit, and the functionally connected regions in this circuit provide testable targets for therapeutic neuromodulation.

Cortical volume alterations in the limbic network in adolescents with high reactive aggression

Previous studies show aggression-related structural alterations in frontal and limbic brain regions. Most studies have focused on overall aggression, instead of its subtypes, and on specific regions instead of networks. This study aims to identify both brain networks and regions that are associated with reactive and proactive subtypes of aggression. Structural MRI data were collected from 340 adolescents (125 F/215 M) with a mean age of 16.29 (SD = 1.20). Aggression symptomology was indexed via the Reactive Proactive Aggression Questionnaire (RPQ). Freesurfer was used to estimate Cortical Volume (CV) from seven networks and regions within specific networks associated with aggression. Two multivariate analyses of covariance (MANCOVAs) were conducted on groups for low versus higher reactive and proactive RPQ scores. Our reactive aggression MANCOVA showed a main effect in CV [F(14,321) = 1.935, p = 0.022,ηp2 = 0.078] across all the 7-Networks. Unpacking this main effect revealed significant volumetric differences in the right Limbic Network (LN) (p = 0.029) and the Temporal Pole (p = 0.011), where adolescents in the higher reactive aggression group showed higher cortical volumes. Such findings are consistent with region/voxel-specific analyses that have associated atypical structure within the LN and reactive aggression. Moreover, the temporal pole is highly interconnected with regions important in the regulation and initiation of reactive aggression.

Investigating the impact of lumping heterogenous conduct problems: aggression and rule-breaking rely on distinct spontaneous brain activity

Accumulating evidence suggests that aggression and rule-breaking may have distinct origins. However, grouping these heterogeneous behaviors into a single dimension labelled Conduct Problems (CP) has become the norm rather than the exception. Yet, the neurobiological features that differentiate aggression and rule-breaking remain largely unexplored. Using a large sample of children and adolescents (n = 1360, 6-18 years old), we examined the common and specific brain activity between CP, aggression, and rule-breaking behaviors. Analyses were conducted using fMRI resting-state data from a 10-minute session to explore the correlations between low frequency fluctuations and both broad and fine-grained CP dimensions. The broad CP dimension was associated with deficits in the precentral gyrus, superior temporal gyrus, and tempo-parietal junction. However, only the superior temporal gyrus was shared between aggression and rule-breaking. Activity of the precentral gyrus was mainly associated with rule-breaking, and the temporo-parietal cortex with aggression. More importantly, voxel-wise analyses on fine-grained dimensions revealed additional specific effects that were initially obscured when using a broad CP dimension. Finally, we showed that the findings specific to aggression and rule-breaking may be related to distinct brain networks and mental functions, especially ventral attention/sensorimotor processes and default mode network/social cognitions, respectively. The current study highlights that aggression and rule-breaking may be related to distinct local and distributed neurobiological markers. Overall, using fine-grained dimensions may provide a clearer picture of the role of neurobiological correlates in CP and their invariance across measurement levels. We advocate for adopting a more thorough examination of the lumping/splitting effect across neuroimaging studies on CP.

Common and distinct neural underpinnings of the association between childhood maltreatment and depression and aggressive behavior

BACKGROUND

Although childhood maltreatment (CM) is widely recognized as a transdiagnostic risk factor for various internalizing and externalizing psychological disorders, the neural basis underlying this association remain unclear. The potential reasons for the inconsistent findings may be attributed to the involvement of both common and specific neural pathways that mediate the influence of childhood maltreatment on the emergence of psychopathological conditions.

METHODS

This study aimed to delineate both the common and distinct neural pathways linking childhood maltreatment to depression and aggression. First, we employed Network-Based Statistics (NBS) on resting-state functional magnetic resonance imaging (fMRI) data to identify functional connectivity (FC) patterns associated with depression and aggression. Mediation analyses were then conducted to assess the role of these FC patterns in the relationship between childhood maltreatment and each outcome.

RESULTS

The results demonstrated that FC within the default mode network (DMN) and between the cingulo-opercular network (CON) and dorsal attention network (DAN) mediated the association between childhood maltreatment and aggression, whereas FC within the reward system and between the CON and the reward system mediated the link between childhood maltreatment and depression.

CONCLUSIONS

We speculate that the control system may serve as a transdiagnostic neural basis accounting for the sequela of childhood maltreatment, and the attention network and the reward network may act as specific neural basis linking childhood maltreatment to depression and aggression, respectively.

An electroencephalography connectome predictive model of craving for methamphetamine

Background

Methamphetamine use disorder (MUD) is characterized by prominent psychological craving and its relapsing nature. Previous studies have linked trait impulsivity and abstinence duration to drug use, but the neural substrates of drug cue-induced craving and its relationship with these traits remain unclear in MUD.

Methods

We acquired high-density resting-state electroencephalography (EEG) after participants watched a five-minute video demonstrating methamphetamine use. Combining precise source imaging to reconstruct brain activities with connectome predictive modeling (CPM), we built a craving-specific network within beta band activity from two independent MUD cohorts (N=144 for model development and N=47 for validation).

Results

This network reveals a unified neural signature for craving in MUD, spanning multiple brain networks including the medial prefrontal, frontal parietal, and subcortical networks. Our findings underscored the mediating role of this craving connectome profile in modulating the relationship between abstinence duration and craving intensity. Moreover, trait impulsivity mediated the relationship between the EEG-derived craving connectome and cue-induced craving.

Conclusion

This study presents a novel predictive model that utilizes sourced connectivity from high-density EEG of resting-state recording to successfully predict methamphetamine craving in abstinent individuals with MUD. These results shed light on the cognitive organization involved in craving, involving cognitive control, attention, and reward reactivity. A comprehensive analysis reveals EEG data's capacity to decipher craving's complex dynamics, facilitating improved understanding and targeted treatments for substance use disorders.

Prediction of individual performance and verbal intelligence scores from resting-state fMRI in children and adolescents

The neuroimaging basis of intelligence remains elusive; however, there is a growing body of research employing connectome-based predictive modeling to estimate individual intelligence scores, aiming to identify the optimal set of neuroimaging features for accurately predicting an individual's cognitive abilities. Compared to adults, the disparities in cognitive performance among children and adolescents are more likely to captivate individuals' interest and attention. Limited research has been dedicated to exploring neuroimaging markers of intelligence specifically in the pediatric population. In this study, we utilized resting-state functional magnetic resonance imaging (fMRI) and intelligence quotient (IQ) scores of 170 healthy children and adolescents obtained from a public database to identify brain functional connectivity markers associated with individual intellectual behavior. Initially, we extracted and summarized relevant resting-state features from whole-brain or functional network connectivity that were most pertinent to IQ scores. Subsequently, these features were employed to establish prediction models for both performance and verbal IQ scores. Within a 10-fold cross-validation framework, our findings revealed that prediction models based on whole-brain functional connectivity effectively predicted performance IQ scores( R = 0.35 , P = 2.2 × 10 - 4 ) but not verbal IQ scores( R = 0.12 , P = 0.20 ). Results of prediction models based on brain functional network connectivity further demonstrated the exceptional predictive ability of the default mode network (DMN) and fronto-parietal task control network (FTPN) for performance IQ scores ( R = 0.71 , P = 2.2 × 10 - 18 ). The above findings have also been validated using an independent dataset. Our findings suggest that the performance IQ of children and adolescents primarily relies on the connectivity of brain regions associated with DMN and FTPN. Moreover, variations in intellectual performance during childhood and adolescences are closely linked to alterations in brain functional network connectivity.

Transdiagnostic Symptom Domains are Associated with Head Motion During Multimodal Imaging in Children

Background

Head motion is a challenge for neuroimaging research in developmental populations. However, it is unclear how transdiagnostic symptom domains including attention, disruptive behavior (e.g., externalizing behavior), and internalizing problems are linked to scanner motion in children, particularly across structural and functional MRI. The current study examined whether transdiagnostic domains of attention, disruptive behavior, and internalizing symptoms are associated with scanner motion in children during multimodal imaging.

Methods

In a sample of 9,045 children aged 9-10 years in the Adolescent Brain Cognitive Development (ABCD) Study, logistic regression and linear mixed-effects models were used to examine associations between motion and behavior. Motion was indexed using ABCD Study quality control metrics and mean framewise displacement for the following: T1-weighted structural, resting-state fMRI, diffusion MRI, Stop-Signal Task, Monetary Incentive Delay task, and Emotional n-Back task. The Child Behavior Checklist was used as a continuous measure of symptom severity.

Results

Greater attention and disruptive behavior problem severity was associated with a lower likelihood of passing motion quality control across several imaging modalities. In contrast, increased internalizing severity was associated with a higher likelihood of passing motion quality control. Increased attention and disruptive behavior problem severity was also associated with increased mean motion, whereas increased internalizing problem severity was associated with decreased mean motion.

Conclusion

Transdiagnostic domains emerged as predictors of motion in youths. These findings have implications for advancing development of generalizable and robust brain-based biomarkers, computational approaches for mitigating motion effects, and enhancing accessibility of imaging protocols for children with varying symptom severities.

The role of functional emotion circuits in distinct dimensions of psychopathology in youth

Several mental disorders emerge during childhood or adolescence and are often characterized by socioemotional difficulties, including alterations in emotion perception. Emotional facial expressions are processed in discrete functional brain modules whose connectivity patterns encode emotion categories, but the involvement of these neural circuits in psychopathology in youth is poorly understood. This study examined the associations between activation and functional connectivity patterns in emotion circuits and psychopathology during development. We used task-based fMRI data from the Philadelphia Neurodevelopmental Cohort (PNC, N = 1221, 8-23 years) and conducted generalized psycho-physiological interaction (gPPI) analyses. Measures of psychopathology were derived from an independent component analysis of questionnaire data. The results showed positive associations between identifying fearful, sad, and angry faces and depressive symptoms, and a negative relationship between sadness recognition and positive psychosis symptoms. We found a positive main effect of depressive symptoms on BOLD activation in regions overlapping with the default mode network, while individuals reporting higher levels of norm-violating behavior exhibited emotion-specific lower functional connectivity within regions of the salience network and between modules that overlapped with the salience and default mode network. Our findings illustrate the relevance of functional connectivity patterns underlying emotion processing for behavioral problems in children and adolescents.

Brain-based graph-theoretical predictive modeling to map the trajectory of anhedonia, impulsivity, and hypomania from the human functional connectome

Clinical assessments often fail to discriminate between unipolar and bipolar depression and identify individuals who will develop future (hypo)manic episodes. To address this challenge, we developed a brain-based graph-theoretical predictive model (GPM) to prospectively map symptoms of anhedonia, impulsivity, and (hypo)mania. Individuals seeking treatment for mood disorders (n = 80) underwent an fMRI scan, including (i) resting-state and (ii) a reinforcement-learning (RL) task. Symptoms were assessed at baseline as well as at 3- and 6-month follow-ups. A whole-brain functional connectome was computed for each fMRI task, and the GPM was applied for symptom prediction using cross-validation. Prediction performance was evaluated by comparing the GPM to a corresponding null model. In addition, the GPM was compared to the connectome-based predictive modeling (CPM). Cross-sectionally, the GPM predicted anhedonia from the global efficiency (a graph theory metric that quantifies information transfer across the connectome) during the RL task, and impulsivity from the centrality (a metric that captures the importance of a region) of the left anterior cingulate cortex during resting-state. At 6-month follow-up, the GPM predicted (hypo)manic symptoms from the local efficiency of the left nucleus accumbens during the RL task and anhedonia from the centrality of the left caudate during resting-state. Notably, the GPM outperformed the CPM, and GPM derived from individuals with unipolar disorders predicted anhedonia and impulsivity symptoms for individuals with bipolar disorders. Importantly, the generalizability of cross-sectional models was demonstrated in an external validation sample. Taken together, across DSM mood diagnoses, efficiency and centrality of the reward circuit predicted symptoms of anhedonia, impulsivity, and (hypo)mania, cross-sectionally and prospectively. The GPM is an innovative modeling approach that may ultimately inform clinical prediction at the individual level.

Neural circuit disruptions of eye gaze processing in autism spectrum disorder and schizophrenia: An activation likelihood estimation meta-analysis

BACKGROUND

Impairment in social cognition, particularly eye gaze processing, is a shared feature common to autism spectrum disorder (ASD) and schizophrenia. However, it is unclear if a convergent neural mechanism also underlies gaze dysfunction in these conditions. The present study examined whether this shared eye gaze phenotype is reflected in a profile of convergent neurobiological dysfunction in ASD and schizophrenia.

METHODS

Activation likelihood estimation (ALE) meta-analyses were conducted on peak voxel coordinates across the whole brain to identify spatial convergence. Functional coactivation with regions emerging as significant was assessed using meta-analytic connectivity modeling. Functional decoding was also conducted.

RESULTS

Fifty-six experiments (n = 30 with schizophrenia and n = 26 with ASD) from 36 articles met inclusion criteria, which comprised 354 participants with ASD, 275 with schizophrenia and 613 healthy controls (1242 participants in total). In ASD, aberrant activation was found in the left amygdala relative to unaffected controls during gaze processing. In schizophrenia, aberrant activation was found in the right inferior frontal gyrus and supplementary motor area. Across ASD and schizophrenia, aberrant activation was found in the right inferior frontal gyrus and right fusiform gyrus during gaze processing. Functional decoding mapped the left amygdala to domains related to emotion processing and cognition, the right inferior frontal gyrus to cognition and perception, and the right fusiform gyrus to visual perception, spatial cognition, and emotion perception. These regions also showed meta-analytic connectivity to frontoparietal and frontotemporal circuitry.

CONCLUSION

Alterations in frontoparietal and frontotemporal circuitry emerged as neural markers of gaze impairments in ASD and schizophrenia. These findings have implications for advancing transdiagnostic biomarkers to inform targeted treatments for ASD and schizophrenia.

Resting-state neural correlates of individual differences in ignored experience and its deleterious effect

Uncovering the neural mechanisms of ostracism experience (including its subclasses of excluded and ignored experiences) is important. However, the resting-state functional brain substrates responsible for individual differences in ostracism experience and its negative effects remain largely undefined. This study explored these issues in a sample of 198 Chinese college students by assessing the amplitude of low-frequency fluctuations and functional connectivity. The findings indicated a positive correlation between ignored experience and the amplitude of low-frequency fluctuations in the right superior frontal gyrus and the functional connectivity between the right superior frontal gyrus and left cerebellum posterior lobe. Additionally, a negative correlation was found between ignored experience and the functional connectivity between the right superior frontal gyrus and the bilateral insula as well as the bilateral inferior parietal lobule. Moreover, the mediation analysis demonstrated that the effects of the functional connectivities of right superior frontal gyrus-left cerebellum posterior lobe and right superior frontal gyrus-right inferior parietal lobule on revenge intention were mediated by ignored experience. Our study offers novel insights into the neural correlates of both individual variations in ignored experience and its typical deleterious effect. These results could deepen our understanding of individual differences in negative experiences and inspire the development of targeted interventions for social stress from the perspective of the brain.

Different whole-brain functional connectivity correlates of reactive-proactive aggression and callous-unemotional traits in children and adolescents with disruptive behaviors

BACKGROUND

Disruptive behavior in children and adolescents can manifest as reactive aggression and proactive aggression and is modulated by callous-unemotional traits and other comorbidities. Neural correlates of these aggression dimensions or subtypes and comorbid symptoms remain largely unknown. This multi-center study investigated the relationship between resting state functional connectivity (rsFC) and aggression subtypes considering comorbidities.

METHODS

The large sample of children and adolescents aged 8-18 years (n = 207; mean age = 13.30±2.60 years, 150 males) included 118 cases with disruptive behavior (80 with Oppositional Defiant Disorder and/or Conduct Disorder) and 89 controls. Attention-deficit/hyperactivity disorder (ADHD) and anxiety symptom scores were analyzed as covariates when assessing group differences and dimensional aggression effects on hypothesis-free global and local voxel-to-voxel whole-brain rsFC based on functional magnetic resonance imaging at 3 Tesla.

RESULTS

Compared to controls, the cases demonstrated altered rsFC in frontal areas, when anxiety but not ADHD symptoms were controlled for. For cases, reactive and proactive aggression scores were related to global and local rsFC in the central gyrus and precuneus, regions linked to aggression-related impairments. Callous-unemotional trait severity was correlated with ICC in the inferior and middle temporal regions implicated in empathy, emotion, and reward processing. Most observed aggression subtype-specific patterns could only be identified when ADHD and anxiety were controlled for.

CONCLUSIONS

This study clarifies that hypothesis-free brain connectivity measures can disentangle distinct though overlapping dimensions of aggression in youths. Moreover, our results highlight the importance of considering comorbid symptoms to detect aggression-related rsFC alterations in youths.

Brain-based graph-theoretical predictive modeling to map the trajectory of transdiagnostic symptoms of anhedonia, impulsivity, and hypomania from the human functional connectome

Clinical assessments often fail to discriminate between unipolar and bipolar depression and identify individuals who will develop future (hypo)manic episodes. To address this challenge, we developed a brain-based graph-theoretical predictive model (GPM) to prospectively map symptoms of anhedonia, impulsivity, and (hypo)mania. Individuals seeking treatment for mood disorders (n = 80) underwent an fMRI scan, including (i) resting-state and (ii) a reinforcement-learning (RL) task. Symptoms were assessed at baseline as well as at 3- and 6-month follow-ups. A whole-brain functional connectome was computed for each fMRI task, and the GPM was applied for symptom prediction using cross-validation. Prediction performance was evaluated by comparing the GPM's mean square error (MSE) to that of a corresponding null model. In addition, the GPM was compared to the connectome-based predictive modeling (CPM). Cross-sectionally, the GPM predicted anhedonia from the global efficiency (a graph theory metric that quantifies information transfer across the connectome) during the RL task, and impulsivity from the centrality (a metric that captures the importance of a region for information spread) of the left anterior cingulate cortex during resting-state. At 6-month follow-up, the GPM predicted (hypo)manic symptoms from the local efficiency of the left nucleus accumbens during the RL task and anhedonia from the centrality of the left caudate during resting-state. Notably, the GPM outperformed the CPM, and GPM derived from individuals with unipolar disorders predicted anhedonia and impulsivity symptoms for individuals with bipolar disorders, highlighting transdiagnostic generalization. Taken together, across DSM mood diagnoses, efficiency and centrality of the reward circuit predicted symptoms of anhedonia, impulsivity, and (hypo)mania, cross-sectionally and prospectively. The GPM is an innovative modeling approach that may ultimately inform clinical prediction at the individual level. ClinicalTrials.gov identifier: NCT01976975.

Unveiling common psychological characteristics of proneness to aggression and general psychopathology in a large community youth cohort

Elevated aggression in individuals with psychiatric disorders is frequently reported yet aggressive acts among people with mental illness are often intertwined with proneness to aggression and other risk factors. Evidence has suggested that both general psychopathology and proneness to aggression may share common psychological characteristics. This study aims to investigate the complex relationship between general psychopathology, proneness to aggression, and their contributing factors in community youth. Here, we first examined the association between proneness to aggression and the level of general psychopathology in 2184 community youths (male: 41.2%). To identify common characteristics, we trained machine learning models using LASSO based on 230 features covering sociodemographic, cognitive functions, lifestyle, well-being, and psychological characteristics to predict levels of general psychopathology and proneness to aggression. A subsequent Gaussian Graph Model (GGM) was fitted to understand the relationships between the general psychopathology, proneness to aggression, and selected features. We showed that proneness to aggression was associated with a higher level of general psychopathology (discovery: r = 0.56, 95% CI: [0.52-0.59]; holdout: r = 0.60, 95% CI: [0.54-0.65]). The LASSO model trained on the discovery dataset for general psychopathology was able to predict proneness to aggression in the holdout dataset with a moderate correlation coefficient of 0.606. Similarly, the model trained on the proneness to aggression in the discovery dataset was able to predict general psychopathology in the holdout dataset with a correlation coefficient of 0.717. These results suggest that there is substantial shared information between the two outcomes. The GGM model revealed that isolation and impulsivity factors were directly associated with both general psychopathology and proneness to aggression. These results revealed shared psychological characteristics of general psychopathology and proneness to aggression in a community sample of youths.

Lower cerebello-cortical functional connectivity in veterans with reactive aggression symptoms: A pilot study

A significant number of veterans experience irritability and aggression symptoms as a result of being exposed to extremely stressful and life-threatening situations. In addition to the well-established involvement of the brain's cortico-subcortical circuit in aggression-related behaviours, a role of the deep cerebellar nuclei (DCN) in reactive aggression has been suggested. In the present study, seed-based resting-state functional connectivity between the DCN and cortico-subcortical areas was explored in veterans with and without reactive aggression symptoms. Nineteen male veterans with reactive aggression symptoms and twenty-two control veterans without reactive aggression symptoms underwent 3T resting-state functional MRI scans. Region-of-interest (ROI) analyses that included the amygdala, hypothalamus and periaqueductal grey as ROIs did not yield significant group-related differences in resting-state functional connectivity with the DCN. However, exploratory whole-brain analysis showed that veterans with reactive aggression symptoms exhibited lower functional connectivity between the DCN and the orbitofrontal cortex compared to control veterans. Our findings provide preliminary evidence for the possible involvement of a cerebello-prefrontal pathway in reactive aggression in male veterans.

Prediction of childhood maltreatment and subtypes with personalized functional connectome of large-scale brain networks

Childhood maltreatment (CM) has a long impact on physical and mental health of children. However, the neural underpinnings of CM are still unclear. In this study, we aimed to establish the associations between functional connectome of large-scale brain networks and influences of CM evaluated through Childhood Trauma Questionnaire (CTQ) at the individual level based on resting-state functional magnetic resonance imaging data of 215 adults. A novel individual functional mapping approach was employed to identify subject-specific functional networks and functional network connectivities (FNCs). A connectome-based predictive modeling (CPM) was used to estimate CM total and subscale scores using individual FNCs. The CPM established with FNCs can well predict CM total scores and subscale scores including emotion abuse, emotion neglect, physical abuse, physical neglect, and sexual abuse. These FNCs primarily involve default mode network, fronto-parietal network, visual network, limbic network, motor network, dorsal and ventral attention networks, and different networks have distinct contributions to predicting CM and subtypes. Moreover, we found that CM showed age and sex effects on individual functional connections. Taken together, the present findings revealed that different types of CM are associated with different atypical neural networks which provide new clues to understand the neurobiological consequences of childhood adversity.

Amygdala-prefrontal connectivity in children with maladaptive aggression is modulated by social impairment

Aggressive behavior is common across childhood-onset psychiatric disorders and is associated with impairments in social cognition and communication. The present study examined whether amygdala connectivity and reactivity during face emotion processing in children with maladaptive aggression are moderated by social impairment. This cross-sectional study included a well-characterized transdiagnostic sample of 101 children of age 8-16 years old with clinically significant levels of aggressive behavior and 32 typically developing children without aggressive behavior. Children completed a face emotion perception task of fearful and calm faces during functional magnetic resonance imaging. Aggressive behavior and social functioning were measured by standardized parent ratings. Relative to controls, children with aggressive behavior showed reduced connectivity between the amygdala and the dorsolateral prefrontal cortex (PFC) during implicit emotion processing. In children with aggressive behavior, the association between reduced amygdala-ventrolateral PFC connectivity and greater severity of aggression was moderated by greater social impairment. Amygdala reactivity to fearful faces was also associated with severity of aggressive behavior for children without social deficits but not for children with social deficits. Social impairments entail difficulties in interpreting social cues and enacting socially appropriate responses to frustration or provocation, which increase the propensity for an aggressive response via diminished connectivity between the amygdala and the ventral PFC.

Prenatal maternal stress and offspring aggressive behavior: Intergenerational and transgenerational inheritance

Even though studies have shown that prenatal maternal stress is associated with increased reactivity of the HPA axis, the association between prenatal maternal stress and fetal glucocorticoid exposure is complex and most likely dependent on unidentified and poorly understood variables including nature and timing of prenatal insults. The precise mechanisms in which prenatal maternal stress influence neuroendocrine signaling between the maternal-placental-fetal interface are still unclear. The aim of this review article is to bring comprehensive basic concepts about prenatal maternal stress and mechanisms of transmission of maternal stress to the fetus. This review covers recent studies showing associations between maternal stress and alterations in offspring aggressive behavior, as well as the possible pathways for the “transmission” of maternal stress to the fetus: (1) maternal-fetal HPA axis dysregulation; (2) intrauterine environment disruption due to variations in uterine artery flow; (3) epigenetic modifications of genes implicated in aggressive behavior. Here, we present evidence for the phenomenon of intergenerational and transgenerational transmission, to better understands the mechanism(s) of transmission from parent to offspring. We discuss studies showing associations between maternal stress and alterations in offspring taking note of neuroendocrine, brain architecture and epigenetic changes that may suggest risk for aggressive behavior. We highlight animal and human studies that focus on intergenerational transmission following exposure to stress from a biological mechanistic point of view, and maternal stress-induced epigenetic modifications that have potential to impact on aggressive behavior in later generations.