Hernandez-Pena, L., Koch, J., Raine, A., & Wagels, L. (2026). Future Directions in Aggression Research: The Contribution of Technology-Integrated Operationalizations. Review of General Psychology. https://doi.org/10.1177/10892680251412856
Aggressive behavior shows striking gender differences. Cross-cultural research shows that men are more likely to engage in physical aggression, while women tend to use indirect forms of aggressive behavior. Aggression is a multifactorial phenomenon influenced by situational, genetic, psychological and other factors. Despite being a transdiagnostic feature in numerous mental disorders, gender-specific differences and the underlying influencing factors have not yet been sufficiently investigated. Many findings originate from older publications and methodologically sound and in particular behavior-based investigations are rare. This article provides a narrative literature review that summarizes the current state of research on gender differences in aggression in selected mental disorders, including substance use disorders, psychotic disorders and borderline personality disorder. The implications for future research and the clinical practice are discussed.
While anyone can behave aggressively, some people are more prone to aggression than others. We present a neuro-cognitive model and consider several inter-individual differences that confer risk for aggression. Forms of atypical cognitive function include a hyperreactive acute threat response, poor emotion regulation, and mechanisms involved in choosing when to aggress. We show dysfunction in the neural systems mediating these functions may account for aggression in people high in psychopathy/callous unemotional traits, irritability/anger, hostility, impulsivity, and low in frustration tolerance. We then review promising interventions including psychological therapies and pharmaceuticals that might influence the neuro-cognitive underpinnings of these constructs. Although there is no overwhelming “one size fits all” approach to treating aggression, identifying the neural mechanisms implicated in these traits may improve individualized treatments.
Brain–computer interfaces (BCIs) can be used to monitor and provide real-time feedback on brain signals, directly influencing external systems, such as virtual environments (VE), to support self-regulation. We piloted a novel immersive, first-person shooting BCI-VE during which the avatars’ movement speed was directly influenced by neural activity in the supplementary motor area (SMA). Previous analyses revealed behavioral and localized neural effects for active versus reduced contingency neurofeedback in a randomized controlled trial design. However, the modeling of neural dynamics during such complex tasks challenges traditional event-related approaches. To overcome this limitation, we employed a data-driven framework utilizing group-level independent networks derived from BOLD-specific components of the multi-echo fMRI data obtained during the BCI regulation. Individual responses were estimated through dual regression. The spatial independent components corresponded to established cognitive networks and task-specific networks related to gaming actions. Compared to reduced contingency neurofeedback, active regulation induced significantly elevated fractional amplitude of low-frequency fluctuations (fALFF) in a frontoparietal control network, and spatial reweighting of a salience/ventral attention network, with stronger expression in SMA, prefrontal cortex, inferior parietal lobule, and occipital regions. These findings underscore the distributed network engagement of BCI regulation during a behavioral task in an immersive virtual environment.
Cheng, A., Lichenstein, S., Chaarani, B., Liang, Q., Babaeianjelodar, M., Riley, S. J., Luo, W., Horien, C., Greene, A. S., Banaschewski, T., Bokde, A. L. W., Desrivières, S., Flor, H., Grigis, A., Gowland, P., Heinz, A., Brühl, R., Martinot, J.-L., Martinot, M.-L. P., … Yip, S. W. (2025). Impulsivity and neuroticism share distinct functional connectivity signatures with alcohol-use risk in youth. Molecular Psychiatry, 31(2), 953–962. https://doi.org/10.1038/s41380-025-03196-6
High levels of trait anger and aggressive behavior are common and problematic phenomena in patients with borderline personality disorder (BPD). In BPD, patterns of reactive aggression often lead to functional impairment affecting important areas of life. Despite the high burden on individuals and their social environment, there are no specific, cost-effective treatments to reduce aggression in BPD. In previous studies, we and others have been able to infer specific biobehavioral mechanisms underlying patterns of reactive aggression in BPD that can be used as potential treatment targets. To address this, we developed a mechanism-based anti-aggression psychotherapy (MAAP) for the group setting that specifically targets the biobehavioral mechanisms underlying outward-directed aggression in BPD. A previously conducted proof-of-concept study had suggested beneficial effects for this neglected group of patients. In this multicenter, confirmatory, randomized-controlled-clinical-trial, MAAP, which consists of multifaceted, evidence-based treatment elements adapted from other sophisticated treatment programs such as Dialectical Behavior Therapy and Mentalization-Based Treatment, is tested for efficacy against a non-specific supportive psychotherapy (NSSP) program focusing on non-specific general factors of psychotherapy at seven different sites in Germany. Both treatment arms, based on one individual and 13 group therapeutic sessions (1.5 h per session, twice a week), are delivered over a period of 7–10 weeks. A total of N = 186 patients will be recruited, half of whom will be cluster-randomized to MAAP. Outcomes are assessed at baseline, immediately, and 4, 12, 20, and 24 weeks post-treatment using ecological momentary assessment, clinical interviews, questionnaires, and online tasks. If proven superior, MAAP can be incorporated into standard psychiatric care, filling a critical gap in the current therapeutic landscape by offering a structured, cost-effective, and evidence-based treatment that directly targets the biobehavioral mechanisms underlying reactive aggression in BPD. By potentially improving clinical outcomes and reducing the burden of reactive aggression in BPD, MAAP could be beneficial for both individuals and their social environments. The study’s large, multicenter design enhances the generalizability of the results, making them more relevant for broader clinical applications.
High-field magnetic resonance imaging to explore brain structure and function remains limited to high-resource settings. Novel, low-field (<0.1 T) imaging offers a more cost-effective/accessible alternative. However, the validity of low-field data at spatial resolutions relevant to research and clinic (vertex-level) remains unclear. Hence, we examine paired high-field (reference) and low-field (single/multi-orientation scans processed through established/novel pipelines) data (12 children [10-12 yrs] in a low- and middle-income country [LMIC]). We assess high-field/low-field correspondence between vertex-level measures of cortical volume, surface area, and cortical thickness; and compare analytic strategies. High/low-field images show weak-to-moderate global correspondence (cortical volume, surface area: Pearson’s r ≤ 0.6, cortical thickness r ≤ 0.3), and weak-to-very strong local correspondence (r ≤ 0.99). Greatest correspondence is achieved with multi-orientation images and a pipeline adjusted for low-resolution images (recon-all-clinical); or image enhancement (SynthSR) plus standard processing (FastSurfer); but agreement varies across brain based on input, analytic strategy, and neuroanatomical feature. We provide an application to interactively explore our results. Thus, low-field imaging can provide reliable, high-resolution estimates of cortical volume and surface area, but not cortical thickness; and analytic approaches should be selected based on multiple considerations. Once validated, this research may help deploy low-field imaging to aid research/evidence-based clinical work in high- and low-resource settings, including LMIC.
Neurodevelopmental conditions, such as autism, are highly heterogeneous at both the mechanistic and phenotypic levels. Therefore, parsing heterogeneity is vital for uncovering underlying processes that could inform the development of targeted, personalized support. We aimed to parse heterogeneity in autism by identifying subgroups that converge at both the phenotypic and molecular levels. An imaging transcriptomics approach was used to link neuroanatomical imaging-derived phenotypes in autism to whole-brain gene expression signatures provided by the Allen Human Brain Atlas. Neuroimaging and clinical data of 359 autistic participants ages 6 to 30 years were provided by EU-AIMS (European Autism Interventions) LEAP (Longitudinal European Autism Project). Individuals were stratified using data-driven clustering techniques based on the correlation between brain phenotypes and transcriptomic profiles. The resulting subgroups were characterized on the clinical, neuroanatomical, and molecular levels. We identified 3 subgroups of autistic individuals based on the correlation between imaging-derived phenotypes and transcriptomic profiles that showed different clinical phenotypes. The individuals with the strongest transcriptomic associations with imaging-derived phenotypes showed the lowest level of symptom severity. The gene sets most characteristic for each subgroup were significantly enriched for genes previously implicated in autism etiology, including processes such as synaptic transmission and neuronal communication, and mapped onto different gene ontology categories. Autistic individuals can be subgrouped based on the transcriptomic signatures associated with their neuroanatomical fingerprints, which reveal subgroups that show differences in clinical measures. The study presents an analytical framework for linking neurodevelopmental and clinical diversity in autism to underlying molecular mechanisms, thus highlighting the need for personalized support strategies.
Neurodevelopmental conditions, such as autism, are highly heterogeneous at both the mechanistic and phenotypic levels. Therefore, parsing heterogeneity is vital for uncovering underlying processes that could inform the development of targeted, personalized support. We aimed to parse heterogeneity in autism by identifying subgroups that converge at both the phenotypic and molecular levels. An imaging transcriptomics approach was used to link neuroanatomical imaging-derived phenotypes in autism to whole-brain gene expression signatures provided by the Allen Human Brain Atlas. Neuroimaging and clinical data of 359 autistic participants ages 6 to 30 years were provided by EU-AIMS (European Autism Interventions) LEAP (Longitudinal European Autism Project). Individuals were stratified using data-driven clustering techniques based on the correlation between brain phenotypes and transcriptomic profiles. The resulting subgroups were characterized on the clinical, neuroanatomical, and molecular levels. We identified 3 subgroups of autistic individuals based on the correlation between imaging-derived phenotypes and transcriptomic profiles that showed different clinical phenotypes. The individuals with the strongest transcriptomic associations with imaging-derived phenotypes showed the lowest level of symptom severity. The gene sets most characteristic for each subgroup were significantly enriched for genes previously implicated in autism etiology, including processes such as synaptic transmission and neuronal communication, and mapped onto different gene ontology categories. Autistic individuals can be subgrouped based on the transcriptomic signatures associated with their neuroanatomical fingerprints, which reveal subgroups that show differences in clinical measures. The study presents an analytical framework for linking neurodevelopmental and clinical diversity in autism to underlying molecular mechanisms, thus highlighting the need for personalized support strategies.
Psychopathy is one of the greatest risk factors for serious and persistent violence. In order to detect its neurobiological substrates, we examined 39 male psychopathic subjects and matched controls using structural MR imaging and the Psychopathy Check-List (PCL-R). Individual brain region volumes were calculated using the Julich-Brain and AAL3 atlases. Associations of region volumes with the PCL-R dimensions among psychopathic subjects and differences between both groups were analysed. PCL-R factor 2 assessing lifestyle and antisocial behaviour showed in the psychopathic sample negative associations with volumes of several regions, including pons, nuclei of basal ganglia, thalamus, basal forebrain (CH-4), cerebellar regions and areas in orbitofrontal, dorsolateral-frontal and insular cortices. These findings suggest dysfunctions in specific frontal-subcortical circuits, which are known to be relevant for behavioral control. In contrast, the interpersonal-affective PCL-R factor 1 showed only weak positive and negative associations with orbitofrontal, dorsolateral-frontal and left hippocampal areas (CA1, subiculum), among others, indicating that involved brain regions might be affected to a variable degree in different individuals. The group comparison yielded a significantly reduced total brain volume in psychopathic subjects relative to controls, while pronounced regional focuses of volume differences were found only in the right subiculum, suggesting an interindividually variable pattern of structural deviations in the brains of psychopathic subjects. In conclusion, these findings are compatible with the dimensionality of the PCL-R construct, and suggest a particulary strong association of antisocial behavior to smaller volumes in widespread subcortical-cortical brain regions.
Perceptual changes in major depressive disorder (MDD) may extend beyond emotional content and include the processing of basic stimulus features. These alterations may ultimately contribute to perceptual bias and anhedonia. To characterize blood oxygen level-dependent (BOLD) signal of perceptual processing, we investigated temporally independent fMRI signal components related to naturalistic stimulus processing in 39 patients with MDD and 36 healthy subjects. Leveraging the capability of multi-echo data to detect BOLD activity changes, we extracted physiologically meaningful group temporal components. For each component that exhibited a significant correlation with the movie content, we localized its underlying brain network and assessed MDD-associated alterations. Two components exhibited significant group differences; one was associated with auditory features (sound pressure level) and one with visual features (temporal contrast of intensity). Notably, these deficits in MDD localized primarily to higher-order processing areas, such as the dorsal prefrontal cortex and insula, rather than primary sensory cortices. For the visual feature component, additional group differences emerged in non-visual primary sensory cortices (auditory and somatosensory) as well as major hubs of the motor system. Our findings support the hypothesis that basic sensory processing deficits represent an inherent feature of MDD which may contribute to anhedonia and negative perceptual bias. These deficits are primarily confined to higher-order processing units, as well as cross-modal primary sensory cortices indicating predominant dysfunction of top-down control and multisensory integration. Therapeutic effects of interventions targeting the prefrontal cortex may be partially mediated by restoring prefrontal control not only over emotional but also sensory processing hubs.
Multi-echo echo-planar imaging (ME-EPI) acquires images at multiple echo times (TEs), enabling the differentiation of BOLD and non-BOLD fluctuations through TE-dependent analysis of transverse relaxation time and initial intensity. Decomposing ME-EPI in tensor space is a promising approach to characterize the distribution of changes across TEs (TE patterns) directly and aid the classification of components by providing information from an additional domain. In this study, the tensorial extension of independent component analysis (tensor-ICA) is used to characterize the TE patterns of neural and non-neural components in ME-EPI data. With the constraints of independent spatial maps, an ME-EPI dataset was decomposed into spatial, temporal, and TE domains to understand the TE patterns of noise or signal-related independent components. Our analysis revealed three distinct groups of components based on their TE patterns. Motion-related and other non-BOLD origin components followed decreased TE patterns. While the long-TE-peak components showed a large overlay on grey matter and signal patterns, the components that peaked at short TEs reflected noise that may be related to the vascular system, respiration, or cardiac pulsation, amongst others. Accordingly, removing short-TE peak components as part of a denoising strategy significantly improved quality control metrics and revealed clearer, more interpretable activation patterns compared to non-denoised data. To our knowledge, this work is the first application of decomposing ME-EPI in a tensor way. Our findings demonstrate that tensor-ICA is efficient in decomposing ME-EPI and characterizing the neural and non-neural TE patterns aiding in classifying components which is important for denoising fMRI data.
Yan, L., Kraaijenvanger, E. J., Wennekers, R., Müller, V. I., Eickhoff, S. B., Fernández, G., Holz, N. E., & Kohn, N. (2025). The effects of childhood adversity: Two specific neural patterns. Neuroscience & Biobehavioral Reviews, 174, 106176. https://doi.org/10.1016/j.neubiorev.2025.106176
Early life stress is associated with alterations in brain function and connectivity during affective processing, especially in the fronto-limbic pathway. However, most of the previous studies were limited to a small set of priori-selected regions and did not address the impact of stress timing on functional connectivity. Using data from a longitudinal birth cohort study (n = 161, 87 females, mean age (SD) = 32.2(0.3)), we investigated the associations between different time points of stress exposure and functional connectivity. We measured stressful life events across development using a modified version of Munich Event List and grouped into four developmental stages: prenatal/newborn (prenatal-3 months), infancy and toddlerhood (3 months-4.5 years), childhood (4.5–11 years), and adolescence (11–19 years). All participants completed an fMRI-based emotion regulation task at the age of 33 years. Task-dependent directed functional connectivity was calculated using whole-brain generalized psychophysiological interactions. The association between life stress and connectivity was investigated within a multiple regression framework. Our findings revealed distinct associations between stress exposure and task-specific functional connectivity, depending on the developmental timing of stress exposure. While prenatal and childhood stress were associated with lower connectivity between subcortex and cognitive networks, stress exposure unique to adolescence was related to higher connectivity from the salience network to the cognitive networks. These results suggest that early life stress alters the connectivity of cognitive and limbic networks, which are important for emotion processing and regulation. Future research should replicate and extend the findings regarding sensitive periods by utilizing diverse paradigms in cognitive, social, and emotional domains.
Physiological responses derived from audiovisual perception during assisted driving are associated with the regulation of the autonomic nervous system (ANS), especially in emergencies. However, the interaction of event-related brain activity and the ANS regulating peripheral physiological indicators (i.e., heart rate variability (HRV) and respiratory rate) is unknown, making it difficult to study the neural mechanism during takeover from the assistance system. In this paper, we established a mapping between the ANS regulation and brain activations of driving events in function magnetic resonance imaging (fMRI)-conditioned audiovisual warnings experiment to add physiological fingerprints for assisted driving. Firstly, we used the general linear model (GLM) to obtain brain activation clusters of driving events and brain activation clusters of peripheral physiological indicators in different frequency bands. Secondly, we redefined the input parameters based on the driving events to calculate the GLM to obtain the brain activation clusters of event-related physiological indicators. Finally, the relationship between the main activation clusters of driving events and the activation of event-related physiological indicators was quantified by the statistical test of the mean-time course of voxels within the region. The results showed that related areas of the brain responsible for movement, visceral autonomic regulation, auditory, and vision actively responded to the audiovisual warnings of automatic driving. The mappings created using them revealed that the correlation between driving event-related activation of brain regions and respiration worked at the onset of audiovisual warnings, especially between the intermediate (IM) and low frequency (LF) bands. For pre-emergency and takeover in audiovisual warnings, the correlations of HRV were dominant, with significant differences among LF, IM and high frequency (HF) bands. At different periods of audiovisual warnings, HRV and respiration play different roles in physiological fingerprints. Compared to respiratory indicators, HRV has higher sensitivity to emergency situations. This study investigates the interaction between driving-related network activity and ANS regulation, revealing the profound connection between driving behavior and neural activity, and contributing to the research of driving assistance systems.
Introduction: Everyday life requires correct processing of emotions constantly, partly occurring unconsciously. This study aims to clarify the effect of emotion perception on different event-related potentials (ERP; P100, N170). The P100 and N170 are tested for their suitability as electrophysiological markers in unconscious processing.
Objective:: Cortisol is a well-established biomarker of stress, assessed through salivary or blood samples, which are intrusive and time-consuming. Speech, influenced by physiological stress responses, offers a promising non-invasive, real-time alternative for stress detection. This study examined relationships between speech features, state anger, and salivary cortisol using a validated stress-induction paradigm.
Background Emotion regulation mediated by cognitive reappraisal can induce activity changes in brain areas and fluctuations in peripheral physiological indicators regulated by the autonomic nervous system (ANS). However, little is known about the relationship between emotional and ANS regulation in major depressive disorder (MDD). In specific, the intermediate band (IM; 0.12–0.18 Hz) is thought to reflect relaxation and emotion processing.