How sexual objectification marks the brain: fMRI evidence of self-objectification and its harmful emotional consequences.

Female Sexual Objectification refers to perceiving and treating women based on their body appearance. This phenomenon may serve as a precursor for dysfunctional behaviors, particularly among females prone to self-objectification and experiencing shame emotions. Understanding this challenging trajectory by disclosing its neural consequences may be crucial for comprehending extreme psychopathological outcomes. However, investigations in this sense are still scarce. The present study explores the neural correlates of female participants' experiences of being objectified and their relationship with self-objectification, emotional responses and individual dispositions in self-esteem, emotion regulation abilities and self-conscious emotion proneness. To this aim, 25 female participants underwent an fMRI experimental session while they were exposed to interpersonal encounters with objectifying or non-objectifying men. Participants' experienced emotions and levels of attention shifted toward their bodies (self-objectification) was reported after each interaction. The results revealed increased brain activity in objectifying contexts, impacting cortical (frontal, occipital and temporal cortex) and subcortical regions (thalamus, and hippocampus) involved in visual, emotion, and social processing. Remarkably, the inferior temporal gyrus emerged as a crucial neural hub associated in opposite ways with self-esteem and the self-conscious emotion of shame, highlighting its role in self-referential processing during social dynamics. This study points out the importance of adopting a neuroscientific perspective for a deeper understanding of sexual objectification, and to shed light on its possible neural consequences.

Narcissus reflected: Grey and white matter features joint contribution to the default mode network in predicting narcissistic personality traits.

Despite the clinical significance of narcissistic personality, its neural bases have not been clarified yet, primarily because of methodological limitations of the previous studies, such as the low sample size, the use of univariate techniques and the focus on only one brain modality. In this study, we employed for the first time a combination of unsupervised and supervised machine learning methods, to identify the joint contributions of grey matter (GM) and white matter (WM) to narcissistic personality traits (NPT). After preprocessing, the brain scans of 135 participants were decomposed into eight independent networks of covarying GM and WM via parallel ICA. Subsequently, stepwise regression and Random Forest were used to predict NPT. We hypothesized that a fronto-temporo parietal network, mainly related to the default mode network, may be involved in NPT and associated WM regions. Results demonstrated a distributed network that included GM alterations in fronto-temporal regions, the insula and the cingulate cortex, along with WM alterations in cerebellar and thalamic regions. To assess the specificity of our findings, we also examined whether the brain network predicting narcissism could also predict other personality traits (i.e., histrionic, paranoid and avoidant personalities). Notably, this network did not predict such personality traits. Additionally, a supervised machine learning model (Random Forest) was used to extract a predictive model for generalization to new cases. Results confirmed that the same network could predict new cases. These findings hold promise for advancing our understanding of personality traits and potentially uncovering brain biomarkers associated with narcissism.

Alteration of prefrontal cortex and its associations with emotional and cognitive dysfunctions in adolescent borderline personality disorder.

The neurobiological mechanism of borderline personality disorder (BPD) in adolescents remains unclear. The study aimed to assess the alterations in neural activity within prefrontal cortex in adolescents with BPD and investigate the relationship of prefrontal activity with emotional regulation and cognitive function. This study enrolled 50 adolescents aged 12-17 years with BPD and 21 gender and age-matched healthy control (HC) participants. Study assessment for each participant included a brain resting-state functional MRI (rs-fMRI), clinical assessment questionnaires such as Borderline Personality Features Scale (BPFS), Difficulties in Emotion Regulation Scale (DERS), Ottawa Self-Injury Inventory and Childhood Trauma Questionnaire (CTQ) and cognitive testing with Stroop Color-Word Test (SCWT). Fractional amplitude of low-frequency fluctuations (fALFF) and seed-based functional connectivity (FC) were obtained from rs-fMRI analysis. Correlation analysis was also performed to evaluate the associations of the neuroimaging metrics such as fALFF and FC with clinical assessment questionnaire and cognitive testing scores. Adolescents with BPD showed increased fALFF values in the right inferior frontal gyrus and decreased activity in the left middle frontal gyrus as compared to the HC group (p < 0.05, cluster size ≥ 100, FWE correction). In adolescents with BPD, increased fALFF in the right inferior frontal gyrus was related to the BPFS (emotional dysregulation), DERS-F (lacking of emotional regulation strategies) and Ottawa Self-Injury Inventory-4 C scores (internal emotional regulation function of self-injurious behavior). The reduced fALFF in the left middle frontal gyrus was associated with the SCWT-A (reading characters) and the SCWT-B (reading color) scores. Additionally, the fALFF values in the left middle frontal gyrus and the right inferior frontal gyrus were related to the CTQ-D (emotional neglect) (p < 0.05). The left middle frontal gyrus exhibited increased FC with the right hippocampus, left inferior temporal gyrus and right inferior frontal gyrus (voxel p < 0.001, cluster p < 0.05, FWE correction). The increased FC between the left middle frontal gyrus and the right hippocampus was related to the SCWT-C (cognitive flexibility) score. We observed diverging changes in intrinsic brain activity in prefrontal cortex, and neural compensatory changes to maintain function in adolescents with BPD. In addition, decreased neural function was closely associated with emotional dysregulation, while increased neural function as indicated by brain activity and FC was associated with cognitive dysfunction. These results indicated that alterations of intrinsic brain activity may be one of the underlying neurobiological markers for clinical symptoms in adolescents with BPD.

Reduced GM-WM concentration inside the Default Mode Network in individuals with high emotional intelligence and low anxiety: a data fusion mCCA+jICA approach.

The concept of emotional intelligence (EI) refers to the ability to recognize and regulate emotions to appropriately guide cognition and behaviour. Unfortunately, studies on the neural bases of EI are scant, and no study so far has exhaustively investigated grey matter (GM) and white matter (WM) contributions to it. To fill this gap, we analysed trait measure of EI and structural MRI data from 128 healthy participants to shed new light on where and how EI is encoded in the brain. In addition, we explored the relationship between the neural substrates of trait EI and trait anxiety. A data fusion unsupervised machine learning approach (mCCA + jICA) was used to decompose the brain into covarying GM-WM networks and to assess their association with trait-EI. Results showed that high levels trait-EI are associated with decrease in GM-WM concentration in a network spanning from frontal to parietal and temporal regions, among which insula, cingulate, parahippocampal gyrus, cuneus and precuneus. Interestingly, we also found that the higher the GM-WM concentration in the same network, the higher the trait anxiety. These findings encouragingly highlight the neural substrates of trait EI and their relationship with anxiety. The network is discussed considering its overlaps with the Default Mode Network.

Borderline shades: Morphometric features predict borderline personality traits but not histrionic traits.

Borderline personality disorder (BPD) is one of the most diagnosed disorders in clinical settings. Besides the fully diagnosed disorder, borderline personality traits (BPT) are quite common in the general population. Prior studies have investigated the neural correlates of BPD but not of BPT. This paper investigates the neural correlates of BPT in a subclinical population using a supervised machine learning method known as Kernel Ridge Regression (KRR) to build predictive models. Additionally, we want to determine whether the same brain areas involved in BPD are also involved in subclinical BPT. Recent attempts to characterize the specific role of resting state-derived macro networks in BPD have highlighted the role of the default mode network. However, it is not known if this extends to the subclinical population. Finally, we wanted to test the hypothesis that the same circuitry that predicts BPT can also predict histrionic personality traits. Histrionic personality is sometimes considered a milder form of BPD, and making a differential diagnosis between the two may be difficult. For the first time KRR was applied to structural images of 135 individuals to predict BPT, based on the whole brain, on a circuit previously found to correctly classify BPD, and on the five macro-networks. At a whole brain level, results show that frontal and parietal regions, as well as the Heschl's area, the thalamus, the cingulum, and the insula, are able to predict borderline traits. BPT predictions increase when considering only the regions limited to the brain circuit derived from a study on BPD, confirming a certain overlap in brain structure between subclinical and clinical samples. Of all the five macro networks, only the DMN successfully predicts BPD, confirming previous observations on its role in the BPD. Histrionic traits could not be predicted by the BPT circuit. The results have implications for the diagnosis of BPD and a dimensional model of personality.

Comparing reappraisal and acceptance strategies to understand the neural architecture of emotion regulation: a meta-analytic approach.

Introduction: In the emotion regulation literature, the amount of neuroimaging studies on cognitive reappraisal led the impression that the same top-down, control-related neural mechanisms characterize all emotion regulation strategies. However, top-down processes may coexist with more bottom-up and emotion-focused processes that partially bypass the recruitment of executive functions. A case in point is acceptance-based strategies. Method: To better understand neural commonalities and differences behind different emotion regulation processes, in the present study, we applied the Activation Likelihood Estimation (ALE) method to perform a meta-analysis on fMRI studies investigating task-related activity of reappraisal and acceptance. Both increased and decreased brain activity was taken into account in the contrast and conjunction analysis between the two strategies. Results: Results showed increased activity in left-inferior frontal gyrus and insula for both strategies, and decreased activity in the basal ganglia for reappraisal, and decreased activity in limbic regions for acceptance. Discussion: These findings are discussed in the context of a model of common and specific neural mechanisms of emotion regulation that support and expand the previous dual-routes models. We suggest that emotion regulation may rely on a core inhibitory circuit, and on strategy-specific top-down and bottom-up processes distinct for different strategies.

Neural dynamics of vicarious physical pain processing reflect impaired empathy toward sexually objectified versus non-sexually objectified women.

Sexually objectified women are perceived as dehumanized. This may affect the behavioral and neural responses underlying the observer's empathic reactions for their physical pain, although this hypothesis still lacks empirical support. In the present study, we measured the electrophysiological activity of 30 participants (14 females and 16 males), in an empathy for physical pain paradigm in which pictures of sexualized and non-sexualized women were presented in painful and non-painful situations. The behavioral results revealed that sexualized women were evaluated as experiencing less pain than non-sexualized women. Neural evidence corroborated this finding showing that the perception of vicarious physical pain is lacking for sexualized women in both event-related potentials (ERPs) and brain oscillation domains. Specifically, the P2 component and the event-related synchronization/desynchronization (ERS/ERD) on the mu frequency band differed between painful and non-painful stimulation exclusively when women were not sexualized. Our results provide the first evidence that the neurophysiological responses to the vicarious experience of physical pain are dampened or even absent for sexualized women. These findings expand our understanding of the neurophysiological signatures of empathic processes and highlight the detrimental effect of a sexual-objectification bias in everyday contexts.

Predicting narcissistic personality traits from brain and psychological features: A supervised machine learning approach.

Narcissism is a multifaceted construct often linked to pathological conditions whose neural correlates are still poorly understood. Previous studies have reported inconsistent findings related to the neural underpinnings of narcissism, probably due to methodological limitations such as the low number of participants or the use of mass univariate methods. The present study aimed to overcome the previous methodological limitations and to build a predictive model of narcissistic traits based on neural and psychological features. In this respect, two machine learning-based methods (Kernel Ridge Regression and Support Vector Regression) were used to predict narcissistic traits from brain structural organization and from other relevant normal and abnormal personality features. Results showed that a circuit including the lateral and middle frontal gyri, the angular gyrus, Rolandic operculum, and Heschl's gyrus successfully predicted narcissistic personality traits (p < 0.003). Moreover, narcissistic traits were predicted by normal (openness, agreeableness, conscientiousness) and abnormal (borderline, antisocial, insecure, addicted, negativistic, machiavellianism) personality traits. This study is the first to predict narcissistic personality traits via a supervised machine learning approach. As such, these results may expand the possibility of deriving personality traits from neural and psychological features.

"Holding in Anger" as a Mediator in the Relationship between Attachment Orientations and Borderline Personality Features.

Insecure attachment and difficulties in regulating anger have both been put forward as possible explanations for emotional dysfunction in borderline personality (BP). This study aimed to test a model according to which the influence of attachment on BP features in a subclinical population is mediated by anger regulation. In a sample of 302 participants, BP features were assessed with the Borderline features scale of the Personality Assessment Inventory (PAI-BOR), attachment was measured with the Experiences in Close Relationships-12 (ECR-12), and trait anger and anger regulation were assessed with the State and Trait Anger Expression Inventory-2 (STAXI-2). The results indicated that anger suppression emerged as a significant mediator of the associations between both anxious and avoidant attachment and BP traits, while anger control resulted as a marginal mediator in the association between attachment avoidance and BP. Suppressing anger may reflect different forms of cognitive or behavioural avoidance of anger, which may differ on the basis of attachment orientations. We argue that these results may have important clinical implications: the promotion of anger regulation in BP should be considered a critical treatment goal.

A Supervised Machine Learning Approach to Classify Brain Morphology of Professional Visual Artists versus Non-Artists.

This study aimed to investigate whether there are structural differences in the brains of professional artists who received formal training in the visual arts and non-artists who did not have any formal training or professional experience in the visual arts, and whether these differences can be used to accurately classify individuals as being an artist or not. Previous research using functional MRI has suggested that general creativity involves a balance between the default mode network and the executive control network. However, it is not known whether there are structural differences between the brains of artists and non-artists. In this study, a machine learning method called Multi-Kernel Learning (MKL) was applied to gray matter images of 12 artists and 12 non-artists matched for age and gender. The results showed that the predictive model was able to correctly classify artists from non-artists with an accuracy of 79.17% (AUC 88%), and had the ability to predict new cases with an accuracy of 81.82%. The brain regions most important for this classification were the Heschl area, amygdala, cingulate, thalamus, and parts of the parietal and occipital lobes as well as the temporal pole. These regions may be related to the enhanced emotional and visuospatial abilities that professional artists possess compared to non-artists. Additionally, the reliability of this circuit was assessed using two different classifiers, which confirmed the findings. There was also a trend towards significance between the circuit and a measure of vividness of imagery, further supporting the idea that these brain regions may be related to the imagery abilities involved in the artistic process.

The Neural Signatures of Shame, Embarrassment, and Guilt: A Voxel-Based Meta-Analysis on Functional Neuroimaging Studies.

Self-conscious emotions, such as shame and guilt, play a fundamental role in regulating moral behaviour and in promoting the welfare of society. Despite their relevance, the neural bases of these emotions are uncertain. In the present meta-analysis, we performed a systematic literature review in order to single out functional neuroimaging studies on healthy individuals specifically investigating the neural substrates of shame, embarrassment, and guilt. Seventeen studies investigating the neural correlates of shame/embarrassment and seventeen studies investigating guilt brain representation met our inclusion criteria. The analyses revealed that both guilt and shame/embarrassment were associated with the activation of the left anterior insula, involved in emotional awareness processing and arousal. Guilt-specific areas were located within the left temporo-parietal junction, which is thought to be involved in social cognitive processes. Moreover, specific activations for shame/embarrassment involved areas related to social pain (dorsal anterior cingulate and thalamus) and behavioural inhibition (premotor cortex) networks. This pattern of results might reflect the distinct action tendencies associated with the two emotions.

Electrophysiological, emotional and behavioural responses of female targets of sexual objectification.

Sexual objectification and the interiorized objectifying gaze (self-objectification) are dangerous phenomena for women's psychological wellness. However, their specific effects on women's socio-affective reactions are still poorly understood, and their neural activity has never been explored before. In the present study, we investigated women's emotional and electrophysiological responses during simulated computer-based objectifying social interactions, and we examined consequent punishing behaviours towards the perpetrator using the ultimatum game. Behavioural results (N = 36) showed that during objectifying encounters women generally felt angrier/disgusted and tended to punish the perpetrator in later interactions. However, the more the women self-objectified, the more they felt ashamed (p = 0.011) and tended to punish the perpetrators less (p = 0.008). At a neural level (N = 32), objectifying interactions modulated female participants' neural signal elicited during the processing of the perpetrator, increasing early (N170) and later (EPN, LPP) ERP components. In addition, only the amplitude of the LPP positively correlated with shame (p = 0.006) and the level of self-objectification (p = 0.018). This finding provides first evidence for the specific time-course of sexual objectification, self-objectification and its associated shame response, and proves that emotional and social consequences of sexual objectification in women may depend on their tendency to self-objectify.

Abnormal Brain Circuits Characterize Borderline Personality and Mediate the Relationship between Childhood Traumas and Symptoms: A mCCA+jICA and Random Forest Approach.

Borderline personality disorder (BPD) is a severe personality disorder whose neural bases are still unclear. Indeed, previous studies reported inconsistent findings concerning alterations in cortical and subcortical areas. In the present study, we applied for the first time a combination of an unsupervised machine learning approach known as multimodal canonical correlation analysis plus joint independent component analysis (mCCA+jICA), in combination with a supervised machine learning approach known as random forest, to possibly find covarying gray matter and white matter (GM-WM) circuits that separate BPD from controls and that are also predictive of this diagnosis. The first analysis was used to decompose the brain into independent circuits of covarying grey and white matter concentrations. The second method was used to develop a predictive model able to correctly classify new unobserved BPD cases based on one or more circuits derived from the first analysis. To this aim, we analyzed the structural images of patients with BPD and matched healthy controls (HCs). The results showed that two GM-WM covarying circuits, including basal ganglia, amygdala, and portions of the temporal lobes and of the orbitofrontal cortex, correctly classified BPD against HC. Notably, these circuits are affected by specific child traumatic experiences (emotional and physical neglect, and physical abuse) and predict symptoms severity in the interpersonal and impulsivity domains. These results support that BPD is characterized by anomalies in both GM and WM circuits related to early traumatic experiences and specific symptoms.

Decoding reappraisal and suppression from neural circuits: A combined supervised and unsupervised machine learning approach.

Emotion regulation is a core construct of mental health and deficits in emotion regulation abilities lead to psychological disorders. Reappraisal and suppression are two widely studied emotion regulation strategies but, possibly due to methodological limitations in previous studies, a consistent picture of the neural correlates related to the individual differences in their habitual use remains elusive. To address these issues, the present study applied a combination of unsupervised and supervised machine learning algorithms to the structural MRI scans of 128 individuals. First, unsupervised machine learning was used to separate the brain into naturally grouping grey matter circuits. Then, supervised machine learning was applied to predict individual differences in the use of different strategies of emotion regulation. Two predictive models, including structural brain features and psychological ones, were tested. Results showed that a temporo-parahippocampal-orbitofrontal network successfully predicted the individual differences in the use of reappraisal. Differently, insular and fronto-temporo-cerebellar networks successfully predicted suppression. In both predictive models, anxiety, the opposite strategy, and specific emotional intelligence factors played a role in predicting the use of reappraisal and suppression. This work provides new insights regarding the decoding of individual differences from structural features and other psychologically relevant variables while extending previous observations on the neural bases of emotion regulation strategies.

"I feel your fear": superior fear recognition in organised crime members.

Individuals who deviate from social norms by committing crimes may have reduced facial emotion recognition abilities. Nevertheless, a specific category of offenders - i.e. organised crime (OC) members - is characterised by hierarchically organised social networks and a tendency to manipulate others to reach their illicit goals. Since recognising emotions is crucial to building social networks, OC members may be more skilled in recognising the facial emotion expressions of others to use this information for their criminal purposes. Evidence of a difference between OC and non-organised crime (NOC) offenders in terms of facial emotion recognition is still lacking. To fill this gap in the literature, we tested 50 OC, 50 NOC offenders, and 50 non-offender controls for their ability to identify six basic emotions (happiness, sadness, fear, anger, disgust, and surprise). All participants underwent a cognitive and psychological evaluation to avoid alternative explanations. Results show that OC members were more able to detect the expression of fear in others as compared to NOC. We interpreted this finding in light of the social context and the behavioural criminal attitude of OC members.

Anxious Brains: A Combined Data Fusion Machine Learning Approach to Predict Trait Anxiety from Morphometric Features.

Trait anxiety relates to the steady propensity to experience and report negative emotions and thoughts such as fear and worries across different situations, along with a stable perception of the environment as characterized by threatening stimuli. Previous studies have tried to investigate neuroanatomical features related to anxiety mostly using univariate analyses and thus giving rise to contrasting results. The aim of this study is to build a predictive model of individual differences in trait anxiety from brain morphometric features, by taking advantage of a combined data fusion machine learning approach to allow generalization to new cases. Additionally, we aimed to perform a network analysis to test the hypothesis that anxiety-related networks have a central role in modulating other networks not strictly associated with anxiety. Finally, we wanted to test the hypothesis that trait anxiety was associated with specific cognitive emotion regulation strategies, and whether anxiety may decrease with ageing. Structural brain images of 158 participants were first decomposed into independent covarying gray and white matter networks with a data fusion unsupervised machine learning approach (Parallel ICA). Then, supervised machine learning (decision tree) and backward regression were used to extract and test the generalizability of a predictive model of trait anxiety. Two covarying gray and white matter independent networks successfully predicted trait anxiety. The first network included mainly parietal and temporal regions such as the postcentral gyrus, the precuneus, and the middle and superior temporal gyrus, while the second network included frontal and parietal regions such as the superior and middle temporal gyrus, the anterior cingulate, and the precuneus. We also found that trait anxiety was positively associated with catastrophizing, rumination, other- and self-blame, and negatively associated with positive refocusing and reappraisal. Moreover, trait anxiety was negatively associated with age. This paper provides new insights regarding the prediction of individual differences in trait anxiety from brain and psychological features and can pave the way for future diagnostic predictive models of anxiety.

Neuroanatomical predictors of real-time fMRI-based anterior insula regulation. A supervised machine learning study.

Increasing evidence showed that learned control of metabolic activity in selected brain regions can support emotion regulation. Notably, a number of studies demonstrated that neurofeedback-based regulation of fMRI activity in several emotion-related areas leads to modifications of emotional behavior along with changes of neural activity in local and distributed networks, in both healthy individuals and individuals with emotional disorders. However, the current understanding of the neural mechanisms underlying self-regulation of the emotional brain, as well as their relationship with other emotion regulation strategies, is still limited. In this study, we attempted to delineate neuroanatomical regions mediating real-time fMRI-based emotion regulation by exploring whole brain GM and WM features predictive of self-regulation of anterior insula (AI) activity, a neuromodulation procedure that can successfully support emotional brain regulation in healthy individuals and patients. To this aim, we employed a multivariate kernel ridge regression model to assess brain volumetric features, at regional and network level, predictive of real-time fMRI-based AI regulation. Our results showed that several GM regions including fronto-occipital and medial temporal areas and the basal ganglia as well as WM regions including the fronto-occipital fasciculus, tapetum and fornix significantly predicted learned AI regulation. Remarkably, we observed a substantial contribution of the cerebellum in relation to both the most effective regulation run and average neurofeedback performance. Overall, our findings highlighted specific neurostructural features contributing to individual differences of AI-guided emotion regulation. Notably, such neuroanatomical topography partially overlaps with the neurofunctional network associated with cognitive emotion regulation strategies, suggesting common neural mechanisms.

Decoding individual differences in expressing and suppressing anger from structural brain networks: A supervised machine learning approach.

Anger can be broken down into different elements: a transitory state (state anger), a stable personality feature (trait anger), a tendency to express it (anger-out), or to suppress it (anger-in), and the ability to regulate it (anger control). These elements are characterized by individual differences that vary across a continuum. Among them, the abilities to express and suppress anger are of particular relevance as they determine outcomes and enable successful anger management in daily situations. The aim of this study was to demonstrate that anger suppression and expression can be decoded by patterns of grey matter of specific well-known brain networks. To this aim, a supervised machine learning technique, known as Kernel Ridge Regression, was used to predict anger expression and suppression scores of 212 healthy subjects from the grey matter concentration. Results show that individual differences in anger suppression were predicted by two grey matter patterns associated with the Default-Mode Network and the Salience Network. Additionally, individual differences in anger expression were predicted by a circuit mainly involving subcortical and fronto-temporal regions when considering whole brain grey matter features. These results expand previous findings regarding the neural bases of anger by showing that individual differences in specific anger-related components can be predicted by the grey matter features of specific networks.

Attachment orientations and emotion regulation: new insights from the study of interpersonal emotion regulation strategies.

A crucial point for the understanding of the link between attachment and emotion regulation concerns the individual tendency in turning to others to alleviate distress. Most previous studies in this field have considered almost exclusively intra-personal forms of emotion regulation, neglecting the role of social interaction in emotion regulation processes. In the present study, instead, we focused on interpersonal emotion regulation. 630 adults were assessed for their attachment orientations, general difficulties in emotion regulation, and habitual intra-personal and interpersonal emotion regulation strategies. Results showed that the imbalance between the hyper-activation and deactivation of the attachment system, which characterize unsecure attachment, reflects a correspondent imbalance in the use of emotion regulation strategies, with an exaggerated dependence on other associated with attachment anxiety and pseudo-autonomy associated to attachment avoidance.

Therapist reactions to patient personality: A pilot study of clinicians' emotional and neural responses using three clinical vignettes from in treatment series.

Introduction: Therapists' responses to patients play a crucial role in psychotherapy and are considered a key component of the patient-clinician relationship, which promotes successful treatment outcomes. To date, no empirical research has ever investigated therapist response patterns to patients with different personality disorders from a neuroscience perspective. Methods: In the present study, psychodynamic therapists (N = 14) were asked to complete a battery of instruments (including the Therapist Response Questionnaire) after watching three videos showing clinical interactions between a therapist and three patients with narcissistic, histrionic/borderline, and depressive personality disorders, respectively. Subsequently, participants' high-density electroencephalography (hdEEG) was recorded as they passively viewed pictures of the patients' faces, which were selected from the still images of the previously shown videos. Supervised machine learning (ML) was used to evaluate whether: (1) therapists' responses predicted which patient they observed during the EEG task and whether specific clinician reactions were involved in distinguishing between patients with different personality disorders (using pairwise comparisons); and (2) therapists' event-related potentials (ERPs) predicted which patient they observed during the laboratory experiment and whether distinct ERP components allowed this forecast. Results: The results indicated that therapists showed distinct patterns of criticized/devalued and sexualized reactions to visual depictions of patients with different personality disorders, at statistically systematic and clinically meaningful levels. Moreover, therapists' late positive potentials (LPPs) in the hippocampus were able to determine which patient they observed during the EEG task, with high accuracy. Discussion: These results, albeit preliminary, shed light on the role played by therapists' memory processes in psychotherapy. Clinical and neuroscience implications of the empirical investigation of therapist responses are discussed.