Stress and reward in the maternal brain of mothers with borderline personality disorder: a script-based fMRI study.

Borderline personality disorder (BPD) is associated with altered neural activity in regions of salience and emotion regulation. An exaggerated sensitization to emotionally salient situations, increased experience of emotions, and dysfunctional regulative abilities could be reasons for increased distress also during parenting. Mothers with BPD tend to have less reciprocal mother-child interactions (MCI) and reveal altered cortisol and oxytocin reactivity in the interaction with their child, which could indicate altered processing of stress and reward. Here, we studied underlying neural mechanisms of disrupted MCI in BPD. Twenty-five mothers with BPD and 28 healthy mothers participated in a script-driven imagery functional magnetic resonance imaging (fMRI)-paradigm. Scripts described stressful or rewarding MCI with the own child, or situations in which the mother was alone. Mothers with BPD showed larger activities in the bilateral insula and anterior cingulate cortex (ACC) compared to healthy mothers during the imagination of MCI and non-MCI. Already in the precursory phase while listening to the scripts, a similar pattern emerged with stronger activity in the left anterior insula (AINS), but not in the ACC. This AINS activity correlated negatively with the quality of real-life MCI for mothers with BPD. Mothers with BPD reported lower affect and higher arousal. An exaggerated sensitization to different, emotionally salient situations together with dysfunctional emotion regulation abilities, as reflected by increased insula and ACC activity, might hinder sensitive maternal behavior in mothers with BPD. These results underline the importance for psychotherapeutic interventions to improve emotional hyperarousal and emotion regulation in patients with BPD, especially in affected mothers caring for young children.

Your smile won't affect me: Association between childhood maternal antipathy and adult neural reward function in a transdiagnostic sample.

Aberrant activation in the ventral striatum (VS) during reward anticipation may be a key mechanism linking adverse childhood experiences (ACE) to transdiagnostic psychopathology. This study aimed to elucidate whether retrospectively reported ACE, specifically maternal antipathy, relate to monetary and social reward anticipation in a transdiagnostic adult sample. A cross-sectional neuroimaging study was conducted in 118 participants with varying levels of ACE, including 25 participants with posttraumatic stress disorder (PTSD), 32 with major depressive disorder (MDD), 29 with somatic symptom disorder (SSD), and 32 healthy volunteers (HVs). Participants underwent functional magnetic resonance imaging during a monetary and social incentive delay task, and completed a self-report measure of ACE, including maternal antipathy. Neural correlates of monetary and social reward anticipation and their association with ACE, particularly maternal antipathy, were analyzed. Participants showed elevated activation in brain regions underlying reward processing, including the VS, only while anticipating social, but not monetary rewards. Participants reporting higher levels of maternal antipathy exhibited reduced activation in the brain reward network, including the VS, only during social, but not monetary reward anticipation. Group affiliation moderated the association between maternal antipathy and VS activation to social reward anticipation, with significant associations found in participants with PTSD and HVs, but not in those with MDD and SSD. Results were not associated with general psychopathology or psychotropic medication use. Childhood maternal antipathy may confer risk for aberrant social reward anticipation in adulthood, and may thus be considered in interventions targeting reward expectations from social interactions.

Do I care for you or for me? Processing of protected and non-protected moral values in subjects with extreme scores on the Dark Triad.

Protected moral values facilitate empathic concern for others, who are exposed to an existential threat, so that one spontaneously helps without taking into account utilitarian cost-benefit considerations. Subjects scoring high on the "Dark Triad" machiavellism, psychopathy, and narcissism are prone to ignore such appeals for selfless help. Until now, data on moral processing and moral decision-making following requests for altruistic help, which directly contrast appeals to protected and non-protected values in subjects with high and low scores on Dark Triad traits, have been missing. In this pilot study 25 healthy subjects with high and 27 with low Dark Triad scores participated in this functional magnetic resonance imaging study. We used a script-driven imagery paradigm to directly contrast requests for selfless help appealing to protected versus non-protected, negotiable moral values. Appeals to protected versus non-protected moral values elicited stronger activations in a large network including insula, amygdala, supramarginal gyrus, and dorsolateral prefrontal cortex. Non-protected values evoked stronger activation in superior frontal sulcus, occipito-temporal junction, and posterior cingulate cortex. During decision-making, high-scorers on the Dark Triad showed increased activations in the superior parietal lobule, precuneus, and intraparietal sulcus. Behaviorally, protected versus non-protected values strongly reduced the reliance on personal cost-benefit calculations in low-scorers, while high-scorers continued to rely on utilitarian deliberations. Data suggest that appeals to protected versus non-protected values activate distinct brain regions associated with strong moral emotions, other-directed cognition, and rule-based decision-making processes. High-scorers display an increased reliance on cost-benefit calculations, which persists even when protected values are threatened.

Abnormal processing of interpersonal cues during an aggressive encounter in women with borderline personality disorder: Neural and behavioral findings.

Inappropriate aggression is a prominent and clinically relevant interpersonal dysfunction of individuals with borderline personality disorder (BPD). Previous studies have shown that individuals with BPD interpret interpersonal signals in a hostile manner, but it is uncertain how this negativity bias impacts decision-making during aggressive encounters. In the present neuroimaging study, 48 medication-free women with BPD and 28 age- and intelligence-matched women played the Social Threat Aggression Paradigm (STAP), a competitive reaction time task in which the winner delivers an aversive sound blast to the loser. Crucially, in the STAP the alleged opponent displays either an angry or neutral facial expression at the beginning of each trial and selects increasingly loud blasts in order to provoke participants. Relative to healthy controls, women with BPD differentiated less between angry and neutral facial expressions, both in terms of aggressive behavior and of activity in medial prefrontal cortex, amygdala, and temporal pole. On the one hand, and contrary to our hypotheses, neural and behavioral responses to angry faces were reduced in women with BPD compared to healthy women. On the other hand, provocation heightened subsequent amygdala responses to neutral faces in BPD, and this was in turn associated with aggressive behavior, supporting a default negativity bias in BPD. The neurocognitive processes by which these alterations might guide aggressive behavior irrespective of interpersonal cues are presented and discussed. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

A Drive towards Thermodynamic Efficiency for Dissipative Structures in Chemical Reaction Networks.

Dissipative accounts of structure formation show that the self-organisation of complex structures is thermodynamically favoured, whenever these structures dissipate free energy that could not be accessed otherwise. These structures therefore open transition channels for the state of the universe to move from a frustrated, metastable state to another metastable state of higher entropy. However, these accounts apply as well to relatively simple, dissipative systems, such as convection cells, hurricanes, candle flames, lightning strikes, or mechanical cracks, as they do to complex biological systems. Conversely, interesting computational properties-that characterize complex biological systems, such as efficient, predictive representations of environmental dynamics-can be linked to the thermodynamic efficiency of underlying physical processes. However, the potential mechanisms that underwrite the selection of dissipative structures with thermodynamically efficient subprocesses is not completely understood. We address these mechanisms by explaining how bifurcation-based, work-harvesting processes-required to sustain complex dissipative structures-might be driven towards thermodynamic efficiency. We first demonstrate a simple mechanism that leads to self-selection of efficient dissipative structures in a stochastic chemical reaction network, when the dissipated driving chemical potential difference is decreased. We then discuss how such a drive can emerge naturally in a hierarchy of self-similar dissipative structures, each feeding on the dissipative structures of a previous level, when moving away from the initial, driving disequilibrium.

Stochastic Chaos and Markov Blankets.

In this treatment of random dynamical systems, we consider the existence-and identification-of conditional independencies at nonequilibrium steady-state. These independencies underwrite a particular partition of states, in which internal states are statistically secluded from external states by blanket states. The existence of such partitions has interesting implications for the information geometry of internal states. In brief, this geometry can be read as a physics of sentience, where internal states look as if they are inferring external states. However, the existence of such partitions-and the functional form of the underlying densities-have yet to be established. Here, using the Lorenz system as the basis of stochastic chaos, we leverage the Helmholtz decomposition-and polynomial expansions-to parameterise the steady-state density in terms of surprisal or self-information. We then show how Markov blankets can be identified-using the accompanying Hessian-to characterise the coupling between internal and external states in terms of a generalised synchrony or synchronisation of chaos. We conclude by suggesting that this kind of synchronisation may provide a mathematical basis for an elemental form of (autonomous or active) sentience in biology.

Discovering Digital Tumor Signatures-Using Latent Code Representations to Manipulate and Classify Liver Lesions.

Modern generative deep learning (DL) architectures allow for unsupervised learning of latent representations that can be exploited in several downstream tasks. Within the field of oncological medical imaging, we term these latent representations "digital tumor signatures" and hypothesize that they can be used, in analogy to radiomics features, to differentiate between lesions and normal liver tissue. Moreover, we conjecture that they can be used for the generation of synthetic data, specifically for the artificial insertion and removal of liver tumor lesions at user-defined spatial locations in CT images. Our approach utilizes an implicit autoencoder, an unsupervised model architecture that combines an autoencoder and two generative adversarial network (GAN)-like components. The model was trained on liver patches from 25 or 57 inhouse abdominal CT scans, depending on the experiment, demonstrating that only minimal data is required for synthetic image generation. The model was evaluated on a publicly available data set of 131 scans. We show that a PCA embedding of the latent representation captures the structure of the data, providing the foundation for the targeted insertion and removal of tumor lesions. To assess the quality of the synthetic images, we conducted two experiments with five radiologists. For experiment 1, only one rater and the ensemble-rater were marginally above the chance level in distinguishing real from synthetic data. For the second experiment, no rater was above the chance level. To illustrate that the "digital signatures" can also be used to differentiate lesion from normal tissue, we employed several machine learning methods. The best performing method, a LinearSVM, obtained 95% (97%) accuracy, 94% (95%) sensitivity, and 97% (99%) specificity, depending on if all data or only normal appearing patches were used for training of the implicit autoencoder. Overall, we demonstrate that the proposed unsupervised learning paradigm can be utilized for the removal and insertion of liver lesions at user defined spatial locations and that the digital signatures can be used to discriminate between lesions and normal liver tissue in abdominal CT scans.

Whole-brain functional connectivity during script-driven aggression in borderline personality disorder.

OBJECTIVE: Intense anger and anger-related aggression are frequently reported by patients with borderline personality disorders (BPD). Recent results suggest that anger-related aggression and its control is associated with a complex interplay of different neural systems in BPD. To further investigate this, we complement standard activation and seed-based connectivity analyses by examining whole-brain changes in functional connectivity during anger and reactive aggression in BPD. METHODS: We reanalyzed functional MRI data from 33 women with BPD, all of them fulfilling BPD criterion 8, "anger proneness", according to DSM-IV, and 30 healthy women. Subjects performed a script-driven imagery task consisting of four phases: baseline, anger-induction by a narrative of interpersonal rejection, a narrative of directing physical aggression towards others, and relaxation. We used a data-driven, spatially constrained spectral clustering approach to parcellate the brain into 200 regions. For each script-phase and subject, we computed the full connectivity matrix using wavelet coefficient correlations in the 0.05-0.10 Hz range. We calculated the individual increase in connectivity from baseline to the anger-induction and physical aggression phases by subtracting the corresponding connectivity matrices per subject, as well as the increase and decrease from the anger-induction to the aggression phase. We then applied permutation-based sampling to determine a combined threshold on the strength of individual connections and the size of the discovered networks for these difference matrices. RESULTS: We discovered a single, large network showing a significantly stronger increase in connectivity from baseline to the aggression phase in female patients with BPD compared to healthy women. This network consisted of regions in the anterior and posterior cingulate cortex, precuneus, dorsomedial prefrontal cortex, superior and middle temporal gyrus, hippocampus, insula, ventrolateral and dorsolateral prefrontal cortex, superior parietal lobe, thalamus, precentral and postcentral gyrus, caudate, pallidum, cerebellum, middle occipital lobe, lingual gyrus, calcarine sulcus, and fusiform gyrus. Hub regions with highest node centrality were found in the right caudate and left thalamus. We found no significant differences for the increase of connectivity from baseline to anger-induction, as well as for the increase or decrease from the anger-induction to the aggression phase. CONCLUSIONS: We identified a large network showing a significantly stronger increase in connectivity from baseline to the aggression phase in female patients with BPD compared to healthy women. The regions constituting this network belong to four previously described functional networks: The frontoparietal cognitive control network, the extended default mode network, the visual system, and the motor system. This stronger increase in connectivity between regions of different functional brain systems associated with cognitive control of behavior, socio-affective and self-referential thinking, as well as salience processing and emotion regulation, visual perception, and action is mediated via hubs in the thalamus and caudate, i.e., core components of the thalamocorticostriatal motor loop essential for action selection and initiation. These findings suggest increased interaction of prefrontal cognitive control processes with thalamocorticostriatal action-selection processes in female patients with BPD during the processing of aggressive action impulses, which are facilitated by states of high emotional salience and associated processes of self-referential and social processing, and ineffective emotion regulation.

Deep active inference.

This work combines the free energy principle and the ensuing active inference dynamics with recent advances in variational inference in deep generative models, and evolution strategies to introduce the "deep active inference" agent. This agent minimises a variational free energy bound on the average surprise of its sensations, which is motivated by a homeostatic argument. It does so by optimising the parameters of a generative latent variable model of its sensory inputs, together with a variational density approximating the posterior distribution over the latent variables, given its observations, and by acting on its environment to actively sample input that is likely under this generative model. The internal dynamics of the agent are implemented using deep and recurrent neural networks, as used in machine learning, making the deep active inference agent a scalable and very flexible class of active inference agent. Using the mountain car problem, we show how goal-directed behaviour can be implemented by defining appropriate priors on the latent states in the agent's model. Furthermore, we show that the deep active inference agent can learn a generative model of the environment, which can be sampled from to understand the agent's beliefs about the environment and its interaction therewith.

Heightened Salience of Anger and Aggression in Female Adolescents With Borderline Personality Disorder-A Script-Based fMRI Study.

Background: Anger and aggression belong to the core symptoms of borderline personality disorder. Although an early and specific treatment of BPD is highly relevant to prevent chronification, still little is known about anger and aggression and their neural underpinnings in adolescents with BPD. Method: Twenty female adolescents with BPD (age 15-17 years) and 20 female healthy adolescents (age 15-17 years) took part in this functional magnetic resonance imaging (fMRI) study. A script-driven imagery paradigm was used to induce rejection-based feelings of anger, which was followed by descriptions of self-directed and other-directed aggressive reactions. To investigate the specificity of the neural activation patterns for adolescent patients, results were compared with data from 34 female adults with BPD (age 18-50 years) and 32 female healthy adults (age 18-50 years). Results: Adolescents with BPD showed increased activations in the left posterior insula and left dorsal striatum as well as in the left inferior frontal cortex and parts of the mentalizing network during the rejection-based anger induction and the imagination of aggressive reactions compared to healthy adolescents. For the other-directed aggression phase, a significant diagnosis by age interaction confirmed that these results were specific for adolescents. Discussion: The results of this very first fMRI study on anger and aggression in adolescents with BPD suggest an enhanced emotional reactivity to and higher effort in controlling anger and aggression evoked by social rejection at an early developmental stage of the disorder. Since emotion dysregulation is a known mediator for aggression in BPD, the results point to the need of appropriate early interventions for adolescents with BPD.

Brain Mechanisms Underlying Reactive Aggression in Borderline Personality Disorder-Sex Matters.

BACKGROUND: Aggression in borderline personality disorder (BPD) is thought to be mediated through emotion dysregulation via high trait anger. Until now, data comparing anger and aggression in female and male patients with BPD have been widely missing on the behavioral and particularly the brain levels. METHODS: Thirty-three female and 23 male patients with BPD and 30 healthy women and 26 healthy men participated in this functional magnetic resonance imaging study. We used a script-driven imagery task consisting of narratives of both interpersonal rejection and directing physical aggression toward others. RESULTS: While imagining both interpersonal rejection and acting out aggressively, a sex × group interaction was found in which male BPD patients revealed higher activity in the left amygdala than female patients. In the aggression phase, men with BPD exhibited higher activity in the lateral orbitofrontal and dorsolateral prefrontal cortices compared with healthy men and female patients. Positive connectivity between amygdala and posterior middle cingulate cortex was found in female patients but negative connectivity was found in male patients with BPD. Negative modulatory effects of trait anger on amygdala-dorsolateral prefrontal cortex and amygdala-lateral orbitofrontal cortex coupling were shown in male BPD patients, while in female patients trait anger positively modulated dorsolateral prefrontal cortex-amygdala coupling. Trait aggression was found to positively modulate connectivity of the left amygdala to the posterior thalamus in male but not female patients. CONCLUSIONS: Data suggest poor top-down adjustment of behavior in male patients with BPD despite their efforts at control. Female patients appear to be less aroused through rejection and to successfully dampen aggressive tension during the imagination of aggressive behavior.

Brain Signal Variability Differentially Affects Cognitive Flexibility and Cognitive Stability.

Recent research yielded the intriguing conclusion that, in healthy adults, higher levels of variability in neuronal processes are beneficial for cognitive functioning. Beneficial effects of variability in neuronal processing can also be inferred from neurocomputational theories of working memory, albeit this holds only for tasks requiring cognitive flexibility. However, cognitive stability, i.e., the ability to maintain a task goal in the face of irrelevant distractors, should suffer under high levels of brain signal variability. To directly test this prediction, we studied both behavioral and brain signal variability during cognitive flexibility (i.e., task switching) and cognitive stability (i.e., distractor inhibition) in a sample of healthy human subjects and developed an efficient and easy-to-implement analysis approach to assess BOLD-signal variability in event-related fMRI task paradigms. Results show a general positive effect of neural variability on task performance as assessed by accuracy measures. However, higher levels of BOLD-signal variability in the left inferior frontal junction area result in reduced error rate costs during task switching and thus facilitate cognitive flexibility. In contrast, variability in the same area has a detrimental effect on cognitive stability, as shown in a negative effect of variability on response time costs during distractor inhibition. This pattern was mirrored at the behavioral level, with higher behavioral variability predicting better task switching but worse distractor inhibition performance. Our data extend previous results on brain signal variability by showing a differential effect of brain signal variability that depends on task context, in line with predictions from computational theories. SIGNIFICANCE STATEMENT: Recent neuroscientific research showed that the human brain signal is intrinsically variable and suggested that this variability improves performance. Computational models of prefrontal neural networks predict differential effects of variability for different behavioral situations requiring either cognitive flexibility or stability. However, this hypothesis has so far not been put to an empirical test. In this study, we assessed cognitive flexibility and cognitive stability, and, besides a generally positive effect of neural variability on accuracy measures, we show that neural variability in a prefrontal brain area at the inferior frontal junction is differentially associated with performance: higher levels of variability are beneficial for the effectiveness of task switching (cognitive flexibility) but detrimental for the efficiency of distractor inhibition (cognitive stability).

Stochastic Dynamics Underlying Cognitive Stability and Flexibility.

Cognitive stability and flexibility are core functions in the successful pursuit of behavioral goals. While there is evidence for a common frontoparietal network underlying both functions and for a key role of dopamine in the modulation of flexible versus stable behavior, the exact neurocomputational mechanisms underlying those executive functions and their adaptation to environmental demands are still unclear. In this work we study the neurocomputational mechanisms underlying cue based task switching (flexibility) and distractor inhibition (stability) in a paradigm specifically designed to probe both functions. We develop a physiologically plausible, explicit model of neural networks that maintain the currently active task rule in working memory and implement the decision process. We simplify the four-choice decision network to a nonlinear drift-diffusion process that we canonically derive from a generic winner-take-all network model. By fitting our model to the behavioral data of individual subjects, we can reproduce their full behavior in terms of decisions and reaction time distributions in baseline as well as distractor inhibition and switch conditions. Furthermore, we predict the individual hemodynamic response timecourse of the rule-representing network and localize it to a frontoparietal network including the inferior frontal junction area and the intraparietal sulcus, using functional magnetic resonance imaging. This refines the understanding of task-switch-related frontoparietal brain activity as reflecting attractor-like working memory representations of task rules. Finally, we estimate the subject-specific stability of the rule-representing attractor states in terms of the minimal action associated with a transition between different rule states in the phase-space of the fitted models. This stability measure correlates with switching-specific thalamocorticostriatal activation, i.e., with a system associated with flexible working memory updating and dopaminergic modulation of cognitive flexibility. These results show that stochastic dynamical systems can implement the basic computations underlying cognitive stability and flexibility and explain neurobiological bases of individual differences.

Hippocampal-dorsolateral prefrontal coupling as a species-conserved cognitive mechanism: a human translational imaging study.

Hippocampal-prefrontal cortex (HC-PFC) interactions are implicated in working memory (WM) and altered in psychiatric conditions with cognitive impairment such as schizophrenia. While coupling between both structures is crucial for WM performance in rodents, evidence from human studies is conflicting and translation of findings is complicated by the use of differing paradigms across species. We therefore used functional magnetic resonance imaging together with a spatial WM paradigm adapted from rodent research to examine HC-PFC coupling in humans. A PFC-parietal network was functionally connected to hippocampus (HC) during task stages requiring high levels of executive control but not during a matched control condition. The magnitude of coupling in a network comprising HC, bilateral dorsolateral PFC (DLPFC), and right supramarginal gyrus explained one-fourth of the variability in an independent spatial WM task but was unrelated to visual WM performance. HC-DLPFC coupling may thus represent a systems-level mechanism specific to spatial WM that is conserved across species, suggesting its utility for modeling cognitive dysfunction in translational neuroscience.

Touchscreen-paradigm for mice reveals cross-species evidence for an antagonistic relationship of cognitive flexibility and stability.

The abilities to either flexibly adjust behavior according to changing demands (cognitive flexibility) or to maintain it in the face of potential distractors (cognitive stability) are critical for adaptive behavior in many situations. Recently, a novel human paradigm has found individual differences of cognitive flexibility and stability to be related to common prefrontal networks. The aims of the present study were, first, to translate this paradigm from humans to mice and, second, to test conceptual predictions of a computational model of prefrontal working memory mechanisms, the Dual State Theory, which assumes an antagonistic relation between cognitive flexibility and stability. Mice were trained in a touchscreen-paradigm to discriminate visual cues. The task involved "ongoing" and cued "switch" trials. In addition distractor cues were interspersed to test the ability to resist distraction, and an ambiguous condition assessed the spontaneous switching between two possible responses without explicit cues. While response times did not differ substantially between conditions, error rates (ER) increased from the "ongoing" baseline condition to the most complex condition, where subjects were required to switch between two responses in the presence of a distracting cue. Importantly, subjects switching more often spontaneously were found to be more distractible by task irrelevant cues, but also more flexible in situations, where switching was required. These results support a dichotomy of cognitive flexibility and stability as predicted by the Dual State Theory. Furthermore, they replicate critical aspects of the human paradigm, which indicates the translational potential of the testing procedure and supports the use of touchscreen procedures in preclinical animal research.

Prefrontal cortical mechanisms underlying individual differences in cognitive flexibility and stability.

The pFC is critical for cognitive flexibility (i.e., our ability to flexibly adjust behavior to changing environmental demands), but also for cognitive stability (i.e., our ability to follow behavioral plans in the face of distraction). Behavioral research suggests that individuals differ in their cognitive flexibility and stability, and neurocomputational theories of working memory relate this variability to the concept of attractor stability in recurrently connected neural networks. We introduce a novel task paradigm to simultaneously assess flexible switching between task rules (cognitive flexibility) and task performance in the presence of irrelevant distractors (cognitive stability) and to furthermore assess the individual "spontaneous switching rate" in response to ambiguous stimuli to quantify the individual dispositional cognitive flexibility in a theoretically motivated way (i.e., as a proxy for attractor stability). Using fMRI in healthy human participants, a common network consisting of parietal and frontal areas was found for task switching and distractor inhibition. More flexible persons showed reduced activation and reduced functional coupling in frontal areas, including the inferior frontal junction, during task switching. Most importantly, the individual spontaneous switching rate antagonistically affected the functional coupling between inferior frontal junction and the superior frontal gyrus during task switching and distractor inhibition, respectively, indicating that individual differences in cognitive flexibility and stability are indeed related to a common prefrontal neural mechanism. We suggest that the concept of attractor stability of prefrontal working memory networks is a meaningful model for individual differences in cognitive stability versus flexibility.