A machine-learning approach for differentiating borderline personality disorder from community participants with brain-wide functional connectivity.

BACKGROUND: Functional connectivity has garnered interest as a potential biomarker of psychiatric disorders including borderline personality disorder (BPD). However, small sample sizes and lack of within-study replications have led to divergent findings with no clear spatial foci. AIMS: Evaluate discriminative performance and generalizability of functional connectivity markers for BPD. METHOD: Whole-brain fMRI resting state functional connectivity in matched subsamples of 116 BPD and 72 control individuals defined by three grouping strategies. We predicted BPD status using classifiers with repeated cross-validation based on multiscale functional connectivity within and between regions of interest (ROIs) covering the whole brain-global ROI-based network, seed-based ROI-connectivity, functional consistency, and voxel-to-voxel connectivity-and evaluated the generalizability of the classification in the left-out portion of non-matched data. RESULTS: Full-brain connectivity allowed classification (∼70 %) of BPD patients vs. controls in matched inner cross-validation. The classification remained significant when applied to unmatched out-of-sample data (∼61-70 %). Highest seed-based accuracies were in a similar range to global accuracies (∼70-75 %), but spatially more specific. The most discriminative seed regions included midline, temporal and somatomotor regions. Univariate connectivity values were not predictive of BPD after multiple comparison corrections, but weak local effects coincided with the most discriminative seed-ROIs. Highest accuracies were achieved with a full clinical interview while self-report results remained at chance level. LIMITATIONS: The accuracies vary considerably between random sub-samples of the population, global signal and covariates limiting the practical applicability. CONCLUSIONS: Spatially distributed functional connectivity patterns are moderately predictive of BPD despite heterogeneity of the patient population.

Bayesian Decision-Making Under Uncertainty in Borderline Personality Disorder.

Bayesian decision theory suggests that optimal decision-making should use and weigh prior beliefs with current information, according to their relative uncertainties. However, some characteristics of borderline personality disorder (BPD) patients, such as fast, drastic changes in the overall perception of themselves and others, suggest they may be underrelying on priors. Here, we investigated if BPD patients have a general deficit in relying on or combining prior with current information. We analyzed this by having BPD patients (n = 23) and healthy controls (n = 18) perform a coin-catching sensorimotor task with varying levels of prior and current information uncertainty. Our results indicate that BPD patients learned and used prior information and combined it with current information in a qualitatively Bayesian-like way. Our results show that, at least in a lower-level, nonsocial sensorimotor task, BPD patients can appropriately use both prior and current information, illustrating that potential deficits using priors may not be widespread or domain-general.

Value Representations of Spite Sensitivity in Psychosis on the Minnesota Trust Game.

BACKGROUND: Spite sensitivity provides a valuable construct to understand persecutory ideation and its underlying neural mechanisms. We examined the relationship between persecution and spite sensitivity in psychosis to identify their neural substrates. METHODS: In a 3T magnetic resonance imaging scanner, 49 participants with psychosis played the Minnesota Trust Game, in which they decided whether to take a small amount of money or trust a partner to choose between fair and unfair distributions of money. In some conditions, the partner benefited from the unfair option, while in others, the partner lost money. Participants who were untrusting in the second condition (suspiciousness) showed heightened sensitivity to spite. Behavioral measures included mistrust during the 2 conditions of the game, which were compared with Brief Psychiatric Rating Scale persecution and computational modeling. Functional connectivity and blood oxygen level-dependent analyses were also conducted on a priori regions during spite-sensitive decisions. RESULTS: Behavioral results replicated previous findings; participants who experienced more persecutory ideation trusted less, specifically in the suspiciousness condition. Functional connectivity findings showed that decreased connectivity between the orbitofrontal cortex-insula and the left frontoparietal network was associated with increased persecutory ideation and estimated spite-guilt (a marker of spite sensitivity). Additionally, we found differences between conditions in caudate nucleus, medial prefrontal cortex, and lateral orbitofrontal cortex activation. CONCLUSIONS: These findings provide a new perspective on the origin of positive symptoms by identifying primary brain circuits that are related to both spite sensitivity and persecutory ideation.

What is the relationship between alexithymia and experiential avoidance? A latent analysis using three alexithymia questionnaires.

Alexithymia is a clinically relevant personality trait characterized by poor emotional awareness and associated with several psychological and physical health concerns. Individuals with high alexithymia tend to engage in experiential avoidance and this may mediate psychological distress. However, little is known about what specific processes of experiential avoidance are involved, and the nature of the relation between alexithymia, experiential avoidance, and psychological distress remains unclear at a latent construct level. To examine this relationship at the latent construct level, a representative sample of 693 U.S. adults completed alexithymia (TAS-20, BVAQ, PAQ), general distress (DASS-21), multi-dimensional experiential avoidance (MEAQ), and general health (PROMIS-G-10) questionnaires. Structural equation modeling revealed that alexithymia significantly predicted experiential avoidance (ß = 0.966, t = 82.383, p < .01), experiential avoidance significantly predicted general distress (ß = 0.810, t = 2.017, p < .05), and experiential avoidance fully mediated the relationship between alexithymia and general distress (ßindirect = -0.159, t = -0.398, p > .05). Correlations between alexithymia and experiential avoidance subfactors revealed a strong relationship to the repression and denial subfactor. Experiential avoidance is a promising target for clinical interventions, though longitudinal research is necessary to elucidate how the relationship between alexithymia and experiential avoidance unfolds over time.

Learning whom to cooperate with: neurocomputational mechanisms for choosing cooperative partners.

Cooperation is fundamental for survival and a functioning society. With substantial individual variability in cooperativeness, we must learn whom to cooperate with, and often make these decisions on behalf of others. Understanding how people learn about the cooperativeness of others, and the neurocomputational mechanisms supporting this learning, is therefore essential. During functional magnetic resonance imaging scanning, participants completed a novel cooperation-partner-choice task where they learned to choose between cooperative and uncooperative partners through trial-and-error both for themselves and vicariously for another person. Interestingly, when choosing for themselves, participants made faster and more exploitative choices than when choosing for another person. Activity in the ventral striatum preferentially responded to prediction errors (PEs) during self-learning, whereas activity in the perigenual anterior cingulate cortex (ACC) signaled both personal and vicarious PEs. Multivariate pattern analyses showed distinct coding of personal and vicarious choice-making and outcome processing in the temporoparietal junction (TPJ), dorsal ACC, and striatum. Moreover, in right TPJ the activity pattern that differentiated self and other outcomes was associated with individual differences in exploitation tendency. We reveal neurocomputational mechanisms supporting cooperative learning and show that this learning is reflected in trial-by-trial univariate signals and multivariate patterns that can distinguish personal and vicarious choices.

Computational validity: using computation to translate behaviours across species.

We propose a new conceptual framework (computational validity) for translation across species and populations based on the computational similarity between the information processing underlying parallel tasks. Translating between species depends not on the superficial similarity of the tasks presented, but rather on the computational similarity of the strategies and mechanisms that underlie those behaviours. Computational validity goes beyond construct validity by directly addressing questions of information processing. Computational validity interacts with circuit validity as computation depends on circuits, but similar computations could be accomplished by different circuits. Because different individuals may use different computations to accomplish a given task, computational validity suggests that behaviour should be understood through the subject's point of view; thus, behaviour should be characterized on an individual level rather than a task level. Tasks can constrain the computational algorithms available to a subject and the observed subtleties of that behaviour can provide information about the computations used by each individual. Computational validity has especially high relevance for the study of psychiatric disorders, given the new views of psychiatry as identifying and mediating information processing dysfunctions that may show high inter-individual variability, as well as for animal models investigating aspects of human psychiatric disorders. This article is part of the theme issue 'Systems neuroscience through the lens of evolutionary theory'.

A Computational Model of Non-optimal Suspiciousness in the Minnesota Trust Game.

This study modelled spite sensitivity, the worry that others are willing to incur a loss to hurt you, which is thought to undergird suspiciousness and persecutory ideation. Two samples performed a parametric, non-iterative trust game known as the Minnesota Trust Game (MTG). The MTG distinguishes suspicious decision-making from otherwise rational mistrust by incentivizing the player to trust in certain situations but not others. In Sample 1, 243 undergraduates who completed the MTG showed less trust as the amount of money they could lose increased. However, only for choices where partners had a financial disincentive to betray the player was variation in the willingness to trust associated with suspicious beliefs. We modified the Fehr-Schmidt (1999) inequity aversion model, which compares unequal outcomes in social decision-making tasks, to include the possibility for spite sensitivity. An anticipated partner's dislike of advantageous inequity (i.e., guilt) parameter included negative values, with negative guilt indicating spite. We hypothesized that the anticipated guilt parameter would be strongly related to suspicious beliefs. Our modification of the Fehr-Schmidt model improved estimation of MTG behavior. Furthermore, the estimation of partner's spite-guilt was highly correlated with choices associated with beliefs in persecution. We replicated our findings in a second sample. This parameter was weakly correlated with a self-reported measure of persecutory ideation in Sample 2. The "Suspiciousness" condition, unique to the MTG, can be modeled to isolate spite sensitivity, suggesting differentiation from inequity aversion or risk aversion. The MTG offers promise for future studies to quantify persecutory beliefs in clinical populations.

Assessing the relationship between the human learned helplessness depression model and anhedonia.

The learned helplessness (LH) model is one of the most commonly used acute stress models to explain depression and it has shown good face and predictive validity. However, despite being able to induce depressed-like behaviors and corresponding psychophysiological changes, there is little evidence showing that the LH paradigm can produce anhedonia, a core symptom seen in all forms of depression in humans. So far a couple of studies showed that rodents bred for helplessness develop anhedonic-like behaviors in response to stress; yet, to the best of our knowledge, no similar human research has tried to investigate the direct relationship between the LH model and anhedonia. In the present study, we use a modified version of the original LH task to experimentally and temporarily induce learned helplessness in college students and then examine if the human LH paradigm induces anhedonia. We aim to 1: address the ill-defined connection between the LH model and anhedonia, and 2: directly assess helplessness in humans as opposed to the majority of non-human animal subjects used in the helplessness literature. We believe that our study will fill an important gap in the learned helplessness model literature, and will advance our understanding of the relationship between depression and perceived control, as well as place limitations to what can and cannot be inferred from non-human animal data in this topic.

Meta-Analysis of white matter diffusion tensor imaging alterations in borderline personality disorder.

Borderline personality disorder (BorPD) is characterized by instability and mood dysregulation, unstable relationships and distorted self-image. Identification of underlying anatomical and physiological changes is crucial to refine current treatments and develop new ones. In this perspective, previous magnetic resonance imaging studies have highlighted alterations associated with BorPD phenotype. In particular, diffusion-weighted imaging/Diffusion tensor imaging (DWI/DTI) has identified many white matter structural alterations in individuals with this diagnosis. Although in its infancy, limiting this line of investigation is a lack of direction at the field level. Hence, the present paper aims to conduct a meta-analysis of DWI/DTI findings in individuals with a diagnosis of BorPD, testing the hypothesis that there are specific white matter alterations associated with BorPD. To this end, we performed a meta-analysis of the existing literature of DWI/DTI in BorPD representing a total of 123 individuals with BorPD and 117 Controls. Our results indicated that individuals with BorPD show regions of reduced fractional anisotropy in the corpus callosum and fornix. These results survived all jack-knife reshuffles and showed no publication bias. This suggests that alterations in these structures may contribute to psychopathology. Further, the present results lend support to extant psychological and biological models of BorPD.

Bayesian decision-making under stress-preserved weighting of prior and likelihood information.

A rich body of empirical work has addressed the question of how stress changes the way we memorize, learn, and make high-level decisions in complex scenarios. There is evidence that stress also changes the way we perceive the world, indicating influences on decision-making at lower levels. Surprisingly, as of yet, little research has been conducted in this domain. A few studies suggest that under stress, humans tend to eschew existing knowledge, and instead focus on novel input or information from bottom-up. Decision-making in the perceptual domain has been modeled with Bayesian frameworks. Here, existing knowledge about structures and statistics of our environment is referred to as prior, whereas sensory data are termed likelihood. In this study, we directly assessed whether stress, as induced by the socially evaluated cold pressure task (SECPT), would modulate low-level decisions, specifically the weight given to sensory information, and how people reacted to changes in prior and sensory uncertainty. We found that while the stress-inducing procedure successfully elicited subjective stress ratings as well as stress relevant physiological paramters, it did not change participants' average reliance on sensory information. Furthermore, it did not affect participants' sensitivity to changes in prior and sensory uncertainty, with both groups able to detect it and modulate their behavior accordingly, in a way predicted by Bayesian statistics. Our results suggest that, contrary to our predictions, stress may not directly affect lower-level sensory-motor decisions. We discuss the findings in context of time scales of the stress reaction, linked to different neural and functional consequences.

A model of risk and mental state shifts during social interaction.

Cooperation and competition between human players in repeated microeconomic games offer a window onto social phenomena such as the establishment, breakdown and repair of trust. However, although a suitable starting point for the quantitative analysis of such games exists, namely the Interactive Partially Observable Markov Decision Process (I-POMDP), computational considerations and structural limitations have limited its application, and left unmodelled critical features of behavior in a canonical trust task. Here, we provide the first analysis of two central phenomena: a form of social risk-aversion exhibited by the player who is in control of the interaction in the game; and irritation or anger, potentially exhibited by both players. Irritation arises when partners apparently defect, and it potentially causes a precipitate breakdown in cooperation. Failing to model one's partner's propensity for it leads to substantial economic inefficiency. We illustrate these behaviours using evidence drawn from the play of large cohorts of healthy volunteers and patients. We show that for both cohorts, a particular subtype of player is largely responsible for the breakdown of trust, a finding which sheds new light on borderline personality disorder.

Dopaminergic Medication Increases Reliance on Current Information in Parkinson's Disease.

The neurotransmitter dopamine is crucial for decision-making under uncertainty but its computational role is still a subject of intense debate. To test potential roles, we had patients with Parkinson's disease (PD), who have less internally-generated dopamine, participate in a visual decision-making task in which uncertainty in both prior and current sensory information was varied and where behavior is often predicted by Bayesian statistics. We found that many aspects of uncertainty processing were conserved in PD: they could learn the prior uncertainty and utilize both priors and current sensory information. As predicted by prominent theories, we found that dopaminergic medication influenced the weight given to sensory information. However, as PD patients learn, this bias disappeared. In addition, throughout the experiment the patients exhibited lower sensitivity to current sensory uncertainty. Our results provide empirical evidence for the idea that dopamine levels, which are affected by PD and the drugs used for its treatment, influence the reliance on new information.

Predicting the knowledge-recklessness distinction in the human brain.

Criminal convictions require proof that a prohibited act was performed in a statutorily specified mental state. Different legal consequences, including greater punishments, are mandated for those who act in a state of knowledge, compared with a state of recklessness. Existing research, however, suggests people have trouble classifying defendants as knowing, rather than reckless, even when instructed on the relevant legal criteria. We used a machine-learning technique on brain imaging data to predict, with high accuracy, which mental state our participants were in. This predictive ability depended on both the magnitude of the risks and the amount of information about those risks possessed by the participants. Our results provide neural evidence of a detectable difference in the mental state of knowledge in contrast to recklessness and suggest, as a proof of principle, the possibility of inferring from brain data in which legally relevant category a person belongs. Some potential legal implications of this result are discussed.

The generalization of prior uncertainty during reaching.

Bayesian statistics defines how new information, given by a likelihood, should be combined with previously acquired information, given by a prior distribution. Many experiments have shown that humans make use of such priors in cognitive, perceptual, and motor tasks, but where do priors come from? As people never experience the same situation twice, they can only construct priors by generalizing from similar past experiences. Here we examine the generalization of priors over stochastic visuomotor perturbations in reaching experiments. In particular, we look into how the first two moments of the prior--the mean and variance (uncertainty)--generalize. We find that uncertainty appears to generalize differently from the mean of the prior, and an interesting asymmetry arises when the mean and the uncertainty are manipulated simultaneously.

Wii Fit balance board playing improves balance and gait in Parkinson disease.

OBJECTIVE: To assess the effect of exercise training by using the Nintendo Wii Fit video game and balance board system on balance and gait in adults with Parkinson disease (PD). DESIGN: A prospective interventional cohort study. SETTING: An outpatient group exercise class. PARTICIPANTS: Ten subjects with PD, Hoehn and Yahr stages 2.5 or 3, with a mean age of 67.1 years; 4 men, 6 women. INTERVENTIONS: The subjects participated in supervised group exercise sessions 3 times per week for 8 weeks by practicing 3 different Wii balance board games (marble tracking, skiing, and bubble rafting) adjusted for their individualized function level. The subjects trained for 10 minutes per game, a total of 30 minutes training per session. MAIN OUTCOME MEASUREMENTS: Pre-and postexercise training, a physical therapist evaluated subjects' function by using the Berg Balance Scale, Dynamic Gait Index, and Sharpened Romberg with eyes open and closed. Postural sway was assessed at rest and with tracking tasks by using the Wii balance board. The subjects rated their confidence in balance by using the Activities-specific Balance Confidence scale and depression on the Geriatric Depression Scale. RESULTS: Balance as measured by the Berg Balance Scale improved significantly, with an increase of 3.3 points (P = .016). The Dynamic Gait Index improved as well (mean increase, 2.8; P = .004), as did postural sway measured with the balance board (decreased variance in stance with eyes open by 31%; P = .049). Although the Sharpened Romberg with eyes closed increased by 6.85 points and with eyes opened by 3.3 points, improvements neared significance only for eyes closed (P = .07 versus P = .188). There were no significant changes on patient ratings for the Activities-specific Balance Confidence (mean decrease, -1%; P = .922) or the Geriatric Depression Scale (mean increase, 2.2; P = .188). CONCLUSIONS: An 8-week exercise training class by using the Wii Fit balance board improved selective measures of balance and gait in adults with PD. However, no significant changes were seen in mood or confidence regarding balance.

Differential representations of prior and likelihood uncertainty in the human brain.

BACKGROUND: Uncertainty shapes our perception of the world and the decisions we make. Two aspects of uncertainty are commonly distinguished: uncertainty in previously acquired knowledge (prior) and uncertainty in current sensory information (likelihood). Previous studies have established that humans can take both types of uncertainty into account, often in a way predicted by Bayesian statistics. However, the neural representations underlying these parameters remain poorly understood. RESULTS: By varying prior and likelihood uncertainty in a decision-making task while performing neuroimaging in humans, we found that prior and likelihood uncertainty had quite distinct representations. Whereas likelihood uncertainty activated brain regions along the early stages of the visuomotor pathway, representations of prior uncertainty were identified in specialized brain areas outside this pathway, including putamen, amygdala, insula, and orbitofrontal cortex. Furthermore, the magnitude of brain activity in the putamen predicted individuals' personal tendencies to rely more on either prior or current information. CONCLUSIONS: Our results suggest different pathways by which prior and likelihood uncertainty map onto the human brain and provide a potential neural correlate for higher reliance on current or prior knowledge. Overall, these findings offer insights into the neural pathways that may allow humans to make decisions close to the optimal defined by a Bayesian statistical framework.

Bayesian models: the structure of the world, uncertainty, behavior, and the brain.

Experiments on humans and other animals have shown that uncertainty due to unreliable or incomplete information affects behavior. Recent studies have formalized uncertainty and asked which behaviors would minimize its effect. This formalization results in a wide range of Bayesian models that derive from assumptions about the world, and it often seems unclear how these models relate to one another. In this review, we use the concept of graphical models to analyze differences and commonalities across Bayesian approaches to the modeling of behavioral and neural data. We review behavioral and neural data associated with each type of Bayesian model and explain how these models can be related. We finish with an overview of different theories that propose possible ways in which the brain can represent uncertainty.

Trust and reciprocity: are effort and money equivalent?

Trust and reciprocity facilitate cooperation and are relevant to virtually all human interactions. They are typically studied using trust games: one subject gives (entrusts) money to another subject, which may return some of the proceeds (reciprocate). Currently, however, it is unclear whether trust and reciprocity in monetary transactions are similar in other settings, such as physical effort. Trust and reciprocity of physical effort are important as many everyday decisions imply an exchange of physical effort, and such exchange is central to labor relations. Here we studied a trust game based on physical effort and compared the results with those of a computationally equivalent monetary trust game. We found no significant difference between effort and money conditions in both the amount trusted and the quantity reciprocated. Moreover, there is a high positive correlation in subjects' behavior across conditions. This suggests that trust and reciprocity may be character traits: subjects that are trustful/trustworthy in monetary settings behave similarly during exchanges of physical effort. Our results validate the use of trust games to study exchanges in physical effort and to characterize inter-subject differences in trust and reciprocity, and also suggest a new behavioral paradigm to study these differences.

Bayesian integration and non-linear feedback control in a full-body motor task.

A large number of experiments have asked to what degree human reaching movements can be understood as being close to optimal in a statistical sense. However, little is known about whether these principles are relevant for other classes of movements. Here we analyzed movement in a task that is similar to surfing or snowboarding. Human subjects stand on a force plate that measures their center of pressure. This center of pressure affects the acceleration of a cursor that is displayed in a noisy fashion (as a cloud of dots) on a projection screen while the subject is incentivized to keep the cursor close to a fixed position. We find that salient aspects of observed behavior are well-described by optimal control models where a Bayesian estimation model (Kalman filter) is combined with an optimal controller (either a Linear-Quadratic-Regulator or Bang-bang controller). We find evidence that subjects integrate information over time taking into account uncertainty. However, behavior in this continuous steering task appears to be a highly non-linear function of the visual feedback. While the nervous system appears to implement Bayes-like mechanisms for a full-body, dynamic task, it may additionally take into account the specific costs and constraints of the task.