Antibody-mediated autoimmunity in symptom-based disorders: position statement and proceedings from an international workshop.

A 2-day closed workshop was held in Liverpool, United Kingdom, to discuss the results of research concerning symptom-based disorders (SBDs) caused by autoantibodies, share technical knowledge, and consider future plans. Twenty-two speakers and 14 additional participants attended. This workshop set out to consolidate knowledge about the contribution of autoantibodies to SBDs. Persuasive evidence for a causative role of autoantibodies in disease often derives from experimental "passive transfer" approaches, as first established in neurological research. Here, serum immunoglobulin (IgM or IgG) is purified from donated blood and transferred to rodents, either systemically or intrathecally. Rodents are then assessed for the expression of phenotypes resembling the human condition; successful phenotype transfer is considered supportive of or proof for autoimmune pathology. Workshop participants discussed passive transfer models and wider evidence for autoantibody contribution to a range of SBDs. Clinical trials testing autoantibody reduction were presented. Cornerstones of both experimental approaches and clinical trial parameters in this field were distilled and presented in this article. Mounting evidence suggests that immunoglobulin transfer from patient donors often induces the respective SBD phenotype in rodents. Understanding antibody binding epitopes and downstream mechanisms will require substantial research efforts, but treatments to reduce antibody titres can already now be evaluated.

Overly Strong Priors for Socially Meaningful Visual Signals Are Linked to Psychosis Proneness in Healthy Individuals.

According to the predictive coding theory of psychosis, hallucinations and delusions are explained by an overweighing of high-level prior expectations relative to sensory information that leads to false perceptions of meaningful signals. However, it is currently unclear whether the hypothesized overweighing of priors (1) represents a pervasive alteration that extends to the visual modality and (2) takes already effect at early automatic processing stages. Here, we addressed these questions by studying visual perception of socially meaningful stimuli in healthy individuals with varying degrees of psychosis proneness (n = 39). In a first task, we quantified participants' prior for detecting faces in visual noise using a Bayesian decision model. In a second task, we measured participants' prior for detecting direct gaze stimuli that were rendered invisible by continuous flash suppression. We found that the prior for detecting faces in noise correlated with hallucination proneness (r = 0.50, p = 0.001, Bayes factor 1/20.1) as well as delusion proneness (r = 0.46, p = 0.003, BF 1/9.4). The prior for detecting invisible direct gaze was significantly associated with hallucination proneness (r = 0.43, p = 0.009, BF 1/3.8) but not conclusively with delusion proneness (r = 0.30, p = 0.079, BF 1.7). Our results provide evidence for the idea that overly strong high-level priors for automatically detecting socially meaningful stimuli might constitute a processing alteration in psychosis.

A multimodal neuroimaging classifier for alcohol dependence.

With progress in magnetic resonance imaging technology and a broader dissemination of state-of-the-art imaging facilities, the acquisition of multiple neuroimaging modalities is becoming increasingly feasible. One particular hope associated with multimodal neuroimaging is the development of reliable data-driven diagnostic classifiers for psychiatric disorders, yet previous studies have often failed to find a benefit of combining multiple modalities. As a psychiatric disorder with established neurobiological effects at several levels of description, alcohol dependence is particularly well-suited for multimodal classification. To this aim, we developed a multimodal classification scheme and applied it to a rich neuroimaging battery (structural, functional task-based and functional resting-state data) collected in a matched sample of alcohol-dependent patients (N = 119) and controls (N = 97). We found that our classification scheme yielded 79.3% diagnostic accuracy, which outperformed the strongest individual modality - grey-matter density - by 2.7%. We found that this moderate benefit of multimodal classification depended on a number of critical design choices: a procedure to select optimal modality-specific classifiers, a fine-grained ensemble prediction based on cross-modal weight matrices and continuous classifier decision values. We conclude that the combination of multiple neuroimaging modalities is able to moderately improve the accuracy of machine-learning-based diagnostic classification in alcohol dependence.

Neural Response Patterns During Pavlovian-to-Instrumental Transfer Predict Alcohol Relapse and Young Adult Drinking.

BACKGROUND: Pavlovian-to-instrumental transfer (PIT) describes the influence of conditioned stimuli on instrumental behaviors and is discussed as a key process underlying substance abuse. Here, we tested whether neural responses during alcohol-related PIT predict future relapse in alcohol-dependent patients and future drinking behavior in adolescents. METHODS: Recently detoxified alcohol-dependent patients (n = 52) and young adults without dependence (n = 136) underwent functional magnetic resonance imaging during an alcohol-related PIT paradigm, and their drinking behavior was assessed in a 12-month follow-up. To predict future drinking behavior from PIT activation patterns, we used a multivoxel classification scheme based on linear support vector machines. RESULTS: When training and testing the classification scheme in patients, PIT activation patterns predicted future relapse with 71.2% accuracy. Feature selection revealed that classification was exclusively based on activation patterns in medial prefrontal cortex. To probe the generalizability of this functional magnetic resonance imaging-based prediction of future drinking behavior, we applied the support vector machine classifier that had been trained on patients to PIT functional magnetic resonance imaging data from adolescents. An analysis of cross-classification predictions revealed that those young social drinkers who were classified as abstainers showed a greater reduction in alcohol consumption at 12-month follow-up than those classified as relapsers (Δ = -24.4 ± 6.0 g vs. -5.7 ± 3.6 g; p = .019). CONCLUSIONS: These results suggest that neural responses during PIT could constitute a generalized prognostic marker for future drinking behavior in established alcohol use disorder and in at-risk states.

Delusion Proneness is Linked to a Reduced Usage of Prior Beliefs in Perceptual Decisions.

Predictive coding theories state an aberrant weighting of prior beliefs and present sensory information as a core computational pathology in psychosis. Specifically, it has been proposed that the influence of prior beliefs which attenuate improbable sensory information is weakened, resulting in an overweighing of this potentially misleading information. However, it is currently unclear whether this alteration is specific to perceptual processes or whether it represents a more pervasive deficit that extends to cognitive processes. Here, we carried out 2 behavioral experiments that probed the usage of priors during perceptual and cognitive processes, respectively, in 123 healthy individuals with varying degrees of delusion proneness. In an audio-visual perceptual discrimination task, participants had to judge the global motion direction of random dot kinematograms. Prior beliefs were induced by auditory cues that probabilistically predicted the global motion direction of the dot kinematograms, allowing us to measure the impact of prior beliefs on perceptual decision making. A control experiment paralleled the design of the perceptual decision making task in the domain of cognitive decision making. By fitting the participants' responses with a probabilistic decision model, we quantified the impact of prior beliefs on participants' decisions in both tasks. With growing delusion proneness, we found a decreased impact of prior beliefs on perceptual but not on cognitive decision making. Our results show that delusion proneness is linked to a specifically reduced usage of prior beliefs in perceptual decisions, thereby empirically substantiating predictive coding theories of psychosis.

Hallucinations and Strong Priors.

Hallucinations, perceptions in the absence of objectively identifiable stimuli, illustrate the constructive nature of perception. Here, we highlight the role of prior beliefs as a critical elicitor of hallucinations. Recent empirical work from independent laboratories shows strong, overly precise priors can engender hallucinations in healthy subjects and that individuals who hallucinate in the real world are more susceptible to these laboratory phenomena. We consider these observations in light of work demonstrating apparently weak, or imprecise, priors in psychosis. Appreciating the interactions within and between hierarchies of inference can reconcile this apparent disconnect. Data from neural networks, human behavior, and neuroimaging support this contention. This work underlines the continuum from normal to aberrant perception, encouraging a more empathic approach to clinical hallucinations.

The Neural Correlates of Hierarchical Predictions for Perceptual Decisions.

Sensory information is inherently noisy, sparse, and ambiguous. In contrast, visual experience is usually clear, detailed, and stable. Bayesian theories of perception resolve this discrepancy by assuming that prior knowledge about the causes underlying sensory stimulation actively shapes perceptual decisions. The CNS is believed to entertain a generative model aligned to dynamic changes in the hierarchical states of our volatile sensory environment. Here, we used model-based fMRI to study the neural correlates of the dynamic updating of hierarchically structured predictions in male and female human observers. We devised a crossmodal associative learning task with covertly interspersed ambiguous trials in which participants engaged in hierarchical learning based on changing contingencies between auditory cues and visual targets. By inverting a Bayesian model of perceptual inference, we estimated individual hierarchical predictions, which significantly biased perceptual decisions under ambiguity. Although "high-level" predictions about the cue-target contingency correlated with activity in supramodal regions such as orbitofrontal cortex and hippocampus, dynamic "low-level" predictions about the conditional target probabilities were associated with activity in retinotopic visual cortex. Our results suggest that our CNS updates distinct representations of hierarchical predictions that continuously affect perceptual decisions in a dynamically changing environment.SIGNIFICANCE STATEMENT Bayesian theories posit that our brain entertains a generative model to provide hierarchical predictions regarding the causes of sensory information. Here, we use behavioral modeling and fMRI to study the neural underpinnings of such hierarchical predictions. We show that "high-level" predictions about the strength of dynamic cue-target contingencies during crossmodal associative learning correlate with activity in orbitofrontal cortex and the hippocampus, whereas "low-level" conditional target probabilities were reflected in retinotopic visual cortex. Our findings empirically corroborate theorizations on the role of hierarchical predictions in visual perception and contribute substantially to a longstanding debate on the link between sensory predictions and orbitofrontal or hippocampal activity. Our work fundamentally advances the mechanistic understanding of perceptual inference in the human brain.

Quantitative neurobiological evidence for accelerated brain aging in alcohol dependence.

The premature aging hypothesis of alcohol dependence proposes that the neurobiological and behavioural deficits in individuals with alcohol dependence are analogous to those of chronological aging. However, to date no systematic neurobiological evidence for this hypothesis has been provided. To test the hypothesis, 119 alcohol-dependent subjects and 97 age- and gender-matched healthy control subjects underwent structural MRI. Whole-brain grey matter volume maps were computed from structural MRI scans using voxel-based morphometry and parcelled into a comprehensive set of anatomical brain regions. Regional grey matter volume averages served as the basis for cross-regional similarity analyses and a brain age model. We found a striking correspondence between regional patterns of alcohol- and age-related grey matter loss across 110 brain regions. The brain age model revealed that the brain age of age-matched AD subjects was increased by up to 11.7 years. Interestingly, while no brain aging was detected in the youngest AD subjects (20-30 years), we found that alcohol-related brain aging systematically increased in the following age decades controlling for lifetime alcohol consumption and general health status. Together, these results provide strong evidence for an accelerated aging model of AD and indicate an elevated risk of alcohol-related brain aging in elderly individuals.

A hierarchical stochastic model for bistable perception.

Viewing of ambiguous stimuli can lead to bistable perception alternating between the possible percepts. During continuous presentation of ambiguous stimuli, percept changes occur as single events, whereas during intermittent presentation of ambiguous stimuli, percept changes occur at more or less regular intervals either as single events or bursts. Response patterns can be highly variable and have been reported to show systematic differences between patients with schizophrenia and healthy controls. Existing models of bistable perception often use detailed assumptions and large parameter sets which make parameter estimation challenging. Here we propose a parsimonious stochastic model that provides a link between empirical data analysis of the observed response patterns and detailed models of underlying neuronal processes. Firstly, we use a Hidden Markov Model (HMM) for the times between percept changes, which assumes one single state in continuous presentation and a stable and an unstable state in intermittent presentation. The HMM captures the observed differences between patients with schizophrenia and healthy controls, but remains descriptive. Therefore, we secondly propose a hierarchical Brownian model (HBM), which produces similar response patterns but also provides a relation to potential underlying mechanisms. The main idea is that neuronal activity is described as an activity difference between two competing neuronal populations reflected in Brownian motions with drift. This differential activity generates switching between the two conflicting percepts and between stable and unstable states with similar mechanisms on different neuronal levels. With only a small number of parameters, the HBM can be fitted closely to a high variety of response patterns and captures group differences between healthy controls and patients with schizophrenia. At the same time, it provides a link to mechanistic models of bistable perception, linking the group differences to potential underlying mechanisms.

A predictive coding account of bistable perception - a model-based fMRI study.

In bistable vision, subjective perception wavers between two interpretations of a constant ambiguous stimulus. This dissociation between conscious perception and sensory stimulation has motivated various empirical studies on the neural correlates of bistable perception, but the neurocomputational mechanism behind endogenous perceptual transitions has remained elusive. Here, we recurred to a generic Bayesian framework of predictive coding and devised a model that casts endogenous perceptual transitions as a consequence of prediction errors emerging from residual evidence for the suppressed percept. Data simulations revealed close similarities between the model's predictions and key temporal characteristics of perceptual bistability, indicating that the model was able to reproduce bistable perception. Fitting the predictive coding model to behavioural data from an fMRI-experiment on bistable perception, we found a correlation across participants between the model parameter encoding perceptual stabilization and the behaviourally measured frequency of perceptual transitions, corroborating that the model successfully accounted for participants' perception. Formal model comparison with established models of bistable perception based on mutual inhibition and adaptation, noise or a combination of adaptation and noise was used for the validation of the predictive coding model against the established models. Most importantly, model-based analyses of the fMRI data revealed that prediction error time-courses derived from the predictive coding model correlated with neural signal time-courses in bilateral inferior frontal gyri and anterior insulae. Voxel-wise model selection indicated a superiority of the predictive coding model over conventional analysis approaches in explaining neural activity in these frontal areas, suggesting that frontal cortex encodes prediction errors that mediate endogenous perceptual transitions in bistable perception. Taken together, our current work provides a theoretical framework that allows for the analysis of behavioural and neural data using a predictive coding perspective on bistable perception. In this, our approach posits a crucial role of prediction error signalling for the resolution of perceptual ambiguities.

Correction: Psychotic Experiences and Overhasty Inferences Are Related to Maladaptive Learning.

[This corrects the article DOI: 10.1371/journal.pcbi.1005328.].

Psychotic Experiences and Overhasty Inferences Are Related to Maladaptive Learning.

Theoretical accounts suggest that an alteration in the brain's learning mechanisms might lead to overhasty inferences, resulting in psychotic symptoms. Here, we sought to elucidate the suggested link between maladaptive learning and psychosis. Ninety-eight healthy individuals with varying degrees of delusional ideation and hallucinatory experiences performed a probabilistic reasoning task that allowed us to quantify overhasty inferences. Replicating previous results, we found a relationship between psychotic experiences and overhasty inferences during probabilistic reasoning. Computational modelling revealed that the behavioral data was best explained by a novel computational learning model that formalizes the adaptiveness of learning by a non-linear distortion of prediction error processing, where an increased non-linearity implies a growing resilience against learning from surprising and thus unreliable information (large prediction errors). Most importantly, a decreased adaptiveness of learning predicted delusional ideation and hallucinatory experiences. Our current findings provide a formal description of the computational mechanisms underlying overhasty inferences, thereby empirically substantiating theories that link psychosis to maladaptive learning.

Enhanced predictive signalling in schizophrenia.

Positive symptoms of schizophrenia such as delusions and hallucinations are thought to arise from an alteration in predictive mechanisms of the brain. Here, we empirically tested the hypothesis that schizophrenia is associated with an enhanced signalling of higher-level predictions that shape perception into conformity with acquired beliefs. Twenty-one patients with schizophrenia and twenty-eight healthy controls matched for age and gender took part in a functional magnetic resonance imaging (fMRI) experiment that assessed the effect of an experimental manipulation of cognitive beliefs on the perception of an ambiguous visual motion stimulus. At the behavioural level, there was a generally weaker effect of experimentally induced beliefs on perception in schizophrenia patients compared with controls, but a positive correlation between the effect of beliefs on perception and the severity of positive symptoms. At the neural level, belief-related connectivity between a region encoding beliefs in the orbitofrontal cortex (OFC) and a region encoding visual motion in the visual cortex (V5) was higher in patients compared with controls, indicating a stronger impact of cognitive beliefs on visual processing in schizophrenia. We suggest that schizophrenia might be associated with a generally weaker acquisition of externally generated beliefs and a compensatory increase in the effect of beliefs on sensory processing. Our current results are in line with the notion that enhanced signalling of higher-level predictions that shape perception into conformity with acquired beliefs might underlie positive symptoms in schizophrenia. Hum Brain Mapp 38:1767-1779, 2017. © 2017 Wiley Periodicals, Inc.

fMRI-based decoding of reward effects in binocular rivalry.

Binocular rivalry is a phenomenon where the simultaneous presentation of two different stimuli to the two eyes leads to alternating perception of the two stimuli. The temporary dominance of one stimulus over the other is influenced by several factors. Here, we studied the influence of reward on binocular rivalry dynamics and its neural representation in visual cortex. Orthogonal rotating grating stimuli were shown continuously, while monetary reward was given during the conscious perception of one stimulus but not the other. Periods of perceptual dominance were assessed both through participants' subjective report and objectively using functional magnetic resonance imaging and multi-voxel pattern analysis. Results did not confirm previous evidence for an effect of reward on perceptual dominance durations. Exploratory post-hoc analyses indicated that knowledge regarding both the reward contingency and the subjective nature of perceptual alternations may have interfered with potential reward effects on perceptual phase durations, suggesting a moderating role of meta-cognitive awareness in reward-based perceptual inference. Future studies of top-down influences on bistable perception should carefully consider the methodological challenges related to meta-cognitive awareness.

Striatal activation as a neural link between cognitive and perceptual flexibility.

Our brain continuously evaluates different perceptual interpretations of the available sensory data in order to enable flexible updates of conscious experience. Individuals' perceptual flexibility can be assessed using ambiguous stimuli that cause our perception to continuously switch between two mutually exclusive interpretations. Neural processes underlying perceptual switching are thought to involve the visual cortex, but also non-sensory brain circuits that have been implicated in cognitive processes, such as frontal and parietal regions. Perceptual flexibility varies strongly between individuals and has been related to dopaminergic neurotransmission. Likewise, there is also considerable individual variability in tasks that require flexibility in cognition, and dopamine-dependent striato-frontal signals have been associated with processes promoting cognitive flexibility. Given the anatomical and neurochemical similarities with regard to perceptual and cognitive flexibility, we here probed whether individual differences in perceptual flexibility during bistable perception are related to individual cognitive flexibility associated neural correlates. 126 healthy individuals performed rule-based task switching during functional magnetic resonance imaging (fMRI) and reported perceptual switching during the viewing of a modified version of the Necker cube. Mean phase duration as measure of perceptual flexibility correlated with task-switching associated activity in the right putamen as part of the basal ganglia. In addition, we found a tentative correlation between perceptual and cognitive flexibility. These results indicate that individual differences in cognitive flexibility and associated fronto-striatal processing contribute to differences in perceptual flexibility. Our findings thus provide empirical support for the general notion of shared mechanisms between perception and cognition.

Learning What to See in a Changing World.

Visual perception is strongly shaped by expectations, but it is poorly understood how such perceptual expectations are learned in our dynamic sensory environment. Here, we applied a Bayesian framework to investigate whether perceptual expectations are continuously updated from different aspects of ongoing experience. In two experiments, human observers performed an associative learning task in which rapidly changing expectations about the appearance of ambiguous stimuli were induced. We found that perception of ambiguous stimuli was biased by both learned associations and previous perceptual outcomes. Computational modeling revealed that perception was best explained by a model that continuously updated priors from associative learning and perceptual history and combined these priors with the current sensory information in a probabilistic manner. Our findings suggest that the construction of visual perception is a highly dynamic process that incorporates rapidly changing expectations from different sources in a manner consistent with Bayesian learning and inference.

Predicting Subjective Affective Salience from Cortical Responses to Invisible Object Stimuli.

The affective value of a stimulus substantially influences its potency to gain access to awareness. Here, we sought to elucidate the neural mechanisms underlying such affective salience in a combined behavioral and fMRI experiment. Healthy individuals with varying degrees of spider phobia were presented with pictures of spiders and flowers suppressed from view by continuous flash suppression. Applying multivoxel pattern analysis, we found that the average time that spider stimuli took relative to flowers to gain access to awareness in each participant could be decoded from fMRI signals evoked by suppressed spider versus flower stimuli in occipitotemporal and orbitofrontal cortex. Our results indicate neural signals during unconscious processing of complex visual information in orbitofrontal and ventral visual areas predict access to awareness of this information, suggesting a crucial role for these higher-level cortical regions in mediating affective salience.