Sensory processing in humans and mice fluctuates between external and internal modes.

Perception is known to cycle through periods of enhanced and reduced sensitivity to external information. Here, we asked whether such slow fluctuations arise as a noise-related epiphenomenon of limited processing capacity or, alternatively, represent a structured mechanism of perceptual inference. Using 2 large-scale datasets, we found that humans and mice alternate between externally and internally oriented modes of sensory analysis. During external mode, perception aligns more closely with the external sensory information, whereas internal mode is characterized by enhanced biases toward perceptual history. Computational modeling indicated that dynamic changes in mode are enabled by 2 interlinked factors: (i) the integration of subsequent inputs over time and (ii) slow antiphase oscillations in the impact of external sensory information versus internal predictions that are provided by perceptual history. We propose that between-mode fluctuations generate unambiguous error signals that enable optimal inference in volatile environments.

Predicting psychotic relapse following randomised discontinuation of paliperidone in individuals with schizophrenia or schizoaffective disorder: an individual participant data analysis.

BACKGROUND: Predicting relapse for individuals with psychotic disorders is not well established, especially after discontinuation of antipsychotic treatment. We aimed to identify general prognostic factors of relapse for all participants (irrespective of treatment continuation or discontinuation) and specific predictors of relapse for treatment discontinuation, using machine learning. METHODS: For this individual participant data analysis, we searched the Yale University Open Data Access Project's database for placebo-controlled, randomised antipsychotic discontinuation trials with participants with schizophrenia or schizoaffective disorder (aged ≥18 years). We included studies in which participants were treated with any antipsychotic study drug and randomly assigned to continue the same antipsychotic drug or to discontinue it and receive placebo. We assessed 36 prespecified baseline variables at randomisation to predict time to relapse, using univariate and multivariate proportional hazard regression models (including multivariate treatment group by variable interactions) with machine learning to categorise the variables as general prognostic factors of relapse, specific predictors of relapse, or both. FINDINGS: We identified 414 trials, of which five trials with 700 participants (304 [43%] women and 396 [57%] men) were eligible for the continuation group and 692 participants (292 [42%] women and 400 [58%] men) were eligible for the discontinuation group (median age 37 [IQR 28-47] years for continuation group and 38 [28-47] years for discontinuation group). Out of the 36 baseline variables, general prognostic factors of increased risk of relapse for all participants were drug-positive urine; paranoid, disorganised, and undifferentiated types of schizophrenia (lower risk for schizoaffective disorder); psychiatric and neurological adverse events; higher severity of akathisia (ie, difficulty or inability to sit still); antipsychotic discontinuation; lower social performance; younger age; lower glomerular filtration rate; benzodiazepine comedication (lower risk for anti-epileptic comedication). Out of the 36 baseline variables, predictors of increased risk specifically after antipsychotic discontinuation were increased prolactin concentration, higher number of hospitalisations, and smoking. Both prognostic factors and predictors with increased risk after discontinuation were oral antipsychotic treatment (lower risk for long-acting injectables), higher last dosage of the antipsychotic study drug, shorter duration of antipsychotic treatment, and higher score on the Clinical Global Impression (CGI) severity scale The predictive performance (concordance index) for participants who were not used to train the model was 0·707 (chance level is 0·5). INTERPRETATION: Routinely available general prognostic factors of psychotic relapse and predictors specific for treatment discontinuation could be used to support personalised treatment. Abrupt discontinuation of higher dosages of oral antipsychotics, especially for individuals with recurring hospitalisations, higher scores on the CGI severity scale, and increased prolactin concentrations, should be avoided to reduce the risk of relapse. FUNDING: German Research Foundation and Berlin Institute of Health.

Sparse models for predicting psychosocial impairments in patients with PTSD: An empirical Bayes approach.

OBJECTIVE: Posttraumatic stress disorder (PTSD) is associated with psychosocial impairments, which represent a relevant focus for therapy. Previous results on the clinical predictors of these psychosocial impairments were inconsistent. The data analyzed in these contexts often suffer from a high number of correlated predictors and small sample sizes, entailing the risk of model overfitting. In Bayesian regression, the problem of overfitting can be mitigated by usage of specific zero-centered (regularizing) prior distributions. In this study, we used the 2 most common Bayesian regression models, the Bayesian Ridge and the Bayesian Lasso, to predict psychosocial impairments in 192 patients of a day clinic for the treatment of PTSD. METHOD: Predictions were based on specific dimensions of PTSD symptoms previously revealed by factor analyses, as well as posttraumatic cognitions, depressive symptoms, comorbid disorders, and demographics. The variance of the prior distribution was estimated through empirical Bayes (maximum marginal likelihood) and an approximation to the posterior distribution was obtained with stochastic variational inference and with a local approximation (Laplace approximation). RESULTS: Severe psychosocial impairments were mainly related to depressive symptoms and symptoms from the amnesia and numbing dimension of PTSD, while gender, posttraumatic cognitions, and reexperience and avoidance symptoms had no impact. As expected, the model coefficients were shrunken to zero when regularizing prior distributions were used, particularly for the Bayesian Lasso. CONCLUSION: Depressive and numbing symptoms are the main clinical correlates of psychosocial impairments in patients with PTSD. Usage of Bayesian and regularized regression can contribute to the generalizability and interpretability of research results. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Neurofunctional alterations of cognitive down-regulation of craving in quitting motivated smokers.

OBJECTIVE: Cognitive down-regulation of craving involves a neural network within the prefrontal cortex. Tobacco use disorder (TUD) and trait impulsivity have been associated with prefrontal cortex impairments and down-regulation deficits. However, general deficits in down-regulation of craving (regarding non-drug-related cues) compared to never-smokers (NS), differential alterations between drug-related and non-drug-related cues, as well as its links to subject characteristics (smoking severity, trait impulsivity) have so far sparsely been investigated in TUD. METHOD: In this study, 78 subjects (37 TUD & 42 NS) underwent functional magnetic resonance imaging while performing a down-regulation of craving task. Two reward cue-types were presented (drug cues and alternative rewards). Subjects applied down-regulation of craving during a LATER condition and up-regulated their craving during a NOW condition. Subjective craving ratings were assessed after each trial. To evaluate down-regulation of craving, we investigated the LATER versus NOW condition. RESULTS: TUD subjects showed no differences in down-regulation on a behavioral level, neither compared to NS nor between the two reward cue-types. On a neurofunctional level, we found a stronger BOLD response in the middle temporal gyrus in TUD subjects compared to NS in the alternative reward condition. No differences between the two reward cue-types were found within TUD subjects. During down-regulation across both reward cue-types, we identified significant negative associations between activation of control areas and smoking severity. CONCLUSIONS: Results neither indicate evidence for the expected general alterations in down-regulation of craving in TUD, compared to NS, nor specific alterations between drug-related and alternative reward cues on a neurofunctional level. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Evidence for a hijacked brain reward system but no desensitized threat system in quitting-motivated smokers: An fMRI study.

BACKGROUND AND AIMS: Several aspects of how quitting-motivated tobacco use disorder (TUD) subjects and never-smokers differ in terms of reward and threat processing remain unresolved. We aimed to examine aberrant reward and threat processes in TUD and the association with smoking characteristics. DESIGN: A between- and within-subjects functional magnetic resonance imaging (fMRI) experiment with a 2 (groups) × 4 (stimulus type) factorial design. The experimental paradigm had four conditions: pictures of (1) cigarettes served as drug-related-positive cues, (2) food as alternative reward cues, (3) long-term consequences of smoking as drug-related-negative cues and (4) neutral pictures as control. SETTING/PARTICIPANTS: Adult participants (n = 38 TUD subjects and n = 42 never-smokers) were recruited in Berlin, Germany. MEASUREMENTS: As contrasts of primary interest, the interactions of group × stimulus-type were assessed. Significance threshold correction for multiple testing was carried out with the family-wise error method. Correlation analyses were used to test the association with smoking characteristics. FINDINGS: The 2 × 2 interaction of smoking status and stimulus type revealed activations in the brain reward system to drug-related-positive cues in TUD subjects (between-subjects effect: P-values ≤ 0.036). As a response to drug-related-negative cues, TUD subjects showed no reduced activation of the aversive brain network. Within the TUD group, a significant negative association was found between response of the aversive brain system to drug-related-negative cues (within-subjects effect: P-values ≤ 0.021) and the number of cigarettes smoked per day (right insula r = -0.386, P = 0.024; left insula r = -0.351, P = 0.042; right ACC r = -0.359, P = 0.037). CONCLUSIONS: Moderate smokers with tobacco use disorder appear to have altered brain reward processing of drug-related-positive (but not negative) cues compared with never smokers.

Aversive drug cues reduce cigarette craving and increase prefrontal cortex activation during processing of cigarette cues in quitting motivated smokers.

Aversive drug cues can be used to support smoking cessation and create awareness of negative health consequences of smoking. Better understanding of the effects of aversive drug cues on craving and the processing of appetitive drug cues in abstinence motivated smokers is important to further improve their use in cessation therapy and smoking-related public health measures. In this study, 38 quitting motivated smokers underwent functional magnetic resonance imaging (fMRI) scanning while performing a novel extended cue-reactivity paradigm. Pictures of cigarettes served as appetitive drug cues, which were preceded by either aversive drug cues (e.g., smokers' leg) or other cues (neutral or alternative reward cues). Participants were instructed to rate their craving for cigarettes after presentation of drug cues. When aversive drug cues preceded the presentation of appetitive drug cues, behavioural craving was reduced and activations in prefrontal (dorsolateral prefrontal cortex) and paralimbic (dorsal anterior cingulate cortex [dACC] and anterior insulae) areas were enhanced. A positive association between behavioural craving reduction and neurofunctional activation changes was shown for the right dACC. Our results suggest that aversive drug cues have an impact on the processing of appetitive drug cues, both on a neurofunctional and a behavioural level. A proposed model states that aversive drug-related cues activate control-associated brain areas (e.g., dACC), leading to increased inhibitory control on reward-associated brain areas (e.g., putamen) and a reduction in subjective cravings.

Markers of muscarinic deficit for individualized treatment in schizophrenia.

Recent clinical studies have shown that agonists at muscarinic acetylcholine receptors effectively reduce schizophrenia symptoms. It is thus conceivable that, for the first time, a second substance class of procholinergic antipsychotics could become established alongside the usual antidopaminergic antipsychotics. In addition, various basic science studies suggest that there may be a subgroup of schizophrenia in which hypofunction of muscarinic acetylcholine receptors is of etiological importance. This could represent a major opportunity for individualized treatment of schizophrenia if markers can be identified that predict response to procholinergic vs. antidopaminergic interventions. In this perspective, non-response to antidopaminergic antipsychotics, specific symptom patterns like visual hallucinations and strong disorganization, the presence of antimuscarinic antibodies, ERP markers such as mismatch negativity, and radiotracers are presented as possible in vivo markers of muscarinic deficit and thus potentially of response to procholinergic therapeutics. Finally, open questions and further research steps are outlined.

Predicting outcome of daycare cognitive behavioural therapy in a naturalistic sample of patients with PTSD: a machine learning approach.

Background: Identifying predictors for treatment outcome in patients with posttraumatic stress disorder (PTSD) is important in order to provide an effective treatment, but robust and replicated treatment outcome predictors are not available up to now. Objectives: We investigated predictors of treatment outcome in a naturalistic sample of patients with PTSD admitted to an 8-week daycare cognitive behavioural therapy programme following a wide range of traumatic events. Method: We used machine learning (linear and non-linear regressors and cross-validation) to predict outcome at discharge for 116 patients and sustained treatment effects 6 months after discharge for 52 patients who had a follow-up assessment. Predictions were based on a wide selection of demographic and clinical assessments including age, gender, comorbid psychiatric disorders, trauma history, posttraumatic symptoms, posttraumatic cognitions, depressive symptoms, general psychopathology and psychosocial functioning. Results: We found that demographic and clinical variables significantly, but only modestly predicted PTSD treatment outcome at discharge (r = 0.21, p = .021 for the best model) and follow-up (r = 0.31, p = .026). Among the included variables, more severe posttraumatic cognitions were negatively associated with treatment outcome. Early response in PTSD symptomatology (percentage change of symptom scores after 4 weeks of treatment) allowed more accurate predictions of outcome at discharge (r = 0.56, p < .001) and follow-up (r = 0.43, p = .001). Conclusion: Our results underscore the importance of early treatment response for short- and long-term treatment success. Nevertheless, it remains an unresolved challenge to identify variables that can robustly predict outcome before the initiation of treatment.

Antecedentes: La identificación de los predictores para el resultado de tratamiento en pacientes con trastorno de estrés postraumático (TEPT) es importante para proporcionar un tratamiento eficaz, pero hasta ahora no se dispone de predictores de respuesta de tratamiento robustos y replicables.Objetivos: Investigamos los predictores de resultado de tratamiento en una muestra naturalista de pacientes con TEPT ingresados a un programa de tratamiento cognitivo conductual tipo hospital de día de ocho semanas, después de una amplia gama de eventos traumáticos.Método: Utilizamos el aprendizaje automático (regresores lineales y no lineales y validación cruzada) para predecir el resultado al alta para 116 pacientes y los efectos sostenidos del tratamiento a los seis meses del alta para 52 pacientes que tuvieron una evaluación de seguimiento. Las predicciones se basaron en una amplia selección de evaluaciones demográficas y clínicas que incluyen edad, género, trastornos psiquiátricos comórbidos, antecedentes de trauma, síntomas postraumáticos, cogniciones postraumáticas, síntomas depresivos, psicopatología general y funcionamiento psicosocial.Resultados: Encontramos que las variables clínicas y demográficas predijeron de manera significativa, pero solo modestamente, el resultado del tratamiento del TEPT al momento del alta (r = 0.21, p= .21 para el mejor modelo) y el seguimiento (r = 0.31, p = .026). Entre las variables incluidas, las cogniciones postraumáticas más severas se asociaron negativamente con el resultado del tratamiento. La respuesta temprana en la sintomatología de TEPT (cambio porcentual del puntaje en los síntomas después de cuatro semanas de tratamiento) permitió predicciones más precisas de los resultados al alta (r = 0.56, p < .001) y el seguimiento (r = 0.43, p = .001).Conclusiones: Nuestros resultados subrayan la importancia de una respuesta temprana al tratamiento para el éxito del tratamiento a corto y largo plazo. No obstante, sigue siendo un desafío sin resolver identificar variables que puedan predecir de manera sólida el resultado antes del inicio del tratamiento.

An active role of inferior frontal cortex in conscious experience.

In the search for the neural correlates of consciousness, it has remained controversial whether prefrontal cortex determines what is consciously experienced or, alternatively, serves only complementary functions, such as introspection or action. Here, we provide converging evidence from computational modeling and two functional magnetic resonance imaging experiments that indicated a key role of inferior frontal cortex in detecting perceptual conflicts caused by ambiguous sensory information. Crucially, the detection of perceptual conflicts by prefrontal cortex turned out to be critical in the process of transforming ambiguous sensory information into unambiguous conscious experiences: in a third experiment, disruption of neural activity in inferior frontal cortex through transcranial magnetic stimulation slowed down the updating of conscious experience that occurs in response to perceptual conflicts. These findings show that inferior frontal cortex actively contributes to the resolution of perceptual ambiguities. Prefrontal cortex is thus causally involved in determining the contents of conscious experience.

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.

Unreliable feedback deteriorates information processing in primary visual cortex.

It is well-established that increased sensory uncertainty impairs perceptual decision-making and leads to degraded neural stimulus representations. Recently, we also showed that providing unreliable feedback to choices leads to changes in perceptual decision-making similar to those of increased stimulus noise: A deterioration in objective task performance, a decrease in subjective confidence and a lower reliance on sensory information for perceptual inference. To investigate the neural basis of such feedback-based changes in perceptual decision-making, in the present study, two groups of healthy human participants (n = 15 each) performed a challenging visual orientation discrimination task while undergoing functional magnetic resonance imaging (fMRI). Critically, one group received reliable feedback regarding their task performance in an intervention phase, whereas the other group correspondingly received unreliable feedback - thereby keeping stimulus information constant. The effects of feedback reliability on performance and stimulus representation in the primary visual cortex (V1) were studied by comparing the pre- and post-intervention test phases between the groups. Compared to participants who received reliable feedback, those receiving unreliable feedback showed a decline in task performance that was paralleled by reduced distinctness of fMRI response patterns in V1. These results show that environmental uncertainty can affect perceptual inference at the earliest cortical processing stages.

Psychotic Experiences in Schizophrenia and Sensitivity to Sensory Evidence.

Perceptual inference depends on an optimal integration of current sensory evidence with prior beliefs about the environment. Alterations of this process have been related to the emergence of positive symptoms in schizophrenia. However, it has remained unclear whether delusions and hallucinations arise from an increased or decreased weighting of prior beliefs relative to sensory evidence. To investigate the relation of this prior-to-likelihood ratio to positive symptoms in schizophrenia, we devised a novel experimental paradigm which gradually manipulates perceptually ambiguous visual stimuli by disambiguating stimulus information. As a proxy for likelihood precision, we assessed the sensitivity of individual participants to sensory evidence. As a surrogate for the precision of prior beliefs in perceptual stability, we measured phase duration in ambiguity. Relative to healthy controls, patients with schizophrenia showed a stronger increment in congruent perceptual states for increasing levels of disambiguating stimulus evidence. Sensitivity to sensory evidence correlated positively with the individual patients' severity of perceptual anomalies and hallucinations. Moreover, the severity of such experiences correlated negatively with phase duration. Our results indicate that perceptual anomalies and hallucinations are associated with a shift of perceptual inference toward sensory evidence and away from prior beliefs. This reduced prior-to-likelihood ratio in sensory processing may contribute to the phenomenon of aberrant salience, which has been suggested to give rise to the false inferences underlying psychotic experiences.

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.

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.

Neuropsychological Testing and Machine Learning Distinguish Alzheimer's Disease from Other Causes for Cognitive Impairment.

With promising results in recent treatment trials for Alzheimer's disease (AD), it becomes increasingly important to distinguish AD at early stages from other causes for cognitive impairment. However, existing diagnostic methods are either invasive (lumbar punctures, PET) or inaccurate Magnetic Resonance Imaging (MRI). This study investigates the potential of neuropsychological testing (NPT) to specifically identify those patients with possible AD among a sample of 158 patients with Mild Cognitive Impairment (MCI) or dementia for various causes. Patients were divided into an early stage and a late stage group according to their Mini Mental State Examination (MMSE) score and labeled as AD or non-AD patients based on a post-mortem validated threshold of the ratio between total tau and beta amyloid in the cerebrospinal fluid (CSF; Total tau/Aß(1-42) ratio, TB ratio). All patients completed the established Consortium to Establish a Registry for Alzheimer's Disease-Neuropsychological Assessment Battery (CERAD-NAB) test battery and two additional newly-developed neuropsychological tests (recollection and verbal comprehension) that aimed at carving out specific Alzheimer-typical deficits. Based on these test results, an underlying AD (pathologically increased TB ratio) was predicted with a machine learning algorithm. To this end, the algorithm was trained in each case on all patients except the one to predict (leave-one-out validation). In the total group, 82% of the patients could be correctly identified as AD or non-AD. In the early group with small general cognitive impairment, classification accuracy was increased to 89%. NPT thus seems to be capable of discriminating between AD patients and patients with cognitive impairment due to other neurodegenerative or vascular causes with a high accuracy, and may be used for screening in clinical routine and drug studies, especially in the early course of this disease.

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.

Cross-Sectional and Longitudinal Relationships between Depressive Symptoms and Brain Atrophy in MS Patients.

Introduction: Depressive symptoms are a frequent and distressing phenomenon in Multiple Sclerosis (MS) patients. Cross-sectional research links these symptoms to reduced brain gray matter volumes in parts of the prefrontal and temporal lobe as well as subcortical structures like the hippocampus, nucleus caudatus and globus pallidus. Nevertheless, prospective relationships between regional gray matter volume and the course of depressive symptoms are poorly understood. Methods: Forty-four patients with relapsing-remitting or secondary progressive MS participated in a prospective study with two assessments of depressive symptoms and high-resolution MRI with an inter-test-interval of 17 months. Relationships between baseline gray matter volume and baseline depressive symptoms, as well as prospective associations between the development of atrophy and depression were assessed using voxel-based morphometry (VBM). Results: Cross-sectional analyses revealed an association between depressive symptoms and gray matter loss in the left temporal lobe. Prospective analysis showed that gray matter losses in the right middle cingulate and middle frontal gyrus at baseline predicted increasing depressive symptoms during follow-up. Increase in depressive symptoms was related to a concomitant increase in atrophy in the left thalamus and right globus pallidus. Discussion: Our results fit well into the concept of a disturbed cortico-striatal-pallido-thalamic loop in depression. In this framework, progressive gray matter loss in limbic basal ganglia structures including globus pallidus and thalamus may lead to depression-typical deficits in hedonic motivation, whereas atrophy of the prefrontal cortex may contribute to maladaptive coping strategies, promoting an unfavorable development of depressive symptoms.