Biased Recognition of Facial Affect in Patients with Major Depressive Disorder Reflects Clinical State

Cognitive theories of depression posit that perception is negatively biased in depressive disorder. Previous studies have provided empirical evidence for this notion, but left open the question whether the negative perceptual bias reflects a stable trait or the current depressive state. Here we investigated the stability of negatively biased perception over time. Emotion perception was examined in patients with major depressive disorder (MDD) and healthy control participants in two experiments. In the first experiment subjective biases in the recognition of facial emotional expressions were assessed. Participants were presented with faces that were morphed between sad and neutral and happy expressions and had to decide whether the face was sad or happy. The second experiment assessed automatic emotion processing by measuring the potency of emotional faces to gain access to awareness using interocular suppression. A follow-up investigation using the same tests was performed three months later. In the emotion recognition task, patients with major depression showed a shift in the criterion for the differentiation between sad and happy faces: In comparison to healthy controls, patients with MDD required a greater intensity of the happy expression to recognize a face as happy. After three months, this negative perceptual bias was reduced in comparison to the control group. The reduction in negative perceptual bias correlated with the reduction of depressive symptoms. In contrast to previous work, we found no evidence for preferential access to awareness of sad vs. happy faces. Taken together, our results indicate that MDD-related perceptual biases in emotion recognition reflect the current clinical state rather than a stable depressive trait

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

Linking unfounded beliefs to genetic dopamine availability

Unfounded convictions involving beliefs in the paranormal, grandiosity ideas or suspicious thoughts are endorsed at varying degrees among the general population. Here, we investigated the neurobiopsychological basis of the observed inter-individual variability in the propensity toward unfounded beliefs. One hundred two healthy individuals were genotyped for four polymorphisms in the COMT gene (rs6269, rs4633, rs4818, and rs4680, also known as val158met) that define common functional haplotypes with substantial impact on synaptic dopamine degradation, completed a questionnaire measuring unfounded beliefs, and took part in a behavioral experiment assessing perceptual inference. We found that greater dopamine availability was associated with a stronger propensity toward unfounded beliefs, and that this effect was statistically mediated by an enhanced influence of expectations on perceptual inference. Our results indicate that genetic differences in dopaminergic neurotransmission account for inter-individual differences in perceptual inference linked to the formation and maintenance of unfounded beliefs. Thus, dopamine might be critically involved in the processes underlying one's interpretation of the relationship between the self and the world

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

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

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

26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 - Meeting Abstracts - Antwerp, Belgium. 15–20 July 2017

This work was produced as part of the activities of FAPESP Research,\ud Disseminations and Innovation Center for Neuromathematics (grant\ud 2013/07699-0, S. Paulo Research Foundation). NLK is supported by a\ud FAPESP postdoctoral fellowship (grant 2016/03855-5). ACR is partially\ud supported by a CNPq fellowship (grant 306251/2014-0)

three fMRI studies

Die dopaminerge Dysfunktion ist ein zentrales Konzept in Pathogenesemodellen der Schizophrenie. Hierbei werden Störungen des Belohnungssystems und Beeinträchtigungen der präfrontalen Kognition als Erklärung für Positiv-, Negativ- und kognitive Symptome herangezogen. Gegenstand der vorliegenden Publikationsdissertation waren krankheitsbedingte Veränderungen sowie pharmakologische und genetische Modulationen dieser beiden Netzwerke. Mit funktioneller Magnetresonanztomographie (fMRT) wurden schizophrene Patienten und gesunde Kontrollen untersucht. Ferner wurde der Val158Met-Polymorphismus der Catechol-O-Methyltransferase (COMT) bestimmt, welcher an interindividuelle Unterschiede im Dopaminabbau geknüpft ist. Es zeigte sich, dass die Verarbeitung von Belohnung mit dem Genotyp interagierte und bei schizophrenen Patienten gestört war. Außerdem ging bei schizophrenen Patienten die Umstellung von konventionellen Antipsychotika auf Olanzapin mit veränderten Aktivierungen im Präfrontalkortex während einer Arbeitsgedächtnisaufgabe einher. Die vorliegenden Arbeiten bestätigen eine Dysfunktion des dopaminergen Belohnungssystems und der präfrontalen Kognition in der Schizophrenie und zeigen, dass Faktoren, die mit dem Dopaminhaushalt interagieren, dabei in signifikanter Weise die Informationsverarbeitung im Gehirn beeinflussen.Dopaminergic dysfunction is a central concept in pathogenetic models of schizophrenia. Disturbances of the reward system and impairments of prefrontal cognition serve as explanatory framework for positive, negative and cognitive symptoms. The subject of the present dissertation were disease-related alterations and pharmacological and genetic modulations of these two networks. Using functional magnetic resonance imaging (fMRI) schizophrenic patients and healthy controls were examined. In addition, the val158met-polymorphism of the catechol-O-methyltransferase (COMT) was analysed, which is associated to interindividual differences in dopamine breakdown. It was shown that reward processing interacted with genotype and was disturbed in schizophrenic patients. Furthermore, switching schizophrenic patients from conventional antipsychotics to olanzapine was accompanied by changes of prefrontal activations during a working memory task. The present findings confirm a dysfunction of the dopaminergic reward system and of prefrontal cognition in schizophrenia and show, that factors that interact with dopamine metabolism influence information processing in the brain in a significant manner

Computational Psychiatry Across Species to Study the Biology of Hallucinations.

Progress in the treatment of severe psychiatric disorders has been slow, despite tremendous advances in neuroscience. In other fields of medicine, the prognosis of many previously devastating disorders has improved thanks to new treatments that were developed based on biological insights gained in animal models. In breast cancer, for example, the study of estrogen receptors in tumors growing in rodents paved the way to novel hormonal therapies. Modeling disease in animals starts with a hypothesized biological dysfunction (eg, uncontrolled cell proliferation) that is inducible by experimental manipulations (eg, carcinogen exposure) and results in quantifiable manifestations (eg, tumor growth). Psychiatric disorders, however, have been challenging to model in animals

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