T64. LINKING SUBCLINICAL PERSECUTORY IDEATION TO INFLEXIBLE SOCIAL INFERENCE UNDER UNCERTAINTY

Abstract Background Persecutory delusions (PD) are a prominent symptom in first episode psychosis and psychosis patients. PD have been linked to abnormalities in probabilistic reasoning and social inference (e.g., attribution styles). Predictive Coding theories of delusion formation suggest that rigid delusional beliefs could be formalized as precise (i.e. held with certainty) high-level prior beliefs, which were formed to explain away overly precise low-level prediction errors (PEs). Rigid reliance on high-level prior beliefs would in turn lead to diminished updating of high-level PEs, i.e. decreased learning and updating of high-level beliefs. Methods We tested the prediction that subclinical PD ideation is related to altered social inference and beliefs about others’ intentions. To that end, N=1’145 participants from the general population were pre-screened with the Paranoia Checklist (PCL) and assigned to groups of high (“high PD”) or low PD tendencies (“low PD”). Participants with intermediate scores were excluded, participants assigned to either group filled in the PCL again after four weeks, only individuals whose score remained inside the cut-offs for either group were subsequently invited to the study. We invited 162 participants and included 151 participants in the analyses based on exclusion criteria defined in an analysis plan, which was time-stamped before the conclusion of data acquisition. Participants performed a probabilistic advice-taking task with dynamic changes in the advice-outcome mapping (volatility) under one of two experimental frames. These frames differentially emphasised possible reasons behind unhelpful advice: (i) the adviser’s possible intentions (dispositional frame) or (ii) the rules of the game (situational frame). Our design was thus 2-by-2 factorial (high vs. low delusional ideation, dispositional vs. situational frame). Participants were matched regarding age, gender, and education in years. In addition to analyses of variance on participants’ behaviour, we applied computational modeling to test the predictions regarding prior beliefs and belief updating mentioned above. Results We found significant group-by-frame interactions, indicating that in the situational frame high PD participants took advice less into account than low scorers (df = (1,150), F = 5.77, p = 0.018, partial η2= 0.04). This was also reflected in the model parameters of the model explaining participants’ learning under uncertainty best in comparison to other learning models (e.g. tonic evolution rate omega2: df = (1,150), F = 4.75, p = 0.03). Discussion Our findings suggest that social inference in individuals with subclinical PD tendencies is shaped by rigid negative prior beliefs about the intentions of others. High PD participants were less sensitive to the attributional framing and updated their beliefs less vs. low PD participants thereby preventing them to make adaptive use of social information in “safe” contexts.

Download Full-text

Aberrant effective connectivity is associated with positive symptoms in first-episode schizophrenia

10.1101/19012773 ◽

2019 ◽

Author(s):

Martin J. Dietz ◽

Yuan Zhou ◽

Lotte Veddum ◽

Christopher D. Frith ◽

Vibeke F. Bliksted

Keyword(s):

Social Cognition ◽

Negative Symptoms ◽

Effective Connectivity ◽

Predictive Coding ◽

First Episode ◽

Positive Symptoms ◽

Prediction Errors ◽

Social Stimuli ◽

Human Connectome Project ◽

First Episode Schizophrenia

AbstractSchizophrenia is a tenacious psychiatric disorder thought to result from synaptic dysfunction. While symptomatology is traditionally divided into positive and negative symptoms, abnormal social cognition is now recognized a key component of schizophrenia. Nonetheless, we are still lacking a mechanistic understanding of how aberrant synaptic connectivity is expressed in schizophrenia during social perception and how it relates to positive and negative symptomatology. We used fMRI and dynamic causal modelling (DCM) to test for abnormalities in synaptic efficacy in twenty-four patients with first-episode schizophrenia (FES) compared to twenty-five matched controls performing the Human Connectome Project (HCP) social cognition paradigm. Patients had not received regular therapeutic antipsychotics, but were not completely drug naïve. Our data reveal an increase in excitatory feedforward connectivity from motion-sensitive V5 to posterior superior temporal sulcus (pSTS) in patients compared to matched controls. At the same time, were less accurate than controls in judging social stimuli from non-social stimuli. Crucially, patients with a higher degree of positive symptoms had more disinhibition within pSTS, a region computationally involved in Theory of Mind. We interpret these within a predictive coding framework where increased feedforward connectivity may encode aberrant prediction errors from V5 to hierarchically higher pSTS and local disinhibition within pSTS may reflect aberrant encoding of the precision of cortical representations about social stimuli.

Download Full-text

Ketamine Affects Prediction Errors about Statistical Regularities: A Computational Single-Trial Analysis of the Mismatch Negativity

10.1101/528372 ◽

2019 ◽

Cited By ~ 5

Author(s):

Lilian A. Weber ◽

Andreea O. Diaconescu ◽

Christoph Mathys ◽

André Schmidt ◽

Michael Kometer ◽

...

Keyword(s):

Mismatch Negativity ◽

Transition Probability ◽

Predictive Coding ◽

Prediction Errors ◽

Hierarchical Bayesian ◽

Single Trial ◽

Perceptual Inference ◽

Trial Analysis ◽

Statistical Regularities ◽

High Level

AbstractThe auditory mismatch negativity (MMN) is significantly reduced in schizophrenia. Notably, a similar MMN reduction can be achieved with NMDA receptor (NMDAR) antagonists. Both phenomena have been interpreted as reflecting an impairment of predictive coding or, more generally, the “Bayesian brain” notion that the brain continuously updates a hierarchical model to infer the causes of its sensory inputs. Specifically, predictive coding views perceptual inference as an NMDAR-dependent process of minimizing hierarchical precision-weighted prediction errors (PEs). Disturbances of this putative process play a key role in hierarchical Bayesian theories of schizophrenia.Here, we provide empirical evidence for this clinical theory, demonstrating the existence of multiple, hierarchically related PEs in a “roving MMN” paradigm. We applied a computational model, the Hierarchical Gaussian Filter (HGF), to single-trial EEG data from healthy volunteers that received the NMDAR antagonist S-ketamine in a placebo-controlled, double-blind, within-subject fashion. Using an unrestricted analysis of the entire time-sensor space, our computational trial-by-trial analysis indicated that low-level PEs (about stimulus transitions) are expressed early (102-207ms post-stimulus), while high-level PEs (about transition probability) are reflected by later components (152-199ms, 215-277ms) of single-trial responses. Furthermore, we find that ketamine significantly diminished the expression of high-level PE responses, implying that NMDAR antagonism disrupts inference on abstract statistical regularities.Our findings are consistent with long-standing notions that NMDAR dysfunction may cause positive symptoms in schizophrenia by impairing hierarchical Bayesian inference about the world’s statistical structure. Beyond their relevance for schizophrenia, our results illustrate the potential of computational single-trial analyses for assessing potential disease mechanisms.

Download Full-text

Towards a Unifying Cognitive, Neurophysiological, and Computational Neuroscience Account of Schizophrenia

Schizophrenia Bulletin ◽

10.1093/schbul/sby154 ◽

2018 ◽

Vol 45 (5) ◽

pp. 1092-1100 ◽

Cited By ~ 15

Author(s):

Andreas Heinz ◽

Graham K Murray ◽

Florian Schlagenhauf ◽

Philipp Sterzer ◽

Anthony A Grace ◽

...

Keyword(s):

Information Processing ◽

Predictive Coding ◽

General Mechanism ◽

Prediction Errors ◽

Psychotic Experiences ◽

Prior Beliefs ◽

Belief Updating ◽

Bayesian Hierarchical ◽

Sensory Data ◽

Neural Computations

Abstract Psychotic experiences may be understood as altered information processing due to aberrant neural computations. A prominent example of such neural computations is the computation of prediction errors (PEs), which signal the difference between expected and experienced events. Among other areas showing PE coding, hippocampal-prefrontal-striatal neurocircuits play a prominent role in information processing. Dysregulation of dopaminergic signaling, often secondary to psychosocial stress, is thought to interfere with the processing of biologically important events (such as reward prediction errors) and result in the aberrant attribution of salience to irrelevant sensory stimuli and internal representations. Bayesian hierarchical predictive coding offers a promising framework for the identification of dysfunctional neurocomputational processes and the development of a mechanistic understanding of psychotic experience. According to this framework, mismatches between prior beliefs encoded at higher levels of the cortical hierarchy and lower-level (sensory) information can also be thought of as PEs, with important consequences for belief updating. Low levels of precision in the representation of prior beliefs relative to sensory data, as well as dysfunctional interactions between prior beliefs and sensory data in an ever-changing environment, have been suggested as a general mechanism underlying psychotic experiences. Translating the promise of the Bayesian hierarchical predictive coding into patient benefit will come from integrating this framework with existing knowledge of the etiology and pathophysiology of psychosis, especially regarding hippocampal-prefrontal-striatal network function and neural mechanisms of information processing and belief updating.

Download Full-text

Inflexible social inference in individuals with subclinical persecutory delusional tendencies

10.31234/osf.io/k36q8 ◽

2019 ◽

Author(s):

Katharina V. Wellstein ◽

Andreea Oliviana Diaconescu ◽

Martin Bischof ◽

Annia Rüesch ◽

Gina Paolini ◽

...

Keyword(s):

Social Context ◽

Prior Beliefs ◽

Significant Group ◽

Persecutory Delusions ◽

Social Inference ◽

Related Information ◽

Advice Taking ◽

Rules Of The Game

It has been suspected that abnormalities in social inference (e.g., learning others' intentions) play a key role in theformation of persecutory delusions (PD). In this study, we examined the association between subclinical PD andsocial inference, testing the prediction that proneness to PD is related to altered social inference and beliefs aboutothers' intentions. Weincluded 151 participants scoring on opposite ends of Freeman's Paranoia Checklist (PCL).The participants performed a probabilistic advice-taking task with a dynamically changing social context (volatility)under one of two experimental frames. These frames differentiallyemphasised possible reasons behind unhelpfuladvice: (i) the adviser's possible intentions (dispositional frame) or (ii) the rules of the game (situationalframe). Our design was thus 2 × 2 factorial (high vs. low delusional tendencies, dispositional vs. situationalframe). We found significant group-by-frame interactions, indicating that in the situational frame high PCLscorers took advice less into account than low scorers. Additionally, high PCL scorers believed more frequentlythat incorrect advice was delivered intentionally and that such misleading behaviour was directed towardsthem personally. Overall, our results suggest that social inference in individuals with subclinical PD tendenciesis shaped by negative prior beliefs about the intentions of others and is thus less sensitive to the attributionalframing of adviser-related information. These findings may help future attempts of identifying individuals atrisk for developing psychosis and understanding persecutory delusions in psychosis.

Download Full-text

Temporal Prediction Errors Affect Short-Term Memory Scanning Response Time

Experimental Psychology (formerly Zeitschrift für Experimentelle Psychologie) ◽

10.1027/1618-3169/a000339 ◽

2016 ◽

Vol 63 (6) ◽

pp. 333-342

Author(s):

Roberto Limongi ◽

Angélica M. Silva

Keyword(s):

Response Time ◽

Short Term Memory ◽

Estimation Error ◽

Predictive Coding ◽

Time Estimation ◽

Prediction Errors ◽

Memory Scanning ◽

Short Term ◽

Term Memory ◽

Temporal Prediction

Abstract. The Sternberg short-term memory scanning task has been used to unveil cognitive operations involved in time perception. Participants produce time intervals during the task, and the researcher explores how task performance affects interval production – where time estimation error is the dependent variable of interest. The perspective of predictive behavior regards time estimation error as a temporal prediction error (PE), an independent variable that controls cognition, behavior, and learning. Based on this perspective, we investigated whether temporal PEs affect short-term memory scanning. Participants performed temporal predictions while they maintained information in memory. Model inference revealed that PEs affected memory scanning response time independently of the memory-set size effect. We discuss the results within the context of formal and mechanistic models of short-term memory scanning and predictive coding, a Bayes-based theory of brain function. We state the hypothesis that our finding could be associated with weak frontostriatal connections and weak striatal activity.

Download Full-text

Machine learning-based ability to classify psychosis and early stages of disease through parenting and attachment-related variables is associated with social cognition

BMC Psychology ◽

10.1186/s40359-021-00552-3 ◽

2021 ◽

Vol 9 (1) ◽

Author(s):

Linda A. Antonucci ◽

Alessandra Raio ◽

Giulio Pergola ◽

Barbara Gelao ◽

Marco Papalino ◽

...

Keyword(s):

Machine Learning ◽

Social Cognition ◽

Attachment Style ◽

Learning Algorithm ◽

Early Recognition ◽

Emotion Perception ◽

First Episode ◽

Support Vector ◽

Significant Group ◽

Early Stages

Abstract Background Recent views posited that negative parenting and attachment insecurity can be considered as general environmental factors of vulnerability for psychosis, specifically for individuals diagnosed with psychosis (PSY). Furthermore, evidence highlighted a tight relationship between attachment style and social cognition abilities, a key PSY behavioral phenotype. The aim of this study is to generate a machine learning algorithm based on the perceived quality of parenting and attachment style-related features to discriminate between PSY and healthy controls (HC) and to investigate its ability to track PSY early stages and risk conditions, as well as its association with social cognition performance. Methods Perceived maternal and paternal parenting, as well as attachment anxiety and avoidance scores, were trained to separate 71 HC from 34 PSY (20 individuals diagnosed with schizophrenia + 14 diagnosed with bipolar disorder with psychotic manifestations) using support vector classification and repeated nested cross-validation. We then validated this model on independent datasets including individuals at the early stages of disease (ESD, i.e. first episode of psychosis or depression, or at-risk mental state for psychosis) and with familial high risk for PSY (FHR, i.e. having a first-degree relative suffering from psychosis). Then, we performed factorial analyses to test the group x classification rate interaction on emotion perception, social inference and managing of emotions abilities. Results The perceived parenting and attachment-based machine learning model discriminated PSY from HC with a Balanced Accuracy (BAC) of 72.2%. Slightly lower classification performance was measured in the ESD sample (HC-ESD BAC = 63.5%), while the model could not discriminate between FHR and HC (BAC = 44.2%). We observed a significant group x classification interaction in PSY and HC from the discovery sample on emotion perception and on the ability to manage emotions (both p = 0.02). The interaction on managing of emotion abilities was replicated in the ESD and HC validation sample (p = 0.03). Conclusion Our results suggest that parenting and attachment-related variables bear significant classification power when applied to both PSY and its early stages and are associated with variability in emotion processing. These variables could therefore be useful in psychosis early recognition programs aimed at softening the psychosis-associated disability.

Download Full-text

The use of remote sensing satellite using deep learning in emergency monitoring of high-level landslides disaster in Jinsha River

The Journal of Supercomputing ◽

10.1007/s11227-020-03604-4 ◽

2021 ◽

Author(s):

Leijin Long ◽

Feng He ◽

Hongjiang Liu

Keyword(s):

Remote Sensing ◽

Southwest China ◽

Influence Factors ◽

Classification Error ◽

Model Parameters ◽

Detection Accuracy ◽

Remote Sensing Images ◽

Jinsha River ◽

Detection Model ◽

High Level

AbstractIn order to monitor the high-level landslides frequently occurring in Jinsha River area of Southwest China, and protect the lives and property safety of people in mountainous areas, the data of satellite remote sensing images are combined with various factors inducing landslides and transformed into landslide influence factors, which provides data basis for the establishment of landslide detection model. Then, based on the deep belief networks (DBN) and convolutional neural network (CNN) algorithm, two landslide detection models DBN and convolutional neural-deep belief network (CDN) are established to monitor the high-level landslide in Jinsha River. The influence of the model parameters on the landslide detection results is analyzed, and the accuracy of DBN and CDN models in dealing with actual landslide problems is compared. The results show that when the number of neurons in the DBN is 100, the overall error is the minimum, and when the number of learning layers is 3, the classification error is the minimum. The detection accuracy of DBN and CDN is 97.56% and 97.63%, respectively, which indicates that both DBN and CDN models are feasible in dealing with landslides from remote sensing images. This exploration provides a reference for the study of high-level landslide disasters in Jinsha River.

Download Full-text

Differentiating the effect of antipsychotic medication and illness on brain volume reductions in first-episode psychosis: A Longitudinal, Randomised, Triple-blind, Placebo-controlled MRI Study

Neuropsychopharmacology ◽

10.1038/s41386-021-00980-0 ◽

2021 ◽

Author(s):

Sidhant Chopra ◽

Alex Fornito ◽

Shona M. Francey ◽

Brian O’Donoghue ◽

Vanessa Cropley ◽

...

Keyword(s):

Antipsychotic Medication ◽

Brain Volume ◽

First Episode Psychosis ◽

Common Finding ◽

First Episode ◽

Control Group ◽

Grey Matter Volume ◽

Long Term Effects ◽

Significant Group ◽

Episode Psychosis

AbstractChanges in brain volume are a common finding in Magnetic Resonance Imaging (MRI) studies of people with psychosis and numerous longitudinal studies suggest that volume deficits progress with illness duration. However, a major unresolved question concerns whether these changes are driven by the underlying illness or represent iatrogenic effects of antipsychotic medication. In this study, 62 antipsychotic-naïve patients with first-episode psychosis (FEP) received either a second-generation antipsychotic (risperidone or paliperidone) or a placebo pill over a treatment period of 6 months. Both FEP groups received intensive psychosocial therapy. A healthy control group (n = 27) was also recruited. Structural MRI scans were obtained at baseline, 3 months and 12 months. Our primary aim was to differentiate illness-related brain volume changes from medication-related changes within the first 3 months of treatment. We secondarily investigated long-term effects at the 12-month timepoint. From baseline to 3 months, we observed a significant group x time interaction in the pallidum (p < 0.05 FWE-corrected), such that patients receiving antipsychotic medication showed increased volume, patients on placebo showed decreased volume, and healthy controls showed no change. Across the entire patient sample, a greater increase in pallidal grey matter volume over 3 months was associated with a greater reduction in symptom severity. Our findings indicate that psychotic illness and antipsychotic exposure exert distinct and spatially distributed effects on brain volume. Our results align with prior work in suggesting that the therapeutic efficacy of antipsychotic medications may be primarily mediated through their effects on the basal ganglia.

Download Full-text

A-8 Developmental Cognitive Phenotypes in First-Episode Psychosis and Longitudinal Cognitive Change Following Antipsychotic Treatment

Archives of Clinical Neuropsychology ◽

10.1093/arclin/acab062.09 ◽

2021 ◽

Vol 36 (6) ◽

pp. 1030-1030

Author(s):

Milena Y Gotra ◽

Elmma Khalid ◽

Madison M Dykins ◽

Scot K Hill

Keyword(s):

Cognitive Ability ◽

Antipsychotic Medication ◽

Cognitive Change ◽

First Episode Psychosis ◽

First Episode ◽

Healthy Controls ◽

Significant Group ◽

Cognitive Improvement ◽

Episode Psychosis ◽

Chronic Psychosis

Abstract Objective The present study applied a developmentally based subgrouping procedure previously examined in chronic psychosis patients to a sample of first-episode psychosis (FEP) and examined change in cognition following treatment with antipsychotic medication. Method Medication naïve FEP patients (n = 119; age = 27.96; 63.9% male; 62.2% White, 32.8% Black, 5.0% Other) recruited during initial hospitalization were categorized into groups based on 1) estimated premorbid intellectual ability and 2) the discrepancy between predicted (modeled on 151 healthy controls) and current cognitive ability. Consistent with findings from chronic psychosis samples, groups were characterized as Preserved (n = 46; average premorbid, no discrepancy), Deteriorated (n = 44; average premorbid, significant discrepancy), and Compromised (n = 29, low premorbid and current cognitive ability). A mixed analysis of variance was used to examine change in a composite cognitive score derived from a comprehensive neuropsychological battery at baseline, 6 weeks, and 12 months. Results There was a significant group by time interaction [Figure 1; F(5.4142.4) = 2.81, p = 0.02] in which the Preserved group performed similar to healthy controls across all time points, the Compromised group demonstrated stable deficits after treatment, and the Deteriorated group diverged from the Compromised group at 6 weeks and 12 months. Discussion There is considerable cognitive heterogeneity in FEP at baseline and after initiation of antipsychotic medication. Findings of cognitive improvement in the Deteriorated group after treatment initiation suggests a differential response to antipsychotic medications that was not found in the Compromised or Preserved groups. Future work may benefit from examining medication and symptom severity as potential factors contributing to the unique change observed in the Deteriorated group.

Download Full-text

NetPyNE, a tool for data-driven multiscale modeling of brain circuits

eLife ◽

10.7554/elife.44494 ◽

2019 ◽

Vol 8 ◽

Cited By ~ 19

Author(s):

Salvador Dura-Bernal ◽

Benjamin A Suter ◽

Padraig Gleeson ◽

Matteo Cantarelli ◽

Adrian Quintana ◽

...

Keyword(s):

Multiple Scales ◽

Network Models ◽

Brain Regions ◽

Data Driven ◽

Model Parameters ◽

Biophysical Modeling ◽

Neuron Network ◽

Graphical Interfaces ◽

Information Theoretic Measures ◽

High Level

Biophysical modeling of neuronal networks helps to integrate and interpret rapidly growing and disparate experimental datasets at multiple scales. The NetPyNE tool (www.netpyne.org) provides both programmatic and graphical interfaces to develop data-driven multiscale network models in NEURON. NetPyNE clearly separates model parameters from implementation code. Users provide specifications at a high level via a standardized declarative language, for example connectivity rules, to create millions of cell-to-cell connections. NetPyNE then enables users to generate the NEURON network, run efficiently parallelized simulations, optimize and explore network parameters through automated batch runs, and use built-in functions for visualization and analysis – connectivity matrices, voltage traces, spike raster plots, local field potentials, and information theoretic measures. NetPyNE also facilitates model sharing by exporting and importing standardized formats (NeuroML and SONATA). NetPyNE is already being used to teach computational neuroscience students and by modelers to investigate brain regions and phenomena.

Download Full-text