Neural Correlates of Aberrant Salience and Source Monitoring in Schizophrenia and At-Risk Mental States-A Systematic Review of fMRI Studies

Associations of Cognitive Expectancies With Auditory Hallucinations and Hallucinatory-Like Experiences in Patients With Schizophrenia

BACKGROUND

Various neurocognitive models explore perceptual distortions and hallucinations in schizophrenia and the general population. A variant of predictive coding account suggests that strong priors, like cognitive expectancy, may influence perception. This study examines if stronger cognitive expectancies result in more auditory false percepts in clinical and healthy control groups, investigates group differences, and explores the association between false percepts and hallucinations.

STUDY DESIGN

Patients diagnosed with schizophrenia with current auditory hallucinations (n = 51) and without hallucinations (n = 66) and healthy controls (n = 51) underwent the False Perception Task under various expectancy conditions. All groups were examined for the presence and severity of hallucinations or hallucinatory-like experiences.

STUDY RESULTS

We observed a main effect of condition across all groups, ie, the stronger the cognitive expectancy, the greater the ratio of auditory false percepts. However, there was no group effect for the ratio of auditory false percepts. Despite modest pairwise correlations in the hallucinating group, the ratio of auditory false percepts was not predicted by levels of hallucinations and hallucinatory-like experiences in a linear mixed model.

CONCLUSIONS

The current study demonstrates that strong priors in the form of cognitive expectancies affect perception and play a role in perceptual disturbances. There is also a tentative possibility that overreliance on strong priors may be associated with hallucinations in currently hallucinating subjects. Possible, avoidable confounding factors are discussed in detail.

Unraveling the brain mechanisms of source monitoring with non-invasive brain stimulation: A systematic review

Background/Objective

Source monitoring refers to the ability to determine the source of memories and encompasses three subprocesses: internal source monitoring, reality monitoring, and external source monitoring. Neuroimaging studies provide valuable insights about neural correlates of source monitoring, but the causal relationship between brain and behavior is lacking. This study aimed to identify brain circuits involved in source monitoring by synthesizing the effects of brain stimulation on source monitoring as a function of the targeted brain regions or circuits.

Method

We conducted a systematic review of interventional studies that have examined the effects of brain stimulation on source monitoring across six databases. The principal outcome was the difference of source monitoring performance between active and control stimulation conditions.

Results

23 studies (920 healthy participants and 54 patients with schizophrenia) were included. Our findings revealed the involvement of i) the lateral prefrontal and temporoparietal cortices in internal source monitoring, ii) the medial prefrontal and temporoparietal cortices in reality monitoring, and iii) the precuneus and the left angular gyrus in external source monitoring.

Conclusions

These findings deepen our understanding of the brain mechanisms of source monitoring and highlight specific stimulation targets to alleviate source monitoring deficits.

A systematic review of performance-based assessment studies on cognitive biases in schizophrenia spectrum psychoses and clinical high-risk states: A summary of 40 years of research

Cognitive models of psychosis have stimulated empirical studies on cognitive biases involved in schizophrenia spectrum psychoses and their symptoms. This systematic review aimed to summarize the studies on the role of cognitive biases as assessed in different performance-based tasks in schizophrenia spectrum psychoses and clinical high-risk states. We focused on five cognitive biases linked to psychosis, i.e., aberrant salience, attentional biases, source monitoring biases, jumping to conclusions, and bias against disconfirmatory evidence. We identified N = 324 studies published in N = 308 articles fulfilling inclusion criteria. Most studies have been cross-sectional and confirmed that the schizophrenia spectrum psychoses are related to exaggerated cognitive biases compared to healthy controls. On the contrary, less evidence suggests a higher tendency for cognitive biases in the UHR sample. The only exceptions were source monitoring and jumping to conclusions, which were confirmed to be exaggerated in both clinical groups. Hallucinations and delusions were the most frequent symptoms studied in the context of cognitive biases. Based on the findings, we presented a hypothetical model on the role of interactions between cognitive biases or additive effects of biases in shaping the risk of psychosis. Future research is warranted for further development of cognitive models for psychosis.

Abnormal effective connectivity of reward network in first-episode schizophrenia with auditory verbal hallucinations

OBJECTIVE

Auditory verbal hallucinations (AVHs) in schizophrenia is proved to be associated with dysfunction of mesolimbic-cortical circuits, especially during abnormal salient and internal verbal resource monitoring processing procedures. However, the information flow among areas involved in coordinated interaction implicated the pathophysiology of AVHs remains unclear.

METHODS

We used spectral dynamic causal modeling (DCM) to quantify connections among eight critical hubs of reward network in 86 first-episode drug-naïve schizophrenia patients with AVHs (AVH), 93 patients without AVHs (NAVH), and 88 matched normal controls (NC) using resting-state functional magnetic resonance imaging. Group-level connection coefficients, between-group differences and correlation analysis between image measures and symptoms were performed.

RESULT

DCM revealed weaker effective connectivity (EC) from right ventral striatum (RVS) to ventral tegmental area (VTA) in AVH compared to NAVH. AVH showed stronger EC from left anterior insula (AI) to RVS, stronger EC from RVS to anterior cingulate cortex (ACC), and stronger EC from VTA to posterior cingulate cortex (PCC) compared to NC. The correlation analysis results were mostly visible in the negative correlation between EC from right AI to ACC and positive sub-score, P1 sub-score, and P3 sub-score of PNASS in group-level.

CONCLUSION

These findings suggest that neural causal interactions between the reward network associated with AVHs are disrupted, expanding the evidence for potential neurobiological mechanisms of AVHs. Particularly, dopamine-dependent salience attribution and top-down monitoring impairments and compensatory effects of enhanced excitatory afferents to ACC, which may provide evidence for a therapeutic target based on direct in vivo of AVHs in schizophrenia.

Self-monitoring deficits in schizophrenia: A cross-sectional study of the underlying cognitive mechanisms

BACKGROUND

People diagnosed with schizophrenia share underlying cognitive deficits in self-monitoring (i.e., identifying the source of self-generated behaviours). This study aimed to investigate whether self-monitoring deficits in schizophrenia are due to a cognitive response bias towards external perceptions or a reduced discriminability of imagined and performed actions. We hypothesised that self-monitoring deficits in individuals with schizophrenia are primarily driven by bottom-up processes, leading to a compromised ability to discriminate between internally generated behaviours as opposed to a cognitive response bias towards performed actions.

METHODS

We recruited 333 participants, including 192 with schizophrenia and 141 healthy controls. As part of the Action-Memory Task, participants were instructed to either imagine or physically perform 36 different actions, half of which were presented as pictograms and half as text. In the test phase, participants indicated whether they had performed or imagined each action, whether it appeared in text or pictogram, or whether it was a new action. Using Signal Detection Theory, the study primarily analysed group differences in discriminability and response-bias.

RESULTS

Participants with schizophrenia made significantly more self-monitoring errors than healthy controls. This was primarily due to significantly lower sensitivity, but not a response bias. Whereas recognition memory errors were driven by both lower sensitivity and a response bias.

CONCLUSIONS

The findings suggest that self-monitoring in schizophrenia was specifically impaired by a compromised discriminability of imagined and performed events and an inability to appropriately compensate by adjusting decision-thresholds. Implications on the role of bottom-up and top-down cognitive mechanisms are discussed.

The self, neuroscience and psychosis study: Testing a neurophenomenological model of the onset of psychosis

AIM

Basic self disturbance is a putative core vulnerability marker of schizophrenia spectrum disorders. The primary aims of the Self, Neuroscience and Psychosis (SNAP) study are to: (1) empirically test a previously described neurophenomenological self-disturbance model of psychosis by examining the relationship between specific clinical, neurocognitive, and neurophysiological variables in UHR patients, and (2) develop a prediction model using these neurophenomenological disturbances for persistence or deterioration of UHR symptoms at 12-month follow-up.

METHODS

SNAP is a longitudinal observational study. Participants include 400 UHR individuals, 100 clinical controls with no attenuated psychotic symptoms, and 50 healthy controls. All participants complete baseline clinical and neurocognitive assessments and electroencephalography. The UHR sample are followed up for a total of 24 months, with clinical assessment completed every 6 months.

RESULTS

This paper presents the protocol of the SNAP study, including background rationale, aims and hypotheses, design, and assessment procedures.

CONCLUSIONS

The SNAP study will test whether neurophenomenological disturbances associated with basic self-disturbance predict persistence or intensification of UHR symptomatology over a 2-year follow up period, and how specific these disturbances are to a clinical population with attenuated psychotic symptoms. This may ultimately inform clinical care and pathoaetiological models of psychosis.

The noise in our brain: A systematic review and meta-analysis of neuroimaging and signal-detection studies on source monitoring in psychosis

OBJECTIVES

Noisy thoughts or perceptions are characteristics of psychosis (PSY) and, they are deeply related to source monitoring (SM) - the ability to discriminate the origin of internal/external experiences.

METHODS

This MOOSE, PRISMA-compliant meta-analysis compared SM performances in PSY compared to healthy controls (HC) focusing on signal-to-noise discrimination in order to: i) test whether neuroimaging procedures (fMRI/EEG) might be a group-specific source of noise for SM; ii) compare error- and accuracy-based indexes; iii) to meta-analyze signal-detection measures (i.e., discrimination index and response bias); iv) to determine the best index capturing SM deficits in psychosis. We conducted a 3-level meta-analysis for each aim to estimate pooled effect-sizes (Cohen's d). SM type, source discrimination and stimulus modality were used as meta-regressors. Heterogeneity (I2), publication bias (Egger's test) and multiple comparisons (Bonferroni correction) were considered.

RESULTS

Sixteen neuroimaging, 44 error/accuracy-based behavioral and 7 signal-detection trials were included (2297 PSY, age range = 18.78-52.6; 1745 HC, age range = 21.1-53.3). The noise generated by neuroimaging procedures slightly influenced error, but not accuracy. Accuracy-based (d = -0.83), but not error-based, indexes showed significant and large SM impairments in PSY compared to HC. Overall SM performance differences between PSY and HC were larger in discrimination index (d = -0.65) and accuracy (d = -0.61), followed by response bias (d = -0.59, ns) and error-based (d = 0.35) indexes.

CONCLUSION

Although both accuracy and discrimination indexes differentiate patients with PSY from HC, discrimination index is more reliable and may better capture the bi-directional nature of the internal/external source confusion.

In vivo mapping of protein-protein interactions of schizophrenia risk factors generates an interconnected disease network

Genetic analyses of Schizophrenia (SCZ) patients have identified thousands of risk factors. In silico protein-protein interaction (PPI) network analysis has provided strong evidence that disrupted PPI networks underlie SCZ pathogenesis. In this study, we performed in vivo PPI analysis of several SCZ risk factors in the rodent brain. Using endogenous antibody immunoprecipitations coupled to mass spectrometry (MS) analysis, we constructed a SCZ network comprising 1612 unique PPI with a 5% FDR. Over 90% of the PPI were novel, reflecting the lack of previous PPI MS studies in brain tissue. Our SCZ PPI network was enriched with known SCZ risk factors, which supports the hypothesis that an accumulation of disturbances in selected PPI networks underlies SCZ. We used Stable Isotope Labeling in Mammals (SILAM) to quantitate phencyclidine (PCP) perturbations in the SCZ network and found that PCP weakened most PPI but also led to some enhanced or new PPI. These findings demonstrate that quantitating PPI in perturbed biological states can reveal alterations to network biology.

Effect of left temporoparietal transcranial direct current stimulation on self-bias effect and retrospective intentional binding paradigm: A randomised, double-blind, controlled study

BACKGROUND

Self-bias effect is expressed as a preferential selection and accelerated perception of self-related sensory information. Intentional binding (IB) is a related phenomenon where the sensory outcome from a voluntary action and the voluntary action itself are perceived to be closer to each other in time in both predictive (voluntary action predicting sensory consequence) and retrospective (sensory consequence features triggering self-related inference) contexts. Recent evidence indicates that self-related visual stimuli can affect retrospective intentional binding (rIB). We aimed to 1) replicate rIB in the auditory context, and 2) investigate the potential role of left temporoparietal junction (l-TPJ), a crucial node for the self-monitoring process, in self-bias effect and intentional binding effect by manipulating l-TPJ activity with neuromodulation [using High-Definition Transcranial Direct Current Stimulation (HD-tDCS)]. We anticipated self-bias and rIB effects to increase with anodal stimulation of l-TPJ in comparison to cathodal-stimulation of l-TPJ.

METHODS

Fourteen, right-handed, healthy participants performed sound-label matching (matching tones to self-and-other labels) and rIB (estimating time interval between a button press and a self/other labelled tone) tasks. Each participant underwent both anodal and cathodal stimulation of l-TPJ in separate sessions (at least 72 h apart). Assignment of HD-tDCS type was random and counter-balanced across participants. Behavioural data was collected at three time points: once at baseline (no-stimulation), and twice after stimulation with HD-tDCS.

RESULTS

Strong self-bias effect was observed across all experimental conditions. Neuromodulation of l-TPJ affected processing of other-labelled tone in the sound-label matching task. rIB was noted in baseline and anodal-HD-tDCS conditions where participants exhibited stronger binding for self-associated stimuli compared to other-associated stimuli.

CONCLUSION

l-TPJ may potentially play a critical role in self-other distinction. This may have possible implications for disorders of self-disturbances like psychosis.

Neurocognitive bases of self-monitoring of inner speech in hallucination prone individuals

Verbal hallucinations in schizophrenia patients might be seen as internal verbal productions mistaken for perceptions as a result of over-salient inner speech and/or defective self-monitoring processes. Similar cognitive mechanisms might underpin verbal hallucination proneness in the general population. We investigated, in a non-clinical sample, the cerebral activity associated with verbal hallucinatory predisposition during false recognition of familiar words -assumed to stem from poor monitoring of inner speech-vs. uncommon words. Thirty-seven healthy participants underwent a verbal recognition task. High- and low-frequency words were presented outside the scanner. In the scanner, the participants were then required to recognize the target words among equivalent distractors. Results showed that verbal hallucination proneness was associated with higher rates of false recognition of high-frequency words. It was further associated with activation of language and decisional brain areas during false recognitions of low-, but not high-, frequency words, and with activation of a recollective brain area during correct recognitions of low-, but not high-, frequency words. The increased tendency to report familiar words as targets, along with a lack of activation of the language, recollective, and decisional brain areas necessary for their judgement, suggests failure in the self-monitoring of inner speech in verbal hallucination-prone individuals.

Bayesian Optimization of Machine Learning Classification of Resting-State EEG Microstates in Schizophrenia: A Proof-of-Concept Preliminary Study Based on Secondary Analysis

Resting-state electroencephalography (EEG) microstates reflect sub-second, quasi-stable states of brain activity. Several studies have reported alterations of microstate features in patients with schizophrenia (SZ). Based on these findings, it has been suggested that microstates may represent neurophysiological biomarkers for the classification of SZ. To explore this possibility, machine learning approaches can be employed. Bayesian optimization is a machine learning approach that selects the best-fitted machine learning model with tuned hyperparameters from existing models to improve the classification. In this proof-of-concept preliminary study based on secondary analysis, 20 microstate features were extracted from 14 SZ patients and 14 healthy controls' EEG signals. These parameters were then ranked as predictors based on their importance, and an optimized machine learning approach was applied to evaluate the performance of the classification. SZ patients had altered microstate features compared to healthy controls. Furthermore, Bayesian optimization outperformed conventional multivariate analyses and showed the highest accuracy (90.93%), AUC (0.90), sensitivity (91.37%), and specificity (90.48%), with reliable results using just six microstate predictors. Altogether, in this proof-of-concept study, we showed that machine learning with Bayesian optimization can be utilized to characterize EEG microstate alterations and contribute to the classification of SZ patients.

Unveiling the Associations between EEG Indices and Cognitive Deficits in Schizophrenia-Spectrum Disorders: A Systematic Review

Cognitive dysfunctions represent a core feature of schizophrenia-spectrum disorders due to their presence throughout different illness stages and their impact on functioning. Abnormalities in electrophysiology (EEG) measures are highly related to these impairments, but the use of EEG indices in clinical practice is still limited. A systematic review of articles using Pubmed, Scopus and PsychINFO was undertaken in November 2021 to provide an overview of the relationships between EEG indices and cognitive impairment in schizophrenia-spectrum disorders. Out of 2433 screened records, 135 studies were included in a qualitative review. Although the results were heterogeneous, some significant correlations were identified. In particular, abnormalities in alpha, theta and gamma activity, as well as in MMN and P300, were associated with impairments in cognitive domains such as attention, working memory, visual and verbal learning and executive functioning during at-risk mental states, early and chronic stages of schizophrenia-spectrum disorders. The review suggests that machine learning approaches together with a careful selection of validated EEG and cognitive indices and characterization of clinical phenotypes might contribute to increase the use of EEG-based measures in clinical settings.

Visual worry in patients with schizophrenia

OBJECTIVE

Worrying is a pervasive transdiagnostic symptom in schizophrenia. It is most often associated in the literature with verbal modality due to many studies of its presence in generalised anxiety disorder. The current study aimed to elucidate worry in different sensory modalities, visual and verbal, in individuals with schizophrenia.

METHOD

We tested persons with schizophrenia (n = 92) and healthy controls (n = 138) in a cross-sectional design. We used questionnaires of visual and verbal worry (original Worry Modality Questionnaire), trait worry (Penn State Worry Questionnaire) and general psychopathology symptoms (General Functioning Questionnaire-58 and Brief Psychiatric Rating Scale).

RESULTS

Both visual and verbal worry were associated with psychotic, anxiety and general symptoms of psychopathology in both groups with medium to large effect sizes. Regression analyses indicated that visual worry was a single significant predictor of positive psychotic symptoms in a model with verbal and trait worry, both in clinical and control groups (β's of 0.49 and 0.38, respectively). Visual worry was also a superior predictor of anxiety and general psychopathology severity (β's of 0.34 and 0.37, respectively) than verbal worry (β's of 0.03 and -0.02, respectively), under control of trait worry, in the schizophrenia group. We also proposed two indices of worry modality dominance and analysed profiles of dominating worry modality in both groups.

CONCLUSIONS

Our study is the first to demonstrate that visual worry might be of specific importance for understanding psychotic and general psychopathology symptoms in persons with schizophrenia.

Hypersensitivity to passive voice hearing in hallucination proneness

Voices are a complex and rich acoustic signal processed in an extensive cortical brain network. Specialized regions within this network support voice perception and production and may be differentially affected in pathological voice processing. For example, the experience of hallucinating voices has been linked to hyperactivity in temporal and extra-temporal voice areas, possibly extending into regions associated with vocalization. Predominant self-monitoring hypotheses ascribe a primary role of voice production regions to auditory verbal hallucinations (AVH). Alternative postulations view a generalized perceptual salience bias as causal to AVH. These theories are not mutually exclusive as both ascribe the emergence and phenomenology of AVH to unbalanced top-down and bottom-up signal processing. The focus of the current study was to investigate the neurocognitive mechanisms underlying predisposition brain states for emergent hallucinations, detached from the effects of inner speech. Using the temporal voice area (TVA) localizer task, we explored putative hypersalient responses to passively presented sounds in relation to hallucination proneness (HP). Furthermore, to avoid confounds commonly found in in clinical samples, we employed the Launay-Slade Hallucination Scale (LSHS) for the quantification of HP levels in healthy people across an experiential continuum spanning the general population. We report increased activation in the right posterior superior temporal gyrus (pSTG) during the perception of voice features that positively correlates with increased HP scores. In line with prior results, we propose that this right-lateralized pSTG activation might indicate early hypersensitivity to acoustic features coding speaker identity that extends beyond own voice production to perception in healthy participants prone to experience AVH.