Did I do that? Abnormal predictive processes in schizophrenia when button pressing to deliver a tone

Sensory Attenuation Deficit and Auditory Hallucinations in Schizophrenia: A Causal Mechanism or a Risk Factor? Evidence From Meta-Analyses on the N1 Event-Related Potential Component

BACKGROUND

Sensory attenuation (SA), the dampened perception of self-generated sensory information, is typically associated with reduced event-related potential signals, such as for the N1 component of auditory event-related potentials. SA, together with efficient monitoring of intentions and actions, should facilitate the distinction between self-generated and externally generated sensory events, thereby optimizing interaction with the world. According to many, SA is deficient in schizophrenia. The question arises whether altered SA reflects a sufficient mechanism to explain positive symptoms such as auditory hallucinations. A systematic association of reduced auditory SA in hallucinating patients would support this hypothesis.

METHODS

We conducted a series of meta-analyses on 15 studies on auditory SA in which the N1 component of event-related potential-electroencephalogram signals was measured during talking (self-generated sensory signals condition) or when listening to prerecorded vocalizations (externally generated sensory signals condition).

RESULTS

We found that individuals with schizophrenia did show some auditory SA because their N1 signal was significantly attenuated in talking conditions compared with listening conditions. However, the magnitude of such attenuation was reduced in individuals with schizophrenia compared to healthy control participants. This phenomenon generalizes independently from the stage of the disease, the severity of positive symptoms, and whether patients have auditory hallucinations or not.

CONCLUSIONS

These findings suggest that reduced SA cannot be a sufficient mechanism for explaining positive symptoms such as auditory hallucinations in schizophrenia. Because reduced SA was also present in participants at risk of schizophrenia, reduced SA may represent a risk factor for the disorder. We discuss the implications of these results for clinical-cognitive models of schizophrenia.

Sense of agency in schizophrenia: A reconciliation of conflicting findings through a theory-driven literature review

The sense of agency is the experience of being the author of self-generated actions and their outcomes. Both clinical manifestations and experimental evidence suggest that the agency experience and the mechanisms underlying agency attribution may be dysfunctional in schizophrenia. Yet, studies investigating the sense of agency in these patients show seemingly conflicting results: some indicated under-attribution of self-agency (coherently with certain positive symptoms), while others suggested over-attribution of self-agency. In this review, we assess whether recent theoretical frameworks can reconcile these divergent results. We examine whether the identification of agency abnormalities in schizophrenia might depend on the measure of self-agency considered (depending on the specific task requirements) and the available agency-related cues. We conclude that all these aspects are relevant to predict and characterize the type of agency misattribution that schizophrenia patients might show. We argue that one particular model, based on the predictive coding theory, can reconcile the interpretation of the multifarious phenomenology of agency manifestations in schizophrenia, paving the way for testing agency disorders in novel ways.

The neural network of sensory attenuation: A neuroimaging meta-analysis

Sensory attenuation refers to the reduction in sensory intensity resulting from self-initiated actions compared to stimuli initiated externally. A classic example is scratching oneself without feeling itchy. This phenomenon extends across various sensory modalities, including visual, auditory, somatosensory, and nociceptive stimuli. The internal forward model proposes that during voluntary actions, an efferent copy of the action command is sent out to predict sensory feedback. This predicted sensory feedback is then compared with the actual sensory feedback, leading to the suppression or reduction of sensory stimuli originating from self-initiated actions. To further elucidate the neural mechanisms underlying sensory attenuation effect, we conducted an extensive meta-analysis of functional magnetic resonance imaging (fMRI) and positron emission tomography (PET) studies. Utilizing activation likelihood estimation (ALE) analysis, our results revealed significant activations in a prominent cluster encompassing the right superior temporal gyrus (rSTG), right middle temporal gyrus (rMTG), and right insula when comparing external-generated with self-generated conditions. Additionally, significant activation was observed in the right anterior cerebellum when comparing self-generated to external-generated conditions. Further analysis using meta-analytic connectivity modeling (MACM) unveiled distinct brain networks co-activated with the rMTG and right cerebellum, respectively. Based on these findings, we propose that sensory attenuation arises from the suppression of reflexive inputs elicited by self-initiated actions through the internal forward modeling of a cerebellum-centered action prediction network, enabling the "sensory conflict detection" regions to effectively discriminate between inputs resulting from self-induced actions and those originating externally.

Multiple processes of vocal sensory-motor interaction in primate auditory cortex

Sensory-motor interactions in the auditory system play an important role in vocal self-monitoring and control. These result from top-down corollary discharges, relaying predictions about vocal timing and acoustics. Recent evidence suggests such signals may be two distinct processes, one suppressing neural activity during vocalization and another enhancing sensitivity to sensory feedback, rather than a single mechanism. Single-neuron recordings have been unable to disambiguate due to overlap of motor signals with sensory inputs. Here, we sought to disentangle these processes in marmoset auditory cortex during production of multi-phrased 'twitter' vocalizations. Temporal responses revealed two timescales of vocal suppression: temporally-precise phasic suppression during phrases and sustained tonic suppression. Both components were present within individual neurons, however, phasic suppression presented broadly regardless of frequency tuning (gating), while tonic was selective for vocal frequencies and feedback (prediction). This suggests that auditory cortex is modulated by concurrent corollary discharges during vocalization, with different computational mechanisms.

More than fulfilled expectations: An electrophysiological investigation of varying cause-effect relationships and schizotypal personality traits as related to the sense of agency

The sense of agency (SoA) is central to human experience. The comparator model, contrasting sensory prediction and action feedback, is influential but limited in explaining SoA. We investigated mechanisms beyond the comparator model, focusing on the processing of unpredictable stimuli, perimotor components of SoA, and their relation to schizotypy. ERPs were recorded from 18 healthy participants engaged in button-pressing tasks while perceiving tones with varying causal relationships with their actions. We investigated the processing of non-causally related tones, contrasted this to causally related tones, and examined perimotor correlates of subjective expectancy and experience of agency. We confirmed N100 attenuation for self-generated stimuli but found similar effects for expectancy-dependent processing of random tones. SoA also correlated with perimotor ERP components, modulated by schizotypy. Thus, neural processes preceding actions contribute to the formation of SoA and are associated with schizotypy. Unpredictable events also undergo sensory attenuation, implying additional mechanisms contributing to SoA.

A systematic review of EEG based automated schizophrenia classification through machine learning and deep learning

The electroencephalogram (EEG) serves as an essential tool in exploring brain activity and holds particular importance in the field of mental health research. This review paper examines the application of artificial intelligence (AI), encompassing machine learning (ML) and deep learning (DL), for classifying schizophrenia (SCZ) through EEG. It includes a thorough literature review that addresses the difficulties, methodologies, and discoveries in this field. ML approaches utilize conventional models like Support Vector Machines and Decision Trees, which are interpretable and effective with smaller data sets. In contrast, DL techniques, which use neural networks such as convolutional neural networks (CNNs) and long short-term memory networks (LSTMs), are more adaptable to intricate EEG patterns but require significant data and computational power. Both ML and DL face challenges concerning data quality and ethical issues. This paper underscores the importance of integrating various techniques to enhance schizophrenia diagnosis and highlights AI's potential role in this process. It also acknowledges the necessity for collaborative and ethically informed approaches in the automated classification of SCZ using AI.

Action prediction in psychosis

Aberrant motor-sensory predictive functions have been linked to symptoms of psychosis, particularly reduced attenuation of self-generated sensations and misattribution of self-generated actions. Building on the parallels between prediction of self- and other-generated actions, this study aims to investigate whether individuals with psychosis also demonstrate abnormal perceptions and predictions of others' actions. Patients with psychosis and matched controls completed a two-alternative object size discrimination task. In each trial, they observed reaching actions towards a small and a large object, with varying levels of temporal occlusion ranging from 10% to 80% of movement duration. Their task was to predict the size of the object that would be grasped. We employed a novel analytic approach to examine how object size information was encoded and read out across progressive levels of occlusion with single-trial resolution. Patients with psychosis exhibited an overall pattern of reduced and discontinuous evidence integration relative to controls, characterized by a period of null integration up to 20% of movement duration, during which they did not read any size information. Surprisingly, this drop in accuracy in the initial integration period was not accompanied by a reduction in confidence. Difficulties in action prediction were correlated with the severity of negative symptoms and impaired functioning in social relationships.

Neurophysiological evidence of motor preparation in inner speech and the effect of content predictability

Self-generated overt actions are preceded by a slow negativity as measured by electroencephalogram, which has been associated with motor preparation. Recent studies have shown that this neural activity is modulated by the predictability of action outcomes. It is unclear whether inner speech is also preceded by a motor-related negativity and influenced by the same factor. In three experiments, we compared the contingent negative variation elicited in a cue paradigm in an active vs. passive condition. In Experiment 1, participants produced an inner phoneme, at which an audible phoneme whose identity was unpredictable was concurrently presented. We found that while passive listening elicited a late contingent negative variation, inner speech production generated a more negative late contingent negative variation. In Experiment 2, the same pattern of results was found when participants were instead asked to overtly vocalize the phoneme. In Experiment 3, the identity of the audible phoneme was made predictable by establishing probabilistic expectations. We observed a smaller late contingent negative variation in the inner speech condition when the identity of the audible phoneme was predictable, but not in the passive condition. These findings suggest that inner speech is associated with motor preparatory activity that may also represent the predicted action-effects of covert actions.

Pre-movement event-related potentials and multivariate pattern of EEG encode action outcome prediction

Self-initiated movements are accompanied by an efference copy, a motor command sent from motor regions to the sensory cortices, containing a prediction of the movement's sensory outcome. Previous studies have proposed pre-motor event-related potentials (ERPs), including the readiness potential (RP) and its lateralized sub-component (LRP), as potential neural markers of action feedback prediction. However, it is not known how specific these neural markers are for voluntary (active) movements as compared to involuntary (passive) movements, which produce much of the same sensory feedback (tactile, proprioceptive) but are not accompanied by an efference copy. The goal of the current study was to investigate how active and passive movements are distinguishable from premotor electroencephalography (EEG), and to examine if this change of neural activity differs when participants engage in tasks that differ in their expectation of sensory outcomes. Participants made active (self-initiated) or passive (finger moved by device) finger movements that led to either visual or auditory stimuli (100 ms delay), or to no immediate contingency effects (control). We investigated the time window before the movement onset by measuring pre-movement ERPs time-locked to the button press. For RP, we observed an interaction between task and movement. This was driven by movement differences in the visual and auditory but not the control conditions. LRP conversely only showed a main effect of movement. We then used multivariate pattern analysis to decode movements (active vs. passive). The results revealed ramping decoding for all tasks from around -800 ms onwards up to an accuracy of approximately 85% at the movement. Importantly, similar to RP, we observed lower decoding accuracies for the control condition than the visual and auditory conditions, but only shortly (from -200 ms) before the button press. We also decoded visual vs. auditory conditions. Here, task is decodable for both active and passive conditions, but the active condition showed increased decoding shortly before the button press. Taken together, our results provide robust evidence that pre-movement EEG activity may represent action-feedback prediction in which information about the subsequent sensory outcome is encoded.

The bodily self from psychosis to psychedelics

The sense of self is a foundational element of neurotypical human consciousness. We normally experience the world as embodied agents, with the unified sensation of our selfhood being nested in our body. Critically, the sense of self can be altered in psychiatric conditions such as psychosis and altered states of consciousness induced by psychedelic compounds. The similarity of phenomenological effects across psychosis and psychedelic experiences has given rise to the "psychotomimetic" theory suggesting that psychedelics simulate psychosis-like states. Moreover, psychedelic-induced changes in the sense of self have been related to reported improvements in mental health. Here we investigated the bodily self in psychedelic, psychiatric, and control populations. Using the Moving Rubber Hand Illusion, we tested (N = 75) patients with psychosis, participants with a history of substantial psychedelic experiences, and control participants to see how psychedelic and psychiatric experience impacts the bodily self. Results revealed that psychosis patients had reduced Body Ownership and Sense of Agency during volitional action. The psychedelic group reported subjective long-lasting changes to the sense of self, but no differences between control and psychedelic participants were found. Our results suggest that while psychedelics induce both acute and enduring subjective changes in the sense of self, these are not manifested at the level of the bodily self. Furthermore, our data show that bodily self-processing, related to volitional action, is disrupted in psychosis patients. We discuss these findings in relation to anomalous self-processing across psychedelic and psychotic experiences.

Altered corollary discharge signaling in the auditory cortex of a mouse model of schizophrenia predisposition

The ability to distinguish sensations that are self-generated from those caused by external events is disrupted in schizophrenia patients. However, the neural circuit abnormalities underlying this sensory impairment and its relationship to the risk factors for the disease is not well understood. To address this, we examined the processing of self-generated sounds in male Df(16)A+/- mice, which model one of the largest genetic risk factors for schizophrenia, the 22q11.2 microdeletion. We find that auditory cortical neurons in Df(16)A+/- mice fail to attenuate their responses to self-generated sounds, recapitulating deficits seen in schizophrenia patients. Notably, the auditory cortex of Df(16)A+/- mice displayed weaker motor-related signals and received fewer inputs from the motor cortex, suggesting an anatomical basis underlying the sensory deficit. These results provide insights into the mechanisms by which a major genetic risk factor for schizophrenia disrupts the top-down processing of sensory information.

Corollary Discharge Dysfunction as a Possible Substrate of Anomalous Self-experiences in Schizophrenia

BACKGROUND AND HYPOTHESIS

Corollary discharge mechanism suppresses the conscious auditory sensory perception of self-generated speech and attenuates electrophysiological markers such as the auditory N1 Event-Related Potential (ERP) during Electroencephalographic (EEG) recordings. This phenomenon contributes to self-identification and seems to be altered in people with schizophrenia. Therefore, its alteration could be related to the anomalous self-experiences (ASEs) frequently found in these patients.

STUDY DESIGN

To analyze corollary discharge dysfunction as a possible substrate of ASEs, we recorded EEG ERP from 43 participants with schizophrenia and 43 healthy controls and scored ASEs with the 'Inventory of Psychotic-Like Anomalous Self-Experiences' (IPASE). Positive and negative symptoms were also scored with the 'Positive and Negative Syndrome Scale for Schizophrenia' (PANSS) and with the 'Brief Negative Symptom Scale' (BNSS) respectively. The N1 components were elicited by two task conditions: (1) concurrent listening to self-pronounced vowels (talk condition) and (2) subsequent non-concurrent listening to the same previously self-uttered vowels (listen condition).

STUDY RESULTS

The amplitude of the N1 component elicited by the talk condition was lower compared to the listen condition in people with schizophrenia and healthy controls. However, the difference in N1 amplitude between both conditions was significantly higher in controls than in schizophrenia patients. The values of these differences in patients correlated significantly and negatively with the IPASE, PANSS, and BNSS scores.

CONCLUSIONS

These results corroborate previous data relating auditory N1 ERP amplitude with altered corollary discharge mechanisms in schizophrenia and support corollary discharge dysfunction as a possible underpinning of ASEs in this illness.

Correlation of motor-auditory cross-modal and auditory unimodal N1 and mismatch responses of schizophrenic patients and normal subjects: an MEG study

Introduction

It has been suggested that the positive symptoms of schizophrenic patients (hallucinations, delusions, and passivity experience) are caused by dysfunction of their internal and external sensory prediction errors. This is often discussed as related to dysfunction of the forward model that executes self-monitoring. Several reports have suggested that dysfunction of the forward model in schizophrenia causes misattributions of self-generated thoughts and actions to external sources. There is some evidence that the forward model can be measured using the electroencephalography (EEG) and magnetoencephalography (MEG) components such as N1 (m) and mismatch negativity (MMN) (m). The objective in this MEG study is to investigate differences in the N1m and MMNm-like activity generated in motor-auditory cross-modal tasks in normal control (NC) subjects and schizophrenic (SC) patients, and compared that activity with N1m and MMNm in the auditory unimodal task.

Methods

The N1m and MMNm/MMNm-like activity were recorded in 15 SC patients and 12 matched NC subjects. The N1m-attenuation effects and peak amplitude of MMNm/MMNm-like activity of the NC and SC groups were compared. Additionally, correlations between MEG measures (N1m suppression rate, MMNm, and MMNm-like activity) and clinical variables (Positive and Negative Syndrome Scale (PANSS) scores and antipsychotic drug (APD) dosages) in SC patients were investigated.

Results

It was found that (i) there was no significant difference in N1m-attenuation for the NC and SC groups, and that (ii) MMNm in the unimodal task in the SC group was significantly smaller than that in the NC group. Further, the MMNm-like activity in the cross-modal task was smaller than that of the MMNm in the unimodal task in the NC group, but there was no significant difference in the SC group. The PANSS positive symptoms and general psychopathology score were moderately negatively correlated with the amplitudes of the MMNm-like activity, and the APD dosage was moderately negatively correlated with the N1m suppression rate. However, none of these correlations reached statistical significance.

Discussion

The findings suggest that schizophrenic patients perform altered predictive processes differently from healthy subjects in latencies reflecting MMNm, depending on whether they are under forward model generation or not. This may support the hypothesis that schizophrenic patients tend to misattribute their inner experience to external agents, thus leading to the characteristic schizophrenia symptoms.

Movement initiation and preparation in subjects with schizophrenia - The amplitude of the readiness potential as a biological marker for negative symptom severity

OBJECTIVE

Despite extensive research, the etiology of negative symptoms is not well understood. Preliminary findings are linking motor disturbances to negative symptom severity. We aimed to further the understanding to what extent motor movement preparation influences negative symptom severity.

METHODS

In a cohort of 31 subjects with schizophrenia and 20 control subjects we recorded the readiness potential amplitude over Cz during spontaneous movements of the right and left thumb. We further assessed negative and positive symptom severity (scale for the assessment of negative and positive symptoms) as well as neurological soft signs (NSS).

RESULTS

In subjects with schizophrenia the severity of negative symptoms was best predicted by the readiness potential amplitude and the NSS subdomain motor coordination. The correlation between deficits in motor coordination and negative symptom severity was partially mediated by the readiness potential amplitude in subjects with schizophrenia.

CONCLUSIONS

Deficits in motor processing are linked to negative symptom severity in schizophrenia. The readiness potential may represent a biological marker of these basal deficits. In combination with the assessment of NSS, the readiness potential may be a marker of the course of negative symptom severity and help clarifying interdependencies between (pre)frontal networks for action initiation and coordination, as well as negative symptoms.

Corollary discharge function in healthy controls: Evidence about self-speech and external speech processing

As we speak, corollary discharge mechanisms suppress the auditory conscious perception of the self-generated voice in healthy subjects. This suppression has been associated with the attenuation of the auditory N1 component. To analyse this corollary discharge phenomenon (agency and ownership), we registered the event-related potentials of 42 healthy subjects. The N1 and P2 components were elicited by spoken vowels (talk condition; agency), by played-back vowels recorded with their own voice (listen-self condition; ownership) and by played-back vowels recorded with an external voice (listen-other condition). The N1 amplitude elicited by the talk condition was smaller compared with the listen-self and listen-other conditions. There were no amplitude differences in N1 between listen-self and listen-other conditions. The P2 component did not show differences between conditions. Additionally, a peak latency analysis of N1 and P2 components between the three conditions showed no differences. These findings corroborate previous results showing that the corollary discharge mechanisms dampen sensory responses to self-generated speech (agency experience) and provide new neurophysiological evidence about the similarities in the processing of played-back vowels with our own voice (ownership experience) and with an external voice.

Current development of biosensing technologies towards diagnosis of mental diseases

The biosensor is an instrument that converts the concentration of biomarkers into electrical signals for detection. Biosensing technology is non-invasive, lightweight, automated, and biocompatible in nature. These features have significantly advanced medical diagnosis, particularly in the diagnosis of mental disorder in recent years. The traditional method of diagnosing mental disorders is time-intensive, expensive, and subject to individual interpretation. It involves a combination of the clinical experience by the psychiatrist and the physical symptoms and self-reported scales provided by the patient. Biosensors on the other hand can objectively and continually detect disease states by monitoring abnormal data in biomarkers. Hence, this paper reviews the application of biosensors in the detection of mental diseases, and the diagnostic methods are divided into five sub-themes of biosensors based on vision, EEG signal, EOG signal, and multi-signal. A prospective application in clinical diagnosis is also discussed.

Early auditory processing dysfunction in schizophrenia: Mechanisms and implications

Schizophrenia is a major mental disorder that affects approximately 1% of the population worldwide. Cognitive deficits are a key feature of the disorder and a primary cause of long-term disability. Over the past decades, significant literature has accumulated demonstrating impairments in early auditory perceptual processes in schizophrenia. In this review, we first describe early auditory dysfunction in schizophrenia from both a behavioral and neurophysiological perspective and examine their interrelationship with both higher order cognitive constructs and social cognitive processes. Then, we provide insights into underlying pathological processes, especially in relationship to glutamatergic and N-methyl-D-aspartate receptor (NMDAR) dysfunction models. Finally, we discuss the utility of early auditory measures as both treatment targets for precision intervention and as translational biomarkers for etiological investigation. Altogether, this review points out the crucial role of early auditory deficits in the pathophysiology of schizophrenia, in addition to major implications for early intervention and auditory-targeted approaches.

Thalamo-cortical circuits during sensory attenuation in emerging psychosis: a combined magnetoencephalography and dynamic causal modelling study

Evidence suggests that schizophrenia (ScZ) involves impairments in sensory attenuation. It is currently unclear, however, whether such deficits are present during early-stage psychosis as well as the underlying network and the potential as a biomarker. To address these questions, Magnetoencephalography (MEG) was used in combination with computational modeling to examine M100 responses that involved a "passive" condition during which tones were binaurally presented, while in an "active" condition participants were asked to generate a tone via a button press. MEG data were obtained from 109 clinical high-risk for psychosis (CHR-P) participants, 23 people with a first-episode psychosis (FEP), and 48 healthy controls (HC). M100 responses at sensor and source level in the left and right thalamus (THA), Heschl's gyrus (HES), superior temporal gyrus (STG) and right inferior parietal cortex (IPL) were examined and dynamic causal modeling (DCM) was performed. Furthermore, the relationship between sensory attenuation and persistence of attenuated psychotic symptoms (APS) and transition to psychosis was investigated in CHR-P participants. Sensory attenuation was impaired in left HES, left STG and left THA in FEP patients, while in the CHR-P group deficits were observed only in right HES. DCM results revealed that CHR-P participants showed reduced top-down modulation from the right IPL to the right HES. Importantly, deficits in sensory attenuation did not predict clinical outcomes in the CHR-P group. Our results show that early-stage psychosis involves impaired sensory attenuation in auditory and thalamic regions but may not predict clinical outcomes in CHR-P participants.

The auditory P2 differentiates self- from partner-produced sounds during joint action: Contributions of self-specific attenuation and temporal orienting of attention

Sensory attenuation of the auditory P2 event-related potential (ERP) has been shown to differentiate the sensory consequences of one's own from others' action in joint action contexts. However, recent evidence suggests that when people coordinate joint actions over time, temporal orienting of attention might simultaneously contribute to enhancing the auditory P2. The current study employed a joint tapping task in which partners produced tone sequences together to examine whether temporal orienting influences auditory ERP amplitudes during the time window of self-other differentiation. Our findings demonstrate that the combined requirements of coordinating with a partner toward a joint goal and immediately adjusting to the partner's tone timing enhance P2 amplitudes elicited by the partner's tone onsets. Furthermore, our findings replicate prior evidence for self-specific sensory attenuation of the auditory P2 in joint action, and additionally demonstrate that it occurs regardless of the coordination requirements between partners. Together, these findings provide evidence that temporal orienting and sensory attenuation both modulate the auditory P2 during joint action and suggest that both processes play a role in facilitating precise interpersonal coordination between partners.

Perception of self-generated and externally-generated visual stimuli: Evidence from EEG and behavior

Efference copy-based forward model mechanisms may help us to distinguish between self-generated and externally-generated sensory consequences. Previous studies have shown that self-initiation modulates neural and perceptual responses to identical stimulation. For example, event-related potentials (ERPs) elicited by tones that follow a button press are reduced in amplitude relative to ERPs elicited by passively attended tones. However, previous EEG studies investigating visual stimuli in this context are rare, provide inconclusive results, and lack adequate control conditions with passive movements. Furthermore, although self-initiation is known to modulate behavioral responses, it is not known whether differences in the amplitude of ERPs also reflect differences in perception of sensory outcomes. In this study, we presented to participants visual stimuli consisting of gray discs following either active button presses, or passive button presses, in which an electromagnet moved the participant's finger. Two discs presented visually 500-1250 ms apart followed each button press, and participants judged which of the two was more intense. Early components of the primary visual response (N1 and P2) over the occipital electrodes were suppressed in the active condition. Interestingly, suppression in the intensity judgment task was only correlated with suppression of the visual P2 component. These data support the notion of efference copy-based forward model predictions in the visual sensory modality, but especially later processes (P2) seem to be perceptually relevant. Taken together, the results challenge the assumption that N1 differences reflect perceptual suppression and emphasize the relevance of the P2 ERP component.

Exploring deep residual network based features for automatic schizophrenia detection from EEG

Schizophrenia is a severe mental illness which can cause lifelong disability. Most recent studies on the Electroencephalogram (EEG)-based diagnosis of schizophrenia rely on bespoke/hand-crafted feature extraction techniques. Traditional manual feature extraction methods are time-consuming, imprecise, and have a limited ability to balance accuracy and efficiency. Addressing this issue, this study introduces a deep residual network (deep ResNet) based feature extraction design that can automatically extract representative features from EEG signal data for identifying schizophrenia. This proposed method consists of three stages: signal pre-processing by average filtering method, extraction of hidden patterns of EEG signals by deep ResNet, and classification of schizophrenia by softmax layer. To assess the performance of the obtained deep features, ResNet softmax classifier and also several machine learning (ML) techniques are applied on the same feature set. The experimental results for a Kaggle schizophrenia EEG dataset show that the deep features with support vector machine classifier could achieve the highest performances (99.23% accuracy) compared to the ResNet classifier. Furthermore, the proposed model performs better than the existing approaches. The findings suggest that our proposed strategy has capability to discover important biomarkers for automatic diagnosis of schizophrenia from EEG, which will aid in the development of a computer assisted diagnostic system by specialists.

CCPNet136: automated detection of schizophrenia using carbon chain pattern and iterative TQWT technique with EEG signals

Objective.Schizophrenia (SZ) is a severe, chronic psychiatric-cognitive disorder. The primary objective of this work is to present a handcrafted model using state-of-the-art technique to detect SZ accurately with EEG signals.Approach.In our proposed work, the features are generated using a histogram-based generator and an iterative decomposition model. The graph-based molecular structure of the carbon chain is employed to generate low-level features. Hence, the developed feature generation model is called the carbon chain pattern (CCP). An iterative tunable q-factor wavelet transform (ITQWT) technique is implemented in the feature extraction phase to generate various sub-bands of the EEG signal. The CCP was applied to the generated sub-bands to obtain several feature vectors. The clinically significant features were selected using iterative neighborhood component analysis (INCA). The selected features were then classified using the k nearest neighbor (kNN) with a 10-fold cross-validation strategy. Finally, the iterative weighted majority method was used to obtain the results in multiple channels.Main results.The presented CCP-ITQWT and INCA-based automated model achieved an accuracy of 95.84% and 99.20% using a single channel and majority voting method, respectively with kNN classifier.Significance.Our results highlight the success of the proposed CCP-ITQWT and INCA-based model in the automated detection of SZ using EEG signals.

SchizoNET: a robust and accurate Margenau-Hill time-frequency distribution based deep neural network model for schizophrenia detection using EEG signals

Objective.Schizophrenia (SZ) is a severe chronic illness characterized by delusions, cognitive dysfunctions, and hallucinations that impact feelings, behaviour, and thinking. Timely detection and treatment of SZ are necessary to avoid long-term consequences. Electroencephalogram (EEG) signals are one form of a biomarker that can reveal hidden changes in the brain during SZ. However, the EEG signals are non-stationary in nature with low amplitude. Therefore, extracting the hidden information from the EEG signals is challenging.Approach.The time-frequency domain is crucial for the automatic detection of SZ. Therefore, this paper presents the SchizoNET model combining the Margenau-Hill time-frequency distribution (MH-TFD) and convolutional neural network (CNN). The instantaneous information of EEG signals is captured in the time-frequency domain using MH-TFD. The time-frequency amplitude is converted to two-dimensional plots and fed to the developed CNN model.Results.The SchizoNET model is developed using three different validation techniques, including holdout, five-fold cross-validation, and ten-fold cross-validation techniques using three separate public SZ datasets (Dataset 1, 2, and 3). The proposed model achieved an accuracy of 97.4%, 99.74%, and 96.35% on Dataset 1 (adolescents: 45 SZ and 39 HC subjects), Dataset 2 (adults: 14 SZ and 14 HC subjects), and Dataset 3 (adults: 49 SZ and 32 HC subjects), respectively. We have also evaluated six performance parameters and the area under the curve to evaluate the performance of our developed model.Significance.The SchizoNET is robust, effective, and accurate, as it performed better than the state-of-the-art techniques. To the best of our knowledge, this is the first work to explore three publicly available EEG datasets for the automated detection of SZ. Our SchizoNET model can help neurologists detect the SZ in various scenarios.

Automated detection of schizophrenia using deep learning: a review for the last decade

Schizophrenia (SZ) is a devastating mental disorder that disrupts higher brain functions like thought, perception, etc., with a profound impact on the individual's life. Deep learning (DL) can detect SZ automatically by learning signal data characteristics hierarchically without the need for feature engineering associated with traditional machine learning. We performed a systematic review of DL models for SZ detection. Various deep models like long short-term memory, convolution neural networks, AlexNet, etc., and composite methods have been published based on electroencephalographic signals, and structural and/or functional magnetic resonance imaging acquired from SZ patients and healthy patients control subjects in diverse public and private datasets. The studies, the study datasets, and model methodologies are reported in detail. In addition, the challenges of DL models for SZ diagnosis and future works are discussed.

Abnormal resting-state functional connectivity underlies cognitive and clinical symptoms in patients with schizophrenia

Introduction

The cognitive and psychotic symptoms in patients with schizophrenia (SZ) are thought to result from disrupted brain network connectivity.

Methods

We capitalize on the high spatiotemporal resolution of magnetoencephalography imaging (MEG) to record spontaneous neuronal activity in resting state networks in 21 SZ compared with 21 healthy controls (HC).

Results

We found that SZ showed significant global disrupted functional connectivity in delta-theta (2-8 Hz), alpha (8-12 Hz), and beta (12-30 Hz) frequencies, compared to HC. Disrupted global connectivity in alpha frequencies with bilateral frontal cortices was associated with more severe clinical psychopathology (i.e., positive psychotic symptoms). Specifically, aberrant connectivity in beta frequencies between the left primary auditory cortex and cerebellum, was linked to greater hallucination severity in SZ. Disrupted connectivity in delta-theta frequencies between the medial frontal and left inferior frontal cortex was associated with impaired cognition.

Discussion

The multivariate techniques employed in the present study highlight the importance of applying our source reconstruction techniques which leverage the high spatial localization abilities of MEG for estimating neural source activity using beamforming methods such as SAM (synthetic aperture morphometry) to reconstruct the source of brain activity, together with functional connectivity assessments, assayed with imaginary coherence metrics, to delineate how neurophysiological dysconnectivity in specific oscillatory frequencies between distinct regions underlie the cognitive and psychotic symptoms in SZ. The present findings employ powerful techniques in spatial and time-frequency domains to provide potential neural biomarkers underlying neuronal network dysconnectivity in SZ that will inform the development of innovations in future neuromodulation treatment development.

Fusion of pattern-based and statistical features for Schizophrenia detection from EEG signals

Schizophrenia (SZ) is a chronic disorder affecting the functioning of the brain. It can lead to irrational behaviour amongst the patients suffering from this disease. A low-cost diagnostic needs to be developed for SZ so that timely treatment can be provided to the patients. In this work, we propose an accurate and easy-to-implement system to detect SZ using electroencephalogram (EEG) signals. The signal is divided into sub-band components by a Fourier-based technique that can be implemented in real-time using fast Fourier transform. Thereafter, statistical features are computed from these components. Further, look ahead pattern (LAP) is developed as a feature to capture local variations in the EEG signal. The fusion of these two distinct schemes enables a thorough examination of EEG signals. Kruskal-Wallis test is utilized for the selection of significant features. Various machine learning classifiers are employed and the proposed framework achieves 98.62% and 99.24% accuracy in identifying SZ cases, considering two distinct datasets, using boosted trees classifier. This method provides a promising candidate for widespread deployment in efficient real-time systems for SZ detection.

Taking subjectivity seriously: towards a unification of phenomenology, psychiatry, and neuroscience

Nearly all psychiatric diseases involve alterations in subjective, lived experience. The scientific study of the biological basis of mental illness has generally focused on objective measures and observable behaviors, limiting the potential for our understanding of brain mechanisms of disease states and possible treatments. However, applying methods designed principally to interpret objective behavioral measures to the measurement and extrapolation of subjective states presents a number of challenges. In order to help bridge this gap, we draw on the tradition of phenomenology, a philosophical movement concerned with elucidating the structure of lived experience, which emerged in the early 20th century and influenced philosophy of mind, cognitive science, and psychiatry. A number of early phenomenologically-oriented psychiatrists made influential contributions to the field, but this approach retreated to the background as psychiatry moved towards more operationalized disease classifications. Recently, clinical-phenomenological research and viewpoints have re-emerged in the field. We argue that the potential for phenomenological research and methods to generate productive hypotheses about the neurobiological basis of psychiatric diseases has thus far been underappreciated. Using specific examples drawing on the subjective experience of mania and psychosis, we demonstrate that phenomenologically-oriented clinical studies can generate novel and fruitful propositions for neuroscientific investigation. Additionally, we outline a proposal for more rigorously integrating phenomenological investigations of subjective experience with the methods of modern neuroscience research, advocating a cross-species approach with a key role for human subjects research. Collaborative interaction between phenomenology, psychiatry, and neuroscience has the potential to move these fields towards a unified understanding of the biological basis of mental illness.

Does temporal irregularity drive prediction failure in schizophrenia? temporal modelling of ERPs

Schizophrenia subjects often suffer from a failure to properly predict incoming inputs; most notably, some patients exhibit impaired prediction of the sensory consequences of their own actions. The mechanisms underlying this deficit remain unclear, though. One possible mechanism could consist in aberrant predictive processing, as schizophrenic patients show relatively less attenuated neuronal activity to self-produced tones, than healthy controls. Here, we tested the hypothesis that this aberrant predictive mechanism would manifest itself in the temporal irregularity of neuronal signals. For that purpose, we here introduce an event-related potential (ERP) study model analysis that consists of an EEG real-time model equation, eeg(t) and a frequency Laplace transformed Transfer Function (TF) equation, eeg(s). Combining circuit analysis with control and cable theory, we estimate the temporal model representations of auditory ERPs to reveal neural mechanisms that make predictions about self-generated sensations. We use data from 49 schizophrenic patients (SZ) and 32 healthy control (HC) subjects in an auditory ‘prediction’ paradigm; i.e., who either pressed a button to deliver a sound tone (epoch a), or just heard the tone without button press (epoch b). Our results show significantly higher degrees of temporal irregularity or imprecision between different trials of the ERP from the Cz electrode (N100, P200) in SZ compared to HC (Levene’s test, p < 0.0001) as indexed by altered latency, lower similarity/correlation of single trial time courses (using dynamic time warping), and longer settling times to reach steady state in the intertrial interval. Using machine learning, SZ vs HC could be highly accurately classified (92%) based on the temporal parameters of their ERPs’ TF models, using as features the poles of the TF rational functions. Together, our findings show temporal irregularity or imprecision between single trials to be abnormally increased in SZ. This may indicate a general impairment of SZ, related to precisely predicting the sensory consequences of one’s actions.

Impaired sense of agency and associated confidence in psychosis

The Sense of Agency (SoA), our sensation of control over our actions, is a fundamental mechanism for delineating the Self from the environment and others. SoA arises from implicit processing of sensorimotor signals as well as explicit higher-level judgments. Psychosis patients suffer from difficulties in the sense of control over their actions and accurate demarcation of the Self. Moreover, it is unclear if they have metacognitive insight into their aberrant abilities. In this pre-registered study, we examined SoA and its associated confidence judgments using an embodied virtual reality paradigm in psychosis patients and controls. Our results show that psychosis patients not only have a severely reduced ability for discriminating their actions but they also do not show proper metacognitive insight into this deficit. Furthermore, an exploratory analysis revealed that the SoA capacities allow for high levels of accuracy in clinical classification of psychosis. These results indicate that SoA and its metacognition are core aspects of the psychotic state and provide possible venues for understanding the underlying mechanisms of psychosis, that may be leveraged for novel clinical purposes.

The Readiness Potential Correlates with Action-Linked Modulation of Visual Accuracy

Visual accuracy is consistently shown to be modulated around the time of the action execution. The neural underpinning of this motor-induced modulation of visual perception is still unclear. Here, we investigate with EEG whether it is related to the readiness potential, an event-related potential (ERP) linked to motor preparation. Across 18 human participants, the magnitude of visual modulation following a voluntary button press was found to correlate with the readiness potential amplitude measured during visual discrimination. Participants' amplitude of the readiness potential in a purely motor-task was also found to correlate with the extent of the motor-induced modulation of visual perception in the visuomotor task. These results provide strong evidence that perceptual changes close to action execution are associated with motor preparation processes and that this mechanism is independent of task contingencies. Further, our findings suggest that the readiness potential provides a fingerprint of individual visuomotor interaction.

SchizoGoogLeNet: The GoogLeNet-Based Deep Feature Extraction Design for Automatic Detection of Schizophrenia

Schizophrenia (SZ) is a severe and prolonged disorder of the human brain where people interpret reality in an abnormal way. Traditional methods of SZ detection are based on handcrafted feature extraction methods (manual process), which are tedious and unsophisticated, and also limited in their ability to balance efficiency and accuracy. To solve this issue, this study designed a deep learning-based feature extraction scheme involving the GoogLeNet model called "SchizoGoogLeNet" that can efficiently and automatically distinguish schizophrenic patients from healthy control (HC) subjects using electroencephalogram (EEG) signals with improved performance. The proposed framework involves multiple stages of EEG data processing. First, this study employs the average filtering method to remove noise and artifacts from the raw EEG signals to improve the signal-to-noise ratio. After that, a GoogLeNet model is designed to discover significant hidden features from denoised signals to identify schizophrenic patients from HC subjects. Finally, the obtained deep feature set is evaluated by the GoogleNet classifier and also some renowned machine learning classifiers to find a sustainable classification method for the obtained deep feature set. Experimental results show that the proposed deep feature extraction model with a support vector machine performs the best, producing a 99.02% correct classification rate for SZ, with an overall accuracy of 98.84%. Furthermore, our proposed model outperforms other existing methods. The proposed design is able to accurately discriminate SZ from HC, and it will be useful for developing a diagnostic tool for SZ detection.

Action-based predictions affect visual perception, neural processing, and pupil size, regardless of temporal predictability

Sensory consequences of one's own action are often perceived as less intense, and lead to reduced neural responses, compared to externally generated stimuli. Presumably, such sensory attenuation is due to predictive mechanisms based on the motor command (efference copy). However, sensory attenuation has also been observed outside the context of voluntary action, namely when stimuli are temporally predictable. Here, we aimed at disentangling the effects of motor and temporal predictability-based mechanisms on the attenuation of sensory action consequences. During fMRI data acquisition, participants (N = 25) judged which of two visual stimuli was brighter. In predictable blocks, the stimuli appeared temporally aligned with their button press (active) or aligned with an automatically generated cue (passive). In unpredictable blocks, stimuli were presented with a variable delay after button press/cue, respectively. Eye tracking was performed to investigate pupil-size changes and to ensure proper fixation. Self-generated stimuli were perceived as darker and led to less neural activation in visual areas than their passive counterparts, indicating sensory attenuation for self-generated stimuli independent of temporal predictability. Pupil size was larger during self-generated stimuli, which correlated negatively with the blood oxygenation level dependent (BOLD) response: the larger the pupil, the smaller the BOLD amplitude in visual areas. Our results suggest that sensory attenuation in visual cortex is driven by action-based predictive mechanisms rather than by temporal predictability. This effect may be related to changes in pupil diameter. Altogether, these results emphasize the role of the efference copy in the processing of sensory action consequences.

Approach to impaired corollary discharge in patients with schizophrenia: An analysis of self-induced somatosensory evoked potentials and fields

Background

Difficulty in distinguishing between self-generated actions and those generated by others is a core feature of schizophrenia. This is thought to be underpinned by the failure of corollary discharge. However, few studies have investigated these events using somatosensory evoked potentials (SEPs) and somatosensory evoked magnetic fields (SEFs).

Methods

The study included 15 right-handed patients with schizophrenia and 16 healthy controls. SEP and SEF were elicited by electrical stimuli to the left median nerve at intervals of 1–3 s. In the external condition, stimuli were externally induced by a machine. In the self-condition, stimuli were induced by tapping the participants’ own right index finger. Peak amplitude at C4’ in SEP and root mean square in 10 channels on the right primary somatosensory area in SEF were analyzed.

Results

Although there was a significant main effect of condition at N20m, and a significant main effect of condition and group at P30m, no significant interactions of condition and group were found in either N20m or P30m. The post-hoc Wilcoxon signed-rank test revealed that the peak value of P30m in the external condition was significantly higher than that in the self-condition in the healthy control group only. In addition, there was a significant positive correlation between the peak value of P30m in the self-condition and a positive symptom score.

Conclusion

In the current study, we did not find abnormalities of corollary discharge in primary sensory areas in patients with schizophrenia. Further investigations with more cases may reveal the possibility of corollary discharge disturbance in the primary sensory cortex.

Sensory suppression and increased neuromodulation during actions disrupt memory encoding of unpredictable self-initiated stimuli

Actions modulate sensory processing by attenuating responses to self- compared to externally generated inputs, which is traditionally attributed to stimulus-specific motor predictions. Yet, suppression has been also found for stimuli merely coinciding with actions, pointing to unspecific processes that may be driven by neuromodulatory systems. Meanwhile, the differential processing for self-generated stimuli raises the possibility of producing effects also on memory for these stimuli; however, evidence remains mixed as to the direction of the effects. Here, we assessed the effects of actions on sensory processing and memory encoding of concomitant, but unpredictable sounds, using a combination of self-generation and memory recognition task concurrently with EEG and pupil recordings. At encoding, subjects performed button presses that half of the time generated a sound (motor-auditory; MA) and listened to passively presented sounds (auditory-only; A). At retrieval, two sounds were presented and participants had to respond which one was present before. We measured memory bias and memory performance by having sequences where either both or only one of the test sounds were presented at encoding, respectively. Results showed worse memory performance - but no differences in memory bias -, attenuated responses, and larger pupil diameter for MA compared to A sounds. Critically, the larger the sensory attenuation and pupil diameter, the worse the memory performance for MA sounds. Nevertheless, sensory attenuation did not correlate with pupil dilation. Collectively, our findings suggest that sensory attenuation and neuromodulatory processes coexist during actions, and both relate to disrupted memory for concurrent, albeit unpredictable sounds.

EEG-Based Schizophrenia Diagnosis through Time Series Image Conversion and Deep Learning

Schizophrenia, a mental disorder experienced by more than 20 million people worldwide, is emerging as a serious issue in society. Currently, the diagnosis of schizophrenia is based only on mental disorder diagnosis and/or diagnosis by a psychiatrist or mental health professional using DSM-5, a diagnostic and statistical manual of mental disorders. Furthermore, patients in countries with insufficient access to healthcare are difficult to diagnose for schizophrenia and early diagnosis is even more problematic. While various studies are being conducted to solve the challenges of schizophrenia diagnosis, methodology is considered to be limited, and diagnostic accuracy needs to be improved. In this study, a new approach using EEG data and deep learning is proposed to increase objectivity and efficiency of schizophrenia diagnosis. Existing deep learning studies use EEG data to classify schizophrenic patients and healthy subjects by learning EEG in the form of graphs or tables. However, in this study, EEG, a time series data, was converted into an image to improve classification accuracy, and is then studied in deep learning models. This study used EEG data of 81 people, in which the difference in N100 EEG between schizophrenic patients and healthy patients had been analyzed in prior research. EEGs were converted into images using time series image conversion algorithms, Recurrence Plot (RP) and Gramian Angular Field (GAF), and converted EEG images were learned with Convolutional Neural Network (CNN) models built based on VGGNet. When the trained deep learning model was applied to the same data from prior research, it was demonstrated that classification accuracy improved when compared to previous studies. Among the two algorithms used for image conversion, the deep learning model that learned through GAF showed significantly higher classification accuracy. The results of this study suggest that the use of GAF and CNN models based on EEG results can be an effective way to increase objectivity and efficiency in diagnosing various mental disorders, including schizophrenia.

Turning a blind eye to motor differences leads to bias in estimating action-related auditory ERP attenuation

Event-related potential (ERP) studies investigating the processing of self-induced stimuli often rely on the assumption that ballistic actions and motor ERPs are constant across different sets of action effects. Since recent studies challenge this motor equivalence assumption, we examined whether neglecting effect-related motor differences can bias the estimation of auditory ERPs in a typical action-related ERP attenuation paradigm. We increased action variability with a force production task and selected an event subset in which the motor equivalence assumption was true. ERP attenuation estimated in this subset was compared with attenuation obtained in the standard task, where motor differences were not controlled. Violation of the motor equivalence assumption resulted in a positive deflection overlapping auditory ERPs elicited by self-induced sounds, resulting in the overestimation of N1- and underestimation of P2-attenuation. This demonstrates that sensory-effect-related motor differences should be considered when separating sensory and motor components in ERPs elicited by self-induced stimuli.

Human Psychological Disorder towards Cryptography: True Random Number Generator from EEG of Schizophrenics and Its Application in Block Encryption’s Substitution Box

Schizophrenia is a multifaceted chronic psychiatric disorder that affects the way a human thinks, feels, and behaves. Inevitably, natural randomness exists in the psychological perception of schizophrenic patients, which is our primary source of inspiration for this research because true randomness is the indubitably ultimate valuable resource for symmetric cryptography. Famous information theorist Claude Shannon gave two desirable properties that a strong encryption algorithm should have, which are confusion and diffusion in his fundamental article on the theoretical foundations of cryptography. Block encryption strength against various cryptanalysis attacks is purely dependent on its confusion property, which is gained through the confusion component. In the literature, chaos and algebraic techniques are extensively used to design the confusion component. Chaos- and algebraic-based techniques provide favorable features for the design of the confusion component; however, researchers have also identified potential attacks on these techniques. Instead of existing schemes, we introduce a novel methodology to construct cryptographic confusion component from the natural randomness, which are existing in the psychological perception of the schizophrenic patients, and as a result, cryptanalysis of chaos and algebraic techniques are not applicable on our proposed technique. The psychological perception of the brain regions was captured through the electroencephalogram (EEG) readings during the sensory task. The proposed design passed all the standard evaluation criteria and validation tests of the confusion component and the random number generators. One million true random bits are assessed through the NIST statistical test suite, and the results proved that the psychological perception of schizophrenic patients is a good source of true randomness. Furthermore, the proposed confusion component attains better or equal cryptographic strength as compared to state-of-the-art techniques (2020 to 2021). To the best of our knowledge, this nature of research is performed for the first time, in which psychiatric disorder is utilized for the design of information security primitive. This research opens up new avenues in cryptographic primitive design through the fusion of computing, neuroscience, and mathematics.

Investigating Motor Preparation in Autism Spectrum Disorder With and Without Attention Deficit/Hyperactivity Disorder

This study investigated motor preparation and action-consequence prediction using the lateralized readiness potential (LRP). Motor impairments are common in autism spectrum disorder (ASD) and attention-deficit/hyperactivity disorder (ADHD), which commonly co-occur. Alterations in predictive processes may impact motor planning. Whether motor planning deficits are characteristic of ASD broadly or magnified in the context of co-morbid ADHD is unclear. ASD children with (ASD + ADHD; n = 12) and without (ASD - ADHD; n = 9) comorbid ADHD and typical controls (n = 29) performed voluntary motor actions that either did or did not result in auditory consequences. ASD - ADHD children demonstrated LRP enhancement when their action produced an effect while ASD + ADHD children had attenuated responses regardless of action-effect pairings. Findings suggest influence of ADHD comorbidity on motor preparation and prediction in ASD.

Tactile suppression stems from specific sensorimotor predictions

Tactile sensations on a moving hand are perceived weaker than when presented on the same but stationary hand. There is an ongoing debate about whether this weaker perception is based on sensorimotor predictions or is due to a blanket reduction in sensitivity. Here, we show greater suppression of sensations matching predicted sensory feedback. This reinforces the idea of precise estimations of future body sensory states suppressing the predicted sensory feedback. Our results shine light on the mechanisms of human sensorimotor control and are relevant for understanding clinical phenomena related to predictive processes.

The ability to sample sensory information with our hands is crucial for smooth and efficient interactions with the world. Despite this important role of touch, tactile sensations on a moving hand are perceived weaker than when presented on the same but stationary hand. This phenomenon of tactile suppression has been explained by predictive mechanisms, such as internal forward models, that estimate future sensory states of the body on the basis of the motor command and suppress the associated predicted sensory feedback. The origins of tactile suppression have sparked a lot of debate, with contemporary accounts claiming that suppression is independent of sensorimotor predictions and is instead due to an unspecific mechanism. Here, we target this debate and provide evidence for specific tactile suppression due to precise sensorimotor predictions. Participants stroked with their finger over textured objects that caused predictable vibrotactile feedback signals on that finger. Shortly before touching the texture, we probed tactile suppression by applying external vibrotactile probes on the moving finger that either matched or mismatched the frequency generated by the stroking movement along the texture. We found stronger suppression of the probes that matched the predicted sensory feedback. These results show that tactile suppression is specifically tuned to the predicted sensory states of a movement.

From Sound Perception to Automatic Detection of Schizophrenia: An EEG-Based Deep Learning Approach

Deep learning techniques have been applied to electroencephalogram (EEG) signals, with promising applications in the field of psychiatry. Schizophrenia is one of the most disabling neuropsychiatric disorders, often characterized by the presence of auditory hallucinations. Auditory processing impairments have been studied using EEG-derived event-related potentials and have been associated with clinical symptoms and cognitive dysfunction in schizophrenia. Due to consistent changes in the amplitude of ERP components, such as the auditory N100, some have been proposed as biomarkers of schizophrenia. In this paper, we examine altered patterns in electrical brain activity during auditory processing and their potential to discriminate schizophrenia and healthy subjects. Using deep convolutional neural networks, we propose an architecture to perform the classification based on multi-channels auditory-related EEG single-trials, recorded during a passive listening task. We analyzed the effect of the number of electrodes used, as well as the laterality and distribution of the electrical activity over the scalp. Results show that the proposed model is able to classify schizophrenia and healthy subjects with an average accuracy of 78% using only 5 midline channels (Fz, FCz, Cz, CPz, and Pz). The present study shows the potential of deep learning methods in the study of impaired auditory processing in schizophrenia with implications for diagnosis. The proposed design can provide a base model for future developments in schizophrenia research.

Neural Substrates and Models of Omission Responses and Predictive Processes

Predictive coding theories argue that deviance detection phenomena, such as mismatch responses and omission responses, are generated by predictive processes with possibly overlapping neural substrates. Molecular imaging and electrophysiology studies of mismatch responses and corollary discharge in the rodent model allowed the development of mechanistic and computational models of these phenomena. These models enable translation between human and non-human animal research and help to uncover fundamental features of change-processing microcircuitry in the neocortex. This microcircuitry is characterized by stimulus-specific adaptation and feedforward inhibition of stimulus-selective populations of pyramidal neurons and interneurons, with specific contributions from different interneuron types. The overlap of the substrates of different types of responses to deviant stimuli remains to be understood. Omission responses, which are observed both in corollary discharge and mismatch response protocols in humans, are underutilized in animal research and may be pivotal in uncovering the substrates of predictive processes. Omission studies comprise a range of methods centered on the withholding of an expected stimulus. This review aims to provide an overview of omission protocols and showcase their potential to integrate and complement the different models and procedures employed to study prediction and deviance detection.This approach may reveal the biological foundations of core concepts of predictive coding, and allow an empirical test of the framework’s promise to unify theoretical models of attention and perception.

Cerebellar stimulation in schizophrenia: A systematic review of the evidence and an overview of the methods

BACKGROUND

Cerebellar structural and functional abnormalities underlie widespread deficits in clinical, cognitive, and motor functioning that are observed in schizophrenia. Consequently, the cerebellum is a promising target for novel schizophrenia treatments. Here we conducted an updated systematic review examining the literature on cerebellar stimulation efficacy and tolerability for mitigating symptoms of schizophrenia. We discuss the purported mechanisms of cerebellar stimulation, current methods for implementing stimulation, and future directions of cerebellar stimulation for intervention development with this population.

METHODS

Two independent authors identified 20 published studies (7 randomized controlled trials, 7 open-label studies, 1 pilot study, 4 case reports, 1 preclinical study) that describe the effects of cerebellar circuitry modulation in patients with schizophrenia or animal models of psychosis. Published studies up to October 11, 2022 were identified from a search within PubMed, Scopus, and PsycInfo.

RESULTS

Most studies stimulating the cerebellum used transcranial magnetic stimulation or transcranial direct-current stimulation, specifically targeting the cerebellar vermis/midline. Accounting for levels of methodological rigor across studies, these studies detected post-cerebellar modulation in schizophrenia as indicated by the alleviation of certain clinical symptoms (mainly negative and depressive symptoms), as well as increased frontal-cerebellar connectivity and augmentation of canonical neuro-oscillations known to be abnormal in schizophrenia. In contrast to a prior review, we did not find consistent evidence for cognitive improvements following cerebellar modulation stimulation. Modern cerebellar stimulation methods appear tolerable for individuals with schizophrenia, with only mild and temporary side effects.

CONCLUSION

Cerebellar stimulation is a promising intervention for individuals with schizophrenia that may be more relevant to some symptom domains than others. Initial results highlight the need for continued research using more methodologically rigorous designs, such as additional longitudinal and randomized controlled trials.

SYSTEMATIC REVIEW REGISTRATION

[https://www.crd.york.ac.uk/prospero/], identifier [CRD42022346667].

Agency Deficits in a Human Genetic Model of Schizophrenia: Insights From 22q11DS Patients

Schizophrenia is a chronic and disabling mental illness characterized by a disordered sense of self. Current theories suggest that deficiencies in the sense of control over one's actions (Sense of Agency, SoA) may underlie some of the symptoms of schizophrenia. However, it is not clear if agency deficits are a precursor or a result of psychosis. Here, we investigated full body agency using virtual reality in a cohort of 22q11 deletion syndrome participants with a genetic propensity for schizophrenia. In two experiments employing virtual reality, full body motion tracking, and online feedback, we investigated SoA in two separate domains. Our results show that participants with 22q11DS had a considerable deficit in monitoring their actions, compared to age-matched controls in both the temporal and spatial domain. This was coupled with a bias toward erroneous attribution of actions to the self. These results indicate that nonpsychotic 22q11DS participants have a domain general deficit in the conscious sensorimotor mechanisms underlying the bodily self. Our data reveal an abnormality in the SoA in a cohort with a genetic predisposition for schizophrenia, but without psychosis, providing evidence that deficits in delineation of the self may be a precursor rather than a result of the psychotic state.

A hybrid decision support system for automatic detection of Schizophrenia using EEG signals

BACKGROUND

Schizophrenia (SCZ) is a serious neurological condition in which people suffer with distorted perception of reality. SCZ may result in a combination of delusions, hallucinations, disordered thinking, and behavior. This causes permanent disability and hampers routine functioning. Trained neurologists use interviewing and visual inspection techniques for the detection and diagnosis of SCZ. These techniques are manual, time-consuming, subjective, and error-prone. Therefore, there is a need to develop an automatic model for SCZ classification. The aim of this study is to develop an automated SCZ classification model using electroencephalogram (EEG) signals. The EEG signals can capture the changes in neural dynamics of human cognition during SCZ.

METHOD

Based on the nature of the SCZ condition, the EEG signals must be examined. For accurate interpretation of EEG signals during SCZ, an automated model integrating a robust variational mode decomposition (RVMD) and an optimized extreme learning machine (OELM) classifier is developed. Traditional VMD suffers from noisy mode generation, mode duplication, under segmentation, and mode discarding. These problems are suppressed in RVMD by automating the selection of quadratic penalty factor (α) and a number of modes (L). The hyperparameters (HPM) of the OELM classifier are automatically selected to ensure maximum accuracy for each mode without overfitting or underfitting. For the selection of α and L in RVMD and HPM in the OELM classifier, a whale optimization algorithm is used. The root mean square error is minimized for RVMD and classification accuracy of each mode is maximized for the OELM classifier. The EEG signals of three conditions performing basic sensory tasks have been analyzed to detect SCZ.

RESULTS

The Kruskal Wallis test is used to select different features extracted from the modes produced by RVMD. An OELM classifier is tested using a ten-fold cross-validation technique. An accuracy, precision, specificity, F-1 measure, sensitivity, and Cohen's Kappa of 92.93%, 93.94%, 91.06% 94.07%, 97.15%, and 85.32% are obtained.

CONCLUSION

The third mode's chaotic features helped to capture the significant changes that occurred during the SCZ state. The findings of the RVMD-OELM-based hybrid decision support system can help neuro-experts for the accurate identification of SCZ in real-time scenarios.

The Role of the Medial Prefontal Cortex in Self-Agency in Schizophrenia

Schizophrenia is a disorder of the self. In particular, patients show cardinal deficits in self-agency (i.e., the experience and awareness of being the agent of one’s own thoughts and actions) that directly contribute to positive psychotic symptoms of hallucinations and delusions and distort reality monitoring (defined as distinguishing self-generated information from externally-derived information). Predictive coding models suggest that the experience of self-agency results from a minimal prediction error between the predicted sensory consequence of a self-generated action and the actual outcome. In other words, the experience of self-agency is thought to be driven by making reliable predictions about the expected outcomes of one’s own actions. Most of the agency literature has focused on the motor system; here we present a novel viewpoint that examines agency from a different lens using distinct tasks of reality monitoring and speech monitoring. The self-prediction mechanism that leads to self-agency is necessary for reality monitoring in that self-predictions represent a critical precursor for the successful encoding and memory retrieval of one’s own thoughts and actions during reality monitoring to enable accurate self-agency judgments (i.e., accurate identification of self-generated information). This self-prediction mechanism is also critical for speech monitoring where we continually compare auditory feedback (i.e., what we hear ourselves say) with what we expect to hear. Prior research has shown that the medial prefrontal cortex (mPFC) may represent one potential neural substrate of this self-prediction mechanism. Unfortunately, patients with schizophrenia (SZ) show mPFC hypoactivity associated with self-agency impairments on reality and speech monitoring tasks, as well as aberrant mPFC functional connectivity during intrinsic measures of agency during resting states that predicted worsening psychotic symptoms. Causal neurostimulation and neurofeedback techniques can move the frontiers of schizophrenia research into a new era where we implement techniques to manipulate excitability in key neural regions, such as the mPFC, to modulate patients’ reliance on self-prediction mechanisms on distinct tasks of reality and speech monitoring. We hypothesize these findings will show that mPFC provides a unitary basis for self-agency, driven by reliance on self-prediction mechanisms, which will facilitate the development of new targeted treatments in patients with schizophrenia.

Movement Planning Determines Sensory Suppression: An Event-related Potential Study

Sensory suppression refers to the phenomenon that sensory input generated by our own actions, such as moving a finger to press a button to hear a tone, elicits smaller neural responses than sensory input generated by external agents. This observation is usually explained via the internal forward model in which an efference copy of the motor command is used to compute a corollary discharge, which acts to suppress sensory input. However, because moving a finger to press a button is accompanied by neural processes involved in preparing and performing the action, it is unclear whether sensory suppression is the result of movement planning, movement execution, or both. To investigate this, in two experiments, we compared ERPs to self-generated tones that were produced by voluntary, semivoluntary, or involuntary button-presses, with externally generated tones that were produced by a computer. In Experiment 1, the semivoluntary and involuntary button-presses were initiated by the participant or experimenter, respectively, by electrically stimulating the median nerve in the participant's forearm, and in Experiment 2, by applying manual force to the participant's finger. We found that tones produced by voluntary button-presses elicited a smaller N1 component of the ERP than externally generated tones. This is known as N1-suppression. However, tones produced by semivoluntary and involuntary button-presses did not yield significant N1-suppression. We also found that the magnitude of N1-suppression linearly decreased across the voluntary, semivoluntary, and involuntary conditions. These results suggest that movement planning is a necessary condition for producing sensory suppression. We conclude that the most parsimonious account of sensory suppression is the internal forward model.

A Generic Approach for Classification of Psychological Disorders Diagnosis using EEG

Electroencephalogram (EEG) is a widely used technique to diagnose psychological disorders. Until now, most of the studies focused on the diagnosis of a particular psychological disorder using EEG. We propose a generic approach to diagnose the different type of psychological disorders with high accuracy. The proposed approach is tested on five different datasets and three psychological disorders. Electrodes having higher signal to noise ratio are selected from the raw EEG signals. Multiple linear and non-linear features are then extracted from the selected electrodes. After feature selection, machine learning is used to diagnose the psychological disorders. We kept the same generic approach for all the datasets and diseases and achieved 93%, 85% and 80% F1 score on Schizophrenia, Epilepsy and Parkinson disease, respectively.

Fronto-Temporal Disconnection Within the Presence Hallucination Network in Psychotic Patients With Passivity Experiences

Psychosis, characterized by hallucinations and delusions, is a common feature of psychiatric disease, especially schizophrenia. One prominent theory posits that psychosis is driven by abnormal sensorimotor predictions leading to the misattribution of self-related events. This misattribution has been linked to passivity experiences (PE), such as loss of agency and, more recently, to presence hallucinations (PH), defined as the conscious experience of the presence of an alien agent while no person is actually present. PH has been observed in schizophrenia, Parkinson's disease, and neurological patients with brain lesions and, recently, the brain mechanisms of PH (PH-network) have been determined comprising bilateral posterior middle temporal gyrus (pMTG), inferior frontal gyrus (IFG), and ventral premotor cortex (vPMC). Given that the experience of an alien agent is a common feature of PE, we here analyzed the functional connectivity within the PH-network in psychotic patients with (N = 39) vs without PE (N = 26). We observed reduced fronto-temporal functional connectivity in patients with PE compared to patients without PE between the right pMTG and the right and left IFG of the PH-network. Moreover, when seeding from these altered regions, we observed specific alterations with brain regions commonly linked to auditory-verbal hallucinations (such as Heschl's gyrus). The present connectivity findings within the PH-network extend the disconnection hypothesis for hallucinations to the specific case of PH and associates the PH-network with key brain regions for frequent psychotic symptoms such as auditory-verbal hallucinations, showing that PH are relevant to the study of the brain mechanisms of psychosis and PE.

The auditory brain in action: Intention determines predictive processing in the auditory system-A review of current paradigms and findings

According to the ideomotor theory, action may serve to produce desired sensory outcomes. Perception has been widely described in terms of sensory predictions arising due to top-down input from higher order cortical areas. Here, we demonstrate that the action intention results in reliable top-down predictions that modulate the auditory brain responses. We bring together several lines of research, including sensory attenuation, active oddball, and action-related omission studies: Together, the results suggest that the intention-based predictions modulate several steps in the sound processing hierarchy, from preattentive to evaluation-related processes, also when controlling for additional prediction sources (i.e., sound regularity). We propose an integrative theoretical framework—the extended auditory event representation system (AERS), a model compatible with the ideomotor theory, theory of event coding, and predictive coding. Initially introduced to describe regularity-based auditory predictions, we argue that the extended AERS explains the effects of action intention on auditory processing while additionally allowing studying the differences and commonalities between intention- and regularity-based predictions—we thus believe that this framework could guide future research on action and perception.