Using concurrent EEG and fMRI to probe the state of the brain in schizophrenia

Simultaneous EEG-fMRI Investigation of Rhythm-Dependent Thalamo-Cortical Circuits Alteration in Schizophrenia

Schizophrenia is accompanied by aberrant interactions of intrinsic brain networks. However, the modulatory effect of electroencephalography (EEG) rhythms on the functional connectivity (FC) in schizophrenia remains unclear. This study aims to provide new insight into network communication in schizophrenia by integrating FC and EEG rhythm information. After collecting simultaneous resting-state EEG-functional magnetic resonance imaging data, the effect of rhythm modulations on FC was explored using what we term "dynamic rhythm information." We also investigated the synergistic relationships among three networks under rhythm modulation conditions, where this relationship presents the coupling between two brain networks with other networks as the center by the rhythm modulation. This study found FC between the thalamus and cortical network regions was rhythm-specific. Further, the effects of the thalamus on the default mode network (DMN) and salience network (SN) were less similar under alpha rhythm modulation in schizophrenia patients than in controls ([Formula: see text]). However, the similarity between the effects of the central executive network (CEN) on the DMN and SN under gamma modulation was greater ([Formula: see text]), and the degree of coupling was negatively correlated with the duration of disease ([Formula: see text], [Formula: see text]). Moreover, schizophrenia patients exhibited less coupling with the thalamus as the center and greater coupling with the CEN as the center. These results indicate that modulations in dynamic rhythms might contribute to the disordered functional interactions seen in schizophrenia.

Unpredictable threat increases early event-related potential amplitudes and cardiac acceleration: A brain-heart coupling study

In the face of unpredictable threat, rapid processing of external events and behavioral mobilization through early psychophysiological responses are crucial for survival. While unpredictable threat generally enhances early processing, it would seem adaptive to particularly increase sensitivity for unexpected events as they may signal danger. To examine this possibility, n = 77 participants performed an auditory oddball paradigm and received unpredictable shocks in threat but not in safe contexts while a stream of frequent (standard) and infrequent (deviant) tones was presented. We assessed event-related potentials (ERP), heart period (HP), and time-lagged within-subject correlations of single-trial EEG and HP (cardio-EEG covariance tracing, CECT) time-locked to the tones. N1 and P2 ERP amplitudes were generally enhanced under threat. The P3 amplitude was enhanced to deviants versus standards and this effect was reduced in the threat condition. Regarding HP, both threat versus safe and unexpected versus expected tones led to stronger cardiac acceleration, suggesting separate effects of threat and stimulus expectancy on HP. Finally, CECTs revealed two correlation clusters, indicating that single-trial EEG magnitudes in the N1/P2 and P3 time-windows predicted subsequent cardiac acceleration. The current results show that an unpredictable threat context enhances N1 and P2 amplitudes and cardiac acceleration to benign auditory stimuli. They further suggest separable cortical correlates of different effects on cardiac activity: an early N1/P2 correlate associated with threat-effects on HP and a later P3 correlate associated with expectedness-effects. Finally, the results indicate that unpredictable threat attenuates rather than enhances the processing of unexpected benign events during the P3 latency.

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.

The Scanner as the Stimulus: Deficient Gamma-BOLD Coupling in Schizophrenia at Rest

Functional magnetic resonance imaging (fMRI) scanners are unavoidably loud and uncomfortable experimental tools that are necessary for schizophrenia (SZ) neuroscience research. The validity of fMRI paradigms might be undermined by well-known sensory processing abnormalities in SZ that could exert distinct effects on neural activity in the presence of scanner background sound. Given the ubiquity of resting-state fMRI (rs-fMRI) paradigms in SZ research, elucidating the relationship between neural, hemodynamic, and sensory processing deficits during scanning is necessary to refine the construct validity of the MR neuroimaging environment. We recorded simultaneous electroencephalography (EEG)-fMRI at rest in people with SZ (n = 57) and healthy control participants without a psychiatric diagnosis (n = 46) and identified gamma EEG activity in the same frequency range as the background sounds emitted from our scanner during a resting-state sequence. In participants with SZ, gamma coupling to the hemodynamic signal was reduced in bilateral auditory regions of the superior temporal gyri. Impaired gamma-hemodynamic coupling was associated with sensory gating deficits and worse symptom severity. Fundamental sensory-neural processing deficits in SZ are present at rest when considering scanner background sound as a "stimulus." This finding may impact the interpretation of rs-fMRI activity in studies of people with SZ. Future neuroimaging research in SZ might consider background sound as a confounding variable, potentially related to fluctuations in neural excitability and arousal.

The effects of attention in auditory-visual integration revealed by time-varying networks

Attention and audiovisual integration are crucial subjects in the field of brain information processing. A large number of previous studies have sought to determine the relationship between them through specific experiments, but failed to reach a unified conclusion. The reported studies explored the relationship through the frameworks of early, late, and parallel integration, though network analysis has been employed sparingly. In this study, we employed time-varying network analysis, which offers a comprehensive and dynamic insight into cognitive processing, to explore the relationship between attention and auditory-visual integration. The combination of high spatial resolution functional magnetic resonance imaging (fMRI) and high temporal resolution electroencephalography (EEG) was used. Firstly, a generalized linear model (GLM) was employed to find the task-related fMRI activations, which was selected as regions of interesting (ROIs) for nodes of time-varying network. Then the electrical activity of the auditory-visual cortex was estimated via the normalized minimum norm estimation (MNE) source localization method. Finally, the time-varying network was constructed using the adaptive directed transfer function (ADTF) technology. Notably, Task-related fMRI activations were mainly observed in the bilateral temporoparietal junction (TPJ), superior temporal gyrus (STG), primary visual and auditory areas. And the time-varying network analysis revealed that V1/A1↔STG occurred before TPJ↔STG. Therefore, the results supported the theory that auditory-visual integration occurred before attention, aligning with the early integration framework.

Don't worry, it won't be fine. Contributions of worry and anxious arousal to startle responses and event-related potentials in threat anticipation

A widely shared framework suggests that anxiety maps onto two dimensions: anxious apprehension and anxious arousal. Previous research linked individual differences in these dimensions to differential neural response patterns in neuropsychological, imaging, and physiological studies. Differential effects of the anxiety dimensions might contribute to inconsistencies in prior studies that examined neural processes underlying anxiety, such as hypersensitivity to unpredictable threat. We investigated the association between trait worry (as a key component of anxious apprehension), anxious arousal, and the neural processing of anticipated threat. From a large online community sample (N = 1,603), we invited 136 participants with converging and diverging worry and anxious arousal profiles into the laboratory. Participants underwent the NPU-threat test with alternating phases of unpredictable threat, predictable threat, and safety, while physiological responses (startle reflex and startle probe locked event-related potential components N1 and P3) were recorded. Worry was associated with increased startle responses to unpredictable threat and increased attentional allocation (P3) to startle probes in predictable threat anticipation. Anxious arousal was associated with increased startle and N1 in unpredictable threat anticipation. These results suggest that trait variations in the anxiety dimensions shape the dynamics of neural processing of threat. Specifically, trait worry seems to simultaneously increase automatic defensive preparation during unpredictable threat and increase attentional responding to threat-irrelevant stimuli during predictable threat anticipation. The current study highlights the utility of anxiety dimensions to understand how physiological responses during threat anticipation are altered in anxiety and supports that worry is associated with hypersensitivity to unpredictable, aversive contexts.

The foundation and architecture of precision medicine in neurology and psychiatry

Neurological and psychiatric diseases have high degrees of genetic and pathophysiological heterogeneity, irrespective of clinical manifestations. Traditional medical paradigms have focused on late-stage syndromic aspects of these diseases, with little consideration of the underlying biology. Advances in disease modeling and methodological design have paved the way for the development of precision medicine (PM), an established concept in oncology with growing attention from other medical specialties. We propose a PM architecture for central nervous system diseases built on four converging pillars: multimodal biomarkers, systems medicine, digital health technologies, and data science. We discuss Alzheimer's disease (AD), an area of significant unmet medical need, as a case-in-point for the proposed framework. AD can be seen as one of the most advanced PM-oriented disease models and as a compelling catalyzer towards PM-oriented neuroscience drug development and advanced healthcare practice.

Perceptual pathways to hallucinogenesis

Recent advances in computational psychiatry have provided unique insights into the neural and cognitive underpinnings of psychotic symptoms. In particular, a host of new data has demonstrated the utility of computational frameworks for understanding how hallucinations might arise from alterations in typical perceptual processing. Of particular promise are models based in Bayesian inference that link hallucinatory perceptual experiences to latent states that may drive them. In this piece, we move beyond these findings to ask: how and why do these latent states arise, and how might we take advantage of heterogeneity in that process to develop precision approaches to the treatment of hallucinations? We leverage specific models of Bayesian inference to discuss components that might lead to the development of hallucinations. Using the unifying power of our model, we attempt to place disparate findings in the study of psychotic symptoms within a common framework. Finally, we suggest directions for future elaboration of these models in the service of a more refined psychiatric nosology based on predictable, testable, and ultimately treatable information processing derangements.

Can neuroimaging-based biomarkers predict response to cognitive remediation in patients with psychosis? A state-of-the-art review

BACKGROUND

Cognitive Remediation (CR) is designed to halt the pathological neural systems that characterize major psychotic disorders (MPD), and its main objective is to improve cognitive functioning. The magnitude of CR-induced cognitive gains greatly varies across patients with MPD, with up to 40% of patients not showing gains in global cognitive performance. This is likely due to the high degree of heterogeneity in neural activation patterns underlying cognitive endophenotypes, and to inter-individual differences in neuroplastic potential, cortical organization and interaction between brain systems in response to learning. Here, we review studies that used neuroimaging to investigate which biomarkers could potentially serve as predictors of treatment response to CR in MPD.

METHODS

This systematic review followed the PRISMA guidelines. An electronic database search (Embase, Elsevier; Scopus, PsycINFO, APA; PubMed, APA) was conducted in March 2021. peer-reviewed, English-language studies were included if they reported data for adults aged 18+ with MPD, reported findings from randomized controlled trials or single-arm trials of CR; and presented neuroimaging data.

RESULTS

Sixteen studies were included and eight neuroimaging-based biomarkers were identified. Auditory mismatch negativity (3 studies), auditory steady-state response (1), gray matter morphology (3), white matter microstructure (1), and task-based fMRI (7) can predict response to CR. Efference copy corollary/discharge, resting state, and thalamo-cortical connectivity (1) require further research prior to being implemented.

CONCLUSIONS

Translational research on neuroimaging-based biomarkers can help elucidate the mechanisms by which CR influences the brain's functional architecture, better characterize psychotic subpopulations, and ultimately deliver CR that is optimized and personalized.

Aperiodic measures of neural excitability are associated with anticorrelated hemodynamic networks at rest: A combined EEG-fMRI study

The hallmark of resting EEG spectra are distinct rhythms emerging from a broadband, aperiodic background. This aperiodic neural signature accounts for most of total EEG power, although its significance and relation to functional neuroanatomy remains obscure. We hypothesized that aperiodic EEG reflects a significant metabolic expenditure and therefore might be associated with the default mode network while at rest. During eyes-open, resting-state recordings of simultaneous EEG-fMRI, we find that aperiodic and periodic components of EEG power are only minimally associated with activity in the default mode network. However, a whole-brain analysis identifies increases in aperiodic power correlated with hemodynamic activity in an auditory-salience-cerebellar network, and decreases in aperiodic power are correlated with hemodynamic activity in prefrontal regions. Desynchronization in residual alpha and beta power is associated with visual and sensorimotor hemodynamic activity, respectively. These findings suggest that resting-state EEG signals acquired in an fMRI scanner reflect a balance of top-down and bottom-up stimulus processing, even in the absence of an explicit task.

Predicting subclinical psychotic-like experiences on a continuum using machine learning

Previous studies applying machine learning methods to psychosis have primarily been concerned with the binary classification of chronic schizophrenia patients and healthy controls. The aim of this study was to use electroencephalographic (EEG) data and pattern recognition to predict subclinical psychotic-like experiences on a continuum between these two extremes in otherwise healthy people. We applied two different approaches to an auditory oddball regularity learning task obtained from N = 73 participants: A feature extraction and selection routine incorporating behavioural measures, event-related potential components and effective connectivity parameters; Regularisation of spatiotemporal maps of event-related potentials. Using the latter approach, optimal performance was achieved using the response to frequent, predictable sounds. Features within the P50 and P200 time windows had the greatest contribution toward lower Prodromal Questionnaire (PQ) scores and the N100 time window contributed most to higher PQ scores. As a proof-of-concept, these findings demonstrate that EEG data alone are predictive of individual psychotic-like experiences in healthy people. Our findings are in keeping with the mounting evidence for altered sensory responses in schizophrenia, as well as the notion that psychosis may exist on a continuum expanding into the non-clinical population.

Artifact Reduction in Simultaneous EEG-fMRI: A Systematic Review of Methods and Contemporary Usage

Simultaneous electroencephalography-functional MRI (EEG-fMRI) is a technique that combines temporal (largely from EEG) and spatial (largely from fMRI) indicators of brain dynamics. It is useful for understanding neuronal activity during many different event types, including spontaneous epileptic discharges, the activity of sleep stages, and activity evoked by external stimuli and decision-making tasks. However, EEG recorded during fMRI is subject to imaging, pulse, environment and motion artifact, causing noise many times greater than the neuronal signals of interest. Therefore, artifact removal methods are essential to ensure that artifacts are accurately removed, and EEG of interest is retained. This paper presents a systematic review of methods for artifact reduction in simultaneous EEG-fMRI from literature published since 1998, and an additional systematic review of EEG-fMRI studies published since 2016. The aim of the first review is to distill the literature into clear guidelines for use of simultaneous EEG-fMRI artifact reduction methods, and the aim of the second review is to determine the prevalence of artifact reduction method use in contemporary studies. We find that there are many published artifact reduction techniques available, including hardware, model based, and data-driven methods, but there are few studies published that adequately compare these methods. In contrast, recent EEG-fMRI studies show overwhelming use of just one or two artifact reduction methods based on literature published 15–20 years ago, with newer methods rarely gaining use outside the group that developed them. Surprisingly, almost 15% of EEG-fMRI studies published since 2016 fail to adequately describe the methods of artifact reduction utilized. We recommend minimum standards for reporting artifact reduction techniques in simultaneous EEG-fMRI studies and suggest that more needs to be done to make new artifact reduction techniques more accessible for the researchers and clinicians using simultaneous EEG-fMRI.

Vocalizing and singing reveal complex patterns of corollary discharge function in schizophrenia

INTRODUCTION

As we vocalize, our brains generate predictions of the sounds we produce to enable suppression of neural responses when intentions match vocalizations and to make adjustments when they do not. This may be instantiated by efference copy and corollary discharge mechanisms, which are impaired in people with schizophrenia (SZ). Although innate, these mechanisms can be affected by intentions. We asked if attending to pitch during vocalizations would take these mechanisms "off-line" and reduce suppression.

METHODS

Event-related potentials (ERP) were recorded from 96 SZ and 92 healthy controls (HC) as they vocalized triplets in monotone (Phrase) or sang triplets in ascending thirds (Pitch). Pre-vocalization activity (Bereitschaftspotential, BP), N1, and P2 ERP components to sounds were compared during vocalization and playback.

RESULTS

N1 was not as suppressed during Pitch as during Phrase. N1 suppression was not affected by SZ in either task when all data were collapsed across pitches (Pitch) and positions (Phrase). However, when binned according to vocalization performance, SZ showed less N1 suppression than HC at longer (>2 s) inter-stimulus intervals (Phrase) and inconsistent suppression across pitches (Pitch). Unlike N1, P2 was more suppressed during Pitch than Phrase and not affected by SZ. BP was greater during vocalization than playback but did not contribute to N1 or P2 effects. Pitch variability was inversely related to negative symptoms.

CONCLUSIONS

Neural processing is not suppressed when patients and controls sing, and corollary discharge abnormalities in schizophrenia are only seen at long vocalization intervals.

Reconciling competing mechanisms posited to underlie auditory verbal hallucinations

Perception is not the passive registration of incoming sensory data. Rather, it involves some analysis by synthesis, based on past experiences and context. One adaptive consequence of this arrangement is imagination-the ability to richly simulate sensory experiences, interrogate and manipulate those simulations, in service of action and decision making. In this paper, we will discuss one possible cost of this adaptation, namely hallucinations-perceptions without sensory stimulation, which characterize serious mental illnesses like schizophrenia, but which also occur in neurological illnesses, and-crucially for the present piece-are common also in the non-treatment-seeking population. We will draw upon a framework for imagination that distinguishes voluntary from non-voluntary experiences and explore the extent to which the varieties and features of hallucinations map onto this distinction, with a focus on auditory-verbal hallucinations (AVHs)-colloquially, hearing voices. We will propose that sense of agency for the act of imagining is key to meaningfully dissecting different forms and features of AVHs, and we will outline the neural, cognitive and phenomenological sequelae of this sense. We will conclude that a compelling unifying framework for action, perception and belief-predictive processing-can incorporate observations regarding sense of agency, imagination and hallucination. This article is part of the theme issue 'Offline perception: voluntary and spontaneous perceptual experiences without matching external stimulation'.

Pitch and Duration Mismatch Negativity and Heschl's Gyrus Volume in First-Episode Schizophrenia-Spectrum Individuals

Background. The mismatch negativity (MMN) brainwave indexes novelty detection. MMN to infrequent pitch (pMMN) and duration (dMMN) deviants is reduced in long-term schizophrenia. Although not reduced at first psychosis, pMMN is inversely associated with left hemisphere Heschl's gyrus (HG) gray matter volume within 1 year of first hospitalization for schizophrenia-spectrum psychosis, consistent with pathology of left primary auditory cortex early in disease course. We examined whether the relationship was present earlier, at first psychiatric contact for psychosis, and whether the same structural-functional association was apparent for dMMN. Method. Twenty-seven first-episode schizophrenia-spectrum (FESz) and 27 matched healthy comparison (HC) individuals were compared. EEG-derived pMMN and dMMN were measured by subtracting the standard tone waveform (80%) from the pitch- and duration-deviant waveforms (10% each). HG volumes were calculated from T1-weighted structural magnetic resonance imaging using Freesurfer. Results. In FESz, pMMN amplitudes at Fz were inversely associated with left HG (but not right) gray matter volumes, and dMMN amplitudes were associated significantly with left HG volumes and at trend-level with right HG. There were no structural-functional associations in HC. Conclusions. pMMN and dMMN index gray matter reduction in left hemisphere auditory cortex early in psychosis, with dMMN also marginally indexing right HG volumes. This suggest conjoint functional and structural pathology that affects the automatic detection of novelty with varying degrees of penetrance prior to psychosis. These brainwaves are sensitive biomarkers of pathology early in the psychotic disease course, and may serve as biomarkers of disease progression and as therapeutic outcome measures.

Theta Phase Synchrony Is Sensitive to Corollary Discharge Abnormalities in Early Illness Schizophrenia but Not in the Psychosis Risk Syndrome

BACKGROUND

Prior studies have shown that the auditory N1 event-related potential component elicited by self-generated vocalizations is reduced relative to played back vocalizations, putatively reflecting a corollary discharge mechanism. Schizophrenia patients and psychosis risk syndrome (PRS) youth show deficient N1 suppression during vocalization, consistent with corollary discharge dysfunction. Because N1 is an admixture of theta (4-7 Hz) power and phase synchrony, we examined their contributions to N1 suppression during vocalization, as well as their sensitivity, relative to N1, to corollary discharge dysfunction in schizophrenia and PRS individuals.

METHODS

Theta phase and power values were extracted from electroencephalography data acquired from PRS youth (n = 71), early illness schizophrenia patients (ESZ; n = 84), and healthy controls (HCs; n = 103) as they said "ah" (Talk) and then listened to the playback of their vocalizations (Listen). A principal component analysis extracted theta intertrial coherence (ITC; phase consistency) and event-related spectral power, peaking in the N1 latency range. Talk-Listen suppression scores were analyzed.

RESULTS

Talk-Listen suppression was greater for theta ITC (Cohen's d = 1.46) than for N1 in HC (d = 0.63). Both were deficient in ESZ, but only N1 suppression was deficient in PRS. When deprived of variance shared with theta ITC suppression, N1 suppression no longer differentiated ESZ and PRS individuals from HC. Deficits in theta ITC suppression were correlated with delusions (P = .007) in ESZ. Theta power suppression did not differentiate groups.

CONCLUSIONS

Theta ITC-suppression during vocalization is a more sensitive index of corollary discharge-mediated auditory cortical suppression than N1 suppression and is more sensitive to corollary discharge dysfunction in ESZ than in PRS individuals.

Multimodal Data Analysis of Alzheimer's Disease Based on Clustering Evolutionary Random Forest

Alzheimer's disease (AD) has become a severe medical challenge. Advances in technologies produced high-dimensional data of different modalities including functional magnetic resonance imaging (fMRI) and single nucleotide polymorphism (SNP). Understanding the complex association patterns among these heterogeneous and complementary data is of benefit to the diagnosis and prevention of AD. In this paper, we apply the appropriate correlation analysis method to detect the relationships between brain regions and genes, and propose "brain region-gene pairs" as the multimodal features of the sample. In addition, we put forward a novel data analysis method from technology aspect, cluster evolutionary random forest (CERF), which is suitable for "brain region-gene pairs". The idea of clustering evolution is introduced to improve the generalization performance of random forest which is constructed by randomly selecting samples and sample features. Through hierarchical clustering of decision trees in random forest, the decision trees with higher similarity are clustered into one class, and the decision trees with the best performance are retained to enhance the diversity between decision trees. Furthermore, based on CERF, we integrate feature construction, feature selection and sample classification to find the optimal combination of different methods, and design a comprehensive diagnostic framework for AD. The framework is validated by the samples with both fMRI and SNP data from ADNI. The results show that we can effectively identify AD patients and discover some brain regions and genes associated with AD significantly based on this framework. These findings are conducive to the clinical treatment and prevention of AD.

Efference copy/corollary discharge function and targeted cognitive training in patients with schizophrenia

INTRODUCTION

During vocalization, efference copy/corollary discharge mechanisms suppress the auditory cortical response to self-generated sounds as reflected in the N1 component of the auditory event-related potential (ERP). N1 suppression during talking is reduced in patients with schizophrenia. We hypothesized that these deficits would recover with auditory training that targets the speech processing system.

METHODS

Forty-nine individuals early in the course of a schizophrenia-spectrum illness (ESZ) were randomly assigned to 40 h of Targeted Auditory Training (TAT; n = 23) or Computer Games (CG; n = 26). The N1 ERP component was elicited during production (Talk) and playback (Listen) of vocalization. Effects of Treatment on Global Cognition, N1 suppression (Talk-Listen), N1 during Talking and Listening were assessed. Simple effects of the passage of time were also assessed in the HC after 28 weeks.

RESULTS

There was a Treatment × Time interaction revealing that N1 suppression was improved with TAT, but not with CG. TAT, but not CG, also improved Global Cognition. However, TAT and CG groups differed in their pre-treatment N1 suppression, and greater N1-suppression abnormalities were strongly associated with greater improvement in N1 suppression.

CONCLUSIONS

In this sample of ESZ individuals, targeted auditory training appeared to improve the function of the efference copy/corollary discharge mechanism which tended to deteriorate with computer games. It remains to be determined if baseline N1 suppression abnormalities are necessary for TAT treatment to have a positive effect on efference copy/corollary discharge function or if improvements observed in this study represent a regression to the mean N1 suppression in ESZ.

TRIAL REGISTRATION

ClinicalTrials.govNCT00694889. Registered 1 August 2007.

Neurofeedback-Based Enhancement of Single-Trial Auditory Evoked Potentials: Treatment of Auditory Verbal Hallucinations in Schizophrenia

Auditory verbal hallucinations depend on a broad neurobiological network ranging from the auditory system to language as well as memory-related processes. As part of this, the auditory N100 event-related potential (ERP) component is attenuated in patients with schizophrenia, with stronger attenuation occurring during auditory verbal hallucinations. Changes in the N100 component assumingly reflect disturbed responsiveness of the auditory system toward external stimuli in schizophrenia. With this premise, we investigated the therapeutic utility of neurofeedback training to modulate the auditory-evoked N100 component in patients with schizophrenia and associated auditory verbal hallucinations. Ten patients completed electroencephalography neurofeedback training for modulation of N100 (treatment condition) or another unrelated component, P200 (control condition). On a behavioral level, only the control group showed a tendency for symptom improvement in the Positive and Negative Syndrome Scale total score in a pre-/postcomparison ( t₍₄₎ = 2.71, P = .054); however, no significant differences were found in specific hallucination related symptoms ( t₍₇₎ = -0.53, P = .62). There was no significant overall effect of neurofeedback training on ERP components in our paradigm; however, we were able to identify different learning patterns, and found a correlation between learning and improvement in auditory verbal hallucination symptoms across training sessions ( r = 0.664, n = 9, P = .05). This effect results, with cautious interpretation due to the small sample size, primarily from the treatment group ( r = 0.97, n = 4, P = .03). In particular, a within-session learning parameter showed utility for predicting symptom improvement with neurofeedback training. In conclusion, patients with schizophrenia and associated auditory verbal hallucinations who exhibit a learning pattern more characterized by within-session aptitude may benefit from electroencephalography neurofeedback. Furthermore, independent of the training group, a significant spatial pre-post difference was found in the event-related component P200 ( P = .04).

Prediction of Speech Sounds Is Facilitated by a Functional Fronto-Temporal Network

Predictive coding postulates that the brain continually predicts forthcoming sensory events based on past experiences in order to process sensory information and respond to unexpected events in a fast and efficient manner. Predictive coding models in the context of overt speech are believed to operate along auditory white matter pathways such as the arcuate fasciculus and the frontal aslant. The aim of this study was to investigate whether brain regions that are structurally connected via these white matter pathways are also effectively engaged when listening to externally-generated, temporally-predicable speech sounds. Using Electroencephalography (EEG) and Dynamic Causal Modeling (DCM) we investigated network models that are structurally connected via the arcuate fasciculus from primary auditory cortex to Wernicke’s and via Geschwind’s territory to Broca’s area. Connections between Broca’s and supplementary motor area, which are structurally connected by the frontal aslant, were also included. The results revealed that bilateral areas interconnected by indirect and direct pathways of the arcuate fasciculus, in addition to regions interconnected by the frontal aslant best explain the EEG responses to speech that is externally-generated but temporally predictable. These findings indicate that structurally connected brain regions involved in the production and processing of auditory stimuli are also effectively connected.

Neurofeedback-Based Enhancement of Single Trial Auditory Evoked Potentials: Feasibility in Healthy Subjects

Previous studies showed a global reduction of the event-related potential component N100 in patients with schizophrenia, a phenomenon that is even more pronounced during auditory verbal hallucinations. This reduction assumingly results from dysfunctional activation of the primary auditory cortex by inner speech, which reduces its responsiveness to external stimuli. With this study, we tested the feasibility of enhancing the responsiveness of the primary auditory cortex to external stimuli with an upregulation of the event-related potential component N100 in healthy control subjects. A total of 15 healthy subjects performed 8 double-sessions of EEG-neurofeedback training over 2 weeks. The results of the used linear mixed effect model showed a significant active learning effect within sessions ( t = 5.99, P < .001) against an unspecific habituation effect that lowered the N100 amplitude over time. Across sessions, a significant increase in the passive condition ( t = 2.42, P = .03), named as carry-over effect, was observed. Given that the carry-over effect is one of the ultimate aims of neurofeedback, it seems reasonable to apply this neurofeedback training protocol to influence the N100 amplitude in patients with schizophrenia. This intervention could provide an alternative treatment option for auditory verbal hallucinations in these patients.

Clustering-Constrained ICA for Ballistocardiogram Artifacts Removal in Simultaneous EEG-fMRI

Combination of electroencephalogram (EEG) recording and functional magnetic resonance imaging (fMRI) plays a potential role in neuroimaging due to its high spatial and temporal resolution. However, EEG is easily influenced by ballistocardiogram (BCG) artifacts and may cause false identification of the related EEG features, such as epileptic spikes. There are many related methods to remove them, however, they do not consider the time-varying features of BCG artifacts. In this paper, a novel method using clustering algorithm to catch the BCG artifacts' features and together with the constrained ICA (ccICA) is proposed to remove the BCG artifacts. We first applied this method to the simulated data, which was constructed by adding the BCG artifacts to the EEG signal obtained from the conventional environment. Then, our method was tested to demonstrate the effectiveness during EEG and fMRI experiments on 10 healthy subjects. In simulated data analysis, the value of error in signal amplitude (Er) computed by ccICA method was lower than those from other methods including AAS, OBS, and cICA (p < 0.005). In vivo data analysis, the Improvement of Normalized Power Spectrum (INPS) calculated by ccICA method in all electrodes was much higher than AAS, OBS, and cICA methods (p < 0.005). We also used other evaluation index (e.g., power analysis) to compare our method with other traditional methods. In conclusion, our novel method successfully and effectively removed BCG artifacts in both simulated and vivo EEG data tests, showing the potentials of removing artifacts in EEG-fMRI applications.