EEG-Based Measures in At-Risk Mental State and Early Stages of Schizophrenia: A Systematic Review

Model-Based Approaches to Investigating Mismatch Responses in Schizophrenia

Alterations of mismatch responses (ie, neural activity evoked by unexpected stimuli) are often considered a potential biomarker of schizophrenia. Going beyond establishing the type of observed alterations found in diagnosed patients and related cohorts, computational methods can yield valuable insights into the underlying disruptions of neural mechanisms and cognitive function. Here, we adopt a typology of model-based approaches from computational cognitive neuroscience, providing an overview of the study of mismatch responses and their alterations in schizophrenia from four complementary perspectives: (a) connectivity models, (b) decoding models, (c) neural network models, and (d) cognitive models. Connectivity models aim at inferring the effective connectivity patterns between brain regions that may underlie mismatch responses measured at the sensor level. Decoding models use multivariate spatiotemporal mismatch response patterns to infer the type of sensory violations or to classify participants based on their diagnosis. Neural network models such as deep convolutional neural networks can be used for improved classification performance as well as for a systematic study of various aspects of empirical data. Finally, cognitive models quantify mismatch responses in terms of signaling and updating perceptual predictions over time. In addition to describing the available methodology and reviewing the results of recent computational psychiatry studies, we offer suggestions for future work applying model-based techniques to advance the study of mismatch responses in schizophrenia.

Genetic analysis of psychosis Biotypes: shared Ancestry-adjusted polygenic risk and unique genomic associations

The Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP) created psychosis Biotypes based on neurobiological measurements in a multi-ancestry sample. These Biotypes cut across DSM diagnoses of schizophrenia, schizoaffective disorder, and bipolar disorder with psychosis. Two recently developed post hoc ancestry adjustment methods of Polygenic Risk Scores (PRSs) generate Ancestry-Adjusted PRSs (AAPRSs), which allow for PRS analysis of multi-ancestry samples. Applied to schizophrenia PRS, we found the Khera AAPRS method to show superior portability and comparable prediction accuracy as compared with the Ge method. The three Biotypes of psychosis disorders had similar AAPRSs across ancestries. In genomic analysis of Biotypes, 12 genes, and isoforms showed significant genomic associations with specific Biotypes in a Transcriptome-Wide Association Study (TWAS) of genetically regulated expression (GReX) in the adult brain and fetal brain. TWAS inflation was addressed by the inclusion of genotype principal components in the association analyses. Seven of these 12 genes/isoforms satisfied Mendelian Randomization (MR) criteria for putative causality, including four genes TMEM140, ARTN, C1orf115, CYREN, and three transcripts ENSG00000272941, ENSG00000257176, ENSG00000287733. These genes are enriched in the biological pathways of Rearranged during Transfection (RET) signaling, Neural Cell Adhesion Molecule 1 (NCAM1) interactions, and NCAM signaling for neurite out-growth. The specific associations with Biotypes suggest that pharmacological clinical trials and biological investigations might benefit from analyzing Biotypes separately.

Electrophysiological measures of patients with unilateral spatial neglect after brain disease: A systematic review

INTRODUCTION

The investigation of brainwave changes during the recovery process of unilateral spatial neglect (USN) has garnered considerable attention in recent years. This paper presents an updated overview of the evolving brainwave patterns during USN rehabilitation, aiming to predict clinical outcomes and guide the selection of effective recovery strategies.

METHODS

A systematic review was conducted, encompassing English literature published up to June 2024. Databases including PubMed, Web of Science, and clinical trials were utilized. The included studies assessed brainwaves using electroencephalography (EEG) in at least one group with USN. However, the diverse nature of these studies posed challenges for a quantitative synthesis.

RESULTS

The final quantitative synthesis comprised 36 studies, incorporating a total of 4517 data points. The analysis revealed abnormalities in alpha, beta, and gamma brainwave activity, along with alterations in the functional monitoring of the alpha band during USN rehabilitation. Additionally, reductions were observed in specific brainwave components such as P1, N1, P2, P300, early directing attention negativity (EDAN), late directing attention positivity (LDAP), and contingent negative variation (CNV). However, findings regarding measures of synchrony, connectivity, and evoked responses across different frequency bands exhibited variability.

CONCLUSIONS

Various indicators of brainwave activity displayed changes at different stages of post-stroke neglect rehabilitation, highlighting the significance of neural network dysfunction in this process. Nonetheless, due to the diversity of the studies, further investigation is necessary to achieve a more comprehensive understanding in future research endeavors.

Genetic Analysis of Psychosis Biotypes: Shared Ancestry-Adjusted Polygenic Risk and Unique Genomic Associations

The Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP) created psychosis Biotypes based on neurobiological measurements in a multi-ancestry sample. These Biotypes cut across DSM diagnoses of schizophrenia, schizoaffective disorder and bipolar disorder with psychosis. Two recently developed post hoc ancestry adjustment methods of Polygenic Risk Scores (PRSs) generate Ancestry-Adjusted PRSs (AAPRSs), which allow for PRS analysis of multi-ancestry samples. Applied to schizophrenia PRS, we found the Khera AAPRS method to show superior portability and comparable prediction accuracy as compared with the Ge method. The three Biotypes of psychosis disorders had similar AAPRSs across ancestries. In genomic analysis of Biotypes, 12 genes and isoforms showed significant genomic associations with specific Biotypes in Transcriptome-Wide Association Study (TWAS) of genetically regulated expression (GReX) in adult brain and fetal brain. TWAS inflation was addressed by inclusion of genotype principal components in the association analyses. Seven of these 12 genes/isoforms satisfied Mendelian Randomization (MR) criteria for putative causality, including four genes TMEM140, ARTN, C1orf115, CYREN, and three transcripts ENSG00000272941, ENSG00000257176, ENSG00000287733. These genes are enriched in the biological pathways of Rearranged during Transfection (RET) signaling, Neural Cell Adhesion Molecule 1 (NCAM1) interactions, and NCAM signaling for neurite out-growth. The specific associations with Biotypes suggest that pharmacological clinical trials and biological investigations might benefit from analyzing Biotypes separately.

Biomarker discovery using machine learning in the psychosis spectrum

The past decade witnessed substantial discoveries related to the psychosis spectrum. Many of these discoveries resulted from pursuits of objective and quantifiable biomarkers in tandem with the application of analytical tools such as machine learning. These approaches provided exciting new insights that significantly helped improve precision in diagnosis, prognosis, and treatment. This article provides an overview of how machine learning has been employed in recent biomarker discovery research in the psychosis spectrum, which includes schizophrenia, schizoaffective disorders, bipolar disorder with psychosis, first episode psychosis, and clinical high risk for psychosis. It highlights both human and animal model studies and explores a varying range of the most impactful biomarkers including cognition, neuroimaging, electrophysiology, and digital markers. We specifically highlight new applications and opportunities for machine learning to impact noninvasive symptom monitoring, prediction of future diagnosis and treatment outcomes, integration of new methods with traditional clinical research and practice, and personalized medicine approaches.

Advances, challenges, and prospects of electroencephalography-based biomarkers for psychiatric disorders: a narrative review

Owing to a lack of appropriate biomarkers for accurate diagnosis and treatment, psychiatric disorders cause significant distress and functional impairment, leading to social and economic losses. Biomarkers are essential for diagnosing, predicting, treating, and monitoring various diseases. However, their absence in psychiatry is linked to the complex structure of the brain and the lack of direct monitoring modalities. This review examines the potential of electroencephalography (EEG) as a neurophysiological tool for identifying psychiatric biomarkers. EEG noninvasively measures brain electrophysiological activity and is used to diagnose neurological disorders, such as depression, bipolar disorder (BD), and schizophrenia, and identify psychiatric biomarkers. Despite extensive research, EEG-based biomarkers have not been clinically utilized owing to measurement and analysis constraints. EEG studies have revealed spectral and complexity measures for depression, brainwave abnormalities in BD, and power spectral abnormalities in schizophrenia. However, no EEG-based biomarkers are currently used clinically for the treatment of psychiatric disorders. The advantages of EEG include real-time data acquisition, noninvasiveness, cost-effectiveness, and high temporal resolution. Challenges such as low spatial resolution, susceptibility to interference, and complexity of data interpretation limit its clinical application. Integrating EEG with other neuroimaging techniques, advanced signal processing, and standardized protocols is essential to overcome these limitations. Artificial intelligence may enhance EEG analysis and biomarker discovery, potentially transforming psychiatric care by providing early diagnosis, personalized treatment, and improved disease progression monitoring.

Neurophysiological patterns reflecting vulnerability to delirium subtypes: a resting-state EEG and event-related potential study

Delirium is a common and acute neurocognitive disorder in older adults associated with increased risk of dementia and death. Understanding the interaction between brain vulnerability and acute stressors is key to delirium pathophysiology, but the neurophysiology of delirium vulnerability is not well defined. This study aimed to identify pre-operative resting-state EEG and event-related potential markers of incident delirium and its subtypes in older adults undergoing elective cardiac procedures. This prospective observational study included 58 older participants (mean age = 75.6 years, SD = 7.1; 46 male/12 female); COVID-19 restrictions limited recruitment. Baseline assessments were conducted in the weeks before elective cardiac procedures and included a 4-min resting-state EEG recording (2-min eyes open and 2-min eyes closed), a 5-min frequency auditory oddball paradigm recording, and cognitive and depression examinations. Periodic peak power, peak frequency and bandwidth measures, and aperiodic offsets and exponents were derived from resting-state EEG data. Event-related potentials were measured as mean component amplitudes (first positive component, first negative component, early third positive component, and mismatch negativity) following standard and deviant auditory stimuli. Incident delirium occurred in 21 participants: 10 hypoactive, 6 mixed, and 5 hyperactive. Incident hyperactive delirium was associated with higher pre-operative eyes open (P = 0.045, d = 1.0) and closed (P = 0.036, d = 1.0) aperiodic offsets. Incident mixed delirium was associated with significantly larger pre-operative first positive component amplitudes to deviants (P = 0.037, d = 1.0) and larger third positive component amplitudes to standards (P = 0.025, d = 1.0) and deviants (P = 0.041, d = 0.9). Other statistically non-significant but moderate-to-large effects were observed in relation to all subtypes. We report evidence of neurophysiological markers of delirium risk weeks prior to elective cardiac procedures in older adults. Despite being underpowered due to COVID-19-related recruitment impacts, these findings indicate pre-operative dysfunction in neural excitation/inhibition balance associated with different delirium subtypes and warrant further investigation on a larger scale.

ℛSCZ: A Riemannian schizophrenia diagnosis framework based on the multiplexity of EEG-based dynamic functional connectivity patterns

Abnormal electrophysiological (EEG) activity has been largely reported in schizophrenia (SCZ). In the last decade, research has focused to the automatic diagnosis of SCZ via the investigation of an EEG aberrant activity and connectivity linked to this mental disorder. These studies followed various preprocessing steps of EEG activity focusing on frequency-dependent functional connectivity brain network (FCBN) construction disregarding the topological dependency among edges. FCBN belongs to a family of symmetric positive definite (SPD) matrices forming the Riemannian manifold. Due to its unique geometric properties, the whole analysis of FCBN can be performed on the Riemannian geometry of the SPD space. The advantage of the analysis of FCBN on the SPD space is that it takes into account all the pairwise interdependencies as a whole. However, only a few studies have adopted a FCBN analysis on the SPD manifold, while no study exists on the analysis of dynamic FCBN (dFCBN) tailored to SCZ. In the present study, I analyzed two open EEG-SCZ datasets under a Riemannian geometry of SPD matrices for the dFCBN analysis proposing also a multiplexity index that quantifies the associations of multi-frequency brainwave patterns. I adopted a machine learning procedure employing a leave-one-subject-out cross-validation (LOSO-CV) using snapshots of dFCBN from (N-1) subjects to train a battery of classifiers. Each classifier operated in the inter-subject dFCBN distances of sample covariance matrices (SCMs) following a rhythm-dependent decision and a multiplex-dependent one. The proposed ℛSCZ decoder supported both the Riemannian geometry of SPD and the multiplexity index DC reaching an absolute accuracy (100 %) in both datasets in the virtual default mode network (DMN) source space.

Schizophrenia genomics: genetic complexity and functional insights

Determining the causes of schizophrenia has been a notoriously intractable problem, resistant to a multitude of investigative approaches over centuries. In recent decades, genomic studies have delivered hundreds of robust findings that implicate nearly 300 common genetic variants (via genome-wide association studies) and more than 20 rare variants (via whole-exome sequencing and copy number variant studies) as risk factors for schizophrenia. In parallel, functional genomic and neurobiological studies have provided exceptionally detailed information about the cellular composition of the brain and its interconnections in neurotypical individuals and, increasingly, in those with schizophrenia. Taken together, these results suggest unexpected complexity in the mechanisms that drive schizophrenia, pointing to the involvement of ensembles of genes (polygenicity) rather than single-gene causation. In this Review, we describe what we now know about the genetics of schizophrenia and consider the neurobiological implications of this information.

Is parental overcontrol a specific form of child maltreatment? Insights from a resting state EEG connectivity study

INTRODUCTION

Recent studies suggest that parental overcontrol could be considered a specific form of childhood trauma (CT). Although previous research has shown that CT alters the functional and structural architecture of large-scale networks in the brain, the neural basis associated with parental overcontrol has not been sufficiently explored. Therefore, the main aim of the current study was to investigate the relationship between parental overcontrol and electroencephalography (EEG) triple network (TN) functional connectivity during the resting state (RS) condition in a non-clinical sample (N = 71; 39 females, mean age 23.94 ± 5.89 SD).

METHODS

EEG was recorded during 5 min of RS with eyes closed. All participants were asked to self-report maternal and paternal overcontrol, CT and general psychopathology. All EEG analyses were performed using the exact low-resolution electromagnetic tomography software (eLORETA).

RESULTS

Our results showed a significant positive correlation between maternal overcontrol and theta connectivity between the salience network and the central executive network. This connectivity pattern was independently associated with maternal overcontrol even when controlling for relevant confounding variables, including the severity of CT and the general level of psychopathology. This neurophysiological pattern may reflect a predisposition to detect and respond to potentially threatening stimuli in the environment, which is typically associated with excessive overcontrol.

CONCLUSIONS

Our findings support the hypothesis that parental overcontrol should be considered a form of CT in all respects independent of the forms traditionally studied in the literature (i.e., emotional abuse, physical abuse, sexual abuse, and physical and emotional neglect).

Resting-state electroencephalogram in drug-free subjects with at-risk mental states who later developed psychosis: a low-resolution electromagnetic tomography analysis

Background and objectives

Several studies have reported on the resting-state electroencephalogram (EEG) power in patients with schizophrenia, with a decrease in α (especially α2) and an increase in δ and β1 power compared with healthy control; however, reports on at-risk mental states (ARMS) are few. In this study, we measured the resting-state EEG power in ARMS, and investigated its features and the relationship between the power of the frequency bands and their diagnostic outcomes.

Methods

Patients with ARMS who were not on any psychotropic medication and met the Comprehensive Assessment of At-Risk Mental State criteria were included. Patients who developed psychotic disorders were labeled as the ARMS-P group, while patients with ARMS who were followed up prospectively for more than 2 years and did not develop psychotic disorders were classified as the ARMS-NP group. EEGs were measured in the resting state, and frequencies were analyzed using standardized low-resolution brain electromagnetic tomography (sLORETA). Seven bands (δ, θ, α1, α2, β1-3) underwent analysis. The sLORETA values (current source density [CSD]) were compared between the ARMS-P and ARMS-NP groups. Clinical symptoms were assessed at the time of EEG measurements using the Positive and Negative Syndrome Scale (PANSS).

Results

Of the 39 patients included (25 males, 14 females, 18.8 ± 4.5 years old), eight developed psychotic disorders (ARMS-P). The ARMS-P group exhibited significantly higher CSD in the β1 power within areas of the left middle frontal gyrus (MFG) compared with the ARMS-NP group (best match: X = -35, Y = 25, Z = 50 [MNI coordinates], Area 8, CSD = 2.33, p < 0.05). There was a significant positive correlation between the β1/α ratio of the CSD at left MFG and the Somatic concern score measured by the PANSS.

Discussion

Increased β1 power was observed in the resting EEG before the onset of psychosis and correlated with a symptom. This suggests that resting EEG power may be a useful marker for predicting future conversion to psychosis and clinical symptoms in patients with ARMS.

Revisiting Rorschach test and Minnesota Multiphasic Personality Inventory-II patterns in Kraepelinian vs. DSM-wise Schizophrenia: How They Differ and What It Means for Diagnosis

OBJECTIVE

This retrospective chart review study aimed to investigate the differences in the Rorschach test and Minnesota Multiphasic Personality Inventory (MMPI)-II profiles among patients with Kraepelinian schizophrenia, those with DSM-wise schizophrenia, and controls. Kraepelinian schizophrenia is characterised by a chronic, deteriorative disease course and a predominance of negative symptoms.

METHODS

Patients with Kraepelinian schizophrenia were selected based on medical record reviews. We then compared their Rorschach test and MMPI-II results with those of the DSM-wise schizophrenia group and the control group.

RESULTS

The Rorschach test revealed a significant increase in DV2 score and a decrease in D score in patients with Kraepelinian schizophrenia compared to those with DSM-wise schizophrenia. In the MMPI-II profiles, patients with Kraepelinian schizophrenia exhibited an elevated L relative to those with DSM-wise schizophrenia.

CONCLUSION

Our results suggested the value of revisiting psychological tests in clinically delineated subgroups, such as Kraepelinian schizophrenia. Although patients fall under the same diagnostic category of schizophrenia, considering different phenotypes is important when interpreting psychological test outcomes. Additionally, our study indicated that both schizophrenia groups did not show as many abnormalities as expected compared to controls. This highlights the potential value of revisiting established profiles of certain psychological tests and calls for further research on other psychological tests.

A Dynamical Systems View of Psychiatric Disorders-Practical Implications: A Review

Importance

Dynamical systems theory is widely used to explain tipping points, cycles, and chaos in complex systems ranging from the climate to ecosystems. It has been suggested that the same theory may be used to explain the nature and dynamics of psychiatric disorders, which may come and go with symptoms changing over a lifetime. Here we review evidence for the practical applicability of this theory and its quantitative tools in psychiatry.

Observations

Emerging results suggest that time series of mood and behavior may be used to monitor the resilience of patients using the same generic dynamical indicators that are now employed globally to monitor the risks of collapse of complex systems, such as tropical rainforest and tipping elements of the climate system. Other dynamical systems tools used in ecology and climate science open ways to infer personalized webs of causality for patients that may be used to identify targets for intervention. Meanwhile, experiences in ecological restoration help make sense of the occasional long-term success of short interventions.

Conclusions and Relevance

Those observations, while promising, evoke follow-up questions on how best to collect dynamic data, infer informative timescales, construct mechanistic models, and measure the effect of interventions on resilience. Done well, monitoring resilience to inform well-timed interventions may be integrated into approaches that give patients an active role in the lifelong challenge of managing their resilience and knowing when to seek professional help.

Pattern glare sensitivity distinguishes subclinical autism and schizotypy

INTRODUCTION

Schizophrenia and autism spectrum disorder are distinct neurodevelopmental disorders sharing clinically relevant behaviours. However, early sensory responses show divergent responses. Individuals with schizophrenia typically exhibit cortical hypo-excitability whereas individuals with autism show cortical hyperexcitability. Identifying reliable neurobiological differences between the disorders can diminish misdiagnosis and optimise treatments.

METHODS

The pattern glare test (PGT) is a simple measure of behavioural hyperexcitability. It measures the number of illusions seen in a static horizontal grating. We collected PGT data from non-clinical adults varying in traits of autism and schizophrenia (schizotypy). 576 undergraduate students completed an online survey consisting of the Schizotypal Personality Questionnaire - Brief Revised, the Autism Spectrum Quotient, and the PGT.

RESULTS

Subclinical autism and schizotypy traits were highly positively correlated. However, only schizotypy scores were significantly predictive of reporting more pattern glare (PG) illusions. When assessing the subcomponents of the schizotypy and autism scores, positive and disorganised schizotypy traits were predictive of reporting more PG illusions. Whereas, subclinical autism factors were not predictive of PG illusions.

CONCLUSIONS

High schizotypy performed the PGT in a manner consistent with behavioural hyperexcitability. The PGT distinguished subclinical autistic traits from schizotypy, suggesting potential clinical application.

Processing of auditory novelty in human cortex during a semantic categorization task

Auditory semantic novelty - a new meaningful sound in the context of a predictable acoustical environment - can probe neural circuits involved in language processing. Aberrant novelty detection is a feature of many neuropsychiatric disorders. This large-scale human intracranial electrophysiology study examined the spatial distribution of gamma and alpha power and auditory evoked potentials (AEP) associated with responses to unexpected words during performance of semantic categorization tasks. Participants were neurosurgical patients undergoing monitoring for medically intractable epilepsy. Each task included repeatedly presented monosyllabic words from different talkers ("common") and ten words presented only once ("novel"). Targets were words belonging to a specific semantic category. Novelty effects were defined as differences between neural responses to novel and common words. Novelty increased task difficulty and was associated with augmented gamma, suppressed alpha power, and AEP differences broadly distributed across the cortex. Gamma novelty effect had the highest prevalence in planum temporale, posterior superior temporal gyrus (STG) and pars triangularis of the inferior frontal gyrus; alpha in anterolateral Heschl's gyrus (HG), anterior STG and middle anterior cingulate cortex; AEP in posteromedial HG, lower bank of the superior temporal sulcus, and planum polare. Gamma novelty effect had a higher prevalence in dorsal than ventral auditory-related areas. Novelty effects were more pronounced in the left hemisphere. Better novel target detection was associated with reduced gamma novelty effect within auditory cortex and enhanced gamma effect within prefrontal and sensorimotor cortex. Alpha and AEP novelty effects were generally more prevalent in better performing participants. Multiple areas, including auditory cortex on the superior temporal plane, featured AEP novelty effect within the time frame of P3a and N400 scalp-recorded novelty-related potentials. This work provides a detailed account of auditory novelty in a paradigm that directly examined brain regions associated with semantic processing. Future studies may aid in the development of objective measures to assess the integrity of semantic novelty processing in clinical populations.

Gamma Oscillations and Potassium Channel Modulation in Schizophrenia: Targeting GABAergic Dysfunction

Impairments in gamma-aminobutyric acid (GABAergic) interneuron function lead to gamma power abnormalities and are thought to underlie symptoms in people with schizophrenia. Voltage-gated potassium 3.1 (Kv3.1) and 3.2 (Kv3.2) channels on GABAergic interneurons are critical to the generation of gamma oscillations suggesting that targeting Kv3.1/3.2 could augment GABAergic function and modulate gamma oscillation generation. Here, we studied the effect of a novel potassium Kv3.1/3.2 channel modulator, AUT00206, on resting state frontal gamma power in people with schizophrenia. We found a significant positive correlation between frontal resting gamma (35-45 Hz) power (n = 22, r = 0.613, P < .002) and positive and negative syndrome scale (PANSS) positive symptom severity. We also found a significant reduction in frontal gamma power (t13 = 3.635, P = .003) from baseline in patients who received AUT00206. This provides initial evidence that the Kv3.1/3.2 potassium channel modulator, AUT00206, may address gamma oscillation abnormalities in schizophrenia.

Frequency Analysis of EEG Microstate Sequences in Wakefulness and NREM Sleep

The majority of EEG microstate analyses concern wakefulness, and the existing sleep studies have focused on changes in spatial microstate properties and on microstate transitions between adjacent time points, the shortest available time scale. We present a more extensive time series analysis of unsmoothed EEG microstate sequences in wakefulness and non-REM sleep stages across many time scales. Very short time scales are assessed with Markov tests, intermediate time scales by the entropy rate and long time scales by a spectral analysis which identifies characteristic microstate frequencies. During the descent from wakefulness to sleep stage N3, we find that the increasing mean microstate duration is a gradual phenomenon explained by a continuous slowing of microstate dynamics as described by the relaxation time of the transition probability matrix. The finite entropy rate, which considers longer microstate histories, shows that microstate sequences become more predictable (less random) with decreasing vigilance level. Accordingly, the Markov property is absent in wakefulness but in sleep stage N3, 10/19 subjects have microstate sequences compatible with a second-order Markov process. A spectral microstate analysis is performed by comparing the time-lagged mutual information coefficients of microstate sequences with the autocorrelation function of the underlying EEG. We find periodic microstate behavior in all vigilance states, linked to alpha frequencies in wakefulness, theta activity in N1, sleep spindle frequencies in N2, and in the delta frequency band in N3. In summary, we show that EEG microstates are a dynamic phenomenon with oscillatory properties that slow down in sleep and are coupled to specific EEG frequencies across several sleep stages.

Proteomic profiling in the progression of psychosis: Analysis of clinical high-risk, first episode psychosis, and healthy controls

BACKGROUND AND HYPOTHESIS

Recent evidence has highlighted the benefits of early detection and treatment for better clinical outcomes in patients with psychosis. Biological markers of the disease have become a focal point of research. This study aimed to identify protein markers detectable in the early stages of psychosis and indicators of progression by comparing them with those of healthy controls (HC) and first episode psychosis (FEP).

STUDY DESIGN

The participants comprised 28 patients in the clinical high-risk (CHR) group, 49 patients with FEP, and 61 HCs aged 15-35 years. Blood samples were collected and analyzed using multiple reaction monitoring-mass spectrometry to measure the expression of 158 peptide targets. Data were adjusted for age, sex, and use of psychotropic drugs.

STUDY RESULTS

A total of 18 peptides (17 proteins) differed significantly among the groups. The protein PRDX2 was higher in the FEP group than in the CHR and HC groups and showed increased expression according to disease progression. The levels of six proteins were significantly higher in the FEP group than in the CHR group. Nine proteins differed significantly in the CHR group compared to the other groups. Sixteen proteins were significantly correlated with symptom severity. These proteins are primarily related to the coagulation cascade, inflammatory response, brain structure, and synaptic plasticity.

CONCLUSIONS

Our findings suggested that peripheral protein markers reflect disease progression in patients with psychosis. Further longitudinal research is needed to confirm these findings and to identify the specific roles of these markers in the pathogenesis of schizophrenia.

[The relationship between the functional state of the brain and clinical and constitutional factors in schizophrenia]

OBJECTIVE

To establish the relationships of functional changes of the brain of patients with schizophrenia with clinical manifestations of the disease and their constitutional and morphological features.

MATERIAL AND METHODS

One hundred and eighteen patients with schizophrenia (64 men and 54 women), aged 33 [29; 40], years were examined. The following clinical and dynamic parameters were used: age of manifestation of the disease, duration of the disease, severity of clinical and psychopathological symptoms according to the PANSS. The anthropometric examination of patients was carried out according to V.V. Bunak's method in V.P. Chitetsov's modification for adult samples with calculation of Rees-Eysenk and Tanner indices. The EEG was recorded and analyzed in a state of calm, relaxed wakefulness with closed eyes with the calculation of the absolute spectral power for theta (4-7 Hz), alpha (8-13 Hz) and beta (14-30 Hz) rhythms.

RESULTS

Significant (p<0.05) direct correlations between the age of the disease manifestation and the spectral power of the beta rhythm in the frontal leads (Fp1, Fp2, F3 and F4) were revealed. Inverse correlations (p<0.05) were found between the duration of the disease in patients with schizophrenia and the spectral power of the alpha rhythm in the left temporal (T3) and right central leads (C4), the spectral power of the beta rhythm in the parietal-occipital (P3, P4, O1,O2) and temporal leads (T3, T4, T5), the spectral power of the theta rhythm in the left occipital (O1) and posterior temporal leads (T5). Significant inverse correlations were also found between the Tanner index and the spectral power of the alpha rhythm in the frontal and temporal leads, between the Rees-Eysenk index and the spectral power of the theta rhythm in the frontal leads.

CONCLUSION

The results indicate the presence of the conjugation of functional changes in the brain of patients with schizophrenia with clinical manifestations of the disease and their constitutional and morphological features. Thus, the assessment of the functional state of the central nervous system in patients with schizophrenia is an important component of the diagnostic search.

Correlations between Negative Symptoms and Cognitive Deficits in Individuals at First Psychotic Episode or at High Risk of Psychosis: A Systematic Review

The present review aims to identify correlations between negative symptoms (NS) and deficits in neurocognition and social cognition in subjects with first-episode psychosis (FEP) and at-high-risk populations (HR). A systematic search of the literature published between 1 January 2005 and 31 December 2022 was conducted on PubMed, Scopus, and PsycInfo. Out of the 4599 records identified, a total of 32 studies met our inclusion/exclusion criteria. Data on a total of 3086 FEP and 1732 HR were collected. The available evidence shows that NS correlate with executive functioning and theory of mind deficits in FEP subjects, and with deficits in the processing speed, attention and vigilance, and working memory in HR subjects. Visual learning and memory do not correlate with NS in either FEP or HR subjects. More inconsistent findings were retrieved in relation to other cognitive domains in both samples. The available evidence is limited by sample and methodological heterogeneity across studies and was rated as poor or average quality for the majority of included studies in both FEP and CHR populations. Further research based on shared definitions of first-episode psychosis and at-risk states, as well as on more recent conceptualizations of negative symptoms and cognitive impairment, is highly needed.

A Network Model of the Modulation of γ Oscillations by NMDA Receptors in Cerebral Cortex

Psychotic drugs such as ketamine induce symptoms close to schizophrenia and stimulate the production of γ oscillations, as also seen in patients, but the underlying mechanisms are still unclear. Here, we have used computational models of cortical networks generating γ oscillations, and have integrated the action of drugs such as ketamine to partially block NMDA receptors (NMDARs). The model can reproduce the paradoxical increase of γ oscillations by NMDA receptor antagonists, assuming that antagonists affect NMDA receptors with higher affinity on inhibitory interneurons. We next used the model to compare the responsiveness of the network to external stimuli, and found that when NMDA channels are blocked, an increase of γ power is observed altogether with an increase of network responsiveness. However, this responsiveness increase applies not only to γ states, but also to asynchronous states with no apparent γ. We conclude that NMDA antagonists induce an increased excitability state, which may or may not produce γ oscillations, but the response to external inputs is exacerbated, which may explain phenomena such as altered perception or hallucinations.

Value of P300 amplitude in the diagnosis of untreated first-episode schizophrenia and psychosis risk syndrome in children and adolescents

BACKGROUND

Identifying the characteristic neurobiological changes of early psychosis is helpful for early clinical diagnosis. However, previous studies on the brain electrophysiology of children and adolescents with psychosis are rare.

METHODS

This study compared P300 amplitude at multiple electrodes between children and adolescents with first-episode schizophrenia (FES, n = 48), children and adolescents with psychosis risk syndrome (PRS, n = 24), and healthy controls (HC, n = 30). Receiver operating characteristic (ROC) analysis was used to test the ability of P300 amplitude to distinguish FES, PRS and HC individuals.

RESULTS

The P300 amplitude in the FES group were significantly lower than those in the HC at the Cz, Pz, and Oz electrodes. The P300 amplitude was also significantly lower in the prodromal group than in the HC at the Pz and Oz electrodes. ROC curve analysis showed that at the Pz electrode, the P300 amplitude evoked by the target and standard stimulus showed high sensitivity, specificity, accuracy, and area under the curve value for distinguishing FES from HC individuals.

CONCLUSIONS

This study found early visual P300 deficits in children and adolescents with FES and PRS, with the exclusion of possible influence of medication and chronic medical conditions, suggesting the value of P300 amplitude for the identification of early psychosis.

Predictors of transition in patients with clinical high risk for psychosis: an umbrella review

Diagnosis of a clinical high-risk (CHR) state enables timely treatment of individuals at risk for a psychotic disorder, thereby contributing to improving illness outcomes. However, only a minority of patients diagnosed with CHR will make the transition to overt psychosis. To identify patients most likely to benefit from early intervention, several studies have investigated characteristics that distinguish CHR patients who will later develop a psychotic disorder from those who will not. We aimed to summarize evidence from systematic reviews and meta-analyses on predictors of transition to psychosis in CHR patients, among characteristics and biomarkers assessed at baseline. A systematic search was conducted in Pubmed, Scopus, PsychInfo and Cochrane databases to identify reviews and meta-analyses of studies that investigated specific baseline predictors or biomarkers for transition to psychosis in CHR patients using a cross-sectional or longitudinal design. Non-peer-reviewed publications, gray literature, narrative reviews and publications not written in English were excluded from analyses. We provide a narrative synthesis of results from all included reviews and meta-analyses. For each included publication, we indicate the number of studies cited in each domain and its quality rating. A total of 40 publications (21 systematic reviews and 19 meta-analyses) that reviewed a total of 272 original studies qualified for inclusion. Baseline predictors most consistently associated with later transition included clinical characteristics such as attenuated psychotic and negative symptoms and functioning, verbal memory deficits and the electrophysiological marker of mismatch negativity. Few predictors reached a level of evidence sufficient to inform clinical practice, reflecting generalizability issues in a field characterized by studies with small, heterogeneous samples and relatively few transition events. Sample pooling and harmonization of methods across sites and projects are necessary to overcome these limitations.

The future of diagnosis in clinical neurosciences: Comparing multiple sclerosis and schizophrenia

The ongoing developments of psychiatric classification systems have largely improved reliability of diagnosis, including that of schizophrenia. However, with an unknown pathophysiology and lacking biomarkers, its validity still remains low, requiring further advancements. Research has helped establish multiple sclerosis (MS) as the central nervous system (CNS) disorder with an established pathophysiology, defined biomarkers and therefore good validity and significantly improved treatment options. Before proposing next steps in research that aim to improve the diagnostic process of schizophrenia, it is imperative to recognize its clinical heterogeneity. Indeed, individuals with schizophrenia show high interindividual variability in terms of symptomatic manifestation, response to treatment, course of illness and functional outcomes. There is also a multiplicity of risk factors that contribute to the development of schizophrenia. Moreover, accumulating evidence indicates that several dimensions of psychopathology and risk factors cross current diagnostic categorizations. Schizophrenia shares a number of similarities with MS, which is a demyelinating disease of the CNS. These similarities appear in the context of age of onset, geographical distribution, involvement of immune-inflammatory processes, neurocognitive impairment and various trajectories of illness course. This article provides a critical appraisal of diagnostic process in schizophrenia, taking into consideration advancements that have been made in the diagnosis and management of MS. Based on the comparison between the two disorders, key directions for studies that aim to improve diagnostic process in schizophrenia are formulated. All of them converge on the necessity to deconstruct the psychosis spectrum and adopt dimensional approaches with deep phenotyping to refine current diagnostic boundaries.

Aperiodic Neural Activity is a Better Predictor of Schizophrenia than Neural Oscillations

Diagnosis and symptom severity in schizophrenia are associated with irregularities across neural oscillatory frequency bands, including theta, alpha, beta, and gamma. However, electroencephalographic signals consist of both periodic and aperiodic activity characterized by the (1/fX) shape in the power spectrum. In this paper, we investigated oscillatory and aperiodic activity differences between patients with schizophrenia and healthy controls during a target detection task. Separation into periodic and aperiodic components revealed that the steepness of the power spectrum better-predicted group status than traditional band-limited oscillatory power in classification analysis. Aperiodic activity also outperformed the predictions made using participants' behavioral responses. Additionally, the differences in aperiodic activity were highly consistent across all electrodes. In sum, compared to oscillations the aperiodic activity appears to be a more accurate and more robust way to differentiate patients with schizophrenia from healthy controls.

Reduced duration mismatch negativity elicited by the multi-feature 'optimal' paradigm in early-phase psychosis

BACKGROUND

MMN and P3a are EEG-derived event related potentials that are thought to be prospective biomarkers for schizophrenia and, potentially, early-phase psychosis (EPP).

METHODS

EPP (n = 12) and healthy control (n = 35) participants listened to a multi-feature optimal paradigm with five deviant types (gap, duration, location, intensity, and frequency).

RESULTS

There was a significant amplitude difference between the EPP and HC group with duration MMN (p =.02). No significant amplitude differences between groups were found for the P3a waveform. There were several correlations for the EPP group with the BNSS, SOFAS, and PANSS-general questionnaires. Length of illness was not associated with MMN or P3a.

CONCLUSIONS

The optimal paradigm is suitable for eliciting multiple deviant types within a short amount of time in both clinical and healthy populations. This study confirms duration MMN deficits within an EPP group and that MMN is related to functional outcomes and positive and negative symptomology.

Online Left-Hemispheric In-Phase Frontoparietal Theta tACS Modulates Theta-Band EEG Source-Based Large-Scale Functional Network Connectivity in Patients with Schizophrenia: A Randomized, Double-Blind, Sham-Controlled Clinical Trial

EEG studies indicated that schizophrenia patients had increased resting-state theta-band functional connectivity, which was associated with negative symptoms. We recently published the first study showing that theta (6 Hz) transcranial alternating current stimulation (tACS) over left prefrontal and parietal cortices during a working memory task for accentuating frontoparietal theta-band synchronization (in-phase theta-tACS) reduced negative symptoms in schizophrenia patients. Here, we hypothesized that in-phase theta-tACS can modulate theta-band large-scale networks connectivity in schizophrenia patients. In this randomized, double-blind, sham-controlled trial, patients received twice-daily, 2 mA, 20-min sessions of in-phase theta-tACS for 5 consecutive weekdays (n = 18) or a sham stimulation (n = 18). Resting-state electroencephalography data were collected at baseline, end of stimulation, and at one-week follow-up. Exact low resolution electromagnetic tomography (eLORETA) was used to compute intra-cortical activity. Lagged phase synchronization (LPS) was used to measure whole-brain source-based functional connectivity across 84 cortical regions at theta frequency (5-7 Hz). EEG data from 35 patients were analyzed. We found that in-phase theta-tACS significantly reduced the LPS between the posterior cingulate (PC) and the parahippocampal gyrus (PHG) in the right hemisphere only at the end of stimulation relative to sham (p = 0.0009, corrected). The reduction in right hemispheric PC-PHG LPS was significantly correlated with negative symptom improvement at the end of the stimulation (r = 0.503, p = 0.039). Our findings suggest that in-phase theta-tACS can modulate theta-band large-scale functional connectivity pertaining to negative symptoms. Considering the failure of right hemispheric PC-PHG functional connectivity to predict improvement in negative symptoms at one-week follow-up, future studies should investigate whether it can serve as a surrogate of treatment response to theta-tACS.

The psychotomimetic ketamine disrupts the transfer of late sensory information in the corticothalamic network

In prodromal and early schizophrenia, disorders of attention and perception are associated with structural and chemical brain abnormalities and with dysfunctional corticothalamic networks exhibiting disturbed brain rhythms. The underlying mechanisms are elusive. The non-competitive NMDA receptor antagonist ketamine simulates the symptoms of prodromal and early schizophrenia, including disturbances in ongoing and task & sensory-related broadband beta-/gamma-frequency (17-29 Hz/30-80 Hz) oscillations in corticothalamic networks. In normal healthy subjects and rodents, complex integration processes, like sensory perception, induce transient, large-scale synchronised beta/gamma oscillations in a time window of a few hundred ms (200-700 ms) after the presentation of the object of attention (e.g., sensory stimulation). Our goal was to use an electrophysiological multisite network approach to investigate, in lightly anesthetised rats, the effects of a single psychotomimetic dose (2.5 mg/kg, subcutaneous) of ketamine on sensory stimulus-induced oscillations. Ketamine transiently increased the power of baseline beta/gamma oscillations and decreased sensory-induced beta/gamma oscillations. In addition, it disrupted information transferability in both the somatosensory thalamus and the related cortex and decreased the sensory-induced thalamocortical connectivity in the broadband gamma range. The present findings support the hypothesis that NMDA receptor antagonism disrupts the transfer of perceptual information in the somatosensory cortico-thalamo-cortical system.

Macroscale EEG characteristics in antipsychotic-naïve patients with first-episode psychosis and healthy controls

Electroencephalography in patients with a first episode of psychosis (FEP) may contribute to the diagnosis and treatment response prediction. Findings in the literature vary due to small sample sizes, medication effects, and variable illness duration. We studied macroscale resting-state EEG characteristics of antipsychotic naïve patients with FEP. We tested (1) for differences between FEP patients and controls, (2) if EEG could be used to classify patients as FEP, and (3) if EEG could be used to predict treatment response to antipsychotic medication. In total, we studied EEG recordings of 62 antipsychotic-naïve patients with FEP and 106 healthy controls. Spectral power, phase-based and amplitude-based functional connectivity, and macroscale network characteristics were analyzed, resulting in 60 EEG variables across four frequency bands. Positive and Negative Symptom Scale (PANSS) were assessed at baseline and 4-6 weeks follow-up after treatment with amisulpride or aripiprazole. Mann-Whitney U tests, a random forest (RF) classifier and RF regression were used for statistical analysis. Our study found that at baseline, FEP patients did not differ from controls in any of the EEG characteristics. A random forest classifier showed chance-level discrimination between patients and controls. The random forest regression explained 23% variance in positive symptom reduction after treatment in the patient group. In conclusion, in this largest antipsychotic- naïve EEG sample to date in FEP patients, we found no differences in macroscale EEG characteristics between patients with FEP and healthy controls. However, these EEG characteristics did show predictive value for positive symptom reduction following treatment with antipsychotic medication.

Emerging Wearable Biosensor Technologies for Stress Monitoring and Their Real-World Applications

Wearable devices are being developed faster and applied more widely. Wearables have been used to monitor movement-related physiological indices, including heartbeat, movement, and other exercise metrics, for health purposes. People are also paying more attention to mental health issues, such as stress management. Wearable devices can be used to monitor emotional status and provide preliminary diagnoses and guided training functions. The nervous system responds to stress, which directly affects eye movements and sweat secretion. Therefore, the changes in brain potential, eye potential, and cortisol content in sweat could be used to interpret emotional changes, fatigue levels, and physiological and psychological stress. To better assess users, stress-sensing devices can be integrated with applications to improve cognitive function, attention, sports performance, learning ability, and stress release. These application-related wearables can be used in medical diagnosis and treatment, such as for attention-deficit hyperactivity disorder (ADHD), traumatic stress syndrome, and insomnia, thus facilitating precision medicine. However, many factors contribute to data errors and incorrect assessments, including the various wearable devices, sensor types, data reception methods, data processing accuracy and algorithms, application reliability and validity, and actual user actions. Therefore, in the future, medical platforms for wearable devices and applications should be developed, and product implementations should be evaluated clinically to confirm product accuracy and perform reliable research.

Predictive Power of Cognitive Biomarkers in Neurodegenerative Disease Drug Development: Utility of the P300 Event-Related Potential

Neurodegenerative diseases, such as Alzheimer's disease (AD), and their associated deterioration of cognitive function are common causes of disability. The slowly developing pathology of neurodegenerative diseases necessitates early diagnosis and monitored long-term treatment. Lack of effective therapies coupled with an improved rate of early diagnosis in our aging population have created an urgent need for the development of novel drugs, as well as the need for reliable biomarkers for treatment response. These issues are especially relevant for AD, in which the rate of clinical trial drug failures has been very high. Frequently used biomarker evaluation procedures, such as positron emission tomography or cerebrospinal fluid measurements of phospho-tau and amyloid beta, are invasive and costly, and not universally available or accessible. This review considers the functionality of the event-related potential (ERP) P300 methodology as a surrogate biomarker for predicting the procognitive potential of drugs in clinical development for neurocognitive disorders. Through the application of standardized electroencephalography (EEG) described here, ERP P300 can be reliably measured. The P300 waveform objectively measures large-scale neuronal network functioning and working memory processes. Increased ERP P300 latency has been reported throughout the literature in disorders of cognition, supporting the potential utility of ERP P300 as a biomarker in many neurological and neuropsychiatric disorders, including AD. Specifically, evidence presented here supports ERP P300 latency as a quantitative, unbiased measure for detecting changes in cognition in patients with AD dementia through the progression from mild to moderate cognitive impairment and after drug treatment.

A scoping review of electroencephalographic (EEG) markers for tracking neurophysiological changes and predicting outcomes in substance use disorder treatment

Substance use disorders (SUDs) constitute a growing global health crisis, yet many limitations and challenges exist in SUD treatment research, including the lack of objective brain-based markers for tracking treatment outcomes. Electroencephalography (EEG) is a neurophysiological technique for measuring brain activity, and although much is known about EEG activity in acute and chronic substance use, knowledge regarding EEG in relation to abstinence and treatment outcomes is sparse. We performed a scoping review of longitudinal and pre-post treatment EEG studies that explored putative changes in brain function associated with abstinence and/or treatment in individuals with SUD. Following PRISMA guidelines, we identified studies published between January 2000 and March 2022 from online databases. Search keywords included EEG, addictive substances (e.g., alcohol, cocaine, methamphetamine), and treatment related terms (e.g., abstinence, relapse). Selected studies used EEG at least at one time point as a predictor of abstinence or other treatment-related outcomes; or examined pre- vs. post-SUD intervention (brain stimulation, pharmacological, behavioral) EEG effects. Studies were also rated on the risk of bias and quality using validated instruments. Forty-four studies met the inclusion criteria. More consistent findings included lower oddball P3 and higher resting beta at baseline predicting negative outcomes, and abstinence-mediated longitudinal decrease in cue-elicited P3 amplitude and resting beta power. Other findings included abstinence or treatment-related changes in late positive potential (LPP) and N2 amplitudes, as well as in delta and theta power. Existing studies were heterogeneous and limited in terms of specific substances of interest, brief times for follow-ups, and inconsistent or sparse results. Encouragingly, in this limited but maturing literature, many studies demonstrated partial associations of EEG markers with abstinence, treatment outcomes, or pre-post treatment-effects. Studies were generally of good quality in terms of risk of bias. More EEG studies are warranted to better understand abstinence- or treatment-mediated neural changes or to predict SUD treatment outcomes. Future research can benefit from prospective large-sample cohorts and the use of standardized methods such as task batteries. EEG markers elucidating the temporal dynamics of changes in brain function related to abstinence and/or treatment may enable evidence-based planning for more effective and targeted treatments, potentially pre-empting relapse or minimizing negative lifespan effects of SUD.

Effects of Atomoxetine on Motor and Cognitive Behaviors and Brain Electrophysiological Activity of Dopamine Transporter Knockout Rats

Changes in dopaminergic and noradrenergic transmission are considered to be the underlying cause of attention deficit and hyperactivity disorder (ADHD). Atomoxetine (ATX) is a selective norepinephrine transporter (NET) inhibitor that is currently used for ADHD treatment. In this study, we aimed to evaluate the effect of atomoxetine on the behavior and brain activity of dopamine transporter knockout (DAT-KO) rats, which are characterized by an ADHD-like behavioral phenotype. Prepulse inhibition (PPI) was assessed in DAT-KO and wild type rats after saline and ATX injections, as well as behavioral parameters in the Hebb-Williams maze and power spectra and coherence of electrophysiological activity. DAT-KO rats demonstrated a pronounced behavioral and electrophysiological phenotype, characterized by hyperactivity, increased number of errors in the maze, repetitive behaviors and disrupted PPI, changes in cortical and striatal power spectra and interareal coherence. Atomoxetine significantly improved PPI and decreased repetitive behaviors in DAT-KO rats and influenced behavior of wild-type rats. ATX also led to significant changes in power spectra and coherence of DAT-KO and wild type rats. Assessment of noradrenergic modulation effects in DAT-KO provides insight into the intricate interplay of monoaminergic systems, although further research is still required to fully understand the complexity of this interaction.

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

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

Potential diagnostic biomarkers for schizophrenia

Schizophrenia (SCH) is a complex and severe mental disorder with high prevalence, disability, mortality and carries a heavy disease burden, the lifetime prevalence of SCH is around 0.7%-1.0%, which has a profound impact on the individual and society. In the clinical practice of SCH, key problems such as subjective diagnosis, experiential treatment, and poor overall prognosis are still challenging. In recent years, some exciting discoveries have been made in the research on objective biomarkers of SCH, mainly focusing on genetic susceptibility genes, metabolic indicators, immune indices, brain imaging, electrophysiological characteristics. This review aims to summarize the biomarkers that may be used for the prediction and diagnosis of SCH.

Correlation Between Resting Theta Power and Cognitive Performance in Patients With Schizophrenia

Objective

Schizophrenia is a mental disorder that is characterized by progressive cognitive impairment. Objective measures of cognitive function may provide reliable neurobiomarkers for patients with schizophrenia. The goal of the current work is to explore the correlation between resting theta power and cognitive performance in patients with schizophrenia.

Methods

Twenty-two patients with schizophrenia and 23 age-, sex-, and education-matched healthy controls were included in this study. The MATRICS Consensus Cognitive Battery (MCCB) was used for cognitive evaluation and the Positive and Negative Syndrome Scale (PANSS) for evaluation of clinical symptoms. EEGs were acquired in the resting state with closed and opened eyes. Between the two groups, we compared the relative theta power and examined their relationship with cognitive performance.

Results

Compared to healthy controls, patients with schizophrenia showed significantly higher theta power, both with eyes closed and open (P < 0.05). When the eyes were open, negative correlations were found in patients with schizophrenia between theta power in the central and parietal regions with processing speed scores, and between the theta power of the Pz electrode and verbal learning and reasoning and problem-solving scores (r ≥ −0.446). In the control group, theta power over the Fz electrode was negatively correlated with processing speed (r = −0.435).

Conclusions

Our findings showed that theta activity increased in certain brain regions during resting state in schizophrenia. Negative associations between resting theta power (increased) over the parietal-occipital regions with MCCB domains scores (decreased) suggest that altered theta activity can be used as a neurobiological indicator to predict cognitive performance.

Deep Convolutional Neural Network-Based Visual Stimuli Classification Using Electroencephalography Signals of Healthy and Alzheimer’s Disease Subjects

Visual perception is an important part of human life. In the context of facial recognition, it allows us to distinguish between emotions and important facial features that distinguish one person from another. However, subjects suffering from memory loss face significant facial processing problems. If the perception of facial features is affected by memory impairment, then it is possible to classify visual stimuli using brain activity data from the visual processing regions of the brain. This study differentiates the aspects of familiarity and emotion by the inversion effect of the face and uses convolutional neural network (CNN) models (EEGNet, EEGNet SSVEP (steady-state visual evoked potentials), and DeepConvNet) to learn discriminative features from raw electroencephalography (EEG) signals. Due to the limited number of available EEG data samples, Generative Adversarial Networks (GAN) and Variational Autoencoders (VAE) are introduced to generate synthetic EEG signals. The generated data are used to pretrain the models, and the learned weights are initialized to train them on the real EEG data. We investigate minor facial characteristics in brain signals and the ability of deep CNN models to learn them. The effect of face inversion was studied, and it was observed that the N170 component has a considerable and sustained delay. As a result, emotional and familiarity stimuli were divided into two categories based on the posture of the face. The categories of upright and inverted stimuli have the smallest incidences of confusion. The model’s ability to learn the face-inversion effect is demonstrated once more.

Increased cingulo-orbital connectivity is associated with violent behaviours in schizophrenia

OBJECTIVE

Although schizophrenia patients are at a heightened risk of exhibiting violent behaviours compared to the general population, few functional neuroimaging studies have explored the aberrant neurocircuitry underpinning such behaviours. This study aimed to identify disrupted resting-state activity and functional connectivity in schizophrenia patients with a history of violence.

METHODS

Resting state functional magnetic resonance imaging data was collected from 62 schizophrenia patients and 25 healthy controls. Voxel-wise analyses of fractional amplitude of low frequency fluctuations (fALFF) were implemented to investigate disrupted regional patterns of spontaneous brain activity. Brain regions which yielded significant differences between groups were subsequently used as data-driven seeds for functional connectivity analyses. Finally, significant alterations (activity and connectivity) were correlated with lifetime violent behaviours.

RESULTS

When compared to healthy controls, schizophrenia patients exhibited reduced fALFF in multiple brain regions including the (subgenual) anterior cingulate cortex (ACC), posterior cingulate cortex, precuneus cortex and left lateral orbitofrontal cortex (OFC). Seed-to-voxel analyses yielded significantly enhanced connectivity between the ACC and left OFC. The heightened functional connectivity between the latter two regions predicted the number of violent behaviours reported by schizophrenia patients.

CONCLUSION

The current study demonstrated that the functional connectivity of brain regions associated with emotion regulation is impaired in schizophrenia and associated with violent antecedents among patients. This result is consistent with predominant theoretical models proposing that the OFC plays a critical role in the neurobiology of violence.

Real-Time Detection and Feedback of Canonical Electroencephalogram Microstates: Validating a Neurofeedback System as a Function of Delay

Recent neurotechnology has developed various methods for neurofeedback (NF), in which participants observe their own neural activity to be regulated in an ideal direction. EEG-microstates (EEGms) are spatially featured states that can be regulated through NF training, given that they have recently been indicated as biomarkers for some disorders. The current study was conducted to develop an EEG-NF system for detecting “canonical 4 EEGms” in real time. There are four representative EEG states, regardless of the number of channels, preprocessing procedures, or participants. Accordingly, our 10 Hz NF system was implemented to detect them (msA, B, C, and D) and audio-visually inform participants of its detection. To validate the real-time effect of this system on participants’ performance, the NF was intentionally delayed for participants to prevent their cognitive control in learning. Our results suggest that the feedback effect was observed only under the no-delay condition. The number of Hits increased significantly from the baseline period and increased from the 1- or 20-s delay conditions. In addition, when the Hits were compared among the msABCD, each cognitive or perceptual function could be characterized, though the correspondence between each microstate and psychological ability might not be that simple. For example, msD should be generally task-positive and less affected by the inserted delay, whereas msC is more delay-sensitive. In this study, we developed and validated a new EEGms-NF system as a function of delay. Although the participants were naive to the inserted delay, the real-time NF successfully increased their Hit performance, even within a single-day experiment, although target specificity remains unclear. Future research should examine long-term training effects using this NF system.

Factors influencing the outcome of integrated therapy approach in schizophrenia: A narrative review of the literature

The integration of pharmacotherapy with psychosocial interventions has an important role to play in the improvement of functional outcome of subjects with schizophrenia (SCZ), in all stages of the disorder. It is essential for the adequate management of unmet therapeutic needs, such as negative symptoms and cognitive dysfunctions which account for most of the functional impairment of subjects with SCZ and do not respond to available antipsychotics. Enhancing the knowledge on factors involved in the effectiveness of integrated treatment plans is an important step forward for SCZ care. This review aims to identify factors that might influence the impact of integrated treatments on functional outcome. Most studies on the impact of psychosocial treatments on functional outcome of subjects with SCZ did not control for the effect of prescribed antipsychotics or concomitant medications. However, several factors relevant to ongoing pharmacological treatment might influence the outcome of integrated therapy, with an impact on the adherence to treatment (e.g., therapeutic alliance and polypharmacotherapy) or on illness-related factors addressed by the psychosocial interventions (e.g., cognitive dysfunctions or motivational deficits). Indirect evidence suggests that treatment integration should consider the possible detrimental effects of different antipsychotics or concomitant medications on cognitive functions, as well as on secondary negative symptoms. Cognitive dysfunctions can interfere with participation to an integrated treatment plan and can be worsened by extrapyramidal or metabolic side effects of antipsychotics, or concomitant treatment with anticholinergics or benzodiazepines. Secondary negative symptoms, due to positive symptoms, sedation, extrapyramidal side effects or untreated depression, might cause early drop-out and poor adherence to treatment. Researchers and clinicians should examine all the above-mentioned factors and implement appropriate and personalized integrated treatments to improve the outcome of SCZ.

Abnormalities of Electroencephalography Microstates in Drug-Naïve, First-Episode Schizophrenia

OBJECTIVE

Microstate analysis is a powerful tool to probe the brain functions, and changes in microstates under electroencephalography (EEG) have been repeatedly reported in patients with schizophrenia. This study aimed to investigate the dynamics of EEG microstates in drug-naïve, first-episode schizophrenia (FE-SCH) and to test the relationship between EEG microstates and clinical symptoms.

METHODS

Resting-state EEG were recorded for 23 patients with FE-SCH and 23 healthy controls using a 64-channel cap. Three parameters, i.e., contribution, duration, and occurrence, of the four microstate classes were calculated. Group differences in EEG microstates and their clinical symptoms [assessed using the Positive and Negative Syndrome Scale (PANSS)] were analyzed.

RESULTS

Compared with healthy controls, patients with FE-SCH showed increased duration, occurrence and contribution of microstate class C and decreased contribution and occurrence of microstate class D. In addition, the score of positive symptoms in PANSS was negatively correlated with the occurrence of microstate D.

CONCLUSION

Our findings showed abnormal patterns of EEG microstates in drug-naïve, first-episode schizophrenia, which might help distinguish individuals with schizophrenia in the early stage and develop early intervention strategies.

Tracing Links Between Early Auditory Information Processing and Negative Symptoms in Schizophrenia: An ERP Study

Background: Negative symptoms represent a heterogeneous dimension with a strong impact on functioning of subjects with schizophrenia (SCZ). Five constructs are included in this dimension: anhedonia, asociality, avolition, blunted affect, and alogia. Factor analyses revealed that these symptoms cluster in two domains: experiential domain (avolition, asociality, and anhedonia) and the expressive deficit (alogia and blunted affect), that might be linked to different neurobiological alterations. Few studies investigated associations between N100, an electrophysiological index of early sensory processing, and negative symptoms, reporting controversial results. However, none of these studies investigated electrophysiological correlates of the two negative symptom domains. Objectives: The aim of our study was to evaluate, within the multicenter study of the Italian Network for Research on Psychoses, the relationships between N100 and negative symptom domains in SCZ. Methods: Auditory N100 was analyzed in 114 chronic stabilized SCZ and 63 healthy controls (HCs). Negative symptoms were assessed with the Brief Negative Symptom Scale (BNSS). Repeated measures ANOVA and correlation analyses were performed to evaluate differences between SCZ and HCs and association of N100 features with negative symptoms. Results: Our findings demonstrated a significant N100 amplitude reduction in SCZ compared with HCs. In SCZ, N100 amplitude for standard stimuli was associated with negative symptoms, in particular with the expressive deficit domain. Within the expressive deficit, blunted affect and alogia had the same pattern of correlation with N100. Conclusion: Our findings revealed an association between expressive deficit and N100, suggesting that these negative symptoms might be related to deficits in early auditory processing in SCZ.

Mismatch Negativity and P3a Impairment through Different Phases of Schizophrenia and Their Association with Real-Life Functioning

Impairment in functioning since the onset of psychosis and further deterioration over time is a key aspect of subjects with schizophrenia (SCZ). Mismatch negativity (MMN) and P3a, indices of early attention processing that are often impaired in schizophrenia, might represent optimal electrophysiological candidate biomarkers of illness progression and poor outcome. However, contrasting findings are reported about the relationships between MMN-P3a and functioning. The study aimed to investigate in SCZ the influence of illness duration on MMN-P3a and the relationship of MMN-P3a with functioning. Pitch (p) and duration (d) MMN-P3a were investigated in 117 SCZ and 61 healthy controls (HCs). SCZ were divided into four illness duration groups: ≤ 5, 6 to 13, 14 to 18, and 19 to 32 years. p-MMN and d-MMN amplitude was reduced in SCZ compared to HCs, independently from illness duration, psychopathology, and neurocognitive deficits. p-MMN reduction was associated with lower "Work skills". The p-P3a amplitude was reduced in the SCZ group with longest illness duration compared to HCs. No relationship between P3a and functioning was found. Our results suggested that MMN amplitude reduction might represent a biomarker of poor functioning in SCZ.

Investigating the Relationships of P3b with Negative Symptoms and Neurocognition in Subjects with Chronic Schizophrenia

Neurocognitive deficits and negative symptoms (NS) have a pivotal role in subjects with schizophrenia (SCZ) due to their impact on patients' functioning in everyday life and their influence on goal-directed behavior and decision-making. P3b is considered an optimal electrophysiological candidate biomarker of neurocognitive impairment for its association with the allocation of attentional resources to task-relevant stimuli, an important factor for efficient decision-making, as well as for motivation-related processes. Furthermore, associations between P3b deficits and NS have been reported. The current research aims to fill the lack of studies investigating, in the same subjects, the associations of P3b with multiple cognitive domains and the expressive and motivation-related domains of NS, evaluated with state-of-the-art instruments. One hundred and fourteen SCZ and 63 healthy controls (HCs) were included in the study. P3b amplitude was significantly reduced and P3b latency prolonged in SCZ as compared to HCs. In SCZ, a positive correlation was found between P3b latency and age and between P3b amplitude and the Attention-vigilance domain, while no significant correlations were found between P3b and the two NS domains. Our results indicate that the effortful allocation of attention to task-relevant stimuli, an important component of decision-making, is compromised in SCZ, independently of motivation deficits or other NS.

Decreased mismatch negativity and elevated frontal-lateral connectivity in first-episode psychosis

Decreased mismatch negativity (MMN) is a proposed biomarker for psychotic disorders. However, the magnitude of the effect appears to be attenuated in first-episode populations. Furthermore, how mismatch negativity amplitudes are related to brain connectivity in this population is unclear. In this study, we used high-density EEG to record duration-deviant MMN from 22 patients with first-episode psychosis (FEP) and 23 age-matched controls (HC). Consistent with past work, we found decreased MMN amplitude in FEP over a large area of the frontal scalp. We also found decreased latency over the occipital scalp. MMN amplitude was negatively correlated with antipsychotic dose. We used Granger causality to investigate directional connectivity between frontal, midline, left, and right scalp during MMN and found reduced connectivity in FEP compared to HC and following deviant stimuli compared to standard stimuli. FEP participants with smaller decreases in connectivity from standard to deviant stimuli had worse disorganization symptoms. On the other hand, connectivity from the front of the scalp following deviant stimuli was relatively preserved in FEP compared to controls. Our results suggest that a relative imbalance of bottom-up and top-down perceptual processing is present in the early stages of psychotic disorders.

Duration Mismatch Negativity Predicts Remission in First-Episode Schizophrenia Patients

Objective: Remission in schizophrenia patients is associated with neurocognitive, social, and role functioning during both the early and chronic stages of schizophrenia. It is well-established that the amplitudes of duration mismatch negativity (dMMN) and frequency MMN (fMMN) are reduced in schizophrenia patients. However, the potential link between MMN and remission has not been established. In this study, we investigated the relationship between MMNs and remission in first-episode schizophrenia (FES) and their association with neurocognitive and social functioning. Method: dMMN and fMMN were measured in 30 patients with FES and 22 healthy controls at baseline and after a mean of 3 years. Clinical symptoms and cognitive and social functioning in the patients were assessed at the time of MMN measurements by using the Positive and Negative Syndrome Scale (PANSS), modified Global Assessment of Functioning (mGAF), Schizophrenia Cognition Rating Scale (SCoRS), and the Brief Assessment of Cognition in Schizophrenia (BACS). Remission of the patients was defined using the criteria by the Remission in Schizophrenia Working Group; of the 30 patients with FES, 14 achieved remission and 16 did not. Results: Baseline dMMN amplitude was reduced in FES compared to healthy controls. Further, baseline dMMN in the non-remitters had decreased amplitude and prolonged latency compared to the remitters. MMN did not change during follow-up period regardless of parameters, diagnosis, or remission status. Baseline dMMN amplitude in FES was correlated with future SCoRS and PANSS total scores. Logistic regression analysis revealed that dMMN amplitude at baseline was a significant predictor of remission. Conclusions: Our findings suggest that dMMN amplitude may be a useful biomarker for predicting symptomatic remission and improvement of cognitive and social functions in FES.