Aberrant activity in conceptual networks underlies N400 deficits and unusual thoughts in schizophrenia

Neural mechanisms of attentional bias to emotional faces in patients with chronic insomnia disorder

OBJECTIVE

This study used event-related potential (ERP) and resting-state functional connectivity (rs-FC) approaches to investigate the neural mechanisms underlying the emotional attention bias in patients with chronic insomnia disorder (CID).

METHODS

Twenty-five patients with CID and thirty-three demographically matched healthy controls (HCs) completed clinical questionnaires and underwent electroencephalography (EEG) and functional magnetic resonance imaging (fMRI) scans. EEG analysis examined the group differences in terms of reaction times, P3 amplitudes, event-related spectral perturbations, and inter-trial phase synchrony. Subsequently, seed-based rs-FC analysis of the amygdala nuclei (including the central-medial amygdala [CMA] and basolateral amygdala [BLA]) was performed. The relationship between P3 amplitude, rs-FC and clinical symptom severity in patients with CID was further investigated by correlation analysis.

RESULTS

CID patients exhibited shorter reaction times than HCs in both standard and deviant stimuli, with the abnormalities becoming more pronounced as attention allocation increased. Compared to HCs, ERP analysis revealed increased P3 amplitude, theta wave power, and inter-trial synchrony in CID patients. The rs-FC analysis showed increased connectivity of the BLA-occipital pole, CMA-precuneus, and CMA-angular gyrus and decreased connectivity of the CMA-thalamus in CID patients. Notably, correlation analysis of the EEG and fMRI measurements showed a significant positive correlation between the P3 amplitude and the rs-FC of the CMA-PCU.

CONCLUSION

This study confirms an emotional attention bias in CID, specifically in the neural mechanisms of attention processing that vary depending on the allocation of attentional resources. Abnormal connectivity in the emotion-cognition networks may constitute the neural basis of the abnormal scalp activation pattern.

Disembodied Language in Early-Onset Schizophrenia

A recent view on schizophrenia phenomenology underlines the impaired relations between the mind and the body. An aberrant feeling of ipseity may be the real source of suffering of the patients from psychosis and impacts general symptomatology. The disturbed connection between thinking processes and environmental stimuli may lead to language disembodiment. In the study, we aimed to experimentally test the presence of disembodied language and investigate its association with symptoms of psychosis in adolescents diagnosed with early-onset schizophrenia spectrum disorders. Assessment of language embodiment was conducted using the Zabór Verbal Task (ZVT) with concurrent linguistic and clinical assessment using the Thought, Language, and Communication Scale (TLCS) and Positive and Negative Symptoms Scale (PANSS). The study group of patients (n = 31) aged 11-18 years, with the diagnosis of schizophrenia spectrum according to Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV) and the International Classification of Diseases (ICD-10) criteria, was compared with a sex- and age-matched healthy control sample (n = 31). Patients with psychosis made more errors in ZVT than healthy controls (p = 0.01) and this parameter did not improve after 6-8 weeks of standard treatment (p = 0.55). A higher number of errors in ZVT were associated with the presence of auditory hallucinations (odds ratio [OR] 1.14; 95% CI 1.02-1.26). ZVT errors coincided with perception disorders, alternatively to the TLCS scores where we observed association with abnormal beliefs. The results of these preliminary studies indicate the value of the phenomenological approach in the diagnosis of schizophrenia spectrum and suggest a potential involvement of language disembodiment in symptomatology.

Semantic priming and neurobiology in schizophrenia: A theoretical review

In this theoretical review we bridge the cognitive and neurobiological sciences to shed light on the neurocognitive foundations of the semantic priming effect in schizophrenia. We review and theoretically evaluate the neurotransmitter systems (dopaminergic, GABAergic and glutamatergic) and neurobiological underpinnings of behavioural and electrophysiological (N400) semantic priming in the pathology, and the main hypotheses on their geneses: a disinhibition of the semantic spread of activation, a disorganised semantic storage or noisy lexical-semantic associations, a psychomotor artefact, an artefact of relatedness proportions, or an inability to mobilise contextual information. We further assess the literature on the endophenotype of Formal Thought Disorder from multiple standpoints, ranging from neurophysiology to cognition: considerations are weaved on neuronal (PV basket cell, SST, VIP) and receptor deficits (DRD1, NMDA), neurotransmitter imbalances (dopamine), cortical and dopaminergic lateralisation, inter alia. In conclusion, we put forth novel postulates on the underlying causes of controlled hypopriming, automatic hyperpriming, N400 reversals (larger amplitudes for close associations), indirect versus direct hyperpriming, and the endophenotype of lexical-semantic disturbances in schizophrenia.

Evaluation of N-400 Evoked Response Potential in schizophrenia: An endophenotype or a disease marker?

N400 evoked response potentials (ERP) reliably map key semantic deficits in schizophrenia. Assessing them as endophenotypes might help in better understanding of schizophrenia risk and their use as biomarkers. We aimed to study N400 as an endophenotype marker by comparing schizophrenia (SCZ), unaffected first-degree relatives (FDR) and healthy controls (HC) and, by assessing its ability to discriminate these groups. Drug naïve or free SCZ probands (n=30), their unaffected FDRs (n=30) and HC (n=30), underwent a 40-channel ERP recording while performing a custom-made, Hindi- sentence context paradigm task, containing congruent and incongruent conditions. Fifteen centro-parietal (CP) leads, further classified into three regions-midline (CPM), right (CPR) and left (CPL) were selected as electrodes-of-interest for assessing N400. During the incongruent condition, compared to both FDRs and HC, SCZ showed significantly longer N400 latency, at CPM, CPR and CPL, and significantly lesser (more negative) amplitude, at CPM; no significant difference was noted between FDR and HC groups. On discriminant functional analysis, significant N400 predictors could accurately classify 73.3% SCZ from HC and 75% of SCZ from FDR. We conclude that N400 deficits, elicited by the incongruent condition of the sentence task, could be potential biomarkers to define disease state in schizophrenia; they may not be endophenotype markers.

Testing Psychosis Phenotypes From Bipolar-Schizophrenia Network for Intermediate Phenotypes for Clinical Application: Biotype Characteristics and Targets

BACKGROUND

Psychiatry aspires to the molecular understanding of its disorders and, with that knowledge, to precision medicine. Research supporting such goals in the dimension of psychosis has been compromised, in part, by using phenomenology alone to estimate disease entities. To this end, we are proponents of a deep phenotyping approach in psychosis, using computational strategies to discover the most informative phenotypic fingerprint as a promising strategy to uncover mechanisms in psychosis.

METHODS

Doing this, the Bipolar-Schizophrenia Network for Intermediate Phenotypes (B-SNIP) has used biomarkers to identify distinct subtypes of psychosis with replicable biomarker characteristics. While we have presented these entities as relevant, their potential utility in clinical practice has not yet been demonstrated.

RESULTS

Here we carried out an analysis of clinical features that characterize biotypes. We found that biotypes have unique and defining clinical characteristics that could be used as initial screens in the clinical and research settings. Differences in these clinical features appear to be consistent with biotype biomarker profiles, indicating a link between biological features and clinical presentation. Clinical features associated with biotypes differ from those associated with DSM diagnoses, indicating that biotypes and DSM syndromes are not redundant and are likely to yield different treatment predictions. We highlight 3 predictions based on biotype that are derived from individual biomarker features and cannot be obtained from DSM psychosis syndromes.

CONCLUSIONS

In the future, biotypes may prove to be useful for targeting distinct molecular, circuit, cognitive, and psychosocial therapies for improved functional outcomes.

Biomarkers and neuromodulation techniques in substance use disorders

Addictive disorders are a severe health concern. Conventional therapies have just moderate success and the probability of relapse after treatment remains high. Brain stimulation techniques, such as transcranial Direct Current Stimulation (tDCS) and Deep Brain Stimulation (DBS), have been shown to be effective in reducing subjectively rated substance craving. However, there are few objective and measurable parameters that reflect neural mechanisms of addictive disorders and relapse. Key electrophysiological features that characterize substance related changes in neural processing are Event-Related Potentials (ERP). These high temporal resolution measurements of brain activity are able to identify neurocognitive correlates of addictive behaviours. Moreover, ERP have shown utility as biomarkers to predict treatment outcome and relapse probability. A future direction for the treatment of addiction might include neural interfaces able to detect addiction-related neurophysiological parameters and deploy neuromodulation adapted to the identified pathological features in a closed-loop fashion. Such systems may go beyond electrical recording and stimulation to employ sensing and neuromodulation in the pharmacological domain as well as advanced signal analysis and machine learning algorithms. In this review, we describe the state-of-the-art in the treatment of addictive disorders with electrical brain stimulation and its effect on addiction-related neurophysiological markers. We discuss advanced signal processing approaches and multi-modal neural interfaces as building blocks in future bioelectronics systems for treatment of addictive disorders.