Effects of Blocking D2/D3 Receptors on Mismatch Negativity and P3a Amplitude of Initially Antipsychotic Naïve, First Episode Schizophrenia Patients

Advancing treatment response prediction in first-episode psychosis: integrating clinical and electroencephalography features

AIMS

Prompt diagnosis and intervention are crucial for first-episode psychosis (FEP) outcomes, but predicting the response to antipsychotics remains challenging. We studied whether adding electroencephalography (EEG) characteristics improves clinical prediction models for treatment response and whether EEG-based predictors are influenced by initial treatment.

METHODS

We included 115 antipsychotic-naïve patients with FEP. Positive and Negative Syndrome Scale (PANSS) and sociodemographic items were included as clinical features. Additionally, we analyzed resting-state EEG data (n = 45) for (relative) power, functional connectivity, and network organization. Treatment response, measured as change in PANSS positive subscale scores (∆PANSS+), was predicted using a random forest regression model. We analyzed whether the most predictive EEG characteristics were influenced after treatment.

RESULTS

The clinical model explained 12% variance in symptom reduction in the training set and 32% in the validation set. Including EEG variables in the model led to a nonsignificant increase of 2% (total 34%) explained variance in symptom reduction. High hallucination symptom scores and a more hierarchical organization of alpha band networks (tree hierarchy) were associated with ∆PANSS+ reduction. The tree hierarchy in the alpha band decreased after medication. EEG source analysis revealed that this change was driven by alterations in the degree and centrality of frontal and parietal nodes in the functional brain network.

CONCLUSIONS

Both clinical and EEG characteristics can inform treatment response prediction in patients with FEP, but the combined model may not be beneficial over a clinical model. Nevertheless, adding a more objective marker such as EEG could be valuable in selected cases.

Unveiling distinct representations of P3a in schizophrenia through two-stimulus and three-stimulus auditory oddball paradigms

BACKGROUND

P3a event-related potential (ERP) is considered a potential biomarker for schizophrenia (SZ), can be elicited through both passive two-stimulus and active three-stimulus auditory oddball paradigms. While both types of P3a reflect involuntary attention shifts, the nuanced understanding of what P3a represents in different contexts is important and rarely studied. This study aims to examine correlations between P3a ERPs elicited from different paradigms and associations of each P3a with cognitive function, clinical symptoms, and antipsychotic medication.

METHODS

Our sample included 178 SZ patients and 127 healthy controls (HC). Data on two-stimulus paradigm, three-stimulus oddball paradigm, Chinese version of MATRICS Consensus Cognitive Battery (MCCB), symptom severity, and medication use were collected.

RESULTS

In both paradigms, SZ group's P3a amplitude was significantly reduced compared to HC's (both p < 0.05). P3a evoked by the two-stimulus paradigm and the three-stimulus paradigm were not correlated (r = -0.06, p = 0.661). Three-stimulus paradigm-P3a was significantly correlated with attention/vigilance (r = 0.27, p = 0.017) in SZ, and with working memory (r = 0.39, p = 0.001) and overall MCCB score (r = 0.25, p = 0.042) in HC. Additionally, the two-stimulus paradigm-P3a correlated with olanzapine equivalent antipsychotic dose (r = -0.26, p = 0.022).

CONCLUSIONS

Our findings offer new insights into the role of P3a in clinical research. P3a ERPs from different paradigms may represent functionally distinct components. The context in which the P3a is elicited should be taken into account when discussing its functional or neurocognitive significance.

Effect of antipsychotic on mismatch negativity amplitude and evoked theta power in drug-naïve patients with schizophrenia

BACKGROUND

Recurrent observations have indicated the presence of deficits in mismatch negativity (MMN) among schizophrenia. There is evidence suggesting a correlation between increased dopaminergic activity and reduced MMN amplitude, but there is no consensus on whether antipsychotic medications can improve MMN deficit in schizophrenia.

METHODS

We conducted clinical assessments, cognitive function tests, and EEG data collection and analysis on 31 drug-naïve patients with schizophrenia. Comprehensive evaluation tools such as PANSS and MCCB. MMN amplitude was analyzed by event-related potential (ERP) approaches, evoked theta power was analyzed by event-related spectral perturbation (ERSP) approaches.

RESULTS

Our findings indicate that antipsychotic treatment significantly improved clinical symptoms, as evidenced by reductions in PANSS positive, negative, general symptoms, and total scores (all p < 0.001). Cognitive function improvements were observed in language learning, working memory, and overall MCCB scores (p < 0.05), although other cognitive domains showed no significant changes. However, no significant improvements were noted in MMN amplitude and evoke theta power after four weeks of antipsychotic treatment (p > 0.05).

CONCLUSION

These results suggest that while antipsychotic medications effectively alleviate clinical symptoms, their impact on MMN amplitude and evoke theta power deficit is limited in the short term. Moreover, the amelioration of cognitive impairment in individuals with schizophrenia is not readily discernible, and it cannot be discounted that the enhancement observed in language acquisition and working memory may be attributed to a learning effect. These findings underscore the complexity of the neurobiological mechanisms involved and highlight the need for further research to optimize individualized treatment strategies for schizophrenia.

TRIAL REGISTRATION

ChiCTR2000038961, October 10, 2020.

An EEG-Based Neuroplastic Approach to Predictive Coding in People With Schizophrenia or Traumatic Brain Injury

Despite different etiologies, people with schizophrenia (SCZ) or with traumatic brain injury (TBI) both show aberrant neuroplasticity. One neuroplastic mechanism that may be affected is prediction error coding. We used a roving mismatch negativity (rMMN) paradigm which uses different lengths of standard tone trains and is optimized to assess predictive coding. Twenty-five SCZ, 22 TBI (mild to moderate), and 25 healthy controls were assessed. We used a frequency-deviant rMMN in which the number of standards preceding the deviant was either 2, 6, or 36. We evaluated repetition positivity to the standard tone immediately preceding a deviant tone (repetition positivity [RP], to assess formation of the memory trace), deviant negativity to the deviant stimulus (deviant negativity [DN], which reflects signaling of a prediction error), and the difference wave between the 2 (the MMN). We found that SCZ showed reduced DN and MMN compared with healthy controls and with people with mild to moderate TBI. We did not detect impairments in any index (RP, DN, or MMN) in people with TBI compared to controls. Our findings suggest that prediction error coding assessed with rMMN is aberrant in SCZ but intact in TBI, though there is a suggestion that severity of head injury results in poorer prediction error coding.

Clustering of antipsychotic-naïve patients with schizophrenia based on functional connectivity from resting-state electroencephalography

Schizophrenia is associated with aberrations in the Default Mode Network (DMN), but the clinical implications remain unclear. We applied data-driven, unsupervised machine learning based on resting-state electroencephalography (rsEEG) functional connectivity within the DMN to cluster antipsychotic-naïve patients with first-episode schizophrenia. The identified clusters were investigated with respect to psychopathological profile and cognitive deficits. Thirty-seven antipsychotic-naïve, first-episode patients with schizophrenia (mean age 24.4 (5.4); 59.5% males) and 97 matched healthy controls (mean age 24.0 (5.1); 52.6% males) underwent assessments of rsEEG, psychopathology, and cognition. Source-localized, frequency-dependent functional connectivity was estimated using Phase Lag Index (PLI). The DMN-PLI was factorized for each frequency band using principal component analysis. Clusters of patients were identified using a Gaussian mixture model and neurocognitive and psychopathological profiles of identified clusters were explored. We identified two clusters of patients based on the theta band (4-8 Hz), and two clusters based on the beta band (12-30 Hz). Baseline psychopathology could predict theta clusters with an accuracy of 69.4% (p = 0.003), primarily driven by negative symptoms. Five a priori selected cognitive functions conjointly predicted the beta clusters with an accuracy of 63.6% (p = 0.034). The two beta clusters displayed higher and lower DMN connectivity, respectively, compared to healthy controls. In conclusion, the functional connectivity within the DMN provides a novel, data-driven means to stratify patients into clinically relevant clusters. The results support the notion of biological subgroups in schizophrenia and endorse the application of data-driven methods to recognize pathophysiological patterns at earliest stage of this syndrome.

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.

Mismatch negativity and P3a amplitude in children with familial high risk of schizophrenia or bipolar disorder - A Danish register-based EEG study

BACKGROUND

Infrequent deviants in a rapid sequence of sounds elicit a negative cortical potential over the frontocentral midline (mismatch negativity, MMN) followed by a positive deflection (P3a). Both cortical potentials are consistently attenuated in patients with schizophrenia (SZ), and, to a lesser degree, in patients with bipolar disorder (BP).

OBJECTIVE

Since it is unclear when MMN and P3a deficits arise relative to the emergence of symptoms, we examined whether MMN and P3a alterations are already detectable in children with familial high risk.

METHODS

Using 128-channel electroencephalography, we recorded auditory MMN and P3a evoked by a deviation in sound duration, frequency, or both in 51 children with familial high-risk for SZ (FHR-SZ), 41 children with familial high-risk for BP (FHR-BP), and 39 population-based children (PBC) at a mean age of 12.10.

RESULTS

MMN amplitude evoked by a duration deviant was larger in children with FHR-BP compared to PBC and FHR-SZ. P3a amplitude in response to a duration ∗ frequency deviant was larger in children with FHR-BP compared to children with FHR-SZ, but not compared to PBC. MMN- and P3a-peak latency did not differ between groups.

CONCLUSIONS

At an age of around 12 years, children with FHR-BP display enhanced neural sensitivity to change detection of duration deviants, while FHR-SZ showed a normal response pattern. Longitudinal recordings in high-risk children during adolescence are required to elucidate the temporal trajectories of MMN and P3a responses and how they relate to the emergence of first clinical symptoms in SZ and BP.

Evaluation of multi-feature auditory deviance detection in Parkinson's disease: a mismatch negativity study

Behavioral studies on auditory deviance detection in patients with Parkinson's disease (PD) have reported contradictory results. The primary aim of this study was to investigate auditory deviance detection of multiple auditory features in patients with PD by means of objective and reliable electroencephalographic (EEG) measurements. Twelve patients with early-stage PD and twelve age- and gender-matched healthy controls (HCs) were included in this study. Patients with PD participated without their regular dopaminergic medication. All subjects underwent an audiometric screening and performed a passive multi-feature mismatch negativity (MMN) paradigm. Repeated-measures analysis of variance (ANOVA) demonstrated no significant differences between patients with PD and HCs regarding MMN mean amplitude and latency for frequency, duration and gap deviants. Nevertheless, a trend towards increased MMN mean amplitude and latency was found in response to intensity deviants in patients with PD compared to HCs. Increased intensity MMN amplitude may indicate that more neural resources are allocated to the processing of intensity deviances in patients with PD compared to HCs. The interpretation of this intensity-specific MMN alteration is further discussed in the context of a compensatory mechanism for auditory intensity processing and involuntary attention switching in PD.

The Deficit of Multimodal Perception of Congruent and Non-Congruent Fearful Expressions in Patients with Schizophrenia: The ERP Study

Emotional dysfunction, including flat affect and emotional perception deficits, is a specific symptom of schizophrenia disorder. We used a modified multimodal odd-ball paradigm with fearful facial expressions accompanied by congruent and non-congruent emotional vocalizations (sounds of women screaming and laughing) to investigate the impairment of emotional perception and reactions to other people's emotions in schizophrenia. We compared subjective ratings of emotional state and event-related potentials (EPPs) in response to congruent and non-congruent stimuli in patients with schizophrenia and healthy controls. The results showed the altered multimodal perception of fearful stimuli in patients with schizophrenia. The amplitude of N50 was significantly higher for non-congruent stimuli than congruent ones in the control group and did not differ in patients. The P100 and N200 amplitudes were higher in response to non-congruent stimuli in patients than in controls, implying impaired sensory gating in schizophrenia. The observed decrease of P3a and P3b amplitudes in patients could be associated with less attention, less emotional arousal, or incorrect interpretation of emotional valence, as patients differed from healthy controls in the emotion scores of non-congruent stimuli. The difficulties in identifying the incoherence of facial and audial components of emotional expression could be significant in understanding the psychopathology of schizophrenia.

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

Introduction: Electrophysiological (EEG) abnormalities in subjects with schizophrenia have been largely reported. In the last decades, research has shifted to the identification of electrophysiological alterations in the prodromal and early phases of the disorder, focusing on the prediction of clinical and functional outcome. The identification of neuronal aberrations in subjects with a first episode of psychosis (FEP) and in those at ultra high-risk (UHR) or clinical high-risk (CHR) to develop a psychosis is crucial to implement adequate interventions, reduce the rate of transition to psychosis, as well as the risk of irreversible functioning impairment. The aim of the review is to provide an up-to-date synthesis of the electrophysiological findings in the at-risk mental state and early stages of schizophrenia. Methods: A systematic review of English articles using Pubmed, Scopus, and PsychINFO was undertaken in July 2020. Additional studies were identified by hand-search. Electrophysiological studies that included at least one group of FEP or subjects at risk to develop psychosis, compared to healthy controls (HCs), were considered. The heterogeneity of the studies prevented a quantitative synthesis. Results: Out of 319 records screened, 133 studies were included in a final qualitative synthesis. Included studies were mainly carried out using frequency analysis, microstates and event-related potentials. The most common findings included an increase in delta and gamma power, an impairment in sensory gating assessed through P50 and N100 and a reduction of Mismatch Negativity and P300 amplitude in at-risk mental state and early stages of schizophrenia. Progressive changes in some of these electrophysiological measures were associated with transition to psychosis and disease course. Heterogeneous data have been reported for indices evaluating synchrony, connectivity, and evoked-responses in different frequency bands. Conclusions: Multiple EEG-indices were altered during at-risk mental state and early stages of schizophrenia, supporting the hypothesis that cerebral network dysfunctions appear already before the onset of the disorder. Some of these alterations demonstrated association with transition to psychosis or poor functional outcome. However, heterogeneity in subjects' inclusion criteria, clinical measures and electrophysiological methods prevents drawing solid conclusions. Large prospective studies are needed to consolidate findings concerning electrophysiological markers of clinical and functional outcome.

A machine-learning framework for robust and reliable prediction of short- and long-term treatment response in initially antipsychotic-naïve schizophrenia patients based on multimodal neuropsychiatric data

The reproducibility of machine-learning analyses in computational psychiatry is a growing concern. In a multimodal neuropsychiatric dataset of antipsychotic-naïve, first-episode schizophrenia patients, we discuss a workflow aimed at reducing bias and overfitting by invoking simulated data in the design process and analysis in two independent machine-learning approaches, one based on a single algorithm and the other incorporating an ensemble of algorithms. We aimed to (1) classify patients from controls to establish the framework, (2) predict short- and long-term treatment response, and (3) validate the methodological framework. We included 138 antipsychotic-naïve, first-episode schizophrenia patients with data on psychopathology, cognition, electrophysiology, and structural magnetic resonance imaging (MRI). Perinatal data and long-term outcome measures were obtained from Danish registers. Short-term treatment response was defined as change in Positive And Negative Syndrome Score (PANSS) after the initial antipsychotic treatment period. Baseline diagnostic classification algorithms also included data from 151 matched controls. Both approaches significantly classified patients from healthy controls with a balanced accuracy of 63.8% and 64.2%, respectively. Post-hoc analyses showed that the classification primarily was driven by the cognitive data. Neither approach predicted short- nor long-term treatment response. Validation of the framework showed that choice of algorithm and parameter settings in the real data was successfully guided by results from the simulated data. In conclusion, this novel approach holds promise as an important step to minimize bias and obtain reliable results with modest sample sizes when independent replication samples are not available.

Accuracy of diagnostic classification algorithms using cognitive-, electrophysiological-, and neuroanatomical data in antipsychotic-naïve schizophrenia patients

BACKGROUND

A wealth of clinical studies have identified objective biomarkers, which separate schizophrenia patients from healthy controls on a group level, but current diagnostic systems solely include clinical symptoms. In this study, we investigate if machine learning algorithms on multimodal data can serve as a framework for clinical translation.

METHODS

Forty-six antipsychotic-naïve, first-episode schizophrenia patients and 58 controls underwent neurocognitive tests, electrophysiology, and magnetic resonance imaging (MRI). Patients underwent clinical assessments before and after 6 weeks of antipsychotic monotherapy with amisulpride. Nine configurations of different supervised machine learning algorithms were applied to first estimate the unimodal diagnostic accuracy, and next to estimate the multimodal diagnostic accuracy. Finally, we explored the predictability of symptom remission.

RESULTS

Cognitive data significantly classified patients from controls (accuracies = 60-69%; p values = 0.0001-0.009). Accuracies of electrophysiology, structural MRI, and diffusion tensor imaging did not exceed chance level. Multimodal analyses with cognition plus any combination of one or more of the remaining three modalities did not outperform cognition alone. None of the modalities predicted symptom remission.

CONCLUSIONS

In this multivariate and multimodal study in antipsychotic-naïve patients, only cognition significantly discriminated patients from controls, and no modality appeared to predict short-term symptom remission. Overall, these findings add to the increasing call for cognition to be included in the definition of schizophrenia. To bring about the full potential of machine learning algorithms in first-episode, antipsychotic-naïve schizophrenia patients, careful a priori variable selection based on independent data as well as inclusion of other modalities may be required.

Sexual dysfunction and hyperprolactinemia in schizophrenia before and after six weeks of D2/3 receptor blockade - An exploratory study

Sexual side-effects along with antipsychotic treatment may be linked to hyperprolactinemia and dopamine D₂ receptor blockade. High prevalence of sexual dysfunction in un-medicated patients challenges the notion of sexual dysfunction as merely a side-effect of antipsychotic medication. Sexual dysfunction was assessed in fifty-six initially antipsychotic-naïve patients with schizophrenia using the UKU (Udvalget for Kliniske Undersøgelser) questionnaire. Serum-prolactin was obtained before and after six weeks of D_2/3 receptor blockade with amisulpride. At baseline 68% of patients reported one or more items of sexual dysfunction (males > females,), but the cumulative load of sexual dysfunction was similar in males and females. After 6 weeks treatment with amisulpride (mean dose 279 mg/day), 65% of patients reported one or more items of sexual dysfunctions (females > males). There was a significant sex*time interaction on mean sexual dysfunction load. All patients developed hyperprolactinaemia, and a significant effect of time and sex was found on s-prolactin (females > males). The results support that patients with schizophrenia report high levels of sexual dysfunction before antipsychotic exposure. After treatment, sexual side-effects were more frequent in females, coinciding with pronounced serum-prolactin increases. These findings suggest sex differences in sexual dysfunction before and after antipsychotic treatment.

Attenuated mismatch negativity in patients with first-episode antipsychotic-naive schizophrenia using a source-resolved method

BACKGROUND

Mismatch negativity (MMN) is a measure of pre-attentive auditory information processing related to change detection. Traditional scalp-level EEG methods consistently find attenuated MMN in patients with chronic but not first-episode schizophrenia. In the current paper, we use a source-resolved method to assess MMN and hypothesize that more subtle changes can be identified with this analysis method.

METHOD

Fifty-six first-episode antipsychotic-naïve schizophrenia (FEANS) patients (31 males, 25 females, mean age 24.6) and 64 matched controls (37 males, 27 females, mean age 24.8) were assessed for duration-, frequency- and combined-type MMN and P3a as well as 4 clinical, 3 cognitive and 3 psychopathological measures. To evaluate and correlate MMN at source-level, independent component analysis (ICA) was applied to the continuous EEG data to derive equivalent current dipoles which were clustered into 19 clusters based on cortical location.

RESULTS

No scalp channel group MMN or P3a amplitude differences were found. Of the localized clusters, several were in or near brain areas previously suggested to be involved in the MMN response, including frontal and anterior cingulate cortices and superior temporal and inferior frontal gyri. For duration deviants, MMN was attenuated at the right superior temporal gyrus in patients compared to healthy controls (p = 0.01), as was P3a at the superior frontal cortex (p = 0.01). No individual patient correlations with clinical, cognitive, or psychopathological measures survived correction for multiple comparisons.

CONCLUSION

Attenuated source-localized MMN and P3a peak contributions can be identified in FEANS patients using a method based on independent component analysis (ICA). This indicates that deficits in pre-attentive auditory information processing are present at this early stage of schizophrenia and are not the result of disease chronicity or medication. This is to our knowledge the first study on FEANS patients using this more detailed method.

Auditory Event-Related Potentials in Antipsychotic-Free Subjects With Ultra-High-Risk State and First-Episode Psychosis

Background: Auditory event-related potentials (ERPs) have been utilized to study defective information processing of patients with schizophrenia. To delineate the pathophysiological processes from pre-psychotic state to first-episode psychosis, a study on subjects from ultra-high-risk (UHR) state to first-episode psychosis, ideally in an antipsychotic-free condition, can add important information to our understanding. Methods: Patients with UHR state or at their first-episode psychosis (FEP) who were drug-naive or only have been temporarily treated with antipsychotics were assessed by auditory ERPs measurement, including P50/N100 (sensory gating) and duration mismatch negativity (MMN; deviance detection). A group of age-matched healthy subjects served as their controls. Results: A total of 42 patients (23 UHR and 19 FEP) and 120 control subjects were recruited, including 21 pure drug-naive and 21 with very short exposure to antipsychotics. Collapsing FEP and UHR as a patient group, they exhibited significant sensory deficits manifested as larger P50 S2 amplitude, larger N100 ratio, and smaller N100 difference, and significantly less deviance detection response revealed by MMN. Such differences were less significant when treating FEP and UHR separately for comparisons. Comparisons of ERP results between drug-naive subjects and antipsychotic-short-exposure subjects revealed no significant difference in any P50/N100 and MMN parameter. Conclusion: Our study is one of the few studies focused on drug-naive or minimally treated patients at pre- or early-psychotic states. Our results exhibited impaired performance in sensory gating and deviance detection shown by certain parameters. A longitudinal study with larger sample sizes will be helpful to provide more evidence to elucidate the role of antipsychotics on an individual's neurophysiological performance at different stages of psychosis.

Sensory prediction errors in the continuum of psychosis

Sensory prediction errors are fundamental brain responses that signal a violation of expectation in either the internal or external sensory environment, and are therefore crucial for survival and adaptive behaviour. Patients with schizophrenia show deficits in these internal and external sensory prediction errors, which can be measured using electroencephalography (EEG) components such as N1 and mismatch negativity (MMN), respectively. New evidence suggests that these deficits in sensory prediction errors are more widely distributed on a continuum of psychosis, whereas psychotic experiences exist to varying degrees throughout the general population. In this paper, we review recent findings in sensory prediction errors in the auditory domain across the continuum of psychosis, and discuss these in light of the predictive coding hypothesis.

Using EEG-Guided Basket and Umbrella Trials in Psychiatry: A Precision Medicine Approach for Cognitive Impairment in Schizophrenia

Due to advances over the last several decades, many fields of medicine are moving toward a precision medicine approach where treatments are tailored to nuanced patient factors. While in some disciplines these innovations are commonplace leading to unique biomarker-guided experimental medicine trials, there are no such analogs in psychiatry. In this brief review, we will overview two unique biomarker-guided trial designs for future use in psychiatry: basket and umbrella trials. We will illustrate how such trials could be useful in psychiatry using schizophrenia as a candidate illness, the EEG measure mismatch negativity as the candidate biomarker, and cognitive impairment as the target disease dimension.

Negative Symptoms and Reward Disturbances in Schizophrenia Before and After Antipsychotic Monotherapy

BACKGROUND

Negative symptoms (NS) are a central part of the symptomatology of schizophrenia, which is highly correlated to the functional outcome. Disturbances of the brain reward system are suggested to be central in the pathogenesis of NS by decreasing motivation and hedonic experiences. In this study, we compared reward-related brain activity in patients improving and not improving in NS after treatment with amisulpride.

METHODS

Thirty-nine antipsychotic-naive patients and 49 healthy controls completed functional magnetic resonance imaging with a modified monetary incentive delay task. Psychopathology of the patients was characterised with Positive and Negative Syndrome Scale (PANSS), and they were treated with individual doses of amisulpride (mean 271 mg) for 6 weeks, after which the examinations were repeated.

RESULTS

Patients improved on positive, general, and total PANSS score after treatment ( P < .001). Fourteen patients had ≥20% improvement of NS, whereas 25 patients improved <20%. At baseline, one-way analysis of variance showed group difference bilaterally in the caudate nucleus and in the right nucleus accumbens (all P < .002), which was caused by decreased reward anticipation activity in the nonimproving patients compared to healthy controls. There was a significant group × time interaction, with the healthy controls and the improvers decreasing and the nonimprovers increasing in reward anticipation activity after treatment, most pronounced in the left caudate nucleus ( P = .001).

DISCUSSION

Patients improving in NS score had a less aberrant reward system at baseline, but reward related activity was reduced over time. Patients not improving in NS showed decreased striatal reward-activity at baseline, which improved over time. Whether this is associated with alteration in working memory and reward learning or with pronounced symptoms within specific domains of NS may be addressed in future studies.

Two subgroups of antipsychotic-naive, first-episode schizophrenia patients identified with a Gaussian mixture model on cognition and electrophysiology

Deficits in information processing and cognition are among the most robust findings in schizophrenia patients. Previous efforts to translate group-level deficits into clinically relevant and individualized information have, however, been non-successful, which is possibly explained by biologically different disease subgroups. We applied machine learning algorithms on measures of electrophysiology and cognition to identify potential subgroups of schizophrenia. Next, we explored subgroup differences regarding treatment response. Sixty-six antipsychotic-naive first-episode schizophrenia patients and sixty-five healthy controls underwent extensive electrophysiological and neurocognitive test batteries. Patients were assessed on the Positive and Negative Syndrome Scale (PANSS) before and after 6 weeks of monotherapy with the relatively selective D₂ receptor antagonist, amisulpride (280.3±159 mg per day). A reduced principal component space based on 19 electrophysiological variables and 26 cognitive variables was used as input for a Gaussian mixture model to identify subgroups of patients. With support vector machines, we explored the relation between PANSS subscores and the identified subgroups. We identified two statistically distinct subgroups of patients. We found no significant baseline psychopathological differences between these subgroups, but the effect of treatment in the groups was predicted with an accuracy of 74.3% (P=0.003). In conclusion, electrophysiology and cognition data may be used to classify subgroups of schizophrenia patients. The two distinct subgroups, which we identified, were psychopathologically inseparable before treatment, yet their response to dopaminergic blockade was predicted with significant accuracy. This proof of principle encourages further endeavors to apply data-driven, multivariate and multimodal models to facilitate progress from symptom-based psychiatry toward individualized treatment regimens.

Systemic oxidative DNA and RNA damage are not increased during early phases of psychosis: A case control study

It has been suggested that patients with schizophrenia develop higher levels of oxidative stress, which may contribute to deteriorating mental illness. In order to examine oxidative stress in the early stages of severe mental illness, we examined the levels of systemic Deoxyribonucleic Acid (DNA) and Ribonucleic Acid (RNA) oxidation, 8-oxo-7,8-dihydro-2'-deoxyguanosine and 8-oxo-7,8-dihydroguanosine, perceived stress and recent life events in patients at ultra high-risk (UHR) of developing psychosis, in antipsychotic naïve patients with first-episode schizophrenia (FES), and in healthy controls. We included 41 UHR patients, 35 FES patients, and 29 healthy controls. There was no difference in the level of DNA/RNA oxidative damage between UHR patients and FES patients compared with healthy controls. We found no association between levels of DNA/RNA oxidative damage and perceived stress/life events. Based on the results, we suggest that DNA and RNA oxidative markers are not increased during the early stages of illness, but further longitudinal studies in first-episode psychosis should be carried out to examine whether DNA and RNA oxidative damage are potential markers of severe illness.