Early detection and treatment of schizophrenia: how early?

Schizophrenia: Communication Disorders and Role of the Speech-Language Pathologist

PURPOSE

This clinical focus article aims to provide a comprehensive overview of schizophrenia and understanding of communication disorders resulting from its psychopathology. Schizophrenia is a spectrum disorder with varying levels of symptom expression. It is characterized by positive and negative symptoms that can cause communication disorders of different severity levels. Communication difficulties manifest as a range of symptoms such as alogia, disorganized speech, and impaired social communication. These challenges may result in receptive and expressive language deficits that lead to misunderstandings, reduced social interactions, and difficulties expressing thoughts and emotions effectively. The purpose of this clinical focus article is to explore the role of the speech-language pathologist (SLP) in assessing and treating communication disorders presented in schizophrenia.

CONCLUSIONS

In order to understand the role of the SLP in assessing and treating communication disorders in schizophrenia, it is imperative to understand the overall course, etiology, assessment, and treatment consideration of this condition. SLPs can provide services in the areas of social skills training and community-based intervention contexts.

Perceptual Functioning

Perceptual disorders are not part of the diagnosis criteria for schizophrenia. Yet, a considerable amount of work has been conducted, especially on visual perception abnormalities, and there is little doubt that visual perception is altered in patients. There are several reasons why such perturbations are of interest in this pathology. They are observed during the prodromal phase of psychosis, they are related to the pathophysiology (clinical disorganization, disorders of the sense of self), and they are associated with neuronal connectivity disorders. Perturbations occur at different levels of processing and likely affect how patients interact and adapt to their surroundings. The literature has become very large, and here we try to summarize different models that have guided the exploration of perception in patients. We also illustrate several lines of research by showing how perception has been investigated and by discussing the interpretation of the results. In addition to discussing domains such as contrast sensitivity, masking, and visual grouping, we develop more recent fields like processing at the level of the retina, and the timing of perception.

New quantification method for monitoring eighteen L-amino acids levels in schizophrenic patients by high-performance liquid chromatography coupled to tandem quadrupole mass spectrometer

A fast, uncomplicated, sensitive, and fully validated high-performance liquid chromatography-tandem mass spectrometry (HPLC-MS/MS) method has been developed for estimating L-amino acids in the plasma of schizophrenic patients. The gradient-elution chromatographic method was implemented with the Luna® PFP column (50 × 2.0 mm, 5-μm), and a mobile phase of 0.1% formic acid in water and methanol was used. The intraday and interday variability of the L-amino acids were less than 13.11%, and their accuracy ranged from 85.14 - 116.75% at the quality control levels and the lower limit of quantification (LLOQ) ranged from 2.5 - 15 nM. The extraction efficiency (apparent recovery) of amino acids from healthy plasma was employed by spiking the plasma with standard amino acids at the quality control levels. Their percentage recoveries ranged from 80.4% to 119.94%. Our method has a short run time and fast sample preparation compared with existing methods, which are suffered from long preparative steps and/or time-consuming analysis, restricted reagents, and suboptimal performance characteristics presently available technologies. Therefore, the proposed HPLC-MS/MS method was effectively applied for monitoring the L-amino acids in the plasma of schizophrenic patients and healthy volunteers.

Using machine learning to improve the discriminative power of the FERD screener in classifying patients with schizophrenia and healthy adults

Background Facial emotion recognition deficit (FERD) seems to be an obvious feature of patients with schizophrenia and has great potential for classifying patients and non-patients. The FERD screener was previously developed to classify patients from healthy adults. However, an obvious drawback of this screener is that the recommended cut-off scores could enhance either sensitivity or specificity (about 0.92) only, while the other one is at an only acceptable level (about 0.66). Machine learning (ML) algorithms are famous for their feature extraction and data classification abilities, which are promising for improving the discriminative power of screeners. This study aimed to improve the discriminative power of the FERD screener using an ML algorithm. Methods The data were extracted from a previous study. Artificial neural networks were generated to estimate the probability of being a patient with schizophrenia or a healthy adult based on the examinee's responses on the FERD screener (168 items). The performances of the ML-FERD screener were examined using a stratified five-fold cross-validation method. Results Across the five subsets of data, the ML-FERD screener showed extremely high areas under the receiver operating characteristic curve of 0.97-0.99. With the optimized cut-off scores, the average sensitivity and specificity of the ML-FERD screener were 0.90 (0.85-0.93) and 0.93 (0.86-1.00), respectively. Limitations The characteristics of patients were not representative, and the age was mismatched to control group. Conclusion The ML-FERD screener appears to have a better discriminative power to classify patients with schizophrenia and healthy adults than does the FERD screener.

Sensitivity and specificity of a facial emotion recognition test in classifying patients with schizophrenia

BACKGROUND

Schizophrenia is a debilitating mental illness that causes significant disability. However, the lack of evidence for functional decline yields difficulty in distinguishing patients with schizophrenia from healthy adults. Since patients with schizophrenia demonstrate severe facial emotion recognition deficit (FERD), FERD measurement appears to be a promising solution for the aforementioned challenge.We aimed to develop a FERD-based screening tool to differentiates patients with schizophrenia from healthy adults.

METHODS

Patients' responses were extracted from a previous study. The most discriminative index was determined by comparing the area under the receiver operating characteristic curve (AUC) of patients' FER scores in 7 domains individually and collectively. The best cut-off score was selected only for the most discriminative index to provide both high sensitivity and specificity (≥ 0.90).

RESULTS

The "number of domains failed" showed the highest discriminative value (AUC = 0.92). Since high sensitivity and specificity could not be achieved simultaneously, two sub-optimal cut-off scores were recommended for prospective users. For users prioritizing sensitivity, the "≥ 2 domains failed" index yields high sensitivity (0.96) with modest specificity (0.66). For users targeting specificity, the "≥ 4 domains failed" indexachieves high specificity (0.92) with acceptable sensitivity (0.72).

LIMITATIONS

Convenience sampling with mild clinical severity and younger healthy adults (< 20 years old) may limit the generalizability.

CONCLUSION

The FERD screener seems to be a discriminative tool with changeable cut-off scores achieving high sensitivity or specificity. Therefore, it may be useful in detecting patients and ruling out adults erroneously suspected of having schizophrenia.

Predictors of study drop-out and service disengagement in patients at clinical high risk for psychosis

PURPOSE

Study drop-out during follow-up and service disengagement frequently occur in patients at clinical high risk for psychosis (CHR-P). However, little is known about their predictors. Therefore, we aimed to analyze the rate and reasons for drop-out and service disengagement in CHR-P patients and investigate their sociodemographic and clinical predictors.

METHODS

Data from 200 patients of the prospective Früherkennung von Psychosen (FePsy) study were analyzed with competing risks survival models, considering drop-out and transition to psychosis as competing events. To investigate whether symptoms changed immediately before drop-out, t tests were applied.

RESULTS

Thirty-six percent of patients dropped out within 5 years. Almost all drop-outs also disengaged from our service. Hence, study drop-out was used as a proxy for service disengagement. Patients with more severe baseline disorganized symptoms and a late inclusion into the study were significantly more likely to disengage. Immediately before disengagement, there was significant improvement in negative symptoms only.

CONCLUSION

A considerable proportion of CHR-P patients disengaged from our clinical study and service. Patients who were included during a later study period with more assessments disengaged more often, which might have been due to more frequent invitations to follow-up assessments and thereby increasing participation burden. Hence, our study provides a cautionary note on high-frequency follow-up assessments. Larger-scale studies evaluating predictors on multiple domains would help to further elucidate drop-out and disengagement.

Early detection of psychosis – Establishing a service for persons at risk

Purpose

The establishment phase of an early detection centre for prodromal psychosis is introduced and characterised, along with its detaining and promoting factors within a universal multi-payer health care system.

Method

Across the first six years (1998–2003), users' characteristics are compared between different diagnostic groups and to the local population statistics; and, for an exemplary 12-months period (3-1-2002 to 2-28-2003), the characteristics of telephone contacts with the service are studied.

Results

Rising steadily in number across the first three years, 872 persons, predominately of German citizenship and higher education, consulted the service until 2003, 326 with first-episode psychosis and 144 not fulfilling criteria for a current or beginning psychosis. Of the 402 putatively prodromal patients, 94% reported predictive basic symptoms, 68.9% attenuated and 20.6% transient psychotic symptoms. Most contacts by persons meeting any prodromal criterion were initiated by mental health professionals (psychiatrists or psychologists) and counselling services.

Conclusion

Supported by public awareness campaigns, an early detection service is well received by its users and private practitioners as reflected by the large proportion of referrals from the latter. However, persons of non-German background as well as of lower education were underrepresented indicating that these sub-groups should be approached by tailored programmes.

Individualized prediction of psychosis in subjects with an at-risk mental state

Early intervention strategies in psychosis would significantly benefit from the identification of reliable prognostic biomarkers. Pattern classification methods have shown the feasibility of an early diagnosis of psychosis onset both in clinical and familial high-risk populations. Here we were interested in replicating our previous classification findings using an independent cohort at clinical high risk for psychosis, drawn from the prospective FePsy (Fruherkennung von Psychosen) study. The same neuroanatomical-based pattern classification pipeline, consisting of a linear Support Vector Machine (SVM) and a Recursive Feature Selection (RFE) achieved 74% accuracy in predicting later onset of psychosis. The discriminative neuroanatomical pattern underlying this finding consisted of many brain areas across all four lobes and the cerebellum. These results provide proof-of-concept that the early diagnosis of psychosis is feasible using neuroanatomical-based pattern recognition.

Clinical and functional ultra-long-term outcome of patients with a clinical high risk (CHR) for psychosis

BACKGROUND

Few studies have followed up patients with a clinical high risk (CHR) for psychosis for more than 2-3 years. We aimed to investigate the rates and baseline predictors for remission from CHR and transition to psychosis over a follow-up period of up to 16 years. Additionally, we examined the clinical and functional long-term outcome of CHR patients who did not transition.

METHODS

We analyzed the long-term course of CHR patients that had been included in the longitudinal studies "Früherkennung von Psychosen" (FePsy) or "Bruderholz" (BHS). Those patients who had not transitioned to psychosis during the initial follow-up periods (2/5 years), were invited for additional follow-ups.

RESULTS

Originally, 255 CHR patients had been included. Of these, 47 had transitioned to psychosis during the initial follow-ups. Thus, 208 were contacted for the long-term follow-up, of which 72 (34.6%) participated. From the original sample of 255, 26%, 31%, 35%, and 38% were estimated to have transitioned after 3, 5, 10, and 16 years, respectively, and 51% had remitted from their high risk status at the latest follow-up. Better psychosocial functioning at baseline was associated with a higher rate of remission. Of the 72 CHR patients re-assessed at long-term follow-up, 60 had not transitioned, but only 28% of those were fully recovered clinically and functionally.

CONCLUSIONS

Our study shows the need for follow-ups and clinical attention longer than the usual 2-3 years as there are several CHR patients with later transitions and only a minority of CHR those without transition fully recovers.

Exploring the predictive power of the unspecific risk category of the Basel Screening Instrument for Psychosis

AIM

Ultrahigh risk (UHR) criteria, consisting of brief limited intermittent psychotic symptoms (BLIPS), attenuated psychotic symptoms (APS) and genetic risk and deterioration (GRD) syndrome are the most widely used criteria for assessing the clinical high-risk state for psychosis (CHR-P). The Basel Screening Instrument for Psychosis (BSIP) includes a further risk category, the unspecific risk category (URC). However, little is known about the predictive power of this risk category compared to other risk categories.

METHODS

Two hundred CHR-P patients were detected as part of the Früherkennung von Psychosen (FePsy) study using the BSIP. Transition to psychosis was assessed in regular intervals for up to 7 years.

RESULTS

Patients meeting only the URC criterion (n = 40) had a significantly lower risk of transition to psychosis than the UHR group (including BLIPS, APS and GRD) (HR 0.19 [0.05; 0.80] (P = 0.024). Furthermore, the URC only risk group had a lower transition risk than the APS without BLIPS group (P = 0.015) and a trendwise lower risk than the BLIPS group (P = 0.066). However, despite the lower transition risk in the URC only group, there were still two patients (5%) in this group with a later transition to psychosis.

CONCLUSIONS

The URC includes patients who have a lower risk of transition than those included by the UHR categories and thereby increases the sensitivity of the BSIP. This offers the possibility of a stratified intervention, with these subjects receiving low intensity follow-up and treatment.

Gender differences in first self-perceived signs and symptoms in patients with an at-risk mental state and first-episode psychosis

AIM

Gender differences in the current symptomatology of patients with psychotic disorders have previously been described in the literature. However, it has not yet been investigated whether gender differences exist in the very first self-perceived signs or symptoms of illness onset. The aim of this study was to investigate this aspect in at-risk mental state (ARMS) and first-episode psychosis (FEP) patients.

METHODS

ARMS and FEP were recruited via the early detection of psychosis (FePsy) clinic Basel, Switzerland. The Basel Interview for Psychosis (BIP) was used to retrospectively assess the first 3 self-perceived signs and symptoms at illness onset. Differences between gender and patient groups on single item and symptom cluster levels were analysed using logistic regression models.

RESULTS

One-hundred-thirty six ARMS (91 men, 45 women) and 89 FEP patients (63 men, 26 women) could be recruited for this study. On a single item level, women more frequently reported "unusual anxiety, fears" and men (at a trend level) "social withdrawal" as being among their 3 first self-perceived symptoms, independent of diagnostic group. On the symptom cluster level, women more frequently reported "increased worrying/anxiety" and (sub-threshold) "hallucinations", independent of diagnostic group. Problems with "thinking, concentration" were reported more frequently by men in the ARMS group only.

CONCLUSION

Our results suggest that only few and relatively small gender differences exist in the first self-perceived signs and symptoms. While men initially mainly notice negative/cognitive symptoms, women first notice (sub-threshold) positive and affective symptoms.

Emerging Temporal Lobe Dysfunction in People at Clinical High Risk for Psychosis

Clinical high-risk (CHR) individuals have been increasingly utilized to investigate the prodromal phases of psychosis and progression to illness. Research has identified medial and lateral temporal lobe abnormalities in CHR individuals. Dysfunction in the medial temporal lobe, particularly the hippocampus, is linked to dysregulation of glutamate and dopamine via a hippocampal-striatal-midbrain network that may lead to aberrant signaling of salience underpinning the formation of delusions. Similarly, lateral temporal dysfunction may be linked to the disorganized speech and language impairments observed in the CHR stage. Here, we summarize the significance of these neurobiological findings in terms of emergent psychotic symptoms and conversion to psychosis in CHR populations. We propose key questions for future work with the aim to identify the neural mechanisms that underlie the development of psychosis.

Mismatch negativity and cognitive performance in the course of schizophrenia

BACKGROUND

Cognitive deficits and abnormal event-related brain potentials (ERP) have been proposed as risk markers for the development of schizophrenia. Evidence is inconclusive whether these markers indicate a risk for the development of psychosis or illness progression.

METHODS

The present study aimed at further clarification by comparing symptom expression (Brief Psychiatric Rating Scale, BRPS), the ERP Mismatch Negativity (MMN), and neuropsychological performance on the MATRICS Consensus Cognitive Battery between healthy controls (HC, n = 38) and individuals at different stages of illness: individuals at risk for psychosis (ARP, n = 33), patients at first admission, thus, early stage (ES, n = 35), chronic schizophrenia patients (CS, n = 25). Moreover, symptom expression was reassessed for ARP and ES at a 6 months follow-up.

RESULTS

MMN was smaller in individuals with manifest psychosis (ES, CS) than in HC, but did not differ between ARP and HC. In contrast, ARP showed similar cognitive deficits as ES and CS, all three groups differing from HC. Lower cognitive performance predicted higher symptom severity at index assessments and 6 months follow-up in ARP and ES, while MMN did not explain additional variance.

CONCLUSION

MMN seems to mark manifest psychosis, independent of early or chronic stage, while cognitive deficits mark early present psychopathology in individuals at risk for and with diagnosed psychosis rather than illness progression.

The relationship between negative symptoms and cognitive functioning in patients at clinical high risk for psychosis

Negative symptoms and neurocognitive performance have been reported to be negatively associated in patients with emerging psychosis. However, most previous studies focused on patients with frank psychosis and did not differentiate between subdomains of negative symptoms. Hence, we aimed to elucidate the specific relationship between negative symptoms and cognitive functioning in patients at clinical high risk (CHR) for psychosis. Data from 154 CHR patients collected within the prospective Früherkennung von Psychosen (FePsy) study were analyzed. Negative symptoms were assessed with the Scale for the Assessment of Negative Symptoms (SANS) and cognitive functioning with an extensive neuropsychological test battery. Regression analyses revealed significant negative associations between negative symptoms and cognitive functioning, particularly in the domains of nonverbal intelligence and verbal fluency. When analyzing each negative symptom domain separately, alogia and asociality/anhedonia were significantly negatively associated with nonverbal intelligence and alogia additionally with verbal fluency. Overall, our results in CHR patients are similar to those reported in patients with frank psychosis. The strong negative association between verbal fluency and negative symptoms may be indicative of an overlap between these constructs. Verbal fluency might have a strong influence on the clinical impression of negative symptoms (particularly alogia) and vice versa.

The Frankfurt Complaint Questionnaire for self-assessment of basic symptoms in the early detection of psychosis-Factor structure, reliability, and predictive validity

OBJECTIVES

Patients with schizophrenia often experience subtle disturbances in several domains of information processing-so-called basic symptoms (BS). BS are already present before onset of frank psychosis and can be assessed by interviews but also by the self-administered Frankfurt Complaint Questionnaire (FCQ). We investigated the factor structure, reliability, and predictive validity for transition to psychosis of the FCQ, comparing previously proposed factor solutions containing 1, 2, 4, and 10 factors.

METHODS

Confirmatory factor analysis was used in a sample of 117 at-risk mental state and 92 first-episode psychosis participants of the Basel FePsy (early detection of psychosis) study.

RESULTS

Although all factor models fitted to the data, the 2- or 4-factor solutions performed best among the models that used at least half of the FCQ items, suggesting the covariance between FCQ items is best explained by 2 to 4 underlying factors. No FCQ-scale predicted transition to psychosis.

CONCLUSION

We could confirm a 2- to 4-factor structure of the FCQ in a sample of at-risk mental state and first-episode psychosis patients using confirmatory factor analysis. Contrary to interview-assessed cognitive-perceptive BS, self-assessed BS do not seem to improve prediction of psychosis. This result reinforces reports of poor correspondence between interview- and questionnaire-assessed BS.

Early detection of psychosis: helpful or stigmatizing experience? A qualitative study

AIM

Despite the large scientific debate concerning potential stigmatizing effects of identifying an individual as being in an at-risk mental state (ARMS) for psychosis, studies investigating this topic from the subjective perspective of patients are rare. This study assesses whether ARMS individuals experience stigmatization and to what extent being informed about the ARMS is experienced as helpful or harmful.

METHODS

Eleven ARMS individuals, currently participating in the follow-up assessments of the prospective Basel Früherkennung von Psychosen (FePsy; English: Early Detection of Psychosis) study, were interviewed in detail using a semistructured qualitative interview developed for this purpose. Data were analysed using Interpretative Phenomenological Analysis.

RESULTS

Most individuals experiencing first symptoms reported sensing that there was 'something wrong with them' and felt in need of help. They were relieved that a specific term was assigned to their symptoms. The support received from the early detection centre was generally experienced as helpful. Many patients reported stigmatization and discrimination that appeared to be the result of altered behaviour and social withdrawal due to the prepsychotic symptoms they experienced prior to contact with the early detection clinic.

CONCLUSIONS

The results suggest that early detection services help individuals cope with symptoms and potential stigmatization rather than enhancing or causing the latter. More emphasis should be put on the subjective experiences of those concerned when debating the advantages and disadvantages of early detection with regard to stigma. There was no evidence for increased perceived stigma and discrimination as a result of receiving information about the ARMS.

Multi-center machine learning in imaging psychiatry: A meta-model approach

One of the biggest problems in automated diagnosis of psychiatric disorders from medical images is the lack of sufficiently large samples for training. Sample size is especially important in the case of highly heterogeneous disorders such as schizophrenia, where machine learning models built on relatively low numbers of subjects may suffer from poor generalizability. Via multicenter studies and consortium initiatives researchers have tried to solve this problem by combining data sets from multiple sites. The necessary sharing of (raw) data is, however, often hindered by legal and ethical issues. Moreover, in the case of very large samples, the computational complexity might become too large. The solution to this problem could be distributed learning. In this paper we investigated the possibility to create a meta-model by combining support vector machines (SVM) classifiers trained on the local datasets, without the need for sharing medical images or any other personal data. Validation was done in a 4-center setup comprising of 480 first-episode schizophrenia patients and healthy controls in total. We built SVM models to separate patients from controls based on three different kinds of imaging features derived from structural MRI scans, and compared models built on the joint multicenter data to the meta-models. The results showed that the combined meta-model had high similarity to the model built on all data pooled together and comparable classification performance on all three imaging features. Both similarity and performance was superior to that of the local models. We conclude that combining models is thus a viable alternative that facilitates data sharing and creating bigger and more informative models.

Improved individualized prediction of schizophrenia in subjects at familial high risk, based on neuroanatomical data, schizotypal and neurocognitive features

To date, there are no reliable markers for predicting onset of schizophrenia in individuals at high risk (HR). Substantial promise is, however, shown by a variety of pattern classification approaches to neuroimaging data. Here, we examined the predictive accuracy of support vector machine (SVM) in later diagnosing schizophrenia, at a single-subject level, using a cohort of HR individuals drawn from multiply affected families and a combination of neuroanatomical, schizotypal and neurocognitive variables. Baseline structural magnetic resonance imaging (MRI), schizotypal and neurocognitive data from 17 HR subjects, who subsequently developed schizophrenia and a matched group of 17 HR subjects who did not make the transition, yet had psychotic symptoms, were included in the analysis. We employed recursive feature elimination (RFE), in a nested cross-validation scheme to identify the most significant predictors of disease transition and enhance diagnostic performance. Classification accuracy was 94% when a self-completed measure of schizotypy, a declarative memory test and structural MRI data were combined into a single learning algorithm; higher than when either quantitative measure was used alone. The discriminative neuroanatomical pattern involved gray matter volume differences in frontal, orbito-frontal and occipital lobe regions bilaterally as well as parts of the superior, medial temporal lobe and cerebellar regions. Our findings suggest that an early SVM-based prediction of schizophrenia is possible and can be improved by combining schizotypal and neurocognitive features with neuroanatomical variables. However, our predictive model needs to be tested by classifying a new, independent HR cohort in order to estimate its validity.

Altered Insular Function during Aberrant Salience Processing in Relation to the Severity of Psychotic Symptoms

There is strong evidence for abnormal salience processing in patients with psychotic experiences. In particular, there are indications that the degree of aberrant salience processing increases with the severity of positive symptoms. The aim of the present study was to elucidate this relationship by means of brain imaging. Functional magnetic resonance imaging was acquired to assess hemodynamic responses during the Salience Attribution Test, a paradigm for reaction time that measures aberrant salience to irrelevant stimulus features. We included 42 patients who were diagnosed as having a psychotic disorder and divided them into two groups according to the severity of their positive symptoms. Whole brain analysis was performed using Statistical Parametric Mapping. We found no significant behavioral differences with respect to task performance. Patients with more positive symptoms showed increased hemodynamic responses in the left insula corresponding to aberrant salience than in patients with less positive symptoms. In addition, left insula activation correlated negatively with cumulative antipsychotic medication. Aberrant salience processing in the insula may be increased in psychosis, depending on the severity of positive symptoms. This study indicates that clinically similar psychosis manifestations share the same functional characteristics. In addition, our results suggest that antipsychotic medication can modulate insular function.

[Why and how to treat psychosis earlier?]

Chronic psychosis, as for instance schizophrenia, usually begins in young adulthood and may cause severe disability. It causes a mean loss of life expectancy of 22 years. Actual models of psychosis do not trace the beginning of psychosis to the first franc psychotic episode only, but to earlier symptoms. In a classical health system only considering the first psychotic episode, the mean duration of untreated illness (DUI) can last several years. Yet this DUI has a direct impact on the prognosis of the disease. Actual international recommendations prescribe to early detect and treat at risk mental states of psychosis, thus reducing DUI. Such an attitude also helps the patient to integrate care in a moment where she/he is fully in condition to consent and to adhere. Generalist practitioners are crucial actors of early detection. We describe here simple and standardized tools helping early detection of high-risk mental states of psychosis in primary care and the appropriate attitude to do it properly. Numerous countries have developed early detection and treatment centers for psychosis. It has been established that such interventions clearly decrease the risk of transition towards chronic psychosis and improve the prognosis. These recent data about early detection and intervention in psychosis are a major step forward in psychiatry practice. It is now necessary to largely develop such actions in France.

Increased superior frontal gyrus activation during working memory processing in psychosis: Significant relation to cumulative antipsychotic medication and to negative symptoms

OBJECTIVES

Impairment in working memory (WM) is a core symptom in schizophrenia. However, little is known about how clinical features influence functional brain activity specific to WM processing during the development of first-episode psychosis (FEP) to schizophrenia (SZ). We compared functional WM-specific brain activity in FEP and SZ patients, including the effects of the duration of illness, psychopathological factors and antipsychotic medication.

METHODS

Cross-sectional study of male FEP (n=22) and SZ (n=20) patients performing an n-back task when undergoing functional magnetic resonance imaging (fMRI). Clinical features were collected by semi-structured interviews and medical records.

RESULTS

The SZ group performed significantly worse than the FEP group in the 2-back condition. The SZ group also showed significantly higher activation in the left superior frontal gyrus in the 2-back versus 0-back condition (2-back>0-back). This frontal activation correlated positively with negative symptoms and with cumulative antipsychotic medication during the year before the fMRI examination. There were no significant correlations between activation and duration of illness.

CONCLUSION

There was greater frontal neural activation in SZ than in FEP. This indicated differences in WM processing, and was significantly related to cumulative antipsychotic exposure and negative symptoms, but not to the duration of illness.

Prediction of psychosis using neural oscillations and machine learning in neuroleptic-naïve at-risk patients

OBJECTIVES

This study investigates whether abnormal neural oscillations, which have been shown to precede the onset of frank psychosis, could be used towards the individualised prediction of psychosis in clinical high-risk patients.

METHODS

We assessed the individualised prediction of psychosis by detecting specific patterns of beta and gamma oscillations using machine-learning algorithms. Prediction models were trained and tested on 53 neuroleptic-naïve patients with a clinical high-risk for psychosis. Of these, 18 later transitioned to psychosis. All patients were followed up for at least 3 years. For an honest estimation of the generalisation capacity, the predictive performance of the models was assessed in unseen test cases using repeated nested cross-validation.

RESULTS

Transition to psychosis could be predicted from current-source density (CSD; area under the curve [AUC] = 0.77), but not from lagged phase synchronicity data (LPS; AUC = 0.56). Combining both modalities did not improve the predictive accuracy (AUC = 0.78). The left superior temporal gyrus, the left inferior parietal lobule and the precuneus most strongly contributed to the prediction of psychosis.

CONCLUSIONS

Our results suggest that CSD measurements extracted from clinical resting state EEG can help to improve the prediction of psychosis on a single-subject level.

The Dark Side of the Moon: Meta-analytical Impact of Recruitment Strategies on Risk Enrichment in the Clinical High Risk State for Psychosis

BACKGROUND

The individual risk of developing psychosis after being tested for clinical high-risk (CHR) criteria (posttest risk of psychosis) depends on the underlying risk of the disease of the population from which the person is selected (pretest risk of psychosis), and thus on recruitment strategies. Yet, the impact of recruitment strategies on pretest risk of psychosis is unknown.

METHODS

Meta-analysis of the pretest risk of psychosis in help-seeking patients selected to undergo CHR assessment: total transitions to psychosis over the pool of patients assessed for potential risk and deemed at risk (CHR+) or not at risk (CHR-). Recruitment strategies (number of outreach activities per study, main target of outreach campaign, and proportion of self-referrals) were the moderators examined in meta-regressions.

RESULTS

11 independent studies met the inclusion criteria, for a total of 2519 (CHR+: n = 1359; CHR-: n = 1160) help-seeking patients undergoing CHR assessment (mean follow-up: 38 months). The overall meta-analytical pretest risk for psychosis in help-seeking patients was 15%, with high heterogeneity (95% CI: 9%-24%, I (2) = 96, P < .001). Recruitment strategies were heterogeneous and opportunistic. Heterogeneity was largely explained by intensive (n = 11, β = -.166, Q = 9.441, P = .002) outreach campaigns primarily targeting the general public (n = 11, β = -1.15, Q = 21.35, P < .001) along with higher proportions of self-referrals (n = 10, β = -.029, Q = 4.262, P = .039), which diluted pretest risk for psychosis in patients undergoing CHR assessment.

CONCLUSIONS

There is meta-analytical evidence for overall risk enrichment (pretest risk for psychosis at 38 monhts = 15%) in help-seeking samples selected for CHR assessment as compared to the general population (pretest risk of psychosis at 38 monhts=0.1%). Intensive outreach campaigns predominantly targeting the general population and a higher proportion of self-referrals diluted the pretest risk for psychosis.

The role of vulnerability factors in individuals with an at-risk mental state of psychosis

BACKGROUND

Several indicators of heightened vulnerability to psychosis and relevant stressors have been identified. However, it has rarely been studied prospectively to what extent these vulnerability factors are in fact more frequently present in individuals with an at-risk mental state for psychosis. Moreover, it remains unknown whether any of these contribute to the prediction of psychosis onset in at-risk mental state individuals.

METHODS

There were 28 healthy controls, 86 first-episode psychosis patients and 127 at-risk mental state individuals recruited within the Basel "Früherkennung von Psychosen" project. Relative frequencies of selected vulnerability factors for psychosis were compared between healthy controls, psychosis patients, those at-risk mental state individuals with subsequent psychosis onset (n = 31) and those without subsequent psychosis onset (n = 55). Survival analyses were applied to determine associations between time to transition to psychosis and vulnerability factors in all 127 at-risk mental state individuals.

RESULTS

The vulnerability factors/indicators such as "difficulties during school education or vocational training", "difficulties during employment", "being single", "difficulties with intimate relationships" and "being burdened with specific stressful situations" were more commonly found in the at-risk mental state and first-episode psychosis group than in healthy controls.

CONCLUSIONS

At-risk mental state and first-episode psychosis individuals more frequently present with vulnerability factors. Individual vulnerability factors appear, however, not to be predictive for an onset of psychosis.

Hippocampal volume correlates with attenuated negative psychotic symptoms irrespective of antidepressant medication

Background

Individuals with at-risk mental state for psychosis (ARMS) often suffer from depressive and anxiety symptoms, which are clinically similar to the negative symptomatology described for psychosis. Thus, many ARMS individuals are already being treated with antidepressant medication.

Objectives

To investigate clinical and structural differences between psychosis high-risk individuals with or without antidepressants.

Methods

We compared ARMS individuals currently receiving antidepressants (ARMS-AD; n = 18), ARMS individuals not receiving antidepressants (ARMS-nonAD; n = 31) and healthy subjects (HC; n = 24), in terms of brain structure abnormalities, using voxel-based morphometry. We also performed region of interest analysis for the hippocampus, anterior cingulate cortex, amygdala and precuneus.

Results

The ARMS-AD had higher ‘depression’ and lower ‘motor hyperactivity’ scores than the ARMS-nonAD. Compared to HC, there was significantly less GMV in the middle frontal gyrus in the whole ARMS cohort and in the superior frontal gyrus in the ARMS-AD subgroup. Compared to ARMS-nonAD, the ARMS-AD group showed more gray matter volume (GMV) in the left superior parietal lobe, but less GMV in the left hippocampus and the right precuneus. We found a significant negative correlation between attenuated negative symptoms and hippocampal volume in the whole ARMS cohort.

Conclusion

Reduced GMV in the hippocampus and precuneus is associated with short-term antidepressant medication and more severe depressive symptoms. Hippocampal volume is further negatively correlated with attenuated negative psychotic symptoms. Longitudinal studies are needed to distinguish whether hippocampal volume deficits in the ARMS are related to attenuated negative psychotic symptoms or to antidepressant action.

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We compared brain structure in high-risk patients with/without antidepressants (AD).

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We found attenuated negative psychotic symptoms (ANS) irrespective of AD.

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We found a significant correlation between ANS and hippocampal volume.

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Results indicate relevance of ANS for clinical high-risk studies.

Population-based initiatives in college mental health: students helping students to overcome obstacles

College students' need for mental health care has increased dramatically, leaving campus counseling and mental health centers struggling to meet the demand. This has led to the investigation and development of extra-center, population-based interventions. Student-to-student support programs are but one example. Students themselves are a plentiful, often-untapped resource that extends the reach of mental health services on campus. Student-to-student programs capitalize on students' natural inclination to assist their peers. A brief review of the prevalence and effects of mental disorders in the college population is provided, followed by a broad overview of the range of peer-to-peer programs that can be available on college campuses. Two innovative programs are highlighted: (1) a hospital- and community-based program, the College Mental Health Program (CMHP) at McLean Hospital, and 2) the Student Support Network (SSN) at Worcester Polytechnic Institute. The subsequent section reviews the literature on peer-to-peer programs for students with serious and persistent mental illness for which there is a small but generally positive body of research. This lack of an empirical basis in college mental health leads the authors to argue for development of broad practice-research networks.

[Assessment of mental states at risk of psychotic transition: validation of the French version of the CAARMS]

This article aims to present the validation study of the French version of the Comprehensive Assessment of at risk mental states (CAARMS), an interview that seeks to determine whether young adults criteria for at-risk (AR) mental states, or psychosis. We assessed 40 young subjects, 15 were considered as "prodromal" (Prd) and 10 as experiencing a first episode of psychosis (PEP) by our expert clinician at the center - centre d'évaluation des jeunes adultes et adolescents, University Hospital Centre, Paris - and 15 were healthy controls matched for age and sex. When assessed with the CAARMS, 73 % (n=11) of the prodromal subjects reached the criteria for AR mental state, four subjects did not reach the criteria for AR, nor psychosis (P) and 100 % of the PEP reached the criteria for P. The three groups were significantly different on CAARMS total score (P<0.001) and subscores ; Prd subjects had intermediate scores between PEP (P<0.001) and controls (P<0.001) scores, PEP showing the highest scores. Post-hoc analysis showed that Prd significantly differed from Controls on each subscale (P<0.001) and that Prd differed from PEP on the "positive symptoms" subscale (P<0.001), as well as on "behavioural change" (P=0.021), owing to difference on the item "impaired role function". We used the brief psychiatric rating scale 24 items with anchor (BPRS24-EA) in addition to with the CAARMS, the AR group showed intermediate scores between controls and P subjects. Total scores of both scales were correlated (r=0.408 ; P=0.043) and the BPRS24-EA "positive symptoms" score was correlated with CAARMS' scores on the "Positive symptoms" subscale (r=0.456, P=0.022), "emotional disturbance" (r=0.506, P=0.01), and "behavioural change" (r=0.666 P=0.001). We found no correlation between BPRS negative and depression subscales and any of the CAARMS' subscales. When looking at its reliability, reliability coefficients (Cronbach's alpha) showed excellent reliability for "positive symptoms", "emotional disturbance", "behavioural change" and "general psychopathology" (respectively r=0.82, 0.75, 0.78, 0.84, 0.83) and moderate reliability for "cognitive change", "negative symptoms" and "motor/physical change" (respectively r=0.39, 0.59, 0.43). Overall, analysis of the results of construct validity, concurrent validity and reliability of the CAARMS indicates that the French version is valid and reliable. It is now available to develop and implement early detection programs in French speaking countries.

Can cognitive deficits facilitate differential diagnosis between at-risk mental state for psychosis and depressive disorders?

AIM

Many studies have provided evidence of cognitive deficits in individuals in an 'At Risk Mental State' (ARMS) for psychosis, which makes neuropsychology potentially useful in the early detection of psychosis. As depression is an important differential diagnosis in prodromal states of psychosis, the specificity of neurocognitive deficits in ARMS individuals as compared with non-psychotic depressive disorders is investigated.

METHODS

Neurocognitive performance of four groups was analysed: 22 ARMS individuals with later transition to psychosis (ARMS-T), 25 ARMS individuals without later transition to psychosis (ARMS-NT), 34 controls with depressive disorders and 76 healthy controls. The subjects were assessed with a neurocognitive test battery covering the domains' intelligence, executive function and attention/ working memory. MANOVAs, ANOVAs and Tukey's tests were applied after adjustment for confounding factors.

RESULTS

ARMS-T showed significant cognitive deficits in working memory and in certain executive function tasks compared with healthy controls as well as with controls with depression. Controls with depression were only impaired in time per move in the tower of Hanoi test when compared with healthy controls.

CONCLUSIONS

The psychosis prodrome seems to be associated with cognitive deficits in the domains of working memory and executive function. In contrast, depressive patients showed no cognitive deficits, but slowing in one executive function task. Neurocognitive testing might therefore contribute to the differential diagnosis between prodromal psychosis and depressive disorders.

Towards the identification of imaging biomarkers in schizophrenia, using multivariate pattern classification at a single-subject level

Standard univariate analyses of brain imaging data have revealed a host of structural and functional brain alterations in schizophrenia. However, these analyses typically involve examining each voxel separately and making inferences at group-level, thus limiting clinical translation of their findings. Taking into account the fact that brain alterations in schizophrenia expand over a widely distributed network of brain regions, univariate analysis methods may not be the most suited choice for imaging data analysis. To address these limitations, the neuroimaging community has turned to machine learning methods both because of their ability to examine voxels jointly and their potential for making inferences at a single-subject level. This article provides a critical overview of the current and foreseeable applications of machine learning, in identifying imaging-based biomarkers that could be used for the diagnosis, early detection and treatment response of schizophrenia, and could, thus, be of high clinical relevance. We discuss promising future research directions and the main difficulties facing machine learning researchers as far as their potential translation into clinical practice is concerned.

Help-seeking and pathways to care in the early stages of psychosis

PURPOSE

Delay in the treatment of a first psychotic episode can have a negative influence on the future course of the disease. In this context, it is important to examine pathways to care to understand factors contributing to delay in access to adequate care.

METHODS

Using the Basel Interview for Psychosis, we examined the help-seeking behaviour of 61 individuals with an at-risk mental state for psychosis and 37 patients with a first episode of psychosis in a low threshold health care system as part of the Basel early detection of psychosis study.

RESULTS

The median duration of untreated illness was 3.4 years, of untreated psychosis 12 months. Eighty-six percent of all individuals sought help of some kind before reaching our specialised early detection outpatient clinic, with a mean number of help-seeking contacts of 1.5 prior to referral. The most frequent first help-seeking contacts were family members or relatives n = 24 (26.7 %), close friends n = 17 (17.9 %), psychiatrists in private practice n = 13 (14.4 %) or general practitioners n = 11 (12.2 %). Most patients consulted other health professionals in the early course of the illness before reaching our specialised service; help-seeking with non-medical institutions was rare. Women had more help-seeking contacts than men before contact with our early detection clinic.

CONCLUSIONS

Family, close friends and medical professionals play an important role in help-seeking leading to specialised psychiatric care. Men seek help less often; specific strategies for encouraging young, at-risk men to seek help should be developed.

Duration of untreated psychosis and cognitive functioning

BACKGROUND

Studies examining the influence of duration of untreated psychosis (DUP) or duration of untreated illness (DUI) on cognition vary with regard to results and methods. This study is the first in this field to include an at risk mental state with later transition to psychosis (ARMS-T) sample and to analyse how the DUI relates to their cognitive functioning. Because methodological operationalization of cognitive functioning in previous studies is highly heterogeneous, we aimed to compare different approaches.

METHOD

60 first episode psychosis (FEP) patients and 24 ARMS-T patients were examined. Associations between DUP, DUI and neurocognitive performance were tested by three different operationalizations of cognition: as the raw outcome measure of different neuropsychological tests, as outcome scores which were normed on a sample of 75 healthy participants, and as the deterioration index (DI).

RESULTS

There were no significant correlations between DUP or DUI and outcome of neuropsychological tests in both normed and raw scores. When adjusted for covariates, DUP and DUI also did not significantly predict any cognitive performance. There was no significant relationship between DUP or DUI and the DI index. However, longer DUP and DUI were significantly associated with stronger negative symptoms.

CONCLUSIONS

This study could not confirm an association between duration of untreated psychosis or duration of untreated illness and neurocognitive performance in the ARMS-T and FEP samples. This could be because schizophrenic psychoses are neurodevelopmental disorders in which most cognitive deficits exist long before the onset of psychiatric symptoms.

Disease prediction in the at-risk mental state for psychosis using neuroanatomical biomarkers: results from the FePsy study

BACKGROUND

Reliable prognostic biomarkers are needed for the early recognition of psychosis. Recently, multivariate machine learning methods have demonstrated the feasibility to predict illness onset in clinically defined at-risk individuals using structural magnetic resonance imaging (MRI) data. However, it remains unclear whether these findings could be replicated in independent populations.

METHODS

We evaluated the performance of an MRI-based classification system in predicting disease conversion in at-risk individuals recruited within the prospective FePsy (Früherkennung von Psychosen) study at the University of Basel, Switzerland. Pairwise and multigroup biomarkers were constructed using the MRI data of 22 healthy volunteers, 16/21 at-risk subjects with/without a subsequent disease conversion. Diagnostic performance was measured in unseen test cases using repeated nested cross-validation.

RESULTS

The classification accuracies in the "healthy controls (HCs) vs converters," "HCs vs nonconverters," and "converters vs nonconverters" analyses were 92.3%, 66.9%, and 84.2%, respectively. A positive likelihood ratio of 6.5 in the converters vs nonconverters analysis indicated a 40% increase in diagnostic certainty by applying the biomarker to an at-risk population with a transition rate of 43%. The neuroanatomical decision functions underlying these results particularly involved the prefrontal perisylvian and subcortical brain structures.

CONCLUSIONS

Our findings suggest that the early prediction of psychosis may be reliably enhanced using neuroanatomical pattern recognition operating at the single-subject level. These MRI-based biomarkers may have the potential to identify individuals at the highest risk of developing psychosis, and thus may promote informed clinical strategies aiming at preventing the full manifestation of the disease.

First self-perceived signs and symptoms in emerging psychosis compared with depression

AIM

To investigate differences between the early symptoms of schizophrenia and depressive disorders.

METHODS

Sixty-one individuals with an at-risk mental state (ARMS), 17 of whom later made the transition to psychosis, 37 patients with a first episode of psychosis and 16 controls with depressive disorders were interviewed about first self-perceived signs and symptoms.

RESULTS

In ARMS and first episode of psychosis, on average, first self-perceived signs or symptoms had occurred about 5-6 years before the interview. In ARMS, including transition to psychosis, 'loss of energy' and 'difficulties concentrating' were the most frequently recalled first signs. There was much overlap for the four most frequently mentioned symptoms in the three groups. As compared with ARMS, controls with depressive disorders significantly more often recalled 'depression' and 'social isolation' as the very first signs of disease.

CONCLUSIONS

Clinicians should consider the development of self-recalled first signs over time carefully when assessing suspected early prodromal stages of schizophrenia and beginning depressive disorder.

Identification of a biological signature for schizophrenia in serum

Biomarkers are now used in many areas of medicine but are still lacking for psychiatric conditions such as schizophrenia (SCZ). We have used a multiplex molecular profiling approach to measure serum concentrations of 181 proteins and small molecules in 250 first and recent onset SCZ, 35 major depressive disorder (MDD), 32 euthymic bipolar disorder (BPD), 45 Asperger syndrome and 280 control subjects. Preliminary analysis resulted in identification of a signature comprised of 34 analytes in a cohort of closely matched SCZ (n=71) and control (n=59) subjects. Partial least squares discriminant analysis using this signature gave a separation of 60-75% of SCZ subjects from controls across five independent cohorts. The same analysis also gave a separation of ~50% of MDD patients and 10-20% of BPD and Asperger syndrome subjects from controls. These results demonstrate for the first time that a biological signature for SCZ can be identified in blood serum. This study lays the groundwork for development of a diagnostic test that can be used as an aid for distinguishing SCZ subjects from healthy controls and from those affected by related psychiatric illnesses with overlapping symptoms.

Gray matters!--mapping the transition to psychosis

Despite many neuroimaging studies on schizophrenia showing brain abnormalities the exact time course of their occurrence is unknown. Studies of gray matter are a powerful tool in biological psychiatry and provide an unprecedented opportunity for brain structure investigations. Here we compared cross-sectional and longitudinal structural neuroimaging studies distinguishing high-risk subjects developing psychosis from those who did not. These investigations on gray matter volumes in the prodromal phase potentially identify core structural markers of impending psychoses and clarify dynamic changes underlying the transition. Subjects at high risk of psychosis show qualitatively similar albeit less severe gray matter abnormalities as patients with psychosis.

Different duration of at-risk mental state associated with neurofunctional abnormalities. A multimodal imaging study

OBJECTIVES

Neurofunctional alterations are correlates of vulnerability to psychosis, as well as of the disorder itself. How these abnormalities relate to different probabilities for later transition to psychosis is unclear. We investigated vulnerability- versus disease-related versus resilience biomarkers of psychosis during working memory (WM) processing in individuals with an at-risk mental state (ARMS).

EXPERIMENTAL DESIGN

Patients with "first-episode psychosis" (FEP, n = 21), short-term ARMS (ARMS-ST, n = 17), long-term ARMS (ARMS-LT, n = 16), and healthy controls (HC, n = 20) were investigated with an n-back WM task. We examined functional magnetic resonance imaging (fMRI) and structural magnetic resonance imaging (sMRI) data in conjunction using biological parametric mapping (BPM) toolbox.

PRINCIPAL OBSERVATIONS

There were no differences in accuracy, but the FEP and the ARMS-ST group had longer reaction times compared with the HC and the ARMS-LT group. With the 2-back > 0-back contrast, we found reduced functional activation in ARMS-ST and FEP compared with the HC group in parietal and middle frontal regions. Relative to ARMS-LT individuals, FEP patients showed decreased activation in the bilateral inferior frontal gyrus and insula, and in the left prefrontal cortex. Compared with the ARMS-LT, the ARMS-ST subjects showed reduced activation in the right inferior frontal gyrus and insula. Reduced insular and prefrontal activation was associated with gray matter volume reduction in the same area in the ARMS-LT group.

CONCLUSIONS

These findings suggest that vulnerability to psychosis was associated with neurofunctional alterations in fronto-temporo-parietal networks in a WM task. Neurofunctional differences within the ARMS were related to different duration of the prodromal state and resilience factors.

Pituitary volume increase during emerging psychosis

BACKGROUND

Morphologic abnormalities of the pituitary gland volume (PV) have been reported in schizophrenia, but at what point in time they occur remains unclear. This study determines PV across different stages of emerging psychotic disorders compared to healthy controls.

METHODS

We compared PV of 36 individuals with an at-risk mental state (ARMS) for psychosis, 23 patients with a first episode psychosis (FEP) and 20 healthy controls (HC). Transition to psychosis was monitored using the BPRS transition criteria according to Yung et al. (Yung, A.R. et al., 1998. Prediction of psychosis. A step towards indicated prevention of schizophrenia. Br. J. Psychiatry Suppl. 172 (33), 14-20). Applying these transition criteria, 16 of the 36 ARMS individuals made the transition to psychosis (ARMS-T) and 20 did not (ARMS-NT). We traced PV manually on 1mm slices of magnetic resonance images in three dimensions (coronal, sagittal and axial) blind to group status. We used univariate analysis of covariance (ANCOVA) with PV as dependent variable, group and sex as between-subject factors and whole brain volume as covariate.

RESULTS

PV increased from HC to ARMS-NT to ARMS-T/FEP. ANCOVA revealed a significant effect of group (F(3,78)=3.0; p=.036) and a sex × group interaction (F(3,78)=6.5; p=.001). Over all groups, women had considerably larger PV than men (F(1,78)=9.8; p=.003).

CONCLUSIONS

Our findings provide further evidence that PV is increased in emerging psychotic disorders, and suggest that this is due to a stress-associated activation of the pituitary gland.

The Self-screen-Prodrome as a short screening tool for pre-psychotic states

BACKGROUND

Early detection of psychosis is an important issue in current research. Early intervention helps to improve the outcome of the disorder. Therefore, a comprehensive examination in large populations, necessary as it might be, is economically almost not feasible. A screening via self-report is more practicable as it helps focus on individuals with high symptom loads.

AIM

To examine aspects of validity of the Self-screen-Prodrome (SPro) as a new screening tool for prodromal states of psychosis in a military sample.

METHOD

938 Swiss conscripts were assessed with the SPro, the Eppendorf Schizophrenia-Inventory (ESI) and the Symptom-Checklist-90-Revised (SCL-90-R). Conscripts with potential psychosis-like pathology (T-transformed Severity Index of the SCL-90-R-subscales Psychoticism [PSYC] and Paranoid Ideation [PARA]≥63) were compared with those not meeting the criteria of this condition (non-cases).

RESULTS

Both groups (cases and non-cases) showed significant differences in their mean scores on SPro and ESI, although only the SPro had satisfactory effect sizes. In hierarchic logistic regression models the SPro turned out to be highly predictive for caseness while ESI-scales were not significant. A cut-off score of ≥2 on the SPro subscale for psychotic risk (SPro-Psy-Risk) was found to identify caseness best with a sensitivity of 74% and a specificity of 61%.

CONCLUSION

The SPro has proven to be a valid and very economic screening tool for general and prodromal pathology in large populations.

EEG spectral power and negative symptoms in at-risk individuals predict transition to psychosis

INTRODUCTION

EEG power in the delta, theta and beta1 bands has been shown to be positively correlated with negative symptoms in first episode psychotic patients. The present study investigates this correlation in an "at risk mental state for psychosis" (ARMS) with the aim to improve prediction of transition to psychosis.

METHODS

Thirteen ARMS patients with later transition to psychosis (ARMS-T) and fifteen without (follow-up period of at least 4 years) (ARMS-NT) were investigated using spectral resting EEG data (of 8 electrodes over the fronto-central scalp area placed according to the 10-20 system) and summary score of the Scale for the Assessment of Negative Symptoms (SANS). Linear regressions were used to evaluate the correlation of SANS and EEG power in seven bands (delta, theta, alpha1, alpha2, beta1, beta2, beta3) in both ARMS groups and logistic regressions were used to predict transition to psychosis. Potentially confounding factors were controlled.

RESULTS

ARMS-T and ARMS-NT showed differential correlations of EEG power and SANS in delta, theta, and beta1 bands (p<.05): ARMS-T showed positive and ARMS-NT negative correlations. Logistic regressions showed that neither SANS score nor EEG spectral power alone predicted transition to psychosis. However, SANS score in combination with power in the delta, theta, beta1, and beta2 bands, respectively, predicted transition significantly (p<.03).

CONCLUSIONS

ARMS-T and ARMS-NT show differential correlations of SANS summary score and EEG power in delta, theta, and beta bands. Prediction of transition to psychosis is possible using combined information from a negative symptom scale and EEG spectral data.

Altered semantic but not phonological verbal fluency in young help-seeking individuals with ultra high risk of psychosis

Some of the cognitive impairments of schizophrenia are already detectable before the onset of the disease, and could help to identify individuals at higher risk of psychosis. In patients with schizophrenia, semantic verbal fluency (VF) is more impaired than phonological fluency. We investigated whether the same profile is present in young patients at Ultra High Risk of psychosis (UHR). One hundred and fifty six young patients (15-30y.o.) consecutively seeking help at our specialized youth mental health center with no definite psychiatric diagnoses were recruited and assessed with the CAARMS. Individuals meeting the criteria for UHR were compared to the remaining patients considered as Help Seeker Controls (HSCo). UHR individuals had a lower mean total semantic fluency score than HSCo. This effect was significant for each semantic category ('animals' and 'fruits'). By contrast, there were no differences in phonological fluency scores between UHR and HSCo either in the total score or when each letter ('P' and 'R') was considered separately. Semantic but not phonological VF differentiated UHR individuals from non-psychotic help-seeking young adults. These results suggest that semantic deficits are present during the prodromal phase, prior to clinical expression of full-blown psychosis, and suggest that prodromes could be associated with alteration in temporal brain areas.

Hippocampus abnormalities in at risk mental states for psychosis? A cross-sectional high resolution region of interest magnetic resonance imaging study

BACKGROUND

Hippocampal volume (HV) reduction is well documented in schizophrenia. However, it is still unclear whether this change is a pre-existing vulnerability factor, a sign of disease progression, a consequence of environmental factors, such as drug use, antipsychotic medication, or malnutrition. The timing of HV changes is not well established, but a lack of macrostructural hippocampal brain abnormalities before disease onset would rather support a neuroprogressive illness model.

AIM

To investigate the timing of HV changes in emerging psychosis.

METHODS

A cross-sectional MRI study of manually traced HVs in 37 individuals with an At Risk Mental State (ARMS) for psychosis, 23 individuals with First-Episode Psychosis (FEP), and 22 Healthy Controls (HC) was performed. We compared left and right HVs corrected for whole brain volume across groups using analysis of covariance (ANCOVA) with gender as a covariate. Sixteen of 37 ARMS individuals developed a psychotic disorder during follow up (ARMS-T). The mean duration of follow up in ARMS was 25.1months.

RESULTS

The overall ANCOVA model comparing left HVs across FEP, ARMS and HC indicated a significant general group effect (p<.05) with largest volumes in ARMS and smallest in FEP. ARMS-T subjects had significantly larger left HVs compared to FE but no HV differences compared to HC (p<0.05). Over all groups, we found an asymmetry between the left and right mean HVs and a strong effect of sex.

DISCUSSION

The present study suggests that macrostructural hippocampal abnormalities probably occur in the context of the first psychotic breakdown.

Neuroimaging predictors of transition to psychosis--a systematic review and meta-analysis

OBJECTIVES

In early stage psychosis research the identification of neurobiological correlates of vulnerability to schizophrenia is an important hurdle.

METHODS

We systematically reviewed the neuroimaging publications on high-risk subjects with subsequent transition to psychosis (HR-T) and conducted a meta-analysis calculating the effect size Cohen's d.

RESULTS

Out of 30 identified studies 25 met the inclusion criteria. Structural (s)MRI studies showed small to medium effect sizes of decreased prefrontal, cingulate, insular and cerebellar gray matter volume in HR-T compared to high-risk subjects without transition (HR-NT). Meta-analysis revealed relatively larger whole brain volumes in HR-T compared to HR-NT subjects (mean Cohen's d 0.36, 95% CI 0.27-0. 46). Compared to HR-NT, HR-T subjects showed in functional imaging studies reduced brain activation in prefrontal cortex, reduced neuronal density, and increased membrane turnover in frontal and cingulate cortex with medium to large effect sizes.

CONCLUSIONS

Despite methodological differences between studies, structural and neurochemical abnormalities in prefrontal, anterior cingulate, medial temporal and cerebellar cortex might be predictive for development of psychosis within HR subjects.

Efficacy of using cognitive status in predicting psychosis: a 7-year follow-up

BACKGROUND

Despite extensive early detection research in schizophrenic psychoses, methods for identifying at-risk individuals and predicting their transition to psychosis are still unreliable. Moreover, there are sparse data on long-term prediction. We therefore investigated long-term psychosis transition in individuals with an At Risk Mental State (ARMS) and examined the relative efficacy of clinical and neuropsychological status in optimizing the prediction of transition.

METHODS

Sixty-four individuals with ARMS for psychosis were identified from all referrals to our early detection clinic between March 1, 2000 and February 29, 2004. Fifty-three (83%) were followed up for up to 7 (mean 5.4) years.

RESULTS

Twenty-one of the 53 staying in follow-up developed psychosis, corresponding to a transition rate of .34 (Kaplan-Meier estimates). Median time to transition was 10 months (range <1-55). Six of all transitions (29%) occurred only after 12 months from referral. Best transition predictors within this population were selected attenuated psychotic symptoms (suspiciousness), negative symptoms (anhedonia/asociality), and cognitive deficits (reduced speed of information processing). With these predictors in an integrated model for predicting transition to psychosis, the overall predictive accuracy was 80.9% with a sensitivity of 83.3% and a specificity of 79.3%.

CONCLUSIONS

Follow-up of ARMS subjects should exceed the usual 12 months. Prediction of transitions could be improved by a stronger weighting of certain early symptoms and by introducing neurocognitive tests into a stepwise risk assessment. Confirmatory research will hopefully further improve risk algorithm, including psychopathology and neuropsychological performance, for clinical application in early detection clinics.

Schizophrenia, "just the facts" 4. Clinical features and conceptualization

Although dementia praecox or schizophrenia has been considered a unique disease entity for the past century, its definitions and boundaries have continued to vary over this period. At any given time, the changing concept of schizophrenia has been influenced by available diagnostic tools and treatments, related conditions from which it most needs to be distinguished, extant knowledge and scientific paradigms. There is significant heterogeneity in the etiopathology, symptomatology, and course of schizophrenia. It is characterized by an admixture of positive, negative, cognitive, mood, and motor symptoms whose severity varies across patients and through the course of the illness. Positive symptoms usually first begin in adolescence or early adulthood, but are often preceded by varying degrees of negative and cognitive symptomatology. Schizophrenia tends to be a chronic and relapsing disorder with generally incomplete remissions, variable degrees of functional impairment and social disability, frequent comorbid substance abuse, and decreased longevity. Although schizophrenia may not represent a single disease with a unitary etiology or pathogenetic process, alternative approaches have thus far been unsuccessful in better defining this syndrome or its component entities. The symptomatologic, course, and etio-pathological heterogeneity can usefully be addressed by a dimensional approach to psychopathology, a clinical staging approach to illness course, and by elucidating endophenotypes and markers of illness progression, respectively. This will allow an approach to the deconstruction of schizophrenia into its multiple component parts and strategies to reconfigure these components in a more meaningful manner. Possible implications for DSM-V and ICD-11 definitions of schizophrenia are discussed.