Interest is growing in the potential effect of gonadal hormones, prolactin, and the hypothalamic-pituitary-gonadal axis in schizophrenic psychoses. Many studies from clinical, epidemiological, and ...fundamental research have confirmed that oestradiol, the main component of oestrogens, can have protective effects in schizophrenic psychoses. Furthermore, many patients with schizophrenic psychoses-even in the untreated prodromal stages-have hyperprolactinaemia and gonadal dysfunction, with oestrogen deficiency in women and testosterone deficiency in men. The understanding of the pathogenetic mechanisms underlying these findings could contribute to a better understanding of the aetiopathogenesis of schizophrenic psychoses and improve therapeutic approaches. In this Series paper, we aim to review methodologically sound studies in this area, propose a theory to explain these findings in the context of psychosis, and suggest therapeutic strategies and implications for further research.
Resting-state EEG microstates are brief (50-100 ms) periods, in which the spatial configuration of scalp global field power remains quasi-stable before rapidly shifting to another configuration. ...Changes in microstate parameters have been described in patients with psychotic disorders. These changes have also been observed in individuals with a clinical or genetic high risk, suggesting potential usefulness of EEG microstates as a biomarker for psychotic disorders. The present study aimed to investigate the potential of EEG microstates as biomarkers for psychotic disorders and future transition to psychosis in patients at ultra-high-risk (UHR). We used 19-channel clinical EEG recordings and orthogonal contrasts to compare temporal parameters of four normative microstate classes (A-D) between patients with first-episode psychosis (FEP; n = 29), UHR patients with (UHR-T; n = 20) and without (UHR-NT; n = 34) later transition to psychosis, and healthy controls (HC; n = 25). Microstate A was increased in patients (FEP & UHR-T & UHR-NT) compared to HC, suggesting an unspecific state biomarker of general psychopathology. Microstate B displayed a decrease in FEP compared to both UHR patient groups, and thus may represent a state biomarker specific to psychotic illness progression. Microstate D was significantly decreased in UHR-T compared to UHR-NT, suggesting its potential as a selective biomarker of future transition in UHR patients.
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.
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.
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.
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.
To date, the MRI-based individualized prediction of psychosis has only been demonstrated in single-site studies. It remains unclear if MRI biomarkers generalize across different centers and MR ...scanners and represent accurate surrogates of the risk for developing this devastating illness. Therefore, we assessed whether a MRI-based prediction system identified patients with a later disease transition among 73 clinically defined high-risk persons recruited at two different early recognition centers. Prognostic performance was measured using cross-validation, independent test validation, and Kaplan-Meier survival analysis. Transition outcomes were correctly predicted in 80% of test cases (sensitivity: 76%, specificity: 85%, positive likelihood ratio: 5.1). Thus, given a 54-month transition risk of 45% across both centers, MRI-based predictors provided a 36%-increase of prognostic certainty. After stratifying individuals into low-, intermediate-, and high-risk groups using the predictor's decision score, the high- vs low-risk groups had median psychosis-free survival times of 5 vs 51 months and transition rates of 88% vs 8%. The predictor's decision function involved gray matter volume alterations in prefrontal, perisylvian, and subcortical structures. Our results support the existence of a cross-center neuroanatomical signature of emerging psychosis enabling individualized risk staging across different high-risk populations. Supplementary results revealed that (1) potentially confounding between-site differences were effectively mitigated using statistical correction methods, and (2) the detection of the prodromal signature considerably depended on the available sample sizes. These observations pave the way for future multicenter studies, which may ultimately facilitate the neurobiological refinement of risk criteria and personalized preventive therapies based on individualized risk profiling tools.
Converging evidence indicates that neural oscillations coordinate activity across brain areas, a process which is seemingly perturbed in schizophrenia. In particular, beta (13-30 Hz) and gamma (30-50 ...Hz) oscillations were repeatedly found to be disturbed in schizophrenia and linked to clinical symptoms. However, it remains unknown whether abnormalities in current source density (CSD) and lagged phase synchronization of oscillations across distributed regions of the brain already occur in patients with an at-risk mental state (ARMS) for psychosis.
To further elucidate this issue, we assessed resting-state EEG data of 63 ARMS patients and 29 healthy controls (HC). Twenty-three ARMS patients later made a transition to psychosis (ARMS-T) and 40 did not (ARMS-NT). CSD and lagged phase synchronization of neural oscillations across brain areas were assessed using eLORETA and their relationships to neurocognitive deficits and clinical symptoms were analyzed using linear mixed-effects models.
ARMS-T patients showed higher gamma activity in the medial prefrontal cortex compared to HC, which was associated with abstract reasoning abilities in ARMS-T. Furthermore, in ARMS-T patients lagged phase synchronization of beta oscillations decreased more over Euclidian distance compared to ARMS-NT and HC. Finally, this steep spatial decrease of phase synchronicity was most pronounced in ARMS-T patients with high positive and negative symptoms scores.
These results indicate that patients who will later make the transition to psychosis are characterized by impairments in localized and synchronized neural oscillations providing new insights into the pathophysiological mechanisms of schizophrenic psychoses and may be used to improve the prediction of psychosis.
Grey matter (GM) volume alterations have been repeatedly demonstrated in patients with first episode psychosis (FEP). Some of these neuroanatomical abnormalities are already evident in the at‐risk ...mental state (ARMS) for psychosis. Not only GM alterations but also neurocognitive impairments predate the onset of frank psychosis with verbal learning and memory (VLM) being among the most impaired domains. Yet, their interconnection with alterations in GM volumes remains ambiguous. Thus, we evaluated associations of different subcortical GM volumes in the medial temporal lobe with VLM performance in antipsychotic‐naïve ARMS and FEP patients. Data from 59 ARMS and 31 FEP patients, collected within the prospective Früherkennung von Psychosen study, were analysed. Structural T1‐weighted images were acquired using a 3 Tesla magnetic resonance imaging scanner. VLM was assessed using the California Verbal Learning Test and its factors Attention Span, Learning Efficiency, Delayed Memory and Inaccurate Memory. FEP patients showed significantly enlarged volumes of hippocampus, pallidum, putamen and thalamus compared to ARMS patients. A significant negative association between amygdala and pallidum volume and Attention Span was found in ARMS and FEP patients combined, which however did not withstand correction for multiple testing. Although we found significant between‐group differences in subcortical volumes and VLM is among the most impaired cognitive domains in emerging psychosis, we could not demonstrate an association between low performance and subcortical GM volumes alterations in antipsychotic‐naïve patients. Hence, deficits in this domain do not appear to stem from alterations in subcortical structures.
Neurocognitive deficits such as in verbal learning and memory have been frequently shown in emerging psychosis. We investigated whether such deficits are likely to originate from brain structural alterations and found antipsychotic‐naïve first episode psychosis patients to present with significantly enlarged volumes of hippocampus, pallidum, putamen and thalamus compared to at‐risk mental state for psychosis patients. Group differences in verbal learning and memory, however, do not seem to stem from these subcortical volumetric alterations.
To investigate the longitudinal latent state-trait structure of the different dimensions of psychosis symptoms in clinical high-risk state (CHRS) and first episode psychosis (FEP) individuals over a ...one year time-span. This paper examines if the symptom clusters Positive Symptoms, Negative Symptoms, Affectivity, Resistance, Activation, and Excitement according to the Brief Psychiatric Rating Scale (BPRS) differ in their trait and state characters in 196 CHRS and 131 FEP individuals. Statistical analysis was performed using latent state-trait analysis. On average, trait differences accounted for 72.2% of Positive Symptoms, 81.1% of Negative Symptoms, 57.0% of Affectivity, and 69.2% of Activation, whereas 15.0% of the variance of Resistance and 13.2% of the variance of Excitement were explained by trait differences. Explorative analyses showed a trait components' increase of 0.408 in Positive Symptoms from baseline up to the 9th month and an increase of 0.521 in Affectivity from baseline up to the 6th month. Negative Symptoms had the highest trait component levels of all subscales between baseline and 6 months. The finding that an increasing proportion of psychosis symptoms is persisting over time underlines the importance of early intervention programs in individuals with psychotic disorders.
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.
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.
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.
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.
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