Reduced sleep spindle density in adolescent patients with early-onset schizophrenia compared to major depressive disorder and healthy controls

OBJECTIVES: During adolescence schizophrenia and major depressive disorder (MDD) increasingly emerge. Overlapping symptomatology during first presentation challenges the diagnostic process. Reduced sleep spindle density (SSD) was suggested as a biomarker in adults, discerning patients with schizophrenia from patients with depression or healthy controls (HC). We aimed to compare SSD in early-onset schizophrenia (EOS), with MDD, and HC, and to analyse associations of SSD with symptomatology and neurocognitive measures. METHODS: Automatic sleep spindle detection was performed on all-night high-density EEG (128 electrodes) data of 12 EOS, 19 MDD, and 57 HC (age range 9.8-19), allowing an age- and sex-matching of 1:2 (patients vs. HC). Severity of current symptoms and neurocognitive variables were assessed in all patients. RESULTS: SSD was defined between 13.75 and 14.50 Hz as within this frequency range SSD differed between EOS vs. HC in bin by bin analyses (12-15 Hz). In EOS, SSD was lower over 27 centro-temporal electrodes compared to HC and over 9 central electrodes compared to MDD. Reduced SSD in EOS compared to MDD and HC was accompanied by a high variability of SSD in all adolescents. SSD did not differ between MDD and HC. In the pooled sample of patients, lower SSD was associated with more severe Positive and Negative Symptoms Scale total score, more impaired memory consolidation and processing speed. CONCLUSION: A high variability of SSD in all adolescents may reflect the evolving character of SSD. The association of reduced SSD with the symptom dimension of impaired cognition cuts across diagnostical entities

Clinical high-risk criteria of psychosis in 8–17-year-old community subjects and inpatients not suspected of developing psychosis

BACKGROUND In children and adolescents compared to adults, clinical high-risk of psychosis (CHR) criteria and symptoms are more prevalent but less psychosis-predictive and less clinically relevant. Based on high rates of non-converters to psychosis, especially in children and adolescents, it was suggested that CHR criteria were: (1) Pluripotential; (2) A transdiagnostic risk factor; and (3) Simply a severity marker of mental disorders rather than specifically psychosis-predictive. If any of these three alternative explanatory models were true, their prevalence should differ between persons with and without mental disorders, and their severity should be associated with functional impairment as a measure of severity. AIM To compare the prevalence and severity of CHR criteria/symptoms in children and adolescents of the community and inpatients. METHODS In the mainly cross-sectional examinations, 8–17-year-old community subjects (n = 233) randomly chosen from the population register of the Swiss Canton Bern, and inpatients (n = 306) with primary diagnosis of attention-deficit/hyperactivity disorder (n = 86), eating disorder (n = 97), anxiety including obsessive–compulsive disorder (n = 94), or autism spectrum disorder (n = 29), not clinically suspected to develop psychosis, were examined for CHR symptoms/criteria. Positive items of the Structured Interview for Psychosis-Risk Syndromes (SIPS) were used to assess the symptomatic ultra-high-risk criteria, and the Schizophrenia Proneness Instrument, Child and Youth version (SPI-CY) was used to assess the 14 basic symptoms relevant to basic symptom criteria. We examined group differences in frequency and severity of CHR symptoms/criteria using χ2 tests and nonparametric tests with Cramer’s V and Rosenthal’s r as effect sizes, and their association with functioning using correlation analyses. RESULTS The 7.3% prevalence rate of CHR criteria in community subjects did not differ significantly from the 9.5% rate in inpatients. Frequency and severity of CHR criteria never differed between the community and the four inpatient groups, while the frequency and severity of CHR symptoms differed only minimally. Group differences were found in only four CHR symptoms: suspiciousness/persecutory ideas of the SIPS [χ2 (4) = 9.425; P = 0.051, Cramer’s V = 0.132; and Z = -4.281, P < 0.001; Rosenthal’s r = 0.184], and thought pressure [χ2 (4) = 11.019; P = 0.026, Cramer’s V = 0.143; and Z = -2.639, P = 0.008; Rosenthal’s r = 0.114], derealization [χ2 (4) = 32.380; P < 0.001, Cramer’s V = 0.245; and Z = -3.924, P < 0.001; Rosenthal’s r = 0.169] and visual perception disturbances [χ2 (4) = 10.652; P = 0.031, Cramer’s V = 0.141; and Z = -2.822, P = 0.005; Rosenthal’s r = 0.122] of the SPI-CY. These were consistent with a transdiagnostic risk factor or dimension, i.e., displayed higher frequency and severity in inpatients, in particular in those with eating, anxiety/obsessive–compulsive and autism spectrum disorders. Low functioning, however, was at most weakly related to the severity of CHR criteria/symptoms, with the highest correlation yielded for suspiciousness/persecutory ideas (Kendall’s tau = -0.172, P < 0.001). CONCLUSION The lack of systematic differences between inpatients and community subjects does not support suggestions that CHR criteria/symptoms are pluripotential or transdiagnostic syndromes, or merely markers of symptom severity

Sleep spindles across youth affected by schizophrenia or anti-N-methyl-D-aspartate-receptor encephalitis

BackgroundSleep disturbances are intertwined with the progression and pathophysiology of psychotic symptoms in schizophrenia. Reductions in sleep spindles, a major electrophysiological oscillation during non-rapid eye movement sleep, have been identified in patients with schizophrenia as a potential biomarker representing the impaired integrity of the thalamocortical network. Altered glutamatergic neurotransmission within this network via a hypofunction of the N-methyl-D-aspartate receptor (NMDAR) is one of the hypotheses at the heart of schizophrenia. This pathomechanism and the symptomatology are shared by anti-NMDAR encephalitis (NMDARE), where antibodies specific to the NMDAR induce a reduction of functional NMDAR. However, sleep spindle parameters have yet to be investigated in NMDARE and a comparison of these rare patients with young individuals with schizophrenia and healthy controls (HC) is lacking. This study aims to assess and compare sleep spindles across young patients affected by Childhood-Onset Schizophrenia (COS), Early-Onset Schizophrenia, (EOS), or NMDARE and HC. Further, the potential relationship between sleep spindle parameters in COS and EOS and the duration of the disease is examined.MethodsSleep EEG data of patients with COS (N = 17), EOS (N = 11), NMDARE (N = 8) aged 7–21 years old, and age- and sex-matched HC (N = 36) were assessed in 17 (COS, EOS) or 5 (NMDARE) electrodes. Sleep spindle parameters (sleep spindle density, maximum amplitude, and sigma power) were analyzed.ResultsCentral sleep spindle density, maximum amplitude, and sigma power were reduced when comparing all patients with psychosis to all HC. Between patient group comparisons showed no differences in central spindle density but lower central maximum amplitude and sigma power in patients with COS compared to patients with EOS or NMDARE. Assessing the topography of spindle density, it was significantly reduced over 15/17 electrodes in COS, 3/17 in EOS, and 0/5 in NMDARE compared to HC. In the pooled sample of COS and EOS, a longer duration of illness was associated with lower central sigma power.ConclusionsPatients with COS demonstrated more pronounced impairments of sleep spindles compared to patients with EOS and NMDARE. In this sample, there is no strong evidence that changes in NMDAR activity are related to spindle deficits

Sleep spindles across youth affected by schizophrenia or anti-N-methyl-D-aspartate-receptor encephalitis

BackgroundSleep disturbances are intertwined with the progression and pathophysiology of psychotic symptoms in schizophrenia. Reductions in sleep spindles, a major electrophysiological oscillation during non-rapid eye movement sleep, have been identified in patients with schizophrenia as a potential biomarker representing the impaired integrity of the thalamocortical network. Altered glutamatergic neurotransmission within this network via a hypofunction of the N-methyl-D-aspartate receptor (NMDAR) is one of the hypotheses at the heart of schizophrenia. This pathomechanism and the symptomatology are shared by anti-NMDAR encephalitis (NMDARE), where antibodies specific to the NMDAR induce a reduction of functional NMDAR. However, sleep spindle parameters have yet to be investigated in NMDARE and a comparison of these rare patients with young individuals with schizophrenia and healthy controls (HC) is lacking. This study aims to assess and compare sleep spindles across young patients affected by Childhood-Onset Schizophrenia (COS), Early-Onset Schizophrenia, (EOS), or NMDARE and HC. Further, the potential relationship between sleep spindle parameters in COS and EOS and the duration of the disease is examined.MethodsSleep EEG data of patients with COS (N = 17), EOS (N = 11), NMDARE (N = 8) aged 7–21 years old, and age- and sex-matched HC (N = 36) were assessed in 17 (COS, EOS) or 5 (NMDARE) electrodes. Sleep spindle parameters (sleep spindle density, maximum amplitude, and sigma power) were analyzed.ResultsCentral sleep spindle density, maximum amplitude, and sigma power were reduced when comparing all patients with psychosis to all HC. Between patient group comparisons showed no differences in central spindle density but lower central maximum amplitude and sigma power in patients with COS compared to patients with EOS or NMDARE. Assessing the topography of spindle density, it was significantly reduced over 15/17 electrodes in COS, 3/17 in EOS, and 0/5 in NMDARE compared to HC. In the pooled sample of COS and EOS, a longer duration of illness was associated with lower central sigma power.ConclusionsPatients with COS demonstrated more pronounced impairments of sleep spindles compared to patients with EOS and NMDARE. In this sample, there is no strong evidence that changes in NMDAR activity are related to spindle deficits

Multimodal Machine Learning Workflows for Prediction of Psychosis in Patients With Clinical High-Risk Syndromes and Recent-Onset Depression

Importance Diverse models have been developed to predict psychosis in patients with clinical high-risk (CHR) states. Whether prediction can be improved by efficiently combining clinical and biological models and by broadening the risk spectrum to young patients with depressive syndromes remains unclear. Objectives To evaluate whether psychosis transition can be predicted in patients with CHR or recent-onset depression (ROD) using multimodal machine learning that optimally integrates clinical and neurocognitive data, structural magnetic resonance imaging (sMRI), and polygenic risk scores (PRS) for schizophrenia; to assess models' geographic generalizability; to test and integrate clinicians' predictions; and to maximize clinical utility by building a sequential prognostic system. Design, Setting, and Participants This multisite, longitudinal prognostic study performed in 7 academic early recognition services in 5 European countries followed up patients with CHR syndromes or ROD and healthy volunteers. The referred sample of 167 patients with CHR syndromes and 167 with ROD was recruited from February 1, 2014, to May 31, 2017, of whom 26 (23 with CHR syndromes and 3 with ROD) developed psychosis. Patients with 18-month follow-up (n = 246) were used for model training and leave-one-site-out cross-validation. The remaining 88 patients with nontransition served as the validation of model specificity. Three hundred thirty-four healthy volunteers provided a normative sample for prognostic signature evaluation. Three independent Swiss projects contributed a further 45 cases with psychosis transition and 600 with nontransition for the external validation of clinical-neurocognitive, sMRI-based, and combined models. Data were analyzed from January 1, 2019, to March 31, 2020. Main Outcomes and Measures Accuracy and generalizability of prognostic systems. Results A total of 668 individuals (334 patients and 334 controls) were included in the analysis (mean [SD] age, 25.1 [5.8] years; 354 [53.0%] female and 314 [47.0%] male). Clinicians attained a balanced accuracy of 73.2% by effectively ruling out (specificity, 84.9%) but ineffectively ruling in (sensitivity, 61.5%) psychosis transition. In contrast, algorithms showed high sensitivity (76.0%-88.0%) but low specificity (53.5%-66.8%). A cybernetic risk calculator combining all algorithmic and human components predicted psychosis with a balanced accuracy of 85.5% (sensitivity, 84.6%; specificity, 86.4%). In comparison, an optimal prognostic workflow produced a balanced accuracy of 85.9% (sensitivity, 84.6%; specificity, 87.3%) at a much lower diagnostic burden by sequentially integrating clinical-neurocognitive, expert-based, PRS-based, and sMRI-based risk estimates as needed for the given patient. Findings were supported by good external validation results. Conclusions and RelevanceThese findings suggest that psychosis transition can be predicted in a broader risk spectrum by sequentially integrating algorithms' and clinicians' risk estimates. For clinical translation, the proposed workflow should undergo large-scale international validation.Question Can a transition to psychosis be predicted in patients with clinical high-risk states or recent-onset depression by optimally integrating clinical, neurocognitive, neuroimaging, and genetic information with clinicians' prognostic estimates? Findings In this prognostic study of 334 patients and 334 control individuals, machine learning models sequentially combining clinical and biological data with clinicians' estimates correctly predicted disease transitions in 85.9% of cases across geographically distinct patient populations. The clinicians' lack of prognostic sensitivity, as measured by a false-negative rate of 38.5%, was reduced to 15.4% by the sequential prognostic model. Meaning These findings suggest that an individualized prognostic workflow integrating artificial and human intelligence may facilitate the personalized prevention of psychosis in young patients with clinical high-risk syndromes or recent-onset depression.</p

Psychotische Störungen: Die Frühbehandlung bei Kindern und Jugendlichen

Studien haben gezeigt, dass die Früherkennung und -behandlung die ­Prognose einer psychotischen Störung deutlich verbessert. Bei Verdacht auf eine psychotische Entwicklung ist daher eine frühzeitige Anmeldung bei Spezialisten wichtig

Jugendliche mit erhöhtem Psychoserisiko : App-unterstützte Behandlung mit dem Therapieprogramm Robin

Jugendliche, die an einer psychotischen Störung erkranken, können in ihrer Entwicklung stark beeinträchtigt werden und haben insgesamt einen schlechteren Krankheitsverlauf als erwachsene Ersterkrankte. Für eine bessere Prognose ist es wichtig, Patienten mit einem erhöhten Psychose-Risiko frühzeitig zu erkennen und zu behandeln. Während es einige Therapiekonzepte für Frühinterventionen mit Erwachsenen gibt, fehlte es bisher an therapeutischen Konzepten für Jugendliche mit At Risk-Symptomen. Robin ist ein innovatives Therapieprogramm und speziell für Jugendliche entwickelt, mit dem Ziel, den jungen Patienten eine Behandlung anzubieten, die altersgerecht, symptom- und ressourcenorientiert ist. Das Therapiemanual ist aufgrund der Heterogenität dieser Patientengruppe modular aufgebaut. Die Module können den Bedürfnissen der Patienten entsprechend individuell zusammengestellt und angewendet werden. Unterstützt wird die therapeutische Behandlung durch die frei verfügbare Smartphone-App «Robin Z». Dieses Buch richtet sich an Psychiater und Psychotherapeuten, die mit Jugendlichen mit erhöhtem Risiko für die Entwicklung einer Psychose arbeiten und das Therapieprogramm Robin oder Teile davon einsetzen und sich so das moderne Smartphone-Nutzungsverhalten ihrer Patienten zu Nutze machen möchten