Dimensional profiling of psychopathology in children and adolescents based on the K-SADS-PL and an analysis of the construct validity of two ADHD symptom dimensions.

OBJECTIVES: The traditional view on psychiatric disorders as categorical and distinct is being challenged by perspectives emphasizing the relevance of dimensional and transdiagnostic assessment. However, most diagnostic instruments are based on a categorical view with a threshold-approach to disease classification. METHODS: We here describe algorithms for dimensionalizing the psychopathological ratings of the widely used diagnostic interview for children and adolescents, the Kiddie-Schedule for Affective Disorders and Schizophrenia - Present and Lifetime Version (K-SADS-PL). We further evaluate the criterion-related construct validity of the dimensionalized attention-deficit/hyperactivity disorder (ADHD) scales using Rasch models in a sample of 590 children (mean age 10.29 (.36), 49% girls). RESULTS: The algorithms generate scores of current symptom load, i.e., the sum of clinician-rated symptoms within each disorder assessed with the interview. We found support for counting symptoms of inattention and hyperactivity/impulsivity, respectively, but not for a single combined ADHD scale. CONCLUSIONS: The algorithms constitute an initial step in creating a framework for clinician-rated dimensional analyses of symptoms derived from the K-SADS-PL, but future studies are needed to further evaluate the construct validity of the remaining scales and the reliability and clinical utility of the method. We believe that our proposed algorithms offer a novel method of dimensional psychopathological assessment, which can be applied in multiple branches of child and adolescent psychiatry.

Maternal inflammation during pregnancy is associated with risk of ADHD in children at age 10.

INTRODUCTION: Maternal inflammation during pregnancy may affect early neurodevelopment in offspring as suggested by preclinical and register data. However, clinical evidence for risk of aberrant neurodevelopment later in childhood is scarce. In the population-based COPSAC2010 mother-child cohort, we investigated associations between maternal inflammation levels during pregnancy and the risk of a diagnosis of ADHD as well as the load of ADHD symptoms in the children at age 10. METHODS: The COPSAC2010 cohort consists of 700 mother-child pairs followed prospectively since pregnancy week 24.Maternal high-sensitivity C-Reactive Protein (hs-CRP) level at week 24 of gestation was investigated in relation to child neurodevelopment by age 10 using logistic and linear regression models with extensive confounder adjustment, including socioeconomic status and maternal polygenic risk of ADHD. The children completed a comprehensive examination of neurodevelopment including categorical (i.e., diagnostic) and dimensional (i.e., symptom load) psychopathology using the Kiddie Schedule for Affective Disorders and Schizophrenia Present and Lifetime Version (K-SADS-PL) and parental rated ADHD-Rating Scale (ADHD-RS). RESULTS: A total of 604 (86 %) of the 700 children in the COPSAC2010 cohort participated in the COPSYCH visit at age 10. Sixty-five (10.8 %) fulfilled a research diagnosis of ADHD (16 girls and 49 boys). Higher maternal hs-CRP level in pregnancy at week 24 (median 5.4 mg/L) was significantly associated with increased risk for a diagnosis of ADHD, adjusted OR 1.40, 95 %CI (1.16-1.70), p = 0.001. Additionally, higher maternal hs-CRP was associated with increased ADHD symptom load in the entire cohort, reflected by ADHD-RS raw scores. DISCUSSION: These clinical data demonstrated a robust association of prenatal maternal inflammation assessed by hs-CRP with a diagnosis of ADHD by age 10. Moreover, maternal inflammation was associated with ADHD symptom load in the complete cohort. Identifying inflammation as an important marker will provide a potential target for future increased awareness and prevention during pregnancy thereby ultimately improving neurodevelopmental outcomes in children.

Influence of psychopathology and metabolic parameters on quality of life in patients with first-episode psychosis before and after initial antipsychotic treatment.

The impact of psychological and physical health on quality of life (QoL) in patients with early psychosis remain relatively unexplored. We evaluated the predictive value of psychopathological and metabolic parameters on QoL in antipsychotic-naïve patients with first-episode psychosis before and after initial antipsychotic treatment. At baseline, 125 patients underwent assessments of psychopathology, prevalence of metabolic syndrome (MetS), and QoL. After 6 weeks of antipsychotic monotherapy, 89 patients were re-investigated. At baseline, the prevalence of MetS was 19.3% (n = 22). After 6 weeks, body weight (1.3 kg, p < 0.001) and body mass index (0.4 kg/m2, p < 0.001) increased, and four additional patients developed MetS. Multivariate linear regression revealed that positive and negative symptoms, and to some degree waist circumference, were predictors of QoL at both time points. Our findings suggest that in the earliest stages of antipsychotic treatment, metabolic side-effects may be less influential on QoL than psychopathological severity.

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

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

Add-On MEmaNtine to Dopamine Antagonism to Improve Negative Symptoms at First Psychosis- the AMEND Trial Protocol.

Background: Antipsychotic drugs are primarily efficacious in treating positive symptoms by blocking the dopamine D2 receptor, but they fail to substantially improve negative symptoms and cognitive deficits. The limited efficacy may be attributed to the fact that the pathophysiology of psychosis involves multiple neurotransmitter systems. In patients with chronic schizophrenia, memantine, a non-competitive glutamatergic NMDA receptor antagonist, shows promise for ameliorating negative symptoms and improving cognition. Yet, it is unknown how memantine modulates glutamate levels, and memantine has not been investigated in patients with first-episode psychosis. Aims: This investigator-initiated double-blinded randomized controlled trial is designed to (1) test the clinical effects on negative symptoms of add-on memantine to antipsychotic medication, and (2) neurobiologically characterize the responders to add-on memantine. Materials and Equipment: Antipsychotic-naïve patients with first-episode psychosis will be randomized to 12 weeks treatment with [amisulpride + memantine] or [amisulpride + placebo]. We aim for a minimum of 18 patients in each treatment arm to complete the trial. Brain mapping will be performed before and after 12 weeks focusing on glutamate and neuromelanin in predefined regions. Regional glutamate levels will be probed with proton magnetic resonance spectroscopy (MRS), while neuromelanin signal will be mapped with neuromelanin-sensitive magnetic resonance imaging (MRI). We will also perform structural and diffusion weighted, whole-brain MRI. MRS and MRI will be performed at an ultra-high field strength (7 Tesla). Alongside, participants undergo clinical and neuropsychological assessments. Twenty matched healthy controls will undergo similar baseline- and 12-week examinations, but without receiving treatment. Outcome Measures: The primary endpoint is negative symptom severity. Secondary outcomes comprise: (i) clinical endpoints related to cognition, psychotic symptoms, side effects, and (ii) neurobiological endpoints related to regional glutamate- and neuromelanin levels, and structural brain changes. Anticipated Results: We hypothesize that add-on memantine to amisulpride will be superior to amisulpride monotherapy in reducing negative symptoms, and that this effect will correlate with thalamic glutamate levels. Moreover, we anticipate that add-on memantine will restore regional white matter integrity and improve cognitive functioning. Perspectives: By combining two licensed, off-patent drugs, AMEND aims to optimize treatment of psychosis while investigating the memantine response. Alongside, AMEND will provide neurobiological insights to effects of dual receptor modulation, which may enable future stratification of patients with first-episode psychosis before initial antipsychotic treatment. Clinical Trial Registration: [ClinicalTrials.gov], identifier [NCT04789915].

Cortico-cognition coupling in treatment resistant schizophrenia.

BACKGROUND: Brain structural alterations and cognitive dysfunction are independent predictors for poor clinical outcome in schizophrenia, and the associations between these domains remains unclear. We employed a novel, multiblock partial least squares correlation (MB-PLS-C) technique and investigated multivariate cortico-cognitive patterns in patients with treatment-resistant schizophrenia (TRS) and matched healthy controls (HC). METHOD: Forty-one TRS patients (age 38.5 ± 9.1, 30 males (M)), and 45 HC (age 40.2 ± 10.6, 29 M) underwent 3T structural MRI. Volumes of 68 brain regions and seven variables from CANTAB covering memory and executive domains were included. Univariate group differences were assessed, followed by the MB-PLS-C analyses to identify group-specific multivariate patterns of cortico-cognitive coupling. Supplementary three-group analyses, which included 23 non-affected first-degree relatives (NAR), were also conducted. RESULTS: Univariate tests demonstrated that TRS patients showed impairments in all seven cognitive tasks and volume reductions in 12 cortical regions following Bonferroni correction. The MB-PLS-C analyses revealed two significant latent variables (LVs) explaining > 90% of the sum-of-squares variance. LV1 explained 78.86% of the sum-of-squares variance, describing a shared, widespread structure-cognitive pattern relevant to both TRS patients and HCs. In contrast, LV2 (13.47% of sum-of-squares variance explained) appeared specific to TRS and comprised a differential cortico-cognitive pattern including frontal and temporal lobes as well as paired associates learning (PAL) and intra-extra dimensional set shifting (IED). Three-group analyses also identified two significant LVs, with NARs more closely resembling healthy controls than TRS patients. CONCLUSIONS: MB-PLS-C analyses identified multivariate brain structural-cognitive patterns in the latent space that may provide a TRS signature.

Lower Vitamin C Levels Are Associated With Less Improvement in Negative Symptoms in Initially Antipsychotic-Naïve Patients With First-Episode Psychosis.

Low levels of vitamin C have been observed in patients with schizophrenia and psychosis, and vitamin C may affect the dopaminergic system. Likewise, antipsychotic medication modulates striatal dopamine D2 receptors. We measured vitamin C levels in 52 patients with first-episode psychoses (24 females, age 23.1 ± 5.2 years) and 57 matched HCs (20 females, age 22.7 ± 4.3 years) before and after 6 weeks where patients received aripiprazole monotherapy (mean dose 10.4 mg ± 4.8 mg). At baseline, patients displayed lower levels of vitamin C (57.4 ± 25.9 µM) than controls (72.7 ± 21.4 µM) (t = 3.4, P = .001). Baseline symptoms and vitamin C levels were not correlated. Higher baseline vitamin C levels were associated with more improvement in negative symptoms (n = 39, R2 = 0.20, F = 8.2, P = .007), but not with age, sex, or p-aripiprazole. Because negative symptoms are generally considered challenging to alleviate, a potential adjunctive effect of vitamin C on treatment response should be tested in future randomized clinical trials.

Prediction of Early Symptom Remission in Two Independent Samples of First-Episode Psychosis Patients Using Machine Learning.

BACKGROUND: Validated clinical prediction models of short-term remission in psychosis are lacking. Our aim was to develop a clinical prediction model aimed at predicting 4-6-week remission following a first episode of psychosis. METHOD: Baseline clinical data from the Athens First Episode Research Study was used to develop a Support Vector Machine prediction model of 4-week symptom remission in first-episode psychosis patients using repeated nested cross-validation. This model was further tested to predict 6-week remission in a sample of two independent, consecutive Danish first-episode cohorts. RESULTS: Of the 179 participants in Athens, 120 were male with an average age of 25.8 years and average duration of untreated psychosis of 32.8 weeks. 62.9% were antipsychotic-naïve. Fifty-seven percent attained remission after 4 weeks. In the Danish cohort, 31% attained remission. Eleven clinical scale items were selected in the Athens 4-week remission cohort. These included the Duration of Untreated Psychosis, Personal and Social Performance Scale, Global Assessment of Functioning and eight items from the Positive and Negative Syndrome Scale. This model significantly predicted 4-week remission status (area under the receiver operator characteristic curve (ROC-AUC) = 71.45, P < .0001). It also predicted 6-week remission status in the Danish cohort (ROC-AUC = 67.74, P < .0001), demonstrating reliability. CONCLUSIONS: Using items from common and validated clinical scales, our model significantly predicted early remission in patients with first-episode psychosis. Although replicated in an independent cohort, forward testing between machine learning models and clinicians' assessment should be undertaken to evaluate the possible utility as a routine clinical tool.