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

Cognitive profiles across the psychosis continuum

Cognitive impairments are core features in individuals across the psychosis continuum and predict functional outcomes. Nevertheless, substantial variability in cognitive functioning within diagnostic groups, along with considerable overlap with healthy controls, hampers the translation of research findings into personalized treatment planning. Aligned with precision medicine, we employed a data driven machine learning method, self-organizing maps, to conduct transdiagnostic clustering based on cognitive functions in a sample comprising 228 healthy controls, 200 individuals at ultra-high risk for psychosis, and 98 antipsychotic-naïve patients with first-episode psychosis. The self-organizing maps revealed six clinically distinct cognitive profiles that significantly predicted baseline functional level and changes in functional level after one year. Cognitive flexibility in particular, as well as specific executive functions emerged as cardinal in differentiating the profiles. The application of self-organizing maps appears to be a promising approach to inform clinical decision-making based on individualized cognitive profiles, including patient allocation to different interventions. Moreover, this method has the potential to enable cross-diagnostic stratification in research trials, utilizing data-driven subgrouping informed by categories from underlying dimensions of cognition rather than from clinical diagnoses. Finally, the method enables cross-diagnostic profiling across other data modalities, such as brain networks or metabolic subtypes.

The Toronto Adolescent and Youth Cohort Study: Study Design and Early Data Related to Psychosis Spectrum Symptoms, Functioning, and Suicidality

BACKGROUND

Psychosis spectrum symptoms (PSSs) occur in a sizable percentage of youth and are associated with poorer cognitive performance, poorer functioning, and suicidality (i.e., suicidal thoughts and behaviors). PSSs may occur more frequently in youths already experiencing another mental illness, but the antecedents are not well known. The Toronto Adolescent and Youth (TAY) Cohort Study aims to characterize developmental trajectories in youths with mental illness and understand associations with PSSs, functioning, and suicidality.

METHODS

The TAY Cohort Study is a longitudinal cohort study that aims to assess 1500 youths (age 11-24 years) presenting to tertiary care. In this article, we describe the extensive diagnostic and clinical characterization of psychopathology, substance use, functioning, suicidality, and health service utilization in these youths, with follow-up every 6 months over 5 years, including early baseline data.

RESULTS

A total of 417 participants were enrolled between May 4, 2021, and February 2, 2023. Participants met diagnostic criteria for an average of 3.5 psychiatric diagnoses, most frequently anxiety and depressive disorders. Forty-nine percent of participants met a pre-established threshold for PSSs and exhibited higher rates of functional impairment, internalizing and externalizing symptoms, and suicidality than participants without PSSs.

CONCLUSIONS

Initial findings from the TAY Cohort Study demonstrate the feasibility of extensive clinical phenotyping in youths who are seeking help for mental health problems. PSS prevalence is much higher than in community-based studies. Our early data support the critical need to better understand longitudinal trajectories of clinical youth cohorts in relation to psychosis risk, functioning, and suicidality.

NetMoST: A network-based machine learning approach for subtyping schizophrenia using polygenic SNP allele biomarkers

Subtyping neuropsychiatric disorders like schizophrenia is essential for improving the diagnosis and treatment of complex diseases. Subtyping schizophrenia is challenging because it is polygenic and genetically heterogeneous, rendering the standard symptom-based diagnosis often unreliable and unrepeatable. We developed a novel network-based machine-learning approach, netMoST, to subtyping psychiatric disorders. NetMoST identifies polygenic risk SNP-allele modules from genome-wide genotyping data as polygenic haplotype biomarkers (PHBs) for disease subtyping. We applied netMoST to subtype a cohort of schizophrenia subjects into three distinct biotypes with differentiable genetic, neuroimaging and functional characteristics. The PHBs of the first biotype (36.9% of all patients) were related to neurodevelopment and cognition, the PHBs of the second biotype (28.4%) were enriched for neuroimmune functions, and the PHBs of the third biotype (34.7%) were associated with the transport of calcium ions and neurotransmitters. Neuroimaging patterns provided additional support to the new biotypes, with unique regional homogeneity (ReHo) patterns observed in the brains of each biotype compared with healthy controls. Our findings demonstrated netMoST's capability for uncovering novel biotypes of complex diseases such as schizophrenia. The results also showed the power of exploring polygenic allelic patterns that transcend the conventional GWAS approaches.

Relating self-disorders to neurocognitive and psychopathological measures in first-episode schizophrenia

AIM

The notion of a disturbed self as the core feature of schizophrenia dates back to the founding texts on the illness. Since the development of the psychometric tool for examination of anomalous self-experience (EASE), self-disorders have become accessible to empirical research. Empirical studies have shown that EASE measured self-disorders predict schizophrenia spectrum in prospective studies and consistently show a selective hyper aggregation of self-disorder in schizophrenia and schizotypal disorders. The aim of this study is to investigate the relationship between self-disorders cognitive deficits and symptoms in schizophrenia.

METHODS

Thirty-five non-acute first-episode patients with schizophrenia and 35 matched healthy controls were evaluated with EASE, cognitive deficits, and symptoms (PANSS positive, negative and general). [Correction added on 28 January 2022, after first online publication: the words, 'evaluated with' were missing and have now been added to the preceding sentence.] RESULTS: The results show that self-disorders and symptoms are correlated among patients with schizophrenia, but not with cognitive deficits. Moreover, with the exception of attentional deficits, neurocognitive impairment was not significantly higher among patients with schizophrenia compared to healthy controls.

CONCLUSIONS

We argue that this adds support to a view of schizophrenia as being characterized by specific traits of pre-reflective self-disturbance, which are related to the severity of symptoms, whereas neurocognitive impairment reflects a separate or distinct aspect of schizophrenia.

Ten-year course of cognition in first-episode non-affective psychosis patients: PAFIP cohort

BACKGROUND

A large body of research states that cognitive impairment in schizophrenia is static. Nevertheless, most previous studies lack a control group or have small study samples or short follow-up periods.

METHOD

We aimed to address these limitations by studying a large epidemiological cohort of patients with first-episode schizophrenia spectrum disorders and a comparable control sample for a 10-year period.

RESULTS

Our results support the generalized stability of cognitive functions in schizophrenia spectrum disorders considering the entire group. However, the existence of a subgroup of patients characterized by deteriorating cognition and worse long-term clinical outcomes must be noted. Nevertheless, it was not possible to identify concomitant factors or predictors of deterioration (all Ps > 0.05).

CONCLUSIONS

Cognitive functions in schizophrenia spectrum disorder are stable; however, a subgroup of subjects that deteriorate can be characterized.

Dopaminergic Activity in Antipsychotic-Naïve Patients Assessed With Positron Emission Tomography Before and After Partial Dopamine D2 Receptor Agonist Treatment: Association With Psychotic Symptoms and Treatment Response

BACKGROUND

Dopamine activity has been associated with the response to antipsychotic treatment. Our study used a four-parameter model to test the association between the striatal decarboxylation rate of ¹⁸F-DOPA to ¹⁸F-dopamine (k₃) and the effect of treatment on psychotic symptoms in antipsychotic-naïve patients with first-episode psychosis. We further explored the effect of treatment with a partial dopamine D₂ receptor agonist (aripiprazole) on k₃ and dopamine synthesis capacity (DSC) determined by the four-parameter model and by the conventional tissue reference method.

METHODS

Sixty-two individuals (31 patients and 31 control subjects) underwent ¹⁸F-DOPA positron emission tomography at baseline, and 15 patients were re-examined after 6 weeks. Clinical re-examinations were completed after 6 weeks (n = 28) and 6 months (n = 15). Symptoms were evaluated with the Positive and Negative Syndrome Scale.

RESULTS

High baseline decarboxylation rates (k₃) were associated with more positive symptoms at baseline (p < .001) and with symptom improvement after 6 weeks (p = .006). Subregion analyses showed that baseline k₃ for the putamen (p = .003) and nucleus accumbens (p = .013) and DSC values for the nucleus accumbens (p = .003) were associated with psychotic symptoms. The tissue reference method yielded no associations between DSC and symptoms or symptom improvement. Neither method revealed any effects of group or treatment on average magnitudes of k₃ or DSC, whereas changes in dopamine synthesis were correlated with higher baseline values, implying a potential effect of treatment.

CONCLUSIONS

Striatal decarboxylation rate at baseline was associated with psychotic symptoms and treatment response. The strong association between k₃ and treatment effect potentially implicate on new treatment strategies.

Abnormal degree centrality in first-episode medication-free adolescent depression at rest: A functional magnetic resonance imaging study and support vector machine analysis

BACKGROUND

Depression in adolescents is more heterogeneous and less often diagnosed than depression in adults. At present, reliable approaches to differentiating between adolescents who are and are not affected by depression are lacking. This study was designed to assess voxel-level whole-brain functional connectivity changes associated with adolescent depression in an effort to define an imaging-based biomarker associated with this condition.

MATERIALS AND METHODS

In total, 71 adolescents affected by major depressive disorder (MDD) and 71 age-, sex-, and education level-matched healthy controls were subjected to resting-state functional magnetic resonance imaging (rs-fMRI) based analyses of brain voxel-wise degree centrality (DC), with a support vector machine (SVM) being used for pattern classification analyses.

RESULTS

DC patterns derived from 16-min rs-fMRI analyses were able to effectively differentiate between adolescent MDD patients and healthy controls with 95.1% accuracy (136/143), and with respective sensitivity and specificity values of 92.1% (70/76) and 98.5% (66/67) based upon DC abnormalities detected in the right cerebellum. Specifically, increased DC was evident in the bilateral insula and left lingual area of MDD patients, together with reductions in the DC values in the right cerebellum and bilateral superior parietal lobe. DC values were not significantly correlated with disease severity or duration in these patients following correction for multiple comparisons.

CONCLUSION

These results suggest that whole-brain network centrality abnormalities may be present in many brain regions in adolescent depression patients. Accordingly, these DC maps may hold value as candidate neuroimaging biomarkers capable of differentiating between adolescents who are and are not affected by MDD, although further validation of these results will be critical.

Cross-sectional study comparing cognitive function in treatment responsive versus treatment non-responsive schizophrenia: evidence from the STRATA study

BACKGROUND

70%-84% of individuals with antipsychotic treatment resistance show non-response from the first episode. Emerging cross-sectional evidence comparing cognitive profiles in treatment resistant schizophrenia to treatment-responsive schizophrenia has indicated that verbal memory and language functions may be more impaired in treatment resistance. We sought to confirm this finding by comparing cognitive performance between antipsychotic non-responders (NR) and responders (R) using a brief cognitive battery for schizophrenia, with a primary focus on verbal tasks compared against other measures of cognition.

DESIGN

Cross-sectional.

SETTING

This cross-sectional study recruited antipsychotic treatment R and antipsychotic NR across four UK sites. Cognitive performance was assessed using the Brief Assessment of Cognition in Schizophrenia (BACS).

PARTICIPANTS

One hundred and six participants aged 18-65 years with a diagnosis of schizophrenia or schizophreniform disorder were recruited according to their treatment response, with 52 NR and 54 R cases.

OUTCOMES

Composite and subscale scores of cognitive performance on the BACS. Group (R vs NR) differences in cognitive scores were investigated using univariable and multivariable linear regressions adjusted for age, gender and illness duration.

RESULTS

Univariable regression models observed no significant differences between R and NR groups on any measure of the BACS, including verbal memory (ß=-1.99, 95% CI -6.63 to 2.66, p=0.398) and verbal fluency (ß=1.23, 95% CI -2.46 to 4.91, p=0.510). This pattern of findings was consistent in multivariable models.

CONCLUSIONS

The lack of group difference in cognition in our sample is likely due to a lack of clinical distinction between our groups. Future investigations should aim to use machine learning methods using longitudinal first episode samples to identify responder subtypes within schizophrenia, and how cognitive factors may interact within this.

TRAIL REGISTRATION NUMBER

REC: 15/LO/0038.

Aberrant triple-network connectivity patterns discriminate biotypes of first-episode medication-naive schizophrenia in two large independent cohorts

Schizophrenia is a complex disorder associated with aberrant brain functional connectivity. This study aims to demonstrate the relation of heterogeneous symptomatology in this disorder to distinct brain connectivity patterns within the triple-network model. The study sample comprised 300 first-episode antipsychotic-naive patients with schizophrenia (FES) and 301 healthy controls (HCs). At baseline, resting-state functional magnetic resonance imaging data were captured for each participant, and concomitant neurocognitive functions were evaluated outside the scanner. Clinical information of 49 FES in the discovery dataset were reevaluated at a 6-week follow-up. Differential features between FES and HCs were selected from triple-network connectivity profiles. Cutting-edge unsupervised machine learning algorithms were used to define patient subtypes. Clinical and cognitive variables were compared between patient subgroups. Two FES subgroups with differing triple-network connectivity profiles were identified in the discovery dataset and confirmed in an independent hold-out cohort. One patient subgroup appearing to have more severe clinical symptoms was distinguished by salience network (SN)-centered hypoconnectivity, which was associated with greater impairments in sustained attention. The other subgroup exhibited hyperconnectivity and manifested greater deficits in cognitive flexibility. The SN-centered hypoconnectivity subgroup had more persistent negative symptoms at the 6-week follow-up than the hyperconnectivity subgroup. The present study illustrates that clinically relevant cognitive subtypes of schizophrenia may be associated with distinct differences in connectivity in the triple-network model. This categorization may foster further analysis of the effects of therapy on these network connectivity patterns, which may help to guide therapeutic choices to effectively reach personalized treatment goals.

EEG microstate features for schizophrenia classification

Electroencephalography (EEG) microstate analysis is a method wherein spontaneous EEG activity is segmented at sub-second levels to analyze quasi-stable states. In particular, four archetype microstates and their features are known to reflect changes in brain state in neuropsychiatric diseases. However, previous studies have only reported differences in each microstate feature and have not determined whether microstate features are suitable for schizophrenia classification. Therefore, it is necessary to validate microstate features for schizophrenia classification. Nineteen microstate features, including duration, occurrence, and coverage as well as thirty-one conventional EEG features, including statistical, frequency, and temporal characteristics were obtained from resting-state EEG recordings of 14 patients diagnosed with schizophrenia and from 14 healthy (control) subjects. Machine-learning based multivariate analysis was used to evaluate classification performance. EEG recordings of patients and controls showed different microstate features. More importantly, when differentiating among patients and controls, EEG microstate features outperformed conventional EEG ones. The performance of the microstate features exceeded that of conventional EEG, even after optimization using recursive feature elimination. EEG microstate features applied with conventional EEG features also showed better classification performance than conventional EEG features alone. The current study is the first to validate the use of microstate features to discriminate schizophrenia, suggesting that EEG microstate features are useful for schizophrenia classification.

Pattern classification as decision support tool in antipsychotic treatment algorithms

Pattern classification aims to establish a new approach in personalized treatment. The scope is to tailor treatment on individual characteristics during all phases of care including prevention, diagnosis, treatment, and clinical outcome. In psychotic disorders, this need results from the fact that a third of patients with psychotic symptoms do not respond to antipsychotic treatment and are described as having treatment-resistant disorders. This, in addition to the high variability of treatment responses among patients, enhances the need of applying advanced classification algorithms to identify antipsychotic treatment patterns. This review comprehensively summarizes advancements and challenges of pattern classification in antipsychotic treatment response to date and aims to introduce clinicians and researchers to the challenges of including pattern classification into antipsychotic treatment decision algorithms.

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

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

Length of stay for inpatient incompetent to stand trial patients: importance of clinical and demographic variables

OBJECTIVE

We investigated clinical and demographic variables to better understand their relationship to hospital length of stay for patients involuntarily committed to California state psychiatric hospitals under the state's incompetent to stand trial (IST) statutes. Additionally, we determined the most important variables in the model that influenced patient length of stay.

METHODS

We retrospectively studied all patients admitted as IST to California state psychiatric hospitals during the period January 1, 2010 through June 30, 2018 (N = 20 041). Primary diagnosis, total number of violent acts while hospitalized, age at admission, treating hospital, level of functioning at admission, ethnicity, sex, and having had a previous state hospital admission were evaluated using a parametric survival model.

RESULTS

The analysis showed that the most important variables related to length of stay were (1) diagnosis, (2) number of violent acts while hospitalized, and (3) age of admission. Specifically, longer length of stay was associated with (1) having a diagnosis of schizophrenia or neurocognitive disorder, (2) one or more violent acts, and (3) older age at admission. The other variables studied were also statistically significant, but not as influential in the model.

CONCLUSIONS

We found significant relations between length of stay and the variables studied, with the most important variables being (1) diagnosis, (2) number of physically violent acts, and (3) age at admission. These findings emphasize the need for treatments to target cognitive issues in the seriously mentally ill as well as treatment of violence and early identification of violence risk factors.

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

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

Current challenges and possible future developments in personalized psychiatry with an emphasis on psychotic disorders

A personalized medicine approach seems to be particularly applicable to psychiatry. Indeed, considering mental illness as deregulation, unique to each patient, of molecular pathways, governing the development and functioning of the brain, seems to be the most justified way to understand and treat disorders of this medical category. In order to extract correct information about the implicated molecular pathways, data can be drawn from sampling phenotypic and genetic biomarkers and then analyzed by a machine learning algorithm. This review describes current difficulties in the field of personalized psychiatry and gives several examples of possibly actionable biomarkers of psychotic and other psychiatric disorders, including several examples of genetic studies relevant to personalized psychiatry. Most of these biomarkers are not yet ready to be introduced in clinical practice. In a next step, a perspective on the path personalized psychiatry may take in the future is given, paying particular attention to machine learning algorithms that can be used with the goal of handling multidimensional datasets.

Identification of a Serotonin 2A Receptor Subtype of Schizophrenia Spectrum Disorders With Pimavanserin: The Sub-Sero Proof-of-Concept Trial Protocol

Background

All current approved antipsychotic drugs against schizophrenia spectrum disorders share affinity for the dopamine receptor (D2R). However, up to one-third of these patients respond insufficiently, and in some cases, side-effects outweigh symptom reduction. Previous data have suggested that a subgroup of antipsychotic-naïve patients will respond to serotonin 2A receptor (2AR) blockade.

Aims

This investigator-initiated, translational, proof-of-concept study has overall two aims; 1) To test the clinical effectiveness of monotherapy with the newly approved drug against Parkinson's disease psychosis, pimavanserin, in antipsychotic-free patients with first-episode schizophrenia spectrum disorders; 2) To characterize the neurobiological profile of responders to pimavaserin.

Materials and Equipment

Forty patients will be enrolled in this 6-week open label, one-armed trial with the selective serotonin 2AR antagonist (pimavanserin 34 mg/day). At baseline, patients will undergo: positron emission tomography (PET) imaging of the serotonin 2AR using the radioligand [¹¹C]Cimbi-36; structural magnetic resonance imaging (MRI); MR spectroscopy of cerebral glutamate levels and diffusion tensor imaging; cognitive and psychopathological examinations; electrocardiogram, and blood sampling for genetic- and metabolic analyses.

Outcome Measures

The primary clinical endpoint will be reduction in the Positive and Negative Syndrome Scale (PANSS) positive score. Secondary clinical endpoints comprise multiple clinical ratings (positive and negative symptoms, depressive-, obsessive-compulsive symptoms, quality of life, social functioning, sexual functioning, and side-effects). PET, MRI, and cognitive parameters will be used for in-depth neuropsychiatric characterization of pimavanserin response.

Anticipated Results

Clinically, we expect pimavanserin to reduce psychotic symptoms with similar effect as observed with conventional antipsychotics, for which we have comparable historical data. We expect pimavanserin to induce minimal side-effects. Neurobiologically, we expect psychotic symptom reduction to be most prominent in patients with low frontal serotonin 2AR binding potential at baseline. Potential pro-cognitive and brain structural effects of pimavanserin will be explored.

Perspectives

Sub-Sero will provide unique information about the role serotonin 2AR in antipsychotic-free, first-episode psychosis. If successful, Sub-Sero will aid identification of a “serotonergic subtype” of schizophrenia spectrum patients, thereby promoting development of precision medicine in clinical psychiatry.

Clinical Trial Registration

ClinicalTrials, identifier NCT03994965.

A Pattern of Cognitive Deficits Stratified for Genetic and Environmental Risk Reliably Classifies Patients With Schizophrenia From Healthy Control Subjects

BACKGROUND

Schizophrenia risk is associated with both genetic and environmental risk factors. Furthermore, cognitive abnormalities are established core characteristics of schizophrenia. We aim to assess whether a classification approach encompassing risk factors, cognition, and their associations can discriminate patients with schizophrenia (SCZs) from healthy control subjects (HCs). We hypothesized that cognition would demonstrate greater HC-SCZ classification accuracy and that combined gene-environment stratification would improve the discrimination performance of cognition.

METHODS

Genome-wide association study-based genetic, environmental, and neurocognitive classifiers were trained to separate 337 HCs from 103 SCZs using support vector classification and repeated nested cross-validation. We validated classifiers on independent datasets using within-diagnostic (SCZ) and cross-diagnostic (clinically isolated syndrome for multiple sclerosis, another condition with cognitive abnormalities) approaches. Then, we tested whether gene-environment multivariate stratification modulated the discrimination performance of the cognitive classifier in iterative subsamples.

RESULTS

The cognitive classifier discriminated SCZs from HCs with a balanced accuracy (BAC) of 88.7%, followed by environmental (BAC = 65.1%) and genetic (BAC = 55.5%) classifiers. Similar classification performance was measured in the within-diagnosis validation sample (HC-SCZ BACs, cognition = 70.5%; environment = 65.8%; genetics = 49.9%). The cognitive classifier was relatively specific to schizophrenia (HC-clinically isolated syndrome for multiple sclerosis BAC = 56.7%). Combined gene-environment stratification allowed cognitive features to classify HCs from SCZs with 89.4% BAC.

CONCLUSIONS

Consistent with cognitive deficits being core features of the phenotype of SCZs, our results suggest that cognitive features alone bear the greatest amount of information for classification of SCZs. Consistent with genes and environment being risk factors, gene-environment stratification modulates HC-SCZ classification performance of cognition, perhaps providing another target for refining early identification and intervention strategies.

Towards Precision Medicine in Psychosis: Benefits and Challenges of Multimodal Multicenter Studies-PSYSCAN: Translating Neuroimaging Findings From Research into Clinical Practice

In the last 2 decades, several neuroimaging studies investigated brain abnormalities associated with the early stages of psychosis in the hope that these could aid the prediction of onset and clinical outcome. Despite advancements in the field, neuroimaging has yet to deliver. This is in part explained by the use of univariate analytical techniques, small samples and lack of statistical power, lack of external validation of potential biomarkers, and lack of integration of nonimaging measures (eg, genetic, clinical, cognitive data). PSYSCAN is an international, longitudinal, multicenter study on the early stages of psychosis which uses machine learning techniques to analyze imaging, clinical, cognitive, and biological data with the aim of facilitating the prediction of psychosis onset and outcome. In this article, we provide an overview of the PSYSCAN protocol and we discuss benefits and methodological challenges of large multicenter studies that employ neuroimaging measures.

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

BACKGROUND

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

METHODS

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

RESULTS

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

CONCLUSIONS

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

Extended-wavelength diffuse reflectance spectroscopy with a machine-learning method for in vivo tissue classification

OBJECTIVES

An extended-wavelength diffuse reflectance spectroscopy (EWDRS) technique was evaluated for its ability to differentiate between and classify different skin and tissue types in an in vivo pig model.

MATERIALS AND METHODS

EWDRS recordings (450-1550 nm) were made on skin with different degrees of pigmentation as well as on the pig snout and tongue. The recordings were used to train a support vector machine to identify and classify the different skin and tissue types.

RESULTS

The resulting EWDRS curves for each skin and tissue type had a unique profile. The support vector machine was able to classify each skin and tissue type with an overall accuracy of 98.2%. The sensitivity and specificity were between 96.4 and 100.0% for all skin and tissue types.

CONCLUSION

EWDRS can be used in vivo to differentiate between different skin and tissue types with good accuracy. Further development of the technique may potentially lead to a novel diagnostic tool for e.g. non-invasive tumor margin delineation.

Machine learning in mental health: a scoping review of methods and applications

BACKGROUND

This paper aims to synthesise the literature on machine learning (ML) and big data applications for mental health, highlighting current research and applications in practice.

METHODS

We employed a scoping review methodology to rapidly map the field of ML in mental health. Eight health and information technology research databases were searched for papers covering this domain. Articles were assessed by two reviewers, and data were extracted on the article's mental health application, ML technique, data type, and study results. Articles were then synthesised via narrative review.

RESULTS

Three hundred papers focusing on the application of ML to mental health were identified. Four main application domains emerged in the literature, including: (i) detection and diagnosis; (ii) prognosis, treatment and support; (iii) public health, and; (iv) research and clinical administration. The most common mental health conditions addressed included depression, schizophrenia, and Alzheimer's disease. ML techniques used included support vector machines, decision trees, neural networks, latent Dirichlet allocation, and clustering.

CONCLUSIONS

Overall, the application of ML to mental health has demonstrated a range of benefits across the areas of diagnosis, treatment and support, research, and clinical administration. With the majority of studies identified focusing on the detection and diagnosis of mental health conditions, it is evident that there is significant room for the application of ML to other areas of psychology and mental health. The challenges of using ML techniques are discussed, as well as opportunities to improve and advance the field.

Effects of clonidine on MMN and P3a amplitude in schizophrenia patients on stable medication

Schizophrenia is a complex brain disease involving several neurotransmitter systems, including aberrant noradrenergic activity, which might underlie cognitive deficits. Clonidine is an α_2A-agonist and previous research has demonstrated that single dosages of clonidine normalize sensori(motor) gating in schizophrenia. Currently, we investigated whether clonidine is able to normalize mismatch negativity (MMN) and P3a amplitude deficits in this same group of patients. This is important, since reports have shown that MMN amplitude is associated with cognitive functioning and daily life functions in schizophrenia. Twenty chronically ill, male schizophrenia patients were tested with the MMN paradigm from the Copenhagen Psychophysiological Test Battery (CPTB) on 5 occasions, separated by a week. Patients received randomized, yet balanced, either a placebo or a single dose (25, 50, 75 or 150 μg) of clonidine (each dose only once) on top of their usual medication on each occasion. Patients were matched on age and gender with 20 healthy controls (HC) who did not receive any treatment. We found decreased MMN and P3a amplitudes in our patients compared to HC. Although clonidine did neither significantly increase MMN nor P3a amplitude in our patients, it did increase certain levels of MMN and P3a amplitude such that these were not significantly different anymore from the healthy controls. Together with our previous reports indicating normalized sensori(motor) gating in the same patients following administration of clonidine, our results could be of potential high clinical relevance in treating schizophrenia. Future studies should focus on longer trial periods to investigate if clonidine also improves cognitive functioning in schizophrenia.

Associations between P3a and P3b amplitudes and cognition in antipsychotic-naïve first-episode schizophrenia patients

BACKGROUND

Cognitive deficits are already present in early stages of schizophrenia. P3a and P3b event-related potentials (ERPs) are believed to underlie the processes of attention and working memory (WM), yet limited research has been performed on the associations between these parameters. Therefore, we explored possible associations between P3a/b amplitudes and cognition in a large cohort of antipsychotic-naïve, first-episode schizophrenia (AN-FES) patients and healthy controls (HC).

METHODS

Seventy-three AN-FES patients and 93 age- and gender-matched HC were assessed for their P3a/b amplitude with an auditory oddball paradigm. In addition, subjects performed several subtests from the Cambridge Neuropsychological Test Automated Battery (CANTAB).

RESULTS

AN-FES patients had significantly reduced P3a/b amplitudes, as well as significantly lower scores on all cognitive tests compared with HC. Total group correlations revealed positive associations between P3b amplitude and WM and sustained attention and negative associations with all reaction time measures. These associations appeared mainly driven by AN-FES patients, where we found a similar pattern. No significant associations were found between P3b amplitude and cognitive measures in our HC. P3a amplitude did not correlate significantly with any cognitive measures in either group, nor when combined.

CONCLUSIONS

Our results provide further evidence for P3a/b amplitude deficits and cognitive deficits in AN-FES patients, which are neither due to antipsychotics nor to disease progress. Furthermore, our data showed significant, yet weak associations between P3b and cognition. Therefore, our data do not supply evidence for deficient P3a/b amplitudes as direct underlying factors for cognitive deficits in schizophrenia.

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

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

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

BACKGROUND

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

METHOD

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

RESULTS

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

CONCLUSION

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

The impact of schizophrenia and intelligence on the relationship between age and brain volume

Age has been shown to have an impact on both grey (GM) and white matter (WM) volume, with a steeper slope of age-related decline in schizophrenia compared to healthy controls. In schizophrenia, the relation between age and brain volume is further complicated by factors such as lower intelligence, antipsychotic medication, and cannabis use, all of which have been shown to have independent effects on brain volume.

In a study of first-episode, antipsychotic-naïve schizophrenia patients (N = 54) and healthy controls (N = 56), we examined the effects of age on whole brain measures of GM and WM volume, and whether these relationships were moderated by schizophrenia and intelligence (IQ). Secondarily, we examined lifetime cannabis use as a moderator of the relationship between age and brain volume.

Schizophrenia patients had lower GM volumes than healthy controls but did not differ on WM volume. We found an age effect on GM indicating that increasing age was associated with lower GM volumes, which did not differ between groups. IQ did not have a direct effect on GM, but showed a trend-level interaction with age, suggesting a greater impact of age with lower IQ. There were no age effects on WM volume, but a direct effect of IQ, with higher IQ showing an association with larger WM volume. Lifetime cannabis use did not alter these findings significantly.

This study points to effects of schizophrenia on GM early in the illness, before antipsychotic treatment is initiated, suggesting that WM changes may occur later in the disease process.

The Group of Treatment Resistant Schizophrenias. Heterogeneity in Treatment Resistant Schizophrenia (TRS)

Schizophrenia is composed of a heterogeneous group of patient segments. Our current notion of the heterogeneity in schizophrenia is based on patients presenting with diverse disease symptom phenotypes, risk factors, structural and functional neuropathology, and a mixed range of expressed response to treatment. It is important for clinicians to recognize the various clinical presentations of resistance to treatment in schizophrenia and to understand how heterogeneity across treatment resistant patient segments may potentially inform new strategies for the development of effective treatments for Treatment Resistant Schizophrenia (TRS). The heterogeneity of schizophrenia may be reduced by parsing patient segments based on whether patients demonstrate an adequate or inadequate response to treatment. In our current concept of TRS, TRS is defined as non-response to at least two adequate trials of antipsychotic medication and is estimated to affect about 30% of all patients with schizophrenia. In this narrative review, the author discusses that the demonstration of inadequate response to antipsychotic drugs (APDs) may infer that some TRS patients may be suffering from a non-dopamine pathophysiology since D2 receptor antagonist-based treatment is ineffective. Preliminary neurobiological findings may further support the pathophysiologic distinction of TRS from that of general schizophrenia. Investigation of the basis for heterogeneity in TRS through the systematic investigation of relevant "clusters" of similarly at risk individuals may hopefully bring us closer to realize a precision medicine approach for developing effective therapies for TRS patient segments.