Using EEG-Guided Basket and Umbrella Trials in Psychiatry: A Precision Medicine Approach for Cognitive Impairment in Schizophrenia

Cognitive training interventions for substance use disorders: what they really offer?

Cognitive training (CT) has emerged as a potential therapeutic approach for substance use disorders (SUD), aiming to restore cognitive impairments and potentially improve treatment outcomes. However, despite promising findings, the effectiveness of CT in real-life applications and its impact on SUD symptoms has remained unclear. This perspective article critically examines the existing evidence on CT for SUD and explores the challenges and gaps in implementing CT interventions. It emphasizes the need for clarity in expectations and decision-making from a public health standpoint, advocating for comprehensive studies that consider a broader range of SUD consequences and utilize measures that reflect patients' actual experiences.

Challenges, unmet needs and future directions - a critical evaluation of the clinical trial landscape in schizophrenia research

INTRODUCTION

Developing novel antipsychotic mechanisms of action and repurposing established compounds for the treatment of schizophrenia is of utmost importance to improve relevant symptom domains and to improve the risk/benefit ratio of antipsychotic compounds. Novel trial design concepts, pathophysiology-based targeted treatment approaches, or even the return to old values may improve schizophrenia outcomes in the future.

AREAS COVERED

In this review of the clinical trial landscape in schizophrenia, we present an overview of the challenges and gaps in current clinical trials and elaborate on potential solutions to improve the outcomes of people with schizophrenia.

EXPERT OPINION

The classic parallel group design may limit substantial advantages in drug approval or repurposing. Collaborative approaches between regulatory authorities, industry, academia, and funding agencies are needed to overcome barriers in clinical schizophrenia research to allow for meaningful outcome improvements for the patients.

Mismatch negativity predicts initial auditory-based targeted cognitive training performance in a heterogeneous population across psychiatric disorders

Auditory-based targeted cognitive training (ATCT) programs are emerging pro-cognitive therapeutic interventions which aim to improve auditory processing to attenuate cognitive impairment in a "bottom up" manner. Biomarkers of early auditory information processing (EAIP) like mismatch negativity (MMN) and P3a have been used successfully to predict gains from a full 40 h course of ATCT in schizophrenia (SZ). Here we investigated the ability of EAIP biomarkers to predict ATCT performance in a group of subjects (n = 26) across SZ, MDD, PTSD and GAD diagnoses. Cognition was assessed via the MATRICS Consensus Cognitive Battery (MCCB) and MMN/P3a were collected prior to completing 1 h of "Sound Sweeps," a representative ATCT exercise. Baseline and final performance over the first two levels of cognitive training served as the primary dependent variables. Groups had similar MMN, but the SZ group had attenuated P3a. MMN and MCCB cognitive domain t-scores, but not P3a, were strongly correlated with most ATCT performance measures, and explained up to 61% of variance in ATCT performance. Diagnosis was not a significant predictor for ATCT performance. These data suggest that MMN can predict ATCT performance in heterogeneous neuropsychiatric populations and should be considered in ATCT studies across diagnostically diverse cohorts.

Accelerating the development of a psychological intervention to restore treatment decision-making capacity in patients with schizophrenia-spectrum disorder: a study protocol for a multi-site, assessor-blinded, pilot Umbrella trial (the DEC:IDES trial)

BACKGROUND

A high proportion of patients diagnosed with schizophrenia-spectrum disorders will at some point in their lives be assessed as not having the capacity to make their own decisions about pharmacological treatment or inpatient care ('capacity'). Few will be helped to regain it before these interventions proceed. This is partly because effective and safe methods to do so are lacking. Our aim is to accelerate their development by testing, for the first time in mental healthcare, the feasibility, acceptability and safety of running an 'Umbrella' trial. This involves running, concurrently and under one multi-site infrastructure, multiple assessor-blind randomised controlled trials, each of which is designed to examine the effect on capacity of improving a single psychological mechanism ('mechanism'). Our primary objectives are to demonstrate feasibility of (i) recruitment and (ii) data retention on the MacArthur Competence Assessment Tool-Treatment (MacCAT-T; planned primary outcome for a future trial) at end-of-treatment. We selected three mechanisms to test: 'self-stigma', low self-esteem and the 'jumping to conclusions' bias. Each is highly prevalent in psychosis, responsive to psychological intervention, and hypothesised to contribute to impaired capacity.

METHODS

Sixty participants with schizophrenia-spectrum diagnoses, impaired capacity and one or more mechanism(s) will be recruited from outpatient and inpatient mental health services in three UK sites (Lothian, Scotland; Lancashire and Pennine; North West England). Those lacking capacity to consent to research could take part if the key criteria were met, including either proxy consent (Scotland) or favourable Consultee advice (England). They will be allocated to one of three randomised controlled trials, depending on which mechanism(s) they have. They will then be randomised to receive, over an 8-week period and in addition to treatment as usual (TAU), 6 sessions of either a psychological intervention which targets the mechanism, or 6 sessions of assessment of the causes of their incapacity (control condition). Participants are assessed at 0 (baseline), 8 (end-of-treatment) and 24 (follow-up) weeks post-randomisation using measures of capacity (MacCAT-T), mechanism, adverse events, psychotic symptoms, subjective recovery, quality of life, service use, anxiety, core schemata and depression. Two nested qualitative studies will be conducted; one to understand participant and clinician experiences and one to investigate the validity of MacCAT-T appreciation ratings.

DISCUSSION

This will be the first Umbrella trial in mental healthcare. It will produce the first 3 single-blind randomised controlled trials of psychological interventions to support treatment decision-making in schizophrenia-spectrum disorder. Demonstrating feasibility will have significant implications not only for those seeking to support capacity in psychosis, but also for those who wish to accelerate the development of psychological interventions for other conditions.

TRIAL REGISTRATION

ClinicalTrials.gov NCT04309435 . Pre-registered on 16 March 2020.

Implication of Electrophysiological Biomarkers in Psychosis: Focusing on Diagnosis and Treatment Response

Precision medicine has been considered a promising approach to diagnosis, treatment, and various interventions, considering the individual clinical and biological characteristics. Recent advances in biomarker development hold promise for guiding a new era of precision medicine style trials for psychiatric illnesses, including psychosis. Electroencephalography (EEG) can directly measure the full spatiotemporal dynamics of neural activation associated with a wide variety of cognitive processes. This manuscript reviews three aspects: prediction of diagnosis, prognostic aspects of disease progression and outcome, and prediction of treatment response that might be helpful in understanding the current status of electrophysiological biomarkers in precision medicine for patients with psychosis. Although previous EEG analysis could not be a powerful method for the diagnosis of psychiatric illness, recent methodological advances have shown the possibility of classifying and detecting mental illness. Some event-related potentials, such as mismatch negativity, have been associated with neurocognition, functioning, and illness progression in schizophrenia. Resting state studies, sophisticated ERP measures, and machine-learning approaches could make technical progress and provide important knowledge regarding neurophysiology, disease progression, and treatment response in patients with schizophrenia. Identifying potential biomarkers for the diagnosis and treatment response in schizophrenia is the first step towards precision medicine.

Transcranial magnetic stimulation as a translational biomarker for AMPA receptor modulation

TAK-653 is a novel α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid receptor (AMPAR)-positive allosteric modulator being developed as a potential therapeutic for major depressive disorder (MDD). Currently, there are no translational biomarkers that evaluate physiological responses to the activation of glutamatergic brain circuits available. Here, we tested whether noninvasive neurostimulation, specifically single-pulse or paired-pulse motor cortex transcranial magnetic stimulation (spTMS and ppTMS, respectively), coupled with measures of evoked motor response captures the pharmacodynamic effects of TAK-653 in rats and healthy humans. In the rat study, five escalating TAK-653 doses (0.1-50 mg/kg) or vehicle were administered to 31 adult male rats, while measures of cortical excitability were obtained by spTMS coupled with mechanomyography. Twenty additional rats were used to measure brain and plasma TAK-653 concentrations. The human study was conducted in 24 healthy volunteers (23 males, 1 female) to assess the impact on cortical excitability of 0.5 and 6 mg TAK-653 compared with placebo, measured by spTMS and ppTMS coupled with electromyography in a double-blind crossover design. Plasma TAK-653 levels were also measured. TAK-653 increased both the mechanomyographic response to spTMS in rats and the amplitude of motor-evoked potentials in humans at doses yielding similar plasma concentrations. TAK-653 did not affect resting motor threshold or paired-pulse responses in humans. This is the first report of a translational functional biomarker for AMPA receptor potentiation and indicates that TMS may be a useful translational platform to assess the pharmacodynamic profile of glutamate receptor modulators.

Hierarchical Pathways from Sensory Processing to Cognitive, Clinical, and Functional Impairments in Schizophrenia

Cognitive impairment is a hallmark of schizophrenia and a robust predictor of functional outcomes. Impairments are found in all phases of the illness and are only moderately attenuated by currently approved therapeutics. Neurophysiological indices of sensory discrimination (ie, mismatch negativity (MMN) and P3a amplitudes) and gamma-band auditory steady-state response (ASSR; power and phase locking) are translational biomarkers widely used in the development of novel therapeutics for neuropsychiatric disorders. It is unclear whether laboratory-based EEG measures add explanatory power to well-established models that use only cognitive, clinical, and functional outcome measures. Moreover, it is unclear if measures of sensory discrimination and gamma-band ASSR uniquely contribute to putative causal pathways linking sensory discrimination, neurocognition, negative symptoms, and functional outcomes in schizophrenia. To answer these questions, hierarchical associations among sensory processing, neurocognition, clinical symptoms, and functional outcomes were assessed via structural equation modeling in a large sample of schizophrenia patients (n = 695) and healthy comparison subjects (n = 503). The results showed that the neurophysiologic indices of sensory discrimination and gamma-band ASSR both significantly contribute to and yield unique hierarchical, "bottom-up" effects on neurocognition, symptoms, and functioning. Measures of sensory discrimination showed direct effects on neurocognition and negative symptoms, while gamma-band ASSR had a direct effect on neurocognition in patients. Continued investigation of the neural mechanisms underlying abnormal networks of MMN/P3a and gamma-band ASSR is needed to clarify the pathophysiology of schizophrenia and the development of novel therapeutic interventions.

Advanced EEG-based learning approaches to predict schizophrenia: Promises and pitfalls

The complexity and heterogeneity of schizophrenia symptoms challenge an objective diagnosis, which is typically based on behavioral and clinical manifestations. Moreover, the boundaries of schizophrenia are not precisely demarcated from other nosologic categories, such as bipolar disorder. The early detection of schizophrenia can lead to a more effective treatment, improving patients' quality of life. Over the last decades, hundreds of studies aimed at specifying the neurobiological mechanisms that underpin clinical manifestations of schizophrenia, using techniques such as electroencephalography (EEG). Changes in event-related potentials of the EEG have been associated with sensory and cognitive deficits and proposed as biomarkers of schizophrenia. Besides contributing to a more effective diagnosis, biomarkers can be crucial to schizophrenia onset prediction and prognosis. However, any proposed biomarker requires substantial clinical research to prove its validity and cost-effectiveness. Fueled by developments in computational neuroscience, automatic classification of schizophrenia at different stages (prodromal, first episode, chronic) has been attempted, using brain imaging pattern recognition methods to capture differences in functional brain activity. Advanced learning techniques have been studied for this purpose, with promising results. This review provides an overview of recent machine learning-based methods for schizophrenia classification using EEG data, discussing their potentialities and limitations. This review is intended to serve as a starting point for future developments of effective EEG-based models that might predict the onset of schizophrenia, identify subjects at high-risk of psychosis conversion or differentiate schizophrenia from other disorders, promoting more effective early interventions.

Decomposing the constituent oscillatory dynamics underlying mismatch negativity generation in schizophrenia: Distinct relationships to clinical and cognitive functioning

Abnormalities in early auditory information processing (EAIP) contribute to higher-order deficits in cognition and psychosocial functioning in schizophrenia. A passive auditory oddball paradigm is commonly used to evoke event-related potential (ERP) measures of EAIP reflecting auditory sensory registration and deviance detection, including mismatch negativity (MMN) and P3a responses. MMN and P3a have been extensively studied in healthy subjects and neuropsychiatric patient populations and are increasingly used as translational biomarkers in the development of novel therapeutics. Despite widespread use, relatively few studies have examined the constituent oscillatory elements and the extent to which sensory registration and deviance detection represent distinct or intercorrelated processes. This study aimed to determine the factor structure and clinical correlates of these oscillatory measures in schizophrenia patients (n = 706) and healthy comparison subjects (n = 615) who underwent clinical, cognitive, and functional characterization and EEG testing via their participation in the Consortium of Genomics in Schizophrenia (COGS-2) study. Results revealed significant deficits in theta-band (4-7 Hz) evoked power and phase locking in patients. Exploratory factor analyses of both ERP and oscillatory measures revealed two dissociable factors reflecting sensory registration and deviance detection. While each factor shared a significant correlation with social cognition, the deviance detection factor had a unique relationship to multiple cognitive and clinical domains. Results support the continued advancement of functionally relevant oscillatory measures underlying EAIP in the development of precognitive therapeutics.