The role of social cognition and neurocognition in functional outcomes in individuals with first episode psychosis

Impaired cognition and poor functioning are closely linked in psychosis; findings from studies of individuals with first episode psychosis (FEP), where intervention may be most effective, are less conclusive. This thesis sought to clarify the contributing role of social cognition (SC) and neurocognition (NC), relative to symptoms, in understanding functional outcome in FEP. Results showed that whilst individuals with poor functioning in FEP have greater SC and NC impairments, negative symptoms is the most robust predictor of later social and role outcomes, with SC and NC having a subordinate role. Exploratory analyses suggest that cognition directly impacts on negative symptoms which in turn may influence functional outcome, highlighting the importance of delineating this relationship. When examining the predictors of treatment response (i.e. improved functioning) following a psychosocial intervention targeting social disability, individuals with 'good' SC were more likely to respond to the intervention. Functional magnetic resonance imaging also provided preliminary evidence of an underlying SC neural network that might be implicated in improved functioning following the intervention. Overall findings show that cognition plays a key role in functional outcomes in FEP. Targeting impaired SC could improve the reach and impact of intervention, to reduce the chances of social disability becoming entrenched

Investigating the role of eEF1A2 in motor neuron degeneration

Abnormal expression of the eukaryotic translation elongation factor 1A (eEF1A) has been implicated in disease states such as motor neuron degeneration and cancer. Two variants of eEF1A are found in mammals, named eEF1A1 and eEF1A2. These two variants are encoded by different genes, produce proteins which are 92% identical but have very different patterns of expression. eEF1A1 is almost ubiquitously expressed while eEF1A2 is expressed only in specialised cell types such as motor neurons and muscle. A spontaneous mutation in eEF1A2 results in the wasted mouse phenotype which shows similar characteristics in the mouse to those seen in human motor neuron degeneration. This mutation has been shown to be a 15.8kb deletion resulting in the complete loss of the promoter region and first non coding exon of eEF1A2 which completely abolishes protein expression. The main aim of this project was to further investigate the role of eEF1A2 in motor neuron degeneration. Firstly, although the wasted phenotype is considered to be caused by a recessive mutation, I established a cohort of aged heterozygote mice to evaluate whether any changes are seen later in life that might model late onset motor neuron degeneration. A combination of behavioural tests and pathology was used to compare wild type and heterozygous mice up to 21 months of age. Whilst results indicate that there is no significant difference between ageing heterozygotes and wildtype controls, there is an indication that female heterozygote mice perform slightly worse that wildtype controls on the rotarod (a behavioural test for motor function). Secondly, I aimed to investigate the primary cause of the wasted pathology by generating transgenic wasted mice expressing neuronal eEF1A2 only. This would complement previous experiments in the lab which studied transgenic wasted mice expressing eEF1A2 in muscle only. Unfortunately the expression of eEF1A2 in the transgenic animals was not neuronal specific. However a transgenic line with expression of eEF1A2 in neurons and skeletal muscle but not cardiac muscle has been generated which clearly warrants further investigation. Thirdly, I wished to assess whether eEF1A2 has any role in human motor neuron degeneration. To achieve this, eEF1A2 expression was investigated in spinal cords from human motor neuron disease (MND) patients. Preliminary data suggests that motor neurons from some MND patients express significantly less eEF1A2 than motor neurons of control samples. Further work is required to confirm these findings. Finally, I investigated the individual roles of eEF1A1 and eEF1A2 in the heat shock response. I used RNAi to ablate each variant separately in cells and subsequently measured the ability of each variant individually to mount a heat shock response. Results indicate a clear role for eEF1A1 but not eEF1A2 in the induction of heat shock. This may explain in part why motor neurons exhibit a poor heat shock response as they express eEF1A2 and not eEF1A1. These experiments shed light on our understanding of the role of eEF1A2 in motor neuron degeneration and uncover many new avenues of future investigation

A synthetic literature review on the management of emerging treatment resistance in first episode psychosis : can we move towards precision intervention and individualised care?

Treatment resistance is prevalent in early intervention in psychosis services, and causes a significant burden for the individual. A wide range of variables are shown to contribute to treatment resistance in first episode psychosis (FEP). Heterogeneity in illness course and the complex, multidimensional nature of the concept of recovery calls for an evidence base to better inform practice at an individual level. Current gold standard treatments, adopting a ‘one-size fits all’ approach, may not be addressing the needs of many individuals. This following review will provide an update and critical appraisal of current clinical practices and methodological approaches for understanding, identifying, and managing early treatment resistance in early psychosis. Potential new treatments along with new avenues for research will be discussed. Finally, we will discuss and critique the application and translation of machine learning approaches to aid progression in this area. The move towards ‘big data’ and machine learning holds some prospect for stratifying intervention-based subgroups of individuals. Moving forward, better recognition of early treatment resistance is needed, along with greater sophistication and precision in predicting outcomes, so that effective evidence-based treatments can be appropriately tailored to the individual. Understanding the antecedents and the early trajectory of one’s illness may also be key to understanding the factors that drive illness course

Heterogeneity in treatment outcomes and incomplete recovery in first episode psychosis:does one size fit all?

The heterogeneity in recovery outcomes for individuals with First Episode Psychosis (FEP) calls for a strong evidence base to inform practice at an individual level. Between 19–89% of young people with FEP have an incomplete recovery despite gold-standard evidence-based treatments, suggesting current service models, which adopt a ‘one-size fits all’ approach, may not be addressing the needs of many young people with psychosis. The lack of consistent terminology to define key concepts such as recovery and treatment resistance, the multidimensional nature of these concepts, and common comorbid symptoms are some of the challenges faced by the field in delineating heterogeneity in recovery outcomes. The lack of robust markers for incomplete recovery also results in potential delay in delivering prompt, and effective treatments to individuals at greatest risk. There is a clear need to adopt a stratified approach to care where interventions are targeted at subgroups of patients, and ultimately at the individual level. Novel machine learning, using large, representative data from a range of modalities, may aid in the parsing of heterogeneity, and provide greater precision and sophistication in identifying those on a pathway to incomplete recovery

Structure and stability of symptoms in first episode psychosis: a longitudinal network approach.

Early psychosis is characterised by heterogeneity in illness trajectories, where outcomes remain poor for many. Understanding psychosis symptoms and their relation to illness outcomes, from a novel network perspective, may help to delineate psychopathology within early psychosis and identify pivotal targets for intervention. Using network modelling in first episode psychosis (FEP), this study aimed to identify: (a) key central and bridge symptoms most influential in symptom networks, and (b) examine the structure and stability of the networks at baseline and 12-month follow-up. Data on 1027 participants with FEP were taken from the National EDEN longitudinal study and used to create regularised partial correlation networks using the 'EBICglasso' algorithm for positive, negative, and depressive symptoms at baseline and at 12-months. Centrality and bridge estimations were computed using a permutation-based network comparison test. Depression featured as a central symptom in both the baseline and 12-month networks. Conceptual disorganisation, stereotyped thinking, along with hallucinations and suspiciousness featured as key bridge symptoms across the networks. The network comparison test revealed that the strength and bridge centralities did not differ significantly between the two networks (C = 0.096153; p = 0.22297). However, the network structure and connectedness differed significantly from baseline to follow-up (M = 0.16405, p = <0.0001; S = 0.74536, p = 0.02), with several associations between psychosis and depressive items differing significantly by 12 months. Depressive symptoms, in addition to symptoms of thought disturbance (e.g. conceptual disorganisation and stereotyped thinking), may be examples of important, under-recognized treatment targets in early psychosis, which may have the potential to lead to global symptom improvements and better recovery