Neuropsychological differences between treatment-resistant and treatment-responsive schizophrenia:a meta-analysis

Antipsychotic treatment resistance affects up to a third of individuals with schizophrenia. Of those affected, 70–84% are reported to be treatment resistant from the outset. This raises the possibility that the neurobiological mechanisms of treatment resistance emerge before the onset of psychosis and have a neurodevelopmental origin. Neuropsychological investigations can offer important insights into the nature, origin and pathophysiology of treatment-resistant schizophrenia (TRS), but methodological limitations in a still emergent field of research have obscured the neuropsychological discriminability of TRS. We report on the first systematic review and meta-analysis to investigate neuropsychological differences between TRS patients and treatment-responsive controls across 17 published studies (1864 participants). Five meta-analyses were performed in relation to (1) executive function, (2) general cognitive function, (3) attention, working memory and processing speed, (4) verbal memory and learning, and (5) visual−spatial memory and learning. Small-to-moderate effect sizes emerged for all domains. Similarly to previous comparisons between unselected, drug-naïve and first-episode schizophrenia samples v. healthy controls in the literature, the largest effect size was observed in verbal memory and learning [dl = −0.53; 95% confidence interval (CI) −0.29 to −0.76; z = 4.42; p < 0.001]. A sub-analysis of language-related functions, extracted from across the primary domains, yielded a comparable effect size (dl = −0.53, 95% CI −0.82 to −0.23; z = 3.45; p < 0.001). Manipulating our sampling strategy to include or exclude samples selected for clozapine response did not affect the pattern of findings. Our findings are discussed in relation to possible aetiological contributions to TRS

Post graduate clinical placements: evaluating benefits and challenges with a mixed methods cross sectional design

Abstract Background Systematic evaluations of clinical placements are rare, especially when offered alongside academic postgraduate courses. An evidence-based approach is important to allow pedagogically-driven provision, rather than that solely governed by opinion or market demand. Our evaluation assessed a voluntary clinical placement scheme allied to a mental health course. Methods Data were collected over academic years 2010/11– 2013/14, from participating students (n = 20 to 58) and clinician supervisors (n = 10–12), using a mixed-methods cross-sectional design. Quantitative evaluation captured information on uptake, dropout, resource use, attitudes and experience, using standardized (the Placement Evaluation Questionnaire; the Scale To Assess the Therapeutic Relationship – Clinical version and the University of Toronto Placement Supervisor Evaluation) and bespoke questionnaires and audit data. Qualitative evaluation comprised two focus groups (5 clinicians, 5 students), to investigate attitudes, experience, perceived benefits, disadvantages and desired future developments. Data were analysed using framework analysis to identify a priori and emergent themes. Results High uptake (around 70 placements per annum), low dropout (2–3 students per annum; 5 %) and positive focus group comments suggested placements successfully provided added value and catered sufficiently to student demand. Students’ responses confirmed that placements met expectations and the perception of benefit remained after completion with 70 % (n = 14) reporting an overall positive experience, 75 % (n = 15) reporting a pleasant learning experience, 60 % (n = 12) feeling that their clinical skills were enhanced and 85 % (n = 17) believing that it would benefit other students. Placements contributed the equivalent of seven full time unskilled posts per annum to local health care services. While qualitative data revealed perceived ‘mutual benefit’ for both students and clinicians, this was qualified by the inherent limitations of students’ time and expertise. Areas for development included fostering learning around professionalism and students’ confidence on placement. Conclusions The addition of healthcare placements to academic postgraduate taught courses can improve their attractiveness to applicants, benefit healthcare services and enhance students’ perception of their learning experiences. Well-positioned and supported placement learning opportunities could become a key differentiator for academic courses, over potential competitors. However, the actual implications for student employability and achievement remain to be established

Post graduate clinical placements: evaluating benefits and challenges with a mixed methods cross sectional design.

BACKGROUND: Systematic evaluations of clinical placements are rare, especially when offered alongside academic postgraduate courses. An evidence-based approach is important to allow pedagogically-driven provision, rather than that solely governed by opinion or market demand. Our evaluation assessed a voluntary clinical placement scheme allied to a mental health course. METHODS: Data were collected over academic years 2010/11- 2013/14, from participating students (n = 20 to 58) and clinician supervisors (n = 10-12), using a mixed-methods cross-sectional design. Quantitative evaluation captured information on uptake, dropout, resource use, attitudes and experience, using standardized (the Placement Evaluation Questionnaire; the Scale To Assess the Therapeutic Relationship - Clinical version and the University of Toronto Placement Supervisor Evaluation) and bespoke questionnaires and audit data. Qualitative evaluation comprised two focus groups (5 clinicians, 5 students), to investigate attitudes, experience, perceived benefits, disadvantages and desired future developments. Data were analysed using framework analysis to identify a priori and emergent themes. RESULTS: High uptake (around 70 placements per annum), low dropout (2-3 students per annum; 5 %) and positive focus group comments suggested placements successfully provided added value and catered sufficiently to student demand. Students' responses confirmed that placements met expectations and the perception of benefit remained after completion with 70 % (n = 14) reporting an overall positive experience, 75 % (n = 15) reporting a pleasant learning experience, 60 % (n = 12) feeling that their clinical skills were enhanced and 85 % (n = 17) believing that it would benefit other students. Placements contributed the equivalent of seven full time unskilled posts per annum to local health care services. While qualitative data revealed perceived 'mutual benefit' for both students and clinicians, this was qualified by the inherent limitations of students' time and expertise. Areas for development included fostering learning around professionalism and students' confidence on placement. CONCLUSIONS: The addition of healthcare placements to academic postgraduate taught courses can improve their attractiveness to applicants, benefit healthcare services and enhance students' perception of their learning experiences. Well-positioned and supported placement learning opportunities could become a key differentiator for academic courses, over potential competitors. However, the actual implications for student employability and achievement remain to be established

Applying the Higher Education Academy framework for partnership in learning and teaching in higher education to online partnership learning communities: A case study and an extended model

As internet access and use increase exponentially, pedagogical practice becomes increasingly embedded in online platforms. We report on an online initiative of engaged student learning, the peer-led, staff-assisted e-helpdesk for research methods and statistics, which we evaluated and redeveloped using the lens and guiding principles of the framework for partnership in learning and teaching of the Higher Education Academy (HEA). The aim of the redevelopment was to steer the initiative towards a more integrative and sustainable implementation, as manifest in the applied construct of an online partnership learning community. Our evolving experience of the e-helpdesk highlighted the central role of the facilitator in engineering and maintaining social presence in the online community. We propose an extended model for building an online partnership learning community, whereby partnership encapsulates all the essential elements of student and staff partnership as outlined in the HEA framework, but is also critically defined by similar parameters of partnership between users and facilitators. In this model, the facilitator’s role becomes more involved in instructional teaching as disciplinary expertise increases, but descending levels of disciplinary expertise can foster ascending levels of independent learning and shared discovery for both users and facilitators.&nbsp; &nbsp

Pattern of neural responses to verbal fluency shows diagnostic specificity for schizophrenia and bipolar disorder

Background: Impairments in executive function and language processing are characteristic of both schizophrenia and bipolar disorder. Their functional neuroanatomy demonstrate features that are shared as well as specific to each disorder. Determining the distinct pattern of neural responses in schizophrenia and bipolar disorder may provide biomarkers for their diagnoses. Methods: 104 participants underwent functional magnetic resonance imaging (fMRI) scans while performing a phonological verbal fluency task. Subjects were 32 patients with schizophrenia in remission, 32 patients with bipolar disorder in an euthymic state, and 40 healthy volunteers. Neural responses to verbal fluency were examined in each group, and the diagnostic potential of the pattern of the neural responses was assessed with machine learning analysis. Results: During the verbal fluency task, both patient groups showed increased activation in the anterior cingulate, left dorsolateral prefrontal cortex and right putamen as compared to healthy controls, as well as reduced deactivation of precuneus and posterior cingulate. The magnitude of activation was greatest in patients with schizophrenia, followed by patients with bipolar disorder and then healthy individuals. Additional recruitment in the right inferior frontal and right dorsolateral prefrontal cortices was observed in schizophrenia relative to both bipolar disorder and healthy subjects. The pattern of neural responses correctly identified individual patients with schizophrenia with an accuracy of 92%, and those with bipolar disorder with an accuracy of 79% in which misclassification was typically of bipolar subjects as healthy controls. Conclusions: In summary, both schizophrenia and bipolar disorder are associated with altered function in prefrontal, striatal and default mode networks, but the magnitude of this dysfunction is particularly marked in schizophrenia. The pattern of response to verbal fluency is highly diagnostic for schizophrenia and distinct from bipolar disorder. Pattern classification of functional MRI measurements of language processing is a potential diagnostic marker of schizophrenia

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

Applying the Higher Education Academy Framework for Partnership in Learning and Teaching in Higher Education to Online Partnership Learning Communities: A Case Study and an Extended Model

As internet access and use increase exponentially, pedagogical practice becomes increasingly embedded in online platforms. We report on an online initiative of engaged student learning, the peer-led, staff-assisted e-helpdesk for research methods and statistics, which we evaluated and redeveloped using the lens and guiding principles of the framework for partnership in learning and teaching of the Higher Education Academy (HEA). The aim of the redevelopment was to steer the initiative towards a more integrative and sustainable implementation, as manifest in the applied construct of an online partnership learning community. Our evolving experience of the e-helpdesk highlighted the central role of the facilitator in engineering and maintaining social presence in the online community. We propose an extended model for building an online partnership learning community, whereby partnership encapsulates all the essential elements of student and staff partnership as outlined in the HEA framework, but is also critically defined by similar parameters of partnership between users and facilitators. In this model, the facilitator’s role becomes more involved in instructional teaching as disciplinary expertise increases, but descending levels of disciplinary expertise can foster ascending levels of independent learning and shared discovery for both users and facilitators

Cognitive performance at first episode of psychosis and the relationship with future treatment resistance: Evidence from an international prospective cohort study

Background: Antipsychotic treatment resistance affects up to a third of individuals with schizophrenia, with recent research finding systematic biological differences between antipsychotic resistant and responsive patients. Our aim was to determine whether cognitive impairment at first episode significantly differs between future antipsychotic responders and resistant cases. Methods: Analysis of data from seven international cohorts of first-episode psychosis (FEP) with cognitive data at baseline (N = 683) and follow-up data on antipsychotic treatment response: 605 treatment responsive and 78 treatment resistant cases. Cognitive measures were grouped into seven cognitive domains based on the preexisting literature. We ran multiple imputation for missing data and used logistic regression to test for associations between cognitive performance at FEP and treatment resistant status at follow-up. Results: On average patients who were future classified as treatment resistant reported poorer performance across most cognitive domains at baseline. Univariate logistic regressions showed that antipsychotic treatment resistance cases had significantly poorer IQ/general cognitive functioning at FEP (OR = 0.70, p = .003). These findings remained significant after adjusting for additional variables in multivariable analyses (OR = 0.76, p = .049). Conclusions: Although replication in larger studies is required, it appears that deficits in IQ/general cognitive functioning at first episode are associated with future treatment resistance. Cognitive variables may be able to provide further insight into neurodevelopmental factors associated with treatment resistance or act as early predictors of treatment resistance, which could allow prompt identification of refractory illness and timely interventions.Funding: This work was supported by a Stratified Medicine Programme grant to J.H.M from the Medical Research Council (grant number MR/L011794/1 which funded the research and supported S.E.S., A.F.P., R.M.M., J.T.R.W. & J.H.M.) E.M’s PhD is funded by the MRC-doctoral training partnership studentship in Biomedical Sciences at King’s College London. J.H.M, E.K, R.M.M are part funded by the National Institute for Health Research (NIHR) Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London. A.P.K. is funded by the NIHR Biomedical Research Centre at South London and Maudsley NHS Foundation Trust and King’s College London. O.A. is further funded by an NIHR Post-Doctoral Fellowship (PDF2018-11-ST2-020). The views expressed are those of the authors and not necessarily those of the NHS, the MRC, the NIHR or the Department of Health. E.M.J. is supported by the UCL/UCLH Biomedical Research Centre. The AESOP (London, UK) cohort was funded by the UK Medical Research Council (Ref: G0500817). The Bologna (Italy) cohort was funded by the European Community’s Seventh Framework Program under grant agreement (agreement No. HEALTH-F2-2010–241909, Project EU-GEI). The GAP (London, UK) cohort was funded by the UK National Institute of Health Research (NIHR) Specialist Biomedical Research Centre for Mental Health, South London and Maudsley NHS Mental Health Foundation Trust (SLaM) and the Institute of Psychiatry, Psychology, and Neuroscience at King’s College London; Psychiatry Research Trust; Maudsley Charity Research Fund; and the European Community’s Seventh Framework Program grant (agreement No. HEALTH-F2-2009-241909, Project EU-GEI). The Oslo (Norway) cohort was funded by the Stiftelsen KG Jebsen, Research Council of Norway (#223273, under the Centers of Excellence funding scheme, and #300309, #283798) and the South-Eastern Norway Regional Health Authority (#2006233, #2006258, #2011085, #2014102, #2015088, #2017-112). The Paris (France) cohort was funded by European Community’s Seventh Framework Program grant (agreement No. HEALTHF2-2010–241909, Project EU-GEI). The Santander (Spain) cohort was funded by the following grants (to B.C.F): Instituto de Salud Carlos III, FIS 00/3095, PI020499, PI050427, PI060507, Plan Nacional de Drogas Research Grant 2005-Orden sco/3246/2004, and SENY Fundatio Research Grant CI 2005-0308007, Fundacion Marques de Valdecilla A/02/07 and API07/011. SAF2016-76046-R and SAF2013-46292-R (MINECO and FEDER). The West London (UK) cohort was funded The Wellcome Trust (Grant Numbers: 042025; 052247; 064607)

Use of schizophrenia and bipolar disorder polygenic risk scores to identify psychotic disorders

BACKGROUND: There is increasing evidence for shared genetic susceptibility between schizophrenia and bipolar disorder. Although genetic variants only convey subtle increases in risk individually, their combination into a polygenic risk score constitutes a strong disease predictor.AimsTo investigate whether schizophrenia and bipolar disorder polygenic risk scores can distinguish people with broadly defined psychosis and their unaffected relatives from controls. METHOD: Using the latest Psychiatric Genomics Consortium data, we calculated schizophrenia and bipolar disorder polygenic risk scores for 1168 people with psychosis, 552 unaffected relatives and 1472 controls. RESULTS: Patients with broadly defined psychosis had dramatic increases in schizophrenia and bipolar polygenic risk scores, as did their relatives, albeit to a lesser degree. However, the accuracy of predictive models was modest. CONCLUSIONS: Although polygenic risk scores are not ready for clinical use, it is hoped that as they are refined they could help towards risk reduction advice and early interventions for psychosis.Declaration of interestR.M.M. has received honoraria for lectures from Janssen, Lundbeck, Lilly, Otsuka and Sunovian.Funding: This work was funded by the Medical Research Council (G0901310), the Wellcome Trust (grants 085475/B/08/Z, 085475/Z/08/Z), the European Union’s Seventh Framework Programme for research, technological development and demonstration (grant 602450). This study was also supported by the NIHR Biomedical Research Centre at University College London (mental health theme) and by the NIHR Biomedical Research Centre at the South London and Maudsley NHS Foundation Trust and Institute of Psychiatry – Kings College London. Further support: NHIR Academic Clinical fellowship awarded to M.S.C.. E.B. acknowledges research funding from: BMA Margaret Temple grants 2016 and 2006, MRC – Korean Health Industry Development Institute Partnering Award (MC_PC_16014), MRC New Investigator Award and a MRC Centenary Award (G0901310), National Institute of Health Research UK post-doctoral fellowship, the Psychiatry Research Trust, the Schizophrenia Research Fund, the Brain and Behaviour Research foundation’s NARSAD Young Investigator Awards 2005, 2008, Wellcome Trust Research Training Fellowship and the NIHR Biomedical Research Centre for Mental Health at the South London and Maudsley NHS Foundation Trust and Institute of Psychiatry Kings College London. The Brain and Behaviour Research foundation’s (NARSAD’s) Young Investigator Award (Grant 22604, awarded to C.I.). The BMA Margaret Temple grant 2016 to J. H.T. European Research Council Marie Curie award to A.D.-R. The infrastructure for the GROUP consortium is funded through the Geestkracht programme of the Dutch Health Research Council (ZON-MW, grant number 10-000-1001), and matching funds from participating pharmaceutical companies (Lundbeck, AstraZeneca, Eli Lilly, Janssen Cilag) and universities and mental healthcare organisations. Amsterdam: Academic Psychiatric Centre of the Academic Medical Center and the mental health institutions: GGZ Ingeest, Arkin, Dijk en Duin, GGZ Rivierduinen, Erasmus Medical Centre, GGZ Noord Holland Noord. Maastricht: Maastricht University Medical Centre and the mental health institutions: GGZ Eindhoven en de kempen, GGZ Breburg, GGZ Oost-Brabant, Vincent van Gogh voor Geestelijke Gezondheid, Mondriaan Zorggroep, Prins Clauscentrum Sittard, RIAGG Roermond, Universitair Centrum Sint-Jozef Kortenberg, CAPRI University of Antwerp, PC Ziekeren Sint-Truiden, PZ Sancta Maria Sint-Truiden, GGZ Overpelt, OPZ Rekem. Groningen: University Medical Center Groningen and the mental health institutions: Lentis, GGZ Friesland, GGZ Drenthe, Dimence, Mediant, GGNet Warnsveld, Yulius Dordrecht and Parnassia psychomedical center (The Hague). Utrecht: University Medical Center Utrecht and the mental health institutions Altrecht, GGZ Centraal, Riagg Amersfoort and Delta. The sample from Spain was collected at the Hospital Universitario Marqués de Valdecilla, University of Cantabria, Santander, Spain, under the following grant support: Carlos III Health Institute PI020499, PI050427, PI060507, Plan Nacional de Drugs Research Grant 2005- Orden sco/3246/2004, SENY Fundació Research Grant CI 2005-0308007 and Fundación Marqués de Valdecilla API07/011. The present data were obtained at the Hospital Marqués de Valdecilla, University of Cantabria, Santander, Spain, under the following grant support: MINECO Exp.: SAF2013-46292-R