Publication: International Journal of Geriatric Psychiatry

Kershenbaum A, Cardinal RN, Chen S, Underwood B, Seyedsalehi A, Lewis JR, Rubinsztein JS

4 November 2020

Previous studies have shown increased rates of death and dementia in older people in specific serious mental illnesses (SMI) such as bipolar disorder or depression.

In this study researchers examined the rates of death and dementia in older people referred into a secondary care psychiatric service across a range of SMIs, using an anonymised dataset across 6 consecutive years with 28,340 patients aged 65 years and older from a single secondary care psychiatric trust in the United Kingdom.

They identified deaths and incident dementia in patients with bipolar disorder/mania, schizophrenia, recurrent depression and anxiety disorders. They compared mortality and dementia rates between these diagnostic groups and in different treatment settings, and also examined mortality rates and dementia rates compared with general population rates.

Patients with schizophrenia showed the highest hazard rate for death compared to other groups with SMIs. Survival was reduced in patients referred to liaison psychiatry services. There were no significant differences between the SMI groups in terms of rates of dementia. However, risks of death and dementia were significantly increased compared to the general population; and older adults referred into an old age psychiatry service showed higher rates of dementia and death than those reported for the general population.

View publication

Publication: Journal of Psychiatric Research

Chen S, Jones PB, Underwood BR, Moore A, Bullmore ET, Banerjee S, Osimo EF, Deakin JB, Hatfield CF, Thompson FJ, Artingstall JD, Slann MP, Lewis JR, Cardinal RN

22 September 2020

COVID-19 has affected social interaction and healthcare worldwide.

Here researchers examined changes in presentations and referrals to the primary provider of mental health and community health services in Cambridgeshire and Peterborough, UK (population ~0·86 million), plus service activity and deaths.

They conducted interrupted time series analyses with respect to the time of UK “lockdown”, which was shortly before the peak of COVID-19 infections in this area, and examined changes in standardized mortality ratio for those with and without severe mental illness (SMI).

Referrals and presentations to nearly all mental and physical health services dropped at lockdown, with evidence for changes in both supply (service provision) and demand (help-seeking).

This was followed by an increase in demand for some services. This pattern was seen for all major forms of presentation to liaison psychiatry services, except for eating disorders, for which there was no evidence of change.

Inpatient numbers fell, but new detentions under the Mental Health Act were unchanged. Many services shifted from face-to-face to remote contacts. Excess mortality was primarily in the over-70s. There was a much greater increase in mortality for patients with SMI, which was not explained by ethnicity.

In conclusion, the research showed that COVID-19 has been associated with a system-wide drop in the use of mental health services, with some subsequent return in activity. “Supply” changes may have reduced access to mental health services for some. “Demand” changes may reflect a genuine reduction of need or a lack of help-seeking with pent-up demand. There has been a disproportionate increase in death among those with SMI during the pandemic.

View publication

Publication: Psychopharmacology

Lim TV, Cardinal RN, Savulich GJ, Moustafa AA, Robbins TW, Ersche KD

1 August 2019

Drug addiction has been suggested to develop through drug-induced changes in learning and memory processes. Whilst the initiation of drug use is typically goal-directed and hedonically motivated, over time, drug-taking may develop into a stimulus-driven habit, characterised by persistent use of the drug irrespective of the consequences.

Converging lines of evidence suggest that stimulant drugs facilitate the transition of goal-directed into habitual drug-taking, but their contribution to goal-directed learning is less clear.

Computational modelling may provide an elegant means for elucidating changes during instrumental learning that may explain enhanced habit formation.

The research team used formal reinforcement learning algorithms to deconstruct the process of appetitive instrumental learning and to explore potential associations between goal-directed and habitual actions in patients with cocaine use disorder (CUD).

They re-analysed appetitive instrumental learning data in 55 healthy control volunteers and 70 CUD patients by applying a reinforcement learning model within a hierarchical Bayesian framework. They used a regression model to determine the influence of learning parameters and variations in brain structure on subsequent habit formation.

The research showed that poor instrumental learning performance in CUD patients was largely determined by difficulties with learning from feedback, as reflected by a significantly reduced learning rate.

Subsequent formation of habitual response patterns was partly explained by group status and individual variation in reinforcement sensitivity. White matter integrity within goal-directed networks was only associated with performance parameters in controls but not in CUD patients.

The data indicate that impairments in reinforcement learning are insufficient to account for enhanced habitual responding in CUD.

View publication

Publication: Psychopharmacology

Robbins TW, Cardinal RN

4 April 2019

Psychopharmacology needs novel quantitative measures and theoretical approaches based on computational modelling that can be used to help translate behavioural findings from experimental animals to humans, including patients with neuropsychiatric disorders.

Here, researchers carried out a brief review which exemplifies this approach when applied to recent published studies of the effects of manipulating central dopaminergic and serotoninergic systems in rodents and marmoset monkeys, and possible comparisons with healthy human volunteers receiving systemic agents or patients with depression and schizophrenia.

Behavioural effects of central depletions of dopamine or serotonin in monkeys in probabilistic learning paradigms are characterised further by computational modelling methods and related to rodent and human data.

Several examples are provided of the power of computational modelling to derive new measures and reappraise conventional explanations of regional neurotransmitter depletion and other drug effects, whilst enhancing construct validation in patient groups. Specifically, effects are shown on such parameters as ‘stimulus stickiness’ and ‘side stickiness’, which occur over and above effects on standard parameters of reinforcement learning, reminiscent of some early innovations in data analysis in psychopharmacology.

Computational modelling provides a useful methodology for further detailed analysis of behavioural mechanisms that are affected by pharmacological manipulations across species and will aid the translation of experimental findings to understand the therapeutic effects of medications in neuropsychiatric disorders, as well as facilitating future drug discovery.

View publication