Rumination Project

Roi Cohen Kadosh

Roi received his PhD in Neuropsychology at Ben-Gurion University in Israel in 2007. After a Postdoc at University College London in 2009, he moved to the University of Oxford to establish his lab. In 2015, due to his academic achievements and only 8 years after his PhD, he received a full professorship in Oxford. In 2021 he moved to the University of Surry to take the role of the Head of School of Psychology.

Roi main research focuses on the psychological and biological factors that shape learning and cognitive achievements with primary focus on mathematical cognition. He was the first to observe that combining numerical training with transcranial electrical stimulation, a non-invasive, painless, portable, and relatively low-cost technique, can enhance numerical learning and mathematical abilities in adults and children with atypical neurodevelopment. Roi has extended his research to design a novel treatment for children with ADHD. He has advised for multiple commercial, NGOs, and governmental organisations. He is the founder of Cognite Neurotechnology Ltd that combines brain stimulation and AI to develop personalised and effective treatments. Roi has repeatedly drawn both scientific and public attention to the ethical implications of using brain stimulation for cognitive enhancement and has discussed its possible advantages and pitfalls.
For his pioneer work Roi has received numerous prestigious awards in the fields of psychology, neuroscience, and education, including the Career Development Award from the Society of Neuroscience, the Spearman Medal from the British Psychological Society, the Paul Bertelson Award from the European Society for Cognitive Psychology, and the Career Development Award from the International Mind, Brain, and Education Society.

Abstract

Rumination is a rigid, repetitive, maladaptive form of self-reflection. Work-related rumination is robustly associated with poor psychological and physical health. Therefore, reducing work-related rumination could ameliorate psychological and physiological markers of ill health. In this project, our main goal is to develop a novel personalised neurostimulation intervention to reduce work-related rumination in school teachers with high levels of work-related rumination. We will first create a model that personalises stimulation parameters based on school teachers’ baseline levels of work-related rumination, aiming to reduce their levels of work-related rumination. We will accomplish that by using a recently developed active machine learning approach–personalised Bayesian optimisation (pBO), and transcranial alternating current stimulation (tACS)—a relatively understudied neurostimulation method in psychiatric disorders and wellbeing. We will compare the effectiveness of our developed algorithm for personalised intervention with a placebo (sham) intervention. Overall, the different steps in this project will pave the way to personalised neurointerventions for people with moderate-to-severe levels of anxious and depressive symptomatology. From a technological development perspective, a successful personalisation, as we aim to develop and validate in this project, will allow researchers and practitioners alike to overcome the limitations of one-size-fits-all psychological and pharmacological treatments. Such an achievement will drive forward personalised medicine from a distant goal to achievable, widely implemented clinical practice, providing each person with the highest possible chance to improve their well-being.

Results and key findings

Work-related rumination is widespread among schoolteachers and is associated with poor mental health, sleep dysfunctions, and physiological disease. In this study, we developed a personalised Bayesian optimisation (pBO) algorithm to tailor transcranial Alternating Current Stimulation (tACS) for UK schoolteachers suffering from high levels of affective work-related rumination. The pBO algorithm adjusted tACS parameters based on individual head circumference and affective work-related rumination levels. The 80 initial burn-in sessions for algorithm refinement, which involved randomly allocated parameters, were followed by 319 personalised home-based stimulation sessions across 65 participants. The pBO algorithm's efficacy was highlighted through its superior performance in optimising stimulation parameters compared to random allocation. The high amplitude-low frequency tACS parameter combination appeared to be the most effective in reducing affective work-related rumination across head circumferences. Additionally, we observed a decrease in the sleep fragmentation index in sessions employing higher-amplitude stimulation, indicative of an improvement in sleep quality. These findings underline the potential of personalised neurostimulation, optimised through pBO, in addressing the complex interplay between psychological well-being and physiological health in high-stress occupations.

Relevant data & publications

In progress. This work is based on the following publication
van Bueren, N. E. R., Reed, T. L., Nguyen, V., Sheffield, J. G., van der Ven, S. H. G., Osborne, M. A., Kroesbergen, E. H., & Cohen Kadosh, R. (2021). Personalized brain stimulation for effective neurointervention across participants. PLoS computational biology, 17(9), e1008886-e1008886.

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Research by: Corundum Neuroscience

Rumination Project

Rumination Project

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