Filippo Corponi will present this month’s Prediction Modelling Group meeting.

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Abstract

Schizophrenia is a heterogenous disease comprising manifold clinical phenotypes which may underlie distinct biological underpinnings. Frontal lobes are a key area of brain dysfunction in schizophrenia. The frontal assessment battery (FAB) is a battery screening for a dysexecutive syndrome in neurodegenerative diseases. We investigated the relationship between frontal lobe impairment and symptom profiles defined along the Positive and Negative Syndrome Scale (PANSS) principal components in patients with acute schizophrenia (n = 516). PANSS principal components were used as clustering variables in a finite-mixture model. ANCOVA was performed to compare mean FAB score across the clinical clusters after adjusting for disease duration. A supervised-learning approach was then implemented to reveal most informative PANSS items. PANSS principal component decomposition was coherent with previous reports. A three-cluster solution emerged: a first profile with high-moderate expression for the positive and excitability/hostility component; a second profile scoring high on depression/anxiety and low on other components; a third profile, comprising the majority of the study population (74%), with a heavy affection on the negative and disorganization dimensions. After controlling for disease duration, frontal lobe impairment significantly differed across the aforementioned clusters, with the third cluster standing out as the worst affected. The PANSS items with the highest predictive value for FAB total score were “N5 - Difficulty in abstract thinking” and “N6 - Lack of spontaneity & flow of conversation”. Negative symptoms and disorganization are specifically mapped to higher levels of frontal lobes dysfunction hinting at similar features with other neurological disorders involving frontal lobes.

Biography

Filippo Corponi joined Department of Biostatistics & Health Informatics in May 2020 as Visiting Research Assistant in Precision Medicine. Filippo graduated in Medicine at University Pisa and in Medical Sciences at School of Advanced Studies Sant’Anna. Filippo is soon to complete his training as a psychiatrist at University of Bologna. During his residency, Filippo spent one year on a clinical and research internship with the Bipolar Disorders Unit, at Hospital Clínic, Barcelona. Filippo research activity has focused on pharmacogenetics and machine learning as start-of-the-art methods to deliver precision medicine, advancing the understanding of mental health and improving outcomes for patients. Areas of application have included mood disorders mainly.

His research interests are: Pharmacogenetics; Machine learning; Mood disorders.

 

Prediction Modelling Group

The Prediction Modelling Group provides a forum for researchers at King's College London’s Institute of Psychiatry, Psychology & Neuroscience (IoPPN) and clinicians at South London and Maudsley NHS Foundation Trust, who are interested in prediction modelling applications for precision medicine.

Prediction models to support clinical decision-making have been around for a long time, but have only become possible on a large scale this century with the arrival of new data such as brain imaging, omics, clinical databases and patient health records, sensors in wearables, smartphones and the internet, and high-performance computing technology that collects, processes, stores and analyses huge amounts of information (so-called ‘big data’).

For more information, please visit out webpages: www.maudsleybrc.nihr.ac.uk/facilities/prediction-modelling-group/