Sebastiano is a Senior Lecturer of Organizational Neuroscience at the Surrey Business School and Honorary Associate Professor at the University of Warwick. Before joining Surrey, he was the Deputy Academic Lead of the Global Research Priority in Behavioural Science and an Assistant Professor of Behavioural Science at the Warwick Business School.
Sebastiano was awarded the inaugural PhD in Management Science from the UCL School of Management and he also graduated in Neuroscience at the University of Trieste and the International School of Advanced Studies, and in Neuroimaging at the University of Edinburgh.
Sebastiano’s research is theoretically and methodologically focused on mapping the scholarly boundaries of the emerging field of organizational neuroscience; empirically, he investigates the interplay between affect and cognition in various kinds of decision-making, such as moral, interpersonal, and strategic. Sebastiano has received several awards for both his teaching and research and his work has appeared in world-leading journals across scientific areas.
Areas of specialism
18 OCT 2019
Parenting, driverless cars and work humour among topics of free talks and events with leading academics
organizational neuroscience; behavioral science; research methods; affect and cognition; decision-making; morality, trust and cooperation; health care
Postgraduate research supervision
PhD students supervised at Surrey Business School:
Leadership (MSc HRM)
Quantitative Methods I (PhD level)
Quantitative Methods II (PhD level)
Chronic Obstructive Pulmonary Disease (COPD) is a highly heterogeneous condition projected to become the third leading cause of death worldwide by 2030. To better characterize this condition, clinicians have classified patients sharing certain symptomatic characteristics, such as symptom intensity and history of exacerbations, into distinct phenotypes. In recent years, the growing use of machine learning algorithms, and cluster analysis in particular, has promised to advance this classification through the integration of additional patient characteristics, including comorbidities, biomarkers, and genomic information. This combination would allow researchers to more reliably identify new COPD phenotypes, as well as better characterize existing ones, with the aim of improving diagnosis and developing novel treatments. Here, we systematically review the last decade of research progress, which uses cluster analysis to identify COPD phenotypes. Collectively, we provide a systematized account of the extant evidence, describe the strengths and weaknesses of the main methods used, identify gaps in the literature, and suggest recommendations for future research
Background: Chronic Obstructive Pulmonary Disease (COPD) is a heterogeneous group of lung conditions that are challenging to diagnose and treat. As the presence of comorbidities often exacerbates this scenario, the characterization of patients with COPD and cardiovascular comorbidities may allow early intervention and improve disease management and care. Methods: We analysed a 4-year observational cohort of 6883 UK patients who were ultimately diagnosed with COPD and at least one cardiovascular comorbidity. The cohort was extracted from the UK Royal College of General Practitioners and Surveillance Centre database. The COPD phenotypes were identified prior to diagnosis and their reproducibility was assessed following COPD diagnosis. We then developed four classifiers for predicting cardiovascular comorbidities. Results: Three subtypes of the COPD cardiovascular phenotype were identified prior to diagnosis. Phenotype A was characterised by a higher prevalence of severe COPD, emphysema, hypertension. Phenotype B was char-acterised by a larger male majority, a lower prevalence of hypertension, the highest prevalence of the other cardiovascular comorbidities, and diabetes. Finally, phenotype C was characterised by universal hypertension, a higher prevalence of mild COPD and the low prevalence of COPD exacerbations. These phenotypes were reproduced after diagnosis with 92% accuracy. The random forest model was highly accurate for predicting hypertension while ruling out less prevalent comorbidities. Conclusions: This study identified three subtypes of the COPD cardiovascular phenotype that may generalize to other populations. Among the four models tested, the random forest classifier was the most accurate at predicting cardiovascular comorbidities in COPD patients with the cardiovascular phenotype.
Porumb, M., Massaro, S., Iadanza, E., & Pecchia, L., (2020), A Convolutional Neural Network Approach to Detect Congestive Heart Failure. Biomedical Signal Processing and Control.
Massaro, S., & Pecchia, L. (2019), Heart rate variability (HRV) analysis: A methodology for organizational neuroscience. Organizational Research Methods, 22, 354-393.
Scarpazza, C., Huang, H., Zangrossi, A., & Massaro, S. (2018), Is interoceptive sensitivity linked to interoceptive awareness in Alexithymia? Journal of Psychosomatic Research, 109, 132.
Cropanzano, R. S., Massaro, S., & Becker, W. J. (2017), Deontic justice and organizational neuroscience. Journal of Business Ethics, 144, 733-754.
Castaldo, R., Montesinos, L., Wan, T.S., Serban, A., Massaro, S., & Pecchia, L. (2017), Heart Rate Variability Analysis and Performance during a Repeated Mental Workload Task. IFMBE Proceedings, 65, 69-72.
Castaldo, R., Montesinos, L., Melillo, P., Massaro, S., & Pecchia, L. (2017), To What Extent Can We Shorten HRV Analysis in Wearable Sensing? A Case Study on Mental Stress Detection. IFMBE Proceedings, 65, 643-646.
Massaro, S. (2015), Neurofeedback in the Workplace: From Neurorehabilitation Hope to Neuroleadership Hype? International Journal of Rehabilitation Research, 38, 276-278.
Massaro, S. (2013), Can WHO survive? An organizational strategy question. The Lancet, 381, 726.
Massaro, S. (2012), Managing Knowledge-Intensive Workers. Nature Biotechnology, 30, 721-723.
Massaro, S. & Jong, S. (2011), Managing Knowledge-Intensive Work: A Trust Based Model. In: Toombs L.A. (Ed.), Best Paper Proceedings of the Academy of Management.
assaro, S. (2012), Breast cancer screening: new technologies for a new debate. British Medical Journal (Online First), 345: e7330/rr/615968.
Massaro, S. (2020, forthcoming), The Organizational Neuroscience of Emotions. In: L. Yang, R. S. Cropanzano, C. Daus, & V. A. M. Tur (Eds.) Cambridge Handbook of Workplace Affect Cambridge University Press: New York, USA.
Healey, M. P., Hodgkinson, G. P., & Massaro, S. (2018), Can Brains Manage? The Brain, Emotion, and Cognition in Organizations. In: L. Petitta, C. E. J. Härtel, N. M. Ashkanasy, W. J. Zerbe (Eds.), Research on Emotion in Organizations - Individual, Relational, and Contextual Dynamics of Emotions, p. 27-58. Emerald Group Publishing Limited: Bingley, UK.
Massaro, S. (2017), Neuroscience Methods: A Framework for Managerial and Organizational Cognition. In: G. P. Hodgkinson, R. J. Galavan, & K. J. Sund (Eds.), Methodological Challenges and Advances in Managerial and Organizational Cognition, New Horizons in Managerial and Organizational Cognition, p. 241-278. Emerald Group Publishing Limited: Bingley, UK.
Massaro, S. (2016), Neuroscientific Methods Applications in Strategic Management. In: G. Dagnino & C. Cinci (Eds.), Strategic Management: A Research Method Handbook, p. 254-282, Routledge: New York, USA.
Massaro, S., & Becker, W. (2015), Organizational Justice Through the Window of Neuroscience. In: D. Waldman & P. Balthazard (Eds.), Organizational Neuroscience, Monographs in Leadership and Management, p. 257-276. Emerald Group Publishing Limited: Bradford, UK.