Andrea Zangrossi

My main research interests concern the use of models for predicting behavioral outcomes based on various types of data (behavioral, psychophysiological, or neuroimaging) for clinical or forensic applications. During my PhD and a research period at the Bernstein Center for Computational Neuroscience (BCCN) in Berlin, Germany, I delved into the study of multivariate methods and machine learning applied to behavioral and structural/functional neuroimaging data. In particular, this approach was aimed at predicting individual characteristics, such as cognitive reserve, from structural magnetic resonance imaging (MRI) data, and at identifying autobiographical memories from brain activation patterns detected through functional MRI.

Subsequently, I applied these methods to predicting cognitive outcomes in patients with neurological conditions such as dementia, stroke, and brain tumors. In recent years, my main focus has been on studying eye movements and their relationship with cognitive functioning in healthy subjects and patients with neurological disorders, with the primary goal of identifying early behavioral markers of dementia. Additionally, I have begun to explore the link between oculomotor dynamics and endogenous brain processes (measured through electroencephalography - EEG) in healthy subjects and dementia patients, both in the lab and using wearable technology.

Concurrently, I have continued to study cognitive aspects relevant to forensic neuroscience, specifically related to autobiographical memory, such as suggestibility and crime-related amnesia. Of particular interest are behavioral or psychophysiological techniques that allow for the identification of individual autobiographical memories (the so-called "memory-detection"). To this end, I am applying the analysis of oculomotor dynamics with the goal of developing indices that enable the implicit identification of an autobiographical memory, with future applications involving criminal offenders.