Lo studio della coscienza in età pediatrica e dei suoi correlati neurofisiologici: implementazione degli strumenti clinici con nuove tecniche di neurofisiologia sperimentale.

Below is the abstract for the final work package (WP4) only. Indeed, the thesis outlines the entire step-by-step process carried out. The machine learning algorithm proposed here is based on the combination of the Spectral Exponent and the Higuchi Fractal Dimension: the first stems from empirical and qualitative observations of the EEG signal, while the second is grounded in theoretical principles. The first three WP have shown that, although they measure different characteristics of brain electrical activity, both measures can discriminate between wakefulness (a state where consciousness is present) and deep sleep (a state without conscious content) in healthy subjects, regardless of age. Moreover, the spectral exponent was successfully applied in a specific physio-pathological model for DoC: hemispherotomy. Abstract Disorders of consciousness (DoC), such as coma, vegetative state and minimally conscious state, pre-sent significant challenges in clinical practice. These conditions, often resulting from severe traumatic brain injuries, anoxic insults or encephalopathies due to toxic-metabolic or immune-mediated causes, are associated with substantial diagnostic and prognostic difficulties. Standardized behavioral scales may fail to detect signs of "covert consciousness" in patients with severe sensory or motor pathway impairments, who are unable to demonstrate self-awareness or awareness of their surroundings. This study aims to develop an advanced neurophysiological tool based on quantitative EEG features to provide prognostic insights in pediatric DoC. Conducted as a single-center prospective observational study between September 2021 and August 2024 at the Department of Women's and Children's Health, University Hospital of Padova, the study also seeks to standardize the multimodal assessment of pedi-atric DoC patients. This includes the integration of standardized clinical scales, neurophysiological techniques and neuroimaging to identify unique features within this population. A comprehensive evaluation of pediatric DoC patients was performed using a multimodal assessment approach adapted from adult guidelines. The study included patients aged 9 months to 18 years admit-ted to the Department during the study period. The assessment encompassed clinical diagnosis using the Coma Recovery Scale for Pediatrics (CRS-P) for children over 1 year of age, structural cerebral MRI and a series of neurophysiological assessments, including standard EEG, SEP, BAEPs, VEPs, and nerve conduction studies. A machine learning algorithm was then developed using two key quantitative EEG features—spectral exponent and Higuchi Fractal Dimension (HFD)—which have demonstrated strong capabilities in discriminating wakefulness from sleep in healthy pediatric subjects. The algo-rithm was initially developed using EEG data from 89 healthy children, trained on 16 additional normal EEG recordings, and subsequently tested on 10 pediatric DoC patients. Among the 10 DoC patients studied, 4 had an unfavorable outcome, while 6 showed improvement to at least a minimally conscious state. Predictive modeling indicated that none of the conventional clinical and instrumental variables could reliably predict outcomes, though the small sample size is a notable limitation. However, the algorithm successfully predicted favorable outcomes in 4 of the 6 cases, cor-rectly identifying wakefulness in EEGs of patients clinically diagnosed as being in a vegetative state. These findings highlight the algorithm's potential to aid in predicting the progression of pediatric DoC. This study successfully developed a machine learning algorithm based on quantitative EEG features, showing promising prognostic capabilities in pediatric disorders of consciousness. Implementing this tool in clinical practice could enhance the accuracy of patient evaluations and significantly contribute to the understanding and management of pediatric DoC.