Dottoranda
saraverde98@outlook.com
Sara Verde è dottoranda in Biotecnologie mediche e Medicina traslazionale.
Ha conseguito la laurea triennale in Biotecnologie per la Salute nel 2021 e la laurea magistrale in Biotecnologie Farmaceutiche nel 2023, entrambe presso l’Università di Napoli “Federico II”.
Ha vinto una borsa di studio finanziata dal POR Campania FSE 2014-2020 ottenendo un attestato di tecnico del trasferimento tecnologico e della valorizzazione industriale della ricerca.
La sua ricerca attuale si concentra sull’esplorazione di nuovi strumenti diagnostici per le malattie rare.
Semantic control enables context-guided retrieval from memory, yet its distinction from domain-general executive control remains debated. We applied transcranial magnetic stimulation (TMS) to the left inferior frontal gyrus (IFG) and pre-supplementary motor area (pre-SMA) to probe their functional relevance for semantic and executive control. Across four sessions, 24 participants received repetitive TMS, followed by semantic fluency, figural fluency, and picture naming tasks. Stimulation of...
Electrophysiological and neuroimaging studies have revealed how the brain encodes various visual categories and concepts. An open question is how combinations of multiple visual concepts are represented in terms of the component brain patterns: are brain responses to individual concepts composed according to algebraic rules? To explore this, we generated "conceptual perturbations" in neural space by averaging fMRI responses to images with a shared concept (e.g., "winter" or "summer"). After...
To-date, brain decoding literature has focused on single-subject studies, that is, reconstructing stimuli presented to a subject under fMRI acquisition from the fMRI activity of the same subject. The objective of this study is to introduce a generalization technique that enables the decoding of a subject's brain based on fMRI activity of another subject, that is, cross-subject brain decoding. To this end, we also explore cross-subject data alignment techniques. Data alignment is the attempt to...
CONCLUSIONS: Our work demonstrated that machine learning models trained with data calculated by a dose-tracking software can provide good estimates of the effective dose just from patient and scanner parameters, without the need for a Monte Carlo approach.
CONCLUSIONS: These results not only validate our method's accuracy and reliability but also establish a foundation for a streamlined, non-invasive approach to dynamic PET data quantification. By offering a precise and less invasive alternative to traditional quantification methods, our technique holds significant promise for expanding the applicability of PET imaging across a wider range of tracers, thereby enhancing its utility in both clinical research and diagnostic settings.
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