Pontillo, Giuseppe (2022) Magnetic resonance imaging and machine learning for brain disorders. [Tesi di dottorato]
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| Tipologia del documento: | Tesi di dottorato |
|---|---|
| Lingua: | English |
| Titolo: | Magnetic resonance imaging and machine learning for brain disorders |
| Autori: | Autore Email Pontillo, Giuseppe giuseppe.pontillo@unina.it |
| Data: | 12 Dicembre 2022 |
| Numero di pagine: | 82 |
| Istituzione: | Università degli Studi di Napoli Federico II |
| Dipartimento: | Ingegneria Elettrica e delle Tecnologie dell'Informazione |
| Dottorato: | Information and Communication Technology for Health |
| Ciclo di dottorato: | 35 |
| Coordinatore del Corso di dottorato: | nome email Riccio, Daniele daniele.riccio@unina.it |
| Tutor: | nome email Riccio, Daniele [non definito] |
| Data: | 12 Dicembre 2022 |
| Numero di pagine: | 82 |
| Parole chiave: | magnetic resonance imaging, machine learning, brain, multiple sclerosis, Fabry disease, schizophrenia. |
| Settori scientifico-disciplinari del MIUR: | Area 09 - Ingegneria industriale e dell'informazione > ING-INF/02 - Campi elettromagnetici Area 09 - Ingegneria industriale e dell'informazione > ING-INF/06 - Bioingegneria elettronica e informatica Area 06 - Scienze mediche > MED/37 - Neuroradiologia |
| Depositato il: | 25 Gen 2023 01:14 |
| Ultima modifica: | 09 Apr 2025 14:13 |
| URI: | http://www.fedoa.unina.it/id/eprint/14669 |
Abstract
Magnetic resonance imaging (MRI) represents a uniquely powerful tool for clinical and basic neuroscience, providing an invaluable window into the in vivo brain. In recent years, different MRI modalities have found their stable place in the clinical and/or research setting, leading to an ever-increasing number of brain imaging studies. In this context, machine learning has emerged as a powerful technique for recognizing patterns on high-dimensional MRI datasets. I present four different case studies in which brain MRI and statistical/machine learning methods are applied in different clinical populations to address specific research questions, including: (i) modelling brain MRI data using unsupervised machine learning to stratify patients with multiple sclerosis; (ii) building a model of healthy aging using deep learning and brain MRIs and testing whether patients with Fabry disease have older appearing brains compared to healthy subjects; (iii) analyzing MRI-based connectivity to study alterations of brain structural and functional networks in schizophrenia; (iv) using quantitative MRI to characterize brain iron and myelin changes in multiple sclerosis.
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