The understanding of human brain mechanisms has captured the imagination of scientists for ages. From the quantitative perspective, there is evidence that damages to brain structure affect brain function and, as a consequence, cognitive aspects. As there is evidence that brain structure might be affected by altered cognition. However, the complex interplay between the human brain and the mind remains still poorly understood. This fact has important clinical consequences, limiting applications devoted to the prevention and treatment of brain diseases. In the present thesis, we aim to enhance our understanding of human brain mechanisms by means of an integrated and data-driven approach, by adopting a systemic perspective and leveraging on tools from computational and network neuroscience. We successfully enhance the state of the art of computational neuroscience in several manners. Firstly, we inspect human cognition by focusing on the geometric exploration of concepts in the human mind to build new datadriven metrics to complement the neurological assessment and to confirm Alzheimer’s disease diagnosis. We formalize a new stochastic process, the potential-driven random walk, able to model the trade-off between exploitation and exploration of network structure, by accounting for local and global information, providing a flexible tool to span from random walk to shortestpath based navigation. Probing the interplay between brain structure and dynamics by means of its Von Neumann entropy, we develop a new framework for the multiscale analysis of the human connectome, which is effective for discerning between healthy conditions and Alzheimer’s disease. Finally, by integrating data from the human brain structural connectivity, its functional response errors as measured by Direct Electrical Stimulation and
semantic selectivity, we propose a new procedure for mapping the human brain triadic nature, thus providing a model-oriented bridge between the human brain and mind. Besides shedding more light on human brain functioning, our findings offer original and promising clues to develop integrated biomarkers for Alzheimer’s disease detection, with the potential of extension for applications to other neurodegenerative diseases and psychiatric disorders.
| Identifer | oai:union.ndltd.org:unitn.it/oai:iris.unitn.it:11572/346541 |
| Date | 13 June 2022 |
| Creators | Benigni, Barbara |
| Contributors | Benigni, Barbara, De Domenico, Manlio, Merler, Stefano |
| Publisher | Università degli studi di Trento, place:TRENTO |
| Source Sets | Università di Trento |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/doctoralThesis |
| Rights | info:eu-repo/semantics/openAccess |
| Relation | firstpage:1, lastpage:216, numberofpages:216 |
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