Title of the Project
“Acquisizione e analisi dei segnali EEG ad alta densità per quantificare la connettività funzionale intrinseca del cervello e la sua integrazione in uno studio multimodale multicentrico” (in English: “Acquisition and analysis of high-density EEG signals to quantify intrinsic brain functional connectivity and its integration in a multicenter multimodal study”), Research Supervisor Prof. Alessandra Bertoldo
Purpose of the Research Grant
The purpose of this Research Grant, which is funded by the European Union in the framework of the Horizon Europe project “EBRAINS 2.0: A Research Infrastructure to Advance Neuroscience and Brain Health”, Principal Investigator Prof. Maurizio Corbetta, is to grant research into:
The project involves the generation of foundational normative atlas by acquiring advanced imaging data from a large, diverse multicentric healthy cohort. The dataset will include structural MRI, resting state fMRI, multi-shell DWI, high-density EEG, and dynamic 18F-FDG PET, providing comprehensive insights into brain anatomy, functional connectivity, white matter fiber orientations, neural electrical activity, and glucose metabolism. The acquired data will be employed to extract relevant imaging features, covering structural, functional, connectivity, activity, and metabolic aspects of the brain. Three main extraction approaches will be used: voxel-based, region-based, and connectivity-based features. Rigorous validation and quality assurance procedures will ensure the accuracy and reliability of the features. Once validated, the curated imaging features will be made available on an open-access platform to promote collaboration and advance understanding of brain function and neurological disorders.
Activity: The research fellow will share responsibilities with other researchers hired in Padua for the same EU project, including the acquisition, anonymization, pre-processing, processing, and integration of neuroimaging and simultaneously acquired HD-EEG signals on the Biograph mMR Siemens hybrid system. They will also take care of cognitive tests and clinical characteristics as part of their shared activities. Specifically, the research fellow will analyze EEG signals both at the scalp level and after reconstruction using source analysis. The analysis methods will be both in the time domain and in the frequency domain. Brain functional connectivity (FC) will be investigated in terms of coupling (non-directed FC) and causality (directed FC). Both approaches will be used, considering (but not limited to) spectral coherence, phase locking value, synergy, transfer entropy, and Granger causality. The research fellow will work actively in close interaction with external collaborators, including neurologists, neuroscientists, and physicists.
Profile: The ideal candidate should have a basic experience in EEG signal analysis and neuroimaging, and preferably a background in bioengineering and/or applied mathematics/physics and related fields. They must have experience in advanced analytical techniques (e.g., multivariate approaches, machine learning, graph theory, etc.). Analytical/mathematical skills are required. Programming and scripting skills (Python, Matlab, Bash) are not mandatory but represent a clear advantage. Additionally, the candidate should be highly motivated and creative, with the ability to work in a dynamic and multidisciplinary research environment and willing to interact with both experimental and theoretical neuroscientists.
Deadline for submitting applications
2 August 2024 at 1:00 pm CEST