Modelling brain connectivity allows characterizing the interplay among different regions, either in the form of functional dependencies (functional and effective connectivity) or backbone of connections (structural connectivity). Besides this, understanding the relationship between such different forms of connectivity and disentangling the link between structural (physical) connections and functionality represents one of the current hot topics in the field. Functional measures are usually derived from functional MRI (BOLD and ASL) or electroencephalographic recordings (EEG) during task activities or while resting, enabling capturing patterns of statistical dependence among neural elements. Moreover, focusing on the temporal dynamics of functional connectivity allows to uncover how brain activity evolves across the brain regions and over time. These approaches result into spatial dynamic states and associated summary measures, in particular persistence, transition, frequency and dwell time that can represent novel indices to discriminate across pathological conditions. In case of effective connectivity, approaches such as dynamic causal modeling, Granger causality and adaptive directed transfer function can be applied to further inform on the directed causal effects.
From a structural point of view, the connectivity is represented by the white matter streamlines linking the different brain regions. Such a link can be quantified by the normalized number of fibers or by summary statistics of microstructural indices collected along the fiber bundles (microstructure-informed connectome).
Brain structure and function can be finally represented in the form of brain networks, comprising a set of nodes (neural elements) and edges (their mutual connections). These networks can be further examined with network science methods extracting measures of segregation, integration or influence.
Rubega M., Storti S.F., Pascucci D.
"Editorial: Chasing brain dynamics at their speed: what can time-varying functional connectivity tell us about brain function?"
Frontiers in Neuroscience, 2023
https://www.frontiersin.org/articles/10.3389/fnins.2023.1223955/full
De Blasi B., Caciagli L., Storti S.F., Galovic M., Koepp M., Menegaz G., Barnes A. and Boscolo Galazzo I.
"Noise removal in resting-state and task fmri: functional connectivity and activation maps"
Journal of Neural Engineering, 17(4):046040, Aug 2020. doi:10.1088/1741-2552/aba5cc.
https://pubmed.ncbi.nlm.nih.gov/32663803/
Caliandro, P., Menegaz, G., Iacovelli, C., Conte, C., Reale, G., Calabresi, P. and Storti S.F.
"Connectivity modulations induced by reach and grasp movements: a multidimensional approach"
Sci Rep, 11(1): 23097, Nov 2021. doi: 10.1038/s41598- 021-02458-x.
https://www.nature.com/articles/s41598-021-02458-x
Formaggio E., Storti S.F., Pastena L., Melucci M., Ricciardi L., Faralli F., Gagliardi R., and Menegaz G.
"How expertise changes cortical sources of EEG rhythms and functional connectivity in divers under simulated deep-sea conditions."
IEEE Trans Neural Syst Rehabil Eng 27(3):450-456, Jan 2019. doi: 10.1109/TNSRE.2019.2894848.
https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8625501
Storti S.F., Boscolo Galazzo I., Pizzini F.B. and Menegaz G.
"Dual-echo ASL based assessment of motor networks: a feasibility study."
J Neural Eng 15(2):026018, Apr 2018. ISSN: 1741-2560. doi: 10.1088/1741-2552/aa8b27.
https://iopscience.iop.org/article/10.1088/1741-2552/aa8b27
Storti S.F., Boscolo Galazzo I., Khan S., Manganotti P. and Menegaz G.
"Exploring the Epileptic Brain Network using Time-Variant Effective Connectivity and Graph Theory."
IEEE J Biomed Health Inform, 21(5):1411-1421, Sep 2017. ISSN: 2168- 2194, doi: 10.1109/JBHI.2016.2607802.
https://pubmed.ncbi.nlm.nih.gov/28113682/
Storti S.F., Boscolo Galazzo I., Montemezzi S., Menegaz G. and Pizzini F.B.
"Dual-echo ASL contributes to decrypting the link between functional connectivity and cerebral blow flow."
Hum Brain Mapp 38(12):5831-5844, Dec 2017. ISSN: 1097-0193. doi: 10.1002/hbm.23804.
https://pubmed.ncbi.nlm.nih.gov/28885752/
Storti S.F., Formaggio E., Manganotti P., and Menegaz G.
"Brain Network Connectivity and Topological Analysis During Voluntary Arm Movements."
Clin EEG Neurosci, 47(4):276– 290, Oct 2016. ISSN: 1550-0594, doi: 10.1177/1550059415598905.
https://pubmed.ncbi.nlm.nih.gov/26251456/
MIUR D.M. 737/2021: “AI4Health: empowering neurosciences with eXplainable AI methods”. PI: G. Menegaz, duration: 24 months. Budget received for 1 RTDa position (ING-INF/06) and 2 one-year AdR fellowships (ING/INF05 e INF01). Funding: 200K. Two main scientific objectives: 1. Exploring the brain connectivity and microstructure modeling through XAI techniques in both healthy and diseased subject (e.g. cancer) 2. Generalizing the developed methodology to other diseases including neurodegenerative (e.g., Parkinson, Alzheimer) and neurological (e.g., stroke, epilepsy) diseases.
PNNR - MNESYS: Nov 2022- Oct 2025 NRRP - MNESYS "A multiscale integrated approach to the study of the nervous system in health and disease (MNESYS) (DN. 1553 11.10.2022)" supported by NEXTGENERATIONEU (NGEU) and funded by the Ministry of University and Research (MUR), National Recovery and Resilience Plan (NRRP). Participation to Spoke 2 (WP2, WP3 and WP4). Silvia Storti task leader for UniVR in WP4 - "T4.5. Neural dynamics and plasticity".