Functional MRI (fMRI) can characterize through Blood Oxygenation Level Dependent (BOLD) signals either the basal brain activity, identifying sensorimotor and cognitive networks, or the activity related to a specific task. Dynamic casual modelling (DCM) is a Bayesian framework that uses BOLD signals to estimate the direct influences (i.e., effective connectivity) and the hierarchical activations between interconnected brain regions during the execution of a task.
Bayesian inference allows DCM to identify whether a brain region exert an excitatory or inhibitory effect on other brain regions on the basis of the strength of their effective connectivity. Also, DCM infer the best model describing a specific functional circuit, e.g. during motor or cognitive tasks, allowing us to improve the knowledge of neural mechanisms underpinning healthy and pathological functionality.
A PhD student will have the possibility to cover various aspects of the investigation of brain functioning with the aim of identifying strategies for diagnosis and personalized therapy. Students have the possibility to spend part of their research activity abroad since the projects are developed within a collaborative network, which comprises MNESYS, CN1, EBRAINS 2.0, and Virtual Brain Twin (VBT) projects.
Fixed Effective Connectivity of the visuomotor network. A) Bayesian Model Selection identifies Model S1.4 (Panel A.1) as the “best model” with posterior probability > 90% both in action execution (AE in panel A.2) and action observation (AO in panel A3). B) Effective connectivity strength in Hz with positive (red) values for excitatory connections and negative (blue) values for inhibitory connections. The strongest excitatory connections are from V1 to SMAPMC, both in AE and AO, and from V1 to M1 only in AE. Connections that present a different excitatory/inhibitory nature in AE and AO are indicated with a *. V1 = bilateral primary visual cortex, M1 = left primary motor cortex, SMAPMC = left supplementary motor and premotor cortex, CC = left cingulate cortex, SPL = left superior parietal lobule, CRBL = right cerebellum.
References
Casiraghi, L., Alahmadi, A. A., Monteverdi, A., Palesi , F., Castellazzi , G., Savini , G., & D’Angelo, E. I see your effort: force-related bold effects in an extended action execution–observation network involving the cerebellum. Cerebral Cortex. 2019, 29(3), 1351-1368.
Lorenzi & Korkmaz et al., 2024. Cerebellar control over inter-regional excitatory/inhibitory dynamics discriminates execution from observation of an action. Preprint bioRxiv 2024.05.21.595114; doi: https://doi.org/10.1101/2024.05.21.595114
Mascheretti et al 2021. Selecting the most relevant brain regions to classify children with developmental dyslexia and typical readers by using complex magnocellular stimuli and multiple kernel learning. Brain Sciences, 11(6), 722.