Magnetic resonance imaging (MRI) is the best non-invasive technique used to investigate brain structure and function in vivo. Several protocols can be set in order to obtain different biophysical meaningful features. Diffusion weighted imaging provides information on brain microstructure and axonal connectivity, quantitative susceptibility imaging localizes the regions with high content of iron and myelin, spectroscopy identifies the main brain metabolites, while functional MRI characterizes the basal brain activity, identifying sensorimotor and cognitive networks.
Brain connectivity will be reconstructed using diffusion tractography and functional MRI to create a personalized avatar of the brain (either human or mouse), which will be combined with computational models to generate synthetic meaningful signals of brain activity. Brain modelling is a promising approach that integrates knowledge across various scales to investigate brain dynamics and characterize the excitatory/inhibitory balance at macroscale level, which cannot otherwise be assessed in vivo both in humans and rodents. From an imaging and macroscale point of view, the human brain is better characterized than the mouse brain, while invasive microscopic recordings in rodents cannot be replicated in humans. Thus, combine these approaches on humans and rodents is needed to bridge the gap between scales, also representing a step ahead toward Brain Digital Twins.
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.
Workflow for virtual brain modelling.
Diffusion and functional MRI data is analyzed to extract brain connectivity that is given as input to The Virtual Brain (TVB).
TVB simulates optimal brain dynamics (time-series) by tuning model parameters against empirical functional connectivity.
This procedure enables to identify a subject-specific excitatory/inhibitory balance based on the value of the parameters: global coupling, inhibitory and excitatory synaptic coupling and recurrent excitation.
References
- Palesi F, Tournier JD, Calamante F, et al. Contralateral cerebello-thalamo-cortical pathways with prominent involvement of associative areas in humans in vivo. Brain Struct Funct. 2015;220(6):3369-3384. doi:10.1007/s00429-014-0861-2
- Palesi F, Tournier JD, Calamante F, et al. Reconstructing contralateral fiber tracts: methodological aspects of cerebello-thalamo- cortical pathway reconstruction. Funct Neurol. 2016;31(4):229-238. doi:10.11138/FNeur/2016.31.4.229
- Palesi F, De Rinaldis A, Castellazzi G, et al. Contralateral cortico-ponto-cerebellar pathways reconstruction in humans in vivo: implications for reciprocal cerebro-cerebellar structural connectivity in motor and non-motor areas. Sci Rep. 2017;7(1):12841. doi:10.1038/s41598-017-13079-8
- Palesi F, Lorenzi RM, Casellato C, et al. The Importance of Cerebellar Connectivity on Simulated Brain Dynamics. Front Cell Neurosci. 2020;14(July):1-11. doi:10.3389/fncel.2020.00240