Abstract #4210

Brain networks topology and dynamics capture neuropsychological heterogeneity in mild cognitive impairment

Anita Monteverdi¹, Sofia Manzon², Matteo Cotta Ramusino³, Francesca Conca¹, Alberto Redolfi⁴, Eleonora Lupi², Marialaura De Grazia², Roberta Maria Lorenzi², Marta Gaviraghi², Lisa Farina⁵, Alfredo Costa^2,3, Anna Pichiecchio^2,5, Stefano Cappa⁶, Fulvia Palesi², Claudia Gandini Wheeler-Kingshott^2,7,8, and Egidio D'Angelo^2,8

¹IRCCS Mondino Foundation, Pavia, Italy, ²Department of Brain and Behavioral Sciences, University of Pavia, Pavia, Italy, ³Unit of Behavioral Neurology, IRCCS Mondino Foundation, Pavia, Italy, ⁴Laboratory of Neuroinformatics, IRCCS Istituto Centro San Giovanni di Dio Fatebenefratelli, Brescia, Italy, ⁵Advanced Imaging and Artificial Intelligence Center, IRCCS Mondino Foundation, Pavia, Italy, ⁶University Institute of Advanced Studies (IUSS), Pavia, Italy, ⁷NMR Research Unit, Queen Square Multiple Sclerosis Centre, Department of Neuroinflammation, UCL Queen Square Institute of Neurology, London, United Kingdom, ⁸Digital Neuroscience Centre, IRCCS Mondino Foundation, Pavia, Italy

Synopsis

Keywords: Alzheimer's Disease, Brain Connectivity, Modelling, The Virtual Brain

Motivation: A deeper understanding of the cellular mechanisms shaping brain network dynamics is crucial in heterogeneous clinical conditions such as mild cognitive impairment (MCI).

Goal(s): We want to show that virtual brain modelling (TVB) can extract information hidden in brain network function and organization and, integrated with graph theory, contribute to the explanation of neuropsychological variance.

Approach: Starting from structural and functional connectomes, TVB and graph theory were combined to investigate brain networks in healthy subjects and MCI at different disease stages.

Results: Graph theory measures together with TVB parameters explained ~70% of neuropsychological scores variance, validating the clinical utility of this new approach.

Impact: With its high correlation with neuropsychological scores, the subject-specific portrait of mild cognitive impairment patients, obtained combining virtual brain modelling and graph theory analysis, can contribute to the development of individualized early intervention.

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