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Goñi & Amico submitted abstract to NetSci 2018

Goñi & Amico submitted abstract to NetSci 2018

Photo of Joaquin Goni
Prof. Goñi
Drs. Joaquín Goñi and Enrico Amico submitted an abstract to NetSci 2018.

Their presentation is titled "Centralized and distributed cognitive task processing in the human connectome". They are collaborating on the project with Prof. Alex Arenas, a complex networks researcher from the Universidad Rovira i Virgili,Tarragona, Spain. NetSci 2018 will be held in Paris June 11-15.

Photo of Enrico Amico

Amico will also compete in the Young Initiative for Best Talk Pitch at NetSci 2018, an event awarding the quality of a young researcher's work while recognizing her/his ability to explain and present the results. Watch his video presentation.



A key question in modern neuroscience is how and to what extent cognitive changes in a human brain can be quantified and captured by functional connectomes (FC) obtained from neuroimaging data. Despite the efforts in trying to characterize connectivity differences between resting-state and different tasks, a systematic analysis on how to measure pairwise (i.e. at the level of FC edges) “cognitive distance” between brain regions at different brain states is still lacking. Developing such a methodology would provide a straight-forward way to quantify differences in cognitive processing across tasks; also, it would help in relating these pairwise differences in task-based FCs to the underlying structural network architecture, another exciting avenue for the brain connectomics community. Here we propose a framework, based on Jensen-Shannon (JS) divergence [1] to map the task-rest “connectivity divergence” between tasks and resting-state FC connections. We show how this information theoretical measure allows for quantifying the amount of connectivity changes in distributed (i.e., between-network) and centralized (i.e., within-network) processing in human functional networks. We use resting-state and seven different task sessions from the Human Connectome Project (HCP) dataset to obtain the most JS-divergent links across tasks. We study how these changes across tasks are associated to different functional brain networks, and use the proposed measure to infer modifications in the information processing regimes of these networks. Furthermore, we show how the connectivity divergence with respect to rest is shaped by the brain structural architecture, and to what extent this relationship depends on the task-based functional scenario at hand. This framework provides a well-grounded mathematical quantification of connectivity changes associated to cognitive processing in large-scale brain networks, that will improve our understanding on the rich repertoire of functional cognitive states observed in the human brain.