Although the brain has been an object of study for a long time, its working principles are still largely unknown. Its highly, non-trivially connected structure shapes a functional network whose activation and synchronization mechanisms represent a major challenge for scientists belonging to different disciplines, from neuroscience to complex system theory.This talk is a contribution to the study of the brain from the perspective of complex network theory. Specifically, resting state functional connectivity networks from 41 mouse brains were measured by functional MRI and analyzed applying clustering algorithms, community detection methods and percolation analysis to gain insight into their modular structure. Statistically significant partitions of functionally connectivity networks from different methods were identified and compared, thus enabling the identification of a set of functionally segregated sub-networks.This study suggests that analytical tools provided by network theory may provide novel insight into the structure of the brain, highlighting non-trivial topological relations between different areas.
Detecting cluster structure of resting state fMRI brain networks of mice: percolation and modularity
Tiziano Squartini
2015
Abstract
Although the brain has been an object of study for a long time, its working principles are still largely unknown. Its highly, non-trivially connected structure shapes a functional network whose activation and synchronization mechanisms represent a major challenge for scientists belonging to different disciplines, from neuroscience to complex system theory.This talk is a contribution to the study of the brain from the perspective of complex network theory. Specifically, resting state functional connectivity networks from 41 mouse brains were measured by functional MRI and analyzed applying clustering algorithms, community detection methods and percolation analysis to gain insight into their modular structure. Statistically significant partitions of functionally connectivity networks from different methods were identified and compared, thus enabling the identification of a set of functionally segregated sub-networks.This study suggests that analytical tools provided by network theory may provide novel insight into the structure of the brain, highlighting non-trivial topological relations between different areas.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
