We present a novel method for analyzing brain functional networks using functional magnetic resonance imaging data, which involves utilizing consensus networks. In this study, we compare our approach to a standard group-based method for patients diagnosed with major depressive disorder (MDD) and a healthy control group, taking into account different levels of connectivity. Our findings demonstrate that the consensus network approach uncovers distinct characteristics in network measures and degree distributions when considering connection strengths. In the healthy control group, as connection strengths increase, we observe a transition in the network topology from a combination of scale-free and random topologies to a small-world topology. Conversely, the MDD group exhibits uncertainty in weak connections, while strong connections display small-world properties. In contrast, the group-based approach does not exhibit significant differences in behavior between the two groups. However, it does indicate a transition in topology from a scale-free-like structure to a combination of small-world and scale-free topologies. The use of the consensus network approach also holds immense potential for the classification of MDD patients, as it unveils substantial distinctions between the two groups.
Topology switching during window thresholding fMRI-based functional networks of patients with major depressive disorder: Consensus network approach
Alexander N. Pisarchik, Andrey V. Andreev, Semen A. Kurkin, Drozdstoy Stoyanov, Artem A. Badarin, Rossitsa Paunova, Alexander E. Hramov; Topology switching during window thresholding fMRI-based functional networks of patients with major depressive disorder: Consensus network approach. Chaos 1 September 2023; 33 (9): 093122. https://doi.org/10.1063/5.0166148
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