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1. Brain language: Uncovering functional connectivity codes

   https://doi.org/10.1109/ACSSC.2017.8335565  

   Vergara, Victor M.; Calhoun, Vince D. (October 2017, 2017 51st Asilomar Conference on Signals, Systems, and Computers)

   The functional connectivity within a specific set of brain networks (or domain) can assume different configurations known as domain states that change with time. Recently, we proposed an information theoretical framework that models the finite set of domain states as elements of an alphabet. Significant bits of information have been found to be shared among domains, but specific domain codification was not explored. This work describes a method to identify code words used to transmit and receive information between the cerebrum and the cerebellum based on dynamic domain connectivity estimated from functional magnetic resonance imaging (fMRI). Following the theory of jointly typical sets, the developed method identifies the codeword length and the specific combination of domain states on each codeword. Resting state fMRI data was taken from 121 subjects with no significant age difference between males and females. Group independent component analysis was utilized to identify important brain networks and group them in a cerebellum and six other domains representing the cerebrum. The amount of information between the cerebellum, the executive control and sensorimotor domains showed a statistically significant number of bits. The proposed method quantified specific temporal sequences of domain states encoded within bits shared between cerebellum and cerebrum. 

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2. Predicting brain structural network using functional connectivity

   https://doi.org/10.1016/j.media.2022.102463  

   Zhang, Lu; Wang, Li; Zhu, Dajiang (July 2022, Medical Image Analysis)
3. Subject identification using edge-centric functional connectivity

   https://doi.org/10.1016/j.neuroimage.2021.118204  

   Jo, Youngheun; Faskowitz, Joshua; Esfahlani, Farnaz Zamani; Sporns, Olaf; Betzel, Richard F. (September 2021, NeuroImage)

   null (Ed.)
4. Attributed Graph Clustering for Functional Connectivity Networks

   https://doi.org/10.1109/ISBI53787.2023.10230566  

   Ortiz-Bouza, Meiby; Aviyente, Selin (April 2023, IEEE)
5. Nonlinear functional network connectivity in resting functional magnetic resonance imaging data

   https://doi.org/10.1002/hbm.25972  

   Motlaghian, Sara M; Belger, Aysenil; Bustillo, Juan R; Ford, Judith M; Iraji, Armin; Lim, Kelvin; Mathalon, Daniel H; Mueller, Bryon A; O'Leary, Daniel; Pearlson, Godfrey; et al (October 2022, Human Brain Mapping)

   Abstract In this work, we focus on explicitly nonlinear relationships in functional networks. We introduce a technique using normalized mutual information (NMI) that calculates the nonlinear relationship between different brain regions. We demonstrate our proposed approach using simulated data and then apply it to a dataset previously studied by Damaraju et al. This resting‐state fMRI data included 151 schizophrenia patients and 163 age‐ and gender‐matched healthy controls. We first decomposed these data using group independent component analysis (ICA) and yielded 47 functionally relevant intrinsic connectivity networks. Our analysis showed a modularized nonlinear relationship among brain functional networks that was particularly noticeable in the sensory and visual cortex. Interestingly, the modularity appears both meaningful and distinct from that revealed by the linear approach. Group analysis identified significant differences in explicitly nonlinear functional network connectivity (FNC) between schizophrenia patients and healthy controls, particularly in the visual cortex, with controls showing more nonlinearity (i.e., higher normalized mutual information between time courses with linear relationships removed) in most cases. Certain domains, including subcortical and auditory, showed relatively less nonlinear FNC (i.e., lower normalized mutual information), whereas links between the visual and other domains showed evidence of substantial nonlinear and modular properties. Overall, these results suggest that quantifying nonlinear dependencies of functional connectivity may provide a complementary and potentially important tool for studying brain function by exposing relevant variation that is typically ignored. Beyond this, we propose a method that captures both linear and nonlinear effects in a “boosted” approach. This method increases the sensitivity to group differences compared to the standard linear approach, at the cost of being unable to separate linear and nonlinear effects. 

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