Brain Connectivity & Machine Learning

High-order interdependencies in the aging brain

Marilyn Gatica, Rodrigo Cofré, Pedro A.M. Mediano, Fernando E. Rosas, Patricio Orio, Ibai Diez, Stephan P. Swinnen, and Jesus M. Cortes. High-order interdependencies in the aging brain. Brain connectivity . In press, 2021. biorxiv: [pdf] article: [pdf]
Background: Brain interdependencies can be studied from either a structural/anatomical perspective (“structural connectivity”-SC) or by considering statistical interdependencies (“functional connectivity”-FC). Interestingly, while SC is by definition pairwise (white-matter fibers project from one region to another), FC is not. However, most FC analyses only focus on pairwise statistics and they neglect higher-order interactions. A promising tool to study high-order interdependencies is the recently proposed O-Information, which can quantify the intrinsic statistical synergy and the redundancy in groups of three or more interacting variables. Methods: We analyzed functional magnetic resonance imaging (fMRI) data obtained at rest from 164 healthy subjects with ages ranging in 10 to 80 years and used O-Information to investigate how high-order statistical interdependencies are affected by age. Results: Older participants (from 60 to 80 years old) exhibited a higher predominance of redundant dependencies as compared to younger participants, an effect that seems to be pervasive as it is evident for all orders of interaction. In addition, while there is strong heterogeneity across brain regions, we found a ‘redundancy core’ constituted by the prefrontal and motor cortices in which redundancy was evident at all the interaction orders studied. Discussion: High-order interdependencies in fMRI data reveal a dominant redundancy in functions such as working memory, executive and motor functions. Our methodology can be used for a broad range of applications, and the corresponding code is freely available.

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