High-order interdependencies in the aging brain

Abstract

Brain interdependencies can be studied either from a structural/anatomical perspective ("structural connectivity", SC) or by considering statistical interdependencies ("functional connectivity", FC). Interestingly, while SC is typically pairwise (white-matter fibers start in a certain region and arrive at another), FC is not; however, most FC analyses focus only on pairwise statistics and neglect highorder interactions. A promising tool to study high-order interdependencies is the recently proposed O-Information, which can quantify the intrinsic statistical synergy and redundancy in groups of three or more interacting variables. In this paper we used the O-Information to investigate how high-order statistical interdependencies are affected by age. For this, we analised functional magnetic resonance imaging (fMRI) data at rest obtained from 164 healthy participants, ranging from 10 to 80 years old. Our results show that older subjects (age ranging from 60 to 80 years) exhibit a higher predominance of redundant dependencies than younger subjects; moreover, this effect seems to be pervasive, taking place at all interaction orders. Additionally, we found that these effects are highly heterogeneous across brain regions, and suggest the existence of a "redundancy core" formed by the prefrontal and motor cortices -thus involving functions such as working memory, executive and motor functions. Our methodology to assess high-order interdependencies in fMRI data has unlimited applications. The code to calculate these metrics is freely available.

Cholinergic neuromodulation of inhibitory interneurons facilitates functional integration in whole-brain models

“…Finally, the graph theoretical analysis used here only considers pairwise interactions, neglecting high-order effects that may contain important information about high dimensional functional brain interactions. Information-theoretical [ 66 , 67 ] and algebraic topological approaches [ 68 – 70 ] may provide complementary insights of high-order interdependencies in the brain.…”

Quantifying Dynamical High-Order Interdependencies From the O-Information: An Application to Neural Spiking Dynamics

“…In a recent paper (Rosas et al, 2019 ), a novel quantity has been introduced to study statistical synergy, the O-information, a metric capable of characterizing synergy- and redundancy-dominated systems and whose computational complexity scales gracefully with system size, making it suitable for practical data analysis; the O-information has been used to study brain aging in Gatica et al ( 2020 ). We remark that the O-information uses equal-time samples of variables, so its output depends only on equal-time correlations in the data-set and is insensitive to dynamic transfer of information; moreover the estimation of O-information does not require a division between predictor and target variables.…”

Cholinergic neuromodulation of inhibitory interneurons facilitates functional integration in whole-brain models

“…Information-theoretical [67,68] and algebraic topological approaches [57,69,70] may provide complementary insights of high-order interdependencies in the brain.…”