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Monday, July 20 • 9:00pm - 10:00pm
P29: High-order interdependencies and the functional balance of segregation-integration in the aging brain

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https://meet.google.com/rzi-rzgd-iac

Marilyn Gatica
, Rodrigo Cofre, Fernando Rosas, Pedro Mediano, Patricio Orio, Ibai Diez, Stephan Swinnen, Jesus Cortes

The interdependencies in the brain can be studied either from a structural/anatomical perspective (“structural connectivity”, SC) or by considering statistical interdependencies (“functional connectivity”, FC). While the SC is essentially pairwise (white-matter fibers start in a certain region and arrive at another), the FC is not, i.e., there is no reason to consider statistical interdependencies pairwise. A promising tool to study high-order interdependencies is the recently proposed O-Information [1]. This quantity captures the balance between redundancies and synergies in arbitrary sets of variables, thus extending the properties of the interaction information of three variables to larger sets. Redundancy is here understood as an extension of the conventional notion of correlation to more than two variables In contrast, synergy corresponds to an emergent statistical relationships that control the whole but not the parts.

In this study, we follow the seminal ideas introduced by Tononi, Sporns, and Edelman [2], which state that high brain functions might depend on the co- existence of integration and segregation. While the latter enables brain areas to perform specialized tasks independently of each other, the former serves to bind together brain areas towards an integrated whole for the purpose of goal- directed task performance. A key insight put forward in [2] is that segregation and integration can coexist and that this coexistence is measurable by assessing the high-order interactions of neural elements. We used the O-Information to investigate how high-order statistical interdependencies are affected by aging. For this, we analyzed fMRI data at rest from 164 healthy participants, ranging from 10 to 80 years old. Our results show an important increase in redundant interdependencies in the older population (age ranging from 60 to 80 years). Moreover, this effect seems to be pervasive, taking place at all interaction orders, suggesting a change in the balance of differentiation and integration towards more synchronized arrangements. Additionally, a redundant core of brain modules was observed, which decreased in size with age. The framework presented here and in detail in [3], provide novel insights into the aging brain revealing the role of redundancy in prefrontal and motor cortices in older participants, thus affecting basic functions such as working memory, executive and motor functions. This methodology may help to provide a better understanding of some brain disorders from an informational perspective, providing “info-markers”, that may lead to fundamental insights into the human brain in health and disease. The code to compute the metrics is available at [4].

References

[1] Rosas F., Mediano Pedro A M, Gastpar Michael, and Jensen Henrik J. Quantifying High-order Interdependencies via Multivariate Extensions of the Mutual Information. Phys. Rev. E, 100, 032305, 2019.

[2] Tononi Giulio, Sporns Olaf, and Edelman Gerald M.. A measure for brain complexity: Relating functional segregation and integration in the nervous system. PNAS, 1994

[3] Gatica Marilyn, Cofré Rodrigo, Mediano Pedro A.M., Rosas Fernando E., Orio Patricio, Diez Ibai, Swinnen S.P , Cortes Jesus M. High order interdependencies in the aging brain bioRxiv 2020.03.17.995886; doi: [https://doi.org/10.1101/2020.03.17.995886](https://doi.org/10.1101/2020.03.17.995886).

[4] [https://github.com/brincolab/High-Order- interactions](https://github.com/brincolab/High-Order-interactions).

Speakers
avatar for Rodrigo Cofre

Rodrigo Cofre

Joint Professor, Institute of Mathematical Engineering, Universidad de Valparaiso
High-order interactions, brain science, complexity, statistical methods, spike train statistics, maximum entropy principle, non-equilibrium statistical mechanics ideas to model phenomena in neuroscience.



Monday July 20, 2020 9:00pm - 10:00pm CEST
Slot 03