Brain signaling becomes less integrated and more segregated with age

Rostam M Razban; Botond B Antal; Ken A Dill; Lilianne R Mujica-Parodi

doi:10.1162/netn_a_00389

Brain signaling becomes less integrated and more segregated with age

Netw Neurosci. 2024 Dec 10;8(4):1051-1064. doi: 10.1162/netn_a_00389. eCollection 2024.

Authors

Rostam M Razban¹, Botond B Antal^{2

3}, Ken A Dill^{1

4

5}, Lilianne R Mujica-Parodi^{1

2

3

6}

Affiliations

¹ Laufer Center for Physical and Quantitative Biology, Stony Brook University, Stony Brook, NY, USA.
² Department of Biomedical Engineering, Stony Brook University, Stony Brook, NY, USA.
³ Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA.
⁴ Department of Physics and Astronomy, Stony Brook University, Stony Brook, NY, USA.
⁵ Department of Chemistry, Stony Brook University, Stony Brook, NY, USA.
⁶ Santa Fe Institute, Santa Fe, NM, USA.

Abstract

The integration-segregation framework is a popular first step to understand brain dynamics because it simplifies brain dynamics into two states based on global versus local signaling patterns. However, there is no consensus for how to best define the two states. Here, we map integration and segregation to order and disorder states from the Ising model in physics to calculate state probabilities, P _int and P _seg, from functional MRI data. We find that integration decreases and segregation increases with age across three databases. Changes are consistent with weakened connection strength among regions rather than topological connectivity based on structural and diffusion MRI data.

Keywords: Aging; Statistical physics; dMRI; fMRI.

Plain language summary

The integration-segregation framework succinctly captures the trade-off that brains face between seamless function (more integration) in light of energetic constrains (more segregation). Despite its ubiquitous use in the field, there is no consensus on its definition with various graph theoretical properties being proposed. Here, we define the two states based on the underlying mechanism of neuronal coupling strength to provide a physical foundation for the framework. We find that younger adults’ brains are close to perfectly balanced between integration and segregation, while older adults’ brains veer off toward random signaling.