From temporal to spatial topography: hierarchy of neural dynamics in higher- and lower-order networks shapes their complexity

Abstract

The brain shows a topographical hierarchy along the lines of lower- and higher-order networks. The exact temporal dynamics characterization of this lower-higher-order topography at rest and its impact on task states remains unclear, though. Using 2 functional magnetic resonance imaging data sets, we investigate lower- and higher-order networks in terms of the signal compressibility, operationalized by Lempel–Ziv complexity (LZC). As we assume that this degree of complexity is related to the slow–fast frequency balance, we also compute the median frequency (MF), an estimation of frequency distribution. We demonstrate (i) topographical differences at rest between higher- and lower-order networks, showing lower LZC and MF in the former; (ii) task-related and task-specific changes in LZC and MF in both lower- and higher-order networks; (iii) hierarchical relationship between LZC and MF, as MF at rest correlates with LZC rest–task change along the lines of lower- and higher-order networks; and (iv) causal and nonlinear relation between LZC at rest and LZC during task, with MF at rest acting as mediator. Together, results show that the topographical hierarchy of lower- and higher-order networks converges with their temporal hierarchy, with these neural dynamics at rest shaping their range of complexity during task states in a nonlinear way.

Reduced emergent character of neural dynamics in patients with a disrupted connectome

“…Indeed, recent advances in the related but complementary account of emergence proposed by Integrated Information Theory [75][76][77][78] present promising avenues for future investigation. Similarly, "hierarchy" is a protean, multi-faceted concept in neuroscience 7,16 , and different operationalisations may bear different relationships with emergence, which should be borne in mind when interpreting the present results. Additionally, although here we capitalised on the availability of functional and diffusion MRI data in the same cohort of patients, future work may also seek to investigate emergence capacity from electrophysiological signals, which have higher temporal resolution and provide a more direct quantification of neuronal activity.…”

“…The copyright holder for this preprint this version posted June 21, 2022. ; https://doi.org/10.1101/2022.06. 16.496445 doi: bioRxiv preprint 2 how specific neuroanatomical regions contribute to brain function, it is also useful to study their dependence on highly distributed spatio-temporal patterns of collective activity arising from the complex interactions between multiple neural systems. At its core, this approach builds on the long-standing conjecture that mental activity may be an emergent phenomenon arising from the collective activity of neurons in the brain [1][2][3][4] .…”

“…The copyright holder for this preprint this version posted June 21, 2022. ; https://doi.org/10.1101/2022.06. 16.496445 doi: bioRxiv preprint organisation of a structural network shapes its ability to influence the functional dynamics that take place over it.…”

“…By making emergence in the brain formally quantifiable, ΦID also makes it possible to contextualise how emergence relates to other fundamental properties of brain functional and structural organisation. Conceptually, emergence is deeply intertwined with the idea of hierarchical organisation, which (in its many possible conceptualisations 7 ) is a fundamental principle in our contemporary understanding of the brain [8][9][10][11][12][13][14][15][16][17] . Thanks to ΦID, we are now in a position to characterise the empirical relationship between emergence and hierarchy in the activity the human brain.…”

“…(which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It isThe copyright holder for this preprint this version posted June 21, 2022. ; https://doi.org/10.1101/2022.06 1623,99 which uses a time-invariant network model on discrete-time of the form 𝒙(𝑡 + 1) = 𝑨𝒙(𝑡) + 𝑩 𝐾 𝒖 𝐾(𝑡) …”