P203 Reciprocity and Hierarchical Organization in the Resting-State Brain: Implications for Efficient Connectivity

Guillermo Montaña-Valverde*1,2, Paula García-Royo2, Wolfram Hinzen1,3, Gustavo Deco2,3

¹ Department of Translation and Language Sciences, Pompeu Fabra University, Barcelona, 08018, Spain
² Center for Brain and Cognition, Computational Neuroscience Group, Department of Information and Communication Technologies, Universitat Pompeu Fabra, Barcelona, 08018, Spain
³ Institució Catalana de Recerca i Estudis Avançats, ICREA, Barcelona, 08010, Spain

*Email: guillermo.montana@upf.edu


Introduction

While the brain is traditionally considered to have a strong hierarchical organization, our findings demonstrate that its structure is more flattened, facilitating more efficient information flow across the network [1]. Using a large resting-state and task-based fMRI dataset, we show that reciprocity – the tendency of a network to have bidirectional connections– strongly correlate with hierarchical organization. This suggests that the brain’s densely interconnected architecture flattens hierarchy, facilitating efficient information flow through shorter average path lengths and enhanced small-worldness. These results aligns with the idea that reciprocity enhances information flow via feedback connectivity [2]. This novel framework lays the foundation for our understanding of whole-brain functional dynamics [3].

Methods
We analyzed open-source fMRI data from the Human Connectome Project (HCP), comprising both resting-state and 7 task-based data from 1000 subjects. Generative effective connectivity (GEC) – an extension of the classic effective connectivity [4] –was estimated from whole-brain modeling for each subject in the DK80 parcellation [5], providing a directed weighted network [6,7]. Hierarchy was then determined by computing measures of coherence and trophic levels on the GEC (Fig. 1a). Reciprocity, defined as the fraction of total connection strength that is bidirectionally shared between regions, captures the balance between feedforward and feedback interactions [8] (Fig. 1b).
Results
We found that the brain in resting-state exhibits a high degree of reciprocity (0.93± 0.02, Fig. 1c), which shows a strong negative correlation with hierarchical coherence (corr=-0.97, p<0.001, Fig. 1d). Conversely, by artificially modulating for more asymmetric interactions, the hierarchy becomes more rigid (Fig. 1e). In addition, a more flattened hierarchy was associated with a shorter average path length (corr=0.97, p<0.001, Fig. 1f), higher average clustering coefficient (corr=-0.95, p<0.001, Fig. 1g), and increased small-worldness (corr=-0.99, p<0.001, Fig. 1h). Furthermore, decreased hierarchical coherence was observed during task performance (Fig. 1i).
Discussion
Overall, our results demonstrate that reciprocity plays a crucial role in shaping the brain’s hierarchical organization. The brain’s nature of high reciprocal connections facilitates information flow and integration, potentially optimizing cognitive processing both at rest and during task performance. On the contrary, a stronger hierarchy, reduces flexibility and adaptability, leading to a worsening in brain connectivity. For this reason, future research in this methodology should explore neuropsychiatric disorders, where changes in hierarchical organization of the brain may underlie altered brain processing. Ultimately, exploring whether targeted interventions that modulate reciprocity can restore optimal hierarchical organization and improve cognitive function.



Figure 1. A. The hierarchy was quantified measuring directedness based on trophic levels. B. Simplified representation of reciprocity. C. Reciprocity in the HCP resting-state dataset. D. Coherence and Reciprocity relation in HCP resting-state. E. Hierarchical representations for different reciprocities. F, G and H. Graph measures correlates with coherence. I. Coherence in 7 tasks compared to resting-state.
Acknowledgements
This study is part of the projectI+D+i Generación de ConocimientoPRE2020-095700, funded by MCIN/AEI/10.13039/501100011033.
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