Assessing High-Order Interdependencies Through Static O-Information Measures Computed on Resting State fMRI Intrinsic Component Networks

Valenti, Simone; Sparacino, Laura; Pernice, Riccardo; Marinazzo, Daniele; Almgren, Hannes; Comelli, Albert; Faes, Luca

doi:10.1007/978-3-031-13321-3_34

Cited by 2 publications

(5 citation statements)

References 27 publications

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“…Highly similar recurrence patterns were found in the MICA data using the same data analysis pipeline (see Supplemental Figure 2). While the alignment of synergistic triads to known functional systems may seem unexpected based on ours and others' previous work indicating that these systems are redundancy dominated [10,12,17], it is consistent with the results in Figure 1d-g that show that globally redundant subsets experience the most synergistic moments.…”

Section: Maximally Synergistic and Maximally Redundant Triadssupporting

confidence: 90%

“…We found that for small subsets, and especially in the optimal triads, these information-rich regions correspond to known functional systems, such as the visual system, the somatomotor system and the default mode network. While past work, and indeed most functional connectivity analyses, have focused on the redundant interactions within these systems [12, 17], our results indicate that a thorough exploration of synergistic interactions within and between these systems is necessary. Since it is well-established that many functional systems have strong relationships to cognition, teasing apart the moments in which those systems are interacting synergistically may provide new windows into cognitive processes.…”

Section: Discussionmentioning

confidence: 92%

“…Higher order synergistic interactions are interactions between three or more brain regions in which there is some information at the level of the whole that is not reducible to the activity of individual brain regions. Several studies have revealed that traditional pairwise methods of studying interactions between regions, such as functional connectivity analyses, almost exclusively capture redundancy and are blind to synergistic interactions [10,12,17]. However, synergistic interactions are thought to be highly relevant to information modification [18] and computation in complex systems [19,20] and have begun to be fruitfully identified in neural systems.…”

Section: Introductionmentioning

confidence: 99%

“…The O-information, a heuristic measure of redundancy/synergy dominance [1,35], has been instrumental in revealing the presence of higher order interactions in the cortex as measured with blood oxygen-level dependent functional magnetic resonance imaging (BOLD fMRI) [10,17,36], in particular because it scales to handle interactions of many components, unlike other statistical approaches, such as the partial information decomposition [9]. Like many other information theoretic metrics, the O-information can be written as an expected value over all of a system's realized states.…”

Section: Introductionmentioning

confidence: 99%

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Time-varying synergy/redundancy dominance in the human cerebral cortex

Pope,

Varley,

Sporns

2024

Preprint

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Recent work has emphasized the ubiquity of higher-order interactions in brain function. These interactions can be characterized as being either redundancy or synergy-dominated by the heuristic O-information [1]. Though the O-information can be decomposed into local values to measure the synergy-redundancy dominance at each point in a time series [2] no such analysis of fMRI dynamics has been carried out. Here we analyze the moment-to-moment synergy and redundancy dominance of the fMRI BOLD signal during rest for 95 unrelated subjects. We present results from several interaction sizes. The whole brain is rarely synergy-dominated, with some subjects never experiencing a whole-brain synergistic moment. Randomly sampled subsets of many sizes reveal that subsets that are the most redundancy dominated on average exhibit both the most synergistic and most redundant time points. Exhaustive calculation of the optimally synergistic and optimally redundant triads further emphasizes this finding, with recurrent nodes frequently belonging to a single coherent functional system. We find that when a triad is momentarily synergistic, it is often split between two instantaneously co-fluctuating communities, but is collectively co-fluctuating when it is momentarily redundant. After optimizing for synergy and redundancy in subsets of size five to seventy-five, we show that this effect is consistent across interaction sizes. Additionally, we find notable temporal structure in all optimized redundant and synergistic subsets: higher order redundant and synergistic interactions change smoothly in time and recur more than expected by chance.

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Section: Maximally Synergistic and Maximally Redundant Triadssupporting

confidence: 90%

Section: Discussionmentioning

confidence: 92%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Time-varying synergy/redundancy dominance in the human cerebral cortex

Pope,

Varley,

Sporns

2024

Preprint

View full text Add to dashboard Cite

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“…We compared redundancy dominated modules to canonical resting-state systems. While some level of similarity was expected (canonical systems are dominated by redundant interactions [54]), we highlight how they differ. Our method led to a clear identification of the visual and somatomotor systems, and the dorsal attention network, which are also the systems more consistently detected across reports.…”

Section: Redundancy Dominated Systems Of the Human Brainmentioning

confidence: 87%

Leveraging multivariate information for community detection in functional brain networks

Puxeddu,

Pope,

Varley

et al. 2024

Preprint

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Embedded in neuroscience is the concept that brain functioning is underpinned by specialized systems whose integration enables cognition and behavior. Modeling the brain as a network of interconnected brain regions, allowed us to capitalize on network science tools and identify these segregated systems (modules, or communities) by optimizing the weights of pairwise connections within them. However, just knowing how strongly two brain areas are connected does not paint the whole picture. Brain dynamics is also engendered by interactions involving more areas at the same time, namely, higher-order interactions. In this paper, we propose a community detection algorithm that accounts for higher-order interactions and finds modules of brain regions whose brain activity is maximally redundant. Compared to modules identified with methods based on bivariate interactions, our redundancy-dominated modules are more symmetrical between the hemispheres, they overlap with canonical systems at the level of the sensory cortex, but describe a new organization of the transmodal cortex. By detecting redundant modules across spatial scales, we identified a sweet spot of maximum balance between segregation and integration of information as that scale where redundancy within modules and synergy between modules peaked. Moreover, we defined a local index that distinguishes brain regions in segregators and integrators based on how much they participate in the redundancy of their modules versus the redundancy of the whole system. Finally, we applied the algorithm to a lifespan dataset and tracked how redundant subsystems change across time. The results of this paper serve as educated guesses on how the brain organizes itself into modules accounting for higher-order interactions of its fundamental units, and pave the way for further investigation that could link them to cognition, behavior, and disease.

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Assessing High-Order Interdependencies Through Static O-Information Measures Computed on Resting State fMRI Intrinsic Component Networks

Cited by 2 publications

References 27 publications

Time-varying synergy/redundancy dominance in the human cerebral cortex

Time-varying synergy/redundancy dominance in the human cerebral cortex

Leveraging multivariate information for community detection in functional brain networks

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