Higher-order connectomics of human brain function reveals local topological signatures of task decoding, individual identification, and behavior DOI Creative Commons
Andrea Santoro, Federico Battiston, Maxime Lucas

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: Dec. 5, 2023

Abstract Traditional models of human brain activity often represent it as a network pairwise interactions between regions. Going beyond this limitation, recent approaches have been proposed to infer higher-order from temporal signals involving three or more However, day remains unclear whether methods based on inferred outperform traditional ones for the analysis fMRI data. To address question, we conducted comprehensive using time series 100 unrelated subjects Human Connectome Project. We show that greatly enhance our ability decode dynamically various tasks, improve individual identification unimodal and transmodal functional subsystems, strengthen significantly associations behavior. Overall, approach sheds new light organization series, improving characterization dynamic group dependencies in rest revealing vast space unexplored structures within data, which may remain hidden when approaches.

Language: Английский

Higher-order connectomics of human brain function reveals local topological signatures of task decoding, individual identification, and behavior DOI Creative Commons
Andrea Santoro, Federico Battiston, Maxime Lucas

et al.

Nature Communications, Journal Year: 2024, Volume and Issue: 15(1)

Published: Nov. 26, 2024

Abstract Traditional models of human brain activity often represent it as a network pairwise interactions between regions. Going beyond this limitation, recent approaches have been proposed to infer higher-order from temporal signals involving three or more However, day remains unclear whether methods based on inferred outperform traditional ones for the analysis fMRI data. To address question, we conducted comprehensive using time series 100 unrelated subjects Human Connectome Project. We show that greatly enhance our ability decode dynamically various tasks, improve individual identification unimodal and transmodal functional subsystems, strengthen significantly associations behavior. Overall, approach sheds new light organization series, improving characterization dynamic group dependencies in rest revealing vast space unexplored structures within data, which may remain hidden when approaches.

Language: Английский

Citations

5
HOI: A Python toolbox for high-performance estimation of Higher-Order Interactions from multivariate data DOI Creative Commons
M. Neri,

Dishie Vinchhi,

Christian Ferreyra

et al.

The Journal of Open Source Software, Journal Year: 2024, Volume and Issue: 9(103), P. 7360 - 7360

Published: Nov. 12, 2024

Neri et al., (2024). HOI: A Python toolbox for high-performance estimation of Higher-Order Interactions from multivariate data. Journal Open Source Software, 9(103), 7360, https://doi.org/10.21105/joss.07360

Language: Английский

Citations

2
Neural interactions in the human frontal cortex dissociate reward and punishment learning DOI Creative Commons
Etienne Combrisson, Ruggero Basanisi, Maëlle C. M. Gueguen

et al.

eLife, Journal Year: 2023, Volume and Issue: 12

Published: Nov. 9, 2023

How human prefrontal and insular regions interact while maximizing rewards minimizing punishments is unknown. Capitalizing on intracranial recordings, we demonstrate that the functional specificity toward reward or punishment learning better disentangled by interactions compared to local representations. Prefrontal cortices display non-selective neural populations punishments. Non-selective responses, however, give rise context-specific interareal interactions. We identify a subsystem with redundant between orbitofrontal ventromedial cortices, driving role of latter. In addition, find dorsolateral insula. Finally, switching mediated synergistic two subsystems. These results provide unifying explanation distributed cortical representations supporting learning.

Language: Английский

Citations

4
Higher-order connectomics of human brain function reveals local topological signatures of task decoding, individual identification, and behavior DOI Creative Commons
Andrea Santoro, Federico Battiston, Maxime Lucas

et al.

bioRxiv (Cold Spring Harbor Laboratory), Journal Year: 2023, Volume and Issue: unknown

Published: Dec. 5, 2023

Abstract Traditional models of human brain activity often represent it as a network pairwise interactions between regions. Going beyond this limitation, recent approaches have been proposed to infer higher-order from temporal signals involving three or more However, day remains unclear whether methods based on inferred outperform traditional ones for the analysis fMRI data. To address question, we conducted comprehensive using time series 100 unrelated subjects Human Connectome Project. We show that greatly enhance our ability decode dynamically various tasks, improve individual identification unimodal and transmodal functional subsystems, strengthen significantly associations behavior. Overall, approach sheds new light organization series, improving characterization dynamic group dependencies in rest revealing vast space unexplored structures within data, which may remain hidden when approaches.

Language: Английский

Citations

3