TY - JOUR
T1 - The arrow of time of brain signals in cognition
T2 - Potential intriguing role of parts of the default mode network
AU - Deco, Gustavo
AU - Sanz Perl, Yonatan
AU - de la Fuente, Laura
AU - Sitt, Jacobo D.
AU - Yeo, B. T.Thomas
AU - Tagliazucchi, Enzo
AU - Kringelbach, Morten L.
N1 - Publisher Copyright:
Copyright: © 2023 Massachusetts Institute of Technology Published under a Creative Commons Attribution 4.0 International (CC BY 4.0) license.
PY - 2023
Y1 - 2023
N2 - A promising idea in human cognitive neuroscience is that the default mode network (DMN) is responsible for coordinating the recruitment and scheduling of networks for computing and solving task-specific cognitive problems. This is supported by evidence showing that the physical and functional distance of DMN regions is maximally removed from sensorimotor regions containing environment-driven neural activity directly linked to perception and action, which would allow the DMN to orchestrate complex cognition from the top of the hierarchy. However, discovering the functional hierarchy of brain dynamics requires finding the best way to measure interactions between brain regions. In contrast to previous methods measuring the hierarchical flow of information using, for example, transfer entropy, here we used a thermodynamicsinspired, deep learning based Temporal Evolution NETwork (TENET) framework to assess the asymmetry in the flow of events, ‘arrow of time’, in human brain signals. This provides an alternative way of quantifying hierarchy, given that the arrow of time measures the directionality of information flow that leads to a breaking of the balance of the underlying hierarchy. In turn, the arrow of time is a measure of nonreversibility and thus nonequilibrium in brain dynamics. When applied to large-scale Human Connectome Project (HCP) neuroimaging data from close to a thousand participants, the TENET framework suggests that the DMN plays a significant role in orchestrating the hierarchy, that is, levels of nonreversibility, which changes between the resting state and when performing seven different cognitive tasks. Furthermore, this quantification of the hierarchy of the resting state is significantly different in health compared to neuropsychiatric disorders. Overall, the present thermodynamics-based machine-learning framework provides vital new insights into the fundamental tenets of brain dynamics for orchestrating the interactions between cognition and brain in complex environments.
AB - A promising idea in human cognitive neuroscience is that the default mode network (DMN) is responsible for coordinating the recruitment and scheduling of networks for computing and solving task-specific cognitive problems. This is supported by evidence showing that the physical and functional distance of DMN regions is maximally removed from sensorimotor regions containing environment-driven neural activity directly linked to perception and action, which would allow the DMN to orchestrate complex cognition from the top of the hierarchy. However, discovering the functional hierarchy of brain dynamics requires finding the best way to measure interactions between brain regions. In contrast to previous methods measuring the hierarchical flow of information using, for example, transfer entropy, here we used a thermodynamicsinspired, deep learning based Temporal Evolution NETwork (TENET) framework to assess the asymmetry in the flow of events, ‘arrow of time’, in human brain signals. This provides an alternative way of quantifying hierarchy, given that the arrow of time measures the directionality of information flow that leads to a breaking of the balance of the underlying hierarchy. In turn, the arrow of time is a measure of nonreversibility and thus nonequilibrium in brain dynamics. When applied to large-scale Human Connectome Project (HCP) neuroimaging data from close to a thousand participants, the TENET framework suggests that the DMN plays a significant role in orchestrating the hierarchy, that is, levels of nonreversibility, which changes between the resting state and when performing seven different cognitive tasks. Furthermore, this quantification of the hierarchy of the resting state is significantly different in health compared to neuropsychiatric disorders. Overall, the present thermodynamics-based machine-learning framework provides vital new insights into the fundamental tenets of brain dynamics for orchestrating the interactions between cognition and brain in complex environments.
KW - Brain
KW - Default mode network
KW - Neuroimaging
KW - Orchestration
KW - Thermodynamics
UR - http://www.scopus.com/inward/record.url?scp=85175052894&partnerID=8YFLogxK
U2 - 10.1162/netn_a_00300
DO - 10.1162/netn_a_00300
M3 - Article
AN - SCOPUS:85175052894
SN - 2472-1751
VL - 7
SP - 966
EP - 998
JO - Network Neuroscience
JF - Network Neuroscience
IS - 3
ER -