Temporal circuit of macroscale dynamic brain activity supports human consciousness

Huang, Zirui; Zhang, Jun; Wu, Jinsong; Mashour, George A.; Hudetz, Anthony G.

doi:10.1126/sciadv.aaz0087

Cited by 163 publications

(264 citation statements)

References 48 publications

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“…First, it identified that the use of entropic measures for the study of consciousness and its (altered) states led the field to substantially advance the previous findings. For instance, they helped verify the theoretical models that envisioned the brain’s large-scale information sharing underlies its conscious processing [ 75 ], that DMN and DAT networks were crucially involved in such processes [ 29 ], and that the conscious brain’s dynamics sustained patterns of long-range coordination which was substantially distinct from its anatomical connectivity [ 28 ]. These studies further validated the utility of entropic measures as potential biomarkers for the study of differential states of consciousness [ 115 , 116 , 117 , 118 , 129 ].…”

Section: Discussionmentioning

confidence: 99%

“…Zhang et al [ 29 ] used the entropy of Markov trajectories [ 83 ] to demonstrate that human consciousness relies on the temporal circuit in which the dynamic is characterized by the balanced and reciprocal accessibility of the default mode network (DMN) [ 84 ] and the dorsal attention network (DAT) [ 85 ]. Their findings provided further support for involvement of these two brain networks as two distinct cortical systems that support consciousness [ 85 , 86 , 87 , 88 ].…”

Section: (Altered) State Of Consciousnessmentioning

confidence: 99%

“…These findings, in turn, help explain the pivotal role of entropy ( Appendix A ) in quantification of the brain’s information processing [ 18 , 19 , 20 , 21 ], given the direct correspondence between the variance and the amount of information [ 22 ]. Today’s applications of entropy for quantification of the brain function range from information capacity of working memory (WM) [ 23 ] and the neural coding [ 24 , 25 ] to the interplay between neural adaptation and behaviour [ 26 ], functional interactivity between the brain regions [ 27 ], and the state of consciousness [ 28 , 29 ].…”

Section: Introductionmentioning

confidence: 99%

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Entropy and the Brain: An Overview

Keshmiri

2020

Entropy

102

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Entropy is a powerful tool for quantification of the brain function and its information processing capacity. This is evident in its broad domain of applications that range from functional interactivity between the brain regions to quantification of the state of consciousness. A number of previous reviews summarized the use of entropic measures in neuroscience. However, these studies either focused on the overall use of nonlinear analytical methodologies for quantification of the brain activity or their contents pertained to a particular area of neuroscientific research. The present study aims at complementing these previous reviews in two ways. First, by covering the literature that specifically makes use of entropy for studying the brain function. Second, by highlighting the three fields of research in which the use of entropy has yielded highly promising results: the (altered) state of consciousness, the ageing brain, and the quantification of the brain networks’ information processing. In so doing, the present overview identifies that the use of entropic measures for the study of consciousness and its (altered) states led the field to substantially advance the previous findings. Moreover, it realizes that the use of these measures for the study of the ageing brain resulted in significant insights on various ways that the process of ageing may affect the dynamics and information processing capacity of the brain. It further reveals that their utilization for analysis of the brain regional interactivity formed a bridge between the previous two research areas, thereby providing further evidence in support of their results. It concludes by highlighting some potential considerations that may help future research to refine the use of entropic measures for the study of brain complexity and its function. The present study helps realize that (despite their seemingly differing lines of inquiry) the study of consciousness, the ageing brain, and the brain networks’ information processing are highly interrelated. Specifically, it identifies that the complexity, as quantified by entropy, is a fundamental property of conscious experience, which also plays a vital role in the brain’s capacity for adaptation and therefore whose loss by ageing constitutes a basis for diseases and disorders. Interestingly, these two perspectives neatly come together through the association of entropy and the brain capacity for information processing.

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Section: Discussionmentioning

confidence: 99%

Section: (Altered) State Of Consciousnessmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Entropy and the Brain: An Overview

Keshmiri

2020

Entropy

102

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“…Although we observed that the GS topography reflected an instantaneous coactivation at the peak time points of GS, it is important to understand whether the GS topography is a single united entity or a combination of different coactivation sets. To address this question, we adopted a data-driven approach (i.e., k-means clustering algorithm) that partitioned the whole-brain frames into spatially congruent CAPs ( Fig 4A) and assigned each fMRI frame to a cluster label [20,32]. The occurrence rate of these CAPs at the peak time point of GS was measured by the number of CAP occurrences divided by the total number of peak time points ( Fig 4A).…”

Section: Decomposing Gs-peak Into a Subset Of Capsmentioning

confidence: 99%

Rest-task modulation of fMRI-derived global signal topography is mediated by transient coactivation patterns

et al. 2020

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Recent resting-state functional MRI (fMRI) studies have revealed that the global signal (GS) exhibits a nonuniform spatial distribution across the gray matter. Whether this topography is informative remains largely unknown. We therefore tested rest-task modulation of GS topography by analyzing static GS correlation and dynamic coactivation patterns in a large sample of an fMRI dataset (n = 837) from the Human Connectome Project. The GS topography in the resting state and in seven different tasks was first measured by correlating the GS with the local time series (GSCORR). In the resting state, high GSCORR was observed mainly in the primary sensory and motor regions, whereas low GSCORR was seen in the association brain areas. This pattern changed during the seven tasks, with mainly decreased GSCORR in sensorimotor cortex. Importantly, this rest-task modulation of GSCORR could be traced to transient coactivation patterns at the peak period of GS (GSpeak). By comparing the topography of GSCORR and respiration effects, we observed that the topography of respiration mimicked the topography of GS in the resting state, whereas both differed during the task states; because of such partial dissociation, we assume that GSCORR could not be equated with a respiration effect. Finally, rest-task modulation of GS topography could not be exclusively explained by other sources of physiological noise. Together, we here demonstrate the informative nature of GS topography by showing its rest-task modulation, the underlying dynamic coactivation patterns, and its partial dissociation from respiration effects during task states.

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“…The spatial topography is already present in rest and carried over to task states 5,6 . At the same time, there is increasing evidence for temporal hierarchy in the cortex with different regions exhibiting different temporal dynamics like slow and fast frequency power 7,8,[17][18][19][20][21][22][9][10][11][12][13][14][15][16] .…”

Section: Introductionmentioning

confidence: 99%

The interplay between information flux and temporal dynamics in infraslow frequencies

Golesorkhi

Tumati

Gómez‐Pilar

et al. 2020

Preprint

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The brain exhibits both spatial and temporal hierarchy with their relationship remaining an open question. We address this issue by investigating the brain's spatial hierarchy with complexity, i.e., Lempel-Zev Complexity (LZC) and temporal dynamics, i.e., median frequency (MF) in rest/task fMRI (including replication data). Our results are: (I) topographical differences in rest between higher-order networks (lower LZC and MF) and lowerorder networks (higher LZC and MF); (II) task-specific increases and task-unspecific decreases in LZC and MF; (III) non-linear topographical relationship with low MF mediating higher LZC rest-task changes as confirmed in various simulations. Together, we demonstrate convergence of spatial (LZC) and temporal (MF) hierarchies in a non-linear topographical way along the lines of higher-order/slow frequency and lowerorder/fast frequency networks.The main aim of our study was to investigate the convergence of spatial and temporal topographies including how that shapes rest and task states. For that purpose, we used the Human Connectome Project (HCP) 7 Tesla fMRI datasets (and the HCP 3T for replication) applying novel measures like Lempel-Ziv complexity (LZC) and median frequency (MF) during different states, i.e., rest and two different task states with two completely different complexity and temporal structure (i.e., movie and retinotopy) (see below).LZC measures the number of distinct patterns in a binary sequence i.e., the regularity or repetitiveness of a signal 34,35 , which reflects the amount of information (number of bits) required to reconstruct a signal 34 . Recently, LZC has been applied in fMRI 36-39 as well as in other imaging modalities including MEG 40-43 and wn spatial 2,43,56 , we h rest and 1,2,12,32,33 , i.e., more toparietalThe second specific aim was to investigate regional temporal dynamics by using median frequency (MF) of ISF in rest and task states. Recent studies demonstrated that lower-and higher-order regions exhibit different intrinsic neural time scales with short and long durations in their temporal receptive windows during task states 7,8,[17][18][19]32,33,[9][10][11][12][13][14][15][16] . Based on these findings and the fact that the duration of intrinsic neural time scale may be related to the length of cycle durations 57 as reflecting the frequency range 7,8 , we hypothesized that lower-order networks would exhibit higher MF, i.e., stronger power in faster ISF frequency ranges with shorter cycle duration. While we assumed that higher-order ones would show lower MF with stronger power in slower ISF frequency ranges, i.e., longer cycle durations, in both rest and task states.The third specific aim consisted in directly linking spatial, i.e., LZC, and temporal, i.e., MF dimensions including their respective topographies. For that purpose, we conducted correlation and regression models as well as simulation. Given the scale-free driven discrepancy in power of slower (strong power) and faster (weaker power) ISF frequency ranges 7,13,58,59 , we assumed non-l...

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Temporal circuit of macroscale dynamic brain activity supports human consciousness

Cited by 163 publications

References 48 publications

Entropy and the Brain: An Overview

Entropy and the Brain: An Overview

Rest-task modulation of fMRI-derived global signal topography is mediated by transient coactivation patterns

The interplay between information flux and temporal dynamics in infraslow frequencies

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