Disruption in structural–functional network repertoire and time-resolved subcortical fronto-temporoparietal connectivity in disorders of consciousness

  1. Rajanikant Panda
  2. Aurore Thibaut
  3. Ane Lopez-Gonzalez
  4. Anira Escrichs
  5. Mohamed Ali Bahri
  6. Arjan Hillebrand
  7. Gustavo Deco
  8. Steven Laureys
  9. Olivia Gosseries
  10. Jitka Annen
  11. Prejaas Tewarie

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    Evaluation Summary

    This study aims to characterise the brain dynamics of different disorders of consciousness by studying patients in a minimally conscious state and those with unresponsive wakefulness syndrome, along with healthy controls. The authors apply elegant analyses to the dynamics of brain functional connectivity to successfully discriminate between healthy controls and patients, revealing reduced metastability and a contracted network repertoire in disorders of consciousness. Overall, the study provides important new information on the mechanisms of disorders of consciousness and the functional brain networks involved.

    This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1 agreed to share their name with the authors.

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Abstract

Understanding recovery of consciousness and elucidating its underlying mechanism is believed to be crucial in the field of basic neuroscience and medicine. Ideas such as the global neuronal workspace (GNW) and the mesocircuit theory hypothesize that failure of recovery in conscious states coincide with loss of connectivity between subcortical and frontoparietal areas, a loss of the repertoire of functional networks states and metastable brain activation. We adopted a time-resolved functional connectivity framework to explore these ideas and assessed the repertoire of functional network states as a potential marker of consciousness and its potential ability to tell apart patients in the unresponsive wakefulness syndrome (UWS) and minimally conscious state (MCS). In addition, the prediction of these functional network states by underlying hidden spatial patterns in the anatomical network, that is so-called eigenmodes, was supplemented as potential markers. By analysing time-resolved functional connectivity from functional MRI data, we demonstrated a reduction of metastability and functional network repertoire in UWS compared to MCS patients. This was expressed in terms of diminished dwell times and loss of nonstationarity in the default mode network and subcortical fronto-temporoparietal network in UWS compared to MCS patients. We further demonstrated that these findings co-occurred with a loss of dynamic interplay between structural eigenmodes and emerging time-resolved functional connectivity in UWS. These results are, amongst others, in support of the GNW theory and the mesocircuit hypothesis, underpinning the role of time-resolved thalamo-cortical connections and metastability in the recovery of consciousness.

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  1. Version published to 10.7554/elife.77462 on eLife
  2. eLife

    Author Response

    Reviewer 1

    Panda and co-workers analyzed RS fMRI recordings from healthy patients and from two types of coma: UWS and MCS. They characterized the time-resolved functional connectivity in terms of metastability (time-variance of the Kuramoto order parameter), spatiotemporal patterns via non-negative tensor factorization, and its relationship to the eigenmodes of structural connectivity. Finding greater metastability and non-stationarity of the DMN network in healthy MCS patients, than in UWS patients, they found that the best discriminators to classify the different DoCs are the number of excursions (nonstability) from the DMN, salience and FPN networks extracted by the NNTF analysis. Interestingly, the data-driven NNTF yielded a novel sub-network comprising the FPN and some subcortical structures. The excursions and dwell times from this FPN subnetwork showed to be significantly lower in the UWS patients than in MCS. Surrogate data testing assures that the different methods and fits are effectively expressing the functional connectivity matrices measured.

    Overall, I think that the results are correct and they advance in the characterization and understanding of the brain under DoC. However, some improvements can be made in the way the results, and the rationale behind them, are presented.

    We thank Prof. Patricio Orio for his assessment.

    While reading the Results section, it is easy to have the impression of a disconnected set of analyses that just happened to be together. In particular, the section about the structural eigenmodes and their relationship with the time-resolved FC seems to have little connection with the rest of the work, except for confirming (yet again) that DoC patients have a less dynamic FC. More elaboration about the relevance of these results, and what they say about DoC (that other dynamical FC analyses don't), is needed both in the introduction and discussion. Although a clear explanation is given in the introduction, the bottom line seems to be yet another measure of metastability. Perhaps, a better explanation of what underlies the 'modulation strength of eigenmodes expression' will be helpful for distinguishing this analysis from others. How novel is the connection that is being done with the structural connectivity and why is this important? Moreover, the eigenmodes analysis has little-to-none importance in the discrimination of patients done at the end; thus, its place within the big picture is hard to evaluate.

    We understand the reviewer’s position. Part one of our work covers time-resolved FC and *spatiotemporal networks in *DoC. Part two covers the relationship between timeresolved FC and eigenmodes of the structural network. The rationale for including part two is the following: there is a lot of literature that shows that eigenmodes of the structural network can be considered as ‘building blocks’ or basis functions/vectors for spatiotemporal networks at the functional level (Aqil et al., 2021; Atasoy et al., 2016, 2018; Deslauriers-Gauthier et al., 2020; Gabay et al., 2018; Gabay and Robinson, 2017; Robinson et al., 2016; Robinson, 2021; Tewarie et al., 2019, 2020; Wang et al., 2017). Ideally to link part one and two, you would take this notion further by analysing if the magnitude eigenmode coefficients differed between UWS, MCS and healthy controls and how this would relate to dwell times or expression of spatiotemporal networks. However, this would lead to an immense multiple testing issue, which would be impossible to overcome with our sample size. An important link between part one and two of our work is the relationship between change in eigenmode expression and metastability. Our measure for metastability is only a proxy for metastability. Lack of change in eigenmode expressions seems to confirm this result of metastability.

    To allow for better integration of part one and two of our work, we have added to the introduction:

    “These eigenmodes can be considered as patterns of ‘hidden connectivity’ that come to expression at the level of functional networks. It has been postulated that eigenmodes form elementary building blocks for spatiotemporal dynamics (Aqil et al., 2021). There is evidence that the well-known resting state networks can be explained by activation of a small set of eigenmodes (Atasoy et al., 2018).”

    We have also clarified in the result section:

    “As resting-state network activity can be explained by activation of structural eigenmodes, we next analyse the role of fluctuations in eigenmode expression over time.”

    Something that I find counter-intuitive and that may confuse some readers, is the (apparent) contradiction between the diminished metastability in the DoC conditions and the reduced dwell times (Figure S1; also "the inability to sequentially dwell for prolonged times in a different set of eigenmodes", as stated in the Discussion). Fewer excursions and shorter dwell times can only mean that some networks are just less visited and maybe this would be enough to distinguish between conditions. Further explaining this will help to understand better the implications of the work.

    We understand the reviewer’s point, however we disagree that diminished metastability is in contradiction with the findings on dwell times. We show that dwell times are reduced in the posterior DMN, FPN and sub-FPTN networks, however, there is very long dwelling in the residual network in DoC. Hence, the brain resides in fewer network states in DoC, which is in agreement with reduced metastability. Our proxy for metastability is the standard deviation of the Kuramoto order parameter. Whenever there are more visits to network states, or switching between network states as is the case for healthy controls in our data, this would lead to phase uncoupling followed by phase synchronization, which would hence boost the standard deviation of the Kuramoto order parameter (a proxy for metastability).

    We agree with the reviewer that the sentence starting “the inability to sequentially dwell for prolonged….” Is confusing. We have now removed this statement.

    We have now added to the result section:

    “These findings of very short dwell times in the posterior DMN, FPN and sub-FPTN and long dwell time in the residual network can be considered as a contraction of the functional network repertoire in DoC, which is in agreement with a loss in metastability in these patients.”

    Finally, some comments about the connection(s) of these analyses with the commonly used FCD analysis (based on sliding windows of pair-wise correlations) will be useful, to put better this work into the big picture of time evolution of the functional connectivity.

    We have now discussed sliding window-based analysis in the context of our work in the methodology section.

    “Lastly, we have used a high temporal resolution method to estimate time-resolved connectivity at every time point instead of a sliding window-based method. Previous studies using sliding window approaches have provided novel insights into brain dynamics of loss of consciousness, such as the brain co-occurrence of functional connectivity patterns, which is known as brain states and its temporal (i.e., rate of pattern occurrence (probability) and between pattern transition probabilities) alteration in loss of consciousness in DoC patients (Demertzi et al., 2019) and anaesthesia induced loss of consciousness (Barttfeld et al., 2014a; Uhrig et al., 2018). However, sliding window approaches have limited sensitivity to non-stationarity in the fMRI BOLD signals (Hindriks et al., 2016) and lack to provide spatial alteration of classical brain functional network. The exploration of the spatiotemporal aspects of well-known resting state networks is an important step forwards for better understanding the relation between brain function and consciousness, in a way that is impossible to achieve at the whole brain level. In addition, recent work on time-resolved connectivity shows that brief periods of co-modulation in BOLD signals are an important driving factor for functional connectivity (Esfahlani et al., 2020; Hindriks et al., 2016).”

    Reviewer 2

    The study is of high significance, rigor, and novelty. Despite the many studies of repertoire, dynamic connectivity, etc., in the study of consciousness, there is (surprisingly, as I confirmed with a literature search) a dearth of application of these approaches to disorders of consciousness. The manuscript is well-written and transparent about its limitations. The author should consider the following recommendations:

    We thank the reviewer for his/her assessment of our work.

    1. There is frequent reference to "subcortical" and related networks, but I see no description in the text of which subcortical structures are involved. Panel N of figure 2 is helpful but I think that more explicit detail is important, especially given the specific predictions of mesocircuit theory.

    We have provided details for the subcortical networks presented in the Panel N of Figure 2. In the manuscript we provide a textual description of the brain areas that are part of the network. To improve the clarity of the description of the network, we also now refer to it as “subcortical fronto-temporoparietal (Sub-FTPN)”.

    In the result section, it read as: “This modulated subcortical fronto-temporoparietal network consist of the following brain regions: bilateral thalamus, caudate, right putamen, bilateral anterior and middle cingulate, inferior and middle frontal areas, supplementary motor cortex, middle and inferior temporal gyrus, right superior temporal, bilateral inferior parietal and supramarginal gyrus.”

    1. Similarly, although the global neuronal workspace does posit a critical role for recurrent frontal-parietal networks, can the authors be more specific about the nodes of the proposed workspace and what they found empirically?

    As above mentioned, we have provided more details about the regions part of the “subcortical fronto-temporoparietal”. As the reviewers rightfully noted, this network also shows some overlap with the Global Neuronal Workspace. We refer to that in more detail in the discussion, highlighting how our functional networks overlap and differ with the two networks (i.e., one feedforward only, one with recurrent activity), and with the predictions of the mesocircuit model. For more detail, please refer to the reply to point 1 of “Recommendations for the authors”.

    1. The classification sensitivity/specificity did not, in my opinion, add much to the manuscript, especially since the number of patients is not remotely close to what would be required for a population-based diagnostic approach. If the authors chose to include this with any reference to diagnosis (highlighted in the introduction and elsewhere), I would encourage a comparison with similar data from other clinical or neuroimagingbased diagnostic approaches. However, I think the value of the study resides more with mechanistic understanding than diagnosis.

    We agree with your suggestions that the primary aim of our work is to provide a mechanistic understanding of loss of consciousness. Therefore, we have removed the classification part from the paper and explain our findings focusing on mechanism of pathological unconsciousness rather than its potential as a clinical diagnostic tool. This change has required several textual edits throughout the manuscript.

  3. eLife

    Evaluation Summary

    This study aims to characterise the brain dynamics of different disorders of consciousness by studying patients in a minimally conscious state and those with unresponsive wakefulness syndrome, along with healthy controls. The authors apply elegant analyses to the dynamics of brain functional connectivity to successfully discriminate between healthy controls and patients, revealing reduced metastability and a contracted network repertoire in disorders of consciousness. Overall, the study provides important new information on the mechanisms of disorders of consciousness and the functional brain networks involved.

    This preprint has been reviewed by eLife. We include the public reviews from the reviewers here; the authors also receive private feedback with suggested changes to the manuscript. Reviewer #1 agreed to share their name with the authors.

  4. eLife

    Review #1 Public Review

    Panda and co-workers analyzed RS fMRI recordings from healthy patients and from two types of comma: UWS and MCS. They characterized the time-resolved functional connectivity in terms of metastability (time-variance of the Kuramoto order parameter), spatiotemporal patterns via non-negative tensor factorization, and its relationship to the eigenmodes of structural connectivity. Finding greater metastability and non-stationarity of the DMN network in healthy MCS patients, than in UWS patients, they found that the best discriminators to classify the different DOCs are the number of excursions (non-stability) from the DMN, salience and FPN networks extracted by the NNTF analysis. Interestingly, the data-driven NNTF yielded a novel sub-network comprising the FPN and some subcortical structures. The excursions and dwell times from this FPN sub-network showed to be significantly lower in the UWS patients than in MCS. Surrogate data testing assures that the different methods and fits are effectively expressing the functional connectivity matrices measured.

    Overall, I think that the results are correct and they advance in the characterization and understanding of the brain under DOC. However, some improvements can be made in the way the results, and the rationale behind them, are presented.

    While reading the Results section, it is easy to have the impression of a disconnected set of analyses that just happened to be together. In particular, the section about the structural eigenmodes and their relationship with the time-resolved FC seems to have little connection with the rest of the work, except for confirming (yet again) that DOC patients have a less dynamic FC. More elaboration about the relevance of these results, and what they say about DOC (that other dynamical FC analyses don't), is needed both in the introduction and discussion. Although a clear explanation is given in the introduction, the bottom line seems to be yet another measure of metastability. Perhaps, a better explanation of what underlies the 'modulation strength of eigenmodes expression' will be helpful for distinguishing this analysis from others. How novel is the connection that is being done with the structural connectivity and why is this important? Moreover, the eigenmodes analysis has little-to-none importance in the discrimination of patients done at the end; thus, its place within the big picture is hard to evaluate.

    Something that I find counter-intuitive and that may confuse some readers, is the (apparent) contradiction between the diminished metastability in the DOC conditions and the reduced dwell times (Figure S1; also "the inability to sequentially dwell for prolonged times in a different set of eigenmodes", as stated in the Discussion). Fewer excursions and shorter dwell times can only mean that some networks are just less visited and maybe this would be enough to distinguish between conditions. Further explaining this will help to understand better the implications of the work.

    Finally, some comments about the connection(s) of these analyses with the commonly used FCD analysis (based on sliding windows of pair-wise correlations) will be useful, to put better this work into the big picture of time evolution of the functional connectivity.

  5. eLife

    Review #2 Public Review

    The study is of high significance, rigor, and novelty. Despite the many studies of repertoire, dynamic connectivity, etc., in the study of consciousness, there is (surprisingly, as I confirmed with a literature search) a dearth of application of these approaches to disorders of consciousness. The manuscript is well-written and transparent about its limitations. The author should consider the following recommendations:

    1. There is frequent reference to "subcortical" and related networks, but I see no description in the text of which subcortical structures are involved. Panel N of figure 2 is helpful but I think that more explicit detail is important, especially given the specific predictions of mesocircuit theory.

    2. Similarly, although the global neuronal workspace does posit a critical role for recurrent frontal-parietal networks, can the authors be more specific about the nodes of the proposed workspace and what they found empirically?

    3. The classification sensitivity/specificity did not, in my opinion, add much to the manuscript, especially since the number of patients is not remotely close to what would be required for a population-based diagnostic approach. If the authors chose to include this with any reference to diagnosis (highlighted in the introduction and elsewhere), I would encourage a comparison with similar data from other clinical or neuroimaging-based diagnostic approaches. However, I think the value of the study resides more with mechanistic understanding than diagnosis.

  6. Version published to 10.1101/2021.12.10.472068v1 on bioRxiv