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

Abstract

Article Figures and data Abstract Editor's evaluation Introduction Results Discussion Methods Data availability References Decision letter Author response Article and author information Metrics 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. Editor's evaluation This is an important paper providing convincing evidence for altered brain dynamics in patients in a minimally conscious state and those with unresponsive wakefulness syndrome relative to healthy control participants. The results indicate reduced metastability and a contracted network repertoire in disorders of consciousness. Overall, the study provides important new information on mechanisms of disorders of consciousness and the functional brain networks involved. https://doi.org/10.7554/eLife.77462.sa0 Decision letter Reviews on Sciety eLife's review process Introduction Diagnosis of the level of consciousness after coma due to severe brain injury is a well-known dilemma in the field of neurology and intensive care medicine. Coma after cardiac arrest or after traumatic brain injury may result in sustained altered states of consciousness. These patients with disorders of consciousness (DoC), irrespective of the aetiology, can be grouped into the unresponsive wakefulness syndrome (UWS) (Laureys et al., 2010), characterized by the presence of eye-opening and reflexive behaviours, and the minimally conscious state (MCS), characterized by consistent but fluctuant wilful conscious behaviours, such as command following or visual pursuit (Giacino et al., 2002; Schnakers et al., 2010). Recovery of consciousness is argued to emerge conjointly with restoration of resting-state functional brain networks (Edlow et al., 2021), which refers to patterns of neuronal interactions inferred by indirect (e.g. functional magnetic resonance imaging – fMRI) or direct (e.g. electro- and magneto-encephalography [EEG/MEG]) measurements. Analysis of these resting-state networks could potentially help in the diagnosis of patients with DoC and provide insight into the mechanisms that results in absence of recovery of consciousness in UWS. Various resting-state networks that play an important role in the recovery of consciousness have been identified, among which the default mode network (DMN), fronto-parietal network (FPN), and the salience network are the most important (Amico et al., 2017; Heine et al., 2012). Recovery of the DMN in combination with recovery of the auditory network could for instance discriminate between MCS and UWS with a very high accuracy (~85%) (Demertzi et al., 2015). The mechanism of resting-state network restoration in DoC is yet unknown, however, thalamic activity and especially thalamo-cortical connectivity may be a driving force behind restorations of cortical network function that sustains conscious states (Fridman et al., 2014; Laureys et al., 2000). Previous work on resting-state networks in DoC have mainly focused on the ‘static’ picture of functional connectivity (Demertzi et al., 2013; Edlow et al., 2021; Giacino et al., 2014; Heine et al., 2012), that is connections are assessed over the entire duration of the (fMRI) recording and fluctuations in connectivity over time are ignored. However, the underlying dynamics of connectivity seem relevant for consciousness (Barttfeld et al., 2014; Luppi et al., 2019) and a static description may therefore be inadequate to provide mechanistic insight into failure of recovery of consciousness in DoC (Demertzi et al., 2019). The analysis of dynamic or time-resolved functional connectivity, as well as the relationship between the underlying anatomical connections and emergent time-resolved functional connectivity (Avena-Koenigsberger et al., 2018; Suárez et al., 2020), may be clinically relevant in patients with DoC. Previous studies have already explored the role of time-resolved functional connectivity in DoC (Del Pozo et al., 2021; Golkowski et al., 2021; Sanz Perl et al., 2021). A recent study demonstrated that network states with long-distance connections occurred less frequently over time in MCS compared to UWS patients (Demertzi et al., 2019), emphasizing disintegration of interactions across the cortex in unconscious states. However, network states reminiscent of the well-known resting-state networks were not retrieved. Cao et al. used two methods to extract time-varying networks, that is independent component analysis and hidden Markov modelling, and revealed clinically relevant differences in network state durations between patients with DoC patients and healthy subjects (Cao et al., 2019), while lacking comparative analysis between patients in MCS and in UWS. In another fMRI study, the authors focused on the posterior cingulate area and the DMN using a spatiotemporal point process analysis and demonstrated decreased occurrence of DMN-like patterns in UWS. Dynamic connectivity analysis has also recently been applied to EEG data, revealing a loss of network integration and increased network segregation in DoC patients (Rizkallah et al., 2019). Spatiotemporal properties of networks have been explored using whole brain modelling, which shows reduction in stability, heterogeneity, and information flow in loss of consciousness (Escrichs et al., 2021; López-González et al., 2021; Panda et al., 2021). Despite the importance of the previously published work, the role of the well-known resting-state networks and especially thalamo-cortical functional connections (Monti et al., 2015) within the context of time-resolved connectivity and DoC has so far not been fully explored. Another important aspect in the context of the emergence or restoration of resting-state networks is the underlying structural network, as anatomical connectivity patterns influence the repertoire of possible functional network states (Deco et al., 2013). It is widely assumed that switching between functional network states is achieved by so-called metastability in the brain (Deco and Kringelbach, 2016), that is winnerless competitive dynamics. A promising and robust approach to analyse the relationship between structural and functional network states is the so-called eigenmode approach (Atasoy et al., 2016; Robinson et al., 2016; Tewarie et al., 2020). With this approach, spatial harmonic components or eigenmodes are extracted from the anatomical network. 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 brain dynamics (Aqil et al., 2021). There is evidence that the well-known resting-state networks can be explained by the activation of a small set of eigenmodes (Atasoy et al., 2018). It can be hypothesized that switching between functional network states, as can be observed in the metastable brain, is accompanied by fluctuations in the expression of eigenmodes Preti and Van De Ville, 2019; therefore, a potential loss of metastability in DoC could co-occur with loss of modulations in eigenmode expression (Barttfeld et al., 2014). In this context, the aim of the current study was fourfold. First, we tested whether loss of metastability and resting-state network activity, derived from time-resolved estimates of functional connectivity, could differentiate between MCS and UWS patients, with a potential extraction of a spatiotemporal thalamo-cortical network state. Second, we analysed whether time-resolved connectivity could be explained by modulations in expression of eigenmodes in DoC, and third, whether potential differences in eigenmode expression in DoC patients would co-occur with a loss of metastability. Finally, we conceptually link the findings of altered spatiotemporal dynamics underlying activity observed in the brain of DoC patients with several consciousness theories to increase our understanding of the mechanism behind pathological states of unconsciousness. Results We included 34 healthy control subjects (HC, 39 [mean] ± 14 years [standard deviation], 20 males), 30 MCS (41 ± 13 years, 21 males) and 14 UWS patients (48 ± 16 years, 7 males). There was no difference in patients with MCS and UWS in terms of age (p > 0.05), gender (p > 0.05), time since injury (p > 0.05), and aetiology (p > 0.05). There was also no difference in age (p > 0.05) and gender (p > 0.05) between HC and DoC patients. Further details about the patient population are described in the methods and Supplementary file 1. Metastability and time-resolved functional connectivity in patients with DoC Time-resolved or dynamic connectivity for all subjects was extracted from the phase information of the fMRI data. We quantified a proxy measure for metastability defined as the standard deviation of the overall phase behaviour over time (i.e. the Kuramoto order parameter). This was followed by the extraction of spatiotemporal patterns using non-negative tensor factorization (NNTF) from phase connectivity data, corresponding to resting-state networks or network states (see Figure 1). Well-known resting-state networks as well as a residual component were used as initial conditions for spatial connectivity patterns for all network states to allow for stable convergence of the algorithm (i.e. DMN, FPN, visual network, sensorimotor network, salience network, and subcortical network; Finn et al., 2015). However, the NNTF algorithm allowed the spatial patterns of these network states to change in order to maximize the explained variance of the data. Temporal statistics from the network states were derived for every network state in terms of excursions from the median proxy for nonstationarity (Zalesky et al., 2014) and state duration (i.e. dwell time). Figure 1 Download asset Open asset Overview of the analysis pipeline. We used the same Shen parcellation for diffusion-weighted MRI (DWI) and functional magnetic resonance imaging (fMRI) data. Time-resolved functional connectivity was estimated using a metric for phase connectivity. A proxy measure for metastability was derived from the phase information. Time-resolved networks were subsequently extracted from the concatenated data from all subjects using non-negative tensor factorization (NNTF). Dwell times and nonstationarity (excursions from the median) were retrieved for each spatial pattern of functional connectivity (six resting-state networks and one ‘residual’ network). At the same time, time-resolved connectivity was predicted on a sample-by-sample basis based on a linear combination of eigenmodes (hidden patterns in the anatomical network). Measures were used for classification and feature ranking. A reduction of metastability was found in DoC patients compared to HCs (Figure 2A). Lower metastability was observed in UWS patients in comparison to MCS patients (Figure 2A). Reduced metastability is expected to occur with loss of switching between resting-state networks and potentially with dwelling within a more limited subset of resting-state networks in DoC. The output of the NNTF algorithm resulted in spatial topographies of some of the well-known resting-state networks, that is the DMN, a separate posterior DMN around the precuneus, the visual network, the salience network, the FPN, and a network consisting of subcortical fronto-temporoparietal regions (Sub-FTPN) (Figure 2I–N, Supplementary file 1B). Note that these network states were not identical to the initial conditions, for example the subcortical network that was provided as initial condition to NNTF was incorporated with the frontoparietal network (Figure 2N) by the NNTF algorithm. This modulated subcortical fronto-temporoparietal network consists 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. At the same time, the sensorimotor network that was provided as initial condition disappeared as state. Excursions from the median were lower for most networks (DMN, visual, Salience, Posterior DMN, and Sub-FTPN) in DoC compared to HC (Figure 2C–F, H). Significant loss of nonstationarity was also found in UWS compared to MCS for the DMN, FPN, and Sub-FTPN (Figure 2C, G, H). The NNTF also yielded a residual state, with a lack of spatial structure, accounting for the variance of connectivity data not explained by the resting-state networks. The residual component had longer dwell times for the decreasing levels of consciousness (Figure 2B). In addition, there were lower dwell times in DoC patients for a specific set of resting-state networks (Salience, Posterior DMN, FPN, and Sub-FTPN), and dwell time was shorter in UWS patients compared to MCS patients only in the Sub-FTPN network (see results in Figure 2—figure supplement 1). 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. Figure 2 with 1 supplement see all Download asset Open asset Metastability and time-resolved functional networks in disorders of consciousness (DoC). (A) Metastability for all groups: healthy controls (HC), minimally conscious state (MCS), and unresponsive wakefulness state (UWS) patients. (B–H) Distributions of nonstationarity (excursions from the median) for the residuals and time-resolved networks. (I–N) Spatial patterns of time-resolved output networks. Abbreviations: DMN, default mode network; FPN, frontoparietal network; Sub-FTPN, subcortical fronto-temporoparietal network. *, ** and *** denote p < 0.05, p < 0.01 and p < 0.001, respectively (Mann–Whitney U tests). The colourbar indicates the strength of that specific area to the overall spatial pattern. Note, the Sub-FTPN appears as modified network from the initially assigned subcortical network. This Sub-FTPN consists 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. Relationship between structural eigenmodes and time-resolved functional connectivity in DoC As resting-state network activity can be explained by the activation of structural eigenmodes, we next analyse the role of fluctuations in eigenmode expression over time. In order to put our findings into context, we first analysed the relationship between static functional networks and structural networks, using the Pearson correlation between static functional connectivity and structural connectivity (SC) for the different groups (Figure 3A). These results show that functional connectivity in DoC patients shows more correspondence with the underlying SC as compared to HCs, as the relationship between structural and functional connectivity was stronger for decreasing levels of consciousness (Figure 3A). Figure 3 Download asset Open asset Relationship between time-resolved connectivity and eigenmodes. (A) The prediction of static functional connectivity based on structural connectivity for all three groups (healthy control [HC], minimally conscious state [MCS], and unresponsive wakefulness syndrome [UWS]) in terms of the Pearson correlation coefficient. (B) The prediction of time-resolved functional connectivity based on eigenmodes. These distributions of eigenmode predictions are accompanied with predictions based on surrogate data. We further illustrate the level of fluctuations in eigenmode expression for all three groups (HC, MCS, and UWS) for dominant (reflecting network integration, C) and non-dominant eigenmodes (reflecting increasing network segregation, D) accompanied with results for surrogate data, ** and *** denote p < 0.01 and p < 0.001, respectively (Mann–Whitney U tests). (E–G) Metastability is strongly correlated to modulations in eigenmode expression within every group. We next obtained the eigenmodes from the SC by extracting the eigenvectors of the graph Laplacian. These eigenmodes can be regarded as distinct spatial harmonics within the SC, where the first eigenmodes correspond to patterns with low spatial frequency and subsequent eigenmodes contain patterns with increasingly higher spatial frequencies. Given their spatial configuration, consecutive eigenmodes can be associated with increasing levels of segregation while the first eigenmodes can be linked with network integration. For every time point we predicted the extent to which phase connectivity could be explained by a weighted combination of the eigenmodes (Tewarie et al., 2020). Since phase connectivity can evolve over time, the weighting coefficients for the eigenmodes can modulate as well, resulting in fluctuations in the strength of the expressions of eigenmodes over time. For every eigenmode, we could then quantify the modulation strength (i.e. how much the eigenmode expression varied over time). In addition to the weighting coefficients, we also obtained the goodness-of-fit for the predictions of time-resolved functional connectivity. The goodness-of-fit for the eigenmode predictions is displayed in Figure 3B, where we show the average correlation between eigenmode predicted FC and empirical FC for the three groups. Results show better predictions for HC and MCS compared to predictions for static FC (median and interquartile range of correlations HC static 0.18 ± 0.04, HC eigenmode 0.39 ± 0.09, Z = −7.1, p < 0.001, MCS static 0.2 ± 0.05, MCS eigenmode 0.35 ± 0.18, Z = −4.8, p < 0.001). In order to test whether these eigenmode predictions of time-varying connectivity could have been obtained by chance, we redid our analysis using surrogate BOLD data (see methods ‘Analysis steps’). Results show that eigenmode predictions for time-resolved connectivity from surrogate data performed significantly worse compared to genuine empirical data (for all comparisons with surrogate data p < 0.001; Figure 3B). We did not test whether the contribution of individual eigenmodes differed between groups since this would come with a serious multiple comparisons problem. Instead, since SC appeared to correlate stronger with static FC in DoC compared to HC, we expected that eigenmode coefficients in DoC patients would hardly change over time, highlighting the observation of a ‘fixed’ structural–functional network relationship in DoC patients. To analyse this lack of change in the structural–functional network relationship over time in DoC patients, we quantified the modulation strength of the weighting coefficients over time (see methods ‘Analysis steps’). We performed this analysis separately for the dominant (1st to 107th eigenmode, first half) and non-dominant eigenmodes (108th to 214th eigenmode, second half). Results for the dominant eigenmodes show a clear reduction in modulation of the eigenmode weighting in DoC patients compared to HCs (Figure 3C), with a significantly lower modulation of eigenmode expression in UWS compared to MCS patients. This result could not be explained by chance, since the same results could not be obtained from surrogate data (Figure 3C). Note that no between group difference was obtained for non-dominant eigenmodes (Figure 3D). We have so far shown a reduction in the modulation strength of eigenmode expressions in DoC patients compared to HC subjects, as well as a loss of metastability in DoC patients and dwelling of the brain in fewer network states in DoC patients. This poses the question of whether these two observations are related. In Figure 3E–G, we show that metastability is strongly correlated with modulations in eigenmode expression for the three groups. This underscores the notion that loss of dynamic modulations in functional network patterns due to a loss of metastability could indeed be related to a reduced modulation of eigenmode expression. Discussion Differentiation between MCS and UWS is key for adequate diagnosis and prognosis in DoC patients as it is connected to medical-ethical end of life decisions. Use of imaging characteristics allows testing of hypotheses about causes of delayed, or failure of, recovery of consciousness. Here, we used state-of-the-art techniques to quantify time-varying functional connectivity, metastability, and the relationship between the underlying anatomical network and time-resolved functional connections. We demonstrated that these advanced techniques were sensitive to detect clinically relevant differences for the diagnosis of MCS and UWS patients. More specifically, we first demonstrated that UWS patients showed reduced metastability, and spend less time in states outside the natural equilibrium state that would favour cerebral processing in a cooperative and coordinated manner to support consciousness. This is accompanied by shorter state durations that the brain spends in the subcortical fronto-temporoparietal configuration in UWS. A loss of nonstationarity was observed in several resting-state networks (i.e. DMN, frontoparietal, and subcortical fronto-temporoparietal) in UWS compared to MCS patients. Finally, we showed that functional brain networks are more ‘fixed’ to the underlying anatomical connections and are less subject to spatial reconfigurations over time in UWS compared to MCS patients. The extent to which these spatial reconfigurations occurred (i.e. expressed as modulations in eigenmode expression) correlated strongly to metastability. Our results are in agreement with several hypothesis and theories for the emergence of consciousness, of which most share the importance of thalamo-cortical connectivity for consciousness (Blumenfeld, 2021; Dehaene et al., 2011; Schiff, 2010). The mesocircuit hypothesis states that deafferentation between the frontal cortex and subcortical regions is crucial in explaining the failure of recovery of consciousness (Schiff, 2010). One of the most novel findings in the current work is the generation of the subcortical fronto-temporoparietal network. Although subcortical connections were, among others, used as initial conditions for the decomposition of the time-varying functional connectivity patterns into resting-state networks, incorporation with fronto-temporoparietal connections emerged from the data-driven NNTF algorithm. Another observation confirmed that this NNTF approach extracted DoC-relevant networks, namely that the sensorimotor network disappeared after optimization of spatial network patterns. This latter result is in line with the fact that somatosensory cortices are not directly involved in the emergence of consciousness, based on current theories (Naccache, 2018). In addition, we found that the subcortical fronto-temporoparietal network showed shorter dwell times in DoC patients compared to HC subjects, with even shorter state durations in UWS compared to MCS patients. Finally, this network also demonstrated a loss of nonstationarity in UWS compared to MCS patients. However, it should be noted that the subcortical fronto-temporoparietal network was not the only network with loss of time-resolved network characteristics; other resting-state networks also showed loss of nonstationarity, such as the DMN and FPN. However, a combination of shorter dwell times and loss of nonstationarity was only found for the subcortical fronto-temporoparietal network. The mesocircuit hypothesis postulates that increased subcortical (specifically thalamic nuclei) inhibition and reduced excitatory output from the thalamus to the cortex (frontal, parietal, temporal, and occipital) leads to weak neural activity and hampers consciousness in DoC patients (Fridman et al., 2014; Giacino et al., 2014). Although our method does not allow to assess excitatory and inhibitory activity, it may be tempting to interpret that the subcortical fronto-temporoparietal network may play a crucial role in orchestrating global network interactions and dwell times. Hence, this subnetwork may be instrumental for the observed loss of nonstationarity in the other subnetworks. Although the importance of functional connections between the thalamus and frontal cortex has been emphasized by the mesocircuit hypothesis, and shown to relate to consciousness in hypothesis-driven functional (e.g., Crone et al., 2018; Fridman et al., 2014; Monti et al., 2015) and structural (e.g., Annen et al., 2016; Weng et al., 2017) neuroimaging studies, this is the first demonstration of the ability of a (semi)data-driven approach to identify this sub-network in the context of time-resolved functional connectivity. Most previous data-driven approaches have been unable to extract such a network (Cao et al., 2019; Demertzi et al., 2019; Rizkallah et al., 2019). Our findings also support the global neuronal workspace (GNW) theory (Dehaene et al., 2011), which emphasizes the importance of long-distance, feedforward and recurrent functional connections, large-scale reverberant networks, and metastable brain states in the emergence and recovery of consciousness (Mashour et al., 2020). Specifically, the GNW postulates that conscious perception should rely on two neural circuits. First, the feedforward circuit encompasses the visual and sensory areas and propagates sensory input to higher order integration areas. Second, in order for sensory input to be accessible to consciousness, recurrent activity in the circuit is required, responsible for ignition in a wide range of high-order functional areas including visual, parieto-temporal, thalamic, and frontal areas (Aru et al., 2020; Baars, 2002; Dehaene and Changeux, 2011; Mashour et al., 2020). The subcortical fronto-temporoparietal network and frontoparietal network obtained in a data-driven fashion conjugates with the latter recurrent circuit proposed to be essential for consciousness by the GNW recurrent circuit. The breakdown of spatio-temporal coordination in the subcortical fronto-temporoparietal network and frontoparietal network was associated to reduced states of consciousness. So far, the importance of metastability has mainly been addressed in the context of recovery of consciousness from anaesthesia (Cavanna et al., 2018). Here, we underscore this finding and demonstrate that a reduction of subcortical–cortical spatiotemporal functional organization into metastable states can even differentiate between UWS and MCS patients aligning with key factors for consciousness as proposed by GNW and mesocircuit hypothesis. Despite the overlapping cortical circuits important for the mesocircuit model and GNW, differences between the two hypotheses exist. While the occipital cortex is considered part of the same mesocircuit, its role is mostly for feedforward propagation in the GNW. We found a separate visual network that conceptually overlaps more with the feedforward network described by the GNW. In line with GNW predictions, the temporal dynamics in this feedforward network did not distinguish different states of consciousness (Dehaene and Changeux, 2011; Mashour et al., 2020). On the other hand, the involvement of subcortical areas in the subcortical fronto-temporoparietal network is specifically predicted by the mesocircuit model (Fridman et al., 2014; Giacino et al., 2014). Last, the two theories highlight different aspects of brain activity required for consciousness. The GNW proposes specific effective connectivity patterns required for stimuli to reach consciousness. The mesocircuit model, on the other hand, proposes that balanced excitatory and inhibitory activity within the network is a prerequisite for conscious states to be supported. Our resting-state study conceptually thus resonates more with the concepts of the mesocircuit model, yet, we might hypothesize that relative preservation of the recurrent network crucial for the GNW meets the minimal requirement for conscious stimulus processing. Our observation of a