The present study delves into the generalization of results obtained previously2 about the dynamics of the human brain and how they are altered in states of loss of consciousness, by employing two complementary datasets of humans under general anaesthesia and N3 sleep. The ability to employ the same method effectively across independent datasets in different scenarios of low consciousness, emphasizes its reliability and underscores its potential to yield consistent outcomes in other datasets. Such cross-applicability is indicative of the robustness of the approach and the generalizability of the results. Specifically, loss of consciousness—whether due to spontaneous sleep, or induced by general anaesthesia—appears to reduce the repertoire of dynamical states that the brain visits, with increased prevalence of the most structurally coupled pattern.Our results demonstrate that sustaining rich brain dynamics is essential for consciousness and can serve as a biomarker for consciousness. While the awake state exhibits a richer dynamic exploration of the functional repertoire as exhibited by the Shannon entropy analyzing the histograms (Figs. 2C and 3C) and the Markov entropy for the Markov chains (Figs. 4B and 5B), the dynamic exploration is consistently reduced under anaesthesia and N3 sleep for all the different choices for the number of clusters. This result is well aligned with the entropic brain hypothesis46,47,48. This diversity in state exploration is also related to recent results in the mouse using calcium imaging under different drugs of general anaesthesia, which show that under anaesthesia, the brain explores less states than the awake brain49, and recent results of the structure-function interdependence of the macaque brain under loss of consciousness induced by three different anaesthetics (sevoflurane, propofol, ketamine) and restoration of consciousness by deep brain stimulation (DBS)50. Furthermore, the more prevalent functional connectivity patterns during anaesthesia correlate with the anatomical connectivity, consistent with previous findings. As in previous studies2, here we find that the slope of occurrence probability versus structure-function correlation increases in the states of low consciousness. Here, we show that those earlier results generalize to other mechanism of loss of conscisouiness such as general anaesthesia and N3 sleep and different number of brain patterns.A notable feature of the phase-based methodology is that it does not need meticulous adjustment of the width of sliding windows. This autonomy from fine-tuning aspects diminishes the potential for bias introduced by window length selection, highlighting again the robustness of the methodology.The sporadic emergence of patterns more present in states of low consciousness during periods of conscious wakefulness, and vice-versa, raises pertinent questions about the boundaries between these brain states. The co-occurrence of seemingly contrasting states within the same individual challenges conventional notions of discrete consciousness states. This observation requires further investigation into the underlying mechanisms that give rise to these sporadic occurrences and offers a unique perspective on the dynamic nature of consciousness32,51,52,53. The alterations induced by propofol in the body and the brain are not only reduced to consciousness, as elucidated by ref. 54. Discrimination between changes stemming directly from the loss of consciousness and those arising from ancillary effects of propofol on cerebral processes remains challenging. A comparable challenge is encountered in the realm of sleep research, as underscored by ref. 55, wherein deep sleep is acknowledged as more than mere unconsciousness. Additionally, diminished vigilance is observed independently of attenuated awareness56.The question arises concerning the generalizability of the findings through alternative brain recording techniques, bypassing the reliance on functional Magnetic Resonance Imaging (fMRI). The prospect of employing different data sources, such as electroencephalography (EEG) or magnetoencephalography, to substantiate the identified consciousness states introduces an avenue for expanding the horizon of the current methodology, and enhances the robustness of the method and the findings25,57,58,59. Generalisations of the present method to EEG are under development60, and may be of particular relevance, for example, for the study of unconsciousness induced by epileptic seizures. Alternatively, other species under general anaesthesia can be studied as has been done in the past with the sliding-window technique29,34. Envisioning a broader context, the study prompts consideration of consciousness-altering scenarios beyond those encountered within wakefulness or unconscious states.However, it is important to acknowledge the limitations inherent in the study. Important gaps remain in understanding how these dynamics are influenced by brain structure and anaesthesia agents. For example, another widely used anaesthetic, sevoflurane, has been shown to resemble propofol in terms of its effects on brain dynamics, both in humans27,35,61 and macaques34,62. Another important limitation is that we do not know which changes are a consequence of propofol reducing consciousness, and which appear because propofol does other things in the brain54. The same problem is present for sleep, since deep sleep is not only loss of consciousness (consciousness may be present as shown by Siclari, Tononi et al.55), and reduced vigilance occurs independently of reduced awareness56. The generality of the method is also partly a weakness because, as can be seen in Figs. 2 and 3, the patterns of general anaesthesia and deep sleep are quite different. This may be because they are obtained from data obtained by different research groups using different data acquisition parameters, with different preprocessing protocols and different participants under different conditions of loss of consciousness and different levels of global signal contribution. In Supplementary Fig. S6, we illustrate that the sleep dataset exhibits significantly higher levels of global signal compared to the anaesthesia dataset. There are multiple reasons why this may have come about. Methodologically, the two datasets were denoised differently; RETROICOR63 (sleep) versus aCompCor (anaesthesia). Although global signal regression is not part of either method, the two procedures may have removed different amounts of global signal. Physiologically, the sleep dataset participants may have been more drowsy even during the awake scans, since they were scanned at 7 pm in the evening. This disparity partially accounts for the pronounced differences between the centroids derived from the two datasets, as well as the presence of hyper-synchronized patterns in the sleep dataset. These findings suggest that our approach remains robust despite varying levels of global signal across different datasets.Future studies could address this issue following the approach taken in ref. 2, which uses data from different research groups and applies a unique preprocessing method and parcellation. Despite these limitations, the method appears to be robust and generalisable; the question then remains: how broad is the generalisability of the present findings?In conclusion, the present study highlights the generalisation and robustness of previous results employing a uniform methodology across varying states exhibited by different cohorts, in different consciousness states. By leveraging the complementary nature of our results, we provide a comprehensive characterization of the dynamic features of brain networks and how consciousness reshapes the dynamics of the human brain and its relation with the connectome. This study contributes to the broader discourse on consciousness and methodology, paving the way for future investigations into the relationship of consciousness and dynamical brain patterns and to identify potential markers that may be utilized in the future to distinguish between conscious and unconscious states.fMRI acquisition, experimental design and processingAnaesthesia data: recruitmentThe propofol data employed in this study have been published before27,64,65,66. For clarity and consistency of reporting, where applicable we use the same wording as our previous studies. The propofol data were collected between May and November 2014 at the Robarts Research Institute in London, Ontario (Canada)27. The study received ethical approval from the Health Sciences Research Ethics Board and Psychology Research Ethics Board of Western University (Ontario, Canada). Healthy volunteers (n = 19) were recruited (18–40 years; 13 males). Volunteers were right-handed, native English speakers, and had no history of neurological disorders. In accordance with relevant ethical guidelines, each volunteer provided written informed consent, and received monetary compensation for their time. Due to equipment malfunction or physiological impediments to anaesthesia in the scanner, data from n = 3 participants (1 male) were excluded from analyses, leaving a total n = 16 for analysis.Anaesthesia data: procedureResting-state fMRI data were acquired at different propofol levels: no sedation (Awake), and Deep anaesthesia (corresponding to Ramsay score of 5). As previously reported27, for each condition, fMRI acquisition began after two anaesthesiologists and one anaesthesia nurse independently assessed Ramsay level in the scanning room. The anaesthesiologists and the anaesthesia nurse could not be blinded to experimental condition, since part of their role involved determining the participants’ level of anaesthesia. Note that the Ramsay score is designed for critical care patients, and therefore, participants did not receive a score during the Awake condition before propofol administration; rather, they were required to be fully awake, alert, and communicating appropriately. To provide a further, independent evaluation of participants’ level of responsiveness, they were asked to perform two tasks: a test of verbal memory recall, and a computer-based auditory target-detection task. Wakefulness was also monitored using an infrared camera placed inside the scanner.Propofol (a potent agonist of inhibitory GABA-A receptors67,68 was administered intravenously using an AS50 auto syringe infusion pump (Baxter Healthcare, Singapore); an effect-site/plasma steering algorithm combined with the computer-controlled infusion pump was used to achieve step-wise sedation increments, followed by manual adjustments as required to reach the desired target concentrations of propofol according to the TIVA Trainer (European Society for Intravenous Aneaesthesia, eurosiva.eu) pharmacokinetic simulation program. This software also specified the blood concentrations of propofol, following the Marsh 3-compartment model, which were used as targets for the pharmacokinetic model providing target-controlled infusion. After an initial propofol target effect-site concentration of 0.6 µg mL−1, concentration was gradually increased by increments of 0.3 µg mL−1, and Ramsay score was assessed after each increment: a further increment occurred if the Ramsay score was lower than 5. The mean estimated effect-site and plasma propofol concentrations were kept stable by the pharmacokinetic model delivered via the TIVA Trainer infusion pump. Ramsay level 5 was achieved when participants stopped responding to verbal commands, were unable to engage in conversation, and were rousable only to physical stimulation. Once both anaesthesiologists and the anaesthesia nurse all agreed that Ramsay sedation level 5 had been reached, and participants stopped responding to both tasks, data acquisition was initiated. The mean estimated effect-site propofol concentration was 2.48 (1.82–3.14) µg mL−1, and the mean estimated plasma propofol concentration was 2.68 (1.92–3.44) µg mL−1. Mean total mass of propofol administered was 486.58 (373.30–599.86) mg. These values of variability are typical for the pharmacokinetics and pharmacodynamics of propofol. Oxygen was titrated to maintain SpO2 above 96%. At Ramsay 5 level, participants remained capable of spontaneous cardiovascular function and ventilation. However, the sedation procedure did not take place in a hospital setting; therefore, intubation during scanning could not be used to ensure airway security during scanning. Consequently, although two anaesthesiologists closely monitored each participant, scanner time was minimised to ensure return to normal breathing following deep sedation. No state changes or movement were noted during the deep sedation scanning for any of the participants included in the study. Written informed consent was asked to all participants before the experiment. All ethical regulations relevant to human research participants were followed.Anaesthesia data: designAs previously reported27, once in the scanner participants were instructed to relax with closed eyes, without falling asleep. Resting-state functional MRI in the absence of any tasks was acquired for 8 min for each participant. A further scan was also acquired during auditory presentation of a plot-driven story through headphones (5 min long). Participants were instructed to listen while keeping their eyes closed. The present analysis focuses on the resting-state data only; the story scan data have been published separately66.Anaesthesia data: fMRI data acquisitionAs previously reported27, MRI scanning was performed using a 3-Tesla Siemens Tim Trio scanner (32-channel coil), and 256 functional volumes (echo-planar images, EPI) were collected from each participant, with the following parameters: slices = 33, with 25% inter-slice gap; resolution = 3 mm isotropic; TR = 2000 ms; TE = 30 ms; flip angle = 75 degrees; matrix size = 64 × 64. The order of acquisition was interleaved, bottom-up. Anatomical scanning was also performed, acquiring a high-resolution T1-weighted volume (32-channel coil, 1 mm isotropic voxel size) with a 3D MPRAGE sequence, using the following parameters: TA = 5 min, TE = 4.25 ms, 240 × 256 matrix size, 9 degrees flip angle27.Sleep data: recruitmentA total of 63 healthy subjects (36 females, mean ± SD, 23.4 ± 3.3 years) were selected from a dataset previously described in a sleep-related study by Tagliazucchi and Laufs69. Participants entered the scanner at 7 PM and were asked to relax, close their eyes, and not fight the sleep onset. A total of 52 minutes of resting-state activity were measured with a simultaneous combination of EEG and fMRI. According to the rules of the American Academy of Sleep Medicine, the polysomnography signals (including the scalp potentials measured with EEG) determine the classification of data into four stages (wakefulness, N1, N2, and N3 sleep).We selected 18 subjects with contiguous resting-state time series of at least 200 volumes to perform our analysis. The local ethics committee approved the experimental protocol (Goethe-Universität Frankfurt, Germany, protocol number: 305/07), and written informed consent was asked to all participants before the experiment. The study was conducted according to the Helsinki Declaration on ethical research. All ethical regulations relevant to human research participants were followed.Sleep data: MRI data acquisitionMRI images were acquired on a 3-Tesla Siemens Trio scanner (Erlangen, Germany) and fMRI acquisition parameters were 1505 volumes of T2-weighted EPIs, TR/TE = 2080 ms/30 ms, matrix 64 × 64, voxel size 3 × 3 × 3 mm3, distance factor 50%; FOV 192 mm2. An optimized polysomnographic setting was employed (chin and tibial EMG, ECG, EOG recorded bipolarly [sampling rate 5 kHz, low-pass filter 1 kHz] with 30 EEG channels recorded with FCz as the reference [sampling rate 5 kHz, low-pass filter 250 Hz]. Pulse oximetry and respiration were recorded via sensors from the Trio [sampling rate 50 Hz]) and MR scanner-compatible devices (BrainAmp MR+, BrainAmpExG; Brain Products, Gilching, Germany), facilitating sleep scoring during fMRI acquisition. The method RETROICOR63, was uses to model physiological noise (Respiration effects and cardiac pulsatility) and use it to denoise the data.Sleep data: brain parcellation AAL90 to extract BOLD time series and filteringTo extract the time series of BOLD signals from each participant in a coarse parcellation, we used the AAL90 parcellation with 90 brain areas anatomically defined in [25]. BOLD signals (empirical or simulated) were filtered with a Butterworth (order 2) band-pass filter in the 0.01–0.1 Hz frequency range.DWI preprocessing and tractography anaesthesia datasetThe diffusion data were preprocessed with MRtrix3 tools. This is the same pipeline adopted in our previous work for clarity and consistency of reporting, where applicable we use the same wording as in our previous publications,70. After manually removing diffusion-weighted volumes with substantial distortion, the pipeline involved the following steps: (i) DWI data denoising by exploiting data redundancy in the PCA domain71 (dwidenoise command); (ii) Correction for distortions induced by eddy currents and subject motion by registering all DWIs to b0, using FSL’s eddy tool (through MRtrix3 dwipreproc command); (iii) rotation of the diffusion gradient vectors to account for subject motion estimated by eddy72 (iv) b1 field inhomogeneity correction for DWI volumes (dwibiascorrect command); (v) generation of a brain mask through a combination of MRtrix3 dwi2mask and FSL BET commands.After preprocessing, the DTI data were reconstructed using the model-free q-space diffeomorphic reconstruction algorithm (QSDR) implemented in DSI Studio (www.dsi-studio.labsolver.org), following our previous work70. The use of QSDR is desirable when investigating group differences because this algorithm preserves the continuity of fiber geometry for subsequent tracking since it reconstructs the distribution of the density of diffusing water in standard space. QSDR initially reconstructs DWI data in native space, and subsequently computes values of quantitative anisotropy (QA) in each voxel, based on which DSI Studio performs a nonlinear warp from native space to a template QA volume in Montreal Neurological Institute (MNI) space. Once in MNI standard space, spin density functions are reconstructed, with a mean diffusion distance of 1.25 mm with three fiber orientations per voxel73.Finally, fiber tracking was carried out using DSI Studio’s own FACT deterministic tractography algorithm, requesting 1000,000 streamlines according to widely adopted parameters: angular cutoff = 55°, step size = 1.0 mm, tract length between 10 mm (minimum) and 400 mm (maximum), no spin density function smoothing, and QA threshold determined by DWI signal in the cerebrospinal fluid. Streamlines were automatically rejected if they presented improper termination locations, based on a white matter mask automatically generated by applying a default anisotropy threshold of 0.6 Otsu’s threshold to the anisotropy values of the spin density function.Brain parcellationFor both BOLD and DWI data, brains were parcellated into 68 cortical ROIs, according to the Desikan-Killiany anatomical atlas74.DWI preprocessing and tractography Sleep datasetThe data was obtained from 16 healthy right-handed participants (11 men and five women, mean age: 24.75 ± 2.54). Data were collected at Aarhus University, Denmark. Participants with psychiatric or neurologic disorders (or a history thereof) were excluded from participation in this study. The MRI data (structural MRI, DTI) were collected in one session on a 3 T Siemens Skyra scanner at Aarhus University, Denmark. The parameters for the structural MRI T1 scan were as follows: voxel size of 1 mm3; reconstructed matrix size 256 × 256; echo time (TE) of 3.8 ms and TR of 2300 ms.The DTI data were collected using TR = 9000 ms, TE = 84 ms, flip angle = 90°, reconstructed matrix size of 106 × 106, voxel size of 1.98 × 1.98 mm with slice thickness of 2 mm and a bandwidth of 1745 Hz/Px. Furthermore, the data were collected with 62 optimal nonlinear diffusion gradient directions at b = 1500 s/mm2. Approximately one nondiffusion-weighted image (DWI; b = 0) per 10 diffusion-weighted images was acquired. Additionally, the DTI images were collected with different phase encoding directions. One set was collected using anterior to posterior phase encoding direction and the second acquisition was performed in the opposite direction. For the parcellation, we used the AAL template to parcellate the entire brain into 90 regions (76 cortical regions, adding 14 subcortical regions, AAL90). The parcellation consists of regions distributed in each hemisphere75. The linear registration tool from the FSL toolbox (www.fmrib.ox.ac.uk/fsl, FMRIB76) was used to coregister the EPI image to the T1-weighted structural image. The T1-weighted image was coregistered to the T1 template of ICBM152 in MNI space. The resulting transformations were concatenated and inversed and further applied to warp the AAL template from MNI space to the EPI native space, where interpolation using nearest-neighbor method ensured that the discrete labeling values were preserved. Thus the brain parcellations were conducted in each individual’s native space. We generated the SC maps for each participant using the DTI data acquired. We processed the two datasets acquired (each with different phase encoding to optimize signal in difficult regions). The construction of these SC maps or structural brain networks consisted of a three-step process. First, the regions of the whole-brain network were defined using the AAL template as used in the functional MRI data. Second, the connections between nodes in the whole-brain network (i.e., edges) were estimated using probabilistic tractography. Third, data were averaged across participants. Similar to the functional data, we applied the AAL90 template using the FLIRT tool from the FSL toolbox (www.fmrib.ox.ac.uk/fsl, FMRIB) to coregister the b0 image in diffusion MRI space to the T1-weighted structural image and then to the T1 template of ICBM152 in MNI space77. The two transformation matrices from these coregistration steps were concatenated and inversed to subsequently be applied to warp the AAL templates from MNI space to the diffusion MRI native space.Brain parcellationFor both BOLD and DWI data, brains were parcellated into 90 ROIs, according to the AAL anatomical atlas75.
