
==== Front
J Neurol
J Neurol
Journal of Neurology
0340-5354
1432-1459
Springer Berlin Heidelberg Berlin/Heidelberg

39090230
12591
10.1007/s00415-024-12591-y
Original Communication
Explaining recovery from coma with multimodal neuroimaging
http://orcid.org/0000-0003-0403-1982
Pozeg Polona 1
Jöhr Jane 2
Prior John O. 3
Diserens Karin 2
Dunet Vincent vincent.dunet@chuv.ch

1
1 https://ror.org/019whta54 grid.9851.5 0000 0001 2165 4204 Departement of Medical Radiology, Lausanne University Hospital and University of Lausanne, Rue du Bugnon 46, 1011 Lausanne, Switzerland
2 https://ror.org/019whta54 grid.9851.5 0000 0001 2165 4204 Acute Neurorehabilitation Unit, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, 1011 Lausanne, Switzerland
3 https://ror.org/019whta54 grid.9851.5 0000 0001 2165 4204 Department of Nuclear Medicine and Molecular Imaging, Lausanne University Hospital and University of Lausanne, 1011 Lausanne, Switzerland
1 8 2024
1 8 2024
2024
271 9 62746288
26 3 2024
6 7 2024
18 7 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by/4.0/ Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
The aim of this prospective, observational cohort study was to investigate and assess diverse neuroimaging biomarkers to predict patients’ neurological recovery after coma. 32 patients (18–76 years, M = 44.8, SD = 17.7) with disorders of consciousness participated in the study. Multimodal neuroimaging data acquired during the patient’s hospitalization were used to derive cortical glucose metabolism (18F-fluorodeoxyglucose positron emission tomography/computed tomography), and structural (diffusion-weighted imaging) and functional connectivity (resting-state functional MRI) indices. The recovery outcome was defined as a continuous composite score constructed from a multivariate neurobehavioral recovery assessment administered upon the discharge from the hospital. Fractional anisotropy-based white matter integrity in the anterior forebrain mesocircuit (r = 0.72, p < .001, 95% CI: 0.87, 0.45), and the functional connectivity between the antagonistic default mode and dorsal attention resting-state networks (r = − 0.74, p < 0.001, 95% CI: − 0.46, − 0.88) strongly correlated with the recovery outcome. The association between the posterior glucose metabolism and the recovery outcome was moderate (r = 0.38, p = 0.040, 95% CI: 0.66, 0.02). Structural (adjusted R2 = 0.84, p = 0.003) or functional connectivity biomarker (adjusted R2 = 0.85, p = 0.001), but not their combination, significantly improved the model fit to predict the recovery compared solely to bedside neurobehavioral evaluation (adjusted R2 = 0.75). The present study elucidates an important role of specific MRI-derived structural and functional connectivity biomarkers in diagnosis and prognosis of recovery after coma and has implications for clinical care of patients with severe brain injury.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00415-024-12591-y.

Keywords

Disorders of consciousness
DWI
fMRI
PET
Brain injury
Recovery
Swiss National Science Foundation320030_189129 320030_189129 Diserens Karin Dunet Vincent University of LausanneOpen access funding provided by University of Lausanne

issue-copyright-statement© Springer-Verlag GmbH Germany, part of Springer Nature 2024
==== Body
pmcIntroduction

Coma is a state of prolonged unarousable unresponsiveness following severe brain injury. Recovery occurs in a gradual but not necessarily definite restoration of arousal, awareness, and responsiveness [1]. Bedside clinical evaluation using neurobehavioral scores, such as the Glasgow Coma Scale [2] or the Coma Recovery Scale-Revised (CRS-R) [3], remains the standard approach to assess the level of impaired consciousness and predict the outcome [4]. Clinical evaluation is indispensable for establishing the proper diagnosis and treatment plan for the patient’s care; however, accurate detection of subtle signs of conscious awareness may often be hindered. The misdiagnosis rate following bedside examination can reach 40% [5, 6], influenced by biases, such as the examiner, the environment, and/or the patient [7, 8]. In the latter, sensory impairments or neurological conditions affecting motor functions, language and praxia may conceal the patient’s ability to interact with the environment despite being conscious and mimic disturbances of consciousness [9].

Complementing clinical examination with neuroimaging can significantly improve the patient’s diagnosis, prognosis, and subsequently their treatment plan. Owen’s et al. [10] seminal study demonstrated the successful use of an active imagery task paradigm during functional MRI (fMRI) to identify covert awareness in a patient behaviorally diagnosed being in a vegetative state. This paradigm has been since applied to larger cohorts of patients, confirming the presence of covert awareness in a proportion of unresponsive patients [11–15]. These patients show the ability to willfully modulate their brain activity following a command by engaging in motor or spatial imagery. Due to a clear dissociation between their motor output and residual cognitive abilities, their condition has been defined as a cognitive motor dissociation (CMD) [16]. While the neuromodulation task showed very good sensitivity in healthy subjects [17], assessing its detection accuracy in behaviorally unresponsive patients is impossible due to the absence of an independent, “ground-truth” measure of awareness, other than behavior [18]. It, therefore, represents a great risk for false negatives since a severe brain injury often gravely impacts functioning in multiple cognitive domains required to perform the fMRI mental imagery task [19].

Apart from the above-mentioned task-fMRI, the application of neuroimaging techniques to improve clinical diagnosis and predict recovery has been intensely studied, using diverse imaging methods, for example, structural imaging to gain qualitative and quantitative information about structural damage, or functional imaging using varying passive task paradigms and task-free methods [20] offering an insight into brain activity. Structural connectivity focused analyses based on microstructure diffusion-weighted imaging (DWI), revealed the significance of specific white matter track integrity in predicting the recovery from coma [21–23]. The anterior forebrain mesocircuit has been suggested as a prominent model to explain the common underlying mechanism for disorders of consciousness of different etiologies [24, 25]. The main components of the circuit: medial frontal and anterior cingulate cortex, central thalamus and the striatum, form a supporting architecture for brain arousal regulation of excitatory input from the brainstem [25]. The level of preserved integrity of the mesocircuit structures and their structural connectivity showed to be correlated with the degree of recovery after coma [23, 26–30].

The anterior forebrain mesocircuit also plays an important interactive role in sustaining and moderating neural activity of the cortical fronto-parietal networks [25, 31]. On the one hand, these networks consist of the default mode network (DMN; the medial prefrontal cortex, the posterior cingulate cortex, precuneus and the angular gyri), which is activated during passive rest conditions, internally oriented attention, and during self-referential processes [32]. On the other hand, the DMN is inhibited during tasks that require externally oriented attention, and activate lateral fronto-parietal and inferior parietal regions [33]. The strongest anti-correlation has been observed with the dorsal attention network (DAN), principally composed of the frontal eye fields and intraparietal sulcus [34, 35]. The anti-correlation between the DMN and DAN is an inherent robust feature of the functional organization of the brain and it underlies a segregation of competitive internal and external cognitive mechanisms [36]. Adequate segregation is thought to reflect the brain’s ability to adapt to a changing surrounding by flexibly allocating attention resources and is an indicator of a healthy neural connectivity [36–38]. Accumulating evidence has shown that the within- and between-network connectivity of the DMN and extrinsic networks assessed during resting-state fMRI (rs-fMRI) is attenuated in patients with less favorable outcome after severe brain injury [39–42], possibly being a promising neuroimaging biomarker to assess residual brain function. Similarly, the studies on brain metabolism using the 18F-FDG PET/CT showed reduced glucose metabolism in severe brain injury patients with less favorable diagnosis [12, 30], particularly in the posterior cingulate and precuneus [43, 44], which are the central nodes of the DMN [45], as well as the highest interconnected hub in the brain [46].

Despite the accumulating knowledge on neural mechanisms of recovery after brain injury and prominent advances in neuroimaging, determining an accurate prognosis in severe brain injury still remains a difficult challenge with critical consequences for the patient. The use of neuroimaging, while valuable, poses non-negligible cost and accessibility issues. It is hence imperative to assess the value of various neuroimaging biomarkers to optimize the use of resources and improve the prediction of coma outcome. Nonetheless, studies using multimodal neuroimaging measures and comparing their role in the prognosis of recovery in the disorders of consciousness are sparse [30, 39, 47, 48]. Therefore, the goal of this study was twofold. We first evaluated univariate associations between the recovery level and multimodal neuroimaging biomarkers, derived from the DWI, rs-fMRI and 18F-FDG PET/CT imaging. In particular, we assessed the mesocircuit structural connectivity and fronto-parietal functional integrity as potential predictors of the recovery after coma. We then compared which combination of the neuroimaging biomarkers can best improve the prediction of the recovery at the post-acute phase with regards to the clinical assessment only. The results of the present study showed that complementing bedside neurobehavioral evaluation with a selective neuroimaging biomarker importantly improves the prediction of recovery after severe brain injury.

Materials and methods

Subjects

Adult patients (≥ 18 years old) admitted to the Acute Neurorehabilitation Unit at the Lausanne University Hospital between the May 1, 2020 and the April 30, 2023 were enrolled in this prospective study. Inclusion criteria were a severe brain injury due to trauma or disease, and a behavioral phenotype of disorders of consciousness based on the clinical consensus for DOC diagnosis, i.e., Coma Recovery Scale—Revised (CRS-R) criteria for coma, unresponsive wakefulness syndrome, or minimally conscious state [3, 49]. Exclusion criteria were artificial coma, premorbid history of developmental, psychiatric or neurological illness resulting in documented functional disabilities at the time of the accident, glucose plasma level > 8.3 mmol/L, an MRI-unsafe device or metal fragment implant (Fig. 1). Only patients with informed consent to participate in the study, obtained from their legal representatives, were included in the study. The study was conducted in compliance with the ethical standards of the Declaration of Helsinki and was approved by the local ethical committee (CER-VD, 142/09).Fig. 1 Inclusion flow diagram and the number of patients with imaging data. In total 32 patients were recruited, of which 29 had a 18F-FDG PET/CT scan, 24 had a diffusion MRI scan and 23 had a resting-state functional MRI (rs-fMRI). 19 patients had data of all three imaging modalities

Clinical evaluations

The patients’ levels of motor, cognitive, and functional recovery were repeatedly assessed with a set of neurobehavioral evaluation tools during their stay in the unit by an experienced neuropsychologist or neurologist. The patients’ evolution was continuously monitored with the CRS-R first prior to their admission to the unit and followed-up every 7 days during their stay at the unit until the recovery of consciousness according to the CRS-R criteria (i.e., functional use of objects and/or functional communication). The first CRS-R evaluation was complemented with the Motor Behavior Tool—revised [50, 51] to detect subtle signs of motor behavior that could indicate a clinical CMD (cCMD) [52]. Patients with cCMD present subtle signs of conscious perception not accounted for by the CRS-R, and in the absence of decortication/decerebration signs reflect bilateral pyramidal pathway lesion.

A multivariate assessment of patient’s recovery was performed at the discharge from the unit using the Disability Rating Scale (DRS) [53], Rancho Los Amigos Levels of Cognitive Functioning Scale (RLAS) [54], and Functional Ambulation Category (FAC) [55].

18F-FDG PET/CT and MR imaging acquisition

In line with the study protocol, each patient underwent two scanning sessions 2 weeks apart. Each session consisted of first 18F-FDG PET/CT scan followed with an MRI scan the following day. The analyses were performed on the neuroimaging data of the first session; however, in the case of missing or insufficient quality data, we used the neuroimaging data of the second session.

MR data were collected on a 3T Siemens Skyra fit scanner (n = 14) and 3T Siemens Magnetom Vida scanner (n = 16; Erlangen, Germany) using the same scanning protocol.

Anatomic T1-weighted 3D magnetization-prepared rapid acquisition gradient echo images were acquired with the TR = 2.3 s, TE = 29.8 ms, flip angle = 9°, dimension = 160 × 240 × 256 voxels, and 1 × 1 × 1 mm voxel size.

Diffusion-weighted MRI (DWI) data were acquired with the neurite orientation dispersion and density imaging (NODDI) technique, using the following protocol: TR = 9.4 s, TE = 105 ms, flip angle = 90°, dimension 128 × 128 × 66 voxels, 2 × 2 × 2 mm voxel size, and 2 mm spacing between slices, 100 frames: 10 at b = 0 s/mm2, 30 at b = 700 s/mm2, 60 at b = 2000s/mm2.

Rs-fMRI data were acquired with a T2*-weighted echo planar imaging sequence (TR = 2 s, TE = 30 ms, flip angle = 80°, dimension 64 × 64 × 35 voxels, 3 × 3 × 3 mm voxel size and 3 mm spacing between slices, 300 frames).

Glucose brain metabolism was assessed using the 18F-fluorodeoxyglucose positron emission tomography (18F-FDG PET/CT) scanning at resting state on a PET/CT scanner (Biograph64 Vision 600, Siemens, Erlangen, Germany). Before radiotracer injection, 20 min of sensorimotor rest were respected in a dark and quiet room. The static 18F-FDG PET/CT images were acquired 30 min post-injection (3 MBq/kg 18F-FDG) with a 3-dimensional static emission for 16 min. The PET scan attenuation was corrected with the information provided by the CT (120 kVp, 40 mA, FOV 50 cm). Images were reconstructed on a 440 × 440 matrix (PSF + TOF 12i5s), 164 slices, pixel size = 0.825 × 0.825, thickness = 1.6 mm.

Neuroimaging data preprocessing and derivation of biomarkers’ values

The neuroimaging data were first evaluated for the image quality, and the scans that exceeded the quality control threshold were excluded from the analyses.

The DWI data were denoised, preprocessed, and used to derived fractional anisotropy (FA) maps as described previously in Pozeg et al. [23]. We quantified the structural connectivity of the forebrain mesocircuit using the multi-scale probabilistic atlas of human connectome [56]. We calculated the mesocircuit structural connectome by extracting the mean FA values across the voxels belonging to the white matter bundles connecting the bilateral regions forming a part of the forebrain mesocircuit (the frontal cortex, precuneus, cingulate cortex, thalamic nuclei, and the basal ganglia). We derived the biomarker of structural connectivity (mesocircuit FA) by averaging the FA values across the entire mesocircuit connectome. The method used to derive the mean structural connectivity value is described in detail in the Supplemental Information.

The anatomical and rs-fMRI data were preprocessed using the default fmriprep pipeline (21.0.2) [57, 58]. Resting-state functional connectivity was assessed with the data-driven, group independent component (IC) analysis [59] by decomposing the preprocessed and smoothed data in 20 spatially ICs. We sorted the ICs into the components presenting resting-state networks (RSN) and noise components through the visual inspection of various signal and noise features [60], and through a comparison to the resting-state networks templates [61]. The mean group spatial t-value maps of the RSN ICs were thresholded at t > 4 and used as brain masks to extract the individual mean spatial map connectivity value (t-value) from each patient’s corresponding IC spatial map. Second, we calculated the within-network connectivity of the DMN by correlating the IC time courses of the DMN components. In the same manner, we also calculated the connectivity (anti-correlations) between the DMN and the executive functioning related networks (EFN) [62].

The 18F-FDG PET/CT images (in bq/ml) were transformed into the standard uptake values (SUV) maps considering the patient’s body weight and dose decay correction. The SUV maps were co-registered with the native anatomical images and normalized to the MNI space. For each patient, the SUV map was normalized by the mean value of the pons to obtain the SUV ratio (SUVr) map. Mean global SUVr value was calculated across the entire brain grey matter, and the mean posterior SUVr value was derived by averaging the SUVr of the posterior cingulate and the precuneus. The neuroimaging preprocessing and biomarkers’ values extraction steps are detailed in the Supplemental Information and illustrated in Fig. 2.Fig. 2 Pre- and post-processing of multimodal neuroimaging data and measures of brain glucose metabolism, structural, and functional connectivity. a Native diffusion-weighted image (DWI) was preprocessed and used to derive a fractional anisotropy (FA) map in native space, which was then normalized to the MNI152 space. The atlas tractography was overlaid with the patient’s normalized FA map. The structural connectivity biomarker, the mesocircuit FA, was calculated by averaging the FA values across the voxels belonging to the white matter bundles connecting the brain regions within the anterior forebrain mesocircuit. b The native rs-fMRI BOLD images were preprocessed and normalized to the MNI152 space with the fmriprep pipeline. The smoothed images were then analyzed with the group independent component analysis (ICA) and decomposed into 20 independent spatial components. These components were then sorted into the resting-state networks and noise. The average spatial maps of the resting-state networks components were thresholded at t-value > 4 and used as masks to extract individual spatial connectivity values. In addition, we extracted the time course signal of the posterior default mode network and dorsal attention network to calculate the between-networks functional connectivity (DMN-DAN anti-correlation). c Native static 18F-FDG PET images in bq/ml were converted into standard uptake values (SUV) maps. The SUV maps were normalized to the MNI152 space. Each patient’s SUV map was then normalized by the mean value of the pons using the anatomical mask to obtain the SUV ratio (SUVr) map. To obtain the biomarker of the brain metabolism in the posterior cingulate and precuneus (posterior SUVr), we averaged the SUVr value using an anatomical mask for this region of interest

Statistical analysis

Outcome index

To reduce data dimensionality, we used principal component analysis on patients’ clinical evaluation scores at discharge, combining DRS, RLAS, and FAC scores. The first principal component, reflecting the most explained variance, was defined as the outcome index, representing overall functional and cognitive recovery from coma. We linearly transformed outcome index scores to a positive scale for clarity, where higher scores indicate more favorable outcomes. The analysis details are presented in the Supplemental Information.

Univariate regressions and linear regression model comparisons

First, we have tested the strengths of associations between the neuroimaging biomarkers and the outcome index measured at the discharge using Pearson’s correlation coefficient. Then, we compared different nested and non-nested linear regression models to determine which neuroimaging biomarkers can best explain the variance of the outcome index and if they can significantly increase the explained variance of the CRS-R score alone. We first built a minimal “clinical” linear model consisting of the CRS-R score at the approximate time of the MRI and PET scans, and the patient’s age, sex, time between the injury and outcome evaluation at the discharge, and the time between the CRS-R and outcome evaluation at the discharge as confounding variables in the model. This “clinical” linear model was then compared to the non-nested minimal “neuroimaging” linear models, containing each of the neuroimaging biomarkers and the corresponding confounding variables using the Vuong test [63] for non-nested models.

In addition, we evaluated if a simple lesion assessment based on anatomical MRI scan can outperform or improve the minimal clinical linear model. To this end we evaluated the lesion load as described in our previous work [64]. An experienced neuroradiologist assessed bilaterally four cortical (frontal, temporal, parietal, and occipital lobe) and five subcortical regions (basal ganglia, thalamus, mesencephalon, pons, and cerebellum). Each region was scored binary: with 0 when no lesion or a smaller lesion was present and with 1, when a larger focal lesion, covering more than 30% of the region’s volume or a diffuse lesion was present. The lesion load was defined as the sum of the lesion scores for all 18 regions and was included as a predictor in the minimal “lesion” model together with the confounding variables.

We subsequently compared the nested minimal “clinical” linear model to more complex linear models containing additional lesion or neuroimaging biomarkers. The nested linear models were compared and evaluated for the model fit using the Akaike Information Criteria (AIC) as well as the χ2 test on log likelihood ratios. The family-wise error rate was controlled by employing Bonferroni method to adjust the α level within each family of tests.

Results

Subjects

In total, 32 patients (11 women) with age range between 18 and 76 years (M = 44.8 years, SD = 17.7) were included in the study. The etiology of brain injury was traumatic (n = 15), hemorrhagic (n = 6), ischemic (n = 1), postanoxic (n = 3), SARS-CoV-2-related encephalopathy (n = 5), encephalitic (n = 1), and other (global rostral midbrain syndrome and corpus callosum infarction in the context of insufficient shunt drainage, n = 1).

All patients were identified as cCMD based on the MBT-r tool (i.e., they all showed signs of conscious perception at the first evaluation). The median CRS-R score prior to or at the admission to the unit was 6 (range: 0–23, IQR = 3) and the median score at the approximate time of the scan was 19 (range: 3–23, IQR = 11). The mean outcome index computed based on the RLAS, DRS, and FAC scores at the discharge from the unit was 5.0 (range: 1.3–7.8, SD = 1.6). The summary of the patients’ demographic and clinical data is presented in Table 1. The flow diagram showing patient selection and number of patients per neuroimaging modality is shown in Fig. 1.Table 1 Patients’ demographics and clinical info. ANR: Acute NeuroRehabilitation

Variable		Shapiro–wilk test of normality	
Age (years)	18–76, M = 44.9 ± 17.8	W = 0.95

p = 0.16

	
Sex	Women	11 (34%)		
Men	21 (66%)		
Etiology	Traumatic	15 (47%)		
Hemorrhagic	6 (19%)		
Ischemic	1 (3%)		
Postanoxic	3 (9%)		
SARS-CoV-2 encephalopathy	5 (16%)		
Encephalitic	1 (3%)		
Other	1 (3%)		
MBT-r classification	cCMD	32 (100%)		
CRS-R diagnosis prior to/at the ANR admission	coma	5 (16%)		
UWS	15 (47%)		
MCS	12 (37%)		
CRS-R initial score prior to/at the ANR admission	0–17, Mdn = 6, IQR = 3	W = 0.93

p = 0.043

	
CRS-R score at scan	3–23, Mdn = 19, IQR = 11	W = 0.86

p < 0.001

	
Time between injury and outcome evaluation (days)	23–160, Mdn = 57, IQR = 27.3	W = 0.88

p = 0.002

	
Time between injury and admission to the ANR (days)	3–104, Mdn = 23, IQR = 17.8	W = 0.81

p < 0.001

	
Time between CRS-R initial score and outcome evaluation (days)	13–88, M = 43.1, SD = 16.5	W = 0.97

p = 0.43

	
Time between CRS-R at scan and outcome evaluation (days)	0–63, M = 24.3, SD = 13.5	W = 0.96

p = 0.20

	
Time between 18F-FDG PET/CT scan and outcome evaluation (days) n = 29	2–60, M = 23.3, SD = 13.1	W = 0.96

p = 0.33

	
Time between DWI scan and outcome evaluation (days) n = 24	1–45, M = 19.3, SD = 11.7	W = 0.95

p = 0.29

	
Time between rs-fMRI scan and outcome evaluation (days) n = 23	3–59, M = 23.1, SD = 14.9	W = 0.94

p = 0.17

	
Outcome index (n = 32)	1.3–7.8, M = 5, SD = 1.6	W = 0.97

p = 0.53

	
P-value < 0.05 was considered significant

Lesion biomarker

The median lesion load score was 3 (range: 0–12, IQR = 4.25). The correlation between the lesion count and the outcome index at the discharge was not significant (Shapiro–Wilk W = 0.87, p = 0.001; Spearman’s ρ = − 0.25, p = 0.168).

Neuroimaging biomarkers

We did not acquire DWI scans for six subjects due to excessive agitation in the scanner, and the DWI data of two subjects were excluded from the subsequent analysis due to insufficient image quality (movement artifacts). In total, we analyzed the DWI data of 24 patients. The association between the mean mesocircuit FA and the outcome index was strong and significant (Shapiro–Wilk W = 0.92, p = 0.06; Pearson’s r = 0.72, p < 0.001, 95% CI: 0.87, 0.45), indicating more preserved white matter fiber integrity of the mesocircuit in patients with better clinical recovery.

We did not acquire rs-fMRI scans for five subjects due to excessive agitation in the scanner, and the rs-fMRI data of subjects was excluded from the subsequent analysis due to insufficient image quality (movement artifacts, n = 1), larger brain deformation preventing image co-registration and normalization (n = 2), and a different rs-fMRI protocol (n = 1). In total, the rs-fMRI data of 23 patients were analyzed. Following the IC decomposition, we identified 13 ICs representing RSN source signals; these were two DMN components: posterior DMN (p-DMN), anterior DMN (a-DMN); four EFNs: dorsal attention (DAN), salience, executive control, and right fronto-parietal network; six primary sensory and motor networks, and the reward network. The mean spatial maps of the DMN and EFN independent components, and their associations with the outcome index are shown in the Supplemental Information, Fig. S3.

The highest correlation between the mean spatial connectivity and the outcome index was found for the p-DMN (Shapiro–Wilk W = 0.97, p = 0.75; Pearson’s r = 0.43, p = 0.040, 95% CI: 0.72, 0.02); however, it did not survive the Bonferroni corrected significance level (α = 0.0045). The association between the DAN network and the outcome index (Shapiro–Wilk W = 0.96, p = 0.49, Pearson’s r = 0.39, p = 0.07, 95% CI: 0.69, − 0.03) was fair/weak and not significant. The other ICs demonstrated weaker and insignificant associations with the outcome index (all r < 0.35, p > 0.05).

The analyses of within- and between-ICs connectivity demonstrated that the connectivity between the p-DMN and DAN ICs was strongly negatively and significantly associated with the outcome index (Shapiro–Wilk W = 0.98, p = 0.82; Pearson’s r = − 0.74, p < 0.001, 95% CI − 0.46, − 0.88). In other words, the patients with stronger negative functional connectivity (anti-correlation) between the p-DMN and DAN showed more favorable clinical indices of recovery. On the other hand, the strength of within-DMN network connectivity (between a-DMN and p-DMN) did not show any association with the outcome index (Shapiro–Wilk W = 0.95, p = 0.24; Pearson’s r = 0.08, p = 0.71, 95% CI: 0.48, − 0.34). The connectivity values between the p-DMN and other EFN were weak and did not show any statistically significant correlation with the outcome index (all p > 0.05); the scatter plots representing their associations with the outcome index are shown in Supplemental material Fig. S3.

The 18F-FDG PET/CT scans could not be acquired for two patients due to their excessive agitation in the scanner. One patient was scanned with a different scanning protocol; therefore, their 18F-FDG PET/CT data were not included in the analyses. In total, we analyzed the data of 29 patients. The associations between the mean global SUVr and the outcome index (Shapiro–Wilk W = 0.94, p = 0.12; Pearson’s r = 0.18, p = 0.34, 95% CI 0.52, − 0.20) were weak and statistically not significant. The association between the posterior SUVr and the outcome index was fair/weak and significant (Shapiro–Wilk W = 0.95, p = 0.15; Pearson’s r = 0.38, p = 0.040, 95% CI: 0.66, 0.02). The scatter plots displaying the most pertinent correlations between neuroimaging biomarkers and the outcome index are shown in Fig. 3.Fig. 3 Outcome index and its correlation with neuroimaging biomarkers at the time of discharge. a Upper: The probabilistic white matter fiber bundles of the anterior forebrain mesocircuit extracted from the human connectome atlas. Lower: scatter plot showing the correlation between the mean fractional anisotropy of mesocircuit and the outcome index. b Upper: the brain mask of the posterior cingulate and precuneus used to extract the mean posterior SUVr value. Lower: scatter plot showing the correlation between the posterior SUVr and the outcome index. c Upper: brain mask representing the resting-state networks of the posterior default mode (DMN) in blue and dorsal attention (DAN) in yellow obtained with the group independent component analysis by thresholding the component’s average spatial t-map. Lower: the scatter plot is showing the correlation between the outcome index and the negative functional connectivity between the DMN and DAN (anti-correlation). d Scatter plot showing the correlation between the Total Coma Recovery Scale—Revised (CRS-R) score and the outcome index. The shaded areas represent the 95% confidence interval of the fitted line

The mean FA of the mesocircuit significantly correlated with both the negative functional DMN-DAN connectivity (n = 20, Pearson’s r =  − 0.70, p < 0.001, 95% CI: − 0.87, − 0.37) and the posterior SUVr (n = 23, Pearson’s r = 0.51, p = 0.013, 95% CI: 0.13, 0.76), whereas the correlation between the posterior SUVr and the negative functional DMN-DAN connectivity was weaker and statistically not significant (n = 22, Pearson’s r =  − 0.38, p = 0.08, 95% CI: − 0.69, 0.05). The scatter plots displaying correlations between the biomarkers are shown in Fig. 4.Fig. 4 Correlations between neuroimaging biomarkers. a Scatter plot representing the correlation between the mean fractional anisotropy value of the mesocircuit and the negative functional connectivity between the default mode and dorsal attention network. b Scatter plot representing the correlation between and the negative functional connectivity between the default mode and dorsal attention network and the posterior standard uptake value ratios (SUVr). c Scatter plot representing the correlation between the mean fractional anisotropy value of the mesocircuit and the posterior SUVr values. The shaded areas represent the 95% confidence interval of the fitted line. The color bar represents the outcome index value

Linear regression model comparisons

We conducted statistical comparisons on linear models featuring single clinical or neuroimaging predictors, as well as their combinations, while accounting for confounding variables. This analysis was restricted to the subset of patients with neuroimaging data from all three modalities (n = 19).

The minimal clinical linear model incorporating only the CRS-R score and the covariates (age, sex, time between the injury and outcome index evaluation, and time between the CRS-R and the outcome index evaluation) explained 75% of variance (adjusted R2, p < 0.001, AIC = 55.0). The CRS-R score significantly predicted the outcome index (B = 0.23, p < 0.001). However, to note, this association weakened with a longer period between assessments, i.e., between CRS-R at/prior to admission and outcome index at the discharge (r = 0.06, p = 0.73). Further details and additional analysis including the CRS-R initial score and diagnosis are available in the Supplemental Information.

The minimal lesion model with the lesion load as the single predictor and covariates explained 26% of variance (adjusted R2) in the outcome index and was statistically not significant (p = 0.11, AIC = 75.7).

Statistically significant was the minimal neuroimaging linear model with the functional p-DMN-DAN anti-correlation as the single predictor and the covariates (adjusted R2 = 0.68, p < 0.001, AIC = 59.8), where the p-DMN-DAN anti-correlation significantly predicted the outcome index (B =  − 4.0, p < 0.001), and the minimal neuroimaging linear model with the structural connectivity biomarker (adjusted R2 = 0.65, p = 0.002, AIC = 61.6), where the mesocircuit FA value significantly predicted the outcome index at the discharge (B = 46.1, p = 0.001).

Comparing the linear models’ explained variance and AIC, none of the minimal lesion or neuroimaging linear models outperformed the clinical linear model. The Vuong test for comparison of non-nested models confirmed the clinical model’s superior goodness of fit (all p > 0.05).

Statistical comparison of the nested models showed that adding the structural connectivity biomarker (mesocircuit FA) to the clinical linear model significantly improved the model fit (adjusted R2 = 0.84, χ2 = 11.8, p = 0.003). The prediction of the outcome was also significantly improved when adding the anti-correlation between the p-DMN and DAN as the fMRI biomarker to the clinical linear model (adjusted R2 = 0.85, χ2 = 13.5, p = 0.001). However, addition of both significant biomarkers at once (the mesocircuit FA and the functional DMN-DAN anti-correlation) did not further improve the prediction of the outcome (adjusted R2 = 0.84, χ2 = 3.1, p > 0.05). Other neuroimaging biomarkers did not show statistically significant improvement of the goodness of fit (all p > 0.05). The statistical tests of model comparisons are shown in Table 2. The linear models’ performance is graphically displayed in Fig. 5.Table 2 Model comparisons statistics

Prediction of the outcome index at discharge from the acute neurorehabilitation unit	
Model	Adj. R2	AIC	Model F (p value)	Predictor t (p value)	Vuong test z (p value)	χ2 test (p value)	
Minimal clinical	
CRS-R + age + sex + time: injury to outcome evaluation + time: CRS-R to outcome evaluation	0.75	55.0	11.8 (< 0.001)	6.5 (< 0.001)		–	
Minimal lesion	
Lesion load + age + sex + time: injury to outcome evaluation + time: CRS-R to outcome evaluation	0.26	75.7	2.3 (0.11)	 − 2.4 (0.035)	2.62 (> 0.99)	–	
Minimal structural	
FA mesocircuit + age + sex + time: injury to outcome evaluation + time: dMRI to outcome evaluation	0.65	61.6	7.5 (0.002)	4.6 (0.001)	0.91 (0.82)	–	
Minimal functional	
p-DMN-DAN anti-correlation + age + sex + time: injury to outcome evaluation + time: rs-fMRI to outcome evaluation	0.68	59.8	8.6 (< 0.001)	 − 5.5 (< 0.001)	0.68 (0.75)	–	
Minimal 18F-FDG PET/CT	
pSUVr + age + sex + time: injury to outcome evaluation + time: PET to outcome evaluation	0.16	78.0	1.7 (0.21)	1.7 (0.11)	3.5 (> 0.99)	–	
Clinical + lesion	
CRS-R + lesion load + age + sex + time: injury to outcome evaluation + time: CRS-R to outcome evaluation	0.74	56.0	9.6 (< 0.001)	5.0 (< 0.001)

0.82 (0.43)

	–	1.04 (0.31)	
Clinical + structural	
CRS-R + FA mesocircuit + age + sex + time: injury to outcome evaluation + time: CRS-R to outcome evaluation + time: dMRI to outcome evaluation	0.84	47.2	14.6 (< 0.001)	4.2 (0.002)

2.2 (0.048)

	–	11.8 (0.003)	
Clinical + functional	
CRS-R + p-DMN-DAN anti-correlation + age + sex + time: injury to outcome evaluation + time: CRS-R to outcome evaluation + time: rs-fMRI to outcome evaluation	0.85	45.5	16.1 (< 0.001)	4.1 (0.002)

 − 3.0 (0.013)

	–	13.5 (0.001)	
Clinical + 18F-FDG PET/CT	
CRS-R + pSUVr + age + sex + time: injury to outcome evaluation + time: CRS-R to outcome evaluation + time: PET to outcome evaluation	0.74	56.2	8.5 (0.001)	5.5 (< 0.001)

1.0 (0.34)

	–	2.8 (0.25)	
Clinical + structural + functional	
CRS-R + FA mesocircuit + p-DMN-DAN anti-correlation + age + sex + time: injury to outcome evaluation + time: CRS-R to outcome evaluation + time: dMRI to outcome evaluation + time: rs-fMRI to outcome evaluation	0.84	48.1	11.2 (< 0.001)	3.3 (0.009)

0.8 (0.45)

 − 1.3 (0.24)

	–	3.1 (0.21)	
P-value < 0.05 was considered significant

Fig. 5 Linear models’ performance comparison. Bar chart showing the models performance metrics: explained variance with adjusted R2 (blue) and Akaike information criterion (AIC, red). Minima linear models: clin = minimal clinical, lesion = minimal lesion pet = minimal 18F-FDG PET/CT, sc = minimal structural, fcDMN-DAN = minimal functional; Nested linear models: clin lesion = clinical + lesion, clin pet = clinical + 18F-FDG PET/CT, clin sc = clinical + structural, clin fcDMN-DAN = clinical + functional, clin sc fcDMN-DAN = clinical + structural + functional

Discussion

In this prospective study, we investigated diverse multimodal neuroimaging biomarkers to predict recovery after coma. Our focus included the indices of DWI-derived structural connectivity, rs-fMRI-derived functional connectivity, and brain glucose metabolism estimated with the 18F-FDG PET/CT imaging. Univariate analysis showed strong and significant associations between the recovery levels and white matter integrity in the anterior forebrain mesocircuit, as well as with functional segregation between the DMN and DAN during rs-fMRI. In multivariate linear regression models, both structural and functional connectivity biomarkers significantly improved the recovery prediction in the post-acute phase.

Our findings demonstrate that patients with stronger structural connectivity in the anterior forebrain mesocircuit display more favorable neurological evolution at the discharge from the acute neurorehabilitation unit. This aligns with the mesocircuit hypothesis, and points to the common underlying neural architecture, necessary for the recovery of consciousness [24, 25]. This neural circuit encompasses the frontal cortices and the striato-pallidal negative loop, which regulates the excitatory thalamo-cortical projections [24]. Lesions impacting the circuit cause disfacilitation of the main anterior frontal cortical targets (anterior cingulate and medial frontal cortex) and result in the down regulation of arousal [65, 66]. The hypothesis is supported by similar studies using DWI showing that greater lesion burden of the structures or of the connecting white matter tracts within the mesocircuit is associated with a worse outcome [23, 27, 28, 64, 67–70].

We also showed that the negative functional connectivity (anti-correlation) between the two antagonistic fronto-parietal networks correlates with the degree of recovery from coma. This finding implicates that the patients with better functional and cognitive recovery profiles displayed more preserved intrinsic cortico-cortical organization of the brain, a necessary property for adequate information integration and processing. In line with the previous research [39, 41, 71, 72], our results show that the strength of anti-correlation between the DMN and DAN could be a promising biomarker for the preserved neural capacity to sustain awareness.

Contrary to prior research, our study only partially replicated the common finding of restored within-connectivity of the DMN [20]. We observed a moderate association between the p-DMN spatial map connectivity and the outcome index, but no correlation in the connectivity strength between the posterior and anterior DMN nodes with the degree of recovery. The lack of correlation might be attributed to the medial prefrontal cortex (mPFC), which serves as both the anterior hub of the DMN, and the salience network, and is considered a functionally heterogeneous region, involved in various cognitive and affective processes [73]. Consequently, our analysis might have not reflected the within-DMN connectivity. As preserved within-DMN connectivity was also reported in unresponsive patients and propofol-induced unconscious subjects [39, 74], it is suggested that this connectivity does not exclusively represent conscious mental activity, but rather a fundamental functional brain organization that is necessary, yet not sufficient for sustenance of consciousness [75, 76]. In addition, the outcome in our study was defined with an interval scale based on the multidimensional neurological evaluation, and not on binary classification based on the Glasgow Outcome Scale [77] or CRS-R recovery of consciousness.

While we found a fair/weak association between the glucose metabolism in the posterior cingulate/precuneus and the outcome index, this biomarker has not shown to significantly improve the prediction of the recovery. Although 18F-FDG PET/CT imaging previously showed a promising role in diagnosing patients with disorders of consciousness [12, 30, 43, 78], the measure is biased by various factors, such as the use of substances, and artifacts including hyperglycemia, resulting in larger variations in glucose metabolism among the subjects [79]. In addition, as already suggested and also observed in our data, 18F-FDG PET/CT imaging might have a higher accuracy to identify patients who will not display any improvement in recovery of consciousness, but a lesser sensitivity to predict recovery in a graded manner [12].

We found significant correlations between the mesocircuit structural connectivity and the DMN-DAN negative functional connectivity, and between the mesocircuit structural connectivity and glucose metabolism in the posterior cingulate/precuneus. This finding further corroborates observations of the interactions between the two system components [25] that are necessary to enable a sufficient arousal of the system through the brainstem-thalamo-cortical projections, and which facilitate adequate communication between high level cortical networks, required for conscious mental activity [41, 68, 80, 81]. Our findings also highlight the important involvement of the posterior cingulate/precuneus in the recovery of consciousness, aligning with previous studies showing reduced functional [82], effective connectivity [83], and metabolic activity [43, 44] within this region in the patients with disorders of consciousness. This brain region displays dense long-range connections with the frontal regions, temporal lobes, parahippocampal areas, and with the pontine regions [84]. Studies demonstrated that the dorsal posterior cingulate/precuneus forms a functional part of the DMN, while its ventral part activates with the central executive network during cognitively demanding tasks [84], suggesting an important modulating role in the interaction between attention and cognition. With its strategic position, it is viewed as a main hub area to integrate internal and external stimuli, and associate them with existing knowledge in order to facilitate an adequate behavioral response [85].

Lastly, we assessed whether incorporating neuroimaging biomarkers improves outcome prediction compared to the CRS-R evaluation alone. None of the neuroimaging biomarkers alone outperforms the CRS-R evaluation. However, adding either the mesocircuit structural connectivity index or the DMN-DAN negative functional connectivity index significantly increased the explained variance in recovery. While the CRS-R remains important for standardized bedside evaluation, there is an overlap with neurobehavioral scales (most notably the DRS) in the measured construct, showing high concurrent validity [3, 86], and therefore, presenting a confounding factor of multicollinearity in the interpretation of the true predictive validity of the scale.

Our study also showed that deploying both structural and functional connectivity biomarkers together does not add additional value to the prediction of the outcome. This has important implications for diagnostic tests planning, especially in settings with limited access to the neuroimaging facilities. When deciding between using the DWI-FA derived structural or the rs-fMRI connectivity, the latter can be affected by the arousal levels and sedation [87], therefore, its validity might be hindered when used in the critical care in the early phase of the injury. In addition, structural connectivity is less prone to time-related changes, and is, unlike functional connectivity, state-independent. As such, it remains a good candidate for the neuroimaging biomarker of recovery from coma. However, a future longitudinal study is essential to confirm its stability and predictive validity across different injury phases and recovery time intervals.

The study has certain limitations, including a small sample size, requiring validation on a larger sample for generalizability. Further research should also explore whether different neuroimaging acquisition protocols, preprocessing pipelines, and noise removal methods yield similar results, ensuring the robustness of findings. Here we have used the FA as a metric for white matter integrity as it is a most commonly used marker of cerebral white matter microstructure. However, its interpretability is reduced in the presence of crossing fibers or edema [88], thus a confirmatory study using advanced diffusion imaging techniques that account for different neurite orientations [89] is needed. In the same line, there is no single way to derive the RSN from the rs-fMRI. We here followed the state-of-the art, open access, and robust pipeline for neuroimaging preprocessing [57] and a commonly used tool for the group independent component analyses [59], in order to facilitate the reproducibility of the current study. Nevertheless, the group ICA RSN spatial maps are inherently sample-dependent. For these reasons, we showed that atlas-based extraction of DMN-DAN inter-network connectivity produces similar results (see Supplemental Information), suggesting a less expertise-demanding and more generalizable approach may be used instead. We acknowledge that reproducibility of neuroimaging studies represents a significant challenge for implementing rs-fMRI-based biomarkers in clinical settings [90]. Standardizing methodology and outcome definitions is crucial to develop reliable and accurate neuroimaging biomarkers for diagnosing and prognosing disorders of consciousness.

In conclusion, our study demonstrates that greater preserved structural connectivity in the anterior forebrain mesocircuit and stronger negative functional rs-fMRI connectivity between DMN and DAN are significantly correlated with a more favorable neurological evolution upon hospital discharge. In multivariate linear regression models, we showed that the individual structural or functional connectivity biomarker, but not their combination, significantly improves the model fit to predict the recovery in the post-acute phase compared solely to the bedside neurobehavioral evaluation. These findings have implications for selecting diagnostic tests, improving the patient identification for potential recovery, planning a targeted therapy, and aiding in life-death decision-making.

Supplementary information

Supplementary information is available at Journal of Neurology online.

Supplementary Information

Below is the link to the electronic supplementary material.Supplementary file 1 (DOCX 19479 kb)

Acknowledgements

We would like to acknowledge dr. Melanie Hirt Price’s contribution to the data management and organization of patients scanning schedules.

Author contributions

P. P., J. J., J. O. P., K. D. and V. D. all contributed to the conception and design of the study; P. P., J. J., and V. D. contributed to the acquisition and analysis of data; P. P. drafted a significant portion of the manuscript and figures; K. D. and V. D. obtained funding for the study; all authors reviewed and validated the final draft.

Funding

Open access funding provided by University of Lausanne. This study was funded by the Swiss National Science Foundation (Grant number: FNS 320030_189129).

Data availability

The structural connectomes, spatial masks and time series from the rs-fMRI group independent component analysis and extracted global and posterior SUVr values are available at the open-source public data repository (https://zenodo.org), 10.5281/zenodo.10581959.

Declarations

Conflict of interest

On behalf of all the authors, the corresponding author states that there is no conflict of interest.

Karin Diserens and Vincent Dunet have contributed equally to this work.
==== Refs
References

1. Posner JB Saper CB Schiff ND Plum F Plum and Posner’s diagnosis of stupor and coma 2007 4 Oxford Oxford University Press
Posner JB, Saper CB, Schiff ND, Plum F (2007) Plum and Posner’s diagnosis of stupor and coma, 4th edn. Oxford University Press, Oxford
2. Teasdale G Maas A Lecky F The Glasgow Coma Scale at 40 years: standing the test of time Lancet Neurol 2014 13 844 854 10.1016/S1474-4422(14)70120-6 25030516
Teasdale G, Maas A, Lecky F et al (2014) The Glasgow Coma Scale at 40 years: standing the test of time. Lancet Neurol 13:844–854. 10.1016/S1474-4422(14)70120-625030516 10.1016/S1474-4422(14)70120-6
3. Giacino JT Kalmar K Whyte J The JFK coma recovery scale-revised: measurement characteristics and diagnostic utility1 Arch Phys Med Rehabil 2004 85 2020 2029 10.1016/j.apmr.2004.02.033 15605342
Giacino JT, Kalmar K, Whyte J (2004) The JFK coma recovery scale-revised: measurement characteristics and diagnostic utility1. Arch Phys Med Rehabil 85:2020–2029. 10.1016/j.apmr.2004.02.03315605342 10.1016/j.apmr.2004.02.033
4. Lucca LF Lofaro D Pignolo L Outcome prediction in disorders of consciousness: the role of coma recovery scale revised BMC Neurol 2019 19 68 10.1186/s12883-019-1293-7 30999877
Lucca LF, Lofaro D, Pignolo L et al (2019) Outcome prediction in disorders of consciousness: the role of coma recovery scale revised. BMC Neurol 19:68. 10.1186/s12883-019-1293-730999877 10.1186/s12883-019-1293-7
5. Schnakers C Vanhaudenhuyse A Giacino J Diagnostic accuracy of the vegetative and minimally conscious state: clinical consensus versus standardized neurobehavioral assessment BMC Neurol 2009 9 35 10.1186/1471-2377-9-35 19622138
Schnakers C, Vanhaudenhuyse A, Giacino J et al (2009) Diagnostic accuracy of the vegetative and minimally conscious state: clinical consensus versus standardized neurobehavioral assessment. BMC Neurol 9:35. 10.1186/1471-2377-9-3519622138 10.1186/1471-2377-9-35
6. Andrews K Murphy L Munday R Littlewood C Misdiagnosis of the vegetative state: retrospective study in a rehabilitation unit BMJ 1996 313 13LP 16 10.1136/bmj.313.7048.13 8664760
Andrews K, Murphy L, Munday R, Littlewood C (1996) Misdiagnosis of the vegetative state: retrospective study in a rehabilitation unit. BMJ 313:13LP – 16. 10.1136/bmj.313.7048.138664760 10.1136/bmj.313.7048.13
7. Candelieri A Cortese MD Dolce G Visual pursuit: within-day variability in the severe disorder of consciousness J Neurotrauma 2011 28 2013 2017 10.1089/neu.2011.1885 21770758
Candelieri A, Cortese MD, Dolce G et al (2011) Visual pursuit: within-day variability in the severe disorder of consciousness. J Neurotrauma 28:2013–2017. 10.1089/neu.2011.188521770758 10.1089/neu.2011.1885
8. Young MJ Bodien YG Giacino JT The neuroethics of disorders of consciousness: a brief history of evolving ideas Brain 2021 144 3291 3310 10.1093/brain/awab290 34347037
Young MJ, Bodien YG, Giacino JT et al (2021) The neuroethics of disorders of consciousness: a brief history of evolving ideas. Brain 144:3291–3310. 10.1093/brain/awab29034347037 10.1093/brain/awab290
9. Pincherle A Rossi F Jöhr J Early discrimination of cognitive motor dissociation from disorders of consciousness: pitfalls and clues J Neurol 2020 10.1007/s00415-020-10125-w 32754829
Pincherle A, Rossi F, Jöhr J et al (2020) Early discrimination of cognitive motor dissociation from disorders of consciousness: pitfalls and clues. J Neurol. 10.1007/s00415-020-10125-w32754829 10.1007/s00415-020-10125-w
10. Owen AM Coleman MR Boly M Detecting awareness in the vegetative state Science 2006 313 1402LP 1402 10.1126/science.1130197 16959998
Owen AM, Coleman MR, Boly M et al (2006) Detecting awareness in the vegetative state. Science 313:1402LP – 1402. 10.1126/science.113019716959998 10.1126/science.1130197
11. Edlow BL Chatelle C Spencer CA Early detection of consciousness in patients with acute severe traumatic brain injury Brain J Neurol 2017 140 2399 2414 10.1093/brain/awx176
Edlow BL, Chatelle C, Spencer CA et al (2017) Early detection of consciousness in patients with acute severe traumatic brain injury. Brain J Neurol 140:2399–2414. 10.1093/brain/awx17610.1093/brain/awx176
12. Stender J Gosseries O Bruno M-A Diagnostic precision of PET imaging and functional MRI in disorders of consciousness: a clinical validation study The Lancet 2014 384 514 522 10.1016/S0140-6736(14)60042-8
Stender J, Gosseries O, Bruno M-A et al (2014) Diagnostic precision of PET imaging and functional MRI in disorders of consciousness: a clinical validation study. The Lancet 384:514–522. 10.1016/S0140-6736(14)60042-810.1016/S0140-6736(14)60042-8
13. Monti MM Vanhaudenhuyse A Coleman MR Willful modulation of brain activity in disorders of consciousness N Engl J Med 2010 362 579 589 10.1056/NEJMoa0905370 20130250
Monti MM, Vanhaudenhuyse A, Coleman MR et al (2010) Willful modulation of brain activity in disorders of consciousness. N Engl J Med 362:579–589. 10.1056/NEJMoa090537020130250 10.1056/NEJMoa0905370
14. Wang F Hu N Hu X Detecting brain activity following a verbal command in patients with disorders of consciousness Front Neurosci 2019 13 976 10.3389/fnins.2019.00976 31572121
Wang F, Hu N, Hu X et al (2019) Detecting brain activity following a verbal command in patients with disorders of consciousness. Front Neurosci 13:976. 10.3389/fnins.2019.0097631572121 10.3389/fnins.2019.00976
15. Bardin JC Fins JJ Katz DI Dissociations between behavioural and functional magnetic resonance imaging-based evaluations of cognitive function after brain injury Brain 2011 134 769 782 10.1093/brain/awr005 21354974
Bardin JC, Fins JJ, Katz DI et al (2011) Dissociations between behavioural and functional magnetic resonance imaging-based evaluations of cognitive function after brain injury. Brain 134:769–782. 10.1093/brain/awr00521354974 10.1093/brain/awr005
16. Schiff ND Cognitive motor dissociation following severe brain injuries JAMA Neurol 2015 72 1413 1415 10.1001/jamaneurol.2015.2899 26502348
Schiff ND (2015) Cognitive motor dissociation following severe brain injuries. JAMA Neurol 72:1413–1415. 10.1001/jamaneurol.2015.289926502348 10.1001/jamaneurol.2015.2899
17. Boly M Coleman MR Davis MH When thoughts become action: an fMRI paradigm to study volitional brain activity in non-communicative brain injured patients Neuroimage 2007 36 979 992 10.1016/j.neuroimage.2007.02.047 17509898
Boly M, Coleman MR, Davis MH et al (2007) When thoughts become action: an fMRI paradigm to study volitional brain activity in non-communicative brain injured patients. Neuroimage 36:979–992. 10.1016/j.neuroimage.2007.02.04717509898 10.1016/j.neuroimage.2007.02.047
18. Peterson A Cruse D Naci L Risk, diagnostic error, and the clinical science of consciousness NeuroImage Clin 2015 7 588 597 10.1016/j.nicl.2015.02.008 25844313
Peterson A, Cruse D, Naci L et al (2015) Risk, diagnostic error, and the clinical science of consciousness. NeuroImage Clin 7:588–597. 10.1016/j.nicl.2015.02.00825844313 10.1016/j.nicl.2015.02.008
19. Brain–computer interfaces for communication with nonresponsive patients-Naci-2012-Annals of Neurology-Wiley Online Library. 10.1002/ana.23656?casa_token=2xyoF-f-54kAAAAA%3AkbbzaoYVuoFaj6TqCKG-o4uxVs23ZCUBhqdlALxDND_yRDJF3X3vzEm6kaCPYUFOvqr0OJ6GqAJNbQ. Accessed 15 Sep 2023
20. Snider SB Edlow BL MRI in disorders of consciousness Curr Opin Neurol 2020 33 676 683 10.1097/WCO.0000000000000873 33044234
Snider SB, Edlow BL (2020) MRI in disorders of consciousness. Curr Opin Neurol 33:676–683. 10.1097/WCO.000000000000087333044234 10.1097/WCO.0000000000000873
21. Bodart O Amico E Gómez F Global structural integrity and effective connectivity in patients with disorders of consciousness Brain Stimulat 2018 11 358 365 10.1016/j.brs.2017.11.006
Bodart O, Amico E, Gómez F et al (2018) Global structural integrity and effective connectivity in patients with disorders of consciousness. Brain Stimulat 11:358–365. 10.1016/j.brs.2017.11.00610.1016/j.brs.2017.11.006
22. Zhang J Wei R-L Peng G-P Correlations between diffusion tensor imaging and levels of consciousness in patients with traumatic brain injury: a systematic review and meta-analysis Sci Rep 2017 7 2793 10.1038/s41598-017-02950-3 28584256
Zhang J, Wei R-L, Peng G-P et al (2017) Correlations between diffusion tensor imaging and levels of consciousness in patients with traumatic brain injury: a systematic review and meta-analysis. Sci Rep 7:2793. 10.1038/s41598-017-02950-328584256 10.1038/s41598-017-02950-3
23. Pozeg P Alemán-Goméz Y Jöhr J Structural connectivity in recovery after coma: connectome atlas approach NeuroImage Clin 2023 37 103358 10.1016/j.nicl.2023.103358 36868043
Pozeg P, Alemán-Goméz Y, Jöhr J et al (2023) Structural connectivity in recovery after coma: connectome atlas approach. NeuroImage Clin 37:103358. 10.1016/j.nicl.2023.10335836868043 10.1016/j.nicl.2023.103358
24. Schiff ND Recovery of consciousness after brain injury: a mesocircuit hypothesis Trends Neurosci 2010 33 1 9 10.1016/j.tins.2009.11.002 19954851
Schiff ND (2010) Recovery of consciousness after brain injury: a mesocircuit hypothesis. Trends Neurosci 33:1–9. 10.1016/j.tins.2009.11.00219954851 10.1016/j.tins.2009.11.002
25. Schiff ND Mesocircuit mechanisms in the diagnosis and treatment of disorders of consciousness Presse Med 2023 52 104161 10.1016/j.lpm.2022.104161 36563999
Schiff ND (2023) Mesocircuit mechanisms in the diagnosis and treatment of disorders of consciousness. Presse Med 52:104161. 10.1016/j.lpm.2022.10416136563999 10.1016/j.lpm.2022.104161
26. Lutkenhoff ES Chiang J Tshibanda L Thalamic and extrathalamic mechanisms of consciousness after severe brain injury Ann Neurol 2015 78 68 76 10.1002/ana.24423 25893530
Lutkenhoff ES, Chiang J, Tshibanda L et al (2015) Thalamic and extrathalamic mechanisms of consciousness after severe brain injury. Ann Neurol 78:68–76. 10.1002/ana.2442325893530 10.1002/ana.24423
27. Weng L Xie Q Zhao L Abnormal structural connectivity between the basal ganglia, thalamus, and frontal cortex in patients with disorders of consciousness Cortex 2017 90 71 87 10.1016/j.cortex.2017.02.011 28365490
Weng L, Xie Q, Zhao L et al (2017) Abnormal structural connectivity between the basal ganglia, thalamus, and frontal cortex in patients with disorders of consciousness. Cortex 90:71–87. 10.1016/j.cortex.2017.02.01128365490 10.1016/j.cortex.2017.02.011
28. Zheng ZS Reggente N Lutkenhoff E Disentangling disorders of consciousness: insights from diffusion tensor imaging and machine learning Hum Brain Mapp 2016 38 431 443 10.1002/hbm.23370 27622575
Zheng ZS, Reggente N, Lutkenhoff E et al (2016) Disentangling disorders of consciousness: insights from diffusion tensor imaging and machine learning. Hum Brain Mapp 38:431–443. 10.1002/hbm.2337027622575 10.1002/hbm.23370
29. Yao S Song J Gao L Thalamocortical sensorimotor circuit damage associated with disorders of consciousness for diffuse axonal injury patients J Neurol Sci 2015 356 168 174 10.1016/j.jns.2015.06.044 26165776
Yao S, Song J, Gao L et al (2015) Thalamocortical sensorimotor circuit damage associated with disorders of consciousness for diffuse axonal injury patients. J Neurol Sci 356:168–174. 10.1016/j.jns.2015.06.04426165776 10.1016/j.jns.2015.06.044
30. Annen J Heine L Ziegler E Function–structure connectivity in patients with severe brain injury as measured by MRI-DWI and FDG-PET Hum Brain Mapp 2016 37 3707 3720 10.1002/hbm.23269 27273334
Annen J, Heine L, Ziegler E et al (2016) Function–structure connectivity in patients with severe brain injury as measured by MRI-DWI and FDG-PET. Hum Brain Mapp 37:3707–3720. 10.1002/hbm.2326927273334 10.1002/hbm.23269
31. Laureys S Faymonville ME Luxen A Restoration of thalamocortical connectivity after recovery from persistent vegetative state Lancet Lond Engl 2000 355 1790 1791 10.1016/s0140-6736(00)02271-6
Laureys S, Faymonville ME, Luxen A et al (2000) Restoration of thalamocortical connectivity after recovery from persistent vegetative state. Lancet Lond Engl 355:1790–1791. 10.1016/s0140-6736(00)02271-610.1016/s0140-6736(00)02271-6
32. Raichle ME The brain’s default mode network Annu Rev Neurosci 2015 38 433 447 10.1146/annurev-neuro-071013-014030 25938726
Raichle ME (2015) The brain’s default mode network. Annu Rev Neurosci 38:433–447. 10.1146/annurev-neuro-071013-01403025938726 10.1146/annurev-neuro-071013-014030
33. Naghavi HR Nyberg L Common fronto-parietal activity in attention, memory, and consciousness: Shared demands on integration? Conscious Cogn 2005 14 390 425 10.1016/j.concog.2004.10.003 15950889
Naghavi HR, Nyberg L (2005) Common fronto-parietal activity in attention, memory, and consciousness: Shared demands on integration? Conscious Cogn 14:390–425. 10.1016/j.concog.2004.10.00315950889 10.1016/j.concog.2004.10.003
34. Buckner RL DiNicola LM The brain’s default network: updated anatomy, physiology and evolving insights Nat Rev Neurosci 2019 20 593 608 10.1038/s41583-019-0212-7 31492945
Buckner RL, DiNicola LM (2019) The brain’s default network: updated anatomy, physiology and evolving insights. Nat Rev Neurosci 20:593–608. 10.1038/s41583-019-0212-731492945 10.1038/s41583-019-0212-7
35. Dixon ML De La Vega A Mills C Heterogeneity within the frontoparietal control network and its relationship to the default and dorsal attention networks Proc Natl Acad Sci 2018 115 E1598 E1607 10.1073/pnas.1715766115 29382744
Dixon ML, De La Vega A, Mills C et al (2018) Heterogeneity within the frontoparietal control network and its relationship to the default and dorsal attention networks. Proc Natl Acad Sci 115:E1598–E1607. 10.1073/pnas.171576611529382744 10.1073/pnas.1715766115
36. Fox MD Snyder AZ Vincent JL The human brain is intrinsically organized into dynamic, anticorrelated functional networks Proc Natl Acad Sci 2005 102 9673 9678 10.1073/pnas.0504136102 15976020
Fox MD, Snyder AZ, Vincent JL et al (2005) The human brain is intrinsically organized into dynamic, anticorrelated functional networks. Proc Natl Acad Sci 102:9673–9678. 10.1073/pnas.050413610215976020 10.1073/pnas.0504136102
37. Esposito R Cieri F Chiacchiaretta P Modifications in resting state functional anticorrelation between default mode network and dorsal attention network: comparison among young adults, healthy elders and mild cognitive impairment patients Brain Imaging Behav 2018 12 127 141 10.1007/s11682-017-9686-y 28176262
Esposito R, Cieri F, Chiacchiaretta P et al (2018) Modifications in resting state functional anticorrelation between default mode network and dorsal attention network: comparison among young adults, healthy elders and mild cognitive impairment patients. Brain Imaging Behav 12:127–141. 10.1007/s11682-017-9686-y28176262 10.1007/s11682-017-9686-y
38. Owens MM Yuan D Hahn S Investigation of psychiatric and neuropsychological correlates of default mode network and dorsal attention network anticorrelation in children Cereb Cortex N Y NY 2020 30 6083 6096 10.1093/cercor/bhaa143
Owens MM, Yuan D, Hahn S et al (2020) Investigation of psychiatric and neuropsychological correlates of default mode network and dorsal attention network anticorrelation in children. Cereb Cortex N Y NY 30:6083–6096. 10.1093/cercor/bhaa14310.1093/cercor/bhaa143
39. Perri CD Bahri MA Amico E Neural correlates of consciousness in patients who have emerged from a minimally conscious state: a cross-sectional multimodal imaging study Lancet Neurol 2016 15 830 842 10.1016/S1474-4422(16)00111-3 27131917
Perri CD, Bahri MA, Amico E et al (2016) Neural correlates of consciousness in patients who have emerged from a minimally conscious state: a cross-sectional multimodal imaging study. Lancet Neurol 15:830–842. 10.1016/S1474-4422(16)00111-327131917 10.1016/S1474-4422(16)00111-3
40. Demertzi A Antonopoulos G Heine L Intrinsic functional connectivity differentiates minimally conscious from unresponsive patients Brain J Neurol 2015 138 2619 2631 10.1093/brain/awv169
Demertzi A, Antonopoulos G, Heine L et al (2015) Intrinsic functional connectivity differentiates minimally conscious from unresponsive patients. Brain J Neurol 138:2619–2631. 10.1093/brain/awv16910.1093/brain/awv169
41. Demertzi A Kucyi A Ponce-Alvarez A Functional network antagonism and consciousness Netw Neurosci 2022 6 998 1009 10.1162/netn_a_00244 38800457
Demertzi A, Kucyi A, Ponce-Alvarez A et al (2022) Functional network antagonism and consciousness. Netw Neurosci 6:998–1009. 10.1162/netn_a_0024438800457 10.1162/netn_a_00244
42. Boly M Tshibanda L Vanhaudenhuyse A Functional connectivity in the default network during resting state is preserved in a vegetative but not in a brain dead patient Hum Brain Mapp 2009 30 2393 2400 10.1002/hbm.20672 19350563
Boly M, Tshibanda L, Vanhaudenhuyse A et al (2009) Functional connectivity in the default network during resting state is preserved in a vegetative but not in a brain dead patient. Hum Brain Mapp 30:2393–2400. 10.1002/hbm.2067219350563 10.1002/hbm.20672
43. Laureys S Lemaire C Maquet P Cerebral metabolism during vegetative state and after recovery to consciousness J Neurol Neurosurg Psychiatry 1999 67 121 122 10.1136/jnnp.67.1.121 10454871
Laureys S, Lemaire C, Maquet P et al (1999) Cerebral metabolism during vegetative state and after recovery to consciousness. J Neurol Neurosurg Psychiatry 67:121–122. 10.1136/jnnp.67.1.12110454871 10.1136/jnnp.67.1.121
44. Chen Y Zhang J How energy supports our brain to yield consciousness: insights from neuroimaging based on the neuroenergetics hypothesis Front Syst Neurosci 2021 15 648860 10.3389/fnsys.2021.648860 34295226
Chen Y, Zhang J (2021) How energy supports our brain to yield consciousness: insights from neuroimaging based on the neuroenergetics hypothesis. Front Syst Neurosci 15:648860. 10.3389/fnsys.2021.64886034295226 10.3389/fnsys.2021.648860
45. Fransson P Marrelec G The precuneus/posterior cingulate cortex plays a pivotal role in the default mode network: evidence from a partial correlation network analysis Neuroimage 2008 42 1178 1184 10.1016/j.neuroimage.2008.05.059 18598773
Fransson P, Marrelec G (2008) The precuneus/posterior cingulate cortex plays a pivotal role in the default mode network: evidence from a partial correlation network analysis. Neuroimage 42:1178–1184. 10.1016/j.neuroimage.2008.05.05918598773 10.1016/j.neuroimage.2008.05.059
46. Hagmann P Cammoun L Gigandet X Mapping the structural core of human cerebral cortex PLOS Biol 2008 6 e159 10.1371/journal.pbio.0060159 18597554
Hagmann P, Cammoun L, Gigandet X et al (2008) Mapping the structural core of human cerebral cortex. PLOS Biol 6:e159. 10.1371/journal.pbio.006015918597554 10.1371/journal.pbio.0060159
47. Amiri M Fisher PM Raimondo F Multimodal prediction of residual consciousness in the intensive care unit: the CONNECT-ME study Brain J Neurol 2023 146 50 64 10.1093/brain/awac335
Amiri M, Fisher PM, Raimondo F et al (2023) Multimodal prediction of residual consciousness in the intensive care unit: the CONNECT-ME study. Brain J Neurol 146:50–64. 10.1093/brain/awac33510.1093/brain/awac335
48. Bruno MA, Fernández-Espejo D, Lehembre R et al (2011) Multimodal neuroimaging in patients with disorders of consciousness showing “functional hemispherectomy.” In: Van Someren EJW, Van Der Werf YD, Roelfsema PR, et al (eds). Progress in Brain Research. Elsevier, pp 323–333
49. Giacino JT Ashwal S Childs N The minimally conscious state Neurology 2002 58 349 353 10.1212/WNL.58.3.349 11839831
Giacino JT, Ashwal S, Childs N et al (2002) The minimally conscious state. Neurology 58:349–353. 10.1212/WNL.58.3.34911839831 10.1212/WNL.58.3.349
50. Pincherle A Jöhr J Chatelle C Motor behavior unmasks residual cognition in disorders of consciousness Ann Neurol 2019 85 443 447 10.1002/ana.25417 30661258
Pincherle A, Jöhr J, Chatelle C et al (2019) Motor behavior unmasks residual cognition in disorders of consciousness. Ann Neurol 85:443–447. 10.1002/ana.2541730661258 10.1002/ana.25417
51. Pignat J-M Mauron E Jöhr J Outcome prediction of consciousness disorders in the acute stage based on a complementary motor behavioural tool PLoS ONE 2016 11 e0156882 e0156882 10.1371/journal.pone.0156882 27359335
Pignat J-M, Mauron E, Jöhr J et al (2016) Outcome prediction of consciousness disorders in the acute stage based on a complementary motor behavioural tool. PLoS ONE 11:e0156882–e0156882. 10.1371/journal.pone.015688227359335 10.1371/journal.pone.0156882
52. Diserens K Meyer IA Jöhr J A focus on subtle signs and motor behavior to unveil awareness in unresponsive brain-impaired patients: the importance of being clinical Neurology 2023 100 1144 1150 10.1212/WNL.0000000000207067 36854621
Diserens K, Meyer IA, Jöhr J et al (2023) A focus on subtle signs and motor behavior to unveil awareness in unresponsive brain-impaired patients: the importance of being clinical. Neurology 100:1144–1150. 10.1212/WNL.000000000020706736854621 10.1212/WNL.0000000000207067
53. Williams MW Clinical utility and psychometric properties of the disability rating scale with individuals with traumatic brain injury Rehabil Psychol 2017 62 407 10.1037/rep0000168 28836811
Williams MW (2017) Clinical utility and psychometric properties of the disability rating scale with individuals with traumatic brain injury. Rehabil Psychol 62:407. 10.1037/rep000016828836811 10.1037/rep0000168
54. Lin K, Wroten M (2021) Ranchos Los Amigos. In: StatPearls. StatPearls Publishing, Treasure Island (FL)
55. Mehrholz J Wagner K Rutte K Predictive validity and responsiveness of the functional ambulation category in hemiparetic patients after stroke Arch Phys Med Rehabil 2007 88 1314 1319 10.1016/j.apmr.2007.06.764 17908575
Mehrholz J, Wagner K, Rutte K et al (2007) Predictive validity and responsiveness of the functional ambulation category in hemiparetic patients after stroke. Arch Phys Med Rehabil 88:1314–1319. 10.1016/j.apmr.2007.06.76417908575 10.1016/j.apmr.2007.06.764
56. Alemán-Gómez Y Griffa A Houde J-C A multi-scale probabilistic atlas of the human connectome Sci Data 2022 9 516 10.1038/s41597-022-01624-8 35999243
Alemán-Gómez Y, Griffa A, Houde J-C et al (2022) A multi-scale probabilistic atlas of the human connectome. Sci Data 9:516. 10.1038/s41597-022-01624-835999243 10.1038/s41597-022-01624-8
57. Esteban O Markiewicz CJ Blair RW fMRIPrep: a robust preprocessing pipeline for functional MRI Nat Methods 2019 16 111 116 10.1038/s41592-018-0235-4 30532080
Esteban O, Markiewicz CJ, Blair RW et al (2019) fMRIPrep: a robust preprocessing pipeline for functional MRI. Nat Methods 16:111–116. 10.1038/s41592-018-0235-430532080 10.1038/s41592-018-0235-4
58. Esteban O Ciric R Finc K Analysis of task-based functional MRI data preprocessed with fMRIPrep Nat Protoc 2020 15 2186 2202 10.1038/s41596-020-0327-3 32514178
Esteban O, Ciric R, Finc K et al (2020) Analysis of task-based functional MRI data preprocessed with fMRIPrep. Nat Protoc 15:2186–2202. 10.1038/s41596-020-0327-332514178 10.1038/s41596-020-0327-3
59. Calhoun VD Adali T Pearlson GD Pekar JJ A method for making group inferences from functional MRI data using independent component analysis Hum Brain Mapp 2001 14 140 151 10.1002/hbm.1048 11559959
Calhoun VD, Adali T, Pearlson GD, Pekar JJ (2001) A method for making group inferences from functional MRI data using independent component analysis. Hum Brain Mapp 14:140–151. 10.1002/hbm.104811559959 10.1002/hbm.1048
60. Griffanti L Douaud G Bijsterbosch J Hand classification of fMRI ICA noise components Neuroimage 2017 154 188 205 10.1016/j.neuroimage.2016.12.036 27989777
Griffanti L, Douaud G, Bijsterbosch J et al (2017) Hand classification of fMRI ICA noise components. Neuroimage 154:188–205. 10.1016/j.neuroimage.2016.12.03627989777 10.1016/j.neuroimage.2016.12.036
61. Thomas Yeo BT Krienen FM Sepulcre J The organization of the human cerebral cortex estimated by intrinsic functional connectivity J Neurophysiol 2011 106 1125 1165 10.1152/jn.00338.2011 21653723
Thomas Yeo BT, Krienen FM, Sepulcre J et al (2011) The organization of the human cerebral cortex estimated by intrinsic functional connectivity. J Neurophysiol 106:1125–1165. 10.1152/jn.00338.201121653723 10.1152/jn.00338.2011
62. Witt ST van Ettinger-Veenstra H Salo T What executive function network is that? an image-based meta-analysis of network labels Brain Topogr 2021 34 598 607 10.1007/s10548-021-00847-z 33970388
Witt ST, van Ettinger-Veenstra H, Salo T et al (2021) What executive function network is that? an image-based meta-analysis of network labels. Brain Topogr 34:598–607. 10.1007/s10548-021-00847-z33970388 10.1007/s10548-021-00847-z
63. Vuong QH Likelihood ratio tests for model selection and non-nested hypotheses Econometrica 1989 57 307 333 10.2307/1912557
Vuong QH (1989) Likelihood ratio tests for model selection and non-nested hypotheses. Econometrica 57:307–33310.2307/1912557
64. Pozeg P Jöhr J Pincherle A Discriminating cognitive motor dissociation from disorders of consciousness using structural MRI NeuroImage Clin 2021 30 102651 10.1016/j.nicl.2021.102651 33836454
Pozeg P, Jöhr J, Pincherle A et al (2021) Discriminating cognitive motor dissociation from disorders of consciousness using structural MRI. NeuroImage Clin 30:102651. 10.1016/j.nicl.2021.10265133836454 10.1016/j.nicl.2021.102651
65. Schiff ND Central thalamic contributions to arousal regulation and neurological disorders of consciousness Ann N Y Acad Sci 2008 1129 105 118 10.1196/annals.1417.029 18591473
Schiff ND (2008) Central thalamic contributions to arousal regulation and neurological disorders of consciousness. Ann N Y Acad Sci 1129:105–118. 10.1196/annals.1417.02918591473 10.1196/annals.1417.029
66. Fridman E Beattie B Broft A Regional cerebral metabolic patterns demonstrate the role of anterior forebrain mesocircuit dysfunction in the severely injured brain Proc Natl Acad Sci 2014 10.1073/pnas.1320969111 24733913
Fridman E, Beattie B, Broft A et al (2014) Regional cerebral metabolic patterns demonstrate the role of anterior forebrain mesocircuit dysfunction in the severely injured brain. Proc Natl Acad Sci. 10.1073/pnas.132096911124733913 10.1073/pnas.1320969111
67. Lant ND Gonzalez-Lara LE Owen AM Fernández-Espejo D Relationship between the anterior forebrain mesocircuit and the default mode network in the structural bases of disorders of consciousness NeuroImage Clin 2016 10 27 35 10.1016/j.nicl.2015.11.004 26693399
Lant ND, Gonzalez-Lara LE, Owen AM, Fernández-Espejo D (2016) Relationship between the anterior forebrain mesocircuit and the default mode network in the structural bases of disorders of consciousness. NeuroImage Clin 10:27–35. 10.1016/j.nicl.2015.11.00426693399 10.1016/j.nicl.2015.11.004
68. Panda R Thibaut A Lopez-Gonzalez A Disruption in structural–functional network repertoire and time-resolved subcortical fronto-temporoparietal connectivity in disorders of consciousness Elife 2022 11 e77462 10.7554/eLife.77462 35916363
Panda R, Thibaut A, Lopez-Gonzalez A et al (2022) Disruption in structural–functional network repertoire and time-resolved subcortical fronto-temporoparietal connectivity in disorders of consciousness. Elife 11:e77462. 10.7554/eLife.7746235916363 10.7554/eLife.77462
69. Rohaut B Doyle KW Reynolds AS Deep structural brain lesions associated with consciousness impairment early after hemorrhagic stroke Sci Rep 2019 9 4174 10.1038/s41598-019-41042-2 30862910
Rohaut B, Doyle KW, Reynolds AS et al (2019) Deep structural brain lesions associated with consciousness impairment early after hemorrhagic stroke. Sci Rep 9:4174. 10.1038/s41598-019-41042-230862910 10.1038/s41598-019-41042-2
70. Franzova E, Shen Q, Doyle K, et al (2023) Injury patterns associated with cognitive motor dissociation. Brain awad197. 10.1093/brain/awad197
71. Threlkeld ZD Bodien YG Rosenthal ES Functional networks reemerge during recovery of consciousness after acute severe traumatic brain injury Cortex 2018 106 299 308 10.1016/j.cortex.2018.05.004 29871771
Threlkeld ZD, Bodien YG, Rosenthal ES et al (2018) Functional networks reemerge during recovery of consciousness after acute severe traumatic brain injury. Cortex 106:299–308. 10.1016/j.cortex.2018.05.00429871771 10.1016/j.cortex.2018.05.004
72. Haugg A Cusack R Gonzalez-Lara LE Do patients thought to lack consciousness retain the capacity for internal as well as external awareness? Front Neurol 2018 9 492 10.3389/fneur.2018.00492 29997565
Haugg A, Cusack R, Gonzalez-Lara LE et al (2018) Do patients thought to lack consciousness retain the capacity for internal as well as external awareness? Front Neurol 9:49229997565 10.3389/fneur.2018.00492
73. Müller NCJ Dresler M Janzen G Medial prefrontal decoupling from the default mode network benefits memory Neuroimage 2020 210 116543 10.1016/j.neuroimage.2020.116543 31940475
Müller NCJ, Dresler M, Janzen G et al (2020) Medial prefrontal decoupling from the default mode network benefits memory. Neuroimage 210:116543. 10.1016/j.neuroimage.2020.11654331940475 10.1016/j.neuroimage.2020.116543
74. Boveroux P Vanhaudenhuyse A Bruno M-A Breakdown of within- and between-network resting state functional magnetic resonance imaging connectivity during propofol-induced loss of consciousness Anesthesiology 2010 113 1038 1053 10.1097/ALN.0b013e3181f697f5 20885292
Boveroux P, Vanhaudenhuyse A, Bruno M-A et al (2010) Breakdown of within- and between-network resting state functional magnetic resonance imaging connectivity during propofol-induced loss of consciousness. Anesthesiology 113:1038–1053. 10.1097/ALN.0b013e3181f697f520885292 10.1097/ALN.0b013e3181f697f5
75. Boly M Phillips C Tshibanda L Intrinsic brain activity in altered states of consciousness Ann N Y Acad Sci 2008 1129 119 129 10.1196/annals.1417.015 18591474
Boly M, Phillips C, Tshibanda L et al (2008) Intrinsic brain activity in altered states of consciousness. Ann N Y Acad Sci 1129:119–129. 10.1196/annals.1417.01518591474 10.1196/annals.1417.015
76. Norton L Hutchison RM Young GB Disruptions of functional connectivity in the default mode network of comatose patients Neurology 2012 78 175 181 10.1212/WNL.0b013e31823fcd61 22218274
Norton L, Hutchison RM, Young GB et al (2012) Disruptions of functional connectivity in the default mode network of comatose patients. Neurology 78:175–181. 10.1212/WNL.0b013e31823fcd6122218274 10.1212/WNL.0b013e31823fcd61
77. McMillan T Wilson L Ponsford J The glasgow outcome scale-40 years of application and refinement Nat Rev Neurol 2016 12 477 485 10.1038/nrneurol.2016.89 27418377
McMillan T, Wilson L, Ponsford J et al (2016) The glasgow outcome scale-40 years of application and refinement. Nat Rev Neurol 12:477–485. 10.1038/nrneurol.2016.8927418377 10.1038/nrneurol.2016.89
78. Hermann B Stender J Habert M-O Multimodal FDG-PET and EEG assessment improves diagnosis and prognostication of disorders of consciousness NeuroImage Clin 2021 30 102601 10.1016/j.nicl.2021.102601 33652375
Hermann B, Stender J, Habert M-O et al (2021) Multimodal FDG-PET and EEG assessment improves diagnosis and prognostication of disorders of consciousness. NeuroImage Clin 30:102601. 10.1016/j.nicl.2021.10260133652375 10.1016/j.nicl.2021.102601
79. Berti V Mosconi L Pupi A Brain: normal variations and benign findings in fluorodeoxyglucose-PET/computed tomography imaging PET Clin 2014 9 129 140 10.1016/j.cpet.2013.10.006 24772054
Berti V, Mosconi L, Pupi A (2014) Brain: normal variations and benign findings in fluorodeoxyglucose-PET/computed tomography imaging. PET Clin 9:129–140. 10.1016/j.cpet.2013.10.00624772054 10.1016/j.cpet.2013.10.006
80. Liu Y Li Z Bai Y Frontal and parietal lobes play crucial roles in understanding the disorder of consciousness: a perspective from electroencephalogram studies Front Neurosci 2023 16 1024278 10.3389/fnins.2022.1024278 36778900
Liu Y, Li Z, Bai Y (2023) Frontal and parietal lobes play crucial roles in understanding the disorder of consciousness: a perspective from electroencephalogram studies. Front Neurosci 16:1024278. 10.3389/fnins.2022.102427836778900 10.3389/fnins.2022.1024278
81. Crone JS Soddu A Höller Y Altered network properties of the fronto-parietal network and the thalamus in impaired consciousness NeuroImage Clin 2013 4 240 248 10.1016/j.nicl.2013.12.005 24455474
Crone JS, Soddu A, Höller Y et al (2013) Altered network properties of the fronto-parietal network and the thalamus in impaired consciousness. NeuroImage Clin 4:240–248. 10.1016/j.nicl.2013.12.00524455474 10.1016/j.nicl.2013.12.005
82. Hannawi Y Lindquist MA Resting brain activity in disorders of consciousness Neurology 2015 84 1272 1280 10.1212/WNL.0000000000001404 25713001
Hannawi Y, Lindquist MA (2015) Resting brain activity in disorders of consciousness. Neurology 84:1272–128025713001 10.1212/WNL.0000000000001404
83. Chen L Rao B Li S Altered effective connectivity measured by resting-state functional magnetic resonance imaging in posterior parietal-frontal-striatum circuit in patients with disorder of consciousness Front Neurosci 2022 15 766633 10.3389/fnins.2021.766633 35153656
Chen L, Rao B, Li S et al (2022) Altered effective connectivity measured by resting-state functional magnetic resonance imaging in posterior parietal-frontal-striatum circuit in patients with disorder of consciousness. Front Neurosci 15:76663335153656 10.3389/fnins.2021.766633
84. Tanglay O Young IM Dadario NB Anatomy and white-matter connections of the precuneus Brain Imaging Behav 2022 16 574 586 10.1007/s11682-021-00529-1 34448064
Tanglay O, Young IM, Dadario NB et al (2022) Anatomy and white-matter connections of the precuneus. Brain Imaging Behav 16:574–586. 10.1007/s11682-021-00529-134448064 10.1007/s11682-021-00529-1
85. Dadario NB Sughrue ME The functional role of the precuneus Brain 2023 146 3598 3607 10.1093/brain/awad181 37254740
Dadario NB, Sughrue ME (2023) The functional role of the precuneus. Brain 146:3598–3607. 10.1093/brain/awad18137254740 10.1093/brain/awad181
86. Onami S Tran D Koh-Pham C Coma recovery scale-revised predicts disability rating scale in acute rehabilitation of severe traumatic brain injury Arch Phys Med Rehabil 2023 104 1054 1061 10.1016/j.apmr.2023.01.007 36736600
Onami S, Tran D, Koh-Pham C et al (2023) Coma recovery scale-revised predicts disability rating scale in acute rehabilitation of severe traumatic brain injury. Arch Phys Med Rehabil 104:1054–1061. 10.1016/j.apmr.2023.01.00736736600 10.1016/j.apmr.2023.01.007
87. Stamatakis EA Adapa RM Absalom AR Menon DK Changes in resting neural connectivity during propofol sedation PLoS ONE 2010 5 e14224 10.1371/journal.pone.0014224 21151992
Stamatakis EA, Adapa RM, Absalom AR, Menon DK (2010) Changes in resting neural connectivity during propofol sedation. PLoS ONE 5:e14224. 10.1371/journal.pone.001422421151992 10.1371/journal.pone.0014224
88. Figley CR Uddin MN Wong K Potential pitfalls of using fractional anisotropy, axial diffusivity, and radial diffusivity as biomarkers of cerebral white matter microstructure Front Neurosci 2022 15 799576 10.3389/fnins.2021.799576 35095400
Figley CR, Uddin MN, Wong K et al (2022) Potential pitfalls of using fractional anisotropy, axial diffusivity, and radial diffusivity as biomarkers of cerebral white matter microstructure. Front Neurosci 15:79957635095400 10.3389/fnins.2021.799576
89. Kamiya K Hori M Aoki S NODDI in clinical research J Neurosci Methods 2020 346 108908 10.1016/j.jneumeth.2020.108908 32814118
Kamiya K, Hori M, Aoki S (2020) NODDI in clinical research. J Neurosci Methods 346:108908. 10.1016/j.jneumeth.2020.10890832814118 10.1016/j.jneumeth.2020.108908
90. Botvinik-Nezer R Wager TD Reproducibility in neuroimaging analysis: challenges and solutions Biol Psychiatry Cogn Neurosci Neuroimaging 2023 8 780 788 10.1016/j.bpsc.2022.12.006 36906444
Botvinik-Nezer R, Wager TD (2023) Reproducibility in neuroimaging analysis: challenges and solutions. Biol Psychiatry Cogn Neurosci Neuroimaging 8:780–788. 10.1016/j.bpsc.2022.12.00636906444 10.1016/j.bpsc.2022.12.006
