Structural and Functional Connectivity of the Ascending Arousal Network for Prediction of Outcome in Patients with Acute Disorders of Consciousness

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Enciso-Olivera, Edgar G. Ordóñez-Rubiano, Rosángela Casanova-Libreros, and 12 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-244211/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 25 Nov, 2021 Read the published version in Scientific Reports → Version 1 posted 9 You are reading this latest preprint version Abstract Object: To determine the role of early acquisition of blood oxygen level-dependent (BOLD) signals and diffusion tensor imaging (DTI) for analysis of the connectivity of the ascending arousal network (AAN) in predicting neurological outcomes after acute traumatic brain injury (TBI), cardiopulmonary arrest (CPA), or stroke. Methods: A prospective analysis of 50 comatose patients was performed during their ICU stay. Image processing was conducted to assess structural and functional connectivity of the AAN. Outcomes were evaluated after 3 and 6 months. Results: Nineteen patients (38%) had stroke, 18 (36%) CPA, and 13 (26%) TBI. Twenty-three patients were comatose (44%), 11 were in a minimally conscious state (20%), and 16 had unresponsive wakefulness syndrome (32%). Univariate analysis demonstrated that measurements of diffusivity, functional connectivity, and numbers of fibers in the gray matter, white matter, whole brain, midbrain reticular formation, and pontis oralis nucleus may serve as predictive biomarkers of outcome depending on the diagnosis. Multivariate analysis demonstrated a correlation of the predicted value and the real outcome for each separate diagnosis and for all the etiologies together. Conclusion: Findings suggest that the above imaging biomarkers may have a predictive role for the outcome of comatose patients after acute TBI, CPA, or stroke. Nuclear Medicine & Medical Imaging Neurology diffusion tensor imaging (DTI) diagnosis neurology Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Predicting neurological outcomes and mortality in patients with acute disorders of consciousness (DOCs) is challenging 1 – 3 , and clinicians continue to question complex connection impairments of the ascending arousal network (AAN) in comatose patients 2 , 4 . Unfortunately, current clinical and radiological tools are not reliable for detecting consciousness or predicting recovery in those with either severe traumatic brain injury (TBI) 5 , cardiac arrest 3 , 6 , 7 , or stroke 7 , 8 . To this end, multiple clinical and radiological tests have been proposed for assessing patients with DOCs, including bedside behavioral assessment 9 , electroencephalography (EEG) 10 , task-based functional magnetic resonance imaging (fMRI) 11 , resting-state-fMRI (rsfMRI) 6 , 7 , 12 , diffusion tensor imaging (DTI) 1 , 2 , 7 , 8 , 13 , and different combinations of these approaches 5 , 12 , 14 . In the absence of reliable prognostic tests, the clinician’s judgment, experience, and communication skills may influence a family’s decision about life-sustaining therapy and lead to premature care decisions before a patient’s prognosis becomes clear 14 . In this regard, investigations have focused on diagnostic tests that might objectify this initial prediction assessment of consciousness outcome 2 , 4 , 6 , 7 . The AAN is an essential component of human consciousness and is formed by a group of subcortical pathways connecting the rostral brainstem tegmentum to the hypothalamus, thalamus, and basal forebrain 2 , 13 . It has been described that DOCs after TBI, cardiac arrest, or stroke are related to axonal injury within the AAN 1 , 13 , 15 . Additionally, clinical and electrophysiological evaluations are insufficient and might be biased by sedation or any clinical condition, such as aphasia 16 . Accordingly, there is uncertainty concerning long-term effects in a broad spectrum of cognitive, behavioral, and functional impairments 16 . Overall, more specialized tests derived from MRI may be able to better characterize microstructural disturbances. Blood oxygen level-dependent (BOLD) imaging utilizes a gradient-echo imaging sequence with parameters sensitive to the oxygen state of hemoglobin, which is used as contrast to delineate regional brain activity 17 . BOLD imaging allows for the analysis of both task-based fMRI and rsfMRI. rsfMRI itself can be used for studying different resting-state neural networks (RSNs) to establish functional connectivity in patients with a DOC 12 . On the other hand, DTI uses anisotropic diffusion to estimate the organization of brain tissue. Additionally, structural analysis of white matter (WM) with DTI techniques, including diffusion tensor tractography (DTT), has allowed physicians to scrutinize the anatomy of the AAN 1 , 8 , 13 , 15 , 18 , revealing the structural connectivity of this network 1 , 19 . Both tools can be employed to determine ascending and descending structural and functional connectivity between AAN brainstem nuclei and many different cortical areas 12 . Nevertheless, the exact biological nature of the structural and functional injury that leads to a DOC remains uncertain. Multiple efforts to elucidate the origin of impaired consciousness have led to the proposition of a compromised state for multiple cortical and subcortical areas and networks that may be involved in this process, including the brainstem 20 , thalamus 6 , 21 , hypothalamus 1 , frontal basal cortex 1 , 4 , 5 , 13 , and other association areas in the parietal lobe 6 . However, varying impairment in these areas may induce any DOC regardless of the etiology of the injury. In this regard, the aim of the present study was to analyze a combination of structural and functional information of the AAN obtained from both DTI and BOLD acquisitions to determine whether early acquisition of DTI and BOLD techniques for analysis of structural and functional connectivity of the AAN can predict neurological outcomes in terms of consciousness in patients with DOCs after TBI, cardiopulmonary arrest (CPA), or stroke. Results Clinical Features Between October 2017 and January 2020, a total of 293 patients were assessed for eligibility criteria, of whom 50 were enrolled. Of the 243 excluded subjects, 104 were excluded for having a GCS score 8 or higher, 58 due to a previous history of any neurological or psychiatric disease, 41 because they were not able to be transferred to the MRI scanner due to their medical condition, 13 because MRI was not performed before death, 10 due to radiologically confirmed brain death during the first 48 hours after being admitted to the ICU, eight due to the family's decision not to participate, and nine due to other reasons (Fig. 1 ). The median age of the enrolled patients was 64 years (IQR: 49–74), and 27 (54%) were female. Nineteen patients (38%) were admitted with stroke, 18 (36%) with hypoxic-ischemic brain injury after cardiac arrest, and 13 (26%) with severe TBI. In regard to the state of consciousness, 23 patients were in coma (44%), 11 in MCS (20%), and 16 in UWS (32%). The median length of stay in the ICU was 13.2 days (IQR: 5.1–21.3). The overall median ICU admission GCS score was 6 (IQR: 3–8); the UWS group had a median score of 8 (IQR: 4–8), the coma group a median score of 6 (IQR: 3–7), and MCS a median score of 5 (IQR: 4–8). The 28 patients (56%) who were discharged from the ICU had a median GCS score of 9 (IQR: 3–12) (Table 1 ). Among them, 17 died during the follow-up before the neuropsychological evaluation in an outpatient setting. Additionally, 4 patients were lost from the study due to loss of contact with their surrogates. Table 1 Clinical and demographic features according to state of consciousness after ICU admission. Coma (n = 23) MCS (n = 11) UWS (n = 16) Total (n = 50) p-value Age median (IQR) 50 (72–86) 48 (73–90) 47 (76–92) 49 (74–88) 0.42 Sex 0.56 Female (%) 13 (56.5) 7 (63.6) 7 (43.8) 27 (54) Male (%) 10 (43.5) 4 (36.4) 9 (56.2) 23 (46) Education 0.16 Illiterate (%) 0 (0) 0 (0) 1 (6.3) 0 (0) High School (%) 9 (39.1) 2 (18.2) 5 (31.2) 16 (32) Primary School (%) 10 (43.5) 5 (45.5) 2 (12.5) 17 (34) Technical (%) 0 (0) 2 (18.2) 2 (12.5) 4 (8) Graduate School (%) 4 (17.4) 2 (18.2) 6 (37.5) 12 (24) Comorbidities 0.82 Hypertension (%) 7 (30.4) 7 (63.6) 8 (50) 22 (44) Diabetes Mellitus (%) 6 (26.1) 3 (27.3) 3 (18.8) 12 (24) Hypothyroidism (%) 2 (8.7) 2 (18.2) 3 (18.8) 7 (14) Dyslipidemia (%) 1 (4.3) 1 (9.1) 0 (0) 2 (4) Acute myocardial infarction (%) 0 (0) 1 (9.1) 1 (6.3) 2 (4) Other (%) 5 (21.7) 5 (45.5) 11 (68.8) 21 (42) ICU admission GCS score median (IQR) 6 (3–7) 5 (4–8) 8 (4–8) 6 (3–8) 0.08 Intracranial Pressure ICP monitoring (%) 2 (8.7) 1 (9.1) 1 (6.25) 4 (8) 0.95 Intracranial hypertension (%) 2 (8.7) 2 (18.2) 1 (6.25) 5 (10) 0.57 Mechanical ventilation (%) 22 (95.7) 10 (90.9) 14 (87.5) 46 (92) 0.65 Sepsis (%) 8 (34.8) 3 (27.3) 8 (50) 19 (38) 0.45 Vital status Alive (%) 12 (52.2) 4 (36.4) 12 (75) 28 (56) 0.17 Deceased (%) 10 (43.5) 5 (45.5) 2 (12.5) 17 (43) *Transferred to a different institution (%) 0 (0) 1 (9.1) 2 (12.5) 3 (6) GCS score at discharge median (IQR) 6 (3–11) 3 (3–14) 11 (9–12) 9 (3–12) 0.15 *Expenses not covered by their health insurance company in our institution P values correspond to comparative measures among the three patient groups. Structural and Functional Connectivity Univariate Analysis A set of 60 individual imaging measurements was completed. However, only sixteen had an area under the curve (AUC) \(\ge\) 0.80. Thus, these specific features were explored as possible predictors with at least an accuracy of 80%. A remarkable finding is that some single regions might serve as biomarkers of consciousness at ICU discharge. Figure 2 illustrates the potential use of single imaging measurements in specific regions to predict the patient’s consciousness at ICU discharge. The ROC curve indicates possible use as an isolated approach for measuring specific regions to predict GCS score at ICU discharge. Based on separate analysis for each variable, the gray matter (GM) FA was found to be a possible predictor in the setting of TBI. The AD, MD, and RD in both the GM and WM, as well as functional connectivity in the PON, and the number of fibers in the locus coeruleus (LC) and the parabrachial complex (PC) are possible predictors in the setting of CPA. Measurements of MD in the whole brain, WM, GM, MRF and PON, of AD in the WM, GM, and MRF, and the RD in the whole brain, WM, and MRF were found to be possible predictors of the state of consciousness at ICU discharge in the setting of a stroke. Separate analysis of automatic DTI, tractrography (Fig. 3 ) and BOLD measurements showed no predictive value for the state of consciousness at ICU discharge. Structural and Functional Connectivity - Multivariate Analysis Figure 4 illustrates the results obtained by GLM for each separate diagnosis; independent variables included the confounding variables and those features obtained from DTI and BOLD acquisitions. This figure shows the correlation between the real outcome and the value predicted by the model. In the setting of TBI, the model reached an adjustment defined by the metric R2 = 0.463; it was 0.84 for stroke and 0.92 for CPA. Moreover, Fig. 5 denotes the correlation between the outcome and the model’s predicted value from a global aspect, grouping patients with TBI, CPA, and stroke into one group. Three different scenarios were explored: (1) including both structural (DTI) and functional (BOLD) features, (2) functional features alone, and (3) structural features alone. The R2 values obtained for each scenario were 0.82, 0.463 and 0.5, respectively. These trends show the great importance of combining both features. Neurological Outcomes Thirty of the 50 patients died during follow-up before the neuropsychological evaluation. Of those remaining, five were lost during follow-up. Tests were performed for the remaining 15 patients. The NeuroPsi demonstrated that 9 (60%) of the patients had normal psychological behavior, 1 (6.6%) presented moderate psychological sequelae, and 5 (33.3%) presented severe cognitive sequelae. Additionally, 40% presented abnormal orientation, 3 (20%) had severe compromise of their attention/concentration, and 4 (26.7%) and 5 (33.3%) had moderate and severe visual memory impairment, respectively. On the other hand, the MoCa test showed that 11 (73.3%) patients had cognitive deficits; 4 (26.7%) had normal scores. Visual memory showed higher cognitive compromise, while the best performance was observed for language, attention, and orientation. Conclusions Our findings suggest that early acquisition of BOLD and DTI for evaluation of structural and functional connectivity of the AAN may represent a tool for predicting outcome in patients with impaired consciousness after acute TBI, CPA, or stroke. DTI and BOLD analysis represent an observer- and operator-dependent task despite the automatic data processing involved, and clinical decision making must be made by physicians in a case-by-case manner. Limitations The limited number of patients recruited for this study represents a notable limitation. Although different etiologies were included, a separate analysis of each group was performed. This study sought to elucidate the prognostic value of early DTI and BOLD acquisitions, yet there are multifactorial limitations to enrolling comatose patients, including the high mortality in these scenarios as well as the social and economic background of a middle-income country. In addition, as this study lacks EEG data, further studies are needed to compare EEG findings with radiological biomarkers. Finally, specific analysis of the emotional, behavioral, and general neurological outcomes should also be addressed for targeted therapeutic assessment. Methods Clinical Data and Study Design This is a prospective, observational, cohort-type diagnostic test study. Patients admitted to the ICU with acute DOC after CPA, stroke or TBI who stayed in the ICU for more than 48 hours were enrolled. Ten healthy volunteer adult subjects were recruited and analyzed as the control group 12 . Patients were admitted to the Hospital Infantil Universitario de San José ICU between October 2017 and January 2020. Inclusion criteria included patients over 18 years old, with either CPA treated within our institution with successful cardiopulmonary resuscitation, stroke (ischemic or hemorrhagic), or TBI, with a neurological evaluation prior to ICU admission consisting of coma (defined as Glasgow Coma Scale [GCS] score of ≤ 6/15 without eye opening) after the initial resuscitation and who could be transferred to the MRI scanner. Exclusion criteria included patients diagnosed with brain death within the first 48 hours of admission to the ICU, those with severe TBI who were considered to be “nonsalvageable” by the neurosurgery staff, and those who had any medical history of a neurological entity prior to the event (e.g., trauma, degenerative disease), and those whose family decided to withdraw them from the study at any time during the follow-up period. Written informed consent for inclusion in the study was obtained from a surrogate for each patient. Authorization by our Institutional Ethics Board to include information for the subjects was requested. This research was performed in accordance with the Declaration of Helsinki. This prospective study was approved by our Institutional Review Board ( Comité de Ética en Investigación con Seres Humanos - CEISH ). Neurological Outcomes The initial bedside cognitive and behavioral assessment was performed by a neurologist (J.H.) in the first 24 hours or up to three days after the event whenever possible. The following tests were added for assessment by the patient’s family: Lawton-Brody Instrumental Activities Scale, Frontal Systems Behavior Scale, and Memory Scale. Any sensory perception disorder was ruled out, and the family was asked about the patient's previous cognitive condition in relation to symptoms associated with previous behavioral changes, schooling, and occupation or any conditions that may bias the cognitive assessment. A subsequent evaluation was made by a neurologist (J.H.) and the ICU staff within the first 7 to 10 days after the initial injury or at the time of discharge from the ICU in the case of a short length of ICU stay. After the second assessment, the patients were categorized into those with coma, unresponsive wakefulness syndrome (UWS) (defined as a state of wakefulness without awareness in which there is preserved capacity for spontaneous or stimulus-induced arousal, evidenced by sleep–wake cycles and a range of reflexive and spontaneous behaviors, with complete absence of evidence for self or environmental awareness), or minimally conscious state (MCS) (defined as a state of severely altered consciousness in which minimal but clearly discernible behavioral evidence of self- or environmental awareness is demonstrated) 45 . Finally, the NeuroPSI (a short neuropsychological test battery for use with Spanish-speaking adults) 46 and the Montreal Cognitive Assessment (MoCA) test 47 were performed by a former neuropsychologist (C.P.H.) for cognitive function assessment at three- and six-month follow-ups whenever possible according to the patient's condition or a fatal outcome. Endpoints for evaluation were defined as an early consciousness status based on the average GCS score for the previous two days before ICU discharge, middle-term consciousness status based on the ability or not to perform the MoCA test (awareness of their selves or their environment) during follow-up, or a fatal outcome. Neuroimaging Data Acquisition A 1.5-T General Electric scanner was used for data acquisition. As reported previously 12 , we acquired one hundred and eighty multislice T2*-weighted functional images using an axial slice orientation and covering the whole brain (slice thickness = 4.5 mm without free space, matrix = 64 x 64 mm, TR = 3000 ms, TE = 60 ms, flip angle = 90° and FOV = 288 x 288 mm). The three initial volumes were discarded to avoid T1 saturation effects. Moreover, axial diffusion weighted imaging (DWI) (slice thickness = 2.5 mm without free space, matrix = 100 x 100, TR = 17000 ms, TE = 96 ms, flip angle = 90°, FOV = 250 x 250 mm, b value = 1000 and gradient directions = 30) was acquired. Finally, structural axial T1 (slice thickness = 1 mm, GAP = 1 mm, matrix = 256 x 256 mm, TR = 670 ms, TE = 22 ms, flip angle = 20° and FOV = 250 x 250 mm) and axial T2 (slice thickness = 6 mm, GAP = 1 mm, matrix = 320 x 320 mm, TR = 6.000 ms, TE = 96 ms, flip angle = 90° and FOV = 220 x 220 mm) images were acquired for anatomical reference. Neuroimaging Data Preprocessing The T1 and rsfMRI data were preprocessed using the approach suggested by Kandeepan et al . 48 In particular, T1 preprocessing included manual removal of the neck, brain extraction using FSL 49 , correction of low-frequency intensity nonuniformity based on the N4 bias field correction algorithm from SimpleITK 50 , image denoising based on the nonlocal means algorithm from Dipy 50 , 51 , and spatial normalization to standard stereotactic Montreal Neurological Institute (MNI) space using the SPM12 normalization algorithm 50 – 52 . The initial six volumes of the fMRI data were discarded to avoid T1 saturation effects. Head motion and slice timing corrections were performed on the fMRI data using FSL, followed by artifact correction using RapidArt 53 . Subsequently, the fMRI data were coregistered to a T1 image using SPM12 and spatially normalized to the MNI space using the SPM12 normalization algorithm. Finally, spatial smoothing of the fMRI data was performed with a Gaussian kernel of 8 mm full width at half maximum, as implemented in SPM12. The spurious variance was reduced by regression of nuisance waveforms derived from time series extracted from regions of noninterest (WM and cerebrospinal fluid). Additional nuisance regressors included the blood oxygen level-dependent imaging (BOLD) time series averaged over the whole brain. The DWI images were preprocessed using the approach suggested by Parra-Morales et al 12 . This process included automatic realignment, correction of eddy-current artifacts by using FSL tools, reslicing to obtain the isotropic voxel size, automatic brain extraction by the BET tool from FSL, and improvement of signal-to-noise rate using the Non-Local mean algorithm from Dipy. Location and Characterization of Regions of Interest Regions of interest (ROIs) for AAN reconstruction were located based on Harvard Ascending Arousal Network Atlas provided by the Martinos Center for Biomedical Imaging, Charleston, Massachusetts, USA 13 , and the cortex nuclei were extracted from Harvard-Oxford Atlas provided by the Harvard Center for Morphometric Analysis 54 . These nuclei were linearly (rigid, translation, and affine transformation) and nonlinearly (symmetric diffeomorphic registration) 55 registered with each subject space. AAN Functional Connectivity Many features were extracted from the distinct imaging acquisition techniques to characterize the connectivity of the AAN with the rest of the brain. Functional connectivity (FC) was estimated using a measure of Pearson’s correlation among the average filtered time courses of eight AAN nuclei and forty-eight cortical nuclei 54 . A bandpass Butterworth filter with cutoff frequencies set at 0.005 Hz and 0.1 Hz was used for this step 56 , which produced three hundred eighty-four FC values (8 AAN nuclei x 48 cortical nuclei). These sets of values were summarized by averaging the quantities to eight representative values associated with each AAN nucleus. AAN Structural Connectivity A constant solid angle model was used to obtain directions from diffusion imaging 57 . This model estimates the orientation distribution function (ODF) at each voxel. This ODF is a function of the distribution of water movement, and its peaks are a suitable estimate for the orientation of each tract at each voxel. Afterwards, a deterministic local fiber tracking algorithm was applied for the WM 58 , with the following set of parameters: min separation angle = 30°, step size = 1 and number of seeds in each voxel = 8. Subsequently, the number of tracts that may connect every possible pair of nuclei between the AAN and the cortex was calculated (8 AAN nuclei x 48 cortical nuclei). These sets of values were also averaged to obtain the eight representative quantities associated with each AAN nucleus. Statistical Analysis Univariate Analysis To assess the predictive ability of image-based features, univariate analysis of each single value from the set of 60 measurements was performed through a receiver operating characteristic (ROC) curve (Fig. 2 ). The measurements were grouped according to the diagnosis, and the outcome was established as indicated in the previous section. The associated area under the curve was then used to determine the predictive ability of a single image-based feature linked to the admission diagnosis. Multivariate Analysis A general linear model (GLM) was employed for multivariate analysis. GLM is able to quantify the variation of a dependent variable in terms of a linear combination of several reference independent variables 59 . GLM was implemented as follows: Declarations Data Availability The data that support the findings of this study are available on request from the corresponding author, [JHM]. The data are not publicly available due to the containing information that could compromise the privacy of research participants (e.g. Patients' names, surrogates' names, IDs). Competing Interests This article is funded by the Administrative Department of Science, Technology, and Innovation of the government of Colombia (Colciencias) under grant number 702–2016. Author Contributions Conceptualization: C.E.O., J.H.M, F.G, E.G.O. Data acquisition: C.E.O., C.J.Z., M.A.H., N.G., C.P., N.A., C.P.H., J.H., Formal analysis: R.C.L, D.R., J.R., C.P., F.G., D.M, C.P.H. E.G.O., J.H.M. Statistical analysis: R.C.L., D.R., J.R., D.M. Writing (original draft): C.E.O, E.G.O., C.J.Z., J.R., D.M., F.G, J.M. Writing (critical review): all authors. All authors read and approved the final manuscript. Acknowledgements To all health personnel who significantly contributed to the acquisition and processing of the data used for this study, including nurses, anesthesiologists, psychologists, and technicians. To all patients and surrogates who contribute to the enrollment process for this study. References Ordonez-Rubiano, E. G. et al. Reconstruction of the ascending reticular activating system with diffusion tensor tractography in patients with a disorder of consciousness after traumatic brain injury. Cureus. 9 , e1723 (2017). Snider, S. B. et al. Disruption of the ascending arousal network in acute traumatic disorders of consciousness. Neurology. 93 , e1281–e1287 (2019). Koenig, M. A. et al. MRI default mode network connectivity is associated with functional outcome after cardiopulmonary arrest. Neurocrit. Care. 20 , 348–357 (2014). Edlow, B. L. et al. Personalized connectome mapping to guide targeted therapy and promote recovery of consciousness in the intensive care unit. Neurocrit. Care. 33 , 364–375 (2020). Edlow, B. L. et al. Early detection of consciousness in patients with acute severe traumatic brain injury. Brain. 140 , 2399–2414 (2017). Wagner, F. et al. Outcome prediction with resting-state functional connectivity after cardiac arrest. Sci. Rep. 10 , 11695 (2020). Velly, L. et al. Use of brain diffusion tensor imaging for the prediction of long-term neurological outcomes in patients after cardiac arrest: a multicentre, international, prospective, observational, cohort study. Lancet Neurol. 17 , 317–326 (2018). Jang, S. H., Chang, C. H., Jung, Y. J., Kim, J. H. & Kwon, Y. H. Relationship between impaired consciousness and injury of ascending reticular activating system in patients with intracerebral hemorrhage. Stroke. 50 , 2234–2237 (2019). Monti, M. M. et al. Willful modulation of brain activity in disorders of consciousness. N. Engl. J. Med. 362 , 579–589 (2010). Kondziella, D. et al. European academy of neurology guideline on the diagnosis of coma and other disorders of consciousness. Eur. J. Neurol. 27 , 741–756 (2020). Owen, A. M. et al. Detecting awareness in the vegetative state. Science. 313 , 1402 (2006). Parra-Morales, A. M. et al. Structural and functional connectivity of ascending reticular activating system in a patient with impaired consciousness after a cardiac arrest: a case report. Med. (Baltim). 98 , e15620 (2019). Edlow, B. L. et al. Neuroanatomic connectivity of the human ascending arousal system critical to consciousness and its disorders. J. Neuropathol. Exp. Neurol. 71 , 531–546 (2012). Edlow, B. L. & Fins, J. J. Assessment of covert consciousness in the intensive care unit: clinical and ethical considerations. J. Head Trauma Rehabil. 33 , 424–434 (2018). Jang, S. H., Kim, O. L., Kim, S. H. & Kim, J. B. The relation between loss of consciousness, severity of traumatic brain injury, and injury of ascending reticular activating system in patients with traumatic brain injury. Am. J. Phys. Med. Rehabil. 98 , 1067–1071 (2019). Galanaud, D. et al. Assessment of white matter injury and outcome in severe brain trauma: a prospective multicenter cohort. Anesthesiology. 117 , 1300–1310 (2012). Glover, G. H. Overview of functional magnetic resonance imaging. Neurosurg. Clin. N. Am. 22, 133–139, vii(2011). Jang, S. H. & Kwon, H. G. Injury of ascending reticular activating system associated with delayed post-hypoxic leukoencephalopathy: a case report. BMC Neurol. 17 , 139 (2017). Tan, X. et al. Structural connectome alterations in patients with disorders of consciousness revealed by 7-tesla magnetic resonance imaging. Neuroimage Clin. 22 , 101702 (2019). Ordonez-Rubiano, E. G. et al. Recovery of consciousness after a brainstem cavernous malformation hemorrhage: a descriptive study of preserved reticular activating system with tractography. J. Clin. Neurosci. 59 , 372–377 (2019). Hindman, J. et al. Thalamic strokes that severely impair arousal extend into the brainstem. Ann. Neurol. 84 , 926–930 (2018). Pujol, J. et al. Largest-scale dissociation of brain activity at propofol-induced loss of consciousness. Sleep. https://doi.org/10.1093/sleep/zsaa152 (2020). Nir, T. et al. Resting-state functional connectivity in early postanaesthesia recovery is characterised by globally reduced anticorrelations. Br. J. Anaesth. 125 , 529–538 (2020). Alexander, A. L., Lee, J. E., Lazar, M. & Field, A. S. Diffusion tensor imaging of the brain. Neurotherapeutics. 4 , 316–329 (2007). Xiong, K. L., Zhu, Y. S. & Zhang, W. G. Diffusion tensor imaging and magnetic resonance spectroscopy in traumatic brain injury: a review of recent literature. Brain Imaging Behav. 8 , 487–496 (2014). Basser, P. J. & Jones, D. K. Diffusion-tensor MRI: theory, experimental design and data analysis - a technical review. NMR Biomed. 15 , 456–467 (2002). Hulkower, M. B., Poliak, D. B., Rosenbaum, S. B., Zimmerman, M. E. & Lipton, M. L. A decade of DTI in traumatic brain injury: 10 years and 100 articles later. AJNR Am. J. Neuroradiol. 34 , 2064–2074 (2013). Zhang, J. et al. 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 (2017). Hammoud, D. A. & Wasserman, B. A. Diffuse axonal injuries: pathophysiology and imaging. Neuroimaging Clin. N. Am. 12 , 205–216 (2002). de la Marquez, C. D. et al. Diffusion tensor imaging biomarkers for traumatic axonal injury: analysis of three analytic methods. J. Int. Neuropsychol. Soc. 17 , 24–35 (2011). Benson, R. R. et al. Global white matter analysis of diffusion tensor images is predictive of injury severity in traumatic brain injury. J. Neurotrauma. 24 , 446–459 (2007). Tang, C. Y. et al. Diffuse disconnectivity in tBi: a resting state fMri anD Dti stuDy. Transl. Neurosci. 3 , 9–14 (2012). Sekhon, M. S., Ainslie, P. N. & Griesdale, D. E. Clinical pathophysiology of hypoxic ischemic brain injury after cardiac arrest: a "two-hit" model. Crit. Care. 21 , 90 (2017). Thornton, J. S. et al. Anisotropic water diffusion in white and gray matter of the neonatal piglet brain before and after transient hypoxia-ischaemia. Magn. Reson. Imaging. 15 , 433–440 (1997). Samuels, E. R. & Szabadi, E. Functional neuroanatomy of the noradrenergic locus coeruleus: its roles in the regulation of arousal and autonomic function part I: principles of functional organisation. Curr. Neuropharmacol. 6 , 235–253 (2008). Chiang, M. C. et al. Parabrachial complex: a hub for pain and aversion. J. Neurosci. 39 , 8225–8230 (2019). Klatzo, I. Pathophysiological aspects of brain edema. Acta Neuropathol. 72 , 236–239 (1987). Sotak, C. H. The role of diffusion tensor imaging in the evaluation of ischemic brain injury - a review. NMR Biomed. 15 , 561–569 (2002). Chaudhary, N. et al. Diffusion tensor imaging in hemorrhagic stroke. Exp. Neurol. 272 , 88–96 (2015). Davis, D. et al. Rapid monitoring of changes in water diffusion coefficients during reversible ischemia in cat and rat brain. Magn. Reson. Med. 31 , 454–460 (1994). Mintorovitch, J. et al. Comparison of diffusion- and T2-weighted MRI for the early detection of cerebral ischemia and reperfusion in rats. Magn. Reson. Med. 18 , 39–50 (1991). Fridman, E. A., Beattie, B. J., Broft, A., Laureys, S. & Schiff, N. D. Regional cerebral metabolic patterns demonstrate the role of anterior forebrain mesocircuit dysfunction in the severely injured brain. Proc. Natl. Acad. Sci. U. S. A. 111, 6473–6478(2014). Threlkeld, Z. D. et al. Functional networks reemerge during recovery of consciousness after acute severe traumatic brain injury. Cortex. 106 , 299–308 (2018). Edlow, B. L., Claassen, J., Schiff, N. D. & Greer, D. M. Recovery from disorders of consciousness: mechanisms, prognosis and emerging therapies. Nat. Rev. Neurol. https://doi.org/10.1038/s41582-020-00428-x (2020). Turner-Stokes, L. Prolonged disorders of consciousness: new national clinical guidelines from the Royal College of Physicians, London. Clin. Med. (Lond.). 14 , 4–5 (2014). Ostrosky-Solis, F., Ardila, A. & Rosselli, M. NEUROPSI: a brief neuropsychological test battery in Spanish with norms by age and educational level. J. Int. Neuropsychol. Soc. 5 , 413–433 (1999). Nasreddine, Z. S. et al. The montreal cognitive assessment, MoCA: a brief screening tool for mild cognitive impairment. J. Am. Geriatr. Soc. 53 , 695–699 (2005). Kandeepan, S. et al. Modeling an auditory stimulated brain under altered states of consciousness using the generalized Ising model. Neuroimage. 223 , 117367 (2020). Woolrich, M. W. et al. Bayesian analysis of neuroimaging data in FSL. Neuroimage. 45 (1 Suppl), S173–186 (2009). Beare, R., Lowekamp, B. & Yaniv, Z. Image segmentation, registration and characterization in R with SimpleITK. J. Stat. Softw. 86 , 8 (2018). Garyfallidis, E. et al. Dipy, a library for the analysis of diffusion MRI data. Front. Neuroinform. 8 , 8 (2014). Penny, W., Friston, K., Ashburner, J., Hiebel, S. & Nichols, T. Statistical parametric mapping: the analysis of functional brain images(Elsevier, 2011). NITRC. NITRC: NeuroImaging Tools & Resources Collaboratory https://www.nitrc.org/plugins/mwiki/index.php/nitrc:Site_Map (2020). Desikan, R. S. et al. An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. Neuroimage. 31 , 968–980 (2006). Avants, B. B., Epstein, C. L., Grossman, M. & Gee, J. C. Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. Med. Image Anal. 12 , 26–41 (2008). Demertzi, A. et al. Multiple fMRI system-level baseline connectivity is disrupted in patients with consciousness alterations. Cortex. 52 , 35–46 (2014). Aganj, I. et al. Reconstruction of the orientation distribution function in single- and multiple-shell q-ball imaging within constant solid angle. Magn. Reson. Med. 64 , 554–566 (2010). Afzali, M., Fatemizadeh, E. & Soltanian-Zadeh, H. Interpolation of orientation distribution functions in diffusion weighted imaging using multi-tensor model. J. Neurosci. Methods. 253 , 28–37 (2015). Monti, M. M. Statistical analysis of fMRI time-series: a critical review of the GLM approach. Front. Hum. Neurosci. 5 , 28 (2011). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 25 Nov, 2021 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Major revision 19 Apr, 2021 Reviews received at journal 30 Mar, 2021 Reviewers agreed at journal 18 Mar, 2021 Reviewers agreed at journal 09 Mar, 2021 Reviewers invited by journal 08 Mar, 2021 Editor assigned by journal 08 Mar, 2021 Editor invited by journal 17 Feb, 2021 Submission checks completed at journal 17 Feb, 2021 First submitted to journal 15 Feb, 2021 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-244211","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":12303255,"identity":"3e9401f9-9903-49a8-9a0f-1ad757d4e0cd","order_by":0,"name":"Cesar O. Enciso-Olivera","email":"","orcid":"","institution":"Fundación Universitaria de Ciencias de la Salud","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Cesar","middleName":"O.","lastName":"Enciso-Olivera","suffix":""},{"id":12303256,"identity":"1fee2288-bd92-432d-a8df-bb9d41d9cf31","order_by":1,"name":"Edgar G. Ordóñez-Rubiano","email":"","orcid":"","institution":"Fundación Universitaria de Ciencias de la Salud","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Edgar","middleName":"G.","lastName":"Ordóñez-Rubiano","suffix":""},{"id":12303257,"identity":"51f8b7db-5717-4eda-b314-360ffba481c8","order_by":2,"name":"Rosángela Casanova-Libreros","email":"","orcid":"","institution":"Fundación Universitaria de Ciencias de la Salud","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Rosángela","middleName":"","lastName":"Casanova-Libreros","suffix":""},{"id":12303258,"identity":"6e6950e3-7e1f-401a-8a32-49aaee3559b5","order_by":3,"name":"Diana Rivera","email":"","orcid":"","institution":"Fundación Universitaria de Ciencias de la Salud","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Diana","middleName":"","lastName":"Rivera","suffix":""},{"id":12303259,"identity":"5cd49c52-c63f-4860-bbbe-50ac0376627d","order_by":4,"name":"Carol J. Zarate-Ardila","email":"","orcid":"","institution":"Fundación Universitaria de Ciencias de la Salud","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Carol","middleName":"J.","lastName":"Zarate-Ardila","suffix":""},{"id":12303260,"identity":"6a251720-dc9a-429f-9a04-3924384acea6","order_by":5,"name":"Jorge Rudas","email":"","orcid":"","institution":"National University of Colombia","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Jorge","middleName":"","lastName":"Rudas","suffix":""},{"id":12303261,"identity":"4103d28e-6f1c-4689-84ed-3726ae45be32","order_by":6,"name":"Cristian Pulido","email":"","orcid":"","institution":"National University of Colombia","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Cristian","middleName":"","lastName":"Pulido","suffix":""},{"id":12303262,"identity":"b36a1296-ade3-4839-8b7c-2072807573b0","order_by":7,"name":"Francisco Gómez","email":"","orcid":"","institution":"National University of Colombia","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Francisco","middleName":"","lastName":"Gómez","suffix":""},{"id":12303263,"identity":"c64fb5b7-8ca6-4ed0-885d-fc9d1481b1df","order_by":8,"name":"Darwin Martínez","email":"","orcid":"","institution":"Universidad Central","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Darwin","middleName":"","lastName":"Martínez","suffix":""},{"id":12303264,"identity":"0e2b70cb-8cd1-472f-8b33-f163d8053f89","order_by":9,"name":"Natalia Guerrero","email":"","orcid":"","institution":"Fundación Universitaria de Ciencias de la Salud","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Natalia","middleName":"","lastName":"Guerrero","suffix":""},{"id":12303265,"identity":"f00b10e1-826d-470a-9a09-dbee2c6dcab9","order_by":10,"name":"Mayra A. Hurtado","email":"","orcid":"","institution":"Fundación Universitaria de Ciencias de la Salud","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Mayra","middleName":"A.","lastName":"Hurtado","suffix":""},{"id":12303266,"identity":"c73c27db-b075-4888-9a49-5ce7cbe91172","order_by":11,"name":"Natalia Aguilera-Bustos","email":"","orcid":"","institution":"Fundación Universitaria de Ciencias de la Salud","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Natalia","middleName":"","lastName":"Aguilera-Bustos","suffix":""},{"id":12303267,"identity":"51066dd1-ecd0-4cc3-917f-5b3508a2da12","order_by":12,"name":"Clara P. Hernández-Torres","email":"","orcid":"","institution":"Fundación Universitaria de Ciencias de la Salud","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Clara","middleName":"P.","lastName":"Hernández-Torres","suffix":""},{"id":12303268,"identity":"4164ee2f-8580-4766-8064-98a6a3dc4eeb","order_by":13,"name":"José Hernandez","email":"","orcid":"","institution":"Fundación Universitaria de Ciencias de la Salud","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"José","middleName":"","lastName":"Hernandez","suffix":""},{"id":12303269,"identity":"2be92f73-d43b-4918-b660-816eef545a3c","order_by":14,"name":"Jorge H. Marín-Muñoz","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuElEQVRIiWNgGAWjYBACxgYgkcBgwwOk2EjSkkaCFig4zEC8FuZph599eLjjvIxue4/Zwx8MdnK6DYQcNjvNeEbimds8ZmeOpRvzMCQbmx0gqCXBmCGxDajlRvIxaQaGA4nbCGtJ/wzUco7H7P7DNskfxGnJAdlyAGgL8zEJHiK1FAO1JAP9kpYmzWNAhF8MZ6dvZvzZZmdvdvyMmeSPCjs5wloaULgGBJSDgDwRakbBKBgFo2CkAwC06j67nx5hPAAAAABJRU5ErkJggg==","orcid":"","institution":"Imaging Experts and Healthcare Services (ImexHS)","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Jorge","middleName":"H.","lastName":"Marín-Muñoz","suffix":""}],"badges":[],"createdAt":"2021-02-15 17:14:02","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-244211/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-244211/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-021-98506-7","type":"published","date":"2021-11-25T13:28:04+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":6411147,"identity":"5e4c39ba-8415-4c28-aac2-8d99bc7636f1","added_by":"auto","created_at":"2021-02-26 21:42:22","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":54412,"visible":true,"origin":"","legend":"Description of the enrollment of patients in the study.","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-244211/v1/3b447240f99cc39b8a3ff4e9.jpg"},{"id":6410834,"identity":"95f40a95-92af-400a-b7f7-6f7580541551","added_by":"auto","created_at":"2021-02-26 21:39:22","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":31093,"visible":true,"origin":"","legend":"Receiver operating characteristic (ROC) curves of single measurements for different cortical and subcortical areas. ROC curves demonstrate the possible predictive value of data comparing true positive and false positive rates for fractional anisotropy, mean diffusivity, and radial diffusivity among patients with TBI (yellow), cardiac arrest (purple), and stroke (green).","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-244211/v1/d93cc081e0a674e01d207dad.jpg"},{"id":6410837,"identity":"300a9a78-aab0-4e1e-bfec-f5099348200c","added_by":"auto","created_at":"2021-02-26 21:39:22","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":96541,"visible":true,"origin":"","legend":"Reconstruction of the tractography of ascending arousal network. (A) A complete reconstruction of the AAN of a normal subject is demonstrated. Major components including the medial forebrain bundle (MFB) projecting to the dorsal frontal cortex, the dorsal raphe (DR), the thalamic-hypothalamic complex (asterisk), and superior and inferior tegmental tracts (TT) projecting to the basal frontal cortex are shown. (B) A reconstruction of the AAN of a comatose patient after a severe traumatic brain injury is observed, denoting a destruction of the tegmental tracts. (C) A reconstruction of the AAN of a patient after a stroke demonstrates a disruption of the tracts in the length of the MFB, the fibers of the DR, and the tegmental tracts. (D) A reconstruction of the AAN is shown in a comatose patient after a cardiac arrest, demonstrating a decrease in the number of fibers of all components of the AAN.","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-244211/v1/ead61e94b3374708f79e6080.jpg"},{"id":6411609,"identity":"07a93687-7b2d-4464-80bc-ae839f01f2c7","added_by":"auto","created_at":"2021-02-26 21:45:22","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":24666,"visible":true,"origin":"","legend":"Multivariate analysis for prediction of outcomes of consciousness by etiology. In the three scenarios (traumatic brain injury, cardiac arrest, and stroke), the outcome for each patient is demonstrated by a linear model. The curves show a direct correlation between imaging and prediction of consciousness outcome, with corresponding R2 values of 0.463, 0.92 and 0,84, respectively.","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-244211/v1/697016f974690da2031c421c.jpg"},{"id":6411149,"identity":"88125add-6808-4e56-9c89-f908c02fabbb","added_by":"auto","created_at":"2021-02-26 21:42:23","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":25093,"visible":true,"origin":"","legend":"Multivariate prediction at the group level. The relationship between outcome and prediction of the general linear model is demonstrated using (a) both the structural and functional descriptors together, (b) the functional descriptors alone, and (c) the structural descriptors alone. The corresponding R2 values are 0.82, 0.46 and 0.5, respectively.","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-244211/v1/c236be349ab2db690e0668a6.jpg"},{"id":15892570,"identity":"06ad1534-195a-4410-96e7-3940ef44fa39","added_by":"auto","created_at":"2021-11-25 13:28:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":662378,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-244211/v1/6147ced9-010f-4db1-9e67-346d68156d0a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Structural and Functional Connectivity of the Ascending Arousal Network for Prediction of Outcome in Patients with Acute Disorders of Consciousness","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePredicting neurological outcomes and mortality in patients with acute disorders of consciousness (DOCs) is challenging\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, and clinicians continue to question complex connection impairments of the ascending arousal network (AAN) in comatose patients\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. Unfortunately, current clinical and radiological tools are not reliable for detecting consciousness or predicting recovery in those with either severe traumatic brain injury (TBI)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e, cardiac arrest\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e, or stroke\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e\u003c/sup\u003e. To this end, multiple clinical and radiological tests have been proposed for assessing patients with DOCs, including bedside behavioral assessment\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e\u003c/sup\u003e, electroencephalography (EEG)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e\u003c/sup\u003e, task-based functional magnetic resonance imaging (fMRI)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e\u003c/sup\u003e, resting-state-fMRI (rsfMRI)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, diffusion tensor imaging (DTI)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, and different combinations of these approaches\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. In the absence of reliable prognostic tests, the clinician\u0026rsquo;s judgment, experience, and communication skills may influence a family\u0026rsquo;s decision about life-sustaining therapy and lead to premature care decisions before a patient\u0026rsquo;s prognosis becomes clear\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e\u003c/sup\u003e. In this regard, investigations have focused on diagnostic tests that might objectify this initial prediction assessment of consciousness outcome\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe AAN is an essential component of human consciousness and is formed by a group of subcortical pathways connecting the rostral brainstem tegmentum to the hypothalamus, thalamus, and basal forebrain\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e. It has been described that DOCs after TBI, cardiac arrest, or stroke are related to axonal injury within the AAN\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e\u003c/sup\u003e. Additionally, clinical and electrophysiological evaluations are insufficient and might be biased by sedation or any clinical condition, such as aphasia\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Accordingly, there is uncertainty concerning long-term effects in a broad spectrum of cognitive, behavioral, and functional impairments\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e. Overall, more specialized tests derived from MRI may be able to better characterize microstructural disturbances.\u003c/p\u003e\n\u003cp\u003eBlood oxygen level-dependent (BOLD) imaging utilizes a gradient-echo imaging sequence with parameters sensitive to the oxygen state of hemoglobin, which is used as contrast to delineate regional brain activity\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e. BOLD imaging allows for the analysis of both task-based fMRI and rsfMRI. rsfMRI itself can be used for studying different resting-state neural networks (RSNs) to establish functional connectivity in patients with a DOC\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. On the other hand, DTI uses anisotropic diffusion to estimate the organization of brain tissue. Additionally, structural analysis of white matter (WM) with DTI techniques, including diffusion tensor tractography (DTT), has allowed physicians to scrutinize the anatomy of the AAN\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e\u003c/sup\u003e, revealing the structural connectivity of this network\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e\u003c/sup\u003e. Both tools can be employed to determine ascending and descending structural and functional connectivity between AAN brainstem nuclei and many different cortical areas\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Nevertheless, the exact biological nature of the structural and functional injury that leads to a DOC remains uncertain. Multiple efforts to elucidate the origin of impaired consciousness have led to the proposition of a compromised state for multiple cortical and subcortical areas and networks that may be involved in this process, including the brainstem\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, thalamus\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, hypothalamus\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e, frontal basal cortex\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, and other association areas in the parietal lobe\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e. However, varying impairment in these areas may induce any DOC regardless of the etiology of the injury. In this regard, the aim of the present study was to analyze a combination of structural and functional information of the AAN obtained from both DTI and BOLD acquisitions to determine whether early acquisition of DTI and BOLD techniques for analysis of structural and functional connectivity of the AAN can predict neurological outcomes in terms of consciousness in patients with DOCs after TBI, cardiopulmonary arrest (CPA), or stroke.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Features\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBetween October 2017 and January 2020, a total of 293 patients were assessed for eligibility criteria, of whom 50 were enrolled. Of the 243 excluded subjects, 104 were excluded for having a GCS score 8 or higher, 58 due to a previous history of any neurological or psychiatric disease, 41 because they were not able to be transferred to the MRI scanner due to their medical condition, 13 because MRI was not performed before death, 10 due to radiologically confirmed brain death during the first 48 hours after being admitted to the ICU, eight due to the family's decision not to participate, and nine due to other reasons (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). The median age of the enrolled patients was 64 years (IQR: 49\u0026ndash;74), and 27 (54%) were female. Nineteen patients (38%) were admitted with stroke, 18 (36%) with hypoxic-ischemic brain injury after cardiac arrest, and 13 (26%) with severe TBI. In regard to the state of consciousness, 23 patients were in coma (44%), 11 in MCS (20%), and 16 in UWS (32%). The median length of stay in the ICU was 13.2 days (IQR: 5.1\u0026ndash;21.3). The overall median ICU admission GCS score was 6 (IQR: 3\u0026ndash;8); the UWS group had a median score of 8 (IQR: 4\u0026ndash;8), the coma group a median score of 6 (IQR: 3\u0026ndash;7), and MCS a median score of 5 (IQR: 4\u0026ndash;8). The 28 patients (56%) who were discharged from the ICU had a median GCS score of 9 (IQR: 3\u0026ndash;12) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e). Among them, 17 died during the follow-up before the neuropsychological evaluation in an outpatient setting. Additionally, 4 patients were lost from the study due to loss of contact with their surrogates.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eClinical and demographic features according to state of consciousness after ICU admission.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eComa\u003c/p\u003e\n\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;23)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eMCS (n\u0026thinsp;=\u0026thinsp;11)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eUWS (n\u0026thinsp;=\u0026thinsp;16)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTotal\u003c/p\u003e\n\u003cp\u003e(n\u0026thinsp;=\u0026thinsp;50)\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ep-value\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAge \u003cem\u003emedian\u003c/em\u003e (IQR)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e50 (72\u0026ndash;86)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e48 (73\u0026ndash;90)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e47 (76\u0026ndash;92)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e49 (74\u0026ndash;88)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.42\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSex\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.56\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFemale (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13 (56.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7 (63.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7 (43.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e27 (54)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMale (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10 (43.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4 (36.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9 (56.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e23 (46)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEducation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.16\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIlliterate (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (6.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHigh School (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9 (39.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (18.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5 (31.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e16 (32)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePrimary School (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10 (43.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5 (45.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (12.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17 (34)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTechnical (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (18.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (12.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4 (8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGraduate School (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4 (17.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (18.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6 (37.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12 (24)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eComorbidities\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.82\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHypertension (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7 (30.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7 (63.6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8 (50)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22 (44)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDiabetes Mellitus (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6 (26.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3 (27.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3 (18.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12 (24)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHypothyroidism (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (8.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (18.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3 (18.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7 (14)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDyslipidemia (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (4.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (9.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAcute myocardial infarction (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (9.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (6.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eOther (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5 (21.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5 (45.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11 (68.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e21 (42)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eICU admission GCS score \u003cem\u003emedian\u003c/em\u003e (IQR)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6 (3\u0026ndash;7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5 (4\u0026ndash;8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8 (4\u0026ndash;8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6 (3\u0026ndash;8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.08\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIntracranial Pressure\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eICP monitoring (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (8.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (9.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (6.25)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4 (8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.95\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIntracranial hypertension (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (8.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (18.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (6.25)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5 (10)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.57\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMechanical ventilation (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22 (95.7)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10 (90.9)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14 (87.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e46 (92)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.65\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSepsis (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8 (34.8)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3 (27.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8 (50)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19 (38)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.45\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eVital status\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAlive (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12 (52.2)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4 (36.4)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12 (75)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28 (56)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.17\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDeceased (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10 (43.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5 (45.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (12.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17 (43)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e*Transferred to a different institution (%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0 (0)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1 (9.1)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 (12.5)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3 (6)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGCS score at discharge \u003cem\u003emedian\u003c/em\u003e (IQR)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6 (3\u0026ndash;11)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3 (3\u0026ndash;14)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11 (9\u0026ndash;12)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9 (3\u0026ndash;12)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"char\" char=\".\"\u003e\n\u003cp\u003e0.15\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\"\u003e*Expenses not covered by their health insurance company in our institution\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\"\u003eP values correspond to comparative measures among the three patient groups.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStructural and Functional Connectivity\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\n\u003cp\u003e\u003cstrong\u003eUnivariate Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA set of 60 individual imaging measurements was completed. However, only sixteen had an area under the curve (AUC) \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\ge\\)\u003c/span\u003e\u003c/span\u003e0.80. Thus, these specific features were explored as possible predictors with at least an accuracy of 80%. A remarkable finding is that some single regions might serve as biomarkers of consciousness at ICU discharge. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the potential use of single imaging measurements in specific regions to predict the patient\u0026rsquo;s consciousness at ICU discharge. The ROC curve indicates possible use as an isolated approach for measuring specific regions to predict GCS score at ICU discharge.\u003c/p\u003e\n\u003cp\u003eBased on separate analysis for each variable, the gray matter (GM) FA was found to be a possible predictor in the setting of TBI. The AD, MD, and RD in both the GM and WM, as well as functional connectivity in the PON, and the number of fibers in the locus coeruleus (LC) and the parabrachial complex (PC) are possible predictors in the setting of CPA. Measurements of MD in the whole brain, WM, GM, MRF and PON, of AD in the WM, GM, and MRF, and the RD in the whole brain, WM, and MRF were found to be possible predictors of the state of consciousness at ICU discharge in the setting of a stroke. Separate analysis of automatic DTI, tractrography (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e) and BOLD measurements showed no predictive value for the state of consciousness at ICU discharge.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\n\u003cp\u003e\u003cstrong\u003eStructural and Functional Connectivity - Multivariate Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e illustrates the results obtained by GLM for each separate diagnosis; independent variables included the confounding variables and those features obtained from DTI and BOLD acquisitions. This figure shows the correlation between the real outcome and the value predicted by the model. In the setting of TBI, the model reached an adjustment defined by the metric R2\u0026thinsp;=\u0026thinsp;0.463; it was 0.84 for stroke and 0.92 for CPA. Moreover, Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e5\u003c/span\u003e denotes the correlation between the outcome and the model\u0026rsquo;s predicted value from a global aspect, grouping patients with TBI, CPA, and stroke into one group. Three different scenarios were explored: (1) including both structural (DTI) and functional (BOLD) features, (2) functional features alone, and (3) structural features alone. The R2 values obtained for each scenario were 0.82, 0.463 and 0.5, respectively. These trends show the great importance of combining both features.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNeurological Outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e\n\u003cp\u003eThirty of the 50 patients died during follow-up before the neuropsychological evaluation. Of those remaining, five were lost during follow-up. Tests were performed for the remaining 15 patients. The NeuroPsi demonstrated that 9 (60%) of the patients had normal psychological behavior, 1 (6.6%) presented moderate psychological sequelae, and 5 (33.3%) presented severe cognitive sequelae. Additionally, 40% presented abnormal orientation, 3 (20%) had severe compromise of their attention/concentration, and 4 (26.7%) and 5 (33.3%) had moderate and severe visual memory impairment, respectively. On the other hand, the MoCa test showed that 11 (73.3%) patients had cognitive deficits; 4 (26.7%) had normal scores. Visual memory showed higher cognitive compromise, while the best performance was observed for language, attention, and orientation.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":""},{"header":"Conclusions","content":"\u003cp\u003eOur findings suggest that early acquisition of BOLD and DTI for evaluation of structural and functional connectivity of the AAN may represent a tool for predicting outcome in patients with impaired consciousness after acute TBI, CPA, or stroke. DTI and BOLD analysis represent an observer- and operator-dependent task despite the automatic data processing involved, and clinical decision making must be made by physicians in a case-by-case manner.\u003c/p\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe limited number of patients recruited for this study represents a notable limitation. Although different etiologies were included, a separate analysis of each group was performed. This study sought to elucidate the prognostic value of early DTI and BOLD acquisitions, yet there are multifactorial limitations to enrolling comatose patients, including the high mortality in these scenarios as well as the social and economic background of a middle-income country. In addition, as this study lacks EEG data, further studies are needed to compare EEG findings with radiological biomarkers. Finally, specific analysis of the emotional, behavioral, and general neurological outcomes should also be addressed for targeted therapeutic assessment.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n\u003cp\u003e\u003cstrong\u003eClinical Data and Study Design\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis is a prospective, observational, cohort-type diagnostic test study. Patients admitted to the ICU with acute DOC after CPA, stroke or TBI who stayed in the ICU for more than 48 hours were enrolled. Ten healthy volunteer adult subjects were recruited and analyzed as the control group\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. Patients were admitted to the Hospital Infantil Universitario de San Jos\u0026eacute; ICU between October 2017 and January 2020. Inclusion criteria included patients over 18 years old, with either CPA treated within our institution with successful cardiopulmonary resuscitation, stroke (ischemic or hemorrhagic), or TBI, with a neurological evaluation prior to ICU admission consisting of coma (defined as Glasgow Coma Scale [GCS] score of \u0026le;\u0026thinsp;6/15 without eye opening) after the initial resuscitation and who could be transferred to the MRI scanner. Exclusion criteria included patients diagnosed with brain death within the first 48 hours of admission to the ICU, those with severe TBI who were considered to be \u0026ldquo;nonsalvageable\u0026rdquo; by the neurosurgery staff, and those who had any medical history of a neurological entity prior to the event (e.g., trauma, degenerative disease), and those whose family decided to withdraw them from the study at any time during the follow-up period. Written informed consent for inclusion in the study was obtained from a surrogate for each patient. Authorization by our Institutional Ethics Board to include information for the subjects was requested. This research was performed in accordance with the Declaration of Helsinki. This prospective study was approved by our Institutional Review Board (\u003cem\u003eComit\u0026eacute; de \u0026Eacute;tica en Investigaci\u0026oacute;n con Seres Humanos - CEISH\u003c/em\u003e).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n\u003cp\u003e\u003cstrong\u003eNeurological Outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe initial bedside cognitive and behavioral assessment was performed by a neurologist (J.H.) in the first 24 hours or up to three days after the event whenever possible. The following tests were added for assessment by the patient\u0026rsquo;s family: Lawton-Brody Instrumental Activities Scale, Frontal Systems Behavior Scale, and Memory Scale. Any sensory perception disorder was ruled out, and the family was asked about the patient's previous cognitive condition in relation to symptoms associated with previous behavioral changes, schooling, and occupation or any conditions that may bias the cognitive assessment. A subsequent evaluation was made by a neurologist (J.H.) and the ICU staff within the first 7 to 10 days after the initial injury or at the time of discharge from the ICU in the case of a short length of ICU stay. After the second assessment, the patients were categorized into those with coma, unresponsive wakefulness syndrome (UWS) (defined as a state of wakefulness without awareness in which there is preserved capacity for spontaneous or stimulus-induced arousal, evidenced by sleep\u0026ndash;wake cycles and a range of reflexive and spontaneous behaviors, with complete absence of evidence for self or environmental awareness), or minimally conscious state (MCS) (defined as a state of severely altered consciousness in which minimal but clearly discernible behavioral evidence of self- or environmental awareness is demonstrated)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e\u003c/sup\u003e. Finally, the NeuroPSI (a short neuropsychological test battery for use with Spanish-speaking adults)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e\u003c/sup\u003e and the Montreal Cognitive Assessment (MoCA) test\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e47\u003c/span\u003e\u003c/sup\u003e were performed by a former neuropsychologist (C.P.H.) for cognitive function assessment at three- and six-month follow-ups whenever possible according to the patient's condition or a fatal outcome. Endpoints for evaluation were defined as an early consciousness status based on the average GCS score for the previous two days before ICU discharge, middle-term consciousness status based on the ability or not to perform the MoCA test (awareness of their selves or their environment) during follow-up, or a fatal outcome.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n\u003cp\u003e\u003cstrong\u003eNeuroimaging Data Acquisition\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA 1.5-T General Electric scanner was used for data acquisition. As reported previously\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e, we acquired one hundred and eighty multislice T2*-weighted functional images using an axial slice orientation and covering the whole brain (slice thickness\u0026thinsp;=\u0026thinsp;4.5 mm without free space, matrix\u0026thinsp;=\u0026thinsp;64 x 64 mm, TR\u0026thinsp;=\u0026thinsp;3000 ms, TE\u0026thinsp;=\u0026thinsp;60 ms, flip angle\u0026thinsp;=\u0026thinsp;90\u0026deg; and FOV\u0026thinsp;=\u0026thinsp;288 x 288 mm). The three initial volumes were discarded to avoid T1 saturation effects. Moreover, axial diffusion weighted imaging (DWI) (slice thickness\u0026thinsp;=\u0026thinsp;2.5 mm without free space, matrix\u0026thinsp;=\u0026thinsp;100 x 100, TR\u0026thinsp;=\u0026thinsp;17000 ms, TE\u0026thinsp;=\u0026thinsp;96 ms, flip angle\u0026thinsp;=\u0026thinsp;90\u0026deg;, FOV\u0026thinsp;=\u0026thinsp;250 x 250 mm, b value\u0026thinsp;=\u0026thinsp;1000 and gradient directions\u0026thinsp;=\u0026thinsp;30) was acquired. Finally, structural axial T1 (slice thickness\u0026thinsp;=\u0026thinsp;1 mm, GAP\u0026thinsp;=\u0026thinsp;1 mm, matrix\u0026thinsp;=\u0026thinsp;256 x 256 mm, TR\u0026thinsp;=\u0026thinsp;670 ms, TE\u0026thinsp;=\u0026thinsp;22 ms, flip angle\u0026thinsp;=\u0026thinsp;20\u0026deg; and FOV\u0026thinsp;=\u0026thinsp;250 x 250 mm) and axial T2 (slice thickness\u0026thinsp;=\u0026thinsp;6 mm, GAP\u0026thinsp;=\u0026thinsp;1 mm, matrix\u0026thinsp;=\u0026thinsp;320 x 320 mm, TR\u0026thinsp;=\u0026thinsp;6.000 ms, TE\u0026thinsp;=\u0026thinsp;96 ms, flip angle\u0026thinsp;=\u0026thinsp;90\u0026deg; and FOV\u0026thinsp;=\u0026thinsp;220 x 220 mm) images were acquired for anatomical reference.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n\u003cp\u003e\u003cstrong\u003eNeuroimaging Data Preprocessing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe T1 and rsfMRI data were preprocessed using the approach suggested by Kandeepan \u003cem\u003eet al\u003c/em\u003e.\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e48\u003c/span\u003e\u003c/sup\u003e In particular, T1 preprocessing included manual removal of the neck, brain extraction using FSL\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e49\u003c/span\u003e\u003c/sup\u003e, correction of low-frequency intensity nonuniformity based on the N4 bias field correction algorithm from SimpleITK\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e\u003c/sup\u003e, image denoising based on the nonlocal means algorithm from Dipy\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e,\u003cspan class=\"CitationRef\"\u003e51\u003c/span\u003e\u003c/sup\u003e, and spatial normalization to standard stereotactic Montreal Neurological Institute (MNI) space using the SPM12 normalization algorithm\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e50\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e52\u003c/span\u003e\u003c/sup\u003e. The initial six volumes of the fMRI data were discarded to avoid T1 saturation effects. Head motion and slice timing corrections were performed on the fMRI data using FSL, followed by artifact correction using RapidArt\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e53\u003c/span\u003e\u003c/sup\u003e. Subsequently, the fMRI data were coregistered to a T1 image using SPM12 and spatially normalized to the MNI space using the SPM12 normalization algorithm. Finally, spatial smoothing of the fMRI data was performed with a Gaussian kernel of 8 mm full width at half maximum, as implemented in SPM12. The spurious variance was reduced by regression of nuisance waveforms derived from time series extracted from regions of noninterest (WM and cerebrospinal fluid). Additional nuisance regressors included the blood oxygen level-dependent imaging (BOLD) time series averaged over the whole brain. The DWI images were preprocessed using the approach suggested by Parra-Morales \u003cem\u003eet al\u003c/em\u003e\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e\u003c/sup\u003e. This process included automatic realignment, correction of eddy-current artifacts by using FSL tools, reslicing to obtain the isotropic voxel size, automatic brain extraction by the BET tool from FSL, and improvement of signal-to-noise rate using the Non-Local mean algorithm from Dipy.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e\n\u003cp\u003e\u003cstrong\u003eLocation and Characterization of Regions of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eRegions of interest (ROIs) for AAN reconstruction were located based on Harvard Ascending Arousal Network Atlas provided by the Martinos Center for Biomedical Imaging, Charleston, Massachusetts, USA\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e\u003c/sup\u003e, and the cortex nuclei were extracted from Harvard-Oxford Atlas provided by the Harvard Center for Morphometric Analysis\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. These nuclei were linearly (rigid, translation, and affine transformation) and nonlinearly (symmetric diffeomorphic registration)\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e55\u003c/span\u003e\u003c/sup\u003e registered with each subject space.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAAN Functional Connectivity\u003c/strong\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec17\" class=\"Section2\"\u003e\n\u003cp\u003eMany features were extracted from the distinct imaging acquisition techniques to characterize the connectivity of the AAN with the rest of the brain. Functional connectivity (FC) was estimated using a measure of Pearson\u0026rsquo;s correlation among the average filtered time courses of eight AAN nuclei and forty-eight cortical nuclei\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e54\u003c/span\u003e\u003c/sup\u003e. A bandpass Butterworth filter with cutoff frequencies set at 0.005 Hz and 0.1 Hz was used for this step\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e56\u003c/span\u003e\u003c/sup\u003e, which produced three hundred eighty-four FC values (8 AAN nuclei x 48 cortical nuclei). These sets of values were summarized by averaging the quantities to eight representative values associated with each AAN nucleus.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAAN Structural Connectivity\u003c/strong\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\n\u003cp\u003eA constant solid angle model was used to obtain directions from diffusion imaging\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e57\u003c/span\u003e\u003c/sup\u003e. This model estimates the orientation distribution function (ODF) at each voxel. This ODF is a function of the distribution of water movement, and its peaks are a suitable estimate for the orientation of each tract at each voxel. Afterwards, a deterministic local fiber tracking algorithm was applied for the WM\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e58\u003c/span\u003e\u003c/sup\u003e, with the following set of parameters: min separation angle\u0026thinsp;=\u0026thinsp;30\u0026deg;, step size\u0026thinsp;=\u0026thinsp;1 and number of seeds in each voxel\u0026thinsp;=\u0026thinsp;8. Subsequently, the number of tracts that may connect every possible pair of nuclei between the AAN and the cortex was calculated (8 AAN nuclei x 48 cortical nuclei). These sets of values were also averaged to obtain the eight representative quantities associated with each AAN nucleus.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec19\" class=\"Section2\"\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv id=\"Sec20\" class=\"Section3\"\u003e\n\u003cp\u003e\u003cstrong\u003eUnivariate Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo assess the predictive ability of image-based features, univariate analysis of each single value from the set of 60 measurements was performed through a receiver operating characteristic (ROC) curve (Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e). The measurements were grouped according to the diagnosis, and the outcome was established as indicated in the previous section. The associated area under the curve was then used to determine the predictive ability of a single image-based feature linked to the admission diagnosis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMultivariate Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec21\" class=\"Section3\"\u003e\n\u003cp\u003eA general linear model (GLM) was employed for multivariate analysis. GLM is able to quantify the variation of a dependent variable in terms of a linear combination of several reference independent variables\u003csup\u003e\u003cspan class=\"CitationRef\"\u003e59\u003c/span\u003e\u003c/sup\u003e. GLM was implemented as follows:\u003c/p\u003e\n\u003c/div\u003e\n\u003c/div\u003e\u003cp\u003e\u003cimg src=\"https://myfiles.space/user_files/58653_1b1c6aeb34a62c68/58653_custom_files/img1614360844.jpg\"\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study are available on request from the corresponding author, [JHM]. The data are not publicly available due to the containing information that could compromise the privacy of research participants (e.g. Patients' names, surrogates' names, IDs).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis article is funded by the Administrative Department of Science, Technology, and Innovation of the government of Colombia (Colciencias) under grant number 702\u0026ndash;2016.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConceptualization: C.E.O., J.H.M, F.G, E.G.O. Data acquisition: C.E.O., C.J.Z., M.A.H., N.G., C.P., N.A., C.P.H., J.H., Formal analysis: R.C.L, D.R., J.R., C.P., F.G., D.M, C.P.H. E.G.O., J.H.M. Statistical analysis: R.C.L., D.R., J.R., D.M. Writing (original draft): C.E.O, E.G.O., C.J.Z., J.R., D.M., F.G, J.M. Writing (critical review): all authors. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo all health personnel who significantly contributed to the acquisition and processing of the data used for this study, including nurses, anesthesiologists, psychologists, and technicians. To all patients and surrogates who contribute to the enrollment process for this study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eOrdonez-Rubiano, E. G. \u003cem\u003eet al.\u003c/em\u003e Reconstruction of the ascending reticular activating system with diffusion tensor tractography in patients with a disorder of consciousness after traumatic brain injury. \u003cem\u003eCureus.\u003c/em\u003e \u003cb\u003e9\u003c/b\u003e, e1723 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSnider, S. B. \u003cem\u003eet al.\u003c/em\u003e Disruption of the ascending arousal network in acute traumatic disorders of consciousness. \u003cem\u003eNeurology.\u003c/em\u003e \u003cb\u003e93\u003c/b\u003e, e1281\u0026ndash;e1287 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKoenig, M. A. \u003cem\u003eet al.\u003c/em\u003e MRI default mode network connectivity is associated with functional outcome after cardiopulmonary arrest. \u003cem\u003eNeurocrit. Care.\u003c/em\u003e \u003cb\u003e20\u003c/b\u003e, 348\u0026ndash;357 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEdlow, B. L. \u003cem\u003eet al.\u003c/em\u003e Personalized connectome mapping to guide targeted therapy and promote recovery of consciousness in the intensive care unit. \u003cem\u003eNeurocrit. Care.\u003c/em\u003e \u003cb\u003e33\u003c/b\u003e, 364\u0026ndash;375 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEdlow, B. L. \u003cem\u003eet al.\u003c/em\u003e Early detection of consciousness in patients with acute severe traumatic brain injury. \u003cem\u003eBrain.\u003c/em\u003e \u003cb\u003e140\u003c/b\u003e, 2399\u0026ndash;2414 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWagner, F. \u003cem\u003eet al.\u003c/em\u003e Outcome prediction with resting-state functional connectivity after cardiac arrest. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e10\u003c/b\u003e, 11695 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVelly, L. \u003cem\u003eet al.\u003c/em\u003e Use of brain diffusion tensor imaging for the prediction of long-term neurological outcomes in patients after cardiac arrest: a multicentre, international, prospective, observational, cohort study. \u003cem\u003eLancet Neurol.\u003c/em\u003e \u003cb\u003e17\u003c/b\u003e, 317\u0026ndash;326 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJang, S. H., Chang, C. H., Jung, Y. J., Kim, J. H. \u0026amp; Kwon, Y. H. Relationship between impaired consciousness and injury of ascending reticular activating system in patients with intracerebral hemorrhage. \u003cem\u003eStroke.\u003c/em\u003e \u003cb\u003e50\u003c/b\u003e, 2234\u0026ndash;2237 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMonti, M. M. \u003cem\u003eet al.\u003c/em\u003e Willful modulation of brain activity in disorders of consciousness. \u003cem\u003eN. Engl. J. Med.\u003c/em\u003e \u003cb\u003e362\u003c/b\u003e, 579\u0026ndash;589 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKondziella, D. \u003cem\u003eet al.\u003c/em\u003e European academy of neurology guideline on the diagnosis of coma and other disorders of consciousness. \u003cem\u003eEur. J. Neurol.\u003c/em\u003e \u003cb\u003e27\u003c/b\u003e, 741\u0026ndash;756 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOwen, A. M. \u003cem\u003eet al.\u003c/em\u003e Detecting awareness in the vegetative state. \u003cem\u003eScience.\u003c/em\u003e \u003cb\u003e313\u003c/b\u003e, 1402 (2006).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParra-Morales, A. M. \u003cem\u003eet al.\u003c/em\u003e Structural and functional connectivity of ascending reticular activating system in a patient with impaired consciousness after a cardiac arrest: a case report. \u003cem\u003eMed. (Baltim).\u003c/em\u003e \u003cb\u003e98\u003c/b\u003e, e15620 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEdlow, B. L. \u003cem\u003eet al.\u003c/em\u003e Neuroanatomic connectivity of the human ascending arousal system critical to consciousness and its disorders. \u003cem\u003eJ. Neuropathol. Exp. Neurol.\u003c/em\u003e \u003cb\u003e71\u003c/b\u003e, 531\u0026ndash;546 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEdlow, B. L. \u0026amp; Fins, J. J. Assessment of covert consciousness in the intensive care unit: clinical and ethical considerations. \u003cem\u003eJ. Head Trauma Rehabil.\u003c/em\u003e \u003cb\u003e33\u003c/b\u003e, 424\u0026ndash;434 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJang, S. H., Kim, O. L., Kim, S. H. \u0026amp; Kim, J. B. The relation between loss of consciousness, severity of traumatic brain injury, and injury of ascending reticular activating system in patients with traumatic brain injury. \u003cem\u003eAm. J. Phys. Med. Rehabil.\u003c/em\u003e \u003cb\u003e98\u003c/b\u003e, 1067\u0026ndash;1071 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGalanaud, D. \u003cem\u003eet al.\u003c/em\u003e Assessment of white matter injury and outcome in severe brain trauma: a prospective multicenter cohort. \u003cem\u003eAnesthesiology.\u003c/em\u003e \u003cb\u003e117\u003c/b\u003e, 1300\u0026ndash;1310 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGlover, G. H. Overview of functional magnetic resonance imaging. Neurosurg. Clin. N. Am. 22, 133\u0026ndash;139, vii(2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJang, S. H. \u0026amp; Kwon, H. G. Injury of ascending reticular activating system associated with delayed post-hypoxic leukoencephalopathy: a case report. \u003cem\u003eBMC Neurol.\u003c/em\u003e \u003cb\u003e17\u003c/b\u003e, 139 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTan, X. \u003cem\u003eet al.\u003c/em\u003e Structural connectome alterations in patients with disorders of consciousness revealed by 7-tesla magnetic resonance imaging. \u003cem\u003eNeuroimage Clin.\u003c/em\u003e \u003cb\u003e22\u003c/b\u003e, 101702 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOrdonez-Rubiano, E. G. \u003cem\u003eet al.\u003c/em\u003e Recovery of consciousness after a brainstem cavernous malformation hemorrhage: a descriptive study of preserved reticular activating system with tractography. \u003cem\u003eJ. Clin. Neurosci.\u003c/em\u003e \u003cb\u003e59\u003c/b\u003e, 372\u0026ndash;377 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHindman, J. \u003cem\u003eet al.\u003c/em\u003e Thalamic strokes that severely impair arousal extend into the brainstem. \u003cem\u003eAnn. Neurol.\u003c/em\u003e \u003cb\u003e84\u003c/b\u003e, 926\u0026ndash;930 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePujol, J. \u003cem\u003eet al.\u003c/em\u003e Largest-scale dissociation of brain activity at propofol-induced loss of consciousness. \u003cem\u003eSleep.\u003c/em\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/sleep/zsaa152\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNir, T. \u003cem\u003eet al.\u003c/em\u003e Resting-state functional connectivity in early postanaesthesia recovery is characterised by globally reduced anticorrelations. \u003cem\u003eBr. J. Anaesth.\u003c/em\u003e \u003cb\u003e125\u003c/b\u003e, 529\u0026ndash;538 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlexander, A. L., Lee, J. E., Lazar, M. \u0026amp; Field, A. S. Diffusion tensor imaging of the brain. \u003cem\u003eNeurotherapeutics.\u003c/em\u003e \u003cb\u003e4\u003c/b\u003e, 316\u0026ndash;329 (2007).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eXiong, K. L., Zhu, Y. S. \u0026amp; Zhang, W. G. Diffusion tensor imaging and magnetic resonance spectroscopy in traumatic brain injury: a review of recent literature. \u003cem\u003eBrain Imaging Behav.\u003c/em\u003e \u003cb\u003e8\u003c/b\u003e, 487\u0026ndash;496 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBasser, P. J. \u0026amp; Jones, D. K. Diffusion-tensor MRI: theory, experimental design and data analysis - a technical review. \u003cem\u003eNMR Biomed.\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e, 456\u0026ndash;467 (2002).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHulkower, M. B., Poliak, D. B., Rosenbaum, S. B., Zimmerman, M. E. \u0026amp; Lipton, M. L. A decade of DTI in traumatic brain injury: 10 years and 100 articles later. \u003cem\u003eAJNR Am. J. Neuroradiol.\u003c/em\u003e \u003cb\u003e34\u003c/b\u003e, 2064\u0026ndash;2074 (2013).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang, J. \u003cem\u003eet al.\u003c/em\u003e Correlations between diffusion tensor imaging and levels of consciousness in patients with traumatic brain injury: a systematic review and meta-analysis. \u003cem\u003eSci. Rep.\u003c/em\u003e \u003cb\u003e7\u003c/b\u003e, 2793 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHammoud, D. A. \u0026amp; Wasserman, B. A. Diffuse axonal injuries: pathophysiology and imaging. \u003cem\u003eNeuroimaging Clin. N. Am.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e, 205\u0026ndash;216 (2002).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ede la Marquez, C. D. \u003cem\u003eet al.\u003c/em\u003e Diffusion tensor imaging biomarkers for traumatic axonal injury: analysis of three analytic methods. \u003cem\u003eJ. Int. Neuropsychol. Soc.\u003c/em\u003e \u003cb\u003e17\u003c/b\u003e, 24\u0026ndash;35 (2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBenson, R. R. \u003cem\u003eet al.\u003c/em\u003e Global white matter analysis of diffusion tensor images is predictive of injury severity in traumatic brain injury. \u003cem\u003eJ. Neurotrauma.\u003c/em\u003e \u003cb\u003e24\u003c/b\u003e, 446\u0026ndash;459 (2007).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTang, C. Y. \u003cem\u003eet al.\u003c/em\u003e Diffuse disconnectivity in tBi: a resting state fMri anD Dti stuDy. \u003cem\u003eTransl. Neurosci.\u003c/em\u003e \u003cb\u003e3\u003c/b\u003e, 9\u0026ndash;14 (2012).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSekhon, M. S., Ainslie, P. N. \u0026amp; Griesdale, D. E. Clinical pathophysiology of hypoxic ischemic brain injury after cardiac arrest: a \"two-hit\" model. \u003cem\u003eCrit. Care.\u003c/em\u003e \u003cb\u003e21\u003c/b\u003e, 90 (2017).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThornton, J. S. \u003cem\u003eet al.\u003c/em\u003e Anisotropic water diffusion in white and gray matter of the neonatal piglet brain before and after transient hypoxia-ischaemia. \u003cem\u003eMagn. Reson. Imaging.\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e, 433\u0026ndash;440 (1997).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSamuels, E. R. \u0026amp; Szabadi, E. Functional neuroanatomy of the noradrenergic locus coeruleus: its roles in the regulation of arousal and autonomic function part I: principles of functional organisation. \u003cem\u003eCurr. Neuropharmacol.\u003c/em\u003e \u003cb\u003e6\u003c/b\u003e, 235\u0026ndash;253 (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChiang, M. C. \u003cem\u003eet al.\u003c/em\u003e Parabrachial complex: a hub for pain and aversion. \u003cem\u003eJ. Neurosci.\u003c/em\u003e \u003cb\u003e39\u003c/b\u003e, 8225\u0026ndash;8230 (2019).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKlatzo, I. Pathophysiological aspects of brain edema. \u003cem\u003eActa Neuropathol.\u003c/em\u003e \u003cb\u003e72\u003c/b\u003e, 236\u0026ndash;239 (1987).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSotak, C. H. The role of diffusion tensor imaging in the evaluation of ischemic brain injury - a review. \u003cem\u003eNMR Biomed.\u003c/em\u003e \u003cb\u003e15\u003c/b\u003e, 561\u0026ndash;569 (2002).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChaudhary, N. \u003cem\u003eet al.\u003c/em\u003e Diffusion tensor imaging in hemorrhagic stroke. \u003cem\u003eExp. Neurol.\u003c/em\u003e \u003cb\u003e272\u003c/b\u003e, 88\u0026ndash;96 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavis, D. \u003cem\u003eet al.\u003c/em\u003e Rapid monitoring of changes in water diffusion coefficients during reversible ischemia in cat and rat brain. \u003cem\u003eMagn. Reson. Med.\u003c/em\u003e \u003cb\u003e31\u003c/b\u003e, 454\u0026ndash;460 (1994).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMintorovitch, J. \u003cem\u003eet al.\u003c/em\u003e Comparison of diffusion- and T2-weighted MRI for the early detection of cerebral ischemia and reperfusion in rats. \u003cem\u003eMagn. Reson. Med.\u003c/em\u003e \u003cb\u003e18\u003c/b\u003e, 39\u0026ndash;50 (1991).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFridman, E. A., Beattie, B. J., Broft, A., Laureys, S. \u0026amp; Schiff, N. D. Regional cerebral metabolic patterns demonstrate the role of anterior forebrain mesocircuit dysfunction in the severely injured brain. Proc. Natl. Acad. Sci. U. S. A. 111, 6473\u0026ndash;6478(2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThrelkeld, Z. D. \u003cem\u003eet al.\u003c/em\u003e Functional networks reemerge during recovery of consciousness after acute severe traumatic brain injury. \u003cem\u003eCortex.\u003c/em\u003e \u003cb\u003e106\u003c/b\u003e, 299\u0026ndash;308 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEdlow, B. L., Claassen, J., Schiff, N. D. \u0026amp; Greer, D. M. Recovery from disorders of consciousness: mechanisms, prognosis and emerging therapies. \u003cem\u003eNat. Rev. Neurol.\u003c/em\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41582-020-00428-x\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTurner-Stokes, L. Prolonged disorders of consciousness: new national clinical guidelines from the Royal College of Physicians, London. \u003cem\u003eClin. Med. (Lond.).\u003c/em\u003e \u003cb\u003e14\u003c/b\u003e, 4\u0026ndash;5 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOstrosky-Solis, F., Ardila, A. \u0026amp; Rosselli, M. NEUROPSI: a brief neuropsychological test battery in Spanish with norms by age and educational level. \u003cem\u003eJ. Int. Neuropsychol. Soc.\u003c/em\u003e \u003cb\u003e5\u003c/b\u003e, 413\u0026ndash;433 (1999).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNasreddine, Z. S. \u003cem\u003eet al.\u003c/em\u003e The montreal cognitive assessment, MoCA: a brief screening tool for mild cognitive impairment. \u003cem\u003eJ. Am. Geriatr. Soc.\u003c/em\u003e \u003cb\u003e53\u003c/b\u003e, 695\u0026ndash;699 (2005).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKandeepan, S. \u003cem\u003eet al.\u003c/em\u003e Modeling an auditory stimulated brain under altered states of consciousness using the generalized Ising model. \u003cem\u003eNeuroimage.\u003c/em\u003e \u003cb\u003e223\u003c/b\u003e, 117367 (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWoolrich, M. W. \u003cem\u003eet al.\u003c/em\u003e Bayesian analysis of neuroimaging data in FSL. \u003cem\u003eNeuroimage.\u003c/em\u003e \u003cb\u003e45\u003c/b\u003e (1 Suppl), S173\u0026ndash;186 (2009).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeare, R., Lowekamp, B. \u0026amp; Yaniv, Z. Image segmentation, registration and characterization in R with SimpleITK. \u003cem\u003eJ. Stat. Softw.\u003c/em\u003e \u003cb\u003e86\u003c/b\u003e, 8 (2018).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGaryfallidis, E. \u003cem\u003eet al.\u003c/em\u003e Dipy, a library for the analysis of diffusion MRI data. \u003cem\u003eFront. Neuroinform.\u003c/em\u003e \u003cb\u003e8\u003c/b\u003e, 8 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePenny, W., Friston, K., Ashburner, J., Hiebel, S. \u0026amp; Nichols, T. Statistical parametric mapping: the analysis of functional brain images(Elsevier, 2011).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNITRC. \u003cem\u003eNITRC: NeuroImaging Tools \u0026amp; Resources Collaboratory\u003c/em\u003e \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.nitrc.org/plugins/mwiki/index.php/nitrc:Site_Map\u003c/span\u003e\u003c/span\u003e (2020).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDesikan, R. S. \u003cem\u003eet al.\u003c/em\u003e An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. \u003cem\u003eNeuroimage.\u003c/em\u003e \u003cb\u003e31\u003c/b\u003e, 968\u0026ndash;980 (2006).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAvants, B. B., Epstein, C. L., Grossman, M. \u0026amp; Gee, J. C. Symmetric diffeomorphic image registration with cross-correlation: evaluating automated labeling of elderly and neurodegenerative brain. \u003cem\u003eMed. Image Anal.\u003c/em\u003e \u003cb\u003e12\u003c/b\u003e, 26\u0026ndash;41 (2008).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDemertzi, A. \u003cem\u003eet al.\u003c/em\u003e Multiple fMRI system-level baseline connectivity is disrupted in patients with consciousness alterations. \u003cem\u003eCortex.\u003c/em\u003e \u003cb\u003e52\u003c/b\u003e, 35\u0026ndash;46 (2014).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAganj, I. \u003cem\u003eet al.\u003c/em\u003e Reconstruction of the orientation distribution function in single- and multiple-shell q-ball imaging within constant solid angle. \u003cem\u003eMagn. Reson. Med.\u003c/em\u003e \u003cb\u003e64\u003c/b\u003e, 554\u0026ndash;566 (2010).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAfzali, M., Fatemizadeh, E. \u0026amp; Soltanian-Zadeh, H. Interpolation of orientation distribution functions in diffusion weighted imaging using multi-tensor model. \u003cem\u003eJ. Neurosci. Methods.\u003c/em\u003e \u003cb\u003e253\u003c/b\u003e, 28\u0026ndash;37 (2015).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMonti, M. M. Statistical analysis of fMRI time-series: a critical review of the GLM approach. \u003cem\u003eFront. Hum. Neurosci.\u003c/em\u003e \u003cb\u003e5\u003c/b\u003e, 28 (2011).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"diffusion tensor imaging (DTI), diagnosis, neurology","lastPublishedDoi":"10.21203/rs.3.rs-244211/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-244211/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObject:\u003c/strong\u003e To determine the role of early acquisition of blood oxygen level-dependent (BOLD) signals and diffusion tensor imaging (DTI) for analysis of the connectivity of the ascending arousal network (AAN) in predicting neurological outcomes after acute traumatic brain injury (TBI), cardiopulmonary arrest (CPA), or stroke.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e A prospective analysis of 50 comatose patients was performed during their ICU stay. Image processing was conducted to assess structural and functional connectivity of the AAN. Outcomes were evaluated after 3 and 6 months.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e Nineteen patients (38%) had stroke, 18 (36%) CPA, and 13 (26%) TBI. Twenty-three patients were comatose (44%), 11 were in a minimally conscious state (20%), and 16 had unresponsive wakefulness syndrome (32%). Univariate analysis demonstrated that measurements of diffusivity, functional connectivity, and numbers of fibers in the gray matter, white matter, whole brain, midbrain reticular formation, and pontis oralis nucleus may serve as predictive biomarkers of outcome depending on the diagnosis. Multivariate analysis demonstrated a correlation of the predicted value and the real outcome for each separate diagnosis and for all the etiologies together.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Findings suggest that the above imaging biomarkers may have a predictive role for the outcome of comatose patients after acute TBI, CPA, or stroke.\u003c/p\u003e","manuscriptTitle":"Structural and Functional Connectivity of the Ascending Arousal Network for Prediction of Outcome in Patients with Acute Disorders of Consciousness","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-02-26 21:39:21","doi":"10.21203/rs.3.rs-244211/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2021-04-19T10:11:00+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2021-03-30T15:41:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"fd7d3797-1a57-4d8f-becd-fdc09807afa5","date":"2021-03-18T08:13:47+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"1fe2aa1f-ee17-4e0a-9814-970391b66d71","date":"2021-03-09T13:40:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2021-03-08T08:48:02+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2021-03-08T08:39:24+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2021-02-17T08:51:35+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2021-02-17T08:30:10+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2021-02-15T17:01:04+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b908a3ea-0bac-467c-8050-96c654c91842","owner":[],"postedDate":"February 26th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":2656609,"name":"Nuclear Medicine \u0026 Medical Imaging"},{"id":2656610,"name":"Neurology"}],"tags":[],"updatedAt":"2021-11-25T13:28:04+00:00","versionOfRecord":{"articleIdentity":"rs-244211","link":"https://doi.org/10.1038/s41598-021-98506-7","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2021-11-25 13:28:04","publishedOnDateReadable":"November 25th, 2021"},"versionCreatedAt":"2021-02-26 21:39:21","video":"","vorDoi":"10.1038/s41598-021-98506-7","vorDoiUrl":"https://doi.org/10.1038/s41598-021-98506-7","workflowStages":[]},"version":"v1","identity":"rs-244211","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-244211","identity":"rs-244211","version":["v1"]},"buildId":"rHA-KDH7Qsr4HCuvH75dn","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}