Dataset Information

Structural and functional connectivity of the ascending arousal network for prediction of outcome in patients with acute disorders of consciousness.

ABSTRACT: 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. 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. 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. Findings suggest that the above imaging biomarkers may have a predictive role for the outcome of comatose patients after acute TBI, CPA, or stroke.

SUBMITTER: Enciso-Olivera CO

PROVIDER: S-EPMC8617304 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature

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Structural and functional connectivity of the ascending arousal network for prediction of outcome in patients with acute disorders of consciousness.

Enciso-Olivera Cesar O CO Ordóñez-Rubiano Edgar G EG Casanova-Libreros Rosángela R Rivera Diana D Zarate-Ardila Carol J CJ Rudas Jorge J Pulido Cristian C Gómez Francisco F Martínez Darwin D Guerrero Natalia N Hurtado Mayra A MA Aguilera-Bustos Natalia N Hernández-Torres Clara P CP Hernandez José J Marín-Muñoz Jorge H JH

Scientific reports 20211125 1

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. 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 ...[more]

PMID: 34824383

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Project description:BackgroundIt is not currently possible to predict which patients will develop chronic disorders of consciousness (DoC) after severe traumatic brain injury (TBI). Although the ascending arousal network (AAN) supports human consciousness, it is unknown which AAN pathways must be preserved for patients to recover consciousness.MethodsSixteen patients with acute traumatic coma and 16 matched healthy controls were scanned with high angular resolution diffusion imaging (HARDI). All patients recovered consciousness (Recovery Cohort). Nine were scanned longitudinally: first in the ICU (Acute), then at ≥5 months post-injury (Follow-up). Six separate patients with post-traumatic DoC were scanned ≥5 months post-injury (Chronic DoC Cohort). For each AAN pathway, we computed the median relative change in Acute-to-Follow-up Connectivity Probability (CP) in the Recovery Cohort. We then used Wilcoxon tests with Bonferroni correction to compare CP in each AAN pathway in the Recovery Cohort at Follow-up versus the Chronic DoC Cohort. In an exploratory analysis, we used principal component analysis (PCA) to determine whether linear combinations of AAN CP values could separate the Chronic DoC Cohort from the Recovery Cohort and the healthy controls.ResultsIn the Recovery Cohort, the largest relative AAN CP changes were in the brainstem-to-thalamus (median [IQR] = 0.7 [0.09, 0.9]) and forebrain-to-occipital lobe (-0.8 [-0.9, -0.8]) pathways. The AAN connections that differed in the cross-sectional analysis between the Recovery Cohort at Follow-up and the Chronic DoC Cohort included brainstem-to-hypothalamus (W = 53, PBonf = 0.02), brainstem-to-temporal lobe (W = 52, PBonf = 0.04), and thalamus-to-temporal lobe (W = 54, PBonf = 0.009). Plotting the first two principal components of AAN connectivity resulted in a linear separation of Chronic DoC patients from other study groups.ConclusionsWe provide evidence for a longitudinal increase in brainstem-thalamic connectivity during recovery of consciousness after traumatic coma. Cross-sectional analyses revealed that brainstem-hypothalamus, brainstem-temporal lobe, and thalamus-temporal lobe connectivity differed between patients who recovered consciousness and those with a chronic DoC. These observations provide the basis for further investigation into AAN connectivity as a biomarker for recovery of consciousness after traumatic coma.

Project description:Prolonged Disorders of Consciousness (PDOC) resulting from severe acquired brain injury can lead to complex disabilities that make diagnosis challenging. The role of machine learning (ML) in diagnosing PDOC states and identifying intervention strategies is relatively under-explored, having focused on predicting mortality and poor outcome. This study aims to: (a) apply ML techniques to predict PDOC diagnostic states from variables obtained from two non-invasive neurobehavior assessment tools; and (b) apply network analysis for guiding possible intervention strategies. The Coma Recovery Scale-Revised (CRS-R) is a well-established tool for assessing patients with PDOC. More recently, music has been found to be a useful medium for assessment of coma patients, leading to the standardization of a music-based assessment of awareness: Music Therapy Assessment Tool for Awareness in Disorders of Consciousness (MATADOC). CRS-R and MATADOC data were collected from 74 PDOC patients aged 16-70 years at three specialist centers in the USA, UK and Ireland. The data were analyzed by three ML techniques (neural networks, decision trees and cluster analysis) as well as modelled through system-level network analysis. PDOC diagnostic state can be predicted to a relatively high level of accuracy that sets a benchmark for future ML analysis using neurobehavioral data only. The outcomes of this study may also have implications for understanding the role of music therapy in interdisciplinary rehabilitation to help patients move from one coma state to another. This study has shown how ML can derive rules for diagnosis of PDOC with data from two neurobehavioral tools without the need to harvest large clinical and imaging datasets. Network analysis using the measures obtained from these two non-invasive tools provides novel, system-level ways of interpreting possible transitions between PDOC states, leading to possible use in novel, next-generation decision-support systems for PDOC.

Dataset Information

Structural and functional connectivity of the ascending arousal network for prediction of outcome in patients with acute disorders of consciousness.

Publications

Structural and functional connectivity of the ascending arousal network for prediction of outcome in patients with acute disorders of consciousness.

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