Consciousness in Neurocritical Care Cohort Study Using fMRI and EEG (CONNECT-ME): Protocol for a Longitudinal Prospective Study and a Tertiary Clinical Care Service

Multimodal Prediction of 3- and 12-Month Outcomes in ICU Patients with Acute Disorders of Consciousness

BACKGROUND

In intensive care unit (ICU) patients with coma and other disorders of consciousness (DoC), outcome prediction is key to decision-making regarding prognostication, neurorehabilitation, and management of family expectations. Current prediction algorithms are largely based on chronic DoC, whereas multimodal data from acute DoC are scarce. Therefore, the Consciousness in Neurocritical Care Cohort Study Using Electroencephalography and Functional Magnetic Resonance Imaging (i.e. CONNECT-ME; ClinicalTrials.gov identifier: NCT02644265) investigates ICU patients with acute DoC due to traumatic and nontraumatic brain injuries, using electroencephalography (EEG) (resting-state and passive paradigms), functional magnetic resonance imaging (fMRI) (resting-state) and systematic clinical examinations.

METHODS

We previously presented results for a subset of patients (n = 87) concerning prediction of consciousness levels in the ICU. Now we report 3- and 12-month outcomes in an extended cohort (n = 123). Favorable outcome was defined as a modified Rankin Scale score ≤ 3, a cerebral performance category score ≤ 2, and a Glasgow Outcome Scale Extended score ≥ 4. EEG features included visual grading, automated spectral categorization, and support vector machine consciousness classifier. fMRI features included functional connectivity measures from six resting-state networks. Random forest and support vector machine were applied to EEG and fMRI features to predict outcomes. Here, random forest results are presented as areas under the curve (AUC) of receiver operating characteristic curves or accuracy. Cox proportional regression with in-hospital death as a competing risk was used to assess independent clinical predictors of time to favorable outcome.

RESULTS

Between April 2016 and July 2021, we enrolled 123 patients (mean age 51 years, 42% women). Of 82 (66%) ICU survivors, 3- and 12-month outcomes were available for 79 (96%) and 77 (94%), respectively. EEG features predicted both 3-month (AUC 0.79 [95% confidence interval (CI) 0.77-0.82]) and 12-month (AUC 0.74 [95% CI 0.71-0.77]) outcomes. fMRI features appeared to predict 3-month outcome (accuracy 0.69-0.78) both alone and when combined with some EEG features (accuracies 0.73-0.84) but not 12-month outcome (larger sample sizes needed). Independent clinical predictors of time to favorable outcome were younger age (hazard ratio [HR] 1.04 [95% CI 1.02-1.06]), traumatic brain injury (HR 1.94 [95% CI 1.04-3.61]), command-following abilities at admission (HR 2.70 [95% CI 1.40-5.23]), initial brain imaging without severe pathological findings (HR 2.42 [95% CI 1.12-5.22]), improving consciousness in the ICU (HR 5.76 [95% CI 2.41-15.51]), and favorable visual-graded EEG (HR 2.47 [95% CI 1.46-4.19]).

CONCLUSIONS

Our results indicate that EEG and fMRI features and readily available clinical data predict short-term outcome of patients with acute DoC and that EEG also predicts 12-month outcome after ICU discharge.

Detecting signatures of consciousness in acute brain injury after stimulation with apomorphine and methylphenidate: protocol for a placebo-controlled, randomized, cross-over study

Introduction

Acute brain injury can lead to states of decreased consciousness, that is, disorder of consciousness (DoC). Detecting signs of consciousness early is vital for DoC management in the intensive care unit (ICU), neurorehabilitation and long-term prognosis. Our primary objective is to investigate the potential of pharmacological stimulant therapies in eliciting signs of consciousness among unresponsive or low-responsive acute DoC patients.

Methods

In a placebo-controlled, randomised, cross-over setting, we evaluate the effect of methylphenidate and apomorphine in 50 DoC patients with acute traumatic or non-traumatic brain injury admitted to the ICU. Patients are examined before and after administration of the trial drugs using (1) neurobehavioural scales to determine the clinical level of consciousness, (2) automated pupillometry to record pupillary responses as a signature for awareness and (3) near-infrared spectroscopy combined with electroencephalography to record neurovascular coupling as a measure for cortical activity. Primary outcomes include pupillary dilations and increase in cortical activity during passive and active paradigms.

Ethics

The study has been approved by the ethics committee (Journal-nr: H-21022096) and follows the principles of the Declaration of Helsinki. It is deemed to pose minimal risks and to hold a significant potential to improve treatment options for DoC patients. If the stimulants are shown to enhance cortical modulation of pupillary function and neurovascular coupling, this would warrant a large multicentre trial to evaluate their clinical impact.

Dissemination

Results will be available on EudraCT, clinicaltrialsregister.eu and published in an international peer-reviewed journal.

Trial registration number

EudraCT Number: 2021-001453-31.

Brain Trauma Imaging

Imaging of mild traumatic brain injury (TBI) using conventional techniques such as CT or MRI often results in no specific imaging correlation that would explain cognitive and clinical symptoms. Molecular imaging of mild TBI suggests that secondary events after injury can be detected using PET. However, no single specific pattern emerges that can aid in diagnosing the injury or determining the prognosis of the long-term behavioral profiles, indicating the heterogeneous and diffuse nature of TBI. Chronic traumatic encephalopathy, a primary tauopathy, has been shown to be strongly associated with repetitive TBI. In vivo data on the available tau PET tracers, however, have produced mixed results and overall low retention profiles in athletes with a history of repetitive mild TBI. Here, we emphasize that the lack of a mechanistic understanding of chronic TBI has posed a challenge when interpreting the results of molecular imaging biomarkers. We advocate for better target identification, improved analysis techniques such as machine learning or artificial intelligence, and novel tracer development.

Multimodal prediction of residual consciousness in the intensive care unit: the CONNECT-ME study

Functional MRI (fMRI) and EEG may reveal residual consciousness in patients with disorders of consciousness (DoC), as reflected by a rapidly expanding literature on chronic DoC. However, acute DoC is rarely investigated, although identifying residual consciousness is key to clinical decision-making in the intensive care unit (ICU). Therefore, the objective of the prospective, observational, tertiary centre cohort, diagnostic phase IIb study 'Consciousness in neurocritical care cohort study using EEG and fMRI' (CONNECT-ME, NCT02644265) was to assess the accuracy of fMRI and EEG to identify residual consciousness in acute DoC in the ICU. Between April 2016 and November 2020, 87 acute DoC patients with traumatic or non-traumatic brain injury were examined with repeated clinical assessments, fMRI and EEG. Resting-state EEG and EEG with external stimulations were evaluated by visual analysis, spectral band analysis and a Support Vector Machine (SVM) consciousness classifier. In addition, within- and between-network resting-state connectivity for canonical resting-state fMRI networks was assessed. Next, we used EEG and fMRI data at study enrolment in two different machine-learning algorithms (Random Forest and SVM with a linear kernel) to distinguish patients in a minimally conscious state or better (≥MCS) from those in coma or unresponsive wakefulness state (≤UWS) at time of study enrolment and at ICU discharge (or before death). Prediction performances were assessed with area under the curve (AUC). Of 87 DoC patients (mean age, 50.0 ± 18 years, 43% female), 51 (59%) were ≤UWS and 36 (41%) were ≥ MCS at study enrolment. Thirty-one (36%) patients died in the ICU, including 28 who had life-sustaining therapy withdrawn. EEG and fMRI predicted consciousness levels at study enrolment and ICU discharge, with maximum AUCs of 0.79 (95% CI 0.77-0.80) and 0.71 (95% CI 0.77-0.80), respectively. Models based on combined EEG and fMRI features predicted consciousness levels at study enrolment and ICU discharge with maximum AUCs of 0.78 (95% CI 0.71-0.86) and 0.83 (95% CI 0.75-0.89), respectively, with improved positive predictive value and sensitivity. Overall, both machine-learning algorithms (SVM and Random Forest) performed equally well. In conclusion, we suggest that acute DoC prediction models in the ICU be based on a combination of fMRI and EEG features, regardless of the machine-learning algorithm used.

Case Report: Resting-State Brain-Networks After Near-Complete Hemispherectomy in Adulthood

Objectives

Understanding the dynamics of reorganized network-level brain functions after hemispherectomy is important for treatment, prognostication, and rehabilitation of brain injury, but also for investigating questions of fundamental neurobehavioral interest: How does the brain promote consciousness despite loss of one hemisphere?

Methods

We studied resting-state functional connectivity (RSFC) in a high-functioning middle-aged man 6 years after functional hemispherectomy following malignant middle cerebral artery infarction, and we compared results to RSFC in 20 healthy controls.

Results

Our analysis indicates increased between-network connectivity for all seven networks examined in the patient's preserved hemisphere, compared to healthy controls, suggesting a shift toward increased between-network connectivity following near-complete loss of one hemisphere during adulthood.

Conclusions

These data corroborate and extend recent findings of increased between-network connectivity in the remaining hemisphere after surgical hemispherectomy for intractable epilepsy during childhood. Our results support a neuroplasticity model with reorganization of distributed brain connectivity within the preserved hemisphere as part of the road to recovery after brain injury, as well as recovery of consciousness and cognitive functions, after hemispherectomy.

Efficacy of Transcranial Direct Current Stimulation Over Dorsolateral Prefrontal Cortex in Patients With Minimally Conscious State

Background

The treatment of patients in a minimally conscious state (MCS) remains challenging. Transcranial direct current stimulation (tDCS) is a non-invasive therapeutic method in treating neurologic diseases by regulating the cortical excitability. The aim is to investigate the effect of tDCS in patients with MCS in this study.

Methods

Eleven patients in MCS were enrolled in the study. All the patients received 5 daily sessions of 20-min sham tDCS, followed by 10 sessions of 20-min real tDCS. The anodal electrode and cathodal electrodes were placed over the left dorsolateral prefrontal cortex (DLPFC) and the right eyebrow, respectively. Assessment of Coma Recovery Scale-Revised (CRS-R) scores and resting-state functional MRI (rs-fMRI) scans was conducted three times in each patient: before tDCS (baseline, T0), post-sham tDCS at week 1 (T1), and post-real tDCS at week 2 (T2). The whole-brain functional connectivity (FC) was obtained by bilaterally computing FC from six seed regions: precuneus, middle frontal gyrus, supplemental motor area, angular gyrus, superior temporal gyrus, and occipital lobe. One-way repeated measure ANOVA was used to compare the differences of CRS-R scores and FC at T0, T1, and T2. The false discovery rate correction of p < 0.001 was adopted for controlling multiple comparisons in FC analysis.

Results

Five patients with MCS showed obvious clinical improvement represented by increased CRS-R scores post- 2-week real tDCS. The CRS-R scores did not change post- 1-week sham treatment. No side effects were reported during the study. The FC of the bilateral supplementary motor area, right angular gyrus, and right superior temporal gyrus were significantly enhanced after 2-week real tDCS compared with that after 1-week sham-tDCS. In addition, FC of bilateral occipital lobe and right precuneus were significantly enhanced post- 2-week real tDCS compared with the baseline.

Conclusion

Our findings indicated that tDCS over DLPFC could serve as a potentially effective therapy for improving the consciousness state in patients with MCS. The FC in rs-fMRI can be modulated by tDCS at both the stimulation site (left DLPFC) and the distant regions.

Impact of MRI on decision-making in ICU patients with disorders of consciousness

BACKGROUND

Recovery of consciousness is the most important survival factor in patients with acute brain injury and disorders of consciousness (DoC). Since most deaths in the intensive care unit (ICU) occur after withdrawal of life-support, medical decision-making is crucial for acute DoC patients. Neuroimaging informs decision-making, yet the precise effects of MRI on decision-making in the ICU are poorly understood. We investigated the impact of brain MRI on prognostication, therapeutic decisions and physician confidence in ICU patients with DoC.

METHODS

In this simulated decision-making study utilizing a prospective ICU cohort, a panel of neurocritical experts first reviewed clinical information (without MRI) from 75 acute DoC patients and made decisions about diagnosis, prognosis and treatment. Following review of the MRI, the panel then decided if the initial decisions needed revision. In parallel, a blinded neuroradiologist reassessed all neuroimaging.

RESULTS

MRI led to changes in clinical management of 57 (76%) of patients (Number-Needed-to-Test for any change: 1.32), including revised diagnoses (20%), levels of care (21%), diagnostic confidence (43%) and prognostications (33%). Decisions were revised more often with stroke than with other brain injuries (p = 0.02). However, although MRI revealed additional pathology in 81%, this did not predict revised clinical decision-making (p-values ≥0.08).

CONCLUSION

MRI results changed decision-making in 3 of 4 ICU patients, but radiological findings were not predictive of clinical decision-making. This highlights the need to better understand the effects of neuroimaging on management decisions. How MRI influences decision-making in the ICU is an important avenue for research to improve acute DoC management.

Emerging Utility of Applied Magnetic Resonance Imaging in the Management of Traumatic Brain Injury

Traumatic brain injury (TBI) is a widespread and expensive problem globally. The standard diagnostic workup for new TBI includes obtaining a noncontrast computed tomography image of the head, which provides quick information on operative pathologies. However, given the limited sensitivity of computed tomography for identifying subtle but meaningful changes in the brain, magnetic resonance imaging (MRI) has shown better utility for ongoing management and prognostication after TBI. In recent years, advanced applications of MRI have been further studied and are being implemented as clinical tools to help guide care. These include functional MRI, diffusion tensor imaging, MR perfusion, and MR spectroscopy. In this review, we discuss the scientific basis of each of the above techniques, the literature supporting their use in TBI, and how they may be clinically implemented to improve the care of TBI patients.

Advanced neuroimaging in traumatic brain injury: an overview

Traumatic brain injury (TBI) is a common condition with many potential acute and chronic neurological consequences. Standard initial radiographic evaluation includes noncontrast head CT scanning to rapidly evaluate for pathology that might require intervention. The availability of fast, relatively inexpensive CT imaging has fundamentally changed the clinician's ability to noninvasively visualize neuroanatomy. However, in the context of TBI, limitations of head CT without contrast include poor prognostic ability, inability to analyze cerebral perfusion status, and poor visualization of underlying posttraumatic changes to brain parenchyma. Here, the authors review emerging advanced imaging for evaluation of both acute and chronic TBI and include QuickBrain MRI as an initial imaging modality. Dynamic susceptibility-weighted contrast-enhanced perfusion MRI, MR arterial spin labeling, and perfusion CT are reviewed as methods for examining cerebral blood flow following TBI. The authors evaluate MR-based diffusion tensor imaging and functional MRI for prognostication of recovery post-TBI. Finally, MR elastography, MR spectroscopy, and convolutional neural networks are examined as future tools in TBI management. Many imaging technologies are being developed and studied in TBI, and some of these may hold promise in improving the understanding and management of TBI. ABBREVIATIONS ASL = arterial spin labeling; CNN = convolutional neural network; CTP = perfusion CT; DAI = diffuse axonal injury; DMN = default mode network; DOC = disorders of consciousness; DTI = diffusion tensor imaging; FA = fractional anisotropy; fMRI = functional MRI; GCS = Glasgow Coma Scale; MD = mean diffusivity; MRE = MR elastography; MRS = MR spectroscopy; mTBI = mild TBI; NAA = N-acetylaspartate; SWI = susceptibility-weighted imaging; TBI = traumatic brain injury; UHF = ultra-high field.