Improving Diagnosis and Prognosis in Acute Severe Brain Injury: A Multimodal Imaging Protocol

Abstract

Multi-modal neuroimaging techniques have the potential to dramatically improve the diagnosis of the level consciousness and prognostication of neurological outcome for patients with severe brain injury in the intensive care unit (ICU). This protocol describes a study that will utilize functional Magnetic Resonance Imaging (fMRI), electroencephalography (EEG), and functional Near Infrared Spectroscopy (fNIRS) to measure and map the brain activity of acute critically ill patients. Our goal is to investigate whether these modalities can provide objective and quantifiable indicators of good neurological outcome and reliably detect conscious awareness. To this end, we will conduct a prospective longitudinal cohort study to validate the prognostic and diagnostic utility of neuroimaging techniques in the ICU. We will recruit 350 individuals from two ICUs over the course of 7 years. Participants will undergo fMRI, EEG, and fNIRS testing several times over the first 10 days of care to assess for residual cognitive function and evidence of covert awareness. Patients who regain behavioral awareness will be asked to complete web-based neurocognitive tests for 1 year, as well as return for follow up neuroimaging to determine which acute imaging features are most predictive of cognitive and functional recovery. Ultimately, multi-modal neuroimaging techniques may improve the clinical assessments of patients' level of consciousness, aid in the prediction of outcome, and facilitate efforts to find interventional methods that improve recovery and quality of life.

Exploring the Significance of Cognitive Motor Dissociation on Patient Outcome in Acute Disorders of Consciousness

“…Besides EEG (and ERPs) and fMRI, the prognostic value of other emerging techniques such as functional near-infrared spectroscopy or transcranial magnetic stimulation combined with EEG (TMS-EEG) will be investigated in the future in the acute clinical setting. 29,30 Finally, an effort has been undertaken to investigate the added value of multimodal assessment, such as done in the study of Amiri et al 31 However, they failed to prove the added value of the combination of EEG and fMRI data and using machine learning to predict 12month outcome while EEGs alone-as well as thorough repeated neurological assessment alone-were good models to predict outcome at that time point. Mortality rates were not addressed systematically, but we found similar death rates for patients with CMD (33%) compared with non-CMD patients (30.5%).…”

Effects of Systemic Physiology on Mapping Resting-State Networks Using Functional Near-Infrared Spectroscopy

“…In particular, the DMN and FPC play a critical role in normal consciousness ( Demertzi et al, 2014 ; Stender et al, 2014 ). Previous work has shown that disruption in these networks could be a potential marker for patients who are likely to suffer from a disorder of consciousness ( Demertzi et al, 2014 ; Stender et al, 2014 ; Kazazian et al, 2021 ). Therefore, extracting these networks at the single-subject level illustrates the potential of fNIRS as a tool for assessing awareness at the bedside, longitudinally and with low cost.…”

Improved clinical outcome prediction in depression using neurodynamics in an emotional face-matching functional MRI task

“…However, this subjective approach might not be beneficial for all patients. It has been hypothesized that objective prognosis based on physiological measurements could support this decision-making and eventually improve the quality of individual treatment strategies (5,6).…”