Functional and Structural Integrity of Frontoparietal Connectivity in Traumatic and Anoxic Coma

Péran, Patrice; Malagurski, Brigitta; Nemmi, Federico; Sarton, Benjamine; Vinour, Hélène; Ferré, Fabrice; Vardon-Bounes, Fanny; Rousset, David; Mrozeck, Segolène; Seguin, Thierry; Riu, Béatrice; Minville, Vincent; Geeraerts, Thomas; Lotterie, Jean Albert; Deboissezon, Xavier; Albucher, Jean François; Fourcade, Olivier; Olivot, Jean Marc; Naccache, Lionel; Silva, Stein

doi:10.1097/ccm.0000000000004406

Cited by 25 publications

(21 citation statements)

References 38 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…different mechanisms of brain injury 10,29,[63][64][65][66][67][68][69][70] . While most studies have evaluated the prognostic value of DMN connectivity, others evaluated connectivity between the DMN and other networks 10 , evaluated connectivity within other networks 10,65,71 , or evaluated whole brain connectivity (i.e., matrices of connectivity between many regions across many networks) 65,67 .…”

Section: Accepted Manuscriptmentioning

confidence: 99%

“…As with advanced MRI for CMD, there are several factors to consider if clinically pursuing advanced MRI for neuroprognostication, which are summarized in Figure 3. First, clinicians should ensure that the patient population is appropriate, as only certain brain injury etiologies -including traumatic brain injury, anoxic brain injury after cardiac arrest, and vascular injury -have been studied in the context of advanced MRI for neuroprognostication 10,29,[63][64][65][66][67][68][69][70] . In contrast, while brain network dysfunction has been observed in other etiologies of disordered consciousness -such as toxic-metabolic encephalopathy 78 -advanced MRI has not yet been shown to have prognostic value in these conditions.…”

Section: Accepted Manuscriptmentioning

confidence: 99%

“…greater insights into DoC 27,51,52,69,85,86 . Second, there is an opportunity to optimize how advanced MRI data are collected, as ongoing research explores techniques to improve resolution 87,88 , eliminate sources of physiologic noise [89][90][91][92][93][94] , and minimize the effects of confounders [72][73][74][75][76][77][78] .…”

Section: Accepted Manuscriptmentioning

confidence: 99%

See 2 more Smart Citations

Applications of Advanced MRI to Disorders of Consciousness

et al. 2022

View full text Add to dashboard Cite

Disorder of consciousness (DoC) after severe brain injury presents numerous challenges to clinicians, as the diagnosis, prognosis, and management are often uncertain. Magnetic resonance imaging (MRI) has long been used to evaluate brain structure in patients with DoC. More recently, advances in MRI technology have permitted more detailed investigations of the brain's structural integrity (via diffusion MRI) and function (via functional MRI). A growing literature has begun to show that these advanced forms of MRI may improve our understanding of DoC pathophysiology, facilitate the identification of patient consciousness, and improve the accuracy of clinical prognostication. Here we review the emerging evidence for the application of advanced MRI for patients with DoC.

show abstract

Section: Accepted Manuscriptmentioning

confidence: 99%

Section: Accepted Manuscriptmentioning

confidence: 99%

See 1 more Smart Citation

Applications of Advanced MRI to Disorders of Consciousness

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Indeed, sMRI has demonstrated the usefulness of white matter fractional anisotropy (FA) [4] and gray matter morphometry [5] to predict neurological outcome after CA. In addition, fMRI studies have identified putative signatures of consciousness by using either static or dynamic resting-state connectivity [6][7][8][9]. Thereby, recent fMRI studies in patients in anoxoischemic coma have shed light on the role of frontal (mesial prefrontal cortex [mPFC]) and posterior parietal (precuneus [PreCun] and posterior cingulate cortex [PCC]) cortices as critical hubs within a putative brain mesocircuit underpinning consciousness emergence and maintain [10].…”

Section: Introductionmentioning

confidence: 99%

Multimodal MRI-Based Whole-Brain Assessment in Patients In Anoxoischemic Coma by Using 3D Convolutional Neural Networks

et al. 2022

Self Cite

View full text Add to dashboard Cite

Background There is an unfulfilled need to find the best way to automatically capture, analyze, organize, and merge structural and functional brain magnetic resonance imaging (MRI) data to ultimately extract relevant signals that can assist the medical decision process at the bedside of patients in postanoxic coma. We aimed to develop and validate a deep learning model to leverage multimodal 3D MRI whole-brain times series for an early evaluation of brain damages related to anoxoischemic coma. Methods This proof-of-concept, prospective, cohort study was undertaken at the intensive care unit affiliated with the University Hospital (Toulouse, France), between March 2018 and May 2020. All patients were scanned in coma state at least 2 days (4 ± 2 days) after cardiac arrest. Over the same period, age-matched healthy volunteers were recruited and included. Brain MRI quantification encompassed both “functional data” from regions of interest (precuneus and posterior cingulate cortex) with whole-brain functional connectivity analysis and “structural data” (gray matter volume, T1-weighted, fractional anisotropy, and mean diffusivity). A specifically designed 3D convolutional neuronal network (CNN) was created to allow conscious state discrimination (coma vs. controls) by using raw MRI indices as the input. A voxel-wise visualization method based on the study of convolutional filters was applied to support CNN outcome. The Ethics Committee of the University Teaching Hospital of Toulouse, France (2018-A31) approved the study and informed consent was obtained from all participants. Results The final cohort consisted of 29 patients in postanoxic coma and 34 healthy volunteers. Coma patients were successfully discerned from controls by using 3D CNN in combination with different MR indices. The best accuracy was achieved by functional MRI data, in particular with resting-state functional MRI of the posterior cingulate cortex, with an accuracy of 0.96 (range 0.94–0.98) on the test set from 10-time repeated tenfold cross-validation. Even more satisfactory performances were achieved through the majority voting strategy, which was able to compensate for mistakes from single MR indices. Visualization maps allowed us to identify the most relevant regions for each MRI index, notably regions previously described as possibly being involved in consciousness emergence. Interestingly, a posteriori analysis of misclassified patients indicated that they may present some common functional MRI traits with controls, which suggests further favorable outcomes. Conclusions A fully automated identification of clinically relevant signals from complex multimodal neuroimaging data is a major research topic that may bring a radical paradigm shift in the neuroprognostication of patients with severe brain injury. We report for the first time a successful discrimination between patients in postanoxic coma patients from people serving as controls by using 3D CNN whole-brain structural and functional MRI data. Clinical Trial Numberhttp://ClinicalTrials.gov (No. NCT03482115).

show abstract

“…Over the past decade, resting-state functional magnetic resonance imaging (rs-fMRI) studies have demonstrated the potential diagnostic and prognostic utility of brain network mapping in patients with a broad range of brain injuries, including traumatic brain injury (TBI) (Guo et al, 2019;Kondziella et al, 2017;Peran et al, 2020;Threlkeld et al, 2018), hypoxic-ischemic injury (HIE) from cardiac arrest (Koenig et al, 2014;Norton et al, 2012;Peran et al, 2020;Pugin et al, 2020;Sair et al, 2018), and hypoxic injury from coronavirus disease 2019 (COVID-19) (Fischer et al, 2020). In addition, rs-fMRI is now used as a pharmacodynamic biomarker in early-stage clinical trials to assess brain responses to targeted therapies aimed at promoting recovery of consciousness (Edlow et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Correcting Cardiorespiratory Noise in Resting-state Functional MRI Data Acquired in Critically Ill Patients

Chan

Sanders

Fischer

et al. 2021

Preprint

View full text Add to dashboard Cite

Resting-state functional MRI (rs-fMRI) is being used to develop diagnostic, prognostic, and therapeutic biomarkers for critically ill patients with severe brain injuries. In studies of healthy volunteers and non-critically ill patients, prospective cardiorespiratory data are routinely collected to remove non-neuronal fluctuations in the rs-fMRI signal during analysis. However, the feasibility and utility of collecting cardiorespiratory data in critically ill patients on a clinical MRI scanner are unknown. We concurrently acquired rs-fMRI (TR=1250ms), cardiac and respiratory data in 23 critically ill patients with acute severe traumatic brain injury (TBI), and 12 healthy control subjects. We compared the functional connectivity results after denoising with cardiorespiratory data (i.e., RETROICOR) with the results obtained after standard bandpass filtering. Rs-fMRI data in 7 patients could not be analyzed due to imaging artifacts. In 6 of the remaining 16 patients (37.5%), cardiorespiratory data were either incomplete or corrupted. In both patients and control subjects, the functional connectivity corrected with RETROICOR did not significantly differ from that corrected with bandpass filtering of 0.008-0.125 Hz. Collectively, these findings suggest that there is a limited feasibility and utility to prospectively acquire high-quality cardiorespiratory data during rs-fMRI in critically ill patients with severe TBI for physiological correction.

show abstract

Functional and Structural Integrity of Frontoparietal Connectivity in Traumatic and Anoxic Coma

Abstract: Supplemental Digital Content is available in the text.

Cited by 25 publications

References 38 publications

Applications of Advanced MRI to Disorders of Consciousness

Applications of Advanced MRI to Disorders of Consciousness

Multimodal MRI-Based Whole-Brain Assessment in Patients In Anoxoischemic Coma by Using 3D Convolutional Neural Networks

Correcting Cardiorespiratory Noise in Resting-state Functional MRI Data Acquired in Critically Ill Patients

Contact Info

Product

Resources

About