Evidence map›Paper›PMID 32563037›Full record

ArticleNeuroImage. Clinical2020

Brain functional connectivity during the first day of coma reflects long-term outcome.

Thomas Kustermann, Nathalie Ata Nguepnjo Nguissi, Christian Pfeiffer, Matthias Haenggi, Rebekka Kurmann, Frédéric Zubler, Mauro Oddo, Andrea O Rossetti, Marzia De Lucia

Open access · goldAbstract read
In one paragraph

Article in NeuroImage. Clinical, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.

0numbers the graph read from it
0cells of the map it votes in
15citing papers in PubMed
1.2field-weighted citation impact, top 22% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

15 citing papers in PubMed, 19 citations in OpenAlex.

  1. Article
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  4. Resting-state EEG for continuous prognostic monitoring and prediction of coma recovery after acute brain injury.Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 4 institutions in 1 country.

Thomas KustermannLaboratoire de Recherche en Neuroimagerie (LREN), University Hospital (CHUV) & University of Lausanne, Switzerland. Electronic address: tfkustermann@gmail.com.
Nathalie Ata Nguepnjo NguissiLaboratoire de Recherche en Neuroimagerie (LREN), University Hospital (CHUV) & University of Lausanne, Switzerland.
Christian PfeifferDepartment of Psychology, University of Zürich, Switzerland.
Matthias HaenggiDepartment of Intensive Care Medicine, Inselspital, Bern University Hospital, University of Bern, Switzerland.
Rebekka KurmannDepartment of Neurology, Inselspital, Bern University Hospital, University of Bern, Switzerland.
Frédéric ZublerDepartment of Neurology, Inselspital, Bern University Hospital, University of Bern, Switzerland.
Mauro OddoDepartment of Intensive Care Medicine, University Hospital (CHUV) & University of Lausanne, Switzerland.
Andrea O RossettiDepartment of Neurology, University Hospital (CHUV) & University of Lausanne, Switzerland.
Marzia De LuciaLaboratoire de Recherche en Neuroimagerie (LREN), University Hospital (CHUV) & University of Lausanne, Switzerland.
University of Lausanne · CHUniversity of Bern · CHUniversity Hospital of Bern · CHUniversity of Zurich · CH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectiveIn patients with disorders of consciousness (DOC), properties of functional brain networks at rest are informative of the degree of consciousness impairment and of long-term outcome. Here we investigate whether connectivity differences between patients with favorable and unfavorable outcome are already present within 24 h of coma onset.

methodsWe prospectively recorded 63-channel electroencephalography (EEG) at rest during the first day of coma after cardiac arrest. We analyzed 98 adults, of whom 57 survived beyond unresponsive wakefulness. Functional connectivity was estimated by computing the 'debiased weighted phase lag index' over epochs of five seconds duration. We evaluated the network's topological features, including clustering coefficient, path length, modularity and participation coefficient and computed their variance over time. Finally, we estimated the predictive value of these topological features for patients' outcomes by splitting the patient sample in training and test datasets.

resultsGroup-level analysis revealed lower clustering coefficient, higher modularity and path length variance in patients with favorable compared to those with unfavorable outcomes (p < 0.01). Within all features, the path length variance in the network provided the best positive predictive value (PPV) for favorable outcome and specificity for unfavorable outcome in the test dataset (PPV: 0.83, p < 0.01; specificity: 0.86, p < 0.01) with above-chance negative predictive value and accuracy. Of note, the exclusion of patients with epileptiform activity (20 in total) eliminates all false positive predictions (n = 6) for path length variance.

interpretationTopological features of functional connectivity differ as a function of long-term outcome in patients on the first day of coma. These differences are not interpretable in terms of consciousness levels as all patients were in a deep unconscious state. The time variance of path length is informative of comatose patients' outcome, as patients with favorable outcome exhibit a richer repertoire of path length than those with unfavorable outcomes.

Indexed as

TimeAdultAgedAged, 80 and overBrainComaConsciousnessConsciousness DisordersElectroencephalographyFemaleHumansMaleMiddle AgedPersistent Vegetative StateWakefulnessCardiac arrestComaEEGFunctional connectivityResting state

Identifiers

PMID32563037
PMCPMC7305428
OpenAlexW3029474614

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC-ND
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.