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Predicting neurologic recovery after severe acute brain injury using resting-state networks

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A Correction to this article was published on 13 October 2023

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Abstract

Objective

There is a lack of reliable tools used to predict functional recovery in unresponsive patients following a severe brain injury. The objective of the study is to evaluate the prognostic utility of resting-state functional magnetic resonance imaging for predicting good neurologic recovery in unresponsive patients with severe brain injury in the intensive-care unit.

Methods

Each patient underwent a 5.5-min resting-state scan and ten resting-state networks were extracted via independent component analysis. The Glasgow Outcome Scale was used to classify patients into good and poor outcome groups. The Nearest Centroid classifier used each patient’s ten resting-state network values to predict best neurologic outcome within 6 months post-injury.

Results

Of the 25 patients enrolled (mean age = 43.68, range = [19–69]; GCS ≤ 9; 6 females), 10 had good and 15 had poor outcome. The classifier correctly and confidently predicted 8/10 patients with good and 12/15 patients with poor outcome (mean = 0.793, CI = [0.700, 0.886], Z = 2.843, p = 0.002). The prediction performance was largely determined by three visual (medial: Z = 3.11, p = 0.002; occipital pole: Z = 2.44, p = 0.015; lateral: Z = 2.85, p = 0.004) and the left frontoparietal network (Z = 2.179, p = 0.029).

Discussion

Our approach correctly identified good functional outcome with higher sensitivity (80%) than traditional prognostic measures. By revealing preserved networks in the absence of discernible behavioral signs, functional connectivity may aid in the prognostic process and affect the outcome of discussions surrounding withdrawal of life-sustaining measures.

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Data availability

The codes used to analyze the data from this study is available at https://github.com/TheOwenLab/Acute-Resting-State. The deidentified fMRI data can be made available from the corresponding author, upon reasonable request.

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Acknowledgements

The authors gratefully acknowledge the dedication of the bedside nurses and MRI technologists for making the acquisition of these data possible. The authors would also like to extend our thanks to the patients and families who participated in this study.

Funding

This research was funded by the Canada Excellence Research Chairs (CERC) program (#215063) and the Canadian Institutes of Health Research (CIHR, #408004).

Author information

Author notes

  1. Matthew Kolisnyk and Karnig Kazazian are co-first authors of this work.

Authors and Affiliations

  1. Western Institute of Neuroscience, Western Interdisciplinary Research Building, Western University, 1151 Richmond Street, London, ON, N6A 3K7, Canada

    Matthew Kolisnyk, Karnig Kazazian, Sergio L. Novi, Conor J. Wild, Adrian M. Owen & Loretta Norton

  2. Temerty Faculty of Medicine, University of Toronto, Toronto, Canada

    Karina Rego

  3. Department of Physiology and Pharmacology, Schulich School of Medicine and Dentistry, Western University, London, Canada

    Sergio L. Novi & Adrian M. Owen

  4. Department of Clinical Neurological Sciences, Schulich School of Medicine and Dentistry, Western University, London, Canada

    Teneille E. Gofton & Derek B. Debicki

  5. Department of Psychology, Western University, London, Canada

    Adrian M. Owen

  6. Department of Psychology, King’s University College at Western University, London, Canada

    Loretta Norton

Authors
  1. Matthew Kolisnyk
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Contributions

MK, KK, AMO, and LN contributed to the conception and design of the study; MK, KK, KR, and LN contributed to the acquisition and analysis of data; MK, KK, KR, SLN, CW, TEG, DD, AMO, and LN contributed to drafting the text or preparing the figures.

Corresponding author

Correspondence to Karnig Kazazian.

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Conflicts of interest

The authors declare no conflicts of interest.

Ethical standard

The study was conducted according to the guidelines of the Declaration of Helsinki of 1964 and later amendments, and approved by the Health Sciences Research Ethics Board of Western University.

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Supplementary file1 (DOCX 1211 KB)

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Kolisnyk, M., Kazazian, K., Rego, K. et al. Predicting neurologic recovery after severe acute brain injury using resting-state networks. J Neurol 270, 6071–6080 (2023). https://doi.org/10.1007/s00415-023-11941-6

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  • DOI: https://doi.org/10.1007/s00415-023-11941-6

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