Cape Town - 2026 ISMRM-ISMRT Annual Meeting and Exhibition
9 May 2026 – 14 May 2026 · Cape Town, South Africa
470-08-136 ISMRM Abstract

Reducing Misdiagnosis in Disorders of Consciousness Via a Resting-State fMRI-based Hierarchical Brain Dynamics Network (HBDN)

Primary: Brain Function and fMRI - fMRI Analysis
Secondary: Analysis Methods - Classification and Prediction
470-08-136 · Analysis: Neuro · Tuesday, 12 May, 4:00 PM–4:55 PM · Traditional Posters | Exhibition Hall
Accepted
Shanshan Chen 1, pan shiwen2, Poly Z.H. Sun2
1Psychology, Xinjiang Normal University, Urumqi, China
2East China Normal University, Shanghai, China
Presenting Author: Shanshan Chen

Synopsis

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References

1. Giacino, J.T., et al., Disorders of consciousness after acquired brain injury: the state of the science. Nat Rev Neurol, 2014. 10(2): p. 99-114.
2. Schnakers, C., et al., Diagnostic accuracy of the vegetative and minimally conscious state: Clinical consensus versus standardized neurobehavioral assessment. BMC Neurology, 2009. 9(1): p. 35.
3. Wang, J., et al., The misdiagnosis of prolonged disorders of consciousness by a clinical consensus compared with repeated coma-recovery scale-revised assessment. BMC Neurol, 2020. 20(1): p. 343.
4. Noirhomme, Q., et al., "Look at my classifier's result": Disentangling unresponsive from (minimally) conscious patients. Neuroimage, 2017. 145(Pt B): p. 288-303.
5. Yang, H., et al., Precise detection of awareness in disorders of consciousness using deep learning framework. Neuroimage, 2024. 290: p. 120580.
6. Kim, Y.-T., et al., Differentiating loss of consciousness causes through artificial intelligence-enabled decoding of functional connectivity. NeuroImage, 2024. 297: p. 120749.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2026/470-08-136