EEG connectivity and network analyses predict outcome in patients with disorders of consciousness – A systematic review and meta-analysis
Danuta Szirmai,
Arashk Zabihi,
Tamás Kói,
Péter Hegyi,
Alexander Schulze Wenning,
Marie Anne Engh,
Zsolt Molnár,
Gábor Csukly,
András Attila Horváth
Affiliations
Danuta Szirmai
Centre for Translational Medicine, Semmelweis University, Budapest, Hungary (Baross utca 22., Budapest, H-1085, Hungary; Corresponding author. Amerikai út 57., Budapest, H-1145, Hungary.
Arashk Zabihi
Centre for Translational Medicine, Semmelweis University, Budapest, Hungary (Baross utca 22., Budapest, H-1085, Hungary
Tamás Kói
Centre for Translational Medicine, Semmelweis University, Budapest, Hungary (Baross utca 22., Budapest, H-1085, Hungary; Mathematical Institute, Department of Stochastics, Budapest University of Technology and Economics, Budapest, Hungary (Műegyetem rkp. 3, Budapest, H-1111, Hungary
Péter Hegyi
Centre for Translational Medicine, Semmelweis University, Budapest, Hungary (Baross utca 22., Budapest, H-1085, Hungary; Institute of Pancreatic Diseases, Semmelweis University, Budapest, Hungary (Tömő u. 25-29, Budapest, H-1083, Hungary; Institute for Translational Medicine, Medical School, University of Pécs, Pécs, Hungary (Szigeti út 12., Pécs, H-7624, Hungary
Alexander Schulze Wenning
Centre for Translational Medicine, Semmelweis University, Budapest, Hungary (Baross utca 22., Budapest, H-1085, Hungary
Marie Anne Engh
Centre for Translational Medicine, Semmelweis University, Budapest, Hungary (Baross utca 22., Budapest, H-1085, Hungary
Zsolt Molnár
Department of Anesthesiology and Intensive Therapy, Semmelweis University, Budapest, Hungary (Üllői út 78., Budapest, H-1082, Hungary; Department of Anesthesiology and Intensive Therapy, Poznan University of Medical Sciences, Poznan, Poland (49 Przybyszewskiego St, Poznan, Poland, 60-355, Poland
Gábor Csukly
Department of Psychiatry and Psychotherapy, Semmelweis University, Budapest, Hungary (Balassa u. 6, Budapest, H-1083, Hungary
András Attila Horváth
Centre for Translational Medicine, Semmelweis University, Budapest, Hungary (Baross utca 22., Budapest, H-1085, Hungary; Neurocognitive Research Center, National Institute of Mental Health, Neurology, Neurosurgery, Budapest, Hungary (Amerikai út 57., Budapest, H-1145, Hungary; Department of Anatomy, Histology and Embryology, Semmelweis University, Budapest, Hungary (Üllői út 26., Budapest, H-1085, Hungary
Outcome prediction in prolonged disorders of consciousness (DOC) remains challenging. This can result in either inappropriate withdrawal of treatment or unnecessary prolongation of treatment. Electroencephalography (EEG) is a cheap, portable, and non-invasive device with various opportunities for complex signal analysis. Computational EEG measures, such as EEG connectivity and network metrics, might be ideal candidates for the investigation of DOC, but their capacity in prognostication is still undisclosed. We conducted a meta-analysis aiming to compare the prognostic power of the widely used clinical scale, Coma Recovery Scale-Revised - CRS-R and EEG connectivity and network metrics. We found that the prognostic power of the CRS-R scale was moderate (AUC: 0.67 (0.60–0.75)), but EEG connectivity and network metrics predicted outcome with significantly (p = 0.0071) higher accuracy (AUC:0.78 (0.70–0.86)). We also estimated the prognostic capacity of EEG spectral power, which was not significantly (p = 0.3943) inferior to that of the EEG connectivity and graph-theory measures (AUC:0.75 (0.70–0.80)). Multivariate automated outcome prediction tools seemed to outperform clinical and EEG markers.
