Application of Fast Perturbational Complexity Index to the Diagnosis and Prognosis for Disorders of Consciousness

• Published in: IEEE transactions on neural systems and rehabilitation engineering Vol. 30; pp. 509 - 518
• Main Authors: Wang, Yong, Niu, Zikang, Xia, Xiaoyu, Bai, Yang, Liang, Zhenhu, He, Jianghong, Li, Xiaoli
• Format: Journal Article
• Language: English
• Published: United States IEEE 2022; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Brain modeling; Complexity; Complexity theory; Computational neuroscience; Consciousness; Consciousness Disorders - diagnosis; Diagnosis; Disorders; DOC; EEG; Electrodes; Electroencephalography; Frequencies; Humans; Indexes; Magnetic fields; Medical diagnosis; Medical prognosis; Nervous system; Oscillators; Parameters; Patients; PCIst; Persistent Vegetative State - diagnosis; Prognosis; Prognostics and health management; Regression models; Sleep and wakefulness; TMS-EEG; Transcranial magnetic stimulation; Unconsciousness; Wakefulness; Wakefulness - physiology
• ISSN: 1534-4320; 1558-0210
• DOI: 10.1109/TNSRE.2022.3154772

Objective: Diagnosis and prognosis of patients with disorders of consciousness (DOC) is a challenge for neuroscience and clinical practice. Transcranial magnetic stimulation combined with electroencephalography (TMS-EEG) is an effective tool to measure the level of consciousness. However, a scientific and accurate method to quantify TMS-evoked activity is still lacking. This study applied fast perturbational complexity index (PCIst) to the diagnosis and prognosis of DOC patients. Methods: TMS-EEG data of 30 normal healthy participants (NOR) and 181 DOC patients were collected. The PCIst was used to assess the time-space complexity of TMS-evoked potentials (TEP). We selected parameters of PCIst in terms of data length, data delay, sampling rate and frequency band. In addition, we collected Coma Recovery Scale-Revised (CRS-R) values for 114 DOC patients after one year. Finally, we trained the classification and regression model. Results: 1) PCIst shows the differences among NOR, minimally consciousness state (MCS) and unresponsive wakefulness syndrome (UWS) and has low computational cost. 2) Optimal parameters of data length and delay after TMS are 300 ms and 101-300 ms. Significant differences of PCIst at 5-8 Hz and 9-12 Hz bands are found among NOR, MCS and UWS groups. PCIst still works when TEP is down-sampled to 250 Hz. 3) PCIst at 9-12 Hz shows the highest performance in diagnosis and prognosis of DOC. Conclusions: This study confirms that PCIst can quantify the level of consciousness. PCIst is a potential measure for the diagnosis and prognosis of DOC patients.