定量脑电图
脑电图
心理学
事件相关电位
创伤性脑损伤
电生理学
神经科学
精神科
作者
Paul E. Rapp,David O. Keyser,A. M. Albano,Rene S. Hernandez,Douglas Gibson,Robert A. Zambon,W. David Hairston,John Hughes,Andrew D. Krystal,Andrew S. Nichols
标识
DOI:10.3389/fnhum.2015.00011
摘要
Measuring neuronal activity with electrophysiological methods may be useful in detecting neurological dysfunctions, such as mild traumatic brain injury (mTBI). This approach may be particularly valuable for rapid detection in at-risk populations including military service members and athletes. Electrophysiological methods, such as quantitative electroencephalography (qEEG) and recording event-related potentials (ERPs) may be promising; however, the field is nascent and significant controversy exists on the efficacy and accuracy of the approaches as diagnostic tools. For example, the specific measures derived from an electroencephalogram (EEG) that are most suitable as markers of dysfunction have not been clearly established. A study was conducted to summarize and evaluate the statistical rigor of evidence on the overall utility of qEEG as an mTBI detection tool. The analysis evaluated qEEG measures/parameters that may be most suitable as fieldable diagnostic tools, identified other types of EEG measures and analysis methods of promise, recommended specific measures and analysis methods for further development as mTBI detection tools, identified research gaps in the field, and recommended future research and development thrust areas. The qEEG study group formed the following conclusions: 1. Individual qEEG measures provide limited diagnostic utility for mTBI. However, many measures can be important features of qEEG discriminant functions, which do show significant promise as mTBI detection tools. 2. ERPs offer utility in mTBI detection. In fact, evidence indicates that ERPs can identify abnormalities in cases where EEGs alone are nondisclosing. 3. The standard mathematical procedures used in the characterization of mTBI EEGs should be expanded to incorporate newer methods of analysis including nonlinear dynamical analysis, complexity measures, analysis of causal interactions, graph theory and information dynamics. 4. and 5. are too long to include here
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