EEG and fMRI agree: Mental arithmetic is the easiest form of imagery to detect |
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| Affiliation: | 1. Key Laboratory of Child Development and Learning Science of Ministry of Education, School of Biological Science and Medical Engineering, Southeast University, Nanjing, Jiangsu 210096, PR China;2. School of Computer Science and Electronic Engineering, University of Essex, Colchester CO4 3SQ, UK;3. Department of Medical Imaging, Bengbu Medical College, Bengbu, Anhui 233030, PR China;4. School of Mathematics and Big Data, Foshan University, Foshan, Guangdong 528000, PR China;1. Department of Radiology, Xuanwu Hospital, Capital Medical University, Beijing, China;2. Beijing Key Lab of Magnetic Resonance Imaging and Brain Informatics, Beijing, China;3. Key Laboratory for Neurodegenerative Diseases, Ministry of Education, China;4. Institute of Artificial Intelligence and Cognitive Engineering, University of Groningen, Groningen, The Netherlands;5. Department of Life Science and Informatics, Maebashi Institute of Technology, Maebashi, Japan |
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| Abstract: | fMRI and EEG during mental imagery provide alternative methods of detecting awareness in patients with disorders of consciousness (DOC) without reliance on behaviour. Because using fMRI in patients with DOC is difficult, studies increasingly employ EEG. However, there has been no verification that these modalities provide converging information at the individual subject level. The present study examined simultaneous EEG and fMRI in healthy volunteers during six mental imagery tasks to determine whether one mental imagery task generates more robust activation across subjects; whether activation can be predicted from familiarity with the imagined activity; and whether EEG and fMRI converge upon the same conclusions about individual imagery performance. Mental arithmetic generated the most robust activation in the majority of subjects for both EEG and fMRI, and level of activation could not be predicted from familiarity, with either modality. We conclude that overall, EEG and fMRI agree regarding individual mental imagery performance. |
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| Keywords: | Consciousness Mental imagery Electroencephalography Functional magnetic resonance imaging Machine learning Disorders of consciousness Command following |
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