The Establishment of Pseudorandom Ecological Microexpression Recognition Test (PREMERT) and Its Relevant Resting-State Brain Activity
| Autor: | Ming Yin, Dianzhi Liu, Deming Shu, Jianxin Zhang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
microexpression M
Precuneus microexpression SD 050105 experimental psychology Temporal lobe Cuneus lcsh:RC321-571 03 medical and health sciences Behavioral Neuroscience 0302 clinical medicine PREMERT medicine 0501 psychology and cognitive sciences lcsh:Neurosciences. Biological psychiatry. Neuropsychiatry Biological Psychiatry Original Research Fusiform gyrus Resting state fMRI medicine.diagnostic_test Ecology 05 social sciences Parietal lobe ALFF difference eyes-open and eyes-closed resting states Psychiatry and Mental health Neuropsychology and Physiological Psychology medicine.anatomical_structure Neurology nervous system Psychology Occipital lobe Functional magnetic resonance imaging 030217 neurology & neurosurgery Neuroscience |
| Zdroj: | Frontiers in Human Neuroscience, Vol 14 (2020) Frontiers in Human Neuroscience |
| ISSN: | 1662-5161 |
| DOI: | 10.3389/fnhum.2020.00281/full |
| Popis: | The EMERT (ecological microexpression recognition test) by Zhang et al. (2017) used between-subjects Latin square block design for backgrounds; therefore, participants could not get comparable scores. The current study used within-subject pseudorandom design for backgrounds to improve EMERT to PREMERT (pseudorandom EMERT) and used eyes-closed and eyes-open resting-state functional magnetic resonance imaging to detect relevant brain activity of PREMERT for the first time. The results showed (1) two new recapitulative indexes of PREMERT were adopted, such as microexpression M and microexpression SD. Using pseudorandom design, the participants could effectively identify almost all the microexpressions, and each microexpression type had significant background effect. The PREMERT had good split-half reliability, parallel-forms reliability, criterion validity, and ecological validity. Therefore, it could stably and efficiently detect the participants’ microexpression recognition abilities. Because of its pseudorandom design, all participants did the same test; their scores could be compared with each other. (2) amplitude of low-frequency fluctuations (ALFF; 0.01–0.1 Hz) in both eyes-closed and eyes-open resting states and ALFF difference could predict microexpression M, and the ALFF difference was less predictive. The relevant resting-state brain areas of microexpression M were some frontal lobes, insula, cingulate cortex, hippocampus, amygdala, fusiform gyrus, parietal lobe, caudate nucleus, precuneus, thalamus, putamen, temporal lobe, and cerebellum. (3) ALFFs in both eyes-closed and eyes-open resting states and ALFF difference could predict microexpression SD, and the ALFF difference was more predictive. The relevant resting-state brain areas of microexpression SD were some frontal lobes, central anterior gyrus, supplementary motor area, insula, hippocampus, amygdala, cuneus, occipital lobe, fusiform gyrus, parietal lobe, caudate nucleus, pallidum, putamen, thalamus, temporal lobe, and cerebellum. (4) There were many similar relevant resting-state brain areas, such as brain areas of expression recognition, microexpressions consciousness and attention, and the change from expression backgrounds to microexpression, and some different relevant resting-state brain areas, such as precuneus, insula, and pallidum, between microexpression M and SD. The ALFF difference was more sensitive to PREMERT fluctuations. |
| Databáze: | OpenAIRE |
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