Multiscale energy reallocation during low‐frequency steady‐state brain response |
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| Authors: | Yifeng Wang Wang Chen Liangkai Ye Bharat B. Biswal Xuezhi Yang Qijun Zou Pu Yang Qi Yang Xinqi Wang Qian Cui Xujun Duan Wei Liao Huafu Chen |
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| Affiliation: | 1. The Clinical Hospital of Chengdu Brain Science Institute, MOE Key Lab for Neuroinformation, University of Electronic Science and Technology of China, Chengdu, China;2. School of Life Science and Technology, Center for Information in BioMedicine, University of Electronic Science and Technology of China, Chengdu, China;3. Department of Biomedical Engineering, New Jersey Institute of Technology, 607 Fenster Hall, University Height, Newark, New Jersey;4. School of Political Science and Public Administration, University of Electronic Science and Technology of China, Chengdu, China |
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| Abstract: | Traditional task‐evoked brain activations are based on detection and estimation of signal change from the mean signal. By contrast, the low‐frequency steady‐state brain response (lfSSBR) reflects frequency‐tagging activity at the fundamental frequency of the task presentation and its harmonics. Compared to the activity at these resonant frequencies, brain responses at nonresonant frequencies are largely unknown. Additionally, because the lfSSBR is defined by power change, we hypothesize using Parseval's theorem that the power change reflects brain signal variability rather than the change of mean signal. Using a face recognition task, we observed power increase at the fundamental frequency (0.05 Hz) and two harmonics (0.1 and 0.15 Hz) and power decrease within the infra‐slow frequency band (<0.1 Hz), suggesting a multifrequency energy reallocation. The consistency of power and variability was demonstrated by the high correlation (r > .955) of their spatial distribution and brain–behavior relationship at all frequency bands. Additionally, the reallocation of finite energy was observed across various brain regions and frequency bands, forming a particular spatiotemporal pattern. Overall, results from this study strongly suggest that frequency‐specific power and variability may measure the same underlying brain activity and that these results may shed light on different mechanisms between lfSSBR and brain activation, and spatiotemporal characteristics of energy reallocation induced by cognitive tasks. |
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| Keywords: | brain signal variability face recognition fMRI frequency specificity steady‐state brain response |
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