Multiscale energy reallocation during low‐frequency steady‐state brain response

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.

Frequency-dependent circuits anchored in the dorsal and ventral left anterior insula

“…Second, although Buzsáki inferred distinctive neural oscillations from slow 4 to slow 2 according to a comparable frequency interval between successive oscillations in a log axis of coordinates 18 , there is yet no consensus on how many frequency bands should the low frequency neural fluctuations be divided into. Tremendous studies have suggested distinctive characteristics in brain disorders and cognitive tasks in different narrow low frequency bands 23,24,59,60 . The cognitive mechanisms of narrow band low frequency BOLD signal fluctuations, however, are largely undetermined.…”

“…Third, a direct test of structure-function relationship is necessary for illuminating the principle of the functional organization of LAI although multiple frequency neural oscillations dramatically expand the space of inter-regional information communication in a limited anatomical relationship 46 . Lastly, the frequency-dependent functional organization of LAI may alter under various brain states, considering that inter-regional relationship and energy distribution in the low frequency range are altered under different brain states 59,61 . Therefore, the current results cannot be generalized to different task states involving distinctive cognitive processes.…”

Altered Brain Signal Variability in Patients With Generalized Anxiety Disorder

“…As the next frontier in brain mapping, the brain signal variability (BSV) reflects the capacity of state transition of neural activities and dynamic range of brain functional systems (16). BSV has been suggested to be an excellent proxy of the characteristics of neural dynamics, cognitive performance, and brain disorders (14, 19–21). Great BSV has been suggested to be associated with increased ability to transfer between brain states (22) and to process varying and unexpected external stimuli (16, 23).…”

Multiple Frequency Bands Analysis of Large Scale Intrinsic Brain Networks and Its Application in Schizotypal Personality Disorder

“…Recently, the low frequency steady-state brain response has been studied by the MFBA (Wang Y. et al, 2014 ; Wang et al, 2015 , 2018 ; Wang Y. et al, 2016 ). It is demonstrated that the cognitive activities can change the power at different frequencies.…”