Low frequency steady-state brain responses modulate large scale functional networks in a frequency-specific means

Abstract

Neural oscillations are essential for brain functions. Research has suggested that the frequency of neural oscillations is lower for more integrative and remote communications. In this vein, some resting-state studies have suggested that large scale networks function in the very low frequency range (<1 Hz). However, it is difficult to determine the frequency characteristics of brain networks because both resting-state studies and conventional frequency tagging approaches cannot simultaneously capture multiple large scale networks in controllable cognitive activities. In this preliminary study, we aimed to examine whether large scale networks can be modulated by task-induced low frequency steady-state brain responses (lfSSBRs) in a frequency-specific pattern. In a revised attention network test, the lfSSBRs were evoked in the triple network system and sensory-motor system, indicating that large scale networks can be modulated in a frequency tagging way. Furthermore, the inter- and intranetwork synchronizations as well as coherence were increased at the fundamental frequency and the first harmonic rather than at other frequency bands, indicating a frequency-specific modulation of information communication. However, there was no difference among attention conditions, indicating that lfSSBRs modulate the general attention state much stronger than distinguishing attention conditions. This study provides insights into the advantage and mechanism of lfSSBRs. More importantly, it paves a new way to investigate frequency-specific large scale brain activities.

Adaptation of brain functional stream architecture in athletes with fast demands of sensorimotor integration

“…To test whether the optimal connectivity distance relates to attentional processes, all subjects were asked to perform a revised attention network test (ANT) (Wang et al, 2016b). Figure Responses were collected via Q (for left targets) and P (for right targets) on the keyboard.…”

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

“…The cognitive processes and ongoing activities are demonstrated to be negatively interactive and phase‐dependent rather than linear addition (He, ; Huang et al, ), making the GLM‐based brain activation inadequate to describe cognitive‐related brain activity. By contrast, the lfSSBR could regulate phase synchronization at multiple frequency bands (Lewis et al, ; Wang et al, ). The phase synchronization is essential to information transfer between brain regions (phase gating hypothesis) and to modulate high‐frequency neural oscillations (phase‐amplitude coupling hypothesis) (Canolty & Knight, ; Florin & Baillet, ; Maris, Fries, & van Ede, ).…”

“…It should be noticed that percent‐signal change or z‐statistics of time series are often used in BSV studies because the MSSD is sensitive to field‐strength; however, the normalization of time series is not often used in lfSSBR or power studies (Guitart‐Masip et al, ; Nomi, Bolt, Ezie, Uddin, & Heller, ; Samanez‐Larkin, Kuhnen, Yoo, & Knutson, ; Wang et al, ; Yang et al, ). In addition, Nomi et al () compared the MSSD with normalized time series and SD with non‐normalized time series, observing a correlation of 0.73 between two indices.…”

“…In lfSSBR, we assess the amplitude of regular fluctuations in a relative long time series rather than transient signal change (Figure ), indicating that it cannot be delineated using traditional hemodynamic response function (Lewis, Setsompop, Rosen, & Polimeni, ; Wang et al, ). Additionally, the lfSSBR represents brain network in frequency‐ and phase‐dependent means (Wang, Liu, Jing, Long, & Chen, ; Wang et al, ), and is associated with particular psychophysiological activity (Wang et al, ). Recent studies have suggested that task‐evoked blood oxygen level dependent (BOLD) signals and ongoing BOLD signal fluctuations have negative and phase‐dependent interaction, challenging the linear superposition model (He, ; Huang et al, ).…”

Noise and non-neuronal contributions to the BOLD signal: applications to and insights from animal studies

“…V Liu et al, 2013; Williams et al, 2010). For comparison, TE values for human studies at 3T are typically around 30–40 ms (Jann et al, 2015; Küblböck et al, 2014; Magnuson et al, 2015; Y.-F. Wang et al, 2016). In addition, coil technology and advanced imaging sequences are usually less available for small animals, unless the researchers are willing and able to build their own.…”