Higher Frequency Network Activity Flow Predicts Lower Frequency Node Activity in Intrinsic Low-Frequency BOLD Fluctuations

Abstract

The brain remains electrically and metabolically active during resting conditions. The low-frequency oscillations (LFO) of the blood oxygen level-dependent (BOLD) signal of functional magnetic resonance imaging (fMRI) coherent across distributed brain regions are known to exhibit features of this activity. However, these intrinsic oscillations may undergo dynamic changes in time scales of seconds to minutes during resting conditions. Here, using wavelet-transform based time-frequency analysis techniques, we investigated the dynamic nature of default-mode networks from intrinsic BOLD signals recorded from participants maintaining visual fixation during resting conditions. We focused on the default-mode network consisting of the posterior cingulate cortex (PCC), the medial prefrontal cortex (mPFC), left middle temporal cortex (LMTC) and left angular gyrus (LAG). The analysis of the spectral power and causal flow patterns revealed that the intrinsic LFO undergo significant dynamic changes over time. Dividing the frequency interval 0 to 0.25 Hz of LFO into four intervals slow-5 (0.01–0.027 Hz), slow-4 (0.027–0.073 Hz), slow-3 (0.073–0.198 Hz) and slow-2 (0.198–0.25 Hz), we further observed significant positive linear relationships of slow-4 in-out flow of network activity with slow-5 node activity, and slow-3 in-out flow of network activity with slow-4 node activity. The network activity associated with respiratory related frequency (slow-2) was found to have no relationship with the node activity in any of the frequency intervals. We found that the net causal flow towards a node in slow-3 band was correlated with the number of fibers, obtained from diffusion tensor imaging (DTI) data, from the other nodes connecting to that node. These findings imply that so-called resting state is not ‘entirely’ at rest, the higher frequency network activity flow can predict the lower frequency node activity, and the network activity flow can reflect underlying structural connectivity.

Dynamic Causal Modeling of Hippocampal Links within the Human Default Mode Network: Lateralization and Computational Stability of Effective Connections

“…The results are not consistent with the well-known strength of the structural links between the MPFC and PCC (Khalsa et al, 2014). We think that this is due to weakening effective connections of MPFC and PCC at rest, as were previously shown by Li et al (2012) and Razi et al (2015), although there are alternative data (Bajaj et al, 2013). Our conclusion can be supported by the data on the weakening of connections between functional networks in the resting state and their strengthening in the task performance (Di et al, 2013).…”

Towards a systems view of IBS

“…These networks and their properties have been assessed by using neuroanatomical and neurophysiological studies in animals, 10 as well as different brain imaging techniques and analyses in humans. 39–46 In humans, several types of networks have been reported: functional brain networks based on evoked responses 37 or intrinsic connectivity of the brain during rest; 39–41,44,45 structural networks based on grey matter parameters 47 and white matter properties; and anatomical networks based on white matter connectivity. 48 Both evoked and resting state studies performed in patients with IBS have demonstrated abnormalities in regions and task-related networks linked to emotional arousal, 49–53 central autonomic control, 6,54–56 central executive control, 51,57,58 sensorimotor processing 6,59–61 and salience detection.…”

Multivariate classification of autism spectrum disorder using frequency-specific resting-state functional connectivity—A multi-center study

“…Slow-6, Slow-3 and Slow-2 frequency bands were discarded as they mainly reflect low-frequency drifting, white matter signals and high-frequency physiological noise (Biswal et al, 1995;Zuo et al, 2010) and classifiers obtained by support vector machine (SVM) are sensitive to noise (Guyon et al, 1996). Recent study suggested that Slow-3 frequency band is also meaningful (Bajaj et al, 2013). As the TR of UCLA site is 3 s, cannot fully cover the whole Slow-3 frequency band, we also discarded the Slow-3 band.…”