Imaging the temporal dynamics of brain states with highly sampled fMRI

Abstract

The spontaneous dynamics of the brain modulate its function from moment to moment, shaping neural computation and cognition. Functional MRI (fMRI), while classically used as a tool for spatial localization, is increasingly being used to identify the temporal dynamics of brain activity. fMRI analyses focused on the temporal domain have revealed important new information about the dynamics underlying states such as arousal, attention, and sleep. Dense temporal sampling – either by using fast fMRI acquisition, or multiple repeated scan sessions within individuals – can further enrich the information present in these studies. This review focuses on recent developments in using fMRI to identify dynamics across brain states, particularly vigilance and sleep states, and the potential for highly temporally sampled fMRI to answer these questions.

Probing the association between resting state brain network dynamics and psychological resilience

“…However, this disadvantage of MB-EPI is more than compensated by the ability to acquire more data in the same time, the possibility to investigate faster dynamics, and to thus better disentangle neural and physiological signals (Yang & Lewis, 2021). Furthermore, we showed empirically for the present data that node flexibility was largely robust against changes in window size.…”

“…In contrast, the use of MB-sequences allows to better resolve the physiological signals in their respective frequency bands. This, in turn, allows for a better control of physiological noise (Risk et al, 2021;Yang & Lewis, 2021). However, we cannot draw a firm conclusion about possible differential influences of motion and noise on the results of our study vs. those of Long et al (2019).…”

“…However, this disadvantage of MB-EPI is more than compensated by the ability to acquire more data in the same time, the possibility to investigate faster dynamics, and to thus better disentangle neural and physiological signals (Yang & Lewis, 2021).…”

“…We cannot rule out that such long window sizes might hamper the sensitivity to small changes, as calculating connectivity matrices over longer periods serves as a smoothing kernel minimizing the ability to resolve TVFC (Vergara et al, 2019). However, this disadvantage of MB-EPI is more than compensated by the ability to acquire more data in the same time, the possibility to investigate faster dynamics, and to thus better disentangle neural and physiological signals (Yang & Lewis, 2021). Furthermore, we showed empirically that node flexibility was largely robust against changes in window size.…”

A temporal sequence of thalamic activity unfolds at transitions in behavioral arousal state

“…In this prior work, the thalamic and cortical dynamics were reported to be synchronous, whereas our study found that the thalamus activates before the cortex deactivates, replicated in our two separate experiments. Our detection of precise temporal sequences can be primarily attributed to the fast temporal sampling of our acquisition, which enhances sensitivity to timing differences 110,111 . However, a second potential contributor to this difference is that prior work aimed to reduce physiological noise by regressing the cerebrospinal fluid (CSF) signal, a common approach in fMRI.…”