Neural and metabolic basis of dynamic resting state fMRI

Abstract

Resting state fMRI (rsfMRI) as a technique showed much initial promise for use in psychiatric and neurological diseases where diagnosis and treatment were difficult. To realize this promise, many groups have moved towards examining "dynamic rsfMRI," which relies on the assumption that rsfMRI measurements on short time scales remain relevant to the underlying neural and metabolic activity. Many dynamic rsfMRI studies have demonstrated differences between clinical or behavioral groups beyond what static rsfMRI measured, suggesting a neurometabolic basis. Correlative studies combining dynamic rsfMRI and other physiological measurements have supported this. However, they also indicate multiple mechanisms and, if using correlation alone, it is difficult to separate cause and effect. Hypothesis-driven studies are needed, a few of which have begun to illuminate the underlying neurometabolic mechanisms that shape observed differences in dynamic rsfMRI. While the number of potential noise sources, potential actual neurometabolic sources, and methodological considerations can seem overwhelming, dynamic rsfMRI provides a rich opportunity in systems neuroscience. Even an incrementally better understanding of the neurometabolic basis of dynamic rsfMRI would expand rsfMRI's research and clinical utility, and the studies described herein take the first steps on that path forward.

Pushing the Limits of EEG: Estimation of Large-Scale Functional Brain Networks and Their Dynamics Validated by Simultaneous fMRI

“…Although unreported so far, the match between dFC states derived from fMRI and EEG data found in the present study was somewhat expected, considering that a number of studies have already found EEG correlates of dFC fluctuations and brain states measured with simultaneous fMRI (Tagliazucchi and Laufs, 2015), motivated by the yet unclear physiological underpinnings of dFC (Thompson, 2018). These studies were mainly focused on healthy subjects (Chang and Glover, 2010;Allen et al, 2017) and epilepsy patients (Laufs et al, 2014;Lopes et al, 2014;Preti et al, 2014;Omidvarnia et al, 2017;Abreu et al, 2019).…”

“…In contrast with the identification of RSNs, whereby templates derived from previous fMRI studies of large populations are available, dFC state templates are yet to be discovered, as this is still a fairly recent research topic (Preti et al, 2017;Thompson, 2018). Thus, the dFC states estimated from the EEG-ESI data can only be cross, rather than independently, validated by the dFC states estimated from the fMRI data.…”

“…Importantly, RSNs are also identified under the assumption that their functional connectivity is static; however, current literature suggests that brain networks in general continuously reorganize in response to both internal and external stimuli at multiple time-scales, resulting in temporal fluctuations of their FC -the so-called dynamic functional connectivity (dFC) (Hutchison et al, 2013;Calhoun et al, 2014;Preti et al, 2017). Specifically, it has been shown that dFC correlates with brain state (stimulation/task, eyes closure vs. eyes open, vigilance, sleep, anesthesia, and drug manipulation) as well as with age, gender and disease [for a review, please refer to Thompson (2018)]. dFC correlates of disease are believed to be particularly relevant for its characterization, and recent studies have demonstrated that dFC discriminates healthy from patient populations better than static FC (Du et al, 2018).…”

Improved Resting-State Functional Dynamics in Post-stroke Depressive Patients After Shugan Jieyu Capsule Treatment

“…Additionally, some studies have found that impaired cognitive function is related to altered functions in specific regions -such as the precuneus, anterior cingulate cortex, and medial precentral gyrus (Bruner et al, 2017) -and those depressive symptoms are related to aberrant functioning in some brain networks, such as the fronto-parietal network and default mode network (DMN) (Korgaonkar et al, 2019). Recently, dynamic functional activity and connectivity analyses have been used to measure temporal flexibility in spontaneous fluctuations (Thompson, 2018). The major advantage of dynamics over static measures is the ability to capture recurring brain activity (Xie et al, 2018), and specific exploration of dynamics may complement deficiencies in static alterations.…”

BOLD and EEG Signal Variability at Rest Differently Relate to Aging in the Human Brain

“…In addition, based on the premise that BOLD fMRI signal variability reflects neural variability as measured by rsEEG, we expected that the corresponding changes in both signal modalities would demonstrate moderate to strong similarity in their spatial distribution. Given the confounding effects of vascular factors during aging on the fMRI signal (D'Esposito et al, 2003;Liu, 2013;Thompson, 2018), we further expected to find the relationship between BOLD and EEG signal variability to be stronger in younger than older adults.…”