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

Abstract

The BOLD signal reflects hemodynamic events within the brain, which in turn are driven by metabolic changes and neural activity. However, the link between BOLD changes and neural activity is indirect and can be influenced by a number of non-neuronal processes. Motion and physiological cycles have long been known to affect the BOLD signal and are present in both humans and animal models. Differences in physiological baseline can also contribute to intra- and inter-subject variability. The use of anesthesia, common in animal studies, alters neural activity, vascular tone, and neurovascular coupling. Most intriguing, perhaps, are the contributions from other processes that do not appear to be neural in origin but which may provide information about other aspects of neurophysiology. This review discusses different types of noise and non-neuronal contributors to the BOLD signal, sources of variability for animal studies, and insights to be gained from animal models.

Opioidergic Regulation of Emotional Arousal: A Combined PET–fMRI Study

“…The BOLD-signal thus only indirectly reflects neuronal activity. It also carries components without neural origin, such as cardiac pulsation and respiration, further decreasing its signal-to-noise ratio [75].…”

Quasi-Periodic Patterns of Intrinsic Brain Activity in Individuals and their Relationship to Global Signal

“…BOLD-based QPPs are correlated with electrical intrinsic brain activity in the infra-slow (<0.1Hz) frequency range Thompson et al, 2014aThompson et al, , 2014bThompson et al, , 2015Keilholz 2014;Grooms et al, 2017). The infraslow electrical signals themselves are still poorly understood but may arise from coordinated interactions between neurons, glia, and the vasculature (see the thorough review in Keilholz et al, 2016 and the speculation on the effect of vascular structure in Thompson et al, 2014a). Like traditional BOLD-based networks of functional connectivity, QPPs have been observed in mice (Belloy et al, 2017), rats Thompson et al, 2014aThompson et al, , 2014bMagnuson et al, 2010), monkeys (Abbas et al, 2016a), and humans (Majeed et al, 2011;Kiviniemi et al, 2016), in states ranging from deeply anesthetized to awake.…”

“…Majeed and colleagues (2011) subsequently developed a patternfinding algorithm to identify QPPs in humans, which involved alternating high and low activity in default mode (DMN) and task positive (TPN) networks. Animal and human studies have shown that QPPs are linked to infra-slow electrical activity Thompson et al, 2014aThompson et al, , 2014bThompson et al, , 2015Keilholz 2014;Grooms et al, 2017) and represent a different type of activity than the higher frequency activity tied to time-varying BOLD correlation between areas (Thompson et al, 2015;Keilholz et al, 2016). Infraslow electrical activity is one of the best candidates for the coordinating mechanisms within and between brain's large-scale networks (see discussion in Thompson et al, 2014aThompson et al, , 2014b for literature review), and BOLD QPPs can reflect aspects of such mechanisms.…”

“…Large-scale patterns such as QPPs are likely to contribute to the global signal, so analysis was performed before and after global signal regression. Slow variations in respiration depth and rate have been shown to correlate with variations in the global signal (Power et al, 2017;Liu et al, 2017;Keilholz et al, 2016; and with variations in the default mode network (Birn et al, 2006 and, so the relationship between respiration and QPPs was also examined. To compare the levels of variability within individuals to variability across individuals, the HCP resting state scans acquired over two subsequent days were analyzed separately.…”

Resting State fMRI in Rodents

“…Anesthetics may also have regionallyspecific effects that could prove troublesome for some rs-fMRI studies. One alternative approach is to use unanesthetized animals (King et al, 2005), although this requires a significant investment of time in training the animals and tends to result in more motion during the scan (Keilholz, Pan, Billings, Nezafati, & Shakil, 2016). The training could also be considered a form of chronic stress.…”