Role of Ongoing, Intrinsic Activity of Neuronal Populations for Quantitative Neuroimaging of Functional Magnetic Resonance Imaging–Based Networks

Abstract

A primary objective in neuroscience is to determine how neuronal populations process information within networks. In humans and animal models, functional magnetic resonance imaging (fMRI) is gaining increasing popularity for network mapping. Although neuroimaging with fMRI-conducted with or without tasks-is actively discovering new brain networks, current fMRI data analysis schemes disregard the importance of the total neuronal activity in a region. In task fMRI experiments, the baseline is differenced away to disclose areas of small evoked changes in the blood oxygenation level-dependent (BOLD) signal. In resting-state fMRI experiments, the spotlight is on regions revealed by correlations of tiny fluctuations in the baseline (or spontaneous) BOLD signal. Interpretation of fMRI-based networks is obscured further, because the BOLD signal indirectly reflects neuronal activity, and difference/correlation maps are thresholded. Since the small changes of BOLD signal typically observed in cognitive fMRI experiments represent a minimal fraction of the total energy/activity in a given area, the relevance of fMRI-based networks is uncertain, because the majority of neuronal energy/activity is ignored. Thus, another alternative for quantitative neuroimaging of fMRI-based networks is a perspective in which the activity of a neuronal population is accounted for by the demanded oxidative energy (CMR O2 ). In this article, we argue that network mapping can be improved by including neuronal energy/activity of both the information about baseline and small differences/fluctuations of BOLD signal. Thus, total energy/activity information can be obtained through use of calibrated fMRI to quantify differences of DCMR O2 and through resting-state positron emission tomography/magnetic resonance spectroscopy measurements for average CMR O2 .

A Non-cognitive Behavioral Model for Interpreting Functional Neuroimaging Studies

“…The concept of the majority of the brains energy not directly supporting function was challenged in 1998 with the finding, using 13 C MRS, that approximately 80% of cortical neuronal energy in the resting state was directly supporting neuronal signaling, as measured quantitatively with glutamate/glutamine cycling ( Sibson et al, 1998 ). Subsequently this result has been replicated repeatedly and extended to GABAergic signaling and glial metabolism ( Yu et al, 2017 ) in animals and humans as well as to independent electrical measurements of signaling ( Hyder and Rothman, 2010 ; Hyder et al, 2011 , 2013a ). In addition multiple studies have shown that it is consistent with measurements of signaling and energetics at the cellular level when scaled up to whole cortex ( Atwell and Laughlin, 2001 ; Yu et al, 2017 ).…”

Maturation of metabolic connectivity of the adolescent rat brain

“…Because metabolic activity measured by FDG uptake reflects the cumulative energy consumption in steady states, while BOLD signal from fMRI reflects fast temporal fluctuation of physiologic factors such as blood flow, blood oxygenation, and cerebral metabolic rate of oxygen, the metabolic network provides distinct information from the functional network on fMRI as well as from the structural network disclosed on diffusion tensor imaging ( Di et al, 2012 ; Wehrl et al, 2013 ). Furthermore, network construction using neuronal activity-coupled metabolism found on FDG PET could take regional metabolic activities into account to disclose energy efficiency rather than only concentrate on the changes of fluctuating neural activities without considering absolute amount of regional perfusion or metabolism ( Hyder et al, 2011 ; Smith et al, 2002 ).…”

The coupling of cerebral blood flow and oxygen metabolism with brain activation is similar for simple and complex stimuli in human primary visual cortex

“…Future work combining measurements of M or R 2 ' with baseline blood flow and blood volume will potentially provide quantitative information about the dynamics of oxygen metabolism. This will include information on both the intrinsic evoked response studied here and also the baseline state of CMRO 2 that has been pursued in other studies (Griffeth, Perthen et al 2011, Hyder, Herman et al 2011). …”