Temporal Dynamics of GABA and Glx in the Visual Cortex

Abstract

Magnetic resonance spectroscopy (MRS) can be used in vivo to quantify neurometabolite concentration and provide evidence for the involvement of different neurotransmitter systems (e.g., inhibitory and excitatory) in sensory and cognitive processes. The relatively low signal-to-noise ratio of MRS measurements has shaped the types of questions that it has been used to address. In particular, temporal resolution is often sacrificed in MRS studies to achieve a signal sufficient to produce a reliable estimate of neurometabolite concentration. Here we apply novel analyses with large datasets from human participants (both sexes) to reveal the dynamics of GABA 1 and Glx in visual cortex while participants are at rest (with eyes closed) and compare this with changes in posterior cingulate cortex from a previously collected dataset (under different conditions). We find that the dynamic concentration of GABA 1 and Glx in visual cortex drifts in opposite directions; that is, GABA 1 decreases while Glx increases over time. Further, we find that in visual, but not posterior cingulate cortex, the concentration of GABA 1 predicts that of Glx 120 s later, such that a change in GABA 1 is correlated with a subsequent opposite change in Glx. Together, these results expose novel temporal trends and interdependencies of primary neurotransmitters in visual cortex. More broadly, we demonstrate the feasibility of using MRS to investigate in vivo dynamic changes of neurometabolites.

Transcranial direct current stimulation (tDCS) over the auditory cortex modulates GABA and glutamate: a 7 T MR-spectroscopy study

“…As a consequence, we assessed the effects of both, anodal and cathodal tDCS on the primary AC and compared it to a repeated baseline measure. Even though there is ample evidence for a stable GABA concentration 53 or even a trend for a decrease in GABA and an increase in Glx during rest 69 , we cannot completely www.nature.com/scientificreports/ rule out potential changes of neurochemical concentrations over time. Second, our observation of an polarity independent, but asynchrone effect of the GABA/Glu ratio might be related to a functional regulation of Glutamic acid decarboxylase (GAD-67).…”

Investigating the neurochemistry of the human visual system using magnetic resonance spectroscopy

“…While fMRS studies have revealed dynamic changes in metabolite levels in response to functional stimulation (Stanley and Raz 2018), clear quantitative relationships between stimulus and response are still to be described. This is an important area to clarify, as recent evidence suggests that metabolite dynamics, even in the resting visual cortex, can reflect reliable, regionally specific interactions between cortical excitation and inhibition (Rideaux 2020). Taken together, these results point towards a complex relationship between metabolite levels during stimulation and rest that have yet to be investigated in detail.…”

Functional MRS studies of GABA and Glutamate/Glx – a systematic review and meta-analysis

“…Some studies observed a fast Glu response early in a working memory task, but not later in the task (Woodcock et al, 2018), while others observed Glu reaching a new steady state 1 to 2 minutes after stimulus onset (Mangia et al, 2007; Schaller et al, 2013). Previous studies of GABA and Glx in response to visual stimulation demonstrated concentration drifts over time in opposite directions while participants were at ‘rest’ before stabilising (in steady state) after around 500 seconds (Rideaux, 2020). As discussed in previous sections, the time courses of neurometabolites in response to stimulus domain are a topic of great interest and require further elucidation.…”

“…Some studies have averaged across a small number of transients but across participants to obtain group-level spectra with higher temporal resolution (Apšvalka et al, 2015; Bednařík et al, 2015; Fernandes et al, 2020). Others have applied a ‘sliding window’ or ‘moving averages’ approach (i.e., average transients in blocks then shifting the averaging over time by a certain transient window width) to detect a dynamic trace of metabolite changes (Mangia et al, 2007; Schaller et al, 2013; Fernandes et al, 2020; Rideaux, 2020).…”