Age‐related differences in GABA levels are driven by bulk tissue changes

Abstract

Levels of GABA, the main inhibitory neurotransmitter in the brain, can be regionally quantified using magnetic resonance spectroscopy (MRS). Although GABA is crucial for efficient neuronal functioning, little is known about age-related differences in GABA levels and their relationship with age-related changes in brain structure. Here, we investigated the effect of age on GABA levels within the left sensorimotor cortex and the occipital cortex in a sample of 85 young and 85 older adults using the MEGA-PRESS sequence. Because the distribution of GABA varies across different brain tissues, various correction methods are available to account for this variation. Considering that these correction methods are highly dependent on the tissue composition of the voxel of interest, we examined differences in voxel composition between age groups and the impact of these various correction methods on the identification of age-related differences in GABA levels. Results indicated that, within both voxels of interest, older (as compared to young adults) exhibited smaller gray matter fraction accompanied by larger fraction of cerebrospinal fluid. Whereas uncorrected GABA levels were significantly lower in older as compared to young adults, this age effect was absent when GABA levels were corrected for voxel composition. These results suggest that age-related differences in GABA levels are at least partly driven by the age-related gray matter loss. However, as alterations in GABA levels might be region-specific, further research should clarify to what extent gray matter changes may account for age-related differences in GABA levels within other brain regions.

The trajectory of cortical GABA levels across the lifespan: An individual participant data meta-analysis of edited MRS studies

“…Interestingly, none of the studies included in this review showed significant changes in segmented tissue content with age. This is surprising because other studies (Maes et al, 2018;Porges et al, 2017a) showed that age-related changes in GABA depend on tissue correction due to atrophy being common in older participants. The potential effect of tissue composition on GABA levels is limited if a study focused on a young cohort where age-related atrophy would be negligible.…”

“…Marenco et al (2018) also show a decrease in GABA with aging. It is important to note that other reports (Hermans et al, 2018;Maes et al, 2018) have compared MRS of GABA between defined groups of older and younger adults rather than with continuity across the lifespan. These findings are consistent with continuous approaches, with older adults having reduced GABA.…”

“…The above paragraph reflects a large number of studies showing that GABA plays an important role in regulating cognitive function in health and disease. It is also well known that age affects cognitive processes both in development and aging, including sensory processing (Koerner and Zhang, 2018;Simmonite et al, 2019), working memory (Mok et al, 2019), motor function (Hermans et al, 2018;Maes et al, 2018;Mikkelsen et al, 2018) and many others, however the potential role of GABA has not been well-explored. Perhaps more immediately, it is not well-known how GABA changes across the lifespan.…”

Aging and GABA

“…In terms of measurement of age-related changes in GABA levels using MRS, a major question of interest is whether brain structure alterations need to be considered. More specifically, the identification of age-related decreases in GABA level in the brain seems to be dependent on whether loss of gray matter is considered in the quantification of GABA levels or not [ 2 ]. Besides improvements in measurement techniques, more insight into the reliability of MRS-based measures over time as well as differences in GABA levels across areas covering the cortical-subcortical territory across the lifespan is warranted.…”

Hippocampal and striatal responses during motor learning are modulated by prefrontal cortex stimulation

“…The Gannet software 3.0 toolkit (Edden, Puts, Harris, Barker, & Evans, 2014) was used for MRS data analysis similar to previous research in our group Maes et al, 2018). We corrected the individual frequency-domain spectra for frequency and phase using spectral registration in the time domain .…”