Impact of tissue correction strategy on GABA-edited MRS findings

Highlights

• The selection of tissue correction approach can fundamentally impact the interpretation of GABA+ results.

• In the frontal region, overall tissue volume decreases with age but GABA+ in the remaining tissue does not decrease with age.

• In frontal regions, GABA+ correlates with MoCA, a marker of cognitive function.

• This work support the use of the α-correction for GABA+ -MRS.

Abstract

Tissue composition impacts the interpretation of magnetic resonance spectroscopy metabolite quantification. The goal of applying tissue correction is to decrease the dependency of metabolite concentrations on the underlying voxel tissue composition. Tissue correction strategies have different underlying assumptions to account for different aspects of the voxel tissue fraction. The most common tissue correction is the CSF-correction that aims to account for the cerebrospinal fluid (CSF) fraction in the voxel, in which it is assumed there are no metabolites. More recently, the α-correction was introduced to account for the different concentrations of GABA+in gray matter and white matter. In this paper, we show that the selected tissue correction strategy can alter the interpretation of results using data from a healthy aging cohort with GABA+ measurements in a frontal and posterior voxel. In a frontal voxel, we show an age-related decline in GABA+ when either no tissue correction (R² = 0.25, p < 0.001) or the CSF-correction is applied (R² = 0.08, p < 0.01). When applying the α-correction to the frontal voxel data, we find no relationship between age and GABA+ (R² = 0.02, p = 0.15). However, with the α-correction we still find that cognitive performance is correlated with GABA+ (R² = 0.11, p < 0.01). These data suggest that in healthy aging, while there is normal atrophy in the frontal voxel, GABA+ in the remaining tissue is not decreasing on average. This indicates that the selection of tissue correction can significantly impact the interpretation of MRS results.

Introduction

As the primary inhibitory neurotransmitter in the human brain, γ-Aminobutyric acid (GABA) has a fundamental role in shaping cortical activity as well as in plasticity and in cognitive function. Measurement of GABA in vivo, in humans is possible using specialized magnetic resonance spectroscopy (MRS) techniques; in this study we apply GABA-edited MEGA-PRESS (Harris et al., 2017). Due to limitations with the method, measurements of GABA are contaminated with macromolecules. Consistent with conventions in the literature, in the current manuscript, when referring to MRS measures of GABA, we will use GABA+ to represent GABA+macromolecules. Previous MRS studies have shown that GABA+ levels in the brain decline with age. Specifically, GABA+/Cr levels in frontal and parietal brain regions have been shown to decrease with age (Gao et al., 2013). This finding was subsequently replicated in comparable voxel locations (Porges et al., 2017) and was associated with age-related changes in cognitive performance. Cognitive performance was strongly correlated with frontal GABA+ concentrations, even after accounting for age, the fraction of cerebral spinal fluid (CSF) in the MRS voxel (a coarse measure of brain atrophy) and behavioral factors such as education or age related normative performance. This is consistent with prior animal work showing age-related declines in GABA+ (He et al., 2016) and a cross-sectional study observing lower GABA+ concentrations in the right hippocampus in healthy elderly controls compared to young controls, though no differences were observed in the anterior cingulate (Huang et al., 2017).

While the majority of age-related atrophy research has been in the context of specific pathologies (e.g. Alzheimer's dementia), age-related atrophy will affect many people as part of the normal aging process. It is well established that even with healthy aging, brain volume declines with age (Hedman et al., 2012, Royle et al., 2013, O'Shea et al., 2016, Nissim et al., 2016). Age-related brain volume loss is not equal between tissue types (i.e., white or gray matter) and the age at which tissue volume begins to decrease also varies (Dotson et al., 2015, Raz et al., 2005, Fjell et al., 2009, Walhovd et al., 2005). Furthermore, the rate of tissue atrophy during healthy aging varies regionally, with frontal and medial temporal atrophy occurring at a more rapid rate than parietal and occipital regions (Dotson et al., 2015, Raz et al., 2005, Fjell et al., 2009). How the heterogeneity of white and gray matter atrophy impacts cognitive decline is unclear (Royle et al., 2013). The impact of this dynamic, regionally variant atrophy necessitates a sophisticated consideration of voxel tissue composition to ensure that the age-related GABA+ changes are not unduly impacted by the tissue composition of the measurement voxel (Harris et al., 2015). The current study implements and compares different tissue corrections for GABA+ in the context of healthy aging. Specifically, the most commonly used approach, the CSF-correction, a more recently proposed tissue correction, here referred to as the α-correction (Harris et al., 2015), and no tissue correction are compared.

Tissue composition of MRS voxels has a significant impact on metabolite quantification (Harris et al., 2015, Gasparovic et al., 2006, Gussew et al., 2012), as metabolite levels (and reference signals) differ between tissue and CSF, but also between gray and white matter. Tissue correction is applied to account for the differences in signal between different tissues within a voxel; however both the use of the term and implementation of the method are inconsistently applied. In the simplest form, tissue correction refers to accounting for the CSF fraction of the voxel (CSF-correction) and is applied by normalizing the voxel by the non-CSF voxel fraction (1-f_CSF), with the implicit assumption that the metabolites in question are not contained in CSF or, if present, do not contribute to the MRS signal. More sophisticated tissue corrections include accounting for the differential relaxation constants and water visibility between white matter, gray matter and CSF (Harris et al., 2015, Gasparovic et al., 2006, Gussew et al., 2012). In addition to the differences in water signal between white matter, gray matter and CSF, GABA+-MRS measurements are further complicated by the differences in GABA+ between tissue types. It is well established that the concentration of GABA+ in gray matter is greater than in white matter (Harris et al., 2015, Bhattacharyya et al., 2011, Choi et al., 2006, Choi et al., 2007, Jensen et al., 2005, Zhu et al., 2011). Recently, Harris et al. (2015) proposed a tissue correction strategy to account for the different concentrations of GABA+ in gray matter and white matter in addition to accounting for differential tissue relaxations and signal constants to better address the underlying dependency of GABA+ on the tissue composition of the voxel.

The primary goal of tissue-correction is to remove the dependency of metabolite measurements, in this case GABA+, on the voxel tissue composition. To most fully address the issues associated with the dependency of measures on underlying tissue composition, the correction needs to accounts for differences in GABA+ concentration between white matter and gray matter (accomplished by applying the constant α) and normalize GABA+ values to a standard voxel composition (in this case, we chose the group-average voxel composition of white matter and gray matter). In the current manuscript, we refer to this entire procedure (correction and normalization) as the α-correction. As shown by Harris et al. (2015), equation (3), the correction is:

$${\mathit{c}}_{\mathit{corr}}={\mathit{c}}_{\mathit{meas}}\left(\frac{1}{\left({\mathit{f}}_{\mathit{GM}}+\mathit{\alpha }{\mathit{f}}_{\mathit{WM}}\right)}\right)\left(\frac{\left({\mathit{\mu }}_{\mathit{GM}}+\mathit{\alpha }{\mathit{\mu }}_{\mathit{WM}}\right)}{\left({\mathit{\mu }}_{\mathit{GM}}+{\mathit{\mu }}_{\mathit{WM}}\right)}\right)$$

where c_corr is the tissue-corrected GABA+ level, c_meas is the measured GABA+ level that accounts for individual tissue T1, T2 and water visibility constants, μ_GM and μ_WM are the group average voxel fractions for gray and white matter, f_GM and f_WM are the individual voxel fractions of gray matter and white matter and α is the ratio of GABA+ in white matter to gray matter, which is assumed to be 0.5. This correction accounts for the for the fact that GABA+ in gray matter is twice that of white matter and normalizes the GABA+ measurement to a standardized voxel, in this case it is the group average voxel fractions of white matter and gray matter. The normalization of the voxel fractions to a standard voxel is similar to registration or normalization of T1-weighted anatomical images in imaging studies. It is common to register and warp an individual's T1-weighted anatomical image to a group template (as per the FSL-VBM tools) or standard space (e.g., MNI or Talairach space) for a structural imaging analysis. The process of this registration impacts the absolute volume of measurements of each anatomical structure for each individual; however, it enables direct comparisons of structural volumes because there is a normalized baseline. Similarly, selecting a normalized voxel composition of white matter and gray matter enables direct comparison GABA+ measures between individuals. The selection of the group-average voxel is based on the desire to normalize the GABA+ measurements to a voxel that is representative of the group and accurately reflects these data. This approach and selection of normalization voxel has been used previously (Harris et al., 2015, Puts et al., 2017). As discussed by Harris et al. (2015), the selection of the voxel fractions for normalization may vary depending on the study; for example, if two groups with different structural changes are being compared, it may be appropriate to use the control group to determine the voxel for normalization. For the present study, the group-average voxel fractions was selected as the normalized voxel that best reflects the population. In absence of normalization to a group-average voxel, but applying the α-ratio in the tissue correction (i.e., applying the first correction term but not the last in the above equation), GABA is corrected to a voxel of pure gray matter. While this may be useful in some cases, the reported GABA values will be inflated and do not represent the tissue examined. For simplicity and to maintain consistency with the literature, we have used the term α-correction to refer to the procedure including: the correction accounting for the CSF-fraction, the difference in GABA concentration between gray matter and white matter (α), and inclusion of all tissue specific relaxation constants; and the normalization procedure. It is important to note that the normalization of GABA levels to the group-average voxel is a normalization procedure in addition what is more conventionally thought of as tissue correction (i.e., the CSF-correction or the GM-correction) that accounts for the gross tissue composition of the voxel used across the sample.

In this paper, we investigate the impact of applying the different tissue corrections to GABA+-MRS data in a large, healthy-aging cohort. Given the expectation that tissue volume declines with age, this cohort provides a unique opportunity to examine the impact of different tissue corrections on the interpretation of GABA+-MRS data. We compare the most commonly applied CSF-based tissue correction with a more sophisticated tissue correction, the α-correction, that accounts for differences in signal and GABA+ concentration between white and gray matter. Two voxel locations are included in this study, a frontal voxel and a posterior voxel. To investigate the relationship between GABA+ levels and functional processes, the Montreal Cognitive Assessment (MoCA) was used as an easily administered test of cognitive performance that is appropriate for this cohort (Nasreddine et al., 2005). This assessment evaluates cognitive performance across multiple cognitive domains including: visuospatial ability, executive function, attention and concentration, memory, language, and time and space orientation (Nasreddine et al., 2005). The total MoCA score is widely used and has strong psychometric properties including consistency and reliability. In particular, MoCA includes items that are sensitive to dysfunction in frontal brain regions, and performance on these tasks often declines with age. We show that the selection of tissue correction strategy can impact the results; specifically, we show differences in the age-GABA+ and MoCA-GABA+ relationships depending on the voxel of interest and the tissue-correction strategy. These results provide support for the application of the α-correction and evidence that previous reports, including our own previous results, may underestimate the influence of age-related atrophy when reporting age-related GABA+ decreases.