Research Article

Neuroscience

The trajectory of cortical GABA across the lifespan, an individual participant data meta-analysis of edited MRS studies

Center for Cognitive Aging and Memory, University of Florida, United States
McKnight Brain Research Foundation, University of Florida, United States
Department of Clinical and Health Psychology, University of Florida, United States
Department of Neuroscience, Columbia University Medical Center, United States
Zuckerman Mind Brain Behavior Institute, Columbia University, United States
Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, United States
F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, United States
Department of Forensic and Neurodevelopmental Sciences, Sackler Institute for Translational Neurodevelopment, Institute of Psychiatry, Psychology, and Neuroscience, King’s College London, United Kingdom

Jun 1, 2021

https://doi.org/10.7554/eLife.62575

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Figures
Tables
Additional files

6 figures, 3 tables and 1 additional file

Figures

Figure 1

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Linear relationships between age and γ-aminobutyric acid (GABA) signal, showing that linear extrapolation over the lifespan is not appropriate.

In each dataset, GABA was scaled relative to the geometric mean. Linear models were fit for each dataset separately. Dark shaded regions represent the 95% credible interval for the interpolated regression line, given the data from each study and the assumption of a linear effect, whereas the light shaded regions represent the 95% credible interval for the extrapolated regression line.

Figure 2

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Non-linear regression models of γ-aminobutyric acid (GABA) signal integrating all data simultaneously.

The shaded region depicts the 95% credible interval for the mean. (Left) Penalized basis spline model. (Right) Log-normal model.

Figure 3

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Posterior estimates of the relative scaling factor $F_{s}$ for each study in the penalized basis spline model (left) and the log-normal model (right), sorted by reference method.

Boxes represent the 80% credible interval for the posterior estimate, whereas whiskers represent the 95% credible interval.

Figure 4 with 1 supplement

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Non-linear regression models of γ-aminobutyric acid (GABA) signal performed using a leave-one-out (LOO) cross-validation approach.

The gray shaded region and black line depict the 95% credible interval for the mean for the model and the colored shaded region and dotted line show the model with the respective data left out. In all cases, a similar overall trajectory to the full data is implied by each of the subsets, albeit with greater variation in the posterior estimates.

Figure 4—figure supplement 1

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To accompany Figure 4, we performed additional analysis on frontal data only (removing Mikkelsen et al. and Simmonite et al., respectively) to investigate whether the inclusion of non-frontal regions biased our data.

These data show no substantial impact on the direction of the slope over time.

Figure 5

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PRISMA 2009 flow diagram of study identification and inclusion.

Figure 6

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Raincloud plot depicting the age distribution in each of the eight included datasets.

Plotted densities are scaled within study. Boxes represent the first and third quartiles, while whiskers represent the range of the data. The central notch corresponds to the bootstrapped confidence interval for median. Kernel density estimates for each dataset were computed using a Gaussian kernel according to Silverman, 1986. The relevant analysis script for these densities is included as supplementary material.

Tables

Table 1

Regression statistics for simple linear fits.

Intercepts are omitted because rescaling causes them to be entirely determined by the slopes and the mean age. Further study details can be found in Tables 2 and 3.

GABA study	Mean slope	Lower and upper bounds (0.025–0.975 quantiles)	Mean residual $σ$	Lower and upper bounds (0.025–0.975 quantiles)	R²
Aufhaus	−0.0008	−0.0055 to 0.0041	0.155	0.125 to 0.194	0.003
Gao	−0.0075	−0.0092 to −0.0058	0.121	0.106 to 0.140	0.459
Ghisleni	0.0034	0.0007 to 0.0061	0.111	0.092 to 0.134	0.106
Mikkelsen	−0.0019	−0.0054 to 0.0016	0.129	0.117 to 0.142	0.005
Porges	−0.0099	−0.0133 to −0.0064	0.172	0.148 to 0.200	0.279
Puts	0.0436	0.0085 to 0.0795	0.215	0.188 to 0.249	0.058
Rowland	−0.0032	−0.0060 to −0.0004	0.176	0.151 to 0.207	0.061
Simmonite	−0.0023	−0.0040 to −0.0005	0.160	0.127 to 0.203	0.156

Table 2

Neuroimaging acquisition and analysis details for eight studies included in the analysis.

Reference method refers to either reference to water (in estimated concentration/H₂O) or as a ratio to creatine plus phosphocreatine (Cr+PCr) and describes whether data was acquired macromolecule-suppressed (γ-aminobutyric acid [GABA]) or as GABA+ macromolecules (GABA+). MRS averages refer to the number of ON + OFF transients. *The manuscript refers to 96 averages. It was clarified with the authors that this referred to 96 ON and 96 OFF averages.

GABA study	Type of scanner	Analysis software	Reference method	Voxel volume (ml³)	MRS means	TE (ms)	TR (ms)	Voxel location
Aufhaus et al., 2013*	3 T Siemens	jMRUI/LCModel	GABA/H₂O	24	192*	68	3000	Medial frontal lobe
Gao et al., 2013	3 T Philips	jMRUI	GABA+/Cr+PCr	27	320	68	2000	Medial frontal lobe
Ghisleni et al., 2015	3 T GE	LCModel	GABA+/H₂O	30	320	68	2000	Left dorsolateral prefrontal lobe
Mikkelsen et al., 2017	3 T GE/Philips/Siemens	Gannet	GABA+/Cr+PCr	27	320	68	2000	Medial parietal lobe
Porges et al., 2017a	3 T Philips	Gannet	GABA+/H₂O	27	320	68	2000	Medial frontal lobe
Puts et al., 2017	3 T Philips	Gannet	GABA+/H₂O	27	320	68	2000	Right precentral sulcus
Rowland et al., 2016	3 T Philips	Gannet	GABA/H₂O	24	256	68	2000	Medial frontal lobe
Simmonite et al., 2019	3 T Philips	Gannet	GABA+/Cr+PCr	22.5	256	68	1800	Medial occipital lobe

Table 3

Descriptive statistics for eight studies included in the analysis.

This gives a basic description of sample size and age range for the eight datasets. Additionally, Figure 6 depicts the distribution of ages using a raincloud plot (Allen et al., 2018).

GABA study	# of subjects	Mean age	Age (SD)	Age range	Reference
Aufhaus	44	35.5	10	21–53	Aufhaus et al., 2013
Gao	96	45.7	14.5	20–76	Gao et al., 2013
Ghisleni	55	27.2	11	13–53	Ghisleni et al., 2015
Mikkelsen	220	26.5	4.9	18–48	Mikkelsen et al., 2017
Porges	86	71.8	10.6	43–92	Porges et al., 2017a
Puts	101	10.3	1.2	8–13	Puts et al., 2017
Rowland	82	38.0	13.7	18–62	Rowland et al., 2016
Simmonite	38	50.1	29.2	18–87	Simmonite et al., 2019

Additional files

Transparent reporting form: https://cdn.elifesciences.org/articles/62575/elife-62575-transrepform-v2.docx
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Eric C Porges
Greg Jensen
Brent Foster
Richard AE Edden
Nicolaas AJ Puts

(2021)

The trajectory of cortical GABA across the lifespan, an individual participant data meta-analysis of edited MRS studies

eLife 10:e62575.

https://doi.org/10.7554/eLife.62575

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Linear relationships between age and γ-aminobutyric acid (GABA) signal, showing that linear extrapolation over the lifespan is not appropriate.

Non-linear regression models of γ-aminobutyric acid (GABA) signal integrating all data simultaneously.

Posterior estimates of the relative scaling factor Fs for each study in the penalized basis spline model (left) and the log-normal model (right), sorted by reference method.

Non-linear regression models of γ-aminobutyric acid (GABA) signal performed using a leave-one-out (LOO) cross-validation approach.

To accompany Figure 4, we performed additional analysis on frontal data only (removing Mikkelsen et al. and Simmonite et al., respectively) to investigate whether the inclusion of non-frontal regions biased our data.

PRISMA 2009 flow diagram of study identification and inclusion.

Raincloud plot depicting the age distribution in each of the eight included datasets.

Regression statistics for simple linear fits.

Neuroimaging acquisition and analysis details for eight studies included in the analysis.

Descriptive statistics for eight studies included in the analysis.

Transparent reporting form

Downloads (link to download the article as PDF)

Open citations (links to open the citations from this article in various online reference manager services)

Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)

Posterior estimates of the relative scaling factor $F_{s}$ for each study in the penalized basis spline model (left) and the log-normal model (right), sorted by reference method.