Figure 2. Task-dependent correlations of GABA change and behavioral improvement.

We measured MRS GABA in the posterior occipito-temporal cortex (Figure 2—figure supplement 1). We pre-processed and fit the data with ProFit (Schulte and Boesiger, 2006). We excluded three participants (FD task) due to fat contamination in the spectra (Figure 2—figure supplement 2). We did not observe significant differences in mean GABA concentration in occipito-temporal cortex before vs. after training (Figure 2—figure supplement 3). Here, we show skipped Pearson’s correlations indicating (a) a significant negative correlation of GABA change in occipito-temporal cortex with learning rate for the Signal-in-noise task (r = −0.43, CI=[−0.74,–0.07]) and (b) a significant positive correlation with Δd’ for the Feature-differences task (r = 0.54, CI=[0.05,0.85]). Correlations of GABA change with Δd’ for the Signal-in-noise task or learning rate for the Feature-differences task were not significant (Figure 2—figure supplement 4a). Negative learning rate or negative Δd’ represents decreased sensitivity during training. As participants were trained only for a single training session and without trial-by-trial feedback, these measures may be noisier and result in negative values. Correlations of GABA change with behavioral improvement remained significant when we removed data from participants with negative learning rate or Δd’. Further, including data from participants that were trained for an additional eighth run showed similar results: the correlations of GABA change with behavioral improvement (learning rate computed with eight training runs; Δd’ (last vs. first training run) remained significantly different (Fisher’s z = 2.4, p=0.02) between tasks (Figure 2—figure supplement 4b).

Figure 2—figure supplement 1. MRS voxel placement.

(a) MRS voxels were placed in the posterior occipito-temporal cortex. Anatomical landmarks (Middle Occipital Gyrus, Superior Temporal Gyrus) were used to guide consistent voxel placement across participants. The color bar indicates the probability of overlap across participants for each 1 × 1×1 mm anatomical voxel included in the MRS voxels. The figure highlights the grey matter voxels on the cortical surface that are present in at least 50% of the participants MRS masks. (b) Grey matter voxels included within the MRS mask, shown on an axial brain slice. When these clusters are mapped on the cortical surface (a) they correspond to separable regions in the lateral and medial occipito-temporal cortex–as the voxel overlap is thresholded between 50% and 100%, extending both anteriorly and posteriorly from the center of the MRS voxel. Despite this cautious voxel placement for each individual participant, there was some variability in the voxel location across participants due to the variability in the shape of the skull and brain morphology (e.g. extent of ventricles posteriorly; extent of the cerebellum superiorly). To control for potential variability of the MRS voxel placement within and between participants, across MRS blocks, we extracted the Talairach coordinates of each participant’s pre- and post-training MRS acquisition voxels. The mean difference in voxel placement within participants was minimal (x = 0.60 mm; y = 0.40 mm; z = 0.52 mm). Only for one participant (Signal-in-noise task) was the difference in the MRS voxel position before and after training larger than two standard deviations above the mean; data for this participant were excluded from further analyses. The following analyses showed that the variability in voxel placement across participants was not statistically significant: a) a multivariate ANOVA showed no significant group differences in MRS voxel positioning before (X: F(1,34)= 0.003, p=0.95; Y: F(1,34)= 0.834, p=0.37; Z: F(1,34)= 1.482, p=0.23) or after (X: F(1,34)= 0.039, p=0.85; Y: F(1,34)= 0.027, p=0.87; Z: F(1,34)= 1.899, p=0.18) training, b) all voxels were within three standard deviations of the average Euclidean distance (5.6 ± 2.5 mm) from the mean coordinates (x = 29.6 ± 3.44 mm, y = -67.3 ± 4.3 mm, z = 3.6 ± 2.3 mm) across participants, c) the average Euclidean distance from the mean MRS voxel coordinates did not differ significantly between tasks (t(34)=0.35, p=0.74).

Figure 2—figure supplement 2. Fat contamination.

In healthy tissue, fat contamination is caused by extracranial lipids when the MRS voxel is placed near the skull, compromising metabolite quantification. We used two empirical criteria to estimate contamination, following previous studies (Schmitz et al., 2017). First, we identified the resonance frequency of the largest peak in each spectrum. This is expected for NAA at 2ppm (a). If the largest peak was observed at a resonance frequency different from 2ppm (b), this indicated that the spectrum was contaminated. Second, we confirmed this contamination by visually inspecting the output of the MRS processing software (Profit, Schulte and Boesiger, 2006) that included two-dimensional spectra (c,d), their fits (e,f) and residual plots (g,h) for each measurement. The data are plotted as contours where closed loops at fixed heights indicate equal signal intensity (Stagg, 2014). The J-resolved dimension (Hz) (y-axis) is plotted against the chemical shift dimension (ppm; x axis). The logarithmic color-scale represents the range of chemical compound concentrations. Only the real part of the phased spectrum is shown, resulting in both positive and negative values (Schulte and Boesiger, 2006). The largest peak of the spectrum is NAA at 2ppm and is indicated by contours colored with the highest values of the color scale (red). Fat contamination results in a noisy spectrum (d) with large residuals (h), indicating a poor fit.

Figure 2—figure supplement 3. GABA concentration before vs. after training.

No significant differences were observed in GABA concentration in occipito-temporal cortex across participants before vs. after training (main effect of MRS block: F(1,34)= 0.06, p=0.81; Task x MRS block interaction: F(1,34)= 0.21, p=0.65). Boxplots denote median and interquartile ranges; indicating variability in GABA concentration across participants.

Figure 2—figure supplement 4. Correlations of GABA change and behavioral improvement.

(a) For the Signal in Noise task, the correlation of GABA change with Δd’ was not significant (r = −0.14, CI=[−0.49, 0.29]) and was not significantly different from the correlation of GABA change with learning rate (Steiger’s z = 1.25, p=0.21). For the Feature differences task, the correlation of GABA change with learning rate was not significant (r = 0.13, CI=[−0.38, 0.62]) but was significantly different from the correlation of GABA change with Δd’ (Steiger’s z = 2.27, p=0.02). (b) Correlations between GABA change and behavioral improvement (learning rate for SN; Δd’ for FD) remained significantly different between tasks (Fisher’s z = 2.4, p=0.02) when we included data from participants that were trained for an additional eighth run.