Learning to optimize perceptual decisions through suppressive interactions in the human brain

Abstract

Translating noisy sensory signals to perceptual decisions is critical for successful interactions in complex environments. Learning is known to improve perceptual judgments by filtering external noise and task-irrelevant information. Yet, little is known about the brain mechanisms that mediate learning-dependent suppression. Here, we employ ultra-high field magnetic resonance spectroscopy of GABA to test whether suppressive processing in decision-related and visual areas facilitates perceptual judgments during training. We demonstrate that parietal GABA relates to suppression of task-irrelevant information, while learning-dependent changes in visual GABA relate to enhanced performance in target detection and feature discrimination tasks. Combining GABA measurements with functional brain connectivity demonstrates that training on a target detection task involves local connectivity and disinhibition of visual cortex, while training on a feature discrimination task involves inter-cortical interactions that relate to suppressive visual processing. Our findings provide evidence that learning optimizes perceptual decisions through suppressive interactions in decision-related networks.

Full text

ARTICLE
Learning to optimize perceptual decisions through
suppressive interactions in the human brain
Polytimi Frangou 1, Uzay E. Emir 2,3, Vasilis M. Karlaftis 1, Caroline Nettekoven 4, Emily L. Hinson 3,4,
Stephanie Larcombe 3, Holly Bridge 3, Charlotte J. Stagg 3,4 & Zoe Kourtzi 1
Translating noisy sensory signals to perceptual decisions is critical for successful interactions
in complex environments. Learning is known to improve perceptual judgments by filtering
external noise and task-irrelevant information. Yet, little is known about the brain mechan-
isms that mediate learning-dependent suppression. Here, we employ ultra-high field mag-
netic resonance spectroscopy of GABA to test whether suppressive processing in decision-
related and visual areas facilitates perceptual judgments during training. We demonstrate
that parietal GABA relates to suppression of task-irrelevant information, while learning-
dependent changes in visual GABA relate to enhanced performance in target detection and
feature discrimination tasks. Combining GABA measurements with functional brain con-
nectivity demonstrates that training on a target detection task involves local connectivity and
disinhibition of visual cortex, while training on a feature discrimination task involves inter-
cortical interactions that relate to suppressive visual processing. Our findings provide evi-
dence that learning optimizes perceptual decisions through suppressive interactions in
decision-related networks.
https://doi.org/10.1038/s41467-019-08313-y OPEN
1Department of Psychology, University of Cambridge, Cambridge, CB2 3EB, UK. 2Purdue University School of Health Sciences, 550 Stadium Mall Drive,
West Lafayette, IN 47907, USA. 3Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of
Oxford, Oxford OX3 9DU, UK. 4Oxford Centre for Human Brain Activity, Wellcome Centre for Integrative Neuroimaging, Department of Psychiatry,
University of Oxford, Oxford OX3 7JX, UK. Correspondence and requests for materials should be addressed to Z.K. (email: zk240@cam.ac.uk)
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Making successful decisions entails extracting meaningful
information from multiple sources in the environment
that are inherently noisy and ambiguous. Experience
and training have been shown to play a key role in optimizing
perceptual decisions1–3by filtering external noise (e.g., when
detecting targets in cluttered scenes) and retuning task-relevant
feature templates (e.g., when discriminating highly similar
objects)4–6. Previous functional magnetic resonance imaging
(fMRI) studies have demonstrated learning-dependent changes in
functional brain activity due to training on perceptual tasks that
involve detecting targets in clutter or discriminating fine feature
differences (for reviews7,8). However, fMRI does not allow us to
distinguish excitatory from inhibitory mechanisms of experience-
dependent plasticity, as BOLD reflects aggregate activity from
both excitatory and inhibitory signals across large neural popu-
lations9. Thus, the inhibitory brain plasticity mechanisms that
support our ability to improve our perceptual decisions by
learning to suppress noisy and task-irrelevant information
through training remain largely unknown.
To investigate inhibitory mechanisms of learning-dependent
plasticity, we employed magnetic resonance spectroscopy (MRS)
that has only recently made it possible to measure γ-aminobutyric
acid (GABA), the primary inhibitory neurotransmitter in the
brain. Previous animal studies have linked decreased GABAergic
activity to learning and synaptic plasticity in primary motor
cortex10,11. In accordance with these findings, human MRS stu-
dies have shown that GABA levels in the primary motor cortex
decrease following interventions that facilitate cortical reorgani-
sation12 and motor training13,14. In the visual cortex, human
MRS studies have shown that GABA levels relate positively to
performance in perceptual tasks15–17. Further, decrease in visual
GABA has been shown to relate to homeostatic plasticity18. Here,
we took advantage of the high spectral resolution afforded by
ultra-high field (7T) MRS to reliably resolve GABA19,20 and take
fast and reliable repeated measurements of functional GABA
during training. This allowed us to test changes in GABA during
training (i.e., while the participants were trained on a task),
extending beyond standard correlational approaches that relate
single measurements of GABA at baseline (i.e., when participants
are at rest) to behavior. Further, we tested whether changes in
GABAergic inhibition during task-specific training relate to
improvement in perceptual decisions.
To probe the brain mechanisms that support learning by
suppressing noisy and irrelevant signals, we employed two
learning tasks that have been shown to rely either on noise fil-
tering or feature template retuning: (1) a signal-in-noise task that
involves extracting a target masked by noise, (2) a feature dif-
ferences task that involves judging fine differences21. Recent
computational investigations22,23 and animal studies propose
dissociable roles for inhibition in learning to interpret noisy
sensory signals vs. tuning fine feature processing. Based on this
work, we hypothesized that distinct GABAergic inhibition
mechanisms are involved in task-dependent learning and plasti-
city. Specifically, we reasoned that decreased GABAergic inhibi-
tion during training would relate to improved ability to detect
targets in clutter, as changes in GABAergic inhibition have been
linked to neural gain, (i.e., changes in information transmission
between neurons24 or the slope of the neural input-output rela-
tionship25). In contrast, we reasoned that increased GABAergic
inhibition would relate to improved performance in fine feature
discrimination, as increased GABAergic inhibition has been
linked to enhanced orientation selectivity in visual cortex16,26–28.
Further, previous neuroimaging and neurophysiology studies
have implicated distinct functional roles for the visual and pos-
terior parietal cortex (PPC) in sensory processing vs. perceptual
decision making, respectively29,30. To test the role of inhibitory
processing in learning for both visual and parietal cortex, we
implemented an imaging protocol that measured GABA in two
voxels (one in occipito-temporal (OCT), one in PPC) in alter-
nating order and allowed us to track changes in GABA in both
areas during training. Interestingly, previous studies have pro-
posed that perceptual learning is implemented by top-down
influences from decision-related areas that re-weight processing
in sensory areas30,31. To test whether learning involves local
processing within visual cortex or suppressive interactions
between decision-related and sensory areas, we combined GABA
measurements in occipito-temporal and posterior parietal cortex
with functional brain connectivity, as measured by resting-state
fMRI. In particular, we tested the hypothesis that learning is
implemented by local inhibitory processing in visual cortex that is
gated by functional interactions between sensory and decision-
related areas. Specifically, we tested whether learning-dependent
changes in visual cortex GABA relate to functional connectivity
between visual and posterior parietal cortex.
Our results reveal distinct GABAergic inhibition mechanisms
in a cortical network that is known to be involved in perceptual
decisions. In particular, increased parietal GABA with training
suggests suppression of task-irrelevant information. In contrast,
changes in occipito-temporal GABA with training relate to
enhanced target detection and discriminability, suggesting
learning-dependent changes in the processing of task-relevant
features. Further, analysis of functional brain connectivity at rest
reveals interactions within this network that relate to GABA
changes and behavioral improvement during training. Learning
to detect targets from clutter is implemented by local connectivity
and disinhibition of the visual cortex. In contrast, learning feature
differences is implemented by interactions between parietal and
visual areas that relate to increased GABAergic inhibition in
visual cortex. Our results provide evidence that learning improves
perceptual decisions through suppressive interactions within
decision-related circuits in the human brain.
Results
Training improves behavioral performance. We tested two
groups of participants on (a) a signal-in-noise (SN) task that
involves extracting shapes (radial vs. concentric Glass patterns)
masked by noise or (b) a feature differences (FD) task that
involves judging fine differences induced by morphing between
two stimulus classes (Fig. 1a). Participants were asked to judge the
identity of the stimulus presented per trial (i.e., radial vs. con-
centric). Our results showed that participants improved in their
judgments within a single training session during scanning
(Fig. 1b), consistent with previous reports showing fast behavioral
improvement early in the training (for a review, see ref. 32). A
linear mixed effects (LME) model with Task and Training Block
(6 blocks, 200 trials per block) as fixed effects showed significantly
improved performance during training across tasks (main effect
of Block: F(1, 249) =9.35, p=0.002). No significant interaction
between Task × Block (F(1, 249) =0.10, p=0.75) was observed,
suggesting similar improvement across tasks (Fig. 1b).
To quantify behavioral improvement due to training in
individual participants, we compared performance at the
beginning of training (i.e., first training block) to the maximum
performance achieved (i.e., when participants achieved highest
accuracy) by each participant during training (Fig. 1b) (see Sup-
plementary Methods). We chose this measure to capture
individual variability across participants that may be more
pronounced in our data, as participants were trained only for a
single training session in contrast to our previous studies that
have shown that performance saturates after multiple training
sessions on similar perceptual tasks33,34. A repeated measures
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two-way analysis of variance (Task × Block) showed significantly
improved performance during training across tasks (main effect
of block: F(1, 45) =59.88, p< 0.0001) but no significant interac-
tion between Task × Block (F(1, 45) =1.07, p=0.31).
Learning-dependent changes in GABA. To test whether
GABAergic inhibition in the visual or parietal cortex changes
with training, we measured GABA (Fig. 2a) before (baseline
block) and during training. During the baseline block, partici-
pants were presented with random dot stimuli, while during the
training blocks participants performed either the SN or the FD
task on Glass pattern stimuli. We tested two MRS voxels—one
centered on the occipito-temporal cortex (OCT voxel) and the
other on the PPC (PPC voxel) (Fig. 2b)—following previous
studies showing that these areas are involved in learning using the
same tasks and stimuli as in our study33,34. Each block comprised
one MRS acquisition per voxel and the order of the voxels within
each block was counterbalanced across participants.
Figure 3shows that OCT GABA/tCr changed in the two tasks
during training in opposite directions. In contrast, PPC GABA/
tCr changed in the same direction (i.e., increased during training)
for both tasks. These effects were supported by an LME analysis
(Task, Voxel, and MRS Blocks as fixed effects) that modeled
GABA before (baseline block) and during (three training blocks)
training in both OCT and PPC. This analysis showed that
learning-dependent changes in GABA levels differed between
tasks and regions (Task × Voxel × Block: F(1, 264) =6.24, p=
0.01). In particular, we found that changes in OCT GABA (OCT
GABA) levels during training differed between tasks (LME model
for OCT GABA with Task and MRS Block as fixed effects; Task ×
Block: F(1, 119) =10.77, p=0.001) (Fig. 3a). In contrast, GABA
changes in the PPC (PPC GABA) during training did not differ
significantly between tasks (LME model for PPC GABA with
Task and MRS Block as fixed effects; Task × Block: F(1, 145) =
0.18, p=0.68) (Fig. 3a). That is, PPC GABA increased during
training independent of Task (LME model for PPC GABA with
MRS Block as fixed effect; main effect of Block: F(1, 147) =6.27,
t=2.50, p=0.01).
Specifically, for the FD task, GABA changes during training did
not differ significantly between OCT and PPC (LME model for
FD task GABA with Voxel and MRS Block as fixed effects; Voxel
× Block: F(1, 143) =0.10, p=0.74). That is, GABA increased
significantly during training independent of Voxel (LME model
for FD GABA with MRS Block as fixed effect; main effect of MRS
Block: F(1, 145) =6.13, t=2.48, p=0.01). In contrast, for the SN
task GABA changes during training differed in the two regions
(LME model for SN task with Voxel and MRS Block as fixed
effects; Voxel × Block: F(1, 121) =13.06, p=0.0004). That is, we
found a significant decrease for OCT GABA (LME model for
SN task with MRS Block as fixed effect; main effect of Block:
Radial
Concentric
Signal-in-noise Feature differences
a
Stimulus
prototypes
0.50 0.60 0.70 0.80 0.90
0.50
0.60
0.70
0.80
0.90
0.50 0.60 0.70 0.80 0.90
Maximum performance
Early performance Early performance
Signal-in-noise (SN)
Feature differences (FD)
b
400 600 800 1000 1200
−4
−2
0
2
4
6
8
10
12
14
Trials
Behavioral improvement (%)
200
Behavioral performance
Stimuli
Fig. 1 Tasks, stimuli, and behavioral results. aStimuli: Example stimuli comprising radial and concentric Glass patterns (stimuli are presented with inverted
contrast for illustration purposes). Stimuli are shown for the signal-in-noise task (SN task, 25% signal, spiral angle 0° for radial and 90° for concentric) and
the feature differences task (FD task, 100% signal, spiral angle 38° for radial and 52° for concentric). Prototype stimuli (100% signal, spiral angle 0° for
radial and 90° for concentric) are shown for illustration purposes only. bBehavioral performance across participants: We calculated improvement in task
performance during training as the difference in mean performance (i.e., mean accuracy) during each training block (200 trials) from the first training block
(200 trials), divided by performance in the first training block. Further, we compared individual participant accuracy early in training (first 200 trials) to
the maximum accuracy achieved per participant during training (200 trials). Participants did not differ significantly in their performance early in training
(t(45) =0.56, p=0.57) between the two tasks. Note that most participants (85%) achieved maximum performance during the last two MRS
measurements. Error bars indicate standard error of the mean across participants
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F(1,58) =16.65, t=−4.08, p=0.0001), but a nonsignificant
increasing trend for PPC GABA (LME model for SN task with
MRS Block as fixed effect; main effect of Block: F(1,63) =3.26, t
=1.80, p=0.08).
We conducted the following control analyses that corroborated
our results (see Supplementary Methods for more details). First,
we demonstrated that the learning-dependent changes we
observed in GABA levels: (a) were not simply driven by GABA
measurements at baseline, (b) could not be simply due to the
order with which the MRS voxels were acquired during training.
Second, we tested whether the learning-dependent changes we
observed in GABA, were simply due to differences in data quality
across multiple MRS measurements. In particular, we showed
that there were no significant differences in the signal-to-noise
Design
MRS
rs-fMRI
Baseline T1 T2 T3
a
Blocks
b
Occipito-temporal (OCT)
Posterior parietal (PPC)
c
Occipito-temporal (OCT) Posterior parietal (PPC)
GABA
Raw data
Baseline
1234
Residual
1234
Fit
ppm ppm
MRS spectra
MRS voxel placement
Fig. 2 Task design, and MR spectroscopy (MRS) voxels. aDesign: Each participant took part in a single session during which we acquired resting-state
functional MRI (rs-fMRI) and MRS data. We collected MRS GABA during one block before (Baseline) and three blocks during training (T1, T2, T3). Each
block comprised two MRS acquisitions: one from occipito-temporal (OCT voxel—black squares) and one from posterior parietal (PPC voxel—gray squares)
cortex. The order of the voxels within each block was counterbalanced across participants. During each block, participants were presented with stimuli for
400 trials (200 trials per MRS voxel acquisition). bMRS voxel placement: We positioned the MRS voxels using anatomical landmarks (superior temporal
gyrus and middle occipital gyrus for OCT and intraparietal sulcus for PPC) on the acquired T1 scan to ensure that voxel placement was consistent across
participants. The mean GM tissue fraction was 44 ± 8% for OCT and 46 ± 7% for PPC and GM tissue content did not differ significantly between the two
MRS Voxels (t(81) =1.17, p=0.24). The average distance of individual MRS voxels from the mean Montreal Neurological Institute (MNI) coordinates
(OCT: x=−38.6 ± 4.4 mm, y=−67.8 ± 4.8 mm, z=1.8 ± 4.4 mm; PPC: x=−31.6 ± 4.0 mm, y=−50.5 ± 6.5 mm, z=40.7 ± 4.7 mm) across participants
was small (i.e., lower than the MRS spatial resolution; 6.9 ± 3.6 mm for OCT and 7.9 ±4.0 mm for PPC and did not differ between the two tasks (t(34) =
0.02, p=0.99 for OCT; t(38) =0.65, p=0.52 for PPC). We computed the overlap across participant MRS voxels for OCT (yellow) and PPC (green)
separately. We illustrate a group MRS mask (sagittal, coronal, axial view) that covers a cortical area that is common in at least 50% of the participants’
MRS voxels. cMRS spectra: Example spectra from the OCT and PPC voxel for one participant (see Supplementary Figure 1 for an average MRS spectrum
across participants and Supplementary Figure 2 for individual participant MRS spectra). We show the GABA fit using LC model (GABA peaks at 1.89 ppm,
2.29 ppm and 3.01 ppm), the residuals, the spectrum comprising all metabolites, the LC Model fit, and the baseline
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ratio (SNR) of MRS measurements over time, nor BOLD effects
on MRS spectra as represented by narrowing of the linewidth. In
addition, we conducted a no-training control experiment on an
independent group of participants (n=8). We measured OCT
GABA before and after exposing the participants to Glass pattern
stimuli for a duration comparable to the training in our main
study. During this time participants performed a fixation task
rather than the SN or FD tasks. We did not observe any
significant differences (t(7) =1.56, p=0.16) between two mea-
surements of OCT GABA, suggesting that the GABA changes we
observed in our main study were specific to task-training. Third,
we showed that the learning-dependent changes we observed in
GABA/tCr (a) could not be explained simply by changes in tCr
concentration over time (Supplementary Figure 5), (b) remained
significant when we referenced GABA to water rather than tCr
concentration (Supplementary Figure 4), (c) were specificto
GABA and did not generalize to Glutamate (Fig. 3b).
Learning-dependent changes in GABA relate to behavior.We
next tested whether the learning-dependent changes we observed
in occipito-temporal and parietal GABA related to behavioral
improvement. Correlating change in OCT GABA
(see Supplementary Methods) to behavioral improvement showed
differences between tasks. Specifically, we observed a significant
negative correlation of OCT GABA change with behavioral
improvement (r=−0.43, CI =[−0.75, −0.02]) for the SN task,
whereas a significant positive correlation (r=0.55, CI =[0.10,
0.78]) for the FD task (Fig. 4a). A linear regression analysis
confirmed this dissociation (OCT GABA change × Task inter-
action: F(1, 29) =9.03, p=0.005), suggesting that lower vs.
higher occipito-temporal GABA relates to improved performance
when learning to detect targets vs. discriminate feature differ-
ences, respectively. This dissociable result between tasks cannot
be simply explained by differences in task difficulty, as partici-
pants showed similar behavioral improvement across tasks
(Fig. 1b).
In contrast to these correlations of OCT GABA change with
behavioral improvement, we did not observe any significant
correlations between PPC GABA change and behavioral
improvement for either task (SN: r=−0.23, CI =[−0.61, 0.19];
FD: r=0.05, CI =[−0.37, 0.43]) nor a significant PPC GABA
change × Task interaction (F(1, 34) =0.57, p=0.45) (Fig. 4b).
Following previous work on the role of the PPC early rather than
later in training35, we next tested the link between PPC GABA
−40
−30
−20
−10
0
10
20
30
40
Baseline T1 T2 T3
Occipito-temporal cortex
GABA/tCr
GABA/tCr change (%)
−40
−30
−20
−10
0
10
20
30
40
Baseline T1 T2 T3
Signal-in-noise
Feature differences
Posterior parietal cortex
a
Glutamate/tCr
Glutamate/tCr change (%)
−40
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−10
0
10
20
30
40
Baseline T1 T2 T3 −40
−30
−20
−10
0
10
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Baseline T1 T2 T3
b
Fig. 3 MR spectroscopy (MRS) measurements of γ-aminobutyric acid (GABA) and glutamate during training. aMRS-measured GABA over time is shown
from two voxels (occipito-temporal, posterior parietal cortex) per task (signal-in-noise, feature differences). For each MRS voxel, we calculated % GABA
change: we normalized GABA/tCr per training block (T1, T2, T3) to GABA/tCr recorded during the baseline block; that is, we computed GABA/tCr change
subtracting GABA/tCr measurements in each of the three training blocks from the baseline block and then divided by GABA/tCr in the baseline block. We
observed maximum (across blocks) 20% GABA change (mean across participants). This is consistent with previous studies measuring GABA at 3T that
have reported changes in GABA between 10 and 15% within a single session (duration of 20–30 min) of stimulation75 or training13. Ultra-high field imaging
(i.e., 7T) has been shown to have increased sensitivity for MR spectroscopy measurements19,20 and may result in enhanced and more accurate detection
of learning-dependent changes in GABA. bMRS-measured glutamate over time is shown from two voxels (occipito-temporal, posterior parietal cortex) per
task (signal-in-noise, feature differences). For each MRS voxel, we calculated % glutamate change: we normalized glutamate/tCr per training block (T1, T2,
T3) to glutamate/tCr recorded during the baseline block; that is, we computed glutamate/tCr change subtracting glutamate/tCr measurements in each of
the three training blocks from the baseline block and then divided by glutamate/tCr in the baseline block. Error bars indicate standard error of the mean
across participants. Error bars are not visible for small changes in metabolite concentrations
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and performance early in training for the two tasks. A linear
regression model with baseline PPC GABA and Task as
predictors of behavioral performance (first training block) showed
asignificant effect of PPC GABA (F(1, 37) =4.69, p=0.04), but no
interaction between PPC GABA and Task (F(1, 37) =0.03, p=
0.85). This result was confirmed by a significant positive
correlation between baseline PPC GABA and performance in
the first training block (r=0.34, CI =[0.06, 0.59]), suggesting
GABAergic inhibition in the parietal cortex before training relates
to suppression of task-irrelevant information early in the training
across tasks. We did not find a significant effect of OCT GABA
(F(1, 33) =2.14, p=0.15) nor an interaction between OCT
GABA and Task (F(1, 33) =2.33, p=0.14), suggesting that this
result linking GABA to performance early in training was specific
to parietal cortex.
Functional connectivity at rest relates to behavior and GABA.
Previous studies have shown that functional connectivity in
motor14 and visual36 networks relates to behavioral improvement
and learning-dependent plasticity. Further, GABAergic inhibition
has been suggested to shape network connectivity37. In particular,
extra-synaptic GABA has been shown to relate to local oscillatory
activity in the high gamma frequency range38 and to inter-
regional functional connectivity39. In the human brain, MRS-
−1 −0.5 0 0.5 1
−10
0
10
20
30
40
OCT GABA/tCr change
Signal-in-noise
Behavioral improvement (%)
Correlations of OCT GABA/tCr change with behavioral improvement
−1 −0.5 0 0.5 1
Feature differences
OCT GABA/tCr change
−10
0
10
20
30
40
a
Correlations of PPC GABA/tCr change with behavioral improvement
PPC GABA/tCr change
Behavioral improvement (%)
Signal-in-noise
−10
0
10
20
30
40
−1 −0.5 0 0.5 1
Feature differences
PPC GABA/tCr change
−1 −0.5 0 0.5 1
−10
0
10
20
30
40
b
Fig. 4 Correlating γ-aminobutyric acid (GABA) change with behavioral improvement. aSkipped Pearson’s correlations showing a significant negative
correlation of GABA/tCr change in occipito-temporal cortex with behavioral improvement for the signal-in-noise task (n=16, r=-0.43, CI =[–0.75,
–0.002]), whereas a significant positive correlation for the feature differences task (n=19, r=0.55, CI =[0.10, 0.78]). We computed GABA/tCr change
for each participant as the difference between GABA/tCr in the training block with maximum performance and GABA/tCr in the baseline block. Note that
the temporal resolution of GABA measurements does not allow us to separate the different processes associated with different events in a trial (e.g.,
stimulus vs. response). Thus, the correlations we report here relate learning-dependent changes in GABA levels to learning-dependent changes in overall
task performance. The plots indicate that for a small number of participants the data deviated from the overall pattern of the correlation; e.g., for some
participants in the SD task, GABA/tCr values were higher rather than lower compared with baseline. Our treatment of the data (i.e., GABA data are
expressed as percent over baseline; behavioral improvement is expressed as percent over early performance) accounts for potential differences across
participants in performance early in training or baseline GABA before training. It is possible that this individual variability was due to the single training
session employed in our study during which participant performance did not saturate (i.e., average 72% maximum performance across participants).
bCorrelations of posterior parietal GABA/tCr change from baseline with behavioral improvement were not significant for the signal-in-noise (n=17,
r=–0.23, CI =[–0.61, 0.19]) or the feature differences task (n=21, r=0.05, CI =[–0.37, 0.43]). Significant correlations are indicated by closed symbols;
nonsignificant correlations by open symbols
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assessed GABA has been linked to functional connectivity as
measured by resting-state fMRI37,40,41, suggesting that GABAer-
gic inhibition may relate to local neural dynamics42. Here we test
whether functional connectivity between the OCT and PPC
relates to behavioral improvement and GABA changes during
training to detect targets in clutter vs. discriminate fine features.
First, we tested whether functional connectivity between these
regions relates to behavioral improvement in each of the two
learning tasks. We extracted the resting-state fMRI (rs-fMRI)
time course only from the gray matter voxels within the OCT
MRS voxel and the PPC MRS voxel (see Supplementary
Methods). We measured functional connectivity by correlating
the rs-fMRI time courses at rest between these two regions (OCT-
PPC connectivity) (see Supplementary Methods). We observed a
significant positive correlation between OCT-PPC connectivity
and behavioral improvement for the FD task (r=0.37, CI =
[0.03, 0.69]), whereas a significant negative correlation for the SN
task (r=−0.72, CI =[−0.90, −0.29]) (Fig. 5). This dissociation
in the relationship of OCT-PPC connectivity and behavioral
improvement between tasks was confirmed by a linear regression
showing a significant interaction between OCT-PPC connectivity
and Task (F(1, 39) =10.72, p=0.002), suggesting that higher
connectivity between parietal and visual cortex facilitates learning
of fine feature differences. We interpret these results according to
previous studies that show both positive and negative values for
resting-state connectivity (e.g.,36). Positive connectivity values
(i.e., correlated signals) are typically interpreted to indicate
integration or cooperation within or between brain regions. In
contrast, negative connectivity values (i.e., anti-correlated signals)
are typically interpreted to indicate segregation of processing or
competition between networks. Thus, the correlations we
observed between resting-state connectivity and behavioral
improvement suggest higher cooperation between OCT and
PPC for learning fine features differences (FD task) than learning
to detect targets from noise (SN task). Further, to test whether
this link between functional connectivity and behavioral
improvement is specific to interactions between parietal and
visual areas, we extracted rs-fMRI for two additional control
regions: early visual cortex and motor cortex. We did not observe
any significant results for correlations of behavioral improvement
and rs-fMRI connectivity between OCT and (a) early visual
cortex (SN: r=0.37, CI =[–0.17, 0.70]; FD: r=–0.22, CI =
[–0.56, 0.15]) or (b) motor cortex (SN: r=–0.26, CI =[–0.61,
0.12]; FD: r=–0.06, CI =[–0.39, 0.42]) (see Supplementary
Methods).
Second, we tested whether OCT-PPC functional connectivity
relates to changes in visual cortex GABA during training. Our
results showed a significant positive correlation between func-
tional connectivity and OCT GABA change for the FD task (r=
0.55, CI =[0.13, 0.87]), but not the SN task (r=0.27, CI =
[–0.08, 0.60]) (Fig. 6a). These correlations were specific to GABA
changes in OCT. That is, there were no significant correlations
between OCT-PPC connectivity and PPC GABA change for the
FD task (r=0.29, CI =[–0.37, 0.69]) nor the SN task (r=0.20,
CI =[–0.31, 0.60]). A multivariate linear regression showed that
OCT-PPC connectivity had a significant effect on GABA change
in OCT (F(1, 14) =20.74, p=0.0005), but not PPC (F(1, 14) =
0.46, p=0.51) for the FD task. The correlation between
functional connectivity and OCT GABA change for the FD task
remained significant (r=0.49, CI =[0.10, 0.82]) when we tested
for percentage GABA change (GABA change/baseline GABA),
suggesting that our results could not be due to differences in
baseline GABA. These results show that parietal-visual cortex
connectivity relates to GABAergic inhibition in visual cortex,
suggesting top-down influences to suppressive processing in
visual cortex for learning fine feature differences.
Interestingly, for the SN task we observed a significantly
negative correlation between connectivity within the OCT (i.e.,
OCT connectivity: connectivity measured as rs-fMRI correlations
across voxels within the OCT MRS voxel, see Supplementary
Methods) and OCT GABA change (r=–0.55, CI =[–0.86,
–0.01]) (Fig. 6b). That is, higher OCT connectivity related to
decreased OCT GABA with training, suggesting that learning to
detect targets in clutter is supported by local connectivity and
decreased GABAergic inhibition within visual cortex. Correla-
tions of functional connectivity and GABA change in the OCT
were not significant for the FD task (r=0.12, CI =[–0.29, 0.51])
and were significantly different from correlations for the SN task
(Z=2.33, p=0.02). The correlation between OCT connectivity
and GABA change for the SN task remained significant
(r=–0.55, CI =[–0.88, –0.003]), when we used percentage
GABA change (GABA change/baseline GABA), suggesting that
our results could not be due to differences in baseline GABA.
Correlating OCT connectivity with PPC GABA change did not
show any significant results (r=0.18, CI =[–0.39, 0.60]),
suggesting that local connectivity relates specifically to GABA
changes in visual cortex.
Taken together, our results suggest that learning fine
discriminations involves interactions between decision-related
(posterior parietal) and sensory (visual) areas that may facilitate
retuning of task-relevant features in visual cortex. In contrast,
learning to detect targets from clutter involves local processing
and decreased inhibition in visual cortex. To further test this
proposal, we performed moderation analyses43 that allowed us to
test whether the influence that an independent variable (i.e.,
GABA change) has on the outcome (i.e., behavioral improve-
ment) is moderated by a moderator variable (i.e., connectivity).
Our results showed that OCT-PPC connectivity moderates the
relationship between OCT GABA and behavior for the FD task (F
(1, 15) =6.19, p=0.03) but not the SN task (F(1, 12) =0.74, p=
0.41). In contrast, local connectivity within OCT moderates the
relationship between GABA change and behavior for the SN task
(F(1, 12) =7.65, p=0.02) but not the FD task (F(1, 15) =2.80,
p=0.11). These moderation analyses suggest that the relation-
ship between learning-dependent changes in GABA and behavior
is moderated by functional connectivity; that is, interactions
between parietal and visual areas for the FD task, whereas local
connectivity within visual cortex for the SN task.
Discussion
Here, we provide evidence that learning improves perceptual
decisions (i.e., learning to detect targets in clutter or discriminate
highly similar features) by suppressive interactions in the human
brain. First, we demonstrate dissociable GABAergic inhibition
mechanisms for learning in a posterior cortical network (i.e.,
occipito-temporal and posterior-parietal cortex) known to be
involved in perceptual decisions. In particular, increased
GABAergic inhibition in the PPC with training suggests sup-
pressive processing of task-irrelevant information. In contrast,
changes in occipito-temporal GABA with training relate to
enhanced target detection and discriminability, suggesting
learning-dependent changes in the processing of behaviorally
relevant features. Second, we provide evidence that interactions
within this network, as measured by functional brain connectivity
at rest, gate suppressive processing of sensory signals. Learning to
detect targets from clutter involves local processing and disin-
hibition in visual cortex, while learning feature differences
involves suppressive interactions between decision-related (par-
ietal) and visual areas.
Previous studies have used MRS to measure GABA in the
context of visual15, sensory-motor44–46, and learning tasks47,48.
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Signal-in-noise
Correlations of OCT-PPC connectivity with behavioral improvement
−0.8 −0.4 0 0.4 0.8
−10
0
10
20
30
40
Behavioral improvement (%)
Feature differences
−0.8 −0.4 0 0.4 0.8
−10
0
10
20
30
40
OCT−PPC connectivity
OCT−PPC connectivity
Fig. 5 Correlating occipito-temporal cortex (OCT)-posterior parietal cortex (PPC) functional connectivity with behavioral improvement. Skipped Pearson’s
correlations showing a significant negative correlation of OCT-PPC connectivity with behavioral improvement for the signal-in-noise task (n=21,
r=−0.72, CI =[−0.90, −0.29]), whereas a significant positive correlation for the feature differences task (n=25, r=0.37, CI =[0.03, 0.69])
−0.6 −0.4 −0.2 0 0.2 0.4 0.6
−1
−0.5
0
0.5
1
aCorrelations of OCT-PPC connectivity with OCT GABA/tCr change
OCT GABA/tCr change
OCT−PPC connectivity
Signal-in-noise
−1
−0.5
0
0.5
1
−0.6 −0.4 −0.2 0 0.2 0.4 0.6
OCT−PPC connectivity
Feature differences
Correlations of OCT connectivity with OCT GABA/tCr change
Signal-in-noise
−1
−0.5
0
0.5
1
0.4 0.6 0.8 1 1.2
OCT connectivity
Feature differences
0.4 0.6 0.8 1 1.2
−1
−0.5
0
0.5
1
OCT connectivity
OCT GABA/tCr change
b
Fig. 6 Correlating functional connectivity with occipito-temporal cortex (OCT) γ-aminobutyric acid (GABA)/tCr change. aSkipped Pearson’s correlations
showing a significant positive correlation of OCT-posterior parietal cortex (PPC) connectivity with OCT GABA/tCr change for the feature differences task
(n=19, r=0.55, CI =[0.13, 0.87]), but not the signal-in-noise task (n=16, r=0.27, CI =[−0.08, 0.60]). We computed GABA/tCr change for each
participant as the difference between GABA/tCr in the training block with maximum performance and GABA/tCr in the baseline block. bSkipped Pearson’s
correlations showing a significant negative correlation of functional connectivity within the occipito-temporal cortex with OCT GABA/tCr change for the
signal-in-noise task (n=16, r=−0.55, CI =[−0.86, −0.01]), but not the feature differences task (n=19, r=0.12, CI =[−0.29, 0.51]). Significant
correlations are indicated by closed symbols; nonsignificant correlations by open symbols
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Here, we exploited the high spectral resolution afforded by ultra-
high field (7T) MRS to reliably resolve GABA from other meta-
bolites19,20 and measure GABA changes over time at faster time
scales than typically recorded at lower field strength. However, as
the temporal resolution of MRS-assessed GABA is in the order of
minutes, it is not possible to separate GABAergic inhibition
related to different processes within a trial (i.e., stimulus pro-
cessing, perceptual judgment, response, feedback); thus, it is likely
that the observed GABA changes relate to aggregate signals across
all processes involved in the tasks performed by the participants.
Despite this, our methodology provides two main advantages
over previous work. First, moving beyond the standard correla-
tional approach that relates single measurements of GABA at
baseline to behavior, we implemented an interventional approach
to test whether GABAergic inhibition changes due to task-specific
training. In particular, we captured learning-dependent changes
in functional GABA during training. Two previous studies
reporting measurements of GABA during training have focused
on motor cortex GABAergic inhibition during training on a
motor learning task13,49. Second, we implemented an MRS pro-
tocol that allows us to track longitudinal changes in GABA during
training from both OCT and PPC that are thought to play distinct
functional roles in perceptual learning29,30. Using this protocol, in
combination with functional connectivity as measured by rs-
fMRI, we tested for the role of suppressive interactions between
sensory and decision-related areas in perceptual learning.
Our findings provide evidence for distinct GABAergic inhibi-
tion mechanisms in PPC vs. OCT for learning. In particular, we
demonstrate a common mechanism across tasks in the PPC. That
is, GABA increases during training may facilitate suppression of
task-irrelevant information (i.e., background clutter when learn-
ing to detect targets, task-irrelevant features when learning fine
differences). It is unlikely that changes in GABA levels with
training reflect reduced attention to the task, as the task remained
sufficiently demanding (i.e., mean maximum performance was
72%) during the single training session employed in our study.
Our findings are consistent with the known role of parietal cortex
in perceptual decision-making and attentional selection. In par-
ticular, the PPC has been implicated in detecting low-saliency
targets by suppressing distractors50, providing a salient repre-
sentation of the environment and top-down attentional feed-
back51, accumulating sensory information52 and directing
attention to task-relevant features29,51. Interestingly, we show that
parietal cortex GABA before training relates to performance early
in training across tasks. This is in contrast with visual cortex
changes in GABA that relate to improvement in behavioral per-
formance during training. This finding suggests that suppression
of task-irrelevant information in the PPC may precede suppres-
sion in the visual cortex related to the processing of task-relevant
features. This is consistent with previous studies showing that
transcranial magnetic stimulation (TMS) in the parietal cortex
disrupts performance in visual discrimination tasks early in the
training compared with TMS in the visual cortex that disrupts
performance after training35.
In contrast to this common GABAergic inhibition mechanism
across tasks in the PPC, our results in the visual cortex suggest
distinct GABAergic mechanisms between tasks that relate to
improved behavioral performance. In particular, decreased
occipito-temporal GABA related to improved performance when
training to detect targets in clutter, while increased occipito-
temporal GABA related to improved performance when training
to discriminate fine feature differences. These results suggest that
learning to detect targets in clutter is implemented by decreased
local GABAergic inhibition that may facilitate noise filtering and
feature detection. Disinhibition of the visual cortex may facilitate
cortical recruitment that enhances probability summation and
SNR for target detection53. This mechanism is consistent with
previous animal studies linking GABAergic inhibition to neural
gain25 and interventional studies showing that blocking
GABAergic inhibition increases neural gain24. In contrast, dis-
criminating highly similar targets is implemented by increased
GABAergic inhibition in the visual cortex that may facilitate
learning through feature template retuning. This mechanism is
consistent with neurophysiological studies linking GABAergic
inhibition to cortical tuning26 and pharmacological interventions
showing that GABA agonists enhance orientation selectivity in
visual cortex27,28, whereas blocking GABAergic inhibition results
in broader neural tuning27. Interestingly, recent work has
implicated different populations of inhibitory interneurons in
neural gain vs. tuning54 that may contribute differentially to
learning by noise filtering vs. feature template retuning. In par-
ticular somatostatin (SOM)-positive interneurons have been
implicated in spatial summation55 and have been shown to gate
plasticity by providing contextual information56. In contrast,
parvalbumin (PV)-positive interneurons have been implicated in
selective inhibition16 that sharpens feature representations after
training57. It is therefore possible that SOM interneurons may
support learning to detect targets from clutter (SN task) through
noise filtering, whereas PV interneurons may support learning
fine differences (FD task) through feature retuning. Thus, our
findings propose testable hypotheses for further animal studies on
the micro-circuits that mediate adaptive behavior and underlie
the macroscopic learning-dependent plasticity as measured by
human brain imaging.
We interpret these links between MRS-assessed GABA, beha-
vior and possible neural mechanisms with caution, as the precise
mechanisms that underlie changes in GABA as measured by MRS
remain under investigation. In particular, it is unclear whether
MRS-assessed GABA represents the entire pool of GABA avail-
able in a voxel. It is possible that the individual GABA pools are
not equally visible using MRS13,58. Another possibility is that
MRS-assessed GABA reflects the exchange between the intra-
cellular and synaptic pools of GABA59. A number of studies using
paired-pulse TMS (e.g., 60,61) have shown that MRS-assessed
GABA does not relate to GABA
A
or GABA
B
activity and there-
fore it is unlikely to reflect synaptic transmission of GABA. It is
more likely that MRS-assessed GABA reflects ambient extra-
cellular GABA that contributes to tonic GABAergic activity (for
reviews e.g., 62,63). This is consistent with animal studies showing
that GABA synthesis64 is associated with Glutamic Acid Dec-
arboxylase (GAD)
67
that is predominantly found throughout the
cell, rather than GAD
65
that is found in axon terminals65. Finally,
we did not observe changes in glutamate simultaneously with the
change in GABA observed during training. Thus, it is unlikely
that the changes in GABA reflect overall changes in the meta-
bolite cycling. GABA undergoes rapid turnover in the mamma-
lian cortex, and GAD activity has been shown to be rapidly
modulated in a variety of physiological processes66 in both
human67 and animal68 studies. Future studies combining invasive
investigations in animals (e.g., two photon imaging of inter-
neurons) with non-invasive MRS are necessary to shed more light
on the basis MRS-assessed GABA.
Finally, investigating functional connectivity within this pos-
terior cortical network revealed suppressive interactions that
relate to our ability to improve perceptual judgments through
training. We demonstrate that higher connectivity between par-
ietal and visual cortex at rest relates to increased GABA levels in
visual cortex during training and behavioral improvement in fine
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feature discrimination. This finding suggests top-down influences
from the parietal cortex on suppressive processing in the visual
cortex for retuning feature templates. Previous theoretical inves-
tigations have proposed that sensory selectivity is enhanced by
suppressive feedback mechanisms that change recurrent proces-
sing in visual cortex23 and relate to attention-guided selection of
behaviorally relevant information69. Our findings suggest that
suppression of task-irrelevant information in the parietal cortex
enhances tuning of task-relevant features in visual cortex through
top-down feedback, consistent with previous proposals that per-
ceptual learning re-weights sensory processing30,31. In contrast,
we show that higher connectivity within the visual cortex (rather
than connectivity between visual and parietal cortex) relates to
decreased GABA in visual cortex and behavioral improvement in
detecting targets from clutter. Lateral interactions within the early
visual cortex are shown to support contextual processing and
shape integration53,70. Further, recurrent processing has been
implicated in robust representations of ambiguous stimuli in
higher visual cortex71, suggesting that local connectivity in occi-
pito-temporal cortex facilitates visual processing under
uncertainty.
In sum, our findings provide evidence for distinct GABAergic
inhibition mechanisms that support our ability to optimize per-
ceptual decisions through training. We propose that local sup-
pressive processing within visual cortex enhances target detection,
whereas top-down suppression from decision-related areas (i.e.,
posterior parietal cortex) enhances retuning of task-relevant
features for fine discrimination in the visual cortex. Decision-
making models have proposed suppressive mechanisms that
resolve competition between neuronal ensembles that represent
behavioral choices72. Our findings provide novel insights in
understanding how these suppressive mechanisms are imple-
mented in decision-related and sensory areas in the human brain
and optimize our ability for perceptual decisions through
training.
Methods
Participants. Fifty participants (32 females; mean age 25.6 ± 3.3 years) took part in
this study. Data from three participants were not useable due to technical pro-
blems. All participants were right handed, had normal or corrected-to-normal
vision, and gave written informed consent. The study was approved the University
of Oxford Central University Research Ethics committee (MSD-IDREC-C1-2014-
081).
Stimuli. We presented participants with Glass patterns generated using previously
described methods33,34. In particular, stimuli were defined by white dot pairs
(dipoles) displayed within a square aperture on a black background. Experiment
stimuli (size =7.9o× 7.9o) were presented in the center of the screen. The dot
density was 3%, and the Glass shift (i.e., the distance between two dots in a dipole)
was 16.2 arc min. The size of each dot was 2.3 × 2.3 arc min2. For each dot dipole,
the spiral angle was defined as the angle between the dot dipole orientation and the
radius from the center of the dipole to the center of the stimulus aperture. Each
stimulus comprised dot dipoles that were aligned according to the specified spiral
angle (signal dipoles) for a given stimulus and noise dipoles for which the spiral
angle was randomly selected. The proportion of signal dipoles defined the stimulus
signal level.
We generated radial (0ospiral angle) and concentric (90ospiral angle) Glass
patterns by placing dipoles orthogonally (radial stimuli) or tangentially (concentric
stimuli) to the circumference of a circle centered on the fixation dot. Further, we
generated intermediate patterns between these two Glass pattern types by
parametrically varying the spiral angle of the pattern from 0° (radial pattern) to 90°
(concentric pattern). We randomized the presentation of clockwise (0° to 90° spiral
angle) and counterclockwise patterns (0° to –90° spiral angle) across participants. A
new pattern was generated for each stimulus presented in a trial, resulting in
stimuli that were locally jittered in their position.
For the SN task, radial and concentric stimuli (spiral angle: 0° ± 90°) were
presented at 24% ± 1% signal level; that is, 76% of the dipoles were presented at
random position and orientation. For the FD task, stimuli were presented at 100%
signal and spiral angle of ± 38o(radial) or ± 52o(concentric) (Fig. 1a). To control
for stimulus-specific training effects, we presented each participant with a newly
generated set of stimuli. To control for potential local adaptation due to stimulus
repetition, we jittered ( ± 1–3°) the spiral angle across stimuli. These procedures
ensured that learning related to global shape rather than local stimulus features.
Experiment design. All participants took part in a single brain imaging session
(Fig. 2a) during which they were randomly assigned and trained on either the SN
or the FD task. We recorded whole-brain rs-fMRI data before training while
participants fixated on a cross at the center for the screen.
Following the rs-fMRI scan, we recorded MRS GABA before and during
training (Fig. 2a). We collected MRS GABA from one baseline block before
training and three blocks during task training. Each block comprised two MRS
acquisitions: one from OCT and one from PPC. The order of the voxels within
each block was counterbalanced across participants. During each training block,
participants were presented with stimuli for 400 trials (200 trials per MRS voxel
acquisition). During the baseline MRS block (400 trials) participants engaged in a
task with similar stimuli as those presented during the training; that is, participants
viewed random dot patterns (0% signal dipoles) and were asked to respond (button
press) as soon as a pattern appeared. This ensured that differences in GABA
between blocks could not be simply attributed to differences in overall alertness.
During the MRS training blocks (3 MRS blocks, 400 trials each), participants were
presented with Glass patterns and were asked to judge and indicate by button press
whether the presented stimulus in each trial was radial or concentric. Two stimulus
conditions (radial vs. concentric Glass patterns; 200 trials per condition), were
presented for each training block. For each trial, a stimulus was presented for
300 ms and was followed by fixation (i.e., blank screen with a central fixation dot)
while waiting for the participant’s response (self-paced training paradigm). Trial-
by-trial feedback was provided by means of a visual cue (green tick for correct, red
‘x’for incorrect) followed by a fixation dot for 500 ms before the onset of the next
trial. Average time to complete a trial ranged between 1.4 s and 1.9s across
participants (1.66 ± 0.16 s for SN; 1.69 ± 0.16 s for FD). Mean trial duration across
participants decreased across MRS blocks for both tasks (LME model for
Trial duration with Task and MRS Block as fixed effects; main effect of Block:
F(1, 113) =4.68, p=0.03) but did not differ between tasks (main effect of Task:
F(1, 113) =0.12, p=0.73; Task × Block: F(1, 113) =0.002, p=0.97), suggesting
that participant became faster in their judgments with training for both tasks. Each
MRS acquisition lasted for 5 min and 56 s, and each MRS training block (400 trials)
for 11 min and 11 s ± 63 s. Thus, in most cases training was completed within the
duration of the training block (i.e., 2 MRS acquisitions × 5 min 56 s). In the event
that the training took longer than the MRS block, the next MRS acquisition was
delayed until completion of the previous training block.
Data acquisition. Experiments were conducted at the Wellcome Centre for Inte-
grative Neuroimaging, using a Siemens 7T Magnetom (Siemens, Erlangen) with a
32-channel head coil.
We acquired structural data (MPRAGE; TR 2200 ms; TE 2.82 ms; slice
thickness 1.0 mm; in-plane resolution 1.0 × 1.0 mm2; GRAPPA factor =4) and
echo planar imaging data (gradient echo-pulse sequences) from 40 slices (TR
2250 ms; TE 28 ms; slice thickness 3.0 mm; in-plane resolution 3.0 × 3.0 mm2;
GRAPPA factor =2, 140 volumes).
We acquired MRS data using a semi-localization by adiabatic selective
refocusing (semi-LASER) sequence73 (64 averages, TR 5010 ms, TE 36 ms). We
measured two MRS voxels (2 × 2 × 2 cm3isotropic), one in the left OCT (OCT
voxel) and one in the left PPC (PPC voxel), avoiding contact with the dura to
minimize macromolecule contamination. We focused on the left hemisphere as
previous fMRI50 and TMS35 studies have shown that the left PPC is involved in
suppressing distracting signals. To cover both areas with the same dielectric pad,
we placed both MRS voxels on the left hemisphere.
Statistical analyses. To compare changes in behavioral performance and neu-
rotransmitter concentrations across blocks, tasks, and MRS voxels, we used a LME
approach. LME models allow modeling of longitudinal data (i.e., multiple mea-
surements over time) and can account for missing values across participants. In
each of the models, we tested both for random (Participants) and fixed (MRS
Block, Voxel, and Task) effects. To relate behavioral improvement to GABA
changes and rs-fMRI connectivity, we computed Pearson skipped correlations
using the Robust Correlation Toolbox74. This method accounts for potential
bivariate outliers and determines statistical significance using bootstrapped con-
fidence intervals (CI) for 1000 permutations. Note that bivariate outliers are not
shown in the data figures. To directly compare the relationship of GABA change
and rs-fMRI connectivity with behavioral improvement between the two tasks, we
used linear regression models with interaction terms. Data distribution assump-
tions of normality and heteroscedasticity of variance were verified using
Shapiro–Wilk and Levene’s tests, respectively. All statistical tests are two sided.
Data availability
Data files have been uploaded on the Cambridge Data Repository: https://doi.org/
10.17863/CAM.33382.
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Received: 16 May 2018 Accepted: 16 December 2018
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Acknowledgements
We would like to thank Adam Berrington and Clark Lemke for help with the data
collection, Joseph Giorgio and Ke Jia for help on preliminary data analysis, Rogier Kievit,
Delia Fuhrmann, and Mark Haggard for advice on statistical analyses, Adrian Garcia for
preliminary work, Jasper Poort for helpful comments. This work was supported by
funding to Z.K. from the Alan Turing Institute, the Biotechnology and Biological Sci-
ences Research Council (grants: H012508, P021255), the European Community’s
Seventh Framework Programme (grant FP7/2007–2013 under agreement PITN-GA
2011–290011), and the Wellcome Trust (grant 205067). C.J.S. holds a Sir Henry Dale
Fellowship, funded by the Wellcome Trust and the Royal Society (102584/Z/13/Z). E.L.
H. is supported by the NIHR Oxford Health Biomedical Research Centre. The Wellcome
Centre for Integrative Neuroimaging is supported by core funding from the Wellcome
Trust (203139/Z/16/Z).
Author contributions
P.F. conceived the study, collected the data, performed the analysis and wrote the
manuscript. U.E.E. provided acquisition and analysis tools for MR Spectroscopy. V.K.
performed the analysis of the rs-fMRI data. C.N., E.L.H. and S.L. collected the data. H.B.
and C.J.S guided the study design and analysis. Z.K. conceived the study, guided the
analysis, and wrote the manuscript.
Additional information
Supplementary Information accompanies this paper at https://doi.org/10.1038/s41467-
019-08313-y.
Competing interests: The authors declare no competing interests.
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