This review summarizes research on the dynamics of MRS-assessed GABA (and, when available, Glu or Glx) in humans following various types of intervention and their associations with or implications for behavioral performance. Figure 3 summarizes the interventions that have been reported to induce GABA modulations in the human brain.

Fig. 3

The presumed associations reported in current literature between human behavior and GABA modulation in brain areas, including the cortex, subcortex, and cerebellum. Please note that the studies reporting no GABA modulation were not visualized in this figure

GABA modulation in human learning

The regulation of GABAergic activity is widely considered a crucial process in facilitating plasticity and learning. In recent work reviewing evidence for the role of ‘baseline GABA’ in behavior, we proposed the GABA-distinctiveness hypothesis, which implies that maintaining appropriate neural suppression in perceptual processing regions via higher baseline GABA levels is associated with more distinctive perceptual performance [6]. Consistent with this hypothesis, the present review identified converging evidence for a beneficial role for the task-induced GABA increase in effectively discerning subtle perceptual differences and possibly building distinct perceptual neural representations in the context of perceptual feature difference learning tasks (section "Perceptual plasticity"). We refer to this as the GABA increase for better neural distinctiveness hypothesis.

Taken together, we tentatively speculate that higher GABA levels at baseline and the increase of GABA levels during task performance may improve the distinctiveness of neural representations. In this context, representations mainly refer to the way that internal or external information is encoded and stored in the brain. This can be expressed at various levels, i.e., from the firing patterns of individual neurons to the activation of complex brain networks.

From the perspective of the cellular level, the distinctiveness of neural representations might be achieved through intricate adjustments and refinements in the functioning and connectivity of neurons, which is known as neuronal fine-tuning. For example, animal studies showed that the tuning curves of neurons are improved as a result of the administration of GABA and GABA agonists. More specifically, in a study by Leventhal et al. [71], it was observed that neuronal tuning became increasingly selective in old monkeys after the administration of GABA and its agonists, resembling the tuning functions observed in younger counterparts [71]. These GABAergic functional changes ultimately resulted in enhanced visual function. This evidence appears to support the GABA increase for better neural distinctiveness hypothesis.

From the perspective of brain activity using fMRI techniques, a higher distinctiveness of neural representations can become expressed by lower levels of overlap among representations as a function of different task conditions. For example, a number of studies have shown that the distributed patterns of brain activation elicited by visual stimuli [72, 73] or motor tasks [74] are less selective in older than in young adults. Although the role of GABA was not tested in the abovementioned studies, it can be speculated that the age-related decline in baseline GABA levels is (at least) partially responsible for the reduced selectiveness of patterns of brain activity in older adults. To directly test the GABA increase for better neural distinctiveness hypothesis, future studies could integrate MRS and fMRI techniques. This integration would allow us to examine the potential correlation between increased GABA levels and reduced overlap in brain activation patterns for different stimuli or task conditions as behavioral skills develop. Additionally, randomized controlled trials combining MRS techniques and pharmacological interventions may offer a promising approach to establishing causal relationships between neuronal GABA levels and human behavior.

However, in contrast to the aforementioned studies supporting a beneficial effect for GABA increases in neural distinctiveness, findings of other studies suggested beneficial effects of training-induced GABA reduction, not only in enhancing perceptual noise filtering capacity (section "Perceptual plasticity" ) but also in (visuo)motor task improvement (section "Motor learning" ). Additionally, limited evidence provides first hints that the reduction of GABA levels is related to better working memory and successful (reinforcement) learning, again suggesting a beneficial role for GABA reduction in learning (section "Attention and working memory" and section "Associative learning" ). Therefore, we coin this as the GABA decrease to boost learning hypothesis, which implies that decreasing neural inhibition through a reduction of GABA boosts human learning.

Regarding the underlying mechanisms, the reduction of GABA levels may decrease the overall inhibition among neuronal ensembles, thereby promoting neural communication and reorganization as underlying mechanisms of human learning. It has been established that the temporary activation of the N-methyl-D-aspartate (NMDA) receptor system is essential for the induction of LTP [75], which serves as the neural basis for learning. Notably, the activation of the NMDA receptor system could be modulated by GABA-mediated synaptic inhibition. This modulation occurs through the effect of GABAB autoreceptors on the presynaptic membrane. By exerting an effect on these receptors, the presynaptic cell inhibits its own GABA release, depolarizing the postsynaptic membrane and facilitating adequate activation of NMDA receptors, permitting the induction of LTP [76]. Given the relationship observed between MRS-assessed GABA levels and human behavior, one might speculate that when a decrease in GABA release from the pre-synaptic neuron is induced by human behavior, the synaptic GABA concentration diminishes, consequently resulting in reduced MRS-assessed GABA levels. This mechanism may account for the consistent GABA reduction observed through MRS in numerous learning conditions. Nevertheless, there may also be other mechanisms coming into play.

GABA modulation according to the different phases of learning

MRS studies in the field of learning have primarily addressed the early stage in the process of encoding, storage, and retrieval of information that takes place during task practice. However, learning also encompasses a phase in which temporary, fragile memories are transformed into a more stable, long-lasting form, known as memory consolidation. Preliminary findings indicate that modulation direction of GABA may vary according to the specific phase (early or late) of task learning. For example, the OCC Glu/GABA ratio exhibited an increase following the learning phase of a visual learning task, whereas it demonstrated a decrease after the overlearning phase [26]. More specifically, within the domain of perceptual learning, only two studies have delved into the role of GABA in learning stabilization or consolidation. The preliminary findings point to a beneficial effect of increased GABA levels in aiding the fast online (not sleep-dependent) consolidation of acquired information and guarding against subsequent interference. Previous studies that made use of zolpidem, a GABAA agonist, to modulate sleep patterns during a daytime nap and overnight sleep showed an improvement in hippocampal-dependent episodic memories after this pharmacological intervention [77, 78], suggesting that increased GABA activity during sleep is related to memory consolidation. Currently, the limited studies on MRS and learning suggest that higher inhibition induced by increased GABA levels during learning supports consolidation and guards against interference. Further research is certainly necessary to validate and substantiate these findings.

Although GABA modulation has been observed in various learning conditions, it is noteworthy that such changes are not unique to learning. GABA modulation has been observed in other conditions as well, as discussed below.

GABA modulation in other human behaviors besides learning

In addition to the study of learning processes, GABA modulation has been observed in some conditions with an implicit learning component as well as in various non-learning conditions, such as exposure to perceptual stimulation, physical exercise, and performance of cognitive tasks.

In the context of perceptual stimulation, a form of adaptive plasticity in perceptual processing areas can be induced. Specifically, the release from inhibition caused by the modulation of Glx and/or GABA co-occurs with most of the stimulation paradigms (3.1 perceptual stimulation). This is in line with results from animal studies, which have shown that stimulus-specific adaptation in the perceptual cortex is exhibited by both excitatory neurons and inhibitory interneurons [79, 80]. Specifically, the neuron’s response to a frequently occurring perceptual stimulus differs from its reaction to a rare stimulus, and this stimulus-specific neuroplasticity is mediated by the excitatory and inhibitory system. At the cellular level, the substantial facilitation caused by an increase in excitation and/or reduction of inhibition is thought to enhance overall cortical responsiveness to perceptual task stimuli [81]. Hence, the release from inhibition (facilitation) in perceptual brain regions induced by visual stimuli may be beneficial for general perceptual information processing.

In the context of physical exercise, increases in GABA have been observed following intensive short-term cardiovascular endurance activity, such as cycling, but GABA modulation has also been connected with yoga. Conversely, decreases in GABA have also been observed after, for example, repeated hand movement practice (section "Physical exercise" ). This suggests that the brain’s adaptive plasticity may vary in response to different types of physical activity intervention. Lastly, it remains to be studied whether modulations of GABA following an exercise intervention benefit subsequent behavioral performance and learning of motor or other tasks. For example, can exercise-induced GABA modulation create the optimal conditions for increased neuronal interactions associated with learning new skills (section "Perceptual plasticity" and section "Motor learning" )?

In the context of cognition, increases in GABA levels have been observed in tasks with high cognitive load levels, such as conflict resolution (section "Inhibition and self-regulation" ). Nevertheless, given the notable variations in cognitive task subtypes, it is premature to draw firm conclusions at this stage.

GABA modulation varies according to the function of brain areas

Converging evidence suggests that GABA modulation exhibits brain regional specificity. On the one hand, this refers to a dynamic GABA modulation in those brain areas that are involved in the performance of a given task, while no strong modulation occurs in brain areas that are less relevant for that task. For instance, given that the SM1 brain region serves as the primary brain region for motor control, it is expected that cycling training results in modulations of GABA levels in the SM1 region and not in the cognitive processing-related brain regions [36]. On the other hand, different directions of GABA modulation in the task-involved brain areas may take place. For example, during the signal-in-noise (SN) task requiring participants to discern the presented patterns embedded in noisy dots, GABA levels increased in the PPC, whereas they decreased in the OCT [23]. Future research should address whether broader changes across the entire brain (global changes) can also be induced by interventions or whether they primarily affect specific brain regions (local changes). Furthermore, under which circumstances can local versus global changes in neurochemical modulation be accomplished?

The effect of perceptual stimulus complexity, task difficulty and cognitive demand on GABA modulation

Using fMRI, a number of studies have shown that the scope of brain activity in the multiple demand system varies depending on the difficulty of the behavioral task [82,83,84]. Nevertheless, there remains a gap in research specifically designed to directly investigate the modulation of MRS-assessed GABA in response to variations in behavioral task difficulty.

In the perceptual domain, it has been shown that presentation of an experimental stimulus, such as a checkerboard or wedges, as compared to a simple fixation cross, is more likely to induce GABA modulation [9,10,11]. Additionally, presentation of abnormal perceptual stimulation, such as monocular deprivation [29], seems to better induce GABA modulation as compared to often used experimental stimuli, such as a checkerboard.

The motor tasks addressed in this review included visuomotor coordination learning, motor sequence learning, visuomotor adaptation learning, and various types of physical exercise, etc. Thus, comparing the difficulty of motor tasks across studies becomes challenging. However, it was reported that training of a visually-guided bimanual coordination task resulted in decreased OCC GABA levels in the more challenging (random practice) as compared with the less challenging (blocked practice) motor learning condition [48].

In the cognitive domain, across studies, it appears that GABA modulation occurs in working memory tasks with relatively higher cognitive load rather than those with lower cognitive load [61, 62]. However, attempts to induce GABA modulation by manipulating visual attentional load in a visual tracking study were unsuccessful [60].

Overall, the current state of the literature does not provide sufficient evidence to draw definitive conclusions on the relationship between task complexity or difficulty and GABA modulation. Future studies may look into GABA modulations of more generic (pre-frontal) brain regions, such as DLPFC, that become activated when increased cognitive effort is required, especially during the initial phase of various types of learning.

Factors influencing the accuracy of MRS-assessed GABA

Quantifying GABA levels accurately is essential and a prerequisite for exploring their associations with behavior. Thus, it is crucial to assess whether proper MRS measurement techniques have been used in the reported studies. Although a detailed comparison of the outputs of different MRS techniques falls outside the main scope of this review, here we list several factors that affect MRS measurement accuracy, including MR field strength, MR sequence type and the methodology employed to eliminate the macromolecule contamination. Additionally, we provide suggestions for improving the overall accuracy of GABA measurement in practice.

MR field strength

Higher MR field strength leads to better signal-to-noise ratios, which contributes to higher MRS measurement accuracy. Therefore, studies conducted at ultra-high MR field strengths (≥ 7T) are likely to obtain more sensitive and reliable results than those conducted under 3T or 1.5T. In this review, 63.4% of the included articles utilized a 3T magnetic field strength, 31.7% employed a 7T field strength (31.7%) and 4.9% employed a 4T field strength.

MRS sequence type

GABA signal overlaps with signals from other molecules in the brain. Therefore, accurate quantification of GABA requires the employment of an edited sequence that can isolate GABA signals from those of overlapping molecules. The edited MRS sequences exploit known J-coupling relationships to distinguish signals originating from low-concentration metabolites, such as GABA, from stronger overlapping signals [85].

Detailed distribution of the type of MR sequences used in the reviewed studies is reported in Table 2. It is noteworthy that over half of the included studies (56%) utilized an edited sequence. Furthermore, the most commonly employed edited sequence was MEGA-PRESS, accounting for 78.3% of the reported studies utilizing the edited sequence. Less than half of the included studies (44%) used a non-edited sequence, which delivers broader spectral information from multiple metabolites rather than focusing specifically on GABA. The choice of non-edited sequences varies across studies, with no clear preference for any specific sequence.

Table 2 The employed MR sequences in the reviewed studiesMacromolecule contamination

Although spectral editing allows the detection and quantification of GABA, the edited signal contains a significant contribution from the macromolecular signal when using conventional editing approaches. It is important to note that failure to account for macromolecule contamination may lead to inaccurate estimation of metabolite levels and misinterpretation of their relationship with human behavior. While macromolecule contamination is an acknowledged problem, there is currently no clear consensus on the best approach to tackle it. Methods used in the correction of macromolecules in the reviewed studies are reported in Table 2. Among the 41 studies analyzed, only 16 employed methods to eliminate the contamination of macromolecules, of which six studies employed an inversion-recovery sequence to acquire a macromolecule spectrum during data collection; two studies used a symmetric-suppression editing, and eight studies used a set of macromolecule basis functions during fitting. A detailed discussion of these three techniques and their caveats has been reported by Mullins et al. [86]. Even though over half of the included studies did not account for contaminating macromolecule signals, it is assumed that this is not a major confounder for studies focused on GABA changes (using repeated measures) rather than those reporting a one-time measurement. This is because macromolecule signals are believed to be relatively stable within healthy participants during multiple measurements in fMRS studies.

MRS measurement time points

The precise time course of neurochemical modulations is still unclear, and therefore, no definite consensus exists on the temporal resolution that is necessary to detect neurometabolic fluctuations with optimal sensitivity and reliability. Stated differently: what is the critical window for GABA modulation to occur in the context of task performance and learning? It appears likely that GABA modulation occurs within milliseconds, but changes over minutes, hours, days, or weeks can also possibly take place. This prompts questions about setting up the appropriate experimental paradigms for capturing the window of change that scientists wish to investigate while taking into account the current MRS imaging constraints (relatively long measuring time: 8–10 min for each brain area).

Figure 2 provides an overview of the MRS timing designs in various studies, summarized as pre-post, pre-during, pre-during-post, and continuous measurement during task performance. It is important to mention that even with similar MRS timing designs, the exact measurement times may differ across studies. For example, most of the studies conducted the post measurement immediately after the end of training, but a few studies acquired MRS data with some time delay following the end of training. The time gap between task completion and MRS measurement may impact results because GABA levels could return to baseline over time. Moreover, adaptation and/or habituation processes may occur rapidly such that the concentrations of neurometabolites recover to the resting state level before or during the post-MRS measurement. In addition, MRS measures assessed during task performance can show different modulation directions depending on the different phases of task performance/learning. Accordingly, scientists should give careful consideration to the design of behavioral tasks when planning the timing of the MRS acquisition and provide highly detailed, accurate, and replicable descriptions of their experimental setup when reporting their findings.

Recent progress in event-related fMRS techniques allows continuous collection of MRS data at a time resolution of seconds during the presentation of intermixed experimental conditions in a sequence of trials [87]. As compared to measuring the GABA modulation via repeated measures, this event-related method appears very promising to detect the dynamics of neurometabolites with a comparably high temporal resolution. However, the other side of the temporal spectrum is also of interest and addresses questions about the sustainability of training-induced neurochemical changes. At least some evidence suggests that neurochemical modulation occurs across a time window of weeks of training, even though it is not yet clear how neurochemical concentrations evolve after training has been completed [47]. It is important to gain deeper insights into the temporal characteristics of task-related neurochemical fluctuations in order to decide upon the appropriate MRS measurement protocol.

Calculating GABA modulation

Throughout the reviewed studies, substantial inconsistencies exist with regard to the methods employed to assess the modulation of GABA at a group level. Here, we use ‘scan 1’ to refer to the first MRI scan across these studies, which is often measured during a resting state before the task or intervention starts. We use ‘scan 2’ to refer to the subsequent MRI scans, which are often measured during or after the performance or training of a task. Initially, most studies (around 60%) addressed the “absolute” GABA changes, which are calculated by the difference between ‘scan 2’ and ‘scan 1’ (scan 2-scan 1). In contrast, around 36% of the studies took the baseline GABA levels into consideration to normalize or standardize the GABA modulation, i.e., GABA modulation was obtained by calculating the difference between ‘scan 2’ and ‘scan 1’, divided by ‘scan 1’ [(scan 2- scan 1)/scan 1]. Furthermore, only a limited number of studies (about 4%) reported their results using both calculation methods. There is currently no gold standard for which method to use. However, it is important to acknowledge that results obtained through different calculation procedures are not always consistent.

Individual differences approach

Even though task- or training-induced changes in GABA and/or Glx at the group level have been demonstrated, it is fruitful to complement this with an individual differences approach to explore whether changes in neurometabolites exhibit different directions across individuals. The same experimental manipulation may cause increases, decreases, or no changes in neurometabolites in different individuals. This prompts questions about the potential interactions between baseline levels of neurometabolites and the directions of modulation as well as their range of modulation. For example, does a higher baseline level of GABA in learners imply that they have a larger window for change available to reduce GABA during task practice? Will the larger modulation further promote inter-neuronal interactions and increase neural plasticity? Preliminary evidence provides some hints that this might be the case. A motor sequence learning study in older adults revealed that higher baseline GABA levels were associated with a larger training-induced reduction of GABA, even though the age of the participants may potentially have mediated this effect. Moreover, in the latter study, a greater reduction of GABA levels following motor training was related to a greater learning magnitude [52]. However, it is noteworthy that the dynamics of neural circuits are highly intricate and non-linear [88, 89], and hence the brain-behavior associations might also follow a non-linear and more complex relationship. Therefore, further research is required to confirm such findings and to explore whether causal interventions to boost baseline GABA promote task-induced GABA change. This may have important implications for those individuals confronted with decreased GABA as a result of normal aging [52,53,54] or in pathological conditions associated with acute or chronic GABA depletion [90, 91].

GABA, Glu and E/I balance

In the present review, GABA levels were the primary focus because recent studies have suggested that GABA-induced disinhibition is tightly related to the learning process [50]. However, it is crucial to bear in mind that the majority of GABA is synthesized directly from Glu [92]. Consequently, there may be an association between GABA and Glu levels during the resting state. Additionally, the modulation of GABA levels may coincide with changes in Glu levels, influencing E/I balance. Moreover, the optimal MRS sequences for GABA and Glu may differ. Therefore, measuring both metabolites simultaneously using a sequence targeted at either GABA or Glu may reduce the accuracy of the other.

Concerning the associations between baseline MRS-assessed GABA and Glu (or Glx) levels, previous research has yielded inconsistent findings [93,94,95]. More specifically, while Rideaux discovered evidence against a positive correlation in both the visual and motor cortices, Steel et al. showed a positive link between MRS-assessed GABA + and Glx levels in the posterior cingulate gyrus. These discrepancies could be explained by different functions of the brain areas investigated. However, the latest work using a large sample size attempting to resolve this inconsistency showed that there is a regionally non-specific common ratio between MRS-assessed GABA and Glu levels [96]. Overall, these results suggest the existence of a inter-individual common ratio between baseline GABA and Glu levels. Concerning the associations between the modulation of MRS-assessed GABA and Glu (or Glx), to the best of our knowledge, there is only one study addressing this question and reporting a positive correlation between both [56].

Here, we summarized and reported the associations between the modulation of MRS-assessed GABA and various human behaviors. Since this is still a relatively new area of research and the current literature focusing on GABA does not always report the E/I balance, we cannot provide conclusive evidence on the effect of GABA modulation on Glu modulation and how the E/I balance changes along with behavior.

We recommend that future studies investigating the associations between human behavior and E/I balance also report the results on independent neuro-metabolites, such as GABA and Glu. This would enable us to discern which metabolites drive the observed correlations between E/I balance and behaviors.