Memories are stored by distributed groups of neurons in the brain, with individual neurons contributing to multiple memories. In a part of the brain called the neocortex, memories are held in a silent state through a balance between excitatory and inhibitory activity. This is to prevent them from being disrupted by incoming information. When a memory is recalled, an area of the brain called the hippocampus is thought to instruct the neocortex to activate the appropriate neuronal network. But how the hippocampus and neocortex coordinate their activity to switch memories ‘on’ and ‘off’ is unclear.

The answer may lie in the fact that neurons in the neocortex consist of two broad types: excitatory and inhibitory. Excitatory neurons increase the activity of other neurons. They do this by releasing a chemical called glutamate. Inhibitory neurons reduce the activity of other neurons, by releasing a chemical called GABA. Koolschijn, Shpektor et al. hypothesized that the hippocampus activates memories by changing the balance of excitatory and inhibitory activity in neocortex.

To test this idea, Koolschijn, Shpektor et al. invited healthy volunteers to explore a virtual reality environment. The volunteers learned that specific sounds in the environment predicted the appearance of particular visual patterns. The next day, the volunteers returned to the environment and viewed these patterns again. After each pattern, they were invited to open a virtual box. Volunteers learned that some patterns led to money in the virtual box, while other patterns did not.

Finally, on day three, the volunteers listened to the sounds from day one again, this time while lying in a brain scanner. The volunteers’ task was to infer whether each of the sounds would lead to money. Given that the sounds were never directly paired with the content of the virtual box, the volunteers had to solve the task by recalling the associated visual patterns. As they did so, the brain scanner measured their overall brain activity. It also assessed the relative levels of excitatory and inhibitory activity in visual areas of the neocortex, by measuring glutamate and GABA.

The results revealed that as the volunteers recalled the visual cues, activity in both the hippocampus and the visual neocortex increased. Moreover, the ratio of glutamate to GABA in visual neocortex also increased which was predicted by activity in the hippocampus. This suggests that the hippocampus reactivates memories stored in neocortex by temporarily increasing excitatory activity to release memories from inhibitory control.

Disturbances in the balance of excitation and inhibition occur in various neuropsychiatric disorders, including schizophrenia, autism, epilepsy and Tourette’s syndrome. Damage to the hippocampus is known to cause amnesia. The current findings suggest that memories may become inaccessible – or may be activated inappropriately – when the interaction between the hippocampus and neocortex goes awry. Future studies could test this possibility in clinical populations.

Memories are thought to be stored across sparse and distributed neuronal ensembles in the brain (Buzsáki, 2010; Josselyn and Tonegawa, 2020). During memory recall, activity across these neuronal ensembles is selectively reinstated to recover enduring representations of the past. This reinstatement is thought to be mediated by the hippocampus, a brain region important for learning and memory (Squire, 1992). Anatomically, the hippocampus sits at the apex of a cortical sensory processing hierarchy (Felleman and Essen, 1991) where inputs received by sensory cortices reach the hippocampus via the entorhinal cortex and other relay regions, which in turn make widespread cortico-cortical connections that project the hippocampal output back to neocortex (Witter, 1993; Witter et al., 1989). This reciprocal anatomical connectivity equips the hippocampus with the necessary architecture to coordinate activity in neocortex. The hippocampus may therefore be considered to provide a ‘memory index’, or summary sketch, for information stored across distributed cortical circuits (Goode et al., 2020; Teyler and DiScenna, 1985; Teyler and Rudy, 2007). Consistent with this view, during memory recall, hippocampal reinstatement predicts subsequent neocortical reinstatement (Pacheco Estefan et al., 2019; Tanaka et al., 2014).

However, the mechanism that allows the hippocampus to coordinate reinstatement across distributed neocortical circuits remains unclear. One possibility is that the hippocampus shapes computations performed by neocortical circuits by modulating the dynamic interplay between excitation and inhibition (EI). At the cellular level, tight coupling between neocortical EI can be observed during both sensory stimulation and spontaneous neural activity (Haider et al., 2006; McCormick et al., 2004; Okun and Lampl, 2008; Wehr and Zador, 2003). This phenomenon has led to the physiological concept of EI balance, where, following changes in excitability, synaptic strength, current, or overall network activity returns to a stable set point via negative feedback (Field et al., 2020). Evidence in humans, animal models, and theoretical models together suggests that EI balance is maintained to hold memories in a silent and dormant state (Barron et al., 2016; Froemke et al., 2007; Vallentin et al., 2016; Vogels et al., 2011), thus protecting memories from interference caused by new learning (Koolschijn et al., 2019; Kuchibhotla et al., 2017). During recall, however, EI balance must be transiently disturbed if memories are to be released from inhibitory control.

Here, we predict that memory recall involves a transient break in EI balance, opening a window to release memories from the blanket of inhibition before network stability is re-established. Moreover, we predict that this transient break in neocortical EI balance is mediated by activity in the hippocampus. To test these predictions, here, we implemented a new imaging sequence in humans that combines functional magnetic resonance imaging (fMRI) with functional magnetic resonance spectroscopy (fMRS) (Ip et al., 2019; Ip et al., 2017). This sequence provides an opportunity to monitor activity in the hippocampus with fMRI while simultaneously measuring time-resolved fluctuations in neocortical glutamate and GABA using fMRS.

MRS provides a unique tool to quantify the concentration of different neural metabolites (De Graaf, 2019; Mangia et al., 2012), including glutamate and GABA, the principle excitatory and inhibitory neurotransmitters in the brain. MRS cannot dissociate between neurotransmitter and metabolic pools of glutamate and GABA (Bak et al., 2006; Magistretti and Allaman, 2015). However, meaningful interpretation of MRS nevertheless derives from a major body of work showing an approximately 1:1 relationship between the rate of glutamine-glutamate cycling, which is necessary for glutamate and GABA synthesis, and neuronal oxidative glucose consumption, which indirectly supports neurotransmitter release among other processes (Rothman et al., 2003; Shen et al., 1999; Sibson et al., 1998). Therefore, while measures of EI balance vary in both definition and granularity, MRS can provide a non-invasive marker for physiologically relevant EI at a coarse spatiotemporal scale. Correspondingly, MRS-derived glutamate and GABA reported during learning and memory paradigms show remarkable consistency with findings reported at the physiological level in animals (Barron et al., 2016; Castro-Alamancos et al., 1995; Floyer-Lea et al., 2006; Froemke et al., 2007; Kolasinski et al., 2019; Lunghi et al., 2015; Trepel and Racine, 2000; Vallentin et al., 2016).

Using the combined fMRI-fMRS sequence, here, we implemented a task designed to engage hippocampal-dependent recall of a visual cue. During memory recall, we report a transient increase in the ratio between MRS-derived glutamate and GABA in neocortex which is selectively predicted by the blood oxygen level-dependent (BOLD) signal in the hippocampus. These findings suggest the hippocampus coordinates memory recall by transiently perturbing neocortical EI balance to release memories stored across distributed neural circuits.

The hippocampus is thought to provide an index for memories stored across distributed neocortical circuits (Goode et al., 2020; Teyler and DiScenna, 1985; Teyler and Rudy, 2007). However, the mechanism by which hippocampal activity coordinates with neocortex to facilitate memory recall has remained unclear. Here, using time-resolved fMRI-fMRS in humans, we show that recall of a visual cue is accompanied by a dynamic increase in the ratio between glutamate and GABA in visual cortex. This transient increase in glu/GABA ratio in visual cortex is selectively predicted by activity in the hippocampus. Accordingly, we propose the hippocampus gates recall of memories stored across distributed neocortical circuits using a disinhibitory mechanism (Figure 5E). This mechanism may explain how a memory index represented by the hippocampus selectively releases otherwise dormant representations stored across distributed neocortical circuits.

By simultaneously acquiring both fMRI and fMRS data, we provide a macroscopic readout of memory recall that reflects the consequence of underlying neural circuit level processes. Insight into the nature of these underlying circuit level processes can be gained from related data from animal models. For example, the neural circuit mechanisms that underlie an increase in glu/GABA ratio during recall may be informed by evidence that the ratio between excitatory and inhibitory synaptic conductances in cortical neurons fluctuate around a stable set point (Anderson et al., 2000; Okun and Lampl, 2008; Wehr and Zador, 2003; Wilent and Contreras, 2005). This overall EI proportionality ensures that neurons and networks are neither hypo- nor hyper-excitable for prolonged periods, allowing memories to be held in a dormant state (Barron et al., 2016; Froemke et al., 2007; Vallentin et al., 2016; Vogels et al., 2011) that is protected from interference caused by new learning (Koolschijn et al., 2019; Kuchibhotla et al., 2017). However, despite overall proportionality, the exact E/I ratio is highly dynamic and transient breaks in EI balance appear necessary for new learning and memory expression (Letzkus et al., 2015). Here, the reported fluctuations in MRS-derived glu/GABA ratio during memory recall may therefore reflect, if indirectly, dynamic changes in EI balance.

Similarly, the reported relationship between the fluctuations in glu/GABA ratio and hippocampal activity may be informed by data from animal models. Of particular relevance are studies in rodents which show that glutamatergic projections from higher-order or interconnected brain regions can target disinhibitory cortical circuits to provide selective EI modulation (Krabbe et al., 2019; Lee et al., 2013; Zhang et al., 2014). For example, to enhance visual discrimination during attentional modulation, projections from the cingulate region of mouse frontal cortex modulate activity in V1 by targeting vasoactive intestinal polypeptide-expressing (VIP+) interneurons, which in turn preferentially target other interneuron subtypes to release excitatory principle cells from inhibitory control (Zhang et al., 2014). During memory recall, hippocampal projections may similarly permit memory reinstatement by targeting disinhibitory circuits in neocortex. The correlation between hippocampal activity and glu/GABA ratio reported here may therefore reflect a mechanism whereby activity in the hippocampus facilitates cortical disinhibition to release otherwise latent cortical associations from inhibitory control.

This interpretation of the data is consistent with the notion that the hippocampus provides a memory index to flexibly reinstate information in extrahippocampal circuits (Goode et al., 2020; Teyler and DiScenna, 1985; Teyler and Rudy, 2007). Moreover, our findings replicate equivalent analyses conducted on fMRI data acquired using the same task (Barron et al., 2020) and are consistent with previous studies in humans showing evidence for coordinated hippocampal-neocortical memory reinstatement (Horner et al., 2015; Pacheco Estefan et al., 2019). When combined with the fMRS data, our results also corroborate findings in humans showing that hippocampal glutamate and GABA can predict mnemonic control (Nikolova et al., 2017; Schmitz et al., 2017). Taken together, we propose a mechanism for hippocampal indexing whereby hippocampal projections control the release of mnemonic representations in sensory cortices by targeting disinhibitory circuits.

Given this interpretation of the data, we emphasise that the relationship between MRS-derived measures of glutamate and GABA and physiological measures of EI balance remains complex. Rapid changes in synaptic glutamate and GABA that accompany neurotransmitter release occur on a timescale that is not possible to detect using fMRS. Moreover, only a fraction of MRS-derived neurometabolite concentration reflects neurotransmitter release. Of the different pools of glutamate and GABA (cytoplasmic, vesicular, or extracellular), MRS is considered most sensitive to unconstrained, intracellular metabolic pools that reside at relatively high concentration in the neuronal cytoplasm (Rae, 2014). Indeed, changes in extracellular GABA of less than 100-fold are unlikely to be detectable using MRS (Myers et al., 2016) and post-mortem studies suggest MRS is not sensitive to intracellular pools that reside in the mitochondria or vesicles (De Graaf and Bovée, 1990; Kauppinen and Williams, 1991).

Interpretation of MRS-derived glutamate and GABA is further complicated by the fact that the release and recycling of glutamate and GABA constitute major metabolic pathways (Bak et al., 2006; Magistretti and Allaman, 2015). Yet, the metabolic and neurotransmitter pools are thought to be tightly coupled during anaesthesia, rest and certain stimulation protocols, with a 1:1 relationship reported between the rate of glutamine-glutamate cycling, which is necessary for glutamate and GABA synthesis, and neuronal oxidative glucose consumption, which indirectly supports neurotransmitter release among other processes (Rothman et al., 2003; Shen et al., 1999; Sibson et al., 1998). Therefore, an increase in synaptic neurotransmission occurs together with an increase in synthesis of exogenous glutamate, which provides a precursor for GABA via the glutamate-glutamine cycle. During sensory stimulation a transient uncoupling has been observed with a short-lived mismatch between glucose utilization and oxygen consumption (Fox et al., 1988; Fox and Raichle, 1986), particularly during stimulation protocols that alternate between high intensity and quiescent periods (Gjedde et al., 2002). Dynamic fluctuations in fMRS-derived glutamate and GABA reported here may therefore reflect transitions to new metabolic steady states (Stanley and Raz, 2018), which could reflect (if indirectly) relative shifts in EI equilibrium at the physiological level. During associative memory recall, the increase in glu/GABA ratio may therefore be interpreted as an increase in synthesis of glutamate relative to degradation, with an opposing effect on GABA.

This interpretation is supported by a handful of previous studies showing event-related changes in MRS glutamate (Apšvalka et al., 2015; Gussew et al., 2010; Lally et al., 2014) and GABA (Cleve et al., 2015), together with a growing body of evidence reporting a relationship between MRS-derived measures of neurometabolites and behaviour (Puts et al., 2011; Scholl et al., 2017; Stagg et al., 2011). Nevertheless, it remains to be established whether unconstrained glutamatergic and GABAergic pools show event-related changes that are MRS-sensitive. To validate this interpretation of event-related fMRS, it is important to leverage animal studies where more sensitive methods can be employed to relate fMRS measures to physiological parameters. Here, by implementing an inference task in VR, we operationalize memory recall using the exact same paradigm previously employed in rodents (Barron et al., 2020). Therefore, in addition to engaging memory-dependent inference, ‘opening the box’ to find a reward in the VR environment approximated the process of rodents finding a reward from a dispenser in a 3D environment. By using VR, the findings presented here may be compared to data acquired in animal models in ongoing future research. In this manner, VR paradigms in humans may provide a basis from which to gain insight into the cellular and circuit mechanisms that underlie macroscopic measures of EI. This may prove particularly useful for establishing a more detailed understanding of the relationship between fMRS-derived measures of glutamate and GABA and physiological measures of EI balance.

Previous MRS protocols typically employ a ‘block’ design, where a static measure of the concentration of glutamate and GABA is achieved by averaging the spectra across a time window that may span several minutes. This approach obscures the temporal dynamics of neurometabolites which more closely relate to fluctuations in EI reported at the physiological level. Similarly, dynamic changes in neurometabolites that accompany cognitive processes and ongoing behaviour are overlooked. Indeed, when the average concentration of Glx and GABA are considered in V1 across time, no significant relationship is observed across subjects (Rideaux, 2021), a result which we also observed when assessing average glutamate and GABA using our dataset. By contrast, with the increase in availability of ultra-high field MRI scanners and the development of more advanced sequences (Stagg and Rothman, 2013), fMRS has emerged as a viable method to detect dynamic changes in neurochemicals in both healthy and clinical populations (Stanley and Raz, 2018).

Although there are currently only a handful of event-related fMRS studies, together with our data, these suggest that fMRS is highly sensitive to detecting task-relevant dynamic changes in glutamate and GABA (Jelen et al., 2018). For example, in the lateral occipital complex, fMRS demonstrates differences in glutamate in response to presentation of objects versus abstract stimuli (Lally et al., 2014), and in the left anterior insula fMRS reveals a transient increase in glutamate with exposure to painful stimuli (Gussew et al., 2010). fMRS-derived glutamate is even sufficiently sensitive to detect repetition suppression effects in the lateral occipital complex (Apšvalka et al., 2015), mirroring analogous effects reported in fMRI (Barron et al., 2016; Grill-Spector et al., 2006). Here, we further illustrate that within a 3 s time window delineated by the question period in the inference task, the temporal resolution of fMRS is sufficient to relate transient changes in glutamate and GABA to associative memory recall. Importantly, we compare data across two conditions (‘remembered’ and ‘forgotten’) to inherently control for: (1) between-subject differences in average GABA and glutamate which are affected by demographic (e.g. age and gender); (2) between-subject differences in spectral quality; (3) between-subject differences in tissue composition; (4) between-subject differences in the effect of other neurochemicals on measures of glutamate and GABA. Such time-resolved, within-subject, and condition-dependent fMRS may provide a promising tool to capture real-time, task-relevant changes in neurometabolites, on a timescale equivalent to task-based fMRI. Assessing whether the temporal resolution of fMRS can be further improved will likely prove an important step in refining fMRS in the future.

During associative memory recall, the transient increase in glu/GABA ratio reported in our data can be accounted for by a significant decrease in the concentration of MRS-derived GABA. Notably, detecting dynamic changes in GABA is challenging for two key reasons: the concentration of GABA in human brain tissue is relatively low and the spectral peaks for GABA overlap with other, more abundant neurochemicals (Andreychenko et al., 2012; Govindaraju et al., 2000; Puts and Edden, 2012). While the most common approach to detecting MRS-derived GABA involves using a J-difference spectral editing technique to separate GABA peaks from overlapping peaks (Bottomley, 1987; Mescher et al., 1998), here we use a non-edited sequence (sLASER). While spectral editing may provide higher precision (Hong et al., 2019), this occurs at the cost of a larger volume of interest, longer TEs and higher susceptibility to motion and drift artefacts due to longer acquisition times, making it less suitable for event-related fMRS (Terpstra et al., 2006; Trabesinger and Boesiger, 2001). Moreover, direct comparisons between edited and non-edited sequences at 7 T reveal no significant difference in the concentration of GABA measurements (Hong et al., 2019). Therefore, together with studies reporting dynamic changes in GABA with sensory stimulation (Lin et al., 2012; Mekle et al., 2017), our data illustrates how a non-edited sequence can provide sufficient data quality for measuring dynamic changes in MRS-derived GABA, which cannot be explained by changes in compounds at higher concentration that have overlapping peaks (i.e. glutamate or NAA, Figure 4—figure supplement 7). Indeed, Monte Carlo simulations reported here validate that non-edited sequences can be used to quantify dynamic changes in GABA (Figure 4G; Figure 4—figure supplement 2). Moreover, compared to spectral editing, our approach comes with the advantage of simultaneously measuring dynamic changes in GABA and glutamate, together with 17 other neurometabolites.

To detect dynamic changes in GABA, it was necessary to disable default priors on the spectral fitting procedure that constrain GABA as a ratio to more stable metabolite concentrations (Figure 4—figure supplement 2, see Appendix 1—supplementary note 1). As a consequence, we were able to detect dynamic changes in both glutamate and GABA across time, as illustrated using Monte Carlo simulations and permutation testing. By comparing the change in metabolite concentration between two conditions (‘remembered’ versus ‘forgotten’), the ratio in GABA between conditions rather than absolute values was the key measure of interest. However, we note that absolute GABA estimates were higher compared to those obtained using default priors that normalise estimates relative to more stable metabolite concentrations. Importantly, the quality of our MRS data was comparable with other studies that have acquired 7 T MRS data from visual cortex (Bednařík et al., 2018; Hong et al., 2019; Mekle et al., 2017; Prinsen et al., 2017). Moreover, the quality of the glutamate estimates was in line with previous studies employing event-related fMRS to assess dynamic changes in glutamate (Apšvalka et al., 2015; Gussew et al., 2010; Lally et al., 2014).

Disturbances in EI balance are thought to underlie a number of neuropsychiatric conditions, including schizophrenia, autism, epilepsy, and Tourette’s syndrome (Robertson et al., 2016; Stanley and Raz, 2018; Taylor et al., 2015). While previous studies report inconsistencies in MRS-derived measures of glutamate and GABA in these patient populations, this may be attributed to differences in brain region, cognitive state, and imaging protocol, among other factors. Here, by using both fMRS and fMRI to reveal a signature change in glu/GABA ratio that relates to hippocampal BOLD signal, behavioural performance, and cognition, our findings present a potential target for clinical investigation. Moreover, our findings show that even in the healthy brain a transient break in EI balance is necessary to support key cognitive processes such as memory recall.

In summary, using time-resolved fMRI-fMRS, we report a transient increase in glu/GABA ratio in V1 during associative recall of a visual cue. This increase in neocortical glu/GABA ratio is predicted by activity in the hippocampus. By unveiling this coordination between the hippocampus and neocortex, we show how the hippocampus may have the capacity to selectively modulate and disinhibit memories represented in neocortex. This mechanism may explain how the hippocampus plays a key role in memory recall, by indexing the release of specific memories stored across distributed neocortical circuits.

All data generated and analysed during this study are included in the manuscript and supporting files. The data and code used in this study are available via the MRC BNDU Data Sharing Platform upon registration. The data is available via https://data.mrc.ox.ac.uk/data-set/fmri-fmrs-inference. The code is available via https://data.mrc.ox.ac.uk/data-set/frms-code.

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Author details

1. Renée S Koolschijn

   Wellcome Centre for Integrative Neuroimaging, University of Oxford, FMRIB, John Radcliffe Hospital, Oxford, United Kingdom

   Contribution

   Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review and editing

   Contributed equally with

   Anna Shpektor

   For correspondence

   renee.koolschijn@keble.ox.ac.uk

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-9553-4213

2. Anna Shpektor

   Wellcome Centre for Integrative Neuroimaging, University of Oxford, FMRIB, John Radcliffe Hospital, Oxford, United Kingdom

   Contribution

   Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Software, Validation, Visualization, Writing – original draft, Writing – review and editing

   Contributed equally with

   Renée S Koolschijn

   Competing interests

   No competing interests declared

3. William T Clarke

   Wellcome Centre for Integrative Neuroimaging, University of Oxford, FMRIB, John Radcliffe Hospital, Oxford, United Kingdom

   Contribution

   Formal analysis, Methodology, Software, Validation, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-7159-7025

4. I Betina Ip

   Wellcome Centre for Integrative Neuroimaging, University of Oxford, FMRIB, John Radcliffe Hospital, Oxford, United Kingdom

   Contribution

   Formal analysis, Methodology, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0003-3544-0711

5. David Dupret

   Medical Research Council Brain Network Dynamics Unit, University of Oxford, Oxford, United Kingdom

   Contribution

   Conceptualization, Funding acquisition, Methodology, Supervision, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-0040-1766

6. Uzay E Emir

   1. Wellcome Centre for Integrative Neuroimaging, University of Oxford, FMRIB, John Radcliffe Hospital, Oxford, United Kingdom
   2. School of Health Sciences, Purdue University, West Lafayette, United States

   Contribution

   Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review and editing

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0001-5376-0431

7. Helen C Barron

   1. Wellcome Centre for Integrative Neuroimaging, University of Oxford, FMRIB, John Radcliffe Hospital, Oxford, United Kingdom
   2. Medical Research Council Brain Network Dynamics Unit, University of Oxford, Oxford, United Kingdom

   Contribution

   Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review and editing

   For correspondence

   helen.barron@merton.ox.ac.uk

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-4575-6472

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