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.

1. 1. Koolschijn RS
   2. Shpektor A
   3. Clarke WT
   4. Betina Ip I
   5. Dupret D
   6. Emir UE
   7. Barron HC

   (2021) MRC BNDU Data sharing platform

   Combined fMRI-fMRS dataset in an inference task in humans.

   https://doi.org/10.5287/bodleian:vmJOOm7KD
2. 1. Koolschijn RS
   2. Shpektor A
   3. Clarke WT
   4. Dupret D
   5. Emir UE
   6. Barron HC

   (2021) MRC BNDU Data Sharing Platform

   Code used for analysis of task-relevant, time-resolved functional Magnetic Resonance Spectroscopy (fMRS).

   https://doi.org/10.5287/bodleian:8JwYayQmD

1. 1. Anderson JS
   2. Carandini M
   3. Ferster D

   (2000) Orientation tuning of input conductance, excitation, and inhibition in cat primary visual cortex

   Journal of Neurophysiology 84:909–926.

   https://doi.org/10.1152/jn.2000.84.2.909

   • PubMed
   • Google Scholar
2. 1. Andreychenko A
   2. Boer VO
   3. de Castro CSA
   4. Luijten PR
   5. Klomp DWJ

   (2012) Efficient spectral editing at 7 T: GABA detection with MEGA-sLASER

   Magnetic Resonance in Medicine 68:1018–1025.

   https://doi.org/10.1002/mrm.24131

   • PubMed
   • Google Scholar
3. 1. Apšvalka D
   2. Gadie A
   3. Clemence M
   4. Mullins PG

   (2015) Event-related dynamics of glutamate and BOLD effects measured using functional magnetic resonance spectroscopy (fMRS) at 3 T in a repetition suppression paradigm

   NeuroImage 118:292–300.

   https://doi.org/10.1016/j.neuroimage.2015.06.015

   • PubMed
   • Google Scholar
4. 1. Bak LK
   2. Schousboe A
   3. Waagepetersen HS

   (2006) The glutamate/GABA-glutamine cycle: aspects of transport, neurotransmitter homeostasis and ammonia transfer

   Journal of Neurochemistry 98:641–653.

   https://doi.org/10.1111/j.1471-4159.2006.03913.x

   • PubMed
   • Google Scholar
5. 1. Barron HC
   2. Vogels TP
   3. Emir UE
   4. Makin TR
   5. O’Shea J
   6. Clare S
   7. Jbabdi S
   8. Dolan RJ
   9. Behrens TEJ

   (2016) Unmasking Latent Inhibitory Connections in Human Cortex to Reveal Dormant Cortical Memories

   Neuron 90:191–203.

   https://doi.org/10.1016/j.neuron.2016.02.031

   • PubMed
   • Google Scholar
6. 1. Barron HC
   2. Reeve HM
   3. Koolschijn RS
   4. Perestenko PV
   5. Shpektor A
   6. Nili H
   7. Rothaermel R
   8. Campo-Urriza N
   9. O’Reilly JX
   10. Bannerman DM
   11. Behrens TEJ
   12. Dupret D

   (2020) Neuronal computation underlying inferential reasoning in humans and mice

   Cell 183:228–243.

   https://doi.org/10.1016/j.cell.2020.08.035

   • PubMed
   • Google Scholar
7. 1. Bednařík P
   2. Tkáč I
   3. Giove F
   4. Eberly LE
   5. Deelchand DK
   6. Barreto FR
   7. Mangia S

   (2018) Neurochemical responses to chromatic and achromatic stimuli in the human visual cortex

   Journal of Cerebral Blood Flow and Metabolism 38:347–359.

   https://doi.org/10.1177/0271678X17695291

   • PubMed
   • Google Scholar
8. 1. Bosch SE
   2. Jehee JFM
   3. Fernández G
   4. Doeller CF

   (2014) Reinstatement of Associative Memories in Early Visual Cortex Is Signaled by the Hippocampus

   The Journal of Neuroscience 34:7493–7500.

   https://doi.org/10.1523/JNEUROSCI.0805-14.2014

   • PubMed
   • Google Scholar
9. 1. Bottomley PA

   (1987) Spatial Localization in NMR Spectroscopy in Vivo

   Annals of the New York Academy of Sciences 508:333–348.

   https://doi.org/10.1111/j.1749-6632.1987.tb32915.x

   • PubMed
   • Google Scholar
10. 1. Brink WM
    2. Webb AG

    (2014) High Permittivity Pads Reduce Specific Absorption Rate, Improve B1 Homogeneity, and Increase Contrast-to-Noise Ratio for Functional Cardiac MRI at 3 T

    Magnetic Resonance in Medicine 71:1632–1640.

    https://doi.org/10.1002/mrm.24778

    • PubMed
    • Google Scholar
11. 1. Brogden WJ

    (1939) Sensory pre-conditioning

    Journal of Experimental Psychology 25:323–332.

    https://doi.org/10.1037/h0058944

    • Google Scholar
12. 1. Bunsey M
    2. Eichenbaum H

    (1996) Conservation of hippocampal memory function in rats and humans

    Nature 379:255–257.

    https://doi.org/10.1038/379255a0

    • PubMed
    • Google Scholar
13. 1. Buzsáki G

    (2010) Neural syntax: cell assemblies, synapsembles and readers

    Neuron 68:362–385.

    https://doi.org/10.1016/j.neuron.2010.09.023

    • PubMed
    • Google Scholar
14. 1. Castro-Alamancos MA
    2. Donoghue JP
    3. Connors BW

    (1995) Different forms of synaptic plasticity in somatosensory and motor areas of the neocortex

    The Journal of Neuroscience 15:5324–5333.

    https://doi.org/10.1523/JNEUROSCI.15-07-05324.1995

    • PubMed
    • Google Scholar
15. 1. Clarke WT
    2. Stagg CJ
    3. Jbabdi S

    (2021) FSL-MRS: An end-to-end spectroscopy analysis package

    Magnetic Resonance in Medicine 85:2950–2964.

    https://doi.org/10.1002/mrm.28630

    • PubMed
    • Google Scholar
16. 1. Cleve M
    2. Gussew A
    3. Reichenbach JR

    (2015) In vivo detection of acute pain-induced changes of GABA+ and Glx in the human brain by using functional 1H MEGA-PRESS MR spectroscopy

    NeuroImage 105:67–75.

    https://doi.org/10.1016/j.neuroimage.2014.10.042

    • PubMed
    • Google Scholar
17. 1. De Graaf AA
    2. Bovée WMMJ

    (1990) Improved quantification of in vivo1H NMR spectra by optimization of signal acquisition and processing and by incorporation of prior knowledge into the spectral fitting

    Magnetic Resonance in Medicine 15:305–319.

    https://doi.org/10.1002/mrm.1910150212

    • PubMed
    • Google Scholar
18. Book

    1. De Graaf RA

    (2019) In Vivo Nmr Spectroscopy: Principles and Techniques

    John Wiley & Sons.

    https://doi.org/10.1002/9781119382461

    • Google Scholar
19. 1. DeVito LM
    2. Kanter BR
    3. Eichenbaum H

    (2010) The hippocampus contributes to memory expression during transitive inference in mice

    Hippocampus 20:208–217.

    https://doi.org/10.1002/hipo.20610

    • PubMed
    • Google Scholar
20. 1. Efron B

    (2000) The Bootstrap and Modern Statistics

    Journal of the American Statistical Association 95:1293–1296.

    https://doi.org/10.1080/01621459.2000.10474333

    • Google Scholar
21. 1. Felleman DJ
    2. Essen DCV

    (1991) Distributed hierarchical processing in the primate cerebral cortex

    Cerebral Cortex 1:1–47.

    https://doi.org/10.1093/cercor/1.1.1-a

    • PubMed
    • Google Scholar
22. 1. Field RE
    2. D’amour JA
    3. Tremblay R
    4. Miehl C
    5. Rudy B
    6. Gjorgjieva J
    7. Froemke RC

    (2020) Heterosynaptic plasticity determines the set point for cortical excitatory-inhibitory balance

    Neuron 106:842–854.

    https://doi.org/10.1016/j.neuron.2020.03.002

    • PubMed
    • Google Scholar
23. 1. Floyer-Lea A
    2. Wylezinska M
    3. Kincses T
    4. Matthews PM

    (2006) Rapid modulation of GABA concentration in human sensorimotor cortex during motor learning

    Journal of Neurophysiology 95:1639–1644.

    https://doi.org/10.1152/jn.00346.2005

    • PubMed
    • Google Scholar
24. 1. Fox PT
    2. Raichle ME

    (1986) Focal physiological uncoupling of cerebral blood flow and oxidative metabolism during somatosensory stimulation in human subjects

    PNAS 83:1140–1144.

    https://doi.org/10.1073/pnas.83.4.1140

    • PubMed
    • Google Scholar
25. 1. Fox PT
    2. Raichle ME
    3. Mintun MA
    4. Dence C

    (1988) Nonoxidative glucose consumption during focal physiologic neural activity

    Science 241:462–464.

    https://doi.org/10.1126/science.3260686

    • PubMed
    • Google Scholar
26. 1. Froemke RC
    2. Merzenich MM
    3. Schreiner CE

    (2007) A synaptic memory trace for cortical receptive field plasticity

    Nature 450:425–429.

    https://doi.org/10.1038/nature06289

    • PubMed
    • Google Scholar
27. 1. Gjedde A
    2. Marrett S
    3. Vafaee M

    (2002) Oxidative and Nonoxidative Metabolism of Excited Neurons and Astrocytes

    Journal of Cerebral Blood Flow and Metabolism 22:1–14.

    https://doi.org/10.1097/00004647-200201000-00001

    • PubMed
    • Google Scholar
28. 1. Goode TD
    2. Tanaka KZ
    3. Sahay A
    4. McHugh TJ

    (2020) An Integrated Index: Engrams, Place Cells, and Hippocampal Memory

    Neuron 107:805–820.

    https://doi.org/10.1016/j.neuron.2020.07.011

    • PubMed
    • Google Scholar
29. 1. Govindaraju V
    2. Young K
    3. Maudsley AA

    (2000) Proton NMR chemical shifts and coupling constants for brain metabolites

    NMR in Biomedicine 13:129–153.

    https://doi.org/10.1002/1099-1492(200005)13:3<129::aid-nbm619>3.0.co;2-v

    • PubMed
    • Google Scholar
30. 1. Grill-Spector K
    2. Henson R
    3. Martin A

    (2006) Repetition and the brain: Neural models of stimulus-specific effects

    Trends in Cognitive Sciences 10:14–23.

    https://doi.org/10.1016/j.tics.2005.11.006

    • PubMed
    • Google Scholar
31. 1. Gussew A
    2. Rzanny R
    3. Erdtel M
    4. Scholle HC
    5. Kaiser WA
    6. Mentzel HJ
    7. Reichenbach JR

    (2010) Time-resolved functional 1H MR spectroscopic detection of glutamate concentration changes in the brain during acute heat pain stimulation

    NeuroImage 49:1895–1902.

    https://doi.org/10.1016/j.neuroimage.2009.09.007

    • PubMed
    • Google Scholar
32. 1. Haider B
    2. Duque A
    3. Hasenstaub AR
    4. McCormick DA

    (2006) Neocortical network activity in vivo is generated through a dynamic balance of excitation and inhibition

    The Journal of Neuroscience 26:4535–4545.

    https://doi.org/10.1523/JNEUROSCI.5297-05.2006

    • PubMed
    • Google Scholar
33. 1. Hess AT
    2. Tisdall MD
    3. Andronesi OC
    4. Meintjes EM
    5. Van Der Kouwe AJW

    (2011) Real-Time Motion and B0 Corrected Single Voxel Spectroscopy Using Volumetric Navigators

    Magnetic Resonance in Medicine 66:314–323.

    https://doi.org/10.1002/mrm.22805

    • PubMed
    • Google Scholar
34. 1. Ho J
    2. Tumkaya T
    3. Aryal S
    4. Choi H
    5. Claridge-Chang A

    (2019) Moving beyond P values: data analysis with estimation graphics

    Nature Methods 16:565–566.

    https://doi.org/10.1038/s41592-019-0470-3

    • PubMed
    • Google Scholar
35. 1. Hong D
    2. Rohani Rankouhi S
    3. Thielen J-W
    4. van Asten JJA
    5. Norris DG

    (2019) A comparison of SLASER and MEGA-SLASER using simultaneous interleaved acquisition for measuring GABA in the human brain at 7t

    PLOS ONE 14:e0223702.

    https://doi.org/10.1371/journal.pone.0223702

    • PubMed
    • Google Scholar
36. 1. Horner AJ
    2. Bisby JA
    3. Bush D
    4. Lin WJ
    5. Burgess N

    (2015) Evidence for holistic episodic recollection via hippocampal pattern completion

    Nature Communications 6:1–11.

    https://doi.org/10.1038/ncomms8462

    • Google Scholar
37. 1. Ip IB
    2. Berrington A
    3. Hess AT
    4. Parker AJ
    5. Emir UE
    6. Bridge H

    (2017) Combined fMRI-MRS acquires simultaneous glutamate and BOLD-fMRI signals in the human brain

    NeuroImage 155:113–119.

    https://doi.org/10.1016/j.neuroimage.2017.04.030

    • PubMed
    • Google Scholar
38. 1. Ip IB
    2. Emir UE
    3. Parker AJ
    4. Campbell J
    5. Bridge H

    (2019) Comparison of neurochemical and bold signal contrast response functions in the human visual Cortex

    The Journal of Neuroscience 39:7968–7975.

    https://doi.org/10.1523/JNEUROSCI.3021-18.2019

    • PubMed
    • Google Scholar
39. 1. Jelen LA
    2. King S
    3. Mullins PG
    4. Stone JM

    (2018) Beyond static measures: A review of functional magnetic resonance spectroscopy and its potential to investigate dynamic glutamatergic abnormalities in schizophrenia

    J. Psychopharmacol (Oxf.) 32:497–508.

    https://doi.org/10.1177/0269881117747579

    • Google Scholar
40. 1. Josselyn SA
    2. Tonegawa S

    (2020) Memory engrams: Recalling the past and imagining the future

    Science 367:eaaw4325.

    https://doi.org/10.1126/science.aaw4325

    • PubMed
    • Google Scholar
41. 1. Kauppinen RA
    2. Williams SR

    (1991) Nondestructive Detection of Glutamate by 1H Nuclear Magnetic Resonance Spectroscopy in Cortical Brain Slices from the Guinea Pig: Evidence for Changes in Detectability During Severe Anoxic Insults

    Journal of Neurochemistry 57:1136–1144.

    https://doi.org/10.1111/j.1471-4159.1991.tb08271.x

    • PubMed
    • Google Scholar
42. 1. Kolasinski J
    2. Hinson EL
    3. Zand APD
    4. Rizov A
    5. Emir UE
    6. Stagg CJ

    (2019) The dynamics of cortical GABA in human motor learning

    J. Physiol 597:271–282.

    https://doi.org/10.1113/JP276626

    • PubMed
    • Google Scholar
43. 1. Koolschijn RS
    2. Emir UE
    3. Pantelides AC
    4. Nili H
    5. Behrens TEJ
    6. Barron HC

    (2019) The Hippocampus and Neocortical Inhibitory Engrams Protect against Memory Interference

    Neuron 101:528–541.

    https://doi.org/10.1016/j.neuron.2018.11.042

    • PubMed
    • Google Scholar
44. 1. Koster R
    2. Chadwick MJ
    3. Chen Y
    4. Berron D
    5. Banino A
    6. Düzel E
    7. Hassabis D
    8. Kumaran D

    (2018) Big-Loop Recurrence within the Hippocampal System Supports Integration of Information across Episodes

    Neuron 99:1342–1354.

    https://doi.org/10.1016/j.neuron.2018.08.009

    • PubMed
    • Google Scholar
45. 1. Krabbe S
    2. Paradiso E
    3. d’Aquin S
    4. Bitterman Y
    5. Courtin J
    6. Xu C
    7. Yonehara K
    8. Markovic M
    9. Müller C
    10. Eichlisberger T
    11. Gründemann J
    12. Ferraguti F
    13. Lüthi A

    (2019) Adaptive disinhibitory gating by VIP interneurons permits associative learning

    Nature Neuroscience 22:1834–1843.

    https://doi.org/10.1038/s41593-019-0508-y

    • PubMed
    • Google Scholar
46. 1. Kuchibhotla KV
    2. Gill JV
    3. Lindsay GW
    4. Papadoyannis ES
    5. Field RE
    6. Sten TAH
    7. Miller KD
    8. Froemke RC

    (2017) Parallel processing by cortical inhibition enables context-dependent behavior

    Nature Neuroscience 20:62–71.

    https://doi.org/10.1038/nn.4436

    • PubMed
    • Google Scholar
47. 1. Lally N
    2. Mullins PG
    3. Roberts MV
    4. Price D
    5. Gruber T
    6. Haenschel C

    (2014) Glutamatergic correlates of gamma-band oscillatory activity during cognition: A concurrent ER-MRS and EEG study

    NeuroImage, New Horizons for Neural Oscillations 85:823–833.

    https://doi.org/10.1016/j.neuroimage.2013.07.049

    • Google Scholar
48. 1. Lee S
    2. Kruglikov I
    3. Huang ZJ
    4. Fishell G
    5. Rudy B

    (2013) A disinhibitory circuit mediates motor integration in the somatosensory cortex

    Nature Neuroscience 16:1662–1670.

    https://doi.org/10.1038/nn.3544

    • PubMed
    • Google Scholar
49. 1. Letzkus JJ
    2. Wolff SBE
    3. Lüthi A

    (2015) Disinhibition, a Circuit Mechanism for Associative Learning and Memory

    Neuron 88:264–276.

    https://doi.org/10.1016/j.neuron.2015.09.024

    • PubMed
    • Google Scholar
50. 1. Lin Y
    2. Stephenson MC
    3. Xin L
    4. Napolitano A
    5. Morris PG

    (2012) Investigating the Metabolic Changes due to Visual Stimulation using Functional Proton Magnetic Resonance Spectroscopy at 7 T

    Journal of Cerebral Blood Flow and Metabolism 32:1484–1495.

    https://doi.org/10.1038/jcbfm.2012.33

    • PubMed
    • Google Scholar
51. 1. Lin A
    2. Andronesi O
    3. Bogner W
    4. Choi IY
    5. Coello E
    6. Cudalbu C
    7. Juchem C
    8. Kemp GJ
    9. Kreis R
    10. Krššák M
    11. Lee P
    12. Maudsley AA
    13. Meyerspeer M
    14. Mlynarik V
    15. Near J
    16. Öz G
    17. Peek AL
    18. Puts NA
    19. Ratai EM
    20. Tkáč I
    21. Mullins PG

    (2021) Minimum Reporting Standards for in vivo Magnetic Resonance Spectroscopy (MRSinMRS): Experts’ consensus recommendations

    NMR in Biomedicine 34:e4484.

    https://doi.org/10.1002/nbm.4484

    • PubMed
    • Google Scholar
52. 1. Lunghi C
    2. Berchicci M
    3. Morrone MC
    4. Russo FD

    (2015) Short-term monocular deprivation alters early components of visual evoked potentials

    J. Physiol 593:4361–4372.

    https://doi.org/10.1113/JP270950

    • PubMed
    • Google Scholar
53. 1. Magistretti PJ
    2. Allaman I

    (2015) A cellular perspective on brain energy metabolism and functional imaging

    Neuron 86:883–901.

    https://doi.org/10.1016/j.neuron.2015.03.035

    • PubMed
    • Google Scholar
54. 1. Mangia S
    2. Tkáč I
    3. Gruetter R
    4. Van De Moortele PF
    5. Giove F
    6. Maraviglia B
    7. Uğurbil K

    (2006) Sensitivity of single-voxel 1H-MRS in investigating the metabolism of the activated human visual cortex at 7 T

    Magnetic Resonance Imaging 24:343–348.

    https://doi.org/10.1016/j.mri.2005.12.023

    • PubMed
    • Google Scholar
55. 1. Mangia S
    2. Giove F
    3. DiNuzzo M

    (2012) Metabolic Pathways and Activity-Dependent Modulation of Glutamate Concentration in the Human Brain

    Neurochemical Research 37:2554–2561.

    https://doi.org/10.1007/s11064-012-0848-4

    • PubMed
    • Google Scholar
56. Book

    1. McCormick DA
    2. Shu YS
    3. Hasenstaub A

    (2004) Balanced recurrent excitation and inhibition in local cortical networks

    In: Hensch TK, Fagiolini M, editors. Excitatory-Inhibitory Balance. Boston, MA: Springer. pp. 113–124.

    https://doi.org/10.1007/978-1-4615-0039-1_8

    • Google Scholar
57. 1. Mekle R
    2. Kühn S
    3. Pfeiffer H
    4. Aydin S
    5. Schubert F
    6. Ittermann B

    (2017) Detection of metabolite changes in response to a varying visual stimulation paradigm using short-TE 1H MRS at 7 T

    NMR in Biomedicine 30:e3672.

    https://doi.org/10.1002/nbm.3672

    • PubMed
    • Google Scholar
58. 1. Mescher M
    2. Merkle H
    3. Kirsch J
    4. Garwood M
    5. Gruetter R

    (1998) Simultaneous in vivo spectral editing and water suppression

    NMR in Biomedicine 11:266–272.

    https://doi.org/10.1002/(sici)1099-1492(199810)11:6<266::aid-nbm530>3.0.co;2-j

    • PubMed
    • Google Scholar
59. 1. Mullins PG
    2. McGonigle DJ
    3. O’Gorman RL
    4. Puts NAJ
    5. Vidyasagar R
    6. Evans CJ
    7. Edden RAE

    (2014) Current practice in the use of MEGA-PRESS spectroscopy for the detection of GABA

    NeuroImage 86:43–52.

    https://doi.org/10.1016/j.neuroimage.2012.12.004

    • PubMed
    • Google Scholar
60. 1. Myers JF
    2. Nutt DJ
    3. Lingford-Hughes AR

    (2016) Γ-aminobutyric acid as a metabolite: Interpreting magnetic resonance spectroscopy experiments

    Journal of Psychopharmacology 30:422–427.

    https://doi.org/10.1177/0269881116639298

    • PubMed
    • Google Scholar
61. 1. Nikolova S
    2. Stark SM
    3. Stark CEL

    (2017) 3T hippocampal glutamate-glutamine complex reflects verbal memory decline in aging

    Neurobiology of Aging 54:103–111.

    https://doi.org/10.1016/j.neurobiolaging.2017.01.026

    • PubMed
    • Google Scholar
62. 1. Okun M
    2. Lampl I

    (2008) Instantaneous correlation of excitation and inhibition during ongoing and sensory-evoked activities

    Nature Neuroscience 11:535–537.

    https://doi.org/10.1038/nn.2105

    • PubMed
    • Google Scholar
63. 1. Oz G
    2. Tkáč I

    (2011) Short-echo, single-shot, full-intensity proton magnetic resonance spectroscopy for neurochemical profiling at 4 t: Validation in the cerebellum and brainstem

    Magnetic Resonance in Medicine 65:901–910.

    https://doi.org/10.1002/mrm.22708

    • PubMed
    • Google Scholar
64. 1. Pacheco Estefan D
    2. Sánchez-Fibla M
    3. Duff A
    4. Principe A
    5. Rocamora R
    6. Zhang H
    7. Axmacher N
    8. Verschure P

    (2019) Coordinated representational reinstatement in the human hippocampus and lateral temporal cortex during episodic memory retrieval

    Nature Communications 10:2255.

    https://doi.org/10.1038/s41467-019-09569-0

    • PubMed
    • Google Scholar
65. 1. Preston AR
    2. Eichenbaum H

    (2013) Interplay of hippocampus and prefrontal cortex in memory

    Current Biology 23:S0960-9822(13)00636-2.

    https://doi.org/10.1016/j.cub.2013.05.041

    • PubMed
    • Google Scholar
66. 1. Prinsen H
    2. de Graaf RA
    3. Mason GF
    4. Pelletier D
    5. Juchem C

    (2017) Reproducibility measurement of glutathione, GABA, and glutamate: Towards in vivo neurochemical profiling of multiple sclerosis with MR spectroscopy at 7T

    Journal of Magnetic Resonance Imaging 45:187–198.

    https://doi.org/10.1002/jmri.25356

    • PubMed
    • Google Scholar
67. 1. Provencher SW

    (1993) Estimation of Metabolite Concentrations from Localized in Vivo Proton NMR Spectra

    Magnetic Resonance in Medicine 30:672–679.

    https://doi.org/10.1002/mrm.1910300604

    • PubMed
    • Google Scholar
68. 1. Puts NAJ
    2. Edden RAE
    3. Evans CJ
    4. McGlone F
    5. McGonigle DJ

    (2011) Regionally Specific Human GABA Concentration Correlates with Tactile Discrimination Thresholds

    The Journal of Neuroscience 31:16556–16560.

    https://doi.org/10.1523/JNEUROSCI.4489-11.2011

    • PubMed
    • Google Scholar
69. 1. Puts NAJ
    2. Edden RAE

    (2012) In vivo magnetic resonance spectroscopy of GABA: A methodological review

    Progress in Nuclear Magnetic Resonance Spectroscopy 60:29–41.

    https://doi.org/10.1016/j.pnmrs.2011.06.001

    • PubMed
    • Google Scholar
70. 1. Rae C

    (2014) A Guide to the Metabolic Pathways and Function of Metabolites Observed in Human Brain 1H Magnetic Resonance Spectra

    Neurochemical Research 39:1–36.

    https://doi.org/10.1007/s11064-013-1199-5

    • PubMed
    • Google Scholar
71. 1. Rideaux R

    (2021) No balance between glutamate+glutamine and GABA+ in visual or motor cortices of the human brain: A magnetic resonance spectroscopy study

    NeuroImage 237:S1053-8119(21)00468-7.

    https://doi.org/10.1016/j.neuroimage.2021.118191

    • PubMed
    • Google Scholar
72. 1. Robertson CE
    2. Ratai EM
    3. Kanwisher N

    (2016) Reduced gabaergic action in the autistic brain

    Current Biology 26:80–85.

    https://doi.org/10.1016/j.cub.2015.11.019

    • PubMed
    • Google Scholar
73. 1. Rothman DL
    2. Behar KL
    3. Hyder F
    4. Shulman RG

    (2003) In vivo NMR Studies of the Glutamate Neurotransmitter Flux and Neuroenergetics: Implications for Brain Function

    Annual Review of Physiology 65:401–427.

    https://doi.org/10.1146/annurev.physiol.65.092101.142131

    • PubMed
    • Google Scholar
74. 1. Schmitz TW
    2. Correia MM
    3. Ferreira CS
    4. Prescot AP
    5. Anderson MC

    (2017) Hippocampal GABA enables inhibitory control over unwanted thoughts

    Nature Communications 8:1311.

    https://doi.org/10.1038/s41467-017-00956-z

    • PubMed
    • Google Scholar
75. 1. Scholl J
    2. Kolling N
    3. Nelissen N
    4. Stagg CJ
    5. Harmer CJ
    6. Rushworth MF

    (2017) Excitation and inhibition in anterior cingulate predict use of past experiences

    eLife 6:e20365.

    https://doi.org/10.7554/eLife.20365

    • PubMed
    • Google Scholar
76. 1. Shen J
    2. Petersen KF
    3. Behar KL
    4. Brown P
    5. Nixon TW
    6. Mason GF
    7. Petroff OAC
    8. Shulman GI
    9. Shulman RG
    10. Rothman DL

    (1999) Determination of the rate of the glutamate/glutamine cycle in the human brain by in vivo 13C nmr

    PNAS 96:8235–8240.

    https://doi.org/10.1073/pnas.96.14.8235

    • PubMed
    • Google Scholar
77. 1. Shibata K
    2. Sasaki Y
    3. Bang JW
    4. Walsh EG
    5. Machizawa MG
    6. Tamaki M
    7. Chang L
    8. Watanabe T

    (2017) Overlearning hyperstabilizes a skill by rapidly making neurochemical processing inhibitory-dominant

    Nature Neuroscience 20:470–475.

    https://doi.org/10.1038/nn.4490

    • PubMed
    • Google Scholar
78. 1. Sibson NR
    2. Dhankhar A
    3. Mason GF
    4. Rothman DL
    5. Behar KL
    6. Shulman RG

    (1998) Stoichiometric coupling of brain glucose metabolism and glutamatergic neuronal activity

    PNAS 95:316–321.

    https://doi.org/10.1073/pnas.95.1.316

    • PubMed
    • Google Scholar
79. 1. Squire LR

    (1992) Memory and the hippocampus: A synthesis from findings with rats, monkeys, and humans

    Psychological Review 99:195–231.

    https://doi.org/10.1037/0033-295x.99.2.195

    • PubMed
    • Google Scholar
80. 1. Stagg CJ
    2. Bachtiar V
    3. Johansen-Berg H

    (2011) The role of GABA in human motor learning

    Current Biology 21:480–484.

    https://doi.org/10.1016/j.cub.2011.01.069

    • PubMed
    • Google Scholar
81. Book

    1. Stagg C
    2. Rothman DL

    (2013) Magnetic Resonance Spectroscopy: Tools for Neuroscience Research and Emerging Clinical Applications

    Academic Press.

    https://doi.org/10.1016/C2011-0-09647-3

    • Google Scholar
82. 1. Stanley JA
    2. Raz N

    (2018) Functional Magnetic Resonance Spectroscopy: The “New” MRS for Cognitive Neuroscience and Psychiatry Research

    Frontiers in Psychiatry 9:76.

    https://doi.org/10.3389/fpsyt.2018.00076

    • Google Scholar
83. 1. Tanaka KZ
    2. Pevzner A
    3. Hamidi AB
    4. Nakazawa Y
    5. Graham J
    6. Wiltgen BJ

    (2014) Cortical Representations Are Reinstated by the Hippocampus during Memory Retrieval

    Neuron 84:347–354.

    https://doi.org/10.1016/j.neuron.2014.09.037

    • PubMed
    • Google Scholar
84. 1. Taylor R
    2. Neufeld RWJ
    3. Schaefer B
    4. Densmore M
    5. Rajakumar N
    6. Osuch EA
    7. Williamson PC
    8. Théberge J

    (2015) Functional magnetic resonance spectroscopy of glutamate in schizophrenia and major depressive disorder: Anterior cingulate activity during a color-word stroop task

    NPJ Schizophrenia 1:15028.

    https://doi.org/10.1038/npjschz.2015.28

    • PubMed
    • Google Scholar
85. 1. Terpstra M
    2. Marjanska M
    3. Henry PG
    4. Tkáč I
    5. Gruetter R

    (2006) Detection of an antioxidant profile in the human brain in vivo via double editing with MEGA-PRESS

    Magnetic Resonance in Medicine 56:1192–1199.

    https://doi.org/10.1002/mrm.21086

    • PubMed
    • Google Scholar
86. 1. Terpstra M
    2. Cheong I
    3. Lyu T
    4. Deelchand DK
    5. Emir UE
    6. Bednařík P
    7. Eberly LE
    8. Öz G

    (2016) Test-retest reproducibility of neurochemical profiles with short-echo, single-voxel MR spectroscopy at 3T and 7T

    Magnetic Resonance in Medicine 76:1083–1091.

    https://doi.org/10.1002/mrm.26022

    • PubMed
    • Google Scholar
87. 1. Teyler TJ
    2. DiScenna P

    (1985) The role of hippocampus in memory: A hypothesis

    Neuroscience and Biobehavioral Reviews 9:377–389.

    https://doi.org/10.1016/0149-7634(85)90016-8

    • PubMed
    • Google Scholar
88. 1. Teyler TJ
    2. Rudy JW

    (2007) The hippocampal indexing theory and episodic memory: updating the index

    Hippocampus 17:1158–1169.

    https://doi.org/10.1002/hipo.20350

    • PubMed
    • Google Scholar
89. 1. Trabesinger AH
    2. Boesiger P

    (2001) Improved selectivity of double quantum coherence filtering for the detection of glutathione in the human brain in vivo

    Magnetic Resonance in Medicine 45:708–710.

    https://doi.org/10.1002/mrm.1094

    • PubMed
    • Google Scholar
90. 1. Trepel C
    2. Racine RJ

    (2000) GABAergic modulation of neocortical long-term potentiation in the freely moving rat

    Synapse 35:120–128.

    https://doi.org/10.1002/(SICI)1098-2396(200002)35:2<120::AID-SYN4>3.0.CO;2-6

    • PubMed
    • Google Scholar
91. 1. Vallentin D
    2. Kosche G
    3. Lipkind D
    4. Long MA

    (2016) Inhibition protects acquired song segments during vocal learning in zebra finches

    Science 351:267–271.

    https://doi.org/10.1126/science.aad3023

    • PubMed
    • Google Scholar
92. 1. Vogels TP
    2. Sprekeler H
    3. Zenke F
    4. Clopath C
    5. Gerstner W

    (2011) Inhibitory Plasticity Balances Excitation and Inhibition in Sensory Pathways and Memory Networks

    Science 334:1569–1573.

    https://doi.org/10.1126/science.1211095

    • PubMed
    • Google Scholar
93. 1. Wehr M
    2. Zador AM

    (2003) Balanced inhibition underlies tuning and sharpens spike timing in auditory cortex

    Nature 426:442–446.

    https://doi.org/10.1038/nature02116

    • PubMed
    • Google Scholar
94. 1. Wheeler ME
    2. Petersen SE
    3. Buckner RL

    (2000) Memory’s echo: vivid remembering reactivates sensory-specific cortex

    PNAS 97:11125–11129.

    https://doi.org/10.1073/pnas.97.20.11125

    • PubMed
    • Google Scholar
95. 1. Wilent WB
    2. Contreras D

    (2005) Dynamics of excitation and inhibition underlying stimulus selectivity in rat somatosensory cortex

    Nature Neuroscience 8:1364–1370.

    https://doi.org/10.1038/nn1545

    • PubMed
    • Google Scholar
96. 1. Wimmer GE
    2. Shohamy D

    (2012) Preference by Association: How Memory Mechanisms in the Hippocampus Bias Decisions

    Science 338:270–273.

    https://doi.org/10.1126/science.1223252

    • PubMed
    • Google Scholar
97. 1. Witter MP
    2. Groenewegen HJ
    3. Lopes da Silva FH
    4. Lohman AH

    (1989) Functional organization of the extrinsic and intrinsic circuitry of the parahippocampal region

    Progress in Neurobiology 33:161–253.

    https://doi.org/10.1016/0301-0082(89)90009-9

    • PubMed
    • Google Scholar
98. 1. Witter MP

    (1993) Organization of the entorhinal—hippocampal system: A review of current anatomical data

    Hippocampus 3:33–44.

    https://doi.org/10.1002/hipo.1993.4500030707

    • PubMed
    • Google Scholar
99. 1. Worsley KJ
    2. Friston KJ

    (1995) Analysis of fMRI time-series revisited--again

    NeuroImage 2:173–181.

    https://doi.org/10.1006/nimg.1995.1023

    • PubMed
    • Google Scholar
100. 1. Zhang S
     2. Xu M
     3. Kamigaki T
     4. Do JPH
     5. Chang WC
     6. Jenvay S
     7. Miyamichi K
     8. Luo L
     9. Dan Y

     (2014) Long-range and local circuits for top-down modulation of visual cortex processing

     Science 345:660–665.

     https://doi.org/10.1126/science.1254126

     • Google Scholar

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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