Even when your body is at rest, your brain remains active. Subjects lying in brain scanners without any specific task to perform show coordinated and reproducible patterns of brain activity. Areas of the brain with similar functions, such as those involved in vision or in movement, tend to increase or decrease their activity in sync, and these coordinated patterns are referred to as resting state networks.

The functions of these networks are unclear—they may support introspection, memory recall or planning for the future, or they may help to strengthen newly acquired skills by enabling the brain to replay previous learning episodes. There is evidence that resting state networks are altered in disorders such as Alzheimer’s disease, autism and schizophrenia, but little is known about how these changes arise or what they might mean.

Now, Stagg et al. have used a type of brain scan called magnetic resonance spectroscopy to gain insights into the mechanisms by which one particular network—the resting motor network—is generated. This network consists of areas involved in planning, monitoring and executing movements, and includes the primary motor cortex, which initiates movements by sending instructions to the spinal cord.

The levels of a chemical called GABA—a neurotransmitter molecule that tends to inhibit the activity of nerve cells—were measured in the primary motor cortex of young healthy volunteers as they lay idle in a scanner. GABA levels were negatively correlated with the amount of coordinated activity within the resting motor network. By contrast, no relation was seen between coordinated activity and the levels of the neurotransmitter glutamate, which tends to increase the activity of nerve cells. Furthermore, when a weak electric current was applied through the subjects’ scalp to their primary motor cortex—a technique previously shown to lower levels of GABA in the region—the resting motor network became stronger.

In addition to providing new information on how the rhythmic patterns of activity seen in the resting brain arise, the work of Stagg et al. contributes to the more general effort to understand the complex patterns of connections within the human brain.

There has been a surge of recent interest in so-called ‘resting state networks’ (RSNs) in the human brain. These robust, distributed networks, most commonly detected using functional MRI, show correlated fluctuations in their resting signal and have revealed networks formed from spatially widespread but anatomically and functionally closely linked regions (Fox and Raichle, 2007; Snyder and Raichle, 2012). Understanding the basis of these resting state networks is of increasing interest, as variation in the strength of functional coupling within these networks has been shown to be highly sensitive to a wide variety of clinical, genetic, or cognitive states (Filippini et al., 2009; Pievani et al., 2011). However, the neurochemical basis for such variations remains poorly understood.

Resting correlations have long been thought to be a functional marker of excitatory connections (Vincent et al., 2007; Shmuel and Leopold, 2008). However, recent simulation studies have predicted that patterns of local oscillatory activity can emerge spontaneously in a coherent fashion across large networks with specific connectional architectures (Cabral et al., 2011). Such local neuronal dynamics depend on neurochemical inhibitory tone, as shown, for example, by changes in local activity within the primary motor cortices (M1s) caused by pharmacological manipulations (Hall et al., 2011). If correlated activity spontaneously emerges through attractor dynamics in distant cortical regions, then extent of these functional correlations should be best predicted by local GABA concentrations.

Here, we carried out a series of experiments to address this hypothesis. In the first three studies, we directly quantified GABA within the left M1 using Magnetic Resonance Spectroscopy (MRS) to test the prediction that the strength of functional connectivity within the motor RSN is related to GABA concentration within M1, a major node of the network, and that this relationship is specific in both neurochemical and anatomical terms. In a fourth study, we performed an intervention study—to directly assess this relationship by testing whether application of anodal (excitatory) transcranial direct current stimulation (tDCS) to M1, an intervention known to decrease local GABA concentration (Stagg et al., 2009; 2011a), resulted in strengthening of functional connectivity within the motor RSN.

MR Spectroscopy data were acquired from the hand region of left primary motor cortex (M1) and all neurochemicals of interest were expressed as a ratio to N-acetyl aspartate (NAA) (Stagg et al., 2009; see Figure 1 for representative spectra).

Figure 1

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We investigated resting functional connectivity using two different analysis approaches: independent component analysis (ICA) and seed-based correlation. For the ICA-based analysis approach, ICA was applied to group level resting fMRI data to define group level RSNs. Next, a standard dual-regression approach was used to regress these group level RSNs of interest against each individual subject’s data. This generates subject level maps of functional connectivity for each RSN of interest. Analysis focused on the motor network, the principal RSN containing M1 (Figure 2A). For the seed-based correlation analysis, we correlated the average resting BOLD timecourses between the left and right M1s as a simpler measure of functional connectivity between left M1, from where our GABA measurements were acquired, and another major network node. To investigate the anatomical specificity of the relationship between GABA and functional connectivity within the motor RSN we correlated the average resting BOLD timecourses between the left M1 and left dorsal premotor cortex (PMd).

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Experiment 1

Experiment 1 considered a cohort of 12 young, healthy subjects, of whom 11 had spectra of sufficient quality for inclusion. Using ICA, we demonstrated a significant inverse correlation between functional connectivity within the motor RSN and GABA concentration within M1 (M1-GABA) in healthy young volunteers (r = −0.71, p=0.01; Figure 2C).

To test the anatomical specificity of this result, we assessed the default mode network (DMN), as this is a well-characterized RSN that does not include M1 (Figure 2B). There was no significant correlation between M1-GABA and functional connectivity within the DMN (r = 0.25, p=0.44) (Figure 2D), and M1-GABA and functional connectivity within the DMN was significantly less correlated than M1-GABA and functional connectivity within the motor RSN (Fisher’s R-to-Z transformation, Z = −2.29, p=0.02).

To test the neurochemical specificity of this result, we measured M1 glutamate (assessed via Glx, a composite measure of glutamate and glutamine). There was no significant correlation between motor RSN functional connectivity and M1-Glx (r = −0.35, p=0.36; Figure 2E), and the correlation between M1-GABA and motor RSN functional connectivity persisted when M1-Glx was co-varied out (r = −0.67, p=0.03).

Additionally, using a seed-based correlation analysis, we demonstrated a significant inverse relationship between the degree of correlation between the two M1s and GABA concentration (r = −0.60, p=0.047; Figure 3A). There was a trend towards an inverse relationship between the degree of correlation between left M1 and left PMD and GABA concentration (r = −0.49, p=0.12; Figure 3—figure supplement 1).

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Experiment 4

Experiment 4 aimed to investigate whether decreasing GABA within M1 using anodal tDCS (Stagg et al., 2009; 2011a), increased functional connectivity within the motor RSN. We acquired resting fMRI data before and immediately after anodal tDCS applied to the left M1 in a separate group of 10 subjects. A significant increase in network functional connectivity after tDCS was observed in the motor RSN (pre: 20.7 ± 3.34, post: 26.7 ± 2.97; t(9) = 2.59, p=0.02; Figure 4). There was also a significant increase in the degree of M1-M1 correlation (t(9) = 1.94, p=0.04; Figure 3B).

Figure 4

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In this study, we investigated the basis of the long-range fluctuations seen in resting fMRI. In three separate cohorts of subjects we have demonstrated a significant negative correlation between the levels of GABA in M1 and the strength of functional connectivity within the motor RSN. This relationship was specific both anatomically and neurochemically: no relationship between glutamate levels and resting connectivity was demonstrated. We believe that the fundamental relationship between resting connectivity within the motor network and M1 GABA demonstrated here is robust, as it is replicated across both ICA and seed-based approaches; whether the MRS and fMRI data were collected on the same day or different days; across different scanners; and in young and old healthy cohorts.

Further, we have shown that a technique that is known to decrease GABA within M1 significantly increases functional connectivity of the motor RSN.

What do these findings tell us about how activity at a cellular level relates to long-range network connectivity? The activity within the motor RSN has been shown to be related to fluctuations in the power of beta oscillations (Brookes et al., 2011), which have in turn been related to GABA activity (Hall et al., 2011). A recent simulation-based paper suggested a positive relationship between local oscillatory power in the gamma band and resting state functional connectivity (Cabral et al., 2011), something supported in the visual network by a relationship between RSN connectivity and local gamma frequency oscillatory activity (Shmuel and Leopold, 2008). Evoked gamma power is also known to be inversely related to extra-synaptic GABA tone (Towers et al., 2004). GABA is present in the human brain in three major pools—in pre-synaptic vesicles, as a metabolite in the cytoplasm, and in the extracellular fluid, where it underlies extra-synaptic GABA tone. It is likely that MRS-assessed GABA, which is sensitive to the total amount of GABA within the voxel, more closely reflects extra-synaptic rather than synaptic GABA activity (Stagg et al., 2011b).

While it is not yet clear what the relationship is between local gamma oscillations and beta oscillations, the negative correlation found between motor RSN functional connectivity and MRS-assessed GABA in M1 is consistent with the idea that fluctuations in the power of oscillations underlie long-range resting functional connectivity.

In combining observational studies (Experiments 1–3) and an intervention study (Experiment 4), the data presented here significantly extends and brings coherence to previous findings. One previous study has observed a correlation between GABA in the posteromedial cortex and the strength of the default mode network (Kapogiannis et al., 2013). Taken together with our findings, this suggests that the relationships we report are not specific to the motor system, but rather a general feature of resting state functional connectivity networks. It is interesting to note, however, that in addition to a relationship with GABA, Kapogiannis et al. demonstrate a significant positive correlation between glutamate in the posteromedial cortex and the strength of the default mode network. There are a number of reasons why we did not find a similar relationship between glutamate and resting state connectivity. Our MRS approach allowed quantification of Glx, a composite measure of glutamate and glutamine, and therefore may be less sensitive to relationships with glutamate specifically. Alternatively, it may be that different networks have different neurochemical profiles, although this seems less likely.

Other previous studies have explored the relationship between task-evoked BOLD responses and GABA: a relationship between GABA levels and BOLD signal in response to a task has been demonstrated in anatomically plausible regions for both motor and visual tasks (Muthukumaraswamy et al., 2009; Stagg et al., 2011a), such that higher local GABA concentrations are associated with smaller task-related BOLD signals. Motor-task related BOLD signals have also been shown to correlate with the strength of the motor RSN across individuals (Kannurpatti et al., 2012). The relationships demonstrated here, build on this prior work and provide a mechanistic explanation for the previous finding that motor learning, which decreases GABA (Floyer-Lea et al., 2006), increases RSN functional connectivity (Albert et al., 2009).

Taken together, these findings relate RSN functional connectivity to local inhibitory tone within the motor cortex, and thereby may reveal an important neural mechanistic explanation for the RSN changes observed in a wide range of states.

All subjects gave their informed consent to participate in the study in accordance with local ethics committee approval (Experiments 1, 3 & 4: Oxfordshire REC A 06/Q1604/2; Experiment 2: East London REC 1 10/H0703/50). All subjects were right-handed and none had a history of any neurological or psychiatric disorder.

1. 1. Albert NB
   2. Robertson EM
   3. Miall RC

   (2009) The resting human brain and motor learning

   Current Biology 19:1023–1027.

   https://doi.org/10.1016/j.cub.2009.04.028

   • Google Scholar
2. 1. Beckmann CF
   2. DeLuca M
   3. Devlin JT
   4. Smith SM

   (2005) Investigations into resting-state connectivity using independent component analysis

   Philosophical Transactions of the Royal Society of London Series B, Biological Sciences 360:1001–1013.

   https://doi.org/10.1098/rstb.2005.1634

   • Google Scholar
3. 1. Brookes MJ
   2. Woolrich M
   3. Luckhoo H
   4. Price D
   5. Hale JR
   6. Stephenson MC
   7. Barnes GR
   8. Smith SM
   9. Morris PG

   (2011) Investigating the electrophysiological basis of resting state networks using magnetoencephalography

   Proceedings of the Montana Academy of Sciences 108:16783–16788.

   https://doi.org/10.1073/pnas.1112685108

   • Google Scholar
4. 1. Cabral J
   2. Hugues E
   3. Sporns O
   4. Deco G

   (2011) Role of local network oscillations in resting-state functional connectivity

   NeuroImage 57:130–139.

   https://doi.org/10.1016/j.neuroimage.2011.04.010

   • Google Scholar
5. 1. Filippini N
   2. MacIntosh BJ
   3. Hough MG
   4. Goodwin GM
   5. Frisoni GB
   6. Smith SM
   7. Matthews PM
   8. Beckmann CF
   9. Mackay CE

   (2009) Distinct patterns of brain activity in young carriers of the APOE-epsilon4 allele

   Proceedings of the National Academy of Sciences of the United States of America 106:7209–7214.

   https://doi.org/10.1073/pnas.0811879106

   • Google Scholar
6. 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

   • Google Scholar
7. 1. Fox MD
   2. Raichle ME

   (2007) Spontaneous fluctuations in brain activity observed with functional magnetic resonance imaging

   Nature Reviews Neuroscience 8:700–711.

   https://doi.org/10.1038/nrn2201

   • Google Scholar
8. 1. Greve DN
   2. Fischl B

   (2009) Accurate and robust brain image alignment using boundary-based registration

   NeuroImage 48:63–72.

   https://doi.org/10.1016/j.neuroimage.2009.06.060

   • Google Scholar
9. 1. Hall SD
   2. Stanford IM
   3. Yamawaki N
   4. McAllister CJ
   5. Ronnqvist KC
   6. Woodhall GL
   7. Furlong PL

   (2011) The role of GABAergic modulation in motor function related neuronal network activity

   NeuroImage 56:1506–1510.

   https://doi.org/10.1016/j.neuroimage.2011.02.025

   • Google Scholar
10. 1. Jenkinson MM
    2. Beckmann CFC
    3. Behrens TEJT
    4. Woolrich MWM
    5. Smith SMS

    (2012) FSL

    NeuroImage 62:782–790.

    https://doi.org/10.1016/j.neuroimage.2011.09.015

    • Google Scholar
11. 1. Kannurpatti SS
    2. Rypma B
    3. Biswal BB

    (2012) Prediction of task-related BOLD fMRI with amplitude signatures of resting-state fMRI

    Frontiers in Systems Neuroscience 6:1–10.

    https://doi.org/10.3389/fnsys.2012.00007

    • Google Scholar
12. 1. Kapogiannis D
    2. Reiter DA
    3. Willette AA
    4. Mattson MP

    (2013) Posteromedial cortex glutamate and GABA predict intrinsic functional connectivity of the default mode network

    NeuroImage 64:112–119.

    https://doi.org/10.1016/j.neuroimage.2012.09.029

    • Google Scholar
13. 1. Muthukumaraswamy SD
    2. Edden RAE
    3. Jones DK
    4. Swettenham JB
    5. Singh KD

    (2009) Resting GABA concentration predicts peak gamma frequency and fMRI amplitude in response to visual stimulation in humans

    Proceedings of the National Academy of Sciences of the United States of America 106:8356–8361.

    https://doi.org/10.1073/pnas.0900728106

    • Google Scholar
14. 1. Pievani M
    2. de Hann W
    3. Wu T
    4. Seeley W
    5. Frisoni G

    (2011) Functional network disruption in the degenerative dementias

    The Lancet Neurology 10:829–843.

    https://doi.org/10.1016/S1474-4422(11)70158-2

    • Google Scholar
15. 1. Power JD
    2. Barnes KA
    3. Snyder AZ
    4. Schlaggar BL
    5. Petersen SE

    (2012) Spurious but systematic correlations in functional connectivity MRI networks arise from subject motion

    NeuroImage 59:2142–2154.

    https://doi.org/10.1016/j.neuroimage.2011.10.018

    • Google Scholar
16. 1. Shmuel A
    2. Leopold DA

    (2008) Neuronal correlates of spontaneous fluctuations in fMRI signals in monkey visual cortex: implications for functional connectivity at rest

    Human Brain Mapping 29:751–761.

    https://doi.org/10.1002/hbm.20580

    • Google Scholar
17. 1. Smith S
    2. Jenkinson M
    3. Woolrich M
    4. Beckmann C
    5. Behrens T
    6. Johansen-Berg H
    7. Bannister P
    8. De Luca M
    9. Drobnjak I
    10. Flitney D
    11. Niazy R
    12. Saunders J
    13. Vickers J
    14. Zhang Y
    15. De Stefano N
    16. Brady J
    17. Matthews P

    (2004) Advances in functional and structural MR image analysis and implementation as FSL

    NeuroImage 23:S208–S219.

    https://doi.org/10.1016/j.neuroimage.2004.07.051

    • Google Scholar
18. 1. Snyder AZ
    2. Raichle ME

    (2012) A brief history of the resting state: the Washington University perspective

    NeuroImage 62:902–910.

    https://doi.org/10.1016/j.neuroimage.2012.01.044

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

    (2011a) The role of GABA in human motor learning

    Current Biology 21:480–484.

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

    • Google Scholar
20. 1. Stagg CJ
    2. Best JG
    3. Stephenson MC
    4. O’Shea J
    5. Wylezinska M
    6. Kincses ZT
    7. Morris PG
    8. Matthews PM
    9. Johansen-Berg H

    (2009) Polarity-sensitive modulation of cortical neurotransmitters by transcranial stimulation

    Journal of Neuroscience 29:5202–5206.

    https://doi.org/10.1523/JNEUROSCI.4432-08.2009

    • Google Scholar
21. 1. Stagg CJ
    2. Bestmann S
    3. Constantinescu AO
    4. Moreno LM
    5. Allman C
    6. Mekle R
    7. Woolrich M
    8. Near J
    9. Johansen-Berg H
    10. Rothwell JC

    (2011b) Relationship between physiological measures of excitability and levels of glutamate and GABA in the human motor cortex

    The Journal of Physiology 589:5845–5855.

    https://doi.org/10.1113/jphysiol.2011.216978

    • Google Scholar
22. 1. Tomassini V
    2. Jbabdi S
    3. Klein J
    4. Behrens T
    5. Pozzilli C
    6. Matthews P
    7. Rushworth M
    8. Johansen-Berg H

    (2007) Diffusion-weighted imaging tractography-based parcellation of the human lateral premotor cortex identifies dorsal and ventral subregions with anatomical and functional specializations

    The Journal of Neuroscience 27:10259–10269.

    https://doi.org/10.1523/JNEUROSCI.2144-07.2007

    • Google Scholar
23. 1. Towers SK
    2. Gloveli T
    3. Traub RD
    4. Driver JE
    5. Engel D
    6. Fradley R
    7. Rosahl TW
    8. Maubach K
    9. Buhl EH
    10. Whittington MA

    (2004) Alpha 5 subunit-containing GABAA receptors affect the dynamic range of mouse hippocampal kainate-induced gamma frequency oscillations in vitro

    The Journal of Physiology 559:721–728.

    https://doi.org/10.1113/jphysiol.2004.071191

    • Google Scholar
24. 1. Vincent JL
    2. Patel GH
    3. Fox MD
    4. Snyder AZ
    5. Baker JT
    6. Van Essen DC
    7. Zempel JM
    8. Snyder LH
    9. Corbetta M
    10. Raichle ME

    (2007) Intrinsic functional architecture in the anaesthetized monkey brain

    Nature 447:83–86.

    https://doi.org/10.1038/nature05758

    • Google Scholar
25. 1. Zuo X-N
    2. Kelly C
    3. Adelstein JS
    4. Klein DF
    5. Castellanos FX
    6. Milham MP

    (2010) Reliable intrinsic connectivity networks: Test–retest evaluation using ICA and dual regression approach

    NeuroImage 49:2163–2177.

    https://doi.org/10.1016/j.neuroimage.2009.10.080

    • Google Scholar

Author details

1. Charlotte J Stagg

   Oxford Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom

   Contribution

   CJS, Conception and design, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   For correspondence

   charlotte.stagg@ndcn.ox.ac.uk

   Competing interests

   The authors declare that no competing interests exist.

2. Velicia Bachtiar

   Oxford Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom

   Contribution

   VB, Acquisition of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

3. Ugwechi Amadi

   Oxford Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom

   Contribution

   UA, Acquisition of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

4. Christel A Gudberg

   Oxford Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom

   Contribution

   CAG, Acquisition of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

5. Andrei S Ilie

   1. Oxford Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
   2. Department of Pharmacology, University of Oxford, Oxford, United Kingdom

   Contribution

   ASI, Acquisition of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

6. Cassandra Sampaio-Baptista

   Department of Pharmacology, University of Oxford, Oxford, United Kingdom

   Contribution

   CS-B, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

7. Jacinta O’Shea

   Oxford Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom

   Contribution

   JO’S, Acquisition of data, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

8. Mark Woolrich

   Oxford Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom

   Contribution

   MW, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

9. Stephen M Smith

   Oxford Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom

   Contribution

   SMS, Analysis and interpretation of data, Drafting or revising the article

   Competing interests

   The authors declare that no competing interests exist.

10. Nicola Filippini

    1. Oxford Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom
    2. Department of Psychiatry, University of Oxford, Oxford, United Kingdom

    Contribution

    NF, Analysis and interpretation of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

11. Jamie Near

    Oxford Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom

    Contribution

    JN, Analysis and interpretation of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

12. Heidi Johansen-Berg

    Oxford Centre for Functional MRI of the Brain (FMRIB), Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom

    Contribution

    HJ-B, Conception and design, Analysis and interpretation of data, Drafting or revising the article

    Competing interests

    The authors declare that no competing interests exist.

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