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Posteromedial cortex glutamate and GABA predict intrinsic functional connectivity of the default mode network
Open accessAbstract
The balance between excitatory glutamatergic projection neurons and inhibitory GABAergic interneurons determines the function of cortical microcircuits. How these neurotransmitters relate to the functional status of an entire macro-scale network remains unknown. The posteromedial cortex (PMC) is the default mode network (DMN) node with the greatest functional connectivity; therefore, we hypothesized that PMC glutamate and GABA predict intrinsic functional connectivity (iFC) across the entire DMN. In 20 healthy men, we combined J-resolved magnetic resonance spectroscopy to measure glutamate and GABA in the PMC and resting fMRI followed by group Independent Components Analysis to extract the entire DMN. We showed that, controlling for age and partial GM volume in the MRS voxel, PMC glutamate and GABA explained about half of the variance of DMN iFC (represented by the network's beta coefficient for rest). Glutamate correlated positively and GABA correlated negatively with DMN iFC; in an alternative statistical model which included the glutamate/GABA ratio, the ratio correlated positively with DMN iFC. Age had no independent association with DMN iFC. No other network was associated with PMC glutamate or GABA. We conclude that regional neurotransmitter concentrations in a network node strongly predict network but not global brain iFC.
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“Higher resting state GABA levels in the DMN are associated with enhanced task-induced deactivation of this network [165–167]”
From Apolipoprotein E4, inhibitory network dysfunction, and Alzheimer’s disease (2019) · cites this paper as [166]
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Cites
Complex brain networks: graph theoretical analysis of structural and functional systems
Bullmore, E., & Sporns, O. (2009) · Nature Reviews Neuroscience · doi:10.1038/nrn2575 · Cited by 11,820
The Brain's Default Network: Anatomy, Function, and Relevance to Disease
Buckner, R. L., Andrews‐Hanna, J. R., & Schacter, D. L. (2008) · Annals of the New York Academy of Sciences · doi:10.1196/annals.1440.011 · Cited by 9,761
A fast diffeomorphic image registration algorithm
Ashburner, J. (2007) · NeuroImage · doi:10.1016/j.neuroimage.2007.07.007 · Cited by 8,122
A theory of cortical responses.
Friston, K. (2005) · Philosophical Transactions of the Royal Society B: Biological Sciences · doi:10.1098/rstb.2005.1622 · Cited by 4,578
Mapping the structural core of human cerebral cortex
Hagmann, P., Cammoun, L., Gigandet, X., Meuli, R., Honey, C. J., Wedeen, V. J., & Sporns, O. (2008) · PLoS Biology · doi:10.1371/journal.pbio.0060159 · Cited by 4,173
Estimation of metabolite concentrations from localized in vivo proton NMR spectra
Provencher, S. W. (1993) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910300604 · Cited by 3,659
What we can do and what we cannot do with fMRI
Logothetis, N. K. (2008) · Nature · doi:10.1038/nature06976 · Cited by 3,612
Buckner, R. L., Sepulcre, J., Talukdar, T., Krienen, F. M., Liu, H., Hedden, T., Andrews-Hanna, J. R., Sperling, R. A., & Johnson, K. A. (2009) · The Journal of Neuroscience · doi:10.1523/jneurosci.5062-08.2009 · Cited by 2,862
Region of interest analysis using an SPM toolbox
Brett, M. (2002) · NeuroImage · Cited by 2,273
Resting-State Functional Connectivity Reflects Structural Connectivity in the Default Mode Network
Greicius, M. D., Supekar, K., Menon, V., & Dougherty, R. F. (2009) · Cerebral Cortex · doi:10.1093/cercor/bhn059 · Cited by 2,175
NEURONAL CIRCUITS OF THE NEOCORTEX
Douglas, R. J., & Martin, K. A. C. (2004) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.27.070203.144152 · Cited by 1,886
Experimentation and Uncertainty Analysis for Engineers
Coleman, H. W., Steck, W. G., & Hurlbut, F. C. (1991) · Journal of Engineering for Industry · doi:10.1115/1.2899692 · Cited by 1,809
Raichle, M. E., & Mintun, M. A. (2006) · Annual Review of Neuroscience · doi:10.1146/annurev.neuro.29.051605.112819 · Cited by 1,630
A Baseline for the Multivariate Comparison of Resting-State Networks
Allen, E. A., Erhardt, E. B., Damaraju, E., Gruner, W. E., Segall, J. M., Silva, R. F., Havlíček, M., Rachakonda, S., Fries, J. F., Kalyanam, R., et al. (2011) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2011.00002 · Cited by 1,329
Neurosteroids: endogenous regulators of the GABAA receptor
Belelli, D., & Lambert, J. J. (2005) · Nature reviews. Neuroscience · doi:10.1038/nrn1703 · Cited by 1,159
A review of group ICA for fMRI data and ICA for joint inference of imaging, genetic, and ERP data
Calhoun, V. D., Liu, J., & Adalı, T. (2008) · NeuroImage · doi:10.1016/j.neuroimage.2008.10.057 · Cited by 1,153
Uddin, L. Q., Clare Kelly, A. M., Biswal, B. B., Xavier Castellanos, F., & Milham, M. P. (2009) · Human Brain Mapping · doi:10.1002/hbm.20531 · Cited by 1,135
Clusters of Hyperactive Neurons Near Amyloid Plaques in a Mouse Model of Alzheimer’s Disease
Busche, M. A., Eichhoff, G., Adelsberger, H., Abramowski, D., Wiederhold, K.-H., Haass, C., Staufenbiel, M., Konnerth, A., & Garaschuk, O. (2008) · Science · doi:10.1126/science.1162844 · Cited by 1,125
Raichle, M. E. (2010) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2010.01.008 · Cited by 1,111
Efficiency of Functional Brain Networks and Intellectual Performance
van den Heuvel, M. P., Stam, C. J., Kahn, R. S., & Hulshoff Pol, H. E. (2009) · Journal of Neuroscience · doi:10.1523/jneurosci.1443-09.2009 · Cited by 1,078
Sperling, R. A., LaViolette, P. S., O'Keefe, K., O'Brien, J., Rentz, D. M., Pihlajamaki, M., Marshall, G., Hyman, B. T., Selkoe, D. J., Hedden, T., et al. (2009) · Neuron · doi:10.1016/j.neuron.2009.07.003 · Cited by 1,027
Neuronal activity regulates the regional vulnerability to amyloid-β deposition
Bero, A. W., Yan, P., Roh, J. H., Cirrito, J. R., Stewart, F. R., Raichle, M. E., Lee, J.-M., & Holtzman, D. M. (2011) · Nature Neuroscience · doi:10.1038/nn.2801 · Cited by 880
Adaptive reconfiguration of fractal small-world human brain functional networks
Bassett, D. S., Meyer-Lindenberg, A., Achard, S., Duke, T., & Bullmore, E. (2006) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0606005103 · Cited by 814
Cellular mechanisms of brain energy metabolism and their relevance to functional brain imaging
Magistretti, P. J., & Pellerin, L. (1999) · Philosophical Transactions of the Royal Society B Biological Sciences · doi:10.1098/rstb.1999.0471 · Cited by 750
GABAergic Hub Neurons Orchestrate Synchrony in Developing Hippocampal Networks
Bonifazi, P., Goldin, M., Picardo, M. A., Jorquera, I., Cattani, A., Bianconi, G., Represa, A., Ben-Ari, Y., & Cossart, R. (2009) · Science · doi:10.1126/science.1175509 · Cited by 695
Homonuclear broad band decoupling and two-dimensional J-resolved NMR spectroscopy
Aue, W. P., Karhan, J., & Ernst, R. R. (1976) · The Journal of Chemical Physics · doi:10.1063/1.431994 · Cited by 688
Functional connectivity density mapping
Tomasi, D. G., & Volkow, N. D. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1001414107 · Cited by 645
Uncovering Intrinsic Modular Organization of Spontaneous Brain Activity in Humans
Yong He, A., Wang, J., Wang, L. Y., Chen, Z. J., Yan, C., Yang, H., Tang, H., Zhu, C., Gong, Q., Zang, Y., et al. (2009) · PLoS ONE · doi:10.1371/journal.pone.0005226 · Cited by 635
Disruption of Functional Connectivity in Clinically Normal Older Adults Harboring Amyloid Burden
Hedden, T., Van Dijk, K. R. A., Becker, J. A., Mehta, A., Sperling, R. A., Johnson, K. A., & Buckner, R. L. (2009) · The Journal of Neuroscience · doi:10.1523/jneurosci.3189-09.2009 · Cited by 598
Jueptner, M., & Weiller, C. (1995) · NeuroImage · doi:10.1006/nimg.1995.1017 · Cited by 522
Measuring functional connectivity using MEG: methodology and comparison with fcMRI
Brookes, M. J., Hale, J. R., Zumer, J. M., Stevenson, C. M., Francis, S. T., Barnes, G. R., Owen, J. P., Morris, P. G., & Nagarajan, S. S. (2011) · NeuroImage · doi:10.1016/j.neuroimage.2011.02.054 · Cited by 519
Localized 1H NMR measurements of gamma-aminobutyric acid in human brain in vivo.
Rothman, D. L., Petroff, O. A. C., Behar, K. L., & Mattson, R. H. (1993) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.90.12.5662 · Cited by 510
Buzsáki, G., Kaila, K., & Raichle, M. E. (2007) · Neuron · doi:10.1016/j.neuron.2007.11.008 · Cited by 474
Association between Functional Connectivity Hubs and Brain Networks
Tomasi, D. G., & Volkow, N. D. (2011) · Cerebral Cortex · doi:10.1093/cercor/bhq268 · Cited by 441
Neuronal-glial glucose oxidation and glutamatergic-GABAergic function.
Hyder, F., Patel, A. B., Gjedde, A., Rothman, D. L., Behar, K. L., & Shulman, R. G. (2006) · Journal of Cerebral Blood Flow & Metabolism · doi:10.1038/sj.jcbfm.9600263 · Cited by 408
Experimentation and Uncertainty Analysis for Engineers
Jacobs, T. (1991) · Journal of Testing and Evaluation · doi:10.1520/jte12616j · Cited by 398
GABA concentrations in the human anterior cingulate cortex predict negative BOLD responses in fMRI
Northoff, G., Walter, M., Schulte, R. F., Beck, J., Dydak, U., Henning, A., Boeker, H., Grimm, S., & Boesiger, P. (2007) · Nature Neuroscience · doi:10.1038/nn2001 · Cited by 359
Magri, C., Schridde, U., Murayama, Y., Panzeri, S., & Logothetis, N. K. (2012) · Journal of Neuroscience · doi:10.1523/jneurosci.3985-11.2012 · Cited by 356
Cerebral energetics and spiking frequency: The neurophysiological basis of fMRI
Smith, A. J., Blumenfeld, H., Behar, K. L., Rothman, D. L., Shulman, R. G., & Hyder, F. (2002) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.132272199 · Cited by 349
Hemodynamic and metabolic responses to neuronal inhibition
Stefanovic, B., Warnking, J. M., & Pike, G. B. (2004) · NeuroImage · doi:10.1016/j.neuroimage.2004.01.036 · Cited by 306
Horn (2010) · Frontiers in Systems Neuroscience · doi:10.3389/fnsys.2010.00033 · Cited by 276
Quantitative proton MR spectroscopic imaging of the human brain
Soher, B. J., van Zijl, P. C. M., Duyn, J. H., & Barker, P. B. (1996) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910350313 · Cited by 271
Characterizing variation in the functional connectome: promise and pitfalls
Kelly, C. A. M., Biswal, B. B., Craddock, R. C., Castellanos, F. X., & Milham, M. P. (2012) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2012.02.001 · Cited by 250
Koch, W., Teipel, S. J., Mueller, S., Büerger, K., Bokde, A. L. W., Hampel, H. J., Coates, U., Reiser, M., & Meindl, T. (2009) · NeuroImage · doi:10.1016/j.neuroimage.2009.12.008 · Cited by 210
Homonuclear 1H double-resonance difference spectroscopy of the rat brain in vivo.
Rothman, D. L., Behar, K. L., Hetherington, H. P., & Shulman, R. G. (1984) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.81.20.6330 · Cited by 209
Baseline GABA concentration and fMRI response
Donahue, M. J., Near, J., Blicher, J. U., & Jezzard, P. (2010) · NeuroImage · doi:10.1016/j.neuroimage.2010.07.017 · Cited by 182
ProFit: two‐dimensional prior‐knowledge fitting of J‐resolved spectra
Schulte, R. F., & Boesiger, P. (2006) · NMR in Biomedicine · doi:10.1002/nbm.1026 · Cited by 177
Behar, K. L., & Ogino, T. (1991) · Magnetic Resonance in Medicine · doi:10.1002/mrm.1910170202 · Cited by 173
Translating Glutamate: From Pathophysiology to Treatment
Javitt, D. C., Schoepp, D., Kalivas, P. W., Volkow, N. D., Zarate, C., Merchant, K., Bear, M. F., Umbricht, D., Hajos, M., Potter, W. Z., et al. (2011) · Science Translational Medicine · doi:10.1126/scitranslmed.3002804 · Cited by 168
Representation, inference, and transcendent encoding in neurocognitive networks of the human brain
Mesulam, M. M. (2008) · Annals of Neurology · doi:10.1002/ana.21534 · Cited by 166
Denoising Source SeparationPreprint
Särelä, J., & Harri Valpola, B. (2005) · CogPrints (University of Southampton) · Cited by 162
Talairach, J., Tournoux, P., & Missir, O. (1993) · Medical Entomology and Zoology · Cited by 156
Granger causal influence predicts BOLD activity levels in the default mode network
Jiao, Q., Lu, G., Zhang, Z., Zhong, Y., Wang, Z., Guo, Y., Li, K., Ding, M., & Liu, Y. (2010) · Human Brain Mapping · doi:10.1002/hbm.21065 · Cited by 141
Chen, S., Ross, T. J., Zhan, W., Myers, C. S., Chuang, K.-S., Heishman, S. J., Stein, E. A., & Yang, Y. (2008) · Brain Research · doi:10.1016/j.brainres.2008.08.028 · Cited by 139
Muthukumaraswamy, S. D., Evans, J., Edden, R. A. E., Wise, R. G., & Singh, K. D. (2011) · Human Brain Mapping · doi:10.1002/hbm.21223 · Cited by 136
Differential modulation of the default mode network via serotonin-1A receptors
Hahn, A., Wadsak, W., Windischberger, C., Baldinger, P., Höflich, A. S., Lošák, J., Nics, L., Philippe, C., Kranz, G. S., Kraus, C., et al. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1117104109 · Cited by 133
Experimentation and Uncertainty Analysis for Engineers, 2nd Ed.
Wahl, T. L. (1999) · Journal of Hydraulic Engineering · doi:10.1061/(asce)0733-9429(1999)125:12(1320 · Cited by 114
Bruyn, G. W. (1994) · Journal of the Neurological Sciences · doi:10.1016/0022-510x(94)90227-5 · Cited by 109
Pfefferbaum, A., Chanraud, S., Pitel, A.-L., Muller-Oehring, E., Shankaranarayanan, A., Alsop, D. C., Rohlfing, T., & Sullivan, E. V. (2011) · Cerebral Cortex · doi:10.1093/cercor/bhq090 · Cited by 104
Improved two‐dimensional J‐resolved spectroscopy
Schulte, R. F., Lange, T., Beck, J., Meier, D., & Boesiger, P. (2006) · NMR in Biomedicine · doi:10.1002/nbm.1027 · Cited by 89
Falkenberg, L. E., Westerhausen, R., Specht, K., & Hugdahl, K. (2012) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1115628109 · Cited by 72
Duncan, N. W., Enzi, B., Wiebking, C., & Northoff, G. (2011) · Human Brain Mapping · doi:10.1002/hbm.21179 · Cited by 70
Homeostatic Disinhibition in the Aging Brain and Alzheimer's Disease
Gleichmann, M., Chow, V. W., & Mattson, M. P. (2011) · Journal of Alzheimer s Disease · doi:10.3233/jad-2010-101674 · Cited by 59
Self-citationNonsynaptic Receptors for GABA and Glutamate
Vizi, E. S., & Mike, Á. (2006) · Current Topics in Medicinal Chemistry · doi:10.2174/156802606777323782 · Cited by 35
Benarroch, E. E. (2007) · Neurology · doi:10.1212/01.wnl.0000257836.09570.e1 · Cited by 31
DiNuzzo, M., Gili, T., Maraviglia, B., & Giove, F. (2011) · Journal of Neurophysiology · doi:10.1152/jn.00416.2011 · Cited by 22
Competition Through Selective Inhibitory Synchrony
Rutishauser, U., Slotine, J.-J. E., & Douglas, R. J. (2012) · Neural Computation · doi:10.1162/neco_a_00304 · Cited by 18
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Metadata
- Authors
- Kapogiannis, D., Reiter, D. A., Willette, A. A., & Mattson, M. P.
- Affiliations
- National Institute on Aging (US)
- Corresponding author
- Kapogiannis, Dimitrios
- Venue
- NeuroImage
- Publisher
- Elsevier BV
- Published
- 2013-01
- Volume
- 64
- Pages
- 112-119
- ISSN
- 1053-8119 (ISSN-L)
- PMC
- PMC3801193
- OpenAlex
- W1971032414
- Cited by
- 211
- References
- 67
- Citations by year
- 2026: 11 · 2025: 14 · 2024: 12 · 2023: 15 · 2022: 15 · 2021: 23 · 2020: 19 · 2019: 17 · 2018: 18 · 2017: 19 · 2016: 14 · 2015: 11 · 2014: 18 · 2013: 4
- FWCI
- 5.57
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- NeuroImage (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- National Institute on Aging
- National Institutes of Health
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Functional Brain Connectivity Studies
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced MRI Techniques and Applications
- Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Advanced Neuroimaging Techniques and Applications
- Keywords
- posteromedial cortex, default mode network, intrinsic functional connectivity, glutamate, GABA, glutamate/GABA ratio, neurotransmitter concentrations, cortical microcircuits, excitation-inhibition balance, resting-state functional connectivity, age effects
- MeSH terms
- Brain Mapping, Cerebral Cortex, gamma-Aminobutyric Acid, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Nerve Net, Neural Pathways, Magnetic Resonance Spectroscopy, Tissue Distribution, Neurotransmitter Agents, Glutamic Acid, Connectome
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