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GABA content within medial prefrontal cortex predicts the variability of fronto-limbic effective connectivity

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An Erratum to this article was published on 08 June 2017

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Abstract

The amygdala-medial prefrontal cortex (mPFC) circuit plays a key role in social behavior. The amygdala and mPFC are bidirectionally connected, functionally and anatomically, via the uncinate fasciculus. Recent evidence suggests that GABA-ergic neurotransmission within the mPFC could be central to the regulation of amygdala activity related to emotions and anxiety processing. However, the functional and neurochemical interactions within amygdala-mPFC circuits are unclear. In the current study, multimodal magnetic resonance imaging techniques were combined to investigate effective connectivity within the amygdala-mPFC network and its relationship with mPFC neurotransmission in 22 healthy subjects aged between 41 and 88 years. Effective connectivity in the amygdala-mPFC circuit was assessed on resting-state functional magnetic resonance imaging data using spectral dynamic causal modelling. State and trait anxiety were also assessed. The mPFC was shown to be the target of incoming outputs from the amygdalae and the source of exciting inputs to the limbic system. The amygdalae were reciprocally connected by excitatory projections. About half of the variance relating to the strength of top–down endogenous connection between right amygdala and mPFC was explained by mPFC GABA levels. State anxiety was correlated with the strength of the endogenous connections between right amygdala and mPFC. We suggest that mPFC GABA content predicts variability in the effective connectivity within the mPFC-amygdala circuit, providing new insights on emotional physiology and the underlying functional and neurochemical interactions.

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Change history

  • 08 June 2017

    An erratum to this article has been published.

Abbreviations

GABA:

γ-Aminobutyric acid

GM:

Grey matter

STAI:

State-Trait Anxiety Inventory scale (Y1 = subscale-1, Y2 = subscale-2).

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Acknowledgements

The work has been supported by the Italian Ministry of Health (Grant No. GR-2010-2313418) and the Wellcome Trust (Grant No. 101253/A/13/Z). This study applies tools developed under NIH R01 EB016089 and P41 EB015909; RAEE also receives salary support from these grants.

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Authors and Affiliations

  1. Department of Neuroscience, Imaging and Clinical Sciences, University “G. d’Annunzio” of Chieti-Pescara, Via dei Vestini, 66100, Chieti, Italy

    Stefano Delli Pizzi, Piero Chiacchiaretta, Giovanna Bubbico, Marco Onofrj, Antonio Ferretti & Laura Bonanni

  2. Institute for Advanced Biomedical Technologies (ITAB), “G. d’Annunzio” University, Chieti-Pescara, Italy

    Stefano Delli Pizzi, Piero Chiacchiaretta, Giovanna Bubbico & Antonio Ferretti

  3. Center of Aging Sciences and Translational Medicine (CeSI-MeT), University “G. d’Annunzio” of Chieti-Pescara, Chieti, Italy

    Stefano Delli Pizzi, Marco Onofrj & Laura Bonanni

  4. Research Centre for Motor Control and Neuroplasticity, KU Leuven, Heverlee, Belgium

    Dante Mantini

  5. Department of Health Sciences and Technology, Neural Control of Movement Lab, Zurich, Switzerland

    Dante Mantini

  6. Department of Experimental Psychology, Oxford University, Oxford, UK

    Dante Mantini

  7. Russell H. Morgan Department of Radiology, The Johns Hopkins University School of Medicine, Baltimore, MD, USA

    Richard A. Edden

  8. F.M. Kirby Center for Functional MRI, Kennedy Krieger Institute, Baltimore, MD, USA

    Richard A. Edden

Authors
  1. Stefano Delli Pizzi
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  2. Piero Chiacchiaretta
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  3. Dante Mantini
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  4. Giovanna Bubbico
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  5. Richard A. Edden
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  6. Marco Onofrj
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  7. Antonio Ferretti
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  8. Laura Bonanni
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Corresponding author

Correspondence to Laura Bonanni.

Additional information

In the original publication of this article, the family name co-author has been published incorrectly; this error has now been corrected.

An erratum to this article is available at https://doi.org/10.1007/s00429-017-1446-7.

Electronic supplementary material

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429_2017_1399_MOESM1_ESM.tiff

Supplementary figure 1. Representative GANNET-edited MR spectra showing the GABA peak (in red). Abbreviations: GABA=γ-Aminobutyric acid (3.02 ppm); mPFC=medial prefrontal cortex (TIFF 12 KB)

429_2017_1399_MOESM2_ESM.tiff

Supplementary figure 2. Scatterplots displaying the relationship a GABA+/tCr content and effective connectivity. The strength of the effective connectivity is reported in Hz. FDR p values are reported for each correlation. Abbreviations: The wordings “mPFC-mPFC” and “AMG-AMG” refer to the intrinsic connectivity within mPFC and amygdala, respectively. RAMG=right amygdala; LAMG=left amygdala; mPFC=medial prefrontal cortex (TIFF 185 KB)

429_2017_1399_MOESM3_ESM.tiff

Supplementary figure 3. Scatterplots displaying the relationship among effective connectivity and STAI-Y1. The strength of the effective connectivity is reported in Hz. FDR p values are reported for each correlation. Abbreviations: The wordings “mPFC-mPFC” and “AMG-AMG” refer to the intrinsic connectivity within mPFC and amygdala, respectively. RAMG=right amygdala; LAMG=left amygdala; mPFC=medial prefrontal cortex (TIFF 61 KB)

Supplementary material 4 (DOC 61 KB)

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Delli Pizzi, S., Chiacchiaretta, P., Mantini, D. et al. GABA content within medial prefrontal cortex predicts the variability of fronto-limbic effective connectivity. Brain Struct Funct 222, 3217–3229 (2017). https://doi.org/10.1007/s00429-017-1399-x

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