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. 2015 Dec 31;11(5):758–766. doi: 10.1093/scan/nsv155

GABA content within the ventromedial prefrontal cortex is related to trait anxiety

Stefano Delli Pizzi 1,2,3,1, Caterina Padulo 1,4,2, Alfredo Brancucci 4, Giovanna Bubbico 1,2, Richard A Edden 5,6, Antonio Ferretti 1,2, Raffaella Franciotti 1,2,3, Valerio Manippa 1,4, Daniele Marzoli 4, Marco Onofrj 1,3, Gianna Sepede 1,2,7, Armando Tartaro 1,2, Luca Tommasi 4, Stefano Puglisi-Allegra 8,9, Laura Bonanni 1,3,✉
PMCID: PMC4847694  PMID: 26722018

Abstract

The ventromedial prefrontal cortex (vmPFC) plays a key role in emotion processing and regulation. vmPFC dysfunction may lead to disinhibition of amygdala causing high anxiety levels. γ-Aminobutyric acid (GABA) inter-neurons within vmPFC shape the information flow to amygdala. Thus, we hypothesize that GABA content within vmPFC could be relevant to trait anxiety. Forty-three healthy volunteers aged between 20 and 88 years were assessed for trait anxiety with the Subscale-2 of the State-Trait-Anxiety Inventory (STAI-Y2) and were studied with proton magnetic resonance spectroscopy to investigate GABA and Glx (glutamate+glutamine) contents within vmPFC. Total creatine (tCr) was used as internal reference. Partial correlations assessed the association between metabolite levels and STAI-Y2 scores, removing the effect of possible nuisance factors including age, educational level, volumes of gray matter and white matter within magnetic resonance spectroscopy voxel. We observed a positive relationship between GABA/tCr and STAI-Y2 scores. No significant relationships were found between Glx/tCr and STAI-Y2 and between tCr/water and STAI-Y2. No differences were found between males and females as regards to age, STAI-Y2, GABA/tCr, Glx/tCr, tCr/water, gray matter and white matter volumes. We suggest a close relationship between GABA content within vmPFC and trait anxiety providing new insights in the physiology of emotional brain.

Keywords: γ-aminobutyric acid, glutamate, ventromedial prefrontal cortex, anxiety

Introduction

The ventromedial prefrontal cortex (vmPFC) plays a key role in many social and affective functions (Damasio et al., 1996; Quirk and Beer, 2006; Fellows, 2011) including emotion processing and implicit emotion regulation (Winecoff et al., 2013). Abnormal functional activity within the vmPFC has been reported in patients with social anxiety disorder (McClure et al., 2007; Monk et al., 2008; Price et al., 2011; Labuschagne et al., 2012; Sladky et al., 2015) and in healthy volunteers with elevated anxiety traits (Etkin et al., 2004; Stein et al., 2007). Histological studies in rodents and primates have documented direct and reciprocal projections between the vmPFC and the amygdala, highlighting the presence of a viable anatomic substrate for the functional interaction (McDonald et al., 1996; Ghashghaei and Barbas, 2002; Kim et al., 2011).

On these grounds, recent models of affective psychopathology have suggested that the vmPFC regulates negative emotions via top–down inhibition of the limbic structures (Etkin et al., 2004; Stein et al., 2007; Phillips et al., 2008; Motzkin et al., 2015). Thus, the vmPFC dysfunction could lead to disinhibition of the amygdala causing limbic system hyperactivity and, consequently, high levels of anxiety (Price, 1999; Quirk and Gehlert, 2003; Rauch et al., 2006; Vidal-Gonzalez et al., 2006; Amting et al., 2010; Myers-Schulz and Koenigs, 2012). In this context, the neurotransmitter balance within the vmPFC could be a relevant factor to investigate.

γ-Aminobutyric acid (GABA) and glutamate are, respectively, the main inhibitory and excitatory neurotransmitters in the mammalian brain (Sieghart, 1995). Although a functional relationship between glutamatergic neurons and GABA inter-neurons within the vmPFC is well documented (Goldman-Rakic, 1999), the understanding of the invivo interactions between these neurotransmitters in mediating social and affective behaviors is still shallow.

GABA inter-neurons of the vmPFC exert powerful inhibitory control over the excitatory output of pyramidal neurons, influencing the flow of information in the vmPFC (Constantinidis et al., 2002). Moreover, it has been demonstrated in animal models that GABA enhancement in the vmPFC reduces GABA-ergic inhibition within the amygdala, promoting its hyperactivity (Akirav and Maroun, 2007; Chefer et al., 2011; Moscarello and LeDoux, 2013; Courtin et al., 2014; Nuss, 2015).

Endogenous brain metabolites can be detected invivo and non-invasively by using proton magnetic resonance spectroscopy (1H-MRS) (Puts and Edden, 2012). Total creatine (tCr) and Glx complex (glutamate+glutamine) are readily detectable with clinical magnetic resonance imaging (MRI) scanners and Point Resolved Spectroscopy sequence (PRESS). Recent advances in 1H-MRS also allow to quantify the GABA content by using spectral editing techniques such as Meshcher-Garwood Point Resolved Spectroscopy sequence (MEGA-PRESS) (Mescher et al., 1998; Mullins et al., 2014).

In the present study, by using 1H-MRS with PRESS and MEGA-PRESS, we aimed to investigate the relationship between the contents of GABA and Glx in the vmPFC and trait anxiety in healthy volunteers. On the basis of the central role of GABA-ergic neurotransmission within the vmPFC in the top–down control of amygdala activity, we hypothesized that the GABA content in the vmPFC could be related to high trait anxiety levels.

Materials and methods

Study sample

This study was approved by the Local Institutional Ethics Committee and was performed according to the Declaration of Helsinki (1997) and subsequent revisions. All healthy volunteers gave written informed consent.

Exclusion criteria were prior history of major medical or psychiatric disorders; head injury or neurological problems; current pregnancy or breastfeeding; history of substance abuse; any pharmacological treatment; tobacco addiction; any contraindication to MRI scanning, including metal implants and claustrophobia. Alcohol and caffeine consumption were prohibited for 12 h prior to the MR measurement (Gao et al., 2013). Considering the possible noise effect of the menstrual cycle phase on GABA (De Bondt et al., 2015), young females were selected in the follicular or luteal phase.

Forty-three participants aged between 20 and 88 years (21 females and 22 males) underwent MR imaging and neuropsychological evaluation.

Neuropsychiatric and neuropsychological evaluation

Before MR session, participants’ state of mind was assessed by a psychiatrist and only those subjects who resulted not to suffer of any current DSM-5 psychiatric disorder were enrolled (American Psychiatric Association, 2013). All participants were tested for trait anxiety using the subscale-Y2 of the State-Trait Anxiety Inventory (STAI-Y) (Spielberger, 1983).

Considering the potential effect of aging on the brain function, older subjects (>50 years) were further evaluated to ascertain normal brain functioning. Specifically, Activities of Daily Living Scale (Katz, 1963) and Instrumental Activities of Daily Life Scale (Lawton and Brody, 1970) were employed for daily functions assessment. Mini Mental State Examination was administered for global cognitive assessment (Folstein et al., 1975). Frontal assessment battery was administered to exclude patients affected by frontal dysfunction (Dubois et al., 2000). Lexical production and phonemic verbal fluency as well as attention were evaluated by means of the verbal fluency test (Tombaugh et al., 1999; Oppenheimer, 2008). Attention skills, sustained attention, divided attention, task coordination and set shifting were evaluated using the Trail Making Test A and B (Rossini and Karl, 1994; Robertson et al., 1996). Attentional matrices were employed to evaluate speed and attention (Abbate et al., 2007). Short-term and long-term verbal memory (BSRT) (Babcock Story Recall Test; Horner et al. 2002) were assessed as well as auditory working memory (Baddeley and Wilson, 2002). Visuo-spatial memory and ability were also investigated (Shin et al. 2006). Finally, the forward and backward Digit Span test was used to evaluate auditory working memory (Wechsler, 1939). Participants who did not reach the cut-off thresholds (reported in the Supplementary Table S1) in all psychometric tests were excluded from the study cohort.

MR protocol

All MR data were acquired by means of a Philips Achieva 3 Tesla scanner (Philips Medical Systems, Best, The Netherlands) equipped with an 8-channel receiver coil. Three-dimensional T1-weighted images were acquired by using Turbo Field-Echo sequence (TFE, TR/TE = 11/5 ms, slice thickness of 0.8 mm). 1H-MRS spectrum was acquired from a voxel of 2.0 (anterior-posterior) × 3.0 (left-right) × 3.0 (cranio-caudal) mm3 placed on the vmPFC (Figure 1A). MEshcher-GArwood Point RESolved Spectroscopy (MEGA-PRESS) sequence (TR/TE = 2000/68 ms, 320 averages) was used to acquire 1024 points within a spectral width of 2000 Hz. MEGA-PRESS generates two sub-spectra, with the editing pulse ON in one and OFF in the other. Specifically, an editing pulse is applied to GABA spins at 1.9 ppm to selectively refocus the evolution of J-coupling to the GABA spins at 3.02 ppm (ON spectra). In the other, the inversion pulse is applied elsewhere so that the J-coupling evolves freely throughout the TE (OFF spectra). Subtracting scans acquired without these pulses (OFF spectra) from scans acquired with the editing pulses (ON spectra) removes overlying tCr signals from the edited spectrum, revealing the GABA signal in the difference spectrum (Mullins et al., 2014) (Figure 1B). Point-resolved spectroscopy sequence (PRESS) (TR/TE = 2000/40 ms, 16-step phase-cycle and 128 averages) with and without water suppression was additionally acquired by using chemically shift selective (CHESS) pulses. One thousand and twenty-four points were acquired with a spectral width of 2000 Hz.

Fig. 1.

Fig. 1.

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Proton magnetic resonance spectroscopy (1H-MRS). (Panel A) A voxel of 3.0 × 3.0 × 2.0 mm3 was placed into the ventromedial prefrontal cortex (vmPFC) by using T1-weighted image as anatomical reference. (Panel B) Representative GANNET-edited MR spectra for assessing GABA/tCr and Glx/tCr. (Panel C) Representative GANNET-edited spectra (in blue) with estimated Glx model indicated in red. (Panel D) Representative GANNET-edited spectra (in blue) with estimated GABA model indicated in red. For panels C and D, residue was shown in black. GABA, γ-aminobutyric acid (3.02 ppm); Glx, glutamate + glutamine (pseudo-doublet peaks at 3.65–3.75 ppm).

1H-MRS analysis

tCr was used as an internal standard reference based on its stable levels reported in normal conditions (Bogner et al., 2009). Because the signal detected at 3.02 ppm is also expected to include contributions from both macromolecules and homocarnosine, in the rest of the manuscript this signal is labeled as GABA + rather than GABA, to underline the potential presence of these other compounds (Rothman et al., 1997; Gao et al., 2013).

GANNET, a MATLAB-based tool (Edden et al., 2014), was used to assess GABA+/tCr and Glx/tCr in each spectrum using default parameters, including frequency and phase correction of time-resolved data using spectral registration (Near et al., 2015). Glx signal was quantified from the 3.75 ppm as pseudo-doublet peaks in the GANNET-edited spectrum. GANNET-estimated signal for GABA and Glx is shown in Figure 1C and D, respectively.

To verify whether the tCr level was effectively stable, PRESS spectra with and without water suppression were analyzed by using JMRUI (Naressi et al., 2001) to calculate the area under the tCr and water peaks, respectively (Figure 2). In detail, spectra with water suppression were filtered for removal of residual water by using the Hankel-Lanczos Singular Values Decomposition algorithm. After autophasing, baseline and frequency shifts correction, a priori knowledge database (tCr, 3.03 ppm) was created to put constraints on the Advanced Magnetic Resonance (AMARES) fitting algorithm within the jMRUI package. Peak shifts were restricted to ± 0.05 ppm of the theoretical location. Spectra with artifact and metabolites fits with Cramer Rao Lower Bounds above 20% were excluded (Delli Pizzi et al., 2015). Thus, water signal was used as an internal reference standard to perform absolute tCr quantification (Christiansen et al., 1993; Delli Pizzi et al., 2012, 2013, 2015). Because of signal quality, PRESS results were available for 32 out of 43 subjects.

Fig. 2.

Fig. 2.

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Absolute quantification of tCr. Representative PRESS spectra acquired with (upper panel) and without (lower panel) water suppression for assessing tCr and water concentrations, respectively. Estimated signals (red) were reported on original signals (blue). Residue was shown in black.

Tissue segmentation within 1H-MRS voxel

For each subject, a GANNET extension was used to obtain the mask of the 1H-MRS voxel. The T1-weighted images were segmented in native space by the recon-all command line (FreeSurfer; Dale et al., 1999). FLIRT tool (FMRIB Software Library, FSL; Smith et al., 2004) was used to co-register the structural images, Freesurfer’s outputs and 1H-MRS voxel mask in a common (native) space. FSL command lines (fslmaths and fslstats) were used to define the gray matter (GM) and white matter (WM) within the 1H-MRS voxel and to measure the tissue volumes. All generated images were viewed in FSL View (Jenkinson et al., 2012) to validate the location of the MRS voxel and the confidence of the tissue segmentation.

Statistical analyses

The demographic, psychometric and 1H-MRS values are presented as mean ± standard error (SE). Pearson’s correlation was computed to assess the relationship between demographic features (age and educational level) and STAY-2. Partial correlations were used to measure the degree of association between each metabolite (GABA+/tCr, Glx/tCr, tCr/water) and STAI-Y2 scores. We removed the known effect of age on GABA (Gao et al., 2013) and the possible effects of WM and GM volume within 1H-MRS voxel (Harris et al., 2015) and of educational level.

The gender effect was assessed using t-test. All statistical tests were two-tailed and the significant P-value threshold was set at 0.05.

Results

Demographic and psychometric results

Table 1 summarizes demographic, psychometric and 1H-MRS data for the whole sample and for males and females separately. Supplementary Table S1 shows the mean ± standard error and cut-off for each test performed in subjects aged over 50 years.

Table 1.

Demographic, psychometric and 1H-MRS outcomes

Outcome All participants (n = 43) Female (n = 21) Male (n = 22) Female vs malea
Age (years) 42.56 ± 3.22 38.52 ± 3.87 46.41 ± 5.12 t 41 = −1.231 P = 0.225
Educational level (years) 12.28 ± 0.54 13.76 ± 0.69 10.86 ± 0.73 t 41 = 2.899 P = 0.006
STAI-Y2 36.09 ± 0.96 37.67 ± 1.35 34.59 ± 1.33 t 41 = 1.641 P = 0.108
GM (mm3) 10539 ± 211.66 10637 ± 336.21 10368 ± 274.94 t 41 = 0.629 P = 0.533
WM (mm3) 8583 ± 116.45 8641 ± 154.50 8528 ± 179.02 t 41 = 0.479 P = 0.635
GABA+/tCr 0.0685 ± 0.002 0.0720 ± 0.003 0.0654 ± 0.003 t 41 = 1.567 P = 0.125
Glx/tCr 0.0808 ± 0.016 0.0806 ± 0.011 0.0810 ± 0.020 t 41 = −0.075 P = 0.940
tCr/waterb 4.40 ± 0.10 4.35 ± 0.14 4.46 ± 0.14 t 30 = −0.523 P = 0.605
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Note. Glx, glutamate + glutamine; STAI-Y, State-Trait Anxiety Inventory scale.

a P value was calculated using the one-way analysis of variance.

bvalues × 10−4; these data are available for 32/43 subjects.

Concerning gender effects, no differences were found between males and females as regards to age (t = −1.23, P = 0.23), STAI-Y2 (t = 1.64, P = 0.11), GABA+/tCr (t = 1.57, P = 0.13), Glx/tCr (t = −0.08, P = 0.94), tCr/water (t = −0.52, P = 0.61), GM (t = 0.63, P = 0.53) and WM (t = 0.48, P = 0.64). Conversely, we found a significant difference between males and females for educational level (t = 2.90, P = 0.006).

Correlations

As shown in Figure 3, we observed a significant relationship between GABA+/tCr and STAI-Y2 scores (r = 0.568, P < 0.001). Conversely, no significant relationships were found between Glx/tCr and STAI-Y2 (r = 0.251, P = 0.123) and between tCr/water and STAI-Y2 (r = −0.115, P = 0.561). Finally, we did not find significant correlations between STAI-Y2 and age (r = 0.109, P = 0.845) and between STAI-Y2 and educational level (r = 0.003; P = 0.984).

Fig. 3.

Fig. 3.

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Scatterplots displaying the correlations between STAI-Y2 scores and the metabolites (GABA/tCr, Glx/tCr and tCr/water).

To control for possible effects of educational level, partial correlations (partialling out age) between STAI-Y2 and educational level were computed for males and females, separately. These analyses yielded no significant results (females: r = −0.393, P = 0.086; males: r = 0.286, P = 0.208). Subsequently, the educational level was correlated (partialling out age) with GABA and Glx. These analyses yielded no statistically significant results (females: GABA+/tCr: r = −0.123, P = 0.626; Glx/tCr: r = 0.220, P = 0.381; males: GABA+/tCr: r = 0.161, P = 0.511; Glx/tCr: r = 0.308, P = 0.199).

Discussion

In our cohort of healthy volunteers, we showed that while the Glx complex (i.e. glutamate and glutamine) within the vmPFC had no correlation with trait anxiety, the GABA content within the vmPFC was positively correlated with trait anxiety. Of note, these results are expressed on the basis of the tCr level, i.e. a neuronal marker of energetic metabolism whose levels are typically stable in normal conditions (Christiansen et al., 1993; Bogner et al., 2009). TCr has been widely used as an internal reference in 1H-MRS studies (Gao et al., 2013; Delli Pizzi et al., 2015). Consistent with the literature, we found that the tCr/water did not correlate with trait anxiety. Hence, this result reinforces the primary role of GABA shown in our study. Importantly, these outcomes are not influenced by age (ranging from 20 to 88 years), gender or educational level.

GABA-ergic neurotransmission in the vmPFC plays a critical role in regulating the amygdala activity (Goldman-Rakic, 1999; Akirav and Maroun, 2007; Chefer et al., 2011; Moscarello and LeDoux, 2013; Courtin et al., 2014; Calhoon and Tye, 2015; Nuss, 2015). In detail, the bottom-up glutamatergic projections send excitatory input from the basolateral amygdala (BLA) to the apical dendrites of pyramidal neurons and to the dendrites of fast-spiking GABA cells in the vmPFC. GABA-ergic interneurons in the vmPFC exert a top–down inhibitory control over the excitatory pyramidal neurons, which in turn regulate the GABA-ergic interneurons within the BLA and the central medial nucleus of amygdala (CeA, which is the principal output pathway from the amygdala) (Bishop, 2007). Specifically, the top–down glutamatergic control from the infralimbic neurons within vmPFC regulates the amygdala output via GABAergic intercalated (ITC) cells (Quirk and Gehlert, 2003; Sotres-Bayon et al., 2004; Bishop, 2007) or exciting GABAergic neurons within the BLA (Constantinidis et al., 2002; Akirav and Maroun, 2007; Bishop, 2007; Chefer et al., 2011; Grace and Rosenkranz, 2002, Moscarello and LeDoux, 2013; Courtin et al., 2014; Nuss, 2015). The activation of the GABA-ergic neurons projecting from CeA to hypothalamus- brainstem and of the glutamatergic neurons from BLA to bed nucleus of the stria terminalis (BNST) leads to somatic manifestation of anxiety (Calhoon and Tye, 2015; Nuss, 2015). Hence, in normal conditions, the primary activations of the BLA and CeA, which are caused by incoming information on a potentially negative emotional stimulus, are adequately compensated by the top–down control system, which restores the normal activity of the BLA and of the CeA (Bishop, 2007; Calhoon and Tye, 2015; Nuss, 2015) (Figure 4A).

Fig. 4.

Fig. 4.

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GABA-mediated circuit between the vmPFC and the amygdala. The basolateral nucleus of amygdale (BLA) receives incoming information on potentially negative emotional signals from the thalamus and the sensory association cortex. The bottom-up glutamatergic projections send excitatory input from the BLA to GABAergic interneurons and glutamatergic fibers in the vmPFC. (Panel A) In normal functioning, the activation of the glutamatergic ‘top-down’ control restores the normal activity of the amygdala by increasing the activity of the GABAergic interneurons within the BLA and within the central medial nucleus of amygdala (CeA). (Panel B) In anxious subjects, the primary activations of the BLA and the CeA, which are caused by incoming information on potentially negative emotional stimulus, could be not adequately compensated by the top–down control system. Specifically, the high GABA content in the vmPFC reduces the top–down control and down-regulates the GABA-mediated inhibition on the BLA and the CeA. Thus, the resulting over-activation of the GABAergic neurons projecting from CeA to hypothalamus- brainstem and of the glutamatergic projections from BLA to and bed nucleus of the stria terminalis (BNST) leads to somatic manifestation of anxiety.

In both humans and animal models, the GABA receptor antagonists produce anxiogenic effects, while the GABA receptor agonists reduce anxiety and stress responses (Kalueff and Nutt, 2007). Specifically to the amygdala, it has been demonstrated on animal models that the infusions of GABA or GABA-receptor agonists into the amygdala reduce levels of fear and anxiety, while the infusions of GABA antagonists tend to have anxiogenic effects (Sanders and Shekhar, 1995; Barbalho et al. 2009). In humans, the administration of benzodiazepines attenuates the activation of the amygdala in the presence of negative emotional stimuli (Del-Ben et al. 2012). Data on GABA within vmPFC in anxiety are limited. Of note, a MRS study by Hasler et al. (2010) has observed a prefrontal GABA decrease in 10 healthy volunteers during a threat-of-shock condition vs a nonthreatening control condition. Recently, it has been demonstrated that the amygdala activity and the connectivity between vmPFC and amygdala are altered in anxious subjects (Stan et al., 2014). Particularly, Bishop et al. (2004) have reported that individuals with both low and high anxiety levels showed an increased amygdala response to attended threat-related stimuli, but only highly anxious subjects showed an augmented amygdala response to unattended threat-related stimuli. Coombs et al. (2014) have shown that both the BLA perfusion and the functional connectivity between BLA and vmPFC were related to anxiety levels in healthy subjects. Recent studies have demonstrated that the vmPFC dysregulation could be directly responsible for amygdala hyperactivity and consequent high anxiety levels (Price, 1999; Quirk and Gehlert, 2003; Rauch et al., 2006; Vidal-Gonzalez et al., 2006; Amting et al., 2010; Myers-Schulz and Koenigs, 2012). Specifically, Simpson et al. (2001) have observed a positive correlation between the activity within vmPFC and both self-reported anxiety ratings and increase in heart rate. Sladky et al. (2015) have recently suggested that the amygdala hyperactivation in patients with social anxiety disorder could be a consequence of reduced inhibitory top–down neuronal control from the vmPFC. Particularly, the reduced glutamatergic stimulation of the ITC cells and of the GABAergic neurons could reduce the GABA-mediated inhibition on the BLA and the CeA (Lewis et al. 2005; Bishop, 2007; Calhoon and Tye, 2015; Nuss, 2015). Hence, in anxious subjects, we hypothesize that the primary activations of the BLA and CeA, which are caused by incoming information on potentially negative emotional stimulus, could be not adequately compensated by the top–down control system (as conversely occurs in non-anxious subjects), causing high anxiety levels (Figure 4B).

We did not find correlations between Glx in the vmPFC and trait anxiety. This finding is in agreement with previous evidence (Chefer et al., 2011) failing to report any correlation between glutamate level in the vmPFC and amygdala activity. However, some technical consideration on the Glx complex should be addressed. First, the Glx complex describes the contributions of glutamate and glutamine. It has been reported that glutamine concentration in the brain is up to 45% higher than glutamate concentration (Jang et al., 2005). Although the glutamine is involved in energy production throughout the body (Aledo, 2004) and it may have some direct effects on neurotransmission (Duncan et al., 2014), it is mainly involved in the recycling of extracellular glutamate (Shen, 2013). Therefore, the glutamate–glutamine cycle may be of relevance to the interpretation of Glx-based results. Second, the glutamate pool within mitochondria and vesicular transporter is not detected by 1H-MRS, leaving up to 30% of this transmitter unmeasured (De Graaf and Bovée, 1990; Kauppinen and Williams, 1991; Duncan et al., 2014). Third, 1H-MRS measures cannot distinguish between synaptic and extra-synaptic glutamate (Duncan et al., 2014). At the synapse, glutamate typically shows an excitatory effect, limited by astrocyte uptake (Scanziani, 2002). At extra-synaptic level, glutamate exerts multiple effects including the modulation of oxidative stress and immune responses, and the direct regulation of blood flow which plays a role in neuronal synchronization (Rodriguez et al., 2013). Therefore, the glutamate content within vmPFC could be determined by heterogeneous processes where the different roles between extra-synaptic and synaptic glutamate must be taken into account.

Conclusions

Our findings suggest a close relationship between GABA content within the vmPFC and trait anxiety providing new insights in the physiology of emotions. Particularly, we hypothesized that subjects with higher content of GABA within the vmPFC could have a less efficient top–down excitatory modulation (glutamatergic) on GABA-ergic interneurons within the amygdala, resulting in greater amygdala activity. This hypothesis is strongly supported by recent evidence, showing a negative correlation between vmPFC activity and anxiety rates and between activity within vmPFC and amygdala (Wager et al., 2009; Kim et al., 2011). Moreover, recent evidence showing a negative correlation between GABA concentration and fMRI BOLD response amplitude (Duncan et al., 2014) suggests a strong coupling between the high GABA content and the reduced vmPFC activity. In this context, further investigations combining 1H-MRS and fMRI will be crucial to better clarify whether/how the GABA content within vmPFC influences the functioning of the vmPFC-amygdala circuit.

Supplementary Material

Supplementary Data
Click here for additional data file. (42.1KB, zip)

Funding

This study was supported by Italian Ministry of Health (Ministero della Salute); Grant number: GR-2010-2313418.

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