Altered directed functional connectivity of the right amygdala in depression: high-density EEG study

Abstract

The cortico-striatal-pallidal-thalamic and limbic circuits are suggested to play a crucial role in the pathophysiology of depression. Stimulation of deep brain targets might improve symptoms in treatment-resistant depression. However, a better understanding of connectivity properties of deep brain structures potentially implicated in deep brain stimulation (DBS) treatment is needed. Using high-density EEG, we explored the directed functional connectivity at rest in 25 healthy subjects and 26 patients with moderate to severe depression within the bipolar affective disorder, depressive episode, and recurrent depressive disorder. We computed the Partial Directed Coherence on the source EEG signals focusing on the amygdala, anterior cingulate, putamen, pallidum, caudate, and thalamus. The global efficiency for the whole brain and the local efficiency, clustering coefficient, outflow, and strength for the selected structures were calculated. In the right amygdala, all the network metrics were significantly higher (p < 0.001) in patients than in controls. The global efficiency was significantly higher (p < 0.05) in patients than in controls, showed no correlation with status of depression, but decreased with increasing medication intake ([Formula: see text]). The amygdala seems to play an important role in neurobiology of depression. Practical treatment studies would be necessary to assess the amygdala as a potential future DBS target for treating depression.

Figure 1

Parametric power spectral density (PSD) of the population subjects representing controls (A) vs patients (B) in the subcortical regions of interest. Power significantly increases within the interval [4–12] Hz (indicated with vertical dashed lines) in theta ([4–8] Hz) and alpha ([8–12] Hz) bands and decreases in delta ([1–4] Hz) and beta ([12–18] Hz) bands in patients compared to controls (p < 0.05) in the subcortical regions of interest. Continuous and dashed lines indicate the results for structures in the right and left hemispheres, respectively.

Figure 2

Boxplots to graphical illustrate the distribution of power of controls (green boxes) and patients (red boxes) in (a) [1–4] Hz, (b) [4–12] Hz and (c) [12–18] Hz. One star (*) stands for significant statistical difference with p < 0.05 and two stars (**) for p < 0.001. Power in [4–12] Hz significantly increases in patients compared to controls in all examined anatomical brain structures.

Figure 3

Local efficiency computed in the two population subjects representing (a) controls and (b) patients. Note that all subcortical regions of interest (ROIs) revealed higher values for patients than controls corresponding to the same tendency observed in all ROIs of the brain at the single-subject level (see Supplementary Figs. S1, S2). The efficiency for each ROI is represented by a sphere centered on the cortical region, whose radius is linearly related to the magnitude. Such information is also coded through a color scale.

Figure 4

Boxplots to graphically illustrate the distribution of (a) local efficiency, (b) clustering coefficient, (c) strength, and (d) outflow in controls (green boxes) and patients (red boxes). One star (*) stands for significant statistical difference with p < 0.05 and two stars (**) for p < 0.001. All network metrics that refer to the right amygdala significantly differ between controls and patients (p < 0.001), applying the Bonferroni correction (p < 0.05/12 → p < 0.0042).

Figure 5

Relationship between the intake of antidepressants/antipsychotics/mood stabilizers (AD/AP/MS) and the global efficiency (GE). Note that higher medication intake is associated with lower GE. The orange dotted line stands for the predicted value of AD/AP/MS for each patient using GE as predictor. For values of the AD/AP/MS medication scale the reader is referred to the legend of Table 2.