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Psychiatry Res. Author manuscript; available in PMC Aug 30, 2010.
Published in final edited form as:
PMCID: PMC2720603
NIHMSID: NIHMS104394

Amygdala hyperactivation in untreated depressed individuals

Abstract

The amygdala participates in the detection and control of affective states, and has been proposed to be a site of dysfunction in affective disorders. To assess amygdala processing in individuals with unipolar depression, we applied a functional MRI (fMRI) paradigm previously shown to be sensitive to amygdala function. Fourteen individuals with untreated DSM-IV major depression and 15 healthy subjects were studied using fMRI with a standardized emotion face recognition task. Voxel-level data sets were subjected to a multiple-regression analysis, and functionally defined regions of interest (ROI), including bilateral amygdala, were analyzed with MANOVA. Pearson correlation coefficients between amygdala activation and HAM-D score also were performed. While both depressed and healthy groups showed increased amygdala activity when viewing emotive faces compared to geometric shapes, patients with unipolar depression showed relatively more activity than healthy subjects, particularly on the left. Positive Pearson correlations between amygdala activation and HAM-D score were found for both left and right ROIs in the patient group. This study provides in vivo imaging evidence to support the hypothesis of abnormal amygdala functioning in depressed individuals.

Keywords: depressive disorder, amygdala, facial expression, functional MRI

1. Introduction

The amygdala plays a central role in the processing of, and memory for, emotionally latent stimuli (Rolls, 2000). As part of a fronto-limbic network involved in mood regulation, the amygdala is a potential locus of dysfunction in affective disorders (Soares and Mann, 1997a; Soares and Mann, 1997b; Phillips et al., 2003). Individuals with unipolar (major) depressive disorders have difficulty discerning affective facial expressions in still photographs (Persad and Polivy, 1993), an ability linked to amygdala activity in healthy subjects (Hariri et al., 2002a). Indeed, functional neuroimaging experiments with healthy subjects have consistently demonstrated that the amygdala is selectively engaged in response to facial stimuli, particularly when for negative expressions including fear (Breiter et al., 1996; Morris et al., 1996), disgust (Phillips et al., 1997), sadness (Blair et al., 1999) or anger (Hariri et al., 2002b). Patients with unipolar depression tend to over-activate the left amygdala in response to fearful or sad faces (Fu et al., 2004; Sheline et al., 2001). This over-activation normalizes with antidepressant treatment (Fu et al., 2004; Sheline et al., 2001), a finding consistent with the observation that the metabolic rate in the left amygdala of affective disorder patients normalizes with chronic treatment (Drevets et al., 1992). However, these findings are not universal (Abercrombie et al., 1998; Irwin et al., 2004), leaving questions about the specificity of left amygdala dysfunction in major depression.

Here we apply an emotional processing task previously demonstrated to be sensitive to amygdala function (Hariri et al., 2002a) in a well characterized sample of unmediated unipolar patients in an attempt to replicate and extend previous findings of amygdala hyperactivation in response to emotionally expressive faces. Specifically, this study assessed activation in response to faces expressing anger and fear in untreated depressed patients as well as healthy controls. We hypothesized that depressed patients would have greater amygdala activation compared to matched healthy controls.

2. Methods

2.1. Subjects

Fourteen patients with unipolar depression and 15 healthy subjects matched for age (mean years: 37.9±14 vs. 37.9±12, respectively, t=.02, p=.99), gender (% female: 64% vs. 53% χ2=.55, p=.71), and handedness (% right handed: 79% vs. 93%, χ2=1.66, p=.40) participated in this study. Subjects were recruited from advertisements soliciting volunteers for ongoing imaging studies. All subjects provided written informed consent in accordance with the local IRB. To be included in the study, patients were required to be at least 18 years of age, have DSM-IV diagnosis of major depressive disorder (First et al., 1996b), have a score of 18 or more on the 21-item Hamilton Rating Scale for Depression (HAM-D (Hamilton, 1960); average rating 20.0±1.4), and be free of psychotropic medication. Patients were excluded if they had history of substance abuse within 6 months of study participation or had any current comorbid Axis I disorder, except for anxiety disorders. The inclusion criteria for healthy controls were: at least 18 years of age, no history of a DSM-IV Axis I disorder (First et al., 1996a), and no first-degree relative with any Axis I disorder. Exclusion criteria for all subjects were presence of chronic illnesses, including hypertension, diabetes, chronic obstructive pulmonary disease (COPD), and liver or kidney diseases; current thyroid dysfunctions; and history of neurological disorders or head trauma resulting in loss of consciousness.

2.2. Activation paradigm

The emotive face identification task employed was previously shown to robustly activate the amygdala (Hariri et al., 2002a). It consisted of nine 32.5 second blocks: 2 blocks of matching a target face with one of two emotive faces (expressing anger or fear), 2 blocks of labeling an emotive face indicating the affect that best described it (angry or afraid), and 5 sensory-motor control blocks interleaved in which subjects matched one of two geometric shapes with a target one. Twelve different images, selected from a standard set of pictures of facial affect (Ekman and Friesen, 1975), were used. Images were presented for 5 seconds in a pseudo-randomized fashion.

2.3. Image acquisition and analysis

Images were acquired on a 2T Prestige whole-body MRI scanner (General Electric Medical System, Milwaukee, Wisconsin). Functional imaging used a gradient echo-planar sequence sensitive to the BOLD effect. Twenty-four axial slices were acquired (TR/TE/flip angle: 2000ms/45ms/90°; voxel = 3.33 × 3.33 × 4 mm). After the functional imaging acquisition, a high resolution 3D T1-weighted image was obtained for anatomical localization (TR/TE/angle: 25ms/5ms/25°; voxel = 1 × 1 × 1 mm).

Functional image analyses were performed with tools available as part of the FSL software package (www.fmrib.ox.ac.uk/fsl/) (Smith et al 2004) and supplemented with utilities developed in-house. To combat potential motion artifacts, each image in a time series underwent spatial registration to the middle data point in the time series. Data were smoothed with a non-linear algorithm designed to preserve image structure by only smoothing voxels thought to be of the same tissue type (5 mm kernel). Each data set was subjected to a multiple-regression analysis, using a pre-whitening technique to account for the intrinsic temporal autocorrelation of BOLD imaging. The threshold of a z (Gaussianised T/F) statistic image was estimated by clusters determined by z > 2.3 and a corrected cluster significance threshold of p = 0.01 (Forman et al., 1995). For each intra-cranial voxel, least-squares coefficients were generated independently reflecting the emotion and control conditions and statistical images tested hypotheses about emotional processing.

To facilitate multi-subject analysis and based on landmarks from the higher resolution anatomical image, statistical images were normalized to a standard coordinate reference frame (Talairach and Tournoux, 1988). Higher-level multi-subject analysis utilized a mixed effects model, where subject was represented as random factor, providing z-images reflecting group activation patterns (depressed and healthy comparison subjects). Group maps were thresholded based on the magnitude (z ≥ 2.3) and extent (cluster significance, corrected for multiple comparisons, p<0.01; 9 contiguous voxels) of activation (Forman et al., 1995).

In addition, anatomically defined regions of interest (ROI) were drawn for the amygdala, bilaterally, and included an 8-mm cube centered around the maximum activation reported previously with the identical task (Hariri et al., 2002a): left -28, -6, -16; right 30, -4, -16. ROIs were overlaid on each subject's images to determine the percent signal change for the affective compared to the control (e.g., sensory motor task) condition. Percent signal change data were entered into a 2 × 2 MANOVA, testing main and interactive effects of diagnostic group (depressed vs. control) and hemisphere (left vs. right), with diagnosis as a between group effect and hemisphere entered as a repeated measure factor. Significant main effects or interactions (p<0.05, two tailed) were decomposed with univariate methods.

Finally, the Pearson correlation coefficients between amygdala activation and HAM-D score were performed for each diagnostic group and hemisphere to assess whether amygdala activation is related to severity of mood symptoms.

3. Results

Voxel-level analysis revealed a network of brain regions engaged by the emotion task, including bilateral posterior fusiform gyri (including putative face-processing area (Grill-Spector et al., 2004)), inferior parietal lobules, frontal eye fields, striate and extra striate cortex, anterior cingulate gyrus, bilateral hippocampal and parahippocampal gyri, and bilateral amygdala (Figure 1). This network of regions was observed in both healthy and patient groups and has been previously reported with this task (Hariri et al., 2002a; Hariri et al., 2002b).

Figure 1
Voxel Level Activation Patterns During An Emotive Facial Discrimination Task

ROI analyses found a significant main effect of diagnosis (F [1,27]=5.96, p=0.02), suggesting that the diagnosis of unipolar depression is associated with amygdala dysregulation (Figure 2). Furthermore, the lack of a significant effect for hemisphere (F [1,27] = 0.05, p>0.05) or a significant hemisphere × group interaction (F [1,27] = 0.82, p>0.05), indicates that this effect may not be lateralized. However, univariate analyses designed to decompose these findings determined that while patients and controls were significantly different within the left amygdala (% signal change (standard deviation): 1.06 (0.3) vs. 0.78 (0.2), respectively; F [1,27] = 7.62, p=0.01), this difference did not reach formal significance on the right (1.02 (0.2) vs. 0.81 (0.3); F [1,27] = 3.60, p=0.07).

Figure 2
Amygdala Region of Interest Analysis

To determine if this amygdalae hyperactivation was associated with gender, the ROI analysis was repeated while including gender as a factor. The effect for gender on amygdala activation was not significant (F [1,27] = 0.29, p=0.59), nor was gender × diagnostic group interaction significant (F [2,26] = 0.07, p=0.77).

Positive Pearson correlations between amygdala activation and HAM-D score were found for both left (r=0.53, p=0.05) and right (r=0.54, p=0.05) ROIs in the patient group, suggesting that increased depressive symptoms was associated with stronger amygdala activation. In contrast, correlations between amygdala activity and HAM-D scores in the healthy subjects were negative and non-significant (left r=-0.40, p=0.22; right r=-0.01, p=0.97).

4. Discussion

Our primary finding is that currently depressed unipolar individuals have greater amygdala activation than non-depressed healthy comparison subjects. This study replicates previous findings of left amygdala hyperactivation when patients with unipolar depression process negative affective faces (Fu et al., 2004; Sheline et al., 2001). We did not find any right amygdala activation change in patients relative to controls, nonetheless the between group effect sizes for the left (Cohen's d=1.15) and right (d=0.85) amygdala regions of interest were both large and suggest comparable group differences for both hemispheres. This assertion is strengthened by comparable correlation coefficients between left and right amygdala activation and clinical ratings of depressive symptomatology (0.53 vs. 0.54), suggesting that relationship between amygdala reactivity to emotive faces and depressive symptoms is similar across hemispheres. Thus, our data support the notion of increased amygdala activation in depressed unipolar subjects but do not support the lateralization of this hyperactivation.

Although Drevets and colleagues (Drevets et al., 1992) found greater resting metabolism in the left amygdala of depressed patients compared to healthy controls, Abercrombie and colleagues (Abercrombie et al., 1998) did not replicate these findings. Indeed, they found that resting metabolic rates of the right amygdala, but not of the left, predicted negative affect in the depressed group (Abercrombie et al., 1998). Irwin and colleagues (Irwin et al., 2004) reported no group differences in task-activated or resting-state amygdala activation in a series of experiments including an fMRI study and two separate PET scans of depressed and healthy individuals. Together, these data suggest that while amygdala hyperactivation is rather consistent across studies and modalities, the lateralization of these findings is in doubt and should be carefully examined in subsequent, well powered, experiments.

This study has some limitations: 1) no subjective ratings of the emotional faces were collected, therefore it was not possible to evaluate if the patients perceived the emotional value of the faces different from the controls; 2) no recognition rates of the emotions in the faces were collected as well, therefore there was no way to ascertain whether the patients and controls had comparable recognition rates of these emotions; 3) as angry and fearful faces were presented at the same time in this experimental paradigm, it could not be investigated if they elicit different BOLD responses in the amygdala and other brain regions.

In conclusion, this study aimed specifically to assess the amygdala response to fearful and angry faces and more generally the amygdala response to emotional stimuli. Our results provide in vivo imaging evidence to support the hypothesis of abnormal amygdala functioning in depressed individuals, replicating and further extending previous findings of amygdala hyperactivation in response to emotional stimuli.

Figure 3
Pearson correlation coefficients between amygdala activation and HAM-D score

Acknowledgments

This research was partly supported by grants MH 01736, MH 068662, RR020571, UTHSCSA GCRC (M01-RR-01346), Dana Foundation, the Krus Endowed Chair in Psychiatry (UTHSCSA), the Veterans Administration (VA Merit Review), and CNPq (“Conselho Nacional de Desenvolvimento Científico e Tecnológico”, Brazil – grant# 200006/04-5).

Footnotes

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