Neural and cognitive response to emotional faces in dizygotic twins at familial risk of depression

fMRI data analysis

We investigated the hypothesis that high-risk twins would show greater fronto-parietal response but no differential amygdala response to fearful faces with whole-brain exploratory analysis and region of interest (ROI) analysis, respectively. The hypothesis that high-risk twins display stronger negative FC between amygdala and prefrontal regions during emotional face processing was investigated with psychophysical interaction (PPI) analyses (details below).

Functional MRI data processing was performed with the FMRI Expert Analysis Tool (FEAT; version 6.00) part of FMRIB's Software Library (FSL, www.fmrib.ox.ac.uk/fsl). Pre-processing included image realignment, non-brain removal, spatial normalization to an MNI (Montreal Neurologic Institute) template and spatially smoothing (Gaussian kernel, 5 mm full-width-half-maximum). The time series in each session were high pass-filtered (to maximum 0.008 Hz). Two experimental conditions -‘fearful faces’ and ‘happy faces’- were modeled separately by convolving trials with a canonical hemodynamic response function (Boynton et al. 1996). At the group level, all analyses employed a full mixed-effects approach (Woolrich et al. 2004), a valid method with low type I errors rates (<5%) (Eklund et al. 2016). Given the exploratory nature of the study, Z (Gaussian T) statistic images were thresholded using clusters determined by Z>2.0 and a cluster significance of p<0.05 corrected for multiple comparisons at a cluster level, consistent with the approach in our MZ twin study (Miskowiak et al. 2015). Three face contrasts were chosen: fear>happy, happy>fear, fear and happy>baseline (inter-blocks with a central fixation cross). For brain regions in which significant group-by-task interactions were observed in the whole-brain analyses, cluster maxima were localized using Talairach coordinates (Talairach and Tournoux 1988). The mean percent signal change in these clusters to fearful and happy faces (vs. baseline) was extracted for illustrative purposes.

Amygdala response was investigated by creating ROIs for the left and right amygdala in standard space with mri3dX (http://www.idoimaging.com/program/160), which uses a stored representation of the Talairach Daemon Database (Lancaster et al. 2000). Mean percent BOLD signal change for each participant to fearful and happy faces was computed in left and right amygdala separately and compared between the groups. We applied Bonferroni correction to adjust for the number of comparisons of signal change from these two amygdala ROIs (p≤ 0.05/2: p≤0.025).

Functional connectivity analysis was performed by extracting for each participant a deconvolved time series for the emotional face blocks vs. baseline cluster identified within the anatomical left and right amygdala, respectively, obtained by using the amygdala structural mask for small volume correction (SVC) in FSL (thresholded at Z>2.0, p<0.05, corrected for multiple comparisons at a cluster level). These time courses were then entered in two separate PPI analyses with the functional left amygdala cluster and the functional right amygdala cluster as the seed region, respectively, along with the two psychological regressors (fear, happy) and the two PPI regressors (fear x time series, happy x time series). These contrast images were then entered into the group level (high-risk vs. low-risk twins) using a mixed-effects analysis across the whole brain to identify brain areas in which regional activity co-varied stronger with that of the left and right amygdala in one of the two groups during fear blocks, happy blocks, and emotional face blocks in general. Z-statistic images were thresholded at Z>2.0, with a cluster threshold of p<0.05, including a multiple-comparison correction. We tested for a linear relationship between significant clusters of differential connectivity between the groups and the behavioral response to fear with Pearson’s correlation analyses for PPI standardized betas versus fear vigilance/ recognition accuracy scores.

Results

Participant characteristics and mood

Demographic information, mood and coping scores for the high-risk (N=22) and low-risk groups (N=20) are presented in Table 1.  The two groups were well-matched for age, gender and education levels (p≥0.6), handedness (p≥0.1), neuroticism (p≥0.4), severe LEs in the lifetime before the baseline assessment (p≥0.07), mood and subjective state (p≥0.2) and coping style (p≥0.3). There was a strong trend towards more stressful LEs in the 7-year period prior to the fMRI study in high-risk twins (Mann-Whitney U=101, p=0.054).

 

Behavioral results

Gender discrimination and faces dot-probe

Gender discrimination during fMRI scanning showed no behavioural differences between risk groups (p≥0.3).

Dot-probe data was lost for one participant (high-risk) due to technical difficulties; the results were therefore analyzed for 41 participants (21 high-risk, 20 low-risk). High-risk twins displayed less vigilance to unmasked (supraliminal) fearful faces than the low-risk group (Mann-Whitney U=115, Z=2.5, p=0.01) (Figure 1A). Post-hoc comparisons of response times to dots replacing fearful vs. neutral faces within each group separately showed that the low-risk group displayed significant fear vigilance (i.e. faster responses to dots replacing fearful faces; t=3.32, df=19, p=0.004), whereas the high-risk group showed neither bias towards nor away from fearful faces (p≥0.4; i.e, greater indifference or ‘attention control’). There was a trend towards lower vigilance to unmasked happy faces in the high-risk vs. low-risk groups (t=1.9, df=39, p=0.07). No differences between groups were found in speed to masked emotional faces (p≥0.5) or accuracy in any condition (p≥0.1).
Facial expression recognition

There was a main effect of emotion on facial expression recognition accuracy (F(5,200)=15.0, p<0.001), reflecting greater accuracy for positive expressions (happy and surprise) than negative (anger, disgust, fear, sadness) expressions across the entire cohort (Wilcoxon signed-rank test: Z=5.2, p<0.001). High-risk twins showed reduced recognition of fear and happiness compared with low-risk twins (F(5,200)=3.3, p=0.01; U=113 Z=2.7, p=0.01; happy: U=133, Z=2.2, p=0.03) in the absence of differences in overall emotion recognition accuracy (p≥0.1) (Figure 1). Examination of fear recognition accuracy across the ten intensity levels revealed reduced recognition of moderate and high intensity fear in high-risk vs. low-risk twins (F(5.4,215)=2.4, p=0.04; 50%: U=159, Z=2.0, p=0.046; 60%: U=141, Z=2.1, p=0.04; 70%: U=130, Z=2.3, p=0.02;  80%: U=129, Z=2.3. p=0.02; 90%: U=132, Z=2.3, p=0.02; 100%: U=89, Z=3.4, p=0.001), see Figure 1. In contrast, the recognition of happiness showed no interaction between group and intensity level (p≥0.2). Speed of facial expression recognition showed no differences between groups (p≥0.3). 

 

Functional brain activation during face processing

Whole-brain fMRI analysis

Emotional faces (vs. baseline) activated a network of prefrontal and occipito-parietal regions across all participants (Figure 2, green network; for peak cluster activations see Table 2). High-risk twins showed reduced neural response to all emotional faces (vs. baseline) in the superior frontal gyrus (SFG), inferior frontal gyrus (IFG) and occipital regions of the face processing network compared with low-risk twins (Figure 2, blue clusters; for peak cluster activations, see Table 2).

Whole-brain analysis of neural response to fearful vs. happy faces revealed no main effect of task across the entire cohort (i.e, no regions were activated more by one vs. the other emotion). However, high-risk twins showed greater activity to fearful vs. happy faces within the left MFG (BA 9) and bilateral superior temporal gyri (STG; BA 22)  (Figure 2, yellow clusters; for peak cluster activations, see Table 2).
Amygdala ROI

Fearful and happy faces produced significant bilateral amygdala activation across all participants (p<0.001), but without differences between high-risk and low-risk groups (p≥0.9 and p≥0.2, respectively). There was a moderate to strong negative correlation between bilateral amygdala response to fearful vs. happy faces and the fear-specific MFG and SFG activity identified in the whole-brain analysis across the entire cohort (MFG: r(40)=-0.6, p≤0.001; right SFG: r(40)=-0.6, p≤0.001; left SFG: r(40)=-0.6, p≤0.001) and within the high-risk group (MGF: r(20)=-0.5, p=0.01; right SFG: r(20)=-0.6, p=0.003) (see Figure 2).

Functional connectivity of the amygdala

Whole-brain PPI analysis with the left amygdala functional cluster (ROI; emotional faces vs. baseline) as the seed region revealed aberrant functional coupling with the MFG (BA 10) and pgACC (BA 24) during emotional face blocks (vs. baseline) in high-risk vs. low-risk twins (see Figure 3; for peaks of the group differences, see Table 2). Post-hoc paired t-tests of the standardized betas in these clusters within each group revealed significant negative FC (anticorrelations) between left amygdala and the mPFC and pgACC clusters in high-risk twins (amygdala–mPFC coupling: t=3.1, df=21, p=0.01; amygdala–pgACC coupling: t=2.8, df=21, p=0.01), whereas low-risk twins co-activated these regions (t=2.9, df=19, p=0.01 and t=2.8, df=19, p=0.01, respectively) (see Figure 3). Whole-brain PPI analysis showed no difference between groups in FC from the right amygdala functional cluster.

Exploratory post-hoc analyses revealed that stronger amygdala-mPFC anticorrelations were associated with decreased fear recognition accuracy across the entire cohort (r(40)=0.3, p=0.03) and within the high-risk group (r(20)=0.4, p=0.050) (Figure 3). While stronger amygdala-pgACC co-activation correlated with greater fear vigilance in the low-risk group (r(18)=0.6, p=0.001), this association was absent in the high-risk group (p≥0.2).