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. Author manuscript; available in PMC: 2024 Mar 27.
Published in final edited form as: Brain Behav Immun. 2022 May 27;104:205–212. doi: 10.1016/j.bbi.2022.05.015

Exaggerated amygdala response to threat and association with immune hyperactivity in depression

Sarah Boukezzi a, Sara Costi a, Lisa M Shin b,c, Seunghee Kim-Schulze a, Flurin Cathomas d, Abigail Collins a, Scott J Russo a,d, Laurel S Morris a,1, James W Murrough a,d,1,*
PMCID: PMC10966680  NIHMSID: NIHMS1971186  PMID: 35636614

Abstract

Background:

Depression is characterized by altered neurobiological responses to threat and inflammation may be involved in the development and maintenance of symptoms. However, the mechanistic pathways underlying the relationship between the neural underpinnings of threat, inflammation and depressive symptoms remain unknown.

Methods:

Twenty participants with major depressive disorder (MDD) and 17 healthy controls (HCs) completed this study. Peripheral blood mononuclear cells (PBMCs) were collected and stimulated ex vivo with lipopolysaccharide (LPS). We then measured a broad array of secreted proteins and performed principal component analysis to compute an aggregated immune reactivity score. Subjects completed a well-validated emotional face processing task during functional magnetic resonance imaging (fMRI). Amygdala activation was measured during perception of threat for the main contrast of interest: fear > happy face. Participants completed the Mood and Anxiety Symptom Questionnaire (MASQ) and the Perceived Stress Scale (PSS). Correlation analyses between amygdala activation, the aggregate immune score, and symptom were computed across groups. A mediation analysis was also performed across groups to further explore the relationship between these three variables.

Results:

In line with our hypotheses and with prior work, the MDD group showed greater amygdala activation in response to threat compared to the HC group [t35 = −2.038, p = 0.049]. Internal consistency of amygdala activation to threat was found to be moderate. Response to an ex vivo immune challenge was greater in MDD than HC based on the computed immune reactivity score (PC1; t35 = 2.674, p = 0.011). Amygdala activation was positively correlated with the immune score (r = 0.331, p = 0.045). Moreover, higher amygdala activation was associated with greater anxious arousal measured by the MASQ (r = 0.390, p = 0.017). Exploring the role of stress, we found that higher perceived stress was positively associated with both inflammatory response (r = 0.367, p = 0.026) and amygdala response to threat (r = 0.325, p = 0.050). Mediation analyses showed that perceived stress predicted anxious arousal, but neither inflammation nor amygdala activation fully accounted for the effect of perceived stress on anxious arousal.

Conclusion:

These data highlight the potential importance of threat circuitry hyperactivation in MDD, consistent with prior reports. We found that higher levels of inflammatory biomarkers were associated with higher amygdala activation, which in turn was associated with anxious arousal. Future research utilizing larger sample sizes are needed to replicate these preliminary results.

Keywords: Inflammation, Major depressive disorder, Threat, Anxious arousal, Amygdala, Leukocytes, Neuroimaging, Functional MRI, Stress

1. Introduction

Depression is a common psychiatric disorder and a leading source of disability and disease burden worldwide (Friedrich 2017). While anhedonia and depressed mood are core symptoms of depression, abnormal threat processing plays a critical role in the development and maintenance of symptoms (Dillon et al. 2014). The study of depression has been conducted focusing primarily on a categorical approach over past decades (Bech and Allerup 1986), however modern research encourages the exploration of specific domains of functioning and their underlying neurobiological mechanisms rather than the investigation of the disorder as a categorical entity (Kozak and Cuthbert 2016). Investigating this condition using both a dimensional and categorical approach might help contribute to the emergence of personalized treatments.

Anxiety disorders, and symptoms of anxious arousal, are highly comorbid with major depressive disorder (MDD) (Costello et al. 2019). Post-traumatic stress disorder (PTSD) is also highly comorbid in MDD, and the perception of threat in both MDD and PTSD are characterized by abnormal anxious arousal (Brown et al. 2001) and amygdala hyper-activation to negative stimuli (Felger 2018). Anxiety and depression might arise from similar biological processes (Griffith et al. 2010; Krueger and Markon 2006) and be phenotypic expressions of a common underlying risk factor. For example, abnormal threat perception associated with higher levels of anxious arousal in depression might contribute to a cascade of temporal processes in the development of depression such that anxiety in some cases precedes depressive symptoms such as anhedonia, fatigue or dysregulated appetite (Slavich and Irwin 2014). From a neural perspective, the inability to cope with stressful situations and regulate negative emotions efficiently in MDD has also been associated with amygdala hyperactivity (Sheline et al. 2001) during resting state (Drevets et al. 1992; Drevets et al., 2002) and task-based functional imaging during negative facial expression viewing (Beesdo et al. 2009; Ma 2015). Neuroimaging studies have also highlighted hypoactive prefrontal activity alongside hyperactive amygdala activity in individuals with MDD (Siegle et al. 2007).

Depression has been characterized by elevated inflammation as demonstrated by several meta-analyses (Enache, Pariante, and Mondelli 2019; Osimo et al. 2020; Strawbridge et al. 2015). Inflammation may adversely impact brain functioning in depression by interacting with both the reward and threat circuitries (Felger 2018). Elevated concentrations of circulating inflammatory factors including Interleukin (IL)-1, IL-6, tumor necrosis factor (TNF), and C-reactive protein (CRP) have been found in depression (Felger, Haroon, and Miller 2015; Goldsmith, Rapaport, and Miller 2016). Prior research has linked exaggerated inflammation to abnormal activation of brain structures regulating motivation and response to reward (Costi et al., 2021; Felger and Lotrich, 2013; Miller et al., 2013). However, to date the relationship between inflammation and functional activation of the threat circuitry in MDD remains under-explored. In healthy subjects, threat-related amygdala activity was positively associated with peripheral inflammation in men but not in women (Swartz, Prather, and Hariri 2017). Amygdala stress reactivity has been positively correlated with inflammation in a sample of children exposed to chronic stressors (Miller et al. 2021).

Inflammatory response has shown to be positively associated with anxious arousal (Costello et al. 2019). Experimentally, inflammation can be induced by the lipopolysaccharide (LPS) administration, either in vivo or ex vivo. LPS activates the innate immune system and induces inflammatory response in both the periphery and the brain as shown in preclinical studies (Hoogland et al. 2015; Li et al. 2017; Mello et al. 2013) and in one clinical study (Lasselin et al. 2017). Our group recently reported on the relationship between peripheral LPS-induced immune activation of peripheral blood mononuclear cells (PBMCs) on brain reward systems and behaviors in patients with MDD and healthy volunteers (Costi et al. 2021). In that study, we found that higher peripheral immune activation was associated with reduced neural responses to reward anticipation within the ventral striatum, as well as with reduced self-reported anticipation of pleasure.

The impact of stress on depressive symptoms has been extensively explored (Gold 2015; Tafet and Nemeroff 2016) and stress is as well-known driver of depression risk and symptom severity (Cohen, Kamarck, and Mermelstein 1983; Lazarus 1966; Lazarus and Folkman 1984). The construct of perceived stress in particular is important, as prior research has shown that the way individuals perceive and interpret stressful events can worsen mental health outcomes and foster psychological distress (Hammen 2005; Pruchno and Meeks 2004). Chronic stress including psychosocial stress and high levels of perceived stress (Martínez de Toda et al. 2019) activates inflammatory responses, and leads to excessive release of pro-inflammatory cytokines (Capuron and Miller 2011). Limited prior work also suggests that inflammation may drive an anxious phenotype within depression, in part through exaggerated sensitivity to threat (Haroon, Raison, and Miller 2012; Slavich and Irwin 2014). A better understanding of the mechanistic pathways by which stress, inflammation, anxiety and the threat circuitry interact and contribute to MDD is critical.

The goal of our study was to determine the relationship between inflammation and amygdala activation during the perception of threat, and their relationship with symptoms of anxious arousal. We examined a large panel of peripheral immune factors from PBMCs stimulated ex vivo with LPS. To interrogate threat circuitry with a particular focus on the amygdala, we asked participants to complete an emotional face-processing task (Hariri et al. 2002) during fMRI. We predicted that both peripheral inflammation and amygdala activation in response to threat will be greater in individuals with MDD relative to healthy control participants. We further hypothesized that peripheral inflammation would be positively correlated with amygdala activation across groups (Miller, Haroon, and Felger 2017). Finally, we explored the influence of perceived stress on relationships between amygdala response to threat, inflammation, and anxious arousal across groups.

2. Materials and methods

2.1. Participants

The current study is a new analysis of a cohort previously reported in (Costi et al. 2021). Participants aged between 18 and 55 years old were recruited at the Icahn School of Medicine at Mount Sinai (ISMMS) in New York City. Participants in the MDD group met the DSM-V criteria for MDD, as assessed by a trained interviewer, using the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders-Fifth Edition Text Revision (DSM-V-TR) Axis I Disorders-Patient (SCID-I/P)(First et al. 2002). Healthy control (HC) participants who did not meet the criteria for any psychiatric disorders were also enrolled. Subjects were excluded if they had another psychiatric or neurological disorder, a history of inflammatory or autoimmune disorder, positive urine toxicology test for illicit drugs, medication or nutritional supplement known to affect inflammation within one week of assessment or any unstable medical illnesses. See (Costi et al. 2021) for further detailed inclusion/exclusion criteria. Participants completed a blood draw for analyses of peripheral inflammatory factors, and completed clinical and neuro-behavioral assessments. All study procedures were approved by the institutional board at ISMMS, participants provided written informed consent and were compensated for their time.

2.2. Clinical assessment

MDD severity was assessed using the Montgomery-Asberg Depression Rating Scale (MADRS) (Montgomery and Asberg 1979). Dimensions of general distress, anhedonia and anxiety were measured with the Mood an Anxiety Symptom Questionnaire (MASQ). This is a validated questionnaire that is based on the tripartite model of affect, proposed to account for comorbidity between depression and anxiety disorders (Watson 1995) and has 3 sub-scores: general distress, anhedonic depression, and anxious arousal. We focused our analyses on the anxious arousal as our symptom domain of interest. Perceived stress was assessed using the perceived stress scale (PSS) (Cohen et al. 1983). This is a widely used instrument for measuring the degree to which an individual has perceived life as unpredictable, uncontrollable, and overloading during previous months, and the degree to which external demands exceed the individual’s perceived ability to cope.

2.3. Blood collection and processing

Procedures related to blood and processing have been described in detail in (Costi et al. 2021). Blood samples were collected in a fasting state at approximately 8 am on the day of the neuroimaging scanning session. Briefly, blood was processed within 2 h of collection and spun at 1500 rpm for 10 min. After plasma collection, cell fraction was diluted with phosphate buffered saline (PBS), and centrifuged for 20 min at 1800 rpm. The peripheral blood mononuclear cells (PBMCs) layer was isolated. The LPS stimulated purified PBMCs were treated with LPS at 0.1 microg/ml in RPMI + 5% FBS at 0.5 Million cell/well in 96 well tissue culture plate at 37 °C for six hours and samples were analyzed using Olink multiplex assay – Inflammatory panel (Olink Bioscience, Uppsala, Sweden). The inflammatory panel included 92 proteins. Briefly, pairs of oligonucleotide-labeled antibodies to each protein were added to the samples and incubated for 16 h at 4°. Each protein was targeted with different epitope-specific antibodies leading to the formation of a double strand oligonucleotide polymerase chain reaction (PCR) target. Amplicons were generated the next day using PCR in 96 well plates. Specific protein was detected via a primed dynamic array Integrated Fluidic Circuit (IFC) 96×96. This was then loaded with 92 proteins and mixed with sample amplicons including inter-plate controls (IPC) and negative controls. Data were normalized using internal controls, IPC and negative controls. One Normalized Protein eXpression (NPX) difference equals the doubling of the protein concentration. Further details on blood processing are provided in our previous paper (Costi et al. 2021).

2.4. MRI acquisition and processing

Neuroimaging data were acquired with a Siemens 3-T MAGNETOM Skyra scanner and 32-channel head coil at ISMMS’s BioMedical Engineering and Imaging Institute (BMEII). Anatomical T1-weighted images were magnetization-prepared 2 rapid gradient-echo sequence (MP2RAGE, TR = 4,000 ms, TE = 1.9, inversion 1/2 time = 633/1,860, field of view = 186 × 162, voxel resolution = 1 × 1 × 1 mm). Functional scans during the emotional face processing task were acquired with a multi-echo, multiband, accelerated echo planar imaging sequence (TR = 882 ms, TE = 11.0, 29.7, 48.4, 67.1, multiband factor = 5, field of view = 560 × 560, voxel resolution = 3 × 3 × 3 mm, flip angle = 45°). Images were robustly preprocessed and denoised motion and physiological noise using multi-echo independent component analysis (ME-ICA) (Kundu et al. 2012).

2.5. Emotional face processing task

We used the emotional face processing (EFP) task adapted from the original one developed by (Hariri et al. 2002). This task has been shown to robustly engage the amygdala activation in both healthy volunteer cohorts and individuals with psychiatric disorders (Avinun et al. 2018; Salgado-Pineda et al. 2010). In this modified version of the task, participants matched one of two cues (shown at the bottom of the screen) to a corresponding target (centered at the top of the screen) via a left or right button press using their dominant hand. Participants were instructed that they would be presented with two different types of cues: shape and face cues. In face-matching conditions, participants indicated which of the two facial cues (one of which was always neutral) matched the emotion of the target face (happy, angry, fear or neutral). In the shape-matching condition, participants matched simple geometric shapes. Our paradigm consisted of two runs of 15 blocks of each angry, happy, neutral, and fear face-matching interleaved with 20 shape-matching control blocks. The entire task was approximately 6 min in duration. Each run consisted of 60 trials (30 face-matching blocks and 30 shape-matching blocks) and each condition-matching block was presented for a maximum of 5000 ms.

2.6. Statistical analyses

Demographic, behavioral and clinical data were analyzed with SPSS software (version 19; SPSS, Inc., Chicago, IL, USA). Demographic data were assessed using X2 and Mann-Whitney U tests when appropriate, and behavioral and clinical data were assessed with independent samples t-test or analysis of variance (ANOVA). Data were assessed for normality; non-normally distributed data were log-transformed prior to analyses. Alpha level was set at 0.05.

Reaction Time (RT) in ms from the stimulus onset relative to subject response and the level of accuracy, corresponding to the percentage of correct responses to stimuli-matching conditions, were computed for the face and shape conditions. RT and accuracy were analyzed using a 2 (Group: HC/MDD) × 4 (Face: fear/angry/happy/neutral) ANOVA.

Proteins with > 50% missing the limit of detection (LOD) values were excluded from the analysis. Outliers, defined as standard deviations > 3 were excluded separately for each immune factor (Sundkvist et al. 2018). The remaining proteins had < 5% missing data. Predictive mean matching was used for imputation of missing data using multivariate imputation by chained equations (MICE) using R software (Buuren and Groothuis-Oudshoorn, 2011). To determine the influence of stimulation, the proteins’ fold change was calculated as the value of the protein released by PBMC following LPS stimulation divided by the un-stimulated value of the protein secreted by PBMC prior to the LPS challenge. The resultant fold-change protein data (42 proteins × 51 subjects) were entered into principal component analysis (PCA) to reduce the dimensionality of the data, while retaining the majority of variability within the data. A scree plot of eigenvalues was computed and inspected to indicate component significance (See Supplemental Methods). The first four components preceded the scree plot eigenvector elbow, all had an eigenvector value>1 and were considered significant. Principal component 1 (PC1) explained a large proportion of the variance (51.07%), with an eigenvector of 13.46. PC2 explained 15.28% variance, with an eigenvector of 4.03. PC3 explained 11.07% variance, with an eigenvector of 2.92. PC4 explained 5.42% variance with an eigenvector of 1.43 (Fig. S1A). Similar to our previous report in a slightly larger, but overlapping sample (Costi et al. 2021), PC1 included highest factor loadings for tumor necrosis factor (TNF), IL-12 receptor subunit beta (IL-12B), C–C motif chemokine 20 (CCL20), IL-6, monocyte chemotactic protein 3 (MCP3) and (CCL23) (Fig. S1B). All subsequent analyses focused on the immune score PC1. The first significant component from the PCA, namely the immune score PC1, was examined for group differences based on individual factor loadings using independent samples t-test, and used for correlations with self-report questionnaires, clinician administered scales, and measures of brain activation using two-tailed Pearson’s correlation.

Neuroimaging data were analyzed using AFNI (Cox and Hyde, 1996). Preprocessing was completed using the afni_proc.py function. First level analysis utilized a general linear model (GLM) with regressors for cues onset (angry, fear, happy, neutral, shape), each including duration modulation using AFNI’s stim_DM function, and convolved with the hemodynamic response function. Our main goal was to determine the neural mechanisms underlying threat circuitry, and more particularly, amygdala activation during threat perception, so our analyses focused on the main contrast of interest: fear > happy face. In order to test our a priori hypothesis, activation for each contrast within the structural amygdala region of interest (ROI) was extracted from FSL’s Harvard-Oxford atlas for each subject and entered into independent samples t-test for group difference comparison.

In light of previous studies suggesting low task-based consistency (Elliott et al. 2020; Infantolino et al. 2018; Lois et al. 2018; Nord et al. 2017), internal consistency of the task-based fMRI measures of interest was calculated. Please see Supplemental Materials for details.

To complement our hypothesis-driven approach focusing on the amygdala, we conducted an exploratory whole-brain analysis. For this, a voxel wise, cluster-defining threshold of p < 0.005 was used plus cluster-wise correction of k > 57 as calculated by AFNI’s new autocorrelation function (ACF) to mitigate against spatial autocorrelation and the incidence of false-positives (Cox et al. 2017; Eklund, Nichols, and Knutsson 2016).

Mediation analyses were run with the Lavaan (Rosseel 2012) package within the R statistical environment (R Core Team 2020). We hypothesized that perceived stress (measured with the PSS) would predict anxious arousal, amygdala activation and inflammation (summarized with the immune score PC1), and that inflammation and amygdala activation would mediate the effect of perceived stress on anxious arousal. Therefore, in our model, PSS was defined as a predictor, PC1 and amygdala activation were defined as mediators, and anxious arousal was defined as the outcome.

3. Results

3.1. Demographic and clinical data

The groups did not differ in terms of age, gender, body mass index (BMI), education, race, ethnicity, employment, or relationship status (Table 1). Participants with MDD had higher scores on the MADRS than HC (t35 = 17.758, p < 0.001), and on the PSS (t35 = 4.482, p < 0.001) indicating higher levels of depression, and perceived stress, respectively. MDD subjects also showed higher scores on the MASQ general distress sub-score (t35 = −6.097, p < 0.001), on the MASQ anhedonia sub-score (t35 = −7.492, p < 0.001) and on the MASQ anxious arousal sub-score (t35 = 3.181, p < 0.001).

Table 1.

Demographic and clinical characteristics of participants

Group MDD (N=20) HC (N=17) Between-group comparisons
Mean (SD) Mean (SD)
Age at enrollment (years) 34.9 (10.3) 37.6 (10.3) t(35)=0.795, p=0.432
Age at depression onset (years) 18.3 (8.1) - -
BMI 25.9 (5.8) 24.4 (4.7) t(35)=−0.911, p=0.369
N (%) N (%)
Male 10 (50.0) 7 (41.2) χ2(1,37)= 0.288, p=0.591
Race
 White/caucasian 13 (65.0) 8 (47.1) χ2(1,37)=1.205, p=0.272
Ethnicity
 Hispanic/latino 6 (30.0) 3 (17.6) χ2(1,37)=0.762, p=0.383
Employment
 Employed 16 (80.0) 13 (76.5) χ2(1,37)=0.068, p=0.795
Education
 Bachelor degree 16 (80.0) 15 (88.2) χ2(1,37)=0.459, p=0.498
Relationship status
 Married 1 (5.0) 3 (17.6) χ2(1,37)=1.524, p=0.217
Psychiatric comorbidities
 Anxiety disorder* 9 (45.0) - -
 PTSD current 5 (25.0) - -
Episode (single/recurrent) 12/8 (60/40) -
Current medication* 3 -
Mean (SD) Mean (SD)
MADRS 27.55 (5.9) 1.18 (1.7) t(35)=17.758, p<0.001
PSS 22.50 (7.4) 10.47 (8.8) t(35)=4.482, p<0.001
MASQ general distress 30.30 (10.3) 12.92 (6.0) t(35)=−6.097, p<0.001
MASQ anhedonia 45.45 (4.11) 27.76 (9.59) t(35)=−7.492, p<0.001
MASQ anxious arousal 14.25 (4.41) 10.71(1.36) t(35)=−3.181, p<0.001
Median (Interquartile range) Median (Interquartile range) -
hs-CRP level 1.4 (0.1–10.2) 1.3 (0.2–5.7) U=173.0, p=0.731
Duration of illness (months) 8.0 (6–48) - -
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BMI, Body Mass Index; hs-CRP, high-sensitivity C-reactive protein; M, Mean; MADRS, Montgomery–Åsberg Depression Rating Scale; MASQ; Mood and Anxiety Symptom Questionnaire (MASQ); MDD, Major Depressive Disorder; HC, Health Control; PSS, Perceived Stress Scale; PTSD, Posttraumatic stress disorder; SD, Standard deviation; Race and ethnicity were reported by the study participants. Anxiety disorder*: n=1 with Social phobia and generalized anxiety disorder (GAD), n=1 with GAD only, n=4 with social phobia only, n=3 with both social phobia and GAD.

Current medication*: Methylphenidate (n=1), Lorazepam (n=1), Adderall (n=1)

3.2. Neuroimaging data and immune markers

In our primary analysis, activation within the bilateral amygdala was examined for the main contrast of interest: fear > happy face (Fig. 1A). Behavioral responses during the fMRI task are reported in Supplemental Results. Consistent with our hypotheses and with prior work, amygdala activation was greater in the MDD group compared to the HC group [t(35) = −2.038, p =0.049] (Fig. 1A). This effect was driven by the right amygdala [t(35) = −2.173, p = 0.037] though there was a trend in the similar direction for the left amygdala [t(35) = −1.407, p = 0.168]. Our data showed moderate internal consistency of brain activation during the task for our contrast of interest (See Supplemental Results for details). Additional whole brain analyses were performed for the fear > happy face contrast (See Supplemental Results).

Fig. 1. Amygdala hyperactivation in depression and heightened peripheral blood mononuclear cells reactivity.

Fig. 1.

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A. Left panel: Amygdala region of interest (ROI). Right panel: Group difference in amygdala activation in the major depressive disorder (MDD) group compared to the healthy control (HC) group. B. Group difference in immune reactivity, computed using the principal component 1 (PC1) from a Principal Component Analysis in the MDD group compared to the HC group (see text for details). C. Correlation between amygdala response to threat (fear > happy faces) during the task in MDD and HC, and PC1 factor loading. *p < 0.05, error bars represent standard error of the mean (SEM).

As expected, the immune score was higher in the MDD group compared to the HC group (PC1; t35 = 2.674, p = 0.011) (Fig. 1B). Across groups, amygdala activation during threat was positively correlated with the immune score (r = 0.331, p = 0.045) (Fig. 1C).

3.3. Relationships between brain, immune, and clinical measures

3.3.1. Neuroimaging data

We investigated the relationship between amygdala activation and clinical measures. MADRS was not associated with amygdala activation across groups (r = 0.219, p = 0.193) or within the MDD group (r = −0.407, p = 0.075). Amygdala activation was positively correlated with anxious arousal across groups (r = 0.390, p = 0.017) but not with general distress (r = 0.196, p = 0.249) or anhedonia (r = 0.312, p = 0.060). Amygdala activation was also positively correlated with PSS (r = 0.325, p = 0.050) (Fig. 2A). There were no correlations within the MDD group only.

Fig. 2. Relationship between amygdala activation, inflammation, anxious arousal and perceived stress.

Fig. 2.

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A. Correlation between amygdala response to threat (fear > happy face) during the task in MDD and HC and score on the Perceived Stress Scale (PSS). B. Correlation between perceived stress and immune activation as represented by principal component 1 (PC1). C. Mediation analysis. Estimates are reported. In this model, PSS score is defined as the predictor, PC1 and amygdala activation are defined as mediators, and Mood and Anxiety Symptom Questionnaire-Anxious Arousal (MASQ-AA) is defined as the outcome. a1: direct effect of PSS on PC1; a2: direct effect of PSS on amygdala activation; b1: direct effect of PC1 on MASQ-AA; b2: direct effect of amygdala activation on MASQ-AA; c: direct effect of PSS on MASQ-AA; a1xb1: indirect effect of PSS on MASQ-AA with PC1 as mediator; a2b2: indirect effect of PSS on MASQ-AA with amygdala activation as mediator c’: total indirect effects *p < 0.05.

3.3.2. Immune markers

Across groups, there was a positive association between symptoms of depression measured by MADRS total and immune score (r = 0.348, p = 0.035). There were no significant associations within the MDD and HC groups separately.

Across groups, immune score positively correlated with general distress (r = 0.401, p = 0.014), but not with the anxious arousal (r = 0.261, p = 0.119) or anhedonia (r = 0.257, p = 0.125) subscales. In the MDD group only, PC1 did not correlate with any of the MASQ subscores. Immune score was positively correlated with PSS across groups (r = 0.367, p = 0.026) (Fig. 2B). Within the MDD group alone, immune score showed a trend towards a positive correlation with PSS (r = 0.433, p = 0.057).

3.3.3. Relationship between immune system, amygdala, anxious arousal and perceived stress

In order to more fully understand the relationship between amygdala activity, immune factors and behavioral measures of symptoms and stress, we conducted a mediation analysis. Results of the mediation analysis across all participants showed that PSS has a direct effect on immune score (βa1 = 0.134, SE = 0.056, z = 2.397, p = 0.017), on amygdala activation (βa2 = 0.033, SE = 0.016, z = 2.088, p = 0.037), and on anxious arousal (βc = 0.140, SE = 0.059, z = 2.385, p = 0.017). Anxious arousal was not predicted by immune score (βb1 = 0.041, SE = 0.154, z = 0.264, p = 0.792), or by amygdala activation (βb2 = 0.951, SE = 0.547, z = 1.739, p = 0.082). Immune score and amygdala activation did not account for the effect of PSS on anxious arousal (Fig. 2C).

4. Discussion

In the current study we aimed to determine the relationship between amygdala activation during perception of threat and inflammatory responses in individuals with depression. We also endeavored to understand the relationships between perceived stress, anxious arousal, inflammatory responses and amygdala reactivity to threat in depression.

In line with previous research, individuals with MDD showed exaggerated peripheral immune reactivity to an inflammatory challenge (e.g., LPS) (Felger 2018) and amygdala hyperactivation to threat (Beesdo et al. 2009) compared to HC. Interestingly, peripheral inflammation was positively associated with amygdala reactivity to threat, anxious arousal, and perceived stress dimensionally across groups. Amygdala activation was also positively correlated with anxious arousal and perceived stress across groups. Finally, perceived stress predicted amygdala reactivity to threat, peripheral inflammatory responses, and anxious arousal, but amygdala activation and inflammation did not fully account for the effect of perceived stress on anxious arousal.

Our results replicate a convergent line of research showing that the amygdala plays a crucial role in the regulation of fear processing (Davis, Rainnie, and Cassell 1994; LeDoux 1996). Indeed, heightened amygdala activation has been described in healthy volunteers in response to negative facial expressions such as fear (Breiter et al. 1996; Morris et al. 1996), disgust (Phillips et al. 1997), sadness (Blair et al. 1999) or anger (Hariri et al. 2002). Exaggerated amygdala activation has been described in both adults (Siegle et al. 2002, 2007) and adolescents with depression (Beesdo et al. 2009).

Peripheral inflammatory responses to an LPS challenge were higher in magnitude in the MDD group compared to the HC group, which is consistent with a growing literature suggesting that abnormal inflammatory signalling plays a role in pathophysiology of depression. In previous studies, higher levels of peripheral and central inflammatory cytokines (including IL-1β, IL-6, and TNF) have been observed in depressed individuals compared to unaffected healthy control individuals (Felger and Lotrich 2013; Haroon et al. 2012; Michopoulos et al. 2017). Cytokines have been shown to impact structures involved in the regulation of fear such as the amygdala (Felger et al. 2016; Miller et al. 2013). Although the exact molecular mechanisms are still unknown, peripheral cytokines can directly access brain regions of the limbic system affecting neuronal circuits (Cathomas et al. 2019; Salvador et al., 2021). With regard to the amygdala, one recent animal study showed that administration of LPS was associated with changes in synaptic plasticity in the basolateral amygdala (BLA) (Zheng et al. 2021). Microglia activation and the production of inflammatory cytokines in the BLA led to the development of anxiety- and depressive-like behaviors. Another study showed that LPS-induced maternal immune activation during embryonic stage induces a pro-inflammatory cytokine profile in the rodent fetal brain that persists during early adulthood in the amygdala (O’Loughlin et al. 2017).

We found that peripheral inflammation was correlated with anxious arousal and perceived stress across groups, with a trend for a significant correlation within the MDD group. Perceived stress is a strong predictor of depression onset (Cristobal-Narváaez, Haro, and Koyanagi 2020), and is strongly linked to peripheral inflammation (Zou, Miao, and Chen 2020). Prior studies that employed a psychosocial stressor as an experimental manipulation showed that psychosocial stress also activates the inflammatory response, which over time may contribute to the development of depressive symptoms (Miller and Raison 2016). Our results add to the literature that perceived stress may influence anxious arousal and peripheral inflammation. However, the lack of significant results for the mediative effects of the amygdala or inflammation on the relationship between perceived stress and anxious arousal suggest that the perceived stress influences those constructs through different mechanistic pathways.

Amygdala activation in response to threat was also correlated with anxious arousal and perceived stress across groups. This is in line with studies showing that amygdala reactivity to fearful facial expressions predicted greater depressive symptoms and higher perceived stress (Prather, Bogdan, and Hariri 2013). Anxious arousal dimensions are a key component of depression, and contribute importantly to the complexity of the disorder, the maintenance of symptoms, and resistance to treatment. While anxiety can be defined as a state of distress, arousal which corresponds to the physiological mechanisms underpinning anxiety, is evoked by uncertain danger, or threat. While studies have shown that inflammation correlates with a widely-distributed network (Yin et al. 2019) and amygdala-ventromedial prefrontal connectivity in association with anxiety in depression (Mehta et al. 2018), recent research suggests that elucidating the neural circuitry underlying certain and uncertain threats will critically contribute to our understanding of the mechanistic pathways underlying mood and anxiety-related disorders. Interestingly, correlations between amygdala activation, anxious arousal and perceived stress were not found in the MDD group alone, but only across both groups, encouraging future research to deconstruct diagnostic categories into constituent domains relevant and testable across units of analysis, as suggested by the Research Domain Criteria (RDoC) initiative (Insel 2014).

Finally, while we could not account for the mediative effect of inflammation or amygdala activation on the relationship between perceived stress and anxious arousal, our path analysis showed that perceived stress predicted inflammation, anxious arousal and amygdala activation. Previous research described the bidirectionality of neuro-immune pathways. Some studies showed that inflammation occurred after psychosocial distress or heightened threat-related neural activity, while others showed that inflammation may also be a precursor of both distress and threat-related neural activity. Increases in inflammation have been associated with greater social disconnection feelings and depressed mood (Eisenberger et al. 2010), while heightened inflammation led to increased threat-related amygdala reactivity (Inagaki et al. 2012; Muscatell et al. 2015). In our study, the measure of perceived stress was retrospective and captured levels of perceived stress one month before the study visit, so this might explain why our model showed that perceived stress predicted inflammation, amygdala activation and anxious-arousal symptoms.

Our study has several limitations. Since the sample size is relatively small, the results of the study should be considered preliminary. In addition, the internal consistency of brain response to amygdala activation during our contrast of interest was only moderate. This is somewhat better than prior reports of low internal consistency of amygdala activation during similar tasks (Infantolino et al. 2018; Kennedy et al. 2021; Nord et al. 2017). We implemented a multi-echo, multi-band acquisition protocol together with robust preprocessing and denoising procedures using multi-echo independent component analysis (ME-ICA) which may have contributed to relatively improved internal consistency. However, only moderate levels of internal consistency limit the utility of our reported finding as a reliable biomarker. We suggest that future research studies should explore and report the internal consistency of neural activation during task-based fMRI. It may be that novel computational techniques (Fu et al. 2008), as well as the continued development of robust denoising procedures such as ME-ICA (Kundu et al. 2012; Kundu et al., 2017) will continue to improve the consistency and reliability of fMRI-based measures.

In conclusion, we report that MDD is associated with heightened amygdala response to threat and this response is positively correlated with peripheral immune reactivity. Our findings add to a growing literature that is beginning to map dysfunction within the inflammatory system to specific symptom domains in mood disorders. Interestingly, a recent meta-analysis showed that anti-inflammatory agents are promising in the treatment of depressive symptoms. Indeed, a quantitative analysis including thirty randomized controlled trials suggested that anti-inflammatory agents reduced depressive symptoms compared with placebo (Bai et al. 2020). Future studies with larger sample sizes are needed to replicate these findings and improve the knowledge of the neurobiological mechanisms underlying depression with the ultimate goal of advancing treatment options.

Supplementary Material

Figure S1
Figure S2
Supplemental Information

Acknowledgments

Research reported in this publication was supported by the National Institute of Mental Health of the National Institutes of Health under Award Number R21MH109771 to JWM. Additional funding for this study was provided by the Ehrenkranz Laboratory for Human Resilience, a component of the Depression and Anxiety Center for Discovery and Treatment at the Icahn School of Medicine at Mount Sinai and by a generous gift from the Gottesman Foundation to Mount Sinai. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or other funding bodies.

Footnotes

Declaration of Competing Interest

In the past 5 years, Dr. Murrough has provided consultation services and/or served on advisory boards for Allergan, Boehreinger Ingelheim, Clexio Biosciences, Fortress Biotech, FSV7, Global Medical Education (GME), Otsuka, and Sage Therapeutics. Dr. Murrough is named on a patent pending for neuropeptide Y as a treatment for mood and anxiety disorders and on a patent pending for the use of ezogabine and other KCNQ channel openers to treat depression and related conditions. The Icahn School of Medicine (employer of Dr. Murrough) is named on a patent and has entered into a licensing agreement and will receive payments related to the use of ketamine or esketamine for the treatment of depression. The Icahn School of Medicine is also named on a patent related to the use of ketamine for the treatment of PTSD. Dr. Murrough is not named on these patents and will not receive any payments. All the other authors report no financial relationships with commercial interests.

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://doi.org/10.1016/j.bbi.2022.05.015.

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Supplementary Materials

Figure S1
NIHMS1971186-supplement-Figure_S1.pdf (399.6KB, pdf)
Figure S2
NIHMS1971186-supplement-Figure_S2.pdf (244.3KB, pdf)
Supplemental Information
NIHMS1971186-supplement-Supplemental_Information.docx (47.9KB, docx)

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