HPA-Axis Hormone Modulation of Stress Response Circuitry Activity in Women with Major Depression

HPA-Axis Hormone Modulation of Stress Response Circuitry Activity in Women with Major Depression
HPA-Axis Hormone Modulation of Stress Response Circuitry Activity in Women with Major Depression

Keywords

depression; stress; HPA-axis; sex differences; fMRI

1. INTRODUCTION

Major depressive disorder (MDD) is the fourth leading cause of disease burden worldwide (Ustun et al., 2004), and the incidence in women is twice that of men (Kendler et al., 2006).Thus, understanding the pathophysiology of MDD has widespread implications for attenuation and prevention (Ustun et al., 2004), particularly in women. Studies have

implicated abnormal hormone dynamics in mood disorders, supporting the involvement of the hypothalamic-pituitary-adrenal (HPA)-axis in MDD (Plotsky et al., 1998, Young and Korszun, 2002). In fact, hormonal dysregulation in women has been found to precede MDD onset (Harlow et al., 2003), suggesting it reflects underlying subsyndromal MDD

expression. Despite these findings, there are few studies characterizing the neurobiological pathways associated with the co-occurrence of mood-related brain circuitry deficits and altered hormone secretion.

Human studies demonstrate consistent HPA-axis abnormalities associated with MDD,specifically hypercortisolemia as reflected by elevated levels of cortisol in plasma,

cerebrospinal fluid, and 24h urine (Carroll et al., 1976a, Carroll et al., 1976b) at baseline and in response to psychological stress (Burke et al., 2005). Lower DHEAS (an androgen precursor produced by the adrenal gland) (Young et al., 2002) levels have been reported,with mixed results regarding adrenocorticotropin hormone (ACTH) levels (Kalin et al.,1982, Linkowski et al., 1985, Deuschle et al., 1997, Posener et al., 2000, Young et al.,

2001). HPA-axis abnormalities such as elevated cortisol, which is positively associated with age (Nelson et al., 1984) and number of episodes (Sher et al., 2004), may be implicated in vulnerability to MDD (or a trait) and not only related to clinical state (Vreeburg et al., 2009).The comorbidity between depression and disordered HPA-axis hormone dynamics is not surprising from a brain circuitry point of view, given that MDD is a disorder which involves hypothalamic nuclei, amygdala, hippocampus, anterior cingulate cortex (ACC), subgenual ACC (sgACC), and medial and orbitofrontal cortex (mPFC, OFC) (Dougherty and Rauch,1997, Mayberg, 1997, Drevets, 1999, Sheline et al., 2001), regions dense in glucocorticoid receptors (McEwen et al., 1986). Most consistently, evidence supports hyperactivation of several of these limbic regions in response to negative stimuli in MDD, as demonstrated by recent meta-analyses of functional neuroimaging studies employing emotion paradigms (Delvecchio et al., 2012, Hamilton et al., 2012). However, the vast majority of these studies have focused on state characteristics (i.e., individuals currently in a major depressive episode). In the few investigations of potential trait markers in the brain [i.e., in remitted depression (rMDD)], results appear less consistent, with mixed reports of hyperactivation or hypoactivation of the amygdala, PFC, and ACC compared to controls (Hooley et al., 2005,Ramel et al., 2007, Wang et al., 2008, Okada et al., 2009, Holsen et al., 2011, Dichter et al.,2012, Kerestes et al., 2012), likely due to differences in study design (i.e., cognitive vs.emotional paradigms).

Collectively, these brain regions implicated in MDD overlap substantially with the neural circuitry involved in arousal and response to stressful events (Fuchs et al., 1985, Keverne,1988, Price, 1999). Neuroimaging studies designed to evoke a stress response in healthy adults report activation in these same regions (hypothalamus, amygdala, hippocampus,

ACC, mPFC, and OFC) using a variety of paradigms, including sadness induction (Ottowitz et al., 2004), passive viewing of negative valence, high arousal stimuli (Goldstein et al.,

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2005, van Stegeren et al., 2007, Cunningham-Bussel et al., 2009, Root et al., 2009,

Goldstein et al., 2010b), and psychosocial stressors such as the Montreal Imaging Stress Task, which involves a mental arithmetic task and continuous negative feedback on task performance (Wang et al., 2007, Pruessner et al., 2008).

Concurrent measurement of endogenous cortisol secretion (usually salivary cortisol) during these stress paradigms has allowed for investigation of relationships between brain activity and HPA-axis responsivity. In response to psychosocial stressors, findings appear highly variable, with some reporting increases in the PFC and OFC related to pre- to post-stressor change in cortisol (Wang et al., 2007) and others showing decreases in the hypothalamus,amygdala, hippocampus, OFC, and ACC in cortisol “responders” (i.e., those classified based on an increase in cortisol following the stressor) (Pruessner et al., 2008). For passive

viewing of negative stimuli paradigms, negative relationships between cortisol reactivity and ventromedial PFC activity (Root et al., 2009) and negative correlations between cortisol levels and activation of the amygdala were observed (Pruessner et al., 2008, Cunningham-Bussel et al., 2009), although the reverse (positive association between limbic activation and cortisol change) has also been shown (van Stegeren et al., 2007, Root et al., 2009). These inconsistencies could be driven by methodological approaches (pre-/post-stress cortisol

change vs. diurnal cortisol amplitude), as well as the substantial individual differences (even amongst health controls) which exist in the cortisol response, introducing significant

variability in the main outcome measure. Exogenous cortisol administration, conversely, is associated with decreased activation of the amygdala and hippocampus during rest in healthy controls (Lovallo et al., 2010).

Few studies have concurrently examined neural functioning and HPA-axis responsivity in depression. In early FDG-PET studies, endogenous plasma cortisol levels (obtained at one timepoint just prior to the PET scan, thus conceptualized as reflecting response to the

“stress” of the scanning environment) were positively associated with glucose metabolism in the amygdala (but not the hippocampus) in individuals with MDD (Drevets et al., 2002b), as well as the pre- to post-treatment change in glucose metabolism in the amygdala (Drevets et al., 2002a). Further, although endogenous salivary cortisol levels did not change in response to an emotional memory fMRI paradigm in either MDD or control subjects, during administration of exogenous cortisol (hydrocortisone vs. placebo), MDD women

demonstrated greater hippocampal activation during encoding of neutral words compared to healthy women (Abercrombie et al., 2011). These studies point to important potential relationships between limbic dysfunction and abnormalities in the HPA-axis in MDD.However, it remains unclear the degree to which individuals with depression demonstrate abnormalities in relationships between endogenous cortisol levels and brain activity, as measured in parallel and in response to an established stress paradigm. Furthermore, studies to date have not focused on potential trait features of depression (i.e., those in remission),despite evidence that brain activity (Hooley et al., 2005, Wang et al., 2008, Okada et al.,2009, Holsen et al., 2011, Dichter et al., 2012, Kerestes et al., 2012) and HPA-axis (Kathol,1985, Deshauer et al., 1999, Bhagwagar et al., 2003, Vreeburg et al., 2009, Morris et al.,2012) abnormalities may persist following recovery and thus signal a potential vulnerability to relapse. Thus, building on our previous findings (Holsen et al., 2011), the current study was designed to address the importance of integrating hormone-brain associations in a sample of women with recurrent MDD in remission (rMDD) using a mild stress response fMRI paradigm (viewing of negative and neutral stimuli) in parallel with plasma hormone assessments of HPA-axis responsivity. We hypothesized that compared to controls, rMDD women would demonstrate significant differences in HPA-axis hormones, dysfunction in hypothalamus, amygdala, hippocampus, ACC, sgACC, OFC, and mPFC in response to a

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mild stress challenge, and that cortisol and/or ACTH levels would account for significant variance in between-group differences in brain activation.

2. EXPERIMENTAL PROCEDURES

2.1. Sample

Participants were selected from the 17,741 Boston and Providence pregnancies from the Collaborative Perinatal Project (CPP), known as the New England Family Study (NEFS)(Goldstein et al., 2010a). The CPP was initiated over 40 years ago to investigate prospectively the prenatal and familial antecedents of pediatric, neurological, and

psychological disorders of childhood (Niswander and Gordon, 1972). Women (recruited 1959–1966) were representative of patients receiving prenatal care, thus unselected for psychiatric status. Across NIMH-funded adult follow-up studies of their offspring, we identified 1214 subjects, 508 diagnosed with DSM-IV major depressive disorder (MDD)and 706 healthy controls (HCs) (ORWH-NIMH P50 SCOR MH082679). Systematic

structured clinical interviews on all potential subjects were conducted by trained M.A.-level clinical interviewers. Expert diagnosticians (including J.M.G. and J. Donatelli) reviewed all information to determine final best estimate diagnoses. Of 508 MDD cases, 205 had recurrent episodes, of which 148 were women. Cases thus represented a truly general population sample of women with MDD.

Of these, we re-recruited for brain imaging 15 recurrent MDD women who were in remission (rMDD) and 15 healthy control women (HC) comparable on ethnicity (all

Caucasian), right-handedness, socioeconomic status, and general intelligence (see Table 1).Selection of this subsample for brain imaging was based on current mood status and other eligibility criteria. Due to recruitment of their mothers during the NEFS study period (1959–1966), all women were between ages 43–50 years with rMDD women slightly older than HCs. rMDD women had generally experienced two or more episodes (two experienced only one lengthy episode rather than discrete multiple episodes); one woman with more than two episodes had a seasonal pattern. Full remission was defined as not having experienced any MDD symptoms for >30 days prior to scanning. One woman was in partial remission due to continuing loss of interest and mild guilt, but no other symptoms. Healthy controls did not meet DSM-IV criteria for any current Axis 1 disorders. Comorbid current (in the rMDD group) and past (rMDD and HC groups) Axis 1 diagnoses are reported in Table 1. Among rMDD, one woman reported a previous MDD psychiatric hospitalization, seven reported previous psychotherapy, thirteen had previous medication trials, and only six were currently taking psychotropic medication (listed in Table 1). HCs were not taking any psychotropic medication. No rMDD or HC subjects were taking oral contraceptives or hormone replacement therapy.

2.2. Procedures

Human subjects and methods approval was granted by Partners Healthcare and Brown University. Written informed consent was obtained from all participants. Subjects tracked their cycles for three to four months. Findings from the late follicular/midcycle menstrual phase (days 10–15 assessed by patient history) are presented here. Groups did not differ on mean cycle day (M =13.56, t=0.24; n.s.). On the morning of the study visit, rMDD women completed the 17-item Hamilton Rating Scale for Depression [HAM-D] with trained study staff to assess current mood state. All subjects scored less than eight [except for the subject in partial remission (HAM-D score = 11)], indicating they were not clinically symptomatic (10 of 15 scored less than four) and thus findings would represent potential MDD “trait”characteristics rather than “clinical state”. Mood and anxiety levels were assessed using the

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Profile of Mood States (POMS) and the Spielberger State-Trait Anxiety Inventory (STAI)administered pre- and post-scanning.

Our method for acquiring HPA-axis hormone level changes in response to the visual stress challenge “in real time” using serial blood samples was applied here. Trained nurses inserted a saline-lock IV line in the non-dominant forearm. Blood acquisition commenced during fMRI scanning, with an in-scanner blood draw (Time 0; 1020h) drawn approximately 5minutes after the subject was introduced into the magnet (after the first few MRI set-up scans) and just prior to the start of the stress paradigm. A 15 min in-scanner blood sample was drawn after the first two runs of the stress paradigm at 1035h, followed by a 30 min in-scanner blood draw at 1050h, and 60 min and 90 min blood draws, out-of-scanner in a quiet room, at approximately 1120h and 1150h, respectively. Subjects remained inside the bore of the magnet during in-scanner blood draws. The timing of these blood draws was based on the expected peak response (following the onset of the visual stress challenge) of ACTH at 15–30 min and cortisol at approximately 60 min. Hormone data were missing or not reported for some subjects due to poor IV access preventing blood acquisition during scanning (four HC; one rMDD) or difficulty with blood draw at one timepoint during scanning (one MDD for ACTH at Time 30).

Approximately 30cc of blood were sampled at each time point, allowed to clot for 45–60min, spun, aliquoted, and stored frozen at ?80 °C. Serum hormones were analyzed in duplicate with commercial immunoassay kits: cortisol (0.04 ug/dL;000000 4.4–6.7%);ACTH (1.5 pg/mL; 2.5– 5.5%): Immunoradiometric Assay (IRMA), DiaSorin, Inc.,

Stillwater, MN. Given significant differences in individual cortisol and ACTH responses,subjects were classified according to whether they demonstrated increases (responders) or decreases (non-responders) in serum hormone levels following onset of the stress paradigm (i.e. from Time 0) through the expected peak response time. For responders, change in each hormone (cortisol, ACTH) was measured by subtracting the basal from the peak level.Functional MRI (fMRI) scanning was conducted using Siemens Tim Trio 3T MR scanner with a 12-channel head coil.180 functional volumes were acquired using a spin echo, T2*-weighted sequence (TR=2000 ms; TE=40 ms; FOV=200×200 mm; matrix=64×64; in-plane resolution=3.125 mm; slice thickness=5 mm; 23 contiguous slices aligned to the AC-PC plane). A visual stimuli task paradigm was presented to evoke a mild stress response,

adapted from the International Affective Picture System (IAPS) and described and validated in our previous publications (Goldstein et al., 2005, Goldstein et al., 2010b, Holsen et al.,2011). Briefly, participants viewed fixation, neutral, and negative [according to affective valence (unpleasant/neutral) and arousal (high/low)] images, and pressed a button when a new image appeared in order to ensure attention. The mild stress paradigm lasted

approximately 18 minutes (3 runs of 6 minutes each). For additional experimental details see (Holsen et al., 2011). After the fMRI session, participants were shown two blocks of negative and neutral images (presented on a laptop computer) and provided subjective ratings of arousal.

2.3. Data analysis

fMRI data were preprocessed using Statistical Parametric Mapping (SPM8) (Wellcome Department of Cognitive Neurology, 2008) and custom routines in MATLAB (Mathworks,Inc., 2000) and included realignment and geometric unwarping of EPI images using magnetic fieldmaps, correction for bulk-head motion, nonlinear volume-based spatial

normalization (standard Montreal Neurological Institute brain template), spatial smoothing with a Gaussian filter (6mm at FWHM), and artifact detection (https://www.360docs.net/doc/0973565.html,/swg/software.htm ) to identify outliers in the global mean image time series (threshold: 3.5) and movement (threshold: 0.7; measured as scan-to-scan movement,

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separately for translation and rotation) parameters. rMDD women had slightly more outliers (M=12.2, SD=13.9) than HC women (M=4.8, SD=4.0), however, this difference was not statistically significant (p>0.05). Outliers were entered as nuisance regressors in the first-level, single-subject GLM analysis. Specific comparisons of interest (negative versus neutral) from single-subject analyses were then tested using linear contrasts, and SPM T-maps created based on these contrasts.

Results from the single-subject level were submitted to second-level random effects analyses of the between group (rMDD vs. HC) contrast. Given hypotheses about specific brain regions, we used the small volume correction approach in SPM8 which limits voxel-wise analyses to voxels within a priori regions of interest (ROIs). Anatomic borders of ROIs (hypothalamus, amygdala, hippocampus, OFC, mPFC, ACC, sgACC) were defined using a manually segmented MNI-152 brain and implemented as overlays on the SPM8 canonical brain using the Wake Forest University PickAtlas ROI toolbox for SPM (Maldjian et al.,2003). False positives were controlled using family-wise error (FWE) correction. Within an anatomic ROI, significant results identified using small volume correction (initial voxel-wise height threshold: p<0.05 uncorrected) are reported as significant if they additionally met the peak-level threshold of p<0.05, FWE-corrected.

Anatomic overlays were used on each subject’s statistical maps to acquire signal change values across ROIs. Values indicated the degree of change in signal detected between negative and neutral stimuli. Average percent signal change (psc) values were obtained using the REX toolbox (Whitfield-Gabrieli, 2009) and used to calculate effect sizes for

group differences (Cohen’s d = 2t/√df) and in brain-hormone GLM analyses. The psc values were defined based on activation clusters from the rMDD vs. HC group contrast and drawn from spheres around the maximum voxel within each cluster.

Behavioral data were analyzed using SPSS (version 19; SPSS Inc., Chicago, IL) using independent t-tests (demographic and clinical characteristics; stimuli ratings) or repeated-measures ANOVAs (mood and anxiety ratings). Correlations between behavioral, hormone,and brain data were calculated using Pearson correlation coefficients. A p<0.05 was designated for statistical significance.

3. RESULTS

Women with rMDD reported higher STAI trait anxiety scores than HC women; however,mean levels in the rMDD group were below the clinical range (see Table 2), consistent with their remission status. Analysis of the pre- and post-scan anxiety ratings (STAI state)

revealed a significant effect of session with increases in anxiety post-scan compared to pre-scan (F=9.87, df=1, p<0.05) and case status (F=6.37, p<0.05), but no session by case interaction (F=1.67, n.s.). However, post-scan, rMDD women rated subjective anxiety higher than HC women (t=2.34, p<0.05; see Table 2). These results imply that rMDD women demonstrated overall significantly higher state anxiety than HC women, and that both groups demonstrated increases in anxiety following the mild stress paradigm (see Table 2). Closer examination of mean ratings indicated that the average state anxiety rating

increased 7 points in rMDD women compared to 3 points in HC women. Thus, the lack of session by case interaction was likely driven by significant variability in the rMDD women in post-scan ratings. Analysis of the pre- and post-mood ratings (POMS) subscales revealed no significant session by case interactions. Subjective stimuli ratings did not differ between groups. Collectively, these findings suggest a somewhat more marked response to the mild stress challenge in rMDD women, with significantly higher anxiety after the challenge,consistent with a lower threshold for stress reactivity despite equivalent stimuli ratings as HC women.

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ACTH and cortisol levels were not significantly different between groups pre-scan (at Time 0; t values 0.9–1.3, n.s.). However, compared to HC women, rMDD women exhibited increased cortisol and ACTH levels over time, with significantly higher levels of ACTH at 30 minutes (t=2.20; p<0.05) and cortisol at 90 minutes following onset of the mild stress paradigm (t=2.17; p<0.05; see Figure 1), while HC women on average did not show this response. In the classification of individual cortisol responsivity, 100% (n=13) of rMDD women were classified as responders compared to 9% of HC women. This classification was significantly different between groups (X 2=20.3; p<0.001), and indicated greater HPA-axis responsivity to stress in rMDD women. For ACTH, groups were not different in their distribution of responders (66.7% of rMDD, n=8; 54.5% of HC, n=6) and non-responders (33.3% of rMDD, n=4; 45.5% of HC, n=5) (X 2=0.35; n.s.).

In parallel with behavioral and hormone responses to the mild stress challenge, rMDD women displayed hyperactivation to negative (vs. neutral) stimuli in the left amygdala (effect size = 1.11) and hippocampus (effect size = 1.33) at p<0.05, FWE-corrected (see Table 3). Percent signal change (psc) values extracted from anatomical regions of interest are plotted in Figure 2. At an uncorrected p<0.05 level, rMDD women showed greater activation in the right hypothalamus, amygdala, hippocampus and ACC and left sgACC at p<0.05 (effect sizes: 0.85– 1.29). There were no regions in which HC women exhibited

greater activation than MDD women. We further examined the potential effects of remission and medication status (in the rMDD women) on the between-group results. Analysis of the 6MDD women on medications compared to the 9 off medications and to controls indicated that although STAI scores were slightly (but not significantly) higher in unmedicated women, hyperactivity in stress response regions remained in both groups. Similarly,

removing the data of the 1 rMDD woman in partial remission did not alter the overall group findings. Thus, hormone and brain results were not driven by the women on medications or the single participant in partial remission (data available on request).

To examine the relationship between cortisol responsivity (as defined as increases in cortisol in response to the mild stress challenge) and amygdala activation during the mild stress paradigm, analyses were restricted to rMDD women, all of whom were classified as cortisol responders. Amongst rMDD women, percent signal change in the right amygdala was

negatively related to peak cortisol (r=?0.61, p<0.05; see Figure 3a) and to change in cortisol at a trend level (r=?0.52, p=0.07; see Figure 3b). Cortisol response was not significantly related to hippocampal hyperactivity either measured as “peak response” or “change over time” (r values |0.14–0.42|, n.s.). Thus, rMDD women with the highest cortisol levels exhibited the lowest amygdala activity in response to stress and lack of response (little correlation) from the hippocampus.

4. DISCUSSION

Extant literature has provided substantial evidence of abnormal stress responsivity in the etiology of MDD, most often focusing on HPA-axis hormones and their targets in the central nervous system in individuals currently in a depressed state. Here, we present evidence that even in remission, women with recurrent MDD display dysregulation on multiple levels involved in their response to a stress challenge. In response to a mild stress challenge,rMDD women (compared to healthy controls): a) reported increased anxiety; b) exhibited increased HPA-axis responsivity; c) displayed hyperactivity in the amygdala and

hippocampus, and d) demonstrated associations between brain hyperactivation and HPA-axis alterations. Findings represent an advance in our understanding of the role of HPA activity and brain activity in rMDD in women compared to healthy women.

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Our sample of rMDD women exhibited HPA-axis reactivity in response to a mild stressor.Remarkably, classification of cortisol responders versus non-responders matched completely with case group status, with all rMDD subjects meeting criteria as responders and all controls as non-responders. Further, as a group, rMDD women demonstrated increases in ACTH following the stress paradigm, with significantly greater levels at T30 compared to healthy controls, which generally showed decreases over the course of blood sampling.These findings are consistent with previous reports of persistent HPA-axis dysregulation in remitted MDD (Kathol, 1985, Deshauer et al., 1999, Bhagwagar et al., 2003, Vreeburg et al.,2009, Morris et al., 2012), and collectively, provide evidence of HPA-axis hyperresponsivity as a potential trait marker of MDD. Studies have suggested that this may signal vulnerability to relapse in individuals demonstrating this phenotype (Morris et al., 2012).

In contrast, there have been reports of hyporeactivity of the HPA-axis in response to

significant psychosocial stress in rMDD women (Ahrens et al., 2008). This discrepancy may be due to sample demographics (our subjects were on average 6 years younger) or procedural differences (our paradigm was designed to invoke a mild stress reaction).Alternatively, HPA-axis responses may differ depending on whether the woman is in an acute depressive episode or following recovery (Bouhuys et al., 2006, Morris et al., 2012),given that some individuals demonstrate normalization in remission while others exhibit persistent abnormalities in ACTH and cortisol responses. Our sample of rMDD women showed activation of ACTH and cortisol secretion following mild stress. Increased ACTH and cortisol suggests abnormalities at the central nervous system (CNS) level of the HPA circuit with hypersecretion of corticotropin-releasing hormone (CRH) by the paraventricular nucleus of the hypothalamus (PVN).

This is supported by our brain imaging findings of hyperactivity in the arousal region, right amygdala, signaling an initiation of the stress response, and hyperactivity in the

hippocampus. However, there was a lack of association between how the CNS responded to stress and how the HPA circuitry functioned. Hyperactivity of the amygdala was related to lower cortisol response and there was little relationship between hippocampus and cortisol response. Although we understand that there was insufficient temporal resolution to

determine how the endogenous changes in cortisol were precisely affecting activity in our regions of interest, we suggest that increased amygdala activity to decreased cortisol

responsivity reflects dysregulation in the feedback from hippocampal glucocorticoid action to the central nucleus of the amygdala, which stimulates release of CRH from the

hypothalamic PVN. PVN abnormalities in MDD have been identified at the postmortem level, particularly in neurons containing ER-°– (Bao et al., 2005), in addition to the well-known abnormalities found in the amygdala and hippocampus. Thus, further research using functional connectivity in women with rMDD is needed to explore these brain-hormone relations.

Our findings of hyperactivation of the amygdala and hippocampus in response to negative stimuli are consistent with findings in currently depressed (Sheline et al., 2001, Fahim et al.,2004) and remitted (Ramel et al., 2007, Hooley et al., 2009) MDD subjects. Further,

amygdala activation has been negatively associated with endogenous (Pruessner et al., 2008,Cunningham-Bussel et al., 2009) and exogenous (Lovallo et al., 2010) cortisol in healthy control subjects. This is not completely consistent (Drevets et al., 2002b, van Stegeren et al.,2007, Root et al., 2009), likely driven by sample selection (currently depressed versus remitted subjects) and methodological approaches (resting state PET, use of a single

baseline cortisol assessment). The fact that we observed similar heightened activations in a sample of recurrent MDD women in remission suggests hyperactivation of stress response circuitry may be a potential characteristic trait of MDD in women and may speak to the cumulative effects of lifelong recurrent MDD. In contrast, in a pilot study of rMDD women,

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we reported hypo activation of stress response circuitry using a similar paradigm (Holsen et al., 2011). However, in that study we targeted younger women (mean age 34), who were all premenopausal with regular menstrual cycles, in contrast to our current sample of older women with several in perimenopause (mean age 46; there was no overlap in ages among these samples). Moreover, in the current study, we were able to further elucidate how dysregulation of stress response regions in rMDD is associated with real-time HPA-axis hormone responses.

Our study design had a number of strengths, including systematic clinical phenotyping,incorporation of multiple levels of measuring the stress response, assessment of serum

hormones in response to a stress challenge in real time during fMRI scanning, and inclusion of recurrent rMDD women, the majority of whom were unmedicated. In fact, our analysis of medication effects yielded null results, indicating that our rMDD versus HC findings were not driven by the small number of women on medications. We understand that that our power to detect group differences was limited by a small sample size and some technical difficulties in the scanner during multiple blood draw timepoints. In addition, the age range of our study sample (43–50) coincides with the perimenopausal transition (marked by significant fluctuations in gonadal hormones) which prevented the straightforward

examination of the effect of HPG-axis hormones on HPA-axis and stress response circuitry hyperactivation in our rMDD women. Finally, we understand that the ideal timing for

cortisol collection may not be early morning (given the diurnal decrease following morning wakening levels). However, because our rMDD group showed ACTH and cortisol increases after a stress paradigm at a time when endogenous cortisol is highest, we may have

underestimated the extent of this activation. Moreover, given that we also collected gonadal hormones, which necessitated fasting, scanning had to be acquired during the early morning time period for subject convenience and comfort.

Some might argue against our use of a mild stress challenge rather than a severe

psychosocial stressor involving components of uncontrollability and social-evaluative threat (Dickerson and Kemeny, 2004). However, previous reports indicate that currently depressed individuals report increased negative affect even in response to daily life stressors (Myin-Germeys et al., 2003), and we would argue that our mild stress challenge has better content validity and important implications for response to “everyday stress” in rMDD subjects in particular. Recent findings in remitted depression suggest that higher cortisol to a low-stress task (but not a high-stress task) predicted risk for onset of subsequent depressive symptoms (Morris et al., 2012), suggesting that mild stress not only produces a stress response in rMDD, but also has the potential to serve as a marker of vulnerability to relapse. Future studies can address some of the limitations in our study by employing larger sample sizes of pre- or post-menopausal women, and collecting hormone and brain data during the late afternoon, when cortisol levels are stable and/or implementing dexamethasone/CRH test in remitted rMDD women. Finally, although we make reference to findings in remitted MDD indicating potential “trait” characteristics, due to the fact that we did not assess brain and hormone functioning prior to MDD onset, we cannot exclude the possibility that these abnormalities may reflect illness “scarring” as a consequence of experiencing multiple depressive episodes.

In conclusion, our findings provide evidence for the importance of understanding the impact of HPA-axis hormone abnormalities on hyperactivity in the brain in response to stress in women with recurrent MDD in remission. Altered neuroendocrine dynamics identified in these women and their impact on brain activity abnormalities are important clues for the potential development of sex-dependent hormonal treatments. Further, we provided support for the necessity of measuring peripheral HPA-axis hormones in parallel with neuroimaging data. Our results represent an important step in understanding potential traits characterized

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by behavioral, neuroendocrine, and brain dysfunction in women with MDD, which contribute to understanding sex differences in MDD.

Acknowledgments

This work was supported by the National Institutes of Health Office for Research on Women’s Health and Mental Health (ORWH-NIMH) P50 MH082679 (Goldstein, P.I.). L.M.H. was also funded by NIMH K01 MH091222. The research was conducted with support from Harvard Catalyst | The Harvard Clinical and Translational Science Center (NIH #UL1 RR025758). We thank Jenn Walch, M.Ed. and Anne Remington for help with project

management, Harlyn Aizley, M.Ed. for clinical interviewing, and Jo-Ann Donatelli, Ph.D. for her contributions to diagnostic review with Dr. Goldstein. The authors also thank Stuart Tobet, Ph.D. and Robert Handa, Ph.D. for their collaborative work with the Goldstein Lab (on P50 MH082679) regarding the impact of hormones on stress response circuitry and mood and anxiety.

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Figure 1.

Cortisol levels in rMDD and healthy control subjects, measured in response to mild stress paradigm during and following fMRI scanning. Error bars represent the standard error of the mean. **rMDD > HC, p<0.05.

NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript

Figure 2.

Significant hyperactivation of stress response circuitry regions in rMDD women in

comparison to healthy control subjects. Activations of hypothesized regions of interest were derived using restriction to within anatomical borders (defined by a manually segmented MNI brain) with the small volume correction tool in SPM8. Activations in Figure 2 are

selected from Table 3, centered on the peak voxel of activation with a p<0.05 (uncorrected).Error bars indicate standard deviations.

NIH-PA Author Manuscript NIH-PA Author Manuscript

NIH-PA Author Manuscript

Figure 3.

Associations between amygdala hyperactivation and cortisol reactivity in response to mild stress paradigm. (a) Correlation between right amygdala psc and peak cortisol response in rMDD women (p<0.05). (b) Correlation between right amygdala psc and change in cortisol in rMDD women (p=0.071).

NIH-PA Author Manuscript NIH-PA Author Manuscript NIH-PA Author Manuscript

NIH-PA Author Manuscript

NIH-PA Author Manuscript NIH-PA Author Manuscript T

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关于时间管理的英语作文 manage time

How to manage time Time treats everyone fairly that we all have 24 hours per day. Some of us are capable to make good use of time while some find it hard to do so. Knowing how to manage them is essential in our life. Take myself as an example. When I was still a senior high student, I was fully occupied with my studies. Therefore, I hardly had spare time to have fun or develop my hobbies. But things were changed after I entered university. I got more free time than ever before. But ironically, I found it difficult to adjust this kind of brand-new school life and there was no such thing called time management on my mind. It was not until the second year that I realized I had wasted my whole year doing nothing. I could have taken up a Spanish course. I could have read ten books about the stories of successful people. I could have applied for a part-time job to earn some working experiences. B ut I didn’t spend my time on any of them. I felt guilty whenever I looked back to the moments that I just sat around doing nothing. It’s said that better late than never. At least I had the consciousness that I should stop wasting my time. Making up my mind is the first step for me to learn to manage my time. Next, I wrote a timetable, setting some targets that I had to finish each day. For instance, on Monday, I must read two pieces of news and review all the lessons that I have learnt on that day. By the way, the daily plan that I made was flexible. If there’s something unexpected that I had to finish first, I would reduce the time for resting or delay my target to the next day. Also, I would try to achieve those targets ahead of time that I planed so that I could reserve some more time to relax or do something out of my plan. At the beginning, it’s kind of difficult to s tick to the plan. But as time went by, having a plan for time in advance became a part of my life. At the same time, I gradually became a well-organized person. Now I’ve grasped the time management skill and I’m able to use my time efficiently.

英语演讲稿:未来的工作

英语演讲稿:未来的工作 这篇《英语演讲稿范文:未来的工作》,是特地,希望对大家有所帮助! 热门演讲推荐:竞聘演讲稿 | 国旗下演讲稿 | 英语演讲稿 | 师德师风演讲稿 | 年会主持词 | 领导致辞 everybody good afternoon:. first of all thank the teacher gave me a story in my own future ideal job. everyone has a dream job. my dream is to bee a boss, own a pany. in order to achieve my dreams, i need to find a good job, to accumulate some experience and wealth, it is the necessary things of course, in the school good achievement and rich knowledge is also very important. good achievement and rich experience can let me work to make the right choice, have more opportunities and achievements. at the same time, munication is very important, because it determines whether my pany has a good future development. so i need to exercise their municative ability. i need to use all of the free time to learn

关于坚持的英语演讲稿

关于坚持的英语演讲稿 Results are not important, but they can persist for many years as a commemoration of. Many years ago, as a result of habits and overeating formed one of obesity, as well as indicators of overall physical disorders, so that affects my work and life. In friends to encourage and supervise, the participated in the team Now considered to have been more than three years, neither the fine rain, regardless of winter heat, a day out with 5:00 time. The beginning, have been discouraged, suffering, and disappointment, but in the end of the urging of friends, to re-get up, stand on the playground. 成绩并不重要,但可以作为坚持多年晨跑的一个纪念。多年前,由于庸懒习惯和暴饮暴食,形成了一身的肥胖,以及体检指标的全盘失常,以致于影响到了我的工作和生活。在好友的鼓励和督促下,参加了晨跑队伍。现在算来,已经三年多了,无论天晴下雨,不管寒冬酷暑,每天五点准时起来出门晨跑。开始时,也曾气馁过、痛苦过、失望过,但最后都在好友们的催促下,重新爬起来,站到了操场上。 In fact, I did not build big, nor strong muscles, not a sport-born people. Over the past few years to adhere to it, because I have a team behind, the strength of a strongteam here, very grateful to our team, for a long time, we encourage each other, and with sweat, enjoying common health happy. For example, Friends of the several run in order to maintain order and unable to attend the 10,000 meters race, and they are always concerned about the brothers and promptly inform the place and time, gives us confidence and courage. At the same time, also came on their own inner desire and pursuit for a good health, who wrote many of their own log in order to refuel for their own, and inspiring. 其实我没有高大身材,也没健壮肌肉,天生不属于运动型的人。几年来能够坚持下来,因为我的背后有一个团队,有着强大团队的力量,在这里,非常感谢我们的晨跑队,长期以来,我们相互鼓励着,一起流汗,共同享受着健康带来的快

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1.How to build a business that lasts100years 0:11Imagine that you are a product designer.And you've designed a product,a new type of product,called the human immune system.You're pitching this product to a skeptical,strictly no-nonsense manager.Let's call him Bob.I think we all know at least one Bob,right?How would that go? 0:34Bob,I've got this incredible idea for a completely new type of personal health product.It's called the human immune system.I can see from your face that you're having some problems with this.Don't worry.I know it's very complicated.I don't want to take you through the gory details,I just want to tell you about some of the amazing features of this product.First of all,it cleverly uses redundancy by having millions of copies of each component--leukocytes,white blood cells--before they're actually needed,to create a massive buffer against the unexpected.And it cleverly leverages diversity by having not just leukocytes but B cells,T cells,natural killer cells,antibodies.The components don't really matter.The point is that together,this diversity of different approaches can cope with more or less anything that evolution has been able to throw up.And the design is completely modular.You have the surface barrier of the human skin,you have the very rapidly reacting innate immune system and then you have the highly targeted adaptive immune system.The point is,that if one system fails,another can take over,creating a virtually foolproof system. 1:54I can see I'm losing you,Bob,but stay with me,because here is the really killer feature.The product is completely adaptive.It's able to actually develop targeted antibodies to threats that it's never even met before.It actually also does this with incredible prudence,detecting and reacting to every tiny threat,and furthermore, remembering every previous threat,in case they are ever encountered again.What I'm pitching you today is actually not a stand-alone product.The product is embedded in the larger system of the human body,and it works in complete harmony with that system,to create this unprecedented level of biological protection.So Bob,just tell me honestly,what do you think of my product? 2:47And Bob may say something like,I sincerely appreciate the effort and passion that have gone into your presentation,blah blah blah-- 2:56(Laughter) 2:58But honestly,it's total nonsense.You seem to be saying that the key selling points of your product are that it is inefficient and complex.Didn't they teach you 80-20?And furthermore,you're saying that this product is siloed.It overreacts, makes things up as it goes along and is actually designed for somebody else's benefit. I'm sorry to break it to you,but I don't think this one is a winner.

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尊敬的领导,老师,亲爱的同学们, 大家好!我是5班的梁浩东。今天早上我坐车来学校的路上,我仔细观察了路上形形色色的人,有开着小车衣着精致的叔叔阿姨,有市场带着倦容的卖各种早点的阿姨,还有偶尔穿梭于人群中衣衫褴褛的乞丐。于是我问自己,十几年后我会成为怎样的自己,想成为社会成功人士还是碌碌无为的人呢,答案肯定是前者。那么十几年后我怎样才能如愿以偿呢,成为一个受人尊重,有价值的人呢?正如我今天演讲的题目是:自主管理。 大家都知道爱玩是我们孩子的天性,学习也是我们的责任和义务。要怎样处理好这些矛盾,提高自主管理呢? 首先,我们要有小主人翁思想,自己做自己的主人,要认识到我们学习,生活这一切都是我们自己走自己的人生路,并不是为了报答父母,更不是为了敷衍老师。 我认为自主管理又可以理解为自我管理,在学习和生活中无处不在,比如通过老师,小组长来管理约束行为和同学们对自身行为的管理都属于自我管理。比如我们到一个旅游景点,看到一块大石头,有的同学特别兴奋,会想在上面刻上:某某某到此一游话。这时你就需要自我管理,你需要提醒自己,这样做会破坏景点,而且是一种素质低下的表现。你设想一下,如果别人家小孩去你家墙上乱涂乱画,你是何种感受。同样我们把自主管理放到学习上,在我们想偷懒,想逃避,想放弃的时候,我们可以通过自主管理来避免这些,通过他人或者自己的力量来完成。例如我会制定作息时间计划表,里面包括学习,运动,玩耍等内容的完成时间。那些学校学习尖子,他们学习好是智商高于我们吗,其实不然,在我所了解的哪些优秀的学霸传授经验里,就提到要能够自我管理,规范好学习时间的分分秒秒,只有辛勤的付出,才能取得优异成绩。 在现实生活中,无数成功人士告诉我们自主管理的重要性。十几年后我想成为一位优秀的,为国家多做贡献的人。亲爱的同学们,你们们?让我们从现在开始重视和执行自主管理,十几年后成为那个你想成为的人。 谢谢大家!

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Dear teacher and colleagues: my topic is on “spare time”. It is a huge blessing that we can work 996. Jack Ma said at an Ali's internal communication activity, That means we should work at 9am to 9pm, 6 days a week .I question the entire premise of this piece. but I'm always interested in hearing what successful and especially rich people come up with time .So I finally found out Jack Ma also had said :”i f you don’t put out more time and energy than others ,how can you achieve the success you want? If you do not do 996 when you are young ,when will you ?”I quite agree with the idea that young people should fight for success .But there are a lot of survival activities to do in a day ,I want to focus on how much time they take from us and what can we do with the rest of the time. As all we known ,There are 168 hours in a week .We sleep roughly seven-and-a-half and eight hours a day .so around 56 hours a week . maybe it is slightly different for someone . We do our personal things like eating and bathing and maybe looking after kids -about three hours a day .so around 21 hours a week .And if you are working a full time job ,so 40 hours a week , Oh! Maybe it is impossible for us at

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