# data[].attributes.attachment_link.title
The Causes of Chemical Imbalance in the Brain

# data[].attributes.body
I am going to be contacting a licensed therapist at CenClear.

# data[].attributes.attachment_link.readable_text
Vonetta M. Dotson, Ph.D., 1, ² Sarah M. Szymkowicz, M.S., ¹ Joshua W. Kirton, M.S, ¹ Molly E. McLaren, B.S., ¹ Mackenzie L. Green, B.S., ¹ and Jessica Y. Rohani ¹

Abstract

Objective

Depression and anxiety and are associated with cognitive deficits and brain changes, especially in older adults. Despite the frequent co-occurrence of these conditions, cognitive neuroscience studies examining comorbid depression and anxiety are limited. The goal of the present study was to examine the unique and combined effect of depressive and anxiety symptoms on cognitive and brain functioning in young and older adults.

Methods

Seventy-one healthy, community-dwelling adults between the ages of 18 and 81 were administered a neuropsychological battery and completed the Center for Epidemiologic Studies Depression Scale (CES-D) and the trait form of the State-Trait Anxiety Inventory (STAI-T). A subset of 25 participants also underwent functional magnetic resonance imaging (fMRI) scanning while completing the n-back working memory task.

Results

Total depressive symptoms, depressed mood symptoms, and somatic symptoms were associated with deficits in speed, working memory and executive functions, especially in older adults. Symptoms of lack of well-being were not associated with any neuropsychological test. Anxiety was associated with better attention and working memory. Moreover, anxiety modified the relationship between depressive symptoms and executive functioning in older adults, as elevated depressive symptoms were associated with worse performance at low levels of anxiety, but not at higher anxiety levels. Similarly, analysis of fMRI data showed that total depressive symptoms and depressed mood symptoms were associated with decreased activity in the superior frontal gyrus at low anxiety levels, but not at high anxiety levels.

Conclusion

Results confirm previous reports that subthreshold depression and anxiety impact cognitive and brain functioning and suggest that the interaction of depression and anxiety results in distinct cognitive and brain changes. Findings highlight the importance of assessing and controlling for symptoms of depression and anxiety in research studies of either condition.

INTRODUCTION

The frequent co-occurrence of depression and anxiety has long been recognized. As many as 40-50% of patients with major depression have comorbid anxiety disorders [ 1- 5]. Evidence suggests that the combination of depression and anxiety leads to worse outcomes and response to treatment compared to either disorder alone [ 6- 11], highlighting the importance of research that examines the interactive effect of depression and anxiety on the broad spectrum of possible outcomes. This may be particularly pertinent for older adults, as research has shown that in many cases, the adverse impact of depression and anxiety is greater at older ages [ 12- 14]. Moreover, the co-occurrence of depression and anxiety may be even higher in older adults. According to a recent report, anxiety symptoms are present in 67% of older adults with subthreshold depression and 87% of those with clinical depression [ 15].

Both depression and anxiety are associated with cognitive deficits and changes in brain structure and function. Numerous studies have documented reduced cognitive functioning in major depression compared to controls, as well as a linear relationship between higher depressive symptoms, even at a subthreshold level, and lower cognitive functioning [ 16- 21]. Deficits are most consistently seen on tasks of episodic memory, working memory, attention, and executive functioning, and are often seen exclusively or disproportionately in older adults compared to young adults [ 13, 14]. Corresponding with these cognitive changes are findings from the neuroimaging literature that document depression-related structural and functional brain changes in a network of frontolimbic regions, which underlie performance on memory, attention, and executive tasks. Findings include reduced regional brain volumes, altered functional activity, and increases in white matter lesions [ 22- 26].

Findings on the relationship between anxiety and cognitive performance are mixed [ 27- 33]. Investigations comparing patients diagnosed with anxiety disorders, such as obsessive-compulsive disorder, generalized anxiety disorder, and post-traumatic stress disorder typically find anxiety-related attentional biases, executive dysfunction, and memory deficits [ 34, 35]. In contrast, although some studies have reported a linear relationship between subthreshold anxiety and cognition in older adults [ 32, 36, 37], many studies in young and older adults show an inverted U-shaped function, such that an intermediate level of anxiety symptoms is associated with optimal cognitive performance, while low and high severity are related to worse functioning [ 16, 38, 39]. Neuroimaging studies of clinical and subthreshold anxiety suggest that increased anxiety is associated with decreased volumes in the hippocampus and other temporal regions [ 40, 41], heightened amygdala and insular activity and reduced prefrontal and temporal activity [ 42- 47].

Despite the frequent comorbidity of depression and anxiety, few studies have examined the unique and interactive effect of the two on cognitive and brain functioning. There is evidence that mixed depression and anxiety in late life is associated with worse visual memory compared to controls [ 48], and a longitudinal study [ 49] found greater decline in memory over 4 years in anxious depression compared to non-anxious depression. In another study, general cognitive status was better predicted by combined depression and anxiety than by either variable alone [ 37]. Similarly, studies in young adults show memory deficits in mixed depression and anxiety compared to controls [ 50, 51], as well as poor verbal fluency, speed, and executive functioning in depression with comorbid anxiety compared to non-anxious depression [ 52]. Functional imaging studies in older adults have found that compared to depression without anxiety, mixed depression and anxiety is associated with great activation in the right hemisphere dorsal anterior cingulate cortex (ACC), posterior cingulate, and precentral gyrus during an executive task, and with lower default mode network activation in the rostral ACC, medial prefrontal cortex (PFC), and orbitofrontal cortex [ 53, 54]. This parallels functional studies in young adults during emotion processing tasks, which have documented increased activation in middle frontal regions and the insula as a function of heightened anxiety in mixed major depression/generalized anxiety disorder patients [ 55], as well as reduced activity in the hippocampus [ 40].

The goal of the present study was to add to the limited cognitive neuroscience research on the combined effect of depression and anxiety in late life. In a sample of young and older adults, we examined the unique and combined effect of subthreshold symptoms of depression and anxiety on neuropsychological test performance and brain activity measured with functional magnetic resonance imaging (fMRI) during a working memory task. We also examined the impact of age on the relationships of depressive and anxiety symptoms with cognitive and brain functioning. We predicted that depressive symptoms would have a linear relationship with deficits in memory, attention, and executive functions, and that this relationship would be modified by symptoms of anxiety. For the fMRI data, based on previous findings of heightened frontal activity during working memory tasks in depression [ 56, 57], we hypothesized that depressive symptoms would be associated with hyperactivity in frontal brain regions, and this effect would be accentuated in individuals with high symptoms of anxiety.

METHODS

Participants

The cognitive sample included 71 healthy, community dwelling adults between the ages of 18 and 81; the neuroimaging sample included a subset of 25 adults between the ages of 18 and 75 who were enrolled after the neuroimaging substudy began, had no MRI contraindications, and were interested in participating in the fMRI portion of the study ( Figure 1 ). Participants were recruited from the University of Florida and the surrounding area via the Claude D. Pepper Registry for Research, local flyer and newspaper advertisements, and public service announcements. All participants were right-handed, native English speakers with normal or corrected-to-normal vision, and at least 9 years of education. Exclusionary criteria included history of major medical or neurological illness, head trauma, learning disorder, current antiepileptic or antipsychotic use, and language comprehension difficulties. Participants were given the mood disorders, psychotic screener, substance use disorders, and anxiety disorders modules of the Structured Clinical Interview for DSM-IV Axis I Disorders [SCID-IV; 58] to assess the presence of major psychopathology, other than unipolar depression or anxiety disorders, that would be an exclusionary criteria. All subjects scored higher than 30 on the Telephone Interview for Cognitive Status [TICS; 59], which has 94% sensitivity and 100% specificity for differentiating demented and cognitively intact individuals at this cutoff score. Participants who underwent fMRI met standard magnetic resonance imaging (MRI) safety criteria. All participants gave written and verbal consent to participate in the study, and the protocol was approved by the University of Florida Health Science Center Institutional Review Board. Sample demographic data are presented in Table 1 .

Table 1

Cognitive Sample

fMRI Sample

Measures

Symptoms of depression were assessed with the Center for Epidemiologic Studies Depression Scale [CES-D; 60], a 20-item self-report measure of the frequency and severity of depressive symptoms experienced in the past week. This widely-used measure has been validated in both young and older community-dwelling adults [ 61, 62] and has a well-replicated factor structure that includes subscales for depressed mood, somatic symptoms, lack of well-being, and interpersonal symptoms [ 63]. Statistical analyses used continuous measures of CES-D total and subscale scores. The interpersonal symptoms scale was not analyzed due to the restricted range of the scale, which includes only 2 items.

The State-Trait Anxiety Inventory [STAI; 64] was administered to measure symptoms of anxiety. The STAI is a 40-item questionnaire that measures situational (i.e., at the time of testing) and dispositional symptoms of anxiety, and yields separate scores for state and trait anxiety, respectively. Only the trait score (STAI-T) was used in the current analyses due to missing state scores at the time of fMRI scanning for some participants.

Participants were administered a neuropsychological battery that focused on measures of episodic and working memory, attention, and executive functions, which are known to be impacted by depression. Measures of episodic memory included the immediate recall and delayed recall scores from the Hopkins Verbal Learning Test, Revised [HVLT-R; 65] and Brief Visual Memory Test, Revised [BVMT-R; 66]. Attention, working memory, and executive functions were measured by the total scores on the Digit Span and Letter-Number Sequencing (LNS) subtests of the Wechsler Adult Intelligence Scale, Third Edition [ 67], and time to completion on the Trailmaking Test [ 68] parts A (TMT-A) and B (TMT-B).

Neuroimaging Procedure

During fMRI scanning, participants completed a letter variant version of the n-back task [ 69]. Participants were shown individual letters and were asked to indicate whether the stimuli were targets (33% of trials) or non-targets by pressing one of two buttons. Four separate conditions were presented: 0-back (target stimulus is the letter "X"), 1-back (target was any letter that was the same as the previous letter), 2-back (target was any letter that matched the letter two back from itself), and 3-back (target was any letter that matched the letter three back from itself). This paradigm allows for working memory to be examined over increasingly difficult trial conditions while maintaining consistency in all other stimulus processing and response demands. Each letter was presented for 750 ms with a fixed inter-stimulus interval of 3250 ms. Prior to each task block, instructions were read aloud to the participant. A fixation cross ("+") presented for 16000 ms preceded the onset of the first letter in each block. Four blocks of 32 trials were presented in each condition and the order of the conditions was pseudo-randomized across participants. Stimuli were presented using an Integrated Functional Imaging System hardware system (IFIS; Psychology Software Tools, Pittsburgh, PA) and a LCD screen viewed by participants via reflective mirrors positioned at the back of the magnet bore. Subject responses were made via an MRI-compatible keypad. Both accuracy and response latency were recorded by the IFIS system. Prior to entering the fMRI scanner, all participants practiced one block of each task condition (0- through 3-back) to ensure that they fully understood the task requirements. If accuracy was below 80 percent on any practice block, the block was repeated.

Image Acquisition and Preprocessing

MRI data was collected using a Philips 3-Tesla scanner at the University of Florida's McKnight Brain Institute. Participants lay supine on the scanner bed and a standard 8-channel head radio-frequency coil was placed over their head. Foam pads were used to minimize head motion. Functional MR images were acquired in 35 axial slices with approximately 30° rotation above the anterior commissure-posterior commissure (AC-PC) line using a T2*-weighted echo planar imaging (EPI) pulse sequence [repetition time (TR) = 2000 ms; echo time (TE) = 30 ms; flip angle (FA) = 90°; field of view (FOV) = 24 cm; 64 × 64 voxels at 3.75 mm3 with .4 mm slice gap]. Seventy-four volumes were acquired per run. Prior to acquiring functional data, a T1-weighted Magnetization Prepared Rapid Gradient Echo (MP-RAGE) high-resolution 3D anatomical scan (170 1-mm thick slices; TR = 2000 ms; TE = 4.13 ms; FA = 8°; FOV = 24 cm; 256 × 256 voxels at 3.75 mm3) was performed to enable standardization of brain space on which to overlay functional data (Talairach and Tournoux, 1988).

Preprocessing and statistical analysis of MRI data was performed using BrainVoyager software (BVQX 2.4.0.2; Brain Innovation, Maastricht, the Netherlands). Functional data were motion-corrected and spatially smoothed with a Gaussian kernel (8 mm full width half-maximum [FWHM]), followed by low-frequency non-linear drifts removal with a temporal high-pass filter of frequencies below 3 cycles per time course. Functional images were aligned with their respective high resolution 3D anatomical volumes using automatic standard BVQX co-registration procedures; manual alignment was subsequently conducted to ensure accurate co-registration. The 3D scans were transformed into Talairach space [ 70] to allow for group averaging.

Data Analysis

Cognitive data analysis

Cognitive data were analyzed using the PROC GLM procedure in SAS version 9.2 (SAS Institute, Cary, NC). Models included continuous measures of CES-D score, STAI trait score, and age as independent variables and race (white/non-white), years of education and sex as covariates. Independent variables were centered around the variable mean. For graphing purposes, an age cutoff of 50 was used for creating young adult and older adult groups. Separate models were performed for each of the aforementioned cognitive measures and for error rates and reaction times (RTs) excluding non-responses on the n-back task.

fMRI data analysis

Whole brain voxel-wise statistical tests and follow-up contrasts on signal intensity in identified regions of interest (ROI) were examined using a two-step general linear modeling (GLM) approach [ 71]. First, an individual fixed-effects GLM was determined for every participant for each task. The GLMs utilized four predictors: 0-back, 1-back, 2-back, and 3-back. Subsequently, separate-subject predictors random-effects GLM analyses were performed to determine main effects for the task (z-transformed time-course, in-brain functional image intensity threshold = 300). Hemodynamic response for each event was estimated by convolving each regressor with a standard two-gamma function (onset = 0, response undershoot ratio = 6, time to response peak = 5 sec, time to undershoot peak = 15 sec, response dispersion = 1, undershoot dispersion = 1) [ 72]. This shifted the predicted hemodynamic response approximately 5 sec to account for expected delay. Parameter estimates (β) were generated, which indicated relative strength of covariance between the data and the hemodynamic response function (HRF). We used a 3D spatial contiguity threshold of 20 voxels and a statistical significance threshold of p ≤ .005 to decrease the likelihood of spurious findings. Localization of suprathreshold clusters of brain activity was verified using the "nearest gray matter" search function provided by Talairach Daemon software [ 73; http://www.talairach.org].

To examine whether anxiety and depressive symptoms impacted the observed working-memory related brain activity, we conducted ROI GLMs over contiguous voxels centering on the areas of activation identified in the whole-brain analysis, from which beta weights were obtained. GLMs that paralleled the cognitive data analyses were conducted on the beta weights for each region.

RESULTS

Cognitive Results

Mean performance data for the cognitive tests are presented in Table 2 ; results of the cognitive data analyses are summarized in Table 3 . An age × CES-D effect was observed for TMT-A [ F(1,59) = 4.61, p =0.04] and LNS [ F(1,59) = 6.19, p = 0.02] ( Figure 2a ). For both measures, higher CES-D scores were associated with worse performance in older adults, but with stable or better performance in young adults. Performance on Digit Span improved as a function of higher STAI-T scores, but this effect was greater in older adults [age × STAI-T; F(1, 59) = 4.79, p = 0.03] ( Figure 3 ). Similarly, older adults with higher STAI-T scores performed better on LNS, although young adults did not show this association [age × STAI-T; F(1,59) = 6.90, p = 0.01] ( Figure 3 ). We observed an age × CES-D × STAI-T effect for TMT-B [F(1,58)= 4.83, p=0.03] ( Figure 2b ). In older adults, higher CES-D scores were associated with worse performance only in those with lower STAI-T scores, while depressive and anxiety symptoms were not associated with TMT-B scores in young adults.

Table 2

Test

Mean (SD)

HVLT-R Immediate Recall

27.66 (4.13)

HVLT-R Delayed Recall

9.68 (1.90)

BVMT-R Immediate Recall

28.39 (5.64)

BVMT-R Delayed Recall

10.68 (1.69)

TMT-A (seconds)

25.28 (10.09)

TMT-B (seconds)

57.63 (29.43)

Digit Span (total score)

19.10 (3.91)

LNS (total score)

11.54 (2.63)

N-back Error Rate

.06 (.05)

N-back Reaction Time (sec)

714.01 (147.37)

Table 3

TMT-A

TMT-B

BVMT-R immediate recall

BVMT-R delayed recall

HVLT-R immediate recall

HVLT-R delayed recall

Digit Span

LNS

CES-D Total Score model

Sex

3.616 (2.310)

−0.750 (6.164)

−0.816 (1.441)

−0.236 (0.451)

−3.549 (1.043)**

−1.187 (0.524)*

−0.693 (1.029)

−1.371 (0.628)*

Age

0.347 (0.068)**

1.210 (0.180)**

−0.141 (0.042)**

−0.025 (0.013)

−0.106 (0.031)**

−0.035 (0.015)*

−0.060 (0.030)

−0.089 (0.018)**

Age x CES-D

0.019 (0.009)*

0.046 (0.024)

0.001 (0.006)

0.002 (0.002)

−0.001 (0.004)

0.002 (0.002)

−0.005 (0.004)

−0.006 )0.002)*

Age x STAI-Trait x CES-D

−0.000 (0.000)

−0.002 (0.001)*

−0.000 (0.000)

−0.000 (0.000)

0.000 (0.000)

0.000 (0.000)

0.000 (0.000)

0.000 (0.000)

Depressed Mood model

Sex

2.804 (2.244)

−1.414 (5.685)

−0.490 (1.362)

−0.189 (0.424)

−2.953 (1.030)**

−0.982 (0.504)

−0.675 (0.987)

−1.144 (0.627)

Age

0.280 (0.064)**

1.237 (0.161)**

−0.156 (0.039)**

−0.036 (0.012)**

−0.078 (0.029)**

−0.034 (0.014)*

−0.050 (0.028)

−0.070 (0.018)**

Age x Mood

0.035 (0.022)

0.166 (0.055)**

−0.012 (0.013)

−0.003 (0.004)

0.004 (0.010)

0.002 (0.005)

−0.011 (0.010)

−0.010 (0.006)

Age x STAI-Trait x Mood

0.000 (0.001)

−0.008 (0.003)**

0.000 (0.001)

0.000 (0.000)

0.000 (0.001)

0.000 (0.000)

−0.000 (0.000)

0.000 (0.000)

Somatic Symptoms model

Sex

2.857 (2.375)

−1.343 (6.018)

−0.663 (1.372)

−0.295 (0.417)

−2.950 (1.066)**

−1.032 (0.506)*

−0.505 (0.992)

−1.060 (0.620)

Age

0.280 (0.058)**

1.063 (0.146)**

−0.123 (0.033)**

−0.025 (0.010)*

−0.084 (0.026)**

−0.029 (0.012)*

−0.047 (0.024)

−0.070 (0.015)**

Age x Somatic

0.022 (0.021)

0.064 (0.053)

0.018 (0.012)

0.010 (0.004)*

0.000 (0.009)

0.008 (0.005)

−0.011 (0.009)

−0.011 (0.006)*

Lack of Well-being model

Sex

3.544 (2.436)**

−0.344 (6.526)

−0.747 (1.461)

−0.199 (0.465)

−3.482 (1.091)**

−1.146 (0.545)*

−0.867 (3.437)**

−1.383 (0.673)*

Age

0.323 (0.071)**

0.997 (0.189)**

−0.151 (0.042)**

−0.029 (0.014)*

−0.091 (0.032)**

−0.035 (0.016)*

−0.057 (0.030)

−0.072 (0.020)**

Analysis of CES-D subscales revealed parallel results for depressed mood effects on TMT-B [age × depressed mood [ F(1,59) = 9.20, p = 0.00; age × depressed mood × STAI-T [ F(1,59) = 7.97, p = 0.01] ( Figure 4a ). Age differences on LNS were greater in individuals with higher somatic symptoms [F (1,60)= 3.97, p=0.05], but were attenuated on BVMT delayed recall as a function of higher somatic symptoms [F(1,60)= 6.56, p=0.01] ( Figure 4b ).

fMRI Results

Analysis of activation associated with increasing working memory load on the n-back task revealed increased activity in a cluster in the frontal lobes that included the superior frontal gyrus (SFG; Brodmann area [BA] 10), prefrontal cortex (BA 9), and inferior frontal gyrus (BA 44), with peak activity in the right SFG, and in the left insula (BA 13) extending into the putamen ( Figure 5a and 5b ). Peak t-values and localization for all observed separable clusters of working memory activation are provided in Table 4 .

Table 4

Region

BA

Side

Coordinate*

Cluster Size

t

p

x

y

z

ROI analysis of fMRI activity within clusters identified in the whole-brain analysis revealed a depressed mood × STAI-T effect for the SFG [ F(1,17) = 4.83, p = .04] ( Figure 5a ). Working-memory related activity in the SFG increased on more difficult trials as a function of higher depressed mood scores in individuals with lower STAI-T scores, but decreased as a function of higher depressed mood scores in those with high STAI-T scores. Higher total CES-D and depressed mood scores were associated with increased activity in the insula [CES-D F(1,16) = 5.00, p = . 04; depressed mood F(1,17) = 6.73, p = . 0.02].

N-back Task Results

Performance data for the n-back are presented in Table 2 . Data were missing for two participants, thus analyses were performed on 23 participants. Higher total depressive symptoms and depressed mood symptoms were associated with more errors [total CES-D F(1,15) = 7.02, p = 0.02; depressed mood F(1,16) = 9.06, p = 0.01]. Error rates generally increased on more difficult trials (i.e., higher working memory load), but individuals with the highest total CES-D and mood subscale scores made more errors on 1-back compared to 2-back trials [working memory load × CES-D F(3,49) = 2.89, p=0.05; working memory load × depressed mood F(3,48) = 3.09, p = 0.04]. For RTs, higher lack of well-being scores were associated with greater increases in RTs at higher working memory load [ F(3,45) = 4.50, p = 0.01], but this effect was reduced at higher STAI-T levels [ F(3,45) = 6.93, p = 0.00].

DISCUSSION

This study examined the effects of depressive symptoms, anxiety symptoms, and their interaction on cognitive and brain functioning. We examined healthy young and older adults who presented with a range of depressive and anxiety symptoms, which allowed use to examine subthreshold symptoms. This focus is important as there is increasing recognition that subthreshold mood symptoms are particularly common in older adults and are associated with similar negative sequelae as clinical mood disorders [ 74, 75]. Despite the frequent comorbidity of depressive and anxiety disorders, cognitive neuroscience studies focusing on the unique and combined effects of depression and anxiety are limited. The present study was designed to address this gap in the literature.

We found effects of both depressive and anxiety symptoms on cognitive and brain functioning. At older ages, total depressive symptoms were associated with slower processing speed, as measured by TMT-A, and with worse working memory performance on LNS. The finding of an interactive effect of age and depressive symptoms is consistent with previous studies showing that older adults may be particularly vulnerable to the adverse impact of depression [ 12- 14]. Indeed, many studies of cognitive functioning in depressed young adults have failed to show effects, while cognitive studies in late-life depression yield more consistent results and generally show deficits in attention, working memory, episodic memory, and executive functions [ 18, 21].

Examination of the CES-D subscales and neuropsychological test performance revealed effects for the depressed mood and somatic symptoms subscales, but not the lack of well-being subscale, consistent with our previous work [ 76- 78]. Higher depressed mood symptoms were associated with deficits in executive functioning on TMT-B in older adults but not young adults. Additionally, age differences in working memory were greater in individuals with elevated somatic symptoms, but age differences in delayed visual memory were reduced as a function of higher somatic symptoms. Perhaps somatic symptoms in older adults are a reflection of physical changes that are correlated with white matter lesions in the brain, which are known to increase as a function of age and depression [ 79, 80]. Based on previous evidence that prefrontal white matter is most susceptible to aging and that such lesions are associated with deficits in frontally-mediated cognitive functions [ 81, 82], somatic symptoms might be expected to have a greater impact on working memory in older adults compared to visual memory.

Regarding anxiety symptoms, we found that higher trait anxiety was associated with better performance on Digit Span and LNS, measures of attention and working memory, and this effect was greater at older ages. Given our non-clinical sample, higher STAI-T scores correspond with moderate levels of anxiety rather than severe levels characteristic of anxiety disorders. Thus, our findings are consistent with studies reporting an inverted U-shape function in the relationship between anxiety and cognitive performance, such that moderate levels of anxiety are associated with better performance, while low and high symptoms are associated with performance deficits [ 16, 38, 39]. Interestingly, we found that anxiety moderated the impact of depressive symptoms on executive functioning. Higher total CES-D and depressed mood symptoms were associated with deficits on TMT-B at low levels of trait anxiety, but not at higher anxiety levels. Since moderate anxiety is associated with better performance on some cognitive measures, it appears that anxiety counteracted the negative impact of depressive symptoms on executive functioning. This is contrary to previous investigations of mixed depression and anxiety, which have shown that comorbid depression and anxiety are associated with worse outcomes, including cognitive deficits, compared to either condition alone [ 37, 49, 52]. This discrepancy may be due to our examination of subthreshold depression and anxiety, rather than clinical conditions. Perhaps the unique and interactive effects of depression and anxiety differ as a function of severity level. This is an important area for future research considering the frequent comorbidity of depression and anxiety and the high prevalence of subthreshold conditions.

A subset of our sample underwent fMRI scanning while they performed the n-back working memory task. Consistent with other working memory studies, we found monotonic increases in activity in the PFC, inferior and superior frontal gyri, putamen and insula at higher working memory loads. Higher total depressive symptoms was associated with reduced activity in the SFG at higher working memory loads in individuals with high anxiety, but with increased activity in those with low anxiety. This suggests that anxiety reduced the heightened frontal activity that has previously been shown in depression during working memory tasks [ 56, 57]. Analysis of n-back task performance showed that higher total depressive symptoms and depressed mood symptoms were associated with more errors on the n-back, consistent with the neuropsychological test results. Given the smaller sample size in the fMRI subsample, we did not examine whether the effect differed in young and older adults. In contrast to the neuropsychological test results, we also found an effect of the lack of well-being subscale on performance. Higher lack of well-being scores were associated with greater increases in RTs at higher working memory load, but higher trait anxiety reduced this effect. This finding parallels the fMRI activation results but should be interpreted with caution due to the small sample size in the neuroimaging subsample.

Taken together, our results confirm previous reports that subthreshold depression and anxiety impact cognitive and brain functioning, add to the growing literature showing that age may moderate these effects, and suggest that the interaction of subthreshold depression and anxiety results in distinct cognitive and brain changes. Findings highlight the importance of assessing and controlling for symptoms of depression and anxiety in research studies of either condition. Results also have implications for clinicians, as they suggest that older adults with comorbid symptoms of depression and anxiety may be at risk for cognitive deficits even in the absence of a diagnosis of major depression or an anxiety disorder. Future work should compare the unique and interactive effect of depression and anxiety on cognitive and brain functioning at both subthreshold and clinical levels, and should examine changes in functioning over time using longitudinal designs.

Acknowledgments

This research was supported by an Age Related Memory Loss award from the McKnight Brain Research Foundation (VMD), and by the National High Magnetic Field Laboratory. This project was performed at the Advanced Magnetic Resonance Imaging and Spectroscopy (AMRIS) facility in the McKnight Brain Institute of the University of Florida.

References

11. Vollrath M, Angst J. Results of the Zurich cohort study: course of anxiety and depression. Psychiatr Psychobiol. 1989;4:307-313. [ Google Scholar]

15. Braam AW, Copeland JR, Delespaul PA, Beekman AT, Como A, et al. Depression, subthreshold depression and comorbid anxiety symptoms in older Europeans: Results from the EURODEP concerted action. J Affect Disord 2013 [[ PubMed] Google Scholar]

59. Brandt J, Spencer M, Folstein M. The Telephone Interview for Cognitive Status. Neuropsychiatry Neuropsychol Behav Neurol. 1988;1:111-117. [ Google Scholar]

60. Radloff LS. The CES-D scale: A self-report depression scale for research in the general population. Appli Psychol Meas. 1977;1:385-401. [ Google Scholar]

78. Kirton JW, Resnick SM, Davatzikos C, Kraut MA, Dotson VM. Depressive Symptoms, Symptom Dimensions, and White Matter Lesion Volume in Older Adults: A Longitudinal Study. Am J Geriatr Psychiatry 2013 [[ PMC free article] [ PubMed] Google Scholar]

# data[].attributes.attachment_link.title
Unique and interactive effect of anxiety and depressive symptoms on cognitive and brain function in young and older adults

# data[].attributes.attachment_link.description
Depression and anxiety and are associated with cognitive deficits and brain changes, especially in older adults. Despite the frequent co-occurrence of these conditions, cognitive neuroscience studies examining comorbid depression and anxiety are limited. ...

# data[].attributes.attachment_link.readable_text
Thanks for visiting. Don't miss your FREE gift.

The Best Diets for Cognitive Fitness, is yours absolutely FREE when you sign up to receive Health Alerts from Harvard Medical School

Sign up to get tips for living a healthy lifestyle, with ways to fight inflammation and improve cognitive health, plus the latest advances in preventative medicine, diet and exercise, pain relief, blood pressure and cholesterol management, and more.

# data[].attributes.attachment_link.title
Brain science suggests "mind wandering" can help manage anxiety - Harvard Health

# data[].attributes.attachment_link.description
The wandering mind can get stuck on negative thoughts and start to "react" to a perceived threat that feels very real-and makes you feel anxious. Naming the negative feeling associated with that thought and then helping your mind wander in a more positive direction can help.

# data[].attributes.attachment_link.readable_text
by Trevor Haynes figures by Rebecca Clements
"I feel tremendous guilt," admitted Chamath Palihapitiya, former Vice President of User Growth at Facebook, to an audience of Stanford students. He was responding to a question about his involvement in exploiting consumer behavior. "The short-term, dopamine-driven feedback loops that we have created are destroying how society works," he explained. In Palihapitiya's talk, he highlighted something most of us know but few really appreciate: smartphones and the social media platforms they support are turning us into bona fide addicts. While it's easy to dismiss this claim as hyperbole, platforms like Facebook, Snapchat, and Instagram leverage the very same neural circuitry used by slot machines and cocaine to keep us using their products as much as possible. Taking a closer look at the underlying science may give you pause the next time you feel your pocket buzz.

Never Alone

If you've ever misplaced your phone, you may have experienced a mild state of panic until it's been found. About 73% of people claim to experience this unique flavor of anxiety, which makes sense when you consider that adults in the US spend an average of 2-4 hours per day tapping, typing, and swiping on their devices-that adds up to over 2,600 daily touches. Most of us have become so intimately entwined with our digital lives that we sometimes feel our phones vibrating in our pockets when they aren't even there.

While there is nothing inherently addictive about smartphones themselves, the true drivers of our attachments to these devices are the hyper-social environments they provide. Thanks to the likes of Facebook, Snapchat, Instagram, and others, smartphones allow us to carry immense social environments in our pockets through every waking moment of our lives. Though humans have evolved to be social-a key feature to our success as a species-the social structures in which we thrive tend to contain about 150 individuals. This number is orders of magnitude smaller than the 2 billion potential connections we carry around in our pockets today. There is no doubt that smartphones provide immense benefit to society, but their cost is becoming more and more apparent. Studies are beginning to show links between smartphone usage and increased levels of anxiety and depression, poor sleep quality, and increased risk of car injury or death. Many of us wish we spent less time on our phones but find it incredibly difficult to disconnect. Why are our smartphones so hard to ignore?

The Levers in Our Brains - Dopamine and social reward

Dopamine is a chemical produced by our brains that plays a starring role in motivating behavior. It gets released when we take a bite of delicious food, when we have sex, after we exercise, and, importantly, when we have successful social interactions. In an evolutionary context, it rewards us for beneficial behaviors and motivates us to repeat them.

The human brain contains four major dopamine "pathways," or connections between different parts of the brain that act as highways for chemical messages called neurotransmitters. Each pathway has its own associated cognitive and motor (movement) processes. Three of these pathways-the mesocortical, mesolimbic, and nigrostriatal pathways-are considered our "reward pathways" and have been shown to be dysfunctional in most cases of addiction. They are responsible for the release of dopamine in various parts of the brain, which shapes the activity of those areas. The fourth, the tuberoinfundibular pathway, regulates the release of a hormone called prolactin that is required for milk production.

While the reward pathways () are distinct in their anatomical organization, all three become active when anticipating or experiencing rewarding events. In particular, they reinforce the association between a particular stimulus or sequence of behaviors and the feel-good reward that follows. Every time a response to a stimulus results in a reward, these associations become stronger through a process called long-term potentiation. This process strengthens frequently used connections between brain cells called neurons by increasing the intensity at which they respond to particular stimuli.

Although not as intense as hit of cocaine, positive social stimuli will similarly result in a release of dopamine, reinforcing whatever behavior preceded it. Cognitive neuroscientists have shown that rewarding social stimuli-laughing faces, positive recognition by our peers, messages from loved ones-activate the same dopaminergic reward pathways. Smartphones have provided us with a virtually unlimited supply of social stimuli, both positive and negative. Every notification, whether it's a text message, a "like" on Instagram, or a Facebook notification, has the potential to be a positive social stimulus and dopamine influx.

The Hands that Pull - Reward prediction errors and variable reward schedules

Because most social media platforms are free, they rely on revenue from advertisers to make a profit. This system works for everyone involved at first glance, but it has created an arms race for your attention and time. Ultimately, the winners of this arms race will be those who best use their product to exploit the features of the brain's reward systems.

Reward prediction errors

Research in reward learning and addiction have recently focused on a feature of our dopamine neurons called reward prediction error (RPE) encoding. These prediction errors serve as dopamine-mediated feedback signals in our brains (). This neurological feature is something casino owners have used to their advantage for years. If you've ever played slots, you'll have experienced the intense anticipation while those wheels are turning-the moments between the lever pull and the outcome provide time for our dopamine neurons to increase their activity, creating a rewarding feeling just by playing the game. It would be no fun otherwise. But as negative outcomes accumulate, the loss of dopamine activity encourages us to disengage. Thus, a balance between positive and negative outcomes must be maintained in order to keep our brains engaged.

Variable reward schedules

How do social media apps take advantage of this dopamine-driven learning strategy? Similar to slot machines, many apps implement a reward pattern optimized to keep you engaged as much as possible. Variable reward schedules were introduced by psychologist B.F. Skinner in the 1930's. In his experiments, he found that mice respond most frequently to reward-associated stimuli when the reward was administered after a varying number of responses, precluding the animal's ability to predict when they would be rewarded. Humans are no different; if we perceive a reward to be delivered at random, and if checking for the reward comes at little cost, we end up checking habitually ( e.g. gambling addiction). If you pay attention, you might find yourself checking your phone at the slightest feeling of boredom, purely out of habit. Programmers work very hard behind the screens to keep you doing exactly that.

The Battle for Your Time

If you've been a Facebook user for more than a few years, you've probably noticed that the site has been expanding its criteria for notifications. When you first join Facebook, your notification center revolves around the initial set of connections you make, creating that crucial link between notification and social reward. But as you use Facebook more and begin interacting with various groups, events, and artists, that notification center will also become more active. After a while, you'll be able to open the app at any time and reasonably expect to be rewarded. When paired with the low cost of checking your phone, you have a pretty strong incentive to check in whenever you can.

Other examples highlight a more deliberate effort to monopolize your time. Consider Instagram's implementation of a variable-ratio reward schedule. As explained in this 60 Minutes interview, Instagram's notification algorithms will sometimes withhold "likes" on your photos to deliver them in larger bursts. So when you make your post, you may be disappointed to find less responses than you expected, only to receive them in a larger bunch later on. Your dopamine centers have been primed by those initial negative outcomes to respond robustly to the sudden influx of social appraisal. This use of a variable reward schedule takes advantage of our dopamine-driven desire for social validation, and it optimizes the balance of negative and positive feedback signals until we've become habitual users.

Question Your Habits
Trevor Haynes is a research technician in the Department of Neurobiology at Harvard Medical School.
Smartphones and social media apps aren't going anywhere anytime soon, so it is up to us as the users to decide how much of our time we want to dedicate to them. Unless the advertisement-based profit model changes, companies like Facebook will continue to do everything they can to keep your eyes glued to the screen as often as possible. And by using algorithms to leverage our dopamine-driven reward circuitry, they stack the cards-and our brains-against us. But if you want to spend less time on your phone, there are a variety strategies to achieve success. Doing things like disabling your notifications for social media apps and keeping your display in black and white will reduce your phone's ability to grab and hold your attention. Above all, mindful use of the technology is the best tool you have. So the next time you pick up your phone to check Facebook, you might ask yourself, "Is this really worth my time?"

For more information:

Tips for building a healthier relationship with your phone

A list of stories from NPR about smartphone addiction

A high-level primer on dopamine and how it affects your brain, body, and mood

An updated overview of trends in screen addiction, including the impact of COVID-19

# data[].attributes.attachment_link.title
Dopamine, Smartphones & You: A battle for your time - Science in the News

# data[].attributes.attachment_link.description
by Trevor Haynes figures by Rebecca Clements "I feel tremendous guilt," admitted Chamath Palihapitiya, former Vice President of User Growth at Facebook, to an audience of Stanford students. He was responding to a question about his involvement in exploiting consumer behavior. "The short-term, dopamine-driven feedback loops that we have created are destroying how society works," he explained.

# data[].attributes.attachment_link.readable_text
Vonetta M. Dotson, Ph.D., 1, ² Sarah M. Szymkowicz, M.S., ¹ Joshua W. Kirton, M.S, ¹ Molly E. McLaren, B.S., ¹ Mackenzie L. Green, B.S., ¹ and Jessica Y. Rohani ¹

Abstract

Objective

Depression and anxiety and are associated with cognitive deficits and brain changes, especially in older adults. Despite the frequent co-occurrence of these conditions, cognitive neuroscience studies examining comorbid depression and anxiety are limited. The goal of the present study was to examine the unique and combined effect of depressive and anxiety symptoms on cognitive and brain functioning in young and older adults.

Methods

Seventy-one healthy, community-dwelling adults between the ages of 18 and 81 were administered a neuropsychological battery and completed the Center for Epidemiologic Studies Depression Scale (CES-D) and the trait form of the State-Trait Anxiety Inventory (STAI-T). A subset of 25 participants also underwent functional magnetic resonance imaging (fMRI) scanning while completing the n-back working memory task.

Results

Total depressive symptoms, depressed mood symptoms, and somatic symptoms were associated with deficits in speed, working memory and executive functions, especially in older adults. Symptoms of lack of well-being were not associated with any neuropsychological test. Anxiety was associated with better attention and working memory. Moreover, anxiety modified the relationship between depressive symptoms and executive functioning in older adults, as elevated depressive symptoms were associated with worse performance at low levels of anxiety, but not at higher anxiety levels. Similarly, analysis of fMRI data showed that total depressive symptoms and depressed mood symptoms were associated with decreased activity in the superior frontal gyrus at low anxiety levels, but not at high anxiety levels.

Conclusion

Results confirm previous reports that subthreshold depression and anxiety impact cognitive and brain functioning and suggest that the interaction of depression and anxiety results in distinct cognitive and brain changes. Findings highlight the importance of assessing and controlling for symptoms of depression and anxiety in research studies of either condition.

INTRODUCTION

The frequent co-occurrence of depression and anxiety has long been recognized. As many as 40-50% of patients with major depression have comorbid anxiety disorders [ 1- 5]. Evidence suggests that the combination of depression and anxiety leads to worse outcomes and response to treatment compared to either disorder alone [ 6- 11], highlighting the importance of research that examines the interactive effect of depression and anxiety on the broad spectrum of possible outcomes. This may be particularly pertinent for older adults, as research has shown that in many cases, the adverse impact of depression and anxiety is greater at older ages [ 12- 14]. Moreover, the co-occurrence of depression and anxiety may be even higher in older adults. According to a recent report, anxiety symptoms are present in 67% of older adults with subthreshold depression and 87% of those with clinical depression [ 15].

Both depression and anxiety are associated with cognitive deficits and changes in brain structure and function. Numerous studies have documented reduced cognitive functioning in major depression compared to controls, as well as a linear relationship between higher depressive symptoms, even at a subthreshold level, and lower cognitive functioning [ 16- 21]. Deficits are most consistently seen on tasks of episodic memory, working memory, attention, and executive functioning, and are often seen exclusively or disproportionately in older adults compared to young adults [ 13, 14]. Corresponding with these cognitive changes are findings from the neuroimaging literature that document depression-related structural and functional brain changes in a network of frontolimbic regions, which underlie performance on memory, attention, and executive tasks. Findings include reduced regional brain volumes, altered functional activity, and increases in white matter lesions [ 22- 26].

Findings on the relationship between anxiety and cognitive performance are mixed [ 27- 33]. Investigations comparing patients diagnosed with anxiety disorders, such as obsessive-compulsive disorder, generalized anxiety disorder, and post-traumatic stress disorder typically find anxiety-related attentional biases, executive dysfunction, and memory deficits [ 34, 35]. In contrast, although some studies have reported a linear relationship between subthreshold anxiety and cognition in older adults [ 32, 36, 37], many studies in young and older adults show an inverted U-shaped function, such that an intermediate level of anxiety symptoms is associated with optimal cognitive performance, while low and high severity are related to worse functioning [ 16, 38, 39]. Neuroimaging studies of clinical and subthreshold anxiety suggest that increased anxiety is associated with decreased volumes in the hippocampus and other temporal regions [ 40, 41], heightened amygdala and insular activity and reduced prefrontal and temporal activity [ 42- 47].

Despite the frequent comorbidity of depression and anxiety, few studies have examined the unique and interactive effect of the two on cognitive and brain functioning. There is evidence that mixed depression and anxiety in late life is associated with worse visual memory compared to controls [ 48], and a longitudinal study [ 49] found greater decline in memory over 4 years in anxious depression compared to non-anxious depression. In another study, general cognitive status was better predicted by combined depression and anxiety than by either variable alone [ 37]. Similarly, studies in young adults show memory deficits in mixed depression and anxiety compared to controls [ 50, 51], as well as poor verbal fluency, speed, and executive functioning in depression with comorbid anxiety compared to non-anxious depression [ 52]. Functional imaging studies in older adults have found that compared to depression without anxiety, mixed depression and anxiety is associated with great activation in the right hemisphere dorsal anterior cingulate cortex (ACC), posterior cingulate, and precentral gyrus during an executive task, and with lower default mode network activation in the rostral ACC, medial prefrontal cortex (PFC), and orbitofrontal cortex [ 53, 54]. This parallels functional studies in young adults during emotion processing tasks, which have documented increased activation in middle frontal regions and the insula as a function of heightened anxiety in mixed major depression/generalized anxiety disorder patients [ 55], as well as reduced activity in the hippocampus [ 40].

The goal of the present study was to add to the limited cognitive neuroscience research on the combined effect of depression and anxiety in late life. In a sample of young and older adults, we examined the unique and combined effect of subthreshold symptoms of depression and anxiety on neuropsychological test performance and brain activity measured with functional magnetic resonance imaging (fMRI) during a working memory task. We also examined the impact of age on the relationships of depressive and anxiety symptoms with cognitive and brain functioning. We predicted that depressive symptoms would have a linear relationship with deficits in memory, attention, and executive functions, and that this relationship would be modified by symptoms of anxiety. For the fMRI data, based on previous findings of heightened frontal activity during working memory tasks in depression [ 56, 57], we hypothesized that depressive symptoms would be associated with hyperactivity in frontal brain regions, and this effect would be accentuated in individuals with high symptoms of anxiety.

METHODS

Participants

The cognitive sample included 71 healthy, community dwelling adults between the ages of 18 and 81; the neuroimaging sample included a subset of 25 adults between the ages of 18 and 75 who were enrolled after the neuroimaging substudy began, had no MRI contraindications, and were interested in participating in the fMRI portion of the study ( Figure 1 ). Participants were recruited from the University of Florida and the surrounding area via the Claude D. Pepper Registry for Research, local flyer and newspaper advertisements, and public service announcements. All participants were right-handed, native English speakers with normal or corrected-to-normal vision, and at least 9 years of education. Exclusionary criteria included history of major medical or neurological illness, head trauma, learning disorder, current antiepileptic or antipsychotic use, and language comprehension difficulties. Participants were given the mood disorders, psychotic screener, substance use disorders, and anxiety disorders modules of the Structured Clinical Interview for DSM-IV Axis I Disorders [SCID-IV; 58] to assess the presence of major psychopathology, other than unipolar depression or anxiety disorders, that would be an exclusionary criteria. All subjects scored higher than 30 on the Telephone Interview for Cognitive Status [TICS; 59], which has 94% sensitivity and 100% specificity for differentiating demented and cognitively intact individuals at this cutoff score. Participants who underwent fMRI met standard magnetic resonance imaging (MRI) safety criteria. All participants gave written and verbal consent to participate in the study, and the protocol was approved by the University of Florida Health Science Center Institutional Review Board. Sample demographic data are presented in Table 1 .

Table 1

Cognitive Sample

fMRI Sample

Measures

Symptoms of depression were assessed with the Center for Epidemiologic Studies Depression Scale [CES-D; 60], a 20-item self-report measure of the frequency and severity of depressive symptoms experienced in the past week. This widely-used measure has been validated in both young and older community-dwelling adults [ 61, 62] and has a well-replicated factor structure that includes subscales for depressed mood, somatic symptoms, lack of well-being, and interpersonal symptoms [ 63]. Statistical analyses used continuous measures of CES-D total and subscale scores. The interpersonal symptoms scale was not analyzed due to the restricted range of the scale, which includes only 2 items.

The State-Trait Anxiety Inventory [STAI; 64] was administered to measure symptoms of anxiety. The STAI is a 40-item questionnaire that measures situational (i.e., at the time of testing) and dispositional symptoms of anxiety, and yields separate scores for state and trait anxiety, respectively. Only the trait score (STAI-T) was used in the current analyses due to missing state scores at the time of fMRI scanning for some participants.

Participants were administered a neuropsychological battery that focused on measures of episodic and working memory, attention, and executive functions, which are known to be impacted by depression. Measures of episodic memory included the immediate recall and delayed recall scores from the Hopkins Verbal Learning Test, Revised [HVLT-R; 65] and Brief Visual Memory Test, Revised [BVMT-R; 66]. Attention, working memory, and executive functions were measured by the total scores on the Digit Span and Letter-Number Sequencing (LNS) subtests of the Wechsler Adult Intelligence Scale, Third Edition [ 67], and time to completion on the Trailmaking Test [ 68] parts A (TMT-A) and B (TMT-B).

Neuroimaging Procedure

During fMRI scanning, participants completed a letter variant version of the n-back task [ 69]. Participants were shown individual letters and were asked to indicate whether the stimuli were targets (33% of trials) or non-targets by pressing one of two buttons. Four separate conditions were presented: 0-back (target stimulus is the letter "X"), 1-back (target was any letter that was the same as the previous letter), 2-back (target was any letter that matched the letter two back from itself), and 3-back (target was any letter that matched the letter three back from itself). This paradigm allows for working memory to be examined over increasingly difficult trial conditions while maintaining consistency in all other stimulus processing and response demands. Each letter was presented for 750 ms with a fixed inter-stimulus interval of 3250 ms. Prior to each task block, instructions were read aloud to the participant. A fixation cross ("+") presented for 16000 ms preceded the onset of the first letter in each block. Four blocks of 32 trials were presented in each condition and the order of the conditions was pseudo-randomized across participants. Stimuli were presented using an Integrated Functional Imaging System hardware system (IFIS; Psychology Software Tools, Pittsburgh, PA) and a LCD screen viewed by participants via reflective mirrors positioned at the back of the magnet bore. Subject responses were made via an MRI-compatible keypad. Both accuracy and response latency were recorded by the IFIS system. Prior to entering the fMRI scanner, all participants practiced one block of each task condition (0- through 3-back) to ensure that they fully understood the task requirements. If accuracy was below 80 percent on any practice block, the block was repeated.

Image Acquisition and Preprocessing

MRI data was collected using a Philips 3-Tesla scanner at the University of Florida's McKnight Brain Institute. Participants lay supine on the scanner bed and a standard 8-channel head radio-frequency coil was placed over their head. Foam pads were used to minimize head motion. Functional MR images were acquired in 35 axial slices with approximately 30° rotation above the anterior commissure-posterior commissure (AC-PC) line using a T2*-weighted echo planar imaging (EPI) pulse sequence [repetition time (TR) = 2000 ms; echo time (TE) = 30 ms; flip angle (FA) = 90°; field of view (FOV) = 24 cm; 64 × 64 voxels at 3.75 mm3 with .4 mm slice gap]. Seventy-four volumes were acquired per run. Prior to acquiring functional data, a T1-weighted Magnetization Prepared Rapid Gradient Echo (MP-RAGE) high-resolution 3D anatomical scan (170 1-mm thick slices; TR = 2000 ms; TE = 4.13 ms; FA = 8°; FOV = 24 cm; 256 × 256 voxels at 3.75 mm3) was performed to enable standardization of brain space on which to overlay functional data (Talairach and Tournoux, 1988).

Preprocessing and statistical analysis of MRI data was performed using BrainVoyager software (BVQX 2.4.0.2; Brain Innovation, Maastricht, the Netherlands). Functional data were motion-corrected and spatially smoothed with a Gaussian kernel (8 mm full width half-maximum [FWHM]), followed by low-frequency non-linear drifts removal with a temporal high-pass filter of frequencies below 3 cycles per time course. Functional images were aligned with their respective high resolution 3D anatomical volumes using automatic standard BVQX co-registration procedures; manual alignment was subsequently conducted to ensure accurate co-registration. The 3D scans were transformed into Talairach space [ 70] to allow for group averaging.

Data Analysis

Cognitive data analysis

Cognitive data were analyzed using the PROC GLM procedure in SAS version 9.2 (SAS Institute, Cary, NC). Models included continuous measures of CES-D score, STAI trait score, and age as independent variables and race (white/non-white), years of education and sex as covariates. Independent variables were centered around the variable mean. For graphing purposes, an age cutoff of 50 was used for creating young adult and older adult groups. Separate models were performed for each of the aforementioned cognitive measures and for error rates and reaction times (RTs) excluding non-responses on the n-back task.

fMRI data analysis

Whole brain voxel-wise statistical tests and follow-up contrasts on signal intensity in identified regions of interest (ROI) were examined using a two-step general linear modeling (GLM) approach [ 71]. First, an individual fixed-effects GLM was determined for every participant for each task. The GLMs utilized four predictors: 0-back, 1-back, 2-back, and 3-back. Subsequently, separate-subject predictors random-effects GLM analyses were performed to determine main effects for the task (z-transformed time-course, in-brain functional image intensity threshold = 300). Hemodynamic response for each event was estimated by convolving each regressor with a standard two-gamma function (onset = 0, response undershoot ratio = 6, time to response peak = 5 sec, time to undershoot peak = 15 sec, response dispersion = 1, undershoot dispersion = 1) [ 72]. This shifted the predicted hemodynamic response approximately 5 sec to account for expected delay. Parameter estimates (β) were generated, which indicated relative strength of covariance between the data and the hemodynamic response function (HRF). We used a 3D spatial contiguity threshold of 20 voxels and a statistical significance threshold of p ≤ .005 to decrease the likelihood of spurious findings. Localization of suprathreshold clusters of brain activity was verified using the "nearest gray matter" search function provided by Talairach Daemon software [ 73; http://www.talairach.org].

To examine whether anxiety and depressive symptoms impacted the observed working-memory related brain activity, we conducted ROI GLMs over contiguous voxels centering on the areas of activation identified in the whole-brain analysis, from which beta weights were obtained. GLMs that paralleled the cognitive data analyses were conducted on the beta weights for each region.

RESULTS

Cognitive Results

Mean performance data for the cognitive tests are presented in Table 2 ; results of the cognitive data analyses are summarized in Table 3 . An age × CES-D effect was observed for TMT-A [ F(1,59) = 4.61, p =0.04] and LNS [ F(1,59) = 6.19, p = 0.02] ( Figure 2a ). For both measures, higher CES-D scores were associated with worse performance in older adults, but with stable or better performance in young adults. Performance on Digit Span improved as a function of higher STAI-T scores, but this effect was greater in older adults [age × STAI-T; F(1, 59) = 4.79, p = 0.03] ( Figure 3 ). Similarly, older adults with higher STAI-T scores performed better on LNS, although young adults did not show this association [age × STAI-T; F(1,59) = 6.90, p = 0.01] ( Figure 3 ). We observed an age × CES-D × STAI-T effect for TMT-B [F(1,58)= 4.83, p=0.03] ( Figure 2b ). In older adults, higher CES-D scores were associated with worse performance only in those with lower STAI-T scores, while depressive and anxiety symptoms were not associated with TMT-B scores in young adults.

Table 2

Test

Mean (SD)

HVLT-R Immediate Recall

27.66 (4.13)

HVLT-R Delayed Recall

9.68 (1.90)

BVMT-R Immediate Recall

28.39 (5.64)

BVMT-R Delayed Recall

10.68 (1.69)

TMT-A (seconds)

25.28 (10.09)

TMT-B (seconds)

57.63 (29.43)

Digit Span (total score)

19.10 (3.91)

LNS (total score)

11.54 (2.63)

N-back Error Rate

.06 (.05)

N-back Reaction Time (sec)

714.01 (147.37)

Table 3

TMT-A

TMT-B

BVMT-R immediate recall

BVMT-R delayed recall

HVLT-R immediate recall

HVLT-R delayed recall

Digit Span

LNS

CES-D Total Score model

Sex

3.616 (2.310)

−0.750 (6.164)

−0.816 (1.441)

−0.236 (0.451)

−3.549 (1.043)**

−1.187 (0.524)*

−0.693 (1.029)

−1.371 (0.628)*

Age

0.347 (0.068)**

1.210 (0.180)**

−0.141 (0.042)**

−0.025 (0.013)

−0.106 (0.031)**

−0.035 (0.015)*

−0.060 (0.030)

−0.089 (0.018)**

Age x CES-D

0.019 (0.009)*

0.046 (0.024)

0.001 (0.006)

0.002 (0.002)

−0.001 (0.004)

0.002 (0.002)

−0.005 (0.004)

−0.006 )0.002)*

Age x STAI-Trait x CES-D

−0.000 (0.000)

−0.002 (0.001)*

−0.000 (0.000)

−0.000 (0.000)

0.000 (0.000)

0.000 (0.000)

0.000 (0.000)

0.000 (0.000)

Depressed Mood model

Sex

2.804 (2.244)

−1.414 (5.685)

−0.490 (1.362)

−0.189 (0.424)

−2.953 (1.030)**

−0.982 (0.504)

−0.675 (0.987)

−1.144 (0.627)

Age

0.280 (0.064)**

1.237 (0.161)**

−0.156 (0.039)**

−0.036 (0.012)**

−0.078 (0.029)**

−0.034 (0.014)*

−0.050 (0.028)

−0.070 (0.018)**

Age x Mood

0.035 (0.022)

0.166 (0.055)**

−0.012 (0.013)

−0.003 (0.004)

0.004 (0.010)

0.002 (0.005)

−0.011 (0.010)

−0.010 (0.006)

Age x STAI-Trait x Mood

0.000 (0.001)

−0.008 (0.003)**

0.000 (0.001)

0.000 (0.000)

0.000 (0.001)

0.000 (0.000)

−0.000 (0.000)

0.000 (0.000)

Somatic Symptoms model

Sex

2.857 (2.375)

−1.343 (6.018)

−0.663 (1.372)

−0.295 (0.417)

−2.950 (1.066)**

−1.032 (0.506)*

−0.505 (0.992)

−1.060 (0.620)

Age

0.280 (0.058)**

1.063 (0.146)**

−0.123 (0.033)**

−0.025 (0.010)*

−0.084 (0.026)**

−0.029 (0.012)*

−0.047 (0.024)

−0.070 (0.015)**

Age x Somatic

0.022 (0.021)

0.064 (0.053)

0.018 (0.012)

0.010 (0.004)*

0.000 (0.009)

0.008 (0.005)

−0.011 (0.009)

−0.011 (0.006)*

Lack of Well-being model

Sex

3.544 (2.436)**

−0.344 (6.526)

−0.747 (1.461)

−0.199 (0.465)

−3.482 (1.091)**

−1.146 (0.545)*

−0.867 (3.437)**

−1.383 (0.673)*

Age

0.323 (0.071)**

0.997 (0.189)**

−0.151 (0.042)**

−0.029 (0.014)*

−0.091 (0.032)**

−0.035 (0.016)*

−0.057 (0.030)

−0.072 (0.020)**

Analysis of CES-D subscales revealed parallel results for depressed mood effects on TMT-B [age × depressed mood [ F(1,59) = 9.20, p = 0.00; age × depressed mood × STAI-T [ F(1,59) = 7.97, p = 0.01] ( Figure 4a ). Age differences on LNS were greater in individuals with higher somatic symptoms [F (1,60)= 3.97, p=0.05], but were attenuated on BVMT delayed recall as a function of higher somatic symptoms [F(1,60)= 6.56, p=0.01] ( Figure 4b ).

fMRI Results

Analysis of activation associated with increasing working memory load on the n-back task revealed increased activity in a cluster in the frontal lobes that included the superior frontal gyrus (SFG; Brodmann area [BA] 10), prefrontal cortex (BA 9), and inferior frontal gyrus (BA 44), with peak activity in the right SFG, and in the left insula (BA 13) extending into the putamen ( Figure 5a and 5b ). Peak t-values and localization for all observed separable clusters of working memory activation are provided in Table 4 .

Table 4

Region

BA

Side

Coordinate*

Cluster Size

t

p

x

y

z

ROI analysis of fMRI activity within clusters identified in the whole-brain analysis revealed a depressed mood × STAI-T effect for the SFG [ F(1,17) = 4.83, p = .04] ( Figure 5a ). Working-memory related activity in the SFG increased on more difficult trials as a function of higher depressed mood scores in individuals with lower STAI-T scores, but decreased as a function of higher depressed mood scores in those with high STAI-T scores. Higher total CES-D and depressed mood scores were associated with increased activity in the insula [CES-D F(1,16) = 5.00, p = . 04; depressed mood F(1,17) = 6.73, p = . 0.02].

N-back Task Results

Performance data for the n-back are presented in Table 2 . Data were missing for two participants, thus analyses were performed on 23 participants. Higher total depressive symptoms and depressed mood symptoms were associated with more errors [total CES-D F(1,15) = 7.02, p = 0.02; depressed mood F(1,16) = 9.06, p = 0.01]. Error rates generally increased on more difficult trials (i.e., higher working memory load), but individuals with the highest total CES-D and mood subscale scores made more errors on 1-back compared to 2-back trials [working memory load × CES-D F(3,49) = 2.89, p=0.05; working memory load × depressed mood F(3,48) = 3.09, p = 0.04]. For RTs, higher lack of well-being scores were associated with greater increases in RTs at higher working memory load [ F(3,45) = 4.50, p = 0.01], but this effect was reduced at higher STAI-T levels [ F(3,45) = 6.93, p = 0.00].

DISCUSSION

This study examined the effects of depressive symptoms, anxiety symptoms, and their interaction on cognitive and brain functioning. We examined healthy young and older adults who presented with a range of depressive and anxiety symptoms, which allowed use to examine subthreshold symptoms. This focus is important as there is increasing recognition that subthreshold mood symptoms are particularly common in older adults and are associated with similar negative sequelae as clinical mood disorders [ 74, 75]. Despite the frequent comorbidity of depressive and anxiety disorders, cognitive neuroscience studies focusing on the unique and combined effects of depression and anxiety are limited. The present study was designed to address this gap in the literature.

We found effects of both depressive and anxiety symptoms on cognitive and brain functioning. At older ages, total depressive symptoms were associated with slower processing speed, as measured by TMT-A, and with worse working memory performance on LNS. The finding of an interactive effect of age and depressive symptoms is consistent with previous studies showing that older adults may be particularly vulnerable to the adverse impact of depression [ 12- 14]. Indeed, many studies of cognitive functioning in depressed young adults have failed to show effects, while cognitive studies in late-life depression yield more consistent results and generally show deficits in attention, working memory, episodic memory, and executive functions [ 18, 21].

Examination of the CES-D subscales and neuropsychological test performance revealed effects for the depressed mood and somatic symptoms subscales, but not the lack of well-being subscale, consistent with our previous work [ 76- 78]. Higher depressed mood symptoms were associated with deficits in executive functioning on TMT-B in older adults but not young adults. Additionally, age differences in working memory were greater in individuals with elevated somatic symptoms, but age differences in delayed visual memory were reduced as a function of higher somatic symptoms. Perhaps somatic symptoms in older adults are a reflection of physical changes that are correlated with white matter lesions in the brain, which are known to increase as a function of age and depression [ 79, 80]. Based on previous evidence that prefrontal white matter is most susceptible to aging and that such lesions are associated with deficits in frontally-mediated cognitive functions [ 81, 82], somatic symptoms might be expected to have a greater impact on working memory in older adults compared to visual memory.

Regarding anxiety symptoms, we found that higher trait anxiety was associated with better performance on Digit Span and LNS, measures of attention and working memory, and this effect was greater at older ages. Given our non-clinical sample, higher STAI-T scores correspond with moderate levels of anxiety rather than severe levels characteristic of anxiety disorders. Thus, our findings are consistent with studies reporting an inverted U-shape function in the relationship between anxiety and cognitive performance, such that moderate levels of anxiety are associated with better performance, while low and high symptoms are associated with performance deficits [ 16, 38, 39]. Interestingly, we found that anxiety moderated the impact of depressive symptoms on executive functioning. Higher total CES-D and depressed mood symptoms were associated with deficits on TMT-B at low levels of trait anxiety, but not at higher anxiety levels. Since moderate anxiety is associated with better performance on some cognitive measures, it appears that anxiety counteracted the negative impact of depressive symptoms on executive functioning. This is contrary to previous investigations of mixed depression and anxiety, which have shown that comorbid depression and anxiety are associated with worse outcomes, including cognitive deficits, compared to either condition alone [ 37, 49, 52]. This discrepancy may be due to our examination of subthreshold depression and anxiety, rather than clinical conditions. Perhaps the unique and interactive effects of depression and anxiety differ as a function of severity level. This is an important area for future research considering the frequent comorbidity of depression and anxiety and the high prevalence of subthreshold conditions.

A subset of our sample underwent fMRI scanning while they performed the n-back working memory task. Consistent with other working memory studies, we found monotonic increases in activity in the PFC, inferior and superior frontal gyri, putamen and insula at higher working memory loads. Higher total depressive symptoms was associated with reduced activity in the SFG at higher working memory loads in individuals with high anxiety, but with increased activity in those with low anxiety. This suggests that anxiety reduced the heightened frontal activity that has previously been shown in depression during working memory tasks [ 56, 57]. Analysis of n-back task performance showed that higher total depressive symptoms and depressed mood symptoms were associated with more errors on the n-back, consistent with the neuropsychological test results. Given the smaller sample size in the fMRI subsample, we did not examine whether the effect differed in young and older adults. In contrast to the neuropsychological test results, we also found an effect of the lack of well-being subscale on performance. Higher lack of well-being scores were associated with greater increases in RTs at higher working memory load, but higher trait anxiety reduced this effect. This finding parallels the fMRI activation results but should be interpreted with caution due to the small sample size in the neuroimaging subsample.

Taken together, our results confirm previous reports that subthreshold depression and anxiety impact cognitive and brain functioning, add to the growing literature showing that age may moderate these effects, and suggest that the interaction of subthreshold depression and anxiety results in distinct cognitive and brain changes. Findings highlight the importance of assessing and controlling for symptoms of depression and anxiety in research studies of either condition. Results also have implications for clinicians, as they suggest that older adults with comorbid symptoms of depression and anxiety may be at risk for cognitive deficits even in the absence of a diagnosis of major depression or an anxiety disorder. Future work should compare the unique and interactive effect of depression and anxiety on cognitive and brain functioning at both subthreshold and clinical levels, and should examine changes in functioning over time using longitudinal designs.

Acknowledgments

This research was supported by an Age Related Memory Loss award from the McKnight Brain Research Foundation (VMD), and by the National High Magnetic Field Laboratory. This project was performed at the Advanced Magnetic Resonance Imaging and Spectroscopy (AMRIS) facility in the McKnight Brain Institute of the University of Florida.

References

11. Vollrath M, Angst J. Results of the Zurich cohort study: course of anxiety and depression. Psychiatr Psychobiol. 1989;4:307-313. [ Google Scholar]

15. Braam AW, Copeland JR, Delespaul PA, Beekman AT, Como A, et al. Depression, subthreshold depression and comorbid anxiety symptoms in older Europeans: Results from the EURODEP concerted action. J Affect Disord 2013 [[ PubMed] Google Scholar]

59. Brandt J, Spencer M, Folstein M. The Telephone Interview for Cognitive Status. Neuropsychiatry Neuropsychol Behav Neurol. 1988;1:111-117. [ Google Scholar]

60. Radloff LS. The CES-D scale: A self-report depression scale for research in the general population. Appli Psychol Meas. 1977;1:385-401. [ Google Scholar]

78. Kirton JW, Resnick SM, Davatzikos C, Kraut MA, Dotson VM. Depressive Symptoms, Symptom Dimensions, and White Matter Lesion Volume in Older Adults: A Longitudinal Study. Am J Geriatr Psychiatry 2013 [[ PMC free article] [ PubMed] Google Scholar]

# data[].attributes.attachment_link.title
Unique and interactive effect of anxiety and depressive symptoms on cognitive and brain function in young and older adults

# data[].attributes.attachment_link.description
Depression and anxiety and are associated with cognitive deficits and brain changes, especially in older adults. Despite the frequent co-occurrence of these conditions, cognitive neuroscience studies examining comorbid depression and anxiety are limited. ...

# data[].attributes.attachment_link.readable_text
Initial deliberations by the DSM-V Anxiety, Obsessive-Compulsive Spectrum, Post-traumatic, and Dissociative Disorders Work Group focused on the nosological structure of the anxiety and depressive disorders. These deliberations were spurred in part by structural modeling studies that highlight a shared internalizing factor ( 1, 2) and recommendations from Watson ( 3) for collapsing mood and anxiety disorders into an over-arching class of "internalizing" disorders, with three subclasses: bipolar disorders (bipolar I, bipolar II, and cyclothymia); distress or "anxious-misery" disorders (major depression (MD), dysthymia, generalized anxiety disorder (GAD) and posttraumatic stress disorder (PTSD)); and the fear disorders (panic disorder (PD), agoraphobia, social phobia (SOP) and specific phobia (SP)). The Work Group recognized that deliberations would be aided by knowing whether the anxiety disorders share features of responding that define them and distinguish them from mood disorders, and/or that differentiate between anxious-misery and fear disorders. The nosological recommendations by Watson and others derive from structural analyses of symptom self-report data, which represent estimation of past responding and prediction of future responding. In this review, such data were complemented with measures of "on-line" cognitive, behavioral, psychophysiological, and neural responding in the presence of aversive stimuli.

This study represents the work of the authors for consideration by the DSM-V Anxiety, Obsessive-Compulsive Spectrum, Post-traumatic, and Dissociative Disorders Work Group. Recommendations provided in this study should be considered preliminary at this time; they do not necessarily reflect the final recommendations or decisions for DSM-V, as the DSM-V development process is still ongoing.

The method of review was based on computer database searches of PubMed and PsychINFO for English language articles from 1994 through 2009, combined with reviews of reference lists from identified manuscripts, as well as the proceedings and/or monographs of the preparatory conference series for DSM-V, particularly the Stress-Induced and Fear Circuitry Disorders. ( 4) Given the vast number of references produced, this review presents representative rather than complete citations. However, meta-analyses were cited when available, and conclusions were drawn when published findings converged from multiple, independently conducted sources (vs. single and/or dependent sources). Also, whenever the literature was divided on a particular topic, representative citations are presented for each argument.

STRUCTURAL ANALYSES OF SYMPTOM SELF-REPORT DATA

Goals

Diagnosis is based on the self-report of symptoms, whether provided solely by the patient or with the aid of a clinician's appraisal. In this section, we explore the structure of symptom self-report data, to evaluate the degree to which symptom reports share features in common across the anxiety disorders, making them distinct from mood disorders, and whether features of symptom self-report distinguish fear disorders from anxious-misery disorders. Later, we address the behavioral, physiological, cognitive, and neural correlates of symptom reports and diagnoses.

We begin with conceptualizing the terms "fear" and "anxiety," since these constructs underlie the symptoms of anxiety disorders. Then, we consider whether self-reported fear and anxiety distinctly differ from each other and from depression. Finally, the relationship between symptoms of fear, anxiety and depression and existing diagnostic categories of anxiety and mood disorder is reported.

Defining fear and anxiety

Evidence for differentiating fear from anxiety

Differentiating fear, anxiety, and depression: evidence from the tripartite model

The large literature testing the tripartite model of fear, anxiety, and depression ( 13) provides additional evidence for a distinction between self-reported somato-visceral symptoms of fear and verbal-subjective symptoms of anxiety. The tripartite model proposes that there are symptoms shared across "anxiety" and depression as well as symptoms unique to each. Shared symptoms typically are represented by a negative affect (NA) or general distress factor. Symptoms of anhedonia and the absence of positive affect are specific to depression whereas symptoms of physiological hyper-arousal are specific to "anxiety."

Using the present terminology, the physiological hyperarousal items would be described as somato-visceral symptoms of fear. The subjective anxiety symptoms often are key markers of general distress. For example, when averaging factor loadings across five different samples (three student samples, an adult sample, and a patient sample), the item "worried a lot about things" had the second largest loading of any item on the general distress factor, ( 14) and the same was true in a sample of child and adolescent psychiatric inpatients ( 15).

Tests of the tripartite model demonstrate that somato-visceral symptoms of fear, symptoms of depression, and subjective symptoms of anxiety (along with other general distress symptoms) can be distinguished from each other. Such a distinction has been demonstrated in many samples, clinical and nonclinical, adult and child (e.g., ( 14- 19)). As mentioned, although these factors can be distinguished from one another, they typically are moderately to highly correlated, albeit with some exceptions (e.g., ( 15)).

Some studies have identified two rather than three factors (e.g., ( 20)). The two-factor findings have been interpreted as symptoms of "anxiety" vs. symptoms of depression, without distinguishing fear from anxiety symptoms. However, these studies tend to rely on an insufficient number of items for independent measurement of fear and anxiety.

Relationship of DSM disorders to self-report symptoms of fear, anxiety, and depression

A similar specificity has been found between positive affect and both MD and SOP. That is, when structural models including multiple disorders and symptom constructs are considered, positive affect only displays significant associations with MD and SOP ( 9, 18). Notably, the similarity between MD and SOP in this regard is at odds with the separation of SOP as a fear disorder from depression as an anxious-misery disorder.

Furthermore, in clinically referred children and adolescents, after controlling for the variance in physiological hyperarousal associated with negative affect, the somato-visceral symptoms of fear were significantly associated with clinical severity ratings for PD only and no other disorders of anxiety or depression ( 22). Positive affect showed significant, negative associations with MD and SOP, whereas negative affect demonstrated significant, positive relations with PD, GAD, and OCD.

Limitations and future directions

The psychometric distinction between symptoms of fear and anxiety is potentially confounded by the measurement of different response systems, since most studies assess only somato-visceral symptoms of fear and only subjective symptoms of anxiety. For example, studies of SP stimuli typically measure how much fear is expected if a specific stimulus was encountered in the coming week, without any indicators of anxiety about the stimulus (e.g., worry about encountering, or avoidance of situations involving the stimulus). Thus, it is unclear whether symptoms load on different factors because they represent differences between symptoms of fear and anxiety or because they represent differences among response systems (i.e., verbal-subjective, overt motor, and somato-visceral).

Another limitation is that most studies employ nonstimulus-specific items. For example, physiological hyperarousal (e.g., heart pounding) items usually refer to the frequency or intensity of symptoms over a specified period (past week or two), without reference to an eliciting stimulus. Individuals who experience fear symptoms in the presence of circumscribed stimuli (e.g., air travel) may not encounter such stimuli over the specified period of time, and thus would score low on fear items. Similarly, even though they may worry about particular circumscribed stimuli as they become more probable (e.g., days before air travel), such individuals may not typically worry about those stimuli and hence would score low on symptoms of anxiety. In contrast, individuals with PD would be more likely to score high on items of fear, as would individuals with GAD be more likely to score high on items of anxiety, because they experience fear and anxiety, respectively, more frequently. In other words, nonstimulus-specific measures appear to represent frequency of fear and anxiety. However, frequency does not take into account the magnitude of response in the presence of specific stimuli or the frequency of exposure to specific triggering stimuli.

Hence, non-stimulus-specific items (as a measure of frequency) may be best complemented by stimulus-specific symptom items (as a measure of magnitude). For example, social anxiety symptom items might include: "I avoid parties" (overt motor acts), "when alone I worry about meeting new people" (verbal-subjective), and "I get tense and irritable from worrying about social situations" (somato-visceral). Social fear items could include: "I try to leave social situations as soon as possible" (overt motor acts), "when speaking in public, I think I look like a fool" (verbal-subjective), and "my palms get sweaty when eating in front of others" (somato-visceral). Patterns of covariation among such items would reveal whether or not a future-oriented social anxiety factor, indicated by avoidance of and worry about potential social situations, was separable from a present-focused social fear factor, indicated by escape from social situations as well as fearful thoughts and physiological arousal during social situations. With this kind of measurement, the extent to which each disorder is marked by symptoms of fear vs. symptoms of anxiety could be determined.

Summary and implications for DSM

The existing literature reveals that self-reported somato-visceral symptoms of fear (e.g., breathlessness) are separable from, yet related to, verbal-subjective symptoms of anxiety (e.g., worry a lot). Although verbal-subjective symptoms of anxiety are not always separable from other general distress symptoms, anxiety symptoms and fear symptoms generally are separable from, yet related to, symptoms representing anhedonia or the absence of positive affect (i.e., depression). The extent of the relationship between factors of fear, anxiety, and depression tends to be moderate-to-large, but can be quite modest depending on item content. While there is some evidence to indicate that several anxiety disorders are significantly, positively related to somato-visceral symptoms of fear, the relationship of these symptoms to PD seems to be stronger than with GAD. These findings lend support to a distinction between fear-based disorders and anxious-misery disorders. Symptoms of anhedonia are especially linked to both MD and SOP, thereby at odds with the proposed categorization of SOP as a fear disorder vs. anxious-misery disorder.

However, as noted, the limitations to existing self-report methodologies weaken the implications for DSM. That is, most studies are limited by the measurement of different response systems to assess fear (i.e., somato-visceral) vs. anxiety (i.e., verbal-subjective) symptoms as well as by restriction to nonstimulus-specific symptoms of fear and anxiety. Even when studies employ stimulus-specific items, they tend to address either fear or anxiety symptoms, but not both. Therefore, it is recommended that self-report items are developed that would better assess fear and anxiety symptoms across all three response modalities as well as in response to a range of stimuli.

EXPERIMENTAL PARADIGMS

Goals

This section reviews aversive conditioning, stress reactivity, and information processing (attention, memory, and appraisal) paradigms to evaluate commonalities and differences in behavioral, psychophysiological, and cognitive responding to aversive stimuli across anxiety and mood disorders. Symptom self-report (reviewed in the earlier section) entails retrospective estimation of how one has responded in the past or prediction of how one might respond in the future. In contrast, in this section we evaluate features of verbal-subjective, overt motor acts, and somato-visceral activity responding as they occur in the presence of specific stimuli. Discordances often exist between symptom self-report on the one hand and online measurement of behaviors and physiology on the other hand. As before, our goal is to evaluate the extent to which these indices of on-line responding are shared across anxiety disorders and differ from mood disorders.

Pavlovian aversive conditioning: explicit threat cue (fear) vs. context (anxiety)

Pavlovian fear conditioning has long been applied as an etiological model for anxiety disorders ( 23). In a typical Pavlovian fear conditioning paradigm, a neutral stimulus is paired with an innately aversive stimulus (unconditional stimulus, US, such as a painful shock) a sufficient number of times for the neutral stimulus to become a reliable predictor of the aversive, and therefore capable of eliciting a conditional response (CR) in the absence of the US. The CR typically is measured by an increase in arousal (e.g., galvanic skin conductance response) and an increase in negative valence (e.g., startle blink reflex). The CR is even acquired to a CS that is masked to prevent conscious awareness (i.e., subliminal), at least with stimuli judged to be "fear-relevant." Pavlovian conditioning also occurs through observation, as when observing others react fearfully in the presence of a given stimulus. Olsson and Phelps ( 24) found that observational conditioning in humans was measurable even to subliminal presentations of the CS. Furthermore, verbal instructions to expect a shock without actual shock delivery have been shown to elicit similar CRs, although only to supraliminal presentations of the CS (e.g., ( 24)). Thus, the Pavlovian conditioning model posits that fears are acquired through associations with aversive events, experienced directly, vicariously, or through informational transmission.

Developments in the basic science of fear conditioning have converged on a distinction between two types of learning, explicit threat cue vs. context learning. Explicit threat cues refer to the specific CSs that predict the US, whereas context refers to the location within which the US is presented. Like the CS, the context becomes predictive of the US and capable of eliciting a CR. Explicit threat cues are presumed to elicit phasic fear responses to certain or imminent threat, whereas contextual cues are presumed to elicit a more sustained anxious response to less certain threat (since the context is a reminder of threat but does not signal the precise timing of its occurrence). In this way, explicit threat cue vs. context learning map onto different defensive responses tied to proximal vs. distal threat on the predator imminence continuum, ( 6) for a related discussion also see Reference ( 25) and parallel the distinctions between self-reported fear and anxiety. Davis and colleagues ( 26) have demonstrated that two distinct neural substrates underlie these responses: phasic fear response to imminent threat (explicit threat cue) is mediated by the amygdala; and anxiety response to uncertain threat (context) is mediated by the bed nucleus of the stria terminalis, BNST (extended amygdala) ( Table 2). Another body of research has established the role of the hippocampus in context conditioning relative to cue conditioning, ( 27) and perhaps more so for complex vs. simple contextual information ( 28).

In humans, the context may be the screen on which the CS and US are presented, ambient lighting, or the room itself. Several studies have investigated context conditioning in humans via the presentation of unpredictable USs. Greater context conditioning is observed following unpredictable than predictable shocks in healthy controls, as measured by startle reflexes during baseline conditions (conditions of anticipation prior to the delivery of an experimental procedure) and during intertrial intervals. In one study, ( 29) the effect of unpredictable shocks was tied to an elevated expectancy for the US, perhaps akin to the state of vigilance and preparation for threat that is believed to be characteristic of anxiety ( 5). In another study, ( 30) participants were less likely to subsequently explore the unpredictable vs. the predictable context for monetary reward; lack of exploration was interpreted as an index of anxious avoidance and further evidence of context conditioning.

Anxiety disorders and explicit threat cue conditioning: simple (CS+) and differential (CS+/CS−).

The clinical literature to date has emphasized explicit threat cue conditioning, with only a few studies examining context conditioning. Within explicit threat cue conditioning, the paradigms tested include simple conditioning (CS+ is paired with a US) or differential or discrimination conditioning (CS+ is paired with a US, and CS− is never paired with the US). The CS− can be viewed as a "safety stimulus" in comparison to the danger CS+, since the CS− signals the absence of the US.

Simple conditioning.

From their 2005 meta-analysis, Lissek et al. ( 34) concluded that anxiety disordered adults show elevated responding to CS+s in simple conditioning paradigms, relative to healthy controls (weighted mean ES = .42). These studies included samples of patients with "neurotic/anxiety" states (four studies), PTSD (two studies), and SOP (one study). Also, the effects of extinction training (albeit with fewer studies) were weaker in anxious adults vs. controls ( 34) (weighted mean ES = .39), although possibly due to elevated acquisition levels. Aside from studies of eye blink conditioning, there have been no additional studies of simple conditioning in relation to anxiety disorders since 2005.

Differential conditioning.

Studies involving differential conditioning in Lissek et al.'s ( 34) meta-analysis included samples of "neurotic/anxious" patients (two studies), PTSD (five studies), SOP (two studies), GAD (two studies), PD (one study), and OCD (one study). Weighed mean ES for strength of discrimination between the CS+ and CS− were lower for differential conditioning than simple conditioning because anxiety disordered adults showed elevated responding to reinforced trials as well as nonreinforced trials, some-times leading to nondiscriminant responding to the CS+ vs. the CS− relative to controls. Thus, anxious individuals were characterized by elevated responding to the CS+ and CS− during acquisition and extinction.

Seven additional studies were identified since 2005, three in adult and four in youth samples. In the adult samples, one study with PTSD individuals showed stronger responding during acquisition to both the CS+ and CS− than healthy controls ( 35) and one study with PD did not; ( 36) the remaining PTSD study found no differences in comparison to traumatized non-PTSD individuals. ( 37) The first two studies found stronger responding during extinction to both the CS+ and CS− than healthy controls ( 35, 36). In child samples, mixed anxious groups showed stronger responding during acquisition to the CS+ and CS− in three of the four studies ( 35, 39) although one was limited to self report. ( 40) Lack of effects in the fourth study ( 41) may be attributable to confounding influences. Also, responding was elevated during extinction in the anxious group in three studies ( 38, 39, 41) but not when measurement was limited to self-report ( 40).

Summary.

Potential mechanisms of elevated responding to CS+ and CS−.

Another possibility is sensitization and lack of habituation. That is, responses to the CS+ and CS− may be elevated by being more negatively impacted and thereby sensitized by the US (indicated by a larger UR), leading to slowed habituation to the CSs. However, the evidence is limited and available results are contradictory: elevated physiological UR to the delivery of the US (e.g., ( 38)) vs. equivalence in subjective ratings of anxiety for the US (e.g., ( 35)). Yet another explanation is that anxious individuals show greater stimulus generalization due to deficits in processing of perceptual information that distinguishes the CS+ from the CS−. Although direct evidence is lacking, evidence pertaining to attentional bias to threat and limited attentional control reviewed in a later section may be relevant here, since over attention to threat may disrupt processing of nonthreatening stimuli.

Davis's ( 43) model, in which pathological anxiety is attributed to abnormalities in inhibitory fear mechanisms, or the failure to inhibit fear in response to safety signals, offers another explanation for elevated responding to the CS−. It also explains elevated responding during extinction in simple and differential conditioning paradigms, since inhibitory processes are heavily involved in extinction ( 44). That is, extinction is associated with neuronal activity that is involved in inhibition of CRs, ( 45- 47) mostly within the medial prefrontal cortex (see the following section). In a study of PTSD, the extinction phase of training was associated with decreased function in the orbito-frontal and medial prefrontal cortex, visual association cortex, and other areas, indicative of impaired inhibitory regulation, whereas controls demonstrated an increase in the medial prefrontal cortex ( 48). Finally, as extinction does not reflect destruction of the CS-UCS association learned during conditioning, but rather, represents the formation of a new association that renders the meaning of the CS+ essentially ambiguous, ( 44) impaired inhibitory responding during extinction may be associated with a failure to reappraise the CS-UCS association as new information comes to hand. Biases in appraisal of ambiguous information (see later section) may contribute to such failures.

Comparing explicit threat cue conditioning across disorders.

Only two studies were located in which an anxiety disorder was compared to another disorder. One study compared children with attention deficit/hyperactivity disorder (ADHD) with and without overanxious disorder ( 49). There were no between group difference in terms of strength of discrimination between CS+ and CS− trials during acquisition or extinction (absolute levels of responding were not reported). In an fMRI study, four individuals with "criminal psychopathy," four individuals with SOP and seven controls underwent differential conditioning ( 50). Healthy controls showed a larger skin conductance to the CS+ than the CS−, whereas the other two groups did not differentiate between the two. Absolute levels of responding to the CS+ and CS− were not reported. The three groups did not differ in their response to the US itself. Clearly, there is a need for more comparative research.

Anxiety disorders and context conditioning.

There are only a few studies of context conditioning in clinical samples. One study reported that baseline startle reflexes (i.e., the waiting period before delivery of experimental procedures) increased in PTSD veterans from the first to the second laboratory session, after having received an aversive event in the first session, relative to veterans without PTSD ( 51). Such elevations were not present when veterans with PTSD knew that no aversive stimuli would be presented during a second experimental procedure, ( 52) and other studies fail to find elevations in baselines when aversive stimuli are either mild or never presented (e.g., ( 53)). Thus, elevation of startle reflexes during baseline is viewed as an index of contextual anxiety in the place where strong aversive events are possible, albeit uncertain in terms of their precise timing. These results imply that PTSD involves elevated sensitivity to contexts associated with threat compared to controls. In a study of PD, ( 54) startle reflexes measured during intertrial intervals (as a measure of context) increased to a greater degree from neutral to predictable to unpredictable conditions in PD relative to controls. These findings imply that, like individuals with PTSD, individuals with PD are more sensitive to contexts of threat than are healthy controls.

Threat of shock paradigms (i.e., shock is expected but not delivered) are purported to induce contextual anxiety by inducing uncertain threat. Grillon and colleagues demonstrated that adults with PD and PTSD show sustained elevations in startle reflexes throughout experiments involving threat of shock relative to controls (e.g., ( 51)). One study demonstrated different effects for PD when it was comorbid with depression ( 55). Specifically, PD without depression showed a facilitation of startle reflexes relative to controls whereas PD with depression showed an attenuation of startle. Clearly, there is need for more evaluation, given the possibility of basic differences in responding to threat of an aversive stimulus between those with anxiety with and without depression.

Moreover, another line of research shows that children and adolescents at risk for emotional dis-orders, by virtue of parental/grandparental anxiety or depression, or the temperamental trait of neuroticism, also show elevated startle reflexes in anticipation of experiments involving threat (at least in females) ( 56) and in anticipation of threat within the experiment ( 57). These findings suggest that contextual anxiety may not only be characteristic of anxiety disorders but also could be a marker of risk for emotional disorders in general.

In summary ( Table 3), the findings from context conditioning suggest that anxiety disorders that have been tested (PTSD and PD) are characterized by hypersensitivity to contexts in which threat will/may be delivered. However, the degree to which this hyper-sensitivity discriminates anxiety disorders from mood disorders, or discriminates fear disorders from other anxiety disorders, is not yet known.

Summary of explicit threat cue and context conditioning and implications for DSM.

Whereas elevated sensitivity to explicit threat cues and safety cues (including cues during extinction) may be characteristic of anxiety disorders, it is unknown whether it is more characteristic of some anxiety disorders vs. other anxiety disorders, and whether it is exclusive to anxiety disorders relative to depression. Similarly, extant data suggest that certain anxiety disorders are characterized by hypersensitivity to contexts in which threat will/may be delivered, but the degree to which this hypersensitivity discriminates anxiety disorders from other disorders, or discriminates fear disorders from anxious-misery disorders is not known. Thus, the literature pertaining to aversive conditioning helps to define features of responding that are common to anxiety disorders, but is insufficient to justify revisions to the organizational structure of the DSM nosology.

Anxiety disorders and stress reactivity

Another extensive body of research measures psy-chophysiological response to a variety of different aversive stimuli, without testing the learning of an association with neutral stimuli. Such "stress reactivity" measurement illuminates differences between anxious groups and controls, and between anxiety disorders. Anticipatory baseline responding in studies of "stress reactivity" can be viewed as an index of contextual anxiety. The acute response measured during actual delivery of stressors can be viewed as an UR (when using generic stressors, such as shock), or a response to personally relevant stimuli (when using disorder-specific stressors, such as trauma reminders for PTSD). Stress reactivity paradigms typically measure skin conductance, heart rate, respiration, muscle tension, or startle reflex to index the somato-visceral response component of fear and anxiety. The most thorough evaluation would entail assessment of generic stressors and disorder-specific stressors across different anxiety disorders and mood disorders. However, instead, almost every study pertains to a single anxiety disorder vs. healthy controls, and usually tests either generic or disorder-specific stressors. Results are summarized in Table 5. Notably, this review does not cover pharmacological probes that exert central nervous system effects, such as meta-chlorophenylpiperazine or yohimbine, but rather is restricted to assessment of reactivity to inherently aversive stimuli that may occur in the natural environment.

Combining the psycho-physiological results from the studies of individual anxiety disorders and the few studies that address more than one anxiety disorder ( Table 5), it may be concluded that PTSD and PD show elevations in baseline anticipation of generic stressors, ( 58, 59) and perhaps more so in PTSD than PD, ( 60) but do not show elevations in acute response to generic stressors relative to controls ( 58, 59). Also, individuals with PD show a stronger baseline response (e.g., ( 61)) but not a stronger physiological acute response to disorder-specific stressors (e.g., ( 62, 63)), although there are occasional exceptions (e.g., ( 64)). The disorder-specific stressors for PD have consisted mostly of carbon dioxide inhalation paradigms. The majority of PTSD individuals additionally exhibit stronger acute physiological response to trauma reminders compared to controls (e.g., ( 69)] although not always (e.g., ( 70, 71)). SOP (nongeneralized) and SP may possess stronger baseline and acute physiological response to disorder-specific stimuli compared to healthy controls (e.g., ( 72- 75)), although baseline and acute response have not been evaluated to highly aversive generic stressors such as shock, and the findings remain tentative. Evidence pertaining to GAD and OCD also is limited by insufficiently aversive generic stressors. Available evidence provides an inconsistent picture regarding baseline differences between GAD and healthy controls ( 76, 77) and for non-anxious individuals to sometimes have stronger autonomic responses to acute generic stressors than individuals with GAD (e.g., ( 77)), which may be due to tonic inhibitory effects in GAD (e.g., ( 78)). Individuals with OCD did not differ from controls during baseline recordings ( 79) although this may be due to the use of mild stressors; they show an elevated autonomic acute response when confronted with relevant stimuli, such as contaminated objects, relative to baseline conditions, although comparisons have not been made with controls (e.g., ( 80)).

Comparing stress reactivity across disorders.

In terms of comparisons across anxiety disorders, the most extensive body of research compares response to carbon dioxide inhalations in PD relative to other disorders. However, as noted, differences between PD and other disorders reside almost always in subjective measures of distress and rarely appear in psychophysiological measurement. Otherwise, research comparing stress reactivity across anxiety disorders, let alone anxiety and depression, is very limited. Only two studies were located. Cuthbert et al. ( 81) compared autonomic and startle reflex among groups with PD, SOP, SP, or PTSD. Participants listened to and imagined neutral scenes, standardized fear scenes (generic), and personalized fear scenes (disorder-specific). Baseline physiology (e.g., heart rate) was higher in PD relative to controls and SOP, and PTSD looked most similar to PD but did not differ significantly from any other group. In terms of acute response, SP and SOP showed the highest physiological response to personal fear scenes, whereas physiological reactivity was limited in PTSD and PD. The fact that the latter two groups had higher rates of co-morbidity including depression led the authors to posit that generalized negative affect is associated with elevated baseline responding and lessened physiological reactivity to specific fear cues. In the second study, Blechert et al. ( 60) compared groups of PD, PTSD, and controls during baseline and during threat of shock (generic). The PTSD group showed higher levels of sympathetic arousal during baseline but lower levels during threat of shock relative to PD. Clearly, further comparative research is needed, using a comprehensive array of psycho-physiological measures, in order to further our understanding of commonalities and differences among the anxiety disorders and in contrast to mood disorders.

Child/adolescent samples.

Results from child samples are mixed. In terms of baseline response, one study showed elevated responding in 9-18 year olds with PD, SOP, overanxious disorder, and separation anxiety disorder compared to controls, before receiving carbon dioxide inhalations; ( 82) another study showed no differences in children with GAD, separation anxiety, SP or SOP compared to children with ADHD, before a stressful arithmetic test ( 83). In terms of acute response, two studies show stronger responses to carbon dioxide inhalations in adolescents with separation anxiety disorder vs. controls (or SOP) ( 84) and to stressful arithmetic tests in children with GAD, separation anxiety, SP or SOP vs. ADHD ( 83). Two other studies show no differences in acute response to carbon dioxide inhalation compared to controls ( 82) or to the Trier Stress Test in children with GAD, SOP, separation anxiety, and/or SP compared to children with recurrent abdominal pain ( 85).

Summary of stress reactivity and implications for DSM.

Findings from stress reactivity studies suggest that some anxiety disorders are characterized by elevated anxiety in anticipation of generic threat (PTSD and PD), and some anxiety disorders are characterized by elevated acute fear to disorder-specific threat (PTSD, specific phobia (SP), and nongeneralized SOP). However, investigations to date are insufficiently comprehensive to draw conclusions regarding groupings of anxiety disorders (i.e., fear vs. anxious-misery) let alone distinctions between anxiety and mood disorders. Thus, there is no evidence to justify revisions to the diagnostic nosology for anxiety disorders based on stress reactivity.

Information processing bias

Information processing refers to cognitive processes of attention, memory, and appraisal. An extensive body of research has evaluated these processes in relation to anxiety and depression. In this section, we evaluate the extent to which indices of information processing are shared across anxiety disorders and differ from mood disorders.

Attentional bias and anxiety.

Numerous studies support the presence of an attentional bias toward threat-related stimuli across a range of anxiety disorders, using the emotional Stroop task, the probe detection task, as well as visual search paradigms, and eye tracking. Furthermore, these effects persist for all anxiety disorders when using subliminal presentations or conditions that restrict conscious awareness of threat stimuli (e.g., ( 86)). Whereas cognitive biases are common to all anxiety disorders, the content of these biases becomes relatively specific, presumably as a result of past history and learning experiences.

For example, an attentional bias toward personally threat-relevant stimuli is characteristic of individuals with GAD (e.g., ( 87)), and SOP (e.g., ( 88)). Also, individuals with SOP show an attentional bias toward angry faces, perhaps followed by an avoidant diversion of attention away from such stimuli (e.g., ( 89)) consistent with a "vigilance-avoidance" pattern. An attentional bias toward phobic stimuli in SP has been observed using several different paradigms (e.g., ( 90)). Also, individuals with PD preferentially allocate attentional resources to stimuli that represent physical and mental threat such as "fatality" and "insane" (e.g., ( 88)). In PTSD, an attentional bias toward trauma cues has been observed in several studies (e.g., ( 91)). The one anxiety disorder where the results are unclear is OCD: several studies that failed to find an attentional bias (e.g., ( 92)) may have used stimuli that were insufficiently relevant. Another study indicated that even though patients with OCD did not show an attentional bias on response latency measures in an emotional Stroop task, they did show a specific neural response during color naming of relevant words ( 93).

A related body of research addresses attention to bodily states, or interoception, mostly in the context of PD. PD is associated with heightened awareness of, or ability to detect, bodily sensations of arousal compared to controls (e.g., ( 96- 99)). Discrepant findings (e.g., ( 100, 101)) exist, but have been attributed to methodological artifact ( 97).

Attentional bias across anxiety disorders.

Attentional bias in youth samples.

Attentional bias in anxiety vs. depression.

Memory bias and anxiety.

Memory bias in anxiety vs. depression.

Thus, in comparison to the rapid attentional bias to threat and limited memory bias seen in anxiety disorders, depression is associated with a delayed attention to threat and greater "rumination" upon and recall of negative information. However, there are no direct comparisons of memory bias in anxiety disorders vs. depression, although lower recall of positive adjectives was associated with dimensional measures of depression symptom levels but not with anxiety symptom levels in an inpatient sample of children and adolescents ( 115).

Appraisal bias and anxiety.

Studies of appraisal biases indicate that individuals with anxiety disorders appraise situations as more threatening compared to controls. For example, PD individuals are more likely to resolve ambiguous stimuli related to physical sensations in a threat-congruent fashion ( 116). Persons with PTSD are faster to respond to threat meanings of ambiguous words and to complete sentences with threatening meanings (e.g., ( 117)). SOP is associated with tendencies to judge negative social events to be more likely and positive social events to be less likely than controls, and to interpret ambiguous social events as more negative and mildly negative social events as more catastrophic than other anxious patients or controls (e.g., ( 118)). Individuals with SPs overestimate negative outcomes, dangers and the extent of harm or injury in relation to their phobic objects (e.g., ( 119)), and compulsive washing correlates with estimates of danger. ( 120) Finally, individuals with high trait anxiety or GAD tend to interpret ambiguous events as threatening (e.g., ( 121)). These findings also are true for anxious children; compared to controls, anxious children expect a larger number of negative outcomes and more negative events to happen to them (e.g., ( 122)). Studies of lexical decisions that assess "on-line" interpretations at the time participants are exposed to ambiguous stimuli have similarly shown a negative bias in relation to trait anxiety and social anxiety (e.g., ( 123)), albeit not always (e.g., ( 124)). No within-study comparisons between anxiety disorders were located.

Appraisal bias in anxiety vs. depression.

Summary of information processing biases and implications for DSM.

Whereas both anxiety disorders and unipolar depression exhibit attentional biases to threat-relevant stimuli, the biases appear to occur at different stages of processing ( Table 6); early, nonconscious stage of processing in anxiety disorders vs. later, more strategy/conceptual-based stage of processing in depression. Memory biases also seem to differ between anxiety and depression. That is, depression is reliably associated with both implicit and explicit memory biases toward negative self-referent information and general autobiographic memory. In contrast, memory biases are unstable, weak phenomena in the anxiety disorders. Conceivably, the information processing features support threat sensitivity in persons with anxiety disorders, whereas they support deeper evaluation and rumination in persons with depression. However, no studies have specifically evaluated the memory features of fear disorders vs. anxious misery disorders. Finally, both anxiety and depression are associated with appraisal biases, to interpret ambiguous information in a negative fashion, albeit more threat-laden in the first case and more negative self-evaluation in the second case.

Thus, aspects of cognitive biases support the existing nosological distinctions between anxiety and depression. However, very few studies directly compare anxiety and depression and none have compared fear disorders vs. anxious-misery disorders. Thus, the cognitive data either do not support or are insufficient at this time to justify changes to the nosological structure of DSM.

Information processing bias and aversive con-ditioning/stress reactivity.

Conceivably, the non-conscious attentional bias toward threat seen in anxiety disorders contributes to various aspects within Pavlovian conditioning: elevated responding to explicit threat cues, cues that should signal safety (CS−), extinction, and contexts associated with threat. For example, an attentional bias to threat may result in more rapid excitatory fear responding to cues and contexts that signal threat. It may also contribute to sensitization and lack of habituation effects, leading to elevated responding to the CS+ and CS−. Similarly, by overattending to threat relevant stimuli, processing of the features that distinguish the CS− from the CS+ may be impaired, thereby supporting stimulus generalization to the CS−. A correlate of such interference effects may be lack of contingency awareness of the relationships among the CS+, CS− and the US, that again contributes to elevated responding to both types of stimuli. Furthermore, attention to bodily states (interoception) is likely to enhance conditioning, since stronger perceived physiological responses to aversive stimuli generally elicit stronger conditioning ( 129). In addition, an attentional bias toward threat may increase stress reactivity, in anticipation of stressors and/or in acute response to stressors.

Similarly, threat-biased appraisals may contribute to the acquisition of conditional fear and anxiety responses, and/or contribute to the weakened extinction of such responses. That is, as a result of an interpretive focus that exaggerates rather than reduces threat, anxious individuals may perceive the US as more threatening than nonanxious individuals, thereby supporting both sensitization and associative excitation mechanisms. In addition, anxious individuals may have difficulty reappraising situations in less threatening ways, thereby interrupting inhibitory learning throughout extinction and at extinction recall, such that responding remains more elevated to the CS+ and/or CS− relative to controls. Similar processes could contribute to elevated anticipatory or acute response to stressors in stress reactivity paradigms.

BRAIN IMAGING AND THE NEUROCIRCUITRY OF ANXIETY DISORDERS

Goals

Brain regions associated with anxiety

A constellation of brain regions has been implicated in mediating the normal functions pertinent to anxiety disorders. First, a network involving the amygdala, ventromedial prefrontal cortex (vmPFC), and the hippocampus has been a focal point for contemporary models of anxiety disorders ( 47, 130). The amygdala plays a critical role in threat assessment, in forming associations regarding danger in the environment (e.g., conditioned fear acquisition; see earlier section), and in mediating response to threat or potential threat via descending projections to regions that mediate autonomic responses (e.g., heart rate, blood pressure, respiration, sweating, etc.) ( 133). The vmPFC and hippocampus provide top-down governance over the amygdala, capable of inhibiting fear responding; for instance, the vmPFC mediates extinction recall via inhibition of the amygdala response to learned threat cues (see earlier section), and the hippocampus provides information that is permissive of extinction recall by providing information regarding safe vs. dangerous contexts ( 47) for review, see Reference ( 134). Importantly, the hippocampus also mediates contextual conditioning ( 30, 31, 135, 136) (see earlier section). Of note, the extended amygdala, including the BNST, is purported to play an important role in anxiety per se, as opposed to fear ( 26, 43) (see earlier section). However, given the challenges in resolving the extended amygdala vs. the amygdala proper, it is important to appreciate that most imaging studies to date that report amygdala findings have not sought to make this distinction. Although, the imminence continuum from anxiety to fear as a function of proximity to threat (see initial section) has been substantiated at the neural level as a shift from vmPFC to periaqueductal gray ( 137).

Second, the orbitofrontal cortex (OFC) is divisible into medial (mOFC) and lateral (IOFC) subterritories ( 138). To the extent that OFC plays an important role in computing, assigning, or weighing value, in the service of decision making and guiding behavior, the mOFC principally mediates positive valuations (e.g., of reward and safety) whereas the IOFC principally mediates negative valuations (e.g., of punishment) (e.g., ( 139)). In this context, mOFC (highly overlapping with vmPFC) plays a role in suppression of fear and anxiety, such as through mediating extinction recall (see earlier section), whereas IOFC with extension in ventrolateral PFC more generally, appears to mediate negative cognitions, obsessions and worry (see first section and earlier section).

Third, the insular cortex mediates interoception, and hence plays a critical role in individuals' awareness of and sensitivity to visceral activity ( 140) (see earlier section). In this context, the insula is implicated in anxiety sensitivity, something which is purported to enhance Pavlovian conditioning, since stronger perceived (as well as actual) physiological responses to aversive stimuli generally elicit stronger conditioning ( 129).

Hypothesized links between brain regions and anxiety symptoms/disorders

Building on knowledge of normal functional anatomy, one can pose a variety of hypotheses seeking to link the observed clinical and behavioral phenomena of anxiety disorders and their neural substrates. For instance, exaggerated responsivity or sensitivity of the amygdala could mediate abnormal threat assessment (see earlier section), exaggerated fear responses including exaggerated autonomic output (see earlier section), or abnormalities in learning about danger in the environment (see earlier section). Further, in addition to vulnerabilities to anxiety conferred by intrinsic abnormality in amygdala function, abnormal amygdala responses could be secondary to insufficient vmPFC function, leading to inability to recall extinction information (see earlier section); or, secondary to abnormal hippocampal function, undermining the capacity to distinguish between safe and dangerous contexts (see earlier section). Cognitive manifestations such as worrying and obsessing are likely mediated by excessive activity in IOFC (and related regions). Interestingly, conditions characterized by globally excessive OFC activity may involve both complaints of such cognitive symptoms (mediated by IOFC) and paradoxically relatively reduced amygdala as well as autonomic responsivity (due to suppression by mOFC), perhaps characteristic of GAD (see earlier section).

Elevated insula activity would be expected as a consequence of normal interoceptive function in the face of elevated autonomic responses (such as secondary to exaggerated amygdala responses) in anxiety disorders. Interestingly however, aberrant insula activity, due to intrinsic dysfunction or hypersensitivity, could also be seen as a correlate of anxiety sensitivity (i.e., interoceptive hypersensitivity) (earlier section), in the absence of elevated autonomic measures or elevated amygdala output, as has been observed in relation to carbon dioxide inhalations and PD (e.g., ( 63)) (earlier section). Aberrant error detection (i.e., false alarms) leading to pathological doubting, as seen in OCD, could be mediated by dACC dysfunction. It is also possible that elevated activity in this region could exacerbate conditioned fear expression as seen with prelimbic cortical stimulation in rodents ( 142). In contrast, deficiency in pgACC may mediate attention biases to disorder-relevant cues in the anxiety disorders (earlier sections).

The above series of hypotheses hints at the range of possibilities as well as the potential heterogeneity of pathophysiological roots for the anxiety disorders. While numerous brain imaging studies have been performed in an effort to elucidate the brain basis of anxiety disorders, to date the aggregate data provide initial support for heuristic models as opposed to conclusive evidence of distinct pathophysiology by diagnosis. In fact, few of the above hypotheses have been investigated systematically across the anxiety disorders. We propose that it would be illuminating to take such a systematic approach, including comparisons with other conditions beyond the anxiety disorders, to test for the specificity of findings.

Here we present three examples for illustrative purposes. The first example pertains to amygdala function. Using a range of symptom provocation and cognitive paradigms, investigators have probed amygdala responses to generic (e.g., emotional faces) and disorder-specific stimuli ( Table 7). In this context, there is evidence that exaggerated response to disorder-specific stimuli is a shared feature of those anxiety disorders tested to date, whereas amygdala responses to generic threat-related stimuli may distinguish among the anxiety disorders (e.g., ( 145)). Consistent with the psychophysiology data from stress reactivity studies (see earlier section), there is preliminary evidence for PTSD and PD to be associated with elevated responses to generic stressors to a greater degree than SOP or SP. Of note, MD is also characterized by exaggerated amygdala responses to emotional faces ( 146) weighing against this being a unique feature for the anxiety disorders. However, there may be other aspects of amygdala function that distinguish between anxiety disorders and depression. For instance, with regard to laterality, there is some suggestion that anxiety disorders may be preferentially characterized by right-lateralized amygdala findings, ( 147) whereas depression is characterized by left-sided amygdala findings; this may reflect lateralized aspects of normal amygdala function in humans ( 148). Further, elevated resting/baseline metabolism within the amygdala has been consistently found in depression, but not in anxiety disorders ( 149).

Second, using a range of symptom provocation and cognitive paradigms, reduced mOFC function has been consistently found in association with anxiety disorders ( 131). Interestingly, increased activation within IOFC has been less consistent across anxiety disorders, but may be a hallmark of OCD, and GAD and other disorders characterized by cognitive anxiety (i.e., worry, obsessions, etc.).

Third, again, based upon data from a range of experimental paradigms, elevated insular signal may be a common finding across anxiety disorders ( 132).

Limitations

Summary

In summary, advances in neuroimaging and translational research of the past ≈20 years have helped to focus the field on neural circuitry implicated in the pathophysiology of anxiety disorders as well as relevant normal functions. While this research has yielded useful heuristic models, much work remains in order to definitively establish the brain basis of anxiety disorders. At this point, measures of amygdala, vmPFC/ OFC, and insula function stand as leading candidates for usefully defining anxiety disorders in future.

CONCLUSION

The goal of this review has been to evaluate commonalities across anxiety disorders in features of responding in self-report estimation/prediction and in on-line responding to aversive stimuli across behavioral, psychophysiological, cognitive domains as well as functional systems at the neural level. We addressed whether a category of fear disorders (i.e., PD, PTSD, SOP, and SP) is distinctly identifiable from anxious-misery disorders (i.e., GAD and depression), and more broadly whether anxiety disorders are distinct from depression.

In terms of symptom self-report, the anxiety disorders are characterized by prototypical fear, com-prised of escape behaviors, physiological arousal, and thoughts of imminent threat, and prototypical anxiety, comprised of avoidant behaviors, tension, and thoughts of future threat. These symptoms are related to, but also distinct from, symptoms of depression. There is some indication that self-reported symptoms of physiological arousal, as a measure of the construct of fear, relate more positively to PD and more negatively to GAD than other anxiety disorders, thereby supporting a distinction between fear and anxious misery disorders. On the other hand, lack of positive affect appears to be strongly associated with both depression and SOP, which is at odds with the proposed subdivision of disorders. However, limitations to self-report methodologies render these findings preliminary and in need of further development.

Together, the bodies of research on Pavlovian conditioning and stress reactivity indicate that, compared to controls, individuals with anxiety disorders show a sensitivity to threat that is expressed in terms of both fear and anxiety responding. More specifically, anxiety disorders are associated with (1) elevated fear responding to cues that signal threat (CS+), (2) elevated fear responding to cues that signal no threat (CS−) when presented in the context of threat, and to cues that formerly signaled threat (i.e., extinction trials), (3) elevated contextual anxiety in contexts and waiting periods (baselines) in which sufficiently aversive stimuli are anticipated, (4) equivalent acute responses to generic stressors, and (5) elevated responses to disorder-specific (personally relevant) stressors. However, these findings have not been fully evaluated across all anxiety disorders and/or are not entirely consistent across all the anxiety disorders that have been tested; only preliminary data suggest greater baseline anticipatory and acute fear responding to disorder-specific stressors in SOP and SP relative to PTSD and PD, and greater baseline anticipatory responding to generic stressors in PTSD and PD.

With respect to our question of "what is an anxiety disorder" these findings imply that anxiety disorders are characterized by elevated sensitivity to threat. However, the nuances of that sensitivity, and whether it discriminates anxiety disorders from depression or fear disorders from anxious misery disorders is undetermined. For these reasons, data from studies of Pavlovian conditioning and stress reactivity do not justify revisions to the DSM nosology for anxiety disorders. Also for these reasons, there is a call for a great deal more research to address the gaps listed above.

Data from measurement of cognitive biases indicates that the anxiety disorders are characterized by a preconscious attentional bias toward personally relevant threat stimuli, and a bias to interpret ambiguous information in a threat-relevant manner. Comparisons across subtypes of anxiety disorders are lacking and thus it is not known whether features of cognitive bias differentiate fear disorders from anxious-misery disorders. However, the cognitive bias data support the nosological distinction between anxiety and depression, with the latter characterized by a slower attentional bias to threat and a stronger memory bias for negative information relative to anxiety. Still, very few studies directly compare cognitive biases in anxiety and depression, such that the data are insufficient at this time to have direct relevance to changes to the nosology of DSM.

Data from neuroimaging studies indicate elevated amygdala responses to disorder-specific threat cues as a common characteristic across anxiety disorders. In contrast, amygdala responses to general threat cues may distinguish among anxiety disorders. However, the various profiles of amygdala response remain to be extended across development, to be well replicated, and to be sufficiently well studied in comparison groups to ascertain their specificity to anxiety vs. mood disorders. Similarly, while there is some indication that anxiety disorders are characterized by elevated insular, lateral OFC, and dorsal ACC responses as well as deficient responses within pgACC, vmPFC, and hippocampus, these profiles also remain to be thoroughly elaborated and replicated. Nevertheless, this implicated circuitry provides a heuristic model for the next step of research, whereby cognitive, behavioral, and physiological findings can be linked to mediating neuroanatomy and brain pathophysiology.

In summary, our review has helped to sharpen our knowledge of the features that characterize anxiety disorders. These features represent an elevated sensitivity to threat, as observed across symptom reporting, behavioral, cognitive and physiological responding, and underlying neural systems. However, the degree to which these features are differentiated between subtypes of anxiety disorders, or discriminate anxiety disorders from depression remain open to further investigation. One of the major barriers to such investigation is the high rate of co-morbidity within and across anxiety disorders and depression, and the associated overlap in diagnostic symptom criteria in some cases. Advances will most likely require dimensional approaches that evaluate both the shared as well as the unique contributions of fear, anxiety, and depression to the various facets of threat sensitivity.

REFERENCES

10 Ree MJ, MacLeod C, French D, Locke V. The State-Trait Inventory for Cognitive and Somatic Anxiety: Development and Validation. Poster Session Presented at the Annual Meeting of the Association for the Advancement of Behavior Therapy, New Orleans, LA; 2000. Google Scholar

# data[].attributes.attachment_link.description
Initiated as part of the ongoing deliberation about the nosological structure of DSM, this review aims to evaluate whether the anxiety disorders share features of responding that define them and make them distinct from depressive disorders, and/or that differentiate fear disorders from anxious-misery disorders.