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Logo of nihpaAbout Author manuscriptsSubmit a manuscriptHHS Public Access; Author Manuscript; Accepted for publication in peer reviewed journal;
J Neuropsychiatry Clin Neurosci. Author manuscript; available in PMC 2018 January 1.
Published in final edited form as:
PMCID: PMC5473777



We conducted a cross-sectional study to investigate the association between anxiety symptoms and cortical thickness as well as amygdalar volume. We recruited 1,505 cognitively normal participants from the Mayo Clinic Study of Aging in Olmsted County, Minnesota, aged ≥70 years, on whom Beck Anxiety Inventory and 3T brain MRI data were available. Even though the effect sizes were small in this community-dwelling group of participants, anxiety symptoms were associated with reduced global cortical thickness and reduced thickness within the frontal and temporal cortex. However, after additionally adjusting for comorbid depressive symptoms, only the association between anxiety symptoms and reduced insular thickness remained significant.

Keywords: anxiety, anxiety symptoms, cortical thickness, aging, normal cognition, neuroimaging


Late life depression has been extensively investigated, however less is known about anxiety in old age. Yet, subsyndromal symptoms of anxiety are prevalent in the elderly, with a reported 12-month prevalence rate of 26.2% for subthreshold anxiety, versus 5.6% for DSM-IV anxiety disorders.(1) In addition, subsyndromal symptoms of anxiety are associated with poor outcomes such as cognitive decline,{2} increased health care cost, and reduced health-related quality of life.{3} Experts have recently suggested conceptualizing anxiety in late life as a dimensional rather than categorical construct.{4} Based on this concept, it is crucial to investigate symptoms of anxiety rather than anxiety disorders. In addition, a National Institute on Aging (NIA) and Alzheimer’s Association (AA) expert panel has called for characterizing and understanding of the presymptomatic phase of Alzheimer’s disease (AD) by using biomarkers such as brain MRI.{5} Investigating the association of subsyndromal anxiety with biomarkers in presymptomatic AD will be a value added approach towards an understanding of presymptomatic AD.

Changes in cortical thickness have been observed in normal aging, mild cognitive impairment (MCI), Alzheimer’s disease dementia{6} and also in psychiatric disorders including major depression{7} and anxiety.{8} An automated method has been developed to accurately measure cortical thickness across the entire brain, providing an opportunity to investigate the structural correlates of anxiety.{9} Only few studies have examined cortical thickness in participants with anxiety disorders{8} or symptoms.{3, 10} Most reports were based on small sample sizes and involved a wide age range, with mostly middle-aged participants.

The amygdala has been shown to play an important role in anxiety, with reports on reduced volume in panic disorder.{11, 12} A small number of studies also reported associations between hippocampal volume and anxiety.{13} Even though, previous studies showed high co-occurrence of anxiety and depression{1416} most previous imaging studies that investigated anxiety did not adjust for co-occuring depression.

We therefore sought to examine the associations between anxiety symptoms and cortical thickness in elderly community-dwelling participants. In addition, we performed further analyses also adjusting for depressive symptoms. Furthermore, we investigated the association between anxiety symptoms and amygdalar as well as hippocampal volume.



Participants were recruited from the Mayo Clinic Study of Aging (MCSA). Details of the study procedures have been previously described.{17} Briefly, the MCSA is an ongoing population-based study in Olmsted County, Minnesota that was designed to study the prevalence, incidence and risk factors of cognitive aging, mild cognitive impairment and dementia. Only participants who had undergone both brain MRI imaging and anxiety symptom assessment were included in this study. When we compared MCSA participants who underwent MRI vs. others, we observed the following: as compared to MCSA participants who did not undergo MRI, participants who underwent MRI scans had lower numbers of medical comorbidities (median: 3 vs. 4, p<0.01), higher z-scores for global cognition (median: 0.52 vs. 0.14, p<0.01), higher scores on the Short Test of Mental Status (median: 35 vs. 34, p<001) and slightly lower numbers of anxiety (median: 1 vs. 2, p<0.01) and depressive (median: 3.5 vs. 4, p<0.01) symptoms. This study was approved by the Mayo Clinic and Olmsted Medical Center Institutional Review Boards, and written informed consent was obtained from every participant prior to enrollment in the study.

Cognitive evaluation

Participants of the MCSA underwent face-to-face cognitive and risk factor evaluations that are published in detail elsewhere.{17} Briefly, the assessment included 1) baseline evaluation (including Clinical Dementia Rating Scale {18} and risk factor ascertainment (including Beck Anxiety Inventory) performed by a nurse or study coordinator; and 2) neurological evaluation performed by behavioral neurologists. Participants also underwent neuropsychological evaluation of 4 cognitive domains – memory (delayed recall trials from the Auditory Verbal Learning Test {19} and the Wechsler Memory Scale-Revised, {20} Logical Memory and Visual Reproduction subtests); language (Boston Naming Test {21} and category fluency); visuospatial (Wechsler Adult Intelligence Scale-Revised, {22} Picture Completion and Block Design subtests); and executive function (Trail Making Test Part B {23} and the Wechsler Adult Intelligence Scale-Revised, Digit Symbol subtest). In the results section, the neuropsychological performance within the separate cognitive domains is expressed as z-score which reflects the number of standard deviations the performance is above the mean.

An expert consensus panel of physicians, neuropsychologists, and nurses/study coordinators reviewed all the data for each participant and made the diagnosis of normal cognition, MCI, or dementia based on published criteria. Only participants with normal cognition were included in this study.

Measurement of anxiety symptoms

Within 150 days of the MRI scan, anxiety symptoms were measured using the Beck Anxiety Inventory (BAI), which is a validated, self-administered questionnaire.{24} The BAI is an ordinal measurement consisting of 21 items that are assessed over the last week. The severity of each symptom is rated ranging from 0 to 3 with total scores ranging from 0 to 63.


Brain MRI scans were performed on 3-T scanners (Signa; GE Healthcare) equipped with an 8-channel phased array coil (GE Healthcare). A 3-dimensional magnetization–prepared rapid gradient echo sequence was performed{25, 26} and images were corrected for bias field and for distortion due to gradient nonlinearity.{27} Cortical thickness, amygdalar and hippocampal volumes were measured with FreeSurfer software, version 5.3.{28} The following regions of interest (ROIs) of cortical thickness labeled by FreeSurfer were included in the analysis: Frontal, temporal, and parietal thickness, as well as global cortical thickness which was the mean thickness of all regions measured by FreeSurfer, including frontal, temporal, parietal and occipital tickness. In addition, we investigated the following subregions within the frontal and temporal cortex: Anterior cingulate cortex, ACC (combined thickness of the rostral, isthmus and caudal ACC cortex), dorsolateral prefrontal cortex, dlPFC (combined superior frontal, rostral middle frontal and caudal middle frontal cortex), ventrolateral prefrontal cortex, vlPFC (combined pars opercularis, pars triangularis and pars orbitalis), and orbitofrontal cortex, OFC (combined lateral and medial orbitofrontal cortex) as labeled by FreeSurfer. For a secondary analysis we calculated amygdalar and hippocampal volumes that were measured by FreeSurfer and were adjusted by total intracranial volume measured by SPM12.{29}.

Participation in imaging is voluntary and is available to all study participants. There are a few exceptions that will not allow a participant to be imaged (e.g., participants with pacemakers). Study participants can decline imaging for any reason. For ethical reason, if they decline no further effort is made to make them change their mind or inquire as to why they are unwilling.

The majority of participants underwent magnetic resonance imaging within 120 days after the administration of the anxiety assessment. About 5% of our cohort had the MRI after 120 days. There was one subject with a maximum of 150 days between the BAI visit date and the MRI.

Measurement of covariates

We defined the following variables as covariates: age, sex, education, medical comorbidity, antidepressant medication, and global cognition. Medical comorbidity was measured via the Charlson index, a widely used weighted index that accounts for number and severity of diseases.{30} Global cognition was measured as a composite z-score computed from memory, executive function, visuospatial and language domain scores from an extensive neuropsychological test battery described in detail elsewhere.{17} Antidepressant medication use was self-reported and included use of SSRI, SNRI, citalopram, tetracyclic, or tricyclic antidepressants.

Depressive and anxiety symptoms often co-occur in late life.{3133} Therefore when investigating the association of anxiety with biomarkers such as cortical thickness, it is critical to adjust for depressive symptoms in order to make sure that the observed association between anxiety and cortical thickness is not confounded by the co-morbid depressive symptoms. It is for this reason that we adjusted for depressive symptoms as measured by Beck Depression Inventory-II (BDI-II).{30} Similar to the BAI, the BDI-II is also an ordinal measurement consisting of 21 items. Depressive symptoms are assessed over the last two weeks and are rated in severity on a 4-point scale ranging from 0 to 3; with a total score ranging from 0 to 63.

Statistical analysis

We conducted a cross-sectional study involving cognitively normal elderly individuals with available MRI and anxiety data. To test for pairwise differences in demographic characteristics, Wilcoxon two-sided rank sum tests were calculated for participants with BAI scores ≥ 1 vs. participants without anxiety symptoms. When we examined the association between anxiety symptoms and MRI measures, we treated anxiety symptoms, measured by BAI, as continuous variable, in order to avoid arbitrary categorical cut-offs. Therefore, we calculated Spearman rank-order correlations between MRI measures (cortical thickness by region of interest, amygdalar and hippocampal volumes) and BAI (as continuous variables) after adjusting for age, sex, education, medical comorbidity, global cognition, and antidepressant medication. We ran an additional analysis in which we also adjusted for depressive symptoms as measured by Beck Depressive Inventory-II (BDI-II).{30} We computed estimates of associations using SAS System, version 9.3 software (SAS Institute, Cary, NC) and visually displayed the data as forest plots using R statistical software, version 3.0.2 (R Foundation for Statistical Computing, Vienna).


The complete demographic characteristics are displayed in Table 1. Only a small percentage of this population-based study met the threshold for clinical anxiety (BAI >10; 5.3 %) and depression (BDI >13; 5.6 %). Participants with anxiety symptoms were significantly older, had fewer years of education, lower scores on the Short Test of Mental Status,{34} lower z-scores for global cognition, higher number of depressive symptoms and higher use of antidepressant medication as compared to participants without anxiety symptoms.

Table 1
Demographic characteristics

When examining anxiety symptoms as continuous variable, we observed associations with reduced thickness in most ROIs in unadjusted models.(Table 2) In multivariate analyses, the higher the BAI scores the lower the cortical thickness of ROI as noted below: global/composite thickness (r= −0.06, p=0.02), frontal (r= −0.07, p=0.01), and temporal (r= −0.07, p<0.01), dorsolateral prefrontal cortex (r= −0.07, p=0.01), insula (r= −0.09, p<0.01), parahippocampal (r= −0.06, p=0.04), and ACC (r= −0.05, p=0.04), after adjusting for age, sex, education, medical comorbidity, antidepressant medication, and global cognition. When we additionally adjusted for depressive symptoms (as measured by BDI-II), only the associations between anxiety symptoms and reduced insular cortical thickness remained significant (r = −0.07, p<0.01). We further observed a trend for a correlation between OFC with anxiety (p=.06) after correction for depression. A secondary analysis did not show associations between anxiety symptoms and amygdalar or hippocampal volume, after adjusting for age, sex, education, medical comorbidity, global cognition, and antidepressant medication use. (Table 2) Figure 1 shows forest plots of the partial Spearman rank-order corellations and Figure 2 shows a boxplot of the BAI scores by sex and age groups.

Figure 1
Forest plots of the Spearman rank-order correlations between BAI and MRI measures
Figure 2
Box plot of BAI scores by sex and age groups
Table 2
Spearman rank-order adjusted correlations (p-value) between anxiety symptoms as measured by BAI and cortical thickness


Herein we report the associations between cortical thickness and anxiety symptoms in a large sample of cognitively normal elderly participants enrolled in the population-based Mayo Clinic Study of Aging. In previous studies we and others have shown that anxiety symptoms increase the risk of developing mild cognitive impairment in cognitively normal elderly participants. This may indicate that anxiety symptoms could be a marker of preclinical AD. Thus we looked at elderly participants who were still free of MCI and dementia. We then sought to examine if there was already a structural correlate to anxiety symptoms. However, since these are cross-sectional data, whatever is reported about structural biomarker relationships is not yet answering this question of preclinical AD indicators.

We observed associations between anxiety symptoms and lower global thickness as well as lower thickness in frontal and temporal cortical regions, after adjusting for age, sex, education, medical comorbidity, antidepressant medication, and global cognition. The results changed markedly after we additionally adjusted for depressive symptoms. Furthermore, as expected, the effect sizes were very small. In contrast to patients from clinical settings, we randomly sampled and recruited participants from the general population with the result that particpants had very mild symptoms of anxiety. Only a small percentage of this population-based study met the threshold for clinical anxiety (BAI >10; 5.3 %).

Several studies examined associations between brain volumes and disorders of anxiety, based on categorical classifications, for example generalized anxiety disorder or panic disorder. They mainly reported altered volumes of the amygdala,{11, 12, 35} insular cortex,{13, 3537} and ACC.{10, 36, 37} Few studies also reported altered volumes in the prefrontal cortex,{3, 10, 12} and temporal regions.{12, 38, 39} However, little is known about the association between cortical thickness and anxiety symptoms in the elderly. Moreover, most previous studies that examined anxiety did not specifically adjust for comorbid depression. Yet, our study findings changed markedly after additionally adjusting for depressive symptoms. While the associations with various regions remained in the same direction, only the association between anxiety symptoms and reduced insula thickness remained statistically significant and the association between OFC with anxiety remained marginally significant after additionally adjusting for depressive symptoms. This finding suggests that many regions that appear to be associated with anxiety could be rather confounded by the presence of co-occurring depressive symptoms. Indeed, our data suggest that only insular thickness may specifically be associated with anxiety symptoms. This is in line with previous studies that have shown an association between reduced insular volume and disorders of anxiety.{13, 3537} The role of reduced insular thickness in anxiety symptoms may be explained by the insula’s high interconnections with other cortical and subcortical regions. Moreover, the insula has been shown to be involved in emotional response and its size and reactivity has been linked to awareness of bodily responses, and especially anxiety.{40}.

We observed no relationship between anxiety symptoms and hippocampal or parahippocampal MRI measures. This may indicate that anxiety may not be a valuable predictor for AD risk in the subclinical stages of AD. The amygdala is probably the most widely investigated region in anxiety, {3, 11, 35} and several studies reported associations between reduced amygdalar volume and anxiety disorders.{11, 12} We only observed significant associations in our unadjusted results. However, similar to other studies that investigated mild anxiety symptoms,{2, 10} we also did not observe significant associations between amygdalar volume and very mild anxiety symptoms in community-dwelling participants after adjusting for multiple possible confounders. This may indicate that in contrast to studies that reported significant associations with clinical anxiety disorders, mild symptoms in community-dwelling individuals may not be severe enough to manifest associations with lower amygdalar volume.

Additionally, we conducted further analyses in order to explore correlations among the components of neuronal circuitry related to anxiety. Indeed, we observed significant correlations between insular cortex and orbitofrontal cortex (r = 0.54, p<.01) as well as between insular cortex and amygdala (r = 0.08, p<.01). This finding is consistent with previous reports.{10}.

Investigators from Italy examined associations between anxiety symptoms as measured by the Hamilton scale for anxiety and amygdalar and hippocampal volume as well as cortical thickness in the OFC and ACC in 121 participants with a median age of 36 years. While, similar to our findings, they did not observe reduced amygdalar or hippocampal volume, they also reported associations between anxiety scores and increased ACC thickness.{10} Whereas they and others reported increased thickness,{2, 9, 36} other studies have reported reduced ACC volume in individuals with panic disorder.{36, 37} We observed weak associations between reduced ACC thickness and anxiety symptoms as measured by BAI after adjusting for age, sex, education, medical comorbitiy, antidepressant medication and global cognition. Discrepancies across studies may be attributable to methodological issues including differences in anxiety assessment and participant age groups. Since age is associated with reduced cortical thickness,{41} it is possible that studies involving younger individuals may show different results and that associations between anxiety symptoms and lower cortical thickness do not manifest until old age. While the mechanisms that may underly reduced cortical thickness are not fully understood, it has been reported that the neuronal number is not reduced in age-related cortical atrophy.{42} Thus, cortical thinning may be rather associated with neuronal shrinkage or degeneration of synapses. While our cross-sectional study does not provide information about the causal relationship between cortical thickness and anxiety symptoms, we hypothesize that underlying pathological neurodegeneration may result in reduction of synapses in cortical circuits involving the insula. This in turn may be seen on MRI as cortical thinning and lead to anxiety symptoms. Alternatively, it is possible that an additional risk factor may synergistically interact with the cortical thinning pathway and lead to anxiety symptoms. It also seems plausible that a confounder such as age may lead to both cortical thinning and anxiety symptoms.

Our study should be interpreted in light of its strengths and limitations. An important strength is that participants were from a large, well-described population-based study thereby reducing selection bias as would occur with a clinic-based setting. The comprehensive clinical evaluation and access to medical record information from the Rochester Epidemiology Project provided the opportunity to adjust for multiple possible confounders including age, sex, education, antidepressant medication use, medical comorbidities, and cognition. In contrast to most previous studies, we also adjusted for depressive symptoms. The comorbidity between anxiety and depression is very high; therefore, we additionally adjusted for depressive symptoms. The observed association between anxiety and cortical thickness remained the same indicating that the association is over and above what can be explained by co-morbid depressive symptoms. Furthermore, the study sample allowed us to investigate associations of anxiety with imaging variables (i.e. cortical thickness, amygdalar and hippocampal volume) in more than 1,500 participants. Additionally, we examined associations between anxiety symptoms and cortical thickness as continuous variables, whereas most previous studies chose categorical classifications.

The study also has potential limitations. The BAI is a self-reported questionnaire and may therefore be subject to recall bias. However, the BAI is a well validated and widely used scale.{24} In addition, the study is limited by its cross-sectional design which does not allow for interpretations of the direction of causality. Therefore, longitudinal studies are needed to replicate our findings. Furthermore, even though the Mayo Clinic Study of Aging extensively evaluates cognition and we were able to exclude participants with MCI or dementia from this study, we could not account for subjective cognitive impairment which can also be accompanied by symptoms of anxiety.

Our findings indicate that a higher level of anxiety symptoms was associated with lower thickness in various cortical regions after adjusting for age, sex, education, medical comorbidity, antidepressant medication and global cognition. This cross-sectional association is informative in that a dimensional approach has raised an important hypothesis that needs to be tested in a future longitudinal cohort study to investigate as to who will eventually develop further neurodegeneration leading to a phenotype of cognitive impairment. However, after additionally adjusting for depressive symptoms, the findings changed markedly, and only the association between anxiety symptoms and reduced insular thickness remained significant. Therefore, this finding may provide insight into the specific structural correlates of anxiety symptoms after adjusting for possible confounding effects of co-occuring depression.



Support for this research was provided by NIH grants: National Institute of Mental Health (K01 MH068351 to Dr. Geda), and National Institute on Aging (U01 AG006786 to Dr. Petersen and K01 AG028573 to Dr. Roberts). This project was also supported by the Robert Wood Johnson Foundation (to Dr. Geda), the Robert H. and Clarice Smith and Abigail Van Buren Alzheimer’s Disease Research Program (to Drs. Petersen and Geda), the European Regional Development Fund: FNUSA-ICRC (No. CZ.1.05/1.1.00/02.0123 to Drs. Stokin and Geda), and the Arizona Alzheimer’s Consortium (to Dr. Geda).


Dr. Knopman serves as Deputy Editor for Neurology®; serves on a Data Safety Monitoring Board for Lundbeck Pharmaceuticals and for the Dominantly Inherited Alzheimer’s Disease Treatment Unit. He has served on a Data Safety Monitoring Board for Lilly Pharmaceuticals; served as a consultant to Tau RX, was an investigator in clinical trials sponsored by Baxter and Elan Pharmaceuticals in the past 2 years; and receives research support from the NIH.

Dr. Jack has provided consulting services for Eli Lily and owns stock in Johnson and Johnson. He receives research funding from the National Institutes of Health (R01-AG011378, RO1 AG041851, U01-AG06786, U01-AG024904, R01 AG37551, R01AG043392), and the Alexander Family Alzheimer’s Disease Research Professorship of the Mayo Foundation.

Dr. Petersen reports being a consultant to Roche Incorporated, Merck and Genentech; and serving as chair of the data monitoring committees of Pfizer Incorporated and Janssen Alzheimer Immunotherapy.


Author contributions: Dr. Geda had full access to all data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Pink, Przybelski, Roberts, Jack, Petersen, Geda.

Acquisition, analysis, or interpretation of data: Pink, Przybelski, Krell-Roesch, Stokin, Roberts, Mielke, Geda.

Drafting of the manuscript: Pink, Geda.

Critical revision of the manuscript for important intellectual content: Przybelski, Krell-Roesch, Stokin, Roberts, Mielke, Spangehl, Knopman, Jack, Petersen.

Statistical analysis: Przybelski.

Obtained funding: Stokin, Petersen, Geda.

Administrative, technical, or material support: Stokin, Roberts, Jack, Petersen, Geda.

Role of sponsor

The funding sources had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Conflict of interest disclosures

All other authors report no disclosures.


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