Hostname: page-component-8448b6f56d-mp689 Total loading time: 0 Render date: 2024-04-23T09:40:46.574Z Has data issue: false hasContentIssue false

Gray matter characteristics associated with trait anxiety in older adults are moderated by depression

Published online by Cambridge University Press:  10 June 2015

Olivier Potvin
Affiliation:
Institut National de la Santé et de la Recherche Médicale, INSERM U897, Bordeaux, France Centre de recherche de l'Institut universitaire en santé mentale de Québec, Québec, Canada
Gwénaëlle Catheline
Affiliation:
Laboratoire de Neurobiologie Intégrative et Adaptative EPHE INCIA UMR 5287 CNRS-Université Segalen Bordeaux 2, Bordeaux, France
Charlotte Bernard
Affiliation:
Laboratoire de Neurobiologie Intégrative et Adaptative EPHE INCIA UMR 5287 CNRS-Université Segalen Bordeaux 2, Bordeaux, France
Céline Meillon
Affiliation:
Institut National de la Santé et de la Recherche Médicale, INSERM U897, Bordeaux, France
Valérie Bergua
Affiliation:
Laboratoire de psychologie EA 4139, Université de Bordeaux, Bordeaux, France
Michèle Allard
Affiliation:
Laboratoire de Neurobiologie Intégrative et Adaptative EPHE INCIA UMR 5287 CNRS-Université Segalen Bordeaux 2, Bordeaux, France
Jean-François Dartigues
Affiliation:
Institut National de la Santé et de la Recherche Médicale, INSERM U897, Bordeaux, France
Nicolas Chauveau
Affiliation:
UMR 825 Inserm, Université Toulouse III - Paul Sabatier, Toulouse, France
Pierre Celsis
Affiliation:
UMR 825 Inserm, Université Toulouse III - Paul Sabatier, Toulouse, France
Hélène Amieva*
Affiliation:
Institut National de la Santé et de la Recherche Médicale, INSERM U897, Bordeaux, France
*
Correspondence should be addressed to: Hélène Amieva, INSERM U897, Université Bordeaux Segalen, 146 Rue Léo Saignat, 33076, Bordeaux cedex, France. Phone: +33557571510. Email: Helene.Amieva@isped.u-bordeaux2.fr.

Abstract

Background:

Structural gray matter characteristics of anxiety remain unclear. The aim of this study was to assess the influence of current depressive symptoms and history of depression on the gray matter characteristics of trait anxiety.

Methods:

Structural magnetic resonance imaging (MRI) data from 393 individuals aged 65 years or older were used. Regions of interest (ROIs) included the amygdala, anterior cingulate cortex (ACC), insula, orbitofrontal cortex (OFC), and temporal cortex. Trait anxiety was measured by the State-Trait Anxiety Inventory (STAI). Depression and depressive symptoms were measured using DSM-IV criteria and the Center for Epidemiological Studies Depression Scale (CESD).

Results:

After adjustments for sociodemographics and health-related variables, anxiety had a significant influence on the gray matter characteristics in all cortical ROIs. First, in participants without depression antecedents, higher trait anxiety was associated with a larger cortical thickness in all cortical ROIs. Second, in participants with a previous history of depression, higher trait anxiety was associated with a smaller cortical thickness in all cortical ROIs.

Conclusions:

These results suggest that anxiety is related to cortical thickness differently in healthy older adults and in older adults with psychiatric antecedents. Anxiety associated with thinner cortical areas could reflect symptoms of a specific type of depression or a vulnerability to develop depression.

Type
Research Article
Copyright
Copyright © International Psychogeriatric Association 2015 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

3C Study Group (2003). Vascular factors and risk of dementia: design of the Three-City Study and baseline characteristics of the study population. Neuroepidemiology, 22, 316325.CrossRefGoogle Scholar
Alexopoulos, G. S. et al. (2005). Remission in depressed geriatric primary care patients: a report from the PROSPECT study. American Journal of Psychiatry, 162, 718724.Google Scholar
Andreescu, C. and Lenze, E. J. (2012). Comorbid anxiety and depression: bete noire or quick fix? British Journal of Psychiatry, 200, 179181.Google Scholar
Andreescu, C., Wu, M., Butters, M. A., Figurski, J., Reynolds, C. F. 3rd and Aizenstein, H. J. (2011). The default mode network in late-life anxious depression. American Journal of Geriatric Psychiatry, 19, 980983.CrossRefGoogle ScholarPubMed
Andreescu, C. et al. (2007). Effect of comorbid anxiety on treatment response and relapse risk in late-life depression: controlled study. British Journal of Psychiatry, 190, 344349.Google Scholar
Andreescu, C. et al. (2009). fMRI activation in late-life anxious depression: a potential biomarker. International Journal of Geriatric Psychiatry, 24, 820828.CrossRefGoogle ScholarPubMed
Asami, T. et al. (2008). Anterior cingulate cortex volume reduction in patients with panic disorder. Psychiatry and Clinical Neurosciences, 62, 322330.CrossRefGoogle ScholarPubMed
Asami, T. et al. (2009). Sexually dimorphic gray matter volume reduction in patients with panic disorder. Psychiatry Research, 173, 128134.Google Scholar
Ashburner, J. and Friston, K. J. (2000). Voxel-based morphometry–the methods. Neuroimage, 11, 805821.Google Scholar
Beekman, A. T., de Beurs, E., van Balkom, A. J., Deeg, D. J., van Dyck, R. and van Tilburg, W. (2000). Anxiety and depression in later life: co-occurrence and communality of risk factors. American Journal of Psychiatry, 157, 8995.Google Scholar
Bergua, V. et al. (2012). The STAI-Y trait scale: psychometric properties and normative data from a large population-based study of elderly people. International Psychogeriatrics, 24, 11631171.Google Scholar
Blackmon, K. et al. (2011). Structural evidence for involvement of a left amygdala-orbitofrontal network in subclinical anxiety. Psychiatry Research, 194, 296303.Google Scholar
Bloch, I. (2005). Fuzzy spatial relationships for image processing and interpretation: a review. Image Vis Comput, 23, 89110.CrossRefGoogle Scholar
Bogg, T. and Roberts, B. W. (2004). Conscientiousness and health-related behaviors: a meta-analysis of the leading behavioral contributors to mortality. Psychological Bulletin, 130, 887919.Google Scholar
Brown, T. A., Chorpita, B. F. and Barlow, D. H. (1998). Structural relationships among dimensions of the DSM-IV anxiety and mood disorders and dimensions of negative affect, positive affect, and autonomic arousal. Journal of Abnormal Psychology, 107, 179192.CrossRefGoogle ScholarPubMed
Clark, L. A., Watson, D. and Mineka, S. (1994). Temperament, personality, and the mood and anxiety disorders. Journal of Abnormal Psychology, 103, 103116.CrossRefGoogle ScholarPubMed
Dew, M. A. et al. (1997). Temporal profiles of the course of depression during treatment. Predictors of pathways toward recovery in the elderly. Archives of General Psychiatry, 54, 10161024.Google Scholar
Etkin, A. and Wager, T. D. (2007). Functional neuroimaging of anxiety: a meta-analysis of emotional processing in PTSD, social anxiety disorder, and specific phobia. American Journal of Psychiatry, 164, 14761488.Google Scholar
Fava, M. et al. (2004). Clinical correlates and symptom patterns of anxious depression among patients with major depressive disorder in STAR*D. Psychological Medicine, 34, 12991308.CrossRefGoogle ScholarPubMed
Fava, M. et al. (2008). Difference in treatment outcome in outpatients with anxious versus nonanxious depression: a STAR*D report. American Journal of Psychiatry, 165, 342351.Google Scholar
Fjell, A. M. et al. (2013). Critical ages in the life course of the adult brain: nonlinear subcortical aging. Neurobiology of Aging, 34, 22392247.Google Scholar
Flint, A. J. and Rifat, S. L. (1997). Anxious depression in elderly patients. Response to antidepressant treatment. American Journal of Geriatric Psychiatry, 5, 107115.Google Scholar
Folstein, M. F., Folstein, S. E. and McHugh, P. R. (1975). “Mini-mental state.” A practical method for grading the cognitive state of patients for the clinician. Journal of Psychiatric Research, 12, 189198.CrossRefGoogle ScholarPubMed
Freeman, S. H. et al. (2008). Preservation of neuronal number despite age-related cortical brain atrophy in elderly subjects without Alzheimer disease. Journal of Neuropathology and Experimental Neurology, 67, 12051212.Google Scholar
Frick, A., Howner, K., Fischer, H., Eskildsen, S. F., Kristiansson, M. and Furmark, T. (2013). Cortical thickness alterations in social anxiety disorder. Neuroscience Letters, 536, 5255.CrossRefGoogle ScholarPubMed
Good, C. D., Johnsrude, I. S., Ashburner, J., Henson, R. N., Friston, K. J. and Frackowiak, R. S. (2001). A voxel-based morphometric study of ageing in 465 normal adult human brains. Neuroimage, 14, 2136.Google Scholar
Greenlee, A., Karp, J. F., Dew, M. A., Houck, P., Andreescu, C. and Reynolds, C. F. 3rd. (2010). Anxiety impairs depression remission in partial responders during extended treatment in late-life. Depression and Anxiety, 27, 451456.Google Scholar
Hardeveld, F., Spijker, J., De Graaf, R., Nolen, W. A. and Beekman, A. T. (2013). Recurrence of major depressive disorder and its predictors in the general population: results from The Netherlands Mental Health Survey and Incidence Study (NEMESIS). Psychological Medicine, 43, 3948.CrossRefGoogle ScholarPubMed
Hayano, F. et al. (2009). Smaller amygdala is associated with anxiety in patients with panic disorder. Psychiatry and Clinical Neurosciences, 63, 266276.CrossRefGoogle ScholarPubMed
Hettema, J. M., Neale, M. C., Myers, J. M., Prescott, C. A. and Kendler, K. S. (2006). A population-based twin study of the relationship between neuroticism and internalizing disorders. American Journal of Psychiatry, 163, 857864.Google Scholar
Hettema, J. M., Prescott, C. A., Myers, J. M., Neale, M. C. and Kendler, K. S. (2005). The structure of genetic and environmental risk factors for anxiety disorders in men and women. Archives of General Psychiatry, 62, 182189.Google Scholar
Hutton, C., Draganski, B., Ashburner, J. and Weiskopf, N. (2009). A comparison between voxel-based cortical thickness and voxel-based morphometry in normal aging. Neuroimage, 48, 371380.Google Scholar
Inkster, B. et al. (2011). Structural brain changes in patients with recurrent major depressive disorder presenting with anxiety symptoms. Journal of Neuroimaging, 21, 375382.CrossRefGoogle ScholarPubMed
Ionescu, D. F., Niciu, M. J., Mathews, D. C., Richards, E. M. and Zarate, C. A. Jr. (2013). Neurobiology of anxious depression: a review. Depression and Anxiety, 30, 374385.Google Scholar
Jones, S. E., Buchbinder, B. R. and Aharon, I. (2000). Three-dimensional mapping of cortical thickness using Laplace's equation. Human Brain Mapping, 11, 1232.Google Scholar
Kessler, R. C., Berglund, P., Demler, O., Jin, R., Merikangas, K. R. and Walters, E. E. (2005). Lifetime prevalence and age-of-onset distributions of DSM-IV disorders in the National Comorbidity Survey Replication. Archives of General Psychiatry, 62, 593602.Google Scholar
Kessler, R. C. et al. (2003). The epidemiology of major depressive disorder: results from the National Comorbidity Survey Replication (NCS-R). JAMA, 289, 30953105.Google Scholar
Kessler, R. C. et al. (2010). Age differences in the prevalence and co-morbidity of DSM-IV major depressive episodes: results from the WHO World Mental Health Survey Initiative. Depression and Anxiety, 27, 351364.Google Scholar
Kuhn, S., Schubert, F. and Gallinat, J. (2011). Structural correlates of trait anxiety: reduced thickness in medial orbitofrontal cortex accompanied by volume increase in nucleus accumbens. Journal of Affective Disorders, 134, 315319.Google Scholar
Lenze, E. J. et al. (2000). Comorbid anxiety disorders in depressed elderly patients. American Journal of Psychiatry, 157, 722728.Google Scholar
Massana, G. et al. (2003a). Parahippocampal gray matter density in panic disorder: a voxel-based morphometric study. American Journal of Psychiatry, 160, 566568.Google Scholar
Massana, G. et al. (2003b). Amygdalar atrophy in panic disorder patients detected by volumetric magnetic resonance imaging. Neuroimage, 19, 8090.Google Scholar
Mohlman, J., Price, R. B., Eldreth, D. A., Chazin, D., Glover, D. M. and Kates, W. R. (2009). The relation of worry to prefrontal cortex volume in older adults with and without generalized anxiety disorder. Psychiatry Research, 173, 121127.Google Scholar
Olesen, J., Gustavsson, A., Svensson, M., Wittchen, H. U. and Jonsson, B. (2012). The economic cost of brain disorders in Europe. European Journal of Neurology, 19, 155162.CrossRefGoogle ScholarPubMed
Pernet, C., Andersson, J., Paulesu, E. and Demonet, J. F. (2009). When all hypotheses are right: a multifocal account of dyslexia. Human Brain Mapping, 30, 22782292.Google Scholar
Potvin, O. et al. (2013). Anxiety and 10-year risk of incident and recurrent depressive symptomatology in older adults. Depression and Anxiety, 30, 554563.Google Scholar
Protopopescu, X. et al. (2006). Increased brainstem volume in panic disorder: a voxel-based morphometric study. Neuroreport, 17, 361363.Google Scholar
Querbes, O. et al. (2009). Early diagnosis of Alzheimer's disease using cortical thickness: impact of cognitive reserve. Brain, 132, 20362047.Google Scholar
Radloff, L. S. (1977). The CES-D scale: a self-report depression scale for research in the general population. Applied Psychological Measurement, 1, 385401.Google Scholar
Rauch, S. L. et al. (2004). A magnetic resonance imaging study of cortical thickness in animal phobia. Biological Psychiatry, 55, 946952.Google Scholar
Rice, D. P. and Miller, L. S. (1998). Health economics and cost implications of anxiety and other mental disorders in the United States. British Journal of Psychiatry. Suppl(34), 49.Google Scholar
Roppongi, T. et al. (2010). Posterior orbitofrontal sulcogyral pattern associated with orbitofrontal cortex volume reduction and anxiety trait in panic disorder. Psychiatry and Clinical Neurosciences, 64, 318326.Google Scholar
Rosellini, A. J. and Brown, T. A. (2011). The NEO five-factor inventory: latent structure and relationships with dimensions of anxiety and depressive disorders in a large clinical sample. Assessment, 18, 2738.Google Scholar
Rubin, D. B. and Schenker, N. (1991). Multiple imputation in health-care databases: an overview and some applications. Statistics in Medicine, 10, 585598.Google Scholar
Schienle, A., Ebner, F. and Schafer, A. (2011). Localized gray matter volume abnormalities in generalized anxiety disorder. European Archives of Psychiatry and Clinical Neuroscience, 261, 303307.Google Scholar
Sheehan, D. V. et al. (1998). The Mini-International Neuropsychiatric Interview (M.I.N.I.): the development and validation of a structured diagnostic psychiatric interview for DSM-IV and ICD-10. Journal of Clinical Psychiatry, 59 (Suppl. 20), 2233.Google Scholar
Sobanski, T. et al. (2010). Temporal and right frontal lobe alterations in panic disorder: a quantitative volumetric and voxel-based morphometric MRI study. Psychological Medicine, 40, 18791886.Google Scholar
Spampinato, M. V., Wood, J. N., De Simone, V. and Grafman, J. (2009). Neural correlates of anxiety in healthy volunteers: a voxel-based morphometry study. Journal of Neuropsychiatry and Clinical Neurosciences, 21, 199205.Google Scholar
Spielberger, C. D. (1983). Manual for the State-Trait Anxiety Inventory (Form Y). Palo Alto: Consulting Psychologist Press.Google Scholar
Stein, M. B., Simmons, A. N., Feinstein, J. S. and Paulus, M. P. (2007). Increased amygdala and insula activation during emotion processing in anxiety-prone subjects. American Journal of Psychiatry, 164, 318327.CrossRefGoogle ScholarPubMed
Studholme, C., Hill, D. L. G. and Hawkes, D. J. (1999). An overlap invariant entropy measure of 3D medical image alignment. Pattern Recog, 32, 7186.Google Scholar
Syal, S. et al. (2012). Grey matter abnormalities in social anxiety disorder: a pilot study. Metabolic Brain Disease, 27, 299309.CrossRefGoogle ScholarPubMed
Sylvester, C. M. et al. (2012). Functional network dysfunction in anxiety and anxiety disorders. Trends in Neurosciences, 35, 527535.CrossRefGoogle ScholarPubMed
Szeszko, P. R. et al. (2008). Gray matter structural alterations in psychotropic drug-naive pediatric obsessive-compulsive disorder: an optimized voxel-based morphometry study. American Journal of Psychiatry, 165, 12991307.Google Scholar
Tzourio-Mazoyer, N. et al. (2002). Automated anatomical labeling of activations in SPM using a macroscopic anatomical parcellation of the MNI MRI single-subject brain. Neuroimage, 15, 273289.Google Scholar
Uchida, R. R. et al. (2003). Decreased left temporal lobe volume of panic patients measured by magnetic resonance imaging. Brazilian Journal of Medical and Biological Research, 36, 925929.CrossRefGoogle ScholarPubMed
Uchida, R. R. et al. (2008). Regional gray matter abnormalities in panic disorder: a voxel-based morphometry study. Psychiatry Research, 163, 2129.Google Scholar
van Tol, M. J. et al. (2010). Regional brain volume in depression and anxiety disorders. Archives of General Psychiatry, 67, 10021011.Google Scholar
VanValkenburg, C., Akiskal, H. S., Puzantian, V. and Rosenthal, T. (1984). Anxious depressions. Clinical, family history, and naturalistic outcome–comparisons with panic and major depressive disorders. Journal of Affective Disorders, 6, 6782.Google Scholar
Watson, D. (2005). Rethinking the mood and anxiety disorders: a quantitative hierarchical model for DSM-V. Journal of Abnormal Psychology, 114, 522536.CrossRefGoogle ScholarPubMed
Winkler, A. M. et al. (2010). Cortical thickness or grey matter volume? The importance of selecting the phenotype for imaging genetics studies. Neuroimage, 53, 11351146.Google Scholar
Yoo, H. K. et al. (2005). Putaminal gray matter volume decrease in panic disorder: an optimized voxel-based morphometry study. European Journal of Neuroscience, 22, 20892094.CrossRefGoogle ScholarPubMed
Supplementary material: File

Potvin supplementary material S1

Supplementary Tables

Download Potvin supplementary material S1(File)
File 32.3 KB