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Volumetric reduction in various cortical regions of elderly patients with early-onset and late-onset mania

Published online by Cambridge University Press:  18 June 2010

Shou-Hung Huang ,
Shang-Ying Tsai ,
Jung-Lung Hsu  and
Yi-Lin Huang
Shou-Hung Huang
Affiliation:
Department of Psychiatry and Psychiatric Research Center, Taipei Medical University Hospital, Taipei, Taiwan
Shang-Ying Tsai*
Affiliation:
Department of Psychiatry and Psychiatric Research Center, Taipei Medical University Hospital, Taipei, Taiwan Department of Psychiatry, School of Medicine, Taipei Medical University, Taipei, Taiwan Department of Psychiatry, Po-Jen General Hospital, Taipei, Taiwan
Jung-Lung Hsu
Affiliation:
Department of Psychiatry, School of Medicine, Taipei Medical University, Taipei, Taiwan Department of Neurology, Shin Kong Wu Ho-Su Memorial Hospital, Taipei, Taiwan
Yi-Lin Huang
Affiliation:
Department of Psychiatry and Psychiatric Research Center, Taipei Medical University Hospital, Taipei, Taiwan
*
Correspondence should be addressed to: Professor Shang-Ying Tsai, Department of Psychiatry, School of Medicine, Taipei Medical University, Taipei, Taiwan. #252 Wu-Hsing Street, Taipei, 110, Taiwan. Phone: +886-2-22344850; Fax: +886-2-27368829. Email: tmcpsyts@tmu.edu.tw.
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Abstract

Background: Few studies have examined alterations of the brain in elderly bipolar patients. As late-onset mania is associated with increased cerebrovascular morbidity and neurological damage compared with typical/early-onset mania, we investigated differences in the volume of various cortical regions between elderly patients with early-onset versus late-onset mania.

Methods: We recruited 44 bipolar patients aged over 60 years, who underwent volumetric magnetic resonance imaging at 1.5 T. The analytic method is based on the hidden Markov random field model with an expectation-maximization algorithm. We determined the volume of each cortical region as a percentage of the total intracranial volume. The cutoff age for defining early versus late onset was 45 years.

Results: The study participants consisted of 25 patients with early-onset mania and 19 patients with late-onset mania; their mean ages were 65.7 years and 62.8 years, respectively. The demographic variables of the two groups were comparable. The volumes of the left caudate nucleus (p = 0.022) and left middle frontal gyrus (p = 0.013) were significantly greater and that of the right posterior cingulate gyrus (p = 0.019) was significantly smaller in the late-onset group. More patients with late-onset mania had comorbid cerebrovascular disease (p = 0.072).

Conclusions: The right posterior cingulate gyrus is smaller and the left caudate nucleus and left middle frontal gyrus are larger in patients with late-onset mania compared with those with early-onset mania. Volumetric change in brain regions may vary in elderly bipolar patients with early and late-onset mania.

Keywords

volumetric MRI studybipolar I disorderelderlylate-onset
Type
Research Article
Information
International Psychogeriatrics , Volume 23 , Issue 1 , February 2011 , pp. 149 - 154
Copyright
Copyright © International Psychogeriatric Association 2010

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References

Alemán-Gómez, Y., Melie-Garcia, L. and Valdés-Hernandez, P. (2006). IBASPM: toolbox for automatic parcellation of brain structures. Paper presented at the 12th Annual Meeting of the Organization for Human Brain Mapping, 11–15 June. Florence, Italy.Google Scholar
Atmaca, M. et al. (2007). Cingulate gyrus volumetry in drug free bipolar patients and patients treated with valproate or valproate and quetiapine. Journal of Psychiatric Research, 41, 821–827.CrossRefGoogle ScholarPubMed
Beyer, J. L. et al. (2004). Hippocampal volume measurement in older adults with bipolar disorder. American Journal of Geriatric Psychiatry, 12, 613–620.CrossRefGoogle ScholarPubMed
Cassidy, F. and Carroll, B. J. (2002). Vascular risk factors in late onset mania. Psychological Medicine, 32, 359–362.CrossRefGoogle ScholarPubMed
Cherbuin, N., Anstey, K. J., Reglade-Meslin, C. and Sachdev, P. S. (2009). In vivo hippocampal measurement and memory: a comparison of manual tracing and automated segmentation in a large community-based sample. PLoS One, 4, e5265.CrossRefGoogle Scholar
DelBello, M. P., Zimmerman, M. E., Mills, N. P., Getz, G. E. and Strakowski, S. M. (2004). Magnetic resonance imaging analysis of amygdala and other subcortical brain regions in adolescents with bipolar disorder. Bipolar Disorder, 6, 43–52.CrossRefGoogle ScholarPubMed
Depp, C. A. and Jeste, D. V. (2004). Bipolar disorder in older adults: a critical review. Bipolar Disorder, 6, 343–367.CrossRefGoogle ScholarPubMed
Fujikawa, T., Yamawaki, S. and Touhouda, Y. (1995). Silent cerebral infarctions in patients with late-onset mania. Stroke, 26, 946–949.CrossRefGoogle ScholarPubMed
Gildengers, A., G. et al. (2004). Cognitive functioning in late-life bipolar disorder. American Journal of Psychiatry, 161, 736–738.CrossRefGoogle ScholarPubMed
Hamilton, M. (1960). A rating scale for depression. Journal of Neurology, Neurosurgery and Psychiatry, 23, 56–62.CrossRefGoogle ScholarPubMed
Harris, G. J. et al. (1994). MR volume segmentation of gray matter and white matter using manual thresholding: dependence on image brightness. American Journal of Neuroradiology, 15, 225–230.Google ScholarPubMed
Hosokawa, T., Momose, T. and Kasai, K. (2009). Brain glucose metabolism difference between bipolar and unipolar mood disorders in depressed and euthymic states. Progress in Neuropsychopharmacology and Biological Psychiatry, 33, 243–250.CrossRefGoogle ScholarPubMed
Jenkinson, M., Bannister, P., Brady, M. and Smith, S. (2002). Improved optimization for the robust and accurate linear registration and motion correction of brain images. Neuroimage, 17, 825–841.CrossRefGoogle ScholarPubMed
Kalpouzos, G. et al. (2009). Voxel-based mapping of brain gray matter volume and glucose metabolism profiles in normal aging. Neurobiology of Aging, 30, 112–124.CrossRefGoogle ScholarPubMed
Konarski, J. Z., McIntyre, R. S., Kennedy, S. H., Rafi-Tari, S., Soczynska, J. K. and Ketter, T. A. (2008). Volumetric neuroimaging investigations in mood disorders: bipolar disorder versus major depressive disorder. Bipolar Disorder, 10, 1–37.CrossRefGoogle ScholarPubMed
Lin, P. I. et al. (2006). Clinical correlates and familial aggregation of age at onset in bipolar disorder. American Journal of Psychiatry, 163, 240–246.CrossRefGoogle ScholarPubMed
Liu, H. C. et al. (1995). Prevalence and subtypes of dementia in Taiwan: a community survey of 5297 individuals. Journal of the American Geriatrics Society, 43, 144–149.CrossRefGoogle ScholarPubMed
Lyoo, I. K. et al. (2006). Regional cerebral cortical thinning in bipolar disorder. Bipolar Disorder, 8, 65–74.CrossRefGoogle ScholarPubMed
Pavuluri, M. N., Passarotti, A. M., Harral, E. M. and Sweeney, J. A. (2009). An fMRI study of the neural correlates of incidental versus directed emotion processing in pediatric bipolar disorder. Journal of the American Academy of Child and Adolescent Psychiatry, 48, 308–319.CrossRefGoogle ScholarPubMed
Roth, R. M. et al. (2006). Functional magnetic resonance imaging of executive control in bipolar disorder. Neuroreport, 17, 1085–1089.CrossRefGoogle ScholarPubMed
Sassi, R. B. et al. (2004). Reduced left anterior cingulate volumes in untreated bipolar patients. Biological Psychiatry, 56, 467–475.CrossRefGoogle ScholarPubMed
Schouws, S. N. et al. (2009). Cognitive Impairment in early and late bipolar disorder. American Journal of Geriatric Psychiatry, 17, 508–515.CrossRefGoogle ScholarPubMed
Smoski, M. J. et al. (2009). fMRI of alterations in reward selection, anticipation, and feedback in major depressive disorder. Journal of Affective Disorder, 118, 69–78.CrossRefGoogle ScholarPubMed
Strauss, J. S. and Carpenter, W. T. Jr. (1972). The prediction of outcome in schizophrenia. I. Characteristics of outcome. Archives of General Psychiatry, 27, 739–746.CrossRefGoogle ScholarPubMed
Subramaniam, H., Dennis, M. S. and Byrne, E. J. (2007). The role of vascular risk factors in late onset bipolar disorder. Internatinal Journal of Geriatric Psychiatry, 22, 733–737.CrossRefGoogle ScholarPubMed
Tsai, S. Y., Lee, H. C., Chen, C. C. and Huang, Y. L. (2007). Cognitive impairment in later life in patients with early-onset bipolar disorder. Bipolar Disorder, 9, 868–875.CrossRefGoogle ScholarPubMed
Vannier, M. W., Butterfield, R. L., Jordan, D., Murphy, W. A., Levitt, R. G. and Gado, M. (1985). Multispectral analysis of magnetic resonance images. Radiology, 154, 221–224.CrossRefGoogle ScholarPubMed
Wylie, M. E. et al. (1999). Age at onset in geriatric bipolar disorder: effects on clinical presentation and treatment outcomes in an inpatient sample. American Journal of Geriatric Psychiatry, 7, 77–83.Google Scholar
Young, R. C. and Klerman, G. L. (1992). Mania in late life: focus on age at onset. American Journal of Psychiatry, 149, 867–876.Google ScholarPubMed
Young, R. C., Biggs, J. T., Ziegler, V. E. and Meyer, D. A. (1978). A rating scale for mania: reliability, validity and sensitivity. British Journal of Psychiatry, 133, 429–435.CrossRefGoogle ScholarPubMed
Zhang, Y., Brady, M. and Smith, S. (2001). Segmentation of brain MR images through a hidden Markov random field model and the expectation-maximization algorithm. IEEE Transactions of Medical Imaging, 20, 45–57.CrossRefGoogle ScholarPubMed