Hostname: page-component-586b7cd67f-t7fkt Total loading time: 0 Render date: 2024-11-22T19:53:26.790Z Has data issue: false hasContentIssue false

Electrophysiological changes in late life depression and their relation to structural brain changes

Published online by Cambridge University Press:  18 June 2010

Sebastian Köhler
Affiliation:
School for Mental Health and Neuroscience, EURON, Maastricht University Medical Centre, Maastricht, The Netherlands
C. Heather Ashton
Affiliation:
School of Neurology, Neurobiology and Psychiatry, University of Newcastle, and Department of Psychiatry, The Royal Victoria Infirmary, Newcastle upon Tyne, U.K.
Richard Marsh
Affiliation:
School of Neurology, Neurobiology and Psychiatry, University of Newcastle, and Department of Psychiatry, The Royal Victoria Infirmary, Newcastle upon Tyne, U.K.
Alan J. Thomas
Affiliation:
Institute for Ageing and Health, Newcastle University, Newcastle upon Tyne, U.K.
Nicky A. Barnett
Affiliation:
Institute for Ageing and Health, Newcastle University, Newcastle upon Tyne, U.K.
John T. O'Brien*
Affiliation:
Institute for Ageing and Health, Newcastle University, Newcastle upon Tyne, U.K.
*
Correspondence should be addressed to: Professor John T. O'Brien, Institute for Ageing and Health, Newcastle University, Wolfson Research Centre, Campus for Ageing and Vitality, Newcastle upon Tyne NE4 5PL, U.K. Phone: + 44 (0)191 248 1310; Fax: + 44 (0)191 248 1301. Email: j.t.o'[email protected].

Abstract

Background: Late life depression is often accompanied by slowed information processing during neuropsychological testing, and this has been related to underlying cerebrovascular disease. We investigated whether changes in electrophysiological markers of information processing might share the same pathological correlates.

Methods: Differences in power spectra frequency, contingent negative variation (CNV), post-imperative negative variation (PINV), and auditory P300a amplitude and latency in 19 patients with DSM-IV major depression aged ≥ 60 years were compared with 25 recordings in age-matched healthy controls. Associations with total brain volume and degree of white matter hyperintensities (WMH) were examined in those who had undergone additional magnetic resonance imaging (MRI).

Results: Compared with healthy controls, patients had more slow-wave delta (group difference: p = 0.024) and theta activity (p = 0.015) as well as alpha activity (p = 0.005) but no decrease in beta band frequency (p = 0.077). None of these changes related differently to brain volume or WMH in patients or controls. Patients further showed prolonged P300a latencies (p = 0.027), which were associated with decreased total brain volume in patients but not controls (interaction by group: p = 0.004). While there were no overall differences in PINV between both groups, patients showed a decrease in PINV magnitude with increasing WMH, a relation that was not seen in controls (interaction by group: p = 0.024).

Conclusion: Patients with late life depression show changes in several electrophysiological markers of cerebral arousal and information processing, some of which relate to brain atrophy and WMH on MRI.

Type
Research Article
Copyright
Copyright © International Psychogeriatric Association 2010

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

Ashton, C. H., Marshall, E. F., Hassanyeh, F., Marsh, V. R. and Wright-Honari, S. (1994). Biological correlates of deliberate self-harm behaviour: a study of electroencephalographic, biochemical and psychological variables in parasuicide. Acta Psychiatrica Scandinavica, 90, 316323.CrossRefGoogle ScholarPubMed
Ashton, H., Golding, J. F., Marsh, V. R., Thompson, J. W., Hassanyeh, F. and Tyrer, S. P. (1988). Cortical evoked potentials and clinical rating scales as measures of depressive illness. Psychological Medicine, 18, 305317.CrossRefGoogle ScholarPubMed
Brenner, R. P. et al. (1986). Computerized EEG spectral analysis in elderly normal, demented and depressed subjects. Electroencephalography and Clinical Neurophysiology, 64, 483492.CrossRefGoogle ScholarPubMed
Butters, M. A. et al. (2004). The nature and determinants of neuropsychological functioning in late-life depression. Archives of General Psychiatry, 61, 587595.CrossRefGoogle ScholarPubMed
Butters, M. A. et al. (2008). Pathways linking late-life depression to persistent cognitive impairment and dementia. Dialogues in Clinical Neuroscience, 10, 345357.CrossRefGoogle ScholarPubMed
Cegalis, J. and Bowlin, J. (1991). VIGIL: Software for the Assessment of Attention. Nashua, NH: Forthought.Google Scholar
Dahabra, S. et al. (1998). Structural and functional abnormalities in elderly patients clinically recovered from early- and late-onset depression. Biological Psychiatry, 44, 3446.CrossRefGoogle ScholarPubMed
Diener, C., Kuehner, C., Brusniak, W., Struve, M. and Flor, H. (2009). Effects of stressor controllability on psychophysiological, cognitive and behavioural responses in patients with major depression and dysthymia. Psychological Medicine, 39, 7786.CrossRefGoogle ScholarPubMed
Kalayam, B., Alexopoulos, G. S., Kindermann, S., Kakuma, T., Brown, G. G. and Young, R. C. (1998). P300 latency in geriatric depression. American Journal of Psychiatry, 155, 425427.CrossRefGoogle ScholarPubMed
Kindermann, S. S., Kalayam, B., Brown, G. G., Burdick, K. E. and Alexopoulos, G. S. (2000). Executive functions and P300 latency in elderly depressed patients and control subjects. American Journal of Geriatric Psychiatry, 8, 5765.CrossRefGoogle ScholarPubMed
Köhler, S., Thomas, A. J., Barnett, N. A. and O'Brien, J. T. (2010a). The pattern and course of cognitive impairment in late-life depression. Psychological Medicine, 40, 591602.CrossRefGoogle ScholarPubMed
Köhler, S., Thomas, A. J., Lloyd, A., Barber, R., Almeida, O. P. and O'Brien, J. T. (2010b). White matter hyperintensities, cortisol levels, brain atrophy and continuing cognitive deficits in late-life depression. British Journal of Psychiatry, 196, 143149.CrossRefGoogle ScholarPubMed
Kraiuhin, C. et al. (1990). Normal latency of the P300 event-related potential in mild-to-moderate Alzheimer's disease and depression. Biological Psychiatry, 28, 372386.CrossRefGoogle Scholar
Lezak, M. D., Howieson, D. B. and Loring, D. W. (2004). Neuropsychological Assessment. Oxford: Oxford University Press.Google Scholar
Lloyd, A. J., Ferrier, I. N., Barber, R., Gholkar, A., Young, A. H. and O'Brien, J. T. (2004). Hippocampal volume change in depression: late- and early-onset illness compared. British Journal of Psychiatry, 184, 488495.CrossRefGoogle ScholarPubMed
McCallum, W. C. (1988). Potentials related to expectancy, preparation and motor activity. In Picton, T. W. (ed.), Human Event-Related Potentials (pp. 427517). New York: Elsevier Publishing Company.Google Scholar
Montgomery, S. and Åsberg, M. (1979). A new depression scale designed to be sensitive to change. British Journal of Psychiatry, 134, 382389.CrossRefGoogle ScholarPubMed
Nyström, C., Matousek, M. and Hallstrom, T. (1986). Relationships between EEG and clinical characteristics in major depressive disorder. Acta Psychiatrica Scandinavica, 73, 390394.CrossRefGoogle ScholarPubMed
O'Brien, J. T. et al. (2002). Cognitive associations of subcortical white matter lesions in older people. Annals of the New York Academy of Sciences, 977, 436444.CrossRefGoogle ScholarPubMed
O'Brien, J. T., Lloyd, A., McKeith, I., Gholkar, A. and Ferrier, N. (2004). A longitudinal study of hippocampal volume, cortisol levels, and cognition in older depressed subjects. American Journal of Psychiatry, 161, 20812090.CrossRefGoogle ScholarPubMed
Polich, J. (2007). Updating P300: an integrative theory of P3a and P3b. Clinical Neurophysiology, 118, 21282148.CrossRefGoogle ScholarPubMed
Pritchard, W. S. (1981). Psychophysiology of P300. Psychological Bulletin, 89, 506540.CrossRefGoogle ScholarPubMed
Rey, A. (1964). Clinical Examination in Psychology. Paris: University of Paris.Google Scholar
Rockstroh, B., Elbert, T., Lutzenberger, W. and Birbaumer, N. (1979). Slow cortical potentials under conditions of uncontrollability. Psychophysiology, 16, 374380.CrossRefGoogle ScholarPubMed
Rockstroh, B., Müller, M., Wagner, M., Cohen, R. and Elbert, T. (1993). “Probing” the nature of the CNV. Electroencephalography and Clinical Neurophysiology, 87, 235241.CrossRefGoogle ScholarPubMed
Roth, M., Huppert, F. A., Mountjoy, C. Q. and Tym, E. (1999). The Cambridge Examination for Mental Disorders of the Elderly –Revised. Cambridge: Cambridge University Press.Google Scholar
Salthouse, T. A. (2000). Aging and measures of processing speed. Biological Psychology, 54, 3554.CrossRefGoogle ScholarPubMed
Scheltens, P. et al. (1993). A semiquantative rating scale for the assessment of signal hyperintensities on magnetic resonance imaging. Journal of the Neurological Sciences, 114, 712.CrossRefGoogle ScholarPubMed
Sheline, Y. I. et al. (2008). Regional white matter hyperintensity burden in automated segmentation distinguishes late-life depressed subjects from comparison subjects matched for vascular risk factors. American Journal of Psychiatry, 165, 524532.CrossRefGoogle ScholarPubMed
Stroop, J. (1935). Studies of interference in serial verbal reactions. Journal of Experimental Psychology, 18, 643662.CrossRefGoogle Scholar
Tecce, J. J., Savignano-Bowman, J. and Meinbresse, D. (1976). Contingent negative variation and the distraction: arousal hypothesis. Electroencephalography and Clinical Neurophysiology, 41, 277286.CrossRefGoogle ScholarPubMed