Department of Old Age Psychiatry, Manchester Mental Health & Social Care NHS Trust, Manchester Royal Infirmary, Manchester
Meadowbrook Unit, Hope Hospital, Salford
Department of Image Science & Biomedical Engineering, University of Manchester, Manchester
Faculty of Medicine, Dentistry and Nursing, University of Manchester, Manchester
Department of Image Science & Biomedical Engineering, University of Manchester, Manchester
School of Psychiatry & Behavioural Sciences, University of Manchester, Manchester, UK
Correspondence: Professor R.C. Baldwin, Department of Old Age Psychiatry, York House, Manchester Mental Health & Social Care NHS Trust, Manchester Royal Infirmary, Oxford Rd, Manchester M13 9BX, UK. Tel: 0161 276 5317; Fax: 0161 276 5303; e-mail: robert.baldwin{at}man.ac.uk
Declaration of interest None. This research was supported by a grantto R.B. from Research into Ageing, England (grant 181).
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ABSTRACT |
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Aims To compare neurological signs in a group of patients with late-onset depression and in healthy controls.
Method Acasecontrol study comparing 50 patients with depression and 35 controls on three measures of central nervous system (CNS) signs: a structured CNS examination, the Neurological Evaluation Scale (NES) and the Webster rating scale for parkinsonism.
Results After adjusting for major depression at the time of evaluation and prescription of tranquillisers, ratings ontwo of the NES sub-scales (complex motor sequencing andothersigns) and on the Webster scale were significantly higher (more impaired) in patients compared with controls (P<0.05). With logistic regression, the NES was the main measure predictive of group outcome. There were no differences in scores of vascular risk or white matter but patients had patients had more atrophy.
Conclusions The findings add to the evidence that late-life depression is associated with organic brain dysfunction, perhaps mediated by neurodegeneration or subtle vascular impairment. The use of the NES in subjects with depression should be replicated.
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INTRODUCTION |
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METHOD |
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Recruitment
Subjects were recruited from four health districts in Greater Manchester,
in the North of England. Three-quarters of the subjects with depression were
referred from local psychiatric out-patient services and the remainder from
primary care physicians within the same locality. All control subjects came
from the same geographical area as the patients. Some were spouses or partners
and some were recruited via advertisement at day centres. All participants
gave informed consent. The research was approved by the relevant local
research ethics committees.
Inclusion criteria
For patients:
For control subjects:
Exclusion criteria
Demographic information
Age, gender, civil status, social class and years of education were
recorded. The National Adult Reading Test (NART;
Nelson, 1991) was administered
to assess premorbid intellectual level. Information was collected about
prescribed psychotropic medication.
Psychiatric measures
All subjects were administered the following:
Physical measures
General measures
Burvill physical illness scale. This scale rates acute and chronic
physical illness for the dimensions of severity (mild,
moderate or severe) and disability (not at
all, little, some, great
deal) for eight body systems
(Burvill et al, 1990). The higher the score, the greater the problem.
Framingham stroke risk factor score (Wolf et al, 1990). Computed for each patient based on the history and physical findings, this score comprises a weighted composite measure of the following factors: age, systolic blood pressure, treatment with antihypertensives, diabetes, cigarette consumption, evidence of cardiovascular disease, atrial fibrillation and left ventricular hypertrophy. The score gives a likelihood of stroke within the next 10 years, expressed as a percentage. Separate scores are provided for males and females. Although not strictly linear, the higher the score, the greater the risk.
Specific neurological measures
Structured central nervous system examination. A research
psychiatrist (S.J.) received training in a protocol developed at the
Manchester Cerebral Function Unit (Neary,
1999). This encompassed cranial nerves, speech articulation, tone
and power in upper and lower limbs, peripheral sensation, tactile
localisation, rapid alternating movements, hand posture, presence of tremor in
limbs and plantar responses. Ratings were either present/absent or
normal/abnormal. The maximum score was 30, with a higher score suggesting more
neurological impairment. The scale was subdivided into items that were mainly
upper motor neuron, pyramidal and/or cortical in nature (21 items) and those
that were mainly subcortical (9 items).
The ten-item Webster evaluation scale for parkinsonism (Webster, 1968). This incorporates bradykinesia, rigidity, posture, upper extremity swing, gait, tremor, facies, seborrhoea, balance and rising from a chair. Each item is rated on a four-point scale from 0 (no abnormality/not present) to 3 (severe difficulty or deficit).
The Neurological Evaluation Scale (NES; Buchanan & Heinrichs, 1989). This comprises four subgroups: sensory integration (stereognosis, graphaesthesia, extinction, right/left confusion); motor coordination (tandem walk, rapid alternating movements, fingerthumb opposition, fingernose test); sequencing of complex motor tasks (fistring test, fistedgepalm test, Ozeretski test of rapid alternating movements, rhythm tapping); and other (Romberg sign, tremor, mirror movements, synkinesis, convergence, gaze impersistence, grasp, snout and suck reflexes). In all there are 28 items and each is scored on a three-point scale (0=no abnormality; 1=mild but definite impairment; 2=marked impairment) except for the snout and suck reflexes, which are scored as either 0 or 2.
Participants were asked not to disclose whether they were patients or control subjects and were asked to avoid giving details about their health.
Neuroimaging evaluation
This was conducted using a 1.5T Phillips Gyroscan scanner (Phillips Medical
Systems, Best, NL). The imaging protocol used was the axial FLAIR
(fluid-attenuated inversion recovery) sequence. Slices were 3.0 mm thick with
no interslice gap. Imaging parameters were TR 11000, TE 140, TI 2600, matrix
256x256 and field of view 230 mm2. Axial T1-weighted
inversion recovery images were matched in anatomical location to the FLAIR
sequence. Images were reconstructed to produce real rather than
modulus images.
Volumetric analysis was performed with the TINA software package (a free open-source image analysis software package: http://www.niac.man.ac.uk/Tina/) using an automated algorithm for assessment of the severity and pattern of cerebral atrophy (Thacker et al, 2002). The analysis was performed on T1-weighted inversion recovery images. The method is designed to allow detection of subtle degrees of atrophy in the prosencephalon without prior knowledge of the location. Atrophy measures were obtained for the left and right sides and for the whole brain. Higher scores indicate more atrophy.
White matter lesions were assessed on a PC workstation using EFilm
viewstation software (EFilm Medical Ltd, Toronto, Ontario, Canada). The
assessment was performed on matched T1-weighted inversion recovery and
T2-weighted FLAIR images using the Scheltens scale
(Scheltens et al,
1993). All ratings were conducted by an experienced
neuroradiologist (A.J.) who was masked to patient group. Inter- and
intra-observer variation for this scale had been established previously in a
group of 60 elderly patients comprising a mixture of subjects: normal subjects
and those with Alzheimers disease, frontotemporal dementia and vascular
dementia. These trials indicated weighted Cohens values in the
range 0.520.89 (good to excellent) for all components of the scale.
Statistical analysis
No data for this population could be found to conduct a power calculation
based on neurological signs. It was not thought valid to base a power
calculation on neuroimaging findings, as in our previous report
(Baldwin et al, 2004).
Data were entered into a Statistical Package for the Social Sciences database
(Version 11.5). Normally distributed data were analysed using t-tests
and non-normally distributed data with MannWhitney testing. For group
differences significant at P<0.05 the data were re-analysed after
exclusion of patients prescribed major tranquillisers and/or meeting the
criteria for major depression at the time of neurological evaluation
(n=13). A Bonferroni correction was made with respect to the main
neurological measures (nine in all, as in
Table 2).
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Logistic regression with forward stepwise selection of variables was used to predict variables significantly associated with group membership. The variables chosen were those that were significant in univariate analysis and included the NES, CNS and Webster total scores. Neuropsychological test results showing significant results from the previous study with the same sample (Baldwin et al, 2004) were included (Rey Auditory Verbal Learning Test Trial I (Rey, 1964), verbal fluency, Haylings test for dysexecutive disorder (Burgess & Shallice, 1997), the Rey copy figure and logical memory test), along with other potential predictors (gender, age and scores on the NART, MMSE, Scheltens and Framingham scales).
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RESULTS |
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Stroke risk on the Framingham scale differs for men and women (Wolf et al, 1990). In this study: for women, the lowest score was 5, equating to a 2.4% 10-year risk; for men the scores equated to a 4.7% 10-year risk. The median score was 14, equating to a 13.3% 10-year risk for women and a 17% 10-year risk for men (Wolf et al, 1990). There were no significant group x gender differences in the Framingham scores.
Neurological findings
The unadjusted P values are presented in the penultimate column of
Table 2 and the last column
shows the P values following adjustment. As outlined in the Method
section, the adjustment took account of: (i) the prescription of major
tranquillisers (cases censored); (ii) whether the patient was suffering major
depression at the time of evaluation (cases censored); 13 cases meeting both
(i) and (ii) were censored; (iii) a Bonferroni correction for multiple
comparisons. After these adjustments, scores on the NES (total), the NES
complex motor sequencing sub-scale, the NES other signs
sub-scale and the Webster scale remained significant at P<0.05 or
less (Table 2).
To check for the possibility that patients from the depression group who were in remission had sufficient symptoms to affect neurological function, the mean Hamilton score for those subjects who had major depression at the time of the investigation (n=8) was compared with the score for those who were not depressed (n=42). The values were 16.1 and 5.1, respectively. Although the residual scores in the remitted group seemed too low to influence the results, the analysis was repeated using the alternative strategy of including all subjects with depression but covarying using the Hamilton score. After Bonferroni correction the results from this analysis were almost identical to the previous ones (NES total score: F=13.25, P<0.01; NES complex motor sequence: F=7.31, P=0.04; NES other: F=8.05, P<0.05; Webster: F=5.96, P<0.05).
No laterality effects were observed with the NES (data not shown, all non-significant).
Of patients prescribed antidepressants (n=39) at the time of evaluation, two-thirds were taking selective serotonin reuptake inhibitors (SSRIs) or newer antidepressants and the remainder were on tricyclics, including lofepramine. Because SSRIs may precipitate or aggravate parkinsonism (Lane, 1998), the mean Webster score for those on tricyclics was compared with that of patients prescribed SSRIs. The scores were 1.87 (s.d.=2.06) and 1.21 (s.d.=1.89), respectively. The total NES scores were very similar (9.67, tricyclics; 9.83, SSRIs).
Neuroimaging
Complete brain volumes were available for 58 of the 85 subjects. Using the
TINA software program, there was significantly greater atrophy among subjects
with depression than the controls, after covarying for skull size and age at
examination (depressed, 196 515 mm; controls, 175 397 mm; F=5.161,
P=0.03). The total Scheltens score of white matter hyperintensity
(available for all 85 subjects) showed higher scores (more white matter
lesions) for the depressed group than controls, but this difference was not
statistically significant (depressed, 11.54; controls, 9.03; F=1.585,
P=0.21).
Logistic regression
The NES total score alone accurately predicted 71.8% of group membership
(CI 1.171.58). One further variable, the Rey Auditory Verbal Learning
Test Trial I, was also significant, improving the prediction to almost 80%
(Table 3). Brain volume data
were only available for 58 subjects. The logistic regression was repeated
(data not shown), adding the whole, left and right brain volumes using this
smaller data set. The NES alone predicted 72.4% correct membership. The
Webster score was the second variable selected, increasing predictive accuracy
to 79.3%. No other variables were selected.
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DISCUSSION |
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The primary hypothesis was supported: that neurological signs consistent with subcorticalfrontal dysfunction are present in late-onset depression.
Limitations
It was not possible to guarantee that the physician carrying out the
neurological examination was blind to the participants group. Although
subjects were asked not to reveal which group they were in, an experienced
physician might guess correctly. This difficulty is no different from that
faced by the originators of the NES. Buchanan & Heinrichs
(1989) comment that there is no
easy solution to this but that a consistency of findings across research
centres (as has happened with the NES) helps to mitigate this criticism.
Replication of our findings in subjects with depression from other centres is
therefore indicated.
Further limitations include the possibility that patients differed from controls in their medical comorbidity, although this was not reflected in either the composite measure of physical morbidity (Burvill physical illness scale) or the stroke risk factor score. The study was probably underpowered, leading to the possibility of type II errors, although there is a consistent pattern in the results. The most commonly used class of antidepressant in this study, SSRIs, has been implicated in causing extrapyramidal side-effects (Lane, 1998). However, the Webster score was (non-significantly) lower among those on SSRIs compared with tricyclics, making antidepressants an unlikely cause for the findings. Although most of those in the depressed group were in remission, a prospective study is needed to address whether neurological abnormality in depressive disorder represents a state or trait. Lastly, the subjects all had late-onset depression (age of onset after 50 years) and it may not be valid to extrapolate the findings to depression in later life with an early onset.
Relevance to current literature
There is little research involving neurological signs in depression. Parker
and colleagues developed a sign-based system, CORE, which
demonstrated greater psychomotor dysfunction in melancholic compared with
non-melancholic major depression, including in later life
(Parker et al, 2003).
Some of the CORE-rated items overlap with the Webster scale used in this
study.
However, age and the presence of white matter lesions are two factors that might confound the findings of neurological abnormality in late-life depression. Extrapyramidal signs occur in the absence of detectable neurological disease and increase with age (Prettyman, 1998). In control subjects white matter lesions increase with age and are associated with demonstrable gait impairment (Whitman et al, 2001). Primitive reflexes (such as the snout and grasp reflex) are also reported in subjects at risk of stroke (Rao et al, 1999). In that study, the control subjects were of similar age to the patients with depression and had similar amounts of white matter lesions, suggesting that these factors themselves are insufficient to explain the neurological findings.
Relevance to vascular depression
Strong links exist between depression and vascular disease
(Thomas et al, 2004). Late-onset depression is associated with a high level of white matter lesions
(OBrien et al,
1996; Krishnan,
2002), and white matter lesions, as visualised by magnetic
resonance imaging, are associated with cerebrovascular risk factors such as
hypertension, cardiac disease and diabetes mellitus
(Longstreth et al,
2001). Depression in association with cerebrovascular risk factors
and white matter lesions is increasingly referred to as vascular
depression (Alexopoulos et
al, 1997; Baldwin &
OBrien, 2002). Abnormal neurological signs in late-life
depression may be particularly relevant to vascular depression because they
could represent a further manifestation of vascular brain disease.
We were surprised, then, that no significant group differences emerged either on the Framingham measure, which incorporates several common cerebrovascular risk factors, or the Scheltens measure of white matter lesion burden. With respect to risk factors for cerebrovascular disease it is increasingly recognised that traditional bedside measures may overlook important mechanisms leading to vascular damage. An example is blood pressure. Alterations in circadian blood pressure rhythms (Sander et al, 2000) and blood pressure at the upper level of normal (Goldstein et al, 2002) and not merely one-off resting readings are implicated in cerebral damage. Altered cerebrovascular reactivity is important in the genesis of white matter lesions (Cupini et al, 2001). In this study there was a trend for higher Scheltens white matter lesion scores in the depressed group compared with controls. This was not statistically significant, possibly due to the small numbers, and our study does not rule out a vascular basis for the neurological signs reported.
Lloyd et al (2004) reported that patients with late-onset depression had more hippocampal atrophy compared with patients with early-onset depression and controls, but had similar amounts of white matter lesions. Of interest is the finding in this study that the depressed group had higher scores on a measure of brain atrophy. Kumar et al (2000), using statistical modelling, have proposed that atrophy and white matter lesions may represent separate pathways to late-life depression, based on neurodegeneration and vascular disease, respectively. Numbers in this study were small, thus necessitating caution, but it is possible that neurodegeneration represents another explanation for altered neurological signs in late-life depression. Suggested mechanisms, besides age, include hypercortisolaemia (Baldwin & OBrien, 2002), inflammation (Penninx et al, 2003) and altered homocysteine metabolism (Naismith et al, 2002). It would be fruitful to explore the role of neurodegeneration in the neurology of late-life depression.
The NES emerged as a significant predictor of whether a participant was from the depressed group or was a control subject. Future studies might explore whether the NES has the potential to predict outcomes, including symptomatic recovery and reversible neuropsychological deficits.
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Clinical Implications and Limitations |
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LIMITATIONS
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ACKNOWLEDGMENTS |
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REFERENCES |
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![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() ![]() |
---|
American Psychiatric Association (1994) Diagnostic and Statistical Manual of Mental Disorders (4th edn) (DSMIV).Washington, DC: APA.
Baldwin, R. C. & OBrien, J. (2002)
Vascular basis of late-onset depressive disorder. British Journal
of Psychiatry, 180, 157
160.
Baldwin, R. C., Jeffries, S., Jackson, A., et al (2004) Treatment response in late-onset depression: relationship to neuropsychological, neuroradiological and vascular risk factors. Psychological Medicine, 44, 125 136.[CrossRef]
Buchanan, R.W. & Heinrichs, D.W. (1989) The Neurological Evaluation Scale (NES): a structured instrument for the assessment of neurological signs in schizophrenia. Psychiatric Research, 27, 325 350.[CrossRef][Medline]
Burgess, P. W. & Shallice, T. (1997) The Hayling and Brixton Test. Bury St Edmunds: Thames Valley Company.
Burvill, P. W., Mowry, B. & Hall, W. D. (1990) Quantification of physical illness in psychiatric research in the elderly. International Journal of Geriatric Psychiatry, 5, 161 170.[CrossRef]
Cupini, Cupini, L. M., Diomedi, M., Placidi, F., et al
(2001) Cerebrovascular reactivity and subcortical
infarctions. Archives of Neurology,
58, 577
581.
Folstein, M. F., Folstein, S.E. & 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, 189 198.[CrossRef][Medline]
Goldstein, I. B., Bartzokis, G., Guthrie, D., et al
(2002) Ambulatory blood pressure in the healthy elderly.
Neurology, 59, 713
719.
Hamilton, M. (1960) A rating scale for depression. Journal of Neurology, Neurosurgery and Psychiatry, 23, 56 62.[Medline]
Krishnan, K. R. R. (2002) Biological risk factors in late-life depression. Biological Psychiatry, 52, 185 192.[CrossRef][Medline]
Kumar, A., Bilker, W., Jin, Z., et al (2000) Atrophy and high intensity lesions: complementary neurobiological mechanisms in late-life major depression. Neuropsychopharmacology, 22, 264 274.[CrossRef][Medline]
Lane, R. M. (1998) SSRI-induced extrapyramidal side-effects and akathisia: implications for treatment. Journal of Psychopharmacology, 12, 192 214.[Medline]
Lloyd, A. J., Ferrier, I. N., Barber, R., et al
(2004) Hippocampal volume change in depression: late- and
early-onset compared. British Journal of Psychiatry,
184, 488
495.
Longstreth, W. T., Diehr, P., Manolio, T. A., et al
(2001) Cluster analysis and patterns of findings findings on
cranial magnetic resonance imaging of the elderly. Archives of
Neurology, 58, 635
640.
Naismith, S., Hickie, I., Ward, P. B., et al
(2002) Caudate nucleus volumes and genetic determinants of
homocysteine metabolism in the prediction of psychomotor speed in older
persons with depression. American Journal of
Psychiatry, 159, 2096
2098.
Neary, D. (1999) Classification of the dementias. Reviews in Clinical Gerontology, 9, 55 64.[CrossRef]
Nelson, H. (1991) Nelson Adult Reading Test (2nd edn). NFERNelson: London.
OBrien, J.T., Ames, D. & Schwietzer, I. (1996) White matter changes in depression and Alzheimers disease: a review of magnetic resonance imaging findings. International Journal of Geriatric Psychiatry, 11, 681 694.[CrossRef]
Parker, G., Snowden, J. & Parker, K. (2003) Modelling late-life depression. International Journal of Geriatric Psychiatry, 18, 1102 1109.[CrossRef][Medline]
Penninx, B.W. J. H., Britchevsky, S. B., Yaffe, K., et al (2003) Inflammatory markers and depressed mood in older persons: results from the Health, Aging and Body Composition Study. Biological Psychiatry, 54, 566 572.[CrossRef][Medline]
Prettyman, R. (1998) Extrapyramidal signs in cognitively intact elderly people. Age & Ageing, 27, 557 560.[Abstract]
Rao, R., Jackson, S. & Howard, R. (1999) Primitive reflexes in cerebrovascular disease: a community study of older people with stroke and carotid stenosis. International Journal of Geriatric Psychiatry, 14, 964 972.[CrossRef][Medline]
Rey, A. (1964) LExamen Clinique en Psychologie.Paris: Presses Universitaires de France.
Sander, D., Winbeck, K., Klingelhofer, J., et al (2000) Extent of cerebral white matter lesions is related to changes on circadian blood pressure. Archives of Neurology, 57, 302 307.
Scheltens, P., Barkhof, F. & Leys, D. (1993) A semiquantitative rating scale for the assessment of signal hyperintensities on magnetic resonance imaging. Journal of the Neurological Sciences, 114, 7 12.[CrossRef][Medline]
Simpson, S., Baldwin, R. C., Jackson, A., et al (1998) Is subcortical disease associated with a poor response to antidepressants? Neurological, neuropsychological and neuroradiological findings in late life depression. Psychological Medicine, 28, 1015 1026.[CrossRef][Medline]
Spitzer, R. L. & Endicott, J. (1979) Schedule for Affective Disorders and Schizophrenia Life-time version (SADL). NIMH Clinical Research Branch Collaborative Program on the Psychobiology of Depression: New York.
Thacker, N. A., Varma, A. R., Bathgate, D., et al
(2002) Dementing disorders: volumetric measurement of
cerebrospinal fluid to distinguish normal from pathologic findings
feasibility study. Radiology,
224, 278
285.
Thomas, A. J., Kalaria, R. N. & OBrien, J. T. (2004) Depression and vascular disease: what is the relationship? Journal of Affective Disorders, 79, 81 95.[CrossRef][Medline]
Webster, D. D. (1968) Critical analysis of the disability in Parkinsons disease. Modern Treatment, 1968, March, 257 282.
Whitman, G. T., Tang, T., Lin, A., et al
(2001) A prospective study of cerebral white matter
abnormalities in older people with gait dysfunction.
Neurology, 57, 990
994.
Wolf, P. A., DAgostino, R. B., Belanger, A. J., et al (1990) Probability of stroke: a risk profile from the Framingham study. Stroke, 22, 312 318.
Received for publication December 22, 2003. Revision received September 16, 2004. Accepted for publication October 30, 2004.