Cholinergic deficiency and inflammation in cognitive dysfunction

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Lemstra, A.W.

Publication date

2008

Link to publication

Citation for published version (APA):

Lemstra, A. W. (2008). Cholinergic deficiency and inflammation in cognitive dysfunction.

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Chapter 4

Can quantitative spectral electroencephalography

predict response to rivastigmine in

patients with dementia?

A.W. Lemstra L.J. Bour W.A. van Gool J.H. Koelman

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ABSTRACT

The aim of this study was to explore if baseline qEEG-parameters have an additional value to clinical measures in discriminating responders from non-responders to cholinesterase inhibitor therapy.

A prospective cohort study was conducted in 53 patients with dementia who started therapy with rivastigmine. Patients were labelled responders and non-responders based on change scores after 3 months in 3 clinical domains. Quanti-tative spectral EEG was performed at baseline and after 3 months of treatment. Twenty-three responders and 13 non-responders were identified. No significant differences in baseline qEEG-parameters were found between groups. A trend was found for increased frontocentral theta-activity in responders. Mean relative alpha power increased in the total study-population. In a logistic regression model qEEG-parameters did not improve the prediction of responders.

QEEG can measure cerebral changes during CEI-treatment but does not predict response to rivastigmine. Our results suggest that qEEG at baseline has no additional value to clinical features in identifying responders to CEI-treatment.

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Quantitative spectral encephalography and response to rivastigmine 49

INTRODUCTION

Cholinergic deficits have been established in Alzheimer’s disease (AD) and to an even larger extent in Dementia with Lewy bodies (DLB) and also Parkinson’s disease dementia (PDD)1. The rationale of cholinesterase inhibitor (CEI) treatment in dementia is based on restoring the depletion of cortical acetyl-choline due to loss of acetyl-cholinergic neurons. Although overall effect of CEIs may be limited, there is a subpopulation of patients with a more satisfactory response, probably due to a more extensive cholinergic deficit 2,3. Up till now it is unclear how to identify patients who will have a favourable response to CEI-treatment. In a recent prospective cohort study we showed that clinical measures of attention

are associated with a favourable response to treatment with rivastigmine 4. Electroencephalography (EEG) can detect changes in brain activity of patients

with dementia 5-7. Decrease of cholinergic activity is thought to be reflected in the EEG by general slowing 8,9. Several studies investigated the potential of electroencephalographic quantitative spectral analysis (qEEG) to predict response to cholinesterase inhibitors. In a review by Lanctot qEEG profile after a single dose of a CEI was consistently found to be correlated with cognitive response 10. If response to CEIs is correlated with the extent of cholinergic deficiency one may expect to find differences in baseline qEEG-parameters between patients who respond to treatment and those who do not. Two studies analysed baseline qEEG parameters in relation to response to CEIs. In one study clinical response to donepezil was post-hoc correlated with increased dominant frequency variability before treatment11. However, in this study baseline qEEG-parameters were not significant predictors of response to therapy. Babiloni et al. found that posterior delta and alpha source magnitude were lower at baseline in responding AD-patients, where response was defined with the MMSE after one year of treatment with donepezil 12.

Our goal was to explore if baseline EEG-parameters have an additional value to clinical measures of attention in discriminating responders from non-responders to cholinesterase inhibitor therapy. This study was carried out in a heterogeneous population of patients with dementia since cholinergic deficiency can occur in a variety of neurodegenerative diseases affecting the cholinergic system.

METHODS Subjects

Patients were participants in a prospective cohort study on predictors of the efficacy of rivastigmine therapy. Detailed inclusion and exclusion criteria are described elsewhere4. In short, patients were included when they suffered from progressive cognitive decline for more than 6 months as reported by patient,

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caregiver and referring physician. Diagnoses made by the referring physician were verified by using the appropriate international classification criteria for the specific diseases (Alzheimer’s disease (AD): NINCDS-ARDA13; Vascular demen-tia (VaD): NINDS-AIREN14; Dementia with Lewy bodies (DLB), dementia accom-panied parkinsonism, hallucinations and fluctuations: consensus criteria suggested by the consortium on DLB15; Parkinson’s disease dementia (PDD): established Parkinson’s disease according to UK Parkinson's disease brain bank criteria16 in combination with dementia according to Diagnostic and Statistical Manual of Mental Disease (fourth edition) – criteria 17).

All patients received rivastigmine starting with 1,5 mg bid, and gradually in-creasing up to the dose best tolerated. Patients were evaluated clinically at base-line and at 3 months after receiving rivastigmine by the Mini Mental State Examination (MMSE, 0-30 points higher score indicating better performance), the Interview for Deterioration in Daily Living (IDDD, 0-44 points, higher score indicat-ing worse performance), and the Neuropsychiatric Inventory (NPI, 0-144 points, higher score indicating worse performance) 18-20. These tests are known to have a high test-retest reliability 19-22. Responders were patients who remained stable or improved after 3 months of therapy in all three clinical domains: cognition (MMSE), activities of daily living (IDDD) and behaviour (NPI). Patients who did not fulfil this criterion were labelled non-responders. In the analysis of clinical predictive measures, two attention tasks emerged as predictors for response to rivastigmine4. Visual reaction time (VRT) did not differ between groups, but variability as indicated by the VRT standard deviation was higher in the responders (VRT-sd). Responders also performed worse on the sustained

atten-tion task (Continuous Performance Test, CPT). We included these tests in the analysis. This study was approved by the local ethical committees. Patients and their caregivers gave written informed consent before entering the study.

Electroencephalography

EEG recordings were performed at baseline and after 3 months of therapy using an online 20-channel EEG system, using the international 10/20 system and Ag/AgCl electrodes. EEGs were recorded in a resting condition with eyes closed. At least twenty epochs of 5 seconds (a total of 100 s or more) were selected by visual inspection. Computerized spectral analysis was performed using fast Fourier transformation on six bipolar derivations: F3-C3, F4-C4, T5-O1, T6-O2, P3-O1, P4-O2. Subsequently an average power spectrum was computed. Quan-titative EEG was compared in the following frequency bands: delta 1.4 – 3.4Hz, theta 3.6 – 7.4Hz, alpha 7.6 – 12.4Hz, beta1 12.6 – 20Hz, beta2 20.2 – 32Hz, gamma 32.2 – 48.2Hz. Absolute mean powers and relative mean powers per

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frequency band were calculated. Relative values were calculated by dividing the absolute power in each frequency band by the total power of the whole spectrum in all six bands and logarithmically transformed to correct for data skewness. Mean left and right relative power of homologous derivations was calculated since no significant differences between left and right were observed. The peak frequency of the power spectrum was determined in the alpha band as the highest amplitude of either the P3-O1 or P4-O2 derivation.

Statistics

Statistical analysis was performed using SPSS 12.0 package for Windows. For normally distributed data Student’s t-test was used to compare variables between groups, otherwise Mann-Whitney U test was applied. A value of P <0.05 was considered statistically significant for all statistical analyses. Logistic regression was performed to analyse predictive value of independent baseline variables.

Table 1. Clinical characteristics of responders and non-responders

Responders (n=23) Non-responders (n=13) Baseline age 73 (50-89) 68 (45-81) sex 4:19 2:11 diagnosis AD 4 2 DLB 12 11 PDD 6 8 VaD 1 1 MMSE 19 (14-25) 23 (15-28) IDDD 18 (5-31) 8 (0-37) NPI 26 (13-70) 15 (0-39)

Change scores after 3 months* MMSE + 4 (-1 -11) +2 (1 - 5) IDDD + 3 (-1 -14) -1 (-5 -5) NPI +15 (1- 54) -2 (-27 – 17)

* displayed values are medians with range in parentheses; + = true positive change indi-cating improvement, - = true negative change indiindi-cating deterioration. AD= Alzheimer's disease, DLB=Dementia with Lewy bodies, PDD= Parkinson's disease dementia, VaD= vascular dementia

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RESULTS

Of the cohort of 53 patients who participated in the rivastigmine response predictor study 42 patients were using rivastigmine after 3 months. Of this cohort 36 patients could be analysed in this study. In 1 patient no baseline EEG was performed, in 5 patients clinical data were missing (3 due to absence of reliable caregiver at 3 months). Based on the predefined criteria, 23 patients were labeled as responders and 13 as non-responders (Table 1). This is a response rate of 55%, according to the criteria of the present study. Of the responders, about half of the patients had a clinical diagnosis of probable DLB.

Mean age between groups did not differ significantly (p=0.141). The baseline data of these patients are comparable to the data of the whole group at the beginning of the study (53 patients + 3 patients who withdrew consent before starting with the treatment) with respect to age (mean 71,4 yrs) gender (F:M = 12:44), and MMSE-score (21, 14-28).

Median dose of rivastigmine at 3 months was 9 mg in responders (range 6-9 mg) and non-responders (range 3-12mg). No significant differences were found between responders and non-responders in baseline EEG-parameters including dominant frequency, total absolute and relative power per frequency band, and relative power per frequency band per derivation. There was a trend for a higher frontocentral relative theta-power in responders (p=0.054) (Table 2). Responders showed a greater increase in alpha power after 3 months of rivastigmine treat-ment in the temporo-occipital (p=0.016) and parieto-occipital region (p=0.042). The total study-population showed a significant decrease in relative mean delta-power (p=0.0001) and increase in relative mean alpha-delta-power (p=0.0001) and beta1-power (p=0.003) after 3 months of therapy.

In a logistic regression model baseline VRT-sd, CPT and frontocentral thetapower were entered as predictors of response to rivastigmine. Responders were signifi-cantly worse on the attentional tasks at baseline (VRT-sd p=0.001, CPT p=0.045). The best predictive model was achieved with the combination of VRT-sd and CPT as predictors (percentage correctly predicted 85.3%, Hosmer-Lemeshow test p=0.193). With frontocentral theta-power added, the model predicted 79.4%.

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Table 2. Summary of qEEG parameters of responders and non-responders to rivastigmine therapy

Baseline a Change after 3 months b

reponders non-responders reponders non-responders

delta fc -0,963 -1,1452 delta fc -0,4589 -0,2307 to -0,9966 -1,1464 to -0,376 -0,2743 po -1,0364 -1,2135 po -0,4068 -0,3345 theta fc -0,4775 -0,7992 theta fc -0,1066 0,0238 to -0,301 -0,4496 to -0,0672 -0,0495 po -0,3197 -0,4811 po -0,0142 -0,0352 alpha fc -1,1565 -1,2508 alpha fc 0,3635 0,1903 to -1,3272 -1,0893 to 0,4578 0,2147 po -1,1565 -0,9645 po 0,3983 0,1785 beta1 fc -2,1839 -2,0906 beta1 fc 0,2601 0,2098 to -2,6853 -2,6697 to 0,1662 0,1714 po -2,5783 -2,5977 po 0,1014 0,1283 beta2 fc -2,9339 -2,5828 beta2 fc 0,1727 0,0859 to -3,3495 -3,3517 to 0,0818 0,0805 po -3,3919 -3,3908 po -0,1065 0,0938

a)displayed are log transformed relative values;

b)Values in this part of table are differences between values at baseline and values 3 months. fc: frontocentral derivation, to:temporo-occipital derivation, po: parieto-occipital derivation bold values indicate significant difference (p<0,05) between responders and non-responders to rivastigmine (see results section); for theta fc at baseline there was a trend (p< 0,06).

DISCUSSION

The object of this prospective cohort study was to investigate if qEEG could add to clinical measures in the identification of responders to therapy with rivastigmine. Since cholinergic neuronal loss occurs in various degenerative dis-eases causing dementia we did not limit our study to a single disease entity. In this study strict criteria were applied in order to distinguish clinical relevant response to treatment. As shown in our former study fluctuations in reaction time and poor sustained attention are features that are associated with a favourable response to rivastigmine 4. Analysing the qEEG-data of patients who were treated with rivastigmine we found a trend for increased baseline frontocentral theta-activity in responders compared to non-responders.

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This might reflect a more severe cholinergic dysfunction: increased theta-activity and decrease in alpha-activity have been described after administration of scopolamine, a muscarinic receptor antagonist 9,23.

In a logistic regression model however this parameter had no additional effect to the clinical measures of attentional deficits in predicting response to rivastigmine treatment.

EEG is a ready available, non-invasive technique that has been widely studied in patients with dementia 5-7. With the increasing use of CEIs in patients with dementia several qEEG-studies were performed to study the effect of these drug on brain function in patients with AD, DLB and PDD 24-26. Overall, a tendency of decrease in slow wave activity and a normalization of the background rhythm (alpha-power) are observed in patients using these drugs. Our results corroborate with these studies. In the total study-population a decrease in delta-power and increase in alpha-power after 3 months of treatment with rivastigmine was found. The increase in alpha-power after treatment was significantly greater in patients classified as responders.

As for the predictive value of qEEG, it is not clear if qEEG is an effective ancillary investigation in this respect. Several studies investigated possible predictors of a favourable CEI response: clinical characteristics such as behavioural profile, disease severity, fluctuating cognition, a diagnosis of DLB or PDD, and older age have been coined as possible predictors of beneficial therapeutic response in retrospective studies27-30. There is only a limited number of studies that looked at the predictive potential of baseline qEEG-parameters11,12. Several authors report that changes in qEEG after short-term use (hours to 1 week) of CEIs can be used to predict favourable response10,31. These findings are consistent, but one could question the practicability of this time consuming procedure. Furthermore, translating differences in qEEG-parameters on group-level to the individual patient could be complicated in terms of quantifying cut-off values. Our results suggest that qEEG at baseline has no additional value to clinical features such as measures of attention. Larger studies in different groups of patients with dementia are warranted to define more exactly the role of qEEG in this respect. However, considering current evidence it is not likely that baseline qEEG will turn out to be a useful tool in discriminating responders to CEI-treatment.

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