Quantitative electroencephalography in mild cognitive impairment: longitudinal changes and possible prediction of Alzheimer’s disease

Neurobiology of Aging 21 (2000) 533–540

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Quantitative electroencephalography in mild cognitive impairment: longitudinal changes and possible prediction of Alzheimer’s disease V. Jelica,*, S-E. Johanssonb, O. Almkvista, M. Shigetac, P. Julina, A. Nordberga, B. Winblada, L-O. Wahlunda a

Karolinska Institutet, NEUROTEC, Division of Geriatric Medicine, B-84, Huddinge University Hospital, S-141 86 Huddinge, Sweden b Department of Social Welfare and Statistics, Stockholm, Sweden c Department of Psychiatry, Jikei University School of Medicine, Tokyo, Japan Received 3 June 1999; received in revised form 3 March 2000; accepted 10 April 2000

Abstract The present study evaluated the clinical course of patients with mild cognitive impairment (MCI), the pattern of electroencephalography (EEG) changes following cognitive deterioration, as well as the potential of neurophysiological measures in predicting dementia. Twenty-seven subjects with MCI were followed for a mean follow up period of 21 months. Fourteen subjects (52%) progressed (P MCI) to clinically manifest Alzheimer’s disease (AD), and 13 (48%) remained stable (S MCI). The two MCI subgroups did not differ in baseline EEG measures between each other and the healthy controls (n ⫽ 16), but had significantly lower theta relative power at left temporal, temporo-occipital, centro-parietal, and right temporo-occipital derivation when compared to the reference AD group (n ⫽ 15). The P MCI baseline alpha band temporo-parietal coherence, alpha relative power values at left temporal and temporo-occipital derivations, theta relative power values at frontal derivations, and the mean frequency at centro-parietal and temporo-occipital derivations overlapped with those for AD and control groups. After the follow-up, the P MCI patients had significantly higher theta relative power and lower beta relative power and mean frequency at the temporal and temporo-occipital derivations. A logistic regression model of baseline EEG values adjusted for baseline Mini-Mental Test Examination showed that the important predictors were alpha and theta relative power and mean frequency from left temporo-occipital derivation (T5-O1), which classified 85% of MCI subjects correctly. © 2000 Elsevier Science Inc. All rights reserved. Keywords: Quantitative electroencephalography; Mild cognitive impairment; Alzheimer’s disease

1. Introduction Much recent research in Alzheimer’s disease (AD) has focused on defining methods for the earliest detection of dementia, preferably in the preclinical stages. The preclinical level of cognitive performance has been useful in predicting the development of dementia [17,31]. Flicker et al. [12] introduced a new operational term, mild cognitive impairment (MCI), for the condition where there is an evidence of subtle neuropsychological deficits in older subjects before functional impairment becomes apparent and dementia is diagnosed. These authors reported subsequently that there was an 80% probability of further decline in subjects with mild cognitive deficits [13]. Twenty of the * Corresponding author. Tel.: ⫹46 8 585 85471; fax: ⫹46 8 585 85470. E-mail address: [email protected] (V. Jelic).

patients in the study of Flicker et al. [13] had a Global Deterioration Score (GDS) score of 3, and, after a 2-year follow-up period, 16 were moderately demented (GDS 4). A follow-up study of a larger cohort of memory-impaired nondemented patients showed that 24% developed AD [34]. Petersen et al. [26] followed over 80 MCI patients for almost 54 months and reported that nearly 55% of cases progressed to manifest dementia. Variation of the incidence rates of AD in different studies might be due to the different entry and follow-up criteria applied, as well as to differences in the length of the follow-up period. Because an unknown proportion of MCI patients will develop dementia and thus require therapy, there is a need to support the early intervention using some additional laboratory measures, such as functional neuroimaging methods. The inclusion of electroencephalography (EEG) examinations in the diagnostic work-up for Alzheimer’s disease is encouraged by its availability and noninvasiveness. Several

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Table 1 Descriptive statistics of the study sample

Total number Gender (male/female) Age (years) Education MMSE (baseline) FSIQ (baseline) MMSE (follow up) Follow-up period (months)

MCI group investigated on two occasions

Reference groups for baseline comparisons

Stable MCI (S MCI)

Progressed MCI (P MCI)

Alzheimer’s disease (AD)

Controls (C)

13 4/9 58.5 (7.9) 47–69 12.7 (4.1) 7–18 28.3 (1.9) 24–30 99.4 (11.2) 83–116 27.8 (2.1) 24–30 20.1 (8.4) 12–36

14 5/9 58.2 (5.9) 49–67 12.4 (4.4) 7–19 26.9 (1.9) 25–30 94.0 (10.8) 80–116 23.1 (2.6) 19–27 21.9 (8.9) 13–39

15 7/8 64.0 (8.1) 51–75 10.5 (3.6) 6–16 21.8 (2.5) 17–25 83.0 (15.4) 63–113 — —

16 8/8 59.9 (11.1) 42–76 11.4 (3.4) 7–18 28.9 (1.1)–27–30 99.4 (7.2) 86–108 — —

Values in the table are means with SD in parentheses, and ranges given for each variable.

studies have shown EEG slowing in AD [7,9,11,25]. It has been reported that an abnormal EEG at the early stage of AD may predict a more severe decline in cognitive functions [16]. However, so far, there have been few longitudinal studies of EEG power spectral changes in Alzheimer’s disease. It has been reported that after 2.5 years follow up, both delta and theta activity significantly increased, whereas alpha and beta activity decreased [8]. Other studies have found that progressive EEG slowing could be detected in only a proportion of early AD cases, with 50% showing no deterioration at 12 months follow up [28,32]. Some authors followed a cohort of healthy subjects and found that low beta power predicted development of cognitive decline after 5 years [11]. Hartikainen et al. [15] found that initially healthy individuals, who after 2 years follow up showed deterioration in learning ability, also had increased delta power. In contrast to these reports, we, in our study, selected at the baseline subjects with objectively verified MCI. In a previous work using discriminant analysis, we defined the best combination of quantitative EEG (qEEG) variables that gave the optimal classification of MCI and AD subjects [18]. We postulated that misclassified MCI subjects might be at risk of developing AD [18]. The comparison of MCI subjects who progress to manifest AD with subjects who remain clinically stable may help us to understand the EEG dynamics of AD. Therefore, in the present study, we evaluated the clinical course of patients with MCI and the incidence of dementia in this group, as well as the pattern of EEG changes following cognitive deterioration and the potential of neurophysiological measures in predicting dementia.

2. Subjects and methods

evidence of deterioration in social or occupational functioning. These subjects performed at least 1 SD below average for their age on neuropsychological tests representing one or more areas of cognition, as was described previously [18]. General levels of cognition were assessed by Mini-Mental Test Examination (MMSE) and Full-Scale Intelligence Quotient [14,36]. For the baseline comparisons of EEG variables, we included 2 reference groups comprising 15 AD patients diagnosed according to the NINCDS-ADRDA criteria and 16 healthy control subjects. The demographic data of the study groups are presented in Table 1. All groups had a similar mean age and education. All patients and healthy subjects underwent general medical, neurological, psychiatric, and neuropsychological investigation, as well as neuroimaging diagnostic procedures, such as magnetic resonance imaging (MRI) and single photon emission computerized tomography (SPECT). No subject received psychotropic medication that may influence EEG recordings. The healthy control group comprised volunteers who were either healthy family members (spouses) of patients investigated at the Geriatric Clinic, Huddinge University Hospital or healthy subjects recruited to the Driving and Aging project through advertisements. Exclusion criteria included any chronic systemic illness, any psychiatric or neurologic disease, family history of dementia, prior history of alcoholism, previous head trauma, and psychotropic medication. The MCI subjects were followed clinically and by serial EEG recordings. The clinical follow-up consisted of physical examinations, functional status assessments, and extended neuropsychological examinations. EEG was recorded on two occasions with an average interval of 21 months (range 12–39 months).

2.1. Study sample

2.2. EEG method

The main study sample consisted of 27 subjects with MCI, who at initial examination did not fulfill NINCDSADRDA criteria for probable AD [20] and did not have

All spontaneous EEGs were recorded in the morning, in a resting awake condition with eyes closed. The EEG data were acquired on a computer-based system (Bio-Logic

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Brain Atlas) from 20 electrode locations, according to the 10/20 system. The EEG was bandpass filtered 1–30 Hz prior to digitizing, using a sampling rate of 128 Hz. Samples were selected by visual inspection, blind to the clinical diagnosis, in order to get a minimum of 15 2-s epochs that were free of eye blink, drowsiness, muscle movements, or any other kind of artifacts. Frequency analysis was performed using a Fast Fourrier Transform (FFT) algorithm with a Hanning window [2]. For calculation of spectral EEG parameters, we used eight bipolar derivations in order to cover topography of the scalp: left and right fronto-central (F3-C3 and F4-C4), left and right centro-parietal (C3-P3, C4-P4), left and right temporal (T3-T5, T4-T6), left and right temporo-occipital (T5-O1, T6-O2). The EEG variables chosen were relative power in four conventional frequency bands, delta (2– 4 Hz), theta (4 – 8 Hz), alpha (8 –13 Hz), beta (13–20 Hz), and mean frequency of averaged spectrum (4 –20 Hz). Relative power was calculated as a quotient between power in one frequency band and total power across all bands, and was expressed as a percentage. All relative power values were transformed using the log[x/(1 ⫺ x)], in order to normalize data distribution. We additionally calculated absolute power by squaring amplitudes in the mentioned frequency bands, because of suggestions that this could be more informative in follow-up studies [33]. Values obtained were transformed using the natural logarithm transformation in order to normalize data distribution. EEG coherence was analyzed in the alpha frequency band. The temporo-parietal coherence in alpha frequency band was shown in our previous work to be useful in discriminating patients from controls [18]. Pairings of bipolar electrode channels were used representing averaged over both hemisphere temporo-parietal coherence, as described elsewhere [24]. Values of EEG coherence were transformed using arcsin 公x transformation [3]. In addition, a method error (E) was calculated as the variability of a single parameter between two paired measurements during a 7-day interval on a separate group of 10 healthy subjects. This was calculated according to the formula E ⫽ 公(⌺d2/N), where d is the difference between the two measurements and N the total number of subjects, expressed as a percentage of the mean multiplied by 100 [4]. 2.3. Statistical analysis Differences in baseline values of qEEG parameters in various derivations among the groups (AD patients, controls, progressed MCI, stable MCI) were evaluated by repeated analysis of variance (ANOVA), where different scalp derivations were treated as repeated measurements on the same subjects. Scheffe´’s post hoc analysis corrected for multiple comparisons was used to evaluate group differences. Changes in qEEG variables in the two MCI subgroups recorded on two occasions with an average 21-month inter-

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val (range 12–39 months) were tested by two-tailed paired t-test. A logistic regression model was applied to study which of the baseline qEEG variables best predicted group membership: progressed MCI (P MCI) or stable MCI (S MCI) after the follow-up period when adjusting for baseline MMSE and follow-up time. Adjustment means that MMSE and the follow-up time were included in the model as control variables and that the linear effect of these variables was partialled out. Because age and gender did not differ between the P MCI and S MCI subgroups, they were not included in the model, in order to reduce the number of covariates. All qEEG variables (four frequency bands and mean frequency for eight derivations and temporo-parietal coherence) were tested for their predictive power by a chunk test. In a chunk test, a group of variables are tested simultaneously [20]. In this case, MMSE, follow-up time, and the qEEG variables in one derivation were tested simultaneously. In total, eight tests were performed, corresponding to the eight bipolar derivations.

3. Results 3.1. Clinical follow up After a mean follow-up period of 21 months (range 12 to 39 months), 14 patients (52%) were diagnosed as AD and were classified as P MCI. The other 13 patients remained clinically stable and were classified as S MCI. The P MCI and S MCI groups had slightly different baseline MMSE values (P ⬍ 0.05), but they did not differ in Full-Scale Intelligence Quotient scores. There was no difference in the length of follow up between these groups (Table 1). MMSE scores after the follow-up period were significantly lower in the P MCI (P ⬍ 0.001) compared to the S MCI group (Table 1). 3.2. Reliability of the method The method errors (expressed as a percentage of the mean) of repeated measurements (spaced 7 days apart) for different parameters in a group of 10 healthy subjects were as follows: absolute delta (45.4%), relative delta (10.8%), absolute theta (25.8%), relative theta (9.4%), absolute alpha (13.8%), relative alpha (15.8%), absolute beta (16.2%), relative beta power (25.6%), mean frequency (1.7%), and temporo-parietal coherence (5.7%). Because, in general, absolute values showed greater variability between the measurements, relative power values were chosen for further calculations and display of the results. 3.3. Baseline comparisons Results of the baseline comparisons in qEEG variables between two MCI subgroups and two reference groups (AD

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Fig. 1. Group comparisons of the baseline qEEG values among 4 studied groups: reference AD group, healthy controls, pregressed MCI (P MCI) and stable MCI (S MCI); P ⬍ 0.05 after correction for multiple comparisons. 䡵, AD patients significantly different from P MCI, S MCI, and controls; `, AD patients significantly different from SMCI and controls; z, AD patients significantly different from controls; ⵧ, no significant difference among the study groups.

and controls) are displayed in Fig. 1. The S MCI and P MCI groups as well as controls when compared to the AD group, had significantly lower theta relative power values at T3-T5, T5-O1, C3-P3, and T6-O2 derivations. Groups did not differ significantly with respect to the beta relative power. The controls and S MCI group had significantly lower delta relative power at derivations T5-O1 and T6-O2, theta relative power at derivations F3-C3, F4-C4, C4-P4 and T4-T6, and higher alpha relative power at derivations T3-T5 and T5-O1, when compared to the reference AD group. The P MCI group did not differ significantly from the AD group, the S MCI group and the controls with respect to these measures. Controls and S MCI cases had significantly higher temporo-parietal coherence compared to the AD

group, while the P MCI group did not differ significantly from the AD, S MCI or control groups in coherence values. Also, none of the EEG measures were significantly different between the P MCI and S MCI groups. Values for the qEEG variables of the respective groups for the T5-O1 derivation are given in Table 2. 3.4. EEG follow-up data EEG follow-up data of the P MCI group are shown in Fig. 2. The 14 MCI patients who progressed to clinically manifest AD (P MCI) had significantly higher theta relative power at derivations T3-T5 (t ⫽ ⫺2.69; P ⫽ 0.02), T4-T6 (t ⫽ ⫺3.06; P ⫽ 0.01), T5-O1 (t ⫽ ⫺2.99; P ⫽ 0.01),

Table 2 EEG baseline comparisons among MCI subgroups, Alzheimer’s disease patients and controls (derivation T5-O1)

Delta relative power Theta relative power Alpha relative power Beta relative power Mean frequency Temporo-parietal coherence

Stabile MCI (S MCI)

Progressed MCI (P MCI)

Alzheimer’s disease (AD)

Controls (C)

F (3,54)

P

5.89 (3.79–7.98) 9.74 (6.88–12.61) 63.73 (54.24–73.22) 20.64 (13.63–27.65) 10.34 (9.90–10.78) 0.76 (0.70–0.82)

9.27 (5.55–13.00) 15.77 (10.06–21.48) 54.11 (42.30–65.92) 20.84 (14.99–26.70) 9.92 (9.55–10.30) 0.68 (0.57–0.78)

16.91 (11.40–22.42) 31.62 (25.25–38.00) 39.18 (31.99–46.36) 12.29 (7.21–17.36) 8.56 (8.04–9.09) 0.62 (0.57–0.67)

4.74 (3.49–5.99) 8.08 (5.79–10.37) 68.64 (62.46–74.81) 18.54 (12.45–24.63) 10.37 (9.82–10.91) 0.74 (0.71–0.77)

11.66 20.94 7.30 3.02 13.90 2.80

0.0001 0.0001 0.0003 0.04 0.0001 0.04

Values are means and 95% confidence intervals. Relative power values are expressed as percentages of the total power spectra, log (x/l-x) transforms were used in statistical analysis.

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Fig. 2. Longitudinal changes in qEEG variables in the PMCI group, two-tailed paired t-test. `, P ⬍ 0.05; 䡵, P ⬍ 0.01; ⵧ, ns.

T6-O2 (t ⫽ ⫺3.09; P ⫽ 0.01), and lower mean frequency T3-T5 (t ⫽ 3.04, P ⬍ 0.01), T4-T6 (t ⫽ 3.18; P ⬍ 0.01), T5-O1 (t ⫽ 3.20; P ⬍ 0.01), and T6-O2 (t ⫽ 2.0; P ⫽ 0.05) on the second occasion, on average 22 months apart. A lower beta relative power was found at T5-O1 (t ⫽ 2.32, P ⫽ 0.04) and T4-T6 derivation (t ⫽ 2.81; P ⫽ 0.02). Subjects who remained stable (S MCI) did not have changes in any of the EEG parameters. Values for qEEG variables recorded at T5-O1 derivation of two MCI subgroups, at both the baseline and after the average 21 months follow-up period, are given in Table 3. Between-group mean differences of the two recording occasions tested whether differences between the two recording times were different between two groups. They were found to be statistically different for the mean frequency at the derivation T5-O1.

3.5. Prediction Logistic regression model applied on baseline EEG variables by means of a chunk test, which tested MMSE, follow-up time, and qEEG variables in one derivation at a time, showed that the best predictors were alpha and theta relative power and mean frequency from T5-O1 derivation. The comparison of predictive power of five logistic regression models is given in Table 4 in order to compare classification accuracies of baseline MMSE or follow-up time only, qEEG variables, or qEEG variables combined with MMSE or follow-up time. Model I used baseline MMSE as the only predictor, model II used baseline MMSE and a follow-up time, model III used baseline qEEG variables only, model IV used baseline MMSE and qEEG vari-

Table 3 EEG follow-up data for the patients with MCI (derivation T5-O1) Stable MCI (S MCI)

Delta relative power Theta relative power Alpha relative power Beta relative power Mean frequency Temporo-parietal coherence

Progressed MCI (P MCI)

Time 1

Time 2

Mean diff. within group

P value

Time 1

Time 2

Mean diff. within group

P value

5.89 9.74 63.73 20.64 10.34 0.76

5.01 9.32 66.76 18.90 10.35 0.74

0.88 0.42 ⫺3.03 1.74 0.01 0.02

0.55 0.91 0.58 0.50 0.95 0.84

9.27 15.77 54.11 20.84 9.92 0.68

8.70 19.98 54.86 16.75 9.43 0.68

0.57 ⫺4.21 ⫺0.75 4.09 0.49 0.00

0.93 0.01 0.56 0.04 0.01 0.93

Mean diff. between group

P value

0.31 4.63 ⫺2.28 ⫺2.35 ⫺0.46 0.02

0.70 0.08 0.92 0.47 0.01 0.80

Values for S MCI and P MCI are group means from two recording occasions (21 months apart on average) with mean differences within respective groups (time 1–time 2). The mean differences between groups are the difference between within group differences (time 1–time 2), e.g. for the delta relative power: 0.88 – 0.57 ⫽ 0.31. Relative power values are expressed as percentages of the total power spectra, log(x/l–x) transforms were used in statistical analysis.

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Table 4 Classification accuracies of the baseline EEG variables Variable

Model I

Model II

Model III

Model IV

Model V

MMSE Follow-up Theta rel pw Alpha rel pw Mean frequency Intercept (␣) Classificantion accuracy of the model

⫺0.4 (⫺0.9–0.10) — — — — 11.8 70%

⫺0.4 (⫺0.9–0.04) 0.1 (⫺0.8⫺0.8) — — — 11.6 70%

— — ⫺2.8 (⫺5.7–⫺0.1)* ⫺3.8 (⫺6.8–⫺0.8)* ⫺3.8 (⫺6.8–⫺0.7)* 34.8 82%

⫺1.3 (⫺1.5–⫺0.1)* — ⫺6.9 (⫺12.5–⫺1.3)* ⫺8.0 (⫺14.3)–⫺1.7)* ⫺7.7 (⫺13.6–⫺1.8)* 104.6 85%

— 0.02 (⫺0.12–0.15) ⫺2.9 (⫺5.8–0.04)* ⫺3.8 (⫺6.7–⫺0.9)* ⫺3.8 (⫺6.9–⫺0.7)* 34.2 81.4%

Values in the table are regression estimates with 95% confidence interval (CI) and predictive values for the baseline EEG variables adjusted for the baseline MMSE scores obtained from a logistic model with group membership (P MCI/S MCI), as dependent variable. * P ⬍ 0.05.

ables, and model V used follow-up time and baseline qEEG variables. The significant variables in models III, IV, and V were alpha and theta relative power and the mean frequency, all from the derivation T5-O1. When the classification accuracies of the different models were compared, it was found that models III, IV, and V were significantly better than models I and II. No statistically significant difference between models III, IV, and V was found. Model IV showed the best classification accuracy, classifying 85% of 27 MCI subjects correctly into the two subgroups: progressed (P MCI) versus stable (S MCI). One patient with P MCI was classified as S MCI, whereas three subjects with S MCI were classified as P MCI. Baseline MMSE values taken in a separate model misclassified five patients from the P MCI group and three subjects from the S MCI group.

4. Discussion The present study shows that 52% of subjects who initially had MCI developed clinically manifest AD after an average follow-up period of 21 months. These results are close to those reported by Petersen et al. [26], except that the follow-up period of their subjects was longer, 54 months on average. Taken together with the data from other studies, which suggest that objective evidence of cognitive impairment can be a reliable predictor of future cognitive decline in the elderly, this study supports the importance of considering the cognitively borderline category as hiding a proportion of preclinical AD cases [1,12,34]. The most important finding of this study is that qEEG variables could be considered as predictors of dementia in subjects with only MCI. It is interesting that the best predictors were combined alpha and theta relative power. Increase of theta power has already been reported in very early stages of the disease [27,33].There are two possible explanations for the reduced alpha power. First, susceptible individuals may have lower values of this main background EEG activity from the beginning. Another possibility is that it is replaced by increasing theta power. The second possi-

bility seems less likely since there was no strong correlation between these two variables, suggesting that they yield different information about brain functions, as suggested by Leuchter et al. [22]. The classification accuracy of baseline EEG measures, when the linear effect of the initial MMSE scores was partialled out, was 85% with only one patient from the P MCI group being misclassified as S MCI. The three S MCI misclassified subjects might be also in the preclinical stage of the disease, but the clinical assessment was not sensitive enough to detect this at follow up. Because the follow-up period of the misclassified S MCI subjects was below the average follow-up interval for the S MCI or P MCI group, it might be as well that classification came before clinical deterioration is evidenced. These subjects may have a higher brain reserve that allows them to compensate for the functional deficits so that they are not evident at a clinical level. Inclusion of the follow-up time in the prediction model did not improve classification accuracy, indicating that the clinical deterioration in MCI is not a function of time only. It is interesting that alpha power is included in the prediction model, since increased theta power has been described as the earliest change occurring during the course of the disease [27] and some authors have found recently that low beta power [11] predicted further cognitive decline in the elderly. However, a recent study performed by Claus et al. [5] showed that both relative alpha and beta powers were significant and independent predictors of mortality in patients with early AD. An earlier study found that alpha activity, absolute and relative amplitudes, provided the most sensitive indicator of differences among healthy individuals [35]. Alpha activity is the main Godground activity of the normal wakening EEG, which is genetically influenced [37]. This baseline EEG pattern may determine the effects of centrally active drugs on EEG. Therefore, it is worth further expanding research in the direction of neurophysiological trait markers because it has important practical implications. It would also be interesting to explore separately in future studies slow and fast alpha bands because they have different functional meanings and can selectively contribute to the slowing in the alpha band [21].

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Another potential explanation of our results could be that the use of relative power is compromised by the fact that all other bands are interrelated. An increase in, for example, theta power will also cause a decrease in relative alpha power even if there was no change in the amount of alpha absolute power. However, the main interest of this study was to find sensitive neurophysiological indices of disease prediction and progression in subjects at risk and not to draw any stronger inferences about the biological significance of different frequency bands. The separate reliability part of this study, performed as repeated measurements on healthy subjects, showed in general higher intraindividual temporal variability for absolute power values, which was especially marked for the slow frequency bands. Therefore, the use of relative power values was justified. The longitudinal effects observed in progressive MCI agree well with the cross-sectional differences between early stage AD patients and healthy subjects published to date [6,7,18,29,30]. Theta relative power and consecutive decrease in the mean frequency were most sensitive to the longitudinal change in patients who progressed to clinically manifest Alzheimer’s disease. This suggests that these parameters are a state marker that changes with the progression of the disease and corresponds to the severity of the clinical picture as it has been suggested by others [16,30]. From the clinical point of view, this may be important because these parameters are candidates for markers of treatment effectiveness at the early stages of AD. It is important to note that temporal and temporo-occipital derivations seem to be most sensitive to these longitudinal effects. Previous studies employing the qEEG technique have generally found that posterior derivations, namely, temporooccipital, temporo-parietal, and occipital-vertex, were most sensitive in the separation of AD patients and healthy subjects [7,9,25]. Furthermore, in our most recent study, we found that qEEG variables from the left temporo-occipital derivation gave the best classification of AD patients and controls in a linear discriminant analysis [19]. It is interesting to note that the values of EEG coherence overlapped at the baseline between the P MCI, S MCI, and AD patients. This is in good agreement with previous findings that temporo-parietal coherence loses its discriminative power when MCI and AD were compared [18]. Dunkin et al. [10] suggested that EEG coherence might be a trait marker that appears early in the course of the disease and remains stable over time. Our data also indicated that the MCI patients who progressed to AD showed no further decline in this measure between the two recording occasions. In conclusion, the results of the current study support the hypothesis that among patients with MCI there is a subgroup who develop AD. The dynamics of EEG changes distinguish the subgroup that further deteriorates, and the important predictors of further clinical deterioration are combined alpha and theta relative power and mean fre-

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quency. This finding has a practical relevance because an early diagnosis of AD is of utmost importance for planning interventional strategies, and qEEG is a widely available and noninvasive method for diagnostic screening.

Acknowledgments This work was supported by the Swedish Medical Research Council, the Gamla Tja¨narinnor Foundation, the Greta Lindenau-Hansells Foundation, the Loo and Hans Ostermans Foundation for Medical Research, the Sandoz Foundation for Gerontological Research, and the Swedish Municipal Pension Institute. All EEG recordings were performed at the Department of Clinical Neurophysiology, Huddinge University Hospital. We thank Anders Persson, head of the Department, and all the staff for the good collaboration.

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