Differences in quantitative EEG between frontotemporal dementia and Alzheimer’s disease as revealed by LORETA

Clinical Neurophysiology 122 (2011) 1718–1725

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Differences in quantitative EEG between frontotemporal dementia and Alzheimer’s disease as revealed by LORETA K. Nishida a,⇑, M. Yoshimura a, T. Isotani b, T. Yoshida c, Y. Kitaura a, A. Saito d, H. Mii a, M. Kato a, Y. Takekita a, A. Suwa a, S. Morita a, T. Kinoshita a a

Department of Neuropsychiatry, Kansai Medical University, 10-15 Fumizono-cho, Moriguchi, Osaka 570-8506, Japan Laboratory for Brain–Mind Research, Faculty of Nursing Shikoku University, Furukawa, Ojin-cho, Tokushima-shi, Tokushima 771-1192, Japan Medical Ambassade du Japon en Algerie, 1 Chemin Al Bakri, Ben Aknoun, Alger, Algerie d Department of Neurology, Kansai Medical University, 10-15 Fumizono-cho, Moriguchi, Osaka 570-8506, Japan b c

a r t i c l e

i n f o

Article history: Accepted 14 February 2011 Available online 10 March 2011 Keywords: Quantitative EEG EEG Frontotemporal dementia Alzheimer’s disease LORETA Beta rhythm Sensorimotor area

h i g h l i g h t s  A comparative study of frontotemporal dementia (FTD), Alzheimer’s disease (AD), and healthy control subjects (NC) was carried out in terms of the cortical localization of oscillatory EEG activity.  As compared to NC, FTD had significantly reduced alpha generators in fronto-temporal cortices, while AD showed significantly stronger delta generators that included the frontal lobes.  ROC curves demonstrate a moderate separation of FTD, AD, and NC groups.

a b s t r a c t Objective: To determine the electrophysiological characteristics of frontotemporal dementia (FTD) and the distinction with Alzheimer’s disease (AD). Methods: We performed analyses of global field power (GFP) which is a measure of whole brain electric field strength, and EEG neuroimaging analyses with sLORETA (standardized low resolution electromagnetic tomography), in the mild stages of FTD (n = 19; mean age = 68.11 ± 7.77) and AD (n = 19; mean age = 69.42 ± 9.57) patients, and normal control (NC) subjects (n = 22; mean age = 66.13 ± 6.02). Results: In the GFP analysis, significant group effects were observed in the delta (1.5–6.0 Hz), alpha1 (8.5– 10.0 Hz), and beta1 (12.5–18.0 Hz) bands. In sLORETA analysis, differences in activity were observed in the alpha1 band (NC > FTD) in the orbital frontal and temporal lobe, in the delta band (AD > NC) in widespread areas including the frontal lobe, and in the beta1 band (FTD > AD) in the parietal lobe and sensorimotor area. Conclusions: Differential patterns of brain regions and EEG frequency bands were observed between the FTD and AD groups in terms of pathological activity. Significance: FTD and AD patients in the early stages displayed different patterns in the cortical localization of oscillatory activity across different frequency bands. Ó 2011 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

1. Introduction Frontotemporal lobar degeneration (FTLD) of non-Alzheimer type dementia (Neary et al., 1998) is a progressive behavioral disorder primarily related to dysfunction of the frontal and anterior temporal lobes. It encompasses a variety of clinical syndromes, which can be divided into three subtypes. The most common subtype is frontotemporal dementia (FTD). The clinical features of FTD are apathy, impulsiveness, lack of inhibition, loss of insight, and

⇑ Corresponding author. Tel.: +81 6 6992 1001; fax: +81 6 6995 2669. E-mail address: [email protected] (K. Nishida).

stereotypic behavior. Its pathological changes are limited to the frontal and temporal lobes in its early stages. In contrast, the most common type of dementia, Alzheimer’s disease (AD), is associated with dysfunction of the parietal lobe, occipital lobe and atrophy of the hippocampus, and its most common symptoms are loss of recent memory and impairment of visuospatial abilities. Many imaging studies have been performed with magnetic resonance imaging (MRI) (Du et al., 2006, 2007; Young et al., 2009), single-photon emission tomography (SPECT) (Lojkowska et al., 2002; Varrone et al., 2002; Waragai et al., 2008) and positron emission tomography (PET) (Ishii et al., 1998; Foster et al., 2007; Mosconi et al., 2008). Pathological changes of FTD patients occur in the frontal and temporal regions, whereas in AD patients the changes are

1388-2457/$36.00 Ó 2011 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved. doi:10.1016/j.clinph.2011.02.011

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observed in the medial temporal lobes, especially in the hippocampus. MRI studies have shown thinning of the cortex in those regions. SPECT and PET studies have shown frontal and anterior temporal hypoperfusion in FTD patients, and parieto-occipital hypoperfusion in early stage AD patients. These imaging methods constitute reliable preclinical tools for distinguishing these diseases. However, MRI, SPECT, and PET by themselves are not sufficient to provide information on disease process and mechanism of dementia especially in the early stages. Furthermore, sometimes these imaging tools are not clinically feasible or impose a heavy burden on the patient because of their invasiveness. In contrast, electroencephalography (EEG) is a non-invasive technique that is very sensitive to changes in the functional state of the human brain. Many studies have shown that AD is associated with visual and quantitative EEG changes (Dierks et al., 1993; Babiloni et al., 2004, 2007, 2009; Yoshimura et al., 2004; Koenig et al., 2005; Rossini et al., 2006; Lehmann et al., 2007; Luckhaus et al., 2008; Park et al., 2008). The EEGs in AD patients are characterized by an increase in slow (delta and/or theta) activity and a decrease in fast (alpha and/or beta) activity. Babiloni reported that occipital delta (2–4 Hz) and alpha1 (8–10.5 Hz) sources in parietal, occipital, temporal, and limbic areas had an intermediate magnitude in MCI subjects compared to mild AD and normal old subjects (Babiloni et al., 2007). In contrast, visual inspection of the EEGs of FTD patients have been reported as almost normal until the advanced stage of the disease (Neary et al., 1998). Julin et al. (1995) reported that when they compared only patients with mild stage (MMSE > 22), the EEG showed significantly less abnormal changes for the frontal lobe dementia (FLD) patients as compared to the AD group. However, Chan et al. (2004) reported no significant difference in the severity of EEG abnormality between the FTLD and AD patient groups despite the fact that visual EEG changes were found in AD patients in early stages. The recent increasing number of quantitative EEG (qEEG) findings for FTD patients indicates otherwise. Passant et al. (2005) found that the EEG was normal in the late-onset group, while it was mildly and variably abnormal in the early-onset group. Some studies have compared AD and FTD groups (Yener et al., 1996; Lindau et al., 2003; Pijnenburg et al., 2004; Yoland et al., 2008; de Haan et al., 2009). Yener found that the most informative qEEG variables for distinguishing FTD from AD were the left parietal theta (4–8 Hz) and Delta (0–4 Hz) frequency bands, the right parietal alpha (8–12 Hz) band, the left frontal theta (4–8 Hz) band, and the left temporal beta2 (18–26 Hz) band. The mean relative powers of the left temporal beta2 (18–26 Hz) and left parietal theta (4–8 Hz) bands were higher for AD than for FTD, while those of the right parietal beta2 (18–26 Hz) and right parietal alpha (8–12 Hz) bands were lower in AD than in FTD. Lindau et al. (2003) reported that FTD did not differ from controls in the delta (1.0–3.5 Hz), and theta (4.0–7.5 Hz) band, but there was tendency in FTD to a larger decrease in alpha (8.0–11.0 Hz), and beta1–beta3 (12.0–23.5 Hz) band compared to controls than in AD. In contrast, the AD group was significantly decreased in the delta (1.0–3.5 Hz) band compared to normal controls. Using graph-theory derived methods applied to scalp-recorded EEG, de Haan et al. (2009) reported that the mean normalized clustering coefficient was smaller in AD compared to controls in the lower alpha and beta frequency bands, and that FTLD showed a non-significant but constant trend in the opposite direction in the higher frequency bands. In addition, they found that the median values of AD and FTLD changed in opposite directions in all frequency bands. These studies show the effectiveness of EEG in assessing dementias, which can be enhanced by using other methods for analyzing EEG data. Thus, it is important to determine the EEG characteristics of FTD patients for the differential diagnosis. Under-

standing its characteristics in early stages will greatly help in treatment selection and social support (Mioshi et al., 2009). The aim of this study was to investigate the electrophysiological characteristics of FTD and the distinction with AD, especially spatial EEG analysis. 2. Materials and methods 2.1. Subjects All the patients that were diagnosed with AD or FTD in the Neuropsychiatry Clinic of Kansai Medical University Takii Hospital between June 2007 and May 2009 were examined as candidates for this study. All patients were diagnosed on the basis of information obtained from an extensive clinical history and physical examination, and excluded mood disorder, schizophrenia, and anxiety disorder. In addition, patients underwent brain MRI, I123-IMP-SPECT, and Mini Mental State Examination (MMSE) (Folstein et al., 1975). All FTD patients showed either frontal temporal mild atrophy on MRI and/or mild hypoperfusion on SPECT. All AD patients showed either mild temporal atrophy or diffusional atrophy on MRI and/or parieto-occipital hypoperfusion on SPECT. An experienced neuroradiologist assessed these atrophies and hypoperfusion by visual inspection of MRIs and with computer-assisted statistical analysis of brain perfusion SPECT images. The severity of dementia was assessed using the clinical dementia rating (CDR) scale (Hughes et al., 1982). Only patients with mild dementia (0.5 6 CDR 6 1.0) were included in this study. For the diagnosis of FTD, the Lund and Manchester criteria (Neary et al., 1998) were employed. Patients with semantic dementia or progressive nonfluent aphasia were excluded from this study. For the diagnosis of probable AD, the diagnostic criteria of the National Institute of Neurological and Communicative Disorders and Strokes–Alzheimer’s Disease and Related Disorders Association (NINCDS–ADRDA) (McKhann et al., 1984) and the Diagnostic and Statistical Manual of Mental Disorder-IV (DSM-IV, 1994) were employed. None of the patients with AD had received anticholinesterase therapy. Nineteen patients with FTD (7 males and 12 females; age range: 51–78 years; mean age ± s.d. = 68.11 ± 7.77; MMSE score ± s.d. = 23.84 ± 3.13), and 19 patients with AD (6 males and 13 females; age range: 50–82 years; mean age ± s.d. = 69.42 ± 9.57; MMSE score ± s.d. = 21.05 ± 3.13) were included in this study. The patients were all right-handed and were not taking psychoactive medication. FTD patients underwent the frontal assessment battery (FAB) (Dubois et al., 2000) which is a simple tool for assessing frontal lobe function and useful method to diagnose of FTD (Oguro et al., 2006) (mean FAB score ± s.d. = 11.42 ± 2.91). We collected the patient’s main and first symptoms hearing from the caregiver. All AD patients exhibited memory loss. At disease onset, the FTD patients were heterogeneously exhibiting the following characteristics: 6/19 with apathy, 6/19 with agitation, 4/19 were disinhibitTable 1 Demographic data of the subjects in this study. FTD (n = 19)

AD (n = 19)

NC (n = 22)

Sex (m:f) Age MMSE

7:12 68.11 ± 7.77 23.84 ± 3.13*

6:13 69.42 ± 9.57 21.05 ± 2.44**

10:12 66.13 ± 6.02 28.72 ± 1.83

FAB Education Duration of the disease CDR (0:0.5:1)

11.42 ± 2.91 10.58 ± 2.71 22.68 ± 9.86

10.74 ± 2.88 20.05 ± 7.56

12.14 ± 2.17 0

0:09:10

0:10:09

22:00:00

P value 0.66 0.41 p < 0.0002 ** p < 0.0001 *

0.099 0.362

FTD: frontotemporal dementia, AD: Alzheimer’s disease; NC: normal control. FTD compared with AD. AD compared with NC.

*

**

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ed, 3/19 with stereotypy, and 6/19 with physical complaints. Twenty-two non-demented, age-matched, right-handed healthy volunteers (12 males and 10 females; age range: 55–76 years; mean age ± s.d. = 66.13 ± 6.02; MMSE score ± s.d. = 28.72 ± 1.83), were recruited as normal controls (NC). The subjects had voluntarily applied to participate in this study with no reward offered. Their medical history and physical and neurological examinations showed no signs of abnormality. Table 1 shows the demographic data of the subjects in this study. There are no statistically significant differences in the demographic variables (age, gender, education, duration of the disease) among the groups (p > 0.05; one-way ANOVA). The Institutional Ethical Review Board of Kansai Medical University approved this study and the written informed-consent was obtained from the patients or the caregivers. 2.2. Recording of EEG Eyes-closed resting EEG was recorded from 19 scalp electrodes in accordance with the international 10/20 system (Fp1, Fp2, F7, F3, Fz, F4, F8, T3, C3, Cz, C4, T4, T5, P3, Pz, P4, T6, O1, O2) referenced to linked ear lobes (A1 and A2). Vigilance controlled EEG recording sessions lasted 15–20 min, with subjects receiving a warning sound when they started to drift towards drowsiness. EEGs were amplified, bandpass filtered to 0.3–30 Hz, sampled at 128 Hz, and stored on a hard disk using EEG-1100 NIHON KODEN system (Nihon Koden, Tokyo, Japan). After each EEG recording, 20 artifact-free epochs of 2-s duration each were randomly selected by visual inspection for analysis, excluding eye-movements, blinks, and drowsiness. The selected data were recomputed against average reference. 2.3. Global field power (GFP) In a first step of analysis, comparisons between groups were based on power spectra of scalp electric potentials. These data do not rely on any assumption or source model, other than the property of wide-sense stationary for the EEG signals. The global field power (GFP) measure (Lehmann and Skrandies, 1980) was used, and is defined as the standard deviation (SD) of the set of scalp potentials, thus corresponding to the whole brain electric field strength. GFP for the spectral amplitude was computed across electrodes to obtain a measure of total amplitude for the seven frequency bands defined by (Kubicki et al., 1979): delta (1.5–6.0 Hz), theta (6.5–8.0 Hz), alpha1 (8.5–10.0 Hz), alpha2 (10.5–12.0 Hz), beta1 (12.5–18.0 Hz), beta2 (18.5–21.0 Hz), and beta3 (21.5–

30.0 Hz). Statistical analyses were performed using SPSS version 12.0 for Windows and the sLORETA software (Pascual-Marqui, 2002) (http://www.uzh.ch/keyinst/loreta) implementing nonparametric randomization statistics with correction for multiple testing (Nichols and Holmes, 2002). Relative (GFP) power for each frequency band (percentage) was calculated as the absolute power of each frequency band divided by absolute power of the total frequency band (sum of all frequency band powers). Repeated measures analysis of variance was used to test the null hypothesis of ‘‘no difference between groups: FTD = AD = NC’’. Upon rejection of this hypothesis, post-hoc tests with correction for multiple testing were performed in order to find which groups and which frequency bands were significantly different. Any significant difference confirmed in this step on scalp potentials validates the second analysis step, where comparisons between groups are based on the sLORETA sources. Statistical analysis on the cortical sources should reveal more detailed structure for the group differences, since scalp GFP is global measure, i.e. an average over all electrodes.

2.4. sLORETA sLORETA computations were carried out in a realistic threecompartment model of the head that included the scalp, skull, and neural tissue. The actual model we used corresponds to the digitized MNI152 template provided by the Brain Imaging Center of the Montreal Neurological Institute (Mazziotta et al., 2001), which is co-registered in the Talairach atlas. Electric neuronal activity is restricted to cortical gray matter, as determined by the Talairach Daemon (Lancaster et al., 2000). The current implementation of the software uses a total of 6239 gray-matter voxels with a resolution of 5 mm. The lead field for this realistic head model was computed using the boundary element method (BEM) (Fuchs et al., 2002). The electrode coordinates were based on the average location of the 10-5 system placement system (Jurcak et al., 2005). We performed voxel-by-voxel tests for comparing the pairs of groups: FTD vs NC, AD vs NC and FTD vs AD. Full correction for multiple testing (for all voxels and all frequency bands) was implemented by means of non-parametric randomization using the maximum-statistic (Nichols and Holmes, 2002). Global subjectwise normalization was performed (Frackowiak, 2004) prior to group comparisons, which is equivalent to the relative global field power of the EEG data used in the previous analysis step. SLORETA software from the KEY Institute was used, which includes not only

Fig. 1. Logarithm of grand average GFP spectra for frontotemporal dementia (FTD), Alzheimer’s disease (AD), and normal control (NC), groups.

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K. Nishida et al. / Clinical Neurophysiology 122 (2011) 1718–1725 Table 2 Individual alpha peak frequencies: descriptive statistics and ANOVA.

Number of subjects Mean alpha peak frequency (Hz) Median Lower quartile Upper quartile Standard deviation

FTD

AD

NC

19 9.35 9.40 8.60 9.80 0.76

19 9.11 9.00 9.00 9.40 0.58

22 9.50 9.40 9.00 10.20 0.80

Parametric ANOVA: p = 0.23 (not significant). Non-parametric Kruskal–Wallis ANOVA by Ranks: p = 0.33 (not significant). FTD: frontotemporal dementia, AD: Alzheimer’s disease; NC: normal control.

Fig. 2. In delta frequency band, the relative GFP value of AD was significantly higher than that of NC (p = 0.005). In alpha1 frequency band, the relative GFP value of NC was significantly higher than that of FTD (p = 0.014). In beta1 frequency band, the relative GFP value of FTD was significantly higher than that of AD (p = 0.014). (FTD: frontotemporal dementia, AD: Alzheimer disease, NC: normal control) ⁄⁄p = 0.005, ⁄ P > 0.05.

source localization, but a full statistical analysis package which corrects for multiple testing. 3. Results 3.1. GFP

band GFP for AD and NC, alpha1 band GFP for FTD and NC, and beta1 band GFP for FTD and AD. Table 4 shows a detailed summary of ROC parameters and statistics. The areas under the curves in all three discrimination cases are significantly different from chance, corresponding to moderate accuracy in separation. In addition, we performed two stepwise multiple correlation analyses, where the seven independent variables were the relative GFP values for each frequency band. In one case, the dependent variable was the MMSE score, and in the other case, the FAB score. For the MMSE score, a positive significant correlation with beta1 GFP was found only for the AD group (p = 0.04). For the FAB score, only the FTD group was tested, and no significant correlations were found.

Fig. 1 shows the logarithm of grand average GFP spectra for FTD, AD and NC groups. These curves have the familiar form of eyes closed EEG spectra, with the characteristic alpha peak well within the 8–12 Hz range. The individual alpha peak frequencies (for each subject) were computed. An ANOVA was performed to assess if there were differences in the alpha peak positions between groups. The results are reported in detail in Table 2, showing no significant differences of the spectral alpha peak position. A detailed statistical analysis of GFP within the frequency bands follows. Given that the individual alpha peak frequencies are not different between groups (Table 2), possible GFP differences in the frequency bands are not confounded by this factor. Repeated measures ANOVA for the three groups of subjects (FTD, AD, and NC), using as dependent variables the seven frequency bands, revealed a highly significant group effect (Wilks test; F = 2.4; df1 = 12; df2 = 104; p = 0.010). Post-hoc comparisons of groups for each frequency band were performed using a non-parametric randomization version of ANOVA with correction for multiple testing. Table 3 and Fig. 2 show mean relative GFP for the FTD, AD, and NC groups. Post-hoc (Tukey) tests indicated that the GFP for AD was higher than that for NC (corrected p = 0.005) in the delta band. In the alpha1 band, the GFP for NC was higher than that for FTD (corrected p = 0.014). And, the GFP for FTD was higher than that for AD (corrected p = 0.014) in the beta1 band. Receiver operating characteristic (ROC) curves were analyzed by means of the SPSS software. Based on the previous GFP analyses, the quality of separation between groups were based on the delta

3.2. sLORETA Fig. 3 compares current density images in Talairach space obtained by sLORETA for the FTD and NC groups. Red areas correspond to significantly higher activity in the FTD group, and yellow areas correspond to significantly higher activity in the NC group (p < 0.047, Log-F-ratio threshold = 0.344). In Fig. 4, in the alpha1 frequency band, NC showed strong activity in the medial frontal gyrus and the frontal lobe. Fig. 5 compares current density images for the AD and NC groups. Blue areas correspond to significantly higher activity in the AD group, and yellow areas correspond to significantly higher activity in the NC group (p < 0.050, Log-F-ratio threshold = 0.186). The solid line surrounds images in the frequency band which had a significant difference of GFP between AD and NC. In Fig. 6, in

Table 3 Post-hoc comparisons of groups using non-parametric ANOVA with randomization and correction for multiple testing. Relative value (%)

FTD

AD

NC

delta theta alpha1 alpha2 beta1 beta2 beta3

43.34 (7.63) 11.59 (4.40) 15.58 (6.06) 8.68 (4.15) 15.77 (4.34) 2.96 (2.44) 2.08 (2.64)

49.33 (11.4) 12.28 (3.91) 17.73 (11.26) 6.35 (1.78) 11.78 (4.34) 1.53 (0.93) 1.00 (0.79)

39.51 (7.27) 10.70 (2.81) 25.4 (14.05) 8.51 (4.35) 13.36 (4.95) 1.612 (0.91) 0.95 (0.56)

Standard deviation was in parentheses. Frontotemporal dementia; FTD, Alzheimer’s disease; AD, normal control; NC. * p > 0.05. ** P > 0.1.

P value FTD vs. NC

AD vs. NC

FTD vs. AD

0.284 0.848 0.014* 1 0.275 0.072** 0.194

0.005* 0.353 0.175 0.096** 0.636 0.993 0.997

0.172 0.968 0.883 0.075** 0.014* 0.056** 0.25

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Table 4 ROC curve analysis.

FTD vs. NC alpha1 band AD vs. NC delta band FTD vs. AD beta1 band

AUC

p Value

0.69 0.78 0.74

0.03 0.002 0.011

95% CI Lower

Upper

0.529 0.637 0.586

0.858 0.928 0.898

Sensitivity

Specificity

0.55 0.74 0.74

0.84 0.73 0.63

AUC: area under the curve. ROC: receiver operating-characteristic. p Value: test for AUC = 0.5 (chance discrimination). 95% CI: confidence interval for AUC. FTD: frontotemporal dementia, AD: Alzheimer’s disease; NC: normal control.

Fig. 3. Compares current density images in Talairach space obtained by sLORETA for the FTD and NC groups. Red areas correspond to significantly higher activity in the FTD group, and yellow areas correspond to significantly higher activity in the NC group (p < 0.047, Log-F-ratio threshold = 0.344). (A: anterior, P: posterior, FTD: frontotemporal dementia, AD: Alzheimer disease, NC: normal control).

Fig. 4. In the alpha1 frequency band, NC showed strong activity in the medial frontal gyrus and the frontal lobe.

Fig. 5. Compares current density images for the AD and NC groups. Blue areas correspond to significantly higher activity in the AD group, and yellow areas correspond to significantly higher activity in the NC group (p < 0.050, Log-F-ratio threshold = 0.186). The solid line surrounds images in the frequency band which is revealed a significant difference of GFP between AD and NC. (A: anterior, P: posterior, FTD: frontotemporal dementia, AD: Alzheimer disease, NC: normal control).

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Fig. 6. In the delta frequency band, activity was significantly higher for AD in widespread areas including the medial frontal gyrus and the frontal lobe, especially in the right hemisphere.

Fig. 7. Compares current density images for FTD and AD. Red areas mean that activity was stronger than the baseline for FTD. Blue areas mean that activity was stronger than the baseline for AD (p < 0.047, Log-F-ratio threshold = 0.280). The solid lines indicate images in frequency bands which has significant differences of GFP between FTD and AD. (A: anterior, P: posterior, FTD: frontotemporal dementia, AD: Alzheimer disease, NC: normal control).

Fig. 8. In the beta1 band, FTD exhibited greater activity in parietal lobe and sensorimortar area.

the delta frequency band, activity was significantly higher for AD in widespread areas including the medial frontal gyrus and the frontal lobe, especially in the right hemisphere. Fig. 7 compares current density images for FTD and AD. Red areas correspond to significantly higher activity in the FTD group, and blue areas correspond to significantly higher activity in the AD group (p < 0.050, Log-F-ratio threshold = 0.280. The solid lines indicate images in frequency band that had significant difference of GFP between FTD and AD. In Fig. 8, in the beta1 frequency band, activity was significantly higher for FTD in parietal lobe and sensorimotor area.

4. Discussion In the present study, GFP analysis revealed significant group effects in the delta, alpha1, and beta1 bands. In a subsequent sLORETA analysis, significant differences in activity were observed in the alpha1 frequency band (NC > FTD) in the orbital frontal and temporal lobe, in the delta frequency band (AD > NC) in widespread areas including the frontal lobe, and in the beta1 band (FTD > AD) in the parietal lobe and sensorimotor area. These significant differences are sufficient for a moderate separation of the groups, as shown in the ROC-curve analyses (see e.g.

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(Yuan et al., 2009) and (Lehmann et al., 2007) for comparative values of ROC area values). We found that the activity in the alpha1 band was significantly higher for NC than for FTD. Our study partly agrees with Lindau et al. (2003) finding that FTD has a tendency to exhibit a decrease in the alpha (8.0–11.0 Hz) band. It should be noted that the frequency bands used in this study (Kubicki et al., 1979) are not identical to those used by Lindau et al. and comparisons should be viewed with a certain amount of caution. However, in the beta (12.0–23.5 Hz) band, we observed an increase in activity for FTD, which is the opposite of the Lindau et al. results. This discrepancy is difficult to understand. One possible explanation is that our FTD patients are different from those of the Lindau et al. study. For instance, we controlled that our subjects have a CDR scale in the range 0.5–1.0, which was not controlled by Lindau. Further evidence that proves the importance of taking into account disease severity when comparing populations was provided by Julin et al. (1995), where it was shown that only when comparing patients with MMSE > 22, a significant difference was found for less abnormal EEG changes in frontal lobe dementia (FLD) as compared to AD. One might speculate that if the Lindau et al. subjects were in a more advanced FTD state, these patients probably had more pathological changes with accompanying decreased beta activity. These same considerations apply to the results of Yener et al. (1996), in which of the 13 FTD patients, one was in very mild stage (CDR 0.5), six in mild stage (CDR 1), 5 in moderate stage (CDR 2), and 1 in severe stage (CDR 3). Using sLORETA to compare FTD and NC, we found significantly lower alpha1 activity for FTD in frontal lobes, particularly in the medial frontal gyrus. Seeley found gray matter atrophy in similar regions in FTD patients (Seeley et al., 2008). Our results are consistent with these findings, especially when considering that in general, metabolism in basal prefrontal cortex correlates directly with alpha band power (Sadato et al., 1998). The overlap of these different imaging modalities in detecting pathological activity in the same brain areas lends validation to the new non-invasive imaging method sLORETA. The GFP of the delta (1.5–6.0 Hz) band activity was higher for the AD group than for the NC group. Taking into account that our wide delta band includes the more popular definition from 1 to 3.5 Hz for delta, and that it overlaps with the popular definition of the theta band (4–7.5 Hz), our results show a very good agreement with those of Huang et al. (2000) and Lindau et al. (2003). A positive significant correlation between MMSE score and beta1 GFP was observed in the AD group. This result is consistent with the decreased beta1 activity for the AD group when compared to normal controls. In other words, the AD subjects have different degrees of memory and general cognitive deficits, with the more severe ones having the least beta1 activity, which is pathologically lower than the normal group. Our results are in agreement with those of Claus et al. (1998) where it was shown that lower parieto-occipital beta was significantly associated with more decline in global cognitive function. Using sLORETA to compare AD and NC, we found that for AD patients, the current density of the delta band increased in frontal, temporal, and occipital regions. Previous results reported by Babiloni et al. (2007) show that AD patients exhibit a widespread increase in the amplitude of delta (2–4 Hz) in temporal and occipital regions. In distinction from the results of Babiloni et al. we observed significant increase of delta generators in frontal regions, which would implicate a frontal lobe dysfunction in AD. These findings are consistent with those of O’Brien et al. (1992), where evidence is provided suggesting that frontal changes can occur early in the course of Alzheimer’s disease. The FTD group exhibited stronger beta1 band activity than the AD group, both for GFP and for the intracranial electrical generators

(sLORETA) in the parietal lobe and sensorimotor areas. Other studies that present evidence in favor of these results are discussed below. Du et al. (2007) reported in an MRI study that AD patients had thinner cortex in parts of bilateral parietal and precuneus regions compared to FTD. In a similar comparative study by Du et al. (2006) but using arterial spin labeling MRI, it was shown that FTD was associated with higher perfusion bilaterally in inferior parietal cortex than AD. Both these structural and functional differences might explain the decreased beta1 activity for the AD group. Other studies that also show decreased function in parietal regions for AD patients compared to FTD are reported (Varrone et al., 2002; Foster et al., 2007). In particular, with respect to the observed increase in beta generators for the FTD patients in sensorimotor areas, this result corresponds to a concomitant decrease in regional cerebral blood flow that has been observed by Oishi et al. (2007) in the sensorimotor areas. They found significant negative correlation between sensorimotor EEG rhythm in the 10–20 Hz range with sensorimotor rCBF. In addition, also supporting these findings, Lojkowska et al. (2002) reported the motor cortex as one of the regions with significant hypoperfusion in FTD as compared to mild AD. From another point of view, the higher beta current density exhibited by FTD patients as compared with AD patients may be the cause of the difference between the main symptoms of FTD and AD (Pijnenburg et al., 2004). Specifically, various somatic symptoms persist in FTD patients, which is likely associated to the high current density of beta1 waves in the sensorimotor area, thus giving rise to the physical symptoms of FTD, in agreement with Poprawski et al. (2007). 5. Limitations and outlook One limitation of this study is the use of only 19 electrodes, which is relatively small for localization. However, it should be emphasized that this does not necessarily imply mislocalization, but rather it decreases the spatial resolution. For instance, using as few as 19 or 27 electrodes, Winterer et al. (2001) and Mulert et al. (2004) have provided cross-modal validation for LORETA across many different Brodmann areas. Accordingly, the localization results in this study consist of wide-spread cortical areas, as should be expected in the case of the pathologies studied here, which affect widely distributed brain areas. The present study suffers from two further limitations. The number of patients was small; and we could not conduct a neuropathological examination, which is usually necessary to make a definitive diagnosis. Further study is needed to compare patient groups according to the stage of the disease and the type of symptoms and to investigate whether or not there is a correlation between results obtained with quantative EEG and those obtained with other tools, such as MRI, SPECT, and PET. In summary, our results suggest that quantative EEG, including sLORETA, provide information that is at least partially (although not sufficiently) useful in distinguishing FTD from AD in the early stages. Acknowledgements This project was sponsored by a grant from the Medical Research Foundation for Senile Dementia of Osaka. We thank Misa Suzuki for help in data collection. References Babiloni C, Cassetta E, Binetti G, Tombini M, Del Percio C, Ferreri F, et al. Resting EEG sources correlate with attentional span in mild cognitive impairment and Alzheimer’s disease. Eur J Neurosci 2007;12:3742–57.

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