Quantitative EEG Markers in Mild Cognitive Impairment: Degenerative versus Vascular Brain Impairment

D. V. Moretti; O. Zanetti; G. Binetti; G. B. Frisoni

doi:10.1155/2012/917537

Quantitative EEG Markers in Mild Cognitive Impairment: Degenerative versus Vascular Brain Impairment

Moretti, D. V.;Zanetti, O.;Binetti, G.;Frisoni, G. B. 2012-07-26 00:00:00 Hindawi Publishing Corporation International Journal of Alzheimer’s Disease Volume 2012, Article ID 917537, 12 pages doi:10.1155/2012/917537 Review Article Quantitative EEG Markers in Mild Cognitive Impairment: Degenerative versus Vascular Brain Impairment D. V. Moretti, O. Zanetti, G. Binetti, and G. B. Frisoni Centro San Giovanni di Dio Fatebenefratelli, TRccs, 25125 Brescia, Italy Correspondence should be addressed to D. V. Moretti, davide.moretti@afar.it Received 28 November 2011; Revised 17 May 2012; Accepted 21 May 2012 Academic Editor: Seishi Terada Copyright © 2012 D. V. Moretti et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. We evaluated the relationship between brain rhythmicity and both the cerebrovascular damage (CVD) and amygdalohippocampal complex (AHC) atrophy, as revealed by scalp electroencephalography (EEG) in a cohort of subjects with mild cognitive impairment (MCI). All MCI subjects underwent EEG recording and magnetic resonance imaging. EEGs were recorded at rest. Relative power was separately computed for delta, theta, alpha1, alpha2, and alpha3 frequency bands. In the spectral band power the severity of CVD was associated with increased delta power and decreased alpha2 power. No association of vascular damage was observed with alpha3 power. Moreover, the theta/alpha1 ratio could be a reliable index for the estimation of the individual extent of CV damage. On the other side, the group with moderate hippocampal atrophy showed the highest increase of alpha2 and alpha3 power. Moreover, when the amygdalar and hippocampal volumes are separately considered, within amygdalohippocampal complex (AHC), the increase of theta/gamma ratio is best associated with amygdalar atrophy whereas alpha3/alpha2 ratio is best associated with hippocampal atrophy. CVD and AHC damages are associated with speciﬁc EEG markers. So far, these EEG markers could have a prospective value in diﬀerential diagnosis between vascular and degenerative MCI. 1. Introduction temporal lobe (MTL) activations in MCI subjects versus controls, during the performance of memory tasks [16, 17]. Mild cognitive impairment (MCI) is a clinical state inter- Nonetheless, fMRI ﬁndings in MCI are discrepant, as MTL mediate between elderly normal cognition and dementia hypoactivation similar to that seen in AD patients [18] that aﬀects a signiﬁcant amount of the elderly population, has also been reported [19]. Recent postmortem data from featuring memory complaints and cognitive impairment on subjects—who had been prospectively followed and clinically neuropsychological testing, but no dementia [1–3]. characterized up to immediately before their death—indicate The hippocampus is one of the ﬁrst and most aﬀected that hippocampal choline acetyltransferase levels are reduced brain regions impacted by both Alzheimer’s disease and mild in Alzheimer’s dementia, but in fact they are upregulated cognitive impairment (MCI; [4–9]). In mild-to-moderate in MCI [15], presumably because of reactive upregulations Alzheimer’s disease patients, it has been shown that hip- of the enzyme activity in the unaﬀected hippocampal pocampal volumes are 27% smaller than in normal elderly cholinergic axons. Previous EEG studies [20–26] have shown controls [10, 11], whereas patients with MCI show a volume a decrease—ranging from 8 to 10.5 Hz (low alpha)—of reduction of 11% [11]. So far, from a neuropathological the alpha frequency power band in MCI subjects, when compared to normal elderly controls [20, 27–30]. However, a point of view, the progression of disease from MCI state to later stages seems to follow a linear course. Nevertheless, recent study has shown an increase—ranging from 10.5 to 13 there is some evidence from functional [12–14]and bio- Hz (high alpha)—of the alpha frequency power band, on the chemical studies [15] that the process of conversion from occipital region in MCI subjects, when compared to normal nondemented to clinically evident demented state is not so elderly and AD patients [30]. These somewhat contradictory linear. Recent fMRI studies have suggested increased medial ﬁndings may be explained by the possibility that MCI 2 International Journal of Alzheimer’s Disease subjects have diﬀerent patterns of plastic organization during 2. Materials and Methods the disease and that the activation (or hypoactivation) 2.1. Subjects of diﬀerent cerebral areas is based on various degrees of hippocampal atrophy. If this hypothesis is true, then EEG 2.1.1. General Considerations about Recruitment. All the changes of rhythmicity have to occur nonproportionally to subjects in the study were recruited from the same cohort the hippocampal atrophy, as previously demonstrated in a in the Memory Clinic of the Scientiﬁc Institute for Research studyofauditoryevokedpotentials[31]. and Care (IRCCS) of Alzheimer’s and psychiatric diseases In a recent study [32], the results conﬁrm the hypothesis “Fatebenefratelli” in Brescia, Italy. All experimental protocols that the relationship between hippocampal volume and EEG had been approved by the local Ethics Committee. Informed rhythmicity is not proportional to the hippocampal atrophy, consent was obtained from all participants or their care- as revealed by the analyses of both the relative band powers givers, according to the Code of Ethics of the World Medical and the individual alpha markers. Such a pattern seems Association (Declaration of Helsinki). The diﬀerence in the to emerge because, rather than a classiﬁcation based on size of the populations (cerebrovascular and degenerative clinical parameters, discrete hippocampal volume diﬀerences 3 impairment) is due to technical reasons linked to the MRI (about 1 cm ) are analyzed. Indeed, the group with moderate analysis. hippocampal atrophy showed the highest increase in the theta power on frontal regions and of the alpha2 and alpha3 powers on frontal and temporoparietal areas. Cerebrovascular Impairment. For the present study, 99 sub- Recently, two speciﬁc EEG markers, theta/gamma and jects with MCI were recruited. Table 1 shows the main alpha3/alpha2 frequency ratio, have been reliably associated features of this group. with the atrophy of amygdalo-hippocampal complex [33], as well as with memory deﬁcits, which are a major risk Degenerative Impairment. For the present study, 79 subjects for the development of AD in MCI subjects [34]. Based on with MCI were recruited. Table 3 shows the main character- the tertiles values of decreasing AHC volume, three groups istics of the group. of AHC growing atrophy were obtained. AHC atrophy is associated with memory deﬁcits as well as with increase 2.2. Shared Procedures of theta/gamma and alpha3/alpha2 ratio. Moreover, when the amygdalar and hippocampal volumes are separately 2.2.1. EEG Recordings. All recordings were obtained in the considered, within AHC, the increase of theta/gamma morning with subjects resting comfortably. Vigilance was ratio is best associated with amygdalar atrophy whereas continuously monitored in order to avoid drowsiness. alpha3/alpha2 ratio is best associated with hippocampal The EEG activity was recorded continuously from 19 atrophy. sites by using electrodes set in an elastic cap (Electro-Cap The role of cerebrovascular (CV) disease and ischemic International, Inc.) and positioned according to the 10– brain damage in cognitive decline remains controversial. 20 International system (Fp1, Fp2, F7, F3, Fz, F4, F8, T3, Although not all patients with mild cognitive impairment C3, Cz, C4, T4, T5, P3, Pz, P4, T6, O1, and O2). The due to CV damage develop a clinically deﬁned dementia, ground electrode was placed in front of Fz. The left and right all such patients are at risk and could develop dementia mastoids served as reference for all electrodes. The recordings in the 5 years following the detection of cognitive decline. were used oﬀ line to rereference the scalp recordings to Cognitive impairment due to subcortical CV damages is the common average. Data were recorded with a band- thought to be caused by focal or multifocal lesions involving pass ﬁlter of 0.3–70 Hz and digitized at a sampling rate of strategic brain areas. These lesions in basal ganglia, thalamus, 250 Hz (BrainAmp, BrainProducts, Germany). Electrodes- or connecting white matter induce interruption of thala- skin impedance was set below 5 kΩ. Horizontal and vertical mocortical and striatocortical pathways. As a consequence, eye movements were detected by recording the electrooculo- deaﬀerentation of frontal and limbic cortical structures is gram (EOG). The recording lasted 5 minutes, with subjects produced. The pattern of cognitive impairment is consistent with closed eyes. Longer recordings would have reduced the with models of impaired cortical and subcortical neuronal variability of the data, but they would also have increased pathways [36]. Even when CV pathology appears to be the possibility of slowing of EEG oscillations due to reduced the main underlying process, the eﬀects of the damaged vigilance and arousal. EEG data were then analyzed and brain parenchyma are variable and, therefore, the clinical, fragmented oﬀ line in consecutive epochs of 2 seconds, with radiological, and pathological appearances may be hetero- a frequency resolution of 0.5 Hz. The average number of geneous. A neurophysiological approach could be helpful in epochs analyzed was 140 ranging from 130 to 150. The EEG diﬀerentiating structural from functional CV damage [35]. epochs with ocular, muscular, and other types of artifacts The quantitative analysis of electroencephalographic (EEG) were discarded. rhythms in resting subjects is a low-cost but still powerful approach to the study of elderly subjects in normal aging, MCI, and dementia. The aim of this study was to compare 2.2.2. Analysis of Individual Frequency Bands. A digital FFT- speciﬁc EEG markers that could be useful for diagnostic based power spectrum analysis (Welch technique, Hanning and prognostic purpose in the investigation of patients with windowing function, no phase shift) computed—ranging cognitive decline. from 2 to 45 Hz—the power density of EEG rhythms with International Journal of Alzheimer’s Disease 3 Table 1: Mean values ± standard error of demographic characteristics, neuropsychological and ARWMC scores of the MCI subgroups. F/M: female/male. Age and education are expressed in years. Group 1: no vascular damage; group 2: mild vascular damage; group 3: moderate vascular damage; group 4: severe vascular damage. Group 1 Group 2 Group 3 Group 4 Subjects (f/m) 27 (18/9) 41 (31/10) 19 (10/9) 12 (9/3) Age 70.1 (±1.7) 69.9 (±1.1) 69.7 (±1.9) 70.5 (±2.4) Education 7.1 (±0.7) 7 (±0.6) 7 (±0.9) 10 (±1.6) MMSE 26.7 (±0.4) 26.5 (±0.4) 27 (±0.4) 26.1 (±0.7) ARWMC scale 0 1–5 6–10 11–15 a 0.5 Hz frequency resolution. Methods are exposed in subjected to diagnostic neuroimaging procedures (magnetic detail elsewhere [32, 35, 37]. Brieﬂy, two anchor frequencies resonance imaging (MRI)) and laboratory blood analysis to were selected according to the literature guidelines [38], rule out other causes of cognitive impairment. that is, the theta/alpha transition frequency (TF) and the The present inclusion and exclusion criteria for MCI individual alpha frequency (IAF) peak. Based on TF and IAF, were based on previous seminal studies [2, 3, 43–46]. we estimated the following frequency band range for each Inclusion criteria in the study were all of the following: (i) subject: delta, theta, low alpha band (alpha1 and alpha2), complaint by the patient or report by a relative or the general and high alpha band (alpha3). Moreover, individual beta and practitioner of memory or other cognitive disturbances; (ii) gamma frequencies were computed. Three frequency peaks Mini-Mental State Examination (MMSE; [39]) score of 24 were detected in the frequency range from the individual to 27/30 or MMSE of 28 and higher plus low performance alpha 3 frequency band and 45 Hz. These peaks were (score of 2/6 or higher) on the clock drawing test [47]; named beta 1 peak (IBF 1), beta 2 peak (IBF 2), and (iii) sparing of instrumental and basic activities of daily gamma peak (IGF). Based on peaks, the frequency ranges living or functional impairment stably due to causes other were determined. Beta1 ranges from alpha 3 to the lower than cognitive impairment, such as physical impairments, spectral power value between beta 1 and beta 2 peak; beta2 sensory loss, and gait or balance disturbances. Exclusion frequency ranges from beta 1 to the lower spectral power criteria were any one of the following: (i) age of 90 years value between beta 2 and gamma peak; gamma frequency and older; (ii) history of depression or psychosis of juvenile ranges from beta 2 to 45 Hz, which is the end of the onset; (iii) history or neurological signs of major stroke; (iv) range considered. The frequency range was determined for other psychiatric diseases, epilepsy, drug addiction, alcohol each patient. On average, the boundaries of the frequency dependence; (v) use of psychoactive drugs including acetyl- bands were as follows: delta 2.9–4.9 Hz; theta 4.9–6.9 Hz; cholinesterase inhibitors or other drugs enhancing brain alpha1 6.9–8.9 Hz; alpha 2 8.9–10.9 Hz; alpha3 10.9–12-9 Hz; cognitive functions; (vi) current or previous uncontrolled or beta1 12,9–19,2 Hz; beta2 19.2–32.4; gamma 32.4–45. In the complicated systemic diseases (including diabetes mellitus) frequency bands determined in this way, the relative power or traumatic brain injuries. spectra for each subject were computed. The relative power All patients underwent (i) semistructured interview density for each frequency band was computed as the ratio with the patient and—whenever possible—with another between the absolute power and the mean power spectra informant (usually the patient’s spouse or a child) by a from 2 to 45 Hz. Finally, the theta/gamma and alpha3/alpha2 geriatrician or neurologist; (ii) physical and neurological relative power ratio were computed and analyzed. The examinations; (iii) performance-based tests of physical func- analysis of other frequencies was not in the scope of this tion, gait, and balance; (iv) neuropsychological assessment study. evaluating verbal and nonverbal memory, attention, and executive functions (Trail Making Test B-A; Clock Drawing Test; [47, 48]), abstract thinking (Raven matrices; [49]), frontal functions (Inverted Motor Learning; [50]), language 2.2.3. Diagnostic Criteria. In this study we enrolled sub- (Phonological and Semantic ﬂuency; Token test; [51]), and jects aﬀerents to the Scientiﬁc Institute of Research and apraxia and visuoconstructional abilities (Rey ﬁgure copy; Care Fatebenefratelli in Brescia, Italy. Patients were taken [52]); (v) assessment of depressive symptoms with the Center from a prospective project on clinical progression of MCI. for Epidemiologic Studies Depression Scale (CES-D; [53]). The project was aimed to study the natural history of Given the aim of the study to evaluate the impact of vascular nondemented persons with apparently primary cognitive damage on EEG rhythms, in this study we did not consider deﬁcits, not caused by psychic (anxiety, depression, etc.) the clinical subtype of MCI, that is, amnesic, nonamnesic, or or physical (uncontrolled heart disease, uncontrolled dia- multiple domain. betes, etc.) conditions. Patients were rated with a series of standardized diagnostic tests, including the Mini-Mental State Examination (MMSE; [39]), the Clinical Dementia Rating Scale (CDRS; [40]), the Hachinski Ischemic Scale 2.2.4. Magnetic Resonance Imaging (MRI) and CV Damage (HIS; [41]), and the Instrumental and Basic Activities of Evaluation. Magnetic resonance (MR) images were acquired Daily Living (IADL, BADL, [42]). In addition, patients were using a 1.0 Tesla Philips Gyroscan. Axial T2 weighted, 4 International Journal of Alzheimer’s Disease proton density(DP), andﬂuidattenuatedinversion recovery Plot of means Delta 2-way interaction (FLAIR) images were acquired with the following acquisition P Alpha 2 F(12.38) = 2.6; P< 0.0025 parameters: TR = 2000 ms, TE = 8.8/110 ms, ﬂip angle = 90 , 4.5 P< 0.0007 ﬁeld of view = 230 mm, acquisition matrix 256 × 256, slice G1 versus G3 thickness 5 mm for T2/DP sequences and TR = 5000 ms, TE = 100 ms, ﬂip angle = 90 ,ﬁeldofview = 230 mm, acquisition 3.5 matrix 256 × 256, slice thickness 5 mm for FLAIR images. Subcortical cerebrovascular disease (sCVD) was assessed 3 using the rating scale for age-related white matter change P = 0.05 2.5 (ARWMC) on T2-weighted and FLAIR MR images. White G1 versus G4 matter changes (WMC) was rated by a single observer (R.R.) in the right and left hemispheres separately in frontal, P< 0.00003 1.5 G1 versus G4 parietooccipital, temporal, and infratentorial areas and basal ganglia on a 4-point scale. The observer of white matter changes was blind to the clinical information of the subjects. Subscores of 0, 1, 2, and 3 were assigned in frontal, parieto- 0.5 Delta Theta Alpha 1 Alpha 2 Alpha 3 occipital, temporal, and infratentorial areas for no WMC, Frequency bands focal lesions, beginning conﬂuence of lesions, and diﬀuse involvement of the entire region, respectively. Subscores of Group 1 Group 3 0, 1, 2, and 3 were assigned in basal ganglia for no WMC, 1 Group 2 Group 4 focal lesion, more than 1 focal lesion, and conﬂuent lesions, Figure 1: Statistical ANOVA interaction among groups, factor and respectively. Total score was the sum of subscores for each relative band power (delta, theta, alpha1, alpha2, and alpha3). In area in the left and right hemisphere, ranging from 0 to the diagram the diﬀerence in delta and alpha2 power among groups 30. As regards the ARWMC scale, the interrater reliability, is also indicated, based on Duncan’s post hoc testing. G1, group 1: as calculated with weighted k value, was 0.67, indicative of no vascular damage; G2, group 2: mild vascular damage; G3, group moderate agreement [54]. We assessed test-retest reliability 3: moderate vascular damage; G4, group 4: severe vascular damage on a random sample of 20 subjects. The intraclass correlation [32, 35]. coeﬃcient was 0.98, values above 0.80 being considered indicative of good agreement. Based on increasing subcortical CV damage, the 99 MCI subjects were subsequently divided in 4 subgroups along the performed statistical analyses to evaluate the speciﬁcity of range between the minimum and maximum ARWMC score the following ratios: theta/alpha1, using as covariate also TF; (resp. 0 and 15). In order to have the higher sensibility to alpha2/alpha3 using as covariate also IAF and alpha1/alpha2 the CV damage, the ﬁrst group was composed by subjects with both TF and IAF as covariate. Moreover, we performed with score = 0. The other groups were composed according to correlations (Pearson’s moment correlation) between CV equal range ARWMC scores. As a consequence, we obtained damage score and frequency markers (TF and IAF), spectral the following groups: group 1 (G1): no vascular damage, CV power, and MMSE. Finally, we performed a control statistical score 0; group 2 (G2): mild vascular damage, CV score 1– analysis with 4 frequency bands, considering alpha1 and 5; group 3 (G3): moderate vascular damage, CV score 6–10; alpha2 as single band (low alpha). This analysis had the aim group 4 (G4): severe vascular damage, CV score 11–15. of verifying if the low alpha, when considered as a whole, has Table 1 reports the mean values of demographic and the same behavior. clinical characteristics of the 4 subgroups. 3. Results 2.2.5. Statistical Analysis. Preliminarily, any signiﬁcant dif- ference between groups in demographic variables, age, 3.1. Vascular MCI. Figure 1 displays the results for ANOVA education, and gender as well as MMSE score was taken analysis of these data showing a signiﬁcant interaction into account. Only education showed a signiﬁcant diﬀerence between group and Band factors (F(12.380) = 2.60;P< between groups (P< 0.03). For avoiding confounding eﬀect, 0.002). Interestingly, Duncan’s post hoc testing showed a subsequent statistical analyses of variance (ANOVA) were signiﬁcant higher delta power in G4 compared to G1 (P< carried out using age, education, gender, and MMSE score as 0.050) and a signiﬁcant higher alpha2 power in G1 compared covariates. Duncan’s test was used for post hoc comparisons. to G3 and G4 (P< 0.000). On the contrary, no diﬀerences For all statistical tests the signiﬁcance was set to P< 0.05. were found in theta, alpha1, and alpha 3 band powers. A second session of ANOVA was performed on EEG Moreover, a closer look at the data, in respect to the alpha1 relative power data. In this analysis, group factor was the frequency, showed a decrease proportional to the degree independent variable and frequency band power (delta, of CV damage very similar to alpha2 band, although not theta, alpha1, alpha2, and alpha 3) the dependent variable. signiﬁcant. On the contrary, in the alpha3 band power this As successive step, to evaluate the presence of EEG trend was not present, suggesting that vascular damage had indexes that correlate speciﬁcally with vascular damage, we no impact on this frequency band. Spectral power density International Journal of Alzheimer’s Disease 5 Table 2: Mean values ± standard error of theta/alpha1, alpha1/alpha2, and alpha2/alpha3 ratios in the MCI subgroups. Group 1: no vascular damage; group 2: mild vascular damage; group 3: moderate vascular damage; group 4: severe vascular damage. Group 1 Group 2 Group 3 Group 4 θ/α10.7(±0.05) 0.77 (±0.05) 1.17 (±0.05) 1.39 (±0.14) α1/α20.46(±0.03) 0.5 (±0.03) 0.53 (±0.05) 0.47 (±0.04) α2/α31.27(±0.12) 1.16 (±0.1) 0.85 (±0.05) 0.79 (±0.07) Table 3: Mean values ± standard deviation of sociodemographic characteristics, MMSE scores, white matter hyperintensities, and hippocampal and amygdalar volume measurements. Hippocampal and amygdalar volumes refer to the whole amygdalohippocampal complex (AHC) and are singularly considered (individual). The t-test refers to AHC versus individual volume in each group. MCI cohort Group 1 Group 2 Group 3 P value (ANOVA) Number of subjects (f/m) 79 (42/37) 27 (14/13) 27 (15/12) 25 (13/12) Age (years) 69.2 ± 2.366.8 ± 6.869.4 ± 8.771.5 ± 6.90.1 Education (years) 7.7 ± 0.88.3 ± 4.56.7 ± 3.18.2 ± 4.60.2 MMSE 27.1 ± 0.427.5 ± 1.527.4 ± 1.526.6 ± 1.80.1 Total AHC volume 6965.3 ± 1248.8 8151.2 ± 436.4 7082.7 ± 266.9 5661.8 ± 720.4 0.00001 AHC-hippocampal volume (mm ) 4891.7 ± 902.6 5771.6 ± 361.1 4935.6 ± 380.9 3967.9 ± 650.3 0.00001 AHC-amygdalar volume (mm ) 2073.5 ± 348.7 2379.6 ± 321.3 2147.1 ± 301.3 1693.9 ± 288.5 0.0001 Individual hippocampal volume (mm ) 4889.8 ± 962.4 5809.6 ± 314.2 4969.4 ± 257.6 3890.1 ± 551.4 0.00001 Individual amygdalar volume (mm ) 2071.7 ± 446.4 2514.4 ± 259.5 2079.2 ± 122.8 1621.6 ± 185.2 0.0001 White matter hyperintensities (mm)3.8 ± 0.53.2 ± 2.84.2 ± 3.84.1 ± 3.60.7 The correlation analysis between CV score and spectral sequence (TR = 5000 ms, TE = 100 ms, ﬂip angle = 90 , band power showed a signiﬁcant positive correlation with ﬁeld of view = 230 mm, acquisition matrix = 256 × 256, and delta power (r = 0.221;P< 0.03) a signiﬁcant negative slice thickness = 5 mm). Hippocampal, amygdalar, and white correlation with alpha1 (r =−0.312;P< 0.002) and alpha2 matter hyperintensities (WMHs) volumes were obtained for power (r= − 0.363;P< 0.0003). The correlations between each subject. The hippocampal and amygdalar boundaries CV score with theta power (r = 0.183; P = 0.07) and were manually traced on each hemisphere by a single tracer alpha3 power (r =−0.002; P = 0.93) were not signiﬁcant with the software program DISPLAY (McGill University, as well as the correlation between CV score and MMSE (r = Montreal, Canada) on contiguous 1.5 mm slices in the −0.07; P = 0.4). coronal plane. The amygdala is an olive-shaped mass of gray Table 2 displays the values of the theta/alpha1 and matter located in the superomedial part of the temporal alpha2/alpha3 power ratio. The statistical analysis of the lobe, partly superior and anterior to the hippocampus. The theta/alpha1 ratio showed a main eﬀect of group (F(3.91) = starting point for amygdala tracing was at the level where it is 15.51;P< 0.000). Duncan’s post hoc testing showed a separated from the entorhinal cortex by intrarhinal sulcus, signiﬁcant increase of the theta/alpha1 ratio between G1 and or tentorial indentation, which forms a marked indent at G2 in respect to G3 and G4 (P< 0.000). Moreover, the the site of the inferior border of the amygdala. The uncinate increase of this ratio was signiﬁcant also between G3 and fasciculus, at the level of basolateral nuclei groups, was G4 (P< 0.04). The statistical analysis of the alpha2/alpha3 considered as the anterior-lateral border. The amygdalo- power ratio showed a main eﬀect of group (F(3.91) = striatal transition area, which is located between lateral 4.60;P< 0.005). Duncan’s post hoc testing showed a amygdaloid nucleus and ventral putamen, was considered as signiﬁcant decrease of the ratio between G1 and G3 (P< the posterior-lateral border. The posterior end of amygdaloid 0.02), G1 and G4 (P< 0.010), and G2 and G4 (P< 0.05). The nucleus was deﬁned as the point where gray matter starts statistical analysis of the alpha1/alpha2 ratio did not show the to appear superior to the alveolus and lateral to the main eﬀect of group (P< 0.2). hippocampus. If the alveolus was not visible, the inferior horn of the lateral ventricle was employed as border [33]. The starting point for hippocampus tracing was deﬁned 3.2. MRI Scans and Amygdalohippocampal Atrophy Evalu- as the hippocampal head when it ﬁrst appears below the ation. MRI scans were acquired with a 1.0 Tesla Philips amygdala, the alveus deﬁning the superior and anterior Gyroscan at the Neuroradiology Unit of the Cittad ` iBrescia border of the hippocampus. The ﬁmbria was included in Hospital, Brescia. The following sequences were used to the hippocampal body, while the grey matter rostral to the ﬁmbria was excluded. The hippocampal tail was traced until measure hippocampal and amygdalar volumes: a high- resolution gradient echo T1-weighted sagittal 3D sequence it was visible as an oval shape located caudally and medially (TR = 20 ms, TE = 5 ms, ﬂip angle = 30 ,ﬁeldofview = to the trigone of the lateral ventricles [32, 35]. The intraclass 220 mm, acquisition matrix = 256×256, and slice thickness = correlation coeﬃcients were 0.95 for the hippocampus and 1.3 mm) and a ﬂuid-attenuated inversion recovery (FLAIR) 0.83 for the amygdala. 6 International Journal of Alzheimer’s Disease White matter hyperintensities (WMHs) were automati- subjects. The diﬀerence in groups subdivision (4 versus 3) cally segmented on the FLAIR sequences by using previously was mandatory to obtain group with volumetric statistical described algorithms [32, 35]. Brieﬂy, the procedure includes signiﬁcant diﬀerence. (i) ﬁltering of FLAIR images to exclude radiofrequency At ﬁrst, we choose to focus on the changes of brain inhomogeneities, (ii) segmentation of brain tissue from cere- rhythmicity induced from hippocampal atrophy alone. Sub- brospinal ﬂuid, (iii) modelling of brain intensity histogram jectsweresubdividedinfourgroupsbased on hippocampal as a Gaussian distribution, and (iv) classiﬁcation of the voxels volume of a normal control sample matched for age, sex, whose intensities were ≥3.5 SDs above the mean as WMHs and education as compared to the whole MCI group. In the [32, 35], Total WMHs volume was computed by counting the normal group the female/male ratio was 93/46, mean age number of voxels segmented as WMHs and multiplying by was 68.9 (SD ± 10.3), mean education was years 8.9 (SD ± the voxel size (5 mm ). To correct for individual diﬀerences 9.4). The mean and standard deviation of the hippocampal in head size, hippocampal, amygdalar and WMHs volumes volume in the normal old population of 139 subjects were were normalized to the total intracranial volume (TIV), 5.72 ± 1.1cm . In this way, 4 groups were obtained: the no obtained by manually tracing with DISPLAY the entire atrophy group with hippocampal volume equal or superior intracranial cavity on 7 mm thick coronal slices of the T1 to the normal mean (total hippocampal volume from 3 3 weighted images. Both manual and automated methods 6.79 cm to 5.75 cm ; G1); the mild atrophy group which used here have advantages and disadvantages. Manual seg- has hippocampal volume within 1.5 SD below the mean mentation of the hippocampus and amygdala is currently of hippocampal normal control value (total hippocampal considered the gold standard technique for the measurement volume from 5.70 to 4.70 cm ; G2); the moderate atrophy of such complex structures. The main disadvantages of group which has hippocampal volume between 1.5 and 3 SD manual tracing are that it is operator dependent and time below the mean of normal hippocampus (total hippocampal consuming. Conversely, automated techniques are more volume from 4.65 to 3.5 cm ; G3); the severe atrophy reliable and less time-consuming but may be less accurate group which has hippocampal volume between 3 and 4.5 SD when dealing with structures without clearly identiﬁable below the mean of hippocampal normal control volume borders. This, however, is not the case for WMHs, which (total hippocampal volume from 3.4 to 2.53 cm ;G4).The appear as hyperintense on FLAIR sequences. rationale for the selection of 1,5 SD was to obtain reasonably Left and right hippocampal as well as amygdalar volumes pathological groups based on hippocampal volume. A SD were estimated and summed to obtain a total volume (indi- below 1.5 could recollect still normal population based on vidual) of both anatomical structures. In turn, total amyg- hippocampal volume. On the other side, a SD over 1.5 could dalar and hippocampal volumes were summed obtaining the not allow an adequate size of all subgroups in study. whole AHC volume. AHC (whole) volume has been divided Subsequently, ANOVA was performed in order to verify in tertiles obtaining three groups. In each group hippocam- (1) the diﬀerence of AHC volume among groups; (2) the pal and amygdalar volumes (within AHC) have been com- diﬀerence of hippocampal and amygdalar volume within puted. The last volumes were compared with the previous AHC among groups; (3) the diﬀerence of hippocampal and obtained individual (hippocampal and amygdalar) volumes. amygdalar volume individually considered among groups; (4) NPS impairment based on ACH atrophy. Moreover, as a control analysis, in order to detect if 3.3. Statistical Analysis and Data Management. The anal- diﬀerence in EEG markers was linked to signiﬁcant diﬀerence in volume measurements, the volume of hippocampus ysis of variance (ANOVA) has been applied as statistical tool. At ﬁrst, any signiﬁcant diﬀerences among groups within AHC was compared with the hippocampal volume in demographic variables, that is, age, education, MMSE individually considered, and the amygdalar volume within score, and morphostructural characteristics, that is, AHC, AHC was compared with the amygdalar volume individually hippocampal, amygdalar, and white matter hyperintensities considered. This control analysis was performed through a paired t-test. (WMHs) volume, were evaluated (Table 1). Greenhouse- Geisser correction and Mauchly’s sphericity test were applied Subsequently, ANOVA was performed in order to check to all ANOVAs. In order to avoid a confounding eﬀect, diﬀerences in theta/gamma and alpha3/alpha2 relative power ratio in the three groups ordered by decreasing tertile ANOVAs were carried out using age, education, MMSE score, and WMHs as covariates. For all statistical tests the values of the whole AHC volume. In each ANOVA, group signiﬁcance level was set at P< 0.05. Duncan’s test was used was the independent variable, the frequency ratios was the dependent variable, and age, education, MMSE score, and for post hoc comparisons. In a ﬁrst study [32] we considered only the hippocampal volume and obtained 4 subgroups WMHs were used as covariates. Duncan’s test was used for based on the hippocampal volume atrophy. In a subsequent post-hoc comparisons. For all statistical tests the signiﬁcance study [33], given the importance of the amigdala in both level was set at P< 0.05. degenerative pathology and brain rhythm generation, we In order to check closer association with EEG markers, hippocampal volume and amygdalar volume within AHC considered the amygdalo-hippocampal volume as a whole, in order to investigate the entire AHC and to focus on the were analyzed separately. A control analysis was carried out hippocampal volume within the AHC itself. In this case we also on the individual hippocampal and amygdalar volumes based on decreasing tertile values for homogeneity with the obtain three groups based on AHC atrophy. Both the ﬁrst and the second studies were performed on the same 79 main analysis. International Journal of Alzheimer’s Disease 7 Plot of means the ﬁrst group (P< 0.03). The amygdalar volume diﬀerence 2-way interaction in the other groups (resp. P = 0.2 and 0.1) as well as F(12.336) = 2.87; P< 0.0009 the diﬀerence in the volume of AHC-hippocampus versus Full scalp individual hippocampus (P = 0.4 in the ﬁrst, P = 0.5 in the Equal size groups second, and P = 0.1 in the third group) was not statistically 4.5 signiﬁcant. Tables 3 and 4 show the results of theta/gamma and alpha3/alpha2 ratio in the groups based on the decrease of 3.5 whole AHC volume as well as, within the same group, the decrease of hippocampal and amygdalar volumes separately 2.5 considered. ANOVA shows results towards signiﬁcance when 2 amygdaloippocampal volume is considered globally in both theta/gamma (F = 2.77;P< 0.06) and alpha3/alpha2 2,76 1.5 ratio (F = 2.71;P< 0.07). When amygdalar and 2,76 hippocampal volumes were considered separately, ANOVA 0.5 results revealed signiﬁcant main eﬀect of Group, respectively, Delta Theta Alpha 1 Alpha 2 Alpha 3 in theta/gamma ratio analysis (F = 3.46;P< 0.03) 2,76 Rhythms for amygdalar and alpha3/alpha2 ratio for hippocampal Group 1 Group 3 (F = 3.38;P< 0.03) decreasing volume. The ANOVA 2,76 Group 2 Group 4 did not show signiﬁcant results in theta/gamma ratio when Figure 2: Statistical ANOVA interaction among group factors and considering hippocampal volume (F = 0.3;P< 0.7) 2,76 relative band powers (delta, theta, alpha1, alpha2, and alpha3), on and in alpha3/alpha2 ratio when considering amygdalar the full scalp region. The groups are based on mean and standard volume (F = 1.46;P< 0.2). The control analysis 2,76 deviations in a normal elderly sample. Group 1: no hippocampal (individual volumes) did not show any signiﬁcant result atrophy; group 2: mild hippocampal atrophy; group 3: moderate neither for hippocampal (theta/gamma, F = 0.3;P< 0.7; 2,76 hippocampal atrophy; group 4: severe hippocampal atrophy. Post alpha3/alpha2, F = 2.15;P< 0.1) nor for amygdalar 2,76 hoc results are indicated in the diagram (see [32, 35]). volume (theta/gamma, F = 0.76;P< 0.4; alpha3/alpha2, 2,76 F = 2.15;P< 0.1). 2,76 3.4. Amigdalohyppocampal Atrophy MCI. Figure 2 displays the results for ANOVA analysis performed on 4 groups of MCI considering growing values of hippocampal atrphy. The 4. Discussion results show a signiﬁcant interaction between group and band power (F(12, 336) = 2, 36);P< 0.007). Duncan’s post MCI and EEG Markers: Degenerative versus Vascular Impair- hoc testing showed that G3 group has the highest alpha2 and ment. A large body of literature has previously demonstrated alpha3 power statistically signiﬁcant with respect to all other that in subjects with cognitive decline an increase of theta groups (P< 0.05;P< 0.006, resp.). The same trend was relative power [32, 33, 35], a decrease of gamma relative present in the subsidiary ANOVA. These results show that the power [32, 33, 35, 55], and an increase of high alpha as relationship between hippocampal atrophy and EEG relative compared to low alpha band are present [33]. On the power is not proportional to the hippocampal atrophy and whole theta/gamma ratio and alpha3/alpha2 ratio could be highlight that the group with a moderate hippocampal considered reliable EEG markers of cognitive decline. volume had a particular pattern of EEG activity as compared The amygdalohippocampal network is a key structure to all other groups. in the generation of theta rhythm. More speciﬁcally, theta Table 3 summarizes the ANOVA results of demographic synchronization is increased between LA and CA1 regions variables, that is, age, education, MMSE score, and mor- of hippocampus during long-term memory retrieval, but phostructural characteristics, that is, hippocampal, amyg- not during short-term or remote memory retrieval [56, dalar, and white matter hyperintensities volume in the whole 57]. Theta synchronization in AHC appears to be apt to MCIcohortaswellasinthe three subgroupsinstudy. improve the neural communication during memory retrieval Hippocampal and aymgdalar volumes are considered as parts [57]. On the other hand, the retrieval of hippocampus- of the whole AHC volume and are individually considered. dipendent memory is provided by the integrity of CA3- Signiﬁcant statistical results were found in hippocampal and CA1 interplay coordinated by gamma oscillations [58]. Our amygdalar volume both within the AHC (resp., F = results conﬁrm and extend all previous ﬁndings. The atrophy 2,76 92.74;P< 0.00001 and F = 33.82;P< 0.00001) and of AHC determines increasing memory deﬁcits. The brain 2,76 were individually considered (resp., F = 157.27;P< oscillatory activity of this MCI state is characterized by an 2,76 0.00001 and F = 132.5;P< 0.00001). The global AHC increase of theta/gamma ratio and alpha3/alpha2 relative 2,76 volume also showed signiﬁcant results (F = 159.27;P< power ratio, conﬁrming the overall reliability of these EEG 2,76 0.00001). Duncan’s post hoc test showed a signiﬁcant markers in cognitive decline. Our results suggest that theta increase (P< 0.01) in all comparisons. The paired t-test synchronization is mainly due to the amygdala activation or showed signiﬁcant diﬀerence between the volume of ACH- as a subsequent ﬁnal net eﬀect within the AHC functioning amygdala and amygdalar volume individually considered in driven by the amygdala excitation. The increase in theta Band power 8 International Journal of Alzheimer’s Disease Table 4: Relative power band ratios in amygdalo-hippocampal complex (AHC), hippocampal and amygdalar atrophy. Hippocampal and amygdalar volumes refer to the whole amygdalo-hippocampal complex (AHC) and are singularly considered (individual). 2 2 Hippocampal + amygdalar volume theta/gamma ratio (μv ) P value alpha3/alpha2 ratio (μv ) P value Group1 1.40 ± 0.35 1.05 ± 0.11 0.06 0.07 Group2 1.43 ± 0.35 1.11 ± 0.14 Group3 1.47 ± 0.44 1.12 ± 0.16 AHC-hippocampal volume Group1 1.39 ± 0.27 1.04 ± 0.11 0.7 0.03 Group2 1.48 ± 0.45 1.11 ± 0.15 Group3 1.43 ± 0.41 1.12 ± 0.14 AHC-amygdalar volume Group1 1.36 ± 0.37 1.04 ± 0.13 0.03 0.2 Group2 1.44 ± 0.36 1.12 ± 0.16 Group3 1.49 ± 0.39 1.09 ± 0.11 Individual hippocampal volume Group1 1.39 ± 0.27 1.04 ± 0.11 0.7 0.1 Group2 1.48 ± 0.45 1.07 ± 0.15 Group3 1.43 ± 0.40 1.10 ± 0.14 Individual amygdalar volume Group1 1.39 ± 0.37 1.04 ± 0.13 0.1 0.4 Group2 1.43 ± 0.36 1.12 ± 0.16 Group3 1.46 ± 0.39 1.09 ± 0.11 activities in AHC, representing an increase in neuronal com- results were conﬁrmed by a correlation analysis that showed munication apt to promote or stabilize synaptic plasticity in a signiﬁcant negative correlation between CV damage score relation to the eﬀort to retention of associative memories and alpha1 and alpha2 band powers. In our results, the CV [59], could be active also during an ongoing degenerative damage did not show any impact on the alpha3 (or high process. The excitation mechanism could be facilitated by the alpha) power. This is a conﬁrmation of what we found in the loss of GABA inhibitory process, determining the decrease of previous study, where no diﬀerences between VaD patients gamma rhythm generation [58, 60]. andnormalelderly (but notinADversusnormalelderly) As regards the CV damage, our results showed that subjects were detected in the alpha3 power. the CV damage aﬀected both delta and low alpha band Together, these results could suggest diﬀerent generators power (alpha1 and alpha2). In the delta band we observed for low alpha and high alpha frequency bands. In particular, a power increase proportional to the CV damage, with a the low alpha band power could aﬀect corticosubcortical signiﬁcant increase in the group with severe CV damage, mechanisms, such as corticothalamic, corticostriatal, and as compared to the no-CV-damage group. The impact of corticobasal ones. This could explain the sensitivity of the the CV damage on the delta power was conﬁrmed by the low alpha frequency band to subcortical vascular damage. signiﬁcant positive correlation between CV damage score On the contrary, the alpha3 band power could aﬀect to and delta power itself. a greater extent those corticocortical interactions based on The increase in the delta band power could be explained synaptic eﬃciency prone to degenerative rather than CV as a progressive cortical disconnection due to the slowing damages [38, 62]. In order to ﬁnd reliable indices of CV of the conduction along corticosubcortical connecting path- damage, we checked the theta/alpha1 band power ratio. ways. Previous studies have shown the reliability of this kind of This result conﬁrmed the increase in the delta band approach in quantitative EEG in demented patients [21]. power we had observed in CV patients, as compared to The importance of this ratio lies in the presence of such normal elderly subjects [37]. It is to be noted that the frequency bands on the opposite side of the TF, that is, increase in the delta band power reﬂects a global state of the EEG frequency index most signiﬁcantly aﬀected by the cortical deaﬀerentation, due to various anatomofunctional CV damage. So, the theta/alpha1 band power ratio could substrates, such as stages of sleep, metabolic encephalopathy, represent the most sensitive EEG marker of CV damage. The or corticothalamocortical dysrhythmia [61]. In the low alpha results showed a signiﬁcant increase of the theta/alpha1 band band power, we observed a signiﬁcant decrease in the alpha power ratio in moderate and severe CV damage groups, as 2 band power for the groups with moderate and severe compared to mild and no-CV-damage groups. This ratio CV damage, as compared to the no-CV-damage group. In increase establishes a proportional increase of the theta band the alpah1 frequency band, there was a similar decrease power relative to the alpha 1 band power with respect to although it did not reach statistically signiﬁcant values. These the CV damage, even though a signiﬁcant increase in the International Journal of Alzheimer’s Disease 9 theta band power per se (or a decrease in the alpha 1 band due to subcortical vascular damage (subcortical vascular power per se) is not present. This could suggest a reliable MCI (svMCI)) has recently been described [74, 75]. In such speciﬁcity for the theta/alpha 1 band power ratio in focusing study, MCI patients with CV etiology developed a distinctive the presence of a subcortical CV damage. clinical phenotype characterized by poor performance on frontal tests and neurological features of parkinsonism without tremor (impairment of balance and gait). These MCI, Cognitive Deﬁcits (Memory and Attention), and EEG Activity. The vulnerability and damage of the connections clinical features could be explained by our results. In CV of hippocampus with amygdala could aﬀect reconsolidation patients, we observed a slowing of the a frequency in the two groups with greater CV damage, as compared to the groups of long-term memory and give rise to memory deﬁcits and behavioural symptoms. Several experiments show that with lesser CV damage. This is in line with a previous study amygdala activity is prominent during the period of intense [37] showing the major eﬀect of the CV damage, in patients arousal, for example, the anticipation of a noxious stimulus with vascular dementia (VaD) versus normal elderly and [63] or the maintenance of vigilance to negative stimuli Alzheimer’s patients. A reasonable (although speculative) [64]. So far, the theta synchronization induced by the explanation of the present results is that the CV-damage- amygdala is deeply involved in endogenous attentional induced slowing of the a frequency start point could be mechanism. Interestingly, the increase of high alpha syn- mainly attributed to the lowering of the conduction time of synaptic action potentials throughout corticosubcortical chronization has been found in internally cued mechanisms of attention, associated with inhibitory top-down processes ﬁbers, such as corticobasal or corticothalamic pathways [76]. [62]. Of note, the amygdala is intimately involved in In fact, experimental models have previously shown that the EEG frequency is due to axonal delay and synaptic time of the anatomophysiological anterior pathways of attention through its connections with anterior cingulated cortex, corticosubcortical interactions [77–79]. Most interestingly, anteroventral, anteromedial, and pulvinar thalamic nuclei other studies have demonstrated that ﬁber myelination [65]. The particular role of amygdala in negative human aﬀects the speed propagation along cortical ﬁbers and emotions could indicate that AHC atrophy is associated with that this parameter is strictly correlated to the frequency range recorded on the scalp. In fact, a theoretical model excessive level of subcortical inputs not adequately ﬁltered by attentive processing, determining fear and anxiety and considering a mean speed propagation in white matter ﬁbers generating cognitive interference in memory performance. of 7.5 m/s (together with other parameters) is associated with a fundamental mode frequency of 9 Hz [80], that is, the Of note, an altered emotional response is very frequent in MCI patients [66, 67]. In a feedback process, this alteration typical mode of scalp-recorded EEG. It is to be noted that could determine a general state of “hyperattention” during a correlation between white matter damage and widespread which top-down internal processes prevail on the bottom- slowing of EEG rhythmicity was found in other studies, up phase, altering attention mechanism and preventing a following the presence of cognitive decline [81], multiple correct processing of sensory stimuli. Focused attention has sclerosis [82], or cerebral tumors [83]. The increase of the been found impaired in MCI patients in particular when theta/alpha1 band power ratio in moderate and severe CV they have to beneﬁt from a cue stimulus [68–72]. This damage groups, as compared to mild and no-CV-damage groups, could be the neurophysiological correlate of the particular state could be useful for maintaining a relatively spared global cognitive performance, whereas it could fail functional slowing of the speed propagation of the electric when a detailed analysis of a sensory stimulus is required. signals in vascular impaired subjects. This “hyperattentive” state could represent the attempt to recollect memory and/or spatial traces from hippocampus Conﬂict of Interests and to combine them within associative areas connected with The corresponding author declares there is no have any direct hippocampus itself. ﬁnancial relation with the commercial identity mentioned in The increase of alpha3/alpha2 ratio in our results sup- the paper that might lead to a conﬂict of interests for any of ports the concomitance of anterior attentive mechanism the authors. impairment in subjects with MCI, even though there are not overt clinical deﬁcits. The major association of the increase of alpha3/alpha2 ratio with the hippocampal formation within References the AHC suggests that this ﬁlter activity is carried out [1] C. Flicker, S. H. Ferris, and B. Reisberg, “Mild cognitive by hippocampus and its input-output connections along impairment in the elderly: predictors of dementia,” Neurology, anterior attentive circuit and AHC. Interestingly, a recent vol. 41, no. 7, pp. 1006–1009, 1991. work has demonstrated that the mossy ﬁber (MF) pathway [2] R. C. Petersen, G. E. Smith, R. J. Ivnik et al., “Apolipoprotein E of the hippocampus connects the dentate gyrus to the auto- status as a predictor of the development of Alzheimer’s disease associative CA3 network and the information it carries is in memory-impaired individuals,” Journal of the American controlled by a feedforward circuit combining disynaptic Medical Association, vol. 273, no. 16, pp. 1274–1278, 1995. inhibition with monosynaptic excitation. Analysis of the MF- [3] R. C. Petersen, R. Doody, A. Kurz et al., “Current concepts in associated circuit revealed that this circuit could act as a mild cognitive impairment,” Archives of Neurology, vol. 58, no. highpass ﬁlter [73]. 12, pp. 1985–1992, 2001. As regards CV impaired subjects, the natural history of a [4] S.E.Arnold,B.T.Hyman,J.Flory,A.R.Damasio,and G. group of subjects at very high risk for developing dementia W. Van Hoesen, “The topographical and neuroanatomical 10 International Journal of Alzheimer’s Disease distribution of neuroﬁbrillary tangles and neuritic plaques [21] V. Jelic, P. Julin, M. Shigeta et al., “Apolipoprotein E ε4allele in the cerebral cortex of patients with alzheimer’s disease,” decreases functional connectivity in Alzheimer’s disease as Cerebral Cortex, vol. 1, no. 1, pp. 103–116, 1991. measured by EEG coherence,” Journal of Neurology Neuro- [5] M. Bobinski, J. Wegiel, H. M. Wisniewski et al., “Atrophy surgery and Psychiatry, vol. 63, no. 1, pp. 59–65, 1997. of Hippocampal formation subdivisions correlates with stage [22] F. Ferreri, F. Pauri, P. Pasqualetti, R. Fini, G. Dal Forno, and P. and duration of Alzheimer disease,” Dementia, vol. 6, no. 4, M. Rossini, “Motor cortex excitability in Alzheimer’s disease: a pp. 205–210, 1995. transcranial magnetic stimulation study,” Annals of Neurology, [6] J. L. Price and J. C. Morris, “Tangles and plaques in nonde- vol. 53, no. 1, pp. 102–108, 2003. mented aging and ’preclinical’ alzheimer’s disease,” Annals of [23] Y. A. L. Pijnenburg, Y. Vd Made, A. M. Van Cappellen Van Neurology, vol. 45, no. 3, pp. 358–368, 1999. Walsum, D. L. Knol, P. Scheltens, and C. J. Stam, “EEG [7] B. Schonheit, ¨ R. Zarski, and T. G. Ohm, “Spatial and temporal synchronization likelihood in mild cognitive impairment and relationships between plaques and tangles in Alzheimer- Alzheimer’s disease during a working memory task,” Clinical pathology,” Neurobiology of Aging, vol. 25, no. 6, pp. 697–711, Neurophysiology, vol. 115, no. 6, pp. 1332–1339, 2004. [24] Z. Y. Jiang, “Study on EEG power and coherence in patients [8] D. A. Bennett, J. A. Schneider, J. L. Bienias, D. A. Evans, and R. with mild cognitive impairment during working memory S. Wilson, “Mild cognitive impairment is related to Alzheimer task,” Journal of Zhejiang University. Science. B, vol. 6, no. 12, disease pathology and cerebral infarctions,” Neurology, vol. 64, pp. 1213–1219, 2005. no. 5, pp. 834–841, 2005. [25] Z. Y. Jiang and L. L. Zheng, “Inter- and intra-hemispheric [9] G. B. Frisoni, A. Prestia, P. E. Rasser, M. Bonetti, and P. EEG coherence in patients with mild cognitive impairment M. Thompson, “In vivo mapping of incremental cortical at rest and during working memory task,” Journal of Zhejiang atrophy from incipient to overt Alzheimer’s disease,” Journal University. Science. B, vol. 7, no. 5, pp. 357–364, 2006. of Neurology, vol. 256, no. 6, pp. 916–924, 2009. [26] L. L. Zheng, Z. Y. Jiang, and E. Y. Yu, “Alpha spectral [10] D. J. A. Callen,S.E.Black,F.Gao,C.B.Caldwell, andJ.P. power and coherence in the patients with mild cognitive Szalai, “Beyond the hippocampus: MRI volumetry conﬁrms impairment during a three-level working memory task,” widespread limbic atrophy in AD,” Neurology, vol. 57, no. 9, Journal of Zhejiang University. Science B, vol. 8, no. 8, pp. 584– pp. 1669–1674, 2001. 592, 2007. [11] A. T. Du, N. Schuﬀ, D. Amend et al., “Magnetic resonance [27] R. Zappoli, A. Versari, M. Paganini et al., “Brain electrical imaging of the entorhinal cortex and hippocampus in mild activity (quantitative EEG and bit-mapping neurocognitive cognitive impairment and Alzheimer’s disease,” Journal of CNV components), psychometrics and clinical ﬁndings in Neurology Neurosurgery and Psychiatry, vol. 71, no. 4, pp. 441– presenile subjects with initial mild cognitive decline or prob- 447, 2001. able Alzheimer-type dementia,” Italian Journal of Neurological [12] G. Gold, C. Bouras, E. Kovar ¨ i et al., “Clinical validity Sciences, vol. 16, no. 6, pp. 341–376, 1995. of Braak neuropathological staging in the oldest-old,” Acta [28] C. Huang, L. O. Wahlund, T. Dierks, P. Julin, B. Winblad, Neuropathologica, vol. 99, no. 5, pp. 579–582, 2000. and V. Jelic, “Discrimination of Alzheimer’s disease and mild [13] V. Della-Maggiore, W. Chau, P. R. Peres-Neto, and A. R. cognitive impairment by equivalent EEG sources: a cross- McIntosh, “An empirical comparison of SPM preprocessing sectional and longitudinal study,” Clinical Neurophysiology, parameters to the analysis of fMRI data,” NeuroImage, vol. 17, vol. 111, no. 11, pp. 1961–1967, 2000. no. 1, pp. 19–28, 2002. [29] T. Koenig,L.Prichep,T.Dierksetal., “Decreased EEG [14] A. Ham ¨ al ¨ ainen, ¨ M. Pihlajamaki, ¨ H. Tanila et al., “Increased synchronization in Alzheimer’s disease and mild cognitive fMRI responses during encoding in mild cognitive impair- impairment,” Neurobiology of Aging, vol. 26, no. 2, pp. 165– ment,” Neurobiology of Aging, vol. 28, no. 12, pp. 1889–1903, 171, 2005. [30] C. Babiloni, G. Binetti, E. Cassetta et al., “Sources of cortical [15] P. Lavenex and D. G. Amaral, “Hippocampal-neocortical rhythms change as a function of cognitive impairment in interaction: a hierarchy of associativity,” Hippocampus, vol. 10, pathological aging: a multicenter study,” Clinical Neurophys- no. 4, pp. 420–430, 2000. iology, vol. 117, no. 2, pp. 252–268, 2006. [16] B. C. Dickerson, D. H. Salat, J. F. Bates et al., “Medial temporal [31] E. J. Golob, R. Irimajiri, and A. Starr, “Auditory cortical lobe function and structure in mild cognitive impairment,” activity in amnestic mild cognitive impairment: relationship Annals of Neurology, vol. 56, no. 1, pp. 27–35, 2004. to subtype and conversion to dementia,” Brain, vol. 130, no. 3, [17] B. C. Dickerson, D. H. Salat, D. N. Greve et al., “Increased hip- pp. 740–752, 2007. pocampal activation in mild cognitive impairment compared [32] D. V. Moretti, C. Miniussi, G. B. Frisoni et al., “Hippocampal to normal aging and AD,” Neurology, vol. 65, no. 3, pp. 404– atrophy and EEG markers in subjects with mild cognitive 411, 2005. impairment,” Clinical Neurophysiology, vol. 118, no. 12, pp. [18] J. Pariente, S. Cole, R. Henson et al., “Alzheimer’s patients 2716–2729, 2007. engage an alternative network during a memory task,” Annals [33] D. V. Moretti, M. Pievani, C. Fracassi et al., “Increase of Neurology, vol. 58, no. 6, pp. 870–879, 2005. of theta/Gamma and Alpha3/Alpha2 ratio is associated [19] M. M. Machulda, H. A. Ward, B. Borowski et al., “Compar- with amygdalo-hippocampal complex atrophy,” Journal of ison of memory fMRI response among normal, MCI, and Alzheimer’s Disease, vol. 17, no. 2, pp. 349–357, 2009. Alzheimer’s patients,” Neurology, vol. 61, no. 4, pp. 500–506, [34] D. V. Moretti, C. Fracassi, M. Pievani et al., “Increase of theta/gamma ratio is associated with memory impairment,” [20] V. Jelic, S. E. Johansson, O. Almkvist et al., “Quantita- Clinical Neurophysiology, vol. 120, no. 2, pp. 295–303, 2009. tive electroencephalography in mild cognitive impairment: [35] D. V. Moretti, C. Miniussi, G. Frisoni et al., “Vascular damage longitudinal changes and possible prediction of Alzheimer’s disease,” Neurobiology of Aging, vol. 21, no. 4, pp. 533–540, and EEG markers in subjects with mild cognitive impairment,” Clinical Neurophysiology, vol. 118, no. 8, pp. 1866–1876, 2007. 2000. International Journal of Alzheimer’s Disease 11 [36] J. H. Kramer,B.R.Reed,D.Mungas,M.W.Weiner, and and qualitative analyses of cognitive impairment,” European H. C. Chui, “Executive dysfunction in subcortical ischaemic Neurology, vol. 36, no. 6, pp. 378–384, 1996. vascular disease,” Journal of Neurology Neurosurgery and [52] P. Caﬀarra, G. Vezzadini, F. Dieci, F. Zonato, and A. Venneri, Psychiatry, vol. 72, no. 2, pp. 217–220, 2002. “Rey-Osterrieth complex ﬁgure: normative values in an Italian [37] D. V. Moretti, C. Babiloni, G. Binetti et al., “Individual population sample,” Neurological Sciences, vol. 22, no. 6, pp. analysis of EEG frequency and band power in mild Alzheimer’s 443–447, 2002. disease,” Clinical Neurophysiology, vol. 115, no. 2, pp. 299–308, [53] L. S. Radloﬀ, “The CES-D scale: a self-report depression scale for research in the general population,” Applied Psychological [38] W. Klimesch, “EEG alpha and theta oscillations reﬂect cogni- Measurement, vol. 1, pp. 385–401, 1977. tive and memory performance: a review and analysis,” Brain [54] L. O. Wahlund, F. Barkhof, F. Fazekas et al., “A new rating scale Research Reviews, vol. 29, no. 2-3, pp. 169–195, 1999. for age-related white matter changes applicable to MRI and [39] M. F. Folstein, S. E. Folstein, and P. R. McHugh, ““Mini mental CT,” Stroke, vol. 32, no. 6, pp. 1318–1322, 2001. state”. A practical method for grading the cognitive state of [55] C. J. Stam, Y. Van Der Made, Y. A. L. Pijnenburg, and P. Schel- patients for the clinician,” Journal of Psychiatric Research, vol. tens, “EEG synchronization in mild cognitive impairment and 12, no. 3, pp. 189–198, 1975. Alzheimer’s disease,” Acta Neurologica Scandinavica, vol. 108, [40] C. P. Hughes, L. Berg, and W. L. Danziger, “A new clinical scale no. 2, pp. 90–96, 2003. for the staging of dementia,” British Journal of Psychiatry, vol. [56] T. Seidenbecher,T.R.Laxmi,O.Stork,and H. C. Pape, 140, no. 6, pp. 566–572, 1982. “Amygdalar and hippocampal theta rhythm synchronization [41] W. G. Rosen, R. D. Terry, and P. A. Fuld, “Pathological during fear memory retrieval,” Science, vol. 301, no. 5634, pp. veriﬁcation of ischemic score in diﬀerentiation of dementias,” 846–850, 2003. Annals of Neurology, vol. 7, no. 5, pp. 486–488, 1980. [57] R. T. Narayanan, T. Seidenbecher, S. Sangha, O. Stork, and H. [42] M. P. Lawton andE.M.Brody,“Assessmentofolder people: C. Pape, “Theta resynchronization during reconsolidation of self-maintaining and instrumental activities of daily living,” remote contextual fear memory,” NeuroReport, vol. 18, no. 11, Gerontologist, vol. 9, no. 3, pp. 179–186, 1969. pp. 1107–1111, 2007. [43] M. Albert,L.A.Smith,P.A.Scherr, J. O. Taylor,D.A.Evans, [58] S. M. Montgomery and G. Buzsaki, ´ “Gamma oscillations and H. H. Funkenstein, “Use of brief cognitive tests to iden- dynamically couple hippocampal CA3 and CA1 regions tify individuals in the community with clinically diagnosed during memory task performance,” Proceedings of the National Alzheimer’s disease,” International Journal of Neuroscience, vol. Academy of Sciences of the United States of America, vol. 104, 57, no. 3-4, pp. 167–178, 1991. no. 36, pp. 14495–14500, 2007. [44] R. C. Petersen,G.E.Smith,S.C.Waring,R.J.Ivnik,E. [59] P. Sauseng, W. Klimesch, M. Doppelmayr, S. Hanslmayr, Kokmen, and E. G. Tangelos, “Aging, memory, and mild M. Schabus, and W. R. Gruber, “Theta coupling in the cognitive impairment,” International Psychogeriatrics, vol. 9, human electroencephalogram during a working memory no. 1, supplement, pp. 65–69, 1997. task,” Neuroscience Letters, vol. 354, no. 2, pp. 123–126, 2004. [45] F. Portet, P. J. Ousset, P. J. Visser et al., “Mild cognitive [60] A. Bragin, G. Jando, Z. Nadasdy, J. Hetke, K. Wise, and G. impairment (MCI) in medical practice: a critical review of Buzsaki, “Gamma (40–100 Hz) oscillation in the hippocam- the concept and new diagnostic procedure. Report of the MCI pus of the behaving rat,” Journal of Neuroscience, vol. 15, no. 1 Working Group of the European Consortium on Alzheimer’s I, pp. 47–60, 1995. Disease,” Journal of Neurology, Neurosurgery and Psychiatry, [61] R. R. Llinas, ´ U. Ribary, D. Jeanmonod, E. Kronberg, and vol. 77, no. 6, pp. 714–718, 2006. P. P. Mitra, “Thalamocortical dysrhythmia: a neurological [46] C. Geroldi, R. Rossi, C. Calvagna et al., “Medial temporal and neuropsychiatric syndrome characterized by magne- atrophy but not memory deﬁcit predicts progression to toencephalography,” Proceedings of the National Academy of dementia in patients with mild cognitive impairment,” Journal Sciences of the United States of America, vol. 96, no. 26, pp. of Neurology, Neurosurgery and Psychiatry, vol. 77, no. 11, pp. 15222–15227, 1999. 1219–1222, 2006. [62] W. Klimesch, P. Sauseng, and S. Hanslmayr, “EEG alpha [47] K. I. Shulman, “Clock-drawing: is it the ideal cognitive oscillations: the inhibition-timing hypothesis,” Brain Research screening test?” International Journal of Geriatric Psychiatry, Reviews, vol. 53, no. 1, pp. 63–88, 2007. vol. 15, no. 6, pp. 548–561, 2000. [63] D. Pare, ´ D. R. Collins, and J. G. Pelletier, “Amygdala oscilla- [48] P. Amodio, H. Wenin, F. Del Piccolo et al., “Variability of trail tions and the consolidation of emotional memories,” Trends making test, symbol digit test and line trait test in normal in Cognitive Sciences, vol. 6, no. 7, pp. 306–314, 2002. people. A normative study taking into account age-dependent [64] M. Garolera, R. Coppola, K. E. Muno ˜ z et al., “Amygdala decline and sociobiological variables,” Aging,vol. 14, no.2,pp. activation in aﬀective priming: a magnetoencephalogram 117–131, 2002. study,” NeuroReport, vol. 18, no. 14, pp. 1449–1453, 2007. [49] A. Basso, E. Capitani, and M. Laiacona, “Raven’s coloured [65] K. A. Young, L. A. Holcomb, W. L. Bonkale, P. B. Hicks, U. progressive matrices: normative values on 305 adult normal Yazdani, and D. C. German, “5HTTLPR polymorphism and controls,” Functional Neurology, vol. 2, no. 2, pp. 189–194, enlargement of the pulvinar: unlocking the backdoor to the limbic system,” Biological Psychiatry, vol. 61, no. 6, pp. 813– [50] H. Spinnler and G. Tognoni, “Italian standardization and 818, 2007. classiﬁcation of neuropsychological tests,” Italian Journal of [66] J. M. Ellison, D. G. Harper,Y.Berlow, andL.Zeranski, Neurological Sciences, vol. 6, supplement 8, pp. 1–120, 1987. “Beyond the “C” in MCI: noncognitive symptoms in amnestic [51] G. A. Carlesimo, C. Caltagirone, and G. Gainotti, “The mental and non-amnestic mild cognitive impairment,” CNS Spec- deterioration battery: normative data, diagnostic reliability trums, vol. 13, no. 1, pp. 66–72, 2008. 12 International Journal of Alzheimer’s Disease [67] L. Rozzini, B. Vicini Chilovi, M. Conti et al., “Neuropsychiatric of Neurology Neurosurgery and Psychiatry,vol. 69, no.2,pp. symptoms in amnestic and nonamnestic mild cognitive 192–198, 2000. impairment,” Dementia and Geriatric Cognitive Disorders, vol. [83] E. S. Goldensohn, “Use of EEG for evaluation of focal 25, no. 1, pp. 32–36, 2007. intracranial lesions,” in Current Practice of Clinical Electroen- [68] P. Johannsen, J. Jakobsen, P. Bruhn, and A. Gjedde, “Cortical cephalography, D. W. Klass and D. D. Daly, Eds., pp. 307–341, responses to sustained and divided attention in Alzheimer’s Raven, New York, NY, USA, 1979. disease,” NeuroImage, vol. 10, no. 3, pp. 269–281, 1999. [69] A. M. Berardi, R. Parasuraman, and J. V. Haxby, “Sustained attention in mild Alzheimer’s disease,” Developmental Neu- ropsychology, vol. 28, no. 1, pp. 507–537, 2005. [70] E. J. Levinoﬀ,D.Saumier,and H. Chertkow,“Focused attention deﬁcits in patients with Alzheimer’s disease and mild cognitive impairment,” Brain and Cognition,vol. 57, no.2,pp. 127–130, 2005. [71] A. Tales, J. Haworth, S. Nelson, R. J. Snowden, and G. Wilcock, “Abnormal visual search in mild cognitive impairment and Alzheimer’s disease,” Neurocase, vol. 11, no. 1, pp. 80–84, 2005. [72] A. Tales, R. J. Snowden, J. Haworth, and G. Wilcock, “Abnor- mal spatial and non-spatial cueing eﬀects in mild cognitive impairment and Alzheimer’s disease,” Neurocase, vol. 11, no. 1, pp. 85–92, 2005. [73] O. C. Zalay and B. L. Bardakjian, “Simulated mossy ﬁber associated feedforward circuit functioning as a highpass ﬁlter,” in Proceedings of the 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS ’06), pp. 4979–4982, September 2006. [74] G. B. Frisoni, S. Galluzzi, L. Bresciani, O. Zanetti, and C. Geroldi, “Mild cognitive impairment with subcortical vascular features: clinical characteristics and outcome,” Journal of Neurology, vol. 249, no. 10, pp. 1423–1432, 2002. [75] S. Galluzzi, C. F. Sheu, O. Zanetti, and G. B. Frisoni, “Dis- tinctive clinical features of mild cognitive impairment with subcortical cerebrovascular disease,” Dementia and Geriatric Cognitive Disorders, vol. 19, no. 4, pp. 196–203, 2005. [76] M. Steriade and R. R. Llinas, “The functional states of the thalamus and the associated neuronal interplay,” Physiological Reviews, vol. 68, no. 3, pp. 649–742, 1988. [77] F. H. Lopes da Silva, A. van Rotterdam, P. Barts, E. van Heusden, and W. Burr, “Models of neuronal populations: the basic mechanisms of rhythmicity,” Progress in Brain Research, vol. 45, no. C, pp. 281–308, 1976. [78] P. L. Nunez, B. M. Wingeier, and R. B. Silberstein, “Spatial-temporal structures of human alpha rhythms: theory, microcurrent sources, multiscale measurements, and global binding of local networks,” Human Brain Mapping, vol. 13, no. 3, pp. 125–164, 2001. [79] B. Doiron, M. J. Chacron, L. Maler, A. Longtin, and J. Bastian, “Inhibitory feedback required for network oscillatory responses to communication but not prey stimuli,” Nature, vol. 421, no. 6922, pp. 539–543, 2003. [80] P. L. Nunez and R. Srinivasan, “A theoretical basis for standing and traveling brain waves measured with human EEG with implications for an integrated consciousness,” Clinical Neuro- physiology, vol. 117, no. 11, pp. 2424–2435, 2006. [81] B. Szelies, R. Mielke, J. Kessler, and W. D. Heiss, “EEG power changes are related to regional cerebral glucose metabolism in vascular dementia,” Clinical Neurophysiology, vol. 110, no. 4, pp. 615–620, 1999. [82] L. Leocani, T. Locatelli, V. Martinelli et al., “Electroencephalo- graphic coherence analysis in multiple sclerosis: correlation with clinical, neuropsychological, and MRI ﬁndings,” Journal MEDIATORS of INFLAMMATION The Scientific Gastroenterology Journal of World Journal Research and Practice Diabetes Research Disease Markers Hindawi Publishing Corporation Hindawi Publishing Corporation Hindawi Publishing Corporation Hindawi Publishing Corporation Hindawi Publishing Corporation http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 International Journal of Journal of Immunology Research Endocrinology Hindawi Publishing Corporation Hindawi Publishing Corporation http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 Submit your manuscripts at http://www.hindawi.com BioMed PPAR Research Research International Hindawi Publishing Corporation Hindawi Publishing Corporation http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 Journal of Obesity Evidence-Based Journal of Journal of Stem Cells Complementary and Ophthalmology International Alternative Medicine Oncology Hindawi Publishing Corporation Hindawi Publishing Corporation Hindawi Publishing Corporation Hindawi Publishing Corporation Hindawi Publishing Corporation http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 Parkinson’s Disease Computational and Behavioural Mathematical Methods AIDS Oxidative Medicine and in Medicine Research and Treatment Cellular Longevity Neurology Hindawi Publishing Corporation Hindawi Publishing Corporation Hindawi Publishing Corporation Hindawi Publishing Corporation Hindawi Publishing Corporation http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Alzheimer's Disease Hindawi Publishing Corporation http://www.deepdyve.com/lp/hindawi-publishing-corporation/quantitative-eeg-markers-in-mild-cognitive-impairment-degenerative-ua0aIpB40y

Loading next page...

References (89)

G. Carlesimo, C. Caltagirone, G. Gainotti (1996)
The Mental Deterioration Battery: normative data, diagnostic reliability and qualitative analyses of cognitive impairment. The Group for the Standardization of the Mental Deterioration Battery.
European neurology, 36 6
M. Folstein, M. Folstein, S. Folstein, S. Folstein, P. McHugh, P. McHugh (1975)
“Mini-mental state”: A practical method for grading the cognitive state of patients for the clinician
Journal of Psychiatric Research, 12
A. Tales, J. Haworth, S. Nelson, Robert Snowden, G. Wilcock (2005)
Abnormal visual search in mild cognitive impairment and Alzheimer’s disease
Neurocase, 11
B. Dickerson, D. Salat, J. Bates, M. Atiya, R. Killiany, D. Greve, A. Dale, C. Stern, D. Blacker, M. Albert, R. Sperling (2004)
Medial temporal lobe function and structure in mild cognitive impairment
Annals of Neurology, 56
J. Kramer, B. Reed, D. Mungas, M. Weiner, H. Chui (2002)
Executive dysfunction in subcortical ischaemic vascular disease
Journal of Neurology, Neurosurgery & Psychiatry, 72
P. Nunez, B. Wingeier, R. Silberstein (2001)
Spatial‐temporal structures of human alpha rhythms: Theory, microcurrent sources, multiscale measurements, and global binding of local networks
Human Brain Mapping, 13
(1987)
[Italian standardization and classification of Neuropsychological tests. The Italian Group on the Neuropsychological Study of Aging].
Italian journal of neurological sciences, Suppl 8
B. Schönheit, R. Zarski, T. Ohm (2004)
Spatial and temporal relationships between plaques and tangles in Alzheimer-pathology
Neurobiology of Aging, 25
T. Seidenbecher, T. Laxmi, O. Stork, H. Pape (2003)
Amygdalar and Hippocampal Theta Rhythm Synchronization During Fear Memory Retrieval
Science, 301
M. Machulda, H. Ward, B. Borowski, J. Gunter, R. Cha, P. O'brien, R. Petersen, B. Boeve, D. Knopman, D. Tang‐Wai, R. Ivnik, Glenn Smith, E. Tangalos, C. Jack (2003)
Comparison of memory fMRI response among normal, MCI, and Alzheimer’s patients
Neurology, 61
L. Leocani, T. Locatelli, V. Martinelli, M. Rovaris, M. Falautano, M. Filippi, G. Magnani, G. Comi (2000)
Electroencephalographic coherence analysis in multiple sclerosis: correlation with clinical, neuropsychological, and MRI findings
Journal of Neurology, Neurosurgery & Psychiatry, 69
V. Della-Maggiore, W. Chau, P. Peres‐Neto, A. Mcintosh (2002)
An Empirical Comparison of SPM Preprocessing Parameters to the Analysis of fMRI Data
NeuroImage, 17
E. Golob, R. Irimajiri, A. Starr (2007)
Auditory cortical activity in amnestic mild cognitive impairment: relationship to subtype and conversion to dementia.
Brain : a journal of neurology, 130 Pt 3
M. Albert, L. Smith, P. Scherr, James Taylor, D. Evans, H. Funkenstein (1991)
Use of brief cognitive tests to identify individuals in the community with clinically diagnosed Alzheimer's disease.
The International journal of neuroscience, 57 3-4
P. Nunez, R. Srinivasan (2006)
A theoretical basis for standing and traveling brain waves measured with human EEG with implications for an integrated consciousness
Clinical Neurophysiology, 117
S. H. Ferris C. Flicker (1991)
Mild cognitive impairment in the elderly: predictors of dementia,
Neurology, 41
V. Jelic, P. Julin, M. Shigeta, A. Nordberg, L. Lannfelt, B. Winblad, L. Wahlund (1997)
Apolipoprotein E ε4 allele decreases functional connectivity in Alzheimer’s disease as measured by EEG coherence
Journal of Neurology, Neurosurgery & Psychiatry, 63
M. Garolera, R. Coppola, K. Muñoz, B. Elvevåg, F. Carver, D. Weinberger, T. Goldberg (2007)
Amygdala activation in affective priming: a magnetoencephalogram study
NeuroReport, 18
R. Zappoli, A. Versari, M. Paganini, G. Arnetoli, G. Muscas, P. Gangemi, M. Arneodo, D. Poggiolini, F. Zappoli, A. Battaglia (1995)
Brain electrical activity (quantitative EEG and bit-mapping neurocognitive CNV components), psychometrics and clinical findings in presenile subjects with initial mild cognitive decline or probable Alzheimer-type dementia
The Italian Journal of Neurological Sciences, 16
Zheng-yan Jiang, Leilei Zheng (2006)
Inter-and intra-hemispheric EEG coherence in patients with mild cognitive impairment at rest and during working memory task
Journal of Zhejiang University SCIENCE B, 7
P. Sauseng, W. Klimesch, M. Doppelmayr, S. Hanslmayr, Manuel Schabus, W. Gruber (2004)
Theta coupling in the human electroencephalogram during a working memory task
Neuroscience Letters, 354
D. Bennett, J. Schneider, J. Bienias, Denis Evans, R. Wilson (2005)
Mild cognitive impairment is related to Alzheimer disease pathology and cerebral infarctions
Neurology, 64
A. Bragin, G. Jandó, Z. Nadasdy, J. Hetke, K. Wise, G. Buzsáki (1995)
Gamma (40-100 Hz) oscillation in the hippocampus of the behaving rat
, 15
H. Spinnler and G. Tognoni (1987)
Italian standardization and classification of neuropsychological tests,
Italian Journal of Neurological Sciences, 6
A. Du, N. Schuff, N. Schuff, D. Amend, M. Laakso, Y. Hsu, W. Jagust, K. Yaffe, J. Kramer, B. Reed, D. Norman, H. Chui, M. Weiner, M. Weiner (2001)
Magnetic resonance imaging of the entorhinal cortex and hippocampus in mild cognitive impairment and Alzheimer's disease
Journal of Neurology, Neurosurgery & Psychiatry, 71
R. Petersen, Glenn Smith, R. Ivnik, E. Tangalos, D. Schaid, S. Thibodeau, E. Kokmen, S. Waring, L. Kurland (1995)
Apolipoprotein E status as a predictor of the development of Alzheimer's disease in memory-impaired individuals.
JAMA, 273 16
C. Babiloni, G. Binetti, E. Cassetta, G. Forno, C. Percio, F. Ferreri, R. Ferri, G. Frisoni, K. Hirata, B. Lanuzza, C. Miniussi, D. Moretti, F. Nobili, G. Rodriguez, G. Romani, S. Salinari, P. Rossini (2006)
Sources of cortical rhythms change as a function of cognitive impairment in pathological aging: a multicenter study
Clinical Neurophysiology, 117
R. Llinás, U. Ribary, D. Jeanmonod, E. Kronberg, P. Mitra (1999)
Thalamocortical dysrhythmia: A neurological and neuropsychiatric syndrome characterized by magnetoencephalography.
Proceedings of the National Academy of Sciences of the United States of America, 96 26
P. Johannsen, J. Jakobsen, P. Bruhn, A. Gjedde (1999)
Cortical Responses to Sustained and Divided Attention in Alzheimer's Disease
NeuroImage, 10
L. Rozzini, B. Chilovi, M. Conti, I. Delrio, B. Borroni, M. Trabucchi, A. Padovani (2007)
Neuropsychiatric Symptoms in Amnestic and Nonamnestic Mild Cognitive Impairment
Dementia and Geriatric Cognitive Disorders, 25
D. Moretti, C. Miniussi, G. Frisoni, O. Zanetti, G. Binetti, C. Geroldi, S. Galluzzi, P. Rossini (2007)
Vascular damage and EEG markers in subjects with mild cognitive impairment
Clinical Neurophysiology, 118
S. Arnold, B. Hyman, J. Flory, A. Damasio, G. Hoesen (1991)
The topographical and neuroanatomical distribution of neurofibrillary tangles and neuritic plaques in the cerebral cortex of patients with Alzheimer's disease.
Cerebral cortex, 1 1
Sean Montgomery, G. Buzsáki (2007)
Gamma oscillations dynamically couple hippocampal CA3 and CA1 regions during memory task performance
Proceedings of the National Academy of Sciences, 104
D. Moretti, C. Fracassi, M. Pievani, C. Geroldi, G. Binetti, O. Zanetti, K. Sosta, P. Rossini, G. Frisoni (2009)
Increase of theta/gamma ratio is associated with memory impairment
Clinical Neurophysiology, 120
J. Pariente, S. Cole, R. Henson, L. Clare, A. Kennedy, M. Rossor, Lisa Cipoloti, M. Puel, J. Démonet, F. Chollet, Richard Frackowiak (2005)
Alzheimer's patients engage an alternative network during a memory task
Annals of Neurology, 58
P. Amodio, Helmut Wenin, F. Piccolo, D. Mapelli, S. Montagnese, Andrea Pellegrini, Carmine Musto, A. Gatta, C. Umilta (2002)
Variability of Trail Making Test, Symbol Digit Test and Line Trait Test in normal people. A normative study taking into account age-dependent decline and sociobiological variables
Aging Clinical and Experimental Research, 14
R. Narayanan, T. Seidenbecher, Susan Sangha, O. Stork, H. Pape (2007)
Theta resynchronization during reconsolidation of remote contextual fear memory
NeuroReport, 18
T. Koenig, L. Prichep, T. Dierks, D. Hubl, L. Wahlund, E. John, V. Jelic (2005)
Decreased EEG synchronization in Alzheimer’s disease and mild cognitive impairment
Neurobiology of Aging, 26
M. Lawton, E. Brody (1969)
Assessment of older people: self-maintaining and instrumental activities of daily living.
The Gerontologist, 9 3
C. Stam, Y. Made, Y. Pijnenburg, P. Scheltens (2003)
EEG synchronization in mild cognitive impairment and Alzheimer's disease
Acta Neurologica Scandinavica, 108
(1979)
Use of EEG for evaluation of focal intracranial lesions
W. Rosen, R. Terry, P. Fuld, R. Katzman, A. Peck (1980)
Pathological verification of ischemic score in differentiation of dementias
Annals of Neurology, 7
D. Moretti, M. Pievani, C. Fracassi, G. Binetti, S. Rosini, C. Geroldi, O. Zanetti, P. Rossini, G. Frisoni (2009)
Increase of theta/gamma and alpha3/alpha2 ratio is associated with amygdalo-hippocampal complex atrophy.
Journal of Alzheimer's disease : JAD, 17 2
M. Bobinski, J. Wegiel, H. Wiśniewski, M. Tarnawski, B. Reisberg, B. Mlodzik, M. Leon, D. Miller (1995)
Atrophy of hippocampal formation subdivisions correlates with stage and duration of Alzheimer disease.
Dementia, 6 4
F. Ferreri, F. Pauri, P. Pasqualetti, R. Fini, G. Forno, P. Rossini (2003)
Motor cortex excitability in Alzheimer's disease: A transcranial magnetic stimulation study
Annals of Neurology, 53
Leilei Zheng, Zheng-yan Jiang, En-yan Yu (2007)
Alpha spectral power and coherence in the patients with mild cognitive impairment during a three-level working memory task
Journal of Zhejiang University SCIENCE B, 8
A. Tales, R. Snowden, J. Haworth, G. Wilcock (2005)
Abnormal spatial and non-spatial cueing effects in mild cognitive impairment and Alzheimer’s disease
Neurocase, 11
Zheng-yan Jiang (2005)
Study on EEG power and coherence in patients with mild cognitive impairment during working memory task
Journal of Zhejiang University SCIENCE B, 6
V. Jelic, Sverker Johansson, O. Almkvist, M. Shigeta, P. Julin, A. Nordberg, B. Winblad, L. Wahlund (2000)
Quantitative electroencephalography in mild cognitive impairment: longitudinal changes and possible prediction of Alzheimer’s disease
Neurobiology of Aging, 21
F. Portet, P. Ousset, P. Visser, G. Frisoni, F. Nobili, P. Scheltens, B. Vellas, J. Touchon (2006)
Mild cognitive impairment (MCI) in medical practice: a critical review of the concept and new diagnostic procedure. Report of the MCI Working Group of the European Consortium on Alzheimer’s Disease
Journal of Neurology, Neurosurgery & Psychiatry, 77
D. G. Harper J. M. Ellison (2008)
Beyond the “C
CNS Spectrums, 13
G. Frisoni, A. Prestia, P. Rasser, M. Bonetti, P. Thompson (2009)
In vivo mapping of incremental cortical atrophy from incipient to overt Alzheimer’s disease
Journal of Neurology, 256
O. Zalay, B. Bardakjian (2006)
Simulated Mossy Fiber Associated Feedforward Circuit Functioning as a Highpass Filter
2006 International Conference of the IEEE Engineering in Medicine and Biology Society
M. Steriade, R. Llinás (1988)
The functional states of the thalamus and the associated neuronal interplay.
Physiological reviews, 68 3
G. Gold, C. Bouras, E. Kövari, A. Canuto, B. Glaría, André Malky, P. Hof, J. Michel, P. Giannakopoulos (2000)
Clinical validity of Braak neuropathological staging in the oldest-old
Acta Neuropathologica, 99
D. Moretti, C. Babiloni, G. Binetti, E. Cassetta, G. Forno, Florinda Ferreric, R. Ferri, B. Lanuzza, C. Miniussi, F. Nobili, G. Rodriguez, S. Salinari, P. Rossini (2004)
Individual analysis of EEG frequency and band power in mild Alzheimer's disease
Clinical Neurophysiology, 115
Richard Goldberg, Jenna Goldberg (1997)
Risperidone for Dementia-Related Disturbed Behavior in Nursing Home Residents: A Clinical Experience
International Psychogeriatrics, 9
D. Moretti, C. Miniussi, G. Frisoni, C. Geroldi, O. Zanetti, G. Binetti, P. Rossini (2007)
Hippocampal atrophy and EEG markers in subjects with mild cognitive impairment
Clinical Neurophysiology, 118
A. Berardi, R. Parasuraman, J. Haxby (2005)
Sustained Attention in Mild Alzheimer's Disease
Developmental Neuropsychology, 28
Y. Pijnenburg, Y. Made, A. Walsum, D. Knol, P. Scheltens, C. Stam (2004)
EEG synchronization likelihood in mild cognitive impairment and Alzheimer's disease during a working memory task
Clinical Neurophysiology, 115
G. Frisoni, S. Galluzzi, Lorena Bresciani, O. Zanetti, C. Geroldi (2002)
Mild cognitive impairment with subcortical vascular features: clinical characteristics and outcome.
Journal of neurology, 249 10
S. Galluzzi, Ching-Fan Sheu, O. Zanetti, G. Frisoni (2005)
Distinctive Clinical Features of Mild Cognitive Impairment with Subcortical Cerebrovascular Disease
Dementia and Geriatric Cognitive Disorders, 19
G. Frisoni, S. Galluzzi, Lorena Bresciani, O. Zanetti, C. Geroldi (2002)
Mild cognitive impairment with subcortical vascular features
Journal of Neurology, 249
W. Klimesch, P. Sauseng, S. Hanslmayr (2007)
EEG alpha oscillations: The inhibition–timing hypothesis
Brain Research Reviews, 53
L. Wahlund, F. Barkhof, F. Fazekas, L. Bronge, M. Augustin, M. Sjögren, A. Wallin, H. Adèr, D. Leys, L. Pantoni, F. Pasquier, T. Erkinjuntti, P. Scheltens (2001)
A New Rating Scale for Age-Related White Matter Changes Applicable to MRI and CT
Stroke: Journal of the American Heart Association, 32
J. Ellison, D. Harper, Y. Berlow, Lauren Zeranski (2008)
Beyond the “C” in MCI: Noncognitive Symptoms in Amnestic and Non-amnestic Mild Cognitive Impairment
CNS Spectrums, 13
R. Petersen, R. Doody, A. Kurz, R. Mohs, J. Morris, P. Rabins, K. Ritchie, M. Rossor, L. Thal, B. Winblad (2001)
Current concepts in mild cognitive impairment.
Archives of neurology, 58 12
W. Klimesch (1999)
EEG alpha and theta oscillations reflect cognitive and memory performance: a review and analysis
Brain Research Reviews, 29
C. Hughes, L. Berg, W. Danziger, L. Coben, R. Martin (1982)
A New Clinical Scale for the Staging of Dementia
British Journal of Psychiatry, 140
D. Paré, D. Collins, J. Pelletier (2002)
Amygdala oscillations and the consolidation of emotional memories
Trends in Cognitive Sciences, 6
E. Levinoff, D. Saumier, H. Chertkow (2005)
Focused attention deficits in patients with Alzheimer’s disease and mild cognitive impairment
Brain and Cognition, 57
K. Young, L. Holcomb, W. Bonkale, P. Hicks, Umar Yazdani, D. German (2007)
5HTTLPR Polymorphism and Enlargement of the Pulvinar: Unlocking the Backdoor to the Limbic System
Biological Psychiatry, 61
P. Lavenex, D. Amaral (2000)
Hippocampal‐neocortical interaction: A hierarchy of associativity
Hippocampus, 10
C. Geroldi, R. Rossi, Cristiana Calvagna, C. Testa, Lorena Bresciani, G. Binetti, O. Zanetti, G. Frisoni (2006)
Medial temporal atrophy but not memory deficit predicts progression to dementia in patients with mild cognitive impairment
Journal of Neurology, Neurosurgery & Psychiatry, 77
P. Caffarra, G. Vezzadini, F. Dieci, Fabrizio Zonato, A. Venneri (2002)
Rey-Osterrieth complex figure: normative values in an Italian population sample
Neurological Sciences, 22
K. Shulman (2000)
Clock‐drawing: is it the ideal cognitive screening test?
International Journal of Geriatric Psychiatry, 15
(2001)
Beyond the hippocampus: MRI volumetry confirms widespread limbic atrophy in AD
B. Szelies, R. Mielke, Josef Kessler, Wolf-Dieter Heiss (1999)
EEG power changes are related to regional cerebral glucose metabolism in vascular dementia
Clinical Neurophysiology, 110
R. Petersen, Glenn Smith, S. Waring, R. Ivnik, E. Kokmen, Eric Tangelos (1997)
Aging, Memory, and Mild Cognitive Impairment
International Psychogeriatrics, 9
B. Doiron, M. Chacron, L. Maler, A. Longtin, J. Bastian (2003)
Inhibitory feedback required for network oscillatory responses to communication but not prey stimuli
Nature, 421
L. Radloff
The CES-D Scale: A Self-Report Depression Scale for Research in the General Population — Source link
L. S. Radloff (1977)
The CES-D scale: a self-report depression scale for research in the general population,
Applied Psychological Measurement, 1
J. Price, J. Morris (1999)
Tangles and plaques in nondemented aging and “preclinical” Alzheimer's disease
Annals of Neurology, 45
C. Caltagirone G. A. Carlesimo (1996)
The mental deterioration battery: normative data, diagnostic reliability and qualitative analyses of cognitive impairment,
European Neurology, 36
B. Dickerson, D. Salat, D. Greve, E. Chua, Erin Rand-Giovannetti, D. Rentz, L. Bertram, K. Mullin, R. Tanzi, D. Blacker, M. Albert, R. Sperling (2005)
Increased hippocampal activation in mild cognitive impairment compared to normal aging and AD
Neurology, 65
Anne Hämäläinen, M. Pihlajamäki, H. Tanila, T. Hänninen, E. Niskanen, Susanna Tervo, P. Karjalainen, R. Vanninen, H. Soininen (2007)
Increased fMRI responses during encoding in mild cognitive impairment
Neurobiology of Aging, 28
F. Silva, A. Rotterdam, P. Barts, E. Heusden, W. Burr (1976)
Models of neuronal populations: the basic mechanisms of rhythmicity.
Progress in brain research, 45
Chaorui Huang, L. Wahlund, T. Dierks, P. Julin, B. Winblad, V. Jelic (2000)
Discrimination of Alzheimer's disease and mild cognitive impairment by equivalent EEG sources: a cross-sectional and longitudinal study
Clinical Neurophysiology, 111
Anna Basso, E. Capitani, M. Laiacona (1987)
Raven's coloured progressive matrices: normative values on 305 adult normal controls.
Functional neurology, 2 2

Publisher: Hindawi Publishing Corporation
Copyright: Copyright © 2012 D. V. Moretti et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
ISSN: 2090-8024
DOI: 10.1155/2012/917537
Publisher site: See Article on Publisher Site

Abstract

Hindawi Publishing Corporation International Journal of Alzheimer’s Disease Volume 2012, Article ID 917537, 12 pages doi:10.1155/2012/917537 Review Article Quantitative EEG Markers in Mild Cognitive Impairment: Degenerative versus Vascular Brain Impairment D. V. Moretti, O. Zanetti, G. Binetti, and G. B. Frisoni Centro San Giovanni di Dio Fatebenefratelli, TRccs, 25125 Brescia, Italy Correspondence should be addressed to D. V. Moretti, davide.moretti@afar.it Received 28 November 2011; Revised 17 May 2012; Accepted 21 May 2012 Academic Editor: Seishi Terada Copyright © 2012 D. V. Moretti et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. We evaluated the relationship between brain rhythmicity and both the cerebrovascular damage (CVD) and amygdalohippocampal complex (AHC) atrophy, as revealed by scalp electroencephalography (EEG) in a cohort of subjects with mild cognitive impairment (MCI). All MCI subjects underwent EEG recording and magnetic resonance imaging. EEGs were recorded at rest. Relative power was separately computed for delta, theta, alpha1, alpha2, and alpha3 frequency bands. In the spectral band power the severity of CVD was associated with increased delta power and decreased alpha2 power. No association of vascular damage was observed with alpha3 power. Moreover, the theta/alpha1 ratio could be a reliable index for the estimation of the individual extent of CV damage. On the other side, the group with moderate hippocampal atrophy showed the highest increase of alpha2 and alpha3 power. Moreover, when the amygdalar and hippocampal volumes are separately considered, within amygdalohippocampal complex (AHC), the increase of theta/gamma ratio is best associated with amygdalar atrophy whereas alpha3/alpha2 ratio is best associated with hippocampal atrophy. CVD and AHC damages are associated with speciﬁc EEG markers. So far, these EEG markers could have a prospective value in diﬀerential diagnosis between vascular and degenerative MCI. 1. Introduction temporal lobe (MTL) activations in MCI subjects versus controls, during the performance of memory tasks [16, 17]. Mild cognitive impairment (MCI) is a clinical state inter- Nonetheless, fMRI ﬁndings in MCI are discrepant, as MTL mediate between elderly normal cognition and dementia hypoactivation similar to that seen in AD patients [18] that aﬀects a signiﬁcant amount of the elderly population, has also been reported [19]. Recent postmortem data from featuring memory complaints and cognitive impairment on subjects—who had been prospectively followed and clinically neuropsychological testing, but no dementia [1–3]. characterized up to immediately before their death—indicate The hippocampus is one of the ﬁrst and most aﬀected that hippocampal choline acetyltransferase levels are reduced brain regions impacted by both Alzheimer’s disease and mild in Alzheimer’s dementia, but in fact they are upregulated cognitive impairment (MCI; [4–9]). In mild-to-moderate in MCI [15], presumably because of reactive upregulations Alzheimer’s disease patients, it has been shown that hip- of the enzyme activity in the unaﬀected hippocampal pocampal volumes are 27% smaller than in normal elderly cholinergic axons. Previous EEG studies [20–26] have shown controls [10, 11], whereas patients with MCI show a volume a decrease—ranging from 8 to 10.5 Hz (low alpha)—of reduction of 11% [11]. So far, from a neuropathological the alpha frequency power band in MCI subjects, when compared to normal elderly controls [20, 27–30]. However, a point of view, the progression of disease from MCI state to later stages seems to follow a linear course. Nevertheless, recent study has shown an increase—ranging from 10.5 to 13 there is some evidence from functional [12–14]and bio- Hz (high alpha)—of the alpha frequency power band, on the chemical studies [15] that the process of conversion from occipital region in MCI subjects, when compared to normal nondemented to clinically evident demented state is not so elderly and AD patients [30]. These somewhat contradictory linear. Recent fMRI studies have suggested increased medial ﬁndings may be explained by the possibility that MCI 2 International Journal of Alzheimer’s Disease subjects have diﬀerent patterns of plastic organization during 2. Materials and Methods the disease and that the activation (or hypoactivation) 2.1. Subjects of diﬀerent cerebral areas is based on various degrees of hippocampal atrophy. If this hypothesis is true, then EEG 2.1.1. General Considerations about Recruitment. All the changes of rhythmicity have to occur nonproportionally to subjects in the study were recruited from the same cohort the hippocampal atrophy, as previously demonstrated in a in the Memory Clinic of the Scientiﬁc Institute for Research studyofauditoryevokedpotentials[31]. and Care (IRCCS) of Alzheimer’s and psychiatric diseases In a recent study [32], the results conﬁrm the hypothesis “Fatebenefratelli” in Brescia, Italy. All experimental protocols that the relationship between hippocampal volume and EEG had been approved by the local Ethics Committee. Informed rhythmicity is not proportional to the hippocampal atrophy, consent was obtained from all participants or their care- as revealed by the analyses of both the relative band powers givers, according to the Code of Ethics of the World Medical and the individual alpha markers. Such a pattern seems Association (Declaration of Helsinki). The diﬀerence in the to emerge because, rather than a classiﬁcation based on size of the populations (cerebrovascular and degenerative clinical parameters, discrete hippocampal volume diﬀerences 3 impairment) is due to technical reasons linked to the MRI (about 1 cm ) are analyzed. Indeed, the group with moderate analysis. hippocampal atrophy showed the highest increase in the theta power on frontal regions and of the alpha2 and alpha3 powers on frontal and temporoparietal areas. Cerebrovascular Impairment. For the present study, 99 sub- Recently, two speciﬁc EEG markers, theta/gamma and jects with MCI were recruited. Table 1 shows the main alpha3/alpha2 frequency ratio, have been reliably associated features of this group. with the atrophy of amygdalo-hippocampal complex [33], as well as with memory deﬁcits, which are a major risk Degenerative Impairment. For the present study, 79 subjects for the development of AD in MCI subjects [34]. Based on with MCI were recruited. Table 3 shows the main character- the tertiles values of decreasing AHC volume, three groups istics of the group. of AHC growing atrophy were obtained. AHC atrophy is associated with memory deﬁcits as well as with increase 2.2. Shared Procedures of theta/gamma and alpha3/alpha2 ratio. Moreover, when the amygdalar and hippocampal volumes are separately 2.2.1. EEG Recordings. All recordings were obtained in the considered, within AHC, the increase of theta/gamma morning with subjects resting comfortably. Vigilance was ratio is best associated with amygdalar atrophy whereas continuously monitored in order to avoid drowsiness. alpha3/alpha2 ratio is best associated with hippocampal The EEG activity was recorded continuously from 19 atrophy. sites by using electrodes set in an elastic cap (Electro-Cap The role of cerebrovascular (CV) disease and ischemic International, Inc.) and positioned according to the 10– brain damage in cognitive decline remains controversial. 20 International system (Fp1, Fp2, F7, F3, Fz, F4, F8, T3, Although not all patients with mild cognitive impairment C3, Cz, C4, T4, T5, P3, Pz, P4, T6, O1, and O2). The due to CV damage develop a clinically deﬁned dementia, ground electrode was placed in front of Fz. The left and right all such patients are at risk and could develop dementia mastoids served as reference for all electrodes. The recordings in the 5 years following the detection of cognitive decline. were used oﬀ line to rereference the scalp recordings to Cognitive impairment due to subcortical CV damages is the common average. Data were recorded with a band- thought to be caused by focal or multifocal lesions involving pass ﬁlter of 0.3–70 Hz and digitized at a sampling rate of strategic brain areas. These lesions in basal ganglia, thalamus, 250 Hz (BrainAmp, BrainProducts, Germany). Electrodes- or connecting white matter induce interruption of thala- skin impedance was set below 5 kΩ. Horizontal and vertical mocortical and striatocortical pathways. As a consequence, eye movements were detected by recording the electrooculo- deaﬀerentation of frontal and limbic cortical structures is gram (EOG). The recording lasted 5 minutes, with subjects produced. The pattern of cognitive impairment is consistent with closed eyes. Longer recordings would have reduced the with models of impaired cortical and subcortical neuronal variability of the data, but they would also have increased pathways [36]. Even when CV pathology appears to be the possibility of slowing of EEG oscillations due to reduced the main underlying process, the eﬀects of the damaged vigilance and arousal. EEG data were then analyzed and brain parenchyma are variable and, therefore, the clinical, fragmented oﬀ line in consecutive epochs of 2 seconds, with radiological, and pathological appearances may be hetero- a frequency resolution of 0.5 Hz. The average number of geneous. A neurophysiological approach could be helpful in epochs analyzed was 140 ranging from 130 to 150. The EEG diﬀerentiating structural from functional CV damage [35]. epochs with ocular, muscular, and other types of artifacts The quantitative analysis of electroencephalographic (EEG) were discarded. rhythms in resting subjects is a low-cost but still powerful approach to the study of elderly subjects in normal aging, MCI, and dementia. The aim of this study was to compare 2.2.2. Analysis of Individual Frequency Bands. A digital FFT- speciﬁc EEG markers that could be useful for diagnostic based power spectrum analysis (Welch technique, Hanning and prognostic purpose in the investigation of patients with windowing function, no phase shift) computed—ranging cognitive decline. from 2 to 45 Hz—the power density of EEG rhythms with International Journal of Alzheimer’s Disease 3 Table 1: Mean values ± standard error of demographic characteristics, neuropsychological and ARWMC scores of the MCI subgroups. F/M: female/male. Age and education are expressed in years. Group 1: no vascular damage; group 2: mild vascular damage; group 3: moderate vascular damage; group 4: severe vascular damage. Group 1 Group 2 Group 3 Group 4 Subjects (f/m) 27 (18/9) 41 (31/10) 19 (10/9) 12 (9/3) Age 70.1 (±1.7) 69.9 (±1.1) 69.7 (±1.9) 70.5 (±2.4) Education 7.1 (±0.7) 7 (±0.6) 7 (±0.9) 10 (±1.6) MMSE 26.7 (±0.4) 26.5 (±0.4) 27 (±0.4) 26.1 (±0.7) ARWMC scale 0 1–5 6–10 11–15 a 0.5 Hz frequency resolution. Methods are exposed in subjected to diagnostic neuroimaging procedures (magnetic detail elsewhere [32, 35, 37]. Brieﬂy, two anchor frequencies resonance imaging (MRI)) and laboratory blood analysis to were selected according to the literature guidelines [38], rule out other causes of cognitive impairment. that is, the theta/alpha transition frequency (TF) and the The present inclusion and exclusion criteria for MCI individual alpha frequency (IAF) peak. Based on TF and IAF, were based on previous seminal studies [2, 3, 43–46]. we estimated the following frequency band range for each Inclusion criteria in the study were all of the following: (i) subject: delta, theta, low alpha band (alpha1 and alpha2), complaint by the patient or report by a relative or the general and high alpha band (alpha3). Moreover, individual beta and practitioner of memory or other cognitive disturbances; (ii) gamma frequencies were computed. Three frequency peaks Mini-Mental State Examination (MMSE; [39]) score of 24 were detected in the frequency range from the individual to 27/30 or MMSE of 28 and higher plus low performance alpha 3 frequency band and 45 Hz. These peaks were (score of 2/6 or higher) on the clock drawing test [47]; named beta 1 peak (IBF 1), beta 2 peak (IBF 2), and (iii) sparing of instrumental and basic activities of daily gamma peak (IGF). Based on peaks, the frequency ranges living or functional impairment stably due to causes other were determined. Beta1 ranges from alpha 3 to the lower than cognitive impairment, such as physical impairments, spectral power value between beta 1 and beta 2 peak; beta2 sensory loss, and gait or balance disturbances. Exclusion frequency ranges from beta 1 to the lower spectral power criteria were any one of the following: (i) age of 90 years value between beta 2 and gamma peak; gamma frequency and older; (ii) history of depression or psychosis of juvenile ranges from beta 2 to 45 Hz, which is the end of the onset; (iii) history or neurological signs of major stroke; (iv) range considered. The frequency range was determined for other psychiatric diseases, epilepsy, drug addiction, alcohol each patient. On average, the boundaries of the frequency dependence; (v) use of psychoactive drugs including acetyl- bands were as follows: delta 2.9–4.9 Hz; theta 4.9–6.9 Hz; cholinesterase inhibitors or other drugs enhancing brain alpha1 6.9–8.9 Hz; alpha 2 8.9–10.9 Hz; alpha3 10.9–12-9 Hz; cognitive functions; (vi) current or previous uncontrolled or beta1 12,9–19,2 Hz; beta2 19.2–32.4; gamma 32.4–45. In the complicated systemic diseases (including diabetes mellitus) frequency bands determined in this way, the relative power or traumatic brain injuries. spectra for each subject were computed. The relative power All patients underwent (i) semistructured interview density for each frequency band was computed as the ratio with the patient and—whenever possible—with another between the absolute power and the mean power spectra informant (usually the patient’s spouse or a child) by a from 2 to 45 Hz. Finally, the theta/gamma and alpha3/alpha2 geriatrician or neurologist; (ii) physical and neurological relative power ratio were computed and analyzed. The examinations; (iii) performance-based tests of physical func- analysis of other frequencies was not in the scope of this tion, gait, and balance; (iv) neuropsychological assessment study. evaluating verbal and nonverbal memory, attention, and executive functions (Trail Making Test B-A; Clock Drawing Test; [47, 48]), abstract thinking (Raven matrices; [49]), frontal functions (Inverted Motor Learning; [50]), language 2.2.3. Diagnostic Criteria. In this study we enrolled sub- (Phonological and Semantic ﬂuency; Token test; [51]), and jects aﬀerents to the Scientiﬁc Institute of Research and apraxia and visuoconstructional abilities (Rey ﬁgure copy; Care Fatebenefratelli in Brescia, Italy. Patients were taken [52]); (v) assessment of depressive symptoms with the Center from a prospective project on clinical progression of MCI. for Epidemiologic Studies Depression Scale (CES-D; [53]). The project was aimed to study the natural history of Given the aim of the study to evaluate the impact of vascular nondemented persons with apparently primary cognitive damage on EEG rhythms, in this study we did not consider deﬁcits, not caused by psychic (anxiety, depression, etc.) the clinical subtype of MCI, that is, amnesic, nonamnesic, or or physical (uncontrolled heart disease, uncontrolled dia- multiple domain. betes, etc.) conditions. Patients were rated with a series of standardized diagnostic tests, including the Mini-Mental State Examination (MMSE; [39]), the Clinical Dementia Rating Scale (CDRS; [40]), the Hachinski Ischemic Scale 2.2.4. Magnetic Resonance Imaging (MRI) and CV Damage (HIS; [41]), and the Instrumental and Basic Activities of Evaluation. Magnetic resonance (MR) images were acquired Daily Living (IADL, BADL, [42]). In addition, patients were using a 1.0 Tesla Philips Gyroscan. Axial T2 weighted, 4 International Journal of Alzheimer’s Disease proton density(DP), andﬂuidattenuatedinversion recovery Plot of means Delta 2-way interaction (FLAIR) images were acquired with the following acquisition P Alpha 2 F(12.38) = 2.6; P< 0.0025 parameters: TR = 2000 ms, TE = 8.8/110 ms, ﬂip angle = 90 , 4.5 P< 0.0007 ﬁeld of view = 230 mm, acquisition matrix 256 × 256, slice G1 versus G3 thickness 5 mm for T2/DP sequences and TR = 5000 ms, TE = 100 ms, ﬂip angle = 90 ,ﬁeldofview = 230 mm, acquisition 3.5 matrix 256 × 256, slice thickness 5 mm for FLAIR images. Subcortical cerebrovascular disease (sCVD) was assessed 3 using the rating scale for age-related white matter change P = 0.05 2.5 (ARWMC) on T2-weighted and FLAIR MR images. White G1 versus G4 matter changes (WMC) was rated by a single observer (R.R.) in the right and left hemispheres separately in frontal, P< 0.00003 1.5 G1 versus G4 parietooccipital, temporal, and infratentorial areas and basal ganglia on a 4-point scale. The observer of white matter changes was blind to the clinical information of the subjects. Subscores of 0, 1, 2, and 3 were assigned in frontal, parieto- 0.5 Delta Theta Alpha 1 Alpha 2 Alpha 3 occipital, temporal, and infratentorial areas for no WMC, Frequency bands focal lesions, beginning conﬂuence of lesions, and diﬀuse involvement of the entire region, respectively. Subscores of Group 1 Group 3 0, 1, 2, and 3 were assigned in basal ganglia for no WMC, 1 Group 2 Group 4 focal lesion, more than 1 focal lesion, and conﬂuent lesions, Figure 1: Statistical ANOVA interaction among groups, factor and respectively. Total score was the sum of subscores for each relative band power (delta, theta, alpha1, alpha2, and alpha3). In area in the left and right hemisphere, ranging from 0 to the diagram the diﬀerence in delta and alpha2 power among groups 30. As regards the ARWMC scale, the interrater reliability, is also indicated, based on Duncan’s post hoc testing. G1, group 1: as calculated with weighted k value, was 0.67, indicative of no vascular damage; G2, group 2: mild vascular damage; G3, group moderate agreement [54]. We assessed test-retest reliability 3: moderate vascular damage; G4, group 4: severe vascular damage on a random sample of 20 subjects. The intraclass correlation [32, 35]. coeﬃcient was 0.98, values above 0.80 being considered indicative of good agreement. Based on increasing subcortical CV damage, the 99 MCI subjects were subsequently divided in 4 subgroups along the performed statistical analyses to evaluate the speciﬁcity of range between the minimum and maximum ARWMC score the following ratios: theta/alpha1, using as covariate also TF; (resp. 0 and 15). In order to have the higher sensibility to alpha2/alpha3 using as covariate also IAF and alpha1/alpha2 the CV damage, the ﬁrst group was composed by subjects with both TF and IAF as covariate. Moreover, we performed with score = 0. The other groups were composed according to correlations (Pearson’s moment correlation) between CV equal range ARWMC scores. As a consequence, we obtained damage score and frequency markers (TF and IAF), spectral the following groups: group 1 (G1): no vascular damage, CV power, and MMSE. Finally, we performed a control statistical score 0; group 2 (G2): mild vascular damage, CV score 1– analysis with 4 frequency bands, considering alpha1 and 5; group 3 (G3): moderate vascular damage, CV score 6–10; alpha2 as single band (low alpha). This analysis had the aim group 4 (G4): severe vascular damage, CV score 11–15. of verifying if the low alpha, when considered as a whole, has Table 1 reports the mean values of demographic and the same behavior. clinical characteristics of the 4 subgroups. 3. Results 2.2.5. Statistical Analysis. Preliminarily, any signiﬁcant dif- ference between groups in demographic variables, age, 3.1. Vascular MCI. Figure 1 displays the results for ANOVA education, and gender as well as MMSE score was taken analysis of these data showing a signiﬁcant interaction into account. Only education showed a signiﬁcant diﬀerence between group and Band factors (F(12.380) = 2.60;P< between groups (P< 0.03). For avoiding confounding eﬀect, 0.002). Interestingly, Duncan’s post hoc testing showed a subsequent statistical analyses of variance (ANOVA) were signiﬁcant higher delta power in G4 compared to G1 (P< carried out using age, education, gender, and MMSE score as 0.050) and a signiﬁcant higher alpha2 power in G1 compared covariates. Duncan’s test was used for post hoc comparisons. to G3 and G4 (P< 0.000). On the contrary, no diﬀerences For all statistical tests the signiﬁcance was set to P< 0.05. were found in theta, alpha1, and alpha 3 band powers. A second session of ANOVA was performed on EEG Moreover, a closer look at the data, in respect to the alpha1 relative power data. In this analysis, group factor was the frequency, showed a decrease proportional to the degree independent variable and frequency band power (delta, of CV damage very similar to alpha2 band, although not theta, alpha1, alpha2, and alpha 3) the dependent variable. signiﬁcant. On the contrary, in the alpha3 band power this As successive step, to evaluate the presence of EEG trend was not present, suggesting that vascular damage had indexes that correlate speciﬁcally with vascular damage, we no impact on this frequency band. Spectral power density International Journal of Alzheimer’s Disease 5 Table 2: Mean values ± standard error of theta/alpha1, alpha1/alpha2, and alpha2/alpha3 ratios in the MCI subgroups. Group 1: no vascular damage; group 2: mild vascular damage; group 3: moderate vascular damage; group 4: severe vascular damage. Group 1 Group 2 Group 3 Group 4 θ/α10.7(±0.05) 0.77 (±0.05) 1.17 (±0.05) 1.39 (±0.14) α1/α20.46(±0.03) 0.5 (±0.03) 0.53 (±0.05) 0.47 (±0.04) α2/α31.27(±0.12) 1.16 (±0.1) 0.85 (±0.05) 0.79 (±0.07) Table 3: Mean values ± standard deviation of sociodemographic characteristics, MMSE scores, white matter hyperintensities, and hippocampal and amygdalar volume measurements. Hippocampal and amygdalar volumes refer to the whole amygdalohippocampal complex (AHC) and are singularly considered (individual). The t-test refers to AHC versus individual volume in each group. MCI cohort Group 1 Group 2 Group 3 P value (ANOVA) Number of subjects (f/m) 79 (42/37) 27 (14/13) 27 (15/12) 25 (13/12) Age (years) 69.2 ± 2.366.8 ± 6.869.4 ± 8.771.5 ± 6.90.1 Education (years) 7.7 ± 0.88.3 ± 4.56.7 ± 3.18.2 ± 4.60.2 MMSE 27.1 ± 0.427.5 ± 1.527.4 ± 1.526.6 ± 1.80.1 Total AHC volume 6965.3 ± 1248.8 8151.2 ± 436.4 7082.7 ± 266.9 5661.8 ± 720.4 0.00001 AHC-hippocampal volume (mm ) 4891.7 ± 902.6 5771.6 ± 361.1 4935.6 ± 380.9 3967.9 ± 650.3 0.00001 AHC-amygdalar volume (mm ) 2073.5 ± 348.7 2379.6 ± 321.3 2147.1 ± 301.3 1693.9 ± 288.5 0.0001 Individual hippocampal volume (mm ) 4889.8 ± 962.4 5809.6 ± 314.2 4969.4 ± 257.6 3890.1 ± 551.4 0.00001 Individual amygdalar volume (mm ) 2071.7 ± 446.4 2514.4 ± 259.5 2079.2 ± 122.8 1621.6 ± 185.2 0.0001 White matter hyperintensities (mm)3.8 ± 0.53.2 ± 2.84.2 ± 3.84.1 ± 3.60.7 The correlation analysis between CV score and spectral sequence (TR = 5000 ms, TE = 100 ms, ﬂip angle = 90 , band power showed a signiﬁcant positive correlation with ﬁeld of view = 230 mm, acquisition matrix = 256 × 256, and delta power (r = 0.221;P< 0.03) a signiﬁcant negative slice thickness = 5 mm). Hippocampal, amygdalar, and white correlation with alpha1 (r =−0.312;P< 0.002) and alpha2 matter hyperintensities (WMHs) volumes were obtained for power (r= − 0.363;P< 0.0003). The correlations between each subject. The hippocampal and amygdalar boundaries CV score with theta power (r = 0.183; P = 0.07) and were manually traced on each hemisphere by a single tracer alpha3 power (r =−0.002; P = 0.93) were not signiﬁcant with the software program DISPLAY (McGill University, as well as the correlation between CV score and MMSE (r = Montreal, Canada) on contiguous 1.5 mm slices in the −0.07; P = 0.4). coronal plane. The amygdala is an olive-shaped mass of gray Table 2 displays the values of the theta/alpha1 and matter located in the superomedial part of the temporal alpha2/alpha3 power ratio. The statistical analysis of the lobe, partly superior and anterior to the hippocampus. The theta/alpha1 ratio showed a main eﬀect of group (F(3.91) = starting point for amygdala tracing was at the level where it is 15.51;P< 0.000). Duncan’s post hoc testing showed a separated from the entorhinal cortex by intrarhinal sulcus, signiﬁcant increase of the theta/alpha1 ratio between G1 and or tentorial indentation, which forms a marked indent at G2 in respect to G3 and G4 (P< 0.000). Moreover, the the site of the inferior border of the amygdala. The uncinate increase of this ratio was signiﬁcant also between G3 and fasciculus, at the level of basolateral nuclei groups, was G4 (P< 0.04). The statistical analysis of the alpha2/alpha3 considered as the anterior-lateral border. The amygdalo- power ratio showed a main eﬀect of group (F(3.91) = striatal transition area, which is located between lateral 4.60;P< 0.005). Duncan’s post hoc testing showed a amygdaloid nucleus and ventral putamen, was considered as signiﬁcant decrease of the ratio between G1 and G3 (P< the posterior-lateral border. The posterior end of amygdaloid 0.02), G1 and G4 (P< 0.010), and G2 and G4 (P< 0.05). The nucleus was deﬁned as the point where gray matter starts statistical analysis of the alpha1/alpha2 ratio did not show the to appear superior to the alveolus and lateral to the main eﬀect of group (P< 0.2). hippocampus. If the alveolus was not visible, the inferior horn of the lateral ventricle was employed as border [33]. The starting point for hippocampus tracing was deﬁned 3.2. MRI Scans and Amygdalohippocampal Atrophy Evalu- as the hippocampal head when it ﬁrst appears below the ation. MRI scans were acquired with a 1.0 Tesla Philips amygdala, the alveus deﬁning the superior and anterior Gyroscan at the Neuroradiology Unit of the Cittad ` iBrescia border of the hippocampus. The ﬁmbria was included in Hospital, Brescia. The following sequences were used to the hippocampal body, while the grey matter rostral to the ﬁmbria was excluded. The hippocampal tail was traced until measure hippocampal and amygdalar volumes: a high- resolution gradient echo T1-weighted sagittal 3D sequence it was visible as an oval shape located caudally and medially (TR = 20 ms, TE = 5 ms, ﬂip angle = 30 ,ﬁeldofview = to the trigone of the lateral ventricles [32, 35]. The intraclass 220 mm, acquisition matrix = 256×256, and slice thickness = correlation coeﬃcients were 0.95 for the hippocampus and 1.3 mm) and a ﬂuid-attenuated inversion recovery (FLAIR) 0.83 for the amygdala. 6 International Journal of Alzheimer’s Disease White matter hyperintensities (WMHs) were automati- subjects. The diﬀerence in groups subdivision (4 versus 3) cally segmented on the FLAIR sequences by using previously was mandatory to obtain group with volumetric statistical described algorithms [32, 35]. Brieﬂy, the procedure includes signiﬁcant diﬀerence. (i) ﬁltering of FLAIR images to exclude radiofrequency At ﬁrst, we choose to focus on the changes of brain inhomogeneities, (ii) segmentation of brain tissue from cere- rhythmicity induced from hippocampal atrophy alone. Sub- brospinal ﬂuid, (iii) modelling of brain intensity histogram jectsweresubdividedinfourgroupsbased on hippocampal as a Gaussian distribution, and (iv) classiﬁcation of the voxels volume of a normal control sample matched for age, sex, whose intensities were ≥3.5 SDs above the mean as WMHs and education as compared to the whole MCI group. In the [32, 35], Total WMHs volume was computed by counting the normal group the female/male ratio was 93/46, mean age number of voxels segmented as WMHs and multiplying by was 68.9 (SD ± 10.3), mean education was years 8.9 (SD ± the voxel size (5 mm ). To correct for individual diﬀerences 9.4). The mean and standard deviation of the hippocampal in head size, hippocampal, amygdalar and WMHs volumes volume in the normal old population of 139 subjects were were normalized to the total intracranial volume (TIV), 5.72 ± 1.1cm . In this way, 4 groups were obtained: the no obtained by manually tracing with DISPLAY the entire atrophy group with hippocampal volume equal or superior intracranial cavity on 7 mm thick coronal slices of the T1 to the normal mean (total hippocampal volume from 3 3 weighted images. Both manual and automated methods 6.79 cm to 5.75 cm ; G1); the mild atrophy group which used here have advantages and disadvantages. Manual seg- has hippocampal volume within 1.5 SD below the mean mentation of the hippocampus and amygdala is currently of hippocampal normal control value (total hippocampal considered the gold standard technique for the measurement volume from 5.70 to 4.70 cm ; G2); the moderate atrophy of such complex structures. The main disadvantages of group which has hippocampal volume between 1.5 and 3 SD manual tracing are that it is operator dependent and time below the mean of normal hippocampus (total hippocampal consuming. Conversely, automated techniques are more volume from 4.65 to 3.5 cm ; G3); the severe atrophy reliable and less time-consuming but may be less accurate group which has hippocampal volume between 3 and 4.5 SD when dealing with structures without clearly identiﬁable below the mean of hippocampal normal control volume borders. This, however, is not the case for WMHs, which (total hippocampal volume from 3.4 to 2.53 cm ;G4).The appear as hyperintense on FLAIR sequences. rationale for the selection of 1,5 SD was to obtain reasonably Left and right hippocampal as well as amygdalar volumes pathological groups based on hippocampal volume. A SD were estimated and summed to obtain a total volume (indi- below 1.5 could recollect still normal population based on vidual) of both anatomical structures. In turn, total amyg- hippocampal volume. On the other side, a SD over 1.5 could dalar and hippocampal volumes were summed obtaining the not allow an adequate size of all subgroups in study. whole AHC volume. AHC (whole) volume has been divided Subsequently, ANOVA was performed in order to verify in tertiles obtaining three groups. In each group hippocam- (1) the diﬀerence of AHC volume among groups; (2) the pal and amygdalar volumes (within AHC) have been com- diﬀerence of hippocampal and amygdalar volume within puted. The last volumes were compared with the previous AHC among groups; (3) the diﬀerence of hippocampal and obtained individual (hippocampal and amygdalar) volumes. amygdalar volume individually considered among groups; (4) NPS impairment based on ACH atrophy. Moreover, as a control analysis, in order to detect if 3.3. Statistical Analysis and Data Management. The anal- diﬀerence in EEG markers was linked to signiﬁcant diﬀerence in volume measurements, the volume of hippocampus ysis of variance (ANOVA) has been applied as statistical tool. At ﬁrst, any signiﬁcant diﬀerences among groups within AHC was compared with the hippocampal volume in demographic variables, that is, age, education, MMSE individually considered, and the amygdalar volume within score, and morphostructural characteristics, that is, AHC, AHC was compared with the amygdalar volume individually hippocampal, amygdalar, and white matter hyperintensities considered. This control analysis was performed through a paired t-test. (WMHs) volume, were evaluated (Table 1). Greenhouse- Geisser correction and Mauchly’s sphericity test were applied Subsequently, ANOVA was performed in order to check to all ANOVAs. In order to avoid a confounding eﬀect, diﬀerences in theta/gamma and alpha3/alpha2 relative power ratio in the three groups ordered by decreasing tertile ANOVAs were carried out using age, education, MMSE score, and WMHs as covariates. For all statistical tests the values of the whole AHC volume. In each ANOVA, group signiﬁcance level was set at P< 0.05. Duncan’s test was used was the independent variable, the frequency ratios was the dependent variable, and age, education, MMSE score, and for post hoc comparisons. In a ﬁrst study [32] we considered only the hippocampal volume and obtained 4 subgroups WMHs were used as covariates. Duncan’s test was used for based on the hippocampal volume atrophy. In a subsequent post-hoc comparisons. For all statistical tests the signiﬁcance study [33], given the importance of the amigdala in both level was set at P< 0.05. degenerative pathology and brain rhythm generation, we In order to check closer association with EEG markers, hippocampal volume and amygdalar volume within AHC considered the amygdalo-hippocampal volume as a whole, in order to investigate the entire AHC and to focus on the were analyzed separately. A control analysis was carried out hippocampal volume within the AHC itself. In this case we also on the individual hippocampal and amygdalar volumes based on decreasing tertile values for homogeneity with the obtain three groups based on AHC atrophy. Both the ﬁrst and the second studies were performed on the same 79 main analysis. International Journal of Alzheimer’s Disease 7 Plot of means the ﬁrst group (P< 0.03). The amygdalar volume diﬀerence 2-way interaction in the other groups (resp. P = 0.2 and 0.1) as well as F(12.336) = 2.87; P< 0.0009 the diﬀerence in the volume of AHC-hippocampus versus Full scalp individual hippocampus (P = 0.4 in the ﬁrst, P = 0.5 in the Equal size groups second, and P = 0.1 in the third group) was not statistically 4.5 signiﬁcant. Tables 3 and 4 show the results of theta/gamma and alpha3/alpha2 ratio in the groups based on the decrease of 3.5 whole AHC volume as well as, within the same group, the decrease of hippocampal and amygdalar volumes separately 2.5 considered. ANOVA shows results towards signiﬁcance when 2 amygdaloippocampal volume is considered globally in both theta/gamma (F = 2.77;P< 0.06) and alpha3/alpha2 2,76 1.5 ratio (F = 2.71;P< 0.07). When amygdalar and 2,76 hippocampal volumes were considered separately, ANOVA 0.5 results revealed signiﬁcant main eﬀect of Group, respectively, Delta Theta Alpha 1 Alpha 2 Alpha 3 in theta/gamma ratio analysis (F = 3.46;P< 0.03) 2,76 Rhythms for amygdalar and alpha3/alpha2 ratio for hippocampal Group 1 Group 3 (F = 3.38;P< 0.03) decreasing volume. The ANOVA 2,76 Group 2 Group 4 did not show signiﬁcant results in theta/gamma ratio when Figure 2: Statistical ANOVA interaction among group factors and considering hippocampal volume (F = 0.3;P< 0.7) 2,76 relative band powers (delta, theta, alpha1, alpha2, and alpha3), on and in alpha3/alpha2 ratio when considering amygdalar the full scalp region. The groups are based on mean and standard volume (F = 1.46;P< 0.2). The control analysis 2,76 deviations in a normal elderly sample. Group 1: no hippocampal (individual volumes) did not show any signiﬁcant result atrophy; group 2: mild hippocampal atrophy; group 3: moderate neither for hippocampal (theta/gamma, F = 0.3;P< 0.7; 2,76 hippocampal atrophy; group 4: severe hippocampal atrophy. Post alpha3/alpha2, F = 2.15;P< 0.1) nor for amygdalar 2,76 hoc results are indicated in the diagram (see [32, 35]). volume (theta/gamma, F = 0.76;P< 0.4; alpha3/alpha2, 2,76 F = 2.15;P< 0.1). 2,76 3.4. Amigdalohyppocampal Atrophy MCI. Figure 2 displays the results for ANOVA analysis performed on 4 groups of MCI considering growing values of hippocampal atrphy. The 4. Discussion results show a signiﬁcant interaction between group and band power (F(12, 336) = 2, 36);P< 0.007). Duncan’s post MCI and EEG Markers: Degenerative versus Vascular Impair- hoc testing showed that G3 group has the highest alpha2 and ment. A large body of literature has previously demonstrated alpha3 power statistically signiﬁcant with respect to all other that in subjects with cognitive decline an increase of theta groups (P< 0.05;P< 0.006, resp.). The same trend was relative power [32, 33, 35], a decrease of gamma relative present in the subsidiary ANOVA. These results show that the power [32, 33, 35, 55], and an increase of high alpha as relationship between hippocampal atrophy and EEG relative compared to low alpha band are present [33]. On the power is not proportional to the hippocampal atrophy and whole theta/gamma ratio and alpha3/alpha2 ratio could be highlight that the group with a moderate hippocampal considered reliable EEG markers of cognitive decline. volume had a particular pattern of EEG activity as compared The amygdalohippocampal network is a key structure to all other groups. in the generation of theta rhythm. More speciﬁcally, theta Table 3 summarizes the ANOVA results of demographic synchronization is increased between LA and CA1 regions variables, that is, age, education, MMSE score, and mor- of hippocampus during long-term memory retrieval, but phostructural characteristics, that is, hippocampal, amyg- not during short-term or remote memory retrieval [56, dalar, and white matter hyperintensities volume in the whole 57]. Theta synchronization in AHC appears to be apt to MCIcohortaswellasinthe three subgroupsinstudy. improve the neural communication during memory retrieval Hippocampal and aymgdalar volumes are considered as parts [57]. On the other hand, the retrieval of hippocampus- of the whole AHC volume and are individually considered. dipendent memory is provided by the integrity of CA3- Signiﬁcant statistical results were found in hippocampal and CA1 interplay coordinated by gamma oscillations [58]. Our amygdalar volume both within the AHC (resp., F = results conﬁrm and extend all previous ﬁndings. The atrophy 2,76 92.74;P< 0.00001 and F = 33.82;P< 0.00001) and of AHC determines increasing memory deﬁcits. The brain 2,76 were individually considered (resp., F = 157.27;P< oscillatory activity of this MCI state is characterized by an 2,76 0.00001 and F = 132.5;P< 0.00001). The global AHC increase of theta/gamma ratio and alpha3/alpha2 relative 2,76 volume also showed signiﬁcant results (F = 159.27;P< power ratio, conﬁrming the overall reliability of these EEG 2,76 0.00001). Duncan’s post hoc test showed a signiﬁcant markers in cognitive decline. Our results suggest that theta increase (P< 0.01) in all comparisons. The paired t-test synchronization is mainly due to the amygdala activation or showed signiﬁcant diﬀerence between the volume of ACH- as a subsequent ﬁnal net eﬀect within the AHC functioning amygdala and amygdalar volume individually considered in driven by the amygdala excitation. The increase in theta Band power 8 International Journal of Alzheimer’s Disease Table 4: Relative power band ratios in amygdalo-hippocampal complex (AHC), hippocampal and amygdalar atrophy. Hippocampal and amygdalar volumes refer to the whole amygdalo-hippocampal complex (AHC) and are singularly considered (individual). 2 2 Hippocampal + amygdalar volume theta/gamma ratio (μv ) P value alpha3/alpha2 ratio (μv ) P value Group1 1.40 ± 0.35 1.05 ± 0.11 0.06 0.07 Group2 1.43 ± 0.35 1.11 ± 0.14 Group3 1.47 ± 0.44 1.12 ± 0.16 AHC-hippocampal volume Group1 1.39 ± 0.27 1.04 ± 0.11 0.7 0.03 Group2 1.48 ± 0.45 1.11 ± 0.15 Group3 1.43 ± 0.41 1.12 ± 0.14 AHC-amygdalar volume Group1 1.36 ± 0.37 1.04 ± 0.13 0.03 0.2 Group2 1.44 ± 0.36 1.12 ± 0.16 Group3 1.49 ± 0.39 1.09 ± 0.11 Individual hippocampal volume Group1 1.39 ± 0.27 1.04 ± 0.11 0.7 0.1 Group2 1.48 ± 0.45 1.07 ± 0.15 Group3 1.43 ± 0.40 1.10 ± 0.14 Individual amygdalar volume Group1 1.39 ± 0.37 1.04 ± 0.13 0.1 0.4 Group2 1.43 ± 0.36 1.12 ± 0.16 Group3 1.46 ± 0.39 1.09 ± 0.11 activities in AHC, representing an increase in neuronal com- results were conﬁrmed by a correlation analysis that showed munication apt to promote or stabilize synaptic plasticity in a signiﬁcant negative correlation between CV damage score relation to the eﬀort to retention of associative memories and alpha1 and alpha2 band powers. In our results, the CV [59], could be active also during an ongoing degenerative damage did not show any impact on the alpha3 (or high process. The excitation mechanism could be facilitated by the alpha) power. This is a conﬁrmation of what we found in the loss of GABA inhibitory process, determining the decrease of previous study, where no diﬀerences between VaD patients gamma rhythm generation [58, 60]. andnormalelderly (but notinADversusnormalelderly) As regards the CV damage, our results showed that subjects were detected in the alpha3 power. the CV damage aﬀected both delta and low alpha band Together, these results could suggest diﬀerent generators power (alpha1 and alpha2). In the delta band we observed for low alpha and high alpha frequency bands. In particular, a power increase proportional to the CV damage, with a the low alpha band power could aﬀect corticosubcortical signiﬁcant increase in the group with severe CV damage, mechanisms, such as corticothalamic, corticostriatal, and as compared to the no-CV-damage group. The impact of corticobasal ones. This could explain the sensitivity of the the CV damage on the delta power was conﬁrmed by the low alpha frequency band to subcortical vascular damage. signiﬁcant positive correlation between CV damage score On the contrary, the alpha3 band power could aﬀect to and delta power itself. a greater extent those corticocortical interactions based on The increase in the delta band power could be explained synaptic eﬃciency prone to degenerative rather than CV as a progressive cortical disconnection due to the slowing damages [38, 62]. In order to ﬁnd reliable indices of CV of the conduction along corticosubcortical connecting path- damage, we checked the theta/alpha1 band power ratio. ways. Previous studies have shown the reliability of this kind of This result conﬁrmed the increase in the delta band approach in quantitative EEG in demented patients [21]. power we had observed in CV patients, as compared to The importance of this ratio lies in the presence of such normal elderly subjects [37]. It is to be noted that the frequency bands on the opposite side of the TF, that is, increase in the delta band power reﬂects a global state of the EEG frequency index most signiﬁcantly aﬀected by the cortical deaﬀerentation, due to various anatomofunctional CV damage. So, the theta/alpha1 band power ratio could substrates, such as stages of sleep, metabolic encephalopathy, represent the most sensitive EEG marker of CV damage. The or corticothalamocortical dysrhythmia [61]. In the low alpha results showed a signiﬁcant increase of the theta/alpha1 band band power, we observed a signiﬁcant decrease in the alpha power ratio in moderate and severe CV damage groups, as 2 band power for the groups with moderate and severe compared to mild and no-CV-damage groups. This ratio CV damage, as compared to the no-CV-damage group. In increase establishes a proportional increase of the theta band the alpah1 frequency band, there was a similar decrease power relative to the alpha 1 band power with respect to although it did not reach statistically signiﬁcant values. These the CV damage, even though a signiﬁcant increase in the International Journal of Alzheimer’s Disease 9 theta band power per se (or a decrease in the alpha 1 band due to subcortical vascular damage (subcortical vascular power per se) is not present. This could suggest a reliable MCI (svMCI)) has recently been described [74, 75]. In such speciﬁcity for the theta/alpha 1 band power ratio in focusing study, MCI patients with CV etiology developed a distinctive the presence of a subcortical CV damage. clinical phenotype characterized by poor performance on frontal tests and neurological features of parkinsonism without tremor (impairment of balance and gait). These MCI, Cognitive Deﬁcits (Memory and Attention), and EEG Activity. The vulnerability and damage of the connections clinical features could be explained by our results. In CV of hippocampus with amygdala could aﬀect reconsolidation patients, we observed a slowing of the a frequency in the two groups with greater CV damage, as compared to the groups of long-term memory and give rise to memory deﬁcits and behavioural symptoms. Several experiments show that with lesser CV damage. This is in line with a previous study amygdala activity is prominent during the period of intense [37] showing the major eﬀect of the CV damage, in patients arousal, for example, the anticipation of a noxious stimulus with vascular dementia (VaD) versus normal elderly and [63] or the maintenance of vigilance to negative stimuli Alzheimer’s patients. A reasonable (although speculative) [64]. So far, the theta synchronization induced by the explanation of the present results is that the CV-damage- amygdala is deeply involved in endogenous attentional induced slowing of the a frequency start point could be mechanism. Interestingly, the increase of high alpha syn- mainly attributed to the lowering of the conduction time of synaptic action potentials throughout corticosubcortical chronization has been found in internally cued mechanisms of attention, associated with inhibitory top-down processes ﬁbers, such as corticobasal or corticothalamic pathways [76]. [62]. Of note, the amygdala is intimately involved in In fact, experimental models have previously shown that the EEG frequency is due to axonal delay and synaptic time of the anatomophysiological anterior pathways of attention through its connections with anterior cingulated cortex, corticosubcortical interactions [77–79]. Most interestingly, anteroventral, anteromedial, and pulvinar thalamic nuclei other studies have demonstrated that ﬁber myelination [65]. The particular role of amygdala in negative human aﬀects the speed propagation along cortical ﬁbers and emotions could indicate that AHC atrophy is associated with that this parameter is strictly correlated to the frequency range recorded on the scalp. In fact, a theoretical model excessive level of subcortical inputs not adequately ﬁltered by attentive processing, determining fear and anxiety and considering a mean speed propagation in white matter ﬁbers generating cognitive interference in memory performance. of 7.5 m/s (together with other parameters) is associated with a fundamental mode frequency of 9 Hz [80], that is, the Of note, an altered emotional response is very frequent in MCI patients [66, 67]. In a feedback process, this alteration typical mode of scalp-recorded EEG. It is to be noted that could determine a general state of “hyperattention” during a correlation between white matter damage and widespread which top-down internal processes prevail on the bottom- slowing of EEG rhythmicity was found in other studies, up phase, altering attention mechanism and preventing a following the presence of cognitive decline [81], multiple correct processing of sensory stimuli. Focused attention has sclerosis [82], or cerebral tumors [83]. The increase of the been found impaired in MCI patients in particular when theta/alpha1 band power ratio in moderate and severe CV they have to beneﬁt from a cue stimulus [68–72]. This damage groups, as compared to mild and no-CV-damage groups, could be the neurophysiological correlate of the particular state could be useful for maintaining a relatively spared global cognitive performance, whereas it could fail functional slowing of the speed propagation of the electric when a detailed analysis of a sensory stimulus is required. signals in vascular impaired subjects. This “hyperattentive” state could represent the attempt to recollect memory and/or spatial traces from hippocampus Conﬂict of Interests and to combine them within associative areas connected with The corresponding author declares there is no have any direct hippocampus itself. ﬁnancial relation with the commercial identity mentioned in The increase of alpha3/alpha2 ratio in our results sup- the paper that might lead to a conﬂict of interests for any of ports the concomitance of anterior attentive mechanism the authors. impairment in subjects with MCI, even though there are not overt clinical deﬁcits. The major association of the increase of alpha3/alpha2 ratio with the hippocampal formation within References the AHC suggests that this ﬁlter activity is carried out [1] C. Flicker, S. H. Ferris, and B. Reisberg, “Mild cognitive by hippocampus and its input-output connections along impairment in the elderly: predictors of dementia,” Neurology, anterior attentive circuit and AHC. Interestingly, a recent vol. 41, no. 7, pp. 1006–1009, 1991. work has demonstrated that the mossy ﬁber (MF) pathway [2] R. C. Petersen, G. E. Smith, R. J. Ivnik et al., “Apolipoprotein E of the hippocampus connects the dentate gyrus to the auto- status as a predictor of the development of Alzheimer’s disease associative CA3 network and the information it carries is in memory-impaired individuals,” Journal of the American controlled by a feedforward circuit combining disynaptic Medical Association, vol. 273, no. 16, pp. 1274–1278, 1995. inhibition with monosynaptic excitation. Analysis of the MF- [3] R. C. Petersen, R. Doody, A. Kurz et al., “Current concepts in associated circuit revealed that this circuit could act as a mild cognitive impairment,” Archives of Neurology, vol. 58, no. highpass ﬁlter [73]. 12, pp. 1985–1992, 2001. As regards CV impaired subjects, the natural history of a [4] S.E.Arnold,B.T.Hyman,J.Flory,A.R.Damasio,and G. group of subjects at very high risk for developing dementia W. Van Hoesen, “The topographical and neuroanatomical 10 International Journal of Alzheimer’s Disease distribution of neuroﬁbrillary tangles and neuritic plaques [21] V. Jelic, P. Julin, M. Shigeta et al., “Apolipoprotein E ε4allele in the cerebral cortex of patients with alzheimer’s disease,” decreases functional connectivity in Alzheimer’s disease as Cerebral Cortex, vol. 1, no. 1, pp. 103–116, 1991. measured by EEG coherence,” Journal of Neurology Neuro- [5] M. Bobinski, J. Wegiel, H. M. Wisniewski et al., “Atrophy surgery and Psychiatry, vol. 63, no. 1, pp. 59–65, 1997. of Hippocampal formation subdivisions correlates with stage [22] F. Ferreri, F. Pauri, P. Pasqualetti, R. Fini, G. Dal Forno, and P. and duration of Alzheimer disease,” Dementia, vol. 6, no. 4, M. Rossini, “Motor cortex excitability in Alzheimer’s disease: a pp. 205–210, 1995. transcranial magnetic stimulation study,” Annals of Neurology, [6] J. L. Price and J. C. Morris, “Tangles and plaques in nonde- vol. 53, no. 1, pp. 102–108, 2003. mented aging and ’preclinical’ alzheimer’s disease,” Annals of [23] Y. A. L. Pijnenburg, Y. Vd Made, A. M. Van Cappellen Van Neurology, vol. 45, no. 3, pp. 358–368, 1999. Walsum, D. L. Knol, P. Scheltens, and C. J. Stam, “EEG [7] B. Schonheit, ¨ R. Zarski, and T. G. Ohm, “Spatial and temporal synchronization likelihood in mild cognitive impairment and relationships between plaques and tangles in Alzheimer- Alzheimer’s disease during a working memory task,” Clinical pathology,” Neurobiology of Aging, vol. 25, no. 6, pp. 697–711, Neurophysiology, vol. 115, no. 6, pp. 1332–1339, 2004. [24] Z. Y. Jiang, “Study on EEG power and coherence in patients [8] D. A. Bennett, J. A. Schneider, J. L. Bienias, D. A. Evans, and R. with mild cognitive impairment during working memory S. Wilson, “Mild cognitive impairment is related to Alzheimer task,” Journal of Zhejiang University. Science. B, vol. 6, no. 12, disease pathology and cerebral infarctions,” Neurology, vol. 64, pp. 1213–1219, 2005. no. 5, pp. 834–841, 2005. [25] Z. Y. Jiang and L. L. Zheng, “Inter- and intra-hemispheric [9] G. B. Frisoni, A. Prestia, P. E. Rasser, M. Bonetti, and P. EEG coherence in patients with mild cognitive impairment M. Thompson, “In vivo mapping of incremental cortical at rest and during working memory task,” Journal of Zhejiang atrophy from incipient to overt Alzheimer’s disease,” Journal University. Science. B, vol. 7, no. 5, pp. 357–364, 2006. of Neurology, vol. 256, no. 6, pp. 916–924, 2009. [26] L. L. Zheng, Z. Y. Jiang, and E. Y. Yu, “Alpha spectral [10] D. J. A. Callen,S.E.Black,F.Gao,C.B.Caldwell, andJ.P. power and coherence in the patients with mild cognitive Szalai, “Beyond the hippocampus: MRI volumetry conﬁrms impairment during a three-level working memory task,” widespread limbic atrophy in AD,” Neurology, vol. 57, no. 9, Journal of Zhejiang University. Science B, vol. 8, no. 8, pp. 584– pp. 1669–1674, 2001. 592, 2007. [11] A. T. Du, N. Schuﬀ, D. Amend et al., “Magnetic resonance [27] R. Zappoli, A. Versari, M. Paganini et al., “Brain electrical imaging of the entorhinal cortex and hippocampus in mild activity (quantitative EEG and bit-mapping neurocognitive cognitive impairment and Alzheimer’s disease,” Journal of CNV components), psychometrics and clinical ﬁndings in Neurology Neurosurgery and Psychiatry, vol. 71, no. 4, pp. 441– presenile subjects with initial mild cognitive decline or prob- 447, 2001. able Alzheimer-type dementia,” Italian Journal of Neurological [12] G. Gold, C. Bouras, E. Kovar ¨ i et al., “Clinical validity Sciences, vol. 16, no. 6, pp. 341–376, 1995. of Braak neuropathological staging in the oldest-old,” Acta [28] C. Huang, L. O. Wahlund, T. Dierks, P. Julin, B. Winblad, Neuropathologica, vol. 99, no. 5, pp. 579–582, 2000. and V. Jelic, “Discrimination of Alzheimer’s disease and mild [13] V. Della-Maggiore, W. Chau, P. R. Peres-Neto, and A. R. cognitive impairment by equivalent EEG sources: a cross- McIntosh, “An empirical comparison of SPM preprocessing sectional and longitudinal study,” Clinical Neurophysiology, parameters to the analysis of fMRI data,” NeuroImage, vol. 17, vol. 111, no. 11, pp. 1961–1967, 2000. no. 1, pp. 19–28, 2002. [29] T. Koenig,L.Prichep,T.Dierksetal., “Decreased EEG [14] A. Ham ¨ al ¨ ainen, ¨ M. Pihlajamaki, ¨ H. Tanila et al., “Increased synchronization in Alzheimer’s disease and mild cognitive fMRI responses during encoding in mild cognitive impair- impairment,” Neurobiology of Aging, vol. 26, no. 2, pp. 165– ment,” Neurobiology of Aging, vol. 28, no. 12, pp. 1889–1903, 171, 2005. [30] C. Babiloni, G. Binetti, E. Cassetta et al., “Sources of cortical [15] P. Lavenex and D. G. Amaral, “Hippocampal-neocortical rhythms change as a function of cognitive impairment in interaction: a hierarchy of associativity,” Hippocampus, vol. 10, pathological aging: a multicenter study,” Clinical Neurophys- no. 4, pp. 420–430, 2000. iology, vol. 117, no. 2, pp. 252–268, 2006. [16] B. C. Dickerson, D. H. Salat, J. F. Bates et al., “Medial temporal [31] E. J. Golob, R. Irimajiri, and A. Starr, “Auditory cortical lobe function and structure in mild cognitive impairment,” activity in amnestic mild cognitive impairment: relationship Annals of Neurology, vol. 56, no. 1, pp. 27–35, 2004. to subtype and conversion to dementia,” Brain, vol. 130, no. 3, [17] B. C. Dickerson, D. H. Salat, D. N. Greve et al., “Increased hip- pp. 740–752, 2007. pocampal activation in mild cognitive impairment compared [32] D. V. Moretti, C. Miniussi, G. B. Frisoni et al., “Hippocampal to normal aging and AD,” Neurology, vol. 65, no. 3, pp. 404– atrophy and EEG markers in subjects with mild cognitive 411, 2005. impairment,” Clinical Neurophysiology, vol. 118, no. 12, pp. [18] J. Pariente, S. Cole, R. Henson et al., “Alzheimer’s patients 2716–2729, 2007. engage an alternative network during a memory task,” Annals [33] D. V. Moretti, M. Pievani, C. Fracassi et al., “Increase of Neurology, vol. 58, no. 6, pp. 870–879, 2005. of theta/Gamma and Alpha3/Alpha2 ratio is associated [19] M. M. Machulda, H. A. Ward, B. Borowski et al., “Compar- with amygdalo-hippocampal complex atrophy,” Journal of ison of memory fMRI response among normal, MCI, and Alzheimer’s Disease, vol. 17, no. 2, pp. 349–357, 2009. Alzheimer’s patients,” Neurology, vol. 61, no. 4, pp. 500–506, [34] D. V. Moretti, C. Fracassi, M. Pievani et al., “Increase of theta/gamma ratio is associated with memory impairment,” [20] V. Jelic, S. E. Johansson, O. Almkvist et al., “Quantita- Clinical Neurophysiology, vol. 120, no. 2, pp. 295–303, 2009. tive electroencephalography in mild cognitive impairment: [35] D. V. Moretti, C. Miniussi, G. Frisoni et al., “Vascular damage longitudinal changes and possible prediction of Alzheimer’s disease,” Neurobiology of Aging, vol. 21, no. 4, pp. 533–540, and EEG markers in subjects with mild cognitive impairment,” Clinical Neurophysiology, vol. 118, no. 8, pp. 1866–1876, 2007. 2000. International Journal of Alzheimer’s Disease 11 [36] J. H. Kramer,B.R.Reed,D.Mungas,M.W.Weiner, and and qualitative analyses of cognitive impairment,” European H. C. Chui, “Executive dysfunction in subcortical ischaemic Neurology, vol. 36, no. 6, pp. 378–384, 1996. vascular disease,” Journal of Neurology Neurosurgery and [52] P. Caﬀarra, G. Vezzadini, F. Dieci, F. Zonato, and A. Venneri, Psychiatry, vol. 72, no. 2, pp. 217–220, 2002. “Rey-Osterrieth complex ﬁgure: normative values in an Italian [37] D. V. Moretti, C. Babiloni, G. Binetti et al., “Individual population sample,” Neurological Sciences, vol. 22, no. 6, pp. analysis of EEG frequency and band power in mild Alzheimer’s 443–447, 2002. disease,” Clinical Neurophysiology, vol. 115, no. 2, pp. 299–308, [53] L. S. Radloﬀ, “The CES-D scale: a self-report depression scale for research in the general population,” Applied Psychological [38] W. Klimesch, “EEG alpha and theta oscillations reﬂect cogni- Measurement, vol. 1, pp. 385–401, 1977. tive and memory performance: a review and analysis,” Brain [54] L. O. Wahlund, F. Barkhof, F. Fazekas et al., “A new rating scale Research Reviews, vol. 29, no. 2-3, pp. 169–195, 1999. for age-related white matter changes applicable to MRI and [39] M. F. Folstein, S. E. Folstein, and P. R. McHugh, ““Mini mental CT,” Stroke, vol. 32, no. 6, pp. 1318–1322, 2001. state”. A practical method for grading the cognitive state of [55] C. J. Stam, Y. Van Der Made, Y. A. L. Pijnenburg, and P. Schel- patients for the clinician,” Journal of Psychiatric Research, vol. tens, “EEG synchronization in mild cognitive impairment and 12, no. 3, pp. 189–198, 1975. Alzheimer’s disease,” Acta Neurologica Scandinavica, vol. 108, [40] C. P. Hughes, L. Berg, and W. L. Danziger, “A new clinical scale no. 2, pp. 90–96, 2003. for the staging of dementia,” British Journal of Psychiatry, vol. [56] T. Seidenbecher,T.R.Laxmi,O.Stork,and H. C. Pape, 140, no. 6, pp. 566–572, 1982. “Amygdalar and hippocampal theta rhythm synchronization [41] W. G. Rosen, R. D. Terry, and P. A. Fuld, “Pathological during fear memory retrieval,” Science, vol. 301, no. 5634, pp. veriﬁcation of ischemic score in diﬀerentiation of dementias,” 846–850, 2003. Annals of Neurology, vol. 7, no. 5, pp. 486–488, 1980. [57] R. T. Narayanan, T. Seidenbecher, S. Sangha, O. Stork, and H. [42] M. P. Lawton andE.M.Brody,“Assessmentofolder people: C. Pape, “Theta resynchronization during reconsolidation of self-maintaining and instrumental activities of daily living,” remote contextual fear memory,” NeuroReport, vol. 18, no. 11, Gerontologist, vol. 9, no. 3, pp. 179–186, 1969. pp. 1107–1111, 2007. [43] M. Albert,L.A.Smith,P.A.Scherr, J. O. Taylor,D.A.Evans, [58] S. M. Montgomery and G. Buzsaki, ´ “Gamma oscillations and H. H. Funkenstein, “Use of brief cognitive tests to iden- dynamically couple hippocampal CA3 and CA1 regions tify individuals in the community with clinically diagnosed during memory task performance,” Proceedings of the National Alzheimer’s disease,” International Journal of Neuroscience, vol. Academy of Sciences of the United States of America, vol. 104, 57, no. 3-4, pp. 167–178, 1991. no. 36, pp. 14495–14500, 2007. [44] R. C. Petersen,G.E.Smith,S.C.Waring,R.J.Ivnik,E. [59] P. Sauseng, W. Klimesch, M. Doppelmayr, S. Hanslmayr, Kokmen, and E. G. Tangelos, “Aging, memory, and mild M. Schabus, and W. R. Gruber, “Theta coupling in the cognitive impairment,” International Psychogeriatrics, vol. 9, human electroencephalogram during a working memory no. 1, supplement, pp. 65–69, 1997. task,” Neuroscience Letters, vol. 354, no. 2, pp. 123–126, 2004. [45] F. Portet, P. J. Ousset, P. J. Visser et al., “Mild cognitive [60] A. Bragin, G. Jando, Z. Nadasdy, J. Hetke, K. Wise, and G. impairment (MCI) in medical practice: a critical review of Buzsaki, “Gamma (40–100 Hz) oscillation in the hippocam- the concept and new diagnostic procedure. Report of the MCI pus of the behaving rat,” Journal of Neuroscience, vol. 15, no. 1 Working Group of the European Consortium on Alzheimer’s I, pp. 47–60, 1995. Disease,” Journal of Neurology, Neurosurgery and Psychiatry, [61] R. R. Llinas, ´ U. Ribary, D. Jeanmonod, E. Kronberg, and vol. 77, no. 6, pp. 714–718, 2006. P. P. Mitra, “Thalamocortical dysrhythmia: a neurological [46] C. Geroldi, R. Rossi, C. Calvagna et al., “Medial temporal and neuropsychiatric syndrome characterized by magne- atrophy but not memory deﬁcit predicts progression to toencephalography,” Proceedings of the National Academy of dementia in patients with mild cognitive impairment,” Journal Sciences of the United States of America, vol. 96, no. 26, pp. of Neurology, Neurosurgery and Psychiatry, vol. 77, no. 11, pp. 15222–15227, 1999. 1219–1222, 2006. [62] W. Klimesch, P. Sauseng, and S. Hanslmayr, “EEG alpha [47] K. I. Shulman, “Clock-drawing: is it the ideal cognitive oscillations: the inhibition-timing hypothesis,” Brain Research screening test?” International Journal of Geriatric Psychiatry, Reviews, vol. 53, no. 1, pp. 63–88, 2007. vol. 15, no. 6, pp. 548–561, 2000. [63] D. Pare, ´ D. R. Collins, and J. G. Pelletier, “Amygdala oscilla- [48] P. Amodio, H. Wenin, F. Del Piccolo et al., “Variability of trail tions and the consolidation of emotional memories,” Trends making test, symbol digit test and line trait test in normal in Cognitive Sciences, vol. 6, no. 7, pp. 306–314, 2002. people. A normative study taking into account age-dependent [64] M. Garolera, R. Coppola, K. E. Muno ˜ z et al., “Amygdala decline and sociobiological variables,” Aging,vol. 14, no.2,pp. activation in aﬀective priming: a magnetoencephalogram 117–131, 2002. study,” NeuroReport, vol. 18, no. 14, pp. 1449–1453, 2007. [49] A. Basso, E. Capitani, and M. Laiacona, “Raven’s coloured [65] K. A. Young, L. A. Holcomb, W. L. Bonkale, P. B. Hicks, U. progressive matrices: normative values on 305 adult normal Yazdani, and D. C. German, “5HTTLPR polymorphism and controls,” Functional Neurology, vol. 2, no. 2, pp. 189–194, enlargement of the pulvinar: unlocking the backdoor to the limbic system,” Biological Psychiatry, vol. 61, no. 6, pp. 813– [50] H. Spinnler and G. Tognoni, “Italian standardization and 818, 2007. classiﬁcation of neuropsychological tests,” Italian Journal of [66] J. M. Ellison, D. G. Harper,Y.Berlow, andL.Zeranski, Neurological Sciences, vol. 6, supplement 8, pp. 1–120, 1987. “Beyond the “C” in MCI: noncognitive symptoms in amnestic [51] G. A. Carlesimo, C. Caltagirone, and G. Gainotti, “The mental and non-amnestic mild cognitive impairment,” CNS Spec- deterioration battery: normative data, diagnostic reliability trums, vol. 13, no. 1, pp. 66–72, 2008. 12 International Journal of Alzheimer’s Disease [67] L. Rozzini, B. Vicini Chilovi, M. Conti et al., “Neuropsychiatric of Neurology Neurosurgery and Psychiatry,vol. 69, no.2,pp. symptoms in amnestic and nonamnestic mild cognitive 192–198, 2000. impairment,” Dementia and Geriatric Cognitive Disorders, vol. [83] E. S. Goldensohn, “Use of EEG for evaluation of focal 25, no. 1, pp. 32–36, 2007. intracranial lesions,” in Current Practice of Clinical Electroen- [68] P. Johannsen, J. Jakobsen, P. Bruhn, and A. Gjedde, “Cortical cephalography, D. W. Klass and D. D. Daly, Eds., pp. 307–341, responses to sustained and divided attention in Alzheimer’s Raven, New York, NY, USA, 1979. disease,” NeuroImage, vol. 10, no. 3, pp. 269–281, 1999. [69] A. M. Berardi, R. Parasuraman, and J. V. Haxby, “Sustained attention in mild Alzheimer’s disease,” Developmental Neu- ropsychology, vol. 28, no. 1, pp. 507–537, 2005. [70] E. J. Levinoﬀ,D.Saumier,and H. Chertkow,“Focused attention deﬁcits in patients with Alzheimer’s disease and mild cognitive impairment,” Brain and Cognition,vol. 57, no.2,pp. 127–130, 2005. [71] A. Tales, J. Haworth, S. Nelson, R. J. Snowden, and G. Wilcock, “Abnormal visual search in mild cognitive impairment and Alzheimer’s disease,” Neurocase, vol. 11, no. 1, pp. 80–84, 2005. [72] A. Tales, R. J. Snowden, J. Haworth, and G. Wilcock, “Abnor- mal spatial and non-spatial cueing eﬀects in mild cognitive impairment and Alzheimer’s disease,” Neurocase, vol. 11, no. 1, pp. 85–92, 2005. [73] O. C. Zalay and B. L. Bardakjian, “Simulated mossy ﬁber associated feedforward circuit functioning as a highpass ﬁlter,” in Proceedings of the 28th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBS ’06), pp. 4979–4982, September 2006. [74] G. B. Frisoni, S. Galluzzi, L. Bresciani, O. Zanetti, and C. Geroldi, “Mild cognitive impairment with subcortical vascular features: clinical characteristics and outcome,” Journal of Neurology, vol. 249, no. 10, pp. 1423–1432, 2002. [75] S. Galluzzi, C. F. Sheu, O. Zanetti, and G. B. Frisoni, “Dis- tinctive clinical features of mild cognitive impairment with subcortical cerebrovascular disease,” Dementia and Geriatric Cognitive Disorders, vol. 19, no. 4, pp. 196–203, 2005. [76] M. Steriade and R. R. Llinas, “The functional states of the thalamus and the associated neuronal interplay,” Physiological Reviews, vol. 68, no. 3, pp. 649–742, 1988. [77] F. H. Lopes da Silva, A. van Rotterdam, P. Barts, E. van Heusden, and W. Burr, “Models of neuronal populations: the basic mechanisms of rhythmicity,” Progress in Brain Research, vol. 45, no. C, pp. 281–308, 1976. [78] P. L. Nunez, B. M. Wingeier, and R. B. Silberstein, “Spatial-temporal structures of human alpha rhythms: theory, microcurrent sources, multiscale measurements, and global binding of local networks,” Human Brain Mapping, vol. 13, no. 3, pp. 125–164, 2001. [79] B. Doiron, M. J. Chacron, L. Maler, A. Longtin, and J. Bastian, “Inhibitory feedback required for network oscillatory responses to communication but not prey stimuli,” Nature, vol. 421, no. 6922, pp. 539–543, 2003. [80] P. L. Nunez and R. Srinivasan, “A theoretical basis for standing and traveling brain waves measured with human EEG with implications for an integrated consciousness,” Clinical Neuro- physiology, vol. 117, no. 11, pp. 2424–2435, 2006. [81] B. Szelies, R. Mielke, J. Kessler, and W. D. Heiss, “EEG power changes are related to regional cerebral glucose metabolism in vascular dementia,” Clinical Neurophysiology, vol. 110, no. 4, pp. 615–620, 1999. [82] L. Leocani, T. Locatelli, V. Martinelli et al., “Electroencephalo- graphic coherence analysis in multiple sclerosis: correlation with clinical, neuropsychological, and MRI ﬁndings,” Journal MEDIATORS of INFLAMMATION The Scientific Gastroenterology Journal of World Journal Research and Practice Diabetes Research Disease Markers Hindawi Publishing Corporation Hindawi Publishing Corporation Hindawi Publishing Corporation Hindawi Publishing Corporation Hindawi Publishing Corporation http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 International Journal of Journal of Immunology Research Endocrinology Hindawi Publishing Corporation Hindawi Publishing Corporation http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 Submit your manuscripts at http://www.hindawi.com BioMed PPAR Research Research International Hindawi Publishing Corporation Hindawi Publishing Corporation http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 Journal of Obesity Evidence-Based Journal of Journal of Stem Cells Complementary and Ophthalmology International Alternative Medicine Oncology Hindawi Publishing Corporation Hindawi Publishing Corporation Hindawi Publishing Corporation Hindawi Publishing Corporation Hindawi Publishing Corporation http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 Parkinson’s Disease Computational and Behavioural Mathematical Methods AIDS Oxidative Medicine and in Medicine Research and Treatment Cellular Longevity Neurology Hindawi Publishing Corporation Hindawi Publishing Corporation Hindawi Publishing Corporation Hindawi Publishing Corporation Hindawi Publishing Corporation http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014 http://www.hindawi.com Volume 2014

Journal

International Journal of Alzheimer's Disease – Hindawi Publishing Corporation

Published: Jul 26, 2012

Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Quantitative EEG Markers in Mild Cognitive Impairment: Degenerative versus Vascular Brain Impairment

Quantitative EEG Markers in Mild Cognitive Impairment: Degenerative versus Vascular Brain Impairment

Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Quantitative EEG Markers in Mild Cognitive Impairment: Degenerative versus Vascular Brain Impairment

Quantitative EEG Markers in Mild Cognitive Impairment: Degenerative versus Vascular Brain Impairment

References (89)

Abstract

Journal

Recommended Articles

There are no references for this article.

Our policy towards the use of cookies